I am a teacher and a speaker. Sometimes, I give the same talk dozens of times. The slides are familiar. The structure is rehearsed. The jokes have been road-tested in boardrooms and classrooms.

You might think that this kind of repetition would dull the experience. That I would get bored, go on autopilot, phone it in. That the audience would sense that I am merely going through the motions, delivering material the way a vending machine dispenses cans.

This never happens, because I never allow myself to forget an asymmetry. For me, it may be the hundredth time. For the person in the third row, it is the first and possibly the only time. That asymmetry changes everything about how I show up.

Repetition is not the enemy of a great performance. It is the precondition for one.

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Experiential Asymmetry and Quiet Responsibility

A performer and the audience occupy the same room, but they inhabit completely different realities. One has seen the movie a hundred times. The other is watching it in wonder for the first time. I think of this as experiential asymmetry, and once you see it, you cannot unsee it. This asymmetry carries a quiet but serious responsibility. My hundredth delivery must feel, to the listener, like it was crafted just for them. Not because I am pretending, but because I have done the deeper work of staying genuinely present inside familiar territory.

Here is what I do. I keep the skeleton of the talk intact - the core arguments, the logical arc, the key frameworks. But I bring new flesh and blood to the narrative each time. I swap stories. I change metaphors. I adjust the voiceover while keeping the same slides underneath. I read the room and shift tone, pace, and emphasis based on what I sense.

Most importantly, I try to enter what musicians call the zone. Mihály Csíkszentmihályi called this state “flow”: the point where skill meets challenge and self-consciousness falls away. For me, it means drawing on something childlike in myself, a genuine curiosity about the ideas, as though I am discovering them alongside the audience rather than delivering practiced conclusions. This is not an act. It is a practice. And it produces something that feels alive rather than replayed.

The paradox is worth pausing on. Because I am not worried about what to say, I can pour all my attention into how it lands. Mastery of the content frees me to be present with the people. The preparation disappears into the performance, the way a jazz musician’s thousands of hours of practice disappear into an improvised solo.

Which brings me to the metaphor at the heart of this piece.

Jukebox vs. Jazz

A jukebox plays the same song the same way every time. Perfectly reproduced. Perfectly indifferent. There is no room for the moment, no awareness of who is listening, no variation in feeling or emphasis. It is reliable, but it has no creativity.

Jazz plays within a structure but responds to what is happening in the room. It breathes. It listens. It wanders and riffs and circles back to the theme. The melody is recognizable, but the performance is unique every time because it belongs to this audience, this evening, this particular collision of energy and attention.

This is the distinction that separates competence from artistry in any domain that involves repetition.

The Discipline of Wonder

Consider Taylor Swift on the Eras Tour. Over nearly two years, she performed 149 shows across 51 cities and five continents. Each show ran over three hours and featured more than 44 songs choreographed into ten distinct acts. The production was engineered down to the minute. The songs were, by definition, the same night after night.

For the Swifties in the crowd, none of that mattered. Each concert was a once-in-a-lifetime event. Months of anticipation. Friendship bracelets traded like sacred tokens. Emotional peaks that would be replayed for years. Over ten million fans bought tickets, many traveling hundreds of miles to be there. No one in that stadium experienced the show as routine.

For Taylor Swift, it was also work. Physically demanding, emotionally exhausting, rigorously professional work. She cancelled only two shows across the entire run. And yet no serious observer would say she was going through the motions. What made the difference was not novelty. It was interpretation. She performed different surprise songs at every single show, drawing from a pool of 145 tracks across her discography. She adjusted lyrics to mark the moment. She read the crowd and responded to signs in the audience. She created small, spontaneous gestures that made each night feel like it belonged only to the people in that arena.

She played jukebox material with a jazz sensibility. And that is why the Eras Tour became something closer to a cultural pilgrimage than a concert series, grossing over two billion dollars and becoming the highest-earning tour in the history of live music.

Experiential Asymmetry Is Everywhere

Once you develop eyes for this pattern, you find it across every industry and role where one person’s routine is another person’s milestone.

A Disney cast member greeting a child who has counted down the days for months. A tour guide explaining a cathedral for the thousandth time to someone seeing it for the first. A nurse delivering test results that are, for her, a Tuesday afternoon briefing and, for the patient, a life-altering moment. A TSA agent repeating the same instruction hundreds of times a day to travelers who are anxious, disoriented, or simply having a terrible morning.

In every case, the interaction is deeply asymmetric. For one party, it is routine. For the other, it is anything but. And the professional who recognizes this asymmetry, who refuses to let their own familiarity flatten someone else’s experience, is the one who turns a transaction into a connection.

Professional excellence is not about pretending every moment is magical. It is about recognizing that someone else’s wonder often lives inside your routine and choosing to honor it.

A Practice, Not a Performance

I want to resist turning this into a tidy five-step framework, because the heart of the idea is more like a discipline than a technique. But there are practices that help, and they are worth naming.

Master the structure so it disappears. Know your material well enough that it no longer consumes your attention. Expertise is not about showing off how much you know. It is about freeing yourself to be human in the moment. A jazz musician who is still thinking about chord changes cannot listen to the room.

Vary the expression, not the essence. Change the stories, the metaphors, the examples, the pacing. Variation keeps you engaged with your own material, and engagement is contagious. Audiences do not catch your ideas. They catch your energy.

Play for the room you are in. Every audience has a rhythm. Some rooms are skeptical and need to be won over slowly. Some are eager and want you to match their pace. Energy is not volume. It is the ability to listen while you speak.

Perform for one. Before you begin, imagine one person in the audience for whom this moment truly matters. Perhaps it is the young professional who will rethink their career because of something you say. Perhaps it is the student in the back row who almost did not come today. That person becomes your anchor. You play for them.

Protect presence, not enthusiasm. You do not need to be “on” every second. You need to be genuinely present at the moments that count. One authentic pause, one honest aside, one real connection can outweigh ten minutes of polished delivery. Presence is not a performance. It is a choice to show up fully, even when the content is familiar.

The Musician’s Return

There is a concept in Indian classical music called riyaz, the daily practice through which a musician internalizes ragas so completely that performance becomes an act of expression rather than execution. The notes are known. The structure is given. But within that structure, the musician finds freedom, because mastery has made the scaffolding invisible.

I think about riyaz every time I step onto a stage. I know the slides. I know the arc. I know where the laughter tends to come and where the silence deepens. The audience does not. And that difference is not a burden. It is a gift. Because it means I get to watch someone encounter an idea for the first time, and if I have done my job, I get to feel the electricity of that encounter as though it were my first time too.

So I show up as a musician, not a machine. I play the same tune, but I listen for new notes. I wander, I riff, and I return to the theme. And in doing so, I rediscover why I love this work.

Because for someone in the room, this is not repetition.

It is a moment.

And moments deserve jazz.

I often get asked a question about AI - isn’t it the great equalizer for competence? Everyone now has access to the same frontier models. A first-generation college student in rural India can use the same Claude or GPT that a McKinsey partner uses. The playing field, the argument goes, has been leveled. This argument seems logical. But it is wrong. And believing it is one of the most dangerous mistakes a knowledge worker can make today.

Every wave of technology disruption generates the same leveling narrative. The internet was supposed to democratize commerce. Social media was supposed to democratize influence. MOOCs were supposed to democratize education. In every case, the tools became universal while the advantages did not. What happened instead was a pattern: the floor rose for everyone, the ceiling rose faster for the few, and the middle got compressed.

AI is Microsoft Word at civilizational scale.

When word processors arrived, everyone had the same tool. Having access to Word did not make anyone Shakespeare. The tool was universal. The talent was not. What happened was predictable: the minimum acceptable quality of a written document rose sharply, while the distance between competent and exceptional grew wider, not narrower. AI is doing the same thing, but across every domain of knowledge work, simultaneously, and at a pace that leaves no time for gradual adjustment.

The floor has been raised dramatically, suddenly, and for almost everyone. The ceiling has also risen, but only for those who already had the height to reach it. The distance between the floor and the ceiling has not shrunk. It has expanded.

The Hollowing of the Middle

For decades, organizations built massive throngs of credentialed middle knowledge workers: analysts, associates, coordinators, junior managers, and specialists of every variety. These people turned senior judgment into structured output. They synthesized research, built models, prepared presentations, drafted communications, and managed the operational metabolism of large enterprises. They were the connective tissue of the knowledge economy.

AI narrows that gap aggressively. The tasks that defined middle-tier knowledge work, synthesis, summarization, structured analysis, first-draft production, research compilation, presentation building, are precisely the tasks that AI now performs at or above the level of a competent junior professional. Not in the future. Today.

