GUEST POST by Greg Satell

In 1965, Intel co-founder Gordon Moore published a remarkably prescient paper, which predicted that the number of transistors on a computer chip would double about every two years. For a half century, this process of doubling has proved to be so remarkably consistent that today it is commonly known as Moore’s Law and has driven the digital revolution.

For most of the past 50 years, Intel has led the industry by doubling transistors faster than its rivals, investing billions into massive fabs to produce the next generation of chips before anyone else. More recently, however, the giant has stumbled, losing ground to firms such as AMD, Qualcomm and Nvidia.

Yet the company recently announced a breakthrough with its Foveros technology that has the potential to put it back on top. No longer just cramming more transistors onto a silicon wafer, it has solved a decades old physics problem to stack chips on top of each other. This has the potential to not only change Intel’s fortunes, but to reshape the industry for years to come.

The Race To The Bottom

Ever since Moore published his famous paper, chip manufacturers have been able to continually come up with new techniques to keep cramming more transistors into the same space. That’s what mad every new generation of chip faster and how we’ve been able to shrink the size of computers from giant mainframes to smartphones we can fit in out pockets.

To put this pace of advancement in perspective, consider that the average smartphone today is more powerful than the computers that put a man on the moon and you get a sense of what an incredible accomplishment this is. We’ve basically gotten innovation for free, every few years new chips come off the line that allow us to do far more than we ever could before.

Yet there are theoretical limits to every technology. The fact is that atoms are only so small and that limits how many transistors we can put on a chip before subatomic forces begin to inhibit their function. We’re beginning to approach that limit now with the current generation of 7nm chips. We may be able to shrink them down to 5nm or 3nm, but probably not further than that.

Yet there is another physical impediment to improving chip performance besides the size of atoms: the speed of light. That is where Intel’s new breakthrough comes in.

The Von Neumann Bottleneck

In 1945, the legendary mathematician John von Neumann came up with a new model for the computer. Previously, computing machines had fixed programs, which allowed them to perform a single function. For example, a desk calculator can do arithmetic for you, but it can’t to run a database. Von Neumann suggested storing programs in memory to add flexibility.

That idea, known as 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.

A related problem is that as technology advances we want to have more specialized chips for advanced applications, like artificial intelligence. The more different kinds of chips, the more information has to move around and the more computing time we lose. Faster chips don’t really solve this problem, we just lose more cycles.

Integrating The Integrated Circuit

If you can’t increase performance by speeding up the chip, then the obvious thing to do is to reduce the distance between chips. That’s the idea behind the architecture called 3D stacking, which integrates different chips together much like the integrated circuit combined various components together on a single chip.

Stacking chips on top of each other would not only vastly reduce the time circuits need to wait for instructions from each other and increase speed significantly. It would also decrease power usage due to far shorter communication paths. It seems like the perfect solution to a thorny problem.

However, this approach comes with its own problems. Because the chips are stacked on top of one another and heat travels upward, they tend to overheat. So while the technique has been used for memory chips, no one has been able to make it work for processing chips.

Until now, that is. That is essentially what Intel has achieved with its Foveros technology. The company has announced that it cracked the problem and that the technology will become available in the latter half of 2019. It is a tremendous achievement and has the potential to put Intel back on top.

What Great Companies Do Differently

Intel rose to dominance largely because of its confidence in Moore’s law. Throughout its history, it invested massive amounts of money into new fabs capable of making next generation of chips. It was a risky strategy, but allowed the company to consistently beat its competitors, over a period spanning decades, in getting faster chips to market.

More recently, however, the company has faltered. It’s fallen behind in getting new 10nm and 7 nm chips to market, forfeiting its performance advantage. To make matters worse, computer scientists found that graphical processing units (GPUs), dominated by rival Nvidia, run artificial intelligence algorithms better than the chips Intel makes.

For many firms, having a successful strategy go awry would be a death knell. Yet rather than than simply trying to execute faster or cut costs, Intel kept exploring. “We’ve been working on this packaging technology for nearly 20 years,” Raja Koduri, a Senior Vice President at the company told Reuters.

Compare that to General Electric, which had its strategy in the power industry similarly disrupted. Unlike Intel, however, it wasn’t able to invent its way out of it. The once innovative firm had mostly abandoned inventing truly new technology since it came out with CT scanners in the 1970s. Today, the company is in dire straits.

Make no mistake. If you don’t explore, you won’t discover. If you don’t discover you won’t invent and if you don’t invent you will be disrupted eventually. Intel had no guarantees that its investment in 3D technology would ever pan out. Yet it kept exploring that and many other things so that it could continue to compete long after its core technology became obsolete.

That’s what great companies do differently.

An earlier version of this article first appeared in Inc.com

Image credit: AdamRicher.com

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GUEST POST by Greg Satell

On April 3rd, 1921, a handful of journalists went to interview a relatively unknown scientist named Albert Einstein. When they arrived to meet his ship they found a crowd of thousands waiting for him, screaming with adulation. Surprised at his popularity, and charmed by his genial personality, the story of Einstein’s arrival made the front page in major newspapers.

It was all a bit of a mistake. The people in the crowd weren’t there to see Einstein, but Chaim Weizmann, the popular Zionist leader that Einstein was traveling with. Nevertheless, that’s how Einstein gained his iconic status. In a way, Einstein didn’t get famous because of relativity, relativity got famous because of Einstein.

This, of course, in no way lessens Einstein’s accomplishments, which were considerable. Yet as Albert-László Barabási, another highly accomplished scientist, explains in The Formula, there is a big difference between success and accomplishment. The truth is that success isn’t what you think it is but, with talent, persistence and some luck, anyone can achieve it.

