https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...
That means that it's worth up to 10% of a developer's salary as a tool. And more importantly, smaller teams go faster, so it might be worth that full 10%.
Now, assume other domains end up similar - some less, some more. So, that's a large TAM.
Huh? Do you mean for official government use?
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And there was never any question as to how social media would make money, everyone knew it would be ads. LLMs can’t do ads without compromising the product.
The Meta app Threads had no ads for the first year, and it was wonderful. Now it does, and its attractiveness was only reduced by 1% at most. Meta is really good at knowing the balance for how much to degrade UX by having monetization. And the amount they put in is hyper profitable.
So let's see Gemini and GPT with 1% of response content being sponsored. I doubt we'll see a user exodus and if that's enough to sustain the business, we're all good.
But inference? Inference is dirt cheap and keeps getting cheaper. You can run models lagging 6-12 years on consumer hardware, and by this I don't mean absolutely top-shelf specs, but more of "oh cool, turns out the {upper-range gaming GPU/Apple Silicon machine} I bought a year ago is actually great at running local {image generation/LLM inference}!" level. This is not to say you'll be able to run o3 or Opus 4 on a laptop next year - larger and more powerful models obviously require more hardware resources. But this should anchor expectations a bit.
We're measuring inference costs in multiples of gaming GPUs, so it's not an impending ecological disaster as some would like the world to believe - especially after accounting for data centers being significantly more efficient at this, with specialized hardware, near-100% utilization, countless of optimization hacks (including some underhanded ones).
Spoiler: they are still going to do ads, their hand will be forced.
Sooner or later, investors are going to demand returns on the massive investments, and turn off the money faucet. There'll be consolidation, wind-downs and ads everywhere.
It depends on what you mean by "compromise" here but they sure can inject ads.. like make the user wait 5 seconds, show an ad, then reply..
They can delay the response times and promote "premium" plans, etc
Lots of ways to monetize, I suppose the question is: will users tolerate it?
Based on what I've seen, the answer is yes, people will tolerate anything as long as it's "free".
That we might come to companies saying "it's not worth continuing research or training new models" seems to reinforce the OP's point, not contradict it.
Edit: I believe that "LLMs transforming society is inevitable" is a much more defensible assertion than any assertion about the nature of that transformation and the resulting economic winners and losers.
I think we'd be more screwed than VR if development ceased today. They are little more than toys right now who's most successsful outings are grifts, and the the most useful tools are simply aiding existing tooling (auto-correct). It is not really "intelligence" as of now.
>I believe that "LLMs transforming society is inevitable" is a much more defensible assertion
Sure. But into what? We can't just talk about change for change's sake. Look at the US in 2025 with that mentality.
The answer was, and will be ads (talk about inevitability!)
Can you imagine how miserable interacting with ad-funded models will be? Not just because of the ads they spew, but also the penny-pinching on training and inference budgets, with an eye focused solely on profitability. That is what the the future holds: consolidations, little competition, and models that do the bare-minimum, trained and operated by profit-maximizing misers, and not the unlimited intelligence AGI dream they sell.
AI on the other hand target businesses and consumers alike. A bank using LLM won’t get ads. Using LLM will be cost of doing business. Do you know what they means to consumers? Price for ChatGPT will go down.
Okay. So AI will be using ads for consumers and make deals with the billionaires. If window 11/12 still puts ads in what is a paid premium product, I see no optimism in thinking that a "free" chatbot will not also resort to it. Not as long as the people up top only see dollar signs and not long term longevity.
>Price for ChatGPT will go down.
Price for ChatGPT in reality, is going up in the meanwhile. This is like hoping grocery prices come down as inflation lessens. This never happens, you can only hope to be compensated more to make up for inflation.
How about tarsnap? https://www.daemonology.net/blog/2014-04-02-tarsnap-price-cu...
As will the response quality, while maintaining the same product branding. Users will accept whatever response OpenAI gives them under the "4o", "6p","9x" or whatever brand of the day, even as they ship-of-Theseus the service for higher margins. I'm yet to see an AI service with QoS guarantees, or even that the model weights & infrastructure won't be "optimized" over time to the customer's disadvantage.
