Season 1. Episode 8. Unscripted with Nevra Ledwon

Nevra Ledwon has spent 25 years in mathematical optimization, the branch of AI that decides which trucks carry which goods, how factories schedule production lines, how airlines crew flights, and how warehouses route their pickers, work that predates the chatbot era by decades and often delivers harder, more measurable business value.

In this conversation, she tells Jeff about the moment that changed her mind: she assumed generative AI, trained on decades of operations research textbooks, could replace the senior PhDs who spend weeks interviewing stakeholders to translate a messy business problem into a solvable model. It couldn't, and the experience gave her new respect for the expertise those specialists bring.

She walks through concrete wins, including a seven-figure reduction in cold storage costs from a hundred-thousand-dollar optimization project, and a European soccer league's season-schedule problem with more possible arrangements than atoms in the universe, solved only through trial-and-error mathematical experimentation no AI could shortcut.

She distinguishes prediction from optimization (prediction anticipates what will happen, optimization prescribes what to do about it) and argues most companies chase marginal prediction accuracy instead of building the ability to adjust plans in real time. Her company, Simple Rose, uses generative AI not to replace operations research experts but to compress the multi-week interview process that used to precede every optimization project, aiming to make a field long reserved for companies like Amazon accessible to a manufacturer of socks or a mid-sized European transit agency.

She closes on what she's watching for next: an AI that stops answering the narrow question asked and starts challenging the premise behind it.