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Discussion (194 Comments)Read Original on HackerNews
You are completely missing the bet these companies are making.
They think can outlast their competitors and capture a larger portion of the pie while the cost of inference keeps going down dramatically.
If you haven't been paying attention, the cost is about 1/100th of what it was in 2024. This is the trajectory pretty much every technology has followed.
Of course there will be market crashes and corrections and things like that and most companies won't survive, but the bet is that whoever survives ends up doing pretty well.
If everyone's running local then why are these larger companies dumping cash into data centres?
You need a cluster of 8-12 H100s to run the largest models locally.
It doesn't make sense to run these locally yet unless your use case also involves making it available for several dozen concurrent users.
We are also within an arms race of training newer larger models with more speed while discontinuing older models.
Gemini/Chatgpt have already discontinued their models from 2024 (iirc) because they are using all their compute in serving/training newer models. Being quite frank, nobody is serving a model from 2024 as the intended use-case while having very little moat as open source models are catching up.
> Of course there will be market crashes and corrections and things like that and most companies won't survive, but the bet is that whoever survives ends up doing pretty well.
How so, by raising the prices? because the current prices aren't sustainable and I feel as if there would certainly be companies which will try for one reason or other to be cheaper to capture the market share because of the larger promise of whoever is able to get as market share. I had once thought about it and I don't think that even in an ideal world, they would end up doing pretty well given no moat.
Also even if a company survives and ends up being one of the survivors and makes profit in the ideal scenario you mention, then within some years other companies will try again and construct more datacenters and end up driving the prices down for everyone, so nobody knows how things might look down for 2-3 years let alone a decade, so I remain a bit skeptic currently so.
I had actually thought some on the economics of datacenters and I found it to be very related to power. The only ones which seems to be making money might be the power generators actually because power is the actual bottleneck rather than GPU's in datacenters from my understanding.
Though the power is raised at the cost of electricity bill increases for everybody including people living in houses. The job prospects are minimal as well, as a nation, aside from just getting investment just for the sake of it because AI's trendy right now, I feel like its a net negative deal for people living there.
Because everyone is buying as they want to run their own models and not pay for a cloud service?
Data centers are real estate. One of the big players in carrier neutral data centers even calls themselves Digitial Realty.
The contents of the DC is not real estate. But neither is the an office or a house or a warehouse.
the historical average is closer to 7%. sustained 12% would be excellent growth for any mature firm
The GPUs are far from worthless after 5 years. E.g. the A100 80GB PCIe version cost around $15k when it was introduced in 2021 and now sells for $10k used.
Things might be slightly worse for the data center servers, but I am sure they will find find buyers.
Which will not be any time soon according to SK Hynix CEO:
> We still forecast that customer demand will remain higher than our supply capacity even beyond 2030
https://www.reuters.com/world/asia-pacific/sk-hynix-ceo-sees...
That's the problem. That's the risk that few (if any) hyperscalers want to take.
Not only that, but they're typically amortized over 5 years, where the actual lifespan usually falls far shorter (1-3 years), adding to the artificial subsidy conditions we see today. So they're gaming the lenders into deferring interest payments as much as possible today so that new competitors don't have the same cheap financing advantage.[0]
0: https://blog.citp.princeton.edu/2025/10/15/lifespan-of-ai-ch...
It's the sort of behaviour that really does end up with people going to prison.
https://www.dpeaflcio.org/factsheets/the-professional-and-te...
In 4 years it better be 10x more important to have than a cell phone is today, or 10x more important than having internet/monitor/pc/printer is for an office worker today.
It's super-intelligence or bust.
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.
They just have such a strong hardware + os ecosystem that they can sit on the sidelines. They'll be able to negotiate with some LLM provider at a good discount when the time is right and put harnesses around it for actual useful features.
Similar in a way to dot com. It's not to say ML won't have practical application in the future, but the likelihood that it will have specifically this form is low and worth waiting until the dust settles and a more commonly accepted utility presents itself.