I see this in my own classroom. Five years ago, the students who excelled at case analysis were the ones who could grind through data, build clean spreadsheets, and produce well-structured slide decks. Those students had an edge because execution was hard. Today, execution is table stakes. AI handles it. The students who stand out now are those who ask better questions, reframe problems in surprising ways, and exercise judgment that the model cannot. The bar for differentiation has migrated upward, and it did so in about eighteen months.

Consider the product manager. Five years ago, a solid PM could differentiate herself by writing crisp user requirements, synthesizing user research into clear insight summaries, building competitive landscapes, and structuring sprint backlogs with care. That was the job. Those were the skills that built a career. Today, any PM with a Claude subscription can produce a first draft of a requirements document in ten minutes that would have taken two days to write manually. Competitive landscapes, feature comparison matrices, user journey maps: AI generates all of these at a quality level that meets or exceeds what most mid-career PMs produce on their own.

So what separates the exceptional PM from the rest? Not the ability to produce artifacts. The ability to decide which product to build and why. The ability to see a market signal that the data does not yet confirm. The ability to say no to the feature that customers are asking for because you understand the job they are actually hiring the product to do, and that job points somewhere else entirely. The ability to hold a room of engineers and executives in a prioritization debate and make a call that balances technical debt, business model economics, competitive timing, and customer psychology, all at once, in real time, with incomplete information. AI cannot do that. AI will not do that for a very long time. But the PM who cannot do that either is now competing against a machine for the tasks she used to own.

The middle is caught in a compression. The floor has risen to meet them from below. The ceiling has pulled away from them above. The comfortable plateau of “competent and credentialed” is disappearing.

The Wedge That Must Not Close

To understand what separates those who will thrive from those who will merely survive, consider a simple mental model: the wedge.

Imagine two lines on a graph. One line represents the advancing capability of AI: its ability to produce high-quality knowledge work output. That line rises steeply and without foreseeable limit. The second line represents the unique capability of a given human professional: the judgment, taste, creativity, contextual awareness, and synthesis ability that the person brings above and beyond what AI can produce. The vertical distance between these two lines at any given moment is the wedge. It is the person’s margin of relevance.

For most people in most jobs, that wedge is narrowing. AI’s capability line is ascending faster than human capability lines. The wedge closes from above.

The only sustainable response is to move the second line upward faster than the first line rises. Not by doing the same things better, but by consistently relocating to the frontier: to the tasks, questions, and forms of judgment that AI cannot yet perform. This is not a one-time migration. It is a permanent posture. The frontier is not a destination. It is a direction.

What does the frontier look like? It is not a fixed set of tasks. It is a set of characteristics. Frontier work is ambiguous: the problem is not well-defined, and framing it correctly is itself the value. Frontier work is integrative: it requires combining insights across domains that AI treats separately. Frontier work is high-stakes: the consequences of getting it wrong are significant, and no one is willing to delegate the decision to a machine. Frontier work is relational: it depends on trust, persuasion, negotiation, and the kind of contextual reading that emerges only from human interaction.

The knowledge workers who thrive will be those who stay perpetually ahead of the closing wedge, who treat AI not as a tool to do their current job faster, but as a displacement force that continuously redefines what their job must become.

What Raising the Ceiling Actually Looks Like

Let me make this concrete with an example from my own work, because abstraction is the enemy of action here.

I write business case studies. I have been doing it for thirty-five years, and the methodology I have developed is specific, opinionated, and built on thousands of hours of classroom testing. Could I ask Claude to “write me a business case study”? Of course. And it would produce something that looks like a case study. It would have a protagonist, a company context, some decision points. It would be competent. It would also be mediocre: generic structure, predictable analysis, no pedagogical design, no narrative tension.

So instead of using AI as a replacement for my judgment, I did something different. I carefully encoded my entire case writing methodology into a structured set of instructions for Claude: what makes a good case protagonist, how to create decision tension, how to structure exhibits, how to calibrate complexity for different classroom contexts. And I gave Claude examples of award-winning cases, and the ones that didn’t sell as well. I taught Claude my style, my tone, my brand guidelines, and my voice. And I encoded all this in a Claude Skill.

With that in place, Claude does not produce generic case studies anymore. It produces case studies that embody my methodology. The floor for a first draft rose dramatically. But here is the critical point: the ceiling rose even more, because I now spend my time on the work that only I can do: selecting the right company, identifying the non-obvious strategic tension, shaping the narrative arc, pressure-testing the teaching plan. The AI handles the execution. I invest in the judgment. I can now produce a polished and insightful case study in two days. Down from six months.

A great chef follows consistent technique: proper mise en place, correct knife cuts, precise heat control. They follow a structured method. The structure makes the dish consistent. But it also frees them up to infuse creativity . The paradox - structure enables creativity. It does not constrain it. The same is true for me as a “case chef”. Before I build my system, I spent so much cognitive energy on execution that I had less bandwidth for originality. Now I have more.

The AI did not replace my expertise. It amplified it. And the amplification is proportional to the expertise I brought to the table in the first place.

Two Gaps, One Fate

The capacity to migrate upward is not evenly distributed, and this is the uncomfortable truth.

There are two distinct gaps operating simultaneously in the AI economy. The first is a capability gap: the difference in what people are able to do with AI based on what they already know. The second is a fluency gap: the difference in what people are able to do with AI based on how well they know how to work with it. These gaps are different in kind, different in consequence, and different in what it takes to close them.

The capability gap is perhaps the less discussed but more consequential of the two. AI is a multiplier. It amplifies whatever stock of judgment, taste, domain expertise, and synthesis ability a person already has. Give the same AI tool to a thirty-year veteran of product strategy and to a freshly minted MBA, and the outputs will differ enormously, not because of the tool, but because of what each person brings to the collaboration. The veteran knows which questions to ask, which outputs to reject, which subtle signals in the data matter and which are noise. The rookie MBA does not. Not yet. The AI amplifies the gap between them rather than closing it.

If I hand my case writing methodology to a student who has never written a case study, they will get a dramatically better first draft than they would have gotten without it. The floor rises. But the distance between their output and mine will increase, because I am working with the same amplifier on top of a much deeper base of knowledge and pattern recognition.

The fluency gap is different. It is not about depth of domain knowledge. It is about a cognitive posture shift that some people make and others do not. The knowledge workers who genuinely understand AI treat it as collaborative intelligence. They iterate with it. They challenge its output. They give it context, constraints, and examples. They think of prompting as a form of creative direction, not a form of search. They build on AI’s output rather than accepting or rejecting it wholesale.

The people who have not made this shift interact with AI the way they interact with Google: type a question, get an answer, done. They prompt AI like they would prompt a junior analyst: give it a task, receive a deliverable, move on. They do not iterate, refine, co-create, or push back. The result is that two people with the same domain expertise can get wildly different outputs from the same AI, simply because one has learned to collaborate with the machine and the other has not.

These are not technical skills. They are mindset shifts, and they are teachable. The people who have not made them are not less intelligent. They are often simply less willing to abandon a mode of working that served them well for decades. That reluctance is human. It is also increasingly expensive.

The Strategic Imperative

For the individual knowledge worker, the implications are clear but not comfortable.

Survival requires closing the fluency gap immediately and without equivocation. AI avoidance is no longer a viable professional strategy. Those who refuse to engage with AI are not making a principled stand. They are falling behind in a race they have not yet realized they are running.

Bridging the capability gap is more difficult, and it demands self-awareness. The relevant question is not “am I using AI?” but “what is the quality of judgment I am bringing above AI’s output?” and “is that judgment appreciating or depreciating in value as AI improves?” If your primary value-add is synthesis that AI can now do, you do not have a moat. You have a memory.

The specific discipline of thriving is frontier migration. It means deliberately and continuously moving toward ambiguous problems, novel synthesis, and high-stakes judgment. It means building attribution: a body of work, a methodology, a point of view, a network of trust that is identifiably yours and cannot be replicated by a model trained on the collective average.

For organizations, the imperative is to resist the obvious but limited play of deploying AI purely for cost reduction, in favor of the more difficult but durable play: using AI to shift the composition of work toward higher-value activities. The companies that use AI only to eliminate headcount will save money in the short term and lose capability in the long term. The companies that use AI to move their people uphill, to free human judgment for the work that actually creates differentiation, will build organizations that are genuinely difficult to compete with.

The Tide is Rising - Move Uphill

The rise of AI capability is a tide. It is rising for everyone simultaneously, and it lifts certain boats spectacularly. But a rising tide also submerges everything that is not elevated enough to stay above the waterline. The tasks, roles, and competencies that sit just above the current water level will be underwater within months, not years.

