The current commitment by hyperscalers is around 1.7T USD, reported liabilities 1.3T and this year global debt related to AI is 570B. So that’s around 3T total. For this to make sense AI must generate 2T in new revenue per year by the end of the decade. And that would be only a 10% ROIC. For context ROIC for big tech is around 35% so at 10% they will be barely breaking even. The SP500 gives 10-12%. With 10% ROIC from AI the only thing investors will be celebrating is that the whole thing didn’t trigger a financial crisis. Data centers are NOT real estate. Buildings and power lines usually last 30-50 years. GPUs become obsolete in 5 years. If hyperscalers need to refinance and their interest rate goes up there’s zero margin for error.
The alarms in this case are that the profits and margins won’t be as high as we’ve come to expect from cloud companies.
Other than Oracle’s questionable spending spree, these big tech companies are still in very good financial positions. The enormous R&D and infrastructure spends are just feeling unusual to investors who got comparable with the unusually high margins and low costs for SaaS companies. Now they have to put a lot of that money back into the business like more normal companies.
If the margins aren't as high then there will be a repricing for all the massive cloud companies, which means several trillions worth of valuations to be cut from the companies.
AWS/Azure/GCP/Oracle/SpaceX/etc neoclouds... are worth a combined 10+Trillion. That going down by 50-70% is going to be insane.
Why? GPUs are replaced every 3 to 5 years. This is going to be an ongoing operational cost forever. It will probably increase more if larger models require bigger VRAM sizes.
Cannot hear what you’re saying with all those alarms blaring non stop since a year. Someone should do something about them, maybe turn them off, I don’t know
> Everyone is in too deep to now admit that there’s a problem
I'm not sure how to square this with the dramatic improvement in LLM capabilities in the last 8-9 months. If anything, it makes the earlier investments look prescient?
"Anthropic and OpenAI generate a lot of revenue with relatively few employees – an estimated $9M and $5.5M in revenue per employee (RPE), respectively. If either company were to go public, it would have a higher RPE than any public tech company on Forbes’ Global 2000 list." https://epoch.ai/data-insights/revenue-per-employee-ai-compa...
This assumes they do not have to increase prices to be profitable, and that they will continue to have customers when customers can switch to open models at similar performance.
As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open model landscape looks like later. Model training and development is expensive, self hosted inference on open models not so much.
(a component of my work is currently building scaffolding so our organization can swap out commercial inference providers for on prem inference infra to derisk against the eventual rug pull when the math gets icky for LLM providers, while consuming as much subsidized tokens as we can until then, when it makes sense to use tokens for work)
The question will be whether customers can switch.
Can you install a near-SOTA model on a cluster in a data center? Of course. Compliance and operations are the sticking points. I work in healthcare IT, and it's amazing how tight the data compliance requirements are. I can't have someone in Canada look at prod data. If we told hospitals that we were handing off PHI/PII to Chinese models, they'd end our relationship due to the long history China has of hacking Western networks and computers. They don't care how open and cheap things are.
Then, you have to keep up-to-date on the latest technology and right-size things in a very fluid market. If you sign a contract for hosting the model on a data center that's running what the SOTA is now in hardware, and someone comes through with a data center hardware or software product that makes that data center contract a disadvantage (maybe it's too expensive and the other party won't budge on the price), you might have to factor that into your offering's price, and that could put you at a disadvantage in your marketplace.
Google, MS, etc. all want to leverage the cloud model to make this be less of an issue for you, for a price. They have the ability to update you with the SOTA stuff in the data centers, because they're the ones driving that SOTA. They can say they host in the US and develop most of their stuff in the US.
Will that be enough of a moat?
Probably not for the levels of spending that are happening now, but over the long term, probably.
How long will a SOTA model be necessary? If day to day work can be achieved on an open weight model, the most evaporates overnight.
Look at any computer in a big company. It isn't the fastest on the market, nor will it have the most RAM or largest monitor or fanciest keyboard. It is good enough at a good enough price point. Once it becomes possible and cheaper to host your own good enough open weight models, with all the benefits of keeping data internal to the company, then the big providers are cooked, so to speak.
My primary role is cybersecurity in a regulated entity in a regulated industry, I am highly confident it is straightforward to do so based on work accomplished in only a couple of weeks. Stand up a router, stand up a Kubernetes cluster if you don't have one, stand up the necessary VMs and compute for serving inference. Two pizza team, in my experience.
