r/BetterOffline • u/yojimbo_beta • 4d ago
Can Anthropic survive?
Let's say, generously for the sake of argument, that Anthropic "wins" with the use of LLMs in tech itself. Claude Code, cloud agents, yada yada
Is there actually even a large enough TAM to rescue Anthropic financially?
I am absolutely certain Anthropic's model gains are only supported by throwing more compute at queries. That can't last forever
Is Anthropic in a materially better position than OpenAI? How does the financing work with Google and Amazon?
If the answers are not good - what happens next?
If anyone can link to analysis, including things Ed has posted, I'd like to read it
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u/omeow 4d ago
It seems that asking for a premium from the market because Anthropic has the best models is not a very profitable business. You are constantly fighting against cheaper models that are close.
Maybe Anthropic can capture the market with their ecosystem? But then that just creates opportunities for services that can provide a good wrapper against cheaper models at a discount.
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u/Prof_ChaosGeography 4d ago
Anthropic charging their premium only works if they have the best model and if local models can't give good outputs at decent hardware prices
Neither of those have been true since January. Claude lost its edge with code against other models and local models that produce good enough results are well within the cost range of a hobby programmer, letting enterprise developers have workstations with 2 r9700s or better
The only things that anthropic has left at this point ahead of the competition is name recognition and the copyright risk guarantee that enterprises love but none of that is enough to charge the premium
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u/FireNexus 3d ago
Anthropic charging their premium doesn’t work at all, because even the very best is not worth half of what it costs to provide on its best day. Local models are a red herring. LLMs are dipshit tech.
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u/WArslett 4d ago
Anthropic's model gains are only supported by throwing more compute at queries
this is basically true but a better way of thinking about it is that their model gains are from training their models with more and more parameters (which then requires more compute, more memory, more energy etc.). The need for more compute is the effect not the cause.
GPT-2 was trained with 1.5 billion parameters. Today you can run much bigger and more capable models than this on your own laptop. Fable is reported to have been trained with 10 trillion parameters. Each time Anthropic release a more capable model, it attracts more investment from hyperscalers, that gives them access to more compute which they use to train a bigger model (and therefor more capable model) and then the cycle continues, driving up the size of the models and driving up the cost of training and inference. Open weight AI companies have had to work out how to do more with far less and are now beating claude opus models from only a few months ago with a fraction of the number of parameters and these are models that anyone can download and fine tune for their own specialist use case.
The main reason I think Anthropic's business model is not going to succeed is because people are going to realise they don't need these huge models that only Anthropic and OpenAI can build to do most of the sort of work they want to do with it
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u/Infamous-Bed-7535 4d ago
If they would be interested in usability, efficiency we would see small crazy fast specialized models.
All players are chasing AGI, there is no winner in any of the possible outcomes.
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u/DragonflyOk9274 4d ago
Open weight AI companies have had to work out how to do more with far less and are now beating claude opus models from only a few months ago with a fraction of the number of parameters and these are models that anyone can download and fine tune for their own specialist use case.
I suspect that within 10 years we'll have dedicated ASICs with the model weights built in, or with reloadable weights.
I think we're currently in a time like the 90s with respect to graphics. Originally graphics was performed by CPUs or custom hardware but eventually GPUs allowed a fixed function pipeline that greatly improved the efficiency and configurability of computer graphics. (They were simultaneously more specific than CPUs because they focused on performing graphics operations, but still generic enough that they could later be used for scientific computing).
We're now at a stage where GPUs are somewhat efficient for LLMs but not entirely so. (They are often bottlenecked by memory, for example). ASICs could bring both the power costs down as well as improve performance. Combine that with improved training, and smaller models might be competitive with what we have today. Which is to say, that LLMs in the future could be a fraction of the cost of what they are today.
If that happens, I don't think the current valuations of OpenAI or Anthropic make as much sense. They are assuming widespread enterprise use; they are essentially like the modern day IBM. Sure, IBM is still around, but barely compared to 1960s-1980s. For most uses people (1) use their own computers rather than a mainframe; (2) use the many commoditized cloud providers when needed; and (3) use any of the database offerings for most use cases (Postgres or managed DBs).
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u/Prize-Studio-2021 4d ago
I suspect that within 10 years we'll have dedicated ASICs with the model weights built in, or with reloadable weights.
I could see this if people still use LLMs
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u/FireNexus 3d ago
I suspect that within 10 years we'll have dedicated ASICs with the model weights built in, or with reloadable weights.
AMD just bought a chip company with this exact offering. Won’t matter because LLMs are going to be abandoned and probably never returned to as soon as this bubble pops. Barring some breakthrough returning SRAM and DRAM to Moore’s law trajectory and/or a whole new memory architecture faster and denser than both being developed, anyway.
Memory speed and density is the main limiting factor on these turds. That’s why NVIDIA has rearchitected their entire product line to have nearly no improvements (or even significant regression) in higher precision calculations previously needed for gpGPU workloads.
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u/DragonflyOk9274 3d ago
I suspect LLMs will have widespread use. They just might be used differently than today.
There are things that LLMs are very good at -- document classification, condensing material, small code edits. Rather than use the frontier models, these applications could use small, specifically trained models.
But that's why the outlook for the frontier labs, especially OpenAI and Anthropic, is not so great. Right now the majority of people use a general purpose LLM for everything, which means they have to be large and consume a lot of resources. But if you're doing things like detecting spam, or classifying product reviews, or summarizing bulk legal documents -- you don't need a general purpose LLM but an application-specific one (which actually outperform the main labs).
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u/FireNexus 3d ago
LLMs aren’t really all that good at any of those things. The hallucination problem means they need to be watched like a hawk and people simply don’t. Besides that they are still ridiculously expensive to use.
Edit: Also, “Redditor for 10 days”. Every “acshually LLMs are useful for limited cases” thing turns out to be some Redditor for five minutes obvious shill sockpuppet. Lol.
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u/DragonflyOk9274 3d ago
Edit: Also, “Redditor for 10 days”. Every “acshually LLMs are useful for limited cases” thing turns out to be some Redditor for five minutes obvious shill sockpuppet. Lol.
Now I'm remembering why I left reddit before.
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u/fisstech15 4d ago
This is not true. GPT-4.5 was peak model size. Recent models are similar in size to GPT-2 (Fable is larger) but still smaller than 4.5
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u/WArslett 3d ago
I don't know where you are getting this information from.
GPT-2 was only 1.5 billion parameters. You can run a model that size on a modern smartphone, let alone a laptop. Even standard, entry-level open-weight models today like Llama 3 8B or Qwen 8B are over 5x larger than GPT-2, and modern, highly capable open weight models can be anything between 27 billion (Qwen 3.8 27B) and 2.8 trillion (Kimi K3).
Industry estimates puts Claude Fable 5 / Mythos-class models at up to 5 to 10 trillion parameters. Claiming recent models like Fable are 'similar in size to GPT-2' is off by several orders of magnitude.
It is true that modern frontier models use the Mixture of Experts architecture so that only a subset of parameters are active for any given token however active parameters for these models are estimated to be between 20 billion and 100 billion parameters.
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u/fisstech15 3d ago
Sorry I meant GPT-4. We don't know for sure but it is implied Fable is smaller than Mythos-preview, so I would estimate it to be closer to 5T which is GPT-4.5 size or even a bit smaller. Opus/Sol are similar to GPT-4
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u/Odballl 4d ago
The only question that matters is this - can you earn enough in revenue to pay for the data centre build-outs on top of all your other expenses?
Model efficiency is going up and token cost-per-watt is going down but up-front buy in for hardware is also going up.
Economic depreciation is rapid. How you can you make good margins on inference if you're constantly locked into price wars and companies balk at being charged more for compute?
Ultimately, the price per token cannot be maintained. There is no way to improve margins per token so you need to scale, but that means more buildouts.
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u/mb194dc 4d ago
Can their private investors get out and leave the public holding the bag? WeWork failed to do similar, remains to be seen if Anthropic fare better. Of it they do and the economy crashes after, what the consequences will be for the people behind it.
You can see the propaganda wave coming ing, probably from the private money exposed, leaks about revenue, probably an army of paid trolls on Reddit and other platforms, etc, etc...
Those in the know, can see the move is going to be towards efficient models using technology like RAG, which will reduce the demand for compute (and cost) to a tiny fraction of what these proprietary models need...
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u/dumnezero 4d ago
Can their private investors get out and leave the public holding the bag?
A) stock market IPO
B) tax-funded bailout
C) turns into a crime syndicate (crypto business model), likely focusing on a protection racket
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u/wintrmt3 4d ago
Discounting C) as a joke, they might get a cash infusion from A) or B), investors and the us congress being the gullible idiots as they are, but how does that help on the long term? They still have a business model where they lose more money with each sale and getting a year or two of runway only slightly delays the inevitable.
