Same thing I was going to type! I think they’re just testing these to see the reaction. Like a reverse auction while OpenAI watches in terror.
I want to see this go out at $800b. I do not believe even $1T will be supported by public markets unless they shrink the float — but that also comes with its own risks.
Most finance people I’m talking to think $200-400B is a fair price. That would be an amazing story in any normal situation but a total disaster here. However sane fundamental financials just don’t support a higher number.
Challenge is while you can play games with the float size to jack up the price as existing shareholders start selling you have to convince more and more people of a sky high price when all the fundamentals and the market pulling things in the opposite direction. Employees have been royally $&@ked in prior IPOs as that story plays out.
I mainly doubt the moat, not necessarily the usefulness. Why will customers stay with anthropic? Many cheaper models seem to catch up within 6-12 months, and there’s also other flagship providers that keep pace and compete on cost (oai, Google). Why should I believe that anthropic’s revenue is going to stay consistent, and not evaporate to other competitors?
Validation is all about assets and future earning potential. Anthropic’s core assets are a model which is shown every day now to be only marginally better than something that’s free. So that asset appears to have very little value. Their future earning potential depends on folks not figuring out they can get a good-enough result for free, only paying for compute. And Anthropic doesn’t own compute, they rent it.
So the whole valuation is based on the idea that folks will happily pay this company a huge markup for inference over the actual cost of compute for a model whose valuation is rapidly tending to zero. Hence why potential investors are starting to freak out.
In competitive environments, such as most businesses, marginally better is enough to create large advantages.
I just don’t see a world where everyone is happy buying the second or third smartest model. The people using the smartest will just out compete them, even if it is marginally more expensive.
> Anthropic sells intelligence, and the most intelligent intelligence.
Most intelligent is a stretch, we don't know how the Chinese architectures would fare if they had the same compute availability as Anthropic and other US labs.
If we extrapolate and consider that they are behind due to artificial export restrictions, once they figure out how to build their own GPUs in a cost-effective/scaled-up way then the American labs' claim of having the best intelligence is at an enormous risk.
They already do a great job being resource constrained, imagine once this constraint is gone!
If the justification for the valuation is "they sell intelligence", then their valuation should be $0. Because their product isn't the least bit intelligent, and routinely gets beaten by humans.
You can be as dismissive as you want but this doesn't reflect the reality.
I can easily spend a LOT less time on describing the same amount to claude than i would in a JIRA ticket and doing code review on the PR of Claude than talking to a person about it. Instead of 1-4 weeks Claude is done in an hour.
Its also already A LOT faster and better of analysing infrastructure issues.
Conservatives' undermining public education makes it inevitable that slop engines will surpass the intelligence of the average human, then double and treble it as the average human's intelligence continues to decline.
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.
can someone explain to me the point of the 'initial valuation', considering ultimately the price will settle lated thorugh trading to perceived value anyway ?
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
My hunch is that it’s better to be late in this scenario because now your moves are under public scrutiny or at least neck-deep. People can pull out, it could slow them down and at the end look worse.
As a privately held company, it functions under different dynamics that in the environment we’re seeing feels better to stay private for longer.
Well they’re struggling to raise cash via new rounds, having slashed both the raise amount and valuation themselves. So in that situation they may go bust before having a chance to go public.
OAI is in the middle of another funding round at $1.25T. If Anthropic with allegedly better financials can't get above $1.1T then why would anyone buy OAI at $1.25T
Or then any numbers they would have publicly presented with even previous scrutiny would have been so bad no one would have bought them at price they want.
I'm not convinced that 2026 was peak of the AI hype cycle.
Arguments are being made that, with the midterms over and 2028 just around the corner, a less sympathetic administration would create significant headwinds, adjusting the valuation of Anthropic and OpenAI down.
What we don't know are what Anthropic and OpenAI have planned and in the works.
A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments. It's likely a significant number of them will start their own business with AI.
AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That said, IPOs are usually liquidity events for LPs and for something really disruptive and unpredictable as AI, I won't be engaging in their IPOs. Only time will tell if this was a good decision, or bad.