I see LLMs inevitably leading to the same place. There will undoubtedly be advertising baked into the models. It is too strong a financial incentive. I can only hope that an open source alternative will at least allow for a hobbled version to consume.
edit: I think this was the podcast https://freakonomics.com/podcast/is-google-getting-worse/
I wonder if instead, could I sell my "attention" instead of others profitting of it?
I'm not excusing the platforms for bad algorithms. Rather, I believe it's naive to think that, but for the behavior of the platform itself that things would be great and rosy.
No, they won't. The fact that nearly every person in the world can mass communicate to nearly every other person in the world is the core issue. It is not platform design.
With LLMs, we know what the revenue source is (subscription prices and ads), but the question is about the lock-in. Once each of the AI companies stops building new iterations and just offers a consistent product, how long until someone else builds the same product but charges less for it?
What people often miss is that building the LLM is actually the easy part. The hard part is getting sufficient data on which to train the LLM, which is why most companies just put ethics aside and steal and pirate as much as they can before any regulations cuts them off (if any regulations ever even do). But that same approach means that anyone else can build an LLM and train on that data, and pricing becomes a race to the bottom, if open source models don't cut them out completely.
Buying better things is one of my main use cases for GPT.
The difference is that Facebook costs virtually nothing to run, at least on a per-user basis. (Sure, if you have a billion users, all of those individual rounding errors still add up somewhat.)
By contrast, if you're spending lots of money per user... well look at what happened to MoviePass!
The counterexample here might be Youtube; when it launched, streaming video was really expensive! It still is expensive too, but clearly Google has figured out the economics.
I guess you'd be surprised to find out that Meta's R&D costs are an order of magnitude higher than OpenAI's training + research costs? ($45B in 2024, vs. about $5B for OpenAI according to the leaked financials.)
I don't know what "moving the goalposts" means. Why were the goalposts there in the first place? The interesting questions here are whether OpenAI can sustain their current cost model long-term, and whether the revenue stream is sustainable without the costs. We'll see, I guess! It's fascinating.
I think what you're not realizing is that OpenAI already has the kind of consumer-facing business that makes Google and Meta hundreds of billions of revenue a year. They have the product, they have the consumer mindshare and usage. All they are missing is the monetization part. And they're doing that at a vastly lower cost basis than Google or Meta, no matter what class of spending you measure. Their unit costs are lower, their fixed costs are lower, their R&D costs are lower.
They don't need to stop R&D to be profitable. Literally all they'd need to do is minimal ads monetization.
There's all kinds of things you can criticize the AI companies for, but the economics being unsustainable really isn't one of them. OpenAI is running a massive consumer-facing app for incredibly cheap in comparison to its peers running systems of a similar scale. It'd be way more effective to concentrate on the areas where the criticism is either obviously correct, or there's at least more uncertainty.
They do not need to. Their costs are already really low given the size and nature of their user base.
> “Can OpenAI do this simple thing” is the whole question!
There was a claim by someone else about OpenAI's unit costs being unsustainably high: I gave the data that shows they aren't. They are in fact quite low compared to those of bigtechs running comparable consumer services.
Then you said that the real problem was OpenAI's R&D costs being so high. I gave the data showing that is not the case. Their R&D costs are very low compared to those of bigtechs running comparable consumer services.
So I take it that you now agree that their unit and R&D costs are indeed low compared to the size of their user base? And the main claim is that they can't actually monetize without losing their users?
It seems hard to be totally confident about that claim either way, we'll only know once they start monetizing. But it is the case that the monetization they'd need to be profitable is going to be comparatively light. It just follows directly out of their cost structure (which is why the cost structure is interesting). They don't need to extract Facebook levels of money out of each user to be profitable. They can keep the ad volumes low and the ad formats inconspicuous to start with, and then boil the frog over a decade.
Like, somebody in the comments for this post said that ChatGPT has recently started showing affiliate links (clearly separated from the answer) for queries about buying products. I hadn't heard about it before now, but that is obvious place to start from: high commissions, high click through rates, and it's the use case where the largest proportion of users will like having the ads rather than annoyed by them.
So it seems that we'll find out sooner rather than later. But I'd be willing to bet money that there won't be any exodus of users from OpenAI due to ads.
