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Leading When You Don’t Know: The Power of Negative Capability 

When New Zealand faced the first wave of COVID-19, Prime Minister Jacinda Ardern didn’t rush to over-promise or posture certainty. Instead, she leaned into transparency, regularly updating citizens with what was known—and, critically, what wasn’t. Her leadership was marked not by decisive bravado, but by a calm willingness to wait, listen, and act when the path became clearer.

This isn’t just a story of pandemic response. It’s an example of leadership at the edge—where accumulated knowledge and traditional decision-making frameworks fall short. In such moments, what matters is not just what a leader knows, but their ability to hold space for not knowing.

This is where the concept of Negative Capability becomes both urgent and transformative.

What Is Negative Capability?

First coined by Romantic poet John Keats, Negative Capability describes the capacity to remain “in uncertainties, mysteries, doubts, without any irritable reaching after fact and reason.” While Keats was reflecting on literary genius, modern scholars and leadership theorists have found in his words a valuable metaphor for navigating complexity and uncertainty.

In leadership, Negative Capability refers to the ability to tolerate ambiguity, suspend judgment, and resist the impulse to impose premature certainty—especially when the stakes are high and the path forward unclear. It is a form of reflective inaction—the deliberate choice to pause, absorb, and wait for the right insight, rather than react defensively or default to what has worked before.

Why Leaders Struggle with Negative Capability

Leadership, especially in Western corporate culture, is often measured by decisiveness, clarity, and confidence. The leader is expected to know, to act, and to inspire trust through their ability to lead from the front. Traditional leadership development prioritizes “positive capabilities”—attributes like visioning, planning, and execution. These are vital in stable environments.

But what happens when the environment is not stable? When the actors are unfamiliar, the rules have changed, and the old playbook no longer applies?

In today’s VUCA world—marked by volatility, uncertainty, complexity, and ambiguity—leadership often unfolds in “radical uncertainty.” Here, the demand to act collides with the reality that we simply don’t yet know the right strategy. Leaders face a paradox: the very qualities that earned them their positions—experience, expertise, confidence—can become liabilities when they prevent them from not acting long enough to sense what is really needed.

The Costs of Premature Action

Consider a common scenario: a tech company begins to lose market share to a disruptive competitor. The board demands a turnaround strategy. The CEO, feeling the weight of expectation, announces a reorganization, lays off staff, and pivots the product line. Six months later, nothing has improved. Why?

Because the leader responded with positive capability—decisive action—before taking the time to understand the deeper dynamics at play: shifting customer expectations, employee morale, and the subtleties of emerging technology trends.

In contrast, a leader drawing on Negative Capability would have paused to reflect more deeply. They might have resisted the urge to act immediately, choosing instead to convene diverse voices, sense the complexity of the situation, and consider new possibilities. This is not indecision—it’s discipline.

Negative Capability in Action: Practical Strategies for Leaders

So how can leaders cultivate Negative Capability? Here are a few grounded strategies:

1. Practice the “Pause”

Create structured pauses in your decision-making process. Before responding to a crisis or making a strategic pivot, ask yourself: What if I waited just a little longer? Create a discipline of pausing, not just for analysis, but for reflection—cognitively and emotionally.

“Don’t just do something, stand there.” — White Rabbit in Alice in Wonderland

2. Adopt a Meta-Perspective

When immersed in a high-stakes situation, practice the “balcony view” — observe yourself and the system neutrally, like looking down from above. What patterns emerge? Who’s reacting from fear or habit? What isn’t being said? This neutral observation disrupts automatic responses and allows for deeper insight.

3. Create Containers for Not-Knowing

Establish spaces—retreats, strategy offsites, or peer dialogue groups—where not knowing is acceptable. Frame these sessions as opportunities to explore complexity rather than solve problems. Psychological safety is key here; people must feel free to admit uncertainty without fear of appearing weak.

4. Normalize Ambiguity in Leadership Culture

Shift your team’s expectations. Instead of always seeking “quick wins,” model tolerance for ambiguity. Share your own moments of uncertainty and how you worked through them. This humanizes leadership and builds collective resilience.

5. Balance Positive and Negative Capabilities

Negative Capability is not the absence of action—it is the capacity to wait until the right action reveals itself. Leadership is often about knowing when to hold back, and when to move decisively. Mastery lies in balancing these twin forces.

