Yes, I wrote about Rock’s Law too, but we don’t know that this is how these models will develop

Evidence point to the same type of scaling law. Compute for a training run grows 4-5x every year.[0] I'm sure this will slow down but the premise remains that weaker competitors will not be able to maintain this pace. We already see labs like Cohere, Mistral, Inflection AI, Adept, Character.ai, and others bow out of the frontier race. I'm also skeptical that Meta, xAI can catch up. Even Google has trouble keeping up.

Even if this isn't true, comparing telecom bits to tokens is wrong. Bits are the same no matter what telecom transfers them. Tokens are not all the same. The quality varies.

We're already seeing a massive divide between frontier models and lesser models in growth rates. Anthropic is adding $10b - $15b every month in ARR. This figure likely dwarfs open source labs. This is all because its models are maybe 10-15% better.

The cost to inference a 1T param frontier model is the same as a 1T param open source model. Therefore, if the frontier model is even 10-15% better, it will gobble up the market over time.

Lastly, even though Claude Code and Codex are the biggest revenue drivers for Anthropic and OpenAI today, I don't believe this will be true 2-5 year from now. I believe selling their tokens via API will be their biggest. The sum of applications in the world will dwarf coding in market size. For example, biotech, finance, physics, engineering, robotics, sensor data, etc. This is why I think OpenAI and Anthropic are becoming more like iOS and Android than AT&T and Verizon. Applications will build on top of OpenAI and Anthropic just like iOS and Android.

[0]https://epoch.ai/blog/training-compute-of-frontier-ai-models...