I hope cheaper inference eventually means faster speeds at the lower tiers. I don't want to settle for 100 t/s, but I don't want to pay $10 per prompt either
When I've raised speeds about local inference I've been told 60-75 t/s is perfectly usable. It makes sense that people aren't talking about speed yet since you either already have a response fast enough to wait for, or you go do something else and check back in a few minutes.
I would love to wait for the latter type of tasks though, because those are typically the ones that require the most work from me to verify and I don't want my attention divided with multitasking.
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.
>the least understood upcoming shift in AI economics.
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?
> So, first, by no measure is GLM5.2 as good as Opus.
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 think the point is that if you’re doing simple, well defined tasks then Opus is overkill and you’d want Sonnet instead. Meaning, GLM5.2 is Sonnet-quality, not Opus-quality.
I think it's interesting to note that in one year we've gone from they're not even close [0] to arguing whether open models are only as good as sonnet or opus.
I see the exact same discussion as we’re having right now there; people stating that local models aren’t as good as the state of the art, but good enough for certain tasks.
I'd guess opus refusals are not an issue for 95%+ of people. Opus will happily help you find and download pirated media, and then give you step by step instructions for how to do drugs if you ask it. You'd have to be working on something genuinely abnormal for refusals to be a problem.
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.
I don’t. although I tried to get it to tell me how to take crack cocaine, and it wouldn’t. But when I was taking grey market peptides, claude walked me through reconstituting, dosing and administering them. I assumed it would behave similarly for other drugs, but it doesn’t seem to
Sure they will, but that doesn't mean that every bad guy should have nuclear weapon. But what I meant here was, that I understand their need to cover themselves of risk.
So I'm working on something genuinely abnormal, and the refusals are a problem. Then what? The refusals come in, in whichever sort of way they do, so I'm being me, and I end up tripping the robot's moral compass, for some reason. Who put them in charge of things?
The cost of running abliterated GLM-5.2 on western inference providers gets close to that of anthropic Opus and is still dumber on everything except the naughty queries you're trying to do. I love uncensored AI too but we need to be realistic here.
I'm trying to break anti-cheat protection in order to mod my own (single-player) game and Opus refuses to help. I don't really care if GLM's dumber at this point if Anthopic's going to be a non-option.
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.
> So, first, by no measure is GLM5.2 as good as Opus.
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.
re: "So, first, by no measure is GLM5.2 as good as Opus."
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.
1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have far 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.
The target audience is different. Coding is mainly a trade of the tech savvy, who like many on r/localllama users do not hesitate to deply on 16GB Vram gpus. Even if so, it is estimated that within 2 years we will be able to run Claude 4.8 on consumer hardware give the rate of improvement of open-weight LLMs, which will put more financial pressure on "paid" labs. It's just a matter of rate of improvement which is shrinking between open-closed models.
A lot of those things you mentioned have sticking power because they’re familiar to folks and migrating to something else is a big deal.
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.
Indeed, as it gets more commoditized it feels more like swapping electricity providers. Who cares whether you get your electricity from IBM or the state of Texas? An amp is an amp.
That's an interesting question. What if we did care? Is this amp from burning dinosaurs or from the sun or from fission? What if we could tag power as coming from oil vs renewables? how would that affect our habits?
I agree with swapping models making it easy. With openrouter, I just change the provider. With reasonix harness, cache hits are basically free. And that's with unsubsidized American providers like Digital Ocean or cloudflare.
I'm using pi-coder with just the free-tier models I can get on openrouter / opencode / kilocode. When I run out of quota on one model I often switch to another model in the same session, and it generally works just fine.
They may pay top dollar but there's all sorts of evidence that they'd very much like to pay radically fewer top dollars than the unsubsidised, off-plan price.
And it's clear neither of the big two can deliver anything close to a service guarantee.
Unlike all your examples, switching out an LLM is both cheap an easy. So easy that every 3 months or so new models are released and people grab them and start using them.
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.
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".
This is just another example of the bitter lesson. In a year a model will come out that will make none of these model specific optimizations you made matter.
I don’t exactly see orgs lining up to switch (and train) their employees between claude desktop and codex and whatever copilot is doing. There’s probably some inertia to those harnesses/integeations on top of the llms themselves.
The inertia is legal and financial. People are paying Anthropic through AWS accounts because the simple reason of not dealing making new contract and legal agreements is enough of reason of the inertia.
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.
Maybe for a fantasy of legal liability of output produced. I haven't heard of any LLM corpo being held liable for any output they generate. Even NYT lawsuit is going nowhere for 3 years in courts already, despite being the most grounded.
Copilot insures your company up to x if someone sues our company. I guess most companies do this. That’s why we can’t use anything else except copilot.
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?”
Bedrock is really out of date with the models it offers, to the extent that I'm not sure they even have plans to update what's on there now they have the deal with Anthropic. They're still offering Qwen 3, not even 3.5 and certainly not 3.6. GLM 5 is the newest z.AI model they have, when it's 5.2 that would be the one to worry Sonnet.
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.
Most large orgs do not need to train end users. They just need to add glm-5.2 to their router and their in house harness will pick it up. Then slowly limit usage on anthropic models and people will swap willingly. It's a simple /model command in every harness.
Yeah, most big orgs are pushing the idea of 'whitelabel' LLMs. Even if they choose to hang on to Claude Opus, they won't name it, they'll just call it the 'extra mode' and 'auto mode' will eventually switch to a local LLM in their harness.
What "training" do you have to do to get a professional developer to switch LLMs or harnesses? Its literally just download the other one, point it to your code base and start typing into that text box instead of the other one.
