The Myths of Innovation

The Myths of Innovation Plot Summary

Introduction

Innovation is often misunderstood, shrouded in myths that distort our perception of how breakthroughs actually happen. We romanticize the "eureka moment," imagining lone geniuses struck by lightning bolts of inspiration that change the world overnight. But these comforting narratives obscure the messy, collaborative, and often mundane reality of how innovation truly works. By examining these myths critically, we can develop a more accurate understanding of innovation as a process rather than an event. The theoretical framework presented challenges our assumptions about creativity, history, methodology, and the reception of new ideas. Through historical examples and practical insights, we learn that innovation is less about magical moments and more about persistence, timing, collaboration, and the complex interplay between ideas and their environments. This understanding not only demystifies innovation but also makes it more accessible to anyone willing to engage in the challenging work of bringing new ideas to life.

Chapter 1: The Myth of Epiphany in Creative Breakthroughs

The myth of epiphany is perhaps the most pervasive misunderstanding about innovation. It's the belief that great ideas arrive in sudden, dramatic flashes of insight—Newton being struck by a falling apple, or Archimedes leaping from his bath shouting "Eureka!" This narrative is compelling because it transforms the complex, often tedious process of innovation into a magical moment, making innovation seem both special and inaccessible to ordinary people. In reality, what appears as a moment of epiphany is typically the culmination of extensive preparation and unconscious processing. Mihaly Csikszentmihalyi's research on creative individuals reveals that innovation follows a pattern: first comes immersion in a domain, followed by a period of incubation where ideas percolate below conscious awareness, then potentially a moment of insight, and finally the extensive work of verification and elaboration. The "eureka moment," if it occurs at all, is merely one piece in this larger process. Consider the stories we tell about innovations like the World Wide Web. While journalists sought to identify the singular moment when Tim Berners-Lee had his breakthrough, he consistently explained that "there was no Eureka moment" but rather "a process of accretion"—gradual growth and development over time. Similarly, the often-told story of eBay being created so the founder's fiancée could trade PEZ dispensers was fabricated for publicity, highlighting our preference for simple, dramatic narratives over complex truths. The most productive way to think about epiphany is as an occasional bonus of working on tough problems, not as the primary mechanism of innovation. Even when powerful moments of insight do occur, they rarely provide complete solutions. Thomas Edison's philosophy that genius is "one percent inspiration and ninety-nine percent perspiration" reflects the reality that the hard work of implementation typically dwarfs the initial insight. Peter Drucker advised that successful entrepreneurs don't wait for the muse to kiss them but simply "go to work." Innovations emerge not from single brilliant ideas but from countless smaller insights accumulated over time. The internet required nearly 40 years of developments in electronics, networking, and software before it resembled what we know today. The refrigerator, laser, and dishwasher were commercial failures for decades before enough technological and cultural barriers were overcome. The myth of epiphany tempts us to focus on the magic moment, but the evidence points to its supporting rather than central role in the innovation process.

Chapter 2: How History Distorts Our Understanding of Innovation

History presents innovation as a neat, linear progression of events, but this orderly narrative is largely an illusion created by hindsight. When we examine artifacts like the Rosetta Stone or study figures like Johannes Gutenberg, we impose our modern understanding onto the past, often missing the chaotic, uncertain reality experienced by those who lived it. The Rosetta Stone wasn't created to help future scholars decipher hieroglyphics—it was an ordinary government announcement that gained extraordinary importance through circumstance. Similarly, Gutenberg wasn't celebrated as a revolutionary figure in his lifetime. He was a craftsman trying to make a living, not someone consciously attempting to transform human knowledge transmission. The neat arcs of progress and clear sense of purpose we attribute to historical innovators are largely constructed through selective storytelling that emphasizes success while omitting failures, dead ends, and the messy context in which innovations actually occurred. This distortion extends to how we view technological evolution. We organize history into convenient "ages"—stone, bronze, iron, or in computing terms, mainframes, personal computers, and the internet. These categorizations create an illusion of inevitability, as if each innovation naturally led to the next in a predetermined sequence. But this perspective, sometimes called "techno-evolutionism," ignores the contingent nature of innovation. The gasoline-powered automobile wasn't inherently superior to electric or steam alternatives; it succeeded due to a complex interplay of technical, economic, and cultural factors specific to its time. When we examine innovation timelines more closely, we discover that each seemingly simple dot represents a chaotic, competitive tangle of events. The personal computer wasn't the product of a single breakthrough but emerged from competing visions, technologies, and business models. Apple, Xerox PARC, IBM, and many others contributed to what eventually became the dominant design, but this outcome wasn't predetermined. Had different decisions been made—if Xerox had commercialized its Alto computer or if Apple had convinced HP to manufacture its machine—the history of computing might look very different. Understanding innovation requires recognizing that progress isn't linear or inevitable. It involves false starts, competing alternatives, and paths not taken. By acknowledging the contingent, messy nature of innovation history, we gain a more realistic perspective on how innovation actually happens and how we might approach it in the present. The most valuable lesson from innovation history isn't which ideas won, but how the process of innovation unfolds in environments of uncertainty and possibility.

