Olivia: A recent study estimated that by 2030, automation could displace up to 800 million jobs globally. Jackson: Whoa. That’s a terrifying number. That’s the kind of statistic that makes you want to go live in a cabin in the woods and learn to churn butter. Olivia: Right? But here's the twist. That same study predicted it could also create nearly 900 million new ones. Jackson: Okay, that’s a serious case of whiplash. So we’re not all doomed to be replaced by robots? Olivia: It seems the real question isn't about replacement, it's about who leads the transition. And that exact question is at the heart of a fascinating book we're diving into today: Leadership by Algorithm by David De Cremer. Jackson: Leadership by Algorithm. That title alone gives me a little bit of anxiety. So, is my next boss going to be an algorithm? Should I be polishing my resume for a robot? Olivia: That is the million-dollar question, and De Cremer is the perfect person to answer it. What’s so interesting is that he's not a tech bro or a Silicon Valley futurist. He's a behavioral scientist who founded a center called 'AI Technology for Humankind.' His entire career is about bridging that exact gap—between the cold, hard logic of technology and the messy, creative, and sometimes irrational world of human beings. Jackson: I like that. It means he’s probably not going to tell us the solution is to just upload our consciousness to the cloud. Olivia: Exactly. He brings a deeply human-centric perspective that’s often missing from this conversation. And his answer to your question—will a robot be your boss—is both yes and no, and the distinction is everything.

Jackson: Okay, a classic 'yes and no.' I'm intrigued. Break it down for me. How can the answer be both? Olivia: De Cremer makes this incredibly sharp distinction right at the start of the book, and it's one of his most powerful quotes: "Humans lead, algorithms manage." Jackson: Humans lead, algorithms manage. That sounds simple, but I have a feeling the details matter. What’s the difference in his view? Olivia: A huge one. He defines 'management' as the set of tasks related to execution, coordination, and optimization. Think about scheduling, tracking performance metrics, allocating resources, processing data. These are things that require consistency, accuracy, and speed. And let's be honest, algorithms are just flat-out better at that than we are. They don't get tired, they don't have bad days, and they can process a million data points in a second. Jackson: That makes perfect sense. It’s like all the stuff on my to-do list that I procrastinate on because it’s tedious. My brain would love to outsource that. But the book uses a pretty stark example of where this can go wrong, right? The story about the hiring algorithm. Olivia: Oh, it’s a perfect and cautionary tale. He talks about a major tech company that, back in 2018, was trying to solve for human bias in their hiring process. A noble goal. They built an AI recruiting tool to analyze resumes and pick the top candidates. Jackson: I can see the appeal. Take the messy human emotions and prejudices out of the equation. Let the data decide. What could go wrong? Olivia: Well, everything. The AI was trained on the company's hiring data from the previous decade. And what did that data reflect? The company's past biases. They had historically hired more men for technical roles. So the algorithm learned that male candidates were preferable. It started penalizing resumes that included the word "women's," as in "women's chess club captain," and it downgraded graduates from two all-women's colleges. Jackson: Oh, man. So instead of eliminating bias, it just automated it and put it on steroids. It’s like they built a robot that was perfectly, efficiently sexist. Olivia: Precisely. And the human managers, the HR team, started noticing this strange pattern. The AI kept spitting out male candidates. It took data scientists digging into the code to realize what was happening. The algorithm was just doing what it was told: find patterns in the past and replicate them for the future. It had no concept of fairness, no understanding of social context, and no ethical compass to say, "Hey, maybe this pattern is a problem." Jackson: That’s terrifying. It’s like a brilliant but completely clueless intern who follows instructions perfectly, even if the instructions are to drive the company off a cliff. So this is what De Cremer means when he says algorithms can manage but not lead? Olivia: Exactly. Management is about executing a process. The AI was a phenomenal manager of the flawed process it was given. But leadership… leadership is something else entirely. De Cremer argues leadership is about three things algorithms can't do. First, it’s about defining a purpose and a vision—the 'why' behind the work. The AI didn't know why diversity was important. Jackson: It just knew how to match keywords. Olivia: Second, leadership is about connection. It's about empathy, inspiration, and building trust. One of the book's best quotes is brutally simple: "It’s all about connecting with others, stupid!" An algorithm can’t connect with you, it can’t understand your fears or your ambitions. Jackson: And the third thing? Olivia: Making ethical judgments. When the hiring algorithm failed, it couldn't correct itself. It couldn't feel remorse or recognize its mistake. It required human leaders to step in, recognize the ethical failure, and make a new decision. That’s leadership. It’s the messy, human, value-driven work that can’t be boiled down to a data set. Jackson: Okay, I get it. The algorithm is the ultimate administrator. It can run the system. But the leader has to design the system, question the system, and know when to break the system. But this brings up a point some of the book's critics have made. They've said it's a great philosophical take, but maybe a little light on the 'how-to.' If AI is handling all the management, what does a human leader's day-to-day actually look like? Are we just babysitting the machines?

