Why Mastery Is Expensive But Competency Scales
Easy to do, hard to master.
I saw this phrase describing a SaaS platform. Simple. Layered. It stuck with me.
So I started applying it elsewhere—skills, workflows, career planning. It turned into a framework for thinking about where to invest time and what mastery actually costs.
Mastery isn’t the only path.
Social media makes it feel like deep expertise is the only winning formula. 10,000 hours. Become world-class. Own your niche.
And sure—mastery can lead to outsized returns. But there are plenty of paths to success that don’t require becoming the best in the world at something. You just need to be strategic about where you go deep and where you stay broad.
That requires thinking about commitment, risk, natural ability, value, and time. Not every skill is worth mastering. Some are worth knowing well enough to be dangerous. Others are worth automating entirely.
Mastery Is Expensive, Competency Scales
Mastery takes time, focus, and obsessive depth most people don’t have. Competency across a dozen high-value skills lets you diagnose fast, connect dots others miss, and know when to bring in experts.
Depth vs. range.
Genetic disposition plays a role here. Some people have the wiring for mastery—the obsessive focus, the tolerance for repetition, the ability to grind on one thing for years.
Others don’t. They consume breadth. They connect dots across domains. They’re competent in twelve things instead of world-class in two.
Neither is better. But you need to know which you are, because the strategy changes.
The framework.
Here’s how I think about skill development across four quadrants:
1. Easy to Do, Hard to Master
This is where I spend most of my time. CRM systems. Coaching. Consulting frameworks. SaaS platforms. Strategy development. Writing. Project management.
My approach: build a broad base of moderate skills with the ability to go deeper when a specific situation demands it. I’m not trying to be the world’s best Salesforce admin. But I can configure it, troubleshoot it, and know when to call in an expert.
Go-to resource: YouTube tutorials, vendor documentation, pattern recognition from repetition.
2. Easy to Do, Easy to Master
I minimize time here. Most manual, stepwise processes fall into this bucket—data entry, basic formatting, repetitive admin tasks.
My goal: automate or outsource anything in this category. If a process can be scripted, documented, or handed off, it should be. These skills don’t differentiate you. They just consume time.
Go-to resources: Scribe for documentation, Zapier for automation, RPA tools, virtual assistants.
3. Hard to Do, Hard to Master
I don’t claim mastery in anything here. Few can. But I maintain competency in several hard skills I’ve developed over my career—programming, sales, lead generation, financial modeling, design thinking, product development.
These require real commitment. If you start down one of these paths without a solid reason, you’ll burn significant energy for minimal gain. But if you choose wisely, competency in hard skills opens doors and commands higher compensation.
Masters in these areas are rare and expensive. Knowing enough to evaluate their work, brief them effectively, and integrate their output is often more valuable than trying to become one yourself.
Go-to resources: Networking, online communities, mentorship, paid courses with real feedback loops.
4. Hard to Do, Easy to Master
I struggled to populate this quadrant. Accounting, maybe? Skills with high barriers to entry but clear paths to competency once you’re in?
Honestly, I don’t invest here. If something is hard to access but easy to master once you do, it’s probably not a leverage point for me.
My personal thesis.
I aim for competency across the value stack of business transformation—enough depth in hard skills to diagnose opportunities, enough breadth to see connections others miss, and enough humility to know when to bring in masters.
The goal isn’t to be the best at any one thing. It’s to be dangerous enough in the right combination of things that I can rapidly assess what’s broken, what’s possible, and who needs to be in the room to execute.
I need to know what mastery looks like so I can help clients stitch together the right resources for ambitious projects. But I don’t need to be the one delivering that mastery in every domain.
The exercise.
Take fifteen minutes and map your own skills across these quadrants:
- Easy to do, hard to master — Where are you building broad competency?
- Easy to do, easy to master — What are you automating or outsourcing?
- Hard to do, hard to master — Where are you committing real energy? Is it worth it?
- Hard to do, easy to master — Does this quadrant even exist for you?
Then ask yourself:
- What’s your commitment level to each category?
- What’s your natural wiring—depth or range?
- What does your skill development thesis look like over the next three years?
This should be unique to you. Don’t copy someone else’s path because it worked for them. Figure out where you have leverage, where you have interest, and where the intersection of those two things creates value.
The takeaway.
Mastery is expensive. It takes time, focus, and often a specific genetic disposition for obsessive depth.
But competency is scalable. You can be competent in a dozen high-value skills and still have time to build a business, stay curious, and avoid burnout.
The trick is knowing which skills are worth going deep on, which are worth staying broad on, and which are worth ignoring entirely.
Most people never ask the question. They just accumulate skills randomly and wonder why nothing compounds.
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Stop Incrementalizing Yourself to Death
Most people run from chaos. That’s a problem.
There’s a soft skill nobody talks about in transformation work: the ability to sit in disorder without panicking. It’s terrifying. It feels reckless. But it’s one of the fastest ways to cut through years of incremental bullshit.
