Latest / Career Skills with Fexingo: Hard Skills, Soft Skills, and Future-Proofing Your Career / Why Most Career Advice Ignores Your Scarcity Leverage
Transcript
- Lucas: Luna, I want to start with a number: in the past twelve months, the number of job postings that mention both a federal certification and an ai related skill has more than doubled. That's from LinkedIn's own data, released last quarter. Luna: That is such a specific pair. Federal certification and AI — those two things feel like they come from different decades. Lucas: Exactly. And that's what makes the combination powerful. Because when you hold a skill that's both regulated and emerging, you're not just another applicant. You have something close to a local monopoly. Luna: Local monopoly — I like that. So the idea is, most career advice tells you to keep adding skills, keep diversifying. But you're saying the real leverage comes from identifying which one of your skills is the hardest for someone else to replicate? Lucas: Right. I call it 'scarcity leverage.' It's not about being the most skilled person in the room. It's about being the only person in the room — or one of very few — who holds a specific, high-demand capability that your employer genuinely needs. Luna: Give me a concrete example. Who's someone you've seen do this well? Lucas: I spoke with a maintenance engineer at a major US airline. Let's call him Mark. He's been with the airline for about eleven years. He holds an FAA Airframe and Powerplant certification — that's the A&P license. It's a federal certification that takes at least eighteen months of training and passing three separate exams. Luna: Okay, that's already a scarce credential. There are only about 160,000 active A&P mechanics in the US, and the industry says they're short thousands. Lucas: Right. So Mark already had scarcity. But here's where it gets interesting. About two years ago, he took a six-month online program in predictive maintenance using machine learning. Not a full degree — a certificate from a community college. He learned how to build models that predict when an engine part is likely to fail, using sensor data. Luna: So now he's an A&P mechanic who can also write code to optimize maintenance schedules. That combination — how many people in the US have both? Lucas: The airline told me they surveyed their own workforce. Out of roughly 14,000 maintenance staff, fewer than forty had both the A&P license and any formal AI training. That's less than three-tenths of one percent. Luna: Three-tenths of a percent. That's not a skill gap — that's a canyon. Lucas: Exactly. And Mark used that leverage. About eight months ago, he went to his manager and said he wanted to move into a new role — a 'maintenance data analyst' position that didn't formally exist yet. He showed them a pilot project he'd run on his own time: he used historical data from one fleet of aircraft and built a model that predicted part failures three days earlier than the standard inspection schedule. Luna: And what happened? Lucas: They created the role. They also gave him a 22 percent raise and a title change. And they paid for him to take two more advanced courses. Because they realized that losing him meant losing access to a capability they couldn't easily hire off the street. Luna: That's the 'scarcity leverage' in action — he created a role that didn't exist by making his own combination too valuable to ignore. But here's my worry: what happens when more people get that same combination? Doesn't the scarcity erode? Lucas: It can. That's the risk. And that's why this strategy requires ongoing attention. You have to think about which of your skills is currently the scarcest, and also ask: is this scarcity likely to persist, or is it a temporary gap that will close in two or three years? Luna: So you need to forecast the supply side of your own skill set. Lucas: Exactly. Let's stay with Mark's example. The supply of A&P mechanics isn't going to spike overnight — it takes 18 to 24 months to get that license, and the training pipeline is limited. That scarcity is structural. On the AI side, more people are learning machine learning every year. So the combination might become less rare over time. But for the next three to five years, Mark probably still has a window. Luna: So he needs to be thinking about his next layer of scarcity. Maybe he adds a cybersecurity component — securing the sensor data and the models. Or he gets involved in regulatory policy around AI in aviation. Lucas: That's exactly the move. Don't just coast on one combination. Keep asking: what's the next skill I can layer on that will keep me in that one-percent or less category? Luna: This is such a different mindset from the 'become a T-shaped person' advice that's everywhere. That advice says: have a broad base and one deep specialty. But what you're describing is more like an L-shaped person with a very specific, weird second skill that makes the deep specialty exponentially more valuable. Lucas: Right. And the key word is 'weird' — or at least, non-obvious. The