GUEST POST by Greg Satell

In 2011, MIT economists Erik Brynjolfsson and Andrew McAfee self-published an unassuming e-book titled Race Against The Machine. It quickly became a runaway hit. Before long, the two signed a contract with W. W. Norton & Company to publish a full-length version, The Second Machine Age that was an immediate bestseller.

The subject of both books was how “digital technologies are rapidly encroaching on skills that used to belong to humans alone.” Although the authors were careful to point out that automation is nothing new, they argued, essentially, that at some point a difference in scale becomes a difference in kind and forecasted we were close to hitting a tipping point.

In recent years, their vision has come to be seen as deterministic and apocalyptic, with humans struggling to stay relevant in the face of a future ruled by robot overlords. There’s no evidence that’s true. The future, in fact, will be driven by humans collaborating with other humans to design work for machines to create value for other humans.

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GUEST POST by Greg Satell

This past December, Amazon announced that it was launching a new quantum computing service, called Braket, joining the ranks of tech giants like Google, Microsoft and IBM. However, it quickly became clear that Amazon was not creating its own technology, but offering that of other companies to its customers through the cloud.

The story gets even stranger when you consider that the purpose of the service is not to do anything of any immediate practical value, but to allow “scientists, researchers, and developers to begin experimenting” with quantum technology. The companies providing the hardware, D-Wave, IonQ, and Rigetti, are themselves fledgling companies.

So why would Amazon promote, with great fanfare, a technology it doesn’t own and that doesn’t really work to customers who can’t use it to solve any practical problems? The reason is that we are entering a new era of innovation in which it is no longer enough to simply move fast and break things. We need to do more than just adapt. We need to learn to prepare.

What Is Quantum Computing?

Quantum computing, as any expert will tell you, uses quantum effects such as superposition and entanglement to compute, unlike digital computers that use strings of ones and zeros. Yet quantum effects are so confusing that the great physicist Richard Feynman once remarked that nobody, even world class experts like him, really understands them.

So instead of quantum effects, think of quantum computing as a machine that works in three dimensions rather than the two-dimensions of digital computers. The benefits of this should be obvious, because you can fit a lot more stuff into three dimensions than you can into two, so a quantum computer can handle vastly more complexity than the ones we’re used to.

Another added benefit is that we live in three dimensions, so quantum computers can simulate the systems we deal with every day, like those in materials and biological organisms. Digital computers can do this to some extent, but some information always gets lost translating the data from a three dimensional world to a two dimensional one, which leads to problems.

I want to stress that this isn’t exactly an accurate description of how quantum computers really work, but it’s close enough for you to get the gist of why they are so different and, potentially, so useful. The three dimensional quantum world can create computing spaces that are exponentially larger than anything we’ve ever seen before, making them ideal for certain tasks, like simulating physical systems and large optimization problems.

The Knowledge Gap

Quantum computing is still very much a nascent technology. The most powerful machines are only about 50 qubits (quantum bits), while it will probably take more than 1000 qubits for a quantum to be really useful. Most experts estimate that will probably take 10-15 years, although it is possible that some practical application may be found in 5-10 years.

However, just as important as the technology gap is the knowledge gap associated with the technology. Quantum computers aren’t just potentially vastly more powerful than digital computers, they’re also very different. They operate according to a different logic, will need new programming languages and algorithmic strategies.

To understand the challenge, consider Daimler’s recent announcement that it is working with IBM to develop advanced batteries. Quantum computer’s ability to simulate physical systems makes them potentially very useful for this type of application. However, no one really knows how to do it, because it’s never been done before.

So over the next 5-10 years, battery scientists will be working with quantum computing scientists to learn how to use and understand the data coming out of a quantum computer, which is unlike anything they’ve ever seen before. Once they figure that out, they will have to explain it to engineers who will build advanced batteries. We’ll have to repeat this same process for every area of application. That will take a lot of time and resource.

An Evolving Quantum Ecosystem

We tend to see things in terms of inventions, but ecosystems are what really drive change. For example, for cars to be useful, they needed a basic infrastructure of roads, gas stations and mechanics. Digital computers needed more than just hardware, but also software developers, systems integrators and others to actually make an impact.

