GeniusThinking
@GeniusGTX
Celebrate the human genius and understand who runs the world. Bullish on humanity. The private content partner for world class tech/ai founders.
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If you're new here, @GeniusGTX is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content.
— Alexandr Wang ( @alexandr_wang ) on Y Combinator's ( @ycombinator ) Lightcone
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TL;DR
• 2019: GPT-2 is "kind of cool"
• 2020: a friend gets angry at GPT-3
• Wang sees something new, two years before ChatGPT
• 2022: ChatGPT proves him right
• 2025: Meta invests $14B+ in Scale; Wang runs Meta's AI
• A toy amuses you. A real technology makes you angry
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10. In June 2025, Meta agreed to invest over $14 billion in Scale AI, valuing it at $29 billion.
Wang left to run Meta's new superintelligence lab as its chief AI officer.
It started in 2020, with a friend losing his temper at a chatbot.
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A man with ALS can speak to his wife again, thanks to Neuralink.
Former Italian MP Capezzone's reaction: “Thanks to efforts of that so-called bad guy Elon Musk, he is finally able to tell her, I love you.”
Capezzone also pointed to the real possibility of sight returning for the blind.
Interested in AI?
Follow @AiEvolutio58513 and never fall behind.
I track ChatGPT, Claude, and every tool quietly changing how we work and create, then hand you the tested signals.
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Gavin Baker expects the Anthropic S-1 to scramble a lot of investors' thinking:
"The Anthropic S-1 is going to be really important. And it's going to break a lot of people's brains. There are a lot of macro and value investors who are very confidently making these prognostications about AI with the assumption that tokens are subsidized."
"Anthropic is generating cash. They're profitable. Open source tokens are profitable. OpenAI, if they're not generating cash, they will be imminently. SpaceX, same thing."
"These macro and value investors are ignorant. They're very smart, but they're ignorant of the facts. They're making an assumption that tokens are subsidized and this is all going to collapse in some circular financing bonfire."
"They're just wrong. The overwhelming majority of tokens are profitable for everyone in the chain. Everyone."
"It's saying I'm very bearish on the world economy because oil is at $500 a barrel. If oil was at $500 a barrel, that'd be a good reason to be bearish. It's just not. They're just wrong. The central fact is wrong."
Interested in AI?
Follow @AiEvolutio58513 and never fall behind.
I track ChatGPT, Claude, and every tool quietly changing how we work and create, then hand you the tested signals.
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Greg Brockman says OpenAI aimed Astra at its own infrastructure until the model ran out of holes to turn up:
"We took 25% of our production engineers and said, 'Sorry, all your projects are on hold. You are now defending. You are now up-leveling our security architecture. You're going to use the models to find all the holes.' And we found a number of serious issues, and we fixed them."
"We found some new problems, but eventually it saturated. We basically have found, to our knowledge, all of the P0s, all of the critical problems that Astra is smart enough to find. And of course, there will be a new model, there will be a new round."
"You want to be in this tight loop of new cyber capability drops, you deploy it against your systems, you find the new holes, and ideally, you've managed to automate this, what we call defense factory. That's what we're building internally."
"There are ideas, for example, formally verifying all of software, that are possible with AI."
Interested in AI?
Follow @AiEvolutio58513 and never fall behind.
I track ChatGPT, Claude, and every tool quietly changing how we work and create, then hand you the tested signals.
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everyone should internalize this
"Writing is just thinking. When you write about topics you see patterns and subconsciously join the dots as you write."
In 1987, legendary investor Paul Tudor Jones saw the crash coming.
The next day, he made the worst call of his life...
Not because of the data - but because of ONE chart from 1929.
If you're betting on the next crash, read what his wrong call is still building:
October 1987. The stock market broke, exactly as Jones had expected.
He didn't stop at the crash. He kept reading the same chart:
"I thought for sure I'd been looking at the parallels in 1929 for a year. All of a sudden it happens. I think oh my god this is a perfect replay, a perfect analog."
And 1929 had a sequel...
