Recent AI model progress feels mostly like bullshit

The core point in this article is that the LLM wants to report _something_, and so it tends to exaggerate. It’s not very good at saying “no” or not as good as a programmer would hope.

When you ask it a question, it tends to say yes.

So while the LLM arms race is incrementally increasing benchmark scores, those improvements are illusory.

The real challenge is that the LLM’s fundamentally want to seem agreeable, and that’s not improving. So even if the model gets an extra 5/100 math problems right, it feels about the same in a series of prompts which are more complicated than just a ChatGPT scenario.

I would say the industry knows it’s missing a tool but doesn’t know what that tool is yet. Truly agentic performance is getting better (Cursor is amazing!) but it’s still evolving.

I totally agree that the core benchmarks that matter should be ones which evaluate a model in agentic scenario, not just on the basis of individual responses.

> The core point in this article is that the LLM wants to report _something_, and so it tends to exaggerate. It’s not very good at saying “no” or not as good as a programmer would hope.

umm, it seems to me that it is this (tfa):

     But I would nevertheless like to submit, based off of internal
     benchmarks, and my own and colleagues' perceptions using these models,
     that whatever gains these companies are reporting to the public, they
     are not reflective of economic usefulness or generality.

and then couple of lines down from the above statement, we have this:

     So maybe there's no mystery: The AI lab companies are lying, and when
     they improve benchmark results it's because they have seen the answers
     before and are writing them down.