If times are changing FOR GOOD, perhaps you're right? But i'm not sure they are.

You are, on some level, suggesting that money is a more compelling argument than an actual argument, because that is your only response.

Instead, I wanted to show people the terms under which OpenAI survives, and how onerous said terms were. It's deeply concerning - and I do not think that's a big thing to say! - how much money they may be burning, and how much money they will take to survive.

LLMs do not "bring the cost of writing software close to $0" on a number of levels.

1. The code is not 100% reliable, meaning it requires human oversight, and human beings cost money. 2. LLMs themselves are not cheap, nor profitable. I am comfortable humoring the idea that someone could run their own models - something which is beginning to happen - to write code. I think that's really cool, but I am also not sure how good said code will be or how practical doing so will be.

Right now, Microsoft is effectively subsidizing the cost of Github Copilot, though they appear to have produced quite a lot of revenue from it.

https://www.benzinga.com/news/24/07/40061358/satya-nadella-s...

However, it seems that Github was not profitable before (https://news.ycombinator.com/item?id=17224136) and I would argue isn't profitable now. It's hard to tell, because Microsoft blends their costs into other business lines.

But also...the reason they might have is that email kind of sucked back then. Of course you wouldn't see the promise in something that was clunky and slow and nobody used.

This isn't a comparable to LLMs, though, because even someone who found them clunky could see why you'd want to send an email versus sending a letter.

"Mass market utility" here refers to its ability to sell at the scale it would need to substantiate its costs. As it stands, LLMs do not have mass-market utility at the scale that they need to substantiate their costs. It is really that simple. If they did, these companies would be profitable, and they would be having a meaningful effect on productivity, which they are not.

See page 4 of this report from Daron Acemoglu of MIT: https://www.goldmansachs.com/images/migrated/insights/pages/...

I doubt that Uncle Sam cares that much about controlling OpenAI.

I also think it's a leap of logic to suggest that the former CTO of Stripe joining is somehow the fix they need, or proof they're going to accelerate.

Also, I fundamentally disagree - Stripe was an obvious business. Explaining what Stripe did wasn't difficult. The established ways of taking money were extremely clunky - perhaps there was RELUCTANCE to change, which is a totally fair thing to bring up, but that doesn't mean it wasn't obvious if you thought about it. What's so obvious about GPT? What's the magic trick here?

Anyway, again, thanks for reading, I know you don't necessarily agree, but you've given me a fair read.

1. I do not know of any article that said that Google was "DOA" or "done" as a result of the choice of a search engine as a business model. In fact, search engines were an already-established industry at the time. If I'm wrong, I'd love to read it, as I imagine it's a fascinating historical document - even if it was horribly wrong!

2. OpenAI's business model and Google Search's business models are totally different. Apples and oranges. The way that OpenAI monetizes, the technology it uses to both deliver a service AND monetize it, the technology stack, the scaling, even the tech they acquire to build it, just totally different.

Again, if you can find an article that had someone in the 90s or 2000s saying "Google is DOA! Search is stupid!" then I'd really really love to read it, genuinely.

"Egregious errors in previous articles" is not a valid argument against current arguments, nor do I agree there were those errors. Nevertheless, we're discussing one particular article today!

"I didn't read the entire gish gallop, but spot-checked a few paragraphs here and there. It's just the kind of innumerate tripe that you should expect from Zitron based on their past performance."

Well that's not very nice! It also means that your argument is made on incomplete data.

"... Sorry, what?

Reducing operating costs does not increase revenue. And I don't know how the author thinks that reducing cost of services would not reduce operating costs."

I'm afraid you misread what I said, likely because you (and I quote) "spot-checked a few paragraphs."

One of the problems OpenAI has is that their cost of revenue - and we don't know it to be exact - is extremely high, higher than the revenue they're actually gaining, otherwise known as an "operating loss." As a result, even if they increase revenue, they'll actually lose more money. On top of that, the argument I was making is that if there's a race to the bottom (one that's already started), they will have to cut costs, making them less money even if they get more customers.

"Reducing prices does not increase costs." Does reducing prices reduce operating expenses? Because if it doesn't, it actually does increase costs, because you're taking home less cash for the same cost. It could be that 4oMini is somehow more efficient - i can find no evidence that that's the case, and if it exists, I will happily update my article.

"So, here's a prime example of the author basing the "analysis" on them personally "seeing no signs" of something they have no expertise to evaluate. There's no source for this claim, and it's pretty crucial for their conclusions that transformers have hit a wall."

I can find no examples of radically-different functionality in GPT or other mass-market transformer-based models. In the event I am wrong, I would be fascinated to read about them, but I would need to understand A) how these functionalities are different and B) how they can be productized. After that, I'd need to understand how this would be profitable, and in turn how this would scale into something truly world-changing.

"But for a given quality of model, haven't the inference costs already gone down by like 90% this year?" Have they?

"It should be pretty obvious to somebody who can read publicly available data that all of the increase over 5 years can't be attributed to AI."

I too read publicly-available data, and my source in this case is "Google."

Forgive the messy copy-paste. https://www.gstatic.com/gumdrop/sustainability/google-2024-e...

In 2023, our total GHG emissions were 14.3 million tCO2e, representing a 13% year-overyear increase and a 48% increase compared to our 2019 target base year. This result was primarily due to increases in data center energy consumption and supply chain emissions. As we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands from the greater intensity of AI compute, and the emissions associated with the expected increases in our technical infrastructure investment.

Hypotheses and hypotheticals are useful tools when writing about something big and messy. Instead of me saying - as I have before - that I believe generative AI is a complete dead end and thus OpenAI is in a really bad way - I took great pains to explain the terms under which they WOULD succeed - how difficult success might be, how much money it would take and how many factors would have to go their way.

If OpenAI pulls it off, it'd be really remarkable. Truly historic! But if they don't, they are in deep, deep doo doo.

I do not THINK Microsoft will put that much money into it. They could! It isn't impossible. But it would be totally unprecedented.

Comments URL: https://news.ycombinator.com/item?id=40885632

Points: 93

# Comments: 47