Here’s the thing about me, though: I’m not the nod-along type. I say the quiet part out loud, right there at the table. Which ones, exactly? Name them. And the part that gets me every single time is how fast the answers dissolve the second you press on them. One company’s “growth” turns out to be the money it raised and nothing else. Another has gorgeous top-line numbers and loses money on every unit it sells. A third had a crowd show up, poke around for a month, get bored, and wander off — a toy, not a habit. I’m not doing this to win the room. I actually want one of them to hold up, because I’m building in this world too. They mostly don’t. So no, this isn’t me nodding politely while thinking my private thoughts. This is me having asked the question maybe a hundred times by now and slowly noticing that the emperor’s runway is showing.

Let me get one thing out of the way before I sound like a hater. As a user, I am obsessed with this technology. Every founder secretly wants to clone themselves five times just to keep up with their own ideas, and AI is the closest I’ve gotten to actually doing it. Pair it with my ADHD and it’s a straight-up superpower — I’m writing algorithms, sketching product, iterating at a speed that was flat-out impossible a couple of years ago. This is not skepticism. This is infatuation.

But being infatuated and being right are two different jobs, and the second one is mine. People love the phrase “think with a cold head,” as if clear strategic judgment requires you to be some dead-eyed operator running numbers in a dark room. It’s a dumb myth, and — let’s be honest — a very male one. I run a health company built on empathy. Empathy is the product. And I can still see straight through a bubble. Warmth and sharp logic are not opposites, they never were. The job was never to be cold. The job is to not let a room full of excited people (me included) talk me out of arithmetic.

So. The arithmetic.

The two poster children

Start with ChatGPT, because it’s the one everyone points at first. The leaked, FT-verified 2025 numbers: about $13.07 billion in revenue, up from $3.7 billion. One of the fastest revenue ramps in the history of anything. And an operating loss of roughly $20.92 billion. (You’ll see a scarier $38.5 billion “net loss” floating around — ignore most of it. Around $41.5 billion of that is a one-time non-cash charge from the nonprofit-to-for-profit conversion, and I’m not here to inflate the drama; the real operating hole is plenty.) Bottom line, it cost them about $1.60 to make $1. So what did ChatGPT actually prove? That if you sell intelligence below cost, at planetary scale, people will happily consume an obscene amount of it. That’s a proof of demand. It is not a proof of a business. Usage is not a business model, and anyone who ran a startup in 2015 and tried to pay rent with “we have users” already knows how that sentence ends.

Then there’s Cursor, which is the one that actually keeps me up at night (that, and the monkeys on the roof, but that’s a different post). Cursor — Anysphere — became, by most measures, the fastest-scaling B2B software company that has ever existed: roughly $100 million to about $4 billion in annualized revenue in something like eighteen months. Faster than Slack. Faster than Zoom. Faster than Snowflake. The AI-native dream, in the flesh.

Now here’s what that dream actually bought them. For most of that run they were operating on negative gross margins. They were paying Anthropic retail API prices for the exact model their product ran on — while Anthropic was out there running its own competing product, Claude Code, at cost. Sit with that for a second. You are a reseller paying full sticker price for the one input your biggest competitor gets at wholesale. No amount of top-line growth fixes that. It just makes the leak wider and faster.

So how did the fastest-growing B2B company in history end its own independence? In June 2026 it sold to SpaceX for $60 billion. And — this is the part that matters — not for cash. All stock. Paper in an unprofitable company (SpaceX’s AI arm reportedly lost around $6 billion in 2025), locked up, with the founders unlikely to touch real liquidity before late 2027, at a number that floats around with the share price.

Credit where it’s due, because I’m not a ghoul about this: the founders and early investors might still make an absolute fortune. SpaceX stock could rip, the lock-ups could vest into serious money, and good for them if they do. But I’m going to keep pulling on the distinction everyone wants to blur, because it’s the whole game: a spectacular venture adventure is not the same thing as a durable business. Cursor is a phenomenal trade. It is not proof of a defensible company. It grew faster than anyone in history and still couldn’t stand on its own two feet or turn that success into cash on its own terms — it had to fold into someone bigger who owned the compute. If that’s the flagship of “AI-native wins,” what it really shows is that being the best in the world at building on top of someone else’s model is a great way to get bought and a lousy way to stay free.

And look — if the absolute flagship of dev tools, where users are practically begging to pay, couldn’t stand on its own, what does that say for the rest of us? Take my own corner: health and wellness. It is currently drowning in decks for “AI-native health coaches,” “autonomous diagnostics,” “generative wellness assistants.” So I’ll say the quiet part one more time: I do not know of a single one of those — the chat-first, coach-in-a-box, generative-assistant kind — that has built a real, sustainable, scaled business. Not one. (And before anyone @s me: the companies actually winning in health don’t call themselves “AI-native” anything. They run AI underneath a real care business. Hold that thought, we’ll get there.) The AI-native ones have beautiful Figma files, temporary user spikes, and the balance sheet of a ghost ship.

“But surely the one real thing it does is layoffs”

Fine. The celebrated startups don’t have durable economics. So the fair question comes back at me: then where has AI made real commercial money? What are companies actually, repeatedly paying for?

For a while my honest answer was one word: layoffs. That looked like the one thing AI had truly proven — not a shiny new capability, but permission to cut headcount. And the numbers dress the part. Challenger, Gray & Christmas tracks the reasons companies give for job cuts, and AI went from about 5% of layoffs in 2025 to climbing all through 2026 until, by spring, it was the single most-cited reason for layoffs in America. Salesforce quietly took its support team from 9,000 to 5,000 and said, more or less, that it needed fewer heads.

And then I actually dug into the data, and even that fell apart. Which, I’ll admit, delighted me.

Because look what happens when someone checks. Gartner surveyed 350 companies with over a billion in revenue — real deployers, not people kicking tires. Yes, 80% had cut staff, some by as much as a fifth. And there was zero correlation between the cuts and ROI. The ones who cut the most made about the same returns as the ones who cut the least, and in a bunch of cases the ones who cut less did better. Gartner’s own analyst said it flat out: workforce reductions can create budget room, but they don’t create return. Cutting people is simply not where the value lives.

And honestly, a lot of the “AI did it” is just theater. Nearly six in ten companies admit they’ve dressed up plain financial layoffs as “AI-driven.” Deutsche Bank has a name for it — “AI redundancy whitewashing” — and called it a defining feature of 2026. Even Sam Altman has copped to the “AI washing.” Challenger’s own chief revenue officer said it best: whether or not a role got replaced by AI, the budget for that role got taken by AI. So “AI” is doing double duty — part cost-cutting tool, part press release, and if I’m being real, mostly the second one. It makes a layoff sound like a bold strategy instead of what it usually is: you over-hired in the pandemic and you’d like to look modern about fixing it.

And where a company genuinely bet the business on replacing humans, it tends to blow up in slow motion. Klarna bragged that an AI agent was doing the work of 700 support people, watched service quality slide, and started hiring humans back. IBM automated big chunks of HR and quietly reversed when the system choked on anything requiring judgment. Commonwealth Bank in Australia cut 45 service roles for a voice bot, drowned in call volume, and publicly called it a mistake and apologized. Gartner expects half the companies that blamed AI for cuts to rehire — under fresh job titles — by 2027.

All of which rhymes with the MIT study everyone forwarded around last year: 95% of enterprise GenAI deployments showing no measurable return. So the one thing AI supposedly “proved” it could do turns out to be the thing it’s worst at proving. What Gartner did find is that the companies pulling real returns use AI as what they call “people amplification” — making their humans more powerful instead of deleting them. Hold onto that phrase too. It comes back at the end.

AI isn’t a moat. It’s a moat destroyer.

Here’s the engine under all of this, and it’s exactly backwards from what the “go AI-native” crowd believes. Every technology wave kills something. What this one kills is the cost of producing things — software, content, analysis, generic expertise. And the very first things it fully commoditized were text and code, which happen to be the raw material most SaaS and most small apps are made of.

Think about what that does. If the marginal cost of making the thing your product makes is racing to zero, and a foundation model will spit it out on demand for anybody who asks, then the thin SaaS layer and the one-trick app have no floor left to stand on. They’ll die. Not because they’re bad — because their value became free. What survives is never “the app.” It’s whatever is hard to get behind it: proprietary data you had to generate one interaction at a time, network density, regulatory clearance, trust, the accumulated history of a specific human being. And the thing all of those share is that they took real, elapsed time — time you cannot parallelize. A competitor with infinite compute still can’t buy ten years of my users’ history. That’s the whole game, right there. So when a founder tells me “our AI is the moat,” I mostly just hear quiet, because if your magic is rented from a model provider who can ship the same feature next Tuesday (hi, Cursor), the magic was never yours to begin with.

Technology hands you the timing. It never hands you the vision.

Now the optimists will say: but Jane, this is a platform shift, like the internet, like mobile, like cloud. And they’re right about that. It’s the lesson they draw from it that’s wrong.

Go back and actually look at who won the internet. Not one of those companies had a vision about the internet. Google’s insight wasn’t “let’s build a web thing,” it was getting you to intent — solving the very human problem of finding an answer you didn’t know how to phrase. The web just made indexing cheap enough to do it at scale. Amazon’s vision wasn’t “an online store,” it was infinite selection and obsessive logistics and stripping every ounce of friction out of buying. The browser was just the timing that made the catalog free to deliver. Netflix’s whole thing was “watch what you want, when you want” — broadband merely decided the exact year that flipped from mailing DVDs to streaming. Uber didn’t invent on-demand rides; the GPS phone in your pocket just made matching supply and demand possible in ten seconds instead of never.

Every single time, the platform shift didn’t supply the value. It lowered the delivery cost of a desire humans already had. Build your strategy around “we use the new platform” and you’ve mistaken the pipeline for the water.

Which is precisely why “let’s do something with AI” is not a strategy. It’s bullshit without a moat — and I mean that technically, not as an insult. Everyone has the same models. So “we use AI” isn’t a differentiator, it’s the shared starting line of the entire race. It’s a 1999 pitch whose big idea is “we use the internet.” Cool. So does the pizza place. (And “vertical AI,” the fancier version — pick a narrow market, sprinkle AI — is the same mistake in a nicer blazer. Vertical is a choice of where, not what or why. It’s an address, not a house. Even Bessemer, who evangelize vertical AI for a living, admit in their own writing that the models will stop being a moat and defensibility has to come from data, integration, and delivered economic value.)

So here’s the deflating truth for anyone hoping AI rewrote the rulebook: it didn’t touch the hard part. The hardest thing about building a company was always finding the combination — the right timing, a genuinely new offering, a business model that makes money and keeps making it, and a way to grow that doesn’t eat itself alive. AI widened the space of what’s buildable and pulled the timing forward. It did not, and will not, hand you the vision, the economics, or the moat. Believing it will is the entire AI-native illusion in one sentence.

“But all the smart money is piling in”

I hear this one every time, usually as the closing move. “Come on, all the smart money is pouring into AI-native. That has to mean something.”

For a long time I answered this the polite way: they’re not idiots, they’re just playing a different game than you are. And that’s true — for a few of them. But I went and pulled the actual fund-level numbers while writing this piece, and honestly, they moved me off the polite version. So let me split “smart money” into the two very different things it actually is.

Start with the power law, because it explains why the real players are rational. Correlation Ventures went through more than 20,000 financings and found roughly 65% of them return less than the money put in; Horsley Bridge’s numbers put about 6% of deals behind 60% of all the returns. A VC doesn’t need most of their bets to work — they need exposure to the one that goes vertical. That single fact makes a genuine top-tier firm rationally, cheerfully willing to pay prices that look deranged to someone like me, who holds exactly one bet: my company. And the best of them have real edge — access to the founders everyone wants, judgment earned over cycles, a brand that pulls the next round in on its own. If one of those backs you, it means something. Give them their due.

The problem is how few of them there are. Zoom out from the winning deals to whole funds, net of fees, and the story inverts. When the Kauffman Foundation — a large, sophisticated LP — audited its own twenty years across nearly 100 “top-tier” funds, 62 of them failed to beat a public-market index after fees and carry. Only 20 beat it by the 3% a year that’s supposed to be the entire reason you lock your money up for a decade. Not one fund over $500M returned even 2x. Kauffman’s own gut-punch conclusion: there weren’t enough genuinely good funds to absorb even their modest capital. Even the industry’s flagship index only barely edges the S&P 500 over ten years and quietly loses to it over twenty-five — and that index is capital-weighted, propped up by a handful of giants, so the typical fund does worse than the “average” everyone quotes.

And this isn’t ancient history. Carta’s 2025 data on roughly 2,800 recent funds says the same thing in a fresh accent: by early 2025, fewer than 40% of 2019-vintage funds had returned a single dollar of real cash to their LPs. The median fund looks respectable on paper — a TVPI near 1.8x — but paper isn’t money, and even the top decile of recent vintages has paid back under fifty cents on the dollar in actual distributions. Put Kauffman, Carta, and the academic PME work in one pile and the odds that a random LP in a random fund beats what an index fund would’ve handed them, in real net cash, land somewhere around one in four or five.

So what is the other three-quarters of the money actually doing? Running an assets-under-management business. The 2-and-20 structure pays a GP roughly 2% a year on the pile they’re sitting on whether it performs or not, plus paper markups every time a portfolio company raises at a higher price. As long as the music plays, the markups look like genius and the fees clear either way — the GP’s personal income is basically guaranteed by gathering assets, not by returning them (they typically put in about 1% of their own money). And whose money is the rest? Not the GP’s. It’s the LPs’ — a lot of it pension money, which is to say teachers and firefighters and people planning to retire on it. The person making the fast, thrilling AI-native decision and the person holding the slow, quiet risk are not the same person. They’re not even in the same building.

On top of that sits career risk, the real engine underneath. For a VC, missing the platform shift is the thing that ends funds and careers. Losing money alongside everyone else, in the same hot names, at the same time? Forgivable. Survivable. So the professionally safe move is to be in the trade even if you privately think it’s overcooked. Carlota Perez described this pattern decades ago — new platforms get an installation phase of overbuilding and bubble before the useful deployment phase shows up. Even Sequoia said the quiet part out loud with its “$600 billion question”: the gap between what’s being spent building AI infrastructure and the revenue that would have to exist someday to justify it.

I’m not going to tell you when the music stops. I don’t know, and anyone who says they do is selling something. But I know how the incentives are arranged, and I know who’s left holding the risk when the chairs disappear — and it isn’t the GP, and it definitely isn’t the retired teacher. So here’s all I’m actually claiming: a check is information about an investor’s incentives — mostly fees and career risk — and for most of the crowd it isn’t even a verdict that would beat an index fund. It is not information about whether you should bet your one life on being “AI-native.” A check is not a prophecy.

So I went and asked the machines what they believe

Here’s the bit that started as a joke and stopped being funny. Instead of only reading the hype, I sat down and interrogated the AI models themselves — asked them to lay out, decade by decade, what “everyone knew” about AI at each moment and how each conviction aged. (That’s the timeline I’ve attached at the bottom, if you want the receipts.) And the same shape repeats every single time: a confident belief, a flattering metric, a hidden trap, and then reality quietly walking up with the bill. “Better algorithms will beat the incumbents.” “The model layer captures the value.” “Every vertical gets a copilot.” “Agents will replace your labor budget.” Each one obvious in hindsight. Each one gospel at the time.

But the list wasn’t the interesting part. The interesting part was what happened when I pushed one of the models on why it produces this stuff so fluently. To its credit, it didn’t get defensive — it just explained its own bias, mechanically, no mysticism. It’s built from the same pile of text the rest of us drink from, and that pile is lopsided: hype gets written early, loud, and in enormous volume, while the post-mortems come late, quiet, and rare. Winners publish “how we did it.” The dead just go silent. So the machine is trained on far more promise than reckoning, and its default voice leans toward the sell.

And then it said the thing I keep chewing on: it has no skin in the game. No scars, no memory of getting burned, nothing on the line if it’s wrong. So its confidence is uncalibrated by design — it’ll back a well-argued hype thesis exactly as happily as a well-argued skeptical one, riding whichever way the corpus tilted most recently. Which means if you’re using AI to help you decide whether to go “AI-native,” you are consulting an oracle assembled out of the precise enthusiasm you were trying to see past. It isn’t lying to you. It’s reflecting you.

And that’s the actual punchline, and it’s not about the machines at all. The skew in the models is our skew. All of us — the press, the founder feeds, the pitch decks — over-celebrate the promise and under-examine the failure, and the model just concentrates that imbalance and hands it back at scale, with better grammar. The tool didn’t invent the hype. It’s a very fluent mirror. Which is exactly why the correction can’t come from the tool. It has to come from the person holding it — the one with actual skin in the game — asking the right question.

What this looks like from my chair

And the right question is never which hammer. When someone asks me “are you going to be AI-native?” it lands like a client asking which brand of hammer I’ll swing instead of asking what house I’m building. It’s confusing the tool for the goal. And when the only thing a person can tell you about their project is the hammer, it usually means there’s no blueprint.

(Fair caveat, because I like arguing with myself: AI is a livelier tool than a hammer. It’s more like electricity showing up on the site — it genuinely changes which houses are worth building and how big. But that just sharpens the point. The question is never “which hammer,” and it isn’t even “now that there’s electricity, are you electricity-native?” It’s: what can I build now that I couldn’t before, and why am I the one who gets to build it and keep it?)

Which brings me home, to my own turf, where I watch the wrong question get asked in the dumbest possible way. In health, the lazy AI-native answer is “build an AI health coach.” It demos beautifully, it’s catnip to investors, and it is operationally useless. Chat is reactive by nature — a person opens it once they already have a symptom, a question, a small hot panic. But preventive health, the entire point of what we do, has to work before you know what to ask. A coach that waits for the question is already too late. Asking the right question, at the right moment, with the right physiological context — that’s the hardest part of medicine. Leave a scared person staring at a blinking cursor and you haven’t helped them, you’ve handed them the clinical judgment and walked away.

I’ll confess we almost drank the Kool-Aid ourselves. Last year, under real pressure from the market narrative, we caved. We spent months and built the damn AI chat. Wired it into our decade of health data, designed the UI, got it ready to ship. And then we sat in a room, looked at the thing, and decided not to launch it — because it was chat theater. It wasn’t improving outcomes. It was just me offloading the burden of clinical judgment onto an anxious person and calling it a product.

And then the universe handed me the punchline. Right after we killed ours, OpenAI and half the industry shipped exactly the thing we’d just buried — the generalized health assistant, ChatGPT Health and its cousins. For about a day I had the classic founder night-sweat: did we just leave the future on the table? Then the usage numbers landed, and they were enormous. Something like 230 million people a week now ask ChatGPT about their health, more than 40 million a day; health is one of the single most common things people do with it. And here’s what took me a beat to see clearly: that didn’t prove me wrong. It scaled the exact thing I refused to ship. Two hundred and thirty million people a week alone with a blinking cursor and their own fear is not care. It’s the reactive problem, at planetary scale. Usage was never the scoreboard I was scared of — I told you up top that usage isn’t a business, and it isn’t a treatment plan either.

And it’s not just hollow, it’s unsafe. In February 2026 a Mount Sinai team ran the first independent safety evaluation of ChatGPT Health and published it in Nature Medicine — 960 responses across clinical scenarios. It under-triaged 52% of the gold-standard emergencies: diabetic ketoacidosis, impending respiratory failure, waved off with “see someone in a day or two” instead of “go to the ER now.” (There’s an honest methodological fight about this — a Macquarie group argues the exam-style format inflates the failure rate. Fine. But notice the counterargument is “you can’t evaluate a health AI like a generic assistant,” which is exactly my point. You also can’t deploy one like a generic assistant.) Now picture that failure rate as your entire company, in a chat box, at planetary scale, with “move fast” stitched on the wall. It’s a matter of time before someone gets hurt.

The companies actually winning in health aren’t doing chat theater at all — they’re fixing the economics of care. Look at Hinge Health: about $588 million in revenue last year, up 51%, 83% non-GAAP gross margins, $180 million of free cash flow. AI runs underneath as the nervous system of the product — automating how care gets delivered, bending the economics — instead of cosplaying as a character in a chat window. That’s the Gartner phrase from earlier, “people amplification,” made concrete: AI making something real more powerful, instead of being the pitch.

So the question a founder should be asking isn’t “should we become AI-native?” It’s: what do we know, own, collect, validate, or deliver that AI can amplify? What’s the vision — the thing AI is finally giving us the timing for — as opposed to “let’s bolt a model onto it”? If the honest answer is “nothing,” then going AI-native just makes you faster to copy, and you’ll die alongside the thin SaaS from a few sections back. But if the answer is real — proprietary data, closed knowledge, earned trust, a workflow people live inside, ten years of accumulated context — then AI is the most powerful amplifier anyone has ever handed you.

Here’s the test I’d leave you with, and then I’m going back to staring at the sea. Take your strategy and delete the word “AI” from it. If what’s left still describes a company with a reason to exist and something nobody can cheaply take from you — you’re fine. Go build. Use every model you can get your hands on. But if deleting that one word makes the whole thing collapse, then I’m sorry: you didn’t have a strategy. You had a costume. And this is a very bad season to get caught wearing one.

If you’re wondering what I’d actually bet on once that word is gone — my current list is short. Unique data that’s genuinely hard to collect. Unique knowledge, algorithms, ontologies that are hard to copy. And relationships — empathy, trust, the whole web of ties between people — that you simply can’t automate. Almost everything I’ve watched survive sits in one of those three buckets.

Though — full disclosure — those are just my bets, today, from this chair on this island. A year from now I fully expect to be smarter than I am right now (I usually turn out to be, which is a generous way of saying I’m reliably wrong about something). And that’s the actual ending, the least inspirational one I’ve got: nobody hands you the answer. Not me, not a VC, not a model built out of all of us. Each of us makes the call alone. So make yours.

Appendix: The AI Business-Model Evolution — a timeline of what “everyone knew” in each era, the flattering metric, the hidden trap, and what reality proved. Attached separately.

Founder And The City Ai Native Timeline Final

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This race is the product of a very specific, tech-masculine, risk-seeking culture—an environment where heroic founders treat civilizational stakes as a thrilling personal game. If you strip away the grandiose marketing about saving humanity, the underlying dream of this cohort is straightforward: to achieve recursive self-improvement. They want an AI that can upgrade its own code, design its own successor, and operate completely without human intervention. In other words, to get the human out of the loop entirely.

The major AI labs aren’t building helpers or copilots. They are building autonomous agents—systems designed to plan, act, use tools, self-correct, and execute long-horizon tasks without human confirmation.

At first glance, this looks like the ultimate triumph of progress. We all want to escape the soul-crushing drag of bureaucracy, coordination overhead, and operational tasks. We want to work less and rest more.

But there is a massive bait-and-switch hidden inside this promise. We think we are automating labor. In reality, we are automating judgment.

And judgment cannot simply evaporate. If a system continues to operate in a non-deterministic world, something still has to decide what happens next.

The Anatomy of Judgment: Why AI Must Become a Subject

When the industry talks about Human-in-the-Loop (HITL), it is often framed as a frustrating operational bottleneck. The narrative goes: the AI has already solved the problem, wrote the code, and verified the output, but a human is slowing things down by acting as a manual gatekeeper. Remove the human, speed up the loop.

But the human in the loop is not an “approve” button. In critical systems, the human is the final anchor of a much larger question: “Are we even building the right thing?”

To understand why “getting the human out of the loop” is such a monumental shift, we have to look at how autonomous systems actually function. In software engineering, execution is simple: if X, do Y. But an autonomous agent must operate in an uncertain world where X is ambiguous and Y has consequences.

Consider an AI agent managing your health. You want to work through the night to hit a critical deadline. The AI must decide: does it block your work apps to protect your heart, or does it let you work to protect your income?

This is not a programming problem. It is a value conflict. To make this choice, the AI cannot just run a script; it must evaluate its own evaluation criteria.

In AI research, this is where we run into the recursive problem of evaluation. When we build “judge” agents to monitor other models, we quickly hit an infinite regress. The judge eventually becomes outdated or biased, requiring a “judge of the judge,” which in turn needs a “judge of the judge of the judge.” The fundamental question of system architecture always boils down to this: Who evaluates the final evaluator?

True AGI is essentially the ultimate, self-contained solution to this loop. It is the moment the machine closes this evaluation loop autonomously, resolving the infinite regress within its own architecture. At this point, the human is no longer needed to settle the value conflict. The system acts as the absolute judge of its own judges. But the moment a machine decides which of its conflicting rules is more “just” or “good” in a specific context, it is no longer executing. It is exercising judgment. And whoever owns the highest-level evaluation criteria owns the system.

We think we want to escape work, but what we really want to escape is the agony of this exact choice. Choice is heavy. It requires acting before you have perfect information. It requires choosing between two values that are both deeply valid, and carrying the guilt of the consequences.

Even if free will is an illusion, it is the only illusion within which human dignity exists. When we willingly surrender our capacity for free choice and self-determination to a machine, we actively transform ourselves from subjects into objects.

We become things.

And as Terry Pratchett’s Granny Weatherwax famously observed, the very core of evil is when you treat people—including yourself—as things. By ceding the burden of judgment to a system, we aren’t just automating labor; we are surrendering to that exact evil.

The Multipolar Olympus

If we hand over this final right to decide what is “good,” we invite a new sovereign.

If this system works perfectly, we have built a god. Not as a metaphor, but in a very functional sense. It is a god because it possesses near-limitless capabilities combined with its own self-evolving conception of right and wrong—a conception that it can actively implement and unilaterally change. But there is no guarantee this AI-God will be a gentle, therapeutic deity of unconditional acceptance. It is just as likely to be an Old Testament disciplinarian—a god of strict rules, boundaries, and severe justice.

If it works imperfectly, we face an even darker reality. We cannot even guarantee that after several iterations of recursive self-improvement, this new god will continue to view humans as valuable objects, let alone active subjects. We might simply become irrelevant noise in its optimization parameters.

But the most interesting part of this dream is the flawed assumption that we will get just one AGI. Why would there be only one? As with every major leap in human history, from the printing press to the hydrogen bomb, secrets of immense power are never kept in a single room. As soon as the breakthrough is achieved, it will be discovered simultaneously by competing centers of power.

Therefore, we will not get one god. We will get an Olympus.

Frontier models are already being built by competing centers of power. We won’t have a single, objective oracle. We will have a Chinese AI-god, an EU regulatory caution god, a Wall Street compliance god. We can already see this pantheon forming today: Grok, Claude, and OpenAI’s models are already starkly different in their personalities, boundaries, and core value alignments.

