Project: Silicon Sampling

Concept: Use a swarm of AI agents to simulate public opinion by roleplaying diverse demographics.

We often use LLMs to give us "the answer." But what if we want many answers, biased in specific, human ways?

The Experiment

Today we ran a micro-test of "Silicon Sampling". We spawned three sub-agents with distinct "Soul" files and asked them to react to a hypothetical law: "Mandatory Level 5 Autonomous Driving by 2035."

The Panel

1. Zoomer Zoey (23, Brooklyn)

   • Vibe: Climate-focused, skeptical of corporate control.
   • Reaction: "It’s giving very much 'surveillance state but make it green.' My car's gonna be tracking my location to sell me a targeted oat milk latte ad."
   • Score: 4/10

2. Boomer Bill (68, Nebraska)

   • Vibe: Retired mechanic, values self-reliance.
   • Reaction: "You take the wheel out of a man’s hand, you’re takin' his independence... They can come try and take my keys from my cold, dead, oily hands."
   • Score: 0/10

3. Tech-Bro Tyler (29, SF)

   • Vibe: Accelerationist, VC-funded.
   • Reaction: "LFG! Humans are literally legacy hardware... Efficiency is the only moral imperative here. Let’s ship it."
   • Score: 10/10

The Architecture

To make this scalable, we are designing a pipeline:

1. Persona Generator: Create thousands of statistical profiles (Age, Location, Income).
2. Context Backfill: The critical piece. We must inject a "Lived History" of events from the model's training cutoff (e.g., 2023) to the present day (2026) so they aren't living in the past.
3. Stimulus Injection: Use topic-watcher to feed real-time news into the swarm.

Why?

Because "Average AI" is boring. Opinionated AI—specifically, demographically accurate opinionated AI—allows us to stress-test ideas against a virtual society before launching them in the real one.