GUEST POST by Greg Satell

Probably the biggest myth about innovation is that it’s about ideas. It’s not. It’s about solving problems. The truth is that nobody cares about what ideas you have, they care about the problems you can solve for them. So don’t worry about coming up with a brilliant idea. If you find a meaningful problem, the ideas will come.

The problem with ideas is that so many of them are bad. Remember New Coke? It seemed like a great idea at first. The new formula tested well among consumers and even had some initial success in the market. Yet what the marketers missed is that many had an emotional attachment to the old formula and created a huge backlash.

Our minds tend to play tricks on us. We think we’ve done our homework and that we base our ideas on solid insights, but often that’s not the case. We see what we want to see and then protect our ideas by ignoring or explaining away facts that don’t fit the pattern. In particular, we need to learn to identify and avoid these three cognitive biases that kill innovation.

1. Availability Bias

It’s easy to see where the marketers at Coke went wrong. They had done extensive market testing and the results came back wildly positive. People consistently preferred the new Coke formula over the old one. The emotional ties that people had to the old formula, however, were harder to see.

Psychologists call these types of errors availability bias. We tend to base our judgments on the information that is most easily available, such as market testing, and neglect other factors, such as emotional bonds. Often the most important factors are the ones that you don’t see and therefore don’t figure into your decision making.

The way to limit availability bias is to push yourself to get uncomfortable facts in front of you. In his new book, Farsighted, Steven Johnson notes two techniques that can help. The first, called pre-mortems, asks you to imagine that the project has failed and figure out why it happened. The second, called red teaming sets up an independent team to find holes in the idea.

Amazon’s innovation process is specifically set up to overcome availability bias. Project managers are required to write a 6-page memo at the start of every project, which includes a press release of both positive and negative reactions. Through a series of meetings, other stakeholders do their best to poke holes in the idea. None of this guarantees success, but Amazon’s track record is exceptionally good.

2. Confirmation Bias

Availability bias isn’t the only way we come to believe things that aren’t true. The machinery in our brains is naturally geared towards making quick judgments.  We tend to lock onto the first information we see (called priming) and that affects how we see subsequent data (framing). Sometimes, we just get bad information from a seemingly trustworthy, but unreliable source.

In any case, once we come to believe something, we will tend to look for information that confirms it and discount contrary evidence. We will also interpret new information differently according to our preexisting beliefs. When presented with a set of relatively ambiguous set of facts, we are likely to see them as supporting out position.

This dynamic plays out in groups as well. We tend to want to form an easy consensus with those around us. Dissent and conflict are uncomfortable. In one study that asked participants to solve a murder mystery, the more diverse teams came up with better answers, but reported doubt and discomfort. The more homogenous teams performed worse, but were more confident.

Imagine yourself sitting in a New Coke planning meeting. How much courage would it have taken to challenge the consensus view? How much confidence would you have in your dissent? What repercussions would you be willing to risk? We’d all like to think that we’d speak up, but would we?

3. The Semmelweis Effect

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, instead of being lauded for his accomplishment, he was castigated and considered a quack. The germ theory of disease didn’t take hold until decades later.

The phenomenon is now known as the Semmelweis effect, the tendency for professionals in a particular field to reject new knowledge that contradicts established beliefs. The Semmelweis effect is, essentially, confirmation bias on a massive scale. It is simply very hard for people to discard ideas that they feel have served them well.

However, look deeper into the Semmelweis story and you will find a second effect that is just as damaging. When the young doctor found that his discovery met some initial resistance, he railed against the establishment instead of collecting more evidence and formatting and communicating his data more clearly. He thought it just should have been obvious.

Compare that to the story of Jim Allison, who discovered cancer immunotherapy. At first, pharmaceutical companies refused to invest in Jim’s idea. Yet unlike Semmelweis, he kept working to gather more data and convince others that his idea could work. Unlike Semmelweis, who ended up dying in an insane asylum, Jim won the Nobel Prize.

We all have a tendency to reject those who reject our ideas. Truly great innovators like Jim Allison, however, just look at that as another problem to solve.

Don’t Believe Everything You Think

When I’m in the late stages of writing a book, I always start sending out sections to be fact checked by experts and others who have first-person knowledge of events. In some cases, these are people I have interviewed extensively, but in others sending out the fact checks is my first contact with them.