Customers can switch (although we can argue the speed and pain of doing so), and the speed at which they do will be a function of cost efficiency and demonstrable value (imho). A recent example of this is Broadcom and VMware [1], for example. When motivated, it can be done. If there is no objective, measured value being delivered, the spend will be cut. If the value delivered is measured, it will be enabled at a lower cost through cost optimization measures (ie self hosting).
This is all to say: there is no moat, the revenue of inference providers is volatile and not assured in any measure. Caveat emptor.
Meanwhile, in real companies, you have to wait 2 months or more to access an API endpoint in preprod.
To setup a cross business kubernetes cluster will take 2 years with unknown results.
On Cloud, in Switzerland, you need to call Microsoft when you need new resources, so much for agility and minute infrastructure provisioning, and I heard the same for AWS.
The technology is too hard to capitalize on. It’s far more democratic than, say, an iPhone, or a search engine. Anyone can download a model to their computer and start toying with it, how do you profit off of that? Even if everyone was constantly tokenmaxxing (which we cannot, since the process gets fucked up if you let it run entirely on its own), it probably still wouldn’t be marginally profitable.
(The claim felt so wild I wanted to check, and indeed, the private Google Cloud for the $125bn Australian pension fund was accidentally deleted by a provisioning misconfiguration. Any others?)
Yes, Google randomly deleted UniSuper for basically the same reason they randomly ban individual customers: they don't care. Relying on them for anything is a huge mistake.
IIRC, the files for Toy Story 2 were accidentally deleted during production, and the film was only saved because someone on maternity leave had a backup at home.
Exponential growth when you're starting from zero is neither difficult nor sufficient in this case. The title of the linked thread is "Dramatic cash burn." So clearly, the revenue did not grow anywhere fast enough.
I know it's easy to forget, and InsideOutSanta kind of anchored the conversation on "revenue", but profit is ultimately what matters. Back when Silicon Valley was merely insane rather than bat-guano crazy insane, it was commonly observed that it's not hard to build a business around selling a dollar for 95 cents. The point being that it doesn't necessarily mean much when you have a business doing that, because of course the demand will be insane. It doesn't mean you have a viable business. You don't know you have a viable business until you transition to selling a dollar for $1.03. Many a VC-funded business that looked successful, even wildly successful, has run aground on that transition, or at least, suddenly stopped looking so wildly successful.
If AI-related expenses are also growing exponentially, and they are growing exponentially faster, it doesn't matter that revenue is growing exponentially.
The AI funding has also now absolutely baked in exponential growth of expenses, because that's how debt works. A slow exponential, hopefully, but an exponential none-the-less.
Something Hacker News needs to be periodically reminded of is that we are the field getting the most out of AI, and it's not even close. That's great for us. But the stocks aren't priced for "a pretty nice coding tool". They're priced for every field in the world getting even more value out of this than our field is getting now. That is, frankly, not happening anywhere near fast enough for the spending and stock valuations. When you don't have all the engineering guardrails that are present in software engineering [1], suddenly the AI is, ahem, exponentially less useful.
As I say in that post, watch your AI actually doing something, even the frontier models. Watch the thinking traces. Watch how many times they bang into a guardrail of some sort; a failing test, a failing compile, a linter failure, a bash script that doesn't work, all those things. How much value would you get out of an AI coding assistant if the first time it banged into a guard rail it was done and you had to stop using it for that task? How much value would you get out of an AI coding assistant if instead it silently failed and just proceeded forward with errors that you lack the infrastructure to easily detect? In the first case, it would be fairly modest, almost certainly not worth the money, and in the second, it would be worth paying to not use.
Even in our field, while the rate of code output has increased substantially, the rate of value generation increase has been quite a bit more modest. I have observed, and heard from a number of other places, that while my own output has increased somewhat we still generally can't plan on being able to work with other teams at much faster a rate than we used to.
There's a viable business here but I can't see how all these companies expect to be returning all this revenue in any financially sensible period of time. They're all spending like if only they spend enough they can own about %900 of the market in three years. They can't all do that, even accounting for "AI makes the market bigger".
And they're wildly vulnerable to some new solution coming out that obsoletes all this spending, like an ASIC that starts running a popular model directly (especially if model capabilities plateau), meaning that all this nVidia GPU spending is so much dead silicon. Or someone comes out with a much more efficient way to train models. There has to be some insight we're missing; humans do not learn what they do by having the entire contents of the Internet poured through their head hundreds of times over. We are far more efficient with our training data. What if someone works out a solution to that and we don't need to spend billions on GPUs but only millions? The whole spending proposition could collapse overnight and the companies that suddenly have three orders of magnitude too much hardware and the debt to match would be up a creek without a paddle.
