Pressure Test Your MBA
Pick a hypothesis. Design probes. Read the data.
People keep asking me some version of the same question:
“How do you do so much?” “Aren’t you tired?” “How do you balance everything?”
The honest answer: I’m running an experiment, and this experiment requires data (and lots of it).
My Hypothesis
I came into Foster with an open question: is Product still the right place for me?
I’d done Product and Program-adjacent work across Boeing, T-Mobile, and TD Bank, and I was good at it. But “good at it” is not the same as “this is the right home.” I came to grad school to actually find out.
It took until about halfway through my first year to start seeing the pattern. What I actually liked wasn’t the Product part. It was the seams. Cross-functional work, the translation layer between technical and commercial, the operational glue. The parts of the role that weren’t really Product, more like running operating rhythm for someone else’s vision.
That’s when I started wondering if I’d been doing a quieter version of Chief of Staff work all along, and whether the Product title had just been the closest available shape for it.
What “Trying Everything” Actually Means
When people see the surface (EVP of Communications for MBAA, Co-President of AAPI, member of: Tech Club, LevelUp, Data and Analytics, the Substack, the consulting practice, the Microsoft project), it can look chaotic.
Sometimes it feels chaotic. But I know if i just zoom out enough, each thing is a probe.
My role as MBAA Comms tests whether I can run executive communication on a real cadence with real stakeholders. The Substack tests whether I can think publicly about AI and have something worth saying. The Fractional CoS practice tests whether founders will pay me to do this work, and whether I’ll still like it when the stakes are real. The Microsoft project tests whether I can do strategic work on an unfamiliar industry, under time pressure, with a team I didn’t pick.
The MBA isn’t the goal. The MBA is the lab. Every club, every project, every awkward networking coffee adds data to the question I came here to answer.
Then AI Learning Lab Showed Up
Mid-Spring quarter, Foster launched the AI Learning Lab: a six-session program that drops MBA students into PE-backed software companies to build working AI prototypes. On paper it’s a “professional development opportunity.” In practice it’s a small startup.
I got involved on the coordination side. Bridging between VC leaders (second-year placements across VoIP, healthcare supply chain, field service, digital marketing, edtech), Career Management at Foster, and the students trying to figure out if this was for them.
About three weeks in, I realized: this is the job.
Not “sort of like CoS work.” This is Chief of Staff work. Translating between three sets of stakeholders with three different incentive structures. Building the operating rhythm. Drafting founder-facing communications. Building the landing page when nobody else had bandwidth. Triaging questions the program lead can’t get to in real time.
I went in with a question. The work really solidifies the answer for me.
The Tactical Takeaways
If you’re earlier in your program, here’s what I’d actually do:
1. Write down your hypothesis before you start signing up. “I want to explore my interests” isn’t a hypothesis (it sounds more like a horoscope). “Is Product still the right place for me?” is. So is “I think I’m built to be a strategic operator at an early-stage company.” The clearer the claim or question, the easier it is to design experiments around it.
2. Pick involvement that probes different facets. Don’t join five clubs that test the same skill. If your hypothesis is about strategic operating, you need probes for written communication, operational rhythm, cross-functional translation, and (most importantly) what you feel like when you’re doing the work for forty hours a week, not four.
3. “Yes” is a default, but only inside the hypothesis. “Say yes to everything” leaves you as the union of everyone else’s priorities. “Say yes inside the hypothesis” is different: you can’t predict which opportunity becomes the real signal. AI Learning Lab wasn’t on my plan. It became the most informative data point of the quarter.
4. Build artifacts as you go, even when no one asks. The landing page, the briefings, the recap posts. Two reasons. First, making them is how you learn whether the work actually fits you. Second, they’re the evidence you can do the thing, which matters when you need someone to bet on you.
5. Re-read your hypothesis every quarter. Some of mine have updated. I came in asking whether Product was still right; the data has clarified that the work I actually want lives closer to CoS, and at an earlier stage than I’d assumed. These check ins reflect the growth and challenges I put against these hypotheses.
The Real Answer
I stopped treating my involvement as extracurricular. The clubs, the projects, the writing, the consulting practice are the MBA, for me. The classes are the textbook. The involvement is the lab.
That’s why AI Learning Lab mattered. Not because it was on a list of things to join, but because it was the moment my question got an answer I didn’t have to argue with.
So when people ask how I do so much, the honest answer is that I’m not optimizing for output. I’m trying to find the version of the work that I actually want to keep doing after the program ends. Most of what I’m involved in is me checking. Some of it sticks. Some of it doesn’t. The point is to know the difference before I have to commit.