Posted by martinald 21 hours ago
> China’s Ministry of Commerce has led meetings over the past month with major AI companies, including Alibaba, ByteDance, and http://z.ai/, to discuss measures that would restrict overseas access to cutting-edge AI models, including models that have not yet been released.
> The discussions reportedly include not only closed-source models but also open-weight models.
> Future regulations could take the form of a tiered framework based on technological capability. Basic open-source AI models may be managed through a filing system, high-performance models may be subject to security reviews, and the most sensitive frontier models may be banned from public release or restricted to use within China
https://www.reuters.com/world/beijing-is-looking-curbing-ove...
It's really hard to tell what's propaganda from what's not.
For examples, this Reuters that you have included here has all tells tale signs of creating fear, uncertainty, and feeding into propaganda. But then again I can't be sure.
It's completely the opposite stance to (a) the actions being taken by chinese companies and (b) the public stance taken by their govt https://english.www.gov.cn/news/202601/08/content_WS695f1b55...
That source is very old, considering how fast everything is changing. It is not impossible if US actions for banning have changed something.
They're also well-positioned to control how groundbreaking US AI companies are allowed to appear to their investors, by offering open models that match what market leaders claim can only be attained with trillion-level spend. That's a strong control on US economy, considering how few stocks are propping up the US stock market, and how these stocks are all dependent on the same beliefs and factors.
Also, regarding China's tech investment in general, the five-year plan is just... available to read. It's not a secret. It explains the strategy, and you can draw a direct line from the plan to their now abundant solar infrastructures and tech achievements, including in AI, which is specifically named.
https://cset.georgetown.edu/publication/china-14th-five-year...
Also, even if China decides it doesn't want to keep the crown jewels of productivity close to home, the US will ban their import. Maybe XzeRo_337 will be torrenting weights and have a VPN to access foreign providers, but Timmy and Ashley are just going to pay for their ChatGPT subs, and Mega corp will pay their Claude Legend token expenses.
China has no real bureaucracy (or any other structure for that matter) because at the end of the day, it's one guy who can do whatever he wants whenever he wants. For commoners and officials there is this faux bureaucracy, but for the elite at the top making decisions, there is zero.
If Xi doesn't want models exported, he's not having a legal delegation go to the supreme court of China to fight for his ruling. It just happens, regardless of whatever anyone else or any piece of paper in the country says, and there is zero recourse anyone who doesn't like it can pursue.
The Ten Eunuchs, if you want one example. Which is to say, their bureaucracies have always worked with single leaders who ostensibly had unilateral uncontested control for life. https://en.wikipedia.org/wiki/Ten_Attendants
And EU leadership completely destroyed Europe's future by betting on depending on US and Chinese models. https://pleias.ai/blog/fable-eu
You could just as well read the european approach as a bet that frontier models will be unable to keep a significant edge over open competition (and thus not worth throwing subsidies at, because any economic advantage is fleeting at best).
Looking at the data and related past experience, this looks like a pretty solid bet (despite the "risk" being hard to quantify).
And that's a bet they will lose 100%. Once the Chinese starts imposing export bans/controlling the access to their models, Europe would be at the mercy of US/China to allow them access or just rely on miserable mistral
There is ton of strong indicators that they will not stay ahead: Assuming that technological progress of any kind follows some form of logistic function (where "gains", in this case "intelligence" become sub-linear at some point) is (long-term) a very conservative and proven assumption, and "automatically" negates your lead over time.
Similarly, purely "intellectual" advantage in disciplines like cryptography/computer chess/algorithm design never really stayed concentrated, either.
To take a simple example, look at the progress of technology over the last ~500 years - it seems to me that the rate of change continues to accelerate despite many of the logistic curves flattening along the way.
There are still huge unanswered questions about whether or not the stacking sigmoids will favor the incumbents. But I would not definitively bet against the people with the most compute data, talent and money.
I'd argue that a lot of important technologies (like circuit design!) started at basically zero in the last century, and the progress was actually exponential for a significant time (=> because we started so low on the curve).
But if you reduce things to a single metric (wealth per person? total energy available to humanity? global industrial/construction output in tons?) I can't think of anything where such "stacking" successful subverted the sigmoid trend (or looks like it will, long-term).
One of the best example is nuclear reactors. By now the know how and technology is fairly mature and open, but not every country gets to build nuclear reactors. Same would be with the frontier models as well.
EU should have already started investing in the infrastructure side in-case they obtain the know how, but your politicians are still bickering on pension reforms and Ukrainian war, etc.
They can just operate and provide normal access to their services, just block AI access. This is already happening, apple would not release new Siri in EU (granted it's due to a regulation clash) but this would be a testing point.
If Europeans are still paying the same prices for sub par services/products to their American counter parts, it's win-win situation for those companies. Just sell a dumbed down non-AI version of service/product in EU for an inflated price.
A not-insignificant portion of the AI/ML research community is in the EU.
Regarding the open weights, I don’t see meta doing that for their future models, especially once they have their own frontier models. Open weight models are kinda marketing strategy where they use it as a bait and switch. A lot of Chinese companies became popular with their open wight models and once they build that reputation they have no incentive to keep on releasing new open weighted models
So basically EU will be left behind unless we start doing something about it now. imo Mistral isn't enough by itself.
