Anyway: Zero, as of right now.
I fully expect to be able to run useful LLMs on a machine I can justify buying for other reasons. I already can on the secondhand kit I own, and I don’t expect the cost-benefit analysis of local LLMs to ever really get worse.
If I ever need to pay for it, it will likely be to shift some of the capacity into the cloud for either business or pragmatic personal reasons (so I can just carry an iPad etc.)
I fully intend my expenditure to be negligible. Because once one realises that outspending others is impossible, only spending minimisation makes sense.
I foresee it potentially making sense for me to move some mature tools off a local LLM to openrouter, maybe. But probably to the same or similar models.
I spend 30 - 60 bucks a year with Horizon Labs.
I spend 25 bucks a month on Cursor. Cursor replaced an OpenAI sub.
Both support hobby projects. If either cost increased I would spend some time testing local alternatives and probably drop them.
Horizon Labs especially, I know that they have been matched by open models and are mostly a convenience at this point.
If I were really forced to.
LLMs provide me about the same value as a car does.
We have benchmarks on our domain and it does there are models that are 2x to 10x cheaper for a small drop in percentage points in accuracy
When I bought my last GPU, running AI models locally was a consideration though not the only one, and I have it set up but haven't used it much yet. I mostly use the free tiers of ChatGPT or Google to write the occasional script for me. I guess they're going to have to inject a truly unfathomable number of ads to get their money's worth.
I have a feeling my experience is closer to an average persons' than a dev, but it doesn't seem like they'll be able to monetize just from devs even if each one is spending thousands a month.
Don't give up just keep trying you can truly build personally life changing things. Don't look at it purely from a how do I sell this lense, just empower yourself with these tools while the getting is good
AI is so important, I want to have it under my control. Even if I have to pay a penalty in terms of capabilities.
If AI allows me to cut my time to do something in half on average or allows me to do 2x more it would be worth it to pay up to what my monthly income was before assuming my income scaled with my output.
The problem is that hasn’t been the case for 50 years
For work, it depends, but if I have to spend more than a few hundreds bucks probably I'll start looking for alternatives (local models, Chinese providers, ecc)
PS: I'm in Italy, I guess in several parts of the world these figures are even smaller.
Maybe it’s just your phrasing but people will only pay for what works, no one is loony enough to support a trillion dollar industry out of the kindness of their heart or spirit of innovation
It may put me at a disadvantage when it comes to quickly slop something together? But so far the free-to-use chat bots do as well for my needs.
Unless we are genuinely pushing to find AGI, at which point nothing matters, LLMs in their current form don't replace knowledge workers but are an effective force multiplier. How good is enough?
For instance, I pay about $1-2 a month for DeepSeek. It's not as sophisticated as Claude, but it still doubles my productivity as a SWE.
If Fable comes out and demands 50x the price of DeepSeek in order for Anthropic to make a profit on it, how much more productive would I be compared to my personal experience + DeepSeek? 3x? 50x?
Is it cost effective for a business to hire someone without SWE experience + Fable verses hiring someone with SWE experience and DeepSeek? When does R&D hit diminishing returns?
There's always working on improving the cost of inference, but I don't think this is an area of R&D that will slow down. The reason is:
1. A better competitor model risks eating away at how much they can charge for inference (i.e. revenue) 2. Whoever unlocks AGI will unlock even more growth 3. Even when you unlock AGI, you'll want to throw gobs of money at it to improve itself and all sorts of things.
> If Fable comes out and demands 50x the price of DeepSeek in order for Anthropic to make a profit on it, how much more productive would I be compared to my personal experience + DeepSeek? 3x? 50x?
You're pricing it wrong and looking at it wrong. First, the per token price doesn't consider that a smarter model can end up using fewer tokens overall to achieve a result. Secondly, if the difference is between failing to accomplish the task and accomplishing the task, suddenly that 50x can seem like a bargain.
> Is it cost effective for a business to hire someone without SWE experience + Fable verses hiring someone with SWE experience and DeepSeek? When does R&D hit diminishing returns?
At this time, someone without SWE experience + <name AI model> vs someone good with SWE experience and <name another AI model> is a no-brainer. The AI model is an accelerant but the "no SWE experience" will be accelerated into a wall. Now maybe that doesn't matter for prototyping and certain other things, but anything in production the lack of experience will hurt them with things they won't even know about or even know how to look for it (e.g. slow, insecure, etc).
Massive assumption there.