most powerful combinations are often ones where the two skills come from different domains entirely. An A&P mechanic plus machine learning. A nurse plus health informatics and a data privacy certification. A teacher plus instructional design for virtual reality. Luna: Let's talk about how someone actually figures out what their scarcest skill is. Most people I talk to can list their skills, but they have no idea how rare each one is in the market. Lucas: There are a few practical steps. First, search for your combination of skills on LinkedIn or Indeed and see how many profiles or jobs match. If it's under a hundred, that's a signal. Second, talk to a recruiter who works in your industry — ask them directly: 'How often do you see someone with both X and Y?' Third, look at the hiring behavior of your own company. If they've had a role open for more than six months, that's a sign that the skill set is scarce. Luna: And once you identify your scarcest skill, you have to protect it — by keeping it current, by making sure it doesn't become commoditized. That might mean investing in continuing education even when your employer isn't asking you to. Lucas: Right. Because the moment your scarcest skill becomes common, your leverage disappears. And you have to rebuild it somewhere else. Luna: I want to push back on one thing, though. Doesn't this whole framework assume that the employer actually values the skill combination? What if you're in a company that doesn't care about predictive maintenance, or doesn't have the data infrastructure to support it? Then your scarcity is theoretical. Lucas: That's a great point. Scarcity only creates leverage if someone with resources wants what you have. So you have to be honest about whether your current employer — or your target employer — actually has a need for that combination. If they don't, you either need to find an employer who does, or you need to reframe your skill in terms of a problem they already care about. Luna: That reframing piece is crucial. Mark didn't walk in and say 'I have a certificate in machine learning.' He showed them a pilot project that predicted part failures and saved money. He translated his skill into a business outcome. Lucas: Exactly. And that's the other half of scarcity leverage: not just having the rare skill, but being able to demonstrate its value in terms the organization understands. Luna: Before we go further, I want to mention something that's been on my mind. We make this show every week — no ads, no sponsors, just the two of us and a producer. And it's only possible because a small group of listeners chip in through buy me a coffee dot com slash fexingo. That support literally funds the research, the editing, the hosting costs. Lucas: Yeah, I'm glad you brought that up. It's not a big fundraising pitch — it's just the reality of how the show stays independent. If you've gotten value out of episodes like this one, and you want to see us keep going, that's the way to make it happen. Luna: And we mean it — no pressure, no guilt. Just a sincere thank you to everyone who already does. It makes a real difference. Lucas: Okay, back to scarcity. Let's talk about one more pitfall. The biggest mistake people make is assuming their scarcest skill is the one they most recently learned. But recency isn't scarcity. Just because you learned a new programming language doesn't mean it's rare. Luna: Right. The scarcest skill might be something you've had for years but never thought to combine with something else. For example, a friend of mine is a graphic designer who also understands accessibility standards — WCAG 2.1 compliance. That combination is surprisingly rare. Most designers don't know it, and most accessibility experts aren't designers. Lucas: That's a perfect example. And that combination is likely to become more valuable as legal requirements around digital accessibility tighten. So she's sitting on leverage she might not even realize she has. Luna: So the action item for listeners this week: take a piece of paper. List your top five skills. Then, for each pair, search online to see how many people or jobs mention that combination. If any pair shows up fewer than a hundred times, that's your leverage. And then ask yourself: who needs this, and how do I show them the value? Lucas: And if you don't have a rare combination today, you can start building one. Pick one skill you already have some depth in, and one emerging skill from a completely different domain. Spend six months getting competent in the second one. That's a faster path to leverage than trying to become an expert in something everyone else is also learning. Luna: I like that. It's a very deliberate, strategic approach to career development — not just 'learn more stuff,' but 'learn the stuff that makes your existing stuff rarer.' Lucas: Exactly. And if you do that consistently, you're not just future-proofing your career. You're building a career that gives you more control — more ability to negotiate, to move, to shape your own role. And that's the whole point.