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Discussion (12 Comments)Read Original on HackerNews
An implication is that successful research in "I don't know" detection could destroy hundreds of billions in shareholder value.
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
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.
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.
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.
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.
This is becoming increasingly important to me. Super smart max reasoning frontier is fine if I leave it running overnight on some prepared set of clearly defined tasks, but when I want to work with the LLM, throughput really matters, and I'll go with a dumber model to get there.
At some point though, it's fast enough and any speed gains beyond that just makes me the bottleneck.
I also am seeing the smaller models gaining big strides lately, closing the gap on frontier models (still a decent sized gap though). I don't even run the small models like Qwen 3.8 27B locally. I just try them out in the cloud to see how they are progressing, and I'm definitely able to be productive.
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...
Presumably not what you intended but this phrase immediately takes me to:
https://www.youtube.com/watch?v=dJFR7xbOIuw&t=42s
Why don't you go talk to your LLM instead of commenting here, then?
Probably a skill issue on your part.
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.
This means that the Frontier labs are under immense pressure to maintain that lead, and could end up in serious trouble if they stumble at all.
The other thing id point out is that a lot of us who are token-sensitive do things like build plans using expensive, smart models, and then execute those plans using cheaper dumber models.
Then there's the fact that we are still in the age of heavily subsidized Frontier subscriptions + tokenmaxxing initiatives from megacorps. Neither of which are sustainable, and will drive more usage to smaller open models once they end.
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.