China is on track to dominate consumer artificial intelligence applications and robotics manufacturing within years, but the United States will maintain its substantial lead in enterprise AI adoption and cutting-edge research, according to Kai-Fu Lee, one of the world’s most prominent 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 both a major venture capital firm and his own AI company — laid out a technology landscape splitting along geographic and economic lines, with profound implications for both commercial competition and national security.
“China’s robotics has the advantage of having integrated AI into much lower costs, better supply chain and fast turnaround, so companies like Unitree are actually the farthest ahead in the world in terms of building affordable, 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 filled with Silicon Valley executives, investors, and researchers, represented one of the most detailed public assessments from Lee about 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 venture capital is flowing in opposite directions in the U.S. and China
At the heart of Lee’s analysis lies a fundamental difference in how capital flows in the two countries’ innovation ecosystems. American venture capitalists, Lee said, are pouring money into generative AI companies building large language models and enterprise software, while Chinese investors are betting heavily on robotics and hardware.
“The VCs in the US don’t fund robotics the way the VCs do in China,” Lee said. “Just like the VCs in China don’t fund generative AI the way the VCs do in the US.”
This investment divergence reflects different economic incentives and market structures. In the United States, where companies 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 gain traction but manufacturing dominates the economy, robotics offers a clearer path to commercialization.
The result, Lee suggested, is that each country is pulling ahead in different domains — and may continue to do so.
“China’s got some challenges to overcome in getting a company funded as well as OpenAI or Anthropic,” Lee acknowledged, referring to the leading American AI labs. “But I think U.S., on the flip side, will have trouble developing the investment interest and value creation in the robotics” sector.
Why American companies 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 actually adopt and pay for AI software.
“The enterprise adoption will clearly be led by the United States,” Lee said. “The Chinese companies have not yet developed a habit of paying for software on a subscription.”
This seemingly mundane difference in business culture — whether companies will pay monthly fees for software — has become a critical factor in the AI race. The explosion of spending on tools like GitHub Copilot, ChatGPT Enterprise, and other AI-powered productivity software has fueled American companies’ ability to invest 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 internet software, China was also well behind because people weren’t willing to pay for software,” he said. “But then advertising models, e-commerce models really propelled China forward.”
Still, he suggested, someone will need to “find a new business model that isn’t just pay per software per use or per month basis. That’s going to not happen in China anytime soon.”
The implication: American companies building enterprise AI tools have a window — perhaps a substantial one — where they can generate revenue and reinvest in R&D without facing serious Chinese competition in 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 kind embedded in social media, e-commerce, and entertainment platforms that billions of people use daily.
“In terms of consumer usage, that’s likely to happen,” Lee said, referring to China matching or surpassing the United States in AI deployment. “The Chinese giants, like ByteDance and Alibaba and Tencent, will definitely move a lot faster than their equivalent in the United States, companies like Meta, YouTube and so on.”
Lee pointed to a cultural advantage: Chinese technology companies have spent the past decade obsessively optimizing for user engagement and product-market fit in brutally competitive markets. “The Chinese giants really work tenaciously, and they have mastered the art of figuring out product market fit,” he said. “Now they have to add technology to it. So that is inevitably going to happen.”
This assessment aligns with recent industry observations. ByteDance’s TikTok became the world’s most downloaded app through sophisticated AI-driven content recommendation, and Chinese companies have pioneered AI-powered features in areas like live-streaming commerce and short-form video that Western companies later copied.
Lee also noted that China has already deployed AI more widely in certain domains. “There are a lot of areas where China has also done a great job, such as 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 companies in a remarkably short time.
“The 10 highest rated open source [models] are from China,” Lee said. “These companies have now eclipsed Meta’s Llama, which used to be number one.”
This represents a significant shift. Meta’s Llama models were widely viewed as the gold standard for open-source large language models as recently as early 2024. But Chinese companies — including Lee’s own firm, 01.AI, along with Alibaba, Baidu, and others — have released a flood of open-source models that, according to various benchmarks, now outperform their American counterparts.
