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Reporting/news around AI is so very interesting these days.

It's really hard to tell what's propaganda from what's not.

For examples, this Reuters that you have included here has all tells tale signs of creating fear, uncertainty, and feeding into propaganda. But then again I can't be sure.

It's completely the opposite stance to (a) the actions being taken by chinese companies and (b) the public stance taken by their govt https://english.www.gov.cn/news/202601/08/content_WS695f1b55...

It absolutely blows my mind that everyone is abundantly aware that money is not shared freely, and yet they are already popping champagne that the CCP are surely going to share power freely.

which is a result of the United States and other western forces (Israel) weaponizing AI to create propaganda

Yep https://news.ycombinator.com/item?id=48816025

And EU leadership completely destroyed Europe's future by betting on depending on US and Chinese models. https://pleias.ai/blog/fable-eu

Makes sense. AI is a munition now, and nations tend to restrict them. Eventually there will be international open source communities building open weights (all you need's a few smart people and a lot of GPUs for training), making the restrictions moot. Until then this will become the norm.

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.

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.

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.

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.

> but I don't see any historical analogues.

The losers are quickly forgotten. Palm, Blackberry, AOL, MySpace. Yahoo, etc.

Software gets replaced all the time too, you even listed one and didn't realize. 15 years ago you'd call office irreplaceable, now you have to add gsuite to the mix, in 15 years there might be others. I know people that have never had office installed on their PC and use spreadsheets daily.

> It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.

Of course. But why pay $25 per million tokens for sonnet when you can pay $3 for GLM? Both probably running on AWS/Azure/Etc. under some third party.

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 :) .

Those solutions have moats:

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.

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.

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.

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.

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.

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.

Also, note that even the highly-regulated sectors invited opensource products and services and allowed data transfer across their network perimeter. That required "blunting" of the security policies, and it did happen.

> 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.

1,2,3 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?

> 4. Many formerly open source infrastructure components like Redis and Elastic Search have Apache equivalents, but they still command healthy margins.

Elastic just had round of layoffs[1]. Elastic still runs operating losses till Q1 2026 [2], albeit a small one, however just breaking even in operating income is hardly "healthy margins". A P/E of 17 is not exactly signaling confidence given they are growing ~20% y-o-y.

[1] https://www.elastic.co/blog/ceo-ash-kulkarni-announcement-to...

[2] https://ir.elastic.co/News--Events/news/news-details/2025/El...

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.

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.

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.

The flaw is here:

> 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.

> 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.

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

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

> Many open source office suites exist yet none compete with the ubiquity of gsuite or office

I think this is more about collaboration being hard to solve. Without collaboration gsuite/office offer nothing.

> 3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time.

Mac OS is free too, just free as in beer.

> It seems that enterprises will pay top dollar for service guarantees, integration, and someone they can sue.

In the grand scheme of things, american enterprise is filthy, filthy, filthy rich. I wouldn't imagine they're the best example of rational spenders.

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.

> 1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins.

No, compute costs collapsed (before mid-2025) because of normal technological progress on all fronts of compute.

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.

. 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.

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.

Operating systems and office suites (like windows and word) have big network effects and high switching costs.

Much less with llm chatbots/coding tools.

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.

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

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

AI costs a lot more than all these, combined.

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?

Cost is a measure of scarcity

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

> 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.

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.

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.

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.

> 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.

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

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.

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.

there's huge margins on GPU time, not tokens.

> I have no idea what people are doing to burn so many tokens

Agentic workflows is what consumes a lot. When you have an automated agentic loop working towards a given goal. If you use an LLM as a support for your own work you don’t end up consuming that much tokens, if you have multiple agents working on things independently, reviewing the work of other agents, etc you very, very quickly burn all your budget

I have the same experience. I literally cannot fathom how people burn the number of tokens they claim to.

Agentic loops are being promoted by the same people selling tokens, abstracting away the cost per token, and doing everything in their power to obfuscate costs.

I think a senior dev/architect + some good models is still the goated combination.

Generating code and building features, even before AI, was never the issue. Stability, knowing what to build when, and boring business problems (licensing, distribution, sales, etc) were the limits.

It seems like you're basing your spend on the subsidized consumer subscriptions. The equivalent API costs for these subscriptions is usually 12-20x.

Any overages (hourly/weekly/model) on these plans gets billed at rack API costs.

