We are living through a period of profound transformation. Artificial intelligence is changing how we access information, solve problems, make decisions, and understand what it means to think.
Yet the central question may not simply be how we adapt to AI. It may be whether the ways we have learned to understand and create change are themselves ready to evolve.
This is the guiding theme behind Everything Is Energy Conversations, a new series on the podcast in which I welcome guests whose work offers us a different way of understanding ourselves and the world around us. These conversations bring together ideas from consciousness, science, metaphysics, organizational leadership, culture, and public policy. My hope is to create a space where different forms of knowledge can meet—and where we can explore important questions that do not belong entirely to any one discipline.
For this inaugural conversation, I am joined by Adrian Wolfberg, Ph.D., a researcher, author, organizational scientist, former U.S. naval officer, and former civilian intelligence officer.
Adrian’s work focuses on how individuals and organizations generate, interpret, communicate, and act upon knowledge under conditions of uncertainty. Drawing upon four decades of experience in national security, organizational research, and knowledge management, he examines why having more information does not necessarily produce greater understanding—or wiser action.
Information Is Not the Same as Understanding
One of the most compelling parts of our conversation begins with two stories from Adrian’s work in national security.
In the first, Adrian recognized an anomaly during a military operation that had potential consequences for the safety of U.S. forces. When he initially shared what he had observed, the information was dismissed because it did not arrive through an expected channel. He had developed the insight himself by recognizing a pattern within the available information.
Later, he was brought before the Chairman of the Joint Chiefs of Staff and asked to explain the situation in fewer than 60 seconds, using language that anyone could understand. The knowledge had not changed—but the way it was framed and communicated had. Once the information could be absorbed, understood, and trusted, it led to action.
His second story concerns a multi-agency effort involving 13 government departments. Each department created knowledge through the lens of its own mission, values, expertise, resources, and responsibilities. The challenge was not a lack of information. It was integrating these different forms of knowledge into a shared understanding that could move through multiple organizational levels and ultimately support a Cabinet-level decision.
These stories illuminate an important distinction: information does not automatically become understanding, and understanding does not automatically become action. Knowledge must travel. It must be interpreted, integrated, communicated, and received within a particular human and organizational context.
Understanding the Present
A central idea in Adrian’s work is the need to develop what he calls an “exquisite understanding of the present.”
Every individual and organization operates through a frame—a way of determining what matters, what belongs within the problem, and what can safely be ignored. These frames allow us to function efficiently and develop expertise. They work well until the situation changes and the frame no longer adequately describes reality.
If we misunderstand what is happening in the present, we may define the wrong problem. We may then make a reasonable decision and take competent action—only to discover that the action never addressed the actual situation.
This is why anomalies are so important. An anomaly may be an early indication that reality has shifted beyond the frame we are using to understand it.
The challenge is learning to notice those signals before a significant failure forces us to reconsider everything.
AI Is More Than Another Tool
Adrian proposes that artificial intelligence is fundamentally different from earlier human tools.
Traditional tools are created for a defined purpose. We generally understand how they are constructed, what they are designed to do, and what outcome we expect when we use them. AI complicates this relationship because it can generate patterns, scenarios, interpretations, and knowledge that were not explicitly placed within it by any one human user.
Adrian describes AI as a kind of thinking partner—a machine intelligence with capacities that greatly exceed our own in certain areas, including speed, information storage, and large-scale pattern recognition.
AI also has profound limitations. It does not possess human context in the way that we do. It cannot determine the meaning or value of a problem independently of the frame it is given. Humans must still understand the situation, define the problem, evaluate the output, recognize what is missing, and decide what should be done.
The question therefore becomes less about whether AI or humanity should lead and more about how human and machine intelligence can be brought into an appropriate relationship.
Simple, Complex, and Wicked Problems
Not every problem should be approached in the same way.
During our conversation, Adrian describes three different dimensions through which we might understand a problem:
Is it routine or novel?
Is it simple or complex?
Is it relatively straightforward, or is it “wicked”?
A wicked problem contains competing values and perspectives that make it difficult for people to agree upon either the nature of the problem or its solution. Healthcare and homelessness are two examples. These problems persist not because we lack information, but because people interpret the issue through different moral, political, institutional, and economic frames.