Similarly, some bosses might believe that they can hire 100 cheap, unmotivated SWEs to replace Linus Torvalds or Fabrice Bellard and achieve something slightly worse. But in certain areas, it doesn't work like that.
on the other hand, the sad reality is many swe are working on dumb crud apps and the code-quality is already very low ime, and the jury is still out if ai tools can long term replace those; but what i have seen first-hand isnt super promising do far...
If you're in the US and you're making 100k a year, that's worth 5k or $416/m. So you can buy two of the most expensive plans on the frontier models.
This focus on cost optimization is insane. Just use the frontier models. Even a marginal bump is worth whatever the hell they're charging, at least for now.
Also where is the evidence that the workers have ever benefited from productivity bumps? The only thing that happens is surplus gets captured by the owners while workers are forced to do more.
Bad deal all around.
There's a non-negligible percentage of the industry who have a pseudo-religious belief in AGI, so I wouldn't be surprised if that was, in fact, the goal.
Who knows, maybe they'll stop once the money dries up.
Deepseek shines for personal usage because it's possible to use it however you want and whenever you want with no session/weekly limits stress because you use the API and it's priced very reasonably.
I think the third coming out Jesus Christ in closer than AGI. Seriously, I dread how much of Silicon Valley is wrapped in this narrative of AGI and Singularity.
How can all these "rationalists" fail to see that this is what religion looks like: Faith and promises of heaven and hell.
R&D costs are hurting profit side and while you can cut that one just becomes irrelevant overnight in this space if you do, hence the problem.
That’s quite the hot take, considering it’s literally an R&D company that got to where it is by doing R&D.
If it's not materials, not energy or taxes, not manufacturing, not licensing or rental fees, then I can only think of R&D.
Unless these frontier providers feel some type of squeeze or constraint the Chinese are well positioned to leave the US bag holders of an NVidia bound system. And if anyone has to wonder how one provider for a critical piece of infrastructure will go, well...
I don't like these products. I have several negative opinions on them. To the extent they work and there is a customer base what marketing could you /possibly/ be engaged in? Doesn't the product sort of market itself? Or another way is this a product that you can market to expand your MAUs?
It's so polarizing I can't imagine how that $5.7B is being spent.
In every way imaginable and then more, looks like beyond the imagination :)
>I don't like these products. I have several negative opinions on them.
You're not alone, and the crowd seems to be building at the same time enthusiasts are proliferating too.
So much widespread negativity I would guess that's about what it's expected to cost to fully overcome resistance and objections. Which must be bigger than we think, they sure have more information than us.
Whether it can physically be as all encompassing as it makes itself out to be or whether it will just be healthily profitable remains to be seen. Kind of like how Uber went from "We'll autonomously drive the world" to "Look, we deliver food, goods, and people to locations and we figured out how to do that in a way that makes profits. Also, ads".
I’m not sure how people are looking at numbers that show, even if we wipe off the enormous R&D expenditures, they are still in the red for inference + sales/marketing + admin and responding “this seems positive”.
It’s like being a sold a car and being told “well if you ignore the fact it has no engine it’s a good buy” yet it also has no wheels.
> Unless there's an assumption that R&D costs have to forever go up in order to increase revenue, I feel like this shows that the AI industry is actually on a path to profitability in the long term.
There are three futures right, I’ll rank them in order of fantasy -
1. Someone achieves AGI. At that point the economics of an individual company don’t even matter.
2. R&D costs do have to forever continue, because LLMs can be continually iteratively improved. Much like chip development, there is no end in sight, at least not on a near term timescale. If you are not continually at the frontier, customers will use a competitor or open/local alternatives.
3. LLMs reach a plateau of functionality. Further gains are minimal, quality reaches the apex of what the technology permits. In this scenario the hyperscalers have no business because open/local models will rapidly reach that same plateau as well.
It also ignores how much of "R&D" is actually needed for the thing they offer to keep working. Looking at the thread everyone seems to be presuming "R&D" is all "training new models", but that is uncertain.
What is counted as R&D is completely arbitrary. These figures are just playing accounting games to attempt to hide the massive ongoing costs.
We’ll see a little better when they IPO and are forced to attempt to make money but I wouldn’t invest in this business.
> Cost of Revenue: $7.5 billion
It's almost too good to be true. Did OpenAI intentionally leak this? It singlehanded eliminate the biggest concern: that tokens are sold at loss.