The open-source question has become a flashpoint in AI development. Lee made an extensive case for why open-source models will prove essential to the technology’s future, even as closed models from companies like OpenAI command higher prices and, often, superior performance.
“I think open source has a number of major advantages,” Lee argued. With open-source models, “you can examine it, tune it, improve it. It’s yours, and it’s free, and it’s important for building if you want to build 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’s why its adoption went through the roof. And I think in the future, open source will also allow people to tune a sovereign model for a country, make it work better for a particular language.”
Still, Lee predicted both approaches will coexist. “I don’t think open source models will win,” he said. “I think just like we have Apple, which is closed, but provides a somewhat better experience than Android… I think we’re going to see more apps using open-source models, more engineers wanting to build open-source models, but I think more money will remain in 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 combination of China’s manufacturing prowess, lower costs, and aggressive investment has created an advantage that will be difficult for American companies to overcome.
When asked directly whether the robotics race was already over with China victorious, Lee hedged only slightly. “It’s not over, but I think the U.S. is still capable of coming up with the best robotic research ideas,” he said. “But the VCs in the U.S. don’t fund robotics the way the VCs do in China.”
The challenge is structural. Building robots requires not just software and AI, but hardware manufacturing at scale — precisely the kind of integrated supply chain and low-cost production that China has spent decades perfecting. While American labs at universities and companies like Boston Dynamics continue to produce impressive research prototypes, turning those prototypes into affordable commercial 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 could prove decisive in commercial markets.
The energy infrastructure gap that could determine AI supremacy
Underlying many of these competitive dynamics is a factor Lee raised early in his remarks: energy infrastructure. “China is now building new energy projects at 10 times the rate of the U.S.,” he said, “and if this continues, it will inevitably lead to China having 10 times the AI capability of the U.S., whether we like it or not.”
This observation connects to a theme raised by multiple speakers at the TED AI conference: that computing power — and the energy to run it — has become the fundamental constraint on AI development. If China can build power plants and data centers at 10 times the rate 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 did not elaborate on what those implications might 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 continue at current rates.
What worries Lee most: not AGI, but the race itself
Despite his generally measured tone about China’s AI development, Lee expressed concern about one area where he believes the global AI community faces real danger — not the far-future risk of superintelligent AI, but the near-term consequences of moving too fast.
When asked about AGI risks, Lee reframed the question. “I’m less afraid of AI becoming self-aware and causing danger for humans in the short term,” he said, “but more worried about it being 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 build products that have problems and holes to be exploited.”
He continued: “I’m very worried about that. In fact, I think some terrible event will happen that will be a wake up call from this sort of problem.”
Lee’s perspective carries unusual weight because of his unique vantage point spanning both Chinese and American AI development. Over a career spanning more than three decades, he has held senior positions at Apple, Microsoft, and Google, while also founding Sinovation Ventures, which has invested in more than 400 companies across both countries. His AI company, 01.AI, founded in 2023, has released several open-source models that rank among the most capable in the world.
For American companies and policymakers, Lee’s analysis presents a complex strategic picture. The United States appears to have clear advantages 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 rather than a single “winner” in AI, the world may be heading toward a technology landscape where different countries excel in different domains — with all the economic and geopolitical complications that implies.
As the TED AI conference continued Wednesday, Lee’s assessment hung over subsequent discussions. His message seemed clear: the AI race is not one contest, but many — and the United States and China are each winning different races.
Standing in the conference hall afterward, one venture capitalist, who asked not to be named, summed up the mood in 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 — may be the defining question of the next decade.
Original Source: https://venturebeat.com/ai/kai-fu-lees-brutal-assessment-america-is-already-losing-the-ai-hardware-war
Original Source: https://venturebeat.com/ai/kai-fu-lees-brutal-assessment-america-is-already-losing-the-ai-hardware-war
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