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In 2014, I left a secure job at Goldman Sachs to start a nonprofit. On paper, it looked like a reckless move: no funding, no team, barely any experience. But it was the best decision I ever made because it taught me that adaptability matters more than certainty. While you cant control President Trumps second term, you can control how you respond to it by learning to work with uncertainty. As his policies rock supply chains, jobs, and lives, the best career plan is the one that can bend and flex. Transformation But lets be clear: Its not just Trump driving this uncertainty. AI and automation are transforming entire industries. Generational shifts are changing how people work and what they value in their careers. No matter whos in the White House, uncertainty is constant. The message couldnt be more explicit: Nothing is guaranteed except the importance of adaptability. This is what I call Trump-proofing your career, and its not about being anti-Trump. Whether you support him or not, his leadership brings unpredictability, and your career plan cant hinge on any one leader or policy. It must be built to flex and shift with the world around you. The old idea of climbing a single career ladder no longer holds up. In today’s job market, staying in the same role for too long can hold you back. According to HRreview, workers who change jobs regularly earn, on average, 31% more than those who stick around in the same job for years. The best plan isnt a perfect five-year road map. Its about treating your career like an ongoing experiment, in which trying new roles, taking smart risks, and building transferable skills is more important than following a linear path. This mindset keeps you adaptable and engaged in a world thats changing faster than any one job can keep up with. The ripple effects of this new reality are already apparent. More than 120,000 U.S. federal workers have lost their jobs or been targeted for layoffs in 2025, a stark reminder that even government work, once considered the gold standard for stability, isnt immune to sudden change. THE PLANNING FALLACY According to psychologists, the planning fallacy is how we fool ourselves into thinking the future will follow our plans. Ive seen this firsthand. At 22, I thought I wanted to work in finance. I had spent years pursuing that path, convinced it was the surest way to build a successful career. But once I got there, I realized that the skills I wanted to develop and the goals I cared about didnt match what I was doing. The daily work didnt challenge me in the ways I needed, and it didnt lead me in a meaningful direction. I realized that sticking with a path that didnt fit was actually riskier than stepping into the unknown. So I did it. I moved back to Canada to build something that felt real and important, which pushed me to grow in the right ways. This led me to founding my nonprofit, Venture for Canada, which raised $80 million and empowered more than 10,000 young professionals to launch their careers. Most people thought I was out of my mind. But I learned that real progress in your career and life happens when youre willing to adapt your skills and goals to match what you and the world at large need most. Not everyone can walk away from a steady paycheck. My story is just one example. But adaptability isnt about giant leaps. Its about small experiments that keep you aligned with what matters most. FOCUS ON OBJECTIVES AND KEY RESULTS One tool thats made a real difference for me is using objectives and key results. OKRs are a great way to break down overwhelming goals into small, measurable steps. Instead of mapping out the next 10 years, focus on the next three months. Pick one meaningful short-term objective, like exploring mission-driven work or building skills in a new sector. Then set two or three key resultssmall, specific actions you can track. At the end of three months, look back. What worked? What didnt? Where do you need to pivot? Heres how I explain this in my upcoming book, The Uncertainty Advantage: First, identify your top three personal values. For example, if youre in marketing, your values might be creativity, collaboration, and growth, which inspire you when the world is unpredictable. Second, set one short-term objective that aligns with those values. Dont worry about the next decade. Focus on what you can start todaysomething specific and achievable, like launching a new marketing campaign that pushes your creative skills and brings your team together. Third, define two or three key results to measure your progress. In this marketing example, your key results might be testing three campaign concepts, meeting with two colleagues to brainstorm fresh ideas, and sharing early results with your manager within the month. Theyre small steps that build momentum, keep you learning, and help you stay adaptable. TREAT YOUR CAREER LIKE AN EXPERIMENT For some, adaptability might mean staying the course in a stable job. For others, it might mean pivoting into something entirely new. The key is to treat your career like an experiment. If you treat your next move as a chance to test what you care about and what you can build, you can shift from panic to purpose. I think of a friend who shifted from teaching to technical program management and now wants to work in AI. He didnt have a 10-year plan. He focused on what sparked his curiosity and where he wanted to grow. It wasnt about having all the answers. It was about testing, learning, and staying true to his values. So heres my challenge to you: Treat your career like the most crucial experiment of your life. Stay curious. Stay connected to what matters. Keep testing new ideas. Because in a world that can shift overnightand it willthe only plan that keeps working is the one youre willing to adapt.
