China is heading in the right direction to dominate consumer artificial intelligence applications and robotics manufacturing inside years, however the United States will maintain its substantial lead in enterprise AI adoption and cutting-edge research, in keeping with Kai-Fu Lee, certainly one of the world’s most distinguished AI scientists and investors.
In a rare, unvarnished assessment delivered via video link from Beijing to the TED AI conference in San Francisco Tuesday, Lee — a former executive at Apple, Microsoft, and Google who now runs each a significant enterprise capital firm and his own AI company — laid out a technology landscape splitting along geographic and economic lines, with profound implications for each industrial competition and national security.
“China’s robotics has the advantage of getting integrated AI into much lower costs, higher supply chain and fast turnaround, so firms like Unitree are literally the farthest ahead on the planet by way of constructing reasonably priced, embodied humanoid AI,” Lee said, referring to a Chinese robotics manufacturer that has undercut Western competitors on price while advancing capabilities.
The comments, made to a room crammed with Silicon Valley executives, investors, and researchers, represented one of the vital detailed public assessments from Lee concerning the comparative strengths and weaknesses of the world’s two AI superpowers — and suggested that the race for artificial intelligence leadership is becoming less a single contest than a series of parallel competitions with different winners.
Why enterprise capital is flowing in opposite directions within the U.S. and China
At the guts of Lee’s evaluation lies a fundamental difference in how capital flows within the two countries’ innovation ecosystems. American enterprise capitalists, Lee said, are pouring money into generative AI firms constructing large language models and enterprise software, while Chinese investors are betting heavily on robotics and hardware.
“The VCs within the US don’t fund robotics the way in which the VCs do in China,” Lee said. “Just just like the VCs in China don’t fund generative AI the way in which the VCs do within the US.”
This investment divergence reflects different economic incentives and market structures. In the United States, where firms have grown accustomed to paying for software subscriptions and where labor costs are high, enterprise AI tools that boost white-collar productivity command premium prices. In China, where software subscription models have historically struggled to achieve traction but manufacturing dominates the economy, robotics offers a clearer path to commercialization.
The result, Lee suggested, is that every country is pulling ahead in numerous domains — and should proceed to accomplish that.
“China’s got some challenges to beat in getting an organization funded in addition to OpenAI or Anthropic,” Lee acknowledged, referring to the leading American AI labs. “But I feel U.S., on the flip side, could have trouble developing the investment interest and value creation within the robotics” sector.
Why American firms dominate enterprise AI while Chinese firms struggle with subscriptions
Lee was explicit about one area where the United States maintains what appears to be a durable advantage: getting businesses to truly adopt and pay for AI software.
“The enterprise adoption will clearly be led by the United States,” Lee said. “The Chinese firms haven’t yet developed a habit of paying for software on a subscription.”
This seemingly mundane difference in business culture — whether firms can pay monthly fees for software — has turn into a critical consider the AI race. The explosion of spending on tools like GitHub Copilot, ChatGPT Enterprise, and other AI-powered productivity software has fueled American firms’ ability to speculate billions in further research and development.
Lee noted that China has historically overcome similar challenges in consumer technology by developing alternative business models. “In the early days of web software, China was also well behind because people weren’t willing to pay for software,” he said. “But then promoting models, e-commerce models really propelled China forward.”
Still, he suggested, someone might want to “discover a latest business model that won’t just pay per software per use or monthly basis. That’s going to not occur in China anytime soon.”
The implication: American firms constructing enterprise AI tools have a window — perhaps a considerable one — where they will generate revenue and reinvest in R&D without facing serious Chinese competition of their core market.
How ByteDance, Alibaba and Tencent will outpace Meta and Google in consumer AI
Where Lee sees China pulling ahead decisively is in consumer-facing AI applications — the type embedded in social media, e-commerce, and entertainment platforms that billions of individuals use day by day.
“In terms of consumer usage, that is prone to occur,” Lee said, referring to China matching or surpassing the United States in AI deployment. “The Chinese giants, like ByteDance and Alibaba and Tencent, will certainly move lots faster than their equivalent within the United States, firms like Meta, YouTube and so forth.”
Lee pointed to a cultural advantage: Chinese technology firms have spent the past decade obsessively optimizing for user engagement and product-market slot in brutally competitive markets. “The Chinese giants really work tenaciously, they usually have mastered the art of determining product market fit,” he said. “Now they need to add technology to it. So that’s inevitably going to occur.”
This assessment aligns with recent industry observations. ByteDance’s TikTok became the world’s most downloaded app through sophisticated AI-driven content advice, and Chinese firms have pioneered AI-powered features in areas like live-streaming commerce and short-form video that Western firms later copied.
Lee also noted that China has already deployed AI more widely in certain domains. “There are a variety of areas where China has also done a terrific job, akin to using computer vision, speech recognition, and translation more widely,” he said.
The surprising open-source shift that has Chinese models beating Meta’s Llama
Perhaps Lee’s most striking data point concerned open-source AI development — an area where China appears to have seized leadership from American firms in a remarkably short time.
“The 10 highest rated open source (models) are from China,” Lee said. “These firms have now eclipsed Meta’s Llama, which was primary.”
