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.
derailing, potentially flame-war inducing, and then topped with a disclaimer.
"Hey, there might be a wolf over there, but in case there isn't please continue listening to me into the future."
Without explaining why and how you believed the text was AI generated it's not even an actionable criticism; you'd might as well have replied with "I don't like your comment." for all the good it does.
> 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.
Either you are getting the smartest engineers, or offer the most freedom to pursue interesting goals that aren't on the frontier. Amazon has neither so they naturally aren't going anywhere in that domain.
I advise a B2B startup that went from 4 developers to just the founder, who is technical but not a developer. It is now profitable and delivering just as many features with arguably better security. N=1
So how is this startup going to be a value add long term? If they can cut all the devs, what moat is there? What's stopping their customers from just prompting their own model?
Seriously asking, why would this look like a good business model to be in? If they don't need anyone besides a technical non-developer to create the product, why would this look like a good business? If it's B2B, then most of their customers will have that type of IT person on staff.
A dry cleaner and a car wash can be a good business. Outsource things that are not your core competency. A moat means you can defend it better, but in this case your business can capture the value of them not having the IT person on staff and not having to spend the tokens. It’s a good old fashioned business.
Wait... Are anthropic and openai just commodity suppliers? Because dry cleaners and car washes both need soap, but I've never heard of a trillion dollar soap company.
If it were true that what OpenAI and Anthropic offer were commodities, that there is no moat, Microsoft could trivially eat their lunch. There would be no OpenAI. Google with its nation-state money, extreme AI talent, chips, datacenters, could eat their lunch.
So mighty Google can't manage it. But it's a commodity that's easily copied.
Somehow HN can't reconcile the contradiction even though it's laughably obvious.
Hey anybody could go MVP a ride hailing app over the weekend. It's a commodity app. Should be super easy with LLMs.
Oh shit, wait, you mean I'd have to take a billion users away from GPT and getting those users is now 50x harder than it was four years ago? You mean it's drastically harder to peel users away from existing services than if you get to start fresh? Ohhhh.
They have gigantic moats and so long as they don't do anything too stupid, those moats will just get stronger with time.
No VC is giving you money to try to run down OpenAI and Anthropic. Nobody is writing that $100 billion check. It's already over, HN is just in its standard denial phase.
The question is "what business value is AI driving?" and the answer was "a solo founder cut all the devs and still delivered a product".
Separate question: "now that AI is here and development isn't a moat any more, how does a business defend itself from competition?"
That second question is a direct result of AI. Five years ago the amount of cost and effort spent on software development was a moat. Now it's not. So that answers the first question completely, I think.
As for the second question - market sector knowledge, customer contacts and industry reputation, all the usual non-technical things that make a moat. Also, we may now be living in a world where software companies cannot expect to be monopolies in their markets any more. This company will have to compete with many similar competitors for market share, like in most other markets. I think this is a good thing. Less enshittification will ensue.
What are some businesses where software was really a moat but the software lacks sufficient complexity that AI can reproduce it?
I can't think of any, I'm sure there are some, but I would really like to know. I just don't think for most business the software is so much of a moat unless it's very complex.
Just imagine if he hired 4 more founders - he could 4x his profitability!
Hopefully his competitors fired enough of their developers to directly compete with them, unless they hire 10 founders and 10x their profitability. But maybe he can just add 2.5 more LLMs and stay competitive.
But there still remains the possibility of error/hallucination. Do you bet on a future where AI will never make any mistakes whatsoever?
I am often told humans make mistakes, too. But this is an elementary logical fallacy because the type, frequency and severity of the problems is never the same.
Also, humans are more likely to do as told, whereas AI often takes liberties (i compiled a book from a github repo and it did things on its own in the book though i had explicitly forbidden that).
I'm not sure if I'm more productive, but in my role where most of my deliverables are technical documents or emails I think my output quality has gone up. Now that I typically use LLMs as a research assistant and editor before sending anything significant to co-workers or customers I'm sure I make fewer mistakes. AI catches a lot of gaps and errors. That's a significant qualitative improvement although I can't quantify that in terms of profit impact.
I think you're letting the LLM write emails that are overly verbose that are shortened on the other side by another LLM and so there are 2 minus habens to both side of a communications that are communicating the same stuff as before but now are paying for altman's yacht in order to do it
Nah, that's not what I'm doing at all. Did you even read my comment? I use it more like a fact checker. It's not perfect but it catches a lot of my errors.
Maybe you could use an AI assistant to help you write better HN comments.
We ship features faster and workers are more independent. They can get the answers they need faster than ever. And i'm talking about the business itself if the business has enabled that. There are a ton of productivity gains.
But make no mistake. Being more productive does not equal higher profits because your customers think you've added value. Both value and productivity are highly subjective terms which is why it is measured differently depending on where you work.
Most businesses adapted to AI by shipping a lot more features in smaller increments of time. What they should have done is become much more ambitious and build moon shots in two quarters instead. You need to go big or go home if you're going all in on AI.
AI is not about productivity. It's about weaponizing the ability to ship fast go-to market features in the blink of an eye when it becomes necessary. If trends change fast and you can't adapt quickly with AI: you're dead. So you need to have AI ready to go, just in case.
GPT 3.5 is 2022. In the past ~18 months the majority of all human software development has been wiped out and the models keep getting better rapidly. AI has already radically overdelivered and the biggest returns are in the next 10, 20, 50 years.
Models are finally good enough that I'm confident that I can repeatably build pretty much any software reliably in less than 2% of the time that I could before 2022. I wouldn't have made that claim 6 months ago.
Even quantized local models like Qwen3.8 are good enough to handle a lot of drudgery. For example, I hooked my wife's computer up to a local model, and now she finally has a clean desktop. She just drops spreadsheets or powerpoints or whatever on her desktop and automation takes care of everything.
The caveat is that I developed software for 30 years, so I understand most of what it produces and can guide it to make fewer mistakes and fix what it can't do. The biggest challenge has been keeping my requirements focused - it's very easy to gold plate everything and prematurely optimize.
