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.

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.

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.

Revenue growth q2 to 3 was significantly lower percentage wise than q1 to q2 and they swung back to net loss.

If you discount my expenses I am actually swimming in cash too.

It is absolutely moronic to discount the value of developing the thing from the profitability of the thing. Models to depreciate with time.

In nearly all cases these metrics just say “if you ignore all the reasons why we’re horribly unprofitable, we’re profitable.” WeWork’s “community adjusted EBITDA” was a textbook example of this.

The real question is "Are frontier training costs growing faster than the productive lifetime value of the GPU infrastructure used to create those models?" Which is a hard question to answer and is something we'll find out it early 2030.

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.

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.

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.

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.

It seems like much of the tulip mania has become the stuff of legend.

From what I have read, it seems closer to baseball card trading than AI or dot com.

You can find baseball and pokemon cards trading for thousands of dollars right now on ebay.

This would seem insane to a culture just valueing the cards as pieces of cardboard.

It would be like hundred of years from now people viewing our society as wanting to buy Mickey Mantle cards instead of houses. It is a very small number of people involved with this. It is not a widespread movement to hold your retirement money in Mickey Mantle cards.

In an interesting way it shows even better though he we are susceptible to these type of mythologies. People don't even question the absurd givens of the narratives of the tulip mania. We just repeat these narratives to each other as if they are historic fact.

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

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 scrap 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 necessarily have to be accessible to the public.

[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'm cautiously bullish on AI in general, and I wouldn't touch an AI stock with a 100m pole, because there are way too many uncautiously bullish investors in the market.

We have, for example, no idea what the long-term implications of AI effectively training on its own output is. Recursive self-improvement is not a law of nature. If anything, the opposite: the overwhelming majority of species are extinct. Evolution is as much a story of dead ends as survival of the fittest. So betting on any one narrow path of AI development to survive seems highly suspect.

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 will 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.

Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation

https://news.ycombinator.com/item?id=50386597

   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

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.

To get enough payload into space that the evaoluation is worth while, he really need to do this datacenter in space thing and sending enough hardware up there to match just one GPU based datacenter would take one Starship launch every single day.

Lets ignore that building Datacenter on earth is stll A LOT easier and cheaper to do and that we shouldn't vaporize non sustainable metals into our atmosphere alone for health reasons.

We have robots, AI (which is so bad that he prevers to rent out his AI hardware instea d of making more money with it directly), 3 Car models and something else combined in two companies with an overall value over 3T.

This setup is still higher valuated as plenty of big car manufacturers, robotics companies and a lot more other companies thrown into the mix together.

What am Im missing here?

I actually calculated and researched these numbers and while it seems that you are dismissive because of your ideological position? i saw and read Elon Musks claim about AI Datacenters in Space and him writing about a dyson sphere and calling it bullshit.

You are not evaluated as the 7/8th most valuable companie on the planet just because you build rockets and send up a little bit of payload. Thats why their P/E is 3 digits.

Over a 100 launches a year, most of them for starlink doesn't sell you the valuation the company currently has. And they have to keep sending up starlink satelites just for renewale of the existing ones. This blocks Space-X of having good margins on it. A normal telecom provider builds a fiber underground and a tower and that stuff just stays there for ages.

And yes you know what happened when people realized this is a thing? Yeah more companies are going into the market. Check out how many will aunch their first rockets in 2027.

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.

> They seem to be happy with their small usages.

Oh yes, we're terribly inferior people. Just living on scraps from the table.

If that were really the case, basically nobody would spend so much money using them.

You should factor in that undeniably most people don't have the means to run even a 27B dense model locally, and therefore do not actually know what they are and are not capable of.

And you might want to look at openrouter's rankings.

If you are comfortable assuming that "doesn't use Claude or Codex" is equivalent to "not a good engineer" or "bad engineer", then I think you are probably lost to more nuanced discussions.

the original comment mentioned only 16GB of VRAM.

