The reality is the product is the easy part.
Actually selling it, maintaining it, selling more of it and running a profitable business is the hard part.
So the AI makes the product really easy to make, but selling it is the hard part.
Why don’t they ask ai how to sell it. After all, isn’t it super intelligent?
I do work for a software company, and sales is part of my job. And yes, sometimes I get the AI to review a tricky sale. Not usually though.
However, the reality is it takes years and years to build relationships, get into markets, get visibility.
Basically embed yourself into a market. So the product is the easy part. The sale is the hard part.
Because its a scam.
A scam that will inevitably end humanity on a global scale…… probably.
Nah mankind will continue, but it’ll definitely hurt when this shitshow implodes and possibly takes out the current economic model and quite a few governments with it.
The real reason isn’t that they can’t turn a profit, it’s that they don’t have to.
They don’t have to be profitable because they aren’t fueling growth with profits, they’re fueling it with debt and investment. Like many have noted, they cannot do that forever and will eventually pass that cost to consumers. A significant portion of their existing cost is driven by research, which they can scale back by lobbying for legislation, so that peers getting a leg up is more difficult and small players catching up is almost impossible.
Also, both Anthropic and OpenAI have planned IPOs, where they will inevitably raise a ton of cash. It’s a game of chicken though. How long can they fuel growth without being publicly accountable to balance sheets and shareholders? Whether we like it or not, they will almost certainly transition to being profitable businesses, but only when it is advantageous for them to do so. Right now, it’s not a priority; the priority is growth to the point of diminishing returns.
I’d wager it’s simply not possible to be profitable using AI for most use-cases. You either burn way too many tokens on your queries or risk your business falling victim to an under-tokenized AI hallucination.
If I recall correctly, shovel companies made a lot of money while only a few of their shovels actually struck gold.
Recent scholarship confirms that merchants made far more money than miners during the gold rush.
The wealthiest man in California during the early years of the rush was Samuel Brannan, a tireless self-promoter, shopkeeper and newspaper publisher. Brannan opened the first supply stores in Sacramento, Coloma, and other spots in the goldfields. Just as the rush began, he purchased all the prospecting supplies available in San Francisco and resold them at a substantial profit.
Better question is if you can really build an app as easily as writing a prompt, why would anyone pay you for your app?
Somehow AI bros like to pretend that AI is some secret lifehack despite it being one of the most mainstream things in human history.
They are going for the gold rush pick axe business model
The difference is there was actual gold in them there hills. LLM’s are hogwash
But there is no gold at the end of that rainbow. It’s more like a selling Florida swampland to real estate developers model.
That’s like asking why doesn’t Adobe make their own content and keep the tools internal.
The AI companies could probably be profitable if they weren’t dumping tons of money into new model research, but then they would stagnate and die to the ones who keep moving forward.
Companies using their AI don’t need to worry about that expense.
It’d be so funny if research hits a breakthrough that absolutely collapses the cost of inference, allowing anyone to self host frontier models, eliminating the ability for all the main AI companies to ever recoup their costs.
Well, except where everyone’s data gets sold at bottom dollar in liquidation. Actually, that’d be pretty funny too.
I don’t know if that’ll happen to frontier models, but I definitely see it happening with models that work well enough for the vast majority of personal use.
To be fair, a reasonable answer would be the same as any reseller or value-added business. They have some skill, capital, or existing market capture that the whole seller does not.
Don’t get me wrong it can still be bullshit, but it’s actually pretty common.
They’re selling hype, not technology
It’s a definite risk. They do steal your inputs and the outputs they send back to you. They will always have even more powerful models that aren’t released yet. If you avoid sending them all of your code, they could hack you to steal it. They will certainly copy any good ideas their competitors come up with (Meta Muse agent, jev) within weeks, as they all copied claude code and open claw. Answer to OP is “just not yet”.
The unified US establishment goal is to create skynet for global and domestic domination. Theoretically, the AI bond villains could make sufficently sophisticated weapon/control systems to threaten the government, but why bother when it already wants to give them all of our money. “Whoever wins AI, wins” means only first to skynet, wins. Zero relevance to any AI customers, who obviously win through the widest competition/choices on what to use to help them. Not the nationality of the oligarchs that profit off them.
https://naturalfinance.blogspot.com/2026/09/a-global-prosperity-manifesto.html
I saw a thread a few minutes ago on a profitable AI business. The business model was hacking websites at scale to steal hundreds of thousands of credit card numbers…
If you come to seminar at the Holiday Inn, I’ll teach you why! Only $500, and at the end you’ll be able to start your own AI powered business and make millions per year!