What would "training" even entail for that? As far as I can tell, using these tools directly is basically identical in terms of what you need to know. If you happen to have a bunch of custom configurations, maybe you need to invest some time into porting them, but it's not clear to me why you think that anyone would need to be trained if they spent months using one tool and then suddenly had t switch to the other.
This is the conversation I plan to have with Okta sales soon. Wait till you see how easy AI makes it to switch to Entra ID or anyone else. It’s tedium not even problem solving.
My problem with the SSO providers is not the technical part, thats "easy". Its the coordinate with the 200+ external and internal vendors / support to redeploy the SSO part which is time consuming. I always say its a ~3 year project, which can be done in 6 months with the right amount of resources, especially if the platform has been running for years.
If you're developing on top of LLM APIs directly, this is definitely not true. There are differences in how context caching works, in what's available through native harnesses, the types of tools you're fine-tuned on (GPT uses apply_patch while Claude uses edit, with different formats), the API surface (Agents SDK, Responses API, Managed Agents), cost structures, and best-practice guidance all around.
Not to mention the meta of account limits, billing, ZDR contracts, etc.
Exactly, as in, really, will they? Where and at what price, especially across an actual enterprise that needs to deploy them to lots of devs? There's much more than just the actual model.
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.
4.7 Flash is a small model that's almost a year old, which is ancient. And yes, your dinky GPU will not run anything worthwhile.
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.
The $5 is so they can see if open weights models are worth using, not so they can use it for a month. (Which you can't; The quota runs out way sooner than a month for any serious usage. Still worth the price of entry.)
If you use DeepSeek v4 Flash as a daily driver, with an occasional usage of DeepSeek V4 Pro and Glam 5.2 when necessary, the monthly quota practically never runs out.
Getting the pay-as-you-go plan with DeepSeek is also a good alternative. When motivation strikes you never get slowed down by quota, and it's cheap enough that even with mostly DeepSeek V4 Pro it's price-competitive with a $5/month subscription. Depending on how bursty your usage pattern is it might even be cheaper
True, but OpenCode Go gives 6x tokens on Flash and 1.5x tokens on DeepSeek Pro. After exhausting the monthly quota, Flash price is the same as directly from DeepSeek, while Pro is 4x pricier.
It really depends on what you're doing, but most LLM usage and agentic runs are pretty interchangeable in my experience, and it's usually trivial to switch.
If anything, you're better off supporting multiple LLMs as backup because most model providers have been so inconsistent with working all the time
Dude it’s not trivial to switch because the behaviors are different!
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.
I don't think they are saying it's trivial but compare say for example switching an organisation from Office or Windows the example that started this. They are not even in the same ballpark.
Makes sense but honestly if you've spent more time testing and working around the nuances to build consistent experience doesn't it mean you actually need more standardization to easily switch models if/when your trusted model is not viable for you provider?
For the public facing consumer functionality I have Gemini Flash running on guardrails directed by a state machine that calls it statelessly everytime. For that, it's strictly locked to a version. I can't afford to suddenly get responses that the SM is not tuned for.
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.
The behaviors of a single model and version can and does change behavior. There’s not only built-in stochasticity, but closed hosted models like Claude are tweaked and changed all the time.
Is it? I switched to Kiro and it's essentially identical.. well a bit better because you get a better idea of what the harness is doing, but otherwise identical.
I should have clarified that it’s not difficult for technical workers to switch, but for your average knowledge worker…it’s not easy switching from Claude Cowork to Codex. I don’t think the switch from Cowork to open source is easy.
Plugins and skills are completely trivial to move and most work with any modem. What is not trivial are Anthropic's new managed agents vendor lock-in offering.
Switching out an LLM? What do you mean by this? Sure some models can run locally but in a company with lots do people they might not be willing to spend to self host a larger model that requires beefier hardware to host, plus all the complexity to scale that out to a bit internal user-base
Most of the AI companies have OpenAi compatible API's, so you just get a subscription from another provider and change the URL that your LLM Agent Harness uses to talk to the AI.
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.
Unlike all your examples, switching out an LLM is both cheap an easy.
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.
GitHub, Slack, and Office have network effects and transition costs.
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.
They don't just need healthy margins, they need to make back almost a trillion dollars in a couple of years. Comparing that to elastic search and redis doesn't make much sense.
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.
The companies don't necessarily need to make back $1T, the investors do, and those investors don't require $1T in profit to do so, they need an asset worth $1T.
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.
Wait what? Why are you measuring valuation as 20x revenue here? If its a public stock (which is what anthropic plans to be soon), it doesn't matter. Otherwise spacex's valuation should be... 18.67 billion x 20 by your logic but its current valuation is over 2 trillion dollars right now.
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
I was arguing a 20x ARR valuation based on a simple 'potential' justification for $1T.
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).
> 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.
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.
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.
I think the big thing here is that paying high margins on a relatively small expense is much more palatable than high margins on a big expense. If a company is spending $1 billion/yr on tokens that a really big incentive to find an alternative where spending $1 million/yr on some SaaS with even higher margins can feel like an easy choice.
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.
> 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 $3 for GLM? Both probably running on AWS/Azure/Etc.
Individuals, sure. For enterprise you can't get monthly plans. You have to pay per token.
It's a bit like saying "nobody pays for Microsoft Office". I certainly don't know anyone personally who has. Students get a free Education License and then your employer provides one for you...
Strange. This is what they got at my company. I do not remember anyone mentioning paying for tokens. Maybe because it is fairly small, couple of hundred people in IT.
Officially it's not available anymore. But there are team plans that are not enterprise, so if you are small enough and fine with the data protection included in those maybe that is what you're working with? And I do know NGOs were offered seat based plans after they were officially not available anymore.
Also: This change came in in March so if you got your contract before then this will only bite once you renew.
Ah past renewal... So probably in next contract there will be a lot of thinking if we still need that.
