When the World Shifts: Rethinking Education in the Age of Change

Note to the Reader: This is the only blog I’m releasing for the Spring 2025 semester, and it’s a long one. I’ve spent these past months reflecting on the many forces reshaping the future of work—and there are simply too many important ideas to fit into a short post. I also want to share some of the thinking behind the vision of Baruch AI Hub and why it matters. Thank you for reading. I hope this piece offers fresh insights and sparks new ideas for your own journey.

I recently picked up a biography of Charles Darwin (by Desmond and Moore)—just out of curiosity about a man who spurred a pivotal moment in human history. Somewhere between those quiet pages, I found his life unexpectedly relatable and yet profoundly significant. When he started his journey aboard the HMS Beagle in 1831, Darwin was a young man, not much older than some of our students today. He worried. He doubted. He battled seasickness and a nagging fear that he didn’t really know what lay ahead.

Over the next five years, as the Beagle traced its winding route from the jagged volcanic shores of the Galápagos to the soaring peaks of the Andes, Darwin’s uncertainty didn’t disappear; it deepened, then evolved. He filled his notebooks with sketches and observations that quietly dismantled old certainties and replaced them with wonders and questions.

What’s remarkable is that Darwin’s discoveries of adaptation and survival didn’t just reshape biology or science; they forced humanity to rethink our place in the world, not as the divinely chosen central figure, but as part of an intricate, adaptive web of life, always evolving, always interconnected with nature.

Reading his story stirred my own memories of standing on unfamiliar shores in the uncertainty of youth. I thought back to a crisp August morning in the early 1980s, stepping off a plane at JFK Airport, buzz cut still sharp from military service in Taiwan, a battered suitcase dragging behind me. Walking through the streets of New York City with two years of naval service behind me and a degree I wasn’t quite sure how to use, I had a vague sense that life here would be faster, louder, and harder than anything I’d known.

Before the navy, my educational path had zigzagged from physics to architecture to engineering. Each switch wasn’t a master plan so much as a response to curiosity, chance, and necessity. I wasn’t searching for a destination; I was searching for questions worth following. I found myself drawn again and again to systems—the delicate dance between human intention, mechanical precision, and unpredictable environments. That fascination became my quiet guide, pulling me across disciplines and industries and eventually into classrooms.

Looking back, every pivot felt like an admission of uncertainty—but also an act of hope. The willingness to unlearn, to reimagine, to start again.

And now, I see that same hope, that same brave uncertainty, in the faces of you, Baruch students. You’re stepping into a world that won’t reward knowledge accumulation, but will demand adaptability, curiosity, and the courage to keep evolving. Your future won’t be defined by a single choice or path, but by how willing you are to keep learning, unlearning, and learning again—across disciplines, technologies, and the unknown.

The Rise of AI—and What It Really Means

Today, as we step deeper into the age of artificial intelligence, I sometimes wonder if we’re living through our own post-Darwinian moment. Just as Darwin’s discoveries unsettled humanity’s sense of place in the natural world, AI is now forcing us to rethink our place in the human world. Yes, it marks another stunning technological leap—a breakthrough with enormous disruptive potential. But here’s the paradox: this moment isn’t really about the technology itself.

Sure, it’s useful to understand how a large language model predicts the next word or how an algorithm sifts through oceans of data. But that’s not the heart of it. It’s about the profound impact AI will have. After all, very few of us truly understand how a steam engine works or the inner mechanics of an automobile—yet we have felt the lasting changes those inventions brought about.

The real question lies in how we respond—how we rethink our roles, value, and sense of purpose in a world where machines now mimic not just our tasks, but our reasoning and creativity, and even provide glimpses of empathy. Like Darwin’s voyage, this moment doesn’t merely shift what we know; it may shake who we believe ourselves to be.

I still remember the first time I worked with a computer, back in college—not the sleek, intuitive machines we know today, but hulking systems that required stacks of punch cards to run even the simplest program. I’d sit at a mechanical punch-card machine, its keys clacking like a heavy typewriter, carefully punching holes into stiff cardstock, one line of code at a time. Every keystroke carried weight, because a single misplaced punch could throw the entire program off. There was no screen, no cursor waiting for me—just the quiet anticipation as I handed my card deck to the operator, watching it disappear into the reader. And then, when the printed output finally emerged, the thrill was undeniable: a small window into a new kind of logic, a glimpse of what it meant to shape something invisible through code, precision, and patience.

But as wondrous as that encounter was, it pales beside what we experience today. With just a few taps on a smartphone or a simple voice command, we can casually ask our devices to translate languages, analyze data, or recommend options, without a second thought about the complex operations behind each action. In doing so, we summon vast computational power: neural networks, generative models, and machine learning systems that don’t just follow instructions, but learn, adapt, and reason. What once demanded meticulous preparation and programming now unfolds instantly, almost effortlessly.

