Comment by georgemcbay | Hacker News Reader

Comment by georgemcbay | Hacker News Reader

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> 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.)

depends on if they're cost efficient. if there comes some kind of "good enough" inference chips and so on (which i believe will happen), then those will simply provide the most tokens per dollar and make open models even cheaper

Yeah; the open model markup goes to zero. The manufacturers of the "good enough" inference chips are probably going to be some subset of NVidia, AMD, Apple, smartphone SoC manufacturers, and (long shot) Intel.

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.

they get 90% of the profit, but the pie would shrink without the massive VC investments from anthropic and openai

> 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.

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