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

I think a LOT of companies really never needed to be on the public market, and its a darn shame that so many go on the stock market, we have this obnoxious culture where you have to fire tons of people if you have a bad quarter just to show you're stopping the bleeding. Companies literally fire and hire x number of people every quarter to keep things going, its ridiculous and unhealthy. Private companies rarely work like this, I'm sure there's exceptions.

Every company I've worked at started off private, and those were their golden years, until some economic hurdle happened so they sold it off to a bigger fish who is on the stock market, who bought them to be more attractive to investors or what have you.

I wish there were an alternative to the stock market where you invest for the long haul, and you cannot take your money out in x number of years. I think this would make more sense. Maybe it doesn't fix the VC peeps want their money back nonsense, but if you could do it even for early stage companies, maybe it could help somewhat.

This reads to me like Anthropic anticipating demand and making a commitment to acquire supply. Not unlike airlines committing to future jet fuel purchases, or Apple committing to future DRAM volume.

Has there been a ton of hype? Absolutely but the value proposition is getting more and more tangible.

Did some of the AI companies over commit in spending? I am sure and they will probably hurt in the long term. I thought Anthropic had been scaling towards profitability at a quick timeline though.

1. Model providers are currently profitable when just counting the cost to serve tokens for inference[1]. They lose money training the next generation of models.

2. Open models don’t work nearly as well. Given that tokens are still relatively cheap, and hallucinations are expensive, I’ve not seen a huge up tick in open model usage for coding agents yet.

3. On the AI economy front, I really have no idea, but AI companies (meta, msft) have already come down in value. It seems investors are at least a little wary of AI over valuation. Of course, the stock market is not the economy, but it’s not clear where warning signs would be. Earnings are healthy.

1: https://martinalderson.com/posts/no-it-doesnt-cost-anthropic...

2: https://www.economist.com/finance-and-economics/2026/04/20/a...

Why do you believe that? Better metric would be price per token of open models served by third party. Last I was tracking the price for similar level model was decreasing by more than 10x year on year, and they are 10-100x cheaper than top properietery models.

Sure you can say that you can't compare them but for sure you can compare the top properietery model of 6 months back to current open models and the gap in time seems to be constant.

a) effective price-per-token is rising b) there is insufficient compute to meet the demand.

And your conclusion is that the industry is circling the drain and due to collapse?

It's only a matter of time until they crash the market. Nobody is making any money, even if the White House is dumping billions in their tools.

...building datacenters will not lower the cost.

The cost (real, not investment hype subsidized one) will only drop with:

* more efficient models * GPU/RAM market going back to reasonable pricing.

The AI bubble pumped the second into unstustainable pricing and progress on first is going.. slowly.

I think this can keep going for at least another 5 years.

Anthropic are scared of open weight models and need to fear-monger towards you to continue paying for their models.

That's the whole point of their 'safety' marketing narrative, account bans, and Dario being the AI scarecrow scaremongering everyone about nonsense like 'Mythos' towards the world.

'Mythos' is already here in the form of open-weight models that also found the same vulnerabilities as Anthropic did.

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.

If Anthropic/OpenAI miss projections, infra providers can somewhat likely still turn around and sell it to the next guy or use it themselves. If they have more demand than expected (as Anthropic currently does), vcs will throw money at them and they can outbid the competition

If they built it themselves and missed projections it's a much more expensive mistake

It's just risk sharing. Infra providers take some of the risk and some of the upside

> 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.

Isn't that almost all that matters when comparing doing something yourself versus paying someone else, in this case Amazon, to do it for you?

If you’re not sure it’s going to blow the socks off, foisting capital investment on partners is a great deal.

See the difference in companies/franchises that always own the land/building and those that always lease.

However there are certain advantages like supply chain that only established companies would have access to. This is also a commitment to spend upto 100B on internal approach and research. I would expect them to come up with their own cpu chip and device design. This will shift the focus to an internal approach. And might make amazon give better prices later down the line

Just a guess.

I do think a ton of businesses would benefit from running their own hardware, but they're not getting five billion dollars to stay on the cloud.

Everybody does right now, right?

But: is it your core competency?

Can your firm afford the distraction?

In the meantime if you work on revenue generating work, that side of PnL is uncapped. So you can either put some engineers on reducing your costs at most by 100% or, if they worked on product ideas they could be working on things that generate over 9000% more revenue.

Wonder if Anthropic is making a mistake by focusing on "consumer" hardware, and not going super specialized.

Companies bake their workflows into these tools. Internal processes start to be written up around specific tools. Once something works, it gets pushed out at scale for all to copy.

Anthropic hit $30B in revenue and this is just the start of coding being deployed at scale. Hard to look past these numbers at this point

As the US sold weapons to many nations in the past, so will China, the US, France, etc sell AI cyber capability to other nations. Likely every modern nation will need some datacenter to host a cluster of the preferred vendor, as nobody's going to trust the US or China with their security.

it will be interesting to see it unfold

[1] https://x.com/kenshii_ai/status/2046111873909891151/photo/2

I have seen this argument made a lot, but llm serving being a commodity makes it _better_ for them not worse.

If it's a commodity, then you are entirely competing on price, and the players that will win on price will be the largest ones, because they can find efficiencies that smaller competitors won't have.

It's actually the small LLM companies that are in trouble if LLM serving commoditizes. They will need to distinguish themselves on features, because they can't compete on price. And even there the big labs will have an advantage.

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 ,-)

> Eventually it will become hard to justify the premium on these models.

On the contrary, the model is the moat.

The model represents embodied capital expenditure in the form of training. Training is not free, and it is not a commodity, it is heavily influence by curation.

Eventually the ever-increasing training expense will reduce the competition to 2-3 participants running cutting edge inference. Nobody else will be able to afford the chips, watts, and warehouse. It's a physics problem - not a lack of will.

If you're a retail user, and a lower-tier model is suitable for your work, you'll have commodity LLM's to help you. Deprecated models running on tired silicon. Corporate surveillance and ad-injection.

But if you're working on high-stakes problems in real time, you're going to want the best money can buy, so you'll concentrate your spend on the cutting-edge products, open API's, a suite of performance monitoring tools and on-the-fly engineering support. And since the cutting edge is highly sought after, it's a seller's market. The cutting edge products buoyed by institutional spend will pull away from the pack. Their performance will far exceed what you're using, because your work isn't important. Hockey stick curve. Haves and Have-Nots.

The economic reality is predetermined by today's physical constraints - paradigm shifting breakthroughs in quantum computing and superconductors could change the calculus but, like atomic fusion power, don't count on it being soon.

I 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.

Anthropic gets access to limited compute resources and Amazon gets demand to justify increased R&D and capex + feedback from the best users in the field.

In my reading, Amazon is giving $5B of usage credits in exchange for shares. If Anthropic works out, it's a good deal for Amazon. If it doesn't, they lose on their invesment sheet, but they got ~ $5B in revenue, so it looks good on their operating sheet. And it helped justify a build out that they can sell to others.

For Anthropic, this lets them operate for more time without having to make numbers work. If Anthropic works out, they'll figure out the $100B commitment later. If it doesn't work out, it's not their problem.

It's probably faster to build up amazon's capacity with amazon's money than to build owned capacity with someone else's money at the scale they're looking to build out.

> Today’s agreement will quickly expand our available capacity, delivering meaningful compute in the next three months and nearly 1GW in total before the end of the year.

They need a bunch of compute, now.

https://www.anthropic.com/news/anthropic-amazon-compute

so basically ...

you could view this as a kind of discount, but instead of paying less later, you get some cash now and then pay full later.

"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."