AI Product Manager

You don't need to be able to code production systems, but you should understand code well enough to have meaningful technical conversations. You should understand what's technically feasible, what trade-offs exist between approaches, and what 'two weeks' or 'two months' of engineering effort actually entails.

You need to understand: how models are trained and evaluated, what accuracy/precision/recall mean and why they matter, data quality and bias, model hallucinations and limitations, inference cost and latency, model versioning and retraining, and the difference between academic performance and production reliability.

Traditional PM is about user needs and business value. AI PM adds technical uncertainty and risk. You need to say 'we might not be able to do this with current AI capabilities.' You also need to manage user expectations around AI limitations, handle ethical considerations, and navigate rapidly changing technology.

Balancing ambition with realistic AI capabilities. It's easy to envision amazing AI products, but much harder to ship products that actually work reliably on real data. The gap between 'AI works great in research papers' and 'AI works reliably for millions of real users' is huge.

You need metrics and monitoring to catch problems early. When accuracy drops, you investigate: Is it data drift? Did users change behavior? Did the training data distribution shift? You decide: retrain the model, refine the feature, add human review steps, or scope down the feature.