![Why Wall Street is ignoring big tech's debt [video]](https://www.youtube.com/s/desktop/8e3d9e40/img/favicon.ico)
Join the discussion
Write your take first — we'll ask for email only when you're ready to publish.
- Hacker News
- Capitalism works with debt for growth. Strange HN doesn't understand thisby arisAlexis
- And capitalism also works with crashes taking unprofitable companies down. And frauds, eventually, gets companies valuation like Enron and FTX to zero. And people go to jail.
Now I'm not saying OpenAI or Anthropic are frauds. What I'm saying is that, eventually, things revert to what is just.
The late 90s SV tech-bros behind pets.com or webvan for example faked it for 18 months to 36 months or so. At some point when the expenses outnumber the revenues, capitalism does its thing.
- Subsets of HN do understand this.
Subsets of HN just want to know how long before they need to withdraw their savings, or sell their position.
I would say growth is even expedited with high allowances for borrowing/leverage, but at the cost of increasing systemic risk.
Obviously the people who will suffer from this are the taxpayers, who don't get a say in the matter, so no one is going to stop and consider whether it's a bad idea to continue issuing more and more debt.
by wanda - TINA, There Is No Alternativeby gedy
- That's funny because this still tracks when you use the Craiglist-interpretation of "Tina" – still, there is no alternative when you're smoking it [0]...
[0] I do not recommend smoking it, with well-over a decade "clean" (currently watching a good neighbor succumb to it, which is awful to witness)
- The papers mentioned:
1. — "The Big Market Delusion: Valuation and Investment Implications" - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3501688
The paper behind the "big market delusion" he discusses.
2. - "Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?" - https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2598
This is the "accrual anomaly" paper Boyle describes.
3. - "The ‘Incomplete Revelation Hypothesis’ and Financial Reporting" - https://publications.aaahq.org/accounting-horizons/article/1...
Paper explains why information can be public yet still not be fully incorporated into prices when extracting it takes effort.
4. - "Limited Arbitrage in Equity Markets" - https://www.hbs.edu/ris/Publication%20Files/Limited%20Arbitr...
Mitchell and Pulvino work on the "limits of arbitrage."
5. "How Pervasive Is Corporate Fraud?" - https://www.chicagobooth.edu/research/rustandy/social-impact...
The paper on about one-third of corporate fraud is detected
6. "There’s never been a better time to commit financial fraud" - https://www.economist.com/business/2026/07/29/theres-never-b...
7. "Firm Data on AI" - https://www.nber.org/system/files/working_papers/w34836/w348...
The Bank of England study on executive AI usage and productivity.
by johnbarron - We now have several voices saying the same:
"Aswath Damodaran: Big Tech Has No Idea How AI Pays Off" - https://news.ycombinator.com/item?id=49229981
by johnbarron - There's a kind of inconsistency in that he ends the video talking about overpriced stocks and how it's hard to short them but the title is about the $1.75tn in debt which seems to mostly be from professional investors, apparently:
>JPMorgan Chase and Morgan Stanley, private equity and credit giants like Blue Owl Capital, BlackRock, and PIMCO, alongside international commercial banks
who probably know what they are doing and read the footnotes.
My guess is that the lending is actually ok because there's a lot of real demand for compute. $1.75 tn is about 1.4% of global GDP which doesn't seem that silly in the AI boom.
by tim333 - Wall Street makes money from issuing debt, trading debt, and IPOs. They need to get the last two mega IPOs off before the curtain call on this era.
But they can only partially influence the market, they can’t control it. The question is if the market will let them get these IPOs off or if the jig is up.
Here’s an exercise for anyone curious: pull up the median stock in the S&P 500. Look at its P/E. Take 15 minutes and look through the company’s financials and figure out what you think about the quality of its earnings.
Then decide if that P/E is appropriate.