Curious: Anybody have comparison what is the difference when company is changing to token based billing? How many times it is bigger than 200$ subscription?
Remember web browsers? compilers? web servers? databases? windows embedded? server operating systems?
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
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
There’s actually a strong case that agents will erode cloud providers’ margins because the lock in migration cost will be much lower in the future. No one ever migrated before because you’d spend $$$ to save $$ then the new vendor would gradually raise your rates negating the savings.
There's a huge case of survivorship bias when trying to recall historical analogues, because in every instance where margins collapsed and competition made the industry a commodity business, the big proprietary names are no longer with us. Here's a selection of examples, though:
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.
There's a really noticeable difference in time frame covered in your examples (80s and 90s) and the one in the comment you're replying to (2010s and 2020s).
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.)
Somewhere else in the comments here, someone else remarked "Individuals perhaps [move to the new models], but not organizations."
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.
Not OP but, EU economy is being squeezed by China on the industrial and tech front, and by the US on the innovation/startup front. It is clear EU is no longer at the technological/economic frontier like it used to be 10-20 years ago. At the same time there are serious demographic, budgetary and political challenges all across the continent. Dragi's report covers some of these. It feels like the whole system might fall into a crisis soon if measures are not taken
We first became powerful because we did the industrial revolution before anyone else, and used more of that capacity to fight the world (and win) than to fight each other.
When we fought each other, after the industrial revolution, that was the Napoleonic Wars and the two World Wars.
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.
Sure, but until then, proportional to each of [population, education/educated workers, capital, instantaneous industrial base, energy supply].
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).
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.
There were a lot of European wars between the Napoleonic Wars and the First World War. I agree with your point overall but there was a lot of fighting each other during that timeframe.
The motivation that the USA entered WWII was not because they were generous, but because the 3rd Reich was effectively becoming a big European nation, so they had to do something to avoid it. A unified Europe is a thread to the USA and Russia and maybe somebody else too.
So you’re saying that the war-ravaged Europe of 1946 that was split by the Iron Curtain and needed Marshall Aid was more powerful and important than today’s EU?
Insane take. But somehow people will go to any lengths to disparage the EU.
Globally in relative terms? Kind of. Sure the gap to the US was huge but it still controlled pretty much entire Africa and much of Asia (with China and Japan obviously not doing great..)
More likely it just slowly declines like Japan. Or if anti-migrant sentiment continues growing at the current rate, it breaks into a race war when the AfD and PFN win the majority of votes in Germany and France respectively.
I dont think these slow indicators and developments hold much water the next few decades. We have seen the first inklings what the brave new multipolar world is going to look like and it does not bode well for much of the world. As long as Russia breaks down before the shit really hits the fan, Europe should be fine(r) than most.
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.
This argument is so flawed in many ways, economies are built by people for people. Extrapolate the numbers, let's say in the COVID Pandemic a country, take USA as example, has 10% percentage of their population killed by it, would that be better to the economy?
There's multiple ways to look at the economy, the raw exchange of dollar currency in a debt chase (shit I need to run faster & pay down this stuff!), there's the productivity of industrial output (shit we need to sell more junk and useless crap!), and there's the stock and productivity in optimal life cycles (Damn, that Nokia is a tank!)
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
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/free 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 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.
Also cases where both happened, eg, Xerox wasn’t wiped out but copiers now have multiple vendors at the high end and have commodity via other brands at the low end.
It pivoted to server market and people who didn't think running linux was professional enough. Workstations were being held afloat by that pivot. Also they were general purpose instead if limiting their workstation market to just one niche.
There’s a difference between proprietary software thats highly profitable maintaining a stronghold over “cheaper” options and a massively overvalued and artificially inflated ecosystem having to confront economic realities.
Still, the clock is ticking. I don't know of any "new" company that would use Oracle instead of e.g. Postgres or would migrate to it. That's probably why they're pretty desperate to jump onto something new before the old source of income fizzles out.
. It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.
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.
For 2 and 3, office software and OSs have strong network effects and up-stack effects, just like CPU instruction sets.
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.
That was intentional - I think Office (or, uh, Microsoft 365 Copilot as it is now called) is frustrating mass-market mediocre software! That said, it consistently lets me do my job, while LibreOffice is often unusable for my purposes.
You missed two things one, a consist thread across all your examples - every market ends with a duopoly along with smaller competitors and two, which of these industries started with multiple billion dollars companies competing with each other?
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?
I don't disagree with your conclusions (enterprises will pay top dollar for service guarantees, integration, and someone they can sue) but by that same logic there is no clear winner with Anthropic/OpenAI. Claude has a habit of going down on me when I need it most and seems to be struggling to even keep 3 nines of availability. They're actively hostile to integration and seem more convinced they should be suing others than behaving in a way that doesn't get them sued.
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.
> It's nobody gets fired for buying IBM all over again.
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.
Nobody got fired for IBM, but it took some battles for IBM to reach that level. Same with AI. Brand images won't develop until the street battles are over and dust settled. Otherwise, Google wouldn't have taken over Yahoo and ChatGPT would have remained the king. That didn't happen. The street fights are still raging in AI and won't settle down any time soon. Cost-concious usage can kick-out Anthropic overnight. Ultimately it's only the cost that matters and that will blunt all other factors, including security concerns and risk aversion etc.
All the more reason to focus on those service guarantees, integration, and lawyers while making the underlying model easily swappable to whoever’s winning the frontier model involution battle at the moment
> I understand the arguments for a margin collapse, but I don't see any historical analogues.
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.
The point in the article that you are not considering is how easy it is to switch to a different model provider right now.
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.
> It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.
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.
The biggest difference between the cloud and AI is that an AWS server might cost 10x of what you would pay for if you bought your own, the overall expenditure is still just a small fraction of the company budget.
