alt Hacker NewsThere's also a failure to misunderstand the arms race nature of it all and the need to be in the lead since everyone is assuming it's a winner take all market. That's probably close to correct.
A data center is a warehouse for sure but the building is only a few percent of the cost. The GPUs are probably 80% of the total cost or more. And they probably only have a 3-5 year primary lifecycle.
Pay the building off over 20 years? Sure but 80% of your capex problems remain. 2 trillion already spent? Just those GPUs need to generate 500 billion a year over the next 5 years.
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> since everyone is assuming it's a winner take all market. That's probably close to correct.
why? I keep seeing this over and over again but I've not seen an explanation. I would really appreciate some insight into this line of thinking, since it's not obvious to me. What is the benefit of holding hundreds of billions of computer hardware when it's not proven that it's even needed.
A "failure to misunderstand" is a fitting turn of phrase.
People not only ignore second order effects, they also don't think about what would actually happen when a "winner" really "took all".
While AI is a tremendously useful technology, business types wildly misunderstand it and its consequences. Greed eats brain.
It's probably closer to 45-60%.
https://epoch.ai/data-insights/ai-datacenter-cost-breakdown
https://www.csis.org/analysis/impact-tariffs-ai-data-center-...
I read somewhere that the expected 3-5 year lifespan of inference GPUs was not holding up to scaring: in practice, the GPUs have the same lifespan as any datacenter CPU or RAM. I think that estimate was based on Bitcoin mining ASICs, which wasn't an apples-to-apples comparison.
but is it a winner takes all market? currently we have models that are close-but-not-quite SOTA which are still heavily used and very profitable because they are _good enough_ (i'm talking about deepseek, of course).
or is the "winner takes all" mentality based on the assumption of being the first to achieve the technological singularity?
Power generation is also extremely expensive ongoing - either through gas turbines that eat fuel or on-grid power that datacenters will need to pay to capitalize (depending on ratepayer agreements of course - but clearly people aren’t stoked about subsidizing these)
Fun part ... all those GPUs are going to shredder after use.
The problem is likely the GPU price, NVidia has a very large margin - but it doesn't seem like the the use case supports it. If NVidia cut the GPU price by 2-4x then these problems likely go away.
I wouldn't be surprised if most hyperscalers are betting on cheaper GPUs on their next upgrade through either pricing pressure on NVidia, competition from AMD/Broadcom or in-house chips.
Normally you'd replace the device because there is more power efficient one on the market so running one at current energy prices costs more than buying new, more power efficient one and saving power.
IF prices of GPU/RAM come back to pre bubble costs, we might see the 5 year cycle of replacement that's relatively common in normal datacenters pre-ai bubble.
At current prices ? those GPUs that are there now will be running there in 10 years still.
The gamble: gunning for market supremacy before investor cash dries up.
The real gamble: betting there is a large enough pool of c-suite that doesn't fundamentally grasp the concept of vertical integration that they're willing to pay to train their competition. Because if you think AI companies aren't training models how to do everything your company does with your data you are deeply delusional.
The real real gamble: praying sovereign governments stay anesthitized to the national security implications of a handful of extra-national organizations operating the conputer security equivalent of WMD with no oversight.
> And they probably only have a 3-5 year primary lifecycle.
I think in 3-5 years close to 100% of current GPUs/RAM will still be in service. New hardware matters only if gains in efficiency are large enough to put you out of business because the competition gets them. Extreme case of that are bitcoin miners. With AI inference there are way too many factors that influence the profitability. It's not like you can't run new model without the new hardware. It can just run on more of the old one. 3-5 years for GPU are estimates when the main application was gaming with new games with new requirements for running on a single GPU being constantly released.
> everyone is assuming it's a winner take all market.
I guess this is the big gamble, the idea that if you just spend enough money as fast as you can you can become the gods of AI forever and can just charge whatever you want for your magic beans.
But what is the moat? If in reality these things become commodities and just compete with each other to spit out cheap tokens then there's no way the margins are there.