When Machines Begin to Reason

I used to teach AI as an engineering professor, and two moments in the rapid rise of AI stunned me. The first was in 2016, when AlphaGo, an AI developed by DeepMind, defeated the world champion in Go, a strategy board game. I wasn’t a Go player, but I knew enough to appreciate its elegance and depth—a game so vast it holds more possible positions than atoms in the universe. Watching AlphaGo play, I was struck not by its efficiency, but by its imagination. It made moves even grandmasters called “beautifully alien”—moves no human had dared. I remember sitting there, stunned, realizing that we weren’t just teaching machines to think like us. We were meeting a form of intelligence that could surprise us and even teach us.

The second moment came in 2020, when DeepMind’s AlphaFold cracked a problem that had stumped scientists for over half a century: how to predict a protein’s three-dimensional structure from its amino acid sequence, known as the protein folding problem. For decades, this problem had been labeled “mathematically intractable,” so complex that even the best methods couldn’t solve it within a reasonable (finite) time. And yet, in what felt like a flash, an algorithm peered into that complexity and emerged with astonishingly accurate answers, opening new doors for drug discovery, accelerating the development of COVID-19 vaccines, and giving scientists an entirely new map of biological possibility to explore.

Moments like these leave me both awed and unsettled. They raise a quiet, urgent question for those of us who are educators: How do we prepare students not just to use these powerful tools, but to think alongside them? To bring human insight, judgment, and creativity into partnership with something that can already outpace us in speed and scale?

Another Tool That Expands the World We See

Go and protein folding aren’t just puzzles. They’re metaphors for the kinds of problems we will attempt—problems too large, too tangled, too dynamic for humans to solve all by ourselves. Yet there is a deeper lesson that endures: When we learn to see the structure of a problem—not just what it appears on the surface—we unlock new ways to solve it.

From telescopes to microscopes, from mass spectrometers to confocal laser microscopy, from the earliest punch-card computers to today’s deep learning models, the pattern repeats. Every time a tool expands what we can see, we’re called to rise to the occasion, to stretch our thinking, to meet that expansion with imagination, purpose, and responsibility.

That’s the work ahead. Not just learning more—but learning to see differently. Because once we understand the structure beneath complexity, we begin to see the path through it.

Cancer treatment offers a powerful example. For decades, progress was slow—not for lack of determination, but because we couldn’t see the intricate molecular networks driving it. Today, with advanced genomic analysis and AI modeling, those hidden structures are coming into view, revealing patterns, pathways, and therapeutic targets we couldn’t imagine before. And cancer is just one case. Across fields—climate modeling, supply chain optimization, mental health diagnostics, even space exploration—AI is beginning to illuminate the underlying architecture of problems long thought too complex to solve. That is its true power: not simply automating tasks, but helping us see what was previously invisible, and opening the door to solutions we couldn’t even begin to conceive of before.

And soon, we may stop talking about AI altogether. It won’t be a buzzword or a headline—just part of the air we breathe. Like electricity or tap water, it will be everywhere, invisible but essential. But ubiquity doesn’t mean we’re ready for what it implies. In fact, I worry that we’re not. Because while the technology advances, the deeper questions remain: What happens to human agency? How do we hold onto ethical clarity in systems too complex for any one person to fully see? How do we preserve the messy, beautiful work of human judgment and care in a world increasingly optimized for speed and efficiency?

These are some of the challenges we face. It’s not just preparing to work with AI, but preparing to live thoughtfully, ethically, and fully human lives alongside it.

AI is just one force in a world shifting on many fronts, each change urging us to rethink how we learn, how we prepare, and how we live. Let’s look more closely at what that means.

Lifelong Learning in an Extended Career

A few years ago, I reconnected with a Baruch alumnus who graduated in the 1960s. His résumé read like a textbook success story in finance. But what stayed with me wasn’t his finance career. It was what came next. In his fifties, he returned to a lifelong passion: photography. At first, it was solitary work, just him and a camera, chasing light along city streets and coastal horizons. But before long, his images began appearing in some of the world’s most renowned museums: the Getty, the Met, the National Gallery, the Whitney.

Still curious and still reaching, he kept going. In his eighties, he finished an online doctoral degree in epidemiology. When I asked him why, he said simply, “Learning makes me happy.” His words have stayed with me. Lifelong learning isn’t just about staying competitive. It’s about staying connected to life’s unfolding complexity. It’s about continuing to grow—not just in knowledge, but in spirit.

That, I’ve come to believe, is the deeper curriculum of a long career: learning not just how to do more, but how to be more questioning, patient, and fully human.

The lesson I took from him was not a solitary one. It’s becoming one of the defining patterns of the future of work. As careers stretch longer and change accelerates, the ability to adapt, retool, and reinvent will matter more than any single credential. The goal isn’t just to stay relevant. It’s to embrace what the world has to offer.

Degrees Open Doors — Skills Keep Them Open

A few months ago, at an alumni gathering in South Florida, I met an alumna who had built a fast-moving career at a major investment bank. But what she shared over cocktails wasn’t about finance. It was about what she had created.

