From Chemical Engineering to AI Founder: A Pivot I Never Planned
I graduated with a ChE degree in 2010 into the wreckage of the financial crisis, never worked a day in the field, and somehow ended up building AI software. Here's how that happened.
I graduated with a Chemical Engineering degree in 2010. The timing was terrible.
The 2008 housing crisis had gutted the job market, and by the time I had my diploma in hand, engineering roles were scarce. The companies that were hiring weren’t hiring new grads without experience. It’s the classic trap — you can’t get experience without a job, and you can’t get a job without experience. For a lot of engineers who graduated around that time, the field we spent four years training for wasn’t an option.
So I did what a lot of people do when their plan falls apart: I followed my connections. Someone knew someone in IT. I took a call, then a meeting, then an opportunity. I learned on the job. Slowly, a career in technology started to take shape.
But I never stopped thinking like an engineer. Chemical engineering rewires your brain in ways that don’t disappear when you change industries. You learn to think in systems. You obsess over constraints. You see every problem as a set of inputs, processes, and outputs that can be optimized. That instinct followed me out of the field and into everything I built after.
The part that didn’t change
Here’s what I’ve always known about myself: I’m a builder. Not a builder in the polished LinkedIn sense — I mean it literally. I like making things. I build furniture in my workshop. I write code to solve my own problems. I tinker with hardware because I find it satisfying to have something real to show for my time.
For most of my career, that builder instinct outpaced my ability to execute on it. I’d have ideas — real ideas, the kind with specific markets and specific problems — and then I’d hit the wall. Building software at a serious level used to require a team, or capital, or years of dedicated development time. Ideas stayed ideas because the gap between conception and prototype was just too wide to close alone.
AI changed that gap.
When the math finally worked
The shift happened gradually and then suddenly. The models got good. Not “impressive demo” good — genuinely useful good. I started prompting my way through problems that used to require hiring someone or learning a skill from scratch. Prototypes that would have taken months started taking weeks. Then days.
More importantly: the cost of being wrong dropped dramatically. When it costs you a few hundred dollars and a few weeks to test an idea, you’ll test more ideas. You’ll kill the bad ones faster. You’ll iterate. The economics of building as a solo founder flipped from impossible to manageable.
I started thinking about all the ideas I’d shelved over the years — the niche markets I’d noticed, the bad software I’d complained about, the problems I’d lived with because building a solution wasn’t worth the investment. Suddenly, those ideas were worth revisiting.
That’s how Modology Studios started. Not from a grand vision, not from a VC pitch, but from the simple realization that I finally had the tools to close the gap between thinking up an idea and shipping it.
What the chemistry degree actually gave me
People sometimes ask if the engineering background is useful. The answer is: more than I expected, just not in the way I thought.
I don’t use reaction kinetics or thermodynamics in my product work. What I use is the engineering mindset: constraints are design parameters, not obstacles. Systems break at their weakest point. Every solution creates new problems downstream, so you have to think ahead. Optimization is an iterative process, not a one-time decision.
Those mental models apply to product development in ways that feel obvious once you’ve internalized them. A product with no constraints is a product with no focus. A launch that doesn’t expose failure modes isn’t really a test. Shipping fast isn’t about lowering your standards — it’s about learning quickly enough to raise them.
The real lesson
The career I have now isn’t the one I trained for, which used to feel like a failure and now feels like the point. The degree taught me how to think. The pivot taught me how to adapt. The years in IT taught me how software actually gets built and sold and used. And AI gave me the leverage to put all of it together.
If you’re holding onto an idea because the execution seems too hard — check whether that’s actually still true. The cost of building has changed. The question is whether you’re going to update your assumptions.
I did. It’s been worth it.