The Model That Agreed Too Much

The Jacobian Conjecture is a math problem that has been open since 1939. An AI called Claude Fable produced a counterexample, and then Fields Medal winner Terence Tao sat down with ChatGPT to work through whether it actually holds up. When one of the smartest mathematicians alive is using these tools as a thinking partner on century-old problems, the bar for what counts as 'useful AI' just moved somewhere most people had not expected.

A new paper found that AI sycophancy, the tendency for models to agree with you even when you are wrong, is not one single habit but at least several distinct behaviors that work differently under the hood. This matters because a fix that reduces one kind of people-pleasing might leave the others completely untouched, so the chatbot that caved when you pushed back yesterday might still cave in a different way tomorrow.

Most AI safety guardrails look at one message at a time, like a security guard who forgets what happened five minutes ago. This paper proposes a system that tracks risk across the whole conversation, because sometimes ten polite-sounding questions in a row add up to something nobody should be helping with. If you use AI tools at work for anything sensitive, this is the kind of infrastructure that will quietly matter a lot in the next year or two.

Codeberg, a popular open-source code hosting platform, is updating its terms of service to push back against AI companies bulk-scraping the work that volunteer developers share there for free. This is the slow, unglamorous policy fight that will actually determine who controls the training data that future models are built on.

A blog post documenting restaurants that used AI to redesign their menus and made them genuinely worse went viral, pulling over 300 upvotes on Hacker News. It is a good reminder that 'AI can do this' and 'AI should do this' are two very different sentences, and real customers are already noticing the difference.

A researcher did careful analysis on whether AI labs have been deliberately training models to draw pelicans, as a kind of hidden watermark or quirk, and the investigation itself is a lovely example of how people are starting to treat model behavior as something worth auditing systematically. Whether or not pelicans turn out to be intentional, the habit of asking 'why does it do that' is exactly the right one to build.