Final Thoughts: Leading into the Unknown

We live in an era where no amount of experience can guarantee the right answer, and where the illusion of control is constantly being shattered by unpredictable change. In such times, perhaps the most courageous act of leadership is not to speak, but to listen. Not to act, but to reflect. Not to know, but to stay with the not-knowing.

Negative Capability is not a replacement for action-oriented leadership—it’s the precondition for wise action in uncertain times. It invites us to become more attuned to the present moment, more accepting of ambiguity, and more open to emergence.

Because sometimes, the answer doesn’t come from what you do next. It comes from what you don’t do yet.

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Cooperation vs. Competition: Resolving Workplace Conflict

Imagine a common workplace hurdle:  two colleagues—Alex from Sales and Priya from Marketing—are jointly preparing a spreadsheet for an upcoming quarterly business review. Late one evening, Alex revises the structure of the spreadsheet, reorders the data, and adds projections. The next morning, Priya logs in and discovers her inputs have been moved or removed. She feels her contributions have been disregarded. Alex believes he improved the document for clarity.

What starts as a simple task quickly spirals into conflict. Communication becomes strained. Collaboration deteriorates. This type of conflict is not uncommon—but how it unfolds depends significantly on the perceived nature of the relationship between the parties involved.

This scenario serves as a practical illustration of psychologist Morton Deutsch’s influential theory of cooperation and competition, a foundational framework in the field of conflict resolution.

Deutsch’s Theory of Cooperation and Competition

In the 1940s, psychologist Morton Deutsch developed a powerful framework for understanding conflict, centered on the crucial concept of interdependence—how our goals are linked to others. According to Deutsch, the way individuals perceive the relationship between their goals determines whether conflict is approached cooperatively or competitively.

He identified two types of interdependence:

  • Positive: Goals of two people are linked in such a way that the probability of one person’s success in achieving the goal is positively correlated to the attainment of the other person’s goal. In other words, the two people sink or swim together. 
  • Negative: In a negative linkage, if one person wins the other loses. 

These perceptions shape the actions individuals take in conflict situations. Actions can be effective (improving the chances of reaching a goal) or bungling (ineffective or even detrimental, worsening those chances). 

When interdependence and actions interact, they give rise to three important relational dynamics that shape the tone and trajectory of conflict:

  1. Mutual Support (Substitutability): How much one person’s actions help or hinder another. In essence, are you working together or against each other? In a cooperative setting, effective actions by team members support each other as they contribute to a shared outcome. In a competitive setting, a bungling action by one might be helpful to the opponent as they look better in comparison. In the spreadsheet example, if Alex’s late-night edits were useful and if Priya perceived the situation as cooperative, she would see the changes as supportive. 
  2. Openness to Influence (Inducibility): This is the willingness of individuals to listen to and be influenced by one another. When people view each other as partners, they are more likely to adapt their ideas and consider alternative perspectives. In contrast, competitive dynamics create defensiveness and resistance to input. In the spreadsheet conflict, if Priya and Alex trust each other’s intentions, they’ll likely integrate feedback and co-create a better result. If they view each other as rivals, they’ll resist collaboration and retreat to siloed efforts.
  3. Attitudes and Emotions: These are the feelings and judgments people hold about each other—such as trust, respect, suspicion, or resentment. Positive interdependence fosters goodwill, patience, and empathy. Negative interdependence breeds anxiety, frustration, and hostility. Over time, these emotional patterns solidify and influence the workplace culture as a whole.

These dynamics—mutual support, openness to influence, and the feelings between team members—are constantly shifting. They’re deeply influenced by how individuals view their interdependence and the actions they take. Deutsch’s research clearly showed that when people are skilled and their actions are effective, a cooperative approach consistently leads to stronger relationships, greater trust, and ultimately, better outcomes than a competitive one.  

Cooperation vs. Competition in Action

Returning to the example of the spreadsheet, let us consider two potential outcomes—one cooperative, one competitive:

Cooperative Resolution: Alex and Priya, guided by a shared understanding of their mutual goal (a successful business review), approach the situation with curiosity. Priya calmly voiced her concerns, and Alex explained his rationale. They agree to jointly review the document and integrate both sets of inputs. Their actions are effective, attitudes remain positive, and each is open to being influenced by the other’s suggestions. Substitutability is high—they both contribute to the same goal—and the relationship is strengthened. In addition, they set new norms on how to make changes to each others’ sections so as not to create confusion. 

