[reissbaker]

According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime. But, since each model has been more and more expensive to train, they keep needing to raise more money to train the next generation of model, and the company balance sheet looks negative: i.e. they spent more this year than last (since the training cost for model N+1 is higher), and the model this year made less money this year than they spent (even if the model generation itself was profitable, model N isn't profitable enough to train model N+1 without raising — and spending — more money).

That's still a pretty good deal for an investor: if I give you $15B, you will probably make a lot more than $15B with it. But it does raise questions about when it will simply become infeasible to train the subsequent model generation due to the costs going up so much (even if, in all likelihood, that model would eventually turn a profit).

[dom96]

> if I give you $15B, you will probably make a lot more than $15B with it

"probably" is the key word here, this feels like a ponzi scheme to me. What happens when the next model isn't a big enough jump over the last one to repay the investment?

It seems like this already happened with GPT-5. They've hit a wall, so how can they be confident enough to invest ever more money into this?

[bcrosby95]

I think you're really bending over backwards to make this company seem non viable.

If model training has truly turned out to be profitable at the end of each cycle, then this company is going to make money hand over fist, and investing money to out compete the competition is the right thing to do.

Most mega corps started out wildly unprofitable due to investing into the core business... until they aren't. It's almost as if people forget the days of Facebook being seen as continually unprofitable. This is how basically all huge tech companies you know today started.

[serf]

>I think you're really bending over backwards to make this company seem non viable.

Having experienced Anthropic as a customer, I have a hard time thinking that their inevitable failure (something i'd bet on) will be model/capability-based, that's how bad they suck at every other customer-facing metric.

You think Amazon is frustrating to deal with? Get into a CSR-chat-loop with an uncaring LLM followed up on by an uncaring CSR.

My minimum response time with their customer service is 14 days -- 2 weeks -- while paying 200usd a month.

An LLM could be 'The Great Kreskin' and I would still try to avoid paying for that level of abuse.

[sbarre]

Maybe you don't want to share, but I'm scratching my head trying to think of something I would need to talk to Anthropic's customer service about that would be urgent and un-straightfoward enough to frustrate me to the point of using the term "abuse"..

[babelfish]

Particularly since they seem to be complaining about service as a consumer, rather than an enterprise...

[StephenHerlihyy]

What's fun is that I have had Anthropic's AI support give me blatantly false information. It tried to tell me that I could get a full year's worth of Claude Max for only $200 dollars. When I asked if that was true it quickly backtracked and acknowledged it's mistake. I figure someone more litigious will eventually try to capitalize.

[nielsbot]

"Air Canada must honor refund policy invented by airline’s chatbot"

https://arstechnica.com/tech-policy/2024/02/air-canada-must-...

[ricardobayes]

It's an interesting case. IMO LLMs are not a product in the classical sense, companies like Anthropic are basically doing "basic research" so others can build products on top of it. Perhaps Anthropic will charge a royalty on the API usage. I personally don't think you can earn billions selling $500 subscriptions. This has been shown by the SaaS industry. But it is yet to be seen whether the wider industry will accept such royalty model. It would be akin to Kodak charging filmmakers based on the success of the movie. Somehow AI companies will need to build a monetization pipeline that will earn them a small amount of money "with every gulp", if we are using a soft drink analogy.

[Barbing]

Thoughts on Ed Zitron’s pessimism?

“There Is No AI Revolution” - Feb ‘25:

https://www.wheresyoured.at/wheres-the-money/

[reissbaker]

Ed Zitron plainly has no idea what he's talking about. For example:

Putting aside the hype and bluster, OpenAI — as with all generative AI model developers — loses money on every single prompt and output. Its products do not scale like traditional software, in that the more users it gets, the more expensive its services are to run because its models are so compute-intensive.

While OpenAI's numbers aren't public, this seems very unlikely. Given open-source models can be profitably run for cents per million input tokens at FP8 — and OpenAI is already training (and thus certainly running) in FP4 — even if the closed-source models are many times bigger than the largest open-source models, OpenAI is still making money hand over fist on inference. The GPT-5 API costs $1.25/million input tokens: that's a lot more than it takes in compute to run it. And unless you're using the API, it's incredibly unlikely you're burning through millions of tokens in a week... And yet, subscribers to the chat UI are paying $20/month (at minimum!), which is much higher than a few million tokens a week cost.

