Nvidia’s Gamble of the Century: Jensen Huang’s “Nuclear Winter” Prophecy and the Birth of an AI Arms Empire

Friends, there are two kinds of great companies in this world. One is the kind that will dig for gold in a gold rush even if they break their heads; the other is the kind that stands alone at a desolate crossroads ten years before the gold rush begins, with shovels and water ready.

Today, when we talk about ChatGPT’s amazing conversational ability, Gemini’s profound understanding of the physical world, and Tesla FSD’s almost intuitive autonomous driving technology, we are actually talking about the name of the same company—Nvidia.

In December 2025, this company’s market value has broken through the sky, becoming the most important “Computing Engine” in human history. In Silicon Valley, on Wall Street, and even in the strategic seminars of every major power, Nvidia’s GPUs (Graphics Processing Units) are regarded as hard currency more precious than oil and gold. Countless tech giants line up just to grab a ticket to the future from Jensen Huang.

But thirty years ago, this was a small company that could go bankrupt at any time.

Please turn the clock of memory back to San Jose, California in 1993. It was an era ruled by the Wintel alliance of Intel and Microsoft. In a roadside diner called Denny’s, three unemployed young men squeezed next to a window full of bullet holes, drawing scribbled sketches on coffee-stained napkins.

Who could have imagined that these young men, regarded as “Toy Makers” by the mainstream chip giants at the time, would foresee today’s AI explosion twenty years ago?

What made that man, always wearing a black leather jacket, keenly smell the chill of “Computing Nuclear Winter” when everyone else was still cheering for Moore’s Law?

What made him dare to brave the punishment of a “50% Stock Plunge” from Wall Street and forcibly stuff an “AI Computing Core“—which no one used at the time—into every graphics card sold to gamers?

And what kind of loneliness and persistence made him stick to running wildly on that seemingly dead-end road like a paranoid for the next decade, facing Intel’s ridicule, AMD’s price war, and shareholders’ anger, until he waited for that thunderclap?

I am the Financial Veteran of [Sage Fellow Traveler].

Today, we will no longer just sigh at the madness of Nvidia’s stock price. We will use the “Evolutionary Force” and “Expansion Force” in the “Four Forces Model” to dismantle Jensen Huang’s gamble of the century spanning twenty years. This is a business epic about “Underlying Logic Reconstruction.”

In this video, you will get:

1. Understand the failure of “Moore’s Law” and the rise of “Accelerated Computing.” We will show you how Jensen Huang found that door to another dimension when general-purpose computing (CPU) hit the physical wall.
2. Reveal the story behind the birth of CUDA architecture. See how an entrepreneur built a software moat stronger than the Great Wall with the anti-business strategy of “Giving Away Computing Power for Free.”
3. We will explore Jensen Huang’s famous “30-Day Crisis” management philosophy. See how this extreme anxiety translates into the strongest survival instinct, keeping a trillion-dollar giant as agile as a startup.

Fasten your seatbelts, let’s travel back to the eve of that “Nuclear Winter” prophecy.

Part I: The Twilight of Moore’s Law and the Prophecy of “Nuclear Winter”

I. The Physical Wall of Computing: The Limit of CPU

(1) The Failure of Moore’s Law

Before entering the core of the story, we need to understand the background of that era.

In the mid-2000s, the entire computer industry actually fell into a deep panic that, although unknown to the public, sent chills down the spines of insiders.

The source of this panic was the golden rule that had ruled the semiconductor industry for decades—”Moore’s Law.” Gordon Moore predicted that the number of transistors on a chip would double every 18 months, performance would double, and costs would halve. For forty years, this law was as precise as a physics axiom. Intel relied on it to become the most profitable tech company on earth.

However, around 2005, engineers desperately discovered that this law hit a wall—the “Thermal Wall” of physics.

As transistors became smaller and denser, the heat emitted by the chip was approaching the core of a nuclear reactor. To prevent the chip from burning out, engineers could no longer continue to unlimitedly increase the CPU’s main frequency (clock speed).

