That's just a different bias purposefully baked into GPT-5's engineered personality on post-training. It always tries to contradict the user, including the cases where it's confidently wrong, and keeps justifying the wrong result in a funny manner if pressed or argued with (as in, it would have never made that obvious mistake if it wasn't bickering with the user). GPT-5.0 in particular was extremely strongly finetuned to do this. And in longer replies or multiturn convos, it falls into a loop on contradictory behavior far too easily. This is no better than sycophancy. LLMs need an order of magnitude better nuance/calibration/training, this requires human involvement and scales poorly.

Fundamental LLM phenomena (ICL, repetition, serial position biases, consequences of RL-based reasoning etc) haven't really changed, and they're worth studying for a layman to get some intuition. However, they vary a lot model to model due to subtle architectural and training differences, and impossible to keep up because there are so many models and so few benchmarks that measure these phenomena.

Don't get me wrong, I get a little tired of it ending turns with "if you want me to do X, say the word." But usually X is actually a good or at least reasonable suggestion, so I generally forgive it for that.

To your larger point: I get that a lot of this comes down to choices made about fine tuning and can be easily manipulated. But to me that's fine. I care more about if the resulting model is useful to me than I do about how they got there.

I want to highlight this realization! Just because a model says something cool, it doesn't mean it's an emergent behavior/realization, but more likely post-training.

My recent experience with claude code cli was exactly this.

It was so hyped here and elsewhere I gave it a try and I'd say it's almost arrogant/petulant.

When I pointed out bugs in long sessions it tried to gaslight me that everything was alright, faked tests to prove his point.

Claude is my loyal assistant who tries its best to do what I tell it to.

GPT-5 is the egotistical coworker who loves to argue and point out what I'm doing wrong. Sometimes it's right, sometimes it's confidently wrong. It's useful to be told I'm wrong even when I'm not. But I'm not letting it modify my code, it can look but not touch.

My experience is claude (but probably other models as well) indeed resort to all sorts of hacks once the conversation has gone for too long.

Not sure if it's an emergent behavior or something done in later stages of training to prevent it from wasting too many tokens when things are clearly not going well.

I think it's enlightening to open up ChatGPT on the web with no custom instructions and just send a regular request and see the way it responds.

Yet the data doesn’t show all that much difference between SOTA models. So I have a hard time believing it.

Like, I think there's definitely value in prompting a dozen LLMs with a detailed description of a CMS you want built with 12 specific features, a unit testing suite and mobile support, and then timing them to see how long they take and grading their results. But that's not how most developers use an LLM in practice.

Until LLMs become reliable one-shot machines, the thing I care most about is how well they augment my problem solving process as I work through a problem with them. I have no earthly idea of how to measure that, and I'm highly skeptical of anyone who claims they do. In the absence of empirical evidence we have to fall back on intuition.

I found this worked suprisingly well, I was certain 'claude' was best, while they like grok and someone else liked ChatGPT. Some AIs just end up fitting best with how you like to chat I think. I do definately also find claude best for coding with as well.

Thing is, everyone knows the benchmarks are being gamed. Exactly how is besides the point. In practice, anecdotally, Opus 4.5 is noticably better than 4, and GPT 5.2 has also noticably improved. So maybe the real question is why do you believe this data when it seems at odds with observations by humans in the field?

> Jeff Bezos: When the data and the anecdotes disagree, the anecdotes are usually right.

https://articles.data.blog/2024/03/30/jeff-bezos-when-the-da...

Most of what I can do now with them I could do half a year to a year ago. And all the mistakes and fail loops are still there, across all models.

What changed is the number of magical incantations we throw at these models in the form of "skills" and "plugins" and "tools" hoping that this will solve the issue at hand before the context window overflows.

Unfortunately, I think that the overlap between actual model improvements and what people perceive as "better" is quite small. Combine this with the fact that most people desperately want to have a strong opinion on stuff even though the factual basis is very weak.. "But I can SEE it is X now".

I would think this also correlates with the type of person who hasn't done enough data analysis themselves to understand all the lies and misleading half-truths "data" often tells. In the reverse also, that experience with data inoculates one to some degree against the bullshitting LLM so it is probably easier to get value from the model.

I would imagine there are all kinds of factors like this that multiple so some people are really having vastly different experiences with the models than others.

Long story short, predicting perpetual growth is also a trap.

I think the models have reached that human training data limitation a few generations ago, yet they stil clearly improve by various other techniques.

You scale your way only out in verifiable domains, like code, math, optimizations, games and simulations. In all the other domains the AI developers still got billions (trillions) of tokens daily, which are validated by follow up messages, minutes or even days later. If you can study longitudinally you can get feedback signals, such as when people apply the LLM idea in practice and came back to iterate later.

It said precisely that to me 3 or 4 days ago when I questioned its labelling of algebraic terms (even though it was actually correct).

A doesn't work. You must frontier model 4.

A works on 4, but B doesn't work on 4. You doing it wrong, you must use frontier model 5.

Ok, now I use 5, A and B work, but C doesn't work. Fool, you must use frontier model 6.

Ok, I'm on 6, but now A is not working as it good as it did on A. Only fools are still trying to do A.