In contrast, even companies who spend hundreds of thousands per employee feel the AI spend right now might be too much.
We will keep using Claude because internal choices made by engineers and internal gatekeeping by engineers make everything else unfeasible, and going back on that would require said engineers to admit that they did something stupid, so it's not likely to happen.
I don't think the parallels are quite as clear for a few reasons:
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.
Examples two and three largely persist due to massive vendor lock in after the vendor has done enough work to capture market share, but that does not seem to be the case for AI labs to my knowledge
> 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.
1,2,3 are dominated by platform stickness or even active lock-in.
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?
In these examples cost of the solution does not generally scale with the use of the solution in the same way we see token use. In the case of LLMs the cost of use scales very differently than seat licensing.
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.
Hyper scalers have a decent margin from a small number of their services and a much more normal if not a loss from many others. Additionally a massive part of their profit is support services and contracts.
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.
good observation! however, I'd argue it's the distribution channel and installation friction did the job in the cases you mentioned.
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.
People might not want to be on update treadmill of proprietary weight models, for enterprise things. Things change a lot between models and they can't guarantee backwards compatibility like you can for deterministic software
I suspect the concern is that model serving is a stateless “simple” problem.
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.
When a hyperscaler is viewed as a software company their stock and value multiplier is much higher than if they are viewed as a commodity with expensive infrastructure costs. There is now not enough compute resources to serve demand. It requires sustained capital to grow compute resources. The costs are uncollapsing due to the overall demand plus the pressure of LLMs. Capital costs matter.
To switch an LLM you just need to open another browser tab and type in your chat query. You cannot say the same for any other kind of software. Traditional software have a large switching cost which acts as a barrier.
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 :) .
Raw cloud computing costs have a fairly small margin over what they could be. Nobody with real purchasing power is paying anywhere near the listed retail rates. It's part of the reason smaller providers e.g. OVH, can remain quite competitive.
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.
I also think we’ll approach a point where increasing intelligence is not really going to suddenly improve most work tasks. I bet that’s already happened actually.
We’re oohing and aahing about models, when the ones a few versions ago did a good enough job for most of the dumb coding, etc we do
The thing is they are inventing new things people will want to do. But for example, "loops", fully hands-off agentic coding etc., seem really unlikely to get much traction because that just isn't how software is designed within its producer/user community.
Requirements evolve in use, and fully hands-off LLMs simply cannot be trusted to only change the things you ask them to change, so I don't think it's likely that products will, in the main, be developed that way.
And if you don't need that fully-hands-off stuff, then the models that run on at least reasonably modest desktop hardware are surprisingly close to being enough.
I don’t think this is true. All the models prior to Fable were honestly dumb as rocks, and Fable is too sometimes, but at least it’s helpful now and not a hindrance.
The future of AI most definitely involves making something twice as good as Fable that is virtually its own employee, and not on reducing inference costs because to be honest Fable isn't actually that expensive.
The real utility behind an AI model (imagining that it can be made twice as good as it is now) would be being able to scale a small business up and down instantly without hiring (to implement a new feature or whatever), which is costly and time consuming these days.
I’ve been on a GLM coding plan since they launched ~year ago and it’s been at „good enough“ since the start. Tangible behind absolute SOTA but like you say most coding isn’t rocket science.
Seems like a pretty pointless post that still centers around output tokens.
In agentic coding, cached input tokens is 90% of the API "cost". It doesn't require GPU compute, and DeepSeek has shown that it can be done 50~100x cheaper with MLA/CSA/HCA, and a whole bunch of disks. This should collapse the margin.
> That is, for your $100/month fee, you get $3600 equivalent of API usage. This is presumably because Anthropic has figured out some clever things to do with model routing and input caching, and also can subsidize with investor money and take a hit on their operating margins.
My take: this is exactly what Anthropic wants everyone to think. In reality, 90% of that $3600 are for cached input tokens, that can be made to cost next to nothing, as shown by DeepSeek.
> The challenge is: when you let a session idle for >1 hour, when you come back to it and send a prompt, it will be a full cache miss, all N messages. We noticed that this corner case led to outsized token costs for users. In an extreme case, if you had 900k tokens in your context window, then idled for an hour, then sent a message, that would be >900k tokens written to cache all at once, which would eat up a significant % of your rate limits, especially for Pro users.
Using the current Opus pricing, that pre-lunch 900k tokens should roughly consist of:
720k input tokens = 0.72 x $5 = $3.6
180k output tokens = 0.18 x $25 = $4.5
900k 1h cached writes = 0.9 x $10 = $9
500M cached input tokens = 500 x $0.5 = $250
$267.1 in total, with 93.6% from cached input tokens. The portion that requires GPU compute is about 3% of the total.
Post-lunch, the 900k tokens should consist of:
900k input tokens = 0.9 x $5 = $4.5
900k 1h cached writes = 0.9 x $10 = $9
So Anthropic is fine with the $267.1 accumulated over 3~4 hours before lunch, but not fine with the $13.5 incurred immediately after lunch. Why?
The only plausible explanation is that the actual cost of caching is way less than the API pricing. If you use a coding plan, Anthropic doesn't really care about your cached input tokens usage. Indeed they want you to show your ccusage screenshots. On the other hand, if you pay by API tokens, the margin is huge for cached input tokens.
Only when you do something that requires a lot of FLOPs, e.g. the post-lunch 900k input tokens, the cost becomes real.
Recently I started getting messages from Clause Code (on a plan). "You're restoring an old session are you sure you don't want to compress the context? This will use a substantial amount of your usage quota"
That's exactly what I said. They do care when FLOPs are involved. Restoring an old session with 900k tokens will require a lot of FLOPs to reprocess the 900k token.
