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.

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.

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.

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.

> 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 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.

> 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.

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.

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.

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

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.

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 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.

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.

> 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.

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

> 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 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.

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

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.

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.

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.

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.

Good luck trying to enforce that outside of the US.

It truly is a pointless article.

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.

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