> without distillation from third-party models

sounds like zero unless they are lying.

So (in my opinion) knowledge benchmarks stagnating for small models is not evidence that small model agentic coding performance improvement will stagnate soon. Small models do not struggle with syntax, the barrier is not knowledge. The barrier is long context coherence and problem solving, which I don't see a bottleneck on improvements for small models in the near horizon as we get more and more high quality reasoning traces to train upon.

Model intelligence and knowledge aren't necessarily directly related. If we can pack greater intelligence and agency at the cost of it forgetting factoids, that would actually be a good thing. We don't need LLMs to memorize facts, we need them to learn how to interact with the world such that they can find the facts that are necessary and surface them to the user.

If we could distill all of the knowledge out of an LLM and just be left with a very agentic model that only knows facts in it's context, I think some very interesting stuff would happen.

Misleading should be removed as a category and replaced with a better hedge like "not sure"

I'm not in tech, I'm in civil engineering so maybe it's just a difference in the types of problems we have in different industries.

I do find it very frustrating when an EIT asks me how to do something and it's clear that they haven't even read the instructions page to the excel sheet that they literally have open. I have time to mentor peers and subordinates but I want them to treat my time with the same respect that they treat their own.

For LLMs the null hypothesis would be that there is no relationship between the input and output tokens. Something that is so obviously not true that it's not even worth calculating the number of sigmas away from the null hypothesis that LLMs are.

So clearly we discarded the null hypothesis sometime in 2017. Now we have a system that is really really good at pattern matching and seems to understand consequences. Is that "seeming" just a ruse or does it really understand stuff? A proper scientists would look at that evidence and put forward the hypothesis that maybe it really does understand stuff and begin working on experiments that would disprove that alternative hypothesis, moving forward with the assumption that the hypothesis is true until disproven or a better hypothesis is proposed that explains previous evidence more accurately. Naysayers saying "you haven't proven that pattern matching becomes understanding to my satisfaction" is not a rebuttal. They need an alternative hypothesis that can make predications that better fit the model and can be tested.

The only rebuttals I've heard are "AI can't actually understand stuff and therefore can't do X" which is a testable hypothesis at least. But Invariably AI eventually does X, just in a different way than anyone really expected.

I think it's only appropriate when you are trying to insult the asker. Like if an employee asks a really dumb question that indicates that they didn't even bother googling the question or asking AI first, then sending them back an AI response is appropriate specifically because it's a bit insulting to do.

In fact it does exist for gpts: https://letmegpt.com/

Personally, If I'm asking for help it's because I've surely exhausted other avenues of approach like googling it or asking chatGPT. I've come to the person because I need their input specifically. The people I work with are professional enough and I've developed such a relationship with them that I don't have the problem the OP is discussing very much.

I don't think I want to live in a world in which they all died out.

Nevertheless, VCs are in fact pretty dumb sometimes and it'd be stupid to invest soley based on stars.

All the foundation model breakthroughs are hoarded by the labs doing the pretraining. That being said, RL reasoning training is the obvious and largest breakthrough for intelligence in recent years.

Pre-ChatGPT, in ~2020, there were about 5,000 new packages per month. Starting in 2025 (the actual year agents took off), there is a clear uptick in packages that is consistently about 10,000 or 2X the pre-ChatGPT era.

In general, the rate of increase is on a clear exponential. So while we might not see a step change in productivity, there comes a point where the average developer is in fact 10X productive than before. It just doesn't feel so crazy because it can about in discrete 5% boosts.

I also disagree with the dataset being a good indicator of productivity. I wouldn't actually suspect the number of packages or the frequency of updates to track closely with productivity. My first order guess would that AI would actually be deflationary. Why spend the time to open source something that AI can gen up for anyone on a case by case basis specific to the project. it takes a certain level of dedication and passion for a person to open source a project and if the AI just made it for them, then they haven't actually made the investment of their time and effort to make them feel justified in publishing the package.

The metrics I would expect to go up are actually the size of codebases, the number of forks of projects that create hyper customized versions of tools and libraries, and other metrics like that.

Overall, I'd predict AI is deflationary on the number of products that exist. If AI removes the friction involved with just making a custom solution, then the amount of demand for middleman software should actually fall as products vertically integrate and reduce dependencies.