There Is Virtually No Limit To Success, But There Is To Accomplishment

Einstein was, without a doubt, one of the great scientific minds in history. Yet the first half of the 20th century was a golden age for physics, with many great minds. Niels Bohr, Einstein’s sparring partner at the famous Bohr–Einstein debates (which Bohr is widely considered to have won) was at least as prominent. Yet Einstein towers over all of them.

It’s not just physicists, either. Why is it that Einstein has become a household name and not, say, Watson and Crick, who discovered the structure of DNA, an accomplishment at least as important as relativity? Even less known is Paul Erdős, the most prolific mathematician since Euler in the 18th century, who had an outrageous personality to boot?

For that matter, consider Richard Feynman, who is probably the second most famous physicist of the 20th century. He was, by all accounts, a man of great accomplishment and charisma. However, his fame is probably more due to his performance on TV following the Space Shuttle Challenger disaster than for his theory of quantum electrodynamics.

There are many great golfers, but only one Tiger Woods, just as there are many great basketball players, but only one Lebron James. The truth is that individual human accomplishment is bounded, but success isn’t. Tiger Woods can’t possibly hit every shot perfectly any more than Lebron James can score every point. But chances are, both will outshine all others in the public consciousness, which will drive their fame and fortune.

What’s probably most interesting about Einstein’s fame is that it grew substantially even as he ceased to be a productive scientist, long after he had become, as Robert Oppenheimer put it, “a landmark, not a beacon.”

Success Relies On Networks

Let’s try and deconstruct what happened after Einstein’s arrival in the United States. The day after thousands came to greet Weizmann and the reporters mistakenly assumed that they were there for Einstein, he appeared on the front pages of major newspapers like The New York Times and the Washington Post. For many readers, it may have been the first time they had heard of any physicist.

As I noted above, this period was something of a heyday for physics, with the basic principles of quantum mechanics first becoming established, so it was a topic that was increasingly discussed. Few could understand the details, but many remembered the genius with the crazy white hair they saw in the newspaper. When the subject of physics came up, people would discuss Einstein, which spread his name further.

Barabási himself established this principle of preferential attachment in networks, also known as the “rich get richer” phenomenon or the Matthew effect. When a particular node gains more connections than its rivals, it tends to gain future connections at a faster rate. Even a slight change in early performance leads to a major advantage going forward.

In his book, Barabási details how this principle applies to things as diverse as petitions on Change.org, projects on Kickstarter and books on Amazon. It also applies to websites on the Internet, computers in a network and proteins in our bodies. Look at any connected system and you’ll see preferential attachment at work.

Small Groups, Loosely Connected

The civil rights movement will always be associated with Martin Luther King Jr., but he was far from a solitary figure. In fact, he was just one of the Big Six of civil rights. Yet few today speak of the others. The only one besides King still relatively famous today is John Lewis and that’s largely because of his present role as a US congressman.

Each of these men were not solitary figures either, but leaders of their own organizations, such as the NAACP, The National Urban League and CORE and these, in turn, had hundreds of local chapters. It was King’s connection to all of these that made him the historic icon we know today, because it was all of those small groups, loosely connected, that made up the movement.

In my book, Cascades, I explain how many movements fail to bring change about by trying to emulate events like the March on Washington without first building small groups, loosely connected, but united by a shared purpose. It is those, far more than any charismatic personality or inspirational speech, that makes a movement powerful.

It also helps explain something about Einstein’s iconic status. He was on the ship with Weizman not as a physicist, but as a Zionist activist and that dual status connected him to two separate networks of loosely connected small groups, which enhanced his prestige. So it is quite possible, if not probable, that we equate Einstein with genius today and not, say, Bohr, because of his political activity as much as for his scientific talent.

Randomness Rewards Persistence

None of this should be taken to mean that Einstein could have become a legendary icon if he hadn’t made truly landmark discoveries. It was the combination of his prominence in the scientific community with the happy accident of Weizmann’s adoring crowds being mistaken for his own, that made him a historic figure.

Still, we can imagine an alternate universe in which Einstein becomes just as famous. He was, for example, enormously quotable and very politically active. (He was, at one time, offered the presidency in Israel). So it is completely possible that some other event, combined with his very real accomplishments, would have catapulted him to fame. There is always an element of luck and randomness in every success.

Yet Einstein’s story tells us some very important things about what makes a great success. It is not, as many tell us, simply a matter of working hard to achieve something because human performance is, as noted above, bounded. You can be better than others, but not that much better. At the same time, it takes more than just luck. It is a combination of both and we can do much to increase our chances of benefiting from them.

Einstein was incredibly persistent, working for ten years on special relativity and another ten for general relativity. He was also a great connector, always working to collaborate with other scientists as well as political figures like Weizmann and even little girls needing help with their math homework. That’s what allowed him to benefit from loosely connected small groups.

Perhaps most importantly, these principles of persistence and connection are ones that any of us can apply. We might not all be Einsteins, but with a little luck, we just might make it someday.

An earlier version of this article first appeared in Inc.com

Image: Wikimedia Commons

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GUEST POST by Greg Satell

Earlier this year, I published an article about no-code software platforms, which was very well received. Before long, however, I began to get angry — and sometimes downright nasty — comments from software engineers who were horrified by the notion that you can produce software without actually understanding the code behind it.

Of course, no-code platforms don’t obviate the need for software engineers, but rather automate basic tasks so that amateurs can design applications by themselves. These platforms are, necessarily, limited but can increase productivity dramatically and help line managers customize technology to fit the task at hand.

Similarly, when FORTRAN, the first real computer language, was invented, many who wrote machine code objected, much like the software engineers did to my article. Yet Fortran didn’t destroy computer programming, but democratized and expanded it. The truth is that value never disappears. It just shifts to another place and that’s what we need to learn to focus on.

