The story
NVIDIA is the defining company of the AI era, and it got there by a bet that looked eccentric for two decades before it looked inevitable. Founded in 1993 to make graphics chips for video games, it spent years turning the GPU — a processor built to render millions of pixels in parallel — into a general engine for any massively parallel computation. When deep learning arrived and turned out to need exactly that kind of parallel maths, NVIDIA already owned the hardware and, crucially, the software the whole field had learned on. In October 2025 it became the first company in history to reach a $5 trillion market value.
The pivotal decision was software, not silicon. In 2006 NVIDIA launched CUDA, a platform that let programmers use its GPUs for general computing, and it poured a decade of investment into it while the market saw no obvious payoff. That patience built the moat: by the time AI exploded, virtually every researcher, framework and model in the field was written for CUDA, and NVIDIA's chips were the only ones they ran on well.
How it makes money
NVIDIA sells the accelerators that train and run AI — the Blackwell generation now, Rubin next — at high margins to the cloud giants and AI labs racing to build ever-larger models. But the hardware sale is only the visible part. What locks customers in is the full stack around it: CUDA, libraries, networking (from its Mellanox acquisition), and reference systems, so that buying an NVIDIA chip means buying into an entire ecosystem competitors can't replicate by matching the silicon alone.
I am still fervently steadfast that NVIDIA is the anchor of the global AI shift.industry analysis, 2025
The moat
NVIDIA's durable advantage is CUDA, not the chip. Two decades of developers learning, building and optimising on CUDA created a network effect: the software everyone writes assumes NVIDIA hardware, which makes NVIDIA hardware the safe choice, which brings more software. A rival with a faster or cheaper chip still faces a world of code written for someone else. Layered on top are switching costs — retraining teams and re-porting models is expensive — and a full-stack integration of chips, networking and software that competitors have to beat all at once. The risks are real: customers designing their own chips, and geopolitics around export controls to China.
Key people
Jensen Huang
Co-founder & CEO since 1993
Chris Malachowsky
Co-founder, engineer
Curtis Priem
Co-founder, chief technologist
The playbook
Key decisions & principles
Own the software, not just the hardware
CUDA, not the GPU, is NVIDIA's moat. A commodity product becomes defensible when it's wrapped in a software ecosystem people build their work on.
Invest through the wilderness
NVIDIA funded CUDA for a decade before the payoff was visible. The deepest moats are dug during the years the market sees no reason to.
Ride network effects
Every developer who learns CUDA makes NVIDIA the default for the next one. Design so that each user makes your product more valuable to the following user.
Sell the whole stack
By integrating chips, networking and software, NVIDIA forces rivals to match everything at once, not just the chip. Bundle the pieces that create the experience.
What to read next
- The Nvidia Way — Tae Kim
- Chip War — Chris Miller
- The Master Algorithm — Pedro Domingos
Why this matters
The strategy behind this business connects to models and moats you can study:
Frequently asked questions
Why did NVIDIA win the AI chip market?
It spent a decade building CUDA, the software the entire AI field learned to build on, so when deep learning exploded, virtually all research and models already ran on — and required — NVIDIA hardware.
What is NVIDIA's moat?
CUDA's software lock-in and developer network effects, plus switching costs and a full-stack integration of chips, networking and software that rivals must beat all at once.
How big is NVIDIA?
In October 2025 it became the first company ever to reach a $5 trillion market value, with roughly $500 billion in AI-chip orders booked through the end of 2026.