This meant that the performance growth of single-core CPUs stagnated. In the “Four Forces Model,” this is a typical “Contraction Force” ceiling. Physical laws began to say “No” to humanity’s greedy demand for computing power.

At that time, the solution given by Intel and AMD was: Since a single core can’t run fast, I’ll put more cores, making “Multi-core CPUs.” Dual-core, quad-core, octa-core… But this was just a stopgap measure and did not solve the fundamental problem of “Stagnation in Computing Power Cost Reduction per Unit.”

Just as everyone was still patching up along the old map, Jensen Huang saw a more terrible future.

He put forward that famous prophecy at an internal meeting: “If we follow this path, computing costs will no longer fall, scientists will be unable to process increasingly massive data, and human technological progress will stagnate. This is the ‘Nuclear Winter’ of the computing field.“

(2) Ferrari vs. Bus

To make everyone understand this fear more intuitively, let’s use an analogy.

The CPU (Central Processing Unit) is like a Ferrari sports car. Its design intention is “Fast.” It possesses extremely complex control units that can handle extremely complex logical judgments—like running the Windows OS, opening a Word document, responding to your mouse click. It can only carry two people at a time, but it runs fast and reacts extremely sensitively.

However, with the explosion of the Internet, the amount of data generated by humans is no longer linear, but exponential. We have to process not just documents, but massive pixels (graphics rendering), massive particles (physics simulation), and massive parameters (neural networks).

Using a CPU to process this data is like using a Ferrari to transport passengers during the Spring Festival travel rush. Even if you build 10,000 Ferraris, you can’t finish transporting them, and the cost is high enough to bankrupt you.

Jensen Huang realized that the world no longer needs faster Ferraris, but a vehicle that can load thousands of people at a time, although the single-person speed is not that fast, but the throughput is huge—a Bus.

Or, a high-speed train loaded with thousands of seats.

II. Jensen Huang’s Bet: GPU is Not Just a Toy

At that time, Nvidia’s GPU (Graphics Processing Unit) was exactly such a “Bus.”

Its design intention was simple: to make game graphics like Quake and World of Warcraft smoother. Every pixel on the screen requires independent color calculation. There are millions of pixels on the screen, which requires millions of simple, repetitive, parallel calculations.

So, the GPU’s belly doesn’t have too many complex logic control units, but is stuffed with thousands of simple computing cores (ALUs). It is dumb, but it has strength in numbers.

When everyone thought GPUs could only be used for gaming and making toys, Jensen Huang’s “Unconventional” “Prism of Human Nature” began to refract a different light.

He is an engineer, but he is also a gambler with profound physical intuition. He made a judgment that seemed like a fantasy at the time: “The future of computing will essentially no longer be logic, but probability. The world is not serial; the world is parallel.” He decided to redefine the GPU from a mere “Graphics Processing Unit” to a “General-Purpose Parallel Computing Processor.” This meant he wanted the graphics card to do the CPU’s work. He wanted that bus transporting pixels to transport physics formulas, weather data, and seismic waves for oil exploration.

Friends, speaking of this, please put yourself in his shoes.

If you were the CEO of Intel or a Wall Street analyst back then, hearing a boss selling game graphics cards suddenly jump out and say: “I want to build rockets; I want to use graphics cards to solve the most complex scientific computing problems of mankind.” What would be your reaction?

You would most likely think he was crazy. Even Nvidia’s own employees thought the boss was crazy. The competition in the graphics card market was extremely fierce at the time; ATI (later AMD) was chasing closely behind, and the price war was bloody. Everyone felt our task was to make the game two frames faster and make the water reflection more realistic. Who would use a graphics card to solve math problems?

But this is exactly the difference between a top strategist and an ordinary businessman. Ordinary people see current demand (Gaming); strategists see future scarcity (Computing Power). What Jensen Huang saw was that ten years later, when data hit like a tsunami, humans would not have a handy weapon. And he wanted to forge this weapon himself.

III. The “Technology Mismatch” I Once Saw

Frankly speaking, reviewing Jensen Huang’s “Fantasy” back then reminds me of an extremely similar technical architecture dispute experienced by the banking industry in the early 2000s.