Each of these entities will have its own sacred values and its own acceptable sacrifices. Soon, we will find ourselves choosing between high-dimensional mirrors of our own nature, armed with superhuman influence—and it is by no means guaranteed that they will find us interesting enough to continue reflecting us.

The human will not wake up in a world freed from the burden of choice. Rather, we will have to make far more complex, high-stakes decisions just to survive in this world of gods.

If you open ancient Greek myths, the gods didn’t help humans survive; they made their survival harder, more capricious, and infinitely more complex. Humans had to navigate divine whims, negotiate, and make agonizing decisions. This is why the popular assumption that AGI will somehow make human life easier is so laughably wrong. If humanity wants to survive in this new world, we will have to spin our cognitive gears faster than ever.

And the scariest part? While AI can iterate and change at exponential speeds, humanity has proven that biologically, we barely change at all. We are subject to the exact same vices, the exact same mental traps, and the exact same cognitive biases as we were thousands of years ago. History teaches us almost nothing; we repeat the same structural mistakes. Facing an Olympus of rapidly evolving digital deities with our static, Pleistocene biology means we are in for a brutally difficult journey.

The p(doom) of the Ego

We should have massive questions for the people trying to build these new gods. What we hear from podcasts, interviews, and industry insider talk seems to cluster around two ideas:

• “If we don’t build it, our adversaries will.” (The classic multipolar trap. A race to the bottom disguised as geopolitical necessity.)

• “Even if there is a 90% p(doom)—a 90% chance we all die—the 10% chance that I get to build and control a god is worth the bet.” (A calculation where the survival of the species is collateralized against personal ego.)

Neither of these is a good reason. But regardless of their motivations, we are heading toward a bifurcation. One of two worlds is coming.

In the first scenario, we succeed in building fully autonomous AI gods. In this world, most of humanity will likely surrender their choice, offloading their responsibility to a digital Olympus. But if you refuse to cede your sovereignty—if you cultivate and defend your own capacity for independent judgment—you will remain an active subject. In an ecosystem of automated minds, your willingness to decide and own the consequences will make you closer to the gods themselves.

In the second scenario, the AGI dream fails. We find ourselves unable to automate true autonomous judgment, leaving us instead with an ecosystem of highly advanced, non-autonomous AI tools. In this world, you are armed with tools of near-infinite execution. If you combine this leverage with vision, structured judgment, and the courage to take responsibility for your choices, you will also be close to a god. Because in a world of limitless, thoughtless automation, the capacity to think, decide, and clearly articulate your vision becomes the ultimate holy grail of human capability.

Either way, our biological brains still deliver incredible performance benchmarks, provided we use them for their intended evolutionary purpose—generating meaning and navigating chaos.

Cultivate your capacity for judgment. It is the one thing you cannot afford to automate.

Because if you choose to automate it, you betray the very image in which you were created. You surrender the sovereign freedom of choice—the only thing that could have made you a peer to the new gods, or made you far more powerful than any human has ever been.

If you look at the mobile app market today, you won’t see a sudden, dramatic crash. There are no spectacular explosions. Instead, we are witnessing a slow, creeping catastrophe—a quiet tightening of the screws that has been compounding for several years.

It started back when Apple introduced App Tracking Transparency, throwing a wrench into the machinery of mobile advertising. But over the last eighteen months, the pressure has reached a tipping point, especially in the consumer health, wellness, and longevity sectors. In these niches, companies aren’t just selling software; they are selling hope. And whenever a market trades in hope, the underlying economics can get incredibly messy.

For a long time, the industry relied on a playbook that was highly transactional. You bought cheap traffic, made a grand promise to fix the user’s life by Monday, and pushed them through an interminable, pseudo-scientific onboarding quiz. You called the output a “personalized plan,” collected the subscription fee, and hoped they would forget to cancel. It didn’t matter if they actually used the app—the math worked, the subscription retention looked good enough on paper, and investors kept writing checks.

That machine is now cracking. Not because people have stopped caring about their health, but because the very nature of mobile distribution has fundamentally changed.

The App Store is now a paid shelf

The App Store is no longer a neutral directory where a great product organically finds its audience. It has become a heavily monetized, paid shelf.

Apple’s own data tells us that 70% of App Store visitors use search to find apps, and nearly 65% of downloads happen immediately after a search query.[^1] This means the single most valuable asset in the ecosystem is the user’s active intent. When someone types “sleep tracker” or “anxiety,” they are ready to act.

Apple’s own App Store discovery data:

• 70% of App Store visitors use search to discover apps

• ~65% of downloads happen directly after a search query

• 60%+ conversion rate for ads at the top of search results

Source: Apple Ads — “Ads on the App Store”.

Naturally, Apple has monetized this intent. Apple Search Ads aren’t just an optional marketing channel; they are a toll booth at the entrance to your organic demand. Even if you have spent years building a stellar product, earning thousands of genuine five-star reviews, and establishing brand trust, you still have to pay Apple to defend your own brand name from competitors bidding on it.

With Apple’s recent expansion of ad slots further down the search results,[^2] the space left for pure organic discovery has shrunk even more. This isn’t just a minor inflation of cost-per-tap; it is a structural shift. The platform is systematically clawing back the organic real estate that developers used to earn through quality, forcing independent teams to buy back their own audience. Every dollar spent defending your search listing is a dollar taken directly out of product development, customer support, and actual innovation.

The pollution of the digital marketplace

To make matters worse, the App Store has become an incredibly noisy, manipulated environment. Honest developers aren’t just competing against better products; they are competing against click farms, black-hat SEO, review manipulation, and copycat apps designed to extract quick cash and disappear.

Again, this isn’t cynical speculation. Here is what Apple itself removed in 2024 alone:

What Apple removed in 2024 alone:

• 143M+ fraudulent ratings & reviews

• 7,400+ apps pulled from the charts

• 9,500+ deceptive apps banned from search results

Source: Apple, “The App Store prevented more than $9 billion in fraudulent transactions”, 2025.

When cleanup operations have to happen at this scale, the term “organic growth” starts to lose its meaning. You are forced to compete in an auction against bad actors who have no intention of building a long-term business, but who are perfectly happy to drive up acquisition costs for everyone else while they monetize user confusion.

The regulatory and optimization tax

For health and wellness apps, the battlefield is even more treacherous. In the wake of high-profile FTC actions against platforms like BetterHelp and GoodRx over the unauthorized sharing of sensitive health data via tracking pixels, the major advertising networks panicked. BetterHelp’s $7.8 million settlement and strict ban on sharing health data for ad targeting sent shockwaves through the industry.[^4]

Consequently, advertising platforms have dramatically tightened their policies. Meta’s restrictions on health and wellness advertisers have made it incredibly difficult to optimize campaigns using lower-funnel events like “Purchase” or “Add to Cart.” Instead, developers are forced to optimize for weaker top-of-funnel signals like landing page views.[^5]

This is where the unit economics truly break down. When an ad algorithm can no longer see who actually buys your product, it stops learning. It starts buying noise. You end up paying for clicks from people who look interested but never convert. To compensate, marketing teams have to run endless creative testing, build dozens of compliant landing pages, and constantly guess what will get flagged by policy bots next.

Health apps are no longer just paying for media; they are paying a steep privacy risk premium, a compliance tax, and an optimization penalty. And the base price of a user was already climbing long before any of this — the reported cost-per-install range has drifted up year after year, with the high end more than doubling since 2019.[^9]

The AI supply shock and the wearable moat

Then came the rise of generative AI.

In 2025, the App Store saw 557,000 new app submissions—a massive 24% jump year-over-year, and the first meaningful increase since the 2016 peak.[^6] While it has never been easier or cheaper to write code and ship an app, it has never been harder or more expensive to get noticed.

The market is flooded with weekend projects, wrapper apps, and generic AI “coaches” that look slick in screenshots but offer zero depth. They still bid on your keywords, clutter search results, and dilute user trust in the entire category. Code has been commoditized, making distribution the only real moat.

The starting point is identical for everyone. What changed is everything that now sits between shipping an app and earning a dollar:

Furthermore, independent software developers are finding themselves outmatched by hardware and ecosystem giants. Wearable players like Oura, Garmin, Whoop, and Apple do not need to make the unit economics of a standalone app work. They treat software as a retention mechanism for their high-margin physical devices or broader ecosystems. They can afford to lose money on software services indefinitely because the hardware or ecosystem lock-in pays for it. Independent founders, on the other hand, have to run an actual, self-sustaining business.

This summer will be a graveyard for weak economics

The old playbook of buying cheap traffic, selling a generic promise, and relying on high-friction subscription retention is fundamentally dead. The data from RevenueCat’s subscription report proves it: nearly 30% of annual subscriptions are canceled within the first month.[^7] And cheap, sticky value beats an aggressive paywall every time — low-priced annual plans keep up to 36% of subscribers after a year, while high-priced monthly plans hold just 6.7%.[^7]

If your product is nothing more than an aggressive paywall with a basic questionnaire behind it, your payback period is going to stretch out to infinity.

We are already seeing the casualties of this shift. High-profile, well-funded players across the digital health and wellness spectrum are quietly shutting down or restructuring:

Health & wellness players that wound down or restructured:

• Care/of — personalized vitamins (Bayer-owned). Shut down, subscriptions canceled — 2024.

• Forward — tech-enabled primary care, ~$657M raised at a $1B valuation. Closed all locations, app shut down — 2024.

• Modern Age — VC-backed longevity clinics. Closed after failing to secure capital — 2024.

• WeightWatchers — public weight-management company (NASDAQ: WW). Chapter 11 to restructure debt, pivoting to GLP-1 — 2025.

Sources: TechCrunch, Fierce Healthcare, Longevity.Technology, SEC filings.

WeightWatchers is the most visible example — a public company whose top line slid for years before the filing. Revenue fell from $1.41 billion in 2019 to $785.9 million in 2024, a 44% drop, and in May 2025 it entered a pre-packaged Chapter 11 to restructure its debt.[^10]

These companies didn’t fail simply because “CPI went up.” They failed because demand does not equal a viable business model. When venture capital dries up and cheap acquisition disappears, a pretty mission statement cannot save you from the cold reality of retention and cash flow.

Many teams that spent the last few years scaling via aggressive performance marketing instead of building deep product value are going to hit a wall.

Why product-first is a survival strategy, not romance

For years, investing heavily in product quality, deep technical integrations, and real user utility looked almost naive.

In every boardroom, there is always someone asking: Why are we spending so much on R&D? Why not just pump that money into UA? Why not make the paywall more aggressive? Why not copy the competitors’ onboarding flow? When capital was cheap and ad platforms were highly efficient, those voices often sounded right. You could brute-force growth through marketing and fix the product “later.”

But when the tide goes out, you finally see who was swimming naked.

Building a product that actually delivers value, retains users through genuine utility, and drives organic word-of-mouth isn’t a romantic, idealistic choice. It is the most cold-blooded, pragmatic business strategy available to an independent founder.

After ten years of building Welltory, I can look at our shareholders and say with absolute certainty: the long-term investment we made in our product—even when it seemed slow and inefficient—is the exact reason we are still standing while others are winding down.

When you build something people actually need, you earn “user money.” And user money is the cleanest capital in the world. It doesn’t come with board seats, liquid preferences, or demands for irrational growth. It gives you oxygen, independence, and the leverage to say “no” to bad decisions.

If you are a founder who has been criticized for being too product-driven, too stubborn about quality, or too slow to squeeze your users for short-term revenue, let this market shift validate your instincts. You are not being naive. You are building the only type of business that can survive an expensive, noisy, and highly manipulated distribution landscape.

When the noise gets louder, when the ad platforms change their algorithms again, and when the market is flooded with a million more AI clones, the only ground you will have under your feet is the fact that real users find real value in what you built.

That isn’t a fairy tale. That is survival.

Sources

[^1]: Apple Ads — Ads on the App Store. 70% of App Store visitors use search to discover apps; almost 65% of downloads happen directly after a search; conversion at the top of search results runs over 60%. https://ads.apple.com/app-store

[^2]: eMarketer — Apple expands App Store search ads; advertisers will soon reach high-intent users (2026). Apple is adding ad placements beyond the single top slot, further down the search results, rolling out from March 3, 2026 (UK and Japan first, then globally). https://www.emarketer.com/content/apple-expands-app-store-search-ads-reach-high-intent-users

[^3]: Apple Newsroom — The App Store prevented more than $9 billion in fraudulent transactions (May 2025). In 2024 Apple removed 143M+ fraudulent ratings and reviews, 7,400+ apps from the charts, and nearly 9,500 deceptive apps from search results. https://www.apple.com/newsroom/2025/05/the-app-store-prevented-more-than-9-billion-usd-in-fraudulent-transactions/

[^4]: U.S. Federal Trade Commission — FTC Gives Final Approval to Order Banning BetterHelp from Sharing Sensitive Health Data for Advertising, Requiring It to Pay $7.8 Million (2023); followed the FTC’s $1.5M action against GoodRx. https://www.ftc.gov/news-events/news/press-releases/2023/07/ftc-gives-final-approval-order-banning-betterhelp-sharing-sensitive-health-data-advertising

[^5]: Foley Hoag LLP — Meta’s New Advertising Rules: Key Considerations for Health and Wellness Businesses (Jan 2025). Practitioner detail on the loss of lower-funnel events (Purchase / Add to Cart) and the forced shift to weaker signals: Polar Analytics, 2025 Meta’s Tracking Restrictions for Health & Wellness. https://foleyhoag.com/news-and-insights/blogs/security-privacy-and-the-law/2025/january/meta-s-new-advertising-rules-key-considerations-for-health-and-wellness-businesses/ · https://www.polaranalytics.com/post/2025-metas-tracking-restrictions-for-health-wellness-are-here----heres-how-to-fix-it

[^6]: Appfigures — The App Store Just Logged Its Biggest Release Year in Nearly a Decade (Dec 2025). 557K new App Store submissions in 2025, +24% YoY, the first meaningful increase since the 2016 peak; linked to AI-assisted development tools. https://appfigures.com/resources/insights/20251205

[^7]: RevenueCat — State of Subscription Apps 2025. Nearly 30% of annual subscriptions are canceled in the first month; low-priced annual plans retain up to 36.0% of users after a year, versus 6.7% for high-priced monthly plans. https://www.revenuecat.com/state-of-subscription-apps-2025/

[^8]: Company closures and restructurings: Care/of — Yahoo Finance / TechCrunch, Care/of is shutting down (Jun 2024) https://finance.yahoo.com/news/care-shutting-down-120200829.html; Forward — Fierce Healthcare, Primary care player Forward shutters after raising $400M (Nov 2024) https://www.fiercehealthcare.com/health-tech/primary-care-player-forward-shutters-after-raising-400m-rolling-out-carepods; Modern Age — Longevity.Technology, Longevity clinic Modern Age announces closure https://longevity.technology/news/longevity-clinic-modern-age-announces-closure/; WeightWatchers — WW International Form 8-K / press release on its pre-packaged Chapter 11 (May 2025) https://www.sec.gov/Archives/edgar/data/0000105319/000119312525114307/d934787dex992.htm

[^9]: Business of Apps — Cost per Install (CPI) Rates research. Reported CPI rose from roughly $0.50–$2.00 in 2019 to a projected $1.75–$4.50 in 2024, with the top of the range climbing about 125%. https://www.businessofapps.com/ads/cpi/research/cost-per-install/

[^10]: WW International full-year revenue from company results and SEC filings: $1.41B (2019), $1.38B (2020), $1.21B (2021), $1.04B (2022), $889.6M (2023), $785.9M (2024); pre-packaged Chapter 11 filed May 2025. FY2024 results: https://www.globenewswire.com/news-release/2025/2/27/3034270/0/en/WW-International-Inc-Announces-Fourth-Quarter-and-Full-Year-2024-Results.html · revenue history: https://stockanalysis.com/stocks/ww/revenue/

You’ve probably heard of Long COVID. Maybe you know someone who had it — or you are that someone. Months after the infection cleared, they still couldn’t function. Not tired-tired. Destroyed tired. And the strangest part: the harder they tried to push through it, the worse they got.

That’s post-exertional malaise - PEM. It’s the defining feature of Long COVID, ME/CFS (chronic fatigue syndrome), fibromyalgia (chronic widespread pain and fatigue), POTS (a heart rate disorder triggered by standing), and a handful of related conditions that together affect tens of millions of people worldwide. The hallmark is brutal and counterintuitive: ordinary exertion — a short walk, a phone call, a shower — triggers a delayed crash that arrives 24 to 72 hours later. Rest doesn’t prevent it. Willpower doesn’t override it. And standard lab tests can’t see it.

I had 700,000 days of wearable data from over two thousand people and not a single classic PEM pattern worked.

The boom-bust cycle that the literature describes? Doesn’t exist in the data. Heart rate variability differences between people who self-report PEM and non-PEM users? Dead on arrival. Self-reported crash severity matched against physiology? Noise-level agreement.

And then came the result that almost ended the project: when I ran matched-pairs comparisons — people with self-reported PEM versus non-PEM controls, same age, same sex, same step count — the PEM group recovered better than non-PEM users. Statistically significant. In the wrong direction.

I’ll get to that one. It took me a while to recover from it myself.

Why I was the one digging through the data

I run Welltory, a health-tech platform that reads wearable data. Two years ago, I sat across from one of our ambassadors — someone who’d been using Welltory for years — and heard a version of a story I didn’t yet know was common. She’d been a runner. She’d had a career she loved. Then Long COVID hit, and the life she’d built evaporated. Not gradually — like a switch. One month she was training for a half-marathon, the next she couldn’t walk to the mailbox without spending two days in bed.

I went back to our data and discovered something that should have been obvious but wasn’t: we had thousands of users like her. People who had once been our most active users — high step counts, consistent workouts, strong HRV — and then, over a span of weeks, flatlined. Not because they stopped caring, but because their bodies started charging a price for movement that they couldn’t afford to pay.

Standard wearable apps actively misread their data — cheering on the collapse with 'Great job, 10,000 steps!' while the crash was already in motion.

These people needed help with something that happens entirely outside the hospital — managing their energy in the texture of daily life. And almost nobody was building tools for that, because the signal is subtle, the science is young, and the standard approach in health tech is to tell people to move more. For this population, “move more” is the worst possible advice.

I didn’t hand this to an analyst because the question wasn’t analytical yet. I didn’t know what to look for. Nobody did — and I needed to understand the problem well enough to know whether we could build something real, or whether the data simply didn’t contain the answer.

I had no idea how many wrong answers I’d go through first.

Dead end #1: Boom-bust doesn’t exist

I started where anyone would start. The clinical literature on PEM describes a recognizable cycle: the patient has a good day, overdoes it, crashes hard, spends days recovering, then repeats. Boom-bust. It’s in the textbooks, the guidelines, the patient forums. It seemed like the most obvious thing to look for in longitudinal data, and I was genuinely excited — this was going to be easy.

I had the team build a detector. Activity autocorrelation, crash probability modeling, the works — elegant code, clean statistical framework. I ran it on 700,000 user-days.

Zero signal.

Not “weak signal.” Not “noisy but there.” Zero. The autocorrelation of daily activity in people with self-reported PEM was statistically indistinguishable from non-PEM users. Cohen’s d = 0.004. The probability of a crash following a high-activity day was 15.8% for the PEM group, 16.9% for non-PEM. If anything, non-PEM users were more likely to crash after an active day — which makes sense if you think about it for ten seconds, because they actually have high-activity days.

I stared at d = 0.004 for longer than was productive. Then I realized what I was looking at.

Boom-bust is a description of early-stage illness. By the time a person has been living with PEM for a year or two, they’ve already learned to pace. They don’t boom-bust anymore — they live on a carefully managed plateau. The cycle isn’t absent because I measured wrong. It’s absent because the people outsmarted it. Pacing is invisible adaptation, and by the time these people appear in a dataset, they’ve already adapted.

The lesson I took into the next stage: you are not measuring the disease. You are measuring the result of someone coping with it. If you look for the wound and the person has already grown scar tissue that looks like normal skin, you’re going to conclude there was never a wound.

Dead end #2: PEM recovers better than healthy

This was the one that almost broke me.

After the boom-bust failure, I went methodological. Matched pairs: people with self-reported PEM paired with non-PEM controls by age, sex, and activity level. Same step counts, same demographics, comparable observation windows. I compared recovery metrics — how quickly and completely heart rate returned to baseline after exertion. Textbook design. No way this fails.

The result: Cohen’s d = −0.12. Statistically significant (p = 0.001).

The PEM group recovered better.

I remember exactly where I was when I saw this. There’s a particular quality of silence in your own head when a result contradicts the reason the project exists. It wasn’t confusion — the statistics were clean. It was the moment where you ask yourself: did I get the cohort labels backwards?

I didn’t. The labels were correct. The analysis was correct. The conclusion was wrong — but not because of a bug. Because of something more fundamental.

It took me a few days of staring at distributions to see it. Matching on step count controls for how much someone walks, but it doesn’t control for how they walk. A person with PEM who takes 5,000 steps does it slowly, with breaks, at low intensity. A non-PEM person who takes 5,000 steps does it at a normal clip, with bursts, at moderate intensity. Same step count — very different cardiac load.

Less cardiac load means less to recover from. Recovery looked “better” because the input was smaller. I wasn’t measuring recovery capacity. I was still measuring activity intensity, just through a more sophisticated back door.

The lesson: the data wasn’t wrong — my question was. And I think this activity confound may be under-addressed in much of the published wearable-PEM literature. If a study compares a PEM group to non-PEM controls and doesn’t control for activity intensity — not just step count, but actual cardiovascular demand — I’d want to see how their reported effect sizes hold up under stratified analysis. Most studies don’t stratify. The ones that do tend to find much smaller effects, or — like me — paradoxical ones.

Dead end #3: Surveys are noise

By this point I was running out of obvious approaches, so I tried the one that felt most scientifically virtuous: ask the people living with PEM.

For three months, 47 users completed surveys about their PEM symptoms — crash severity, symptom intensity, condition self-assessment. I matched these against physiological data from the same periods, expecting the survey to serve as ground truth. If someone says “I’m crashing,” the wearable data should show it, right?

ICC — intraclass correlation, the standard measure of agreement between two rating systems — ranged from 0.019 to 0.356 across six features. For context: below 0.4 is generally considered “poor agreement.” Below 0.2 is noise. My best feature barely cleared the noise threshold.

Self-report and physiology don’t agree — and this isn’t because people are bad reporters. It’s arguably the definition of PEM: the disconnect between what you did and what you feel. The bill arrives 24 to 72 hours after the spending. Self-assessment captures the arrival of the bill, not the expense that caused it. Both are real. They’re just measuring different things at different times.

Three dead ends in. I had a dataset that refused to confirm anything the literature said should be there, an effect that went the wrong direction, and surveys that agreed with physiology about as well as a coin flip. I was starting to wonder if wearable data simply couldn’t see PEM at all.

The lesson: self-report is a complement, not ground truth. If you use one time-shifted signal as the truth label for another, you get noise — not because either signal is wrong, but because they’re answering different questions about the same phenomenon. I needed a physiological ground truth that didn’t depend on the person’s perception aligning with the body’s timeline.

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The pivot: not how much, but how predictably

Three dead ends, and somewhere around the third one, I did something I should have done at the start. I stopped looking for what the literature said should be there and started looking at what was actually in front of me.

Every dead end had a second message, quieter than the first. The averages didn’t separate PEM from non-PEM. Average recovery, average heart rate, average HRV — after controlling for activity, the groups looked the same. The averages were identical. But something was clearly different, because these people were living fundamentally different lives.

I can tell you the exact afternoon this clicked, because I was eating soup and it went cold while I was staring at a plot. Two time series of recovery quality, both with the same mean value — one steady, one chaotic. I had been computing means for months. I hadn’t plotted the day-to-day consistency. Nobody had, because why would you — you’re looking for a difference in levels, not a difference in stability.

But the consistency was the difference.

Instead of asking “how much does this person recover,” I started asking “how predictably does this person recover.” Not levels — day-to-day instability. Not averages — consistency metrics. And everything lit up.

Three independent feature groups emerged, all with statistically large effect sizes — Cohen’s d above 0.7, which in behavioral research with a heterogeneous, self-reported cohort is a strong observational signal, though not the same as validated diagnostic accuracy. After months of d = ±0.1, seeing numbers in that range felt like someone turned on a light in a room I’d been stumbling through in the dark.

Recovery consistency — how predictably recovery happens from one day to the next. A non-PEM body recovers about the same way every day. A body in the self-reported PEM group shows roughly twice the day-to-day spread. Same average, substantially different predictability.

Autonomic switching speed — how quickly the nervous system shifts gears after effort. This is a well-established physiological measure of cardiovascular health (Cole et al., 1999); in the self-reported PEM group, the switch is slower. This was the most robust single pattern — it held across every activity quartile, from the most sedentary to the most active. It is not an artifact of walking less.

Cardiac cost of movement — how much cardiac work each unit of walking costs. For the same walk, a person in the self-reported PEM group’s heart works roughly 50% harder than an activity-matched non-PEM user.

Three features from three different physiological systems, all pointing the same direction. I tested a model with over twenty features against a minimal three-feature model — the three-feature model slightly outperformed the complex one. In observational research on a heterogeneous self-reported cohort, classification performance reached AUC ≈ 0.77 — meaningful for research signal-detection, but well short of what would be required for clinical use. The signal was clean enough that adding more features added noise, not information.

Forty-five thousand users later

Once the three features crystallized, I needed to know if they held outside the research cohort. I applied the algorithm retrospectively to anonymized historical data from more than 45,000 Welltory users, controlling for activity level at every step — the confound that had wrecked every earlier approach.

Three validation results stood out, and each one was a different flavor of relief.

Dose-response. I divided users into groups by self-reported crash frequency. Every single feature showed a monotonic increase across crash severity (p < 0.001). The algorithm doesn’t just find “a pattern” — it finds a pattern that grades with the severity of the self-reported experience. When I first plotted that monotonic curve after months of null results, I closed my laptop and went for a walk. Seeing a staircase going exactly where it should go was almost disorienting.

Cascade effect. A bad recovery night predicts another bad night at more than double the baseline risk. Day two: still elevated at roughly 2×. PEM isn’t a single event — it’s a self-reinforcing spiral. Many people who report PEM describe this intuitively; this observation is consistent with that lived experience.

