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▲ noosphr a day ago

The difference is that all those things needed physical infrastructure to be linearly build out.

AI models can be deployed on the same datacenter as the old one and be either orders of magnitude cheaper to run, or orders of magnitude more capable.

The only thing that can pop this bubble is if we're not smart enough.

▲TheOtherHobbes 21 hours ago | parent | next [-]

That's not the difference. The difference is the distinction between core infra, B2B services, and B2C services.

The companies that sold core infra during dot com mostly died, because they invested in fiber etc far too quickly. Likewise with server factories like Sun.

The B2B and B2C survivors were patchy. A few did extremely well but there were a lot of casualties.

The biggest winners offered B2G - business to government - disguised as search and social media. They were actually selling individual user profiling and targeted ads, and that turned out to be the biggest money maker.

The reason Anthropic and OpenAI appear wildly overvalued is because their offering isn't AI in some abstract sense, it's the next generation of surveillance and persuasion technology. But now with corporate surveillance in addition to the traditional profiling and targeting.

You can tell a lot about what someone is doing - and the industry is doing in general - by looking at everyone's code bases. You don't need to train on them or steal keys to extract immense value.

Potentially there's also the option to eventually make claims on corporate IP developed with AI tech.

Anyone who thinks the companies who pulped and torrented whole libraries of books for their training data would never, ever, pinky promise, try to extract value from the code they're generating is being very naive.

▲charcircuit a day ago | parent | prev | next [-]

>AI models can be deployed on the same datacenter as the old one

This hasn't historically been true. The clusters have been getting bigger along with the models over time. And now we may even want to have these computers have rapid access to extra compute or caches of the internet or other data stores.

▲ZenoArrows a day ago | parent | prev | next [-]

> AI models can be deployed on the same datacenter as the old one

Not if the datacenters are in space, you can upgrade the models for a little while but once you need to upgrade the silicon you need to launch new hardware.

▲justinator a day ago | parent | prev [-]

And data centers are practically free to build, are welcomed everywhere, cost little to run, and never need to have their infrastructure upgraded!

▲anamax a day ago | parent | next [-]

Google hasn't retired any of its now ten-year-old V1 TPUs or any of the subsequent generations so it's unclear that AI infrastructure "needs" upgrades.

Of course, Google could be wrong about the economics of running old AI processors. Or maybe TPUs are special.

▲appplication a day ago | parent | next [-]

I do wonder how much of their inference is running on 10-year old TPUs. I’d imagine not much, if only because 10 years ago the justification for massive compute was quite different than today.

▲ a day ago | parent [-]
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▲raddan a day ago | parent | prev [-]

Citation needed.

▲easterncalculus 17 hours ago | parent | prev [-]

> are welcomed everywhere

This is a perfect circle of reasoning. The bubble exists because people say the bubble exists and reject data centers, therefore creating the bubble?