Discussion

Anthropic takes $5B from Amazon and pledges $100B in cloud spending in return

spwa4: > At the heart of this deal is Amazon’s custom chips: Graviton (a low-power CPU) and Trainium (an Nvidia competitor and AI accelerator chip). The Anthropic deal ...Yeah, totally not desperately seeking investment to keep the party going ...

ozgrakkurt: So they are basically taking debt from amazon which is not a financial institution?

iot_devs: Someone can explain to me what's the expectations for these AI labs?I mostly see their products as commodity at this point, with strong open source contenders.Eventually it will become hard to justify the premium on these models.

nl: $30B ARR says otherwise.

Sayrus: ARR says nothing about the ability of these companies to retain customer once subsidies stop.

gabrielsroka: $25B https://news.ycombinator.com/item?id=47844891

mossTechnician: $5B is part of a contact, the remaining $20B is just a non-binding statement that doesn't hold the same weight (but somehow commands the same media fanfare).

secondcoming: all your GPUs are belong to us

ChrisArchitect: https://www.anthropic.com/news/anthropic-amazon-computehttps://www.aboutamazon.com/news/company-news/amazon-invests...

shubhamjain: If you think you need to spend $100B, does using a third-party cloud provider still make sense? It doesn’t matter what sweet deal Amazon is pitching—in that scenario, you’d want to own your stack. Especially in a hyper-competitive field like this, where margins are going to matter a lot soon.It feels like these hyperscalers are just raising as much as they can giving extremely rosy projections becauses these sooner or later peak is going to be reached (if that hasn’t happened already)

loveparade: Good lucking getting GPUs.

mitchell_h: I watched some explain how deepseak got good and the Chinese approach to LLM training. Really wish I could remember it. The premise was China thinks of LLMs not as a thing separate from hardware, but gains efficiencies at each layer of the stack. From Chips to software, it's all integrated and purpose built for training.Wonder if Anthropic is making a mistake by focusing on "consumer" hardware, and not going super specialized.

jubilanti: So you watched some random video from some random YouTuber, didn't even remember who made it, so much so you didn't even remember that deepseek isn't spelled "deapseak", didn't bother to even find it or verify, and then you go asserting your memory as fact on a serious discussion forum.Comments like yours add nothing to the discussion.

Culonavirus: Only Google and xAI build their own, no? I don't think it's that easy to vertically integrate massive datacenters into a software company. Both Google and xAI (Tesla, SpaceX) have a massive wealth of experience when it comes to building factories.

jeffbee: New level of glazing Elon Musk unlocked. xAI has a vertical integration advantage because Tesla once moved into an old Toyota factory and because once they paid Panasonic to put a Tesla sign outside a Panasonic battery factory. Incredible content.

petesergeant: I would struggle to dislike Elon more, but this seems like you’re some kind of weird anti-Musk fanatic

credit_guy: Here’s the answer to your queation (from the article)> The Anthropic deal specifically covers Trainium2 through Trainium4 chips, even though Trainium4 chips are not currently available. The latest chip, Trainium3, was released in December. On top of that, Anthropic has secured the option to buy capacity on future Amazon chips as they become available.

deskamess: So it comes down to how much of that $100 bn is in the 'option', I guess. Then it's not an expense at all.

wg0: The best thing for humanity, economy, technology, society, progress and environment is that this scam should come down ASAP.

MeetingsBrowser: Going from a company with no experience building and operating datacenters to a company with 100B worth of compute is a multi-decade high risk goal.

loveparade: I give it one to two more years before open source models have fully caught up. Products are commodities and models are commodities too. GPUs cores are still hard to get for inference at scale right now. They need a platform with lock in but unsure what that would look like and why it wouldn't be based on open source models.

alex_duf: What does "fully caught up" mean in the context of an ever evolving technology? I think I'm in support of open weight models (though there are safety implications), but these things aren't cheap to train and run. This fact alone gives no incentive for leading labs to release cutting edge open weight models. Why spend the money then give the product for free?Now if "fully caught up" means today's level of intelligence is available for free in two years, by then that level of intelligence means very little

stavros: Yeah I don't understand it, it's a marathon with three companies perpetually a minute ahead, and people keep saying "I expect the stragglers to catch up".The only thing I can see them meaning is what you said, "in a minute the stragglers will be where the leaders were a minute ago", which, yeah, sure.

mrbombastic: It makes perfect sense if you think things cannot improve indefinitely

inciampati: They do approximate any function... within the range they're trained on. And that range is human limited, at least today.

