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 cost of inference goes down 100x, would need 100x more demand. This makes overspending on GPU even more irrational. Jevons this, Jevons that but ultimately irrelevant. At end of day, leading players, hungergame winner candidates is saddling themselves with so much debt, even if they survive, post crash they are immediately uncompetitive against new entrant with blank slate and newer gen, more efficient GPUs that will be cheaper to buy/operate post crash when hardware prices will revert to mean.
It doesn't matter if some of the current players survive, they've basically stabbed and weakened themselves so much any healthy upstart in the future can wipe them out unless they lock in legislative protection... safety regulations, ban open source models etc.
That is the new bet, regulatory capture moat, because economic bet is entirely lost, especially with open models eroding mote.
A company losing out on a risky bet and failing, letting a new upstart rise up is pretty natural. A large fraction of experts from the failed companies continue at the new ones, business as usual. There are engineering teams at $BIGTECH now full of OS, database, or compiler experts from XP, Sun, HP etc.
This natural ability of companies to take risky bets is what made silicon valley successful.
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.
I've seen people happily use AI that takes several minutes to generate text or edit an image because to them they already aren't using their computer when they tell it to start; they just grab their phone and walk away and come back only to check in on it.
I feel like people here and on other technology discussions -- although it's worse here -- don't seem to parse what being the minority means.
They know they're one of the few to have access to such incredible hardware -- whether it be rented or purchased for way too much cash -- but they only see their own kin; their own ilk. They only compare themselves to the best.
The reality is that nobody expects data centre speed nor power in their own home and are satisfied to just go "haha its thinking" and let their computer quietly tick in the background as opposed to paying outragious prices for subscriptions or hardware.
I'm editing photos with 32GB of DDR4 RAM at 3200mhz alongside a 3050 with a nearly decade-old mobo and ryzen 5800XT and at most? It takes 30 seconds and that's allowing the gpu to use my actual RAM as 'fallback' memory.
There are photoshop filters -now- that take longer than that before the LLM craze even began. Hell; I can train a lora in an hour or two.
This obsession with "more specs more data faster and faster" isn't going to win; it already isn't winning.
Deal with it or get wrecked.
Who's running local? Image generation can make sense to run locally, but frontier LLM make no sense to run on your own hardware.
They promise updates.
Because everyone is buying as they want to run their own models and not pay for a cloud service?
Over 14 years, Uber burnt ~31 Billion dollars and in nearly whereas the amount invested within AI seems to be within Trillions at this point and in near future with a product which doesn't have much moat and shaky financials on profit.
The only relevant number is the price to serve a frontier or near-frontier model.
Also, lost revenue from other services being degraded by shifting resources to supporting training/serving models (Google Search...)?
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.
the historical average is closer to 7%. sustained 12% would be excellent growth for any mature firm
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.
AND - they're operating well past their estimated service lifetime.
That's the problem. That's the risk that few (if any) hyperscalers want to take.
I hope you remember furniture.com, pets.com, webvan.com, kozmo.com and many others.
Amazon.com (during 1998) in some sense is the exception, not the norm from the bloodbath in stock markets during the dot com bubble.
(I highly recommend the book How the internet happened for more knowledge about things before, during and after the dot com bubble.)
Based on what? No AI company has ever made a cent in profit (exept for Nvidia lmao).
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...
Independent estimates sort of show around 2027-28 from what I remember.
I remember reading some article which said that RAM prices are already going down from its peak slowly (IIRC I can be wrong, I usually am but 3-5% month from its absolute peak) but the current RAM prices are still astronomical given past rates but the RAM prices will slow down hopefully sooner rather than later.
https://en.wikipedia.org/wiki/DRAM_price_fixing_scandal
Also, I don't recall any era where "proof" is required for allegations. "Evidence" is probably prudent, but proof is usually produced when the accused gets ends up in court.
Unless you time travelled from a feudal era where they throw you in jail for slandering the nobleman or something.
I understand that you might've wanted to know more sources but its better to ask them directly in good faith as I am more than happy to provide sources and have a good faith discussion! :)
> are you just playing pretend?
(This part feels a bit personally rude to me. I would prefer if you could omit this part of the messages in future or take care of that, Hackernews is a lovely community for the most part so lets keep it that way :-D)
I appreciated reading the comment of @hnfong. The DRAM Price fixing scandal and multiple criminal investigations are something that I didn't even know of!
