Thought Series: The Other Side of Fear


Thought Series provides actionable ideas and anchors for reflection on your life or your work.

Technology is meant to complement us, not dominate us. If automation is where people developing technology can take us and what they want to accomplish, it strikes me that we as human beings need to lean in to our humanity and what psychologist Carl Rogers referred to it as our ‘human-beingness’ even more. In order to remain as relevant as possible, we need to develop the skills that robots cannot simulate, poll dancing aside.

Critical thinking, ethics, and policy will be very important to our future. We need to regain some of the knowledge we have lost in our pursuit to become one with The Machine of the Industrial Revolution. The basis of how we understand (ourselves and others and the universe), therefore, lies in the anatomy of the brain and its capacity to cope with complex human reactions such as intelligence, thinking, and learning.

Master wood turner Eric Hollenbeck put it this way, “It’s like the Train of Society, going down the track, is scooping up more and more information— ‘scooping more, ‘scooping more, ‘scooping more—at an impressive speed. For some reason, it can only hold so much. This forces the person in the caboose to start throwing information off, as fast as he can, making room for the information coming on in the front. The problem is, we are throwing off the information it took us twenty-five thousand years to glean.”

Rather than being completely replaced, jobs are going to be reinvented. Our jobs are merely bundles of different tasks. Some (or many) of those tasks will be automated. But like evolving from the typewriter to the computer, or going to the library to now using a search engine, technology will basically redefine the kinds of things that we do and how we do them. (Librarians, by the way, are still better than a search engine because they are better at forming good questions.)

At some point in the future, we will learn that even something as human as creativity is actually fairly mechanical. There will be, I believe, an algorithm for creativity. But robots are going to be creative in a different way than humans are. For instance, a robot’s attempt at comedy or dance would be different than a human’s. They will never intrinsically understand what it means to “be human” in the way that we do. Even with such deep intelligence at their disposal, they will never do things exactly like we do them, and there is tremendous value in this difference of perspective, of skill, and of execution.

The convergence between man and machine has become adopted by people of every walk of life, from the poorest farmer to the richest billionaire. The relationship we have with machines has spread widely, been adopted quickly, and evolved to an unprecedented level of intimacy. No longer for the super curious dancing on the fringe of early adoption, the web/internet/computer is now part of mainstream society.

Is that panic, or excitement you are feeling?


Since the robots are here, and here to stay, we shouldn’t be fighting them. If we do, we’ll lose (if our national math/science scores are any indication). We should be figuring out how to work with them.

In 1997, the first big challenge to human exceptionalism was the IBM Deep Blue, who beat the reigning chess master at the time, Gary Kasparov. And when Kasparov lost, some thought this was the end of chess. Who’s going to play competitively because computers are always going to win? But that didn’t happen.

Playing against computers actually increased the extent to which chess became popular. And, on average, the best players became better playing against the artificial minds. Technology raised their game. Even Kasparov, who lost, speculated on the unfairness of being matched to a database that had access to every single chess move ever. So he invented a new, freestyle chess league, where you can play any way you want. You can play as an AI or you can play as a human or you can play as a team of AI and humans.

In the past couple of years the best chess player on the planet is not an AI. And it’s not a human. It’s the team that Kasparov refers to centaurs; it’s the team of humans and AI. They are complementary. AIs and humans think differently. This is reflected in other disciplines. The world’s best medical diagnostician is not Watson, or a human doctor. It’s the team of Watson plus a doctor.

This idea of teaming, or collaborating with something that can be creative, make decisions, and develop consciousness (different than ours) requires us to learn to develop more self-awareness, increase our autonomy, and make better decisions. We are running on a different substrate, and it’s not a zero-sum game.

There is inherent beauty in this symmetry between machines and humans. That, if humans get to gain more awareness of themselves and gain mastery in something unique to them, we can work with machines to tackle something even greater.

At its essence, artificial intelligence is math and data. Math and data have rules. What is difficult about the problems that need to be solved today is that deep neural networks of the brain have a multidimensional space where there is no “sense” to be made. Or, at least we are still unable to make sense of the rules at play. At a certain point, we just don’t know what it all means (yet).

If you relate to the metaphor of the brain in terms of a computer and the way that it receives, processes and stores information, you can appreciate that incoming information is acted upon by a series of processing systems. Each of these systems accepts, rejects or transforms the information in some way, resulting in some form of response.