Why Robots Aren’t Taking Our Jobs

Ever since the financial crisis we’ve been hearing about robots taking our jobs. Yet just the opposite seems to be happening. In fact, we increasingly find ourselves in a labor shortage. Most tellingly, the shortage is especially acute in manufacturing, where automation is most pervasive. So what’s going on?

The fact is that automation doesn’t actually replace jobs, it replaces tasks. To understand how this works, think about the last time you walked into a highly automated Apple store, which actually employs more people than a typical retail location of the same size. They aren’t there to ring up your purchase any faster, but to do all the things that a machine can’t do, like answer your questions and solve your problems.

I recently came across an even more stark example when I asked Vijay Mehta, Chief Innovation Officer for Consumer Information Services at Experian about the effect that shifting to the cloud had on his firm’s business. The first order effect was simple, they needed a lot less technicians to manage its infrastructure and those people could easily be laid off.

Yet they weren’t. Instead Experian shifted a lot of that talent and expertise to focus on creating new services for its customers. One of these, a cloud enabled “data on demand” platform called Ascend has since become one of the $4 billion company’s most profitable products.

Now think of what would have happened if Experian had merely seen cloud technology as an opportunity to cut costs. Sure, it would have fattened its profit margins temporarily, but as its competitors moved to the cloud that advantage would have soon been eroded and, without new products its business would soon decline.

The Outsourcing Dilemma

Another source of disruption in the job market has been outsourcing. While no one seemed to notice when large multinational corporations were outsourcing blue-collar jobs to low cost countries, now so-called “gig economy” sites like Upwork and Fiverr are doing the same thing for white collar professionals like graphic designers and web developers.

So you would expect to see a high degree of unemployment for those job categories, right? Actually no. The Bureau of Labor Statistics expects demand for graphic designers to increase 4% by 2026 and web developers to increase 15%. The site Mashable recently named web development as one of 8 skills you need to get hired in today’s economy.

It’s not hard to see why. While it is true that a skilled professional in a low-cost country can do small projects of the same caliber as those in high cost countries, those tasks do not constitute a whole job. For large, important projects, professionals must collaborate closely to solve complex problems. It’s hard to do that through text messages on a website.

So while it’s true that many tasks are being outsourced, the number of jobs has actually increased. Just like with automation, outsourcing doesn’t make value disappear, but shifts it somewhere else.

The Social Impact

None of this is to say that the effects of technology and globalization hasn’t been real. While it’s fine to speak analytically about value shifting here and there, if a task that you spent years to learn to do well becomes devalued, you take it hard. Economists have also found evidence that disruptions in the job market have contributed to political polarization.

The most obvious thing to do is retrain workers that have been displaced, but it turns out that’s not so simple. In Janesville, a book which chronicles a small town’s struggle to recover from the closing of a GM plant, author Amy Goldstein found that the workers that sought retraining actually did worse than those that didn’t.

When someone loses their job, they don’t need training. They need another job and removing yourself from the job market to take training courses can have serious costs. Work relationships begin to decay and there is no guarantee that the new skills you learn will be in any more demand than the old ones you already had.

In fact, Peter Capelli at the Wharton School argues that the entire notion of a skills gap in America is largely a myth. One reason that there is such a mismatch between the rhetoric about skills and the data is that the most effective training often comes on the job from an employer. It is augmenting skills, not replacing them that creates value.

At the same time, increased complexity in the economy is making collaboration more important, so often the most important skills workers need to learn are soft skills, like writing, listening and being a better team player.

You Can’t Compete With A Robot By Acting Like One

The future is always hard to predict. While it was easy to see that Amazon posed a real problem for large chain bookstores like Barnes & Noble and Borders, it was much less obvious that small independent bookstores would thrive. In much the same way, few saw that ten years after the launch of the Kindle that paper books would surge amid a decline in e-books.

The one overriding trend over the past 50 years or so is that the future is always more human. In Dan Schawbel’s new book, Back to Human, the author finds that the antidote for our overly automated age is deeper personal relationships. Things like trust, empathy and caring can’t be automated or outsourced.

There are some things a machine will never do. It will never strike out in a little league game, have its heart broken or see its child born. That makes it hard — impossible really — for a machine ever to work effectively with humans as a real person would. The work of humans is increasingly to work with other humans to design work for machines.

That why perhaps the biggest shift in value is from cognitive to social skills. The high paying jobs today have less to do with the ability to retain facts or manipulate numbers (we now use a computer for those things), but require more deep collaboration, teamwork and emotional intelligence.

So while even the most technically inept line manager can now easily produce an application that it would have once required a highly skilled software engineer, to design the next generation of technology, we need engineers and line managers to work more closely together.

An earlier version of this article first appeared in Inc.com

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GUEST POST by Greg Satell

One of the most rewarding things about writing my book Mapping Innovation was talking to the innovators themselves. All of them were prominent (one recently won the Nobel Prize), but I found them to be the among the kindest and most generous people you can imagine, nothing like the difficult and mercurial stereotype.

At first, this may seem counterintuitive, because any significant innovation takes ambition, drive and persistence. Yet a study at the design firm IDEO sheds some light. It found that great innovators are essentially knowledge brokers who place themselves at the center of information networks. To do that, you need to build trust.

A recent report from Accenture Strategy analyzing over 7000 firms found this effect to be even more widespread than I had thought. When evaluating competitive agility, it found trust “disproportionately impacts revenue and EBITDA.” The truth is that to compete effectively you need to build deep bonds of trust throughout a complex ecosystem of stakeholders.