Instead you'll see a slow ratchet effect: as OpenAI increases their level of ad-based monetization for ChatGPT, the less popular chatbots will follow a step or two behind. Basically let OpenAI establish the norms for frequency and norms and take the minimal heat from it, but not try to become some kind of anti-ad champions promising free service with no ads in perpetuity.
The reason I expect this is that we haven't seen it happen in other similar businesses. Nobody tried to for example make a search engine with no monetization. They might have tried e.g. making search engines that promised no personalized ad targeting, but nobody tried just completely disowning the entire business model.
Do you know why it's so expensive? I'd thought serving html would be cheaper, particularly at Facebook's scale. Does the $30B include the cost of human content moderators? I also guess Facebook does a lot of video now, do you think that's it?
Also, even still, $10 per user has got to be an order of magnitude less than what OpenAI is spending on its free users, no?
I don't know about Facebook specifically, but in general people underestimate the amount of stuff that needs to happen for a consumer-facing app of that scale. It's not just "serving html".
There are going to be thousands of teams with job functions to run thousands of services or workflows doing something incredibly obscure but that's necessary for some regulatory, commercial or operational reason. (Yes, moderation would be one of those functions).
> Also, even still, $10 per user has got to be an order of magnitude less than what OpenAI is spending on its free users, no?
No. OpenAI's inference costs in 2024 were a few billion (IIRC there are two conflicting reports about the leaked financials, one setting the inference costs at $2B/year, the other at $4B/year). That's the inference costs for both their paid subscription users, API users, and free consumer users. And at the time they were reported to have 500M monthly active users.
Even if we make the most extreme possible assumptions for all the degrees of freedom (all costs can be assigned to the free users rather than the paid ones, the higher number for total inference spend, monthly users == annual users), the cost per free user would still be at most $8/year.
And yes these are still businesses. If they can't find profitability they will drop it like it's hot. i.e. we hit another bubble burst that tech is known to do every decade or 2. There's no free money anymore to carry them anymore, so perfect time to burst.
So while I understand how it looks from a financial perspective, I think that perspective is distorted in terms of what causes those outcomes. Many of the unprofitable aspects directly support the profitable ones. Not always, though.
The social media applications have strong network effects, this drives a lot of their profitability.
* sure, there are differences, see the benchmarks, but from a consumer perspective, there's no meaningful differentiation
The AI bubble is so big that if it pops, it will have dramatic effects on the economy.
Twitter has never been consistently profitable
Twitter has never been consistently profitable.
ChatGPT also has higher marginal costs than any of the software only tech companies did previously.
From where I'm standing, the models are useful as is. If Claude stopped improving today, I would still find use for it. Well worth 4 figures a year IMO.
only because software engineering pay hasn't adjusted down for the new reality . You don't know what its worth yet.
The only way I see compensation "adjusting" because of LLMs would need them to become significantly more competent and autonomous.
Not sure what GP meant specifically, but to me, if $200/m gets you a decent programmer, then $200/m is the new going rate for a programmer.
Sure, now it's all fun and games as the market hasn't adjusted yet, but if it really is true that for $200/m you can 10x your revenue, it's still only going to be true until the market adjusts!
> The competent people do get a productivity boost though.
And they are not likely to remain competent if they are all doing 80% review, 15% prompting and 5% coding. If they keep the ratios at, for example, 25% review, 5% prompting and the rest coding, then sure, they'll remain productive.
OTOH, the pipeline for juniors now seems to be irrevocably broken: the only way forward is to improve the LLM coding capabilities to the point that, when the current crop of knowledgeable people have retired, programmers are not required.
Otherwise, when the current crop of coders who have the experience retires, there'll be no experience in the pipeline to take their place.
If the new norm is "$200/m gets you a programmer", then that is exactly the labour rate for programming: $200/m. These were previously (at least) $5k/m jobs. They are now $200/m jobs.
High level languages also massively boosted productivity, but we didn't see salaries collapse from that.
> And they are not likely to remain competent if they are all doing 80% review, 15% prompting and 5% coding.
I've been doing 80% review and design for years, it's called not being a mid or junior level developer.
