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 really hope the future is local models, because the idea that all companies will need to buy tokens from a handfull of tech companies to run their businesses or make their products is awful.
I've been ripped (on this site) for making this prediction. While the frontier (i.e. The toughest problems) will always push the limits of available computing power, what typical people do with AI won't always be on the frontier, nor will what appears on the frontier necessarily be lucrative. The frontier used to be playing Chess, Atari, and Go. These were challenging and really advanced AI, but they didn't make a lot of money. Now a phone can do these things.
You're an example of this process. You used to require a frontier model running on a data centre. Algorithms improved and, perhaps, your hardware as well, but your requirements didn't go up. Now you can do what you need to do with local hardware. You're one AI user who might not need to send money to Anthropic going forward unless you find a new frontier application that interests you.
This is why I'm a little bearish when it comes to the valuation of the AI giants. Yes, there are some extremely lucrative things on the frontier right now but, in the future, people may shift a lot of what they're doing now to local hardware while new things on the frontier may not produce the kind of profits that will pay back trillions in debt. AI companies have bet big on themselves being able to constantly and perpetually find new applications, and also that those new applications will make money.
I think we will see good enough local models, excellent cloud models, and then simpler focused models embedded on silicon running on every small device.
> the idea that all companies will need to buy tokens from a handfull of tech companies to run their businesses or make their products is awful.
To the leadership of these tech companies that actually is the dream. Sam Altman said the quiet part aloud recently: "From a global allocation perspective, actually, it's quite terrible to have everybody have a bunch of the world's limited supply of DRAM sitting in their own computer, and being used maybe 5% of the time."
And it certainly fits into the neofeudal worldview of other relevant Big Tech leaders. Be it Peter "I no longer believe that freedom and democracy are compatible" Thiel, or unnamed bigwigs discussing if shock collars are effective to prevent their staff from revolting after a doomsday scenario [2].
We shall own nothing and rent everything, that is the mindset these people believe.
(And if you ask me: the AI IPOs are part of that strategy. Assuming OpenAI and Anthropic both IPO, they will get part of every American's and most other countries' pension funds... and when they collapse, a lot of that value will be gone. The initial investors into SpaceX, OAI and Anthropic all will have made their money and put it aside, and we will be left holding bags)
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.
Here's a stress test of sorts that I've had it working on the last couple of days: We have a C -> microcode compiler written in C (most of that was work done 'back' in the Opus 4.6/4.8 era). I gave it the docs for that compiler and asked it to implement it in OCaml (since it's not very common, but also is a good implementation language for compilers). It can use the first compiler as a golden to compare against. It's essentially: C language parser -> AST -> IR -> [optimization passes] -> microcode (+ some verilog code). It's basically at the point now where the original compiler has been. Not only does this generate microcode it also has to generate some Verilog code that loads and runs the microcode as well as run simulations in Verilator to confirm functionality. I asked it to come up with some new optimizations that the original compiler doesn't have and it came up with a couple that in some cases reduce microcode lines by up to 30% (less microcode in this machine means more performant). These are ideas that Claude didn't come up with when it was working on the original compiler back in the 4.6/4.8 era(and I recall prompting it several times asking it if there might be other optimizations we could perform).
So all in all, I put it at Opus 4.8 level, at least. And in some ways it's exceeding 4.8.
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
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).
I am in the same boat. Sun servers were still the best that money could buy when they lost to the Linux revolution. Linux was _good enough_ and destroyed their margins.
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.
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.
it feels like this is a bit similar to the famous dropbox comment.
I guess maybe another anecdotal comment, but I'm an engineer myself, have a home server for simple stuff, and I pay $200 a month to chatGPT for a pretty simple usage and really don't see myself go and run my own open source model, especially at the speed at which they evolve and change.
99% of the world will not setup their own open source model, just like 99% of the world is not setting up their own servier with ssh copy whatever the dropbox comment was saying.
If you were paying API prices (for ZDR), I suspect you would be eyeing local hosting. But if you’re comfortable selling your data, then the subscription prices are going to be hard to beat, for however long they may last.
I’m migrating that way as well, but I’m still struggling in creating a nicely tuned harness to achieve what I really want:
Opus for planning and the very difficult parts, Qwen for the mundane ones. It still involves a lot of manual model switching (but opencode and Pi make that relatively easy).
I've got a prototype cluster of 5 devices running Qwen3.8-27B assigned different roles and sharing outputs between each other. Beats Claude Opus MAX in SWE and Terminal benchmarks.
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.
There's basically no point in paying for these subscriptions anymore. Sure they are marginally better at some things. But they are also marginally worse at others and the implications suck. I stopped all my subscriptions and keep like 20 bucks in open router just in case. Never need it, but it's there...
The non-anecdotal part is to me as interesting as the anecdotal part. The price of RAM has gone up a great deal which is going to make it harder for others to get this sweet setup that you have.
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.
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.
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.
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.
> 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)
> 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.
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.
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 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.)
I've been using the $32/month (AUD) subscription, but finally dipped into extra credits to get a task over the line yesterday after I hit my 5-hr limit.
And my god, $15 of extra credits ended up giving me only 30 minutes of Claude Code before running out.
The subscription prices can not be financially sustainable. There is no way that is remotely close to the actual cost of running.
The other way of looking at this is that all the customers paying API rates are giving Anthropic way more than the cost of serving their traffic. They reported an 85% gross margin in their S-1, so this clearly seems to be the case.
Okay but even with a 85% margin the true cost for 30 minutes of usage would be $2.25. At that cost $34 should last only 7.5 hours. I would imagine the average user uses more than that?
API is definitely holding things up. But they excluded the training from those profit calculations. There's a ton of wordplay indulged online about how that's capex etc etc. But without the training they don't have the thing to run inference on so it's very fitting with the pattern of being evasive with finances.
