To Smart Investors,
I’m happy to announce that I’ve finished putting together the first guides on The Oracle Indicator:
My readers' first biggest issue is how to go from 0 to $100k.
Doing that by trading stocks in a year or two is very difficult.
The second biggest issue is going from $100k to $1M.
That can be done by trading stocks reasonably quickly.
Then everything becomes easier.
This rant is my dig at higher education, the 10 years I sacrificed to become a quant, and whether there is a better way.
MIT’s campus is a paradox—a place where logic and lunacy collide. The Stata Center, with its jagged angles and gravity-defying staircases, isn’t just architecture; it’s a manifesto.
Designed to unsettle, it whispers: Clarity is overrated.
I once watched a grad student trace the building’s tilted glass panels with their finger, muttering, “Finally, a structure that mirrors my sleep schedule.” Every corridor hummed with the static of unsolved problems, and the air smelled of solder, stale coffee, and desperation.
The furniture alone could fund a startup.
Sleek ergonomic chairs, priced like luxury sedans, littered open-concept labs where students hunched over code like medieval scribes. One chair’s manual claimed it “optimized posture for breakthrough thinking.” It felt like sitting on a hedgehog. But this was MIT—where even discomfort was engineered to sharpen focus.
The Algorithm of Insomnia: Nights That Forge Futures
The building never slept. Floors reserved for theoretical work buzzed with a unique breed of madness. Whiteboards glowed under fluorescent lights, tattooed with equations that looked like alien graffiti. One scrawled note read: “If this proof works, I’ll name my firstborn ‘P vs NP.’” Midnight was rush hour. Labs transformed into battlegrounds where students wrestled with problems that defied convention—and sanity.
I remember a week when the entire sixth floor became a war room. A team had locked itself in, fueled by cold pizza and existential dread, racing to crack an optimization puzzle before a rival university. Their final solution, submitted three minutes before the deadline, later became the backbone of a quantum encryption startup. MIT’s unwritten rule? Sleep when you’re dead—or when your code compiles.
The Hidden Economy: Coffee, Curry, and Collisions
Free food was the campus’s dark matter—invisible, essential, and everywhere. Pop-up buffets materialized in stairwells: trays of samosas appearing beside robotics projects, taco bars erupting near quantum computing labs. Rumor had it a student once traded a machine learning hack for a semester’s supply of cold brew. Outside, food trucks hawked surreal hybrids—kimchi poutine, ramen burgers—as if daring us to rethink all conventions.
But the real magic unfolded in unscripted collisions. A conversation between a robotics prodigy and a visiting AI researcher over curry stains led to a breakthrough in neural network efficiency. No agendas, no Zoom invites—just two minds sparking over spilled lentils. MIT thrived on these accidents. The lesson? Innovation isn’t scheduled; it’s stumbled into.
Failures as Foundations: Cracks Where the Light Gets In
Let’s debunk the myth: MIT isn’t about avoiding failure—it’s about mining it. I once spent months building a decentralized protocol, only to watch it crumble under a first-year’s casual critique: “Elegant. Useless.” Humiliating? Yes. Liberating? Absolutely. The campus wore its flops like badges. A hallway bulletin board once showcased “The Greatest Misses of 2010”—a rogue’s gallery of collapsed hypotheses and overfit models.
Imposter syndrome was the unsung curriculum. You’d sit through seminars where terms flew like shrapnel, thinking, “I’m a tourist here.” But that discomfort was the point. In markets and academia alike, the moment you stop feeling like a fraud is the moment you’ve stopped growing.
The Machinery of Curiosity: Tools That Shape Thought
Labs at MIT were cathedrals of gadgetry. 3D printers hummed day and night, spitting out fractal sculptures and prototype drones. A room dubbed “The Junkyard” housed deconstructed servers, analog synthesizers, and a disassembled Tesla—part shrine, part threat (“Fix me”). Tools here weren’t just instruments; they were collaborators. One student repurposed a VR headset to visualize blockchain transactions, turning abstract ledgers into glowing constellations.
Even the trash had lessons. A discarded heap of circuit boards became a freshman’s art project—a commentary on e-waste that went viral. MIT’s creed: Constraints aren’t cages; they’re launchpads.
The Silent Mentors: Walls That Teach
The campus itself was a professor. Lecture halls bled into lounges, their glass walls scribbled with fading markers—half-baked proofs, doodles of black holes, existential complaints (“Why is time linear? Asking for a friend”). Even the bathrooms were pedagogical. Stalls featured “Toilet Papers”—actual arXiv preprints taped to doors. (Pro tip: Never underestimate thinking time.)
One stairwell’s graffiti stuck with me: “The best ideas come from the wrong questions.” It encapsulated MIT’s soul. While the world chased answers, we were rewarded for dismantling the questions themselves.
The Price of Genius: Shadows in the Ivory Tower
But let’s not romanticize. For every triumph, there were silent breakdowns—students sleeping under desks, panic attacks before quals, the relentless drumbeat of “not enough.” I knew someone who coded for 72 hours straight, only to hallucinate error messages in their soup. The culture’s dark edge? Brilliance burns. Yet, oddly, these trials bonded us. Shared delirium forged deeper connections than any team-building retreat.
The real education wasn’t in the lectures. It was in the 3 a.m. debates over whether AI ethics (and, yes, AI existed since the 80s) should be baked into algorithms or sprinkled on top. It was in the collective gasp when a crypto experiment literally caught fire.
The Contrarian’s MIT: A User’s Manual
If I returned today, I’d hack the system differently:
Chase dead ends. The best discoveries live in problems labeled “impossible.”
Steal shamelessly. Robotics insights can remodel financial models; biology hacks can optimize code.
Protect your weirdness. MIT’s secret isn’t IQ—it’s stubborn, glorious eccentricity.
Why MIT Still Wins in the Age of AI
In a world obsessed with instant expertise and ChatGPT hacks, MIT is a counterattack. It’s where you learn that LLMs can’t replicate the sweat-soaked thrill of a eureka moment. That innovation isn’t a prompt—it’s a pilgrimage. The same manic energy that birthed the internet now fuels labs tackling fusion energy and brain-computer interfaces.
But here’s the twist: MIT’s edge isn’t its tech. It’s its ethos. A belief that messiness precedes mastery, that confusion is the tax on growth. While Silicon Valley chases scale, MIT teaches depth. While Wall Street worships speed, we worshipped rigor.
The Final Equation: Regret vs. Resolve
Would I do it again? Yes—but with fewer all-nighters and more midnight bike rides through empty labs. I’d trade theorems for conversations with the nihilist poet who coded in FORTRAN for fun. I’d ask the janitors what they overheard (they’re the campus’s silent philosophers).
Because MIT isn’t a degree. It’s a lens—one that turns obstacles into curiosities, failures into data points, and hubris into kindling. And if you survive it, you’ll spot the MIT mindset everywhere: in traders dissecting risk like entropy, in founders chasing “impossible” markets, in anyone who’s ever whispered, “What if we tried it sideways?”
With every good wish, I remain
Yours sincerely in Christ,
Dr. Jack Roshi
Applied Mathematics Department, MIT
Lead Quant and Board Member, Sabre Capital GroupOpinions are my own