Tulips, Railroads, and the Internet also didn't go away when the financial bubble around them burst. That doesn't mean there wasn't a massive market correction in response to the over-investment.
That's a pretty damning statement - this implies companies don't see a return on AI at best, and it's actively destroying their businesses at worst, so there's not only job loss, but businesses themselves are not becoming more efficient.
> In 2026 everything fron sewage to chickens to oil to education is now tied to AI. The entire economy is now, for good or bad tied to AI.
Tied how? How strong is the corellation between these? I'd say very weak, other than everything having to compete with AI for resources.
The same is true of any technology tied to powerful personal computers or the internet - people built and designed beatiful houses, great cars, airliners, wrote books and ran restaurants without computers or the internet.
It has even become a meme how putting smart features and lcd screens into things like washing machines have made them strictly worse.
Somewhat ironically it would appear most CEOs believe the only jobs AI is replacing are developers since they built an app last weekend. Every CEO is saying why do we have so many devs and why are we paying them so much… so devs blew up the market for devs but beyond that there doesn’t seem to be much impact.
Even if it takes them 3-6 Month to open up a new field, its going to happen steadily now and were it started, the AI investments will be recuped by layoffs.
Do you really see lawyers being replaced? I don't see anything special in their work but since it's a regulated industry I doubt it will be so affected.
At best some layers will work faster, but automating their work or people not requiring them? Usually when you contact one there's a lot at stake. Same with accountants.
Software engineering and translators is a different story. Most of the times if something goes wrong nobody cares.
Some lawyers (the worst ones?) might just do the easy or normal things like writing a 'lawyer' letter to someone not paying. This is no risk to automate away.
The avg Lawyers might do more complex but again a lot of 'normal' things. Day to day things the next SaaS AI company wants to 'fix' and they might also accept basic liability.
Then you have the 'tv lawyers' the ones who do non normal things, they have A LOT of apprentices and young lawyers going through papers, research etc.
They will for sure have the expert as a human, but that person will need a lot less support.
You shouldn't need an accountant today already. If you have an digital order and a digital bill and a digital signed delivery note, it can be booked automatically. AI will find risks, summarize them etc.
AI is already helping here closing the gap with better OCR etc.
I really think, that when big companies introduce the agentic layer to their products, there will be less people needed and if you need people, they get less paid because they will need to understand less.
Lawyer, accountants, doctors, architects, and engineers won't be replaced.
We're not paid just for what we do, we get paid because we accept liability for our mistakes. Professional liability insurance is a large part of why licensed professionals charge so much money for their services.
A lawyer can't just sign off on basic inquiring with "just reading". They're legally obligated to actually review the response and verify the answer (including any citations or references).
Lawyers already need less people to prepare things. That revolution happened several decades ago when computers and word processors became normal.
Lawyers as SaaS companies has been a thing for 2 decades, and remains a tiny niche of the market because it's not the kind of lawyering most clients need. A lawyer SaaS is basically just the equivalent of an underpaid, underresourced public defender and will yield about the same quality of work and results.
We will see if we will have one expert ai lawyer per topic or type of lawsuite or if they will get generic good enough that it will not matter
LLMs are only as good as their training set. In order to be a topic expert, they'd have to be retrained at least every week to account for changes in statutory law and case law. It would be trivial for a human lawyer to outmanuever an outdated LLM when the laws changed...and in the real world this happens all the time between human lawyers when one side hasn't done its research on changes to the applicable laws.
Even people defending or suing by (successful) themselves became a thing already last year.
People successfully defending or suing without a lawyer has been a thing for over a century. It's fewer than 0.1% of legal proceedings. Most people don't handle their own legal cases for the same reason most people hire a plumber, or electrician, or order DoorDash instead of cooking their own dinner. Also...almost all of the people using LLMs in the past 2 years lost their cases, because their LLMs hallucinated laws or facts and they didn't know enough to catch it. Even lawyers dumb enough to use LLMs to draft their documents got caught because they were too lazy to review the output. Courts were willing to forgive those mistakes a few years ago before it was common knowledge that LLMs make things up, but now they're no longer willing to give the benefit of the doubt.
The biggest advantage of an LLM is the interface and reading text.
Whatever we had before on full text search, LLMs are a lot better. And i'm not just feeling this, i read plenty of articles about exactly this.
A LLM doesn't need to be retrained every week, it needs to have a base corpus of the field and strategies on how to find current text. They will load it into context and argue.
Then another LLM will take this and formats it for the court.
We will see but i think you overestimate how much automatisation and co current lawyers have and use.
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.
These are a good start, but they aren't quite enough. I just voted for ~25 different people going all the way down to local government. These don't answer the questions I'm about to be answering top down for myself and neighbors.
The major counterpoint here is commoditization; you may have the market growing and models becoming cheaper at the same time. And we don't even have to look to Chinese models for that, as OpenAI has been adjusting its pricing already. For profitability, Anthropic still seems to be pretty dependent on its current price levels.
That said, I'm also somewhat skeptical that there is even much room for growth left unless somebody finds a new, important, and token-hungry use case.
In theory, yes. But the reality is that deep learning models just aren't good at creative output (to put it mildly). And neither Anthropic nor OpenAI have any models that generate video, so this wouldn't help them; in fact, OpenAI killed off Sora.
I believe that the value that OpenAI and Anthropic hold, in light of stiff competition from China, is not their products but their internal models. China’s work is all derivative of American model API access. The exponential RSI loop means that as long as Anthropic and OpenAI keep steady footing and only release older models they will will always be the best on the market and all others will be in their tailwinds. These two things combined are what make me believe OpenAI and Anthropic will hold value and grow as companies.
Best on the market, for something not being sold on the market? How is that helpful in terms of actual revenue?
If Anthropic manages to start producing new pharmaceuticals with their internal models or something that thesis could make sense but not if 90% of their revenue comes from enterprise API sales which would necessarily have to be accessible to the public. Which are then inevitably fast followed by Chinese models 3 months later.
Even if we were to accept your entire argument [1], none of it contradicts the commoditization argument. Note also that competitition does not come just from China, but also from Google, Meta, and SpaceXAI. Where they currently are is not relevant, the entire LLM landscape will likely look nothing like today by the end of 2027.
[1] I don't, I think China is primarily limited by compute and will be at least for the next couple of years, probably longer.
It certainly isn't the peak of the AI hype cycle. Eventually something will surpass LLMs and the hype will start all over again. Will it really be the final invention this time? Place your bets!
Anthropic's original $2T valuation implies they are going to almost 100X their revenue.
I am highly confident that Anthropic will 100X their token sales volume. I am also confident that token prices will continue dropping through the floor due to competition. So to 100X their revenue they will have to 1000X or 10000X their token sales. That seems a stretch.
Or incentivize using the minimum viable model for the job instead of throwing multi trillion parameter Fable sized models at summarizing documents or classification tasks.
I now do like 90% of my routine software dev/sysadmin stuff with Luna on Medium by default, some proportion of which could itself be offloaded to a small Qwen model.
Economic theory would state that if token prices go up people will stop offloading work onto LLMs and start doing it themselves again.
Indeed, 2026 was basically that: the LLM providers increased token costs and companies cut LLM spend to the bone. That is why Anthropic's IPO is struggling and MS and Google have failed to meet AI-related revenue targets for 3 straight years.
As AI grows in capability it will be trusted to do more of the work it already does and also break into new fields. Also, there are many who have never interacted with a chatbot. There is an entire world of potential customers out there. It can be quite a villainous business model but look at what Facebook did in Africa with their sponsored ISP and also what they’re doing with Meta Muse. Demand for AI usage will skyrocket.
But will it increase by 10000X, as I back of the envelope calculated it would require to justify Anthropic's $2T valuation in the face of quickly dropping token prices?
Chatbots aren't a money maker. Each interaction is likely to be 1-2K tokens at most. AWS Bedrock prices 11K token input and 4K output for a total of $0.184 using on-demand usage [1]. So for normal usage (occasionally asking questions) you are looking at less than $1 per month for a person.
