How Nvidia Built a $5 Trillion AI Empire: The Complete Story of GPU Dominance

The Story: From Video Games to AI

On April 5, 1993, three engineers — Jensen Huang (formerly of AMD and LSI Logic), Chris Malachowsky (formerly of Sun Microsystems), and Curtis Priem (formerly of IBM and Sun) — agreed to found a company at a booth in Denny's in San Jose, California. With $40,000 in the bank, Nvidia was born.

The company initially bet on graphics processing (GPU) for video games. Huang later explained: "Video games were simultaneously one of the most computationally challenging problems and would have incredibly high sales volume. Those two conditions don't happen very often."

The road wasn't easy. In 1996, Huang laid off more than half the workforce — cutting from 100 to 40 employees — to focus on the RIVA 128. When it launched in August 1997, the company had only one month's payroll left. This experience birthed the unofficial motto: “Our company is thirty days from going out of business.”

CUDA: Nvidia's Real Competitive Moat

If there's one thing that explains why Nvidia dominates AI, it's CUDA. In the early 2000s, the company invested over $1 billion in a software platform that allowed developers to use GPUs for general-purpose computing — not just graphics.

This far-sighted investment proved transformative. In 2009 came what's called the “Big Bang” of deep learning: Google Brain researchers, led by Andrew Ng, discovered that Nvidia's GPUs could accelerate neural networks by 100x. Suddenly, the GPU was no longer just for gaming — it was the engine of artificial intelligence.

Today, CUDA is the de facto standard in AI development. Libraries like TensorFlow, PyTorch, JAX — essentially everything used for AI model training — are optimized for Nvidia GPUs via CUDA. This creates massive ecosystem lock-in: even if someone builds a faster chip, migrating millions of developers is nearly impossible.

The Chips Powering AI: From A100 to Blackwell

A100 (Ampere, 2020)

The A100 GPU, announced in May 2020, was the first GPU specifically designed for AI data centers. Built on the Ampere architecture, it dominated every model training — from GPT-3 to DALL-E. It was the GPU that ignited the AI gold rush.

H100 (Hopper, 2023)

The H100, based on the Hopper architecture (named after mathematician Grace Hopper), was a quantum leap. Priced at $25,000-$40,000 each, demand was so intense that even tech moguls were “begging” Huang. Oracle's Larry Ellison revealed that during a dinner at Nobu in Palo Alto, he and Elon Musk were “begging” for H100s: “We were eating sushi and begging.” GPUs were transported to data centers by armored car.

H200 & Blackwell B200 (2024)

The H200 added HBM3e memory for faster inference processing. In March 2024, Huang unveiled the Blackwell architecture (named after mathematician David Blackwell), featuring B200 GPUs. In November 2024, Morgan Stanley reported that “the entire 2025 Blackwell production was already sold out.”

Vera Rubin (2026)

At CES 2026, Huang revealed the new Vera Rubin platform, signaling the next generation of AI hardware. Alongside it came the open-source Alpamayo model for autonomous driving, and the Nemotron 3 model suite (Nano, Super, Ultra) with MoE architecture up to 500 billion parameters.

The Financial Explosion: Staggering Numbers

Nvidia's recent financials don't look like a tech company's — they look like science fiction:

Revenue growth was vertical: from $26.9B (FY2023) to $60.9B (FY2024) to $130.5B (FY2025) — nearly a fivefold increase in two years. Net income skyrocketed to $72.9B, with profit margins that every CEO on the planet envied.

Market capitalization set record after record: $1T (May 2023), $2T (March 2024 — in just 180 days, while Apple and Microsoft needed 500+), $3.3T (June 2024 — world's most valuable company), $4T (July 2025 — first company in history), $5T (October 2025). Nvidia was worth more than every publicly traded company in the United Kingdom combined.

The DeepSeek “Seismic Shock”

In January 2025, a Chinese startup called DeepSeek managed to shake Wall Street to its foundations. DeepSeek developed an advanced AI model at significantly lower cost and computing power — challenging the whole logic that “you need Nvidia GPUs for everything.”

The result? Nvidia lost nearly $600 billion in market cap in a single day — the largest one-day value loss for any company in U.S. history. The DeepSeek app even surpassed ChatGPT on the App Store as the #1 free app.

