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.
I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks. I think it is fundamentally a crisis of trust.
Simon Willison writes about a situation where letters were taken from him, prompting a discussion on dwarf behavior rather than dwarf AI. He notes that dwarf AI does not exist, and that dwarves sometimes misbehave. The piece is tagged with AI and game-design, referencing Tarn Adams, co‑creator of Dwarf Fortress.
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .
By Haden Pelletier
On the Navier–Stokes Millennium Prize Problem introduces an impressive result from OpenAI, who used an unreleased model to produce a resolution to the Navier–Stokes existence and smoothness problem, o...
My hypothesis is that there is a new opportunity for Extensible Software on the web . LLMs radically lower the cost of authoring extensions, and modern sandbox primitives lower the deployment cost and provide good security boundaries.
The article reflects on the shift in perspective after the release of Fable, a new model that promised to solve many coding challenges at a comparable or lower cost. Prior to Fable, developers felt it was pointless to invest heavily in coding tools or context strategies, as newer models would likely render them obsolete. However, Fable’s performance was so impressive that, despite its high cost, it prompted a reevaluation of how work was distributed across different models such as Opus, 5.6, K3, and GLM.