Where there is a difference between the computer and the brain is in the type of processing of which each is capable. Computers are only capable of processing one bit of information at a time before moving on to the next bit, whereas the brain often engages in a multitude of bits of information simultaneously. There is also an issue about predictability, with the computer always reacting to the same input in exactly the same manner, whereas the brain may be subjected to emotional or environmental pressures that cause differences in reaction.

In short, we just don’t know the rules of the human brain. Therein lies the great fear of and opportunity for humankind: learning, guiding, or controlling these rules.

Thought Series: Why learning to learn well is fundamental to our survival

Thought Series provides actionable ideas and anchors for reflection on your life or your work.

You can’t blink now without seeing articles on the pace of change, exponential growth, or the need to innovate. Over 60% of all executives now believe disruption will hit their industries hard in the next year. Artificial Intelligence will only accelerate this momentum. The majority of organizations have recognized that company culture, as it impacts decision making and strategic integration, is a major driver of successful transformation. People know change is coming, but do not have the skills and support to drive the transformation. It doesn't matter the industry - management consultingfinancial serviceseducation. Everyone's at risk.

Then, there are these old chestnuts...

  • The only constant is change.

  • People don't resist change; they resist being changed.

  • Change before you have to.

The problem is that organizations of all sizes can be challenged on how to cope with change. All wrestle with their reality and go through denial about the need to change.

Enter the field of change management.

Change management has its origins in the 1960's when business was much more predictable. As a formal discipline, it has been around since the 1990’s. However, references to change and change management can be found in the psychological literature more than 40 years earlier. Psychologists described “change” as the unfreezing, moving, and refreezing of thoughts or behaviors. These developments described how people internalized change and their experience with it, though the researchers did not apply these concepts to an organizational setting.

In the 1990s the topic of change and change management was applied to organizations, and managers and leaders took notice of the new groundswell of articles and books such as John Kotter’s “Leading Change” and Spencer Johnson’s “Who Moved My Cheese”.

Most change models are still based on old-school thinking, tools, and techniques. No wonder 70% of all change efforts fail. In the past, leaders had months and years to implement change. Now, change needs to be understood and addressed at the moment while it is occurring. The response to change needs to be implemented in days and weeks.


Here are three barriers to learning, common behaviors that lead to beliefs to which we all succumb, that I believe account for the failure of our ability to contend with change:


When confronted with the choice to continue with the status quo or accept change, few us will opt for change. We like to stick with what we believe works.


Behavioral psychology explains why we think change is bad:

Change is a threat. --> Threat leads to a loss of food. --> Loss of food leads to death.

So you notice things changing in the world (the robots are coming, the politics are more polarized than ever) and you're one step to it all being all over.

So we learn a trick or two that works, and we use those tricks over and over, until they are status quo.

Inertia makes it hard to turn. What gives us momentum, gives us power: that's the power of scale. Scale is a force. When we have committed our lives to going in a straight line, and a revolution comes along requiring us to take a turn, we don't understand the new strategy and paradigms it's creating, or the tactics it requires, we get left behind.

CONSIDER: What is shifting in our culture is the death of the industrial age. That is at the heart of all the shifts going on. Having a solid understanding of strategy (understanding the systems in play), tactics (the skills and capabilities required to manipulate strategy), and emotional labor (caring enough to really fail at something) are how we make a difference in the world. There's so much confusion now in the business world, a world that 50 years ago had virtually no confusion, about these three concepts, but we rarely separate them into these three different groups of problems and work them together.



In order to make sense of complex concepts, we use models to simplify our understanding. We seek templates, models, and prototypes versus gaining direct involvement with the problems we are trying to solve. In doing so, we give up proximity to the particulars in favor of distance and simplification.


When describing complexity, most change management frameworks assume that the process of change is linear. Here are several examples. They all have a beginning, middle, and end because that is how we understand things.

Losing proximity to the nuances of the problem we are trying to solve and the need for simplicity in how we think run counter to the ongoing learning that needs to occur when reckoning with change. We can no longer give up proximity to the particulars of these issues in favor of distance and simplification.

