Lot of focus on Anthropic's $42B loss in their (reported) IPO numbers. Having read the highlights on the train this morning that's not the key number for me.
~$8B operating loss and ~$4B in revenue on a $2T valuation with ~$518B in forward commitments and 2 customers ~25% of sale sis the "WTF" for me.
For anyone stuck on the $42B: most of that is a non cash mark on financing instruments because the valuation went up. The operating number is the one that matters.
My initial thoughts:
1. That's around x430 price to revenue. Mature mega caps generally sit around x8-15, a "hyper grower" x20-40. x430 is a narrative price. "We're destined to become a utility for cognition, not a software company". Which is a bold bet, and a risk for us (look at how utility companies price)
2. That $8B operating loss on $4.6B of sales means the blended cost of training, capacity and serving still swamps revenue. Whether each API token is a loss leader isn't in the highlights specifically, but t does suggest the company as a whole is still paying up for volume and the next model. The bet is hook-in now, pricing power later, once enough products, services and enterprises rely on them.
3. 25% of revenue in two customers? Well, for all us small builders creating around the pace of change Claude supports, with the trade off being we're reliant on the model to manage the codebase, that's a helluva risk. When the prices go up, or the rate limiting comes, that's directional.
4. $518B of forward commitments over the coming years only works if the IPO pricing/follow ons show up, or revenue scales into the 10s/100s of billions. If it doesn't, that's a problem. $20B in cash doesn't fund that commitment, so it looks a bit circular: the IPO is the funding plan.
5. At this stage, with a Capex bubble the likes of which the world has never seen, if that begins to cool a x400+ valuation starts to crack pretty quickly, along with the willingness of hyperscalers (AWS/GCP etc.) to continue to keep stretching receivable shaped compute.
I've been looking forward to seeing these numbers for a while. And they're as entertaining as I thought they might be.
One of the most interesting thought experiments surrounds the AI productivity boom. Something lots of enterprise level orgs are now down the road with. These numbers support, as I mentioned above, a view that resource may be cheap now, but the business model is that it won't be cheap always. Food for thought.
New paradigm or old risk? Pick your poison.