The biggest question I ask myself is why am I even still doing this. The smart people are probably already trying to figure out how to pipe raw matter and energy to their homes to build replicators.
The future is already here, it's just not evenly distributed.
Astra, Fable, Opus 5.5 are drastically beyond the capabilities of the median software developer. It's not remotely close.
The bottom 2/3 of the human market is entirely dead. Their jobs are gone (AI-shored) they just haven't all gotten the forced retirement or pink slips yet, while they click a few more prompts for a few more pay checks. The frontier pushing forward will knock out most of the remaining top 1/3 without breaking a sweat over the next few years. The really good developers will graduate into babysitting AI as managers.
It is legitimately sad, it's millions of high paying jobs just in the US alone. It's going to be brutal on the housing market. Automation came for another field.
Software engineering isn't unique, if this is true then it's 99% of all white collar work. Those same software engineers are going to help automate every other job on their way out.
I am improving my Spanish and staring at the massive savings that I have built up from my SV job. When my job goes and the industry is in flames, I am moving to Bolivia.
If you have a big mortgage and no savings, it’s going to be tough. Stop taking the Waymo now.
There is definitely an unanswered question as to what the metric, but no, JavaScript is not 90+% by any useful metric. Most code is not a website, and JavaScript penetration outside of the client-side code on a website and some GUI applications on various operating systems is almost nonexistent.
Remember that all of the code in operation includes code like the microcontroller in your mouse, the hundreds of chips in your car doing things like keeping the engine running or "window goes up when I push the button," the DVD menu (which is actually Java code), not to mention all of the staid systems responsible for things like writing your paycheck every other week.
It means in corporations across the globe, the majority of code is no longer being written by humans. That is over and it will only keep dwindling by the quarter.
Writing lines of code by hand is all of human software development? Pretty sure in all of those companies its still software engineers writing the code, even if they're using LLMs to do it.
Unless you're making the claim that software engineers aren't involved or that only writing code is software development.
Nonsense. Nearly every software house in the world has just as many or nearly as many programmers as they did 7 years ago when GPT-2 was "too dangerous to release." You live in a very tiny bubble if you think most businesses are building software without humans. Total horse shit.
And in before ‘JumpCrissCross chimes in about inference being profitable while ignoring all the investment costs needed to get inference to where it is.
The full balance sheet captures the info. If the AI companies suck up all of our salaries and spend that money on R&D/training, they still aren’t profitable, right?
I mean, if anything summarizes where we have gotten to as an industry this quote is sure it. From Uber using VC funds to subsidize growth to where the AI players are at...
I mean, it literally does. Something like 70+ billion of those 80 billion are accounting adjustments because their debt is supposed to be handled in stock and their stock price skyrocketed.
While I was being facetious, Anthropic is nowhere close to being profitable in the normal, i.e., GAAP meaning of the term. It's only using unicorn fantasy lalaland financials that they are "profitable."
Before someone else jumps in talking about "unit economics": unit economics only matters if you can sell the units long enough to make the initial (capitalized) investment (i.e., R&D) back. Models aren't commercially viable long enough for the unit economics to work.
Why have they needed to worry about profitability?
They have been chasing extreme growth with cheap money. That enabled them to hyper scale at a pace the world has rarely seen before. They have made the correct choice so far.
The absolute dumbest thing they could have done up to this point is slow down to try to turn a profit. They have not needed to. Market capture is the sole thing they should be focused on. Here's a hint: how many users does GPT have now versus three years ago?
There appears to be very widespread confusion about what purpose turning a profit serves and how business operations actually work.
Go back in time and tell Uber in ~2013-2015 that it needs to immediately become profitable and not focus on capturing the market.
Market capture didn't work so well for Uber, and it won't work for AI, and even less so for AI because you can't get a driver in China to take you to LAX airport, but you can use cheaper Chinese AI.
Uber has to pay back all the billions of dollars it wasted trying to get people to use their platform, and now I can't afford most Uber or Lyft rides, it's way too expensive. So did market capture really work if they had to raise their prices so much as to make the service unworkable for most people? The only time I take uber is if my company pays for it, and that is getting less often.
AI is also going to have to become way too expensive so they can pay back the billions (trillions?) it is borrowing. And, when everyone is laid-off or fired because AI took their job, then who's going to be paying for AI? I guess the companies that lay off all the humans will, but when the wealthy business owners realize they've replaced everything with AI and nobody is buying what they are selling because there's no more jobs, then what?
Sounds like they should worry about profitability, and a lot of other things too.
Too dangerous to release was 2019. Today it's only very useful for coding and other niche industries. It promised to automate all work and to usher in a utopia. Instead it helps programmers further enshittify Big Tech offerings. That's well short of any promise or expectation set out by boosters, paid and otherwise, for the last 7-ish years. Where's the civilization upending change? Where's the Beef(tm)?
Can you name 3 ways is positively impacted the average non tech persons life in a meaningful way? Meaningful would be more than the invention of the microwave, but not as important as a vaccine.
Help with health issues is turning into a big one.
My mother-in-law has a complicated set of medical conditions and sees half a dozen different doctors. Fable with max thinking came up with a bunch of interactions between drugs and conditions that her doctors hadn't considered. We've run its recommendations by one doctor so far and he agreed with everything.
My brother has one friend who used a frontier AI to diagnose his wife's uncommon cancer, and insisted on a test for that. It was correct and she's fine now since they caught it early. Brother has another friend whose wife had back pain after surgery; medical team didn't find what was wrong, he uploaded the scan, and the AI said "oh they left in a staple," which turned out to be correct.
I've started using it to work on a cookbook for the medically prescribed diet I'm on.
Microwaves, or microwave ovens? Because I can name all sorts of ways that microwaves have improved communications. So many long-haul telco links, and remote radio/TV stations, have been accomplished by microwave. It's a powerful method of transmission.
Unless you're specifically meaning microwave ovens, which are of "limited utility" in a full-service kitchen. I'd have to agree there.
My previous microwave oven was so intensely powerful, that I needed to dial down the power to "7/10" so that it would not destroy my frozen meals. I could never run it at 10/10. My current appliance is very nice, and has an automatic button to run until doneness. Works every time now.