Competitive Breakdown: Priya reacts defensively, assuming Alex is trying to take over the project. Alex feels unappreciated and digs in. Communication becomes guarded, and each begins working on their own version of the report. Substitutability disappears, attitudes harden, and inducibility vanishes. The final presentation is disjointed, and leadership notices the lack of cohesion. The competitive breakdown not only damaged the report but also left lingering resentment between Alex and Priya, impacting future collaborations.

These contrasting outcomes underscore the value of Deutsch’s insights: conflict is not inherently destructive. The determining factor lies in how individuals interpret their interdependence and choose to act.

Strategies for Leaders

Leaders have a critical role to play in shaping the environment that determines whether conflicts become constructive or destructive. Below are three evidence-based strategies drawn from Deutsch’s theory:

1. Reframe Conflicts to Highlight Positive Interdependence Help team members view their goals as interconnected rather than opposed. Facilitate discussions that highlight how each team member’s contribution is essential. In the spreadsheet example, a manager could emphasize that both Sales and Marketing bring vital perspectives to the business review and that their input is complementary.

2. Design Incentives to Reinforce Cooperation Audit reward structures to ensure they do not unintentionally foster competition. Avoid systems where individuals are pitted against one another for limited recognition or rewards. Instead, design performance metrics that reward collaborative outcomes, knowledge sharing, and collective success.

3. Establish Norms and Processes for Constructive Dialogue Promote group norms that encourage respectful disagreement, active listening, and open communication. Use structured meeting formats with clear roles and turn-taking to ensure all voices are heard. Leaders should model openness to influence and reinforce norms that make it safe to express dissent without fear of reprisal.

Conclusion

Morton Deutsch’s work offers a powerful lens through which to understand and transform workplace conflict. By implementing Deutsch’s principles, leaders can transform their workplaces into hubs of collaboration and innovation. In doing so, even the most routine workplace conflicts—like a disagreement over a spreadsheet—can become opportunities for greater trust, innovation, and shared achievement.

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Organizational Play: The Unexpected Path to Radical Innovation

In a captivating TED talk, author Steven Johnson illustrates how the invention of a seemingly simple flute by cavemen 40,000 years ago unexpectedly paved the way for the development of the modern computer. A series of inventions—music boxes, toy robots, and the like—initially perceived as mere amusements, laid the groundwork for innovations that would ultimately revolutionize entire industries. As he explains, “Necessity isn’t always the mother of invention. The playful state of mind is fundamentally exploratory, seeking out new possibilities in the world around us. And that seeking is why so many experiences that started with simple delight and amusement eventually led us to profound breakthroughs.”

Yet, the power of play as a catalyst for innovation is often overlooked in the corporate world. When most companies think about innovation, their first instinct is to lean heavily on market research, analyzing customer feedback, and refining processes to meet immediate needs. However, this approach often leads to incremental improvements rather than groundbreaking advancements.

The Rise of AI and the Need for Transformational Innovation

With the proliferation of generative AI in recent years, most companies are adopting AI in their workflows, leading to more efficient or more capable product offerings (barring current AI challenges like hallucinations). But the more transformational breakthroughs — the killer apps — remain elusive.

The challenge lies in the fact that most company structures and processes are geared toward incremental innovation. Trying to squeeze out transformational innovation from an organizational machinery fine-tuned for incrementalism is a difficult task. The very elements that enable transformative breakthroughs are often the exact opposite of what most companies are structured for.

The Unpredictability of Transformational Ideas

Transformational ideas are inherently difficult to predict, even by the people who might have worked on an earlier, related problem. When the Musa brothers in Baghdad made the first programmable music box, using interchangeable metal cylinders to encode music, they could not have predicted that their invention would later inspire the French inventor, Jacques de Vaucanson, to use the same mechanism to create programmable looms. This unexpected connection highlights the unpredictable nature of innovation.

In other words, companies that rely solely on predicting the next big thing will likely miss out on creating breakthrough ideas. Their existing processes, which often rely heavily on customer feedback and market research, can only lead to predictable improvements on existing products.