We’re beginning to see a similar ecosystem for around quantum technologies. The journal Nature recently published an analysis which showed that over $450 was invested in more than 50 quantum startups between 2012 and 2018. However, only part of that has gone to quantum hardware manufacturers.

For example QxBranch, Zapata and QCWare focus on software, tools and services to support quantum computing, much like firms like Accenture and Deloitte do with digital technology. Others, such as ID Quantique, QuintessenceLabs and MagiQ offer quantum cryptography. Still others are working on quantum communication and other services.

So while practical applications for quantum computing are still years away, there is already an active ecosystem forming around the technology. Obviously, Amazon believed that it could no longer afford to be shut out and figured that leveraging its strong market position in cloud services would be the best way in.

Learning To Prepare

Over the past few decades the focus has been on adapting to rapid change. Digital technologies forced firms to become more agile, more responsive and more innovative. However, what Amazon’s move into quantum technologies should make clear is that adapting isn’t enough any more. You have to prepare.

Certainly this is true for quantum technologies, which are vastly more complex—and potentially vastly more powerful—than anything we’ve ever seen before. That’s why companies are willing to pay to experiment with the technology, because they know if they wait to adapt it may very well be too late.

It also shows why it is becoming so important to have an ecosystem strategy. Amazon didn’t even try to go it alone, but entered the market with partners (and other quantum companies take a similar approach). In an ecosystem driven world, power doesn’t lie at the top of value chains, but at the center of networks and you get to the center by widening and deepening connections.

Make no mistake. The future will not be digital and digital strategies are unlikely to succeed in a post-digital world that will likely includes not only quantum technologies, but neuromorphic computing, synthetic biology, and other things as well. We can’t just move fast and break things anymore. We need to learn to prepare for a world we can’t see, much less understand, yet.

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GUEST POST by Greg Satell

When I first arrived in Poland in 1997, it seemed like the entire country was being rebuilt out of the Marriott hotel. Multinational companies looking to set up shop in the formerly communist country would rent temporary offices at the Regus Center there to get operations rolling. Later, they would move into more conventional space.

It was a good model that provided a useful service, but I was surprised to see that 20 years later that same model, in the form of a company called WeWork was being given a $47 billion valuation for an upcoming IPO. I was somewhat less surprised to see that valuation crash and burn almost as soon as the prospectus came out.

This is becoming a common tale of woe. Theranos, once the darling of Silicon Valley, was exposed as a fraud. Uber, the poster child for the sharing economy, saw its stock price collapse after it once again posting massive losses. It’s time to face facts. These are not isolated incidents but indicative how Silicon Valley investors misjudge the physical economy.

The WeWork Business Model

The WeWork business model is essentially the same as I saw in Poland two decades ago. The company acquires space from building owners using long term leases and then rents that same space out to businesses and entrepreneurs in the form of individual desks or offices. By charging more for the space in pieces than it pays for the whole, it can earn a profit.

A look at the financials of IWG, the parent of Regus, shows how this model can make for a decent business. In 2018, the company earned an operating profit of 154 million British pounds on 2.5 billion in revenues. That translates into 6% margins, which is respectable for a real estate business. So WeWork was, in a sense, following a tried and true path.

The problem comes when you start comparing IWG’s business with WeWork. While IWG had revenues of almost $7000 a desk and expenses just over $6000, WeWork had revenues of just over $4000 a desk but expenses of more than $8000. So While IWG was making a few hundred dollars per desk, WeWork was losing thousands!

What makes the comparison between the two companies even more curious is that while IWG, which is slightly bigger than We Work in terms of square footage is valued in the public markets at just under $4 billion, WeWork, as noted above, was privately valued by its venture funders at $47 billion?

How can a company that’s losing money hand over fist be valued more than ten times higher than a similar business that’s already profitable? Apparently, all it takes is a little Silicon Valley pixie dust.

The New Economy Promise Of Network Effects And Increasing Returns

One of the oldest principles of economics is that of diminishing returns. As you produce more of something, supply costs tend to increase while customers become less enthusiastic and demand less. While there are also positive returns to scale, such as improved efficiencies and reputation, the forces of diminishing returns tend to limit growth.

