George S. Patton is widely quoted as saying:

“If everyone is thinking alike, then somebody isn’t thinking.”

Taken literally, that cannot always be true. Sometimes there really is an obvious answer, and several intelligent people may independently arrive at exactly the same conclusion.

But in a complex situation, Patton’s observation becomes much more interesting.

Put a small group of thoughtful people around a table, and they bring different experiences, knowledge, values, assumptions, and perspectives. They notice different things. They interpret what they see differently. No one person has the complete picture.

Even when they eventually reach the same conclusion, they are unlikely to arrive there by precisely the same route.

So perhaps the important warning is not about agreement itself. It is about agreement without first surfacing a difference.

If everyone immediately agrees about something complex, it is worth becoming curious. What are we not seeing? What assumptions are we sharing? Is somebody holding back? Does someone see things differently but not feel comfortable saying so?

And difference does not necessarily mean outright disagreement. Someone may support a decision but see a risk others have missed. Someone else may agree, but for quite different reasons. Another may say, “Yes, but only if…”

These differences matter. They are part of collective sensemaking. As people voice their different perspectives, question one another and build on each other’s thinking, something else can happen too: interthinking. The group can come to understand things that none of its members could have worked out quite so well on their own.

A group can disagree vigorously, explore its differences and still arrive at the same decision. That is very different from everyone simply nodding in agreement from the outset.

The more complex the issue, the less we should expect thoughtful people to see it in exactly the same way.

When everyone does appear to see it the same way, that is not necessarily a problem. But it is a good reason to ask a few more questions.

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One of the subtle jokes at the heart of The Hitchhiker’s Guide to the Galaxy is not the number 42, but what surrounds it.

Scientists believe they are experimenting on white mice. Later, it is revealed that this is the wrong way around. The white mice are hyper-intelligent beings running the experiment. Humans are part of it, unknowingly. The reversal is funny, but it is not the real point.

The deeper joke comes when we learn what the experiment is for.

The mice want the Answer to the Ultimate Question of Life, the Universe, and Everything. They eventually get an answer: 42. But the answer turns out to be useless because no one knows the question. So an even greater computer is built. The Earth itself is designed to discover the Ultimate Question over millions of years. Humanity emerges as part of that process, not as its purpose.

Just as the computation is about to be completed, the Earth is destroyed. This is where Douglas Adams stops playing with science fiction and starts playing with us.

The story is not really about intelligence, computers, or mice. It is about our faith that meaning can be isolated, extracted, and delivered in a neat form. A number. A formula. A final answer.

The joke is that even with immense intelligence, extraordinary machinery, and millions of years, meaning never arrives as a self-contained object. Detached from the question that gave rise to it, even the right answer becomes meaningless.

We often search for frameworks, metrics, slogans, and summaries that promise clarity while bypassing the slower work of making sense together. We want certainty without conversation.

Adams seems to be pointing to something deeper. Meaning is not something waiting at the end of a calculation. It emerges through inquiry. It depends on questions that remain alive, on context that continues to evolve, and on interpretation that can never quite be finished.

In other words, meaning is relational. It does not exist independently of the people, experiences, and conversations that give it life. Separate it from those, and it becomes something like 42. Precise, perhaps. But empty.

The white mice are clever. The computers are powerful. Yet they make the same mistake we often make. They assume meaning can be computed. Adams quietly suggests that it has to be lived.

Perhaps that is the real joke. And perhaps it is also an invitation: not to search harder for the ultimate answer, but to keep asking better questions.

People often say that the biggest problem with AI is hallucination. It confidently invents facts, references, or quotations that simply are not true. This is certainly a problem, but it is one I can deal with. I can check facts. I can verify sources. I can compare what the AI says with other evidence.

What worries me more is something much harder to detect: sycophancy.

When an AI tells me that my idea is insightful, original, or brilliant, it is much harder to know whether that judgement has substance or is simply what I would like to hear. Unlike a factual error, it is often not obvious how to verify it.

This matters because praise is persuasive. We all like to think our ideas are good, and a confident, articulate AI can reinforce that belief. If we're not careful, we can end up mistaking encouragement for critical evaluation.

So whenever an AI tells me how insightful one of my ideas is, my response is, "Right. Now forget the compliments. Tear the idea to pieces. Tell me everything that's wrong with it." That's the conversation I really want. I don't need a digital cheerleader. I need a thoughtful conversational partner, one that questions my assumptions, points out weaknesses, and helps me see what I may have overlooked.