Deepseek claim they are earning 38 dollars per GPU hour on inference. If you compare Deepseek Flash's off-peak pricing with Sonnet at API rates, Deepseek is 16x cheaper per 1M output tokens, and 13x to 33x cheaper for input, depending on whether or not it's a cache hit. Inference is very profitable.
There are moments where I have a 2 hour , 4 instances in separate Kitty/Ghost windows , Opus 5.5 hammering away on each - and only see a 1% increase on my Max x20. Then there are other days where 10 minutes of a single xH Opus doing 1/10th of the work will kill 2%.
Anthropic is super opaque with what inference costs at any given moment on the subscription and cache is just super inconsistent.
A Claude/OpenAI sub is still cheaper than DeepSeek API pricing though. Also, I suspect Claude and OpenAI models to be way larger (probably in the 2-3T range) than that of Deep Seek.
Yeah and soon we'll discover it's because they calculate the cost of inference as near 0 because they use AWS / NVidia / etc. credits that they got "for free" (aka investments from those companies) instead of paying for them, or some other accounting bullshit.
You wanna know the real price? It's easy, just check what AWS, GCP or Azure charge for tokens.
We recently switched from paying API prices via Cursor to a Claude subscription. Before we'd be averaging $100-$300 per day, now we have the €90/mo subscription and I haven't even reached the 5h limit yet.
My guess is a mixture of both... There's a reason why enterprise customers have to use API based billing.
(We also had a stint of using Grok 4.7 and that was the same price for our usage. Opus 5.5 seemed smarter though. After taking into account caching both average around $0.60 per 1M tokens)
You might not be representative of the average user. Usually with subscription-based services, you have a minority of hyperusers that the company loses money on and a large majority who only use their subscription occasionally.
> And my god, $15 of extra credits ended up giving me only 30 minutes of Claude Code before running out.
I've been running into the same thing, and adding separate OpenAI seats for employees rather than paying obscene overages to Anthropic. Thankfully Opus has decreased usage quite a bit, and made this less of an issue as well.
Opus 5.5 is incredibly verbose compared to GPT 6.1. I mean substantially. They are both great generating the code I need. But I wonder how much more this extra vebosity costs me with Opus.
I found this to be a shocking experience as well. Compare the 20x (now ultra 10x or whatever) plan running with 90% cache hit vs some extra credits you put on top. It's pissing in the wind.
> The subscription prices can not be financially sustainable. There is no way that is remotely close to the actual cost of running.
Welcome to... any subscription system ever? If every person with a gym subscription actually went there say three times a week, or every person living in a dorm paying flat rate for electricity started mining bitcoin 24/7, these systems would collapse. That doesn't mean it's not sustainable, it just is how subscription pricing works, by having some people use its offers a ton and others barely at all
This metaphor gets repeated a lot for some reason but using a gym subscription has an upper bound. If one subscription could be using 10000 different machines at the same time their model would be entirely different.
because "building things with claude-code" isn't the product they're selling, not really.
they're setting a precedent that artificial intelligence should be free, or at least at-cost (free from profit), but robot labor (API usage) should not. I am all in favor of this dynamic. They built / maintain the robots, so they profit from their labors.
I think every comment is talking about their 20 dollar plan. Enterprise pricing is 20/seat+ api use prices. That pricing is all that matters, the personal use tier is probably tiny
>> The subscription prices can not be financially sustainable.
Even if they are a loss leader, they serve a valuable purpose: people who get used to using AI a lot via the subscription then lobby their companies to start using it. IMHO this is one of the reasons Anthropic was able to make major headway into enterprise.
My impression is that none of those systems seems to be efficient.
We see time after time systems that solves most 80% of the case to be 90% cheaper.
And the way it is, it seems to me that nor openai nor Anthropic can keep the current prices.
Some say that if Anthropic keep delivering improvements and be the first it will make it but for that, you need to disregard all competition from other llm companies and from other competitor systems.
And even though they have the smartest model ever, if someone offers something 90% of it for 90% cheaper it will be worth it.
We already see plenty of companies reducing ai cost because they don't see enough roi
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?
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.
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.
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).
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.
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 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.
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.
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.
> 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?
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.
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.
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.
> 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.
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.
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.
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!
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, 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
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.
> a less sympathetic administration would create significant headwinds,
I find myself wondering what "less sympathetic" and "significant headwinds" mean in the minds of people in the upper echelons of the AI industry.
Rules for training frontier models? Requirements for renewables to power data centers? Actually having to pay people for the content you train on? Data center rules that would end them going behind the backs of local electorates to get governments to approve the building of data centers?
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.
Strata and Qwen 3.8 flash next is an 180B model running at 70 tps on my 5070 ti and 64gb of DDR4 RAM at Q3 quants and so far I can't tell much difference between it and any other non local model for my workflows/coding needs. Not saying local is better all the time, but it's certainly a lot more capable with minimal hardware than you're giving it credit for. Qwen 4.0 is right around the corner too.
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.
Having currently a very tech-intensive chat running for more than one week, this is where the frontier models really shine: Its no more 2024 or so, where they lost context on large discussions, you can do really great work with the frontier models - the local models will be a thing in a few years, if RAM & CPU developed further, this will happen: in 2035 like, you will be able to run "Claude-like experience" on your iPhone.
> 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'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.
I think the key to making it make sense is that people have wildly different demands for quality. You will see people say that these small local models have improved quickly and are "good enough for most work". While these models have improved and very fast, it still might be more of a comment on their expectations than the product.
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 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.
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.
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.
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.
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.
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.
Because local models are significantly worse? I'm not so sure what your question is. What else did you expect? If you could just build Claude Code at home, Anthropic would be valued at closer to $1.1M than to $1.1T.