Meanwhile, they don't really care if you use hundreds of millions of cached input tokens, which doesn't consume any FLOP.
This article only promises to get into "the coming AI margin collapse" in a yet to be published "part two". This part only makes the point that GLM 5.2 is pretty good (no shit).
> I'd be very surprised if it wasn't more than 50% cheaper for nearly all workflows, for a very similar level of quality.
If your using pure API ... providers like neuralwatt cut that cost down even more by using energy as the actual cost. So GLM 5.2 is more expensive then GLM 5.1 on their service (those thinking tokens), compared to API costs, its dirt cheap. And way more tokens then the zai subscription delivers.
We are seeing a move towards more realistic pricing on actual consumption based usage. Be it DeepSeek, Xiaomi (MiMo), or zai's GLM via neuralwatt.
The main issue facing subscriptions a-la-carte usage, is that a lot of the heavy hitters really drain the resources. And that as a business model can not survive without ...
a) increasing the prices.
b) everything goes to actual token/energy usage based billing but with more realistic pricing, and not the bloated API prices that are focused on companies.
We shall see what the future holds but things will change.
If they did I wouldn't have had to go to DDG. It's not like it's a big jump over what used to be. I left claw-marks in Google Search, if they drove me off they're in trouble, because I didn't want to accept reality for quite some time.
I wonder if this is an alternative (and better) revenue stream vs ads for search engines: Offer a competing web search for LLMs as an alternative to Google, and charge enterprises and LLM providers for it.
I know Brave do this already. Not sure about DDG (I wonder if their agreement with Bing would allow it?)
Kagi assistant IMO does a great job giving relevant material to the LLM. It's a pretty neat way for a search engine to charge a premium, to offer a good model on top of their results.
Yes, margin on model inference is high with some providers. If you just wanted inference (at cost), you'd buy a GPU, or rent one from AWS or Microsoft. But you're not paying OpenAI/Anthropic for inference. You're paying them for a platform. Every feature OpenAI/Anthropic bake into their applications, models, online services, etc - anything that isn't pure LLM text generation - is a custom integrated add-on service that LLM weights do not include. Even if open weights became cheaper and better than OpenAI/Anthropic, most people would still pay for OpenAI/Anthropic, because they give you things the weights alone don't give you.
Comparing Z.ai GLM 5.2 to Claude Code w/Opus 4.8 is like comparing Linux Kernel 7.0 to Microsoft Windows 11. If you don't know much about computers, you'd say these are the same things. If you know a lot about computers, you know the latter has a thousand extra things that make a huge difference in what it does out of the box. Which one you use speaks to what kind of customer you are.
Sure, GLM 5.2 doesn't have vision; but an AI power user can plumb together any VLM with the text generation of GLM 5.2 in most AI harnesses, just like a Linux power user can combine the Linux kernel with KDE Desktop. Most people don't use Linux and KDE, because it's unpopular, difficult to use, hard to get support for. Instead they pay for Windows or Mac, because there's lots of support, with a giant company pouring money and effort into filling all the usability gaps, making it seamless.
Most people don't pay for the cheapest possible thing. They pay for the thing they can afford that improves their life while making it easier. An open weight alone is almost completely unusable by itself (like the Linux kernel), compared to an AI platform (a completely usable system). If you're constantly wondering about when open weights will reach parity with OpenAI/Anthropic, you're a Linux person. If you just pay $20/$50/$100 for OpenAI/Anthropic without thinking about it, you're a Windows/Mac person. There is nothing wrong with either of these groups, but they are fundamentally different, and always will be. An LLM weight is simply a different category of thing than an entire AI platform/provider.
How long will that $4.40 rate persist? Until we know more about the real unit economics it will be damn near impossible to rely on steady inference costs or make them predictable at the enterprise level. Gonna be a wild ride for awhile.
GPU/RAM/etc prices could continue to rise. If the world leaders decide it's time to build the robot armies, then that could price out the civilian uses for GPUs.
How long did it take vLLM to implement deepseeks sparse attention from the r1 paper?
Does ananyone outside deepseek has a working code for the v4 compressed attention?
Has any other provider managed to bypass CUDA and program the compute engines in their native assembly language to get 10% more performance out of them?
If you look at https://openrouter.ai/z-ai/glm-5.2#providers there's about 28 providers, including z.ai and Alibaba. Most outside of China. I've never seen so many providers for a model on there before, glm 5.2 is popular.
IMHO, cheaper inference means higher costs overall :) because everyone will use more thus driving up the investment required to stay current or to compete.
Switching models is also kind of easy but not plug-and-play. Most harnesses out there do very poor job with the open weight models. Unlike Opus, GLM 5.2 ends up in loops and hallucinates a lot more. If your harness is built on the expectation that the LLM will perform well, then switching to GLM 5.2 will be an uphill struggle. We had to refactor our harness and introduce more defences because of GLM.
The cost savings are substantial. Obviously it really depends on your workloads but it is noticeable cheaper for agentic work. Coding - I don't know. We do have some coding agents on GLM 5.2 and what I noticed with some landing page experiments that the results between GLM and Opus are identical - they might be using the same training data? Obviously Opus is still substantially better model. I don't think there is an argument to be made here but GLM 5.2 is cost effective and really good too.
Overall, we switched all of our internal agents to GLM 5.2 and because it is Open Weight we are in talks to get the model from certain geo locations giving us more freedom as well as extra protection.
Overall I think this industry will be in much better place because of GLM 5.2 and whatever open-weight models come next.
It’s important that none of these entities can collude to price fix. Having China be the competitor ensures that.
Basic macroeconomics 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?
So the federal government industrial policy is the thing that supposedly will keep the prices on "A and O" high in the US while the rest of the world will get comparable AI competing to get cheaper and cheaper?
