As long as the SOTA models are 'ahead' then there will be a big premium.
GLM 5.2 and the coming AI margin collapse
91–100 of 495 posts
Re: GLM 5.2 and the coming AI margin collapse
#92Earlier quoted context omitted.
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…
Linux has a very stable userspace syscall ABI. About as stable as Windows, and much more stable than MacOS or the BSDs. I agree with everything else though.
Yeah, Linux-the-kernel does have a stable ABI indeed, but this is not relevant for most ISV desktop software out there. In my comment above I was referring to Linux-the-OS (aka GNU/Linux). The userspace libs don't have a stable ABI at all, and this is a widely discussed problem. Other operating systems built on top of Linux-the-kernel don't have this problem, Android has a really stable ABI.
Re: GLM 5.2 and the coming AI margin collapse
#93I'm not convinced raw costs matter: 1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins. 2. Many open source office suites exist yet none compete with the ubiquity of gsuite or office. GitHub, Slack are similar examples. 3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time. 4. Many formerly open source infrastructure…
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.
Re: GLM 5.2 and the coming AI margin collapse
#94Earlier quoted context omitted.
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.…
>switching out an LLM is both cheap an easy. 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.
Agents will make all of these migrations trivial. I expect margin collapse across a lot of tech darlings.
Re: GLM 5.2 and the coming AI margin collapse
#95GLM is the model that will sink the frontier labs.
Recall last year deepseek? And 18 month's later? What changed?
Re: GLM 5.2 and the coming AI margin collapse
#96in cursor benchmark glm5.2 is on par with gpt 5.5 medium and sonnet for the same task from results and cost perspective.
The speed of generation for both gpt 5.5 medium and sonnet 5 will be dramatically faster. source : https://cursor.com/evals
I don't get the hype. It's near SOTA model that is not deepseek of this world. It an expensive to run model, and under certain tasks it is comparably cheap as closed source ones.
Re: GLM 5.2 and the coming AI margin collapse
#97I think the profits depend on how well they manage their fleet purchases (or possible sub-leasing?) to get high utilization without overloading or idle racks.
Because accelerators like H200, B300 etc. are highly parallel and designed to run like 200 or maybe 300 sequences at once (depends on the model, just guessing). I assume they finance the hardware and that cost per device or rack is the same whether each unit is handling 10 requests or 150 requests (aside from electricity).
And probably international customers factor into it to get good utilization over more of the night time. And it likely is something that they look at quarterly more seriously than monthly. The biggest risk to profits might be a downturn in business that causes some portion of the financed AI accelerators to go idle or get low utilization for some weeks (that they can't sublease).
Re: GLM 5.2 and the coming AI margin collapse
#98I'm not convinced raw costs matter: 1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins. 2. Many open source office suites exist yet none compete with the ubiquity of gsuite or office. GitHub, Slack are similar examples. 3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time. 4. Many formerly open source infrastructure…
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…
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.
Re: GLM 5.2 and the coming AI margin collapse
#99Earlier quoted context omitted.
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.…
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…
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 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.
Re: GLM 5.2 and the coming AI margin collapse
#100Earlier quoted context omitted.
I switched to yearly Cline pass because it was too cheap haha
I can't find on their website some indication of what kind of usage I can get out it, otherwise I'd be interested.
$6 a month I plan to use deepseek v4 flash mainly which should provide closer to 5x the usage on the cheaper ones but no set number