Notes from the AI Coding Transition – Evan Schwartz

Notes from the AI Coding Transition

05 Aug, 2026

Like many other software engineers, my coding workflow has changed dramatically since the start of 2026. And like many others, I've felt some mix of awe, grief, frenetic productivity, atrophying skills, and understanding less while shipping more. In this moment where the field is undergoing this rapid shift, I've found it helpful to read others' takes on their processes, what they're doing to keep their brains engaged, and their genuinely mixed feelings.

Before writing up my own thoughts, I went back through the relevant essays and blog posts from the last ~7 months to find the ones that resonated with me the most. Below are the posts that I especially liked and lines that stuck out from them, either because they gave me some idea about how I might want to use AI or just because they had a particularly incisive description of our field's situation. (Quotes are exact and the bold text is my added emphasis.)

If you've read others that you thought were particularly on point, please send them my way!

February 7, Nolan Lawson: We mourn our craft

I didn’t ask for the role of a programmer to be reduced to that of a glorified TSA agent , reviewing code to make sure the AI didn’t smuggle something dangerous into production.

If you would like to grieve, I invite you to grieve with me. We are the last of our kind, and those who follow us won’t understand our sorrow. Our craft, as we have practiced it, will end up like some blacksmith’s tool in an archeological dig, a curio for future generations.

February 9, Margaret Storey: How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt

Even if AI agents produce code that could be easy to understand, the humans involved may have simply lost the plot and may not understand what the program is supposed to do, how their intentions were implemented, or how to possibly change it.

Peter Naur reminded us some decades ago that a program is more than its source code. Rather a program is a theory that lives in the minds of the developer(s) capturing what the program does, how developer intentions are implemented, and how the program can be changed over time.

Cognitive debt tends not to announce itself through failing builds or subtle bugs after deployment, but rather shows up through a silent loss of shared theory. As generative and agentic AI accelerate development, protecting that shared theory of what the software does and how it can change may matter more for long-term software health than any single metric of speed or output.

February 15, Simon Willison: Deep Blue

the sense of psychological ennui leading into existential dread that many software developers are feeling

Simon : All of the chess players and the Go players went through this a decade ago and they have come out stronger.

February 15, Tom Wojcik: Finding the Right Amount of AI

The Shen-Tamkin study identified six distinct AI interaction patterns among developers. Three led to poor learning: full delegation, progressive reliance, and outsourcing debugging to AI. Three preserved learning even with full AI access: asking for explanations, posing conceptual questions, and writing code independently while using AI for clarification. The differentiator wasn’t whether developers used AI, it was whether they stayed cognitively engaged.

metrics don’t capture what’s happening underneath. The mental fatigue of reviewing code you didn’t write all day. The boredom of babysitting an agent instead of solving problems . The slow, invisible erosion of the hard skills that made you good at this job in the first place. You stop holding the architecture in your head because the agent handles it. You stop thinking through edge cases because the tests pass. You stop wanting to dig deep because it’s easier to prompt and approve. There’s no spark in you anymore.

February 25, Ivan Turkovic: AI Made Writing Code Easier. It Made Being an Engineer Harder.

Here is something that gets lost in all the excitement about AI productivity: most software engineers became engineers because they love writing code.

Not managing code. Not reviewing code. Not supervising systems that produce code. Writing it. The act of thinking through a problem, designing a solution, and expressing it precisely in a language that makes a machine do exactly what you intended . That is what drew most of us to this profession. It is a creative act, a form of craftsmanship, and for many engineers, the most satisfying part of their day.

this is different because it is not asking engineers to learn a new way of doing what they do. It is asking them to stop doing the thing that made them engineers in the first place and become something else entirely.

a mid-level backend engineer is now expected to understand product strategy, review AI-generated frontend code they did not write, think about deployment infrastructure, consider...