I want to tell you how I first learned what a DCF model was. It wasn't in class. It wasn't from a textbook. It wasn't from a mentor who had done it before. It was at 11 PM, sitting at my desk with three browser tabs open, a YouTube video with 200,000 views from a guy I'd never met, a Reddit thread I found two days before a first-round interview, and a template I'd reverse-engineered from a publicly available company filing.
That is not how this should work.
The Problem
Finance recruiting, investment banking, private equity, wealth management, M&A, is one of the most structurally opaque systems in professional life. The information asymmetry between candidates is not subtle. It's enormous, and it's deliberate.
Kids at target schools with dedicated alumni networks, prep courses designed around their program's recruiting season, and upperclassmen mentors who literally hand down their interview materials walk into the process with a completely different toolkit than someone like me. International student. Non-target school. No one in my immediate network who had navigated this before. No roadmap.
The dirty secret of finance recruiting is that the people who succeed most reliably are often not the best analytical minds in the room. They're the people who had access, access to prep resources, access to people who've done it, access to the institutional knowledge that gets passed down informally in dining halls and fraternity chapters at schools with established Wall Street pipelines. That knowledge, what questions to expect, how to frame your story, what a specific firm cares about, is enormously valuable. And it's not evenly distributed.
"Talent is evenly distributed. Opportunity is not."
I don't believe that's meritocracy. I believe that's a network effect masquerading as one. And I believe AI can break it.
The Platform
Wallstreet.ai is built around three core problems that I experienced firsthand and that I hear from every finance student I talk to who didn't come from a traditional pipeline.
First: interview preparation. Finance interviews are both behavioral and technical, and most candidates underestimate one of the two. Behavioral prep is about your story, the leadership narratives, the failure reflections, the "why finance" framework that needs to feel authentic and specific rather than memorized and generic. Technical prep is the accounting, the valuation, the LBO logic. Wallstreet.ai uses AI to simulate real interview scenarios, give specific and honest feedback on responses, and help candidates identify where they're weak before a real interviewer does. The goal is not to help you sound good. The goal is to make you genuinely better prepared.
Second: resume optimization. Finance recruiting is keyword-specific and context-sensitive in ways that most candidates don't fully understand. The way you describe an experience, the precision of the language, the specificity of the metrics, the framing relative to the role you're targeting, matters more than most people realize. A resume that gets you an interview at a boutique M&A firm might not clear the screen at a bulge bracket. Wallstreet.ai analyzes your resume against specific roles, identifies what's missing or mis-framed, and gives you actionable direction. Not generic advice. Specific changes.
Third: market awareness. Walking into a finance interview without genuine market context is one of the most reliable ways to kill your candidacy. Not just knowing "the market is volatile", but knowing which deals are live in your target sector, what the macro environment means for deal flow, what a specific firm's recent transactions tell you about their thesis. Wallstreet.ai aggregates deal news, company profiles, and market commentary in a format designed to make users conversationally credible, not just technically prepared.
The Vision
The product ambition is focused. But the strategic vision is bigger. Finance careers, the ones at the firms that matter, the ones that build the kind of professional foundation you spend the rest of your career benefiting from, disproportionately go to candidates from well-resourced backgrounds. Some of that is structural and hard to change. But a significant part of it is information asymmetry, and that is solvable.
AI can function as a personalized coach for anyone with an internet connection and the discipline to use it seriously. There's no technical reason that the quality of your interview preparation should correlate with your zip code, your school's alumni network, or whether you had an older sibling who went through the same recruiting process two years ahead of you. Wallstreet.ai is built on the belief that leveling that field is both a good business and a good thing to do.
The focus on Latin American and first-generation candidates is personal. I am both. And I know what it cost me, in time, in anxiety, in missed opportunities, to navigate a system without a map. The students coming behind me shouldn't have to figure it out the way I did.
What Building It Has Taught Me
Building a product in early stages, even a platform that is still evolving, forces a kind of intellectual honesty that I've found genuinely uncomfortable and genuinely valuable. You have to be able to articulate the problem clearly enough that a stranger understands it immediately. You have to make product decisions with incomplete information and own the consequences. You have to know what to build first and what to deprioritize, which is harder than it sounds when everything feels important.
It's taught me that the best products are not built from clever ideas, they're built by people who felt the problem personally and stayed close to the user throughout the process. Every time I talk to another finance student who describes navigating recruiting the way I did, alone, underresourced, figuring it out in the margins, the problem sharpens. That urgency is the most reliable fuel I've found.
It's also taught me to be comfortable with early. The platform is early. The product will evolve significantly. That's not a weakness to manage around, it's the condition of building something genuinely new. The thing that matters is whether the problem is real and whether the direction is right. I'm increasingly confident that both are true.
Finance recruiting is broken. AI can help fix it. I'm building the tool that makes that possible. That's the bet.