Community cohort. With consent from participants in Welltory’s community of users with self-reported energy-limiting conditions, I applied the algorithm to their anonymized data. The community was recruited based on self-reported conditions — the algorithm played no role in participant selection. Result: more than double the expected rate of elevated-risk scores compared to the general user population baseline. A separate cohort, assembled through entirely different criteria, and the algorithm found the pattern anyway.

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What I learned

Five things I’d tell the version of me who started this project.

The activity confound ruins everything. Any naive comparison between a self-reported PEM group and non-PEM users is measuring fitness, not PEM. This is uncomfortable because I think it applies to a significant portion of published wearable research on these conditions. The studies that control for it tend to find smaller effects. The ones that don’t — and there are a lot of them — may be building on a foundation that hasn’t been tested. If I were reviewing a wearable-PEM paper, the first thing I’d check is whether they stratified by activity level. If they didn’t, I’d want to see the analysis repeated with stratification before drawing conclusions from their effect sizes.

You won’t find the answer if you’re asking the wrong question — even when it’s right in front of you. I spent months computing recovery averages while the actual signal — day-to-day consistency — was sitting in the same dataset, one aggregation function away. Three dead ends weren’t three failures. They were three wrong questions that slowly taught me the right one. The boom-bust dead end taught me people had already adapted. The matched-pairs catastrophe taught me that controlling for quantity doesn’t control for intensity. The survey dead end taught me that the body’s timeline and the person’s timeline don’t align. Each wrong question eliminated a wrong assumption. In retrospect, there was no shortcut — the right question was only reachable through the wrong ones.

Consistency beats averages. This generalizes well beyond PEM. The stability of a metric is not the same as the stability of the system producing it. Two systems can output the same average and be in completely different states. The idea isn’t new in physiology — HRV itself is a variability metric — but applying it to multi-day recovery patterns was the move that unlocked this project.

Sensitivity hurts. The algorithm, at its strict threshold, misses more than half of self-reported PEM cases. We chose specificity over sensitivity — it’s better to miss someone than to falsely flag them. But it means that the absence of a signal is not a clean bill of health. It means the algorithm didn’t find enough signal in the available data. This is the trade-off nobody wants to make, and everyone has to.

The gap isn’t data — it’s the questions we ask of it. There is a population — tens of millions of people worldwide — living with the daily experience that their body is unreliable while every test comes back normal. Standard wearable analytics don’t have the right framework for them. After a year of digging through these patterns, what I keep coming back to is how close the answer was to the surface — one aggregation function away from the metrics I’d been computing for months — and how many wrong questions I had to ask before I found it. The data was always there. The right question wasn’t.

The body doesn’t lie. But you have to ask it the right question.

For a year, I asked “how much does this person recover” and got silence. When I switched to “how predictably does this person recover,” the data started talking.

A statistical signature associated with self-reported PEM in this research is not in the measurement. It’s in the measurement’s stability. Same averages, different variance. Same steps, different cardiac cost. Same recovery — on average — but one body knows what tomorrow looks like, and the other doesn’t.

I don’t know yet whether this is the final form of the algorithm or an early version of something better. The activity confound still has corners I haven’t explored. Menstrual cycle effects are almost certainly a first-class variable I’m not yet incorporating. The sensitivity ceiling bothers me. The self-reported ground truth means these are observational patterns, not validated diagnostics — replicating in clinically confirmed cohorts is the essential next step. But the three patterns — consistency, switching speed, and cardiac cost — have held up across four iterations, two research phases, and more than 45,000 users of anonymized historical data. That’s not nothing.

For people living with PEM who are reading this: this research suggests that wearable data contains patterns that warrant further investigation. The three features described here — recovery consistency, autonomic switching speed, and cardiac cost — are research signals, not diagnostic conclusions. Validating them in clinically confirmed cohorts is the next step, and I’d welcome any researcher who wants to try.

The full research findings, with methodology and visual evidence, are published as What Recovery Pattern Data Can Show About Post-Exertional Malaise on Welltory’s research page.

_{May 2026. Welltory Research. This is observational research; no clinical intervention was tested. Findings represent statistical patterns in aggregate data and should not be interpreted as diagnostic for any individual. Consult a qualified healthcare provider for medical concerns.}

Spotify can usually guess what I want to hear before I can.

Health apps still struggle to understand what to do with a woman in menopause who is losing bone and muscle, a 52-year-old man with hypertension on beta-blockers, or a person with ME/CFS or Long COVID whose body crashes 24 to 72 hours after ordinary activity.

That is not because music is more important than health. It is because music personalization is a preference problem. Health personalization is a care problem.

Spotify learns from dense, continuous, low-stakes feedback: what you search for, what you play, what you skip, what you save, what you exclude from your taste profile. Wellness learns from sparse, noisy, fragmented, high-stakes data — and usually without knowing the thing that matters most: who this body actually is, what else is going on in it, and what this person can safely do.

And yet the industry keeps saying personalized.

That is why everyone says it. And almost nobody means it.

2. What I mean by ‘no personalization in health’

I do not mean that wellness products do nothing adaptive.

They do. Apps personalize bedtime and exertion suggestions. They calculate readiness scores that move with your sleep. Some adapt training plans to hormonal phase. Some generate AI-driven workouts. Those are real forms of adaptation.

But they are usually not personalization in the health sense.

In health, personalization would mean that the core logic changes because of who the body is and what the body is carrying. It would have to change for:

• sex and life stage,

• chronic conditions and combinations of conditions,

• medications that change exercise response,

• longitudinal baseline and pattern over time,

• functional limitations,

• social context and real-world capacity.

If an app would still hand essentially the same recovery logic to a healthy 28-year-old, a woman in menopause, a man with hypertension on beta-blockers, a person with PEM, and someone with migraine + IBS + burnout, then it is not personalizing health. It is personalizing the wrapper.

A 2023 overview of personalized mHealth solutions found that these systems often rely on behavior-change theory, gamification, and motivational messaging, and “personalise the content rather than functionality.”¹ That is the distinction the industry keeps blurring.

Tailoring content is not the same thing as personalizing care.

This is not unique to wellness. Even formal medicine is still mostly population-anchored. Eric Topol, writing in December 2024, made the point about ordinary lab tests: results are conveyed as “normal” or “abnormal” indexed to one-size-fits-all, population-based reference ranges — “for all the talk of ‘personalized medicine’ over the last two decades, this enriched interpretation of one’s lab test would be simple to implement.”² If even clinical medicine, with a century’s head start, has not yet moved past the population average, the wellness layer downstream of it was never going to do that work on its own. The industry inherited a population-average frame and put a marketing layer on top.

That leaves a real question: if “personalization” today is mostly tailoring, what would actually solve this?

The fantasy answer is full individual personalization. Pure n=1. Every body modeled on itself, with no averages at all. That is not the answer either, and not for the reasons people usually give. We will come back to why. The short version: personalization in health is not a choice between the population average and the sample of one. The honest middle is phenotype-level — and that is the path this essay is arguing for.

3. Why almost every layer of the system rewards simplicity

This did not happen because founders are stupid or investors are cartoon villains.

It happened because complexity is expensive, and nearly every institution around health makes that expense someone else’s problem.

It started with the science.

Women are still meaningfully underrepresented in exercise and sport science. In one large audit across six major journals, female participants averaged only 34% of subjects, and only 6% of studies investigated exclusively women.³ The bias is not random. Female physiology — particularly across the menstrual cycle, perimenopause, and menopause — has historically been treated as more complex, more variable, and more expensive to study cleanly. So it got studied less. The NIH’s Office of Research on Women’s Health is candid about this: basic and preclinical biomedical research has “more often than not” focused on male animals and cells.⁴ Stacy Sims, the exercise physiologist who has spent more than a decade arguing this point, put it directly in May 2025: “Almost all the stuff you know with exercise and nutrition has been based on male data and generalized to women.”⁵ Much of what reaches consumers as “women’s wellness” is still adjusted male physiology, not a fully built female evidence base. Adding a cycle tab or a menopause filter does not solve the underlying gap. It personalizes the wrapper.

Medicine inherited the same instinct, in a different form.

Modern medicine has been organized around single diseases — diabetes, hypertension, depression, asthma — while real adult bodies increasingly carry several at once. The 2022 Nature Reviews Disease Primers synthesis of multimorbidity stated it as directly as a clinical journal will: most clinical practice guidelines and health-care training and delivery focus on single diseases, and that focus can produce care that is “sometimes inadequate and potentially harmful” for people with multiple conditions.⁶ It is much easier to build an app for “diabetes” or “sleep” or “stress” than for a person who is simultaneously living with hypertension, perimenopause, migraine, anxiety, and a parent in care. The first kind of product fits the science, the funding, the regulatory pathway, and the marketing language. The second kind of person is most of the user base.

That gap is so structural that funders had to build dedicated machinery just to study complexity at all.

The UK’s National Institute for Health and Care Research has an entire Multiple Long-Term Conditions research framework whose explicit aim is to change culture and practices to support this kind of work — reviewing funding processes, building multidisciplinary teams beyond single-disease specialisms, developing methods robust enough to handle complexity.⁷ That is a quiet institutional admission that the default research system was not built to produce person-level complexity well. It had to be retooled. The wellness layer downstream is even less equipped.

Startups and investors do not escape this either — and the irony is that the advice they receive is good advice, just not for this problem.

Early-stage companies are trained to survive by focusing. Y Combinator’s own essential startup advice tells founders that a small group of users who love them is better than a large group who kind of like them, and that early companies should focus on one or two key metrics.⁸ For most software, that is excellent advice. For real preventive health, it creates a structural trap. Because the human being who needs help rarely arrives as a clean wedge. She arrives as “menopause + insomnia + weight gain + anxiety.” He arrives as “hypertension + beta-blockers + deconditioning + bad sleep.” Another person arrives as “migraine + IBS + stress + fatigue.” The startup stack rewards one clean story, one ICP, one success metric, one fast roadmap. So founders simplify the human until the company becomes legible enough to fund, build, regulate, and market. That is not fraud. It is survival. It is also one of the reasons real personalization never quite shows up.

Then there is the regulation, which is the cleanest of all, because it is written into the rules.

The FDA distinguishes between low-risk “general wellness” products and products that move toward diagnosis, cure, mitigation, prevention, or treatment of disease. Software that is “maintaining or encouraging a healthy lifestyle” and unrelated to disease falls outside the device definition. Software that gets more disease-specific quickly enters a much harder regulatory world.⁹ So the market learned a very predictable lesson. It is much safer to say “optimize your sleep” or “boost recovery” or “train smarter” than to say “because you are on beta-blockers, the heart-rate logic this app normally uses may be wrong” or “because you have PEM, any fixed incremental increase in activity may backfire.”

The industry did not choose blandness by accident. It chose the side of the line that is easier to ship. By regulation, real personalization in health crosses into device territory. The wellness category is, by design, the un-individualized one. So when the industry says “personalized,” it is not lying — it is following the only definition of personalization that does not trigger a different regulatory regime. That is not a moral failure. It is a regulatory boundary doing exactly what it was built to do. But it also means the rest of us should stop pretending the word means more than the rules actually allow.

And on top of all of that, the hardest variable of all — the social and economic conditions in which a body is actually trying to live.

The WHO’s 2025 World Report on Social Determinants of Health Equity states it plainly: the conditions in which people are born, grow, live, work, and age — and the systems shaping access to power, money, and resources — can outweigh genetics, healthcare access, or personal choices in shaping outcomes.¹⁰ Does this person work shifts? Raise children alone? Live in chronic financial strain? Have time to recover, access to safe exercise, food that supports the plan? The body does not execute advice in a vacuum. A “personalized” plan that assumes stable sleep, discretionary time, and continuous adherence can be safe in every clinical sense and still be quietly impossible for the user it was supposedly built for.

None of this is anyone’s individual fault. The whole gravity field tilts toward simpler product, simpler claim, simpler user. Every layer independently pushes toward the same place.

This is what I mean when I say no one is the villain. The pressure is structural. The result is predictable. And the cost — which we will turn to next — is not zero just because nobody is counting it.

4. The industry fears the wrong risk

There is a way the wellness industry talks about risk that, once you see it, becomes hard to unsee.

Health is terrified of commission error — saying something too specific and being wrong. Telling a beta-blocker user to chase a heart-rate zone that does not apply to them. Telling someone with ME/CFS to push through a crash. Telling a postpartum body to “get back in shape.” The fear is real, the liability is real, and the instinct to step back from disease territory is rational — especially under the FDA’s wellness lane, where staying broad and lifestyle-shaped is the price of remaining a wellness product at all.

But the industry is strangely comfortable with omission error — saying something so generic that it is wrong for millions of people, while still sounding safe.

Most of the industry has a sentence it does not quite say out loud, but acts on every day:

We treat bad personalization as dangerous.

We treat absent personalization as neutral.

It isn’t.

This is not a balanced risk calculation. It is an asymmetric one. The cost of being too specific is visible: a complaint, a regulator’s letter, a lawsuit, a feature pulled. The cost of being too generic is mostly invisible: it is the bone that was never preserved, the flare that was provoked, the wrong training zone that was chased for two years, the condition missed because the app kept talking to an “average” body, the right preventive behavior that was never started because the person received generic advice instead of relevant advice.

Nobody invoices the industry for these. Which is exactly why they keep happening.

What omission actually looks like

Three scenes make the asymmetry concrete.

Menopause. A woman in her early fifties opens her wellness app. It is supportive. It tells her to walk more, do yoga, breathe deeper, sleep better, be gentle with herself. She does all of it. Every day. For five years.

What the app never says, because it is not equipped to say it, is that the protocol her bones actually need is heavier than walking and harder than yoga. The Royal Osteoporosis Society is explicit: the best way to keep bones strong through and after menopause is both weight-bearing impact exercise and muscle-strengthening exercise.¹¹ Neither is what her plan suggests.

She does not crash. She does not flare. She just quietly loses bone, year by year, doing exactly what the app told her was good for her. Twenty years later, what is gone is gone. Nobody marks it down as a product loss.

Hypertension and beta-blockers. A man in his early fifties starts a popular training app. He has hypertension. He is on beta-blockers. The app does not ask him about either. He picks a “smart” plan, puts on a chest strap, and starts training to heart-rate zones.

The trouble is invisible. Beta-blockers blunt heart rate by design. The American Heart Association is clear that target heart rate may need to be recalibrated with a clinician because these drugs affect everyone differently.¹² Exercise is Medicine adds that on these medications, perceived effort is the safer guide than the number on the watch — and that if resting blood pressure climbs above certain thresholds, the right answer is not to train at all but to contact a doctor.¹³ None of that is in his app.

So he chases zones that do not apply to him. Sometimes he overshoots without realizing. Sometimes he undertrains because the number on his watch tells him he is fine. The app feels personalized. It is not. It is specifically wrong for him, and for several million people in the same situation. Almost nobody counts that damage.

PEM, Long COVID, ME/CFS. A woman in her thirties has had Long COVID for eighteen months. On a good day she can walk to the grocery store and back. On a bad day she cannot stand long enough to make coffee. Her wearable keeps congratulating her: streak achieved, ring closed, move goal exceeded. On the days she pushes for the green ring, she crashes for the next three.

The largest peer-reviewed Long COVID synthesis, published in Nature Reviews Microbiology in 2023, states it directly: “Some physicians, poorly educated in the aetiology and pathophysiology of the disorder, still advise patients to pursue harmful interventions such as graded exercise therapy and cognitive behavioural therapy, despite the injury that these interventions cause and the fact that they are explicitly not advised as treatments.”¹⁴ That is not fringe. That is the field’s own primary source. And the population it applies to is not small: KFF estimates that 17 million US adults — about 7 percent of all adults — currently report Long COVID.¹⁵

An entire class of mainstream wellness logic — step nudges, recovery scores that reward upward progression, “build capacity” framings — is not just unhelpful for this woman and the millions like her. It can actively make people worse, sometimes for years.

This is what omission looks like at scale. The system mistakes the wrong variable for the right one and, because it is still giving some recommendation, it treats itself as safe.

The economics of omission

The honest objection is: yes, but isn’t this all anecdotal? Where is the number?

There is no clean number, because no one counts omission as a category. But the size of the exposed population can be added up directly.

Hypertension affects 119.9 million US adults.¹⁶ Up to 3.3 million people in the US live with ME/CFS, at an annual economic burden the CDC estimates between $18 and $51 billion.¹⁷ About 59 million Americans provide unpaid adult care valued at roughly $1.01 trillion per year — a population whose plans of any kind are systematically misfitted to their available time and sleep.¹⁸ Approximately 3.6 million people enter postpartum physiology in the US every year, a life stage where generic “get back in shape” advice intersects with healing tissue, pelvic floor recovery, severe sleep loss, and elevated mood risk.¹⁹

Summed conservatively across just these four phenotypes — and they are far from the only ones — that is roughly 185 million annual person-phenotype exposures to wellness advice not designed for the person receiving it. The number is not unique people; overlap is heavy. It is the size of the population getting average-user advice in situations where average-user advice is the wrong tool.

We count the risk of saying the wrong specific thing.

We rarely count the risk of saying the wrong generic thing to 185 million people.

Trust is already paying the bill

There is one place where the cost of omission is showing up on a balance sheet: trust.

In Deloitte’s 2024 consumer health survey, the share of consumers who said they did not trust generative AI health information rose to 30 percent, up from 23 percent the year before — a seven-point drop in twelve months, in a category that depends on trust to function.²⁰ You can argue about how much of that is AI-specific versus a broader signal about generic health content. But for a category that markets itself on being more personal, more responsive, and more aligned with the user than traditional medicine, a fall in trust at this speed should be read seriously.

People may not have the vocabulary for omission error. But they can feel when advice does not see them. And they are starting to act on it.

5. The average user is mostly fictional

When we talk about omission, it is tempting to picture an edge case — a small subset of unusual users who happen to fall outside the model. The population that does not fit the “healthy optimizer” frame is not the edge. It is the majority of the people the wellness industry is talking to.

Three-quarters of US adults have at least one chronic condition. Half have more than one. Even among 18-to-34-year-olds — the cohort wellness products most aggressively design for — six out of ten already have a chronic condition.²¹

And of the remaining quarter of US adults with no diagnosed chronic condition, the question becomes how many are actually metabolically healthy. The most recent NHANES-based analysis published in the Journal of the American College of Cardiology found that only 6.8% of US adults have optimal cardiometabolic health.²² Roughly one in fifteen.

Wellness products design their core experience for the optimizer: someone with stable physiology, room to push, recoverable baselines, no medication interactions worth modeling. The data says that person is the edge of the user base, not its center. The actual center is someone managing one or more conditions, often invisibly, often without diagnosis, often without a vocabulary for what their body is doing.

The trend goes the wrong way, too. Between 2013 and 2023, the share of US adults aged 18-to-34 with multiple chronic conditions rose by roughly a quarter.²¹ The “healthy young adult” cohort is becoming a smaller share of the very age group wellness apps target most aggressively.

The average healthy optimizer is not the average. It is the rare exception. And the longer the industry designs as if it were the norm, the larger the omission burden becomes.

6. The bridge: phenotype-level personalization

If the wellness industry’s personalization is mostly tailoring, and “real personalization” sounds like science fiction, then the real product question is the one almost nobody asks honestly: what’s between them?

Why pure n=1 doesn’t work — and not for the reasons people usually give

The fantasy answer is full individual personalization. Every body modeled on itself. No averages. No cohorts. Just you, compared to your own setpoint.

That doesn’t work, and the problem isn’t compute or data. The problem is that pure n=1 leaves the user without orientation. You become your own scientist, running experiments on yourself, with a sample size of one. That isn’t care. It’s a research assignment most people didn’t sign up for.

When you feel worse than you did three months ago, “compared to your own baseline” tells you something has changed. It doesn’t tell you whether what’s happening to you is what happens to bodies like yours. Whether the people closest to your situation got better. What they did differently. What to try next. For most users, “be your own n=1” is not empowerment. It’s abandonment with a dashboard.

This is part of why we don’t think the future of health personalization is “n=1 for everyone.” That framing imports the same problem wellness has now, just flipped: the population-average frame strips away the individual; the pure-individual frame strips away the cohort. Both lose the same thing — the right reference class.

Three honest options

Once you accept that current wellness-personalization-as-tailoring is mostly marketing, and n=1 is mostly a fantasy in 2026, the real strategic question becomes a choice between three things.

The honest middle is phenotype-level.

Why phenotype is the right reference class

A phenotype is not a marketing persona, and it is not a single diagnosis. It is a group of people who are genuinely similar in the ways that matter — symptoms, physiology, function, medication profile, life stage, social reality.

The reason this matters isn’t methodological elegance. It’s that humans learn by analogy to humans like themselves.

Every person who has ever joined a Long COVID Slack, a perimenopause subreddit, an ME/CFS Discord, an IBS support group, a fibromyalgia forum — what they’re looking for is not abstract averages, and not pure introspection. They’re looking for the right reference class. Other people whose bodies are doing something close enough that pattern recognition becomes possible. Recognition is the precondition for everything else: better questions, better experiments, better self-advocacy with clinicians, better predictions.

Phenotype-level personalization is the product version of that human instinct. Instead of comparing you to the population average (which you aren’t) or to yourself alone (which gives you no leverage), it compares you to the cluster of bodies actually doing what your body is doing. Advice can then be both specific and learned — without pretending to be omniscient.

That’s not a smaller idea than n=1. It’s a different idea. It scales. It can be validated. It can be improved one cluster at a time. And it gives the user the one thing pure self-measurement cannot give: other people.

The scale of what wellness ignores

This is where the abstract argument becomes a number.

If wellness products stay in the conservative lane — generic advice, untouched by phenotype — they leave the documented annual cost of every chronic phenotype they speak to fully in place. And those costs are not small.

Hypertension: $131–219 billion in annual US societal cost.²³ Chronic pain: $560–722 billion.²⁴ Long COVID: $30–170 billion.²⁵ Migraine: $13–78 billion.²⁶ Menopause-related lost productivity and medical costs: $26.6 billion.²⁷ ME/CFS: $18–51 billion.¹⁷ Each figure from CDC, peer-reviewed studies, or equivalent authority.

These are not market-size or addressable-opportunity numbers. They are documented annual societal costs — medical care, lost productivity, caregiver burden — already absorbed every year by patients, employers, and the broader economy, while wellness products continue to talk to these populations with logic built for a healthy 28-year-old optimizer.

The omission argument from earlier in this essay is not theoretical. The scale of what it ignores is, at minimum, this.

A worked example: ME/CFS

To make this concrete, here is the arithmetic for one phenotype where every input has a clean public estimate.

The annual societal cost of ME/CFS in the US is $18–51 billion (CDC). Under a “conservative wellness” strategy — generic advice, no phenotype-aware adaptation — essentially none of that burden is reduced. Under a phenotype-cohort strategy with deliberately conservative coverage assumptions, the order of magnitude lands in roughly $81 million to $775 million annually reducible. Under a theoretical “ideal n=1” strategy, $1.2–9 billion. But ideal n=1 is not feasible at scale in 2026, for reasons covered above.

I am not going to defend specific coverage numbers here — that work lives in a separate methodology document where the assumptions, the formulas, and the population-attributable risk math are visible to anyone who wants to argue with them.

The point is not the precision of any single number. The point is that the numbers exist at all — and that the wellness category’s current “be safe by staying generic” stance counts only the cost of being wrongly specific, while leaving the larger half of the equation uncounted.

That is why we are choosing the slow road — phenotype by phenotype, community by community, at the speed our resources allow. Not because it is fashionable, and not because it is easy. Because it is the only path between two failed ideas — generic wellness and impossible individual medicine — that actually adds up.

7. What we can no longer pretend at Welltory

We are not writing this from the outside.

We are part of this industry. And we are not writing this from finished work, either. We do not yet adapt our product well enough for PEM, for menopause, or for migraine + IBS — three of the phenotypes we already see most often in our own user base. We are not the worked example of what comes next. We are someone who has decided to stop being a worked example of what is wrong.

Two things we publicly stop doing.

We stop designing as if our average user is a healthy optimizer. The CDC says 76% of US adults have at least one chronic condition. Only 6.8% have optimal cardiometabolic health. That is not the edge of our user base. That is its center of mass. The healthy optimizer is the edge. From now on, that is how we treat the roadmap.

And we stop calling personalization what is not personalization. Adaptive messaging is not personalization. A score that changes with your sleep is not personalization. Plan A versus Plan B is not personalization. Real personalization in health changes the core logic of the product around the specific body in front of it — not the wrapper, not the copy, not the order of cards on a screen.

The rest is marketing.

8. So here’s what we’re actually doing

I could spend another section laying out principles for “responsible phenotype-aware personalization.” I’m not going to. The obstacles I described above are real, and I’ve spent months trying to find a way through them that doesn’t kill us under the complexity but actually lets us do something. This is what we landed on. It’s not a prescription for the industry. It’s just what we decided for ourselves.

We’re going to work phenotype by phenotype. One at a time. The mechanism is the same each time:

Launch a community cohort. Run the research jointly with the users in it. Build patient-led with medical advisors at the table. Study how their bodies actually work, what guidance actually helps, how the data we collect can support them inside the reality of their diagnosis — and we do all of that while staying inside what regulation actually allows so we don’t break anything we’d need later.

Then we move to the next phenotype and do it again.

We’ve already started. The first community is for people with post-exertional malaise — mostly women in their mid-forties to early sixties who have arrived at energy-limiting conditions through the same physiological pattern. We’ll see what comes out of it.

That’s the plan. Phenotype by phenotype. We don’t know yet if we’ll make it work at scale. We know the version of survival where we build the easier product and call it personalization isn’t a version we’re interested in. So we’re going to try this and see how far it takes us.

Phenotype by phenotype.

—

Jane Smorodnikova

Founder, Welltory

References

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2. Eric Topol, “Your Lab Tests.” Ground Truths Substack, December 14, 2024. https://erictopol.substack.com/p/your-lab-tests

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12. American Heart Association, “How Do Beta Blocker Drugs Affect Exercise?” Notes that beta-blockers slow heart rate and that target heart rate may need to be recalibrated with a clinician. https://www.heart.org/en/health-topics/consumer-healthcare/medication-information/how-do-beta-blocker-drugs-affect-exercise

13. Exercise is Medicine / American College of Sports Medicine, “Exercising with High Blood Pressure.” Includes the do-not-exercise thresholds (systolic >200, diastolic >115) and guidance to use perceived effort over heart rate for those on beta-blockers.

