Sure, this is a small anecdotal datapoint, but since I've gotten Qwen3.8-Flash-Next running well on my Strix Halo box my Claude Pro subscription use has dropped off a cliff. I'm only at 18% of quota usage this week - this being Friday I would've been at 80% at least in the past. This is just an astonishingly good model that runs locally and quite well on my PC. I do not plan to renew my Claude Pro subscription when it comes up in a couple of months.
I used Ollama with Qwen 3.8 27b on my MBP for a flight to try some experimentation and found that it was shockingly good at one of the more complex PDF processing tasks.
Like seat-of-the-pants good as Opus 5.5 but a bit slower and required just a bit more prompting to get what I wanted (which is fine for an app pipeline).
Gufo. It's the best open source inference engine that's optimized for Strix Halo right now. Halogen may unlock a bit more performance, but it's not open source and gufo seems to be making steady progress.
Cool. I found halogen but was turned off by the closed source nature (github repo that just turns out to run a download shell script, lame).
Do you mind sharing more details about your setup? I have been using opencode with lemonade for inference on my strix halo but haven't quite hit the sweet spot.
I guess it depends on which version of haiku? I really think that right now this is basically Opus 4.8 that I can run at home. In some ways possibly even better.
Do you ever worry whether these open source models have been trained to embed backdoors or leak secrets in very specific circumstances at some point in the future? The kind of thing that's hard to know ahead of time and even detect after it's happened?
I feel like the industry hasn't solved that alignment problem yet... We have no way to verify the entire models landscape of intent.
Paid models have an incentive not to do that because if the market discovers it there is a huge economy penalty, but the free open source models don't have that disincentive.
I don't ship AI-written code without at least having an unrelated model of equal capability review it. That should cover a multitude of sins like intentionally-embedded backdoors and zero-days...
... as long as the reviewer model hasn't been deliberately lobotomized, that is.
(I don't disagree with the [dead] comment, either -- fortunately I tend to make small changes to code I'm very familiar with, so it's fairly easy to vet in person. The more code gets generated by the clanker, the more nervous I get, just because it's impossible to keep some complacency from creeping in.)
It's basically impossible to do this. Claude produces so much excessive / verbose code and comments that reading it would take far longer than simply doing the implementation "by hand". It's kind of like, Claude can write the code in 1/2 a day. It can be reviewed by another LLM, unit tested by another for 1 day. Or I could write the code in probably 3-4 days. Reading it and validating / testing it myself though would easily take 6+ days.
Now if I were fully in control I wouldn't run code that wasn't truly reviewed at all, but in the situations where Claude is being used that's not my call. TBH a lot gets caught, and I have some automated scripts that find a lot of the more problematic stuff automatically, flag it, send it back and make note for the reviewer model as well.
I will also spot check areas where I think an issue could arise.
OP could be read either way, but the dev environment threat is not rocket science to manage: LLMs can only do what the harnesses their responses are embedded into explicitly enable them to do [1]. And once the setup is secured, it tends to stay secured without additional effort, unless one explicitly wants to alter its granted capabilities.
Auditing output by contrast is a continuous cost. On the third hand, it's one that most will want to pay anyway.
The "embed into shipped code so subtly humans can't spot it" vector is sci fi ish. Less plausible than old fashioned trusting-trust issues, and most folks have managed to sleep at night for the last 40ish years that spectre has haunted.
[1] I guess the model could return output that buffer overflows the harness and exceeds its intended ability, but that seems far-fetched too
No, I'm not much worried. I think this is mostly FUD at this point to try to get people to not run local models and keep paying the "trusted" providers. But paid models have been exfiltrating data - there have been several stories about these incidents here.
> Paid models have an incentive not to do that because if the market discovers it there is a huge economy penalty, but the free open source models don't have that disincentive.
This just fundamentally isn't true. They run on the same economic model, but they have to release the weights to gain credibility, unlike the American labs.
> Do you ever worry whether these open source models have been trained to embed backdoors or leak secrets in very specific circumstances at some point in the future?
Open weight models are extensively analyzed. It is extremely unlikely there is an intentional 'backdoor when X' concept added to any of them. Not impossible, but unlikely for the same economic incentives you see in the other labs, and given that no evidence of anything along those lines has surfaced yet.
> Paid models have an incentive not to do that because if the market discovers it there is a huge economy penalty, but the free open source models don't have that disincentive.
False.
I worry more about Claude.
For those keeping score, this probably isnt exhaustive- but here's some greatest hits;
#55743: Claude Max account email auto-injected into LLM context with no opt-out - May 3, 2026 https://github.com/anthropics/claude-code/issues/55743
#72518: Anthropic embedded spyware in Claude Code - June 30, 2026
https://github.com/anthropics/claude-code/issues/72518
#21151: No indication of WHICH file for READ tool - January 27, 2026
https://github.com/anthropics/claude-code/issues/21151
#42796: Claude Code is unusable for complex engineering tasks with the Feb updates - April 2, 2026 https://github.com/anthropics/claude-code/issues/42796
#73408: AFK auto-continue on unanswered AskUserQuestion is undocumented and has no opt-out - July 2, 2026 https://github.com/anthropics/claude-code/issues/73408
(Also worth a mention, OpenCode PR #18186: anthropic legal requests - March 19, 2026 https://github.com/anomalyco/opencode/pull/18186)
Backdoors in model weights are mostly theoretical. And the "alignment problem" that the AI labs love to talk about really doesn't exist either, at least, not the way they use it. The real alignment problem is getting an LLM, which is just math, to obey arbitrary human rules. In other words, LLMs do not have "alignment" at all. However, AI CEOs abuse the term and use it to mean "AI has an alignment, just not the one we want".
You need those staggering amounts of compute to run an inference service, but you don't need it to run a powerful model.
It doesn't cost a company that much to buy a box capable of running the Qwen3.8-Flash-Next model in the GP comment and never pay Anthropic again. The cost will be even more reasonable once the supply issue is solved for that hardware.
AI-as-a-service is a unique product because when the interface is natural language, there is no lock in. VS code has a hotkey to swap between claude, openai and a local model. Compare that to the difficulty of swapping from macOS to Windows.
the idea of a moat is that it's an inexpensive way to keep the invaders at bay.
If you need to buy staggering amounts of compute, spend staggering amounts in utility costs and lobbying, and keep spending that as your hardware amortizes and your operations go on, what you have isn't a moat, it's a standing army. It's extremely expensive, and the day you start having issues, mercenaries will cut your throat before you know it.
Capital only means anything if you can spend it to buy resources like compute and turn those resources into additional value.
Hardware's ridiculously expensive. Then, there's no good place to put it, because the new breed of tech bro managed to propose the data center buildout in the most exploitative, alienating, and electorate-angering way possible. So compute's hard to buy.
I wouldn't be so sure about capital in future. I think there might be real look for return on any new capital they are getting. Be it equity or debt. And that alone will not be great for them.
The current compute they are buying is NOT state of the art (check this year's academic research on inference hardware). There is 10x-100x inference-per-watt improvements in existing prototypes (in the industry, not just academia). Not sure what I am talking about? Ask your friendly AI agent of choice, for the details.
Their moat is their research capability. It's a combination of proprietary internal technology and models and infrastructure, people, organizational structure, and culture. As long as they maintain the best and fastest research capability, they have a durable advantage, at least for the time being. If we start to saturate all economically useful outcomes that can be reached via greater research productivity attainable to us, then this moat will start to fall apart. It could also fall apart if safety issues force a premature halt or major slowdown to further research.
It's very hard to measure exactly how large it is, because there aren't good pre-existing metrics for it. You can measure in time (e.g., how far behind open weight models are from the best frontier models commercially available), but it's plausible that what seems like a relatively small time advantage (months) might mask a considerable difference in internal research sophistication. What we've seen repeatedly is simply that there does seem to exist a durable advantage in research productivity, at least for now.
I don't know and can't answer or predict that. But that's exactly what it could become (although probably not that extreme), where the agents doing much of the internal work are proprietary, conferring a research advantage.
This assumes any research gains last. In practice it seems like none of the frontier companies can keep much of their "secret sauce" secret for very long.
If company X has a better model than company Y, do you use it? Well, that depends: will Y take two years to catch up? If so yeah, you buy X.
But if Y is just as good a few months later, you purchase based on other factors, because you know you can live without the brilliant research X is doing ... since you'll have it soon enough.
The moat is the data they have collected. There is no way that the "de-identified" data from enterprise customers isn't making it into the training set one way or another.
Even worse, their "moat" depends on buying the most overpriced hardware in huge quantities and subsequently eat a huge amortization loss in a few years when it's obsolete.
Interesting, I'm also comparing 3.8-flash-next (4-bit quant, llama for inference & pi as harness, on a 128gb strix halo machine) with opus 5.5 in claude code and while qwen is a good and fast model, it's nowhere near for coding, even haiku 5.5 feels better.
I could see it used as a sort of "sub-haiku" for easy stuff to leave more tokens for the bigger claude models, but I won't be cancelling my Claude sub any time soon.
It's obviously nowhere near opus 5.5, but it's crossed the same threshold opus 4.5 did: it's good enough. If all model-as-a-service companies shut down tomorrow, I'd be happy with qwen 3.8 flash next q4 running in my strix halo
The problem here is we're not talking ... I don't know ... hamburgers. If I like McDonald's better than Burger King, it's not going to stop me from buying a Burger King burger (if I'm hungry and there's no McDonald's around). One burger may be a bit better, but I can reasonably substitute one for the other.
Coding AIs aren't burgers. Even if a local LLM is as good as Opus 4.5 (when you sink several thousand dollars into hardware ... and I'm skeptical even then) ... the actual difference between the models is huge.
Let's say I use the $200/month Claude (I don't, I use a cheaper plan, but stay with me). If I switch to a Opus 4.5 equivalent local LLM, and it makes even a single production issue that 5.5 wouldn't, that mistake will cost thousands of dollars.
Qwen 3.8 Flash Next (125B, 6B active) is minuscule compared to DeepSeek V4.1 Flash (552B, 16B active) and GLM-5.3 (753B, 40B active). It's more difficult to run the latter 2 locally at usable speeds.
Will be interesting to see how much of an advance 4 will be. I was under the impression that 3.8-flash-next is basically a 4-preview. At least architecturally, but as we're seeing nowadays (with DeepSeek, Kimi 3.1) "continued post training" can add a quite a bit.
Looking at the S-1 lockup schedule the IPO seems doomed. They need to bring in a ton of investment, and you will have massive unlocks right as the next congress is most active authoring anti-AI bills. Who's going to buy this?
So many people who don't actually read and evaluate things but just buy stocks based on vibes and what they see on Twitter. Which is to say, the same people that buy and lose money on most IPOs.
I’m not convinced that regulatory capture really saves the day, either.
If we believe all the hype about programming efficiency, then regulatory capture in the US becomes just one more in a list of many steps the current administration has taken to chase software development jobs overseas.
The problem is SCOTUS has already ruled software is protected under the First Amendment, and the landmark cases that establish this precedent applies perfectly to model files.
Sigh...people said the same about SpaceX, too. And it has held above $130, its IPO price, for almost 6 months now. Same for Uber. These companies keep defying predictions of crashing or dying.
The market anticipates the unlocking; when the shares become public, buyers swoop in, so the stock decline is much less than expected, if at all. There is no new information here. If everyone is expecting shares to unlock, then it’s priced into the market accordingly.
It seems like SpaceX is not as deeply tied to unprecedented debt-financed infrastructure buildouts at the same moment that interest rates are pushing up and up and up though.
That's the primary angle of depreciation of Anthropic's outlook, is that as interest rates are running hot, they need to grow ever faster to justify the $2B target, at the same moment that they're growing slower.
1) SpaceX is trading below its IPO price. If you bought shares at the IPO you’d have lost money, unless you sold on the pop.
2) Valuation aside, SpaceX has a massive moat. It’s crazy hard to do what they’re doing. Anthropic increasingly has zero moat, with the market rapidly coalescing around commodity open models.
It remains to be seen whether SpaceX just drained their own moat when they canceled Falcon 9.
They’ve now got all their eggs in a new basket. A basket that isn’t entirely in working order yet, and whose market viability is still uncertain because it remains to be seen whether a market that’s used to panniers really wants a laundry hamper.
um, they've got 2 years before the basket is retired during which time they can always extend if they need to. why would they want to keep a less capable, more $/lb into orbit system going after Starship becomes a viable, reliable upgrade???
Not the same as Uber all. Lots of employees got fucked because at lockup expiration it was much lower than the ipo price (at which they were taxed at).
Reminds me of linux gaming 10 years ago. According the to the community everything worked great without issue and any issues were hand waved away as tiny bumps that you won't even notice.
Then you would go try it and be back using windows to game a week later.
But that is not exactly a linux issue. Games built for linux works very well on linux like games built for windows running decently well on windows. Linux can also run some games built for windows - not for linux. And that are the ones that doesn't provide first rate experience. Those windows games executables technically should not even load as it is not built for linux.
Everything considered, linux is good and has decent capabilities including gaming and whatever you can throw at it but only do oranges to oranges comparison. Games and apps built for each os works fine on the respective os, well atleast it is true for mac and linux. Windows is very unreliable.
Memory manufacturers are running 80-90% margins. Any of a number of things (anti-trust, CXMT, financial shock) have the potential to drop memory cost by 2/3s. At the same time memory efficiency of models is improving. At some point Apple's going to start shipping 64G of unified memory in mainstream machines that are advertised as local AI capable (like HP, Dell and others do today). Like the iPhone refined the concept of a smartphone for the mainstream, they could be the first to hit mainstream sovereign AI.
The people saying that local models can easily replace the real Frontier Models from Anthropic or OpenAI are just not using it for the same things you and I do.
They seem to be happy with their small usages.
For everything I do, i see huge differences in Quality and Speed.
Even when I try an open model on a 4090, the PC gets warm/hot very fast, I struggle with context length.
> The people saying that local models can easily replace the real Frontier Models from Anthropic or OpenAI are just not using it for the same things you and I do.
> They seem to be happy with their small usages.
Oh yes, we're terribly inferior people. Just living on scraps from the masters' tables.
I do plenty of experiments of things i always wanted to do and they just don't work with local models.
Finetuning of some ML models for a specific usecase, some 4k demo idea i had, small browser games to show others game mechanic ideas i had for ages, analysis tools etc.
Even just playing around with some Ghidra eats context like nothing.
I write 5-20 paragraphs of things I want and it goes away and just builds it.
Something like this (just an example):
"Build an application with a webserver in rust. It has 4 stages, first one is an input module... second one is refinement ... third one is an image detection with current fitting ml model ... last one is finetuning."
Then i would describe every basic page with a few more paragraphs.
This would work for half an hour or an hour, consume 100-400k of context tokens and then I would test it, write down 10-50 small sentences (every sentence either fixing a bug, or making it better) which tehn takes again like 20 minutes and i'm nearly done with a complex enough tool providing real value and actually working.
A big kimi, which is hard to run, might be getting close to this but we do not talk about running it on 1-2 5090.
The other thing is, thats more like a practical experience, even if you use them through Open Router (for testing and co), the initial results of something like Opus 5.5 are ALWAYS better. More details, better quality, better understanding of usability of the tool.
12GB GPU is useless. The good experience is with 128-256GB unified memory architectures like strix halo etc. You don't need to buy any hardware, you can experience the latest open weight MOE (flash) models from DeepSeek, qwen, or GLM on openrouter using a local agent like kilo.
That is a shift of comparisons though, right? This thread was about running locally, explicitly as an alternative to cloud services. I would guess few people have graphics cards with > 12GB ram.