Imo, this is just the tech version of virtue signaling. Everyone knows local models are objectively just worse than frontier/cloud closed source models for most commodity hardware setups, yet people insist on using it because they want to be contrarian or "stick it to the man."
Your experience is average because it _is_ supposed to be average. The people using local models tradeoff taking the moral/technological high ground vs actually using something high quality to get a job done quickly and with high quality, and largely autonomously. They are ok with subpar output because they will handhold the model more, or the (subpar relative to the frontier) outputs are sufficient for their toy use case while being cheap and accessible, with the added benefit of being able to run it on their private data without sending it to a 3rd party.
Almost no one at any job where software is the main economic output is using a local model to code. If you actually require intelligence to get a hard job done, you should just use a frontier model.
> If you actually require intelligence to get a hard job done, you should just use a frontier model.
Or, you know, your own intelligence.
What I do not understand as an engineer is how our community has gone from believing in open source for controlling destiny, security, etc., from avoiding unnecessary corporate capture (think of all the rants about Swift!), from believing in heterogeneity and portability, to just saying, look, if you're not using the product of one of two loss-making AI unicorns to do all of your work, you're just being a contrarian weirdo who is doing it for performative reasons.
I guess if that is your definition of contrarian — that everyone else is doing it so why aren't we? — then I am.
But in my experience it's not so different to "why aren't you on social media?" — which was an argument from insecurity and the desperate need to fit in.
I personally believe in independent thinking. I have things I want to learn about how all this actually works, I have questions about my own life and future, and I'm not going to simply chuck them away because you or anyone else thinks it's just being contrarian and we shouldn't question the direction everyone is taking. So I am learning the tech with a stack I control, while continuing to do the job I do.
You can do what you want. You're probably right, and I'll be quite happily wrong and continue in my wrongness even though, based on how you characterise it, this kind of independent thinking appears to unsettle you.
So for a sense of scale, un-quantized 1 parameter == 1 byte of ram. 12Gb means you could have roughly 12b parameters, frontier models like Kimi k3 has 3 trillion parameters, so you'd need ~3tb of vram.
> 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.
I really strongly agree with this statement. A bunch of people aren't even engineers, and their abilities aren't that good either, yet they keep saying that open-source models can replace OpenAI or Anthropic's models.
If that were really the case, basically nobody would spend so much money using them.
With my DGX Spark + 5080 + 3060 setup, I've tried all kinds of approaches, including quantization, acceleration, fine-tuning, post-training, and everything else.
It still can't compare to Opus 5.5 max in terms of speed, capability, and stability.
And when I calculate all the money I've invested, directly subscribing to the service would actually be cheaper, more efficient, and more stable.
Open source is great, but don't glorify it.
The architecture of the code I develop has never been simple. If you're someone who doesn't even work on things that need to be deployed to something like k8s, then basically, open-source models are indeed enough for you.
I (all other issues aside) honestly think we have lost all sanity when the term "low" (in any fashion) can end up associated with 1.1 TRILLION.-
There is a serious, malignant, dangerous disarray somewhere in our callibration of value, of worth, of what things are worth. Something, many things, are wrong here.-
I think you’re talking about the headline, and it’s clearly not really calling it ‘low.’ that said, things are relative, and relative to the market’s position on AI and AI related spend, anthropic’s overwhelmingly optimistic spending commitments, how they predict their machine god will take over the world, etc. - 1.1 is indeed “low”
I don't trust the market to calibrate "value" the same way that I subjectively define it.
But I trust the market to calibrate value about 10,000x more than I trust any person or group of people to do so. The thought of that is frankly terrifying.
1.1T is 27% less than what they are aiming at, and yet, it’s still absolutely mind boggling.
People love to reign in AI companies these days (and part of it is warranted) but the triple digit annual increase in revenue at Anthropic and others (SpaceX being in that category as well) is something never seen before.
Some people are being too quick (or too willing) to disregard the obvious immense economic impact of AI and company (and I’m not talking about the next-day-ai-crash-incoming economic impact).
Classic mistake of confusing the usefulness of a technology with the first movers ability to extract long term value.
Anthropics S1 numbers are hugely optimistic given the commoditisation that has hit, continued to hit, in Q3 and the temporarily hold on chip supply that is not going to hold for too long
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.
can someone explain to me the point of the 'initial valuation', considering ultimately the price will settle lated thorugh trading to perceived value anyway ?
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.
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.
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.
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.
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.
> 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.
Everyone is explaining what initial valuation is without explaining the point of initial valuation. The point is that existing private investors get to exit with a sizable profit while leaving the risk of future growth to the public market.
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.
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.
> 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.
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.
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
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.
Inference is projected to continue to become increasingly commoditized so you need to wrap it in compelling and differentiated product people are willing to pay for.
Training, people argue, continue to be very capital intensive so moat there. What I'm wondering, is anyone trying out ideas the lower the training cost materially? I'm not thinking distilling because that, by definition, would always be behind the frontier. Other ways?
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.
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
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!
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?
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.
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.
using claude code with pro subscription and deepseek api. claude is project manager and deepseek is developer. combo seems pretty effective and efficient.
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.
> 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.
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.
F500 already outsource everything in the name of cost efficiency, but you're telling me, with the opportunity to cut token costs 40x, they're going to draw the line here?
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.
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.
The Facebook valuation some 10 years ago was like… 300 million? I mean, I get it, but jeez, when I see headlines like this “as low as(!)”, I need to pinch myself.
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?
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.
> Anthropic hit 9 billion ARR in Dec 2025 and 80 billion in Oct 2026
This is a bit misleading and based on projections of prior reported months. Growth has already slowed, Q3 in dollars was less than Q2. The timing aligns largely with the growth (since mid-summer) of open weight models.
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.
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.
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).
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.
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.
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.
