https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...
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.
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.
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.
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.
Twitter has never been consistently profitable.
ChatGPT also has higher marginal costs than any of the software only tech companies did previously.
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.
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
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.
Twitter has never been consistently profitable
But hey, why not get ahead of the curve? With BrightlyAI™, you get powerful conversational intelligence - always on, always free. Whether you're searching for new gear, planning your next trip, or just craving dinner ideas, BrightlyAI™ brings you personalized suggestions from our curated partners—so you save time, money, and effort.
Enjoy smarter conversations, seamless offers, and a world of possibilities—powered by BrightlyAI™: "Illuminate your day. Conversation, curated."
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.
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.
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.
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.
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...
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.
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...