A few more will have Macs with 48GB RAM though. (Indeed if you push it a little, 36GB unified is possibly enough for Muse Glimmer; I have not tested this theory)
There is, for sure, a limiting factor in the x86 architecture, but I think the RTX Spark will change that a bit, and maybe AMD's subsequent Ryzen AI/Strix Halo chips.
AMD Ryzen AI/Strix Halo is for running locally on your hardware. Openrouter is for evaluating the models you'll be running locally, before buying the hardware.
In any case, 128-256GB unified memory systems running MOE models is the "cheap" option, RTX6000 is the step above it.
12GB is not a lot, you are stuck on low quantisations of older models with that. What exactly did you try?
The newer/larger models are good though, not as good as Claude, but not every task requires a model of that level. Maybe yours do, but for simple coding tasks, summarising text, etc a small local model runs just fine, and is a lot cheaper than Claude
What graphics cards have more than 12GB, and how much does it cost to buy one? What about for others saying you ideally want 32GB? (That split will determine if a 5080 will do it)
12GB GPU is insufficient for most purposes yes. Even 16GB is a notable improvement, but yes 32GB cards such as the 5090 or R9700 are a world of difference.
Given we're discussing a cost comparison (i.e. cutting a Claude subscription in lieu of running locally), how many months of claude $100 or $20/month plans would it cost to buy a 5090? I am showing it as 88 and 440 respectively. I.e. a 7 and 36 year payoff. Not even close to being worth it.
The Claude plans are probably still heavily subsidized. Expect prices to increase, especially after the IPO. Self-hosting models for personal use is probably overkill, but I can see companies doing it pretty soon since they can probably run 24/7 workloads and fully utilize the hardware.
Great point. And, 3 years ago, a 4090 cost $1600, so maybe that will happen again some day. (Same/similar card, or a future card. I remember when GPUs used to get better every few years instead of staying roughly the same but costing several times the original amount!)
"SemiAnalysis, a research and analysis company specializing in the semiconductor and AI industries, found that a $200/month subscription plan is far more generous than previously thought. According to its calculations, while it's widely believed that a $200/month plan maxes out at roughly $2,000/month in tokens (assuming API pricing), the actual numbers are closer to $14,000 for ChatGPT and $8,000 for Claude. "
Good enough? Quite probably, unless you're in the "coding is solved, specify loops and walk away" frame of mind.
32GB (or say a 48GB-unified RAM unified machine) gets you Qwen 3.8 27B at a good adaptive Q4, maybe Q5. Muse Glimmer at Q4 and Q5, etc.
These models can code well enough in an agentic way, I find, and the chat-to-assistant scenario is just fine in Unsloth Desktop or LM Studio.
A slightly lighter harness helps; a lighter system prompt is entirely possible, especially for Muse Glimmer, which is post-trained so effectively for agentic helper tasks that it barely seems to need a system prompt at all except for a "plan" mode.
Well it's certainly the service-dependent (addicted!) model that the big two want.
But will you want to do that when your tokens are metered at non-subsidised prices, and yet there is absolutely no clarity (ever!) about token efficiency, how the model decides to spend your money, how it might ever decide to stop wasting your money and get to the point, etc.?
The specify-loops-and-walk-away model absolutely points to local/on-premises/private cloud AI.
Because otherwise you're letting Anthropic or OpenAI decide how to spend your money. And they are going to be biased towards spending more of it.
I don't think Qwen 3.8 is quite there yet with the loops stuff. Definitely seems to be better at deeper and deeper one-shot puzzles.
I've try every new model when it comes out from both OpenAI and Anthropic. I cannot justify leaving Codex and Astra 6. The token burn on Anthropic is always astronomical for me. Codex just works, and I see no reason to move away from it.
I switched from Codex to Claude Code w/ Opus 5.5, and it's been fine. I haven't hit limits in my $100/mo plan, doing full time dev work all day. But I agree. Codex sub + Astra is also great. Both are excellent options, and I don't see a reason to go with a cheaper / less capable model, frankly.
> The results are ALWAYS terrible compared to CLaude Code.
How much effort did you spend to make the local setup work well?
Local models are not plug and play, you need to adjust settings to make them behave intelligently.
On my side one model looking super dumb right after install ended up being 'agentic' after using the right chat template, fixing how tool calls are setup, fine-tuning plenty of params values, and adjusting the whole setup to my own hardware. Claude.ai (free) was great at helping me sort that all out.
You can maybe replicate Opus 4.5 to 4.7 if you have some moderately expensive hardware. And that is definitely good enough for many people.
The smallest model that can even pretend to be a decent coding model is Qwen3.8 27B, which you realistically need 32GB of VRAM to run without breaking or limiting it in some really noticeable way. This gets you something like a slightly less capable Opus 4.5 with a much better work ethic.
In general, more memory = larger models = more intelligence.
However even if you had a private data center you wouldn't be able to beat Opus 5.5, Astra or Gemini 4. The open weights models are a tier below these three.
I'm not convinced that 2026 was peak of the AI hype cycle.
Arguments are being made that, with the midterms over and 2028 just around the corner, a less sympathetic administration would create significant headwinds, adjusting the valuation of Anthropic and OpenAI down.
What we don't know are what Anthropic and OpenAI have planned and in the works.
A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments. It's likely a significant number of them will start their own business with AI.
AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That said, IPOs are usually liquidity events for LPs and for something really disruptive and unpredictable as AI, I won't be engaging in their IPOs. Only time will tell if this was a good decision, or bad.
Tulips, Railroads, and the Internet also didn't go away when the financial bubble around them burst. That doesn't mean there wasn't a massive market correction in response to the over-investment.
There's a beautiful tulip garden just down the street from me, and an outdoor weekly market where tulip bulbs are sold. Tulips are very much still around, I assure you.
Yes, that's my entire point. There was also a railroad hype cycle and an internet hype cycle that took in massive amounts of overinvestment, and all three things remained as regular things in the economy with bigger or smaller roles after the hype cycles ended.
I don't mean went extinct. I mean the idea that tulips could be an asset class is now laughable, which is not true of the other two. The impact of the tulip industry on GDP is utterly imperceptible.
> Tulips, Railroads, and the Internet also didn't go away when the financial bubble around them burst. That doesn't mean there wasn't a massive market correction in response to the over-investment.
Your Railroads and the Internet parallels are so very apt. We certainly are very productive even in 2026 off those but almost no one remembers how many large behemoths were wiped out in the U.S. because of them.
Yes, it does seem to me that the AI of 2026 will be like the Railroads and the Internet - fundamentally transformative technology that changes how things are done into the future but there will be blood in the short term, which is why I would not be participating in the IPOs directly
That's a pretty damning statement - this implies companies don't see a return on AI at best, and it's actively destroying their businesses at worst, so there's not only job loss, but businesses themselves are not becoming more efficient.
> In 2026 everything fron sewage to chickens to oil to education is now tied to AI. The entire economy is now, for good or bad tied to AI.
Tied how? How strong is the corellation between these? I'd say very weak, other than everything having to compete with AI for resources.
The same is true of any technology tied to powerful personal computers or the internet - people built and designed beatiful houses, great cars, airliners, wrote books and ran restaurants without computers or the internet.
It has even become a meme how putting smart features and lcd screens into things like washing machines have made them strictly worse.
Somewhat ironically it would appear most CEOs believe the only jobs AI is replacing are developers since they built an app last weekend. Every CEO is saying why do we have so many devs and why are we paying them so much… so devs blew up the market for devs but beyond that there doesn’t seem to be much impact.
Even if it takes them 3-6 Month to open up a new field, its going to happen steadily now and were it started, the AI investments will be recuped by layoffs.
Do you really see lawyers being replaced? I don't see anything special in their work but since it's a regulated industry I doubt it will be so affected.
At best some layers will work faster, but automating their work or people not requiring them? Usually when you contact one there's a lot at stake. Same with accountants.
Software engineering and translators is a different story. Most of the times if something goes wrong nobody cares.
Some lawyers (the worst ones?) might just do the easy or normal things like writing a 'lawyer' letter to someone not paying. This is no risk to automate away.
The avg Lawyers might do more complex but again a lot of 'normal' things. Day to day things the next SaaS AI company wants to 'fix' and they might also accept basic liability.
Then you have the 'tv lawyers' the ones who do non normal things, they have A LOT of apprentices and young lawyers going through papers, research etc.
They will for sure have the expert as a human, but that person will need a lot less support.
You shouldn't need an accountant today already. If you have an digital order and a digital bill and a digital signed delivery note, it can be booked automatically. AI will find risks, summarize them etc.
AI is already helping here closing the gap with better OCR etc.
I really think, that when big companies introduce the agentic layer to their products, there will be less people needed and if you need people, they get less paid because they will need to understand less.
Lawyer, accountants, doctors, architects, and engineers won't be replaced.
We're not paid just for what we do, we get paid because we accept liability for our mistakes. Professional liability insurance is a large part of why licensed professionals charge so much money for their services.
Only popular with companies that want to make a quick buck and tech people (hey! that's me!).
You really don't know what we have here, do you? Let me illustrate what you can do with Qwen3.8@q4 locally with about 2TB of storage and a small cloud server to host a website.
Locally you scrape and process every elected official and start tracking what they're doing, what they're promising, and what they're not doing. You build embeddings and make them all geotagged in a database and just start with the largest cities.
The website provides public facing search. User drops in a zipcode, city, state, specific rep's name and gets what's relevant to their results.
All of this is to say, we literally have the technology to hold accountable our representatives and then show everyone objectively who's doing good and bad for their constituents.
It wont take much to solve the problem of bad representation after these last two years. The hard part is literally making sure people going to a polling station can immediately know who the heck the 10 - 30 different people are they're voting for, and track them efficiently within their own specific parameters.
Regulation is coming for more than just the inference providers.
The major counterpoint here is commoditization; you may have the market growing and models becoming cheaper at the same time. And we don't even have to look to Chinese models for that, as OpenAI has been adjusting its pricing already. For profitability, Anthropic still seems to be pretty dependent on its current price levels.
That said, I'm also somewhat skeptical that there is even much room for growth left unless somebody finds a new, important, and token-hungry use case.
In theory, yes. But the reality is that deep learning models just aren't good at creative output (to put it mildly). And neither Anthropic nor OpenAI have any models that generate video, so this wouldn't help them; in fact, OpenAI killed off Sora.
I believe that the value that OpenAI and Anthropic hold, in light of stiff competition from China, is not their products but their internal models. China’s work is all derivative of American model API access. The exponential RSI loop means that as long as Anthropic and OpenAI keep steady footing and only release older models they will will always be the best on the market and all others will be in their tailwinds. These two things combined are what make me believe OpenAI and Anthropic will hold value and grow as companies.
Best on the market, for something not being sold on the market? How is that helpful in terms of actual revenue?
If Anthropic manages to start producing new pharmaceuticals with their internal models or something that thesis could make sense but not if 90% of their revenue comes from enterprise API sales which would necessarily have to be accessible to the public. Which are then inevitably fast followed by Chinese models 3 months later.
Even if we were to accept your entire argument [1], none of it contradicts the commoditization argument. Note also that competitition does not come just from China, but also from Google, Meta, and SpaceXAI. Where they currently are is not relevant, the entire LLM landscape will likely look nothing like today by the end of 2027.
[1] I don't, I think China is primarily limited by compute and will be at least for the next couple of years, probably longer.
It certainly isn't the peak of the AI hype cycle. Eventually something will surpass LLMs and the hype will start all over again. Will it really be the final invention this time? Place your bets!
Anthropic's original $2T valuation implies they are going to almost 100X their revenue.
I am highly confident that Anthropic will 100X their token sales volume. I am also confident that token prices will continue dropping through the floor due to competition. So to 100X their revenue they will have to 1000X or 10000X their token sales. That seems a stretch.
Or incentivize using the minimum viable model for the job instead of throwing multi trillion parameter Fable sized models at summarizing documents or classification tasks.
I now do like 90% of my routine software dev/sysadmin stuff with Luna on Medium by default, some proportion of which could itself be offloaded to a small Qwen model.
Economic theory would state that if token prices go up people will stop offloading work onto LLMs and start doing it themselves again.
Indeed, 2026 was basically that: the LLM providers increased token costs and companies cut LLM spend to the bone. That is why Anthropic's IPO is struggling and MS and Google have failed to meet AI-related revenue targets for 3 straight years.
As AI grows in capability it will be trusted to do more of the work it already does and also break into new fields. Also, there are many who have never interacted with a chatbot. There is an entire world of potential customers out there. It can be quite a villainous business model but look at what Facebook did in Africa with their sponsored ISP and also what they’re doing with Meta Muse. Demand for AI usage will skyrocket.
But will it increase by 10000X, as I back of the envelope calculated it would require to justify Anthropic's $2T valuation in the face of quickly dropping token prices?
Chatbots aren't a money maker. Each interaction is likely to be 1-2K tokens at most. AWS Bedrock prices 11K token input and 4K output for a total of $0.184 using on-demand usage [1]. So for normal usage (occasionally asking questions) you are looking at less than $1 per month for a person.
Using the models for data processing (a lot of input/output tokens) and for role-playing will use more tokens and thus cost more. However, those use cases are not common when comparing the percentage of the user base.
The money maker is agentic coding where people get the models to perform long-running tasks. This is why Open AI and Anthropic have been pushing these hard. However, there is pushback on this, especially among businesses looking for value for money/cut costs [2] like Apple [3], [4].
> A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments. It's likely a significant number of them will start their own business with AI.
What's you're point there? People laid off because of AI will immediately shift into AI-centric small business, and start hyping AI?
That doesn't make sense to me. People being laid off are likely to be bitter about the cause of their layoff, and even if they all start their own businesses (which is unlikely), most of those will fail, leading to more bitterness.
Many people who are being laid off will not find another spot in their industry and will need to pivot. Most of that subset of people will go to existing companies and take traditional roles. The small few remaining might take it as an opportunity to start a new business, and when they do so, they are likely to take risks on technology and business operations which will include AI. Is that so hard to believe?
I don't buy for a second that people laid off over AI will be so darn mad at AI that they won't use it, but I don't know where the hell the markets for all of these hundreds of thousands or millions of new small businesses are.
If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop. They can use AI to help them run that. Getting paid $175K per to write ruby CRUD was a moment that is over, hope you didn't spend all your salary on toys and rent.
They will try to start businesses out of necessity as stable employment is not an option, and they will use AI since it's almost mandatory now for any technical productivity.
> What we don't know are what Anthropic and OpenAI have planned and in the works.
Wouldn't they tell us before IPO? Unless it's not that promising...
> AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That does not mean the current players profit from this and the infrastructure investments were necessary at that scale. There is competition, there seem to be enormous efficiency gains and it's questionable that demand for even bigger models is large enough.
And there are worries about the Democratic midterm election sweep causing tighter scrutiny of new data centers and forcing Anthropic to pay wholesale for chips, or worse.
I don’t agree with you. Their product is a fully scalable almost human tier intelligence. I think it’s worth way more than $1 trillion but they have to get their capex in order first so they can provide dirt cheap tokens.
Their lifespans are 2 to 4 years with always on, heavy usage. Even then the problem isn’t the lifespan. It’s the lack of supply right now. I assume with more built out production capacity GPUs get cheaper too. The whole vertical stack needs to get cheaper and it will in time.
Spot prices for 5 year old A100s are still going up recently, because inference is so valuable. This meme that GPUs are a quickly depreciating asset is outdated.
If the company cant convert the product into earnings it really doesnt matter what the product is. It can be a magic machine that turns water into wine. They still have to find people willing to pay for it.
