Improving predictions with Monte Carlo ranges

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// TOPICS COVERED

(00:00:00) Welcome + Why We’re Talking About AI in Cycling Training

(00:00:30) Environmental Impact of AI: Water Usage and Perspective

(00:06:15) Where TrainerRoad Uses AI (and Where It Doesn’t)

(00:09:30) TrainerRoad AI Results: Fewer Failed Workouts & Early Feedback

(00:14:40) Power Records Chart: A Better Way to Understand Your Workouts

(00:17:30) Why FTP Isn’t Driving the Model (and What Actually Is)

(00:31:00) Group Workouts Return + Fatigue Detection Toggles

(00:40:20) Failure Rates, Workout Difficulty, and Why Some Failure Is Good

(00:43:36) Why Your FTP Prediction Changes (and How to Think About It)

(00:53:03) Triathlon Support + Workout Scoring Updates

(01:00:22) Wrap-Up + Leadville Update

In this episode, TrainerRoad CEO Nate Pearson and Coach Jonathan Lee explain how TrainerRoad uses AI in training—what it does (like improving workout recommendations and predictions) and what it doesn’t do (it’s not a chat-style LLM running the whole app). They address environmental impact concerns, clear up common misconceptions around FTP and AI, and share recent updates like the Power Records Chart, group workouts returning, and new controls for fatigue detection and workout scoring—ultimately focusing on how these changes help athletes get more consistent, achievable training and better results.

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