How to Become Valuable in the AI Era — The Ikigai Framework

The job market is shifting faster than most people realize, and the panic is understandable. AI is automating tasks that used to require expensive engineers. Tools are replacing roles that seemed secure. New graduates are asking themselves whether they should even learn to code.

But John Kim’s framing of the problem is different. He’s not telling people to fear AI or retrain. He’s telling them to think deeper.

“I think AI or not, I think it’s actually always the same,” he says. “You have to be valuable. You have to be valuable.”

The question is how. And for that, Kim points to an old framework with new urgency: ikigai.

The Ikigai Intersection

Ikigai is the Japanese concept of finding your “reason for being.” In career terms, it’s the intersection of four things:

1. What you like doing — your genuine interests, not what you think pays the most or what your parents want
2. What you’re good at — your actual strengths, based on feedback from people who know you
3. What the world needs — problems that exist right now, in your market
4. What you can be paid for — the economic value of solving those problems

Most career advice focuses on #2 or #4. Get good at a skill that pays. Build expertise in a high-demand area.

Kim’s insight is that the AI era makes #1 and #3 more critical than ever.

The First Question: What Do You Actually Like?

“I think you have to understand like what, I think they call it like professional ikigai or something where basically it’s like what you like doing, what you’re good at and whether it’s like valuable for that society or whatever at that current time aligning is truly a huge blessing.”

Most people skip this question. They’re told that what they like is a luxury — “do what you love, but do it on your own time.” So they optimize for what pays, or what’s prestigious, or what their network respects.

The problem is that by the time you hit 30 or 40, you realize you’ve spent a decade building skills in something that doesn’t excite you. When AI offers to automate that work, you don’t feel relieved — you feel trapped, because you’ve built your career around tasks that made you miserable.

“I think getting out of, making sure you find that and you probably already know,” Kim says. “On the first part of like what you like, it’s a deep, deep introspection.”

The introspection doesn’t have to take months. It’s often simpler than you think. When you were in school, what did you do in your free time? What problems do you think about even when you’re not being paid to think about them? What conversations energize you?

Kim didn’t wake up excited about recruiting from first principles. He stumbled into it. But once he realized the problem — the mismatch between talented people and companies that needed them — he couldn’t stop thinking about it. That obsession became a business.

The Second Question: What Are You Actually Good At?

People usually underestimate their strengths. You’ve probably heard feedback about what you’re good at your whole life — you just didn’t catalog it as evidence.

“You probably already know. You probably growing up heard that you’re like, wow, you’re always so good with your words or. well, you’re really good at spotting really nice looking furniture or whatever that is. You’ve probably already heard that before from people around you.”

The pattern matters more than the individual data point. If three different people said you’re good at spotting talent, that’s a signal. If everyone says you’re good at explaining complex things clearly, that’s a signal. If you’ve heard “you’re organized” a hundred times, that matters.

These aren’t profound observations. But they’re more reliable than your self-assessment, because other people are observing your actual behavior, not your aspirations.

The Third Question: What Does the World Need Right Now?

This is where the AI era creates urgency.

“I think if you then apply the AI era of what is useful to the society, I think deeply think about what AI is very, very good at and what humans are very, very good at and distinguish them.”

AI is very good at:

• Pattern matching across massive datasets
• Optimization (finding the best solution to a defined problem)
• Consistency (doing the same task the same way 10,000 times)
• Speed (processing information instantly)

Humans are very good at:

• Relationships and trust (convincing someone to take a leap)
• Judgment calls in ambiguous situations (deciding when rules should be broken)
• Creativity (making novel connections between domains)
• Empathy (understanding unspoken needs)

Notice which roles are surging in 2024: sales, recruiting, product management, and business development. These are almost entirely human skills. “Sales roles are having a surge right now. Like who knew, who knew that sales roles might be actually more valuable than software engineers.”

Kim doesn’t mean software engineering is dying. He means the nature of what makes a valuable software engineer has changed.

