Stanford CRFM's David Hall argues critical training process knowledge for frontier AI models remains private despite open weights

Frontier AI models are built from thousands of small decisions: data sourcing, filtering, mixtures, curricula, scaling experiments, optimizer choices, kernels, evals, failed runs, and protocols for deciding what gets scaled.

This is process knowledge.

This process knowledge is the real secret sauce of modern AI labs.

And it rarely enters the scientific record.

Open weights reveal the model. A technical report may gesture at the recipe. But the process that produced the model usually remains private.

The result is that academia and the public are increasingly locked out of understanding how frontier AI is actually built.

If open labs and academia are going to contribute meaningfully to AI, more of this process knowledge has to become public.

This is understandable. Businesses manufacture knowledge for business ends.

But that means transparency is often tactical: a way to establish credibility, attract attention, recruit talent, or capture mindshare.

Once those goals are achieved, disclosure shrinks.

Not every detail can be open.

But the default should move toward legibility, reproducibility, and public accumulation rather than private inheritance.

I wrote about why more of that process knowledge should become public, and why Marin is organized around open development of frontier AI.

https://openathena.ai/blog/open-development-of-frontier-ai/