Range Is The New Black - Part II
Why Developing Range Is the Most Important Thing You Can Do for Managing and Developing Your Career
If you missed the first part of this - please go back here so you have the full context of what this is about, and then come back to this article.
Chapter 1: The Cult of the Head Start
In almost every industry, we reward people who specialize early. If someone “knew they wanted to be a doctor since age 8” or “wrote their first line of code at 10,” we treat it as a badge of honor. In tech, it’s no different. The faster you declare your domain the more credibility you’re often given. It’s the gospel of the head start: pick a lane early, go deep in that area, and enjoy the benefits of becoming invaluable specialist.
But what if that advice, while occasionally true, is often dangerously incomplete? Especially now.
David opens Range by challenging this cultural narrative head-on. He does it not by critiquing it theoretically, but by showing us two real-world cases: Tiger Woods and Roger Federer. These aren’t just sports stories, they’re opposing archetypes of how excellence can emerge.
Tiger vs. Federer: Two Paths to Greatness
Tiger Woods is the canonical example of early specialization. At just seven months old, he mimicked his father’s golf swing. By two, he was on national TV. By three, he was shooting under par. His life was golf and nothing else. This is the prototypical tech professionals we’ve come to admire and ask others to emulate.
Roger Federer, on the other hand, couldn’t have had a more different trajectory. As a kid, he played soccer, basketball, skateboarding, wrestling, skiing and yes, some tennis. His parents encouraged sampling, not specialization. He didn’t take tennis seriously until his mid-teens. No intensive coaching. No travel circuit. No deep athletic seasoning. Just wide play. And yet, Federer would go on to become one of the greatest tennis players of all time.
These two athletes both reached the pinnacle of their fields, but through radically different developmental paths.
And this is where David makes his key point: while Tiger’s path works in a “kind” environment, Federer’s path is better suited to a “wicked” environment.
Kind vs. Wicked Environments: What’s the Difference?
A “kind” learning environment is predictable, rules-based, and has near-immediate feedback. Think of golf, chess, classical music. Any field where repetition leads directly to mastery. You swing, you miss, you adjust. It’s a quick feedback loop. Specialization here is rewarded early and often.
But most things in life including most careers, especially in tech do not operate this way.
A “wicked” environment is one where the rules aren’t clear, the feedback is slow or inaccurate, and the situation evolves constantly. Think of entrepreneurship, product management, leading engineering teams, navigating cross-functional influencing where the success signals are noisy and messy.
In these environments, the habits and instincts you build from early specialization can actually work against you because the patterns don’t repeat predictably.
That’s where generalists like Federer have the edge: they learn to improvise, adapt, and reason through ambiguity. They learn how to learn, not just how to execute a playbook. Their edge isn’t technical precision, it’s strategic fluency. You learn to not only embrace ambiguity, but anticipate ambiguity. Those are the norms of a wicked environment.
What This Means for Tech Careers
Let’s talk about “Tiger Path” professionals in tech. You know the ones. They picked a specialty early, let’s say Ops/Engineering. They became the go-to infra person, solved every scaling problem, fine-tuned the performance of workloads.
Early on, this pays off. They are indispensable.
But then, the context shifts.
Suddenly, the team needs someone to decide whether to refactor the architecture to support a new business unit. Or whether to sunset a platform and migrate to cloud-native tooling. Or how to frame the value of the team’s work to leadership. The environment becomes wicked. The problem space is no longer technical, it’s systemic, strategic, and cross-functional.
And here’s the hard truth: if you’ve only lived inside the “kind” world of technical execution, your instincts may not translate. In fact, they might actively limit you.
Now imagine someone on a “Federer Path.” Early in their career, they bounced between frontend, data modeling, and dev tooling. They explored product conversations, joined a sprint with the sales enablement team, worked on both successful and failed launches. Their path looked non-linear. Maybe even slower. But it built one thing early: range.