You know talk about Open Router and 27B dense model?

We are not comparing an even more expensive Hardwaresetup to run local models with proper context window and parameters.

Can i run some open model on a super expensive Mac Book or co with some kind of okayisch results? Yeah sure but you know what the t/s will be and yeah Frontier is still Frontier and Kimi, GLM, DeepSeek are not Frontier. They lag half a year.

You are not getting Opus 5 or Sonnet at home for now.

Why are you so offended by this?

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 do plenty of experiments of things i always wanted to do and they just don't work with local models.

Finetuning of some ML models for a specific usecase, some 4k demo idea i had, small browser games to show others game mechanic ideas i had for ages, analysis tools etc.

Even just playing around with some Ghidra eats context like nothing.

I write 5-20 paragraphs of things I want and it goes away and just builds it.

Something like this (just an example):

"Build an application with a webserver in rust. It has 4 stages, first one is an input module... second one is refinement ... third one is an image detection with current fitting ml model ... last one is finetuning."

Then i would describe every basic page with a few more paragraphs.

This would work for half an hour or an hour, consume 100-400k of context tokens and then I would test it, write down 10-50 small sentences (every sentence either fixing a bug, or making it better) which tehn takes again like 20 minutes and i'm nearly done with a complex enough tool providing real value and actually working.

A big kimi, which is hard to run, might be getting close to this but we do not talk about running it on 1-2 5090.

The other thing is, thats more like a practical experience, even if you use them through Open Router (for testing and co), the initial results of something like Opus 5.5 are ALWAYS better. More details, better quality, better understanding of usability of the tool.

And yes Sonnet 5.5 is crazy good

> 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.

There is, for sure, a limiting factor in the x86 architecture, but I think the RTX Spark will change that a bit.

I'm not familiar with these. How much does one cost, and from what store / what product name?

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?

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.

  - 4090: Going for $7500
  - 64GB M5 Max: Starts at $3,800
  - R9700: $2700

But it's also an asset that barely depreciates!

Especially right now — you could buy and extensively use an M5 Max for a year and sell it for almost exactly the same amount at the end. Maybe even more, at the current rate.

Furthermore, it can do other things for you: host your VMs, edit high def video, whatever. Even if it stops being useful to serve LLMs, it might be useful to run as a server for other tasks for a whole decade.

Depends what you want, doesn't it? Or are we all expected to conform?

For me personally, right now I have an M1 Max macbook pro, and that is my local AI machine, because my needs are exploratory and I want to run it here. I want to learn by comparing models. I want to learn how to run them and advise others how to (local inference is not a zero-sized market).

My next step up will be to take the apps I'm looking at building that run against these small models, and host those apps online with some sort of cloud inference — openrouter, digitalocean, cloudflare. Again — if I can avoid it, my apps won't develop a dependency on any AI provider.

If I ever find I want to vaguely describe some desktop application before bed and come down the next morning to an aggravating slop version of it, I'll consider Claude.

As for a local machine, I've looked at the M5 Max and the R9700; haven't pushed the button yet because I don't need it enough.

I've also looked at the M5 Pro, which in practice is about as fast at token generation as my M1 Max, and smokes it at prefill. That alone might be enough for a "leave it running" mini AI box, because agentic coding is so prefill-heavy. But I am a laptop person for the next six months or so I think.

As an aside there's that other thing which is somewhat interesting, though constrained:

https://www.olares.com/one

Uses the 5090 mobile chip, which is pretty good speedwise (about the same as the R9700) but 24GB. Small, tidy, on-prem. I think they have missed the boat on pricing, but if they can ship in volume, maybe not.

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.

"SemiAnalysis, a research and analysis company specializing in the semiconductor and AI industries, found that a $200/month subscription plan is far more generous than previously thought. According to its calculations, while it's widely believed that a $200/month plan maxes out at roughly $2,000/month in tokens (assuming API pricing), the actual numbers are closer to $14,000 for ChatGPT and $8,000 for Claude. "

https://cybernews.com/ai-news/chatgpt-claude-usage-costs-ope...

