[atleastoptimal]

The music would have a risk of "stopping" if these deals were backed by a speculative entity. However AI actually has real value/revenue, and is not a speculative product (i.e. people aren't buying tokens to resell them, a token is "consumed" at moment of inference)

[Terr_]

That's like saying "nobody is speculating in Enron stock" simply because there was electrical power that was sold for real revenue and consumed.

[atleastoptimal]

Enron collapsed due to legitimate fraud. To imply Enron is an apt comparison requires assertion that AI companies are actually cooking the books. Is that what you are saying?

[minraws]

The ARR were fine but showing skewed quarterly profitability numbers by slowing down research due to hitting compute capacity suggests otherwise.

I am certain Anthropic spent less on building the next model this quarter if they make it to profitability due to the shear fact that they don't have enough compute.

Which solves the profitability problem with relative ease momentarily.

Also just to confirm, AI subscriptions are definitely being sold at a loss how big I don't know but these models are much harder to run.

API is definitely being sold at a decent profit.

So if you rate limit users and do usage billing + lower research costs which is a money pit temporarily.

(Proof is the fact that we don't have a new pre training run since 4.5 yet, they used to do one every 2 releases)

4.9 will probably be the same.

Next model Mythos doesn't seem to have a successor yet and was trained previous quarter most likely, they don't seem to have pre trained another one just improved Mythos if at all.

As much as I am into AI these attempts to show that there can be a profitable quarter seem like cooking the books, even if we assume no shady dealings otherwise.

Unless one of the Labs can say for certain training is going to stop they can't be profitable and I don't think training can stop because marginal gains is all they have.

8-12 months behind narrative for Chinese labs literally is going to kill the company that stops training first.

If we assume only a 3-6 month gap once China has more compute, then well then even if they keep training the lack of ability to arbitarily scale data centers in US, will kill them first.

DeepSeek V5 might actually just end the AI race for good.

Also given Mythos is atleast a 10x model compared to Opus, then it's pricing is likely going to be 10x as well so well token prices are likely never coming down, especially if these companies want to IPO.

[atleastoptimal]

Why would V5 kill the AI race? Do you believe that there are diminishing returns on model intelligence when applied to real-world tasks?

I think there are accelerating returns: i.e. a models are still not good enough to be “drop in” remote workers, but once that threshold is passed, the value of each token of inference has a far higher multiplier.

This justifies the buildup. However not everyone agrees that model intelligence will continue scaling thus they assert that eventually the economics will hit a wall.

>Also given Mythos is atleast a 10x model compared to Opus, then it's pricing is likely going to be 10x as well so well token prices are likely never coming down, especially if these companies want to IPO.

I don't know why people say this when cost per unit of intelligence has been going down continuously over the past few years. When Opus 3 was first released, its API cost was $15.00 per million input tokens and $75.00 per million output tokens. Opus 4.8. which is significantly better, is $5.00 per 1 million input tokens and $25.00 per 1 million output tokens

[minraws]

Assuming 2-3 years from now when V5 is out China would have mostly caught up in compute, and honestly that's it China can scale up compute a lot faster than US maybe a few countries can match it, or help match it but won't happen while US Iran thing is going on.

Further the human costs in the loop for AI training are insanely low or atleast substantially lower outside of US, so sure without the Nvidia upcharge I think everyone else who can use Compute from China is at an advantage.

If the assumption is AI is scaling issue then China will win because they can do infrastructure. Maybe if US wasn't in a trade war with rest of the planet there was some hope but I don't think so.

Once Deepseek figures out the new compute and can get it on par with Nvidia's clusters even if by using 4x the energy(cause they can). I don't think OpenAI or Anthropic can maintain a lead, if they don't have a lead the pricing difference will kill the AI race.

The best case scenario is OpenAI and Anthropic are dead in 2-5 years once China is caught up.

The worst case scenario where AI is not a productive boost is that well the thing pops.

Either way I don't see how this works out. Sure US govt could bomb China that's always an option.

[supern0va]

>The ARR were fine but showing skewed quarterly profitability numbers by slowing down research due to hitting compute capacity suggests otherwise.

I have to say, I find this really puzzling. We know for a fact that Anthropic are making bank on metered inference. That's their biggest source of profitability, we are seeing software companies start to majorly adopt coding agents over just the last few months.

Right as the biggest driver of enterprise adoption is accelerating, and it's tied to their biggest profit vector, you find it suspect that their profits are increasing significantly?

Also, can you clarify what you mean by "slowing down research" exactly? Do you mean they're not doing big pretraining runs? Less compute available for researchers? Scaled back RL?

>Also just to confirm, AI subscriptions are definitely being sold at a loss how big I don't know but these models are much harder to run.

Maximum usage of AI subscriptions is a loss, but do we actually know how that nets out? Has anyone done any research to try to figure that out?

[fc417fc802]

> can you clarify what you mean by "slowing down research"

He is claiming that they have been investing less in R&D and that this is juicing their numbers in an unsustainable way given how close the competition is to catching up. His evidence is the content and cadence of model releases recently. (I'm not taking a position one way or the other, just clarifying for you.)

> Maximum usage of AI subscriptions is a loss, but do we actually know how that nets out?

They almost certainly don't have to care. All the enterprise accounts use the API pricing AFAIK and that appears to be profitable and is expected to be the vast majority of the usage in the medium to long term (if it isn't already).

[zamalek]

> API is definitely being sold at a decent profit.

Where do you get this from?

Enterprise plans are being cancelled or limited all over the place (Uber, Microsoft). I doubt Anthropic would be leveraging a loss leader with their consumer plans, while catastrophically hemorrhaging customers on the enterprise.

They are either operating at a loss (possibly a minor one), or a minor profit (which is chasing customers away).

If they were comfortably profitable they wouldn't need to participate in the circular deal circus.

[minraws]

It would be insane, if they can't serve the models at a profit sure at current GPU prices the profit might be 10% or lower. But at realistic gpu prices it would have been close to 30-60% based on how big the models actually are and how much they have optimized the stack to serve them.

1T parameter models like Kimi K2.6 can be served for 1/10 to 1/5 of the price of opus 4.8 for perspective.

Sure opus is 2x the size and hosting might be non linearly scaling so still it should be around 50% margin at regular gpu prices.

If it isn't I would be very surprised.

Also for enterprises we joke but Google is not paying same rates as us there are big massive enterprise discounts. I have heard upto 20-30%... OpenAI is supposedly even more generous.

I don't think API is being sold at a loss at the end of the day even if the API profits are marginal 10-20% because of insane GPU prices now.

[Terr_]

Please address the primary point first: Selling some product does not disprove speculation.

In the case of Enron, people were obviously speculating in its stock, and that remains true regardless of why it collapsed later, or even whether it collapsed at all.

I say "first" because if you still can't agree that speculation in AI stocks even exists, then it's pointless to discuss what people might be doing to exploit or encourage it.

[atleastoptimal]

Speculation exists for every security. However wrt revenue numbers, Anthropic/OpenAI’s revenues are largely made of companies/individuals purchasing tokens. Enron’s was accounting which stated future potential revenue as current earnings. They are not the same. Enron pulled off a lot of shady schemes to hide their accounting practices. All of the “circular deals” AI labs are doing are publicly known and clear to see, so its not like anyone who knows what a circular deal simply knows something everyone doesn’t.

Also to be more specific about our point of disagreement, I think we are referring to speculation in different domains. When I brought it up, I am referring to the fact that any companies whose revenue is driven by a speculative bubble (like what precipitated the 2008 crisis) would be at risk of massive losses "if the music stops". Anthropic/OpenAI aren't flipping assets. It is true that VC funding is based on speculation, but their core business model is producing massive revenue growth on selling tokens.

[fc417fc802]

It's an interesting point that the token revenue will presumably survive a crash in stock prices. But (IIUC) much of the new infrastructure is funded using stock is it not? So it seems like token revenue theoretically surviving doesn't address the risk to the rest of the economy here. And if the economy takes a large enough hit then presumably so will token spend because someone has to pay for that after all.

Sure their actual immediate revenue is driven by concrete numbers but when the rest of the economy is reorganizing itself based on their projected future revenue is the former observation still relevant?

[atleastoptimal]

That is true, if all the new data centers don’t produce revenue then there will be a crash. However you’d have to bet that the models won’t stop getting better, or if they still keep getting better, that somehow better models does not translate to increased productivity. Would it be wise to look at how AI has progressed over the last 5 years and make that bet?

[bigbuppo]

Remember when nvidia asked us to stop calling them enron because unlike enron they actually admit to doing all the things enron did so it's not illegal?

[suggala]

Circular dealing or round tripping is a form of cooking books and sometimes results in accounting fraud. Especially when circular revenue is booked without cash flow growth. Do you see cash flow growth on any side of these transactions.

[0xbadcafebee]

The value of AI companies is speculative just like the railroads were. Railroads also have real value. But you have to have everything ready to use those railroads to make money, or they're just steel bars in the dirt and a big loud heavy thing that moves along the horizon. Too much speculative investment in the railroads (in part) led to the panic of 1873, because just having a promise of a return isn't the same thing as having the return.