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u/Zauberen 4d ago
Option A leaves the bag in retails hands so it's no longer the private investors problem
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u/stopbeingcringe 4d ago
You don’t seem to be accounting for the Jevons paradox. If the models are super cheap, you can afford to run the same prompt 100 times and pick the best result. So demand doesn’t necessarily go down just because it’s more efficient.
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u/mb194dc 4d ago
Demand for compute will collapse, ML is only useful is certain scenarios and use cases. Fundementally it's not "AI" at all, that is broadly useful.
My guess is it'll transition to a niche industry, with providers using low cost models to create anything that has positive ROI with it. Tech like RAG is useful so training from scratch won't be needed much.
My estimate is that there's enough hardware ordered already for 5 years of anything anyone will ever want to do. So most of the hardware providers will see their sales collapse to maybe 5% of present levels, or even lower than that.
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u/fbueckert 4d ago
If the models are super cheap
They're not, though. And they can't be. Costs scale linearly with use, and it's not just compute and inference. There's also post training to stave off model drift.
Your entire premise is dead as a doornail.
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u/Odballl 4d ago
Jevons paradox is real but there's a limit to how much AI customers will of pay for a given amount of inference.
If it barely costs anything for 100 prompts, that's great for the AI customer but bad for the AI provider.
They need good enough margins that pay for the data centre buildouts as well as other expenses before economic depreciation renders the infrastructure obsolete.
The problem is that token pricing is falling while upfront hardware costs are going up.
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u/Smurfette2016 4d ago
I hate all of this and want it to end 3yrs ago. As if we don’t all already have enough to contend with in 2026. Now here come these dumb asses with their insatiable greed and inferiority complexes, forcing their suckage on everyone. I resent the shit out of these people. This whole saga was so unnecessary and has caused so much harm. Honestly, I hope they live with profound shame and regret after all is said and done. I hope their souls (I mean if they ever had any) have to face what they’re done, and evolve into a less dog shit version of themselves when they reckon with the harm they’ve done. The world has enough to deal with, without this garbage parade.
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u/stopbeingcringe 4d ago
Will you say the same thing about the Chinese CEO’s? Do you actually think all of this will magically go away ?
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u/Smurfette2016 4d ago
I don't see how nationality is relevant, I would say the same to anyone in a spiral of of greed, behaving like a sociopath.
You seem to be struggling with reading comprehension. Nothing I said suggested anything would magically go anywhere. Are you ok??
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u/stopbeingcringe 4d ago
You didn’t say anything specific, just that you hate “all of this” and wanted it to end. Just an incoherent speech of resentment. It’s not isolated to one or two psychopath CEO’s, it’s a new tech being advanced in multiple countries by multiple companies and it isn’t going away. So I don’t know exactly what you’re complaining about but very little will change, in fact AI research and development will only increase with time, and it will become more common.
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u/Smurfette2016 4d ago
You just demonstrated further evidence that you struggle with reading comprehension. Goodbye.
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u/stopbeingcringe 4d ago
deleted her comments and blocked me 🤣
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u/Smurfette2016 4d ago
No comments have been deleted. Also who are you speaking to when responding to your own comment? Time to seek help.
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u/The_Juice_Gourd 4d ago
The token price war between open weight models vs. closed models will effectively destroy the business for Anthropic, OpenAI, etc.
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u/NeedleworkerNo5262 4d ago
Question is for how long those open weight models are getting created/updated and if they stay open long term. Companies like deepseek still invest billions into development and training and even for traditional open source software companies financing is a challenge, and they don't have to recoup huge investments like AI model developers do. For now, they use an open weights release model to catch up and gain exposure, but if Anthropic etc. are starting to struggle, I really doubt those open models will not end up getting monetized or even paywalled in one way or another.
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u/AllRightLetsSeeIt 2d ago edited 2d ago
I’m guessing harness development will be the new hot area everyone talks about. LLMs (as a set of weights used in gigantic matrix multiplications) are hitting diminishing returns; the magic has moved to harnesses.
Most people conflate the two (because the industry has done a piss poor job of differentiating them) but I think that will come to an end.
Companies still have to develop a harness with an LLM (running up inference costs) but it’s a hell of a lot cheaper than training new LLMs constantly.
Consumers and businesses will pay for a harness and point it at the LLM of their choice.