Solutions in Search of Problems

Many offbeat ideas are quickly shut down with questions like “What is the customer pain point?” or “This looks like a solution looking for a problem.” While this feedback is relevant when the goal is to evolve existing products, it can stifle radical innovation. Most radical ideas are actually the reverse – they are solutions looking for problems.

Consider the invention of the sticky note. Spencer Silver, a scientist at 3M, was working on making a super-strong adhesive but accidentally ended up creating a weak adhesive that could be peeled off easily and was reusable. He was intrigued by this new adhesive and spent several years giving seminars and talking to people within the company to find ways to commercialize it, but couldn’t find a good use. It wasn’t until another colleague, Art Fry, recognized the potential of the adhesive as a bookmark that the sticky note was born. This example illustrates the importance of recognizing the potential of solutions even before a clear problem has been identified.

Instead of relying solely on customer feedback, companies need to develop a different approach to evaluate solutions that don’t yet have a well-defined problem. One effective strategy is to focus on complexity. If a solution was non-trivial and involved overcoming significant challenges, it could be a good candidate for creating a future competitive advantage.

The Exploration vs. Exploitation Mindset

To thrive in their environments, most animals toggle between exploration and exploitation. Exploration, akin to play, empowers animals to uncover new possibilities and problem-solving approaches. Research suggests that animals engaging in more play are often better prepared to adapt to environmental challenges, boosting their survival and reproductive prospects.

However, when faced with threats, exploration can be risky. In such situations, we tend to resort to using our existing knowledge to solve the immediate problem. This natural tendency highlights the tension between exploration and exploitation.

This biological function of exploration—stepping away from immediate survival needs to tinker with novel ideas—closely aligns with what companies must do to thrive in an uncertain and competitive landscape. Play allows us to probe beyond the “local optima,” or safe solutions, to discover entirely new paradigms. 

However, the conditions under which successful exploration occurs are very different from the exploitation phase. Exploration works best when people feel safe and supported in their environment. A playful, low-stress environment is essential for serious play to thrive.

A New Path For Radical Innovation

Successfully balancing transformational and incremental innovation is a persistent challenge for organizations. While many companies excel at incremental innovation, they often struggle to cultivate an environment that fosters radical breakthroughs. Existing approaches, such as corporate hackathons or Google’s “20% time,” have often fallen short of their intended goals. These initiatives, while valuable in promoting experimentation, often fail to fully embrace the conditions necessary for deep exploration.

What if we could create a new model, an “innovation sabbatical” for radical innovation to thrive? Imagine an extended period, lasting 2-3 months each year, where employees get a a dedicated space for radical exploration, free from the daily demands of their regular work. This would act as an extended hackathon, but with a crucial difference: it would be designed to foster a distinct culture that prioritizes deep exploration. Within this sabbatical environment, traditional hierarchies would fade, replaced by an emphasis on collaboration and a playful, low-stress atmosphere. Evaluation would shift away from immediate business needs, focusing instead on the ingenuity and complexity of the ideas generated. 

The goal with an innovation sabbatical is not for companies to predict the next big idea but to create an additional pathway where transformational ideas get a chance to flourish by ensuring that resources, incentives and motivation are all aligned in the right way.  

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The Science Behind Storytelling: Why Our Brains Crave Narratives

“Once upon a time…” These four words have captivated audiences for centuries, signaling the start of a story. But what is it about stories that so powerfully captures our attention and leaves a lasting impression? The answer may lie in the way our brains learn and process information.

How Our Brains Learn: A Baby’s Perspective

A baby is constantly facing an influx of sensory information that its underdeveloped brain isn’t capable of handling. So how does it make sense of all that information? She relies on her adult caretakers to help her understand what is important and what is not. An example can clarify how this learning process works. 

  • Say you are going on a walk with your toddler and you see the neighbor’s cat. 
  • You excitedly point to the cat, in the high-pitched and exaggerated voice that only parents use, and go  “Oh look, a kitty cat” 
  • The high-pitch sound stands out from all the other audio sounds the baby is hearing. At the same time, her body releases some chemicals like dopamine (to put her in alert state) and noradrenaline (to focus attention). 
  • You might then tell her how cute the cat looks and the cheery tone of your voice tells her that the cat is a “good” thing and not something to be afraid of. And simultaneously her body releases a bit of dopamine that signals relief. 

