Thread

1. stult · 1790021389 · focus · HN ↗

   I have generally been bullish on data centers independent of how AI demand/load evolves. People will find a way to use that compute, even if it isn't the precise use we expect. As scale increases and the price per computation comes down, we will be able to brute force solutions to problems that otherwise would be intractable or prohibitively expensive to solve. And there are effectively an infinite set of those problems.

   This tracks the evolution of how we use cloud computing and GPUs over the last 20 years. Cloud computing was originally just about saving companies from needing to maintain their own server racks, but has unlocked previously unserviced demand by allowing people to build an app or service and scale it to meet rapidly rising use without needing to invest a ton upfront in hardware. Suddenly a hobbyist could spin something up in their spare time that previously required many thousands of dollars of investment. Or when someone wants to run a single large scale computation, they can do it without needing to waste capital on maintaining idling servers to meet an occasional demand spike, like when a company I used to work at moved from running atmospheric calculations on a server in a closet to the cloud and were able to achieve double digit accuracy increases with the increased scale, while spending less overall on batch computing jobs.

   GPUs, as the name implies, were created for graphics, and primarily for gaming graphics, but then more or less accidentally ended up enabling the present AI boom, which depends on a scale of computation that would have been impossible with older CPU architectures. Maybe someone at some point predicted this, but I think for the vast majority of people, it was extremely surprising that a niche gaming product would enable an industrial revolution level technological leap forward.

   LLMs are just one way that increased compute scale unlocks seemingly magical results, but they are far from the only example and I have no doubt that there are many unknown examples remaining to be discovered yet.

   1. LetsGetTechnicl · 1790022176 · focus · HN ↗

      Yeah but general purpose data centers that allow hobbyists to spin up an app or a service easily is exactly that: general purpose. AI data centers are built for the very specific kind of math that LLM's require. Even the GPU's can't even be used for something like cloud gaming, which isn't very popular anyways.

      1. stult · 1790023624 · focus · HN ↗

         That math is very much general purpose. It's just linear algebra under the hood, and linear algebra is a wildly useful toolkit with an unbounded set of potential applications, including many applications other than LLMs. That's how something developed for gaming evolved into something used for text processing and generation. For example, we have barely begun to scratch the surface of what we can achieve with robotics, and I have no doubt that further advances in robotics will generate an enormous amount of computer vision work that GPUs are extremely well designed to handle.

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