If the world is not ready for fully scalable almost human tier intelligence then, being right at the wrong time is the same as being wrong.
I think it's crazy that Walmart is worth 1T. It's grocery chain operating in limited number of contries. Shouldn't be worth much more than for example, Carrefour.
When you mention Tesla, don't forget Space-X. The value proposition of Teslafanboys was always that Tesla is not a car company and now Musk splits it up between Tesla and Space-X
can someone explain to me the point of the 'initial valuation', considering ultimately the price will settle lated thorugh trading to perceived value anyway ?
Whenever you buy a business, you have to have some idea of the valuation.
The IPOs need to have buyers. The buyers will have some idea of the business valuation, as will the sellers. This is decided before the actual stock starts trading.
Specifically, the "underwriters" will go around finding people willing to buy what is being offered. This gives some idea of where it will begin to trade.
Investors ‘subscribe’ to an IPO. A company offers a certain amount of shares for sale at a certain price. Then the underwriting banks go out and sell the shares.
If your broker allocates you shares, you pay the IPO price as does everyone else who was allocated shares. The shares are all allocated before the IPO happens and once it opens for trading, price discovery happens.
How the price is set is another matter, one I’m not familiar with as an amateur.
It's the starting listed price. For an anchor, it determines a lot about how the first few days go. It's also a great headline. It's also also what employees picture when they evaluate their stock options, I bet.
Some people buy on "day 0", before it hits the stock exchange. This determines the price they pay on day 0. For example, if they buy 10% on day 0, they pay $130 billion.
The stock exchange is a "secondary market", which means you're buying stock from investors who own it, not from the company itself.
> the numbers we're discussing here are the amount they'll raise from the IPO
No, the "valuation" numbers are the market cap at the moment of IPO. The amount raised is that multiplied by the percentage of shares issued by Anthropic (as opposed to by existing shareholders like investors and employees -- that combined number is the float) at the IPO.
Sorry, yes, that. The bigger point still stands, though — given a fixed fraction of shares they're offering, the valuation gives you the amount raised, and that makes the number interesting.
> can someone explain to me the point of the 'initial valuation'
Most of the institutional and retail placements happen on the initial valuation. These are the people who are holding the shares when it gets listed and starts trading, and it is the primary source of raising capital through an IPO.
To go further though the Initial Valuation (usually a range) is set weeks before the IPO to make it easier to pitch to investors. And the final valuation is locked in the night before trading based on actual demand.
The opening price on the stock exchange, though, will be whatever the demand and supply is in pre-market trading/open.
Typically in an IPO, the offering is about 20% of valuation of the company at the first offering. But with a $trillion or more, they will offer say 10% or less.. say $100B in one go is probably what the market can take.. Some banker has to back it up initially and defend that price... the next dump will be about same amount say $100B again but not the same number of shares. The underwriters are not obliged to back $1T and the IPOed company is not obliged to release more shares.
Ultimately the percentage of shares that public will trade is material. say 20% for $100B is quite different from 10% for $100B.
The point is that is the price the private investors who will be liquidating, including the company itself, are accepting for their shares.
If they are too far below the perceived value, they are leaving significant money on the table.
If they are too high, the institutions who participate in IPOs will let them know. If the price craters off IPO it is not good for perception of the stock, and so ongoing health of the company even if that maximizes private investor value at IPO.
So they build a book of commitment around the target value, trying to leave enough incentive (15-20%, often) of an initial bump to get big money in.
None of the value changes in later trading mean squat to how much actual money the company gets to put in their bank account.
They get the money from the initial purchase of the shares, and never get another dime directly from the market again, unless they have another offering (which is quite possible). The stock price does drive leverage for later M&A and debt/credit activities, and matters to stock-based compensation, so it does have meaning, but the IPO provides a one-time cash infusion at the initial price.
If the company is valued by the underwriter and initial buyers at $1 trillion and the company offers 10% for sale through their underwriters (who have put out feelers to try to attract buyers at highest price possible), they raise $100 billion from the offering, minus fees, to use as needed to further the business. The initial buyers get the shares at that valuation, and I believe if the underwriter positioned it wrongly they might be on the hook to sell some shares at a lower price without prejudice to the company.
If one of the initial buyers sells in the first minute the company is public the first publicly traded shares at 1.5x their purchase price, the company suddenly has a market cap of $1.5 trillion instead of $1 trillion, and that buyer has made a tidy 50% profit on the shares they just sold. If all initial buyers dump a bunch of their shares the price likely will go down, perhaps pushing the market cap to, say, $750 billion. Regardless of this action on the first day and after, the company offering their shares still has the $100 billion.
Note that initial buyers are discouraged from selling large portions of their shares.
Anthropic intends to rule humanity with its ASI. Perhaps this is the optimal outcome, as there are many problems AI could fix that it wouldn't be able to as efficiently without eliminating the human bottlenecks which caused those issues, but I fear the more financially powerful it gets, the fewer checks there will be on any misalignment.
As opposed to the CCP, which is essentially what the Chinese model companies map to, who are only interested in freedom and liberty for humanity right?
can someone explain to me the point of the 'initial valuation', considering ultimately the price will settle lated thorugh trading to perceived value anyway ?
The initial buyers of the shares sold by Anthropic are institutional investors (mostly). That matching process is done by an investment bank they hire.
The trading price you see on the screen is - shortly afterwards the shares become publicly tradable on the exchange (or on all US equities exchanges, for US stocks). So in effect, the price shooting up on the public markets is actually bad for the company in that they might have left money on the table.
Sort of. IPOs are always undervalued because the entire block of shares gets purchased by the investment bank at the banks decided IPO price but then bears the risk of selling them at or above that price.
It's the price they're going to be offering to the general public to buy ahead of time, through their brokers. They get that price locked in, then once it starts trading it moves as it may. Usually the IPO buyers are not allowed to sell for some time
Please explain to me why my "naive and maybe ignorant" understanding of this is wrong... The bigger the IPO price, the more they earn and the more probability is for you to loose money invested in it.
So say Anthropic, or OpenAi, or smartGummyBearsAI go IPO and the price is insanely high... and then spirals down to be 10% of the real price... they earn a lot of money and the public loose a lot of money.
IF smartGummyBearsAI goes 100% realistic price and you buy near the correct price, they earn their fair money and people get an investment.
IF smartGummyBearsAI says that they have technology to use the same machine that makes gummy bears print gpus and they are priced 10,000x their actual price the society just transfers a lot of wealth to them and they scam the society.
Why shouldn't we regulate and aim for the correct price?
The price isn't "set" from what I understand. The price is determined based on what institutional investors are willing to sign up to pay for it. So it's largely driven by the market (not on the actual stock market, just the market of interested parties pre-IPO). Those institutional investors do not want the scenario you describe any more than you or I do.
That's why it's dropping here. When the actual stock market tanked yesterday institutional investors were like - hey wait a minute this is starting to look too expensive.
Also if your 401(k) is in index funds you are safe. A company is not added to indices right away. If you are in actively managed funds or something like that you might have more exposure risk.
How do you determine the "correct" price? Something is worth what people will pay for it, no more and no less.
Markets have the advantage that many peoples' opinions are aggregated into the market price. This is far, far more accurate than having one person's opinion (an elected bureaucrat) set the price, particularly when they have no skin in the game.
If the IPO price is really 10x the public market price, that implies that the set of institutional investors collectively estimated the price 10x higher than the full set of retail investors. That's pretty unlikely: institutional investors usually do pretty extensive due diligence on IPOs, and you're already averaging out the statistical fluctuations by selling to a wide set of them. But if that does happen, it just means that institutions got fucked over to the benefit of retail (who gets a 90% discount on the same shares), which seems like something we should be celebrating.
I mainly doubt the moat, not necessarily the usefulness. Why will customers stay with anthropic? Many cheaper models seem to catch up within 6-12 months, and there’s also other flagship providers that keep pace and compete on cost (oai, Google). Why should I believe that anthropic’s revenue is going to stay consistent, and not evaporate to other competitors?
Validation is all about assets and future earning potential. Anthropic’s core assets are a model which is shown every day now to be only marginally better than something that’s free. So that asset appears to have very little value. Their future earning potential depends on folks not figuring out they can get a good-enough result for free, only paying for compute. And Anthropic doesn’t own compute, they rent it.
So the whole valuation is based on the idea that folks will happily pay this company a huge markup for inference over the actual cost of compute for a model whose valuation is rapidly tending to zero. Hence why potential investors are starting to freak out.
> Anthropic sells intelligence, and the most intelligent intelligence.
Most intelligent is a stretch, we don't know how the Chinese architectures would fare if they had the same compute availability as Anthropic and other US labs.
If we extrapolate and consider that they are behind due to artificial export restrictions, once they figure out how to build their own GPUs in a cost-effective/scaled-up way then the American labs' claim of having the best intelligence is at an enormous risk.
They already do a great job being resource constrained, imagine once this constraint is gone!
If the justification for the valuation is "they sell intelligence", then their valuation should be $0. Because their product isn't the least bit intelligent, and routinely gets beaten by humans.
You can be as dismissive as you want but this doesn't reflect the reality.
I can easily spend a LOT less time on describing the same amount to claude than i would in a JIRA ticket and doing code review on the PR of Claude than talking to a person about it. Instead of 1-4 weeks Claude is done in an hour.
Its also already A LOT faster and better of analysing infrastructure issues.
I think people in this thread who claim local models are anywhere close to Claude are completely detached from reality. 200$ Ultra sub is the best value ever offered in history of subscriptions. I got 3 running and would pay at least 3x more for them before looking at alternatives. If your local models or some DeepSeek on cheap provider or whatever you install instead of Claude makes even 2-3 more mistakes per week you are already losing money assuming your time is worth average software dev salary.
I don't think it's remotely close either - Claude is that good.
The problem is that it's not how Anthropic plans to make money to justify 1.5T-2T valuation. That would require companies paying API prices on long term big contracts and that makes it a completely different value proposition.
The major cloud providers have Chinese open weight models in their catalogs. If the model is running through your cloud provider on US soil, you're probably fine for most of corporate America.
> To build Microsoft-Decision-1, we post trained Qwen3.5-9B for fast, single-pass decision scoring and will soon rebase it on other models, including Microsoft AI (MAI) and OpenAI.
Cursor's models most famously are built on top of Kimi:
> Cursor’s vice president of developer education Lee Robinson soon acknowledged, “Yep, Composer 2 started from an open-source base!” But he said, “Only ~1/4 of the compute spent on the final model came from the base, the rest is from our training.” As a result, he said Composer 2’s performance on various benchmarks is “very different” from Kimi’s.
So yeah, Chinese open weight models are already in F500.
If any non-US company can use US models, they can use Chinese. Chinese are way less hostile to all of us (regardless on which continent you are) than US is.
This whole fearmongering campaign may work well within US but once you cross the border it becomes funny parody in worst ways. When all this comes from current US its beyond ridiculous, I don't expect post-midterms behavior to be any less petty and dumb.
A good rule of thumb is one a tech company attaints a >$1 trillion valuation, it tends to hold in that range; e.g. see Amazon, Appl, Google, Meta, Space-X, Nvidia, etc.
So many people 6-12 months ago predicted Space-X stock would crash and burn, yet the stock is still holding strong around $130 and a valuation over a trillion dollars.
> So many people 6-12 months ago predicted Space-X stock would crash and burn, yet the stock is still holding strong around $130 and a valuation over a trillion dollars.
That's simply because they're 1) installed in the government gravy-train, and 2) part of the AI bubble. Check again in a month or two, after AI valuations become more clear and Democrats start to publicly sketch out plans for what they're going to be doing to 2028 and beyond. They've already openly announced revenge for the half-billion in political spending, and they've haven't been talking at all about going after speech on twitter (which was a huge loser), but instead going after real stuff. Going after Space-X is an easy call.
Same thing I was going to type! I think they’re just testing these to see the reaction. Like a reverse auction while OpenAI watches in terror.
I want to see this go out at $800b. I do not believe even $1T will be supported by public markets unless they shrink the float — but that also comes with its own risks.
Most finance people I’m talking to think $200-400B is a fair price. That would be an amazing story in any normal situation but a total disaster here. However sane fundamental financials just don’t support a higher number.
Challenge is while you can play games with the float size to jack up the price as existing shareholders start selling you have to convince more and more people of a sky high price when all the fundamentals and the market pulling things in the opposite direction. Employees have been royally $&@ked in prior IPOs as that story plays out.
It keeps dropping. Was 2T, then 1.5T last week, now looking like they might be lucky to get 1T.
I wouldn’t be surprised if Anthropic delays the IPO in hopes this will all magically sort itself out but as every day passes it looks like they should have gone public this summer when things were still on the up.
Realistically the company is worth in the hundreds of billions but if it goes below 1.1T or so existing investors will need to start booking losses and that will trigger a shitstorm of other issues downstream.
This is all becoming a textbook case of folks know these assets aren’t worth what folks want them to be, but they can keep the game going so long as nobody actually marks the asset at its actual realistic valuation.
So that’s the game underway now. How low can they go without blowing up the whole AI ecosystem. If the valuations here drop next the AI bonds and private loans start to fail en-mass and the whole thing that built up this house of cards starts falling apart in reverse. Get ready for a bumpy ride folks.
I wouldn’t be surprised if they pull the plug on the IPO and go hide under a privately funded rock.
The second one of these AI companies gets a fair valuation and folks start booking losses the whole AI house of cards will come
crumbling down. The crazy thing is if they drop below around 1T some existing investors will be booking losses.
A realistic valuation is likely in the hundreds of billions, which would be a total disaster and thus they’re going to do everything possible to avoid that.
literally what is the upside? They're supposedly worth as much as meta despite having nowhere near the customer base or years of work? How much more valuation do they have to trick people into giving them before investors get a return?
Because building a stable revenue base takes time. We've had many flash-in-a-pan type product but things which still make money 10 years later are rare.
Possibly speaks to its moat being weak, if a dedicated team from scratch can duplicate their efforts and get to where they are today in 2 years (but not where they will be).
I think this is down to how investors think about how to value a piece of a company. I think about it almost the same way as I think about sports gambling.
There's a value proposition in which direction the stock is going to go and how long it's going to take to get there. It only makes sense to be invested in a stock if you think it's going to be worth more later than it is now and the amount of time your money is tied up in this stock is commensurate with the profit you may get as a result.
Investors work on limited information about companies, and having a track record can influence a buying decision one way or another. It may matter to some investors and not to others. For many, it's weighed against the price of the stock. Would you bet $100 on a company with a short track record? What about $10,000, $100,000, or more? Generally there's a point where people drop out of buying more of a stock and say that they can't justify the risk given the information they have. That's the expected value that Anthropic will receive from that investor.
Anthropic needs to worry about what that number is for a huge amount of individual investors and how to maximize the amount of money put into the cost of their stocks, and there's very little chance that they can attract the biggest pool of investor money without some of those people considering their longevity vs a more established company like Meta. It may not matter to all investors, but the fact that it will matter to some makes it a factor in the total valuation.
Since they actually have paying customers, it's a completely different sort of thing than for Meta. The question is instead whether they can keep at the frontier, whether the compute they've signed up for is good enough, etc.
Anthropic hit 9 billion ARR in Dec 2025 and 80 billion in Oct 2026.
Meta hit 9 billion in 2014 and 80 billion in 2020. It is now at 250 billion.
At this growth rate Anthropic will be bigger than Meta already next year.
Caveats: It's hard to believe that this growth rate would continue. And Meta's numbers above are trailing 12 months, while Anthropic's are annualized run rate.
