Hacker News Reader

Comment by awongh | Hacker News Reader

I get the structural comparison they are trying to make.

But mortgages are not a frontier AI lab.

They try to draw a comparison to the valuation of the real estate and the valuation of the hyper scalers in the markets.

I would argue that the demand and valuation of a house is less elastic than AI. While a house’s value may continue to appreciate in the market there is an upper bound for the price of a house set by people’s income. We don’t know yet what the value of AI is. The underlying product, the model keeps improving and therefore increases its value. A house is still fundamentally a house a year later and doesn’t intrinsically appreciate in value.

From gpt-3 to gpt-5.5 there’s been a massive change in the underlying value of the product and company in a way that simply doesn’t happen with a house. That’s where the analogy breaks down.

View on HN

Funny how you're echoing exactly what the author said in the article

> This is not precisely 2008. GPUs are not houses; take-or-pay contracts are not mortgage-backed securities; OpenAI is not a subprime borrower in Stockton, and artificial intelligence may well be the most consequential technology of the century, which is more than anyone could ever say for a McMansion in the Inland Empire.

> The bear case in this piece is not that artificial intelligence will fail, or that the demand is fake, or that the technology disappoints. It is narrower: that the financing structure can break before the demand arrives, because the obligations are fixed and front-loaded in commencement while the revenue is variable and back-loaded in adoption - and a fixed obligation meeting a lagging revenue stream is a solvency problem regardless of how transformative the underlying technology turns out to be.

> The industry will spend the next eighteen months debating whether artificial intelligence is a bubble, which is the wrong question, asked at the wrong layer. The technology is real; so were the houses. The question is narrower: what happens when instruments underwritten at the teaser meet their reset schedule, and who is holding the paper when the obligations cannot be met as written

Ie, if you spent $10M buying a house, it doesn't matter if it will be worth $100M in the future. If you're unable to make your mortgage payments in the interim, you're going to lose everything

thanks this is a helpful way of summarizing it

"The underlying product, the model keeps improving and therefore increases its value."

That's not exactly true. Yes, the fundamental capabilities of the models do seem to be growing dramatically, but the economic value of any particular model may be steady, or even falling, because of commoditization, or other issues external to the model itself.

Without a moat, improvement in model capability does not necessarily translate into economic value--and the labs need economic value to pay their obligations.

The economic value may be real but the profits may not be.

One thing that's clear is that there is no leading vendor in this space and there may never be one. To some extent premium models can charge a premium price but it's going to be a competitive market and the likes of Anthropic and OpenAI will not be able to sustain monopoly pricing.

The AI debate has often been flattened into "Do you believe in the long term viability of the tech?" when there is also another question that needs to be asked in "Do you believe in the long term viability of these companies' business models?" It's a lot easier to believe the former than the latter.

It's entirely possible for this tech to be humanity altering in the long term while we are also in a huge bubble that could pop at any moment. In that way the housing analogy is apt. The utility of the houses themselves didn't change, the problem was purely with financial markets until eventually those markets made it everyone's problem.

As of now the compute is fully utilized, progress is rapid, and both OpenAI's and Anthropic's revenue is growing fast.

This could change, but there is no sign of it yet.

No one knows whether other companies are going to catch up, with what compute, or if OpenAI or Anthropic will be able to conquer robotics or medicine.

Yes exactly the same as during the Dot com bubble.

Notably a lot of those companies went down, a lot of investors lost money, but the internet and e-commerce proved to be just as big as people expected back then, maybe even bigger.

Yes I that was in reply to OP making similar point about AI

Almost, but not quite.

Consider

https://www.ft.com/content/6f3ada65-c56c-499c-8eb6-008fac589...

I hate to say it, but the reality (AI, today) might be closest to PG's "intuition",to wit, it doesn't pay to be left of center-right

For "previous bubbles", I have a different, apolitical,take

Having lived to adulthood before Amazon started, I remember the massive debt to profit ratio of many dotcom companies.

Most importantly, I stick to Amazon's lesson: Sometimes, it's not about who's first. The winner can be determined by who's left.

That is much less of a systemic issue though. It could kill OpenAI or Anthropic, but it wouldn't render these large GPU data centers obsolete since whatever replaces them, be it open models or cheaper closed models, would probably still require a lot of GPU compute.

Less bad, yes. But still bad. There will definitely be demand for the compute, the question is whether it will be enough to keep the value of it at the levels you’d expect if the tenants were in a monopoly/duopoly world. Competition puts downward pressure on margins and that’d translate into pressure on data center leases.

Could put pressure on data center margins, but not revenue, and not necessarily gross profit.

"probably still requires a lot of GPU compute" is something that would be very desirable to undercut.

There's a galaxy of potential AI markets that are local-only for compliance/privacy/deployment environment reasons, and an equally large and overlapping set of use cases where you won't be able to bolt a rack of thousand-watt GPUs on the side of the device.

