NVIDIA’s Jensen Huang on Reasoning Models, Robotics, and Refuting the “AI Bubble” Narrative

**Sarah Guo** (0:05)
Jensen, thanks so much for joining us today.

**Jensen Huang** (0:07)
So great to have you guys. What an amazing year.

**Sarah Guo** (0:10)
What a year.

**Jensen Huang** (0:11)
Happy Hanukkah, Merry Christmas. Happy New Year coming up. Yep, happy holidays.

**Elad Gil** (0:16)
So with everything that's happened in 2025, and being in the middle of the vortex with it, what do you reflect on and say, this surprised you most, or this is the biggest change?

**Jensen Huang** (0:27)
Let's see, there's some things that didn't surprise me. 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 the connection of all of the models to search. I'm pleased that 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 just generally improve the quality and the accuracy of the answers. I'm hugely proud of that. I think the whole industry addressed one of the biggest skeptical responses of AI, which is hallucination and generating gibberish and all of that stuff. I thought that this year, the whole industry, everything from every field, from language to vision to robotics to self-driving cars, the application of reasoning and the grounding of the answers.
Big leaps, would you guys say, this year?

**Sarah Guo** (1:39)
And things like open evidence, too, for medical information, where doctors are not really using that as a trusted resource. Like Harvey, for legal, you're really starting to see AI emerge as one of these things that's become a trusted tool or counter party for experts to actually be able to do what they do much better.

**Jensen Huang** (1:54)
That's right. And 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 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 I'm so pleased that these tokens are now profitable, that people are generating, I heard somebody, I heard today that OpenEvidence, speaking of them, 90% gross margins. I mean, those are very profitable tokens. And so they're obviously doing very profitable or very valuable work. Cursor, their margins are great. Cloud's margins are great. For the enterprise use of OpenAI, their margins are great. So anyways, it's really terrific to see that we're now generating tokens that are sufficiently good, so good in value, that people are willing to pay good money for. And so I think these are really great grounding for the year. I mean, some of the things that, the narrative that, of course, the conversation with China really occupied a lot of my time this year.
Geopolitics, the importance of technology in each one of the countries. I spent more time traveling around the world this year than just by any time they hit. All of my life combined, my average elevation this year is probably about 17,000 feet. So it's nice to be here on the ground with you guys. And so I think geopolitics, the importance of AI to all the nations, all worth talking about later. You know, of course, I spent a lot of time on expert control and making sure that our strategy is nuanced and really grounded and promotes national security. But recognizing the importance of various facets of national security, a lot of conversations around that. You know, of course, lots of conversation about jobs, the impact of AI, energy, labor shortage. I mean, boy, we covered everything, did we not?

**Elad Gil** (4:05)
Everything was AI.

**Jensen Huang** (4:06)
Everything was AI. Yeah, it was incredible.

**Sarah Guo** (4:09)
Yeah, AI was definitely the center of the storm for like every one of those themes. Maybe when we can start with actually these jobs, because there are jobs in employment, because when I look at the traditional AI community, even before things were scaling and even before AI was really working, there was a strong sort of doomsday component in the people working on AI oddly enough. The people who are most trying to push the field forward were often the people who are most pessimistic, which is very odd. Why would you do both at once? I feel like that narrative is taken over some subset of media or some set of other things, despite all the things that we think are very positive about what AI has done. That's going to help with health care, with education, with productivity, with all these other areas.