However, under certain conditions, such as when network effects are present and high upfront expenses combined with low marginal costs, instead of diminishing returns you get increasing returns. That completely changes the way a business makes money and how it’s valued.

Consider the case of Microsoft Windows in the 90s. As the dominant operating software, everybody wanted to use it because everybody else was using it. Customers were hesitant to go to the trouble of learning new software to switch and the marginal copy of the software cost Microsoft nearly nothing. That’s what made the business so valuable.

Today, companies like Google, Facebook, and Apple enjoy similar advantages from increasing returns. They are worth more than ordinary businesses because they function, at least to some extent, as platforms. That’s why today, if you’re an entrepreneur looking to impress investors, it helps if you can make a case that your business is, in fact, a platform.

The Platform Fallacy

As noted above, the coworking space business model is fairly straightforward. You lease space for a certain amount, rent it out to others for another amount and pocket the difference. It’s not exactly rocket science, but not terribly profitable either. The 6% operating margins that IWG earns is solid, but not stratospheric.

Yet WeWork argued, in great Silicon Valley fashion, that it was something more. Rather than just a real estate play, WeWork convinced investors that the offices were, in fact, “a platform for creators that transforms buildings into dynamic environments for creativity, focus, and collaboration.” It would also collect and analyze data to optimize its revenue machine.

The key to the strategy, of course, was to leverage network effects. As more and more tenants signed up, the more valuable a desk at WeWork would be because the company was offering more than just office space, but a place for cool, hip entrepreneurs to collaborate with other, cool, hip entrepreneurs.

This is, in effect, the platform fallacy, the idea that a platform business is inherently more profitable than any other kind of business. Amazon struggled for years to become profitable and, even today, makes most of its money from cloud computing, not its retail platform. Uber may never hit break even. Sure there have been some tremendous successes, such as Facebook, but platform-based companies fail all the time.

The Silicon Valley Myth

In hindsight, the WeWork debacle looks ridiculous. Deep pocketed investors pumped billions into a decades-old business model that has a history of mediocre margins in the hopes that it would somehow miraculously become something different. Yet if we dismiss the whole story as simply a tale of foolishness and hubris, we lose sight of an important lesson.

WeWork investors have had great success with this investing approach in the past. Softbank, for example, which was the company’s largest investor, also bought early stakes in Yahoo!, Alibaba and Slack. However, those were software companies and applying the same investing approach to real estate was a serious error.

That, in essence, is the Silicon Valley myth, that a model that has largely been successful in digital technology can be applied to any business, anywhere. As we begin to enter a new era of innovation that will driven more by physical atoms and less by virtual bits, we’re likely to find that the Silicon Valley approach becomes less viable and relevant.

As Mark Twain is reputed to have said, “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.” WeWork’s investors thought that by pumping tons of money into a conventional business they could transform it into a super-profitable platform. They were terribly wrong, but they won’t be the last ones to try.

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GUEST POST by Greg Satell

The history of technological advancement is marked by milestones. Einstein published his theory of relativity. Fleming’s discovered penicillin. Watson and Crick uncovered the structure of DNA. While none of these had any practical significance at the time — and wouldn’t for decades — they all ushered in new eras of technology.

Google’s recent announcement that it had achieved quantum supremacy can be seen in a similar light. While the company proved that its quantum computer could solve a particular problem in minutes that would take a conventional supercomputer thousands of years, that problem itself is of little practical value.

However, the same could be said about the achievements of Einstein, Fleming and Watson and Crick. They were important not in and of themselves, but because of the possibilities they would unleash later on. The truth is that quantum supremacy is a harbinger for a future we can’t see yet. It marks a new era that will take us in completely new directions.

How The Death Of Logic Led To A Universe of Ones And Zeros

The modern concept of computing began, strangely enough, with a hole at the center of logic known as Russell’s paradox, which showed that, under certain conditions, a proposition can be both true and untrue. The dilemma led to a series of problems posed by the mathematician David Hilbert to resolve what was seen as a foundational crisis in mathematics.

The matter at hand centered largely around three principles: completeness, consistency and computability. To the horror of many, in 1931 25 year-old Kurt Gödel published his incompleteness theorems, which showed that a system could be complete or consistent, but not both. Five years later, Alan Turing showed that not every number is computable either.