Using the models for data processing (a lot of input/output tokens) and for role-playing will use more tokens and thus cost more. However, those use cases are not common when comparing the percentage of the user base.
The money maker is agentic coding where people get the models to perform long-running tasks. This is why Open AI and Anthropic have been pushing these hard. However, there is pushback on this, especially among businesses looking for value for money/cut costs [2] like Apple [3], [4].
There's an entire universe of companies outside the MAG7 or VC-backed startups. I used to work for a US company, employing 60.000 people worldwide, which made the switch. But you will never hear about it because it's not a "tech" company.
> A large population of extremely skilled and talented people are being laid off by their employers to make space for AI investments. It's likely a significant number of them will start their own business with AI.
What's you're point there? People laid off because of AI will immediately shift into AI-centric small business, and start hyping AI?
That doesn't make sense to me. People being laid off are likely to be bitter about the cause of their layoff, and even if they all start their own businesses (which is unlikely), most of those will fail, leading to more bitterness.
Many people who are being laid off will not find another spot in their industry and will need to pivot. Most of that subset of people will go to existing companies and take traditional roles. The small few remaining might take it as an opportunity to start a new business, and when they do so, they are likely to take risks on technology and business operations which will include AI. Is that so hard to believe?
I don't buy for a second that people laid off over AI will be so darn mad at AI that they won't use it, but I don't know where the hell the markets for all of these hundreds of thousands or millions of new small businesses are.
If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop. They can use AI to help them run that. Getting paid $175K per to write ruby CRUD was a moment that is over, hope you didn't spend all your salary on toys and rent.
> If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
You assume they are competent. In my experience a monopoly's profit margins makes it very easy for incompetence to flourish.
> The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop.
Really? Experience engineers are better off starting a lawn care business or a bicycle repair shop? I do not believe this for a second. There are many areas engineering skills can be used in.
They will try to start businesses out of necessity as stable employment is not an option, and they will use AI since it's almost mandatory now for any technical productivity.
> What we don't know are what Anthropic and OpenAI have planned and in the works.
Wouldn't they tell us before IPO? Unless it's not that promising...
> AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That does not mean the current players profit from this and the infrastructure investments were necessary at that scale. There is competition, there seem to be enormous efficiency gains and it's questionable that demand for even bigger models is large enough.
> 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?
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.
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
Reminds me of linux gaming 10 years ago. According the to the community everything worked great without issue and any issues were hand waved away as tiny bumps that you won't even notice.
Then you would go try it and be back using windows to game a week later.
But that is not exactly a linux issue. Games built for linux works very well on linux like games built for windows running decently well on windows. Linux can also run some games built for windows - not for linux. And that are the ones that doesn't provide first rate experience. Those windows games executables technically should not even load as it is not built for linux.
Everything considered, linux is good and has decent capabilities including gaming and whatever you can throw at it but only do oranges to oranges comparison. Games and apps built for each os works fine on the respective os, well atleast it is true for mac and linux. Windows is very unreliable.
Memory manufacturers are running 80-90% margins. Any of a number of things (anti-trust, CXMT, financial shock) have the potential to drop memory cost by 2/3s. At the same time memory efficiency of models is improving. At some point Apple's going to start shipping 64G of unified memory in mainstream machines that are advertised as local AI capable (like HP, Dell and others do today). Like the iPhone refined the concept of a smartphone for the mainstream, they could be the first to hit mainstream sovereign AI.
The people saying that local models can easily replace the real Frontier Models from Anthropic or OpenAI are just not using it for the same things you and I do.
They seem to be happy with their small usages.
For everything I do, i see huge differences in Quality and Speed.
Even when I try an open model on a 4090, the PC gets warm/hot very fast, I struggle with context length.
> In a work tree build a feature where each column cog action menu has a "Column statistics" item - clicking that item opens a modal dialog which displays statistics about that column - for text columns that's most common values, stats about value length - for numeric columns it's more interesting than that. Let's do a bit of percentile analysis in there too. Under the hood it works by firing off SQL queries using SQLite window functions and suchlike. For the moment let's have that option only available if SQL queries are allowed against the database, and the SQL queries are built in JavaScript.