At that time, the bank’s core accounting system ran entirely on expensive Mainframes (like IBM hosts). Mainframes are the extreme of “CPU Thinking“; they are extremely stable and handle logic extremely powerfully, but the price is ridiculously high, and scalability is poor.

With the later rise of Internet finance, transaction volume began to explode. Someone (usually a young CTO) proposed: Can we use cheap PC server clusters (Distributed Architecture) to replace Mainframes?

At that time, the old experts in the bank firmly opposed it. They slapped the table and said: “That’s a toy! How can PC servers handle financial data? That’s unreliable! Who is responsible if something goes wrong?”

This is like people mocking using graphics cards for computing back then—you use a toy for playing games to do serious business?

But facts proved that with the emergence of “Double 11” (Singles’ Day Shopping Festival) with tens of thousands of concurrent transactions per second, a single mainframe could not withstand it even if it worked to death. Only cheap, parallel, infinitely stackable distributed architecture could digest that monstrous flood peak.

Jensen Huang’s situation back then was even harder than this. Because the bank’s distributed transformation was forced at least after the demand explosion (Double 11). While Jensen Huang decided to reconstruct the company’s genes for a non-existent market when the demand was not even a shadow.

IV. Expensive Admission Ticket

The idea was beautiful, but reality was skinny.

To make graphics cards capable of general-purpose computing, hardware alone was not enough. Because graphics cards originally only understood “Drawing” instructions. To make it solve math problems, a new set of software language understandable by human scientists was needed. This gave birth to that word later worth trillions—CUDA.

However, to promote CUDA, Jensen Huang had to solve the “Chicken and Egg” problem: If no one uses CUDA, there will be no software support; if there is no software support, no one will buy CUDA graphics cards.

To break this dead loop, Jensen Huang made an extremely “Anti-Business,” even “Betraying Shareholders” decision.

This decision almost bankrupted Nvidia and made Wall Street analysts hate him to the bone. In the next part, we will reveal this decade-long “Lonely Persistence.” See how Jensen Huang braved the infamy of “Bankrupting the Company” to pass the cost on to gamers to feed an invisible future.

This is a gamble about faith.

Part II: Evolutionary Force: The Birth of CUDA and the Decade-Long Night

I. The Gamble of 2006: Taxing the Future

(1) The “Forced Bundling” of Every Chip

Time came to 2006. This was the year Nvidia’s destiny turned, and also the year Jensen Huang showed near-crazy strategic will.

This year, Nvidia launched the epoch-making G80 architecture, which later became the famous GeForce 8800 graphics card. But the core secret of this card was not how strong its gaming performance was, but that it carried a brand new thing—CUDA.

Simply put, CUDA is a key, a key that allows programmers to bypass graphics instructions and directly call the massive computing power of the GPU.

To promote this key, Jensen Huang made a decision that business school professors at the time would definitely consider “Suicidal“: He ordered that from now on, every GPU produced by Nvidia, whether a high-end card sold to Hollywood for special effects for thousands of dollars, or an entry-level card sold to college students for gaming in dorms for a few hundred dollars, must integrate CUDA computing cores.

What does this mean?

This means the number of transistors on the chip skyrocketed. In an era where silicon wafers were expensive, this directly led to larger chip area, lower yield rate, soaring power consumption and heat, and most importantly—Significantly Increased Cost.

For 99% of users at the time—gamers—this was simply unreasonable. They paid a high price for a graphics card, and as a result, 30% or more of the circuit area (CUDA cores) was specifically used for scientific computing. For gaming, this part of the circuit was completely idle and wasted.

It’s like you go to buy a family car, and the car factory boss insists on stuffing a Boeing 747 jet engine into it, telling you “This represents the future,” and then making you pay 30% more for it, and the car consumes more fuel.

Gamers were angry; they mocked Nvidia’s cards as “Nuclear Bombs” (describing high heat). Competitor ATI (later AMD) took the opportunity to launch graphics cards with simpler architecture, cheaper prices, and specifically optimized for games, rubbing Nvidia into the ground.

