If Anthropic's revenue story doesn't materialize and the AI bubble bursts, these companies are in a world of hurt. It's probably not fatal for Google or Amazon but would be a major drag on the stock price (perhaps why Google has dropped from 400 to 315 over the last couple months as the AI story soured), and it could be company-ending for Broadcom or SpaceX if they end up building out infrastructure to service these commitments.

I remember before I left Google, one of the TGIF questions (which are now AI syntheses of many peoples' individual opinions) was "What happens if AI is a bubble and it bursts?" His response was "If that happens, the whole industry is in trouble." I see now what he meant.

- 2026: $97.08B

- 2027: $97.08B

- 2028: $97.08B

- 2029: $97.08B

- 2030: $66.96B

- 2031: $66.96B

- 2032: $40.09B

- 2033: $40.09B

- 2034: $14.59B

- 2035: $10.00B

- 2036: $10.00B

Total: $637.0B

Considering everything that is cancellable, ie. hard commitment only, it's:

- 2026: $56.79B

- 2027: $56.79B

- 2028: $56.79B

- 2029: $56.79B

- 2030: $56.79B

- 2031: $56.79B

- 2032: $29.93B

- 2033: $29.93B

- 2034: $10.00B

- 2035: $10.00B

- 2036: $10.00B

Total: $431B

If they go bankrupt, there are some more cancellations available, also from the other side – if some providers don't deliver, it can be lower as well.

Got the numbers on a spreadsheet. Of course this is a super simplistic version assuming equal yearly commitments.

I'd think it's more sound to just look at what percent of their current spend is on compute? Seems to be 50-60%

Last week I build my new project on free plan medium Luna OpenAI, totally fine.

A lot of new SaaS companies is just integrating AI into their products. Would be nice to follow this if they increase prices / how they are doing.

Not sure how big part of customers are random human coders / big tech / small-medium SaaS companies...

From S-1, some use-cases in many companies doesn't look convincingly.

It's just like a utility with the slight difference that it can be sold at a distance without significant cost for transportation, in many ways it is the ultimate digital good. Unlike Spotify copyright is not an issue, unlike your electricity, gas, internet or water company there is no last mile problem. Unlike Google there are a ton of qualified contenders already (Google had to be better than Altavista, which wasn't hard, but they were so much better they managed to lock up search for long enough to cement their position).

Anthropic has none of the advantages and all of the disadvantages. On top of that all AI companies seem to be run by megalomaniacs.

In the long-term, the marginal cost of a request is zero so prices will plunge and a lot of the fixed investment will get stuck. The difference between the cost of training a model/infra and serving a request from that model is a clear problem for economics.

What happens with Anthropic specifically is less clear because they, as every company in the same position in the last 150 years has done, are going to push for regulation. A lot of the risk has also been shifted onto neoclouds. The value of the customer relationship is, however, effectively zero. A core part of their strategy has been to build stuff out like CC to differentiate that but, ultimately, that won't work.

The other strategy they have employed is trying to get other companies to stop training new models. Again, seems unlikely to work.

There is no scenario in which they become Google search outside of government intervention. Obviously this is a key part of their strategy now but at best, this will only work in the US and Europe (I would actually say they are much further along in Europe where there is a strong tendency towards corporatism and centralisation of power, they could be "Europe's AI").

Top models, capable of discovering and inventing stuff, held back from public, and available only as for-rent research service (or whatever the relevant bureaucratic term is) to big corporations and government agencies like NASA, DARPA, NSF, NIST, DOE, etc. only.

I have a feeling that we, the public, will stop getting access to more intelligent models starting next year.. i mean, not the incremental ones, but, major ones.. like the one you got going from opus 5 to opus 5.5.

Not even just from Chinese models, but from companies/labs like Mercury

We've seen businesses moving everything to the cloud before AI, we'll see more of that as regulatory requirements are increasing.

Given the trillion-dollar spend, the theory is that every many woman and child in the western world will devote a substantial portion of their income (like 5-10%) towards some sort of AI subscription.

or...

That doesn't happen. All the AI investments go south and every man woman and child in the western world will devote a substantial portion of their income (like 5-10%) to bailing out an economic system that went all-in on AI. Either way, the money has already been spent.

It's a lose/lose situation here for Anthropic (and OpenAI) whatever happens.

It's very possible given the current environment that LLMs go the way of airlines. Low margin businesses that are essential to world economics with heavy price competition.