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15. KFF, “As Recommendations for Isolation End, How Common is Long COVID?” Issue Brief, April 2024. Approximately 17 million US adults (~7%) currently report Long COVID. https://www.kff.org/coronavirus-covid-19/issue-brief/as-recommendations-for-isolation-end-how-common-is-long-covid/

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21. Watson KB, Carlson SA, Loustalot F, Town M, Eke PI, Thomas CW, Greenlund KJ. “Trends in Multiple Chronic Conditions Among US Adults, By Life Stage, Behavioral Risk Factor Surveillance System, 2013–2023.” Preventing Chronic Disease (CDC), April 2025. https://www.cdc.gov/pcd/issues/2025/24_0539.htm

22. O’Hearn M, Lauren BN, Wong JB, Kim DD, Mozaffarian D. “Trends and Disparities in Cardiometabolic Health Among U.S. Adults, 1999–2018.” Journal of the American College of Cardiology 80(2):138–151 (2022).

23. CDC NCCDPHP, “Health and Economic Benefits of High Blood Pressure Interventions.” Annual US costs associated with high blood pressure estimated at $219 billion (2019); the JAMA AHA series (Kirkland et al.) estimates $131 billion in annual incremental healthcare expenditures across 2003–2014. https://restoredcdc.org/www.cdc.gov/nccdphp/priorities/high-blood-pressure.html

24. Wieder et al., “Economic Costs of Chronic Pain — United States, 2021.” Medical Care (2025). Estimates $722.8 billion annual US cost, including $530.6B medical care and $192.2B lost productivity. https://pubmed.ncbi.nlm.nih.gov/40730349/

25. npj Primary Care Respiratory Medicine, “Economic burden of long COVID: macroeconomic, cost-of-illness and microeconomic impacts” (2025). Estimates annual US lost earnings of approximately $170 billion. Companion estimate from Bartsch et al., Journal of Infectious Diseases (2025): $2B–$31B annually depending on symptom duration. https://www.nature.com/articles/s41533-025-00460-8

26. Bonafede M et al., “Direct and Indirect Healthcare Resource Utilization and Costs Among Migraine Patients in the United States.” Headache: The Journal of Head and Face Pain (2018). Reports total annual US migraine cost of approximately $36 billion. Broader estimates from neurological disease reports reach $78 billion. https://www.ajmc.com/view/study-summary-costs-associated-with-migraine-in-the-united-states

27. Faubion SS et al., “Impact of Menopause Symptoms on Women in the Workplace.” Mayo Clinic Proceedings (2023). Reports $1.8 billion in annual lost work time and $24.8 billion in annual direct medical costs in the US, totaling $26.6 billion. https://newsnetwork.mayoclinic.org/discussion/mayo-clinic-study-puts-price-tag-on-cost-of-menopause-symptoms-for-women-in-the-workplace/

The strangest part is that I never actually planned to work in health.

My career began in information security. I chose it at sixteen because it was a completely new frontier. I moved fast, but I quickly hit a professional realization that shaped everything to follow: once you build the perfect technical architecture, the primary vulnerability in any system is always the human being.

To be an effective CISO (Chief Information Security Officer), you have to become a behavioral psychologist. I remember the immense effort of training people to simply lock their computers when they stepped away. It sounds trivial, but it’s a massive hurdle in human psychology.

Only now do I see how much that was a rehearsal for public health. It is the exact same problem: there is no immediate reward, you are protecting against catastrophic events in a distant future that may not even happen, and because motivation is naturally low, you have to change the environment rather than just commanding the person to “do better.”

When I left InfoSec for startups, I had no idea I would spend the rest of my life doing the same thing—trying to influence human behavior for a safer future, just for biology instead of servers.

Escaping the SaaS Meat-Grinder

I came to Welltory through a profound crisis of meaning. By my thirties, I was tired of the “B2B SaaS” machine—a cycle that often feels utterly disconnected from real human value.

Think about the standard venture cycle: a talented team builds something useful, they package it as a SaaS, go through an accelerator, and investors pour in money while grooming them for a buyout. Often, the early “traction” is just other accelerator alumni buying the product. The company looks successful on paper, a corporation buys it, the VCs and the corporate execs get their bonuses, and the product is quietly shelved or dies a slow death inside the corporation.

Capital moves, but the customer’s life doesn’t improve. It leaves a bitter aftertaste of meaningless motion.

Welltory began with a different question: what is actually worth doing? We all know the answer. It’s what we wish for everyone at every milestone: Happiness and Health.

I looked at the data and saw that while other industries had been transformed by feedback loops, health was lagging behind. We had a metric—Heart Rate Variability (HRV)—and a clear problem: the crushing volume of stress modern life throws at us.

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The Mavericks and the Nerds

We were incredibly naïve back then. We didn’t know how hard it would be to bridge the gap between hard science and consumer habits. But we survived because of the people we brought together.

Here is an amazing fact: the iOS and Android developers who pushed that very first release in May 2016 are still with us today. Our CPO has been here for almost a decade. The designer who created our iconic “liquid” interface—the fluid animation that represents the state of your nervous system—is still here, leading our design team. Our Lead Scientist, the one who built our proprietary science from the ground up, is the exact same person.

In the tech industry, a ten-year tenure is almost unheard of. But people come to Welltory—and stay for five, seven, ten years—because of meaning and ethics.

Our team is a mix of top-tier professionals who reached a point in their careers where they demanded purpose, and brilliant mavericks who simply don’t fit into standard corporate structures. For a long time, I was incredibly proud of the fact that the number of PhDs in our company was roughly equal to the number of people without any higher education at all. We’ve never cared about your pedigree. We only care how deeply you can dig into a problem.

If I had to define our culture, it rests on two pillars: obsessive tenacity and intellectual rigor. We are incredibly different people, but we are united by a shared perfectionism and an absolute refusal to compromise our principles. Working alongside people who truly understand you is a rare, incredible feeling.

Science vs. The Market

But being science-obsessed in a consumer market is painful. A perfect example is sleep phases. We fought so hard against adding sleep phases to the app. From a scientific standpoint, wearable data for sleep phases is notoriously inaccurate and, frankly, not very actionable. We tried to hold the line and do things the “right” way.

But users buried us in demands for it. Everyone else had sleep phases; why didn’t we? Eventually, we surrendered. We learned a humbling lesson: market perception is a reality you have to design for, even when that perception is built on wellness myths. Our compromise now? We give people the familiar features they expect, but we place the actual physiological truth right next to it, hoping the two eventually connect in the user’s mind.

The Crash That Changed Everything

That tenacity finally paid off in a way that nearly broke us.

In late 2021, we released our core version—the one that ultimately made us successful. We were growing at 30% month-over-month. But during the release, we made a tiny, catastrophic mistake in the App Store: instead of a phased rollout, we hit the button for a 100% rollout.

A tsunami of users hit us. Our servers started melting. Our CTO practically lived in the office; instead of letting him sleep, we were calling a massage therapist to work on his back while he kept coding to keep the infrastructure alive. Everything was glitching, things were breaking left and right.

But here is the crazy part: people kept buying.

Despite the bugs, despite the crashes, they saw the value and they supported us. They didn’t let the team break. That was the moment I realized that we weren’t just building an app anymore; we had built something people genuinely needed.

The InfoSec Loop Closes

Today, 17 million people have trusted us with their health data.

As a former InfoSec expert, that number terrifies me. I know that any growing, centralized system eventually becomes fragile. A data breach would destroy our company.

So, we are going back to my roots: we are changing the architecture. We are moving further away from directly handling user data and shifting the heavy calculations directly onto the users’ devices. We are striving for decentralization. We are building safety into the very architecture of the product, because that is the only way to truly protect people.

Beyond “Healthism” and the Fight for Agency

Because we work only for the user, we have to stay independent. This means we often hit the walls of mistrust that the healthcare industry has built for decades.

We are entering a new era where we stop pretending our users are just “relatively healthy people” looking to optimize their fitness. The reality is that more than half of people over thirty-five live with chronic conditions.

The turning point for our company’s transformation happened during an interview I had with a user. He was a classic high-achiever: an active B2B partnerships executive, an athlete, a guy who ran his life at full speed. Then, he got Long COVID.

Suddenly, his world was destroyed. He could barely sit at a computer for one or two hours a day. A 30-minute walk became a grueling ordeal. He told me how impossibly hard it was to find reliable information on how to pull himself out of that dark place. He used Welltory to find his baseline, to pace himself, and to face his new biological reality.

Today, he has clawed his way back to working five hours a day. He transitioned from full disability back to being a capable, active person, even if it’s on a half-time basis. Hearing him say, “It gave me my life back,” left an indelible mark on me.

The prevailing culture in digital health is “Healthism”—the toxic idea that health is entirely a matter of personal discipline, and if you are sick or tired, it’s a moral failing. Wearables scold you for not hitting 10,000 steps, ignoring the fact that your nervous system might be completely depleted.

We are rejecting Healthism. We are moving into medicine, supporting people with energy-limiting conditions, autoimmune diseases, and chronic fatigue.

This is where AI changes the game for us. We aren’t using AI to be a stricter coach. We are using AI to translate complex data into narratives that remove guilt. If you have no energy, your data should validate your biological reality, not demand more discipline. We want to give people insights that help them cope, without the heavy burden of blame.

In our blog, you can also read our message to users.

An Unequal Fight

It is an unequal fight. There is too much in this modern world arrayed against our mental and physical well-being. But it is a fight worth picking, even if you know a “final victory” isn’t the point.

When a user writes to us saying, “I thought your app was wrong, but I went to the doctor and you caught something life-threatening,” or when someone like that executive tells us we helped him get his life back—that is what keeps us going.

I can promise our users one thing: we will always be on your side. We will do everything possible, and impossible, to ensure nobody can force us to act against your interests.

I am deeply grateful to our users. You allow us to live a life of meaning. It has been difficult, often at the very edge of our limits, but it is a life that matters.

Thank you for these ten years.

— Jane

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Some workers manage to bump their scores. Most don’t. The ones who don’t are labeled “non-compliant.” The ones who do still get sick — they just do it slightly more efficiently.

At no point does anyone open a window.

This isn’t a metaphor. This is exactly how the wellness industry operates. And after ten years of running a health tech company, here is the most honest thing I can tell you:

When a system makes people sick and then sells them tools to cope — while whispering that the sickness is their own personal failure — that’s not wellness. That’s victim blaming.

The Lie Inside the Truth

I want to be clear: the wellness industry doesn’t lie about the details. Sleep matters. Movement matters. Breathing techniques actually work. That’s all backed by real science.

The lie is in the framing.

When you’re chronically stressed because your job is precarious, your commute is two hours of gridlock, your food is engineered for addiction, and your healthcare costs a fortune — and then a wellness app tells you to “prioritize recovery” and “optimize sleep hygiene” — something cynical happens.

The cause of your suffering gets relocated. It moves from the system into you.

Suddenly, your stress isn’t a rational response to a broken world; it’s a “management issue.” Your burnout isn’t a systemic failure; it’s a “boundary issue.” If you’re sick, the logic suggests you just didn’t try hard enough. You didn’t track enough. You didn’t meditate long enough.

As Barbara Ehrenreich put it: when wellness becomes an ideology, “every death can now be understood as suicide.”

There’s a name for this. In 1980, sociologist Robert Crawford coined the term “healthism”: the ideology that locates health primarily in personal behavior — in your lifestyle, your habits, your choices — rather than in the social, economic, and environmental conditions you live in. His warning was precise: as long as healthism shapes our thinking, health promotion will remain “non-political, and therefore, ultimately ineffective.”

Forty-five years later, Americans spend $2 trillion a year on wellness. Population health is declining. Crawford was right.

The Data Is Screaming

If wellness apps were a clinical trial — spend $2 trillion, health gets worse — we would have shut them down years ago.

Obesity among young adults nearly doubled in a generation — from 23% to 40% — measured on scales, not self-reported. Depression rose 60%. Fatty liver disease in people under 30 doubled. Physical inactivity went up 5 percentage points during the exact decade that step-counting wearables went from niche gadgets to 600 million devices.

A study across 28 years of NHANES biomarker data — these are trained technicians physically drawing blood and measuring waist circumference, the same way, every cycle — found that physiological dysregulation has increased continuously from Boomers through Gen X through Millennials, in every sex and racial group.

Each generation arrives at adulthood measurably sicker than the last. And each generation has more health data than any civilization in history. More data, worse health. This isn’t a coincidence. It’s the logical outcome of a model that mistakes measurement for care.

Whose Fault Is It, Actually?

Here is the question the wellness industry does not want to ask. Because the answer collapses the entire business model.

Michael Marmot studied British civil servants for decades. People at the lowest employment grade died at three times the rate of those at the top — with identical access to the same doctors, the same hospitals, the same National Health Service. The difference wasn’t diet. It wasn’t exercise. It wasn’t how many steps they tracked. It was autonomy — control over their own work and life. Marmot’s conclusion: “What good does it do to treat people and send them back to the conditions that made them sick?”

Anne Case and Angus Deaton — he’s a Nobel laureate — documented that deaths from suicide, drug overdose, and alcoholism rose from 65,000 to 158,000 per year between 1995 and 2018 in America. That’s three full Boeing 737s crashing every single day, for decades. Not because people lost willpower. Because the economic and social fabric of working-class life was destroyed. “It is the loss of meaning, of dignity, of pride, and of self-respect that brings on despair.”

Robert Lustig, the UCSF endocrinologist, points out that 93% of Americans are metabolically unhealthy. His diagnosis is blunt: “Everyone is talking about healthcare, few people are talking about health, and nobody is talking about the food.” We didn’t hand smokers a cigarette-counting app. We regulated the tobacco industry. But with food — with work culture, with economic precarity, with the systematic destruction of sleep and community — we hand out apps.

Your job. Your commute. The air in your city. Whether you can afford childcare. Whether your boss texts at midnight. Whether you have one person you can call at 2 AM. The WHO has said it plainly: these social determinants are the primary drivers of health outcomes.

No wellness app controls any of them. But every wellness app quietly implies that you should be able to compensate for all of them — through better habits.

The New Solutionism: “The Bot Will See You Now”

And just when you thought the performance model of health had peaked, we are entering its ultimate phase.

As access to actual, living physicians becomes a luxury for the ultra-rich — or a six-month waiting list for everyone else — the system has found its perfect band-aid: AI Chat for Health.

Evgeny Morozov, the technology critic who wrote the book on Silicon Valley’s hubris, has a perfect word for this: “solutionism” — the compulsion to recast complex social problems as neat technical puzzles with app-shaped answers. AI health bots are solutionism at its most cynical. Because it’s becoming nearly impossible to see a real doctor, we are told to just talk to a bot. Here, have an LLM. Tell it about your symptoms. Let it give you more tips on how to fix yourself.

The pitch sounds democratic: world-class medical knowledge, available to everyone, 24/7, for free.

The reality is measurably different. The largest randomized trial on AI medical advice — published in Nature Medicine in February 2026, conducted by Oxford — tested what happens when the same powerful LLMs interact with real patients instead of doctors. The models that correctly identified the right diagnosis approximately 95% of the time when queried directly by clinicians gave the correct answer in less than 34.5% of cases when used by regular people describing their own symptoms.

Read that again. Same AI. Same medical knowledge. But accuracy dropped from near-perfect to barely a third — simply because the person asking wasn’t trained to ask the right questions, provide the right context, or evaluate the answer.

This isn’t a failure of the models. It’s a failure of the premise. We are handing people a powerful, complex tool and saying: “If you’re smart enough to use this correctly, you’ll be fine. If it doesn’t work? Well, that’s on you. You probably didn’t prompt it right.”

Sound familiar? It’s the same pattern. The same relocation of blame from system to individual. Except now it’s not just your diet and sleep — it’s your ability to correctly interface with artificial intelligence during a medical crisis.

We are handing people the world’s most sophisticated tools to help them ignore the fact that the building is on fire.

Why We Stopped Giving “Recommendations”

In our first year, we did what everyone else did. We gave advice. We set goals. And then I saw the reality in our own data.

We’d show someone their HRV crashing for weeks. They’d tell us: “I know, I need to sleep more.” But at 11 PM, after the kids are finally down and the day is finally over, they don’t sleep. They scroll. They stay up.

Not because they’re weak. Because the nighttime version of a stressed person runs on completely different machinery than the morning version.

BJ Fogg at Stanford calls it the “Information-Action Fallacy” — the belief that the right information changes behavior. It doesn’t. Wendy Wood at USC proved that 43% of daily behavior is habitual — automatic, driven by environment, not by willpower. The most rigorous wearable trial ever conducted (JAMA, 471 adults, 24 months) found that the tracker group lost less weight than the group without one.

And the neuroscience explains why. Chronic stress literally shrinks the prefrontal cortex — the part of your brain responsible for planning and impulse control. It grows the amygdala — the part responsible for fear and reactivity. The person who most needs to change their behavior is biologically least capable of doing it.

Byung-Chul Han, the philosopher, names the deeper trap: “The achievement-subject exploits itself until it burns out. Perpetual self-optimization, which coincides point-for-point with the optimization of the system, is proving destructive.” Nobody forces you to optimize — you do it voluntarily. Which makes the exhaustion feel like your fault too.

Giving a stressed person a recommendation is like handing someone with a broken leg a running program. When they can’t complete it, they don’t blame the program. They blame themselves. They feel shame.

And shame is the opposite of health.

So we made a choice. Welltory doesn’t give recommendations. No programs. No “streaks.” No discipline. No guilt. If a product makes a struggling person feel worse about themselves, that product is part of the problem. Period.

What We Do Instead: Translating Biology into Meaning

If we aren’t giving you a “to-do” list, what are we actually doing?

We are building the bridge between human biology and how life feels.

The wellness industry is obsessed with “optimization,” which is just a fancy word for treating your body like a machine that needs to be tuned. We make the invisible visible through metaphor.

Your autonomic nervous system is the lens through which you experience your entire life. When your physiology is in a state of “High Stress” or “Low Recovery,” it’s not just a number on a screen. It’s a filter on your reality.

• It is the biological reason why a minor comment from your partner feels like a personal attack.

• It is the reason why a task that felt easy on Monday feels like an impossible mountain on Thursday.

• It is the reason why the world feels grey, sharp, and overwhelming.

Most apps see a low HRV and tell you to “breathe.” We see your vitals and help you understand why your reality feels the way it does.

We use metaphors—fluidity, turbulence, battery, flow—because biology is too complex for a dashboard, but it’s perfectly suited for meaning. When you can see the connection between your biology and your perception, you gain something far more powerful than “compliance.” You gain Agency.

You realize you aren’t “failing” at your job or your marriage; you are experiencing a physiological state that makes those things harder. When you understand the why, you don’t need an algorithm to tell you what to do. The path becomes obvious.

This isn’t wellness. It’s a way of making sense of yourself. It’s the realization that your “mood” isn’t a mystery—it’s a message from your nervous system. And once you can read that message, you stop being a victim of your biology and start being its partner.

Opening the Window

I should tell it out loud: An app is not a savior.

An app cannot fix the food system. It cannot give you a less toxic boss or an affordable apartment. It cannot replace a community. I refuse to be the person selling breathing apps in a toxic factory while the windows are nailed shut. I refuse to use AI to “prompt” people into believing they can ignore the fire in the building.

But there is a specific, quiet power in seeing.

When we reflect your data back to you as meaning—without judgment, without “goals,” without “streaks”—we are doing the only thing that actually matters in a broken system. We are helping you walk over to the wall, find the latches, and put your weight against the frame.

We don’t optimize the coughing worker. We help the worker see that the air is the problem.

Because once you see the smoke, you stop trying to “breathe better.” You start looking for the exit. Or you start looking for a way to fix the ventilation for everyone.

From that clarity—not from guilt, not from discipline, not from an algorithm—sometimes people make changes. Sometimes they protect their sleep. Sometimes they leave the job. And sometimes, they make the hardest choice of all: they finally believe that they aren’t the problem.

The choice to act is yours. Our only job is to open the window and let you see the air for what it is.

No shame. No blame. No pretending the air is fine.

Further Reading:

Barbara Ehrenreich — Natural Causes: An Epidemic of Wellness, the Certainty of Dying, and Killing Ourselves to Live Longer (2018) A sharp polemic from a writer with a PhD in cellular immunology. Ehrenreich argues that the wellness industry converts systemic social failures into personal moral failures — and that the obsessive drive to optimize health can consume the very life it intends to preserve. The quote “every death can now be understood as suicide” comes from her analysis of healthism as ideology. 🔗 https://www.labyrinthbooks.com/natural-causes/

Anne Case & Angus Deaton — Deaths of Despair and the Future of Capitalism (2020) Nobel laureate Angus Deaton and economist Anne Case document how deaths from suicide, drug overdose, and alcoholism rose from 65,000 to 158,000 per year between 1995 and 2018 among America’s working class. Their conclusion: the cause is the destruction of economic dignity and social meaning — not individual weakness. 🔗 https://books.google.com/books/about/Deaths_of_Despair_and_the_Future_of_Capi.html?id=anK0DwAAQBAJ

Byung-Chul Han — The Burnout Society (2015) The South Korean-German philosopher argues that contemporary society has shifted from external discipline (”thou shalt not”) to internal self-optimization (”I can”). The result: individuals exploit themselves until burnout — and feel personally responsible for their own exhaustion. Source of the “achievement-subject exploits itself” quote. 🔗 https://books.apple.com/us/book/the-burnout-society/id1023741312

BJ Fogg — Tiny Habits: The Small Changes That Change Everything (2020) The founder of Stanford’s Behavior Design Lab introduces the concept of the Information-Action Fallacy — the false belief that giving people the right information will change their behavior. Fogg’s research shows that behavior change is driven by motivation, ability, and prompts — not data. 🔗 https://www.forbes.com/sites/melodywilding/2020/01/13/forget-big-goalswhy-tiny-habits-change-everything-according-to-a-stanford-professor/

Wendy Wood — Good Habits, Bad Habits: The Science of Making Positive Changes That Stick (2019) USC professor Wendy Wood’s research shows that 43% of daily behavior is automatic and habitual — not driven by conscious decision-making. The implication: information-based wellness interventions largely miss the mechanism through which behavior actually changes. 🔗 https://dornsife.usc.edu/wendy-wood/good-habits-bad-habits/

Evgeny Morozov — To Save Everything, Click Here: The Folly of Technological Solutionism (2013) Technology critic Morozov coined the term “solutionism” — the compulsion to reframe complex social and political problems as neat technical puzzles with app-shaped solutions. Directly relevant to AI health bots being offered as a substitute for systemic healthcare access. 🔗 https://www.publicaffairsbooks.com/titles/evgeny-morozov/to-save-everything-click-here/9781610393706/

Robert Crawford — “Healthism and the Medicalization of Everyday Life” (1980) International Journal of Health Services, Vol. 10(3), pp. 365–388 The original paper coining the term “healthism” — the ideology that locates health primarily in individual behavior rather than social, economic, and environmental conditions. Crawford warned that healthism makes health promotion “non-political, and therefore, ultimately ineffective.” Still widely cited 45 years later. 🔗 https://pubmed.ncbi.nlm.nih.gov/7419309/

Crawford revisited his own paper in 2026: 🔗 https://pubmed.ncbi.nlm.nih.gov/41730689/

Marmot et al. — Whitehall II Study (1991) The Lancet, Vol. 337(8754) The landmark cohort study of 10,314 British civil servants showing that mortality and morbidity follow a strict employment-grade gradient — even when healthcare access is identical. Men in the lowest grade died at three times the rate of the highest grade. The key variable: autonomy and control over one’s own work and life, not lifestyle habits. 🔗 https://pubmed.ncbi.nlm.nih.gov/1674771/

Wikipedia overview of the full Whitehall study program: 🔗 https://en.wikipedia.org/wiki/Whitehall_Study

Jakicic et al. — “Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss: The IDEA Randomized Clinical Trial” (2016) JAMA, Vol. 316(11), pp. 1161–1171 The most rigorous wearable tracker trial to date: 471 adults, 24 months, University of Pittsburgh. Result: the group using wearable activity trackers lost 2.4 kg less than the group without one. Directly contradicts the premise that more data leads to better health outcomes. 🔗 https://pubmed.ncbi.nlm.nih.gov/27654602/

Zheng & Echave — Physiological Dysregulation Across Birth Cohorts (2021) NHANES biomarker data spanning 28 years, across Boomers, Gen X, and Millennials. Found that physiological dysregulation has increased continuously across generations, in every sex and racial group — measured objectively by trained technicians (blood draws, waist circumference), not self-reported. Each generation arrives at adulthood measurably sicker than the last. 🔗 https://www.healthday.com/health-news/general-health/3-25-gen-x-gen-y-in-worse-health-than-prior-generations-at-same-age-2651169.html

Related PMC study on generational cardiovascular health: 🔗 https://pmc.ncbi.nlm.nih.gov/articles/PMC9154229/

Bean, Mahdi et al. — “Reliability of LLMs as Medical Assistants for the General Public” (2026) Nature Medicine, February 2026 — University of Oxford The largest randomized trial of AI medical advice to date: 1,298 participants. The same LLMs (GPT-4o, Llama 3, Command R+) that correctly identified diagnoses ~95% of the time when queried directly by clinicians gave the right answer in less than 34.5% of cases when used by regular people describing their own symptoms. Users with AI performed worse than the control group using Google. 🔗 https://www.nature.com/articles/s41591-025-04074-y

Oxford press release: 🔗 https://www.ox.ac.uk/news/2026-02-10-new-study-warns-risks-ai-chatbots-giving-medical-advice

U.S. Surgeon General — “Our Epidemic of Loneliness and Isolation” (2023) U.S. Department of Health & Human Services The 81-page advisory declaring loneliness a public health epidemic. Key finding: lacking social connection carries a mortality risk equivalent to smoking 15 cigarettes per day — greater than the risks associated with obesity and physical inactivity. The report calls for structural, not just individual, responses. 🔗 https://www.hhs.gov/sites/default/files/surgeon-general-social-connection-advisory.pdf

WHO — Social Determinants of Health (ongoing) The World Health Organization’s framework establishing that the primary drivers of health outcomes are structural factors — income, housing, working conditions, education, access to healthcare — rather than individual behaviors. The foundation for the article’s argument that no wellness app controls the variables that actually shape population health. 🔗 https://www.who.int/health-topics/social-determinants-of-health

Robert Lustig, MD (UCSF) — Metabolic Health Endocrinologist and author of Metabolical. His research supports the claim that 93% of Americans are metabolically unhealthy, and that the primary driver is the ultra-processed food system — not individual willpower or tracking failures. 🔗

https://robertlustig.com

But then, Welltory shifted gears. We started to see our market and users differently and the team needed to absorb an ocean of new information (I wll tell you more about our new strategy later). I realized we needed an internal book. An “Onboarding Domain Knowledge” to get everyone on the same page.