The bubble isn’t in just AI, the bubble is everywhere, and AI is its largest manifestation.
by yeezyszn - its almost as if we over-financialized our economy...by andrekandre
- It might be more accurate to say that many retail investors are ignoring big tech’s debt.by steelkilt
- That does not explain the buy recommendations for SpaceXby lokar
- to modify sinclair: it is difficult to get a man to understand something, when his bonus depends on his not understanding it
- >> when his bonus depends on his not understanding it
As he explains here: https://youtu.be/NufJ7g63KSY?t=1233
by johnbarron - Is Sam Altman even allowed to rent a car with his level of debt?by amelius
- he just rings up his microsoft daddy
- The creativity of the financial industry is quite insane. Combined with the scale-at-all-cost strategy in AI companies, this is a ticking time bomb, no doubt.
I think it is easy to forget that a revolutionary technology does not automatically make a viable business model. I think we are yet to see the real winners in this game.
by bnfcl - I agree with you. Look at railroads, cars, computers, and other technologies that really did end up being world-changing. How many of the original companies are still around?
Sometimes I feel like OpenAI will end up like Fairchild Semiconductor: an early innovator that quickly became an historical footnote.
by Breza - I don't know a single white collar worker who isn't using AI for their job. Not like forced, but like "Oh damn, this bot thing can do a lot of tedious leg work for me".
To think that people won't pay $60-$80/mo to continue using it is wild to me. In a white collar environment it pays for itself in a few hours of use.
If you focus on how much value AI brings to people (mostly in time saved), the bubble hardly looks bubbly.
Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.
I'm sure there will be some losers, but unlike the dot-com boom, the entirety of the population already has all the tools needed to leverage AI.
by WarmWash - The reason uber didn't loose that much business is because it replaced the established businesses and left most consumers with little alternative.
That's not the case with AI - unless the frontier labs achieve AGI or some sort of super intelligence that lets them create infinite economic value (at which point, any further discussion is pointless for obvious reasons), the average worker can do most of their tasks with 5% of the api costs using an open source model from China and achieve the same results.
by borzi - That’s not the argument though. The argument is that conversion rates as per OpenAI are about 5% and to serve the other 95% you need to build significant infrastructure that costs hundreds of billions annually. So for every white collar worker that you know and uses it and pays for it there are 20 free loaders. And mind you, 5% conversion rate is extremely high already as a number per se. It won't magically reach 20% in a couple of years. Chances are it will fall particularly for the enterprise segment due to the advent of the Chinese models.by elorant
- > Did Uber die when a trip across town went from $3 to $13? No. It's giving more rides than ever, 10x more than when it was $3.
Uber has a fairly large moat: the regulatory mess they’ve waded through and the collection of drivers willing to drive for them. Sure, it’s probably easier to start a competitor now than 20 years ago, but there’s still a substantial network effect — a transportation service needs a lot of cars to provide good service, and they need a lot of users to keep those cars busy.
An AI service like chatgpt.com or claude.ai costs literally billions of dollars to develop, except that several Chinese companies are currently happy to spend that money and release the models and weights for free. And even a small company can rent the datacenter capacity they need, at least at moderate scale, and they only need enough scale to reliably fill up the batches on a handful of servers. There is no obvious network effect — a tiny Uber with a total of two cars in a city is useless, but a tiny AI provider that only services 2000 users is just fine as long as someone else provides the models weights.
And the costs to run these services keep coming down. The numbers people are reporting for unquantized DeepSeek v4 Flash 0731 on 2x RTX 6000 Pro [0] using open weight models and open source inference stacks are astounding, and one single copy of this could probably cover the entire inference usage of a decent sized business doing the kind of desk work that OpenAI and Anthropic could need to monetize to cover their expenses. (Using bespoke models has value but may reduce available usage of a single inference machine. Quantizing can make everything cheaper and is often good enough.)
This is not to say that these companies won’t make it on the strength of their enterprise offerings (never underestimate the willingness of enterprises to overpay for services they’re already using or by becoming the next Googles and finding other revenue sources beyond just inference.