There are tons of private datasets where a model will be fine tuned to provide useful domain specific responses.

i keep thinking how crazy it is we went from ChatGPT releasing in 2022 to multi-trillion IPOs 4 years later. the industry still hasn't agreed on how to generally use anything. crazy times ahead.

In LLM world model provider has the opportunity to understand and use the information you value as a company. Append this with regulatory requirements and local, even if less able models, are the way to go

For certain AI use cases, AI will be like a utility. For other user cases you need to pay a premium for the frontier. For other use cases AI will be local.

Even in the industries you mention (water and electricity) you have all sorts of businesses. Like people selling water in fancy cans for a large markup or batteries vs wall power.

Of all scenarios, based on the data we have now, this seems to be the least likely one.

Already happening, which is requiring a lot of enterprise DC power retrofits.

This

Neither do Anthropic and OpenAI.

I like Qwen as much as the next person, but that's a bit to credulous. Those models would not get the compute allocation nor distribution they do if it was not aligned with broader policy.

It's a shame "AI" cannot be made to run on bitcoin mining dedicated hardware because they mastered that in only a few years

My last role swapping from Haiku/Sonnet to GLM dropped costs by 90-95% and increased quality.

Money talks.

People are naturally sticky. If they're satisfied with a product, they'll stick around unless something comes along that is so much better it gets people talking. Open AI's 1B+ customers won't give a shit about Chinese models. Enterprise customers won't move to Chinese models, especially just to save money. They are a non issue, especially if the M.O is just going to be playing catch-up.

It’s not unusual for someone to spend $5k in a week. 2 guys at my place just spend $150,000 over 3 months building out a factory.

We are aggressively exploring open weight models now.

But also, I don't think you should use individual consumer behavior as a proxy for how the overall LLM market behaves. Current estimates are that individual users represent a pretty small share of overall genai revenue. Most of it comes from enterprise contracts. Those might be more price sensitive because the CFO gets a say in which vendor they choose.

For a couple years now I've been accustomed to choice of foundation model being a heavily scrutinized design decision because it's just about the easiest way to manage COGS. I can certainly imagine scenarios where, given the option to pay 1/2 as much in inference costs but with a 5% reduction in output quality, the boss would say, "Nah, that's a bad deal." But I have yet to encounter one in the wild.

For AI? Prompt comes in, tokens come out. The value of the 'ecosystem' is roughly zero to me. Switching costs between models are essentially zero. The UI and UX is the same whichever LLM runs underneath.

If you do use llm some, then the $20 tier makes sense, but that's not where the money is. If you use it a lot then a $100 sub probably makes sense. But if you're a BigCo that heavily uses it and pay API prices and suddenly you pay $200 million a month, well, maybe it's worth buying a bunch of hardware and stick it in a basement somewhere, and you won't upload all your secrets to your future competitor to boot. If US labs didn't care about the competition from Chinese models they wouldn't lobby so hard for banning the evil, unaligned, super dangerous communist models that want to steal our freedoms and make our gas expensive.

Ultra-cheap models like Deepseek are also very nice if you need to burn billions and billions of tokens.

How many people use an iPhone to its maximum? Isn't it the same with models? I think engineer's like to flex that they need a top of the line frontier model for their shitty corp projects. A Chinese model will write your Jenkinsfile just fine.

LLMs don't have that. For at least 90% of users, the model in the back end is completely irrelevant to them. Switching to Kimi won't lose them their iTunes music.

Wait until you are forced onto an enterprise plan...

What a lot of people don't seem to realize is how much of the edge of the 'frontier' is in the tooling, the prompts and the harness. The LLMs themselves are already all reasonably close if you keep all the other stuff constant. Sure, one will have an edge in one domain or another. But that edge mostly translates to a degree of performance rather than some absolute. And if you allow for a few more toolcalls, a web search or a longer context then that difference is much smaller than it might seem at first glance.

Claude code is very polished, but I can get the same jobs done using Cursor CLI or any number of open-source coding harnesses (I like Pi, personally). Most plugins/skills/mcps are portable between harnesses with minimal or no effort.

I use the same skills, rules, and MCPs across claude code, cursor, and pi. My tools/plugins aren't identical between harnesses, but porting them from one to another is just a prompt away.

Google Workspace revenues are something like $25bn/q, and that's the entire workspace division, (including enterprise gmail sales, so the productivity suite is meaningfully smaller than that figure). It's taken Google a long time to carve that slice out of Microsoft's entrenched market position. It's not small money, but they'd basically be going to war with two of the largest and most profitable companies in history on their turf.

I don't think they can credibly take that business plan to the market and say it'll be worth $1.75tn. They're all-or-nothing on being in the model business, and everything else is superfluous.

The same one that leaked itself a few months ago?

as a person, i would absolutely not pay more than $1K for ai, because at that point i can just get myself a bartening job

Like if you want to design a survey you can send out, and collaboratively edit and test the survey, ask for changes in layout, wording, etc, and design the statistical analysis to be used, before sending the survey out where end users can fill it out right in the doc, while internal users/admins can ask the agent about its results?

Honest question -- that all works pretty well in claude. Amazon, Google, Microsoft, Meta all pretty similar?

Companies don't mind paying extra to have a another company that does nothing but said thing do said thing for them. It even actually often ends up being cheaper/more profitable.

No Western company will be allowed to choose a Chinese cloud provider by their legal department or by their stakeholders. Which leaves only the US cloud providers as viable sources (as us, Europeans, have been too busy building our revenues based on turism and other such non-tech thingies instead of building real stuff).

I don't really see it frankly. Admittedly I never tried it (I only played a bit with local models) but conceptually how is it much harder than all the other cloud services that already exist? My educated guess is that building a competitor to Netflix, Youtube or Dropbox seem technically a lot harder to get running than offering inference on demand for instance.

It seems that the main barrier for competing in that market at the moment is getting your hands on the GPUs/RAM and a datacenter to put them in.

In my case: robotics, complex document analysis (typically 800K context).

I'm sorry and I'm sure you are being honest in knowing non-tech people using it, but this is on the whole false. The vast majority of people not only have no interest in "personal software" or using Claude for programming, but the slice of the population that does actively interact with AI is using free chatbots as a nu-Google. Most tech people don't even want the "personal software" thing, I don't know why this line is being pushed as if your grandma is going to make her own calorie counter app one day.

What's your source to the contrary?

also, they previously already got some investments on a close to 1T valuation (in may).

Anthropic can take the parts of Google it needs FAR faster than Google can do the reverse.

I've brought my old GTX 1070 out of retirement for for some molecular dynamics these days because its "free" except for eh electricity and have Ben pleasantly surprised at what it can get done chugging along in the background. I'm considering buying some more, as it's kind of a steal in pricing.

There's very little comparison to be had here. Also, Anthropic likely have investors with way too much leverage for them to stop.

The pattern seems to be that it makes people more productive when you measure it in terms of raw work product volume. But not all work product is equally valuable, and we have yet to see a clear pattern of increased economic productivity. Instead we just keep finding new and interesting bits of jaggedness in the frontier.

Of course nobody can prove those economic gains will not manifest at some point in the future. I can’t prove that Jesus isn’t coming soon, either. But I also can’t prove which millenarian denomination, if any, he would choose to bless in the event that he does come back.

It's part of the reason so many of the dark enlightenment dorks are pushing more towards automation of control.

There have been no signs of "super productivity" in the larger economy due to AI. Boosts in productivity have, so far, been seen exclusively in sectors like tech.

Businesses are extracting value now - for the average user I see it as a convenience that they will pick the cheapest provider for..

It's a race to the bottom for most as there's not a major differentiator.

Anthropic is selling $3 for $1, of course the growth is fast.

Also another aspect: think of the most highly paid workers right now in the US and the companies they work for. Assume that normal people no longer have jobs due to AI. How are these companies going to continue making revenue to pay Anthropic? Anthropic might get a quick buck but a long term growth business is not macro-economically possible for frontier AI businesses.

You're also assuming that basically the entirety of the market would go to Anthropic, rather than other providers, which makes no sense.

You are vastly over-estimating the market for AI. The extreme majority of people would never pay a dime for it.

OpenAI, Google, the many Chinese labs all have products and several of them likely have better financial positions. At the moment Anthropic have one of the best models but it needs to stay sufficiently above competition for them to capture a large share of the market.

At the same time this year we've seen a move for model diversification as cheaper models become "good enough". If this IPO was 6 months ago I think it might have gone better for them.

Yes exactly, that's why I listed them. It's much easier for them to capture a higher revenue compared to software companies, and OP (and Amodei) want to make us believe an AI company could ever reach that revenue.

They paid for their compute in equity in the company, not cash. Therefore their losses don’t show up as cash. I don’t understand how people are this gullible.

But on the flip side I don't think aiming for 1M businesses spending $40k a month in token in average is going to be more viable. And I'm not sure having 1000 corporations spending $40M a month is a option either.