NVIDIA’s Jensen Huang on Reasoning Models, Robotics, and Refuting the “AI Bubble” Narrative
episode
No Priors: Artificial Intelligence | Technology | Startups
1h 16m
2 speakers
8 chapters
transcribed 26 days ago
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What did Jensen Huang say about the biggest AI surprises of 2025?
Enson, thanks so much for joining us today. So great to have you guys. What an amazing year. What a year. Happy Hanukkah. Merry Christmas. Happy New Year coming up. Yep. Happy holidays. Mm-hmm.
So uh with everything that's happened in twenty twenty five, um, and you know, being in the middle of the vortex with it, what do you reflect on and say, like this surprised you most or this is the biggest change?
Let's see, there there's some things that didn't surprise me. Like for example, the scaling laws didn't surprise me because we already knew about that. The technology advancement didn't surprise me. I was pleased with the improvements of grounding. I was pleased with the improvements of reasoning. I was pleased with uh uh the connection of all of the models to to to search. I'm pleased that it that uh there are now routers that are in front of these models so that it could, depending on the confidence of the answers, go off and do necessary research and and just generally improve the quality and The accuracy of answers.
Mm.
I'm hugely proud of that. I think the whole industry addressed one of the biggest skeptical responses of AI, which is hallucination and um generating gibberish and all of that stuff. I I thought that this year, the whole industry, everything from every and every field from language to vision to robotics to self-driving cars, the application of reasoning. And the grounding of the of of of of the answers, um, big big leaps, would you guys say this year?
Yeah. I mean, things like open evidence too for medical information where doctors are not really using that as a trusted resource, like you uh Harvey for legal, you're you're really starting to see AI emerge as one of these things, it's become a trusted tool or counterparty for, you know, experts to actually be able to do what they do much better.
That's that's right. And so so in a lot of ways, I was expecting it, but I'm still pleased by it. I'm proud of it. I'm proud of all of the industry's work in this area. I'm really pleased and and uh uh and probably a little bit surprised. in fact, that token generation rate for inference, especially reasoning tokens, are growing so fast, several exponentials at the same time as it seems. And uh and I'm so pleased that that these tokens are now profitable. That people are generating. I heard somebody uh hurts hurt today that that open evidence, speaking of them, 90% gross margins. I mean, those are very profitable tokens. Yeah. And so they're obviously doing very profitable work, very valuable work. Cursor, their margins are great.
Uh Claude's margins are great. For the enterprise use of OpenAI, their margins are great. Um, so, anyways, it's really terrific to see that that um we're now generating. tokens that are sufficiently good, so good in value that that people are willing to pay good money for. And so I I think these are are really great rounding for the year. I mean some of the things that The narrative that that um Uh of course the conversation with China really, really, you know, occupied a lot of my my time this year, geopolitics, uh the importance of technology in each one of the countries. Uh I spent more time traveling around the world this year than just about any time in the history, all of my life combined. You know, my average elevation this year is probably about 17,000 feet, you know.
So so it's nice to be here on the ground with you guys. Um, and so so I think uh geopolitics, the importance of AI to all the nations, uh all worth talking about later. You know, of course, uh, I spent a lot of time on expert control and and making sure that our strategy is nuanced and uh really grounded and um uh promotes national security, but recognizing the importance of various uh various facets of national security. Um, a lot of conversations about On that. Um, you know, of course, of course, uh lots of conversation about jobs, the impact of AI, uh, energy, um, uh labor shortage. I mean, boy, we covered everything, did we not? Everything did we not?
Yeah. Everything was AI.
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Chapters
8 chapters
1
What did Jensen Huang say about the biggest AI surprises of 2025?
0:05–9:38
2
How does AI impact jobs, productivity, and the task‑vs‑purpose framework?
9:38–20:41
3
Why are robotics and AI essential for solving global labor shortages?
20:41–30:56
4
What is the “layer‑cake” model of AI technology and why does it matter?
30:56–39:46
5
How are open‑source AI and Chinese research influencing the U.S. AI ecosystem?
39:46–49:11
6
What are the cost trends for AI training, inference, and token generation?
49:11–58:40
7
What is Jensen Huang’s outlook for 2026 – US‑China relations, energy, and emerging “ChatGPT moments”?
58:40–1:09:14
8
Is there an AI bubble, and how should policymakers balance regulation with innovation?
1:09:14–1:16:18
Speakers
2 identifiedMore from No Priors: Artificial Intelligence | Technology | Startups
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