Your Kid’s Education Is Already Outdated. Now What?

A few months ago, something unexpected happened to me. After a few years of managing an AI team at a tech company, I found myself writing code again — something I’d assumed was behind me. A team member went on extended leave, and I had to step in. At fifty-something, with fading recall for syntax details and frameworks that had moved on without me, I braced for a humbling experience.

Instead, the opposite happened. With AI as my coding partner, I became remarkably productive — possibly more so than some of the younger, sharp-minded data scientists I’ve worked alongside. Not because of any innate superiority, but because thirty years of experience — combined with a curiosity and desire to build that I never lost — gave me something AI couldn’t replicate: the ability to scope problems, identify what actually matters, and know when to push forward versus step back. AI handled the syntax and boilerplate. I handled the judgment.

It felt great — for a few weeks. Then the deeper implication hit me. If one experienced person paired with AI can do the work of a small team, what happens to that team? And more importantly, what happens to the junior roles that used to be the entry point for building a career?

The Data Is Already In

This isn’t a hypothetical concern. In March 2026, Anthropic — the company behind the AI model Claude — published a study that moved beyond speculation to measure what AI is actually doing in workplaces right now. Using real usage data from their own platform, they found that 75% of computer programming tasks, 70% of customer service tasks, and 67% of data entry tasks are already being performed or automated through Claude.

The paper is careful to note that this hasn’t translated into mass job losses yet — unemployment rates for exposed workers haven’t meaningfully changed. But two findings should give parents pause. First, the workers most exposed to AI aren’t in factories or warehouses — they’re more educated, higher-paid, and working in exactly the kinds of careers our educational system steers kids toward. Second, there is early evidence that hiring of younger workers has already slowed in the most exposed fields.

The researchers described one hypothetical scenario to illustrate the scale of what could happen: a “Great Recession for white-collar workers.” They weren’t predicting it — they were showing that their framework could detect it if it came. But the gap between what AI can theoretically do and what it’s currently doing is enormous, and it’s closing.

This is not a conversation we can postpone. The disruption hasn’t fully arrived, but the early signals are clear. The time to rethink education is now.

When a Machine Outperformed a Math Olympiad Coach

Carnegie Mellon math professor Po-Shen Loh — who also serves as the national coach of the US International Math Olympiad team — recently witnessed something that challenged his deepest assumptions about human capability. Google’s AI solved four out of six International Math Olympiad problems, which are specifically designed to require genuine originality and creative thinking. By Loh’s own admission, that performance exceeded what he himself could achieve.

Consider the weight of that statement: a man who has spent decades training the most gifted young mathematicians in America, acknowledging that a machine surpassed him in the domain he has devoted his life to. This wasn’t a test of memorization or pattern-matching. It was a test of creativity.

The question Loh now poses is both simple and profound: if artificial intelligence can be creative, what remains uniquely human?

What the Hiring Side Looks Like

As someone who works in AI and manages data scientists, I can tell you that what’s happening in industry does not align with what educational institutions are preparing students for. I’ve recently changed how I evaluate candidates, and the shift has been dramatic. Degrees, GPAs, and technical skill inventories have become far less interesting to me. AI can bridge most of those gaps now.

What I look for instead is character. Curiosity. Grit. The instinct to dig into an unfamiliar problem rather than freeze. A genuine desire to solve something real rather than merely complete an assignment. These are the qualities that actually predict success in an AI-augmented workplace, and they are precisely the qualities that most educational institutions fail to cultivate.

A Generation Caught in the Transition

Millions of students are currently sitting in classrooms absorbing skills that are already obsolete — memorizing code syntax, formulas, and frameworks that AI can produce in seconds. Schools sense that something is wrong, and I believe many educators are genuinely trying to adapt. But the system itself is locked in place by standardized testing, curriculum committees, accreditation requirements, and bureaucratic inertia. It moves far too slowly for the pace of change we’re experiencing.

The students caught in that transitional gap are the ones who pay the price.

Loh captures this beautifully with an analogy I haven’t been able to shake: using AI to do your homework is like driving your car for a one-mile run. How much exercise do you get? None. And the loss isn’t just the writing skill itself — it’s the cognitive exercise that writing develops. When children outsource thinking to machines, they don’t merely lose the ability to compose sentences. They lose the ability to reason independently. And without that ability, they lose something even more critical: the capacity to recognize when someone is misleading them.

The Risk Calculus Has Flipped

For parents reading this, I want to challenge an assumption that most of us have been operating under. The conventional path — good grades, good college, good degree, good job — made sense for decades. It was the safe bet.

But it may now be the risky one.