We're at the dot com boom stage of AI. You could say it didn't deliver because boo.com didn't make a profit or because the average person still used paper maps, but that was just because of momentum.
It's the same with AI. The technology is here now. My friends in financial advice and market research have not lost their jobs immediately, because of momentum.
I find it kind of odd that you equate a small oligopoly of intermediaries gatekeeping information as "democratization". Similarly Google maybe made it easy to find for a while, but that was/is not democracy either.
Linux (the kernel) and Git have done more in this regard, but think of FOSS in general. Without it, there wouldn't be Android. Without it, Windows NT would've won server space in the same way Windows won desktop space.
It is tools which are the commons (such as FOSS) and tools such as Wikipedia. But also open standards. LLMs cannibalize on these tools.
Furthermore, no customer wants an AI as customer bot. Especially elder people who are a bit computer illiterate can't find the real support (dark patterns). Can AI help a customer support representative? Oh, absolutely. AI can help a skilled person become better/quicker at certain tasks. But it cannot replace them, and it shouldn't either. If we do, we get all these 'fantastic' tools like *claw.
My conclusion is that this technology is operating in roughly the same way as the personal computer, and that it's going to take similar timelines for the effects to manifest. We still haven't nailed down all the stuff AI is good at, and it's capabilities in known-good domains are increasing consistently. Engineers on my team are chaining together fascinating workflows that I imagine could be entire products, but things just aren't mature enough yet.
Advances are still coming rapidly -- we have quite a bit of time before this settles.
Actually AI has been over promising and under delivering for about 70 years. Whether or not you think the juice is finally worth the squeeze, the distance we've come is impressive.
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.
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.
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).
What does that have to do with anything? And still, so what?
Do the rich hate him because he exploits the working class? Because they think the way he got rich is obscene? No! It's because he won't shut up, and says the quiet parts out loud. They only hate his personality. You would know if they hated his policies, which they don't, because he'd be out on his senile ass before you could say dementia.
> That they even bothered to do all that stuff is good. That others don’t is bad
good and bad isn't what Meta and AWS is running with. If AWS trains willy nilly on user data like Meta does, AWS is toast. AWS is already taking a huge risk by now using their own take of AWS FDEs competing against their established ecosystem of consulting companies that used to recommend all 583 of AWS products to host a static webpage. Turning of more customers by training on their data is too much of a risk. That's the "good" - a risk analysis.
Meta doesn't care if you stop using Facebook, Whatsapp etc. Frankly, if you are embedded in the Facebook, Whatsapp ecosystem, you don't have a way out as a normie. Even if you are technically capable, good luck publishing your custom Mattermost build for iOS.
Meta has a significant amount of investment in hardware, originally due to Metaverse. The way to monetize that investment is inference and training. That's a do or die decision for Meta. Meta isn't releasing open-weight models because they are "good".
Meta is releasing open-weight models for the same reason Alibaba, NVIDIA or IBM does. Or Google releases Gemma.
There's no "good" or "bad" - the moment Alibaba, NVIDIA, IBM or Google's objective functions change, "Do no evil" will quickly morph into "Do as much evil as necessary without the costs of getting caught exceeding E(outcome)"
That explains a lot. They are anti “move fast and break things” which is great news for enterprises or anyone seeking stability but terrible for innovation.
I don’t condone the use of pirated content for training. But anything that was bought is above the board as far as I’m concerned. Can let the lawyers fight out the minutia and inevitable licensing specifics after the fact (book publishers are hawks).
Fun fact, did you know that every library in the world has an independent contract with every major book publisher?
In cases I am familiar with, they added a condition that a physical book can only be lent X times. After that, the library must purchase a new copy (even if the original copy is in pristine condition). Yeah, hawks.
This is what I've heard from my friends both there and at Meta. It definitely doesn't carry the prestige it once did. It's more like, "willing to get yelled at, be afraid of being put on a PIP every day, and trying to ensure output outpaces your peers". That's sad, but it's where the industry is at so many places right now.
Amazon offers good signing bonuses that pay out over two years, so it's very common to peace out right at the 2-year mark when your take-home pay abruptly goes way down and the grind is no longer worth it. That's what I did, and I noticed it almost everywhere: there's a constant revolving door of inexperienced people. I don't understand how they keep it going with so much turnover, but somehow they've built a system that kinda withstands it, except in frontier AI apparently.
I think it's coasting on that prestige of being FAANG. People still want it on their resume. I share this from time to time but I interviewed there at the same time I interviewed at Facebook (right before the name change). And the difference was night and day. Amazon truly treated me like cattle. Get in, do the automated Leetcode/HackerRank thing. Canned conversations. Facebook at least _seemed_ like they were rooting for my success the entire time.
If you chart money in your pocket per unit time, it takes a dip around 18 months where your average pay is less than your first month’s pay. (When I was there, using RSU numbers for that time.) Guess when average quitting time was?
HR swore it wasn’t on purpose and I was somehow measuring pay “wrong” by looking at money in my pocket over time; but knowing their HR, I’m sure the valleys in transition from signing bonus to vesting are intentional.
Because all humans are 100% fungible with all other humans. So, you can take whatever old humans you have, throw them away (or make them want to leave), and then replace them with all new humans, with zero negative effect on anything. Rinse and repeat.
Human experience means nothing. Anything that does have meaning gets encoded in the system for the mindless humans to implement.
That’s my take on the view from the top, based on my time working at AWS.
Was s3 the last time Amazon showed lab grade innovation? Lambda?
I could be wrong because I haven't paid much attention but Amazon has just fast followed everyone else innovation for a awhile so it's in like one ecosystem yeah?
I'm not trying to be snarky, I just haven't thought about aws as having any innovation for a while and I always figured it was because ruthless companies that churn through people as disposable commodities generally don't do tech innovation.
I'm talking on the AWS side, on the logistics side they clearly are doing insane things on supply chain perf optimization.
Dunno about that, but my experience with AWS is their stuff generally works, the other big 2 doesn't.
