My bet: @thinkymachines will soon make more money than @AnthropicAI. Not by winning the race to build one standardized frontier model. By becoming the Palantir FDE for enterprise custom models. The playbook: 1. Release the best American open-weight model. 2. Drive widespread enterprise adoption. 3. Charge the largest companies 7–9 figures to post-train and run custom models behind their own firewall. The model rests on three bets: 1. Large enterprises will increasingly demand their own models with their own data, and this is how they differentiate and win. 2. Enterprises won’t need just one model. They’ll continuously need new models for different workflows, departments, and proprietary datasets. That creates extremely sticky, recurring revenue. 3. Autoresearch will make custom model development increasingly scalable. Tinker can become the interface enterprises use to post-train their own models—with @thinkymachines providing the expertise and infrastructure behind it. FDE, infra, everything, huge contracts. 4. Eventually, maybe everyone wants their OWN model, and autoresearch and training inside tinker on top of @thinkymachines's base model will make it happen. Meanwhile, Henry-ford-styled, standardized models will makes no margins. OpenAI and Anthropic will have their API margins squeezed by Deepseek/GLM/Grok/Meta etc, and their consumer subscriptions are loss centers. The fat margin will move to customization: proprietary data, post-training, evals, deployment, and infrastructure. If this thesis is right, @thinkymachines isn’t building just another frontier lab. It’s building the highest-value layer between frontier research and enterprise model ownership. Turns out, the best business model for enterprise is NOT to sell commodity API access. Sell them their own models. I’m extremely bullish on this approach. @miramurati may be the most commercially savvy frontier-lab leader. I have to admit it.

We’re hearing from GPU capacity sellers that customers have been rotating from H100s to B200s, but this change isn’t fully reflected in 12-month forward rental prices. Halfway through 2026, the @ComputeDesk H100 index is up ~40% while the newer B200 return sits at ~27%. For most of the year, surging demand for training and interference hit the H100 fleet first while B200 capacity was unfilled. The H100-B200 spread started to close only over the last few weeks in response to a marketwide demand spike.