High Capacity

Is the U.S.–China AI competition really a winner-take-all race to AGI? Why has China embraced open-source AI? And are the biggest frontier models actually the greatest security threat?
In this episode, I speak with Alvin Wang Graylin, a Digital Fellow at the Stanford Digital Economy Lab, a Senior Fellow at the Asia Society Policy Institute’s Center for China Analysis, a professor of AI and technology policy at the University of Washington, and co-author of Our Next Reality.
We discuss:
• China’s AI strategy and its focus on spreading AI throughout the economy
• Why the “decisive strategic advantage” theory may misread the U.S.–China AI relationship
• The prisoner’s dilemma, the stag hunt, and the case for cooperation on AI safety
• China’s open-source AI ecosystem and the debate over open versus closed models
• Why small, specialized AI systems may pose greater security risks than frontier models
• How export controls may be accelerating Chinese AI innovation
• China’s push into robotics, manufacturing, and physical AI
• How China’s technology industry has changed over the past three decades
Read Alvin’s essays, “Misdiagnosing the U.S.–China AI Race” and “The Biggest AI Models Are Not the Biggest Threats.”
You can also follow Alvin on X and LinkedIn, or read his Substack, The Abundanist.
For transcripts and more information, visit the High Capacity newsletter.
Follow host Kyle Chan on X.

How do users in China get access to Claude and ChatGPT through the gray market? What’s really driving China’s apparent “AI optimism”? How is China trying to deal with deepfakes and AI-generated content?

In this episode, I speak with Zilan Qian, a researcher at the Oxford China Policy Lab about the hidden side of China’s AI boom, including:
How Chinese users use “transfer stations” to access American AI models like Claude and ChatGPT
Why Chinese users still want to use Claude and ChatGPT even though there are strong Chinese mode
Why China’s apparent AI optimism is really driven by anxiety about falling behind
China’s push to label AI-generated content—and the problems with watermarks, detection, and false positives

Links:
Zilan’s Oxford page
Zilan on Substack and Twitter / X
How to Buy Cheap Claude Tokens in China
China’s AI optimism isn’t what it seems
Labelling AI-Generated Content in China

How did China build a world-leading EV industry? China didn't just pick winners. Local governments and private entrepreneurs played a critical role alongside a broader national push into batteries and electric vehicle technology.

In this episode, I talk with Fengming Lu, an assistant professor at Australian National University, about China's decades-long effort to build a world-class auto industry, its pivot to EVs, and what lessons other countries might learn.

We discuss:
• How Chinese local governments act like VC investors
• What China learned from foreign automakers like GM, Volkswagen
• The rise of upstarts like Geely, Chery, and BYD
• What caused a wave of Chinese EV startups like NIO, Xpeng, Li Auto
• The "Hefei model"

Links:
• The Rise of China’s Electric Vehicle Industry: Strategic Alliances Between Local Governments and Private Capital
• Fengming's personal website
• Fengming on Twitter / X

For transcripts and more information, check out the High Capacity newsletter.

Follow host Kyle Chan on Twitter / X

Why is China’s biotech industry advancing so quickly? I talk with Abigail Coplin of Vassar College to unpack China’s biotech strategy, BGI, genomics, AI-driven drug discovery, gene therapies, biotech regulation, and what the U.S.-China biotech race means for innovation, national security, and the future of medicine.

Why has China emerged so quickly as a global biotech powerhouse?
What gives Chinese biotech companies an edge over their Western competitors?
How did BGI become one of the world’s most influential genomics companies?
Why are China’s gene and cell therapies advancing so rapidly?
What should U.S. policymakers know about China's biotech industry?

Links:
Abigail’s academic profile
Abigail’s policy paper for the Penn Project on the Future of U.S.-China Relations: “Getting Biotechnology Right: Managing Risk and Reward Between the U.S. and China in the Life Sciences”
Yu Zhou and Abigail Coplin. 2022. “Innovation in a Science-Based Sector: The Institutional Evolution behind China’s Emerging Biopharmaceutical Innovation Boom.” The China Review.

For transcripts and more information, check out the High Capacity newsletter.
Follow host Kyle Chan on Twitter / X.

What happened with Anthropic's Mythos and Fable models? Why did Anthropic's approach to AI safety trigger such strong backlash? What does this mean for the future of AI regulation? What role does open-source AI play in this new era of AI control? How will this impact Chinese AI labs and open-source AI models?

In this episode, I talk with Nathan Lambert, a leading expert on open-source AI. He previously led post-training at Ai2, founded The ATOM Project, and writes the popular Interconnects AI newsletter.

Topics covered:
- Anthropic, Mythos, Fable, and the Claude controversy
- Are Anthropic’s AI safety claims sincere?
- Silent degradation, trust, and researcher backlash
- Open science, open-source AI, and safety
- How will Anthropic's new safety features impact Chinese AI labs?
- The “AGI era” of AI governance
- Is open-source AI safer or riskier?
- Why US developers use Chinese open-source models
- What Nathan learned from visiting China’s AI labs
- Are Chinese AI labs AGI-pilled?Follow Nathan Lambert:
Interconnects newsletter
The ATOM Project
X/Twitter: @natolambert
Follow Kyle Chan:
High Capacity newsletter
X/Twitter: @kyleichan

#AI #ArtificialIntelligence #Anthropic #mythos #fable #Claude #OpenSourceAI #ChinaAI #AIGovernance #AISafety