What Makes a Real Team? Lessons from Katzenbach and Smith

If you’ve ever been part of a group that called itself a team but never quite felt like one, you’re not alone. The word ‘team’ gets thrown around liberally in organisations—but according to Jon Katzenbach and Douglas Smith, authors of the influential 1993 book The Wisdom of Teams, most groups that use the label don’t actually qualify.

Their research offers a precise framework for understanding what separates genuine teams from groups that merely share a conference room.

The Definition That Changed Everything

Katzenbach and Smith proposed a definition that has become a touchstone for organisational thinking: a team is a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they hold themselves mutually accountable.

Every word matters here. Small number—because coordination breaks down at scale. Complementary skills—because diversity of capability is the whole point. Common purpose—because without shared direction, you just have individuals occupying the same space. And mutual accountability—perhaps the hardest element of all, because it asks people to answer not just to a boss, but to each other.

The Team Performance Curve

One of the model’s most useful contributions is what the authors call the Team Performance Curve, which maps different types of groups against their performance impact.

At the bottom sits the working group. This isn’t a failure state—it’s simply a collection of individuals who come together primarily to share information and best practices. There’s no collective work product, no mutual accountability. A weekly department meeting where people give updates often falls into this category, and that’s perfectly fine when no true collaboration is needed.

Next comes the pseudo-team, and this is where things get problematic. A pseudo-team has been told it’s a team and may even believe it, but hasn’t done the work to become one. There’s no common purpose, no real joint work, no mutual accountability. Pseudo-teams actually perform worse than working groups because they carry all the overhead of team coordination without any of the benefits.

The potential team represents a group that recognises the need for collective performance and is genuinely trying to improve. They’re working on clarifying purpose, establishing goals, and building accountability. Most organisational teams live here—somewhere on the journey but not yet arrived.

A real team has cracked the code. Members have developed complementary skills, committed to a shared purpose, and hold each other accountable for results. The collective work product exceeds what individuals could produce separately.

Finally, at the top of the curve, sits the high-performance team. These rare groups have all the characteristics of real teams plus a deep personal commitment to one another’s growth and success. Members genuinely care about each other as people, not just as contributors. High-performance teams deliver results that far exceed expectations.

Why This Matters

The model’s power lies in its diagnostic clarity. When a team is struggling, leaders often reach for the wrong interventions—team-building exercises, personality assessments, reorganisations. Katzenbach and Smith’s framework asks simpler questions first: Do we have a clear common purpose? Are we producing joint work products? Are we holding each other accountable?

If the answer to these questions is no, trust falls and personality profiles won’t help. The team needs to do the harder work of establishing genuine shared commitment.

When Teams Aren’t the Answer

Perhaps the model’s most counterintuitive insight is that teams aren’t always appropriate. They require significant investment in time and emotional energy. For routine work that can be divided cleanly among individuals, a working group is more efficient. Teams make sense when the challenge is complex, when diverse skills must be integrated, and when the stakes justify the coordination costs.

Knowing when not to form a team is just as valuable as knowing how to build one.

Putting It Into Practice

If you’re leading or participating in a group that aspires to be a real team, Katzenbach and Smith’s model suggests focusing on the fundamentals: get small enough to maintain real connection; ensure you have the right mix of skills; hammer out a purpose that everyone genuinely owns; set specific performance goals that require collective effort; develop a working approach that holds everyone accountable to each other—not just to the leader.

It’s not glamorous work, but it’s the work that separates teams that deliver from groups that merely meet.


The Wisdom of Teams remains essential reading three decades after its publication because its insights cut through the noise. In a business culture that often treats ‘team’ as a feel-good synonym for ‘group’, Katzenbach and Smith remind us that real teams are built through discipline, commitment, and a willingness to answer to one another.


Further Reading

Katzenbach, J. R., & Smith, D. K. (1993). The discipline of teams. Harvard Business Review, 71(2), 111–120.

Katzenbach, J. R., & Smith, D. K. (1993). The wisdom of teams: Creating the high-performance organization. Harvard Business School Press.

Lencioni, P. M. (2002). The five dysfunctions of a team: A leadership fable. Jossey-Bass.

Marshall, R.W. (2023). The team fruit bowl. Leanpub.

Whitmore, J. (2017). Coaching for performance: The principles and practice of coaching and leadership (5th ed.). Nicholas Brealey Publishing.

The Quintessential Mindset: 79 Essential Organisational Memes

What makes a truly exceptional software development or collaborative knowledge work organisation? Over fifty years of experience has taught me that it’s not about processes, tools, or even technical skills. It’s about the collective assumptions and beliefs—the “memes”—that define an organisation’s culture. In my book Quintessence (Marshall, 2021), I presented a blueprint for highly effective organisations by examining 79 key memes that shape how work gets done.

This post summarises each meme from the quintessential perspective—the perspective of organisations that achieve effectiveness levels some five times higher than the average. Think of this as a map of the ideal future state for your organisation, an “idealised design” in Russell Ackoff’s terms. Rather than telling you how to get there, this guide shows you what “there” looks like:

These foundational memes establish the context for quintessential thinking.

1. Change

Quintessential organisations regard change as their friend and constant companion. Rather than resisting or merely managing change, they embrace it as a source of innovation and opportunity. They organise their work to accommodate change easily, building it into business-as-usual rather than treating it as an exceptional event. Change is the oxygen of their existence.

2. Discussion

Discussion, dialogue, and conversation are the lifeblood of quintessential organisations. They constantly seek open, candid exchange of ideas and knowledge, recognising that speaking one’s mind can be challenging. These organisations invest heavily in developing capabilities for skilful dialogue, using techniques like Nonviolent Communication to handle difficult topics and inevitable conflicts.

3. Undiscussables

There are no taboo topics in quintessential organisations. Even when discussions make people feel nervous or uncomfortable, everyone recognises the necessity to work through such feelings and help each other in tackling difficult subjects. The most challenging topics are often the most worthy of discussion, and these organisations maintain zero tolerance for undiscussability.

4. Courage

Quintessential organisations value courage highly—courage to speak out, take risks, challenge convention, and question each other’s assumptions. They understand that the psychological limits of leaders become the psychological limits of the organisation. Courage means acting when much is at stake, though not recklessly, and showing transparency and radical candour in all interactions.

5. Needs

These organisations recognise that people have needs and celebrate this essential aspect of humanity. They understand the power of attending to folks’ needs and build this understanding into all policies and practices. The wide range of human needs—from autonomy and mastery to safety and belonging—guides their efforts, connecting directly to effectiveness and the bottom line.

6. Who Matters?

Quintessential organisations carefully identify and track “The Folks That Matter”—customers, staff, investors, suppliers, and others whose needs are central to how work works. They invest significant effort in understanding stakeholder symmetry and finding the appropriate balance of competing claims. All work is geared towards meeting these folks’ needs whilst maximising the amount of work NOT done.

7. Collective Mindsets

These organisations embrace the idea that groups and organisations have collective mindsets—sets of interlocking assumptions and beliefs that strongly influence success. They act intentionally on their collective mindset, understanding that organisational effectiveness is a function of these prevailing beliefs. They know that shifting mindsets requires wholesale replacement of one memeplex with another.

8. Success

Success is defined as achieving the organisation’s purpose whilst meeting the needs of all the Folks That Matter. It’s not a binary condition but a sliding scale, measured operationally with clear, quantified definitions. Quintessential organisations balance success across short, medium, and long-term horizons, ensuring no key stakeholders’ needs go unattended.

9. Culture

Quintessential organisations view culture as a read-only manifestation of collective assumptions and beliefs. Rather than trying to “change culture” directly, they shift the underlying assumptions and beliefs to better align with purpose. They recognise that when assumptions and beliefs are misaligned with ambitions, success becomes elusive, and they work to harmonise these elements.

10. Structure

Structure is seen as a lever for effectiveness, with fluidity being paramount. Quintessential organisations prefer self-organising, adaptable structures that can morph as circumstances demand, much like an octopus. They avoid fixed hierarchies in favour of distributed decision-making, flat organisations, and value stream orientations. Any fixedness of structure is considered antithetical to business agility.

11. Coevolution

These organisations understand that innovations in mindset must coevolve with changes to rules, policies, and procedures. Old rules that were designed to bypass old limitations will block the benefits of new thinking. They remain constantly alert to the need for simultaneous evolution of assumptions, beliefs, and operating procedures.

12. Talent

Quintessential organisations have an ambivalent relationship with talent, recognising Deming’s insight that 90–95% of performance comes from the system, not individual ability. They value individuals and their rate of improvement but prioritise interpersonal relationships and interactions over individual talent. They approach talent obliquely, focusing on expanding the organisation’s capacity to create its future.

13. Relationships

These organisations believe that relationships between people—not people themselves—are their greatest asset. They prioritise time and resources for studying motivation, engagement, sociology, and psychology. Relationships are loving and altruistic rather than transactional, with people seeking what’s alive in each other and attending to everyone’s needs.

14. Remuneration

Quintessential organisations understand that compensation is a form of extrinsic motivation with deleterious effects on knowledge work. They pay people according to what they need rather than arbitrary criteria, and invite employees to set their own salaries and rates. This removes one of the workplace’s most contentious issues whilst treating people like trusted adults.

15. Flow of Value

Flow of value is an essential operating principle, with these organisations seeking economies of flow rather than economies of scale. They monitor the rate of flow, lead times, cycle times, and process cycle efficiency, constantly working to improve continuous flow through reducing WIP, making flow visible, and applying Theory of Constraints principles.

16. Waste

Whilst not ignored, waste is seen as less significant than improving flow. Quintessential organisations define waste peculiarly: sacrificing some folks’ needs without effectively meeting other folks’ needs. They remain conscious of Shingo’s seven (eight) forms of waste, particularly the eighth waste—wasted human potential—whilst prioritising flow improvements over waste reduction.

17. Purpose

These organisations have crystal-clear, collectively-defined purposes that evolve regularly with input from all the Folks That Matter. They understand that purpose—”attending to the needs of all the Folks That Matter”—is more than words on a page. Purpose permeates all corners of the organisation, from overall organisational goals to individual sense of meaning.

18. Case For Action

Quintessential organisations use Cases for Action to answer “Why are we doing this?” and improve clarity amongst everyone involved. These systematic approaches to building shared understanding of current business challenges enhance their ability to adapt and reorient quickly. They believe that if people believe in the “why”, they’ll accomplish any “what”.

19. Vision

Vision provides shared understanding of what the organisation desires to achieve in five to ten years. It helps inspire employees, aids decision-making, attracts talent, improves focus, and communicates purpose. These organisations invest heavily in involving everyone in building and keeping the vision current through consensus, dialogue, and communication.

20. Effectiveness

Quintessential organisations strive for effectiveness—doing the right things—regarding efficiency as a pernicious distraction. They’re some five times more productive than organisations focused on efficiency, believing it’s better to do the right thing wrong than the wrong thing right. They recognise that the greatest risk in software development is doing the wrong things altogether.

21. Efficiency

Whilst not ignored, efficiency is viewed with suspicion because focus on it detracts from effectiveness. These organisations understand the crucial distinction between the two and believe efficiency can lead to dysfunctional behaviours like keeping teams busy unnecessarily. They balance measures of efficiency with measures of effectiveness, with “needs met” being the quintessential measure of both.

22. The Role of “The System”

Systems thinking permeates all aspects of quintessential organisations. They understand that the way work works—”the system”—governs organisational performance far more than individual efforts. They differentiate between “the system” (how work actually gets done) and “the process” (how work is intended to work), focusing on the interactions between parts rather than parts taken separately.

23. Part/Whole Thinking

These organisations regard dividing organisations into parts and managing them separately as folly. They understand that local optimisation of parts inevitably leads to sub-optimisation of the whole. Having examined the risks of vertical silos, they choose flat, whole-organisation models like horizontal value streams over functional departments.

24. Innovation

Innovation means implementing more effective strategies for attending to folks’ needs. Quintessential organisations regard innovation in how work works as paramount, believing this leads inexorably to product innovation. They delegate innovation to workers who own the way work works, making innovation a daily occurrence through kaizen. Innovation is the oxygen of their existence.

25. Variability

These organisations understand what unpredictable variation does to business costs and make Statistical Quality Control a cornerstone of their approach. They make variability visible and reduce it wherever sensible, recognising that the major cause lies within the system. Understanding variation changes management theory so fundamentally as to make it unrecognisable from what came before.

26. Learning

Learning is seen as essential to long-term competitiveness, but only when behaviour actually changes. Quintessential organisations are archetypal learning organisations where people continually expand their capacity to create desired results. They allow time for reflection, recognise that learning depends on embodied experiences rather than opinions, and understand that organisational learning happens through shifts in collective assumptions.

27. The Nature of Work

Work is defined as the application of knowledge to developing and delivering products and services. Quintessential organisations make no distinction between work and serious play, trusting people to attend to needs voluntarily rather than through obligation. Work serves multiple social functions beyond economic necessity—providing community, identity, self-esteem, and purpose.

28. The Way the Work Works

In quintessential organisations, the way work works is owned by the people doing the work, not their managers. This enhances buy-in, motivation, innovation, autonomy, and sense of community. Workers answer two questions: What needs are we attending to, and what means will we use to meet those needs? They’re trusted to make changes that may seem contentious or alien.

29. Improvement

Improvement means any change to the way work works that results in better meeting folks’ needs. These organisations place significant emphasis on continuous improvement (kaizen) of effectiveness rather than process improvement. They understand the connection between improvement and quality, pursue both incremental improvements and radical kaikaku, and collectively seek Big Hairy Audacious Goals.

30. Play

Quintessential organisations believe there’s a central place for play—activity voluntarily engaged in for enjoyment. They encourage people to play rather than work, trusting them to align play with organisational needs through shared purpose. Combinatory play is seen as essential to productive thought, and having a licence to play contributes significantly to effectiveness.

31. Utilisation

These organisations regard slack as desirable and busyness as wildly dysfunctional. They understand queueing theory: as utilisation increases, delays increase exponentially. They coach people in working fewer hours and focus on achievements rather than busyness. Utilisation has a place only at the constraint or bottleneck; elsewhere, slack capacity is actively maintained.

32. Doctrine

Quintessential organisations value having an explicit, ever-evolving doctrine shared by everyone. Doctrine provides guidance towards common purpose, promotes productive debate, articulates principles for decision-making, aids recruitment, and enables harmony with stakeholders. Specific elements include organising intent, main effort, and critical vulnerability drawn from auftragstaktik concepts.

33. Quality

Quality is the result of a carefully constructed cultural environment and must be the fabric of the organisation. These organisations follow Crosby’s Four Absolutes: quality is conformance to requirements, the system is prevention, the performance standard is zero defects, and measurement is the price of nonconformance. Quality and continuous improvement are woven together into one eternal golden braid.

34. ZeeDee

ZeeDee (Zero Defects) means the only acceptable number of errors is zero—getting things right first time, every time. This philosophy requires a collective shift in assumptions about quality and leads to pride in work, improved relationships, increased effectiveness, and more satisfaction. Quality is free because prevention costs less than inspection and rework.

35. Cost of Focus

Cost of Focus is the cost incurred when failing to include all needs of all the Folks That Matter in the Needsscape. Quintessential organisations are vigilant in ensuring they attend to all relevant needs, minimising the likelihood of overlooking key individuals and groups. They regularly discuss how scope decisions are made and invite practices to track and update the Needsscape.

36. Cost of Delay

Cost of Delay calculates the impact of time on value creation and capture. These organisations use it to prioritise decisions, understanding that delays in meeting needs have tangible financial costs. All commercial resource-allocation and delivery-prioritisation decisions use Cost of Delay numbers, maximising the economic benefits of their portfolio and helping evaluate queues, capacity, batch sizes, and variability.

37. Risk

Risk is viewed as a friend signalling opportunity. Rather than just identifying and managing risks, quintessential organisations explicitly identify and manage the opportunities risks signal. They believe in the risk-reward equation and invite everyone to train in risk appreciation, opportunity management, and exploitation. They systematically identify, assess, monitor, control, and report on risks.

38. Quantification

These organisations improve and clarify communications by inviting quantification of all things, especially folks’ needs. Pervasive quantification enables more meaningful discussions less obscured by subjective language, increasing the probability of delivering what people actually need. Quantification aids clear thinking and good communication even without subsequent measurement.

39. Metrics and Measures

Quintessential organisations have a love-hate relationship with metrics. Measures collected by those who own their work can illuminate issues and track progress, but measures collected by others cause significant problems. They measure very few things, focusing only on those relevant to decision-making, and continually review their selection as things change.

40. Hierarchy

These organisations place themselves far towards the flat end of the hierarchy spectrum. They embrace self-organisation and self-management, seeing little need for traditional management or hierarchy. They believe hierarchy dissuades innovation, promotes compliance, enables dullards to hide, damages morale, and causes good people to leave.

41. Management

Quintessential organisations see no need for traditional management or managers. As organisations transition to quintessence, managers relinquish control and embrace roles of enablement, resourcing, and support. Eventually, the term “manage” ceases to be relevant, and there are no managers at all—only people working together to attend to needs.

42. Single Wringable Neck

These organisations choose group accountability over individual accountability. Understanding the deleterious effects of blame and accountability, they dwell instead on joy, voluntary action, and nonviolence. The concept of the single wringable neck—having one person to blame—has no place. People find joy in service and delight in attending to each other’s needs.

43. Ownership and Control

Assigned or delegated ownership implies accountability, which quintessential organisations avoid. People voluntarily take on things that need doing to delight the Folks That Matter, without feeling accountable in the conventional sense. When conflicts occur, people work things out through mutual exploration, skilful dialogue, and Nonviolent Communication.

44. Teams

These organisations believe people collaborating together are more effective than individuals working alone. Key factors for performance include attentiveness to needs, camaraderie, dependability, shared purpose, achievement, and personal fulfilment. They embrace novel approaches like ensemble collaboration and mob programming, understanding that team success depends on setting team goals above personal self-interest.

45. Silos or Value Streams

Quintessential organisations recognise that value flows horizontally through value streams, making vertical silo structures nonsensical. Silos lead to delays, queues, bottlenecks, and narrow specialisms. They structure themselves horizontally, understanding that an organisation’s major deficiencies arise from how parts interact, not from actions taken separately.

46. Projects, Products or Other

These organisations eschew projects in favour of flow, embracing #NoProjects. They believe running temporary projects is highly ineffective for building sustainable products. Instead of continually creating and disbanding teams, they have standing teams and long-running value streams, moving work to people rather than people to work.

47. Time Horizons

Quintessential organisations consider multiple time horizons when looking to the future, believing there are significant benefits in balancing attention to current performance and opportunities for growth. They use frameworks like the Three Horizons model to inform this perspective, ensuring they don’t sacrifice long-term success for short-term gains.

48. Speed

Speed has its place as a consequence of the needs of the Folks That Matter, but there’s no point doing things faster than needed. These organisations regard naked emphasis on speed as detracting from the social dimensions of community. They’re more comfortable with natural rhythms and rely on effectiveness to achieve the pace required rather than exhortations.

49. Homogeneity

Quintessential organisations embrace diversity—of thought, approach, experience, and demography. They see diversity as contributing to innovation, market growth, joy, and performance. Whilst encouraging groups to harmonise diverse assumptions and beliefs, they actively discourage uniformity and regimentation for its own sake, relying on the wisdom of crowds.

50. Reuse

These organisations are sceptical about reuse promises, having sufficient experience to know that significant investment is required for effective reuse—often a dubious proposition in terms of ROI. They neither bias towards rejection nor embracing of reuse, preferring to evaluate and decide on the merits of each specific case.

51. Software or Not

Quintessential organisations deliberate on the most effective means to address needs rather than automatically assuming software is appropriate. They help customers first develop manual solutions using simple physical means before considering software automation. They ensure they have skills to supply a broad range of solutions, particularly those that forego software.

52. Cadence

A strong, regular cadence provides confidence, accomplishment, improved morale, psychological safety, and encourages collaboration. These organisations invite collaborative groups to adopt rhythms derived from the needs of the Folks That Matter as one aspect of self-organisation. They believe choice of cadence from needs is more effective than imposed or arbitrary cadence.

53. Decision-making

Quintessential organisations give control to employees at every level through the Advice Process. They recognise that for people to make effective decisions, they must be aligned with purpose and understand goals and decision-making criteria. They use mechanisms like “I intend to…” statements, thinking out loud, and the Three Names rule to decentralise decision-making.

54. Evidence

These organisations insist on and rely on evidence to make decisions—specifically, evidence collected by those directly involved. They value sound, critically-appraised, non-partisan evidence over cargo-culted management ideas and personal experience, which are susceptible to errors and bias. Effectiveness is due in no small part to evidence-based decision-making.

55. Transparency

Quintessential organisations champion radical transparency, publicly sharing information including diversity data, growth metrics, pricing, processes, hiring, salaries, strategies, revenues, and financials. Benefits include engaged employees, increased autonomy, more trust, improved feedback, clarity, alignment, and open relationships. Radical transparency is a multiplier for effectiveness.

56. Technical Competencies

These organisations value skills but not traditional software skills as much. They favour skilful dialogue, relationship building, Nonviolent Communication, evidence-based decision making, compassion, attending to needs, and administration. They enumerate necessary technical skills and invite employees to acquire them with organisational support, postponing or declining assignments when skills are absent.

57. Mudballs

Quintessential organisations keep on top of the internal state of all their products, understanding the value and risks of technical debt. They know that poor internal state constrains their ability to respond to market changes, slows development, and negatively impacts morale. They use technical debt strategically, balancing Cost of Delay with debt interest.

58. Financials

These organisations reject standard cost accounting in favour of Throughput Accounting and eschew separate finance departments. They orient financial planning towards front-line decision-making needs, embrace Beyond Budgeting principles, and use financial resources to drive competitive position improvements. Business units become smaller, more numerous, and more entrepreneurial.

59. Governance

Governance is seen as an opportunity to add effectiveness by staying on top of investments and mitigating risks. These organisations apply principles of joy, volunteerism, and constructive conflict. They ensure strong, high-functioning collaborative governance groups whose members trust and challenge one another, creating a culture of open dissent and nonconformance.

60. Workplaces

Quintessential organisations provide a range of workspace options matching groups’ and individuals’ collaboration needs: ensemble collaboration spaces, individual offices, discussion areas, kitchens, meeting rooms, obeya spaces, and remote options. They understand the benefits and costs, invite staff to define and design spaces, and allow people to choose what suits their current mode.

61. OrgCogDiss

Organisational Cognitive Dissonance—having different belief systems present simultaneously—is recognised as a major risk. These organisations anticipate the risks of balkanisation and conflict, inviting people to prepare mitigating actions. A major risk is people deciding to leave rather than adapt their existing assumptions and beliefs.

62. Violence

Violence includes the intentional use of power or threat of force resulting in psychological harm or deprivation. Quintessential organisations invite people to reject workplace violence—including threatening behaviour, verbal abuse, physical attacks, psychological bullying, and subtle coercion—in favour of nonviolence and volunteerism. Violence is a frequent topic maintaining folks’ alertness.

63. Nonviolence

These organisations believe social change, relationships, and interactions flourish when people refrain from harming others. They promote nonviolence as a fundamental operating principle and enthusiastically promote Nonviolent Communication. Nonviolence makes a major contribution to peaceful resolution of conflicts without rancour or residual ill-feeling.

64. Engineering

Quintessential organisations recognise the value of an engineering approach, particularly set-based concurrent engineering (SBCE). They see building technically successful products as insufficient—products must meet the full set of relevant needs. Software is seen as an occasionally necessary evil to minimise and eliminate, with core business being providing solutions, not writing software.

65. PowerPoint and Slide Decks

These organisations eschew slide-based presentations in favour of more effective means like interactive dialogue sessions, World Cafe, master classes, collaborative learning, and structured socialising. They avoid situations where one person presents to an assembly, believing speaking and presenting are poor means for conveying ideas and information.

66. HR

Quintessential organisations recognise the dysfunctions of partitioning human resource management into a separate silo. Wishing to improve effectiveness, they distribute most functions into their value streams. Only a very few functions—payroll, legal support, complaints, and administrative hiring support—are collected into an internal service value stream.

67. Recruiting

The key question for each candidate is “will they fit?” These organisations are acutely aware of their collective assumptions and select new hires with complementary beliefs. They eschew conventional recruiting in favour of making “bad hires”—hiring for fit six months to a year hence rather than now, so new hires move culture forward rather than reinforcing the status quo.

68. Batch Size

Quintessential organisations believe the most effective form of flow is single-piece continuous flow, making the optimum batch size most often one piece. Smaller batches reduce feedback delays, afford more improvement opportunities, help decision-making, and reduce the impact of sunk cost bias. Larger batches lead to longer cycles and longer waits to learn how well needs were met.

69. Theory of Constraints

These organisations know every system has a limiting constraint and that focusing improvement efforts on this constraint is the fastest and most effective way to improve. They pursue effectiveness via the five steps of TOC: identify the constraint, exploit it, subordinate everything else, elevate it, and repeat. They embrace TOC’s thinking tools and core assumptions throughout.

70. Beliefs About People

Quintessential organisations hold clear Theory-Y beliefs: people are concerned for others, capable of learning, intelligent, naturally self-motivated, take pride in good work, and enjoy challenges. They believe people find joy in attending to each other’s needs and that love and compassion feature large in relationships. They strive to avoid demotivation from Theory-X postures.

71. Morale

These organisations pay constant attention to morale across the organisation, measuring, tracking, and visualising it. They understand impacts on morale from organisational behaviours and decisions. Positive factors include needs attended to, fellowship, equity, camaraderie, fulfilling work, accomplishments, purpose, autonomy, trust, mastery, alignment, clarity, resources, dialogue, and positivity.

72. Motivation

Quintessential organisations pay close attention to folks’ motivations out of concern for wellbeing, not to extract more work. They seek to enrich jobs with opportunities for mastery and achievement. They reject the search for discretionary effort as exploitation, believing all effort is discretionary and voluntary. Obligation to achieve minimums smacks of violence.

73. Engagement

These organisations do not seek “engagement” or engaged employees, regarding the concept as an irrelevant distraction. Their approach to manifesting the highly effective organisation naturally results in what others might describe as employee engagement, making it a consequence rather than a goal.

74. Psychological Safety

The question of explicit psychological safety is moot given their predilection for nonviolence and attending to needs. If issues manifest, their propensity for dialogue and peaceful conflict resolution comes to the fore. They see psychological safety as a consequence of culture, not something requiring explicit address, whilst remaining mindful of amygdala hijack during stress.

75. Giants

Quintessential organisations look up to people who occupy special places in their thoughts and from whose works their collective assumptions arise. They have shared role models—giants like Deming, Goldratt, Ackoff, Ohno, Senge, Seddon, and Rosenberg—whom they revere and acknowledge regularly, widely, and publicly, standing on their shoulders to see further.

76. Meetings

These organisations acknowledge the egregious waste caused by conventional meetings and strive to keep formal, structured meetings to a minimum. The few meetings that occur serve only to make decisions, not discuss or share information. They use alternative forms like walks, fikas, and Lean Coffees. As Jeff Bezos noted, if you need more than two pizzas to feed everyone, there are too many people.

77. Complexity

Quintessential organisations choose to look at situations as if they were always inherently simple. By proceeding as if reality is simple and harmonious, they’re less likely to get trapped seeking sophisticated explanations or complicated solutions. They embrace Inherent Simplicity as a practical approach, believing clear thinking comes from examining cause-effect relationships systematically.

78. Status

These organisations have a distinctly Scandinavian feel regarding status distribution, embracing Hofstede’s Cultural Dimensions Theory. They align with egalitarian cultures featuring flat structures, decentralised decision-making, participative management, and emphasis on power distribution. They believe egalitarian cultures enable more effective collaboration.

79. Conflict

Quintessential organisations see conflict as a consequence of people having different preferred strategies for attending to needs. They see disagreements as positive when illuminating different underlying assumptions and beliefs. Productive conflict is equivalent to a strenuous workout—it builds strength and resilience. They invite folks to engage in productive conflict through challenging questions and mining for conflict.


Conclusion

These 79 memes form an interlocking system—a memeplex—that defines the quintessential organisation. Each meme reinforces the others, creating a whole far greater than the sum of its parts. The journey to quintessence isn’t about implementing these ideas piecemeal, but about a rolling wholesale replacement of one memeplex with another.

The question isn’t whether these ideas are “right” or “wrong” in some absolute sense. The question is: how wide is the gap between your organisation’s current collective assumptions and beliefs, and those of the quintessential organisation? That gap represents the challenge—and the opportunity—ahead of you.

As Russell Ackoff observed, the most effective way of creating the future is by closing or reducing the gap between the current state and the idealised design. May this guide illuminate that ideal for you.


Further Reading

Ackoff, R. L. (1999). Re-creating the corporation: A design of organizations for the 21st century. Oxford University Press.

Ackoff, R. L., Magidson, J., & Addison, H. J. (2006). Idealized design: Creating an organization’s future. Wharton School Publishing.

Marshall, R. W. (2018). Hearts over diamonds: Serving business and society through organisational psychotherapy. Falling Blossoms. https://leanpub.com/heartsoverdiamonds

Marshall, R. W. (2021). Memeology: Surfacing and reflecting on the organisation’s collective assumptions and beliefs. Falling Blossoms. https://leanpub.com/memeology

Marshall, R. W. (2021). Quintessence: An acme for software development organisations. Falling Blossoms. https://leanpub.com/quintessence

 

The Software Quality and Productivity Crisis Executives Won’t Address

Reflections From the Long View – 50 Years in Software Consulting

Over my five decades in software consulting, I’ve witnessed remarkable transformations in our industry—from punch cards to cloud computing, from mainframes to microservices to AI coding. But in the past few years, I’ve observed something deeply troubling: a significant decline in interest in—and concern over—both software quality and productivity.

At first, I dismissed it as the bias of age. Perhaps I was simply romanticising the past. So I did what any good consultant does: I looked for data to either validate or refute my impression.

What I found was worse than I imagined. The decline is not only real and measurable—it’s being actively ignored by the very people with the power to address it. We don’t have a technology crisis. We have a leadership crisis—one that may require us to embrace #NoLeadership and take matters into our own hands.

But here’s something that troubles me even more: I suspect that without a long-term perspective, folks might never have noticed this decline at all. If you’ve only been in the industry for five or ten years, the current state might seem normal. You might assume that 75% project failure rates, $1.52 trillion in technical debt, and 69% of developers losing significant time to inefficiencies is simply “how software development works.” It isn’t. It’s how software development has deteriorated under leadership neglect.

The Crisis in Numbers

Let me establish a baseline. Recent industry surveys reveal:

  • 75% of business and IT executives expect their software projects to fail (Beta Breakers, 2024)
  • Only 39% of projects actually meet success criteria (Beta Breakers, 2024)
  • Technical debt in the US reached $1.52 trillion in 2022 (Consortium for Information & Software Quality, 2022)
  • 69% of developers report losing 8+ hours weekly to inefficiencies—20% of their time (Atlassian, 2024)
  • 91% of CTOs identify technical debt as their biggest challenge (STX Next, 2023)

That last statistic is crucial. Hold onto it.

What Executives Know Privately

Internal surveys—the ones shared only amongst technology leadership—paint a clear picture of awareness:

According to the STX Next Global CTO Study, 91% of CTOs cite technical debt as their biggest challenge heading into 2024 (STX Next, 2023). Not second biggest. Not a concern. Their biggest challenge.

A 2024 survey by Morning Consult and Unqork found that 80% of business and technology leaders acknowledge that technical debt causes delays, cancelled projects, and higher costs (Oliver Wyman, 2024).

McKinsey research shows that CIOs estimate technical debt amounts to 20–40% of the value of their entire technology estate, and some 30% of CIOs surveyed believe that more than 20% of their technical budget ostensibly dedicated to new products is diverted to resolving issues related to tech debt (McKinsey & Company, 2023).

Protiviti’s Global Technology Executive Survey found nearly 70% of organisations view technical debt as having a high level of impact on their ability to innovate (Protiviti, 2024).

The message is unambiguous: executives know.

What Executives Do About It

Here’s where the story becomes damning.

Industry research consistently recommends that companies allocate 15–20% of their IT budgets to proactive technical debt management. According to Oliver Wyman’s 2024 analysis, only a small minority of companies actually earmark this recommended amount (Oliver Wyman, 2024).

Instead, most organisations don’t tackle technical debt until it causes an operational meltdown. At that point, they end up allocating 30–40% of their budget to massive emergency transformation programmes—double the recommended preventive investment (Oliver Wyman, 2024). It’s like Y2K again and again.

Let me put this plainly: executives know the problem, know the solution, know the cost of prevention, yet consistently choose to wait for the crisis.

But it gets worse.

The Priority Charade

I examined CIO priority surveys from 2022–2024 across multiple sources. Here’s what consistently makes executives’ top 5 priorities:

  1. Cybersecurity
  2. AI/GenAI adoption
  3. Digital transformation
  4. Cloud migration
  5. Cost optimisation

Here’s what’s conspicuously absent:

  • Software quality
  • Developer productivity
  • Technical debt remediation
  • Testing (QC) and QA investment

Despite 91% of CTOs citing technical debt as their biggest challenge, it doesn’t make the top five priorities in any major CIO survey from 2022–2024.

Think about that disconnect. The thing keeping CTOs awake at night doesn’t even warrant a place in their stated priorities.

The Public Deception

The gap between private knowledge and public communication is where this crosses from negligence into active deception.