From Value Chain To Value Ecosystem

In Michael Porter’s landmark book, Competitive Advantage, the Harvard professor argued that the key to long-term success was to dominate the value chain by maximizing bargaining power among suppliers, customers, new market entrants and substitute goods. The goal was to create a sustainable competitive advantage your rivals couldn’t hope to match.

Many of the great enterprises of the 20^th century were built along those lines. Firms like General Motors under Alfred Sloan, IBM under Thomas J. Watson (and later, his son Thomas Watson Jr.) as well as others so thoroughly dominated the value chains in their respective industries that they were able to maintain leading positions in their industries for decades.

Clearly, much has changed since Porter wrote his book nearly 40 years ago. Today, we live in a networked world and competitive advantage is no longer the sum of all efficiencies, but the sum of all connections. Strategy, therefore, must be focused on widening and deepening links to resources outside the firm.

So you can see why trust has taken on greater importance. Today, firms like General Motors and IBM need to manage a complex ecosystem of partners, suppliers, investors and customer relationships and these depend on trust. If one link is broken anywhere in the ecosystem, the others will weaken too and business will suffer.

The Cost Of A Trust Event

The study was not originally designed to measure the effect of trust specifically, but overall competitive agility. It looked at revenue growth and profitability over time and then incorporated metrics measuring Sustainability and Trust to get a larger picture of a firm’s ability to compete.

The Accenture Strategy analysis is wide ranging, incorporating over 4 million data points. It also included Arabesque’s S-Ray data from over 50,000 sources to come up with a quantitative score and rate companies on their sustainability practices, as well as a proprietary measurement of trust across customers, employees, investors, suppliers, analysts, and the media.

Yet when the analysts began to examine the data, they found that the trust metrics disproportionately affected the overall score. For example, a consumer focused company that had a sustainability-oriented publicity event backfire lost an estimated $400 million in future revenues. Another company that was named in a money laundering scandal lost $1 billion.

All too often, acting expediently is seen as being pragmatic, because cutting corners can save you money up front. Yet what the report makes clear is that companies today need to start taking trust more seriously. In today’s voraciously competitive environment, taking a major hit of any kind can hamstring operations for years and sometimes permanently.

Where Trust Hits The Hardest

When the issues of trust come up, we immediately think about consumers. With social media increasing the velocity of information, even a seemingly minor incident can go viral, causing widespread outrage. That kind of thing can send customers flocking to competitors.

Yet as I dug into the report’s data more deeply, I found that the effect varied widely by industry. For example, in manufacturing, media and insurance, the cost of a trust incident was fairly low, but in industries such as banking, retail and industrial services, the impact could be five to ten times higher.

What seems to make the difference is that industries that are most sensitive to a trust event have more complex ecosystems. For example, a retail operation needs to maintain strong relationships with hundreds and sometimes thousands of suppliers. Banking, on the other hand, is highly sensitive to the cost of capital. A drop in trust can send costs surging.

Further, in industries like high tech and industrial services, companies need to stay on the cutting edge to compete. That requires highly collaborative partnerships with other companies to share knowledge and expertise. Once trust is lost, it’s devilishly hard to earn back and competitors gain an edge.

Building Resiliency

The trust problem is amazingly widespread. Accenture found that 54% of firms in the study experienced some kind of trust event and these can come from anywhere: a careless employee, a data breach, a defective product, etc. Yet Jessica Long, one of the Accenture Strategy Managing Directors who led the study, told me that a company can improve its resiliency significantly.

“It’s not so much a matter of preventing a trust event,” she says. “The world is a messy place and things happen. The real difference is how you respond and the resiliency you’ve built up through forging strong foundations in the crucial components of competitive agility: growth, profitability, sustainability and trust.”

Think about Steve Jobs and Apple, which encountered a number of trust events during his tenure. However, because he so clearly demonstrated his commitment to “insanely great” products, customers, employees and partners were more forgiving than they would be with another company. Or, more recently, the scandal when two men were arrested at a Starbucks store. Because Howard Schultz has built a reputation for fairness and because he acted decisively, the impact was far less than it could have been.

Perhaps most crucial is to build a culture of empathy. One of the things that most surprised me about the innovators I researched for my book is that many seemed almost as interested in me and my project as I was in them. I could see how others would want to work with them and share information and insights. It was that kind of access that led them to solve problems no one else could.

What the Accenture report shows is that the same thing is true for profit seeking companies. The best strategy to build trust is to actually be trustworthy. Think about how your actions affect customers, employees, partners and other stakeholders and treat their success as you would your own.

An earlier version of this article first appeared in Inc.com

Image: MaxPixel

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GUEST POST by Greg Satell

When I speak at conferences, I’ve noticed that people are increasingly asking me about the unintended consequences of technological advance. As our technology becomes almost unimaginably powerful, there is growing apprehension and fear that we will be unable to control what we create.

This, of course, isn’t anything new. When trains first appeared, many worried that human bodies would melt at the high speeds. In ancient Greece, Plato argued that the invention of writing would destroy conversation. None of these things ever came to pass, of course, but clearly technology has changed the world for good and bad.

The truth is that we can’t fully control technology any more than we can fully control nature or each other. The emergence of significant new technologies unleash forces we can’t hope to understand at the outset and struggle to deal with long after. Yet the most significant issues are most likely to be social in nature and those are the ones we desperately need to focus on.

The Frankenstein Archetype

It’s no accident that Mary Shelley’s novel Frankenstein was published at roughly the same time as the Luddite movement was in full swing. As cottage industries were replaced by smoke belching factories, the sense that man’s creations could turn against him was palpable and the gruesome tale, considered by many to be the first true work of science fiction, touched a nerve.

In many ways, trepidation about technology can be healthy. Concern about industrialization led to social policies that helped mitigate its worst effects. In much the same way, scientists concerned about the threat of nuclear Armageddon did much to help establish policies that would prevent it.