Generally speaking, on AWS you can buy compute, storage, network and other basic 'goods' and just like a supermarket where you can get either flour and eggs, a cake mix or a ready-made cake, AWS is willing to serve it to you in various levels of ready-ness, and you can start at the level you want, and build your own layers on top if you don't like AWS's solutions.
And AWS's solutions are generally built like that as well, there's no magic to Lambda or Fargate or EKS, its just they run the same EC2s, but with some proprietary magic that lifts some complexity from your shoulders, at the cost of generally some markup, and loss of some otherwise desirable features.
But the core thing is that AWS's offerings are generally solid, so as AI evolves and companies become more willing to take on more complexity, they will move to lower level services, but not necessarily out of AWS itself.
> I could be wrong because I haven't paid much attention but Amazon has just fast followed everyone else innovation for a awhile so it's in like one ecosystem yeah?
A lot of innovation at AWS is invisible on the outside. They pioneered hyperscale datacenters with publicly accessible capacity, software-defined networks, secure VMs, and so on.
> I'm not trying to be snarky, I just haven't thought about aws as having any innovation for a while and I always figured it was because ruthless companies that churn through people as disposable commodities generally don't do tech innovation.
I worked at AWS before COVID. It was really a varied experience, but it was not _bad_ overall. We prided ourselves on making sure our APIs are solid, stable, and backwards compatible. When we had to make a backwards-incompatible change, we actually contacted every affected customer and ensured that their workflows wouldn't be broken.
Oh for sure, these many years AWS has set a high bar in the service space for conscientiousness in offerings. When I compare to my personal work experiences with gcp and azure the amount of times I've been rug pulled over the years has been essentially zero for AWS.
I just played with Nova after seeing this submission. I gave it some real problems for which I used Gemini. It markedly "dumb" in comparison, losing sight of facts that I presented in the first prompt of the chat, and other subsequently introduced, and its responses contain repetitions of the same point-form advice. It's like GPT-3 level or something.
Well except for the regular HN anti-AI crowd. Usually the same people that have wished for communism and never ever looked in detail into the 20+ states that failed that way.
- Single point of billing and vendor is still king in enterprise - look at how much of Ant's revenue flows through AWS Marketplace
- With OSS, models may move to a more of an AWS-shaped problem: host with 5 9s, provide the infra and shovels, do it as cheap as possible, fit them neatly into rest of AWS stack. I think this is actually a huge risk to OpenAI and Anthropic, as more commodization gives hyperscalers more advantage
- Still owns a huge chunk of Anthropic and leverage over compute
With this in mind, I'm not sure why they need a frontier model vs. the playbook they did with Linux (i.e. Kimi-K3-AWS-Enterprise-Edition). They are kind of playing both sides right now I guess, but could really stir things up if they wanted to.
Tomorrow (October 28th) is the 1 year anniversary of being laid off from the Amazon Devices team. Nearly all of the senior people on my team were laid off at the same time as part of the "pivot to AI" strategy. Hurray for "commitment to accessibility".
This outcome for their years of squandered effort honestly doesn't surprise me as, other than the Graviton/Trainium work, the Amazon efforts were scattershot, half-baked and underinvested. Only the Siri team is more pathetic than the Alexa team. Well maybe Co-pilot stands between those two somewhere.
their innovation seems to be limited to renaming the forced sidebar on amazon.com every few months and not allowing me to dismiss their useless features.
The chatbot sidebar seems to also be the only way of accessing certain Amazon features, like the price history. And it seems to be implemented in a particularly silly way - clicking the "price history" button prompts the chatbot to show you the price history, rather than just showing it.
I think the forced-chat price history thing is a dirty way of trying to get people used to talking to the chatbot. Can't think of any other reason it would be designed so stupidly
Ublock hides the sidebar from me, but it does still take up the whitespace on the side of the screen. Now it won't remember that I dismissed it and it pops back in on every new screen. I am just used to the screen being narrower now. But it is still annoying.
Amazon Nova is their model? I worked for a company that was being pushed into trying to use AWS's "Nova Act" [0], which I guess was their python-based wrapper to do things in Playwright. I didn't think it used Nova the model under the hood though, or maybe it did. Either way, Nova Act was horrible in terms of performance - it was so...damn...slooooow. Much easier to just use Playwright.
It would seem that they can experience most of the benefit of AI without most of the cost; anthropic is operating at the frontier and shows that even while supplying the best models and token sales to enterprise, they lose massive amounts of money.
Amazon makes 60B+ in profit per year, they don't need to tank that by losing it on training AI / selling tokens.
There are plenty of open weight models out there for it to use with most of the benefit and very little of the downside.
Alexa+ is pretty terrible. It's surprising how no one has beat them in the home device market. Alexa stagnated and died a product death while still being the standard market option for 5+ years. I was hoping LLMs would improve it but I haven't seen any evidence, even after purchasing their new line.
I might try Google's home option again but I'm not a fan of giving Google second chances since they seem to care even less about committing to products.
> Amazon’s standout AI model — organized under the name “Nova” — is about the farthest thing from a household name.
I have tried asking people if they've ever used an AI product from certain companies, and you only seem to get "no", or surprise that they have any AI products, for some.
I'm not at all close to knowing their internals with books in particular but for most products Amazon has made a significant effort to remove themselves from direct product stocking wherever possible (instead preferring to warehouse other people's stock for efficient shipping). I would guess that they aren't particularly well positioned with publishers at this point... Also, most publishers probably loathe Amazon at this point for all the times Amazon has tried to fleece them w.r.t. Kindle.
It's possible for both a new technology to be hugely beneficial in the long run & for early investors to lose a lot of money funding it (if they over-invested / over-paid).
Values drive behavior. Who owns / runs futurism and what are their values?
Journalism / media as activity needs to be sustainable and unfortunately in our current world it's seems very difficult to sustainably remain high integrity. Sometimes it may be greed, but most of the time I think it's survival.
