If an adversary of the United States wanted to slow us down, I think they couldn't wish for almost anything better than these silly moratoriums on building our data centers. I've been alarmed at the amount of lobbying that a handful of businesses have been doing, saying that open models are dangerous. I think that's false. I think the concept that distillation is a major factor has been overstated.
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Hello everyone. I'm James H. Homeman, deputy opinion editor at the Washington Post. Welcome to the Post's Building America podcast about how America builds, manufactures, and innovates better for the future. Today, we're speaking with Andrew about how investing in AI infrastructure will shape America's long-term competitiveness. Andrew is a true pioneer in AI. He led the Google Brain Team, which launched much of the tech giants AI work. He directed the Stanford AI lab and has authored over 200 papers on AI and related issues. He has since co-founded deep learning.ai, which provides technical training. And he's managing general partner of a venture fund. He is also on the board of directors of Amazon whose executive chairman Jeff Bezos owns the post. Andrew, welcome.
Thank you, James. It's always good to be with the post.
Well, I for those who don't know your incredible story. Take us back to the start. Uh you conducted research on neural networks that led to the formation of Google Brain. What was it that you pitched to Google founder Larry Page back in 2010?
At that time I had this controversial idea that if we build really big neuronet networks and feed a lot of data to them that they will learn incredible things. Um and so when I started the Google brain team the number one direction I gave the team was let's build really really big AI systems and feed a lot of data and with that oversimplified direction we wound up um having some incredible accomplishments. I think that culture of the Google brain team led it to later invent the transformer neuronet network which is really the one key technology that the Google brain team openly published and that OpenAI adopted, many others adopted and sparked off the modern AI revolution. It was really driven by this one idea that I saw in my very early data at Stanford that the bigger you build AI systems the smarter they get and that idea is still carrying us forward today.
I mean, fast forwarding to 2026, are you surprised by the speed in which we've gotten to this place, or did you kind of expect that this would be where we'd end up? Obviously, a lot of this was theoretical uh and now it's the biggest thing in the world by far.
Even back then, I was very optimistic about where this would go and I remain very optimistic about where we can get in the future. But, uh, exactly how far we are and how far we could go. Some days I'm excited, some days I wonder why we aren't even further along. And I think what we're seeing is that um as amazing as AI technology is, and it is amazing, businesses do not feel like it's helping them yet. And one of the challenges is no company ever gained competitive advantage just by buying a ChatGPT or Microsoft co-pilot license. The problem is finding the right use cases and um that's a people change management having the right people understand the technology marry it to the actual use cases and of course supported also by the infrastructure that you talked about.
Yeah, I saw that you've just uh unveiled open worker an open-source desktop agent that produces finished work rather than just a conversation. Is that part of the the answer to what you're talking about?
Uh yeah. So uh my my my friend um Rahab Prasad and I have been working on a open version of uh AI that you don't just chat with um but that actually does work for you. So a lot of people still will use chat or Claude or Gemini or tools like that to chat and get back an answer. But today AI could go further instead of just chatting with me. it can produce a finished polished document rather than just give me things to copy paste or it can actually send an email for you or send a you know short message uh in in in Slack or messaging system for you um or build a dashboard for you and so open worker is a a fully open source you know free uh uh piece of software to let people do that on their computer and I'm actually excited about uh uh tools like um uh Claude's co-work and uh ChatGPT work uh and uh Gemini anti-gravity which are all good tools and open worker is a free open source version that anyone can use.
And there's a lot of talk right now in DC certainly about open versus closed open weight models closed uh and you've done a lot on the the open side you see a lot of the the frontier models developing these closed models where you know people have to pay for tokens that's become sort of you're you're seeing companies push back on that as the the costs and the bills start to run up. How do you think about the open versus closed debate, if you can call it that?
Yeah. So, to sustain competitive advantage in America, one of the most important things we have to do is um support and sustain open models. So just to drown to terminology um uh by the way I think I'm the only person that both Sam and Dario has worked for and so actually you know support uh OpenAI, Anthropic, I I really hope they do well and have fantastic IPOs and so on. So I really hope these great American businesses will succeed. At the same time the tension the success of these businesses cannot be at the cost of shutting down everyone else's access to open models. So, OpenAI, Anthropic, uh, Google, um, have done a great job training closed proprietary models that they sell access to, uh, for, you know, decently high price. Uh, open models are models that people have published on the internet, free for anyone to use. And those are extremely performant, very intelligent, and often much cheaper. And so, I've been alarmed at the amount of lobbying that a handful of businesses have been doing. um saying that open models are dangerous. I think that's false. Uh or saying that open models are uh uh you know kind of somehow not as good as the closed models which they just don't get. Uh open models are a form of um gives businesses more choice. It means you're not locked in to one closed proprietary provider. In fact, one piece of advice I often give to business leaders is I can't forecast in a year or even six months what is going to be the top model. So as we build our businesses, one of the most important things to do is to preserve optionality. So I'm not locked in. I I use OpenAI, I use Claude, I use Gemini, I use uh you know a lot of different models but I don't let myself be locked into any one of them because a year from now we want to use the best one whether it's the open model that we cheaper or closed proprietary model so preserving optionality is very important and the world supply chain is using open models so making sure that America continues have access to that will be important for us as well.