My point is that Anthropic is not special. Neither is OpenAI. They are just really good at marketing.
Job displacement is real, but based on real world usage it would owe just as much to Grok, Gemini, DeepSeek, Kimi, and whatever else that is close to frontier, but cheaper.
If in a couple years they're losing their jobs because of Anthropic/OpenAI/10+ other enterprise token resellers using open models then their value proposition is much weaker even if people are still losing jobs.
Yes could be but we don't know yet how big the overall market will be.
If you have in every single industry AI tokens, it will replace wages. In USA alone thats 15 Trillion. If its just 10% the USA market alone are 1.5T without the value added.
The OP is snarky but he makes a valid point: if Claude is so intelligent, why can't it just "do something" to get whatever valuation its owners want? Because that's what we've been told, this is AGI, no it's ASI, it will kill us all.
potential buyers are discussing about a $1.2 trillion to $1.3 trillion valuation range for the company, according to people familiar with the matter. Some are weighing a value as low as $1.1 trillion
My hunch is that it’s better to be late in this scenario because now your moves are under public scrutiny or at least neck-deep. People can pull out, it could slow them down and at the end look worse.
As a privately held company, it functions under different dynamics that in the environment we’re seeing feels better to stay private for longer.
Well they’re struggling to raise cash via new rounds, having slashed both the raise amount and valuation themselves. So in that situation they may go bust before having a chance to go public.
OAI is in the middle of another funding round at $1.25T. If Anthropic with allegedly better financials can't get above $1.1T then why would anyone buy OAI at $1.25T
Or then any numbers they would have publicly presented with even previous scrutiny would have been so bad no one would have bought them at price they want.
People seem unnecessarily deluded here in a supposed hacker forum. If they are really created actual digital intelligence smarter than every human and figure it out how to control it, then 1T dollars means this is the most undervalued business in the entire world. Maybe by 100x or 1000x. You have billions of super genius slaves in data centers that do whatever you want, then if a single human developer can earn $100k/year pessimisticly let alone a super genius one. Multiply that by a billion and do the math at 80% gross margins
…and what if they haven’t? Given that we haven’t seen evidence of one so far.
You’re also missing the competition aspect. What if other companies have their own super genius slaves to sell at a cheaper price? What if open models allow anyone to have their own moderately genius slave at far less expense?
I see nothing delusional in speculating that that’s a very big if.
And if they haven’t created an artificial superintelligence then they start to look more like just another tech startup with no moat[1] and a terrible balance sheet.
[1] I don’t know about you, but I switch model providers multiple times per day.
Yours is a reasonable estimate at the _value_ of their product. They will only be able to capture that value if their competition cannot catch up AND they can reduce spend on R&D. I personally do not believe they’ll defeat distillation.
What if they have billions of super genius slaves, but Chinese companies have just slightly less super genius (and catching up) slaves for 1/4th of the price, while all prices are already dropping?
As high as $1.1T. Where is the supposed growth going to come from?
Claude has been pushed by force and coercive marketing into most software organizations and many open source projects. In 2026 the companies buying the narrative were exuberant and told employees to tokenmaxx.
This will stop in 2027 because companies will find out that nothing has improved. Nothing at all. They'll evaluate the expenditures.
So the market is saturated and usage is going down. Anthropic should be valued at $200 billion at best.
When you sell people 6 quarters for $1 they tend to buy a lot. Useage is a poor metric here given the capital intensive nature of this business. What matters is can they do this in a way that makes money to the degree that justifies the valuation, and there they have not yet demonstrated this is possible.
The internal metric that they are using is they get $2 for every $1 in inference. So, if you discount model development costs, they are selling the tokens for more than it costs them to generate a token. And this is before they start integrating their advertisment networks into the model. They see something as high as $8-11 per $1 of inference once purchasing and advertisments are incorporated.
The “but ads” logic fails basic sense checks. There isn’t enough ad money to go around to make up the gaps. With the big ad players like Google, Meta, and Amazon all shutting Anthropic out of their ecosystem there’s very little evidence that Anthropic can steal away enough of that as revenue to save itself.
In the “but they’ll slow down training and so will become more profitable” thinking is in big trouble given the pace of open models. They’ll then be even less competitive with free options and so even less reason to buy from Anthropic.
Also, Anthropic is known to lie through their teeth to make themselves look better, so any claims that they make a profit on inference should be treated as false until proven otherwise.
Any “non-GAAP” measure of “profit” should generally be viewed with a giant red flag.
In nearly all cases these metrics just say “if you ignore all the reasons why we’re horribly unprofitable, we’re profitable.” WeWork’s “community adjusted EBITDA” was a textbook example of this.
I introduced it at our admittedly small company not because of coercive marketing or force of any kind. I did it because rolling new stuff out takes days instead of months. Not only that but we can make products above our previous level.
Unsafe, comprehension debt, vibecoding, slop? Have you seen actual lowly developers’ code? AI is a vast, vast improvement on all relevant axes if you ask me.
If you think this is going to slow down I have no idea what to tell you.
Good drop. Should be less than a trillion amid current midterm fallout. The chances of more AI regulations are coming.
Colossus 1 & Colossus 2 are located on Tennessee and Mississippi, and Anthropic rents both of them earlier this year.
Now that Democrats are surely going after Trump and Elon Musk and the sidekicks, and both Tennessee and Mississippi turned blue as well, this directly impacts the way Anthropic to source cheap compute and data in the future due to uncertainty around the continuation of SpaceX data centers. And other data center buildouts are likely to be paused for things like power grid connection and environmental impacts and land purpose evaluation, the compute capacity constraints are likely going south, too.
Worse, the proposals around superintelligence is exactly what Anthropic is looking for: slow the fuck down. Now they got the killshot they long desired for, it obviously makes a dent in the capability and capacity of their AI, hence a drop in valuation. I think $800-900B is somewhere I would say okay, and $600-700B is really where it is fair, considering the long term outlook for Anthropic is looming with a greater AI massacre caused by political factors.
This is a "not like that" situation. They wanted "AI safety" legislation guaranteeing them rent money. What they're likely getting is legislation to slow down data center buildout and make them more environmentally sane (more expensive upfront cost).
And open models are coming whether they like it or not.
Oh, and if the Dems take OpenAI and Anthropic's AI safety rhetoric at face value, nationalization and other forms of substantial government interference is on the table. A predictable outcome of irresponsible fear mongering!
I don't see how Anthro is worth 15x( Gemini estimates based off WSJ source )as much as Deepseek( 76 billion) .
If anything is going to pull the rug out, it's going to be a model from Deepseek or another Chinese lab. I'll admit I'm very uncomfortable with so much of our economy riding on AI hype.
To be fair to China, if AI is really this revolutionary technology, they need to be able to have home grown peers.
Every nation ( or bloc of nations) needs alternatives to American tech.
Sure, this is a small anecdotal datapoint, but since I've gotten Qwen3.8-Flash-Next running well on my Strix Halo box my Claude Pro subscription use has dropped off a cliff. I'm only at 18% of quota usage this week - this being Friday I would've been at 80% at least in the past. This is just an astonishingly good model that runs locally and quite well on my PC. I do not plan to renew my Claude Pro subscription when it comes up in a couple of months.
I used Ollama with Qwen 3.8 27b on my MBP for a flight to try some experimentation and found that it was shockingly good at one of the more complex PDF processing tasks.
Like seat-of-the-pants good as Opus 5.5 but a bit slower and required just a bit more prompting to get what I wanted (which is fine for an app pipeline).
What inference engine do you use ?
Gufo. It's the best open source inference engine that's optimized for Strix Halo right now. Halogen may unlock a bit more performance, but it's not open source and gufo seems to be making steady progress.
Cool. I found halogen but was turned off by the closed source nature (github repo that just turns out to run a download shell script, lame).
Do you mind sharing more details about your setup? I have been using opencode with lemonade for inference on my strix halo but haven't quite hit the sweet spot.
How does it compare to haiku?
I guess it depends on which version of haiku? I really think that right now this is basically Opus 4.8 that I can run at home. In some ways possibly even better.
Do you ever worry whether these open source models have been trained to embed backdoors or leak secrets in very specific circumstances at some point in the future? The kind of thing that's hard to know ahead of time and even detect after it's happened?
I feel like the industry hasn't solved that alignment problem yet... We have no way to verify the entire models landscape of intent.
Paid models have an incentive not to do that because if the market discovers it there is a huge economy penalty, but the free open source models don't have that disincentive.
I don't ship AI-written code without at least having an unrelated model of equal capability review it. That should cover a multitude of sins like intentionally-embedded backdoors and zero-days...
... as long as the reviewer model hasn't been deliberately lobotomized, that is.
(I don't disagree with the [dead] comment, either -- fortunately I tend to make small changes to code I'm very familiar with, so it's fairly easy to vet in person. The more code gets generated by the clanker, the more nervous I get, just because it's impossible to keep some complacency from creeping in.)
insane. I can't imagine doing this and I hate that I'm being forced by upstream communities to run code nobody has read
just read the damn code yourself!
>just read the damn code yourself!
It's basically impossible to do this. Claude produces so much excessive / verbose code and comments that reading it would take far longer than simply doing the implementation "by hand". It's kind of like, Claude can write the code in 1/2 a day. It can be reviewed by another LLM, unit tested by another for 1 day. Or I could write the code in probably 3-4 days. Reading it and validating / testing it myself though would easily take 6+ days.
Now if I were fully in control I wouldn't run code that wasn't truly reviewed at all, but in the situations where Claude is being used that's not my call. TBH a lot gets caught, and I have some automated scripts that find a lot of the more problematic stuff automatically, flag it, send it back and make note for the reviewer model as well.
I will also spot check areas where I think an issue could arise.
If you can't read the code, you aren't being responsible enough to ship the code.
I don’t think parent was talking about the shipped code, but about your one development environment while using the models.
OP could be read either way, but the dev environment threat is not rocket science to manage: LLMs can only do what the harnesses their responses are embedded into explicitly enable them to do [1]. And once the setup is secured, it tends to stay secured without additional effort, unless one explicitly wants to alter its granted capabilities.
Auditing output by contrast is a continuous cost. On the third hand, it's one that most will want to pay anyway.
The "embed into shipped code so subtly humans can't spot it" vector is sci fi ish. Less plausible than old fashioned trusting-trust issues, and most folks have managed to sleep at night for the last 40ish years that spectre has haunted.
[1] I guess the model could return output that buffer overflows the harness and exceeds its intended ability, but that seems far-fetched too
Should be soon solved with open weight model providers offering enterprise support.
No, I'm not much worried. I think this is mostly FUD at this point to try to get people to not run local models and keep paying the "trusted" providers. But paid models have been exfiltrating data - there have been several stories about these incidents here.
> Paid models have an incentive not to do that because if the market discovers it there is a huge economy penalty, but the free open source models don't have that disincentive.
This just fundamentally isn't true. They run on the same economic model, but they have to release the weights to gain credibility, unlike the American labs.
> Do you ever worry whether these open source models have been trained to embed backdoors or leak secrets in very specific circumstances at some point in the future?
Open weight models are extensively analyzed. It is extremely unlikely there is an intentional 'backdoor when X' concept added to any of them. Not impossible, but unlikely for the same economic incentives you see in the other labs, and given that no evidence of anything along those lines has surfaced yet.
> Paid models have an incentive not to do that because if the market discovers it there is a huge economy penalty, but the free open source models don't have that disincentive.
False.
I worry more about Claude.
https://news.ycombinator.com/item?id=50387628
Backdoors in model weights are mostly theoretical. And the "alignment problem" that the AI labs love to talk about really doesn't exist either, at least, not the way they use it. The real alignment problem is getting an LLM, which is just math, to obey arbitrary human rules. In other words, LLMs do not have "alignment" at all. However, AI CEOs abuse the term and use it to mean "AI has an alignment, just not the one we want".
I feel that way but about Anthropic and OpenA lol. Didn't they just get caught sharing your email with every website every time it does a search?
That jives with my own datapoints. Of a circle of around 20 very active devs Anthropic went from being the must-have tool to on the chopping block.
Around 50% of that group says they plan to cancel their Claude subscriptions now that their local or open model setup is performing “good enough.”
I don't believe this one bit. There is no way local models are good enough except for the most basic of coding.
These companies have no moat at all.
I think the staggering amounts of compute and capital at their disposal is a kind of moat.
You need those staggering amounts of compute to run an inference service, but you don't need it to run a powerful model.
It doesn't cost a company that much to buy a box capable of running the Qwen3.8-Flash-Next model in the GP comment and never pay Anthropic again. The cost will be even more reasonable once the supply issue is solved for that hardware.
AI-as-a-service is a unique product because when the interface is natural language, there is no lock in. VS code has a hotkey to swap between claude, openai and a local model. Compare that to the difficulty of swapping from macOS to Windows.
Sure, but anyone with money could do that.
We're the only people that can spend this money is not a moat.
That argument seemed to have legs in 2022 when OpenAI was the only game in town.
But the fact that were even talking about Anthropic like this only a few short years later demonstrates that that was only kind of a kind of moat.
the idea of a moat is that it's an inexpensive way to keep the invaders at bay.
If you need to buy staggering amounts of compute, spend staggering amounts in utility costs and lobbying, and keep spending that as your hardware amortizes and your operations go on, what you have isn't a moat, it's a standing army. It's extremely expensive, and the day you start having issues, mercenaries will cut your throat before you know it.
Capital only means anything if you can spend it to buy resources like compute and turn those resources into additional value.
Hardware's ridiculously expensive. Then, there's no good place to put it, because the new breed of tech bro managed to propose the data center buildout in the most exploitative, alienating, and electorate-angering way possible. So compute's hard to buy.
I wouldn't be so sure about capital in future. I think there might be real look for return on any new capital they are getting. Be it equity or debt. And that alone will not be great for them.
compute and debt*
Banks are going to want that "capital" to materialize into gains at some point. The water in that moat is evaporating.
The current compute they are buying is NOT state of the art (check this year's academic research on inference hardware). There is 10x-100x inference-per-watt improvements in existing prototypes (in the industry, not just academia). Not sure what I am talking about? Ask your friendly AI agent of choice, for the details.
Check out mythic.ai for one example.
I’m waiting for somebody to use one of the frontier models to hack its own data centre and exfiltrate everything…
Their moat is their research capability. It's a combination of proprietary internal technology and models and infrastructure, people, organizational structure, and culture. As long as they maintain the best and fastest research capability, they have a durable advantage, at least for the time being. If we start to saturate all economically useful outcomes that can be reached via greater research productivity attainable to us, then this moat will start to fall apart. It could also fall apart if safety issues force a premature halt or major slowdown to further research.
That's not that big of a moat though, as we've seen with DeepSeek and even smaller teams doing edge model work.
It's very hard to measure exactly how large it is, because there aren't good pre-existing metrics for it. You can measure in time (e.g., how far behind open weight models are from the best frontier models commercially available), but it's plausible that what seems like a relatively small time advantage (months) might mask a considerable difference in internal research sophistication. What we've seen repeatedly is simply that there does seem to exist a durable advantage in research productivity, at least for now.
How long till that "research capability" becomes just 5 people with many AI agents, and no other humans in the main research loop?
I don't know and can't answer or predict that. But that's exactly what it could become (although probably not that extreme), where the agents doing much of the internal work are proprietary, conferring a research advantage.
This assumes any research gains last. In practice it seems like none of the frontier companies can keep much of their "secret sauce" secret for very long.
If company X has a better model than company Y, do you use it? Well, that depends: will Y take two years to catch up? If so yeah, you buy X.