Watching friends in real estate drown in the complexity of endless zoning rules, data sets, and client requests, she built something quietly powerful: an AI assistant tailored to their world, a tool that didn’t replace agents but liberated them. It gave them time back to connect with clients, close deals, and build trust and relationships.

Her success wasn’t a departure from her education; it was an extension of it. Her training gave her a lens. But it was her willingness to keep learning, to cross disciplines, to build something useful that brought her idea to life.

This, I believe, is what it means to thrive today. Academic foundations matter because they teach us how to see. But turning that vision into action means staying open, staying agile, and staying brave enough to build.

Ask What’s Needed, Not Just What Your Job Is

I heard another story, just as powerful, from an alumna who once led interpreter services at a hospital. One day, she stopped to speak with an older Angolan refugee with a quiet presence, who was mopping the floor. She greeted him in Portuguese, knowing that was the national language of his home country.

“Sir, what did you do back home?” she asked.

He paused. Then, in perfect English, he said: “I’m a geophysicist.”

They stood there, silently, tears welling in their eyes. In that moment, everything changed. Not just for him, but for her.

She went on to found an employment agency dedicated to helping refugee professionals find work in their fields. That geophysicist? She helped him land a job with the U.S. Army Corps of Engineers.

Her story isn’t just about leadership. It’s about seeing what others overlook, about asking beyond “What’s my job?” to “What’s needed here?”

That question echoes through all workplaces and fields. And it brings me to one final memory from long ago.

The Future of Teams Is Everywhere

On the navy frigate where I served, I lived shoulder to shoulder with men from places I’d never been—men who’d grown up in fishing villages and factory towns. Some hadn’t finished middle school. Others held worldviews wildly different from my own. We spoke different dialects, carried different burdens, dreamed of different futures. And yet, crammed together in those narrow steel corridors, waking to the same alarms, and facing the same tempests at sea, something happened. We forged bonds that felt deeper and truer than many friendships I’d known before.

There’s a certain honesty that comes from sharing fear, hunger, laughter, and the long, aching boredom between ports. We trusted each other, not because we agreed, but because we had to. And in that trust, I learned something that has stayed with me ever since: trust isn’t built on sameness. It was built from learning to work alongside difference, to rely on those whose worldviews and ways might never match your own, but whose commitment to the mission was just as strong.

Today, as teams span continents and time zones, that lesson feels more relevant than ever. The tools we use to connect have changed, but the essence of collaboration remains the same. Trust. Purpose. Shared effort.

Building the Bridge to What’s Next: The Baruch AI Hub

And so, we find ourselves at a turning point—not just of technology, but of human possibility. We see it already in the alumni who rediscovered photography, built an AI tool to empower real estate agents, and helped a geophysicist return to his field. These stories point to deeper ideas: that modern careers span decades and demand reinvention; that fulfillment comes not just from mastery, but from rediscovery; and that leadership often begins with awareness—and courage, with a question.

All of this points to a single truth: The future of work will not be defined by any one skill, credential, or platform. It will be shaped by our ability to create meaning from change.

And that perspective brings us to something new. We’re expanding our campus into a space at 63 Madison Avenue, one that will house classrooms, offices, and a vision I’m excited to share: the Baruch AI Hub. Let me explain.

I hope I have convinced you AI will reshape every industry faster and more profoundly than any wave of innovation before it. According to McKinsey, by 2030, more than 1.7 million jobs in New York City alone will be directly influenced by generative AI. Baruch can—and must—be at the forefront of that transformation.

But the idea of AI Hub is not so much about technology. It’s about people.

It’s a space for professional reinvention. A place where mid-career professionals—starting with our 175,000 alumni—can return not just to update their résumés, but to reimagine their roles. Because the truth is, many of the world’s most stubborn problems remain unsolved not for lack of effort, but because we haven’t yet fully understood their underlying structure—and AI helps us to see what we couldn’t see before. But to take advantage of that will require some human ingenuity. The AI Hub will bring together people with three key characteristics:

• Scholars with deep domain knowledge: honed in fields where Baruch has significant academic expertise, such as finance, auditing, advanced mathematics, public policy, and more.
• Specialists with technological fluency: the ability to explore new tools and technology platforms that expose hidden patterns and possibilities.
• Executives with broad industry experience: the practical wisdom to turn insight into practice and implementation, ensuring that ideas serve real people in real-world contexts.

By fostering collaboration among academics, technologists, and practitioners, the AI Hub will become more than a space for professional upskilling. It will be a place for co-creation that can bridge education and work, classrooms and boardrooms, and potential and progress. And as we help professionals navigate this next era, we will bring their insight back to our students, enriching Baruch’s classrooms with relevance and possibility.

This is how we stay true to Baruch’s mission: by helping our community not just keep up with change, but lead it—thoughtfully, boldly, and together.

Darwin’s journey aboard the Beagle wasn’t about knowing where he’d end up. It was about being open to what he could learn along the way. His spirit of discovery, resilience, and seeing what others overlooked is what we need in today’s shifting world.

We don’t know exactly where this transformation will take us. But we do know how to prepare: with curiosity, courage, and a commitment to learning that never ends.