A college senior asked me recently how to stand out when he graduates.
His name is Jacob. He is about to start a construction internship — his first real job. He has been learning AI tools. He wanted to know whether it would actually make a difference.
I told him the honest answer: yes, but not in the way most people think.
It is not about having AI on your resume. It is about what you can actually do with it that the person next to you cannot. And more importantly, it is about whether you can show it.
What Is Happening at the Company Level
When I work with companies on AI implementation, I keep seeing the same pattern.
They are not laying people off across the board. The hiring has not stopped. But the math has changed.
What used to require five junior employees — research, drafting, data entry, summarizing, coordinating — is increasingly handled by AI workflows. One well-built agent does the work that three people used to do. That means companies can hire fewer people, upskill the ones they have, and come out ahead on productivity.
The phrase I keep hearing: we would rather train two strong people to do the work of five than hire the five.
This is not a prediction. I am watching it happen right now in the companies and founders I work with. And it has real implications for anyone entering the workforce, or trying to stay ahead in it.
The Moat Is Real
AI fluency — genuinely knowing how to use AI tools to produce, build, and deliver — is becoming a career differentiator in a way that certifications and courses are not.
Here is the distinction I keep drawing: a LinkedIn badge tells me you completed a course. A built tool tells me you can actually ship something.
That gap matters. Because the companies doing the math I described above are not looking for people who know about AI. They are looking for people who can use AI to produce results they can see and measure.
At SXSW earlier this year, I was talking about exactly this. The people who are pulling ahead in the current market are not necessarily the most credentialed. They are the ones who are AI native — who have built enough things to know what is possible, what is worth automating, and how to get good output from AI tools consistently.
The ones who figured this out two years ago have a significant head start. But it is not too late. The moat is still available. The compounding just starts whenever you start.
What I Told Jacob: Build in Public
When Jacob asked me how to stand out, my answer was not take a course. It was: build something real and show it.
Build in public means picking a problem that actually exists — ideally in your industry — and building an AI tool or workflow to solve it. Then documenting that process and putting it somewhere people can see it.
Not a vague project description on a resume. The actual thing you built, with a clear explanation of the problem it solves and the results it produces.
A resume describes what you have done. A built tool demonstrates what you can do. Those are not the same thing, and employers increasingly know the difference.
In construction, Jacob understands what this looks like on a job site. You do not get hired to manage projects by writing about projects. You get hired because you have done it and have something to show for it. AI is the same.
How to Start
If you are trying to build AI fluency — and the career moat that comes with it — the path is simpler than most people make it.
Start with your own friction. What is annoying in your day-to-day work? What takes too long? What requires repetitive steps that never quite seem worth the time? Those are the problems AI tools solve best.
Build the smallest useful version. You do not need to build a complex system. You need to build something that works and saves you time. A workflow that summarizes your emails. A tool that drafts responses from a template. A script that formats data you would otherwise do by hand.
Document it. Screenshot the before and after. Write two paragraphs explaining what you built and why. Post it somewhere — LinkedIn, GitHub, a personal site, a Notion page. The act of documenting forces clarity, and the act of publishing creates the portfolio.
Repeat. AI fluency compounds. Each thing you build makes the next one faster. Each problem you solve with AI teaches you what is possible in adjacent problems. The gap between someone with fifty reps and someone with five reps is enormous — and it keeps growing.
The Timing Is Good
The honest message I give people in Jacob position: the timing is good, but it will not stay this way forever.
Right now, being genuinely AI-fluent — not just curious, but actually building things — still sets you apart. Most people are still in the I have used ChatGPT a few times category. The people doing real work with AI tools have a visible edge.
That window will narrow as more people catch up. The moat will still exist, but the barrier to entry will be higher. What makes you stand out today might just make you competitive in three years.
The people I watch pulling ahead are the ones building now. Not waiting to feel ready, not waiting for the perfect tool or the right course. Just picking a problem and building.
That is AI fluency. And it is the new career moat.
Want to build real AI fluency fast? The 4-Day AI Sprint is a hands-on program for getting dramatically better results from AI tools — by actually building things, not just learning about them.
