I’m currently at the halfway point of medical school — two years in, just started rotations last week, Psychiatry is my first rotation. These are my thoughts and reflections on the assumptions I had going into medical school, and my reflections on them now, two years in, 50% of the way through. The caveat is that these reflections are all specifically things that I got wrong, because I find it a worthy endeavor to reflect on incorrect predictions and assumptions. People far too often brush those under the rug, which I think is foolish, because I believe what you were incorrect about leaves the greatest opportunity for you to tune your prediction engines and get increasingly better and more accurate with future predictions. In any case, I will soon also share things that I turned out to be correct about. I hope you enjoyed this reflection. I dictated it to my car while driving home to see my parents. Perhaps I will dictate the reflection of things I was correct about on my drive back to Connecticut in a few days.

Thinking that I could run my agency while still doing well in med school.

The whole reason my first business was a services business was because I knew that I had more time than money, so I could sell my time to people who have more money and less time. I didn’t realize that by going to medical school, now I have less time and less money.

Lesson learned: Always be clear about what you have that the other person does not.

Getting attracted to B2C businesses because they seemed more exciting.

I thought maybe B2B businesses were too boring and not something I wanted to do for the rest of my life. And I got attracted to B2C businesses because it seemed like all my friends, anybody I knew, only knew about B2C businesses and nobody really knew about any B2B businesses.

Lesson learned: Alex Hormozi has a good metaphor for this: the idea of distraction, by things that look good in fleeting moments, but in the long run make you lose out on something that was much better. He calls it the woman in the red dress. Do not get distracted by the woman in the red dress.

Thinking medical school wouldn’t actually be that hard.

When I was premed, I used to talk a lot about how premeds just love to complain. It seemed like they socialized — bonded — based on complaining about things. I remember being somewhat annoyed by this because I was also involved in some engineering, computer science, and business classes, as well as an international relations class, and I noticed that a lot of the folks in those other classes were working just as hard, if not sometimes harder than the premeds, especially the engineering kids, but they just weren’t complaining about it as much. This left me with a sour taste in my mouth, thinking that people in medicine just like to think they’re doing the hardest thing in the world. And to be honest, I do think that at the premed stage this was pretty accurate. So I thought it would be the same case in medical school — that it must not actually be that difficult, and it’s probably the same personalities who have just been socialized on getting closer to people based on common misery and complaining.

I turned out to be somewhat wrong about this. Medical school truly is genuinely really hard. A lot of people in my class and the people I know in medical schools across the country are genuinely some of the hardest workers, with an abnormally gifted talent for learning at a rapid pace and remembering at simply superhuman levels — and I say this with no exaggeration. In terms of pure academics, these are some of the most brilliant and talented people, frankly, in the world, if you’re talking about raw horsepower when it comes to learning and remembering.

Lesson learned: No matter what the thing is, becoming “knowledgeable” in it is an immense amount of work. No if, ands, or, buts.

My romanticized vision of what medicine was.

I grew up thinking medicine was the echelon of higher education. I imagined myself among the ranks of the absolute greats. I thought I would feel closer to Darwin, Sydenham, Galen, Hippocrates, Avicenna, and William Osler. I pictured medicine as an intense, serious discipline of excellence—of scientific rigor, exactness, resoluteness, discovery, and the true scientific method: the humble acceptance that we know nothing for certain. We hold what we know lightly, knowing it may be overturned tomorrow, and yet we must still act decisively to heal and protect the vulnerable.

In many ways, it is exactly that. But I was surprised by how much the broader currents of society now shape daily medical practice and training. Doctors are increasingly expected to address not just disease but the full spectrum of social and environmental factors affecting health. This is not to say it’s inherently good or bad—it simply reflects the world we live in today. Still, I’ll admit it wasn’t what I expected when I entered. I had envisioned a sharper focus on pathophysiology, diagnostics, and therapeutics as the core of professional training.

Lesson learned: It is usually foolish to expect that the cultural zeitgeist will not penetrate every aspect of society. Your default assumption should be that it does.

Bringing the wrong mindset from entrepreneurship into medicine.

This one’s a bit of a weird one. After having spent one to two years running my own businesses, much of the confidence a beginner needs to go from zero to one — to launch, make their first sales, put marketing content out there — comes from absorbing this mentality: others do not actually care about you as much as you think they do. People are not observing and ridiculing and thinking about you as much as you think they are. These fears that are preventing you from putting yourself out there are, in fact, a selfish aggrandizement of the role you play in other people’s lives. You have a very minuscule role in people’s lives and they do not think about you. Nobody cares about you, so do what you need to do. They’re not noticing you. Only you think they are.

This line of reasoning is absolutely true in the business world, and it helped me immensely. So when I began medical school, I carried the same line of reasoning with me.

In this new environment, it turned out to be oh so wrong.

I stood out like a peacock fanning his feathers — like a tall poppy, like the guy who couldn’t shut up about everything else he was doing. I drew attention—sometimes positive, sometimes not—because the rubric wasn’t built for the personality I had cultivated over the past two years in business. I certainly earned the love and appreciation and respect of many — for the same things people have always known to love and respect me for, which is my utmost honesty, my sincerity, and my willingness to say what I deem to be true with no hedging, to say what needed to be said, and to be the voice for perspectives not in the room when I felt I was able to do so.

But suddenly everything was indeed being monitored. Suddenly everybody did indeed care. Suddenly I no longer represented just myself — I allegedly represented an institution, a profession. I went from being a young underdog with nothing to lose and everything to gain, and instead became more like the egotistical diva on a sports team who doesn’t realize that what they’ve joined is bigger than themselves. I failed to realize this early on. I know it now, and I walk that line much more carefully.

Lesson learned: Always know what game you’re playing, and what the rules are. If you’re playing soccer when everybody’s playing lacrosse, you’re gonna lose badly, look like an idiot while doing so, and your team will suffer. On that note, always know if you’re laying an individual sport or a team sport. Medicine is a team sport.

My guilty secret going in.

This one’s a bit of a weird one. Before I came to medical school, I had one guilty secret—one I was slightly ashamed of and felt somewhat paradoxical about. Deep down, I believed that much of what we do in medicine was less powerful than the fundamentals: consistent sunshine, exercise, real food, sleep, and close human relationships. I thought these alone could help most people live as healthy a life as possible, treat or prevent a huge amount of illness, avoid unnecessary harm, and reach the highest quality of life their genes would allow.

This was my guilty secret as I began medical school and took the oath of Hippocrates. And though I still believe those fundamentals are profoundly important, I’ve simply seen too much to remain that simplistic.

I’ve now witnessed how medications and interventions can meaningfully change trajectories in severe cases where lifestyle measures alone aren’t enough. My early view underestimated the complexity of human biology and the real suffering that requires more targeted help. On psychiatry especially, I’ve been struck by how thoughtfully my attendings and residents approach treatment—working hard to use the minimum effective dose and prioritizing non-pharmacologic tools whenever possible. The patients who stay on medication long-term are usually those for whom the risks of stopping are clearly greater.

Lesson learned: Allow yourself to hold convictions, but stay open to being wrong. People are usually trying to do the right thing to the best of their abilities and judgment. Not everyone has the same tools or data, but that doesn’t make them malicious—it often just reflects the limits of their environment and training.

Thinking medical school would give me an unfair advantage in healthcare innovation.

I thought that being in medical school would give me a massive competitive advantage — that I would have access to decision makers within hospitals, and as somebody who is entrepreneurial with a background in software engineering, marketing, and design, I had the absolutely perfect skill set to develop solutions as soon as I came across problems, get them in front of decision makers, and get them implemented rapidly. It turned out I was absolutely delusional about this. Healthcare might be the slowest moving sales cycle I will ever come across.

Despite being on good terms with some of the most senior people in various hospitals, there are endless steps and gatekeepers no matter who you know and no matter what your entry point is. I developed a system I thought was a guaranteed win — a platform that would preemptively detect billing claim denials before they were denied and auto-suggest ways to get them approved before submission to the clearinghouses, with a billing department trained on a machine learning model using the hospital’s own data locally, to minimize claim denial rates. I built this MVP within a weekend of an email going out to our larger community at the hospital about hiring a consulting agency to address problems in revenue cycle management. I thought this was the best possible timing. I walked straight to the CEO’s office. He directed me to the Senior VP of Billing, who expressed some interest, and then seemed to never quite respond again.

That was my first lesson: healthcare is a very slow sales cycle. Persistence is required. Hospitals don’t want to buy systems that are brand new — they want to buy systems that help them sleep better at night because they know they’re vetted and de-risked. That’s a lot harder to provide, and you have to get a lot more creative in demonstrating that with confidence. I thought all I would need was a degree. I was wrong. I would need much, much more.

Lesson learned: Proximity, credentials, timing, all of these matter. But persistence and relentlessness. Are probably what matter most. You don’t actually need to be an insider if you’re a relentless outsider. And if you think that being an insider alone will give you an advantage you’ll probably won’t do the things that the relentless outsider is doing and they’ll end up winning. So even if you are an insider, it does not substitute any work. You still must do all the work that you would have had to do as an outsider. It is an add-on, not a replacement.

The first: people should be left to their own devices. Let things unfold. What’s meant to happen, happens. Interference is arrogance dressed up as care.

The second: the natural state of anything left alone is decay. Businesses that aren’t grown are dying. Relationships that aren’t tended drift apart. Institutions that aren’t reformed calcify. The world doesn’t coast—it corrodes.

I’ve watched this tension play out in religion, politics, business, parenting. And for a long time, I thought the goal was to pick a side. To figure out which philosophy was right and commit.

That was the wrong question.

Newton Already Told Us

In biology, there’s a concept called homeostasis—the tendency of a system to resist change and return to equilibrium. You push a cell out of its preferred state, and it mobilizes everything it can to get back.

Newton said the same thing in different clothes: every action has an equal and opposite reaction. We learn this in physics class and immediately forget to apply it anywhere else.

But the insight is profound. Every ordered system builds reverse potential as you push it in any direction. The harder you push, the more latent energy accumulates in the opposite direction. The system doesn’t disappear. It waits.

This is why overcorrection is so common and so predictable. It’s not a failure of wisdom—it’s a law.

You’ve Seen This Everywhere

Politics swings left. The cultural immune system kicks in, and it swings hard right. Then the cycle repeats. We call it polarization. It’s actually just Newton.

A company spends a decade obsessing over margins—clean GAAP accounting, disciplined cost structures, earnings beats every quarter. Then they wake up and realize a scrappier competitor owns the customer relationship they forgot to protect.

Parents who raise their kids in strict religious households sometimes produce the most aggressively secular adults you’ll ever meet. Not because religion is wrong. Because pressure, sustained without relief, creates its own escape velocity.

You can almost always predict the overcorrection by studying the original correction long enough. The pendulum doesn’t lie about where it’s going.

So What Do You Do With That?

Here’s where most frameworks stop. They diagnose the pattern—oscillation, balance, entropy—and leave you nodding along with no better sense of how to actually move through the world.

I think there are two honest strategies. Not one right answer. Two.

The first: Commit fully, knowing the reversal is coming—and go hard enough that what you capture on the way up more than compensates for what you lose on the way back down. This is the culture warrior. The true believer. The founder who bets everything on a single thesis and wins, or doesn’t. You’re not trying to escape the pendulum. You’re extracting maximum value from one arc of its swing, before the other takes over.

The risk is real. If your timing is off, or your execution falters, you can get buried by the very momentum you were riding. But the upside is also real: concentrated conviction, when right, compounds.

The second: Never fully commit to a direction. Stay nimble. Read the room obsessively. Ride whatever has momentum, then shift when the tide turns. This is the trend-hopping influencer who somehow always seems to be talking about the right thing at the right time. They’re not lucky—they’re watching more carefully than everyone else.

The cost here is different. You don’t build something enduring. You don’t shape culture—you follow it. There’s an intellectual honesty to admitting that, and also a quiet frustration that comes with it.

What This Is Really About

I’m not prescribing one over the other. The honest answer is that most of us operate somewhere between them—sometimes fully committed, sometimes opportunistic, rarely consistent.

But the meta-point matters: knowing the pendulum exists changes how you move through any system. You stop being surprised by reversal. You start asking, where is the momentum right now, and where is the reverse energy building? You make decisions with the full picture instead of half of it.

From Obama to Trump to Biden to Trump. From lean startups to enterprise moats. From strict households to libertine children. The pattern is everywhere once you start seeing it.

Better to know, than to not.​​​​​​​​​​​​​​​​

p.s. I’ve had to stray from my weekly writing habit over the past month as I prepare to take my first medical licensing exam this Saturday. It has been the absolute hardest thing I’ve yet done, and though I’m scoring well on practice exams, I still doubt myself throughout it. Once this is over, God willing, I will return to my regular writing.

This was written as I simply needed an outlet to get the thoughts out of my head so that I can go back to studying.

My response: “This will increase demand and pay for radiologists.”

What followed was a two-hour argument that touched on ATMs, the iPhone, cardiac surgery, and the entire history of medicine. At some point I realized I had written more in that chat than most essays I’ve published. So here we are.

The debate about AI and professional obsolescence is usually framed as binary. Either AI kills the job or it doesn’t. Either you’re a techno-optimist who thinks everything will be fine, or a doomer who thinks we’re all replaceable. Both camps argue from vibes. Neither has a good framework for why some professions survive technology and others don’t.

I think there are three conditions that determine the answer. Understanding them clarifies not just radiology’s future, but every profession currently eyeing AI with either hope or dread.

The paradox that started this

Before the conditions, you need to understand Jevons Paradox, because it’s the reason the intuitive answer is usually wrong.

In 1865, economist William Stanley Jevons noticed something counterintuitive: as steam engines became more fuel-efficient, Britain consumed more coal, not less. Cheaper efficiency didn’t reduce demand. It expanded it. Lower cost unlocked new uses nobody had imagined before.

The ATM is the modern textbook example. When automated teller machines proliferated through the 1980s and 1990s, the obvious prediction was fewer bank tellers. The ATM could handle deposits, withdrawals, balance checks, the core of what tellers did. In 1988, the average urban bank branch needed about 21 tellers. By 2004, ATMs had cut that to 13.

But here’s what actually happened. Between 1970 and 2006, the number of bank tellers in the United States roughly doubled, from 268,000 to 608,000. Cheaper branches meant more branches. More branches meant more tellers, even at lower headcount per location. Jevons won.

The natural conclusion: technology doesn’t kill jobs, it creates them. ATMs didn’t destroy tellers.

Then the iPhone came along.

Mobile banking eliminated the premise of the branch entirely. By 2022, teller employment had collapsed from 332,000 full-time positions in 2010 to 164,000. Not a delayed ATM shock. A completely different technology, attacking not the task but the institution itself.

So the ATM story isn’t proof that technology always creates jobs. It’s proof that the outcome depends on which technology, attacking what, in what context.

Which brings me to the three conditions.

Condition 1: Is the automation partial or complete?

The most important question to ask about any technology is not “what can it do?” but “what can’t it do, and does the remaining human role still matter?”

ATMs automated one task within a multi-task job. Tellers still handled loan inquiries, resolved disputes, cross-sold products, built relationships. The machine took the repetitive core; the human held the contextual periphery. And that periphery turned out to be valuable, more valuable actually, as routine transactions moved to machines and tellers became relationship bankers. Their wages went up. Banks started hiring more college graduates for the role.

Mobile banking was different in kind, not degree. It didn’t automate a task within branch banking. It automated the reason to go to a branch at all. The question stopped being “how do we staff this branch more efficiently?” and became “why does this branch need to exist?” When that question flips, there’s no peripheral role to retreat into. The institution dissolves, and the job with it.

For radiology, this is the first diagnostic question. AI is advancing rapidly on image interpretation, the core diagnostic read. But radiologists spend only 36% of their time on direct image interpretation. The rest is consultation, clinical integration, procedure guidance, peer review, communicating with referring physicians. These functions are relational, contextual, and harder to automate.

The radiologist who only reads scans is in the position of the teller whose only job was counting cash, vulnerable to the first wave. The radiologist who reads scans and runs tumor boards and performs interventional procedures and advises on imaging strategy is more like the teller who became a relationship banker, with the machine having clarified their value rather than erased it.

But most analyses miss something: the human role in radiology isn’t just surviving alongside AI. It’s producing what AI needs to keep advancing. More on this below.

Condition 2: Does lower cost actually expand the market?

Jevons Paradox requires an elastic market. Lower cost has to unlock new demand, not just make existing demand cheaper to serve.

This is where ATMs and mobile banking diverge most clearly.

ATMs made branches cheaper to operate, and banks responded by opening more of them. Urban branch density increased 43% between 1988 and 2004. More branches meant more customers, more complex transactions, more need for human judgment at the window. The efficiency gain grew the pie and kept humans in it.

Mobile banking made transactions cheaper too, dramatically so. But it didn’t generate more banking activity requiring human presence. It routed existing activity away from places where humans worked. The efficiency gain accrued to consumers and to bank margins, and it didn’t expand the market in a way that preserved teller jobs so much as shrink the institutional context those jobs depended on.

The question this raises for radiology: when AI makes imaging reads cheaper and faster, does that expand the market in a way that still requires radiologists?

The structural case is stronger than it looks. Global imaging volume is growing faster than radiologist supply. The UK projects a 40% shortage of radiologist consultants by 2028. The US faces a projected gap of 122,000 radiologists by 2032. Cheaper, faster AI-assisted reads could make imaging more accessible, enable broader screening programs, and generate clinical volume the current workforce simply cannot handle. That’s the ATM outcome, efficiency enabling expansion, expansion requiring expertise.

But there’s a deeper version of this argument, one that I haven’t seen articulated clearly enough.

Clayton Christensen’s The Innovator’s Prescription makes an observation about the history of medicine that cuts to the heart of this debate. Every time medicine developed a new diagnostic technology, a new way of seeing something previously invisible, it didn’t just improve care. It discovered new disease. New disease created new clinical categories. New categories required new specialists. The EKG didn’t just help cardiologists see hearts more clearly; it revealed arrhythmias nobody had characterized before, which required cardiologists to exist as a distinct specialty in the first place. The endoscope didn’t just help surgeons; it created gastroenterologists. MRI didn’t augment existing neurology; it birthed neuroradiology.

The pattern is consistent enough to feel like a law: new visibility creates new disease categories, which create new high-value subspecialties.

AI reading imaging at population scale is the next iteration of this. When a model processes a million chest X-rays, it doesn’t just read them faster than a human. It finds patterns no individual radiologist could see, subtle correlations across thousands of cases that no human career is long enough to accumulate. Some of those patterns will be noise. But some will describe real pathology that currently has no name, no ICD code, no treatment protocol, and no specialist.

