2026.06.02 16:03

Jensen Huang opens up about his plan to upload his consciousness to a humanoid robot in space within his lifetime. "It's a reasonable thing to expect the end of disease," Huang says. "It's a reasonable thing to expect that pollution will be drastically reduced." "It's a reasonable thing to expect that traveling at the speed of light is actually in our future." The third one has a catch. He doesn't mean speed of light for long distances. Even at light speed, the math kills interstellar travel. He means short ones, where the destination matters. Like Earth to a humanoid robot Huang plans to launch on a one-way trip. Then Huang explained the mechanism: "Very soon, I'm gonna put a humanoid on a spaceship — my humanoid — and we're gonna send it out as soon as possible." "It's gonna keep improving and enhancing along the flight." "All of my consciousness has already been uploaded to the internet." "Take all my inbox, take everything that I've done, everything I've said. It's been collected and becoming my AI." "When the time comes, we'll just send that at the speed of light, catch up with my robot." The humanoid is the carrier. The inbox is the passenger. Speed of light is the courier. And Huang says all of this is reasonable to expect. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow @GeniusGTX for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( @nvidia ), NVIDIA CEO, on Lex Fridman's ( @lexfridman ) podcast

显示更多

GeniusThinking@GeniusGTX

2026.05.25 12:40

NVIDIA CEO Jensen Huang says one scaling law multiplies AI faster than NVIDIA can hire engineers. Most people know three AI scaling laws. Pre-training. Post-training. Test-time. Each one multiplies intelligence by throwing more compute at a different stage. Jensen Huang says there's a fourth and it's the one that will dominate... Agentic scaling law. "During test time, that agentic system goes off and does research, bangs on databases, uses tools," Huang says. "And one of the most important things it does is spawn off a whole bunch of sub-agents." That's the multiplier. One AI worker can become a team. Then a department. Then a company. "It's so much easier to scale NVIDIA by hiring more employees than it is to scale myself," Huang says. Now imagine scaling without a payroll constraint. "The agentic scaling law — it's kind of like multiplying AI," Huang says. "We could spin off agents as fast as you want to spin off agents." Each agent spins off sub-agents. Each sub-agent spins off more. The compute requirement compounds inside a single query. And every agent generates new data, new experiences, new edge cases. "Wow, this is really good. We ought to memorize this," Huang says. "That data set comes back to pre-training." The four scaling laws don't compete. They feed each other. Agentic systems produce data, which feeds pre-training, which smartens the base model, which enables better agents, which produce more data. A flywheel that compounds forever. The companies pricing in three scaling laws are mispricing the fourth. The fourth eats the other three for lunch. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow @GeniusGTX for content on the greatest minds in economics, psychology, and history. — Jensen Huang ( @nvidia ), NVIDIA CEO, on Lex Fridman's ( @lexfridman ) podcast

显示更多