Rohan Kodialam, cofounder and CEO of Sphinx, is building AI agents that treat data as its own language—one most models and humans still fail to understand. In this episode, he unpacks why data science has lagged behind software engineering, how AI can finally close the gap between business questions and answers, and what happens when small teams gain the analytical power of a thousand person quant desk. What You'll Learn • How AI models that actually see data can unlock insights traditional transformers miss • Why enterprises must rethink dashboards and embrace real time ad hoc analysis • Where AI truly saves the most time across the data lifecycle and why modeling is not the hardest part • How decoupling statistics from business context gives teams freedom to focus on strategy and creativity • Why success in data science now means reclaiming human creativity while automating repetitive work Timestamped Highlights [01:44] Why data is fundamentally different from text and code and why most AI models struggle with it [06:39] The cultural problem with ad hoc being a dirty word in enterprises and why that mindset is changing [11:09] Where AI tools actually fit into the data science workflow [17:09] How to measure success when using an AI data scientist [21:04] What happens when a small team gains the data firepower of a hedge fund quant operation [24:37] Why bad data science is worse than none and why quality matters more than hype A Thought That Stuck With Us “We are cutting the time to completion by 20x, 40x, even 50x and that remaining human review is not a bottleneck. It is the feature that keeps AI accountable.” Worth Following Connect with Rohan Kodialam on X (@KodialamRo) or LinkedIn and learn more about Sphinx AI and how they are transforming enterprise data science. If This Resonated Share this with someone in the data world who is tired of waiting weeks for insights that should take minutes. Follow The Tech Trek for more conversations about how people and technology create lasting impact.