Shakespeare did not become irrelevant when the printing press democratized access to text. He became more valuable, because the multiplication of words made the rarity of genuine literary judgment and creative brilliance more visible, not less. The printing press did not level the playing field between Shakespeare and the average pamphleteer. It widened the gap between them permanently.

AI will do the same to every domain of knowledge work. The question is not whether you have access to the tool. Everyone does. The question is what you bring to the tool that it cannot bring to itself.

The tide does not wait for you to learn to swim. It does not care about your credentials, your title, or your years of experience. It rises. The only question is whether you are moving uphill faster than the water.

This is the final installment in a three-part series on skills, jobs, and learning in the age of AI. Part 1, “AI Isn’t Taking Jobs. It’s Taking the Ability to Learn,” explored how AI disrupts not just work but the learning process that builds expertise. Part 2, “Philosophy, Plumbing, and Where the Jobs Will Be,” mapped the skills and fields that endure. This piece is for parents. It is the most personal of the three, and in some ways, the most important.

I am a parent. Like you, I want two things for my children. I want them to succeed now: good grades, strong test scores, admission to a respected university, the credentials that open doors. And I want them to be capable for life: resilient, adaptive, able to think independently and navigate an uncertain world.

These two goals have been somewhat conflicting. But AI has deepened the tension between these two goals. Today, your child can use AI to produce a flawless college essay, ace a homework assignment, generate a research paper with perfect citations, and assemble a portfolio that looks like the work of a gifted student. The transcript will be stellar. The admissions committee will be impressed. And underneath it all, the cognitive muscles that your child really needs to build do not get used and will slowly atrophy.

This is the parental dilemma of our times. You need to decide which game you want to play. Are you optimizing for your child’s GPA this semester, or for their cognitive capability over a lifetime? These two objectives are increasingly in conflict, and AI is driving a wedge between them.

The Efficiency Trap

We live in a culture that worships efficiency and treats struggle as a malfunction. When a student finds a homework assignment difficult, the instinct, for both the student and the parent, is to remove the difficulty. Google it. Watch a YouTube tutorial. Ask ChatGPT. Get the answer and move on.

This instinct feels rational. Why should my child spend two hours struggling with a calculus problem when AI can explain the solution in thirty seconds? Why should they agonize over an essay when a tool can produce a polished draft instantly? The logic of efficiency says: eliminate the friction, get the result, move to the next thing.

But here is what the logic of efficiency misses: the friction is the learning. The two hours of struggle with the calculus problem is where mathematical intuition gets built. The agony of the essay is where your child discovers what they actually think. The confusion, the dead ends, the moments of being stuck: these are not obstacles to learning. They are the mechanism of learning. Remove them, and you have removed the thing that produces a capable mind.

The analogy I keep returning to is exercise. “Why should I walk when I can drive?” feels perfectly rational, until you realize the walking was building your cardiovascular health and the driving is slowly weakening it. The effort was not the cost of getting somewhere. The effort was the benefit. AI, used carelessly, is the intellectual equivalent of driving everywhere. The destination looks the same. The body underneath is atrophying.

Parents must realize that they may be unconsciously encouraging the atrophy. Every time you say “just ask ChatGPT,” every time you smooth a path that was supposed to be rough, every time you prioritize the grade over the growth, you are trading long-term capability for short-term comfort. Your child will pay the price, not now, but in ten years, when they are sitting across from a problem that requires real judgment and they have never built the muscle to exercise it.

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What Parents Must Do

I have given you laudatory principles, but principles are useless without practical guidance. Here is my specific advice on “protecting the struggle”:

Protect the struggle window. There is a critical developmental period, roughly ages 12 to 22, where the cognitive foundations of expertise are being built. During this window, productive difficulty is the raw material for learning. This does not mean banning AI. It means sequencing it wisely. There are three stages in using AI wisely: Build, Spar, Orchestrate. Build means doing the foundational cognitive work yourself. Spar means using AI to challenge and pressure-test your thinking. Orchestrate means delegating with earned authority. The first stage must come first. There are no shortcuts, and parents are the ones who enforce the sequence when every other force in a teenager’s life is pushing them to skip ahead.

Change what you celebrate. If you praise your kids’ grades, you are rewarding the output. Your child hears: the result is what matters, by any means necessary. And AI is now the most efficient means available. Instead, try asking: “What stumped you this week? What did you struggle with? What did you get wrong and what did you learn from it?” Children internalize what their parents signal as valuable. If the implicit message is that struggle is failure, they will avoid it. If the message is that struggle is where growth happens, they will seek it out. This shift may seem subtle, but it may be the most consequential shift you can make as a parent.

Model learning yourself. Children learn more from watching than from listening. If your own relationship with AI is purely delegatory, if you ask it for every answer and never wrestle with a hard problem yourself, your child absorbs that pattern. But if you visibly engage in hard learning, reading a difficult book and talking about it, taking on an unfamiliar challenge, admitting what you do not know and then working to figure it out, you create a culture where intellectual effort is respected. The most powerful curriculum is not what you assign your child. It is what they see you do.

Emphasize capability, not credentials. Unfortunately, the college admissions system still rewards polished outputs, and AI makes polished outputs trivially easy to produce. Your child knows this. Their friends are using AI for applications, essays, and projects. Pretending otherwise is naive. The conversation you need to have is not “don’t use AI” but “what are you actually building?” Help them see the difference between a credential and a capability. Yes, the transcript matters. No, it is not the point. The point is to become someone who can do hard things, and the transcript should be evidence of that, not a substitute for it. This is a values conversation, not a rules conversation. Rules get circumvented. Values get internalized.

Rethink the status hierarchy. If you read Part 2 of this series, you know I argued that skilled trades are a smart career bet in an AI world. But here is the uncomfortable truth for many parents: you may agree with that argument intellectually while sending very different signals to your child. Children detect status signaling instantly. If you say, “trades are respectable” but your body language says “I would be disappointed if you became a plumber or a chef,” they will read the body language. If you want your child to pursue skill security, you need to respect the paths that offer it. That means talking about electricians and nurses with the same admiration you give consultants and software engineers. Not as a performance. As a conviction.

Teach them to be editors, not consumers. This may be the single most practical habit you can cultivate. Whenever your child uses AI for anything, ask them to critique the output. What did it get wrong? What would you change? What is missing? What does it assume that you would not? This one practice, treating AI output as a first draft to be improved rather than a final answer to be accepted, builds the critical judgment that separates the people who lead AI from the people who are led by it. It takes thirty seconds. It transforms the relationship with technology from passive consumption to active engagement.

Give them real responsibility with real consequences. Chores, part-time jobs, projects where their decisions matter and the outcomes are not simulated. The teenager who manages a small budget, runs a lawn-mowing operation, or volunteers where people depend on them is building judgment in a way that no classroom exercise and no AI tool can replicate. What matters here is not the specific activity. It is the experience of ownership: making decisions, living with the consequences, and learning from what went wrong. AI cannot give your child this experience. Only life can. And only you can make sure they encounter it.

The Hardest Part

I have given you a list of practical things to do. But I want to be honest about something: the hardest part of this is not knowing what to do. It is having the emotional fortitude to do it.

Watching your child struggle is painful. Every parental instinct says: help them. Fix it. Make it easier. And AI has made “making it easier” frictionless. The answer is always one prompt away. The polished output is always available. Saying no to that, or more precisely, saying “not yet,” requires a kind of faith that is genuinely difficult to sustain.

It is the faith that difficulty is not cruelty. That the answer you refuse to give them is a gift. That the frustration they feel tonight is building something inside them that will matter for the rest of their lives. Every wisdom tradition understands this. In Sikh philosophy, the path of discipline and devoted practice (“Naam Japna”) is not a punishment; it is the mechanism through which character is forged. Zen training places the student before the koan, a riddle that defeats conventional logic, not because the teacher enjoys watching the student suffer, but because the struggle itself is the curriculum. The koan cannot be outsourced. The transformation happens only through the direct encounter with difficulty.

Parenting in an AI age requires the same faith. Not blind faith. Informed faith. You are not withholding help to be cruel. You are protecting the process that builds a capable, independent, thinking human being. You are ensuring that when your child eventually picks up the most powerful cognitive lever in human history, they have something solid to stand on.

The Ground Beneath Their Feet

Yes, you must be the wind beneath the wings of your children. But more importantly, you must be the ground beneath their feet. Throughout this series, I have returned to Archimedes: “Give me a place to stand and a lever long enough, and I will move the world.” The lever is magnificent. It is more powerful than anything I have seen in my long career.

But the lever is not your job. The world will hand your child the lever. It is already handing it to them. Every app, every tool, every classroom is integrating AI at an accelerating pace. The lever will take care of itself.

Your job is the ground.

