Most people assume OpenAI and Anthropic quietly own the entire AI economy. One chart just showed the ground moving under them, and it’s not moving the way you’d guess. I came across this breakdown from the creator who dug into the Vercel data, and I couldn’t stop thinking about it.
Here’s the pattern break. The old story: closed models like ChatGPT and Claude eat everything. The new reality the original poster spotted: open weights models (the free-to-download, run-anywhere kind, mostly out of China) are now grabbing the majority of token volume. DeepSeek even passed Anthropic in token share, 25.2% versus 24.5%.
But here’s the twist that makes this so interesting.
🔑 The split nobody expected
Volume and money are going in opposite directions. Even though DeepSeek moves more tokens, the creator points out that Anthropic captures 64.6% of model spend versus DeepSeek’s 2.8%. That’s roughly 23x more dollars. Cheap models win usage. Frontier models win revenue.
Why? Because on the hardest problems, the right answer is worth a fortune. Think high-frequency trading, where a tiny edge is worth billions. The expert Gavin Baker frames it cleanly: frontier tokens are maybe 10 to 25% of volume but 60 to 90% of the economic value.
💡 Old way vs new way
- Old way: rent intelligence from one or two labs, hand over your data, hope they never compete with you.
- New way: grab an open weights model, run it yourself or through cheap inference providers, fine-tune it on your own data, and own the output end to end.
The price gap is wild. The creator compares Claude Fable 5 at $50 per million output tokens against DeepSeek V4 Flash at 18 cents. For most everyday tasks, why pay premium?
One sharp caution he flags from Bindu Reddy: judge cost per completed task, not per token. A cheaper model that burns more tokens can cost the same. His example: Kimmy K3 finished a task for 84 cents versus 96 cents for a pricier rival. Closer than the sticker price suggests.
🛠️ Why open weights matter (per the post)
- Ownership: your data, your context, your output.
- Bargaining power: dozens of hosting options, not two.
- Customization: feed it your business knowledge and squeeze more intelligence for the same price.
- Real companies already do this. Harvey fine-tuned Kimmy K3 into a legal expert that tops benchmarks. Airbnb uses Qwen. Perplexity uses DeepSeek.
The CTO of Thomson Reuters gave a line the creator loved: renting a model is like renting a house. Roof over your head, but zero equity. Build on open weights and you’re compounding real assets.
How to start, based on his advice:
- Download an open weights model (Qwen, GLM, or Kimmy are near frontier now).
- Run it locally or through a cheap inference provider.
- Feed it your own data and fine-tune for your use case.
- Benchmark it against your real tasks, not generic tests.
One worry the original poster leaves us with: most top open models come from China. If US companies build on them, and those models get co-designed around Chinese chips, we could drift into a real dependency down the road.
Want the full chart-by-chart walkthrough and the numbers behind the flip? Watch the full video, it’s worth your time.