I think the way to parse the current title "DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]" is that there was a leak that DeepSeek will pause fundraising because they perceive there is a compute gap with the US.
I am also guessing that the majority of the people who read this title will think that DeepSeek is pausing this fundraising because some comments they made about the compute gap were leaked. That is not the case.
Maybe: "Leaked Deepseek transcripts reveal plan to pause fundraising due to compute gap"
I don't know what "compute gap" means in this context though and it's not clear that that's why they plan to pause fundraising or if the title is conflating.
I wanted to post this which explains the wording but I thought the transcript was more interesting. Sorry. Maybe mods can help me to put what follows as auxiliary link. I don't know how.
Most of that is paywalled, but this one paragraph in the Bloomberg article suggests it might be more to do with investors leaking information:
"The suspension stemmed in part from Liang’s frustration over online reports about his comments to investors during his first financing deal"
The part of the transcript I'd seen floating around online was this part from around 1 hour 26 min:
"With the largest models available today, we simply cannot afford to train them. Even if we spent all five hundred billion yuan, we still wouldn't be able to do so. Even if we could accumulate the resources, we wouldn't have the means to utilize them. The current largest model
requires approximately 800 billion activations; domestically, we are still at a scale of several dozen billion activations, and even the largest
domestic model may only require several dozen billion activations—a difference of an order of magnitude. To train a model of the same size
as an AI system, we would need around 50,000 GB300 GPUs or Huawei 950 GPUs, totaling two hundred thousand cards. This is merely training; research has not yet been considered. Therefore, the biggest gap between us and the United States lies in resources."
Here's something I really don't understand: If as alleged Chinese open weight models are catching up with US anyway, and the performance is near US frontier model level but Chinese can do it with a fraction of cost, and eventually AI model will be commodified, wouldn't that means that the billion or even trillion dollars that US labs spend have only diminishing returns and the lead is only temporary?
So why Deepseek also want to go down that route? Is having the absolute frontier really that important, given that the performance difference is just transient and costly?
there's a lot of propganda from these state backed enterprises. I think the fraction of the cost label is debatable given the evidence of mass gpu smuggling through third parties like Singapore, which China can't exactly openly admit to, unless of course we're talking about distilling, which is probably a lot cheaper than training a model from scratch (there's also the fact taht labour is still relevatively cheap in china compared to China which might matter e.g. Anthropics claim against Alibaba (although this could all be automated and with agents, not sure)
> The campaign allegedly used nearly 25,000 fraudulent accounts to run 28.8 million exchanges with Claude between April and June 2026
Deepseek is funded by their hedge fund, high flyer. They intentionally cap their token prices to basically recoup server costs. The meeting transcript describes it as a moral commitment, that they don’t care about trends like image and video generation, and world model “hype”. They only care about reasoning, chain of thought and continuous learning.
There is an immense pot of gold at the end of this rainbow and if the theories about ASI are in the general correct direction, only one winner will get it.
It makes no difference if the pot do actually exist, because the prospect of it being real make not getting it the end of your company.
AI's already commoditized, but the fundraising plans for the US labs assumes a winner take all endgame where one lab will pull arbitrarily ahead of everyone else. I have no idea why DeepSeek is making that bad assumption now too. Maybe the investors have drunk the Kool-Aid.
Maybe if "AGI" is some sort of fundamentally different approach than the general purpose AI ("GAI"?) tools that we currently have, it will be a winner-takes-all technology, but now we're speculating on the market structure of a fictional technology that's significantly less thought-through than, say, stuff from the original Star Trek. ("The Ultimate Computer" aged ridiculously well. If it was produced in 2026, it would be a satire targeting LLMs. I digress.)
If we don't assume some sort of unknown technological step function in the next fundraising cycle, then what we'll get is a commodity industry. It takes a few dozen people to make a frontier model, plus a giant pile of minerals and electricity. This looks more like a steel mill than a software company.
