A Data Scientist’s Approach to AI

The Pragmatic AI Podcast is sponsored by Tighten.  T-I-G-H-T-E-N. We will take your AI ideas, prototypes or even vibe-coded apps, and we'll take them to production. Scalable and secure. Check us out at tighten.com. In this episode, Matt Stauffer talks with Apoorva Joshi, DevRel at MongoDB, about what it looks like to come to AI from the inside — seven years as a data scientist in cybersecurity before she ever touched a foundation model. They get into how she evaluates whether a problem needs machine learning at all, why she gives models her brain dump instead of asking for a first draft, the proxy service she built so workshop customers never have to juggle LLM API keys, and why her trust level still sits around seventy percent. The conversation also goes somewhere heavier: what happens to an economic model built on the value of human cognitive work, why so much AI discourse quietly assumes coding agents represent all of AI, and the question of where meaning comes from when the thing that gave it to you gets automated. Matt Stauffer on X - https://x.com/mattstauffer Tighten Website - https://tighten.com/ Apoorva on LinkedIn - https://www.linkedin.com/in/apoorvajoshi95/ Mongo DB Builder Blog - https://www.mongodb.com/company/blog/channel/builder-blog ----- Editing and transcription sponsored by Tighten - https://tighten.com/