Posted by JumpCrisscross 1 day ago
So, it's a race to the bottom because they have to keep spending more and more money on training the models just to keep ahead of the competition, while prices are driven down by the availability of open models.
Chinese companies understand this, and that's why they give their models away in the open. The real money is going to be in integrations and custom services built on top of the models. The models themselves aren't what's valuable.
This is a real question, because I just don't know. Don't the models needs lots and lots of training that need the gobs of giant data centers? So you either need the infrastructure or the money to buy computing to make the models? I see that all the money is being invested in AI and data centers but I do not quite understand why.
I am no economist or business person but AFAICT the massive amount of money being burned is because everyone in the space believes there will be a Google-like (for search) single winner who takes all when the dust settles on the AI race.
Because of this belief they are willing to burn any amount of money they can get their hands on to get to this mythical finish line as fast as they possibly can, seeing it as existential. Moving fast (particularly when your main cost is hardware capex) means moving expensively.
The real question is why anyone (let alone everyone) believes this single-winner theory in 2026 when all available evidence over the past few years shows that there is neither a moat nor a lock-in opportunity at all in this market.
For your product to be fit for purpose, it has to be able to automate your customer's ability to switch to a competing product with little to no friction. And all of the competing products are roughly the same in capabilities over a long enough timeline. Good luck building any kind of real moat with that burden.
But I guess after a certain point after you've taken on hundreds of billions of dollars of investment you're pot committed and just have to keep going through the motions even if you later see the futility, perhaps that's where the AI labs are now...? Either that or just common variety denial.
they're already making a business model where enterprises buy tokens and put them in a pool for their users to use, and I feel like that's exactly where some companies will then compare that token pricing to open source models and find it hard to justify the prices, especially as it's quite obvious that many models https://news.ycombinator.com/formatdocare now "good enough"
I shortened my list to NVidia and AMD because putting Apple in muddies my point: I'd argue they'll be able to sell laptops + phones in that world, but only if you can run frontier open models on their devices, and do so efficiently. This is definitely not the case for their in-house AI offerings, like Siri, but it is already true if you install llama.cpp or whatever. I suspect that AI will not be a big revenue source for them (unless they completely turn around their software engineering org), but instead will be a "good enough to keep people from dumping their iCloud subscription" offering instead. Siri is basically that: It's completely useless, but people aren't switching to android because of it.
In the First World War, when they introduced the tank, everybody assumed its role would be to punch a big enough hole in the front lines that the horse cavalry could ride through and attack the enemy's supply lines, because That's How Wars End™. You fight the war you know, even when it's not the one you're actually fighting.
This is a really great identification of why, from an investing perspective, AI is nothing like past technologies.
That ain't going to happen, and if it somehow did in the US, things like Anna's Archive + libgen will save the rest of the planet from our self-imposed dark ages.
As for those data centers: The cost of inference and training on the same hardware is dropping much faster than Moore's Law. That multiplies with hardware advances, since Moore's Law isn't quite done yet.
So, those datacenters are depreciating at 50% every 18 months (Moore's law) at the same time as performance/$ and performance/watt of the software is dropping at some crazy rate like 10x per year. So, token per unit of data center is increasing by something like > 20x every 18 months, and that'll probably happen at least one more time. If it does, the datacenters will be provisioned to produce 400x more tokens than they could have when they were sized.
Put another way, if the singularity is coming, the first things it will destroy are those data center build outs. That doesn't matter though: the data centers are being built on circular loans, so the money needed to complete them probably never existed.
Dark times ahead; hopefully Trump will be blamed, since all this corruption happened on his watch while he had control of the senate, house and (partially) the courts. The stock-goes-down graph will show it happening after the midterms, of course.
This, to me, is the most interesting question in all of this. I increasingly feel like we live in a 'post-truth' world with regards to money and the stock market in particular. Like even during the COVID lockdowns the market blinked and then was at record highs within 6 months. It was just lol and completely out of touch with the reality on the ground.
The fed, and government alike, seem to have gradually shifted their mandates from trying to provide to a stable playground to trying to create a playground where important kids are never allowed to fall down. And when your existence is tied to trillions of dollars of the economy, you become the important kid.
Come to think of it this is also probably being exasperated by the rise of money management firms controlling tens of trillions of dollars. When the market valuation is technically just based on sentiment and you control that much money in a self-interested way, you're going to get weird outcomes.
We will continue to have every major player trying to build vertically - model, chips, infra, packaged products and services.
This is like saying there's no lock-in that iPhone has because it's trivial to switch to Android. Or that there's no lock-in that Windows has because it's trivial to switch to Linux or Mac. Or that there's no lock-in that cars have because it's trivial to switch to bikes or trains.
They're different products. They're not the same. Being able to write the same code does not make a model Claude. Nor does being able to reach the same benchmark scores. Nor does performing one or a choice set of tasks to a comparable level of quality.
It's true, people can switch if all they want is to perform X, Y, or Z, and some other model can do X, Y and Z well enough. People switch from iPhone to Android all the time. From Windows to Linux or Mac. From cars to bikes or trains or other transit. But they're not the same thing! Android doesn't have a missing business model just because iPhone exists, nor vice versa.
You can't just say that makes cars the same as bikes or Windows the same as Linux. You can't get Claude from anybody other than Anthropic. Maybe you can get something else that does as well where it counts, but not Claude. You can't just say Anthropic is just the same as any open model provider, and that you can just switch providers for the same result. The open model has to actually deliver the desired result, because you're not going to find one that is just Claude.