Yet the initial fears almost always prove to be unfounded. While the Luddites burned mills and smashed machines to prevent their economic disenfranchisement, the industrial age led to a rise in the living standards of working people. In a similar vein, more advanced weapons has coincided with a reduction of violent deaths throughout history.

On the other hand, the most challenging aspects of technological advance are often things that we do not expect. While industrialization led to rising incomes, it also led to climate change, something neither the fears of the Luddites nor the creative brilliance of Shelley could have ever conceived of.

The New Frankensteins

Today, the technologies we create will shape the world as never before. Artificially intelligent systems are automating not only physical, but cognitive labor. Gene editing techniques, such as CRISPR, are enabling us to reengineer life itself. Digital and social media have reshaped human discourse.

So it’s not surprising that there are newfound fears about where it’s all going. A study at Oxford found that 47% of US jobs are at risk of being automated over the next 20 years. The speed and ease of gene editing raises the possibility of biohackers wreaking havoc and the rise of social media has coincided with a disturbing rise of authoritarianism around the globe.

Yet I suspect these fears are mostly misplaced. Instead of massive unemployment, we find ourselves in a labor shortage. While it is true that the biohacking is a real possibility, our increased ability to cure disease will most probably greatly exceed the threat. The increased velocity of information also allows good ideas to travel faster and farther.

On the other hand, these technologies will undoubtedly unleash new challenges that we are only beginning to understand. Artificial intelligence raises disturbing questions about what it means to be human, just as the power of genomics will force us to grapple with questions about the nature of the individual and social media forces us to define the meaning of truth.

Revealing And Building

Clearly, Shelly and the Luddites were very different. While Shelley was an aristocratic intellectual, the Luddites were working class weavers. Yet both saw the rise of technology as the end to a way of life and, in that way, both were right. Technology, if nothing else, forces us to adapt, often in ways we don’t expect.

In his 1954 essay, The Question Concerning Technology the German philosopher Martin Heidegger sheds some light on these issues. He described technology as akin to art, in that it reveals truths about the nature of the world, brings them forth and puts them to some specific use. In the process, human nature and its capacity for good and evil is also revealed.

He gives the example of a hydroelectric dam, which reveals the energy of a river and puts it to use making electricity. In much the same sense, Mark Zuckerberg did not “build” a social network at Facebook, but took natural human tendencies and channeled them in a particular way. After all, we go online not for bits or electrons, but to connect with each other.

Yet in another essay, Building Dwelling Thinking, he explains that building also plays an important role, because to build for the world, we first must understand what it means to live in it. The revealing power of technology forces us to rethink old truths and reimagine new societal norms. That, more than anything else, is where the challenges lie.

Learning To Ask The Hard Questions

We are now nearing the end of the digital age and entering a new era of innovation which will likely be more impactful than anything we’ve seen since the rise of electricity and internal combustion a century ago. This, in turn, will initiate a new cycle of revealing and building that will be as challenging as anything humanity has ever faced.

So while it is unlikely that we will ever face a robot uprising, artificial intelligence does pose a number of troubling questions. Should safety systems in a car prioritize the life of a passenger or a pedestrian? Who is accountable for the decisions an automated system makes? We worry about who is teaching our children, but scarcely stop to think about who is training our algorithms.

These are all questions that need answers within the next decade. Beyond that, we will have further quandaries to unravel, such as what is the nature of work and how do we value it? How should we deal with the rising inequality that automation creates? Who should benefit from technological breakthroughs?

The unintentional consequences of technology have less to do with the relationship between us and our inventions than it does between us and each other. Every technological shift brings about a societal shift that reshapes values and norms. Clearly, we are not helpless, but we are responsible. These are very difficult questions and we need to start asking them. Only then can we begin the cycle of revealing truths and building a better future.

An earlier version of this article first appeared in Inc.com

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GUEST POST by Greg Satell

A decade ago, many still questioned the relevance of digital technology. While Internet penetration was already significant, e-commerce made up less than 4% of retail sales. Mobile and cloud computing were just getting started and artificial intelligence was still more science fiction than reality.

Yet today, all of those things are not only viable technologies, but increasingly key to effectively competing in the marketplace. Unfortunately, implementing these new technologies can be a thorny process. In fact, research by McKinsey found that fewer than one third of digital transformation efforts succeed.

For the most part, these failures have less to do with technology and more to do with managing the cultural and organizational challenges that a technological shift creates. It’s relatively easy to find a vendor that can implement a system for you, but much harder to prepare your organization to adapt to new technology. Here’s what you need to keep in mind:

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GUEST POST by Greg Satell

I study innovators for a living. Every year, I interview dozens of men and women who’ve achieved remarkable things. For my own part, I publish about a hundred articles a year and my second book, Cascades, will come out this spring. While my achievements pale in comparison to many of those I interview, many believe my work to be original.

The most destructive myth about creativity is that there are innate traits that allow some people to be creative, while others, who lack these, cannot. The truth is that in decades of research on creativity, nobody has been able to identify any such traits. In my experience, great innovators come in all shapes and sizes.

Still, despite the diversity of original innovators themselves, there are some common principles in how they approach their work and these are things that anyone can apply. That doesn’t mean everyone can be world famous, but the evidence clearly shows that anyone can be creative and, even if it’s not a major breakthrough, make some contribution to the world.

1. Explore

In 2006, Jennifer Doudna got a call from a colleague at the University of California at Berkeley, Jillian Banfield, who she knew only by reputation. Banfield’s area of research interest, obscure bacteria living in extreme conditions, was only tangentially related to Doudna’s work, studying the biochemistry of RNA and other cell structures.