The hole at the center of logic would remain. Yet there was a silver lining to the hole affair. In proving his theory, Turing also created the concept of a universal computer, today known as a Turning machine. The idea, in a nutshell, was that a sequence of ones and zeroes, if it was long enough, could compute any computable number.

Turing’s idea became the basis for today’s digital economy. Using incredibly long sequences of ones and zeroes, computer chips allow us to do everything from sending emails and writing documents to searching for information and playing video games. It’s an amazing achievement, but also inherently limited.

Computing In Three Dimensions

Every technology eventually hits theoretical limits and digital computing is at that point today. The crux of the problem is that there are only so many transistors that we can fit onto a silicon wafer before subatomic effects start kicking in and begin mixing up all those ones and zeroes. So to advance further we need to come up with new ways of computing things.

One of the most viable options is quantum computing, which uses quantum effects like entanglement and superposition to calculate things. Unfortunately, quantum effects are pretty confusing. In fact, the great physicist Richard Feynman once remarked that nobody, even world class experts like him, really understands them.  So that explanation isn’t very helpful

A simpler and reasonably accurate explanation is that while digital technology computes in two dimensions (strings of ones and zeroes), quantum technology computes in three dimensions. The benefits of this should be obvious, because you can fit a lot more stuff into three dimensions than you can into two, so quantum computer can handle vastly more complexity than digital computers.

Another added benefit is that we live in three dimensions, so quantum computers can simulate the systems we deal with every day, like those in materials and biological organisms. Digital computers can do this to some extent, but some information always gets lost translating the data from a three dimensional world to a two dimensional one, which leads to problems.

However, the opposite is also true. Quantum computers need to translate classical problems into quantum space in order to compute them. That’s why the idea of quantum supremacy has gotten so much attention. For the first time, Google was able to show that its quantum computer was able to overcome that handicap and solve a classical problem faster than a digital computer.

The Evolving Quantum Ecosystem

We use milestones to mark the beginning of a new era. Einstein’s paper on relativity changed physics forever. Alexander Fleming’s discovery of penicillin created the new field of antibiotics. Watson and Crick’s discovery of the structure of DNA created genetics. In this sense, the attainment of quantum supremacy is a milestone event.

However, take a closer look and you’ll find that it takes decades to go from a discovery in a lab to a practical impact in the real world. The problem is that we need more than technology to create value, we need ecosystems of suppliers, customers, service providers and complementary products to create a real market.

For example, while the integrated circuit was invented in 1958 and the first PC was created at Xerox in 1973, it wasn’t until the late 90’s that we saw a real impact on productivity. It took that long to build up a strong ecosystem of software suppliers, systems integrators and networking technology (including the Internet), to deliver on the promise of digital technology.

As the journal Nature reports, we can see a similar ecosystem forming in quantum technology today. In addition to companies that are building quantum computing hardware, there is increasing investment in quantum software, communication, tools and sensors that will be necessary to make quantum technology useful.

Reimagining Computing For A New Era Of Innovation

It’s tempting to look at the achievement of quantum supremacy and see an accelerated version of the digital age. However, that’s almost certainly not going to be the case. Quantum computing isn’t just a more powerful version of today’s technology, it is a fundamentally different way of computing that will be applied to very different tasks.

To understand how different the future will be, think back to the early days of digital computing when the machines filled entire rooms. Nobody looked at those things and saw a device that would fit into your pocket and you would use to watch videos and send emoticons. Things that truly change the world always arrive out of context for the simple reason that the world hasn’t changed yet.

What is clear is that the digital era is ending, but much like Turing and the death of logic, this end also marks a new beginning. Quantum technology, as well as others such neuromorphic computing, will change our conception of what problems a computer can solve. Much like with digital computing, that will open up possibilities that are unimaginable today.

That’s why the challenge — and the opportunity— today lies in the later stages of the ecosystem, the consumers of the technology. That’s where real world problems get solved. The value of a technology does not lie in its inherent capabilities, but how people learn to collaborate and apply those capabilities to deliver a meaningful impact on the world.