Chapter 3: The Fallacy of Innovation Methodology

The myth of methodology suggests that innovation can be reduced to a systematic formula—a step-by-step process that, if followed correctly, guarantees creative breakthroughs. This belief is appealing because it promises to remove risk and uncertainty from the inherently unpredictable work of creating something new. Like a chemistry experiment with precise instructions, we imagine that innovation methodologies can produce consistent results regardless of context or circumstance. However, this perspective fundamentally misunderstands the nature of innovation. True innovation involves venturing into the unknown, exploring possibilities that haven't been mapped. As one anonymous innovator put it, "By definition innovation is a charge into the unknown." While established processes can guide routine work, they cannot eliminate the uncertainty inherent in creating something genuinely new. Even "rocket science," with its rigorous methodologies, experiences catastrophic failures like the Apollo 13 mission or the Challenger disaster. Innovation begins in various ways, not through a single prescribed path. Some innovations emerge from dedicated work on well-defined problems, as with Watson and Crick's discovery of DNA's structure or Google's development of search algorithms. Others start with one goal but pivot when unexpected opportunities emerge—Post-it Notes began with a "failed" adhesive that found an entirely different application. Still others arise from curiosity (Velcro was inspired by burrs sticking to clothing after a hike), necessity (Craigslist began as a simple email list for local events), or the pursuit of wealth (though many successful companies, including Google and Apple, initially tried to sell their ideas to established firms before reluctantly going it alone). The challenges of innovation are numerous and interconnected. Innovators must find ideas, develop solutions, secure funding and sponsorship, solve reproduction and scaling problems, reach potential customers, beat competitors, achieve proper timing, and maintain financial viability throughout the process. With each challenge carrying roughly a 50% chance of success, the probability of overcoming all eight challenges is less than 1%. This explains why innovation is difficult and why success stories are rare. Rather than seeking a foolproof methodology, successful innovators develop attitudes and approaches that improve their odds. These include self-knowledge (understanding which environments and challenges bring out their best work), balancing intense focus with periodic perspective-taking, starting small and growing ambitions as successes accumulate, and acknowledging the role of luck and predecessors. As Isaac Newton famously noted, innovators "stand on the shoulders of giants"—building on existing knowledge rather than creating from nothing. The most successful approach to innovation isn't a rigid methodology but a flexible mindset that embraces both the uncertainty and the opportunity of creating something new.

