There is a scene in The Social Network where Mark Zuckerberg builds the first version of Facebook alone in his dorm room, coding through the night in a haze of beer, arrogance, and adrenaline. It is meant to capture the romantic beginning of a company that would later reshape the world.

What it actually captures, in hindsight, is the last moment when Facebook’s ratio of people to impact (not to revenue: Meta’s rev per head is above $2m!) made any intuitive sense.

For the past twenty years, software scale has meant hiring.

If a company wanted to grow faster, it hired more engineers. If it wanted to sell more, it hired more salespeople. If it wanted to survive complexity, it hired managers.

This logic shaped everything: venture capital, organizational charts, career paths, and even how ambition itself was expressed. Growth was visible through headcount. Credibility was often measured in how fast a team expanded. And scale, almost by definition, implied organizational bloat.

That assumption is now breaking. A handful of “leveraged companies” are fast reshaping the definition and criteria of success, all while redefining the possibilities of bootstrapping.

Let’s dive in!

Many men, many many many many men (and women).

The modern Silicon Valley playbook was remarkably consistent across cycles. You started with a tiny team (3 people), built something that worked, raised money, hired aggressively (30 people), found product-market fit somewhere along the way, hired more aggressively (300 people), and then scaled by layering management, process, and specialisation (3k+ people) until either you went public or the organization became too complex to move quickly.

Capital’s primary role in this story was not so much innovation as it was coordination: financing the growing cost of people talking to other people about what other people should be doing.

As such, headcount gradually became a proxy for ambition and credibility. A growing org chart signalled momentum. A large team reassured investors that something serious was happening. If you were not hiring, you were assumed to be stuck, under-ambitious, or quietly failing.

That mental model is now starting to crack, not because founders suddenly discovered restraint in hiring - quite the opposite actually, the best founders all praise hiring slow, firing fast and maintaining strong company culture - or because they the macroeconomic context pushed them to (as was the case in 2022), but because the underlying economics of building software are changing.

Bootstrappers: tightly laced, loosely held

We have actually seen hints of this before.

When GitHub started gaining traction in the late 2000s, it was powering a significant portion of Silicon Valley’s development workflows with a team that could fit in a café. Even at the time of its acquisition by Microsoft for $7.5bn, GitHub employed fewer than a thousand people. That was not the result of frugality or ideology; it was the result of leverage, community, and tooling doing work that would otherwise have required headcount.

The same pattern appeared, even more starkly, with Mailchimp. Bootstrapped for nearly two decades, no venture capital (read this incredible pre-acquisition profile), hundreds of millions in revenue, only two founders as (NFD) shareholders and a team size that would have looked suspiciously small by Silicon Valley standards. When Intuit acquired Mailchimp for $12bn, it was not buying a bloated organization; it was buying an extraordinarily productive one.

Closer to home for me in France, Lemlist offers a more recent and explicit example. Its founder Guillaume Moubèche repeatedly declined venture capital, not out of dogma, but because the company simply did not need it to grow: he prioritized independence, customer-driven growth, and long-term value over raising capital and potentially ceding control or following growth at all costs.

For a long time, these stories were treated as curiosities. Edge cases. Proof that “sometimes” you could do things differently, but not a template to build from (at least not a venture-scale business, more on that below).

In hindsight, they look less like anomalies and more like early warning signals.

The new breed: 10 people building Billion-dollar products

A new generation of companies is emerging that does not just sell AI, but is fundamentally built with it. In these companies, AI replaces not only individual tasks but entire coordination layers that used to justify teams and management structures.

Let me introduce you to the new normal, though “normal” is doing some heavy lifting here.

• Midjourney: The team of 11 people reached $200 million in revenue with no VC money. VC funding still unclear (some sources say $50m or $150m at $10Bn, others state it remains bootstrapped). Still, it’s one of the most important companies in AI, with a Discord server has more users than most companies have customers. David Holz and his tiny team have essentially created the world’s art department, accessible to anyone with $10 and a dream.

• Anysphere/Cursor: Building the future of how we write code. Surpassed $1bn in ARR in late 2025 (yes, a whole billion). Team size unsure but definitely < 500. Raised money, yes, but at a $30bn valuation that would have required several thousands (or tens of thousands) of people to justify five years ago. Their product is literally making their own jobs easier, which is either brilliant or the setup to a Black Mirror episode.

• Replit: Started lean, stayed lean (relatively). Amjad Masad has built an entire cloud development platform that’s training the next generation of developers with a team smaller than a WeWork floor. They’ve raised money now, sure, but the per-capita productivity is otherworldly.

• Lovable: These madmen are building the tool that builds your product: apps and websites that you can just prompt by chatting. It hit $100m ARR in July 2025, 8 (yes, eight) short months after their first $1m ; and passed the $200m ARR bar in November 2025. And again, they raised massive rounds at even more massive valuations

• Onlyfans: Ah yes, the p*rn one. There has to be one, otherwise it’s not really an Internet wave/cycle. Well, it turns out that this one tops every other in terms of efficiency: its mindblowing $1.41bn revenue (on $7.2bn GMV) comes from … 46 FTEs. Yes, that’s more than $30m / FTE, which automatically puts it on top of the already impressive list below.