Hallucinations can mislead us about the world. Sycophancy can mislead us about ourselves. In the long run, I think the second is the more subtle and dangerous problem.

Perhaps the best way to use AI is not to ask whether it agrees with us, but whether it helps us think more clearly. We can invite it to question our assumptions, expose weak arguments, and offer alternative perspectives. That way, we learn not just from its answers, but from the conversation itself.

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I found myself wondering the other day who was the first person to recognize that a star is really just another sun, far away.

I had always assumed the idea must have emerged gradually, somewhere between Copernicus and Newton. To my surprise, Giordano Bruno first articulated it most clearly. What makes this so striking is that he did so without observational evidence, relying instead on imagination and philosophical reasoning.

Others had hinted at related ideas long before him. Anaxagoras suggested that the Sun was itself a star. Aristarchus of Samos proposed that the Earth moved around the Sun. But Bruno went much further. In the late sixteenth century, he argued that all the points of light in the night sky are suns like our own, spread throughout an infinite universe, each likely accompanied by its own worlds.

In On the Infinite Universe and Worlds (1584), he wrote of “innumerable suns” and “countless earths” orbiting them. He had no telescope and no measurements. He had a coherent vision that made sense of the cosmos as a whole.

For many years, I believed that Bruno was executed for supporting heliocentrism. This turns out to be a simplification. His cosmological ideas contributed to the case against him, but he was condemned primarily for theological reasons, including his rejection of core Christian doctrines and his belief that God and nature were one. His vision of an infinite universe reflected the same worldview that made him unacceptable to the Church.

What is remarkable is how closely later scientific developments came to resemble the universe he imagined, even though none of them delivered certainty in the strict sense.

Here is how the idea that stars are suns evolved, always through inference rather than final proof:

• Giordano Bruno (1584) proposed an infinite universe filled with innumerable suns and worlds. This was pure speculation, grounded in philosophy rather than observation.

• Galileo Galilei (1610s) used a telescope to show that the Milky Way consists of countless faint stars. This did not show what stars are, but it made Bruno’s vision harder to dismiss.

• Isaac Newton (1687) described a universe governed by the same laws of motion and gravity everywhere. Stars could now be treated as objects like the Sun, subject to the same physics, though this rested on untested assumptions about their nature.

• Friedrich Bessel (1838) measured stellar parallax for 61 Cygni, establishing that stars lie at immense distances. This strengthened the case that they could be objects comparable to the Sun, but again by indirect reasoning.

• William Huggins (1864) demonstrated, using spectroscopy, that starlight contains the same chemical elements as sunlight. This was a powerful convergence of evidence, yet it still relied on interpretation. Similar spectra strongly suggest similar physical processes, but they do not logically compel that conclusion.

Each step added weight and coherence. None closed the case. The story is not one of speculation giving way to proof, but of increasingly strong explanations surviving repeated attempts to undermine them.

Bruno’s imagination extended further still. He suggested that these distant suns had planets. For centuries, this remained an ungrounded conjecture. Today, planets around other stars have been detected indirectly.

Space missions such as the Kepler Space Telescope and the TESS monitor the brightness of large numbers of stars, looking for tiny, regular dips in light. The most coherent explanation for these repeated patterns is that a planet is transiting the star.

An independent line of evidence comes from radial velocity measurements, which detect the minute wobble of a star caused by the gravitational pull of an unseen companion. Here again, the planet itself is rarely observed. Its existence is inferred from its effects.

These inferences are not all of the same kind or strength. Parallax measurements are closer to geometry. Spectroscopy depends on physical models. Transit and radial velocity methods rely on statistical regularities and consistency over time. Together they form a tightly interlocking picture, one that is extraordinarily difficult to explain away, yet still not immune in principle to revision.

This is the deeper lesson running through the whole story. Science does not aim at certainty in the absolute sense. It aims for explanations that are workable, coherent, and withstand scrutiny. The confidence we place in them comes not from proof, but from the absence of better alternatives.

That does not make scientific knowledge fragile or arbitrary. On the contrary, it explains why it is so reliable in practice while remaining open in principle. What we have is not truth carved in stone, but understanding held lightly, always subject to challenge.

When I look up at the night sky now, I find myself thinking of Bruno. He had no telescope, no measurements, and no way of knowing what those tiny points of light really were. Yet he imagined that each might be another sun, perhaps with planets of its own, perhaps even with life. It was an astonishing leap of imagination, one that I find every bit as inspiring as the evidence that later emerged.