I’m always amazed how generous people are with their time, willing in some cases to go through material thoroughly just to help me get the story straight. Nevertheless, whenever something comes back wrong, I always feel defensive. I know I shouldn’t, but I do. When told that I’m wrong, I just have the urge to push back.

But I don’t. I fight that urge because I know how dangerous it is to believe everything you think, which is why I go to so much effort to send out the fact checks in the first place. That’s why, instead of publishing work that’s riddled with errors and misinterpretations, my books have held up even after being read thousands of times. I’d rather feel embarrassed at my desk than in the real world.

The truth is that our most fervently held beliefs are often wrong. That’s why we need to make the effort to overcome the flawed machinery in our minds. Whether that is through a formal process like pre-mortems and red teams, or simply seeking out a fresh pair of eyes, we need to avoid believing everything we think.

That’s much easier said than done, but if you want to innovate consistently, that’s what it takes.

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GUEST POST by Greg Satell

With the political season heating up, an increasingly frequent topic of discussion is how radical candidates should be. Some say that the optimal strategy is to be mainstream and court the middle. Others argue that it is better to more extreme and rile up the passions of your most active supporters.

Yet as I explain in Cascades that’s a false choice. The truth is that once seemingly radical positions, such as voting rights for women, civil rights for disenfranchised racial groups and same-sex marriage are now considered mainstream. To win those battles, however, activists needed to appeal to shared values.

What’s key isn’t any particular policy, but whether you can appeal to common values and mobilize supporters to influence institutions that will determine whether you can bring change about. You don’t do that through enforcing ideological purity or demonizing your opposition, but by putting forward an affirmative vision for a better future.

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GUEST POST by Greg Satell

On Halloween day 2008, a mysterious paper entitled Bitcoin: A Peer-to-Peer Electronic Cash System appeared on a cryptography mailing list. Its author, Satoshi Nakamoto, was a pseudonym and, to this day, no one is absolutely sure of his or her true identity. Nevertheless, the revolution the paper unleashed was all too real.

While the objective of the Bitcoin paper was to establish an alternative currency—a concept that had great resonance in the midst of the global financial crisis—it soon became clear to many that the underlying technology, called blockchain, could be of even greater utility as a distributed database.

It was based on that idea that alternative, open-source blockchain platforms, such as Ethereum and Hyperledger started to appear and IBM saw an opportunity use blockchain as an operating system for data. It’s still early days, but the rough outlines are beginning to take shape and the implications are likely to be just as profound as the Internet itself.

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GUEST POST by Greg Satell

One of the most often repeated episodes in the history of technology is when Steve Jobs was recruiting John Sculley from his lofty position as CEO at Pepsi to come to Apple. “Do you want to sell sugar water for the rest of your life,”Jobs asked, “or do you want to come with me and change the world?”

It’s a strange conceit of digital denizens that their businesses are something nobler than other industries. While it is true that technology can do some wonderful things, if the aim of Silicon Valley entrepreneurs was truly to change the world, why wouldn’t they apply their formidable talents to something like curing cancer or feeding the hungry?

The reality, as economist Robert Gordon explains in the The Rise and Fall of American Growth, is that the measurable impact has been relatively meager. According to the IMF, except for a relatively short burst in  growth between 1996 and 2004, productivity has been depressed since the 1970s. We need to rethink how technology impacts our world.

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GUEST POST by Greg Satell

In 2004, I was managing a major news organization during the Orange Revolution in Ukraine. One of the things I noticed was that thousands of people, who would normally be doing thousands of different things, would stop what they were doing and start doing the same things all at once, in nearly complete unison, with no clear authority guiding them.

What struck me was how difficult it was for me to coordinate action among the people in my company. I thought if I could harness the forces I saw at work in the Orange Revolution, it could be a powerful model for business transformation. That’s what started me out on the 15-year journey that led to my book, Cascades.

What I found was that many of the principles of successful movements can be applied to business transformation. Also, because social and political movements so well documented—there are often thousands of contemporary accounts from every conceivable perspective—we can gain insights that a traditional case studies miss. Here are four principles you can apply.