Four years of tuition, potentially massive debt, and a curriculum designed for a world that no longer exists — all while other young people are learning to work directly with AI, building real products, and developing practical judgment through experience rather than coursework. I’m not arguing against education; quite the opposite. I’m arguing for educating children fiercely — but perhaps not through the channels the current system offers.

This leads to a question that I believe is among the most urgent of our time: why do we educate at all? The historical answer was straightforward — survival, economic participation, productive contribution. But as AI increasingly handles the “doing,” that rationale is eroding. What is education for in a world where machines can execute most cognitive tasks more efficiently than humans?

This isn’t a school board question. It’s a civilizational one.

And I believe the answer, paradoxically, is simpler than most people expect.

Teach Kids to Feel

I realize how that sounds. “Teach kids to feel” doesn’t appear on any standardized test, and it won’t impress a college admissions committee. It doesn’t pay the bills — not yet. But let me explain why I believe it should be the central aim of education going forward.

Thinking is a tool — a powerful and necessary one. But feeling is what directs the tool. Thinking tells you how to do something; feeling tells you why it matters and for whom you’re doing it. A person who can think brilliantly but doesn’t care about anyone is, functionally, just a more efficient machine. A person who cares deeply but can’t reason is vulnerable to manipulation. You need both capacities, but caring has to lead.

Loh warns that people who can’t think independently become susceptible to persuasive narratives that may not serve their interests. That’s true, and it’s alarming. But I’d push the argument one step further: what causes a person to notice that something is wrong in the first place? It’s rarely a logical deduction. It’s a feeling — a sense that something doesn’t sit right, that someone is being harmed, that an outcome isn’t just. That gut-level awareness is empathy doing its job.

Without empathy, we cannot build meaningful guardrails around AI. These systems are, in the most literal sense, heartless — not as an insult, but as a statement of fact. They do not feel. The humans who oversee, direct, and constrain AI therefore must feel deeply enough for both. If we want artificial intelligence to serve human interests, then the people making decisions about its deployment need to genuinely care about human wellbeing.

That is what education should be building: not just competent thinkers, but deeply feeling people who notice when something is wrong because they care — not merely because the data flags an anomaly.

The Irony: Empathy Produces Better Engineers

Here is what I find most compelling about this argument. Teaching children to feel — to feel for others — doesn’t just make them better human beings. It makes them better engineers, better product designers, and better entrepreneurs.

In a world where AI handles the analytical heavy lifting, the differentiator between a good product and a great one is understanding how people actually feel. What frustrates them. What brings them joy. What makes them feel seen or dismissed. Only humans can grasp these things with real accuracy, because we experience them ourselves. We know what it feels like to be hurt, to be grateful, to be ignored, to be loved. AI can approximate these emotions through pattern recognition, but we live them.

That lived experience is precisely what consumers value. People don’t want perfect output — they want someone who gets them. And the ability to accurately perceive and respond to another person’s emotional state is, at its core, what empathy is. It’s not a personality trait; it’s a trainable skill. And it may be the most economically valuable skill of the coming decade.

Nobody can predict the future with certainty, but if I were placing a bet, I would wager that the ability to feel deeply and demonstrate trained empathy will be what gets the next generation hired — far more than any GPA or technical certification.

Empathy as Job Security

This connects directly to another of Loh’s central arguments: in the AI era, no one survives professionally in isolation. The critical factor is whether other people want to work with you — and people choose collaborators who genuinely care. As Loh puts it bluntly: if you’re not motivated by creating value for others, you’re a bad partner, and nobody will choose you.

Loh has put this philosophy into practice. He built an educational platform where high school students teach math to younger kids, coached by professional actors on communication and presence. He doesn’t teach them how to solve specific problems — he gives them problems they’ve never seen and watches how they respond. “I don’t expect you to solve it,” he says. “I want to see how you think.” Where most schools train students to never be surprised, Loh trains them to thrive in surprise.

He’s not alone in this perspective. Even Sal Khan — whose Khan Academy has taught traditional academic skills to millions of students worldwide — now emphasizes the primacy of human skills: communication, collaboration, and critical thinking. In a recent interview, Khan offered a striking admission: “If I had to pick between an amazing teacher and amazing technology for my own kids, I’d pick an amazing teacher every time.” This from the man whose career is built on educational technology.

The System Hasn’t Caught Up. One Person Showed Me What’s Possible.

I want to be honest about a tension in everything I’ve argued so far. Most companies still hire for technical skills. Job postings still list specific tools, programming languages, and years of experience as requirements. Parents see this, and they rationally push their children toward credentials and specialization — because that’s what the current system visibly rewards.

