A Crazy Week in San Francisco: Three Encounters, Many Lessons
These are just interesting events that happened & lessons that I've extracted for myself in my quest to become a better founder :)
Notes from the field. March 2026.
Three completely different meetings this week. Three completely different worlds: space cannons, self-driving cars, and personalized medicine. Each one left me with something I don’t want to forget.
TLDR of founder lessons:
You cannot beat a giant at their own game. Take asymmetric, contrarian bets.
If the physics is theoretically possible, every failure is a skill issue.
First-principles people don’t rely on others’ opinions. They read, build, experiment, then form conviction & back it to the hilt.
Founder-problem fit beats market analysis. The best founders lived the problem.
De-risk the co-founder question early. Build with someone you’ve already built with.
GTM has to start by picking a wedge, not selling to the whole market. Also, distribute via believers first.
San Francisco rewards showing up. Every conversation is a potential door.
1. The Space Cannon Guy
I was working at a WeWork when I noticed a guy having lunch alone. I walked up, made small talk, asked him what he does.
His name is Mike Grace. He runs a company called LongShot Space.
Mike is an ex-Army veteran, deployed twice in Afghanistan. He built his last company and sold it to the Department of Defence for millions. His most recent project before LongShot? Helping Ukraine build missile attack systems to fight Russia.
That’s the resume of the guy casually eating lunch at a WeWork.
He invited me to a cigar lounge that evening. Over fine whisky and cigars (he taught me how to pick, cut, and light one), he explained what LongShot does.
The concept: There are five known methods to send a payload into orbit. Rockets are just one of them, and a deeply inefficient one. Only about 5-7% of a rocket’s total weight can be payload. The rest is fuel: and most of that fuel is just there to lift the other fuel. This is the tyranny of the rocket equation, and it’s what makes rockets inherently expensive.
LongShot is building a space cannon. Literally. A massive pneumatic gun where the “bullet” is a satellite or payload. The projectile accelerates through a barrel using compressed hydrogen gas, injected from the sides in stages, until it reaches orbital speeds. In this system, up to 50% of total weight can be payload: making launches potentially 50x to 100x cheaper than rockets.
Thanks to Andre, who handles business development for LongShot, for showing me around their prototype facility at Alameda: a former U.S. Navy cannon testing facility just outside San Francisco. The prototype is a steel tube, roughly 100 meters long with a six-foot diameter. I didn’t see it fire, but just seeing the physical setup was wild.
The strategic bet: SpaceX is reportedly gearing up for an IPO around mid-2026, potentially the world’s largest, which could make Musk a trillionaire. SpaceX would effectively become the monopoly on sending satellites to orbit. It nearly is already.
Mike’s positioning is simple: when SpaceX IPOs, he wants to be the challenger. The guy investors look at and say, “Okay, there’s someone else.” But you cannot challenge Musk by building cheaper rockets. That’s his game, and he’s been at it for over two decades. You challenge him with a fundamentally different technology. An asymmetric bet.
The “why now” question: If the physics of space cannons has been known since World War II (the German V-3 cannon used the same multi-injection principle), why is this happening now?
The answer isn’t physics. It’s the market. SpaceX created an explosion in satellite demand (Starlink alone has 6,000+ satellites). The U.S. Air Force is investing in alternative launch methods. Sam Altman, Tim Draper, and Space Fund are backing LongShot. In the 1990s, there was no market demand, no investor appetite, and no institutional support for “a giant space cannon.” Now there is.
The irony: Musk built the very market conditions that make his own disruption possible.
The Lesson: You Cannot Beat a Giant at Their Own Game
If you’re going up against a dominant player, you don’t compete on the same axis. You find a completely different approach where you have an unfair advantage and they have a structural disadvantage. SpaceX is optimized for rockets: their entire infrastructure, team, IP, and capital structure is built around rockets. A space cannon isn’t a marginal improvement: it’s a category reset. That’s the only kind of bet that has a shot against a monopoly.
Asymmetric doesn’t mean small. It means different. Mike isn’t building a hobby project. He’s building a 10-kilometer cannon designed to shoot things at Mach 29. The ambition is enormous. The approach is just orthogonal.