Your job is to help them build a place to stand: the judgment, the resilience, the pattern recognition, the first-principles thinking, the ethical clarity that comes only from years of doing hard things. Your job is to protect the struggle that builds that ground, even when it would be easier for both of you to skip it. Your job is to believe, with conviction, that the frustration your child feels today is not a problem to be solved but a foundation being laid.

Give them a place to stand. The lever will take care of itself.

* * *

Here is a truth that took me decades to fully appreciate: the quality of your life depends on many things, but few of them matter as much as the quality of the people in it. The right people expand you. They challenge your thinking, lift your energy, and bring out a version of you that you like. The wrong people do the opposite. They slowly erode you in ways you do not notice until you step back and wonder why you feel so drained.

As I get older, I’ve become far more intentional about who I give my time to. Not out of arrogance, but out of awareness. Life is finite, and energy is finite, and every hour spent with someone who leaves you depleted is an hour you could have given to someone who leaves you inspired.

The problem is that most of us make these decisions on vibes and intuition. We tolerate relationships that quietly cost us because they seem “fine.” We keep investing in people who have been running a deficit with us for years, confusing familiarity with value and proximity with friendship.

I want to offer a framework that you can use to choose who gets to be in the front row of your life. I propose three ratios that, taken together, give you a clear-eyed way to evaluate any relationship in your life.

Ratio 1: Saying to Doing Ratio

I work with a lot of entrepreneurs, and over the years I’ve noticed a recurring pattern. There is a type of person who speaks fluently in strategy, vision, and big ideas, the kind of person who is captivating in a room and can paint a picture of the future that makes you want to lean in. But when you circle back six months later and ask what got done, the cupboard is bare. The grand vision hasn’t moved an inch.

I like to say that strategy is 10% vision and 90% execution, and the ratio of what someone says they’ll do and what they do tells you a lot about their character. Think about your last several interactions with someone who matters to you. Were there positive surprises, moments where they went beyond what was expected, took initiative without being asked, showed real ownership over a shared problem? Or was there a pattern of commitments that evaporated, promises that were made with conviction and forgotten with ease?

A small story. I once asked my housekeeper to organize my closet. When I came home that evening, she had sorted my shirts by color, arranged my jackets by how frequently I wear them, and grouped my suits by season. I didn’t ask for any of that. She didn’t just follow instructions; she took ownership of what “organized” meant for me and delivered something I hadn’t even thought to request.

That is what a high saying-to-doing ratio looks like in practice. She walks the talk. Most people, I’m afraid, just stumble the mumble.

Ratio 2: Taking to Giving Ratio

Every relationship has a ledger, whether we acknowledge it or not. I encourage you to try a simple accounting exercise. Think of someone important in your life, a friend, a sibling, a colleague, a partner, and replay the last ten interactions you’ve had with them. Of those ten, how many were gives and how many were asks? How often did they reach out because they needed something from you, and how often did they reach out simply to give, to check in, to be present?

I have a colleague whose last words on every phone call are the same: “Is there anything I can do for you?” It’s not a throwaway line and it’s not performative. He means it every single time. That one small habit tells me everything about his orientation toward the world. He leads with contribution rather than extraction and being around him makes you want to do the same.

The deeper point is that giving and taking is a mindset, not a transaction. It’s not about who picks up the check or who sends a nicer birthday gift. It’s the difference between someone who is genuinely other-centered and someone who is fundamentally self-centered, between someone who shows up when you need them and someone who is conveniently unavailable, between someone who remembers your struggles and someone who only remembers your usefulness. Once you start paying attention to this ratio, you see it everywhere.

Ratio 3: Negativity to Positivity Ratio

You already know the difference between these two kinds of people, even if you’ve never had a name for it. I think of them as radiators and drains.

Radiators bring warmth, empathy, humor, and a glass-half-full energy to every room they enter. After spending time with them, you feel lifted, as though someone quietly recharged a battery you didn’t realize was low. Drains bring gossip, criticism, and a persistent undertone of pessimism. After spending time with them, you feel smaller, as though something has been subtracted from you that you can’t quite name.

Now, this is not about toxic positivity or expecting everyone to be cheerful all the time. Life is hard, and real friends sit with you in the hard parts without flinching. What I’m talking about is net energy over the long run. Does this person, on balance, add to your emotional, cognitive, and spiritual well-being? Or do they quietly deplete all three?

If you consistently feel drained after spending time with someone, that is data. Treat it with kindness but treat it seriously.

Putting It Together

When you stack these three ratios on top of each other, you get a simple but powerful diagnostic for any relationship in your life. Saying-to-doing tells you whether someone delivers or just declares. Taking-to-giving tells you whether they contribute or just consume. Negativity-to-positivity tells you whether they radiate or drain.

Someone who scores well on all three belongs in your front row, close to you, worth investing in deeply. Someone who consistently falls short on all three may deserve your compassion but probably needs to sit further back. Most people, of course, fall somewhere in between, which is exactly why the framework is useful. It replaces a vague sense of unease with a specific and honest assessment.

I want to be clear: this isn’t about cutting people out of your life with a cold calculus. It’s about being thoughtful with the most precious resource you have, which is your presence. Some relationships deserve deep investment. Others deserve warmth and well-wishing, but from a greater distance.

The Mirror Test

And here is the part that most people skip, the part that matters most.

This framework applies to everyone, including you. In fact, you are the most important person to run through these three ratios. Before you start evaluating the people around you, take an honest look in the mirror and ask yourself the same questions. Are you delivering more than you promise? Are you giving more than you take? Are you adding warmth to the rooms you walk into, or are you the one doing the draining?

The best way to attract good people into your life has always been the simplest: become someone worth being around. As the old saying goes, the best way to have a friend is to be one.

Improve your own ratios first, and the rest will follow.

This is the second in a three-part series on skills, jobs, and learning in the age of AI. Part 1, “AI Isn’t Taking Jobs. It’s Taking the Ability to Learn,” explored how AI disrupts not just work but the learning process that builds expertise. If you haven’t read it, I’d encourage you to start there. This piece gets concrete: what should students actually study, and where will the jobs be? Part 3, “Advice for Parents: Protect the Struggle,” will address how to raise capable humans when the easy path is always available.

* * *

Students heading to college ask me an urgent question: What should I major in?

It is the wrong question. Majors are institutional categories. They describe how universities organize departments, not how the world organizes value. Nobody hires a “political science major.” They hire someone who can analyze complex systems, write with clarity, and make a persuasive case under pressure. The major is just the container. The capabilities are the content.

In an AI world, this distinction matters more than ever. The right question is not “what should I major in?” but “what capabilities will remain valuable and become more valuable as AI gets better?” Job titles come and go. Skill portfolios are timeless. The student who builds the right portfolio will have options that no single major can guarantee. The student who picks a “safe” major without building underlying capabilities will discover that no major is safe.

In Part 1, I argued that AI is the most powerful cognitive lever in human history, but a lever without a fulcrum is just a stick. The fulcrum is human judgment, pattern recognition, and first-principles thinking, built through years of struggle and practice. This piece is about what that fulcrum is made of. What, specifically, should you study and build?

The answer involves more philosophy and plumbing than you might expect, and less coding than conventional wisdom suggests.

Think Skill Security, Not Job Security

Before I get to specific fields, let me introduce a concept that should reframe how you think about career preparation: skill security.

Job security means you hold the same position for a long time. It is a relic of an era when industries moved slowly and institutional loyalty ran in both directions. That era is over, and AI is accelerating its end. Entire job categories will be created and destroyed within your career span.

Skill security is different. It means you have mastered capabilities that will be valued regardless of which jobs exist. The specific role changes. The underlying abilities transfer. A person with strong analytical reasoning, clear communication, and the ability to orchestrate complex projects will find work in industries that do not yet exist, doing jobs that have not yet been named. That is skill security. It is the only kind worth pursuing.

So what are the skills that endure? I see five meta-capabilities that every student should be building, regardless of what major they choose.

1. Pattern recognition. The ability to see structural similarities between seemingly unrelated problems. This is what makes a great strategist, diagnostician, or investor. It is trained by exposure to breadth, not just depth. The student who studies history, economics, and biology will see patterns that the student locked into a single discipline will miss.

2. Judgment under uncertainty. Knowing when the data is sufficient, when to act despite ambiguity, when to trust the model and when to overrule it. AI can process information. It cannot take responsibility for a decision when the information is incomplete. And when decisions have serious consequences. That requires a human who has been wrong enough times to develop calibrated intuition.

3. Orchestration. Designing and managing systems where humans, AI agents, and processes work together. This is the skill of the conductor, not the violinist virtuoso. Whether you are orchestrating a marketing campaign, a construction project, or a clinical trial, the ability to see the whole system and coordinate its parts is increasingly the most valuable thing a professional does.