Understanding the nature of a problem helps us determine what AI can reasonably contribute and where human judgment must remain central.
The more novel, complex, and value-laden a problem becomes, the more important context, creativity, discernment, and human responsibility become.
Earned Confidence and Apparent Confidence
We also explore the role of confidence in leadership and in our interactions with AI.
Confidence can help people act. It creates trust, coordination, and momentum. But Adrian makes an important distinction between earned confidence and apparent confidence.
Earned confidence is grounded in knowledge, experience, competence, and integrity. Apparent confidence creates the impression of certainty without necessarily possessing the understanding required to justify it.
This distinction has always mattered in human leadership, but it becomes especially important in the age of generative AI. A large language model can deliver information in a polished, calm, and convincing voice even when its answer is incomplete or incorrect.
Fluency is not the same as truth. Confidence is not the same as knowledge. A compelling answer is not necessarily an accurate one.
As AI becomes increasingly sophisticated, our ability to recognize these distinctions will become even more important.
Attention, Reading, and Critical Thinking
The conversation then turns toward the effects of smartphones and large language models on attention, anxiety, reading, and the development of critical and creative thinking.
Reading allows the mind to travel. Whether we are reading fiction or nonfiction, we encounter unfamiliar perspectives, consider possibilities, follow sustained arguments, and exercise our ability to imagine and evaluate.
When our attention becomes increasingly fragmented—and when summaries continually replace the experience of reading and thinking through something ourselves—we risk weakening precisely the capacities we most need in order to work well with AI.
AI can recognize patterns, process extraordinary amounts of information, and generate possibilities at a speed no human can match. But human beings must still frame the question, understand the context, assess the output, challenge false confidence, and decide what deserves to be acted upon.
If we surrender our critical and creative capacities while AI becomes more powerful, we enter that relationship with what Adrian describes as both hands metaphorically tied behind our backs.
Reframing Our Relationship with Time
One of the most thought-provoking ideas in this conversation is Adrian’s invitation to reconsider our relationship with time.
Modern organizational life often places us under constant deadline pressure. When time appears to control us, our frame narrows. We become more likely to rely upon what is already familiar, overlook anomalies, and move quickly toward an answer before we have adequately understood the problem.
AI will always operate faster than we do. Attempting to compete with its speed may therefore be the wrong goal.
Our task may be to reclaim enough time to do what human beings must do well: pay attention, understand context, recognize change, ask better questions, and engage in critical and creative thought.
Changing the way we change may begin with stepping out of autopilot long enough to ask:
What is actually happening?
What problem are we attempting to solve?
What assumptions are shaping the way we see it?
What belongs to human intelligence—and what can appropriately be entrusted to AI?
Education and the Future of Human Intelligence
As we close, Adrian raises an essential question about education.
A generation of children is now growing up not only as digital natives, but with generative AI as part of everyday reality. How should education evolve so that these students learn to work with AI without weakening the human capacities necessary to question, guide, and evaluate it?
This is not simply a technological question. It is a question about the kind of human beings we are preparing future generations to become.
If we want to meet this moment consciously, we will need to protect and strengthen curiosity, sustained attention, reading, creativity, critical thinking, discernment, and the ability to remain present with uncertainty.
AI may transform how we work and think. But the quality of that transformation will depend, in no small part, upon the quality of consciousness we bring into relationship with it.
Listen to the Conversation
In this episode, Adrian and I explore:
Why more information does not necessarily produce greater understanding
How insight moves from observation into meaningful action
The importance of framing, reframing, and recognizing anomalies
Routine, complex, novel, and wicked problems
The difference between earned and apparent confidence
Why AI should be understood as more than another human tool
The effects of smartphones and AI on attention, reading, anxiety, and critical thinking
How reclaiming our relationship with time can strengthen human judgment
What education may need to preserve in the age of AI
About Adrian Wolfberg
Adrian Wolfberg, Ph.D., is a researcher, author, organizational scientist, and the founder and CEO of Organizational Insight Consulting LLC. His work examines knowledge, insight, leadership, organizational learning, decision-making, and the relationship between human and machine intelligence.
He is the author of Climate Security Intelligence and Who Leads When AI Thinks?
Learn more about Adrian and his work: