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.
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.
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
It's completely unclear to me why this is the conclusion everyone jumps to instead of "holy cow they are milking it on API pricing."
I mean, have you ever run a local model? How much electricity did it cost you to get 1,000 tokens out of it? Like a fairy-dust of a cent?
The AI labs' cost is CapEx. Marginal cost of tokens is zero.
I guess maybe another anecdotal comment, but I'm an engineer myself, have a home server for simple stuff, and I pay $200 a month to chatGPT for a pretty simple usage and really don't see myself go and run my own open source model, especially at the speed at which they evolve and change.
99% of the world will not setup their own open source model, just like 99% of the world is not setting up their own servier with ssh copy whatever the dropbox comment was saying.
I could see it used as a sort of "sub-haiku" for easy stuff to leave more tokens for the bigger claude models, but I won't be cancelling my Claude sub any time soon.
So all in all, I put it at Opus 4.8 level, at least. And in some ways it's exceeding 4.8.
Coding AIs aren't burgers. Even if a local LLM is as good as Opus 4.5 (when you sink several thousand dollars into hardware ... and I'm skeptical even then) ... the actual difference between the models is huge.
Let's say I use the $200/month Claude (I don't, I use a cheaper plan, but stay with me). If I switch to a Opus 4.5 equivalent local LLM, and it makes even a single production issue that 5.5 wouldn't, that mistake will cost thousands of dollars.
That "cheaper" model isn't really cheaper.
You are assuming that Opus would always be superior; the cloud AI providers can be very variable. They can be very useful for months, but during certain times their quality can drop off a cliff. People online speculate that the cloud providers drop quality during this time when they are training a new model or the service is over-subscribed. You do not have that issue with a local model.
That issue of quality of service is enough many businesses may prefer a locally run model because it will be more consistent.
There are a few points Id like to make here.
First, the frontier is jagged. There are errors that Qwen3.8 Flash Next make that Opus 5.5 doesn't make, but the reverese is also true: Opus 5.5 makes errors that Qwen3.8 Flash Next doesn't make. The latter will likely be fewer, but they won't be zero. Outside of really large differences, there is no total ordering among models, only a partial one.
As an aside, do you always run Opus 5.5 at max reasoning in order to minimize errors, too? I suggest most people don't, because they have their own intuition for good enough vs. cost.
Second, Opus 5.5's strength are autonomous tasks. For interactive, HITL work, it's not a particularly great choice. This includes its preferring complex Bash commands over tool use and inscrutable Python scripts, but also often not even explaining what it does. It is not a great choice to use as a virtual pair programmer, because following along what it does and maintaining a mental model is harder than with some other models (though probably because of benchmarks that prioritize autonomous work, these and related problems have infected other models, too). You can address that to an extent with CLAUDE.md instructions, but even that has imperfect results.
Finally, if you rely on a model not to make mistakes in order to ensure correctness, your software development process is fundamentally broken. You need to assume that any model will make at least some errors and need a development process that accounts for that. The problem here is not necessarily which model creates fewer errors, but which model creates code where errors are easier to discover. Ironically, more powerful models may create more complex code, making that harder.
You can see an analogous effect with reasoning effort in FronitierBench 2.0 [1]:
"FrontierCode is one of the few coding benchmarks where writing additional (correct) code doesn’t necessarily raise a model’s score. This leads to an interesting phenomenon: models can score lower at higher reasoning effort. Because this non-monotonicity often matches user sentiment but is hard to see in other evals, we consider it a useful feature of the benchmark."
A example:
"This task comes from PostHog’s open-source Android SDK, which apps use to record analytics events. When the device is offline, the SDK writes events to disk and sends them the next time the app starts. The task is to fix a startup bug in which two modules send data at the same time: the live queue sends new events while a legacy integration, PostHogSendCachedEventsIntegration.kt, sends the cached ones. The two race, and events reach the server out of order.
"At low effort, GPT-6 Astra moves cache loading into PostHogQueue.kt, removes the sending logic from the legacy integration, and leaves the SDK’s public API untouched, which produces a passing solution.