What happened to China because they were third movers in the race to the nuclear weapons? Nothing. They were pretty behind for a while technologically. The wheels have turned. Stop looking at the world as a short strategy game match, or like a Hollywood movie where everything is all-or-nothing. Many parts of south America are way behind in IT, automotive, space, yet many people have meaningful and happy lives there.
I'm yet to accept that the power-block I'm living within being first in the AI race provides a meaningful life to me.
In the US the federal government has much more power but nobody is attributing the term good fortunes of the AI industry to the recent federal policy there. (There's long term stuff that does make a difference in hoovering up the global R&D talent and concentrating capital, but that's a different kettle)
EU budget is about 1% of the GDP of the countries (from https://commission.europa.eu/topics/budget_en)
There's very little betting on any particular AI models going on by the commission/EP. The Pleias article claims a 2020 eurocrat whitepaper determined how things go, but that's fantastical.
1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins.
2. Many open source office suites exist yet none compete with the ubiquity of gsuite or office. GitHub, Slack are similar examples.
3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time.
4. Many formerly open source infrastructure components like Redis and Elastic Search have Apache equivalents, but they still command healthy margins.
I understand the arguments for a margin collapse, but I don't see any historical analogues. It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.
It's nobody gets fired for buying IBM all over again.
1. Memory chip margins collapsed so much in the 80s that Intel exited the memory chip business entirely. At the time, they were known much more as a memory chip company than a microprocessor company.
2. Margins for high-end workstations collapsed in the face of cheaper IBM PC clones and an explosion of MS Windows software. This led directly to the deaths of SGI, Sun, Symbolics, Lucid, LMI, etc.
3. Proprietary UNIX variants like HP-UX, IRIX, AIX, and SCO Unix have basically completely died out, replaced by lower-cost proprietary OSes like Windows and MacOS, or by open-source descendants of Linux and BSD.
4. Many commercial database vendors like Oracle, dBase, Sybase, FoxPro, and Microsoft (SQL Server and Access) found themselves very much under margin pressure from PostGres, MySQL, and SQLite. Oracle survived thanks to their massive installed base and legal department, and Microsoft survived because they could cross-subsidize from their OS and Office monopolies, but dBase, Sybase, and FoxPro are no longer with us.
Is that just two people with different go-to examples? Or is there something going on here?
(I don't mean this as a leading question to some conclusion in my back pocket, I genuinely have no clue.)
The US's corporate problems in the 1980s and early 1990s existed when strong international competition existed.
It began to change with things like https://en.wikipedia.org/wiki/1986_U.S.%E2%80%93Japan_Semico... and https://en.wikipedia.org/wiki/Plaza_Accord.
It was accelerated further with things like anti-circumvention clauses in Free Trade agreements (see Cory Doctrow's recent highlighting of this: https://pluralistic.net/2026/01/01/39c3/) and then had more gasoline thrown on the fire in the ZIRP/easy money era post GFC, culminating with the bazooka of stimulus unleashed post-covid.
My best guess is we are now going to witness ~20 years of slow unwind. You can already see signs of this in things like RoW/EM stocks outperforming the S&P, treasury yields diverging from other "safe haven" soverign bonds (e.g. swiss), gold price rising, Europe starting to get serious about addressing the Draghi report's findings, European defence spending increasing, China starting to act like the "adult in the room" wrt the recent Iran/US blow-up etc. Essentially, countries/blocs attempting to re-assert sovereignty that has been willingly diluted over the last ~30 years to mainly America's benefit.
That's illustrative. The mechanism by which organizations are forced to update their technology, move to more competitive suppliers, and cut costs is a recession. In one, every business that doesn't do so goes bankrupt, and what's left are the more efficient businesses that have adopted technology effectively.
We haven't had a real recession since 2009. (2020 was an odd case, because it was effectively brought on by government edict and so it actually killed a number of efficient but unlucky sectors while doing nothing to clean out the dead wood in major corporations). The next one is likely to be a doozy, because the economy is filled with bullshit jobs, bullshit corporations, and bullshit products.
No, brought on by a novel pathogen that killed 10 million people. It would’ve been much worse without government action.
With decisive government action (see New Zealand), millions less people would have died, and the economy would have done better.
Compared to the very porous land borders of the US.
If 10% of the population went away, it would affect 1 & 2, but in any true practical lens, there's a ton of cheap empty houses, while on the other hand building repairable stuff that lasts or enough cheaply is where economies move to more complex technologies by saving time and effort in useless endeavour of debt chases or consumption-oriented wasteful productivity
The US, EU, China are teetering on the edge of a crisis. Russia is well on its way.
I feel like 2008 was just a warmup to what may be coming.
When we fought each other, after the industrial revolution, that was the Napoleonic Wars and the two World Wars.
> and ever since the creation of the EU it's been becoming less and less important on the world stage.
I wouldn't say it was "ever since the creation of the EU", but rather "roughly between WW1 and decolonisation". Post-Cold-War the EU has taken over from the former global importance of the member states, e.g. https://en.wikipedia.org/wiki/Brussels_effect
That said, east and South Asia are regaining their multi-millennia history of being the world's dominant power by virtue of having roughly half the total world population.
And to agree up-thread, there's plenty going that can rapidly turn the EU's economy into a disaster if not handled expertly.
If human+ level AI takes off one would expect to see a great decoupling of power from population.