The general consensus is that its late 27-28 and I would like to share some of the sources that you were asking.
I don't know if they are lying or not but the interview on reuters was conducted on the same day that they went on Nasdaq
> "We forecast that next year will be the worst year in the industry's history from the supply perspective," Kwak told Reuters in an interview on Friday, the day the South Korean memory chipmaker began trading on Nasdaq.[From (1)]
> Multiple analysts push meaningful relief to late 2027 at earliest — with some estimates stretching into 2028[0]
> UBS likewise expects the global DRAM industry to remain undersupplied until at least the second quarter of 2028.[1]
The above one [1] reference is literally from the reuter article itself which was referenced in the parent comment
> Across the U.S., China and Europe, DDR5 RAM prices fell sharply. For example, after reaching an all-time high of $500, Corsair’s Vengeance 32GB kit is now down to $369 on Amazon. Memory has fallen more than 20% from recent peak prices, and stock prices of major memory factories like Micron, Samsung and SK Hynix have seen drops of over 20%.[2]
[0]: https://iqondigital.com/learn/pc-news/when-will-ram-prices-d...
[1]: https://www.reuters.com/world/asia-pacific/sk-hynix-ceo-sees...
[2]: https://www.tomsguide.com/computing/hardware/ram-prices-are-...
have a nice day with the sources and let me know if you want a good faith discussion!
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.
There’s nothing fraudulent at all here just people using terms they really aren’t comfortable with.
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.
There's two separate things here: 1) the underlying models, and 2) how they are applied to real world tasks. Both are changing at tremendous speed. The underlying models, 1), can be purchased as a component, so there's little advantage to vertical integration, as long as there's a robust competitive market, and boy is there ever. And Apple was never going to be the big B2B provider for LLM compute and models, such sales are just not in their corporate DNA.
For 2), application of AI in Apple's products, nobody knows how to do that correctly yet, and it's going to a wild few years of people trying things and failing before something comes to pass. Waiting to see where to actually find a competitive edge totally makes sense. Microsoft's Copilot bungling was far worse than Apple AI's bungling, for example. I can't even get access to Microsoft Office anymore, it seems, I'm instead confronted with a dumb chat interface that doesn't even know how to launch a new document or open existing ones. At least Apple never destroyed their brand like that...
What have they lost by doing this? Did anyone switch from iPhones to something else?
Google's Assistant was and is better than Siri's. How many switched to Android because of it?
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.
But wait it gets worse: since Meta hasn't got a platform comparable to a mainstream PC or mobile device OS, the Meta glasses business is vulnerable and subscale, as impressive as owning half the TAM is. Since Meta glasses aren't a companion to an existing platform (not even AndroidAR, even though they run Android) they will become a second tier choice as soon as Apple or Samsung ship a smart glasses product.
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.
As in convincing?
Or accurate?
> this is just classic quarterly public company earnings BS, where public markets dont reward innovation investment
Genuine question; but aren't these treating stocks as speculative and on vibes? One can say that these comments could be true for the first signs of cracking of dot com bubble. Sure, Web eventually succeeded but many tech giants from dot com era (AOL/Yahoo and so many more) eventually went to dust for spending too much time on the innovative bandwagon.
During the Dot-com bubble really tried to give this example but IIRC there were companies like pets.com who lost 2$ for every 1$ of sale so how a company treats its financials do matter a lot.
The market doesn't seem to reward innovation sometimes because there have been times the first persons to innovative have actually really failed to capitalize on that innovation and many extremely innovative businesses like Airlines (We can literally fly speak of innovation!) have been terrible businesses investment-wise generally speaking.
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.
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.
https://eco3min.fr/en/big-tech-capex-revenue-ratio-quarterly...
Source?
Also, TPUv2 was introduced in 2018, and still not completely retired in all regions, from accounting pov, it has been written down to 0, but they are still working.
With AI every new generation with both massive hardware and software stack changes from Nvidia makes prior chips extremely inefficient to run, basically we are comparing an ASIC industry to a general purpose compute industry where all work loads are the same shape and size and so on.
Margins for ASIC based mining companies or ASIC solutions providers were never high, Optane and other weird solutions are niche and great for a specific category or moment in time, but they become obsolete pretty quickly.
The fear is we don't know if this Capex can stop. The worst type of fear is if this Capex will stop then what? Someone is very overpriced in this market, the cloud companies, the hardware providers or both.