ForrestN: I think this "Mythos" situation, whether real or hype, points to the endgame here. Eventually, when you have a model powerful enough to have big consequences in the world, you stop worrying about selling it to consumers and start either a) using it to rule the world or b) watch as it gets nationalized. If you have a machine powerful enough to automate everything, why sell access to it when you could just...be all things to all people? Use the god machine yourself to take over more and more of the economy?

lokar: I disagree. The point of the mythos hype is to get regulation to cut off competitors.

inciampati: Didn't OAI just try that 18 months ago?

anonyfox: Sounds like moneygrab is accelerating before consumer grade local models are getting good enough for local inference in few years. Huge house of cards here. Demand skyrocketing until it’s suddenly dropping entirely with ondevice inference.

bwfan123: > consumer grade local models are getting good enough for local inferenceI am waiting for that. Perhaps a taalas kind of high-performance custom hw coding llm engine paired with an open-source coding-agent. Priced like a high-end graphics card which would be pay off over time. It will be a replay of the ibm-mainframe to PC transition of a previous era.

JumpCrisscross: > I am waiting for thatSame, and I think we're close. "The original 1984 128k Mac model was $2,495, and the 1985 512k Mac was $2,795" [1]. That's $8 to 9 thousand today. About the price of a 32-core, 80-GPU M3 Ultra Mac Studio with 256 GB RAM.[1] https://blog.codinghorror.com/a-lesson-in-apple-economics/[2] https://www.bls.gov/data/inflation_calculator.htm

sensanaty: I'm no economist, but how exactly does this make sense? Amazon is basically just giving them 5B which will then be used to repay them back 20x that amount??

FatherOfCurses: I'd bet that Amazon is getting access to chat data (no matter what Anthropic says publicly) and possibly even the ability to change the model to drive business to either Amazon retail or AWS."Claude I'm evaluating whether I should host my app on AWS or Google Cloud. Provide me with an analysis on my options." "After a detailed analysis, AWS is clearly your better option."

coredog64: Let me inject something as an ex-AWS employee: Amazon doesn't capture very much value from Bedrock inference of the Anthropic models (or, put another way, Amazon gave Anthropic an outsized share of the Claude Bedrock revenue). If it was me at the negotiating table, I would be asking for a larger cut of Bedrock revenue rather than violating customer trust by getting chat content access.

etempleton: In a rationale business yes, but when everything is basically some form of growth signal to investors to extract even more money from them before the music stops it doesn’t matter.

jinushaun: Isn’t this kind of like the Nvidia/OpenAI deal? Just circulating debt/money

Symmetry: With NVidia/OpenAI actual graphics cards did change hands. Vendor financing, like when a car dealership gives you a loan to buy a new car, is actually pretty normal.

nashashmi: [delayed]

neya: I remember seeing this extremely shocking graph of top AI companies on Facebook or somewhere on how the money just keeps changing hands between a handful of companies. Almost seemed like a scam.

Aurornis: Money doesn’t just flow around with nothing exchanged. The money is in payment for goods and services.It’s common even for smaller companies to do mutually beneficial business with each other. It’s actually helpful to do business with people who are also your customers because you have a relationship with them and you also have leverage: They are extra incentivized to treat you well because they don’t want to upset any of the other business you have with them.

XCSme: And so the bubble keeps bubbling...

MrBuddyCasino: xAI built a datacenter in a few weeks, if I remember correctly.

Aurornis: That’s PR hype. They built it quickly, but they didn’t go from deciding they wanted a data center to having it running in weeks.You can’t even get the hardware at that scale without months or years of order lead time. NVidia doesn’t have warehouses full of compute hardware waiting for someone to come get it.

MeetingsBrowser: xAI built the Colossus data center in 122 days (just the physical construction time).Colossus initially had ~200k GPUs. 100B buys you ~1 million high end GPUs running 24/7 for a year at AWS retail prices.

Aurornis: Initial Colossus buildout was 100K GPUsThey also reused an existing building that happened to be in the right place at the right time. The larger data center buildouts would almost always need new, dedicated construction.