Asia's diverse, but I'd say they seem to be doing pretty well with rapid improvements across all fronts.
In comparison, the US's weaker (not weak-weak, just weaker) areas currently seem to be educated workers, instantaneous industrial base, and energy supply (relative to rapidly growing demand from compute); while the EU's weaker areas currently seem to be capital and energy supply (from supply shock, as it doesn't have the compute). The US and EU both have coming demographic issues, but not as soon as the other stuff becomes more important. People talk about China having demographic issues too, but they're a dictatorship, they can make it shift if they care to.
(And Russia's losing a lot of people, more educated people, capital markets, industrial base, and energy supply).
China has a significantly bigger problem with demographics than the EU does, it is just on a slightly longer fuse, compare:
https://ourworldindata.org/grapher/children-born-per-woman?f...
The big drop in Chinese fertility is going to be very disruptive in the near future, because it is much less gradual than European trends and the retiree/workers ratio is going to spike much harder because of that.
Having full authoritarian control is not gonna change anything now because it is already much too late (action would have been required like 35 years ago).
Best they can do is get through it somewhat smoothly.
edit: This is an even better visualization (projected working age population fraction)
https://ourworldindata.org/grapher/population-young-working-...
Even then the US might not have done much if the Nazis hadn’t kept attacking US shipping.
Currently, Europe can stand up against tech. Apple could easily prohibit iPhones from going into France but I doubt it cutting off the entire EU.
Europe collectively is about 26.7% of their 2025 revenue, according to SEC filings, so I bet they'd care.
https://www.sec.gov/Archives/edgar/data/320193/0000320193250...
Insane take. But somehow people will go to any lengths to disparage the EU.
Including the warmongering angry midget next to the US, EU, and China is funny. Russia's economy, before they decided to shoot themselves in the face, was the size of the Netherlands. Whether they are in a recession or not is irrelevant to anyone but them.
More relevantly, they were one of the world's petrol stations, and now they're not.
Yeah, it sure feels true.
There's even a book about it, you know, to help people cope with it:
https://press.princeton.edu/books/hardcover/9780691276786/on...
We are seeing the later start to unravel.
The UX is the same regardless the provider. You send in a prompt, it spits back an answer.
In all your other cases, the cost to switch is losing support and a difficult transition period. But in the case of LLMs, there was no support to begin with. The transition is basically updating your current harnesses to know about the other models.
I think the comparison most apt is the rise of AMD. Sure, it never(?) achieved market dominance, but it did ultimately make a huge dent. And a big part of that was because AMD x86 was pretty close and pretty compatible with Intel x86 at a fraction of the cost.
Not to mention the meta of account limits, billing, ZDR contracts, etc.
If anything, you're better off supporting multiple LLMs as backup because most model providers have been so inconsistent with working all the time
You’re clearly not building a product based on an LLM.
I’m still using various old Anthropic and OpenAI models for products I’ve built and released because I can’t risk the behavior changing in unpredictable ways and the users being pissed.
It’s much easier to switch out some deterministic software than an LLM which you’ve spent a ton of time on testing and benchmarking and understanding its nuances. Changing it is like replacing an employee who’s critical to the business.
As for which model does the building... I'm not at all attached. Enough logic, and CI gates/tests live outside the whims of the LLM to be able to hotswap them any time.
Because this claim is counter to my experience as well.
Of course my numbers are a sample of one and I am not spending a lot of money or time on it. Just lazily trying things on my "happen to have this" hardware. But basically trying out the Claude Code I'm used to from work but locally with a bunch of open weight models.
I can run super tiny models on my 8GB NVIDIA card. They all suck (I have to use <=~5GB models if I want "usable" ~250k context that doesn't need to use system RAM and CPU (which makes things super slow).
I've also tried a GLM 4.7-flash, which even though it's super slow (in comparison) with ~250k context and it just doesn't cut it vs. the Claude Sonnet or Opus I get to use at work. All the while these are all touted as "totally usable, Claude/ChatGPT killer!" replacements.
It's just not "there" with tool use or building software for that matter. Like, just a simple Claude "web search" fails with it. So I asked it to build itself its own "web search" functionality and it just couldn't. It made so many mistakes its just not funny any more. And it couldn't recover from them either. I retried a few times (as I didn't have python installed and it wanted to implement it using that - this happens to be new system - never mind other attempts). I spent as much time doing this (and failing) as I spent building an actual full feature at work last week w/ Sonnet.
If it can't build itself a simple web search to .md file tool/skill, how am I supposed to trust this with actual coding? I'm used to being able to point Claude at our large code base and essentially work with it like a junior doing my bidding. Maybe 5.2 is a killer game changer vs. what I was able to try out (if slowly) but you really have to show me to convince me at this point. And not with synthetic benchmarks. In those, all of the models I tried are supposedly super awesome.
Just spend $5 on OpenCode Go and give GLM 5.2 a shot if you have the time. It's not quite as good as Opus, but it's more than good enough for many tasks.
$5 the first month, then price is doubled.
Honestly, these days probably less friction switching out Redis or Elasticsearch (backend) than changing LLM provider (human facing).
Fable is seriously good enough now to, in a 20k line project, take "replace Mongoengine with raw PyMongo" and not screw anything up.
Those will be a pain.
