When AI writes almost all code, what happens to software engineering?
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Highly relevant to the article's central theme about AI's capability shift in coding. This paradox explains why tasks humans find hard (like playing chess) are easy for computers, while tasks we find easy (like physical coordination) are hard for AI. The article describes a reversal where coding—traditionally considered skilled work—is becoming easier for AI, which directly relates to this paradox and provides deeper context for understanding this technological shift.
This winter break was an opportunity for devs to step back from day-to-day work and play around with side projects – including using AI agents to juice up those half-baked or incomplete ideas. At least, that’s what I did with a few features I’d meant to build for months, but didn’t get around to during 2025: related to self-service group subscriptions for larger companies, and my custom-built admin panel for The Pragmatic Engineer.
Unexpectedly, LLMs like Opus 4.5 and GPT 5.2 did amazing jobs on the mid-sized tasks I assigned them: I ended up pushing a few hundred lines of code to production simply by prompting the LLM, reviewing the output, making sure the tests passed (and new tests I prompted also passed!), then prompting it a bit more for some final tweaking.
To add to the magical feeling, I then managed to build production software on my phone: I set up Claude Code for Web by connecting it to my GitHub, which let me instruct the Claude mobile app to make changes to my code and to add/run tests. Claude duly created PRs that triggered GitHub actions (which ran the tests Claude couldn’t) and I found myself reviewing and merging PRs with new functionality purely from my mobile device while travelling. Admittedly, it was low-risk work and all the business logic was covered by automated tests, but I hadn’t previously felt the thrill of “creating” code and pushing it to prod from my phone.
This experience, also shared by many others, suggests to me that a step change is underway in software engineering tooling. In this article – the first of 2026 for this publication – we explore where we are, and what a monumental change like AI writing the lion’s share of code could mean for us developers.
Today, we cover:
Latest models create “a-ha” moments. It’s not just devs working at AI vendors who noticed much more capable models, but also independent software engineers.
Why now? Model releases in November and December seem to have been the tipping point: Opus 4.5, GPT-5.2 and Gemini 3.
The bad: declining value of expertise. Prototyping, being a language polyglot or a specialist in a stack are likely to be a lot less valuable, looking ahead.
The good: software engineers more valuable than before. Tech lead traits in more demand, being more “product-minded” to be a baseline at startups, and being
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