We found Rev transcriptions to be very accurate when folks like Anthony Fauci and Governor Cuomo were talking during the covid times. Probably too accurate for the tastes of those two professional prevaricators.
From Rev, January
NVIDIA at CES 2026
NVIDIA founder and CEO Jensen Huang speaks at the Consumer Electronics Show in Las Vegas. Read the transcript here.
Speaker 1 (00:00):
Ready, go.
Speaker 2 (17:41):
Welcome to the stage, NVIDIA founder and CEO, Jensen Huang.
Jensen Huang (17:55):
Hello, Las Vegas. Happy New Year. Welcome to CES. Well, we have about 15 keynotes worth of material to pack in here. I'm so happy to see all of you. You got 3,000 people in this auditorium. There's 2,000 people in a courtyard watching us. There's another 1,000 people apparently in the fourth floor where they're supposed to be NVIDIA show floors, all watching this keynote. Of course, millions around the world are going to be watching this to kick off this new year.
(18:27)
Well, every 10 to 15 years, the computer industry resets. A new platform shift happens. From mainframe to PC, PC to internet, internet to cloud, cloud to mobile. Each time the world of applications target a new platform. That's why it's called a platform shift. You write new applications for a new computer. Except this time, there are two simultaneous platform shifts, in fact, happening at the same time.(19:03)
While we now move to AI, applications are now going to be built on top of AI. At first, people thought AIs are applications, and in fact, AIs are applications, but you're going to build applications on top of AIs. In addition to that, how you run the software, how you develop the software, fundamentally changed. The entire fibulary stack of the computer industry is being reinvented. You no longer program the software, you train the software. You don't run it on CPUs, you run it on GPUs.(19:43)
Whereas applications were prerecorded, pre-compiled and run on your device, now applications understand the context and generate every single pixel, every single token, completely from scratch, every single time. Computing has been fundamentally reshaped as a result of accelerated computing, as a result of artificial intelligence. Every single layer of that five layer cake is now being reinvented.(20:14)
Well, what that means is some $10 trillion or so of the last decade of computing is now being modernized to this new way of doing computing. What that means is hundreds of billions of dollars, a couple of hundred billion dollars in VC funding each year is going into modernize and inventing this new world. What it means is a hundred trillion dollars of industry, several percent of which is R&D budget is shifting over to artificial intelligence.(20:45)
People ask, "Where is the money coming from?" That's where the money's coming from. The modernization of AI to AI, the shifting of R&D budgets from classical methods to now artificial intelligence methods and enormous amounts of investments coming into this industry, which explains why we're so busy. This last year was no difference. This last year was incredible. This last year … There's a slide coming.(21:16)
This is what happens when you don't practice. It's the first keynote of the year. I hope it's your first keynote of the year, otherwise you have been pretty busy. This is our first keynote of the year. We're going to get the spiderwebs out. So 2025 was an incredible year. It seemed like everything was happening all at the same time, and in fact, it probably was. The first thing, of course, is scaling laws.(21:44)
In 2015, the first language model that I thought was really going to make a difference, made a huge difference. It was called BERT. 2017, transformers came. It wasn't until five years later, 2022, that ChatGPT moment happened and it awakened the world to the possibilities of artificial intelligence. Something very important happened a year after that. The first O1 model from ChatGPT, the first reasoning model, completely revolutionary, invented this idea called test time scaling, which is a very commonsensical thing.(22:24)
Not only did we pre-train a model to learn, we post-train it with reinforcement learning so that it could learn skills. Now we also have test time scaling, which is another way of saying thinking. You think in real time. Each one of these phases of artificial intelligence requires enormous amount of compute, and the computing law continues to scale. Large language models continue to get better. Meanwhile, another breakthrough happened, and this breakthrough happened in 2024.(22:55)
Agentic systems started to emerge. In 2025, it started to proliferate just about everywhere. Agentic models that have the ability to reason, look up information, do research, use tools, plan futures, simulate outcomes, all of a sudden started to solve very, very important problems. One of my favorite agentic models is called Cursor, which revolutionized the way we do software programming at NVIDIA. Agentic systems are going to really take off from here.(23:29)
Of course, there were other types of AI. We know that large language models isn't the only type of information. Wherever the universe has information, wherever the universe has structure, we could teach a large language model, a form of language model to go understand that information, to understand its representation and to turn that into an AI. One of the biggest, most important one is physical AI. AIs that understand the laws of nature.(23:58)
Then of course, physical AI is about AI's interacting with the world, but the world itself has information, encoded information, and that's called AI physics. In the case of physical AI, you have AI that interacts with the physical world, and you have AI physics, AI that understands the laws of physics....
....MUCH MORE
The timestamps are cued to the video of Mr. Huang's speech, also on the Rev page.
