Jul 31, 2026 · 1h 15m
Startups build curation engines to teach artificial intelligence human taste
Why AI has no taste and how to fix it (w/ Thais Castello Branco) | E2319
As generative AI floods the internet with generic content, teaching machine models human-like taste and aesthetic judgment has become the next major frontier in technology.
- 1Taste Labs is building a community of human tastemakers to train AI models on subjective aesthetics and design preferences.
- 2The sudden liquidation of Leopold Aschenbrenner's Situational Awareness fund highlights shifting dynamics in AI investment.
- 3Robotic automation is transforming physical infrastructure, demonstrated by new autonomous systems installing solar panels.
Don't miss
Thais Castello Branco explains the business model and philosophy behind commodifying human curation to train large language models.
The brief
Artificial intelligence can write code and analyze data, but it notoriously struggles with aesthetic quality, often churning out generic design and writing. Taste Labs founder Thais Castello Branco is trying to solve this by training models on human preference data.
By leveraging a community of tastemakers, Taste Labs aims to build a curation infrastructure that injects subjective human taste into AI. The goal is to move past mechanical outputs and eliminate the low-quality aesthetic known as AI slop.
Beyond design aesthetics, the tech landscape is shifting rapidly. The episode covers the liquidation of Leopold Aschenbrenner's prominent Situational Awareness fund and the controversial addition of Google Earth's new AI image generation features.
The hosts also look at physical automation, highlighting a robotic arm from Grit Robotics that can autonomously install solar panels, signaling a massive leap forward for rapid, low-cost clean energy deployment.
Featuring
Taste Labs
Google EarthBooks & mentions
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