The future is not local models because by the time you think of what to pipe through the LLM, the super intelligent AGI owned by Anthropic or OpenAI (but not both) would have already mapped it out, generated it and had it in production. You can, however, still pursue living as a hobby.
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
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.
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.
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
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.
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?
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?
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.
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 $1 they tend to buy a lot. Useage is a poor metric here given the capital intensive nature of this business. What matters is can they do this in a way that makes money to the degree that justifies the valuation, and there they have not yet demonstrated this is possible.
I introduced it at our admittedly small company not because of coercive marketing or force of any kind. I did it because rolling new stuff out takes days instead of months. Not only that but we can make products above our previous level.
Unsafe, comprehension debt, vibecoding, slop? Have you seen actual lowly developers’ code? AI is a vast, vast improvement on all relevant axes if you ask me.
If you think this is going to slow down I have no idea what to tell you.
I think people in this thread who claim local models are anywhere close to Claude are completely detached from reality. 200$ 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 argument is not that they are as good, but that more models have reached the threshold for being useful. You do not need the best for most tasks these days. Freedom from closed model vendors also has value.
This is why token prices are falling and there are expectations for commoditization. In another year or two, you will have Opus 5.5 in much smaller models, assuming capability distillation remains as good as it has been from mega -> mini models.
Yeah I get it but I don't think it hurts Anthropic sub offerings. 200$ or 500$ or whatever it's going to be is low enough that even slight edge is worth paying for business use case. They will likely go into local proprietary models as well (we train our model on your data and you run it on your premises so you meet data retention regulation).
While I am a big fan of open models and I hope I can run local Opus 5.5 level model locally in the future I will still be using Opus 7.5 or whatever it's going to be for anything business related then. Again, the problem here is that they are not making enough from those subs and API is 20x+ more expensive.
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.
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.
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?
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 really hope the future is local models, because the idea that all companies will need to buy tokens from a handfull of tech companies to run their businesses or make their products is awful.
I've been ripped (on this site) for making this prediction. While the frontier (i.e. The toughest problems) will always push the limits of available computing power, what typical people do with AI won't always be on the frontier, nor will what appears on the frontier necessarily be lucrative. The frontier used to be playing Chess, Atari, and Go. These were challenging and really advanced AI, but they didn't make a lot of money. Now a phone can do these things.
You're an example of this process. You used to require a frontier model running on a data centre. Algorithms improved and, perhaps, your hardware as well, but your requirements didn't go up. Now you can do what you need to do with local hardware. You're one AI user who might not need to send money to Anthropic going forward unless you find a new frontier application that interests you.
This is why I'm a little bearish when it comes to the valuation of the AI giants. Yes, there are some extremely lucrative things on the frontier right now but, in the future, people may shift a lot of what they're doing now to local hardware while new things on the frontier may not produce the kind of profits that will pay back trillions in debt. AI companies have bet big on themselves being able to constantly and perpetually find new applications, and also that those new applications will make money.
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I think we will see good enough local models, excellent cloud models, and then simpler focused models embedded on silicon running on every small device.
Why only a handfull? There can be (maybe already are) hundreds of LLM API providers.
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> the idea that all companies will need to buy tokens from a handfull of tech companies to run their businesses or make their products is awful.
To the leadership of these tech companies that actually is the dream. Sam Altman said the quiet part aloud recently: "From a global allocation perspective, actually, it's quite terrible to have everybody have a bunch of the world's limited supply of DRAM sitting in their own computer, and being used maybe 5% of the time."
And it certainly fits into the neofeudal worldview of other relevant Big Tech leaders. Be it Peter "I no longer believe that freedom and democracy are compatible" Thiel, or unnamed bigwigs discussing if shock collars are effective to prevent their staff from revolting after a doomsday scenario [2].
We shall own nothing and rent everything, that is the mindset these people believe.
(And if you ask me: the AI IPOs are part of that strategy. Assuming OpenAI and Anthropic both IPO, they will get part of every American's and most other countries' pension funds... and when they collapse, a lot of that value will be gone. The initial investors into SpaceX, OAI and Anthropic all will have made their money and put it aside, and we will be left holding bags)
[1] https://www.ign.com/articles/openai-ceo-sam-altman-thinks-it...
[2] https://www.rte.ie/news/business/2025/0511/1512086-doomsday-...
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> the idea that all companies will need to buy tokens from a handfull of tech companies to run their businesses or make their products
Did you somehow become incapable of doing things without models? Most of us have not.
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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.
Here's a stress test of sorts that I've had it working on the last couple of days: We have a C -> microcode compiler written in C (most of that was work done 'back' in the Opus 4.6/4.8 era). I gave it the docs for that compiler and asked it to implement it in OCaml (since it's not very common, but also is a good implementation language for compilers). It can use the first compiler as a golden to compare against. It's essentially: C language parser -> AST -> IR -> [optimization passes] -> microcode (+ some verilog code). It's basically at the point now where the original compiler has been. Not only does this generate microcode it also has to generate some Verilog code that loads and runs the microcode as well as run simulations in Verilator to confirm functionality. I asked it to come up with some new optimizations that the original compiler doesn't have and it came up with a couple that in some cases reduce microcode lines by up to 30% (less microcode in this machine means more performant). These are ideas that Claude didn't come up with when it was working on the original compiler back in the 4.6/4.8 era(and I recall prompting it several times asking it if there might be other optimizations we could perform).
So all in all, I put it at Opus 4.8 level, at least. And in some ways it's exceeding 4.8.
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
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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).
And only slower because hardware
I am in the same boat. Sun servers were still the best that money could buy when they lost to the Linux revolution. Linux was _good enough_ and destroyed their margins.
I would have preferred to have open Solaris flourish along with Linux.
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.
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These companies have no moat at all.
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.
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I think the staggering amounts of compute and capital at their disposal is a kind of moat.
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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.
