The article discusses how computer programming fundamentally involves problem‑solving with computers and managing complexity. It argues that these skills will remain valuable and sustain a viable career even as AI tools become more prevalent.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Simon Willison.
The article highlights Paul Dix’s astonishment that AI was able to generate one million lines of code and then refine it over several months into a reliable software product now used by millions of developers. Dix argues that this achievement is far more impressive than merely translating code between languages, emphasizing that with a verification system and clear guidance, AI can produce and iteratively improve highly complex, sophisticated software until it functions perfectly.
Laurie Voss argues that while the cost of writing code has fallen dramatically, the costs of reviewing, fixing, and operating software are rising and will continue to do so. She emphasizes that the true expense lies in understanding user needs, precisely defining requirements, and ensuring a pleasant user experience—costs that are unique to each software product and do not scale with reuse. As software demand grows without an upper limit, these user‑centric costs will dominate the overall development effort.
Simon Willison reflects on the evolving role of software developers in the age of AI, noting that while AI can produce high‑quality code, it also enables poor execution that leads to project failures. He argues that the industry is beginning to recognize the continued need for human collaboration and expertise to truly innovate. The piece highlights the tension between automation and the essential human element in software creation.
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
Simon Willison reflects on how programming used to be a rewarding career, describing it as a job where developers solved complex logic puzzles for high pay and had full control over strategy and timelines. He notes that coding agents and large language models (LLMs) now handle many of these puzzles, offering a "gift that keeps on giving" for those who enjoy building systems without caring deeply about the underlying problems. However, for programmers who found joy in directly solving problems, this shift represents a significant loss.
Simon Willison discusses how the current trend of mining open mathematical problems in a non-renewable way could make these problems scarce. He notes that rumors of a problem can trigger large AI-driven efforts to solve it before original researchers can fully develop their work. This shift may discourage sharing promising research, potentially reversing centuries of open science and harming the field’s future.