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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

#11

Post by otabdeveloper4 »

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.

Probably a skill issue on your part.

Re: If this is true, the hyperscalers are toast

#12

Post by tialaramex »

root-parent wrote: post #4
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 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. The lack of logic and risk management on this statement, is so strong, I hope humans are all quickly substituted by LLMs. Lets just do it and be done with it...

> Lets just do it and be done with it...

Presumably not what you intended but this phrase immediately takes me to:

https://www.youtube.com/watch?v=dJFR7xbOIuw&t=42s

Re: If this is true, the hyperscalers are toast

#13

Post by Animats »

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.

Re: If this is true, the hyperscalers are toast

#14

Post by root-parent »

tialaramex wrote: post #12
root-parent wrote: post #4

Earlier quoted context omitted.

>> 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. The lack of logic and risk management on this statement, is so strong, I hope humans are all quickly substituted by LLMs. Lets just do it and be done with it...

> Lets just do it and be done with it... Presumably not what you intended but this phrase immediately takes me to: https://www.youtube.com/watch?v=dJFR7xbOIuw&t=42s

Great movie...yeah I think I was inspired by the scene... :-)

Re: If this is true, the hyperscalers are toast

#15

Post by cucumber3732842 »

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.

Re: If this is true, the hyperscalers are toast

#16

Post by hyperhello »

> 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?

Re: If this is true, the hyperscalers are toast

#17

Post by ch_sm »

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.

Re: If this is true, the hyperscalers are toast

#18

Post by throwthrowuknow »

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”.

Re: If this is true, the hyperscalers are toast

#19

Post by embedding-shape »

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…

Another "cool but we don't know how yet" thing would be a "confidence interval" so we know how much to trust LLM responses. Or while we're fantasizing, they could just know everything all the time regardless of training data. The "if a solid" part is easy to imagine, hard to implement :)

Re: If this is true, the hyperscalers are toast

#20

Post by kzrdude »

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?

It beats a frontier model from 12 months according to this bench: https://news.ycombinator.com/item?id=49334544

It is not the whole story, and knowledge is very lacking, but it has gotten a lot of attention. That model together with DeepSeek V4 Flash are the highlights of this summer on the open/local models side.

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