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Its possible no AI lab has any unique edge, and success is a combination of (a) having access to GPUs (b) having access to large amounts of data (c) know about the handful of techniques to build an LLM, of which nearly all are likely open source and documented in papers. So the cycle of growth is (a) and (b), get more GPUs and get more data and you have a better model.

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Yea, this reads as LLMs are a pretty obvious technology to develop(for the highly intelligent researchers who are there). Also there's probably a lot of actual divergence in model capabilities and skills that concealed by the fairly narrow set of tests we run them against nowadays. Like wasn't Grok 4.20 super targeted at non-coding tasks.

Why is everyone ignoring the pattern that has existed since training models became a thing? At first it sucks. Then it's better than humans. Just by using it you generate training data that makes it better over time.

GPUs might explain the remarkably concurrent timing. Data access doesn't really explain it unless all labs simultaneously got access to some treasure trove of data.

> Data access doesn't really explain it unless all labs simultaneously got access to some treasure trove of data.

They have data from their competitors model outputs. It is very hard to serve an LLM without also exposing how it works.

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