The Speed of Change: Unlearning as a Survival Skill

“In the long run, the sharpest weapon of all is a kind of resilient adaptability.” - The Art of Learning

We all have songs that instantly transport us back to specific memories. For me, certain books do the same—and The Art of Learning by Josh Waitzkin is one of them. For those unfamiliar, Josh was the child chess prodigy inspiration behind the 1993 film Searching for Bobby Fischer. I first read his book in the aftermath of the Great Financial Crisis, a time when I was actively seeking ideas that could challenge what I thought I knew and help open my mind to the future. Around the same time, Daniel Pink’s A Whole New Mind and Dr. Norman Doidge’s The Brain That Changes Itself helped shape my thinking on learning, psychology, plasticity, and the nature of intelligence. In those years, books were real-time tools. The 1990s were dubbed “the decade of the brain,” and the flood of literature that followed explored cognition and adaptability in ways that still resonate. Among them, Waitzkin’s work stands out as the most impactful for me when it comes to approaching macroeconomics at a time of exponential change.

Years later, I wrote a paper titled The Art of Unlearning, where I argued that in a world defined by exponential change, true learning would require unlearning—an intentional process of letting go of outdated frameworks and remaining open to new ideas. It was clear that reading news to try and interpret the present was like moving at the speed of a bike, when we’re now going 200 miles per hour—where the present and future blur together, and by the time you’ve made sense of "now," it’s already behind you. To stay in the race, you have to unlearn as quickly as you learn—because anchoring to the past is the brake that keeps you from adapting in real time. This philosophy has guided my journey in using, analyzing, and imagining the future of AI and the profound ways it will transform our lives. It has also shaped my views on artificial intelligence’s impact on economics, business cycles, and capital structures, which I now write about for 22V Research. Waitzkin’s book, often viewed as a manual for peak performance, offers something deeper: a framework for dismantling what we know to make space for what we need to learn next. I am often asked how to learn to use AI in work and life, and I think starting with this book and Josh’s journey will help.

After achieving national fame as a chess prodigy, Josh Waitzkin made the unexpected decision to walk away from the game entirely. He shifted his focus to mastering a new discipline: the martial art of Tai Chi, specifically the competitive form known as push hands. It was a profound transition—not just between skills, but between mindsets. Tai Chi push hands is rooted in balance, adaptability, and sensing change in real time—making it a fitting analogy for how we must now engage with AI. Imagine riding the same escalator every day. You know its speed and rhythm. But one day, you step onto it and it accelerates dramatically. Your brain, conditioned by past experience, is unprepared. You lose balance. But if you had anticipated the change—if you had trained for unpredictability—your adjustment would be smoother. This is the world we now live in: an AI-driven economy where the rules are shifting faster than our cognitive defaults can process. In this environment, the ability to “learn how to learn” is no longer optional—it’s a survival skill. And this kind of learning isn’t additive; it’s subtractive. It begins with letting go—of ego, of control, and of what we think we already know. For now, the acceleration of AI is easy to overlook because it’s mostly happening in software. But that won’t last. As AI becomes embodied—in humanoids, in full self-driving systems—the visual, physical presence of change will hit much harder. Preparing for that shift will challenge even the most adaptable among us. And more importantly, I don’t believe you can truly understand the present—especially in markets and macroeconomics—without understanding AI.

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“Growth comes at the point of resistance.”

Waitzkin makes it clear that true learning doesn’t happen when everything is going well—it begins when we hit internal walls. In The Art of Learning, he shares how trying to apply his chess-trained precision to Tai Chi initially led to repeated failure. His muscle memory, rooted in logic and calculation, clashed with the fluid, reactive demands of a contact-based martial art. But those failures weren’t dead ends—they were thresholds. Each one exposed the limitations of his old knowledge and opened the door to something new.

This lesson has never been more relevant. As AI reshapes entire industries, professionals are discovering that what once made them successful may now hold them back. The marketer who relied on gut instinct must now master algorithmic targeting. The lawyer trained on precedent must now partner with predictive models. The doctor who memorized protocols must now work alongside diagnostic AI. These aren’t just skill shifts—they’re identity shifts. And they require the humility to face resistance, not as failure, but as a cue to evolve.

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“We have to be willing to let go of our sense of knowing, to step into the unknown with humility.”

One of the greatest barriers to AI is not technical—it’s psychological. It’s one thing to talk about AI’s potential, but it's another to personally confront what it asks of us: to begin again. For professionals who have spent years or decades mastering their field, this can feel like erasure. It’s not just about learning a new tool. It’s about stepping into unfamiliar territory without a map and accepting that what made you successful before may no longer apply.

Adoption requires the courage to start from zero. And it’s precisely here that many get stuck—not because they lack the intelligence to understand AI, but because they resist the vulnerability of not being the expert anymore. As Waitzkin reminds us, the beginner’s mindset isn’t a weakness—it’s a superpower. Children learn quickly because they haven’t yet hardened around fixed beliefs—they’re open, curious, and unafraid of failure. Their brains are wired for experimentation, not preservation. In a time of exponential change, we must cultivate that same openness. The ability to adopt is the ability to let go. The ones who thrive will not be those who cling to expertise, but those who embrace the humility of starting over.

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“Making smaller circles.”

Waitzkin’s concept of “making smaller circles” is about simplifying without losing depth—distilling complex ideas into internalized, intuitive knowledge. This doesn’t happen quickly. He emphasizes a long-term, organic process of transformation—slow, often invisible progress made through repetition and refinement. In an era dominated by AI, this lesson feels countercultural.

