Restraint

What Live Performance Teaches About Building AI That Knows When to Stop

People tend to think a better system responds faster, but a good performer knows when to wait. Over 15+ years building human-facing technology, timing and restraint have decided whether an interaction stays safe. What does a system do when a moment calls for it to stay quiet? Years on stage taught Shannon that knowing when to hold back is part of the job. Give any sensitive product a built-in pause, so it can wait until the moment is right. Most products are tuned to keep people engaged; restraint asks what each person actually needs. Design for restraint, and users leave feeling looked after.

About Shannon Harvey

Shannon Harvey, podcast guest

Founder & AI Safety Researcher

15+ years Founding and leading studios and research initiativesApple TV+ Virtual production leadership on Silo and HijackProduction Park Established its research labReflection Labs Founder, independent governance for human-facing AI

Shannon Harvey is a founder, researcher and innovation leader focused on AI safety, human behaviour and responsible technology. He is currently developing research and practical testing frameworks to help companies identify and reduce the emotional, psychological and social risks created by human-facing AI.

With 15+ years of experience spanning technology, immersive media, live production, research and academia, Shannon brings a unique perspective shaped by both systems engineering and a background in performance. He has led complex technical and creative projects, including virtual production for Apple TV+ productions such as Silo and Hijack, research at Production Park, and platform adoption work with disguise. He was also Course Leader for the BA (Hons) Visual Production degree at Backstage Academy, now the Academy of Live Technology.

Shannon originally trained and performed as an opera singer before moving into theatre, and large-scale live production. His experience of performance, audience interaction and safety engineering has shaped the way he thinks about AI: particularly the importance of understanding context, vulnerability, trust and what can happen when systems behave unexpectedly.

His current research explores the often-overlooked side of AI safety - what happens when people trust, depend on or emotionally engage with AI systems, particularly in high-trust and vulnerable situations.

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