Everyone eventually wants to be a landlord and a banker (essentially a debt landlord).

It does seem like the tempo and volume of the music is getting louder and louder as the number of chairs is subtly decreasing, doesn’t it?

And I think Oracle got into it as well, and later suffered

Everyone using Claude code on a personal subscription is default opted in to getting their data trained on. Private troves of data like are seen to potentially end up in a winner take all scenario. More data, better models, attracts more users, results in more exclusive data (what Altman calls the data flywheel).

None of them have any moat, OpenAI already lost the lead [1] and no one is "winning". It is just a race to the bottom as they burn through GPUs that won't even last that long.

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

$30B ARR says otherwise.

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.

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?

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 ,-)

>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.

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.

Sure: If you can't get enough compute by ordering it yourself, make deals with anyone who promises to get you more compute.

I think it could make sense to not want to own the stack if you think it's going to cost you velocity/focus? Which is probably the play here. But I'm not certain at all.

Good lucking getting GPUs.

Classic time value of money situation. They get access to the HW now so they can continue to grow the business. Of course, if you think AI is just pets.com redux, I can see how you'd think it's already peaked. All those years of very important people insisting Bezos couldn't just pull a switch on reinvesting all the revenue into growing Amazon and then he did exactly that comes to mind.

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.

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.

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.

I think these pledges offload some of the risk onto Amazon/Oracle/etc

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

If you’re sure it’s going to go gangbusters you want to get it all in-house asap.

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.

I imagine it comes down to if they want to buy hardware every generation, that gets very expensive and depreciates quickly. You've then got a whole load of assets on your books that are technically obsolete for the bleeding edge. This way, AWS buys and maintains the hardware and OpenAI doesn't need to claim it as depreciation ?

Just a guess.

That is a project you can work on at any point in the future and the more you delay it the more certain your investment will be about what you really need. But those additions to the PnL are capped to the costs.

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.

Look at GPU and RAM prices and data center rollout. We have quickly reached Earth's capacity for compute - it is a lot like the housing market. Once there is global saturation, the price to buy becomes increasingly high EVERYWHERE. Let's also not forget that Anthropic moves the market with their purchases and usage. They might literally be unable to buy capacity they need (or project to) and are doing this deal to pave a roadmap for the near-term and to keep global prices (somewhat) down.

From my understanding, if you want to use native Claude in AWS Bedrock, it runs from an AWS datacenter. I'm guessing that's why regardless of running your own stack... they still need a footprint in all the major clouds.

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.

$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).

I was wondering the same thing. I think it's something like, they're going to pay for infra anyways, so Amazon pushes them to allocate their spend to AWS in exchange for 5B.

5 billion now vs 10 billion per year in spend on compute that you had to buy anyways (not necessarily at aws)

> Amazon is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future. This builds on the $8 billion Amazon has previously invested.

> 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

in exchange for service that presumably a) costs something to amazon to operate (so not pure 100B profit) and b) anthropic would have to spend anyway to operate their business.

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.

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."

I'm already living in this future. In a decent execution framework, with context management, memory via unix, and mechanisms for web search and access, local models are effectively on par with frontier ones. And they can often be much faster. I'll keep paying fees for the AI companies until they stop truly subsidizing and leading. They are getting close to the edge of utility, but we can use their services now to bootstrap their own demise. Long live running your own software on your own computer.

> consumer grade local models are getting good enough for local inference

I am waiting for that. Perhaps a taalas kind of high-performance custom hw coding llm engine paired with an open-source coding-agent. On my desktop.