> OTOH, the pipeline for juniors now seems to be irrevocably broken
I constantly get junior developers handed to me from "strategic partners", they are just disguised as senior developers. I'm telling you brother, the LLMs aren't helping these guys do the job. I've let go 3 of them in July alone.
What do you think a product manager is doing?
It doesn't sound like you are disagreeing with me: that role you described is one of manager, not of programmer.
> High level languages also massively boosted productivity, but we didn't see salaries collapse from that.
Those high level languages still needed actual programmers. If the LLM is able to 10x the output of a single programmer because that programmer is spending all their time managing, you don't really need a programmer anymore, do you?
> I've been doing 80% review and design for years, it's called not being a mid or junior level developer.
Maybe it differs from place to place. I was a senior and a staff engineer, at various places including a FAANG. My observations were that even staff engineer level was still spending around 2 - 3 hours a day writing code. If you're 10x'ing your productivity, you almost certainly aren't spending 2 - 3 hours a day writing code.
> I constantly get junior developers handed to me from "strategic partners", they are just disguised as senior developers. I'm telling you brother, the LLMs aren't helping these guys do the job. I've let go 3 of them in July alone.
This is a bit of a non-sequitor; what does that have to do with breaking the pipeline for actual juniors?
Without juniors, we don't get seniors. Without seniors and above, who will double-check the output of the LLM?[1]
If no one is hiring juniors anymore, then the pipeline is broken. And since the market price of a programmer is going to be set at $200/m, where will you find new entrants for this market?
Hell, even mid-level programmers will exit, because when a 10-programmer team can be replaced by a 1-person manager and a $200/m coding agent, those 9 people aren't quietly going to starve while the industry needs them again. They're going to go off and find something else to do, and their skills will atrophy (just like the 1-person LLM manager skills will atrophy eventually as well).
----------------------------
[1] Recall that my first post in this thread was to say that the LLM coding agents have to get so good that programmers aren't needed anymore because we won't have programmers anymore. If they aren't that good when the current crop starts retiring then we're in for some trouble, aren't we?
You keep saying this, but I don't see it. The current tools just can't replace developers. They can't even be used in the same way you'd use a junior developer or intern. It's more akin to going from hand tools to power tools than it is getting an apprentice. The job has not been automated and hasn't been outsourced to LLMs.
Will it be? Who knows, but in my personal opinion, it's not looking like it will any time soon. There would need to be more improvement than we've seen from day 1 of ChatGPT until now before we could even be seriously considering this.
> Those high level languages still needed actual programmers.
So does the LLM from day one until now, and for the foreseeable future.
> This is a bit of a non-sequitor; what does that have to do with breaking the pipeline for actual juniors?
Who says the pipeline is even broken by LLMs? The job market went to shit with rising interest rates before LLMs hit the scene. Nobody was hiring them anyway.
In that case it seems to depend on what you mean by "replacing", doesn't it? It doesn't mean a non-developer can do a developers job, but it does mean that one developer can do two developer's jobs. That leads to a lot more competition for the remaining jobs and presumably many competent developers will accept lower salaries in exchange for having a job at all.
I find this surprising. I figured the opposite: that the quality of body shop type places would improve and the productivity increases would decrease as you went "up" the skill ladder.
I've worked on/inherited a few projects from the Big Name body shops and, frankly, I'd take some "vibe coded" LLM mess any day of the week. I really figured there was nowhere to go but "up" for those kinds of projects.
On the other end, I know a guy who writes deeply proprietary embedded code that lives in EV battery controllers and he's found LLMs useless.
And they would not be incompetent at targeting. If they were to use the chat history for targeting, they might have the most valuable ad targeting data sets ever built.
A quick search shows that click on ads targeting developers are expensive.
Also there is a ton of users asking to rewrite emails, create business plans, translate, etc.
You could even loudly proclaim that the are ads are not targeted by users which HN would love (but really it would just be old school brand marketing).
Citation needed? I can't sit on a bus without spotting some young person using ChatGPT
You don't need every individual request to be profitable, just the aggregate. If you're doing a Google search for, like, the std::vector API reference you won't see ads. And that's probably true for something like 90% of the searches. Those searches have no commercial value, and serving results is just a cost of doing business.
