Your AI model can be 95% accurate and still be too dangerous to deploy. Eric Siegel (@predictanalytic), co-founder of Gooder AI and author of The AI Playbook, has spent more than 35 years studying machine learning. We discuss: •Why companies should invest at least as much in predictive AI as generative AI. •Why improving training data often delivers more business value than adding model complexity. •How technical metrics like precision, recall, and accuracy fail to demonstrate actual financial returns. •Why an AI system that's correct 95% of the time can still be commercially unusable. •How predictive reliability layers can identify risky AI interactions and escalate them to humans. •Why Siegel considers AI extinction scenarios another form of technology hype. "my P doom is zero." If an AI agent needs human oversight for its riskiest decisions, should we consider that a failure of autonomy or the smarter business model? 🎧 Watch, listen, and follow on your favorite platform: buff.ly/WVYymbO 💬 Join the conversation on your favorite social network: buff.ly/8atYlCQ 📝 Read the full episode breakdown on our blog: buff.ly/W99UUc2