“Knowing how to write TypeScript is no longer valuable. It’s free,” he says. “So you have to think about that.”

A junior engineer who just learned TypeScript has the same core skill as ChatGPT + Claude + GitHub Copilot. That’s not valuable anymore.

What is valuable is an engineer who understands what users actually want, can design systems that scale to 100x, and can make decisions when the spec is ambiguous. That requires product sense, architecture thinking, and judgment. AI can’t teach you those — you have to develop them through experience.

The Integration

Here’s what makes the ikigai framework powerful in the AI era:

If you’re good at something that AI does better, you need to either move into a domain where that skill is less valuable, or pair it with a human skill that AI can’t replace.

For example, if you’re an excellent coder but AI can code, you have options:

1. Become a technical PM (coding + product sense)
2. Become an architect (coding + system design)
3. Become a consultant (coding + client relationships)

All three combine your core skill with something AI struggles with: judgment, design, or trust.

“I think if you really deeply, so the answer is not with me or anyone else. I think it’s with the person like you. And I think doing the deep, deep introspection with these three pillars in mind, I think is probably the right way to think about it.”

The work is personal. No framework can tell you what you actually like. No article can tell you what the world needs in your specific market or domain. The introspection is on you.

But the structure is clear: find where your genuine interests, your actual strengths, and the world’s needs overlap. Then deepen that intersection relentlessly. That’s where you become valuable. That’s where AI makes you more capable, not replaceable.

FAQ

Is it too late for me to change careers in the AI era?

No, but timing matters. The sooner you identify what you like and what you’re good at, the sooner you can build credibility in that domain. Someone pivoting at 25 has an easier time than someone pivoting at 45. But both are possible. The “too late” feeling comes when you spend another five years building the wrong thing.

My strength is something AI is obviously good at. What do I do?

Pair it with a human skill. If you’re a great analyzer of data, become a strategist (analysis + judgment). If you’re good at writing, become a brand strategist (writing + positioning). The combination of what you’re good at plus what AI can’t do is where the value is.

How do I know if what I like is actually valuable?

You’ll know because other people are paying for it, or will pay for it. If you like interior design but you’re in an industry with no budget for interior design, you need to either find an industry that values it, or pair it with something adjacent that does. Passion alone doesn’t create value. But passion + skill + market need does.

Should I specialize or generalize in the AI era?

Specialize early. The broader skill you have, the more likely AI can replicate it. “Good at many things” loses to AI. “World’s best at recruiting for healthcare ML roles” beats AI because it’s a specific combination of judgment + network + domain expertise.

What if I discover I don’t like what I’m good at?

Change. Sooner is better than later. Yes, you have to take a step backward sometimes. But staying in a role you dislike for the sake of expertise is a losing game. You’ll be fighting against your own motivation, and AI won’t stay complacent while you’re depressed.

The ikigai framework sounds time-consuming. Can I do it quickly?

Three days of honest reflection is better than a year of rumination. Spend 30 minutes writing about what genuinely excites you. Spend 30 minutes listing feedback you’ve gotten. Spend 30 minutes looking at the current job market. By day two, patterns usually emerge.

I’m a new graduate. Should I learn AI tools or traditional skills?

Learn the traditional skill deeply, then wrap it with AI. A journalist who learns to write well, then learns to use AI to amplify distribution, is more valuable than a journalist who only knows how to use AI. Foundation first, tools second.

Is the ikigai framework universally true, or just for tech?

It’s universally true. Every industry is being impacted by AI or automation. The question “what do humans do that machines don’t?” is now relevant everywhere. The framework applies whether you’re in medicine, construction, law, or trading.

What if I can’t align all four quadrants perfectly?

You don’t need perfect alignment. You need two of the four to be very strong. Great (good at + valuable) will get you hired but might feel joyless. Passionate (love it + good at it) will keep you motivated. Valuable (needed + people pay for it) will keep you employed. The sweet spot is getting at least two of these, and ideally three.