I’m waiting for somebody to use one of the frontier models to hack its own data centre and exfiltrate everything…
it feels like this is a bit similar to the famous dropbox comment.
I guess maybe another anecdotal comment, but I'm an engineer myself, have a home server for simple stuff, and I pay $200 a month to chatGPT for a pretty simple usage and really don't see myself go and run my own open source model, especially at the speed at which they evolve and change.
99% of the world will not setup their own open source model, just like 99% of the world is not setting up their own servier with ssh copy whatever the dropbox comment was saying.
You don't have to rub it yourself though. There's lots of pure inference providers which are way cheaper than the big guys and have cheaper models
If you were paying API prices (for ZDR), I suspect you would be eyeing local hosting. But if you’re comfortable selling your data, then the subscription prices are going to be hard to beat, for however long they may last.
99% of the world is also not spending $200/month for chatGPT. They're using whatever free shit they can get to ask basic questions.
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I’m migrating that way as well, but I’m still struggling in creating a nicely tuned harness to achieve what I really want:
Opus for planning and the very difficult parts, Qwen for the mundane ones. It still involves a lot of manual model switching (but opencode and Pi make that relatively easy).
Curious how much ram you need to run this at acceptable performance
I've got a prototype cluster of 5 devices running Qwen3.8-27B assigned different roles and sharing outputs between each other. Beats Claude Opus MAX in SWE and Terminal benchmarks.
How much does such a box cost?
About 4000 eur
This. The problem is now the same monopoly cartel problem.
Strix halo is double the cost because of...
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.
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There's basically no point in paying for these subscriptions anymore. Sure they are marginally better at some things. But they are also marginally worse at others and the implications suck. I stopped all my subscriptions and keep like 20 bucks in open router just in case. Never need it, but it's there...
The non-anecdotal part is to me as interesting as the anecdotal part. The price of RAM has gone up a great deal which is going to make it harder for others to get this sweet setup that you have.
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?
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.
The latest Deepseek Flash is much, much larger than Qwen3.8 Flash next
Qwen-3.8-flash-next hardware costs motorcycle level money, DeepSeek hardware starts at mid range car money.
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.
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.
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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.
> 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
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> 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.
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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.
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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?
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 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.)
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I've been using the $32/month (AUD) subscription, but finally dipped into extra credits to get a task over the line yesterday after I hit my 5-hr limit.
And my god, $15 of extra credits ended up giving me only 30 minutes of Claude Code before running out.
The subscription prices can not be financially sustainable. There is no way that is remotely close to the actual cost of running.
The other way of looking at this is that all the customers paying API rates are giving Anthropic way more than the cost of serving their traffic. They reported an 85% gross margin in their S-1, so this clearly seems to be the case.
Okay but even with a 85% margin the true cost for 30 minutes of usage would be $2.25. At that cost $34 should last only 7.5 hours. I would imagine the average user uses more than that?
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API is definitely holding things up. But they excluded the training from those profit calculations. There's a ton of wordplay indulged online about how that's capex etc etc. But without the training they don't have the thing to run inference on so it's very fitting with the pattern of being evasive with finances.
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Deepseek claim they are earning 38 dollars per GPU hour on inference. If you compare Deepseek Flash's off-peak pricing with Sonnet at API rates, Deepseek is 16x cheaper per 1M output tokens, and 13x to 33x cheaper for input, depending on whether or not it's a cache hit. Inference is very profitable.
There are moments where I have a 2 hour , 4 instances in separate Kitty/Ghost windows , Opus 5.5 hammering away on each - and only see a 1% increase on my Max x20. Then there are other days where 10 minutes of a single xH Opus doing 1/10th of the work will kill 2%.
Anthropic is super opaque with what inference costs at any given moment on the subscription and cache is just super inconsistent.
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A Claude/OpenAI sub is still cheaper than DeepSeek API pricing though. Also, I suspect Claude and OpenAI models to be way larger (probably in the 2-3T range) than that of Deep Seek.
Again, if you ignore training.
That is like saying selling hamburgers is profitable if you exclude the cost of beef.
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You are probably better off upgrading to Max for one month. I’ve done that a few times.
or a second subscription
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> no way that is remotely close to the actual cost
Anthropic is running 85% gross margins on inference.
Less development and operating costs
Yeah, on the API, not subscriptions. Those are a loss leader
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Yeah and soon we'll discover it's because they calculate the cost of inference as near 0 because they use AWS / NVidia / etc. credits that they got "for free" (aka investments from those companies) instead of paying for them, or some other accounting bullshit.
You wanna know the real price? It's easy, just check what AWS, GCP or Azure charge for tokens.
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We recently switched from paying API prices via Cursor to a Claude subscription. Before we'd be averaging $100-$300 per day, now we have the €90/mo subscription and I haven't even reached the 5h limit yet.
My guess is a mixture of both... There's a reason why enterprise customers have to use API based billing.
(We also had a stint of using Grok 4.7 and that was the same price for our usage. Opus 5.5 seemed smarter though. After taking into account caching both average around $0.60 per 1M tokens)
You might not be representative of the average user. Usually with subscription-based services, you have a minority of hyperusers that the company loses money on and a large majority who only use their subscription occasionally.
Because, like with other subscriptions, most people don’t use even half of it.
OTOH less than $10 gets me more DS4.1 than I can use in a month.
> And my god, $15 of extra credits ended up giving me only 30 minutes of Claude Code before running out.
I've been running into the same thing, and adding separate OpenAI seats for employees rather than paying obscene overages to Anthropic. Thankfully Opus has decreased usage quite a bit, and made this less of an issue as well.
Opus 5.5 is incredibly verbose compared to GPT 6.1. I mean substantially. They are both great generating the code I need. But I wonder how much more this extra vebosity costs me with Opus.
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> The subscription prices can not be financially sustainable. There is no way that is remotely close to the actual cost of running.
