Some basic math supports it. A GB300 NVL72 is about $6.5 million. Lets say that you need $6 million worth of cooling and another $6 million worth of electricity. At current rates, that's 720 billion tokens worth of Claude Opus 4.7. At 100,000 tokens per second, it pays for itself in about 3 months.

Obviously this is an extremely rough calculation. I can even be off by a factor of 10 and it's still a pretty good return.

We don’t know the models sizes, requirements, and optimisations, but we could take a guess using the infrastructure costs of the largest open weight alternatives that perform slightly worse.

In my opinion, it’s a profitable kind of service. They probably don’t pay the public prices for the cloud GPUs though.

> There is absolutely no evidence to support this.

Analysts like Semi-Analysis have done a lot of modeling and estimates on the topic.

But two can play this game: There is absolutely no evidence to support that API prices do not have profitable unit economics.

Mr.Truth-teller Amodei confirmed it that APIs are profitable at Anthropic.

also, like super mario kart, SOTA models from the rear will be continually released because theyre sunk costs and open weights will advertise for themselves. Also, its clear FOMO is a DDoS attack on any perceived leader because theres no way they dont oversell.

Lastly, theyll realize like every good capitalist, theres more profit in exclusiveness and cutiing out customers.

Their CEO is on record as saying this. You may think he's lying, but that's just your opinion; given the pricing and how it stacks relative to the pricing of inference providers of comparable open source models (who are certainly charging above cost!), I am inclined to believe Anthropic on this.

Don't forget the organisational overhead. You'll need managers and communication overhead between developers grows superlinear (see Brook's law).

The arithmetic is a little different in every country because of local rates of pay and taxation but it's worth remembering that in most of the world except for the richer parts of the US developers do not get paid what those US developers have been making in recent years. There are a few exceptions but the norm is several times less even in major economies in Europe or Asia.

Another challenge for US tech companies is that - if you'll forgive the bluntness - their "brand" is now toxic in most of the world. Almost everyone is trying to distance themselves from US tech as fast as they can. Governments and big businesses are starting to invest seriously in alternative solutions and local resources. It will happen over time but I don't see much the US tech companies or the US government can do to stop the train now the wheels are turning.

So there's a serious risk for US tech companies now of a double whammy where their already relatively high R&D costs increase even further and yet they're also facing much stronger competition in international markets or maybe even excluded from some of those markets entirely.

If we also reach the seemingly inevitable point that "capable enough" LLMs can run locally - or at least as a private resource provided internally by large organisations - there is very little moat left to protect not only US Big Tech whose stocks have been heavily driven by expected returns from AI but the whole US tech industry that is banking on productivity gains from that AI tech. Then they also won't be able to capture most of the entire global supply of components like GPUs/RAM/SSDs because it won't be cost effective any more - and that is one of the few practical moats they have built (however accidentally) that would be a significant barrier to direct competitors setting up shop in places like Europe and Asia.

It's going to be interesting to see how US tech companies respond to these effects over the next 5-10 years. The giants are all aboard the AI train and can't back down now so there will probably be some casualties there if - as again seems inevitable - the bubble bursts at some point. But then there's a very long tail of still very successful US tech companies that might be paying US salaries and using AI-based tools but aren't themselves focussed on developing or providing those AI-based tools and they're the ones who are going to need to find new ways to compete effectively within that kind of time frame.

The 1.4x multiple doesn’t work when you get to engineering salaries.

I get paid a decent salary for my expertise and this wild thing called remembering what happened yesterday. My point is my compute is powered by bananas and coffee, not gigawatts of energy and all of the RAM.

I wouldn't call my $2k Strix Halo computer "massively expensive", and it runs e.g. Qwen 3.6 27b brilliantly, with tons of memory to spare and is a full x86 powerhouse pulling 120w at absolute max.

IMO the programming world is far too myopic about / insistent on using laptops, especially macbooks. Just because a crappy deal exists doesn't mean everyone is forced to take it. Local AI is a high performance computing problem and laptops are fundamentally a crappy form factor for it; buy an efficient desktop computer and be surprised at what's possible even with today's crazy prices.