The purpose of the call was to interest Doudna in studying an emerging phenomenon that was recently discovered in microbiology, a strange sequence of DNA found in bacteria. The function of the sequences were not yet clear, but some early evidence suggested that they might be involved in some kind of immune function, helping bacteria to defend themselves against viruses.

Intrigued, Doudna began to research the sequences, called CRISPR, in her own lab and, in 2012, discovered that they could be used as a powerful new tool for editing genes. Today, CRISPR is creating a revolution in genomics, completely redefining what was considered to be possible in just a few short years.

Many have observed the role of serendipity in innovation, such as in Alexander Fleming’s chance discovery of penicillin. Yet in every case, once you look a little deeper, you find that even the most unexpected discoveries were the product of intense exploration. Like Fleming and penicillin, Doudna wasn’t looking for a gene editing technology, but she was investigating a wide number of phenomena that were previously unexplained.

The first step for innovation is exploration. All who wander are not lost.

2. Combine

I’m a relentless fact checker. Over the years, I’ve found that even if you’ve done significant research, reading papers and interviewing experts, it’s amazingly easy to get things wildly wrong. I’ve also found that fact checking can lead you to new information you didn’t know existed. So before I publish anything of significance, I always make sure to reach out to someone who can correct my foolishness before it becomes public.

That’s why when I was finishing up Cascades, I reached out to Duncan Watts to look over two chapters on the science of networks, a field which he helped pioneer. As usual, Duncan was gracious and helpful, and pointed me towards a paper of his that I might want to include. He did so somewhat apologetically, not wanting to push his work on me, but observed that since I had  largely based both chapters on his work already, it was probably okay.

This was entirely true. Much of the first half of my book is based on Duncan’s ideas. What’s more, much of the second half of the book is based on insights from my friend Srdja Popović , who trains activists around the world to create revolutionary movements. There are a number of others as well, all of who shared their wisdom with me.

None of this, of course, was at all original, but the combination is. In fact, the key insight of the book is that Duncan’s mathematical models and the on-the-ground tactics of Srdja and others are intensely related. They can inform each other in ways that both men, who are mostly unfamiliar with each other’s work, had not addressed and, I believe, are important.

3. Refine

I first got interested in Duncan’s work in 2006. I was running a large digital business at the time and, with social networks becoming a powerful force online, I thought that learning some basic concepts of network science would be useful. Much to my surprise, I found that the ideas had a powerful resonance in an unexpected area.

Two years earlier, I had found myself in the middle of the Orange Revolution in Ukraine. What struck me at the time was how nobody seemed to have the first idea what was happening or why — not the journalists I worked with everyday, or the political and business leaders I would meet with regularly, nobody.

So I was excited to find, in Duncan’s work, a mathematical explanation for many of the seemingly inexplicable things that I had seen and experienced first-hand. Yet still, I had only a faint sense of what I was on to. Sure, there were obvious connections and possibilities, but I had no real framework to make the insights actionable.

That was 12 years ago (and 15 since the Orange Revolution began) and I’ve been working to refine those initial ideas ever since. Over that period, there has been no shortage of blind allies and wrong turns. Nevertheless, I kept at it and continued to learn. It took over a decade before I was able to pull everything together into something worth publishing.

4. Validate

The connection between Duncan and Srdja’s work wasn’t completely out of the blue. In fact, Duncan had made a short reference to Otpor, the movement which Srdja had helped lead, and its overthrow of Serbian dictator Slobodan Milošević in his book, Six Degrees. Yet there was no guarantee that the significance went any further than that.

So I began to widen my search. I looked at social movements throughout history to see if similar patterns held or whether the Orange Revolution in Ukraine and similar events in Serbia were anomalies. I struck up a working friendship with Srdja, read his book, Blueprint for Revolution and pored through the training materials on his organization’s website.

Yet to be truly useful, I needed to see if the same concepts could be applied more broadly. So I also researched and spoke to a number of leaders in other fields, such as corporate executives and people who led movements to transform heathcare, education and other things. Anywhere I could find anyone that created transformational change, I sought them out to find how they were able to succeed where so many others failed.

What I found was that while there were vast difference among changemakers, they had all eventually arrived at similar principles that made them successful, which I could validate. It took me nearly 15 years, but the journey that began with that initial connection between two vastly different sets of ideas eventually became something that I could consider to be coherent and useful.

In that way, my experience reflects many of the innovators of vastly greater accomplishment that I research and study. Truly original work doesn’t emerge fully formed from a brainstorm or sudden epiphany. It’s long years that follow, combining, refining and validating that makes the difference between an errant idea and something useful.

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GUEST POST by Greg Satell

We tend to think of innovation as a single event, but the truth is that it’s an extended process of discovery, engineering and transformation. First, a scientist discovers a new phenomenon, then others figure out to use that knowledge to create a viable solution to an existing problem and finally a particular industry or field is transformed.

This process usually takes about 30 years, mostly because of the time it takes for a true transformation takes place. A revolutionary new technology needs to build up an ecosystem of suppliers, build a standard set of tools and practices and develop markets that have learned to make productive use out of it.

It appears that genomics is hitting that point now. Although the cost to sequence a genome has been falling at a rate that far exceeds Moore’s law, for some time now, we’ve seen little effect in the marketplace. That’s beginning to change though. As we move beyond just reading genomes, to being able to actually write them, the genomics revolution is upon us.

How CRISPR Changed The Game

In 1987, a molecular biologist named Yoshizumi Ishino came across something unexpected in his lab at Osaka University. While researching a particular bacterial gene, he accidentally cloned a strange set of repeated sequences. Over the years, similar sequences were found independently at other labs, but nobody could figure out what their function was.