However, the DeepSeek “threat” proved short-lived. Huang argued that investors “got it wrong” in their reaction, while the need for more AI compute — even more efficient compute — ultimately means more GPUs, not fewer. The stock fully recovered within weeks.

Geopolitical Chess: Nvidia vs China

The U.S.-China confrontation over AI chips is one of the most critical geopolitical issues of our era, and Nvidia sits at the center:

• October 2022: The U.S. imposes an embargo on advanced chip exports to China. The H100 is placed on the control list.
• November 2022: Nvidia builds a special A800 GPU that meets the restrictions.
• 2023: Development of the H20 — a chip specifically for China, with 96GB HBM3 memory but drastically reduced compute power (296 vs 1,979 TFLOPs on H100).
• August 2025: The Chinese government orders domestic companies not to purchase even the H20. Nvidia halts production of 300,000 units it had ordered.
• September 2025: Huang says he's “disappointed” after the RTX Pro 6000D ban.
• January 2026: The U.S. approves export of H200 chips to China under specific conditions.

Nvidia is now required to pay 15% of its China chip sale revenues to the U.S. government. Meanwhile, it's developing the new B30A chip (Blackwell architecture) as the H20's successor for the Chinese market.

Acquisitions & Investments: Expanding in Every Direction

Nvidia isn't growing only organically. In recent years, a series of strategic moves have strengthened its ecosystem:

• Mellanox ($6.9B, 2019): Networking hardware for data centers — critical for GPU interconnect.
• Groq ($20B, Dec 2025): Nvidia's largest acquisition, inference technology. Drew criticism for potentially avoiding regulatory scrutiny.
• SchedMD (Dec 2025): Behind the open-source workload manager Slurm, essential in the HPC ecosystem.
• CentML ($400M+, Jul 2025): Canadian AI optimization startup.
• AI21 Labs (~$2-3B, in negotiation): Israeli LLM company, “acquihire” of 200 specialists.
• $5B investment in Intel (Sep 2025): ~4% stake, bolstering Intel foundry.
• $100B deal with OpenAI (Sep 2025): MoU for AI data centers — though by Jan 2026 it was “on ice.”

Competition: Who Can Challenge Nvidia?

92% dominance doesn't mean zero competition. Several players are trying to break the monopoly:

AMD (MI300X)

AMD, under Lisa Su, is the closest competitor with its MI300X GPUs. While technically competitive, the problem remains: AMD lacks an ecosystem like CUDA, and its market share stays in single digits.

Google TPU

Google develops its own Tensor Processing Units (TPU) internally. TPU v5 is reliable but primarily used within Google Cloud — it's not sold on the open market.

Amazon Trainium & Microsoft Maia

Major cloud providers are developing custom chips: Amazon's Trainium, Microsoft's Maia 100. The goal is reducing dependence on Nvidia, but performance and maturity still lag behind.

Huawei Ascend

China is investing through Huawei in Ascend AI chips as a national alternative. While hundreds of thousands of units are being produced, their performance significantly trails Nvidia's H100/Blackwell, especially for training large models.

Jensen Huang, addressing competition at GTC 2025, stated that AI data center revenue will reach $1 trillion by 2028 — a market so large it fits many players, but Nvidia will always hold the lion's share thanks to the CUDA ecosystem.

AI Hardware in 2026: What's Coming

Looking ahead, several trends are shaping the future:

• Vera Rubin & beyond: The new Vera Rubin platform promises even greater performance per watt. Huang is targeting an annual cycle of new architectures — something unheard of in the chip industry.
• $1T AI data center market: Nvidia's projections see $1 trillion in cumulative data center revenue by 2028. That means a market for hundreds of millions of GPUs.
• Agentic AI & Reasoning: New chips are designed specifically for “thinking” AI — autonomous agents that can make decisions, not just generate text.
• Robotics: Nvidia sees humanoid robots and autonomous vehicles as the “next wave” of AI. Isaac GR00T N1 and Alpamayo-R1 are the first steps.
• Energy & Sustainability: Data centers consume massive energy. Nvidia is investing in Project Aurora — AI software that regulates energy consumption in real time.

Nvidia is no longer just a chip company. It's the “central bank” of AI — the infrastructure on which a new industrial revolution is being built. With Jensen Huang at the helm, a company that started at a Denny's now powers the most powerful AI systems on the planet — and is gearing up for even more.