# Is bigger always better when it comes to data in AI?

> I explore why bigger isn't always better when it comes to data in AI — and why, in healthcare especially, the real problem is data quality, not quantity. Curated, structured, interoperable data is what will make trustworthy clinical AI possible.

URL: https://www.ch-healthtech.com/insights/bigger-always-better-when-it-comes-data-ai
Markdown: https://www.ch-healthtech.com/insights/bigger-always-better-when-it-comes-data-ai.md
Published: 2025-07-21
Updated: 2026-05-06
Author: Christian Hein
Tags: technology/artificial-intelligence, technology/machine-learning, technology/synthetic-data, technology/digital-health, industry/medical-terminology-standards, function/innovation-management

---


Is bigger always better when it comes to data in AI?

Not according to Scott Wu (CEO, @Cognition) on his recent podcast with Harry Stebbings of 20VC: He predicts the future isn't ultra-large datasets, but "a small set of highly curated data for exactly the use case that you care about."

This really hit home. In my work at SNOMED International, I see firsthand how the lack of curated data holds healthcare back.

For years, we've known that healthcare data has a quality problem, not a quantity problem. To build trustworthy clinical AI, we need to move from a sea of unusable information to clean, structured, and interoperable data. That's where standards like SnomedCT come in.

https://lnkd.in/enR3EjXP