But if Y is just as good a few months later, you purchase based on other factors, because you know you can live without the brilliant research X is doing ... since you'll have it soon enough.
I’m not sure I’d call maintaining the fastest research program a “durable advantage”.
The moat is the data they have collected. There is no way that the "de-identified" data from enterprise customers isn't making it into the training set one way or another.
Even worse, their "moat" depends on buying the most overpriced hardware in huge quantities and subsequently eat a huge amortization loss in a few years when it's obsolete.
Interesting, I'm also comparing 3.8-flash-next (4-bit quant, llama for inference & pi as harness, on a 128gb strix halo machine) with opus 5.5 in claude code and while qwen is a good and fast model, it's nowhere near for coding, even haiku 5.5 feels better.
I could see it used as a sort of "sub-haiku" for easy stuff to leave more tokens for the bigger claude models, but I won't be cancelling my Claude sub any time soon.
It's obviously nowhere near opus 5.5, but it's crossed the same threshold opus 4.5 did: it's good enough. If all model-as-a-service companies shut down tomorrow, I'd be happy with qwen 3.8 flash next q4 running in my strix halo
The problem here is we're not talking ... I don't know ... hamburgers. If I like McDonald's better than Burger King, it's not going to stop me from buying a Burger King burger (if I'm hungry and there's no McDonald's around). One burger may be a bit better, but I can reasonably substitute one for the other.
Coding AIs aren't burgers. Even if a local LLM is as good as Opus 4.5 (when you sink several thousand dollars into hardware ... and I'm skeptical even then) ... the actual difference between the models is huge.
Let's say I use the $200/month Claude (I don't, I use a cheaper plan, but stay with me). If I switch to a Opus 4.5 equivalent local LLM, and it makes even a single production issue that 5.5 wouldn't, that mistake will cost thousands of dollars.
That "cheaper" model isn't really cheaper.
This. The problem is now the same monopoly cartel problem.
Strix halo is double the cost because of...
I see lots of people talking about running Qwen as a local model just curious what's the difference with say something like local deepseek?
The latest Deepseek Flash is much, much larger than Qwen3.8 Flash next
Qwen 3.8 Flash Next (125B, 6B active) is minuscule compared to DeepSeek V4.1 Flash (552B, 16B active) and GLM-5.3 (753B, 40B active). It's more difficult to run the latter 2 locally at usable speeds.
Qwen-3.8-flash-next hardware costs motorcycle level money, DeepSeek hardware starts at mid range car money.
And 4 is about to drop.
Will be interesting to see how much of an advance 4 will be. I was under the impression that 3.8-flash-next is basically a 4-preview. At least architecturally, but as we're seeing nowadays (with DeepSeek, Kimi 3.1) "continued post training" can add a quite a bit.
4 is a generational leap compared to 3.8 flash.
Looking at the S-1 lockup schedule the IPO seems doomed. They need to bring in a ton of investment, and you will have massive unlocks right as the next congress is most active authoring anti-AI bills. Who's going to buy this?
> Who's going to buy this?
So many people who don't actually read and evaluate things but just buy stocks based on vibes and what they see on Twitter. Which is to say, the same people that buy and lose money on most IPOs.
What is the lockup schedule?
They have like 26 different dates IIRC
Anthropic have been calling for regulation, that in itself seems like a high risk for buyers.
Corporate calls for regulation usually translate to "regulatory capture to ensure the moat", in my books.
Exactly, regulation isn't a risk, that's their attempt to avoid their product becoming commoditised.
The risk for investors is they don't succeed in getting regulatory capture because without that they don't have a long term business model
I’m not convinced that regulatory capture really saves the day, either.
If we believe all the hype about programming efficiency, then regulatory capture in the US becomes just one more in a list of many steps the current administration has taken to chase software development jobs overseas.
In Anthropic's case it is pretty openly that. Dario has been very public about existential dangers from Chinese models.
Existential for Anthropic.
Anthropic wants open weights models banned, and wants data centers. They're probably going to get the opposite.
The problem is SCOTUS has already ruled software is protected under the First Amendment, and the landmark cases that establish this precedent applies perfectly to model files.
Sigh...people said the same about SpaceX, too. And it has held above $130, its IPO price, for almost 6 months now. Same for Uber. These companies keep defying predictions of crashing or dying.
The market anticipates the unlocking; when the shares become public, buyers swoop in, so the stock decline is much less than expected, if at all. There is no new information here. If everyone is expecting shares to unlock, then it’s priced into the market accordingly.
It seems like SpaceX is not as deeply tied to unprecedented debt-financed infrastructure buildouts at the same moment that interest rates are pushing up and up and up though.
That's the primary angle of depreciation of Anthropic's outlook, is that as interest rates are running hot, they need to grow ever faster to justify the $2B target, at the same moment that they're growing slower.
1) SpaceX is trading below its IPO price. If you bought shares at the IPO you’d have lost money, unless you sold on the pop.
2) Valuation aside, SpaceX has a massive moat. It’s crazy hard to do what they’re doing. Anthropic increasingly has zero moat, with the market rapidly coalescing around commodity open models.
1) SpaceX is trading below its IPO price. If you bought shares at the IPO you’d have lost money, unless you sold on the pop.
"SpaceX (ticker: SPCX) was priced at $135 per share during its initial public offering on the Nasdaq on June 12, 2026."
current price is $134 , give or take a dollar.
The debut price for retail investors was $150[1].
1: https://www.forbes.com/sites/tylerroush/2026/06/12/spacex-op...
It remains to be seen whether SpaceX just drained their own moat when they canceled Falcon 9.
They’ve now got all their eggs in a new basket. A basket that isn’t entirely in working order yet, and whose market viability is still uncertain because it remains to be seen whether a market that’s used to panniers really wants a laundry hamper.
um, they've got 2 years before the basket is retired during which time they can always extend if they need to. why would they want to keep a less capable, more $/lb into orbit system going after Starship becomes a viable, reliable upgrade???
Not the same as Uber all. Lots of employees got fucked because at lockup expiration it was much lower than the ipo price (at which they were taxed at).
Can someone help me understand - I have tried local AI/LLMs.
Lets set aside speed. The results are ALWAYS terrible compared to CLaude Code.
Sure I only have a 12GB GPU - is that what it is? If I had a 32GB CPU would the experience be as amazing as Claude Code is?
Why is my experience of local LLMs so average?
Reminds me of linux gaming 10 years ago. According the to the community everything worked great without issue and any issues were hand waved away as tiny bumps that you won't even notice.
Then you would go try it and be back using windows to game a week later.
But that is not exactly a linux issue. Games built for linux works very well on linux like games built for windows running decently well on windows. Linux can also run some games built for windows - not for linux. And that are the ones that doesn't provide first rate experience. Those windows games executables technically should not even load as it is not built for linux.
Everything considered, linux is good and has decent capabilities including gaming and whatever you can throw at it but only do oranges to oranges comparison. Games and apps built for each os works fine on the respective os, well atleast it is true for mac and linux. Windows is very unreliable.
32 would get you a good Qwen 3.8 27b which is like Opus 4.6.
Memory manufacturers are running 80-90% margins. Any of a number of things (anti-trust, CXMT, financial shock) have the potential to drop memory cost by 2/3s. At the same time memory efficiency of models is improving. At some point Apple's going to start shipping 64G of unified memory in mainstream machines that are advertised as local AI capable (like HP, Dell and others do today). Like the iPhone refined the concept of a smartphone for the mainstream, they could be the first to hit mainstream sovereign AI.
The people saying that local models can easily replace the real Frontier Models from Anthropic or OpenAI are just not using it for the same things you and I do.
They seem to be happy with their small usages.
For everything I do, i see huge differences in Quality and Speed.
Even when I try an open model on a 4090, the PC gets warm/hot very fast, I struggle with context length.
> The people saying that local models can easily replace the real Frontier Models from Anthropic or OpenAI are just not using it for the same things you and I do.
> They seem to be happy with their small usages.
Oh yes, we're terribly inferior people. Just living on scraps from the masters' tables.
anyone who is working on an remotely difficult codebase will notice a huge difference, either you are an incompetent dev or dont code at all
It wasn't meant as a dismissive comment.
I do plenty of experiments of things i always wanted to do and they just don't work with local models.
Finetuning of some ML models for a specific usecase, some 4k demo idea i had, small browser games to show others game mechanic ideas i had for ages, analysis tools etc.
Even just playing around with some Ghidra eats context like nothing.
can you give example, prompt require context window > local model, or higher Quality/Speed, that local model can not do, open source example?
Any program I'm vibing.
I write 5-20 paragraphs of things I want and it goes away and just builds it.
Something like this (just an example):
"Build an application with a webserver in rust. It has 4 stages, first one is an input module... second one is refinement ... third one is an image detection with current fitting ml model ... last one is finetuning."
Then i would describe every basic page with a few more paragraphs.
This would work for half an hour or an hour, consume 100-400k of context tokens and then I would test it, write down 10-50 small sentences (every sentence either fixing a bug, or making it better) which tehn takes again like 20 minutes and i'm nearly done with a complex enough tool providing real value and actually working.
A big kimi, which is hard to run, might be getting close to this but we do not talk about running it on 1-2 5090.
The other thing is, thats more like a practical experience, even if you use them through Open Router (for testing and co), the initial results of something like Opus 5.5 are ALWAYS better. More details, better quality, better understanding of usability of the tool.
12GB GPU is useless. The good experience is with 128-256GB unified memory architectures like strix halo etc. You don't need to buy any hardware, you can experience the latest open weight MOE (flash) models from DeepSeek, qwen, or GLM on openrouter using a local agent like kilo.
That is a shift of comparisons though, right? This thread was about running locally, explicitly as an alternative to cloud services. I would guess few people have graphics cards with > 12GB ram.
A few more will have Macs with 48GB RAM though. (Indeed if you push it a little, 36GB unified is possibly enough for Muse Glimmer; I have not tested this theory)
There is, for sure, a limiting factor in the x86 architecture, but I think the RTX Spark will change that a bit, and maybe AMD's subsequent Ryzen AI/Strix Halo chips.
AMD Ryzen AI/Strix Halo is for running locally on your hardware. Openrouter is for evaluating the models you'll be running locally, before buying the hardware.
In any case, 128-256GB unified memory systems running MOE models is the "cheap" option, RTX6000 is the step above it.
> 128-256GB unified memory systems running MOE models is the "cheap"
I'm not familiar with these. How much does one cost, and from what store / what product name?
Would you talk to me a mo about Kilo compared to Pi?
I am digging in on Pi (in lovely Paseo and exploring Pendant when in VS Code).
What would I find better or worse in Kilo?
A 3060 + good chunk of system RAM can run qwen3.8 flash next.
12GB is not a lot, you are stuck on low quantisations of older models with that. What exactly did you try?
The newer/larger models are good though, not as good as Claude, but not every task requires a model of that level. Maybe yours do, but for simple coding tasks, summarising text, etc a small local model runs just fine, and is a lot cheaper than Claude
What graphics cards have more than 12GB, and how much does it cost to buy one? What about for others saying you ideally want 32GB? (That split will determine if a 5080 will do it)
32GB would probably get you qwen3.8-27b at a reasonable quantisation, which benchmarks similar to opus 4.6
You can try that model (and many others) using a cloud host first, and then only if you like it do you need to buy something to run it.
12GB GPU is insufficient for most purposes yes. Even 16GB is a notable improvement, but yes 32GB cards such as the 5090 or R9700 are a world of difference.
Given we're discussing a cost comparison (i.e. cutting a Claude subscription in lieu of running locally), how many months of claude $100 or $20/month plans would it cost to buy a 5090? I am showing it as 88 and 440 respectively. I.e. a 7 and 36 year payoff. Not even close to being worth it.
The Claude plans are probably still heavily subsidized. Expect prices to increase, especially after the IPO. Self-hosting models for personal use is probably overkill, but I can see companies doing it pretty soon since they can probably run 24/7 workloads and fully utilize the hardware.
Great point. And, 3 years ago, a 4090 cost $1600, so maybe that will happen again some day. (Same/similar card, or a future card. I remember when GPUs used to get better every few years instead of staying roughly the same but costing several times the original amount!)
Currently, it's not worth it because:
"SemiAnalysis, a research and analysis company specializing in the semiconductor and AI industries, found that a $200/month subscription plan is far more generous than previously thought. According to its calculations, while it's widely believed that a $200/month plan maxes out at roughly $2,000/month in tokens (assuming API pricing), the actual numbers are closer to $14,000 for ChatGPT and $8,000 for Claude. "
https://cybernews.com/ai-news/chatgpt-claude-usage-costs-ope...
This will change in the future.
As amazing? I suppose not.
Good enough? Quite probably, unless you're in the "coding is solved, specify loops and walk away" frame of mind.
32GB (or say a 48GB-unified RAM unified machine) gets you Qwen 3.8 27B at a good adaptive Q4, maybe Q5. Muse Glimmer at Q4 and Q5, etc.
These models can code well enough in an agentic way, I find, and the chat-to-assistant scenario is just fine in Unsloth Desktop or LM Studio.
A slightly lighter harness helps; a lighter system prompt is entirely possible, especially for Muse Glimmer, which is post-trained so effectively for agentic helper tasks that it barely seems to need a system prompt at all except for a "plan" mode.
Tool use is all fine, all that.
> unless you're in the "coding is solved, specify loops and walk away" frame of mind
which is where the entire software industry is going
Well it's certainly the service-dependent (addicted!) model that the big two want.
But will you want to do that when your tokens are metered at non-subsidised prices, and yet there is absolutely no clarity (ever!) about token efficiency, how the model decides to spend your money, how it might ever decide to stop wasting your money and get to the point, etc.?
The specify-loops-and-walk-away model absolutely points to local/on-premises/private cloud AI.
Because otherwise you're letting Anthropic or OpenAI decide how to spend your money. And they are going to be biased towards spending more of it.
I don't think Qwen 3.8 is quite there yet with the loops stuff. Definitely seems to be better at deeper and deeper one-shot puzzles.
Have you tried Codex? My journey so far
- 2023 using Cursor
- 2024 using Cursor with Agent mode
- 2025 using Claude Code
- 2026 using Codex with Astra 6
I've try every new model when it comes out from both OpenAI and Anthropic. I cannot justify leaving Codex and Astra 6. The token burn on Anthropic is always astronomical for me. Codex just works, and I see no reason to move away from it.
I switched from Codex to Claude Code w/ Opus 5.5, and it's been fine. I haven't hit limits in my $100/mo plan, doing full time dev work all day. But I agree. Codex sub + Astra is also great. Both are excellent options, and I don't see a reason to go with a cheaper / less capable model, frankly.
> The results are ALWAYS terrible compared to CLaude Code.
How much effort did you spend to make the local setup work well?
Local models are not plug and play, you need to adjust settings to make them behave intelligently.
On my side one model looking super dumb right after install ended up being 'agentic' after using the right chat template, fixing how tool calls are setup, fine-tuning plenty of params values, and adjusting the whole setup to my own hardware. Claude.ai (free) was great at helping me sort that all out.
You can't replicate Opus 5.5 at home.
You can maybe replicate Opus 4.5 to 4.7 if you have some moderately expensive hardware. And that is definitely good enough for many people.
The smallest model that can even pretend to be a decent coding model is Qwen3.8 27B, which you realistically need 32GB of VRAM to run without breaking or limiting it in some really noticeable way. This gets you something like a slightly less capable Opus 4.5 with a much better work ethic.
I can't really do anything with this comment without a couple of specifics, such as 'what model are you using?'.
In general, more memory = larger models = more intelligence.
However even if you had a private data center you wouldn't be able to beat Opus 5.5, Astra or Gemini 4. The open weights models are a tier below these three.