And once the environment turns wicked, range becomes leverage.
Why This Matters Now in the AI Era
AI is systematically absorbing many of the kind-environment tasks in tech: writing boilerplate code, debugging syntax errors, generating SQL queries, even suggesting infrastructure diagrams. It rewards those with execution depth but commodifies that depth quickly.
The last thing you want is to be narrowly specialized in a task AI can now do faster than you.
But wicked problems? The fuzzy, strategic, human ones? Those still require range: how to negotiate tradeoffs, assess risk, integrate product-market context, and work across disciplines.
We’re moving from a world where being a “Tiger” gave you a ten-year head start to one where being a “Federer” makes you future-proof.
Practical Strategy: What You Can Do
Audit your “environment”:
Are most of your problems kind (repetitive, with clear feedback) or wicked (ambiguous, cross-functional, change management)? If the latter, are you equipped with enough range to navigate them?
Shift from efficiency to flexibility:
Don’t optimize for faster delivery. Optimize for broader understanding. Take on projects with uncertain outcomes. Volunteer to work on initiatives outside your core stack.
Delay final specialization:
Just because you’re successful in your niche now doesn’t mean you’ve found your long-term “fit.” Allow yourself a career sampling period, no matter how many years of experience are behind you. Think about the active years of experience in front of you where you can make meaningful contribution.
Bottom line: Early specialization makes you faster.
Range makes you adaptable.
And in a world that’s changing faster than your domain can keep up, adaptability always wins.
Chapter 2: How the Wicked World Was Made
In Chapter 2, David dives deeper into the distinction between “kind” and “wicked” learning environments. It’s the concept introduced in Chapter 1 but now rigorously defined. And he starts by examining one of the most high-stakes, feedback-critical professions out there: medicine.
The setup is simple: how do people learn to make decisions? What makes someone an expert? And what happens when the environment they’re operating in doesn’t give them the kind of feedback needed to learn from their experience?
The implications, as we’ll see, go far beyond medicine. It directly impacts how you, as a tech professional, navigate your career in an AI-dominated future.
The Cardiac Diagnosis Story: When Experience Doesn’t Help
One of the most memorable studies David explores involves emergency room physicians diagnosing chest pain.
The researchers discovered something strange: experienced ER doctors often performed worse than novices when it came to accurately diagnosing heart attacks. Their error rates were higher, even though they had years of practice. Why?
Because they were relying on instinct built in a wicked learning environment — one where feedback is slow, incomplete, and ambiguous.
When patients came in with chest pain and were misdiagnosed, but then went to another hospital or died later at home, the original doctor often never knew. The learning loop was broken. And over time, the wrong mental models solidified.
Contrast that with a kind learning environment, like chess or firefighting. You make a move, the result is immediate, and the pattern reinforces or corrects your instincts quickly. These are domains where experience reliably builds expertise.
But medicine — like much of modern life — is different. It’s complex. Multivariable. Pattern-defying. That’s wickedness in action.
Wickedness in Tech: You’re Swimming in It
Now let’s bring this home.
You might think, “Well, I’m not diagnosing heart attacks. I’m building systems.” But think more carefully about how you develop judgment at work.
Let’s say you advocate for redesigning a system to support a growing customer segment. It requires capital. You are in a capital constrained environment. No investments occur. No redesign or re-engineering takes place. The highly anticipated customer segment growth does not come to last, because the systems were never scaled to meet the levels of satisfaction through the customer experience delivered based on legacy implementation. A year later, customer churn spikes in that segment, but no one traces it back to that architectural tradeoff. If you are not in a double digit growth industry (Silicon Valley I am not looking at you), then this will sound familiar.
These are wicked feedback loops. Outcomes are decoupled from decisions. Signals are distorted or delayed. You might be right and still look wrong. Or wrong and still look like a star.
And the biggest trap? You start believing your own faulty patterns.
