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.
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— Jensen Huang (
@nvidia ), NVIDIA CEO, on Lex Fridman's (
@lexfridman ) podcast