It's a strategy as old as time, but it's a strategy that usually fails. Spending a lot of money on customer capture only works when customers are actually solidly captured. Most markets have fairly heavy competition and customers will only stay captured as long as there is no substantial cost to staying captive.

Take Uber as an example: yes they've raised prices to become profitable, but not to the insanely profitable levels they could if they had a true monopoly. People will stay on Uber when the competition is still at a roughly equivalent price, but will switch if Uber raises its prices enough.

Uber Eats is different, since its a 3 sided market where the cost is paid by the restaurant rather than the user.

AI appears it's going to be more like Uber the car service. Claude can charge $200/month, but charging $2000/month seems unlikely to work. I'm sure many would be willing to pay $2000/month if they had no alternative, but there are alternatives.

> They want to get established as the de facto standard and get a whole bunch of people on their platform so by the time they need to "get profitable" they have a captive audience, a leg-up on other labs. It's a tale as old as time, that's why ubers used to be cheaper than cost.

Some of that is seeking to kill competitors before they can get established. That's normal and has been around for generations, if not since trading was invented.

But most of what we've seen during the "enshitification age" has been to burn money until you achieve a critical mass of users. However, this only really applies to social platforms where the point of it is communicating with people you know. That's the lock-in. You convinced Grandma to join Bookface and now you feel bad leaving if she doesn't leave at the same time, and more importantly, who wants to join Google Square if nobody else uses it?

That's not going to work for AI platforms.

What I do see potentially working is one method that email platforms use to lock in users: having tons of data you can't export/migrate. If you spent lots of time training your AI by feeding it your data, that's going to make it harder to leave.

So far none of them have capitalized on this (probably due to various technical reasons) but I expect it to start eventually.

> they make a ton of money on inference

So it is stated, but is it actually true? I am not convinced.

Besides, it's not as if they can suddenly stop training models, the moment you do that you've spelled a death sentence for profitablity because Google and open source will very quickly undercut a 15 year break even timeline.

from what i understand, the issue with inference is it doesn't scale as user count grows the way traditional saas scales. In typical saas adding users requires very little additional capacity. However with inference, supporting more users requires much more capacity to be added. I don't know if it's quite linear but it certainly requires more infrastructure to support additional LLM users than say a web application.

Afaik Anthropic still loses money for their main product in this space: Claude Code and their Max plans.

Not really. They are burning money on hardware, resources and payroll without meaningful return prospects.

Such as? Where do we find these open weight model providers? Why is hardly anyone talking about them or sharing links (here or elsewhere) if they are so wonderful and profitable?

CoreWeave's cash flow do not look too healthy.

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Some of these AI companies that promised AGI are going to find out that they're actually IDE plugin subscription companies