> we are the field getting the most out of AI, and it's not even close.
Just emphasizing that as, due to spending far too much time online the past week, I've been seeing a fair bit of this. "AI is definitely gaining popularity because all the software companies I know are going all in on it."
For certain values of ‘dramatic improvement’. Is lots more important work being done with LLMs? Not much sign of it yet, they’ve been helpful for experts at times (e.g. vuln research or maths research) but that hardly justifies the vast sums for Google investors.
Presumably at some point you need a measurable productivity return yea? Maybe organizations are not built around skill and aptitude so much as liability, which LLMs cannot provide barring (very welcome and also very unlikely) legislation in the US.
The infamous 2025 MIT study that found almost all AI pilots in companies were failing, also found that virtually every worker was using AI many times a week if not daily.
Turns out people just use their personal AI accounts rather than company ones. Which would make sense if you want to claim the work the AI does as your own.
>Presumably at some point you need a measurable productivity return yea?
At what point? This technology is brand new. Did you think we were going to double productivity in 3 years?
Capacity is being built. It's hard to build data centres, there are no chips, there is no memory, it's hard to get talent, we don't have the energy to power the facilities.
No one knows where this is going. We are scratching the surface. There is an absolute boom happening, and yet every day I have log onto Hacker News and read this nonsense about everything falling apart. Are we living in the same universe??? So-called "technologists" saying, "meh, it's not that cool". Okay.
Guess what? You're not Michael Burry. Nobody cares or will care that you "called it". Look around this place: you aren't even slightly contrarian.
"We would be profitable if we had the resources but we don't," isn't the smackdown argument you seem to believe it is.
There used to be a thing where successful tech companies were profitable right out of the gate, and very successful companies doubled those profits for years, and companies who bought and used the tech could point to clear, actioned, benefits and cost savings.
Now it's all "This will be really, really profitable one day, probably, if the omens align and we can deal with all of the problems."
No, because the LLMs will keep getting more efficient and capable. Distillation and quantization will mean firms spending trillions on giant data centres are left holding the bag. I suspect Apple ends up laughing all the way to the bank.
I'm not sure I've seen what I would call dramatic improvement since maybe GPT4?
Sure, things got better. But I'd call it iterative more than revolutionary. I still wouldn't trust any of the models to do anything meaningful unattended. They all still do dumb shit all the time.
Plus, even if they were genuinely dramatically better, the businesses sure as hell aren't. They're burning money left and right, they have no moat, Chinese open models are basically equivalent these days. What's the path to profitability, or hell, break-even? How do you envision this being anything but a giant money pit?
I mostly use anthropic models, but there was a big step function when claude code came out, and it’s been incremental or a plateau since then.
Opus 4.6 and 4.8 are basically indistinguishable from Fable and Sonnet 5. 4.7 was a hot mess. The guardrails on 4.8 and 5.0 make them worse than 4.6 for many tasks. So, even if Fable is theoretically better, refusals/downgrades make it a worse product in practice. Who cares if it outperforms on 1-2% of real world tasks if 5-10% of tasks are blocked?
I’d bet most people could be downgraded to a 12 month old frontier model, and not notice for a week or so.
Anthropic’s big problem is that open weight models are 0-6 months behind. So, their product is commoditized and margins are never going to be good.
It doesn’t matter… are those companies using AI getting a positive ROI? So far there is no signs it is the case, unless you’re yourself selling AI stuff
Because there are almost no "We used AI to save money, improve our services, and gain more customers" success stories.
There's a lot of "We fired a lot of people because we're sheep and now we're having to hire some of them back" stories. And a lot of "A few engineers are doing a lot more, but we're not quite sure how to turn that into actual money" stories. And even more "We told everyone to tokenmaxx, and they did, and then we realised it was costing too much, so we stopped," stories.
But there really hasn't been a deluge of "AI has cut costs and increased profits while also improving quality" stories.
There has been a small outbreak of vibe-startups offering fairly generic services - mostly marketing and adjacent - who are doing okay, possibly.
Sarcasm over a legitimate question really? After about 4 years I think it's totally acceptable to ask where the profit is on any company's 10-K. Where are even the revenues on a 10-K?