If AI/ML were monstrously useful in its current form the companies pushing it would not need to be hawking products; people would be bashing their doors down. I think that's why in areas where it's more directly applied to a known problem set (like Pharma research, and I'm hoping someone with Pharma expertise can pipe up here) there has been more natural pickup.
Coming from trading and markets, ML has been a part of the mix in quantitative strategies for...well, nearly 20 years (by definition I suppose). Spaces with obvious utility will see rapid adoption. Worth waiting that out, honestly.
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.
Zuck has 60% voting power, otherwise he would have been fired over metaverse and then model delays
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.
And then the $85bn to be repaid too.
Also, looks like I got it wrong and they've only raised $45B to date. The rest will come as part of the ATM offering program that begins in Q3.
more details here: https://www.sec.gov/Archives/edgar/data/1652044/000119312526...
https://www.reuters.com/business/alphabet-sells-bonds-worth-...
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?
Even if, hypothetically, Fable or a Fable-class model could seriously replace some headcount, it'll only gain further traction of it's actually cheaper than hiring humans. $50/MTok is expensive. Wouldn't be unreasonable to expect somewhere between ~$3k-$5k/month/developer in spend. Cheaper than a Junior in the HCoL areas (in the US), but not much cheaper in lower-to-average COL areas. Most acceleration will come from having the headcount + giving said headcount $3k-$5k/month in token budget, so now it just becomes a very expensive dev tool rather than a headcount replacement tool.
The idea that a $30k/year API bill will replace 2 $100k developers falls part outside of SFC/NYC. No CFO of a mid-market company in a LCOL area is signing off on $3k/month/dev API bills. They'll just hire juniors and cap their spend at $200/month.
I'd be with you if you claimed that the revenue hasn't translated into substantial profits. Being able to spend a lot of money to get less money back is not that impressive. But revenue by itself is on a dramatic rise as capabilities improve
Completely false.
AI and AI related revenues are growing exponentially.
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.
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?
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.
You'd think if there had been that many dramatic improvements I'd have to babysit an LLM less frequently.
Nobody cares about your personal hangups about AI. Tons of people are building with 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.
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?
https://www.youtube.com/watch?v=gUkbdjetlY8
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.
If you listen to Tesla's recent conference call they are going to making solar panels all the way back to making the silicon ingots and totally vertically integrate. Elon lamented on a previous call that nobody wants to get involved in these primary industries and he has to do it all himself unless he puts his whole supply chain in China. For example, Tesla recently opened a state of the art lithium refinery in Texas cause nobody outside of China does that anymore. He's opening a new fab, because everyone else is too hesitant to expand to meet the capacity he needs.
> 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.
https://fortune.com/2026/03/10/google-ceo-sundar-pichai-692-...
I'm using it a lot less.
Don't think Google can point to past revenue an indicator of future revenue, they need to establish new streams of revenue.
I believe that performance-per-Watt is going to be the only metric that matters. We already have 6 year old hardware (A100) that cannot run the latest models. There will also be new capabilities (eg quantization methods).
I'm not concerned with Alphabet's cash burn rate to be honest. These tech companies are typically shielding themselves from the consequences of this by using Special Purpose Vehicles ("SPVs") where the GPUs themselves are the secured assets for the loans. Even the physical buildings and infrastructure isn't owned by the SPV. Those are rented from another vehicle. So investors are pouring money in to buy GPUs for Google, Amazon, etc. Even SpaceX is partly-insulated by using an xAI SPV.
All of this is I think is a huge risk for OpenAI and Anthropic. The risk to SpaceX is a stock collapse because the AI aspect was always overstated (IMHO).
I think Google will be fine. What is funny is that this is almost using Private Equity type tactics against other investors. Things like the structcures in which the real estate and physical buildings are held in separate entities and the SPVs end up off balance sheet.
[1]: https://ciphertalk.substack.com/p/nobody-knows-what-a-used-g...
[2]: https://news.ycombinator.com/item?id=48917135
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.