Ed Zitron repeats his claim many, many, excruciatingly many times throughout the article, and it seems quite central to the point he's trying to make. But he's wrong, and wrong enough that I think you should doubt that he knows much about what he's talking about.

(His entire blog seems to be a series of anti-tech screeds, so in general I'm pretty dubious he has deep insight into much of anything in the industry. But he quite obviously doesn't know about the economics of LLM inference.)

[Barbing]

Thank you for your analysis!

[mandevil]

I mean, this is how semiconductors have worked forever. Every new generation of fab costs ~2x what the previous generation did, and you need to build a new fab ever couple of years. But (if you could keep the order book full for the fab) it would make a lot of money over its lifetime, and you still needed to borrow/raise even more to build the next generation of fab. And if you were wrong about demand .... you got into a really big bust, which is also characteristic of the semiconductor industry.

This was the power of Moore's Law, it gave the semiconductor engineers an argument they could use to convince the money-guys to let them raise the capital to build the next fab- see, it's right here in this chart, it says that if we don't do it our competitors will, because this chart shows that it is inevitable. Moore's Law had more of a financial impact than a technological one.

And now we're down to a point where only TSMC is for sure going through with the next fab (as a rough estimate of cost, think 40 billion dollars)- Samsung and Intel are both hemming and hawing and trying to get others to go in with them, because that is an awful lot of money to get the next frontier node. Is Apple (and Nvidia, AMZ, Google, etc.) willing to pay the costs (in delivery delays, higher costs, etc.) to continue to have a second potential supplier around or just bite the bullet and commit to TSMC being the only company that can build a frontier node?

And even if they can make it to the next node (1.4nm/14A), can they get to the one after that?

The implication for AI models is that they can end up like Intel (or AMD, selling off their fab) if they misstep badly enough on one or two nodes in a row. This was the real threat of Deepseek: if they could get frontier models for an order of magnitude cheaper, then the entire economics of this doesn't work. If they can't keep up, then the economics of it might, so long as people are willing to pay more for the value produced by the new models.

[m101]

Except it's like second tier semi manufacturer spending 10x less on the same fab in one years time. Here it might make sense to wait a bit. There will be customers, especially considering the diminishing returns these models seem to have come across. If performance was improving I'd agree with you, but it's not.

[majormajor]

Do they have a function to predict in advance if the next model is going to be profitable?

If not, this seems like a recipe for bankruptcy. You are always investing more than you're making, right up until the day you don't make it back. Whether that's next year or in ten or twenty years. It's basically impossible to do it forever - there simply isn't enough profit to be had in the world if you go forward enough orders of magnitude. How will they know when to hop off the train?

[ikr678]

Back in my day, we called this a pyramid scheme.

[Avshalom]

if you're referring to https://youtu.be/GcqQ1ebBqkc?t=1027 he doesn't actually say that each model has been profitable.

He says "You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish cartoon example that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model is actually, in this example is actually profitable. What's going on is that at the same time"

notice those are hypothetical numbers and he just asks you to assume that inference is (sufficiently) profitable.

He doesn't actually say they made money by the EoL of some model.

[9cb14c1ec0]

That can only be true if someone else is subsidizing Anthropic's compute. The calculation is simple: Annualized depreciation costs on the AI buildout (hundreds of billions, possibly a trillion invested) are more that the combined total annualized revenue of the inference industry. A more realistic computation of expenses would show the each model line very deeply in the red.

[oblio]

> According to Dario, each model line has generally been profitable: i.e. $200MM to train a model that makes $1B in profit over its lifetime.

Surely the Anthropic CEO will have no incentive to lie.

[nielsbot]

Not saying he's above lying, but I do believe there are potential legal ramifications from a CEO lying. (Assuming they get caught)

[viscanti]

Well how much of it is correlation vs causation. Does the next generation of model unlock another 10x usage? Or was Claude 3 "good enough" that it got traction from early adopters and Claude 4 is "good enough" that it's getting a lot of mid/late adopters using it for this generation? Presumably competitors get better and at cheaper prices (Anthropic charges a premium per token currently) as well.

[yahoozoo]

What about inference costs?