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PeterHolzwarth | on: DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]
Article grabbed at random that provides some more context (tho could use more):

https://www.cyberkendra.com/2026/07/deepseek-pauses-fundrais...

"The Hangzhou AI lab has told prospective investors in its second fundraising round that it is suspending the deal, people familiar with the matter told Bloomberg on Saturday, days after remarks attributed to founder Liang Wenfeng about US-China AI competition circulated widely online."

And:

"Tencent's technology outlet published a 118-item version covering AGI strategy, chip supply, pricing, and retention. In it, Liang reportedly framed China's disadvantage as an arithmetic problem rather than a talent one: "The biggest gap between us and the US is in resources.""

"The specifics were unusually candid. Liang is said to have told investors he needed 200,000 Huawei 950 chips to train a frontier model but received 16,000, adding that "Huawei's problem is still insufficient capacity" and expecting the crunch to last at least three years. He also floated narrowing the gap with US labs to three to six months using a fraction of their computing."

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cyanydeez | parent
this bodes well for continuing to refine smaller models and open sourcing them.

There's a delusion that what America's AI companies are doing is "best"; the chinese should realize that the forefront is bloated and there's likely hundreds of speed ups viable. Pushing open weights will continue to grind down the bloat.

Aboutplants | root | parent | next
I was gonna say, this just puts more pressure to deliver ground breaking research with limited resources. And if history teaches us anything it’s that scarcity produces ingenuity.
milkshakes | root | parent
http://www.incompleteideas.net/IncIdeas/BitterLesson.html

> One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.

kaashif | root | parent | next
Right, and if you come up with an efficiency gain that makes scaling better, e.g. a 50% reduction in required compute. Or even asymptotic improvements e.g. moving from quadratic to linear. Then you're much much better off.

There is nothing about the bitter lesson that says just be dumb and pour money into a hole, you still have to invent the methods to scale well, and being under immense pressure with constraints seems likely to produce that research.

Enginerrrd | root | parent
It reminds me a bit of the tyranny of the rocket equation. You can always scale your fuel to get a little more delta V, with ever diminishing returns. …but for something like a DEEP space/interstellar mission, it almost always pays to wait a few more years for a faster propulsion system because you’ll get there fastest by always delaying your launch and chasing better technology.

I’m not sure how well the analogy holds up, or if there’s anything to be learned from it though.

RetroTechie | root | parent
https://en.wikipedia.org/wiki/Interstellar_travel#Wait_calcu...

Certainly applies more general imho. Constrained by some resource -> invest resources elsewhere, and/or invest in reducing the constraint(s) encountered.

andai | root | parent | next
The implication here is that the only gains left to be had are from scale. That we are already maximally efficient. If that's true, then how has OpenAI repeatedly bragged about reducing the cost of their models by orders of magnitude? (And DeepSeek Flash even more so, of course.)

But we have not been maximally efficient, we keep gaining efficiency. If we keep gaining efficiency, why should we assume it is impossible to gain more?

anon373839 | root | parent | next
So what? There are physical and economic ceilings on dumb computation scaling.
cyanydeez | root | parent
americas tech stack always ends up bloated. not everything is worth learning.

endlessly knowing about pokemon is not delivering value proposition

cancer also grows carelessly.

strictnein | root | parent
> "There's a delusion that what America's AI companies are doing is "best""

Not sure if the word "delusion" is the correct word here? It has not been proven in either direction. We can all see lots of possible issues with it, but it is also possible that it could be what is needed to unlock key capabilities.

We can see that the Chinese models have been getting better, but OpenAI is out there supporting 10 million active users with their frontier models, and now we know that Deepseek can't even get what they need to properly train models.

3eb7988a1663 | root | parent
They can't get hardware because the US has put restrictions on how much can be sold to China. There is not a technical or know-how limitation, but political. Deepseek could otherwise write some checks to NVidia for what they want.

Thanks to the import restrictions, I expect Chinese GPU hardware to be competitive within a few years.