If there were one steel mill on earth they could demand infinite margins. This is why most countries treat steel production as a national security issue and subsidize competition. LLMs will be the same, or we'll end up with some conglomerate named OpenAnthropicMicrappleGrokGoogXidiazon that acquires literally every other business. That will be the end of capitalism.
"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."
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.
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.
> 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.
> "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.
Curious what the fundamental limit on Huawei's capacity is. China has shown if nothing else they know how to scale when they want to. If it came down to just building more of what they know how to do, it would be happening. Is there more to it?
It's funny that you mention this because with the current US administration it works in a similar fashion... see Anthropic not cooperating with the US military and getting their new shiny model "paused" few weeks later (and officials like Hegseth being pretty open about it beforehand, signalling to them that criticizing the US admin/not cooperating will hurt their business: https://xcancel.com/SecWar/status/2027507717469049070 )
I'm not defending China at all, just noticing a detestable trend.
And it’s not even reading between the lines and being a conspiracy theorist. The current U.S. regime has made it abundantly clear that they will gladly operate in bad faith.
Being rational and predictable is likely a more important quality than ideology now that the Americans are threatening everyone and forcing us all to pick sides.
If this is true it almost sounds like DeepSeek is following the Anthropic playbook of trying to pressure the local government into aligning with their corporate agenda through scare tactics. So I wouldn't be surprised if Liang Wenfeng "disappears" for a little while from the public eye in a few weeks.
Perhaps there is an opportunity for China to close the compute gap by renting compute from hyperscalers through a complex web of shell entities similarly to how the US procured titanium for the SR-71 during the Cold War.
Trump reversed course on the NVIDIA ban. It's now China that is blocking their companies from buying NVIDIA chips. So the shell entities would be to get around Chinese, not USian restrictions
That's not true. First there is still a licensing and quota scheme on the US side for the H200s. Secondly China blocked them for use in inferencing. Thirdly Chinese companies don't want them for training because newer chips are more cost effective.
> Objectively speaking, if I can spend two billion this year, it would indicate that our procurement department has achieved outstanding performance. The main gap between us and the United States lies in resources, while the disparity in personnel is minimal—there is virtually no difference, as we are essentially the same team of people, possibly from China.
> With the largest models available today, we simply cannot afford to train them
It seems they're largely talking about literally purchasing NVIDIA H200 chips. Important context is that Trump first started the trade war with China largely focusing on banning anything that could improve the Chinese domestic semiconductor industry. It was a blatant attempt to prevent China from progressing up the value chain to high tech. China's response is the reason they went from a miniscule player in EVs to the world's largest manufacturer (same for other high tech industries like LIDAR, solar, etc). In his second term, Trump blocked NVIDIA from selling chips to China. China again responded with astounding progress on their domestic semiconductor industry which led to Trump backing down on the ban. However, China shocked everyone by banning their own companies from buying NVIDIA in order to support the domestic semiconductor industry. Obviously China is still years away from EUV but it now produces most of its own >14nm chips and is rapidly growing
Wow, that is fascinating, I didn't realize China was now blocking foreign chips, lol. It's not a definitive indicator, but I feel that doesn't bode well for US dominance in this area -- when your competitor thinks they'd be helping _you_ by using your resources, that's not great.
I don't know that it's clear that the motivation is that it's "helping" their competitors directly Maybe the motivation is "if we rely on these, then the next time a US president arbitrarily decides to block us from buying them, we won't have the infrastructure already in place to be able to work around this". It seems more betting on a shorter-term cost with less uncertainty in the long term rather than a shorter-term win with a lot harder to quantify risks in the long term.
I think the point is more than by banning the NVIDIA hardware they are forcing local development of potentially competitive hardware, basically giving Huawei a subsidy or leg-up.
They've achieved self-sufficiency in >14nm chips in remarkable timing. Unfortunately for DeepSeek, it's the <14nm chips that are needed for massive training tasks. I wouldn't be surprised if China backs down and lets them purchase the chips given that they are still years away from being able to make them themselves. Either that or the gov't steps in and forces them to share resources
And even if Huawei's Ascend 910C can compete with NVIDIA's H200, CUDA is still a large moat
Bypassing the CUDA moat is, in fact, one of the major tasks Deepseek set for itself. Their efforts in this area are likely one of the main reasons for their slow release cadence, culminating in their v4 inference setup that runs on Huawei Ascend chips.