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Conversations begin to die the moment we become convinced we’re right.

It rarely sounds like certainty. More often, it sounds like this:

“I know how this works.”
“I’ve heard this before.”
“They’re simply wrong.”

The moment we think like that, curiosity fades, and the conversation begins to close down.

Here’s a simple thought experiment. The next time someone expresses a view you strongly disagree with, resist the urge to rebut it. Instead, ask yourself:

“What if they’re seeing something that I’m not?”

Not because they’re necessarily right. They may not be. But because that question keeps the conversation alive.

I’ve found that some of the most valuable conversations I’ve had began with someone saying something I thought was plainly wrong. Occasionally, they were. But just often enough, they weren’t. Or they were pointing to something I had completely overlooked.

Being curious doesn’t mean abandoning your convictions. It means holding them lightly enough that new evidence, new experiences, and other people still have a chance to change your mind.

In an age where everyone seems eager to defend a position, perhaps the more interesting question is not “How do I prove I’m right?”

It’s “What am I missing?”

Because when we stop trying to win and stay curious instead, the conversation continues. And that’s often where the real learning begins.

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Organizational culture does not change because someone at the top announces a new direction. It is shaped in the conversations we have and in those that never quite take place.

In every organization, there are individuals who influence what happens in subtle but important ways. They bring people together across boundaries. They make space for issues that are usually left unsaid. They help groups work through differences rather than avoid them.

They are practising Conversational Leadership, whether or not they recognise it, and whether or not they have any formal authority.

Such work does not require permission, a programme, or a title. It is a choice about how we engage with others, day to day.

When more of us begin to work in this way, something shifts. Not because anyone is directing it from above, but because we are thinking, relating, and acting together differently.

So perhaps it comes down to this.

What might begin to shift if more of us chose to work this way?

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There’s a story from Plato that I’ve always liked. Socrates tells it. It’s about the Egyptian god Theuth, the great inventor, who turns up in front of King Thamus with a new invention: writing.

Theuth says this will improve memory. People will be wiser. They’ll be able to record things and won’t forget.

And Thamus says, “ No, you’ve got this wrong. Writing won’t strengthen memory; it will weaken it. People will rely on marks on a page instead of remembering for themselves. They’ll read a lot, but they won’t really understand. They’ll have the appearance of wisdom without the substance.”

It’s a very straightforward exchange. The inventor sees the benefit. The ruler sees the danger.

And yes, writing did change us. It shifted memory outside of our heads. It altered how knowledge travels. But it didn’t destroy thinking. It transformed it. It made possible philosophy, science, law, administration, and whole new ways of organising society.

Now here we are again.

AI turns up, and we hear a very similar worry. This will erode memory. This will weaken thinking. This will give us fluent answers, but not real understanding.

There’s something in that concern. If we simply outsource judgement, if we stop questioning, if we treat generated text as truth, then yes, we hollow something out.

But we’ve been here before. Every time we externalise knowledge, we have to relearn what it means to understand.

And this is where Conversational Leadership really matters.

In the age of AI, answers are cheap. What is scarce is good judgement. What is scarce is the ability to think together, to challenge assumptions, to test ideas in dialogue rather than accept them because they sound plausible.

AI can generate text. But it cannot take responsibility. It cannot sit in the tension of disagreement. It cannot care about the consequences in the way we do.

So the task now is not to compete with the machine. It is to strengthen our capacity to converse, to reason together, to exercise judgement collectively.

That, to me, is the real lesson of Thamus and Theuth. The technology will change the conditions, but the human work remains.

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In a divided world, it is easy to slip into the habit of seeing those who think differently as adversaries. Political opponents, people with different beliefs, people whose lives do not resemble our own. Once that framing takes hold, understanding becomes difficult, and the conversation becomes defensive or confrontational.

Conversational Leadership starts from a different place. It asks us to see others, not as enemies to be defeated, but as people we might come to understand. Not always agree with. Not always like. But understand.

This is not a sentimental idea. It is a practical one.

When we treat people as adversaries, we stop listening. When we treat them as potential friends, or at least as fellow human beings, we stay curious. We leave room for learning, for surprise, and for better judgement in complex situations.

One insight underpinning Conversational Leadership is that none of us sees the whole picture. Our own experience, history, and beliefs shape what we notice and what we miss. That is not a flaw; it is simply human. The only way we extend our understanding is by engaging with perspectives different from our own.

