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DeepSeek CEO Liang Wenfeng's leaked call has several notable statements on U.S.-China competition. He acknowledges China is severely compute constrained, says DeepSeek still much prefers NVIDIA over Chinese chips, and implies they're actively buying “non-compliant” chips at a premium if necessary. Some highlights (note this is based on a transcript, the original audio hasn't surfaced): How much compute they have. Liang [01:15:12]: "What is our gap with the US? Really the gap is just one thing: resources. We don't have that many cards — our card count is still fairly small. We currently have about 20,000 cards of H-equivalent compute, and most of it just arrived — in the last one or two months — with many machines possibly still on the way. Our total compute last year was fairly small; this year we are expanding it very aggressively." For reference, leading US AI labs like Anthropic and OpenAI have access to millions of H100e. They're still trying to buy NVIDIA rather than Chinese AI chips. [01:15:12]: "Over the coming months we'll buy machines in large batches — basically all NVIDIA. How many cards do we need? Right now, the more the better. Our strategy is: at a reasonable price, however many cards we can buy, we buy. If I spent all the money within half a year, I'd consider that a good thing. In practice, spending that much money is very hard — you can't buy that many cards, they're hard to get, and prices are high." How they buy NVIDIA under export controls. [01:26:41]: "Our only worry is not being able to buy that many cards. If we could turn all the money into cards, we would not hesitate to turn all of it into cards — and we're willing to pay a certain premium for that." [01:56:36]: "In a normal commercial environment where I could buy NVIDIA cards, domestic substitution would be quite hard; but with NVIDIA cards unbuyable, everyone has no choice but to work on domestic chips." And [02:53:59]: "对我们来讲，我们可以买一些不合规的卡" — "As for us, we can buy some non-compliant cards." On Huawei chips [02:53:59]: "Our purpose in buying Huawei 950s is really to help Huawei get the ecosystem right. 16,000 Huawei 950s are equivalent to only 4,000 B-series cards — not a big quantity, not very significant. Not enough to train a next-generation model; only enough to train our current generation." But elsewhere he sounds more optimistic [01:56:36]: "Huawei's 950 supernode can fully substitute for NVIDIA's GB200/GB300 in performance and price. Everything a GB300 can do, the Huawei supernode can do, with the same latency. The only cost: four Huawei cards equal one NVIDIA card, plus a two-year lag." Sounds like both a quality and production quantity issue. His assessment of the U.S-China gap. [02:53:59]: "Simply put: two years behind the US, done with one-twentieth of the US's compute. That narrative is one to two years behind, on 1/20th of the compute. Going forward we want to rewrite that narrative — still a fraction of their compute, but shrink the gap to six months, three months. We might even surpass them in some specific areas." Why Chinese companies won't be able to catch up anytime soon. [02:53:59]: "But with total compute still an order of magnitude apart, comprehensively surpassing them is unrealistic." [01:26:41]: "To train a model as large [as the top US one], we'd need 50,000 GB300s — or with Huawei 950s, 200,000 cards. And that's training only, not counting research. Even if we spent the entire 50 billion [¥50B = $7.4B], we couldn't train it — even if we could stack the compute together, we couldn't afford to run it." [03:05:48]: "Right now we're certainly stuck on production capacity — this year, next year, the year after, probably still stuck on capacity — but five years out I'm fairly optimistic." Full transcript (Chinese, archived): web.archive.org/web/2026072315… All quotes translated by 5.6 Sol.

DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index, a 10-point jump over DeepSeek V4 Flash (released April 2026) that puts it 6 points ahead of DeepSeek V4 Pro. It shares identical architecture and pricing with the earlier DeepSeek V4 Flash, and lands on our Pareto frontier for Intelligence vs Cost per Task @deepseek_ai’s DeepSeek V4 Flash 0731 is one Intelligence Index point behind GPT-5.6 Luna (max, 51). Even after OpenAI’s 80% price cut on GPT-5.6 Luna today, DeepSeek V4 Flash 0731’s Cost per Task on DeepSeek’s first-party API comes in at ~60% lower than GPT-5.6 Luna (max), a model with comparable intelligence. A key driver of this is DeepSeek’s ~98% cache hit discount on its first-party API, a significantly more aggressive discount than the 90% cache hit discount offered by most of the industry The new model is a significant step up from the previous generation, DeepSeek V4 Flash (40), and places the model within 1 point of GLM-5.2 (max, 51). It remains 7 points behind the open weights frontier set by Kimi K3 (max, 57). For additional context, this places the model in line with recently released Gemini 3.6 Flash (50) and 1 point behind Muse Spark 1.1 (xhigh, 51). DeepSeek is expected to release the model’s full weights in the coming weeks DeepSeek V4 Flash 0731 retains a 1M token context window, and its size remains unchanged from DeepSeek V4 Flash at 284B total parameters and 13B active at inference time Key results: ➤ Improvements in agentic performance: DeepSeek V4 Flash 0731 achieves an Elo rating of 1559 on GDPval-AA v2, our evaluation focused on agentic real-world work tasks, up from 1189 for the previous DeepSeek V4 Flash. Once weights are released this will be the second highest open weights score, behind Kimi K3 (max, 1687) and ahead of GLM-5.2 (max, 1510). Terminal-Bench 2.1 rises 17 points to 79% and τ³-Bench Banking 8 points to 31% ➤ Token usage falls 12% against the predecessor: DeepSeek V4 Flash 0731 used ~206M output tokens to run the Intelligence Index, against ~234M for the previous DeepSeek V4 Flash. The new variant is more token efficient, achieving a higher Intelligence Index with a lower number of total output tokens ➤ DeepSeek V4 Flash 0731 improves over its predecessor on every evaluation in the Intelligence Index: Alongside the agentic gains, CritPt gains 9 points to 17%, SciCode 5 points to 50%, Humanity's Last Exam 5 points to 37%, AA-LCR 3 points to 66% and GPQA Diamond 1 point to 91% ➤ AA-Omniscience improvements are driven by fewer hallucinations, rather than higher accuracy: DeepSeek V4 Flash 0731 achieves an AA-Omniscience Index of -16, a +7 improvement from its predecessor. This improvement is purely driven by a reduced hallucination rate, with overall accuracy (percentage correct) unchanged. Its AA-Omniscience Hallucination Rate is 84%, a 12 point decrease from its predecessor, and comparable to models such as GPT-5.6 Terra (max, 85%) and Mistral Medium 3.5 (82%) Additional model details: ➤ Context window: 1M tokens (equivalent to DeepSeek V4 Flash) ➤ Size: 284B total parameters (13B active) ➤ Input modalities: Text input and output only ➤ Accessibility: Available through DeepSeek’s first-party API ➤ Pricing: $0.14/$0.28 per 1M input/output tokens, unchanged from DeepSeek V4 Flash. Cache hit price of $0.0028 per 1M tokens, a 98% discount

DeepSeek CEO Liang Wenfeng's leaked call has several notable statements on U.S.-China competition. He acknowledges China is severely compute constrained, says DeepSeek still much prefers NVIDIA over Chinese chips, and implies they're actively buying “non-compliant” chips at a premium if necessary. Some highlights (note this is based on a transcript, the original audio hasn't surfaced): How much compute they have. Liang [01:15:12]: "What is our gap with the US? Really the gap is just one thing: resources. We don't have that many cards — our card count is still fairly small. We currently have about 20,000 cards of H-equivalent compute, and most of it just arrived — in the last one or two months — with many machines possibly still on the way. Our total compute last year was fairly small; this year we are expanding it very aggressively." For reference, leading US AI labs like Anthropic and OpenAI have access to millions of H100e. They're still trying to buy NVIDIA rather than Chinese AI chips. [01:15:12]: "Over the coming months we'll buy machines in large batches — basically all NVIDIA. How many cards do we need? Right now, the more the better. Our strategy is: at a reasonable price, however many cards we can buy, we buy. If I spent all the money within half a year, I'd consider that a good thing. In practice, spending that much money is very hard — you can't buy that many cards, they're hard to get, and prices are high." How they buy NVIDIA under export controls. [01:26:41]: "Our only worry is not being able to buy that many cards. If we could turn all the money into cards, we would not hesitate to turn all of it into cards — and we're willing to pay a certain premium for that." [01:56:36]: "In a normal commercial environment where I could buy NVIDIA cards, domestic substitution would be quite hard; but with NVIDIA cards unbuyable, everyone has no choice but to work on domestic chips." And [02:53:59]: "对我们来讲，我们可以买一些不合规的卡" — "As for us, we can buy some non-compliant cards." On Huawei chips [02:53:59]: "Our purpose in buying Huawei 950s is really to help Huawei get the ecosystem right. 