In time scientists increasingly began to expect that these sequences, which came to be known as CRISPR (clustered regularly interspaced short palindromic repeats), had some immune related function. They also discovered a variety of CRISPR associated proteins (Cas), that appeared to work in tandem with the genomic sequences.

Things came to a head in 2012, when a team led by Jennifer Doudna at the University of California at Berkeley was able to show in her lab that CRISPR, along with an associated protein called Cas9, could be used to edit genes far more accurately and far more cheaply than anything that anyone had ever seen before.

Doudna’s breakthrough opened up the floodgates. In just 6 short years, CRISPR has become a standard tool in the genomic toolbox, sparking a string of breakthroughs as well as countless startup companies and billions of dollars of invested capital.

The Rise Of Synthetic Biology

When the Human Genome Project was completed in 2003, it was a revelation. Now scientists could actually read the language of life and begin to understand, on a basic molecular level, how many diseases come to pass as well as how biological products, such as spider silk, are made. Since that time, the costs for reading DNA have dropped by well over 90%.

Yet as Andrew Hessel, CEO of Humane Genomics, a seed-stage company developing virus-based therapies for cancer, explained to me, the true revolution will come when the value of a sequenced genome exceeds the cost to produce one. He believes we’re beginning to hit that inflection point now as the ecosystem of tools is beginning to both mature and accelerate.

“Writing DNA with high accuracy and efficiency allows us more control of the things we can do downstream in a clinical or industrial environment,” Ted Tisch, COO at Synthego, a company that offers CRISPR-based tools to its customers, told me. “What we’re doing is to provide platforms to scale that process, make it more reproducible, more efficient and more able to move new solutions to market quickly.”

“There are thousands of diseases that can be attributed to an error in a single letter of genetic code,” he continued. “Many of these only afflict a few dozen people worldwide and discovering treatment is not economically viable. With these new tools, however, I believe we’re on the brink of a revolution, much like what happened in digital technology, where costs come down to such an extent that the once impossible or impractical becomes not only possible but commonplace.”

Beyond Medicine And Agriculture

While much of the excitement around genetic engineering has been around medical applications, especially cancer and genetic diseases such as Sickle Cell Anemia, Cystic Fibrosis and Multiple Sclerosis, the potential goes far beyond that. Christina Agapakis, Creative Director at Ginkgo Bioworks, told me that her company is working to make genomic based products a key component of the industrial economy.

“I think that in ten years we’ll see biology as more than just medicine and agriculture, but increasingly as a key component in our everyday products, helping to create better products at lower cost that are healthier and more environmentally friendly,” she says. For example, Adidas’s recent launch of biodegradable shoes made from genetically engineered spider silk gives us a glimpse of what we can expect in the future.

“At Ginkgo, we’re working to make biology easier to engineer,” she told me excitedly, “transforming living cells into factories that create products like flavors and fragrances, food ingredients like vitamins and even materials for manufacturing products, in some cases replacing materials usually made from petroleum with renewable sources.”

There is also great potential for leveraging genomics to advance information technology. For example, a company called Catalog is working to use DNA, which is a million times more information dense than today’s flash drives, to store massive amounts of data. It expects to have its first prototype ready by early next year.

Bracing For Impact

In the 1880s, the revolutionary technologies of internal combustion and electricity just began to emerge as practical applications. However, the immediate impact was relatively muted. In fact, as Robert Gordon explains in The Rise and Fall of American Growth, we didn’t see a productivity boost until the 1920s.

Nevertheless, when the impact hit, it fueled a massive boom in productivity that lasted 50 years, until the 1970s. By comparison, the digital revolution was relatively meager, only producing a relatively short increase in productivity in the late 1990s and early 2000s. There is simply more value in atoms than there are in bits.

That’s why there is so much about genomics to be excited about. “The programming of biology is the future,” says Hessel of Humane Genomics. “When you can just print out code that can reprogram a cell or even make a cell, that is one of the most powerful technologies that humans have ever had access to.”

We’re beginning to enter a new era of innovation, in which rather than just improving our ability to move bits across screens and from server to server, we’re increasingly using bits to drive atoms in the physical world. The advance in genomics, along with similar progress being made in materials science and robotics, may very well fuel a new era of abundance and prosperity.

An earlier version of this article first appeared in Inc.com

Image: NTNU Flickr

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GUEST POST by Greg Satell

In 1999, the day before his eighth startup went public, Steve Blank decided to retire at the age of 45. With time to reflect, he sat in a ski lodge and began to write a memoir with a “lessons learned” section at the end of each chapter. “In hindsight, it was a catharsis of moving from one part of my life to another,” he told me.

“I was 80 pages when in I realized there was a pattern. When I sat inside the building things didn’t go very well, but when I got outside the building things turned around and got much better,” he remembers. It was that insight that would lead him to a second, even more storied career as a business guru.

Today, almost 20 years later, the Lean Startup has become a full-fledged movement, complete with an entire ecosystem of books, conferences and practitioner-consultants. It has also moved far beyond startups to encompass government labs, large bureaucracies and major enterprises. The biggest question today is not whether it is a better way to build companies, but where it’s going next.

The Road To The Lean Startup

Blank’s original epiphany about the importance of “getting out of the building” did indeed lead to a book, but it wasn’t a memoir. The Four Steps To The Epiphany, published in 2005, soon became a bible for startup entrepreneurs. Its key insight, that a startup is not a smaller and newer version of an established company, but “a temporary organization used to search for a repeatable and scalable business model” pointed towards an entirely new way of doing things.

He began teaching entrepreneurship classes at Stanford and UC Berkeley and noticed another problem that needed solving. Throughout his career, whenever he encountered a complex issue that needed to be resolved, he would draw it up on a whiteboard. Yet strangely, no one had come up with way to do the same for a business.