[0] I admit to some skepticism that the pile of sketchy, obviously AI-generated patches to SGLang that allegedly achieve this are doing what the say they’re doing, but the point stands.
by amluto Google funds Anthropic | v Anthropic promises to rent Google's TPUs | v Google guarantees the infrastructure needed to fulfil Anthropic's promise | v Wall Street lends against Google's guarantee | v Borrowed money buys Google-designed TPUs | v The TPU purchases "prove" demand for Google TPUs | v Anthropic's compute capacity and valuation rise | v Google's investment in Anthropic rises in value | v Higher valuations justify still more financing | +--------------------------------------+ | v DO IT AGAINby root-parent- $80 per month in a 5,000 people Enterprise is 400k per month which translates to ~5m per year. That is easily in the 8-16% of their total IT budget. I don’t know you, but companies don’t like to increase recurring cost. Is AI good? Yes. Is it clear how it generates enough ROI to justify a material IT cost increase? No. This is the problem now, not how much white collar workers use AI, but what is the productivity narrative of it. Because AI is so generic, there is no clear specialized use case, like when Salesforce invented the CRM for example.by acivitillo
- And I know very few white collar workers who are, beyond "I guess Google Search is AI now". Each their own bubble, I guess.
AI has some value in some scenarios. Most models still fail badly enough often enough that I personally would not pay $80 for it. I would make use of it if it were free, or $10 / month, but it would have to significantly improve if I were to ask my employer to pay $150 / month for it - let alone $1500 / month.
The evidence is obvious: everyone and their dog has access to highly subsided AI agents right now. Literally every single company is pushing its employees to trial AI - some even forcing AI use. So why aren't we seeing a giant white-collar productivity boom yet?
The idea that every single white-collar worker is going to get a $800 / month AI subscription is ludicrous. The big question is, can the AI companies survive off everyone getting a $5 / month subscription, with a handful of workers getting a $500 / month one?
by crote - The real question is not whether there will be a market for AI, it is obvious that there will be one (as it was obvious the Internet was a gigantic thing in the dotcom boom). It is rather whether the value will be in the models (and if so in which?) or the infrastructure or somewhere else.
Interesting thoughts from Dwarkesh Patel on the future of models [1]. In summary, no moat if a client can switch to a competitor from a dropdown when all models are similar-ish (so models are commodities), but to really compete with humans, AI needs to start "learning on the job", i.e. accumulating experience like an employee (as opposed to the training / inference approach where the model isn't learning after it is released). If your model does that, then you have vendor lock-in as you can't replace an experienced model with a competitor. Then the value is in the model.
As in the 90s, this could go in all sort of different directions, hard to make predictions. And remember the 90s: the value was in the browser (you had to purchase it, eg Netscape), then the browser came for free with the OS (no value in the browser), then the browser was the most important thing in the world as it was control over the default search engine, everyone thought portals was the most important thing to control, so ISPs had their own applications accessing the internet from their own portal, etc - none of those people were stupid.
by cm2187 - But the question is how much are people willing to pay for AI.
I use AI every day at work, but I only pay $20. That's the most I will ever want to pay. And, so far, it gives me everything I need.
If OpenAI or Anthropic suddenly said "Sorry, the game is up. You'll have to pay $100/month now", I would 100% look into cheaper Chinese solutions.
I suspect the AI subscription (or API) economy is whale economy. You have a small, small percentage of users that are happy to pay whatever it takes - while the vast majority will either use the free tier, and then the next group will pay for the cheapest or next cheapest subscription.
EDIT: And I'll echo what another user wrote here. The VAST majority of office users around the world don't work for tech companies flush in cash. Even adding something like $20 - $100 subscriptions to every user is a serious enough financial obligation that it needs to go through budget planning / boards. Even more so for all the government workers around the world.
by TrackerFF