30 Years Ago Today, a Computer Beat the World Chess Champion
30 Years Ago Today, a Computer Beat the World Chess Champion
February 10, 2026
On February 10, 1996, IBM's Deep Blue became the first computer to defeat a reigning world chess champion in a regulation game. Garry Kasparov - widely considered the greatest chess player in history - sat across from a 1.4-ton machine and lost.
The world was stunned. Chess had long been considered the ultimate test of human intellect. If a machine could beat us at that, what was left?
What Deep Blue actually was
Deep Blue wasn't "intelligent" in any meaningful sense. It was a brute-force calculator - a custom-built system with 480 special-purpose chess chips that could evaluate 200 million positions per second. It didn't understand chess. It didn't have intuition. It simply out-calculated the most brilliant human mind on the planet.
The hardware itself was remarkable for its time. Deep Blue ran on an IBM RS/6000 SP system - essentially a parallel computing cluster. Each of its 30 nodes contained a PowerPC processor paired with 16 custom VLSI chess chips. The total system could sustain around 11.38 GFLOPS of processing power.
For context: a single NVIDIA B200 GPU today delivers around 4,500 TFLOPS of FP8 performance. That's roughly 400,000 times more compute than the machine that shook the foundations of human exceptionalism.
From chess to everything
What makes the Deep Blue anniversary relevant in 2026 isn't the chess match itself - it's the trajectory it revealed.
In 1996, it took a purpose-built supercomputer to play one game well. Today, a single GPU can run large language models, train neural networks, generate images, process genomics data, simulate physics, and yes - play chess at a level that would embarrass Deep Blue. All at the same time.
The shift from narrow, single-purpose compute to general-purpose GPU acceleration is arguably the most important infrastructure transition since the internet. And we're still in the early innings.
The speed of the curve
What's truly staggering is the pace. Deep Blue took years to build, cost millions, and required a dedicated team of IBM researchers and grandmaster consultants. It played chess and nothing else.
Fast forward 30 years and a developer anywhere in the world can spin up a B200 on packet.ai, connect to a Jupyter notebook, and start training a model that would have seemed like science fiction in 1996 - all within minutes, for a few dollars an hour.
That compression - from years and millions to minutes and dollars - is the real story. Not just that compute got faster, but that it got radically more accessible. The barriers that once limited serious computing power to IBM research labs and government agencies have effectively disappeared.
What comes next
If the last 30 years took us from a chess computer to artificial general intelligence knocking on the door, it's worth pausing to think about where the next 30 take us.
The models being trained today on GPU infrastructure are already capable of things that would have seemed impossible even five years ago. They write code, reason through complex problems, generate photorealistic images, and hold conversations that pass for human.
And this is still early. The compute is getting cheaper. The models are getting better. The access is getting easier.
Deep Blue proved that raw computation could overcome human intuition in a narrow domain. What we're seeing now is raw computation augmenting human capability across every domain - science, medicine, engineering, creative work, business.
Kasparov himself came to this conclusion. After losing to Deep Blue, he didn't retreat into bitterness. He pioneered "Advanced Chess" - the idea that a human working with a computer is stronger than either alone. He was right. And 30 years later, that principle scales to everything.
The best question to ask on this anniversary isn't "what can machines do?" It's "what can you do with a machine?"
We think the answer should be: anything you want, starting in minutes.
packet.ai - on-demand GPUs from vetted providers. No contracts. No commitments. Just compute.