That does not mean pretending differences do not matter. It does not mean tolerating harm or abandoning deeply held values. It means choosing empathy over contempt and curiosity over certainty where possible.

A striking example comes from Northern Ireland. The public image of Ian Paisley was combative and uncompromising. Yet in later life, he formed a genuine working relationship, and even a friendship, with Martin McGuinness, a man who had once been his sworn political enemy. Their shared laughter during the peace process surprised many people. It also revealed something important. People are often more complex than their public positions suggest.

We rarely know someone until we actually meet them.

There are, of course, limits. Conversational Leadership is not naive. Some people deny others’ humanity. Some conversations are unsafe or conducted in bad faith. In those cases, protecting dignity and well-being takes precedence over dialogue. Choosing not to engage can be a wise and ethical decision.

The point is not that all conversations are possible. It is that many more are possible than we assume.

Other stories reinforce this. Danish politician Özlem Cekic chose not to respond to hate mail with anger. Instead, she invited her critics for coffee and conversation. Those meetings created space for understanding and revealed shared concerns.

A similar dynamic shows up in the story of Derek Black. Raised within a white nationalist family and culture, Black’s ideology began to soften not through confrontation but through friendships and sustained conversation with people from different backgrounds. His journey out of that world highlights how dialogue can open minds.

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Conversation does not always change minds. But it often changes the quality of the relationship, and that alone matters.

The next time we encounter someone whose views clash with our own, it is worth pausing. What happens if we approach them not as an enemy, but as someone we might learn from? What happens if the goal is understanding, not winning?

We are not always friends. Sometimes we face real opposition. But most people are not our enemies. They are fellow travellers, trying to make sense of the same uncertain world.

Seeing that clearly is not a weakness. It is a quiet form of leadership.

For a longer, more reflective exploration of this theme, see the post We Are Not Enemies But Friends in my blook, Conversational Leadership.

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We often say that the difference between Gen-AI and we humans is that it does not really understand. But what do we mean by that, exactly?

Take something simple, an apple. We understand an apple through lived experience. We have held one, smelled it, tasted it, bitten into it. We know its weight in the hand, its sound when it falls, its crispness on the tongue. Our understanding is sensory, emotional, and contextual.

Gen-AI has none of this. It has never touched, tasted, or smelled an apple, yet it can write about one with remarkable accuracy. Having absorbed countless descriptions, it can produce a vivid account of colour, texture, and flavour. On the surface, it seems to understand. But what it knows is entirely linguistic, detached from the world that gives those words their meaning.

The difference between our descriptions and those produced by AI lies in meaning, experience, and subjective context.

Our descriptions are rooted in a rich web of emotional, intuitive, and cultural understanding.

We give the apple meaning. It might be a healthy snack, a symbol of education, a fruit of the autumn harvest, or an object that helped Newton think about gravity. Our descriptions can carry emotion or memory: “It reminds me of my grandmother’s apple pie,” or “I love the tart snap of a fresh Granny Smith.” We can even describe an apple through an unusual lens, its sound, a feeling, or a moment, drawing on imagination and intuition.

An AI’s description, while often eloquent, is entirely built from data and patterns. It identifies the words most commonly associated with “apple”: round, red, smooth, and sweet. It can generate these with precision and consistency, but the description has no inner life. It cannot feel delight or nostalgia; it cannot care. Its attention is oriented toward the visual, grounded in vast collections of images and text. Its strength is precision, not perspective.

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While our accounts are holistic and meaningful, an AI’s account is technical and objective. The first arises from lived experience, the second from statistical correlation. Both may produce beautiful sentences, but only one carries the weight of being in the world.

Of course, not all of our understanding is grounded in direct experience. Much of what we know about galaxies, microbes, or the past comes through the words of others. In that sense, the line between human and machine is not as absolute as it seems. We, too, rely on patterns of description, sometimes mistaking familiarity of language for true understanding.

Yet we have the capacity to reconnect meaning to experience. We can taste the apple again, recall its scent, feel its crispness, and remember what it is to know something through contact.

Perhaps that is what distinguishes understanding from description: not what can be said, but what has been lived, and what we can feel to matter.

To understand is to enter into a relationship with people, with things, with ideas. AI can imitate the language of relationships, but it stands outside of them. Our task is to keep knowledge alive by remaining in conversation with the world that gives it meaning.

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We often assume that Knowledge Management originated in the 1990s. That it arrived with databases, intranets, and strategy decks promising to “leverage intellectual capital.” But that is a fiction. Knowledge Management began much earlier, 60,000 to 70,000 years ago, not in offices or labs, but around a fire.