By serving those unmonetizable queries the search engine is making a bet that when you need to buy a new washing machine, need a personal injury lawyer, or are researching that holiday trip to Istanbul, you'll also do those highly commercial and monetizable searches with the same search engine.
Chatbots should have exactly the same dynamics as search engines.
Techies are also great for network growth and verification for other users, and act as community managers indirectly.
Software guys are doing much, much more than treating LLM's like an improved Stack Overflow. And a lot of them are willing to pay.
All of which made it much less likely that users would bolt in response to each real monetization step. This is very different to the current situation, where we have a shifting landscape with several AI companies, each with its strengths. Things can change, but it takes time for 1-2 leaders to consolidate and for the competition to die off. My 2c.
It would be a hilarious outcome though, “we built machine gods, and the main thing we use them for is to make people click ads.” What a perfect Silicon Valley apotheosis.
Which is still too much trust
I know I don't have as much of a filter as I ought to!
https://www.lesswrong.com/s/pmHZDpak4NeRLLLCw/p/TiDGXt3WrQwt...
you think those people don't believe the magic computer when it talks?
Many people have a lot of trust in anything ChatGPT tells them.
For example, the more product placement opportunities there are, the more products can be placed, so sooner or later that'll become an OKR to the "content side" of the business as well.
Which may be for the best, because people shouldn’t be implicitly trusting the bullshit engine.
Traditional banner ads, inserted inline into the conversation based on some classifier seem a far better idea.
Basically, they can stop investing in research either when 1) the tech matures and everyone is out of ideas or 2) they have monopoly power from either market power or oracle style enterprise lock in or something. Otherwise they'll fall behind and you won't have any reason to pay for it anymore. Fun thing about "perfect" competition is that everyone competes their profits to zero
This is why AI companies must lose money short term. The moment improvements plateau or the economic environment changes, everyone will cut back on research.
Actually, I'd be very curious to know this. Because we already have a few relatively capable models that I can run on my MBP with 128 GB of RAM (and a few less capable models I can run much faster on my 5090).
In order to break even they would have to minimize the operating costs (by throttling, maiming models etc.) and/or increase prices. This would be the reality check.
But the cynic in me feels they prefer to avoid this reality check and use the tried and tested Uber model of permanent money influx with the "profitability is just around the corner" justification but at an even bigger scale.
Is that true? Are they operating inference at a loss or are they incurring losses entirely on R&D? I guess we'll probably never know, but I wouldn't take as a given that inference is operating at a loss.
I found this: https://semianalysis.com/2023/02/09/the-inference-cost-of-se...
which estimates that it costs $250M/year to operate ChatGPT. If even remotely true $10B in revenue on $250M of COGS would be a great business.
> The cost of the compute to train models alone ($3 billion) obliterates the entirety of its subscription revenue, and the compute from running models ($2 billion) takes the rest, and then some. It doesn’t just cost more to run OpenAI than it makes — it costs the company a billion dollars more than the entirety of its revenue to run the software it sells before any other costs.
[0] https://www.lesswrong.com/posts/CCQsQnCMWhJcCFY9x/openai-los...
I think I trust the semianalysis estimate ($250M) more than this estimate ($2B), but who knows? I do see my revenue estimate was for this year, though. However, $4B revenue on $250M COGS...is still staggeringly good. No wonder amazon, google, and Microsoft are tripping over themselves to offer these models for a fee.
Also the semianalysis estimate is from Feb 2023, which is before the release of gpt4, and it assumes 13 million DAU. ChatGPT has 800 million WAU, so that's somewhere between 115 million and 800 million DAU. E.g. if we prorate the cogs estimate for 200 DAU, then that's 15x higher or $3.75B.
That's a great point, but I think it's less important now with MCP and RAG. If VC money dried up and the bubble burst, we'd still have broadly useful models that wouldn't be obsolete for years. Releasing a new model every year might be a lot cheaper if a company converts GPU opex to capex and accepts a long training time.
> Also the semianalysis estimate is from Feb 2023,
Oh! I missed the date. You're right, that's a lot more expensive. On the other hand, inference has likely gotten a lot cheaper (in terms of GPU TOPS) too. Still, I think there's a profitable business model there if VC funding dries up and most of the model companies collapse.