Basically, the US govt will say that foreign models and providers are a security risk and ban them. If the US has shares of Anthropic/OAI due to a sovereign wealth fund, it'll be billed as domestic industry protectionism too.
Considering conditions within a single market is still microeconomics, I agree though its tough to see where firms will get market power from so profit will tend toward zero. I thought the same about GPUs though and nvidia still doesnt seem to have any real datacenter competition in sight.
The fact that these Chinese models are getting close to “Opus-grade” despite costing 6x-8x less is huge.
As the token bills start to come in, those economics will be harder to ignore (regardless of the origin of the LLM); especially as there will be many CIOs sweating over their quick and costly AI initiatives showing little ROI.
My hope is that the EU also steps up their own competition in the frontier model space so that it’s not just China v USA.
they're not near opus at all, anyone using the models in a real working environment will tell you the same thing. on paper they have impressive benchmarks, but that's not realistic to actual use.
I've been using GLM 5.2 a lot this past week, it's been replacing Opus 4.8. I mostly do front-end web development and haven't noticed much of a quality difference.
Sure, "it's just frontend", but that's actual use enough for me to take it seriously.
I think it depends on your use case. For my personal projects (a mix of webdev & some Rust desktop apps) it's honestly very close to Opus 4.8 (which I use in my day job).
I don't feel like I'm missing out after cancelling my personal Claude subscription, whereas I used to feel that way a few months ago.
How fast is glm 5.2 in western hosts? It's doing everything I want it to, but going through PRC host it takes like 5-10 times longer. Not sure if that is nature of modest or PRC computer infra/routing.
> Of course, this was a hugely poor read of where the costs actually lie in AI. Training - while no doubt capex intensive - is a fixed, up-front cost. You spend hundreds of millions to train a model, then you are "done".
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.
These models rely on knowledge that are embedded in their weights, if a new library is released, a new linux version comes out, some new protocol succeeds the previous one, you want your llm to know about it. Sure you can just add that into the context window, but that has its own problems.
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
it does not require training a model from scratch for it to be updated. the entire LLM training process is iterative. essentially each step (there are hundreds/thousands for a training run) yields a complete, usable model. tokens with updated data can be added on top essentially at any time in the future.
the economics of this are a little counterintuitive.
is there a market saturation point for intelligence? how about for software? it seems like the more you have the more you want because you're trying to do more things.
as the models get smarter I get busier because I'm doing more things...
There's definitely a saturation point depending on the complexity of the problem you're solving. For example, any model can write you a small shell script to resize a video with ffmpeg for you right now, so it doesn't matter whether you're using a local Qwen model, GLM, or Fable. They'll all do a roughly comparable job and you'll end up with a working script that does the job.
Then you have things like CRUD apps, where a model needs to write some SQL, make a service endpoint, serialize some JSON, etc. Here a local model might have a bit more trouble juggling all the pieces, but any hosted model will do just fine. If your day to day job involves working on CRUD apps, then it's basically a solved problem now.
The cases where frontier models matter are when you're solving genuinely complex problems, but that's not what most people are doing day to day. So, paying an order of magnitude for a model that has capabilities to solve problems outside the range of problems you actually work becomes a waste of money.
There's going to be a market for these models from people who really do work on complex things on regular basis, but the question is how big that market is. Additionally, open models keep getting better, and GLM 6 or DeepSeek v5 could be another big jump in capability where they fully close the gap with Fable. At that point, even more of the market becomes covered by these models leaving truly complex cases on the frontier.
The question is that of cost. Sure, there's no downside to having a smarter model, but if you have to pay orders of magnitude to use it, then it's just a waste of money. If you can get around on a bicycle then you're not gonna buy a monster truck.
You're right of course when you're talking about people. I believe there is a lot of low-hanging fruit for optimizing the performance of these models. that's actually what my startup is doing.
I don’t understand the argument here. The article doesn’t describe a collapse or the breadcrumbs for it. The only argument I can put together is companies hosting the open source models in house or use some service like Amazon that could potentially host them and so replace the frontier models. Data center and specifically infra to host llms is still the main sticking point given the security concerns about data going to china. The article doesn’t make these arguments coherently
> GLM is the model that will sink the frontier labs.
this is the claim you are making here, no one else claimed that.
two obvious issues here -
1. GLM itself is a frontier lab, ranked No.3 in the world in July 2026, ahead of Google, Meta and xai. GLM is not going to sink itself.
2. GLM won't sink OpenAI, it will significantly restrain OpenAI's profit margin. OpenAI will still be able to get stupidly high market cap, but not trillions, hundreds of billions will be far more likely.
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[ 3.1 ms ] story [ 107 ms ] threadSomehow no one talks about LLM speed.
Partnership you mean?, Cerebras went public and are trading at around 45B in market cap.
While OAI could in theory cough up that kind of money, it would massively hamper their existing committed capital outlays.
maybe i should get some cerebras stock then, ty for the pointer
1. https://openai.com/index/cerebras-partnership/
When I've raised speeds about local inference I've been told 60-75 t/s is perfectly usable. It makes sense that people aren't talking about speed yet since you either already have a response fast enough to wait for, or you go do something else and check back in a few minutes.
I would love to wait for the latter type of tasks though, because those are typically the ones that require the most work from me to verify and I don't want my attention divided with multitasking.
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.
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?
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.
[0] https://news.ycombinator.com/item?id=44623953
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.
ChatGPT didn't care and just gave advice.
They just started with it not helping with software security.
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.
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.
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.
On those measures it is better.
1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have far 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.
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.
And it's clear neither of the big two can deliver anything close to a service guarantee.
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.
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
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
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.
Not to mention the meta of account limits, billing, ZDR contracts, etc.