Her brain then captures all of the information related to this event — including context like the neighborhood, the name, the image and the emotional state — and stores it as a “searchable rule”. The next time she walks by the neighbor’s house, her brain pulls up this knowledge about the cat, and she gets excited to pet the cat. Suppose, at another time you happen to be on a hike and see a different cat. Now, the knowledge that your toddler has about cats doesn’t match perfectly – it’s a different location and a different type of cat. Depending on other existing bits of information (e.g. knowledge about aggressive animals in the wild), her brain might pick a different rule and suggest a more cautionary approach. 

The Story-Learning Connection

This learning process has striking similarities to how artificial intelligence (AI) is trained. Both require labeled data and multiple examples to generalize information. However, human brains have a unique ability to learn continuously by integrating discrete “units” of information into our existing knowledge base. Given what we now know about how our brains work, it seems likely that this unit of information corresponds to what lies between the cortisol and dopamine waves. The presence of this emotional signature tells the brain to take a snapshot of the moment and store it with additional metadata. This metadata, like the labels that we assign to this information (e.g. “cat”, “neighbor”, etc.), help in searching this database of knowledge at a later time. 

This also helps explain why we find stories so compelling. Stories are packaged perfectly in the form our brain needs to process a learning unit. “Once upon a time…”, “…and they lived happily ever after” which map to the rise (and fall) of cortisol and dopamine provide the ideal bookends for this learning unit.

Our affinity for the narrative form explains a lot about learning and how we make meaning. Here are three ways stories play a role for us in society:

  • Bedtime Stories: Bedtime stories, a tradition for many generations, are an ideal medium for communicating cultural values. Most folk tales don’t just tell a story but also explicitly call out a moral value, which is essentially a label for an abstract concept, at the end. When children hear different stories for the same moral they are able to build a deeper understanding of the moral concept and the different ways it can manifest. 
  • Pretend Play: When toddlers engage in pretend play they simulate novel scenarios with all the features of a story – setting, conflict, resolution. The simulation allows the child to vividly experience the emotions in the story and thereby learn from it. Engaging in pretend play with children is a great way for parents to recognize what learning their child is taking away from the situation and reframe it for them if needed.
  • Conspiracy Theories: Unfortunately, our learning mechanism can also be hacked in unhealthy ways. The narrative structure also explains why conspiracies, even though untrue and easily verifiable, are so effective. Most conspiracies start with an outrageous claim to grab attention, label the story with a moral value and suggest an action to resolve the situation. When delivered by someone you trust, which is how we started learning in the first place, the conspiracy is easily accepted and integrated into our knowledge base. 

Conclusion: The Enduring Power of Storytelling

Stories are not just a form of entertainment; they are fundamental to how we learn, make sense of the world, and connect with others. We are not certain why stories are so powerful, but one possible explanation is that the narrative structure is recognized by our brain as a unit of learning allowing it to be integrated well into existing knowledge structures. By understanding the science behind storytelling, we can harness its power for education, communication, and personal growth. So, the next time you hear “Once upon a time…,” remember that you’re not just embarking on a journey of imagination, but also engaging in a deeply ingrained learning process that has shaped humanity for millennia.

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Can AI Have Ethics?

Imagine finding yourself marooned on a deserted island with no other human beings around. You’re not struggling for survival—there’s plenty of food, water, and shelter. Your basic needs are met, and you are, in a sense, free to live out the rest of your days in comfort. Once you settle down and get comfortable, you start to think about all that you have learned since childhood about living a good, principled life. You think about moral values like “one should not steal” or “one should not lie to others” and then it suddenly dawns on you that these principles no longer make sense. What role do morals and ethics play when there is no one else around? 

This thought experiment reveals a profound truth that our moral values are simply social constructs designed to facilitate cooperation among individuals. Without the presence of others, the very fabric of ethical behavior begins to unravel. 

This scenario leads us to a critical question in the debate on artificial intelligence: can AI have ethics?

Ethics as a Solution to Cooperation Problems

Human ethics have evolved primarily to solve the problem of cooperation within groups. When people live together, they need a system to guide their interactions to prevent conflicts and promote mutual benefit. This is where ethics come into play. Psychologists like Joshua Greene and Jonathan Haidt have extensively studied how ethical principles have emerged as solutions to the problems that arise from living in a society.