IMTDb: The problem is that at that scale, the alternative is building your own data centers. You'd probably want at least 2 in the US, 2 in Europe, 2 in Asia, maybe 1 in Africa and 1 in LATAM. So 8-10, and you need at least half of them ready "on time."What does "on time" mean? You'll need to negotiate with local authorities, some friendly, some not. Data centers aren't exactly popular neighbors these days. Then negotiate with the local power utility. Fingers crossed the political landscape doesn't shift and your CEO doesn't sign a contract with an army using your product to pick bombing targets, because you'll watch those permits evaporate fast.Then there's sourcing: CPUs, GPUs, memory, networking. You need all of it. Did you know the lead time for an industrial power transformer is 5+ years? Don't get me started on the water treatment pumps and filters you can't even get permitted without. What will you do in the meantime ? You surely aren't gonna get preferential treatment from AWS / Google / ... if they know you are moving away anyway. Your competition will.The risk and complexity are just too big. AI/LLM is already an incredibly complex and brittle environment with huge competition. Getting distracted building data centers isn't enticing for these companies, it's a death sentence.

amluto: Other than data sovereignty, does the data center location really matter that much? Current inference systems are not exactly low latency.

Aurornis: It’s the power and water needs.Large data centers consume as much power as a small city. The location decision is about being able to connect to a power grid that is ready to supply that.Evaporative cooling also needs steady water supply. There are data centers which don’t operate on evaporative cooling but it’s more equipment intensive and expensive.Latency doesn’t matter. You can get fast enough internet connected to these sites much more easily than finding power.

muyuu: the prospect that any of those big players will be able to pay back 100s of billions with profit on top sounds fantastical to meit will be interesting to see it unfold

zozbot234: The maxed out 512GB RAM Mac Studio is no longer available from Apple and is now pushing $20 thousand in the secondary market. And we might not even see a new Mac Studio release from Apple before October.

electroly: For AI inference you don't need to geographically distribute your data centers. Latency, throughput, and routes don't matter here. When it's 10 seconds for the first token and then a 1KB/sec streamed response, whatever is fine. You can serve Australia from the US and it'll barely matter. You can find a spot far outside populated areas with cheap power, available water, and friendly leadership, then put all of your data centers there. If you're worried about major disasters, you can pick a second city. You definitely don't need a data center in every continent.You're not wrong about the rest but no AI company would ever build a data center in every continent for this, even if they were prepared to build data centers. AI inference isn't like general purpose hosting.

TSiege: latency absolutely matters? this is such a weird thing to say. for training sure, but customers absolutely want low latency

1738384848: thank you for the aerious discussion my good sir I tip my hat to you

johnbarron: Please, some of us are long NVIDIA...let us cope in peace. :-)Here is the thing nobody wants to say out loud or they are too dumb to realize. AI is intelligence, and intelligence has almost never been the binding constraint on productivity.So you will get no productivity increase from the AI bubble. Yes, you read that correctly.The test is simple, if raw brainpower were the bottleneck, you could 10x any company by hiring 200 PhDs. In practice you get 200 brilliant people writing unread memos, refactoring things that worked, and forming a committee to rename the committee. Smart has always been cheaper and more abundant than the discourse pretends.Every real productivity revolution came from somewhere else like energy (steam, electricity), capital stock (machines that do the physical work), or coordination (railroads, shipping containers, the assembly line, the internet).None of these raised the average IQ of the workforce, they changed what a given worker could move, reach, or coordinate with. Solow old line basically still holds. The output per worker grows when you give the worker better tools and infrastructure, not better neurons.Meanwhile the actual bottlenecks in a modern firm are regulatory approval, legacy systems, procurement cycles, customer adoption, internal politics, and physical supply chains that don't care how clever your email was. A smart brains intern at every desk produces more artifacts, not more throughput, and in a lot of organizations, more artifacts is actively negative ROI.Jevons does not save you either, cheaper cognition mostly means more slide decks, not more GDP.So the setup is that models are commoditizing on one side, and on the other side a product whose core value add (more intelligence, faster) is aimed at a constraint that was never really binding. This of course a rough combo for a trillion dollar capex supercycle.Fun for the trade, while it lasts, but there is no thesis. Just dont tell CNBC and short NVDA on time ,-)

paganel: > Jevons does not save you either,There's also a very strong Trurl and Klapaucius [1] component to this AI craziness, as in I remember a passage in Lem's The Cyberiad where either Trurl or Klapaucius were "discussing" with an intelligent/AGI robot and asking it for stuff-to-know/information, at which point said AGI robot started literally inundating them with information, paper on top of paper on top of paper of information. At that point it doesn't even matter if that information is correct or smart or whatever, because by that point the very amount of said information has changed everything into a futile endeavour.[1] https://en.wikipedia.org/wiki/The_Cyberiad

imtringued: Why does this matter? The deal is supposed to last 10 years. If you don't pay AWS to order Nvidia GPUs for you, Nvidia won't have to deliver them to AWS, they will have exactly the same quantity of GPUs, but this time they can deliver to you.