Its not practical to expect these subsidies to last for very long.

They are offloading thinking to the AI too.

It then has to look over everything see how it connects together and then decide the best way to do something.

Giving it small and very focused plans when you already understand the system gets it done fast and cheap.

Because we pay retail consumer prices (subscriptions). Those same tokens cost many thousands on enterprise billing:/

These are probably mostly the enterprise customers - they may use the same amount of tokens as you do, but they have to pay the API price. From my experience the API is significantly more costly. We had one user ask for and receive usage credits on Claude, the bill the next day was to the tune of $400.

Asking claude to implement a single feature that takes under 30 minutes consumes 10-30 dollars of tokens in api costs.

To be clear, some of the token expense is because it's encouraged, and it's encouraged at some companies so that people will break out of their existing workflows to hopefully find useful new ways of working or building

There is more to it than this, but much of the cost structure around subscriptions etc is specifically designed to allow for that experimentation.

There are good cynical takes, here, too. At the current model costs I don't need to optimize my expenses, but that could change if it climbs eg above 30% of my salary^

Note: this is an easy thing to prove ROI on. If I'm writing 5-6x more code and reviewing commensurately more code, and those PRs are better-tested and get us to shipping quality features faster, this is easy to justify and we are not that price sensitive

^ https://x.com/SemiAnalysis_/status/2070915302058041450

My guess is that they never close sessions, I always restart sessions after a few messages to clean context

5.6?

>I have no idea what people are doing to burn so many tokens

Shepherded the writing of on the order of a half a million lines of code

Ignorance. Bad code hygiene and poor prompting. As someone who barely codes, I had a few old vibe coded projects from pre agent days when gemini ide had basically no usage limits that ballooned to multi 1000+ line files with backlog of bugs. I only stopped because dumb models starts breaking down at the point and project was serviceable for my needs. Come agentic coding and models smart enough to fix issues, but codebase is so filthy it does it wildly inefficiently. Like a few prompts would consume my 5 hour quota. Took a few days to get a decend agent.md up and refactor codebase etc and now I'm sipping tokens. I'm sure many people are still in that boat. Many of us literally don't know any best practices and can't tell agents how to behave.

In retrospect, I should have just spend a few days learning the basics, but you don't know what you don't know. And part of me can't help but feel companies aren't exactly prompting agents to be courteous when onboarding newbies because they want people like me to get hooked, and token maxxing on their end helps. I spent few $100 more than I should getting subs/tiers I didn't need, but at the time it was small $$$ for productivity gains from going from 0-1.

> The frontier LLM labs run on a huge fixed cost and very low marginal cost.

> Imagine that you want to buy a few B300s to run GLM 5.2 and rent the service out to other people. How could this business be viable and sustainable in the first place?

My understanding is the frontier labs have huge fixed costs and relatively low marginal costs because they have to bear the cost of training the model/R&D, and then amortise that cost over their userbase.

By contrast, if I buy a few B300s and run GLM5.2 and rent the service out to other people, I can be profitable at a comparatively very small scale because I got the model for free.

I agree, but there is prestige to consider. Many people are motivated to buy the best, even if it's much more expensive. "We're building a mission critical application here. Sure the API costs are much higher, but it's worth it."

I literally burned through 20USD in a couple of hours on openrouter with deepseek v4 pro and opencode tasks - i'm sure i did something wrong

What do you use as interface to OpenRouter? I, too, am looking into using an API to see if I can reduce costs (I use OpenAI + Github Copilot, currently). TensorX instead of OpenRouter (because it's in Europe, and EURouter wanted 15% more money from me :P), but I'm not sure if I want to change a configuration in vscode every time I want to switch the model in the Claude extension (and having an API key in my settings feels iffy too >_>)

> Not even doing anything crazy, just parallel long form work on 2 projects... and at most 3 parallel code review sub-agents.

I had to read this sentence twice.

I switched to yearly Cline pass because it was too cheap haha

They also have GLM-5V-Turbo. https://docs.z.ai/guides/vlm/glm-5v-turbo

You left out the one that will: federal government industrial policy

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 metaphor breaks quickly because as a first approximation the electricity "quality" does not depend on the provider, and will not change overtime. That's not true of LLM output.

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

Anthropic has essentially trained one big model for more than one year.

From Opus 4 to 4.8 all improvements were in RL and post training. Expensive, but not as intensive.

