If this is true, the hyperscalers are toast
klementoninvesting.substack.com
If this is true, the hyperscalers are toast
1–10 of 108 posts
Re: If this is true, the hyperscalers are toast
#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 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.
Re: If this is true, the hyperscalers are toast
#3As 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.
Re: If this is true, the hyperscalers are toast
#4"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...
Re: If this is true, the hyperscalers are toast
#5As 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.
Their point isn’t that local models are better or even as good more but that if you can do 50%+ of tasks with local then that’s 50% of tokens that aren’t captured as compute done in data centers.
Re: If this is true, the hyperscalers are toast
#6As 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.
You completely missed the thesis here, and that is supported by the numbers being presented. It is that a large share of ordinary inference can be routed away from the hyperscalers.
Re: If this is true, the hyperscalers are toast
#7As 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.
Their point isn’t that local models are better or even as good more but that if you can do 50%+ of tasks with local then that’s 50% of tokens that aren’t captured as compute done in data centers.
> As you can see, on average, SLMs are as good if not better than LLMs in 81.2% of the cases, with the LLMs having a significant advantage only in areas like engineering, life sciences, transportation and computer sciences.
So what I’m reading here is “LLMs have a significant advantage” in the most critical areas that have practically infinite demand for more intelligence.
Re: If this is true, the hyperscalers are toast
#8As 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?
Re: If this is true, the hyperscalers are toast
#9Haven'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.
Re: If this is true, the hyperscalers are toast
#10As 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.
While that's true, the open / local models are getting good enough. Given time and the technology trend people may prefer a private local model for most use cases. Nobody is arguing that a Ferrari isn't a faster car, but the Honda is the more practical choice.