Those discoveries will need humans to validate them, characterize them, study them, and ultimately treat them. That work creates new subspecialties, and those subspecialties will be high-acuity and high-value almost by definition, the kind of complex, rare, consequential cases that command the upper end of the compensation curve.

This is why the bifurcation argument is more optimistic than it sounds. The middle of radiology, routine reads on standard cases, faces real pressure. But the frontier of radiology, the part that exists at the edge of what AI has taught us to see, expands every time AI finds something new. The question isn’t whether there will be enough work. It’s whether the profession moves fast enough to claim it.

Condition 3: Is there a regulatory or institutional moat?

The third condition is the one most people forget.

Branch banking survived ATMs partly because it was regulated, liability-laden, and institutionally structured around physical presence. The moat wasn’t just economic; it was legal and relational. Customers needed branches for mortgages, disputes, complex products. Regulators required certain functions to happen in person. That structure bought tellers decades.

Mobile banking eventually breached that moat, not by defeating the regulations, but by changing what customers wanted so fundamentally that the structure protecting branches became irrelevant. The moat dried up not because someone filled it in but because the river that fed it changed course.

For radiology, the moat is deeper. The most important word in healthcare is licensure. You cannot practice medicine without it. You cannot bill Medicare for a diagnostic read without a credentialed physician signing off. You cannot deploy an autonomous AI for clinical diagnosis without FDA clearance, and as of 2025, not a single FDA-cleared radiology AI tool is approved for fully autonomous diagnosis without physician oversight.

This is structural, not incidental. The regulatory architecture around physician practice was built over a century, is defended by powerful professional associations, and serves genuine patient safety interests that make it politically durable. Any scenario where AI fully replaces radiologists requires not just technical capability but regulatory permission, liability restructuring, and insurance coverage changes, and each of these moves slowly and faces organized resistance.

The mobile banking lesson is worth sitting with. The moat protecting branch banking didn’t fail because regulators capitulated. It failed because consumer behavior shifted so completely that the moat became beside the point. If patients and health systems eventually prefer and trust autonomous AI reads, regulatory structures follow rather than lead.

The moat is real. It is not permanent. But it is almost certainly durable enough to outlast the careers of anyone currently in radiology training, and in a profession with 30-year careers, that is worth something.

The part nobody is talking about: humans are the training data

Here is the argument that reframes everything, and I have not seen it made clearly anywhere.

Think about CAPTCHAs. For roughly twenty years, every time you squinted at a distorted image of a street address or clicked the traffic lights in a grid of photos, you weren’t just proving you were human. You were labeling training data. Google used those interactions, billions of them, contributed unknowingly by ordinary internet users, to train the computer vision models that now power autonomous vehicles, AI image recognition, and a significant fraction of modern machine learning infrastructure.

The AI didn’t arrive from nowhere. Humans built it, one labeled image at a time, without realizing that’s what they were doing.

Radiology is in the same relationship with medical AI, and this changes how you should think about the profession’s future entirely.

Every scan a radiologist reads and annotates, every report they generate, every finding they describe with clinical precision, becomes potential training data for the next generation of imaging AI. The models that will eventually surpass human performance on standard reads are being trained right now on the outputs of human radiologists. The AI that threatens radiology’s routine work is, in a very literal sense, built on radiologists’ work product.

This creates a dependency that is easy to miss. AI imaging models don’t self-improve in a vacuum. They need new labeled data, especially for rare findings, novel pathology, and edge cases systematically underrepresented in existing training sets. The AI that finds a new cancer phenotype in a population-scale dataset still needs expert radiologists to validate that finding, characterize its clinical significance, and generate the annotated cases that train the next model to detect it reliably.

The better AI gets at finding new things, the more it needs expert humans to confirm what it found, because the stakes of validating a potentially novel finding are considerably higher than the stakes of confirming a routine pneumonia. The frontier keeps moving. The human role at that frontier doesn’t diminish; if anything, it becomes more consequential at each iteration.

This is why the new diagnostic technology loop, the Christensen insight, is self-reinforcing rather than self-terminating. AI finds a new pattern. Radiologists validate it. Validation generates labeled data. Labeled data trains better AI. Better AI finds more subtle patterns. More subtle patterns require more expert validation. The loop accelerates, but the human node doesn’t disappear; it becomes more specialized and more valuable as the loop tightens.

New hospitals are already building revenue streams around emerging imaging subspecialties. Those programs need enough radiologists to generate the case volume that AI then trains on to detect the new thing at scale. You can’t shortcut this. You can’t train a model to detect a rare cardiac imaging biomarker without having enough expert radiologists reading enough cardiac imaging to label enough positive cases. The human and the machine are in a genuinely symbiotic relationship, not a replacement relationship, at least at the frontier.

The routine nighthawk reads? That’s the cash-handling teller, under pressure, probably headed for compression. The frontier radiologist who sits at the edge of what AI can currently see is something different entirely, a role that gets more valuable as the AI gets better, not less.

The history of medicine is actually this argument

The three conditions and the training data loop aren’t novel theory. They’re what medicine has been doing for two centuries.

The general practitioner nearly disappeared. In 1931, 84% of American physicians identified as general practitioners. By 2019, family and internal medicine physicians, the successors to GPs, constituted about 25% of the physician workforce. Technology didn’t kill generalism. It fragmented it into 40 specialties and 89 subspecialties, because each new technology made something visible that was previously invisible and created enough new clinical demand to justify a new specialist.

Cardiac surgery didn’t exist before the bypass machine. C. Walton Lillehei at the University of Minnesota performed the first successful open heart operation in 1954, using a patient’s parent as the living heart-lung machine, because mechanical bypass wasn’t ready yet. By 1955, the only two places in the world performing open heart surgery were 90 miles apart in Minneapolis and Rochester. The bypass machine didn’t threaten surgery. Technology was cardiac surgery.

In none of these cases did technology produce a clean elimination. What it produced was bifurcation, transformation, or creation, outcomes that looked very different depending on where in the profession you sat and how fast you moved.

The objection worth taking seriously

My friend Joe (name anonymized) pushed back on the whole framework, “You’re following Altman and Dario’s siren song.”

He’s not wrong to push back. AI is categorically different from every previous technology because it attacks all three conditions simultaneously and doesn’t have an obvious ceiling. ATMs had a clear ceiling; they couldn’t do relationship banking. Mobile banking had one too; it couldn’t handle a mortgage dispute requiring human judgment. Every previous automation had a functional boundary that defined the surviving human role.

With multimodal AI, that boundary is genuinely unclear. The technology isn’t specialized. It synthesizes clinical history, drafts reports, suggests differentials, explains findings to patients, and each capability expansion narrows the peripheral role that radiologists retreat into.

My response to Joe isn’t that he’s wrong about the ceiling being unclear. It’s that the ceiling question matters less than how fast the institutions around it can actually move. Healthcare can’t move fast and break things. Hospitals don’t. Regulators don’t. Insurance companies don’t. Medical boards don’t. Branch banking’s moat eventually fell, but it fell over fifteen years, which is a long time in a profession with 30-year careers. The tellers who adapted in 2010 had a full career ahead of them by the time contraction hit.

The honest answer to Joe is that if AI eventually achieves general-purpose clinical cognition, the three conditions probably can’t hold indefinitely. But the training data loop provides a floor even then. General clinical AI needs humans to validate novel findings. As long as AI keeps finding new things, and the evidence suggests it will, there’s a human role in confirming what it found and training it to find it better. That role is smaller than the current radiologist workforce. But it is higher-acuity, higher-paid, and more interesting. Which is, historically, how medicine has always responded to the tools it builds.

What this actually means

The most common mistake in these debates is treating “will technology change this profession?” as the same question as “will technology eliminate this profession?”

They are very different questions.

Bank tellers changed beyond recognition, from cash handlers to relationship bankers, and then contracted sharply when mobile banking made the branch optional. They weren’t eliminated by ATMs. They were transformed by ATMs and then hollowed out by something nobody predicted when ATMs were deployed.

Cardiac surgeons didn’t exist before 1954. They weren’t threatened by the bypass machine. They were created by it.

Radiology will follow the same logic. The bottom of the distribution, routine reads on standard cases, faces real pressure. The top, subspecialists at the frontier of AI-discovered pathology, interventionalists, imaging informaticists, has a different calculus entirely. And underneath all of it sits a layer most people miss: the radiologist isn’t just surviving alongside AI. They’re building it, one annotated finding at a time, the same way you built Google’s computer vision by clicking on fire hydrants for a decade without knowing that’s what you were doing.

The profession will bifurcate. The middle will hollow out, the frontier will expand, and the radiologists who understand that their work product is the substrate the next generation of AI trains on, and who position themselves accordingly, will be fine.

Which is, as I said in that group chat, the entire history of medicine.

During the day, common laborers built the structure. Then, on the night of every fourth day, he hired a second crew to tear it back down.

Build. Destroy. Build again.

The second crew was the nobles. They worked in the dark, invisibly, so that no one would see them doing manual labor. They got paid. Their dignity stayed intact. The economy kept breathing.

I’ve been sitting with this story for years, and I think I finally understand why it won’t leave me alone. It’s not really about economics. It’s about a single, uncomfortable idea that we’ve spent our entire generation trying to avoid:

Visibility is not the same thing as value. And we’ve built almost everything around the assumption that it is.

The Stage and the Dark

In 1959, sociologist Erving Goffman published a book called The Presentation of Self in Everyday Life. His central argument was deceptively simple: we are all, all the time, performing.

We have a front stage (the version of ourselves we show the world) and a backstage (the version we hide.) And the gap between them is more about survival than it is about dishonesty.

Goffman wrote this before the internet.

Before social media collapsed the distance between front stage and backstage into almost nothing. Before every action, every achievement, every struggle became something you could either post or deliberately not post — and both choices carried economic weight.

The nobles of Oudh were managing exactly this gap. Their front stage identity was “nobleman.” Their backstage reality was “laborer.” Asaf ud-Daulah didn’t just feed them. He protected the architecture of their performance. He gave them a backstage where they could do what needed to be done without it contaminating who they were supposed to be.

This is not an 18th-century problem. This is the central tension of being a professional in 2024. The startup founder who can’t publicly acknowledge they’re struggling, because the moment the market perceives weakness, it becomes real. The person job-searching while employed, performing stability everywhere while quietly applying. The medical student grinding through a credential loop, maintaining the appearance of someone who belongs inside it — because the moment you look like you don’t, the loop stops protecting you.

Everyone is managing front stage and backstage now. The question is whether you’re doing it consciously or by accident.

What We Actually Mean When We Say “Value”

Here’s the thing that the Oudh story breaks open if you stare at it long enough.

The laborers who built during the day and the nobles who demolished at night were doing roughly equivalent physical work. Same effort. Same hours. Same output, in terms of what their bodies were doing. But they weren’t being compensated the same way, and they weren’t serving the same function. The laborers were producing a building. The nobles were producing the preservation of a social order.

The nobles’ work was worth more — not because it was harder, but because of what it protected.

Now scale that up. Think about why a McKinsey analyst making PowerPoint decks is paid more than a nurse keeping someone alive. The analyst’s output — a slide deck — is less obviously valuable than the nurse’s output — a human life. And yet the economics say otherwise. Why?

It’s not because the market is broken, exactly. It’s because “value” was never really about the work. It was always about the social agreement around the work. The analyst sits inside a network of prestige, exclusivity, and narrative that inflates their compensation far beyond what the raw output would justify. The nurse sits inside a network that does the opposite.

Thorstein Veblen figured out half of this in 1899. His theory of conspicuous consumption — the idea that people buy expensive things not because they need them but to signal their position — is usually taught as a lesson about wastefulness. But Oudh flips it completely. The nobles weren’t consuming conspicuously. They were working inconspicuously. They were hiding their labor specifically to preserve their signal.

Veblen understood that status is communicated through what you show. Asaf ud-Daulah understood something Veblen didn’t articulate: status is equally communicated through what you don’t show. The information you withhold is just as strategic as the information you broadcast.

This is what “quiet luxury” actually is. Not an aesthetic trend. An information-asymmetry strategy. The old-money family that doesn’t put logos on anything isn’t being humble. They’re refusing to play a game they’ve already won. The information gap — “you can’t tell how much I have” — is the signal itself.

The Gospel of Visibility and Why It’s Wrong

There is a dominant ideology in the world of 25-year-old professionals right now, and it goes something like this: build in public. Show your work. Document the journey. Be transparent about the process.

It’s compelling. It’s democratizing. It’s also, in many cases, exactly backwards.

The nobles of Oudh would have been destroyed by this philosophy. If they’d built in public — if their labor had been visible — the very thing Asaf ud-Daulah was protecting would have collapsed. Their social position, their future economic opportunities, their ability to function within the system that sustained them — all of it depended on the darkness.

This doesn’t mean visibility is always wrong. It means visibility is a tool, not a default. And we’ve turned it into a default.

The “build in public” gospel assumes that the building is the point. That the output is what matters, and more eyes on the output = more value extracted. But Oudh teaches us something different: sometimes the most important work is the work nobody sees. Sometimes the demolition is the load-bearing wall. Remove it, and everything falls.

The Japanese have a concept for this — ma (間). It refers to the negative space in art, architecture, music. The pause. The empty room. The silence between notes. Ma isn’t the absence of something. It’s a presence in its own right — the space that gives everything else its shape and meaning.

The demolition in Oudh was ma. It wasn’t nothing. It was the thing that made the building possible.

The Loop Is Not a Failure

Here’s where this connects to something most people in their 20s are living but won’t name directly.

Medical school. Law school. PhD programs. Early-stage startups that haven’t found product-market fit. These are all loops. You go in. You do the work. The visible output — the career, the income, the product — is years away, or maybe never comes. In the meantime, you are cycling. Building and destroying, building and destroying, in a pattern that looks, from the outside, like it’s going nowhere.

The standard move is to feel guilty about this. To treat the loop as a failure state — something to escape as quickly as possible. To optimize for the moment when the loop ends and the “real” output begins.

But Nassim Taleb would call this a misunderstanding of optionality. Taleb’s core argument in Antifragile is that the best position in an uncertain world isn’t to predict what happens next. It’s to maintain the ability to respond when it does. The loop — the credential treadmill, the startup that’s still figuring it out — isn’t stalling. It’s optionality. It’s staying in the game while the situation clarifies. It’s keeping your options open precisely because you don’t yet know which option will matter.

Asaf ud-Daulah’s Imambara was optionality made physical. The building wasn’t finished. It was being built and partially demolished in a cycle. But the cycle kept 20,000 people alive, kept the social order intact, and kept the option of a completed building on the table. When the famine passed, the loop ended. The building finished. It still stands today.

The loop wasn’t the failure. The loop was the strategy.

The only real failure is being in a loop and not knowing it. Mistaking the holding pattern for the destination. Optimizing for the wrong metric — visible output — when the actual metric is survival, optionality, and the quiet preservation of something that can’t be seen.

The Question Nobody Asks

We’re trained to ask: What are you building?

It’s the default question in every room full of ambitious 25-year-olds. Every pitch, every networking event, every casual conversation that’s really an audition. What are you building? What’s the product? What’s the traction? What’s the timeline?

These are all visibility questions. They all assume the output is what matters.

But Asaf ud-Daulah’s real achievement wasn’t the Bara Imambara. It was the night shift. It was the decision to look at a crisis and see not just an economic problem but a human one — and to design a solution that solved both simultaneously, even when the solution to the human problem was invisible by necessity.

The question he was actually answering was different. It wasn’t What am I building?

It was: What am I protecting?

Those are not the same question. And the second one — the one we almost never ask — is usually the one that determines whether any of it actually works.

What are you protecting? Your ability to stay in the game. Your identity. Your optionality. The people around you. The structure that, if it holds, will eventually produce something visible. Something real. Something that lasts.

The building is the thing everyone points to. The darkness is the thing that made it possible.

Know which one you’re actually working on.

My biostatistics professor had just said something. Not dramatically—almost in passing, the way you mention rain in the forecast. He moved to the next slide. My classmates kept writing.

And I just sat there, hand frozen, because I’d realized something that made me quietly furious.

I had paid over $200,000 for an undergraduate education at a good school. Took statistics. Got solid grades. Learned p-values, confidence intervals, hypothesis testing. The whole apparatus.

And somehow, nobody had ever told me the most important thing about any of it.

The Gap You Don’t Notice Until You Do

Here’s what every college stats class teaches you: how to determine if a result is “statistically significant.” You learn the threshold (p < 0.05), you learn the tests, you learn to calculate whether something is probably real versus probably just chance.

Here’s what they don’t teach you: that “real” and “meaningful” are completely different questions.

My professor said it plainly: “Statistical significance tells you if an effect exists. Clinical significance tells you if it matters.”

Then he moved on. Like this wasn’t the entire point.

I raised my hand. “Can you go back to that slide?”

After class, I approached him. “That distinction—that’s the most important thing you said today, isn’t it?”

He smiled, a little wearily. “I hope so. I try to emphasize it.”

“I don’t think it landed. You said it the same way you said everything else.”

“You’re probably right.”

When Truth Becomes Meaningless

Consider what this actually means.

You can prove something works—mathematically, rigorously, published in peer-reviewed journals—and it can still be essentially useless.

A pharmaceutical company studies a new weight loss supplement. They recruit 10,000 people, follow them for six months. Results: the supplement group loses 0.8 pounds more than placebo.

With 10,000 participants, that’s statistically significant. It’s real. Not luck, not noise, not measurement error. The supplement genuinely causes slightly more weight loss.

The company’s marketing: “Clinically proven! Statistically significant results!”

Both statements are true.

Also true: 0.8 pounds over six months is nothing. You could lose that by skipping dessert twice.

Statistical significance: Yes, the effect is real. Clinical significance: No, the effect doesn’t matter.

My $200,000 education taught me how to detect the first. It never taught me to ask about the second.

The Math Makes It Worse

Here’s the mechanism that breaks people’s brains once they see it:

With enough participants, you can prove anything has an effect, no matter how small.

Sample size and detectable effect size are inversely related. Study 100 people, you can only detect large effects. Study 10,000 people, you can detect tiny effects. Study 100,000 people, you can prove that almost anything does something.

The math doesn’t care if that something matters.

This means every headline screaming “Study Proves X!” might be technically accurate while being practically meaningless. Not fraud. Not bad science. Just the natural consequence of how statistical testing works.

What This Looks Like in Practice

Your doctor tells you to start taking a statin for cholesterol. “Studies show it reduces heart attack risk.”

The studies are real. The effect is real. Statistically significant, replicated, solid evidence.

