What if the surest way to fail at something ambitious is to have a clear plan to achieve it? What if a robot that doesn’t know it’s trying to walk learns faster than one explicitly trained to walk?
Kenneth Stanley is one of the most provocative minds in artificial intelligence. Former professor and team leader at OpenAI, he co-authored the cult classic Why Greatness Cannot Be Planned—a manifesto that challenges everything we think we know about achievement. His research discovered something shocking: in AI experiments, robots seeking novelty learned to walk better than robots explicitly trained to walk. Over the last two years, Ken has taken these theories from the lab into the real world. As Senior Vice President of Open-Endedness at Lila Sciences, he’s building scientific super intelligence—AI that autonomously discovers new biology and chemistry. He’s also the founder of Maven, a social network designed to replace popularity contests with genuine serendipity. His work reveals a profound truth: the path to greatness is paved with stepping stones you can’t predict, and the greatest discoveries come from following what’s interesting, not what’s planned.