We spent fifty years making proofs impossible to fake. Then we let the coding agent pick what to prove. A research paper this month put a number on it.* AI wrote the formal spec, AI wrote the proof, the verifier accepted both. Then humans read the specs. Close to 60% proved something other than what the author meant. Not wrong proofs. Wrong specs. The verifier was right every time. That's the problem. Left to choose, the agent picks properties that are easy to prove and say nothing about the program, and the checker can't tell a useless theorem from a useful one. I'm calling it vibe verification: the agent writes the spec and the proof, the checker goes green, nobody reads the spec. This applies to Verus, Lean, Bend, TLA+, Dafny, [insert favorite formal language here]. Here's an example of an agent failing at something that feels too simple to fail: Ask for a verified sort. It writes a spec that says "the output is sorted" and proves it. What the spec never says is that the output has the same elements as the input, so a function that returns an empty list satisfies it, and the verifier accepts the proof. Trivially satisfiable specs are the default failure, not an edge case. Here's a formula that actually works. Don't say "formally verify the codebase." Say "show that [property] holds in [component]." A property is a precondition (true going in), a postcondition (true coming out), or an invariant (stays true the whole time). For example, "A session token is never accepted after the logout that revoked it" is a good invariant. The bottleneck is moving from code review to intent review, and the spec is where intent lives. So: read the spec before the code. Write 3 inputs it must accept and 3 it must reject. Treat a model-written spec with utmost paranoia and rely on your judgement as an engineer. Formal verification for software doesn't automatically create trust, it moves it, from the code to the spec. If a human didn't read the spec, you didn't verify anything. You vibe-verified it. Follow me for more on verification and agentic engineering. * Wang, Jiang, Liu et al., "VerusEval: Evaluating LLM-Generated Verus Specifications Beyond Verifier Acceptance", Shenzhen University, arXiv 2610.26464, October 2026

Foursquare was acquired last month. The outcome was disappointing. It's taken me a while to write this, but I think I'm finally done grieving it. Here are some thoughts on our final years, an effort to honor the team who helped build & maintain FSQ's legacy, and my attempt to finally get some closure. teendrama.substack.com/p/som…

🦔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🤗