Chamath reveals his company's AI token costs are doubling every 45 days but productivity is only up 5% "I sat down with my CTO today, I said how are we doing on token spend. And he said the most incredible thing, he said right now, our token costs are doubling every 45 days. I said well what is the downstream productivity? And he said maybe 5% max." "So my costs are doubling every 45 days, my upside is essentially flat. He said honestly, what we're finding out is that you need to use a lot more tokens to get to this next iteration of improvement because we've effectively already asymptoted." "We're going to take a step back and try to figure out what to do. I don't know how many other companies will actually go through this reckoning now, but the point is everybody in the next three or four years will for sure go through it." "I suspect that if you can get out now, you should get out now before all of that starts to seep into the water table. Because I think that's probably what allows you to get out at a huge price and raise a huge amount of money."

How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching. Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work. Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task. Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented. Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted. Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect. The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable. Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.

The framing here is exactly backwards. This is the strongest AI bull signal anyone has published this year. Uber deployed Claude Code to 5,000 engineers in December. By March, 84% were classified as agentic coding users. By April, 95% used AI tools monthly. 70% of all committed code came from AI systems. The tool worked so well that four months of usage consumed the entire annual budget. Read that again. The "crisis" is that engineers loved the tool so much they used it 3x more than finance predicted. Uber's finance team built their models around fixed seats and low-frequency calls. What they got was 5,000 engineers running parallel agent workflows eight hours a day. The budget model broke because the adoption model worked. Run the math on the alternative. A senior engineer at Uber costs $350-400K fully loaded. 5,000 of them run about $1.75 billion in annual compensation. AI tools producing 70% of their code output for somewhere between $60-100M a year is a 15-20x return on the AI spend. The "blown budget" is a rounding error on the engineering payroll it's augmenting. Microsoft's move is even more straightforward. They invested $13 billion in OpenAI. They own GitHub Copilot. The Experiences & Devices division canceling Claude Code licenses by June 30, the last day of Microsoft's fiscal year, and migrating to Copilot CLI is a vendor consolidation play dressed up as cost management. Claude models still run inside Copilot. The interface changed. The capability didn't. The companies that set up internal leaderboards ranking teams by AI usage, that coined "tokenmaxx" as a strategy, that rewarded maximum consumption, and then panicked when the bill arrived aren't experiencing an AI cost crisis. They're experiencing a forecasting crisis. The CFO built the budget for a chatbot. The engineers got an agent. Goldman Sachs projects token consumption will grow 24x by 2030. The companies scrambling to cap budgets today are going to look like the enterprises that limited employee internet access in 2003 because bandwidth was expensive.