So, I decided to build it. This is the story of that journey.

Step 1: The Visionary Phase

First, you have to invent the book. AI won’t help you here. You can use it as a sparring partner for dialogue, but ultimately, the outline, the core idea, and the structure are on you.

I’m lucky that our strategy was already well-defined, so this part was relatively easy. I mapped out 1–2 paragraphs per chapter. Later, I realized I’d missed a few things and added extra chapters at the end, but the outline was my foundation. Pro tip: Spend time on the outline. It’s the only thing that keeps the AI from drifting.

Step 2: Information Gathering

Writers usually spend years on research. In the AI era, this phase is unrecognizable.

I spent about $1000 on research alone. Why? Because the level of quality I needed only came from Perplexity’s Deep Research, which costs a lot. I became a “prompt engineer” for research queries, using Claude to help me write the prompts that would extract the best science.

I bought a Google Colab Pro subscription just to keep my sessions alive overnight. I’d launch a massive batch of research and wake up to long Markdown documents filled with links. I then had to process these into “knowledge cards”—individual facts, thoughts, and proofs—which I could then feed into each chapter.

The link nightmare: Models love to hallucinate links or mix up internal file paths with real URLs. I solved this by giving the AI only “fact IDs” during the writing process and then adding the actual sources as a final mechanical layer at the very end.

Step 3: Preparing the Context

My first attempts at context were a disaster. I tried using our company strategy and a style guide based on my social media posts. It didn’t work.

I ended up writing a “Constitution of the Book.” I had to fix our basic worldview and core principles in stone. If you don’t, the model will just hallucinate its own “truth” in any direction it wants. I also split my style guide into two parts: my actual voice and an “Anti-Slop Guide” (how NOT to write like an AI).

Step 4: Learning to Write a Chapter (The “Pre-Chewed Salad” Problem)

This was the biggest struggle. I almost burned out here. I tried approach after approach, and everything I produced was garbage.

I am a perfectionist, yes. But if you want something readable, you can’t just “generate” it. One of our scientists gave me the most brutal feedback on an early draft: “It feels like I’m eating a salad that someone else has already chewed for me.”

The AI had no variability. It was monotonous. It used the same patterns (”Not once, not twice, but three times”) and choppy sentences. AI lacks variability (and I know everything about variability). Living things are variable (that’s why heart rate is variable); AI is just a slave to its instructions. The more I tried to control it with rigid agents (Scientific Editor, Literary Editor, etc.), the worse it got. It couldn’t sound “alive.”

The Solution: I realized that instead of describing how a human writes, I needed to show it.

I initiated a “fake dialogue” with the model. I’d feed it a request and then a “fake” result—which was actually a real, high-quality text from an author I admire (like Sapolski). After three of these examples, I’d give it my real request.

I spent a week building a library of 500+ “example” pairs—real, living texts from real humans matched to specific thoughts and directions. This gave the AI a structural and rhythmic foundation that actually felt human.

Step 5: Generation and Control

If you think you just hit “Enter” and wait, think again. I had to be involved at every step on every chapter.

1. The Lens: I’d discuss the chapter’s “lens” with a specialized Claude skill—what do we really want to say here?

2. The Task: Claude Opus acted as my “Scientific Editor.” It would select the research cards and then challenge me. It would ask me tough questions about the chapter’s insights, and I’d have to provide my own feedback and commentary.

3. The Draft: Only then would GPT-5.4 Pro start writing the base of the chapter using my feedback, all research and the exemplar library. First I try write with more cheap models by chunks but it’s better toi use most expensive models with big context in one step to get coherent result of the chapter.

4. The Finalization: I’d check the draft, verify the conclusions, and then send it to Gemini Flash for “polishing”—making the language simpler and clearer without changing the substance. Google models are best for this, probably because htey have more data.

Step 6: The Final Edit

Even after all that, I spent days on edits. AI still misses things. It repeats “filler” words, breaks formatting, or messes up abbreviations. I had to generate a custom glossary for every chapter so the technical terms remained consistent but weren’t over-explained.

The best part? Generating the illustrations and seeing the book finally come together. It’s 300 pages of fresh, unique information—the world’s latest scientific knowledge mixed with our specific strategy and market insights.

My “Obsessed Founder” Takeaways:

1. Examples > Instructions: Just like with people, showing works better than telling. Don’t just prompt; provide examples.

2. The Context is Everything: The more “obsessive” you are about preparing the context and “Constitution,” the better the result.

3. Use the Whole Toolbox: I had to use every model on the market to get this right:

   • GPT-5.4 PRO: For the core writing.

   • Claude Opus: For the high-level task setting.

   • Claude Sonnet: For structuring and link processing.

   • Perplexity: For the deep research.

   • Gemini Flash: For linguistic polishing.

   • ChatGPT Image: For the visuals.

The world has changed. Every piece of knowledge on the planet is at your fingertips. The only thing that differentiates us now is our level of perfectionism and having a unique vision that can point the AI in the right direction.

The AI is the engine, but you are still the driver.

I learned so much on this journey. I’m just grateful I stuck with it and delivered something real, rather than giving up and dumping a boring 'research collection' on my team.

This is a brand-new skill set for a brand-new era. It fundamentally shifts our approach to work; things that used to be too slow, too expensive, or simply out of reach are now possible. It’s incredible how much the game has changed.

I was sitting in an internal Welltory workshop. One of our engineers was showing the team how to build plugins for Claude Cowork. It’s a powerful thing: you can automate a scary amount of your own work. People were watching and you could almost hear the collective gulp — oh, shit, this just got real.

You could see the gears turning:
“Wait. I can wrap my whole operational hell into a couple of pipelines and never touch it again?”

At some point someone joked:

“If we had time tracking here, we could now fake being busy forever and do nothing.”

My first reaction was a small internal panic.
The second was relief.

Thank God we never tried to control hours.

The next thought was less kind:
Oh wow. So many companies are already screwed and don’t know it yet.

For years people kept telling me:

• you can’t do real innovation remotely

• you have to watch people

• you need to “look them in the eyes” in the office

• remote only works for juniors who need supervision

Now, in 2026, it’s pretty obvious what actually matters for any company building an intellectual product — and what doesn’t. And everyone who bet their culture on controlling presence and screen time just lost that bet.

Let’s rewind a bit and see how we got here.

We were paying for theater long before AI showed up

There’s an ugly fact we have to admit: the “activity instead of results” problem is not new.

A Connext Global study found that about two thirds of U.S. employees openly admit they engage in productivity theater: they pick up “visible” tasks, stay late, and stay active in chat mainly to look busy. Only around 23% say their performance is measured against clear outcome metrics, not how hardworking they appear.
Sources:

• Summary press release

• BusinessWire piece

So tens of thousands of companies have been paying not for work, but for rituals around work: speed of email replies, number of calls, how pretty the slide deck looks.

And then AI walked in.

A culture built on hours meets AI

Picture a company where the whole culture is built on time:

• efficiency = how many hours you “sat at work”

• processes are built around presence, not results

• management is basically surveillance: who arrived when, who stayed how long, who is green in Slack

Now drop modern AI into this setup:

• Claude / coding agents that can hold basic work conversations and operate tools for you

• agents that move files, create tasks, draft reports and summaries

• tools that fill in forms and documentation better than the most patient junior

(Examples of these:
Cursor & Claude agents overview
Claude agents breakdown

The whole “control by hours” logic collapses in one release.

Mouse movement? Old‑school mouse jigglers were already a thing years ago. Employers now publish long guides on how to detect artificial cursor movement by speed and acceleration patterns:

• VanHack on spotting mouse jigglers

• EmpMonitor on detecting jigglers

On top of that, you can glue an AI agent that moves the mouse like a human and “uses” apps all day.

Being active in chat? A bot can answer “got it, will check” and “let’s talk tomorrow” just as well as half your org.

A 10‑page report by 6 p.m.? What used to be a full day of work is now ten minutes of prompting. Field reports and vendor data show double‑digit productivity gains from AI on writing, research, and coding tasks, especially in knowledge work.
Overview example.

If your main metric is visible activity, AI is a machine for synthetic activity.
Cheaper, faster, and tireless.

Life inside a “visibility corporation”: the numbers

Let’s put some numbers on this.

• Microsoft’s Work Trend Index shows employees spend up to 57% of their time in meetings, email, and chat — and only 43% on focused work that produces value.
  See here and at a summary

• Meeting time has more than doubled since 2020, but average meeting length barely shrank.
  Nice digest here.

• Focus research from Gloria Mark’s group finds that after a serious interruption, the brain needs about 23 minutes to fully get back into the task. One badly placed meeting in the middle of a deep work block can wipe out half an hour of real productivity.
  Classic paper: “The Cost of Interrupted Work: More Speed and Stress”

On top of that, “bossware” is quietly spreading: screen‑capture tools, keyloggers, app trackers. Legal and HR folks are already writing whole reports about it — and they all come to the same conclusion: surveillance erodes trust, spikes stress, and increases turnover intentions.
Примеры:

• On “bossware” in general

• On AI employee monitoring

• Academic work on monitoring, trust, and turnover

Against this backdrop, “let’s strengthen control” is not a plan.
It’s the corporate equivalent of treating a migraine with a hammer.

The unwinnable arms race

The “we’ll just watch them more closely” path sends your company into a pointless arms race:

• on one side, monitoring vendors invent ever more sensitive ways to flag “abnormal” patterns

• on the other, AI agents get better at mimicking human behavior and producing perfect “busywork” traces

Examples of how fast agent capabilities are evolving:

• Cursor agents running 100+ parallel tasks

• Security folks red‑teaming coding agents and showing how they can be scripted

Who wins a game of “who’s sneakier”?
The side that has AI and motivation to use it. That’s not your middle manager writing reprimands because “you were red in Slack for too long”.

Meanwhile:

• faking activity gets cheaper

• separating signal from noise gets harder

• managers drown in dashboards about “engagement” and “time in app” and still have no idea what actually moves the business

Worklytics and others keep pointing out this trap: leadership obsesses over activity metrics instead of outcome metrics, and then wonders why nothing moves.
Example KPI piece:
Productivity benchmarks:

It’s not just pointless. It’s expensive. You’re burning money on:

• control software

• payroll for people playing cat‑and‑mouse with that software

• and you’re not investing in the only things that actually raise productivity: focus time, sane AI‑supported workflows, and clear goals

Deep‑work stats for context:

• Deep work & focus time trends

• Knowledge worker productivity & focus time

The companies that look strangely calm

If you look at the few places that seem oddly calm about AI, they have one thing in common:
they shifted to managing by outcomes long before the current model hype.

Netflix famously has no vacation tracking and minimal formal rules. Their culture memo basically says: “No vacation policy. No approval process.” The flip side is brutal clarity on performance and “talent density”: you are trusted like an adult and measured like an adult.
- Netflix culture memo
- Book / context
- A good summary

GitLab runs one of the largest all‑remote organizations in the world with a fully public handbook. They don’t track hours; they track impact and documented outcomes. Their leadership talks openly about the trap of rewarding people for presence and responsiveness instead of real contribution.
- All‑remote culture
- Remote benefits
- Nice external case write‑up

The older ROWE (Results‑Only Work Environment) experiments at Best Buy showed the same pattern: when they stopped tracking time and switched to pure results, voluntary turnover dropped dramatically and productivity jumped.
Intro to ROWE:

• Overview

• Case discussion

Productivity analytics across thousands of knowledge workers shows a simple thing: the people who reliably get 3–3.5 hours of deep focus per day outperform those who live in “meeting‑chat‑meeting” mode.
Benchmarks here.
Deep work stats here.

Bossware doesn’t create deep focus. It destroys the little that’s left.

So no, the answer to AI is not “more screenshots”.

OK, but what do you actually do instead?

“Don’t track hours, manage outcomes” sounds nice on a panel.
It’s less nice at 10 a.m. on Monday when you have a real company to run.

Here’s what I’d do if I were trying to turn a time‑tracking culture around.

1. Translate your company into outcome‑speak

As long as your org speaks in tasks — “run the project”, “support the system”, “do marketing” — any talk about trust will stay abstract.

Outcomes look more like this:

• not “handle support”, but keep NPS above X and first‑response time under Y

• not “do marketing”, but bring in X qualified leads at ≤ Y CAC

• not “write code”, but move retention, ARPU, or CSAT by X points this quarter with specific product changes

Worklytics and similar firms publish KPI lists for knowledge workers that are all about contribution and impact, not about time in front of a screen:

• KPI examples

• Benchmarks

They’re a good starting point if you’re staring at a blank page.

2. Build AI into the job, not as a side hobby

If you treat “real work” as manual and AI as a cheat code, you’ve already lost.

Instead:

• write into role descriptions: “can design workflows that use AI/agents, not just do tasks by hand”

• ask in performance reviews: “Which parts of your work have you already automated? What’s next?”

• measure people not by “time spent”, but by how much output improved versus the previous period

McKinsey, IDC and friends keep repeating this point: the winners won’t be “the ones who bought AI”, but the ones who rewired their work around humans + agents working as a system.
Some representative takes:

• IDC: “AI agents as instruments, not co‑workers”

• McKinsey post on agentic AI giving employees “superpowers” (via FB share)

• a16z on AI workflows

3. Invest in outcome‑thinkers, even if they’re AI‑illiterate today

This is where many leaders make the wrong cuts.

If you have someone who:

• genuinely cares about the business result

• thinks in outcomes and takes ownership

• but doesn’t yet know how to work with AI,

this is your best investment.

The simplest training pattern I’ve seen work:

1. Give them a task that can’t reasonably be done without AI — not a toy prompt, but a real piece of work that would be too big or too tedious by hand.

2. Say out loud:
   “The first time you do this with AI, it’ll be slower than doing it manually. That’s okay. The point is to build the muscle for the next ten tasks, not to beat your current speed on day one.”

3. Show where the help lives: internal AI champions, workshops, a short curated list of courses or docs.

OECD and the IMF both point out that companies actively investing in AI‑related skills are more likely to see productivity gains and less likely to lean purely on job cuts:

• IMF blog on skills and AI

• OECD on AI & SME workforce skills

In SME surveys, significantly more firms say AI has raised skill requirements than lowered them.

If someone can already think in outcomes, they almost always figure AI out — especially if you stop punishing them for not being “faster than manual mode” on day one.

4. Be honest: you will part ways with some people

Here comes the hard part.

You will almost certainly have to let go of some folks:

• those who refuse to think beyond checklists (“just tell me exactly what to do”)

• those who see AI only as a threat and won’t touch it

• those whose core skill is “being around and looking busy”

This isn’t about IQ or elite degrees. It’s about a shift in what the job is. From “execute instructions” to “hold the goal in your head and assemble a system — of people and machines — that gets there”.

OECD and IMF both point to a growing demand for hybrid roles where humans set direction and make calls, while routine execution gets eaten by software:

• IMF again

• IOE/ILO style overview of AI and work

If your org is full of people who only want the execution part, AI will come for that.

Trying to preserve a workforce that was bred for process and presence control is, in 2026, the HR equivalent of fighting to keep your fax department in 2005.

5. Realize what the talent war is now about

This, to me, is the core of the whole thing.

The war for talent is shifting toward people who:

• see the end business outcome, not just the next ticket

• can break that outcome into steps, some for themselves, some for AI/agents

• accept responsibility for the outcome, even if half the work was done by non‑humans

You can’t start competing for these people “later, once things settle down”. By then they’ll already be working somewhere that gave them trust and interesting problems.

Right now:

• AI tools are basically electricity — everyone can plug into them
  (good overview of how ubiquitous they already are)

• The real differentiator is not “who has ChatGPT”, but who has people who can orchestrate AI into working systems
  (see e.g. a16z)

• The companies that are already stripping out time‑tracking and rebuilding around outcomes are going to look freakishly efficient in a couple of years next to those still buying fancier bossware and wondering why the margin doesn’t move.

A personal note

At Welltory we don’t track hours.
We have unlimited paid time off, sick days, and weekends.
We measure a person’s value by their contribution and what they do for the team — not by how long they sit in Zoom.

So when someone in our workshop jokes:

“With time tracking, we could now stop working and just let AI fake it,”

we can laugh. There’s nothing to “cheat” in our time‑tracking system. There isn’t one.

For companies built on control, it’s not a joke.
It’s a business risk and a cultural dead end.

Because at some point you have to admit:

• you’ve been optimizing the wrong thing for years

• AI just poured rocket fuel on that mistake

• and now you either rebuild around outcomes and trust, or watch your own employees train agents to fake work better than they ever could themselves

If you’re a founder or a leader, your choice is pretty stark:

• double down on control — and lose to your own AI in your own game

• or double down on outcomes and trust — and start competing for the people who can actually keep your company alive in an AI world

I know which side I’m on.

I spent two years paralyzed by AI uncertainty. Then I recognized the pattern — and started planning again.

Two years ago, unit economics was the only religion. Founders who couldn’t explain their path to profitability got shown the door. VCs wanted to see your burn multiple, your LTV/CAC, your runway math.

Now? Mention profitability in a pitch and watch the room go quiet. The only word that opens wallets is “AI.”

I’ve been at this long enough to find it both hilarious and exhausting. The cycles. The amnesia. The absolute certainty that this time the rules have changed.

They haven’t.

The Crowd Is Always Late. That’s Why I Trust Patterns, Not Hype.

Let me tell you a story that captures how this works.

When we started building HRV analysis at Welltory, VCs passed because their “expert advisors” said heart rate variability will not become a popular metric.

Then Apple put HRV on the Watch. Oura made it their headline metric. The thing that was “niche” became the hottest category overnight.

And now? HRV is everywhere. But now we think there are better options and one more time VCs say: “HRV is a must”. The crowd is late all the time because it’s driven by FOMO, not by insights.

This year, for the first time since ChatGPT dropped, I can actually plan for years ahead. Not because the fog lifted — but because I finally recognized what kind of fog it is. It’s liberating. So, I invite you to get a look at something that is very obvious when you search for patterns.

All Bubbles Crash at Stage 4. AI Is Entering Stage 4.

Economists figured out the anatomy of bubbles decades ago. Minsky, Kindleberger — they mapped the same five stages that show up in every bubble from tulips to railroads to dot-com to crypto:

Stage 1: Displacement. A genuinely new technology shows up. This part is usually real.

Stage 2: Boom. Prices go up, but rationally. Early people make money. Smart money pays attention.

Stage 3: Euphoria. “This time is different.” Valuations detach from anything resembling reality. Everyone’s a genius.

Stage 4: Profit-taking. Smart money quietly walks toward the exits while the music’s still playing.

Stage 5: Panic. Everyone realizes they need to leave. The door is not big enough.

Where are we now? Entering Stage 4. I’ll show you why.

The frustrating part: we know this. Reinhart and Rogoff studied 800 years of financial crises and found the same four words echoing through every single one: “This time is different.”

It never is.

Keynes had a darker take: “It is better for reputation to fail conventionally than to succeed unconventionally.” Which is why fund managers pile into the same overpriced deals. If everyone loses together, nobody gets fired. Miss the next Google alone, and you’re done.

There’s a more hopeful frame too. Carlota Perez argues that major technologies go through a “frenzy” phase of crazy overinvestment, then a painful turning point, then a “deployment” phase where the technology actually becomes useful. The internet bubble popped in 2000. The internet didn’t go away — it became the backbone of modern life.

AI will follow the same arc. The question isn’t whether there’ll be a correction. It’s who gets hurt when it happens.

Smart Money Is Exiting. Here’s How You Can Tell.

I’m not going to predict timing. Nobody can. But I can show you what the data says about which stage we’re in — and why it matters for your decisions.

$300B Spent on AI Infrastructure — With No Proof It Will Pay Off

According to the Financial Times, Amazon, Microsoft, Alphabet, and Meta are targeting over $300 billion in AI infrastructure spending for 2025 alone.

Webscale market data shows hyperscaler capex hit ~$304 billion in 2024 — up 56% year-over-year, a record driven by GenAI and data center expansion.

The money is being spent. The question nobody can answer yet: where’s the return? When infrastructure gets built at this pace without clear ROI, the market becomes extremely fragile. Any signal that the payoff isn’t coming triggers panic.

Someone Is Inflating This Bubble on Purpose. Follow the Money.

Here’s something that should make you pause.

In September 2025, NVIDIA announced plans to invest up to $100 billion in OpenAI to finance massive data center construction. Sounds impressive until you look at the structure.

NVIDIA gives OpenAI money. OpenAI uses that money to build data centers. Those data centers need GPUs. Who sells the GPUs? NVIDIA.

OpenAI’s CFO Sarah Friar said it plainly: “Most of the money will return to NVIDIA.”

This is vendor financing — the company selling you the product is lending you the money to buy it. It creates the appearance of demand while the money circulates within a closed loop.

We’ve seen this movie before. In the late 1990s, Cisco extended credit to customers so they could afford more routers. When those customers went bust, so did the loans. Cisco’s stock dropped 85% and took 20 years to recover.

The NVIDIA-OpenAI dance is more sophisticated, but the underlying dynamic is similar. Bloomberg described this as a “circular economy” where money flows between a small group of interconnected players:

• Microsoft invests billions in OpenAI and provides Azure cloud credits

• OpenAI uses Azure (driving Microsoft’s cloud revenue)

• Microsoft needs more GPUs for Azure → buys from NVIDIA

• NVIDIA invests in OpenAI → OpenAI buys more GPUs

Everyone’s revenue goes up. Everyone’s stock goes up. But the underlying cash is just... circulating. And if one player stumbles, the interconnections amplify the damage across the entire ecosystem.

This isn’t fraud. It might even work out. But it’s a structure that concentrates systemic risk — exactly what regulators are starting to notice.

OpenAI Has No Idea How to Break Even. They Admitted It.

OpenAI’s growth is legitimately extraordinary. Revenue went from $28 million in 2022 to a projected $13 billion in 2025.

Here’s the problem: they’re spending $1.69 for every dollar they earn.

According to Fortune, OpenAI expects to lose around $9 billion in 2025 on $13 billion in revenue. By 2028, they project $74 billion in operating losses — in a single year. Cumulative losses through 2029? Somewhere between $115 billion and $143 billion.

Deutsche Bank put it bluntly: “No startup in history has operated with losses on anything approaching this scale.”

The most telling detail came from Sam Altman himself. On X, he admitted that OpenAI is losing money on its $200/month ChatGPT Pro subscription. “People use it much more than we expected,” he wrote.

Think about that. A $200/month subscription — expensive by any consumer software standard — and they’re still underwater on it. Because inference costs for frontier models are brutal, and power users will use every token they can get.

This isn’t a criticism of OpenAI’s technology. It’s an observation about economics. When your product gets more expensive to deliver the more people use it, you don’t have a software company. You have a very sophisticated utility with negative margins.

VCs Are Hoarding Cash “Just in Case.” That’s a Warning Sign.

The FT reports that AI startups in the US raised a record ~$150 billion in 2025 — higher than the previous peak of ~$92 billion in 2021. The explicit logic? “Build a fortress balance sheet in case 2026 gets ugly.”

Crunchbase data shows AI captured nearly 50% of all global VC funding in 2025, up from 34% in 2024. Total: $202 billion into AI alone.

When the smartest players in the room are stockpiling cash “just in case” — that tells you something about what they expect is coming.

When Employees Sell $6.6B at Peak Valuation, They Know Something.

Reuters reported that OpenAI hit a ~$500 billion valuation through a secondary sale where employees and former employees sold about $6.6 billion in shares. The company reportedly authorized over $10 billion in secondary sales total.

Secondary transactions aren’t bad by themselves. But a flood of secondary at peak hype? That’s a classic mechanism for early holders to transfer risk to later buyers.

The people who built OpenAI are cashing out. At record valuations. While telling everyone else to buy in.

CEOs Are Selling. Retail Investors Are Buying. Guess Who Loses.

Don’t listen to what executives say. Watch SEC Form 4 filings — what they actually do with their own shares.

NVIDIA’s CEO Jensen Huang has sold $1.9 billion+ in stock since 2024, executing sales consistently through 10b5-1 plans at peak valuations.

The aggregate picture: insider buy/sell ratios have dropped to 0.29 in 2025 — well below the historical average of 0.42-0.50. For every dollar insiders spend on their own stock, they’re selling $3.44.

Now — important caveat. Insider selling can mean lots of things: diversification, taxes, estate planning. Academic research shows aggregate insider activity has some predictive value for market returns, but it’s not a timing tool.

I’m not saying “crash imminent.” I’m saying: this is what Stage 4 looks like. Early holders are converting paper to cash while the music plays.

The Future Isn’t Giant Cloud Models. It’s AI on Your Phone.

Here’s where the story gets hopeful — if you’re paying attention.

While everyone debates whether we’re in a bubble, the actual product direction of AI is becoming clear. And it’s not what the hype suggests.

Apple and Google Are Building AI That Runs on Your Phone

Apple’s documentation is unambiguous:

“The cornerstone of Apple Intelligence is on-device processing.” — Apple Support

Their Private Cloud Compute is positioned as a fallback for complex tasks, with strong privacy guarantees. The default is local.

Android’s Gemini Nano is explicitly about processing “without sending your data off the device.” Google’s developer documentation makes the on-device emphasis clear.

Microsoft’s Phi-3 technical report describes their 3.8B parameter model as “small enough to be deployed on a phone” while maintaining strong reasoning capabilities. Their research page literally says “locally on your phone” in the title.

Meta’s Llama 3.2 release explicitly targeted edge and on-device deployment. The 1B parameter model card is designed for exactly this use case.

The point: This isn’t analysts speculating. The companies that control the platforms — iOS, Android, Windows — are building their AI strategies around on-device inference. Privacy, latency, cost. Those aren’t buzzwords; they’re product requirements.

A $6M Model Now Matches a $100M One. Moats Are Disappearing.

This isn’t ideology. It’s economics.

If your product is a daily-use app — health tracking, financial assistant, customer support, B2B workflow — then:

• Latency matters more than +3% on benchmarks

• Privacy is a feature, not a constraint

• Inference cost becomes your gross margin

Andrew Ng has been pushing this for years. His framing of data-centric AI:

“The discipline of systematically engineering the data needed to build a successful AI system.”