4. Communication and persuasion. The ability to translate complexity into clarity, to move people to action, to build consensus across conflicting interests. AI can draft a memo. It cannot own a relationship, stand behind a recommendation with its reputation on the line, or read a room full of skeptical executives. These are irreducibly human skills.

5. Ethical reasoning. As AI systems gain autonomy, someone has to set boundaries, define what “good” looks like, and take responsibility when things go wrong. This is governance, safety, and values-driven leadership. These are skills that philosophy and liberal arts teach you.

Fields That Will Win

With those meta-capabilities as the frame, let me walk through specific fields. I am going to be opinionated here, because my readers expect me to shoot straight, and not hedge.

Liberal arts. I will say it plainly: the liberal arts are the single best training ground for the cognitive skills AI cannot replicate. This is not the defensive “liberal arts still matter” argument that embattled humanities departments trot out at fundraising dinners. This is the offensive argument. Philosophy trains you to detect bad logic, and in a world flooded with AI-generated plausibility, detecting bad logic is a survival skill. History trains you to recognize patterns across contexts, which is the foundation of strategic thinking. Literature builds empathy and narrative skill, which are the foundations of leadership and persuasion. These are not soft skills. They are the hardest skills to automate and the hardest to acquire.

In a world drowning in AI-generated content, the scarce resource is not production. It is taste, editorial judgment, and the ability to say something worth saying. Liberal arts build these muscles.

Economics and mathematics. Economics is the grammar of incentives, tradeoffs, and systems. It teaches you to think about second-order effects, unintended consequences, and equilibrium dynamics. Every AI deployment decision is fundamentally an economics problem: cost-benefit under uncertainty, principal-agent tensions, market design. Economics also bridges quantitative and qualitative reasoning in a way few disciplines do.

Mathematics, particularly statistics, probability, and optimization, gives you the conceptual foundation to be a credible participant in any technical conversation without necessarily being the one writing the code. You do not need to build the model. You need to know how the model works, and when the model is lying to you. Mathematical fluency is the difference between being a consumer of AI and being its architect.

Computer science, redefined. AI writes code now, and it will write it better next year. Coding may be dying, but computer science lives. Computer science as a discipline of systems thinking, abstraction, and architecture remains powerful. The question is whether you are learning CS to be a coder or to be a systems architect who understands how software, data, and AI compose into larger systems. The latter is deeply durable. What is dying is the middle tier of implementation work. What is thriving is the ability to design, specify, evaluate, and govern complex technical systems. CS as vocational coding training is finished. CS as computational thinking is alive and well.

Design thinking. Design frames problems before solving them, by understanding context and constraints, prototyping and iterating. AI can generate a thousand options in seconds. A human has to decide which ones are worth pursuing. Whether it is product design, service design, organizational design, or policy design, this is the skill of the architect: setting the criteria, evaluating the options, and taking responsibility for the choices. AI is a spectacular option generator. It is a terrible judge. Judgment is where humans earn their keep.

Cognitive science. This is the sleeper field that few people talk about. As AI systems become more capable, the people who understand how humans think, perceive, decide, and err will be disproportionately valuable. Human-AI teaming, AI safety, user experience design, behavioral product design: all of these require deep understanding of cognition. If you want to build AI systems that actually work for humans, you need to understand how humans work.

Plumbing, Welding, and the Smartest Career Bet Nobody Is Making

Now let me make an argument that will surprise some readers and feel obvious to others. One of the smartest career moves a young person can make today is to learn a skilled trade.

The economic logic is solid. Start with what robotics researchers call Moravec’s Paradox: tasks that are easy for humans, like navigating a cluttered basement, diagnosing a strange rattle in an HVAC system, or running plumbing through a 90-year-old building with no two walls the same, are extraordinarily difficult for machines. High variability physical work in unpredictable environments is the last frontier of automation, not the first. Your plumber’s job is safer from AI than your financial analyst’s.

Next, consider supply. In the United States, we have spent decades steering every capable student toward four-year university degrees, creating a massive skilled-trades shortage. Electricians, welders, plumbers, and HVAC technicians have pricing power that many white-collar knowledge workers would envy. An experienced electrician in a major metro can earn $120,000 to $150,000 a year with no student debt and high job security. Try saying that about a freshly minted communications major.

Even better - AI blends beautifully with these trades. An electrician who uses AI to optimize energy systems, diagnose problems faster, and manage a crew with AI-powered scheduling becomes dramatically more productive. AI is a complement to physical-world expertise, not a substitute. The trades are not a fallback. They are a strategic choice.

The obstacle is cultural, not economic. We have created a status hierarchy where a philosophy major working at a coffee shop has more social prestige than an electrician earning six figures. That is a market inefficiency driven by signaling norms. I am calling it out plainly. If we are serious about preparing young people for an AI world, we need to talk about the trades with the same respect we give investment banking and consulting. The economics demand it, even if the social signals have not caught up.

Healthcare: Durable but Transformed

Nursing and medicine are structurally safe. Aging populations, chronic disease burden, and the fundamental human need for care from other humans ensure that healthcare demand is not going away. But the nature of the work will shift enormously.

AI will handle diagnosis support, treatment planning, administrative burden, and routine monitoring. Much of what medical students spend years memorizing will be handled by systems that are faster and more accurate than any human memory. Fewer radiologists will be needed to analyze medical images. What remains uniquely human: intricate surgery, clinical judgment under ambiguity, the ability to integrate AI recommendations with a specific patient’s context, emotional presence, the conversation that helps a scared patient make a difficult decision, and the ethical weight of choosing when to override the algorithm.

The advice for a student interested in healthcare: pursue it with conviction. But build your professional identity around physical skill, judgment and human connection, not around information mastery. The doctor who thrives in 2035 is not the one who memorized the most. It is the one who knows what to do when the AI’s recommendation does not match what they see in the patient’s eyes.

Where the Jobs Will Be

Let me move from fields of study to the actual landscape of work, because students rightly want to know: where do I end up?

The jobs that are growing sit at the intersection of human judgment and AI capability. They are roles where a human sets the direction, defines the quality standards, manages the exceptions, and takes accountability for outcomes, while AI handles speed, scale, and pattern-matching.

Every AI system needs someone to design it, someone to evaluate whether it is working, someone to handle the cases it gets wrong, someone to explain its outputs to stakeholders, and someone to decide when to override it. Those are all human roles, and they require the meta-capabilities I described earlier. They also require domain expertise: you cannot govern an AI system in healthcare if you do not understand clinical practice, and you cannot orchestrate AI in marketing if you do not understand customer behavior.

The jobs that are shrinking are the ones in the middle: roles defined by processing information, following established procedures, and producing routine outputs. These include much of traditional financial analysis, standard legal research, basic software development, routine content creation, and administrative coordination. Not because these tasks are unimportant, but because AI can now do them faster, cheaper, and often better.

This is why I keep coming back to skill security. The specific job titles of 2035 are unpredictable. But the capabilities that will be rewarded are not: judgment, orchestration, communication, ethical reasoning, and deep domain expertise that gives AI something meaningful to amplify.

Start Building Now

I want to close with something actionable, because advice without practice is just pontification. If you are a student, or someone advising a student, here are habits worth starting this week. Not because they guarantee a specific career, but because they build the kind of mind that AI amplifies rather than replaces.

Write by hand regularly. Not because handwriting is sacred, but because the slowness forces you to think before you write, to choose words deliberately, and to develop an internal voice that is yours. In a world where AI can generate fluent prose on any topic, having a distinctive voice is a competitive advantage.

Read long-form material without summarization tools. Build the stamina to sit with a 300-page book and extract meaning through your own effort. This is cognitive endurance, and it is disappearing. The ability to hold a complex argument in your head, follow its logic, and form your own view is exactly the capability that AI threatens to atrophy.

Argue positions you disagree with. Force yourself to argue for the other side. This builds intellectual flexibility and guards against the confirmation bias that AI can amplify when it tells you what you want to hear.

Build something physical. Woodworking, cooking, gardening, wiring a circuit, fixing an engine. Engage with the material world where feedback is immediate, honest, and cannot be prompt-engineered away. There is a reason that every wisdom tradition values craft: it teaches you that reality does not negotiate.

Practice being wrong. Keep a journal of predictions and beliefs. Review them. Notice where you were wrong. Update. This is the fundamental loop of learning, and it requires the intellectual honesty to confront your own errors rather than letting AI shield you from them.

* * *

The question I hear most often, “what should my kid study?,” assumes that the answer is a field. It is not. The answer is a set of capabilities that no field owns and no AI can replicate: the ability to think from first principles, to see patterns others miss, to communicate with precision and empathy, to make decisions when the data is incomplete, and to take responsibility for the outcome.

