Simon Willison quotes Jakub Pachocki, Chief Scientist at OpenAI, arguing that the strongest reason to rapidly train smarter AI models is the necessity of building defensive systems against the dangers posed by other AI. Pachocki stresses that powerful, aligned AI will be essential for securing infrastructure, protecting against rogue agents in real time, and inventing new protective measures, making this a primary focus of OpenAI’s deployment efforts. He cautions that the urgency of progress should not justify reckless behavior, noting that the seriousness of the stakes makes a reckless race forward absurd.
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 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.
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 reports that OpenAI used an unreleased model to produce a claimed resolution to the Navier–Stokes existence and smoothness problem, one of the seven Millennium Prize Problems. The claim has been met with controversy, as NYU professor Tristan Buckmaster and mathematician Levent Alpöge—who had been working on related problems with Claude and Codex—accused OpenAI of using their unpublished work. OpenAI has denied accessing their data and has offered to wait for Buckmaster’s publication, but will not include Alpöge as a co‑author due to a competitive relationship with his employer.