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Comment by dbalatero

6 months ago

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...

This echoes a lot of the rhetoric around "but how will facebook/twitter/etc make money?" back in the mid 2000s. LLMs might shake out differently from the social web, but I don't think that speculating about the flexibility of demand curves is a particularly useful exercise in an industry where the marginal cost of inference capacity is measured in microcents per token. Plus, the question at hand is "will LLMs be relevant?" and not "will LLMs be massively profitable to model providers?"

  • Social networks finding profitability via advertising is what created the entire problem space of social media - the algorithmic timelines, the gaming, the dopamine circus, the depression, everything negative that’s come from social media has come from the revenue model, so yes, I think it’s worth being concerned about how LLMs make money, not because I’m worried they won’t, because I’m worried they Will.

    • I think this can't be understated. It also destroyed search. I listened to a podcast a few years ago with an early googler who talked about this very precipice in early google days. They did a lot of testing, and a lot of modeling of people's valuation of search. They figured that the average person got something like $50/yr of value out of search (I can't remember the exact number, I hope I'm not off by an order of magnitude). And that was the most they could ever realistically charge. Meanwhile, advertising for just Q4 was like 10 times the value. It meant that they knew that advertising on the platform was inevitable. They also acknowledged that it would lead to the very problem that Brin and Page wrote about in their seminal paper on search.

      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/

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    • Social networks will have all of those effects without any effort by the platform itself because the person with more followers has more influence so the people on the platform will do all they can to get more.

      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.

    • oh, I 100% agree with this. The way the social web was monetized is the root of a lot of evil. With AI, we have an opportunity to learn from the past. I think a lesson here is "don't wait to think critically about the societal consequences of the next Big Tech Thing's business model because you have doubts about its profitability or unit economics."

  • > This echoes a lot of the rhetoric around "but how will facebook/twitter/etc make money?" back in the mid 2000s.

    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.

    • You're either overestimating the cost of inference or underestimating the cost of running a service like Facebook at that scale. Meta's cost of revenue (i.e. just running the service, not R&D, not marketing, not admin, none of that) was about $30B/year in 2024. In the leaked OpenAI financials from last year, their 2024 inference costs were 1/10th of that.

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  • > This echoes a lot of the rhetoric around "but how will facebook/twitter/etc make money?"

    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.

    • It won’t be ads. Social media target consumers, so advertising is dominant. We all love free services and don’t mind some attraction.

      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.

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  • The thing about facebook/twitter/etc was that everyone knew how they achieve lock-in and build a moat (network effect), but the question was around where to source revenue.

    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.

    • ChatGPT also makes money via affiliate links. If you ask ChatGPT something like "what is the best airline approved cabin luggage you can buy?" you get affiliate links to Amazon and other sites. I use ChatGPT most of the time before I buy anything these days… From personal experience (I operated an app financed by affiliate links). I can tell you that this for sure generates a lot of money. My app was relatively tiny and I only got about 1% of the money I generated but that app pulled in about $50k per month.

      Buying better things is one of my main use cases for GPT.

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  • Yep. Remember when Amazon could never make money and we kept trying to explain they were reinvesting their earnings into R&D and nobody believed it? All the rhetoric went from "Amazon can't be profitable" to "Amazon is a monopoly" practically overnight. It's like people don't understand the explore/exploit strategy trade-off.

  • > LLMs might shake out differently from the social web, but I don't think that speculating about the flexibility of demand curves is a particularly useful exercise in an industry where the marginal cost of inference capacity is measured in microcents per token

    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.

    • The point I'm making is that, even in the extreme case where we cease all additional R&D on LLMs, what has been developed up until now has a great deal of utility and transformative power, and that utility can be delivered at scale for cheap. So, even if LLMs don't become an economic boon for the companies that enable them, the transformative effect they have and will continue to have on society is inevitable.

      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.

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  • No one ever doubted that Facebook would make money. It was profitable early on, never lost that much money and was definitely profitable by the time it went public.

    Twitter has never been consistently profitable.

    ChatGPT also has higher marginal costs than any of the software only tech companies did previously.

  • Well, given the answers to the former: maybe we should stop now before we end up selling even more of our data off to technocrats. Or worse, your chatbot shilling to you between prompts.

    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.

  • what I struggle with is that the top 10 providers of LLMs all have identical* products. The services have amazing capabilities, but no real moats.

    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 point is that if they’re not profitable they won’t be relevant since they’re so expensive to run.

    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.

    • You’re not thinking evil enough. LLMs have the potential to be much more insidious about whatever it is they are shilling. Our dystopian future will feature plausibly deniable priming.

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    • I can run an LLM on my RTX3090 that is at least as useful to me in my daily life as an AAA game that would otherwise justify the cost of the hardware. This is today, which I suspect is in the upper part of the Kuznets curve for AI inference tech. I don't see a future where LLMs are too expensive to run (at least for some subset of valuable use cases) as likely.

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    • > LLMs can’t do ads without compromising the product.

      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.

    • Well, they haven't really tried yet.

      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.

    • I was chatting with Gemini about vacation ideas and could absolutely picture a world where if it lists some hotels I might like, the businesses that bought some LLM ad space could easily show up more often than others.

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    • > LLMs can’t do ads without compromising the product.

      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".

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  • No one ever doubted that Facebook would make money. It was profitable early on, never lost that much money and was definitely profitable by the time it went public.

    Twitter has never been consistently profitable

That's fixable, a gradual adjusting of the free tier will happen soon enough once they stop pumping money into it. Part of this is also a war of attrition though, who has the most money to keep a free tier the longest and attract the most people. Very familiar strategy for companies trying to gain market share.

  • That assumes that everyone is willing to pay for it. I don't think that's an assumption that will be true.

    • Consider the general research - in all, it doesn't eliminate people, but let's say it shakes out to speeding up developers 10% over all tasks. (That includes creating tickets, writing documentation, unblocking bugs, writing scripts, building proof of concepts, and more rote refactoring, but does not solve the harder problems or stop us from doing the hard work of software engineering that doesn't involve lines of code.)

      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.

    • Those that aren't willing to pay for it directly, can still use it for free, but will just have to tolerate product placement.

    • It very much does not assume that, only that some fraction will have become accustomed to using it to the point of not giving it up. In fact, they could probably remain profitable without a single new customer, given the number of subscribers they already have.

  • Absolutely, free-tier AI won’t stay "free" forever. It’s only a matter of time before advertisers start paying to have their products woven into your AI conversations. It’ll creep in quietly—maybe a helpful brand suggestion, a recommended product "just for you," or a well-timed promo in a tangential conversation. Soon enough though, you’ll wonder if your LLM genuinely likes that brand of shoes, or if it's just doing its job.

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  • Competition is almost guaranteed to drive price close to cost of delivery especially if they can't pay trump to ban open source, particularly chinese. With no ability to play the thiel monopoly playbook, their investors would never make their money back if not for government capture and sweet sweet taxpayer military contracts.