Chapter 4: Why People Resist New Ideas

Contrary to popular belief, people don't naturally embrace new ideas—they resist them. This resistance isn't irrational but deeply rooted in human psychology and evolutionary history. Our ancestors who cautiously approached new situations were more likely to survive than those who enthusiastically embraced every novelty. We've inherited this caution, preferring the safety of the familiar to the uncertainty of the new. This explains why even revolutionary ideas like Einstein's theories, Galileo's heliocentric model, and Darwin's evolution faced significant resistance before gaining acceptance. The resistance to innovation operates on multiple levels. At the psychological level, new ideas create cognitive dissonance by challenging existing beliefs and mental models. This discomfort manifests as skepticism, criticism, or outright rejection. Socially, innovations often threaten established power structures, expertise, and identities. Those who have invested in existing systems—whether they're telegraph operators facing the telephone or executives who built their careers on outdated technologies—have strong incentives to resist change that might diminish their status or render their skills obsolete. This phenomenon, which Clayton Christensen termed "the innovator's dilemma," explains why established organizations struggle to embrace disruptive innovations. As organizations age and succeed, they become increasingly committed to optimizing their existing business rather than exploring potentially disruptive new directions. The same conservatism that protects their current success prevents them from adapting to changing circumstances. Western Union rejected Alexander Graham Bell's telephone, established search companies dismissed Google's approach, and major studios turned down Star Wars—all because these innovations threatened their existing business models. Everett Rogers' research on the diffusion of innovations identified five factors that determine how quickly new ideas spread: relative advantage (perceived benefit compared to existing solutions), compatibility (alignment with existing values, practices, and needs), complexity (how difficult the innovation is to understand and use), trialability (how easily it can be tested before full adoption), and observability (how visible the benefits are to others). Innovations that score highly on these dimensions spread more quickly than those that don't, regardless of their objective merits. Understanding resistance to innovation helps explain why great ideas often fail to gain traction. It's not enough for an innovation to be objectively superior—it must overcome psychological, social, and organizational barriers to adoption. Successful innovators recognize that resistance is normal and develop strategies to address the deeper concerns that underlie superficial objections. By framing innovations in ways that reduce perceived risks and align with existing values, they increase the chances that their ideas will overcome the natural human tendency to resist change.

Chapter 5: The Lone Inventor Myth and Collaborative Reality

The myth of the lone inventor—the solitary genius who single-handedly creates breakthrough innovations—is deeply embedded in our cultural narratives. We celebrate figures like Thomas Edison, Alexander Graham Bell, and Steve Jobs as heroic individuals who changed the world through sheer brilliance and determination. This narrative is compelling but fundamentally misleading. It distorts our understanding of how innovation actually happens and creates unrealistic expectations for aspiring innovators. Historical examination reveals that most significant innovations emerge from collaborative processes rather than individual efforts. Even celebrated "lone inventors" relied heavily on teams, predecessors, and contemporaries. Edison's laboratory at Menlo Park employed dozens of engineers and craftsmen whose contributions were essential to his success. The Wright brothers built on decades of aeronautical research by others. Einstein's revolutionary theories depended on mathematical concepts developed by predecessors like Faraday, Lavoisier, Newton, and Galileo. Innovation is inherently collaborative, building on existing knowledge and combining ideas from multiple sources. The myth of the lone inventor gained prominence during the Renaissance when Western culture began acknowledging individual creativity—a significant shift from earlier periods when creative acts were attributed to divine inspiration. However, this new emphasis on individual genius obscured the collaborative nature of innovation. Even Renaissance masters like Michelangelo and da Vinci had apprentices, studied others' works, and benefited from powerful patronage networks. Their achievements, while remarkable, were not solely the product of isolated individual genius. This misconception persists partly because of how we document and remember innovation. Patent law, designed to protect intellectual property, reinforces the notion that ideas belong to individuals rather than emerging from complex networks of influence and collaboration. Media narratives simplify complex innovation histories into stories with singular heroes because these narratives are more compelling and easier to understand than the messy reality of collaborative creation. Guy Kawasaki, former Apple fellow and innovation expert, advises aspiring innovators to "find a few soulmates," noting that successful companies are typically started and built by teams, not individuals. History may recognize one person as "the innovator," but success almost always requires a group of talented, committed people working together. This collaborative reality extends beyond formal teams to include the broader ecosystem of ideas, technologies, and social contexts that make innovation possible. Every innovation, from spreadsheets to smartphones, combines multiple existing ideas in new ways. Recognizing this interconnected nature of innovation helps us approach it more realistically and effectively than the misleading myth of the lone genius inventor.

