OpenRouter is one of the most insane stories in tech. Co-founded by OpenSea founder, Alex Atallah. It has become one of the most important companies in AI. Scaled to 100s of millions in revenue and wildly profitable. They process 25 TRILLION tokens every week and will do 1 QUADRILLION tokens this year. As a result, Stripe have reportedly offered to buy them for $10BN. Last round was $1.3BN just months ago… so what happens now? I sat down for a chat with OpenRouter Founder, @alexatallah and have summarized my notes below: 1. This Is Going to Be the Biggest Market in the History of Tech AI model inference is set to become one of the largest markets in technology. Even as token prices fall dramatically, Jevons Paradox means usage can grow far faster than costs decline, driving massive overall compute consumption across an increasingly multi-model future. 2. Why None of the Routing Products Being Created Today Will Compete With OpenRouter Building AI gateways has become trendy, but copycat routers that treat routing as a side feature are playing to exist rather than to win. True routing requires relentless focus on optimizing latency, cost, and constantly shifting model quality while giving developers maximum flexibility. 3. Model Labs Have Every Incentive to Come After Your Startup Eventually Startups building thin wrappers around AI models face an existential threat if they occupy workflows that frontier labs consider strategic. Releases like Claude Design show how labs can move up the application layer, absorb valuable use cases, and lock entire enterprise teams into their ecosystems. 4. One New Model Every 10 Hours and the Myth of Model Consolidation Model creation is accelerating as hardware companies, Neo Labs, and agent frameworks continuously release specialized models. Rather than consolidating around a single winner, developers are embracing diverse models optimized for different tasks, reinforcing a fragmented, multi-model future. 5. America Is Still Very, Very Behind in Open-Weight AI The U.S. remains significantly behind China in the race for open-weight models. Chinese labs benefit from aggressive state support and open-source momentum, while American efforts face business model and capital constraints. Closing the gap will require dedicated access to compute and sustained investment. 6. How Open-Weight Models Are Rapidly Closing the Gap on Frontier AI Models like Kimi K3 and the 5.2 generation show how quickly open-weight intelligence is closing the gap with proprietary frontier models. Using low-cost open models for deterministic subtasks beneath a frontier orchestrator can maintain output quality while sharply reducing inference costs. 7. Why We Need to Think About Employee Cost Completely Differently In the AI era, employee cost is no longer just a fixed salary. It increasingly includes the variable inference spend of the AI tools each employee deploys. Companies will need to evaluate workforce performance through a cost-to-productivity lens that accounts for both human compensation and AI consumption. (links in comments)