The systems that build star performers

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View our Twitter (X) feed View our Youtube channel View our Instagram feed View our Substack feed Search Popular SearchesCritical thinkingPhilosophyEmotional IntelligenceFree Will Latest Videos Latest Articles The systems that build star performers

Many top performers start behind — and overtake the early leaders later.

by Rachel Barr January 29, 2026 A tortoise wearing a blue "1st Place" ribbon on its shell, posed against a plain light background.

Elena Scotti Key Takeaways

Across fields from sports to science, the highest performers are often not early standouts, but slow developers.
Systems that favor early identification and specialization tend to suppress the exploration, adaptability, and cross-domain thinking that drive long-term excellence.
Star performers often emerge from environments optimized not for speed or certainty, but for resilience, experimentation, and intelligent failure.

If you were asked to build a future bestselling author, how would you go about it?

Chances are, you’d start young, scouting for early signs of promise. You’d probably reinforce that raw talent right away, sending your protégé to writing workshops and private tutors. You might line their shelves with Pulitzer winners, assign the classics, fast-track an English degree — tracing a path right up to the gates of publishing.

What you probably wouldn’t include is a thawing patch of Arctic soil.

As a young environmental scientist, David Epstein spent his days hunched over a plot of warming permafrost, monitoring carbon emissions bubbling up from the melt. When I asked him about those years, he laughed. “I was shaping up to be an average scientist.”

Nothing about that scene looked like a straight road to success in journalism, and yet just a few years later Epstein landed at Sports Illustrated, climbing fast to become its youngest senior writer. From there, he began chasing the edge cases of human performance that would inspire his first bestselling book, The Sports Gene.

If you’re a fan of tidy origin stories, that’s your ending: STEM graduate discovers his true calling, becoming a star reporter and a bestselling author. Roll credits.

We like that reading because it lets us keep our favorite myths about success intact. Epstein might have wasted his talents, we think, had he not figured out his real path before the clock ran out.

But for Epstein, that version misses the point entirely.

In his telling, the permafrost plots gave him an advantage that couldn’t have been planned. The same skills that had blended into the wallpaper in environmental science provided a competitive edge in the newsroom.

“You can take something that’s normal in one milieu,” he told me, “and move it over somewhere else where it’s seen as invention.”

What had set Epstein apart was his fish-out-of-water fresh perspective. He learned that piecing together the connective tissue between disciplines could lead him to find solutions that single-track specialists missed — the provocation behind his second bestseller, Range: Why Generalists Triumph in a Specialized World.

Range traces the making of star performers through Olympic coaching pipelines, military academies, and Silicon Valley labs, unseating the myths of mastery by leading us to where it’s most reliably found: spaces that allow broad curiosity to roam.

If mastery were a matter of discipline alone, it would be easy enough to teach. Instead, Epstein turns our attention outward, asking us to see mastery in a way perhaps only an environmental scientist would think to: as an ecosystem.

Front-runners fall behind

It’s one thing to accept that an author might arrive on bestseller lists by roundabout means; why should a writer’s career be derailed by a few scenic routes? But try extending that same indulgence to Olympic podiums and the Nobel Banquet Hall — in those arenas, the margins are razor-thin, and mastery demands long marination. In the high temples of human performance, we imagine time as an ever-diminishing commodity. Early commitment and uninterrupted focus feel non-negotiable.

This intuition is the central nerve running through our talent development systems, which are designed, almost exclusively, for early identification, selection, and specialization. We’ve institutionalized the logic that the sooner you can identify the top percentile, the better your odds of developing high achievers. Talent surfaces early, sets its teeth, and never lets go. It’s a clean rationale that lends itself to infrastructure we can easily build.

One small problem.

When researchers tallied up the data from a combined cohort of around 35,000 top-tier performers, they found that early front-runners were usually overtaken. In this newly published synthesis, datasets that included Olympians, Nobel laureates, and world-class musicians were reanalyzed, tracking the trajectories of the highest performers — and as it turns out, they often lagged behind their peers initially.

If you wanted to ruin a child’s chances of one day being the best in their field, you could hardly do better than to make them a prodigy, it seems.