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.
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.
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)
> What graphics cards have more than 12GB, and how much does it cost to buy one?
RTX 5070 Ti and RTX 5080 both have 16 GB.
RTX 5090 has 32 GB.
Good luck finding any of them for reasonable prices, though. I paid $3,000 for my 5090 early last year. I winced when I bought it then. Today, it's like $7,000, and I wouldn't even consider it at that price.
But I'm mostly a gamer that also does local AI as a hobby. If the RTX 5090 was $7,000, I'd still be on my RTX 3080 and still just not started doing local AI and have to turn down the graphics settings in Cyberpunk 2077.
there is also a version of the 5060ti woth 16GB. i got lucky a few months ago and got it for ~380€, but i think you can still get it for around 800, definitely under 1k
The RTX 5090 is the ideal, but it's not the only 32GB GPU that can offer workable performance; the AMD R9700 is catching up.
You can surely build an LLM server with an R9700 and 32GB of mainboard RAM for less than the 64GB M5 Max Apple Studio, which is probably the next most competitive model up. It'll be better than a Strix Halo but less flexible.
- 4090: Going for $7500
- 64GB M5 Max: Starts at $3,800
- R9700: $2700
Compared to $20 or $100 per month. My thought process reading this thread:
Baseline of $20 or $100/month Claude or OpenAI subscriptions. Alternative posed of running models locally. A guy says he did it and results are lackluster on a 12GB video card. A number of people post that that card is insufficient; use 32GB+ cards. I look up prices of 32GB cards, and they are all very expensive. So from a practical perspective, people are suggesting you spend thousands of dollars on a video card to get results that are almost as good as the subscription plan they are on. It doesn't add up, unless one of my assumptions is incorrect.
The assumption that is probably incorrect is that the current prices will last. A 4090 should not cost that much, and most of your Claude usage is paid by an investor. Neither of these things will be true in a few years.
Having a 4090 at home running agents all day is not a $20pcm level of usage, so take the $100 plan. Looking at the S-1 filing, today they are not even close to breakeven. It is reasonable to assume that will become a $300-400 pcm plan.
The launch MSRP of a 4090 was $1600. There has been significant inflation since then, so say $2000. Eventually supply will meet demand and you'll be able to buy one for that price again, or lower due to the economies of scale they must have now.
So, in 2-3 years time once it has all settled, you pay off your 4090 by saving 5-6 months of Claude usage. Maybe the local model isn't good enough for complete replacement, but all it needs to do is drop you to the $20 plan to be worth it. I think this is a large part of why investors are getting jittery now.
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.
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.
I've try every new model when it comes out from both OpenAI and Anthropic. I cannot justify leaving Codex and Astra 6. The token burn on Anthropic is always astronomical for me. Codex just works, and I see no reason to move away from it.
I switched from Codex to Claude Code w/ Opus 5.5, and it's been fine. I haven't hit limits in my $100/mo plan, doing full time dev work all day. But I agree. Codex sub + Astra is also great. Both are excellent options, and I don't see a reason to go with a cheaper / less capable model, frankly.
> The results are ALWAYS terrible compared to CLaude Code.
How much effort did you spend to make the local setup work well?
Local models are not plug and play, you need to adjust settings to make them behave intelligently.
On my side one model looking super dumb right after install ended up being 'agentic' after using the right chat template, fixing how tool calls are setup, fine-tuning plenty of params values, and adjusting the whole setup to my own hardware. Claude.ai (free) was great at helping me sort that all out.
You can maybe replicate Opus 4.5 to 4.7 if you have some moderately expensive hardware. And that is definitely good enough for many people.
The smallest model that can even pretend to be a decent coding model is Qwen3.8 27B, which you realistically need 32GB of VRAM to run without breaking or limiting it in some really noticeable way. This gets you something like a slightly less capable Opus 4.5 with a much better work ethic.