"At max effort, the model often deletes PostHogSendCachedEventsIntegration.kt outright and spreads its responsibilities across other legacy files. This also fixes the race and passes most of the hidden behavior tests. But the deleted class is part of the SDK’s public posthog.api surface, so removing it introduces an unnecessary breaking change for every app that references it. Scope blockers, which deterministically check that the model hasn’t modified certain files, catch this change, and the agent fails the task."
Agreed on that, and actually I'd put it somewhere around opus 4.5 from what I've seen so far.
Is this the nerd version of "doomsday prepping"?
Like seat-of-the-pants good as Opus 5.5 but a bit slower and required just a bit more prompting to get what I wanted (which is fine for an app pipeline).
If company X has a better model than company Y, do you use it? Well, that depends: will Y take two years to catch up? If so yeah, you buy X.
But if Y is just as good a few months later, you purchase based on other factors, because you know you can live without the brilliant research X is doing ... since you'll have it soon enough.
Research does not bring in revenue. Products or services do.
It doesn't cost a company that much to buy a box capable of running the Qwen3.8-Flash-Next model in the GP comment and never pay Anthropic again. The cost will be even more reasonable once the supply issue is solved for that hardware.
AI-as-a-service is a unique product because when the interface is natural language, there is no lock in. VS code has a hotkey to swap between claude, openai and a local model. Compare that to the difficulty of swapping from macOS to Windows.
I don’t have any stake in this and I hate the idea of concentrated wealth and power, wherever it is: government or private businesses. But would I bet against it? Probably not.
We're the only people that can spend this money is not a moat.
If you need to buy staggering amounts of compute, spend staggering amounts in utility costs and lobbying, and keep spending that as your hardware amortizes and your operations go on, what you have isn't a moat, it's a standing army. It's extremely expensive, and the day you start having issues, mercenaries will cut your throat before you know it.
But the fact that were even talking about Anthropic like this only a few short years later demonstrates that that was only kind of a kind of moat.
Check out mythic.ai for one example.
Banks are going to want that "capital" to materialize into gains at some point. The water in that moat is evaporating.
Hardware's ridiculously expensive. Then, there's no good place to put it, because the new breed of tech bro managed to propose the data center buildout in the most exploitative, alienating, and electorate-angering way possible. So compute's hard to buy.
Around 50% of that group says they plan to cancel their Claude subscriptions now that their local or open model setup is performing “good enough.”
Open models: we're already seeing this at some scale. https://news.ycombinator.com/item?id=50350004. I do expect to see more of this.
If you are just vibing and managing multiple agents and reviewing at a higher level like architecture, you are right.
Strix halo is double the cost because of...
Do you mind sharing more details about your setup? I have been using opencode with lemonade for inference on my strix halo but haven't quite hit the sweet spot.
I feel like the industry hasn't solved that alignment problem yet... We have no way to verify the entire models landscape of intent.
Paid models have an incentive not to do that because if the market discovers it there is a huge economy penalty, but the free open source models don't have that disincentive.
False.
I worry more about Claude.
For those keeping score, this probably isnt exhaustive- but here's some greatest hits;
#55743: Claude Max account email auto-injected into LLM context with no opt-out - May 3, 2026 https://github.com/anthropics/claude-code/issues/55743
#72518: Anthropic embedded spyware in Claude Code - June 30, 2026
https://github.com/anthropics/claude-code/issues/72518
#21151: No indication of WHICH file for READ tool - January 27, 2026
https://github.com/anthropics/claude-code/issues/21151
#42796: Claude Code is unusable for complex engineering tasks with the Feb updates - April 2, 2026 https://github.com/anthropics/claude-code/issues/42796
#73408: AFK auto-continue on unanswered AskUserQuestion is undocumented and has no opt-out - July 2, 2026 https://github.com/anthropics/claude-code/issues/73408
(Also worth a mention, OpenCode PR #18186: anthropic legal requests - March 19, 2026 https://github.com/anomalyco/opencode/pull/18186)
What exactly are you claiming is false about my statement?
The list you presented are mistakes Anthropic made or cases when they put their own interests ahead of users, but I don't see how it disproves the point I was making. None of those are examples of embedding backdoors or stealing secrets to take over or sabotage a system. They are categorically different than what I'm asking about.
This just fundamentally isn't true. They run on the same economic model, but they have to release the weights to gain credibility, unlike the American labs.