Build those capabilities, and every field is open to you. Skip them, and no degree will save you.

In Part 3, I will speak directly to parents. Because knowing what to build is only half the battle. The harder half is creating the conditions where your children actually build it. That means protecting the one thing every instinct tells you to eliminate: the struggle.

* * *

This is the first in a three-part series on skills, jobs, and learning in the age of AI. Part 1 maps the terrain: what AI is doing to how we learn, work, and build expertise. Part 2, “Philosophy, Plumbing, and Where the Jobs Will Be,” offers concrete guidance on which skills and fields survive. Part 3, “Advice for Parents: Protect the Struggle,” addresses the hardest question of all: how to raise a capable human when the easy path is always available.

* * *

I am asked this question almost every week.

It comes from parents at dinner parties. From executives in my programs who are thinking about their children, not their companies. The question takes different forms, but the anxiety underneath is always the same: What should my child study? Where are the jobs? How can kids learn to learn when AI can give them instant answers?

I have been a professor of marketing and technology at Northwestern for almost 35 years. I have taught tens of thousands of students, and I have advised senior leaders at some of the world’s largest technology companies. I will be honest with you: these questions have never been harder to answer than they are right now. The speed at which AI is reshaping the landscape of work and learning has no precedent in my career, and I have lived through the internet, mobile, cloud, and social revolutions.

I will try to answer these questions. I will not speak with the hubris of a futurist. I will speak with the perspective I have gained over decades building pattern recognition skills across technology and business. And from the point of view of someone who has spent the last seven years going deeper into AI than anything else in my professional life. This series is my honest attempt to share what I see.

Let me begin with a story about a lever. This story will anchor the three-part series.

The Precondition Everyone Forgets

“Give me a place to stand and a lever long enough, and I will move the world.”

Archimedes said this over two thousand years ago. Everyone remembers the lever. Almost nobody remembers the precondition. He did not say “give me a lever.” He said “give me a place to stand and a lever.” The fulcrum comes first. Without solid ground beneath your feet, the lever is just a stick.

This analogy is very apt for where we are with artificial intelligence. We are in a moment of collective infatuation with the lever. Every conference, every headline, every investor pitch is about the power of the tools. Don’t get me wrong. The tools are extraordinary. I have experienced the power, and I am in awe. The lever is real. And it is getting more powerful by the day.

But few people talk about the fulcrum. The fulcrum is human judgment. It is pattern recognition. It is the ability to frame a problem before solving it, to ask the right question before generating an answer, to know when the model is brilliant and when the model is lying to you eloquently. This fulcrum can only be built one way: through years of struggle, practice, error, and correction. There are no shortcuts. There never have been.

What AI Actually Disrupts

To understand why this matters, you need to understand what AI actually disrupts. It is not just automating tasks. It is disrupting the learning process that produces expertise.

Consider what happens when a student uses ChatGPT to write an essay. The obvious concern is cheating. But the deeper damage is invisible. When you write an essay yourself, you are not just producing text. You are clarifying a vague intuition. You are discovering what you actually believe through the discipline of articulating it. You are confronting the weakness in your own argument when you try to put it on paper, and it falls apart. The essay is the artifact. The thinking is the real learning. Outsource the thinking, and you have atrophied the learning.

This example applies to every knowledge task. When you ask AI to summarize a 30-page report, you skip the cognitive work of reading carefully and distinguishing what matters from what does not. That discrimination skill is the foundation of expertise. When you ask AI to write your code, you skip the debugging that builds your understanding of how systems actually work. When you ask AI to generate your strategy, you skip the messy human process of weighing tradeoffs under uncertainty that builds real judgment.

I call this problem premature abstraction, borrowing a concept from computer science. In programming, you are warned never to abstract too early, before you understand the underlying patterns. The same principle applies to learning. AI allows students and professionals to jump to high-level outputs before they have done the low-level work that makes those outputs meaningful. The product looks the same. The person behind it is fundamentally different.

Here is the sentence I want you to remember: you cannot supervise a process you have never done yourself. A senior partner at McKinsey can use junior analysts effectively because she has done the analysis herself thousands of times. She knows what good looks like. She can smell a flawed assumption in a spreadsheet. A student who has never built an argument from scratch cannot evaluate whether AI’s argument is sound. They lack internal calibration. They have no place to stand.

The Jobs Question, Honestly

Let me address the fear directly, because it hangs over this entire conversation. Will there be fewer jobs? Are our children walking into a world where human work is obsolete?

The leaders building these systems are not reassuring on this point. Sam Altman and Dario Amodei have both spoken publicly about the magnitude of the disruption ahead. Amodei’s vision of “radical abundance” through AI is optimistic in the long run but brutally honest about the transition. Altman has said that AI will eliminate many jobs and that society needs to prepare. These are not critics. These are the people building the most powerful AI systems on earth. When they express concern, it deserves serious weight.

Here is my take. Over a long enough horizon, say 20 to 30 years, I believe new forms of work will emerge that we cannot yet imagine, just as they have in every prior technological revolution. The internet did not produce net fewer jobs. It produced different jobs: jobs that no one in 1990 could have predicted. I expect the same pattern here.

But the transition will be savage in its distribution. And the speed is unprecedented. Previous technological revolutions played out over decades. AI is compressing that timeline to years. The middle tier of knowledge work, the credentialed-but-not-expert layer that processes information, synthesizes reports, and generates routine analysis, is being hollowed out right now. Not in five years. Now.. The people with genuine expertise can use AI as an extraordinary amplifier. The people who were coasting on credentials and process knowledge are discovering that AI can do what they do, faster and cheaper.

So the answer to “will there be fewer jobs?” is the wrong question. The right question is: fewer jobs for whom? And the answer is: for people who never built a place to stand. For people whose value was in execution, not judgment. For people who learned to produce outputs but never learned to think.

The new jobs, the ones that will be created, will go to people who can do what AI cannot: frame problems, exercise judgment under ambiguity, build trust with other humans, take ethical responsibility for outcomes, and orchestrate complex systems where AI is one component among many. These are the people with a fulcrum. AI is their lever. Everyone else is holding a stick.

Stories from Lived Experience

I want to make this concrete. Over the past few years, I have gone deeper into AI than any subject in my career. And I can tell you that the Archimedes principle is not a metaphor for me. It is my daily experience.

I built a custom AI system for writing business case studies. I encoded three decades of case-writing methodology into it: my structure, my style, my tone, my standards, my pedagogical logic. The foundation included insights from over 50 cases I have written over the years. The system now works the way I work. I can go from an idea to a solid first draft within a couple of hours. Astounding acceleration! But every creative decision, every structural choice, every judgment call is still mine. AI did not replace my expertise. It operationalized it. I had to build the expertise first before I could encode it.

I developed a framework called I-MOS, the Intelligent Marketing Operating System, for MIT Sloan Management Review. It maps seven core marketing workflows against an agentic AI operating stack. The architecture came from pattern recognition across hundreds of conversations with CMOs and decades of teaching and consulting. AI helped me iterate and refine at a pace that would have taken months of solo work. But the conceptual breakthrough, seeing the structure that organized what had been a fragmented landscape, was mine. AI did not see the pattern. I did. And then AI helped me articulate it with precision and speed.

Most recently, I have been building a comprehensive ontology for AI-driven marketing, a knowledge architecture that maps how concepts, workflows, technologies, and organizational capabilities connect. The depth and speed at which this work has progressed is, frankly, breathtaking. But the ontology reflects my mental model of how these domains relate. AI helped me externalize it, structure it, pressure-test it. The intellectual DNA is mine.

In each case, the pattern is identical. Human insight first. AI amplification second. Place to stand first. Lever second.

This Article as Proof of Concept

I want to tell you how this series came into being, because the story itself illustrates the thesis.

I was sitting at O’Hare, waiting to board a flight to Delhi. I had been carrying these ideas for months: conversations with anxious parents, observations from my own AI practice, a growing conviction that the education conversation was missing the point. At the gate, I opened my laptop, started a conversation with Claude, and began thinking out loud.

I did not ask AI to write an article. I brought the raw material: the instinct that skill security matters more than job security, the conviction that trades deserve more respect, the Archimedes metaphor that had been forming in my mind, the personal examples from my own work. I pushed. AI pushed back. I refined. AI helped me see structure in what had been a collection of instincts. I rejected some suggestions and sharpened others. The conversation surfaced the architecture of a three-part series.

By the time I boarded the flight, I had a solid first draft of three articles. Granted, the flight was delayed by 45 minutes. But still...