I reviewed annual reports and earnings call transcripts from major technology companies for 2023–2024. Here’s what CEOs told shareholders:

Microsoft’s CEO claimed GitHub Copilot increases developer productivity by ‘up to 55%, whilst helping them stay in the flow and bringing the joy back to coding’ (Microsoft, 2023).

Google boasted that ‘more than a quarter of all new code at Google is generated by AI’ (Clouded Judgement, 2024).

Amazon, Meta, and other tech giants celebrated AI productivity gains and record revenues in their annual reports.

Now here’s what these same executives didn’t mention in their shareholder communications:

  • The 75% project failure expectation that their own industry acknowledges
  • The $1.52 trillion in technical debt
  • The 69% of developers losing 8+ hours weekly to inefficiencies
  • The fact that only 39% of projects meet success criteria
  • The 91% of CTOs who cite technical debt as their top concern

I read through annual shareholder letters from JPMorgan Chase, Caterpillar, Chubb, Microsoft, and SoftwareOne. Not one CEO mentioned software quality concerns, technical debt, or developer productivity issues in their 2023–2024 letters to shareholders.

Not. One.

The Productivity Paradox

This creates a particularly egregious contradiction. Microsoft trumpets 55% productivity gains from AI tools in earnings calls (Microsoft, 2023). Meanwhile, independent research shows:

  • 69% of developers lose 8+ hours weekly to inefficiencies (Atlassian, 2024)
  • Only 20% of professional developers report being happy with their jobs (Stack Overflow, 2024)
  • Tech worker burnout jumped from 49% to 68% in just three years (Techotlist, 2024)
  • Developer productivity is neither well-understood nor enabled, according to Atlassian’s research (InfoWorld, 2024)

So which is it? Are developers 55% more productive, or are they losing 20% of their time to inefficiencies and burning out at record rates?

The answer: executives are measuring—and reporting—what makes their stock price rise, not what’s actually happening on the ground.

The Investment That Never Happens

Perhaps the most damning evidence is in the budgets. McKinsey’s research identifies the solution clearly: proactive technical debt management through consistent investment (McKinsey & Company, 2023). Deloitte’s 2024 Tech Trends report notes that leading companies target 15% of IT budgets for technical debt management (Deloitte, 2024).

Yet Oliver Wyman’s analysis found that only a small minority of companies actually allocate this recommended amount. The majority wait for crisis, then spend 30–40%—double the preventive cost (Oliver Wyman, 2024).

It’s the equivalent of knowing your building needs foundation repairs, having engineers tell you it will cost £100,000 now or £250,000 after collapse, and choosing to wait for the collapse.

Except we’re not talking about one building. We’re talking about the entire industry making this choice simultaneously.

The Trend Is Accelerating—And Why It Takes Decades to See It

The executive action gap isn’t static—it’s widening. Between 2020 and 2024:

2020–2021: The pandemic forced rapid digital transformation. Organisations rushed to cloud, creating massive technical debt. Software supply chain failures accelerated by 650% (Consortium for Information & Software Quality, 2022).

2021–2022: Rather than addressing the debt created, executives doubled down on speed. Cybercrime losses from software vulnerabilities rose 64% in 2020–2021, then another 42% in 2021–2022 (Consortium for Information & Software Quality, 2022).

2022–2023: 72% of organisations reported falling behind in digital transformation because of technical debt, with rushed cloud transitions cited as the primary cause (ReadyWorks, 2023).

2023–2024: Instead of addressing mounting technical debt, executives shifted focus to AI. For the first time, AI and Machine Learning vaulted to #2 in executive priorities, having not appeared in the top five the previous year (Evanta, 2024).

At each stage, executives had the data. At each stage, they chose the next shiny object over addressing fundamental problems.

Here’s what troubles me: if you started your career in 2019 or 2020, this accelerating decline is all you’ve ever known. You entered the industry during the pandemic rush, when corners were being cut at unprecedented rates. You’ve never experienced a development environment where quality was genuinely prioritised, where technical debt was proactively managed, where 75% project failure wasn’t expected.

You might think this is normal.

It’s not. In the 1990s and 2000s, whilst projects certainly failed and technical debt existed, there was a prevailing culture that quality mattered. Teams pushed back on unreasonable deadlines. Architects had authority to insist on sound technical decisions. “Technical debt” was something you reluctantly accumulated and felt obligated to address, not something you casually accepted as inevitable.

The shift has been gradual enough that it’s nearly invisible year-to-year. But viewed across decades, it’s stark. We’ve normalised dysfunction.

This is why long-term perspective matters. Trends that seem acceptable in isolation become alarming in context. A developer who’s experienced 15 different organisations over 30 years can see patterns that someone at their first or second company cannot. The boiling frog doesn’t notice the temperature rising. Those of us who remember when the water was cooler do.

The CrowdStrike Crystallisation

On 19 July 2024, a single CrowdStrike update crashed 8.5 million Windows machines globally, grounding flights, disabling emergency services, and causing an estimated $10 billion in damage. The root cause? A missing array bounds check—Computer Science 101 error handling that nobody implemented (Trendy Tech Tribe, 2025).

This wasn’t sophisticated. This was basic software craftsmanship that failed at every level of the deployment pipeline.

Three months later, in October 2024 earnings calls, did executives mention this as a wake-up call about software quality? Did they announce new quality initiatives?

No. They continued celebrating AI productivity gains whilst CTOs privately listed technical debt as their #1 concern.

What struck me most about the CrowdStrike incident wasn’t that it happened—any system can have bugs. What struck me was the industry’s reaction: brief panic, followed by… nothing. No industry-wide soul-searching. No renewed focus on basic software quality. Just a continuation of the same practices that led to it.

Twenty years ago, an incident of this magnitude would have triggered genuine introspection. Now? It’s Tuesday.

That normalisation of failure is what long-term perspective reveals. It’s not that we’ve stopped noticing problems—it’s that we’ve stopped being concerned about them.

What This Reveals About Executive Leadership

After 50 years in this industry, I can state with confidence: we are witnessing a systematic failure of executive integrity.

Executives aren’t ignorant. They have the data. They commission the surveys. They attend the conferences where CTOs present their concerns. They know that:

  • 91% of CTOs cite technical debt as the biggest challenge
  • 75% of projects are expected to fail
  • 69% of developers lose significant time to inefficiencies
  • Only 39% of projects meet success criteria
  • The recommended 15–20% investment in technical debt management yields better long-term returns than crisis spending

Yet they choose:

  1. Not to allocate recommended budgets for technical debt management
  2. Not to make quality a strategic priority despite CTOs’ and developers’ concerns
  3. Not to mention these challenges in public communications to shareholders
  4. To celebrate AI productivity gains whilst developers report record inefficiency
  5. To focus on the next hype cycle (AI) rather than address fundamental problems

This isn’t a failure of knowledge. It looks to me like a failure of courage and integrity. A failure of the very concept of leadership.

Why Executives Choose Inaction

The calculus is simple and cynical:

Short-term wins are rewarded. Announcing AI adoption, digital transformation, and cost cutting drives stock prices up. Announcing a 15% budget allocation to technical debt management does not.

The crisis is slow. Technical debt doesn’t cause immediate, visible collapse. It degrades gradually. By the time it causes catastrophic failure, the executive who created it has often moved on. The average tenure of a Fortune 500 CEO is less than 8 years (and median in 2022 was 4.8 years). Why invest in quality improvements that won’t pay dividends until you’re long gone?

Metrics are gamed. Report AI productivity gains whilst ignoring developer efficiency losses. Celebrate revenue growth whilst hiding project failure rates. Show cost optimisation whilst deferring necessary maintenance.

Accountability is absent. No executive has faced consequences for the $1.52 trillion in technical debt. No CEO has been fired for the 75% project failure rate. No board has demanded answers for why only 39% of projects meet success criteria.

Historical memory has faded. Here’s where long-term perspective becomes crucial: many current executives have never worked in an environment where quality was genuinely prioritised. They entered leadership during the “move fast and break things” era. They’ve optimised their entire careers for velocity over sustainability. They literally don’t know what they’re missing. (See also: the Software Crisis – since 1968)

The system rewards those who optimise for optics over outcomes—and increasingly, it’s selecting for leaders who’ve never experienced anything different.

The Cost of This Failure

Whilst executives optimise for quarterly earnings, the real costs accumulate:

Financial: The US alone spends $2.41 trillion annually on poor software quality, with $1.52 trillion in technical debt (Consortium for Information & Software Quality, 2022). By waiting for crisis instead of investing preventively, companies spend double the recommended amount.

Human: 83% of developers experience burnout (Haystack Analytics, 2021). For the first time ever, senior developers report lower job satisfaction than juniors (Stack Overflow, 2024). The industry is haemorrhaging experienced talent—the very people who remember when things were different and might push for change.

Innovation: With 30% of IT budgets consumed by technical debt management and 20% of developer time lost to inefficiencies, there’s little capacity left for actual innovation.

Security: Software supply chain failures increased 650% between 2020 and 2021 (Consortium for Information & Software Quality, 2022). Cybercrime losses continue accelerating. Each shortcut creates new vulnerabilities.

Trust: When executives celebrate productivity gains whilst developers report record inefficiency, when they commission CTO surveys showing 91% cite technical debt as their top concern yet never mention it to shareholders, they erode trust throughout the organisation.

Institutional Knowledge: Perhaps most insidiously, as experienced developers get sick of the eternal indifference and leave, we’re losing the people who remember that it didn’t use to be this way. Each generation of new developers enters an environment slightly worse than the one before, with no reference point for what’s been lost.

The Case for #NoLeadership

Here’s what five decades in this industry has taught me: waiting for executive leadership to fix this crisis is futile. They know what needs to be done. They choose not to do it.

Perhaps it’s time we stop waiting for them.

#NoLeadership isn’t a complaint—it’s a philosophy. It’s the recognition that real change in software quality and productivity won’t come from boardrooms optimising for quarterly earnings. It will come from engineers, developers, and teams who decide to take ownership themselves.

But there’s a challenge here, especially for those without long-term perspective: if you’ve never experienced a development environment where quality was prioritised, how do you know what to fight for? If you entered the industry in the past 5-10 years, you might genuinely believe that constant firefighting, accumulating technical debt, and 75% project failure rates are just “how software works.”

They’re not. And #NoLeadership starts with understanding that the current state isn’t inevitable—it’s a choice we’re collectively making. A choice we can unmake.

Consider what happens when we embrace #NoLeadership:

Teams set their own quality standards. Don’t wait for executives to prioritise quality. Build it into your definition of done. Make technical debt visible on every sprint board. Refuse to call something “done” that you wouldn’t be proud to support.

Engineers allocate their own time. If leadership won’t dedicate 15% of budget to technical debt, developers can dedicate 15% of their sprint capacity. One day per sprint. Non-negotiable. Not asking permission—just doing it.

Quality becomes a team responsibility. Stop escalating quality decisions upward. If your team knows a codebase needs refactoring, refactor it. If tests are missing, write them. If documentation is lacking, create it. Own your craft.

Transparency replaces theatre. Track real metrics: hours lost to technical debt, actual bug rates, time spent on rework, developer satisfaction. Make them visible. Not to shame anyone, but to make the invisible visible.

Peers hold each other accountable. Code reviews become about long-term maintainability, not just immediate functionality. Pull requests that create obvious technical debt get challenged. Not by managers—by peers who know they’ll inherit the mess.

We share stories of how it used to be—and could be again. Those of us with long-term perspective have a responsibility to tell younger developers that the current dysfunction isn’t normal. To share what sustainable software development looks like. To paint a picture of what they’re entitled to demand.

This isn’t anarchism. It’s professionalism. It’s recognising that waiting for executives to prioritise what they’ve demonstrated they won’t prioritise is a losing strategy.

What #NoLeadership Looks Like in Practice

I’ve seen pockets of this emerging organically:

The team that implemented “Tech Debt Tuesdays”—dedicating every Tuesday to addressing technical debt, regardless of what management priorities were set. Their velocity appeared to drop 20%. Their actual productivity—measured in sustainable output and reduced rework—increased significantly.

The engineers who created a “Technical Debt Registry”—documenting every shortcut, every deferred refactoring, every known issue. Made it public within the organisation. No permission asked. Just transparency about what was really happening.

The developer who started tracking “Time to Real Done”—measuring not when a feature shipped, but when it was actually stable, tested, documented, and maintainable. Shared the data widely. Made it impossible to ignore the gap between “shipped” and “done.”

The teams adopting “Two-Track Development”—running parallel tracks for new features and quality improvement. Not asking whether they could dedicate time to quality. Just doing it as part of their professional responsibility.

The teams embracing the Antimatter Principle—attending to folks’ needs as a matter of course.

The senior developers who mentor juniors on what sustainable development looks like—not by lamenting “the old days,” but by teaching practices and standards that newer developers never learned because they entered an industry already in crisis.

These aren’t acts of rebellion. They’re acts of practitioners’ integrity. They’re what happens when craftspeople decide that waiting for permission to do good work is itself unprofessional.

And critically, they’re how we preserve and transmit knowledge of what sustainable software development looks like—creating reference points for those who’ve never experienced it.

The Limits and Risks

Let me be clear: #NoLeadership isn’t a panacea, and it comes with real constraints.

You can’t allocate budget you don’t control. Teams can manage their own time, but they can’t approve capital expenditure for infrastructure improvements or hire additional staff.

You may face consequences. Some organisations will punish teams who prioritise quality over velocity metrics, even when that quality improves long-term outcomes. Career risks are real.

Coordination remains necessary. #NoLeadership works for team-level quality decisions. System-wide architectural changes still require coordination that often needs executive sponsorship.

Not everyone will join you. Some teams will continue optimising for the metrics management rewards. You may end up maintaining higher quality in isolation whilst surrounded by deteriorating systems.

Without historical context, it’s hard to know what’s worth fighting for. If you’ve never seen sustainable development practices in action, how do you know which battles matter? This is where mentorship and knowledge sharing become crucial.

These limitations matter. #NoLeadership can’t replace executive action—it can only compensate for executive inaction within the sphere teams actually control.

But here’s what it can do: it can prevent your team’s work from contributing to the crisis. It can create islands of quality in seas of technical debt. It can demonstrate that another way is possible. And it can preserve your own professional integrity whilst the industry around you optimises for quarterly earnings.

Most importantly, it can create reference points. When a junior developer experiences your team’s practices—where quality matters, where technical debt is managed, where 75% failure isn’t expected—they learn what’s possible. They gain perspective that might take decades to acquire otherwise. They become carriers of institutional knowledge about what sustainable software development looks like.

That’s how cultures change: one team at a time, creating examples that others can point to and say, “That’s what we might be doing.”

A Challenge to the Industry

To executives reading this: the data is unambiguous. You know what needs to be done. The question is whether you’ll do it, or whether your teams will be forced to work around your inaction.

To CTOs and technology leaders: if 91% of you cite technical debt as your biggest challenge, why doesn’t it appear in executive priorities? Stop keeping your concerns private. Make them public. Make them impossible to ignore. Risk it.

To senior developers and engineers with long-term perspective: You have a special responsibility. Share your experience. Mentor those who’ve never known anything but crisis mode. Paint a picture of what sustainable development looks like. Your historical perspective is valuable—use it to help others understand that the current state isn’t inevitable.

To developers and engineers: you don’t need permission to do good work. You need the courage to prioritise it even when incentives push you toward shortcuts. #NoLeadership a.k.a. #Fellowship means taking ownership of quality within your sphere of control.

To those new to the industry: If you’ve entered software development in the past 5-10 years, I invite you to consider something crucial: the environment you’re in—the constant firefighting, the technical debt, the 75% failure rates—this isn’t “just how software development works.” It’s how software development has deteriorated. Seek out the older developers, the ones who’ve been doing this for decades. Ask them how things used to be. Learn what you’re entitled to demand.

To teams: you can start today. Dedicate 15% of your sprint to quality. Make technical debt visible. Track real metrics. Hold each other accountable. Don’t wait for executive prioritisation that isn’t coming.

The Path Forward

After 50 years, I’ve learned this: sustainable software quality requires both executive commitment AND professional autonomy. Ideally, we’d have both.

But if we must choose between waiting for executive action that isn’t coming and taking professional responsibility for the work we control, I choose the latter.

#NoLeadership is about recognising when waiting for executive direction means accepting executive dysfunction. It’s about developer communities deciding that their craft matters more than their executives’ quarterly earnings optimisation.

The data shows executives know what needs to be done. They choose not to do it. Perhaps it’s time we stop waiting for them to change their minds and start changing what we can control.

Every sprint in which you dedicate time to quality is a choice. Every pull request you review for maintainability is a choice. Every piece of technical debt you document is a choice. Every standard you refuse to compromise is a choice. Every story you share about how development used to be—and could be again—is a choice.

Make better choices. Don’t wait for permission.

The industry won’t be fixed by executive leadership that’s demonstrated it won’t lead on quality. It will be fixed by teams who decide that professional integrity matters more than executive approval. By experienced developers who share their perspective with those who’ve never known anything different. By practitioners who refuse to accept that the current dysfunction is inevitable.

That’s #NoLeadership. Not a lament—a commitment to integrity.

And for those of us with long-term perspective, it’s also a responsibility: to notice the trends others can’t see, to remember what’s been lost, and to fight to restore what made this profession worth pursuing in the first place.


Are you practising #NoLeadership in your work? What quality standards have you set for yourself regardless of executive priorities? And if you’ve been in the industry long enough to see the trends—what changes have you noticed? Share your experiences in the comments.

Further Reading

Ali, J. (2024, June 4). 268% higher failure rates for Agile software projects, study finds. Engprax. https://www.engprax.com/post/268-higher-failure-rates-for-agile-software-projects-study-finds/

Atlassian. (2024, July 15). New Atlassian research on developer experience highlights a major disconnect between developers and leaders. Work Life by Atlassian. https://www.atlassian.com/blog/developer/developer-experience-report-2024

Beta Breakers. (2024). Software survival in 2024: 2023 statistics and quality assurance. https://www.betabreakers.com/blog/software-survival-in-2024-understanding-2023-project-failure-statistics-and-the-role-of-quality-assurance/

Clouded Judgement. (2024, November 1). Amazon, Google, Microsoft & Meta on AI and CapEx. https://cloudedjudgement.substack.com/p/clouded-judgement-11124-amazon-google

Consortium for Information & Software Quality. (2022, December 6). Software quality issues in the U.S. cost an estimated $2.41 trillion in 2022. Synopsys News. https://news.synopsys.com/2022-12-06-Software-Quality-Issues-in-the-U-S-Cost-an-Estimated-2-41-Trillion-in-2022

Cortex. (2024). The 2024 state of developer productivity. https://www.cortex.io/report/the-2024-state-of-developer-productivity

Deloitte. (2024). Tech trends 2024: Core workout—From technical debt to technical wellness. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2024/tech-trends-core-it-modernization-needed-for-tech-wellness.html

Evanta. (2024). Top 3 priorities for CIOs in 2024: 2024 Leadership Perspective Survey. https://www.evanta.com/resources/cio/survey-report/top-3-priorities-for-cios-in-2024

Haystack Analytics. (2021, July 12). 83% of developers suffer from burnout, Haystack Analytics study finds. https://www.usehaystack.io/blog/83-of-developers-suffer-from-burnout-haystack-analytics-study-finds

InfoWorld. (2024, July 15). Developer productivity poorly understood, report says. https://www.infoworld.com/article/2520803/developer-productivity-poorly-understood-report-says.html

McKinsey & Company. (2023, April 25). Breaking technical debt’s vicious cycle to modernize your business. McKinsey Digital. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/breaking-technical-debts-vicious-cycle-to-modernize-your-business

Microsoft. (2023, October 25). Microsoft fiscal year 2024 first quarter earnings conference call. Microsoft Investor Relations. https://www.microsoft.com/en-us/investor/events/fy-2024/earnings-fy-2024-q1

Oliver Wyman. (2024, July). 5 actions to reduce technical debt in businesses. https://www.oliverwyman.com/our-expertise/insights/2024/jul/reducing-technical-debt.html

Protiviti. (2024). Technical debt remains a major burden. https://www.protiviti.com/us-en/global-technology-executive-survey-tech-debt-major-burden

ReadyWorks. (2023). 2023 CIO priorities: Overcoming challenges and making progress. https://www.readyworks.com/blog/2023-cio-priorities-overcoming-challenges-and-making-progress

Stack Overflow. (2024). Professional developers: 2024 Stack Overflow Developer Survey. https://survey.stackoverflow.co/2024/professional-developers

STX Next. (2023, November 14). STX Next report highlights the challenge of technical debt. Technology Magazine. https://technologymagazine.com/articles/stx-next-report-highlights-the-challenge-of-technical-debt

Techotlist. (2024). Tech burnout 2025: Digital overload. https://techotlist.com/blogs/programming-languages-and-development-trends/tech-burnout-2025-digital-overload

Trendy Tech Tribe. (2025, December 15). Why software is getting worse: The 2025 quality crisis. https://trendytechtribe.com/tech/software-quality-crisis-2025

People are NOT our greatest asset; it’s the relationships BETWEEN people that is the greatest asset

We’ve all heard it countless times. It’s printed on office walls, declared in annual reports, and repeated in town halls: ‘Our people are our greatest asset.’ It’s meant to be inspiring, a testament to an organisation’s commitment to its workforce. But this well-intentioned phrase is fundamentally missing the point.

The truth is otherwise – and more much powerful: People are NOT our greatest asset; it’s the relationships BETWEEN people that is the greatest asset.

The Limitations of Individual Talent

Let’s be clear: talented individuals matter hardly a jot. Skills, expertise, creativity, and dedication all come a distant second to relationships. Here’s the uncomfortable reality: you can assemble a room full of the most brilliant, capable people in your industry and still end up with a dysfunctional organisation that fails to deliver results.

We’ve all seen it. The team of stars that never quite gels. The merger of two successful companies that somehow produces mediocrity. The leadership team full of impressive résumés that can’t seem to make a decision. Individual excellence, in isolation, guarantees nothing.

Why? Because organisations don’t function through the sum of individual capabilities. They function through the quality of connections, interactions, and collaborative exchanges between people.

From Vague Platitudes to Clear Action

Here’s where the shift from the clueless platitude of ‘people are our greatest asset’ to ‘relationships are our greatest asset’ becomes transformative: it changes everything about what you actually do.

When organisations declare they’re investing in people, what does that mean in practice? Better salaries? More training courses? Improved benefits? Free fruit in the kitchen? These aren’t bad things, but they’re scattergun approaches that hope something sticks. The focus remains frustratingly vague, and leaders are left wondering: ‘Are we doing enough? Are we doing the right things? Why is the needle not moving?’

In contrast, when you recognise that relationships are your greatest asset, the path forward becomes remarkably clear. Suddenly, you have specific, observable, measurable phenomena to work with. You can ask concrete questions: Who talks to whom? Where do silos exist? Which teams collaborate effectively and which don’t? Who are the connectors? Where are the bottlenecks? Who’s isolated? Which cross-functional relationships are missing?

Focus on people confuses action because:

  • Individual development is endless and unbound—there’s always another skill to learn, another qualification to pursue, another workshop to attend
  • It’s difficult to know when you’ve invested ‘enough’ in any given person
  • The return on investment is opaque—did that training course actually improve anything?
  • It can inadvertently promote individualism and internal competition rather than collaboration
  • It defaults to generic solutions (send everyone on the same leadership course) that ignore context

Focus on relationships clarifies action because:

  • You can map the current state—literally draw it out and see where connections are strong or weak
  • You can identify specific gaps—’Marketing and product development never speak’ is actionable; ‘we need better people’ is not
  • You can design targeted interventions—’Let’s create a weekly cross-functional stand-up’ beats ‘let’s improve communication skills’
  • You can measure change—relationship density, collaboration patterns, and information flow are all observable
  • You can see immediate impact—when two previously siloed teams start working together, results follow quickly

Consider the difference in leadership conversations:

People-focused approach: ‘We need to invest in our people. Let’s increase the training budget and improve our benefits package.’

Relationship-focused approach: ‘Our network analysis shows that the engineering team is completely disconnected from customer support. No wonder our products miss the mark. Let’s create a monthly session where support shares customer insights directly with engineering, and let’s pair each engineer with a support person for a day each quarter.’

The first is well-meaning but vague. The second is specific, actionable, and directly addresses a visible problem in the organisation’s relationship infrastructure.

This clarity extends to every level. Want to improve innovation? Don’t just ‘invest in creative people’—look at whether people from different disciplines actually interact. Want to speed up decision-making? Don’t just ’empower people’—examine whether decision-makers have strong relationships with those who have the information they need. Want to improve employee retention? Don’t just ‘value people’—investigate whether individuals have meaningful connections with colleagues or feel isolated.

The beauty of focusing on relationships is that it makes the invisible visible. You’re no longer operating on faith that your ‘investment in people’ will somehow pay dividends. You’re working with the actual architecture of how your organisation functions—and you can see, adjust, and improve it in real time.

What Makes Relationships the Real Asset

When we shift our focus from people to the relationships between them, everything changes. Here’s what organisational relationships actually create:

Trust and psychological safety. Relationships built on trust allow people to take risks, admit mistakes, ask for help, and challenge ideas without fear. This is where innovation happens. A brilliant individual who doesn’t trust their colleagues will hoard information and play it safe. That same person, embedded in trusting relationships, becomes exponentially more valuable.

Knowledge flow and collective intelligence. The most valuable knowledge in any organisation isn’t stored in individual heads—it’s created in the spaces between them. When an engineer talks to a customer service representative, when a junior employee questions a senior leader, when cross-functional teams actually collaborate rather than just coordinate, new insights emerge that no individual could have generated alone.

Resilience and adaptability. Strong relationship networks create organisational resilience. When someone leaves, strong relationships mean their knowledge and connections aren’t lost entirely. When crises hit, it’s the strength of relationships that determines whether teams pull together or fall apart. You can’t download resilience from a talented individual’s brain—it exists in the fabric of how people relate to each other.

Speed and efficiency. Consider two scenarios: In one, you need information from another department and must navigate formal channels, wait for responses, and overcome territorial barriers. In another, you can call someone you’ve built a relationship with and get what you need in a five-minute conversation. Strong relationships are the organisation’s nervous system, allowing information and decisions to flow at the speed of trust rather than the speed of bureaucracy.

Meaning and engagement. People don’t just work for salaries or even for purpose statements on walls. They stay and give their best work when they’re part of something larger than themselves—when they feel connected to others in meaningful ways. Relationships are what transform a workplace from a collection of individuals into a community.

The Practical Implications

If relationships are truly our greatest asset, how should this change what organisations do?

Design for connection, not just individual productivity. Stop optimising exclusively for individual efficiency. Steve Jobs understood this when he designed Pixar’s headquarters in the late 1990s. Rather than separating animators, computer scientists, and executives into different buildings, he brought everyone under one roof with a massive atrium at its centre. He deliberately placed the post boxes, cafeteria, meeting rooms, and even the toilets in the central atrium, forcing people to leave their offices and cross paths with colleagues they might not otherwise encounter. As Brad Bird, director of The Incredibles and Ratatouille, observed: ‘The atrium initially might seem like a waste of space…But Steve realised that when people run into each other, when they make eye contact, things happen.’ John Lasseter, Pixar’s chief creative officer, confirmed the impact: ‘Steve’s theory worked from day one…I’ve never seen a building that promoted collaboration and creativity as well as this one.’

Consider: Does your office layout foster serendipitous encounters or isolate people? Do your meeting structures allow for relationship-building or just agenda items? Does your remote work policy account for the relationship-building that happens in informal moments?

Measure what matters. We measure individual performance obsessively. But when was the last time your organisation assessed the quality of relationships across teams? Network analysis can reveal who’s connected, who’s isolated, and where silos exist.

Companies like Four Groups have pioneered methods to make this practical and actionable. Their 4G approach helps organisations understand and predict how relationships and culture impact performance, moving beyond simple org charts to visualize actual working relationships through tools like their Visual Team Builder. Crucially, they’ve demonstrated how to quantify relationship quality and link it directly to financial metrics and KPIs—transforming what was once seen as a “soft” intangible into measurable business intelligence. When a UK-based organisation can predict how a proposed reorganisation will impact collaboration patterns before implementing it, or identify which relationship breakdowns are costing them in delivery delays, relationships shift from being merely important to being strategically manageable.

Employee engagement surveys might ask not just ‘do you feel valued?’ but ‘do you have strong working relationships with colleagues across the organisation?’ (See also: Gallup’s book ‘First Break All The Rules‘).

Invest in relationship infrastructure. Training programmes shouldn’t focus solely on building individual skills. Invest in cohort-based learning that builds cross-functional relationships. Create mechanisms for people to collaborate across boundaries—innovation labs, rotation programmes, cross-functional projects with real stakes. Budget time and resources for team-building that actually builds relationships, not just awkward icebreakers.

Hire and promote for relational capacity. Yes, skills and experience matter. But also evaluate: Can this person build trust? Do they create bridges or walls? Are they generous with knowledge and credit? Can they navigate disagreement constructively? Patrick Lencioni identifies three essential virtues that make someone an ideal team player: they must be humble (putting the team above themselves), hungry (self-motivated and driven to contribute), and people smart (emotionally intelligent in their interactions with others). Individuals who possess all three virtues don’t just perform well—they elevate the performance of everyone around them through the quality of relationships they build. The most talented individual who damages relationships is ultimately a serious net negative to the organisation.

Protect relationships during transitions. Reorganisations, redundancies, and rapid growth all strain relationship networks. Before making major changes, map the critical relationships that make work flow. Ask: How will this change impact collaboration patterns? What relationships are at risk? How can we preserve and rebuild connection?

Give relationships time. Trust and strong working relationships can’t be rushed. Be wary of constant reorganisations, aggressive hiring sprees without proper integration, or cultures that celebrate constant churn. Relationship capital takes time to build and can be destroyed quickly.

The Paradox at the Heart of Organisations

Here’s the beautiful paradox: when you focus on relationships rather than just individuals, people actually become more valuable. Not because they’ve changed, but because they’re now embedded in a network that amplifies their capabilities, compensates for their weaknesses, and multiplies their impact.

A talented designer in isolation is somewhat valuable. That same designer, with strong relationships with engineers who understand her vision, product managers who seek her input early, and junior designers she mentors—that designer’s impact extends far beyond what she could create alone. This is where Lencioni’s virtues of being humble, hungry, and people smart become force multipliers: humility allows for genuine collaboration without ego barriers, hunger drives the relationship-building effort required to make things happen, and being people smart ensures those connections are productive rather than destructive.

The organisation that declares ‘our people are our greatest asset’ is making a promise to invest in individuals. That’s good, but flawed. The organisation that recognises relationships as its greatest asset makes a different commitment: to cultivate the conditions where people can connect, collaborate, and create value together in ways that transcend and far exceed individual contribution.

Moving Forward

The next time you’re tempted to simply parrot ‘our people are our greatest asset’, pause and go deeper. Ask yourself: Are we actually creating an environment where relationships can flourish? Are we measuring, protecting, and investing in the connective tissue that makes this organisation more than a collection of talented individuals?

Because at the end of the day, individual talent can walk out the door. But strong organisational relationships—the patterns of trust, collaboration, and collective capability that exist between people—that’s what’s truly irreplaceable. That’s the asset worth protecting and developing above all others.

Attending to people and their needs is undoubtedly important. But it’s what happens between people that makes the real difference.


Further Reading

Baker, W. E. (2000). Achieving success through social capital: Tapping the hidden resources in your personal and business networks. Jossey-Bass.

Coyle, D. (2018). The culture code: The secrets of highly successful groups. Bantam Books.

Cross, R. L., & Parker, A. (2004). The hidden power of social networks: Understanding how work really gets done in organizations. Harvard Business School Press.

Isaacson, W. (2011). Steve Jobs. Simon & Schuster.

Lencioni, P. M. (2002). The five dysfunctions of a team: A leadership fable. Jossey-Bass.

Lencioni, P. M. (2016). The ideal team player: How to recognize and cultivate the three essential virtues. Jossey-Bass.

Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.

When the Guardians Guard Themselves: Parallels Between UK Policing and Software Development

I’ve recently become interested in UK policing (and its widespread failures). In particular, many YouTube videos on Auditing (of police and security staff behaviours). Also known as e.g. First Amendment Auditing in the USA. I’m interested not least because of the parallels I’ve come to see between poor UK policing and the parlous performance of the software development industry.

We don’t often think of police officers and software developers in the same breath. One wears a uniform and carries the power of the state; the other wears hoodies and carries the power to shape our digital lives. Yet scratch beneath the surface, and you’ll find uncomfortable parallels in how both professions can drift from their stated purpose.

Caveat: In those instances where police are well-informed and competent, I commend them for their professionalism and contribution, and thank them sincerely for their service. Sadly this seems rarer than the ill-informed and incompetent.