Those years were Nvidia’s darkest days. The stock price once fell by 70%. Wall Street analysts roared at Jensen Huang on conference calls: “No one needs to solve math problems with a graphics card! You should focus on gaming! You should cut that damn CUDA!“

(2) The Price of Silence and Persistence

Facing overwhelming skepticism, Jensen Huang did not defend himself, nor did he retreat.

He withstood the huge pressure from the board of directors and endured the pain of the stock price halving. He said at an internal meeting: “We are not taxing gamers; we are investing in the future. If I don’t take this loss-making step first in this chicken-and-egg cycle, that future of democratized computing power will never come.“

This is an extremely lonely persistence. In the “Four Forces Model,” this represents a tragic game between strong “Evolutionary Force” and realistic “Contraction Force.” Jensen Huang used every penny earned from the gaming business to subsidize the CUDA ecosystem that was not yet visible. He was like a person building a highway in the desert; no matter how well the road was built, if no cars ran on it, it was a dead end leading to bankruptcy.

II. Strategic Layout: Free Seeds of Computing Power

(1) If You Can’t Sell It, Give It Away

Since commercial companies wouldn’t pay, and Wall Street didn’t understand, Jensen Huang decided to turn his gaze to those who needed computing power most but lacked money most—Scientists.

He started running around universities and laboratories all over the world. At Tokyo Institute of Technology in Japan, at ETH Zurich in Switzerland, at Stanford in the US. As long as you were doing scientific research and wanted to use CUDA, Nvidia would give graphics cards for free, and even send engineers to your door to teach you how to write code hand in hand.

This was an extremely clever move of “Equilibrium Force.” Jensen Huang bypassed the utilitarian commercial market and sowed the seeds in the pure academic circle.

(2) The Prairie Fire Underground

Gradually, things changed.

Although in the mainstream market, Nvidia was still fighting a price war with AMD. But in the underground scientific research circle, an undercurrent began to surge.

Physicists found that running molecular dynamics simulations with CUDA was dozens of times faster than CPU; oil companies found that processing geological exploration data with CUDA took only days instead of months; Wall Street quants found that calculating option pricing models with CUDA could capture arbitrage opportunities faster than competitors.

Although these demands seemed niche at the time and could not support a trillion-dollar market value. But they were like sparks, starting to burn out another universe parallel to the CPU-ruled iron curtain.

Jensen Huang held the GTC (GPU Technology Conference) every year. In the early years, there were often only a few scientists in plaid shirts sitting sparsely in the audience, looking sympathetically at this man in a leather jacket giving a passionate speech on stage.

But he still spoke every year, released new CUDA versions every year, and told everyone every year: “Accelerated Computing is coming.“

He was waiting. Waiting for a Tipping Point. Waiting for a moment when this highway paved for ten years would instantly be bustling with traffic.

Part III: Expansion Force: From AlexNet to AI Arms Dealer

I. The Singularity of 2012: AlexNet

(1) When Deep Learning Met GPU

This tipping point finally arrived in 2012.

In this story, two key figures need to be remembered. One is Geoffrey Hinton, known as the “Godfather of AI,” and the other is his student Alex Krizhevsky. At that time, the mainstream of AI was “Logicism,” while the “Deep Learning” (Neural Networks) insisted on by Hinton was regarded as heresy by the academic circle because of the huge calculation volume and poor effect, and was even ridiculed as “Pseudoscience.”

In 2012, the ImageNet image recognition competition was about to be held. Alex wanted to try using deep neural networks to compete. But he faced a huge problem: This type of algorithm involved too much matrix calculation volume; running it on the top CPU at the time might take months to train one round.

At the end of his rope, he remembered the CUDA function “Given for Free” in the game graphics card.

So, he went and bought graphics cards. Not enterprise-level high-end cards, just two ordinary Nvidia GTX 580 game cards that could be bought on Amazon.

These two graphics cards thoroughly changed the course of carbon-based civilization.

Using the parallel computing capability of GPU, they completed training in just one week. In the competition, the model named AlexNet won the championship with a crushing advantage, reducing the error rate of image recognition from 26% directly to 15%.