I'm not convinced that 2026 was peak of the AI hype cycle.
Arguments are being made that, with the midterms over and 2028 just around the corner, a less sympathetic administration would create significant headwinds, adjusting the valuation of Anthropic and OpenAI down.
What we don't know are what Anthropic and OpenAI have planned and in the works.
A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments. It's likely a significant number of them will start their own business with AI.
AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That said, IPOs are usually liquidity events for LPs and for something really disruptive and unpredictable as AI, I won't be engaging in their IPOs. Only time will tell if this was a good decision, or bad.
At a minimum we should continue to see smarter and more efficient models in 2027.
If datacenter production sees significant headwind then it’s very bad for ANT. Securing compute is listed as one of their major risks to the SEC.
Tulips, Railroads, and the Internet also didn't go away when the financial bubble around them burst. That doesn't mean there wasn't a massive market correction in response to the over-investment.
Tulips kinda did go away
I don't believe they literally meant Tulips but rather its hype cycle. I commented on the other two that can be taken literally
There's a beautiful tulip garden just down the street from me, and an outdoor weekly market where tulip bulbs are sold. Tulips are very much still around, I assure you.
I think bloppe was talking about the Tulip hype cycle that took out many generations.
Yes, that's my entire point. There was also a railroad hype cycle and an internet hype cycle that took in massive amounts of overinvestment, and all three things remained as regular things in the economy with bigger or smaller roles after the hype cycles ended.
See https://en.wikipedia.org/wiki/Railway_Mania and https://en.wikipedia.org/wiki/Pets.com for some info on the other hype cycles I mentioned.
I don't mean went extinct. I mean the idea that tulips could be an asset class is now laughable, which is not true of the other two. The impact of the tulip industry on GDP is utterly imperceptible.
Same thing could happen with the idea of selling tokens
> Tulips, Railroads, and the Internet also didn't go away when the financial bubble around them burst. That doesn't mean there wasn't a massive market correction in response to the over-investment.
Your Railroads and the Internet parallels are so very apt. We certainly are very productive even in 2026 off those but almost no one remembers how many large behemoths were wiped out in the U.S. because of them.
Yes, it does seem to me that the AI of 2026 will be like the Railroads and the Internet - fundamentally transformative technology that changes how things are done into the future but there will be blood in the short term, which is why I would not be participating in the IPOs directly
> A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments.
Outside of programming and a couple of other sectors I’m not sure that is true. It isn’t visible in the unemployment figures.
> Outside of programming and a couple of other sectors I’m not sure that is true. It isn’t visible in the unemployment figures.
The unemployment figures is mostly a political projection, like CPI.
In 2026 everything fron sewage to chickens to oil to education is now tied to AI. The entire economy is now, for good or bad tied to AI.
What most people either didn't notice or understand is that the U.S. was entering a recession and would have been in one if it hadn't been AI
> What most people either didn't notice or understand is that the U.S. was entering a recession and would have been in one if it hadn't been AI
Is it maybe more accurate to say that the US is in a recession, outside of the industries that are fueling AI?
Life's pretty recession-y for everyone else imo
That's a pretty damning statement - this implies companies don't see a return on AI at best, and it's actively destroying their businesses at worst, so there's not only job loss, but businesses themselves are not becoming more efficient.
Yeah, pretty much spot on.
> In 2026 everything fron sewage to chickens to oil to education is now tied to AI. The entire economy is now, for good or bad tied to AI.
Tied how? How strong is the corellation between these? I'd say very weak, other than everything having to compete with AI for resources.
The same is true of any technology tied to powerful personal computers or the internet - people built and designed beatiful houses, great cars, airliners, wrote books and ran restaurants without computers or the internet.
It has even become a meme how putting smart features and lcd screens into things like washing machines have made them strictly worse.
Everything is tied to the internet or electricity before that, yet the Mag 7 isn't all ISPs and utility companies.
Somewhat ironically it would appear most CEOs believe the only jobs AI is replacing are developers since they built an app last weekend. Every CEO is saying why do we have so many devs and why are we paying them so much… so devs blew up the market for devs but beyond that there doesn’t seem to be much impact.
The capabilites are here though.
Even if it takes them 3-6 Month to open up a new field, its going to happen steadily now and were it started, the AI investments will be recuped by layoffs.
Artists -> Translators -> Journalists -> Software engineers -> mathematicians -> lawyers -> Accountants -> ...
Its already disruptive
Do you really see lawyers being replaced? I don't see anything special in their work but since it's a regulated industry I doubt it will be so affected.
At best some layers will work faster, but automating their work or people not requiring them? Usually when you contact one there's a lot at stake. Same with accountants.
Software engineering and translators is a different story. Most of the times if something goes wrong nobody cares.
Some lawyers (the worst ones?) might just do the easy or normal things like writing a 'lawyer' letter to someone not paying. This is no risk to automate away.
The avg Lawyers might do more complex but again a lot of 'normal' things. Day to day things the next SaaS AI company wants to 'fix' and they might also accept basic liability.
Then you have the 'tv lawyers' the ones who do non normal things, they have A LOT of apprentices and young lawyers going through papers, research etc.
They will for sure have the expert as a human, but that person will need a lot less support.
You shouldn't need an accountant today already. If you have an digital order and a digital bill and a digital signed delivery note, it can be booked automatically. AI will find risks, summarize them etc.
AI is already helping here closing the gap with better OCR etc.
I really think, that when big companies introduce the agentic layer to their products, there will be less people needed and if you need people, they get less paid because they will need to understand less.
Lawyer, accountants, doctors, architects, and engineers won't be replaced.
We're not paid just for what we do, we get paid because we accept liability for our mistakes. Professional liability insurance is a large part of why licensed professionals charge so much money for their services.
If a lawyer can just sign off of a lot of basic inquries with just reading and approving, you will need less.
If a lawyer needs less people preparing everything, the industry will also shrink.
Lawyer as SaaS companies will just introduce some insurance and will clean up from the bottom until AI can do all of it.
We will see if we will have one expert ai lawyer per topic or type of lawsuite or if they will get generic good enough that it will not matter.
Even people defending or suing by (successful) themselves became a thing already last year.
Only popular with companies that want to make a quick buck and tech people (hey! that's me!).
You really don't know what we have here, do you? Let me illustrate what you can do with Qwen3.8@q4 locally with about 2TB of storage and a small cloud server to host a website.
Locally you scrape and process every elected official and start tracking what they're doing, what they're promising, and what they're not doing. You build embeddings and make them all geotagged in a database and just start with the largest cities.
The website provides public facing search. User drops in a zipcode, city, state, specific rep's name and gets what's relevant to their results.
All of this is to say, we literally have the technology to hold accountable our representatives and then show everyone objectively who's doing good and bad for their constituents.
It wont take much to solve the problem of bad representation after these last two years. The hard part is literally making sure people going to a polling station can immediately know who the heck the 10 - 30 different people are they're voting for, and track them efficiently within their own specific parameters.
Regulation is coming for more than just the inference providers.
Got a website for this? A politician leaderboard sounds like it could do some good, even with Goodhart’s law.
got one of those already https://www.theyworkforyou.com/ https://www.govtrack.us/
The major counterpoint here is commoditization; you may have the market growing and models becoming cheaper at the same time. And we don't even have to look to Chinese models for that, as OpenAI has been adjusting its pricing already. For profitability, Anthropic still seems to be pretty dependent on its current price levels.
That said, I'm also somewhat skeptical that there is even much room for growth left unless somebody finds a new, important, and token-hungry use case.
Generating a full length movie script and then the video?
In theory, yes. But the reality is that deep learning models just aren't good at creative output (to put it mildly). And neither Anthropic nor OpenAI have any models that generate video, so this wouldn't help them; in fact, OpenAI killed off Sora.
I believe that the value that OpenAI and Anthropic hold, in light of stiff competition from China, is not their products but their internal models. China’s work is all derivative of American model API access. The exponential RSI loop means that as long as Anthropic and OpenAI keep steady footing and only release older models they will will always be the best on the market and all others will be in their tailwinds. These two things combined are what make me believe OpenAI and Anthropic will hold value and grow as companies.
Best on the market, for something not being sold on the market? How is that helpful in terms of actual revenue?
If Anthropic manages to start producing new pharmaceuticals with their internal models or something that thesis could make sense but not if 90% of their revenue comes from enterprise API sales which would necessarily have to be accessible to the public. Which are then inevitably fast followed by Chinese models 3 months later.
Even if we were to accept your entire argument [1], none of it contradicts the commoditization argument. Note also that competitition does not come just from China, but also from Google, Meta, and SpaceXAI. Where they currently are is not relevant, the entire LLM landscape will likely look nothing like today by the end of 2027.
[1] I don't, I think China is primarily limited by compute and will be at least for the next couple of years, probably longer.
Weren't DeepSeek the first to release chain of thought/reasoning models? [1]
They are also (like other models we know about) experimenting with the architecture. [2]
[1] https://arxiv.org/abs/2501.12948
[2] https://sebastianraschka.com/blog/2026/kimi-k3-architecture-...
They were not. That was OpenAI with o1
You can be the best in a $100B market, but that won't make you worth $2T.
It certainly isn't the peak of the AI hype cycle. Eventually something will surpass LLMs and the hype will start all over again. Will it really be the final invention this time? Place your bets!
Anthropic's original $2T valuation implies they are going to almost 100X their revenue.
I am highly confident that Anthropic will 100X their token sales volume. I am also confident that token prices will continue dropping through the floor due to competition. So to 100X their revenue they will have to 1000X or 10000X their token sales. That seems a stretch.
If datacenters hit significant headwinds, it will drive token prices up as more people start offloading work onto LLMs.
However this effect could just serve to make those who own the compute ridiculous money, while crushing those who need to lease compute.
Or incentivize using the minimum viable model for the job instead of throwing multi trillion parameter Fable sized models at summarizing documents or classification tasks.
I now do like 90% of my routine software dev/sysadmin stuff with Luna on Medium by default, some proportion of which could itself be offloaded to a small Qwen model.
But I wonder if that offloading will happen if prices rise past a certain point and it becomes not worth it.
Economic theory would state that if token prices go up people will stop offloading work onto LLMs and start doing it themselves again.
Indeed, 2026 was basically that: the LLM providers increased token costs and companies cut LLM spend to the bone. That is why Anthropic's IPO is struggling and MS and Google have failed to meet AI-related revenue targets for 3 straight years.
As AI grows in capability it will be trusted to do more of the work it already does and also break into new fields. Also, there are many who have never interacted with a chatbot. There is an entire world of potential customers out there. It can be quite a villainous business model but look at what Facebook did in Africa with their sponsored ISP and also what they’re doing with Meta Muse. Demand for AI usage will skyrocket.
But will it increase by 10000X, as I back of the envelope calculated it would require to justify Anthropic's $2T valuation in the face of quickly dropping token prices?
It only needs to increase just enough to keep the forward looking carrot to be continued to be dangled.
Chatbots aren't a money maker. Each interaction is likely to be 1-2K tokens at most. AWS Bedrock prices 11K token input and 4K output for a total of $0.184 using on-demand usage [1]. So for normal usage (occasionally asking questions) you are looking at less than $1 per month for a person.
Using the models for data processing (a lot of input/output tokens) and for role-playing will use more tokens and thus cost more. However, those use cases are not common when comparing the percentage of the user base.
The money maker is agentic coding where people get the models to perform long-running tasks. This is why Open AI and Anthropic have been pushing these hard. However, there is pushback on this, especially among businesses looking for value for money/cut costs [2] like Apple [3], [4].
[1] https://aws.amazon.com/bedrock/pricing/
[2] https://www.ibm.com/think/insights/tokenmaxxing-dead-long-li...
[3] https://www.bloomberg.com/news/articles/2026-11-04/apple-cut...
[4] https://9to5mac.com/2026/11/04/report-apple-reduces-employee...
Also, there are many who have never interacted with a chatbot.
Something like 99% of the people who have interacted with an LLM chatbot lose interest after a month.
100% of people who have to interact with an LLM chatbot taking the place of a human customer sales or service agent jump to a competitor.
Outside of the tech bubble, the LLM hype is already over.
>Something like 99% of the people who have interacted with an LLM chatbot lose interest after a month.
This is not remotely true. It literally couldn't be true, considering Open AI have 1.2 Billion weekly active ChatGPT users.
I use Gemini instead of Google most of the time and I think most people now go to chatgpt or gemini for things they used to Google.
> As AI grows in capability
The issue is, at some point for AI to grow more in usage it will need to decrease in cost.
We are already seeing this with some companies reducing AI usage or moving to open models due to high costs.
AI will surely boom there's no doubt on that, but it won't necessarily produce the revenue that Anthropic investors hope for.
3 companies have moved.
To be fair, when I buy stocks, I care less whether the company is worth 1 million, 1 billion or 1 trillion at the time I buy.
I care about whether it's going to grow in value/price from that point on or not.
And it's hard for me to imagine it growing even higher than the current valuation.
> A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments. It's likely a significant number of them will start their own business with AI.
What's you're point there? People laid off because of AI will immediately shift into AI-centric small business, and start hyping AI?
That doesn't make sense to me. People being laid off are likely to be bitter about the cause of their layoff, and even if they all start their own businesses (which is unlikely), most of those will fail, leading to more bitterness.
Many people who are being laid off will not find another spot in their industry and will need to pivot. Most of that subset of people will go to existing companies and take traditional roles. The small few remaining might take it as an opportunity to start a new business, and when they do so, they are likely to take risks on technology and business operations which will include AI. Is that so hard to believe?
I don't buy for a second that people laid off over AI will be so darn mad at AI that they won't use it, but I don't know where the hell the markets for all of these hundreds of thousands or millions of new small businesses are.
If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop. They can use AI to help them run that. Getting paid $175K per to write ruby CRUD was a moment that is over, hope you didn't spend all your salary on toys and rent.
They will try to start businesses out of necessity as stable employment is not an option, and they will use AI since it's almost mandatory now for any technical productivity.
I'm not convinced that 2026 was peak of the AI hype cycle.
Agree. these valuations will hold. Pundits made the same prediction after Facebook's IPO in 2012 that everything would soon crash. here we are...
AI will keep going up. The perspective of the investor is on whether OpenAI and Anthropic will be the next Google, Apple, or the next Yahoo, MySpace.
> What we don't know are what Anthropic and OpenAI have planned and in the works.
Wouldn't they tell us before IPO? Unless it's not that promising...
> AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That does not mean the current players profit from this and the infrastructure investments were necessary at that scale. There is competition, there seem to be enormous efficiency gains and it's questionable that demand for even bigger models is large enough.
I've been trying to generate some product videos with opus 5.5 and I use the API because I don't like subscriptions.
I burned about $150 of tokens over a few hours of chatting, although opus 5.5 did generate some 10-30kb html files at a time, containing the "videos".
I think subscriptions are extremely subsidized still and what happened yesterday was just a small dip of what's to come.
Did they consider doing a Dutch auction, like Google did in its IPO[1]?
[1]: https://www.cnbc.com/2014/08/19/es-took-off-but-the-auction-...
And there are worries about the Democratic midterm election sweep causing tighter scrutiny of new data centers and forcing Anthropic to pay wholesale for chips, or worse.
You'd think restricting build-outs would ease pressure on component supplies.
Damodaran placed a fair value of $1.47T minus discount for really bad corporate structure. So $1.3T looks exactly right.
And its kind of circular way of reasoning. As the investors would've actually watched the video and included it in their calculations.
$1.3T is still looney tunes for a company that literally has zero plan to turn a profit.