The Parallels

UK Policing Software Development
Claimed Expertise Without Competence
Officers don’t know the laws they enforce Developers don’t understand security, privacy law, or accessibility requirements
Can’t de-escalate. Contaminate crime scenes, lose evidence Write insecure code, break production, create unmaintainable systems
Ignorant of social dynamics in situations they police Ignorant of social dynamics in systems they build, and the way they build them
Rely on intimidation rather than skill Rely on cargo-culting from e.g. Stack Overflow rather than understanding
Lack of Training and Education
Minimal training, no ongoing professional development Brief bootcamps or degrees followed by on-the-job fumbling
Resist learning about law, psychology, de-escalation Resist learning beyond their immediate tech stack
Promote based on time served, not demonstrated competence Promote based on political skill, not technical excellence
‘Learn on the job’ means repeating the same mistakes ‘Self-taught’ often means learning bad practices
Management Through Psychological Violence
Fear: Implicit threat of arrest, detention, violence Fear: Constant anxiety about firing, PIPs, being ‘managed out’
Obligation: ‘You’re required to comply with us’ Obligation: Unreasonable demands framed as professional duty
Guilt: ‘If you’ve done nothing wrong, why won’t you cooperate?’ Guilt: ‘You’re letting the team down’ for having boundaries
Shame: Public stop and search, handcuffs in front of neighbours Shame: Public callouts in standups, humiliating code reviews
Abandonment of Service
Supposed to serve public, actually control, belittle and intimidate Supposed to support teams, actually extract, undermine and pressure
Citizens avoid police rather than seeking help Developers manage managers rather than being helped by them
‘Protect and serve’ is mythology ‘We remove blockers’ is mythology
Leadership Vacuum
Chief Constables ‘shocked’ by scandals everyone saw coming CTOs ‘shocked’ by toxic cultures flagged in every exit interview
Good at career advancement, bad at actual leadership Good at TED talks and earnings calls, bad at building teams and thus products
Commission reports that gather dust Commission surveys they ignore
Asleep at wheel for responsibilities, awake for their careers Talk about ‘psychological safety’ while heedlessly maintaining fear cultures
Accountability Vacuum
Complaints process investigates itself, finds no wrongdoing HR protects company, not employees; toxic managers stay employed
Officers face minimal consequences for misconduct Managers face no consequences for destroying team morale
System protects its own Institution may settle; individuals remain untouchable
Selectively Applied Rules
Police delete data, access systems improperly, investigate themselves favourably, breach GDPR Management ignores estimates, violates work-life balance, exempts itself from processes
Axt as if rules for civilians don’t apply to officers Rules for developers don’t apply to managers
Ego and Authority
Cannot handle challenges to authority; uniform becomes argument Cannot handle challenges to decisions; title becomes argument
Question an officer → arrested for obstruction Question a manager → labelled insubordinate or ‘not a team player’
Professional identity wrapped up in being right Cannot admit error without existential threat to self-image
Insulation From Consequences
Officer never sees trauma of wrongful arrest Manager never see the depression/burnout they cause
Victims carry the damage; officer continues career Developers carry the damage; manager moves to next role
No feedback loop connecting actions to harm Promoted away before consequences manifest
Institutional Self-Service
Protects reputation over serving public Maintains management hierarchy over building good software
Scandals prompt PR, not reform Problems prompt ‘initiatives’ that never materialise
People’s rights seen as obstacles Developer wellbeing seen as obstacle
Systematic Trampling of Rights
Stop and search, false arrests, violations of liberty, assaults Privacy violations, dark patterns, algorithmic discrimination
‘Necessary for public safety’ justifies rights violations ‘Necessary for business model’ justifies rights violations
Public has no recourse; police investigate themselves Users have no recourse; arbitration clauses, no transparency
The Victims Have No Choice
Can’t opt out of policing, can’t fire police Can’t easily leave job (mortgage, work visa, market conditions)
Power asymmetry enables coercive model Power asymmetry enables coercive model
Compliance or escalating consequences Compliance or unemployment

The Expertise Paradox

UK policing has faced persistent criticism over officers who seem remarkably uninformed about the very laws they are charged with enforcing. We’ve seen arrests for ‘non-crime hate incidents’ that chill free speech, wrongful detentions based on misunderstandings of legislation, and officers confidently citing powers they simply don’t possess.

But the knowledge gap runs deeper than just legal ignorance. Police often demonstrate stunning obliviousness to the social dynamics of the situations they wade into. They treat mental health crises as compliance problems. They escalate situations that required de-escalation. They fail to recognise how their mere presence, their tone, their body language transforms a situation. They reduce complex human problems—domestic disputes, neighbourhood conflicts, community tensions—to simple matters of law enforcement, ignoring the interpersonal relationships, power dynamics, and social contexts that actually drive these situations. And blithely ignore that they’re there to serve the public.

Software development displays the same dual failure. Developers ship features without understanding privacy law, accessibility requirements, or basic security principles. But they’re equally ignorant of the social dimensions of what and how they build—how systems affect human behaviour, relationships, and social structures. And how their toxic behavious influence the social dynamic of society at large.

But here’s the thing: they’re often not even competent at the narrow technical domain in which they claim expertise.

Police officers who don’t know the law also frequently don’t know proper enforcement tactics. They can’t de-escalate. They don’t know how to interview witnesses properly. They contaminate crime scenes. They lose evidence. They can’t write coherent reports. They misuse the powers they do have. They rely on intimidation rather than skill. The incompetence isn’t just about the legal framework—it extends to the basic craft of policing itself.

Watch body camera footage of UK police interactions and you’ll see officers who:

  • Don’t know when they have the power to search and when they don’t
  • Can’t articulate the legal basis for their actions when challenged
  • Escalate situations through their own ineptitude and ego
  • Make unlawful arrests because they’re ignorant of the threshold for arrest
  • Confuse personal offence with criminal offence
  • Apply powers they don’t understand and/or don’t have to situations they’ve misread

This isn’t ‘good at tactics but bad at law’ or ‘good at law but bad at social dynamics’. It’s incompetence compounded by incompetence, wrapped in the authority of the uniform.

Software developers display the same layered incompetence:

They write insecure code riddled with vulnerabilities. They hardcode credentials in version control. They build systems that fall over under minimal load. They create race conditions. They ignore error handling. They produce spaghetti code that no one, including themselves, can maintain six months later. They don’t understand the frameworks they use. They cargo-cult solutions from e.g. Stack Overflow without comprehending what the code does. They break production regularly because they never learnt proper testing or deployment practices.

This isn’t ‘good at coding but bad at privacy law’ or ‘good at algorithms but bad at social dynamics’. They’re not good at the core technical skills either. They’ve just learnt enough to be dangerous, enough to be employed, but no where near enough to be regarded as competent.

The ‘expertise paradox’ is that there’s no expertise at all—just hubris, institutional protection, and the complexity of the domain preventing outsiders from easily recognising the incompetence.

In both cases, we see professionals granted significant power whilst being dangerously incompetent in their supposed area of expertise and dangerously ignorant of the broader context in which that alleged expertise must operate. And no one within these systems seems to care.

The Training Vacuum: Learning Nothing and Proud of It

What’s striking about both ‘professions’ is not just the incompetence, but the systematic failure to address it through training and education—and often, the active resistance to learning.

UK Policing’s Training Failures

Police training in the UK is notoriously brief and inadequate. New officers receive initial training that focuses heavily on powers of arrest and use of force, with minimal attention to de-escalation, mental health awareness, or the actual laws they’ll be enforcing. Once on the job, ongoing professional development is sporadic at best.

But worse than the lack of formal training is the cultural resistance to education. Officers who try to learn more about mental health, community relations, or even the finer points of law are often viewed with suspicion by colleagues. ‘Street smarts’ are valued over book learning. Experience (even if it’s years of repeating the same mistakes) is prized over education. The officer who quotes case law or suggests evidence-based practices risks being labelled a ‘college boy’ or not a ‘real cop’.

Promotion is based largely on time served and political navigation, not demonstrated competence or continued learning. There’s no requirement for ongoing education in law, psychology, or social dynamics. An officer can go an entire career without seriously updating their knowledge or skills, just repeating whatever they learnt (or failed to learn) in their initial training.

The result: officers who are confidently incompetent, who’ve internalised bad practices as ‘the way things are done’, and who actively resist new information that might challenge their behaviours and self-image as experienced professionals.

Software Development’s Training Failures

Tech loves to tout its meritocracy and culture of learning, but the reality is quite different. Many developers enter the field through:

  • Bootcamps that teach just enough to get hired but not enough to be at all competent
  • Computer science degrees that focus on theory whilst ignoring practical software engineering
  • Self-teaching that often means copying patterns without understanding them
  • On-the-job learning that frequently means absorbing the bad practices of whoever happens to be senior

Once employed, the expectation is that developers will ‘keep up’ with new technologies on their own time. But what does this look like in practice? Skimming blog posts. Watching conference talks at 2x speed. Trying whatever framework is currently trendy without understanding why. The learning is shallow, scattered, and driven more by CV-building than actual competence development. And rarely if ever budgeted, paid for.

Formal training? Most companies provide minimal budget for it, and even when they do, developers often don’t use it because they’re too busy meeting deadlines to take time for learning. Mentorship? The senior developers are too busy (or too incompetent themselves) to mentor effectively. Code review as education? Often devolves into nitpicking or ritual humiliation rather than genuine knowledge transfer. Aside: In fifty years I’ve never met a single senior developer that understood software development in the round.

And like police officers, many developers actively resist deeper learning:

  • ‘I don’t need to understand how databases work; I just use an ORM’
  • ‘I don’t need to learn security; that’s the security team’s job’
  • ‘I don’t need to understand algorithms; I can just Google it’
  • ‘I don’t need to read documentation; I’ll just try things until something works’
  • ‘I don;t need to understand people, I don’t work with people’

This isn’t framed as ignorance—it’s framed as pragmatism, as ‘shipping’ rather than ‘gold-plating’. The developer who wants to actually understand what they’re doing, who reads academic papers, who studies fundamentals, risks being seen as slow, perfectionistic, or not ‘agile’ enough.

Promotion often has little to do with actual technical competence and everything to do with visibility, political skill, and being friends with the right people. You can be deeply incompetent but still advance if you’re good at talking about your work, taking credit for others’ efforts, and avoiding blame when things go wrong.

The result: swathes of developers who are confidently incompetent, who’ve internalised cargo-culted practices as ‘best practices’, and who actively resist deeper learning that might reveal how much they don’t know.

The Shared Resistance to Growth

Both professions share several toxic attitudes towards learning:

‘Experience is all that matters’: Years in the role are treated as proof of competence, even when those years were spent repeating the same mistakes or stagnating. The experienced incompetent is given more authority than the well-educated newcomer.

‘Real work vs. theory’: Both professions valorise ‘practical’ knowledge over ‘theoretical’ understanding, as if the two were opposed rather than complementary. Police dismiss academic research on effective policing. Developers dismiss computer science and psychology fundamentals. Both claim they’re too busy doing real work to learn how to do it better.

‘If it ain’t broke, don’t fix it’: Except it is broken. Systems are failing, people are being harmed, but because the professional isn’t personally experiencing the consequences, they see no need to improve. The police officer’s methods work fine (for them, sod the public or society at large). The developer’s code works fine (until it doesn’t, but they’ll be at a different company by then).

‘Learning is for juniors’: Once you’ve reached a certain level, both professions treat continued learning as beneath you. You’re supposed to know everything already. Admitting you need to learn something new is admitting you’re not as senior as you claim.

‘Too busy to learn’: Both professions create such dysfunctional, high-pressure environments that there’s never time for learning. Police are too busy responding to calls. Developers are too busy delivering sprints. The system is unintentionally designed to prevent the very learning that might fix the system (POSIWID).

The Institutional Enablement

Both institutions enable, even force, this resistance to learning:

Police forces don’t mandate ongoing education. They don’t assess whether officers actually know the law. They don’t measure competence; they measure activity (arrests, tickets, responses). An incompetent officer who generates numbers is valued over a competent one who doesn’t.

Tech companies don’t mandate ongoing education. They don’t assess whether developers actually understand what they’re doing. They don’t measure quality and needs met; they measure velocity (features shipped, story points completed, commits made). An incompetent developer who ships quickly is valued over a competent one who ships slowly because they’re doing things right.

Both institutions have created cultures where learning is discouraged, ignorance is protected, and incompetence is rewarded as long as it produces the metrics leadership say they care about.

The Compounding Problem

This lack of training and resistance to education compounds every other problem:

  • Incompetent officers remain incompetent, becoming senior incompetent officers who role model and train the next generation in their incompetence
  • Incompetent developers remain incompetent, becoming senior incompetent developers who set technical direction and review others’ code
  • Officers who don’t understand the law continue violating rights, because they never learnt what rights people have, or even the very concept of rights
  • Developers who don’t understand security continue shipping vulnerabilities, because they never learnt what secure code looks like
  • Police who never learnt de-escalation continue escalating situations
  • Developers who never learnt proper system design continue building unmaintainable systems

The expertise paradox isn’t just that there’s no expertise—it’s that both professions have created cultures that actively prevent expertise from developing. They’ve built institutions where ignorance is acceptable, learning is optional, and incompetence can flourish for entire careers as long as it produces compliance and velocity.

You can’t fix incompetence if you won’t acknowledge it exists. And you can’t acknowledge incompetence if your professional identity depends on being the expert. So both professions remain trapped: incompetent practitioners who resist the learning that might make them competent, protected by institutions that value activity over ability and metrics over mastery.

Management Through Violence: Fear, Obligation, Guilt, and Shame

But perhaps the most damning parallel lies in how both institutions actually operate day-to-day. Both policing and software management have abandoned any pretence of service, support, or genuine help. Instead, they’ve embraced management through what can only be called psychological violence: fear, obligation, guilt, and shame.

UK Policing’s Coercive Model

Modern UK policing has largely abandoned community policing, problem-solving, and service. Instead, it operates through intimidation and coercion.

Fear: The implicit (and sometimes explicit) threat of violence underlies every police interaction. Comply or face escalation. Cooperation isn’t requested; compliance is demanded. The message is clear: we have the power to arrest you, detain you, search you, and there’s very little you can do about it. Even when you’ve done nothing wrong, the fear of what police could do shapes the interaction.

Obligation: Police frame their authority as an obligation you owe them. You’re obligated to answer questions. Obligated to provide identification. Obligated to explain yourself. Obligated to submit to searches. The framing is never ‘we’re here to help’, it’s ‘you’re required to comply with us’. Refusal to submit to demands you’re not legally required to comply with is treated as suspicious, obstructive, hostile, or grounds for escalation.

Guilt: Even innocent interactions with police are designed to make you feel you’ve done something wrong. ‘Why are you nervous?’ ‘What are you hiding?’ ‘If you’ve done nothing wrong, why won’t you cooperate?’ The assumption is guilt; you must prove innocence. The interaction itself becomes evidence that you’re probably up to something.

Shame: Stop and search in public. Handcuffs in front of your neighbours. Being questioned loudly where others can hear. The public nature of police actions creates shame by design. It’s a form of social punishment that occurs before any determination of guilt, and continues to affect you even when you’re released without charge.

This isn’t service. This isn’t helping. This is control through coercion, compliance through intimidation. The person being policed isn’t a citizen to be served; they’re a potential threat to be managed, a suspect to be controlled.

Software Management’s Parallel Coercive Model

Look at how most software development teams are actually managed, and you’ll see the exact same dynamics replicated with eerie precision.

Fear: Management by threat is endemic in tech, despite all the talk of ‘psychological safety’ and ‘blameless post-mortems’.

Fear of being fired. Not just the formal ‘performance improvement plan’ (which everyone knows is a documented path to termination), but the constant low-grade anxiety about redundancies, stack-ranking, ‘right-sizing’, or simply being deemed ‘not a culture fit’. Fear permeates daily work.

Fear of speaking up in meetings. Say something wrong and you’ll be judged as not smart enough. Challenge a decision and you’re ‘not a team player’. Ask for clarification and you reveal you don’t understand. So you stay quiet and nod along.

Fear of pushing back on impossible deadlines. Question the estimate and you’re ‘not showing ownership’. Refuse to work weekends and you’re ‘not passionate enough’. Warn that quality will suffer and you’re ‘being negative’. So you say nothing and start working evenings.

Fear of admitting you don’t understand something. The tech industry worships the myth of the 10x developer who intuits everything. Admitting ignorance marks you as lesser. So people spend hours Googling in private rather than asking the senior engineer for ten minutes of their time.

Fear of reporting problems. Found a major security flaw? Better think carefully about whether raising it will make you look bad for not catching it earlier, or make your team look bad for writing it, or make management angry about the delay in shipping. Sometimes it’s safer to say nothing and hope someone else finds it.

Fear of your code being reviewed. Not the constructive fear of healthy peer review, but the dread of ritual humiliation. Will the senior developer use your pull request to demonstrate their superiority? Will your architectural choices be questioned in ways that make you look incompetent? Will this be used against you in performance reviews?

Fear of being ‘managed out’. Once management decides you’re not working out, there’s no real process, no real recourse. They’ll make your life miserable until you quit, or they’ll find a reason to let you go. Even in the UK. And everyone around you will watch it happen and learn the lesson: don’t become a target.

The fear isn’t occasional. It’s ambient. It shapes every decision, every interaction, every email. Just as citizens learn to fear police encounters, developers learn to fear management’s gaze.

Obligation: Just as police frame authority as obligation, tech management frames unreasonable demands as professional duties. The language is careful—they can’t legally compel you—but the social and economic pressure creates the same effect.

You’re obligated to work evenings and weekends when deadlines loom. Deadlines that were set without your input, that were impossible from the start, that ignore your warnings about feasibility. But now they’re deadlines, and failing to meet them reflects poorly on you, not on the manager who set them.

Obligated to be ‘team players‘. Which means accepting dysfunction without complaint. The broken deployment process that wastes hours? Work around it; fixing it isn’t your responsibility. The terrible decisions made by the architect? Implement them anyway; it’s not your place to question. The manager’s pet feature that makes no sense? Build it; they’re the decision-maker.

Obligated to ‘take ownership’. Which means accepting blame for failures whilst credit flows upward to management. The project failed because of fundamental resourcing issues? Doesn’t matter; you owned it, you failed. The system went down because of infrastructure problems you warned about? You should have been more insistent, should have escalated better, should have found a workaround.

Obligated to ‘be passionate’. Which means accepting below-market compensation because ‘you believe in the mission’. Which means volunteering for extra work to ‘show initiative’. Which means pretending that writing CRUD apps for a marketing company is fulfilling your life’s purpose.

Obligated to ‘be culture fits’. Which means conforming to whatever behavioural norms the dominant group has established. The bro culture that makes crude jokes? Laugh along or be excluded. The workaholic culture where presence equals productivity? Stay late even when you’ve finished. The aggressive culture where loudest voice wins? Either get loud or get ignored. The nerfing culture where everyone is busy (literally) nerfing each other all day every day. And bugger the disruption.

Obligated to participate in the charade. Agile ceremonies that accomplish nothing but must be attended. OKRs that everyone knows are fiction but must be written. Performance reviews that bear no relationship to actual work but must be taken seriously. ‘All-hands’ meetings that communicate nothing but attendance is tracked. The obligation isn’t to do productive work; it’s to perform participation in management’s theatrical production.

R.D. Laing’s profound psychological observation perfectly captures the intricate dance of corporate survival:

“They are playing a game. They are playing at not playing a game. If I show them I see they are, I shall break the rules and they will punish me. I must play their game, of not seeing I see the game.”

The framing is never ‘we’re asking for your voluntary extra effort and will compensate and appreciate you accordingly’. It’s ‘this is what’s expected; this is what professionals do; this is what commitment looks like’. The obligation is presumed, not negotiated. And failing to meet these obligations has consequences—subtle, unwritten, but very real.

Guilt: Tech management weaponises guilt with surgical precision.

Missed a deadline? You let the team down. Never mind that the deadline was arbitrary, the requirements kept changing, or you were understaffed. You failed your teammates. They’re working late because of you. They’re stressed because of you. Look at their faces in the standup—that disappointment is your fault.

Need to take holiday? You’re abandoning your teammates during a critical time. Sure, the company offers ‘unlimited holiday’, but taking more than two weeks a year marks you as not sufficiently dedicated. And taking holiday during a sprint? During a launch? During literally any time because there’s always something critical? You’re making everyone else pick up your slack. Don’t you care about the team?

Can’t work weekends? You’re not committed enough. Your teammates are sacrificing their personal time. Are you really okay with being the person who cares less? Who tries less? Who wants it less? What does that say about you?

Pointed out a problem with the project? You’re being negative. You’re not a ‘solutions person’. You’re bringing down morale. Why are you so focused on what’s wrong instead of what’s right? Can’t you see we’re all trying our best here? Your negativity is toxic.

Insisted on doing things properly? You’re slowing everyone down. You’re letting perfect be the enemy of good. You’re not being pragmatic. Your obsession with quality and folks’ needs is blocking the team from shipping. Do you value your principles more than your teammates’ success?

Asked for the resources to do the job right? You’re not being scrappy enough. You’re making excuses. Real engineers make things work with whatever they have. Why can’t you figure it out like everyone else? Are you just not as capable as you claimed to be?

Refused to implement a bad idea? You’re not trusting your manager’s judgement. You’re being insubordinate. You’re letting your ego get in the way. Who are you to question decisions made by people with more experience, more context, more responsibility? Maybe you’re not senior enough for this role after all.

The guilt is constant and multidirectional. Guilty for not working enough. Guilty for complaining about work. Guilty for having boundaries. Guilty for not having more impact. Guilty for being stressed. Guilty for making others stressed. Guilty for not being grateful enough for the opportunity. Guilty for expecting more from the opportunity.

And here’s the mechanism that makes it so effective: the guilt is socially reinforced. It’s not just your manager making you feel guilty—it’s other developers who’ve internalised the same guilt and will judge you for not carrying your share. It’s the colleague who humble-brags about working all weekend. It’s the teammate who sighs heavily when you leave at 5pm. It’s the developer who casually mentions they haven’t taken holiday in two years. They’re not just victims of the system; they’ve become enforcers of it.

Shame: Public humiliation is a standard management tool, dressed up in the language of ‘transparency’ and ‘accountability’.

Being called out in stand-ups for missing estimates. Not privately, where it could be handled constructively, but in front of the entire team. ‘So your task was supposed to be done Tuesday, and here we are on Thursday…?’ The implication hangs in the air. Everyone’s eyes on you. Everyone noting that you failed.

Having your code torn apart in reviews that are less about improving quality and more about establishing dominance hierarchies. The senior developer who doesn’t just point out issues but does so with contempt. ‘I don’t understand how anyone could think this was acceptable.’ ‘Did you even test this?’ ‘This is exactly what I warned about in the architecture review, but apparently no one was listening.’ It’s not feedback; it’s ritual degradation, performed publicly to establish who matters and who doesn’t.

Being made an example of when projects fail. The post-mortem that’s supposedly ‘blameless’ but somehow keeps coming back to your decisions. The retrospective where your mistakes get documented in bullet points on a screen everyone can see. The all-hands where leadership explains what went wrong and everyone in the company knows it was your team, your module, your code.

Being compared unfavourably to other developers. ‘Why is it taking you three days when Sarah did something similar in one?’ ‘The other team already shipped this feature.’ ‘I’m not seeing the same velocity from you that I see from the rest of the squad.’ The comparison is the point. You’re not just failing; you’re failing relative to others, and everyone can see it.

Being excluded from important meetings or decisions as visible punishment. You raised concerns about the architecture? Suddenly you’re not invited to the architecture meetings anymore. You pushed back on a deadline? You’re no longer consulted on scheduling. You questioned the product direction? The product meetings happen without you. The exclusion is public—everyone sees you’re no longer in the room—and the message is clear: dissent has consequences.

Having your mistakes become teaching moments. ‘Let me tell you about something that happened recently…’ followed by a thinly-veiled description of your error, presented to the team, the department, sometimes the company as a cautionary tale. Anonymised just enough to be deniable, specific enough that everyone knows exactly who they’re talking about.

Being stack-ranked. Your performance quantified, compared, ordered. Your value to the company expressed as a number or a position on a list. And often, these rankings aren’t confidential—they’re discussed in calibration sessions with multiple managers, shared with skip-levels, sometimes leaked to the team. You’re not just being judged; you’re being publicly graded like a schoolchild, and everyone gets to see whether you’re honours roll or remedial.

Just as public police interactions create social shame designed to punish before any determination of guilt, public management actions in tech create professional shame designed to control behaviour. The punishment isn’t just the consequence; it’s the public nature of it. It’s the fact that your peers watched it happen. That they’ll remember. That it becomes part of your reputation within the company.

And like police interactions, the shame persists beyond the incident. Long after the standup where you were called out, people remember you as ‘the person who missed that deadline’. Long after the code review humiliation, you’re ‘the person who writes bad code’. The shame becomes identity.

The Abdication of Support

What’s missing from both is any genuine commitment to support, service, or help.

Police are supposed to serve the public. The phrase ‘protect and serve’ (USA) is literally part of the mythology. But in practice, when’s the last time you had an interaction with police that felt like service? When did they help you solve a problem, support you through difficulty, or make you feel safer? For most people, especially those in marginalised communities, police interactions are dangers to be avoided, not resources to be utilised.

Police aren’t there to help you. They’re there to manage you, control you, extract compliance from you. And if you have a problem—if you’ve been victimised, if you need help—their response is often to treat you as the problem. Reporting a crime? They’ll question whether you’re telling the truth. Been assaulted? They’ll ask what you were wearing, whether you’d been drinking. And wave you off. Need help with a mental health crisis? They’ll treat it as a threat scenario. The institution has abandoned the service model entirely in favour of the control model.

Managers in tech are supposed to support their teams. Remove blockers. Provide resources. Shield teams from unreasonable demands. Develop people’s skills. Create environments where good work can happen. This is what management books say. This is what management training teaches. This is what managers claim in their self-evaluations.

But in practice, how many developers feel genuinely supported by their management?

Management doesn’t remove blockers—they are blockers. They create process overhead, demand status updates, insist on meetings, and then wonder why development is slow. When you point out actual blockers—slow CI/CD, poor tooling, inadequate infrastructure—they tell you to work around it. They’ll spend weeks debating whether to approve a £500/month tool that would save the team hours daily, but they won’t hesitate to demand the team work weekends to hit an arbitrary deadline.

Management doesn’t provide resources—they starve teams and demand miracles. Understaffed by design, because ‘doing more with less’ is a management virtue. Don’t have enough people? Just work more efficiently. Don’t have the right skills on the team? Just figure it out. Need training? Here’s an O’Reilly subscription; teach yourself on your own time. Need equipment? Use your personal laptop; we’ll give you a small stipend. Need infrastructure? Make do with what we have; there’s no budget for improvements.

Management doesn’t shield teams from unreasonable demands—they transmit unreasonable demands and then pressure teams to accept them. The CEO wants a feature by end of quarter? Make it happen. Sales promised something that doesn’t exist? Build it. The deadline is impossible? Not with that attitude. The requirements keep changing? Be agile. The scope keeps expanding? Adjust. Management’s job has become playing telephone between executive fantasy and developer reality, and always, always the pressure flows downward.

Management doesn’t develop people’s skills—they exploit the skills people already have. Training budget? Only if it’s directly applicable to the current sprint. Time for learning? Only if you do it outside work hours. Mentorship? That’s the senior developer’s problem, except they’re too busy hitting deadlines to actually mentor. Career development? Here’s a list of competencies you need to demonstrate; figure out how to demonstrate them whilst also shipping features. Management is extraction, not cultivation.

Management doesn’t create environments where good work can happen—they create environments where frantic work happens. The goal isn’t good, it’s fast. The metric isn’t quality, it’s velocity. The pressure isn’t sustainable, it’s constant. They’ve created systems that burn people out and then blamed the people for burning out. ‘We need to talk about your wellbeing’ really means ‘you’re not keeping up the pace, and that’s a you problem’.

Most developers feel like management is something to be managed—people to be kept happy with status updates, to be shielded from bad news, to be navigated carefully. You learn to tell managers what they want to hear, not what’s actually happening. You learn to translate their demands into something actually achievable and let them think they got what they asked for. You learn to work around them, not with them. And then one day you wake up in Russia.

When’s the last time a developer said ‘I’m so glad I can go to my manager for help with this problem?’ More often you hear: ‘Don’t tell my manager about this problem; they’ll just make it worse’. The manager isn’t a resource; they’re a risk.

Both institutions have replaced support with control, service with compliance, help with coercion.

And crucially: both institutions defend this transformation by claiming it’s necessary. Police claim they can’t coddle criminals; they need to maintain authority. Management claims they can’t coddle developers; they need to maintain accountability. Both are really saying the same thing: we can’t do our jobs if we have to actually care about the people we have power over. Better to just intimidate them into compliance.

The Internal Mirror

And here’s where it gets truly dark: both institutions use the exact same coercive model internally, on their own people.

Police officers themselves are managed through fear, obligation, guilt, and shame. Speak up about misconduct? You’re a grass, you’ve broken the blue line, you’ll be ostracised. Question orders? You’re insubordinate. Fail to meet arrest quotas? You’re not pulling your weight. Show weakness? You’re not cop material. The same psychological violence police inflict externally is inflicted internally to maintain the culture. Officers learn to fear their own colleagues and superiors as much as civilians fear officers. Cop is as cop does.

Software developers are managed through fear, obligation, guilt, and shame, and they internalise it to the point where they enforce it on each other. The developer who works weekends makes everyone else feel guilty for having boundaries. The developer who never asks for help makes everyone else feel ashamed for needing help. The developer who accepts every unreasonable deadline makes everyone else look uncommitted by comparison. Management doesn’t even need to apply much pressure—developers do it to themselves and each other.

Both institutions create cultures where the abused become abusers, where the victims of coercion become the enforcers of it. You survive by submitting, and then you justify your submission by demanding others submit too. Otherwise, you’d have to face that you didn’t have to submit—that you had choices, and you chose compliance. Better to make compliance seem like the only option, for everyone.

The Toll

The psychological impact is severe in both cases.

People who’ve been policed through fear, obligation, guilt, and shame don’t feel safer—they feel threatened by the very institution that claims to protect them. They avoid police rather than seeking their help. They teach their children to fear police rather than trust them. Communities become antagonistic to the police, and police respond with even more aggressive control tactics. The spiral continues.

Developers managed through fear, obligation, guilt, and shame don’t produce better work—they produce compliant mediocrity and eventually burn out. They learn to hide problems rather than surface them. They optimise for looking productive rather than being effective. They keep their heads down, collect paycheques, and count the days to either leaving or being made redundant.

The talented ones—the ones with options—leave. They walk into the office one Monday and realise they can’t do this anymore, that their mental health is worth more than the paycheque. They quit, sometimes without another job lined up, because anything is better than this. (Been there, many times, myself).

The ones who stay either burn out or adapt. Burnout looks like depression, anxiety, exhaustion. It looks like going through the motions. It looks like not caring anymore because caring hurts too much. It looks like disengagement, cynicism, the slow death of professional pride.

Adaptation looks like becoming the same kind of coercive manager who abused them. They learn that guilt, fear, obligation, and shame are effective management tools. They’ve been on the receiving end; they know how well it works. When they get promoted, they perpetuate the cycle. The abused become abusers. The spiral continues.

Why This Happens

Both institutions have embraced coercive models because they’re easier than actually being good at the job.

Real policing—community engagement, problem-solving, de-escalation, earning trust—requires skill, patience, and emotional intelligence. It requires treating people as people. It requires admitting you don’t have all the answers. It requires building relationships over time. It’s hard. It’s exhausting. It requires abilities many officers don’t have and training most forces don’t provide.

It’s so much easier to just rely on the implicit threat of state violence. Walk in with authority, demand compliance, and if they don’t comply, escalate. It works, in the sense that you get compliance. People submit. You can tell yourself you’re doing your job. And you never have to develop the harder, more human skills that real policing requires.

Real management—developing people, building trust, creating clarity, removing obstacles, fostering genuine collaboration—requires skill, patience, and emotional intelligence. It requires treating developers as humans with context, constraints, and expertise you might lack. It requires admitting you don’t have all the answers. It requires building relationships, understanding what motivates each person, creating environments where different people can thrive. It’s hard. It’s exhausting. It requires abilities many managers don’t have and training most companies don’t provide.

It’s so much easier to just rely on the implicit threat of employment insecurity. Demand deliverables, set deadlines, and if people can’t meet them, apply pressure through fear, obligation, guilt, and shame. It works, in the sense that you get compliance. People submit. They work nights and weekends. They keep their objections to themselves. You can show your superiors that you’re ‘getting results’. And you never have to develop the harder, more human skills that real management requires.

Both institutions attract people who are drawn to having power over others but lack the competence or inclination to exercise that power responsibly. And both institutions protect those people, allowing them to continue using coercion as a substitute for capability.

The Victims

In both cases, the people subjected to this coercive control have limited recourse.

If police treat you poorly, you can’t fire them. You can’t choose different police. You can’t opt out of the system. You’re subject to their power whether you consent or not. Your only options are compliance or escalating consequences. You can file a complaint, but the police investigate themselves. You can sue, but qualified immunity protects officers. You can try to avoid police entirely, but that’s not really an option for most people. You’re trapped in the power relationship.

If management treats you poorly, you can try to leave, but you need the job. And in tech, despite the mythology of abundant opportunities, the reality is more constrained. You might have a mortgage, family obligations, visa requirements. The job market might be soft. You might be at a senior level where opportunities are scarce. You might be in a specialised field with few employers.

And even if you can leave—what makes you think the next place will be better? Every developer has stories about toxic management. It’s not a bad company; it’s the industry. The next job will likely be the same coercive model with different faces. So you endure. You comply. You internalise the guilt and shame. You live with the fear and obligation. You stay because the alternative is uncertainty and potential poverty.

In both cases, the power asymmetry means the coercive model works—not because it’s effective at achieving stated goals (safer communities, better software, more successful products), but because the victims have no choice but to submit to it.

The ultimate parallel: both institutions claim to serve (the public, the business) whilst actually serving themselves. And they maintain their power not through competence or service, but through the systematic application of fear, obligation, guilt, and shame to those unfortunate enough to fall under their authority.