What the studies show: for people with no prior heart disease, you need to treat 100-300 people with statins for five years to prevent one heart attack.

Not one death. One heart attack.

So 100-300 people take daily medication for five years. They deal with potential side effects—muscle pain, fatigue, whatever else. They spend money on pills and doctor visits. They turn themselves into patients.

And one person benefits. The other 99-299 were going to be fine anyway.

Is this good medicine?

I genuinely don’t know. It depends entirely on how you weigh the tradeoffs. If you’re terrified of heart attacks and don’t mind pills, maybe it’s worth it. If you’d rather not medicalize your life for a 1-in-200 shot, maybe not.

Both positions are defensible. The statistics don’t resolve this—they just quantify the situation so you can disagree more precisely.

But here’s what makes me want to flip tables: your education taught you to trust “statistically significant” as a quality seal without teaching you that significance says nothing about magnitude.

The Question That Changes Everything

In medicine, we have a metric called Number Needed to Treat (NNT). It asks: how many people do you need to treat to help one person?

Some calibration:

• Antibiotics for strep throat: NNT ≈ 4

• Blood pressure meds after a stroke: NNT ≈ 11

• Aspirin during a heart attack: NNT ≈ 40

• Intensive BP control for healthy people: NNT ≈ 61

• Statins for primary prevention: NNT ≈ 100-300

All of these interventions are “statistically significant.” They all work, in the technical sense.

But NNT of 4 versus NNT of 300 represents wildly different clinical realities.

I don’t know if other fields have an equivalent metric. They should. Because this is the only question that matters: How much do you have to do to get one unit of the thing you actually care about?

Why Nobody Teaches This First

I’ve thought a lot about why this gap exists.

Statistics courses teach you the machinery: how to run tests, calculate p-values, interpret confidence intervals. The mechanics are complex enough that they fill a semester. By the time you’ve learned how to determine if something is significant, there’s no time left to ask what significance actually means.

Or maybe it’s this: teaching mechanical procedures is easier than teaching judgment. You can test whether students can calculate a p-value. You can’t easily test whether they can weigh the clinical or practical importance of a finding.

But the result is that people leave college knowing how to detect effects without knowing how to evaluate whether those effects matter.

That’s not a gap. That’s a chasm.

The Broader Pattern

This isn’t unique to statistics.

We teach people to write code without teaching them to think about whether the code should exist.

We teach financial modeling without teaching when models mislead more than they illuminate.

We teach argumentation without teaching when you should change your mind.

The technical skills are easier to package, test, and grade. The judgment is harder to systematize.

But the judgment is the entire point.

What I’m Left With

I’m now $600,000 deep into my education—undergrad plus medical school. And the single most valuable thing I’ve learned might be this distinction between statistical and clinical significance.

Not anatomy. Not biochemistry. Not diagnostic algorithms. The idea that numbers can be true without being meaningful.

That should’ve been week one of intro statistics.

Instead, it was an aside in a medical school lecture that most people missed because it was delivered in the same tone as everything else.

The professor knew it mattered. He tried to emphasize it. But knowing something is important and making people feel its importance are different skills.

I caught it because I happened to be paying attention at the right moment. Most of my classmates didn’t. Not because they’re not smart—because nothing in the delivery signaled that this was the point.

What You Can Do With This

You probably don’t care about medical studies specifically. But you do encounter claims: supplements that “work,” diets that are “proven,” products that are “scientifically validated.”

Your new filter:

What’s the actual effect size? Not whether it’s significant, but how big it is. Going from 4% risk to 3% is different from 40% to 30%, even if both are “25% reductions.”

How many people were studied? Bigger studies detect smaller effects. A massive study proving a tiny effect is not the same as a small study proving a big effect.

What’s being compared? New thing versus placebo? Versus doing nothing? Versus best available alternative? Each comparison tells you something different.

Would this matter to me? A statistically significant improvement in something I don’t care about is worthless.

You don’t need to understand the formulas. You just need to know these questions exist.

The Expensive Lesson

I paid a lot of money to learn that truth and importance are separate dimensions.

You can have things that are true but unimportant. You can have things that are important but uncertain. The overlap is smaller than you’d think.

Education taught me to find truth. It didn’t teach me to evaluate importance.

That’s the gap.

And closing that gap—learning to ask not just “is this real?” but “does this matter?”—might be worth more than everything else combined.

It shouldn’t cost $300,000 to figure that out.

But apparently, it did.

Everyone talks about the thing. I want to talk about the thing after the thing.

The side effects. The unintended consequences. The second-order impacts that actually matter, and how you can capitalize on them today. Early.

That’s what I’ve been writing about for 10 months - I just didn’t have a name for it.

If you’re still here, you’re probably someone trying to build something while pursuing a traditional career path (med school, law school, PhD, consulting, banking, engineering.) You’re resource-constrained. You refuse to be put in one box. You want to understand how things actually work, not just how they’re supposed to work

Side Effects is for you.

Let’s go.

Ali

It’s Christmas today. For as long as I can remember—at least for the last ten years—these weeks in December have been an odd mix of merry and somber for me. The holidays stack in a strange way. December begins, and I begin my accounting of things coming up and things gone. On the 25th is Christmas, a holiday my family and I didn’t quite celebrate growing up but have gradually begun to enjoy. Then, on December 31st, comes New Year’s Eve, followed immediately by my birthday on January 1st.

Growing up, I joked that I was particularly lucky—double the celebrations, double the gifts. Ironically, with time, this lineup has caused more headache than joy. Splitting myself across family, friends, and significant others has become its own logistical puzzle. Sometimes my family wants New Year’s and my birthday. Sometimes it’s friends. Sometimes it’s someone else entirely.

That dynamic is mildly annoying, sure, but it’s insignificant. That’s not what’s on my mind today, and it’s not what makes this liminal space between the 25th and the 1st feel so reflective.

What’s on my mind is an accounting.

On Failure and Seeing Reality Clearly

It’s time to be honest with myself. I have not achieved many of the things I wanted to have achieved by this age. You can say I had unrealistic goals. You can play perspective games ad nauseam to pacify and justify lack of progress. Or you can simply admit you’ve failed.

I prefer the latter.

I have a deep disdain for people who refuse to see reality for what it is. Vision is a skill—a necessary one—but vision without an understanding of present failures is just aspiration untethered from truth. The line between being visionary and being lost in abstraction is thin, and it comes down to whether you can simultaneously inhabit the future and engage honestly with the present.

You have to understand the here to build the there.
You have to understand the now before you earn the right to talk about what’s next.

So, with that framing, here is the accounting.

Money

I used to tell myself I wouldn’t get married until I made my first million dollars or hit age 30. The reality is simple: I’m nowhere close to $1M, and I’m much closer to 30 than I’d like.

In material terms, I am effectively broke. I have roughly $10,000 invested and around $150,000 in medical school debt—debt that will likely grow to ~$350,000 by graduation. I have no real assets. I manage a modest brokerage portfolio for my family because they don’t understand investing themselves, a dynamic many immigrant families will recognize: your children are your retirement plan.

If I treat myself like a company—and I often do—and imagine I had investors who believed in me, I would have to tell them honestly: we are not on track to meet our stated goals.

What makes this harder is that I was on track.

When I was 23, running JANUS out of coffee shops and my parents’ basement, I thought I was failing. In hindsight, I was anything but. I had no business experience, no network, no precedent, and no real hard skills beyond being able to learn quickly and communicate. Yet I was closing four- and five-figure deals, projecting six figures in revenue within months, and growing cash at roughly 4.5% month over month—about a 65% CAGR.

For context: the stock market grows at ~8% annually. A fund manager hitting 15% in a good year is considered exceptional. I was compounding at 65% and had absolutely no idea.

I shut JANUS down because I couldn’t maintain quality while in medical school. The work going out wasn’t good enough. Clients deserved better. Fixing the company properly would have required rebuilding systems, retraining people, implementing QA, and absorbing short-term chaos—all while risking my performance in medical school and potentially derailing the next four years.

It was a catch-22, but the solution was simple: hit the eject button.

Months later, while reviewing statements and documents, it finally hit me how miscalibrated my self-assessment had been. I thought I was failing when I was, by almost any objective measure, doing remarkably well.

In short: I was on track. I changed course. Now I’m not.

Body

I failed here too.

Before medical school, I trained six days a week at 5am for years. When school started, that entire system collapsed. I gained 10–15 pounds. I lost routines. Even early wake-ups became inconsistent once the behaviors attached to them disappeared.

This isn’t complicated. Systems broke. I paid the price.

Relationships

I’ve always been bad at maintenance.

I have a wide circle of friends whom I care about deeply and whose values I respect. I also intentionally keep people around me who see the world differently than I do, because I think that’s how ideas get battle-tested.

Where I fail is consistency.

I either respond immediately or not at all—not out of disregard, but because the message leaves my mind entirely. There’s no active ignoring, no “I’ll get to this later.” It simply disappears. Friends understandably don’t love this. At best, they tolerate it.

Intent doesn’t matter much here. The outcome does. And the outcome is that people feel unappreciated.

That’s on me.

What Matters (and What Doesn’t)

Health, wealth, and community. Those are the pillars. Everything else is downstream.

I don’t include faith in that list—not because it’s unimportant, but because it’s moved in the opposite direction. Over the past year, I’ve become more religious and spiritual. This started when I was building JANUS and needed something larger than myself to anchor risky decisions. It’s continued since.

A soul in search of growth benefits from an organizing principle, and for thousands of years, God has been the strongest one humanity has found. I understand why now.

So What Now?

The most honest answer is: I’m not entirely sure.

What I do know is how I think about bets.

Asymmetric Bets

The first is Capital Alpha, my investment and finance newsletter. It documents the growth of a portfolio that started at $1,000. I add $100–200 per month, publish my theses, and leave a public record.

In three to four years, one of two things will be true:

• I failed cheaply.

• Or I succeeded publicly.

If I fail, I lose $1,000. If I succeed, I gain not just capital but an undeniable record of judgment and discipline. The downside is capped. The upside compounds. That’s the kind of bet worth making.

The second bet is writing—what you’re reading right now.

I didn’t fully realize what this was doing until strangers started referencing things I’d written, reaching out for perspective, or telling me they looked forward to reading my work. Writing clarifies my thinking in a way few things do. I enter flow. The jumbled threads in my head arrange themselves into something coherent.

I write so that when I meet someone serious, we can skip the surface-level conversation and talk substance.

Because this comes naturally to me, and because one piece can reach many people, it’s asymmetric. Low marginal cost. Potentially high leverage. I’d be foolish not to continue.

This isn’t a resolution piece. It’s not New Year’s yet.

It’s simply an honest accounting—of failures, and of the long bets I’m still willing to make.

There are more. I’ll write about them soon.

For now, this is enough truth for one Christmas.

There’s a peculiar madness that afflicts modern reading: the belief that understanding comes through dissection. We’ve been trained to approach texts like forensic pathologists—scalpel in hand, ready to slice open every sentence, examine every word under fluorescent lights, catalog every comma. We call this “close reading.” We call it scholarship. We call it critical thinking.

I call it a profound misunderstanding of how human beings actually communicate.

Consider how you learned your first language. You didn’t parse grammatical structures. You didn’t analyze the morphological composition of words or diagram sentences on a whiteboard. You absorbed meaning through context, repetition, immersion—through something far more holistic and intuitive than analysis. A child hears “don’t touch that, it’s hot” and learns not just the literal meaning of those words but the urgency in the voice, the protective intent, the relationship between touching and pain. The meaning lives not in the words themselves but in the whole gestalt of the communication.

Yet somewhere along the way, we decided this natural human capacity for understanding was insufficient. We decided that true comprehension required breaking everything down into smaller and smaller pieces, as if meaning were simply the sum of its parts, as if understanding were a matter of inventory rather than insight.

This is not how ideas have traveled through human history. The great philosophical traditions—Confucian, Socratic, Buddhist—were oral for generations before they were written. The Iliad and the Odyssey were sung for centuries before Homer (if Homer even existed as a single person) put them to parchment. The meaning wasn’t in the words; the words were merely the vehicle for something larger, something that lived in the telling and the hearing and the space between speaker and listener.

When we over-analyze text, we make the same category error a mechanic would make if he tried to understand a symphony by taking apart the violin. Yes, you need the violin. Yes, the violin must be constructed in a particular way for the music to be possible. But the music isn’t in the wood or the strings or the varnish. The music emerges from the relationship between instrument, musician, composer, and listener. Disassemble the violin and you’ve destroyed the very thing you claimed to be studying.

Let me offer you an analogy from the digital world—a world we’ve created that mirrors, in imperfect ways, the deeper structures of reality itself.

Take a JPEG image. It’s a file that communicates information—visual information encoded in a particular format. Now suppose you take this image and you compress it, export it, upload it to a server, download it, compress it again, re-export it, upload it somewhere else, download it, compress it once more. Do this enough times and something terrible happens. Artifacts appear. The edges blur. Colors shift. Details vanish. Eventually, the image becomes so degraded that it’s nearly unrecognizable. The original meaning—what the photograph was meant to show you—has been lost not through a single catastrophic error but through the accumulated damage of too many operations.

This is compression loss. Each individual operation seems harmless, even necessary. But the cumulative effect is destruction.

The Constitutional Catastrophe

Watch what happens when constitutional scholars debate the Second Amendment. They’ll spend hours arguing about the comma placement in “A well regulated Militia, being necessary to the security of a free State, the right of the people to keep and bear Arms, shall not be infringed.” Does “well regulated” mean government oversight or simply “well-functioning”? What did “militia” mean in 1791? Does “bear” mean carry or does it mean something more specific about military service? What is the relationship between the dependent clause and the independent clause?

Each word becomes a battleground. Each interpretation spawns counter-interpretations. Someone brings up the Federalist Papers. Someone else brings up anti-Federalist writings. Someone cites usage patterns in 18th-century Virginia legal documents. The debate fractals into infinity.

Here’s what they’re missing: with each word they dissect, they’re introducing a new operation—a new opportunity for error. And I don’t mean error in the casual sense. I mean error in the precise statistical sense of Type I error: false positives that accumulate when you run too many calculations.

Think about what happens mathematically when you test multiple hypotheses. If you run a single statistical test with 95% confidence, you have a 5% chance of a false positive. Run twenty such tests and you’re almost guaranteed to find something “significant” that’s actually just noise. The math doesn’t lie: the more operations you perform, the more likely you are to find patterns that don’t exist.

Now apply this to textual analysis. Every word you isolate for special scrutiny becomes a new test, a new opportunity to find meaning that was never intended. Constitutional scholars aren’t discovering the “true” meaning of the Second Amendment through their word-by-word analysis. They’re generating artifacts—interpretative artifacts that are the direct result of their analytical method, not properties of the text itself.

The same madness afflicts religious scholarship. Watch an Islamic debate, where Imams spend lifetimes parsing individual words of the Quran or a Hadith, building arguments on arguments on arguments, interpretations stacked like a house of cards where removing any single reading threatens to collapse the entire edifice. Or watch Christian theologians debate the precise meaning of Greek words in the New Testament—as if the meaning of “logos” in John 1:1 can be isolated from the entire communicative context in which it was embedded.

The Divergence Problem

Here’s the deeper issue they’re blind to: humans don’t share identical definitions for words, even within the same culture and language. We operate with fuzzy, overlapping understanding—rough shapes rather than precise definitions.

Ask me to define “understand” and I’ll give you an answer. Ask yourself to define it and you’ll give a different answer. Not wildly different—we’re operating within the same linguistic and cultural space. We have the same general shape, the same rough outline. But the exact pixel-by-pixel, color-by-color definition? Different.

This isn’t a bug in human communication. It’s a feature. It’s precisely this fuzziness that allows communication to work at all. We don’t need perfect definitional alignment to transmit meaning. We need good enough alignment. The gestalt is enough.

But watch what happens when you force word-by-word analysis. You’re now demanding that I provide an exact definition for every single word. And since my definition will differ slightly from yours—and yours will differ from the next person’s, and theirs from the next—each word becomes a point of divergence.

One word, one divergence. Two words, two divergences. Ten words, ten divergences. A full paragraph? You’ve compounded the error dozens of times. By the time you’ve analyzed your way through an entire text, the cumulative divergence is so massive that you’re no longer discussing the same text. You’re discussing the artifacts created by your analytical method.

This is the compression loss I mentioned earlier, but now you can see it more clearly. Each time you define a word using other words, you’re re-encoding the message. You’re running another compression operation. And each operation introduces loss. Define “understand” using “comprehend” and “grasp” and “perceive”—but now those words need defining too. So you define “comprehend” using “understand” and “recognize” and “apprehend.” Notice what just happened? You’ve created a circular loop where each word is defined by other words that themselves need defining.

Constitutional scholars do this constantly. They’ll argue about what “regulated” means by citing other contemporary uses of “regulated,” which requires interpreting those other contexts, which requires defining more words in those contexts, which requires more interpretation, which requires more definition. It’s turtles all the way down. And at each level, you’re introducing new opportunities for divergence, new compression artifacts, new Type I errors.

The religious scholars do it too. The word “salvation” needs to be understood in terms of “redemption” and “grace,” which need to be understood in terms of “sin” and “faith,” which need to be understood in terms of “law” and “covenant,” which need to be understood… and suddenly you’re 2,000 years deep in commentary on commentary on commentary, and nobody remembers what the original point was.

The Exponential Accumulation

Here’s where it gets mathematically devastating: these errors don’t add linearly. They compound exponentially.

If I have a 5% margin of interpretative divergence on one word, and a 5% margin on the next word, I don’t have a 10% divergence for the two-word phrase. The divergences interact. They multiply. By the time I’ve parsed a sentence word by word, my cumulative divergence from the original intended meaning could be 50%, 75%, 90%. By the time constitutional scholars have spent 200 years analyzing every word of a document, they’ve diverged so far from the original meaning that their debate has become entirely self-referential—a conversation about their own interpretative framework rather than about the Constitution itself.

This is why Supreme Court decisions read like theological texts. They’re not interpreting a document. They’re interpreting interpretations of interpretations. They’re citing precedent that cited precedent that cited precedent, each layer adding its own compression artifacts, its own divergence from the source.

And the tragedy is that everyone involved thinks they’re being more rigorous, more careful, more precise. They think their word-by-word analysis is bringing them closer to truth. But the mathematics tells us the opposite: every additional operation pushes them further away.

The original authors of the Constitution didn’t intend for their words to be analyzed this way. When James Madison wrote about the separation of powers, he wasn’t encoding some hidden message that required centuries of scholarly decryption. He was communicating an idea—a straightforward, graspable idea about how to structure government to prevent tyranny. The idea was clear to his contemporaries, who shared his cultural context, his linguistic intuitions, his background assumptions.

