You’ve been appointed as Minister of Science and Technology. Everyone in the conference room holds perfect strategy documents, dense execution pathways, clear KPIs. You feel slightly uneasy inside, but begin approving: “Let’s do this.”

Six months later, you discover all plans are on track, but nothing exciting is happening. The damning thing is, you can’t explain why—all the indicators are green.

Kenneth Stanley and Joel Lehman used a programming example to illustrate why. They ask: If you want to evolve an AI that can accomplish a completely specific goal (like navigating a maze fastest), what’s the most effective method?

The intuitive answer: directly optimize for this goal. Select mutations that get closer to the finish line with each step.

In reality? These directly optimized AIs often get stuck in local optima, never finding the real exit. Because each step moves toward what seems like the right direction, there’s no motivation to explore those places that “seem like detours”—yet precisely those detours can bypass dead ends.

Now reverse it. What if instead of directly optimizing “escape the maze,” you optimize for “exploring the most new states” (Novelty Search)?

Something remarkable happens. The AI starts wandering. It enters dead ends, then backs out. It explores corners, corridors, all kinds of circuitous routes. In pursuing “novelty,” it accidentally discovers a direct path to the exit—a path it would never have found if optimizing directly.

This experiment later changed Stanley’s view of human history.

Turning Points in the Story

“Why Greatness Cannot be Planned” validates Novelty Search with historical cases. The case selection is unbeatable:

The Wright brothers. They wanted to build airplanes, but spent time not on engine engineering, but in wind tunnel experiments studying wing shapes and airflow behavior. Those seemingly completely unrelated experiments became the foundation for their later ability to control flight. Every “deviation from plan” experiment was a stepping stone to later success.

The microwave oven. Percy Spencer, working at a radar company, discovered microwaves could melt chocolate. That wasn’t his goal. The goal was developing better radar. But this “accidental discovery” later changed every household’s kitchen.

GPUs. The video game industry needed faster graphics processing, driving GPU development. No one in the 1990s said: “We need GPUs to train large neural networks.” But those chips optimized for gaming later became the infrastructure for deep learning.

Common thread? All achievers were pursuing some point of interest, then got led in completely different directions. Each “deviation” wasn’t waste, but construction of a possibility space.

This isn’t motivational fluff. This is a mathematically verifiable pattern: systems employing Novelty Search often discover more powerful solutions than systems employing Goal Search.

From Policy to Personal

The book has a disturbing middle chapter: education policy.

Governments worldwide are doing the same thing—defining “success,” then designing education systems to maximize these success metrics. Test scores, college admission rates, employment rates. Each measurable, so seemingly scientific.

But what’s the result? They’ve trained a generation skilled at walking pre-planned routes, yet unable to innovate when encountering problems without preset paths.

Stanley’s argument: If you truly want to teach creative people, you need to let them explore. Let them “waste time” on seemingly unrelated projects. Let them pursue quirky interests, even when these interests aren’t in the curriculum.

This completely opposes modern corporate doctrine. Modern corporations say: focus. Set goals. Every minute should advance toward the goal.

Stanley’s response: That kind of focus traps you in local optima, from which you’ll never escape.

My Own Three Stepping Stones

Reading this book, I was re-examining my own twenty years of entrepreneurial trajectory. Frankly, I didn’t start with a “five-year plan.” I had some instincts and interests.

First Stepping Stone: iShelly. Online financial media. Strictly speaking, this differed from everything I did later. But what did I learn in that process? Web traffic, content marketing, community management. These skills were used in every subsequent project.

Second Stepping Stone: nvesto. From financial media to investment consulting. On the surface, “deepening” the financial field, but actually I was exploring a completely different business model—from advertising-driven to consulting-driven. It failed, but that failure taught me the real cost of client trust, and why professional services can’t be done with media logic.