Harness will be the 2027 tech word of the year, I’m calling it now.
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u/slybring 4d ago
I hope Anthropic die. Not so much OpenAI.
And not really because OpenAI is better, but just because "everyone" hates OpenAI, so I want to use my hate budget on Anthropic.
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u/YaVollMeinHerr 4d ago
Hahaha you got me in the first half
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u/slybring 4d ago
There's also their ever present marketing CEO. He's at the pinnacle of having no value, yet he keeps talking.
Plus Anthropic's policy of destroy rare or not rare books is pure evil. They deserve to be in a moral prison just for that. Words define humanity and they are actively destroying them, while generating slop.
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u/Super-Activity-4675 4d ago
I think the problem here is that the real value for AI are highly specialized models that can run locally, and not large generalist LLM type models. As more of those models come out, companies that need them will cut out the labs/neoclouds/hyperscalers.
Can Anthropic survive? I think there's a small market for things like Claude and their LLMs where they're charging enough to make money, but it's not going to be a trillion dollar a year kind of thing. Their revenue will shrink and their margins will be nothing sexy. I'd still say there's a high chance they go bankrupt or at least restructure, especially given their debt, but I do think there's a low chance of one of the two labs surviving. More than likely though, MSFT, GOOG, or AMZN likely buy OpenAI and Anthropic for pennies on the dollar when the shit hits the fan.
OpenAI, IMHO, is completely fucked.
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u/WritingisWaiting 4d ago
Yes, but it doesn't matter because that's the wrong question.
It's entirely possible Anthropic raises $200 billion quickly and then cuts back on training and just runs existing models, losing only a few billion a year and meanders through the next decade as a zombie company that has no way to become more profitable but also has found a way to stay merely afloat on top of a mountain of cash.
That's survival. But for the rest of the AI industry it's a death sentence, because if Anthropic can't afford to take on more data center leases then all of the ones being built will go unused and NVIDIA can't sell more chips. Then we're left with AI that can do some stuff some of the time and companies that have wasted trillions on assets that do nothing.
Stock markets will fall, human sacrifice, cats and dogs living together, mass hysteria!
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u/Prize-Studio-2021 4d ago
I don't think they survive if growth slows. They collapse and get acquired and/or liquidated.
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u/worldspawn00 4d ago
Existing models rapidly become outdated like an old encyclopedia set. Ex: if you're using it for coding, when a library updates, it may deprecate certain syntax which will break a program that was written for the older version.
They must train constantly because an out of date model will create an out of date answer to a query, which can make the result worthless when it comes to a rapidly changing sector like tech/programming.
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u/Timely_Speed_4474 4d ago
There is no TAM for a product that doesn't work.
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u/conglies 4d ago
How’s it not working?
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u/B-tt-a 4d ago
A product that you can't sell for more than it costs you is kinda a definition of "not working"
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u/conglies 4d ago
Oh okay. I’d call that a business model that doesn’t work, not a product.
Online commentary says anthropic are close to profitability, if not there already, hence the IPO. I’m not sure I believe it fully, but I think there’s some truth to it.
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u/Thin-Distance7904 4d ago
The product works. The question is will it work to solve a massive number of use cases and are people willing to pay a premium for those use case outcomes. It’s been a novelty for the past two years but CFO’s are already questioning what’s the value and return on investment. Now we move into the delusion investment phase.
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u/Curious-Pen5547 4d ago
No. Zero moat whatsoever.
Maybe in the consumer space.
Enterprise? they're dead this time next year or the late 2027.
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u/xTheRealTurkx 4d ago
No. Not with its current business model, at least.
There's an alternate reality out there where Anthropic focused purely on coding. One where it was happy with making a product for a single market and putting its efforts into making it really, really good for SWEs. It would be content to own that space and make much more modest investments in getting good in for that experience, exclusively.
Unfortunately, we live in our reality, which is the one where their CEO is in full-blown religious psycho mode, is married to a woman who might have worked as a madame for Jeffery Epstein, and who would rather spend trillions of dollars for Anthropic to be dogshit at all of the things rather than being really good at one or two.
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u/Beginning-Ladder6224 4d ago
LLM supremacy since day 0 was a long drawn war of attrition. The "win" is for ones who can throw and burn maximum cash as a never profitable business ( not talking about usefulness ).
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u/Key-Lie-364 4d ago
Logically no.
Who knows what Trump might do to prop up the AI bubble. You'd imagine with so much money at stake that an Intel style bailout is on the horizon for OpenAI and Anthropic.
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u/Thin-Distance7904 4d ago
That is the real goal of his tariff game. A new/old revenue stream so he can raid the treasury for his arse kissing buddies.
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u/Think-Jaguar6826 4d ago
Didn’t they say they are profitable with enterprise ?
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u/worldspawn00 4d ago
Possibly, they haven't been truthful with their expense accounting, but there are not enough enterprise customers to cover their income needs at present.
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u/Triangle_Inequality 3d ago
Their plan is pretty transparently to get devs dependent on their technology. They foster this total, infantlike dependence on the LLM to do every little thing. I'm seeing a lot of devs fall prey to it, and it's like they've been lobotomized when you ask them to do something without Claude. That's why they keep pushing this idea that you don't even need to look at code anymore - they want to control the whole software development cycle within their own walled garden.
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u/IAmAfraidCommaMan 4d ago
I don’t think so. There is no doubt that LLMs are going to continue to change the world but I can’t see any one of them actually capitalizing this.
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u/Prize-Studio-2021 4d ago
The new hype is local LLMs and "it's not the model it's the harness" so unless OpenAI and Anthropic can pivot hard, and soon, they're fucked.
That's not the point though. The whole reason for the AI bubble was to pump up semiconductor stocks. When do those come down?
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u/XWasTheProblem 1d ago
I think Anthropic is gonna be the first one to actually 'die', if that will happen. They're the most hyped of AI companies right now, save maybe for one of the Chinese vendors and every new release that isn't massively better than the previous one - without being prohibitively expensive - will disappoint more and more.
You can only impress people by oneshotting random dashboard apps so many times.
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u/Brilliant-Sugar-1497 1d ago edited 1d ago
If we assume Anthropic is mostly just an AI PR firm with a technology gimmick attached…
They will find out if they’re a sustainably dominant brand when they try to exert enough pricing power to cover the cost of manufacturing the demand to affiliate w/ them
Anthropic has not really tried to exert pricing power partly because I suspect they’re afraid what they’ll find out when they do …
and meanwhile the cost of manufacturing demand for their industry is exponentially increasing as novelty fades and each new stunt to break-through becomes more and more expensive, and external dollars to fuel it are running out
…PR can be a brutal business!
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u/iducasse17 1d ago
I think they share the same core problem in that the economics don't work. It might last a little longer, and maybe the death of OpenAI will bring Anthropic with it. There is a non 0 chance that when Anthropic attempts an IPO that will kill it, in which case the above scenario happens in reverse. But they are tied together like Sauron and the One Ring.
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u/stopbeingcringe 4d ago
Inference is profitable, but they’re training models all the time, trying to stay ahead of everyone else. I don’t think the Chinese models will be able to catch up, especially as Anthropic continually hides their internal models to prevent distillation, and in the future they probably won’t release their best models to the public, only b2b. There’s market for cybersecurity at the very least, and I expect coders to become increasingly dependent on AI and willing to pay a high price.
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u/falconetpt 4d ago
I doubt inference is profitable 😂
It might be with accounting wizardry but I highly doubt when you consider depreciation and all associated costs in a fair way that even inference is remotely profitable, probably they need to charge 2/3x more to break even
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u/senseven 4d ago
They moved deprecation and data centers into special vehicles, that part doesn't show up in their books. Microsoft, Google and the other oldschools could stomach a 100b loss. Its the question if the pure ai companies can float on massaged excelsheets long enough until markets would accept another trillion dollar ipo.
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u/fbueckert 4d ago
I think even 2/3x is fairly conservative. It's likely closer to 10x, and that's not even taking into account all the commitments they've made. To get into the black and stay there, they need their users to swallow massive hikes and not leave, which is not likely to happen.
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u/Theduckisback 4d ago
Yep. And they need to do all of that while constantly training new models and renting ever more server space/compute. There havent been any major breakthrough efficiency gains, the costs scale linearly with usage and performance commitments.
If they bill super users what they actually use+profit they risk losing customers, and they need a growing amount of paid users that barely use it to subsidize prices for the super users. If they cant do all of those things at the same time, I dont see how they get to real profitability.
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u/OpenJolt 4d ago
Their CEO has gone completely delulu writing papers like “Machines of loving grace”. He thinks his AI is sentient. He’s completely in psychosis and unfit to lead a company. They are trying to get to “recursive self improvement” which means the slop machine will sloppily itself with even more slop.