HSBC say they need to turn a 13b revenue to 200b by 2030 AND also find another 204b, in order to become profitable.
Its a little less arbitrary than that. Cost of Revenue/Cost of Sales/Cost of Goods Sold are clear, if you're following GAAP. To label these expenses as cost of revenue, they must meet the matching principle in that the expenses must be directly tied to the generation of specific revenue. If you didn't make that "sale" then that specific cost would not exist.
Other operating expenses come later on the income statement.
Total Revenue - Cost of Revenue = Gross Profit first, then you subtract OpEx from there for EBIT.
For OpenAI, I'd assume cost of revenue is almost directly inference costs + customer support & app dev.
It's not going to happen.
Not saying anyone is wrong in pointing at the buildouts for AI and questioning its feasibility. Just making the argument for why I personally only look at operational costs and revenue because it's the only real-ish value I can look at and judge if a business can grow sustainably.
As a counter point, the red flag to all of this is R&D costs growing for each model release. If that continues and revenue cannot outstrip it, then these companies have a problem and it'll probably be that just 1-2 frontier labs can survive this once the dust settles.
Operating loss went from ~$8.8B to ~$20.9B — roughly 2.4x.
Doesn't seem like a domesday scenario.
Ceteris paribus, those figures imply a $45bn loss this year, $90bn loss next year and $110bn loss in 2028 before breakeven in 2029.
That's $250bn of losses to be financed from 2026 onwards. (They raised ~$120bn, $25bn up front and the rest based on milestones. So Another ~$125bn uncovered.) That only works if OpenAI stays a fundraising darling. So not a doomsday sceanario. But perilous, and dependent on short-term trends extending into long-term curves.
> Operating loss went from ~$8.8B to ~$20.9B — roughly 2.4x.
> Doesn't seem like a domesday scenario.
Those two lines are moving up and to the right, but are not parallel.
It all depends on where those two lines meet (the break-even point): too far in the future and the company will be dead anyway. Almost all companies will eventually be profitable; the problem is that the majority of them will need constant cash injections to keep the lights on.
Like the old aviation saying: even a brick will fly if it has enough thrust. doesn't make the brick a plane, though.
the brick has a lot of thrust but there is a airplane behind it, and it's moving on its own
https://www.reuters.com/technology/openai-considers-drastic-...
Ads, maybe, but not only are they already walking back recent price hikes, the paying customers were hitting the brakes even on the original price.
Note that this data you see (their increased revenue) came from a period where they were onboarding customers who were competing to see who used the most tokens.
IOW, this is the best-case scenario for them - customers with no cap on token spend.
But... the caps from customers came in before they hiked prices. Then they hiked prices. That resulted in a short-term boost to revenue to compensate for the caps. Now they are talking about walking back those hikes. That means they are going to find an equilibrium lower than their best-case scenario.
I do wonder if this comparison is really meaningful. It looks like if they can grow infinitely, then at some point they should be profitable. However, that's already a somewhat sad story ("in the limit as x->inf, we'll actually _make_ money!"). And there are of course limitations. Anthropic, Google, open models etc are all real competitors, and it seems to me that there will only be one winner. If openAI is losing money faster than the others, then it may not survive long enough to reach that eventual profitability. And finally, the human population is limited. There isn't a true infinity that the pattern can extend to. If we've only reached 10% of the TAM that's fine, but if we're at like 70% (which personally I suspect is about right), then this looks bad.
With so many free models available the ai companies are going to struggle to convert active free users to paid.
the biggest reason for this is that the digital ad market is a duopoly (charitably a triopoly if you count Amazon in), if all of the LLM companies start to go into ads that's going to be a much more competitive market for ad buyers. It's not going to be so straight forward when both customers and merchants have ten different places to go.
Also not to forget that ChatGPT has zero moat, unlike social Facebook and Google.
I think that AI is going to become just another utility people pay to stay relevant. Same as their internet, electricity or gas.
I'm guessing that might be so in certain professions, but I would expect the employer to pay for that. For the rest of us, it seems unlikely. At least for me, I don't have a need of a device to generate text for me. And I bet most people are are in the same boat as me.
They don’t care about making money at present
Basically, it's a company that's not sustainable for two separate reasons. The first one is that they have an extremely high overhead. SG&A of 55% is really bad. The seconds reason is that their R&D costs are truly astronomical. They could probably cut those costs to some extent, but they're not going to cut them to nothing. They're already losing ground to Anthropic even with this much R&D.