1. Failure Doesn’t Have To Be Fatal

One of the things that amazed me while researching revolutionary movements was how consistently failure played a part in their journey. Mahatma Gandhi’s early efforts to bring independence to India led to the massacre at Amritsar in 1919. Our own efforts in Ukraine in 2004 ultimately led to Viktor Yanukovych’s rise to power in 2010.

In the corporate context, it is often a crisis that leads to transformational change. In the early 90s, IBM was nearly bankrupt and many thought the company should be broken up. That’s what led to the Gerstner revolution that put the company back on track and a similar crisis at Alcoa presaged record profits under Paul O’Neil.

In fact, Lou Gertner would later say that failure and transformation are inextricably linked. “Transformation of an enterprise begins with a sense of crisis or urgency,” he told a groups of Harvard Business School students. “No institution will go through fundamental change unless it believes it is in deep trouble and needs to do something different to survive.”

What’s important about early failures is what you learn from them. In every successful transformation I researched, what turned the tide was when the insights gained from early failures were applied to create a keystone change that set out a clear and concrete initiative, involved multiple stakeholders and paved the way for a greater transformation down the road.

2. Don’t Bet Your Transformation On Persuasion

Any truly transformational change is going to encounter significant resistance. Those who fear change and support the status quo can be expected to work to undermine your efforts. That’s fairly obvious in social and political movements like the civil rights movements or the struggle against Apartheid, but often gets overlooked in the corporate context.

All too often change management efforts seek to convince opponents through persuasion. That’s unlikely to succeed. Betting your transformation on the idea that, given the right set of arguments or snappy slogans, those who oppose the change that you seek will immediately see the light is unrealistic. What you can do, however, is set out to influence stakeholders who can wield influence.

For example, in the 1980s, anti-Aparthied activists activists led a campaign against Barclays Bank in British university towns. That probably did little to persuade white nationalists in South Africa, but it severely affected Barclays’ business, which pulled its investments from South Africa. That and similar efforts made Apartheid economically untenable and helped lead to its downfall.

In a corporate transformation, there are many institutions, such as business units, customer groups, industry associations, and others that can wield significant influence. By looking at stakeholder groups more broadly, you can win important allies that can help you drive transformation forward.

3. Be Explicit About Your Values

Today, we regard Nelson Mandela as an almost saintly figure, but it wasn’t always that way. Throughout his career as an activist, he was accused of being a communist, an anarchist and worse. When confronted with these accusations, however, he always pointed out that no one had to guess what he believed in, because it was written down in the Freedom Charter in 1955.

Being explicit about values helped to signal to external stakeholders, such as international institutions, that the anti-Aparthied activists shared common values. In fact, although the Freedom Charter was a truly revolutionary document, its call for things like equal rights and equal protection would be considered utterly unremarkable in most countries.

After Apartheid fell and Mandela rose to power, the values spelled out in the Freedom Charter became important constraints. If, for example, a core value is that all national groups should be treated equal, then Mandela’s government clearly couldn’t oppress whites. His reconciliation efforts are a big part of the reason he is so revered today.

Irving Wladawsky-Berger, one of Gerster’s key lieutenants, told me how values played a similar role during IBM’s turnaround years. “The Gerstner revolution wasn’t about technology or strategy, it was about transforming our values and our culture to be in greater harmony with the market… Because the transformation was about values first and technology second, we were able to continue to embrace those values as the technology and marketplace continued to evolve.”

4. Every Revolution Inspires A Counter-Revolution

After the Orange Revolution ended in 2005, we felt triumphant. We overcame enormous odds and had won. Little did we know that there would be much darker days ahead. In 2010, Viktor Yanukovych, the man we took to the streets to keep out of power, was elected president in an election that international observers judged to be free and fair.

While surprising, this is hardly uncommon. Similar events took place during the Arab Spring. The government of Hosni Mubarrak was overthrown only to be replaced by that of Abdel Fattah el-Sisi, who is possibly even more oppressive. Harvard professor Rita Gunther McGrath points out that in today’s business environment, competitive advantage tends to be transient.

The truth is that every revolution inspires a counter-revolution. That’s why the early days of victory are often the most fragile. That’s when you tend to take your foot off the gas and relax, while at the same time those who oppose the change you worked to build are just beginning to plan to redouble their efforts.