In this case AI is part of what is threatening journalist survivability, so it kind of makes sense.
not a fan of recent AI wave. but if I wanted to be long on it I'd pick robots, drones and AMI (essentially +1 on Yann LeCun). if Amazon were to be refocusing/doubling-down on those areas too I'd respect it
Hard to innovate on AI when you're firefighting 30+ sevs a week.
funny story actually. I was recruiting an engineer from another stressful team. I pitched to him that we have about 4-6 sevs a quarter, and I was worried he would balk.
He said "that's nothing, when I was at Amazon we had 30 Sevs a week" .
My impression of Amazon is that it’s a doer and is scrappy and gets things done at scale quickly. I just don’t see them as a place that is at the forefront of research.
Too much backstabbing from legacy employees inside amazon to allow for grassroots innovation. Good work will get stolen or stamped out by coworkers. Vicious employee base intent on collecting their paychecks
I wonder if the best play now is not to play and wait until the landscape is clear. They are making partnerships now (e.g OpenAI), and investments (e.g. Anthropic).
It doesn't make too much sense for Amazon to invest too much of their own money into AI.
They already sell a lot of the shovels for training and inference and make ample money from that, and there are enough open weight models on the market to make it infeasible to build a large enough moat.
The remainder of your quote matters more, I think:
> Instead, labor and computing power are being diverted toward a singular “frontier-model effort” led by Pieter Abbeel, director of the Berkeley Robot Learning Lab whose robotics company Covariant was hoovered up by Amazon in 2024. At the time Abbeel joined the tech giant, Covariant’s founders were working on AI models for robots, which lines up pretty well with Amazon’s push to automate everything it possibly can.
Amazon can't hope to compete in the LLM genAI (what is colloquially known as AI anyway) space. That is toast, the race is over for them, too much competition, no way to make money.
But industrial automation? Globally, Amazon employs about 1M people in fulfillment/operations roles, the utter majority of their headcount. If there is one company on this world that has the money and compute capacity to spare for a high performance, real world application of fusion between modern machine learning and robotics, it's Amazon - and to make life easier for Amazon, unlike every other competitor they don't have to pay or partner with another company for real world testing. They can just go and deploy a robot in their warehouse if they want to.
Achieve a breakthrough in a fully automated picker robot, and they can save half a million people on their payroll, if not more, they'd save shrink loss and handling mishaps as well. That is one hell of an opportunity to save money by R&D. And if they patent everything they do, they can lock their competitors out of that technology for a loooong time.
They could revert to the pre-LLM craze where Alexa was actually able to answer prompts in a reasonable amount of time. Alexa is a glorified egg timer for nearly all users - it's super convenient to use a voice prompt to start/stop timers while cooking and being able to play music on voice demand is kinda alright. It genuinely doesn't need to answer hard questions.
The new AI powered Alexa beta basically can't do egg timer, music, or light switch duties as reliably as the old Alexa, which basically makes it useless.
You'd think with all the data collected by these tech companies, they'd understand the common usage scenarios of their products. But instead we have AI Alexa that can't egg timer, or Gemini Assistant that can't navigate or make phone calls.
Wait so Microsoft gutted their AI Division and Microsoft looks like it did as well from the list of "departments" that are in the approved buyout list. Does this mean they are just going to be resellers of the big models companies going forward?
Outdated, sensationalist reporting off July 29 numbers that's already useless in the fast pace nature of the AI space. This is endemic of the last 5 years of "journalism".
The title is clickbait - Amazon is redirecting resources toward another major AI project and that does not, by itself, establish an overall "retreat from AI". The last 5 years have been a time of flux and now companies are starting to focus on niches and gaps in the market.
AI takes substantial focus and investment and we are compute constrained - AWS is correctly consolidating and refocusing by scaling back several of its existing Nova projects (Canvas, Reel) and concentrating resources on a new flagship model effort led by Pieter Abbeel.
If the claim is "sustained failure", what failed against which objectives? Technical capabilities? customer adoption? economics? delivery? The article does little to establish any. Consumer name recognition is a weak substitute for evidence about an enterprise model business!
A good report would have included benchmarks, revenue, or profitability evidence supporting the article's broad failure claim - this lazy "vibecoded" report doesn't even come near.
It credits Bloomberg while linking to Business Insider. That is a straightforward sourcing error - a journalism 101 no no.
If this is the new generation of shoddy, sensationalist "reporting", good riddance!
The frontier of AI has gotten so spicy that the delivery risk of pursuing this rabbit is astronomical. Knowing when to quit and use someone else's solution is critical at this scale.
If someone started from zero, how long do we think it would take for them to reach the cost/capability front currently provided by gpt-6.1-terra? How long until OAI figures out a way to distill that down again for pennies on the dollar?
Apple seems to have this sorted. You won't find them chasing OAI, Google and Anthropic around the playground.
Let someone else take on the research and training costs, and charge a massive premium for your hardware.
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.
no.
I think you're wrong and I won't ignore it.
derailing, potentially flame-war inducing, and then topped with a disclaimer.
"Hey, there might be a wolf over there, but in case there isn't please continue listening to me into the future."
Without explaining why and how you believed the text was AI generated it's not even an actionable criticism; you'd might as well have replied with "I don't like your comment." for all the good it does.
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.
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.
We all feel more productive. But did your rust rewrite or my fancy react dashboard produce more value?
Seriously asking, why would this look like a good business model to be in? If they don't need anyone besides a technical non-developer to create the product, why would this look like a good business? If it's B2B, then most of their customers will have that type of IT person on staff.
So mighty Google can't manage it. But it's a commodity that's easily copied.
Somehow HN can't reconcile the contradiction even though it's laughably obvious.
Hey anybody could go MVP a ride hailing app over the weekend. It's a commodity app. Should be super easy with LLMs.
Oh shit, wait, you mean I'd have to take a billion users away from GPT and getting those users is now 50x harder than it was four years ago? You mean it's drastically harder to peel users away from existing services than if you get to start fresh? Ohhhh.
They have gigantic moats and so long as they don't do anything too stupid, those moats will just get stronger with time.
No VC is giving you money to try to run down OpenAI and Anthropic. Nobody is writing that $100 billion check. It's already over, HN is just in its standard denial phase.
