They might generate 10b ARR, but they lose a lot more than that. Their paid users are a fraction of the free riders.
https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...
https://www.wheresyoured.at/openai-is-a-systemic-risk-to-the...
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.
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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