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E-Commerce
For centuries, weve believed that the act of thinking defines us. In what is widely considered a major philosophical turning point, marking the beginning of modern philosophy, secular humanism, and the epistemological shift from divine to human authority, the French philosopher and mathematician René Descartes (15961650) famously concluded that everything is questionable except the fact that we think, Cogito, ergo sum(I think, therefore I am). Fast-forward a few hundred years, however, and in an age where generative AI can produce emails, vacation plans, mathematical theorems, business strategies, and software code on demand, at a level that is generally undistinguishable from or superior to most human output, perhaps its time for an update of the Cartesian mantra: I dont think . . . but I still am. Indeed, the more intelligent our machines become, the less we are required to think. Not in the tedious, bureaucratic sense of checking boxes and memorizing facts, but in the meaningful, creative, cognitively demanding way that once separated us from the rest of the animal kingdom. The irony, of course, is that only humans could have been smart enough to build a machine capable of eliminating the need to think, which is perhaps not a very clever thing. Thinking as Optional Large segments of the workforce, especially knowledge workers who were once paid to think, now spend their days delegating that very function to AI. In theory, this is the triumph of augmentation. In practice, its the outsourcing of cognition. And it raises an uncomfortable question: if we no longer need to think in order to work, relate to others, and carry out so-called knowledge work, what is the value we actually provide, and will we forget how to think? We already know that humans aren’t particularly good at rationality. Nobel laureates Daniel Kahneman and Amos Tversky showed that we mostly operate on heuristics (fast, automatic, and error-prone judgments). This is our default System 1 mode: intuitive, unconscious, lazy. Occasionally, we summon the energy for System 2(slow, effortful, logical, proper reasoning). But it’s rare. Thinking is metabolically expensive. The brain consumes 20% of our energy, and like most animals, we try to conserve it. In that sense, as neuroscientist Lisa Feldman Barrett noted, the brain is not for thinking; its for making economic, fast, and cheap predictions about the world, to guide our actions in autopilot or low energy consumption mode. So what happens when we create, courtesy of our analytical and rather brilliant System 2, a machine that allows us to never use our brain again? A technology designed not just to think better than us, but instead of us? Its like designing a treadmill so advanced you never need to walk again. Or like hiring a stunt double to do the hard parts of life, until one day, theyre doing all of it, and no one notices youve left the set. The Hunter-Gatherer Brain in a High-Tech World Consider a parallel in physical evolution: our ancestors didnt need personal trainers, diet fads, or intermittent fasting protocols. Life was a workout. Food was scarce. Movement was survival. The bodies (and brains) weve inherited are optimized to hoard calories, avoid unnecessary exertion, and repeat familiar patterns. Our operating model and software is made for hungry cavemen chasing a mammoth, not digital nomads editing their PowerPoint slides. Enter modernity: the land of abundance. As Yuval Noah Harari notes, more people today die from overeating than from starvation. So we invented Ozempic to mimic a lack of appetite and Pilates to simulate the movement we no longer require. AI poses a similar threat to our minds. In my last book I, Human, I called generative AI the intellectual equivalent of fast food. It’s immediate, hyper-palatable, low effort, and designed for mass consumption. Tools like ChatGPT function as the microwave of ideas: convenient, quick, and dangerously satisfying, even when they lack depth or nutrition. Indeed, just like you wouldnt choose to impress your dinner guests by telling them that it took you just two minutes to cook that microwaved lasagna, you shouldnt send your boss a deck with your three-year strategy or competitor analysis if you created with genAI in two minutes. So dont be surprised when future professionals sign up for thinking retreats: cognitive Pilates sessions for their flabby minds. After all, if our daily lives no longer require us to think, deliberate thought might soon become an elective activity. Like chess. Or poetry. The Productivity Paradox: Augment Me Until Im Obsolete Theres another wrinkle: a recent study on the productivity paradox of AI shows that while the more we use AI, the more productive we are, the flip side is equally true: the more we use it, the more we risk automating ourselves out of relevance. This isnt augmentation versus automation. Its a spectrum where extreme augmentation becomes automation. The assistant becomes the agent; the agent becomes the actor; and the human is reduced to a bystander . . . or worse, an API. Note for the two decades preceding the recent launch of contemporary large language models and gen AI, most of us knowledge workers spent most of their time training AI on how to predict us better: like the microworkers who teach AI sensors to code objects as trees or traffic lights, r the hired drivers that teach autonomous vehicles how to drive around the city, much of what we call knowledge work involves coding, labelling, and teaching AI how to predict us to the point that we are not needed. To be sure, the best case for using AI is that other people use it, so we are at a disadvantage if we dont. This produces the typical paradox we have seen with other, more basic technologies: they make our decisions and actions smarter, but generate a