For decades, velocity, time to market and features all drove the industry. But AI puts speed in everyone's hands.
But quality is still hard. Maybe harder than ever.
Separate question: "now that AI is here and development isn't a moat any more, how does a business defend itself from competition?"
That second question is a direct result of AI. Five years ago the amount of cost and effort spent on software development was a moat. Now it's not. So that answers the first question completely, I think.
As for the second question - market sector knowledge, customer contacts and industry reputation, all the usual non-technical things that make a moat. Also, we may now be living in a world where software companies cannot expect to be monopolies in their markets any more. This company will have to compete with many similar competitors for market share, like in most other markets. I think this is a good thing. Less enshittification will ensue.
I can't think of any, I'm sure there are some, but I would really like to know. I just don't think for most business the software is so much of a moat unless it's very complex.
[0] https://www.afr.com/technology/ai-can-make-free-copies-of-ad...
Hopefully his competitors fired enough of their developers to directly compete with them, unless they hire 10 founders and 10x their profitability. But maybe he can just add 2.5 more LLMs and stay competitive.
Watch what the open weight models can do. Once they can manage your codebase, you’re safe from gouging.
I am often told humans make mistakes, too. But this is an elementary logical fallacy because the type, frequency and severity of the problems is never the same.
Also, humans are more likely to do as told, whereas AI often takes liberties (i compiled a book from a github repo and it did things on its own in the book though i had explicitly forbidden that).
Maybe you could use an AI assistant to help you write better HN comments.
But make no mistake. Being more productive does not equal higher profits because your customers think you've added value. Both value and productivity are highly subjective terms which is why it is measured differently depending on where you work.
Most businesses adapted to AI by shipping a lot more features in smaller increments of time. What they should have done is become much more ambitious and build moon shots in two quarters instead. You need to go big or go home if you're going all in on AI.
It's 1987: The PC is underdelivering.
It's 2000: the Internet is underdelivering.
It's 2010: smartphones are underdelivering.
Even quantized local models like Qwen3.8 are good enough to handle a lot of drudgery. For example, I hooked my wife's computer up to a local model, and now she finally has a clean desktop. She just drops spreadsheets or powerpoints or whatever on her desktop and automation takes care of everything.
The caveat is that I developed software for 30 years, so I understand most of what it produces and can guide it to make fewer mistakes and fix what it can't do. The biggest challenge has been keeping my requirements focused - it's very easy to gold plate everything and prematurely optimize.
The biggest question I ask myself is why am I even still doing this. The smart people are probably already trying to figure out how to pipe raw matter and energy to their homes to build replicators.
The future is already here, it's just not evenly distributed.
The bottom 2/3 of the human market is entirely dead. Their jobs are gone (AI-shored) they just haven't all gotten the forced retirement or pink slips yet, while they click a few more prompts for a few more pay checks. The frontier pushing forward will knock out most of the remaining top 1/3 without breaking a sweat over the next few years. The really good developers will graduate into babysitting AI as managers.
It is legitimately sad, it's millions of high paying jobs just in the US alone. It's going to be brutal on the housing market. Automation came for another field.
If you have a big mortgage and no savings, it’s going to be tough. Stop taking the Waymo now.
What the hell does this even mean? Literally half of the code in operation I the world is still COBOL. The fuck is this nonsense wiped out
There’s no way this is even remotely true. JavaScript alone is probably 90+%, considering it’s on almost every website.
Remember that all of the code in operation includes code like the microcontroller in your mouse, the hundreds of chips in your car doing things like keeping the engine running or "window goes up when I push the button," the DVD menu (which is actually Java code), not to mention all of the staid systems responsible for things like writing your paycheck every other week.
Unless you're making the claim that software engineers aren't involved or that only writing code is software development.
And in before ‘JumpCrissCross chimes in about inference being profitable while ignoring all the investment costs needed to get inference to where it is.
I mean, if anything summarizes where we have gotten to as an industry this quote is sure it. From Uber using VC funds to subsidize growth to where the AI players are at...
Before someone else jumps in talking about "unit economics": unit economics only matters if you can sell the units long enough to make the initial (capitalized) investment (i.e., R&D) back. Models aren't commercially viable long enough for the unit economics to work.
They have been chasing extreme growth with cheap money. That enabled them to hyper scale at a pace the world has rarely seen before. They have made the correct choice so far.
The absolute dumbest thing they could have done up to this point is slow down to try to turn a profit. They have not needed to. Market capture is the sole thing they should be focused on. Here's a hint: how many users does GPT have now versus three years ago?
There appears to be very widespread confusion about what purpose turning a profit serves and how business operations actually work.
Go back in time and tell Uber in ~2013-2015 that it needs to immediately become profitable and not focus on capturing the market.
Uber has to pay back all the billions of dollars it wasted trying to get people to use their platform, and now I can't afford most Uber or Lyft rides, it's way too expensive. So did market capture really work if they had to raise their prices so much as to make the service unworkable for most people? The only time I take uber is if my company pays for it, and that is getting less often.
AI is also going to have to become way too expensive so they can pay back the billions (trillions?) it is borrowing. And, when everyone is laid-off or fired because AI took their job, then who's going to be paying for AI? I guess the companies that lay off all the humans will, but when the wealthy business owners realize they've replaced everything with AI and nobody is buying what they are selling because there's no more jobs, then what?
Sounds like they should worry about profitability, and a lot of other things too.
Hate to say it, but in this case you’re not the market so you are irrelevant.
Here’s data from Chicago, USA, showing Uber and Lyft dominate the market now, so yes, it worked.
https://toddwschneider.com/dashboards/chicago-taxi-ridehaili...
This isn’t the “hallucinate everything” style of model like the earlier GPT models…
Downside is that OpenAI is already having ads on their chat interface, so I guess it will be crap any time soon.
My mother-in-law has a complicated set of medical conditions and sees half a dozen different doctors. Fable with max thinking came up with a bunch of interactions between drugs and conditions that her doctors hadn't considered. We've run its recommendations by one doctor so far and he agreed with everything.