2. The Tesla Autopilot Guy
Through Viram (CEO of Vested), I got connected with Dhaval Shroff. Dhaval is an Autopilot AI engineer at Tesla: not just any engineer. He’s the guy who, in December 2022, pitched the idea of an end-to-end neural network planner to Elon Musk: essentially “ChatGPT, but for cars.” That conversation is documented in Walter Isaacson’s biography of Musk. Dhaval is described as a trusted aide to Musk.
His background: Indian. Undergrad in Mumbai, Master’s in Robotics at CMU. Joined Tesla Autopilot as an intern in June 2014. Converted to full-time. Moved from driver assistance, to Autopilot R&D, to Autopilot AI. About 12 years now. One company. One bet.
Why he stayed: I asked him: how is it that you’ve been at Tesla for over a decade and never left? Plenty of self-driving companies would have thrown money at him.
His answer: conviction in a contrarian bet.
The context: twelve years ago, every serious self-driving project incl. Waymo, and everyone following Waymo’s lead was building on LIDAR and radar systems. Tesla was the only company that said we’re going pure vision plus neural networks. No LIDAR. No radar. Just cameras (the “eyes”) feeding data to a neural network (the “brain”): mimicking how humans actually drive.
Musk’s reasoning: humans drive using their eyes and their brain. If we want cars to truly drive like humans, replicate that loop: optical input, neural processing, action output. Not laser beams bouncing off objects.
This was wildly contrarian at the time. Almost every expert thought Tesla was wrong. Dhaval had options to join other self-driving companies, but they were all going the Waymo way. He figured if he was going to take a bet, he’d rather take the contrarian one.
On Andrej Karpathy: Karpathy joined Tesla as head of AI in 2017 and was another reason Dhaval stayed.
What made Karpathy special: he is incredibly hands-on. He doesn’t rely on other people’s opinions or someone else’s research summaries. He reads the white papers himself. Builds his own logic on top of them. Gets his hands dirty and experiments personally. Only then does he form an opinion. But once he does, his conviction is absolute, because it’s grounded in first-principles work he did himself.
Karpathy was also deeply interested in what every team member was building. He’d sit with engineers, including on weekends. That level of involvement and excitement was contagious. He wasn’t a figurehead: he was in the trenches.
On Elon Musk: Dhaval’s take is that the guy is ruthless. His operating principle is deceptively simple:
Determine what is possible according to the laws of physics.
If the physics says it’s theoretically possible, then any failure to achieve it is an execution problem: a skill issue.
When Musk decided to go all-in on the vision-only bet, many team members told him it probably wasn’t possible. His response: “Is the physics theoretically sound?” Yes. “Then it’s a skill issue. Go find better people.”
He recruits the best people from the best universities and companies. He finds capital from the best sources. And he goes absolutely relentless on the mission.
On Musk’s product philosophy: Every product Musk builds has to have civilizational-scale impact. He doesn’t do niche. Tesla had to be affordable enough for every person on Earth to eventually own one. Same logic across all his companies (space migration for everyone etc). If the technology can’t serve every human on the planet, it’s not worth his time. (Which is why he doesn’t do Louis Vuitton type products).
What people get wrong about Tesla: It always comes back to the contrarian bet. People underestimated the vision-only approach for years.
Once you have conviction in a bet like that, you back it to the hilt. All in.
The Lessons
Contrarian conviction is the real moat. Dhaval could have joined any self-driving company. He stayed at Tesla because he believed in the contrarian approach. You don’t beat the incumbent by doing what they do slightly better. You beat them by doing something fundamentally different that they can’t easily switch to.
First-principles people are rare and magnetic. Most senior leaders in tech operate on summarized information: someone else’s research, someone else’s analysis, someone else’s opinion. Karpathy reads the papers, builds the prototypes, runs the experiments, and then forms an opinion. That’s why people follow him. As a founder, this is the standard I want to hold myself to.
“Is the physics possible?” is the only question that matters. Musk’s framework is brutal but clarifying. If something is theoretically possible, then every objection is an execution problem. This reframes every “we can’t do this” into “we haven’t figured out how to do this yet.”
Sidenote: I AM LITERALLY ONE DEGREE OF SEPARATION FROM ELON FKN MUSK WHAAAAT!
