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Wall Street is growing skeptical of the data center boom

72 points · 87 comments · mikhael

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. XorNot · 1790021483 · focus · HN ↗
  The eventual utility of the Internet did not stop a lot of people losing a lot of money in the dotcom crash.
  1. stult · 1790021775 · focus · HN ↗
    Fair, but my point is that a data center is not pets.com. They are a much more flexible asset, and are even more flexible than AI itself. Pets.com failed because the physical delivery infrastructure and consumer buying habits that support Chewy today did not yet exist and took years to create. Data centers can be repurposed from supporting the current generation of LLM-based AI models to an infinite variety of use cases. Pets.com could only ever hope to deliver pet food.
    motionlessveloc · 1790023460 · focus · HN ↗
    And Amazon.com could only ever hope to deliver physical books?
    Anyway, it's clear an AI data center has utility for crunching AI inference, and that there is and will continue to be demand for AI inference. The trillion dollar question is whether you can make money doing this, and so far the answer is no.
    <a href="https://isaiprofitable.com/" rel="nofollow">https://isaiprofitable.com/
    stult · 1790025807 · focus · HN ↗
    You are not disproving but rather reinforcing my point. Amazon survived the dotcom bubble precisely because they could hope to do more than deliver physical books. It was a more flexible, generally applicable business model than pets.com, and they started out targeting a market more susceptible to ecommerce conversion than pet food delivery proved to be. Similarly, a company selling a product that can only be used for the limited use cases we have found for chatbot-style text generating LLMs will be more likely to fail than a company selling something that is more generally useful. Data centers are more generally useful than LLMs, and the companies building them are more likely to survive than those whose fortunes are pinned entirely to unrealistically high expectations for replacing white collar workers with LLMs.
    XorNot · 1790030212 · focus · HN ↗
    Data centers aren't just physical buildings though - the vast bulk of data centre costs is the racks of servers in them, and those are being bought and installed on credit at very high costs.
    Its not about "is a computer thoeretically useful" its about if it's going to deliver value to cover the cost of installing it in the first place over it's expected lifetime.
    You aren't substituting generic compute: you're substituting highly optimized GPU dense server space. The application is more limited then it looks.

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