You invite a friend over, but raccoon appears. Then pigeon appears. Then friend appears but at the last second suddenly becomes a banana. You remember you are out of bananas so you order more and also some cola zero cans on your local grocery delivery app. You are back to the party, but now you have 5 friends in the room, and you run de-duplication query. Now half of your friend is sitting at the sofa, and another half becomes a quarter of banana. Suddenly bananas arrive so you need to open the door. Once you are back there are no friends, pigeons or raccoons but also no bananas and no cola - all the delivery results are gone. This seems to be urgent and important, gotta fix this first before going back to that friend invitation...

i’m not sure “variable rewards” is the right term, but i do agree with the op that it is very similar to gambling.

regarding your examples, i think the difference is that with ai, you’re literally sitting in front of a machine, pressing a button, and (almost instantly) getting a result that, if not desired, can immediately be tried for again. you even spend “tokens” to do this, and at least in my native language, “token” brings to mind the coins you’d stick in a slot machine

Same vibe as people saying "addicted to sugar" or "sugar hijacks your reward system"

Sugar is the original point of the reward system!

No, if I use a ruler or a pocket calculator they will reliabley give me the correct result. There is no gambling.

I would agree that 1-2 years ago models were more "slot machine"-esque - sometimes the output was good, sometimes the output was bad. And as a result, I primarily used them for auto-complete functionality and bouncing ideas around. In those workflows, you can easily ignore it if the spin is wrong.

Not everyone has the desire to work around the system, and many are diametrically opposed to the concept of AI. They get this perception that it's a slot machine because of that inconsistency, and then do the human thing of assuming that other people must just be flawed if they're different from them. They're "addicted to gambling".

Obviously, things have changed. Open models can still be like that, but are often so fast and cheap at iterating it doesn't matter. SOTA models aren't perfect, but are to the point that they're generally much better than the average developer.

But once that perception set in and the meme spreads, it's really hard for some to break out of it. Especially at the pace AI development has been moving. It's just that simple.

Listen Pal, this is load bearing. If you know what is good for you then you will stop asking questions. —Claude

>Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do.

I was working on a side project recently. I had spent months designing the data model in my spare time, thinking through how to make it as elegant and durable to change as possible in the long term, since (if I launched it) the repercussions for getting it wrong would be significant.

Once I had a working design, it probably would have been several more months to build a working prototype and start testing it.

Instead, Claude knocked out the prototype for me in an afternoon. And it immediately became clear that it didn't work: not because the data model didn't solve all the problems I wanted it to solve, but because it didn't fit the shape of how I quickly learned a normal person would need/want to interact with the product. I was so focused on the long term, that I never thought about what the first five minutes of a user with hands on the thing would need. And the changes needed would be significant.

Maybe there's some variable reward mechanism. But I sure was glad to be able to pull that particular slot machine handle and learn that than waste even more of my time on what was a dead end.

> It was a gamble against your own time and patience.

At this point, what do the words even mean? Your own patience and available time are always completely random and fairly distributed across a large enough data set?

> Maybe I'd waste hours down the wrong rabbit holes trying to find a library that worked for my use case. Maybe I'd waste a day trying to get an API to do something it turned out it couldn't do. Maybe I'd have to redo my entire approach because of some factor I hadn't considered.

Our ignorance isn't random chance. As we research and experiment, we reduce the problem area.

You're absolutely right. And it matters.