Introduction: What Makes Us More Than Machines?

“The most important thing about AI may be that it shows us what it can’t do, and so reveals who we are and what we have to offer.”- David Brooks

From my front porch I can see the adobe house where one of humanity’s earliest evolutionary thinkers, Juan Ignacio Molina, grew up nearly 300 years ago. Molina spent his childhood roaming around these forests and streams, on a farm in the Maule Valley of central Chile, observing with fascination and curiosity the abundance of biodiversity here. He went on to write extensively about the natural history of the region, classifying many species of flora and fauna. In 1776, he published his first book articulating a view of lifeforms not as fixed but continuously evolving—33 years before Lamarck published the first systematic theory of evolution and 83 years before Darwin shook the world with Origin of Species.

As I retrace Molina’s footsteps in this place, I wonder how he came to such profound insight. He belonged to a world dominated by a view of nature as a collection of fixed forms. He didn’t have the extensive dataset Darwin later had, who traveled around the world collecting samples that enabled a comprehensive cross-analysis. Where did such breakthrough insight come from? Why here? 

Now, at this very peculiar moment in history, we all find ourselves wrestling with some profound questions. What does it mean to be human? What separates us, if anything, from the machines? What roles will we play in this rapidly approaching future, where it’s possible that our machines can do everything we once did and more? What will we have of value to contribute? What will happen to us if we don’t figure it out? 

Just as Molina glimpsed the evolutionary patterns invisible to his age, we too must learn to perceive the emerging patterns of ours. What is required of us to see through the dominant views of our day, tapping into new sources of insight that shift our understanding to new levels? How does this capacity relate to our future value-adding roles as human beings?   

Discovering our place in an AI-integrated world

As AI technology advances at breakneck speed, there is great fear mixed in strange ways with great hope. There is rising anxiety, with the realization that everything is changing in profound, unprecedented, and unpredictable ways.

Two dominant views seem to currently pervade the conversation:

One view is that AI will inevitably replace us humans in the marketplace. It will be able to do everything we can do but far better, faster, and cheaper. This camp is further split into those optimistic about a future of leisure and abundance, and those who are concerned about the consequences of lacking purposeful and meaningful roles, or the consequences of vast concentrations of power and control.  

Secondly, there is the view that argues AI will enhance our ability to create value in the marketplace. The mantra is you won’t be replaced by AI, you will be replaced by someone who is better at using AI. Most advocates of this view will admit many jobs are going away, but are optimistic that new jobs will be created, similar to other major technological revolutions of the past.   

As of this writing in Q3 of 2025, the evidence suggests AI has not yet had a major impact on job displacement. While some sectors have been affected, overall unemployment in the U.S., for example, has remained relatively stable since the explosion of generative AI nearly 3 years ago.  Some companies who made layoffs with the aim of becoming “AI-first,” like the Swedish fintech company Klarna, have reportedly re-hired many people due to disappointing results.

Many claim this is evidence that AI has been overhyped, which may be true. Yet we also know first-hand how rapidly this technology is advancing. Who can say what it will be capable of in even the short term, much less years down the road? It is becoming clearer and clearer that no one really knows.

What is clear is that we find ourselves in a process of navigating a significant transition, into a new world that is calling on us to adapt like never before. How we engage in this process will arguably determine what roles this technology will play—and what roles we will play—in the emerging future. 

What is required of us to navigate this process effectively? How can we become more than passive observers or innocent bystanders? How can we develop ourselves into more effective, conscious participants, capable of realizing and actualizing the potential we want to bring into existence?      

A Guiding Premise

Here is a guiding premise, which we will explore and test moving forward: as human beings, we have a unique capacity, though largely underdeveloped, for realizing and actualizing the evolutionary potential of unique, complex living systems—a capacity that requires consciousness which machines do not possess by nature.

Of course, I can’t dismiss the possibility that I’m wrong about machines becoming “conscious” someday. Consciousness is not something we understand well. We’ll dig into this later in the book, exploring what it means to be conscious and how it relates to what may separate us from the machines. Even if machines do gain some form of consciousness, is there something unique about human consciousness that relates to our roles within living systems?    

There does seem to be a solid consensus that our current machines, as clever as they seem, are not conscious. They are great at analyzing data, crunching numbers, and calculating probabilities—already much better than we are and getting better fast. As we’ve seen, this number-crunching even translates into generating impressive works of art and music, to our surprise, delight, and rising existential dread.

One way to look this, borrowing from Dave Snowden’s Cynefin framework, is that what AI does falls into one of two domains: simple work or complicated work. From this view, simple work involves doing something for which there is a clear best practice or procedure to follow.

Complicated work, on the other hand, is like a challenging puzzle requiring analysis and domain-specific expertise. With the right amount of data and know-how, cause and effect can be identified and the puzzle can be solved. In other words, the way forward is knowable with the right knowledge.   

