DeepSeek 梁文锋投资会议纪要解读：开源商业模式与 AGI 愿景

What does Liang Wenfeng want? The CEO of DeepSeek is now richer than either Dario Amodei or Sam Altman. Backed by his careful watch and the immense wealth of his hedge fund, a little-known group of researchers in Hangzhou has managed to consistently command the world’s attention — and they keep giving their work away for free. The motivations behind Liang’s theory of action have puzzled China+AI watchers since the beginning.

In late July, leaked minutes from a four-hour meeting between Liang and investors circulated around the internet. Like everyone else, we’ve been pouring over the minutes to understand Liang the CEO, DeepSeek the company, and Liang the man. Liang comes off as a genuinely unusual character among China’s tech elite: a person who sincerely holds a specific and highly personalized worldview, if not an entire ideology. Whereas others in his walk of life are highly attuned to commercial trends and political winds, he positions himself as squarely focused on realizing his vision for technology — and it’s hard to doubt his conviction when he has invested so much of his literal worth into this quest.

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His is a confidently singular thesis: that there is an inevitable causal relationship between automated learning and generalized intelligence, and that the pursuit of such advanced machine intelligence is the only problem worth solving right now. Being Chinese means working within the constraints of current hardware limitations, but DeepSeek frames their own ambitions on a much farther horizon. Whether or not they can, of course, might not be up to them.

At ChinaTalk, we’ve been covering the DeepSeek story since before R1. The following are our thoughts and observations after reading the leaked minutes. We get into:

• How Chinese open models work as a business model;
• Why, according to Liang, learning is the path to AGI;
• What China’s political economy looks like from the vantage point of Hangzhou;
• How DeepSeek is managed differently;
• And what the Liang Wenfeng-Demis Hassabis comparison reveals and misses.

The business logic of open source, revealed

If the weights are open, how exactly does DeepSeek make money off of its models? Liang told investors that the lab’s biggest source of revenue comes from business customers. When R1 came out in 2025, there was a wave of Chinese businesses and government entities connecting DeepSeek to their internal systems. While much of that was a fad, some of those corporate customers apparently stuck. Since all their models since R1 have been released openly under the MIT license, DeepSeek does not earn revenue from on-premise deployments by corporate entities, so those actual corporate customers must be paying for API tokens.

The DeepSeek team published an analysis in February 2025 showing that on an average day, R1’s API earned the lab US$562,027, at a 545% profit margin. Fast forward to now, and the hypothetical figure Liang apparently gave in the investor meeting for DeepSeek’s enterprise-end revenue this year is in the hundreds of millions of US dollars. With enough growth in this area, he figured, DeepSeek could even turn a net profit soon, paving the way for a successful IPO. Failing that, and API sales to individual consumers have room for growth as well.

That being said, Liang has little interest in consumers. He confesses that at one point last year, the lab even considered sunsetting its consumer products given how little effort went into maintaining them. But DeepSeek’s chatbot and individual API users were incredibly loyal, apparently, so the team ultimately decided to keep the lights on. (I wonder how many of these are roleplayers…)

One of the many memes that circulated around the Chinese internet after Liang’s comments leaked; this one was posted to Xiaohongshu by user @lunerpine. The caption reads, “Don’t know what the point of users is… I’ll just feed them for now.”

From Learning to AGI

Minor problems like keeping users around do not concern Liang. As long as the upper limits of machine intelligence are still out of view, there is more (technological, societal, and literal business) value in pursuing that than there is in building business models based on what is available today. More capable models easily pull the rug out from under competitors, especially when the latter have fallen into path dependencies based on older technology. Once truly generalized and convincingly superior intelligence arrives, creating to-C and to-B products will be trivial. Before then, the vast majority of DeepSeek’s resources will go into probing the upper limits of intelligence — and nothing else.

Liang describes this work as the “main quest” of AGI, which, to him, covers research in the realm of general-purpose intelligence, agents, chain-of-thought, etc. He is careful not to commit to specific subfields or theories, but does explicitly exclude some trajectories. World models, for example, are to him “irrelevant” for the pursuit of higher levels of intelligence, even though he thinks embodied AI is largely inevitable. He argues that once AI research is based on “self-iteration,” these systems will then solve for embodied intelligence in the physical world. Before then, however, he believes the most important problem right now is learning.

AI training today relies heavily on high-quality, labelled data being fed into models — not autonomous and truly continuous learning. Liang cautions investors not to think of “learning” as a sub-technology the way they do with “agents,” but instead to think of it as a problem to be solved. (Indeed, one takeaway from reading Liang in his own words is that despite being a finance veteran, he seems to see the world as an unfolding kaleidoscope of puzzles the way an academic researcher does.) The path to AGI, in his eyes, runs through mechanisms that allow models to keep acquiring knowledge. He is quite honest that he and his team don’t know what that path looks like yet, but he’s asking investors to bet on their clarity of thought.

China, According to DeepSeek

Throughout the conversation, Liang seems to implicitly assume that future AI systems — and eventually AGI — will be open. While America will maintain its capability advantage for now, China, in his eyes, will play the role of token factory at global scale, pushing the price of intelligence down as it did for countless other industries during its manufacturing boom.

Liang appears to have somewhat settled into the national-champion role. Phrases like “historic mission” slip through when he discusses how the hardware chokehold China faces will be eroded and finally dismantled, as if the forces of history make that inevitable. Liang expects Nvidia’s CUDA moat to erode, and — with limited specifics — expressed cautious optimism about training on Huawei chips. In fact, he presents DeepSeek’s relationship with Huawei as cordial and regards working with domestic GPUs as an inevitability. Huawei apparently allocated 16,000 Ascend 950 GPUs to DeepSeek, which is fewer than what it sold to “bigger internet companies” (probably ByteDance, Alibaba, and Tencent). Revealingly, Liang remarked that these competitors need Huawei chips more than DeepSeek does because DeepSeek can “acquire some noncompliant chips,” and that DeepSeek’s main rationale for spending on Huawei is to support a domestic hardware ecosystem.

Liang does not want to be an out-and-proud part of China, Inc. He knows, however, that Chinese domestic integration is inevitable, and sees value in aligning with Beijing on tech sovereignty. The trouble is convincing investors that they should really feel so optimistic about model training on domestic GPUs — it’s the topic that collected the most number of questions during the Q&A.

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