This will change in the future.

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

But will you want to do that when your tokens are metered at non-subsidised prices, and yet there is absolutely no clarity (ever!) about token efficiency, how the model decides to spend your money, how it might ever decide to stop wasting your money and get to the point, etc.?

The specify-loops-and-walk-away model absolutely points to local/on-premises/private cloud AI.

Because otherwise you're letting Anthropic or OpenAI decide how to spend your money. And they are going to be biased towards spending more of it.

I don't think Qwen 3.8 is quite there yet with the loops stuff. Definitely seems to be better at deeper and deeper one-shot puzzles.

- 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 outputs are totally sufficient for their toy use case while being cheap.

Almost no one at any job where software is the main economic output is using a local model to code.

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 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.

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?"

Found your issue.

If that were really the case, basically nobody would spend so much money using them.

I'm lost on how you can be snarky while others already are loosing their jobs because of Anthropic/OpenAI

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.

Sure that argument is a lot stronger. But the market might be big enough for Gemini, Anthropic and OpenAI.

Grok is a lot worse then Frontier and still is owned by a company for very little trust.

The chinese model are intersting, they do leverage Frontiermodels to train, this might be a moat for a while.

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.

If you have in every single industry AI tokens, it will replace wages. In USA alone thats 15 Trillion. If its just 10% the USA market alone are 1.5T without the value added.

My company is building an agentic layer in its product, google and the other hyperscalers do that too.

I already want to get rid of one person in my team because he was slow before but was able to contribute something, now I want to have his position as AI tokens.

No i don't say that 10% of the entire USA market is gonna go to anthropic.

I said what the market overall could look like. USA is not the only country out there.

[1] Say >10% of people of employable age suddenly becomes superfluous with not much prospect of finding new jobs (since AI eats those too).

But no one knows if there is a ceiling or progress might slow down massivly.

Is warning people so wrong if you interpolate the current progress and just look ahead a few years? Might just be as stupid as some North Korean/Russian hacker throwing some abliberated model against some computer system and having some cascading effect on water or energy grid.

For plenty of people AI already took their jobs, jobs they liked and enjoyed doing.

50b would be a fair valuation.

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 don't even know my specifications, why are you parroting something useless?

then maybe it's on you?

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.

[1]: https://www.cnbc.com/2014/08/19/es-took-off-but-the-auction-...

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.

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.

Then you have to consider automations and user facing products. A quota based subscription is not enough or risks being shut off after too many tokens are used. These are the primary places open weights are disrupting closed, expensive models.

Eventually, token vendors will be commoditized and the average user will have an effectively unlimited plan, but we are not there yet.

At which point, the stock is well-traded on the open market and has found a "correct" market price, with no necessary relationship to the valuation at IPO.

1793898170 | Michael Burry: Anthropic Is "Betting Its Existence on Optimism" | https://mrkt30.com/michael-burry-anthropic-s1-commitments-op... | https://news.ycombinator.com/item?id=50381531

1793902395 | So, about that Anthropic IPO | https://www.ft.com/content/2fbd60ee-060a-4957-b9c5-3b5a0ce88... | https://news.ycombinator.com/item?id=50382331

1793918987 | Michael Burry says Anthropic is 'betting its existence' on optimism | https://www.businessinsider.com/michael-burry-anthropic-ipo-... | https://news.ycombinator.com/item?id=50385642

1793923469 | Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation | https://www.wsj.com/finance/major-investors-are-skeptical-an... | https://news.ycombinator.com/item?id=50386375

1793923694 | Anthropic warns that it's close to RSI capability, a.k.a. self-building AI | https://techcrunch.com/2026/11/02/anthropic-warns-that-its-v... | https://news.ycombinator.com/item?id=50386406

1793924267 | Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation | https://www.wsj.com/finance/major-investors-are-skeptical-an... | https://news.ycombinator.com/item?id=50386483

1793925071 | Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation | https://www.wsj.com/finance/major-investors-are-skeptical-an... | https://news.ycombinator.com/item?id=50386597

1793934067 | About That Anthropic IPO | https://www.wsj.com/livecoverage/stock-market-today-dow-sp-5... | https://news.ycombinator.com/item?id=50387632

1793965329 | Is Anthropic About to Delay the Biggest IPO? | https://www.zerohedge.com/ai/anthropic-about-delay-biggest-i... | https://news.ycombinator.com/item?id=50391165

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.

In the S-1 filing Q3 was profitable even with training included, if you exclude stock based compensation.

Q2 was profitable on all operating costs (incl. hardware, employees, training, inference). They were not profitable on non operating costs though. That’s partially interest, but mostly, when their valuation goes up, they’ll have to issue more stock (absolute value) to early investors after IPO.

Their profitability will likely crator as they start building their own data centres after the IPO, so that’s the best results they’ll have in a while.

The median user is using AI as a search engine, meme creator, and/or chatbot… at best.

The additional cost created by the subset of programmers using subscriptions rather than tokens/api can easily be categorized as a marketing expense.

Remember that I referred to median user here.

> Do you have any data you're basing that claim on, or are you just speculating based off of you projecting?

Anecdata? I guess it depends on what you count as “data”.

I am currently training a lot of people directly or indirectly (train the trainer type of stuff) on how to use AI professionally. I always start with “what do you currently use AI for?”. The answers I listed above are largely the categories that people have told me and the folks I train, with the exceptions being a handful of non-technical folks who have leaned into AI and have used agents and/or created apps of some sort.

To add some categories that I didn’t initially mention, some folks also use AI for drafting/writing documents and for creating meeting transcripts.

> AI maximalists would have you believe that people are using these things as therapists and having conversations with it that are more intimate than any lovers'.

Chatbot… this is something I hear, but I’m guessing I don’t hear all of the examples since it’s personal rather than professional. But some folks certainly say something like “I treat AI like a non-judgmental friend that I can ask personal questions to”.

> AI minimalists would have you believe that no one is using these thing except at gunpoint, and even then.

In my experience, this is almost exclusively the domain of old/older people who are happy to let tech pass them by and young people who are virtue signaling to their peers (or at least trying to).

There are precisely zero professionals I k ke

> Unless you have some reason for us to believe you that isn't just "trust me bro", your portrayl of the median user is as suspect as any other.

It seems like you’re new to HN. I understand your skepticism. There is a relatively high density of actual professionals here. Trust me on that, bro.

My data set numbers in the 100s, and it is not a statistically representative cross-section of the US or world market. That said, I’m guessing that I have a fairly good range of data points for the US market.

> some folks certainly say something like “I treat AI like a non-judgmental friend that I can ask personal questions to”.

is why I believe that these things are stickier than I think you believe. I'm obviously biased by my own personal usage of AI and where I live and my own bubble which isn't the same as your bubble. Thank you for the context of your dataset. It's larger and more professional than mine. Mine is not professional at all and definitely a bubble of whom I happen to run into at a non-random selection of events.

We'll have to see what the stock market decides.

Classic

> or are you just speculating based off of you projecting?

This is also a strange thing to say. Why would you assume speculation or jump to projection as the basis for it? Nothing in the comment that you are replying to hints at projection.

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"

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

Overall it would go through weekly usage within 40 minutes, if not for 5h session limit.

Running on 10000 computers will just hit all the limits at once.

And there's a hard place that everyone freaks out at API prices and on-demand Opex models outside of a niche (I use API for personal research for quite focused queries and slightly more reassurance on being the customer and not the product) but this is very competitive + not sticky.

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.

Anthropic is running 85% gross margins on inference.

You wanna know the real price? It's easy, just check what AWS, GCP or Azure charge for tokens.

The API Gross margin is even higher.