Tech is real, impact is gigantic, long term winners hard to predict, capex spending hard to recoup soon, if ever.
And differently than internet or rails, you don't build once and maintain later, but enter a loop of ever increased spending to keep on top of the arms race and ever exploding usage.
They've literally rated the debt as too big to fail in order to get foreign sovereign wealth funds (mostly gulf states) to agree to put up the money for loans. This has been happening this entire time.
Specifically, that the US economy is not doing well. And that the investors who don't know a thing about AI will continue to sing its praises for everyone who is willing to believe fairytales. Until the crash comes.
I see everyone around me doing way more work, of way more depth, than they ever did before using AI models. I see my company and friends of mine all paying large sums of money to Anthropic, Google, OpenAI to use AI models, and do more work than we did before.
So Google is investing in infrastructure which is HIGHLY in demand, there is much more demand than supply, and then they are making money from this infrastructure...
That's a good thing for Google, and as an investor in Google, I am glad they are making these investments.
What would be the best thing to do with ones investments considering these alarms?
Say you had some money in cash rn, what should one do? Wait for a crash and buy stuff up cheap? Put it in some safe category?
This stuff is stressing me out and I do believe it's gonna come crashing down sooner or later, but I don't know enough about investments to know how to best come out unscathed.
Diversify! Historically, the average length of a recession has been 12-24 months. So set up a system whereby you won’t screw’s yourself over by selling when things are low, but instead you can weather the storm.
Build a rainy day fund. Determine how much cash you will need if you are out of a job and how long you think that will last, allocate some portion of that amount into low risk bonds. Russ way if you need cash you aren’t selling investments at a big loss.
If you have enough liquidity put some in real estate as a forced savings vehicle as it’s harder to liquidate than stocks. Then just sit out any coming storm.
I'm thinking Apple has been really smart in their AI strategy here.
It seems a mistake to make unprecedentedly large capital expenditures, in a very very crowded space, without much evidence of a moat. Presumably people thought the moat would be singularity-like self-improvement of AI, but the singularity is merely a religious concept, and nobody should take religious myth as fact, it's merely narrative for orientation and inspiration.
i dont understand the concern. they are putting up great financials. you have to invest ahead of the outcome. this is just classic quarterly public company earnings BS, where public markets dont reward innovation investment. they just want crank the handle financials.
The bigger issue is on the model front, can Google compete; Gemini doesnt seem to be able to compete on the heavy expert end; they are doing well on lighter faster models.
People bought Google for the torrential free cashflow, that looks like its gone forever with this new capital intensive model. If that the case then it needs to be valued like a heavy industrial rather than a capital light tech company.
I've given up on Gemini. It sounds smart but most of what it tells me ends up being wrong or misleading. I might actually hand $20/mo to OpenAI. It's been far more helpful with the random collection of legal and health problems I've thrown at it. My recent comment history is going to make me come across like a shill for them but, holy crap, GPT has been doing amazing things for me at work as well.
I don't get it either... Google has so much talent yet they just can't seem to get it right.
Looking at cash burn is looking at the wrong end of the horse. Some companies, like Meta, have burned huge piles of cash in pursuit of, for example, the Metaverse and they've got nothing to show for it, not even a slight increment in ad tech, and yet they earned enough to shrug it off.
There's a big difference between Google spending tens of billions on AI infrastructure and what Oracle is doing. Oracle is spending to get on a bandwagon. Google is transforming their business, so far seemingly correctly. If AI flops big-time, Google will be left with some stranded assets, but it won't be existential the way it would be to Oracle.
> The search giant now expects to spend between $195 billion and $205 billion in capital expenditures, its finance chief Anat Ashkenazi said on a conference call with analysts. The company said last quarter that it planned to spend between $180 billion and $190 billion this year.
Only google serves its own model - increasing its cloud revenue. The growth chart shows linear increase over time, indicating exponential growth if cloud revenue for google.
Meta at least has a theory that it will transform their ad business. No guarantees but it seems like a pretty decent theory, with direct connections to revenue.
They just raised $85 billion and they're sitting on a mountain of cash - if their spending didn't increase in this context, it'd be bad management. The real story here is that they have decided to spend that mountain of cash on AI CapEx.
> "If there is a huge demand for shipping goods internationally,
> investing in ships and planes isn't burning money.
> There is massive demand for compute in the world right now"
Emphasis on right now. CapEx makes sense if the demand is forecast to deliver enough profit over the expected lifespan of the investment to recoup the cost and margin.
