Jun Park outlines Simile's data strategy: they don't go after expert programmers or scientists but everyday people, ensuring demographic representativeness. The critical data challenge is two-fold: sourcing the right people and asking the right experiments that get at the core of who they are, including their life stories and hardest decisions. ✦ AI generated
Jun Park · 20VC · 2026-08-01 · original ↗
starts at this moment · 10:00
“Is data collection acquisition the hardest element of building simulation models for you?”
My fundamental thesis here is for AI companies of this generation you need to have an interesting data strategy that's going to be defensible and for us really the data collection challenge comes from two angles one is actually sourcing people sourcing people here is a little bit different than what other language model companies might consider to be their people or their population. We don't go after these expert programmers or expert scientists. We go after people like us like everyday people living their everyday life. That's what we care about is are they representative? Do we actually have the same representation of people as we do in the world that we live in? And then actually asking the right questions to these people. What are the experiments? What are the questions that actually get at the fundamental core nature of who they are? Some of the questions we actually ask at the start of our data collection at times is actually saying something like tell us the story of your life. Where did you grow up? What did you experience? What were some of the hardest problems that you had to tackle or decisions you had to make? Tell us a lot about these people.
verbatim transcript · starts at 10:00
10:02is an important piece of simil for sure u my fundamental thesis here is for AI companies of this generation you need to have an interesting data strategy that's going to be defensible and for us really the data collection challenge comes from two angles one is actually sourcing people sourcing people here is a little bit different than what other language model companies might consider to be their people or their population. We
10:25don't go after these expert programmers or expert scientists. We go after people like us like everyday people living their everyday life. Um that's but what we care about is are they representative? Do we actually have the same representation of people as we do in the world that we live in? And then actually asking the right questions to these people. What are the experiments? What are the questions that actually get
10:50at the fundamental core nature of who they are? Some of the questions we actually ask at the start of our data collection at times is actually saying something like tell us the story of your life. Where did you grow up? What did you experience? What were some of the hardest problems that you had to tackle or decisions you had to make? Tell us a lot about these people. And that's what
11:10we try to do >> in terms of people don't want to predict the future. Just so we can drill down on that. I thought they do like Starbucks if they can predict that Frappuccino sales will be down in two quarters, they can amend bluntly their buying cycle, they can change how much they purchase. Isn't that valuable? And what am I missing? But that's the thing. The reason why they want to know is so they
11:33can change their strategy. So certainly talking about oh how much u resources they actually need to actually serve this market that is a kind of changing in behavior but fundamentally it is about counterfactuals. So well we have this market that we want to serve we want to maximize our value as a company. What do we need to do to make sure that we react to this dip in the market