Yes, but --- using something they call "adjusted operating income".
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".
>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
source? this seems false. reportedly the adjusted profitability includes inference and amortized training costs
> 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, the newspaper said.
Yes the company known for famously training 1 model
Isn't training necessary for the end-product? How is it not a cost to generate the output if you can't generate the output without having done the training? Seems more like saying that a car is profitable product when you don't have to account for the steel that its made from. Seems completely disingenuous.
Is it really capex though? New models are being constantly trained, released at least quarterly, while old ones become obsolete. Training costs vary, but never disappear.
New models are constantly being trained, but they don't have to be constanatly trained. If OpenAI or Anthropic 'just' wanted to be a profitable business, they could ease up on that, but they're both racing to create a machine to can automate all or most human labour.
If they stop racing, the wave of open models will pass them and their inference margins will drop to zero. A realistic model of their operating costs surely must include ongoing training.
>If they stop racing, the wave of open models will pass them and their inference margins will drop to zero.
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.
1. They absolutely would, eventually, if open models surpassed Ant & OAI's flagships. Keep in mind "surpassed" encompasses both output quality and cost-saving architecture innovations like DSA, which may not be possible to apply to old models (and may take advantage of new hardware!)
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.
1. Mainstream users don't care about benchmarks or whether some open weight model has technically surpassed GPT-X on a leaderboard. They care about whther GPT does what they want it to do. Capable Open source models already exist, and that hasn't caused ordinary chatGPT users to abandon chatGPT for them. Hell Anthropic exists, and that didn't cause that either. OpenAI still dwarfs Anthropic in the consumer space. Obviously, sufficiently large differences in capability can eventually matter like when Anthropic blew everyone away in coding at one point, but that's very different from saying OpenAI has to train a frontier model every few months or inference margins go to zero.
2. Nobody said anything about a fixed target. Not sure why you interpreted 'slow down' as 'freeze current models forever'. And no i don't think they would catch up.
>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.
Enterprises absolutely care about benchmarks (especially internal ones, but the headline benchmaxxed ones too), and the Ant coding thing is a great example. How long did that last again? A few months? Illustrates my point perfectly. Switching is easy. Why would a business have any loyalty to one text->text endpoint over another? The consumer market may be less responsive to quality, sure, but it is more responsive to cost which I mentioned. It's also just not as big.
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.
Enterprises have loyalty, but an obvious step-change in ability will sway. No, i don't think open source will zoom past the frontier (the necessary step change) just because they put the brakes on. Like i said, there's a reason Open Source is perpertually behind. If frontier labs decide to stop chasing AGI, then what, open source providers will step in to bleed a lot more money without being able to distill from the frontier ? To what end ?
> To what end?
To chase AGI, of course! An "open" lab does not have to stay open forever.
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.
Seeing a lot of tricks similar to how ridesharing companies tried to be "profitable" before going to IPO. Caveat: Thing have materially improved but really Uber is carried by its insane Ads margins
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.
> idea of removing model training from your costs is a little wild tbh
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.
I think part of the big push to "slow down AI development" is to add some sort of regulatory pressure that will give them sort cover to train less models and slow their burn rates
I can’t count the number of times I’ve heard variants of ‘they’re losing money on every query’ and ‘I’m getting 10k worth of tokens for $200’ over the last year. People clearly believed serving margins were -ve
That's a bit hyperbolic. It's closer to using EBITDA as your "earnings" and bucketing model costs in a rapid depreciation model (which is fair, I'd assume a model is good for more than just 1 year...
Ok, but a more honest comparison here would be removing the cost of hiring architects and hiring lawyers to draft contracts, not all the per-house costs.
> before accounting for revenue shared with distribution partners, including Amazon (AMZN.O), opens new tab, and the cost of training its model, the newspaper said.
"We are profitable when we ignore our costs".
I wonder what other funny strategy they may employ to claim 80% margins.
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[ 0.23 ms ] story [ 44.1 ms ] threadThis 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
Yes the company known for famously training 1 model
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 no i don't think they would catch up.
>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.
This is an open question!
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.
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.
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.
Active competition requires constant reinvestment and does not allow them to milk their trained models long enough (except poor Haiku maybe).
"We are profitable when we ignore our costs".
I wonder what other funny strategy they may employ to claim 80% margins.