This logic seems mad. If people only need SLMs then hyperscalers can also centrally host higher-efficiency models, and still gain efficiencies of scale and convenience over hosting locally.

From what’s presented this seems to be the lower end of Q&A and reasoning tasks and not long horizon agentic work. I agree that the search engine replacement AI usage is something that can run anywhere (though it’s still better run in the cloud for speed, context length, sandboxing and convenience) but this isn’t the engine of AI growth.

Also, the average consumer is not going to be running a local model until they are built into the hardware they already buy and when they are, who is supplying the weights? They’ll likely be shipped as an ASIC (or MSIC) at that point anyways. Those will use a licensed model from the current leaders. The whole argument sounds like saying that cloud services shouldn’t be profitable because everyone has a computer at home or to meme “we have AI at home”.

I don't think they are toast, I mean they will be in some trouble because all of them have fallen victim to fomo and started building out with so much debt for capacity that may or may not be needed nor achieve the returns that they want. I think there's a future where "personal software" meaning highly custom apps generated by an agent is a thing that doesn't mean everything will become that, same for local LLMs but all of this is still too far. The main issue is that hyperscalers or big tech in general have become too powerful they can just buy their way in and out of legislation as they please, sorry I mean lobby ... funny how if you rename something it becomes legal or illegal

"If", sure.

How many developers here don't see a difference between the latest LLMs and SLMs they can run on their own computer? I tried running a smaller model locally, and it's not usable for me.

I know people like to "predict" things, so that if they happen they can then say "I am a visionary, I predicted it" and start their blog posts with "as I predicted long ago (because I am a visionary), ...".

> The research report estimates that the addressable market in the US for SLMs has grown to about $10tn or one-third of the entire US GDP of $30tn. There isn’t much left for LLMs to thrive in, and every year, their advantage over SLMs is shrinking.

I stopped counting the number of times "estimates" said that a market would absolutely explode, and it absolutely didn't. Those are in the business of being a broken clock.

If something better comes, it will be better. Sure. And we would like to have something better, because it would be better.

One of the big things to think about is whether local LLMs will be things companies want to deploy.

If you think of for e.g. some proprietary piece of software that wants to embed an LLM they've fine tuned or trained, they will want to make back some of their research cost right. So they are not going to want to put this on-device even if the hardware is there, unless there's some way of locking it down. I suspect we'll need on-hardware validation/verification and a way of preventing extraction of weights for this move to happen for many use cases.

Haven't the SLMs been distilled using the LLMs?

If this is correct I see a future where the hyperscalers are funded by the businesses integrating siloed SLMs in their software.

Also the defence/intelligence industry will always want to keep an edge so don't be surprised if they stick around and we see favourable regulations for them similarly to how the government turns a blind eye to social media platforms because they increase the footprint of mass surveillance.

I wouldn't be surprised if the hyperscalers became software auditors and any piece of critical software was required to have a regulated security audit before it could enter production. Selling the poison and the cure is a great business model.

The paper underlying this blog post is fundamentally flawed because of benchmark ceilings. If we define only simple tasks like asking what is the capital of France, all models will converge to 100%, obviously. But as bigger models get more capable we want them to replace more and more complex tasks, in as short time as possible.

Then of course there is the economics of it. Do people prefer to spend $5000 upfront to get things done 5x slower, or would they rather pay $20 a month for that?

I believe it is true, and likely there exists a class of even smaller models than what they call "small".

You can imagine a reasoning model as a huge set of rules that generate the next statement from previous statements (written in context). In that sense, a reasoning model can be compared to a logical theory - you have certain deduction rules which can generate new judgments.

Often, logical theories are structured that the rules are remade into axioms, and the deduction rule is only modus ponens (which corresponds to function application and is a building block of program execution).

In the case of an LLM, the set of rules (or axioms) they have in the theory is quite large, but most likely semantically unsound (with respect to their their own representation of truth) - that's why LLM's make mistakes.