But this is the same inertia, the same lag, that plagues education. Companies are stuck in the same trap as schools: measuring what’s easy to quantify rather than what actually predicts success. The organizations that recognize this shift earliest will have an enormous competitive advantage. The ones that don’t will follow the trajectory of BlackBerry — dominant in one era, irrelevant in the next.

I know this because I experienced the alternative firsthand.

In 2018, while searching for a new position, a friend introduced me to a man named Ian Kitajima, who served as head of business development at a multidisciplinary innovation lab in Hawaii. The company worked across nanotechnology, engineering, and defense — all kinds of science. They had a team doing AI, but nobody was doing applied deep learning yet. It was still early days — deep learning existed, but real-world applications were rare. Ian wanted to change that.

What happened next was unlike any hiring process I’d experienced. Rather than screening my resume against a checklist, Ian met with me for one hour every week, in person, for six months. He didn’t care that I’d never used Linux. He didn’t care that my Python experience was minimal. He simply told me what he was passionate about, what he envisioned building, and he wanted to understand who I was as a person.

It was honestly refreshing.

So I learned. In the evenings, on my own time, I bought a new hard drive and installed Linux. I purchased an NVIDIA graphics card. I taught myself everything I needed — because the curiosity and the persistence were already there. Ian had simply recognized them before anyone else did.

He hired me. I became the lead AI engineer for a major federal project from the US Department of Energy. Later, I won a few grants myself and became the principal investigator for those projects. That job transformed my career.

Ian didn’t hire my skills. He hired who I was. And it worked.

Something Remarkable Is Happening

I recognize that much of what I’ve described might sound alarming. Schools are failing to adapt, jobs are compressing, and the traditional safety net of credentials and specialization is fraying. But I believe something genuinely remarkable is emerging beneath the anxiety.

For generations, our economic and social systems have rewarded output — your productivity, your resume, your measurable contributions. In that framework, deeply human qualities like empathy, curiosity, integrity, and emotional intelligence were categorized as “soft skills.” Nice to have. Secondary to the real qualifications.

Those qualities were always valuable. The system simply didn’t pay for them.

AI changes that equation permanently. When machines handle the production of output, what remains valuable is everything the machine cannot be: genuinely understanding another person’s problem, persisting through ambiguity, connecting ideas across seemingly unrelated domains, caring enough to notice when something isn’t right.

David Epstein explored this dynamic extensively in his book Range: Why Generalists Triumph in a Specialized World. After studying the careers of the world’s most successful athletes, scientists, inventors, and artists, Epstein reached a conclusion that contradicts conventional wisdom: in complex, unpredictable environments — which describes virtually every professional field in the AI era — generalists consistently outperform specialists. People who sampled widely, connected ideas across disciplines, and specialized later in their development achieved more than those who narrowed early. And notably, the kinds of problems that reward narrow specialization are precisely the kinds that AI handles most effectively.

Who you are is finally what counts.

I explored a version of this thesis in an earlier article I called “The Empathy Economy,” where I argued that empathy itself will become a form of economic value — something people actively seek out and pay for in an age of AI-generated perfection.

The Mirror

I want to close with an observation that has stayed with me.

If you’ve ever watched Star Trek, you’re familiar with Spock and Data — two of the franchise’s most beloved characters. Spock, half-Vulcan, suppresses emotion in favor of logic. Data, an android, lacks the capacity for emotion entirely. And yet, across decades of storytelling, these are consistently the characters who teach audiences the most about what it means to be human.

Every time Spock chooses loyalty over logic, every time Data struggles to understand why humans laugh, cry, or sacrifice themselves for people they love — those moments function as a mirror. They don’t teach us about technology. They illuminate what we value most about ourselves.

I believe AI is performing the same function right now, on a civilizational scale. It is systematically stripping away the things we assumed made us exceptional — our accumulated knowledge, our specialized skills, our productive output — and revealing what was underneath all along: the caring, the curiosity, the stubbornness, and the fundamental desire to see other people thrive.

I think we will survive this transition. I think we’ll do considerably more than survive. But only if we stop measuring human worth by what people produce, and start recognizing what was always there — waiting to be valued.

And to my fellow hiring managers: consider taking a chance on who someone is, not just what they know. Someone took that chance on me, and it worked.

The question isn’t whether AI will replace us. The question is whether it will finally teach us who we’ve been all along.

I don’t have all the answers. Nobody does. That’s kind of the point.

This article isn’t meant to prescribe solutions — it’s meant to start a conversation. With your children, with other parents, with your community, and with yourself. Because the conversation needs to happen now. Not after the system catches up. Now.

Further reading and watching: