>The bank's analysts then assumed Anthropic gross profit margins of 60%, and estimated that 75% of related costs are spent on AWS cloud services.

Not estimate, assumption.

They had pretty drastic price cuts on Opus 4.5. It's possible they're now selling inference at a loss to gain market share, or at least that their margins are much lower. Dario claims that all their previous models were profitable (even after accounting for research costs), but it's unclear that there's a path to keeping their previous margins and expanding revenue as fast or faster than their costs (each model has been substantially more expensive than the previous model).

I've been wondering about this generally... Are the per-request API prices I'm paying at a profit or a loss? My billing would suggest they are not making a profit on the monthly fees (unless there are a bunch of enterprise accounts in group deals not being used, I am one of those I think)

but those AI/ML researchers aka LLM optimization staff are not cheap. their salaries have skyrocketed, and some are being fought for like top-tier soccer stars and actors/actresses

The leaders of Anthropic, OpenAI and DeepMind all hope to create models that are much more powerful than the ones they have now.

A large portion of the many tens of billions of dollars they have at their disposal (OpenAI alone raised 40 billion in April) is probably going toward this ambition—basically a huge science experiment. For example, when an AI lab offers an individual researcher a $250 million pay package, it can only be because they hope that the researcher can help them with something very ambitious: there's no need to pay that much for a single employee to help them reduce the costs of serving the paying customers they have now.

The point is that you can be right that Anthropic is making money on the marginal new user of Claude, but Anthropic's investors might still get soaked if the huge science experiment does not bear fruit.

Because discounted dollar bills are still a tangible asset, but churning language models are intangible?

Maybe for those of us not-too-clever ones, what is the bet? Why is it different? Would be pretty great to have like a clear articulation of this!

> the Madoff scam is only ~$70b in total

Incorrect - that was the fraudulent NAV.

An estimate for true cash inflow that was lost is about $20 billion (which is still an enormous number!)

No, I'm scamming myself. Halving my fortune because I believe karma will somehow repay me ten fold some time later.

Or I'm just saying revenue numbers alone don't prove anything useful when you have deep pockets.

From the acquirer’s perspective, you’re right. (Bonus: it diminishes your own employees’ ability to leave and fundraise to compete with you.)

From an ecosystem perspective, acquihires trash the funding landscape. And from the employees’ perspective, as an investor, I’d see them being on an early founding team as a risk going forward. But that isn’t relevant if the individual pay-off is big.

You want those people specifically. To get them, you need to hire them for a lot more money than you pay your current folks. That causes a lot of resentment with folks and messes up things like salary bands, etc.

But since they own equity in the current company, you can give them a ton of money by buying out that equity/paying acquisition bonuses that are conditional on staying for specific amounts of time, etc. And your current staff doesn't feel left out because "it's an acquisition" the way they would if you just paid some engineers 10x or 100x what you pay them.

I left out the part that the motivations for the acquirers were not to save money or to be slimy. It was the only way to get around overzealous government regulators making it harder to acquirer companies.