It's completely unclear to me why this is the conclusion everyone jumps to instead of "holy cow they are milking it on API pricing."
I mean, have you ever run a local model? How much electricity did it cost you to get 1,000 tokens out of it? Like a fairy-dust of a cent?
The AI labs' cost is CapEx. Marginal cost of tokens is zero.
How much electricity do you think a few DGX Sparks consume?
I found this to be a shocking experience as well. Compare the 20x (now ultra 10x or whatever) plan running with 90% cache hit vs some extra credits you put on top. It's pissing in the wind.
Be careful!
They lure you in to do "automatic top up", which gets quite expensive if you dont watch it :-)
> The subscription prices can not be financially sustainable. There is no way that is remotely close to the actual cost of running.
Welcome to... any subscription system ever? If every person with a gym subscription actually went there say three times a week, or every person living in a dorm paying flat rate for electricity started mining bitcoin 24/7, these systems would collapse. That doesn't mean it's not sustainable, it just is how subscription pricing works, by having some people use its offers a ton and others barely at all
This metaphor gets repeated a lot for some reason but using a gym subscription has an upper bound. If one subscription could be using 10000 different machines at the same time their model would be entirely different.
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because "building things with claude-code" isn't the product they're selling, not really.
they're setting a precedent that artificial intelligence should be free, or at least at-cost (free from profit), but robot labor (API usage) should not. I am all in favor of this dynamic. They built / maintain the robots, so they profit from their labors.
I think every comment is talking about their 20 dollar plan. Enterprise pricing is 20/seat+ api use prices. That pricing is all that matters, the personal use tier is probably tiny
Don't worry, I've probably sent Claude 5 messages on my personal sub the past month, so I got you covered.
>> The subscription prices can not be financially sustainable.
Even if they are a loss leader, they serve a valuable purpose: people who get used to using AI a lot via the subscription then lobby their companies to start using it. IMHO this is one of the reasons Anthropic was able to make major headway into enterprise.
My impression is that none of those systems seems to be efficient. We see time after time systems that solves most 80% of the case to be 90% cheaper. And the way it is, it seems to me that nor openai nor Anthropic can keep the current prices.
Some say that if Anthropic keep delivering improvements and be the first it will make it but for that, you need to disregard all competition from other llm companies and from other competitor systems.
And even though they have the smartest model ever, if someone offers something 90% of it for 90% cheaper it will be worth it.
We already see plenty of companies reducing ai cost because they don't see enough roi
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?
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.
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Anthropic wants open weights models banned, and wants data centers. They're probably going to get the opposite.
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Especially since it seems to be a call for Trump, specifically, to control the models.
What is the lockup schedule?
They have like 26 different dates IIRC
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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.
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.
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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).
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.
> 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 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.
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.
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.
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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.
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.
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> 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?
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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.
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> 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
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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
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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.
> 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.
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.
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.
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.
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Generating a full length movie script and then the video?
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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!
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.
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, 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
Tulips kinda did go away
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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 one of those already https://www.theyworkforyou.com/ https://www.govtrack.us/
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Got a website for this? A politician leaderboard sounds like it could do some good, even with Goodhart’s law.
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> a less sympathetic administration would create significant headwinds,
I find myself wondering what "less sympathetic" and "significant headwinds" mean in the minds of people in the upper echelons of the AI industry.
Rules for training frontier models? Requirements for renewables to power data centers? Actually having to pay people for the content you train on? Data center rules that would end them going behind the backs of local electorates to get governments to approve the building of data centers?
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...
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?
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.
Strata and Qwen 3.8 flash next is an 180B model running at 70 tps on my 5070 ti and 64gb of DDR4 RAM at Q3 quants and so far I can't tell much difference between it and any other non local model for my workflows/coding needs. Not saying local is better all the time, but it's certainly a lot more capable with minimal hardware than you're giving it credit for. Qwen 4.0 is right around the corner too.
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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.
This:
Having currently a very tech-intensive chat running for more than one week, this is where the frontier models really shine: Its no more 2024 or so, where they lost context on large discussions, you can do really great work with the frontier models - the local models will be a thing in a few years, if RAM & CPU developed further, this will happen: in 2035 like, you will be able to run "Claude-like experience" on your iPhone.
can you give example, prompt require context window > local model, or higher Quality/Speed, that local model can not do, open source example?
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> 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.
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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.
This matches my experience, Claude limits with Fable blows up the entire 20x usage in like an hour, whereas Astra can run a couple days.
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.
I think the key to making it make sense is that people have wildly different demands for quality. You will see people say that these small local models have improved quickly and are "good enough for most work". While these models have improved and very fast, it still might be more of a comment on their expectations than the product.
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)
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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.
A 3060 + good chunk of system RAM can run qwen3.8 flash next.
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.
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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?
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.
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.
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
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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.
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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.
Because local models are significantly worse? I'm not so sure what your question is. What else did you expect? If you could just build Claude Code at home, Anthropic would be valued at closer to $1.1M than to $1.1T.
Imo, this is just the tech version of virtue signaling. Everyone knows local models are objectively just worse than frontier/cloud closed source models for most commodity hardware setups, yet people insist on using it because they want to be contrarian or "stick it to the man."
Your experience is average because it _is_ supposed to be average. The people using local models tradeoff taking the moral/technological high ground vs actually using something high quality to get a job done quickly and with high quality, and largely autonomously. They are ok with subpar output because they will handhold the model more, or the (subpar relative to the frontier) outputs are sufficient for their toy use case while being cheap and accessible, with the added benefit of being able to run it on their private data without sending it to a 3rd party.
Almost no one at any job where software is the main economic output is using a local model to code. If you actually require intelligence to get a hard job done, you should just use a frontier model.
> If you actually require intelligence to get a hard job done, you should just use a frontier model.