I am also guessing that the majority of the people who read this title will think that DeepSeek is pausing this fundraising because some comments they made about the compute gap were leaked. That is not the case.
I don't know what "compute gap" means in this context though and it's not clear that that's why they plan to pause fundraising or if the title is conflating.
https://www.bloomberg.com/news/articles/2026-07-25/deepseek-...
"The suspension stemmed in part from Liang’s frustration over online reports about his comments to investors during his first financing deal"
The part of the transcript I'd seen floating around online was this part from around 1 hour 26 min:
"With the largest models available today, we simply cannot afford to train them. Even if we spent all five hundred billion yuan, we still wouldn't be able to do so. Even if we could accumulate the resources, we wouldn't have the means to utilize them. The current largest model requires approximately 800 billion activations; domestically, we are still at a scale of several dozen billion activations, and even the largest domestic model may only require several dozen billion activations—a difference of an order of magnitude. To train a model of the same size as an AI system, we would need around 50,000 GB300 GPUs or Huawei 950 GPUs, totaling two hundred thousand cards. This is merely training; research has not yet been considered. Therefore, the biggest gap between us and the United States lies in resources."
So why Deepseek also want to go down that route? Is having the absolute frontier really that important, given that the performance difference is just transient and costly?
It makes no difference if the pot do actually exist, because the prospect of it being real make not getting it the end of your company.
Unless I’ve missed some advancement?
Maybe if "AGI" is some sort of fundamentally different approach than the general purpose AI ("GAI"?) tools that we currently have, it will be a winner-takes-all technology, but now we're speculating on the market structure of a fictional technology that's significantly less thought-through than, say, stuff from the original Star Trek. ("The Ultimate Computer" aged ridiculously well. If it was produced in 2026, it would be a satire targeting LLMs. I digress.)
If we don't assume some sort of unknown technological step function in the next fundraising cycle, then what we'll get is a commodity industry. It takes a few dozen people to make a frontier model, plus a giant pile of minerals and electricity. This looks more like a steel mill than a software company.
If there were one steel mill on earth they could demand infinite margins. This is why most countries treat steel production as a national security issue and subsidize competition. LLMs will be the same, or we'll end up with some conglomerate named OpenAnthropicMicrappleGrokGoogXidiazon that acquires literally every other business. That will be the end of capitalism.
This axiom not being true (and I'd bet against it) means your overall conclusion is false.
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."
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.
> 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.
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.
I'm not defending China at all, just noticing a detestable trend.
Being rational and predictable is likely a more important quality than ideology now that the Americans are threatening everyone and forcing us all to pick sides.
US incumbent party criticism is nothing like CCP criticism.
https://theaviationgeekclub.com/in-1960s-russia-sold-titaniu...
https://nationalinterest.org/blog/buzz/titanium-russia-was-s...
> With the largest models available today, we simply cannot afford to train them
It seems they're largely talking about literally purchasing NVIDIA H200 chips. Important context is that Trump first started the trade war with China largely focusing on banning anything that could improve the Chinese domestic semiconductor industry. It was a blatant attempt to prevent China from progressing up the value chain to high tech. China's response is the reason they went from a miniscule player in EVs to the world's largest manufacturer (same for other high tech industries like LIDAR, solar, etc). In his second term, Trump blocked NVIDIA from selling chips to China. China again responded with astounding progress on their domestic semiconductor industry which led to Trump backing down on the ban. However, China shocked everyone by banning their own companies from buying NVIDIA in order to support the domestic semiconductor industry. Obviously China is still years away from EUV but it now produces most of its own >14nm chips and is rapidly growing
And even if Huawei's Ascend 910C can compete with NVIDIA's H200, CUDA is still a large moat