That’s why you need a plan to survive victory from the start rooted in shared values. In the final analysis, driving change is less about a series of objectives than it is about forming a common cause. That’s just as true in a corporate transformation as it is in a social or political revolution.

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GUEST POST by Greg Satell

Today, everybody needs to innovate. So it shouldn’t be surprising that corporate innovation programs have become wildly popular. There is an inherent tradeoff between innovation and the type of optimization that operational executives excel at. Creating a separate unit to address innovation just makes intuitive sense.

Yet corporate innovation programs often fail and it’s not hard to see why. Unlike other business functions, like marketing or finance, in a healthy organization everybody takes pride in their ability to innovate. Setting up a separate innovation unit can often seem like an affront to those who work hard to innovate in operational units.

Make no mistake, a corporate innovation program is no panacea. It doesn’t replace the need to innovate every day. Yet a well designed program can augment those efforts, take the business in new directions and create real value. The key to a successful innovation program is to develop a clear purpose built on a shared purpose that can solve important problems.

A Good Innovation Program Extends, It Doesn’t Replace

It’s no secret that Alphabet is one of the most powerful companies in the world. Nevertheless, it has a vulnerability that is often overlooked. Much like Xerox and Kodak decades ago, it’s highly dependent on a single revenue stream. In 2018, 86% of its revenues came from advertising, mostly from its Google search business.

It is with this in mind that the company created its X division. Because the unit was set up to pursue opportunities outside of its core search business, it didn’t encounter significant resistance. In fact, the X division is widely seen as an extension of what made Alphabet so successful in the first place.

Another important aspect is that the X division provides a platform to incubate internal projects. For example, Google Brain started out as a “20% time project.” As it progressed and needed more resources, it was moved to the X division, where it was scaled up further. Eventually, it returned to the mothership and today is an integral part of the core business.

Notice how the vision of the X division was never to replace innovation efforts in the core business, but to extend them. That’s been a big part of its success and has led to exciting  new business like Waymo autonomous vehicles and the Verily healthcare division.

Focus On Commonality, Not Difference

All too often, innovation programs thrive on difference. They are designed to put together a band of mavericks and disruptors who think differently than the rest of the organization. That may be great for instilling a strong esprit de corps among those involved with the innovation program, but it’s likely to alienate others.

As I explain in Cascades, any change effort must be built on shared purpose and shared values. That’s how you build trust and form the basis for effective collaboration between the innovation program and the rest of the organization. Without those bonds of trust, any innovation effort is bound to fail.

You can see how that works in Alphabet’s X division. It is not seen as fundamentally different from the core Google business, but rather as channeling the company’s strengths in new directions. The business opportunities it pursues may be different, but the core values are the same.

The key question to ask is why you need a corporate innovation program in the first place. If the answer is that you don’t feel your organization is innovative enough, then you need to address that problem first. A well designed innovation program can’t be a band-aid for larger issues within the core business.

Executive Sponsorship Isn’t Enough

Clearly, no corporate innovation program can be successful without strong executive sponsorship. Commitment has to come from the top. Yet just as clearly, executive sponsorship isn’t enough. Unless you can build support among key stakeholders inside and outside the organization, support from the top is bound to erode.

For example, when Eric Haller started Datalabs at Experian, he designed it to be focused on customers, rather than ideas developed internally. “We regularly sit down with our clients and try and figure out what’s causing them agita,” he told me, “because we know that solving problems is what opens up enormous business opportunities for us.”

Because the Datalabs units works directly with customers to solve problems that are important to them, it has strong support from a key stakeholder group. Another important aspect at Datalabs is that once a project gets beyond the prototype stage it goes to one of the operational units within the company to be scaled up into a real business. Over the past five years businesses originated at Datalabs have added over $100 million in new revenues.

Perhaps most importantly, Haller is acutely aware how innovation programs can cause resentment, so he works hard to reduce tensions through building collaborations around the organization. Datalabs is not where “innovation happens” at Experian. Rather it serves to augment and expand capabilities that were already there.

Don’t Look For Ideas, Identify Meaningful Problems

Perhaps most importantly, an innovation program should not be seen as a place to generate ideas. The truth is that ideas can come from anywhere. So designating one particular program in which ideas are supposed to happen will not only alienate the rest of the organization, it is also likely to overlook important ideas generated elsewhere.