Or, you know, your own intelligence.
What I do not understand as an engineer is how our community has gone from believing in open source for controlling destiny, security, etc., from avoiding unnecessary corporate capture (think of all the rants about Swift!), from believing in heterogeneity and portability, to just saying, look, if you're not using the product of one of two loss-making AI unicorns to do all of your work, you're just being a contrarian weirdo who is doing it for performative reasons.
I guess if that is your definition of contrarian — that everyone else is doing it so why aren't we? — then I am.
But in my experience it's not so different to "why aren't you on social media?" — which was an argument from insecurity and the desperate need to fit in.
I personally believe in independent thinking. I have things I want to learn about how all this actually works, I have questions about my own life and future, and I'm not going to simply chuck them away because you or anyone else thinks it's just being contrarian and we shouldn't question the direction everyone is taking. So I am learning the tech with a stack I control, while continuing to do the job I do.
You can do what you want. You're probably right, and I'll be quite happily wrong and continue in my wrongness even though, based on how you characterise it, this kind of independent thinking appears to unsettle you.
I can't really do anything with this comment without a couple of specifics, such as 'what model are you using?'.
So for a sense of scale, un-quantized 1 parameter == 1 byte of ram. 12Gb means you could have roughly 12b parameters, frontier models like Kimi k3 has 3 trillion parameters, so you'd need ~3tb of vram.
32 would get you a good Qwen 3.8 27b which is like Opus 4.6.
12 GB isn't a lot. You need a lot of VRAM or RAM for context window.
> 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.
I really strongly agree with this statement. A bunch of people aren't even engineers, and their abilities aren't that good either, yet they keep saying that open-source models can replace OpenAI or Anthropic's models.
If that were really the case, basically nobody would spend so much money using them.
With my DGX Spark + 5080 + 3060 setup, I've tried all kinds of approaches, including quantization, acceleration, fine-tuning, post-training, and everything else.
It still can't compare to Opus 5.5 max in terms of speed, capability, and stability.
And when I calculate all the money I've invested, directly subscribing to the service would actually be cheaper, more efficient, and more stable.
Open source is great, but don't glorify it.
The architecture of the code I develop has never been simple. If you're someone who doesn't even work on things that need to be deployed to something like k8s, then basically, open-source models are indeed enough for you.
>Sure I only have a 12GB GPU
Found your issue.
I (all other issues aside) honestly think we have lost all sanity when the term "low" (in any fashion) can end up associated with 1.1 TRILLION.-
There is a serious, malignant, dangerous disarray somewhere in our callibration of value, of worth, of what things are worth. Something, many things, are wrong here.-
I think you’re talking about the headline, and it’s clearly not really calling it ‘low.’ that said, things are relative, and relative to the market’s position on AI and AI related spend, anthropic’s overwhelmingly optimistic spending commitments, how they predict their machine god will take over the world, etc. - 1.1 is indeed “low”
I don't trust the market to calibrate "value" the same way that I subjectively define it.
But I trust the market to calibrate value about 10,000x more than I trust any person or group of people to do so. The thought of that is frankly terrifying.
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.
You can just cancel the subscription...
Dear god, you can use sonnet for that.
opus was the only one who did well, argon too
you don't even know my specifications, why are you parroting something useless?
1.1T is 27% less than what they are aiming at, and yet, it’s still absolutely mind boggling.
People love to reign in AI companies these days (and part of it is warranted) but the triple digit annual increase in revenue at Anthropic and others (SpaceX being in that category as well) is something never seen before.
Some people are being too quick (or too willing) to disregard the obvious immense economic impact of AI and company (and I’m not talking about the next-day-ai-crash-incoming economic impact).
Classic mistake of confusing the usefulness of a technology with the first movers ability to extract long term value.
Anthropics S1 numbers are hugely optimistic given the commoditisation that has hit, continued to hit, in Q3 and the temporarily hold on chip supply that is not going to hold for too long
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.
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can someone explain to me the point of the 'initial valuation', considering ultimately the price will settle lated thorugh trading to perceived value anyway ?
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.
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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.
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.
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.
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.
> 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.
Everyone is explaining what initial valuation is without explaining the point of initial valuation. The point is that existing private investors get to exit with a sizable profit while leaving the risk of future growth to the public market.
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.
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.
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.
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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.
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.
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-...
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.
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
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.
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Not that crazy, software sales are global from day 1.
I know quite a few paying Anthropic customers here (Norway). Not a single customer of Walmart.
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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
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.
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$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.
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The moat really is shallow to non-existent.
Both Anthropic and OpenAI are trying to build more tooling to try and lock companies into their ecosystems.
But I can’t help thinking that they are impacted by the very thing they have created.
You can now build your own enterprise tooling using the very models that they have created.
Inference is projected to continue to become increasingly commoditized so you need to wrap it in compelling and differentiated product people are willing to pay for.
Training, people argue, continue to be very capital intensive so moat there. What I'm wondering, is anyone trying out ideas the lower the training cost materially? I'm not thinking distilling because that, by definition, would always be behind the frontier. Other ways?
Really though we all know how this will play out. Google, Amazon, Microsoft are going to buy out both labs and we will all be centralized again
Here is a video overview of Anthropic's IPO filing by a finance expert. [0] https://youtu.be/PfjsIfXms4o
Still too high
Dupe: https://news.ycombinator.com/item?id=50394906
Anyone have a sense of what a valuation like that would mean for employees trying to cash out?
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.
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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
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.
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.
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.
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.
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> 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!
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?
This is a very competitive market. You can't command fat margins in a very competitive market.
Huh? If a Chinese open weights model comes out that is nearly as good, the valuation drops to very little.
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.
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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.
using claude code with pro subscription and deepseek api. claude is project manager and deepseek is developer. combo seems pretty effective and efficient.