There's enough hype and exuberance in the AI market that it's likely some players are going to be left holding the bag with a write-down on assets.
Serious investors look at balance sheets, less then what CEOs say. Elon Musk -- as an example-- says all kinds of things that don't really happen. Mark Zuckerberg is arguably less grandiose. When FB changed their name to Meta, said they were committed to the metaverse the stock didn't dump. When the really big investments in consumer VR hit Meta's balance sheet, there was a big drop.
Think of it as the difference between the waiter describing dishes with ingredients you don't really understand (or maybe even taste) vs presenting the bill for the meal.
If a company’s value was completely representated within their balance sheet, you would just run a computer program and be done. The problem is 1) balance sheets can be manipulated in legal ways to support a specific narrative 2) growth is governed by vision + strategy + execution.
For example, Apple the year before the iPhone got launched isn’t an attractive investment. They’re a one hit wonder with the iPod saving them from bankruptcy and the market has been fully saturated. The year the iPhone gets released their balanced sheet hasn’t really changed.
It could absolutely harm their long term value but keep in mind Alphabet and the other hyperscalers are generally flush with cash. Is this a lot of debt? Absolutely but the businesses are generating a lot of cash too.
Basically all of Big Tech is betting it all on Red that this whole AI business pays off before they end up losing everything. And I get it, it would be unwise to stay behind and ignore what could very easily turn out to be humanity's greatest invention since pizza. But still, is there seriously no other way to go about it instead of collectively running head first, hands behind at a breakneck pace, while risking the complete collapse of ... well, everything? I suppose not, especially considering it's a technology with potentially massive military and social impact on a global scale, or even beyond that if we're being particularly delusional. Though one has to wonder who will end up paying the tab, and I think that we all know the answer to that.
This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term
Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries
Google’s response?
Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for
A few examples to illustrate:
For all of its history until recently, Google operated on a 2nd price auction model
I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid
This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much
However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding
It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem
Making thing worse, Google also recently nerfed keyword targeting precision
Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting
This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed
But now, even if you bid on a specific term or phrase using the strictest exact
-match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)”
The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off
So now exact match is broad match, and broad match is just meaningless spam
This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants)
This is how you grow revenue atop declining search volumes
Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day
And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off
Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target
These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow
Google operated a benevolent monopoly for the better part of 25 yrs
Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future
This is now no longer the case
At the alter of AI capex, Google is sacrificing the golden goose
> Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day
When I worked on Google Ads (I left in 2020), I remember this one tripping a lot of people. As I remember it, the limit for a single day is indeed 2x daily budget, but over a month it will average to it. This is supposed to give more flexibility to the auto bidder.
Are you asserting that there exists a time period to analyze that is not arbitrary and meaningless? If so, which?
The reason why 2026 specifically is interesting is because it wasn't until late December of last year that AI models started to demonstrate particularly interesting capabilities, while we finally got IPO announcements for OpenAI and Anthropic. Assuming that the market works at all, it should be pricing in these events.
If what they're referring to is coding agents, I can say they were pretty crappy as of November last year, and they've come a long way. Much more useful and usable now.
Amusing how volatile these stocks are. In any case, my intent was not to boost Alphabet but to denounce the overheated state of the tech sector in general, so having the entirety of the Mag 7 fall below the baseline is no skin off my nose.
Other than Oracle’s questionable spending spree, these big tech companies are still in very good financial positions. The enormous R&D and infrastructure spends are just feeling unusual to investors who got comparable with the unusually high margins and low costs for SaaS companies. Now they have to put a lot of that money back into the business like more normal companies.
AWS/Azure/GCP/Oracle/SpaceX/etc neoclouds... are worth a combined 10+Trillion. That going down by 50-70% is going to be insane.
I'm not sure how to square this with the dramatic improvement in LLM capabilities in the last 8-9 months. If anything, it makes the earlier investments look prescient?
As an analogy, Uber could crank up rates after the VC growth play was over to stoke revenue and profits because they have a duopoly with Lyft. LLM consumers can switch to Kimi models fairly trivially today, and whatever the frontier open model landscape looks like later. Model training and development is expensive, self hosted inference on open models not so much.
https://www.wheresyoured.at/the-openai-bubble/ has the math.