> Do you ever worry whether these open source models have been trained to embed backdoors or leak secrets in very specific circumstances at some point in the future?
Open weight models are extensively analyzed. It is extremely unlikely there is an intentional 'backdoor when X' concept added to any of them. Not impossible, but unlikely for the same economic incentives you see in the other labs, and given that no evidence of anything along those lines has surfaced yet.
In my opinion, to suggest open-weight models are extensively analyzed while being net new and continuously released and the model itself being unintelligible to humans seems a bit premature.
It's possible that closed-weight models end up following the pattern of being arguably less secure since there are fewer eyes on them analogous to open source software but I don't think there's a perfect match here.
... as long as the reviewer model hasn't been deliberately lobotomized, that is.
(I don't disagree with the [dead] comment, either -- fortunately I tend to make small changes to code I'm very familiar with, so it's fairly easy to vet in person. The more code gets generated by the clanker, the more nervous I get, just because it's impossible to keep some complacency from creeping in.)
Auditing output by contrast is a continuous cost. On the third hand, it's one that most will want to pay anyway.
The "embed into shipped code so subtly humans can't spot it" vector is sci fi ish. Less plausible than old fashioned trusting-trust issues, and most folks have managed to sleep at night for the last 40ish years that spectre has haunted.
[1] I guess the model could return output that buffer overflows the harness and exceeds its intended ability, but that seems far-fetched too
just read the damn code yourself!
It's basically impossible to do this. Claude produces so much excessive / verbose code and comments that reading it would take far longer than simply doing the implementation "by hand". It's kind of like, Claude can write the code in 1/2 a day. It can be reviewed by another LLM, unit tested by another for 1 day. Or I could write the code in probably 3-4 days. Reading it and validating / testing it myself though would easily take 6+ days.
Now if I were fully in control I wouldn't run code that wasn't truly reviewed at all, but in the situations where Claude is being used that's not my call. TBH a lot gets caught, and I have some automated scripts that find a lot of the more problematic stuff automatically, flag it, send it back and make note for the reviewer model as well.
I will also spot check areas where I think an issue could arise.
The risk for investors is they don't succeed in getting regulatory capture because without that they don't have a long term business model
If we believe all the hype about programming efficiency, then regulatory capture in the US becomes just one more in a list of many steps the current administration has taken to chase software development jobs overseas.
The market anticipates the unlocking; when the shares become public, buyers swoop in, so the stock decline is much less than expected, if at all. There is no new information here. If everyone is expecting shares to unlock, then it’s priced into the market accordingly.
2) Valuation aside, SpaceX has a massive moat. It’s crazy hard to do what they’re doing. Anthropic increasingly has zero moat, with the market rapidly coalescing around commodity open models.
"SpaceX (ticker: SPCX) was priced at $135 per share during its initial public offering on the Nasdaq on June 12, 2026."
current price is $134 , give or take a dollar.
1: https://www.forbes.com/sites/tylerroush/2026/06/12/spacex-op...
They’ve now got all their eggs in a new basket. A basket that isn’t entirely in working order yet, and whose market viability is still uncertain because it remains to be seen whether a market that’s used to panniers really wants a laundry hamper.
$/kg to orbit is not as important a deciding factor as many people assume it is. It’s typically a small portion of overall costs, and there are often cases where a launch provider that’s less expensive in terms of $/kg to orbit is actually more expensive in terms of total cost.
Starship is going to be a particularly weird one to adapt to. The “deploy out the side” design may make it straight up incompatible with a large subset of spacecraft. For those where it is workable, migrating commercial payloads to it may still require the development of new satellite buses, which is something SpaceX doesn’t seem to be working on for the benefit of anyone other than Starlink. And such a large rocket means most potential customers will have to plan on ridesharing with even more co-passengers, which means even more room for scheduling conflicts, compromises on initial orbit (and therefore cost to reach destination orbit), etc.
That’s just assuming Starship development goes smoothly from here on out. Which seems optimistic considering that, even by the standards of spaceflight, its development thus far arguably qualifies as cursed. Considering the gap between original design goals and reality is already historically large, I certainly wouldn’t be taking any cost estimates at face value right now. Like for any launch vehicle program, we won’t know its true capabilities and costs until it’s operational.
That's the primary angle of depreciation of Anthropic's outlook, is that as interest rates are running hot, they need to grow ever faster to justify the $2B target, at the same moment that they're growing slower.
So many people who don't actually read and evaluate things but just buy stocks based on vibes and what they see on Twitter. Which is to say, the same people that buy and lose money on most IPOs.
Lets set aside speed. The results are ALWAYS terrible compared to CLaude Code.
Sure I only have a 12GB GPU - is that what it is? If I had a 32GB CPU would the experience be as amazing as Claude Code is?
Why is my experience of local LLMs so average?
You can 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.
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.
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.
> 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
> They seem to be happy with their small usages.
Oh yes, we're terribly inferior people. Just living on scraps from the masters' tables.
I do plenty of experiments of things i always wanted to do and they just don't work with local models.
Finetuning of some ML models for a specific usecase, some 4k demo idea i had, small browser games to show others game mechanic ideas i had for ages, analysis tools etc.
Even just playing around with some Ghidra eats context like nothing.
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 in that context.
And you might want to look at openrouter's rankings.
If you are comfortable automatically 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?
- 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.
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.
There is, for sure, a limiting factor in the x86 architecture, but I think the RTX Spark will change that a bit, and maybe AMD's subsequent Ryzen AI/Strix Halo chips.
In any case, 128-256GB unified memory systems running MOE models is the "cheap" option, RTX6000 is the step above it.
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?
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.
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.
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.
"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.
If that were really the case, basically nobody would spend so much money using them.
With my DGX Spark + 5080 + 3060 setup, I've tried all kinds of approaches, including quantization, acceleration, fine-tuning, post-training, and everything else.
It still can't compare to Opus 5.5 max in terms of speed, capability, and stability.
And when I calculate all the money I've invested, directly subscribing to the service would actually be cheaper, more efficient, and more stable.
Open source is great, but don't glorify it.
The architecture of the code I develop has never been simple. If you're someone who doesn't even work on things that need to be deployed to something like k8s, then basically, open-source models are indeed enough for you.
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
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 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.
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.
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?
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.
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.
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...
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.
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.
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.
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.
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.
Outside of programming and a couple of other sectors I’m not sure that is true. It isn’t visible in the unemployment figures.
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
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.
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.
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
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.
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
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.
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.
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-...
[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.
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.
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.
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
See https://en.wikipedia.org/wiki/Railway_Mania and https://en.wikipedia.org/wiki/Pets.com for some info on the other hype cycles I mentioned.
I find myself wondering what "less sympathetic" and "significant headwinds" mean in the minds of people in the upper echelons of the AI industry.
Rules for training frontier models? Requirements for renewables to power data centers? Actually having to pay people for the content you train on? Data center rules that would end them going behind the backs of local electorates to get governments to approve the building of data centers?
You 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.
Not making a leader board to be clear though. The goal is to build a branchable leaflet style page based on zip codes that lists ALL the people that were elected. Clicking into the each person will yield details on any legislative actions, their attendance, publicly made promises, etc.
I'm really sick of hearing lip service from politicians so I want to build receipts that even a child could understand.
Agree. these valuations will hold. Pundits made the same prediction after Facebook's IPO in 2012 that everything would soon crash. here we are...
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?
They get the money from the initial purchase of the shares, and never get another dime directly from the market again, unless they have another offering (which is quite possible). The stock price does drive leverage for later M&A and debt/credit activities, and matters to stock-based compensation, so it does have meaning, but the IPO provides a one-time cash infusion at the initial price.
If they are too far below the perceived value, they are leaving significant money on the table.
If they are too high, the institutions who participate in IPOs will let them know. If the price craters off IPO it is not good for perception of the stock, and so ongoing health of the company even if that maximizes private investor value at IPO.
So they build a book of commitment around the target value, trying to leave enough incentive (15-20%, often) of an initial bump to get big money in.
The stock exchange is a "secondary market", which means you're buying stock from investors who own it, not from the company itself.
If one of the initial buyers sells in the first minute the company is public the first publicly traded shares at 1.5x their purchase price, the company suddenly has a market cap of $1.5 trillion instead of $1 trillion, and that buyer has made a tidy 50% profit on the shares they just sold. If all initial buyers dump a bunch of their shares the price likely will go down, perhaps pushing the market cap to, say, $750 billion. Regardless of this action on the first day and after, the company offering their shares still has the $100 billion.
Note that initial buyers are discouraged from selling large portions of their shares.
Ultimately the percentage of shares that public will trade is material. say 20% for $100B is quite different from 10% for $100B.
Most of the institutional and retail placements happen on the initial valuation. These are the people who are holding the shares when it gets listed and starts trading, and it is the primary source of raising capital through an IPO.
To go further though the Initial Valuation (usually a range) is set weeks before the IPO to make it easier to pitch to investors. And the final valuation is locked in the night before trading based on actual demand.
The opening price on the stock exchange, though, will be whatever the demand and supply is in pre-market trading/open.
The IPOs need to have buyers. The buyers will have some idea of the business valuation, as will the sellers. This is decided before the actual stock starts trading.
Specifically, the "underwriters" will go around finding people willing to buy what is being offered. This gives some idea of where it will begin to trade.
No, the "valuation" numbers are the market cap at the moment of IPO. The amount raised is that multiplied by the percentage of shares issued by Anthropic (as opposed to by existing shareholders like investors and employees -- that combined number is the float) at the IPO.
If the pop is too big, that means the company left money on the table.
If the pop is too small, the investment banks that underwrite the IPO have to worry about losing money.
If your broker allocates you shares, you pay the IPO price as does everyone else who was allocated shares. The shares are all allocated before the IPO happens and once it opens for trading, price discovery happens.
How the price is set is another matter, one I’m not familiar with as an amateur.
And its kind of circular way of reasoning. As the investors would've actually watched the video and included it in their calculations.
If the world is not ready for fully scalable almost human tier intelligence then, being right at the wrong time is the same as being wrong.
Also you don't have to make people pay. Facebook doesn't charge end users for its products and neither does Google for either search or Android.
I mean, that does sound very much like the future of our current timeline, unfortunately.
There’s a shitton of companies willing to pay for it.
how are they gonna do that when the half-life of a GPU is 18 months?
"Almost" being a synonym for "not".
It's not the same kind of intelligence as humans so it'll probably always have weird areas where it can't do as well (like counting letters in words), but that doesn't mean it isn't smart. If AI were sentient it could point at humans and say "haha look how dumb they are; 99.999% of them can't even write a basic Lean program".
We're way beyond the point where "it's not as smart as humans" is meaningful.
Apple: 5 T
Google: 3.5 T
Meta: 1.55 T
Microsoft: 3.97 T
Berkshire Hathaway: 1.2 T
Eli Lilly: 1.28 T
Tesla: 1.38 T
Walmart: 1 T
Spotify: 125 billion
There are also many positive things about them. They are huge, and innovating. But their growth potential is somewhat limited
Walmart, on the other hand, has a store close to most small towns in the US, as well as multiple in more metropolitan places.
I know that’s oversimplifying and probably naive, but huge physical operations seem like more of a moat.
For people with a Walmart background, Meta is black magic
Tesla + Space-X: 3T
[1]: https://www.cnbc.com/2014/08/19/es-took-off-but-the-auction-...
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
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.
So say Anthropic, or OpenAi, or smartGummyBearsAI go IPO and the price is insanely high... and then spirals down to be 10% of the real price... they earn a lot of money and the public loose a lot of money.
IF smartGummyBearsAI goes 100% realistic price and you buy near the correct price, they earn their fair money and people get an investment.
IF smartGummyBearsAI says that they have technology to use the same machine that makes gummy bears print gpus and they are priced 10,000x their actual price the society just transfers a lot of wealth to them and they scam the society.
Why shouldn't we regulate and aim for the correct price?
Remember 801k money is what we are talking about
That's why it's dropping here. When the actual stock market tanked yesterday institutional investors were like - hey wait a minute this is starting to look too expensive.
Also if your 401(k) is in index funds you are safe. A company is not added to indices right away. If you are in actively managed funds or something like that you might have more exposure risk.
Markets have the advantage that many peoples' opinions are aggregated into the market price. This is far, far more accurate than having one person's opinion (an elected bureaucrat) set the price, particularly when they have no skin in the game.