(2) Trisolarans Discovered Earth

This moment was like Ye Wenjie pressing that red transmission button in The Three-Body Problem.

AI scientists all over the world seemed to suddenly wake up overnight: It turns out that the “Philosopher’s Stone” we have been searching for decades has always been hidden in that graphics card for playing games! It turns out that Deep Learning is not pseudoscience; it just lacked a good engine!

The formula of “Deep Learning + GPU” instantly ignited the entire tech world.

It was at the very moment of hearing this signal that Jensen Huang showed an amazing, even “Gambler-like” business sense. He didn’t hesitate at all. When no one else reacted, he immediately turned the ship around and bet the entire company’s life and fortune on AI.

Four years later in 2016, this gamble welcomed its “Normandy Landing.”

We must remember that famous photo: Billionaire Jensen Huang personally turned into a “Deliveryman” and pushed the world’s first supercomputer specifically developed for AI, DGX-1, to OpenAI’s office. On that heavy machine, he solemnly signed a line: “To Elon and the OpenAI Team! For the future of computing and humanity!“

Elon Musk at that time looked at this machine, excited like a child getting a new toy. This was not just the delivery of a machine, but the most symbolic “Torch Relay” in the history of Silicon Valley. It was this machine that later trained the early GPT models. The door to the AI era was completely pushed open at this moment.

II. The Flywheel of Monopoly: The Hegemony of Software-Hardware Integration

(1) Why Irreplaceable?

When the AI tide truly hit, Intel, AMD, and Google all panicked and started building their own AI chips. But they desperately found that they were facing not an Nvidia, but an insurmountable high wall.

This wall is not hardware, but Software.

Because in the past ten years, through “Giving Away Computing Power for Free” and sticking to CUDA, Jensen Huang had already corralled millions of scientists and developers worldwide into the CUDA ecosystem.

All AI code is written in CUDA; all mainstream frameworks are optimized based on CUDA at the bottom layer.

This is like everyone in the world is used to speaking English, and all libraries store English books. Suddenly you jump out and say, I invented a new language (chip) more concise than English, but you need to translate all books again. Who would pay attention to you?

This is the return of “Long-Termism.” CUDA, which Jensen Huang forcibly promoted amidst curses back then, has now become a moat stronger than nuclear weapons.

(2) The Successor of Moore’s Law

Nvidia, having obtained the admission ticket, started running at a terrifying speed.

Jensen Huang proposed “Huang’s Law“: GPU AI inference performance will more than double every year. This far exceeds Moore’s Law of doubling every 18 months.

From Pascal architecture to Volta, from Ampere to Hopper, and then to the latest Blackwell. Nvidia is no longer a graphics card company; it has become a “Data Center Company.” It sells not chips, but entire “Computing Power Factories.”

III. Veteran’s Story: The “Shovel Seller” I Once Saw

Frankly speaking, watching Nvidia’s dominance today reminds me of a scene I saw in the financial circle when the mobile internet exploded.

At that time, countless startups making Apps emerged like carp crossing the river. Group buying, ride-hailing, food delivery… they died batch after batch. Even the final winners experienced a near-death cash-burning war.

But who earned the most steadily? Those selling servers, building base stations, infrastructure manufacturers like Huawei and Cisco.

In the financial circle, this is called “Certainty Premium.” In a gold rush, digging for gold is a probability game; you might dig up gold or rocks. But selling shovels, selling jeans, selling water is a certainty business. As long as someone wants to dig for gold, he has to buy your shovel.

Jensen Huang is the one who made the shovel to the extreme. He not only sells shovels but even wrote the Mining Guide (Software Library). You can only understand this guide using his shovel.

This is not selling hardware; this is clearly collecting future “Tolls.”

IV. The Run Without a Finish Line

Standing at the peak of a trillion-dollar market value, logically Jensen Huang should be able to breathe a sigh of relief.

But if you have seen his recent interviews, you will find that he is still as anxious as when he drew sketches in Denny’s thirty years ago, still wearing that leather jacket that seems never to be changed, and still shouting “We are on the verge of bankruptcy” every day.