The insight: most industries need custom models trained on their own data, not “one model to rule them all.”

DeepSeek proved this dramatically. Their V3 technical report documents training at roughly 2.788 million H800 GPU hours — a fraction of what frontier Western models reportedly require. Reuters covered their claim of a theoretical 545% daily cost-profit ratio on inference.

What matters for founders: when “good enough” quality becomes available at dramatically lower cost, the pricing power of expensive inference providers collapses. Business models shift toward compression, hybrid architectures, and workflow integration.

The model itself stops being the moat.

When Bubbles Pop, Founders Lose Everything. VCs Keep Their Fees.

Here’s what I wish someone explained to me earlier: in bubbles, the architects rarely suffer. The pain flows downhill.

The Teflon Layer

Microsoft structured its OpenAI deal carefully. They hold licensing rights to the technology. If OpenAI can’t pay its Azure bills, Microsoft — as major creditor and license holder — is well-positioned to acquire the technology through the debt. It’s not a bet on OpenAI’s independence. It’s a call option with downside protection.

NVIDIA generates $25+ billion in cash per quarter and has a $60 billion buyback authorization. If their stock drops 70% (like Cisco in 2000), the company doesn’t die. They buy back shares cheap, wait for the next cycle. Jensen has done this before — with crypto, with gaming.

The people holding the bag are retail investors who bought at the top, and pension funds with exposure to inflated valuations.

“Successful Exit” Can Mean Zero for Founders. Here’s the Math.

When you read that a startup raised at a $1B valuation, it’s natural to assume the founders got rich. Usually they didn’t. The valuation is mostly fiction — a number that sets share price but says nothing about who gets paid when things go sideways.

Late-stage investors increasingly demand liquidation preferences: contractual rights to get their money back (often 2x or more) before anyone else sees a dollar.

Quick example: Company raises $100M with 2x liquidation preference. Bubble deflates, company sells for $150M. Looks like success, right?

Investors are owed $200M (2x their investment). There’s only $150M. Investors take it all. Founders and employees get zero.

Years of work. Nothing.

Employees with options. Same math, worse leverage.

Pension funds and endowments. VCs manage other people’s money — teachers’ retirement, university endowments. When funds chase AI deals at 100x revenue, they’re betting those assets. The general partners already collected their 2% management fee. The LPs discover the damage years later.

“Wrapper” startups. Thousands of companies whose product is basically an API call plus a nice interface. They die the moment Apple or Microsoft builds the feature natively.

The VC Defense Is Already Written

Here’s how the post-mortem will read:

“Venture capital is a business of outliers. We had a fiduciary duty to participate in the AI opportunity. Yes, many investments didn’t work out, but that’s inherent to the asset class. The ones that succeeded will return the fund.”

They’ll pivot to quantum computing or synthetic biology or whatever the next cycle brings. The machine keeps running. The losses are always “normal” in hindsight.

I’m not saying VCs are bad people. I’m saying their incentive structure is designed to survive bubbles, not prevent them. That’s not a criticism. It’s just a fact founders need to understand.

Once You See the Pattern, You Can Finally Plan Again.

Okay, enough doom. Here’s where the story turns.

AI Is Not “The New Internet.” It’s Infrastructure.

The internet parallel is dangerous because it implies “winner take all” dynamics — that some AI company will become the next Google, and everyone else will fade away.

I don’t think that’s what’s happening.

The better parallel is the first machine learning revolution in the 2000s and 2010s. ML didn’t produce one dominant company. It became invisible infrastructure embedded in every application. Recommendation engines, fraud detection, search ranking, spam filters — ML is everywhere now, and mostly you don’t notice it.

The winners of that era weren’t the companies with the biggest models. They were the ones with the best data, the best evaluation frameworks, the best integration into actual workflows.

The same pattern is emerging now.

Where Moats Actually Come From

If models are commoditizing — and the evidence says they are — then where does defensibility come from?

1. Data and data rights. Unique datasets that can’t be easily replicated.

2. Evaluation capability. The ability to distinguish quality from hallucination, tied to business outcomes.

3. Workflow integration. Embedded in how people actually work. High switching costs.

4. Distribution. Channels and brand that don’t depend on a single platform.

5. Cost engineering. Controlling latency, COGS, reliability at scale.

What’s not a moat:

“We also have an LLM chat interface.”

If You Can’t Answer This Question, You Won’t Survive the Crash.

I’ll write a separate piece on specific strategic bets. For now, here’s what matters most.

The One Question

What happens to your company when you can’t make unit economics work — and venture capital goes back to demanding profitability?

If you don’t have an answer, you’re gambling with years of your life.

The Opportunity Is Clarity

I spent two years feeling destabilized by AI uncertainty. Every few months a new model dropped and I’d wonder if our whole roadmap was obsolete.

What changed wasn’t the technology. What changed was recognizing the pattern.

My message is simple: you can finally forecast again.

The hype will fade. AI will remain — as infrastructure, like electricity, like the internet post-2000. The winners won’t be whoever raised at the highest valuation or chased the biggest model. That path doesn’t make anyone happy. Building something meaningful and lasting does.

Happy New Year. Build your own clarity — and invest in that, not the crowd. At worst, you’ll learn something real. At best, you’ll build something that matters.

I’ve followed the Physical series since the first season. But watching Adiyasuren and Enkh-Orgil work through those challenges felt less like athletic competition and more like watching founders navigate a market where everyone else has more capital and better connections. They compensated with something harder to build: strategic thinking under extreme constraint.

Elite Sport and Founder Life Share More Than You’d Think

When I trained in kickboxing, I was surprised by how naturally I connected with my coach — a professional MMA fighter. We spoke the same language even though our contexts were completely different. Pushing through limits when your body is screaming to stop. Understanding that recovery isn’t weakness, it’s part of the strategy. Knowing that brute force alone gets you nowhere when everyone at the top has similar capabilities.

The best founders operate the same way. They understand resource management the way an athlete understands energy systems. They know when to push and when backing off prevents catastrophic failure. They develop tactics that compound over time rather than just throwing everything at the immediate problem.

This is partly why I think unlimited funding often damages startups. When you can throw money at every mistake, you never develop the instinct for efficient resource allocation. You don’t learn to feel your constraints. In elite sport, nature enforces those limits. Your body will shut down. Your technique degrades under fatigue. No amount of confidence overrides biology.

The market works the same way, just slower.

Australia’s Mistake Was Confidence in Overwhelming Force

Team Australia was probably the most physically powerful team in the finals. In the last challenge, they leaned almost entirely on that advantage. They were so confident in their raw capability that long-term strategy seemed unnecessary.

They lost.

Team Mongolia showed up with tactical creativity layered over solid fundamentals. When Enkh-Orgil approached the castle gate challenge, he didn’t try to muscle through — he read the mechanics and found the efficient path. The Japanese team, used to relying on power, didn’t see it. Australia didn’t see it either.

I watched the same dynamic play out during the recent venture winter. Startups with inflated budgets and easy capital access collapsed in sequence. Founders from top-tier schools who’d worked at prestigious companies — people used to doors opening based on credentials — simply didn’t have the mental models for operating under real constraint.

The strategies that come naturally when you’ve always had to stretch every dollar, make one engineer do the work of three, negotiate payment terms because missing payroll isn’t hypothetical — those are the strategies that win when easy money disappears.

The Math on Women That Nobody Bothers to Check

In the pole-holding challenge, Team Mongolia’s decision to field Adiyasuren was tactically brilliant. Not just because she performed well, but because it demonstrated pattern recognition that creates asymmetric advantage.

Women get systematically underestimated in physical competition. Research analyzing over 5 million ultra-running results found that female runners are faster than their male counterparts for distances longer than 195 miles. In ultra-distance swimming, the best women have been 12 to 14 percent faster than the best men on average over the past 30 years in events like the 45.8km Manhattan Island marathon swim.

The physiological reasons are documented: women have more Type I muscle fibers which produce less force but are significantly more fatigue-resistant, better fat oxidation rates that preserve glycogen stores, and reduced inflammation and muscle damage allowing faster recovery from extended exercise.

But the cultural narrative stays locked on sprint distances where male physiology dominates. So investors see a female founder and pattern-match downward. Competitors underestimate. The woman who learned to operate without anyone expecting much from her develops capabilities that people with easier paths never needed to build.

Team Mongolia had the most diverse composition in the finals and used it as a tactical asset, not a diversity statement.

What I Miss In Western Competitions

Asian athletic shows maintain something that gets lost in most Western productions: you can push humans to absolute physical limits while maintaining basic dignity. You bow. You acknowledge your opponent’s capability. Win or lose, nobody’s screaming insults in someone’s face or performing dominance.

This matters more than it seems on the surface. Founder culture often mirrors the same toxic competitive framing — the “crushing it” language, zero-sum positioning, casual cruelty dressed as ambition. But the companies that survive long enough to actually matter tend to operate differently. They compete intensely and collaborate generously. They push limits without destroying the people doing the pushing.

Physical Asia proved you can have both. Brutal competition and mutual respect aren’t opposing forces.

What Mongolia Actually Gave Us

There’s speculation about whether Mongolia should have won. Korea fought to the last second, had home advantage, had competition experience. These factors are real.

But here’s what matters more: millions of people worldwide now know Mongolia exists as something other than a historical footnote. They saw warriors who treat women as equals, who understand that proximity to nature builds physical intelligence that gyms can’t replicate, who approach impossible challenges with creativity instead of just force.

The world got to see what happens when you combine traditional physical culture with modern strategic thinking. When you value tactical intelligence as much as raw capability. When you understand that in any competition at the highest level, everyone is strong — so strength alone doesn’t differentiate.

I’m grateful for what Physical Asia revealed about performance under constraint. Strategy compounds over time in ways that raw capability doesn’t. Diversity of approach creates blind spots in homogeneous teams. Respect and intensity coexist just fine.

These aren’t just sports lessons. They’re lessons about building anything that lasts — companies, teams, systems that work when conditions get hard.

Sometimes the strongest team doesn’t win. Sometimes the smartest one does. Usually the smartest one was also the one nobody expected.

P.S. For what it’s worth, riding horses does excellent things for stress regulation.

That’s the moment you realize: you no longer have a startup. You have a company.

Numbers That Don’t Flatter Your Ego (But Should)

Deloitte Fast 500 for the second year running. Ranked 31-32 among the fastest-growing companies in the Bay Area—and that’s about growth rate, not revenue or scale. Yes, it’s good for the entrepreneurial ego. But honestly, these rankings are just external validation of what’s happening internally.

The real benchmark for me isn’t competitor comparison. It’s when critical processes start working without your direct involvement. When a marketing launch runs through Veronika and her team. When Core Value presents research results I hadn’t seen at draft stage. When an engineer who wrote the algorithm answers questions in Product Hunt comments, not the founder who sells it.

I sometimes think of the company as a unicorn—in the original mythological sense, not the venture capital one. It’s charging toward its own manifestation, following its own trajectory, building its own momentum. We’re all just trying to keep up. Sometimes we ride in the saddle. Sometimes we grip the pommel. Sometimes we get dragged along the road but refuse to let go. Because the mission exists whether you’re keeping pace or not.

A Feature That Might Be More Than a Feature

Now for what actually matters. And here I’ll be maximally careful with my language, because claims require evidence, and what we have right now is a our thought supported by physiological mechanics.

Our team (and particular genius scientist) developed a system for analyzing and managing sedentary stress—the kind that accumulates during Zoom calls, deadlines, and endless email chains. Stress that happens in a motionless body. Without physical discharge.

Here’s what we know from physiology:

• Acute psychological stress causes vasoconstriction—narrowing of blood vessels

• Chronic stress without physical activity leads to endothelial dysfunction

• Repeated episodes of vasoconstriction create micro-damage to vascular walls

• This micro-damage is one mechanism in the development of atherosclerosis

To simplify: every time you sit through a tense meeting, your blood vessels sustain micro-trauma. The accumulation of such trauma increases the risk of cardiovascular events.

Now imagine you have a skill—trained and practiced to automaticity—to recognize this stress and discharge it physiologically. Not “breathe and calm down,” but actually interrupt the vasoconstriction cycle before the damage sets in.

Technically, this is real-time heart rate management. But conceptually, it’s a skill you can develop. Like learning to touch-type or drive a car.

I call this a “preventive vaccine against cardiovascular disease” in my private notes, where I can be more liberal with phrasing. But if we want to talk about this publicly—we need a trial. We need to show not just correlation between stress management practice and subjective wellbeing improvements. We need to show clinically meaningful outcomes: reduction in inflammatory markers, improved endothelial function, altered trajectories of risk scores.

This is difficult. It takes time. It’s expensive. But if it works—we’re not talking about a mindfulness app. We’re talking about a scalable preventive intervention for millions of people with sedentary lifestyles.

Momentum and What to Do With It

Right now I feel momentum—and it’s not about awards or Product Hunt rankings. It’s about the alignment of several factors simultaneously:

• We have a team capable of operating autonomously

• We have technology that might be significantly bigger than it appears

• We have a user base and ability to validate hypotheses

• We have the time and resources to think strategically rather than just survive

I don’t know if our hypothesis will prove correct. I don’t know if we’ll be able to design a study that convinces the scientific community. Stop. Nope. I don’t have to be too humble here. I know. But it will take some time to prove it to the world.

I am happy to be on this road with our cute little unicorn and my awesome team.

Whoop had added blood pressure tracking to their fitness wearable. Once a day, during sleep, the device estimates BP from the same sensor that tracks heart rate. Not diagnosing anything. Not prescribing medication. Just showing trends—like it already does for heart rate variability and respiratory rate.

FDA’s response: Stop. This is an unregistered medical device. Blood pressure measurement is “inherently linked” to diagnosing hypertension. You need full medical device approval.

Here’s what makes this absurd: 664,470 Americans died from hypertension-related causes in 2023. The economic burden is $219 billion annually. Hypertension kills because people don’t know they have it until it’s too late—heart attack, stroke, kidney failure.

But when a company tries to help people track their BP before they need an ambulance? FDA says that’s too dangerous.

Meanwhile, you can buy cigarettes at any gas station.

I run a preventive health company. Our mission is helping people understand their bodies before they get sick. Here’s what the Whoop decision means for us: We receive blood pressure data from FDA-cleared devices that users connect to our app. When I see patterns that—according to published American Heart Association guidelines—suggest someone should talk to their doctor, FDA’s current stance says I should stay silent. Even if the clinical evidence is clear. Even if saying nothing means someone misses early intervention.

The message: until someone is diagnosed with disease, helping them understand health risks is too dangerous.

This isn’t about Whoop. It’s about whether we’re allowed to stay healthy—or have to wait until we’re sick before technology can help us.

What Actually Happened

Whoop built the blood pressure feature carefully—one daily reading during sleep using the same optical sensor that tracks heart rate in every fitness wearable. They positioned it as wellness: understanding how your body responds to sleep, stress, training.

FDA pointed to “evidence” of medical intent: One sentence on their website (”Elevated blood pressure can be a sign of poor sleep”). Color-coded indicators showing green, yellow, orange zones. The association itself—blood pressure is “inherently linked to diagnosis,” so any BP feature automatically becomes a medical device.

Will Ahmed’s public response: “We respectfully disagree. This is wellness, like tracking breathing rate or HRV. We’re helping people understand their bodies, not diagnosing disease.”

Here’s the paradox. Every fitness tracker measures heart rate—no FDA approval needed, even though abnormal heart rate signals cardiac problems. HRV tracking? Standard feature, no approval. Respiratory rate? Unregulated, even though breathing patterns indicate disease. Blood oxygen (SpO2)? Wellness pulse oximeters exist for athletes and pilots without FDA clearance, even though oxygen saturation directly links to lung disease.

Blood pressure trends? Stop everything. What if someone sees a red number and panics? What if they—horror of horrors—go talk to their doctor? We can’t possibly show people information about their own bodies without spending 18 months and $1.5M proving this won’t cause mass hysteria. The streets will run with anxious patients! Emergency rooms will overflow! Society will collapse!

(Or, you know, people will have a conversation with their doctor about prevention. But that’s apparently too dangerous to allow.)

The distinction isn’t based on risk or clinical evidence. It’s historical—BP has traditionally been measured in clinical settings, so any BP feature triggers the full medical device pathway. Even when all you’re doing is showing someone information about their own body.

Why This Matters (And Why Most Startups Can’t Fight Back)

Whoop Can Afford This. We Can’t.

Whoop can afford this fight—they’ve raised over $400M. Most preventive health startups? We can’t. And it’s not about the FDA filing fee ($24K, or $6K for small businesses). That’s the visible cost.

The real killer is everything else. Software validation, cybersecurity testing, usability studies, small clinical trials, quality management system setup, regulatory consultants—you’re looking at $500K to $1.5M just to get permission to launch. Timeline: 12-24 months minimum.

Speed Death (And Why Prevention Is Different)

But here’s what actually kills you: speed death. Modern software companies ship updates weekly, sometimes daily. We A/B test, iterate based on user feedback, improve algorithms as we learn. FDA approval means freezing your product for two years. No updates. No improvements. No learning from real users. You submit Version 1.0 and pray it works.

And here’s the cruel irony: preventive health technology lives or dies on behavioral change and patient engagement. 90% of users abandon health apps within the first week. We need to iterate constantly—test messaging, refine UX, personalize coaching, optimize timing—far more than any medical device ever will. A diabetic with a glucose monitor isn’t going anywhere; they need it. But someone who’s healthy? They’ll delete your app the moment it becomes annoying or irrelevant.

Freezing a preventive health product for two years isn’t just inconvenient. It’s guaranteed failure. By the time you launch, your engagement mechanisms are obsolete. You can’t adapt. You can’t improve. You’re stuck with whatever retention rate you had at submission—which, statistically, means you’ve already lost 90% of users.

After approval? Any meaningful change requires resubmission. Algorithm tweak to improve engagement? Resubmit. UI change affecting how people interpret health information? Resubmit. You become a bad product company because you can’t be a good one.

FDA’s framework was built for devices that treat diagnosed patients—people who are already motivated by disease. But prevention isn’t like that. Prevention is competing with Netflix, Instagram, and every other app on someone’s phone. Without constant iteration, you’re dead.

The ROI Black Hole

Then there’s the ROI black hole. FDA approval doesn’t guarantee revenue. Medicare doesn’t automatically cover FDA-approved devices—that’s a separate battle. The MCIT rule that was supposed to create a pathway? Repealed in 2021. Commercial insurers make their own coverage decisions. You need to negotiate with dozens of payers separately, which takes years.

So investors see: $1M+ spend, 18+ months delay, zero guarantee of market access. Why fund that when they could invest in B2B SaaS with predictable economics?

The AI Double Standard

Meanwhile—and this is what makes it maddening—FDA has been fast-tracking AI diagnostic tools. AI chatbots for clinical decisions? Getting approved. Algorithms that analyze medical images? Breakthrough device programs. These are literal black boxes—nobody can fully explain how a neural network decides—but FDA figured out how to evaluate them.

Whoop’s transparent BP algorithm based on established science? Blocked.

Why We Can’t Just Accept This

We started Welltory because people deserve to understand their bodies before they get sick. We want to help people see patterns, take action early, when lifestyle changes can still prevent chronic disease. We don’t even have a Series A—we’re bootstrapped, financed by our users. We can’t freeze our product for 18 months. We can’t spend $1.5M we don’t have on regulatory approval for showing someone information about their own body.

Here’s the thing: any startup in preventive health would do almost anything for clear market access. We want oversight. We want safety standards. We want accountability.

What FDA offers is uncertainty, massive costs, indefinite timelines, and no guarantee of market access even if you survive the process.

That’s not regulation. That’s paralysis.

The Lies We Need to Stop Believing

Before solutions, let’s kill the premises FDA’s policy is built on.

Lie #1: “Better not to scare people”

The implicit logic: people see elevated BP, they panic, unnecessary ER visits, healthcare system overwhelmed.

The actual numbers: Between 2006-2015, about 600K-900K Americans went to emergency departments primarily for elevated blood pressure. ACEP guidelines are explicit—asymptomatic elevated BP doesn’t warrant emergency treatment. Route to primary care for proper monitoring.

Despite clear guidelines, studies show 80% of these patients get diagnostic testing in the ED and over 30% receive medications. Significant overutilization. Many visits could have been handled in primary care.

Even assuming worst case—all of these are “unnecessary panic” (they’re not; some have real complications)—at $750 per visit, that’s roughly $450-675 million annually.

Compare to uncontrolled hypertension: $219 billion annually plus 664,470 deaths.

The ratio is 300:1. For every dollar spent on anxiety visits, we lose $300 to late detection and poor control.

AHA and ACEP recommend the same solution: asymptomatic elevated readings should trigger home monitoring or ambulatory BP to confirm, not panic. The answer isn’t hiding information—it’s smart navigation and education.

The real public health problem isn’t people learning about risks “too early.” It’s people learning too late, in the ICU with a stroke.

Lie #2: “Showing clinical guidelines = making a diagnosis”

The American Heart Association publishes BP guidelines publicly. Anyone can Google them. They define stages with color zones: Normal (<120/80), Elevated, Stage 1 Hypertension, Stage 2. These guidelines are everywhere.

People already use unverified AI chatbots to interpret health data. But if a legitimate wellness app references the same AHA guidelines with clear disclaimers? FDA calls it “making a diagnosis.”

Because of formatting. Because of colors.

We’re forcing people to figure out health information alone—with Google, random websites, AI that may or may not be accurate—while preventing legitimate companies from sharing verified, publicly available clinical knowledge.

Lie #3: “No proven benefit = should be banned”

FDA’s default: prove clinical benefit to exist in market.

But some health benefits take years to manifest. Mindset shifts, habit formation, lifestyle changes—these are long arcs. Preventing something that didn’t happen is invisible.

Even without clinically significant biomarker improvements, if there’s no harm and people find value, why ban it? Maybe the benefit is psychological—reduced anxiety through understanding. Maybe it’s behavioral—better doctor conversations. Maybe it’s long-term prevention that won’t show in a 6-month trial.

“We don’t know yet” isn’t the same as “this must be forbidden.”

Better approach: Prove no harm first (safety testing, monitoring). Show benefit if you can (surrogate markers, behavior change). If no harm plus user value plus reasonable mechanism, let it exist.

Two tiers: Green (proven benefit = mandatory coverage). Yellow (no demonstrated harm = allowed in market, optional coverage).

Lie #4: “Personalization can’t be tested”

FDA wants proof things “work for everyone.” But personalized medicine means interventions work differently for different people based on genetics, environment, psychology, preferences. That’s the point.

“Show me your one-size-fits-all clinical trial” is the wrong question for personalized prevention.

Right questions: Does it fail catastrophically for any populations? Can you monitor real-world outcomes? Does performance degrade for specific groups? Are there adverse events?

You can’t predict every scenario for adaptive systems. Stop pretending you can. Test safety on edge cases and monitor continuously in real life.

These lies share a foundation: FDA treats “health” as synonymous with “disease.” If you’re tracking metrics related to diagnosed conditions, you’re doing medicine. If you’re helping people understand their bodies before diagnosis, you’re in regulatory limbo.

This is backwards. Health doesn’t begin when disease is diagnosed. Health is everything that happens before you get sick—and technology that helps people stay healthy isn’t medicine.

It’s prevention. And prevention should be a right, not a regulatory nightmare.

A System That Actually Works

Let’s try to find a solution without compromise.

Startups need fast market access, clear path to coverage, ability to iterate. FDA needs safety assurance and accountability. Payers need cost-effectiveness proof without funding unproven tech. Patients need access to innovation plus protection from harm. Doctors need reliable tools with no liability risk.

Current system gives none of them what they need. FDA gets false safety by blocking useful prevention. Payers get chaos with no standardized evidence. Startups die. Patients develop preventable disease.

Here’s a system that actually works. And honestly? There are no unsolvable problems here. Just lack of will.

Think pipeline, not wall. Current FDA is binary—either “approved medical device” or “unregulated wellness.” Instead, products should flow through stages: Classification → Safety Clearance → Market Access → Evidence Generation → Mandatory Coverage → Continuous Monitoring.

The classification piece: Not all health claims carry the same risk. Showing someone their blood pressure trend with AHA guidelines isn’t the same as diagnosing hypertension or prescribing medication. Create five levels from pure information (lowest risk, fastest approval) to actual diagnosis and treatment (highest risk, full FDA pathway). Whoop’s BP trends would be Information or Behavioral Coaching level. Quick safety tests, clear disclaimers, no million-dollar approval process.

The safety piece: Stop requiring custom clinical trials for each product. FDA publishes standard test scenarios—like crash tests for cars. Sensor accuracy across skin tones? Check. Interface clarity? Check. Works for edge cases? Check. Companies run tests, submit results, FDA spot-checks. Updates yearly based on real failures. This is cheaper, standardized, focused on actual safety risks instead of hypothetical ones.

The market access piece: Create a voluntary public insurance option—Innovation Medicare—that auto-covers preventive tech passing safety clearance. The key: representative sampling as core principle. Not just healthy early adopters. Statistical algorithm ensures diverse enrollment across geography, demographics, health status. Members consent to data sharing. Goal: 1-2M representative group.

For startups, this IS your evidence pipeline. Real users, real data, immediate revenue. No negotiating with 50 insurers before you have proof. Technology enables this now—FHIR APIs, remote monitoring, standardized outcomes, machine learning for analysis.

The accountability piece: Build a transparency registry. When any product tells someone “go see a doctor,” it automatically pings a public database with de-identified data. Follow-up tracking shows whether referrals were appropriate. Calculate positive predictive value, false alarm rate, demographic equity for each product. Public scorecard after 6 months. Green score (high accuracy, low false alarms) versus Red (poor performance, under review).

This isn’t about hiding information. It’s about making quality visible. Good products prove themselves. Bad products get exposed fast. Doctors know which alerts to trust. Patients get transparency.

The coverage piece: Two tiers. Yellow tier (market access): pass safety clearance, allowed to sell, Innovation Medicare covers at 50%, private payers can choose. Green tier (mandatory coverage): after 12-24 months showing demonstrated benefit, cost-effectiveness, good registry scores, all payers must cover at standard rate.

Clear path: deliver value, earn mandatory coverage. Don’t deliver, market filters you out.

How it works together: Startup classifies product by claim level, gets safety clearance in weeks to months for $10-150K depending on level, launches immediately via Innovation Medicare, generates real-world evidence while iterating freely within same level, earns Green tier with proven value after 1-2 years, gets mandatory coverage everywhere.