Anthropologists refer to it as the Cultural Big Bang or the Great Leap Forward. Yuval Harari, in Sapiens, called it the Cognitive Revolution. Whatever name you choose, something remarkable happened. After millions of years of slow biological evolution, we began to evolve culturally.

In a brief period, we began painting on cave walls, crafting tools for specific purposes, and burying our dead with ritual. What changed was not our anatomy but our capacity to share meaning.

Three abilities emerged that transformed us:

• Language: the power to communicate complex ideas and intentions.

• Gossip: the social glue that allowed larger groups to cooperate and build trust.

• Abstraction: the capacity to talk about things that don’t physically exist: love, justice, gods, and dreams.

This was the original knowledge revolution. For the first time, we could preserve knowledge beyond our own minds and transmit it through stories, songs, and shared practices. We stopped being creatures of instinct and started becoming custodians of memory.

Knowledge Management, in this sense, is as old as humanity itself. It began when we learned to remember together, when information ceased to die with the individual and started to live in the collective.

What we call Knowledge Management today is simply the latest version of this ancient practice. The tools have changed, from fireside tales to Zoom calls, but the purpose remains the same. We are still struggling with the same challenge our ancestors faced: how to keep what we’ve learned alive, how to pass it on, and how to make sense of it together.

Perhaps it’s time to acknowledge the truth. Knowledge Management didn’t begin with consultants or technology. It started with conversation, as people talked, taught, and wondered aloud. It began the moment we became human.

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In Zen and the Art of Motorcycle Maintenance, Robert Pirsig describes a classroom experiment run by his earlier self, Phædrus, while teaching English in Montana. For one quarter, he returned essays with comments but no grades. The short-term reaction exposed the habits the system had created. Strong students kept going, middling students drifted, and weaker ones vanished. Over time, the best students relaxed into the work, others raised their effort, and the classroom became more engaged than usual. By the end, the most capable students supported the system while those most reliant on grades opposed it.

The moment the grades disappeared, the educational machinery showed its foundations. Many students saw only a void because their motivation had never been cultivated from within. Schooling had trained them to work for measurement rather than meaning. Pirsig calls it a “slave mentality.” Without an external metric to chase, they were forced to confront questions the institution usually keeps submerged: What am I learning? Why am I here? What is good writing, and how would I know?

This is the terrain Alfie Kohn maps in Punished by Rewards. Kohn argues that incentives function as control devices that suppress curiosity and narrow learning to the performance of correctness. Pirsig offers the lived evidence. The system not only fails to foster independence but also actively discourages it. Remove the evaluative scaffolding, and you immediately see who has been taught to think and who has been taught merely to comply.

Seen from today’s vantage point, the pattern takes on a new form. Students who are happy to submit AI-generated essays are not doing anything unexpected. They are behaving exactly as the structure has conditioned them to act. If the purpose of the work is to obtain a grade, then outsourcing becomes a rational move. AI does not create the problem; it exposes it. The real crisis is not technological but institutional.

This is where Conversational Leadership points toward a different orientation. It shifts attention from performance to participation, from products to inquiry, and from compliance to shared meaning-making. It sits comfortably alongside Rupert Wegerif’s work on dialogic education at Cambridge, which emphasises learning as something that happens in and through dialogue rather than in the pursuit of correct answers.

What Pirsig glimpsed in his classroom experiment is the same underlying truth: when people are invited into genuine dialogue, they begin to think with others, rather than perform for others. That is the capacity grading systems suppress and the one our educational future most needs.

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Much of the current discussion about the impact of Artificial Intelligence (AI) on Knowledge Management actually concerns Information Management. The focus is often on smarter databases, automated tagging and categorization, semantic search, predictive analytics, and intelligent systems that organize, retrieve, and distribute information more efficiently.

AI undoubtedly improves how organizations handle information. It can classify documents, summarize content, identify experts, personalize recommendations, and surface hidden patterns within vast datasets. These developments enhance efficiency and accessibility, but they fall under the field of Information Management, not Knowledge Management.

Knowledge Management, in its deeper sense, is not about the storage and manipulation of data. It is about the processes through which people think together, make sense of complex and uncertain situations, and act with understanding. It involves conversation, reflection, and shared inquiry. These are the social and cognitive dimensions of working with knowledge. Knowledge emerges through interaction, not through capture or codification.

