CoolestBeans

It demonstrates why Anthropic has to IPO ASAP. Private investors are beginning to tap out. Anthropic is selling junior debt over 10% where it was about 8.5% last offer. If Anthropic wants to meet its compute purchase obligations (via actual business), it needs to start tapping public markets ASAP as its position is getting weaker. The size of the float and how much is primary vs secondary sale is the data we're missing right now. If the float is small and from secondary sales, the IPO is an exit and investors would be wise to stay away as Anthropic's runway stays where it is and therefore would require a re-acceleration of revenue growth to survive. If it is large and mostly from primary sales, it is a legit fund raise which extends the runway and revenue growth can be allowed to slow because of the pricing pressure you mentioned.

The issue is that investors expected the legacy revenue sources to compensate for the negative cash flows required for the AI build out. The expectation was that the traditional business would provide a risk backstop because they would be uncorrelated to AI related revenue. The Q3 earnings provide some evidence that the old business may not be the backstop everyone hoped and worse AI may be cannibalizing that revenue. Even if AI manages to compensate, the unit economics of AI is far worse than traditional software, meaning Google et al would have effectively traded a money printer business for a commodity utility, a horrible trade by nearly any standard you can imagine. Of course this is just one quarter but Wall Street is naturally going to hate that possibility being live which it is now.

It means the spending spree is on hold. The credit window for the neoclouds remains closed. Now the hyperscalers are feeling the pain. The narrative was that the hyperscalers could lean on their prodigious cash flows if the data center rush didn't pan out. This round of earnings reports adds some doubt to that narrative which is spooking everyone. Hyperscalers are still projecting a further increase in capex in 2027, but one has to wonder how long that will last if conditions stay the same.

The next step is whether data center capex discipline begins filtering to Nvidia and the DRAM semis revenues. At that point pure play companies must drive revenue growth or else.

Unknown. AI usage went up exponentially this year but a lot of that was driven by access to frontier and near-frontier models and a shift to agents. Does that constitute a decrease in prices because $ per unit of intelligence (whatever that could mean) has gone down? Maybe.

One issue is that token usage and revenue seem to be decoupling in real time. For Jevons paradox to be true, prices need to be going down but total spend on AI has to be going up. That's technically true right now but total usage is going way up while total spend is going up slightly. Another problem is that we may be witnessing the end of "tokenmaxxing" with Anthropic's revenue growth decelerating and OpenRouter's token volume plateauing. Two points of data do not make evidence but the possibility that AI usage is an S curve rather than exponential is live.

The rates environment directly dictates what capital is available to OpenAI and at what terms. When potential investors can get x% virtually guaranteed, then they are less willing to give cash for equity to OpenAI.

1. They are starting to do it. Take a look at OpenAI's product portfolio, there's a galaxy of attempts at application.

2. Selling tokens via long running agents is the only thing that's provably working with respect to revenue growth.

3. They need revenue yesterday. Increases in borrowing rates means access to capital isn't what it was in the 10s. Their runway is much shorter than you would imagine.

4. Some of the decision makers at these companies believe AI will exhibit Jevons Paradox and that any decrease in price will result in induced demand.

5. The frontier models are still subject to the limitations of AI. They're not good at everything. Better to have someone else learn that on their dime rather than yours.

Everyone in this thread is being pedantic about what optimization is but missing the point that code is a precise description of some virtual machine that is realized with near perfect accuracy by the compiler. Yeah you don't know exactly what the silicon is doing with a modern compiler but you do know that the final result of the program for any input will be defined by whatever the correct interpretation of the program is, as if the structure the programming language implies actually existed.

LLMs are not this, and they cannot be this because it is their ability to operate on vague instructions that make them as powerful as they are. They can take an imprecise natural language description of a process and realize it as a process in a programming language. The problem is that understanding and manipulating the precise problem description is your job as a software engineer. The LLM can and will make its best attempt based on what exists in its training data, but it cannot know what is the precise behavior that actually fulfills goals and I think, contrary to the parent article, it cannot determine where the edges are to ask you with enough certainty that you will be able to take your hands completely off the wheel.

I think the nature of LLMs also explains why it is good at the verticals it is good at. Porting is an extremely well defined, replicate the behavior on another platform. There are far fewer vagueries in that. Game dev is not extremely well defined, but the consequences of imprecision do not have a lot on the line. Furthermore, games are pretty self-contained, so there's not a lot of guessing about what could happen or what the behavior of a counterparty system could be. With some pretty big exceptions, it is all there for the LLM to see in C++ (or whatever).

I think it's a combination of it not being a GAAP revenue number and the timing. If you have flexibility in the report of the revenue number people assume you will choose the most flattering one especially before an IPO. If this is the most flattering number they can demonstrate, people are going to worry about what the not so flattering data says.

The other part is how big the debt load is downstream. Anthropic is one of the revenue engines that has to feed the rest of the supply chain, clouds, SPVs, semiconductors, etc. They have to be pulling very large numbers to justify everyone else's finances.

I think the growth is real and tremendous and that's probably more important than this blip in revenue growth. But things tend to look peachy until they're not and so everyone is on watch for cracks in the foundation.

But it is only two points of unclear data among a sea of exponential growth stories. It is just that the total debt amount and rates environment mean the industry has to hit an extremely small target which prices everything toward perfection.

I don't think you can draw an accurate narrative from two data points but I'm going to do it anyway. This and the slowing in revenue growth at Anthropic are not terrific signs. This should be the era of exponential growth for the AI industry. Even if the unit economics are sound at these prices, companies really need more revenue to service their current debt obligations and for now that can't come from price it has to come from volume. Furthermore, this is OpenRouter's statistics which partially represents the cost conscious section of the market and this doesn't line up with an induced demand story.

Again, two data points do not make a full story but if you're an AI bull this is not what you want to see.

The traditional pedagogy still works for those students who could be disciplined enough to not use AI on problem sets and projects. But that's not a realistic expectation. You can learn how to use AI on the job. It is very rare someone gains the baseline knowledge of algorithms on the job.

Programs are swapping capstone projects and courses for comprehensive oral examinations and that's partly from input from industry that there's been a skill drop. But that feels like a panic stopgap and we really are going to struggle with this for a while.

The bigger issue is that undergrad students learning in the era of AI is really a terrible environment for actually learning the fundamental theory that gives someone a long and productive career. And the workplace has never been a good place to learn that stuff. The talent pipeline from colleges has always been a steady trickle despite eras when signups into computer science was a flood. With AI, that trickle has become a scant drip.

Following is my experience and constantly pestering my friends across the industry for anecdotes. It isn't even that AI moves the bottleneck, the thing it solves was never the bottleneck in the first place (except for certain narrow verticals). AI both can't and doesn't do your thinking for you and worse yet, it robs you of the environment where you do your best thinking about data structures, algorithms, organization, etc. Code. Of course we can do that in natural language, but it's hardly the right tool for the job.

All this being said, I don't think AI usage will meaningfully go down in the long run cause it's a very powerful dopamine loop and tech companies are very good at making those. How many people report social media making them unhappy and how many people do you know who have actually quit? Or to paraphrase Jim Gaffigan, nobody says they eat at McDonalds, but the sign outside says "billions served". Somebody's lying (and it's not McDonalds).