GLM-5.2 is not as good as Opus, it's better. I can abliterate GLM-5.2 and have it work on projects that Opus refuses.

What if your measure is cost? Or zero-data-retention? Or diversity of inference providers?

On those measures it is better.

> 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.

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.

> 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.

GLM seems to suffer less of this.

Aren't the American AI labs desperately struggling to find a market beyond just agentic coding?

The current top comment in https://lobste.rs/s/ua1gxl/glm_5_2_coming_ai_margin_collapse correctly zoomed into cached input tokens, but landed on the opposite conclusion:

> 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.

> MLA/CSA/HCA

Aren’t these techniques all “lossy” compression, and one of the reasons people complain about loss in quality as the context size grows larger?

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’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.

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.

Well, Microsoft just started offering Kimi K2.7 through Copilot hosted on Azure.

https://github.blog/changelog/2026-07-01-kimi-k2-7-is-now-av...

Cursor Composer 2 and 2.5 are also fine tunes of Kimi K2.5

It looks like politics don't matter when it comes to economics.

It wasn't really a secret, the main constraint is lack of compute resources

Are you running unquantized GLM-5.2 and getting in loops or quantized?

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?)

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.

Which race? As an information-providing "oracle" type model, maybe.

For practical agentic tasks? Not even close. Gemini is blatantly incompetent at tool use in an agentic harness. Even their own.

You can point Claude to another model if you want. See ANTHROPIC_BASE_URL: https://code.claude.com/docs/en/env-vars

Someone on HN made a comment in one of these threads that we could bake the weights into something like Cerebras's wafer scale chips and serve essentially the entire world off a single wafer, which is a pretty wild thing to think about. You'd have to make new hardware any time you trained a model but that seems really worth it.

FWIW I've seen subagents remain open for followups on latest Claude.

Multiple providers (who need to make a profit) offer the same 4.40 rate for glm-5.2. It's not subsidized.

Deepseek's 0.86 or whatever is likely subsidized but alternate providers offer it for a price comparable to glm-5.2.

GLM feels faster and more reliable in my experience. Anthropic and OpenAI models would hem and haw or straight up timeout during peak times.

There's definitely a saturation point depending on the complexity of the problem you're solving. For example, any model can write 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 what you need.

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 on 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 end up being 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.

Another thing to consider is that most big problems can be broken down into smaller ones. That's the basis for how programming languages are structured. We have primitives which are arranged into functions, that get bundled into classes or namespaces, and so on. So, you don't need an infinitely capable model to solve big problems. You just need to be able to break large problems into smaller ones, and a model that's smart enough to decompose a problem to the point where it becomes tractable.

Opus is good but not consistently good. That’s a problem. I’m paying the same but not getting the same results.

A year ago I wasn't using Deepseek. Now I am. I guess what changed is which models people are using most for coding.

> 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.

Which raises the question, which are the fastest frontier models? are the enterprise hosted Anthropic models faster than what Anthropic serves?

Somehow no one talks about LLM speed.

GLM 5.2 has a Fast variant at 200-400 tps.

It truly is a pointless article.

needs to be higher up

This take may be frowned upon in HN, but I suspect it's much closer to the median sentiment.

Don't Anthropic and OpenAI both already have military contracts? They are already growing into that fabric I think.

Good luck trying to enforce that outside of the US.

Pi.dev is great and with only a little customization made even previous gen open weights feel superior.

It also doesn't feel like they're trying to sell me on transhumanism all the time.

It also doesn't get mysteriously downgraded. It's just consistent, even before 5.2.

5.2 is great in a lot of ways - but it's best quality is that it gives some pushback and isn't nearly as synchophantic

Interesting, so point 2 means that a lot of the hardware being installed now won't be able to run the frontier models of 2029? How does that change the demand for compute/models in the future, I can imagine that even if OpenAI/Anthropic have a moat in 2029 there will be so much older hardware and such a hangover from that investment boom that there will be very little installed capacity that can run it

On 1 and 3, the obvious move is to shift the bulk of the harness behind a new API that's not based on raw LLM access. Then they get to hide secret sauce behind that API and all three go from commodity to premium while simultaneously being able to try out whatever tricks they can get away with to reduce their own inference costs. I'm almost surprised this hasn't happened already.

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.

its open-weight. I think you can find a host for GLM-5.2 in the 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.