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.
> The idea of removing model training from your costs is a little wild tbh.
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.
> Anthropic has told shareholders that its adjusted operating income will be positive for a second straight quarter, the Financial Times reported on Sunday, citing multiple people with knowledge of the matter.
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.
Typically R&D is not part COGS. It’s absolutely part of the bottom line when you will typically recognize the cost over some period of time to try to get a true picture of the business.
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.
This is how all companies calculate gross margins, which is why they're called gross. Gross profit is revenue minus the cost of goods sold. For example, if you sell wheat for $1 and spend $0.20 of labor and raw materials (e.g. fertilizer) to grow it, your gross margin is 80%. This will not include equipment like tractors. AI companies, like all others, include this in their quarterly reports because they are legally, professionally, and customarily required to.
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.
The way I read it, they are convincing the investors that they can fool the larger population convincingly. At the end of the day the investor term is misnomer for big institutional investors, given that these people are managing other people money where they always make out a certain percentage of fees despite the outcome.
I still question it. It's very light on details by just casually dropping they have 80% margins. I smell bullshit on this.
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.
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
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
> 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.
Well then you haven't listened to Ed Zitron or any of the other AI bubble doomers. His contention is that its worthless and they lose money on every query.
But isn't he taking all the costs into account, that created the experience? Rather than just literally the inference/serving infrastructure? Bananas way of calculating things if so, doesn't match reality at all.
Zitron is worthless–lying about numbers and not correcting the record when you're called out means you aren't trustworthy. Worse than that if you directionally agree with him, which I do.
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 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.
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.
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.
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
> 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.
> 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
The “opens new tab” sometimes drives me insane when using TTS to listen to Reuters articles. I’m assuming they are using the wrong CSS/markup for the purpose.
Or they've hit diminishing returns that will collapse their valuation, so saying "this is a threat to humanity" sounds better than "This is about as good as the tech is going to be for a long time" to investors.
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...
The models don’t exist without training. I don’t see how excluding the training cost from the thing they are selling(inference) is perfectly reasonable and not just an accounting trick.
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?
Generally speaking, IDK about Anthropic specifically, they don't train purely from scratch though. A good chunk of the setup is reused previous models can serve as a basis for the next model. Plus there are methods that also use the previous model as a warm start.
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
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.
> models don’t exist without training. I don’t see how excluding the training cost from the thing they are selling(inference) is perfectly reasonable
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.
This concern trolling on behalf of people that can't read to the end of a sentence, about a conversation that they aren't even part of.
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.
I would like them to communicate something else though (ie reveal what their training costs are, how much revenue share they have and with who) so I could have some idea of their actual profit margins. They're not public yet so they're within rights to withhold that, but it does mean this isn't very useful information for a lot of questions one might have?
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.
> The idea of removing model training from your costs is a little wild tbh.
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.
> Anthropic has told shareholders that its adjusted operating income will be positive for a second straight quarter, the Financial Times reported on Sunday, citing multiple people with knowledge of the matter.
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.
If you are sharing revenue (royalties, licenses based on revenue, costs that scale directly with revenue) doesnt that count as cost of goods sold?
Typically R&D is not part COGS. It’s absolutely part of the bottom line when you will typically recognize the cost over some period of time to try to get a true picture of the business.
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.
This is how all companies calculate gross margins, which is why they're called gross. Gross profit is revenue minus the cost of goods sold. For example, if you sell wheat for $1 and spend $0.20 of labor and raw materials (e.g. fertilizer) to grow it, your gross margin is 80%. This will not include equipment like tractors. AI companies, like all others, include this in their quarterly reports because they are legally, professionally, and customarily required to.
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.
Andy Fastow would be envious.
I believe this is a new accounting method called “phantasmagorical accounting.”
The way I read it, they are convincing the investors that they can fool the larger population convincingly. At the end of the day the investor term is misnomer for big institutional investors, given that these people are managing other people money where they always make out a certain percentage of fees despite the outcome.
> The profitability of being able to serve a query wasn't really under question
In one sense, yes, but I do see people question it regularly.
I still question it. It's very light on details by just casually dropping they have 80% margins. I smell bullshit on this.
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.
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
I think the latter half of your post is missing
Hehe, he reached limit of his tokens /s
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
> 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.
> 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 didn't realize how much money Uber makes from ads.... Why does every business devolve into an ad platform?
Might be because most hit their maximum growth but need to keep growing indefinitely or risk becoming a “mature” company?
Because consumers don't care much about ads and prefer them to even minor cost differences.
Any business where people are looking at a screen should probably sell ads.
Vomit. We should ban 90% of ads. They are mostly net negative on society.
Well then you haven't listened to Ed Zitron or any of the other AI bubble doomers. His contention is that its worthless and they lose money on every query.
But isn't he taking all the costs into account, that created the experience? Rather than just literally the inference/serving infrastructure? Bananas way of calculating things if so, doesn't match reality at all.
isn't that when the training costs are actually accounted for?
Not for operating profit. Training is a one-time cost that gets amortized across every query.
And either way, the training of new base models will eventually slow from the current frantic pace.
>Not for operating profit. Training is a one-time cost that gets amortized across every query.
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.