The Systematic Trampling of Rights

This coercive management culture doesn’t just make developers miserable—it creates the conditions for systematic rights violations against users.

When developers work in environments of fear, when they’re constantly guilted about deadlines, when they’re shamed for pushing back, when their entire employment hinges on compliance—they stop caring about user rights. They can’t afford to.

The developer who knows the feature violates privacy but ships it anyway because questioning it would make them a target. The team that implements dark patterns because management demands ‘conversion optimisation’ and refusing would be ‘not being a team player’. The engineer who sees the security flaw but says nothing because raising it would mean admitting their code has problems. The architect who designs invasive surveillance features because their manager’s promotion depends on hitting engagement metrics.

This is how rights get trampled: not primarily through malice, but through organisations that manage their people so coercively that ethical concerns become luxury goods no one can afford.

Just as police officers subject to coercive internal cultures become more likely to violate civilian rights (because they’ve learnt that might makes right, that questioning orders has consequences, that covering for colleagues is survival), developers subject to coercive management become more likely to build rights-violating products (because they’ve learnt that objecting has consequences, that deadline pressure justifies cutting corners, that going along is survival).

The internal culture of psychological violence produces external consequences of actual harm.

The Leadership Vacuum – A Fish Rots From the Head

If front-line failures reveal the expertise paradox, leadership failures reveal something worse: institutional rot from the top down.

UK policing’s problems don’t stop with constables on the beat. Chief Constables and senior officers preside over forces where misconduct flourishes, yet seem perpetually surprised when scandals emerge. They speak at conferences about ‘lessons learnt’ from the same mistakes that keep recurring. They commission reports that gather dust. They announce reform initiatives that never quite materialise.

These senior officers are masters of looking busy whilst accomplishing nothing. They’re skilled at navigating political pressure, managing their own reputations, and securing the next career move. What they’re not skilled at is actually reforming their organisations or supporting their officers in doing better work.

When Sarah Everard was murdered by a serving Metropolitan Police officer, senior leadership expressed shock—despite years of warnings about the force’s culture. When strip-search scandals emerge, when stop-and-search data shows persistent racial bias, when officers are caught in WhatsApp groups sharing racist and misogynistic content, senior officers act surprised. Every. Single. Time.

They knew. Everyone knew. The culture was visible to anyone who bothered to look. But looking would have required acknowledging the problem, and acknowledging the problem would have required fixing it, and fixing it would have been hard work that might not pay off before their next career move. So they looked away.

The pattern is clear: asleep at the wheel when it comes to their actual responsibilities, wide awake when it comes to their own careers.

Tech Leadership’s Parallel Failure

Tech executives and engineering managers display remarkably similar characteristics. They’re excellent at:

  • Delivering quarterly earnings calls that move stock prices
  • Pivoting to whatever is trendy (mobile! cloud! AI! crypto! AI again!)
  • Acquiring and then destroying smaller competitors
  • Giving TED talks about innovation and ‘changing the world’
  • Collecting compensation packages that would make Victorian robber barons blush
  • Talking about ‘growth mindset’ and ‘psychological safety’ in all-hands whilst running organisations based on fear

What they’re not excellent at:

  • Actually supporting the developers who work for them
  • Creating sustainable work environments
  • Addressing the toxic management culture everyone complains about in exit interviews
  • Building organisations where technical excellence matters more than political skills
  • Taking responsibility when things go wrong (that’s what VPs are for)
  • Protecting their teams from unreasonable demands (they’re usually the source of them)

Engineering VPs and CTOs talk endlessly about ‘building great engineering cultures’ whilst presiding over teams that are demoralised, burnt out, and haemorrhaging senior talent. They commission surveys on developer happiness, get back results showing pervasive problems, and then… do nothing. Or worse, they blame the developers for not being resilient enough, passionate enough, committed enough.

They’ll implement ‘wellness programmes’ and ‘mental health days’ whilst maintaining the exact management practices that destroy mental health in the first place. They’ll talk about ‘work-life balance’ whilst promoting the managers who demand weekend work. They’ll claim to value ‘technical excellence’ whilst rewarding the developers who ship fast and break things. They’ll say they want ‘honest feedback’ whilst retaliating against people who provide it.

When toxic managers are finally too obvious to ignore, leadership acts shocked. ‘We had no idea this was happening.’ Really? In an organisation where people work closely together, where exit interviews consistently mention the same problems, where team surveys flag the same issues quarter after quarter? They knew. They just didn’t care enough to do anything about it.

Like Chief Constables, they’ve mastered the art of looking like leaders whilst failing at actual leadership:

  • They give great interviews about engineering excellence whilst presiding over codebases held together with duct tape and prayer
  • They talk about diversity and inclusion whilst their hiring and promotion practices perpetuate homogeneity
  • They champion ‘blameless post-mortems’ whilst running organisations where blame flows freely and downward
  • They praise ‘innovative thinking’ whilst punishing anyone who suggests doing things differently
  • They mandate ‘psychological safety’ training whilst maintaining fear-based management cultures
  • They implement tools to measure developer productivity whilst ignoring that what they’re measuring is performance under duress, not actual productivity

Both police leadership and tech leadership have perfected a particular kind of wilful blindness: they structure their days, their metrics, and their attention so they can plausibly claim not to have known about problems that were obvious to everyone below them.

They create layers of middle management to insulate themselves from ground truth. They measure what makes them look good rather than what would reveal problems. They attend conferences and talk about best practices whilst their own organisations violate those practices systematically. They commission studies and ignore the findings. They announce initiatives that never materialise into actual change.

And when disasters inevitably occur, both groups deploy the same playbook: express shock, promise to ‘take this seriously’, announce an investigation or review, wait for the news cycle to move on. Real accountability never arrives. Real change never happens. And six months later, a nearly identical scandal emerges.

The fish, as they say, rots from the head.

The Managerial Class That Shouldn’t Exist

Management is a cargo-culted idea

Here’s an uncomfortable truth: most managers in tech shouldn’t be managers. They don’t have the skills for it. They don’t have the temperament for it. They often don’t even want to do it—they just want the title and the pay.

Tech has created a system where the only path to higher compensation and status is through management. You can be a brilliant individual contributor, but there’s a ceiling. To break through, you need to manage people. So developers become managers not because they want to support and develop people, but because they want to advance their careers.

This is like making every police officer who wants a pay rise become a sergeant, regardless of whether they have any skill at leadership, training, or managing others. You’d get exactly what we have: an incompetent management layer that uses coercion to compensate for lack of actual management ability.

The senior developer who was great at coding becomes a manager and suddenly they’re terrible at their job. But they can’t admit it, because admitting it would mean giving up the salary and status. So they fake it. They copy what they’ve seen other managers do. And what have they seen? Fear, obligation, guilt, and shame. It works, in the sense that people comply. So they do more of it.

The industry has created a management class that’s incentivised to extract value from developers whilst providing minimal support, that’s promoted based on their ability to ‘drive results’ (make people work harder) rather than their ability to create environments where good work happens naturally.

And at the executive level? They’re even more divorced from the actual work, even more focused on metrics that have nothing to do with actual software quality or developer wellbeing, even more skilled at protecting their own positions whilst claiming ignorance about obvious problems.

Both institutions have leadership that’s asleep at the wheel—or worse, wide awake and choosing to look away because fixing the problems would threaten their own positions.

The Accountability Vacuum

When UK police officers break the rules, the consequences are often… minimal. The complaints process is labyrinthine. Internal investigations frequently find ‘no case to answer’. Officers who lie, abuse their position, or even commit crimes sometimes receive brief suspensions or early retirement with full pensions. The system protects its own.

Managers in tech operate in a remarkably similar accountability vacuum. Toxic manager destroying team morale? HR will ‘coach’ them whilst the team members who complained start looking for new jobs. Manager creating hostile work environment? Maybe they get moved to a different team to ruin. Manager discrimination in hiring or promotion? Company settles quietly, manager stays employed.

Individual developers face consequences for technical failures—missed deadlines, production bugs, security breaches. But managers face almost no consequences for management failures—toxic cultures, burnt-out teams, discrimination, retaliation.

Both professions have constructed moats around individual accountability. The institution may occasionally face consequences, but the individuals within it remain largely untouchable.

Rules for Thee, Not for Me

Perhaps nothing illustrates institutional rot quite like selectively applied rules.

UK police have been caught deleting data they’re required to retain, accessing systems for personal reasons, and investigating their own misconduct in ways that would never pass muster if applied to civilians. The rules apply to everyone except those who enforce them.

In software development organisations, this manifests everywhere:

  • Management demands accurate estimates from developers, then ignores those estimates and commits to different timelines with stakeholders
  • Management preaches ‘work-life balance’ whilst expecting developers to work weekends and answer messages at night
  • Management talks about ‘psychological safety’ whilst maintaining fear-based cultures
  • Management demands ‘accountability’ from developers whilst facing none themselves
  • Management institutes ‘no blame’ policies whilst systematically blaming developers for systemic failures
  • Management requires developers to follow processes that management itself ignores when convenient
  • Management demands detailed documentation from developers whilst providing none about their own decisions
  • Executives talk about ‘shared sacrifice’ during lean times whilst protecting their bonuses and share grants

The rules, it seems, are for managing other people (the hoi poloi)

The Ego Problem

UK policing has a well-documented problem with officers who cannot handle challenges to their authority. Question an officer’s legal basis for action, and you might find yourself arrested for ‘obstructing police’ or ‘causing harassment, alarm, or distress’. The uniform becomes the argument.

Management in tech has the same ego problem:

The manager who cannot accept that their technical understanding is outdated. They made technical decisions ten years ago; they must still be the technical expert. Challenge their understanding and you’re ‘not respecting experience’.

The manager who cannot handle being questioned in meetings. They’ve decided on a direction; questioning it is insubordination, not legitimate technical discourse.

The manager who takes any pushback as personal disrespect. You’re not disagreeing with the plan; you’re disrespecting them.

The manager whose ego is so wrapped up in being right that they’d rather see projects fail than admit error. Admitting the architecture they championed is flawed would mean admitting they were wrong, which is existentially threatening.

The manager who confuses authority with correctness. ‘Because I’m the manager’ becomes the argument, just as ‘because I’m the police’ becomes the argument.

In both cases, professional identity becomes so intertwined with being right that being wrong becomes unthinkable. And in both cases, this ego protects the professional from recognising how their actions harm others.

The Insulation From Consequences

Police officers rarely experience the downstream effects of their errors. The person wrongly arrested carries the trauma; the officer faces no consequence and therefore learns nothing. The system provides no feedback loop.

Managers are similarly insulated from their mistakes:

The toxic manager who destroys team morale? They don’t see the depression, the anxiety, the family stress their employees experience. They just see that people aren’t performing well, which confirms that more pressure is needed.

The manager who sets impossible deadlines? They don’t work the weekends. They don’t experience the burnout. They just see the feature shipped (barely, and full of bugs), and conclude that pressure works.

The manager who drives away senior talent? They’re not the ones trying to work with the depleted team. They’ve moved to a different role by the time the consequences manifest.

The manager who creates hostile work environments? They don’t experience the hostility. They’re at the top of the local hierarchy. For them, the environment is fine.

Both professions can rise through the ranks whilst leaving damage in their wake, never forced to confront the human cost of their professional negligence. The rights they’ve trampled remain abstractions; the people whose lives they’ve damaged remain invisible.

The Institutional Capture

Perhaps the deepest parallel is this: both institutions increasingly serve their own interests rather than their stated purpose.

UK policing often seems more concerned with protecting its reputation than protecting the public. Criticism is met with defensive briefings, not reform. Scandals prompt PR campaigns, not cultural change. Rights are seen as obstacles to institutional effectiveness rather than constraints on institutional power.

Software organisations have undergone similar capture. What began as a field dedicated to building useful tools has become increasingly focused on:

  • Extracting maximum value from developers’ labour (longer hours, more pressure, less support)
  • Maintaining management’s power and status (hierarchy for its own sake)
  • Protecting the organisation from accountability (binding arbitration, NDAs, forced rankings that justify redundancies)
  • Prioritising management comfort over developer wellbeing or product quality
  • Building empires rather than effective teams

The mission statement says one thing; the incentive structure points elsewhere. And in both cases, the people whose rights are being trampled are treated as resources to be extracted from, not humans deserving of dignity and respect.

Management talks about ‘our greatest asset is our people’ whilst treating people as disposable. They talk about ‘innovation’ whilst punishing anyone who suggests doing things differently. They talk about ‘excellence’ whilst rewarding political skills over technical competence. They talk about ‘values’ whilst managing through fear, obligation, guilt, and shame.

The stated purpose is building great software. The actual purpose is maintaining the management hierarchy that extracts value from developers whilst insulating itself from accountability.

What Makes These Parallels Possible?

These aren’t coincidences. They emerge from similar structural conditions:

Complexity as a shield: Both law and code are sufficiently complex that outsiders can’t easily evaluate professional competence. This opacity creates space for incompetence to hide—and for coercive management to go unchallenged.

Professional closure: Both fields have successfully convinced society that only their members can judge their work. Challenge a police officer’s behaviour? You’re not qualified. Question a technical or management decision? You don’t understand the business context.

Weak external accountability: Neither profession faces robust external oversight. Police complaints boards are often staffed by former officers. Corporate management faces minimal external accountability for how they treat employees.

Misaligned incentives: Police are judged on arrests and crime statistics, not on whether justice was served or communities feel safer. Managers are judged on delivered features and met deadlines, not on whether they’ve created sustainable, healthy, effective teams.

Leadership that optimises for self-preservation: Both fields promote people who are good at managing up and protecting the institution, not people who are good at the actual job. Chief Constables and CTOs rise by avoiding scandal, not by fixing problems.

The pretence that social dynamics don’t matter: Both institutions operate as if human behaviour, relationships, and social structures are irrelevant to their work. This makes it impossible to address problems that are fundamentally social in nature—like toxic cultures built on fear, obligation, guilt, and shame.

Power without responsibility: Both institutions wield enormous power over people’s lives—police through the monopoly on legitimate force, management through control of employment and therefore economic survival. But both have successfully avoided commensurate responsibility for how that power is used.

The normalisation of psychological violence: Both have operated long enough that managing through fear, obligation, guilt, and shame has become ‘just how things are done’. People no longer recognise it as violence; they think it’s normal management.

Economic dependence: Just as you can’t opt out of policing, most developers can’t opt out of employment. This captive population allows coercive practices to persist because the victims have nowhere else to go.

A Way Forward?

Acknowledging these parallels isn’t counsel for despair. It’s a call for structural reform in both fields:

Real accountability: Individual professionals must face meaningful consequences for abusing their power. Toxic managers need to face actual consequences, not just ‘coaching’. The protection of incompetent, abusive management needs to end.

External oversight: Both professions need robust oversight from outside their own ranks. Just as police need civilian oversight with real teeth, tech companies need independent channels for reporting management abuse—not HR departments that protect the company.

Realigned incentives: Stop measuring and rewarding the wrong things. Managers should be evaluated on team health, retention of top talent, and sustainable delivery—not just shipped features. Police should be evaluated on community trust and safety, not arrest numbers.

Cultural change: Both fields need to rebuild professional cultures that prize service over self-interest, support over coercion, development over extraction. This means explicitly rejecting management through fear, obligation, guilt, and shame.

Mandatory, ongoing training: Real training in the actual skills needed for the job. For police: law, de-escalation, mental health, social dynamics. For managers: actual management skills, emotional intelligence, team development. Not one-off workshops, but continuous professional development that’s evaluated and required.

Leadership accountability: Senior officers and executives must face consequences when they preside over toxic cultures. Being ‘shocked’ when problems surface should not be a defence—it should be an indictment.

End the promotion trap: Create viable career paths for individual contributors that offer similar compensation and status to management roles. Stop forcing people into management positions they’re not suited for.

Reject the mythology: Stop pretending that fear-based management ‘gets results’. It gets compliance and burnout. Stop pretending that technical skill is all that matters. Stop pretending social dynamics don’t exist. Stop pretending that ‘experience’ equals competence when that experience is just years of repeating the same mistakes.

Acknowledge the social dimension: Both fields must stop pretending that human relationships, power dynamics, and social structures are irrelevant to their work. You cannot build healthy organisations whilst pretending humans aren’t social beings.

Ground truth mechanisms: Create channels that force leadership to confront what’s actually happening, not what they want to believe is happening. Anonymous surveys that actually get read and acted on. Exit interviews that are taken seriously. Skip-levels that are genuinely candid.

Union power: Developers need collective bargaining power to push back against coercive management. Individual developers can be crushed; organised labour can negotiate.

Professionalisation that means something: Both fields need to develop what it actually means to be a professional—not just having a job title, but having genuine expertise backed by continuous education, ethical standards that are enforced, and accountability for failures.

The parallel exists because the problem is structural, not individual. Good people in bad systems still produce bad outcomes.

Until we reckon with the ways both UK policing and software development have become institutions that serve themselves rather than the people they claim to serve, we’ll continue to see the same patterns: incompetence masquerading as expertise, power exercised without constraint, psychological violence normalised as standard practice, leadership that’s asleep at the wheel—or worse, wide awake and choosing to look away—and a systematic trampling of human dignity in service of institutional convenience.

The first step in fixing a system is acknowledging it’s broken. The second step is acknowledging that all systems are, at their core, systems of human relationships. The third step is acknowledging that management through fear, obligation, guilt, and shame is violence, and it needs to stop. The fourth step is acknowledging that neither profession has the expertise it claims, and that without serious, ongoing training and education, that won’t change.

In both cases, we’re long overdue for that honesty.

And one idea I propose: Make it a criminal offense to misrepresent one’s authority or statutory powers to e.g. the general public.


Further Reading

Ashworth, A., & Zedner, L. (2014). Preventive justice. Oxford University Press.

Bradford, B., Milani, J., & Jackson, J. (2017). Identity, legitimacy and ‘making sense’ of police use of force. Policing: An International Journal, 40(3), 614–627. https://doi.org/10.1108/PIJPSM-06-2016-0085

Burnham, D. (1996). The rise of the computer state. Random House.

Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.

Gilliom, J., & Monahan, T. (2013). SuperVision: An introduction to the surveillance society. University of Chicago Press.

Her Majesty’s Inspectorate of Constabulary and Fire & Rescue Services. (2022). An inspection of vetting, misconduct, and misogyny in the police service. HMICFRS.

Home Office. (2023). Police workforce, England and Wales: 31 March 2023. Home Office.

Independent Office for Police Conduct. (2022). Annual report and statement of accounts 2021-22. IOPC.

Loader, I., & Sparks, R. (2011). Public criminology? Routledge.

Lyon, D. (2018). The culture of surveillance: Watching as a way of life. Polity Press.

Maslach, C., & Leiter, M. P. (2016). Understanding the burnout experience: Recent research and its implications for psychiatry. World Psychiatry, 15(2), 103–111. https://doi.org/10.1002/wps.20311

Newburn, T. (2015). Literature review: Police integrity and corruption. Her Majesty’s Inspectorate of Constabulary.

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.

Reiner, R. (2010). The politics of the police (4th ed.). Oxford University Press.

Schein, E. H. (2010). Organizational culture and leadership (4th ed.). Jossey-Bass.

Schradie, J. (2019). The revolution that wasn’t: How digital activism favors conservatives. Harvard University Press.

Waddington, P. A. J. (1999). Police (canteen) sub-culture: An appreciation. British Journal of Criminology, 39(2), 287–309. https://doi.org/10.1093/bjc/39.2.287

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Profile Books.

Why Software Developers REALLY Hate Teamwork (And What That Tells Us About Organisations)

For fifty years, we’ve been telling ourselves the same story: software developers are antisocial hermits who despise collaboration. They’d rather code alone in dark rooms than work with other human beings. It’s just their nature, right?

Wrong.

The real story is far more interesting—and far more damning of how we run organisations.

The Stereotype Isn’t Wrong, It’s Manufactured

Here’s what I’ve learnt from five decades in this industry: Yes, many developers do exhibit antisocial behaviour. Yes, they often resist teamwork. Yes, they guard their code jealously and bristle at collaboration.

But this isn’t an innate characteristic of people who happen to be good at programming. It’s a rational response to profoundly dysfunctional organisational design.

We’ve built organisations that systematically manufacture antisocial behaviour in developers, then blamed the developers for it.

The Real Culprits

Let me be specific about what creates the ‘antisocial developer’ syndrome:

1. Individual Performance Metrics in a Team Sport

Most organisations evaluate developers on their personal contribution to the team’s output. Lines of code. Commits. Tickets closed. ‘Their’ features shipped.

You can NEVER reward solo performance and expect team behaviour.

When promotion, bonuses, and performance reviews depend on demonstrating individual achievement, teammates become competitors. Helping someone else succeed actively harms one’s own metrics. Knowledge hoarding becomes rational. Taking credit becomes essential.

The system demands antisocial behaviour.

As Deming (2000) observed, the system in which people work accounts for 90-95% of performance. Yet we persist in evaluating and rewarding individuals as if they operate in isolation.

2. The Interruption Problem (Poorly Managed)

Yes, programming requires deep focus. Research shows developers are interrupted every 6-9 minutes on average, with each interruption costing 20+ minutes of productivity (Parnin & Rugaber, 2011). But here’s the thing: developers don’t resent teammates. They resent poorly-managed collaboration.

There’s a massive difference between:

  • Dave interrupting you with an under-specified question he could have researched himself
  • A structured pairing session where you’re both solving a gnarly problem together

The first is a failure of organisational design around communication norms. The second is effective teamwork.

When organisations fail to distinguish between these, developers learn to protect themselves by avoiding all collaboration. Can you blame them?

3. Hopeless Management Role Models

Most developers have spent years watching their managers:

  • Model territorial, siloed behaviour
  • Compete with each other for resources and credit
  • Optimise for their department and their own personal well being at the expense of the whole
  • Play politics rather than solve problems

Why would developers learn to collaborate well when the people with power and authority consistently demonstrate that collaboration is for suckers?

As Schein (2016) notes, culture is fundamentally shaped by what leaders pay attention to, measure, and control. When leaders model competitive rather than collaborative behaviour, that’s what the organisation learns.

4. The Myth of the 10x Developer

We celebrate the lone genius. The Linus Torvalds figure. The person who ‘doesn’t need anyone else’. We’ve built an entire mythology around individual brilliance.

This mythology is toxic for team dynamics. It tells developers: ‘Your value is in being smarter than everyone else, not in making everyone else smarter’.

When your professional identity is built on individual exceptionalism, collaboration feels like madness.

5. Fifty Years of Learnt Helplessness

After decades of ‘teamwork’ characterised by:

  • Endless meetings where three people talk and five zone out
  • ‘Collaboration’ that’s really just more interruptions
  • ‘Agile’ ceremonies that are bureaucratic theatre
  • ‘Team building’ exercises that everyone hates

…developers have learnt that ‘teamwork’ is just NewSpeak for ‘waste my time’.

Research on meeting effectiveness shows that organisations do little to assess the return on meeting investment or take steps to ensure meetings are productive (Rogelberg et al., 2012). The problem isn’t that developers hate teamwork. The problem is that what most organisations call ‘teamwork’ actively makes the work worse.

The Rational Calculation: All Downside, No Upside

Let’s be brutally honest about the economics of teamwork in most organisations. From a developer’s perspective, collaboration is a totally losing proposition.

Consider the incentive structure:

What You Lose Through Teamwork

Time and focus. Every collaboration costs you productive hours. Explaining your approach to someone. Reviewing their code. Answering questions. Attending coordination meetings. This time doesn’t appear on your performance scorecard as ‘value delivered’.

Individual metrics. When you help a colleague solve their problem, their ticket gets closed, their feature ships, their contribution shows up in the metrics. Yours doesn’t. You’ve just spent several hours making someone else look more productive whilst making yourself look less so.

Competitive advantage. In organisations where promotion is a zero-sum game, making your colleagues better directly harms your relative standing. If five people are competing for two senior positions, why would you help the other four improve?

Knowledge as currency. The more you share what you know, the less valuable you become. In organisations that value individual expertise over collective capability, teaching others is literally giving away your competitive edge.

Exposure of weaknesses. Real collaboration means admitting when you don’t know something. In organisations that worship the ‘brilliant individual’, admitting ignorance can damage your reputation and career prospects.

Association with failure. When teams fail, everyone gets tainted. When individuals fail, only they get blamed. Risk-averse developers learn quickly: avoid team accountability.

What You Gain Through Teamwork

In most organisations? Precisely nothing.

You don’t get promoted for making others better. There’s no ‘best mentor’ bonus. Your performance review doesn’t include ‘improved team capability’ as a metric. Your salary doesn’t increase because you helped three colleagues solve problems.

The organisation may give lip service to ‘being a team player’, but when it comes to actual rewards—promotion, compensation, recognition, interesting projects—these go to the people who demonstrate individual achievement.

The Inevitable Conclusion

Developers aren’t stupid. They can do this maths.

Time spent collaborating = -X hours of individual output + 0 career benefit = Net loss

Time spent coding alone = +X hours of individual output + potential career benefit = Net gain

Given this equation, the rational choice is obvious: minimise collaboration, maximise individual output, protect your knowledge, avoid team commitments where possible.

We then have the gall to call this ‘antisocial behaviour’ and blame the developers for being insufficiently ‘team-oriented’.Cha!

The Violence of Forced Collaboration

Here’s where it gets particularly pernicious. Organisations that recognise developers are avoiding teamwork often respond by mandating it:

  • Compulsory pair programming sessions
  • Required attendance at ‘collaboration’ meetings
  • Forced team-building exercises
  • Performance reviews that penalise ‘not being a team player’

This is what Rosenberg (2003) calls violence—using fear, obligation, guilt, and shame to coerce behaviour. And it doesn’t work. You cannot force genuine collaboration through threats and sanctions.

What you get instead is performative teamwork. Developers going through the motions, visibly ‘collaborating’ whilst mentally calculating how to minimise the damage to their individual metrics. The worst of both worlds: the overhead of collaboration without any of the benefits.

The System Is Working As Designed

The tragic irony is that the system is working perfectly. We’ve designed organisations that:

  1. Measure and reward individual contribution
  2. Create competition for scarce promotions and recognition
  3. Make helping others a net loss for the helper
  4. Punish collaboration through opportunity cost
  5. Then wonder why people don’t collaborate

This isn’t a bug. This is the system working exactly as designed. We’ve built organisations where collaboration is economically irrational, then blamed workers for being economically rational.

As Deming (1986) repeatedly emphasised: ‘A bad system will defeat a good person every time’. You can have the most altruistic, team-oriented developers in the world, and this system will turn them antisocial within weeks.

The Evidence: It Doesn’t Have To Be This Way

Here’s where this all stops being theory and becomes proven practice.

Four Years of Praxis

I know this because I didn’t just think about it or write about it. I built it. For four years (1996-2000), I ran a software consultancy called Familiar where we proved—in the messy reality of client work, commercial pressures, and market competition—that developers can be intensely collaborative, social, and team-oriented when the system supports it rather than punishes it.

This wasn’t a controlled experiment in a laboratory. This was a real business with real clients, real deadlines, real financial pressures, and real human beings with all their complexities and needs.

And it worked. Spectacularly.

What We Actually Did

Let me be specific, because the details matter:

We eschewed individual performance reviews entirely. No metrics comparing one person to another. No competition for ratings or rankings. No individual targets. Why would you hoard knowledge when helping your colleague has no downside?

Result: Knowledge flowed freely. People actively sought opportunities to teach and learn from each other. The expertise of one became the expertise of all.

Everyone set their own compensation. Transparently. Everyone knew what everyone else was taking. You’d think this would lead to chaos or greed. It didn’t. When you trust people and treat them like adults, they tend to behave like adults.

Result: People chose lower salaries than we would have assigned them, whilst being happier with the result. The guideline was simple: ‘You’ll have to live with the consequences’. That plus transparency was sufficient.

We made the social side of the organisation as important as the business side. Company-funded weekends away with partners. Regular dining out together. Meetings in each other’s homes. An office designed for socialising as much as working—lounge, sofas, library, kitchen.

Result: Work and social connection became indistinguishable. People actually wanted to come together. Collaboration happened naturally because people genuinely cared about each other.

We had no managers. Everyone was a peer. Fifteen people at the peak, all colleagues, all fellows. Without hierarchy modelling antisocial behaviour, people naturally collaborated. Without career ladders, there was no competition for scarce promotions.

Result: Decisions got made faster. People took ownership. No one was ‘just following orders’. And cf. Auftragstaktik.

Constant dialogue was the norm. Not because we mandated it, but because we facilitated it (often by me). When people feel psychologically safe and aren’t competing with each other, they actually want to talk through problems together.

Result: Problems got solved faster. Better solutions emerged. Conflicts got resolved before they festered. Learning happened continuously.

The Results Were Extraordinary

The virtuous circle was real and measurable:

  • Fun led to great work. People doing work they enjoyed, with people they liked, produced better results than stressed people competing against each other.
  • Great work led to high margins. Clients were delighted. They came back. They referred others. We could charge premium rates because we delivered exceptional value.
  • High margins led to funds for more fun. Which led to better work. Which led to higher margins. Round and round.

Commercially, we were successful. The work was profitable. Clients loved working with us. Our reputation grew.

But more importantly—and this is what most organisations miss entirely—everyone was happy. People loved coming to work. They brought their whole selves. They cared about each other’s wellbeing, not just the project deliverables.

Why It Ended (And What That Tells Us)

Familiar ended in 2000 when I left. It stumbled on for two to three years after, then folded.

This isn’t a failure of the model. It’s proof of something important: this kind of organisation requires someone holding the vision and facilitating the constant dialogue.

It wasn’t the size (15 people is manageable). It wasn’t the market (we were commercially successful). It wasn’t the people (they thrived in this environment).

It was that the cultural maintenance—the facilitation, the vision-holding, the constant attention to the social dynamic—requires active stewardship. When I left, no one else could hold that mirror.

This tells us something crucial about replication: you can’t just copy the practices (no reviews, self-set salaries, flat structure). You need someone who understands the underlying principles deeply enough to maintain them.

This Isn’t Unique to Familiar

The patterns I saw at Familiar align with research on self-managing organisations (Laloux, 2014), psychological safety (Edmondson, 2019), and intrinsic motivation (Pink, 2009).

Other organisations have discovered similar approaches:

  • W.L. Gore & Associates (no managers, self-organising teams)
  • Valve Corporation (flat structure, self-allocation to projects)
  • Semco Partners (radical transparency, self-set salaries)
  • Buurtzorg (self-managing nursing teams)

The pattern is repeatable. Familiar proved it could work in software development. Others have proven it can work in manufacturing, healthcare, gaming, and more.

The Uncomfortable Implication

If it worked for four years in a real commercial environment, with real financial pressures and real human beings, then the ‘antisocial developer’ truly is a manufactured artefact of oh so common dysfunctional organisational design.

We can’t dismiss this as:

  • ‘Too small to scale’ (organisations with thousands have done similar)
  • ‘Only works in special circumstances’ (we had normal commercial pressures)
  • ‘Only works with special people’ (we had normal developers)
  • ‘Sounds nice but isn’t practical’ (it was profitable and sustainable)

The uncomfortable truth is that we know how to create organisations where developers are collaborative, social, and team-oriented. We’ve known for decades. The research supports it. The examples exist.

We just choose not to, because it requires changing assumptions about control, measurement, and hierarchy that most leaders aren’t willing to question.

This blog post isn’t theory. It’s a report from the field.

What This Means for Your Organisation

If you’re running a software organisation and wondering why your developers seem antisocial:

Don’t blame the developers.

Look at your performance review system. Look at your meeting culture. Look at how your managers behave. Look at whether you’re rewarding individual heroics or collective success. Look at whether you’ve made collaboration genuinely valuable or just performative.(It’s always just performative)

The developers aren’t the problem. The developers are responding rationally to the system you’ve built.

Every organisation gets the behaviour it designs for. If you’ve designed a system that rewards individual achievement, punishes time spent helping others, promotes people who hoard knowledge, and models territorial behaviour at the management level—congratulations, you’ve systematically manufactured antisocial developers.

The Harder Truth

Here’s the really uncomfortable part: fixing this requires changing fundamental assumptions and beliefs about how work should work.

You can’t just add some new ‘collaboration tools’ or run some team-building exercises. You need to change:

  • How performance is measured (if at all)
  • How people are compensated
  • What behaviours get promoted
  • How managers operate (or whether you need managers)
  • What ‘productivity’ even means

This is not a process improvement project. This is a wholesale rethinking of organisational design.

Ackoff (2006) describes this as ‘idealized design’—imagining what you would create if you could start from scratch, then working backwards to get there. But idealized design requires confronting the assumptions embedded in current practice.

And most organisations won’t do it. Because admitting that the ‘antisocial developer’ is something we created means admitting that our entire approach to running organisations is fundamentally broken.

It’s easier to just keep blaming the developers.


After 50+ years in this industry, I’ve learnt this: The most expensive assumption in software development isn’t a technical one. It’s the assumption that people problems are people problems, when they’re actually systems problems.

The developers are fine. It’s the organisations that need therapy.

Further Reading

Ackoff, R. L., Magidson, J., & Addison, H. J. (2006). Idealized design: Creating an organization’s future. Wharton School Publishing.

Deming, W. E. (2000). The new economics for industry, government, education (2nd ed.). MIT Press.

DeMarco, T., & Lister, T. (2013). Peopleware: Productive projects and teams (3rd ed.). Addison-Wesley.

