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.
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.
I do find it pretty extraordinary that Anthropic's revenue growing from $4.8bn in Q1 to $11.5bn in Q2 to $16.8bn in Q3 is seen as a disappointment, but they did indeed stop doubling on a per-quarter basis.
I don't have the numbers handy, but they spent drastically more than that each quarter. Growing revenue is all well and good, but profit does eventually matter, especially if debt dries up.
If you're looking for indicators one way or another, I have yet to see any economic productivity indicators that would show any correlation between productivity and LLM use.
> This and the slowing in revenue growth at Anthropic are not terrific signs.
The CEOs of both Broadcom [0] and Intel [1] publicly noting that the various LLM companies are going to have a difficult time paying for the things they need to pay for and Oracle [2] continuing to prohibit LLM-generated code in OpenJDK due to both copyright and licensing concerns [3] are also not terrific signs.
[2] ...that is, the "law firm with a great big sales department and a software development division tucked into a broom closet somewhere" company ;)...
I think part of the issue is that SWE work is slowing down on needing more context and token throughput (see Opus 5.5) and new industries that _can_ adopt AI right now just don't need insane amounts of tokens flowing through it, for instance Legal (in the context of people using LLMs for Legal correctly), where you kind of just need the best model and context is maybe a few dozen PDFs relevant to your task.
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.
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.
Yes, I believe programs like this aim to address this issue too. The traditional ways of learning might no longer be sufficient or reliable enough to produce appropriately skilled workers in the era of AI. Maybe something like this program or other apprenticeship programs would bridge that gap by focusing on juniors who are truly interested in the field.
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.
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).
The moat for audio effects but especially guitar effects has never been just the quality of algorithm. Yes I know you and the teams at companies like Strymon and Universal Audio and whatnot have slaved over getting every detail right. But what makes Strymon the go to choice for so many players isn't just the sound you can get out of it. Because you can get those sounds elsewhere now.
The moat a physical pro audio product is its interface and its branding. And I know that's tough to hear especially if you're an engineer but its true. People buy Strymon pedals because they know exactly what they're getting and they know its going to be in a format where if you want to dial in a tone without getting into the weeds of menus you can (but if you need to advanced features you can also do that). If people wanted the cheapest way to get those sounds, they could always hook up to a live VST host rig. But you get Strymon because you know that brand name and you know you're going to get a box that you can twiddle the dials on the floor and yes you are also getting that as close to perfect algorithm as well. The sound is part of Strymon's branding but it isn't all of their branding.
Look at it this way, pretty much every original circuit design in the analog guitar pedal space has been copied, duplicated, sold for pennies on the dollar, modified, whatever. But those companies still exist and do well because their moat has never been the circuit, its the branding.
Anyway, I hope you find some peace in the next few days. Its hard to argue that your moat hasn't shrunk and maybe you're in the process of building up that customer base. You gotta think about the copycats and it sucks, but you always had to do that. Its just harder now.
And, the stability of a hardware instrument or effect is considerably higher than that of a computer with 10s of software pieces (and licensing engines) interacting.
And in live music, this is rather critical; some musicians get it instantly, some need a single gig (even in a rehearsal) with their computer setup going dark or blocked or whatever to get it.
I wrote something similar and decided to wait until the morning before sending. You said it better but I'm posting to hopefully give it more weight.
And don't forget, Behringer has been around and reverse engineering successful products as a business model. And I don't think they are putting anyone out of business because customers people know what they're getting when they buy knock-offs.
Came here to mention Behringer. They have proce advantage but are still cheap and poorly made. May be good for entry level musicians, anyone worth their salt stays away.
> cheap and poorly made
You clearly haven't used anything they made in the last 5-10 years. There's a reason that their hardware synth clones (and original like the Neutron) have flown off shelves.
Japanese keiretsu also usually have a bank at their center or have both horizontal and vertical integrations. Where they don't have integration, they at least have financial integration. Whatever Musk is doing is far less cohesive in every way.
A payments platform is not a bank. Beyond the services being different and the customers being different (consumers vs businesses), both had to partner with... well a bank, to provide the actual financial services.
The only charitable part of this idea is that it will insulate the stock price from the actual balance sheet. It asks investors not if they want to invest in an EV automaker but in "Elon Musk Inc" essentially. He's been doing some variation of this his whole career, this would just give him the ability to dilute the numbers of each component industry further. The problem is the brand name of Elon Musk is worth a lot less than it was in the 10s.
The more constraining the language, the better. For example, languages with strong static typing are better for agents because they reject entire classes of errors. This is why Rust is a particularly good target for coding agents. Its hard to write data races, draw null pointer exceptions, access freed memory, double free memory, etc. Not only that, but the feedback loop becomes shorter because the error happens in the compiler and not when that code path gets run (which may never happen because its so hard to write comprehensive tests). This helps correct the stochastic nature of agents and bring them back on track when they wander.
This is the antithesis of the idea that more powerful languages are better but it makes sense. For humans, it costs a lot for the language to get in the way all the time. Yes, a constraining language prevents you from writing errors but it also interrupts flow, forces you to describe processes that are meaningless at the application level, and usually is more verbose. But for an agent, the marginal cost of an additional line of code is less. Both the literal cost but also the opportunity cost.
Its not that Rust is a good or bad language per se, its just that agents don't care about ergonomics while humans do. And when you don't have to care about ergonomics this lets you push the tradeoffs way outside the range that would be even remotely acceptable for us. The flip side is that so far, we are in the mix so you can't go all the way to constraints... not that I can imagine what that would look like.
I remember this being one of the first things I had to learn in my first "real" job. Like someone literally had to tell me there's not much of a difference between coding six hours in a day and one. Later on I learned there's just a class of tasks that just take time for your unconscious brain to break it down to understand it. Algorithms are like this. A lot of things that have unintuitive physical components are like this, e.g. bending on harmonica. Some things you just have to show up every day, give it your best effort and then something will click. Programming is like that.
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