The speed of AI adoption tempts us to look for quick hacks or plug-and-play solutions. But meaningful fluency in AI—true integration—requires time and curiosity. It’s not enough to prompt a model. We must understand why it responds the way it does, how data moves through it, and where its limitations lie. This is pattern recognition over time. It’s learning how to think with machines, not just use them. Waitzkin’s call to deepen, rather than rush, offers a blueprint for developing AI intuition in a world obsessed with shortcuts.

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“Learn the macro from the micro.”

Waitzkin discovered that letting go of structured systems and embracing spontaneity was essential to his growth. In Tai Chi, this meant responding to force not with force, but with flow—meeting tension with softness. This looseness, once seen as a weakness, became a strength. He learned to trust in responsiveness over rigidity.

The same lesson applies in the age of AI. These systems are probabilistic, not rule-based. They work best when guided, not commanded. Analysts, teachers, and business leaders who cling to linear thinking—who expect tidy inputs to yield tidy outputs—struggle with AI. Success doesn’t come from control, but from calibration: from testing, iterating, and adapting. The professionals who thrive are those who embrace ambiguity rather than resist it. Just like Waitzkin, they learn that flexibility—not precision—is what leads to mastery in complex systems.

This principle shapes the way I use AI. My process is simple: I consume real-time information—podcasts, YouTube videos, interviews—and when something sparks curiosity, I pause to ask a question. Often it’s about how a traditional relationship has broken down, and more importantly, why. That question—“why?”—is at the heart of learning. It’s also the central idea behind another influential book from the era of brain-centric literature: Start with Why by Simon Sinek. One recent example: I saw a post on X about how Disney was already seeing benefits from AI. I’m especially interested in non-tech companies seeing early gains, because AI’s impact is macro in nature—more like the internet or electricity than software. I looked up Disney’s revenue per employee and saw it rising sharply, which led me to wonder what role AI might be playing. I’ll be publishing a paper on this for 22V next week. That inquiry led me to revisit the broader relationship between workers, productivity, and economic output—using Okun’s Law as a starting point. What followed was a conversation with ChatGPT that challenges some of the most established assumptions in macroeconomics.

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Okun’s Law Q&A

Q: Does Okun’s Law still hold in a world of AI-driven productivity?

A: Not in the way it used to. Okun’s Law is built on the assumption that economic growth and employment are tightly linked—that you need more workers to produce more output. But AI breaks that relationship. As machines and algorithms increasingly perform tasks once handled by humans, we can now generate more economic output without increasing labor—and in some cases, while reducing it. This means GDP can rise even as employment stagnates. In an AI-driven economy, productivity gains are no longer necessarily tied to job creation. Instead, they are increasingly tied to capital investment in technology, data infrastructure, and software—decoupling growth from traditional labor dynamics. In this way, AI doesn’t just change how we work—it challenges the very models we’ve used to understand the economy for decades.

Q: Has labor flexibility changed since the rise of the smartphone?

A: Dramatically. The smartphone, combined with cloud software and communication tools, has untethered work from fixed locations and traditional hours. This shift gave rise to the gig economy, remote work, and a new class of contingent labor. Today, businesses can scale labor up or down with far greater speed and precision than in past economic cycles. This flexibility dampens the traditional signals of downturns—like mass layoffs or unemployment spikes—because companies can trim hours, shift to freelancers, or automate tasks rather than eliminate full-time jobs. In this way, the smartphone era has redefined labor dynamics, contributing to why recessions may appear “transitory” or more fragmented in data, even when structural adjustments are underway.

This is why when I say there are no recessions anymore, this relationship has changed forever. To continue using a rearview mirror economic philosophy from before the smartphone and especially AI is, to me, the very definition of refusing to unlearn.

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“The obstacle is the path.”

Perhaps the most difficult part of unlearning is the emotional toll. Waitzkin built his identity around mastery—first in chess, then in structured thought. Stepping into a more chaotic martial discipline forced him to not only rebuild technique but to confront the ego built on expertise. For many navigating today’s AI transition, this feels familiar.

Credentials, status, and institutional knowledge are being disrupted by technologies that can outperform humans in narrow domains. That sense of loss—of status, of control, of predictability—is real. But it’s not the end of value; it’s the beginning of reinvention. As Waitzkin learned, embracing chaos doesn’t mean giving up—it means evolving. And it’s in that discomfort that our most human capabilities—creativity, intuition, judgment—can reemerge with new power.

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“The art of learning is to become resilient, not perfect.”

Waitzkin’s final—and perhaps most essential—message is that learning is not about flawless execution, but about durable transformation. It’s not a ladder you climb once; it’s a cycle you return to again and again. In the AI era, this perspective is liberating. No one will master it all. Tools will evolve, systems will break, paradigms will shift—and then shift again. There are new capabilities and applications emerging every day. Just this week, listening to Sergey Brin on the All-In Podcast and Eric Schmidt on a TED Talk, I heard two of the smartest technologists alive admit how overwhelmed they are by the speed of AI’s progress. Their takeaway was clear: stay resilient, stay in motion, and be ready to move on to the next new thing—quickly.

What matters most is not having the perfect answer, but having the mindset to begin again—without ego, without fear, and with a sense of curiosity. We’re entering a future where every form of expertise will be tested. But if we embrace the mindset Waitzkin models—humble, open, and resilient—we won’t just survive disruption. We’ll grow because of it.