But we’ve lost access to that direct understanding. And instead of admitting this loss, instead of saying “we’re separated by 230 years of cultural evolution and we can only approximate the original meaning,” we’ve convinced ourselves that if we just analyze carefully enough, if we just examine every word closely enough, we can recover the perfect, original intent.

We can’t. The method itself prevents it.

A Case Study in Destruction

“Judge a man by the content of his character”

Let me show you exactly how this destruction happens in real time. Take one of the most famous sentences in American oratory: Martin Luther King Jr.’s line from the “I Have a Dream” speech—“I have a dream that my four little children will one day live in a nation where they will not be judged by the color of their skin but by the content of their character.”

Now watch what happens when we apply the over-analytical method:

The Over-Analytical Approach:

First, we need to define “judged.” Does King mean formal legal judgment? Social evaluation? Private personal assessment? The word “judged” has theological connotations—is he invoking a religious framework? We should examine other uses of “judged” in King’s speeches to establish his personal lexicon. But wait, those other uses are in different contexts, so we need to analyze those contexts. What did “judgment” mean in 1963 versus 1955? Was it evolving?

Now, “color of their skin”—is this literal or metaphorical? Obviously he means race, but why use the metonym “color” instead of saying “race” directly? Is there significance to the choice of “color”? Does this tell us something about his theory of racial categories? Are racial categories based on skin color or is skin color merely a visible marker of deeper differences? We need to examine the history of racial classification to understand what “color” signified in 1963.

Then there’s “content of their character.” What is “content”? Does he mean the internal substance as opposed to external appearance? But what constitutes substance versus appearance in character? And “character”—does he mean moral virtue? Personality traits? Habits of action? Aristotelian virtue ethics or something else? We should probably consult the Greek philosophers to understand different theories of character. And what does “of” mean here—is it possessive, descriptive, compositional?

But here’s where it gets truly destructive. Once you’ve opened the door to analyzing “character,” you can’t help but ask: what determines character? Is character formed by individual choices or by social circumstances? If a person grows up in poverty without access to quality education, can they develop the same “content of character” as someone who grew up with every advantage? And if not, isn’t King’s formulation actually demanding equal outcomes rather than equal treatment? Doesn’t judging by character implicitly require that everyone has had equal opportunity to develop good character?

And now we’re off to the races. One group of scholars argues that King was advocating for affirmative action and reparations—because you can’t fairly judge character without first ensuring equal opportunity to develop character. Another group argues King was advocating for pure colorblindness and meritocracy—character is precisely what you judge when you strip away all racial considerations. The sentence fractures into opposing political camps, each claiming King’s authority for their position.

Conservatives say: “See? King wanted us to ignore race entirely and just look at individual merit and moral character. Affirmative action violates King’s dream.” Liberals say: “No, King understood that character is shaped by opportunity, and centuries of oppression mean we can’t fairly judge character until we’ve corrected for systemic disadvantage. King was calling for equity, not just equality.”

Notice how we’ve turned this into an analytical circle where we can’t define any word without defining other words, and we’ve introduced at minimum a dozen interpretative operations, each with its own margin for divergence. By the time you’ve had four scholars debate these definitions for an hour, you’ve lost the message entirely. You’re arguing about whether King was a virtue ethicist or a consequentialist, whether his theology was orthodox or liberal, whether “content” implies essentialism or social construction, whether he supported race-conscious policies or race-neutral policies.

The Holistic Approach:

King is saying: stop judging people by their race; judge them by their character instead.

That’s it. That’s the whole message. A child can understand it. Anyone listening to that speech in 1963 understood it immediately. The meaning isn’t hidden in the etymological roots of “judged” or the philosophical implications of “content.” The meaning is right there, clear and direct.

And it goes both ways, which is so obvious it shouldn’t need stating: a Black person of strong moral character should be recognized as such. A Black person of poor character should be recognized as such. A white person of strong character should be recognized as such. A white person of poor character should be recognized as such. King has negated race as a factor in judgment. He’s made it irrelevant. The instruction is symmetrical, universal, clear.

And here’s the crucial part: the holistic understanding leads to action. If you understand King’s message the natural way, you know exactly what to do. When you’re hiring someone, evaluating someone, forming an opinion about someone—look at their actions, their choices, their virtues, their character. Don’t look at their race. It’s a clear moral instruction with immediate practical application.

But if you spend three hours analyzing every word? You end up with a PhD dissertation and no clearer sense of what King wants you to actually do. Worse, you might end up arguing that “character” is itself socially constructed and therefore King’s distinction between race and character is incoherent. You might conclude that there’s no such thing as “content” separate from social perception, and therefore King’s dream is philosophically naive. Or you go the other direction and conclude that King was secretly advocating for a massive expansion of the welfare state to ensure equal character development opportunities. You’ve used your analytical sophistication to obliterate the message and render yourself incapable of the very moral action King was calling for.

This is the compression loss in its purest form. The over-analytical approach took a clear signal and turned it into noise. Each word you examined became another compression operation, another opportunity for divergence, another Type I error. By the time you finished your analysis, the accumulated artifacts had destroyed the original image entirely. What should unite us—a shared moral vision of human dignity independent of race—instead becomes another front in our culture war, with each side citing the same sentence as proof of their opposing positions.

The Same Destruction, Constitutional Edition

“Congress shall make no law…abridging the freedom of speech”

Let’s watch this same process destroy the First Amendment. The text reads: “Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise thereof; or abridging the freedom of speech, or of the press; or the right of the people peaceably to assemble, and to petition the Government for a redress of grievances.”

Focus on the speech clause: “Congress shall make no law…abridging the freedom of speech.”

The Over-Analytical Approach:

What is “speech”? Does it include symbolic speech like flag burning? Does it include commercial advertising? Does it include campaign contributions, which are a form of political expression? What about lies? What about threats? What about pornography? We need to examine 18th-century usage of “speech” to determine the boundaries. But speech meant something different then—there was no broadcast media, no internet, no modern advertising industry. So do we interpret based on what they meant by speech in 1791, or do we apply the principle to modern forms of communication?

Now, what does “abridging” mean? Does it mean any restriction whatsoever, or does it mean substantive restriction? If Congress passes a time, place, and manner restriction—you can protest but not at 3 AM in a residential neighborhood—is that “abridging”? The word “abridge” means to shorten or curtail, but how much curtailment counts as abridging?

And “freedom”—does this mean absolute freedom or freedom within reasonable bounds? The Founders clearly didn’t think freedom meant the freedom to commit crimes, so freedom must have some implicit limitations. But what are those limitations? Are they written into the concept of “freedom” itself, or are they external constraints on an otherwise absolute freedom?

What about “the freedom of speech”—the definite article “the” suggests a specific, known concept of freedom. So we need to research what “the freedom of speech” meant in 1791. But it meant different things to different Founders. Some thought it meant freedom from prior restraint but not freedom from subsequent punishment. Others thought it meant broader protection. Which conception is authoritative?

And here’s where it gets politically explosive. Once you start analyzing, you can make the text say almost anything:

Progressives argue: Speech has always been subject to reasonable regulation. The Founders didn’t intend to protect dangerous misinformation, hate speech, or campaign spending that corrupts democracy. “Speech” means individual human expression, not corporate propaganda. “Freedom” means freedom within a framework of democratic equality. Therefore, campaign finance limits, hate speech codes, and content moderation are consistent with the First Amendment.

Conservatives argue: “No law” means no law. “Abridging” means any restriction. The text is absolute in its prohibition. Political speech is the core concern, so campaign contributions are protected speech. Corporate speakers have the same rights as individuals because corporations are associations of individuals. Therefore, campaign finance limits, speech codes, and government pressure on platforms are all unconstitutional.

Notice what happened? We’ve taken a clear, direct instruction and fractured it into opposing interpretations based on word-by-word analysis. Each side claims their reading is the “correct” interpretation based on careful textual analysis. But they’re not discovering meaning in the text. They’re generating artifacts through their analytical method. Each definitional choice introduces divergence, and the divergences compound until you’ve created two entirely different First Amendments from the same sentence.

The Holistic Approach:

The government can’t restrict your speech.

That’s what the sentence means. That’s what everyone who ratified it understood it to mean. When someone in 1791 read “Congress shall make no law…abridging the freedom of speech,” they understood: the new federal government we’re creating doesn’t have the power to control what you say.

Yes, there are edge cases. Threats aren’t protected. Fraud isn’t protected. Incitement to imminent violence isn’t protected. But these limitations were understood implicitly because they fall outside what anyone meant by “speech” in the relevant sense. When the Founders said “speech,” they meant expression of ideas, opinions, beliefs—the kind of thing that happens in political discourse, religious debate, philosophical argument, journalism. They didn’t mean any sound that comes out of your mouth.

And here’s the practical upshot: if you understand the sentence holistically, you have clear guidance. Is the government trying to stop you from expressing an idea or opinion? That’s presumptively unconstitutional. Is someone trying to use “speech” as cover for criminal conduct (like fraud or blackmail)? That’s not what the amendment protects. The vast majority of cases become straightforward.

But if you’ve spent decades analyzing every word, you end up with a jurisprudence so complex that Supreme Court justices can’t agree on basic questions. Citizens United—are campaign contributions speech? Well, it depends on what you mean by “speech” and whether corporations are “speakers” and whether money is “expression” and whether the government interest in preventing corruption is compelling enough to justify the restriction. Nine justices, four different opinions, no consensus.

The Founders would be baffled. They wrote: the government can’t restrict your speech. How did that become a source of endless controversy? The answer: compression loss. Too many analytical operations. Too many interpretative layers. Too many opportunities for divergence. The original signal is gone, replaced by artifacts of our own creation.

The natural human capacity for understanding—the same capacity that let every ratifier immediately grasp what “Congress shall make no law…abridging the freedom of speech” meant—gets it right. The sophisticated analytical method gets it catastrophically wrong.

The Way Out: Recognizing the Nature of Society

The alternative isn’t lazy reading or willful ignorance. You still need to understand the basic semantic content. When the Constitution says “Congress shall make no law,” you need to know what “Congress” means and what “law” means. You need baseline literacy.

But there’s an enormous difference between baseline literacy and the kind of pathological over-analysis that dominates constitutional and religious scholarship. One gives you access to the meaning. The other destroys it.

The solution is to read the way humans naturally communicate: holistically, intuitively, with trust in your capacity to grasp the gestalt without having to explain exactly how you grasp it. Read the way you learned your first language—through immersion, through pattern recognition, through that mysterious direct perception that precedes and transcends analysis.

When the Founders wrote about the “general welfare” or when Jesus spoke about the Kingdom of God, they were pointing to something. Something real, something graspable, something that their audiences understood without needing a PhD in textual analysis. The meaning was right there on the surface, obvious to anyone embedded in that cultural and linguistic context.

We’ve lost some of that context. Fine. We can’t recover it through analysis. We can only recover it through imaginative reconstruction—through trying to inhabit the worldview and assumptions of the original speakers, through trying to feel what they felt and see what they saw. This is art, not science. It’s more like method acting than like mathematics.

But there’s something deeper here, something we’ve forgotten because we’ve become so enamored with our analytical sophistication. We already have the answer to this problem. It’s built into our organic biology and the social systems we’ve developed over thousands of years.

Human society, whether we like it or not, ultimately operates on one of two principles: rule by the mighty, or rule by the majority. Often these converge—the majority is the mighty by sheer volume of numbers. But not always. Sometimes David beats Goliath. Sometimes a determined minority can shape the rules of the game.

The first step is recognizing what kind of society we actually live in and collectively deciding what kind of society we want. America was not founded as pure majority rule. If it were, the popular vote would determine the executive branch. It doesn’t. We have the Electoral College, which means a president can win without winning the popular vote—as we’ve seen multiple times in recent history. This reveals something fundamental: America is, in practice, a system where influential minorities can dictate outcomes. Whether you like this or not is irrelevant to the fact that it is so.

This is like being born into a household. You don’t choose your parents. You don’t choose their rules. But you have options: you can change the parents (elect new leaders), you can change the household rules (amend the Constitution), you can change yourself and your beliefs (accept the system as it is), or you can leave the household (emigrate).

Now, I’m not advocating that anyone leave the country—though even that statement has become so politically charged that it requires a disclaimer. But having lived across five different countries by age twelve, I can tell you that the concept isn’t as radical as it’s been made out to be. Almost every immigrant understands this intimately. People come to America because they’ve made a judgment: America’s system is better run than the system they’re leaving. That’s the entire reason for immigration.

There’s a peculiar irony in Americans who claim to despise American systems but refuse to even temporarily explore countries with the socialist or communist policies they advocate for. Not as a political statement, but simply as an exercise in moral honesty—to find out if those systems actually produce the outcomes you imagine. But I digress.

The point is this: when there’s disagreement about the gestalt understanding of a text, we need to determine what the strongest interpretation is within our society and use that as the working definition. And this will change over time. It must change, because society changes, circumstances change, technology changes. But the underlying forms remain constant.

This is where Plato had it exactly right, though perhaps not in the way he intended.

Plato’s Forms and the Problem of Mapping

Plato argued that behind every instance of a thing in our world exists a perfect, eternal Form. Every chair you see is an imperfect instantiation of the Form of Chair. Every act of justice is an imperfect reflection of the Form of Justice itself. The Forms are unchanging, eternal, perfect. The instances are changing, temporal, flawed.

Most people misunderstand Plato as making a metaphysical claim about some other realm where these Forms exist. But there’s a more useful way to understand him: he’s describing a fundamental feature of how concepts work and how meaning transmits across time and context.

When the Founders wrote about “freedom of speech,” they weren’t describing a specific set of technologies or situations. They were pointing to a Form—the principle of free expression, the idea that government shouldn’t control what people say and think. The specific instantiations change: in 1791 it was newspapers and pamphlets and speeches in the town square. In 2024 it’s Twitter and podcasts and YouTube videos. But the Form remains the same.

The problem we face isn’t that we don’t know what the Form is. The problem is mapping old instantiations to new instantiations. This is a mapping problem, not a textual analysis problem.

Consider Political Action Committees. Do we really believe that the concept of pooling money to influence politicians didn’t exist in the time of the Founders? That’s absurd. Money existed. Political influence existed. Collective action existed. Wealthy merchants could absolutely organize dinners with politicians, offer financial support, coordinate their efforts to push certain policies. The Form was identical to what PACs do today. The instantiation is different—modern PACs have formal legal structures, file disclosure forms, run television ads instead of printing handbills. But the underlying activity, the Form of the thing, is the same.

So the question isn’t “Would the Founders have wanted PAC money to count as speech?” The question is: “Does PAC spending instantiate the Form that the Founders were protecting when they wrote about speech?” And that’s a mapping question, not a textual analysis question.

This is where our obsession with word-by-word analysis completely fails us. You can’t answer a mapping question by analyzing the word “speech” more carefully. You need to understand what Form the word was pointing to, and then determine whether modern instantiations are instances of that same Form.

Take another example: digital surveillance. The Fourth Amendment protects against “unreasonable searches and seizures” of “persons, houses, papers, and effects.” Did the Founders mean to protect emails? They didn’t know what emails were. But they understood the Form: people have a right to private communication and private property free from arbitrary government intrusion. The question is whether emails instantiate the Form that “papers” pointed to in 1791.

The answer is obviously yes. An email is functionally identical to a letter—it’s private written communication between individuals. The technology changed but the Form remained constant. The mapping is straightforward. Similarly, your phone’s location data might instantiate the Form of “papers and effects”—it’s personal information about your life and movements that you reasonably expect to remain private.

But here’s where it gets interesting: we can have genuine disagreements about mapping without needing to analyze the text more carefully. Some people argue that financial contributions to campaigns don’t instantiate the Form of “speech” because speech is about expressing ideas, not about deploying resources. Others argue that spending money to amplify your message is just a modern instantiation of the same Form the Founders were protecting—the ability to disseminate your political views.

This is a legitimate debate. But it’s not a debate about what “speech” meant in 1791. It’s a debate about whether campaign spending maps to the Form that “speech” was pointing at. And we can’t resolve this debate through more careful textual analysis. We can only resolve it by understanding the Form clearly and then collectively deciding, as a society, whether the mapping holds.

And here’s the crucial part: we resolve these mapping questions the way humans have always resolved collective questions—through power. Either the power of the majority, or the power of the mighty, or some combination of the two. The Supreme Court decides that campaign spending is speech, and that becomes the operative mapping. Not because they’ve discovered the one true interpretation through careful analysis, but because they have the power to make their interpretation stick.

This is not cynical. This is how human societies have always functioned. The Constitution works not because we’ve all agreed on the precise meaning of every word, but because we’ve agreed on a process for determining which interpretations will be binding. The process involves textual interpretation, yes, but also precedent, social values, practical consequences, and ultimately the power dynamics of nine justices voting.

When we pretend that constitutional interpretation is about recovering the original textual meaning through careful analysis, we’re lying to ourselves. What we’re actually doing is mapping old Forms to new instantiations, and using the text as a constraint on how much mapping we can do before we’re no longer talking about the same Form.

Sometimes the mapping is obvious: the First Amendment’s protection of “the press” clearly maps to television news, despite television not existing in 1791. Sometimes it’s contested: does “the press” map to individual bloggers, or only to institutional journalism? These are genuine questions, but they’re not questions we answer by analyzing the word “press” more carefully. We answer them by understanding the Form that “press” pointed to—some notion of independent information dissemination that checks government power—and then fighting over whether bloggers instantiate that Form.

And we already have mechanisms for resolving these fights. They’re not particularly elegant, and they don’t produce perfect certainty, but they work well enough to keep society functioning. Courts make rulings. Legislatures pass laws. The executive enforces or doesn’t enforce. Elections shift power. Constitutional amendments change the rules. It’s messy and political and involves power dynamics we might not like.

But it’s real. It’s how societies actually operate. And it’s far more honest than pretending we can resolve our disagreements by analyzing the text more carefully, when what we’re really doing is fighting over how to map old Forms to new instantiations in a world the Founders never imagined.

The text isn’t hiding secrets. The meaning isn’t buried in the etymological roots of individual words. The Founders were pointing at Forms—principles, concepts, structures—that their audiences immediately recognized. We can still recognize those Forms. We just need to stop staring at the words and start looking at what they’re pointing to. And then we need to do the hard political work of collectively deciding how those Forms map to our modern world.

That’s the way out. Not through more analysis, but through less. Not through more sophistication, but through more honesty about what we’re actually doing when we interpret these texts. We’re not recovering hidden meanings. We’re negotiating how ancient Forms apply to modern instantiations. And we do this through the same mechanisms human societies have always used: power, persuasion, precedent, and collective decision-making.