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.
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.
In fact, most benchmarks show this! Most benchmarks have similar performance for the same classes of models.
On top of this, there are tools like open router, or even the openai SDK which trivially allows you to swap endpoints for the LLM!
If you're using the agents SDK from openai or something, then yeah it's not interchangeable but that's you doing it wrong
For now
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.
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 switching models is just a command.
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.
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.
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.
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).
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.
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.
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.
> 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 $3 for GLM? Both probably running on AWS/Azure/Etc.
It's a bit like saying "nobody pays for Microsoft Office". I certainly don't know anyone personally who has. Students get a free Education License and then your employer provides one for you...
Also: This change came in in March so if you got your contract before then this will only bite once you renew.
https://support.claude.com/en/articles/9797531-what-is-the-e...
Curious: Anybody have comparison what is the difference when company is changing to token based billing? How many times it is bigger than 200$ subscription?
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
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.)
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.
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.
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.
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.
One would also expect to see sharp declines in population from just such an event, so I wouldn't celebrate too much.
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...
Even then the US might not have done much if the Nazis hadn’t kept attacking US shipping.
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.
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
No government action whatsoever was taken in Sweden. None. Death rates were no better or worse than adjacent countries that intervened extensively.
Ergo, government intervention was irrelevant one way or another.
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...
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/free 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 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.
We are seeing the later start to unravel.
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.
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.
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?
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.
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.
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.
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.
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.
In contrast, even companies who spend hundreds of thousands per employee feel the AI spend right now might be too much.
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.
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.
Much less with llm chatbots/coding tools.
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.
No, compute costs collapsed (before mid-2025) because of normal technological progress on all fronts of compute.
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
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.
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.
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.
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 :) .
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.
This isn't SAP or a CRM or some other business where workflow = moat.
We’re oohing and aahing about models, when the ones a few versions ago did a good enough job for most of the dumb coding, etc we do
Requirements evolve in use, and fully hands-off LLMs simply cannot be trusted to only change the things you ask them to change, so I don't think it's likely that products will, in the main, be developed that way.
And if you don't need that fully-hands-off stuff, then the models that run on at least reasonably modest desktop hardware are surprisingly close to being enough.
The future of AI most definitely involves making something twice as good as Fable that is virtually its own employee, and not on reducing inference costs because to be honest Fable isn't actually that expensive.
The real utility behind an AI model (imagining that it can be made twice as good as it is now) would be being able to scale a small business up and down instantly without hiring (to implement a new feature or whatever), which is costly and time consuming these days.
In agentic coding, cached input tokens is 90% of the API "cost". It doesn't require GPU compute, and DeepSeek has shown that it can be done 50~100x cheaper with MLA/CSA/HCA, and a whole bunch of disks. This should collapse the margin.
> That is, for your $100/month fee, you get $3600 equivalent of API usage. This is presumably because Anthropic has figured out some clever things to do with model routing and input caching, and also can subsidize with investor money and take a hit on their operating margins.
My take: this is exactly what Anthropic wants everyone to think. In reality, 90% of that $3600 are for cached input tokens, that can be made to cost next to nothing, as shown by DeepSeek.
> The challenge is: when you let a session idle for >1 hour, when you come back to it and send a prompt, it will be a full cache miss, all N messages. We noticed that this corner case led to outsized token costs for users. In an extreme case, if you had 900k tokens in your context window, then idled for an hour, then sent a message, that would be >900k tokens written to cache all at once, which would eat up a significant % of your rate limits, especially for Pro users.
Using the current Opus pricing, that pre-lunch 900k tokens should roughly consist of:
720k input tokens = 0.72 x $5 = $3.6
180k output tokens = 0.18 x $25 = $4.5
900k 1h cached writes = 0.9 x $10 = $9
500M cached input tokens = 500 x $0.5 = $250
$267.1 in total, with 93.6% from cached input tokens. The portion that requires GPU compute is about 3% of the total.
Post-lunch, the 900k tokens should consist of:
900k input tokens = 0.9 x $5 = $4.5
900k 1h cached writes = 0.9 x $10 = $9
So Anthropic is fine with the $267.1 accumulated over 3~4 hours before lunch, but not fine with the $13.5 incurred immediately after lunch. Why?
The only plausible explanation is that the actual cost of caching is way less than the API pricing. If you use a coding plan, Anthropic doesn't really care about your cached input tokens usage. Indeed they want you to show your ccusage screenshots. On the other hand, if you pay by API tokens, the margin is huge for cached input tokens.
Only when you do something that requires a lot of FLOPs, e.g. the post-lunch 900k input tokens, the cost becomes real.
Anthropic was extremely capacity constrained at that point. They still are but not to that extent.
I'd note that OpenAI offers 24 hour caching. I'd be surprised if Anthropic hasn't optimised their caching for Claude code too.
The principles remain though.
So it seems they do care.
Meanwhile, they don't really care if you use hundreds of millions of cached input tokens, which doesn't consume any FLOP.
Aren’t these techniques all “lossy” compression, and one of the reasons people complain about loss in quality as the context size grows larger?
If your using pure API ... providers like neuralwatt cut that cost down even more by using energy as the actual cost. So GLM 5.2 is more expensive then GLM 5.1 on their service (those thinking tokens), compared to API costs, its dirt cheap. And way more tokens then the zai subscription delivers.
We are seeing a move towards more realistic pricing on actual consumption based usage. Be it DeepSeek, Xiaomi (MiMo), or zai's GLM via neuralwatt.
The main issue facing subscriptions a-la-carte usage, is that a lot of the heavy hitters really drain the resources. And that as a business model can not survive without ...
a) increasing the prices. b) everything goes to actual token/energy usage based billing but with more realistic pricing, and not the bloated API prices that are focused on companies.
