alt Hacker NewsPeople who claim that the Chinese open weight models have some type of manifest advantage don't realize that the close weight models have a huge advantage as well: the researchers from OpenAI, Anthropic, Google, xAI, Meta are not dumb, they can read the white papers written by DeepSeek, Moonshot, etc, and they can inspect all those architectures and they can pick and choose the best tricks there are out there, and of course, they have access to their own in-house secret sauces.
Sure, any model that is not at the frontier can use the frontier model to generate synthetic high quality training data, so this can reduce significantly the training costs.
But at the scale of OpenAI, Anthropic and Google, it is quite likely that the (raw) training cost is very high anymore. Here's a few heuristics:
1. All the hyperscalers see a huge demand for inference. They can't deploy datacenters quickly enough to satiate all the demand they see. But, it's is impossible for the inference demand to be constant throughout a day or a week. If you use the times when the demand is lower than the peak demand (which is almost all the time) to dedicate the spare compute capacity to training, then your the cost of training compute is zero.
2. It is likely that increasingly a higher cost of the "training" is actually setting the guardrails, which is essentially post-training. As we've seen, without proper guardrails, the US Government won't allow you to serve inference. Anthropic was hit directly, but OpenAI delayed their 5.6 release as well to make sure the US Government is ok. This part of the training cost can't be reduced easily by using synthetic data generated by other models.
3. The frontier labs are also investing more and more in building an ecosystem around their models.
I am not a frontier lab insider, but take a look at the jobs posted on the Anthropic career page [1]. There are 74 jobs in "AI Research and Engineering" and by my count at most 15-20 are related to pure model training (of pre-training or RL type), and the rest are post-training, safety and security, alignment, interpretability, productivity and lots and lots of other things.
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If the Google and meta engineers are not dumb how come they consistently trail behind the frontier labs and even the Chinese labs with a fraction of the funding.
Probably bad leadership
>they have access to their own in-house secret sauces.
I remember some feature lauded by Gemini was reverse engineered by the open weights guys in < 30 days.
If they dont publish some technical information its hard to protect in the US, but conversely, once it is published smart people from outside the copyrightosphere can start working to reverse engineer it.
>3. The frontier labs are also investing more and more in building an ecosystem around their models.
Theres nothing there that isnt immediately replaceable.
It’s giving desperate!
People who claim that Postgres has some type of manifest advantage don't realize that Oracle has a huge advantage as well…etc