For a few months, maybe. Then they become obsolete and, in some cases like coding, useless.
If they stop training today what happens? Does training always have to be at these same levels or will it level off? Is training fixed? IE, you can add 10x the subs and training costs stay static.
IMO, there is a great business in there, but the market will likely shrink to ~2 players. ChatGPT has a huge lead and is already Kleenex/Google of the LLMs. I think the battle is really for second place and that is likely dictated by who runs out of runway first. I would say that Google has the inside track, but they are so bad at product they may fumble. Makes me wonder sometimes how Google ever became a product and verb.
OpEx is larger than revenue. CapEx is also larger than the total revenue on the lifetime of a model.
Different investors use different ratios and numbers (ARR, P/E, EV/EBITDA, etc) as a quick initial smoke screen. They mean different things in different industries during different times of a business’ lifecycle. BUT they are supposed to help you get a starting point to reduce noise. Not as a the 1 metric you base your investing strategy on.
Even being generous it seems like it'd be too noisy to even assist in informing a good decision. Don't the overwhelmingly vast majority of businesses see periodic ebbs and flows over the course of a year?
Here is how it sort of happens sometimes:
- You are an analyst at some hedge fund.
- You study the agriculture industry overall and understand the general macro view of the market segment and its parameters etc.
- You pick few random agriculture company (e.g: WeGrowPotatos Corp.) that did really really solid returns between 2001 and 2007 and analyze their performance.
- You try to see how you could have predicted the company's performance in 2001 based on all the random bits of data you have. You are not looking for something that makes sense per se. Investing based on metrics that make intuitive sense is extremely hard if not impossible because everyone is doing that which makes the results very unpredictable.
- You figure out that for whatever reason, if you sum the total sales for a company, subtract reserved cash, and divide that by the global inflation rate minus the current interest rate in the US; this company has a value that's an anomaly among all the other agriculture companies.
- You call that bullshit The SAGI™ ratio (Sales Adjusted for Global Inflation ratio)
- You calculate the SAGI™ ratio for other agriculture companies in different points in time and determine its actual historical performance and parameters compared to WeGrowPotatoes in 2001.
- You then calculate that SAGI™ ratio for all companies today and study the ones that match your desired number then invest in them. You might even start applying SAGI™ analysis to non-agriculture companies.
- (If you're successful) In few years you will have built a reputation. Everyone wants to learn from you how you value a company. You share your method with the world. You still investigate the business to see how much it diverges from your "WeGrowPotatoes" model you developed the SAGI ratio based on.
- People look at your returns, look at your (1) step of calculating SAGI, and proclaim that the SAGI ratio paramount. Everyone is talking about nothing but SAGI ratio. Someone creates a SAGIHeads.com and /r/SAGInation and now Google lists it under every stock for some reason.
It's all about that (sales - cash / inflation - interest). A formula that makes no sense; but people are gonna start working it backwards by trying to understand what does "sales - cash" actually mean for a company?
Like that SAGI is bullshit I just made up, but EV is an actual metric and it's generally calculated as (equity + debt - cash). What do you think that tells you about a company? and why do people look at it? How does it make any sense for a company to sum its assets and debt? what is that? According to financial folks it tells you the actual market operation size of the company. The cash a company holds is not in the market so it doesn't count. the assets are obviously important to count, but debt for a company can be positive if it's on path to convert into asset on a reasonable timeline.
I don't know why investors in the tech space focus too much on ARR. It's possible that it was a useful metric with traditional internet startups model like Google, Facebook, Twitter, Instagram, Reddit, etc where the general wisdom was it's impossible to expect people to pay a lot for online services. So generating any sort of revenue almost always correlated with how many contracts do you get to signup with advertisers or enterprises and those are usually pretty stable and lucrative.
I highly recommend listening to Warren Buffets investing Q&As or lectures. He got me to view companies and the entire economy differently.
To steelman the original concept, annual revenue isn't a great measure for a young fast-growing company since you are averaging all the months of the last year, many of which aren't indicative of the trajectory of the company. E.g. if a company only had revenue the last 3 months, annual revenue is a bad measure. So you use MRR to get a better notion of instantaneous revenue, but you need to annualize it to make it a useful comparison (e.g. to compute a P/E ratio), so you use ARR.
