Comment by SubiculumCode
12 hours ago
The compute deficit of Chinese Ai companies is real, and it IS THE ONLY competitive advantage that Western companies have.
The only way the U.S. keeps that edge is to prevent distillation. The only way Chinese companies can make up for the deficit in compute is to distill. There innovation in great supply on every side of the Ocean. Its about the chips. And in terms of national security, for the U.S., and for China, its about the chips and the distillation that undermines that advantage. This is an arms race.
If compute or access to training data were the only issues, then companies like Meta and X.ai (Grok) should be doing better, even Google for that matter. Musk even admitted that Grok used training data from OpenAI models.
https://techcrunch.com/2026/04/30/elon-musk-testifies-that-x...
While there is no moat as such, there is still a lot of expertise that goes into training SOTA models. There's a reason Google was willing to pay $2.7B just to get Noam Shazeer back to improve Gemini.
You got that wrong. The forcing function of compute scarcity is an advantage not a detriment. The amount of investment pulverized in performative model training and dead ends (Hi Sora) should make this obvious.
If saying “plz don’t distill me” is your moat, you don’t have a moat.
No. What will happen is it will turn dark. No public release. National Security uses only, or in carefully vetted industry settings.
There’s huge issue with that approach. It’s not a multi trillion dollar business.
yeah nope, it won't happen, the snowball is already rolling
Good luck not crashing the markets and the economy.
And good luck not staying behind when you can't monetize your gargantuan investments and have little incentives to make your models better as the world moves on.
1 reply →
Define compute deficit?
They've been bringing out open weight models competitive with frontier models. How could they do that if they had a compute deficit?
If they need to divert inference resources to train models, this counts as a compute deficit to me.
I'm using GLM-5.2 daily for my own stuff, and during Chinese business hours, specially on their afternoon, it's a festival of rate limits.
I believe this article is about the technique they may or may not have used.
> The only way the U.S. keeps that edge is to prevent distillation.
For how long ? year ? how long till model that is year behind will be fine for 90%+ use cases ?
Putting aside agentic coding, that is to say, if you judge LLMs as a consumer technology (an old-fashioned idea for the inward-looking tech industry admittedly), then open weights LLMs, even quite small ones like Gemma 4, can likely already satisfy 90% of applications with a bit of help from search and browse tools.
Much of the arms race for better LLMs exists to satisfy only the IT industry's needs.