Chapter 6: Finding Good Ideas: Challenges and Strategies

The myth that good ideas are hard to find stems from adult experiences rather than inherent human limitations. Children naturally generate creative ideas through play and exploration, but as we age, educational and professional systems train us to prioritize efficiency, conformity, and risk avoidance over creative exploration. The result isn't a loss of creative capacity but a suppression of the playful, exploratory mindset that facilitates idea generation. Contrary to popular belief, humans are naturally creative problem-solvers. Our species' evolutionary advantage lies not in physical strength but in our ability to adapt through creative thinking. Howard Gardner explains that creative individuals maintain childlike qualities into adulthood—they aren't "bothered by inconsistencies, departures from convention, non-literalness" and willingly pursue unusual ideas that others reject as impractical or inappropriate. The challenge isn't finding ideas but maintaining the openness and playfulness that allows us to recognize and develop them. The environment in which ideas are evaluated dramatically affects their survival and development. Many organizations create idea-killing environments where new suggestions are immediately criticized or dismissed with phrases like "we tried that already," "we don't do it that way here," or "it won't make enough money." These responses reflect a fundamental misconception about innovation: that good ideas will be immediately recognizable as such. In reality, revolutionary ideas often appear strange, impractical, or unimpressive when first proposed. The computer mouse was initially just "a block of wood on a cord," and Ford's first automobile was a flimsy gas-powered cart that compared poorly to horses. Successful idea generation requires understanding the mind as a filter that can be adjusted. Most of the time, we operate with many filters active—rejecting ideas that seem impractical, unconventional, or risky. Finding good ideas requires temporarily turning off these filters to explore possibilities more freely. Alex Osborn, who coined the term "brainstorming," identified four principles for effective idea finding: produce as many ideas as possible, encourage wild ideas, build upon others' suggestions, and avoid premature judgment. These principles create an environment where ideas can flourish before being subjected to critical evaluation. The most productive approach to innovation combines periods of open exploration with phases of focused development. As Linus Pauling, winner of two Nobel Prizes, observed, "The best way to have a good idea is to have lots of ideas." This approach recognizes that innovation is not about efficiency but exploration—trying many possibilities to discover which ones have potential. By creating environments that support this exploration and developing the ability to adjust our mental filters appropriately, we can overcome the artificial scarcity of ideas and tap into our natural creative capacity.

Chapter 7: Managing Innovation: Leadership Myths and Realities

The myth that managers inherently know how to foster innovation stems from a fundamental confusion between power and talent. Just because someone has the authority to make decisions doesn't mean they have the wisdom or experience to make good ones, particularly when it comes to innovation. This misconception is especially problematic because traditional management approaches often work against the conditions that innovation requires. Professional management emerged from industrial-era efforts to optimize and control production processes. Frederick Taylor, Henry Ford, and other early management theorists focused on designing systems that minimized variation and maximized efficiency—goals that directly conflict with the exploration and experimentation essential to innovation. This legacy continues to influence management practices today, even in organizations that claim to prioritize innovation. As Amy Edmonson of Harvard Business School notes, "Management 101 is based on the assumption that we know with a high degree of certainty what needs to happen," but innovation requires embracing uncertainty rather than eliminating it. Effective innovation management requires addressing five key challenges. First, leaders must nurture the "life of ideas" by creating environments where suggestions flow freely and receive thoughtful consideration rather than immediate criticism. Second, they must design physical and social environments that support creative collaboration, as exemplified by Bob Taylor at Xerox PARC, who created a culture where open debate and idea-sharing flourished. Third, managers must protect innovation efforts from organizational threats, whether budget cuts, political opposition, or premature demands for results. Steve Jobs physically separated the Macintosh team from the rest of Apple to shield it from corporate politics and conventional thinking. The fourth challenge is execution—transforming promising ideas into tangible results. This requires balancing idealism with practical constraints, making difficult trade-offs, and maintaining momentum through inevitable setbacks. As Jobs told the Macintosh team, "Real artists ship," emphasizing that innovation ultimately requires delivering finished products, not just generating ideas. Finally, innovation leaders must excel at persuasion, convincing others to support ideas that may initially seem risky or impractical. This requires persistence more than charisma—as Jobs noted, "about half of what separates successful entrepreneurs from non-successful ones is pure perseverance." Successful innovation management looks very different from traditional control-oriented approaches. Rather than dictating solutions or enforcing rigid processes, effective innovation leaders focus on asking good questions, removing obstacles, protecting creative teams from organizational politics, and creating environments where talented people can do their best work. They recognize that their role is not to have all the answers but to create conditions where innovative solutions can emerge and develop.

