Danielle Tichner

A first for me: At a VC dinner last night, they handed out syringes of peptides alongside a “future of food” meal (It was great!)

AI turns water brown. You might disagree. You might even have some evidence to the contrary. But you have to ask yourself: is this really worth losing my job over? AI turns water brown.

bull case for southern portugal data centres: elite solar for power + desal (proven), cold atlantic upwelling year round for cooling, access to spanish nuclear, lots of land, and a strategic fibre position linking europe, america, africa and asia. underpriced infra geography.

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗

We are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such as our customers’ enterprises, organizations, and repositories), we are closely monitoring our infrastructure for follow-on activity.

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

Wild: macOS on Apple Silicon is supposed to be one of the hardest consumer targets in the world. Anthropic’s most powerful AI model Claude Mythos helped crack it in 5 days 😳 Researchers at Calif .io used an early Claude Mythos Preview to discover and exploit a vulnerability in macOS running on Apple M5 silicon. The target had Apple’s newest memory protection system fully enabled. Of course, we must note that AI did not hack Apple. It’s that AI compressed elite exploit research into a much tighter loop: find → test → write → iterate → chain. The details are pretty wild: → April 25: Bruce Dang finds the bugs → April 27: Dion Blazakis joins → May 1: full working exploit → Starts from an unprivileged local account → Uses normal system calls → Ends with a root shell Calif was very careful about Mythos’s role. The model did not magically invent new attack primitives. It recognized bug classes it had generalized from, helped write and iterate exploit code, and moved the team faster. Humans still handled the hardest parts, especially bypassing Apple’s newest mitigation layer. Which means that the future of cybersecurity probably won’t be where AI replaces hackers. It will be the one where AI gives elite hackers a second brain, a faster loop, and far more attempts per week. Wild times.

In case you were wondering where we're at in the cycle, I just heard about a seed round where the founder is selling secondary