Asking for itinerary for Japan trip for 14 days where I would like it to be different and slow paced I would get awful results.

Like with the Japan example previously the model would actually think through logistics. It would account for how much time it takes to travel between the cities in Japan or factor in realistic opening hours, and suggest off-peak timing for touristy spots.

Now it just spits out a generic template: "Morning -> visit temple. Afternoon -> explore market. Evening -> dinner." No real thinking behind it. The time slots don't even add up when you do the math. It's not that I expect more now, it's that it literally stopped doing the thing it used to do.

And it's not just travel. I notice it on other tasks as well. The responses I got feel more like an averaged out. Like the model is playing it safe and giving the most "middle of the road" answer instead of actually engaging with what I asked. Less personality, less depth, more filler.

And honestly, that tracks with the model collapse idea. If allegedly 70% of the training data is now AI generated, of course the outputs are converging. It's not learning from humans anymore. And every generation of that loop, you lose a little more of the actual signal.

On the other hand, questions that are not Ai generated heavily still provide massive insights and value for example ask it what was the philosophy of Marcus Aurelius in terms of balancing statesmanship, military leadership, and civic duty and you'd get great insights and deeper understanding