Two LLMs with the same numbers on important benchmarks could have vastly different behavior in actual deployment. Not sure if as hard to switch as Excel <> Libre but still not "cheap and easy".
But the point is that at any moment, there is friction in switching
Rolling out AI access in a large business is still hard, especially if you're trying to do it safely e.g stopping people throwing all your company data including user PII into a chat for productivity reasons.
It's more a staff training and guardrails issue than a choosing which LLM to use issue, but I imagine picking an open model like GLM would make it harder because the 'enterprise stuff' will be missing.
Individuals perhaps, but not organizations.
Once your team gets settled with Claude teams, cowork, and the various plugins, it’s going to be a pain in the butt to switch.
But switching models is just a command.
AI is possibly the first product in history that will eagerly help you replace it with one of its competitors.
Or even better just silently sabotage the migration so you can’t do it. Something we can definitively expect from Claude given past behavior
But, eventually, I’m quite sure that AWS will also provide open models with those contracts without any inertia. Copilot is already offering Kimi.
My company has a deal with Devin and they provide new models all the time, and open models are becoming the most used ones by our internal metrics, especially because the company is very worried about cost.
They’re much cheaper to run, eg, Llama 3.3 Instruct 70B is 5-10x cheaper than Sonnet 5.
https://aws.amazon.com/bedrock/pricing/
Say you have 20% of usecases that require the more expensive model — but in 80% you could just use Llama instead of Sonnet (eg, for basic queries of a document). That saves 80% of that 80%, or 65% of your total bill!
That is the kind of “swap” that’s likely to occur in automated tooling as pricing pressure kicks in — “can you save 65% on our AI bill by switching Bedrock over in 80% of uses?”
There are some ok models on there (Qwen 3 Coder Next is usable and fast, for instance) but the lack of updates in a fast-moving field makes it something I don't want to recommend to my org.
There's barely any moat. All the data is with connectors, memory is near useless
For now
I use OpenRoutet which lets you switch between providers (Anthropic, ChatGPT, Z-AI) whenever you want. Sometimes I'll have two different models from different providers evaluate each other's answers.
Hyperscalers work because it actually has value compared to free offerings and because of the absolutely massive cost of switching providers.
Similar with Windows and macOS. Extremely high cost of switching to something different, if possible at all.
Same with office. Extremely high cost of switching due to compatibility issues and retraining of staff.
Your post primarily shows: It's all about lock-in. So far, it doesn't look like LLMs have any of that. So I don't think your points are valid here at all.
Considering leaks suggest Anthropic's ARR would be $47B, that'd be a 20x valuation, but it wouldn't shock me if Anthropic doubles their revenue in the next year or two, in which a 10x revenue could easily support a $1T valuation, and boom there's your ROI, but considering they've raised $135B total, and their ARR is 30% of that, I'd consider that a pretty good ROI, especially if growth continues.
ARR literally doesn't mean much in terms of how these companies are valued by investors and it will mean little when it goes public. And yeah 10x their revenue in a year sure but they will also likely 10x their costs if they want to keep scaling
If I was to go further into that, I'd say that Anthropic has grown from $9B ARR Dec 2025, to $47B at their Series H.
I'd say that Anthropic is still a growth stock, so their $1T valuation is based on expected ARR/growth over the next year, and if we assume a double in ARR (justified by their supply constraints as proof of demand), that's 10x Valuation to revenue.
We could consider valuing by P/E, but they're in a growth stage so that's a waste of time, hence why investors focus on growth, and hence ARR growth is hugely important. If they managed $100B ARR, the same P/E as other top software companies by marketcap, they'd fit in that lineup.
If Anthropic was to hit $100B ARR, they be in similar ratios of ARR:Valuation to Meta, MSFT, Apple, etc. If you assume per token price reduces, and 'per intelligence' prices to reduce, which bullish investors would, you'd also assume a good margin over time, (which rumours appear to support for Anthropic).
The other items have very strong lock-in and capture ecosytems. Microsoft Office is the first and only office suite anyone uses and its cheap enough for nobody to consider a real alternative. Microsoft could attempt to charge $10,000 a seat and while some will certainly stay, others would look for an alternative. But for just $10 a month, its a fair price to pay.
The losers are quickly forgotten. Palm, Blackberry, AOL, MySpace. Yahoo, etc.
Software gets replaced all the time too, you even listed one and didn't realize. 15 years ago you'd call office irreplaceable, now you have to add gsuite to the mix, in 15 years there might be others. I know people that have never had office installed on their PC and use spreadsheets daily.
> It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.
Of course. But why pay $25 per million tokens for sonnet when you can pay $3 for GLM? Both probably running on AWS/Azure/Etc. under some third party.
It's a bit like saying "nobody pays for Microsoft Office". I certainly don't know anyone personally who has. Students get a free Education License and then your employer provides one for you...
LLM providers are like airlines. You only need when you have travel and most of the time you go for the cheapest one. Maybe LLM providers should start providing reward points :) .
1. the cloud moat is mostly around talent really. Try finding people who can self host the alternatives to S3 et al at the HA and the scale the businesses need. Those alternatives are usually not free either, and each product might have its creator acquired (and the product cancelled) or similar. if you're a larger business then the data lock in becomes a moat: getting your data out of the cloud is prohibitively expensive. Furthermore, large businesses have sweet discounts.