Yeah, I mean part of the lobbying campaign that you are referring to, they're trying to sort of they're basically trying to portray open models as as one and the same as Chinese models and then they're making kind of this national security argument, but it it doesn't necessarily need to be or stay that way. Uh and I mean I also wonder about the tension in the Chinese system of having open models and whether the government there will continue to allow that over the long term.
When ChatGPT was first released a few years ago, America was decisively ahead in of China in uh generative AI technology. Since then, China has played its hand really well. One of the things that China did really well was embrace open models because it turns out that when you release models freely for anyone to use, it helps the whole world. Yes, but it helps you even more than it helps the whole world. So the openness, freedom of communication, fast diffusion of knowledge has meant China has rapidly accelerated and is approaching maybe not quite at par but approaching par with um the leading Chinese models. In contrast, because uh a lot of American work has gone into closed proprietary models, it's just much harder to know if you have a question, who should I call up to understand how to do this modeling thing.
Um and maybe one company recruit another engineer for, you know, a very high price tag and then there's a little bit of diffusion of knowledge. But I think this has um uh the the the lobbying against open models has hampered American AI development. We do have some great open American models. I think um Nvidia's Neotron models are strong models. Uh the uh Thinking Machines Inkling lab feels like a very strong showing. I I'd love to see more of this. I I think that there's room for proprietary models and open models to succeed.
And the important thing is to maintain a level playing field so that any model, you know, so that so that people have choice to use the best model. How much do you think that the gains that the open Chinese models have made are the result of distillation versus their own progression and the kind of the democratized nature of an open model.
So it is clear that we have great technical innovations in America and China has great technical innovations. In fact um many of the US frontier labs are actively reading a lot of the open research that the Chinese lab published. I mean you have to be dumb not to. So America labs are definitely benefiting from tons of Chinese research lab innovations. I think the concept that distillation is a major factor has been overstated, vastly overstated. Um you know there's a question of what the world should consider fair. AI labs all around the world took data off the open internet and use it to distill knowledge from the internet into their AI models. Is it fair for them to turn around and say, 'I've distilled the internet into my model. If anyone distills my model from here on out, that's not fair.' Um, I think that's an open question of what society should consider fair or not. Um, the the the some of the claims that uh uh some I think that uh you know, Kimi K was released uh uh recently uh I guess with weights coming soon. Um given that the Fable model was available only for a short period of time, the idea that Kimi K was trained primarily by distilling Fable, I just find that very hard to believe. Uh uh given, you know, there just couldn't have been that much fable data and how could Kimi K have been trained in such a short time.
That's really that's a really good perspective because I think that's definitely not part of the conversation in DC. But that that's a great point. Yeah, there's certain parties that eager to throw whatever they can at the wall to uh attack open source models including Chinese but also uh when you take an open model whether it's released from a Chinese lab or American lab and it run them on American infrastructure you know it basically becomes you really American infrastructure can control that model for example when um I you know in the US publish code online that is used by some other country well that other country controls it from them or no. And so I think the amount of FUD to slow down uh uh American adoption. Oh, one unfortunate thing, the whole world's going to use open models. A lot of the world is going to use open models. And so American underinvestment in open models means that there are many nations, for example, in Africa, DeepSeek adoption is through the roof. We don't see this that much in the US, but in places where they're a little bit more price sensitive, Chinese models have really gained tremendous market share. And this is regrettable because I would like American open models or American models to be more competitive all around the world. And it is a competitive massive competitive advantage disadvantage. If um an open model allows you to get intelligence at I don't know 1/5th or one third or one the cost then if um for everyone wanting to build AI applications if your supply of intelligence costs three times more that's a very fundamental uh business disadvantage.
I guess I'm sort of curious how do you sustain the like a business model for an open model? Uh you know if you're charging for tokens, if you're Anthropic or OpenAI that's helping obviously there's some profit but it's also eventually hopefully uh but you're it's also helping do this big buildout. Where does the kind of the money come from for the capital expenditures when it when you're talking about open AI models?