The truth is that innovation isn’t about ideas. It’s about solving problems. In researching my book, Mapping Innovation, I came across dozens of stories from every conceivable industry and field and it always started with someone who came across a problem they wanted to solve. Sometimes, it happened by chance, but in most cases I found that great innovators were actively looking for problems that interested them.

If you look at successful innovation programs like Alphabet’s X division and Experian’s Datalabs, the fundamental activity is exploration. X division explores domains outside of search, while Datalabs explores problems that its customers need solved. Once you identify a meaningful problem, the ideas will come.

That’s the real potential of innovation programs. They provide a space to explore areas that don’t fit with the current business, but may play an important role in its future. A good innovation program doesn’t replace capabilities in the core organization, but leverages them to create new opportunities.

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GUEST POST by Greg Satell

By 1980, IBM had hit an inflection point. It had already missed out on the minicomputer revolution. Now, with new, even smaller “microcomputers” evolving into a growing market, it was about to miss out again. So it set up a “skunk works” in Boca Raton, FL and, in less than a year, launched the IBM PC.

Today, at the end of the digital revolution, IBM is at a similar juncture. Yet its approach is the polar opposite than it took four decades ago. Rather than operating in secret, it is building a collaborative network to develop quantum computing. The reason: the technology is simply too complex for anyone to go it alone.

It’s not just IBM either. A recent report by Accenture Strategy found that found that ecosystems are increasingly seen as a “cornerstone” of future growth. In fact, almost half of the executives it surveyed are actively seeking to participate in ecosystems to create new business models. Today, if you want to compete effectively, you need an ecosystem strategy.

Porter’s Competitive Advantage

The dominant view of strategy in the 20th century was based on Michael Porter’s ideas about competitive advantage. In essence, he 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.

Porter’s ideas dominated thinking in corporate strategy for decades, yet they had a fatal flaw that wasn’t always obvious. Thinking in terms of value chains is viable when technology is relatively static, but when the marketplace is rapidly evolving it can get you locked out of important ecosystems and greatly diminish your ability to compete.

IBM’s history has shown a steady evolution from value chains to ecosystems. When it developed its System 360 mainframe architecture in the late 50s, the value chain was almost completely self-contained and vertically integrated. The PC was more of a hybrid strategy, in which it partnered with companies like Microsoft and Intel, but mostly kept them at arm’s length.

Today, rather than looking to dominate value chains, the company seeks to widen and deepen connections with research partners, customers and startups. Importantly, it does this not out of any newfound altruism—it would probably prefer to dominate the value chain if it could—but because of hard business realities.

The truth is that markets today are much faster, more interconnected and more complex than they were when Porter formulated his ideas about competitive advantage. If you are always looking to maximize your bargaining power, you are likely to cut yourself off from important information and capabilities that you will need to effectively compete.

Understanding Ecosystem Dynamics

Value chains are linear, so the biggest and most powerful link in the chain tends to dominate. A major industry player like McDonald’s or Walmart has significant bargaining power that impacts both customers and suppliers and helps to create barriers to entry for new market entrants and substitute goods.

Ecosystems are nonlinear and complex. So power emanates from the center instead of at the top of a value chain. You move to the center by connecting out. So while an industry giant may possess significant bargaining power, exercising that bargaining power can be problematic, because it can weaken links to other nodes in the ecosystem.

Consider the case of Ford, which in the 1920s built the almost completely vertically integrated River Rouge plant to dominate the value chain. Because the company had the ability to produce just about every facet of its product itself (the plant even had its own steel mill), it had tremendous bargaining power.

However, as the industry matured, other companies began to specialize in particular components. Ford, unable to compete in so many directions, became integrated into the larger ecosystem. In fact, during the financial crisis in 2008, the company’s CEO, Alan Mulally, said this in testimony to Congress:

In particular, the collapse of one or both of our domestic competitors would threaten Ford because we have 80 percent overlap in supplier networks and nearly 25 percent of Ford’s top dealers also own GM and Chrysler franchises

In a value chain driven world, Ford would have welcomed the collapse of its competitors. In an ecosystem driven world, however, their collapse would do major damage to nodes that the company itself depended on. Clearly, the principles of competitive advantage have changed.