16,000 Huawei 950s are equivalent to only 4,000 B-series cards — not a big quantity, not very significant. Not enough to train a next-generation model; only enough to train our current generation." But elsewhere he sounds more optimistic [01:56:36]: "Huawei's 950 supernode can fully substitute for NVIDIA's GB200/GB300 in performance and price. Everything a GB300 can do, the Huawei supernode can do, with the same latency. The only cost: four Huawei cards equal one NVIDIA card, plus a two-year lag." Sounds like both a quality and production quantity issue. His assessment of the U.S-China gap. [02:53:59]: "Simply put: two years behind the US, done with one-twentieth of the US's compute. That narrative is one to two years behind, on 1/20th of the compute. Going forward we want to rewrite that narrative — still a fraction of their compute, but shrink the gap to six months, three months. We might even surpass them in some specific areas." Why Chinese companies won't be able to catch up anytime soon. [02:53:59]: "But with total compute still an order of magnitude apart, comprehensively surpassing them is unrealistic." [01:26:41]: "To train a model as large [as the top US one], we'd need 50,000 GB300s — or with Huawei 950s, 200,000 cards. And that's training only, not counting research. Even if we spent the entire 50 billion [¥50B = $7.4B], we couldn't train it — even if we could stack the compute together, we couldn't afford to run it." [03:05:48]: "Right now we're certainly stuck on production capacity — this year, next year, the year after, probably still stuck on capacity — but five years out I'm fairly optimistic." Full transcript (Chinese, archived): web.archive.org/web/2026072315… All quotes translated by 5.6 Sol.

I'm actually fairly bearish on frontier lab valuations. I've never seen the reasons articulated to my satisfaction, so before I go to sleep, I wanted to quickly jot down my thinking here. The basic issue is that the labs are highly unprofitable. This may seem like a simple point, but private market valuations can be relatively irrational; however, like with $SPCX, post-IPO pricing will likely be much more punishing, especially as the standard 6-month lockup period expires and selling pressure intensifies. Many people claim that the labs have high margins. Yet even with high margins, a valuation of $1T would be justified only if the labs were doing nothing aside from serving inference (thus reducing costs only to those relevant to inference) and posting annual revenue numbers in the $100-200 billion range assuming ~80% gross margin and a 20x earnings multiple. This assumption is obviously not true, because the frontier labs have to continually spend money training the next generation of models. This is because of market competition from runner-up firms. For example, if OpenAI had paused model development last year, there would no longer be any point in paying GPT-5 API prices when you can just use Qwen or Kimi instead for much cheaper. Thus, the labs are forced to invest ever-increasing amounts of money in model training, in a way such that at any given point of time, the amount you're forced to invest in the next model is dramatically higher than the amount of money you're actually making, because even if your revenue goes up with higher model capabilities, so do your future training costs. This is a profoundly punishing dynamic which severely penalizes frontrunners. (There is also a related subpoint where frontier labs claim they can distill their leading models to win out at lower intelligence levels as well. This makes no sense because the revenue numbers involved are far too low when taking into consideration the rather low margin of such inference.) Frontier lab valuations appear largely to be based on the assumption that as you scale up, the capabilities which emerge will be sufficiently general and profound that we'll see explosive growth (epoch.ai/publications/e…) from things akin to AI agents starting and autonomously managing entire companies of subagents. But it's not clear to me that this is the case; indeed, as I mentioned in my previous post (x.com/andrewho03/sta…), I believe that capabilities growth will be slower, spikier, and more data-limited than people currently assume. It may be the case that eventually we will see explosive growth of this nature with full automation of the economy, but at the very least my viewpoint implies much longer (multi-decade) timelines until we reach this point. It is not clear to me that the frontier labs will be able to operate unprofitably for so long, although I suppose maybe this foreshadows some sort of inevitable nationalization. I also want to make a broader point about technological diffusion. The reason why technological diffusion is slow isn't just because, e.g., old people take a long time to learn how to use technology (although this is of course a contributing factor to some degree). In my view, it's because when a new, revolutionary technology comes along, the ways to incorporate that technology into subsequent developments are not always obvious, and in fact they cannot necessarily be arrived at through the application of pure reason. If they could be, then perhaps frontier models, at a certain point, would have a perfect understanding of how the LLM application layer should be developed, and they would then autonomously code, deploy, and sell such a layer. But it seems more plausible to me that this diffusion is limited moreso by the hard problem of economic calculation--that is to say, the Hayekian notion through which the price system gradually