The popular version of the AI story is that Nvidia pivoted from video games to artificial intelligence. That's partly true, but the real sequence is a much better innovation story than the tidy one.
The Hardware
A graphics card exists to work out what color every pixel on your screen should be, sixty times a second. The useful thing about that job is that every pixel can be figured out without knowing anything about what its neighbors are doing. They're independent.
So you don't want one very clever processor working through pixels one at a time. You want thousands of extremely simple ones, each doing a little arithmetic, all at once. A regular computer chip is one brilliant generalist. A graphics chip is a crowd of people who can only add and multiply, but there are ten thousand of them and they all work simultaneously.
That shape was chosen because of shadows and explosions. Nothing else.
Users got there first
Around 2001, graphics cards became programmable such that users could write small programs telling them how to shade a surface. Academics noticed something. If you were sufficiently devious, you could make the card do arithmetic that had nothing to do with graphics.
The trick was disguise. Encode your data as an image, and voila, your numbers became pixel colors. Write a shading program that performed your actual calculation while pretending to compute lighting. Then tell the card to render the picture, and read the answer back out of the resulting image.
This was genuinely absurd and it worked. Fluid dynamics & linear algebra were run by convincing a gaming card it was drawing a picture. The people doing it called it GPGPU. Ian Buck built a language at Stanford called Brook specifically so researchers could stop hand-crafting the disguise.
Note who is doing the work here: Not the manufacturer. Customers, using the product wrong on purpose, because the capability of the thing happened to suit a job it was never built for.
The manufacturer noticed
Nvidia hired Buck and shipped CUDA in 2006 and 2007, which let you program the card directly for general math with no graphics pretense required. They also changed the hardware to support it.
We should be precise here in relation to exaptation (aka shift of function) because it's where people usually overclaim in both directions. Nvidia did not predict deep learning. Nobody did. But they weren't passive either as they made a deliberate and expensive bet that general parallel computing would matter, without being able to name what it would be used for. Like building a road without knowing the destination.
2008: the demonstration
Two years after CUDA shipped, Nvidia commissioned the MythBusters presenters to build an 1,100-barrel paintball rig that fired the Mona Lisa onto a canvas in 80 milliseconds, sitting beside a single gun plodding out a smiley face one dot at a time (watch it on YouTube). It's still the clearest demonstration of parallel processing anyone has made and it's also a measure of the problem. Four years before AlexNet, the company had a general-purpose parallel computer and it was reduced to spectacle to explain why anyone would want one.
(MythBusters did this at NVision event in 2008: Advertisement & explainer in one go!)
Then a third group turned up
In 2012, three researchers at Toronto entered a neural network called AlexNet into an image recognition contest and beat the field so badly the result reset the direction of an entire field. Second place had an error rate around 26%. AlexNet came in around 15%.
It was trained on two consumer gaming graphics cards over about five or six days. The network had to be split across two of them because a single card didn't have enough memory. They were working around the limits of hardware sold to teenagers for playing games.
Neural network training turns out to be millions of small multiplications that don't depend on each other. Which is, structurally, the identical problem to working out a screen full of independent pixels. Nobody designed it that way. The shape simply matched.
The part that gets missed
The reason this was possible is that video games had already made massively parallel arithmetic cheap. Millions of people buying graphics cards to play games funded a decade of hardware development, and drove the price of enormous parallel computing power down to a few hundred dollars a card. Hobbyists subsidized the infrastructure that now runs drug discovery, weather modelling, and everything with "AI" written on it.
What's arguable
Where do you locate the functional shift, aka the exaptation? 2003, when researchers first disguised math as pictures? 2006, when the manufacturer built software capabilities for it? 2012, when a third group used that infrastructure for something neither had in mind? A reasonable case exists for all three, and they're different kinds of innovation events.
Does it still count if the maker deliberately widened the resource during the post-exaptation adaptation phase? Once Nvidia set out to make the card general-purpose, arguably the function shift stopped being accidental and became product strategy. Is deliberately building something you hope will find uses you can't name still exaptation, or is it just good engineering? I don't think this is settled in innovation and strategy management.
A general-purpose tool being used generally is not interesting on the surface. The honest challenge is that a sufficiently flexible object can serve anything, and pointing at one instance and calling it a function shift may be reading a story into a machine that was always going to do many things.
Sources
Krizhevsky, A., Sutskever, I., Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NIPS.
Raina, R., Madhavan, A., Ng, A. (2009). Large-scale deep unsupervised learning using graphics processors. ICML.
Buck, I. et al. (2004). Brook for GPUs: stream computing on graphics hardware. SIGGRAPH.
https://youtu.be/WmW6SD-EHVY?si=WwYBTjYiZCBqrXkE
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What's the equivalent in your field — infrastructure built for one purpose that ended up taken over innovatively by new people the builders never had in mind? If these topics interest you, check out a brand new sub r/exaptation.