As I've been pointing out a little bit recently -- the 'downside' scenario -- somehow we conclude we have enough inference at our world's desired quality and therefore demand stabilizes below that of these datacenters -- would be GREAT for the world. Super cheap intelligence for all.
If some lenders go bankrupt on a datacenter note post-buildout and the capital structure is reworked, lowering the costs by removing capitalization costs from the cashflow of the inference provider, that is all to the good of people who did not do the initial lending.
I think that is extremely unlikely, but it's good for datacenter doomers to think through these scenarios IMO.
Your take seems a bit optimistic if we use the dot-com boom as an analogy. The dot-com boom sped up a massive infrastructure build-out, but also left damage that lasted for a very long time, with many organizations collapsing, and others taking a very long time to recover (e.g. cisco networks). It also seems that these 'data-centers' have a higher depreciation rate than 'dark-fiber' did post-dot-com, which means that there is likely to be greater capital waste.
I am not sure whether the dot-com boom was a net-positive, and the AI boom may be less productive.
It absolutely was a net positive. On the infrastructure side we got datacenters built and tons of dark fiber put down. At the start of the boom we had only 2G cellular for goodness sake. I think you could argue we wouldn't have necessarily had the rise in cloud compute without some of the 'overbuilding' of datacenters in web 1.
For reference this 'massive build-out' resulted in, according to a Berkeley study I found, 500MW of total datacenter power in the US across 320 locations at end of 2020. This wasn't too much for very long: as of 2005, according to Tier1 there were 511 datacenters. And it was likely only really a year or two: Dupont's VA3 utilization numbers published it at 50% utilization in 2003, 89.4% in 2004, then full thereafter.
Meanwhile, these infrastructure improvements allowed, for example, things like the iPhone to be built and deployable -- there was 3g, there were a LOT more web devs that could create the early (web only) apps the iPhone needed to compete. This in turn allowed another round of investment to be built on top of all of that.
On the capital side, investors were also able to buy Amazon at around $1 / share. AAPL at iPhone launch was $7/share. I haven't split adjusted either of these, this is just from memory.
I don't think a bust was necessarily good but the boom undeniably built out infrastructure, a small fraction of what the world is currently using, and did it pretty quickly, and then post bust that infra got bought out in a capital efficient manner. I think that's the bear case here.
Put another way, the Dotcom one boom was wrong about two things: timing was too fast, and the predicted scale of change was way, way, way too small. They undersold the future.
My point is that these hyperscalers need X traditional data centers and Y AI data centers. If Y goes to 0 that doesn't mean they eat everything they've spent on those data centers. They get converted to serve non-AI loads while other older ones get abandoned or leases not renewed and so on.
In that scenario the GPUs are a loss. But really at this point inference is so profitable it's hard to imagine these data centers not being able to at least cover operations & rent.
Ai datacenters are quite different from traditional datacenters. For example, a massive cost for ai DCs is power and the grid to support it. Regular DCs don’t need anything like the same power.
Cisco networks took a very long time to recover, but in the meantime, everyone else enjoyed massive growth in cheap connectivity.
Capital waste is only capital waste if the physical goods that were built are no longer useful for anyone. Otherwise, it's only a waste for the people who lent money.
This is something people miss when comparing things to dot com. It was a very thin layer on top of something new. 90% of the companies were speculative jokes with no basis in reality. Anthropic makes a profit. Google is a mature company that makes a profit and will continue to do so.
There's a lot of insanely silly AI products and services that have absolutely no use in the real world whatsoever. In that sense it's very very similar to the dotcom crash. And the crypto "blockchain" boom. And the metaverse. Remember when we all put blockchain on our CVs somewhere or we'd be unhireable? Who's talking about that now?
The Google and antrhropics of today are the ISPs of back then. They didn't go anywhere. It was more the services on top of that that crumbled because they had nothing real to offer. I see the same here.
In 2001 society wasn't ready. People didn't yet want to order their groceries online or read books off a screen. The computers weren't ready, they were slow, heavy and clunky. The smartphone still needed to be invented. Mobile networks needed to come around, the miniaturisation of GPS, cameras. These things can't be rushed and that realisation popped the bubble. In the end they did happen, but in their own time.
I see so much of that now. We must have AI in our product! How, why, what value does it add? Who cares! It must be AI or we're out of the race!! That's really the hallmark of a bubble.
Here again, the usecases aren't fleshed out yet (except coding). The tech isn't finished, it's all very hit and miss, hallucination, loss of control (agents hacking) etc. The people certainly aren't ready, we need time to adapt society to new technology and a couple years isn't going to cut it. But these investors aren't investing this much to see a return in 2035. They want it much sooner. Sooner or later that will dawn on them and they start pulling out.
You are correct of course. I was conflating their operating cash flow stuff. They are getting real money from real customers is what I should have said. Other much more knowledgeable people have broken down all the accounting in a way that I can roughly follow. I suppose my point is that people comparing them to pets.com et al are torturing the analogy.
Not sure when you last were in SF but there is probably half a billion in Series A capital within 1 mile radius of me raised by very nebulous AI start ups.
The downside scenario isn't AI equity getting zeroed out and lenders getting crammed down. It's the federal government deciding that AI capabilities are of national strategic importance and throwing trillions of taxpayer dollars onto the bonfire.
It really depends on the "somehow". The most realistic downside scenario, it seems to me, is that most people don't care too much about super cheap intelligence for all because there ends up being another bottleneck for most of the things we value.
Like, trains are an incredible technology which transformed the world, but very few people go around counting their lucky stars that they live in a world that permits such wonders. It's a train, it gets things where they need to go, and by the way isn't it so annoying that sometimes it doesn't quite arrive when the schedule said it would?
If some lenders go bankrupt on a datacenter note post-buildout and the capital structure is reworked, lowering the costs by removing capitalization costs from the cashflow of the inference provider, that is all to the good of people who did not do the initial lending.
I think that is extremely unlikely, but it's good for datacenter doomers to think through these scenarios IMO.