To put it differently, even if OpenAI cut its R&D and inference costs by half, they would still be leaking money like a sieve.
Gemini is number 3 in this race
= SG&A stands for Selling, General, and Administrative expenses
Anthropic is also likely losing money, right?
If you're building a model that lasts a few months before it's no longer the most current one, and maybe a year before it's completely unusable by anybody, then that should just be COGS.
Doing that, however, would betray the real problem with this business model.
Revenue is still growing faster than costs and gross margins have continued to improve.
The real question is when they can start spending less on R&D and still compete.
Do we know how bad/good misAnthropic is doing financially?
If they manage to keep those customers for several years without more sales, that bit looks like a normal "high-touch" business.
They shouldn't look like a "high-touch" business, but their unitary numbers look way better than I expected. They just need to grow some 10 times to star making a profit... Maybe 100 to cover the opportunity cost of their capital.
It's just a matter of finding 5 billion people willing to pay US prices :)
But it is still better than I expected.
It's getting businesses to pay $2k/mo or more per professional employee, like a lot of Anthropic customers.
Anthropic is ahead of them there, but that is how they win.
Isn't Anthropic currently killing that market though? I've been hearing about a lot of businesses pulling back after having experienced the reality.
If they're the only ones who ̶a̶r̶e̶ ̶w̶i̶l̶l̶i̶n̶g̶ ̶t̶o̶ ̶b̶e̶ ̶t̶h̶e̶ ̶e̶n̶g̶i̶n̶e̶ ̶f̶o̶r̶ ̶a̶u̶t̶o̶n̶o̶m̶o̶u̶s̶ ̶k̶i̶l̶l̶b̶o̶t̶s̶ can draw a reciprocation dingle-arm to reduce soinosoidal repleneration, then "I'm sure the government will buy it" [0]
This is how you know ads are inevitable. YouTube is probably a good indicator of how BigLabs will operate for free users.
They just have to become the world dominant LLM provider by a large margin, and become a high-value ad provider with world scale.
I mean... They just have to repeat a Google. That would make them sustainable and a reasonable value for the investment.
The problem is you can't just separate training costs from inference costs. If OpenAI just didn't train a new model for the next five years, sure, they'd do OK. Assuming all those dirt cheap Chinese models nipping at their heels don't make up the gap while OpenAI is resting on their laurels.
Without being a frontier model (read: continuous, incredibly expensive training), they effectively don't have much to sell. So inference and training costs are intertwined to some extent.
And the network effect which ruled for the last 20 years seems to have relaxed its death grip just a bit (of course it is still there as having more customers using your tools and models provides more training data, etc., yet the current network effect doesn't seem to have that high exponential value like before)
And then the reality turns out not to be the case - you have to continuously spend on R&D to avoid getting your lunch eaten by someone else.
This isn't a social media network with lockin either. People can and will just switch to whatever whenever they feel like it. Maybe it becomes a defacto standard like google but if someone is much better than you, well...
Totally untrue.
This includes running inference for ~1b free users.
What is untrue about this?
Would love to hear some details on that one...
Or was that a typo and you meant the $200/mo plan instead maybe? That one I could believe, assuming no or frugal subagent use that is.
> Interesting. I'm mostly using Claude, so perhaps I'm not nearing the limits, but I do use Codex (for coding and reviews occasionally) and use chatgpt for second opinion many times, including "pro" research. Never got to my limits. But again, not my main go to tool.
Alphabet: ~$4.5T value / ~$403B revenue ≈ 11× revenue
Microsoft: ~$2.9T value / ~$282B revenue ≈ 10× revenue
OpenAI: ~$850B value / ~$13B revenue ≈ 65× revenue
Can someone explains that logic?
Should these companies be valued the same?
By who? Public money is looking for dividends (profits) not growth?
This is because people here are quietly realizing that they fell for the "token-maxxing" marketing drive which was complete BS for you to gamble more money on tokens as the big AI labs gave heavily subsidized token prices they cannot afford.
Jevon's paradox does not exist at those companies, but it certainly exists at the Chinese AI Labs at Deepseek, Alibaba, z.AI and Xiaomi.
Good callout. All these "trends" in AI were definitely from the AI companies themselves in order to push the sales of more tokens. What's after agent orchestration? Whatever it is, it will involve a big spend.
Luckily, we've seen this before. Doom and gloom when smartphones came out. And then the same again when mobile development was preferred and there was an outcry from the web dev crowd and constant downvoting of phone apps.
