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.
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…)
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.
A New Theory of Management
Liang clearly takes great pride in the company he’s built. To investors, he pitched a story of a team that commits to its work not only because of the substance or the mission, but also because of a culture that’s rare in China’s tech industry. DeepSeek researchers, apparently, rarely work overtime. Management strives to ensure that “mandatory” tasks — those based on central goals decided collectively — do not take up more than half of employees’ time at work. The other half is entirely self-directed, and employees are supported in pursuing whatever research projects interest them as long as there is compute available.
Of course, Liang is not doing this entirely out of the goodness of his heart (though he does seem to have a refreshingly normal view of human performance, having once noted that he believes a person can only concentrate for six to eight hours a day). In the meeting, he argued that a relaxed environment is a necessity for research. Good research ideas come from exploring idle curiosity, and there would be no room for such curiosity if researchers were constantly preoccupied with pressure. To maintain such an environment, Liang focuses on “saying no” to projects that do not relate to their main mission; he told investors that this is why they intentionally did not make improvements to many existing products.
Will a new theory of technology and productivity finally defeat the dreaded 996? Rather than jumping onto every vertical and rushing to be first, as is the norm across much of China’s internet industry, Liang is betting that focus and discipline will win the day in the AI chapter. Unlikely in Silicon Valley, where the AI gold rush is unleashing extremely workaholic founders and pushing even longer hours onto employees, Liang doesn’t sound like he is racing against time. (He’s certainly not ending up in the permanent underclass personally.) In his eyes, it seems, the horizons for machine intelligence are still so wide that there is much more work to be done before anyone should panic. It’s in the interest of the follower, after all, to hope that the race remains far from over. But maybe Liang is also less worried about the consequences of such a race: throughout the four-hour conversation, the dangers of potential AGI never come up. The pursuit of it seems inherently worthy to Liang.
Is Liang Wenfeng the Demis of China?
Liang is inspired by the history of Bell Labs but does not see DeepSeek as an exact replica. When investors asked about the tension between DeepSeek’s research idealism and the urgency of constructing a sustainable business, Liang argued that missions do not cost good companies revenue. Rather, worthy missions make for even stronger paths to commercialization. In other words, not only is research possible inside for-profit companies, but sometimes better outcomes happen when there is capital involved.
There’s another AI CEO who arrived at a similar conclusion — Demis Hassabis. Sebastian Mallaby’s biography of Hassabis, The Infinity Machine, documents how DeepMind became a Google subsidiary. Former Google CEO Larry Page presented Hassabis, who resented business development work, two future scenarios: either spend the prime of his career gathering Google-level resources for the real work DeepMind hopes to accomplish, or do that work right now on Google’s back. To Hassabis, the choice was obvious.
But Demis’s bet did not turn out the way he wanted. Google DeepMind became much more entwined in Google’s corporate labyrinth than he expected. He complained of becoming just another employee from the perspective of Mountain View, as he was no longer the sole deciding vote steering what DeepMind produced. Unlike Liang, who regards harnessing commercial incentives for “pure” AGI development as entirely possible, Mallaby describes Hassabis as disappointed in what transpired, even though he remains convinced of the necessity of keeping powerful technology in the hands of those who understand it most.
On August 5th, all this tension ripped wide open. Hassabis has resigned from Google and will pivot to working on AI-assisted drug discovery at Isomorphic Labs, as well as researching “societal impacts of AGI” per a Google spokesperson. The siren call of the mission pulled him away from a disheartening situation — but also away from his life’s work at DeepMind.
Hassabis should certainly not be jealous of Liang’s personal predicament. While Hassabis was merely constrained by the complex politics of a world-famous company, Liang’s firm now symbolizes the US-China AI race. To shield his company from the eye of the storm, Liang takes pains to shape the domestic narrative around DeepSeek. There is to be no heroism or glorification of specific researchers; he repeatedly emphasized that DeepSeek is made up of “regular people” whose secret sauce is teamwork. He mentions at one point that the government “won’t give [them] a cent” should the company fail. This shouldn’t be taken literally given the asymmetric relationship between the Communist Party’s coercive potential and a small (if well-funded) lab; it was probably to assure his audience of investors that sound financial management won’t take a backseat as DeepSeek pours more of itself into AGI.
After leaks of this very meeting spiraled across the internet, Liang was reportedly so furious that he paused a new funding round and pushed back IPO plans once more. More than anything, he demands complete trust that does not dilute his exact vision — and whoever leaked this clearly does not fully trust the plan. The question is: who, among the audience that day, had reasons to want to poke holes in Liang’s vision?
What's striking about Liang isn't the technology, it's the posture. Every previous framing of Chinese tech strategy has been catch-up logic — close the gap, localize the supply chain, achieve parity. Liang's bet is the opposite: skip the commercialization phase entirely and go straight for the frontier, treating today's products as irrelevant compared to whoever reaches general intelligence first. That's not an incremental strategic adjustment. It's a company deciding the entire "catching up" framework no longer applies to it — and open-sourcing the models isn't generosity, it's a wager that speed matters more than moat.
Why Deepseek Free Code while West's ones not? My view is simple (even if wrong/limited): Millennial Confucian Ethics embedded in many (the majority?) of chinese individuals makes it far more easier for one of their genius (Liang) to share their knowledge than from the typical greedy, selfish westerner.