Like those ER physicians, you begin to trust gut feel that’s been trained in a messy, ambiguous environment. You overfit to patterns that don’t generalize. You optimize for what gets rewarded locally, not what’s right systemically.
AI Makes the Environment Even More Wicked
Here’s where it gets even more interesting and relevant to your career.
AI is removing kind-environment tasks at scale. Repetitive queries, straightforward scripts, basic code translation many of these are now accelerated or being worked on to be replaced. That means what remains on your plate is disproportionately wicked.
You are no longer going to be being asked to “code the thing.” You are going to be asked to:
Decide which thing is worth coding
Translate ambiguous business goals into system architecture
Influence a cross-functional group with competing priorities
Make calls under uncertainty, where tradeoffs are murky and outcomes are months away
This is wicked territory. And if you’re used to feedback-rich, rule-based environments, this new terrain can feel disorienting.
But it’s also where the opportunity lies, because most of us are still trying to operate like it’s a kind environment. Which means those who ask the question “Ok, what do I need to prepare myself to be ready for a wicked environment” will develop an edge.
Why Range Helps You Navigate Wickedness
This is where David’s thesis and your career strategy start to converge quite powerfully.
In wicked environments, what works isn’t deep repetition. It’s broad pattern recognition.
The doctors who performed best at diagnosing cardiac events? They didn’t have more years of ER experience. They had exposure to multiple domains of medicine. They had developed conceptual models, not rote responses.
In your career, that means the more domains you’ve touched product, ops, sales support, finance ops, systems design, market / social research, organizational design (to name a few), the better you’ll perform when the rules are unclear and feedback is messy.
You’ll see analogies others miss. You’ll avoid overfitting to short-term patterns. You’ll frame decisions in ways that account for ambiguity, not in spite of it.
Practical Strategy: Building a Wicked-Proof Career
Shift from solution muscle to sensing muscle
Stop asking, “What’s the best tool for this problem?” Start asking, “How is this problem evolving, and who else is affected by it?”
Create your own feedback loops
Don’t wait for outcomes to validate your decisions. Build a practice of retrospective inference: six months after a project, ask, “What did I believe? What happened? What does that teach me?”
Learn across functions, not just within your stack
Read about decision-making. Study organizational behavior. Understand how incentives shape architecture choices. Range lives in the overlaps.
Say yes to ambiguous projects
Join the working group with unclear outcomes. Take the product strategy sprint with no defined metrics. Build the muscle of navigating fog. That’s where range thrives.
Bottom line:
Kind environments reward mastery. Wicked environments reward metacognition. It is the ability to think about how you’re thinking.
As AI clears out kind problems, your real value lies in how well you navigate the rest. And that means becoming the kind of person who doesn’t just solve problems, but reframes them entirely.
Chapter 3: When Less of the Same Is More
After laying the groundwork for how wicked environments require a different kind of thinking, David now turns to a question that challenges one of the most accepted beliefs in professional development: Does early, narrow focus actually help you succeed faster?
The answer, it turns out, is not only no, but in many fields, the opposite is true.
This chapter is where David begins to dismantle the myth that linear paths and early clarity about your goals are the fastest (or even most efficient) ways to succeed. In fact, he introduces one of the most powerful career levers most professionals overlook: sampling.
The Army Officer Study: Fast Doesn’t Mean Forward
One of the more unexpected stories David shares is a large-scale study on U.S. Army officers. The study followed their career trajectories and evaluated their performance across time.
It found that officers who sampled more assignments early in their careers — rotating through different posts, roles, and units were initially slower to advance. They didn’t shoot up the ranks like their peers who stuck to one track.
But here’s what happened later: the samplers surpassed their more narrowly focused peers. They led more effectively, scored higher on assessments of judgment and strategic decision-making, and ultimately rose to more senior ranks.
Early specialists got the head start. But generalists won the race.