It would be desirable to break the logical theory represented by LLM into a smaller set of axioms, which would:

a) remove rules easily deductible from the smaller core of axioms (for example, LLM doesn't need to remember "Socrates is mortal", as it can derive it from "Socrates is a man" and "all men are mortal")

b) remove rules that have low value (facts that aren't used often or have weak validity) which cause ruleset to become unsound

I suspect that's what SLM distillation is doing, to some extent.

The question is, how far this process can go? I personally believe there is a useful logic for commonsense reasoning that has less than thousand rules (still several orders more than your typical mathematical logic, but orders less than SLMs). These axioms do not contain much facts about the world, but that could be added.

So I believe there is a sweet spot (deductive core, encyclopedic shell) which we have not yet found (it's a little bit more formal language than natural language) but is very efficient for general reasoning.

A remaining advantage of large language models is that as they get larger, they tend to hallucinate less, simply because the odds of the training set containing a desired answer improve with size. If a solid "I don't know" detector is developed for inference, then you can try a small language model first.

An implication is that successful research in "I don't know" detection could destroy hundreds of billions in shareholder value.

If you are like Google or Apple and you are delivering AI to a mass market unwilling to pay a lot for it, you are absolutely going to drive AI processing to endpoint devices. You are also going to spend what it takes in R&D make a hybrid system that knows when to use local compute or cloud compute. That's going to be the bulk of the workload.

The paper focuses on "intelligence per watt (IPW)", as a way to compare SLMs vs LLMs.

What might happen is that a chunk of the market, whatever its size will be, will end up going to SLMs run on iphones or Macbooks, and eat some of the revenues from LLMs, because not everyone needs the most powerful LLM all the time.

This is up to October 2025 though, yes? Obviously things are continually moving but Opus 4.5 launched in November and that was a recognised step change in capability. An up to date comparison would be interesting.

Given how often the "experts" on CNBC and Bloomberg TV use the term hyperscalers, my approach is to completely disregard anything written or spoken by any person who uses the term.

I think one of the watershed developments will be fast models.

Imagine current frontier models at 20k tokens/second.

There's also video models, world models, robotics simulations, the matrix...

Small language model gave satisfactory healthcare output in 100% of cases?

Complete nonsense.

> they provide a better or at least as good an answer as LLMs in 62.5% of the cases.

Are we going to scrap hospitals because a vet could do the job 62.5% of the time?

The economics also point away from everyone buying a big RAM Mac that sits idle 99% of the time. SLM and own hardware sounds efficient and “free” but it is nothing of the sort when you factor everything in (and forfeit the sharing efficiencies of API)

SLMs are great esp for task specific fine tunes but this take isn’t it

> If their results are true, then we will hardly need any data centres in the future, and the hyperscalers are wasting hundreds of billions of dollars in investments.

What if they get sufficiently powered and watered industrial warehouses close to where the successful people live?

Cool, they scored well on all the "make complex calculations and I'll vibe check your results based on my own domain experience" things I use the average LLM chatbot for.

So maybe in 10yr I'll be able to run a SLM on a 5yo laptop and not have Google or whoever hoover up everything.

As much as I want local and open-weights models to succeed, nothing beats a paid frontier model for now. Anybody who claims otherwise is simply not a daily user of such models. So this "investor" here should invest sime time in actually using the various LLM models and get a real taste of what it's like.

I think it’s a bit more complicated.

There is a likely US scenario and a rest of the world scenario. It will be interesting to see if China acts on the overextension of the US Military in the Middle East. Taiwan will be a big play for both and crucial to the hyperscalers.

But since the US is dabbling in piracy again and telling people what they can do and not do with their shit, it’s not too far-fetched that everyone that is not a global superpower is at risk of getting bombed to smithereens if they are a danger to US AI supremacy.

This is such a crazy timeline, predicting even like a single year ahead feels like looking into a medieval glass ball.

But we are humans, I am confident we will find a way to fuck this up royally for everyone. Brace for impact.