They actually wanted to build general computational agents,

sort of the way generative agents are supposed to be.

But they didn't have the techniques to do it.

They basically didn't have the large language moral.

And like, the work that you've done is one of those 100%, right?

There's like a spark of genius.

What does it mean to actually reflect your behavior?

But the set of companies that come out of it

are always part of this enthusiast era, right?

Like you couldn't have predicted Yahoo, you couldn't have predicted Amazon, like you knew

something was going to happen.

Can we run simulation so we can learn more about ourselves?

A few weeks ago, the A16Z infrastructure team ran an event in the San Francisco office.

The topic, generative Agents. These are autonomous

characters designed to simulate human behavior derived from a recent but

game-changing paper called Generative Agents, Interactive Similakra of Human Behavior.

Developers from all around the city came to hear the lead author, June Park, speak

alongside A16Z general partner, Martin Kasato.

And in this panel, they discuss how this paper and the advancements in large language models

have opened a new window, expanding the dynamism of simulation, which instead of binary logic,

we're using probabilistic thinking, and the ability to incorporate new information.

So what does that really mean? Well instead of your character in

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