This week on Dev Interrupted, Anush Elangovan, VP of AI Software at AMD, returns to unpack the rapid shift toward an agentic software development lifecycle. Anush introduces the concept of "Agentic IO," a workflow where engineers focus strictly on high-level goals while AI handles the complex implementation. The conversation also highlights the expanding productivity wingspan of modern developers, the power of local open source models, and why speed remains the ultimate competitive moat.

1. The build versus buy debate continues

We recently reached out to our community to settle the debate around building versus buying in the age of agentic orchestrators. Rob Zuber, the CTO at CircleCI, nailed the core philosophy: you have to anchor your decisions in whether the tool provides direct value to your customers and doubles down on your domain expertise. If a tool just helps you do your job better behind the scenes, there are experts out there who can build it more effectively. Do not let the hype convince you that AI is ready to autonomously spin up full SaaS platforms without deep, strategic product guidance.

Read: The AI SaaSpocalypse is a mirage

Dev Interrupted

The AI SaaSpocalypse is a mirage

In February 2026, roughly a trillion dollars in software market cap evaporated in a few weeks. Wall Street gave it a name: the SaaSpocalypse. ServiceNow, Salesforce, and Intuit are only a few of the many examples of double-digit declines year-over-year. The thesis driving these sell-offs is that if AI agents can do what enterprise software does, then en…

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14 days ago · 12 likes · 1 comment · Andrew Zigler and Ben Lloyd Pearson

2. The hidden power of native primitives

Sometimes you do not need a shiny new AI tool to solve your automation problems. You just need a good Linux kernel. This piece is a fantastic deep dive into systemd timers, a native primitive that sits right under our noses in the command line. It comes with built in sophistication to prevent thundering herd problems and manages log rotations automatically. It is a refreshing reminder that the foundational technologies we rely on were built for the long haul, and occasionally, they are still the absolute best tools for the job.

Read: You Don’t Love systemd Timers Enough

3. Microsoft steps out of OpenAI’s shadow

Microsoft finally made its move at Build 2026, unveiling a new flagship family of foundation models. For the massive swath of developers living inside Microsoft’s ecosystem, this brings deep, native integrations without relying exclusively on their old partnership with OpenAI. The release includes everything from a heavy reasoning model to MAI-Code-1-Flash, a lightweight tool built specifically for edge efficiency and speed. I’m very bullish that personalizing AI services with your own data is the frontier we should all be optimizing for, and Microsoft has an impressive platform and ecosystem to provide this end to end.

Read: Introducing MAI-Code-1-Flash

4. Stanford’s new rulebook for coding assistants

Stanford just published a fascinating set of guidelines for how AI coding assistants should behave in their CS336 course. Rather than letting the agent generate outright solutions or refactor code, the syllabus constrains the tool to act strictly as a Socratic tutor. We often use AI transactionally just to get to an answer, but forcing the model to challenge you and ask guiding questions is one of its most underrated capabilities. Crucially, this Socratic rulebook is not intended to slow down the coding process. Instead, you can use this approach in your own codebase to ensure your developers are learning deeply rather than just taking the easiest path.

Read: AI Agent Guidelines for CS336 at Stanford

5. Manufacturing trust in the age of fast code

Kent Beck just published an excellent piece arguing that AI assisted development is creating a dangerous imbalance. We are currently generating code much faster than we are building the necessary trust to support it. To fix this, you have to deliberately introduce speed bumps into your process. For example, I use a custom skill called /scrutinize that acts as an adversarial prompt, specifically designed to poke holes in my work before I get too deep. You have to slow down and create mechanical ways to verify correctness, otherwise that unchecked speed will eventually catch up to your organization.

Read: Trust Factory

6. The first-ever Gartner Magic Quadrant for an engineering leader’s biggest challenge right now

AI is changing how we build software, but how do you choose the right platform to measure its impact? To get the visibility you need into productivity, bottlenecks, and real ROI, you need a trusted evaluation method.

Gartner just released the first-ever Magic Quadrant for Developer Productivity Insight Platforms, naming LinearB a Leader. Download your complimentary report to understand why this category matters right now and why LinearB is recognized for our vision and workflow automation.

Get the Gartner report

7. Academia’s battle against AI generated noise

Mathematicians are pushing back against a flood of unsubstantiated, AI generated proofs from commercial tech companies, warning that these massive model drops often lack fundamental replicability and leave academics unable to properly respond. This dynamic perfectly mirrors what we are seeing in the software development life cycle. Code review queues are getting absolutely drowned out by AI generated noise as tokenmaxxing takes Goodhart’s Law to its logical extreme. As my co-host Ben Lloyd Pearson pointed out, our own data at LinearB shows that AI generated code can actually take five times longer to get through the review process because of a lack of human ownership, adding more friction than velocity in most cases. Turns out mathematicians are feeling it too.

Read: Mathematicians warn of AI threats to profession as industry encroaches