(a component of my work is currently building scaffolding so our organization can swap out commercial inference providers for on prem inference infra to derisk against the eventual rug pull when the math gets icky for LLM providers, while consuming as much subsidized tokens as we can until then, when it makes sense to use tokens for work)
Can you install a near-SOTA model on a cluster in a data center? Of course. Compliance and operations are the sticking points. I work in healthcare IT, and it's amazing how tight the data compliance requirements are. I can't have someone in Canada look at prod data. If we told hospitals that we were handing off PHI/PII to Chinese models, they'd end our relationship due to the long history China has of hacking Western networks and computers. They don't care how open and cheap things are.
Then, you have to keep up-to-date on the latest technology and right-size things in a very fluid market. If you sign a contract for hosting the model on a data center that's running what the SOTA is now in hardware, and someone comes through with a data center hardware or software product that makes that data center contract a disadvantage (maybe it's too expensive and the other party won't budge on the price), you might have to factor that into your offering's price, and that could put you at a disadvantage in your marketplace.
Google, MS, etc. all want to leverage the cloud model to make this be less of an issue for you, for a price. They have the ability to update you with the SOTA stuff in the data centers, because they're the ones driving that SOTA. They can say they host in the US and develop most of their stuff in the US.
Will that be enough of a moat?
Probably not for the levels of spending that are happening now, but over the long term, probably.
Look at any computer in a big company. It isn't the fastest on the market, nor will it have the most RAM or largest monitor or fanciest keyboard. It is good enough at a good enough price point. Once it becomes possible and cheaper to host your own good enough open weight models, with all the benefits of keeping data internal to the company, then the big providers are cooked, so to speak.
Customers can switch (although we can argue the speed and pain of doing so), and the speed at which they do will be a function of cost efficiency and demonstrable value (imho). A recent example of this is Broadcom and VMware [1], for example. When motivated, it can be done. If there is no objective, measured value being delivered, the spend will be cut. If the value delivered is measured, it will be enabled at a lower cost through cost optimization measures (ie self hosting).
This is all to say: there is no moat, the revenue of inference providers is volatile and not assured in any measure. Caveat emptor.
[1] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
To setup a cross business kubernetes cluster will take 2 years with unknown results.
On Cloud, in Switzerland, you need to call Microsoft when you need new resources, so much for agility and minute infrastructure provisioning, and I heard the same for AWS.
it has to be some amazing router and while the models are open-weights, the knowhow to run them efficiently surely is not?
Meanwhile the cost/benefit analysis doesn't move much even if you are paying 2x for tokens, and you don't need anything on prem.
(The claim felt so wild I wanted to check, and indeed, the private Google Cloud for the $125bn Australian pension fund was accidentally deleted by a provisioning misconfiguration. Any others?)
Turns out you can fuck up self hosting too.
Completely false.
AI and AI related revenues are growing exponentially.
Or are you just adding nonsense about "yeah but yeah but no value"?
Please try and provide one for such strong claims.
If AI-related expenses are also growing exponentially, and they are growing exponentially faster, it doesn't matter that revenue is growing exponentially.
The AI funding has also now absolutely baked in exponential growth of expenses, because that's how debt works. A slow exponential, hopefully, but an exponential none-the-less.
Something Hacker News needs to be periodically reminded of is that we are the field getting the most out of AI, and it's not even close. That's great for us. But the stocks aren't priced for "a pretty nice coding tool". They're priced for every field in the world getting even more value out of this than our field is getting now. That is, frankly, not happening anywhere near fast enough for the spending and stock valuations. When you don't have all the engineering guardrails that are present in software engineering [1], suddenly the AI is, ahem, exponentially less useful.
As I say in that post, watch your AI actually doing something, even the frontier models. Watch the thinking traces. Watch how many times they bang into a guardrail of some sort; a failing test, a failing compile, a linter failure, a bash script that doesn't work, all those things. How much value would you get out of an AI coding assistant if the first time it banged into a guard rail it was done and you had to stop using it for that task? How much value would you get out of an AI coding assistant if instead it silently failed and just proceeded forward with errors that you lack the infrastructure to easily detect? In the first case, it would be fairly modest, almost certainly not worth the money, and in the second, it would be worth paying to not use.
Even in our field, while the rate of code output has increased substantially, the rate of value generation increase has been quite a bit more modest. I have observed, and heard from a number of other places, that while my own output has increased somewhat we still generally can't plan on being able to work with other teams at much faster a rate than we used to.
There's a viable business here but I can't see how all these companies expect to be returning all this revenue in any financially sensible period of time. They're all spending like if only they spend enough they can own about %900 of the market in three years. They can't all do that, even accounting for "AI makes the market bigger".
