What Liang Wenfeng Told DeepSeek's Investors About Compute

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DeepSeek’s Theory of the AI Gap<br>Liang Wenfeng says talent, model capability, and applications all trace back to compute. His investor transcript also reveals where that theory begins to contradict itself.

Poe Zhao<br>Jul 23, 2026

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This is a preview of what Hello China Tech does three times a week: reading China’s AI, chip, and robotics sectors through primary sources and verifiable claims. Subscribe free to get every new analysis as it publishes.<br>In late July 2026, Tencent Tech, Tencent’s technology news outlet, published a lightly edited transcript of a nearly 4-hour investor meeting with DeepSeek founder Liang Wenfeng. The 118-item transcript covers AGI strategy, chip supply, pricing, team retention, and the company’s first external fundraise, a round exceeding Rmb 50bn ($7.4bn) that we analyzed last month. DeepSeek has not confirmed the record. This article selects from the transcript, reorganizes it by theme, and compares it with Hello China Tech’s existing DeepSeek coverage.<br>Since January 2025, the prevailing Western reading of DeepSeek has centered on efficiency. A comparatively small Chinese lab appeared to show that frontier-level performance could be approached without the compute budgets of the largest US labs. Bloomberg framed the company as championing China’s “bid to flood the world with cheap AI.” Liang offered a different account of the gap with the US:<br>“All the differences we see, including talent, model capability, and applications, can be attributed to differences in compute resources.” (#56)

In his account, the advantages held by DeepSeek’s American competitors reduce to one variable. They can deploy more compute.<br>The Confession

The efficiency record is real. Liang’s investor-facing account, however, frames efficiency as an adaptation to scarcity.<br>“The biggest gap between us and the US is in resources. On one hand, we can’t buy enough chips domestically. On the other, our capital investment is far less than America’s. The salary share is small. The bulk is compute.” (#55)<br>“Spending Rmb 20bn this year would mean our procurement team did an exceptional job. It is extremely difficult to spend that much. You can’t buy that many chips, and the prices are high.” (#54)

He argued that the talent gap follows from the same constraint.<br>“Talent is not the bottleneck. Resources are the biggest bottleneck. Resources first affect talent development: less compute means fewer experimental opportunities, so our talent base overall is weaker than America’s. The talent gap is fundamentally a compute gap.” (#43)

On the US-China timeline gap, the transcript is notably imprecise. In a single exchange (#57), Liang described DeepSeek as 12 months behind, 12 to 18 months behind, 6 to 12 months behind, and then summarized: “to put it simply, two years behind, using one-twentieth of the compute.” The range is wide enough that it should be read as conversational approximation rather than a calibrated estimate. The transcribed text appears muddled at this point. What remains consistent is the aspiration:<br>“We want to rewrite that narrative. A fraction of the compute, but closing the gap to 6 months, 3 months.” (#58)

On scaling, he was direct.<br>“We believe in scaling. Bigger is always better. What stops us from scaling is compute, not desire. We train a model at this size not because it is enough, but because that is all our resources allow.” (#59, #60)<br>“When Silicon Valley says scaling has hit a ceiling, that is for Silicon Valley. We in China are nowhere near that point.” (#61)

Liang extended the resource argument to data. High-quality annotation, he said, offers China no meaningful cost advantage.<br>“There is no cost advantage for data annotation in China. Especially for high-end data, there is no cost advantage.” (#107)

This challenges a common assumption among Western investors that Chinese AI companies benefit from systematically lower labor costs. For high-end annotation, by Liang’s account, costs converge globally. DeepSeek cannot match American annotation spending simply by hiring at lower cost, so it relies more heavily on its own researchers for high-quality data work.<br>“You could say that half our core researchers, our most important people, are labeling data right now.” (#109)

Liang prefaced this with “you could say,” signaling characterization. The actual proportion may differ. But a substantial share of DeepSeek’s senior research staff appears to be doing data work that better-capitalized US labs can fund at a different scale.<br>This is a clean narrative, perhaps too clean. The transcript is not fully consistent on its own terms. In the discussion of team retention, Liang said money and resources were “not problems” (#38, #41). Elsewhere, he described resources as the biggest bottleneck (#43) and the largest source of the gap with US labs (#55). DeepSeek may have enough capital to survive...