How My Economics Degree Made Me a Better AI Engineer

Jack Felke — Fri, 16 Jan 2026 21:13:36 +0000

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.

Hello, DEV! My Journey into Coding Starts Here 🚀

Jack Felke — Fri, 16 Jan 2026 20:37:36 +0000

Hey everyone! 👋

This is my very first post on DEV, and I couldn't be more excited to join this amazing community of developers!

A Little About Me

I'm just getting started on my coding journey, and I've already learned that the dev community is one of the most welcoming and supportive spaces on the internet. Whether you're a seasoned senior engineer or someone who just wrote their first "Hello, World!" program, there's a place for you here.

Why I'm Here

I decided to join DEV for a few reasons:

To Learn - There's so much incredible content here, from tutorials and guides to thoughtful discussions about best practices and industry trends.

To Connect - Programming can sometimes feel isolating, especially when you're learning on your own. I'm looking forward to engaging with others, asking questions, and hopefully helping fellow beginners along the way.

To Document My Journey - They say teaching is the best way to learn, so I plan to share what I'm learning as I go. Expect posts about my wins, my struggles, and everything in between.

What's Next?

Right now, I'm exploring the fundamentals and trying to build a solid foundation. I'm particularly interested in web development, and I'm curious about so many technologies that I honestly don't know where to start!

If you have any advice for a newcomer, I'd love to hear it in the comments. What do you wish you knew when you first started coding?

Thanks for reading my first post! Here's to the beginning of something great. 🎉

Happy coding! 💻

DEV Community: Jack Felke