What is he worried about? Is he worried about Google TPU catching up? Or worried about the ultimate physical limit of Moore’s Law?

Or, is this “Blade Warrior in Leather Jacket” laying out the next battlefield grander than AI? When silicon-based computing power can finally simulate carbon-based life, will Nvidia’s next stop be cracking God’s password?

In the next part, we will delve into Jensen Huang’s inner world, exploring the “Prism of Human Nature” of this “Paranoid” and his ultimate vision for future “Digital Biology” and “Metaverse.”

Part IV: The 30-Day Crisis and the Philosophy of Accelerated Computing

I. Prism of Human Nature: The Eternal “30 Days from Bankruptcy”

(1) The Paranoid’s Way of Survival

Friends, in this world, usually only two types of companies live long: one is the monopolist, sleeping on the moat; the other is the paranoid, running on fear.

Although Nvidia has formed a de facto monopoly on AI chips, Jensen Huang is a thorough paranoid.

Even though Nvidia’s market value has surpassed Apple and Microsoft, and he has become a strong contender for the world’s richest man, you still see that 60-year-old man, energetic as a young lad, running and shouting on stage at every annual launch event in a black leather jacket.

He has a catchphrase on his lips, not “Changing the World,” but: “Remember, we are only 30 days away from going out of business.“

This is not Versailles-style modesty; this is a survival instinct carved into the bones.

If you understand Jensen Huang’s childhood, you will understand. As a Chinese immigrant sent to a boarding school in Kentucky, USA by his parents at the age of 9, he grew up in an environment full of bullying and uncertainty. He needed to survive under the fists of punks and learn to observe guests’ faces when waiting tables at Denny’s.

This early experience endowed him with extreme sensitivity and insecurity.

Inside Nvidia, there is no strict hierarchy. Jensen Huang has no independent office; he wanders around the campus and finds any table to work. He will email the most junior intern directly to ask about a technical detail.

He fears Big Company Disease, fears layers of information filtering. He wants to ensure he always hears the sound of gunfire. Because he knows well that today, with Moore’s Law failing, the speed of technology iteration is exponential. Just dozing off for a month might lead to being thoroughly overturned by a new technological route (like photonic computing, quantum computing).

(2) Intellectual Honesty: The Courage to Admit Mistakes

This sense of crisis also derived another extremely charming trait of Jensen Huang—”Intellectual Honesty.”

In this tech circle full of PPT car-making and pie-in-the-sky promises, admitting “I was wrong” is an extremely expensive thing. But Jensen Huang dares.

In the early years, Nvidia almost went bankrupt due to a wrong route for Sega’s console chip. Jensen Huang did not hide it but confessed directly to the Sega CEO: “I was wrong, but I need your money to save my life.” His honesty moved the other party and exchanged for life-saving funds.

Later, when the mobile internet exploded, Nvidia also tried to seize the mobile phone market with Tegra chips. But when he found he couldn’t beat Qualcomm and MediaTek, Jensen Huang did not hold on for face like some giants but decisively cut losses and admitted failure: “This is not our battlefield.“

Immediately, he withdrew all resources from mobile phones and bet everything on the then-barren fields of autonomous driving and AI.

It was this extreme honesty about mistakes that allowed him to correct course quickly and avoid running to death on the wrong path.

II. The Ultimate Handshake Between Carbon-Based and Silicon-Based

(1) The Engine of Entropy Reduction

Putting business aside, if we stand at the height of civilization, what exactly is Nvidia doing?

Physics tells us that the essence of the universe is Entropy Increase, moving from order to disorder, from life to death. And Computing, essentially, is a process of fighting against entropy increase. By consuming energy, we turn chaotic data into ordered information.

What Nvidia provides is no longer simple game graphics cards, but humanity’s strongest weapon to fight against entropy increase and understand the universe.

From simulating trends of global warming to simulating plasma turbulence in nuclear fusion reactors, to simulating the binding process of drug molecules with billions of atoms. CUDA is not just accelerating games; it is accelerating humanity’s understanding of the physical world.