Take the Twitter saga—whatever you think of Elon, there’s a method worth examining. He laid off half the workforce in one swing. Brutal? Absolutely. Chaotic? By design.
But here’s what happened: the system broke in specific places. Fast. He learned in weeks what would’ve taken consultants eighteen months and millions in discovery fees to figure out. Which 100 roles actually mattered. Which systems were load-bearing. Which processes were theater.
You can hate the approach. But you can’t ignore the speed.
Disorder Uncovers What Matters
Most organizations study problems until the market moves. Controlled chaos cuts through years of planning by spiking the system and watching what actually breaks.
Chaos as a diagnostic tool.
Most organizations incrementalize themselves to death. They pilot programs. They form committees. They study the problem until the market moves and the problem changes.
Controlled chaos does something different—it creates variance fast. You spike the system, watch what breaks, and identify the vital few variables that actually move outcomes. The critical Xs that drive your most important Ys.
In transformation work, we do this deliberately. We dump everything into one space—a war room, a virtual whiteboard, whatever. All the diagnostics, all the analyses, all the conflicting data. It looks like a disaster. Clients panic.
That’s the point.
Ambiguity is a feature, not a bug.
We coach teams to work the chaos. Test hypotheses. Find connections nobody saw when everything was siloed. Build consensus around what success actually looks like—not what the strategic plan from 2019 says it should look like.
Slowly, patterns emerge. The puzzle assembles itself. A strategic path becomes visible that nobody could’ve planned from a conference room.
This is a form of productive suffering. Grinding through disorder hardens the outcomes. The strategy that survives chaos is antifragile—it gets stronger under pressure because it was forged under pressure.
When chaos doesn’t work.
If your business runs well and has solid foundations, don’t inject chaos. Incremental planning works fine when systems are healthy.
But if you’re dysfunctional? If you’re in rapid change and legacy processes are anchors? If every meeting ends with “let’s form a working group to explore this further”?
You don’t need more structure. You need controlled demolition.
The takeaway.
Get comfortable being uncomfortable. Chaos reveals truth faster than consensus ever will. The answers are already in your organization—they’re just buried under process, politics, and the fear of breaking things.
Sometimes you have to break things to find out what matters.
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From 20 Years Experience, 10 Lessons on Financial Forecasting
What twenty years of financial modeling taught me.
Early on, I built credibility through complexity. Multi-tab spreadsheets with hundreds of inputs, sensitivity analyses following statistical distributions, thousands of outputs, and forecasts stretching twenty years out.
I could command a boardroom with the stories these models told. I believed the numbers.
Then I checked the receipts. Short-term? Decent. Long-term? Garbage. Accuracy varied wildly by industry maturity, but the pattern held: complexity didn’t equal precision.
80/20 Rule
Also known as pareto’s principle: roughly 80% of effects come from 20% of causes. Find the vital few, ignore the trivial many.
A decade later, I stripped it all down.
Fewer inputs. Fewer assumptions. Leaned hard into the 80/20 rule—find the variables that actually move the business, ignore the rest.
The 80/20 principle comes from Vilfredo Pareto, an Italian economist who noticed that 80% of Italy’s land belonged to 20% of the population. The pattern showed up everywhere he looked. Today we call it the Pareto Principle: roughly 80% of effects come from 20% of causes. In financial modeling, that means most of your forecast accuracy comes from a handful of key drivers. Nail those, and the rest is noise.
The simpler models weren’t perfect. But they performed better. And teams could actually use them without a decoder ring.
The lesson: less is more. Here are ten things I wish I’d known earlier.
- Understand the goals of the model. Whether revenue, cash flow, market share, P&L, headcount, or volume, design a simple model focused on the core output. And resist the tendency to build the all-in-one model.
- Think modularly, for example, inputs, calculation engine, and outputs.
- Resist complex embedded formulas at first. Further, make it easy for others to edit and understand the logic.
- Use writing as an analogy to modeling. Work quickly to write a first draft, or MVM, a minimal viable model. And then begin an editing process until you create a structured and logical result.
- Subtractive is better than additive. Sculptors start with a block and chisel away until they execute their vision. I attempt to do the same. Start with knowns and chisel away based on confident subtractions. The additive approach, or bottoms-up, can provide a nice check and balance but tends to overshoot reality.
- Start with a shorter duration with realistic assumptions, then expand. Working backward from a target is valuable, but it often leads to disconnected results — so do both. Working the model from the goal also helps to assess input sensitivity.
- Show your results as scenarios; base case, moderate, and aggressive. Don’t fall in love with a single number or scenario.
- Focus on the inputs rather than argue about the outcomes — make it easy for reviewers to challenge inputs and see the results in real time.
- As confidence builds around the model, add new modules or consolidate formulas to reduce the size. Redo the editing step.
- Start from scratch with your models, or at the very least, start with other’s models that you trust and wholly understand.
Modeling anything takes a bit of art and science and most importantly – reps.
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