Amazon Is Gutting Its AI Division After Sustained Failure
futurism.com295 points by dpflan 13 hours ago
295 points by dpflan 13 hours ago
They have no doubt realized that hosting third party models is far more profitable.
Let someone else take on the research and training costs, and charge a massive premium for your hardware.
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.
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It's just basic economic theory applied to current IT market conditions.
Thanks for the reply. Yes, indeed, it's a great analysis.
I use AI so much that I too started hearing myself using the same phrasing as AI... not happy about it.
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my comment has been satisfactorily answered by the person it was addressed to.
it doesn't concern you.
> 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.
Seems like the smartest move. Microsoft went all in and have really not got much out of their efforts.
> Microsoft went all in and have really not got much out of their efforts.
The problem was rather that Microsoft had no real plan what they wanted to get out of AI. If they had a plan, they could have laser-focused their all-in on AI.
Instead, Microsoft just AI-slopped a lot of their bread-and-butter products, making them much worse than before. If AI is as revolutionizing and beloved by users as Microsoft believes, Microsoft could easily have created an offering like "Windows AI Pack" or "Microsoft AI" that should sell like hot cakes.
On the other hand, if you have to force AI functionality onto the users, it should be considered as a strong sign that perhaps it is not something that the users want.
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 best of the best do work at Amazon. Their cloud infra is world class. They just cannot compete in the ai/ml space with different economics. It’s an operational company and not one to do tech innovation.
Basically you want to run an LLM, Amazon. You want to build an LLM not Amazon
Anyone that's been in the Amazon offices in the last few years is not likely to have witnessed a culture that fosters lab-grade innovation.
Your comment was likely focused on dysfunctional parts of the culture there (and that seems to be how most replies to you interpreted it as well). There's truth to that, sure. But I think respect for data sensitivity and security is also a big part of what makes the culture ill-suited for frontier AI lab work.
When I was at AWS, we had data access locked down very tightly. I was trying to train a model to predict how long various dynamic workloads running on behalf of customers would take. Just getting access to anonymized production data from a DB that my team owned took well over a month and had me compromise heavily on what attributes I could access. I also spent a decent amount of time in meetings with lawyers and compliance folks to explain and justify all our data classifications for PII, retention, access controls, etc. We would review our data policies quite frequently. We needed break-glass procedures to access prod data in our own systems, and it was uncommon to need to resort to that. Even when you broke glass, certain classes of sensitive data would still not be visible to you. On the plus side, We did get quite good at operating and maintaining systems without needing to see sensitive info.
If you contrast that with Meta's disregard for privacy or the way frontier labs disregard the law, it's easy to see why Amazon would struggle in this space.
I read your comment as “amazon acts too responsibly to be a frontier lab,” and that worries me. That they even bothered to do all that stuff is good. That others don’t is bad.
I did indeed mean it that way, but I agree with you. It sucks, but it's just the unfortunate reality of the world. Many of the most disruptive and successful startups made it there by ignoring legal or moral limitations.
Frankly reading your comments was a relief. I'd rather they take my privacy (and security) seriously. I don't need or want any AI features or technology from Amazon, thanks very much.
Amazon has a history of fostering off-shoot companies that are given more freedom to innovate[1][2]. Keeping AI development in-house under Bedrock was probably their mistake. There's a big difference between running AI and developing new AI models.
Their core selling point is customer trust that their product works as advertised.
From my interactions with them, is that they really care about customer success, and everything including AI is a means to an end to them.
I think building a cool product with undefined reliability and capabilities, then selling fantasies about what it will possibly be able to do down the line isn't really what they do - or what their customers are paying for.
I agree, from my personal experience. AWS support at most tiers I’ve used is excellent. Granted, the larger account you have, the friendlier the service, but even when using it for smaller personal projects, I’ve always been pleased with the level of support and devotion to customer experience (except the console experience, that is not fun).
Don’t know to comment about the other areas of their business but AWS has always been excellent for me.
Incentives can produce locally rational and globally harmful behavior. (See also: "Moloch", although I'm less of a fan of the concept lately since it focuses exclusively on the harmful aspects of emergent systems, deleting the neutral and positive ones in the process.)
A relevant metaphor is the nuclear arms race. Nobody wants a world full of nukes pointed at each other, but if the other guy is gonna do it...
The argument in this case is, we can't slow down, because China certainly won't, and do you want those guys to build the machine God first? So it's "rational" to maximize p(accidental_skynet).
I interned there for a summer working on evaluating text generation and despite the fact that I used no consumer data whatsoever, but rather a well-known public research dataset, I also couldn't egress from the machines I ran training on. Not even reports on the training (admittedly, this was 2021 when they had just introduced this new system). So it was even more of a damper on research progress than you might imagine.
If the people that do stuff well fail, what does that leave us with?
With the need to enact political change so incentives align for the benefit of society at large.
This indeed. Economic democracy is as important as political democracy.
Careful there... people who cannot even describe or define what communism is will tag you as a communist.
what is communism and are there verifiable reports of it accomplishing what it promises?
Where is there verifiable proof that capitalism is not rule by the rich and powerful?