Why? Because by moving through different roles, the samplers developed transferable skills, pattern recognition, and a wider lens for assessing problems. When the complexity of leadership kicked in, they had the range to handle it. Their early “slowness” was an investment in long-term versatility.
What Tech Professionals Get Wrong About Sampling
Let’s translate this to a tech career.
Early-career developers, analysts, or engineers are often told: “Pick your specialization fast. Go deep. Own a vertical. Make yourself indispensable.”
That sounds great until the context changes. Which it always does.
And Then Came AI…
Here’s where it gets critical in today’s landscape.
AI is reducing the value of ultra-specialized execution. Writing optimized code, querying large datasets, even deploying infrastructure much of that is now being co-piloted, if not outright replaced, by a machine.
But what AI can’t do at least not yet is bridge disciplines. It can’t integrate product context with data limitations and engineering tradeoffs. It can’t negotiate incentives between teams. It can’t hold a mental model that spans five loosely coupled systems and predict where the next problem will appear.
That kind of thinking requires range.
And range comes from sampling.
A gig is not forever, so why not adopt a “I am going to sample this job”
Let’s acknowledge something. Sampling doesn’t always feel strategic while you’re doing it. Just know that hiring managers don’t think anyone they hire are going to stick with the job you take forever. Sooner or later high performers seek the next challenge. Be deliberate about it. And if you so feel, share it. And, of course, there is always a right way to frame it.
Taking a lateral move from ML to DevOps, or rotating into a data governance project, or shadowing a product manager for a quarter, none of those feel like the “next big thing.” Especially when others in your peer network seem to be making vertical ascension in their careers within their specialization.
You worry: Am I falling behind? Am I diluting my expertise?
But if David’s research shows anything, it’s this: non-linear progress is not a weakness. It’s exactly what builds the judgment needed in wicked environments.
And in AI-driven tech, where everything is fluid, judgment beats optimization every time.
Practical Strategy: Designing Your Sampling Phase
Map the adjacency graph
If your core skill is backend engineering, what’s one adjacent domain that’s tightly coupled but not your core? API design? Frontend integration? Dev tooling? Sampling doesn’t mean abandoning, it means expanding.
Declare a “range window”
Set a 6–12 month intentional sampling period. Don’t drift. Pick areas to explore (e.g. “I want to understand business metrics and product prioritization”) and look for projects that expose you to those skills.
Use side bets wisely
Sampling doesn’t have to happen only within your full-time role. Contribute to internal tools, mentor outside your domain, build a tool that solves a non-engineering problem. These side bets become strategic range builders.
Reframe your story
When asked about your career trajectory, don’t apologize for variety. Position it as strategic versatility. Say: “I intentionally explored multiple domains early to better navigate ambiguity and make higher-leverage decisions.” Why? Because ultimately you own your career. There is agency in choosing what you want to do with your career, so why not be intentional about it. And also own what story you want to tell.
Final Thought: You’re Not Late, You’re Building Optionality
In tech, we treat the person who climbed the ladder fastest as the gold standard.
But as David shows through military officers, scientists, athletes, and musicians, the people who climb the highest often didn’t start on the ladder at all. They wandered, explored, cross-trained and then took off with more force and better direction than anyone else.
If your path looks messy, non-linear, or a little slow, that might be the best indicator of future readiness in a world where AI will remove the straight lined paths.
In next part of this series we’ll begin looking at the next big idea Epstein provides:
Learning, Fast and Slow, where we’ll look at why the people who seem to struggle more during learning often outperform in the long term and how embracing inefficient learning can give you the edge in a fast-moving industry.
Until next time,
Vijay
If this helped you reframe your thinking about your career development please consider sharing with others. (And, feel free to post it to your LinkedIn network). More content like this in the future. Stay tuned.
Vijay, these practical strategies are inspirational! I'm particularly eager to try shifting from the solution muscle to start building sensing muscle. After all, I won't be able to fully leverage AI if I couldn't precisely identify and frame the problems at the first place!