My brother has one friend who used a frontier AI to diagnose his wife's uncommon cancer, and insisted on a test for that. It was correct and she's fine now since they caught it early. Brother has another friend whose wife had back pain after surgery; medical team didn't find what was wrong, he uploaded the scan, and the AI said "oh they left in a staple," which turned out to be correct.
I've started using it to work on a cookbook for the medically prescribed diet I'm on.
Unless you're specifically meaning microwave ovens, which are of "limited utility" in a full-service kitchen. I'd have to agree there.
My previous microwave oven was so intensely powerful, that I needed to dial down the power to "7/10" so that it would not destroy my frozen meals. I could never run it at 10/10. My current appliance is very nice, and has an automatic button to run until doneness. Works every time now.
2. greatest hacking tool in history
3. greatest corporate espionage tool in history (stealing of Navier-Stokes solution)
4. greatest accountability transfer tool in history (whoops it was the AI that shot those missiles)
5. greatest capital raising tool in history (we use AI in our startup, and you should give us money)
6. greatest scamming tool in history (cloning people voices for scam calls, etc..)
Edit:Whoops, I didn't read "positively"
It's the same with AI. The technology is here now. My friends in financial advice and market research have not lost their jobs immediately, because of momentum.
"democratization of information", maybe if you were too lazy to click on a google link
It is tools which are the commons (such as FOSS) and tools such as Wikipedia. But also open standards. LLMs cannibalize on these tools.
Furthermore, no customer wants an AI as customer bot. Especially elder people who are a bit computer illiterate can't find the real support (dark patterns). Can AI help a customer support representative? Oh, absolutely. AI can help a skilled person become better/quicker at certain tasks. But it cannot replace them, and it shouldn't either. If we do, we get all these 'fantastic' tools like *claw.
Advances are still coming rapidly -- we have quite a bit of time before this settles.
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.
[1] https://en.wikipedia.org/wiki/Annapurna_Labs
[2] https://en.wikipedia.org/wiki/Amazon_Lab126
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.
Don’t know to comment about the other areas of their business but AWS has always been excellent for me.
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).
Do the rich hate him because he exploits the working class? Because they think the way he got rich is obscene? No! It's because he won't shut up, and says the quiet parts out loud. They only hate his personality. You would know if they hated his policies, which they don't, because he'd be out on his senile ass before you could say dementia.
good and bad isn't what Meta and AWS is running with. If AWS trains willy nilly on user data like Meta does, AWS is toast. AWS is already taking a huge risk by now using their own take of AWS FDEs competing against their established ecosystem of consulting companies that used to recommend all 583 of AWS products to host a static webpage. Turning of more customers by training on their data is too much of a risk. That's the "good" - a risk analysis.
Meta doesn't care if you stop using Facebook, Whatsapp etc. Frankly, if you are embedded in the Facebook, Whatsapp ecosystem, you don't have a way out as a normie. Even if you are technically capable, good luck publishing your custom Mattermost build for iOS.
Meta has a significant amount of investment in hardware, originally due to Metaverse. The way to monetize that investment is inference and training. That's a do or die decision for Meta. Meta isn't releasing open-weight models because they are "good".
Meta is releasing open-weight models for the same reason Alibaba, NVIDIA or IBM does. Or Google releases Gemma.
There's no "good" or "bad" - the moment Alibaba, NVIDIA, IBM or Google's objective functions change, "Do no evil" will quickly morph into "Do as much evil as necessary without the costs of getting caught exceeding E(outcome)"
Metaverse was 4-5 years ago already wasn't it? That hardware is verging on obsolete.
I don’t condone the use of pirated content for training. But anything that was bought is above the board as far as I’m concerned. Can let the lawyers fight out the minutia and inevitable licensing specifics after the fact (book publishers are hawks).
Fun fact, did you know that every library in the world has an independent contract with every major book publisher?
In cases I am familiar with, they added a condition that a physical book can only be lent X times. After that, the library must purchase a new copy (even if the original copy is in pristine condition). Yeah, hawks.
They're a good data point on where the robotics labor frontier is.
00000010: 626f 74
Amazons interviews were so formulaic and it’s interviewers so robotic it was almost sickening.
.. and then got let go at 22 months in favour of the AI budget.
Gotta respect the hustle.
HR swore it wasn’t on purpose and I was somehow measuring pay “wrong” by looking at money in my pocket over time; but knowing their HR, I’m sure the valleys in transition from signing bonus to vesting are intentional.
Human experience means nothing. Anything that does have meaning gets encoded in the system for the mindless humans to implement.
That’s my take on the view from the top, based on my time working at AWS.
I could be wrong because I haven't paid much attention but Amazon has just fast followed everyone else innovation for a awhile so it's in like one ecosystem yeah?
I'm not trying to be snarky, I just haven't thought about aws as having any innovation for a while and I always figured it was because ruthless companies that churn through people as disposable commodities generally don't do tech innovation.
I'm talking on the AWS side, on the logistics side they clearly are doing insane things on supply chain perf optimization.
Generally speaking, on AWS you can buy compute, storage, network and other basic 'goods' and just like a supermarket where you can get either flour and eggs, a cake mix or a ready-made cake, AWS is willing to serve it to you in various levels of ready-ness, and you can start at the level you want, and build your own layers on top if you don't like AWS's solutions.
And AWS's solutions are generally built like that as well, there's no magic to Lambda or Fargate or EKS, its just they run the same EC2s, but with some proprietary magic that lifts some complexity from your shoulders, at the cost of generally some markup, and loss of some otherwise desirable features.
But the core thing is that AWS's offerings are generally solid, so as AI evolves and companies become more willing to take on more complexity, they will move to lower level services, but not necessarily out of AWS itself.
A lot of innovation at AWS is invisible on the outside. They pioneered hyperscale datacenters with publicly accessible capacity, software-defined networks, secure VMs, and so on.
> I'm not trying to be snarky, I just haven't thought about aws as having any innovation for a while and I always figured it was because ruthless companies that churn through people as disposable commodities generally don't do tech innovation.