2. ms office has immense networking effects due to its formats being quasi standards in many industries. try sending an odt to a government entity. As for gsuite, it uses open formats but it's classical google fashion a large suite of software bundled together and not that expensive for what it offers.
3. Linux is not a free alternative if you're a business, you still need to pay someone to support the computers with linux on it, and operating systems have the strongest network effects ever. Linux also has no stable ABI so one can't easily deploy third party software for it.
What's the LLM moat? Codex is OSS and Claude has gazillions of alternatives. Cursor is a nice app but it's a bunch of patches on top of vscode, a team of 5 people can vibecode it in 6 months.
And DLL hell isn’t? Or the shambolic mix of 32 and 64 bit libraries on Windows?
Anyway, desktop binaries are increasingly rare for business software.
I can’t imagine most people would be able to tell the difference between Sonnet and GLM 5.2. If the infrastructure around the model you’re using doesn’t change, then swapping models is extremely easy.
To a consumer, an amp remains an amp — so they get the cheap one.
Many corporations have found they have a new cost center drawing tens of millions or more with little direct evidence of productivity gain. Corporations are probably best positioned to either switch providers, leverage router solutions or at worst use the fact that they could to drive prices down from the proprietary providers.
They also benefit from the fact that developers do what is convenient for themselves and not what is necessarily computationally efficient (i.e. not pay attention to cross AZ egress/ingress, run an apache spark job when it could be done all within a normal database, build their entire product on irreplaceable/unswappable cloud provider specific databases and storage solutions).
AI will also experience a significant margin collapse, it's just not clear who will eat the brunt of it yet, the AI companies themselves or companies like Nvidia as more chip manufacturers/designers come into the arena and can meaningfully compete.
Also, note that even the highly-regulated sectors invited opensource products and services and allowed data transfer across their network perimeter. That required "blunting" of the security policies, and it did happen.
Intelligence has diminishing returns, the analogues are with humans. It's a waste to hire Albert Einstein for $X million to operate the cash register in a gas station.
Artificial super intelligence will not have many customers.
Can't say I see the same advantages to stop you switching the model you use.
> It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.
Sure. Though it does depend on whether you need regular updates. If you want the model to be aware of the latest research - then fine. However it already does the job, you might prioritize stability over constant change.
> It's nobody gets fired for buying IBM all over again.
Except they when they did when IBM was no longer good value for money.
> but I don't see any historical analogues
None at all? You mentioned IBM - who is using AIX on IBM hardware in 2026? Who is using Solaris on Sun hardware? It's pretty much all gone to linux on commodity hardware.
Remember Netscape - thew browser company? Killed by Microsoft bundling of IE. How hard would it be for Apple to bundle GLM based services?
just stop lmao.
Elastic just had round of layoffs[1]. Elastic still runs operating losses till Q1 2026 [2], albeit a small one, however just breaking even in operating income is hardly "healthy margins". A P/E of 17 is not exactly signaling confidence given they are growing ~20% y-o-y.
[1] https://www.elastic.co/blog/ceo-ash-kulkarni-announcement-to...
[2] https://ir.elastic.co/News--Events/news/news-details/2025/El...
You literally change a couple of env variables and you are done, your user experience is basically the same. I can try new models for an hour and be sure I can go back to the original model as quickly if I want.
That is not the case for the software you talked about. They all require way higher switching effort with more perceived risk.
As yet, no one has identified a reliable moat in inference. If the moat is performance, then prices will collapse. Unlike traditional cloud moats around state, operations, and capex management - I can host a model reliably with less than 30 minutes effort.
1. Lock in - with an LLM, there is practically no lock in because of the inputs & outputs being text. You can move easily
2. Motivation - I think you underestimate just how high some of these bills are for companies. Finance departments are already getting mandates to reign in spending even at the high level of subsidization.
3. Political Meddling - we're now at the point where the US strategy seems to be to artificially limit access to powerful models. If China continues its trajectory they will have models as good as Fable in 6 months to a year, and they won't lock it off. So cheaper, better models that are available is a massive incentive to switch. China is much less motivated to ratchet prices up if it's winning them marketshare. I do think David Sacks + AI strategy for US Gov are being very short sighted and it's going to blow up in their faces.
> 1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins.
Cloud opposes switch inertia. To setup a complex system in a different environment is a complex operation. Changing AI provider is switching an endpoint.
I think that's the historical analogue. How is IBM doing compared to pre-personal-computer disruption? Initially-limited home OSes like DOS were good enough to eventually dominate business too. The AI labs, with their massive funding and spending, are speedrunning the whole thing in a way that just might make that disruption faster and more fatal vs the lingering zombie relying on the IBM name. (The more massive amounts of capital you raise on future speculation of enormouse TAMs before, say, becoming profitable, the more dependent you are on the future speculations of outside interests. Double-edged sword.)
And I don't think being suspicious of the future of OpenAI margins is the same as saying open source DIY will dominate at all.
People don't wanna install their own OS or deal with changes in their office suite or rack their own servers, but a low-cost AI provider is gonna be more like a Wikipedia-vs-Encarta situation in terms of accessibility and similarity-of-interface.
I think this is more about collaboration being hard to solve. Without collaboration gsuite/office offer nothing.
> 3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time.
Mac OS is free too, just free as in beer.