(2) The Dawn of Digital Biology

Jensen Huang has recently been talking about a new vision—Digital Biology.

He believes that life is essentially a kind of code. DNA is the program written by God. If we have enough computing power, we can simulate the process of life in a computer.

Imagine, in the future, developing new drugs will no longer require trial and error on mice and volunteers, but direct simulation on a digital twin human body in Nvidia’s Metaverse platform. This will reduce the cost of new drug R&D by ten thousand times, allowing terminal illnesses like cancer and Alzheimer’s to be thoroughly conquered.

This is the ultimate direction of Nvidia’s “Evolutionary Force“—using silicon-based computing power to repair carbon-based life.

When that day comes, we might find that Jensen Huang’s gamble on graphics cards back then meant far more than money; it was a ticket to immortality won by humanity for itself.

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Conclusion: Running at the Speed of Light

Friends, reviewing Nvidia’s thirty-year history, this is a magnificent expedition from a “Mocked Toy Merchant” to a “Civilization Ferryman.”

It tells us that great strategies often look like a stupid mistake at the start.

Without the sketch by those three young men by the bullet-hole window, without the lonely persistence facing Wall Street’s insults for ten years, without the crazy gambling nature of smashing every penny of profit into the CUDA black hole, there would be no trillion-dollar glory in this AI era today.

As the saying goes: “To test jade, one must burn it for three full days; to distinguish timber, one must wait for seven years.” [1]

Jensen Huang is like the person sharpening a sword in the long night. The world laughed at him for being foolish and silly, until the sword qi came out, chilling nineteen continents with its light. [2]

I am the Financial Veteran of [Sage Fellow Traveler].

In this era where AI computing power determines national destiny and personal fate, we may not become Jensen Huang, but we can learn his “Foresight” and “Persistence.”

When you identify a correct path, even if the whole world says it’s wrong, even if you have to walk alone in the dark for ten years, you must grit your teeth and go on. Because the road to the stars and the sea is never on the crowded plain, but on that dangerous peak no one dares to walk.

If you also want to see the future direction clearly in the noise, please follow me, like, and share this video. Let us find our own anchor in the wave of accelerated computing together.

Core Content Summary
This episode “Nvidia’s Gamble of the Century” aims to deconstruct how Jensen Huang led Nvidia from a marginal game graphics card company to evolve into the “King of Arms” in the AI era. The core logical chain is as follows:

1. Foresight (Nuclear Winter Prophecy): As early as 2006, Jensen Huang foresaw the physical limit of Moore’s Law (CPU single-core performance), i.e., “Computing Nuclear Winter,” thus betting that GPU parallel computing (Bus Mode) was the only way out for the future.
2. Gamble (CUDA Strategy): To build a software ecosystem, Jensen Huang forcibly implanted CUDA cores in all game graphics cards, leading to soaring costs and plummeting stock prices. This was “Taxing the Future,” using gamers’ money to feed the dream of scientific computing.
3. Persistence (Free Sparks): In the ten years when the commercial market didn’t buy it, by giving away computing power to scientists for free, he sowed the sparks of deep learning in the academic circle.
4. Explosion (Singularity Moment): The birth of AlexNet in 2012 verified the power of “GPU + Deep Learning,” and Nvidia instantly turned from a toy merchant into an “Infrastructure Monopolist” of the AI era.
5. Philosophy (30-Day Crisis): Jensen Huang’s paranoia of “Only 30 days from going out of business” and “Intellectual Honesty” culture are the soul of Nvidia crossing cycles and fighting big company disease.

[Translation Notes for Cultural References]

[1] “Shi yu yao shao san ri man…” (试玉要烧三日满…): From a poem by Bai Juyi. Translated as “To test jade…” Meaning true value takes time and trials to reveal.
[2] “Jian qi yi chu, guang han shi jiu zhou” (剑气一出，光寒十九洲): “Sword qi came out, chilling nineteen continents…” A very poetic and powerful imagery describing the moment of revealing true power/success after long obscurity.