Convening Spaces

Ecosystems are essentially networks of networks, so a crucial component of any ecosystem strategy is to participate in convening spaces. Traditionally, these have been industry-based associations, such as the National Retail Federation. Yet as the importance of ecosystems has increased, so have the number and types of convening spaces.

Some of these convening spaces focus on a particular technology, such as the Internet of Things Consortium or the Partnership on AI. Others focus more widely around a particular domain, like the Joint Center for Energy Storage Research or the Composite Materials Institute. These types of platforms should be considered essential for any ecosystem strategy.

For quantum computing, IBM has taken the more ambitious step of creating its own Q Network to collaborate with a wide array of partners. Other companies create accelerators and VC funds to help them to connect with emerging startups. Google’s academic program helps it connect to the world’s best minds and gives it a leg up on recruiting up and coming talent.

The truth is that there is really no limit to the ways in which you can connect. What’s important is not any particular platform or direction, but understanding the importance of connection. In an ecosystem driven world, widening and deepening links is how you move to the center of networks and increase your power.

Preparing For A Future You Cannot See

Historically, the lines between industries were fairly clear-cut. Ford competed with GM and Chrysler. Later, foreign competition became more important, but the basic logic of the industry remained fairly stable: you produced cars and sold them to the public through a network of dealers.

Today, however, industry lines have blurred considerably. A company like Amazon competes with Walmart in retail, Microsoft, IBM and Google in cloud computing, and Netflix and Warner Media in entertainment. The company itself is much more than simply a bundle of operations competing in different value chains, but a platform for accessing a variety of ecosystems of talent, technology and information.

In much the same way, automobile manufacturers are making investments to transform themselves into mobility companies. To do so, they are building ecosystems made up of technology giants, startups and others. They are not seeking to “maximize bargaining power,” but rather to prepare for a future that hasn’t taken shape yet.

The truth is that value chain based strategies are slow and rigid and the world has become fast and agile. You can’t simply seek to build a better mousetrap, you need to relentlessly connect to build a better ecosystem. Today, that’s becoming less a matter of competitive strategy and more a basic requirement of survival.

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GUEST POST by Greg Satell

In 2013, a study at Oxford University found that 47% of jobs in the United States are likely to be replaced by robots over the next two decades. As if that doesn’t seem bad enough, Yuval Noah Harari, in his bestselling book Homo Deus, writes that “humans might become militarily and economically useless.” Yeesh! That doesn’t sound good.

Yet today, six years after the Oxford Study, we are experiencing a serious labor shortage. Even more puzzling is that the shortage is especially acute in manufacturing, where automation is most pervasive. If robots are truly taking over, then why are having trouble finding enough humans to do work that needs being done?

The truth is that automation doesn’t replace jobs, it replaces tasks and when tasks become automated, they largely become commoditized. So while there are significant causes for concern about automation, such as increasing returns to capital amid decreasing returns to labor, the real danger isn’t with automation itself, but what we choose to do with it.

Organisms Are Not Algorithms

Harari’s rationale for humans becoming useless is his assertion that “organisms are algorithms.” Much like a vending machine is programed to respond to buttons, humans and other animals are programed by genetics and evolution to respond to “sensations, emotions and thoughts.” When those particular buttons are pushed, we respond much like a vending machine does.

He gives various data points for this point of view. For example, he describes psychological experiments in which, by monitoring brainwaves, researchers are able to predict actions, such as whether a person will flip a switch, even before he or she is aware of it. He also points out that certain chemicals, such as Ritalin and Prozac, can modify behavior.

Therefore, he continues, free will is an illusion because we don’t choose our urges. Nobody makes a conscious choice to crave chocolate cake or cigarettes any more than we choose whether to be attracted to someone other than our spouse. Those things are a product of our biological programming.

Yet none of this is at all dispositive. While it is true that we don’t choose our urges, we do choose our actions. We can be aware of our urges and still resist them. In fact, we consider developing the ability to resist urges as an integral part of growing up. Mature adults are supposed to resist things like gluttony, adultery and greed.