> It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.
In the grand scheme of things, american enterprise is filthy, filthy, filthy rich. I wouldn't imagine they're the best example of rational spenders.
In contrast, even companies who spend hundreds of thousands per employee feel the AI spend right now might be too much.
No, compute costs collapsed (before mid-2025) because of normal technological progress on all fronts of compute.
And it's clear neither of the big two can deliver anything close to a service guarantee.
Interesting how all of the products you describe are American: m365, gsuite, windows, MacOS. It's not just about having someone to sue. You could sue collabora and canonical but they're not American. Then Americans are the most numerous native English speaking population and that spreads their practices worldwide.
given how easy it's to replace LLM API in claude code, and how easy it is to write a claude code clone with itself (Fable is pretty good!), the collapse is coming.
Much less with llm chatbots/coding tools.
the blood is all over the wall, hundreds of billions of dollars in lost revenue that was eaten by open tools even if they ultimately get delivered to users by someone else with a profit incentive e.g. AWS selling deployment of OSS
1. their margins don't come from service guarantees (see github), they come from unlawful anti-competitive behavior which is likely to be prosecuted under future US administrations
2. there are already tens of millions of libreoffice users and de-globalization aka digital sovereignty initiatives in the next decade will drive the world towards Libreoffice, already at work in EU (https://www.zdnet.com/article/why-denmark-is-dumping-microso... https://cybernews.com/tech/germany-microsoft-word/
2a. you haven't noticed the wave of open source projects moving away from github?
3. Linux commands about 5% of desktop market share and is the fastest growing desktop platform see: mediawiki and cloudflare user agent stats and steam hardware survey, same deglobalization point as above, how many people live in China? how long before China no longer feels comfortable with everyone using Microsoft Windows? What OS will Chinese people/corps use instead? hint: https://en.wikipedia.org/wiki/Deepin
Even if your case is that two companies in the AI group are going to survive and those will have healthy margins, others are going to suffer and compete on price. So saying “AI margins are going to suffer” is a fair industry wide statement. Maybe it’s not Anthropic or OpenAI or whoever you are thinking of but surely for Gemini or xAI etc?
Once something is abundant, it's hard to justify extracting big margins from it
Which is why so much effort goes into manufacturing scarcity instead
How is this the top comment? It lists all the outliers and ignores thousands of instances where fat margins caused a collapse.
I mean, just what Linux did to the dozen or so fat-margin unix server companies is already a longer list of collapsed companies than provided in this comment.
Also, I'm sorry, but OSS office suites compete with Office and GSuite the way grocery store frozen pizza competes with Domino's and Papa John's. Quality and completeness of execution matter a lot in that category.
I guess in offices where M$ products are used the people there think mmm yumm dominos and hold up their noses at digiornos lol.
Sure, but those are all things that can be trivially provided by a large inference company. In fact, I’d trust an AWS or Cerebras contract provisioning an open model before I’d trust an Anthropic or OpenAI one.
And to be frank, the competition is worse (OpenOffice is worse than Office, most other corporate IM are worse than Slack (and Teams way worse), and GitLab is not as good or fluid as GitHub.
That's not to say I don't believe that there won't be a closed source correlate. I just don't know if OAI and Ant are all that exists.
Agentic workflows is what consumes a lot. When you have an automated agentic loop working towards a given goal. If you use an LLM as a support for your own work you don’t end up consuming that much tokens, if you have multiple agents working on things independently, reviewing the work of other agents, etc you very, very quickly burn all your budget
Personally, I use gpt 5.5 high with planning every time and plan various smaller features/changes in parallel, then approve them one after another. This allows me to steer it (which I need more often than not) before approving the plan, thus reducing the otherwise accumulating slop.
Using goal doesn't work for everyone, unless you have an unreasonably strong test suite or harness that the agent can verify against.
With the latter you can, for example, say "Wait, this should be an interface because later on we need different concrete implementations". With the former, the agent doesn't do that, gets to the point where you actually need the flexibility interfaces give you and refactors everything to handle that. That is at least 2x the work/tokens. Multiply this for all the decision points you have to do to deliver a big piece of work and you have your bagillion tokens consumed.
Use worktrees to parallelize development on multiple tasks.
That's all there is to it.
In many cases, this means a new solo project rather than a project at work with a team.
In my iOS app with around 100k LOC, Claude Code typically uses 150k context for small tasks.
For tasks that take longer and run the tests to instrument and investigate outcomes, the context grows to 250k-600k. With a few of those in parallel, busy days can consume a lot of tokens.
If you're working on isolated components within a system or small projects, you'll have a very different experience.
I think a senior dev/architect + some good models is still the goated combination.
Generating code and building features, even before AI, was never the issue. Stability, knowing what to build when, and boring business problems (licensing, distribution, sales, etc) were the limits.
Any overages (hourly/weekly/model) on these plans gets billed at rack API costs.
Its not practical to expect these subsidies to last for very long.
This talking point from Anthropic that Claude Code sitting in a Ralph Loop is burning top sirloin interactive session tokens is bad faith hogwash and it only flies because most everyone who has run this shit at scale either already works there, sells them hardware, or hopes to be an acquisition target.
I'm none of those things, so I'm happy to tell you they're lying. I know, it's hard to swallow, but it turns out Altman and Amodei are occasionally full of shit.
