There is intense competition in the AI industry, especially with AI firms in China driving down prices, so it seems likely that LLM's and software products may become fungible commodities that trade close to marginal cost = marginal revenue = minimum average total cost. In other words, most of the profits from AI and software development will be competed away, eventually.

Data center hardware, by contrast, is a scarce commodity with rapidly growing demand, and therefore likely to capture most of the profits from AI and software development, if current market conditions persist.

Today's IT business trends represent a remarkable change in the industry compared to the situation 50 years ago, when startups like Microsoft and Oracle, whose business models were based exclusively on software development, challenged and overthrew the dominant player, IBM, whose primary source of revenue was from hardware.

> Let someone else take on the research and training costs, and charge a massive premium for your hardware.

the linked article is from July, it is almost three months old.

Now there may be a very relevant recent story; it has the potential to reduce the flow of new open weight models. This could change quite a few expectations, which are based on open weight models:

"China’s STAR 50 Index Falls 40%, Erasing $480 Billion as AI Stocks Crash"

https://www.technology.org/2026/10/27/china-ai-stock-bubble-...

https://www.reuters.com/world/china/china-ai-bubble-has-alre...

Reuters says there is a run, but also deeper reasons:

"There has been no obvious selling trigger in China, although traders said high prices, tax bills and the threat of the US or other nations banning Chinese equipment in data centers all contributed."

So far I don't see any discussions of this story on HN, interestingly.

Just as there is product-market fit, there is also product-company fit. Amazon as a company has their own cost of capital, earnings expectations, talent distribution, and organizational capacity. It is not a place where the best of the best work in mainline product development, nor is it a place that is willing or able to go through long periods of losing money in order to rapidly improve the product. The Amazon of old did that, and had different capabilities, but today's Amazon is not the same company.

Notice that you also see Google really struggling (and falling behind) in the AI race. This, despite massive resources and being responsible for key pioneering research. Same reason why IBM has failed in this area. The big established companies tend to shut their brightest people into research departments where they write papers and produce demos while the main cash cow areas remain undisturbed, so they have collections of bright people and they have money, but they rarely let their brightest people make big bets with the money, whereas startups do.

It's also why poor people spend more on lottery tickets than rich people.

The article implies they are wrapping up their current models, and instead aiming to go all in on a frontier class model?

“Instead, labor and computing power are being diverted toward a singular “frontier-model effort””

Sounds like the opposite of what you're suggesting they're doing?

Seems like the smartest move. Microsoft went all in and have really not got much out of their efforts.