We shall see what the future holds but things will change.
This is why Google will win the race over most of its competitors. They own search.
I know Brave do this already. Not sure about DDG (I wonder if their agreement with Bing would allow it?)
Market share is currently Google (91%), Bing (4%), Yandex (<2%), Baidu (<1%), Brave (<1%)
Google can and do already monetize automated search from AI models.
Heck, if they wanted to, Google could turn off search and make you go through their AI model to get information.
Imagine that. That's how powerful they are.
For practical agentic tasks? Not even close. Gemini is blatantly incompetent at tool use in an agentic harness. Even their own.
Comparing Z.ai GLM 5.2 to Claude Code w/Opus 4.8 is like comparing Linux Kernel 7.0 to Microsoft Windows 11. If you don't know much about computers, you'd say these are the same things. If you know a lot about computers, you know the latter has a thousand extra things that make a huge difference in what it does out of the box. Which one you use speaks to what kind of customer you are.
Sure, GLM 5.2 doesn't have vision; but an AI power user can plumb together any VLM with the text generation of GLM 5.2 in most AI harnesses, just like a Linux power user can combine the Linux kernel with KDE Desktop. Most people don't use Linux and KDE, because it's unpopular, difficult to use, hard to get support for. Instead they pay for Windows or Mac, because there's lots of support, with a giant company pouring money and effort into filling all the usability gaps, making it seamless.
Most people don't pay for the cheapest possible thing. They pay for the thing they can afford that improves their life while making it easier. An open weight alone is almost completely unusable by itself (like the Linux kernel), compared to an AI platform (a completely usable system). If you're constantly wondering about when open weights will reach parity with OpenAI/Anthropic, you're a Linux person. If you just pay $20/$50/$100 for OpenAI/Anthropic without thinking about it, you're a Windows/Mac person. There is nothing wrong with either of these groups, but they are fundamentally different, and always will be. An LLM weight is simply a different category of thing than an entire AI platform/provider.
Deepseek's 0.86 or whatever is likely subsidized but alternate providers offer it for a price comparable to glm-5.2.
They have published tons of articles dedicated to performance and efficiency engineering. Feel free to have a look...
Does ananyone outside deepseek has a working code for the v4 compressed attention?
Has any other provider managed to bypass CUDA and program the compute engines in their native assembly language to get 10% more performance out of them?
There is your answer.
i mean i guess my employers wouldn't know the difference
but i'd like to play it safe and keep everything in america
Switching models is also kind of easy but not plug-and-play. Most harnesses out there do very poor job with the open weight models. Unlike Opus, GLM 5.2 ends up in loops and hallucinates a lot more. If your harness is built on the expectation that the LLM will perform well, then switching to GLM 5.2 will be an uphill struggle. We had to refactor our harness and introduce more defences because of GLM.
The cost savings are substantial. Obviously it really depends on your workloads but it is noticeable cheaper for agentic work. Coding - I don't know. We do have some coding agents on GLM 5.2 and what I noticed with some landing page experiments that the results between GLM and Opus are identical - they might be using the same training data? Obviously Opus is still substantially better model. I don't think there is an argument to be made here but GLM 5.2 is cost effective and really good too.
Overall, we switched all of our internal agents to GLM 5.2 and because it is Open Weight we are in talks to get the model from certain geo locations giving us more freedom as well as extra protection.
Overall I think this industry will be in much better place because of GLM 5.2 and whatever open-weight models come next.
Basic macroeconomics 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.
As the token bills start to come in, those economics will be harder to ignore (regardless of the origin of the LLM); especially as there will be many CIOs sweating over their quick and costly AI initiatives showing little ROI.
My hope is that the EU also steps up their own competition in the frontier model space so that it’s not just China v USA.
Sure, "it's just frontend", but that's actual use enough for me to take it seriously.
I don't feel like I'm missing out after cancelling my personal Claude subscription, whereas I used to feel that way a few months ago.
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.
is there a market saturation point for intelligence? how about for software? it seems like the more you have the more you want because you're trying to do more things.
as the models get smarter I get busier because I'm doing more things...
Then you have things like CRUD apps, where a model needs to write some SQL, make a service endpoint, serialize some JSON, etc. Here a local model might have a bit more trouble juggling all the pieces, but any hosted model will do just fine. If your day to day job involves working on CRUD apps, then it's basically a solved problem now.
The cases where frontier models matter are when you're solving genuinely complex problems, but that's not what most people are doing day to day. So, paying an order of magnitude for a model that has capabilities to solve problems outside the range of problems you actually work becomes a waste of money.
There's going to be a market for these models from people who really do work on complex things on regular basis, but the question is how big that market is. Additionally, open models keep getting better, and GLM 6 or DeepSeek v5 could be another big jump in capability where they fully close the gap with Fable. At that point, even more of the market becomes covered by these models leaving truly complex cases on the frontier.
the smarter the model the more capable it is to marshal resources to accomplish the goal.
this means you don't need a smart person to be able to take advantage of this. a smart model will be able to do the same.
You're right of course when you're talking about people. I believe there is a lot of low-hanging fruit for optimizing the performance of these models. that's actually what my startup is doing.
There's the sanctions already implemented, next step might be giving these companies government funding, just like they do with military companies.
Recall last year deepseek? And 18 month's later? What changed?
this is the claim you are making here, no one else claimed that.
two obvious issues here -
1. GLM itself is a frontier lab, ranked No.3 in the world in July 2026, ahead of Google, Meta and xai. GLM is not going to sink itself.
2. GLM won't sink OpenAI, it will significantly restrain OpenAI's profit margin. OpenAI will still be able to get stupidly high market cap, but not trillions, hundreds of billions will be far more likely.