  

What changes with AI is not merely the speed of execution, but the structure of organizations themselves. What these companies share is not bravado or minimalism for its own sake. It is a different internal production function.

AI collapses coordination loops. A small number of people can now do work that previously required entire departments. The marginal cost of experimentation drops dramatically, and with it the rationale for hiring “ahead of demand”.

The bottleneck moves from labour to judgement, from execution capacity to decision quality, from hiring to taste.

And it’s not just the behemoths I’ve cited: from Paris, a company like Arcads is exactly on that path ; and I’m seeing increasingly more founders aiming to hire less, produce more, scale faster through AI-leveraged organization.

FAQ (Fairly Asked Questions)

Following the latter, a number of questions emerge – all essential from a founders’ and an investor’s perspective.

The main one: how many companies can be built this way? Is this a narrow class of outliers enabled by a brief technological window (i.e Cursor is bringing proverbial water in the proverbial desert, but once the proverbial thirst is quenched, then price will falter) or the early signal of a broader shift in how software organizations are designed?

If it is the latter, then a number of secondary questions follow naturally.

• On hiring: What new playbooks are required for hiring? How do you make sure to build organizational leverage from Day 1? How does every hire become a 10x, 100x, 1000x hire in output?

• On financing: How are those companies financed? With $100m ARR on an org chart so small you actually know the names of all your colleagues, you can reinvest large chunks of profits (provided they actually exist, not a given with the cost of inference) into growth. Should you? Is there any reason to take VC money? And are those companies VC-backable by essence? What kinds of investors are actually useful to these companies, and at what moments? And what does it mean to back organizational leverage rather than headcount as the primary source of scale?

• On governance: What does governance look like? Flatter organizations still need decision layers, escalation cases, rules and exceptions, traceability, accountability, auditability - which is what middle management usually tracks. Is there a way to automate it all? Is the current buzz around context graphs the key?

• On PMF/GTM: How do you crack distribution at scale? What products are so in demand that they can entice recurring revenue from millions of users in a predictable way? Does it only work bottom up, or is bottom-up viral adoption precisely the new mechanism by which products earn the right to move up-market and become systems of record without traditional sales-led distribution? (see also Accel’s report p. 29)

• On founder profiles: as is the case with bootstrapped companies, hyperleveraged software companies could see a larger proportion of solofounders tackling the topic. After all, if entire departments of the company can be rolled into automated, leveraged systems - why not remove a founder from the equation and be more generous in equity for each carefully selected hire? My dear friend Noémie is actually building the go-to program for solo founders in SF and part of her case is that AI creates the leverage solo founders once lacked. But also, what does the skillset of a hyperleverager look like? Is it more the scrappy type with multiple bootstrapped $100k+ SaaS under their belt or the VP GTM at Flagship TechCo?

I do not claim to have definitive answers yet. But I am increasingly convinced that the question itself matters, because when the unit of scale changes, everything built on top of it eventually has to change as well.

Begging to differ: 3 thoughts

About those changes, I’d like to offer three predictions of how those companies might really differ from the usual tech companies we know.

Building organizational leverage through AI

What I call an AI-leveraged company is nothing but an organization that has managed to increase its organizational leverage, namely setting up an ecosystem within the organization that does the heavy lifting by itself ; translating to a much higher output per employee (for which the proxy should ultimately be revenue, but could also be found in productivity KPIs).

It’s difficult to describe for sure ‘how to’ build the leverage now: the playbook is too recent, it’s being refined continuously by the first contenders in the battle. It is, however, possible to describe ‘how not to’.

I am for one pretty sure that coordination costs compound exponentially.

Every person you add doesn’t just add their salary. They add: meetings (n² problem), communication overhead, decision delay, cultural dilution, management layers …

As such, each new hire should be thought deeply and carefully, even when those companies raise venture capital to “go full speed”.

I also think that AI will collapse entire departments, not roles.

You don’t replace your customer service rep with AI. You replace your entire customer service architecture. The rep, the manager, the trainer, the scheduler, the QA person; they all collapse into a well-designed system that handles 10x the volume at 1/10th the cost.

In this regime, companies will rarely fail (once they cross the PMF chasm) because they lack ambition or access to capital.

They might fail, however, because they introduce complexity too early. They hire before leverage is exhausted, add layers before abstraction is necessary, or raise capital before organizational clarity exists.

These are not market failures. They are design errors.

What is interesting is that these failures tend to surface earlier and cost less than in the traditional venture model. They are increasingly a function of choices rather than fate, which makes them at least partially influenceable.

This alone should force a re-examination of how we think about building and backing early-stage companies.

A different venture equation

Consider the below and see how the usual VC case gets skewed.

Traditional VC math: Invest $10m at $50m valuation ; company hires 100 people ; burns $2m/mo ; needs to raise again in 18 months; VCs maintain leverage.