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GUEST POST by Greg Satell

In a disruptive era, the only viable strategy is to adapt and that is especially true today. With change seeming to accelerate with each passing year, every organization must transform itself. Those who are unable to change often find that they are unable to compete and soon disappear altogether.

There has been a long running debate about whether change should be top-down or bottom-up. Some say that true change can only take hold if it comes from the top and is pushed through the entire organization. Others argue that you must first get buy-in from the rank-and-file before any real change can take place.

As I explain in Cascades, the truth is that transformation isn’t top-down or bottom-up, but happens from side-to-side. Change never happens all at once and can’t simply be willed into existence. It can only happen when people truly internalize and embrace it. The best way to do that is to empower those who already believe in change to bring in those around them

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GUEST POST by Greg Satell

In 1936, Alan Turing’s published a breakthrough paper describing a universal computer which could be programmed to do any task. Essentially, he argued that rather than having different machines for different tasks, a single machine, using a system of ones and zeroes, could be programmed to do any task.

Today, we can see Turing’s vision writ large. Digital technology pervades just about everything we do, from producing documents to navigating the physical world. Although the basic technology has evolved from vacuum tubes to transistors to integrated circuits, modern computers are essentially scaled up versions of that initial idea.

Yet even the most powerful ideas have their limits. While it is true that digital computers can perform almost any informational task, the technology is approaching theoretical barriers and we can no longer rely on a single technology to power the future. At Intel, scientists are working to create a new vision in which computing is no longer universal, but heterogeneous.

Intel’s Challenge

Few companies have benefited from Turing’s vision than Intel. In 1959, the company’s co-founder Robert Noyce helped pioneer the integrated circuit. In 1965, Gordon Moore came up with his eponymous law that predicted that the number of transistors on a microchip would double every two years. For half a century, the company has prospered by cramming more and more transistors onto silicon wafers.

Yet every technology eventually hits theoretical limits and that’s where Moore’s law stands today. There are physical limits to the number of transistors that can fit in a limited space and how fast we can send information through them. We will likely hit these limits in the next 5-10 years.

As a general rule, businesses that owe their success to a single idea or technology don’t survive past its relevance. Kodak, despite what many assume, invested significant resources in digital photography, but couldn’t replace the enormous profits it made from developing film. When Xerox’s copier business was disrupted, the company lost its dominance. The list goes on.

The odds would seem to be stacked against Intel, but the company has embarked on a multi-decade plan to rise to even greater heights. The strategy rests on three basic pillars: optimizing traditional chip architectures for specific tasks, shortening the distance between chips and inventing new computing architectures.

Optimizing Digital Architectures

While Turing proved that a universal computer could perform any calculation, that doesn’t mean that’s the best or most efficient way to do it. Think about all the things that we do with a computer today, from writing documents and preparing analyses to watching videos and playing games and it becomes obvious that we can improve performance through specialization.

Intel has invested in two technologies that optimize chip architecture for specific tasks. The first, called ASIC, is hardware-based. The chip is designed at the factory to perform a particular function, such as run an AI algorithm or mine bitcoin. It can greatly increase efficiency, but obviously reduces flexibility.

The second technology, called FPGA, is software based, which provides much greater flexibility. So, for example, at an e-commerce data center a chip can be optimized to process transactions during the day and then reprogrammed, in microseconds, to analyze marketing trends at night. Deploying ASIC and FPGA chips can improve performance by as much as 30%-50%.

In a heterogeneous computing environment, ASICs and FPGAs provide very different roles. ASICs are best suited for applications for which there is a large addressable market, which is why Google and Microsoft use them to run their AI algorithms. FPGA’s are more useful for smaller scale applications in which the economics don’t favor devoting an entire fab to their manufacture.

Integrating The Integrated Circuit

The von Neumann architecture has long been the standard for how computers are run. It consists of a set of chips, including a central processing unit, a control unit, and memory chips as well as other types of chips that provide long term data storage, graphics capability and so on.

These provide a computer with full functionality, but come with a built-in problem. It takes time for information to travel from one chip to another. At first this wasn’t a big deal, but as chips have become faster they need to wait longer and longer, in terms of computing cycles, to get the information they need to do their work.