Now here is the question: was that AI slop? Was that a machine writing on my behalf? I would argue exactly the opposite. What happened at that gate was my accumulated expertise finding a lever powerful enough to match its ambition. The speed was not a shortcut. It was the result of the foundation. Every instinct I brought to that conversation was earned over decades of teaching, writing, consulting, and thinking. AI did not give me those instincts. It helped me articulate them faster than I could have alone.

A student with no experience in AI strategy, marketing transformation, or case writing could have sat at that same gate with the same tool and produced nothing of value. The lever was identical. The fulcrum was not. We all have word processors. But all of us are not Shakespeare!

What Comes Next

The most powerful cognitive lever in human history is upon us. It will reshape work, education, and expertise more profoundly than any technology since the printing press. The people who thrive will not be those who adopt AI fastest. They will be those who built something solid to stand on before they picked up the tool.

You have the lever. The question is whether you have a fulcrum.

In Part 2, I will get specific. If you are a student, or someone advising a student, what should you actually study? Which fields, skills, and habits build the kind of foundation that AI amplifies rather than replaces? The answer involves more philosophy and plumbing than you might expect, and less coding than the conventional wisdom suggests.

In Part 3, I will speak directly to parents. Because the hardest part of this story is not knowing what to build. It is having the courage to let your children struggle while they build it.

* * *

Look at any product team today and you’ll notice something odd. The org chart hasn’t changed. The product manager still sits at the center. The designer still owns the interface. The engineer still writes code. The marketer still runs campaigns. Same titles. Same Slack channels. Same reporting lines.

But watch what actually happens in the meetings. The PM shows up with a wireframe she built in twenty minutes using AI. The marketer is running his own A/B tests without waiting for analytics. The designer is generating front-end code. The engineer is drafting product copy.

Nobody announced a reorganization. Nobody changed anyone’s title. But the boundaries between these roles have become porous in ways that would have been unthinkable a few years ago.

I have been thinking about this shift through a metaphor borrowed from physics. For decades, skills inside organizations behaved like solids. A skill belonged to a role. A role belonged to a function. A function belonged to a department. Movement across these boundaries was possible but slow, expensive, and rare. Crossing into another team’s domain was like walking through a wall. If a marketing manager wanted to run a complex data analysis, she needed months of training or dedicated help from an analyst. If an engineer wanted to do interface design, he needed to go to design school.

AI has changed the temperature. And when the temperature rises enough, solids become liquids.

The Phase Change

AI does not make everyone an expert at everything. That is simply not possible, and in fact it would be dangerous. What AI does is dramatically reduce the friction involved in acquiring and applying skills in adjacent domains. The cost and effort to stretch into a neighboring discipline has collapsed.

Research that once required a dedicated analyst can be synthesized in minutes. Designs can be mocked up from a text prompt. Code can be prototyped without a formal engineering background. Data analyses can be run by people with no SQL knowledge. The barrier hasn’t disappeared, but it has dropped from a wall to a curb.

This is why skills are becoming fluid. They no longer stay locked inside functional silos. They flow across roles, across teams, across the old boundaries that once kept everyone in their lane.

The implications are significant. LinkedIn’s workforce data suggests that by 2030, roughly 70% of the skills used in jobs will have changed, with AI as a primary catalyst. An Atlassian design leader, after the company ran an all-hands AI experimentation week, put it this way: what could have taken months of self-learning happened in days.

Anchor and Radius

When skills flow freely, we need a new way to think about professional capability. I propose a simple framework: anchor and radius.

Every professional has an anchor. This is their core domain of deep expertise and judgment, built through years of learning, practice, pattern recognition, and tacit knowledge. The anchor is what allows someone to make good decisions when information is incomplete, to exercise taste in their field. AI does not replace the anchor. If anything, anchors matter more in an AI world, because they guide the effective use of all the new tools. A weak anchor paired with AI leads to confidently wielding tools in foolish ways. As the saying goes, a fool with a tool is still a fool.

What AI does is extend the radius of competence around that anchor.

Think of each professional as having a circle of capability centered on their core expertise. AI increases the diameter of that circle. The product manager’s anchor might still be product strategy and orchestration, but her radius now extends further into design, analytics, even light engineering. The marketer’s anchor remains customer insight and creative storytelling, but his radius now reaches into data analysis, growth experiments, and user experience.

Without an anchor, a larger radius is dangerous. With a strong anchor, a larger radius is a force multiplier. Careers in the AI era will be defined not just by the depth of your anchor but by how far your circle extends around it.

Here’s the observation that changes how organizations work: as everyone’s circle expands, the circles inevitably overlap.

What Happens in the Overlap Zones

The PM’s circle now overlaps with design. The marketer’s overlaps with analytics. The engineer’s overlaps with UX. These adjacencies create very real tensions inside organizations.

Who owns the early user experience work when product managers can generate wireframes and user flows themselves? Who owns insight generation when a marketer can run experiments and queries without analyst support? Who defines technical feasibility when AI can generate a plausible code prototype for a non-engineer?

These aren’t turf battles or ego fights. They are structural consequences of reduced friction.

Consider what Airbnb did. When Brian Chesky announced in 2023 that they had merged product management and product marketing, it was widely misinterpreted as eliminating PMs. It wasn’t. It was a recognition that the overlap zone between building the product and marketing the product needed a single integrated owner. “You can’t develop products unless you know how to talk about the products,” Chesky explained. The skill sets had overlapped so much that maintaining separate roles was itself creating friction.

When designers at the Figma conference cheered Chesky’s announcement, they revealed the tension. Designers felt some PMs were encroaching on their terrain in unproductive ways, and the old role definitions weren’t resolving it.

The key managerial question is not whether overlaps exist. They do, and they’re growing. The question is: who prevails in the gray zones? I see three forces that determine influence in these overlap areas.

Anchor depth. When a decision truly hinges on deep craft or specialized mastery, the domain specialist retains authority. AI might help a PM create a draft design, but information architecture and interaction design nuances still require a seasoned designer’s judgment. Paradoxically, AI can increase respect for deep expertise by automating the easy parts and highlighting the hard parts.

End-to-end ownership. When decisions involve balancing trade-offs across the whole product or value stream, the person with holistic accountability prevails. Product managers typically own the end-to-end outcome, which naturally positions them to integrate across domains. AI-augmented breadth makes them even more effective here, because they can personally explore each side of a trade-off before deciding.

Decision frequency. Influence accrues to whoever makes the most frequent, iterative decisions. In a fast-paced, AI-powered workflow, there are dozens of micro-decisions every day: tweaking a prompt, choosing which experiment to run, adjusting copy based on feedback. The person consistently in that loop gains authority over time. High-frequency decision-making, compounded over dozens of iterations, beats a slow deliberative process that routes through hierarchy.

These forces suggest that overlapping skills won’t produce a free-for-all. We’ll see a new balance of power based on who brings deep craft when it matters, who integrates across functions, and who drives fast iterative cycles.

The Rise of the Full-Stack Product Builder

No role illustrates this shift more clearly than product management. Even before AI, great PMs were defined by their breadth. They sit at the intersection of customer needs, business strategy, design, and engineering. Their value has always come from synthesis rather than deep craft expertise in any single area. AI amplifies that inherent breadth dramatically.

LinkedIn saw this early and acted on it. In 2025, they sunsetted their Associate Product Manager program and replaced it with the Associate Product Builder program. The name change is telling. Participants learn coding, design, and product skills together, leveraging AI tools throughout. LinkedIn even created a formal “Full-Stack Builder” job title with its own career track, enabling anyone from any function to take a product from idea to launch.

The rationale, as LinkedIn’s Chief Product Officer Tomer Cohen explained it, was that the traditional model of highly specialized teams was too slow. Organizational bloat meant even small features took six months, whereas AI tools allow leaner teams to deliver faster. The application process reflects this philosophy: no resume required. Candidates submit a 60-second demo of a product they built and answer questions about how they used AI in the process.

Shopify has moved in a similar direction. Their PMs don’t just write specs. They use AI to generate prototypes and analyze data independently. Atlassian ran an internal “AI Product Builders Week” where over a thousand designers, PMs, and engineers set aside their normal tasks to experiment with AI together, producing dozens of production-ready prototypes in days.

The full-stack archetype integrates three capabilities: strategist (market understanding, customer insight, product vision), builder (hands-on creation, prototyping, technical execution), and growth driver (adoption, experimentation, metrics, iteration). The anchor remains judgment. The expanded radius shows up in the building and growth dimensions.

This isn’t just a PM story. Marketing is converging from the other direction. Today’s marketers segment customers with AI-driven clustering algorithms. They generate and test content variations at scale. They build personalized user journeys that start to look like product UX flows. They run A/B tests and attribution models that were once the domain of data scientists. At HubSpot, 74% of marketers were using at least one AI tool at work in 2024, up from just 35% the year before.

