Krispin Ibyiza

It started as a habit I set for myself freshman year. I had no background in markets, no finance family, no Bloomberg terminal — just a physics textbook and the sense that the world moved to forces I didn’t understand yet.

So I started reading. Every morning, before class, I open the FT and the WSJ. Not to trade, not because anyone told me to — just to understand. What keeps me coming back is that these aren’t only news for me. They’re a real-time feed of how the world actually works: how geopolitics moves commodity prices, how central bank language shifts bond yields, how a single earnings miss can crater a sector.

Physics is where I learned to look for the mechanism behind any phenomenon, and the market feels like that same instinct applied to the economy. When the Fed raises rates and tech stocks fall, I find myself tracing it like a problem set — a discounting mechanism responding to the cost of future cash flows. When oil spikes after a geopolitical event, I’m watching a supply shock in real time.

A couple of years in, I notice I can look at a market event and start building a mental model almost on reflex: what caused it, who’s exposed, what might happen next. That’s not anything I was taught in a lecture. It’s pattern recognition I’ve picked up by showing up every morning, and that’s probably why it sticks.

I still miss things, and I’m still learning. But reading the news like a physics problem — asking why, not just what — is the habit I find myself most grateful for.

In physics, I don’t get to memorize a formula and call it understanding. I have to derive it — start from the simplest assumptions I can defend, and build up from there. That process is the most transferable thing I’ve picked up at Amherst, and it followed me into everything else.

When I started reading seriously about business and markets, I noticed I wanted the same thing from the material. I didn’t want to just learn the vocabulary — DCFs, multiples, margins — and move on. I wanted to know what each one was actually modeling about the world. Why does the discount rate matter? What is it really saying about time, risk, and uncertainty?

That’s where physics shows up for me. When I look at a company, I don’t start with the multiples. I start with: what does this business actually do? Where does the money come from? What has to be true for this model to work? That’s the same question I’m asking before I set up a differential equation — just applied to a different system.

The parallel runs deeper than mindset. In mechanics, every system has constraints that bound what’s possible. In business, those constraints are unit economics, market size, competitive dynamics. In both, the elegant answer isn’t the complicated one — it’s the one that identifies the binding constraint and works from there.

I don’t think physics automatically makes anyone good at markets. But the habit of asking what must be true for this to work? is the tool I reach for the most, and the one I most want to keep sharpening.

I didn’t build MarketCap to get users. I built it to see how a real-time data platform actually works — what breaks, what scales, and what costs money I didn’t expect. The 9 users it has are a side effect; the education was the point.

That’s how I think about every product I build. When I started on Aura, an AI-powered skincare app, the goal wasn’t to disrupt the beauty industry. It was to understand how you take a raw AI capability, turn it into something a real person would trust and use, and build the operational layer around it — auth, onboarding, data storage, rate limits, cost controls.

There’s a kind of understanding I only seem to get by shipping. Reading about API rate limits isn’t the same as hitting them at 2am and having to fix it. Understanding database indexing on paper isn’t the same as watching my own query slow to a crawl with 10,000 rows. Building is how I turn concepts into something concrete for myself.

This matters to me in a specific way. I want to work in equity research, trading, and business development — not in software itself. But the analysts I admire tend to carry a builder’s intuition for the companies they look at. They understand the operational reality of a business because they’ve run one, built one, or broken one.

Building software is my version of that. Each project is a working model of a real system — not a line on a résumé, but something I had to make decisions about, debug at the edges, and explain to people who didn’t care how it worked under the hood.

That’s the education I’m building alongside my physics degree — one system at a time.