Edmondson, A. C. (2019). The fearless organization: Creating psychological safety in the workplace for learning, innovation, and growth. John Wiley & Sons.

Laloux, F. (2014). Reinventing organizations: A guide to creating organizations inspired by the next stage of human consciousness. Nelson Parker.

Marshall, R. W. (2021). Quintessence: An acme for software development organisations. Falling Blossoms. https://leanpub.com/quintessence

Marshall, R. W. (2024). The team fruit bowl: A fruity look at teambuilding. Leanpub. https://leanpub.com/theteamfruitbowl

Parnin, C., & Rugaber, S. (2011). Programmer information needs after memory failure. In Proceedings of the 2011 IEEE 19th International Conference on Program Comprehension (pp. 123-132). IEEE.

Pink, D. H. (2009). Drive: The surprising truth about what motivates us. Riverhead Books.

Rogelberg, S. G., Shanock, L. R., & Scott, C. W. (2012). Wasted time and money in meetings: Increasing return on investment. Small Group Research, 43(2), 236-245. https://doi.org/10.1177/1046496411429170

Schein, E. H. (2016). Organizational culture and leadership (5th ed.). John Wiley & Sons.

Seddon, J. (2019). Beyond command and control: A guide to working in the service industry. Vanguard Consulting.

Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization (Revised ed.). Currency.

The Uncomfortable Truth: Why Developer Training Is a Waste of Time

There’s an entire industry built around “improving” software developers. Conferences, workshops, bootcamps, online courses, books, certifications—billions of dollars spent annually on the promise that if we just train developers better, we’ll get better software. It’s time to say what many of us have privately suspected: it’s all just theater.

Here’s why investing in developer training is increasingly pointless, and why organisations would be better served directing those resources elsewhere:

  1. Nobody’s actually interested in improvement
  2. Developers don’t control what actually matters
  3. GenAI has fundamentally changed the equation

Let’s examine each of these uncomfortable truths.

1. Nobody’s Actually Interested in Improvement

Walk into any development team and ask who wants to improve their craft. Hands will shoot up enthusiastically. Now watch what happens over the next six months. The conference budget goes unused. The book club fizzles after two meetings. The internal tech talks attract the same three people every time. The expensive training portal shows a login rate of less than 15%. Personal note: I have seen this myself time and again in client organisations.

The uncomfortable reality is that most developers have found their comfort zone and have little to no genuine interest in moving beyond it. They’ve learned enough to be productive in their current role, and that’s sufficient. The annual performance review might require them to list “professional development goals” but these are box-checking exercises, not genuine aspirations. When developers do seek training, it’s often credential-seeking behavior—resume-building for the next job search, a.k.a. mortgage-driven development, not actual skill development for their current role.

This isn’t unique to software development. In most professions, once practitioners reach competence, the motivation for continued improvement evaporates. The difference is that in software, we’ve created an elaborate fiction that continuous learning is happening when it definitely isn’t. The developers who genuinely seek improvement are self-motivated outliers who would pursue it regardless of organisational investment. They don’t need your training programs; they’re already reading papers, experimenting with new technologies, and pushing boundaries on their own time.

2. Developers Have No Control Over What Actually Matters

Even if a developer emerges from training enlightened about better practices, they return to an environment that makes applying those practices simply impossible. They’ve learned about continuous deployment, but the organisation requires a three-week approval process for production releases. They’ve studied domain-driven design, but the database schema was locked in five years ago by an architecture committee. They’ve embraced test-driven development, but deadlines leave no time for writing tests, and technical debt is an accepted way of life.

The factors that most impact software quality—architecture decisions, technology choices, team structure, deadline pressures, hiring practices, organisational culture, the social dyname—are entirely outside individual developers’ control. These are set by management, architecture boards, or historical accident. Having developers trained in excellent practices but embedded in a dysfunctional system is like teaching someone Olympic swimming techniques and then asking them to compete while chained to a cinder block. (See also: Deming’s Red Bead experiment).

Moreover, the incentive structures in organisations reward maximising bosses’ well being, not e.g. writing maintainable code. Developers quickly learn that the skills that matter for career advancement are political navigation, project visibility, stakeholder management and sucking up—not technical excellence. Training developers in better coding practices while maintaining perverse incentives is simply theater that lets organisations feel good about the charade of “investing in people” while changing absolutely nothing that matters.

3. GenAI Has Fundamentally Changed the Equation

The emergence of generative AI has rendered much of traditional developer training obsolete before it’s even delivered. When Claude or GPT can generate boilerplate code, explain complex algorithms, refactor legacy systems, and even architect solutions, what exactly are we training developers to do? (Maybe AI has a more productive role to play in helping developers maximise their bosses’ well being).

The skills we’ve traditionally taught—memorising syntax, understanding framework details, knowing design patterns, debugging techniques—are precisely the skills that AI handles increasingly well. We’re training developers for skills that are being automated even as we conduct the training. The half-life of technical knowledge has always been short in software, but AI has accelerated this to the point of absurdity. By the time a developer completes a course on a particular framework or methodology, AI tools have already internalized that knowledge and can apply it faster and more consistently than any human (usual AI caveats apply).

The argument that developers need to “understand the fundamentals” to effectively use AI is wishful thinking from an industry trying to justify its existence. Junior developers are already shipping production code by describing requirements to AI and validating outputs. The bottleneck isn’t their understanding—it’s organisational factors like the social dynamic, relationships, requirements clarity and system architecture. Training developers in minutiae that AI handles better is like training mathematicians to use slide rules in the calculator age.

The Hard Truth

The developer training industry persists not because it works, but because it serves organisational needs that have nothing to do with actual improvement. It provides HR with checkboxes for professional development requirements. It gives managers a feel-good initiative to tout in interviews and quarterly reviews. It offers developers a sanctioned way to take a break from the grind. Everyone benefits except the balance sheet.

If organisations genuinely wanted better software, they’d stop pouring money into training programs and start fixing the systems that prevent good work: rigid processes, unrealistic deadlines, toxic relationships, flawed shared assumptions and beliefs, and misaligned incentives. They’d hire fewer developers at higher salaries, giving them the time and autonomy to do quality work. They’d measure success by folks’ needs met rather than velocity and feature count. But that would require admitting that the problem isn’t the developers—it’s everything else. And that’s a far more uncomfortable conversation than simply booking another training workshop.

The Comfortable Lie: Why We Don’t Actually Learn From Our Mistakes

We love a good comeback story. The entrepreneur who failed three times before striking it rich. The developer who learnt from a catastrophic production incident and never made ‘that mistake’ again. We tell these stories because they’re comforting—they suggest that failure has a purpose, that our pain is an investment in wisdom.

But what if this narrative is mostly fiction? What if, in the contexts where we most desperately want to learn from our mistakes—complex, adaptive systems like software development—it’s not just difficult to learn from failure, but actually impossible in any meaningful way?

The Illusion of Causality

Consider a typical software development post-mortem. A service went down at 2 AM. After hours of investigation, the team identifies the culprit: an innocuous configuration change made three days earlier, combined with a gradual memory leak, triggered by an unusual traffic pattern, exacerbated by a caching strategy that seemed fine in testing. The conclusion? ‘We learnt that we need better monitoring for memory issues and more rigorous review of configuration changes.’

But did they really learn anything useful?

The problem is that this wasn’t a simple cause-and-effect situation. It was the intersection of dozens of factors, most of which were present for months or years without issue. The memory leak existed in production for six months. The caching strategy had been in place for two years. The configuration change was reviewed by three senior engineers. None of these factors alone caused the outage—it required their precise combination at that specific moment.

In complex adaptive systems, causality is not linear. There’s no single mistake to point to, no clear lesson to extract. The system is a web of interacting components where small changes can have outsized effects, where the same action can produce wildly different outcomes depending on context, and where the context itself is always shifting.

The Context Problem

Here’s what makes this especially insidious: even if we could perfectly understand what went wrong, that understanding is locked to a specific moment in time. Software systems don’t stand still. By the time we’ve finished our post-mortem, the team composition has changed, two dependencies have been updated, traffic patterns have evolved, and three new features have been deployed. The system we’re analysing no longer exists.

This is why the most confident proclamations—’We’ll never let this happen again’—are often followed by remarkably similar failures. Not because teams are incompetent or negligent, but because they’re trying to apply lessons from System A to System B, when System B only superficially resembles its predecessor. The lesson learnt was ‘don’t deploy configuration changes on Fridays without additional review’, but the next incident happens on a Tuesday with a code change that went through extensive review. Was the lesson wrong? Or was it just irrelevant to the new context?

The Narrative Fallacy

Humans are storytelling machines. When something goes wrong, we instinctively construct a narrative that makes sense of the chaos. We identify villains (the junior developer who made the change), heroes (the senior engineer who diagnosed the issue), and a moral (the importance of code review). These narratives feel true because they’re coherent.

But coherence is not the same as accuracy. In the aftermath of failure, we suffer from hindsight bias—knowing the outcome, we see a clear path from cause to effect that was never actually clear at the time. We say ‘the warning signs were there’ when in reality those same ‘warning signs’ are present all the time without incident. We construct a story that couldn’t have been written before the fact.

This is why war stories in software development are simultaneously compelling and useless. The grizzled veteran who regales you with tales of production disasters is imparting wisdom that feels profound but often amounts to ‘this specific thing went wrong in this specific way in this specific system at this specific time’. And the specifics are rarely defined. The lesson learnt is over-fitted to a single data point.

Emergence and Irreducibility

Complex adaptive systems exhibit emergence—behaviour that arises from the interaction of components but cannot be predicted by analysing those components in isolation – c.f. Synergetics (Buckminster Fuller). Your microservices architecture might work perfectly in testing, under load simulation, and even in production for months. Then one day, a particular sequence of requests, combined with a specific distribution of data across shards, triggers a cascade failure that brings down the entire system.

You can’t ‘learn’ to prevent emergent failures because you can’t predict them. They arise from the system’s complexity itself. Adding more tests, more monitoring, more safeguards—these changes don’t eliminate emergence, they just add new components to the complex system, creating new possibilities for emergent behaviour.

The Adaptation Trap

Here’s the final twist: complex adaptive systems adapt. When you implement a lesson learnt, you’re not just fixing a problem—you’re changing the system. And when the system changes, the behaviours that emerge from it change too.

Add comprehensive monitoring after an outage? Now developers start relying on monitoring as a crutch, writing less defensive code because they know they’ll be alerted to issues. Implement mandatory code review after a bad deployment? Now developers become complacent, assuming that anything that passed review must be safe. The system adapts around your interventions, often in ways that undermine their original purpose.

This isn’t a failure of implementation—it’s a fundamental characteristic of complex adaptive systems. They don’t have stable equilibrium points. Every intervention shifts the system to a new state with its own unique vulnerabilities.

So What Do We Do?

If we can’t learn from our mistakes in any straightforward way, what’s the alternative? Are we doomed to repeat the same failures for ever?

Not quite. The solution is to stop pretending we can extract universal lessons from specific failures and instead focus on building systems that are resilient to the inevitable surprises we can’t predict.

This means designing for graceful degradation rather than preventing all failures. It means building systems that can absorb shocks and recover quickly rather than systems that need to be perfect. It means accepting that production is fundamentally different from any testing environment and that the only way to understand system behaviour is to observe it in production with real users and real data.

It also means being humble. Every post-mortem that ends with ‘we’ve identified the root cause and implemented fixes to prevent this from happening again’ is cosplaying certainty in a domain defined by uncertainty. A more honest conclusion might be: ‘This is what we think happened, given our limited ability to understand complex systems. We’re making some changes that might help, but we acknowledge that we’re also potentially introducing new failure modes we haven’t imagined yet.’

The Productivity of Failure

None of this means that failures are useless. Incidents do provide value—they reveal the system’s boundaries, expose hidden assumptions, and force us to confront our mental models. But the value isn’t in extracting a tidy lesson that we can apply next time. The value is in the ongoing process of engaging with complexity, building intuition through repeated exposure, and developing a mindset that expects surprise rather than seeking certainty.

The developer who has been through multiple production incidents isn’t valuable because they’ve learnt ‘lessons’ they can enumerate. They’re valuable because they’ve internalised a posture of humility, an expectation that systems will fail in ways they didn’t anticipate, and a comfort with operating in conditions of uncertainty.

That’s not the same as learning from mistakes. It’s something both more modest and more useful: developing wisdom about the limits of what we can learn.


The next time you hear someone confidently declare that they’ve learnt from a mistake, especially in a complex domain like software development, be sceptical. Not because they’re lying or incompetent, but because they’re human—and we all want to believe that our suffering has purchased something more substantial than just the experience of suffering. The truth is messier and less satisfying: in complex adaptive systems, the best we can hope for is not wisdom, but the wisdom to know how little wisdom we can extract from any single experience.


Further Reading

Allspaw, J. (2012). Fault injection in production: Making the case for resilience testing. Queue, 10(8), 30-35. https://doi.org/10.1145/2346916.2353017

Dekker, S. (2011). Drift into failure: From hunting broken components to understanding complex systems. Ashgate Publishing.

Dekker, S., & Pruchnicki, S. (2014). Drifting into failure: Theorising the dynamics of disaster incubation. Theoretical Issues in Ergonomics Science, 15(6), 534-544. https://doi.org/10.1080/1463922X.2013.856495

Fischhoff, B. (1975). Hindsight ≠ foresight: The effect of outcome knowledge on judgment under uncertainty. Journal of Experimental Psychology: Human Perception and Performance, 1(3), 288-299. https://doi.org/10.1037/0096-1523.1.3.288

Hollnagel, E., Woods, D. D., & Leveson, N. (Eds.). (2006). Resilience engineering: Concepts and precepts. Ashgate Publishing.

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.

Leveson, N. G. (2012). Engineering a safer world: Systems thinking applied to safety. MIT Press.

Perrow, C. (1999). Normal accidents: Living with high-risk technologies (Updated ed.). Princeton University Press. (Original work published 1984)

Roese, N. J., & Vohs, K. D. (2012). Hindsight bias. Perspectives on Psychological Science, 7(5), 411-426. https://doi.org/10.1177/1745691612454303

Woods, D. D., & Allspaw, J. (2020). Revealing the critical role of human performance in software. Queue, 18(2), 48-71. https://doi.org/10.1145/3406065.3394867

The Great Listening Crisis of 2347

A Short Story

The thing about the end of human civilization is that nobody was actually paying attention when it happened.

I should know. I was there.

My name is Dr. Sarah Chen, and I’m—well, was—the Chief Communications Officer aboard the interstellar colony ship Probable Cause. Yes, that’s actually what they named it. The Naming Committee thought they were being hilarious. The Naming Committee, incidentally, did not listen to the hundreds of formal objections filed about the name. This could have been our first clue.

The crisis began at 0927 hours on a Tuesday, which is when Captain Morrison called the senior staff meeting to discuss what he termed “a minor navigational anomaly.”

“We’re heading directly into a black hole,” said Lieutenant Park, our navigator, before Morrison had even finished his opening remarks.

“Thank you, Sarah,” Morrison said to me, not making eye contact with Park. “Now, as I was saying, we need to discuss the crew morale initiative—”

“Captain,” Park interrupted, because she hadn’t yet learned that interrupting Morrison was like trying to stop a freight train with a strongly worded letter. “Black. Hole. We have maybe six hours before the gravitational effects become catastrophic.”

“I appreciate your enthusiasm,” Morrison said, still looking at his tablet, “but let’s stay on topic. The crew has been complaining about the quality of the synthetic coffee.”

I watched Park’s face do something that would have been fascinating under different circumstances. It was the precise moment when a competent professional realizes they’re living in a farce.

“Sir,” she tried again, her voice tight. “I’m telling you we’re going to die.”

“And I’m telling you,” Morrison said, finally looking up with that practiced expression of patient condescension that probably worked great at his TED talk, “that we need to maintain crew morale. Now, Raj, what’s the status on the coffee situation?”

Chief Engineer Patel looked up from his phone. “Sorry, what?”

“The coffee,” Morrison repeated.

“Oh, yeah, totally.” Patel nodded vigorously. He had clearly not been listening and was just agreeing so as to get back to his phone. “We’ll definitely get right on that.”

“Excellent,” Morrison said.

Park tried to interrupt three more times. Each time, someone spoke over her. By the fourth attempt, she just stood up and left. Morrison didn’t notice. He was too busy explaining his vision for a crew talent show.

In the hallway, I caught up with Park.

“I heard you,” I said.

She spun around. “Did you? Did you actually hear me, or are you just saying the words?”

It was a fair question. I thought about it. Had I really heard her? Or had I just detected the sounds coming from her mouth while I was busy thinking about what I was going to say next?

“Six hours?” I asked.

“Give or take.”

“What do we need to do?”

“I need access to the main navigation controls. But Morrison locked me out after I ‘exceeded my authority’ by trying to change course without his approval.”

“When did you try to change course?”

“Fifteen minutes ago. While he was ‘actively listening’ to Jenkins complain about the smell in the recycling bay.”

I pulled up my communicator. “Let me call—”

“I already sent seventeen urgent messages to the bridge crew,” Park said. “Martinez replied ‘lol.’ Thompson sent back a thumbs up. Wilson told me he was really focused on being present in the moment and couldn’t deal with my negative energy right now.”

“Wilson’s been doing that mindfulness course,” I said.

“I noticed.” Park’s voice was flat. “Very present. Very in the moment. Presently about to be in the moment when we cross the event horizon.”

We tried the direct approach next. Park and I went to the bridge together. We brought charts. We brought data. We brought a simulation that literally showed the ship being spaghettified.

Commander Oakes listened politely while checking his messages. When Park finished, he nodded thoughtfully.

“That’s really interesting,” he said. “But have you considered that maybe the black hole is a metaphor?”

“A metaphor for what?” Park’s voice had reached a pitch I didn’t know human vocal cords could achieve.

“For the darkness we all carry inside us,” Oakes said. “I’ve been reading this amazing book about—”

“It’s not a metaphor!” Park shouted. “It’s an actual goddamn black hole! With actual goddamn gravity! That is actually going to actually kill us!”

“I hear that you’re feeling frustrated,” Oakes said in the tone people use when they’ve just learned about active listening but understood none of it. “And I want you to know that your feelings are valid.”

“My feelings?” Park looked like she might actually explode.

“I’m sensing a lot of anger,” Oakes continued, making concerned eye contact while obviously thinking about something else. “Have you tried the meditation app I recommended?”

That’s when Park grabbed the fire extinguisher.

I managed to stop her before she brained him with it, but it was a close thing.

“Five hours,” she told me, breathing hard. “We have five hours left and nobody will listen for five consecutive seconds.”

I had an idea. It was a terrible idea, but all the good ideas required people to actually listen, so terrible was what we had left.

“The PA system,” I said. “Ship-wide announcement.”

“Morrison will just talk over it.”

“Not if we lock him in his quarters first.”

Park smiled for the first time that day. It was not a reassuring smile.

Fifteen minutes later, Morrison was “temporarily confined for his own safety” (he’d been practicing his juggling for the talent show and kept dropping the balls), and I had access to the PA system.

“Attention all crew,” I said, my voice echoing through every corridor of the ship. “This is Dr. Chen. Stop what you’re doing and listen. Not listening-to-reply. Not fake listening. Not listening while you think about what you’re going to have for lunch. Actually listening. Because in four hours and forty-two minutes, we’re going to die.”

I paused. On the security monitors, I could see people stopping, looking up at the speakers.

“Lieutenant Park has been trying to tell us this all morning. None of us listened. Not really. We heard sounds coming out of her mouth and we thought ‘that’s nice’ and went back to our own thoughts. We nodded and said ‘uh-huh’ and didn’t process a single word. We interrupted. We changed the subject. We got defensive. We did everything except actually listen.”

Another pause. More people were stopping now.

“Here’s what’s alive in Lieutenant Park right now,” I continued, borrowing from the backgrounder I’d read last year about NVC listening. “She’s desperate. She’s terrified. And she knows exactly how to save us if we’ll just give her the chance.”

I switched the PA over to Park.

Her voice was steady now. Clear. “We need to execute an emergency burn in four hours and thirty-eight minutes. Not before—we won’t have enough power. Not after—we’ll be past the point of no return. Chief Patel, I need you to redirect all non-essential power to the engines. Dr. Yamamoto, I need the medical bay on standby for potential G-force injuries. Martinez, I need you to stop texting and actually pilot the ship when I give the word.”

Silence.

Then: “Copy that, Lieutenant.” Patel’s voice, serious for once.

“Medical bay standing by.” Yamamoto.

“Phone’s off. Ready when you are.” Martinez.

One by one, the crew responded. Actually responded. Actually listened.

We executed the burn at exactly the right moment. The Probable Cause shuddered, groaned, and pulled away from the black hole event horizon with about twelve hundred kilometers to spare.

Later, after the excitement died down and we’d checked that everyone survived with all their limbs intact and in the right places, I found Park in the observation deck, staring at the stars.

“Thank you,” she said. “For listening.”

“I should have listened the first time,” I said. “We all should have.”

“Yeah, well.” She shrugged. “We’re human. We’re kind of terrible at it.”

“We could get better.”

“We could.” She turned to look at me. “Think we will?”

The next morning, Captain Morrison called a meeting to discuss implementing a new “active listening protocol.” He talked for forty-five minutes straight without letting anyone else speak. Half the senior staff was checking their phones. Oakes kept trying to bring up the subject of his meditation app.

I caught Park’s eye across the table. She raised an eyebrow.

“Black hole?” I mouthed silently.

She checked her console and shook her head. Then she paused, looked again, and her eyes went wide.

“Captain,” she said.

Morrison kept talking.

“Captain,” she said louder.

He held up one finger in a “wait a moment” gesture and continued his explanation of the importance of really hearing what people are saying.

“CAPTAIN!” Park shouted.

“Please don’t interrupt, Lieutenant. I’m trying to make an important point about listening.”

Park looked at me. I looked at the fire extinguisher still sitting in the corner from yesterday.

“You know what?” Park said, standing up. “Never mind. Forget I said anything.”

“Thank you,” Morrison said. “Now, as I was saying about the art of truly hearing another person—”

I give us six hours. Maybe seven.

But at least we’ll go down proving that humanity’s greatest skill has always been its absolutely remarkable ability to not pay attention to anything that matters.

 

THE END

Is Leadership the Answer?

Do you assume that leadership is a positive thing? What are the consequences of that assumption?

Resetting: An Invitation to Own What Comes Next

I haven’t published here in quite some time. Not from lack of ideas—if anything, the opposite. After over 50 years of studying and practising software development, management and organisational dynamics, I have more to say than ever about the human dimensions of our work. But I’ve realised I can approach this better.

I was writing from what I thought was important, what I wanted to explore, what I believed needed saying. And whilst there’s nothing inherently wrong with that, it misses something fundamental that I’ve spent decades learning: ownership matters, and invitation is how ownership happens.

The Gap Between Writing and Connection

Here’s what I’ve come to understand: when I write from my agenda alone, I’m imposing a curriculum. I might be right about what’s valuable or useful, but rightness isn’t the point. The point is whether what I’m offering connects with where you are, what you’re wrestling with, what you’re ready to explore.

This maps directly to what I’ve learnt about organisations. Whether you’re leading a development team, managing a department, or setting strategy at the executive level, you’re navigating social complexity. Organisations operate on collective assumptions and beliefs that are often invisible:

  • What constitutes good work
  • How decisions really get made
  • Who gets to challenge the status quo
  • What trade-offs are acceptable
  • What problems are worth solving
  • How people might better relate to each other across hierarchies
  • Etc.

These assumptions shape everything, but they’re rarely examined because no one thinks to invite that examination.

And here’s the ironic part: I’ve been doing the same thing with this blog. Operating on my assumptions about what you needed, never actually inviting you into the conversation about what this space could be.

Ironic, given that for 20 years I’ve been emphasising the Antimatter Principle—attending to people’s actual needs rather than our assumptions about them. Apparently I still have things to learn about practising what I preach. This is exactly the kind of blind spot that self-awareness is supposed to catch, and it took my recent hiatus for me to reconnect with the principle.

The Reset

So here’s what I’m proposing—really, what I’m inviting:

Tell me what you want to explore.

Not just topics, though those matter. Also formats and media. Do you want:

  • Short reflections or deep dives?
  • Case studies from real organisations or conceptual frameworks?
  • Dialogues and Q&A or essays?
  • Written posts, recorded conversations, podcasts, video shorts, or something else entirely?

I’m super interested in what you’re actually curious about. What challenges are you facing—whether that’s:

  • Team dynamics and collaboration in software development
  • Middle management’s squeeze between strategic directives and ground-level realities
  • Executive decisions about culture, structure, and organisational transformation
  • The gap between what leadership espouses and what actually happens
  • How self-awareness (individual and collective) shapes organisational outcomes
  • Navigating technical decisions with human implications
  • Making sense of resistance, politics, and power

And here’s the question that matters most: How can my experiences and insights from a long career help you?

What are you trying to understand? What are you trying to change? What patterns have you noticed but can’t quite name yet?

There are over 1500 posts in the archives here. (and my books and white papers, too). Feel free to mine them for ideas—topics you’d like to see revisited, expanded, updated, or challenged. What sparked something for you but for which you need more exploration? What made sense years ago but feels different now? What concepts need translating for today’s context?

Why This Matters

This isn’t about making the blog more ‘user-friendly’. It’s about something much deeper.

When you own the direction of your learning—when you’re invited to shape what we explore together rather than passively receiving what I decide to present—something shifts. You engage differently. You bring your own experiences into dialogue with what’s offered. You’re more likely to actually use what we discuss because it’s connected to your genuine needs and curiosity, not my assumptions about what you could be asking.

And just as importantly: I’ll learn from what you ask for. Your questions will reveal the collective assumptions and challenges in organisations right now. Your format preferences will show me how people are actually trying to integrate these ideas into their work. This becomes a genuine exchange, not a broadcast. Seems more in tune with the current zeitgeist?

The Invitation

So: What do you want to explore about the human dimensions of work—in software development, in management, in organisational life?

What problems are you facing that feel stubborn or invisible? What assumptions have you started to question? Where do you see the gap between what people say matters and what actually drives decisions? What have you noticed about how self-awareness—yours, your team’s, your organisation’s—changes what’s possible?

What format would actually be useful to you? And how can my half-century of experience serve what you’re trying to learn or accomplish?

You can reach me:

I’ve been thinking about what I want to say for a while now. I’m more interested in learning what you want to explore.

Let’s see what we can discover together!

Further Reading

Argyris, C. (1991). Teaching smart people how to learn. Harvard Business Review, 69(3), 99–109.

DeMarco, T., & Lister, T. (1999). Peopleware: Productive projects and teams (2nd ed.). Dorset House.

Marshall, R. W. (2019). Hearts over diamonds: Serving business and society through organisational psychotherapy. Leanpub.

Marshall, R. W. (2021a). Memeology: Surfacing and reflecting on the organisation’s collective assumptions and beliefs. Leanpub.

Marshall, R. W. (2021b). Quintessence: An acme for highly effective software development organisations. Leanpub.

Schein, E. H. (2010). Organizational culture and leadership (4th ed.). Jossey-Bass.

Senge, P. M. (2006). The fifth discipline: The art and practice of the learning organization (Rev. ed.). Currency/Doubleday.

Weinberg, G. M. (1992). Quality software management: Vol. 1. Systems thinking. Dorset House.

This Company Eliminated All Managers and Turned Every Product Team Into a Profitable Startup

Whilst most tech companies are debating the merits of OKRs versus alternative goal-setting frameworks, a Chinese appliance manufacturer has been quietly running one of the world’s most radical organisational experiments. Haier’s “Rendanheyi” model—which translates roughly to “inverted triangle of individual-goal combination”—has transformed a near-bankrupt refrigerator company into a global giant by eliminating traditional management and replacing it with market-driven entrepreneurship.

But could this model work for software development organisations? The answer might surprise you.

What Is Rendanheyi?

Rendanheyi flips traditional corporate structure on its head. Instead of hierarchical departments managed through cascading objectives, Haier operates as an ecosystem of thousands of small, autonomous business units called “self-managed teams” or SMTs. Each unit:

  • Operates as an independent business with full P&L responsibility
  • Serves real customers (either external customers or other internal units)
  • Buys and sells services from other units at market rates
  • Shares in financial outcomes through profit-sharing and ownership stakes
  • Has direct access to resources without middle management gatekeeping

The result? No middle managers, no annual planning cycles, no OKRs—just market forces driving alignment and accountability.

Why Traditional Goal-Setting Falls Short in Software Development

Before exploring how Rendanheyi might apply to software organisations, it’s worth acknowledging why conventional approaches often struggle:

The Innovation Problem: OKRs assume you can predict what success looks like. But breakthrough software products often emerge from experimentation that defies predetermined key results.

The Measurement Trap: Software development involves significant creative and problem-solving work that’s difficult to capture in quarterly metrics. Teams often end up optimising for what’s measurable rather than what’s valuable.

The Speed Penalty: Complex goal-setting processes add overhead and delay decision-making in an industry where speed to market is crucial.

The Scaling Challenge: As software organisations grow, maintaining alignment through cascading objectives becomes increasingly bureaucratic and disconnected from actual customer value.

Rendanheyi in Software: The Natural Fit

Software development organisations might actually be ideal candidates for Rendanheyi-style transformation, for several reasons:

Digital Products Enable Clean Unit Separation

Unlike manufacturing, where supply chains create complex interdependencies, software products can often be cleanly separated into distinct services, features, or platforms. A music streaming service, for example, could organise around units like:

  • Recommendation Engine Team (sells personalised playlists to other units)
  • Audio Infrastructure Team (provides streaming services to product teams)
  • Mobile Experience Team (builds customer-facing apps, buys services from backend teams)
  • Analytics Platform Team (sells data insights to all other units)

Natural Market Pricing Mechanisms

Software organisations already use internal concepts that mirror market pricing:

  • Infrastructure costs (AWS bills, computational resources)
  • Engineering time (sprint capacity, story points)
  • User engagement metrics (DAU, retention, conversion rates)

These existing metrics could form the basis of an internal market where teams buy and sell services from each other.

Rendanheyi Through the Lens of Quintessence

The “Quintessence” framework—which describes the ideal software development organisation—provides a compelling lens through which to evaluate Haier’s model. The alignment is remarkably strong, suggesting that Rendanheyi might represent one of the closest real-world implementations of quintessential organisational principles.

Strong Convergence Areas

Elimination of Traditional Management: Both Rendanheyi and Quintessence completely reject traditional hierarchical management. The quintessential organisation has “no managers” and emphasises that people doing the work should own “the way the work works.” Haier’s elimination of middle management and direct connection between autonomous units mirrors this perfectly.

Flow Over Silos: Quintessence emphasises horizontal value streams rather than vertical departmental structures. Haier’s approach of organising around customer-facing business units rather than functional departments aligns with this principle of organising around value flow rather than organisational convenience.

Trust and Autonomy: Both frameworks treat people as capable adults rather than resources to be controlled. Theory-Y assumptions about human nature—that people find joy in collaborative work and naturally take ownership—align with Haier’s trust in unit leaders to make entrepreneurial decisions.

Distributed Decision-Making: Quintessence advocates for the “Advice Process” and pushing decisions to where information originates. Haier’s model of autonomous decision-making within market constraints serves a similar function.

Key Tensions and Philosophical Differences

Market Mechanisms vs. Needs-Based Coordination: This represents the most interesting tension. Haier uses internal markets and P&L responsibility as coordination mechanisms, whilst Quintessence emphasises collaborative attention to “folks’ needs” through dialogue and consensus. However, these approaches might be more complementary than contradictory—internal markets serve as a mechanism for surfacing and meeting stakeholder needs.

Individual vs. Collective Accountability: Haier emphasises individual entrepreneurial ownership within small units, whilst Quintessence explicitly prefers group accountability and rejects “single wringable neck” approaches. Though Haier’s focus on small teams (10-15 people) somewhat bridges this gap.

Financial Incentives: Haier relies heavily on profit-sharing and market-based rewards, whilst Quintessence views extrinsic motivation sceptically, preferring intrinsic motivation through purpose, mastery, and autonomy. This represents a fundamental difference in assumptions about what motivates human behaviour.

Assessment: 75-85% Alignment

Haier achieves remarkable alignment with Quintessence principles—perhaps 75-85%—which is extraordinary for any large-scale implementation. The core philosophy of trusting people, eliminating bureaucracy, focusing on customer value, and enabling self-organisation aligns strongly.

The main gaps centre around Quintessence’s emphasis on nonviolence, psychology, and broader stakeholder consideration beyond customers and profits. Haier’s internal market mechanisms, whilst effective, might create competitive pressures that Quintessence would view as potentially harmful to interpersonal relationships.

Learning from Toyota’s Chief Engineer Model

Toyota’s Chief Engineer (CE) system offers another organisational model worth considering alongside Rendanheyi. The CE has complete responsibility for a vehicle programme from conception through production, coordinating across functional departments without formal authority over the specialists involved.

Key aspects of Toyota’s model that complement Rendanheyi thinking:

Cross-Functional Integration: The CE integrates expertise from multiple disciplines—similar to how Haier’s business units must coordinate across traditional functional boundaries.

Responsibility Without Authority: The CE must influence and coordinate without commanding—developing skills in consensus-building and collaborative decision-making that would serve software organisations well.

Long-Term Product Ownership: Unlike project-based structures, the CE maintains responsibility throughout the product lifecycle, similar to how Haier units maintain ongoing customer relationships.

Market-Driven Decisions: The CE makes trade-offs based on customer needs and market constraints rather than internal politics or resource optimisation.