When I read "the worst possible thing for me to get" I had assumed it would be evidence that inference/Codex is fundamentally unprofitable (as Ed often blogs about) but there isn't enough information here to support that argument either: revenue is still greater than cost of revenue, and the major losses are clearly delineated.
I'm not sure where they'd get that idea from? If inference was fundamentally unprofitable, I don't think we'd have seen the massive CapEx spend & VC cash flooding into AI, it'd be a negative gross margin trap if that were the case.
It looks unprofitable because of the massive CapEx spend right now to build data centers.
People that think inference is not profitable are mistaking the total compute cost as inference cost, when really it needs separated into training compute vs. inference compute.
The bigger question is, is when does training slow down, if at all? If we hit plateaus with LLMs, at that point inference becomes nearly pure profit once you own the compute (and a hardware refresh cycle every 3-5 years).
LLMs eventually hitting a dead end for more advanced capabilities is what would spell trouble for the labs. Any existing hyperscaler cloud can run inference all day long, as long as they have access to a model. They don't need OpenAI or Anthropic for that. The frontier labs entire valuations rely purely on them staying ahead of the commodity curve. The moment they can't do that, they're done.
The question is, can OpenAI survive if customers start tokenminning? A pure inference business could be profitable but that’s not the business OpenAI are in. OpenAI has a billion users that OpenAI loses money on.
Ed's claim is that they haven't shown inference to be profitable. Which is true? And that he personally believe it is unprofitable (his personal opinion, not what his data report).
I think that's a meaningful distinction with your statement
People ignore all his horrendous takes from last year and still eat this years “analyses” like it’s Gods words.
He has been predicting the doom for years and years now and it is strange to see HN still putting credence here.
This is what he said around a week back
“ One of my sources has come forward and brought me a story that will possibly burst the AI bubble. The reason they brought this to me is that I’ve shown — and will continue to show — that I actually give a shit about this industry and the people in it.
If you’re wondering what the story is, know that it’s the information I’ve wanted for years, delivered as I have always wanted it, and I will treat it with the reverence it deserves. Imagine what the worst possible thing for me to get would be and you’re probably close.
I expect it to be out in the next two weeks, and you’ll know exactly when it runs. There’ll be a podcast and a newsletter, and very likely follow-on coverage elsewhere.
I can guarantee you it’ll be worth it, and you’ll be stunned by what I report.”
This is qanon tier stuff. He’s been pulling this shtick for a while and people still haven’t caught on.
No idea why his shit keeps getting submitted.
So everything else is kind of academic. Of course they were losing money in 2025, they had a technology that was kind of cool - clearly eventually going to deliver something great, but they didn't actually have anything somebody should pay for. Now they have a thing that people will pay for. So who cares what they lost in 2025?
So what's important today is - how competitive are they with Anthropic in delivering that product. How do the economics of companies using AI agents for coding work. That's all. I don't think there's really an argument about them losing money on inference any more.
There is, put simply, a huge, huge information gap about the uniqueness of these commercial services.
There's an open question about how open weights models will be funded when they can't be used in a war between these companies, but the reality is that the amount Apple is paying Google for the right to distill Gemini, for example, is strongly indicative of the total size of the consumer market. Because pretty soon everyone's phones will be doing what local models can do.
Global markets will ultimately learn that coding agents are, at a first approximation, the only source of revenue for this stuff over the medium term at least, and the value proposition for consumer AI in the long term (beyond being a feature of a phone) hasn't yet been invented, and any that might exist depend on micropayments architectures that don't exist.
I guess we'll see if people will pay a premium for Anthropic in ~6 months, 12 months, etc. If not, well, it's a race to commodity.
Some of my coworkers even use Sonnet (the default in Claude Code for the 20 USD subscription) and see no reason to change even though that model is definitely "outdated" compared to current SOTA.
It will become profitable. Local models and local on-laptop inference will get good enough. This argument has been made for decades. It's not like everyone is walking around hosting email and photos on their personal machines. Sometimes it takes a large investment to make servers and clouds for this stuff possible.
We need to get away from this idea that in order for one thing to succeed, the other must fail. We also need to stop thinking in binary and accept that all these things (profitability, local models, powerful laptops, etc.) can all happily coexist.