Olivia: That is the perfect question, because it leads directly to the most exciting and forward-looking part of the book. De Cremer's vision is so much bigger than just babysitting. He proposes a model he calls 'co-creation,' and he uses the best possible analogy to explain it: Iron Man. Jackson: Iron Man! Okay, now you’re speaking my language. I’m listening. Olivia: Think about the relationship between Tony Stark and his AI assistant, J.A.R.V.I.S. Who is the leader and who is the follower? It’s not that simple, is it? Jackson: Not at all. Tony has the crazy ideas, the vision. He's the one who says, "I'm going to build a suit of armor in a cave with a box of scraps!" or "Let's invent a new element." He's the creative, strategic, purpose-driven force. Olivia: Exactly. That's the human leader. He sets the impossible goal. But could he do it alone? Absolutely not. J.A.R.V.I.S. is the one running the diagnostics, calculating the flight paths, managing the suit's power levels, and analyzing terabytes of data in milliseconds. J.A.R.V.I.S. is the ultimate manager. Jackson: Right! And they work together seamlessly. Tony provides the 'what' and the 'why,' and J.A.R.V.I.S. provides the 'how.' They constantly learn from each other. Tony has a new idea, J.A.R.V.I.S. tells him if it's feasible and runs the simulations, and based on that data, Tony refines his vision. Olivia: That is co-creation. It’s not human versus machine. It’s human vision amplified by machine intelligence. De Cremer argues this is the future of work. The leader's job is no longer to be the smartest person in the room who has all the answers. The leader's job is to be the best question-asker in the room. Their role is to define the mission, to inspire the team—which now includes both humans and algorithms—and to act as what he calls an "orchestral conductor." Jackson: An orchestral conductor. I love that. They're not playing every instrument, but they are the ones who know the music, who bring all the different parts together—the violins, the percussion, the AI—to create a symphony. Olivia: You've got it. The conductor ensures everyone is playing in harmony and toward a common purpose. They're focused on the big picture, the emotion, the story the music is telling. They're not micromanaging how the first violinist holds their bow. Jackson: But we're not all billionaire superheroes or world-famous conductors. How does a regular manager at a mid-sized company apply this Iron Man model? What does being an 'orchestral conductor' look like on a Tuesday morning in a normal office? Olivia: It means a fundamental shift in focus. Instead of spending your day chasing down status reports and approving expense forms—all 'management' tasks an AI could do—you spend your time on uniquely human 'leadership' work. You're coaching your team members. You're fostering psychological safety so people feel free to take creative risks. You're walking the floor and asking questions, trying to understand the challenges your team is facing so you can frame the right problem for your AI tools to help solve. Jackson: So my job becomes less about managing the work and more about managing the energy and direction of the team. Olivia: Precisely. You’re the keeper of the purpose. You're the one who reminds everyone why their work matters, especially when they're just seeing their little piece of the puzzle. The algorithm can optimize the path, but the leader has to make sure everyone is excited about the destination. It’s a shift from being a taskmaster to being a coach and a visionary.

Jackson: It’s fascinating how these two ideas connect. The reason we can even think about the Iron Man model is because we first accept that management and leadership are different things. By handing over the administrative burden of management to algorithms, we're not becoming obsolete. We're being forced to level up. Olivia: That's the deep insight of the whole book. The rise of AI isn't a threat to human leadership; it's a clarification of it. It strips away all the noise, all the bureaucratic tasks that we often mistake for leadership, and it leaves us with the core, essential, and deeply human elements: purpose, connection, and ethical judgment. Jackson: So the future of leadership isn't about learning to code or becoming a data scientist to compete with the machines. It's about becoming more human. Olivia: It's about doubling down on the skills that machines don't have. Empathy, creativity, strategic thinking, moral courage. The McKinsey data we started with showed that hundreds of millions of jobs might be displaced, but even more could be created. This is what those new jobs will demand. They will demand leaders who can conduct the orchestra of humans and AI. Jackson: Wow. So the ultimate challenge that AI presents to us isn't technological at all. It's a human one. It’s a call to become better, more thoughtful, more connected leaders than we’ve ever been before. Olivia: That’s the perfect way to put it. And it leaves us with a really practical question to reflect on. What is one 'management' task you do every week—something repetitive, data-driven, or administrative—that you would gladly give to an algorithm? Jackson: Oh, easily my expense reports and scheduling meetings. I would give those away in a heartbeat. Olivia: And what 'leadership' activity would you do with all that free time you just got back? Jackson: That’s the real question. I’d hope to spend more time just talking to my team, brainstorming, and thinking about the bigger picture instead of being buried in the day-to-day grind. It’s a powerful thought. We encourage everyone listening to think about that for themselves. What would you stop doing, and what would you start? Let us know what you come up with. Olivia: This is Aibrary, signing off.