August 2026
Her case that America's AI lead is measured by the wrong scoreboard: model count and benchmark wins, when what decides adoption is whether ordinary workers can actually use the thing.
Transcript
We have a lot of experience at OpenAI implementing AI transformations. And so we have a lot of insight and data about what works and what really makes a difference. And it has been consistently true that the biggest predictor of whether an organization will have a successful AI transformation or not is the extent to which their C-suite uses it. The people at the very top of the organization, it's whether they themselves are using the technology in their own work. And that is something that's important for government to really take on board because it's not enough to just have a bottom-up approach where you just give everybody access to the tool and hope for the best. I always say, you don't get fit by reading about working out and you do not get good at understanding how AI is going to transform your organization by reading about it or going to a panel discussion about it. You have to be using it and getting in reps and sets yourself.
And what's the biggest mistake that you see leaders making when they try to adopt AI?
Probably the biggest one is building model gardens. And what I mean by that is the belief that the thing that you really need is just access to the maximum number of tools in a single place so that people can have access to 12 or 20 different models at their fingertips and that that's what success looks like. That to me is a mistake for a number of reasons. One, it's really expensive. You're buying every model and you're making it all available. Number two, Brenda from HR does not need 12 choices when she's typing a query into a chatbot. That does not actually materially benefit. And in fact, I think it creates a lot of friction in terms of how the general population uses AI. I do think that model choice is really important, but for basic usage of chats, I do think that model gardens are something that we will look back on and think that was probably not a good thing for us to have spent that much effort on.
America and China are taking fundamentally different Approaches to AI. What are the differences and what are the implications?
I think there are a few big differences. Number one, trust in AI is just wildly different. And that opens up a very different surface area for China to explore and to create public support for things that it's doing that I don't see here in the United States. China is also much more heavily involved in the development of AI than the U.S. government is. And some of that is a feature of the way that their society operates very differently from ours. The CCP and the Chinese government in particular has more of a direct seat at the frontier AI table than the U.S. government currently does. Although we are starting to see the U.S. assert its prerogatives in different ways, mostly around model safety and model releases. But I still think that fundamentally there's a different relationship that China has with the AI industry than the relationship that the U.S. has. In some ways, for the better, in some ways, for the worse. I also think that the Chinese government and the CCP have moved faster to recognize AI as fundamentally an infrastructure play and to make strategic investments in infrastructure as part of their strategy. That is happening in the U.S. AI ecosystem, but it's happening because the companies are making those investments and because private industry is making those investments, not because the government has decided it wants to make a big play in that area.
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