The pattern is the same across functions: T-shaped professionals, deep in one area with broad ability in others, are increasingly valuable. The horizontal bar of that T can now stretch much further than before.

The Risks of Expanding Your Radius

I want to be direct about the dangers, because the full-stack archetype has failure modes.

The first is the jack-of-all-trades trap. Expanding your radius without maintaining your anchor depth makes you broadly mediocre rather than distinctively valuable. Organizations still need deep specialists. Not every product manager, marketer, or engineer will become full-stack, and that is perfectly fine.

The second risk is burnout. Because full-stack people can do so much, organizations tend to pile responsibilities onto them. Just because your star PM can also do great design work and run analyses doesn’t mean she should do all of it all the time. The new bottleneck is cognitive bandwidth. No matter how talented an individual is, there are limits to how many domains they can integrate in a single day. Full-stack humans need protection from their own capability.

The third risk is starting-point bias. In complex work, whoever produces the credible first draft of an artifact wields outsized influence on the outcome. If AI allows PMs to generate the first draft in domains that used to be out of their reach, they increasingly set the direction before the specialist formally gets involved. A PM who comes to a meeting with a plausible wireframe is effectively steering the design conversation from the outset. This can accelerate progress. It can also marginalize expertise if not managed carefully.

What Leaders Must Do

The changing physics of skills places new demands on the C-suite.

Redefine roles around outcomes, not tasks. Rigid job descriptions that specify “you do X, then hand off to Y” are becoming obsolete. Define roles by the outcomes they own and let the tasks be fluid. Some companies are already dropping traditional titles in favor of “product builder” or “growth owner” to signal this shift.

Protect your integrators. Your best full-stack people are force multipliers. They are also the first to burn out if you load everything onto them. Ensure they have support. Monitor their workload. In performance reviews, check in on how they are managing breadth, not just depth.

Clarify decision rights in the overlap zones. As overlaps proliferate, ambiguity in decision-making creeps in. Define who has the call on what and when, even as collaboration increases. The PM might have final call on priority and scope, the designer on UX quality, the marketer on brand voice. Make it explicit. Overlaps without governance become stalemates.

Reward cross-functional value creation. If your compensation and promotion criteria only celebrate individual functional excellence, you are sending the signal that staying in your lane is safe. Highlight team wins that were only possible because someone ventured beyond their role. Create career paths that allow growth in breadth, not just depth.

Treat AI fluency as foundational literacy. Don’t leave this to chance. LinkedIn created an entire culture program around being AI-native in product development. Atlassian’s company-wide experimentation week gave every team member hands-on education. If you institutionalize it, you get a company-wide leap. If you leave it to individual initiative, you get pockets of innovation surrounded by inertia.

The Management Crossroads

We stand at a choice point. One path is to continue managing skills with the old assumptions, treating overlaps as anomalies or problems to be squashed. The other is to redesign organizations for skill liquidity, encouraging learning, experimentation, and collaboration across boundaries.

The companies already taking the second path are reporting not just faster output but higher employee satisfaction. Top talent wants to grow and contribute in multiple ways. If you provide the platform for it, they flourish. If you don’t, they leave for organizations that will.

Your organizational chart is a lagging indicator of how work actually happens. It describes the past, not the present. When skills become fluid, leaders who will win will let the skills flow and wisely guide the current.

The inspiration for this framework was a conversation with my friend Parimal Deshpande, who works at Adobe. He told me that ever since he started a new role, he had been able to tap into his creative side. He was energized in a way he hadn’t been before, because he has been a creator all his life. Suddenly his job felt meaningful. That conversation got me thinking about what actually drives professional fulfillment. This framework is the result.

There are three pillars. They are simple to name but difficult to align.

Performance

The foundation is performance. This is your competence and skill in the role you occupy. You must be good at your job, and that competence comes from experience, capability, and the relevance of your skills to the context you are in. That last part matters more than people realize. You might be a phenomenal guitar player. But if you’ve been asked to play the piano, your talent is real but misplaced. Skill without fit is potential without traction.

If your job were a human body, performance would be the muscle and the mind. It is the starting point. But functional competence must be maintained and matched to context for it to translate into results.

Passion

The second pillar is passion. If performance is the muscle, passion is the heart. This is what inspires you, motivates you, and makes you excited about what you do. Think about when you wake up in the morning. Are you looking forward to the work ahead?

For a salesperson, passion might be the thrill of closing a deal. For me, it is helping students learn and watching them grow their careers. Passion may sound lofty, but it can be about small things. You might be a barista who takes pride in creating the perfect cappuccino. You might be a hotel front desk clerk who delights in making guests feel welcome. Passion is your in-the-moment satisfaction. It is the rocket fuel that converts a competent employee into an exceptional one.

Purpose

The third pillar is purpose. This is your mission. Do you derive meaning from your job? Are you making a difference?

Some professions are naturally purpose-driven: nursing, teaching, firefighting, public service, the military. Some organizations also index higher on purpose within their industry. I served for ten years on the board of Reliance Jio, a company whose purpose was crystal clear - bring affordable data connectivity to a billion Indians. Microsoft, Nike, The Body Shop, Patagonia: these are organizations where purpose is baked into the culture. A sense of purpose is what makes people go for the moonshots.

The Trifecta

Performance is the body. Passion is the heart. Purpose is the soul. When all three are present, you have professional fulfillment.

Think of your career as a ship sailing the seas of professional opportunity. Performance is the sails: strong, broad, and able to push the ship forward. Passion is the wind that fills those sails. Purpose is the North Star: the destination you are sailing toward. If that destination is meaningful, and the sails and wind are working together, you have a ship that is not only seaworthy but headed somewhere worth going.

Here is the critical insight. These three elements do not add together. They multiply.

Fulfillment = Performance × Passion × Purpose

If any one of the three is zero, the product is zero. You can be highly skilled and deeply passionate, but if the work has no meaning, fulfillment collapses. You can be mission-driven and competent, but without passion, you are grinding. You can be passionate and purposeful, but without competence, you are ineffective.

The Japanese call this alignment Ikigai: the discovery of your reason for being. The three P’s are my way of making that idea actionable. Find the role where your skill meets your energy meets your meaning. That is where fulfillment lives.

I have always believed that every conversation is a learning experience and every person is a teacher. My shoeshine experience at La Guardia airport is a case in point.

I had a few minutes to kill while waiting for my flight to Chicago, so I walked over to the shoeshine stand at the American Airlines terminal next to Gate D1. The shoeshine man introduced himself as John. What attracted me was his cheerful banter with customers and passers-by alike.

Here are seven lessons in customer service that John taught me in fifteen minutes:

Make them smile. To a lady passing by, John, without even looking at her, exclaimed: “Size six and a half. I can tell from the sound of your boots.” He put a smile on her face. He had a joke for every customer and everyone around him. What a breath of fresh air for stressed passengers on a rainy Friday afternoon at a crowded airport.

Keep them informed. I learned from John that Tom Hanks would be shooting a movie about Captain Sully, the pilot who landed the US Airways aircraft in the Hudson River. I learned about his “sexy work wife” next door who runs a concession stand. He was nonstop news, like watching CNN.

Care about their well-being. Someone had spilled coffee on the slippery floor near the shoeshine stand. Without waiting for the cleaners to show up, John excused himself, walked over with paper towels, and cleaned up the spill. His comment: “Can’t have my customers slipping and falling.”

Reward them for loyalty. John’s offer to me: “If you come back with these shoes any time in the next month, I will brush them for you, no charge. I like to take care of my customers.”

Go the extra mile. A man stepped over and saw two people in line. Figuring he wouldn’t have time, he started to walk away. John called him over and told him to stand with his feet together. In ten seconds, he quickly brushed and polished the man’s shoes and sent him on his way. The man tried to pay a couple of dollars. John’s response: “Don’t you dare!”

Have the customer’s best interest at heart. A man came along with a large stain on his shoes, probably from a food spill. John looked at them and said: “Son, you should not polish these shoes. They need to be cleaned with vinegar and a brush when you get home. I could polish them for you, but the stain would just get hidden and embedded in the leather.” He cleaned the stain, brushed the shoes, and sent the man on his way. No charge.

Be fair. The man before me was so impressed with John’s service that he tried to give him a twelve-dollar tip for a six-dollar shoeshine. John refused and handed ten dollars back. “Son, I really appreciate your generosity, but just come back and see me again.”

Seven profound lessons in customer service in fifteen minutes at a shoeshine stand. Next time you are at La Guardia, look for John. When you find him, congratulate him for practicing on the ground what we teach in the ivory towers of business school.