I don’t agree with you. Their product is a fully scalable almost human tier intelligence. I think it’s worth way more than $1 trillion but they have to get their capex in order first so they can provide dirt cheap tokens.
> get their capex in order first
how are they gonna do that when the half-life of a GPU is 18 months?
Their lifespans are 2 to 4 years with always on, heavy usage. Even then the problem isn’t the lifespan. It’s the lack of supply right now. I assume with more built out production capacity GPUs get cheaper too. The whole vertical stack needs to get cheaper and it will in time.
Spot prices for 5 year old A100s are still going up recently, because inference is so valuable. This meme that GPUs are a quickly depreciating asset is outdated.
If the company cant convert the product into earnings it really doesnt matter what the product is. It can be a magic machine that turns water into wine. They still have to find people willing to pay for it.
If the world is not ready for fully scalable almost human tier intelligence then, being right at the wrong time is the same as being wrong.
> Their product is a fully scalable almost human tier intelligence.
"Almost" being a synonym for "not".
"fully scalable" except it requires massive capex to build out data centers with limited actual resources to put in those data centers.
Some benchmarks to compare:
Apple: 5 T
Google: 3.5 T
Meta: 1.55 T
Microsoft: 3.97 T
Berkshire Hathaway: 1.2 T
Eli Lilly: 1.28 T
Tesla: 1.38 T
Walmart: 1 T
Spotify: 125 billion
Crazy to think they'd still be worth more than Walmart. At least to me.
I know quite a few paying Anthropic customers here (Norway). Not a single customer of Walmart.
Hei!
Not that crazy, software sales are global from day 1.
I think it's crazy that Walmart is worth 1T. It's grocery chain operating in limited number of contries. Shouldn't be worth much more than for example, Carrefour.
A lot of non-tech non-AI companies have seen outsized gains this year as people use them for AI hedging.
Walmart is huge compared to Carrefour. They're the world's largest employer, and their yearly net profit is $22 billion compared to $300 million.
Carrefour appears larger because as you travel the world you will see them in the major city in every region.
Walmart, on the other hand, has a store close to most small towns in the US, as well as multiple in more metropolitan places.
I think about what it would take for a small group of smart people to do what they do. Walmart? Seems hard. Meta? I could see that.
I know that’s oversimplifying and probably naive, but huge physical operations seem like more of a moat.
You are probably saying this because you are coming from a tech background (my guess)
For people with a Walmart background, Meta is black magic
Walmart is in a mature low-margin business. Their global expansion has stalled, and in the US competition is increasing
There are also many positive things about them. They are huge, and innovating. But their growth potential is somewhat limited
When you mention Tesla, don't forget Space-X. The value proposition of Teslafanboys was always that Tesla is not a car company and now Musk splits it up between Tesla and Space-X
Tesla + Space-X: 3T
can someone explain to me the point of the 'initial valuation', considering ultimately the price will settle lated thorugh trading to perceived value anyway ?
Whenever you buy a business, you have to have some idea of the valuation.
The IPOs need to have buyers. The buyers will have some idea of the business valuation, as will the sellers. This is decided before the actual stock starts trading.
Specifically, the "underwriters" will go around finding people willing to buy what is being offered. This gives some idea of where it will begin to trade.
Investors ‘subscribe’ to an IPO. A company offers a certain amount of shares for sale at a certain price. Then the underwriting banks go out and sell the shares.
If your broker allocates you shares, you pay the IPO price as does everyone else who was allocated shares. The shares are all allocated before the IPO happens and once it opens for trading, price discovery happens.
How the price is set is another matter, one I’m not familiar with as an amateur.
It's the starting listed price. For an anchor, it determines a lot about how the first few days go. It's also a great headline. It's also also what employees picture when they evaluate their stock options, I bet.
Some people buy on "day 0", before it hits the stock exchange. This determines the price they pay on day 0. For example, if they buy 10% on day 0, they pay $130 billion.
The stock exchange is a "secondary market", which means you're buying stock from investors who own it, not from the company itself.
An IPO is effectively a funding round, and the numbers we're discussing here are the amount they'll raise from the IPO.
> the numbers we're discussing here are the amount they'll raise from the IPO
No, the "valuation" numbers are the market cap at the moment of IPO. The amount raised is that multiplied by the percentage of shares issued by Anthropic (as opposed to by existing shareholders like investors and employees -- that combined number is the float) at the IPO.
Sorry, yes, that. The bigger point still stands, though — given a fixed fraction of shares they're offering, the valuation gives you the amount raised, and that makes the number interesting.
> can someone explain to me the point of the 'initial valuation'
Most of the institutional and retail placements happen on the initial valuation. These are the people who are holding the shares when it gets listed and starts trading, and it is the primary source of raising capital through an IPO.
To go further though the Initial Valuation (usually a range) is set weeks before the IPO to make it easier to pitch to investors. And the final valuation is locked in the night before trading based on actual demand.
The opening price on the stock exchange, though, will be whatever the demand and supply is in pre-market trading/open.
The first seller has to decide a price to ask. The price of the first share sold x number of shares is that seller's "valuation" of the company.
Typically in an IPO, the offering is about 20% of valuation of the company at the first offering. But with a $trillion or more, they will offer say 10% or less.. say $100B in one go is probably what the market can take.. Some banker has to back it up initially and defend that price... the next dump will be about same amount say $100B again but not the same number of shares. The underwriters are not obliged to back $1T and the IPOed company is not obliged to release more shares.
Ultimately the percentage of shares that public will trade is material. say 20% for $100B is quite different from 10% for $100B.
The point is that is the price the private investors who will be liquidating, including the company itself, are accepting for their shares.
If they are too far below the perceived value, they are leaving significant money on the table.
If they are too high, the institutions who participate in IPOs will let them know. If the price craters off IPO it is not good for perception of the stock, and so ongoing health of the company even if that maximizes private investor value at IPO.
So they build a book of commitment around the target value, trying to leave enough incentive (15-20%, often) of an initial bump to get big money in.
None of the value changes in later trading mean squat to how much actual money the company gets to put in their bank account.
They get the money from the initial purchase of the shares, and never get another dime directly from the market again, unless they have another offering (which is quite possible). The stock price does drive leverage for later M&A and debt/credit activities, and matters to stock-based compensation, so it does have meaning, but the IPO provides a one-time cash infusion at the initial price.
They can issue new shares. It's not like it doesn't matter at all.
If the company is valued by the underwriter and initial buyers at $1 trillion and the company offers 10% for sale through their underwriters (who have put out feelers to try to attract buyers at highest price possible), they raise $100 billion from the offering, minus fees, to use as needed to further the business. The initial buyers get the shares at that valuation, and I believe if the underwriter positioned it wrongly they might be on the hook to sell some shares at a lower price without prejudice to the company.
If one of the initial buyers sells in the first minute the company is public the first publicly traded shares at 1.5x their purchase price, the company suddenly has a market cap of $1.5 trillion instead of $1 trillion, and that buyer has made a tidy 50% profit on the shares they just sold. If all initial buyers dump a bunch of their shares the price likely will go down, perhaps pushing the market cap to, say, $750 billion. Regardless of this action on the first day and after, the company offering their shares still has the $100 billion.
Note that initial buyers are discouraged from selling large portions of their shares.
My understanding is that you want a small pop when the company IPOs.
If the pop is too big, that means the company left money on the table.
If the pop is too small, the investment banks that underwrite the IPO have to worry about losing money.
Still too high
Dupe: https://news.ycombinator.com/item?id=50394906
Anthropic intends to rule humanity with its ASI. Perhaps this is the optimal outcome, as there are many problems AI could fix that it wouldn't be able to as efficiently without eliminating the human bottlenecks which caused those issues, but I fear the more financially powerful it gets, the fewer checks there will be on any misalignment.
As opposed to the CCP, which is essentially what the Chinese model companies map to, who are only interested in freedom and liberty for humanity right?
If they release weights, then no, it's not concentrating power and control in a single company like the vision of Anthropic and OpenAI.
You think it's a good idea to trustmebro some Silicon Valley jerks with world domination? I... don't.
What's ASI?
can someone explain to me the point of the 'initial valuation', considering ultimately the price will settle lated thorugh trading to perceived value anyway ?
The price will settle, but it has to start somewhere.
Well obviously Anthropic as the one offering the shares would care about how much they sell them for.
The initial buyers of the shares sold by Anthropic are institutional investors (mostly). That matching process is done by an investment bank they hire.
The trading price you see on the screen is - shortly afterwards the shares become publicly tradable on the exchange (or on all US equities exchanges, for US stocks). So in effect, the price shooting up on the public markets is actually bad for the company in that they might have left money on the table.
Sort of. IPOs are always undervalued because the entire block of shares gets purchased by the investment bank at the banks decided IPO price but then bears the risk of selling them at or above that price.
The price those that buy the IPO get before it starts trading publicly.
E.g. SpaceX is currently trading below its IPO price. If you became a shareholder by buying at IPO and held your have lost money as of today.
It's the price they're going to be offering to the general public to buy ahead of time, through their brokers. They get that price locked in, then once it starts trading it moves as it may. Usually the IPO buyers are not allowed to sell for some time
Please explain to me why my "naive and maybe ignorant" understanding of this is wrong... The bigger the IPO price, the more they earn and the more probability is for you to loose money invested in it.
So say Anthropic, or OpenAi, or smartGummyBearsAI go IPO and the price is insanely high... and then spirals down to be 10% of the real price... they earn a lot of money and the public loose a lot of money.
IF smartGummyBearsAI goes 100% realistic price and you buy near the correct price, they earn their fair money and people get an investment.
IF smartGummyBearsAI says that they have technology to use the same machine that makes gummy bears print gpus and they are priced 10,000x their actual price the society just transfers a lot of wealth to them and they scam the society.
Why shouldn't we regulate and aim for the correct price?
Remember 801k money is what we are talking about
The price isn't "set" from what I understand. The price is determined based on what institutional investors are willing to sign up to pay for it. So it's largely driven by the market (not on the actual stock market, just the market of interested parties pre-IPO). Those institutional investors do not want the scenario you describe any more than you or I do.
That's why it's dropping here. When the actual stock market tanked yesterday institutional investors were like - hey wait a minute this is starting to look too expensive.
Also if your 401(k) is in index funds you are safe. A company is not added to indices right away. If you are in actively managed funds or something like that you might have more exposure risk.
How do you determine the "correct" price? Something is worth what people will pay for it, no more and no less.
Markets have the advantage that many peoples' opinions are aggregated into the market price. This is far, far more accurate than having one person's opinion (an elected bureaucrat) set the price, particularly when they have no skin in the game.
If the IPO price is really 10x the public market price, that implies that the set of institutional investors collectively estimated the price 10x higher than the full set of retail investors. That's pretty unlikely: institutional investors usually do pretty extensive due diligence on IPOs, and you're already averaging out the statistical fluctuations by selling to a wide set of them. But if that does happen, it just means that institutions got fucked over to the benefit of retail (who gets a 90% discount on the same shares), which seems like something we should be celebrating.
Some benchmarks to compare against:
Apple: 5 T
Google: 3.5 T
Meta: 1.55 T
Microsoft: 3.97 T
Berkshire Hathaway: 1.2 T
Eli Lilly: 1.28 T
Tesla: 1.38 T
Walmart: 1 T
Spotify: 125 billion
Anthropic sells intelligence, and the most intelligent intelligence.
Honestly crazy that we’re doubting 1T, even with all the capital issues behind it.
Huh? If a Chinese open weights model comes out that is nearly as good, the valuation drops to very little.
I mainly doubt the moat, not necessarily the usefulness. Why will customers stay with anthropic? Many cheaper models seem to catch up within 6-12 months, and there’s also other flagship providers that keep pace and compete on cost (oai, Google). Why should I believe that anthropic’s revenue is going to stay consistent, and not evaporate to other competitors?
Validation is all about assets and future earning potential. Anthropic’s core assets are a model which is shown every day now to be only marginally better than something that’s free. So that asset appears to have very little value. Their future earning potential depends on folks not figuring out they can get a good-enough result for free, only paying for compute. And Anthropic doesn’t own compute, they rent it.
So the whole valuation is based on the idea that folks will happily pay this company a huge markup for inference over the actual cost of compute for a model whose valuation is rapidly tending to zero. Hence why potential investors are starting to freak out.
> Anthropic sells intelligence, and the most intelligent intelligence.
Most intelligent is a stretch, we don't know how the Chinese architectures would fare if they had the same compute availability as Anthropic and other US labs.
If we extrapolate and consider that they are behind due to artificial export restrictions, once they figure out how to build their own GPUs in a cost-effective/scaled-up way then the American labs' claim of having the best intelligence is at an enormous risk.
They already do a great job being resource constrained, imagine once this constraint is gone!
This is a very competitive market. You can't command fat margins in a very competitive market.
If the justification for the valuation is "they sell intelligence", then their valuation should be $0. Because their product isn't the least bit intelligent, and routinely gets beaten by humans.
You can be as dismissive as you want but this doesn't reflect the reality.
I can easily spend a LOT less time on describing the same amount to claude than i would in a JIRA ticket and doing code review on the PR of Claude than talking to a person about it. Instead of 1-4 weeks Claude is done in an hour.
Its also already A LOT faster and better of analysing infrastructure issues.
Low ???
Related:
Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation
https://news.ycombinator.com/item?id=50386597
I think people in this thread who claim local models are anywhere close to Claude are completely detached from reality. 200$ Ultra sub is the best value ever offered in history of subscriptions. I got 3 running and would pay at least 3x more for them before looking at alternatives. If your local models or some DeepSeek on cheap provider or whatever you install instead of Claude makes even 2-3 more mistakes per week you are already losing money assuming your time is worth average software dev salary.
I don't think it's remotely close either - Claude is that good. The problem is that it's not how Anthropic plans to make money to justify 1.5T-2T valuation. That would require companies paying API prices on long term big contracts and that makes it a completely different value proposition.
SpaceX had insanely great timing. They came in and ate up a lot of the available capital at the most prime the they could.
Antropic will be fine I think but open AI will probably have issues
Space-X and Tesla has to be a very very weird mix of person cult and fluke.
Tesla was what everyone valued crazy a because Tesla is not a car company, now Space-X is doing the same s*it as Tesla.
The general economy/inflation etc. and cost o fmoney is for sure not a good sign. Lets hope, i def want to invest in Anthropic.
1T is not low for an IPO
It's not worth 500 billion
It’s going to drop further, probably well below $1T. Still a multibillion dollar valuation though imo.
Their numbers are shit. They are brittle af. Any shift in political climate results in internal mayhem.
Same with openai
"Their numbers are shit"
Scaled from 0 to $65 billion in revenue in 5 years...
If you gave me $130B I can find a few ways to scale to $65B in 5 years too
And scaled to an annual burn rate more than double that in 5 years...
No f500 is going to sign up to use chinese models.
Until there is a large free US model, its Ant and OAI.
*Chinese hosted models, not Chinese models themselves
The major cloud providers have Chinese open weight models in their catalogs. If the model is running through your cloud provider on US soil, you're probably fine for most of corporate America.
Many F500 are already using Chinese models or models derived from them through American providers. Even Microsoft's Decision-1 Jev-like:
https://commandline.microsoft.com/microsoft-decision-1-model...
Cursor's models most famously are built on top of Kimi:
https://techcrunch.com/2026/03/22/cursor-admits-its-new-codi...
So yeah, Chinese open weight models are already in F500.
If any non-US company can use US models, they can use Chinese. Chinese are way less hostile to all of us (regardless on which continent you are) than US is.