What if the next "DeepSeek shock" is something we can run for non-toy use cases on our box of old Android phones? The GPU data centres could be expensive albatrosses very quickly.

There's probably a case that right now, the bigger-is-better paradigm protects incumbents-- a smaller model will always have a FOMO factor unless it can be proven competitive, so that leaves the playing field to those who can afford to train and deploy a new Fable or Sol every few months.

100%. I have sometimes wondered if China is playing the long game with the open weight models trying to tank the margins of the US labs so these profits don’t materialize and the US economy (currently predicated on the net that they will) suffers. I think this is a coherent strategy beyond just “don’t let the US control AI as a strategic asset.”

It's the domestic policy version of "commoditise your complement".

As the model keeps improving, to the extent it has some utility, it will decrease in value.

The dynamics are interesting. If all businesses get productivity increases from AI, the margins they could have claimed are competed away. The model companies also have their margins competed away because of open models. The only companies that have a moat are the ones with capital as a barrier to entry and even then there is cut throat competition.

We might end up with massive consumer surplus from AI because no business will be able to raise prices due to competition. This is why it's so important that we don't allow for regulatory capture in this space.

Yes and much like the technological leaps of the past, everyone’s standard of living goes up.

> The underlying product, the model keeps improving and therefore increases its value.

I think this is true, but a customer's willingness to spend is based on _perceieved_ value, not actual value. For many companies, the _perceived_ value of AI has been trending down as internal projects fail and cost skyrocket, even as models on paper improve.

And the Open Weight models keep improving.

For many tasks, you don’t need a frontier model.

The value is going up, but the pricing is going down, right? At least at the token level. So the usage would have to go up dramatically to compensate for that.

> there is an upper bound for the price of a house set by people’s income

Isn't that essentially true here too? The money to pay these expected future AI prices is coming from someone's income. Sure, the pie will be growing at the same time, but enough?

Individuals and families buy houses(ideally). Entities like corporations buy work, often intelligent work. If they can crank up outputs and profits via more intelligent work done by models, I think the ceiling is global demand for the entity's product. Which is still a bound, and ultimately set by individual consumers.

The question is whether the consumers will have the income to spend. So I kind of agree in the end I guess.

The performance of the recent open weight models is making it harder for the bug players to justify their pricing.

> We don’t know yet what the value of AI is. The underlying product, the model keeps improving and therefore increases its value.

i dont know. i think we already know how far these things go. Buying tons of data on mercor to slightly improve one domain has not really even displaced ppl in that domain. i really cant tell the difference between opus 4.8 and 5

very few domains in the world are closed like math.

I mostly agree but at the same time the change in underlying value from opus4.6 to Fable has not been as dramatic, and I'm not really sure if a "better Fable" is something most people even need. To the point where a faster and more cost effective model is preferable

Many would argue that opus5 is a regression in value despite what benchmarks say.

1. The massive amounts of GPUs being purchased have far shorter valuable lifespans than a house.

2. A model's value seems to be depreciating at an unbelievable rate. The most expensive top SOTA models (GPT-5, Opus 4.1) a year ago are far less capable than GPT-5.6 Luna. Compared to when those models were new, Luna costs 85% less than GPT-5 and 98% less than Opus 4.1. That's good for us consumers, but if a lab stumbles for 6-12 months, a lot of their value goes away. Especially with open models only months behind the SOTA closed models.

I feel like the idea that GPUs wear out in 3 years is as far as I can tell, generally unfounded.

The GPU build out will keep pumping tokens until the cost to replace is less that the cost to maintain. It is effectively sunk cost.

Even if it is 5 or 7 years, houses last decades. Even cars last longer than GPUs. GPUs are a highly depreciating asset. They'll last longer than 3 years, but might not be very cost effective (tokens per Wh) compared to the latest AI accelerators that are available.

I wonder if it will be that the U.S. (and allied countries) will be using the latest 1-2 generations of AI accelerators, and if the 3-5+ year old stuff will be sold to Chinese datacenters since that will still be the most powerful tech we'll be allowed to export to them?

You pointed out correctly that house prices are bounded by incomes, then immediately made the same mistake with AI - someone has to want to pay for this stuff. It also has to compete against free models, which are not only almost as good, but they pull ahead sometimes.

Saying “the product increased in value” is only true if someone buys it!

I'm still paying OpenAI $20/month for a product that has gotten massively more valuable. That's the problem. They aren't getting any more money from me for a product which is much more valuable.

GPUs depreciate at a far, far faster rate than houses do. And then they need to be replaced. Who's paying for that? OpenAI and Anthropic don't even have positive free cash flow.

I think its well corrected by the increase in competition that meets or exceeds the quality. The house may have gotten nicer but now you are selling a single room

Reply on news.ycombinator.com