I don't see how we reconcile this without a massive wave of repricing, ofc markets can stay irrational and we don't see the actual books but AI doesn't have so much revenue. Suddenly the AI token/cloud revenue won't 100x in a year or two...
Especially when intelligence will continue to get cheaper, the margin compression is a massive risk.
All the data centers for hyper scalers were a miniscule part of their story the real moat was the software layer on top otherwise Hetzner would be priced like an Amazon as well.
Something is shaky with this market I don't know what it's very opaque even as an insider working on for big tech and startups. I have no clue who falls first and which bottleneck cracks but there is not enough revenue for tokens, we will see a strong 2-3x growth in the next few years, from here which is absurd, but it's not enough, not nearly enough. If the capex keeps high and increasing.
Ofc they can stop the capex and the otherside gets repriced it's not like nvidia, micron and co aren't worth trillions.
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.
Depends on the advantage it gives people and marketing of that advantage. You'd be surprised at how overpowered the average workplace laptop is. Each company I've been at has had at least some people who do non-technical roles using high-end hardware. Why? Because the account executive wants the fast machine and they get what they want.
You can apply the same to GenAI. Humans are notoriously bad at estimating actual needs when it comes to resource consumption. Best to have it and not need it than need it and not have it, especially if your competition just shelled out for SOTA.
And that's not even taking into consideration regulatory and customer concerns about where the AI you're serving requests with came from.
We have already seen tech workers at big name companies get whiplash from "leaderboards showing people using the most tokens!" as a good thing one month to being pressured to using fewer tokens a month later.
You'd be surprised. I've seen people request upgrades and get them. Not even execs, just employees. Even the average devices are probably overpowered these days. Chromebooks could do the vast majority of work in a corporate environment. Try getting workers to accept them, though.
Regardless, there are probably enough reasons to use proprietary Western AI for the time being, particularly in regulated industries, that poop won't completely hit the fan. Particularly if the CTOs start searching the phrase "Operation Aurora".
> We have already seen tech workers at big name companies get whiplash from "leaderboards showing people using the most tokens!" as a good thing one month to being pressured to using fewer tokens a month later.
Indeed. What do you do when you want a group of people to do something that they'd be otherwise adverse to doing at all? You rank them on it and let them get in a competition over who can do it the most. That's what tokenmaxxing was about: getting people to use the AI at all. Now that the people are using AI, we move onto another objective, which is getting them to use the tokens efficiently. The trick is measuring that efficiency.
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) [2].
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...
[2] Microsoft considers replacing ChatGPT and Claude with Kimi K3 to save $600M - https://news.ycombinator.com/item?id=49022984 - July 2026
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.
> To setup a cross business kubernetes cluster will take 2 years with unknown results.
Do you seriously believe those times will not go down 95% if the CEO pushes for it to get done yesterday because it will save the company millions in expenses?
it has to be some amazing router and while the models are open-weights, the knowhow to run them efficiently surely is not?
If the second one were as easy as the first, I wouldn't have to be online at 9:00 to deploy stuff to prod tonight; the team in India would handle it. But customers write into the contracts that only US-based employees interact with prod systems. No amount of cajoling will get them to change their minds; they have data sovereignty, international telecommunications treaties, and HIPAA compliance to worry about. So I'll be pressing buttons tonight.
Could you swap out Anthropic or OpenAI or Google or whoever's models for Kimi? Yes. They're like other software these days, they're modular. What isn't modular is regulatory and geopolitical concern.
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.
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.
(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.
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.
Or are you just adding nonsense about "yeah but yeah but no value"?
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.
A new study is needed.
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."
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.
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.
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.
This is another unbelievable claim. I actually used frontier models from 12 months ago and they were completely different.
You'd think if there had been that many dramatic improvements I'd have to babysit an LLM less frequently.
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.
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?
Build your emergency fund first if you don't have one. 6-12 months of salary in cash or CDs. Then dollar-cost average into well diversified equities. Don't watch them day-to-day. You're concerned about their value in 20-30 years, not tomorrow.
Sure, spread investments across stocks and bonds and treasuries from different markets.
But you can also diversify more broadly beyond economic capital to cultural and social capital. Learn new skills and build networks of generalized reciprocity with others before you (or they) need help.
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.
> 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.
> "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"
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'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.
https://fortune.com/2026/03/10/google-ceo-sundar-pichai-692-...
(still somewhat outraged Reuters has a paywall now. Also BBC, CNN...)
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...
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.