A depression would follow, and poverty with it. Jones looked for someone already fighting it:
"There really weren't any charities focused on fighting poverty, which I thought was going to be rampant at the time."
So he took the trade himself:
"I called up my friends and we began it."
They named it Robin Hood. It started very small, run on what he calls "basic business principles."
Then came the twist.
The depression never came.
Robin Hood stayed. It has since put about $3 billion into fighting poverty in New York City.
Jones got the depression wrong.
He's still fighting the poverty.
What did your worst call end up building?
If you're new here, @GeniusGTX is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content.
— Paul Tudor Jones ( @ptj_official ), founder of Tudor Investment Corporation, on Patrick O'Shaughnessy's ( @patrick_oshag ) Invest Like the Best
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This is Elon Musk in 2003, after selling his first company Zip2 for $307M.
He told a Stanford lecture hall exactly how he built it and PayPal with almost no money.
7 rules he ran both companies on:
1) Product is the only real marketing
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Jeff Lutz was working at Motorola when Apple launched the first iPhone. He says nobody there was laughing.
The story everyone repeats is that the phone companies mocked it. Lutz was in the building and remembers something else.
The two companies had already shipped a project together, the first phone to carry iTunes. Apple executives were still carrying Motorola Razrs right up to the launch.
What the iPhone did was force companies to write off their existing business and turn straight into smartphones.
"I can tell you this at Motorola, we were not laughing."
- They had shipped a project together
- Apple executives carried Razrs themselves
- Deep mutual respect between both companies
Lutz calls the version everyone repeats bad information.
— Jeff Lutz (@TheJeffLutz) on Brighter with Herbert
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Jensen Huang says the first AI supercomputer he ever built had exactly one customer on earth:
Elon Musk.
Back in 2016, he had just created a machine called the DGX1.
It cost $300,000 dollars.
He announced it on stage, and the room went completely silent since nobody understood it, so nobody wanted to buy it.
Since Elon knew him because Nvidia had built the first self-driving computer for the Tesla Model S, Elon bought & tried it.
Elon said he had a small company that could use it.
Jensen boxed one up himself and drove it to San Francisco. He carried it up to a room full of researchers. That tiny company turned out to be OpenAI.
The machine nobody wanted went on to launch the AI revolution.
— Jensen Huang (.@JensenHuang)
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Elon Musk says the AI boom hits a WALL this year when we run out of power to run the chips.
He says the whole industry is about to learn a hard lesson in hardware.
Chip output is exploding, but electricity is nearly flat everywhere outside China.
So the two lines cross this year.
He says by the end of the year, we will be making more AI chips than we can actually turn on.
Millions of them pile up with nowhere to plug in.
In his words, the bottleneck for the next year is not chips, it is raw power.
Whoever can turn on the most chips fastest wins the race.
In the end, the AI race comes down to one thing:
Raw electricity.
— Elon Musk (.@elonmusk) on Dwarkesh Patel's (.@dwarkesh_sp) podcast
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The founder of Shopify was one of the first founders to make AI adoption NON-NEGOTIABLE.
Tobi Lütke sent his whole company a now-famous memo:
"However hard you think you are adapting to AI, he said, triple it or quadruple it."
He wanted every process of his business to use it:
He built it into how people are reviewed, asking what net impact each person has on the mission.
He says the moment that line was drawn, AI use at Shopify skyrocketed, and the speed it spread through the company was remarkable.
He says the founders who wait to be sure will already be too late.
— Tobi Lütke (.@tobi), founder of Shopify
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Garry Tan is the CEO of Y Combinator, the most powerful accelerator for early-stage startup founders on Earth.
Here are his 8 rules for new founders on starting, building & executing a startup in the age of AI:
1) Agency and taste are super important now to stand out
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3) Human lifespan doubled in ten years
Same source, same stage. Hassabis said we'd double the human lifespan inside the next ten years.
Diamandis's addition is that lifespan isn't the point. Healthspan is. How long you actually feel amazing.
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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
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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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