And they're wildly vulnerable to some new solution coming out that obsoletes all this spending, like an ASIC that starts running a popular model directly (especially if model capabilities plateau), meaning that all this nVidia GPU spending is so much dead silicon. Or someone comes out with a much more efficient way to train models. There has to be some insight we're missing; humans do not learn what they do by having the entire contents of the Internet poured through their head hundreds of times over. We are far more efficient with our training data. What if someone works out a solution to that and we don't need to spend billions on GPUs but only millions? The whole spending proposition could collapse overnight and the companies that suddenly have three orders of magnitude too much hardware and the debt to match would be up a creek without a paddle.
[1]: https://jerf.org/iri/post/2026/programming_is_engineering/
Just emphasizing that as, due to spending far too much time online the past week, I've been seeing a fair bit of this. "AI is definitely gaining popularity because all the software companies I know are going all in on it."
Turns out people just use their personal AI accounts rather than company ones. Which would make sense if you want to claim the work the AI does as your own.
At what point? This technology is brand new. Did you think we were going to double productivity in 3 years?
Capacity is being built. It's hard to build data centres, there are no chips, there is no memory, it's hard to get talent, we don't have the energy to power the facilities.
No one knows where this is going. We are scratching the surface. There is an absolute boom happening, and yet every day I have log onto Hacker News and read this nonsense about everything falling apart. Are we living in the same universe??? So-called "technologists" saying, "meh, it's not that cool". Okay.
Guess what? You're not Michael Burry. Nobody cares or will care that you "called it". Look around this place: you aren't even slightly contrarian.
There used to be a thing where successful tech companies were profitable right out of the gate, and very successful companies doubled those profits for years, and companies who bought and used the tech could point to clear, actioned, benefits and cost savings.
Now it's all "This will be really, really profitable one day, probably, if the omens align and we can deal with all of the problems."
https://github.com/microsoft/BitNet
A good tech demo doesn’t matter to the business if the products don’t become profitable at the scale the investment chased.
Sure, things got better. But I'd call it iterative more than revolutionary. I still wouldn't trust any of the models to do anything meaningful unattended. They all still do dumb shit all the time.
Plus, even if they were genuinely dramatically better, the businesses sure as hell aren't. They're burning money left and right, they have no moat, Chinese open models are basically equivalent these days. What's the path to profitability, or hell, break-even? How do you envision this being anything but a giant money pit?
You'd think if there had been that many dramatic improvements I'd have to babysit an LLM less frequently.
LLM conversations online are so weird. Whenever I read things like this it’s like I’m living in a different world than the other person.
GPT4 was almost useless compared to what we have available today.
Opus 4.6 and 4.8 are basically indistinguishable from Fable and Sonnet 5. 4.7 was a hot mess. The guardrails on 4.8 and 5.0 make them worse than 4.6 for many tasks. So, even if Fable is theoretically better, refusals/downgrades make it a worse product in practice. Who cares if it outperforms on 1-2% of real world tasks if 5-10% of tasks are blocked?
I’d bet most people could be downgraded to a 12 month old frontier model, and not notice for a week or so.
Anthropic’s big problem is that open weight models are 0-6 months behind. So, their product is commoditized and margins are never going to be good.
Nobody cares about your personal hangups about AI. Tons of people are building with it.
Yes.
>So far there is no signs it is the case
How could you possibly know this?
There's a lot of "We fired a lot of people because we're sheep and now we're having to hire some of them back" stories. And a lot of "A few engineers are doing a lot more, but we're not quite sure how to turn that into actual money" stories. And even more "We told everyone to tokenmaxx, and they did, and then we realised it was costing too much, so we stopped," stories.
But there really hasn't been a deluge of "AI has cut costs and increased profits while also improving quality" stories.
There has been a small outbreak of vibe-startups offering fairly generic services - mostly marketing and adjacent - who are doing okay, possibly.
But established tech? Doubt.
The point is, is anyone getting any value from it?
No, you're right, no one is getting any value from it.
Tech is real, impact is gigantic, long term winners hard to predict, capex spending hard to recoup soon, if ever.
And differently than internet or rails, you don't build once and maintain later, but enter a loop of ever increased spending to keep on top of the arms race and ever exploding usage.
I see everyone around me doing way more work, of way more depth, than they ever did before using AI models. I see my company and friends of mine all paying large sums of money to Anthropic, Google, OpenAI to use AI models, and do more work than we did before.