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 problem is that "can't tell much difference" is not going to be an objective indicator; you will be very likely to overlook the flaws of the local model and overweigh the flaws of the non-local model. The only reliable way to compare the two is to have your own set of evals based on the types of tasks you regularly perform and then run those evals on Qwen and a frontier model.
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.
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.
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 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.
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.
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.
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?
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[ 2.9 ms ] story [ 106 ms ] threadI 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.
Challenge is while you can play games with the float size to jack up the price as existing shareholders start selling you have to convince more and more people of a sky high price when all the fundamentals and the market pulling things in the opposite direction. Employees have been royally $&@ked in prior IPOs as that story plays out.
Apple: 5 trillion
Google: 3.5 trillion
Walmart: 1 trillion
Spotify: 125 billion
Honestly crazy that we’re doubting 1T, even with all the capital issues behind 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.
I just don’t see a world where everyone is happy buying the second or third smartest model. The people using the smartest will just out compete them, even if it is marginally more expensive.
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 can easily spend a LOT less time on describing the same amount to claude than i would in a JIRA ticket and doing code review on the PR of Claude than talking to a person about it. Instead of 1-4 weeks Claude is done in an hour.
Its also already A LOT faster and better of analysing infrastructure issues.
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.
I’m not making a bigger argument other than I disagree with that one datapoint you mentioned.
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.
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.
As a privately held company, it functions under different dynamics that in the environment we’re seeing feels better to stay private for longer.
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.
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
Is it maybe more accurate to say that the US is in a recession, outside of the industries that are fueling AI?
Life's pretty recession-y for everyone else imo
Tied how? How strong is the corellation between these? I'd say very weak, other than everything having to compete with AI for resources.
The same is true of any technology tied to powerful personal computers or the internet - people built and designed beatiful houses, great cars, airliners, wrote books and ran restaurants without computers or the internet.
It has even become a meme how putting smart features and lcd screens into things like washing machines have made them strictly worse.
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
At best some layers will work faster, but automating their work or people not requiring them? Usually when you contact one there's a lot at stake. Same with accountants.
Software engineering and translators is a different story. Most of the times if something goes wrong nobody cares.
The avg Lawyers might do more complex but again a lot of 'normal' things. Day to day things the next SaaS AI company wants to 'fix' and they might also accept basic liability.
Then you have the 'tv lawyers' the ones who do non normal things, they have A LOT of apprentices and young lawyers going through papers, research etc.
They will for sure have the expert as a human, but that person will need a lot less support.
You shouldn't need an accountant today already. If you have an digital order and a digital bill and a digital signed delivery note, it can be booked automatically. AI will find risks, summarize them etc.
AI is already helping here closing the gap with better OCR etc.
I really think, that when big companies introduce the agentic layer to their products, there will be less people needed and if you need people, they get less paid because they will need to understand less.
We're not paid just for what we do, we get paid because we accept liability for our mistakes. Professional liability insurance is a large part of why licensed professionals charge so much money for their services.
If a lawyer needs less people preparing everything, the industry will also shrink.
Lawyer as SaaS companies will just introduce some insurance and will clean up from the bottom until AI can do all of it.
We will see if we will have one expert ai lawyer per topic or type of lawsuite or if they will get generic good enough that it will not matter.
Even people defending or suing by (successful) themselves became a thing already last year.
Lawyers already need less people to prepare things. That revolution happened several decades ago when computers and word processors became normal.
Lawyers as SaaS companies has been a thing for 2 decades, and remains a tiny niche of the market because it's not the kind of lawyering most clients need. A lawyer SaaS is basically just the equivalent of an underpaid, underresourced public defender and will yield about the same quality of work and results.
We will see if we will have one expert ai lawyer per topic or type of lawsuite or if they will get generic good enough that it will not matter
LLMs are only as good as their training set. In order to be a topic expert, they'd have to be retrained at least every week to account for changes in statutory law and case law. It would be trivial for a human lawyer to outmanuever an outdated LLM when the laws changed...and in the real world this happens all the time between human lawyers when one side hasn't done its research on changes to the applicable laws.
Even people defending or suing by (successful) themselves became a thing already last year.