If the IPO price is really 10x the public market price, that implies that the set of institutional investors collectively estimated the price 10x higher than the full set of retail investors. That's pretty unlikely: institutional investors usually do pretty extensive due diligence on IPOs, and you're already averaging out the statistical fluctuations by selling to a wide set of them. But if that does happen, it just means that institutions got fucked over to the benefit of retail (who gets a 90% discount on the same shares), which seems like something we should be celebrating.
Apple: 5 T
Google: 3.5 T
Meta: 1.55 T
Microsoft: 3.97 T
Berkshire Hathaway: 1.2 T
Eli Lilly: 1.28 T
Tesla: 1.38 T
Walmart: 1 T
Spotify: 125 billion
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.
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.
Major Investors Are Skeptical Anthropic IPO Can Achieve $1.5T Valuation
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.
Their numbers are shit. They are brittle af. Any shift in political climate results in internal mayhem.
Same with openai
So many people 6-12 months ago predicted Space-X stock would crash and burn, yet the stock is still holding strong around $130 and a valuation over a trillion dollars.
That's simply because they're 1) installed in the government gravy-train, and 2) part of the AI bubble. Check again in a month or two, after AI valuations become more clear and Democrats start to publicly sketch out plans for what they're going to be doing to 2028 and beyond. They've already openly announced revenge for the half-billion in political spending, and they've haven't been talking at all about going after speech on twitter (which was a huge loser), but instead going after real stuff. Going after Space-X is an easy call.
Until there is a large free US model, its Ant and OAI.
https://commandline.microsoft.com/microsoft-decision-1-model...
> To build Microsoft-Decision-1, we post trained Qwen3.5-9B for fast, single-pass decision scoring and will soon rebase it on other models, including Microsoft AI (MAI) and OpenAI.
https://techcrunch.com/2026/03/22/cursor-admits-its-new-codi...
> Cursor’s vice president of developer education Lee Robinson soon acknowledged, “Yep, Composer 2 started from an open-source base!” But he said, “Only ~1/4 of the compute spent on the final model came from the base, the rest is from our training.” As a result, he said Composer 2’s performance on various benchmarks is “very different” from Kimi’s.
This whole fearmongering campaign may work well within US but once you cross the border it becomes funny parody in worst ways. When all this comes from current US its beyond ridiculous, I don't expect post-midterms behavior to be any less petty and dumb.
Who is the “all of us”?
Not the UK
https://www.independent.co.uk/bulletin/news/china-security-t...
or the EU
https://data.consilium.europa.eu/doc/document/ST-11631-2026-...
Definitely not Taiwan, Japan, South Korea, Vietnam, India....
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 wouldn’t be surprised if Anthropic delays the IPO in hopes this will all magically sort itself out but as every day passes it looks like they should have gone public this summer when things were still on the up.
Realistically the company is worth in the hundreds of billions but if it goes below 1.1T or so existing investors will need to start booking losses and that will trigger a shitstorm of other issues downstream.
This is all becoming a textbook case of folks know these assets aren’t worth what folks want them to be, but they can keep the game going so long as nobody actually marks the asset at its actual realistic valuation.
So that’s the game underway now. How low can they go without blowing up the whole AI ecosystem. If the valuations here drop next the AI bonds and private loans start to fail en-mass and the whole thing that built up this house of cards starts falling apart in reverse. Get ready for a bumpy ride folks.
The second one of these AI companies gets a fair valuation and folks start booking losses the whole AI house of cards will come crumbling down. The crazy thing is if they drop below around 1T some existing investors will be booking losses.
A realistic valuation is likely in the hundreds of billions, which would be a total disaster and thus they’re going to do everything possible to avoid that.
Caveats: It's hard to believe that this growth rate would continue. And Meta's numbers above are trailing 12 months, while Anthropic's are annualized run rate.
Also, ARR as in Annualized Run Rate is a bullshit, non-standard metric only used to mislead investors
This is a bit misleading and based on projections of prior reported months. Growth has already slowed, Q3 in dollars was less than Q2. The timing aligns largely with the growth (since mid-summer) of open weight models.
https://www.regenerator1.com/p/anthropics-growth-just-flatte...
Since they actually have paying customers, it's a completely different sort of thing than for Meta. The question is instead whether they can keep at the frontier, whether the compute they've signed up for is good enough, etc.