I worked at AWS before COVID. It was really a varied experience, but it was not _bad_ overall. We prided ourselves on making sure our APIs are solid, stable, and backwards compatible. When we had to make a backwards-incompatible change, we actually contacted every affected customer and ensured that their workflows wouldn't be broken.
> Amazon Is Gutting Its AI Division [July]
https://news.ycombinator.com/item?id=49082344
This isn't a blip on anyone's radar.
- Single point of billing and vendor is still king in enterprise - look at how much of Ant's revenue flows through AWS Marketplace
- With OSS, models may move to a more of an AWS-shaped problem: host with 5 9s, provide the infra and shovels, do it as cheap as possible, fit them neatly into rest of AWS stack. I think this is actually a huge risk to OpenAI and Anthropic, as more commodization gives hyperscalers more advantage
- Still owns a huge chunk of Anthropic and leverage over compute
With this in mind, I'm not sure why they need a frontier model vs. the playbook they did with Linux (i.e. Kimi-K3-AWS-Enterprise-Edition). They are kind of playing both sides right now I guess, but could really stir things up if they wanted to.
This outcome for their years of squandered effort honestly doesn't surprise me as, other than the Graviton/Trainium work, the Amazon efforts were scattershot, half-baked and underinvested. Only the Siri team is more pathetic than the Alexa team. Well maybe Co-pilot stands between those two somewhere.
They are just changing their strategy to invest in a single model? It's not like their AI department is in shambles...
[0] https://nova.amazon.com/act
Amazon makes 60B+ in profit per year, they don't need to tank that by losing it on training AI / selling tokens.
There are plenty of open weight models out there for it to use with most of the benefit and very little of the downside.
I might try Google's home option again but I'm not a fan of giving Google second chances since they seem to care even less about committing to products.
I have tried asking people if they've ever used an AI product from certain companies, and you only seem to get "no", or surprise that they have any AI products, for some.
Amazon is not going to pay for AI internally, and sooner or later they will have to add something internal.
It will always remain weird to me that a publication called Futurism is so outright negative towards our newest technologies.
Values drive behavior. Who owns / runs futurism and what are their values?
Journalism / media as activity needs to be sustainable and unfortunately in our current world it's seems very difficult to sustainably remain high integrity. Sometimes it may be greed, but most of the time I think it's survival.
In this case AI is part of what is threatening journalist survivability, so it kind of makes sense.
“So… what happened to the previous team?”
Source: https://www.businessinsider.com/amazon-overhauls-ai-strategy...
Couple comments then:
https://news.ycombinator.com/item?id=49082344
If true, Amazon is not really gutting its AI division.
funny story actually. I was recruiting an engineer from another stressful team. I pitched to him that we have about 4-6 sevs a quarter, and I was worried he would balk.
He said "that's nothing, when I was at Amazon we had 30 Sevs a week" .
I didn't feel so bad .
This is something that requires an ethos completely different from how amazon works and the way jeff bezos sees the world.
you want edge inference, from decision to small models.
That's it. It will be 2c, please.
They already sell a lot of the shovels for training and inference and make ample money from that, and there are enough open weight models on the market to make it infeasible to build a large enough moat.
> Instead, labor and computing power are being diverted toward a singular “frontier-model effort”
It’s a consolidation, not a withdrawal.
> Instead, labor and computing power are being diverted toward a singular “frontier-model effort” led by Pieter Abbeel, director of the Berkeley Robot Learning Lab whose robotics company Covariant was hoovered up by Amazon in 2024. At the time Abbeel joined the tech giant, Covariant’s founders were working on AI models for robots, which lines up pretty well with Amazon’s push to automate everything it possibly can.
Amazon can't hope to compete in the LLM genAI (what is colloquially known as AI anyway) space. That is toast, the race is over for them, too much competition, no way to make money.
But industrial automation? Globally, Amazon employs about 1M people in fulfillment/operations roles, the utter majority of their headcount. If there is one company on this world that has the money and compute capacity to spare for a high performance, real world application of fusion between modern machine learning and robotics, it's Amazon - and to make life easier for Amazon, unlike every other competitor they don't have to pay or partner with another company for real world testing. They can just go and deploy a robot in their warehouse if they want to.
Achieve a breakthrough in a fully automated picker robot, and they can save half a million people on their payroll, if not more, they'd save shrink loss and handling mishaps as well. That is one hell of an opportunity to save money by R&D. And if they patent everything they do, they can lock their competitors out of that technology for a loooong time.
You'd think with all the data collected by these tech companies, they'd understand the common usage scenarios of their products. But instead we have AI Alexa that can't egg timer, or Gemini Assistant that can't navigate or make phone calls.
Latency is the #1 UX killer on voice apps, and the latency was terrible compared to Alexa. I used it for like 15 minutes and reverted.
All anyone was talking about was how much worse it was for common tasks.
The title is clickbait - Amazon is redirecting resources toward another major AI project and that does not, by itself, establish an overall "retreat from AI". The last 5 years have been a time of flux and now companies are starting to focus on niches and gaps in the market.
AI takes substantial focus and investment and we are compute constrained - AWS is correctly consolidating and refocusing by scaling back several of its existing Nova projects (Canvas, Reel) and concentrating resources on a new flagship model effort led by Pieter Abbeel.
If the claim is "sustained failure", what failed against which objectives? Technical capabilities? customer adoption? economics? delivery? The article does little to establish any. Consumer name recognition is a weak substitute for evidence about an enterprise model business!
A good report would have included benchmarks, revenue, or profitability evidence supporting the article's broad failure claim - this lazy "vibecoded" report doesn't even come near.
It credits Bloomberg while linking to Business Insider. That is a straightforward sourcing error - a journalism 101 no no.
If this is the new generation of shoddy, sensationalist "reporting", good riddance!
If someone started from zero, how long do we think it would take for them to reach the cost/capability front currently provided by gpt-6.1-terra? How long until OAI figures out a way to distill that down again for pennies on the dollar?
Apple seems to have this sorted. You won't find them chasing OAI, Google and Anthropic around the playground.
https://blog.google/company-news/inside-google/company-annou...