Anthropic IPO buyers see valuation as low as $1.1T

they're setting a precedent that artificial intelligence should be free, or at least at-cost (free from profit), but robot labor (API usage) should not. I am all in favor of this dynamic. They built / maintain the robots, so they profit from their labors.

I'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.

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.

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

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

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.

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.

Like seat-of-the-pants good as Opus 5.5 but a bit slower and required just a bit more prompting to get what I wanted (which is fine for an app pipeline).

I 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.

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.”

Strix halo is double the cost because of...

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.

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.

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.

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.

- 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.

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

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.

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.

Then you would go try it and be back using windows to game a week later.

Your experience is average because it _is_ supposed to be average. The people using local models tradeoff taking the moral/technological high ground vs actually using something high quality to get a job done quickly and with high quality, and largely autonomously. They are ok with subpar output because they will handhold the model more, or the (subpar relative to the frontier) outputs are sufficient for their toy use case while being cheap and accessible, with the added benefit of being able to run it on their private data without sending it to a 3rd party.

Almost no one at any job where software is the main economic output is using a local model to code. If you actually require intelligence to get a hard job done, you should just use a frontier model.

If 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.

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.

Found your issue.

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.

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.

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.

Outside of programming and a couple of other sectors I’m not sure that is true. It isn’t visible in the unemployment figures.

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.

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.

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?

Agree. these valuations will hold. Pundits made the same prediction after Facebook's IPO in 2012 that everything would soon crash. here we are...

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.

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.

Tesla + Space-X: 3T

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.

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.

Honestly crazy that we’re doubting 1T, even with all the capital issues behind it.

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.

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.

Until there is a large free US model, its Ant and OAI.

Scaled from 0 to $65 billion in revenue in 5 years...

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.

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.

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.

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

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.

I’m not making a bigger argument other than I disagree with that one datapoint you mentioned.

Unsafe, comprehension debt, vibecoding, slop? Have you seen actual lowly developers’ code? AI is a vast, vast improvement on all relevant axes if you ask me.

If you think this is going to slow down I have no idea what to tell you.

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.