𝗜𝗸𝗶𝗴𝗮𝗶 𝗮𝘀𝗸𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗿𝗲 𝗴𝗼𝗼𝗱 𝗮𝘁. 𝗪𝗶𝘁𝗵 𝗔𝗜, 𝗱𝗼𝗲𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿?

The popular version of Ikigai, that Venn diagram we’ve all shared for the last decade, sits at the intersection of four things: what you love, what you’re good at, what the world needs, and what you can get paid for.

It’s been the North Star for career strategy since it went viral in 2014. But AI just rewrote the most important circle: “What you’re good at.”

Your skills used to be your moat. Years of practice. Developed taste. The craft of execution. Now? That circle is becoming “Things AI made easy.”

If your value was execution – building, writing, designing – you’re in a precarious spot. The work is easier to produce now. It still pays (for now). But we are entering a time of polished mediocrity at scale: surfaces without substance.

I’m redrawing the diagram. Strikethrough “What You’re Good At” Replace it with “Things AI Made Easy.”

The only work that holds value now is what’s interesting, needed by the world, and paid—but not easy. This requires judgment. The ability to know what problem actually deserves your focus before you delegate it to a tool.

Because here’s what’s true: AI doesn’t know if its answer is useful. You do.

Organizations are starting to realize they don’t need “Execution Experts” anymore. They need “Decision Architects”, people who know what deserves executing.

That skill hasn’t been automated. But it’s the only one left that keeps the center of the diagram from collapsing.

𝗪𝗵𝗮𝘁 𝗰𝗮𝗻’𝘁 𝗯𝗲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂?