Why The Next AI Breakthroughs Will Be In Reasoning, Not Scaling
Harj Taggar (host), Garry Tan (host), Jared Friedman (host), Diana Hu (host), Diana Hu (host)
In this episode of Y Combinator, featuring Harj Taggar and Garry Tan, Why The Next AI Breakthroughs Will Be In Reasoning, Not Scaling explores aI’s Next Leap: Reasoning Engines Transform Science, Chips, And Startups The hosts discuss Sam Altman’s recent AGI/ASI essay and argue that the next major AI breakthroughs will come from reasoning-focused models like OpenAI’s o1, not just larger model scaling. They highlight how o1’s chain-of-thought and reinforcement-learning-inspired architecture unlocks step-function improvements in hard domains such as chip design, CAD/airfoils, and complex customer support. Several YC-backed companies are showcased as concrete examples of o1 enabling system-level engineering, advanced physics simulations, and high-accuracy automation that older models like GPT-4o couldn’t reliably handle. The conversation closes with implications for startup moats, the centrality of evals and proprietary data, and how reasoning models may usher in a “Star Trek” style age of physical-world abundance if steered well.
AI’s Next Leap: Reasoning Engines Transform Science, Chips, And Startups
The hosts discuss Sam Altman’s recent AGI/ASI essay and argue that the next major AI breakthroughs will come from reasoning-focused models like OpenAI’s o1, not just larger model scaling. They highlight how o1’s chain-of-thought and reinforcement-learning-inspired architecture unlocks step-function improvements in hard domains such as chip design, CAD/airfoils, and complex customer support. Several YC-backed companies are showcased as concrete examples of o1 enabling system-level engineering, advanced physics simulations, and high-accuracy automation that older models like GPT-4o couldn’t reliably handle. The conversation closes with implications for startup moats, the centrality of evals and proprietary data, and how reasoning models may usher in a “Star Trek” style age of physical-world abundance if steered well.
Key Takeaways
Reasoning-focused models mark a step-change beyond simple scaling.
OpenAI’s o1 introduces chain-of-thought and reinforcement-learning-style training that lets models ‘think through’ problems, enabling capabilities (like complex chip system design) that GPT-4o could not handle with the same prompts.
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AI can now meaningfully automate expert-level hardware and engineering tasks.
Companies like Diode Computer and Camfer use o1 to perform high-level PCB system design, component selection, and multi-equation airfoil simulations—work that previously required specialized electrical or mechanical engineers.
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Eval sets and proprietary workflows are becoming core startup moats.
The hosts argue that writing tens of thousands of high-quality eval cases, especially using non-public, domain-specific data, is a durable advantage when everyone accesses similar base models.
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Advanced reasoning dramatically increases automation viability in messy domains.
Gigaml’s customer support product jumped from about 70% error in hard cases to roughly 5% error (around 85% accuracy) using o1 and rigorous evals, making AI agents credible for complex, non-rules-based support.
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Strong technical teams matter more, not less, in the o1 era.
While some fear AI will commoditize engineering, the panel believes the highest value will accrue to teams that can push models the final 10%—through clever prompts, evals, UI, integrations, and domain-specific reasoning.
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The biggest upside is in the physical world: atoms, not just bits.
Given o1’s strength in math and physics, the hosts see major new opportunities in mechanical, electrical, chemical, and bioengineering—areas like fusion, fluid mechanics, and advanced materials that can drive real-world abundance.
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We’re early in a rapidly compounding capability curve.
The current o1-preview is already transformative, with full o1, o2, and o3 expected soon, and Altman targeting up to four orders of magnitude more compute—implying today’s impressive abilities are the worst these systems will ever be.
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Notable Quotes
“It’s the worst that these models are ever going to be right now, right this moment.”
— Gary
“What was missing from its ability to actually do science and accelerate technological progress is it needs to be able to think through things.”
— Jared
“They went from 0% accuracy to 85% accuracy.”
— Diana (about Gigaml’s o1-powered customer support)
“All of the value is probably going to be captured by the strongest technical teams who can build on top of whatever the base level of tech is and get the final 10%.”
— Harj
“It can’t just be helping people click a little bit faster. It’s gotta be things that actually create real-world abundance for everyone.”
— Gary
Questions Answered in This Episode
How does o1’s chain-of-thought and reinforcement learning approach fundamentally differ from traditional next-token prediction models?
The hosts discuss Sam Altman’s recent AGI/ASI essay and argue that the next major AI breakthroughs will come from reasoning-focused models like OpenAI’s o1, not just larger model scaling. ...
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What kinds of engineering and scientific problems are likely to be unlocked first as o1 and its successors improve?
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How can startups practically build and maintain large, high-quality eval sets that become defensible moats?
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What governance or safety mechanisms are needed if AI systems start designing chips and physical systems beyond typical human oversight?
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In a world where base models handle more reasoning, where should human experts focus their effort to stay uniquely valuable?
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Transcript Preview
I remember about a year ago, one of these conversations around, "Are we going to have AGI? What would that look like?" One- one of the arguments for it was that, "Well, like, at some point the AI will get good enough to just, like, design chips better than, like, humans can and then it will just, like, eliminate one of its bottlenecks for, like, getting greater intelligence." And so it feels we're on the pathway to that in a way that we just weren't before.
The last episode, we were talking about, you know, what are you going to do with these two more orders of magnitude? Since then, Sam has, uh, told me that he actually wants to go to four orders of magnitude. It's the worst that these models are ever going to be right now, right this moment. Week to week, you know, there are things that you couldn't do maybe a month ago that you could do really, really well right now. So that sounds like a pretty crazy moment in history. (laughs) Welcome back to another episode of The Light Cone. I'm Gary. This is Jared, Harj, and Diana. And at Y Combinator, we've funded companies worth more than $600 billion, and we fund hundreds of companies every single year. So we're right there on the edge of seeing what is going to work, both in startups and in AI. Recently, Sam Altman wrote this pretty wild essay that predicted that AGI and ASI are coming within thousands of days. Seeing him on Monday, he actually directly estimated, you know, between 4 and 15 years. Have you guys read this essay yet and what do you think?
Yeah, I read it, and one- one of the interesting places where I think we have a unique perspective is that we were- we had a front row seat to the very beginnings of OpenAI 'cause OpenAI basically spun out of YC. And so what was cool to me reading this essay is that it's literally the same ideas that Sam was talking about in 2015 when he started OpenAI. Like, he's been talking about this, like, basically since I've known the guy. Um, and in 2015, when he said these things, he sounded kind of like a crazy person-
(laughs)
... and not that many people took him seriously. And now, 10 years later, it turns out he was right, and actually, we were much closer to AGI than anybody thought in 2015. And now it doesn't sound crazy at all, it sounds, like, totally plausible.
I mean, the essay itself is pretty much the most techno-optimist thing I've read in a really long time. Some of the things that he says are coming are pretty wild. Space colonies, fixing the climate problem, um, y- intelligence on tap, being able to solve abundant energy. Uh, yeah, I think he's basically ushering in this sort of Star Trek future on the back of literally human intelligence, being able to figure out all of physics.
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