Yes yes, I understand proximity is not access. And access is not influence. The real question is: what do I do with this proximity?
3. The Personalized Medicine Play
I went to Mountain View for an event at the Google DeepMind office. After the event, I spoke with one of the DeepMind research scientists. I told him about my journey, what I’m building, why I’m in SF. He told me about a company called Diadia Health and connected me with the founders.
The core insight: If two people both catch a common cold, their bodies respond very differently. Their DNA is different. Their physiology is different. Their metabolism, hormone levels, genetic variants: all different. And yet, both get prescribed the same paracetamol at the same dose.
Diadia Health’s thesis is that medicine should be personalized: not at the level of “here are your symptoms,” but at the level of your actual genetic makeup. Their AI platform analyzes hundreds of biomarkers and generates personalized treatment protocols.
They focus on root causes, not symptoms. And the reason this works now is because AI can reason across millions of data points simultaneously: something no clinician, no matter how skilled, can do alone.
The founding story: The CEO is Elena Ikonomovska, PhD: former Google and Reddit AI scientist (she was Reddit’s first data scientist, later Head of AI at Change.org). She didn’t start Diadia because she saw a market opportunity on a spreadsheet. She started it because it happened to her. She had unexplained symptoms, her lab results kept coming back “normal,” and she spent years being dismissed by doctors. When she finally understood her own genetic profile, everything changed. Standard lab reference ranges were established decades ago using predominantly male subjects. They don’t account for genetic individuality, especially in women.
Her co-founder and CTO is Andrii Yasinetsky: ex-Uber, ex-Google, serial technical founder. They’d already co-founded a company together before (Mnemonic), so the team risk was de-risked before Diadia even started.
The distribution: They’re going B2B2C. They sell to functional medicine clinicians as a clinical decision-support tool: not directly to patients. Practitioners in functional medicine already believe in root-cause analysis, so initial adopters don’t need to be convinced of the philosophy, just the tool. They’ve validated across 12+ clinical sites and thousands of patient cases. Now they’ve also opened a direct-to-consumer path where women can order their own lab work and share reports with providers.
The Lessons
Founder-problem fit is real. Elena didn’t pick “personalized medicine” from a market study. She lived the problem. She was the patient who was dismissed. That kind of conviction doesn’t come from TAM analysis: it comes from rage and personal experience.
The co-founder question matters. Elena and Andrii had already built and shipped a company together before Diadia. They knew each other’s working style, conflict patterns, and technical strengths. In a space as complex as genomics + AI + healthcare, you can’t afford a co-founder learning curve.
Pick a wedge, not a market. Diadia isn’t trying to be “personalized medicine for everyone.” They started with a specific, underserved niche: women with complex hormonal conditions who keep getting told their labs are “normal.” That’s a wedge with real emotional urgency and a clear expansion path.
Distribution through believers. Going to functional medicine practitioners first: people who already think in terms of root causes: is a masterclass in picking your initial channel. You don’t waste energy convincing skeptics. You arm believers with better tools.
The Meta-Lesson
Three encounters. Three completely different domains: space, autonomous vehicles, healthcare. Same pattern:
Take asymmetric, contrarian bets. LongShot isn’t building a cheaper rocket. Tesla didn’t build a better LIDAR system. Diadia isn’t building a slightly improved blood test. Each of them looked at the dominant approach in their industry and said: that’s the wrong axis.
Founder-problem fit beats market analysis. Elena lived her problem. Mike’s military background gave him a unique lens on launch systems. Dhaval’s love for robotics since childhood made the Tesla bet feel obvious to him. In every case, the founder’s personal history made the contrarian bet feel like the only bet.
San Francisco rewards showing up. I walked up to a stranger at a WeWork and ended up touring a space cannon facility. I stayed after a DeepMind event and got connected to a precision medicine startup. A friend introduced me to a guy who demos self-driving software to Elon Musk. None of this was planned. All of it happened serendipitously.
The density of ambition here is unlike anywhere else. Every lunch, every event, every random conversation is a potential door. I hope I keep walking :)
Week one of many. Let’s see what next week brings.
Crazy incidents, and definitely a depth of writing.
Incredible experiences, inspiring to read and informative as heck too..