When I tell people I studied Economics at Arizona State University and now work as an AI Engineer building chatbots, I usually get a confused look. “How did that happen?”
The truth is, my economics background didn’t just accidentally lead me to AI—it actively made me better at it. Here’s what I’ve learned bridging these two worlds.
The Unexpected Overlap
Economics and machine learning share more DNA than most people realize. Both fields are obsessed with the same fundamental questions: How do we make predictions with incomplete information? How do we optimize outcomes given constraints? How do we model complex systems with many interacting variables?
During my time at ASU, I spent countless hours working with regression analysis, statistical modeling, and data interpretation. Sound familiar? These are the exact foundations of supervised machine learning.
Skills That Transferred Directly
1. Thinking in Models
Economists are trained to build simplified representations of complex systems. We learn that models aren’t meant to capture every detail—they’re meant to capture the important details that help us make better decisions.
This mindset is invaluable in ML. When I’m designing a chatbot system at All Digital Rewards, I’m constantly asking: What variables actually matter here? What can we safely ignore? How do we balance model complexity against practical utility?
2. Understanding Incentives and Behavior
Economics taught me to think about why people do what they do. This has been surprisingly useful when building AI systems that interact with humans.
When designing our customer support chatbots, I don’t just think about technical accuracy—I think about user behavior. What motivates someone to use the chatbot vs. call a human? How do we design responses that actually solve problems rather than just technically answer questions?
3. Comfort with Uncertainty
Every economist knows that forecasts are uncertain. We learn to think in probabilities, confidence intervals, and error terms from day one.
In AI, this translates to a healthy relationship with model performance. I don’t expect 100% accuracy—I expect to understand where and why my models fail, and to communicate uncertainty appropriately to stakeholders.
4. Data Storytelling
Economic analysis is useless if you can’t explain it to non-economists. I spent years learning to translate complex statistical findings into actionable insights for people without technical backgrounds.
Now, as an AI engineer, I bridge the gap between our technical team and business stakeholders daily. Being able to explain what our AI does—and what it can’t do—in plain language has been one of my biggest assets.
The Transition Wasn’t Magic
I won’t pretend the path was obvious. After graduating in December 2023, I had to actively build the technical skills I was missing.
I started with Stanford’s Supervised Machine Learning course to formalize my ML knowledge. I learned Python and Node.js to actually build things. I got hands-on experience during my AI research internship, where I went from analyzing data to building systems that use it.
The economics foundation gave me the thinking patterns. The technical skills gave me the tools. Both were necessary.
What I’d Tell Economics Students Curious About AI
If you’re studying economics and interested in AI/ML, you’re in a better position than you might think:
Lean into econometrics. The statistical techniques you’re learning are directly applicable to ML. Don’t just memorize formulas—understand why they work.
Learn to code. Your analytical skills are valuable, but you need to express them in code. Python is your friend. Start with data analysis libraries like pandas, then branch into ML frameworks.
Don’t abandon your domain knowledge. The world needs AI engineers who understand business, finance, and human behavior. Your economics training gives you context that pure CS graduates often lack.
Build projects. Theory only gets you so far. I learned more building my first chatbot than I did reading about chatbots.
The Bigger Picture
We’re in an era where AI is becoming embedded in every industry. The engineers who will have the most impact aren’t just those who understand the algorithms—they’re the ones who understand the problems.
Economics trained me to think about real-world systems, trade-offs, and human behavior. AI gives me the tools to build solutions at scale. Together, they’re more powerful than either alone.
I’m still early in my AI engineering journey, but I’m grateful for the unconventional path that got me here. If you’re considering a similar transition—or wondering if your “unrelated” background is holding you back—I hope this helps.
What unexpected skills have helped you in your tech career? I’d love to hear about other non-traditional paths in the comments.
Currently building AI-powered customer experiences at All Digital Rewards. Previously confused everyone by double-majoring in regression analysis and Python.