Epstein raised this alarm years ago in Range, writing that the best predictors of future elite performance often hide inside messy developmental stories. Among his sources was Chelsea Warr, a top performance strategist formerly at UK Sport, where she rebuilt athlete development systems ahead of the 2012 London Olympics. She defined two types of athletes: fast risers, the 12-year-olds dominating age-group competitions, and slow bakers, who hover around the middle of the pack, either between growth spurts or bouncing between sports, before emerging into excellence much later.

Trials and talent scouts scoop up the early standouts, while everyone else is cut adrift. It’s intended to be diagnostic, a means of identifying potential so it can be preserved. In practice, early selection becomes an intervention, hoarding resources for the front-runners and stacking the odds against anyone hoping to catch up to them.

“If we disadvantage the other people so much by deselecting them, then they’re never going to become those slow bakers,” says Epstein.

And yet some still do. Looking back through the records, Warr found that the majority of medalists hadn’t looked like sure things at age 12, even as every lever in the system was tuned to favor those who did.

This insight led her to an important policy shift. She argued that, with fewer deselections and more ways in, UK Sport would see more of that late-developing potential surfacing at the podium. Rather than continuing to rely on a single, high-stakes point of entry, she pushed for looser age cut-offs and routes for late developers to join or rejoin the system.

Fast risers succeed within systems tailored for them; slow bakers succeed despite not fitting the system, and they do so at a far higher rate. It’s a pattern that emerges beyond athletics.

Nobel Prize winners often underperform relative to their peers early in their careers, publishing less and securing tenure later. They wander between fields, collaborate more broadly, and become sharper problem solvers in the process. In the short term, they’re penalized for it, but in the long term, that same tendency pays off — if they can make it through the system.

If there’s one constant in our misunderstanding of mastery, it is time.

We need a different picture of what excellence looks like while it is forming. Systems optimized for early performance are, by definition, mechanisms for suppressing everything that develops on a longer fuse. They disincentivize the messier, more wandering developmental arcs that most reliably predict future excellence, disadvantaging the people who could become extraordinary if given the time.

And if there’s one constant in our misunderstanding of mastery, it is time.

The 10,000-hour rule, which seemed to promise that mastery could be engineered by little more than persistence, is a perfect example. Epstein dismantled it long ago in The Sports Gene, and the empirical disproofs have been mounting ever since. Still, the myth limps on, surviving, I think, by slotting itself so neatly into our most reflexive intuitions about success.

We worry about lost time, wasted time, and the perceived impossibility of catching up to a head start, all the while withdrawing from the slow, exploratory processes that don’t yield clear and immediate utility. Specialize early, count the hours, rack up the reps — and mastery is mathematically inevitable.

Only the clock doesn’t do the learning. We do.

The benefit of lateral experience is that it forms a mental model not just of a single task, but of how systems work in general. It doesn’t yield easily to metrics or timelines, but it wins out across time because the worlds we inhabit — socially, professionally, and physically — don’t reward repetition nearly as often as they reward intelligent adaptation.

Cognition and sensorimotor functions emerged from the evolutionary pressures of an unpredictable earthly environment. That’s why intelligent learning systems have mechanisms for handling uncertainty. In humans, that is largely a function of the hippocampus, the brain’s central hub for learning and memory. It drafts multiple renderings of reality — what was, what is, and what might be — using spatial and relational representations to generalize across time and context. It allows us to flexibly recombine past experience to model futures we’ve never directly encountered.

Hyperspecialization short-circuits that mechanism. Borrowing a term from machine learning, it leads to overfitting. The mind and body become superbly effective at solving the same problem, using the same tools, under the same conditions, while losing flexibility elsewhere.

The trouble is that this isn’t always visible from the surface. Hyper-specialized cardiac surgeons, for example, have fewer complications than their less-specialized peers. That seems like good news — until you learn they’re also more likely to perform unnecessary procedures and be more resistant to new findings that undermine the validity of the procedure they happen to be specialized for. As Epstein puts it, “You’re less likely to have complications for a surgery you didn’t need.”

This phenomenon — when expertise becomes a cage — is called cognitive entrenchment. Brains trained within rigid constraints are efficient at pattern completion; they see a few cues and fill in the rest from prior experience, suppressing competing possibilities. That’s wonderful if you’re playing the same endgame again, but it’s a losing strategy, and potentially even dangerous, if the task has shifted under your feet — and most do.