So Google is investing in infrastructure which is HIGHLY in demand, there is much more demand than supply, and then they are making money from this infrastructure...
That's a good thing for Google, and as an investor in Google, I am glad they are making these investments.
https://www.youtube.com/watch?v=gUkbdjetlY8
Don't think that day is far when "software people" are paid as if they were taxi drivers.
Say you had some money in cash rn, what should one do? Wait for a crash and buy stuff up cheap? Put it in some safe category?
This stuff is stressing me out and I do believe it's gonna come crashing down sooner or later, but I don't know enough about investments to know how to best come out unscathed.
Build a rainy day fund. Determine how much cash you will need if you are out of a job and how long you think that will last, allocate some portion of that amount into low risk bonds. Russ way if you need cash you aren’t selling investments at a big loss.
If you have enough liquidity put some in real estate as a forced savings vehicle as it’s harder to liquidate than stocks. Then just sit out any coming storm.
If we assume this takes down the US economy and bonds, what then? International bonds/stocks? Won't those also be too entangled? Precious metals?
It seems a mistake to make unprecedentedly large capital expenditures, in a very very crowded space, without much evidence of a moat. Presumably people thought the moat would be singularity-like self-improvement of AI, but the singularity is merely a religious concept, and nobody should take religious myth as fact, it's merely narrative for orientation and inspiration.
The bigger issue is on the model front, can Google compete; Gemini doesnt seem to be able to compete on the heavy expert end; they are doing well on lighter faster models.
I don't get it either... Google has so much talent yet they just can't seem to get it right.
There's a big difference between Google spending tens of billions on AI infrastructure and what Oracle is doing. Oracle is spending to get on a bandwagon. Google is transforming their business, so far seemingly correctly. If AI flops big-time, Google will be left with some stranded assets, but it won't be existential the way it would be to Oracle.
> The search giant now expects to spend between $195 billion and $205 billion in capital expenditures, its finance chief Anat Ashkenazi said on a conference call with analysts. The company said last quarter that it planned to spend between $180 billion and $190 billion this year.
No return? Annual earnings have kept increasing at 20-40% for the last 4 years.
Plus there's this:
https://www.theregister.com/paas-and-iaas/2026/07/22/google-...
> Google Cloud is killing it
> It's Alphabet's fastest-growing business and now makes up more than a fifth of the juggernaut's revenue and operating profit
Also the ~4% drop is really not a big swing for earnings. This looks like a non story
If there is a huge demand for shipping goods internationally, investing in ships and planes isn't burning money.
There is massive demand for compute in the world right now, Google is investing in that area. That's a good thing.
There's enough hype and exuberance in the AI market that it's likely some players are going to be left holding the bag with a write-down on assets.
Think of it as the difference between the waiter describing dishes with ingredients you don't really understand (or maybe even taste) vs presenting the bill for the meal.
For example, Apple the year before the iPhone got launched isn’t an attractive investment. They’re a one hit wonder with the iPod saving them from bankruptcy and the market has been fully saturated. The year the iPhone gets released their balanced sheet hasn’t really changed.
https://x.com/MaxAnderson/status/2080229375773941871 https://xcancel.com/MaxAnderson/status/2080229375773941871 --- As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty:
This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term
Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries
Google’s response?
Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for
A few examples to illustrate:
For all of its history until recently, Google operated on a 2nd price auction model
I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid
This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much
However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding
It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem
Making thing worse, Google also recently nerfed keyword targeting precision
Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting
This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed
But now, even if you bid on a specific term or phrase using the strictest exact -match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)”
The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off
So now exact match is broad match, and broad match is just meaningless spam
This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants)
This is how you grow revenue atop declining search volumes
Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day
And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off
Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target
These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow
Google operated a benevolent monopoly for the better part of 25 yrs
Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future
This is now no longer the case
At the alter of AI capex, Google is sacrificing the golden goose
When I worked on Google Ads (I left in 2020), I remember this one tripping a lot of people. As I remember it, the limit for a single day is indeed 2x daily budget, but over a month it will average to it. This is supposed to give more flexibility to the auto bidder.
I have to laugh to keep from crying.
The reason why 2026 specifically is interesting is because it wasn't until late December of last year that AI models started to demonstrate particularly interesting capabilities, while we finally got IPO announcements for OpenAI and Anthropic. Assuming that the market works at all, it should be pricing in these events.
What are you referring to here?
They have (massively) outperformed it in 2025 though.