For software organisations, a hybrid approach might combine Haier’s entrepreneurial autonomy with Toyota’s integration model—autonomous product teams with designated integrators responsible for cross-team coordination and long-term product vision.

Built-in Customer Feedback Loops

Software development organisations already have rapid feedback mechanisms through user analytics, A/B testing, and deployment metrics. This real-time customer data will drive market dynamics more effectively than quarterly OKR reviews.

A Rendanheyi Software Organisation in Practice

Imagine a mid-sized SaaS company reorganising around Rendanheyi principles:

Team Structure

Instead of traditional engineering, product, and design departments, the company forms customer-focused units:

  • Onboarding Experience Squad (responsible for new user activation)
  • Core Platform Team (provides APIs and infrastructure services)
  • Enterprise Features Unit (builds B2B functionality)
  • Growth Engine Team (drives user acquisition and retention)

Internal Market Dynamics

Teams operate on market principles:

  • The Growth Engine Team “buys” A/B testing infrastructure from the Core Platform Team at rates based on computational costs and engineering time
  • The Enterprise Features Unit “pays” the Onboarding Squad based on how many enterprise customers successfully complete setup
  • Teams can choose to build internally or “buy” from other teams based on cost and quality

Profit and Loss Responsibility

Each unit tracks financial performance:

  • Revenue attribution: Customer subscription revenue is attributed to teams based on feature usage and customer feedback
  • Cost allocation: Infrastructure, support, and development costs are allocated based on actual resource consumption
  • Profit sharing: Teams share in the financial success of their contributions

Autonomous Decision Making

Teams make independent choices about:

  • Technology stack and architecture decisions
  • Hiring and team composition
  • Feature priorities based on customer value
  • Whether to build, buy, or partner for new capabilities

The Benefits for Software Development

This approach could solve several persistent challenges in software organisations:

Faster Innovation: Teams can experiment and pivot without waiting for approval or updating company-wide roadmaps.

Better Customer Focus: When teams’ success is directly tied to customer value rather than internal metrics, product decisions improve.

Natural Scaling: As the organisation grows, new teams can form organically around customer needs rather than requiring top-down reorganisation.

Reduced Bureaucracy: No need for complex planning processes, alignment meetings, or quarterly business reviews.

Talent Retention: Engineers and designers get entrepreneurial ownership and direct impact on business outcomes.

The Challenges and Considerations

However, implementing Rendanheyi in software development isn’t without significant challenges:

Technical Architecture Requirements

Software systems would need to be architected for independence:

  • Microservices architecture to enable teams to deploy independently
  • Clear API boundaries to facilitate internal markets
  • Robust monitoring and billing to track resource usage across teams

Cultural Transformation

The shift from employee to entrepreneur mindset is profound:

  • Risk tolerance: Team members must be comfortable with financial accountability
  • Collaboration vs. competition: Internal markets could create unhealthy competition between teams
  • Leadership development: Teams need entrepreneurial skills, not just technical expertise

Measurement and Pricing Complexity

Creating fair internal markets is challenging:

  • How do you price shared infrastructure like security, compliance, or platform services?
  • What happens to teams working on foundational technology that doesn’t directly drive revenue?
  • How do you handle cross-team dependencies that don’t fit clean market models?

Regulatory and Compliance Constraints

Software companies often face regulatory requirements that don’t align with fully autonomous units:

  • Data privacy regulations may require centralised oversight
  • Security standards might mandate consistent practices across teams
  • Financial reporting may require traditional departmental structures

Implementation Strategies for Software Organisations

For software companies interested in experimenting with Rendanheyi principles, here are some practical starting points:

Start with Product Teams

Begin by giving product development teams more autonomy and P&L visibility. Let them make technology choices, prioritise features based on customer data, and share in the financial outcomes of their work.

Create Internal Service Markets

Identify shared services (infrastructure, design systems, analytics) and experiment with market-based allocation. Let teams choose between building internally or “buying” from shared service teams.

Implement Transparent Cost Allocation

Make infrastructure costs, engineering time, and other resources visible to teams. Start charging teams for their actual resource consumption rather than treating these as free shared resources.

Develop Customer-Centric Metrics

Move beyond engineering metrics (story points, velocity) to customer value metrics (feature adoption, customer satisfaction, revenue attribution).

Experiment with Team Formation

Allow teams to form organically around customer problems or business opportunities rather than maintaining static organisational boundaries.

The Future of Software Organisation

Rendanheyi represents a fundamentally different approach to organisational design—one that treats internal operations like external markets and replaces management hierarchy with entrepreneurial ownership. For software development organisations facing scaling challenges, innovation bottlenecks, and talent retention issues, it offers a compelling alternative to traditional approaches.

Whilst full implementation requires significant commitment and cultural change, the principles behind Rendanheyi—customer focus, market accountability, entrepreneurial ownership, and autonomous decision-making—can be adopted incrementally by forward-thinking software organisations.

The question isn’t whether your next quarterly planning cycle should use OKRs or another goal-setting framework. The question is whether you’re ready to move beyond goal-setting entirely and trust market forces to drive alignment and performance.

For software organisations willing to make this leap, the rewards could be transformational: faster innovation, better customer outcomes, and a more engaged, entrepreneurial workforce that thinks like owners because they actually are owners.

The future of software development might not look like traditional corporate hierarchies at all. It might look more like a marketplace of entrepreneurial teams, competing and collaborating to create customer value. And that future might be closer than we think.

Further Reading

Hamel, G. (2011). The big idea: First, let’s fire all the managers. Harvard Business Review, December 2011.

Hamel, G., & Zanini, M. (2020). Humanocracy: Creating organizations as amazing as the people inside them. Harvard Business Review Press.

Marshall, R. W. (2021). Quintessence: An acme for software development organisations. Falling Blossoms. [Available on Leanpub]

Hamel, G., & Zanini, M. (2018). The end of bureaucracy. Harvard Business Review, November-December 2018.

Kennedy, M. N. (2003). Product development for the lean enterprise: Why Toyota’s system is four times more productive and how you can implement it. Oaklea Press.

Pink, D. H. (2009). Drive: The surprising truth about what motivates us. Riverhead Books.

Reinertsen, D. G. (2009). The principles of product development flow: Second generation lean product development. Celeritas Publishing.

Sobek, D. K., Ward, A. C., & Liker, J. K. (1999). Toyota’s principles of set-based concurrent engineering. MIT Sloan Management Review, 40(2), 67-83.

Why Curiosity Beats Shame in Software Retrospectives

There’s a moment in therapy that therapists call ‘the shift’—when you stop drowning in your patterns and start watching them with fascination. You realise you’ve been having the same argument with your partner for three years, and instead of feeling like a broken record, you start laughing. ‘Oh, there I go again, catastrophising about the dishes.’ The pattern doesn’t vanish overnight, but something fundamental changes: you’re no longer at war with yourself.

What if software teams could experience this same shift?

The Drama We Know By Heart

Every team has their recurring drama. Maybe it’s the sprint planning meeting that always runs two hours over because nobody can agree on story points. Perhaps it’s the deployment Friday that inevitably becomes deployment Monday because ‘just one small thing’ broke. Or the code review discussions that spiral into philosophical debates about variable naming or coding standards more generally, whilst the actual logic bugs slip through unnoticed.

We know these patterns intimately. We’ve lived them dozens of times. Yet most teams approach retrospectives like a tribunal, armed with post-its and grim determination to ‘fix our dysfunction once and for all.’ We dissect our failures with the energy of surgeons operating on ourselves, convinced that enough shame and analysis will finally make us different people.

But what if we’re approaching this backwards?

The Mice Would Find Us Fascinating

Douglas Adams had it right when he suggested that mice might be the truly intelligent beings, observing human behaviour with scientific curiosity. Imagine if we could watch our team dynamics the way those hyperintelligent mice observe us—with detached fascination rather than existential dread.

‘Interesting,’ the mice might note. ‘When the humans feel time pressure, they consistently skip the testing phase, then spend three times longer fixing the resulting problems. They repeat this behaviour with remarkable consistency, despite claiming to have “learned their lesson” each time.’

The mice wouldn’t judge us. They’d simply observe the pattern, maybe take some notes, perhaps adjust their experiment parameters. They wouldn’t waste energy being disappointed in human nature.

The Science of Predictable Irrationality

Behavioural economists like Dan Ariely have spent decades documenting how humans make decisions in ways that are wildly irrational but remarkably consistent. We’re predictably bad at estimating time, systematically overconfident in our abilities, and reliably influenced by factors we don’t even notice. These aren’t bugs in human cognition—they’re features that served us well in evolutionary contexts but create interesting challenges in modern day work environments.

Software teams exhibit these same patterns at scale. We consistently underestimate complex tasks (planning fallacy), overvalue our current approach versus alternatives (status quo bias), and make decisions based on whoever spoke last in the meeting (recency effect). The beautiful thing is that once you name these patterns, they become less mysterious and more laughable.

Curiosity as a Debugging Tool

When we approach our team patterns with curiosity instead of judgement, something magical happens. The defensive walls come down. Instead of ‘Why do we always screw this up?’ we start asking ‘What conditions reliably create this outcome?’

This shift from shame to science transforms retrospectives from group therapy sessions into collaborative debugging. We’re not broken systems that need fixing—we’re complex systems exhibiting predictable behaviours under certain conditions. Complex systems can be better understood through observation, and sometimes influenced through small experiments, though the outcomes are often unpredictable

Consider the team that always underestimates their stories. The shame-based approach produces familiar results: ‘We need to be more realistic about our estimates.’ (Spoiler alert: they won’t be.) The curiosity-based approach asks different questions: ‘What happens right before we make these optimistic estimates? What information are we missing? What incentives and other factors are shaping our behaviour?’

The Hilariously Predictable Humans

Once you start looking for patterns with curiosity, they become almost endearing. The senior developer who always says ‘this should be quick’ right before disappearing into a three-day rabbit hole. The product manager who swears this feature is ‘simple’ whilst gesturing vaguely at convoluted requirements that would make a vicar weep. The team that collectively suffers from meeting amnesia, forgetting everything discussed five seconds after the meeting ends.

These aren’t character flaws to be eliminated. They’re what Dan Ariely would call ‘predictably irrational’ behaviours—systematic quirks in how humans process information and make decisions. The senior developer genuinely believes it will be quick because they’re anchored on the happy path scenario (classic anchoring bias). The product manager sees simplicity because they’re viewing it through the lens of user experience, not implementation complexity (curse of knowledge in reverse). The team forgets meeting details because our brains are optimised for pattern recognition, not information retention across context switches.

We’re not broken. We’re just predictably, irrationally human.

Practical Curiosity: Retrospective Questions That Transform

Instead of ‘What went wrong this sprint?’ you might like to try:

  • ‘What hilariously predictable human things did we do again?’
  • ‘If we were studying ourselves from the outside, what would be fascinating about our behaviour?’
  • ‘What patterns are we executing so consistently that we could almost set our watches by them?’
  • ‘Under what conditions do we make our most questionable decisions?’
  • What shared assumptions inevitably led to this sprint’s outcomes?
  • ‘What would the mice find interesting about how we work?’

These questions invite observation rather than judgement. They make space for laughter, which is the enemy of shame. And they reduce the role of shame—the antithesis of learning.

The Liberation of Accepting Our Programming

Here’s the paradox: accepting our patterns makes them easier to change. When we stop fighting our humanity and start working with it, we find leverage points we never noticed before.

The team that always underestimates might not become perfect estimators, but they can build buffers into their process (Cf. TOC). The developer who disappears into rabbit holes can set timers and check-in points (such as Pomodoros). The product manager can be paired with someone who thinks in implementation terms.

We don’t have to become different people. We just have to become people who understand ourselves better.

AI as a Curiosity Amplifier

Here’s where artificial intelligence might genuinely help—not as a problem-solver, but as a curiosity amplifier. AI excels at exactly the kind of pattern recognition that’s hard for humans trapped inside their own systems.

Pattern Recognition Beyond Human Limits

AI could spot correlations across longer timeframes than teams naturally track. Perhaps story underestimation always happens more, or less, after certain types of client calls, or when specific team members are on holiday. Maybe over-architecting solutions correlates with unclear requirements, or planning meetings grow longer when the previous sprint’s velocity dropped.

These are the kinds of subtle, multi-factor patterns that human memory and attention struggle with, but that could reveal fascinating insights about team behaviour.

Systematic Curiosity Drilling

More intriguingly, AI could help teams ask better layered questions: ‘We always over-architect when requirements are vague → What specific types of vagueness trigger this? → What makes unclear requirements feel threatening? → What would need to change to make simple solutions feel safe when requirements are evolving?’

This is the kind of systematic curiosity that therapists use—moving from ‘this is problematic’ to ‘this is interesting, let’s understand the deep logic.’ AI could be brilliant at sustaining that investigation without getting distracted or defensive.

The Crucial Cautions

But here’s what AI absolutely cannot do: the therapeutic shift itself. The moment of laughing at your patterns instead of being tormented by them? That’s irreplaceably human. AI risks creating surveillance anxiety—the sense that someone (or something) is always watching and judging.

There’s also the fundamental risk of reinforcing the very ‘fix the humans’ mentality this approach seeks to avoid. AI pattern recognition could easily slide back into ‘here are your dysfunctions, now optimise them away.’

The sweet spot might be AI as a very patient, non-judgmental research assistant—helping teams investigate their own behaviour more thoroughly. The humans still have to do the laughing, the accepting, and the choosing. But AI could make the curiosity richer and more evidential.

Just remember: the mice observed the humans with detached fascination, not with algorithms for improvement.

The Recursive Gift

The most beautiful part of this approach is that it’s recursive. Once your team learns to observe its patterns with curiosity, you’ll start applying this same gentle scrutiny to your retrospectives themselves. You’ll notice when you slip back into judgement mode and laugh about it. You’ll develop patterns for catching patterns.

You’ll become a team that’s as interested in how you think as in what you build. And that might be the most valuable code you ever debug.

The Pattern That Doesn’t Disappear

Your recurring drama won’t vanish. The sprint planning will probably still run long sometimes. The ‘quick fix’ will occasionally become a weekend project. But your relationship to these patterns will transform. You’ll work on them without the crushing weight of believing you should be different than you are.

And in that space—between pattern and judgement, between observation and criticism—you’ll find something remarkable: the room to actually change.

The mice would be proud.


Further Reading

Adams, D. (1979). The hitchhiker’s guide to the galaxy. Harmony Books.

Ariely, D. (2008). Predictably irrational: The hidden forces that shape our decisions. Harper.

Netó, D., Oliveira, J., Lopes, P., & Machado, P. P. (2024). Therapist self-awareness and perception of actual performance: The effects of listening to one recorded session. Research in Psychotherapy: Psychopathology, Process and Outcome, 27(1), 722. https://doi.org/10.4081/ripppo.2024.722

Williams, E. N. (2008). A psychotherapy researcher’s perspective on therapist self-awareness and self-focused attention after a decade of research. Psychotherapy Research, 18(2), 139-146.

Just Who is this Guy (FlowChainSensei) Anyway? And Why is He Qualified to Comment on Agile Software Development?

Claude and I wrote me a new bio.

In the world of software development discourse, few voices are as provocative—or as polarising—as the one behind the handle FlowChainSensei. If you’ve spent any time in Agile circles online, you’ve likely encountered his scathing critiques of Agile software development practices. But who exactly is this mysterious figure who claims that ‘40 million brilliant minds‘ are now ‘spending their days in fruitless stand-ups and retrospectives’ and that ‘Agile has zero chance of delivering on its promises’?

Meet Bob Marshall: The Organisational AI Therapist

FlowChainSensei is the online persona of Bob Marshall, who currently describes himself as an ‘Organisational AI therapist’. This isn’t just a catchy title—Bob brings serious credentials that explain why his voice carries weight in discussions about the future of both software development and organisational effectiveness.

Backgrounder

Bob’s career trajectory reveals the depth of his software development expertise. His first 20 years were spent in the trenches as a developer, analyst, designer, architect and code troubleshooter—roles that gave him intimate knowledge of how software actually gets built and where things go wrong. This hands-on experience was followed by some 15 years helping a multitude of clients improve their software development approaches, before evolving into his current therapeutic practice.

This progression from practitioner to consultant to therapist reflects an increasingly sophisticated understanding of where the real problems lie in software development—not in the technical details, but in the human and organisational systems that create the context for technical work.

Five Decades in the Vanguard

Bob’s most compelling qualification is his longevity and verifiable involvement in the field. With 53 years in software development—including creating back in 1994 some practices that later became known as Agile—he has demonstrable evidence of being in the thick of it and the vanguard even before Agile happened.

During the period from 1994-2000, Bob was instrumental in creating what he calls ‘European Agile’ and created the Javelin software development method. This wasn’t someone learning about Agile from a certification course—Bob has documentation, project records, and verifiable traces of his involvement in developing the foundational practices years before the Agile Manifesto was even written.

From Agile Pioneer to Organisational Psychotherapist

What sets Bob apart from other Agile worthies is his evolution beyond traditional consulting approaches. He spent fours years as founder and CEO of Familiar, the first 100% Agile software house in Europe, but that was decades ago. He hasn’t been a consultant for over 20 years.

Instead, Bob developed what he calls ‘Organisational Psychotherapy’—bringing psychotherapy techniques out of the therapy room and into the organisation as a whole. He’s documented this approach extensively in his book Hearts over Diamonds: Serving Business and Society through Organisational Psychotherapy (Marshall, 2019).

The Therapeutic Alliance: Why Relationships Trump Solutions

Bob’s approach inverts everything most people expect from organisational change work. Where consultants diagnose problems and provide solutions, Bob creates space for organisations to surface their own unconscious assumptions. The key insight: it’s the therapeutic relationship itself that enables change, not any specific techniques or frameworks.

This relationship-centred approach explains why his work feels ‘alien’ to most business frameworks. People can’t categorise it as consulting, coaching, training, or change management because it operates from completely different assumptions about how transformation happens. As Bob notes in his therapeutic practice: voluntary participation is fundamental—nobody can be forced into genuine therapeutic engagement.

Organisational Cognitive Dissonance: The Hidden Driver of Readiness

One of Bob’s notable contributions is his analysis of organisational cognitive dissonance—what happens when organisations simultaneously hold incompatible belief systems. In his seminal 2012 post on OrgCogDiss, he explains how this internal tension creates the conditions for genuine change.

Unlike external pressure, which organisations can often rationalise away, cognitive dissonance is internal and harder to dismiss. Bob observed that this dissonance typically resolves itself within a nine-month half-life—but often not in the direction change agents hope for. Organisations either fully adopt new approaches or revert to old patterns, but they can’t sustain internal contradictions indefinitely.

This insight explains why so many transformation efforts create enormous organisational pain but ultimately fail. The dissonance gets resolved through exits and resistance rather than genuine adoption, leaving organisations depleted but not actually transformed.

The Memeplex Problem: Why Piecemeal Change Fails

Bob’s work on ‘memeplexes’—interlocking systems of organisational beliefs—reveals why most change initiatives fail. You can’t swap out individual beliefs when they’re part of an interlocking system. Trying to introduce ‘self-organisation’ into a command-and-control organisation without addressing the entire supporting structure of beliefs about authority, expertise, and planning just creates internal contradictions.

He explores this concept further in his book Memeology: Surfacing and Reflecting on the Organisation’s Collective Assumptions and Beliefs (Marshall, 2021). Most failed transformations are attempts to graft elements from one memeplex onto another incompatible one, creating the very cognitive dissonance that eventually leads to rejection of the new elements.

Beyond Agile: The Quintessence Alternative

Bob isn’t just a critic—he’s developed alternatives. His framework ‘Quintessence’, detailed in his book Quintessence: An Acme for Highly Effective Software Development Organisations (Marshall, 2021), represents what he calls ‘the radical departure from Agile norms, based as it is on people-oriented technologies such as sociology, group dynamics, psychiatry, psychology, psychotherapy, anthropology, cognitive science and modern neuroscience’.

But true to his therapeutic approach, Bob doesn’t push Quintessence as a solution to be implemented. Instead, he creates conditions where organisations might naturally evolve toward more effective ways of working based on their own insights and readiness.

The Evidence Question: Why Facts Don’t Change Minds

Bob makes a provocative observation about the role of evidence in organisational change: assertions often carry more weight than verifiable facts because ‘nobody’s opinion is swayed by evidence’. This isn’t cynicism—it’s recognition that evidence gets interpreted within existing paradigms until something else creates readiness for change.

Drawing on Thomas Kuhn’s work on paradigm shifts, Bob notes that evidence alone never creates fundamental change—it gets reinterpreted within existing frameworks until organisations become ready to see things differently. This readiness comes from organisational stress and cognitive dissonance, not from logical argument.

The Readiness Challenge: Why Most People Don’t Engage

Bob sees the general lack of engagement with his writing as corroboration for Gallup’s data on employee engagement—few are yet ready to own improvement efforts and their own motivation. Most people remain trapped in patterns where they expect solutions to be provided rather than taking responsibility for their own transformation.

This explains why his therapeutic approach focuses on creating conditions for readiness rather than trying to convince people with evidence or argument. Until someone genuinely wants to change, all the insights in the world won’t help them.

Why His Critique Resonates

Bob’s perspective resonates with many practitioners experiencing Agile fatigue because he articulates what they feel but struggle to express. His recent post ‘How We Broke 40 Million Developers‘ struck a chord by describing how modern practices often feel performative rather than productive.

The Bottom Line: Qualified by Experience and Understanding

Is Bob qualified to comment on Agile (and the alternatives)? Absolutely. His five+ decades in software development, his demonstrable involvement in creating pre-Agile practices, his experience sucessfully founding and running the first 100% Agile software house in Europe, and his deep work in organisational psychology and change give him a unique vantage point.

His perspective is clearly informed by his therapeutic practice and his promotion of alternative approaches. And his core challenge remains valid: after more than 20 years of Agile domination, are we better at attending to people’s needs? Are users getting products and services that genuinely serve them better?

The Therapeutic Difference

What makes Bob’s voice distinctive isn’t just his comments on Agile—it’s his deep insights and understanding of how organisational change actually works. His therapeutic approach recognises that transformation happens through relationships and readiness, not through evidence and argument. Organisations change when they’re ready to see themselves differently, not when they’re presented with compelling data about their dysfunction.

This insight challenges the entire ‘evidence-based’ approach to organisational improvement. Bob suggests that meaningful change happens through shifts in readiness and perspective, with evidence becoming compelling only after those shifts occur, not before.

Whether you see Bob as a wise elder statesman or a contrarian voice, his decades of experience and unique therapeutic perspective offer valuable insights into why so many development efforts and ‘Agile transformation’ efforts fail—and what might work better.

Of course, you have to have the motivation to do better for any of Bob’s insights to be of any help to you.

Further Reading

Argyris, C. (1990). Overcoming organizational defenses: Facilitating organizational learning. Allyn & Bacon.

Bridges, W. (2004). Managing transitions: Making the most of change (2nd ed.). Da Capo Press.

Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press.

Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press.

Marshall, R. W. (2019). Hearts over diamonds: Serving business and society through organisational psychotherapy. Leanpub.

Marshall, R. W. (2021a). Memeology: Surfacing and reflecting on the organisation’s collective assumptions and beliefs. Leanpub.

Marshall, R. W. (2021b). Quintessence: An acme for highly effective software development organisations. Leanpub.

Marshall, R. W. (2012, November 16). OrgCogDiss. Think Different. Retrieved from https://flowchainsensei.wordpress.com/2012/11/16/orgcogdiss/

Rogers, C. R. (1951). Client-centered therapy: Its current practice, implications, and theory. Houghton Mifflin.

Seligman, M. E. P. (2011). Flourish: A visionary new understanding of happiness and well-being. Free Press.


Bob Marshall blogs at Think Different and his books on Organisational Psychotherapy, Memeology, and Quintessence are available through Leanpub.

The Agile Paradox: Why Developers Can’t Find a Way Out

How management mandates create the very problem they’re meant to solve


I’ve had the same conversation dozens of times over the past few years. A developer, usually over coffee or in the quiet corner of a conference, leans in and says something like: ‘I’m so tired of Agile. The ceremonies feel meaningless, the estimates are fiction, and we’re not actually being agile at all. Isn’t there something better?’

The frustration is real, and it’s widespread. But here’s the kicker: when I ask what alternative they’d prefer, the conversation usually stalls. Not because developers lack ideas—they have plenty—but because they’ve internalised a harsh truth. In most organisations, no alternative to Agile is ‘viable’ precisely because management has mandated Agile as the solution.

This creates a perfect catch-22 that keeps teams trapped in approaches that often aren’t working for them.

The Agile Promise vs Reality

Let’s be clear: Agile methodologies weren’t born from management consultants or process enthusiasts. They emerged from developers who were frustrated with rigid, documentation-heavy approaches that seemed designed to prevent software from being built. The original Agile Manifesto was a rebellion against exactly the kind of top-down mandate we see today.

But somewhere along the way, Agile became the thing it was meant to replace. Instead of ‘individuals and interactions over processes and tools’, we got Scrum Masters obsessing over story point velocity. Instead of ‘responding to change over following a plan’, we got sprint commitments treated as unbreakable contracts.

The irony is thick.

The Viability Trap

Here’s where the organisational dynamics get truly perverse. When management declares Agile as the standard approach, they’re not just choosing a process—they’re making it the only process that can succeed within the organisational context.

Consider what ‘viable’ means in a corporate environment:

  • Resource allocation: Only Agile-compatible roles get budget approval
  • Tool selection: The company invests in Jira, Azure DevOps, or other Agile-focused platforms
  • Performance metrics: Success is measured in story points, sprint velocity, and burndown charts
  • Career advancement: Understanding Agile ceremonies becomes a prerequisite for leadership roles
  • Vendor relationships: Third-party teams and consultants are selected based on Agile experience

In this ecosystem, suggesting an alternative approach isn’t just a process change—it’s asking the organisation to abandon significant investments and restructure fundamental management assumptions about how work gets measured and managed.

The Innovation Stifling Effect

The mandate creates a particularly insidious problem: it prevents the organic experimentation that led to Agile in the first place. The most effective development approaches often emerge from teams trying to solve specific problems in specific contexts. But when there’s only one ‘approved’ way to work, this natural evolution stops.

I’ve seen teams that would benefit enormously from approaches like:

  • Kanban-style continuous flow for maintenance-heavy projects
  • Shape Up-style cycles for product development with unclear requirements
  • Lean startup approaches for experimental features
  • Traditional waterfall for compliance-heavy or well-understood domains
  • Quintessence for a total, yet incremental and self-paced overhaul of shared assumptions and beliefs about the very nature of work

But suggesting any of these becomes an uphill battle against established process, tooling, and metrics—not because they wouldn’t work better, but because the organisation has made them unviable by design (and mandate).

The Hidden Costs

This viability trap creates costs that rarely show up in sprint retrospectives:

Developer burnout increases when people feel trapped in ineffective processes they can’t change. The psychological impact of being forced to participate in ceremonies you believe are wasteful is significant.

Innovation slows when teams spend more energy navigating process requirements than solving actual problems. Every stand-up spent discussing why estimates were wrong is time not spent making the product better.

Talent retention suffers as experienced developers seek environments where they have more autonomy over how they work. The best developers often have options, and rigid process adherence isn’t typically what attracts them (understatement!).

Breaking the Cycle

So how do we break out of this trap? Solution suggest we recognise that viability of alternatives is as much about the organisation as it is about development practices.

Start with principles, not processes. Instead of mandating specific ceremonies or frameworks, organisations might choose to focus on outcomes: shipped software, customer satisfaction, team health. Let teams experiment with how they achieve these goals.

Measure what matters. Story point velocity tells you very little about whether you’re building the right thing or building it well. Focus on metrics that actually correlate with business success and the needs of (all the) Folks That Matter™.

Create safe spaces for experimentation. Allow teams to try different approaches for specific projects or timeframes. Make it clear that experiments are encouraged, and not career-limiting moves.

Invest in tooling flexibility. Choose platforms and tools that can support multiple approaches rather than locking into Agile-specific solutions (bin JIRA)

Most importantly, recognise that context matters. The approach that works for a startup building an MVP is different from what works for a team maintaining critical infrastructure. One size fits all is exactly the kind of thinking that led to Agile rebellion in the first place.

The Path Forward

The developers crying out for alternatives aren’t anti-process or anti-collaboration. They’re pro-effectiveness. They want to build great software and have positive working relationships with their colleagues. They’re frustrated because they can see that the current approach isn’t achieving those goals, but they’re powerless to change it.

The path forward invites courage from both developers and management.

Until then, we’ll continue to have conversations in coffee shop corners about how things could be better, whilst the next sprint planning meeting looms on the calendar.

The real tragedy isn’t that Agile doesn’t work—it’s that we’ve created organisational structures that prevent us from discovering what would work better. And that’s the most un-agile thing of all.


What alternatives have you wanted to try but couldn’t? How has your organisation balanced consistency in the way the work works with team autonomy? The conversation continues in the comments and, hopefully, in conference room discussions everywhere.

Further Reading

Abrahamsson, P., Salo, O., Ronkainen, J., & Warsta, J. (2002). Agile software development methods: Review and analysis (Technical Report No. 478). VTT Publications.

Anderson, D. J. (2010). Kanban: Successful evolutionary change for your technology business. Blue Hole Press.

Boehm, B., & Turner, R. (2003). Balancing Agility and discipline: A guide for the perplexed. Addison-Wesley Professional.

British Computer Society. (2023, November 7). The uncomfortable truth about Agile. https://www.bcs.org/articles-opinion-and-research/the-uncomfortable-truth-about-agile/

Cockburn, A. (2006). Agile software development: The cooperative game (2nd ed.). Addison-Wesley Professional.

Dalmijn, M. (2020, May 6). Basecamp’s Shape Up: How different is it really from Scrum? Serious Scrum. https://medium.com/serious-scrum/basecamps-shape-up-how-different-is-it-really-from-scrum-c0298f124333

DeMarco, T., & Lister, T. (2013). Peopleware: Productive projects and teams (3rd ed.). Addison-Wesley Professional.

Hunt, A. (2015, December 20). The failure of Agile. Atlantic Monthly. https://blog.toolshed.com/2015/05/the-failure-of-agile.html

Noble, J., & Biddle, R. (2018). Back to the future: Origins and directions of the “Agile Manifesto”—views of the originators. Journal of Software Engineering Research and Development, 6(1), Article 15. https://doi.org/10.1186/s40411-018-0059-z

Poppendieck, M., & Poppendieck, T. (2003). Lean software development: An Agile toolkit. Addison-Wesley Professional.

Serrador, P., & Pinto, J. K. (2015). Does Agile work? A quantitative analysis of Agile project success. International Journal of Project Management, 33(5), 1040-1051.

Singer, R. (2019). Shape Up: Stop running in circles and ship work that matters. Basecamp. https://basecamp.com/shapeup

Thomas, D. (2019). Agile is dead (long live Agility). In Proceedings of the 2019 ACM SIGPLAN Symposium on Scala (pp. 1-1).

The Agile Manifesto: Rearranging Deck Chairs While Five Dragons Burn Everything Down

Why the ‘Sound’ Principles Miss the Dragons That Actually Kill Software Projects

The Agile Manifesto isn’t wrong, per se—it’s addressing the wrong problems entirely. And that makes it tragically inadequate.

For over two decades, ‘progressive’ software teams have been meticulously implementing sprints, standups, and retrospectives whilst the real dragons have been systematically destroying their organisations from within. The manifesto’s principles aren’t incorrect; they’re just rearranging deck chairs on the Titanic whilst it sinks around them.

The four values and twelve principles address surface symptoms of dysfunction whilst completely ignoring the deep systemic diseases that kill software projects. It’s treating a patient’s cough whilst missing the lung cancer—technically sound advice that’s spectacularly missing the point.

The Real Dragons: What Actually Destroys Software Teams

Whilst we’ve been optimising sprint ceremonies and customer feedback loops, five ancient dragons have been spectacularly burning down software development and tech business effectiveness:

Dragon #1: Human Motivation Death Spiral
Dragon #2: Dysfunctional Relationships That Poison Everything
Dragon #3: Shared Delusions and Toxic Assumptions
Dragon #4: The Management Conundrum—Questioning the Entire Edifice
Dragon #5: Opinioneering—The Ethics of Belief Violated

These aren’t process problems or communication hiccups. They’re existential threats that turn the most well-intentioned agile practices into elaborate theatre whilst real work grinds to a halt. And the manifesto? It tiptoes around these dragons like they don’t exist.

Dragon #1: The Motivation Apocalypse

‘Individuals and interactions over processes and tools’ sounds inspiring until you realise that your individuals are fundamentally unmotivated to do good work. The manifesto assumes that people care—but what happens when they don’t?

The real productivity killer isn’t bad processes; it’s developers who have mentally checked out because:

  • They’re working on problems they find meaningless
  • Their contributions are invisible or undervalued
  • They have no autonomy over how they solve problems
  • The work provides no sense of mastery or purpose
  • They’re trapped in roles that don’t match their strengths

You can have the most collaborative, customer-focused, change-responsive team in the world, but if your developers are quietly doing the minimum to avoid getting fired, your velocity will crater regardless of your methodology.

The manifesto talks about valuing individuals but offers zero framework for understanding what actually motivates people to do their best work. It’s having a sports philosophy that emphasises teamwork whilst ignoring whether the players actually want to win the game. How do you optimise ‘individuals and interactions’ when your people have checked out?

Dragon #2: Relationship Toxicity That Spreads Like Cancer

‘Customer collaboration over contract negotiation’ assumes that collaboration is even possible—but what happens when your team relationships are fundamentally dysfunctional?