Third Stepping Stone: Good Food Machine, Generous Market, Semi-Acre Garden. Suddenly jumping to agricultural-food ecosystems. Seemed like wasting the expertise I’d accumulated in finance. But in that process I understood what real supply chains are, what farmers’ real situations are, what “the difficulty of changing an ecosystem” means.

Now: Our AI platform. Circular economy digitization. Why can I see what others can’t? Because I’ve walked those circuitous paths. Finance×AgriFood×Architecture×AI. Every “digression” is the foundation for what I can see at this moment.

If I had “planned” twenty years ago to become a circular economy AI expert, I would have missed all these stepping stones. I would have been like a fake expert, knowing only surface knowledge, without real tactile understanding.

Why Organizations Become Death Mechanisms

Stanley’s sharpest critique in the book: goal-oriented systems naturally oppose novelty.

In a highly goal-oriented organization, people who “deviate from goals” are considered unprofessional. “Wasting time” on seemingly unrelated projects is seen as laziness. Cross-boundary experiments are viewed as distraction.

Yet it’s precisely in these activities that organizations despise where real innovation emerges.

So the more successful large organizations become, the more they set clear goals, the harder it becomes to generate game-changing innovation. They become highly optimized systems, perfectly solving past problems, but with no defense against future problems.

This explains why Kodak’s engineers invented the world’s first digital camera as early as 1975, yet the company refused commercialization—not a technical problem, but a clear, definite goal (maximizing the film market) that eliminated motivation to explore new possibilities. They personally created the technology that would disrupt them, yet were blind to it due to goal-oriented thinking.

Treasure Hunter Mentality

Stanley uses a metaphor: goal-oriented people are measuring tapes. They know where the destination is, how far in a straight line, wanting only to arrive fastest.

Innovators are treasure hunters. They have curiosity, adventurous spirit. They don’t know where treasure is, but walking along, they discover better things.

This doesn’t mean having no goals. Rather: the best goals often come from possibilities accidentally discovered in the process, not predetermined at the starting point.

In the circular economy field, what I’m doing now—urban mining digitization, material flow tracking, carbon footprint visualization—no one said ten years ago “this will change the world.” These all emerged through interaction with actual cases, failure, correction, exploration.

My assumption wasn’t “I want to change circular economy.” The assumption was “this field is interesting, let me see what I can discover.” The results far exceeded initial imagination.

Uncomfortable Implications

Pushing Stanley’s logic to its conclusion yields a disturbing conclusion:

If you can completely plan your success, then your ambitions might be too small.

True greatness often looks like waste. Looks inefficient. Looks like taking detours.

This means if you want to build an organization capable of real innovation, you must give people permission to “waste time.” You must reward novelty, not just progress. You must trust stepping stones, even when they seem unrelated in the short term.

Google’s 20% time policy (employees can spend 20% of work time on any project) was once mocked as wasteful. But AdSense came from this 20% time (though Gmail is often mistakenly cited as a 20% project, creator Paul Buchheit stated it wasn’t). It’s like organized Novelty Search.

More radical application: VC investment. The best startup investments might not be teams with clear business plans, but entrepreneurs with “interesting ideas, currently without clear exits.” Because it’s precisely in pursuing the interesting that they’ll discover opportunities that “planning-type” entrepreneurs will never see.

Conclusion: Abandon the Map, Bring a Compass

Stanley’s final sentence is simple: Stop planning what your life should become, start pursuing what makes you feel alive.

This isn’t motivational fluff. This is mathematically grounded advice.

Multiple experiments prove Novelty Search discovers more powerful solutions than Goal Search. Pursuing novelty goes further than straight-line rushing toward goals.

My twenty years of entrepreneurial experience confirms this. Every decision that most looked like “waste” later became the most valuable asset.

So if you’re now at some crossroads in life—not knowing whether you should “stick to the plan” or “pursue interests”:

Please choose interests. Find those stepping stones that seem outside the plan but truly make you curious.

All people in history who changed the world were walking such paths. Not the shortest path, but the most interesting path.

And interesting is often the only direction to greatness.