The Death of "Pretty Good"

You've spent years getting good at several things, and that middle ground just became the most exposed position in the market.

"Jack of all trades, master of none." The dismissal has been around for centuries. The comeback, "but oftentimes better than a master of one," is surprisingly recent.¹

What matters: the tension between the two is as old as professional work. AI just resolved it.

Both sides win. The middle loses.

When "Pretty Good" Became Table Stakes

For three decades, career advice converged on one shape: the T. Go broad, go deep in one area, be employable forever.

McKinsey was using the concept internally in the 1980s. IDEO's Tim Brown popularized it.

It worked, because being "pretty good" at something required real effort. The gap between "knows nothing about design" and "can put together a clean layout" represented years of learning.

You're an indie hacker, a product manager, a senior engineer. You've built competence across product, design, marketing, maybe finance. That profile was the gold standard.

Then the tool that's "pretty good at everything" showed up. Broad competence stopped being scarce overnight. Anyone with a browser tab and some curiosity can produce a passable landing page, write decent marketing copy, analyze a dataset, draft a legal agreement. The horizontal bar of the T is now available to everyone.

The market for professional capability is developing a barbell shape. Weight at both ends, nothing in the middle.

On one end: the deep specialist who knows more about a narrow domain than anyone in the room, including the AI. On the other: the extreme generalist who uses AI as a depth multiplier across every domain they touch.

The person in the middle, competent at three things and deep in none, used to be the safest hire. Now they're the easiest to replace.

Medicine Already Ran This Experiment

This pattern has a century of precedent.

In the early 1900s, a doctor was a doctor. They set bones, delivered babies, prescribed medicines, and performed surgeries. Then knowledge accumulated.

Anesthesiology split off. Cardiology became its own field. Cardiology fractured into interventional cardiology, electrophysiology, heart failure, cardiac imaging. Today the American Board of Medical Specialties certifies physicians across more than 40 specialties and nearly 90 subspecialties.²

The general practitioner didn't disappear. Their role transformed. They became the coordinator, the person who knows enough to ask the right questions and route you to the right specialist.

They don't compete with the cardiologist on cardiac knowledge. They compete on breadth of judgment and the ability to see the whole patient.

Sound familiar? The GP is the generalist. The subspecialist is the depth player. The middle, "pretty good at cardiology but not a cardiologist," has been dead in medicine for decades.

AI is doing to every profession what accumulating medical knowledge did to doctoring. Just faster.

The Two Edges AI Can't Reach

What makes both extremes defensible is the same thing: they operate where AI runs out of road.

The specialist's edge is the frontier. AI can retrieve and synthesize existing knowledge faster than any human. But the frontier of a field, where the next discovery or insight hasn't been codified yet, is beyond AI's reach. The security researcher discovering a new class of vulnerability. The tax attorney navigating a regulatory edge case with no precedent. AI's training data ends where the frontier begins. The deeper you go, the less AI can follow, because depth at the frontier means creating knowledge that doesn't exist yet.

The generalist's edge is the mesh. Where the specialist goes deeper than AI can follow, the generalist goes wider than AI can hold. They maintain context across many domains simultaneously, making judgment calls that require understanding how a product decision affects engineering, how an engineering choice shapes marketing, how a marketing angle constrains pricing.

AI can simulate excellence in any single domain. It can simulate cross-domain thinking when prompted. What it can't do is bring your specific judgment to the intersection. The way you weigh tradeoffs is shaped by every domain you've worked in, every failure you've internalized, every pattern you've noticed across fields that don't normally talk to each other. Two generalists with identical range will make different calls, because their mesh is built from different scar tissue.

The indie hacker who ships a feature, writes the launch post, adjusts the pricing, and handles the first support ticket before lunch isn't just switching tasks. They're applying a coherent point of view across everything. AI amplifies their reach in each domain. The point of view that connects the domains is theirs alone.

The Debate That Proves the Point

Here's the part nobody seems to notice.

Search for "AI and the future of careers" and you'll find article after article arguing generalists will win because adaptability and cross-domain thinking are what matter now. Dig into industry hiring data and you'll find the opposite claim: specialists are more sought after than ever, because deep expertise is irreplaceable.