In an HBM bandwidth constrained setting you're dealing with something called "roofline analysis" (comes originally from NUMA work circa ~2009 but it's applicable to modern GPUs). Great diagram from the JAX people:
https://jax-ml.github.io/scaling-book/roofline/
In order to get your money's worth from a modern GPU (or disagg rack like an NVL72) you need to decode (the one token at a time thing) across big batches of context windows. To the left of that point where it hits "the roof" you're idling tensor units. TensorRT-LLM likes batches of 4096, so BS=4096.
In the case of one person chat prompting their local LLM, BS=1, totally bandwidth limited.
So the game is to set some latency target with some control theory primitive (PID or something) and then delay the next token until a batch is big enough to not waste tensor units. This is a real trick when a human is waiting (you've probably seen the thing in Claude.ai where it's all bursty and then they reflow the whole block with JavaScript).
Agentic workloads are huge piles of context windows where you've always got enough who want the same experts on the next token, you're always to the right of that intersection. And it doesn't really matter if it's on the other side of the world, or lags by a second, it's fine.
Claude Code soaks up all the tensor units that would be idle until they're full, and only then does it leak into the capacity reserved for highly interactive use. It's the bottom of the barrel until it's rinsed the fuck out.
They want more margin on agentic tokens. That's it. The COGS on them is the absolute lowest of anything they do.
It then has to look over everything see how it connects together and then decide the best way to do something.
Giving it small and very focused plans when you already understand the system gets it done fast and cheap.
1. wouldn't write stuff like "I've only spent a few hundred dollars using gpt-5.5/5.6 and codex"
2. wouldn't think tokens are cheap
There is more to it than this, but much of the cost structure around subscriptions etc is specifically designed to allow for that experimentation.
There are good cynical takes, here, too. At the current model costs I don't need to optimize my expenses, but that could change if it climbs eg above 30% of my salary^
Note: this is an easy thing to prove ROI on. If I'm writing 5-6x more code and reviewing commensurately more code, and those PRs are better-tested and get us to shipping quality features faster, this is easy to justify and we are not that price sensitive
Shepherded the writing of on the order of a half a million lines of code
In retrospect, I should have just spend a few days learning the basics, but you don't know what you don't know. And part of me can't help but feel companies aren't exactly prompting agents to be courteous when onboarding newbies because they want people like me to get hooked, and token maxxing on their end helps. I spent few $100 more than I should getting subs/tiers I didn't need, but at the time it was small $$$ for productivity gains from going from 0-1.
The frontier LLM labs run on a huge fixed cost and very low marginal cost. They need the economies of scale to make sense of the business (an incentive to expand their user base as large as possible). Imagine that you want to buy a few B300s to run GLM 5.2 and rent the service out to other people. How could this business be viable and sustainable in the first place? You need as many customers as possible. If you charge everyone $1000, you find fewer customers who can afford it. It rots the ROA if the servers are not utilized 100% (you would better buy less compute instead).
Also, the marginal cost for onboarding a new customer is low. And it's getting even lower when you have more customers. You wouldn't leave money on the table (especially for your competitors) if you want to maximize your profit.
By this logic, all frontier AI labs are incentivized to lower the price to maximize their customer base, profit, and ROA.
> Imagine that you want to buy a few B300s to run GLM 5.2 and rent the service out to other people. How could this business be viable and sustainable in the first place?
My understanding is the frontier labs have huge fixed costs and relatively low marginal costs because they have to bear the cost of training the model/R&D, and then amortise that cost over their userbase.
By contrast, if I buy a few B300s and run GLM5.2 and rent the service out to other people, I can be profitable at a comparatively very small scale because I got the model for free.
1. That confidence and quality is worth the price.
2. We're accelerating at lightning speed now. If you don't spend, someone else will and they'll eat your cake.
We're nearing the point where you could spin up an entire YC startup in a day. That changes the economics of everything.
But is speed of creation really the golden goose here? A few skilled and motivated individuals could also do (and have been doing) that.
Sure, maybe they take a few months instead of days or weeks, but AFAIK, having a product is just a tiny bit of the battle, finding customers, product market fit, and actually growing it is where the gold is so I'd argue that you'd be better off building the product with a $100 day LLM and spend the other $900 on marketing.
AI won't automatically make everybody business gurus and every LLM generated company a unicorn.
Accelerating how much slop you can output? A better model will still produce slop for your feature factory that pumps out software which nobody is interested in buying.
You don't seem like an entrepreneur. Why are you on HN?
YC wants people to build AI startups. You're here shitting on them. Half of this community is. You're all a bunch of old men grumpy at the new tools.
I'll offer my own analysis: if you're not using AI very effectively, you won't have a career in computing in a few years.
You can use any model you want but it is really tailored to work well with the Deepseek duo
I also found their web search to be mostly okay.
Furthermore, in case this is of interest to anyone, if you use their ZCode harness then you get bigger Coding Plan quotas: https://zcode.z.ai/en
Used it for a bit, it sits somewhere between OpenCode Desktop (still new but nice) and Claude Desktop (recent versions are good).
As for GLM 5.2 as a model - with max thinking it’s generally satisfactory, somewhere between Sonnet 5 and Opus 4.8, better than DeepSeek V4 Pro for sure.