In his book Moral Tribes, Joshua Green proposes that morality developed as a solution to the “Tragedy of the Commons,” a dilemma faced by all groups. Consider a tribe where people sustain themselves by gathering nuts, berries, and fish. If one person hoards more food than necessary, their family will thrive, even during harsh winters. However, food is a finite resource. The more one person takes, the less remains for others, potentially leading to the tribe’s collapse as members starve. Even if the hoarder’s family survives, the tribe members are likely to react negatively to such selfish behavior, resulting in serious consequences for the hoarder. This example illustrates the fundamental role of morality in ensuring the survival and well-being of the group.

Our innate ability to recognize and respond to certain behaviors forms the bedrock of morality. Haidt defines morality as “a set of psychological adaptations that allow otherwise selfish individuals to reap the benefits of cooperation.” This perspective helps explain why diverse cultures, despite differences in geography and customs, have evolved strikingly similar core moral values. Principles like fairness, loyalty, and respect for authority are universally recognized, underscoring the fundamental role of cooperation in shaping human morality.

The Evolution of Moral Intuitions

Neuroscience has begun to uncover the biological mechanisms underlying our moral intuitions. These mechanisms are the result of evolutionary processes that have equipped us with the ability to navigate complex social environments. For instance, research has shown that humans are wired to find violence repulsive, a trait that discourages unnecessary harm to others. This aversion to violence is not just a social construct but a deeply ingrained biological response that has helped our species survive by fostering cooperation rather than conflict.

Similarly, humans are naturally inclined to appreciate generosity and fairness. Studies have shown that witnessing acts of generosity activates the reward centers in our brains, reinforcing behaviors that promote social bonds. Fairness, too, is something we are biologically attuned to; when we perceive fairness, our brains release chemicals like oxytocin that enhance trust and cooperation. These responses have been crucial in creating societies where individuals can work together for the common good.

The Limits of AI in Understanding Morality

Now, let’s contrast this with artificial intelligence. AI, by its very nature, does not face the same cooperation problems that humans do. It does not live in a society, it does not have evolutionary pressures, and it does not have a biological basis for moral intuition. AI can be programmed to recognize patterns in data that resemble ethical behavior, but it cannot “understand” morality in the way humans do.

To ask whether AI can have ethics is to misunderstand the nature of ethics itself. Ethics, for humans, is deeply rooted in our evolutionary history, our biology, and our need to cooperate. AI, on the other hand, is a tool—an extremely powerful one—but it does not possess a moral compass. It knows about human moral values strictly from a knowledge perspective, but it’s unlikely to ever create these concepts internally by itself simply because AI has no need to cooperate with others. 

The Implications of AI in Moral Decision-Making

The fact that AI cannot possess ethics in the same way humans do has profound implications for its use in solving human problems, especially those that involve moral issues. When we deploy AI in areas like criminal justice, healthcare, or autonomous driving, we are essentially asking a tool to make decisions that could have significant ethical consequences.

This does not imply that AI should be excluded from these domains. However, we must acknowledge AI’s limitations in moral decision-making. While AI can contribute to more consistent and data-driven decisions, it lacks the nuanced understanding inherent in human morality. It can inadvertently perpetuate existing biases present in training datasets, leading to outcomes that are less than ethical. Moreover, an overreliance on AI for ethical decision-making can hinder our own moral development. Morality is not static; it evolves within individuals and societies.  Without individuals actively challenging prevailing norms and beliefs, many of the freedoms we cherish today would not have been realized.

Conclusion

Ultimately, the question of whether AI can have ethics is not just meaningless; it is the wrong question to ask. AI does not have the capacity for moral reasoning because it does not share the evolutionary, biological, and social foundations that underlie human ethics. Instead of asking if AI can be ethical, we should focus on how we can design and use AI in ways that align with human values.

As we continue to integrate AI into various aspects of society, the role of humans in guiding its development becomes more critical. We must ensure that AI is used to complement human judgment rather than replace it, especially in areas where ethical considerations are paramount. By doing so, we can harness the power of AI while maintaining the moral integrity that defines us as human beings.