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Comment by jsheard | Hacker News Reader

> Almost every model trained by the majors has paid for itself with inference fees.

Even if we assume this is true, the downstream customers paying for that inference also need it to pay for itself on average in order for the upstream model training to be sustainable, otherwise the demand for inference will dry up when the music stops. There won't always be a parade of over-funded AI startups burning $10 worth of tokens to bring in $1 of revenue.

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My employer spends $100k/month or more on OpenAI fees. Money well spent, in both product features and developer process. This is just one fairly small random startup. Thousands of companies are spending this money and making more money because of it.

Curious what makes you think the money is well spent.

I can maybe digest the fact that it helped prototype and ship a bit more code in a shorter time frame... but does that warrant in enough new customers or a higher value product that would justify $100k a month?!

Some of that $100k/month might be powering the features, rather than supporting development.

yeah it's probably 80% going to product features (processing/classifying data, and agentic workflow features), and 20% to dev tools

Probably 80% of that money goes towards product features that are crucial to retention and acquisition of customers, and the business is profitable. Could those features exist without AI integrations? Some yes, but the data would be limited/inferior, other features would not be possible at all.

The 20% spent on dev tooling seems well-spent. About 10 devs on the team, and all at least 2x (hard to measure exactly, but 2x seems conservative) more productive with these tools.

Isn't most of OpenAI revenue from end users and not revenue from token sales? For Anthropic, it is the opposite where almost all of their revenue comes from API usage. So even if AGI/ASI don't pan out, OpenAI will have a great consumer-focused inference business where they build useful applications (and new devices) using existing state-of-the-art LLMs and stop investing heavily in the next gen model training? I think potentially just replacing Google Search and smartphones with a new AI device would be massive consumer businesses that OpenAI could potentially go after without any major advancements in AI research.

I’m the other way — the cost of launching a creative / interesting software company / project just got cut to 1% or so. (I said launching. Maintaining … obviously not quite as good on the numbers).

I propose software creation, and therefore demand for software creation are subject to Jevon’s Paradox.

Tokens that can be purchased for $10 may or may provide the purchaser with almost any dollar denominated result, from negative-billions* to postive-billions**.

Right now, I assume more the former than the latter. But if you're an optimistic investor, I can see why one might think a few hundred billion dollars more might get us an AI that's close enough to the latter to be worth it.

Me, I'm mostly hoping that the bubble pops soon in a way I can catch up with what the existing models can already provide real help with (which is well short of an entire project, but still cool and significant).

* e.g. the tokens are bad financial advice that might as well be a repeat of SBF

** how many tokens would get you the next Minecraft?

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