AI thrives in these “lower” domains of work, where humans will arguably have less and less to offer as AI continues to advance.  

In contrast, the premise about unique human capacity above is about complex work. The shift into the complex domain is a shift into working with the inherent uncertainty and continuous, unpredictable and emergent processes of evolving living systems.

With AI handling more and more of the lower domains, this represents a shift from a knowledge economy, where ‘experts’ were once paid to solve problems based on their hard-earned knowledge, to an understanding economy, where we must engage in a process of exploring the unknown—and unknowable.   

One good example of complex work is entrepreneurship. As any experienced entrepreneur can tell you, creating something unique and valuable doesn’t happen by crunching numbers behind closed doors. It requires gaining an understanding of a unique, dynamic and complex system through observing and interacting with that system over time. Arguably, it also requires things that remain somewhat of a mystery to us, like intuition, embodied sense-making, ethical discernment, and relational awareness.

Reid Hoffman, the founder of LinkedIn, recently said in response to a question on AI’s potential impact on jobs: “We all need to think more entrepreneurially.” What does that mean in your specific context? What does that require you to develop?

A Brief Story of Discovery

In February of 2010, I was sleeping on the second floor of an old farmhouse in northern Patagonia when an 8.8-magnitude earthquake shook the country to its core. I expected the little house to crumble, violently swaying back and forth as things flew off shelves and crashed to the floor.  It was a humbling reminder that underneath the illusion of human control, there are greater forces at work.

The days following the earthquake provided some insights that had a lasting impact on my thinking and the direction of my work. As my plans to return to Santiago to begin my first teaching job were disrupted, I also had some extra time to explore the small library they had assembled there.

One of the books was called Permaculture: A Designers Manual by Bill Mollison. It resonated for me like few books have. Mollison writes, “Permaculture is a philosophy of working with, rather than against nature; of protracted and thoughtful observation rather than protracted and thoughtless labor.” It is a principled approach to designing living systems that are resilient and continuously evolving. I was living in an example of it in practice: as millions were reeling from disaster, we were relatively unaffected on the farm. Something clicked. I felt called to dig in deeper.

Permaculture led me down a path where I discovered many brilliant thinkers and practitioners working in what may be called the broader field of regenerative development and design. This includes pioneers in the field of organizational development like the late Carol Sanford, who I had the good fortune of working with for several years in her Change Agent Development community. It also includes Ben Haggard, Pamela Mang and others at the Regenesis Institute, who have played a huge role in helping me deepen my own thinking and practice within a global community of practitioners.

This experience has blended in interesting ways with my work over the past 15 years in the field of learning and education. In this time, I’ve worn many hats: as a teacher, curriculum developer, instructional designer, performance consultant, learning strategist and coach. In my own complex entrepreneurial journey, I’ve worked with many different kinds of organizations, including large Fortune 500 companies, fast-growth startups, government agencies, mission-driven non-profits, and leading educational institutions. 

Through this experience, it’s become clear to me that in the context of our rapidly changing world, our approach to learning and human development must shift in some very fundamental ways if we are to adapt to what is not only coming, but to what is here. We are largely stuck in paradigms that not only hinder our ability to adapt in the face of complexity and uncertainty, but may in fact threaten the survival of our species.

Catalyzing a shift in how we develop into new roles

A key shift we’ll continue to explore is from an approach to learning and development that focuses on accumulating knowledge, to one that aims at developing understanding. By and large, our current approaches to learning have focused on learning to do what AI is already better than us at, through programming best practices (simple work) or building analytical problem-solving skills (complicated work).

In other words, we’ve been treating learners (and ourselves as self-directed learners) like machines. Yet as machines, we can’t compete.

Many attempts to transcend this mechanistic approach to learning and development fall short. These learner-centered or human-centered approaches emphasize autonomy and self-actualization, but end up producing a lack of shared direction, coherence, and motivation. As a result, they end up sliding back into more command-and-control approaches focused on programming target behaviors.

Both Carol Sanford and the Regenesis Institute work with a developmental framework technology created by the English Mathematician and Philosopher J.G. Bennett, which he called Systematics. The purpose of Systematics is to enable us to develop an understanding of whole systems. As our understanding evolves, so does our capacity to create new levels of unique value across multiple scales: as individuals, teams, organizations, communities, industries, and so on.

Bennett was a student of the philosopher G.I. Gurdjieff, who created an approach to developing higher human capacities through daily life and work (as opposed to secluding oneself in a monastery somewhere), which he called the Fourth Way Work, or simply “The Work.” Gurdjieff’s aim was helping people evolve out of a machine-like state of low-level, automated, reactive thinking and action.

The Triad of Experience

Through this developmental process, we will engage with several frameworks directly from Bennett or derived from his original work on Systematics. The first we’ll practice with is a framework he calls The Triad of Experience: