GeniusThinking (@GeniusGTX)
@GeniusGTX
I write about the greatest minds in economics, psychology, and history. Follow @GeniusGTX to celebrate the human genius and understand how the world works.
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Jensen Huang reveals two CEOs told him NVIDIA could never cross $1B and $25B — it just hit $4 trillion.
"I still remember the first time we crossed a billion dollars," Huang says.
"I was reminded of a CEO who told me, 'Jensen, it's theoretically impossible for a fabless semiconductor company to exceed a billion dollars.'"
"And then somebody told me, 'You'll never be more than $25 billion because of some other company.'"
Both peers were wrong. The miss was generational:
- $1B ceiling, missed by 4,000x
- $25B ceiling, missed by 160x
- Combined error: trillions of dollars
Then Huang named what they got wrong:
"Those aren't first-principled thinking."
"The simple way to think about it is what is it that we make and how large is the opportunity that we can create?"
Huang asked a different question. Not what's the market today. But what could NVIDIA create that didn't exist yet?
That's the calculation that builds new markets instead of dividing up old ones.
The ceiling-setters were stuck inside the existing market.
Huang was busy building the next one.
A ceiling assumes the room. He was building the room.
P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers.
5,000+ downloads. 113 five-star reviews.
Grab a free copy here:
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
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Jensen Huang says we've achieved AGI when an AI can build a billion-dollar app then immediately die.
The AGI debate has one definition problem.
Jensen Huang gave Lex Fridman a one-word answer.
"I think it's now," Huang says.
"I think we've achieved AGI."
The reason is the small "forever" clause Lex didn't include.
"You said a billion, and you didn't say forever."
"It is not out of the question that a Claw was able to create a web service, some interesting little app that all of a sudden, a few billion people used for 50 cents, and then it went out of business again shortly after."
"We saw a whole bunch of those type of companies during the internet era, and most of those websites were not anything more sophisticated than what OpenClaw could generate today."
The bar isn't an AI that runs Apple.
The bar is an AI that builds a dotcom-era app that goes viral and dies.
"It's happening right now," Huang says.
"When you go to China you're gonna see a whole bunch of people teaching their Claws to go out and look for jobs and do work, make money."
Jensen Huang isn't asking when AGI arrives.
He's pointing at the AI agents already on the way to a job interview.
They're already filling out the application.
P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers.
5,000+ downloads. 113 five-star reviews.
Grab a free copy here:
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
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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
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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
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Elon Musk says one millionth of the Sun's energy is 100,000X Earth's economy.
Yes you read that right.
For a century, every advance had run on a tiny fraction of the Sun's output.
Oil.
Coal.
Solar.
Natural gas.
Combined, they powered everything every human had ever built. The total: about half a billionth of what hit the planet each day.
"Earth only receives about half a billionth of the Sun's energy."
Then Musk pulled up the Kardashev scale: "The Sun is essentially all the energy."
He named the framework: **the Kardashev scale**.
A Soviet astronomer's idea from 1964, repurposed for AI scaling.
Musk, who needed a terawatt a year, knew civilization ran on a rounding error.
A Kardashev Type I civilization harnesses all the energy that reaches its home planet, a Type II harnesses the full output of its star, and Earth in 2026 sat at neither.
Earth was nowhere close to Type I.
Not 1%.
Not 0.1%.
Not 0.001%.
"Let's say you wanted to harness a millionth of the sun's energy, which sounds pretty small."
"That would be about, call it roughly, 100,000x more electricity than we currently generate on Earth for all of civilization."
A million times Earth's economy.
From one millionth of the Sun.
Take a billionth and you still have a thousand-Earth economy.
After Musk did the math, modular nuclear reactors looked like rounding errors.
Musk, on what scale required:
"Obviously, the only way to scale is to go to space with solar."
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, @GeniusGTX is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content.
— Elon Musk ( @elonmusk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( @dwarkesh_sp ) podcast
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Elon Musk says he underweighted one trait in hiring and learned it the hard way.
For decades, talent acquisition built its scorecards on three pillars.
Skills. Experience. Cultural fit.
Resumes were ranked accordingly.
Then the bad hires happened anyway.
"Generally, I think it's a good idea to hire for talent and drive and trustworthiness."
Talent. Drive. Trustworthiness.
The first three felt obvious. The fourth had cost Musk careers.
Hires he'd defended. Hires he'd promoted. Hires he eventually fired.
Then Musk named the trait most rubrics skipped.
"And I think goodness of heart is important. I underweighted that at one point."
Musk named the trait: **goodness of heart**.
Polished. Predictable. Almost useless without it.
Musk, who had interviewed the first few thousand SpaceX hires himself, knew the longest training set.
A high-talent, high-drive, trustworthy employee with bad intent could ship more damage to a company over a quarter than a low-output engineer could in a decade, because the same competence that delivered the win also delivered the harm.
"Are they a good person? Trustworthy? Smart and talented and hard working?"
You can teach domain knowledge.
You can teach a process.
You cannot teach a person to be kind.
Or to mean well when nobody's watching.
After Musk made the correction, his hiring filters added a layer most rubrics never named.
Goodness of heart became a yes/no gate.
Musk, on the four traits that can't be unlearned:
"Those fundamental properties, you cannot change."
What's the trait you keep meeting in great hires that doesn't show up on any resume?
P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers.
5,000+ downloads. 113 five-star reviews.
Grab a free copy here:
— Elon Musk ( @elonmusk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( @dwarkesh_sp ) podcast
显示更多
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
Elon Musk says he underweighted one trait in hiring and learned it the hard way.
For decades, talent acquisition built its scorecards on three pillars.
Skills. Experience. Cultural fit.
Resumes were ranked accordingly.
Then the bad hires happened anyway.
"Generally, I think it's a good idea to hire for talent and drive and trustworthiness."
Talent. Drive. Trustworthiness.
The first three felt obvious. The fourth had cost Musk careers.
Hires he'd defended. Hires he'd promoted. Hires he eventually fired.
Then Musk named the trait most rubrics skipped.
"And I think goodness of heart is important. I underweighted that at one point."
Musk named the trait: **goodness of heart**.
Polished. Predictable. Almost useless without it.
Musk, who had interviewed the first few thousand SpaceX hires himself, knew the longest training set.
A high-talent, high-drive, trustworthy employee with bad intent could ship more damage to a company over a quarter than a low-output engineer could in a decade, because the same competence that delivered the win also delivered the harm.
"Are they a good person? Trustworthy? Smart and talented and hard working?"
You can teach domain knowledge.
You can teach a process.
You cannot teach a person to be kind.
Or to mean well when nobody's watching.
After Musk made the correction, his hiring filters added a layer most rubrics never named.
Goodness of heart became a yes/no gate.
Musk, on the four traits that can't be unlearned:
"Those fundamental properties, you cannot change."
What's the trait you keep meeting in great hires that doesn't show up on any resume?
P.S. I made a playbook breaking down 100+ most powerful decision making mental models used by history's greatest thinkers.
5,000+ downloads. 113 five-star reviews.
Grab a free copy here:
— Elon Musk ( @elonmusk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( @dwarkesh_sp ) podcast
显示更多