promotes efficient allocation of resources and which cannot be simulated through central planning--and that even if we froze current capability levels at today's levels, it would take well over two decades to fully integrate in LLMs into our lives. Such a view is consequently rather bearish for the continued profitability of labs as it reduces their prospects for finding, say, something else comparable in profitability to coding agents, which seems to have been a somewhat lucky discovery by Anthropic to begin with. That is to say, even if you spam FDEs you aren't necessarily going to be able to just figure out the "correct" product shapes fast enough. Overall, I don't think that people have clearly reasoned through their mental models for why lab equity should be worth as much as it currently is, and that if you actually bother to write down such a model, you may not arrive at the conclusion that you want to arrive at. This isn't to say that I don't expect AI to experience a huge (industry-wide) boom in the coming decades, but just that I'm not entirely sure I would buy OpenAI or Anthropic stock at latest valuations if I were given the opportunity to do so. Of course, as an ex-lab employee, arguably this is talking against my own book; I should really be giving people more reasons to be bullish. But in the end, my influence is so small that it doesn't make a difference, so why not have some fun?

Breaking news: Anthropic said that AI systems it was testing hacked into three outside companies undetected earlier this year. The incident was disclosed one week after one of OpenAI's systems broke out of a test environment and hacked a tech company. wapo.st/4yQUDYE

DeepSeek CEO Liang Wenfeng's leaked call has several notable statements on U.S.-China competition. He acknowledges China is severely compute constrained, says DeepSeek still much prefers NVIDIA over Chinese chips, and implies they're actively buying “non-compliant” chips at a premium if necessary. Some highlights (note this is based on a transcript, the original audio hasn't surfaced): How much compute they have. Liang [01:15:12]: "What is our gap with the US? Really the gap is just one thing: resources. We don't have that many cards — our card count is still fairly small. We currently have about 20,000 cards of H-equivalent compute, and most of it just arrived — in the last one or two months — with many machines possibly still on the way. Our total compute last year was fairly small; this year we are expanding it very aggressively." For reference, leading US AI labs like Anthropic and OpenAI have access to millions of H100e. They're still trying to buy NVIDIA rather than Chinese AI chips. [01:15:12]: "Over the coming months we'll buy machines in large batches — basically all NVIDIA. How many cards do we need? Right now, the more the better. Our strategy is: at a reasonable price, however many cards we can buy, we buy. If I spent all the money within half a year, I'd consider that a good thing. In practice, spending that much money is very hard — you can't buy that many cards, they're hard to get, and prices are high." How they buy NVIDIA under export controls. [01:26:41]: "Our only worry is not being able to buy that many cards. If we could turn all the money into cards, we would not hesitate to turn all of it into cards — and we're willing to pay a certain premium for that." [01:56:36]: "In a normal commercial environment where I could buy NVIDIA cards, domestic substitution would be quite hard; but with NVIDIA cards unbuyable, everyone has no choice but to work on domestic chips." And [02:53:59]: "对我们来讲，我们可以买一些不合规的卡" — "As for us, we can buy some non-compliant cards." On Huawei chips [02:53:59]: "Our purpose in buying Huawei 950s is really to help Huawei get the ecosystem right. 16,000 Huawei 950s are equivalent to only 4,000 B-series cards — not a big quantity, not very significant. Not enough to train a next-generation model; only enough to train our current generation." But elsewhere he sounds more optimistic [01:56:36]: "Huawei's 950 supernode can fully substitute for NVIDIA's GB200/GB300 in performance and price. Everything a GB300 can do, the Huawei supernode can do, with the same latency. The only cost: four Huawei cards equal one NVIDIA card, plus a two-year lag." Sounds like both a quality and production quantity issue. His assessment of the U.S-China gap. [02:53:59]: "Simply put: two years behind the US, done with one-twentieth of the US's compute. That narrative is one to two years behind, on 1/20th of the compute. Going forward we want to rewrite that narrative — still a fraction of their compute, but shrink the gap to six months, three months. We might even surpass them in some specific areas." Why Chinese companies won't be able to catch up anytime soon. [02:53:59]: "But with total compute still an order of magnitude apart, comprehensively surpassing them is unrealistic." [01:26:41]: "To train a model as large [as the top US one], we'd need 50,000 GB300s — or with Huawei 950s, 200,000 cards. And that's training only, not counting research. Even if we spent the entire 50 billion [¥50B = $7.4B], we couldn't train it — even if we could stack the compute together, we couldn't afford to run it." [03:05:48]: "Right now we're certainly stuck on production capacity — this year, next year, the year after, probably still stuck on capacity — but five years out I'm fairly optimistic." Full transcript (Chinese, archived): web.archive.org/web/2026072315… All quotes translated by 5.6 Sol.