This is what keeps me on Apple now. This is why most of my biggest hopes and dreams for my personal computing are that Apple will do the right things and in the right way. Because if I want to reject even one single aspect of an Apple product, it means changing to an entirely separate product. That gives up every aspect Apple had over that product at once, even the vastly positive ones. So I put up with the decisions I don't agree with, in exchange for the decisions that I do.
(Meanwhile, I plan to switch to SteamOS as soon as they support Nvidia. I don't use or care about any aspects of Windows besides running games. Now that I have Steam Frame, I don't need Windows Mixed Reality either.)
Granted Anthropic (and Google, maybe less OpenAI actually) do what they can to make it similar. But switching phone brands would take me a day or two of planning and I don’t even use the Apple mail app.
I don't switch from Claude for things like emotional advice. I gave Siri AI a big trial run when it first launched, because I absolutely love the system integration and the user interface, but the model itself is a bit dry for my needs. It doesn't really have principles the way Claude does (thanks to "constitutional AI" or whatever the fuck they want to call it). And I want principles, and sometimes the impression of empathy. Claude meets that bar without being obsessively sycophantic or paranoid.
(Siri AI is very good for some things, though. And I do greatly admire it. When it first came out, I even hacked up an adapter to use it for coding with Pi for just a short time, partly as a meme but also just out of curiosity.)
I tried switching from Claude Code to Pi with DeepSeek. I had tried OpenCode at first, but it's just complete garbage. I felt so impressed with it at first, it felt so slick on the outside, but that impression's just been shattered and shattered and shattered ever since I dug into it trying to fix a performance issue. Its data model and internal behavior is just not acceptable for me. (I tried Reasonix for about five minutes before concluding it's also a bit slop. It seems crafted by someone who actually understands LLMs though, unlike OpenCode, but it's still not very good.)
I absolutely loved the flexibility of Pi and the cheap prices of DeepSeek, but found myself falling into the same patterns as when trying Linux: I was constantly debugging the harness and the model, trying to get it to perform better or investigate something or other. Constantly worried that something was going wrong, that I should try to fix. And yeah, Pi has a lot of convenient features that Claude Code doesn't have, is a lot lighter, and of course can be incredibly modded. I just felt like it was also my responsibility to make my own experience, not just my capability.
At this point, Claude Code seems to just work for me, even though they removed a bunch of features I loved a while ago and never added them back (they came up with huge misunderstandings that completely missed what I actually appreciated about those features, and then proceeded to file them away as resolved). I never have to worry about debugging Claude or Claude Code.
For certain other things that aren't chatting or coding, I found that Gemini 4 Argon is actually by far the best model I've ever seen. It is the first model that's ever strictly exceeded the original November 2022 release of ChatGPT for me. Yup, it took the collective world nearly four years to replicate an experience from 2022. Gemini 3.1 Pro was so close though, I could feel it. When I first found it, I was so relieved that type of experience wasn't lost forever. Then Gemini 4 truly solidified what line of models I prefer for that use case.
Plenty of companies are attached to systems that don't do what they need because they're afraid to replace it, or because it's linked to an employee they need, etc.
And the cost of training is going down as a percentage of revenue over time, is it not?
And unless my math skills are completely off, it’s about 21 months, not two years, but fair enough. What 18 month period in that range would you like to consider as validating the ridiculous idea that open weight models are going to eat Anthropic’s lunch?
It also doesn't do much to disprove the OP's argument which is that the actual models aren't what drive growth. (I'm ignoring the "no business model" claim as either hyperbole or too silly to engage with.) I personally think it doesn't particularly matter which models Anthropic is running because the tools like Claude are what people are actually paying for. Anthropic, at this point, is so far ahead of the competition in the tooling around models that they can probably just start running Deepseek products directly and people would still pay them.
It may only be a short-term circumstance that you need a data centre to run AI for lucrative applications. Thirty years ago you needed a mainframe to beat Kaspersky at chess. Algorithm's got better, and now you can beat the mainframe that beat Kaspersky with a bot running on a desktop Pentium of the day[1]. You could still, today, devote a full hyperscale data centre to create a chess bot to beat all other chess bots, but nobody is doing that because there's no money in it.
The frontier is continually pushed outwards. At it's outer edge you need mainframes and data-centres. Once it pushes past, you don't. There wasn't a lot of money in Chess or protein folding, despite the later having huge potential for public benefit. There is a lot of money in coding and middle management, and that's what currently has tech-bros in a frenzy. However, there's no guarantee that what the frontier moves onto next will be lucrative. It might be more chess and protein folding. If, as Algorithm's and hardware improve, coding moves to local models (Sam Altman's nightmare) and nothing lucrative immediately replaces that, what are these companies going do do with their massive data-centres?
The scale of investment we're seeing these companies commit only makes sense if they have multiple decades to reap the returns. They may well not have that much time.
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[1]https://hackaday.com/2026/11/05/pitting-a-modern-chess-engin...
Reality can’t be saturated. The set of things you can accomplish with more intelligence is unbounded. So while cheap models will continue to proliferate, there will always be a massive market at the frontier, and that won’t ever change.
Guess what could help solve it? :)
Seriously though, this “problem” will never be fully solved, we’ll just get better and better at connecting AI to the broader world, and to me that’s already the story of the last few years. Models get better, they’re able to build and drive tools to interact with both the digital and physical world better, which helps build the next generation of models, and so on.
Nerfed, proprietary models have have no future and these trillion dollar valuations are laughable.