The Myth of Goal-Driven Greatness | Generated by AI
Why Greatness Cannot Be Planned is a 2015 book by Kenneth O. Stanley (the AI researcher who invented NeuroEvolution and the novelty search algorithm) and Joel Lehman. Its core message is radical but backed by both computer science experiments and real-world examples:
Main Thesis
Greatness (major breakthroughs, world-changing innovations, or even personal achievements) is almost never achieved by directly pursuing it through explicit, step-by-step objectives. Instead, it emerges as a byproduct of following “interesting” or “novel” paths – what the authors call the pursuit of interestingness rather than goals.
Chasing ambitious objectives directly is not just inefficient – it is actively counterproductive.
Key Arguments from the Book
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The Objective Paradox
Big goals (e.g., “cure cancer”, “build AGI”, “become a billionaire”) create a deceptive illusion of progress. You set intermediate stepping stones, optimize hard for them, and end up stuck on local optima while missing the truly revolutionary discoveries that lie off the beaten path. -
Stepping-Stone Trap
Almost every historical example of greatness (the light bulb, penicillin, microwave oven, Post-it notes, evolution of the eye, Google’s PageRank, etc.) did not come from optimizing toward the final goal. The eventual “great” outcome was not even an objective at the time it was discovered – it was a side effect of exploring something else that seemed merely interesting or strange at the moment. -
Novelty Search vs. Objective-Driven Search (the core experiment)
Stanley’s famous AI experiments showed that trying to evolve robots to walk by rewarding distance traveled (objective-based) fails or plateaus quickly. But if you ignore the objective entirely and simply reward behavioral novelty (“do something different from what you and your ancestors have done before”), the same system discovers walking, running, jumping, flipping, etc., far faster and more creatively. The walking behavior emerges naturally once enough interesting intermediate behaviors have been collected. - Why This Happens
- Objectives create blinders; they force you to discard anything that doesn’t immediately move the metric.
- Interestingness (novelty, surprise, curiosity) is an open-ended compass that naturally collects diverse “stepping stones” without knowing in advance which ones will later connect to something great.
- Greatness is usually non-deceptive only in hindsight; from the starting point it looks deceptive (progress appears to go backward or sideways for a long time).
- Implications for Life, Science, Business, Education, AI
- Stop obsessing over 5-year plans, OKRs, milestones, and KPIs when the real goal is ambiguous or grand.
- Reward exploration, serendipity, and “weird” ideas instead of short-term measurable progress.
- In research and invention, collect interesting artifacts (the authors call them “treasures”) even if you don’t know what they’re for yet.
- Personal advice: follow what fascinates you, not what “should” lead to success. Many people who became legends (Steve Wozniak, J.K. Rowling, Van Gogh, etc.) were not optimizing for fame or money at the time.
One-Sentence Summary of the Book’s Message
“If you want something truly great, stop trying to get it. Instead, follow whatever is most interesting right now, accumulate a large and diverse collection of stepping stones, and greatness will find you as an accidental side effect.”
The book is short (≈150 pages), very readable, and mixes philosophy, computer science evidence, and historical anecdotes. It has become a cult classic in AI research (especially in open-endedness and quality-diversity algorithms) and among people who are skeptical of hyper-goal-oriented productivity culture.
If you’re into AI, innovation, or just tired of New Year’s resolutions that never lead anywhere significant, it’s one of the most mind-opening books out there.