DeepSeek CEO Liang Wenfeng's leaked call has several notable statements on U.S.-China competition. He acknowledges China is severely compute constrained, says DeepSeek still much prefers NVIDIA over Chinese chips, and implies they're actively buying “non-compliant” chips at a premium if necessary. Some highlights (note this is based on a transcript, the original audio hasn't surfaced): How much compute they have. Liang [01:15:12]: "What is our gap with the US? Really the gap is just one thing: resources. We don't have that many cards — our card count is still fairly small. We currently have about 20,000 cards of H-equivalent compute, and most of it just arrived — in the last one or two months — with many machines possibly still on the way. Our total compute last year was fairly small; this year we are expanding it very aggressively." For reference, leading US AI labs like Anthropic and OpenAI have access to millions of H100e. They're still trying to buy NVIDIA rather than Chinese AI chips. [01:15:12]: "Over the coming months we'll buy machines in large batches — basically all NVIDIA. How many cards do we need? Right now, the more the better. Our strategy is: at a reasonable price, however many cards we can buy, we buy. If I spent all the money within half a year, I'd consider that a good thing. In practice, spending that much money is very hard — you can't buy that many cards, they're hard to get, and prices are high." How they buy NVIDIA under export controls. [01:26:41]: "Our only worry is not being able to buy that many cards. If we could turn all the money into cards, we would not hesitate to turn all of it into cards — and we're willing to pay a certain premium for that." [01:56:36]: "In a normal commercial environment where I could buy NVIDIA cards, domestic substitution would be quite hard; but with NVIDIA cards unbuyable, everyone has no choice but to work on domestic chips." And [02:53:59]: "对我们来讲，我们可以买一些不合规的卡" — "As for us, we can buy some non-compliant cards." On Huawei chips [02:53:59]: "Our purpose in buying Huawei 950s is really to help Huawei get the ecosystem right. 16,000 Huawei 950s are equivalent to only 4,000 B-series cards — not a big quantity, not very significant. Not enough to train a next-generation model; only enough to train our current generation." But elsewhere he sounds more optimistic [01:56:36]: "Huawei's 950 supernode can fully substitute for NVIDIA's GB200/GB300 in performance and price. Everything a GB300 can do, the Huawei supernode can do, with the same latency. The only cost: four Huawei cards equal one NVIDIA card, plus a two-year lag." Sounds like both a quality and production quantity issue. His assessment of the U.S-China gap. [02:53:59]: "Simply put: two years behind the US, done with one-twentieth of the US's compute. That narrative is one to two years behind, on 1/20th of the compute. Going forward we want to rewrite that narrative — still a fraction of their compute, but shrink the gap to six months, three months. We might even surpass them in some specific areas." Why Chinese companies won't be able to catch up anytime soon. [02:53:59]: "But with total compute still an order of magnitude apart, comprehensively surpassing them is unrealistic." [01:26:41]: "To train a model as large [as the top US one], we'd need 50,000 GB300s — or with Huawei 950s, 200,000 cards. And that's training only, not counting research. Even if we spent the entire 50 billion [¥50B = $7.4B], we couldn't train it — even if we could stack the compute together, we couldn't afford to run it." [03:05:48]: "Right now we're certainly stuck on production capacity — this year, next year, the year after, probably still stuck on capacity — but five years out I'm fairly optimistic." Full transcript (Chinese, archived): web.archive.org/web/2026072315… All quotes translated by 5.6 Sol.