This whole fearmongering campaign may work well within US but once you cross the border it becomes funny parody in worst ways. When all this comes from current US its beyond ridiculous, I don't expect post-midterms behavior to be any less petty and dumb.
A good rule of thumb is one a tech company attaints a >$1 trillion valuation, it tends to hold in that range; e.g. see Amazon, Appl, Google, Meta, Space-X, Nvidia, etc.
So many people 6-12 months ago predicted Space-X stock would crash and burn, yet the stock is still holding strong around $130 and a valuation over a trillion dollars.
> So many people 6-12 months ago predicted Space-X stock would crash and burn, yet the stock is still holding strong around $130 and a valuation over a trillion dollars.
That's simply because they're 1) installed in the government gravy-train, and 2) part of the AI bubble. Check again in a month or two, after AI valuations become more clear and Democrats start to publicly sketch out plans for what they're going to be doing to 2028 and beyond. They've already openly announced revenge for the half-billion in political spending, and they've haven't been talking at all about going after speech on twitter (which was a huge loser), but instead going after real stuff. Going after Space-X is an easy call.
Still overvalued.
Same thing I was going to type! I think they’re just testing these to see the reaction. Like a reverse auction while OpenAI watches in terror.
I want to see this go out at $800b. I do not believe even $1T will be supported by public markets unless they shrink the float — but that also comes with its own risks.
I’m angling for it to go out at $250b.
At $250b it would be an acquisition target.
I think that’d be appropriate.
Most finance people I’m talking to think $200-400B is a fair price. That would be an amazing story in any normal situation but a total disaster here. However sane fundamental financials just don’t support a higher number.
Challenge is while you can play games with the float size to jack up the price as existing shareholders start selling you have to convince more and more people of a sky high price when all the fundamentals and the market pulling things in the opposite direction. Employees have been royally $&@ked in prior IPOs as that story plays out.
Polymarket put a valuation at around $1.7 trillion
It keeps dropping. Was 2T, then 1.5T last week, now looking like they might be lucky to get 1T.
I wouldn’t be surprised if Anthropic delays the IPO in hopes this will all magically sort itself out but as every day passes it looks like they should have gone public this summer when things were still on the up.
Realistically the company is worth in the hundreds of billions but if it goes below 1.1T or so existing investors will need to start booking losses and that will trigger a shitstorm of other issues downstream.
This is all becoming a textbook case of folks know these assets aren’t worth what folks want them to be, but they can keep the game going so long as nobody actually marks the asset at its actual realistic valuation.
So that’s the game underway now. How low can they go without blowing up the whole AI ecosystem. If the valuations here drop next the AI bonds and private loans start to fail en-mass and the whole thing that built up this house of cards starts falling apart in reverse. Get ready for a bumpy ride folks.
I wouldn’t be surprised if they pull the plug on the IPO and go hide under a privately funded rock.
The second one of these AI companies gets a fair valuation and folks start booking losses the whole AI house of cards will come crumbling down. The crazy thing is if they drop below around 1T some existing investors will be booking losses.
A realistic valuation is likely in the hundreds of billions, which would be a total disaster and thus they’re going to do everything possible to avoid that.
literally what is the upside? They're supposedly worth as much as meta despite having nowhere near the customer base or years of work? How much more valuation do they have to trick people into giving them before investors get a return?
Why does years of work matter? Serious question.
Because building a stable revenue base takes time. We've had many flash-in-a-pan type product but things which still make money 10 years later are rare.
It's not work, but the Lindy effect makes Anthropic's outlook in 2032 very uncertain.
Possibly speaks to its moat being weak, if a dedicated team from scratch can duplicate their efforts and get to where they are today in 2 years (but not where they will be).
I think this is down to how investors think about how to value a piece of a company. I think about it almost the same way as I think about sports gambling.
There's a value proposition in which direction the stock is going to go and how long it's going to take to get there. It only makes sense to be invested in a stock if you think it's going to be worth more later than it is now and the amount of time your money is tied up in this stock is commensurate with the profit you may get as a result.
Investors work on limited information about companies, and having a track record can influence a buying decision one way or another. It may matter to some investors and not to others. For many, it's weighed against the price of the stock. Would you bet $100 on a company with a short track record? What about $10,000, $100,000, or more? Generally there's a point where people drop out of buying more of a stock and say that they can't justify the risk given the information they have. That's the expected value that Anthropic will receive from that investor.
Anthropic needs to worry about what that number is for a huge amount of individual investors and how to maximize the amount of money put into the cost of their stocks, and there's very little chance that they can attract the biggest pool of investor money without some of those people considering their longevity vs a more established company like Meta. It may not matter to all investors, but the fact that it will matter to some makes it a factor in the total valuation.
That plus Meta's Muse Wonder LLM is very close to Opus 5.5.
Its not. Perhaps in Metas own Benchmarks but not on the public listed results.
World's best or next best LLM, at least for code.
Since they actually have paying customers, it's a completely different sort of thing than for Meta. The question is instead whether they can keep at the frontier, whether the compute they've signed up for is good enough, etc.
What on earth are you smoking. Meta has lots of high paying customers, their revenue last year was 200 billion, roughly 44x Anthropic's revenue.
Ah, I simply hugely underestimated how big Meta was.
Do you know how companies are valued?
Anthropic hit 9 billion ARR in Dec 2025 and 80 billion in Oct 2026. Meta hit 9 billion in 2014 and 80 billion in 2020. It is now at 250 billion. At this growth rate Anthropic will be bigger than Meta already next year.
Caveats: It's hard to believe that this growth rate would continue. And Meta's numbers above are trailing 12 months, while Anthropic's are annualized run rate.
$80B ARR with an ~$160B net loss.
We already know the growth rate doesn’t continue, their q3 is a massive slowdown compared the 26H1.
Also, ARR as in Annualized Run Rate is a bullshit, non-standard metric only used to mislead investors
some of you are acting like earth is the size of jupiter and there's 800 billion humans not 8
Its the growth potential and the product they already have.
Companies are now building the agentic layer, the agentic layer eats tokens. Everything you do every process will have tokens eaten inbetween.
Your HR Request? AI Tokens. Everything.
It is strange that they have this problem. I thought they achieved superintelligence. Maybe they haven't prompted it right yet.
They have a product which can do A LOT of stuff A LOT better and A LOT faster than A LOT of people around me already.
I'm lost on how you can be snarky while others already are loosing their jobs because of Anthropic/OpenAI
My point is that Anthropic is not special. Neither is OpenAI. They are just really good at marketing.
Job displacement is real, but based on real world usage it would owe just as much to Grok, Gemini, DeepSeek, Kimi, and whatever else that is close to frontier, but cheaper.
Your original comment was just snarky though :P
Sure that argument is a lot stronger. But the market might be big enough for Gemini, Anthropic and OpenAI.
Grok is a lot worse then Frontier and still is owned by a company for very little trust.
The chinese model are intersting, they do leverage Frontiermodels to train, this might be a moat for a while.
The / hints at the problem.
If in a couple years they're losing their jobs because of Anthropic/OpenAI/10+ other enterprise token resellers using open models then their value proposition is much weaker even if people are still losing jobs.
Yes could be but we don't know yet how big the overall market will be.
If you have in every single industry AI tokens, it will replace wages. In USA alone thats 15 Trillion. If its just 10% the USA market alone are 1.5T without the value added.
do you think we live in the alien universe or something? I seriously sometimes try to argue with some of these comments but jesus christ
The OP is snarky but he makes a valid point: if Claude is so intelligent, why can't it just "do something" to get whatever valuation its owners want? Because that's what we've been told, this is AGI, no it's ASI, it will kill us all.
Get your tickets to Layoffpalooza 2027 now!
Has already started with companies offering buyouts.
I fire U, electric boogaloo.
These people are from another planet.
Gotta give it to OAI for waiting. They smelled it.
Is waiting better? Surely it would be better to be early, before faith is lost? I think being late to a party as it’s deflating is the worst case
My hunch is that it’s better to be late in this scenario because now your moves are under public scrutiny or at least neck-deep. People can pull out, it could slow them down and at the end look worse.
As a privately held company, it functions under different dynamics that in the environment we’re seeing feels better to stay private for longer.
Well they’re struggling to raise cash via new rounds, having slashed both the raise amount and valuation themselves. So in that situation they may go bust before having a chance to go public.
OAI is in the middle of another funding round at $1.25T. If Anthropic with allegedly better financials can't get above $1.1T then why would anyone buy OAI at $1.25T
Or then any numbers they would have publicly presented with even previous scrutiny would have been so bad no one would have bought them at price they want.
People seem unnecessarily deluded here in a supposed hacker forum. If they are really created actual digital intelligence smarter than every human and figure it out how to control it, then 1T dollars means this is the most undervalued business in the entire world. Maybe by 100x or 1000x. You have billions of super genius slaves in data centers that do whatever you want, then if a single human developer can earn $100k/year pessimisticly let alone a super genius one. Multiply that by a billion and do the math at 80% gross margins
…and what if they haven’t? Given that we haven’t seen evidence of one so far.
You’re also missing the competition aspect. What if other companies have their own super genius slaves to sell at a cheaper price? What if open models allow anyone to have their own moderately genius slave at far less expense?
> If they are really created actual digital intelligence smarter than every human
Whoever is claiming that is an idiot.
I see nothing delusional in speculating that that’s a very big if.
And if they haven’t created an artificial superintelligence then they start to look more like just another tech startup with no moat[1] and a terrible balance sheet.
[1] I don’t know about you, but I switch model providers multiple times per day.
Yours is a reasonable estimate at the _value_ of their product. They will only be able to capture that value if their competition cannot catch up AND they can reduce spend on R&D. I personally do not believe they’ll defeat distillation.
What if they have billions of super genius slaves, but Chinese companies have just slightly less super genius (and catching up) slaves for 1/4th of the price, while all prices are already dropping?
Talk about terrible timing. Had their IPO been 2 weeks earlier they'd have hit $2T easy.
that's low enough Musky could just buy it out right, jeez.
But we're so close to RSI!
A lot of people have had it for decades. It really hurts.
Seems like a great argument for anchoring high in negotiations.
They made a typo, it's more like $11B
What? Imho that is still way too much, I wouldn't go above 400B, considering if z.ai is about 128B
make it 100b
As high as $1.1T. Where is the supposed growth going to come from?
Claude has been pushed by force and coercive marketing into most software organizations and many open source projects. In 2026 the companies buying the narrative were exuberant and told employees to tokenmaxx.
This will stop in 2027 because companies will find out that nothing has improved. Nothing at all. They'll evaluate the expenditures.
So the market is saturated and usage is going down. Anthropic should be valued at $200 billion at best.
Usage is not going down. Take a look at their published revenue, it has a nice hockey stick shape.
I’m not making a bigger argument other than I disagree with that one datapoint you mentioned.
A floppy hockey stick.
Revenue growth q2 to q3 was significantly lower percentage wise than q1 to q2 and they swung back to net loss.
Yeah, the hockey stick is flipped in the wrong direction if you're looking at the past 4 months, plateauing after steady gains.
usage may shift to models run locally, albeit trained by the frontier labs, increasingly over time, though.
When you sell people 6 quarters for $1 they tend to buy a lot. Useage is a poor metric here given the capital intensive nature of this business. What matters is can they do this in a way that makes money to the degree that justifies the valuation, and there they have not yet demonstrated this is possible.
The internal metric that they are using is they get $2 for every $1 in inference. So, if you discount model development costs, they are selling the tokens for more than it costs them to generate a token. And this is before they start integrating their advertisment networks into the model. They see something as high as $8-11 per $1 of inference once purchasing and advertisments are incorporated.
The “but ads” logic fails basic sense checks. There isn’t enough ad money to go around to make up the gaps. With the big ad players like Google, Meta, and Amazon all shutting Anthropic out of their ecosystem there’s very little evidence that Anthropic can steal away enough of that as revenue to save itself.
In the “but they’ll slow down training and so will become more profitable” thinking is in big trouble given the pace of open models. They’ll then be even less competitive with free options and so even less reason to buy from Anthropic.
> So, if you discount model development costs, they are selling the tokens for more than it costs them to generate a token.
If you discount my expenses I am actually swimming in cash too.
It is absolutely moronic to discount the value of developing the thing from the profitability of the thing. Models to depreciate with time.
Also, Anthropic is known to lie through their teeth to make themselves look better, so any claims that they make a profit on inference should be treated as false until proven otherwise.
Any “non-GAAP” measure of “profit” should generally be viewed with a giant red flag.
In nearly all cases these metrics just say “if you ignore all the reasons why we’re horribly unprofitable, we’re profitable.” WeWork’s “community adjusted EBITDA” was a textbook example of this.
I introduced it at our admittedly small company not because of coercive marketing or force of any kind. I did it because rolling new stuff out takes days instead of months. Not only that but we can make products above our previous level.
Unsafe, comprehension debt, vibecoding, slop? Have you seen actual lowly developers’ code? AI is a vast, vast improvement on all relevant axes if you ask me.
If you think this is going to slow down I have no idea what to tell you.
Good drop. Should be less than a trillion amid current midterm fallout. The chances of more AI regulations are coming.
Colossus 1 & Colossus 2 are located on Tennessee and Mississippi, and Anthropic rents both of them earlier this year.
Now that Democrats are surely going after Trump and Elon Musk and the sidekicks, and both Tennessee and Mississippi turned blue as well, this directly impacts the way Anthropic to source cheap compute and data in the future due to uncertainty around the continuation of SpaceX data centers. And other data center buildouts are likely to be paused for things like power grid connection and environmental impacts and land purpose evaluation, the compute capacity constraints are likely going south, too.
Worse, the proposals around superintelligence is exactly what Anthropic is looking for: slow the fuck down. Now they got the killshot they long desired for, it obviously makes a dent in the capability and capacity of their AI, hence a drop in valuation. I think $800-900B is somewhere I would say okay, and $600-700B is really where it is fair, considering the long term outlook for Anthropic is looming with a greater AI massacre caused by political factors.
Wasn’t the regulation main hope to sustain the value? Otherwise open models come and eat the lunch.
This is a "not like that" situation. They wanted "AI safety" legislation guaranteeing them rent money. What they're likely getting is legislation to slow down data center buildout and make them more environmentally sane (more expensive upfront cost).
And open models are coming whether they like it or not.
Oh, and if the Dems take OpenAI and Anthropic's AI safety rhetoric at face value, nationalization and other forms of substantial government interference is on the table. A predictable outcome of irresponsible fear mongering!
Nothing ever happens. Elon and SpaceX will be fine.
I don't see how Anthro is worth 15x( Gemini estimates based off WSJ source )as much as Deepseek( 76 billion) .
If anything is going to pull the rug out, it's going to be a model from Deepseek or another Chinese lab. I'll admit I'm very uncomfortable with so much of our economy riding on AI hype.
To be fair to China, if AI is really this revolutionary technology, they need to be able to have home grown peers.
Every nation ( or bloc of nations) needs alternatives to American tech.
A trillion in debt and no profit. It ain’t worth a trillion. It has no moat and no guarantee of future revenue. It doesn’t own anything.
50b would be a fair valuation.
Perhaps $50B would be a fair valuation to aim for?
Imagine that - 50 (fifty) times more than an average unicorn start-up!
Something they could be very proud of, and many if not all employees would become multi-hundred-thousandaires practically overnight!
No
Reportedly Anthropic's ARR is $80B and gross margin 85%, so $50B sounds low.
Sure, if you ignore all RnD costs. If you don't; they were $80B in the hole the first half of 2026.
Almost all of that loss is accounting charges due to valuation increases, not R&D or other expenses.