Both are right. They're describing the same barbell from different ends.

The natural objection: can't you just stay T-shaped and add AI fluency? Keep your broad competence, learn the tools, hold your position? You can try. But watch what happens. If you use AI to deepen your specialty, you're drifting toward the specialist end. If you use AI to extend your reach across more domains, you're drifting toward the generalist end. AI fluency doesn't preserve the middle. It sorts you out of it.

Recent IMF research confirms the pattern: new skills are boosting employment overall but deepening polarization, with gains concentrating at the extremes while middle-skilled workers see no significant benefit.³

The question worth asking: why does anyone still think the middle is safe?

Both Ends Have a Trap

The barbell is descriptive, not a guarantee. Sitting at an end doesn't mean you're safe. It means you have a different kind of exposure.

The specialist's trap is depth in a dead end. You can be the world's foremost authority on a technology, a legal framework, a therapeutic technique, and the domain can shift under you. Depth is only defensible when the frontier you're exploring still connects to something the world needs. Go deep in the wrong direction and your irreplaceable knowledge becomes irrelevant knowledge.

The generalist's trap is subtler and harder to see from the outside.

Breadth can become a hiding place. The person who touches five domains well enough to contribute to each but never commits deeply enough that any single one could expose them. They look like a strategic generalist. They feel like one. But the tell is in the output.

You can usually tell the difference by asking one question: does this person's breadth produce something that wouldn't exist without the specific combination? If the domains generate something together that none of them could alone, the mesh is real. If they just coexist on a resume, the breadth is a different kind of "pretty good."

The Unstated Baseline

All of this comes with a condition most discussions skip past.

You have to be using AI.

A specialist who ignores AI is building a sandcastle at low tide. A less experienced specialist who uses AI well will close the gap faster than either of them expects. Your decade of depth buys you a window, and it's narrowing.

A generalist who refuses AI is trying to span multiple domains on raw bandwidth alone. Someone with half your experience and twice your AI fluency will cover more ground, faster, with fewer blind spots.

The barbell only works if you're actively using the tools that created it. Neither end is defensible without them.

But here's the thing the AI fluency advice is already running into. AI fluency itself is becoming "pretty good."

Right now, knowing how to use these tools well is an edge. As the tools get more intuitive and the population of fluent users grows, that edge dulls. Being good at prompting will be as differentiating as being good at Googling.

When AI fluency becomes table stakes, what's left is what makes the barbell's ends defensible now. Frontier knowledge you created. Or judgment built from your specific combination of domains and failures.

The tool proficiency gets you to the table. The taste is the game.

Pick Your End

The safest position in any professional field used to be the middle. Competent at several things, deep enough in one or two to stay relevant.

That ground is disappearing.

Pick an end of the barbell. Go deeper than AI can follow, or go wider than AI can hold. Both come with their own traps. The specialist risks depth in the wrong direction. The generalist risks breadth without a real mesh connecting it.

But the middle's trap is worse. You don't fall into it. You stay in it, comfortably, while the ground erodes.

At least at the ends, you chose.

Rabbit Hole

If this angle landed, you might also dig into:

• Your Job Already Changed. You Just Didn't Notice. - How AI quietly redefined what "good" means in your role
• The Uber-Engineer Doesn't Write Code - What happens when one person directs the whole production
• Every Venture Is Either a Commodity or a Brand - The same hollowing-out, applied to businesses instead of careers

1. "Jack of all trades" first appeared in print in 1612. "Master of none" was tacked on in the late 1700s. The positive ending, "but oftentimes better than a master of one," doesn't show up in print until around 2007. It's widely attributed to Shakespeare, who died a century before even the negative version was recorded.
2. The American Board of Medical Specialties (ABMS) certifies physicians across its 24 member boards in more than 40 general specialties and nearly 90 subspecialty areas. The actual number of practiced subspecialties is higher, as many specializations exist without formal board certification.
3. IMF Staff Discussion Note, "Bridging Skill Gaps for the Future: New Jobs Creation in the AI Age" (January 2026). The research finds that demand for new skills, especially in IT and AI, boosts average wages and employment but deepens labor market polarization, with gains concentrating at both extremes of the skill spectrum while middle-skilled workers see no significant benefits.