This essay is based on a conversation with Varun Goyal, a 25-year-old AI startup engineer based in California. Edited for length and clarity.
In 2022, I was at a crossroads between Quant Trading and Tech. It was my final year as an undergraduate student. I was blinded by my first high salary and fame, so I joined the company as a quantitative summer strategist.
I learned a lot and worked with incredible people, but I was sure I should go back to school. I decided to move from India to America for my Masters degree.
Last year, when I was about to complete my master's degree, Quant's path was even clearer. With AI, we were in the first stages of the AI revolution, and as the industry changes every day, it was much more troubling and unpredictable in AI.
After interviewing both fields, I chose an AI startup. The time was longer and the basic wages were lower than I got with Quant, and it was a fierce year, but I have no regrets about my decision.
My mastering program gave me time to explore both career options
When I moved to the US to earn my Masters in Computer Science from the University of Illinois Urbana-Champaign, I already knew I wanted to pursue Quant, AI, and Computer Science courses.
Both AI and Quant roles require algorithmic skills in core computer science.
During my masters I had time to pursue research and talk to people from advanced industries. This was the biggest advantage for me when I was deciding what I wanted my daily life and career to be like ten years from now.
When I entered my master's degree, I was leaning heavily towards Quant as the initial compensation is almost always high, but these conversations helped me to look beyond the starting salary. I've heard about the daily work itself, the culture and the opportunities for growth in each field.
Quantum trading has its advantages and disadvantages
Someone I knew was working in school for two or three years and working for some of the best Quant Firms in New York and Chicago. I definitely didn't want to skip that option, but when I talked to some of them, a few things stood out to me.
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In large companies, teams often compete and you can't really talk about your work outside of the group. As an extrovert who thrives in a communal environment, I felt that was restrictive. I also felt that the exit options on Quant were more constrained.
It is typical to grow into roles such as senior quant researcher, strategic owner, and portfolio manager. These are great paths, but are tied to highly specialized expertise. It's good to specialize if you like it, but I was terrified of it because I wasn't sure if I wanted to stay in the field forever.
Working with AI has given me more options
I applied to both industries and got some quantum interviews, but decided to join an AI startup.
Quant reminded me of a single-lane speed track: fast, sharp, but narrow, but the AI felt like a racing track with many turns and curves.
When I was in Master, there was an AI boom. It kept me up at night in the best possible way. I've begun to lean towards working in AI. Because it is redesigning human productivity capabilities from scratch. This is very exciting.
With AI, especially at startups, the daily challenges were broader, less predictable, and the mix of freedom and freshness appealed to me. I was able to imagine the path to the founding of Big Tech, venture capital and leadership role, and I was able to begin honing all of these skills early in my career.
AI ultimately kept my options open with what I could do later in life, and working at a startup provided the collaborative environment I wanted.
I got a lower base salary than what I got with Quant, but it's worth it
At AI startups, base salaries still offer excellent standard of living, but the real advantage comes from fairness. The opportunity to learn is everywhere in AI startups. Part of the appeal is the opportunity to learn how to make your company successful, rather than optimizing within an established machine.
The biggest surprise I've had with a startup over the past year is that I'm learning external coding. I'm learning to lead and sell employment, adaptation, product directions. These skills make me more than just an engineer. I'm trying to jump on customer meetings to understand their needs, contribute to hiring decisions, and learn with shadows from founders where I can.
The year was pretty intense in races featuring a variety of AI startups.
Without launching features and tools in a new research paper within a few days, it can be stressful because I basically lost it to that competition, but it keeps me sharp.
At AI, I am in uncomfortable seats every few months to keep up with the pace of the market, but I am happy with my decision. I've been pushed to grow that much.
My advice for candidates interested in both Quant and AI
If I was in the decision-making stage again, one thing I think is that it's difficult to move from Quant to AI. Having spent a year with AI, I find it a bit uncomfortable to jump into Quant.
Quant is difficult if you want to narrow your skills down to specialized very quickly and expand outside. However, I have several undergraduate friends. That's possible.
I love working for AI. We are like the builders of machinery of the Industrial Revolution and I think we are laying the foundation for how the world actually uses AI. There is a lot to build and this is an opportunity for the brightest graduates to take.
Are there any AI career journey stories to share? Please contact this reporter, Agnes Applegate aapplegate@businessinsider.com.
