Good Enough is now the New Baseline, Great requires more Taste, Range and Soul

For the first time in history, average is being automated at scale. AI has leveled the playing field—at least on the surface. Today, anyone with a browser and a decent prompt can generate a business strategy, a client email, a rebrand concept, a full marketing campaign or a LinkedIN thought leadership post in seconds. AI delivers “good” on demand.

That sounds like progress. And in many ways, it is.

But when everyone can create something decent—fast—what’s the real value of decency?

What we’re witnessing is the automation of competence, the elimination of inefficiency and the standardization of “good enough”. In this new landscape, AI isn’t just replacing talent. It’s just raising the floor—and exposing who was never standing that tall to begin with.

The Mediocratization of Everything

AI is closing the gap between bad and good. But it’s stretching the distance between good and great into a canyon.

We’re entering an Age of Mediocrity. A flood of content, strategies, visuals, and ideas that are solid but soulless, crisp but forgettable. The kind of work that feels like it was written by a machine, even when it wasn’t - that’s because most of us were never that great to begin with. We just had more time, more resources, or the luxury of human bottlenecks to hone our outputs. Now that anyone can produce “decent” at scale, average work is becoming the norm—and indistinguishable from machine-made output.

And AI is not to blame, it just amplifies the situation that has been building underneath all the time. In her 2011 study, K.H. Kim described a “Creativity Crisis” that has been growing since the 1990s. U.S. creativity scores have consistently declined, especially in young children. And divergent thinking and originality have dropped significantly.

We’ve built systems that optimize for correct and not creative, efficiency over originality and standardized outputs over personal insight. That’s a breeding ground for “good enough” and we’ve used all of that data and thinking to build the AI models that we now use to “stand out”. In “The Curse of Recursion” (2023), the Princeton researchers found that generative models trained on outputs from other generative models cause “model collapse,” producing increasingly bland and homogenized content over time. It’s basically a feedback loop of mediocrity—prompting outputs from outputs, remixing the remixed, delivering “good enough” at scale.

And in this sea of “good enough,” real taste will become more valuable than ever.

The shift in talent priorities

In a recent company-wide email, Shopify CEO Tobi Lütke made it clear: headcount is no longer the growth lever. Talent is. Employees must prove why a task can’t be done with AI before asking for more people or budget. The performance bar is about working effectively with AI—not in spite of it. And when “good enough” comes without increase of a headcount, greatness will the shopping item for recruitment.

The same pattern is unfolding in law. At Harvey AI, now used by most of the top 10 U.S. law firms, the most valuable legal minds aren’t the ones doing the rote research. They’re the ones guiding the AI, shaping outputs, and applying human judgment to what’s already been streamlined. These companies will not be looking for more people. They will be on the hunt for better minds—people with judgment, taste, and the ability to lead in an environment where the rules are changing daily. When standard work comes as a standard, companies need unicorns that can see the patterns beyond the machines and connect the dots for bigger outputs.

Which brings us directly to one of the most important shifts of the Age of Mediocrity.

Range will be the Competitive Advantage in an AI World

In Range, David Epstein writes how in a world defined by complexity, ambiguity, and rapid change, it’s the generalists—not specialists—who thrive. They know how to think across domains, apply analogies from one field to another, and adapt to new situations quickly. That is the world we’re headed now, especially post-AI, a world of wickedly complex problems.

“The more constrained and repetitive a challenge, the more likely it will be automated,” Epstein writes, “while great rewards will accrue to those who can take conceptual knowledge from one problem or domain and apply it in an entirely new one.”

Specialists thrive in what Epstein calls kind learning environments—systems with clear rules, repeated patterns, and fast feedback loops (like chess or golf). But the modern business landscape is becoming more complex every day. There’s unclear rules, unpredictable feedback, and no obvious playbook.

Taste, Range, and Soul

The future competitive edge of a talent isn’t just skills they possess - it’s discernment. It’s the ability to know what matters before the market catches up or pull insight from psychology, art, history, and code—and fuse it into something new. It’s the ability to understand human emotion in a world of cold, hyper-efficient tools. Yes, we humans still make buying decision based on our emotions and then rationalize them.

Having taste will be a key attribute. And taste is only learned through exposure—through variety, experimentation, play. Epstein found that in his research on musicians: the most exceptional performers weren’t the ones who started young with rigid, deliberate practice. They were the ones who explored many instruments before specializing. The figlie del coro of 18th-century Venice. The jazz improvisers, the exceptional kids who didn’t over-practice one thing—they built range.

This is where work-talent match quality matters the most - the fit between your work and your strengths—not just persistence—drives success. And we only find that fit through sampling, not sticking with the first path we fall into.

This is where analogical thinking becomes your power tool. Breakthrough thinkers—like Charlie Munger—don’t solve problems with brute force. They draw comparisons, reason across boundaries and see patterns others, including AI miss.

Two-Tier Talent Economy

The new structure of the workforce might look something like this in the future:

1. The AI-enabled majority: Efficient, fast, and competent. Armed with prompts and capable of producing endless decent output.

2. The high-impact few: Adaptive, creative, emotionally intelligent, culturally literate, and strategically weird.

AI is automating the middle and eliminating the need for brute force and narrow expertise.

This creates a growing need for humans who:

• Combine creativity with computation

• Connect disparate ideas across industries

• Take risks no algorithm would approve

• Build things that feel alive

For companies it means that they have to start hiring differently. Prioritizing soft skills over hard ones, range over resume and curiosity over credentials.

For builders, strategists, creatives, and operators it means that they need to optimize for perspective and not just productivity. Play with ideas, meander across disciplines and let their inputs diversify before their outputs conform.

Average is now being automated, polished and scaled. But greatness still takes guts.

And range.