This problem, known as the von Neumann Bottleneck, has stymied computer scientists for decades. Yet in January, Intel announced that it had solved the problem with its new Foveros technology, based on decades of research, which employs a method called 3D stacking.

Essentially, 3D stacking integrates the integrated circuit. In a typical chipset, different types of chips, such as a CPU, a memory chip and a graphics chip are set up side-by-side. The company’s new Foveros technology, however, is able to stack chips vertically, greatly reducing the distance between chips and improving overall performance.

Inventing New Architectures

The third, and most ambitious pillar, is inventing completely new computing architectures, where the company is investing in two futuristic technologies: neuromorphic and quantum computing.

Neuromorphic computing involves chips that are based on the human brain. Because these chips do not compute in a linear fashion like conventional chips, but are massively parallel, they are potentially thousands of times more computationally efficient for some applications and millions of times more energy efficient. Likely applications include robotics and edge computing.

Quantum computing leverages quantum effects, such as superposition and entanglement, to create almost unimaginably large computing spaces that can handle enormous complexity. Potential applications include large and complex simulations, such as those of chemical and biological systems, large optimization problems, like in logistics and artificial intelligence.

Both of these technologies have the potential to power computing for decades to come, but neither is commercially viable today. Executives at Intel told me they expect that neuromorphic chips could possibly begin to have an impact within five years, but quantum computing may take as long as 10-15 years.

Preparing For The Challenge Ahead

Decades from now, we will probably come to see the digital revolution as a quaint, simpler time. Every few years a new generation of computer chips would come of the line that worked exactly the same as the previous generation, but were better, faster and opened up entirely new possibilities. This gave entrepreneurs and product designers a high level of predictability.

For example, when Steve Jobs imagined the iPod as “a thousand songs in my pocket,” he knew it wasn’t technically feasible. But he also knew that it would be in just a few years. So he waited for a hard drive with the technical specifications he needed to come on the market and, when it did, bought the whole production run.

Yet those days are ending. “We see the future as heterogeneous computing,” Mike Davies, Director of the Neuromorphic Computing Lab at Intel told me. “In the future we’ll be using different architectures, such as quantum, neuromorphic, classical digital chips and so on, for different applications. That will enable us to fit the tool to the job that much more effectively.”

That idea has great promise, but it also presents great challenges. We will need to design systems that optimize specific operations, but are not so inflexible that we can’t scale them. With new architectures like neuromorphic and quantum, we will also need to develop new programming languages and algorithmic approaches.

That’s the challenge that heterogeneous computing presents. It will usher in a new era of technology that is far more powerful, but also far more complex than anything we’ve ever seen before.

Image credit: Pixabay

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GUEST POST by Greg Satell

There is a line of thinking that says that the world is built on ideas. It was an idea that launched the American Revolution and created a nation. It was an idea that led Albert Einstein to pursue relativity, Linus Pauling to invent a vaccine and for Steve Jobs to create the iPhone and build the most valuable company in the world.

It is because of the power of ideas that we hold them so dear. We want to protect those we believe are valuable and sometimes become jealous when others think them up first. There’s nothing so rapturous as the moment of epiphany in which an idea forms in our mind and begins to take shape.

Clearly, ideas are important, but not as many believe. America is what it is today, for better or worse, not just because of the principles of its founding, but because of the actions that came after it. We revere people like Einstein, Pauling and Jobs not because of their ideas, but what they did with them. The truth is that although possibilities are infinite, ideas are limited.

The Winklevoss Affair

The muddled story of Facebook’s origin is now well known. Mark Zuckerberg met with the Winklevoss twins and another Harvard classmate to discuss building a social network together. Zuckerberg agreed, but then sandbagged his partners while he built and launched a competing site. He would later pay out a multimillion dollar settlement for his misdeeds.

Zuckerberg and the Winklevoss twins were paired in the news together again recently when Facebook announced that it’s developing a new cryptocurrency called Libra. As it happens, the Winklevoss twins have been high profile investors in Bitcoin for a while now. The irony was too delicious for many in the media to ignore. First he stole their idea for Facebook and now he’s doing the same with cryptocurrencies!

Of course this is ridiculous. Social networks like Friendster and Myspace existed before Facebook and many others came after. Most failed. In much the same way, many people today have ideas about starting cryptocurrency businesses. Most of them will fail too. The value of an initial idea is highly questionable.