Private investors will of course demand more detailed numbers like churn and an exact breakdown of "recurring" revenue. The real issue is that these aren't public companies, and so they have no obligation to report anything to the public, and their PR team carefully selects a couple nice sounding numbers.
Any number that there isn't a law telling companies how to calculate it will always be a joke.
So I guess this rules out most SV venture capital
The money is there. Investors believe this is the next big thing, and is a once in a lifetime opportunity. Bigger than the social media boom which made a bunch of billionaires, bigger than the dot com boom, bigger maybe than the invention of the microchip itself.
It's going to be years before any of these companies care about profit. Ad revenue is unlikely to fund the engineering and research they need. So the only question is, does the investor money dry up? I don't think so. Investor money will be chasing AGI until we get it or there's another AI winter.
[1]: https://www.businessofapps.com/data/chatgpt-statistics/
I imagine they would’ve flicked that switch if they thought it would generate a profit, but as it is it seems like all AI companies are still happy to burn investor money trying to improve their models while I guess waiting for everyone else to stop first.
I also imagine it’s hard to go to investors with “while all of our competitors are improving their models and either closing the gap or surpassing us, we’re just going to stabilize and see if people will pay for our current product.”
Yeah, no one wants to be the first to stop improving models. As long as investor money keeps flowing in there's no reason to - just keep burning it and try to outlast your competitors, figure out the business model later. We'll only start to see heavy monetization once the money dries up, if it ever does.
I think there's an element of FOMO - should someone actually get to AGI, or at least something good enough to actually impact the labor market and replace a lot of jobs, the investors of that company/product stand to make obscene amounts of money. So everyone pumps in, in hope of that far off future promise.
But like you said, how long can this keep going before it starts looking like that future promise will not be fulfilled in this lifetime and investors start wanting a return.
Funny seeing that comment on this post in particular, tho. When OP says “I’m not sure it’s a world I want”, I really don’t think they’re thinking about corporate revenue opportunities… More like Rehoboam, if not Skynet.
This might be true (or not), but for sure not on this site.
LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them,
Whether it's true for any of the mainstream LLM companies or not is anyone's guess, since their financials are either private or don't separate out LLM inference as a line item.
What's happening here is pretty clear to me: Its a form of enshittification. These companies are struggling to find a price point that supports both broad market adoption ($20? $30?) and the intelligence/scale to deliver good results ($200? $300?). So, they're nerfing cheap plans, prioritizing expensive ones, and pissing off customers in the process. Cursor even had to apologize for it [3].
There's a broad sense in the LLM industry right now that if we can't get to "it" (AGI, etc) by the end of this decade, it won't happen during this "AI Summer". The reason for that is two-fold: Intelligence scaling is logarithmic w.r.t compute. We simply cannot scale compute quick enough. And, interest in funding to pay for that exponential compute need will dry up, and previous super-cycles tell us that will happen on the order of ~5 years.
So here's my thesis: We have a deadline that even evangelists agree is a deadline. I would argue that we're further along in this supercycle than many people realize, because these companies have already reached the early enshitification phase for some niche use-cases (software development). We're also seeing Grok 4 Heavy release with a 50% price increase ($300/mo) yet offer single-digit percent improvement in capability. This is hallmark enshitification.
Enshitification is the final, terminal phase of hyperscale technology companies. Companies remain in that phase potentially forever, but its not a phase where significant research, innovation, and optimization can happen; instead, it is a phase of extraction. AI hyperscalers genuinely speedran this cycle thanks to their incredible funding and costs; but they're now showcasing very early signals of enshitifications.
(Google might actually escape this enshitification supercycle, to be clear, and that's why I'm so bullish on them and them alone. Their deep, multi-decade investment into TPUs, Cloud Infra, and high margin product deployments of AI might help them escape it).
[1] https://www.reddit.com/r/cursor/comments/1m0i6o3/cursor_qual...
[2] https://www.reddit.com/r/ClaudeAI/comments/1lzuy0j/claude_co...
[3] https://techcrunch.com/2025/07/07/cursor-apologizes-for-uncl...