edzitron

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.

"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.

"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."