From this perspective, the impact of AI on Knowledge Management is both profound and paradoxical. AI can generate text, suggest insights, and simulate reasoning, but it does not understand meaning, context, or purpose. Its abilities force a closer examination of what distinguishes human knowing from information processing.

Used narrowly, AI risks reinforcing a mechanistic view of knowledge as something that can be managed through automation and retrieval. Used thoughtfully, it can catalyze deeper reflection. It can help surface connections, prompt new questions, and support collective sensemaking. The value lies not in replacing human understanding but in supporting the conversations through which understanding develops.

The true impact of AI on Knowledge Management will depend on how it is applied. If it is confined to optimizing Information Management, its effect will be limited to efficiency gains. If it is used to enrich the relational and conversational practices through which knowledge emerges, it has the potential to expand human capability in the face of growing complexity.

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A report is a place where knowledge goes to die. So is a book. Once knowledge is written down, it stops moving. It loses its life.

Think of a butterfly. A living butterfly is alive in motion, sensing, responding, adapting. It belongs to the world around it. Its life is inseparable from its flight.

Now think of a butterfly pinned in a display case. It still looks like a butterfly, but it isn’t one. It can’t fly, it can’t feed, it can’t feel the air. What’s left is only the form, the idea of a butterfly, not the living thing itself.

That’s what happens when knowledge is turned into information. Knowledge lives in people, in conversation, in action, in shared understanding. It changes as we change.

Information, on the other hand, is static. It has the shape of knowledge, but not its life.

We need our reports, our books, our databases, but we shouldn’t confuse them with living knowledge.

If we want knowledge to stay alive, we have to keep it in motion. Talk about it. Question it. Share it. Let it evolve.

Otherwise, all we’ll have are beautifully pinned butterflies, perfectly preserved, and quite dead.

Reports and books are where knowledge goes to die.

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We often treat ChatGPT as a clever search engine: we ask, it answers. But what happens when we start to think with it instead of just querying it?

Most people use generative AI tools like ChatGPT in a simple, transactional way. We ask a question and receive an answer. It’s a one-step process: straightforward, efficient, and helpful.

Yet this barely begins to tap into ChatGPT's much larger potential as a conversational partner — as a partner in thinking.

Used this way, ChatGPT functions like a smart reference tool: helpful, but limited. A slightly more sophisticated approach is to ask a question, get an answer, and then follow up. Depending on the response, we might probe deeper, refine our inquiry, and continue the exchange.

This creates a sequence of questions and answers — a chain of inquiry rather than a single interaction.

Still, even this remains essentially one-directional. We are the ones asking, and ChatGPT is the one answering.

What we rarely do, and what ChatGPT will not do unless we explicitly invite it, is turn the tables and let it ask us questions. This is a subtle but significant shift. When ChatGPT begins asking its own questions, the dynamic changes. The exchange is starting to feel more like a conversation than an interview or a search.

You might, for instance, ask ChatGPT a question and receive a response. Then you could say:

“That’s an interesting answer. I’d like to have more of a conversation about this. Could you start by asking me questions about why I’m interested in this topic, or what it means to me personally?”

Once ChatGPT begins to ask questions, it invites reflection. You might answer the first question, and then it asks another.

But here lies another subtle trap: falling into a reversed pattern where ChatGPT asks and you answer, just as before, but in the opposite direction. That, too, is only half a conversation.

The real potential emerges when you begin to alternate — sometimes answering, sometimes asking back. ChatGPT poses a question, you respond, and in your response, you pose a question of your own.

The dialogue begins to flow more naturally. It is no longer about turns or structure, but about mutual inquiry. You explore ideas together, each prompting the other to think differently, to notice connections, and to see things afresh.

This is where ChatGPT becomes not just a tool for producing answers but a partner in thinking. It helps you articulate, challenge, and extend your own ideas. The exchange becomes richer, more exploratory, and — while still not quite human — closer to what we might genuinely call a conversation.

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Give This a Try

Start by choosing a subject that genuinely interests you, something you’d like to understand more deeply. Then tell ChatGPT that you’d like to have a conversation about it, not just a question-and-answer exchange.

Explain clearly the kind of turn-taking you want to see, as described above. Say that you’d like both of you to ask and answer questions, taking turns in a more natural, conversational flow rather than following a fixed sequence.

Once you begin, you’ll probably notice that ChatGPT slips back into its default answer mode from time to time, giving straightforward replies rather than engaging in dialogue. When that happens, simply remind it that you’re trying to have a conversation and that you'd like it to keep asking you questions, too.

