The company doesn't begin to exist once it trades publicly.

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.

The stock exchange is a "secondary market", which means you're buying stock from investors who own it, not from the company itself.

No, the "valuation" numbers are the market cap at the moment of IPO. The amount raised is that multiplied by the amount of shares issued by Anthropic (as opposed to by existing shareholders like investors and employees -- that combined number is the float) at the IPO.

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.

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.

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, but the IPO provides a one-time cash infusion at the initial price.

> unless they have another offering (which is quite possible).

?

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.

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.

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.

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.

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...

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.

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

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

There are also many positive things about them. They are huge, and innovating. But their growth potential is somewhat limited

Tesla + Space-X: 3T

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!

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 environmental impacts, the 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.

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?

That's why it's dropping here. When the market tanked institutional investors were like - hey wait a minute this is starting to look too expensive.

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.

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.

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 current SCOTUS majority will change any ruling as needed to satisfy the wishes of the oligarchs.

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.

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.

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.

And $/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 more expensive in terms of $/kg to orbit is actually more expensive in terms of total cost.

And 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.

And 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, won’t know its true capabilities and costs until it’s operational.

Their numbers are shit. They are brittle af. Any shift in political climate results in internal mayhem.

Same with openai

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

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....

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 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.

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.

And its kind of circular way of reasoning. As the investors would've actually watched the video and included it in their calculations.

how are they gonna do that when the half-life of a GPU is 18 months?

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.

There’s a shitton of companies willing to pay for it.

"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.

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.

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.

... as long as the reviewer model hasn't been deliberately lobotomized, that is.

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.

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

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.

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 but I don't see how it disproves my main point. None of those are examples of embedding backdoors or leaking secrets to take over or sabotage a system.

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.

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.

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.

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.

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.

Banks are going to want that "capital" to materialize into gains at some point. The water in that moat is evaporating.

Check out mythic.ai for one example.

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.

That doesn't seem like much of a moat. What happens when a couple researchers go on new parent leave at the same time, or someone's productivity drops for a quarter because a parent passed, or gets divorced, or whatever?

Continually staying 6 months ahead of the distilled open-weight models means you need to keep flipping heads (nobody goes on vacation) until the end of time. The geometric distribution describes that, and it ain't pretty for the expectation value of these companies.

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.

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. I LOST MONEY ON THE CHEAP AI, I DIDN'T SAVE!

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.

As is known, LLMs are amazing when verification is cheap, and much less useful when it's expensive.

It actually did. There was an older yacc-based implementation that had a lot of architectural limitations. We decided that it was better to start over with a better architecture, but we did have that older one around to compare generated microcode initially.

But if, as you say, both had an oracle then this is a really interesting outcome.

To be fair, I think the differences between frontier and open models is pretty small (I've been swapping back between Claude and GLM over the last week).

The one thing that I notice is that GLM keeps trying to blame external factors for any non-matches (we're rebuilding an external tool for internal use), whereas Claude adheres to my prompt much better (i tell it to only blame these factors if it's ruled out everything else).

I'm not convinced that change is worth 3.5-5x cost differentials though (I only used Claude because we have the small business package, which is way cheaper than API rates).

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.

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."

[1] https://cognition.com/blog/frontier-code-2.0

What are you talking about, you're fully dependent on an LLM (Opus or whatever) not making a mistake or else you lose thousands of dollars, in production? How is that okayed?

Try it before you go super far into the world of this "must suck".

Most people aren't going full vibe code. If you stear these models, they are almost identical. Seriously. The major score changes in a lot of the absolute latest models are just models doing more things (often when they shouldn't). Having a slightly lower scoring model is actually better in some ways. I digress.

If your model can make a production mistake you are using it wrong. Sorry not sorry.

There are other benefits. Your data is private, it's not sold. Your privacy is kept, that's priceless. No one can degrade your workflows. Uptime is you are online whenever you are online not 3 9s. You also won't get refused asking basic security questions.

Your math doesn't math for a lot of people. It might for you, but I would encourage you to think a little more.

Is this the nerd version of "doomsday prepping"?

