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Comment by TacticalCoder | Hacker News Reader

I understand that things can go wrong and there can be security issues, but I see at least two other issues:

1. what if, ChadGPT style, ads are added to the answers (like OpenAI said it'd do, hence the new "ChadGPT" name)?

2. what if the current prices really are unsustainable and the thing goes 10x?

Are we living some golden age where we can both query LLMs on the cheap and not get ad-infected answers?

I read several comments in different threads made by people saying: "I use AI because search results are too polluted and the Web is unusable"

And I now do the same:

"Gemini, compare me the HP Z640 and HP Z840 workstations, list the features in a table" / "Find me which Xeon CPU they support, list me the date and price of these CPU when they were new and typical price used now".

How long before I get twelve ads along with paid vendors recommendations?

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> what if the current prices really are unsustainable and the thing goes 10x?

Where does this idea come from? We know how much it costs to run LLMs. It's not like we're waiting to find out. AI companies aren't losing money on API tokens. What could possibly happen to make prices go 10x when they're already running at a profit? Claude Max might be a different story, but AI is going to get cheaper to run. Not randomly 10x for the same models.

From what I've read, every major AI player is losing a (lot) of money on running LLMs, even just with inference. It's hard to say for sure because they don't publish the financials (or if they do, it tends to be obfuscated), but if the screws start being turned on investment dollars they not only have to increase the price of their current offerings (2x cost wouldn't shock me), but some of them also need a (massive) influx of capital to handle things like datacenter build obligations (10s of billions of dollars). So I don't think it's crazy to think that prices might go up quite a bit. We've already seen waves of it, like last summer when Cursor suddenly became a lot more expensive (or less functional, depending on your perspective)

This is my understanding as well. If GPT made money the companies that run them would be publicly traded?

Furthermore, companies which are publicly traded show that overall the products are not economical. Meta and MSFT are great examples of this, though they have recently seen opposite sides of investors appraising their results. Notably, OpenAI and MSFT are more closely linked than any other Mag7 companies with an AI startup.

https://www.forbes.com/sites/phoebeliu/2025/11/10/openai-spe...

Going public is not a trivial thing for a company to do. You may want to bring in additional facts to support your thesis.

Going public also brings with it a lot of pesky reporting requirements and challenges. If it wasn't for the benefit of liquidity for shareholders, "nobody" would go public. If the bigger shareholders can get enough liquidity from private sales, or have a long enough time horizon, there's very little to be gained from going public.

Fwiw I’m not making a comment on companies which have transitioned from private to public or doing any sort of strategic analysis related to that. I’m merely saying if we index on publicly traded companies which have made substantial AI investments, the story isn’t super clear, i.e. as an investor there is not yet a solid positive thesis for the economics of LLM that would rate the tech as a buy. It doesn’t help that information is sparse here and that AI investments in general is clustered around a few megacap stocks with other interests outside of AI writ large

Sam Altman is on record saying that OpenAI is profitable on inference. He might be lying, but it seems an unlikely thing to lie about.

Dario Amodei has said that their models actually have a good return, even when accounting for training costs [0]. They lose money because of R&D, training the next bigger models, and I assume also investment in other areas like data centers.

Sam Altman has made similar statements, and Chinese companies also often serve their models very cheaply. All of this makes me believe them when they say they are profitable on API usage. Usage on the plans is a bit more unknown.

[0] https://youtu.be/GcqQ1ebBqkc?si=Vs2R4taIhj3uwIyj&t=1088

Their whole company has to be profitable, or at least not run out of money/investors. If you have no cash you can't just point to one part of your business as being profitable, given that it will quickly become hopelessly out-of-date when other models overtake it.

Yeah, that’s the whole game they’re playing. Compete until they can’t raise more and then they will start cutting costs and introducing new revenue sources like ads.

They spend money on growth and new models. At some point that will slow and then they’ll start to spend less on R&D and training. Competition means some may lose, but models will continue to be served.

Other models will only overtake as long as there is enough investor money or margins from inference for others to continue training bigger and bigger models.

We can see from inference costs at third party providers that the inference is profitable enough to sustain even third party providers of proprietary models that they are undoubtedly paying licensing/usage fees for, and so these models won't go away.

We can also look at the inference costs at 3rd party inference providers.

> Sam Altman has made similar statements, and Chinese companies also often serve their models very cheaply.

Sam Altman got fired by his own board for dishonesty, and a lot of the original OpenAI people have left. I don't know the guy, but given his track record I'm not sure I'd just take his word for it.

As for chinese models..: https://www.wheresyoured.at/the-enshittifinancial-crisis/#th...

From the article:

> You’re probably gonna say at this point that Anthropic or OpenAI might go public, which will infuse capital into the system, and I want to give you a preview of what to look forward to, courtesy of AI labs MiniMax and Zhipu (as reported by The Information), which just filed to go public in Hong Kong.

> Anyway, I’m sure these numbers are great-oh my GOD!

> In the first half of this year, Zhipu had a net loss of $334 million on $27 million in revenue, and guess what, 85% of that revenue came from enterprise customers. Meanwhile, MiniMax made $53.4 million in revenue in the first nine months of the year, and burned $211 million to earn it.

> From what I've read, every major AI player is losing a (lot) of money on running LLMs, even just with inference.

> It's hard to say for sure because they don't publish the financials (or if they do, it tends to be obfuscated)

Yeah, exactly. So how the hell the bloggers you read know AI players are losing money? Are they whistleblowers? Or they're pulling numbers out of their asses? Your choice.

Some of it's whistle blowers, some of it is pretty simple math and analysis. Some of it's just common sense. Constantly raising money isn't sustainable and just increases obligations dramatically.. if these companies didn't need the cash to keep operating, they probably wouldn't be asking for tens of billions a year because it creates profit expectations that simply can't be delivered on.

Where did u get this notion from? you must not be old enough to know how subscription services play out. Ask your parents about their internet or mobile billings. Or the very least check Azures, AWS, Netflix historical pricing.

Heck we were spoiled by “memory is cheap” but here we are today wasting it at every expense as prices keep skyrocketing (ps they ain’t coming back down). If you can’t see the shift to forceful subscriptions via technologies guised as “security” ie. secure boot and the monopolistic distribution (Apple, Google, Amazon) or the OEM, you’re running with blinders. Computings future as it’s heading will be closed ecosystems that are subscription serviced, mobile only. They’ll nickel and dime users for every nuanced freedom of expression they can.

Is it crazy to correlate the price of memory to our ability to localize LLM?

> Ask your parents about their internet or mobile billings. Or the very least check Azures, AWS, Netflix historical pricing.

None of these went 10x. Actually the internet went 0.0001~0.001x for me in terms of bits/money. I lived through dial-up era.

Seems much more likely the cost will go down 99%. With open source models and architectural innovations, something like Claude will run on a local machine for free.

How much RAM and SSD will be needed by future local inference, to be competitive with present cloud inference?

I asked Gemini deep research to project when that will likely happen based on historical precedent. It guessed October 2027.

> what if the current prices really are unsustainable and the thing goes 10x?

What if a thermonuclear war breaks out? What's your backup plan for this scenario?

I genuinely can't tell which is more likely to happen in the next decade. If I have to guess I'll say war.

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