What do you value a company at that has gotten to $14b in revenue in 3 years and has 60%+ margin on inference? Just out of curiosity.
But project out forwards.
- What happens when Google builds a similar model? Or even Meta, as far behind as they are? They have more than Anthropic in cash flow to pour into these models.
- What happens when OSS is "enough" for most cases? Why would anyone pay 60% margins on inference?
What is Anthropic's moat? The UX is nice, but it can be copied. And other companies will have similarly intelligent models eventually. Margins will then be a race to the bottom, and the real winners will be GPU infra.
Google and Meta might be the only real threats against this given how much cash they have and so far Meta is just flopping
Others are made of different stuff, and are going to go right back to work, even though they could go off to a beach for the rest of forever somewhere.
Doesn't this require their private market valuations to go well into the trillions?
+ r&d costs
Of course, if one does not "pay" for investment, benefits are easily made ..
You MUST accrue the lifetime value of the assets against the capital expense (R&D in this case) to determine the answer to this question.
The company (until this announcement) had raised $17B and has a $14B revenue rate with 60% operating margin.
It is only negative on margin if you assume the prior 14B (e.g. Claude 4.6 plus whatever’s unreleased) will have no value in 24 months. In that case, well, they probably wasted money training.
If you think their growth rate will continue, then you must only believe the models have a useful 9 months or so life before they are break even.
Anthropic is, according to Dario, profitable on every model <<—- they have trained if you consider them individually. You would do best to think “will this pattern continue?”
Or are you suggesting that in fact each model comes out ahead over its lifespan, and all this extra cash is needed because the next model is so much more costly to train that it is sucking up all the profits from the current, but this is ok because revenue is expected to also scale?
Basically every model trained so far has made money for Anthropic and OpenAI. Well maybe not GPT4.5 - we liked you but we barely knew thee..
The cash spend is based on two beliefs a) this profitability will continue or improve, and b) scaling is real.
Therefore, rational actors are choosing to 2-10x their bets in sequence, seeing that the market keeps paying them more money for each step increase in quality, and believing that either lift off is possible or that the benefits from the next run will translate to increased real cash returns.
What's obscure to many is that these capital investments are happening time shifted from model income. Imagine a sequence of model training / deployments that started and finished sequenced: Pay $10m, make $40m. Pay $100m, make $400m. Pay $1bn, make $4bn. Pay $10bn, (we are here; expectation is: make $40bn).
If you did one of those per year, the company charts would look like: $30m in profits, $300m in profits, $3bn in profits. And in fact, if you do some sort of product-based accrual accounting, that's what you would see.
Pop quiz, if you spend in the first month your whole training budget, and the cycles all start in November, what would the cash basis statement look like for the same business model I just mentioned?
-$10m, -$60m, -$600m, $-6bn.. This is the same company with different accounting periods.
Back in reality, shortly into year 1, it was clear (or a hopeful dream) that the next step (-100 / +400) was likely, and so the company embarked on spending that money well ahead of the end of the revenue cycle for the first model. They then did it again and again. As a result naive journalists can convince engineers "they've never made money". Actually they've made money over and over and are making more and more money, and they are choosing to throw it all at the next rev.
Is it a good idea or not to do that is a question worth debating. But it's good to have a clear picture of the finances of these companies; it helps explain why they're getting the investment.
> clear picture of the finances of these companies
Since they are not publicly traded companies, presumably there is no legal duty for the officers to be clear or even honest about these numbers?
But even assuming good faith, my understanding is that the scale of the current build out is so huge that revenues would need to exceed the size of many entire industries to have a chance to turn a profit.
The numbers are right there in the announcement - that investment will come with a pref, likely 1x. So, can Anthropic make 17b with their current revenue growth and inference margin? That’s the only question an investor needs to feel comfortable on to participate in this round.
Realistically - imagine all training from now fails for the world and everyone just shifts to inference - Anthropic would need only clear like 3 or 4b a year in net income to be worth more than this round’s pref in a sale. Meanwhile that 17b will have been sent to workers and data center providers who will book it as revenue and margin.