Why experience matters more (not less) in the age of AI
A career-changer’s perspective on learning, judgment, and AI
Hi friends,
The last time I wrote to you, I was on the job hunt for my first fulltime software engineering job. Well, I have just passed the 1 year mark on the engineering team at a startup called SimpleClosure — with a promotion under my belt — and I could not be happier with this new path I’m on.
I’ve been building real products with real constraints and real consequences. And while I’ve learned plenty of technical things, my biggest learning wasn’t technical at all.
It turns out, in this new career, I wasn’t starting from square one. I just thought I was.
On paper, my transition into software engineering looked like a hard reset and that’s how I stepped into my new role: quite literally as a junior employee, in title and in scope.
Truthfully, that ended up being an asset. I wasn’t afraid to ask “dumb questions,” and I approached my work with the openness and curiosity that had long been replaced with authority in my career as a founder and creative.
But as the months progressed, and the projects I worked on became more complex, I realized something:
The real work wasn’t just about writing code. It was about deciding what to build, why to build it, how pieces fit together, and what tradeoffs to make. It was about communicating intent, navigating constraints, and making judgment calls with incomplete information.
And all that really wasn’t new at all.
Those were the same muscles I’d built over years — systems thinking, problem framing, prioritization, clarity under ambiguity. I didn’t have the deepest technical skill set in the room, but I could see the shape of the problem, and that mattered more than I expected.
On top of that, AI made it possible to punch above my coding years. AI is very good at writing pretty good code, but it only works if you know how to ask the right questions.
What matters now isn’t who can churn out lines of code the fastest or memorize every technical trick. It’s who can clearly articulate the goal, define constraints, evaluate quality, and decide what’s worth iterating on. AI is incredibly good at responding to clarity (and can fail spectacularly without it).
AI doesn’t replace experience — it rewards it.
It rewards people who know how to frame a problem, who understand systems well enough to spot second-order effects, and who have developed taste and judgment over time. The cost of iteration is lower than ever, which means learning happens faster, but only if you know what to ask for and how to tell whether the result is good.
The irony is that the very things that felt “non-technical” turned out to be my biggest advantage, especially now.
When I decided I wanted to switch careers the advice I kept getting was to look at my skills and to figure out how to transfer them. It took me until now to realize I did just that.
I’m still learning every day, but I’ve stopped worrying about “catching up.” Turns out all those years of figuring things out, solving problems, and seeing the bigger picture actually pay off in software too.
Until next time,
Aja
P.S. (Shameless plug for my husband) If you or anyone you know is a founder or thinking about becoming a founder, Charlie’s new book, Founder Unfriendly, is now available for preorder! It’s an honest and direct take on startup fundraising written for the 99% of founders who struggle to raise capital.
P.P.S. I would love to hear from you! What have you been thinking about? What’s exciting you these days? How are you using AI in your day to day? Please respond and let me know! (I still care about non-software things, too 😊)