Zitron is worthless–lying about numbers and not correcting the record when you're called out means you aren't trustworthy. Worse than that if you directionally agree with him, which I do.
He makes his money and fame by spewing bulshit as many grifters before him.
Which unfortunately probably hides the real truth. That large labs do have potential problems with long term profitability.
Who do you think is being tricked exactly? I can guarantee it isn't the team of lawyers and analysts at their investors firms.
"profitable without COGS" doesn't actually mean anything at all does it?
Profitable without Capex, i.e. gross margin. Inference is included in COGS. Capex (training) is not. That's reasonable.
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
How is this different from planes or cars? If Ford or Boeing zero line their R&D...well, we know what happens.
Two big differences are low switching costs and a higher rate of innovation.
> Two big differences are low switching costs and a higher rate of innovation
The concept this entire thread seems to need is the difference between fixed and variable costs.
You’re the only person I’ve seen on here with strong foundations who should be allowed to talk about all things finance and valuation (besides me).
lol
>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 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.
> but they don't have to be constanatly trained
This is an open question!
This also includes revenue sharing with distribution partners, which should probably be in COGS.
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.
It's an accounting trick.
In what case is capex (for any company) not required for the end product? Are you arguing capex is disingenuos as a concept?
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.
If my company sells widgets and dongles, capex for the dongle line does not figure into widget COGS
Is that basically: We'd be making money, if we didn't have to build the product?
I imagine there are many other businesses that would be profitable if they excluded all of their largest costs from their reporting.
Fun fact: most, if not all, tech startups are actually “profitable” under this assumption.
Turns out building houses is super profitable once you remove the cost of building materials, labor, and land.
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.
> removing the cost of hiring architects and hiring lawyers to draft contracts
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.
I believe that their desire to slow down AI development is just for profits.
Active competition requires constant reinvestment and does not allow them to milk their trained models long enough (except poor Haiku maybe).
Blockbuster was profitable if you only considered its Milk Duds sales.
Is this community adjusted EBITDA?
GAPP or ACSOI? Adjusted Consolidated Segment Operating Income from the groupon days....
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".
https://www.msn.com/en-us/technology/artificial-intelligence...
>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
source?
Listed at the end of my post.
this seems false.
Source showing this in accordance with GAAP (Generally Acceptable Accounting Practices)?
that says only that the gross margin calculation excludes profit sharing and training. You should read it more carefully
edit: def not gaap profitable or they would have said that to investors. and their stock-based comp is surely astronomically high on paper.
It's really easy to be profitable when you exclude all of your expenses
very convoluted, number game but probably works for casual investors who just want to put money in something.
these are not casual investors - anthropic is not public
> 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.
GAAP or non GAAP profitable?
non GAAP. If they were GAAP profitable they either wouldn't be sharing or would be IPOing already.
> If they were GAAP profitable they either wouldn't be sharing or would be IPOing already
They'd be in their quiet period...
surprised by 80% margin that doesn't include training cost.
> 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
The “opens new tab” sometimes drives me insane when using TTS to listen to Reuters articles. I’m assuming they are using the wrong CSS/markup for the purpose.
Well fortunately now we're gonna be Pacing the Frontier.
I cynically wonder how much of this "pacing" is a mutual decision to reduce training expenditure costs as OpenAI and Anthropic plan to IPO.
Or they've hit diminishing returns that will collapse their valuation, so saying "this is a threat to humanity" sounds better than "This is about as good as the tech is going to be for a long time" to investors.
My guess is a ton. According to https://www.thebignewsletter.com/p/monopoly-round-up-just-st..., they are asking for exception to anti trust as this type of collaboration would normally be massive anti trust violation.
[flagged]
Pathetic! At my company, we have a 100% margin before accounting for cost!
This is Enron-level fraud. What would Ford/GM/Toyota's gross margins be without the cost of manufacturing vehicles?
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...
It's gross margin. Not including training in gross margin is perfectly reasonable.
The models don’t exist without training. I don’t see how excluding the training cost from the thing they are selling(inference) is perfectly reasonable and not just an accounting trick.
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?
Generally speaking, IDK about Anthropic specifically, they don't train purely from scratch though. A good chunk of the setup is reused previous models can serve as a basis for the next model. Plus there are methods that also use the previous model as a warm start.
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?
That’s maybe a reasonable argument but it’s not one Anthropic is making.
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.
> If the article is to be believed they aren’t including their training costs
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.
> I bet anyone’s finances look great if you just start ignoring all the money they owe.
Lol and truth.
> models don’t exist without training. I don’t see how excluding the training cost from the thing they are selling(inference) is perfectly reasonable
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.
This concern trolling on behalf of people that can't read to the end of a sentence, about a conversation that they aren't even part of.
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.
I would like them to communicate something else though (ie reveal what their training costs are, how much revenue share they have and with who) so I could have some idea of their actual profit margins. They're not public yet so they're within rights to withhold that, but it does mean this isn't very useful information for a lot of questions one might have?
> would like them to communicate something else though
Then wait for the S-1. They aren't communicating internal financials to you.
Sure, it leaves many people with unanswered questions.
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
Unfortunately, just stating that something is reasonable does not make it true.
More similar to if they took out the cost to design the cars and all the many levels of corporate overhead they have in the form of middle management.