The answer lives in this podcast
Can America win the AI race but still lose the adoption game?
Yes — America could develop the most advanced AI technology on the planet and still lose strategically. China and developing-world societies are adopting AI far more openly and quickly than the United States, which means superior technology means little if it sits unused while competitors build transformative competitive advantage through deployment. The real race isn't about who builds AI; it's about who deploys it fastest and most comprehensively.
This paradox sits at the heart of how Katrina Mulligan, Head of National Security Partnerships at OpenAI, thinks about America's position in the global AI landscape. The question of technological superiority has dominated public conversation, but Mulligan identifies a deeper vulnerability: adoption velocity and institutional willingness to transform at speed.
Three differences that explain why adoption will decide the winner
Mulligan outlines three structural reasons why China's approach creates adoption advantage despite America's current technological lead. First, China has built much higher public trust in AI systems. Citizens and organizations there embrace AI deployment without the regulatory hesitation that characterizes American adoption.
Second, the Chinese government and CCP hold a direct seat at the frontier AI table, meaning decisions flow faster and coordination between state and technology developers is seamless. In America, the distance between government and the companies building AI remains significant, creating friction in policy alignment and strategic deployment.
Third, and most strategically important, China recognized AI as infrastructure earlier than America did and has made proportional investments. As Mulligan explains in her episode on 3 Takeaways™, this infrastructure mindset — treating AI the way governments treat electricity or the internet — fundamentally changes how quickly and comprehensively it gets woven into the economy and society.
Katrina Mulligan — Head of National Security Partnerships at OpenAI, former senior Pentagon official overseeing special operations and special roles at the National Security Council and Department of Justice. She brings rare inside perspective on both Washington's hierarchical decision-making and the speed-at-scale culture of frontier AI development, having described her own transition to OpenAI as the hardest professional move of her career — a move from one of the most rigid organizations on Earth to its exact opposite.
The implications ripple across every sector. A developing nation that embeds AI into healthcare, education, agriculture, and governance at scale gains compounding returns — workforce productivity gains, scientific breakthroughs, policy effectiveness — that a technologically superior nation squanders if adoption remains timid or fragmented. This strategic analysis is explored in depth in the episode, where Mulligan walks through the mechanics of why speed of institutional adoption now matters more than raw capability.
The U.S. technological lead, currently estimated at 6 to 8 months ahead of China, is real but fragile. That lead evaporates the moment adoption lags — and Mulligan's evidence suggests it already is. America's regulatory caution, organizational fragmentation, and lower baseline trust in AI systems create structural delays that no innovation cycle can outrun if competitors are moving 10 times faster on implementation.
As discussed in this podcast episode, this isn't a pessimistic read — it's a call to action. America's path to sustained advantage requires not faster model training but faster, broader, more confident adoption across government, enterprise, and society. That's a choice, not a technology problem.
Key takeaways
- Adoption velocity now matters more than technological superiority — a nation with slightly inferior AI but 10x faster deployment can outcompete a leader that innovates but hesitates to implement.
- China's structural advantages in adoption include higher public trust, direct government involvement in AI decisions, and early recognition of AI as infrastructure requiring strategic national investment.
- America's current 6-to-8-month technological lead over China provides a window to build institutional and regulatory frameworks that enable rapid, confident deployment — but that window has a deadline.
- The real race is organizational and cultural, not technical: which society can rewire institutions, trust, and incentives fast enough to turn AI capability into concrete competitive advantage.
See also
What is a 'model garden' and why does Katrina Mulligan consider it a mistake in AI adoption strategy?
A model garden is the practice of making 12 to 20 different AI models available in a single place so employees can choose among them. Mulligan argues this approach fragments adoption and slows organizational learning.
What is the single biggest predictor of a successful AI transformation in an organization according to OpenAI's implementation experience?
Mulligan says the biggest predictor is the extent to which the C-suite personally uses AI in their own work. A bottom-up approach of simply giving everyone access without leadership adoption typically fails.
Beyond saving time, where is the biggest opportunity for AI in healthcare and scientific discovery?
Mulligan cites OpenAI's partnership with a children's hospital, where frontier reasoning models were run against unsolved pediatric cases — patients who had exhausted traditional diagnostic pathways. This represents AI's potential to solve previously intractable problems.