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.
"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.
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.
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?
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.
> 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.
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
The article claims that the finances fall apart because OpenAI won't hit the growth it wants. It's an interesting thing to say when using their product to write 100% of the article.
wow excellent piece. Gary Marcus had a long post about this article on his substack.
scary stuff
"And look at what this implies about OpenAI’s valuation as it moves toward an IPO:
OpenAI’s equity - valued north of $850 billion - is functionally the junior tranche of a capital structure whose senior claims, the take-or-pay compute obligations, exceed any revenue path management itself has articulated.
On those numbers, the equity is effectively underwater, and the market has not priced it that way because it still treats those obligations as service agreements rather than what they are economically: debt.
Even if OpenAI can meet those obligations, OpenAI’s unaudited financial statements - as of March 31, 2026 - disclose $665 billion in non-cancellable compute commitments (management’s more recent plan runs to $750 billion). These commitments are take-or-pay in structure - which, as established above, is debt.
Carry the net present value of those obligations as senior debt - roughly $450–500 billion, the same methodology rating agencies have used for decades to capitalize take-or-pay contracts as debt - and a company the market prices as debt-free carries a senior claim worth more than half its entire equity value."
and the 2008 analog
"Millions of subprime borrowers were, at that moment, paying the low introductory rate on a two-year adjustable rate mortgage - the 2/28 ARM. A low fixed-rate for two years, then the rate reset to a payment 30% to 50% higher. During those first two years the loan performed beautifully: the borrower paid, the servicer collected, and the bond paid its coupon. Nothing looked wrong because the whole complex - housing, mortgages, securitization - was sitting inside the teaser period.
The AI boom has rebuilt this exact structure, and the market is once again underwriting the teaser.
It has a reset wall of its own - a schedule of dated, contractual, non-negotiable payment shocks - hiding inside the trillions of dollars of compute contracts signed by OpenAI and other frontier labs since 2024."
Marcus believes that the underlying technology doesn't work. If that's true, then of course the whole thing will crash as soon as everyone realizes this.
This article is mostly making a different argument (though it contradicts itself in some places), which is that even if the underlying technology does work, and is ultimately going to create quadrillions of dollars of value and transform society, if it takes more than another 1–2 years for that to happen, then there'll still be a crash, because that's when the data center construction bills come due and the labs (especially OpenAI) don't yet have the money to pay them.
It argues primarily against a hypothetical optimist who believes that everything is fine because the cash flow numbers currently work out, on the grounds that this hypothetical optimist hasn't realized that the labs' recurring expenses are scheduled to spike in 1–2 years when the data centers come online and the labs have to start paying for them.
I am not sure that anyone is actually making this mistake (i.e., trying to predict the future by looking at labs' present cash flows). The better counterargument is what Matt Levine used to call "Netflix Theory": if the large capital investors who own stakes in the labs still believe in their valuations (which they should, if the technology works and the quadrillions are coming, which we're assuming here for the sake of argument), then they will be very highly motivated not to let their investment be seized by the labs' creditors. So the labs will not have too much difficulty raising or borrowing enough money to pay the bills.
It’s an interesting comparison. The housing market is linked to the value of the house though which is subject to crashes in value without a corresponding drop in demand.
With AI it comes down to whether the large companies orders and building of datacenters aligns with token demand. There is years worth of lag there so they kinda have to front load this by necessity
> Every ARM reset was known, dated, and contractually inevitable from the moment of origination. Aggregate those reset schedules and you get the most damning exhibit of the era: the reset wall.
One of my distinct memories from this era is watching CNBC where a guest said exactly the same thing.
As the interview went on, he became more animated and used stronger language to the point of:
"You don't get it, THEY ARE GOING TO BE PICKING PEOPLE OFF THE FLOOR when these ARM rates reset"
I would guess this was right about 2006 which lines up with the article.
It would be nice if the article cited the actual contracts the labs have signed so that others could also read them and draw their own conclusions (maybe it does and I got tired of the slop-like writing style too early?).
Are the contracts actually take-or-pay-style? What are the terms? What are the amounts of compute and money involved for each future time period? What happens if the datacenter costs spiral upwards? What happens to the datacenter investment if the buyer goes bankrupt, goes public, or gets acquired (potentially by the datacenter owner)?
I personally think the datacenter build-outs are going to generally be a big swing and a miss. The problem 1-2 years ago was making models good enough to be useful for a variety of tasks. Now we have that. The next problem is making the models efficient enough to run a profitable business. Recent Chinese lab model releases (because they're already constrained on compute resources) and OpenAI price cuts on Luna seem to indicate that this transition to chasing efficiency has already started.
> The next problem is making the models efficient enough to run a profitable business.
In that world, what does Oracle do with a bunch of data centres which 1. It needs to pay for and 2. nobody needs. This is what the article is about: building supply far in advance of demand.
People only want cash during the crash so the value of everything goes down. It doesn't matter if you bond has a known 8% yield when held to maturity; the market can't hold it to maturity so its current value drops.
Like go find 2008 in the graph of BND (Vanguard Bond ETF) vs SPY (SNP500) [1]. Let me know how you'd know when to sell your bonds for stocks.
If you can actually time the market, sure. I cannot, so I don't pull money out of my stock indexes; I just send a larger fraction of my new investments into bonds.
IMO there is overinvestment in compute and this will turn out to be a bubble.
But all of the money was anyway just lying around doing nothing. An enormous amount of capital has been building since the 80's thanks to corporate profits. A small sector of the population is so rich they don't know what to do with their capital.
Some other commentators said this was AI slop. I don't have enough expertise to say if it really is, but it sure has the "claudish" cadence of AI generated text that makes it hard to take it seriously. The authoritative and strong statements, the use of phrasing with colons and dashes, the use of bold, etc.