You’ll find that it quickly adjusts and returns to a more interactive, reflective exchange.

The point is to play with it. Treat the process as an experiment in thinking together, exploring ideas rather than seeking definitive answers.

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Some years ago, I was thinking about the history of humanity and wondered how many people like ourselves—Homo sapiens—had ever lived. I found a website that performed the calculation (the calculation has changed slightly in recent years), and the number at the time came out to be about 100 billion. It struck me that 100 billion wasn’t such a huge number. Given the roughly 8 billion people alive today, I was surprised to realise that something like 8 percent of all the humans who had ever lived were alive at that moment.

That number has stayed with me ever since. I often think about what it means for the story of humanity, both past and future. And so, when a friend recently told me that the number was meaningless and that the calculation was a stupid one, it forced me to stop and think about why the number held significant meaning for me.

For me, it helps to see where we are in the long story of humanity. When I think about 100 billion lives, I think about the conversations, decisions, and dreams that came before us. I think about how much we inherit from them, and how much responsibility rests with us now. We are many, and the choices we make ripple out across the planet and into the future.

There is also another twist. We are approaching peak population. With falling birth rates worldwide, the human population is likely to start declining before the end of this century. This decline may be catastrophic, bringing with it social and economic consequences we can hardly imagine. The figure of 100 billion is not just about the past; it may also mark a turning point. Future generations may see our time as the peak of human presence on Earth.

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When I think about those hundred billion people who came before us, I can’t help but see them as not so different from us. They felt the same emotions, the same fears and desires, the same small joys and quiet disappointments. They fell in love, they argued, they worried about their children, they struggled to make a living, they fell ill, and they grew old. They were as human as we are.

And yet they are gone. Many of them have been gone for hundreds, thousands, or even hundreds of thousands of years. There is no trace of most of them. Unless one holds religious beliefs, it is as if they never existed at all. Billions of lives lived, billions of stories lost to time.

Still, I find myself thinking of them with gratitude. Life for most of them was hard, dangerous, often short, full of hunger, illness, and loss. But through all of that, they kept going. In ways large and small, through invention, courage, and persistence, they made it possible for us to be here.

That thought moves me. I want to thank them, not as a sentimental gesture but as a quiet recognition. We live on the foundation of their struggles and their hopes. Whatever progress we enjoy, whatever comfort or freedom we take for granted, was built on their shoulders.

We are living at a high-water mark in the human story, with all the opportunities and risks that come with it. And perhaps that is why this “useless” number stays with me: because it reminds me how extraordinary it is to be alive at this point, when the story of humanity feels both vast and precarious.

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What I always enjoy most about events isn’t the presentations, it’s the conversations they spark. Listening to experts can be interesting, occasionally inspiring, but the real value comes when you sit down with other people, ask questions, and explore ideas together. That is when things start to feel alive.

I recently went to a launch event that ticked many of the usual boxes. There were a handful of short presentations, followed by a panel discussion with five experts. The panel itself was excellent, engaging, lively, and worth listening to. At the end, the audience had the opportunity to ask three or four questions, and that was it.

The real opportunities for conversation came before and after. There was half an hour for coffee at the start, which was great if you arrived early, as I did. Afterwards, a buffet lunch was served in a large room, where people could chat and network, which worked well.

But during the event itself, there was no space for conversation. As I sat there, I found myself wondering how easy it would have been to change that.

What I would have loved to see was a different approach to the panel. Imagine if, instead of all five panellists staying up on stage, each gave a short introduction to their area of expertise and then moved to sit at separate tables around the room. People in the audience could choose which conversation to join, and even move between them if they wished.

It is somewhat similar to Open Space (OST), albeit in a lighter, modified form. In a full OST session, the participants decide the topics and host the conversations. In this version, the experts themselves would anchor the discussions. The principle is the same: you choose the conversation that interests you, and you are free to move if something else draws your attention.

I think half an hour of that would have been fascinating. It would have given everyone the chance to ask their own questions, share experiences, and hear directly from the experts in a more personal way. I suspect the energy from those conversations would have flowed straight into the lunch afterwards, making the networking even richer.

The beauty is that it wouldn't have required a significant redesign. The presentations could still have taken place in the lecture theatre. Only the panel would shift into this more conversational format, ideally in a larger open space nearby.

For me, that is the point. Events do not need to be turned upside down to make them more conversational. A few small changes, such as reshaping a panel into a set of smaller discussions, can turn an event from being simply informative into something people really remember, because they were part of it.