Good enough may be sufficient for personal projects, but in a competitive landscape where you're using a "good enough" model and your rivals are using a frontier model like Opus 5.5, you will be at a significant disadvantage.

So all in all, I put it at Opus 4.8 level, at least. And in some ways it's exceeding 4.8.

Strix halo is double the cost because of...

I guess maybe another anecdotal comment, but I'm an engineer myself, have a home server for simple stuff, and I pay $200 a month to chatGPT for a pretty simple usage and really don't see myself go and run my own open source model, especially at the speed at which they evolve and change.

99% of the world will not setup their own open source model, just like 99% of the world is not setting up their own servier with ssh copy whatever the dropbox comment was saying.

If it were not for the x86 architecture they would almost have models that could answer those queries on their desks. (Gemma 4 E4B QAT is surprisingly good at this stuff; Gemma 4 12B QAT is great at it).

If I was Sam Altman I would consider giving Intel and AMD money to not develop unified memory architectures.

To the leadership of these tech companies that actually is the dream. Sam Altman said the quiet part aloud recently: "From a global allocation perspective, actually, it's quite terrible to have everybody have a bunch of the world's limited supply of DRAM sitting in their own computer, and being used maybe 5% of the time."

And it certainly fits into the neofeudal worldview of other relevant Big Tech leaders. Be it Peter "I no longer believe that freedom and democracy are compatible" Thiel, or unnamed bigwigs discussing if shock collars are effective to prevent their staff from revolting after a doomsday scenario [2].

We shall own nothing and rent everything, that is the mindset these people believe.

[1] https://www.ign.com/articles/openai-ceo-sam-altman-thinks-it...

[2] https://www.rte.ie/news/business/2025/0511/1512086-doomsday-...

Thiel's comment is also pro-individual-ownership - in context he was talking about the danger that in a mass democracy the people as a whole would vote to have the government restrict the freedom and confiscate the property of successful people (particularly prescient in light of the recent California attempt at a billionaire wealth tax). RAM being expensive is better than RAM being rationed by a democratically-elected government so no one person has too much, as judged by the voting masses.

no, he was lying by omission

"it's quite terrible" only if you ignore all the problems with plans they promote and measure it only in metrics Altman wants to measure, as they support his plans

Thiel has benefited from wealth being wielded by rich media owners to convince the masses decades long trends in deflation of buying power through inflation of costs are good for them

With such a system giving him a boost he was successful before Millennials and GenZ were adults or even born

Altman came along during ZIRP; being successful when the government is intentionally printing money is not really success

Which might be true, I'm willing to accept that, but it does not change the fact his message was incredibly tone deaf.

Cost of living in general is through the roof for years now, many people haven't had the financial ability to meaningfully upgrade their computers for just about the same timeframe - first Bitcoin drove CPU prices through the roof, then GPU prices, once that died down due to ASICs there were a few fads that "mined" RAM, HDD and SSD, and in parallel to the latter Covid hit, disrupting supply chains. After Covid was somewhat over, the Russians invaded Ukraine, then we got the various MENA conflicts after Oct 7th, and now the Iran war is wrecking everything - and ever since AI entered the picture somewhat three years ago, first GPUs and now everything else has exploded in price. Entry level smartphones have all but disappeared. And don't forget our existing computers got less and less useful because first Microsoft pushed the garbage called Windows 11 onto everyone it could and second, everyone and their dog went for Electron resource hog apps.

For Altman to now say "your unused RAM could be of much better use in our servers" is like a giant fat fucking middle finger, telling us to sacrifice even more of our needs just for the opportunity of him and his fellow rich bastard friends to make more money, all while he and his fellow rich bastard friends are executing the setup of a financial crisis that will make 2007 look fucking tame.

> Thiel's comment is also pro-individual-ownership - in context he was talking about the danger that in a mass democracy the people as a whole would vote to have the government restrict the freedom and confiscate the property of successful people (particularly prescient in light of the recent California attempt at a billionaire wealth tax).