The real collaboration killers that the manifesto ignores entirely:

  • Trust deficits: When team members assume bad faith in every interaction
  • Ego warfare: When technical discussions become personal attacks on competence
  • Passive aggression: When surface civility masks deep resentment and sabotage
  • Fear: When people are afraid to admit mistakes or ask questions
  • Status games: When helping others succeed feels like personal failure

You hold all the retrospectives you want, but if your team dynamics are toxic, every agile practice becomes a new battlefield. Sprint planning turns into blame assignment. Code reviews become character assassination. Customer feedback becomes ammunition for internal warfare.

The manifesto’s collaboration principles are useless when the fundamental relationships are broken. It’s having marriage counselling techniques for couples who actively hate each other—technically correct advice that misses the deeper poison. How do you collaborate when trust has been destroyed? What good are retrospectives when people are actively sabotaging each other?

Dragon #3: Shared Delusions That Doom Everything

‘Working software over comprehensive documentation’ sounds pragmatic until you realise your team is operating under completely different assumptions about what ‘working’ means, what the software does, and how success is measured. But what happens when your team shares fundamental delusions about reality?

The productivity apocalypse happens when teams share fundamental delusions:

  • Reality distortion: Believing their product is simpler/better/faster than it actually is
  • Capability myths: Assuming they can deliver impossible timelines with current resources
  • Quality blindness: Thinking ‘works on my machine’ equals production-ready
  • User fiction: Building for imaginary users with imaginary needs
  • Technical debt denial: Pretending that cutting corners won’t compound into disaster

These aren’t communication problems that better customer collaboration can solve—they’re shared cognitive failures that make all collaboration worse. When your entire team believes something that’s factually wrong, more interaction just spreads the delusion faster.

The manifesto assumes that teams accurately assess their situation and respond appropriately. But when their shared mental models are fundamentally broken? All the adaptive planning in the world won’t help if you’re adapting based on fiction.

Dragon #4: The Management Conundrum—Why the Entire Edifice Is Suspect

‘Responding to change over following a plan’ sounds flexible, but let’s ask the deeper question: Why do we have management at all?

The manifesto takes management as a given and tries to optimise around it. But what if the entire concept of management—people whose job is to direct other people’s work without doing the work themselves—is a fundamental problem?

Consider what management actually does in most software organisations:

  • Creates artificial hierarchies that slow down decision-making
  • Adds communication layers that distort information as it flows up and down
  • Optimises for command and control rather than effectiveness
  • Makes decisions based on PowerPoint and opinion rather than evidence
  • Treats humans like interchangeable resources to be allocated and reallocated

The devastating realisation is that management in software development is pure overhead that actively impedes the work. Managers who:

  • Haven’t written code in years (or ever) making technical decisions
  • Set timelines based on business commitments rather than reality
  • Reorganise teams mid-project because a consultant recommended ‘matrix management’ or some such
  • Measure productivity by story points rather than needs attended to (or met)
  • Translate clear customer needs into incomprehensible requirements documents

What value does this actually add? Why do we have people who don’t understand the work making decisions about the work? What if every management layer is just expensive interference?

The right number of managers for software teams is zero. The entire edifice of management—the org charts, the performance reviews, the resource allocation meetings—is elaborate theatre that gets in the way of people solving problems.

Productive software teams operate more like research labs or craftsman guilds: self-organising groups of experts who coordinate directly with each other and with the people who use their work. No sprint masters, no product owners, no engineering managers—just competent people working together to solve problems.

The manifesto’s principles assume management exists and try to make it less harmful. But they never question whether it has any value at all.

Dragon #5: Opinioneering—The Ethics of Belief Violated

Here’s the dragon that the manifesto not only ignores but actually enables: the epidemic of strong opinions held without sufficient evidence.

William Kingdon Clifford wrote in 1877 that

‘it is wrong always, everywhere, and for anyone, to believe anything upon insufficient evidence’
(Clifford, 1877).

In software development, we’ve created an entire culture that violates this ethical principle daily through systematic opinioneering:

Technical Opinioneering: Teams adopting microservices because they’re trendy, not because they solve actual problems. Choosing React over Vue because it ‘feels’ better. Implementing event sourcing because it sounds sophisticated. Strong architectural opinions based on blog posts rather than deep experience with the trade-offs.

Process Opinioneering: Cargo cult agile practices copied from other companies without understanding why they worked there. Daily standups that serve no purpose except ‘that’s what agile teams do.’ Retrospectives that generate the same insights every sprint because the team has strong opinions about process improvement but no evidence about what actually works.

Business Opinioneering: Product decisions based on what the CEO likes rather than what users require. Feature priorities set by whoever argues most passionately rather than data about user behaviour. Strategic technology choices based on industry buzz rather than careful analysis of alternatives.

Cultural Opinioneering: Beliefs about remote work, hiring practices, team structure, and development methodologies based on what sounds right rather than careful observation of results.

The manifesto makes this worse by promoting ‘individuals and interactions over processes and tools’ without any framework for distinguishing between evidence-based insights and opinion-based groupthink. It encourages teams to trust their collective judgement without asking whether that judgement is grounded in sufficient evidence. But what happens when the collective judgement is confidently wrong? How do you distinguish expertise from persuasive ignorance?

When opinioneering dominates, you get teams that are very confident about practices that don’t work, technologies that aren’t suitable, and processes that waste enormous amounts of time. Everyone feels like they’re making thoughtful decisions, but they’re sharing unfounded beliefs dressed up as expertise.

The Deeper Problem: Dysfunctional Shared Assumptions and Beliefs

The five dragons aren’t just symptoms—they’re manifestations of something deeper. Software development organisations operate under shared assumptions and beliefs that make effectiveness impossible, and the Agile Manifesto doesn’t even acknowledge this fundamental layer exists.

My work in Quintessence provides the missing framework for understanding why agile practices fail so consistently. The core insight is that organisational effectiveness is fundamentally a function of collective mindset:

Organisational effectiveness = f(collective mindset)

I demonstrate that every organisation operates within a “memeplex“—a set of interlocking assumptions and beliefs about work, people, and how organisations function. These beliefs reinforce each other so strongly that changing one belief causes the others to tighten their grip to preserve the whole memeplex.

This explains why agile transformations consistently fail. Teams implement new ceremonies whilst maintaining the underlying assumptions that created their problems in the first place. They adopt standups and retrospectives whilst still believing people are motivated, relationships are authentic, management adds value, and software is always the solution.

Consider the dysfunctional assumptions that pervade conventional software development:

About People: Most organisations and their management operate under “Theory X” assumptions—people are naturally lazy, require external motivation, need oversight to be productive, and will shirk responsibility without means to enforce accountability. These beliefs create the very motivation problems they claim to address.

About Relationships: Conventional thinking treats relationships as transactional. Competition drives performance. Hierarchy creates order. Control prevents chaos. Personal connections are “unprofessional.” These assumptions poison the collaboration that agile practices supposedly enable.

About Work: Software is the solution to every problem. Activity indicates value. Utilisation (of eg workers) drives productivity. Efficiency trumps effectiveness. Busyness proves contribution. These beliefs create the delusions that make teams confidently ineffective.

About Management: Complex work requires coordination. Coordination requires hierarchy. Hierarchy requires managers. Managers add value through oversight and direction. These assumptions create the parasitic layers that impede the very work they claim to optimise.

About Knowledge: Strong opinions indicate expertise. Confidence signals competence. Popular practices are best practices. Best practices are desirable. Industry trends predict future success. These beliefs create the opinioneering that replaces evidence with folklore.

Quintessence (Marshall, 2021) shows how “quintessential organisations” operate under completely different assumptions:

  • People find joy in meaningful work and naturally collaborate when conditions support it
  • Relationships based on mutual care and shared purpose are the foundation of effectiveness
  • Work is play when aligned with purpose and human flourishing
  • Management is unnecessary parasitism—people doing the work make the decisions about the work
  • Beliefs must be proportioned to evidence and grounded in serving real human needs

The Agile Manifesto can’t solve problems created by fundamental belief systems because it doesn’t even acknowledge these belief systems exist. It treats symptoms whilst leaving the disease untouched. Teams optimise ceremonies whilst operating under assumptions that guarantee continued dysfunction.

This is why the Qunitessence approach differs so radically from ‘Agile’ approaches. Instead of implementing new practices, quintessential organisations examine their collective assumptions and beliefs. Instead of optimising processes, they transform their collective mindset. Instead of rearranging deck chairs, they address the fundamental reasons the ship is sinking.

The Manifesto’s Tragic Blindness

Here’s what makes the Agile Manifesto so inadequate: it assumes the Five Dragons don’t exist. It offers principles for teams that are motivated, functional, reality-based, self-managing, and evidence-driven—but most software teams are none of these things.

The manifesto treats symptoms whilst ignoring diseases:

  • It optimises collaboration without addressing what makes collaboration impossible
  • It values individuals without confronting what demotivates them
  • It promotes adaptation without recognising what prevents teams from seeing their shared assumptions and beliefs clearly
  • It assumes management adds value rather than questioning whether management has any value at all
  • It encourages collective decision-making without any framework for leveraging evidence-based beliefs

This isn’t a failure of execution—it’s a failure of diagnosis. The manifesto identified the wrong problems and thus prescribed the wrong solutions.

Tom Gilb’s Devastating Assessment: The Manifesto Is Fundamentally Fuzzy

Software engineering pioneer Tom Gilb delivers the most damning critique of the Agile Manifesto: its principles are

‘so fuzzy that I am sure no two people, and no two manifesto signers, understand any one of them identically’

(Gilb, 2005).

This fuzziness isn’t accidental—it’s structural. The manifesto was created by ‘far too many “coders at heart” who negotiated the Manifesto’ without

‘understanding of the notion of delivering measurable and useful stakeholder value’

(Gilb, 2005).

The result is a manifesto that sounds profound but provides no actionable guidance for success in product development.

Gilb’s critique exposes the manifesto’s fundamental flaw: it optimises for developer comfort rather than stakeholder value. The principles read like a programmer’s wish list—less documentation, more flexibility, fewer constraints—rather than a framework for delivering measurable results to people who actually need the software.

This explains why teams can religiously follow agile practices whilst consistently failing to deliver against folks’ needs. The manifesto’s principles are so vague that any team can claim to be following them whilst doing whatever they want. ‘Working software over comprehensive documentation’ means anything you want it to mean. ‘Responding to change over following a plan’ provides zero guidance on how to respond or what changes matter. (Cf. Quantification)

How do you measure success when the principles themselves are unmeasurable? What happens when everyone can be ‘agile’ whilst accomplishing nothing? How do you argue against a methodology that can’t be proven wrong?

The manifesto’s fuzziness enables the very dragons it claims to solve. Opinioneering thrives when principles are too vague to be proven wrong. Management parasitism flourishes when success metrics are unquantified Shared delusions multiply when ‘working software’ has no operational definition.

Gilb’s assessment reveals why the manifesto has persisted despite its irrelevance: it’s comfortable nonsense that threatens no one and demands nothing specific. Teams can feel enlightened whilst accomplishing nothing meaningful for stakeholders.

Stakeholder Value vs. All the Needs of All the Folks That Matter™

Gilb’s critique centres on ‘delivering measurable and useful stakeholder value’—but this phrase itself illuminates a deeper problem with how we think about software development success. ‘Stakeholder value’ sounds corporate and abstract, like something you’d find in a business school textbook or an MBA course (MBA – maybe best avoided – Mintzberg)

What we’re really talking about is simpler, less corporate and more human: serving all the needs of all the Folks That Matter™.

The Folks That Matter aren’t abstract ‘stakeholders’—they’re real people trying to get real things done:

  • The nurse trying to access patient records during a medical emergency
  • The small business owner trying to process payroll before Friday
  • The student trying to submit an assignment before the deadline
  • The elderly person trying to video call their grandchildren
  • The developer trying to understand why the build is broken again

When software fails these people, it doesn’t matter how perfectly agile your process was. When the nurse can’t access records, your retrospectives are irrelevant. When the payroll system crashes, your customer collaboration techniques are meaningless. When the build and smoke takes 30+ minutes, your adaptive planning is useless.

The Agile Manifesto’s developer-centric worldview treats these people as distant abstractions—’users’ and ‘customers’ and ‘stakeholders.’ But they’re not abstractions. They’re the Folks That Matter™, and their needs are the only reason software development exists.

The manifesto’s principles consistently prioritise developer preferences over the requirements of the Folks That Matter™. ‘Working software over comprehensive documentation’ sounds reasonable until the Folks That Matter™ require understanding of how to use the software. ‘Individuals and interactions over processes and tools’ sounds collaborative until the Folks That Matter™ require consistent, reliable results from those interactions.

This isn’t about being anti-developer—it’s about recognising that serving the Folks That Matter™ is the entire point. The manifesto has it backwards: instead of asking ‘How do we make development more comfortable for developers?’ we might ask ‘How do we reliably serve all the requirements of all the Folks That Matter™?’ That question changes everything. It makes motivation obvious—you’re solving real problems for real people. It makes relationship health essential—toxic teams can’t serve others effectively. It makes reality contact mandatory—delusions about quality hurt real people. It makes evidence-based decisions critical—opinions don’t serve the Folks That Matter™; results do.

Most importantly, it makes management’s value proposition clear: Do you help us serve the Folks That Matter™ better, or do you get in the way? If the answer is ‘get in the way,’ then management becomes obviously a dysfunction.

What Actually Addresses the Dragons

If we want to improve software development effectiveness, we address the real dragons:

Address Motivation: Create work that people actually care about. Give developers autonomy, mastery, and purpose. Match people to problems they find meaningful. Make contributions visible and valued.

Heal Toxic Relationships: Build psychological safety where people can be vulnerable about mistakes. Address ego and status games directly. Create systems where helping others succeed feels like personal victory.

Resolve Shared Delusions: Implement feedback loops that invite contact with reality. Measure what actually matters. Create cultures where surfacing uncomfortable truths is rewarded rather than punished.

Transform Management Entirely: Experiment with self-organising teams. Distribute decision-making authority to where expertise actually lives. Eliminate layers between problems and problem-solvers. Measure needs met, not management theatre.

Counter Evidence-Free Beliefs: Institute a culture where strong opinions require strong evidence. Enable and encourage teams to articulate the assumptions behind their practices. Reward changing your mind based on new data. Excise confident ignorance.

These aren’t process improvements or methodology tweaks—they’re organisational transformation efforts that require fundamentally different approaches than the manifesto suggests.

Beyond Agile: Addressing the Real Problems

The future of software development effectiveness isn’t in better sprint planning or more customer feedback. It’s in organisational structures that:

  • Align individual motivation with real needs
  • Create relationships based on trust
  • Enable contact with reality at every level
  • Eliminate management as dysfunctional
  • Ground all beliefs in sufficient evidence

These are the 10x improvements hiding in plain sight—not in our next retrospective, but in our next conversation about why people don’t care about their work. Not in our customer collaboration techniques, but in questioning whether we have managers at all. Not in our planning processes, but in demanding evidence for every strong opinion.

Conclusion: The Problems We Were Addressing All Along

The Agile Manifesto succeeded in solving the surface developer bugbears of 2001: heavyweight processes and excessive documentation. But it completely missed the deeper organisational and human issues that determine whether software development succeeds or fails.

The manifesto’s principles aren’t wrong—they’re just irrelevant to the real challenges. Whilst we’ve been perfecting our agile practices, the dragons of motivation, relationships, shared delusions, management being dysfunctional, and opinioneering have been systematically destroying software development from within.

Is it time to stop optimising team ceremonies and start addressing the real problems? Creating organisations where people are motivated to do great work, relationships enable rather than sabotage collaboration, shared assumptions are grounded in reality, traditional management no longer exists, and beliefs are proportioned to evidence.

But ask yourself: Does your organisation address any of these fundamental issues? Are you optimising ceremonies whilst your dragons run wild? What would happen if you stopped rearranging deck chairs and started questioning why people don’t care about their work?

Because no amount of process optimisation will save a team where people don’t care, can’t trust each other, believe comfortable lies, are managed by people who add negative value, and make decisions based on opinions rather than evidence.

The dragons are real, and they’re winning. Are we finally ready to address them?

Further Reading

Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., … & Thomas, D. (2001). Manifesto for Agile Software Development. Retrieved from https://agilemanifesto.org/

Clifford, W. K. (1877). The ethics of belief. Contemporary Review, 29, 289-309.

Gilb, T. (2005). Competitive Engineering: A Handbook for Systems Engineering, Requirements Engineering, and Software Engineering Using Planguage. Butterworth-Heinemann.

Gilb, T. (2017). How well does the Agile Manifesto align with principles that lead to success in product development? Retrieved from https://www.gilb.com/blog/how-well-does-the-agile-manifesto-align-with-principles-that-lead-to-success-in-product-development

Marshall, R.W. (2021). *Quintessence: An Acme for Software Development Organisations. *[online] leanpub.com. Falling Blossoms (LeanPub). Available at: https://leanpub.com/quintessence/ [Accessed 15 Jun 2022].

Praising the CRC Card

For the developers who never got to hold one

If you started your career after 2010, you probably never encountered a CRC card. If you’re a seasoned developer who came up through Rails tutorials, React bootcamps, or cloud-native microservices, you likely went straight from user stories to code without stopping at index cards. This isn’t your fault. By the time you arrived, the industry had already moved on.

But something was lost in that transition, and it might be valuable for you to experience it.

What You Missed

A CRC card is exactly what it sounds like: a Class-Responsibility-Collaborator design written on a physical index card. One class per card. The class name at the top, its responsibilities listed on the left, and the other classes it works with noted on the right. Simple. Physical. Throwaway.

The technique was developed by Ward Cunningham and Kent Beck in the late 1980s, originally emerging from Cunningham’s work with HyperCard documentation systems. They introduced CRC cards as a teaching tool, but the approach was embraced by practitioners following ideas like Peter Coad’s object-oriented analysis, design, and programming (OOA/D/P) framework. Peter Coad (with Ed Yourdon) wrote about a unified approach to building software that matched how humans naturally think about problems. CRC cards are a tool for translating business domain concepts directly into software design, without getting lost in technical abstractions.

The magic wasn’t in the format—it was in what the format forced you to do.

The Experience

Picture this: You and your teammates sitting around a conference table covered in index cards. Someone suggests a new class. They grab a blank card and write ‘ShoppingCart’ at the top. ‘What should it do?’ someone asks. ‘Add items, remove items, calculate totals, apply promotions,’ comes the reply. Those go in the responsibilities column. ‘What does it need to work with?’ Another pause. ‘It needs Product objects to know what’s being added, a Customer for personalised pricing, maybe a Promotion when discounts apply.’ Those become collaborators.

But here’s where it gets interesting. The card is small. Really small. If you’re writing tiny text to fit more responsibilities, someone notices. If you have fifteen collaborators, the card looks messy. The physical constraint was a design constraint. It whispered: ‘Keep it simple.’

Aside: In Javelin, we also advise keeping all methods to no more than “Five Lines of Code”. And Statements of Purpose to 25 words or less.

The Seduction

Somewhere in the 2000s, we got seduced. UML tools (yak) promised prettier diagrams. Digital whiteboards now offer infinite canvas space. Collaborative software lets us design asynchronously across time zones. We can version control our designs! Track changes! Generate code from diagrams!

We told ourselves this was progress. We retrofitted justifications: ‘Modern systems are too complex for index cards.’ ‘Remote teams need digital tools.’ ‘Physical methods don’t scale.’

But these were lame excuses, not good reasons.

The truth is simpler and more embarrassing: we abandoned CRC cards because they felt primitive. Index cards seemed amateur next to sophisticated UML tools and enterprise architecture platforms. We confused the sophistication of our tools with the sophistication of our thinking.

What We Actually Lost

The constraint was the feature. An index card can’t hold a God class. It can’t accommodate a class with dozens of responsibilities or collaborators. But more importantly, it forced you to think in domain terms, not implementation terms. When you’re limited to an index card, you can’t hide behind technical abstractions like ‘DataProcessor’ or ‘ValidationManager.’ You have to name things that represent actual concepts in the problem domain – things a business person would recognise. The physical limitation forced good design decisions and domain-focused thinking before you had time to rationalise technical complexity.

Throwaway thinking was powerful. When your design lived on index cards, you could literally throw it away and start over. No one was attached to the beautiful diagram they’d spent hours or days perfecting. The design was disposable, which made experimentation safe.

Tactile collaboration was different. There’s something unique about physically moving cards around a table, stacking them, pointing at them, sliding one toward a teammate. Digital tools simulate this poorly. Clicking and dragging isn’t the same as picking up a card and handing it to someone.

Forced focus was valuable. You couldn’t switch to Slack during a CRC card session. You couldn’t zoom in on implementation details. The cards kept you at the right level of abstraction—not so high that you were hand-waving, not so low that you were bikeshedding variable names.

The Ratchet Effect

Here’s what makes this particularly tragic: once the industry moved to digital tools, it became genuinely harder to go back. Try suggesting index cards in a design meeting today. You’ll get polite smiles and concerned looks. Not because the method doesn’t work, but because the ecosystem has moved backwards. The new developers have never seen it done. The tooling assumes digital. The ‘best practices’ articles all recommend software solutions.

We created a ratchet effect where good practices became impossible to maintain not because they were inadequate, but because they felt outdated.

For Those Who Never Got the Chance

If you’re reading this as a developer who never used CRC cards, I want you to know: you were cheated, but not by your own choices. You came into an industry that had already forgotten one of its own most useful practices. You learned the tools that were available when you arrived.

But you also inherited the complexity that came from abandoning constraints. You’ve probably spent hours in architecture meetings where the design sprawled across infinite digital canvases, where classes accumulated responsibilities because the tools could accommodate any amount of complexity, where the ease of adding ‘just one more connection’ led to systems that no one fully understood.

You’ve felt the pain of what we lost when we abandoned the constraint.

A Small Experiment

Next time you’re designing something new, try this: grab some actual index cards. Write one class per card. See how it feels when the physical constraint pushes back against your design. Notice what happens when throwing away a card costs nothing but keeping a complex design visible costs table space.

You might discover something we lost when we got sophisticated.

Do it because CRC cards were actually superior to modern digital tools for early design thinking. We didn’t outgrow them – we abandoned something better for something shinier.

Sometimes the simpler tool was better precisely because it was simpler.

The industry moves fast, and not everything we leave behind should have been abandoned. Some tools die not because they’re inadequate, but because they’re unfashionable. The CRC card was a casualty of progress that wasn’t progressive.

Further Reading

Beck, K., & Cunningham, W. (1989). A laboratory for teaching object-oriented thinking. SIGPLAN Notices, 24(10), 1-6.

Coad, P., & Yourdon, E. (1990). Object-oriented analysis. Yourdon Press.

Coad, P., & Yourdon, E. (1991). Object-oriented design. Yourdon Press.

Coad, P., North, D., & Mayfield, M. (1995). Object-oriented programming. Prentice Hall.

Coad, P., North, D., & Mayfield, M. (1996). Object models: Strategies, patterns, and applications (2nd ed.). Prentice Hall.

Wirfs-Brock, R., & McKean, A. (2003). Object design: Roles, responsibilities, and collaborations. Addison-Wesley.

Your Software Requirements Are Worthless

Every day, software teams burn millions of pounds building the wrong thing because they mistake fuzzy feelings and opinioneering for engineering specifications

Software teams continue writing requirements like ‘user-friendly’, ‘scalable’, and ‘high-performance’ as if these phrases mean anything concrete.

They don’t.

What they represent is ignorance (of quantification) disguised as intellectual laziness disguised as collaboration. When a product manager says an interface should be ‘intuitive’ and a developer nods in agreement, no communication has actually occurred. Both parties have simply agreed to postpone the hard work of thinking and talking until later—usually until users complain or products break.

The solution isn’t better communication workshops or more stakeholder alignment meetings. It’s operational definitions—the rigorous practice of quantifying every requirement so precisely that a computer could verify compliance.

What Are Operational Definitions?

An operational definition specifies exactly how to measure, observe, or identify something in terms that are meaningful to the Folks That Matter™. Instead of abstract concepts or assumptions, operational definitions state the precise criteria, procedures, or observable behaviours that determine whether something meets a standard—and why that standard creates value for those Folks That Matter™.

The term originates from scientific research, where researchers must ensure their experiments are replicable. Instead of saying a drug ‘improves patient outcomes’, researchers operationally define improvement as ‘a 15% reduction in Hamilton Depression Rating Scale scores measured by trained clinicians using the 17-item version at 6-week intervals, compared to baseline scores taken within 72 hours of treatment initiation, with measurements conducted between 9-11 AM in controlled clinical environments at 21°C ±2°C, amongst patients aged 18-65 with major depressive disorder diagnosed per DSM-5 criteria, excluding those with concurrent substance abuse or psychotic features’.

This example only scratches the surface—a complete operational definition would specify dozens more variables including exact clinician training protocols, inter-rater reliability requirements, patient positioning, statistical procedures, and missing data handling. This precision is what makes scientific breakthroughs reproducible and medical treatments safe.

The Software Development Challenge

Software teams constantly wrestle with ambiguous terms that everyone assumes they understand:

  • ‘This feature should be fast’
  • ‘The user interface needs to be intuitive’
  • ‘We need better code quality’
  • ‘This bug is critical’

These statements appear clear in conversation, but they’re loaded with subjective interpretations. What’s ‘fast’ to a backend engineer may be unacceptably slow to a mobile developer. ‘Intuitive’ means different things to designers, product managers, and end users.

Worse: these fuzzy requirements hide the real question—what specificaly do the Folks That Matter™ actually need?

How Operational Definitions Transform Software Teams

1. Connect Features to the Needs of the Folks That Matter™

Consider replacing ‘the API should be fast’ with an operational definition: ‘API responses return within 200ms for 95% of requests under normal load conditions, as measured by our monitoring system, enabling customer support agents to resolve inquiries 40% faster and increasing customer satisfaction scores by 15 points as measured on <date>.’

This eliminates guesswork, creates shared understanding across disciplines, and directly links technical decisions to the needs of the Folks That Matter™.

2. Turn Subjective Debates Into Objective Decisions

Operational definitions end pointless arguments about code quality. Stop debating whether code is ‘maintainable’. Define maintainability operationally:

  • Code coverage above 80% to reduce debugging time by 50%
  • Cyclomatic complexity below 10 per function to enable new team members to contribute within 2 weeks
  • No functions exceeding 50 lines to support 90% of feature requests completed within single sprint
  • All public APIs documented with examples to achieve zero external developer support tickets for basic integration

Each criterion ties directly to measurable benefits for the Folks That Matter™.

3. Accelerate Decision Making

With operationally defined acceptance criteria, teams spend less time in meetings clarifying requirements and more time attending to folks’ needs. Developers know exactly what ‘done’ looks like, and the Folks That Matter™ verify completion through measurable outcomes.

4. Bridge Cross-Functional Disciplines

Different roles think in different terms. Operational definitions create a common vocabulary focused on the needs of the Folks That Matter™:

  • Product: Transform ‘User-friendly’ into ‘Users complete the checkout flow within 3 steps, with less than 2% abandonment at each step, increasing conversion rates by 12% and generating £2M additional annual revenue
  • Design: Transform ‘Accessible’ into ‘Meets WCAG 2.1 AA standards as verified by automated testing and manual review, enabling compliance with federal accessibility requirements and expanding addressable market by 15%
  • Engineering: Transform ‘Scalable’ into ‘Handles 10x current load with response times under 500ms, supporting planned user growth without additional infrastructure investment for 18 months

5. Evolutionary Improvement

Operational definitions evolve as the needs of the Folks That Matter™ become clearer. Start with basic measurements, then refine scales of measure as you learn what truly drives value. A ‘fast’ system might initially mean ‘under 1 second response time’ but evolve into sophisticated performance profiles that optimise for different user contexts and business scenarios.

Real-World Implementation: Javelin’s QQO Framework

Some teams have already embraced this precision. Falling Blossoms’ Javelin process demonstrates operational definitions in practice through Quantified Quality Objectives (QQOs)—a systematic approach to transforming vague non-functional requirements into quasi or actual operational definitions.

Instead of accepting requirements like ‘the system should be reliable’ or ‘performance must be acceptable’, Javelin teams create detailed QQO matrices where every quality attribute gets operationally defined with:

  • Metric: Exact measurement method and scale
  • Current: Baseline performance (if known)
  • Best: Ideal target level
  • Worst: Minimum acceptable threshold
  • Planned: Realistic target for this release
  • Actual: Measured results for actively monitored QQOs
  • Milestone sequence: Numeric targets at specific dates/times throughout development

A Javelin team might operationally define ‘reliable’ as: ‘System availability measured monthly via automated uptime monitoring: 99.5% by March 1st (MVP launch), 99.7% by June 1st (full feature release), 99.9% by December 1st (enterprise rollout), with worst acceptable level never below 99.0% during any measurement period.’

This transforms the entire conversation. Instead of debating what ‘reliable enough’ means, teams focus on achievable targets, measurement infrastructure, and clear success criteria. QQO matrices grow organically as development progresses, following just-in-time elaboration of folks’ needs. Teams don’t over-specify requirements months in advance; they operationally define quality attributes exactly as needed for immediately upcoming development cycles.

This just-in-time approach prevents requirements from going stale whilst maintaining precision where it matters. A team might start with less than a dozen operationally defined QQOs for an MVP, then expand to hundreds as they approach production deployment and beyond—each new QQO addressing specific quality concerns as they become relevant to actual development work.

Toyota’s Product Development System (TPDS) demonstrates similar precision in manufacturing contexts through Set Based Concurrent Engineering (SBCE). Rather than committing to single design solutions early, Toyota teams define operational criteria for acceptable solutions—precise constraints for cost, performance, manufacturability, and quality. They then systematically eliminate design alternatives, at scheduled decision points, that fail to meet these quantified thresholds, converging on optimal solutions through measured criteria rather than subjective judgement.

Both Javelin’s QQOs and Toyota’s SBCE prove that operational definitions work at scale across industries—turning fuzzy requirements into systematic, measurable decision-making frameworks that deliver value to the Folks That Matter™.

Practical Examples in Software Development

User Story Acceptance Criteria

Before: ‘As a user, I want the search to be fast so I can find results quickly.’

After: ‘As a user, when I enter a search query, I should see results within 1 second for 95% of searches, with a loading indicator appearing within 100ms of pressing enter.’

Bug Priority Classification

Before: ‘This is a critical bug.’

After: ‘Priority 1 (Critical): Bug prevents core user workflow completion OR affects >50% of active users OR causes data loss OR creates security vulnerability.’

Code Review Standards

Before: ‘Code should be clean and well-documented.’

After: Operationally defined code quality standards with measurable criteria:

Documentation Requirements:

  • 100% of public APIs include docstrings with purpose, parameters, return values, exceptions, and working usage examples
  • Complex business logic (cyclomatic complexity >5) requires inline comments explaining the ‘why’, not the ‘what’
  • All configuration parameters documented with valid ranges, default values, and business impact of changes
  • Value to the Folks That Matter™: Reduces onboarding time for new developers from 4 weeks to 1.5 weeks, cuts external API integration support tickets by 80%

Code Structure Metrics:

  • Functions limited to 25 lines maximum (excluding docstrings and whitespace)
  • Cyclomatic complexity below 8 per function as measured by static analysis tools
  • Maximum nesting depth of 3 levels in any code block
  • No duplicate code blocks exceeding 6 lines (DRY principle enforced via automated detection)
  • Value to the Folks That Matter™: Reduces bug fix time by 60%, enables 95% of feature requests completed within single sprint

Naming and Clarity:

  • Variable names must be pronounceable and searchable (no abbreviations except industry-standard: id, url, http)
  • Boolean variables/functions use positive phrasing (isValid not isNotInvalid)
  • Class/function names describe behaviour, not implementation (PaymentProcessor not StripeHandler)
  • Value to the Folks That Matter™: Reduces code review time by 40%, decreases bug report resolution from 3 days to 8 hours average

Security and Reliability:

  • Zero hardcoded secrets, credentials, or environment-specific values in source code
  • All user inputs validated with explicit type checking and range validation
  • Error handling covers all failure modes with logging at appropriate levels
  • All database queries use parameterised statements (zero string concatenation)
  • Value to the Folks That Matter™: Eliminates 90% of security vulnerabilities, reduces production incidents by 75%

Testing Integration:

  • Every new function includes unit tests with >90% branch coverage
  • Integration points include contract tests verifying interface expectations
  • Performance-critical paths include benchmark tests with acceptable thresholds defined
  • Value to the Folks That Matter™: Reduces regression bugs by 85%, enables confident daily deployments

Review Process Metrics:

  • Code reviews completed within 4 business hours of submission
  • Maximum 2 review cycles before merge (initial review + addressing feedback)
  • Review comments focus on maintainability, security, and business logic—not style preferences
  • Value to the Folks That Matter™: Maintains development velocity whilst ensuring quality, reduces feature delivery time by 25%

Performance Requirements

Before: ‘The dashboard should load quickly.’

After: ‘Dashboard displays initial data within 2 seconds on 3G connection, with progressive loading of additional widgets completing within 5 seconds total.’

The Competitive Advantage

Teams that master operational definitions gain significant competitive advantages:

  • Faster delivery cycles from reduced requirement clarification—deploy features 30-50% faster than competitors
  • Higher quality output through measurable standards—reduce post-release defects by 60-80%
  • Improved confidence from the Folks That Matter™ from predictable, verifiable results—increase project approval rates and budget allocations
  • Reduced technical debt through well-defined standards—cut maintenance costs whilst enabling rapid feature development
  • Better team morale from decreased frustration and conflict—retain top talent and attract better candidates

Most importantly: organisations that operationally define their quality criteria can systematically out-deliver competitors who rely on subjective judgement.