dependency that erodes our adaptational capabilities to the point that if we are detached from our tech our incompetence is exposed. Ever had to spend an entire day without your smartphone? Not sure what you could do. Other than talk to people (but they are probably on their smartphones). Weve seen this before. GPS has eroded our spatial memory. Calculators have hollowed out basic math. Wi-Fi has made knowledge omnipresent and effort irrelevant. AI will do the same to reasoning, synthesis, and yes, actual thinking. Are We Doomed? Only If We Stop Trying Its worth noting that no invention in human history was designed to make us work harder. Not the wheel, not fire, not the microwave, and certainly not the dishwasher. Technology exists to make life easier, not to improve us. Self-improvement is our job. So, when we invent something that makes us mentally idle, the onus is on us to resist that temptation. Because heres the philosophical horror: AI can explain everything without understanding anything. It can summarize Foucault or Freud without knowing (let alone feeling) pain or repression. It can write love letters without love, and write code without ever being bored. In that sense, its the perfect mirror for a culture that increasingly confuses confidence with competence: something that, as Ive argued elsewhere, never seems to stop certain men from rising to the top. What Can We Do? If we want to avoid becoming cognitively obsolete in a world that flatters our laziness and rewards our dependence on machines, well need to treat thinking as a discipline. Not an obligation, but a choice. Not a means to an end, but a form of resistance. Here are a few ideas: Be deliberately cognitively inefficientRead long-form essays. Write by hand. Make outlines from scratch. Let your brain feel the friction of thought. Interrupt the autopilotAsk yourself whether what youre doing needs AI, or whether its simply easier with it. If its the latter, try doing it the hard way once in a while. Reclaim randomnessAI is great at predicting what comes next. But true creativity often comes from stumbling, wandering, and not knowing. Protect your mental serendipity. Use genAI to know what not to do, since its mostly aggregating or crowdsourcing the wisdom of the crowds, which is generally quite different from actual wisdom (by definition, most people cannot be creative or original). Teach thinking, not just promptingPrompt engineering may be useful, but critical reasoning, logic, and philosophical depth matter more. Otherwise, were just clever parrots. Remember what it feels like to not knowCuriosity starts with confusion. Embrace it. Lean into uncertainty instead of filling the gap with autocomplete. As Tom Peters noted, if you are not confused, you are not paying attention. Thinking Is Not Yet Extinct, But It May Be Endangered AI won’t kill thinking. But it might convince us to stop doing it. And that would be far worse. Because while machines can mimic intelligence, only humans can choose to be curious. Only we can cultivate understanding. And only we can decide that, in an age of mindless efficiency, the act of thinking is still worth the effort, even when it’s messy, slow, and gloriously inefficient. After all, I think, therefore I am was never meant as a productivity hack. It was a reminder that being human starts in the mind, even if it doesnt actually end there.
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E-Commerce
Enterprises are on track to pour $307 billion into AI in 2025more than $35 million dollars every hour. Yet most of that cash will never see daylight: an S&P Global survey found that 42 percent of companies scrapped most of their AI projects this year. The problem isnt funding or ambition; it is a failure to see that the moonshots need to be balanced by sure things, the stretch goals by easy wins. AI’s true transformative power emerges not from any single initiative but when leaders orchestrate a portfolio of projects that runs the gamut from the revolutionary to the routine. The organizations that will thrive in this new era are those that pursue both the audacious bets that can redefine their industry and the mundane victories that provide the resources to fund the journey. These modern alchemists understand that transformation requires both vision and groundwork, both aspiration and application. And they know that going all in on a single idea offers an almost guaranteed path to failure. The Innovation Portfolio Just as financial portfolios balance risk and return across diverse investments, organizations approaching AI need to develop what we call an “innovation portfolio”a carefully curated collection of AI initiatives that offer multiple paths to transformation while effectively managing risk. This portfolio approach responds to a fundamental truth about innovation: long-term success requires a pipeline of projects that vary in their size, scope, risk, and transformative power. The portfolio and financial management approach allows organizations to maintain a comprehensive view of potential AI projects and to systematically manage their development. Think of it as the difference between a chess grandmaster who sees the entire board versus a novice fixated on individual pieces. The portfolio approach enables leaders to understand how different AI initiatives interact, where synergies might emerge, and how risks in one area might be balanced by stability in another. Crucially, it also lets leaders orchestrate a combination of big and small bets, long- and short-term plans, that fit the businesss needs and resources. Some projects will deliver value immediately while others represent longer-term bets on emerging capabilities that might fundamentally reshape entire industries. By maintaining a portfolio that encompasses both time horizons and risk profiles, organizations create the conditions for