Episode summary
She Knows the 250 People Building AI. Here's What They Actually Believe.
Invest Like the Best
· · Hosts: Patrick O'Shaughnessy · Guests: Sarah Guo
Summary
Sarah Guo argues that AI has become a violently competitive, globally strategic landscape, but she resists framing it as a simple war between a few labs. Her view is that individual founders and researchers still matter a great deal, and that investors can change outcomes by backing people with high agency, strong technical judgment, and the capital to keep pushing the frontier. She says her firm is trying to stay close to a relatively small set of people who are actually advancing the field, rather than simply following consensus.
A major theme is how conviction is built when there is no historical playbook. Sarah says she often starts with an instinctive read on people and ideas, then spends time stress-testing the thesis, learning the field, and looking for what she does not understand. That approach has led her into areas like legal AI, robotics, and biology, where she believes model capability is broadening what is economically possible.
She also argues that compute is becoming a central constraint on the future of AI and on national competitiveness. Sarah says the U.S. will need more compute independence, which means more energy, infrastructure, supply-chain resilience, and a willingness to build at industrial scale. On open source, she thinks the models are already too widespread to contain and that broad access will accelerate adoption across business and research.
Key takeaways
- Sarah Guo thinks frontier AI is so competitive that the real edge comes from identifying and supporting the small set of people actually moving the field forward.
- She says conviction investing means starting with a strong instinct, then stress-testing the thesis until the gaps in understanding are closed.
- Sarah believes compute is becoming a strategic bottleneck and that the U.S. needs more compute independence to stay competitive.
- She argues open-source models are already too widespread to stop and will help spread AI into more businesses and use cases.
- Sarah says biology, robotics, and legal AI are becoming investable because model capability is changing what is technically and economically feasible.
Notable quotes
“It is a violently competitive landscape. I think people are very concerned that is a globally competitive landscape”
“I believe in like the great man and great woman theories of history”
“if you don't feel like you have any grounded intuition on the on the bet itself, what are we doing here”
“I think compute is one people”
How the episode opens
It is a violently competitive landscape. I think people are very concerned that is a globally competitive landscape. There's insecurity in that because I think it's a bit narrative breaking as well.
The belief is like with recursive self-improvement of AI research models that can improve the models themselves. We are a year two years away from some sort of exponential intelligence. Sarah, where to begin?
Uh, what will hopefully be a really fun conversation, I guess, because you and I are interested in so many of the same things. I'm just curious what's on your mind today. I think we're both feeling a little frenzied and it's been going for a while and it kind of feels like if anything it might get more frenzied and more chaotic both for what you do but also the world around what you do.
So just in in this moment like what yeah what does it feel like? What's on your mind? An obvious thing for anyone like thinking about how to navigate this period as an investor is just like well like how does it unfurl and how's it different from the past um and what do I do if I uh can't back test right I was talking to a investor friend last night and the analogy he gave me was like I keep saying I want to press the brakes as hard as I can but I'm not doing it I'm going 90 miles an hour.
And I do think the question is like, do you miss the opportunity on one side or, you know, do you make the mistake of every boom bus cycle in technology history? I think it's a complex question.
Do you think that we got it right in the profile that we wrote that you're sort of making a specific I think we called it a wager, a bet, a positioning, whatever you want to call it.
From the episode’s captions: roughly the first two minutes, lightly cleaned. Keep watching on YouTube →
Topics
- ai investing
- frontier ai beliefs
- compute race
- open-source ai
- robotics and drug discovery
Companies and tickers mentioned
- Conviction
Watch the episode
Watch “She Knows the 250 People Building AI. Here's What They Actually Believe.” on YouTube
Summary, takeaways and quote selection by Octagon. Quotes are short excerpts; the full conversation is in the episode.
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