People successfully defending or suing without a lawyer has been a thing for over a century. It's fewer than 0.1% of legal proceedings. Most people don't handle their own legal cases for the same reason most people hire a plumber, or electrician, or order DoorDash instead of cooking their own dinner. Also...almost all of the people using LLMs in the past 2 years lost their cases, because their LLMs hallucinated laws or facts and they didn't know enough to catch it. Even lawyers dumb enough to use LLMs to draft their documents got caught because they were too lazy to review the output. Courts were willing to forgive those mistakes a few years ago before it was common knowledge that LLMs make things up, but now they're no longer willing to give the benefit of the doubt.
Whatever we had before on full text search, LLMs are a lot better. And i'm not just feeling this, i read plenty of articles about exactly this.
A LLM doesn't need to be retrained every week, it needs to have a base corpus of the field and strategies on how to find current text. They will load it into context and argue.
Then another LLM will take this and formats it for the court.
We will see but i think you overestimate how much automatisation and co current lawyers have and use.
Mine already has been.
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.
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.
If Anthropic manages to start producing new pharmaceuticals with their internal models or something that thesis could make sense but not if 90% of their revenue comes from enterprise API sales which would necessarily have to be accessible to the public. Which are then inevitably fast followed by Chinese models 3 months later.
[1] I don't, I think China is primarily limited by compute and will be at least for the next couple of years, probably longer.
They are also (like other models we know about) experimenting with the architecture. [2]
[1] https://arxiv.org/abs/2501.12948
[2] https://sebastianraschka.com/blog/2026/kimi-k3-architecture-...
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.
However this effect could just serve to make those who own the compute ridiculous money, while crushing those who need to lease compute.
I now do like 90% of my routine software dev/sysadmin stuff with Luna on Medium by default, some proportion of which could itself be offloaded to a small Qwen model.
Indeed, 2026 was basically that: the LLM providers increased token costs and companies cut LLM spend to the bone. That is why Anthropic's IPO is struggling and MS and Google have failed to meet AI-related revenue targets for 3 straight years.
Using the models for data processing (a lot of input/output tokens) and for role-playing will use more tokens and thus cost more. However, those use cases are not common when comparing the percentage of the user base.
The money maker is agentic coding where people get the models to perform long-running tasks. This is why Open AI and Anthropic have been pushing these hard. However, there is pushback on this, especially among businesses looking for value for money/cut costs [2] like Apple [3], [4].
[1] https://aws.amazon.com/bedrock/pricing/
[2] https://www.ibm.com/think/insights/tokenmaxxing-dead-long-li...
[3] https://www.bloomberg.com/news/articles/2026-11-04/apple-cut...
[4] https://9to5mac.com/2026/11/04/report-apple-reduces-employee...
Something like 99% of the people who have interacted with an LLM chatbot lose interest after a month.
100% of people who have to interact with an LLM chatbot taking the place of a human customer sales or service agent jump to a competitor.
Outside of the tech bubble, the LLM hype is already over.
This is not remotely true. It literally couldn't be true, considering Open AI have 1.2 Billion weekly active ChatGPT users.
The issue is, at some point for AI to grow more in usage it will need to decrease in cost.
We are already seeing this with some companies reducing AI usage or moving to open models due to high costs.
AI will surely boom there's no doubt on that, but it won't necessarily produce the revenue that Anthropic investors hope for.
many companies are now using both, it's only been a few months since it really picked up
Do we know if current pricing is sustainable?
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.
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.
If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop. They can use AI to help them run that. Getting paid $175K per to write ruby CRUD was a moment that is over, hope you didn't spend all your salary on toys and rent.
> If these giant monopolies couldn't squeeze revenue out of these employees using AI, why would we think that they will be able to do it by themselves?
You assume they are competent. In my experience a monopoly's profit margins makes it very easy for incompetence to flourish.
> The vast majority of them if they want to be entrepreneurs will be better off starting a new lawn care business, or a bicycle repair shop.
Really? Experience engineers are better off starting a lawn care business or a bicycle repair shop? I do not believe this for a second. There are many areas engineering skills can be used in.