sustainable innovation rather than sporadic breakthroughs. The CEO as Chief AI Orchestrator The transformative power of AI is so great that it demands a fundamental change in the role of the CEO. In this new landscape, AI strategy cannot be delegated to the CTO alone. The CEO must become the chief orchestrator of the AI portfolio, balancing competing priorities while maintaining strategic coherence. While a foundational AI tech literacy is essential for making informed decisions, this doesn’t mean that CEOs need to understand the technical minutiae at a highly granular level. Instead, they must excel in three critical areas: Vision Setting: The CEO must articulate how AI aligns with organizational purpose. When employees grasp AI’s significance beyond its ability to deliver financial gains, adoption accelerates and resistance diminishes. Resource Allocation: Making tough decisions about which AI initiatives receive funding and attention is vital. This demands the courage and authority to discontinue promising projects that don’t align with strategic priorities. Cultural Transformation: Most critically, CEOs must embody the shift in mindset that AI requiresembracing uncertainty, celebrating intelligent failures, and demonstrating continuous learning. When the CEO publicly shares their AI learning journey, including their mistakes, it empowers organizational experimentation. The Macro-Micro Balance A successful AI portfolio should operate on two levels simultaneously. At the macro level, you’re asking profound questions: How might artificial general intelligence reshape entire industries? What happens when AI agents take over most knowledge work? How should a company be reconfigured to make the most of a hybrid human-AI workforce. These aren’t philosophical musingsthey’re strategic imperatives that guide long-term positioning. But here’s where organizations often stumble: they become so intoxicated by grand visions that they neglect the micro-level victories that are necessary to fuel the journey. At the same time as planning for whole-of-organization transformation, you also need to ask what your company can do this quarter. Can you use an algorithm to optimize delivery routes? Is there a commercially available chatbot you can use to process customer inquiries? The mundane funds the miraculous. Strategic Priority Mapping Not all AI initiatives deserve equal resources. Comprehensive frameworks for harnessing AI’s potential and managing its risks, such as the OPEN and CARE frameworks, provide systematic tools for evaluating capacities and needs. For instance, the OPEN frameworks FIRST assessment provides a tool for rapid viability screening Feasibility: Can current technology deliver your vision? Don’t confuse science fiction with strategic planning. Investment: What’s the true costnot just dollars, but organizational attention and cultural capital? Risk/Reward: Map the potential downside as well as the upside. Remember, though, that the biggest risk might be doing nothing. Strategic Priority: How closely does this idea align with our core purpose? An AI initiative that is at odds with your organizations identity and goals is doomed regardless of its technical merit. Time Frame: Can you sustain investment long enough to see returns? Many AI projects fail not because they were wrong, but because they are too early. The Continuous Evolution Model Static strategies die in dynamic environments. Your AI portfolio needs built-in adaptation mechanisms: Regular Rebalancing: Quarterly reviews of project mix. Are you maintaining appropriate risk levels? Have new capabilities opened fresh opportunities? Learning Loops: Every experiment feeds strategic understanding. Failed projects often teach more than successful ones. Cultural Evolution: Organizations must embrace perpetual beta. Yesterday’s mindset won’t create tomorrow’s success. From Theory to Practice A financial services firm might simultaneously pursue: A moonshot project using AI to predict market movements with unprecedented accuracy A medium-risk initiative automating compliance reporting Several low-risk projects improvig customer service chatbots Each initiative serves distinct portfolio purposes. The moonshot could transform the business model entirely. Compliance automation delivers clear ROI within 18 months. Chatbot improvements show immediate returns while building AI capabilities. The CEOs role is to ensure that each initiative receives appropriate resources while maintaining portfolio balancenot picking favorites, but orchestrating the symphony. The Transcendence Factor Ultimately, successful AI portfolios recognize a profound truth: AI isn’t just about efficiency or cost reductionit’s about transcending current limitations entirely. But transcendence requires groundwork. Like alchemists purifying base materials before transformation, your AI journey begins with the mundanecleaning data, upskilling teams, running small experiments. These pedestrian activities build toward something greater: a point at which AI doesn’t just improve existing business operations but enables entirely new possibilities that were previously unimaginable. Who will win? The organizations that will thrive in the age of AI won’t be those that bet everything on a single strategy. The winners will be those who build diversified portfolios that balance transformational ambitions with incremental improvements, macro visions with micro victories, human wisdom with machine capabilities. For CEOs, this balancing act isn’t optional. Leaders who treat AI as just another type of new technology have already lost. Those who recognize its power to fundamentally transform both companies and markets are the ones who will write the next chapter in business history.
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E-Commerce
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