“We were puzzling over how to represent a business graphically,” Blank remembers, when his teaching assistant Ann Miura-ko, who is now a top VC in Silicon Valley, told him that she had come across a book with exactly what they were looking for and wanted to show him. “No,” he said, “I want to show you this PhD thesis I came across first. It’s exactly what we need!”

It turned out that both the book and the thesis were written by the same former beach volleyball player from Lausanne, Switzerland. His name was Alex Osterwalder and, as luck would have it, he would soon be visiting Palo Alto. Steve arranged to meet him for coffee and then invited Alex to spend the weekend at his ranch for an extended discussion.

That initial encounter would lead to a close friendship and a long and fruitful partnership. Steve would combine Osterwalder’s Business Model Canvas with the Customer Development process he outlined in The Four Steps To The Epiphany. Eric Ries would later add elements of agile software development and the concept of a minimum viable product to form what we now know as the Lean Startup.

Bringing Government-Funded Science To The Marketplace

In 2011, Blank was driving on the Stanford University campus when the announcement of a troop surge in Afghanistan came over the radio. Just then, a call came in on his cell phone and when he answered, the caller announced, “The US government needs your help,” to which Blank replied, “Sorry, the US government already had my help for 4 years in Vietnam.”

A bit flustered, the caller tried to explain that Blank wasn’t being drafted into the army. “I’m from the NSF,” he said.

“What’s the NSF?” Blank asked.

“The National Science Foundation and we think you’ve invented the scientific method for entrepreneurship,” came the reply.

With that, Steve pulled over to the side of the road and began to listen intently. The caller, Errol Arkilic, was a Program Manager for SBIR/STTR, which offers grants for the commercialization  of government-funded science. While there had been some notable successes over the years, such as Qualcomm and Symantec, overall the results had been disappointing. Even breakthrough discoveries were having a hard time finding traction in the marketplace.

Arkilic wanted Blank to help design a program to train SBIR/STTR grantees in the Lean Startup process and he wanted to start in 90 days. Blank agreed and, once again, a seemingly chance encounter led to a long and fruitful partnership.

Today, the program that Blank and Arkilic spawned, called I-Corps, is taught at 86 separate sites and has led to the formation of dozens of companies. It has tripled the success rate of commercialization grants and I-Corps is now mandated for every government agency that offers a SBIR/STTR program.

Hacking 4 Defense

While Blank was teaching his “Lean LaunchPad” course at Stanford, one student, a highly decorated ex-Special Forces operator, blurted out that Blank’s methods seemed a lot like what they were doing at the Rapid Equipping Force (REF). “What’s a Rapid Equipping Force?”, Blank asked, repeating the pattern that had previously led to the development of I-Corps.

As it turned out, the former Director of the REF, retired Colonel Pete Newell, had recently relocated to Palo Alto and had a small office in town. Blank went to meet him for a short coffee, but what was scheduled for 20 minutes turned into a four-hour discussion. The two men realized that not only did their methods have a lot in common, they also had a lot to offer each other.

The meeting also reminded Steve of a problem that had been nagging at him for some time. As a Vietnam veteran, it bothered him that most of his students, many of whom will go on to be leaders in society, never get any experience with government service. His discussion with Newell led him to believe that he could apply elements of his Lean LaunchPad class to solve problems in government.

Yet Blank also realized that his methods would need to be adapted for organizations that don’t serve paying customers. So he and Newell began to collaborate with Alex Osterwalder to adapt the Business Model Canvas for government agencies and nonprofits and created the Mission Model Canvas for that purpose.

Together, they developed the Hacking 4 Defense program, which allows top minds at some of the country’s best schools to work on solving problems for the Defense Department. While it began at Stanford, the program has spread to dozens of universities. It has also moved beyond its military roots with programs like Hacking 4 Energy and Hacking 4 Impact.

The Journey Continues

What most people fail to realize about the Lean Startup is that it was never really about being lean, or even about startups for that matter, but a continual search to find a better way to solve problems. Perhaps not surprisingly, Blank sees no shortage of problems to be solved just about everywhere he looks.

One thing in particular that concerns him is the lack of civic engagement, particularly among the talented young professionals he sees at places like Stanford and UC Berkeley. “Most of my students believe that the only thing you need to be a citizen is maybe paying your taxes, not even voting, and then they’re unhappy with the government they get,” he told me.

“The social fabric has disconnected over the last 40 or 50 years,” Blank continues. “The internet has fragmented the media market and with it much of the common experience. What kind of common things can we do that allow us to share some common values? How do you create radical change in government?”

He didn’t offer any answers to these questions, but I got the feeling that sometime in the future, a seemingly random phone call or a chance meeting for coffee will lead to an extended discussion, a new partnership and a practical solution to a thorny problem. The search continues and we’ll all be better for it.

An earlier version of this article first appeared in Inc.com

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GUEST POST by Greg Satell

Data, as many have noted, has become the new oil, meaning that we no longer regard the information we store as merely a cost of doing business, but a valuable asset and a potential source of competitive advantage. It has become the fuel that powers advanced technologies such as machine learning.

A problem that’s emerging, however, is that our ability to produce data is outstripping our ability to store it. In fact, an article in the journal Nature predicts that by 2040, data storage would consume 10–100 times the expected supply of microchip-grade silicon, using current technology. Clearly, we need a data storage breakthrough.

One potential solution is DNA, which is a million times more information dense than today’s flash drives. It also is more stable, more secure and uses minimal energy. The problem is that it is currently prohibitively expensive. However, a startup that has emerged out of MIT, called Catalog, may have found the breakthrough we’re looking for: low-cost DNA Storage.

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