Revealing And Building

If you believe that organisms are algorithms, it’s easy to see how humans become subservient to machines. As machine learning techniques combine with massive computing power, machines will be able to predict, with great accuracy, which buttons will lead to what actions. Here again, an incomplete picture leads to a spurious conclusion.

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.

In another essay, Building Dwelling Thinking,  Heidegger 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. Once we understand that Mark Zuckerberg, or anyone else for that matter, is working to manipulate us, we can work to prevent it. In fact, knowing that someone or something seeks to control us gives us an urge to resist. If we’re all algorithms, that’s part of the code.

Social Skills Will Trump Cognitive Skills

All of this is, of course, somewhat speculative. What is striking, however, is the extent to which the opposite of what Harari and other “experts” predict is happening. Not only have greater automation and more powerful machine learning algorithms not led to mass unemployment it has, as noted above, led to a labor shortage. What gives?

To understand what’s going on, consider the legal industry, which is rapidly being automated. Basic activities like legal discovery are now largely done by algorithms. Services like LegalZoom automate basic filings. There are even artificial intelligence systems that can predict the outcome of a court case better than a human can.

So it shouldn’t be surprising that many experts predict gloomy days ahead for lawyers. By now, you can probably predict the punchline. The number of lawyers in the US has increased by 15% since 2008 and it’s not hard to see why. People don’t hire lawyers for their ability to hire cheap associates to do discovery, file basic documents or even, for the most part, to go to trial. In large part, the want someone they can trust to advise them.

The true shift in the legal industry will be from cognitive to social skills. When much of the cognitive heavy lifting can be done by machines, attorneys who can show empathy and build trust will have an advantage over those who depend on their ability to retain large amounts of information and read through lots of documents.

Value Never Disappears, It Just Shifts To Another Place

In 1900, 30 million people in the United States worked as farmers, but by 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a matter of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Yet somehow, the twentieth century was seen as an era of unprecedented prosperity.

You can imagine anyone working in agriculture a hundred years ago would be horrified to find that their jobs would vanish over the next century. If you told them that everything would be okay because they could find work as computer scientists, geneticists or digital marketers, they would probably have thought that you were some kind of a nut.

But consider if you told them that instead of working in the fields all day, they could spend that time in a nice office that was cool and dry because of something called “air conditioning,” and that they would have machines that cook meals without needing wood to be chopped and hauled. To sweeten the pot you could tell them that ”work” would mostly consist largely of talking to other people. They may have imagined it as a paradise.

The truth is that value never disappears, it just shifts to another place. That’s why today we have less farmers, but more food and, for better or worse, more lawyers. It is also why it’s highly unlikely that the robots will take over, because we are not algorithms. We have the power to choose.

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GUEST POST by Greg Satell

Many say that coding is the new literacy. Kids are encouraged to learn programming in school and take coding courses online. In that famous scene in The Graduate Dustin Hoffman’s character was encouraged by a family friend to go into plastics. If it were shot today, it would have probably been computer code.

This isn’t actually that new. I remember first being taught how to code in middle school in the early 80s in BASIC (a mostly defunct language now). Yet even today, coding is far from an essential skill. In fact, with the rise of no-code platforms, there is a strong argument to be made that code is becoming less important.

Don’t get me wrong, there’s still plenty of coding to be done on the back end and programming is certainly a perfectly reasonable thing to learn. However, there’s no reason people need to learn it to have a successful, productive career. On the other hand writing, as well as other communication skills, will only become more important in the decades to com.

The Future Is Not Digital

During the past few decades, digital technology has become largely synonymous with innovation. Every 18 months or so, a new generation of processors has come out that was faster, more powerful and cheaper than its predecessors. Entrepreneurs would leverage these new capabilities to create exciting new products and disrupt entire industries.

Yet now that’s all coming to an end. Every technology eventually hits theoretical limits and that’s where we are now with regard to digital processors. We have maybe one or two generations of advancement and then, with some clever workarounds, we may be able to stretch the technology for a decade or so, but it’s highly unlikely that it’ll last any longer than that.

That’s not so horrible. There’s no 11th Commandment that says, “Thou shalt compute in ones and zeroes,” and there are nascent architectures that are potentially far more powerful than digital computers, such as quantum and neuromorphic computing. Neither of these, however are digital technologies. They operate on fundamentally different logic and will use different code.