Stop operating on the patient. Step back. Look at the whole. The Forms were always clear. The instantiations change. Our job is mapping, not analysis. And we already know how to do that—we’ve been doing it for thousands of years.​​​​​​​​​​​​​​​​

This essay is part of that project: examining my reasoning publicly, inviting critique, building a track record of honest thinking. If I’m wrong about data abundance, I want to be proven wrong with evidence, not dismissed with appeals to authority. And if I’m right, I want the argument to be strong enough that it moves the field forward.

The trust problem starts with meeting patients where they are—at home, online, thinking through these questions themselves. This is how we begin.

We had a class one day about medical tourism. The professor opened with a simple exercise: raise your hand if you’d get a whole-body MRI if someone offered you one for free.

About half the class raised their hands.

What followed was a carefully constructed ninety minutes on the dangers of overdiagnosis—the incidental findings that lead to unnecessary biopsies, the anxiety of ambiguous results, the inequities of boutique medicine. As one practical example, they showed us [company redacted]’s website and had us critique how it is allegedly marketed to worried wealthy people (side note: as someone who worked in marketing, naive consumers have no sense of marketing funnels and do not understand the differing messaging for bottom of funnel content versus top of funnel content. For anyone in marketing it’s no surprise at all that a company’s website is focused on mid-to-bottom of funnel messaging. Additionally, for anyone who has ever built a business, one of the first lessons you learn is to start top of market. Variables are minimized and you don’t need to worry about scale. You generate enough cash flow to improve the core product before aiming to move downmarket with economies of scale. This is Business 101.)

At the end, the professors asked us to vote again on the same question. I think they expected fewer hands—that we’d been educated out of our naive enthusiasm for more information.

The class fell into silence, broken only by a handful of awkward, quiet, honest laugh as we all looked around to see more hands up now than at the beginning.

The lead professor’s face was a mix of shock and disappointment. I was trying not to grin. The professor sees the anxiety whole body MRIs cause. I like that they represent a patient taking agency over their health. Both are true.

In the beginning of class I had already reached out to one of the executives from the referenced company— a medical doctor himself. (As a side note, if running JANUS taught me anything, it’s that you can just message decision-makers directly and cut through the marketing noise. This is probably the most valuable skill I learned and I am eternally grateful to that doctor, and so many others, who graciously shared their time in reinforcing this as a positive action.) He responded immediately. Told me he’d had similar experiences in his M1 year decades ago—except back then it was about lung cancer screening CTs, and everyone was still using paper charts. The technology was new, the establishment was skeptical, and medical students were being taught to distrust it.

This was clearly a discussion worth having. But the decades of elapsed time suggests it needs to be had differently, and assessed more earnestly.

What follows, is an attempt to begin in that earnest, open discussion, publicly.

There’s a peculiar sensibility in medicine— as soon as we picked up on it, a friend and I began calling it an “anti-abundance mentality.”

It’s a reflex that greets every new data source like continuous monitors, whole-body imaging, or patient portals with suspicion rather than curiosity. The default assumption is that more information is dangerous until proven otherwise.

This strikes tech people, like Roon, as backwards. In tech, data abundance is obviously good. More signals mean better models, faster iteration, clearer patterns. The marginal cost of storage approaches zero, so why wouldn’t you capture everything?

But healthcare has internalized scarcity as virtue. To be fair though, it’s for reasons that aren’t entirely stupid. Yet some still are.

Why Doctors Learned to Say No

Medical training is an extended exercise in restraint. You learn early that every intervention carries risk—radiation exposure, false positives that trigger cascading procedures, the anxiety of knowing something ambiguous about your body. Resources are genuinely finite in ways tech resources aren’t: operating room time, specialist availability, someone’s actual kidney.

More importantly, you’re taught that ordering lots of tests is the mark of a bad doctor. It suggests you don’t know what you’re looking for. The impressive attending is the one who needs fewer data points to reach the right diagnosis. Clinical acumen is demonstrated through parsimony.

This makes sense in context. Shotgunning labs because you’re intellectually lazy is bad medicine. The doctor who orders a comprehensive metabolic panel, complete blood count, lipid panel, thyroid function, vitamin levels, and tumor markers for a patient with a headache isn’t being thorough—they’re avoiding the harder work of clinical reasoning.

So restraint becomes sophistication. Scarcity becomes a North Star.

When the Constraint Disappears

The problem is that this mentality persists even after the original constraint vanishes.

Continuous glucose monitors were initially dismissed as “too much data”—why would a diabetic need to know their glucose every five minutes? The quarterly A1C was considered sufficient. Then studies showed that CGMs dramatically improved outcomes. Patients could see patterns in real-time, adjust behavior, prevent dangerous swings. Suddenly “too much data” became standard of care.

Genetic sequencing costs have collapsed by orders of magnitude, yet it remains locked behind specialist gatekeeping. The technology says abundance; the reimbursement model and professional norms say scarcity.

Whole-body MRI screening gets taught as dangerous excess—”you’ll find things that don’t matter and end up doing unnecessary biopsies.” Maybe. Or maybe early detection of treatable cancers saves lives and the incidental findings are manageable with better protocols. We won’t know without trying, but the default posture is resistance.

Most tellingly: patients having access to their own medical data is still treated as potentially dangerous rather than obviously empowering. The paternalism is barely concealed—you might not understand it, you might worry unnecessarily, you might make bad decisions. Better to keep the information with the professionals.

Why It Persists

Training trauma. Medical education rewards doing less and punishes shotgunning tests. This becomes identity, not just technique. After years of being graded on restraint, abundance feels professionally threatening.

Legitimate bad experiences. Every doctor has seen the patient whose incidental finding led to biopsy, led to complication, led to worse outcome than if they’d never known. These stories have weight. The precautionary principle gets overapplied: since abundance can cause harm, we should default to scarcity.

Payment models. Even as technology gets cheaper, reimbursement still treats testing as expensive. The system architecture assumes scarcity when the underlying economics have shifted. Insurance will pay for a comprehensive metabolic panel but not for continuous metabolic monitoring, even though the latter might prevent the expensive emergency that quarterly testing misses.

Status games. Clinical judgment is demonstrated through needing less data to diagnose. If abundance-tools make diagnosis easier, they threaten this status marker. The doctor who needs an AI reading of continuous vitals to catch early sepsis seems less impressive than the one who spots it from experience and intuition. This isn’t always the case, but there is some degree of selective pressure applied towards assessing pre/post-test probabilities.

The deeper issue is that scarcity mentality is load-bearing for quality in the current system. It prevents genuine waste and harm. You can’t just remove it without replacing its function.

The Conceptual Fix

What needs to happen is a clean separation between two different things that are currently conflated:

Abundance of data is often good. Information is cheap to gather, store, and analyze. Continuous monitoring, comprehensive panels, patient-generated health data—these should default to “yes” when the technology allows and the patient wants it.

Abundance of action is often bad. Interventions carry real risk and cost. You shouldn’t do procedures that won’t change management. Selective decision-making is correct. This is the same in business, as any business owner understands. You have a million “brilliant ideas” but each change comes with immense starting costs and training friction that get scaled across the entire team/ operation; so if you do change anything, you really want to make sure its gains will account for the temporary loss across your whole team.

The error is applying intervention-logic to information-gathering. They’re different categories requiring different heuristics.

This requires retraining an entire profession to think about data differently than they currently do. Information and intervention are treated as the same type of thing—both require justification, both should be minimized unless proven necessary. That made sense when tests were expensive and invasive. It doesn’t make sense when a sleep mask can log biometrics passively and cheaply.

The Processing Problem

The real reason doctors resist more data isn’t philosophical—it’s practical. They don’t have good tools to process it.

A continuous glucose monitor generates thousands of data points. If the doctor has to eyeball raw traces during a fifteen-minute appointment, abundance is genuinely burdensome. But if the system says “A1C equivalent is 7.2%, nocturnal hypoglycemia pattern on Tuesday nights, likely related to Monday dinner timing,” then abundance becomes useful.

Healthcare needs intelligence layers between data collection and clinical decision-making. Right now we have abundance at the collection layer (CGMs, wearables, genomics) and extreme scarcity at the interpretation layer (one doctor’s attention during a brief appointment). The gap is obvious.

This is solvable with better software, better models, better interfaces. It’s not a fundamental constraint—it’s an infrastructure problem.

The Risk Reframe

The current calculation is: “What’s the harm of this extra test?” Radiation, false positive cascade, patient anxiety, cost.

The reframe needs to be: “What’s the harm of not having this information when we need it?”

The patient whose cancer would have been caught on a whole-body MRI but wasn’t. The diabetic whose dangerous patterns weren’t visible in quarterly A1C checks. The cardiac event that continuous monitoring would have predicted but standard vitals missed.

CGMs won this argument through evidence, not rhetoric. Studies showed better outcomes. The abundance skeptics were proven wrong by data about data. Early cancer detection and longitudinal health tracking will need to win the same way—demonstrate that they change outcomes, not just generate information.

Follow the Incentives

The current reimbursement model financially rewards doing less. Value-based care was supposed to fix this but mostly just created administrative overhead.

What you need are payment models that reward better outcomes with whatever resources required—abundant testing or minimal testing, but optimizing for the right thing. This probably means working within capitated models or direct-to-consumer where the incentive structure is already different.

This is the unsexy answer: reimbursement reform. But it matters. The culture follows the incentives more than the arguments.

The Gradual Path

This doesn’t get “fixed” system-wide through a single policy change or persuasive essay. What happens instead is gradual erosion through specific victories:

• CGMs for diabetes (won)

• Liquid biopsies for cancer screening (winning)

• Continuous vital monitoring for early deterioration (fighting)

• Consumer genomics for disease risk (contested)

• Whatever gets built next for sleep, metabolics, longevity

Each success creates precedent. Eventually the mentality shifts, but it’s a decades-long accumulation of proof points.

The culture has internalized scarcity as virtue, and virtues don’t change through argument. They change when the world makes the old virtue obsolete and the new virtue necessary.

For now, the leverage is in picking one specific instance of abundance—one category of information that’s cheap to gather and valuable to have—and proving it matters. Execute well enough that it becomes undeniable. The broader culture will follow, slowly, after enough specific battles are won.

The scarcity trap in medicine is real, but it’s not permanent. It’s just slower to escape than the technological advancement curve suggests it should be.

If medicine is art as much as it is science, then we must recognize that a part of the art is in the language we use, and much of the language we use to speak about data and information is reductionist and from bad-faith assumptions.

My proposition here, of diverging abundance of data from abundance of action, is an attempt give hospital systems the logical framework to become pro-data without becoming overly-reactive and wasteful. I hope it is taken in earnest and provokes consideration.

Medicine’s default should shift toward data abundance when we can demonstrate that the information improves outcomes and when we have infrastructure to process it meaningfully, but each modality needs to prove itself rather than inheriting blanket skepticism.

Post-mortem of my argument

Rebuttals You Might Make Against My Argument

I underestimate legitimate medical concerns. The “incidentaloma” problem with whole-body MRI screening isn’t just theoretical hand-wringing. Studies have shown that aggressive screening in asymptomatic populations can lead to net harm through the cascade of interventions on clinically insignificant findings. This isn’t just doctors being backwards—it’s evidence-based concern about iatrogenic harm.

The CGM analogy doesn’t fully transfer. CGMs work because diabetes has clear, actionable thresholds and frequent monitoring demonstrably improves outcomes. Whole-body MRI screening doesn’t have the same evidence base yet. The question isn’t “is data good?” but “does this specific data source improve outcomes net of harms?” That’s an empirical question for each modality.

I am missing the false positive problem. When you screen low-prevalence conditions in asymptomatic populations, most positive findings are false positives. This is basic epidemiology. A test with 95% specificity sounds great until you apply it to a million healthy people and generate 50,000 false alarms. The math here is real, not just medical conservatism.

My Response to Each Rebuttal

On “Legitimate Medical Concerns” and Incidentalomas

The incidentaloma critique assumes the current management protocols for ambiguous findings are fixed and optimal. They’re not. The cascade problem is real, but it’s a protocol design failure, not an inherent property of information abundance.

When CGMs first emerged, physicians could have made identical arguments: “We’ll find glucose fluctuations that don’t matter and patients will panic and over-treat.” But we didn’t conclude “therefore don’t use CGMs.” We developed better interpretation guidelines, better patient education, better decision thresholds. We solved the protocol problem.

The medical establishment treats incidentalomas as an argument against gathering data rather than an argument for developing better response protocols. That’s backwards. If whole-body MRI screening creates management challenges, the answer is better management protocols, not information avoidance.

Moreover, the harm calculus is suspiciously one-sided. Every analysis of incidentaloma risk carefully quantifies the harms of false positives and cascading interventions. But the harms of missed diagnoses due to information scarcity are systematically underweighted because they’re invisible—we never know about the cancer that would have been caught early, the aneurysm that would have been monitored, the condition that would have been preventable.

Absence of evidence gets treated as evidence of absence. “We don’t have RCTs proving whole-body MRI screening improves outcomes” becomes “therefore we should assume it’s harmful” rather than “therefore we should study it properly.” That asymmetry reveals the bias.

On the CGM Analogy “Not Transferring”

This critique proves my point rather than refuting it. Yes, CGMs had to demonstrate outcome improvements through studies. And they did. But what matters is that the medical establishment resisted even doing those studies because of prior assumptions about “too much data.”

The pathway was: technology becomes feasible → medical culture resists → contrarians push for trials → evidence emerges → grudging acceptance. The resistance happened before the evidence phase, not because of evidence. That’s the cultural problem I’m identifying.

Saying “whole-body MRI doesn’t have the same evidence base yet” is technically true but misleading. The question is whether the default posture should be “this is probably harmful until proven otherwise” or “this is worth studying rigorously.” Medicine currently defaults to the former, and that default itself prevents the evidence generation.

Furthermore, the claim that “diabetes has clear actionable thresholds” wasn’t obvious before CGMs. CGMs revealed patterns and thresholds we didn’t know to look for. That’s exactly what exploratory data collection does—it shows you what’s actionable. You can’t know what information will be useful until you have it.

On the False Positive Problem

Yes, I understand basic epidemiology. The false positive problem is real. But here’s what this critique misses:

We already accept false positive burdens all the time when we think the benefit justifies it. Mammography in average-risk women generates plenty of false positives. We do it anyway because the lives saved outweigh the harms. PSA screening generates massive false positive rates—we debated whether it was worth it, not whether false positives exist.

The question isn’t “do false positives exist” but “does the benefit/harm ratio favor testing?” And crucially: we can’t know that ratio without collecting the data to find out.

Moreover, the false positive argument assumes we’re stuck with current sensitivity/specificity profiles and current management protocols. We’re not. Better imaging, better biomarkers, better AI interpretation, better clinical guidelines for managing ambiguous findings—all of these can shift the ratio.

Saying “false positives make screening harmful” treats the entire system as static. That’s exactly the scarcity mentality I’m critiquing: assuming we’re stuck with current constraints rather than engineering around them.

Finally, the false positive critique is selectively applied. Nobody says “we shouldn’t collect vitals every 4 hours in hospitals because most abnormal vitals are false alarms.” We accept the false positives because we think catching the true positives matters. The question is why that same logic doesn’t apply to other abundant data sources.

On “Not Addressing When Data Improves Outcomes”

I actually did address this—perhaps not explicitly enough. The answer is: we find out by collecting the data and studying it rigorously, not by refusing to collect it based on prior assumptions.

The current system says: “Prove this new abundant data source improves outcomes before we adopt it, but we’ll resist the studies that would generate that proof, and we’ll interpret ambiguous evidence in the most conservative possible way.”

That’s a Catch-22 designed to preserve the status quo.

The pathway should be:

1. Technology makes new data collection feasible

2. Observational studies explore whether the data reveals useful patterns

3. RCTs test whether acting on those patterns improves outcomes

4. If yes, adopt; if no, abandon

But medicine currently shorts-circuits this at step 1: “We don’t have RCTs proving this works, so we shouldn’t even do the exploratory work to see if there are useful patterns.”

The CGM story proves this. The randomized evidence came after years of observational use by contrarian endocrinologists who thought the data might be useful. If the medical establishment had successfully prevented that exploratory phase, we’d never have gotten the RCT evidence.

On “First Do No Harm” vs. “Try Things and See What Works”

This is framed as if they’re opposed. They’re not. “First do no harm” applies to interventions, not information gathering.

Getting a whole-body MRI is not an intervention. Looking at your own glucose data is not an intervention. Having continuous vitals monitoring is not an intervention. These are information-gathering activities with minimal direct harm.

The harm comes from subsequent actions taken in response to information. That’s precisely why my data/action distinction matters. You can gather abundant data and maintain appropriate restraint in how you respond to it.

“First do no harm” has been weaponized into “first gather no information that might lead someone to take action.” That’s not the same thing. That’s paralysis disguised as prudence.

The Core Issue

The core issue is that the medical establishment has developed sophisticated arguments for why information abundance is dangerous, and those arguments sound like evidence-based caution but function as motivated reasoning to preserve existing practice patterns.

Every new data source faces the same gauntlet: it won’t work, we don’t need it, false positives, incidentalomas, anxiety, we don’t have RCTs, it’s not cost-effective. Then the contrarians prove it works anyway, and the establishment grudgingly accepts it while claiming they were appropriately cautious.

At some point you have to ask: if this pattern repeats with CGMs, pulse oximetry, cardiac telemetry, liquid biopsies, and every other monitoring innovation, maybe the problem isn’t that each individual technology needs to overcome legitimate skepticism. Maybe the problem is that the default posture of skepticism toward information abundance is systematically wrong.

I’m not arguing for reckless adoption of every monitoring technology. I’m arguing that the burden of proof should be symmetrical: show me evidence that abundant data causes net harm, and show me evidence that it causes net benefit. Right now the burden is asymmetric—abundant data must prove itself beneficial while scarcity is assumed safe. That asymmetry is the cultural problem.