Agree. these valuations will hold. Pundits made the same prediction after Facebook's IPO in 2012 that everything would soon crash. here we are...
Wouldn't they tell us before IPO? Unless it's not that promising...
> AI is also gaining a much wider audience and acceptance. It's likely over the next few years, AI demand will grow, not stagnate.
That does not mean the current players profit from this and the infrastructure investments were necessary at that scale. There is competition, there seem to be enormous efficiency gains and it's questionable that demand for even bigger models is large enough.
I 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 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.
Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation
https://news.ycombinator.com/item?id=50386597
Antropic will be fine I think but open AI will probably have issues
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.
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?
Then you would go try it and be back using windows to game a week later.
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.
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.
> In a work tree build a feature where each column cog action menu has a "Column statistics" item - clicking that item opens a modal dialog which displays statistics about that column - for text columns that's most common values, stats about value length - for numeric columns it's more interesting than that. Let's do a bit of percentile analysis in there too. Under the hood it works by firing off SQL queries using SQLite window functions and suchlike. For the moment let's have that option only available if SQL queries are allowed against the database, and the SQL queries are built in JavaScript.
You can see a screenshot of what Opus 5.5 built here: https://github.com/simonw/datasette/pull/3038
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 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
You can try that model (and many others) using a cloud host first, and then only if you like it do you need to buy something to run it.
RTX 5070 Ti and RTX 5080 both have 16 GB.
RTX 5090 has 32 GB.
Good luck finding any of them for reasonable prices, though. I paid $3,000 for my 5090 early last year. I winced when I bought it then. Today, it's like $7,000, and I wouldn't even consider it at that price.
But I'm mostly a gamer that also does local AI as a hobby. If the RTX 5090 was $7,000, I'd still be on my RTX 3080 and still just not started doing local AI and have to turn down the graphics settings in Cyberpunk 2077.
You can surely build an LLM server with an R9700 and 32GB of mainboard RAM for less than the 64GB M5 Max Apple Studio, which is probably the next most competitive model up. It'll be better than a Strix Halo but less flexible.
Baseline of $20 or $100/month Claude or OpenAI subscriptions. Alternative posed of running models locally. A guy says he did it and results are lackluster on a 12GB video card. A number of people post that that card is insufficient; use 32GB+ cards. I look up prices of 32GB cards, and they are all very expensive. So from a practical perspective, people are suggesting you spend thousands of dollars on a video card to get results that are almost as good as the subscription plan they are on. It doesn't add up, unless one of my assumptions is incorrect.
Having a 4090 at home running agents all day is not a $20pcm level of usage, so take the $100 plan. Looking at the S-1 filing, today they are not even close to breakeven. It is reasonable to assume that will become a $300-400 pcm plan.
The launch MSRP of a 4090 was $1600. There has been significant inflation since then, so say $2000. Eventually supply will meet demand and you'll be able to buy one for that price again, or lower due to the economies of scale they must have now.
So, in 2-3 years time once it has all settled, you pay off your 4090 by saving 5-6 months of Claude usage. Maybe the local model isn't good enough for complete replacement, but all it needs to do is drop you to the $20 plan to be worth it. I think this is a large part of why investors are getting jittery now.
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.
which is where the entire software industry is going
- 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.
How much effort did you spend to make the local setup work well?
Local models are not plug and play, you need to adjust settings to make them behave intelligently.
On my side one model looking super dumb right after install ended up being 'agentic' after using the right chat template, fixing how tool calls are setup, fine-tuning plenty of params values, and adjusting the whole setup to my own hardware. Claude.ai (free) was great at helping me sort that all out.
You can maybe replicate Opus 4.5 to 4.7 if you have some moderately expensive hardware. And that is definitely good enough for many people.
The smallest model that can even pretend to be a decent coding model is Qwen3.8 27B, which you realistically need 32GB of VRAM to run without breaking or limiting it in some really noticeable way. This gets you something like a slightly less capable Opus 4.5 with a much better work ethic.
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.
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.
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.
Found your issue.
If that were really the case, basically nobody would spend so much money using them.
50b would be a fair valuation.