This represents a major shift. Meta’s Llama models were widely viewed because the gold standard for open-source large language models as recently as early 2024. But Chinese firms — including Lee’s own firm, 01.AI, together with Alibaba, Baidu, and others — have released a flood of open-source models that, in keeping with various benchmarks, now outperform their American counterparts.
The open-source query has turn into a flashpoint in AI development. Lee made an in depth case for why open-source models will prove essential to the technology’s future, at the same time as closed models from firms like OpenAI command higher prices and, often, superior performance.
“I feel open source has various major benefits,” Lee argued. With open-source models, “you may examine it, tune it, improve it. It’s yours, and it’s free, and it is vital for constructing if you desire to construct an application or tune the model to do something specific.”
He drew an analogy to operating systems: “People who work in operating systems loved Linux, and that is why its adoption went through the roof. And I feel in the longer term, open source can even allow people to tune a sovereign model for a rustic, make it work higher for a specific language.”
Still, Lee predicted each approaches will coexist. “I do not think open source models will win,” he said. “I feel identical to we’ve got Apple, which is closed, but provides a somewhat higher experience than Android… I feel we’ll see more apps using open-source models, more engineers wanting to construct open-source models, but I feel extra money will remain within the closed model.”
Why China’s manufacturing advantage makes the robotics race ‘not over, but’ nearly decided
On robotics, Lee’s message was blunt: the mixture of China’s manufacturing prowess, lower costs, and aggressive investment has created a bonus that might be difficult for American firms to beat.
When asked directly whether the robotics race was already over with China victorious, Lee hedged only barely. “It’s not over, but I feel the U.S. remains to be able to coming up with one of the best robotic research ideas,” he said. “But the VCs within the U.S. don’t fund robotics the way in which the VCs do in China.”
The challenge is structural. Building robots requires not only software and AI, but hardware manufacturing at scale — precisely the type of integrated supply chain and low-cost production that China has spent many years perfecting. While American labs at universities and firms like Boston Dynamics proceed to supply impressive research prototypes, turning those prototypes into reasonably priced industrial products requires the manufacturing ecosystem that China possesses.
Companies like Unitree have demonstrated this advantage concretely. The company’s humanoid robots and quadrupedal robots cost a fraction of their American-made equivalents while offering comparable or superior capabilities — a price-to-performance ratio that might prove decisive in industrial markets.
The energy infrastructure gap that might determine AI supremacy
Underlying a lot of these competitive dynamics is an element Lee raised early in his remarks: energy infrastructure. “China is now constructing latest energy projects at 10 times the speed of the U.S.,” he said, “and if this continues, it can inevitably result in China having 10 times the AI capability of the U.S., whether we prefer it or not.”
This commentary connects to a theme raised by multiple speakers on the TED AI conference: that computing power — and the energy to run it — has turn into the basic constraint on AI development. If China can construct power plants and data centers at 10 times the speed of the United States, it could simply outspend American competitors in training ever-larger models and running them at ever-greater scale.
Lee noted this dynamic carries “very real national security implications for the U.S.” — though he didn’t elaborate on what those implications is likely to be. The comment appeared to reference growing concerns in Washington about technological competition with China, particularly in areas like AI-enabled military systems, surveillance capabilities, and economic competitiveness.
Despite the United States currently hosting several times more AI computing power than China, Lee warned that “this lead is growing” for now but could reverse if energy infrastructure investments proceed at current rates.
What worries Lee most: not AGI, however the race itself
Despite his generally measured tone about China’s AI development, Lee expressed concern about one area where he believes the worldwide AI community faces real danger — not the far-future risk of superintelligent AI, however the near-term consequences of moving too fast.
When asked about AGI risks, Lee reframed the query. “I’m less afraid of AI becoming self-aware and causing danger for humans within the short term,” he said, “but more nervous about it getting used by bad people to do terrible things, or by the AI race pushing people to work so hard, so fast and furious and move fast and break things that they construct products which have problems and holes to be exploited.”
He continued: “I’m very nervous about that. In fact, I feel some terrible event will occur that might be a get up call from this kind of problem.”
Lee’s perspective carries unusual weight due to his unique vantage point spanning each Chinese and American AI development. Over a profession spanning greater than three many years, he has held senior positions at Apple, Microsoft, and Google, while also founding Sinovation Ventures, which has invested in greater than 400 firms across each countries. His AI company, 01.AI, founded in 2023, has released several open-source models that rank amongst probably the most capable on the planet.
For American firms and policymakers, Lee’s evaluation presents a posh strategic picture. The United States appears to have clear benefits in enterprise AI software, fundamental research, and computing infrastructure. But China is moving faster in consumer applications, manufacturing robotics at lower costs, and potentially pulling ahead in open-source model development.
The bifurcation suggests that reasonably than a single “winner” in AI, the world could also be heading toward a technology landscape where different countries excel in numerous domains — with all of the economic and geopolitical complications that suggests.
As the TED AI conference continued Wednesday, Lee’s assessment hung over subsequent discussions. His message seemed clear: the AI race just isn’t one contest, but many — and the United States and China are each winning different races.
Standing within the conference hall afterward, one enterprise capitalist, who asked to not be named, summed up the mood within the room: “We’re not competing with China anymore. We’re competing on parallel tracks.” Whether those tracks eventually converge — or diverge into entirely separate technology ecosystems — would be the defining query of the following decade.
