BREAKING: Revolut moved ~2,000 engineers onto self-hosted GLM for coding and pushed Claude Code and Codex to the exception list. Per David Abitbol, who leads AI ops there: 56 Nvidia B200s, a blended 30 cents per million tokens, roughly a tenth of what they paid Anthropic and OpenAI. Engineering lead time improved 70%. Here's the part that matters: the gains showed up when tokens stopped being rationed. Engineers quit saving AI for the hard tickets and pointed it at everything. Frontier models become the exception, open weights become the default. That's a pricing problem for every lab heading to IPO. h/t @OliverMolander Would you move your team off frontier APIs if the bill dropped 90%?

Sequoia's thesis that the next $1T company will sell work, not software, is the most important reframe in AI right now. The argument: if you sell a copilot, you're competing with every new model release. But if you sell the outcome, books closed, contracts reviewed, claims handled, every AI improvement makes your margins better, not your product obsolete. The key insight most people miss: for every $1 spent on software, ~$6 is spent on services. The entire SaaS playbook was about capturing the software dollar. The AI playbook is about capturing the services dollar, at software margins. Not "AI for accountants." The AI accounting firm. Not "AI for lawyers." The AI law firm. The companies that figure this out won't look like SaaS companies. They'll look like services firms rebuilt on software infrastructure. That's a fundamentally different company to build, fund, and scale. And most founders are still building copilots. I went deeper on this in AI Market Fit: Sequoia Capital: The next $1T company won’t sell software. It will sell the work.

Dario Amodei: some tokens Claude produces are worth a few cents, others could be worth tens of millions of dollars. His examples: 1. Telling someone to restart their Mac: the model has said it 10 million times, worth a dollar or less 2. Telling a pharma company to move an aromatic ring to the other end of a molecule: potentially tens of millions 3. Expect pay-for-results pricing, or labor-like pricing by the hour 4. It's a new industry, so many models will be tried Today pricing is simpler: per the S-1, usage-based pricing was 80% of H1 2026 revenue and subscriptions 20%. Full breakdown here:

BREAKING: Palantir's Alex Karp says OpenAI will never IPO. His theory: nationalization is the only real exit. When asked what the S-1 risk factors look like, Karp's answer was simple: there is no S-1. The liability exposure from frontier AI is so large that no public market can absorb it. The only entity big enough to backstop it is a government. If Karp is right, OpenAI doesn't become the next Google. It becomes a utility. Or a weapon. The most valuable AI company in the world may have no clean path to public markets. Is nationalization actually the most likely outcome for frontier AI labs?

Hugging Face got hit by an AI-led cyberattack. Their CEO's response? Existing cyber laws are probably enough to govern advanced AI. "I'm not even sure that we need to reinvent the wheel." That's a remarkable position to hold after your own company becomes a case study for why the current framework might be failing. The attack wasn't a phishing scam or a script kiddie — it was AI-driven. That's a qualitatively different threat model than what most cyber law was written to handle. The argument for regulatory restraint usually comes from people who haven't had skin in the game. Here, the CEO has skin in the game and still lands there. That's either principled consistency or a dangerous blind spot dressed as pragmatism. The market is going to find out which one pretty fast.