[jbreckmckye]

I don't think so.

His argument is not "this tech doesn't work", but rather "these businesses aren't economically viable"

And that the smoke and mirrors accounting and perpetual thirst for more billions indicates just how unviable it is

Whilst he does dunk on LLM capabilities, the framing is the business angle - can Anysphere etc. actually form a moat and make a profit?

[simianwords]

>His argument has never been "this tech doesn't work", but rather "these businesses aren't economically viable"

Why? because of cost?

[jbreckmckye]

Cost, debt, difficulty forming a moat, gap between what the product promises and what it can do, and the difficulty actually raising capital required.

His style is acerbic and (imo) excessive sometimes. But he's also one of a minority of journos actually looking at the numbers and adding them up. Which seems to be a rarity

[simianwords]

cost is going down 20x, 30x over the years so he's wrong about this.

[orwin]

That doesn't matter if the free models are as performant in 6 months. I will never personally pay for a model I can have for free. ChatGPT 5 used to be my preferred model as a DMing help tool, now deepseek and LeChat are the one I use, and are better at what OpenAI model use to be better at. And I think the models hit their limit for my usecase, I don't need better one. I never 'reprompt' anymore, and just roll/improvise with what I got.

[simianwords]

i find it interesting that in no case do you allow openapi to profit

- if the costs go up then they can't make profits

- if the costs go down then you won't pay for them

[orwin]

It's hard to sell something I can have for free.

The only way for openAI to get my subscription back would be my country making open-weight ai or deepseek illegal. It was worth the price tbh, but they can't compete with free.

[jbreckmckye]

Those are very large reductions - can I ask you for a source?

And why is the error bar so large?

[simianwords]

https://epoch.ai/data-insights/llm-inference-price-trends

> The rate of decline varies dramatically depending on the performance milestone, ranging from 9x to 900x per year

[hiddencost]

Disagree. He's cherry picking an extremely limited subset of numbers, based on a weak understanding of the industry and a lack of access to a lot of private data, and taking advantage of vulnerable people.

[danlitt]

>taking advantage of vulnerable people

What on earth do you mean by this? Who is getting taken advantage of?

[jbreckmckye]

I'm not sure how anyone can respond to that, without asking you to divulge that private data

[JanneVee]

Well from my point of view. When they talk about gigawatt datacenters, then yes it is economically nonviable. You just need to know the scale of a gigawatt to realize that we need to start building power plants and fortifying the power grid to ship a gigawatt of power to a single location. Until the build out which takes years mind you, it is competing with other consumers of power. Lets take another huge consumer of power like a large steel mills use 100 megawatt. So if that power becomes more expensive because of datacenters, then the price of steel will go up. And if the price of steel goes up it affects a lot of things in the economy.

We are facing a situation that the short term effects are on memory and storage prices going up and lack of jet engines. Long term we wont be able to build actual buildings and ships without financing it with even more debt than today and everyone in the economy is going to service that debt through the price.

[simianwords]

but the costs of inference have been going down 20x to 30x over the years. so how can you tell it is nonviable? unless you are saying they are not paying market rate for the inference

[JanneVee]

So, they still booked up all the ram and ssd in the world and still going to use gigawatts of power. The price of energy production is not going to go down 20x and 30x it just means that they can cram in more inference on the same energy consumption if the cost goes down. But they aren't paying the market rate for inference because everything is subsidized with debt and investors money to scale as fast as possibly. They are flushed with money and that is why they can book up all silicon production.

[surgical_fire]

I have no idea if costs indeed came down 20x-30x.

This claim sounds extremely fancy when AI companies bleed money, and will keep bleeding money in the foreseeable future.

I don't pretend to know the future. Maybe LLMs become economically viable and are the future, maybe not. I don't really care either way, to be frank.

And I use LLMs, btw. I pay for a ChatGPT account, but I find it only moderately useful. I always sort of question myself upon renewal date if it is worth the 20 bucks I spend monthly on it.

In no small part I keep using it to keep myself up to date on the best practices of using them in case it becomes standard.

[simianwords]

https://epoch.ai/data-insights/llm-inference-price-trends

Do you have any reason to not believe it? It’s expected for costs to come down

[surgical_fire]

The graph you linked seems to compare different OpenAI models in terms of "price per million tokens".

I am very skeptical of any financial information that comes from OpenAI. I have no idea how truthful those numbers are, or how creatively they can be collected to paint a rosier future for them.

Even if the numbers are truthful, I have no idea how the calculate price there. Is it in terms of cost of compute they rent? Is this cost subsidized or not?

Also, I don't know this "epoch.ai" website, I don't know their stance. The website name itself does not inspire my confidence on their reporting of anything related to AI. "Eat meat, says the butcher" vibes and all.

You can claim that the AI bleeds money because training is expensive, but inference is cheap. So it will only be financially viable when they stop training models? So they would need to stop improving their capabilities entirely for it to make any sense, is that your claim?

Even if I take this claim at face value (and that would take a lot of faith I don't have to give), it doesn't sound as good as you think it does.

[simianwords]

I don't think Ed doesn't comment about the actual tech. Here are some things he has said before and please tell me if these still hold in the spirit?

> You cannot "fix" hallucinations (the times when a model authoritatively tells you something that isn't true, or creates a picture of something that isn't right), because these models are predicting things based off of tags in a dataset, which it might be able to do well but can never do so flawlessly or reliably.

ChatGPT is fairly reliable.

>Deep Research has the same problem as every other generative AI product. These models don't know anything, and thus everything they do — even "reading" and "browsing" the web — is limited by their training data and probabilistic models that can say "this is an article about a subject" and posit their relevance, but not truly understand their contents. Deep Research repeatedly citing SEO-bait as a primary source proves that these models, even when grinding their gears as hard as humanely possible, are exceedingly mediocre, deeply untrustworthy, and ultimately useless.

This is untrue in spirit.

> You can fight with me on semantics, on claiming valuations are high and how many users ChatGPT has, but look at the products and tell me any of this is really the future.

Imagine if they’d done something else.

Imagine if they’d done anything else.

Imagine if they’d have decided to unite around something other than the idea that they needed to continue growing.

Imagine, because right now that’s the closest you’re going to fucking get.

This is what he said in 2024. He really thought ChatGPT is not in the future.

There are so many examples and its clear that he's not good faith and has consistently gotten the spirit wrong.

[energy123]

This guy sounds like an uninformed jackass.

Look at Gemini 3.1 Pro on the AA-Omniscience Index, which measures hallucinations. It's 30, previous best was 11.

https://artificialanalysis.ai/evaluations/omniscience

With the amount of talent working on this problem, you would be unwise to bet against it being solved, for any reasonable definition of solved.

[disgruntledphd2]

> With the amount of talent working on this problem, you would be unwise to bet against it being solved, for any reasonable definition of solved.

I'm honestly not sure how this issue could be solved. Like, fundamentally LLMs are next (or N-forward) token predictors. They don't have any way (in and of themselves) to ground their token generations, and given that token N is dependent on all of tokens (1...n-1) then small discrepancies can easily spiral out of control.

[simianwords]

To solve it doesn't mean we have to eliminate it completely. I think GPT has solved it to enough extent that it is reliable. You can't get it to easily hallucinate.

[disgruntledphd2]

It depends on how much context is in the training data. I find that they make stuff up more in places where there isn't enough context (so more often in internal $work stuff).