ATRIUMsearch → argument graph
PredictionVideo · 89:09 — 90:39

Video generation is just a stepping stone toward a future 'dopamine generator' where companies like Meta wire AI-created content directly to engagement signals (likes, viewing behavior) to maximize the dopamine response and keep users coming back.

Pash argues that video generation models like Meta's Muse Spark are just the prerequisite technology for an eventual 'dopamine generator' that optimizes content directly against engagement data to keep users hooked. ✦ AI generated

Pash · The Cognitive Revolution · 2026-07-08 · original ↗

starts at this moment · 89:09

the the video gen is kind of a prerequisite to the dopamine uh generator. Uh but the dopamine generator is coming, right? And and that's the ultimate goal. Ultimate goal is not a video gen. The ultimate goal is dopamine gen, right?

verbatim transcript · starts at 89:09

Transcript · around this moment

89:09the video gen is kind of a prerequisite to the dopamine uh generator. Uh but the dopamine generator is coming, right? And and that's the ultimate goal. Ultimate goal is not a video gen. The ultimate goal is dopamine gen, right? >> Yeah. Uh mixed feelings on that to put it mildly. But um >> every everyone everyone is scared of meta, right? like when when Meta decides to go for it because I think I think

89:36people in the industry kind of know what is possible but what most people most researchers have not focused on and you know having researchers focus on that or having the best researchers focus on that is something that is scary I think so but you know people have been talking about it for a while right it's not it's It's not it's not exactly, you know, a new idea.

90:06>> Yeah. Well, we've seen a version of it, right? I mean the the the sort of 40 sycopants, the the sycopant aocalypse uh was at least in part driven by a perhaps you know naive would be probably harsh but a less sophisticated than it needed to be use of the thumbs up thumbs down signal in chat GBT that fed back into uh post training process and obviously

90:35you know created uh the sick fancy problem. So I've been generally I mean I thought that was a really interesting moment. It showed how much you know we still don't know and the fact that we can be surprised and the fact that we can be surprised post launch in such a meaningful way where I guess you have to put it to a large extent to the differences between how

90:59people use models at OpenAI and how the rest of the world interacts with them. um you know and they've presumably closed that blind spot significantly since then. Um but it was also quite telling that they did take it down. You know they're not um I think clearly OpenAI is like interested in serving users, interested in serving customers, but they're not fully uh you know committed or they're not

Around this claim