To me it read like a combination of human written and AI slop, as if the author had Claude write it and then rewrote portions of it, or the author wrote an initial version and told Claude to "punch it up, without rewriting the whole thing entirely".
Regardless of who or what actually wrote it the piece was much longer than it needed to be (though I do think it highlights a very real problem).
This post is just AI slop. The details of these contracts aren't disclosed as far as I know.
You need to understand the contracts, the acceleration of demand, how the various inputs into supply scale (energy, chips, data centers) etc to say something sensible about this.
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.
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.
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.
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.
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.
> 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?
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.
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.
For many tasks, you don’t need a frontier model.
scary stuff
"And look at what this implies about OpenAI’s valuation as it moves toward an IPO:
OpenAI’s equity - valued north of $850 billion - is functionally the junior tranche of a capital structure whose senior claims, the take-or-pay compute obligations, exceed any revenue path management itself has articulated.
On those numbers, the equity is effectively underwater, and the market has not priced it that way because it still treats those obligations as service agreements rather than what they are economically: debt.
Even if OpenAI can meet those obligations, OpenAI’s unaudited financial statements - as of March 31, 2026 - disclose $665 billion in non-cancellable compute commitments (management’s more recent plan runs to $750 billion). These commitments are take-or-pay in structure - which, as established above, is debt.
Carry the net present value of those obligations as senior debt - roughly $450–500 billion, the same methodology rating agencies have used for decades to capitalize take-or-pay contracts as debt - and a company the market prices as debt-free carries a senior claim worth more than half its entire equity value."
and the 2008 analog
"Millions of subprime borrowers were, at that moment, paying the low introductory rate on a two-year adjustable rate mortgage - the 2/28 ARM. A low fixed-rate for two years, then the rate reset to a payment 30% to 50% higher. During those first two years the loan performed beautifully: the borrower paid, the servicer collected, and the bond paid its coupon. Nothing looked wrong because the whole complex - housing, mortgages, securitization - was sitting inside the teaser period.
The AI boom has rebuilt this exact structure, and the market is once again underwriting the teaser.
It has a reset wall of its own - a schedule of dated, contractual, non-negotiable payment shocks - hiding inside the trillions of dollars of compute contracts signed by OpenAI and other frontier labs since 2024."
This article is mostly making a different argument (though it contradicts itself in some places), which is that even if the underlying technology does work, and is ultimately going to create quadrillions of dollars of value and transform society, if it takes more than another 1–2 years for that to happen, then there'll still be a crash, because that's when the data center construction bills come due and the labs (especially OpenAI) don't yet have the money to pay them.
It argues primarily against a hypothetical optimist who believes that everything is fine because the cash flow numbers currently work out, on the grounds that this hypothetical optimist hasn't realized that the labs' recurring expenses are scheduled to spike in 1–2 years when the data centers come online and the labs have to start paying for them.
I am not sure that anyone is actually making this mistake (i.e., trying to predict the future by looking at labs' present cash flows). The better counterargument is what Matt Levine used to call "Netflix Theory": if the large capital investors who own stakes in the labs still believe in their valuations (which they should, if the technology works and the quadrillions are coming, which we're assuming here for the sake of argument), then they will be very highly motivated not to let their investment be seized by the labs' creditors. So the labs will not have too much difficulty raising or borrowing enough money to pay the bills.
With AI it comes down to whether the large companies orders and building of datacenters aligns with token demand. There is years worth of lag there so they kinda have to front load this by necessity
One of my distinct memories from this era is watching CNBC where a guest said exactly the same thing.
As the interview went on, he became more animated and used stronger language to the point of:
"You don't get it, THEY ARE GOING TO BE PICKING PEOPLE OFF THE FLOOR when these ARM rates reset"
I would guess this was right about 2006 which lines up with the article.
Are the contracts actually take-or-pay-style? What are the terms? What are the amounts of compute and money involved for each future time period? What happens if the datacenter costs spiral upwards? What happens to the datacenter investment if the buyer goes bankrupt, goes public, or gets acquired (potentially by the datacenter owner)?
I personally think the datacenter build-outs are going to generally be a big swing and a miss. The problem 1-2 years ago was making models good enough to be useful for a variety of tasks. Now we have that. The next problem is making the models efficient enough to run a profitable business. Recent Chinese lab model releases (because they're already constrained on compute resources) and OpenAI price cuts on Luna seem to indicate that this transition to chasing efficiency has already started.
In that world, what does Oracle do with a bunch of data centres which 1. It needs to pay for and 2. nobody needs. This is what the article is about: building supply far in advance of demand.
People only want cash during the crash so the value of everything goes down. It doesn't matter if you bond has a known 8% yield when held to maturity; the market can't hold it to maturity so its current value drops.
Like go find 2008 in the graph of BND (Vanguard Bond ETF) vs SPY (SNP500) [1]. Let me know how you'd know when to sell your bonds for stocks.
[1]: https://www.google.com/finance/beta/quote/SPY:NYSEARCA?keymo...
But all of the money was anyway just lying around doing nothing. An enormous amount of capital has been building since the 80's thanks to corporate profits. A small sector of the population is so rich they don't know what to do with their capital.
I find it amusing that even the AI skeptic articles are written with AI these days.
To me it read like a combination of human written and AI slop, as if the author had Claude write it and then rewrote portions of it, or the author wrote an initial version and told Claude to "punch it up, without rewriting the whole thing entirely".
Regardless of who or what actually wrote it the piece was much longer than it needed to be (though I do think it highlights a very real problem).
You need to understand the contracts, the acceleration of demand, how the various inputs into supply scale (energy, chips, data centers) etc to say something sensible about this.