I think it might have accelerated things but on a much more basic level, there seems to be no real moat in synthesizing the world’s knowledge into LLMs.

My spend on DeepSeek is not much and I regularly top up my balance every month as my support for all the good work DeepSeek is doing for the open science.

Architectural/algorithmic tweaks do advance the efficiency frontier nicely. But raw intelligence mostly comes from data (not just its sheer quantity, but also how it's curated & cleansed) and the scaling law. The know-how about data curation doesn't seem to get published much, even among the open-weight labs, though.

So boring to see conversations moved over to Chinese models when that’s not even what we’re talking about here. This is about Mistral.

I'm not sure I like this framing - so much of AI research has been academic, in the open, building on others people's work. Much less comp sci generally, math & philosophy, etc. The idea that rich companies can just build stuff in secret because they have resources is a fantasy.

Also, the field moves fast, but slower than people do. Researchers and engineers switch companies every year or two, and the know-how walks out the door with them.

How it should be. Knowledge should not be copyrighted. The world will be a better place with such information democratized

There would be a lot of competition even without DeepSeek. Workers can freely exfiltrate trade secrets without noncompetes in California.

>instruction manuals in paper form

So the most common way to publish manuals?