I guess you will teach me how to build courses using AI and sell them for $500 at a fancy resort?
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The AI tech they peddle is impressive and has some uses; but it is nowhere near ready to live up to the hype they created in which you can just pay $10K a year for a license and get rid of a $250K employee
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The AI tech they sell for $10K a year, actually costs $20K to run and they know nobody would pay the full cost, let alone the cost + their profit margin
PS: those are made up numbers to answer the question, while not real numbers, they represent the points I am trying to make
You are right.
The AI tech they sell for $10K a year, actually costs $20K to run
Open AI and anthropic make a profit on inference / api usage. It’s only really the training that’s dragging down their bottom line.
Well unless they have reach a level where training is not needed (which will be never for an LLM), what you told me is that you make a profit freezing ice cream but lose it on the eggs, cream and sugar necessary to make it
The model releases are getting shorter and shorter. close to a month now. There is no actual profitability not because the price isn’t higher than cost of ingredients, but because they don’t count throwing away the ice cream mixer, and factory every month, when they should if replacing them is part of the process.
Training is not needed to run inference on the existing models, it’s needed to make new models. If LLMs plateau and there’s no use in training new models, or the government regulates them and they can’t train new frontier models, then they can still run inference on the currently existing models and make a profit.
This is more like making a profit for making ice cream but losing a bunch of money researching new recipes for ice cream. If you decide the recipe is good enough and stop doing that research you can still make money making and selling ice cream.
yeah? so a model trained in 2 year old data is just as good? as a model trained today?
If you decide the recipe is good enough and stop doing that research you can still make money making and selling ice cream.
So they are just choosing to lose money? they have a perfectly good, profitable product but they choose to lose money… ok bud
Your point only makes sense if we agree the current models are still not good enough to make a profitable product and thus, they need to keep pushing
so a model trained in 2 year old data is just as good? as a model trained today?
Yes, assuming a plateau in LLM capability the only reason you’d train on an updated corpus is to get fresh info. That’s not worth it because:
- The model will most likely be wrapped in a harness that has search capabilities, so it can use that to fetch fresh info
- 2 year old data may actually be better as the Internet becomes increasingly tainted with LLM content
Most of the improvement from new models isn’t coming from expanding or updating the corpus, its coming from increasing the parameter size and reinforcement learning.
So they are just choosing to lose money?
No, they are competing. If open AI decides to stop training new models right now then anthropic will and take all there business as the switching cost for models is low. That’s why they want the government to regulate it, so they can have a ceasefire to start taking in profits from inference without worrying about their competitor making a new better model and eating there lunch.
if that were true, and the current models were actually good, there would be wide adoption of the current models while people wait for the next leap… instead, you get glimpses of the terrible numbers like 3% of user base is actually paying for these things
maybe we torture our analogies to death but you make it sound like they have the formula for coca cola but instead of selling that, they are burning cash trying to find an even better formula
when the reality is that they have something that is technically drinkable and not officially poison and they are selling it like the next elixir of life (while they burn cash trying to catch up to their own hype)
People are adopting the current models, mostly coders right now. Most people using LLMs are using them for basic information search, ie. Google AI overviews. Those tasks don’t require the top models and don’t burn that many tokens so the companies keep them free to get the public exposed to AI.
Then there’s the 3% of people who are the power users using it for work, especially coders. For productivity the top models do perform better and the stakes are higher so they need to perform better. The top models aren’t needed for every task, but they shine as an orchestrator of smaller models handling more basic tasks. Coding also requires a lot more tokens, to read all the existing code; to do “thinking” which generates a bunch of output tokens to imitate reasoning; then to generate the code itself; then to review it, adjust after a review…
All of that equates to large bills for token spend to anthropic or open AI. My Claude bill on my company account is approaching $2,000 this month, and that’s about average / what’s expected from an engineer at my company. I’d bet that every engineer in silicon valley is burning through a similar amount as well.
This is why both open AI and anthropic are both seeing extremely high revenue growth. Anthropic ended 2025 with $9b in revenue , they are now on track to hit $100b revenue this year, for reference / the analogy coca cola made $47b this year, it’s a larger revenue then every other software company except Microsoft. THAT IS INSANE, no company has ever seen that kind of growth. Yes it’s being weighed down by training costs so they aren’t profitable but if they even double there revenue next year that could change.
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