Anthropic tells investors it will be profitable for second straight quarter
reuters.com
reuters.com
The idea of removing model training from your costs is a little wild tbh.
The profitability of being able to serve a query wasn't really under question (nor is the margin expected to be anything less than 80%+) I think.
Which unfortunately probably hides the real truth. That large labs do have potential problems with long term profitability.
And either way, the training of new base models will eventually slow from the current frantic pace.
But people are saying its not part of the calculation to generate the cost of a query...
>And either way, the training of new base models will eventually slow from the current frantic pace.
Sure, maybe.
Yeah, I didn't believe they'd claim something like that. But yes indeed, from the article:
> Anthropic's gross margins are above 80% before accounting for revenue shared with distribution partners, including Amazon (AMZN.O), opens new tab, and the cost of training its model,
Is this how all AI companies calculate if they're profitable or not, by removing the highest costs? What a circus.
Note this claim is about „operating profit“, which commonly is the revenue - operating expenses (COGS, rent, payroll). This does not include RnD cost.
>Anthropic's gross margins are above 80% before accounting for revenue shared with distribution partners, including Amazon (AMZN.O), and the cost of training its model, the newspaper said.
Gross margin is typically (revenue - COGS) / revenue. Thus, both statements above seem generally in line with commonly accepted accounting standards.
It’s also easier to strip it out of the picture to think about how much it costs to serve the next token. If you can have great economics to serve the next token (profitable) you can always figure out ways to further reduce your R&D costs.
Now they are absolutely intertwined but I don’t think this is ever as big of an issue that people make it out to be. Replace token with any widget, this is how businesses measure themselves.
1) gross margins are 80% w/o revenue sharing.
2) gross margins are positive w/ revenue sharing.
So that means Anthropic is making money on every token, and customers are willing to pay 80% margins (some of which might go to e.g. Bedrock to serve the model).
The real circus is commenters on HackerNews thinking this number means Anthropic is cooking the books or that it's actually profitable. Gross margin is meaningless for an AI company, since most of their expenses are R&D and infrastructure (the two things excluded from gross margin), but they have to report it anyway.
In one sense, yes, but I do see people question it regularly.
It is certainly not above them to play accounting tricks to pretend to be anywhere near profitable.
If you create a machine that can turn a dollar into 5, you don't dillute ownership of the machine, you use your fabulous profits to expand production. Anthropic, on the other hand, raises money like crazy, and seems desperate to IPO.
HN had long debates about whether AI inference could even be affordable from a compute perspective.
It's one of several metrics and tries to estimate steady-state profitability. It's the only one being leaked because it's the most sensational one. But don't assume cash-flow profitability is negative just because you don't know it.
In your car example the training is much like setting up the manufacturing line.
I think the issue here is that the capex depreciates super fast since the models obsolete really fast.
1. ChatGPT's ~billion weekly active users aren't going to give a shit about some open source model, and neither would most of Anthropic's Enterprise cutomers.
2. Open AI and Anthropic are in a race between themselves, not open source model trainers. There's a reason those models are consistently several months behind and often perform much worse than benchmarks indicate. In the first place, they're only as close as they currently are from the distillation attacks on Anthropic and OpenAI. If they slowed down, they would slow down too.
2. "They're only as close..." is not natural law. You really think open weights couldn't catch up to a fixed target if Beijing makes it a priority? And what happens to their valuations if they abandon the goal of building AGI? There is no strategic alternative to constant training for these companies, which is why they're, uh, constantly training.
2. Nobody said anything about a fixed target. Not sure why you interpreted 'slow down' as 'freeze current models forever'.
>And what happens to their valuations if they abandon the goal of building AGI?
The capabilities these companies already have, combined with their growing userbases, revenue and distribution are plausibly enough to sustain trillion dollar businesses already. OpenAI is a company with a billion active users that has started running ads that reached ARR of $1 billion in the first 2 months and Anthropic is a company that hit $11B+ in revenue last quarter after a pretty massive jump.
Well, I'm not really talking about freezing models forever either, I'm saying that nonstop training is a necessary part of their business. I don't think slowing down is untenable, I just think it's silly not to expect & account for ongoing training costs. That's all my original comment meant.
I also don't understand why you think the open labs couldn't catch up to a given level of quality. If something's been done twice already, why can't a well funded team of experts somewhere else do it a third time? Sounds like wishful thinking.
Are you confident that a step change in open source is unlikely? The industry seems to disagree, considering billions are being spent on them and billions are being spent to stay ahead of them. Open step changes have happened before (eg R1, or heck, self-attention). By the way, AIs themselves are quite good at writing GPU kernels now.
How is this different from planes or cars? If Ford or Boeing zero line their R&D...well, we know what happens.
The concept this entire thread seems to need is the difference between fixed and variable costs.
lol
This is an open question!
This is reportedly a sort of "Enron" accounting which excludes some really big expenses like revenue sharing, the cost of model training and hardware deploymments which are kept off the corporate balance sheet using "special finance vehicles".
https://www.msn.com/en-us/technology/artificial-intelligence...
source? this seems false. reportedly the adjusted profitability includes inference and amortized training costs
Listed at the end of my post.
this seems false.
Source showing this in accordance with GAAP (Generally Acceptable Accounting Practices)?
edit: def not gaap profitable or they would have said that to investors. and their stock-based comp is surely astronomically high on paper.
For multi-unit houses, sure. The actual analogy is closer to removing the cost of the cement plant from every house. Fixed versus variable costs.
"We are profitable when we ignore our costs".
I wonder what other funny strategy they may employ to claim 80% margins.
Active competition requires constant reinvestment and does not allow them to milk their trained models long enough (except poor Haiku maybe).
Yes the company known for famously training 1 model
They'd be in their quiet period...
If I am building a widget and have a widget factory that cost money to build and operate, is it reasonable to only use the cost of shipping my widgets to my buyer as the costs for my gross margin?
Just taking a wild guess, but I'd assume the .5 releases are built on the previous and the Majors (3, 4, 5) are more extensive retrains?
If the article is to be believed they aren’t including their training costs.
Also lol at reporting it as above 80% without accounting for the revenue sharing as well.
I bet anyone’s finances look great if you just start ignoring all the money they owe.
Lol and truth.
In this metric. Revenues aren't a scam because they don't include costs.
We're getting a partial picture. But concluding adversely based on selective disclosure that isn't in control of the person disclosing doesn't make a lot of sense. Especially when it's a compelling narrative.
Anthropic is clear on what they are communicating. If every message had to be dumbed down to the level of the least attentive person to run across a message third hand, we could communicate and nothing but grunts.
Then wait for the S-1. They aren't communicating internal financials to you.
I wish all companies would provide me with their confidential information.
I find it fascinating that so many people default to Indignant outrage and critique because they want more
Planes and cars take a lot of capital to develop. That's a fixed cost. Building them consumes labour and resources. Those are variable costs. It's useful to know the slop of the variable-cost function.