That latest drug for pancreatic cancer? Yeah, all human. After the trillions already spent, AI hasn't come up with any new medications, no new inventions to save lives... Nothing
This is not happening in a vacuum. A lot of index funds and retirement accounts have bought into AI and AI adjacent companies, many with stakes in OpenAI. If OpenAI keels over, even when private, it will affect a lot of americans. If they IPO, it's even worse.
But: how are they calculating the cost of revenue? Do they have rapidly depreciating assets that are also needed to produce that revenue? (Starlink has this issue.) Will their cost per arithmetic operation for inference rise or fall? (Anthropic is paying xAI an absolutely insane amount to lease GPUs. They must be betting that they will not need to repeat that.) Is a large portion of the cost allocated to R&D actually being used to support their revenue?
I certainly believe that the cost of inference can be plenty low for them to make a profit, but a more granular breakdown would make it easier to evaluate.
The whole point of the company is that they are investing a huge amount of money upfront in order to make models that are better and better, and thus have a higher productivity multiplier.
They are very profitable on inference, they just know that the race to AGI requires a huge amount of investment, compute, getting the best researchers, etc.
That ship has sailed long ago into the IPO sunset.
> As OpenAI’s worth rose, the increased value of those investor rights created a roughly $30bn charge, added the person. The charge is not expected to recur following the restructuring, they said.
> Stripping out the charge and other non-cash expenses, such as stock-based compensation of staff and computing credits from Microsoft, OpenAI’s losses were $8bn, according to the person.
If R&D costs don't go up - where does the moat come from? Cheaper players catch up with 'good enough' and will erode their revenue. Most of human tasks just don't require that much intelligence.
They're racing toward 'superintelligence' that recursively self-improves.
No indication we're anywhere close to reaching it.
Going to be an interesting year to say the least.
Look, for coding and a lot of other things, AI is awesome.
But the here's the killer. I have a dinky 16gb VRAM card, and that's kind of the sweet spot for the level of AI I actually want. I don't want it doing too much, I'd rather create slowly than have it one shot something that I have to then pore over later.
Feels like a company investing kazillions in, i don't know, air-conditioning or building wi-fi. Yes, it's going to be around, and also no one's gonna need THAT MUCH.
I was watching a World Cup match last week and one of the TV ads during half time was something to the tune of ChatGPT being used by kids to improve their street soccer skills. This was Brazilian TV. Anyone even remotely familiar with Brazil would find this ad deeply, thoroughly out of touch. I can't think of a worse chatbot pitch than that.
Glad to see more sane takes in the comments. All these articles on their current financials are missing the point.
OpenAI is doing pretty well.
Capital expenditure is required to deliver on 1) better models 2) better infra and 3) better products. Insane CapEx is required to do all the above + compete with Google, Meta, Microsoft, Apple, Anthropic, etc. etc. etc. who are all trying to do the same. These financials are sane, considering the scenario.
I feel like the 1080 ti is like a prophet of the current crisis, these companies are buying $10k paperweights per user to MAYBE... LUCKILY... charge what... $200 a year? and that is for every 1/100 users.
this same 10k hardware will be outdated in a couple of years...
It just doesn't make financial sense, if you couldn't sell standalone GPUs that people PAID for with HBM in them, what makes you think that you can sell a POSSIBLE subscription utilizing a $10k+ GPU?
This is the most obvious bubble of all time.
"According to the narrative story in Genesis 11, the city received the name "Babel" from the Hebrew verb bālal,[e] meaning to jumble or to confuse, after Yahweh distorted the common language of humankind.[11] According to Encyclopædia Britannica, this reflects word play due to the Hebrew terms for Babylon and "to confuse" having similar pronunciation.[7]" (Wikipedia)
Just as with the two previous bubble, we’re seeing companies hemorrhaging huge amounts of money, and when the dust settles the market is going to crash big time like it did with the two previous bubbles.
Unlike previous bubbles, this bubble isn’t giving people high paying jobs until everything crashes (programmers with the dot-com bubble; construction people during the real estate bubble), but it very annoyingly is making memory and SSD storage cost far too much causing computers to cost about 150% as the cost two years ago before the AI bubble was in full force, forcing Apple to make a “MacBook Neo” model with the absolute minimum of ram and SSD storage space.
Like the dot-com bubble, we will have very few winners left (with dot-com, the big winners were Amazon and Google) but unlike the previous bubble, it’s incredible how political this particular bubble is (i.e. the controversy around Grok).
AI companies are black holes for money the way delivery companies are (or were, considering the money people are willing to pay these days).
Most of them will disappear alongside the money people have bet on them.