The purpose of the high API prices is, in addition to trying to get more money, to anchor the high price, and then make the subscriptions seem like a great deal. But inference is just not that expensive, not when provided at scale and with huge batches.

You can see this with the various Chinese providers doing inference on open models, which are unlikely to cost less to run than the models at the frontier AI labs. A single 8-GPU GB300 cluster doing high-throughput batch inference on Kimi K3.1 Pro, which is the largest open weights model available, can do something like 300 000 tokens per second, that is, 10T tokens per year. That cluster, with current insane inflated prices, costs something like $1M to buy, and $50k to run per year in electricity. At $10/Mtok, that pays itself back in 4 days. Do you think that that's a reasonable price?

The subscriptions were probably priced with reasonable positive margins.

The huge expenses are in training and pushing the models forwards, with the gigantic RLAIF runs we know they are doing. Then once you got the model, you can scale up inference at low cost and want to serve as many people as possible.

They lure you in to do "automatic top up", which gets quite expensive if you dont watch it :-)

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.

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.

Anthropic is super opaque with what inference costs at any given moment on the subscription and cache is just super inconsistent.

That is like saying selling hamburgers is profitable if you exclude the cost of beef.

If under the scenario you outline where they don't need to upgrade then AI is commoditized and margins are razor thin. And that is fine. Great even! But it doesn't justify Anthropic's trillion+ dollar valuation.

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)

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?

AS LOW AS?! This wording has to be intentional... Because I would use "as high as", myself.

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.-

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.

Not that it's somehow "not people" (of course it is).

"Prediction" markets have rendered this clearly false of late.

> the reason I trust the market is the "skin in the game"

The market expects economic bailouts and other assistance during downturns, because individual investors with 401(k)s demand it, despite the perversions of market incentives that result.

Now people are disappointed with a thousand times that.

the absurdity

Simple as. I hope this helps.

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.

People love to reign in AI companies these days (and pay of it is warranted) but the triple digit annual increase in revenue at Anthropic and others (SpaceX send to be 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).

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

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

That said, Anthropic is an interesting company. I mostly use local models like Qwen 3.8 Next Flash, but when I occasionally use Opus 5.5 for difficult I enjoy the experience.

- Nervous about Redis being written in C? Switch to Garnet.

- Bun ported from Zig to Rust for $165,000 at API pricing -> https://bun.com/blog/bun-in-rust

- Microsoft ports Copilot runtime to Rust for $120,000 in API costs without blocking other development work -> https://github.blog/ai-and-ml/generative-ai/migrating-the-gi...

- Someone made a "clean room port" of Total Annihilation in golang, looks like it started in August and is now the most convenient way to play Total Annihilation. https://github.com/nanolathe-gg/nanolathe

- You only have to decompile / reverse engineer an existing game once and now you can have an agent occasionally add features.

Once you do the port and have worked out the regression issues, assuming you are switching to a safer language with better guarantees, you are now off of the old codebase and don't have to pay the costs of the old codebase. It's just a one time burst of token usage.

Costs of tokens will continue to fall as more hardware either becomes rentable (datacenter comes online) or moves into the used market, as workloads move onto users individual machine (because smaller models are becoming more capable and getting better inference engines and harnesses).

You can also task an agent with researching for "list the top 5 performance improvements we might be able to make" and have it research overnight, then the next day start a fleet of agents iterating through the findings that seem the most workable . Once you gain the performance win, that's basically permanent.

I think that token usage is a very elastic market, you can choose to spin up a fleet of agents or... you can stay human-in-the-loop, or you can type code yourself if you don't have a token budget.

So, yeah, I don't think Anthropic or OpenAI are worth much when hot on their heels are open-weight models hosted by major cloud providers + Nvidia and cloud providers themselves building their own models.

Token volume is increasing though -> https://ramp.com/data/ai-index?metric=token-volume&detail=ma...

Companies are cutting down on AI cost (adding per person budgets, using smaller models). Anthropic has also been forced to reduce token prices due to competition.