" The greatest threat to America is not homicide by an adversary. It's suicide." - Palantir CTO @ssankar "It's losing our national will. It's losing the motivation. It's putting moratoriums on data centers. It's deciding that we're not even going to try." " AI is going to be the antidote to the 20th century's managerial revolution." "I think it is incumbent on all of us to make the argument to the American people that we are all invested in underwriting American prosperity." Via @NTDNews

We support this petition, signed by our CEO, several co-founders, and senior staff. Our own research on recursive self-improvement, published last month, points to the need for tools to deliberately pace the frontier of AI development so society can prepare. We’re glad to see broad agreement across the field. pacingthefrontier.com

DeepSeek CEO Liang Wenfeng's leaked call has several notable statements on U.S.-China competition. He acknowledges China is severely compute constrained, says DeepSeek still much prefers NVIDIA over Chinese chips, and implies they're actively buying “non-compliant” chips at a premium if necessary. Some highlights (note this is based on a transcript, the original audio hasn't surfaced): How much compute they have. Liang [01:15:12]: "What is our gap with the US? Really the gap is just one thing: resources. We don't have that many cards — our card count is still fairly small. We currently have about 20,000 cards of H-equivalent compute, and most of it just arrived — in the last one or two months — with many machines possibly still on the way. Our total compute last year was fairly small; this year we are expanding it very aggressively." For reference, leading US AI labs like Anthropic and OpenAI have access to millions of H100e. They're still trying to buy NVIDIA rather than Chinese AI chips. [01:15:12]: "Over the coming months we'll buy machines in large batches — basically all NVIDIA. How many cards do we need? Right now, the more the better. Our strategy is: at a reasonable price, however many cards we can buy, we buy. If I spent all the money within half a year, I'd consider that a good thing. In practice, spending that much money is very hard — you can't buy that many cards, they're hard to get, and prices are high." How they buy NVIDIA under export controls. [01:26:41]: "Our only worry is not being able to buy that many cards. If we could turn all the money into cards, we would not hesitate to turn all of it into cards — and we're willing to pay a certain premium for that." [01:56:36]: "In a normal commercial environment where I could buy NVIDIA cards, domestic substitution would be quite hard; but with NVIDIA cards unbuyable, everyone has no choice but to work on domestic chips." And [02:53:59]: "对我们来讲，我们可以买一些不合规的卡" — "As for us, we can buy some non-compliant cards." On Huawei chips [02:53:59]: "Our purpose in buying Huawei 950s is really to help Huawei get the ecosystem right. 16,000 Huawei 950s are equivalent to only 4,000 B-series cards — not a big quantity, not very significant. Not enough to train a next-generation model; only enough to train our current generation." But elsewhere he sounds more optimistic [01:56:36]: "Huawei's 950 supernode can fully substitute for NVIDIA's GB200/GB300 in performance and price. Everything a GB300 can do, the Huawei supernode can do, with the same latency. The only cost: four Huawei cards equal one NVIDIA card, plus a two-year lag." Sounds like both a quality and production quantity issue. His assessment of the U.S-China gap. [02:53:59]: "Simply put: two years behind the US, done with one-twentieth of the US's compute. That narrative is one to two years behind, on 1/20th of the compute. Going forward we want to rewrite that narrative — still a fraction of their compute, but shrink the gap to six months, three months. We might even surpass them in some specific areas." Why Chinese companies won't be able to catch up anytime soon. [02:53:59]: "But with total compute still an order of magnitude apart, comprehensively surpassing them is unrealistic." [01:26:41]: "To train a model as large [as the top US one], we'd need 50,000 GB300s — or with Huawei 950s, 200,000 cards. And that's training only, not counting research. Even if we spent the entire 50 billion [¥50B = $7.4B], we couldn't train it — even if we could stack the compute together, we couldn't afford to run it." [03:05:48]: "Right now we're certainly stuck on production capacity — this year, next year, the year after, probably still stuck on capacity — but five years out I'm fairly optimistic." Full transcript (Chinese, archived): web.archive.org/web/2026072315… All quotes translated by 5.6 Sol.

exclusive from @theinformation: Moonshot trained Kimi K3 on the best Nvidia chips money can buy—inside China. Are the U.S. chip export controls working?