References

Continuous Glucose Monitoring (CGM)

Initial Resistance Phase: When the first CGM devices reached the market in 1999-2000, enthusiasm waned even within the scientific community due to unexpected sensor output drift and limited clinical utility over the FDA-approved three-day implantation period. PubMed Central The FDA explicitly stated that the first CGM was only supplemental to standard home glucose-monitoring devices and should be used occasionally, not for everyday use. ScienceDirect Early sensors’ utility was limited due to significant drift in sensitivity, and as a result, there was less enthusiasm concerning CGM in the early days of the technology. AJMC

The Resistance Arguments: The objections followed the predictable pattern. Physicians argued patients didn’t need continuous data when quarterly A1C tests were sufficient. Barriers to use included lack of FDA approval for insulin dosing, cost and variable reimbursement, need for recalibrations, and lack of training for physicians regarding interpretation of CGM results. PubMed Central Physicians were cited as major barriers to implementation, facing demands on time that were impossible to meet during brief clinical visits, lack of reasonable reimbursement, potential medical-legal liability, and uncertainties associated with the new intervention. PubMed Central

Contrarian Success: Despite institutional resistance, endocrinologists who believed in the technology pushed forward with clinical use and studies. By 2016—seventeen years after the first device—the accuracy of CGM sensors was so good that the FDA approved continuous glucose readings to replace fingerstick blood sugar testing altogether. HealthCentralIt is now accepted that CGM increases quality of life by allowing informed diabetes management decisions as a result of more optimized glucose control, leading to better health and a reduction in diabetic complications. PubMed Central

Pulse Oximetry

Initial Resistance Phase: In Japan, the first commercial pulse oximeter was considered a useful research device but not a clinically viable option; only 200 devices were sold. PubMed Central The technology was invented in 1972, but it will likely surprise younger physicians to know that the modern pulse oximeter was not invented until the early 1970s and did not become commercially available until the 1980s. Even in the late 1990s there was still debate regarding the utility of routine pulse oximetry for ED patients. Acponline

The Resistance Arguments: Early studies foreshadowed widespread use but illustrated that the problem was one of practicality—the early commercial device, although functional, was difficult to use in clinical practice. Chestnet It took nearly 40 years from early proof-of-concept to widespread adoption.

Establishment Acceptance: It wasn’t until 1988 that Dr. Thomas Neff suggested we should consider oxygen saturation by pulse oximetry as a “fifth vital sign,” a concept that definitely took hold. Acponline By the late 1980s, pulse oximetry was considered a standard of care for monitoring patients during anesthesia and joined the ECG as a routine monitor for all critically-ill patients. Chestnet

Cardiac Telemetry

The Overuse Problem (Resistance in Reverse): Cardiac telemetry presents an interesting inversion—the technology was adopted, but the medical establishment has spent decades trying to restrict its use, demonstrating the same pattern of institutional inertia. Studies indicate telemetry monitoring is often overused in intermediate level of care settings, with analysis showing 33% of telemetry days did not meet appropriate indications. PubMed Central

Cultural Resistance to Evidence-Based Guidelines: AHA telemetry monitoring practice standards had not been fully embedded or adhered to within the system, leading to wide variation in what was considered appropriate telemetry monitoring use. System norms mentioned among interviewees may inhibit appropriate use—for example, the norm that everyone on the cardiac floor receives telemetry monitoring. PubMed Central Physicians expressed that a concern for clinical deterioration, rather than explicit concern for development of arrhythmia, drove most telemetry use—notably because telemetry does not replace more frequent vital sign checks and may lead to a false sense of security. PubMed Central

Liquid Biopsy (Currently Fighting the Battle)

Current Resistance Phase: Liquid biopsy perfectly demonstrates this pattern in real-time. Adoption of liquid biopsy to exclude patients from targeted therapy has seen much slower clinical adoption, mostly due to concerns for false negatives. At this time, negative liquid biopsy sequencing should be followed up by tissue biopsy sequencing. College of American Pathologists Integrating liquid biopsies into current clinical workflows requires overcoming logistical challenges, including the need for healthcare professional training on interpretation and limitations. The lack of standardization in liquid biopsy protocols presents a significant barrier to widespread adoption. PubMed Central

The Familiar Objections: False positives may lead to unnecessary treatments, exposing patients to potential side effects and causing psychological distress. False negatives can result in delayed diagnosis and treatment. Regulatory and ethical considerations play a crucial role, with the evolving regulatory landscape needing to address implications of early detection such as managing incidental findings and risk of overdiagnosis. PubMed Central

Evidence of Contrarian Success: Despite resistance, landmark trials like PADA-1 demonstrated improved progression-free survival using liquid biopsy to guide therapy, and the plasmaMATCH study confirmed 96-99% concordance between liquid biopsy and tissue sequencing, supporting broader adoption in clinical practice. Nature Liquid biopsy-based tests are gaining popularity for early cancer diagnosis, with multi-cancer early detection tests like Galleri, CancerSEEK, and OneTest showing promise for detecting multiple cancers at early, treatable stages. PubMed Central

The Pattern Is Clear

In each case, the sequence is identical:

1. Technology becomes feasible (CGM 1999, pulse oximetry 1970s, liquid biopsy 2000s)

2. Initial enthusiasm from inventors/researchers

3. Medical establishment resistance citing practical concerns, lack of evidence, false positive risks, cost, and workflow disruption

4. Contrarians persist with clinical use and studies despite institutional barriers

5. Evidence accumulates over 10-20 years proving clinical benefit

6. Grudging acceptance as technology becomes standard of care

7. Retrospective claims that the delay was appropriate caution rather than institutional inertia

The time lag is remarkably consistent: approximately 15-25 years from proof-of-concept to mainstream acceptance, not because the technology needed that long to mature, but because institutional medicine needed that long to overcome its default skepticism toward information abundance.

The conversation drifted through the usual territory until John posed a question that stuck with me: “I think everyone who helps someone else only does it because there’s something in it for them.”

He’s a religious guy, and he used religion to prove his point—when he helps a stranger, part of him knows he’s accumulating blessings, credit in God’s ledger. The altruism isn’t pure. There’s always a transaction.

He turned to Victor. “When’s the last time you helped someone out of your way?” Victor paused, thought about it, couldn’t come up with anything recent. This bothered him—not because the question was unfair, but because it revealed something he hadn’t wanted to see.

Then John turned to me.

My answer came out before I’d fully thought it through: “I help people because if I don’t, the thought lingers. Like I neglected something I was supposed to do.”

John wanted this to be about religion. But as I explained it more, I realized it wasn’t. What I actually felt was that by not helping, I’d be casting a vote—a vote for becoming someone who doesn’t help. And that every choice like this, no matter how small, was a vote for the kind of person I’m becoming.

That reframing seemed trivial at the time. Dinner moved on. But I’ve thought about it constantly since, because I think it reveals something about human psychology that, once you see it, explains a lot of self-sabotage.

The Wrong Question

Most people, when facing a decision, ask: “What should I do?”

This framing is almost always wrong, because it treats the decision as isolated. Should I go to the gym today? Should I send that email? Should I help this person? Each choice appears as its own little dilemma, to be evaluated on its own merits.

But decisions aren’t isolated. They’re patterned. And patterns compound.

The person who skips the gym “just today” is running an experiment: what happens when I skip? The answer, usually, is nothing. No immediate punishment. The world continues. So the next time the decision arises, the skip-option has been validated. It’s slightly easier to choose. Not because anything external changed, but because a precedent now exists.

This is how patterns form. Not through dramatic choices, but through tiny precedents that accumulate until they become defaults.

The Right Question

The better framing is: “What pattern am I reinforcing?”

This question does something the first one doesn’t—it connects the present choice to a trajectory. It forces you to see the decision not as a single point, but as a vote in an ongoing election. An election where the winner becomes your identity.

When you ask “should I go to the gym today?” you can easily rationalize a no. You’re tired. You went yesterday. One day won’t matter. All true, in isolation.

But when you ask “what pattern am I reinforcing?”—you have to confront something harder. You’re either reinforcing the pattern of someone who trains consistently, or the pattern of someone who finds reasons not to. There’s no third option. Inaction is itself a vote.

Why Your Brain Sabotages You

Here’s where it gets interesting. Humans are famously bad at intuiting exponential functions. We can feel linear growth—ten dollars a day for a year feels like it’s worth about $3,650, and it is. But compound growth breaks our intuitions completely. A penny doubled daily for 30 days feels like it should be worth a few dollars. It’s actually $5.3 million.

This isn’t just a math curiosity. It’s a deep flaw in human cognition that sabotages our decision-making constantly.

The tragedy is this: exponential curves start slower than linear ones. Put them on the same graph and, for the first several periods, the linear line is actually ahead. Exponential growth only overtakes linear growth later—sometimes much later. Which means that at the moment of decision, when you’re comparing options, the exponentially better choice will often feel worse.

Going to the gym once feels like nothing. Eating a salad feels like something—immediate, tangible, virtuous. Both are good decisions. But the gym compounds in ways the salad doesn’t. The salad gives you roughly what you’d expect: some nutrients, some fiber, slightly better digestion that day. The gym gives you something that’s nearly invisible at first but accumulates into a completely different body, energy level, mental clarity, and self-image over years. The first few gym sessions might actually feel less rewarding than the first few salads. That’s the trap.

This is true for almost every high-value behavior. The things that compound dramatically tend to feel the most pointless in the early stages, precisely because exponential curves start slow. Writing one page feels meaningless compared to reading an interesting article. Sending one networking email feels awkward compared to browsing LinkedIn passively. Having one difficult conversation feels painful compared to just letting things slide.

In every case, the higher-leverage choice feels worse initially.

The Discomfort Heuristic

This suggests a counterintuitive heuristic: if you’re choosing between two reasonably good options and one feels noticeably more uncomfortable or pointless in the short term, that’s often a signal that it’s the exponential bet.

Not always. Some things feel bad because they’re actually bad. But among choices that are plausibly beneficial, short-term discomfort or apparent meaninglessness often correlates with long-term compounding.

The workout that feels like a waste of time. The cold email that feels awkward. The skill practice that feels tedious. The boundary-setting conversation that feels harsh. The early-morning routine that feels brutal. These tend to be the exponential plays. And they tend to feel worse than their linear alternatives precisely when you need to start them—at the beginning, when the exponential curve is at its flattest.

Your brain will try to talk you out of these choices using perfectly reasonable logic. One session won’t matter. You can start tomorrow. The linear alternative is good too. This is all true in the immediate frame. It’s catastrophically false in the cumulative frame.

The Tactic

Here’s something concrete you can use immediately.

The next time you face a decision—especially a small one where you’re tempted to take the easier path—complete this sentence:

“If I choose X, I’m voting to become the kind of person who ___.”

Fill in the blank honestly.

If I skip the gym, I’m voting to become the kind of person who skips when it’s inconvenient. If I send the email, I’m voting to become the kind of person who handles uncomfortable tasks promptly. If I help this stranger, I’m voting to become the kind of person who helps. If I walk past, I’m voting to become the kind of person who walks past.

Then ask a second question: “Is this a linear or exponential bet?”

Linear bets give you roughly what they appear to give you. Exponential bets appear to give you almost nothing at first, then everything later. The discomfort of the early stages is a feature, not a bug—it’s the price of admission to the steep part of the curve that most people never reach because they quit while the curve is still flat.

The Hidden Leverage

This reframing reveals where the real leverage exists in your life.

Most people think high-leverage decisions are the big ones: what career to pursue, who to marry, where to live. Those matter, but they’re rare—a handful per decade.

The decisions you make dozens of times daily—how you spend your morning, whether you keep small promises, how you respond to minor friction, whether you do the slightly harder thing or slightly easier thing—these feel low-leverage because their individual stakes are low. But they’re the highest-leverage decisions you make, because they’re the raw material of exponential curves.

One gym session is nothing. A thousand gym sessions is a different person.

One awkward networking email is nothing. A thousand such emails is a different career.

One moment of helping a stranger is nothing. A thousand such moments is a different character.

The question is whether you can tolerate the flat part of the curve long enough to reach the steep part. Most people can’t. Not because they lack discipline, but because their intuitions are calibrated for linear growth, and exponential growth feels like a lie until it suddenly doesn’t.

The Identity Flip

Back at that dinner, John wanted to know why people help each other. It’s a fine philosophical question. But I think the more useful question is mechanical: what does the pattern of helping or not helping actually do to you over time?

And the answer is that it compounds. Not linearly—you don’t just become “a little more helpful” with each act. You become a different kind of person, with different defaults, different instincts, different possibilities available to you. The same is true for every pattern you run.

This sounds abstract, but the implication is concrete: if you want to change who you are, you need to change your relationship with discomfort. Not dramatically—you don’t need to become an ascetic. But you need to learn to recognize the feeling of “this seems pointless right now” as a possible signal that you’re on an exponential curve, in the flat part, where most people quit.

The things that will change your life the most are probably the things that feel the least rewarding in the beginning. That’s not a motivational platitude. It’s a mathematical property of exponential functions, applied to human behavior.

The question is never “what should I do?”

The question is “what pattern am I reinforcing, and does it compound?”

You’re answering it every day, whether you realize it or not.

I was in third or fourth grade when I first realized adults lie to children—not in the obvious ways, like about Santa Claus or the Tooth Fairy, but in more insidious ways that reveal something broken in how we think.

The librarian and teachers kept repeating this phrase: “Don’t judge a book by its cover.” They said it with the kind of moral certainty adults reserve for things they want to be true. But even as a kid, I could see they were full of shit.

The thing that struck me wasn’t just that it was false—it was that it was obviously false. If you don’t judge books by their covers, why do publishers spend millions on cover design? Why did the teacher herself pick up Charlotte’s Web instead of the book next to it? Some judgment must have occurred. The cover communicated something, and she responded to it. That’s literally what covers are for.

What bothered me wasn’t the advice itself—maybe they meant something like “don’t only judge books by their covers,” which would at least be coherent. What bothered me was the gap between what they preached and what they practiced. They were lying, and worse, they seemed to believe their own lie.

I was too young to articulate it this way at the time. My rebellion was simpler: a defiant insistence that yes, I do judge books by their covers, and so does everyone else. It was the intellectual equivalent of a kid yelling “the emperor has no clothes.” I was confident I was right, but I couldn’t yet explain why the adults were wrong in such a specific, systematic way.

The First Crack

Looking back now at 25, I think that was the moment I first glimpsed something important about how the world works. Not that people lie—every kid knows that—but that people lie to themselves. They construct elaborate narratives about who they are and what they believe, and then they defend these narratives even when reality contradicts them.

The book cover thing was trivial, but the pattern wasn’t. People say one thing and do another. They preach principles they don’t practice. Sometimes this is conscious hypocrisy, but more often it’s something stranger: they genuinely can’t see the contradiction. They’ve told themselves the story so many times that they’ve forgotten it’s a story.

And here’s the really unsettling part: they want you to adopt their delusions too. It’s not enough for them to believe that judging books by their covers is wrong while doing it anyway. They need you to believe it too, to participate in the same collective fiction. There’s something almost desperate about it.

The Harder Problem

At this point, observing that people deceive themselves isn’t interesting anymore. It’s just a baseline assumption about how humans work. What’s interesting now is the second-order problem: if this was obvious to me in third grade, what’s obvious to a 35-year-old or 45-year-old that I’m missing now?

The liars have gotten better. They’re older, more articulate, better at constructing persuasive narratives. They have professional credentials and sophisticated arguments. They speak with passion and conviction—often because they genuinely believe what they’re saying, having internalized their own lies so thoroughly that they’re no longer distinguishable from beliefs.

This is much harder to detect than a teacher contradicting herself about book covers. When you’re young, adult hypocrisy is like a neon sign. But sophisticated self-deception in adults, by other adults, is camouflaged. It hides behind complexity, expertise, good intentions, and social consensus.

The scary question is: what have these better liars already convinced me of? What beliefs am I carrying around right now that are just more sophisticated versions of “don’t judge a book by its cover”—things that sound right, that everyone around me agrees with, but that don’t survive contact with reality?

The Mirror

But there’s an even more uncomfortable question lurking beneath that one: what lies am I telling myself?

I’m 25 now. I’m one of the adults. It’s entirely possible—likely, even—that I’m doing exactly what that librarian did. Saying things that sound wise and principled while acting in ways that contradict them. The only difference is that I can’t see it yet. Just like she probably couldn’t.

This bothers me for two reasons. The obvious one is that I’d like to be closer to the truth. I’d like congruence between what I believe and what I do, between what I say and what I practice. There’s something inherently valuable in that alignment, independent of outcomes.

But there’s a second reason that I find more interesting: I wonder if the very act of watching for incongruence—of constantly checking whether your actions match your words—creates its own kind of value.

Maybe the person who’s perpetually on the lookout for gaps between their stated beliefs and revealed preferences becomes, almost as a side effect, more honest. More self-aware. More confident, even, because their self-image is grounded in reality rather than aspiration.

It’s not that hunting for incongruence necessarily makes you truthful. You could spot all your contradictions and decide not to fix them. But there might be a correlation. The kind of person who habitually checks their beliefs against their behavior might be more likely to develop genuine integrity—not because they’re more moral, but because they’ve made it harder to fool themselves.

Pattern Recognition

I’ve started to notice that the book cover problem has a signature. It shows up whenever there’s a large gap between stated and revealed preferences. When what people say they value and what they actually do with their time, money, and attention point in different directions.

It shows up in moral advice that, if followed literally, would make you worse off—and that the advice-giver clearly doesn’t follow themselves. “Be yourself” from people who carefully curate every aspect of their presentation. “Follow your passion” from people who chose lucrative careers. “Don’t judge books by their covers” from people who judge books by their covers.

It shows up in explanations that are optimized for sounding good rather than being true. The kind of thing that gets nods of approval in conversation but doesn’t generate any useful predictions about the world.

The tricky part is that these patterns are easy to see in domains you understand well and nearly impossible to see in domains you don’t. That third-grade version of me could spot the book cover lie because I had direct, repeated experience of how book selection actually works. But in areas where I lack that ground truth—politics, relationships, career advice, what makes people happy—I’m as vulnerable as anyone else to confident-sounding bullshit.

And this includes my own bullshit. The stories I tell about myself.

The Vigilance Question

So I keep coming back to this question: is there value in vigilance itself? Not just in achieving congruence, but in the active practice of looking for incongruence?

I suspect the answer is yes, but not in a straightforward way. It’s not that checking for contradictions automatically makes you more honest—you could just become better at rationalizing them. But the habit of checking creates a certain kind of friction. It makes self-deception more expensive, more effortful. And over time, that friction might push you toward truth almost accidentally, the way water finds the easiest path downhill.

There’s also something about confidence here that I’m still working out. I think real confidence—the kind that isn’t brittle or defensive—comes from having an accurate map of your own abilities and beliefs. When you know what you actually think (as opposed to what you wish you thought) and what you can actually do (as opposed to what you imagine you could do), you can move through the world with less anxiety. You’re not constantly worried about being exposed as a fraud, because you’ve already done the exposing yourself.