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When I scroll through X (Twitter), I encounter a constant stream of statements, often conflicting. Many I agree with, many I don’t. That in itself is not a problem. On issues of values, such as abortion, differences of opinion are natural and even healthy. People hold different values, so they arrive at different conclusions.

What I do struggle with, however, are outright falsehoods. A lie can be deliberate or inadvertent. When it is clear that someone knowingly lies, the problem is obvious. More difficult is the grey area when people spread falsehoods without thinking, perhaps because they want a figure to be true or because it simply feels right to them.

Take a recent example in the UK. Tommy Robinson claimed that three million people had joined protests in London. Anyone looking at the footage could see that the crowd was large, but the media put the number between 110,000 and 150,000. That is a big difference. And it isn’t hard to test the claim. With today’s tools, such as ChatGPT, you can do a quick calculation. Assume a typical London street, filled building to building, with about four people per square metre. To fit three million people, the line would stretch for roughly 39 miles (the distance from London to Reading). By contrast, 150,000 people would fill about two miles. Clearly, those numbers are worlds apart.

If you don’t believe me, try it yourself. Ask ChatGPT to do the calculations. I think you’ll find that 150,000 is closer to the truth. Now, I didn’t see all the footage or every part of the protest, so it may well have been higher than 150,000. It could have been 250,000, or even 500,000. But it certainly wasn’t a million, two million, let alone three million.

Whether Robinson deliberately lied or inflated the number because it “felt” right is hard to know. But the point is this: a little critical thinking, or even a simple calculation, shows the truth. Too often, people skip that step and pass along demonstrably false claims.

And there’s another consequence when the person making such claims is a well-known celebrity or political figure. Each time they share something that can so easily be shown to be false, they chip away at their own credibility. Even when they later make statements that cannot easily be tested, people remember that they have either lied before or failed to think critically. In the long run, exaggeration erodes trust and works against them.

So please, when you come across a figure or a statement that can easily be checked, where there is no grey area, where it’s a matter of right or wrong, take a moment to do that check. It doesn’t take long, it’s not difficult, and in today’s noisy world of social media, I find myself doing it more and more.

The film drama "The Journey" is a fictional account of the true story of how Northern Ireland's political adversaries, Ian Paisley and Martin McGuinness, formed an unlikely political alliance.

You can watch a clip from the start of the film and a clip from the end of the film, and if you have access to Amazon Prime, you can watch the entire movie here.

I am sure you are familiar with the troubles in Northern Ireland, which lasted approximately 30 years from the late 1960s to 1998. However, here is a short video summarizing the life of Ian Paisley, which provides an overview of the Troubles and the eventual Good Friday Agreement.

I am well aware that the story of the journey is fictitious, as this article in the Guardian makes clear. Still, the two men did strike up a remarkable friendship, given their political views were so polarised.

Despite the fiction, I still enjoyed the story, as it is a beautiful example of what conversation across divides can achieve.

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Here is the email in full:

“I grow little of the food I eat, and of the little I do grow I did not breed or perfect the seeds.
I do not make any of my own clothing.
I speak a language I did not invent or refine.
I did not discover the mathematics I use.
I am protected by freedoms and laws I did not conceive of or legislate, and do not enforce or adjudicate.
I am moved by music I did not create myself.
When I needed medical attention, I was helpless to help myself survive.
I did not invent the transistor, the microprocessor, object oriented programming, or most of the technology I work with.
I love and admire my species, living and dead, and am totally dependent on them for my life and well being.”

Source: Steve Jobs Archive

Jobs is remembered as an uncompromising innovator, but here he reveals something different: a recognition that even the most celebrated lives are built on the work of countless others. Farmers, teachers, scientists, artists, caregivers, lawmakers, and many more, most of them long forgotten.

This is not only a statement of humility. It is a statement of relationship. Jobs is reminding us that no life, no achievement, no act of creativity stands alone.

In the practice of Conversational Leadership, this recognition matters. Conversation is one of the ways we honour our dependence on one another. Through conversation, we share knowledge we did not invent, stories passed down through generations, and ideas sparked by those who came before us. Conversation itself is a collective inheritance.

Jobs died in October 2011, just a year after writing these words. At the height of his influence, he paused to reflect not on what he had built, but on what had been given to him.

For me, that is the heart of Conversational Leadership: to see that independence is largely an illusion, and to cultivate the conversations that keep us connected to the larger web of human effort and imagination.

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