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A Reddit user questions the sustainability of AI's growth, fearing a financial bubble burst due to massive capital expenditure on data centers and chips. They observe that AI primarily makes existing products cheaper or better, rather than creating significant new demand or allowing for price increases. The user wonders how this "low key" AI adoption can avoid market collapse, especially if companies cannot avoid paying for compute resources they might not fully utilize, echoing concerns from figures like Ed Zitron about a potentially negative outcome.
Hey,
I'm not a hater or a fanboy, I'm just an average person using AI where using AI makes sense and being annoyed by AI being stuffed into things where it doesn't belong. I'm sure AI will transform the world, but I'm also pretty sure not next year and not completely.
What I'm really wondering, though, is how AI can become "stable" without a massive financial bubble bursting. All the AI we're using today is running on existing hardware. The spending for new data centers and chips all comes in top of the current capacity and already, the capex is absolutely insane. I know that people like Ed Zitron say that this will end really bad, but is there a numbers based study or article where somebody lays out how it all could work out without everybody being replaced by AI? Currently, what I see, is that AI is just making stuff cheaper or better, but not really creating massive additional demand. Nobody spends twice as much on Photoshop because of AI, nobody pays more for a fridge because it can say hi in the morning. Rather, people expect the AI to be a standard enhancement of the product at a similar price - just like more RAM or a newer processor used to be expected in new PCs.
Is there any meaningful way how such a "low key" AI adoption could not crash the market? Is there some way for the companies to wiggle out of paying for the compute if they don't need it?