Will the U.S. LOSE the AI Race to China? – Helen Toner, ex OpenAI Board Member

Is the U.S. LOSING the AI race to China?

China and the U.S. are neck and neck in the AI race for global dominance. Former OpenAI board member Helen Toner (now at Georgetown’s CSET) joins us in Washington, D.C. to break down China vs U.S. strategies—open-source diffusion vs big tech global dominance— and what “winning” actually means.

Helen has recently spent time in China and works at the center of U.S. AI policy—offering a rare inside view of both ecosystems and who’s truly ahead.

Helen explains:

- Who’s ahead right now and how to measure it (frontier AI vs adoption/diffusion)

- Open-source vs closed: DeepSeek, Qwen, Kimi, Gemma, Llama vs OpenAI, Anthropic, Google

- Compute & chips: NVIDIA dependence, export controls, and why compute concentration matters

- AGI timelines: whether “AI 2027” holds up and why short timelines cooled after GPT-5

- “AI+” strategy: applying AI to manufacturing, healthcare, and finance vs pure frontier bragging rights

- What governments should do now: transparency, auditing, AI literacy, and measurement science

Who do you think is winning and WHY – China or the U.S.? Drop one evidence-backed reason (links welcome). We’ll pin the best reply.

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Chapters

00:00 – Two strategies, one AI race

01:00 – Open-source China vs Big-Tech USA

03:37 – Not one race: choose your finish line

04:04 – Who’s actually open? DeepSeek, Qwen/Kimi, Llama, Gemma, GPT-OSS

06:26 – Frontier bragging rights vs real-world adoption

07:46 – China’s “AI Plus” play (AI + industry)

10:06 – Is the US still ahead at the frontier?

12:04 – GPT-5 reality check & AGI timelines

20:58 – Compute decides: chips, export controls, auto-ML engineers

23:04 – What we need now: transparency, audits, AI literacy

28:02 – Standards in practice: de-facto beats de-jure

30:56 – Next 5 years: closed peaks, open bow wave

37:55 – Final take: which path wins?

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