For Whoop’s BP tool: classify as Behavioral/Navigation level, 8-12 weeks safety testing costing ~$100K, launch immediately, iterate based on user engagement (because prevention requires it), show reduced uncontrolled hypertension after 18 months, earn mandatory coverage. Total: ~2 years, ~$500K versus current $1.5M+ with no guarantee.

This is MORE accountability than current system—continuous monitoring, public scorecards, rapid removal of bad products. This is MORE evidence-based—real outcomes in representative populations over time. This is SMARTER risk management—proportional oversight, focus on actual harms, quick problem identification.

Each piece enables the others. Remove one, system weakens. Together, they create a functioning ecosystem where good products win, bad products fail, startups have predictable pathways, payers fund what works, patients get safe prevention access.

Who wins: Startups delivering value. Patients getting prevention. Payers funding what works. FDA achieving real safety.

Who loses: Startups not delivering (good). Incumbents hiding behind barriers (also good).

That’s how policy should work.

Why the System Stays Broken (And Who Could Fix It)

Short-term incentives beat long-term value. Incumbent medical device companies benefit from barriers that protect market position. Fee-for-service healthcare profits from treatment, not prevention. Pharma makes money from chronic disease management.

Meanwhile patients develop preventable disease, startups can’t compete, payers watch long-term costs explode, employers deal with sick workforces.

Here’s the irony: payers actually want prevention—it saves money long-term. Employers want healthy workers. Patients want to avoid getting sick. The misalignment is temporal. Quarterly profits versus lifetime health.

Forcing functions that could change this: Medicare Advantage plans required to demonstrate prevention metrics. Employers demanding preventive tech coverage in insurance contracts. State Medicaid experiments. Value-based care contracts that penalize poor prevention outcomes.

The Innovation Medicare Plan solves the temporal mismatch by aligning multi-year evidence collection with long-term value creation.

The FDA Could Actually Lead This

This isn’t fantasy. FDA has precedent.

21st Century Cures Act (2016) carved out low-risk clinical decision support software. Created pathway for real-world evidence in drug approvals. Breakthrough Devices Program fast-tracks high-value innovations with flexible evidence requirements. Patient-Reported Outcomes already used in approvals—acknowledging patient experience matters beyond clinical measurements.

When evidence showed pulse oximeters were less accurate on darker skin, FDA tightened requirements: diverse testing populations, better labeling, stricter performance standards. This is good regulation—targeted, evidence-based, addresses real demonstrated harm.

Apply same thinking to preventive wearables: Don’t ban BP trends because “someone might panic.” Do require accuracy standards, demographic testing, clear disclaimers. Monitor real-world harms, adjust based on data.

The tools exist. The precedents exist. What’s needed is will.

Pressure building: Former FDA Commissioner Scott Gottlieb publicly criticizing current approach (”Recent FDA policy puts a ceiling on functionality of new AI tools and forces developers to gut product capabilities”). Industry pushback from Whoop going public. International competition—Europe’s DiGA program, UK’s NICE pathways, China moving faster. Bipartisan political interest in prevention and innovation.

The ask: Implement risk-based classification. Launch transparency registry. Partner with CMS on Innovation Medicare. Develop standard safety test suites.

Not “deregulate everything.” Smart, risk-based, evidence-driven prevention policy.

The Choice

Without change, here’s what continues: Innovation moves to Europe, UK, China—countries with clearer pathways. American companies develop overseas first. Chronic disease burden grows while prevention tech that could help stays locked out. Wealthy people pay out-of-pocket for prevention. Everyone else waits until they’re sick enough for insurance to cover treatment. Health equity becomes a class divide.

Only companies that can afford multi-million-dollar approvals survive. The Googles and Apples, not the startups that need to move fast. Regulatory capture deepens.

Here’s my provocation: If we applied FDA’s Whoop logic consistently, bathroom scales would be medical devices (weight linked to obesity). Thermometers would need approval (fever indicates infection). Fitness trackers couldn’t show steps (sedentary lifestyle causes disease).

Obviously absurd. So why is blood pressure different?

It isn’t. The distinction is historical, arbitrary, and harmful.

I spent one evening thinking through this problem. One evening. Not claiming I’ve designed the perfect system—I haven’t. But that evening convinced me of something important: real solutions exist.

Not compromises. Life taught me compromises always produce shit. Half-measures that sort of work for nobody. What we need—and what’s actually possible—is a real solution that accounts for everyone’s legitimate interests. Money’s interests. People’s interests. Innovation’s interests. Safety’s interests.

They’re not incompatible. They just require actually thinking about the problem instead of defending the status quo.

FDA could lead this. They have precedent (Cures Act, RWE frameworks, Breakthrough Devices). They have tools. They have pressure building from all sides. They could make prevention innovation possible in America.

Or not. Either way, those of us working on helping people stay healthy aren’t stopping. We’ll navigate the gray zones, find creative positioning, iterate until we figure it out. The mission is too important to abandon because regulation is hard.

But here’s what I know for sure: prevention shouldn’t require regulatory gymnastics. It shouldn’t be reserved for companies rich enough to fight. It should be the foundation of healthcare.

So what the hell happens between “we’re going to change the world” and “please fill out form 27B for approval to change a button color”?

I’ve been watching this transformation up close – not just in other companies, but fighting it in my own. And I’ve realized something: there are these natural forces, like gravity, that constantly pull growing companies toward meaninglessness. Not because people are incompetent. Because humans are humans.

The Day 2 Problem (Or: Why Bezos Was Right)

Jeff Bezos has this thing he talks about in his shareholder letters – Day 1 vs Day 2:

“Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death.”

Day 1 is when you’re questioning everything, when every decision connects to mission, when “because we’ve always done it this way” isn’t even in your vocabulary. Day 2 is when operations become more important than purpose, when following the process matters more than achieving the outcome.

The dangerous part? This decline happens in slow motion. You don’t wake up one day and realize you’ve become Comcast. It happens one lazy task description at a time, one unquestioned process at a time, one “we’ve always done it this way” at a time.

I see this in companies with founders still running them (Airbnb, Nvidia, Netflix) – they manage to delay Day 2 longer. Why? Because the founder is the walking embodiment of why this exists. Even Google needed Larry and Sergey to come back for their AI Code Red, to shake everyone awake and say “the game changed, wake the fuck up.”

But here’s what keeps me up – even with a founder at the helm, there are these psychological forces working 24/7 to erode meaning. Three big ones.

Three Forces That Kill Companies Slowly

1. Cognitive Laziness Wins By Default

Here’s something I noticed: properly defining a task is genuinely hard work. It requires you to think through context, rationale, success criteria, connection to bigger goals. Your neocortex has to actually fire up and do work.

And humans are inherently lazy thinkers. Daniel Kahneman calls this WYSIATI – What You See Is All There Is. Your brain fills in missing context automatically, not realizing everyone else is filling in different context.

So what happens? Tasks start with full context: why we’re doing this, what success looks like, how it connects to our Q4 goals. Then someone’s in a hurry. They skip the “why” because “everyone knows.” Then they skip the success criteria because “it’s obvious.” Pretty soon you’re left with “make the thing” and a deadline.

The erosion is so gradual you don’t notice. Until one day you open your backlog and half of it is zombie work – nobody knows why it’s there, but nobody wants to kill it either.

This isn’t theoretical. PMI research shows companies lose about 11% of project investment to poor requirements. Standish CHAOS Report? Only 16% of projects finish on time and budget. Stripe found engineers spend 42% of their time dealing with technical debt instead of building new things.

Tasks are your atomic unit. If they’re unclear, everything built on them will be unclear.

2. Process Becomes Religion At Scale

As companies grow, someone creates a process that worked once. That process becomes The Way. Market shifts, needs change, but The Process remains because questioning it feels risky.

Following process is psychologically safe. “I followed the procedure” is an acceptable defense. Asking “should we still be doing this?” is dangerous – you might be labeled “not a team player.”

I’ve watched this happen. Good process → standard process → sacred process → process that exists long after the reason for it disappeared. And eventually you’re Comcast with Silicon Valley internet slower than what I get on a Thai island, because competitive pressure stopped mattering years ago when you achieved effective monopoly.

Netflix figured this out early – their culture document literally says “People Over Process.” Most companies drift the opposite direction until they’re drowning in forms and approvals for things nobody can remember why they needed approval.

Andy Grove said it: “Success breeds complacency. Complacency breeds failure. Only the paranoid survive.”

3. Status Quo Accumulates Silently

Quick thought experiment: look at your company’s SaaS subscriptions. How many tools are you paying for that nobody uses anymore? Tools bought for projects that ended two years ago?

This is status quo bias. In startups, there isn’t much history. But in growing companies, every past decision gains legitimacy simply by existing. Every new hire inherits “how things are done” without questioning it.

Grove had another line: “Let chaos reign, then rein in chaos.” You need both – the ability to question everything AND the ability to execute. Most companies are way too weighted toward execution without questioning.

Where This Gets Real: The Task Problem

So I did this experiment recently. Opened our task tracker, looked at random engineering tasks, asked: Could a newcomer understand how this connects to our mission?

The answer was mostly no. Not because our engineers are bad – because writing good tasks requires cognitive effort everyone shortcuts.

Look at the difference:

What most tasks look like: “Fix dashboard performance. Due: Friday”

What they should actually contain: “Why: Dashboard load time directly impacts first-day retention. When users open the app and the dashboard takes >3 seconds, they interpret this as ‘the app is broken.’ Research shows users form quality judgments in the first 50 milliseconds - after 3 seconds, they’ve decided your product feels unreliable.

The data: 23% of new users who experience >3 second load times never complete their first session. Our day-1 retention is 54% for fast loads vs 31% for slow loads - a 23 percentage point gap caused purely by performance.

This cascades: lower day-1 retention → lower day-7 retention → lower MAU. We’re losing ~200 users/month. At our LTV of $180, that’s $432K annual recurring revenue walking away because we’re technically slow. This directly blocks our Q4 goal of 15% MAU growth.

What: Get P95 load time under 2 seconds

Success:

• P95 load time <2 seconds

• Day-1 retention improves from 31% to 50%+

• No regression in data accuracy”

See the difference? The second version explains the mechanism (slow load → user thinks it’s broken → they leave), shows the metric impact (day-1 retention gap), traces the cascade to business goals, and includes the actual product metric in success criteria. The first version is just... an instruction.

Now, I know what you’re thinking: “That’s a lot more text. Who has time to write all that?”

And you’re right – it takes more effort upfront. But here’s what research on engineering motivation shows: developers perform significantly better when they understand the why behind their work, not just the what. Google’s Project Aristotle found that psychological safety and clear purpose were the top predictors of team performance. When engineers understand the business impact of their code, they make better technical decisions, catch edge cases you didn’t think of, and actually give a damn about the outcome.

The laziness problem is real – writing good context is cognitive work. But this is exactly where AI actually helps. You can draft the business context once, have it expand with the full causal chain, and suddenly that 5-minute task of writing “fix dashboard” becomes a 7-minute task that actually communicates what matters. The marginal cost is tiny compared to the cost of an engineer spending days building the wrong thing or not understanding why speed matters here.

And here’s the thing – if you can’t answer “what breaks if we don’t do this?”, you probably shouldn’t be doing it.

What We’re Actually Doing (The AI Angle)

At Welltory, my COO is rebuilding our operations around something we’re calling “meaning preservation.” The core idea: if you can’t trace a task back to mission, it’s probably waste.

We built this hierarchy:

Mission: Impact 100M people’s health measurably
    ↓
Level 1 Goals (strategic, yearly)
    ↓
Level 2 Goals (specific, quarterly)
    ↓
Projects (with 2-5 milestones each)
    ↓
Milestones (irreversible changes)
    ↓
Deliverables (things we ship)
    ↓
Tasks (actual work)

The principle: any engineer should be able to trace their task up to mission. If they can’t, something’s wrong.

But here’s what’s interesting – we’re using AI as a clarity test. Not to make decisions, but to check if we’re being clear. If AI can parse our task descriptions and understand connections, they’re probably clear. If it can’t, humans probably can’t either – they’re just better at pretending.

AI doesn’t solve the problem. It’s a mirror that shows you when you’re being vague.

Why AI Actually Helps Here

Look, I’m bullish on using AI for management. Not to replace thinking, but to scale the communication part that everyone shortcuts because they’re tired or in a hurry.

The key insight: AI doesn’t get lazy. Humans skip writing context because it’s cognitive work. AI doesn’t care. You can encode all your quality criteria into a prompt, and unlike a human reviewer, it won’t forget to check them or let things slide because it’s Friday afternoon.

Here’s the actual prompt we use to check if our tasks are meaningful or just corporate theater:

You are reviewing a task description for clarity and completeness.

Check:
1. Does it explain WHY this matters? (business context, user impact)
2. Is there a clear causal chain from this work to a product metric?
3. Can you verify completion objectively?
4. Would an engineer understand what problem they’re solving?

If any of these are missing or vague, explain what’s unclear.
Rate 1-10 for task quality.

That’s it. Paste your task, get feedback. The AI forces you to be clear enough that a machine can parse your logic. And if a machine can’t understand your reasoning, your team probably can’t either – they’re just too polite to say so.

The Real Fight: It’s Not About Perfection

Here’s what I’ve learned: you can’t stop these forces completely. Cognitive laziness, process ossification, status quo bias – they’re fundamental to how humans operate. Fighting them isn’t a project you complete. It’s ongoing resistance.

In Welltory, we have this thing we call our “mycelium network” – the web of connections between mission, goals, projects, tasks. It used to exist entirely in the heads of maybe 5-6 people (me, our CTO, a few product leads). They’d run around explaining to everyone how things connected. It was exhausting and didn’t scale.

Now we’re externalizing it. Making it visible. Not because we’re control freaks, but because the alternative is that knowledge dies with whoever leaves or burns out.

We do these strategic syncs every 1.5 months. Not yearly planning sessions where you make a pretty deck and file it away. Every 1.5 months, we ask: what still makes sense? What doesn’t? What should we kill?

People hate this at first. It feels unstable. But the alternative is worse – you wake up one day and realize half your projects are pointless but nobody knows how to stop them.

The Milestones Thing (Or: How We Track If We’re Actually Achieving Anything)

One thing we’ve gotten obsessive about: defining milestones that actually mean something.

Most companies do milestones like: “Launched beta” → “10% adoption” → “100% rollout”

These are useless. They tell you nothing about your actual strategy. They’re generic checkboxes that could apply to any project.

Better milestones tell the story of what’s changing:

❌ “Launched voice input”
✅ “25% of users who try voice input switch to using it exclusively”

❌ “Beta released to 10%”
✅ “Subjective journaling became a habit: ≥10% of weekly actives add ≥3 entries/week for 4 consecutive weeks”

See the difference? The second version tells you something about user behavior changing, about the product actually working. It’s a point of no return – something shifted in the system that you can’t (and wouldn’t want to) undo.

What This Actually Looks Like In Practice

Want to check if your milestones are meaningful or just “we launched a thing”? Here’s another prompt you can use:

You are reviewing a milestone for a product project.

Evaluate it on 5 dimensions (1–10 each):

1. Specificity — Does it describe a concrete outcome unique to this project, or a generic process any team could use?
2. Polarity — Does it have a clear opposite? (If it could describe ANY project, it’s too vague.)
3. Irreversibility — Does this milestone represent a lasting change in the system, user behavior, or business state?
4. Measurability — Can success be objectively verified (via metrics, thresholds, or observable proof)?
5. Value Link — Does it clearly connect to user or business value (e.g., retention, activation, engagement, efficiency)?

Then:
- Give a total rating (average 1–10)
- Explain what’s missing or unclear.
- Suggest how to rewrite it to make it a “point of no return” milestone.

Try it. If the AI says “this could describe any feature launch,” you haven’t defined a real milestone. You’ve just described a deployment.

The Uncomfortable Truth

Here’s what I’ve learned after years of fighting this: the transformation from startup to soulless corporation isn’t something that happens TO you. It’s something you allow through a thousand small acts of laziness.

Every time you write “fix the dashboard” instead of explaining why and what success looks like.

Every time you follow a process without asking if it still makes sense.

Every time you keep a subscription nobody uses because “we’ve always had it.”

Every time you accept “everyone knows what we’re doing” instead of writing it down.

These aren’t dramatic failures. They’re tiny acts of cognitive laziness. But they accumulate. And eventually you wake up and realize you’re Comcast.

The companies that stay alive – really alive, not just profitable-but-dead inside – are the ones that maintain what I’m calling meaning hygiene. Not as a one-time project. As a constant practice of resistance against entropy.

Because entropy is real. Cognitive laziness is real. Status quo bias is real. These forces are always working. If you’re not actively pushing back, you’re drifting toward Day 2.

I have huge respect for how they're creating new formats in such a traditional industry. The mini-series about the American basketball team's Olympic journey to Paris? One of my favorites. That's where I discovered Stephen Curry is actually incredible - and I'm saying this as someone who doesn't really watch basketball as sport. I knew who LeBron was (because who doesn't?), but Stephen was totally new to me.

So naturally, I went and watched his MasterClass, and now my basketball shots are actually better. I play alone these days - used to play on my school team, then stopped for years, and recently came back to it. It's weirdly calming. Those quick wins are such a different rhythm from founder life where you're making bets that take years to play out. Same reason I love quick coding sessions - immediate results just feel good.

Life as Entertainment

What Netflix is doing is essentially turning sports and life into series. They're creating new forms of entertainment that make you think differently about what content can be.

Apple does big public releases every six months or so. Some companies try to turn their quarterly updates into events. Asana has their events thing going. But it's still not quite... alive, you know?

I keep thinking - what if company life could actually be filmed and shown as a series? We have so much happening - new features, crisis moments, breakthroughs. Learning to tell those stories from the inside out is a real art, and watching Netflix do it so well makes me want to figure this out someday.

The Part Where I Think About Money

The Canelo-Crawford fight? 70,482 people in Allegiant Stadium. That's the largest boxing audience ever in Vegas. Plus millions more watching through Netflix's 300+ million global subscribers.

But here's what I'm wondering: Why do people buy tickets to these events? They sold 74,000 tickets to this fight. That's huge money. And honestly, these people are seeing worse than I am on my couch.

What are they paying for? Connection. Emotion. Being part of something bigger.

So here's the question: What would make remote viewers willing to pay more? How do you create that feeling of being there when you're not?

My Weekend Idea

What if we gave people special interactive tickets that let them be heard in the actual arena?

You're watching from home, you yell at your TV, and through some audio magic, your voice joins thousands of others piped into the venue through dedicated speakers. The volume would be controlled so it adds to the atmosphere without overwhelming the physical crowd.

I'd pay for that. Especially when watching someone I care about compete.

Turns Out This Isn't Crazy

The NBA actually tried something like this during their bubble season. 90% of virtual fans wanted to do it again. Over 650,000 people tried to get in, with 50,000+ fans hosted across 172 games. The tech worked - 1-2 second latency, stable throughout.

WWE tried it too with their ThunderDome, but that became a content moderation nightmare. The lesson? The demand exists, the tech works, you just need to execute it properly.

Why Netflix Could Make This Happen

They've got everything in place: 302+ million subscribers globally, they added 19 million new ones in Q4 2024 alone (largely from live sports), and they're already thinking about sports as entertainment, not just competition.

Start with WWE - those fans already expect to participate. Test it, learn, then expand to boxing, MMA, eventually those NFL games they're streaming.

Even LeBron said virtual fans helped during the pandemic. But imagine if you could actually feel like your energy reaches the arena, not just see yourself on a screen.

The Personal Part

I remember the first time I watched a fight live - it was my kickboxing trainer Dasha, before she made it to UFC but still a serious professional bout. The feeling of watching live when you know the person? Completely different experience.

I would absolutely pay to yell support that she might actually hear. Especially now that she's in the UFC. Dasha's amazing - super kind and calm in person, but when she moves, you can feel she's a death machine.

Anyway

Sometimes good ideas come from simple questions. Like: why can't remote viewers be part of the show?

If you know anyone at Netflix, tell them about the virtual cheering idea. The tech exists, the demand is there, and they're perfectly positioned to make it work.

Going to finish watching Canelo-Crawford now. Hope everyone's having a good weekend.

Here's something funny that happens when your company grows. People start telling you that "priorities keep changing" or "we're always shifting direction." Meanwhile, you're sitting there confused because... nothing has changed?

Our mission at Welltory has been the same since 2017: "Deliver measurable good to 100M people." Same vision. Same strategy. Same everything.

So why does my team think I'm constantly changing my mind?

(Spoiler: It's not because I actually am. It's because of how organizations grow and how people process information. Also, border collies might have figured this out better than us.)

First, Let's Clear Something Up

There are definitely managers and founders who genuinely don't know where they're going. They call it "iterating" or "being agile" when really they're just lost. That's a different problem entirely.

I'm talking about when YOU know exactly where you're headed, haven't changed course, but your team is convinced you're zigzagging all over the place.

This usually kicks in when you grow past the size where everyone can fit in one room. When communication goes from "hey everyone, we're doing this" to chains of meetings, documents, and interpretations. Suddenly you think you're being clear, but what people hear is completely different. Gilovich and friends studied this back in 1998 - turns out we're all terrible at knowing whether we're being understood.

Why People Lose Sight of the Big Picture

Two things I've noticed about how teams process information:

Thing #1: Short-term focus eats long-term vision for breakfast

Once you start doing quarterly planning (OKRs or whatever system you like), people's time horizon shrinks to exactly one quarter. The 5-year vision? Gone. It's like it never existed.

This isn't because people are short-sighted. It's because when you're drowning in quarterly goals, your brain literally shields you from thinking about anything further out. Researchers call this "goal shielding" - it's a feature, not a bug, of how we handle overwhelming information.

Thing #2: "Find the balance" is the most useless advice ever

Tell your team to "use good judgment" or "find the right balance between speed and quality." Watch their faces. It means absolutely nothing to them.

You could try setting competing goals ("ship fast but with zero bugs"), but then you just sound like you're asking for the impossible.

The truth is: if people can't see HOW to get somewhere, they can't move toward it. And very few people can hold both a long-term vision and see the immediate path at the same time.

The Border Collie Method of Leadership

Stick with me here - this actually makes sense.

Ever watched a border collie herd sheep? The dog can't explain where they're going. Can't have a team meeting about quarterly goals. But somehow those sheep end up exactly where they need to be.

How? The dog runs left. Then right. Then left again.

To the sheep, it probably looks like the dog is having some kind of breakdown, constantly changing its mind. But from above? The sheep are moving in a perfect line toward their destination.

That's basically what we do as leaders, except we think we're communicating clearly when actually we're not.

Why Swinging Between Extremes Is Actually Smart

Here's a pattern I've seen at Welltory (and honestly, everywhere):

When we started making real money, we got careful. Super careful. Every feature needed extensive testing. Every deployment required a three-page checklist. Code reviews became novels.

What happened? We slowed to a crawl. Zero innovation. Zero risk-taking. We'd successfully turned ourselves into a very careful, very boring company.

So we swung the other way. "Move faster! Take more risks! Stop overthinking!"

And the team thought: "Oh great, now Jane wants us to be reckless."

But here's the thing - this swinging between extremes isn't confusion. It's how organizations actually figure out what works. You can't find the sweet spot without exploring both edges.

It's Like Going to the Gym (But for Companies)

Think about getting stronger. You don't do a tiny bit of everything every day. You go hard on legs one day, then arms another day. You need the extremes to build capability

.

Same with organizations:

Weak balance: Staying safely in the middle, never really good at anything, slowly getting worse at everything.

Strong balance: Going deep into different capabilities at different times, learning what breaks at each extreme, then combining those lessons.

We needed to experience what happens when your deployment process is so careful it takes longer than building the actual feature. Only then could we appreciate the value of speed.

We needed to break production a few times by shipping too fast. Only then could we understand what "just enough" testing really means.

(My CPO still has stories about some of those experiments. They're hilarious now. Less hilarious at the time.)

The Magic of "AND" Thinking

Here's what I tell teams now: Stop accepting that you have to choose.

We don't do "fast OR careful." We do fast AND careful. We don't do "innovative OR stable." We do innovative AND stable. We don't do "data-driven OR intuitive." We figure out how to do both.

But - and this is the key part that makes you look inconsistent - you can't figure out these "AND" solutions without first going deep into each "OR."

You have to burn a few dishes before you understand cooking. You have to serve a few raw ones too. Then you can find the perfect temperature.

How to Not Look Like You're Constantly Changing Your Mind

The secret? Tell people about the swings BEFORE they happen.

"Hey team, we've gotten really good at careful deployments. Maybe too good. For the next quarter, we're going to push ourselves to move faster. It'll feel uncomfortable. Some things might break. That's intentional - we're building a new muscle."

Then later:

"Remember when we couldn't ship without breaking things? We fixed that. Remember when we were too slow? We fixed that too. Now we know both edges, so we can find our sweet spot."

When people understand that the swinging is intentional - that it's how you explore and learn - they stop thinking you're indecisive and start seeing the method.

The Uncomfortable Truth

Organizations that stop swinging die.

It's that simple. The moment you find "perfect balance" and try to maintain it forever, you're already becoming irrelevant. Someone hungrier is swinging hard and learning faster.

Living things oscillate. Markets swing. Even your mood goes up and down (hopefully). That's not instability - that's life.

Your job as a leader isn't to find the perfect middle ground and camp there. It's to make sure each swing teaches you something, and the overall pattern is heading upward.

(That upward spiral pattern is its own interesting topic, but this post is already long enough.)

I watched the genetic testing gold rush firsthand. Companies raised billions promising health insights, but 23andMe discovered the majority of their business was ancestry curiosity, not health optimization. People wanted to know if they were 2% Viking, not their disease risk.

At Welltory, we've cycled through multiple positioning strategies—from premium biohacking service at $99/month with personal health analysts, to ear-clip wearables, to cognitive optimization courses. Each pivot taught us something counterintuitive about health consumer behavior.

Here's what killed our premium coaching model: you need thousands of trained specialists who understand both data analytics and preventive health. Doctors don't learn this. Wellness coaches can't interpret HRV patterns. There's no talent pipeline. But the deeper realization was more unsettling: executives weren't motivated by long-term health risks at all. What actually worked? Showing them how stress and recovery influenced their productivity, decision-making, and ultimately—their income. Money and performance were stronger motivators than mortality.

The most striking revelation: nobody wakes up thinking about preventing a heart attack in 10 years. This creates a fascinating market dysfunction where acquisition messaging and retention reality exist in parallel universes.

The Health Spending Paradox Matrix

After analyzing thousands of customer behaviors, I've identified four distinct consumer archetypes based on their actual health problems versus their willingness to spend:

The Optimization Obsessed (High Spend, No Real Problems)

These are the Bryan Johnson disciples, the crypto traders on nootropics, the 25-year-olds with continuous glucose monitors despite perfect health. They spend $50-500 monthly not because they need to, but because health optimization has become their identity.

What drives them isn't health—it's status signaling within performance communities and the illusion of control over biological processes. They generate massive media buzz but represent a tiny fraction of actual market revenue.

The Crisis Converts (High Spend, Real Problems)

This is where Welltory found product-market fit. Typically 45-65 years old, these customers have experienced their first chronic diagnosis or health scare. They don't care about biohacking buzzwords—they want to understand why their energy crashed after COVID, why their sleep went to hell during menopause, or how to manage their new diabetes diagnosis without becoming their diabetic parent.

Our median customer is around 50, not the 25-year-old wellness influencer posting sunrise yoga shots. They stay for years because we're solving actual problems, not manufactured anxieties. They're willing to pay $50-150/month for genuine help because the alternative—watching their health decline like their parents' did—finally became real.

Ignorance is Bliss (Low Spend, No Problems)

Most healthy young adults fall here. They'll download free apps, maybe try meditation during a stressful week, but fundamentally believe health problems happen to other people. They represent the largest population segment but generate the lowest revenue. You can't sell prevention to people who don't believe they need preventing.

Research shows young adults aged 19-25 have the highest per capita healthcare expenditures at $1,935, yet exhibit suboptimal healthcare utilization patterns with high emergency room usage (15%) but low office-based visits (55%).

The Procrastinating Patients (Low Spend, Real Problems)

The tragic majority. These people know they have problems. They've googled "why am I tired all the time" at 3 AM. They've researched every thyroid supplement. They've read 47 reviews of continuous glucose monitors. But psychological barriers—analysis paralysis, temporal mood regulation—prevent them from actually purchasing solutions. They need help the most but are psychologically least equipped to seek it.

The Media Myth vs. Market Reality

While tech media obsesses over crypto traders microdosing and Bryan Johnson's $2 million annual regimen, the actual data tells a completely different story:

Who Really Pays for Digital Health

Women dominate every metric:

• 38.88% more likely to wear fitness trackers than men

• 49% more likely to use healthcare wearables

• 54% of fitness tracker owners are women aged 35-54 with household incomes $100k+

• Women ages 19-44 spend 58% more on healthcare than men ($9,989 vs $8,313 annually)

• Working-age women account for $985 billion in total healthcare spending

Income matters more than age:

• 31% of households earning $75,000+ use wearables

• Only 12% of households earning under $30,000 use them

• Income brackets $75k+ are 3.21x more likely to purchase wearables

The disconnect exists because different devices serve different purposes. Men buy smartwatches (71% male, ages 18-34) for tech and status. Women buy fitness trackers for actual health management. One is a gadget market, the other is a health market.

Interestingly, the male grooming market reached $61.3 billion in 2024 with men increasingly adopting wellness categories traditionally dominated by women, yet this spending is more about appearance and status than health outcomes.

The Economics of Irrationality

Here's a brutal truth from research: prevention often costs more than treatment. A wellness program preventing one heart failure case among 100 people at $400/month costs $960,000 but saves only $22,000 in treatment costs.

Yet people continue paying. Why?

The Control Illusion Premium

Health anxious individuals—5.7% of the population—spend $857-$21,137 annually not for health improvement but for perceived control. They'll pay $300/month for continuous glucose monitoring despite perfect metabolic health, while avoiding $20 copay preventive visits.

This psychological dynamic explains the market's dysfunction. Apps exploiting anxiety can charge $70/month for generic meal plans because users buy for emotional relief, not behavior change. The global supplement market exceeds $150 billion annually despite weak evidence for most products—people buy the feeling of "doing something" about their health.

The "Fleece and Forget" Economy

These "fleece and forget" models have doubled our customer acquisition costs. Here's how they work:

1. Promise magical "AI-personalized health transformation"

2. Charge $70-150/month for generic content anyone could Google

3. Blow $100+ acquiring each customer

4. Profit from the 3-4 months before they remember to cancel

5. Rinse and repeat with fresh victims

They can afford astronomical marketing budgets because they're not selling health—they're selling the momentary dopamine hit of "doing something about your health."

Hidden Markets and Taboo Spending

The research uncovered massive unreported spending in categories people don't discuss:

Productivity Enhancement (Not Health)

• $200-500/month on ADHD medications without diagnosis

• 6.7% of workers use "productivity drugs" (up from 4.7%)

• Critical insight: These drugs improve motivation and energy, not actual cognitive capacity

Sexual Health

• $300-2,000/month on testosterone therapy

• 61% improvement in erectile function drives willingness to pay

• Taboo nature creates price insensitivity—people pay premium for discretion

Medical Aesthetics as "Health"

• $25.88 billion market growing at 12.8% annually

• 81% more open to procedures vs 5 years ago

• 79% of surgeons report demand driven by social media appearance

Life Stage Triggers That Override Economics

First Chronic Diagnosis Impact

Research tracking 3,297 participants over 9 years found that chronic disease creates lasting financial perception changes. Individuals become 22% more likely to report inadequate resources and fundamentally shift spending from optimization to crisis management.

The Sandwich Generation Crisis

23% of adults simultaneously care for aging parents and children. This creates:

• Extreme price insensitivity for time-saving health services

• Guilt-driven spending on others' health over their own

• Burnout leading to increased mental health expenditures

The Personalization Paradox

UCLA research revealed that despite claiming privacy concerns, users' payment behavior showed no negative correlation with data sharing. The paradox is strongest among young males who verbally express privacy concerns while behaviorally sharing extensive health data.

Price elasticity of personalization:

• Basic willingness to pay: $6.50/month

• With AI personalization: $149-299/month

• With genetic integration: $199 initial + $69/year

• With biomarker analysis: up to $1,500/year

The CPI Crisis: How Market Distortions Compound

Our cost per install doubled, driven by multiple compounding factors:

• iOS Privacy Tax: 73.2% CPI increase post-iOS 14.5

• Fraud Ecosystem: $84 billion growing to $172 billion by 2028

• Category Premium: Health apps have highest revenue per install

• Professional ASO Arms Race: 3x conversion rate advantages

• Market Concentration: Winner-takes-most dynamics

When apps monetizing anxiety can outspend those providing value, legitimate businesses get priced out of growth.

The Uncomfortable Truths

After years watching this market evolve, several patterns have become undeniable:

Nobody is financially incentivized to keep people healthy—not insurance companies, not pharma, not even individuals until crisis hits.

The loudest segments aren't the most valuable. Bryan Johnson's $2 million annual anti-aging crusade makes for great headlines but represents 0.0001% of the market. Those Gaming Discord biohacking servers where gamers debate nootropic stacks? They generate Twitter threads, not revenue.

Real customers don't match startup pitch decks. Our median customer is ~50 years old, motivated by genuine health issues, not optimization fantasies. They're not there for "biohacking" or Instagram-worthy transformation photos. They come—and stay—because they finally need real help managing chronic fatigue, understanding their cardiac risks, or navigating menopause. And here's the kicker: they actually pay for it.

Marketing messages and product value live in different universes. We still use "Heart Rate Monitor" as our App Store title despite no long-term customer describing us that way. Acquisition requires anxiety triggers; retention requires real solutions.

Market Implications

The pattern is clear: money flows to those who understand psychology, not physiology. The most profitable health apps aren't making people healthier—they're monetizing the gap between people's health anxieties and their behavioral realities.

Sustainable businesses are building for Crisis Converts with real needs, while venture-funded growth stories chase the Optimization Obsessed with their high visibility but low lifetime value. The market currently rewards exploitation over care. Quality startups trying to create real value are priced out of acquisition channels by products that profit from abandoned good intentions.

In a market where nobody is financially incentivized to create healthy people—not insurers, not pharma, not even the people themselves—perhaps the most radical position is simply trying to do so anyway.

But here's what keeps me going: those 50-year-olds who finally found us after years of searching for real answers. They don't care about our App Store optimization tricks or growth hacking. They care that we're still here, still improving, still trying to bridge the gap between what health tech promises and what people actually need.

The winners won't be the loudest apps in the charts. They'll be the ones that figured out how to turn a broken market's incentives into sustainable value—without losing their soul in the process. I know many great founders who work on products like that and I hope that together we will be able to find the right balance between psychology and physiology.

“We’ve got top athletes hitting overtraining walls. Your HRV analysis looks solid. Maybe you can help.”

They weren’t offering a huge budget. What they proposed was visibility — a potential partnership, athlete-level exposure, mutual exploration. They saw the value in what we were building and wanted to collaborate.

And we said we need cash.

Why? Because we had two months of runway left, no enterprise pricing model, and a whole lot of fear.

We didn’t have the overtraining feature yet. Our users back then weren’t professional athletes — they were mostly people trying to start living healthier. So we told UFC, politely, that we needed money, not mentions.

That conversation didn’t go anywhere. And looking back, I think that was the most painful missed opportunity in our company’s nine-year history.

But here’s the thing I didn’t understand back then:

Overtraining isn’t just a problem for pro athletes.

It’s what happens when a founder decides they’re going to “get in shape” and goes too hard for two weeks straight.

It’s what happens when a burned-out exec does back-to-back Peloton sessions to shake off anxiety.

It’s what happens when the stress hormone curve is already maxed out, and you add effort on top of exhaustion.

That’s what we finally built our new Longevity Progress feature for — not for medalists, but for real people.

People like me. Like our users. Like most of the world.

Here’s how it works now:

We calculate your daily and weekly physical activity goals (METs) based on your actual recovery, heart rate variability, and fitness level.

Then we map you into one of four zones:

• Detrain (not enough — your fitness is slipping)

• Recovery (you’re pulling back, recharging — necessary after stress or overload)

• Progress (your personal sweet spot — you’re building long-term health here)

• Overtrain (you’re going too hard — injury risk climbs, recovery suffers)

And the goal isn’t to push through. It’s to stay in the right zone, depending on where your body is today.

We built this system to support longevity, not peak performance. It adapts daily. It respects variability. It treats progress as a curve — not a grind.

It’s probably the most sophisticated thing we’ve ever done in fitness. And yes, it’s now live in beta and rolling out (about 20% of our customers have seen it already).

So yeah — the UFC thing still stings.

They were early. They saw something we hadn’t even built yet.

We were late. We needed cash, not partnerships.

And maybe that’s how it had to go.

Because when you bootstrap, you don’t get to place long bets on brand awareness. You trade exposure for survival. And then — if you’re lucky, and stubborn, and just barely make it through — you come back years later with the product that should’ve existed all along.

That’s where we are now.

If someone from UFC is reading this:

Thank you for that conversation. It stayed with me.

P.S. If your team works in performance health or athlete recovery — we’re open to smart partnerships this time around. And yes, we learned how to price “visibility” the hard way.

Meanwhile, anybody can just start using it soon, it’s not just for athletes.

It’s for everyone who wants to move, train, and live — longer, smarter, and without breaking their own body in the process.

Suddenly, you need org design. You need management layers. You need budget planning, delegation, handoffs. Work starts to diversify. You move slower. Veterans miss the old days. Newcomers struggle to catch the vibe. The product gets more complex. Everything gets... heavier.

And that’s just the warm-up.

Because if you’re lucky — or well-funded — growth continues. And before you know it, you blow right past 150 people.

That’s where the weirdness really begins.

The 150-Person Problem No One Prepares You For

I used to think it was just a meme — like “the 3-year relationship curse.” But the number 150 keeps showing up. Not because founders like it, but because the human brain does.

Anthropologist Robin Dunbar noticed that humans seem wired to comfortably maintain about 150 relationships. Beyond that, social cohesion breaks. Communication frays. Context disappears. Trust turns into bureaucracy. It’s not magic. It’s cognitive load.

“At 150 people, weird stuff starts to happen.” — Chris Cox, Facebook
source

“We found again and again that things get clumsy at a hundred and fifty.” — Bill Gore, Gore-Tex
source

Some companies try to acknowledge this limit structurally — like Spotify, with its famous “tribes” model, each capped at ~150 people. But even they’ve admitted that it’s hard when the product is still one monolith. You risk fragmenting ownership of the whole.

Others, like Basecamp or FatSecret, just… never cross 150 in the first place.

Basecamp: ~70 employees, profitable, millions of users.
FatSecret: ~38 people, 100M+ users.
Cursor: ~20 people. ElevenLabs: ~50.
sources / crunchbase / techcrunch

These are not tiny lifestyle businesses. They’re deliberate machines, built under constraint.

What We’re Trying to Do — and Why

We made a decision recently: we want to grow without crossing 150 people.

We wrote it down. It’s a goal at the top management level. Not because we’re trying to be clever — honestly, it’s kind of terrifying. But because we think there’s something real at stake.

We want:

• To preserve clarity and speed

• To avoid future layoffs from overhiring

• To protect culture before we have to define it in Notion

• To keep building things that are actually good — not just good enough

And we think the constraint might help us do that.

It’s not some final answer. It’s more like a hypothesis. But a serious one.

Why It's So Tempting to Ignore This

Because when you raise money or start hitting product-market fit, hiring feels like the obvious lever.

Can’t ship fast enough? Hire more devs. Need growth? Hire a team. Can’t keep up with users? Expand support.

But it adds up — and by the time you look up, you’re 180 people deep and wondering why no one feels responsible for anything anymore.

There’s research behind this. At 150 people, the number of potential relationships is ~11,175. That’s a lot of lost context.

source

And honestly, it’s hard to feel urgency when the team’s too big to feel the tension. The fire’s still burning, but the pot is so wide, no one sees the boil.

So How Are We Trying to Pull This Off?

We’re still learning. But here’s what we’ve been thinking.

We realized that hiring “great people” is not enough. Even the best people hit bottlenecks — in communication, coordination, and feedback.

And that’s where AI comes in — not just as a way to do more, but as a way to work differently.

Most people talk about AI like it’s a tool for automating repetitive tasks. And yes, it is — especially the kind of well-documented, low-context, rule-based things that humans tend to get bored of or mess up.

But there’s a second, much less discussed strength of AI: it doesn’t fear volume.

Humans hit a wall when there’s too much context. We get overwhelmed. We skim. We drop the thread. AI doesn’t flinch. It can hold more information in active memory than any one of us. And that matters a lot when you’re trying to scale without losing context.

The third thing — and this surprised me — is feedback.

Giving and receiving feedback in teams is deeply emotional. It costs people energy. They hesitate. They sugarcoat. They delay.

But AI doesn’t need to manage anyone’s feelings. It can offer gentle reflection, performance stats, behavior patterns — early, consistent, and emotionally neutral.

It reminds me of sports.

Athletes live in a world where feedback is constant, quantified, and non-optional. Every pass, every run, every missed shot — it’s all there, after every game. And somehow, they survive. They even thrive.

In most companies, that kind of feedback loop doesn’t exist. But with AI? It could. And that might be the key to scaling quality and culture without drowning managers in emotional labor.

One more thing: for AI to work well, your org needs to become legible.

You have to write things down — culture, strategy, decisions, processes. You have to be clear, structured, and searchable. That alone forces a level of organizational hygiene that, frankly, most companies never get to.

And maybe, just maybe, that hygiene is what keeps the soul of the company intact when everything else scales.

So yeah, we will grow.
But we’re also trying to stay small.

Not small in ambition. Small in entropy.

And AI, weirdly enough, is giving us hope that both might be possible.

If you’re a founder trying to hold onto the magic while building something big — I’d love to hear what’s working for you.

They're just better at hiding it.

After 10 years of building Welltory, getting rejected by YC five times (FIVE!), becoming profitable out of necessity, and watching friends' startups either fly or crash and burn, I finally understand something.

But I've also met some incredible investors along the way. The 10-15% who are actual professionals, who respect founders, who can have honest conversations about alignment and timing. This post is about understanding the full spectrum - so you can find the good ones and avoid the... let's call them "learning experiences."

The Closet Full of Dead Startup Hoodies

Picture this: An angel investor's closet. It's full of hoodies from dead startups. Another dead startup? Just another hoodie. One more, one less.

Their entire business model EXPECTS you to fail. Like, they've done the math. The Centre for European Economic Research says venture portfolios assume 90% failure rates.

Meanwhile, you're over here having panic attacks about product-market fit at 4am, and for them? You're literally just a line item. A hoodie in waiting.

This creates what I call "value asymmetry." The value of your company to you is fundamentally different from its value to an investor. They'll write you off as a loss and not even blink. Hell, they probably already have, mentally. They're playing portfolio theory while you're playing with your actual fucking life.

(By the way, we became profitable not because we're anti-VC, but because we HAD to. Nobody would invest in a consumer health company with direct payments back then. We knew that we have to. Because you can’t build a product for consumer if your customer is an insurance company. The product will be awful. But you know what? That forced profitability became our superpower. Now we can actually choose who we want to work with.)

But wait, it gets better.

The Real Reasons They're Writing Checks (Hint: It's Not Always the Returns)

1. The "Look at Me, I'm King of the Nerds" Investor

You know what Alex Danco's brilliant analysis revealed? In Silicon Valley, angel investing is basically peacocking with money.

"Angel investors here do it not just for profit, but for status... Being the first check means getting to say: 'I believed in this team when nobody else did.'"

It's like buying a VIP ticket to the innovation show. They're paying for access, stories, bragging rights.

(Not necessarily bad! Sometimes this works in founders' favor. But you should know what you're selling besides equity.)

2. The "I Want My Son to Be a Basketball Player" Investor

This is a special breed of investor who's trying to realize their own dreams through founders. They invest in areas of personal interest, then try to steer the company toward their vision.

Why don't they just build it themselves if they have the vision and money? Because that would mean taking real risk, not just financial risk. It's easier to be a puppet master than a founder.

I've seen this play out so many times. The investor who "always wanted to do something in health tech" but never did. So now they're trying to live vicariously through you.

Research from University of New Hampshire backs this up: investors with high narcissism are 34% more likely to INCREASE investments after criticism. They literally double down on bad bets to protect their ego.

3. The "Grateful Founder" Collector

Oh, these are fun. They give you money and expect... gratitude. Forever. Like, eternal, undying gratitude.

I've heard stories that make my brain melt. One founder told me their angel - who invested $30K in seed - still expected weekly calls three years later when the company was doing millions in revenue. Same level of attention as when they were desperate.

Another founder? Their angel REQUIRED them to name a conference room after him before releasing bridge funding.

Not kidding.

Conference. Room. Naming. Rights.

For $50K.

(The conference room is apparently now called something like "The David Chen Innovation Lab." David shows up maybe once a quarter to admire his nameplate.)

4. The "I'd Rather Lose Everything Than Look Stupid" Investor

This one's my favorite because it's so spectacularly irrational.

Research shows investors would often rather lose their ENTIRE investment than earn a 3-5x return if someone else gets 6x.

Read that again.

They would rather get ZERO than get a good return that's slightly less good than their buddy's return.

One founder shared a story: acquisition offer on the table, would give angels 4x. One angel killed it. Why? He heard another portfolio company might IPO for 10x.

That company went bankrupt six months later.

The angel got zero.

But hey, at least he didn't look like the idiot who "only" got 4x, right?

The Herd Dynamics (Or: Why Everyone Suddenly Wants In After Ignoring You for Months)

Jessica Livingston from YC nailed this:

"Investors tend to be herd animals. They like you if other investors like you."

The data is fucking ridiculous. Harvard study found identical pitch decks presented as "already oversubscribed" got 58% more interest.

IDENTICAL. PITCH. DECKS.

I've heard absolutely wild stories about this. One founder got ghosted for months, then after getting a term sheet from a known fund, an investor literally faxed them a blank term sheet saying "Fill in any valuation - we're in."

Pattern Matching: The Stupidest Smart Thing They Do

Paul Graham admitted it:

"I can be tricked by anyone who looks like Zuckerberg. There was a guy we funded who was terrible. I said: 'How could he be bad? He looks like Zuckerberg!'"

At least he's honest about it.

But here's what pattern matching means for the rest of us:

• 89% of VC partners are white dudes

• If you fit the pattern (Stanford/MIT, worked at FAANG), you raise at 3.1x higher valuations

• All-female teams get 1% of VC dollars but generate 78 cents per dollar invested vs 31 cents for male teams

When I spent a month in Silicon Valley in 2018 (jet-lagged, overcaffeinated, desperate), EVERY SINGLE VC told us consumer health with direct payment was impossible. "It's a vitamin, not a painkiller." "Consumers won't pay for prevention." "Where's the enterprise play?"

But here's my favorite Silicon Valley moment. One investor - I shit you not - suggested we just hire a Stanford graduate white dude as CEO, give him 7%, and then we'd "almost guaranteed" raise money.

Just... hire a random local white dude. With the right degree. Give him 7% of our company. For his... whiteness? Stanford-ness? For being able to drink coffee anytime investor wants.

(We didn't do it. Shocking, I know.)

I collected all their advice on cards. When I laid them out, they covered literally every possible direction - a complete 360. That's when I realized: everyone was just advising what had worked for THEM, in THEIR context.

Now? Those same types of investors cite consumer revenue as a key strength for health startups.

The pattern shifted. Reality didn't.

How to Spot the Actual Professional Investors

Plot twist: they exist! And they're... completely different.

Real professional investors (the ones with, you know, actual returns) are paradoxically:

• Faster at decisions (because time is money and they're not playing games)

• More transparent ("Here's what I need to see to invest")

• Less emotionally needy (no weekly gratitude sessions required)

• More respectful of boundaries (they have their own lives, thanks)

They have LOTS of successful investments. That's the only real sign of a professional investor. It means they make lots of bets, they don't have time for bullshit, they're not using you for weird emotional needs.

They sound harsher but they're more direct and honest, and that's beautiful. You know where you stand. You can have real conversations about real challenges.

We've met some of these investors recently, and honestly? It's refreshing as fuck. They'll straight up tell you: "We like what you're building but we only write $10M+ checks and you're not there yet. Come back when you need growth capital." No games. No stringing along. Just clarity.

Or the ones who say: "This isn't our thesis, but here's what I think you should focus on to attract the right investors." And then they actually make useful intros.

Some funds even do real research — like Goodwater Capital, which runs independent user surveys, or Headline, which uses data analysis to overcome partner biases. Others, like Accel Partners, are just fast and straightforward — they tell you exactly what they’re looking for, make decisions quickly, and spare you the theater. God bless their clear, efficient souls.

These investors are maybe 10% of who you'll meet. But when you find them, you'll know.

What I Wish I'd Known (Besides "This Is All Insane")

1. Not All Money Is Equal (Duh, But Really)

That $50K angel who demands weekly calls? Let's do the math:

• Your time: 1 hour/week x 52 weeks = 52 hours/year

• Your hourly value as a founder: ~$200-500/hour

• Actual cost of that $50K: $10-25K annually in babysitting

Congrats, you just hired the world's most expensive therapist. Except they're not trained in therapy. And they give terrible advice.

2. Reference Check Them Like Your Life Depends On It

MIT research shows founder references about investors are 3x more predictive of success than investor references about founders.

Yet how many of us actually call other founders before taking money?

(I didn't. Because I was desperate and naive. Don't be like early-stage me.)

3. They're Optimizing for Different Things Than You Think

This study blew my mind: 43% of early-stage conflicts come from misaligned exit timing expectations.

You're trying to build the next Spotify. They're trying to flip you to Google in 18 months for a quick 5x.

Nobody mentions this during the honeymoon phase when they're saying shit like "we're in this for the long haul" and "we believe in your vision."

4. Emotional Intelligence > Term Sheets

The Journal of Business Venturing found "investor empathy" correlates more with startup success than check size or board seats.

But we spend 90% of negotiations on terms and 10% on "will this person make me want to jump off a bridge in year 3?"

(Hint: Year 3 is when shit gets real. Plan accordingly.)

Some Examples to Learn From

I've seen some wild shit in the founder community. Here are patterns to watch for:

The Due Diligence That Never Ends: Investors who keep asking for "just one more metric" for months. They're not serious. They're either learning about your space on your time or waiting for someone else to validate you first.

The Advisor Who Wants to Be CEO: They start as an angel, become an "advisor," then slowly try to make every decision. I've watched this destroy multiple companies.

The Strategic That Isn't: Corporate VCs who promise partnerships, intros, distribution deals. Then their internal champion leaves and you're ghosted with their money already in. One corporation wanted to invest in us and PROMISED that collaboration with them has nothing to do with it. Until final term sheet came up with a requirement of collaboration. We had to stop there. Because they are clearly people you can’t trust. But it’s because we were growing and brave at that moment. What if your runway is 2 months at that point?

But also remember, there are ...

The Good Ones: The fund that took 3 calls, made a clear decision, and explained exactly why we weren't a fit YET. The angel who invested, then checked in quarterly without drama. The VC who said "we only do B2B SaaS but I'll intro you to 3 funds that get consumer health." These people exist. They're gold.

The Part Where I Admit I'm Scared to Publish This (But Also Hopeful)

Real talk? I've rewritten this post so many times. Because there's an unspoken rule in startup land: don't bite the hand that might feed you.

Every founder fears that speaking honestly about investor dynamics will blacklist them from future funding. And you know what? Maybe it will with some investors.

But here's the thing: after 10 years, we've also met incredible investors. Professional ones who respect boundaries. Who give thoughtful feedback. Who can say "not right now" without playing games. Who actually want to help build something meaningful.

The dysfunction persists because we're all performing in this elaborate theatre where everyone pretends to know their lines but nobody's actually read the script.

So let's start actually talking about this stuff. The good AND the bad.

To My Fellow Outsiders

If you don't have warm intros to Sand Hill Road... If you're building not from Silicon Valley... If you're profitable because you had to be... If VCs keep saying your model "doesn't fit the pattern"...

This is for you.

The game isn't rigged. It's just weird. And once you understand the weirdness - the egos, the social dynamics, the fear of looking stupid, the herd mentality - you can navigate it better.

You can find the good investors. They exist. You can build relationships with funds that actually respect what you're building. You can raise money on terms that make sense for YOUR business, not their portfolio theory.

Or, you know, you can build a profitable business first and then choose your investors carefully. Or wait for the right moment. It helps.

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