Related:
Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation
https://news.ycombinator.com/item?id=50386597
anthropic futures on binance dropped again today. why don't they just do a direct listing and then atm share sales ?
Low ???
1T is not low for an IPO
1793898170 | Michael Burry: Anthropic Is "Betting Its Existence on Optimism" | https://news.ycombinator.com/item?id=50391165
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
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.
No f500 is going to sign up to use chinese models.
Until there is a large free US model, its Ant and OAI.
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.
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.
*Chinese hosted models, not Chinese models themselves
F500 already outsource everything in the name of cost efficiency, but you're telling me, with the opportunity to cut token costs 40x, they're going to draw the line here?
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.
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"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
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And scaled to an annual burn rate more than double that in 5 years...
They made a typo, it's more like $11B
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.
Don't think SpaceX is a cult of personality mix. Making rocketry cheap is how they got to where they are.
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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.
The Facebook valuation some 10 years ago was like… 300 million? I mean, I get it, but jeez, when I see headlines like this “as low as(!)”, I need to pinch myself.
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?
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.
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
> Anthropic hit 9 billion ARR in Dec 2025 and 80 billion in Oct 2026
This is a bit misleading and based on projections of prior reported months. Growth has already slowed, Q3 in dollars was less than Q2. The timing aligns largely with the growth (since mid-summer) of open weight models.
https://www.regenerator1.com/p/anthropics-growth-just-flatte...
$80B ARR with an ~$160B net loss.
some of you are acting like earth is the size of jupiter and there's 800 billion humans not 8
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.
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Why does years of work matter? Serious question.
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.
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).
It's not work, but the Lindy effect makes Anthropic's outlook in 2032 very uncertain.
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.
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.
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.
Do you know how companies are valued?
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.
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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.
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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.
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Get your tickets to Layoffpalooza 2027 now!
Has already started with companies offering buyouts.
I fire U, electric boogaloo.
Compare to SpaceXSI, they lack missiles, datacenters, and a profitable internet business so... yeah.
The future is not local models because by the time you think of what to pipe through the LLM, the super intelligent AGI owned by Anthropic or OpenAI (but not both) would have already mapped it out, generated it and had it in production. You can, however, still pursue living as a hobby.
Simple as. I hope this helps.
These people are from another planet.
> Anthropic IPO buyers see valuation as low as $1.1T
AS LOW AS?! This wording has to be intentional... Because I would use "as high as", myself.
And after they went way out of their way to state in their S-1 that they would never dream of valuing the company at $1 more than it was worth?
As "low" as 1.1T. What a world we live in where 1.1T is considered "low"...
Gotta give it to OAI for waiting. They smelled it.
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.
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.
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
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.
Still at least $1.1T too high.
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?
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.
> If they are really created actual digital intelligence smarter than every human
Whoever is claiming that is an idiot.
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?
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.
If a frog had wings...
You are making claims with no basis, caveat-ed by "if," and then calling everyone here fools for not basing their beliefs on your wishful thinking.
wasn't 1.3 just a couple of hours ago and 1.5 yesterday?
But we're so close to RSI!
A lot of people have had it for decades. It really hurts.
that's low enough Musky could just buy it out right, jeez.
"as low as $1.1T"
the absurdity
Seems like a great argument for anchoring high in negotiations.
It's not worth 500 billion
Talk about terrible timing. Had their IPO been 2 weeks earlier they'd have hit $2T easy.
What? Imho that is still way too much, I wouldn't go above 400B, considering if z.ai is about 128B
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.
When you sell people 6 quarters for $1 they tend to buy a lot. Useage is a poor metric here given the capital intensive nature of this business. What matters is can they do this in a way that makes money to the degree that justifies the valuation, and there they have not yet demonstrated this is possible.
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usage may shift to models run locally, albeit trained by the frontier labs, increasingly over time, though.
A floppy hockey stick.
Revenue growth q2 to q3 was significantly lower percentage wise than q1 to q2 and they swung back to net loss.
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I introduced it at our admittedly small company not because of coercive marketing or force of any kind. I did it because rolling new stuff out takes days instead of months. Not only that but we can make products above our previous level.
Unsafe, comprehension debt, vibecoding, slop? Have you seen actual lowly developers’ code? AI is a vast, vast improvement on all relevant axes if you ask me.
If you think this is going to slow down I have no idea what to tell you.
Imagine 'as low as' next to 1.1 Trillion Dollars.
make it 100b
I think people in this thread who claim local models are anywhere close to Claude are completely detached from reality. 200$ 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 argument is not that they are as good, but that more models have reached the threshold for being useful. You do not need the best for most tasks these days. Freedom from closed model vendors also has value.
This is why token prices are falling and there are expectations for commoditization. In another year or two, you will have Opus 5.5 in much smaller models, assuming capability distillation remains as good as it has been from mega -> mini models.
Yeah I get it but I don't think it hurts Anthropic sub offerings. 200$ or 500$ or whatever it's going to be is low enough that even slight edge is worth paying for business use case. They will likely go into local proprietary models as well (we train our model on your data and you run it on your premises so you meet data retention regulation).
While I am a big fan of open models and I hope I can run local Opus 5.5 level model locally in the future I will still be using Opus 7.5 or whatever it's going to be for anything business related then. Again, the problem here is that they are not making enough from those subs and API is 20x+ more expensive.
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.
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Nothing ever happens. Elon and SpaceX will be fine.
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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.
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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.
What's ASI?
Artificial super intelligence
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.
I don't think being ruled by ASI is by default safe whether it's Chinese or American.
You think it's a good idea to trustmebro some Silicon Valley jerks with world domination? I... don't.
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!
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.
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No
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and I behind 100M, in that case everyone who buys if they are worth more including them will get more right?
No - the company is initially offering shares, so a higher IPO valuation means that the company gets more liquidity.