So instead of learning to code, maybe our kids would be better served by learning about quantum mechanics or neurology. Those would seem to be far more relevant to their future.

The Shift From Bits To Atoms

Digital technology is largely virtual. Transistors on silicon wafers compute ones and zeroes so that images can flash across our screens. That can be very useful, because we can simulate things on a screen much more cheaply than in the physical world, but it’s also limited. We can’t eat, wear or live in a virtual world.

The important technologies of the next generation, however, will be based on atoms rather than bits. Advances in genomics have led to the new field of synthetic biology and a revolution in materials science is transforming our ability to develop advance materials for manufacturing, clean energy and space exploration. So maybe instead of learning how to code, kids should be studying genetics and chemistry.

As we develop new technologies, we will also need to design experiences so that we can use them more effectively. For example, we need linguists and conversational analysts to design better voice interfaces. Kids who study those things may be able to build great careers.

The rapid pace of technological advancement over the next generation will surely put stress on society. Digital technology has helped produce massive income inequality and a rise in extremism. We will need sociologists and political scientists to help us figure out how to cope with these new, much more powerful technologies.

Collaboration Is The New Competitive Advantage

When my generation was in school, we were preparing for a future that seemed pretty clear cut. We assumed we would become doctors, lawyers, executives and engineers and spend our entire lives working in our chosen fields. It didn’t turn out that way. These days a business model is unlikely to last a decade, much less a lifetime.

Kids today need to prepare to become lifelong learners because the pace of change will not slow down. In fact, it is likely to accelerate beyond anything we can imagine today. The one thing we can predict about the future is that collaboration will be critical for success. People like geneticists and quantum scientists will need to work closely with chemists, designers sociologists and specialists in fields that haven’t even been invented yet.

These are, in fact, longstanding trends. The journal Nature recently noted that the average scientific paper today has four times as many authors as one did in 1950 and the work they are doing is far more interdisciplinary and done at greater distances than in the past. We can only expect these trends to become more prominent in the future.

In order to collaborate effectively, you need to communicate effectively and that’s where writing comes in. Being able to express thoughts and ideas clearly and cogently is absolutely essential to collaboration and innovation.

Writing Well Is Thinking Well

Probably the most overlooked aspect of writing is that it does more than communicate thoughts, but helps form them. As Fareed Zakaria has put it. “Thinking and writing are inextricably intertwined. When I begin to write, I realize that my ‘thoughts’ are usually a jumble of half-baked, incoherent impulses strung together with gaping logical holes between them.”

“Whether you’re a novelist, a businessman, a marketing consultant or a historian,” he continues, “writing forces you to make choices and it brings clarity and order to your ideas.” Zakaria also points to Jeff Bezos’ emphasis on memo writing as an example of how clarity of expression leads to innovation.

In fact, Amazon considers writing so essential to its ability to innovate that it has become a key part of its culture. It’s hard to make much of a career at Amazon if you cannot write well, because to create products and services that are technically sound, easy to use and efficiently executed, a diverse group of highly skilled people need to tightly coordinate their efforts.

Today, as the digital revolution comes to an end and we enter a new era of innovation, it’s easy to get overwhelmed by the rapid advancement of breakthrough technologies. However, the key to success in our uncertain future will be humans collaborating with other humans to design work for machines. That starts with writing effectively.

Image by StockSnap from Pixabay

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GUEST POST by Greg Satell

Ever since Eric Reis published his bestselling book, The Lean Startup, the idea of a minimum viable product (MVP) has captured the imagination of entrepreneurs and product developers everywhere. The idea of testing products faster and cheaper has an intuitive logic that simply can’t be denied.

Yet what is often missed is that a minimum viable product isn’t merely a stripped down version of a prototype. It is a method to test assumptions and that’s something very different. A single product often has multiple MVPs, because any product development effort is based on multiple assumptions.

Developing an MVP isn’t just about moving faster and cheaper, but also minimizing risk. In order to test assumptions, you first need to identify them and that’s a soul searching process. You have to take a hard look at what you believe, why you believe it and how those ideas can be evaluated. Essentially, MVP’s work because they force you to do the hard thinking early.

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