Chapter 8: Why the Best Ideas Don't Always Win

The belief that the best ideas naturally triumph in the marketplace is a persistent myth deeply rooted in American meritocratic ideals and hero narratives. We want to believe that superior innovations will inevitably succeed through their inherent quality, as captured in the misattributed quote, "If you build a better mousetrap, the world will beat a path to your door." This comforting notion suggests that innovation is primarily about creating objectively superior solutions, with market success following automatically. Reality tells a different story. History is littered with technically superior innovations that failed while objectively inferior alternatives thrived. The QWERTY keyboard layout, designed to prevent mechanical typewriter jams rather than optimize typing efficiency, remains dominant despite better alternatives. VHS videotapes defeated the technically superior Betamax format. HTML and JavaScript, far from being the best possible web development technologies, became ubiquitous through timing and accessibility rather than technical excellence. These examples demonstrate that an innovation's objective quality is only one factor in its success or failure. Multiple secondary factors often prove more influential than an innovation's inherent merits. Cultural values significantly impact adoption—the Japanese invented firearms before Europeans but initially rejected them because they conflicted with cultural values around honor and craftsmanship. Dominant designs create powerful inertia—once a technology becomes established, alternatives must offer dramatic improvements to justify the costs of switching. Political considerations shape innovation outcomes as people in positions of power protect their interests. Economic factors determine whether an innovation's benefits justify its costs. And the subjective nature of "goodness" means that different stakeholders value different aspects of an innovation—consumers often prioritize convenience or aesthetics over technical excellence. This creates what might be called the goodness/adoption paradox. From an expert's perspective, the "best" solution might be technically sophisticated, comprehensive, and elegant. But these qualities often make innovations more difficult to adopt by increasing complexity, raising costs, or requiring significant behavior changes. The most successful innovations typically find a sweet spot between expert-defined quality and ease of adoption, balancing technical excellence with practical considerations like compatibility with existing systems, learning requirements, and immediate utility. The World Wide Web exemplifies this paradox. When Tim Berners-Lee created HTML, experts criticized its technical limitations compared to more sophisticated hypertext systems. But these very limitations—its simplicity and accessibility—enabled rapid adoption. Had Berners-Lee prioritized technical sophistication over ease of use, the web might have remained a specialized tool rather than transforming global communication. Understanding this balance between quality and adoptability helps explain why technically superior innovations often fail while seemingly inferior alternatives succeed, challenging the myth that the best ideas inevitably win.

Summary

The myths surrounding innovation—from the dramatic eureka moment to the lone genius inventor to the inevitable triumph of superior ideas—serve as comforting narratives that ultimately mislead us about how innovation actually works. The theoretical framework presented throughout this exploration reveals innovation as a messy, collaborative process shaped by historical context, psychological factors, organizational dynamics, and market forces beyond the control of individual innovators. Innovation is neither magical nor methodical but rather a human endeavor characterized by uncertainty, persistence, and adaptation. By recognizing that breakthroughs emerge not from sudden inspiration but sustained effort, not from isolated genius but collaborative networks, not from perfect methodologies but flexible approaches, and not solely from technical excellence but from navigating complex adoption factors, we gain a more realistic and ultimately more empowering understanding of innovation. This perspective doesn't diminish innovation's importance but makes it more accessible, allowing us to approach it with appropriate expectations and strategies rather than waiting for lightning to strike or formulas to follow. The greatest insight may be that innovation, for all its mythology, remains fundamentally human—reflecting both our creative potential and our complex social nature.

About Author

Loading...

Scott Berkun

Scott Berkun is the author of four popular books, Making Things Happen, The Myths of Innovation, Confessions of a Public Speaker and Mindfire: Big Ideas for Curious Minds. His work as a writer and speaker have appeared in the The Washington Post, the New York Times, Wired, the Economist, Fast Company, Forbes, CNBC, MSNBC, CNN, National Public Radio and other media. His many popular essays and entertaining lectures can be found for free on his blog at http://www.scottberkun.com, and he tweets at https://twitter.com/berkun.

Read more