Different people have similar ideas all the time. In fact, in a landmark study published in 1922 identified 148 major inventions or discoveries that at least two different people, working independently, arrived at the same time. So the fact that both the Winklevoss twins and Zuckerberg wanted to launch a social network was meaningless.

The truth is that Zuckerberg didn’t have to pay the Winklevoss twins because he stole their idea, but because he used their trust to actively undermine their business to benefit his. His crime wasn’t creation, but destruction.

The Semmelweis Myth

In 1847, a young doctor named Ignaz Semmelweis had a major breakthrough. Working in a maternity ward, he discovered that a regime of hand washing could dramatically lower the incidence of childbed fever. Unfortunately, the medical establishment rejected his idea and the germ theory of disease didn’t take hold until decades later.

The phenomenon is now known as the Semmelweis effect, the tendency for people to reject new knowledge that contradicts established beliefs. We tend to think that a great idea will be immediately obvious to everyone, but the opposite usually happens. Ideas that have the power to change the world always arrive out of context for the simple reason that the world hasn’t changed yet.

However, the Semmelweis effect is misleading. As Sherwin Nuland explains in The Doctor’s Plague, there’s more to the story than resistance to a new idea. Semmelweis didn’t see the value in communicating his work effectively, formatting his publications clearly or even collecting data in a manner that would gain his ideas greater acceptance.

Here again, we see the limits of ideas. Like a newborn infant, they can’t survive alone. They need to be nurtured to grow. They need to make friends, interact with other ideas and mature. The tragedy of Semmelweis is not that the medical establishment did not immediately accept his idea, but that he failed to steward it in such a way that it could spread and make an impact.

Why Blockbuster Video Really Failed

One of the most popular business myths today is that of Blockbuster Video. As the story is usually told, the industry giant failed to recognize the disruptive threat that Netflix represented. The truth is that the company’s leadership not only recognized the problem, but developed a smart strategy and executed it well.

The failure, in fact, had less to do with strategy and tactics than it did with managing stakeholder networks. Blockbuster moved quickly to launch an online business, cut late fees and innovated its business model. However, resistance from franchisees, who were concerned that the changes would kill their business, and from investors and analysts, who balked at the cost of the initiatives, sent the stock price reeling.

From there things spiraled downward. The low stock price attracted the corporate raider Carl Icahn, who got control of the board. His overbearing style led to a compensation dispute with Blockbuster’s CEO, John Antioco. Frustrated, Antioco negotiated his exit and left the company in July of 2007.

His successor, Jim Keyes, was determined to reverse Antioco’s strategy, cut investment in the subscription model, reinstated late fees and shifted focus back to the retail stores in a failed attempt to “leapfrog” the online subscription model. Three years later, in 2010, Blockbuster filed for bankruptcy.

The Fundamental Fallacy Of Ideas

One of the things that amazed me while I was researching my book Cascades was how often movements behind powerful ideas failed. The ones that succeeded weren’t those with different ideas or those of higher quality, but those that were able to align small groups, loosely connected, but united by a shared purpose.

The stories of the Winklevoss twins, Ignaz Semmelweis and Blockbuster Video are all different versions of the same fundamental fallacy, that ideas, if they are powerful enough, can stand on their own. Clearly, that’s not the case. Ideas need to be adopted and then combined with other ideas to make an impact on the world.

The truth is that ideas need ecosystems to support them and that doesn’t happen overnight. To make an idea viable in the real world it needs to continually connect outward, gaining adherents and widening its original context.  That takes more than an initial epiphany. It takes the will to make the idea subservient to its purpose.

What we have to learn to accept is that what makes an idea powerful is its ability to solve problems. The ideas embedded in the American Constitution were not new at the time of the country’s founding, but gained power by their application in the real world. In much the same way, we revere Einstein’s relativity, Pauling’s vaccine and Jobs iPhone because of their impact on the world.

As G.H. Hardy once put it, “For any serious purpose, intelligence is a very minor gift.” The same can be said about ideas. They do not and cannot stand alone, but need the actions of people to bring them to life.

Image credit: Pixabay

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