CONSIDER: We need to remind ourselves to engage with the actual substance of a problem, not just a model. This requires us to revisit goals and strategies based on the learning that occurs from the process of intervening in the change itself. Moving fast requires creating feedback loops so you can adjust as needed based on what you see and experience - not by following a step by step approach with little flexibility. Like Design Thinking, it may be useful to jump back to a previous step and do it over based on what's been learned.



The values within the structures we embrace emphasize efficiency, mechanization, standardization, and automation—enabling powerful forces that drive production, convenience, and reliability. They seek the ‘right answer’ to a prescribed question. The inertia behind these values drives towards homogenization.


Values of standardization tend to generate problems with relatively clear end states. If something isn’t efficient, troubleshooting persists until the wrinkle is smooth and systems run according to plan.

We have a bias to concluding what we start. We need closure. This bias runs counter to truly gaining the intimacy needed with complex problems.

While the systems designed to support us have enhanced our lives, they are breaking down. Systems of scale allow more of us to do more than any one of us could do alone. And, they also block. With convenience, we have less need to master feeling, judgment, and sensing. We don’t even see it happening. Slowly we lose the capacity to troubleshoot the machines that support us. Process replaces feel; rules replace judgment; policy replaces our need to think critically. When ambiguous questions arise, we have less practice with the struggle of finding solutions. In the name of stability and convenience, we lose the opportunity to engage the problem with any meaningful intimacy.

CONSIDER: When we address change, we typically focus on assessing the current state, defining the desired end state, and then bridging the gaps between the two via a gap analysis. This approach offers a logical end state. The ideal future is defined at the start of the change process and everything done from that point on hammers it home. But how often do people, or organizations, or economies freeze for the time you are working on your solution? In short, there is no closure. The environment you operate in is not fixed, but an emerging ecology that needs to be tended and responded to. Neither the pace of change nor disruptive technology will wait for you to implement your change. Customers don’t wait around either. Change processes that myopically focus on a pre-defined future risk having that future disrupted before it arrives.


Embracing the emotional labor of change, gaining proximity to the nuances of the problem we are trying to solve, and questioning the explicit and implicit values that guide the structures in which we reckon with tension, are the forces we need to embrace in order to learn to learn well. Change, real change, demands that we really integrate the idea of ongoing learning. Superbugs, homelessness, inequality, and global warming are all examples of ongoing, complex problems that can’t be solved without more effectively managing the tension of our beliefs:


We can learn to respond and not react. We can learn to re-frame threats into challenges and opportunities. The threat-challenge idea and its effects may rest on the assumption that people are prone to consistently interpret situations as a threat or a challenge based on their life experiences. But that doesn’t mean that this tendency is a life sentence that we always think this way. If you actively re-frame stressful situations as challenges and your elevated heart rate as excitement, you can improve your health, well-being, and performance level, all at the same time.


Business as ‘unusual’ will not feel natural at first. At some point, we might even need new words to describe it. Eventually, we will need to reinvent what it means to lead or to work in an organization. To be as close to creative problem solving as possible you must learn to improvise and adapt. You can no longer pay lip service to these terms. To improvise means "to work with what is available." It is the antithesis to preparation. To adapt means "to adjust to new conditions." Both infer the need to respond to a shift in the environment around you. The opportunity for you is to be that agent of evolution. Waiting for the DNA to evolve will take too long. A random feature that is created when a strand of DNA, or an idea, is altered and then transferred creates a mutation. Seeking or creating positive mutations can increase an organization’s resilience to change.


Complexity needs to be managed, not solved. That means we need to get adept at managing and leveraging tension between two opposing forces: open/closed; stability/innovation, etc. Leveraging is about getting more with less. When you go too far to one side, you lose out on the benefits of the other.

James Carse summarizes his argument in Finite and Infinite Games,

There are at least two kinds of games: finite and infinite. Finite games are those instrumental activities - from sports to politics to wars - in which the participants obey rules, recognize boundaries and announce winners and losers. The infinite game - there is only one - includes any authentic interaction, from touching to culture, that changes rules, plays with boundaries and exists solely for the purpose of continuing the game. A finite player seeks power; the infinite one displays self-sufficient strength. Finite games are theatrical, necessitating an audience; infinite ones are dramatic, involving participants.

We are slowly acknowledging that we are in an infinite game, playing by old rules.