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If this is true, the hyperscalers are toast

klementoninvesting.substack.com

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Re: If this is true, the hyperscalers are toast

#21

Post by mtklein »

hdgvhicv wrote: post #8
nubg wrote: post #3

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.

How does a current local model compare to the best frontier model 12 months ago. Or 24 months ago?

I have found qwen 3.8's coding quality using opencode to be similar to claude or gpt from 6-9 months ago, except much slower.

Re: If this is true, the hyperscalers are toast

#23

Post by everyone »

ch_sm wrote: post #17
palata wrote: post #2

"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…

> I tried running a smaller model locally, and it's not usable for me. If you have the hardware, a MacBook Pro for Qwen 3.6 35B A3B and Gemma 4 26B A4B for example, they are absolutely usable, both in terms of speed and quality. Anecdotally, I can use Qwen for day-to-day coding tasks in TS and Go, without hickups.

You let a hiccup slip through in your comment though.

Re: If this is true, the hyperscalers are toast

#24

Post by [deleted] »

palata wrote: post #2

"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…

[deleted]

Re: If this is true, the hyperscalers are toast

#25

Post by embedding-shape »

philipallstar wrote: post #22

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.

You have to remember that articles like these are written for finance people who don't understand the underlying technology, by finance people who don't understand the underlying technology. In this case, the author is a "CFA Institute Enterprising Investor", previously a CIO and basically their entire life been "money, money & money", so hardly surprising they're pulling a lot of assumptions based on what they read.

Read the paper the author talks about yourself instead (https://arxiv.org/abs/2511.07885), and also, contrary to what the author says in the article, do not do investments based on single papers made from academic studies, regardless of how much money this guy tells you you can make.

Re: If this is true, the hyperscalers are toast

#26

Post by amelius »

philipallstar wrote: post #22

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.

The logic seems mad to me because SLMs can simply not hold as much information as an LLM.

Maybe if you combine an SLM with a database (as a tool) then it could work, but someone should first prove that.

Re: If this is true, the hyperscalers are toast

#27

Post by embedding-shape »

ch_sm wrote: post #17
palata wrote: post #2

"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…

> I tried running a smaller model locally, and it's not usable for me. If you have the hardware, a MacBook Pro for Qwen 3.6 35B A3B and Gemma 4 26B A4B for example, they are absolutely usable, both in terms of speed and quality. Anecdotally, I can use Qwen for day-to-day coding tasks in TS and Go, without hickups.

I'm unable to find a local model that comes close to the effectiveness of GPT models in Codex, and I have 96GB of VRAM available and tried every local model under the sun so far. Neither of those you mention I'd say are good enough for day to day software engineering for me, but I'm also really strict about code quality and iterate on what outputs agents give me a lot before I'm happy.

With local models, this iteration cycle takes maybe 30 minutes for a single fix or feature, rather than 10 minutes with GPT+Codex, as there is so many corrections and iterations needed, although I will say that the speed I'm able to get locally makes it more fun that any of the remote models.

Re: If this is true, the hyperscalers are toast

#28

Post by Zigurd »

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.

Re: If this is true, the hyperscalers are toast

#29

Post by simonebrunozzi »

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.

Re: If this is true, the hyperscalers are toast

#30

Post by root-parent »

Animats wrote: post #13

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 shar…

>> A remaining advantage of large language models is that as they get larger, they tend to hallucinate less

First time I hear that...not really true.

"Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors" - https://arxiv.org/abs/2607.00447

"Calibrated Language Models Must Hallucinate" - https://arxiv.org/abs/2311.14648

"TruthfulQA: Measuring How Models Mimic Human Falsehoods" - https://arxiv.org/abs/2109.07958

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