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Engineering management after the cost of code collapsed

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Why This Matters

The collapse in the cost of producing code due to AI tools like LLMs is reshaping traditional engineering management practices. Leaders must critically evaluate their assumptions, as many longstanding rules may no longer hold true, prompting a shift towards more data-driven and flexible approaches. This evolution impacts how organizations scale, onboard, and coordinate teams in the modern tech landscape.

Key Takeaways

I have been a director of engineering for a bit over three years now, and I still hear and read what I call the "old rules" repeated over and over: a director should not spend time coding, good work takes time, protect the team from the business, get consensus before you commit, etc.

For a while I thought the people repeating these lines were behind. Then we introduced LLMs in my org and the cost of producing code dropped, and I started checking each rule against the assumption underneath it. The surprising realization was that about half of the old rules were resting on assumptions that broke and the other half were resting on assumptions that did not, and a few of those matter more now than they did before.

What follows is a cleaned-up version of notes I accumulated over the past year. Gemini 4 helped with the editing.

What we actually know

The cost of producing plausible code has collapsed and it is not going back. Almost every claim beyond that is either unproven or wrong.

That AI tooling has made engineering orgs dramatically faster: unproven.

That code review, documentation, and onboarding are obsolete: wrong.

That you can run the same roadmap with half the people: a bet, not a fact.

If you rebuild your management practices on the narrow claim, you will be right. If you rebuild them on the broad claims you are gambling with other people's careers and calling it a conclusion.

Focus on auditing assumptions

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