Start Today

Choose one ambiguous term your team uses frequently and spend 30 minutes defining it operationally. Ask yourselves:

  1. What value does this QQO deliver to the Folks That Matter™?
  2. What specific, observable criteria determine if this value is achieved?
  3. What scale of measure will we use—percentage, time, count, ratio?
  4. How will we measure this, and how often?
  5. What does ‘good enough’ look like vs. ‘exceptional’ for the Folks That Matter™?

Aim for precision that drives satisfaction of folks’ needs, not perfection. Even rough operational definitions linked to the needs of the Folks That Matter™ provide more clarity than polished ambiguity.

Implementation Strategy

Start Small and Build Consensus

Begin by operationally defining one or two concepts that cause the most confusion in your team. Start with:

  • Definition of ‘done’ for user stories linked to specific value for the Folks That Matter™
  • Bug severity levels tied to business impact measures
  • Performance benchmarks connected to user experience goals
  • Code standards that enable measurable delivery improvements

Define Scales of Measure

Write operational definitions that specify not just the criteria, but the scale of measure—the unit and method of measurement. Include:

  • Measurement method: How you will measure (automated monitoring, user testing, code analysis)
  • Scale definition: Units of measure (response time in milliseconds, satisfaction score 1-10, defect rate per thousand lines)
  • Measurement infrastructure: Tools, systems, and processes needed
  • Frequency: How often measurements occur and when they’re reviewed
  • Connection to the Folks That Matter™: What business need each measurement serves

Evolve Based on Learning

Operational definitions evolve as you learn what truly drives meeting the needs of the Folks That Matter™. Start with basic measurements, then refine scales as you discover which metrics actually predict success. Regular retrospectives can examine not just whether definitions were met, but whether they satisfied the intended needs of the Folks That Matter™.

Document and Automate

Store operational definitions in accessible locations—team wikis, README files, or project documentation. Automate verification through CI/CD pipelines, monitoring dashboards, and testing frameworks wherever possible. The goal is measurement infrastructure that runs automatically and surfaces insights relevant to the needs of the Folks That Matter™.

Conclusion

Operational definitions represent a paradigm shift from ‘we all know what we mean’ to ‘we are crystal clear about what value we’re delivering to the Folks That Matter™’. In software development, where precision enables competitive advantage and the satisfaction of the needs of the Folks That Matter™ determines success, this shift separates organisations that struggle with scope creep and miscommunication from those that systematically out-deliver their competition.

Creating operational definitions pays dividends in reduced rework, faster delivery, happier teams, and measurable value for the Folks That Matter™. Most importantly, it transforms software development from a guessing game into a needs-meeting discipline—exactly what markets demand as digital transformation accelerates and user expectations rise.

Operational definitions aren’t just about better requirements. They’re about systematic competitive advantage through measurable satisfaction of the needs of the Folks That Matter™.

Take action: Pick one fuzzy requirement from your current sprint. Define it operationally in terms of specific needs of the Folks That Matter™. Watch how this precision changes every conversation your team has about priorities, trade-offs, and success.

Further Reading

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.

Beck, K. (2000). Extreme programming explained: Embrace change. Addison-Wesley.

Cockburn, A. (2004). Crystal clear: A human-powered methodology for small teams. Addison-Wesley.

DeMarco, T. (1982). Controlling software projects: Management, measurement, and estimation. Yourdon Press.

DeMarco, T., & Lister, T. (2013). Peopleware: Productive projects and teams (3rd ed.). Addison-Wesley.

Falling Blossoms. (2006). Our Javelin™ process (Version 2.0a). Falling Blossoms.

Gilb, T. (1988). Principles of software engineering management. Addison-Wesley.

Gilb, T. (2005). Competitive engineering: A handbook for systems engineering management using Planguage. Butterworth-Heinemann.

Gilb, T., & Graham, D. (1993). Software inspection. Addison-Wesley.

Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56-62.

Kennedy, M. N., & Harmon, K. (2008). Ready, set, dominate: Implement Toyota’s set-based learning for developing products and nobody can catch you. Oaklea Press.

Morgan, J. M., & Liker, J. K. (2006). The Toyota product development system: Integrating people, process, and technology. Productivity Press.

Sobel, A. E., & Clarkson, M. R. (2002). Formal methods application: An empirical tale of software system development. IEEE Transactions on Software Engineering, 28(3), 308-320.

W3C Web Accessibility Initiative. (2018). Web content accessibility guidelines (WCAG) 2.1. World Wide Web Consortium.

Ward, A. C. (2007). Lean product and process development. Lean Enterprise Institute.

Weinberg, G. M. (1985). The secrets of consulting: A guide to giving and getting advice successfully. Dorset House.

Yourdon, E. (1997). Death march: The complete software developer’s guide to surviving ‘mission impossible’ projects. Prentice Hall.

The Human Factor: Why Psychology is Tech’s Most Undervalued Discipline

From cognitive biases to team dynamics, the psychological insights that could revolutionise how we build products, manage teams, run businesses and drive innovation

Silicon Valley has conquered machine learning, perfected continuous deployment, and built systems that serve billions. Yet for all its technical mastery, the tech industry repeatedly fails at something far more fundamental: understanding people.

The evidence is overwhelming. Digital transformations fail at rates between 70-95%, with an average failure rate of 87.5% (Bonnet, 2022). Software projects consistently run over budget and behind schedule, wasting £millions. Developer burnout has reached epidemic proportions. User adoption of new features remains stubbornly low despite e.g. sophisticated A/B testing.

The common thread? These aren’t technical failures—they’re human failures. Failures of communication, motivation, decision-making, relationships, and understanding what actually drives behaviour.

The Industry’s Psychological Blind Spot

Walk through any tech office and you’ll witness a fascinating paradox. Engineers who can optimise algorithms to microsecond precision struggle to understand why their perfectly logical user interface confuses customers. Engineering gurus who architect fault-tolerant distributed systems can’t figure out why their teams are demotivated. Product managers who obsess over conversion metrics completely miss the emotional journey that determines whether users actually adopt their features.

This isn’t incompetence—it’s a systematic blind spot. Technical education trains us to think in features, algorithms, and deterministic outcomes. We learn to eliminate variables, optimise for efficiency, and build predictable solutions. But humans are gloriously, frustratingly unpredictable.

The blind spot runs deeper than individual ignorance. There’s a cultural disdain for anything psychology-related (interesting in itself from a psychology perspective). Mention “team dynamics” in a planning meeting and watch the eye-rolls. Suggest that cognitive biases might be affecting architectural decisions and you’ll be dismissed as pushing tree-hugging, woke “soft skills” nonsense. The tech industry has convinced itself that psychology is touchy-feely therapy speak, irrelevant to the serious business of building software and running businesses.

This dismissal comes at a massive cost. When we ignore psychology, we build products that solve the wrong problems, create team environments that burn out our best people, and make flawed decisions based on biases we don’t even recognise.

The Data-Driven Case for Psychology

Ironically, one of history’s most influential systems thinkers understood psychology’s business value perfectly. W. Edwards Deming—the statistician whose principles revolutionised manufacturing quality and helped rebuild Japan’s post-war economy—made psychology one of the four pillars of his “System of Profound Knowledge”. And from his persepctive, the most important of the four.

Deming didn’t treat psychology as a nice-to-have add-on. He argued that managers must understand human nature, motivation, and behaviour to build effective ways of working. His famous insight that 94% of quality problems stem from systems and management—not worker incompetence—was fundamentally psychological. Yet tech management, which claims to worship data-driven decision making, has ignored these insights from one of the most successful data-driven thinkers in history.

Modern research backs up Deming’s intuition. Studies consistently show that psychological factors are among the strongest predictors of software project success:

Research on agile development teams found that human-related factors—quality of relationships, team capability, customer involvement, and team dynamics—are the critical success factors, far outweighing technical considerations (Barros et al., 2024).

Studies of developer performance demonstrate that emotional states directly impact problem-solving abilities, with “happy developers” significantly outperforming their stressed counterparts on analytical tasks (Graziotin et al., 2014).

Analysis of team effectiveness reveals that personality traits and interpersonal dynamics have measurable impacts on code quality, delivery timelines, and innovation rates (Acuña et al., 2017).

The data is clear: psychology isn’t optional. It’s a core competency that determines whether technical brilliance translates into business success.

The Psychology Toolkit for Tech

Psychology isn’t a monolithic field—it’s a rich ecosystem of frameworks and insights that can transform how we approach technical challenges. Let’s explore just a few of the most powerful tools.

Cognitive Biases: The Bugs in Human Reasoning

Just as we debug code, we need to debug our thinking. Cognitive biases are systematic errors in reasoning that affect every decision we make, including the technical ones:

Confirmation Bias leads engineers to seek information that supports their preferred solution whilst ignoring alternatives. That’s why teams often stick with familiar technologies even when better options exist.

Sunk Cost Fallacy keeps teams investing in failing projects because of previous effort. We’ve all seen projects that should have been killed months ago but continued because “we’ve already invested so much.”

Planning Fallacy explains why developers consistently underestimate task complexity. It’s not laziness—it’s a predictable cognitive bias that affects every developer (and managers, too).

Availability Heuristic makes recent incidents seem more likely than they actually are, leading to over-engineering for problems that rarely occur. Aka Gold plating.

Understanding these biases doesn’t eliminate them, but it enables us to build processes that account for them. Code reviews help catch confirmation bias. Time-boxed experiments limit sunk cost fallacy. Historical data counteracts planning fallacy.

User Psychology: Beyond A/B Testing

Most product teams approach users like they approach code—looking for deterministic patterns and optimal solutions. But users don’t behave logically; they behave psychologically.

Loss Aversion: People feel losses more acutely than equivalent gains. This affects everything from pricing strategies to feature adoption. Users will stick with inferior solutions rather than risk losing what they already have.

Mental Models: Users approach new interfaces with existing expectations. Fighting these mental models creates friction; aligning with them creates intuitive experiences.

Choice Overload: Contrary to Silicon Valley dogma, more options don’t always create better outcomes. Too many choices can paralyse users and reduce satisfaction even when they do choose.

Social Proof: People follow what others do, especially in uncertain situations. This is why testimonials, usage statistics, and “trending” indicators can dramatically impact adoption.

Motivation Theory: What Actually Drives Performance

The tech industry’s approach to motivation is remarkably naive: pay people well, give them interesting problems, and assume they’ll perform. But decades of research reveal motivation is far more complex.

Self-Determination Theory identifies three psychological needs that drive intrinsic motivation:

Autonomy: People need control over their work. Micromanagement destroys motivation even when well-intentioned. The most productive developers choose their own tools, approaches, and priorities within clear constraints.

Competence: People need to feel effective and capable. This means providing appropriate challenges, learning opportunities, and recognition for growth. Boredom and overwhelm both kill motivation.

Relatedness: Humans need connection and shared purpose. Remote work and competitive environments can undermine this need, leading to disengagement even when technical work is satisfying.

Companies that design roles around these three needs see higher productivity, lower turnover, and more innovation. Companies that ignore them burn through talent despite offering competitive salaries.

Eric Berne’s Transactional Analysis: A Framework for Management

Among psychology’s many insights, one framework stands out for its practical application to management challenges: Eric Berne’s Transactional Analysis (TA).

Developed in the 1950s, TA provides a simple but powerful model for understanding interpersonal dynamics. Berne identified three “ego states” that everyone operates from:

Parent: The inherited voices of authority figures. When we’re in Parent mode, we’re either nurturing (“Let me help you”) or criticising (“You’re doing it wrong”).

Adult: Rational, present-moment thinking. This is where we process information objectively and respond appropriately to current situations.

Child: Our emotional, spontaneous, creative self. This includes both our playful, innovative side and our adapted, compliant side.

Every conversation involves transactions between these ego states. Understanding these patterns can transform management, team and group effectiveness, particularly in the fraught dynamics between management and workers.

TA in Action: Management vs Workers

The Micromanaging Manager

Situation: Sarah, an engineering manager, constantly checks on her senior developers, questions their technical decisions, and demands detailed status reports. Team productivity plummets and two experienced engineers start looking elsewhere.

Traditional Analysis: “Sarah needs to trust her team more. The developers are being defensive.”

TA Analysis: Sarah operates from Criticising Parent (“I need to oversee everything”), which triggers her developers’ Rebellious Child (“Stop treating us like incompetent children”). The developers’ Adult expertise gets bypassed entirely.

Solution: Sarah shifts to Adult state: “What obstacles are blocking your progress? How can I help remove them?” This invites Adult-to-Adult collaboration rather than Parent-to-Child control and confrontation.

The Blame-First Post-Mortem

Situation: After a production incident, CTO Mark runs a post-mortem focused on “who made the mistake.” Junior developer Jenny, who deployed the problematic code, sits silently while Mark questions her testing procedures. The team leaves feeling demoralised rather than enlightened.

TA Analysis: Mark operates from Criticising Parent (“Someone needs to be held accountable”), triggering Jenny’s Adapted Child (shame and withdrawal). Other team members also shift to Child state, afraid they’ll be next.

Solution: Mark engages Adult state: “Let’s understand what systemic issues allowed this to reach production. How do we improve our processes?” This frames the incident as a learning opportunity rather than a blame assignment.

The Innovation Killer

Situation: Technical architect David consistently rejects new ideas from his team with responses like “That’s not how we do things” or “That technology is too risky.” The team stops proposing improvements and settles into maintenance mode.

TA Analysis: David operates from Criticising Parent, prioritising control over innovation. His team’s Natural Child (creativity and enthusiasm) gets suppressed, and they shift to Adapted Child—compliant but disengaged.

Solution: David engages Adult state when evaluating proposals: “Walk me through your thinking. What problems does this solve and what risks do we need to mitigate?” This validates creative thinking while maintaining appropriate oversight.

The Abdication Executive

Situation: VP of Engineering Lisa assigns a complex microservices migration with minimal guidance: “You’re smart people, figure it out.” Three months later, teams are building incompatible services and the project is behind schedule and over budget.

TA Analysis: Lisa operates from Free Child—enthusiastic but irresponsible, delegating without providing necessary structure. Her team is forced into Adapted Child, trying to guess her expectations while being set up for failure.

Solution: Lisa engages Adult state to provide context and constraints: “Here’s why we’re migrating, here are our business and technical constraints, and here’s how we’ll measure success. What approach do you recommend?” This treats her team as professional partners rather than subordinates.

Beyond TA: The Broader Psychology Toolkit

Transactional Analysis is just one tool in a comprehensive psychology toolkit. Other frameworks provide equally valuable insights:

Group Dynamics: Bruce Tuckman’s model of team development—forming, storming, norming, performing—explains why new teams struggle initially and how to accelerate their progression to high performance.

Change Psychology: Understanding why people resist change (loss of control, uncertainty, increased complexity) enables more effective technology adoption and organisational transformation.

Decision Science: Research on how people actually make decisions (versus how we think they should) can improve everything from user interface design to enterprise sales processes.

Behavioural Economics: Insights like anchoring effects, framing bias, and loss aversion can dramatically improve product design, pricing strategies, and user engagement.

The Business Case for Psychological Literacy

Understanding psychology isn’t about being nice—it’s about being effective especially in the domain of people. Companies that develop psychological literacy see measurable improvements:

Better Product-Market Fit: When you understand user psychology—their biases, emotions, and decision-making patterns—you can design experiences that truly resonate rather than just optimising random metrics.

Higher Team Performance: Research consistently shows that team dynamics, motivation, and emotional states directly impact code quality, innovation rates, and delivery speed.

More Effective Fellowship: Fellows who understand frameworks like TA, motivation theory, and cognitive biases make better decisions, communicate more effectively, and build higher-performing teams.

Improved Change Management: Understanding the psychology of change—why people resist it, how they adopt new behaviours, what motivates transformation—enables more successful technology adoptions and organisational changes.

Stronger Customer Relationships: Sales, support, and customer success teams become far more effective when they can recognise psychological patterns and respond appropriately.

Building Psychological Literacy

Developing psychological competence means building skills in several areas:

Pattern Recognition: Learning to identify psychological patterns in yourself and others—ego states in interactions, cognitive biases in decision-making, team dynamics that help or hinder performance.

Framework Fluency: Understanding proven models like TA, motivation theory, cognitive bias research, and team psychology. These aren’t abstract theories—they’re practical tools for solving real problems.

Emotional Intelligence: Developing the ability to recognise and work with emotions rather than pretending they don’t exist or dismissing them as irrelevant to technical work.

Systems Thinking: Recognising that human systems are as complex and important as technical systems. Team dynamics, user behaviour, and organisational culture follow patterns that can be understood and optimised.

Research Literacy: Understanding how to evaluate psychological research and apply evidence-based insights rather than relying on intuition or management fads.

This doesn’t require everyone to become psychologists. It means recognising that psychology offers evidence-based tools for solving the human problems that consistently derail technical projects.And one or two people on a team, with psychology skills, are distinct assets.

The Future Competitive Advantage

Your current tech stack will become obsolete. Your architecture will be rewritten. Your product features and products will be replaced. But organisations that master the human elements of the technology business will build lasting competitive advantages.

The companies that thrive in the next decade won’t just have better engineers—they’ll have better people smarts. They’ll understand what motivates their teams, what drives their customers, and what biases affect their decisions. They’ll build products that work for real humans rather than idealised users. They’ll create environments where people do their best work rather than burning out.

Psychology isn’t a “soft skill” addition to technical competence—it’s a force multiplier that makes everything else more effective. When you understand how people actually think, feel, and behave, you can design better experiences, create more effective teams, make better decisions, and build more successful organisations.

The tech industry’s next breakthrough won’t come from a new programming language or cloud service. It’ll come from finally bridging the gap between technical excellence and psychological mastery.

Because at the end of the day, all technology is about people. The sooner we start working with psychology in mind, the sooner we’ll build things that actually work for the beautifully complex humans who use them.

Further Reading

Acuña, S. T., Gómez, M., & Juristo, N. (2017). An examination of personality traits and how they impact on software development teams. Information and Software Technology, 86, 101-122.

Barros, L. B., Varajão, J., & Helfert, M. (2024). Agile software development projects–Unveiling the human-related critical success factors. International Journal of Information Management, 75, 102737.

Berne, E. (1961). Transactional analysis in psychotherapy: A systematic individual and social psychiatry. Grove Press.

Berne, E. (1964). Games people play: The psychology of human relationships. Grove Press.

Bonnet, D. (2022, September 20). 3 stages of a successful digital transformation. Harvard Business Review.

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268.

Deming, W. E. (1982). Out of the crisis. MIT Press.

Graziotin, D., Wang, X., & Abrahamsson, P. (2014). Happy software developers solve problems better: psychological measurements in empirical software engineering. PeerJ, 2, e289.

Heath, C., & Heath, D. (2013). Decisive: How to make better choices in life and work. Crown Business.

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.

Pink, D. H. (2009). Drive: The surprising truth about what motivates us. Riverhead Books.

Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.

Tuckman, B. W. (1965). Developmental sequence in small groups. Psychological Bulletin, 63(6), 384-399.

You’re the Mark

A critical examination of how Agile software development transformed from a liberation movement into a wealth extraction mechanism

Twenty-three years ago, seventeen software developers gathered at a ski resort in Utah and thrashed out the Agile Manifesto—a mere 68 words that would spawn a multi-billion dollar global rent-seeking industry. What began as a febrile attempt to improve the lot of software developers has metastasised into the most successful rent-seeking operation in corporate history. Today’s Agile ecosystem extracts enormous wealth from organisations worldwide while delivering pretty much zero value, creating a perfect case study in how good intentions can be weaponised for profit.

A Note to Developers

Most developers reading this already suspect something’s amiss. You’ve likely developed a nuanced, perhaps conflicted relationship with Agile practices—simultaneously recognising their theatrical aspects whilst navigating hiring expectations that demand fluency in the ceremonies. You may have already internalised that story pointing is largely kabuki theatre, that many retrospectives produce no meaningful change, and that velocity metrics often obscure rather than illuminate actual progress.

The challenge isn’t ignorance—it’s entrapment. Even developers who see through the performance still find themselves trapped by industry demand. Job descriptions demand Agile experience. Performance reviews measure engagement with Agile processes. Career advancement often requires demonstrating enthusiasm for practices you privately reject. This creates a sophisticated form of professional Stockholm syndrome where intelligent people participate in systems they recognise as dysfunctional at best because non-participation means no job, no income.

Some of you have found ways to work effectively within or around Agile constraints—delivering value despite the overhead rather than because of it. Others have embraced pragmatic subsets whilst ignoring the more theatrical elements. Still others have built careers on Agile coaching or Scrum mastery and face the uncomfortable reality that your expertise has become part of the rent-seeking apparatus.

The analysis that follows isn’t an attack on your intelligence or choices—it’s an attempt to name the economic forces that have shaped an industry where intelligent people spend increasing amounts of time on activities they know add next to no value, at best.

The Anatomy of Agile Rent Seeking

Rent seeking, in economic terms, occurs when individuals or organisations manipulate the environment to increase their share of existing wealth without creating new value. The modern Agile industry exhibits every hallmark of classic rent-seeking behaviour—extracting wealth from existing economic activity without creating new value.

The Certification Racket

The most obvious manifestation is the explosion of Agile certifications. Scrum Alliance alone has issued over 1.3 million certifications, each demanding fees, training courses, and periodic renewal. These credentials have no regulatory backing, no standardised curriculum, and no measurable correlation with improved outcomes. Yet they’ve become de facto requirements for countless positions.

Consider the absurdity: you can become a ‘Certified Scrum Master’ with a two-day course and $1,400, despite having never managed a software project. The certification teaches you to facilitate meetings and maintain a backlog—activities that competent professionals have done for decades without special training. But the certificate creates artificial scarcity and justifies premium salaries for what amounts to administrative work—classic rent-seeking through credentialism.

The Consultant Multiplication Complex

Agile has created an entire consulting ecosystem that extracts wealth by feeding on organisational anxiety. Anxiety about achieving appreciable ROI from Agile investments they’ve already made or are about to make. Companies spend millions on Agile coaches, transformation consultants, and implementation specialists who lack deep technical expertise but excel at selling process theatre.

These consultants arrive with identical playbooks: conduct ‘maturity assessments,’ implement story point estimation, establish retrospective ceremonies, and create elaborate metrics dashboards. They transform simple development work into elaborate rituals that require their ongoing presence to maintain. The process becomes the product, and the consultants extract rent as indispensable guardians of the process.

Tool Vendor Capture

The Agile ecosystem has spawned specialised software tools that extract rent through expensive, long-term contracts whilst locking organisations into vendor dependency. Jira, Azure DevOps, and dozens of competitors have convinced companies they need sophisticated ‘Agile project management platforms’ to track work that developers previously managed with simple task lists.

These tools don’t improve development velocity—they hinder it with excessive overhead and forced workflows. But they generate subscription revenue whilst creating switching costs that trap organisations. The tools become shelfware that teams work around rather than with, yet the contracts auto-renew annually.

The Value Creation Mirage

Proponents argue that Agile creates value through faster delivery, better collaboration, and improved quality. But where’s the evidence?

The Productivity Paradox

Despite decades of Agile adoption, software productivity remains stagnant or has declined by many measures. The average enterprise software project still runs over budget and behind schedule. Technical debt continues to accumulate. Developer satisfaction surveys consistently rank process overhead as a top frustration.

Meanwhile, the most productive software teams practice development methods that bear no resemblance to ceremonial Agile. They focus on technical excellence, autonomous teams, and minimal process overhead. Their success comes from owning and paying attendtion to the way the work works, and removing obstacles, not from following ceremonies—yet the Agile industry extracts zero rent from these approaches, which explains why they’re rarely promoted.

The Innovation Slowdown

Agile’s emphasis on incremental delivery and user story decomposition actively discourages breakthrough innovation. The methodology breaks everything into small, measurable chunks that can be completed in two-week sprints. This works for maintenance programming but stifles the sustained, exploratory work that produces real advances.

The pressure for continuous delivery means teams avoid ambitious architectural changes or experimental features that disrupt their velocity metrics. Innovation requires periods of unproductive exploration that Agile frameworks penalise.

The Parasitic Nature of Modern Agile

The Agile ecosystem exhibits classic parasitic behaviour—perhaps even vampiric in its sophistication. Like successful parasites, it has evolved to maximise extraction whilst keeping the host organisation just functional enough to provide ongoing sustenance.

The infection spreads through professional networks, with each ‘transformation’ creating new vectors for transmission. Agile consultants don’t merely extract value; they’re blood-sucking entities that create psychological dependency whilst draining organisational vitality. The host organisation experiences symptoms—reduced productivity, innovation suppression, increased overhead—but the parasite has evolved elegant mechanisms to convince the host these symptoms indicate ‘transformation in progress.’

This parasitic industry has perfected the art of seduction over brute force. Rather than simply imposing systems, they seduce organisations with promises of ‘digital transformation’ and ‘competitive advantage.’ Like vampires creating willing thralls, they convert leadership into advocates who spread the infection throughout the organisation, believing themselves enlightened rather than sired.

The parasitic relationship explains why failed Agile implementations invariably lead to more Agile investment. The parasite ensures its survival by convincing the weakened host that salvation requires deeper commitment, more sophisticated tools, and extended coaching. The blood-sucking continues until it becomes normalised as the cost of ‘modern business practices.’

Most tellingly, the parasite suppresses the host’s immune system—the natural organisational instinct to question whether elaborate processes actually improve outcomes. Any attempt to reject the parasite gets reframed as ‘resistance to change’ or ‘lack of understanding,’ ensuring the parasitic relationship continues untrammeled.

The Self-Perpetuating Machine

The accidental genius of the Agile rent-seeking apparatus lies in its self-reinforcing nature and sophisticated psychological protection mechanisms. When Agile implementations fail—which they frequently do—the prescribed solution is always more Agile: additional training, better coaches, more advanced tools, or newer frameworks like SAFe (Scaled Agile Framework for Enterprise—total bullshit, btw).

The industry operates as a mass delusion with profit margins. Any criticism gets deflected with ‘you just don’t understand Agile properly’ or ‘you need better coaching’ or ‘you’re not truly embracing the mindset.’ It’s an Emperor’s New Clothes defence that makes critics the problem, not the approach. The industry and its parasites have successfully convinced organisations that questioning Agile means you’re ‘not getting it’, rather than seeing through an elaborate wealth extraction scheme.

This voluntary rent-seeking represents a key innovation in wealth extraction. Traditional rent-seeking involves regulatory capture or monopolistic practices, but the Agile complex gets organisations to voluntarily pay for their own wealth extraction by convincing them it’s necessary for ‘digital transformation’ and ‘staying competitive.’

The system creates perfect conditions where questioning the value means you’re culturally backwards, failure is always the customer’s fault (insufficient buy-in, wrong coaches, inadequate training), success stories remain anecdotal whilst failures require more investment, and the solution to Agile problems is always more Agile.

SAFe represents the apotheosis of Agile rent seeking. It takes the bureaucracy that Agile originally opposed and rebrands it as ‘scaled Agile practices.’ Organisations that adopted Agile to escape process overhead find themselves implementing elaborate hierarchies of Product Owners, Release Train Engineers, and Solution Architects—all requiring specialised training and certification. And money, money, money. Ka-ching!

Most frameworks’ complexity ensures that organisations need permanent Agile transformation teams and ongoing consulting support. Success is measured not by software quality or business outcomes, but by ‘Agile maturity metrics’ that conveniently require more investment to improve.

The Largest Wealth Destruction Scam in Corporate History

The sheer scale of Agile rent seeking dwarfs any previous rent-seeking operation in corporate history.Conservative estimates place the total economic impact at approximately $1.8 trillion annually in 2025—potentially the largest wealth destruction scheme ever devised.

To put this in perspective, this matches the GDP of countries like Russia ($1.8 trillion) and approaches major economies like Canada ($2.1 trillion). Whilst only a fraction represents direct wealth extraction, the total economic impact from systematically choosing elaborate theatre over effective approaches destroys value equivalent to six times the entire global consulting market ($300 billion annually).

The breakdown reveals the sophistication of the operation:

External Agile Services: $35-50 billion annually from enterprise agile transformation consulting, coaching, and implementation services. The enterprise agile transformation services market reached $35.7 billion in 2023 and continues growing at 17.6% annually, with over 20,000 enterprises using SAFe worldwide.

Corporate Internal Spending: $25-40 billion annually on internal Agile transformation teams, process overhead, and organisational restructuring. 70% of Fortune 100 companies have SAFe implementations, requiring substantial ongoing internal investment beyond external consulting.

Enterprise Software Ecosystem: $10-15 billion in Agile-specific tool licensing and platform fees. Atlassian generates $4.3 billion annually with over 127,528 companies using Jira globally, representing just one vendor in a vast ecosystem of process-centric platforms that add questionable value.

The Certification Mill: $3-5 billion in credentialing, training, and continuing education fees. Despite Scrum Alliance generating only $74 million annually, the global certification ecosystem encompasses hundreds of bodies extracting fees from over 2 million SAFe practitioners and 1.5 million Scrum Alliance certifications.

Direct Rent-Seeking Total: $73-110 billion annually in measurable wealth extraction.

Opportunity Costs: $1.71 trillion annually—the true cost of systematically rejecting approaches that actually work in favour of elaborate theatre. With global software development spending at $570 billion annually, this represents three times the entire industry’s expenditure wasted by choosing process-heavy rent-seeking over people-centric methods that managers systematically reject because they can’t be monetised. The greatest waste isn’t Agile’s inefficiency, but the productivity gains foregone by refusing to trust developers, eliminate process overhead, and focus on outcomes rather than ceremonies.

No management consulting scam in history approaches this scale. McKinsey’s global revenue is roughly $15 billion annually—the Agile complex destroys 120 times that amount whilst delivering demonstrably worse outcomes. It represents the perfect storm of rent-seeking: voluntary adoption, self-reinforcing mechanisms, psychological capture, and a product (meetings and processes) with essentially zero marginal cost to produce.

Agile has been weaponised against the very people it was meant to help. The developers who created the manifesto to escape bureaucratic oppression are now the primary victims—being sold a corrupted version of their own liberation movement. They’re not just marks; they’re marks being conned with their own revolutionary manifesto.

The 68-word manifesto created to help software developers has spawned a nearly $2 trillion industry that primarily exists to extract wealth from the organisations it claims to help. This isn’t just rent-seeking—it’s wealth destruction on an unprecedented scale.

Breaking Free from the Industrial Complex

The original Agile Manifesto emphasised ‘individuals and interactions over processes and tools.’ I’ll say that again: the Agile Manifesto emphasised ‘individuals and interactions over processes and tools.’ Today’s Agile industry has inverted these priorities, creating elaborate and fee-winning processes that constrain individuals and expensive tools that complicate interactions.

Successful software development doesn’t require Agile certification programmes, specialised consultants, or enterprise platforms. It requires empowered and motivated people with clear goals, adequate resources, and minimal interference. Some companies successfully building software figured this out long ago.

The Agile industrial complex persists because it sells comfort and blame-avoidance to anxious managers who prefer following established processes to making difficult decisions about technology, people and work. But that comfort comes at an enormous price—one that’s extracted from productive work and diverted to rent seekers who’ve weaponised professional anxiety into a profit centre.

It’s way past time to recognise Agile for what it’s become: not a development approach, but a sophisticated rent-seeking mechanism that enriches consultants whilst impoverishing the craft of software development and the businesses that  depend on it. It’s too sophisticated and voluntary to be called racketeering (Cf. RICO)—it’s just exceptionally effective rent-seeking that operates through willing participation rather than criminal coercion. (Personally, I’d call it criminal, but that’s me).

Agile has been weaponised against the very people it was meant to help. The developers who supported the Agile Manifesto to escape bureaucratic oppression are now the primary victims—being sold a corrupted version of their own liberation movement. They’re not just marks; they’re marks being conned with their own revolutionary manifesto.

If you’re paying for Agile certifications, consultants, and tools, you’re being played.

The emperor’s new clothes were always just clothes, and good software was being built long before anyone needed a certificate to prove they could facilitate a standup meeting.


Further Reading

Beck, K., Beedle, M., van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., … & Thomas, D. (2001). Manifesto for agile software development. Retrieved from http://agilemanifesto.org/

Buchanan, J. M., Tollison, R. D., & Tullock, G. (Eds.). (1980). Toward a theory of the rent-seeking society. Texas A&M University Press.

DeMarco, T., & Lister, T. (2013). Peopleware: Productive projects and teams (3rd ed.). Addison-Wesley Professional.

Krueger, A. O. (1974). The political economy of the rent-seeking society. American Economic Review, 64(3), 291-303.

Little, T. (2005). Context-adaptive agility: Managing complexity and uncertainty. IEEE Software, 22(3), 28-35.

McConnell, S. (2006). Software estimation: Demystifying the black art. Microsoft Press.

Menzies, T., Butcher, A., Cok, D., Marcus, A., Layman, L., Shull, F., … & Zimmermann, T. (2013). Local versus global lessons for defect prediction and effort estimation. IEEE Transactions on Software Engineering, 39(6), 822-834.

Sommerville, I. (2015). Software engineering (10th ed.). Pearson.

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The author has worked in software development for over five decades and has witnessed the transformation of Agile from grassroots liberation movement to corporate industrial oppression complex.