That’s what Epstein was running into with his surgeons: brains that perform brilliantly on the distribution they were trained on, but then argue with reality when the distribution shifts.

Brains and institutions alike are both drawn to exploit what already looks promising, often at the expense of exploring what’s merely interesting, awkward, or unfinished. For most of our evolution, that served us because exploration was non-negotiable, but we modern humans can opt out and are actively encouraged to do so in nearly every formal system for high achievement.

Slow bakers pull ahead

If Nobel laureates are slow bakers, you’d hope to see Warr-style contingencies built into the academic career track. When I asked Epstein where that machinery most obviously misfires, he pointed me to the work of Arturo Casadevall, an infectious disease researcher with a mission to fix academic culture from the inside. Casadevall and his colleague Ferric Fang published a paper dissecting the hypercompetitive chase for research funding. Agencies like the NIH send grant proposals to a rotating cast of experts who read, score, and then rank applications. Careers live or die on the fine gradations between those numbers.

The more interdisciplinary a proposal is, the less likely it is to be funded. Short-term funding success and long-term intellectual payoff are, in that region of idea-space, pulling in opposite directions. Not only that, but it turns out that evaluators can only reliably score the bottom 10-20% of applications. After scrapping the obvious duds, they were no better than a coin toss. The result is the academic equivalent of Warr’s deselection problem.

If experts are reasonably good at spotting the duds but almost powerless to rank the rest, then, according to Casadevall, we should stop asking them to. You’d cut off the bottom 20% and enter the rest into a funding lottery.

That may sound heretical, but science effectively already runs a lottery while insisting it’s a precision instrument. Scientists spend weeks gaming out what reviewers want to hear, rewriting fundable ideas that simply fell on the wrong side of an arbitrary line. A modified lottery would save them from a system that has poor long-range vision, encouraging the kind of slow-baker brilliance that fuels Nobel-winning discoveries.

It’s not just a thought experiment. New Zealand’s Health Research Council already allocates some of its grants by lottery. Early evaluations suggest that the funded projects are at least as productive as those chosen under traditional peer review, with the added benefit of greater diversity in topics and investigators.

Systems that demand immediate answers, linear progress, and constant proof of usefulness will reliably produce one thing: people who stop exploring.

If Casadevall’s lottery is a bid to stop the system from deselecting slow bakers, it still leaves another problem unsolved: What happens after the money arrives? The road to innovation is paved with dead ends, and you don’t get breakthrough performers if those failures are punished. Somewhere in the pipeline, somebody has to underwrite intelligent risk.

When Epstein went looking for examples of that, he saw that 3M was routinely ranked as a top innovator. “The Post-it company?” He was initially confused, until he discovered that they employ roughly 8,500 scientists and expect a quarter of their revenue to come from products that didn’t exist five years earlier. They make everything from surgical adhesives to aerospace materials.

Inside that machine, he was introduced to Jayshree Seth, a corporate scientist who climbed all the way up to the tiny elite tier of 3M’s top-ranked inventors. On paper, her trajectory looks scattered in the same way those Nobel slow bakers do. She calls what she does “mosaic building.” As Epstein tells it, she spends a lot of time walking around asking people what they’re working on, and then getting them in the same room with other teams who are orbiting the same problem. By cross-pollinating different groups of thinkers, Seth disturbs the pattern-completion machinery of each mind, clearing the ground for alternative insights to land.

A healthy innovation space allows ideas to move, but that is perhaps secondary to what happens when they lead nowhere. One of Seth’s ambitious cross-divisional projects failed, which, under short-term optimization logic, should have counted against her. Instead, 3M promoted her. The question she’d framed was judged important enough that the company was willing to eat the miss. In Epstein’s words, they “reward intelligent failure.” It creates a home for visionaries like Seth, encouraging their expansion by backing them through the hit-and-miss process of invention.

Systems that demand immediate answers, linear progress, and constant proof of usefulness will reliably produce one thing: people who stop exploring.

Short-term metrics are not only poor predictors of long-term success, but in many cases directly hostile to the outcomes we desire. They are our failed attempt to make an unpredictable environment more predictable, even though we already have a perfectly good mechanism — one a few millennia in the making — for handling uncertainty. The systems that build star performers are the ones that allow that process to occur naturally.

Rachel Barr Full Profile