The person who habitually checks for misalignment between belief and action might develop this kind of confidence naturally. Not because they’re perfect—they’ll find plenty of contradictions—but because they’ve at least looked. They know where the gaps are. They’re not operating under a comforting delusion that could shatter at any moment.

The Real Lesson

The librarian probably meant well. Maybe she was trying to teach us not to be superficial, to give things a chance before dismissing them. That’s a reasonable lesson. But by packaging it in an obvious falsehood, she accidentally taught me something more valuable: that the way people explain the world is often decorative rather than functional. It’s social signaling dressed up as wisdom.

The real skill isn’t detecting lies versus truth. It’s detecting which explanations are load-bearing—which ones actually help you navigate reality—versus which ones are just there to make you or someone else feel good.

At 25, I’m still working on this. I can spot the obvious stuff, the third-grade-level contradictions. But I’m sure I’m surrounded by more sophisticated versions of the same thing, lies that have been refined over generations until they sound like common sense.

And I’m sure I’m telling some of those lies myself. Right now. To you, to others, to myself.

The question I keep coming back to is: what would a 45-year-old version of me see that I can’t see yet? What am I confidently wrong about right now? What obvious lie am I telling myself?

I don’t know. But I’m pretty sure that whatever it is, it seems just as self-evidently true to me now as “don’t judge a book by its cover” seemed to that librarian.

The best I can do is keep looking. Not because vigilance guarantees truth, but because the kind of person who keeps looking is more likely to find it. And maybe, over time, that vigilance itself builds something valuable: a version of myself that’s harder to bullshit, including by me.

That might be the real lesson from third grade. Not “watch out for liars”—that’s too easy. But “watch out for the liar in the mirror, and check often, because he’s gotten very good at his job.”

I didn’t know it at the time, but that random spreadsheet was the first time I ever tried to audit my relationships.

And the reason I’m telling you this story is because it revealed something that changed my entire trajectory: the people you surround yourself with will determine your standard for “normal.”

The Open Loop: Why Your Circle Is Probably Holding You Back

Here’s the uncomfortable truth: if you want extraordinary outcomes in life, you need to be out of the ordinary. But most of us spend our days surrounded by people who reinforce the average.

It’s not because they’re bad people. It’s because the bell curve of life pulls everyone toward the middle. And when your circle is average, “average” starts to feel safe.

But average never built a meaningful business. Average never transformed healthcare. Average never pulled a family out of generational poverty, or pushed a medical student to innovate beyond textbooks, or created wealth that lasts for generations.

So here’s the real question: how do you know if your circle is silently pulling you back toward average?

That’s what this essay is about.

Step 1: Recognize the Environment Problem

The best piece of advice I ever got came from Nick Soman. We were on a call before I had started JANUS, Brask, or any of the projects I’m building today. Out of nowhere he said:

“You’re a CEO, and you’re in the wrong environment.”

If anyone else had said it, I would’ve rolled my eyes. But here was a guy who had built and sold a company, who lived and breathed the world I wanted to enter. And he didn’t just see me as I was—he saw where I fit.

That’s when it clicked: my problem wasn’t that I didn’t have ambition, or discipline, or ideas. My problem was that I was playing in the wrong arena.

Step 2: Define What “Out of the Ordinary” Looks Like

Here’s the paradox: everyone says they want to achieve “extraordinary” results, but very few actually define what “out of the ordinary” looks like in their day-to-day.

Think of the bell curve. On the left, you’ve got the people who underperform. In the middle? The vast majority—good, decent, hardworking, but average. On the far right, you’ve got the 1–2% who are doing something exceptional.

Now ask yourself: what behaviors put you in the middle of that curve?

• Consuming the same content everyone else consumes.

• Spending time with people who never challenge you.

• Chasing comfort over growth.

And what behaviors push you toward the right tail?

• Asking uncomfortable questions.

• Doing the reps when no one’s watching.

• Saying no to environments that drag you back.

This is where your relationship audit begins. If you want to build something extraordinary—whether that’s a startup, a career in medicine, or a life of impact—you cannot only absorb inputs from the middle.

Step 3: Decide What You Will Tolerate

Your circle is less about who you add and more about what you tolerate.

When I was running JANUS, I noticed something. My clients who built the best companies weren’t necessarily smarter than anyone else. But they were ruthless about what they allowed in their orbit. They didn’t tolerate mediocrity.

And I realized: it’s the same in life. If you tolerate people who:

• Complain but never act,

• Validate your excuses instead of challenging your thinking,

• Or drain energy instead of amplifying it…

…you are signaling to yourself that this is your standard.

Auditing your circle means drawing a line. You don’t need to “cut people off” in some dramatic way. But you do need to consciously invest less energy where the ROI is low.

Step 4: Build a Circle That Normalizes Your Ambition

So, how do you build the right circle? Here’s the formula I use:

1. Seek challenge. Join rooms where you feel slightly out of place. If you’re the smartest person in the room, you’re in the wrong room.

2. Normalize excellence. Surround yourself with people who make your big goals feel small, because they’ve already done it or are doing it.

3. Contribute back. This is key. Eventually, you’ll reach the point where you can sit at that table. Don’t just take. Bring value—ideas, energy, support.

I like to compare it to dating. When someone asks, “What do you bring to the table?”—most people flinch. But that’s the exact question you should be asking yourself in every relationship.

Step 5: Repeat the Audit

This isn’t a one-time exercise. Your circle drifts. People change. You change.

Every few months, ask yourself:

• Who’s pushing me forward?

• Who’s holding me back?

• Am I showing up as someone worth investing in?

When you repeat this process, you’ll notice something fascinating: your tolerance for mediocrity shrinks, your circle sharpens, and your sense of “normal” shifts upward.

Closing the Loop

Remember that spreadsheet I started in August 2023? I never finished it. But the act of starting it was enough to wake me up. It forced me to confront a simple truth: if you want to be extraordinary, you cannot afford to surround yourself with average inputs.

Today, I can trace almost every meaningful step forward—every project, every opportunity, every collaboration—back to a decision I made about my circle.

So if you’re in your own “relationship audit” phase right now, don’t overthink it. Start small. Ask the hard questions. And commit to building a circle that elevates your standard of normal.

Because in the end, that’s what decides whether you land in the middle of the curve—or break out to the far right.

Action Steps You Can Take Today

• Write down the 5 people you spend the most time with. Do they reflect who you want to become?

• Audit one relationship: ask, “Is this person pushing me forward, holding me back, or keeping me in place?”

• Join one environment this month that feels uncomfortable but inspiring. (A mastermind, a meetup, a class, even a new Twitter circle.)

• Write down what you bring to the table. Be brutally honest.

She wasn’t some career educator. She never worked a formal job. She wasn’t trained in pedagogy. She could barely use a computer. Yet she built my education, brick by brick, in ways that most people in the wealthiest country in the world would call extreme, if not impossible.

I’ll never forget the time we flew from Lebanon to Pakistan to visit my grandmother. The nearest airport was four hours from our destination. Most parents would have let the kid sleep, stare out the window, or play games. My mother hopped into the backseat with me, pulled out next year’s math textbook, and sat with me for the entire ride. Chapter after chapter, exercise after exercise, she made sure I pushed through it. By the time we arrived, I had already finished half of the upcoming year’s curriculum.

This wasn’t a one-off. It was her way of life. Before cooking dinner, she would scribble practice problems for me on a sheet of paper, leave me to work them out while she cooked, and check my answers when she came back. If I had to read a book for school and write an essay, she would read the entire book herself—cover to cover—so she could fairly critique my writing, point out what I had missed, and force me to expand my arguments. She wasn’t even particularly good at math herself, so she would phone an aunt, an uncle, or occasionally my dad to help me work through a problem. But she never let me hit a wall alone.

She drilled discipline into us in small, almost symbolic ways. When we came home from school, we weren’t allowed to toss our backpacks into a corner like every other kid. We had to carry them to our desks and place them gently. The message was unmistakable: how dare you treat your education like a burden you can’t wait to drop? If you can’t even bear the weight of your backpack with respect, you don’t deserve the education inside it. At the time, it felt harsh. In hindsight, it was true.

By the time I was 10 or 11, she didn’t need to press so hard anymore. The system she built had already become self-sustaining. She would walk into my room and find me reading random Wikipedia pages for fun, teaching myself things neither she, nor my teachers, nor anyone else knew about. I remember I’d go down Wikipedia rabbit holes learning about quarks and blackholes and the Bohr model. She had flipped the switch from external pressure to internal drive. At that point I wasn’t doing schoolwork anymore—I was learning to teach myself.

And here’s the real crazy part I still have a hard time fathoming sometimes: English was not even her first language. It was her second. And yet she insisted on making it my first because she figured I’d learn Urdu passively through social interactions.

Think about how insane that is. Imagine you half-learned French or Spanish as an adult—not fluent, just “good enough”—and then decided that when your child was born, their first language would be French or Spanish. You’d buy the textbooks you barely understood yourself, you’d teach day after day for a decade, and somehow, you’d succeed so completely that your child grew up fluent. That’s what she did with English. She willed it into being. And let’s not forget this wasn’t a woman who ever thought I’d end up in America, she just thought it’d make me sharper.

So she did most of this in Pakistan, where the infrastructure itself fought our every step. Electricity would cut out for hours every day. The internet was unreliable well into my teens, and video/ YouTube was almost inaccessible at any consistent quality. Textbooks were outdated, libraries nonexistent, and schools obsessed with rote memorization instead of critical thinking. My mother didn’t like the way my schools taught and decided to take charge herself, even though she didn’t have the training or the resources.

This is why I find it baffling that in the United States—the richest country in the world—children routinely fall below grade level despite billions poured into schools, reforms, and technology. The uncomfortable truth is this: a disengaged parent in a wealthy nation will raise a child less prepared than a committed parent in a poor one.

And before people make stupid claims about the poorest Americans: I’m not talking about them. Their struggles are real and shaped by unique, crushing economic inequalities. I’m talking about middle-class American families—people with reliable electricity, stable internet, and schools stocked with resources. The shocking thing is that a child in Lahore, Pakistan, on the other side of the world, with outdated textbooks, failing infrastructure, and electricity for only half the day, can actively compete with kids in middle-class America. And it’s not just me, it’s also thousands of other Pakistani kids, from similar backgrounds, who manage to earn competitive financial aid offers to come study here after scoring in the 98th+ percentile on the SATs— an exam literally catered to the American school system.

That should unsettle us. Not because it condemns those families, but because it shows we are focused on the wrong things. We obsess over policy tweaks, gadgets, and programs while neglecting the foundation: parenting, discipline, and values.

My mother, with no job, no wealth, and English as her second language, made English my first, math my discipline, and curiosity my lifelong habit. That is not easy. That is not something any policy or technology can replicate. It took willpower, sacrifice, and conviction.

And so when I look at America’s educational struggles, I don’t buy the excuses. I don’t believe that another billion dollars in funding will solve the problem if the family itself has abdicated its role. If a woman in Pakistan, facing every imaginable disadvantage, could still pull this off, then what excuse does the wealthiest society in history really have?

The answer is none. If we want to fix education, stop looking first to Washington, Silicon Valley, or the school board. Start looking at the dinner table conversations, the living room, the backpack ritual, the parent who decides whether curiosity lives or dies.

Because the truth is simple: parenting, not policy, is the driver of education. And the sooner we admit that, the sooner we’ll stop failing our children.

P.S. I think this is why Kumon is so popular in Asian communities in America. It is the closest thing to productizing this experience, and its results show that it is extremely effective.

This story is trivial on the surface and profound underneath. It reveals the hidden scaffolding of culture: it is not built by invisible laws or forces of nature. It is built by people—individuals—who dared to assert that their preference, their taste, their decision should become reality. The world is littered with the fingerprints of those who refused to wait for permission.

But most people, confronted with this reality, recoil. To admit that society is shaped by the strongest personalities is to admit that you, too, could shape it if only you stepped forward. That truth is too heavy for most to bear. It’s easier to believe in inevitability—systems, institutions, “the way things are.” It’s easier to resign yourself to the backseat and call it humility or realism.

Modern culture thrives on this sense of helplessness. It whispers that you are a mere consumer, not a creator. That the rails are already laid, that the best you can do is ride along and maybe choose your seat. But this is a lie. Every building, every law, every tradition, every pixel on your screen was once arbitrary. Once, someone said: I like blue better than green. And the world bent around that choice.

You are allowed to do the same.

The battleground of ideas is not fair, and it has never been. The loudest, most consistent, most relentless voices set the tone of their era. That has always been the case, and it always will be. The question is not whether this is fair. The question is whether you will accept the weight of your own preferences as worthy of shaping reality—or whether you will surrender them, pretending they don’t matter, while others build the world in their image.

Most people resign themselves to the backseat of life. But the front seat is empty, waiting for anyone willing to grab the wheel and say, I like blue.

Imagine, for a moment, writing down every single flaw, every failing, every fear. Make the list as brutally honest as possible—your impatience, your insecurity, your mistakes, your illnesses, your struggles. Then ask a simple question: What if God let this happen to me for a reason? What if these weaknesses are not arbitrary curses but invitations to growth?

One of my favorite reminders to myself is this question: If God wanted to make someone tough, would he give them an easy life?

When we reframe weakness in this way, something extraordinary happens. Each burden becomes a potential gift, each wound a source of wisdom. A person who has wrestled with anxiety may be uniquely equipped to guide others through storms of fear. Someone who has known the sting of poverty might carry an unmatched compassion for the marginalized. A leader who once failed profoundly may have the humility and patience to help others avoid similar ruin. In this light, weakness becomes a teacher—and more than that, a calling.

The paradox is that our struggles create a familiarity that cannot be faked. You cannot lead people out of darkness unless you have known what it feels like to walk there yourself. You cannot speak with authority about resilience unless you have endured. The authority of lived weakness is not in theory, but in testimony. And so the very things we once sought to bury become the foundation upon which our most authentic influence is built.

This is the alchemy of weakness: to recognize that the list of flaws we once despised is, in fact, a catalog of unique strengths. Where others see shame, we can see training. Where others see limitation, we can see preparation. Our wounds are not simply healed; they are transfigured into wisdom and power that can only come from having lived them.

So perhaps the challenge is not to rid ourselves of weakness, but to reinterpret it. To accept that the things that make us feel small may be the very things that make us indispensable. To see our lives not as accidents, but as carefully tailored paths where even the jagged stones were placed with purpose.

In the end, we lead not despite our weaknesses, but because of them.

P.S. I plan on writing a book of my personal life philosophy. I will be writing this book, in public, in realtime. For now, over the past 5 years I have collected the most important maxims I have discovered through personal trials. You can find them here: https://blog.thisisalimirza.com/meditations/

I will be turning each of those maxims into a chapter of the book. Each chapter will contain a personal story from my life putting the maxim in context, some stories from others in history who lived them, as well as a section on how to implement the maxim in your life.

This will probably take me around 2 years. I will continue adding maxims to that page, and eventually I will share a live public google document where I will be writing out the rest of the book as it takes form. I think this will be fun.

The rigor of the program demands discipline and quick recall, yet I have noticed it often emphasizes memory and application over the kind of open-ended, creative problem-solving I once encountered in courses like Game Theory or Discrete Mathematics.

Those earlier experiences gave me frameworks to explore models, manipulate assumptions, and seek optimal outcomes, and I find myself still drawn to that way of thinking.

This inclination to optimize, to search for structure and clarity, has shaped how I view service and contribution. Much of our society celebrates forms of service that are visible, immediate, and emotionally resonant, like volunteering.

Yet, I often find myself asking: what is the true cost of these actions, and what is the most effective way to use one’s time and talent?

The only 3 drivers of action

Take volunteering as an example. On the surface, it is unpaid labor freely given. But that labor is never without cost; the opportunity cost is always time. So why would someone give that time away? Often, it is in exchange for meaning, purpose, or status. These are not bad motivations. But they raise an important question: what happens when the work that best uses one’s skills and training is not volunteering at all, but the very work one is already being paid to do?

Consider a surgeon. In an ideal world, would we prefer to see that surgeon distributing food at a shelter, or saving lives in the operating room? Clearly, the highest value comes from applying rare, specialized skills where they matter most. Yet paradoxically, our culture sometimes treats the second choice as less noble, simply because it is compensated. Why should this be so?

This leads to the larger question: what is the best way to measure contribution? Hours worked do not tell the full story. An hour from one person may not equal an hour from another. Social approval is equally flawed — charisma can win followers more easily than quiet, life-saving work. Money, however, offers a clearer metric. No one parts with it easily. If someone pays, it is because they received something they valued more than the money itself.

A better use of your time & money

Even saving and investing money carries this logic. Investment directs capital into systems that are proven to deliver value at scale. A thousand dollars placed into Apple does not just sit in an account; it flows into designing devices, supporting research, creating jobs, and building tools that people demonstrably want. In this way, investment itself can be a far more powerful act of service than an isolated hour of volunteerism.

This perspective does not diminish the human importance of kindness or selfless acts. Rather, it challenges us to see service through a wider lens — one that accounts for unmeasured externalities, aligns incentives, and recognizes that the most effective contribution often comes from leaning into our greatest skills and opportunities.

God gave us minds capable of building systems that improve the world. The task before us is not to reject money or specialization, but to understand them, align them with real human needs, and use them wisely. If we succeed in this, we may find that true service is not in choosing between “noble” and “profitable,” but in seeing how the two can become one and the same.

There’s a viral app that’s been making a lot of waves lately.

It’s called Cal AI. It is basically like MyFitnessPal except powered by an AI Camera. You use it by taking a picture of whatever you’re eating and it’ll use an AI model to infer what’s in the picture, estimate quantity, ingredients, and arrive at a total calorie estimate.

The idea is that it makes it really easy to calorie count and help you on your weight loss/ gain process.

They’ve been claiming around $3.6 Million / month in revenue, and I do believe them. I like to think people have no reason to lie usually. It’s not always true, but I’d simply rather have that belief than the opposite one. So let’s keep the working assumption that they indeed make $3.6M/month in revenue.

That’s impressive! They’re also a bunch of young 20 somethings, a few of them are just freshman, and one of the four founders is actually at the University of Miami, my alma matter!

What’s all the talk about?

But here’s the thing— yesterday, they announced that they will be launching a course teaching other people how to build similar apps as they have. Ostensibly I have no issue with this personally. I believe everything in the world comes at a cost, and so you either pay with money or you pay with time. You simply must decide for every “purchase” which currency you’d like to use. I prefer to spend money on things that I don’t want to spend time on— I think everyone does. I also see courses as a similar situation: I prefer to spend $1,000 on a course that will teach me exactly how 4 young guys in their 20s built a set of apps that gets them $3.6M/month. To me, I could probably figure that out on my own too but it’ll probably take me a decent few months of trial and error. And sure, I’ve learned a lot of things through trial and error but one of those things was also that the only thing that matters most is getting the job done, and not all time expenditures are worth the same as others. So personally, I’d gladly cough up $1k for their course, rapidly implement and I’d probably see a return on my investment pretty quickly. If I really wanted I’d calculate the Internal Rate of Return (IRR) of the venture and determine if it beats the returns I get from JANUS or other ventures, or even the medical path.

But I’m not going to do any of that because I am entering a season of “no”. I want to say “no” to a lot more things than I say “yes” to. I think I’ve said yes to so many things over the past years that I have developed a very, very unique skillset. I feel it is about time I focus intensely on momentum, and every “no” is an active decision to preserve momentum. Every “yes” is an expenditure of said momentum. So, that being said, I will not be purchasing this course, because app development is not a priority for me right now.

If it is for you, feel free to check it out: https://appmafia.com/

First, let’s look at the numbers

Now, that all being said, what I also wanted to do in this newsletter piece is address a very common misconception I see in many people, and I see us business folks take advantage of this far too often. The honest ones will usually point out the difference but the one’s that benefit from optics will usually not, and you’ll be left with a wrapped sense of reality and you’ll make poor decisions. I want to prevent you from making poor decisions as best as I can determine them to be poor.

• So, App Mafia, the team behind Cal AI claims to make $3.6M/month revenue. As mentioned, let’s assume this is true. Regardless, since it is all App Store driven sales, Apple takes a cut of 30% on every transaction.

• That leaves them with $2.52M/month.

• Now, they probably spend around 80% of that to get the sales and attention in the first place. This is almost always a combination of Meta Ads, UGC, paid acquisition.

• That leaves them with: $0.5M/month.

• Of this, there’s around a 40% federal tax rate on them.

• So that leaves them with $0.3M/month.

• Now since it’s a team of 4 young guys, that net’s them each $75,000/month.

That is a great amount! It’s certainly more money than most people will know what to do with, and at that young of an age, they’re doing well for themselves. If that’s what you’re thinking right now, I’m not here to argue with you— I don’t even disagree!

Where the circles I’m in are taking interest in this story however is that it is interesting to see how quickly $3.4 MILLION gets diluted down to $75k.

That’s a 2% net margin on their viral business.

It’s worse than the average 6-10% net margins of a restaurant. It’s way worse than the 50-75% net margins I have running JANUS. It’s even more worse than the 90% net margins of selling courses.

What does this tell us about their incentives?

And this is my final point. This is why, allegedly someone who is making $3.6/month will take time out of their business hours to instead start recording content for a course business. The only reason that makes business sense for them is because they finally looked at their numbers and realized that their business, is in fact, quite sh**.

They work far more than most, for far less margins. It’s also a business they can never sell because there is absolutely no inherent MOAT, so there isn’t really an exit path either. So, of course these guys figured it’s time to cash out by selling a course.

That’s not to say it’s a bad thing at all— they clearly know what they are doing and its not like their results aren’t real. It is however important that folks understand THEIR incentive and reason for this. The business model they want to sell you on does make money, it made them $75k/month for around a year, but they themselves eventually found something else to be worth more, and they are exiting it by teaching you the model.

Lot’s of cases like these around. This is also probably why course gurus get attacked for being scammy.

I like to never lay judgement on people. There is no point in judging other’s actions since given enough crossover between their life and yours, you’d probably make the same decisions.

I, instead, prefer to assess motivations, strategies, and intent.

I hope this read was somewhat educational for you, and helps you get a better snap judgement of the numbers you see people plaster online.

Have a good upcoming week.

Cheers,

Ali

The real difference lies in how they go about achieving these outcomes—and more importantly, in what incumbents are willing to neglect.

One of the most common blind spots is personalized attention to existing clients. Established businesses often assume their size, track record, and brand are enough to keep clients around. That assumption is exactly what allows newer startups to wedge their way into the market and begin siphoning away customers, often without the incumbent realizing what’s happening until it’s too late.

This isn’t speculation. It’s a playbook. At startup incubators like Y Combinator, founders are explicitly trained to find these wedges and exploit them. They’re taught to identify where big companies grow complacent, to attack those weak points with laser precision, and to build disruptive momentum from there.

If incumbents want to survive, they need to pay attention.

Why Community is the Ultimate Lock-In

One of the simplest and most cost-effective defenses against disruption is cultivating community. Building a community around your product or service creates some of the strongest lock-in imaginable—and compared to other strategic investments, it costs very little.

Consider Apple.

Every year, Apple hosts WWDC, its Worldwide Developers Conference. On the surface, it’s a technical event for developers. But look closer, and you’ll see it’s one of the most powerful community-building tools in business.

Developers walk away not only with knowledge, but with a renewed sense of loyalty. They feel valued, seen, and inspired. Apple, in turn, gets mindshare—every single year, for over two decades. The result? It becomes almost unthinkable for a developer to suddenly prioritize Android, even when Android is innovating faster in certain areas.

This is the key point: innovation alone doesn’t guarantee loyalty. Community does.

Lessons from Healthcare’s Quiet Giant

I saw this firsthand when I worked at Epic, the largest B2B healthtech company in the world.

Epic’s software runs 100% of the top 20 hospitals in America and over 80% of all hospitals nationwide.

Inside their campus bathrooms, plastered across the walls, is a simple mantra: “Perception = Reality.”

Epic lives by this principle.

Sure they provide software; but really their secret is in how they cultivate relationships. Their annual User Group Meeting (UGM) isn’t just a conference—it feels more and more like a festival each year (with costumes and amusements all part and parcel of the show!) Epic invests heavily in making it a can’t-miss event, even once flying out client executives from a financially distressed hospital to ensure their attendance.

Why? Because they also understand what Apple also understands: longitudinal relationships are everything.

Bringing clients together year after year creates a sense of belonging, loyalty, and shared identity. And when your business is interwoven with a client’s community, you’re no longer just a vendor—you’re indispensable.

The Wake-Up Call for Incumbents

The message for incumbents is clear: disruption doesn’t come out of nowhere. It starts in the gaps you leave behind. Personalized attention and community-building aren’t just nice-to-haves—they’re existential defenses against competitors eager to pry open cracks in your armor.

Startups are trained to look for wedges. I can tell you this because I am a 25 year old medical student who works with startups on a weekly basis for the past 2 years and will inevitably build one myself soon too. I am telling you point-blank, unless you figure this out, your business is probably one of the boomer businesses most likely to be disrupted by the folks I meet on a regular basis. We’ve all seen how quickly some startups can topple incumbents— I implore you to look for the pattern, but if you want to save yourself the time, just take my word for it.

If you’re an incumbent, your job is to close these wedges before anyone else gets the chance to Trojan Horse your market. Build communities. Show up consistently. Make your clients feel like insiders, not just account numbers.

In the end, perception really is reality. And if your clients perceive that you’ve stopped caring, the reality will be that they’re already halfway out the door.

It seems arbitrary. A thousand dollars isn’t a life-changing sum, especially if you’re used to earning a stable income. But psychologically, the shift is profound. Once you cross that threshold, your entire perception of value, time, and opportunity transforms forever.

I’ve experienced this shift firsthand—and I've since watched others experience it repeatedly. Here’s why it happens, why it matters far more than you might expect, and exactly how you can create that shift yourself.

Mental Accounting: Why Independent Money Feels Different

To understand why your first independent earnings feel disproportionately meaningful, you first need to understand something behavioral economists call mental accounting.

Mental accounting means that people mentally categorize money differently, depending on how it's earned and spent. Even though every dollar is technically the same, money doesn’t actually feel the same psychologically.

• A paycheck feels stable, predictable, and constrained.

• Independent income—especially your first significant earnings—feels powerful, surprising, and liberating.

Your first independently earned $1,000 doesn’t just feel like ordinary money. It feels like a fundamentally new category. Because it didn't come from someone else's approval, someone else's decision, or a system outside your control, it feels uniquely yours—directly connected to your personal choices, skills, and decisions.

This new reference point permanently resets how you think about value.

Reference Points: How Your Baseline Shifts Permanently

Earning $1,000 independently does something subtle but powerful to your thinking: it creates a new reference point.

A reference point is simply your baseline measure of value. If you've only ever earned money at $15 per hour, your mental reference point for value is hourly wages. Every purchase is subconsciously measured in how many hours of work it costs.

When I first started earning independent income by selling marketing videos at around $2,000 each, my reference point shifted from "hours worked" to "videos sold."

This seems minor, but it changed everything:

Suddenly, everything I considered buying wasn’t measured in hours or dollars—but in terms of the new reference point I'd created. For example, consider food. Before, I carefully budgeted and worried about grocery prices. But after selling a few videos, I realized something slightly absurd but fundamentally important:

"I could literally eat Chipotle twice a day, every single day, and it would only cost about $1,000 a month. One half-hour sales call could land me a $2,000 project—covering two months of meals. So why was I spending hours going to the grocery store, picking ingredients, cooking meals, and cleaning dishes?"

This wasn't actually about Chipotle. It was about the radical shift in my perception of value. Cooking and cleaning stopped looking like sensible frugality and started looking like wildly inefficient uses of time.

This kind of shift—funny and trivial though it may sound—symbolizes a far deeper change. Once your reference point resets, your entire relationship to time, effort, and opportunity cost shifts dramatically.

The New Reality Problem: Why You Can’t Go Back

Here’s the critical insight most people miss:

Once your reference point changes, you can’t comfortably go back. Your desires, realities, and possibilities all shift irreversibly. What once felt rational or responsible now seems painfully inefficient, even irrational.

This sounds scary, but it’s powerful—because this new discomfort motivates fundamentally different decisions and actions. Things you never considered possible suddenly feel accessible. Things that previously seemed prudent now feel unnecessarily restrictive.

You start seeing your surroundings in terms of opportunity cost rather than absolute cost. Your choices become driven by leverage—by how efficiently you can achieve meaningful outcomes, rather than how cheaply you can survive day-to-day.

That’s why the first independent $1,000 changes everything: It resets your mental baseline in a way that permanently alters your behavior.

How to Actually Earn Your First Independent $1,000

Given how powerful this shift is, the obvious next question is: how do you actually achieve it?

Here’s the method I used personally—and recommend to anyone else looking for that critical first $1,000:

1. Identify One Clear Skill

You don’t need ten skills—you just need one skill that people clearly value.
For me, this was initially making simple but effective marketing videos.

2. Package It Cleanly

Make your offer simple, clear, and well-defined.
“I'll create a professional marketing video explaining your software product for $2,000.” Clear, simple, and precise.

3. Sell Manually First (Don’t Overcomplicate)

Forget websites, funnels, or branding initially. Just sell manually: reach out to your network, send direct messages, find one interested person, and make your offer clearly.
Keep the first sale as simple as possible.

4. Anchor High, Deliver Value

Most people undercharge initially. Resist this.
Anchor your price high enough that you can hit your $1,000 goal in one or two sales—not twenty.
Deliver clearly on your promise. Your first sale doesn’t need to scale. It just needs to exist.

5. Experience the Shift Fully

Once you earn that first independent $1,000, explicitly acknowledge the shift in your thinking. Notice how your reference points immediately reset. Feel how your perception of value, time, and effort has changed.

From that moment onward, everything looks different.

Why This Matters More Than You Think

The first independent $1,000 matters disproportionately—not because it’s a large amount of money—but because it permanently resets how you see money itself.

The shift from dependent money (salary) to independent money (directly earned through your own agency) changes your fundamental relationship with value. Your baseline recalibrates, your reference points shift, and new desires—previously invisible or unrealistic—become obviously achievable.

I experienced this firsthand, and I’ve never looked at value, money, or even simple choices (like cooking vs. buying Chipotle) the same way again.

The practical takeaway is simple:

If you haven't earned your first independent $1,000 yet, prioritize it now—not because of the money itself, but because of how it will change your thinking forever.

And once your thinking changes, your entire reality changes with it.

— Ali

I figured I’d keep newsletter subscribers in the loop, since I understand that it can be hard to keep track of what I am getting done across all my various channels, from LinkedIn to Twitter, to Instagram, to even GitHub if anybody ever checks that.

So here it is, August Edition:

JANUS

Going through a rebrand. I am transitioning from deliverables to outcome oriented sales. What this means is that until now many of my clients treated JANUS as a vendor for videos. I do not like that. You cannot grow a business to internet power-law scales being a widget-merchant. So, I’m re-orienting us towards more outcome-oriented-sales. This means we will diagnose a problem in our prospect’s business, and we will effectively guarantee to solve that fundamental problem. We will work with the prospect to determine how much that problem is costing them right now. Once we have a dollar amount on the prospect’s inaction (how much not doing something is costing them) as well as a dollar amount for taking action (how much they can save or generate by fixing the problem), we will get paid by a percentage of that outsized return that we provide them.

So, for example, recently I had a prospective client who has a health-tech software business, that let’s patients directly text the medical practice to do everything from scheduling to answers from the doctor. No clunky interface, no nonsense, just, easy, simple texting. At the early stages, I’ve noticed a lot of business owners are price-minded, which is not the right way to think about it. It’s a very consumerist perspective to look at the price first. As a business, the price means very little relative to the more important metric: expected return.

So for this prospective client, their signature product is a $99/month subscription. If we estimate my prospective client’s clients stay on for around 12 months, thats an expected lifetime value (LTV) of $1,188. That means one singular client is worth $1,188 to my prospective client. So from there capturing the value upside becomes relatively trivial. If we can realistically expect that the work JANUS will do for this business will generate them at least ONE client that they wouldn’t have otherwise signed, then JANUS will have generated them $1,188 at a minimum as soon as we get them one client (even if the client is on a monthly plan, we use LTV to project future earnings.) With this the math simple comes out to: how many users can I expect JANUS to get for this business? I expect around 5. Why? Because we’ve gotten past clients way more. So that means I expect JANUS to generate this person $5,940 in LTV. Of that, we will charge 5%. Which comes out to a pretty low $297. Realistically speaking, that’s not worth my time. So, I probably wouldn’t accept this prospective client’s request for work unless I think there is some other intangible upside like brand growth, network growth, case-study potential.

That's pretty much it for updates on JANUS and how I’m thinking about pricing right now.

Let me know how you like the new website (though its not fully done): https://www.janusny.com/

BRASK

Brask Group is something I’m fairly excited about but I talk about with restraint because its a longer term play. The core problem I wanted to address with Brask is that in my work building JANUS and growing my own network, I constantly came across amazingly talented people in different areas, who would benefit significantly from knowing each other. I always try to make connections between them and do warm introductions to get things going, but I wanted to systematize, and accelerate this. That’s the idea behind Brask. It’s a curated community of talented, bright minds, collectively committed to thinking big, and doing things that will change the world. Most of us will probably fail, but we won’t care, because we’ll just keep going at it until we win. I’ve come to learn, for entrepreneurs, its somewhat easier to accept this reality of our personality than to try and deny and hide it. We are relentless psychopaths. Brask is a group chat for those kind of people, inspired by Benjamin Franklin’s Junto Club.

You can join for free right now: https://www.braskgroup.com/

Sitr

Oh boy, Sitr.

I made the first version of this literally in a couple weekends, sitting in a Starbucks in Farmington, Connecticut. I made it because I noticed that the folks trying to put on our 1st year medical school formal were using a really clunky bonze-age process of Google Forms, messy spreadsheets, and paper. I figured it had to be a mess, and I confirmed this when I spoke with them. So, I made Sitr quickly as a way to address that immediate local need. In all honesty, I was *extremely* anxious about everything leading up to its official “launch”. I had never built something that was actually used by many people at once. I barely knew what I was doing, I had no clue if the random Heroku server I deployed the project on would be able to sustain the load, I overpaid for the server out of pocket because I just wanted this to work and didn’t want to take any risks and cause a headache for the event organizers I was trying to help. Shoutout to them for being so nice about it all, and also, thankfully, it all worked with no issues at all.

It was an absolutely surreal feeling to walk into class the following day and see my web application open on everybody’s screen as they clicked around to find out what seats and tables their friends had selected for the upcoming formal.

Today, around 5 months later, I built in all the things I didn’t have the time to do back then. I built a proper dashboard, a cohesive design style, a functioning log-in/log-out system, the ability for the user to create infinite events and edit them as need arises. It has gone from being “okay dude text me what you guys need and I’ll edit the code on the backend to make that happen” to complete DIY, “you want to remove that attendee? cool, just click the delete button on their name!” This was much harder than it may seem, as I’ve recently learned most people have absolutely no idea how complicated websites are behind-the-scenes. People have used things like Wix, and Squarespace and think that that is how these things are made. They have no idea that quite literally every micro-interaction from the way a button’s color changes, to where on the page text shows up all needs to be manually coded into a website.

I digress. The point is, Sitr is pretty damn cool now, and its fully-functional, and it solves an immediate very specific problem for event planners: seating charts.

You can sign up for it here before I put up a Stripe paywall and start charging for it: https://usesitr.com

Med Atlas

Not a lot about this right now but basically it’s going to be a one-place for all the needs of anybody who is in healthcare as a physician or physician-to-be. It’ll have tools and resources to help premeds, med students, residents, and even physicians. It has its own private community you can join to chat with any of these people as well, and collaborate on anything you might want. It has crowdsourced reviews of various medical schools, residency programs, and yes, even jobs at hospitals and private practices as an attending. It has tools to help premeds build their class schedule, guides for med students to find research, negotiating tips for residents and more.

One-stop-shop for all physician or physician-to-be needs + community.

All of this for a one-time payment of $29 until we hit 100 members. Then the price will go up to $99. It’ll always be a one-time-payment, that is: no monthly subscription. Pay once, join the community, benefit forever.

Right now it’s not quite ready, but you can get a sense of it as I build it out at: https://medatlas-omega.vercel.app/

Okay, that’s it for now.

This was quite fun to write, and quite nice because it gave me, myself, a cohesive overview of the different moving parts right now as well. The other thing I didn’t mention is that of course, from now until March I am focused also on dedicating 6hrs per day towards studying for the USMLE Step 1 exam. I am in medical school training to be a physician because I deeply believe we need more physicians who understand systems, technology, and business; these are the forces that shape our patient’s lives before they get to the clinic. My hypothesis is fairly simple: the best doctor is he who understands his patient the most. That’s it. Everything I do is geared towards comprehensively understanding my patients. It just so happens that I firmly believe that learning by doing is the only real learning.

Let me know if you have any ideas or thoughts on anything I’ve mentioned here. I genuinely welcome any and all communication that comes my way. I would love to hear questions, criticism, and all.