Oh for fucks sake. Taxes aren't theft, they provide for the environment that makes these rich fucks able to earn money in the first place. Thiel is worth 38 billion dollars. Even if you'd tax the guy 50% of his wealth, he'd still have more than one single human and their descendants can ever feasibly gamble away.

> RAM being expensive is better than RAM being rationed by a democratically-elected government so no one person has too much, as judged by the voting masses.

At least in a rationed system that allocates supply fairly, ordinary people wouldn't have to run with barely working hardware because they can't afford to buy something new and modern for once as all the new stuff is hoarded by the rich like a LotR dragon hoards his gold pile.

And as evidenced by this week's elections - the people are fed up beyond belief. Either we the people get some relief, the sooner the better, or the situation may indeed devolve into open riots.

if you assume crazy fairy land that is not going to happen, then you can assume cheap and plentiful RAM for better results

And now this mf'er (or to be more precise, alleged sf'er [1]) goes on the record to say it's bad if we the people have RAM.

Fuck him, fuck everything he stands for.

[1] https://www.theguardian.com/technology/2026/oct/20/judge-dis...

Oh hell no. The hardware vendors want to make as much money as possible, with Micron going as far as to fully exit the consumer market entirely so they can suck as much money out of the AI market as possible.

How that one can fly by antitrust enforcement is beyond me.

[1] https://investors.micron.com/news/press-release/2025/Micron-...

Did you somehow become incapable of doing things without models? Most of us have not.

The above comment said:

> need to buy tokens from a handfull of tech companies to run their businesses or make their products

That's clearly indicating necessity.

That would indeed be concerning if it was the case, but it's not.

So now the case is just "you can pay more money to do things faster".

And guess what? That's been the case for centuries if not millennia. Nothing special or concerning at all.

And so your analogy is misleading. You're trying to imply that only a tiny number (2-3?) of companies make the tools, and nobody else does. You either pay those companies, or you don't get them.

That's just false. There are hundreds of companies that will sell you tokens - just not for frontier models. The correct analogy is how Samsung, Google, and Apple make the best phones in the world, so you can buy from one of them - or you can buy a still-good but not bleeding-edge phone from another vendor. And everyone is fine with that.

Correct. I have to pay Anthropic to use Claude models. Open-weight models are significantly worse than frontier cloud models. Their cell phone-equivalent would call your boss when you try to call your spouse, randomly delete photos you took with it, fail to send text messages half the time, power off when it still has 50% battery.

Still incorrect.

Models are always stochastic. They always have a chance of failure. That includes the most expensive frontier models.

You're making an invalid comparison to cellphones by claiming that there are cellphones that spontaneously and regularly do those things (which is wrong) vs flagship phones that don't (which is extremely wrong).

Either that, or you're claiming that the difference between frontier models and open models is the difference between actual flagship phones and these hypothetical ultra-buggy phones. Which is also wrong, because of the above fact that frontier models and open models are both stochastic, and because on many public benchmarks (e.g. Artificial Analysis) the gaps between the best frontier models and the best open models are far smaller than your analogy claims.

The fact is that, while frontier models are better than open models, you can still do a lot of work with open models. You're in no way "screwed" like you're trying to imply either with the incorrect phone analogy or the incorrect power tools analogy above.

You're an example of this process. You used to require a frontier model running on a data centre. Algorithms improved and, perhaps, your hardware as well, but your requirements didn't go up. Now you can do what you need to do with local hardware. You're one AI user who might not need to send money to Anthropic going forward unless you find a new frontier application that interests you.

This is why I'm a little bearish when it comes to the valuation of the AI giants. Yes, there are some extremely lucrative things on the frontier right now but, in the future, people may shift a lot of what they're doing now to local hardware while new things on the frontier may not produce the kind of profits that will pay back trillions in debt. AI companies have bet big on themselves being able to constantly and perpetually find new applications, and also that those new applications will make money.

I am definitely building more and more ambitious projects as models improve. Both personally and at work

Opus for planning and the very difficult parts, Qwen for the mundane ones. It still involves a lot of manual model switching (but opencode and Pi make that relatively easy).