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This as-told-to essay is based on a conversation with Varun Goyal, a 25-year-old AI startup engineer, based in California. It’s been edited for length and clarity.
In 2022, I was standing at the crossroads between quant trading and tech. It was my final year of undergrad. Blinded by the initial high salary and prestige, I joined a firm as a quantitative strategist for the summer.
I was learning so much and working with incredible people, but I was increasingly convinced that I should go back to school. I decided to move from India to the States for my master’s.
When I was about to graduate with my master’s last year, the path in quant was clearer. I could accelerate my career quickly, whereas in AI, it was much messier and unpredictable because we’re still in the first stages of the AI revolution, and the industry is changing every day.
After interviewing in both fields, I chose an AI startup. The hours are long, the base pay is lower than I would’ve gotten in quant, and it’s been an intense year, but I don’t regret my decision, and here’s why.
My master’s program gave me time to explore both career options
When I moved to the US to get my master’s in computer science from the University of Illinois Urbana-Champaign, I already knew that I wanted to pursue courses in quant, AI, and computer science.
Both AI and quant roles require core computer science algorithm skills.
During my master’s, I had more time to pursue research and talk to senior industry folks. This was the biggest benefit for me when I was deciding what I wanted my daily life and career to look like 10 years down the line.
When I entered my master’s, I leaned heavily toward quant because the initial compensation there is almost always higher, but these conversations helped me see beyond the starting paycheck. I heard about the day-to-day work itself, the culture, and the opportunities for growth in each field.
Quantum trading has pros and cons
People I knew who were two or three years out of school were working at some of the best quant firms in New York and Chicago. I definitely didn’t want to skip that option, but a few things stood out to me as I spoke to some of them.
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At bigger firms, teams often compete, and you can’t really discuss your work outside your group. As an extrovert who thrives in a collaborative environment, that felt restrictive. The exit options in quant felt more constrained, too.
It’s typical to grow into roles like senior quant researcher, strategy owner, or portfolio manager. Those are excellent paths, but they’re tied to very specialized expertise. It’s good to be specialized if you like that, but I feared it because I didn’t know if I wanted to stay in the field forever.
A job in AI gave me more options down the line
I applied for both industries and had a few quant interviews, but I decided to join an AI startup.
Quant reminded me of a single-lane speed track: fast, sharp, but narrow, while AI felt more like a racing track with a lot of turns and curves.
When I was in my master’s, the AI boom was happening. It kept me up at night in the best way possible. I started leaning toward working in AI because it’s redesigning the capacity of human productivity from the ground up, which is so exciting.
In AI, especially at startups, the day-to-day challenges are broader, less predictable, and that mix of liberty and freshness appealed to me much more. I could imagine paths into founding, venture capital, and leadership roles in Big Tech, and I could start honing all of those skills early in my career.
AI ultimately left my options more open with what I could do later in life, and working at a startup provided the collaborative environment I wanted.
I took a lower base salary than what I could’ve earned in quant, but it’s worth it
In AI startups, the base salary still provides a good standard of living, but the real upside comes from equity. The opportunity to learn is everywhere in an AI startup. Part of the appeal is the chance to learn how to make a company successful rather than optimizing within a well-established machine.
The biggest surprise at the startup this past year has been how much I’m learning outside coding. I’m learning to hire, adapt, lead the product direction, and sell. These skills make me more than just an engineer. I’m trying to jump on customer meetings, understand their needs, contribute to hiring decisions, and shadow and learn from the founders where I can.
The year has been pretty intense with the race that various AI startups are in.
If we don’t launch the features and tools in a new research paper within a few days, we’ve basically lost that one to the competition, so it can be stressful, but it also keeps me sharp.
In AI, I’m in an uncomfortable seat every few months trying to keep up with the market pace, but I’m happy with my decision. I’ve been pushed to grow so much.
My advice for candidates interested in both Quant and AI
If I were in the decision stage again, one thing I’d consider is the difficulty of moving from quant to AI or vice versa later in you’re career. Having spent one year in AI, I’d feel a little uncomfortable jumping into quant.
Quant narrows your skills down to a specialty very quickly, and if you want to expand outside that, it’s hard. But I have some friends from undergrad who did a year or two in quant and then later moved over. It’s possible.
I love working in AI. I believe we’re like the builders of the machines in the Industrial Revolution, laying the foundations for how the world will actually use AI. There’s a lot left to be built, and this is the opportunity the brightest graduating minds should take.
Do you have an AI career journey story to share? Contact this reporter, Agnes Applegate, at aapplegate@businessinsider.com.
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