This week marked a historic moment on Wall Street as NVIDIA became the first company ever to reach a $4 trillion market capitalization. The chip giant has officially surpassed both Microsoft and Apple to become the world's most valuable company, with Apple now trailing by over 30% and Microsoft falling nearly 20% behind.

It's hard to believe that just two and a half years ago, NVIDIA was worth less than $400 billion. Back then, ChatGPT had just launched, and AI was still viewed as "that interesting thing OpenAI built." Most people still thought of NVIDIA as simply the company that made graphics cards for gamers.

The transformation has been nothing short of remarkable. Since late 2022, NVIDIA's stock has surged over 870%, with net income increasing 17-fold. The company's valuation journey tells the story: $1 trillion in May 2023, $2 trillion by February 2024, $3 trillion in June 2024, and now $4 trillion in July 2025. To put this in perspective. That’s like adding an entire trillion-dollar company—every single year—for four years straight.

What's particularly striking is that NVIDIA's revenue growth has actually outpaced its stock performance. While the stock has averaged 70% annual growth over the past 15 years, some quarters have seen sales jump over 100% year-over-year. This is startup-level growth for a $4 trillion company, with gross margins exceeding 70%.

CEO Jensen Huang's personal wealth has soared to over $145 billion, placing him ahead of Warren Buffett as the 7th richest person globally. At this trajectory, he could easily break into the top five.

The demand for NVIDIA's chips is being driven by four major customers who account for 40% of the company's revenue: Microsoft, Amazon, Google, and Meta. These tech giants are collectively spending $320 billion this year on AI infrastructure, with most of that money flowing directly to NVIDIA.

Oracle's Larry Ellison captured the sentiment perfectly after dining with Elon Musk and Jensen Huang, reportedly saying, "Please... take our money." This isn't hyperbole—companies are literally prepaying billions just to secure a place in NVIDIA's delivery queue.

While it makes sense for Microsoft and Amazon to be major GPU buyers given their cloud businesses, Meta's massive chip purchases are more intriguing. The company doesn't sell cloud services and hasn't dominated the AI chatbot space. Its LLaMA language model made little impact, and LLaMA 4, released in 2025, was widely considered a disappointment by industry insiders.

So why is Meta buying NVIDIA chips at such scale? The answer lies in Mark Zuckerberg's ambitious new goal: building Artificial Superintelligence (ASI).

In June, Meta announced a $14.3 billion investment to establish a dedicated superintelligence research lab. This isn't about achieving Artificial General Intelligence (AGI) or building better chatbots—Meta is aiming to create ASI, intelligence that surpasses human capabilities across all domains.

The Meta Superintelligence Lab (MSL) represents Zuckerberg's attempt to leapfrog the competition entirely. His public commitment is clear: "I will do everything I can to make Meta the leader in the new era of intelligence."

To achieve this goal, Zuckerberg understands that success depends on acquiring the best people in the field. Meta is now offering compensation packages worth $100 million to attract top AI researchers, particularly targeting talent from OpenAI. These packages typically combine cash with equity distributed over 4-5 years.

The strategy is working. Meta has successfully recruited key figures behind OpenAI's GPT-4 Mini, GPT-5's voice capabilities, and multilingual processing systems. Media outlets have dubbed this group the "AI Avengers."

The crown jewel of this recruitment effort is Alexandr Wang, a 27-year-old MIT dropout who founded Scale AI and became a billionaire in his early twenties. Meta recently acquired a 49% stake in Scale AI for $14.3 billion, reportedly to secure Wang's services as Chief Intelligence Officer. Silicon Valley insiders call him the "AI talent magnet," and Meta now controls that magnet.

Meta's aggressive recruitment extends beyond high-profile acquisitions. The company reportedly offered $200 million to a former Apple AI executive, prompting Apple to refuse to match the offer and lose the engineer. OpenAI's Sam Altman is publicly trying to maintain his team, stating that "none of our top executives have accepted these offers," but the pressure is intense across all levels of the organization.

The current AI hiring market represents the most competitive labor environment in decades. Engineering compensation packages now routinely reach $50-100 million, putting AI talent on par with professional athletes in terms of earning potential.