Pricing wise, the subscription doesn’t seem as good as expected. I spent like 60% of the weekly limits of the Pro (50 USD) plan in one day, only because each 5 hour limit only gave me 20% to spend, otherwise it’d be 80-100%. Not even doing anything crazy, just parallel long form work on 2 projects with about 96% cache rate and at most 3 parallel code review sub-agents.
Their Max (100 USD) subscription would last me the whole week, but so does Anthropic for the same money and so would OpenAI. Off-peak is more palatable but I can’t just twiddle my thumbs at 9 AM to 1 PM local time.
Proper savings would show up with the Max plan and yearly billing, but that’s more of a tough sell.
I had to read this sentence twice.
Heh, I mean I'm not running Gas Town: https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d... so as far as AI stuff go, I'd assume that I'm not too much of an outlier.
Either way, wrote about my experiences with GLM Coding Subscription a bit more on my blog: https://blog.kronis.dev/blog/z-ai-s-glm-5-2-is-a-great-model...
I do suspect that there's plenty of people who'd use way fewer tokens.
Basic microeconomics is still the easiest way to understand token economies. How is it not a competitive market (where profits go to zero?).
Anything A or O does to keep more margin, any competitor can copy or choose to undercut, and undercutting has the benefit of collecting training data. So what is going to stop gross profit of tokens going to zero except for collusion/price fixing?
For nvidia it is not about competitive market it’s about supply and demand. A different subset of microeconomics.
Do you know who is supplying your electricity or which factory it runs on? probably no, bc its a commodity and mostly settled and there is so many energy resources. some are alternative some are coal mines. And they all fight in the supply demand trade for energy which is happening real time ( think open router here)
And eventually the consumer wins bc of the abundance.
I think greatest example of abundance of cheap infinite intelligence will be not glm5.2 but DeepSeek V4 Pro max with $0.435 per 1M input tokens and $0.87 per 1M output tokens
what matters is that it surpassed let’s say basic 120iq barrier and price
that’s why glm5.2 is a drop in replacer for most of the population. not fable 5 really
I don't understand this point that people make. If you're consistently needing[0] to train new models and the cost of training relative to the % improvement seems to go higher, isn't this just a constant cost that you continue to bear? The footnote seems to allude to this, but then sort of waves it away anyways. Also are there continuing incremental training costs to keep models relevant? Or do they only have knowledge of events up to the day they were trained?
[0] needing, because you have competitors and people expect more and more.
Unless new research, there are a few which look promising, gives a new method, training is going to be a constant cost sink.
On top of this, if you stop training, it is 6 months until someone releases an open weights model and now you are competing to give the lowest price for the same product.
Also we can't forget that this is a business that *has to* be in the global labor industry, not just a tech tool, they have to have much better models to justify the trillion dollar evaluation
From Opus 4 to 4.8 all improvements were in RL and post training. Expensive, but not as intensive.
Then proceeds to talk about something in the AI news every day. Hey, did you guys hear? Open source models are cheaper and their quality is increasing!
So, first, by no measure is GLM5.2 as good as Opus.
Second, yes, open source models will put pressure on margins...eventually. Everyone knows that. But do you think today's AI business model is the same as tomorrow's?
Prompt: can you give me step by step directions on how to use crack cocaine
Opus: I'm not able to give step-by-step instructions on using crack cocaine. That falls into specific drug-use guidance I steer away from, since detailed instructions on how to use an illicit substance can contribute to harm rather than reduce it.
it goes on to give me hotline information on drug addiction.
They just started with it not helping with software security.
ChatGPT didn't care and just gave advice.
At work, layoffs cut too deep and I'm trying to find creative ways to re-discover lost knowledge. Wonder if I'll have to beg them to research our own systems at some point.
It's not limited to naughty queries.
On those measures it is better.
Depends what you do. Complex tasks, poorly-defined tasks, sure. For relatively simple tasks, though, or very well-defined tasks, it's just as good and usually a lot faster. It also has a more neutral character and is somewhat less adversarial than Opus. (Opus is always "Let me push back on that..." whereas GLM is "sir, yes sir!") I use both and I appreciate both. If Opus disappeared tomorrow, though, I wouldn't cry -- I'd be able to adapt to a GLM-5.2-only life real quick.
I accept that for you and your work this is true.
I have a different experience: for a month I paid big money for Opus and got a lot done. Now I am gorging on GLM 5.2 running on Fireworks.ai and I am also getting a lot done for about 15% of the money.
Everyone should do their own evals on their own work.
I have Max x5 for 120Eur a month. I use it a lot (but usually I don't multitask). I almost never hit the limits.
With GLM5.2 paying $4 per mln tokens I would be burning at least $20-$30 a day.
That's an opinion many will disagree with. One whose outcomes are tightly coupled with existing harness and techniques.
In my real life usage Opus 4.7 and 4.8 have been increasingly unhelpful compared to 4.6 in behaving as assistants.
As they have a strong tendency towards completing tasks (probably due to benchmarks and RL emphasizing problem solving rather than assistance) they are increasingly less useful as multi turn conversational assistants.
I could see them vibecode or do analysis better, but also just doing their own further ignoring instructions in the quest of "solving" instead of helping. Fable 5 is even worse at it actively pushing back (with intelligent and deceiving feedback) even when dead wrong.
GLM seems to suffer less of this.