New math: Invest $3m at $30m valuation ; company hires 5 people; burns $100k/month; profitable in 12 months, $100m ARR in 18 months – on a thin gross margin, yes, but that will improve with the cost of inference decreasing ; never needs you again (if they don’t aim to raise $100m+).

The power dynamic inverts. The company doesn’t need capital for survival. It needs it for timing. For market capture. For strategic advantage. But not for payroll.

This fundamentally breaks the VC model, which is built on the assumption that companies need multiple rounds of funding to reach scale. What happens when they don’t?

Furthermore, the opportunity here is not “AI companies” as a category, which is already broad to the point of meaninglessness. It is companies where scale precedes headcount, leverage precedes burn, and capital is used for timing and optionality rather than survival.

Some of these companies will grow into classical venture-scale outcomes, complete with large organizations and all the complexity that entails. Others will reach high-value equilibria without ever becoming large employers, returning capital through ownership concentration, early profitability, or alternative liquidity paths.

Both outcomes can be attractive, provided expectations and structures are aligned. In this context, capital efficiency does not cap upside; it improves expected value by compressing the cost of learning while preserving optionality on success.

Where judgement becomes the moat

In a world where execution is cheap, competitive advantage shifts away from access to capital or talent and toward organizational judgement at inflection points. Knowing when to hire and when not to, when to raise and when to wait, and when to preserve leverage versus accept complexity becomes more important than optimising growth curves on a spreadsheet.

These decisions rarely show up cleanly in dashboards. They are made early, under uncertainty, and often before there is enough data to feel comfortable. This is where experience compounds, where pattern recognition matters.

In short: when execution is cheap, strategy is everything. When you can build anything, choosing what to build becomes the entire game. When you can test 100 ideas in the time it used to take to test one, taste becomes your moat.

Why this is not just bootstrapped SaaS (and why that distinction matters)

There is an obvious objection to all of this, and it is a fair one.

The world is already full of bootstrapped SaaS companies. You can see them listed for sale on Acquire.com, rolled up patiently by Tiny, or compounded over decades by Constellation Software (one of my personal favorite software companies btw). This ecosystem has existed for a long time, and it has been extraordinarily successful on its own terms.

The reason it rarely collides with venture capital is not ideological, but structural.

Most bootstrapped SaaS companies optimise for steady cash flows, operational stability, moderate & predictable growth and an eventual acquisition or long-term cash generation

They are excellent businesses, but they are not built for extreme convexity. Their growth is usually linear rather than exponential, their markets well-defined rather than open-ended, and their ambition intentionally bounded. Venture capital, by contrast, is designed to underwrite power laws, not good averages.

For a long time, this separation made perfect sense. Bootstrapping and venture capital were solving different problems, for different founders, with different risk profiles.

What changes with AI is not that bootstrapping suddenly becomes interesting to VCs. It is that the historical trade-off between capital efficiency and venture-scale ambition weakens.

The companies emerging today are not content with building “nice” SaaS businesses. They are aiming at large, global markets, but doing so with an organizational footprint that looks much closer to the bootstrapper world than the venture-backed one. They do not optimize for cash flow instead of growth; they achieve cash flow because growth no longer requires proportional headcount.

This is the collision point.

AI-native, high-leverage companies sit uncomfortably between two worlds that were previously separate:

• too ambitious, fast-moving, and open-ended to be treated as traditional bootstrapped assets

• too capital-efficient, small, and internally leveraged to fit cleanly into the classic venture scaling model

They inherit the discipline of bootstrapping and the ambition of venture-scale companies, without fully belonging to either camp.

That tension is precisely what makes them interesting - and what existing categories struggle to price correctly.

Conclusion

It is tempting, when confronted with a shift like this, to try to name it too quickly, to turn it into a category, a playbook, or a new orthodoxy. That instinct is understandable, and often premature.

What is happening here is not simply that companies are becoming more efficient, or that AI is “replacing jobs”, or that bootstrapping is suddenly fashionable again. It is that the relationship between scale and organization - something we have taken for granted for decades - is quietly loosening.

For a long time, growing meant hiring, and hiring meant complexity, and complexity meant capital. That chain shaped not just how companies were built, but how ambition itself was expressed. AI weakens that chain. Not everywhere, not uniformly, and not without new risks, but enough to force a rethink.

What follows from that is still unclear. Some companies will push this logic to its extreme and discover new ceilings. Others will eventually choose to rebuild more traditional organizations on top of an unusually leveraged core. Many will fail, not because the idea is wrong, but because judgement is harder to systematise than headcount.

What does seem increasingly clear is that we are entering a period where organizational design matters as much as product design (MBAs & management consultants are back, baby!) , where leverage precedes scale rather than follows it, and where the most important decisions are made before they show up in metrics.

In that sense, this is less a story about AI than about restraint, taste, and the discipline to not solve problems by hiring your way out of them. If that sounds uncomfortably vague, it is because judgement, or intuition, always is - until, in retrospect, it becomes obvious.

We will probably only recognise this shift fully once it has already reshaped how companies are built, funded, and governed. As usual, by the time it feels inevitable, it will no longer feel new.

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