This exceeds even the early 2000s IT boom and the smartphone app gold rush of the 2010s. The key difference is that while demand is higher than ever, the pool of qualified AI talent is much smaller, creating an unprecedented supply-demand imbalance.

Silicon Valley has become home to the most competitive labor market in modern history, with companies scrambling to retain talent and engineers being recruited like NBA free agents. The AI elite are effectively writing their own tickets, choosing their destinations based on nine-figure compensation packages.

Companies controlling the fundamental infrastructure—chips like NVIDIA's and the labs building advanced AI systems—will define the next two decades of technological development.

Competition is no longer primarily about products or services. Success depends on recruiting the people capable of building the next breakthrough technologies.

The AI landscape is consolidating around a few major players—OpenAI, Meta, Anthropic, and Google. There's likely room for only 3-4 global winners, with everyone else relegated to purchasing their technology.

What we're witnessing extends far beyond NVIDIA's market performance or Meta's recruitment strategy. This represents the emergence of a new kind of arms race—one fought not with traditional weapons, but with GPUs and PhD-level talent.

In this competition, victory won't go to whoever spends the most money. It will go to whoever can attract and retain the people who know how to spend that money most effectively. The age of AI infrastructure has begun, and the companies that master this new paradigm will shape the future of technology and society.

At 24, Alexandr Wang became the youngest self-made billionaire in history. His startup journey has moved like a missile—fast, precise, and impossible to ignore. He grew up in a town built for nuclear secrets, dropped out of MIT at 19, and received a $4 million investment before he had even named his company. Five years later, his company was powering autonomous vehicles, tagging military drone footage, and training the most powerful AI systems on the planet.

In 2025, at just 28 years old, Wang walked into Meta to lead its most secretive and ambitious AI lab. This is the story of how he got there—and why his fingerprints are on nearly every major AI breakthrough today.

Born in Los Alamos, New Mexico—the very town where the atomic bomb was developed—Wang was raised in a home steeped in science. His parents were physicists at the Los Alamos National Lab. His neighbors were rocket scientists and plasma engineers.

By his teens, Wang was already a national math competitor and avid coder. At 17, he landed a coveted internship at Quora in Silicon Valley, where Facebook's former CTO gave him a piece of advice: "Four years of college is overrated. Two is underrated."

Inspired by DeepMind's AlphaGo victory over the world champion in 2016, Wang saw AI's trajectory accelerating. At 19, he left MIT and headed straight to Y Combinator with a notebook full of half-formed ideas.

There, he reconnected with fellow dropout Lucy Guo. They didn’t start with a product or a pitch deck. Just a realization: everyone was building AI models, but no one had the clean, labeled data those models needed. That was the bottleneck. That was the opportunity.

And so, Scale AI was born.

Scale AI combined smart software with a distributed workforce of human annotators. It quickly became indispensable for self-driving car companies like Waymo, Uber, and GM Cruise, and later expanded to government contracts, including the Pentagon's Project Maven.

By 2019, it was a unicorn. By 2021, Wang was a billionaire.

Wang’s work caught the attention of the U.S. government. Scale AI began supporting everything from military drone footage analysis to satellite imagery in warzones, and even built custom large language models for secure military networks.

Wang wasn’t just building a startup—he was shaping national security infrastructure.

When ChatGPT hit the public in late 2022, the generative AI gold rush began. And Scale AI? It was already there. It had been providing data to OpenAI, Meta, and nearly every foundation model developer.

By late 2024, the company had raised $1 billion from Amazon, NVIDIA, Meta, and others—solidifying itself as the AWS of AI training data.

In June 2025, Meta bought a 49% stake in Scale AI for $14.3 billion. But instead of walking away rich, Wang stepped in deeper—joining Meta as the head of its new superintelligence lab.

Now, he’s leading the next chapter of the AI revolution—from the inside.

Today, Scale AI's infrastructure underpins nearly every major AI deployment—from defense to finance, from chatbots to robots. Wang’s journey, from a kid in Los Alamos to a national AI architect, is more than just a startup story.

It’s the blueprint of a new era.

And he’s just getting started.