Will AI predict people like we predict the weather? (alternate title “A field guide to synthetic personas for market research”)

Official Schedule Context

Official Description

Large language models can now stand in for humans in surprising ways, from predicting personality

types to replicating their responses in market research. Like weather forecasting, once considered

impossible and now so routine we take it for granted, LLMs are in the early, unreliable-but-

improving stage of simulating how populations think and respond. Teams are already using LLMs as

synthetic survey respondents for concept testing, UX exploration, and early market validation. In

the past year, the field has gotten both more promising and more tricky. The real question is no

longer "can LLMs simulate people?", but whether the simulation is validated for the decision you

want to make. New methods show that how you ask an LLM matters as much as which model you use and

can dramatically improve fidelity to real human responses. Meanwhile validation studies show

accuracy can mask subgroup distortion and that seemingly minor choices can reshape the simulated

population entirely. This talk gives entrepreneurs, engineers, and PMs an overview of the techniques

and a framework for validating synthetic respondents before making decisions. Even if you never

build a synthetic persona, this is one of the richest windows into LLM behavior under the hood and

these lessons apply to any system where you're trusting an LLM to represent something about the real

world.

Related YouTube Video

How LLMs work for Web Devs: GPT in 600 lines of Vanilla JS - Ishan Anand (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

Transcript Status

Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

People

Notes

Supporting Slides

Slide Evidence