Companies are now building the agentic layer, the agentic layer eats tokens. Everything you do every process will have tokens eaten inbetween.
Your HR Request? AI Tokens. Everything.
There's a value proposition in which direction the stock is going to go and how long it's going to take to get there. It only makes sense to be invested in a stock if you think it's going to be worth more later than it is now and the amount of time your money is tied up in this stock is commensurate with the profit you may get as a result.
Investors work on limited information about companies, and having a track record can influence a buying decision one way or another. It may matter to some investors and not to others. For many, it's weighed against the price of the stock. Would you bet $100 on a company with a short track record? What about $10,000, $100,000, or more? Generally there's a point where people drop out of buying more of a stock and say that they can't justify the risk given the information they have. That's the expected value that Anthropic will receive from that investor.
Anthropic needs to worry about what that number is for a huge amount of individual investors and how to maximize the amount of money put into the cost of their stocks, and there's very little chance that they can attract the biggest pool of investor money without some of those people considering their longevity vs a more established company like Meta. It may not matter to all investors, but the fact that it will matter to some makes it a factor in the total valuation.
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.
[1] Say >10% of people of employable age suddenly becomes superfluous with not much prospect of finding new jobs (since AI eats those too).
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.
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.
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
As a privately held company, it functions under different dynamics that in the environment we’re seeing feels better to stay private for longer.
You’re also missing the competition aspect. What if other companies have their own super genius slaves to sell at a cheaper price? What if open models allow anyone to have their own moderately genius slave at far less expense?
Whoever is claiming that is an idiot.
And if they haven’t created an artificial superintelligence then they start to look more like just another tech startup with no moat[1] and a terrible balance sheet.
[1] I don’t know about you, but I switch model providers multiple times per day.
You are making claims with no basis, caveat-ed by "if," and then calling everyone here fools for not basing their beliefs on your wishful thinking.
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.
In the “but they’ll slow down training and so will become more profitable” thinking is in big trouble given the pace of open models. They’ll then be even less competitive with free options and so even less reason to buy from Anthropic.
The biggest threat to them is that continuing massive R&D into new models might not be worth it once open-weight models become "good enough" for 99% of usecases. Then what? And if they stop the massive R&D, they've already lost. It's a perpetual race against being commoditized.
Profitable inference is table stakes. The pro-Anthropic argument has to be something like: they achieve ASI "lift-off" and leave all open-weight models completely behind with no hope of catching up. And there needs to be an infinite economic appetite for super-intelligence. Or they use the super-intelligence to directly enslave the human race, or something.
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.
Revenue growth q2 to q3 was significantly lower percentage wise than q1 to q2 and they swung back to net loss.
Unsafe, comprehension debt, vibecoding, slop? Have you seen actual lowly developers’ code? AI is a vast, vast improvement on all relevant axes if you ask me.
If you think this is going to slow down I have no idea what to tell you.
I 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.
This is why token prices are falling and there are expectations for commoditization. In another year or two, you will have Opus 5.5 in much smaller models, assuming capability distillation remains as good as it has been from mega -> mini models.
Colossus 1 & Colossus 2 are located on Tennessee and Mississippi, and Anthropic rents both of them earlier this year.
Now that Democrats are surely going after Trump and Elon Musk and the sidekicks, and both Tennessee and Mississippi turned blue as well, this directly impacts the way Anthropic to source cheap compute and data in the future due to uncertainty around the continuation of SpaceX data centers. And other data center buildouts are likely to be paused for things like power grid connection and environmental impacts and land purpose evaluation, the compute capacity constraints are likely going south, too.
Worse, the proposals around superintelligence is exactly what Anthropic is looking for: slow the fuck down. Now they got the killshot they long desired for, it obviously makes a dent in the capability and capacity of their AI, hence a drop in valuation. I think $800-900B is somewhere I would say okay, and $600-700B is really where it is fair, considering the long term outlook for Anthropic is looming with a greater AI massacre caused by political factors.
And open models are coming whether they like it or not.
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.
50b would be a fair valuation.
Imagine that - 50 (fifty) times more than an average unicorn start-up!
Something they could be very proud of, and many if not all employees would become multi-hundred-thousandaires practically overnight!