Large language models, when prompted correctly, can extract realistic human behaviors because they are trained on vast amounts of human behavior and sentiment data expressed on the web, and this capability is domain-agnostic.
Joon explains the origin of Simile's approach: LLMs trained on web data can, when probed correctly, produce realistic human behaviors across any domain, leading to the creation of generative agents. ✦ AI generated
Joon Sung Park · 20VC · 2026-08-01 · original ↗
starts at this moment · 1:57
“Can you explain what happened and how that potentially led to the early days of Simily?”
We had this idea that large language models are often used for simple tasks like classification, simple generation, but we thought that these models actually had a lot more potential. One of the early observations that we made was that these models are trained on so much of human behavior data, sentiment data that were expressed on the web. So, if you poke at them sort of the right angle, you could actually extract a lot of realistic human behaviors out of them. I thought that was really interesting, and it was also practically interesting in that it was domain agnostic.
verbatim transcript · starts at 1:57
1:57classification, simple generation, but we thought that these models actually had a lot more potential. One of the early observations that we made was that these models are trained on so much of human behavior data, sentiment data that were expressed on the web. So, if you poke at them sort of the right angle, you could actually extract a lot of realistic human behaviors out of them. I thought that was really interesting, and
2:20it was also practically interesting in that it was domain agnostic. So, if you look at the literature in computer science for many decades, we've always had the vision of creating agents that are meant to be generalizable, that are meant to really be able to act like human in any environment. And my mind went to, well, maybe we have that opportunity here. So, what we ended up doing was, well, if we are to fast
2:44forward many years into doing this, what would be the most ambitious vision that we might have? And that was creating entire lived experience of a town. So, the idea here was we would make a game town. And we would populate it with 25 NPCs, so non-player characters, except these characters would actually wake up in the morning, do their routines, go to work, have relationships, and do all that.
3:06They would actually remember their interactions. They would actually plan their days. And some of the surprising things you end up seeing was the simulation itself was set the day before Valentine's Day, and you'd actually see these agents come together, have parties, like self-organize. So, they would actually plan parties, they would decorate the cafe, and so forth. We thought that was really interesting. Now, two fundamental contribution from