Comment by tocs3
2 days ago
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 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.
I feel like there is a case to be made that they will be the dominant players, but if we're talking about them charging a heavy premium and trying to target enterprise customers, then I don't know how well that'll go
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"
That turns into a world where NVidia + AMD get 90% of the profit. Supermicro and friends get the other 10%. (Excluding cloud markups.)
3 replies →
> The real question is why anyone (let alone everyone) believes this single-winner theory in 2026
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.
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.
This is a really great identification of why, from an investing perspective, AI is nothing like past technologies.
The models are costly to make, but they're hard to monetize because there's not enough difference between them. It's a general commodity, so profit margins are thin because what companies can charge ends up being pushed down to the cost of inference.
There's a finite amount of training data available, and multiple companies have downloaded all of it. Arguably, trying to get a monopoly on training data is equivalent to getting monopoly access to the open internet + books.
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.
> 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.
Chancellor On The Brink