Why is there so much AI fundraising activity in China?
Eddie: It all comes down to compute. Over the past year, Chinese labs realized the same thing US labs did: if you want to compete at the frontier, you can’t avoid spending billions on advanced chips and data centers.
Take DeepSeek. It used to pride itself on not raising external capital, relying instead on Liang Wenfeng’s quant hedge fund High-Flyer. That’s no longer realistic. The next generation of models is so compute-hungry that even DeepSeek has gone on a fundraising push and is now preparing for a potential IPO.
You see the same pattern with MiniMax, Z.ai, Moonshot, and others looking at Hong Kong listings or secondary share sales. All of this is essentially for raising money to buy compute.
How much of this is funded by Beijing versus private capital?
Laurie: It’s a genuine mix of three: private capital and VCs; big tech and large private companies, as well as state-backed and state-owned funds.
Chinese researchers know they operate with far less compute than labs like OpenAI or Anthropic. That forces them to be more disciplined: fewer wild experiments, more targeted bets.
On the state side, you have vehicles like China’s national semiconductor investment fund taking stakes in companies such as DeepSeek. The government is strategic, not omnipresent — it wants influence in key players, but a lot of the frenzy is still driven by market investors chasing the next AI champion.
Is China having the same AI safety debate as the US?
Eddie: The tone is very different. In the US, you hear a lot of existential-risk talk. In China, the focus is more pragmatic. AI is seen as a way to squeeze growth and efficiency out of a tough economy — weak consumer sentiment, tight job markets, pressure on productivity.
Regulators like the Cyberspace Administration of China absolutely discuss AI agent risks, but it’s a dry, policy-driven debate, not a public moral panic. The dominant question is still ‘how do we deploy this to help the economy?’ rather than ‘should we slow this down?’
How big is China's AI data center build-out?
Eddie: Much smaller than many people think. When we looked through thousands of Chinese government documents on AI data centers, we found that OpenAI’s Stargate alone — a proposed half-trillion-dollar mega-infrastructure plan — would cost about five times more than everything China has spent on AI data centers in the past five years. In China, typical AI data center projects cost hundreds of millions of dollars each and are spread across hundreds of smaller sites.
With Nvidia constrained by US export controls, what chips is China using?
Eddie: Until 2023-24, Nvidia NVDA.O dominated a China AI chip market Nvidia’s CEO Jensen Huang has called a $50 billion opportunity. Export controls have effectively forced Nvidia to retreat from the highest end of that market.
That’s opened space for Huawei, which has become the de facto primary supplier for advanced AI workloads in China. Huawei knows it’s unlikely to match Nvidia’s single-chip performance anytime soon, so its strategy is to build the best chip it can under sanctions, and use advanced packaging and network design to link thousands of chips into large systems, or “super nodes.” The goal is to try to compete at the system level, not the chip level.
The big questions are if Huawei's system architecture meaningfully narrows the gap with Nvidia, and if Huawei can scale fabrication and improve yields enough to meet domestic demand.
Until both answers are clearly yes, China’s AI compute capacity will remain constrained.
Why is China so strong on open-source models?
Laurie: Initially, this wasn’t a political directive. Chinese labs recognized they were behind US frontier labs, so opening weights was a pragmatic way to accelerate collective learning and let researchers build on each other’s work.
Later, Beijing embraced open-weight models as a diplomatic tool, pitching them as a global public good for countries that lack their own frontier AI.
The problem is monetization. Many open-weight labs struggle to turn popularity into revenue.
What about robotics and humanoids? Is China really ahead of the US?
Eddie: On hardware, China is very strong. Companies like Unitree, UBTECH, and others have made impressive gains in mobility and balance, dynamic control, and visually striking performance demos — running, jumping, kung fu routines.
But what really matters is embodied AI software: models that let robots truly understand and operate in the physical world. That’s where the bottleneck is. Timelines are highly uncertain. As Unitree’s founder Wang Xingxing put it, a “ChatGPT moment” for embodied AI could be 2 to 3 years away if things go fast, or 5 to 10 years away if progress is slower.
If that software breakthrough doesn’t come, we could see failed business models, bankruptcies, and a sharp reset in robotics valuations.
Laurie: On cutting-edge embodied AI models, US startups are still slightly ahead. A recent example is a US company that showed a humanoid learning a new task from a single short video, with no lengthy retraining. When I asked a Chinese founder about it, he acknowledged that no Chinese team has matched that yet.
The race now is to build robots that can do general-purpose tasks, autonomously, with minimal data and training, and still hit high reliability. That’s where the real gap lies.
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