Founder And The City

Beyond “Healthism”: Why I’m Rejecting the Performance Model of Health

Thu, 09 Apr 2026 06:27:40 GMT

Imagine a factory where the air is toxic. The workers start coughing. Management doesn’t fix the ventilation. Instead, they hand out breathing apps. The app tracks your respiration, monitors your blood oxygen, and pings you with “personalized recommendations.” Breathe deeper. Try the 4-7-8 technique. Your breathing score is 67 — you can do better.

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.

How I Built a 300-Page internal book about modern health & wellness with a multi-model AI pipeline

Jane Smorodnikova — Sat, 04 Apr 2026 13:35:43 GMT

I’ve wanted to write a book my entire life. But let’s be real: between running Welltory and managing my entrepreneurial ADHD brain, the sheer discipline required for a 300-page manuscript felt like a fever dream.

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.

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

Examples > Instructions: Just like with people, showing works better than telling. Don’t just prompt; provide examples.
The Context is Everything: The more “obsessive” you are about preparing the context and “Constitution,” the better the result.
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.

AI, fake productivity, and why “time‑tracking founders” are in trouble

Tue, 24 Mar 2026 06:18:13 GMT

Recently I had a weird moment.

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:

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:

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.
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.”
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.

The AI Bubble Survival Guide

Jane Smorodnikova — Tue, 30 Dec 2025 14:34:23 GMT

Inside the AI Bubble: Who’s Cashing Out, Who Gets Burned, and How to Survive

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?

Data and data rights. Unique datasets that can’t be easily replicated.
Evaluation capability. The ability to distinguish quality from hallucination, tied to business outcomes.
Workflow integration. Embedded in how people actually work. High switching costs.
Distribution. Channels and brand that don’t depend on a single platform.
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.

What Physical Asia Teaches Us

Jane Smorodnikova — Sun, 23 Nov 2025 17:38:38 GMT

I was born up on the border with Mongolia. Literally one mountain over. Part of my ancestry traces back to northern Mongolian tribes, and my family historically bred horses. So when Physical Asia aired and Team Mongolia made it to the finals, I had skin in the game beyond just good television.

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.

When Your Company Stops Being You

Jane Smorodnikova — Sat, 22 Nov 2025 13:13:50 GMT

This week, I didn’t see our Product Hunt video before it went live. Not because I wasn’t invited to the discussion—I was just buried in other critical priorities, and the team launched without me. We won the day. Then the week. Outranked Google and Oura in our category. I learned about it after the fact, reading comments where our engineers were answering questions about features they’d built with their own hands.

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.

The FDA Says You Can't Know Your Blood Pressure Until You're Sick Enough

Jane Smorodnikova — Sat, 25 Oct 2025 13:50:26 GMT

July 15, 2025. Will Ahmed opens an FDA warning letter that perfectly captures everything broken about American healthcare regulation.

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.

Meaning Hygiene, or How Companies Turn Into Soulless Monsters

Jane Smorodnikova — Sun, 12 Oct 2025 07:37:33 GMT

Every soul-crushing corporation you’ve ever worked for – the ones with meaningless meetings, projects that go nowhere, that weird hollow feeling when you ask “why are we doing this?” – they all started as startups. Passionate founders. Clear mission. Tight team that actually gave a damn.

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.

Netflix, Virtual Crowds, and Why I'm Yelling Business Ideas at My TV

Jane Smorodnikova — Sun, 14 Sep 2025 10:59:14 GMT

Watched Canelo-Crawford on Netflix last weekend, lying on my couch like any reasonable person would. And honestly? Netflix is doing something really cool with sports entertainment.

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.

Why Your Team Thinks You're a Bipolar Asshole

Jane Smorodnikova — Sun, 07 Sep 2025 11:14:02 GMT

Why Your Team Thinks You Can't Make Up Your Mind (When Nothing Has Changed)

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.)

Consumer and Purchasing Behavior in Health & Fitness: The Health Spending Paradox

Jane Smorodnikova — Sun, 17 Aug 2025 14:40:36 GMT

After years in this market, watching genetic testing companies pivot from health insights to ancestry entertainment, and seeing wearables evolve from revolutionary health tools to expensive step counters, one pattern remains constant: the fundamental disconnect between who media says buys health products and who actually opens their wallets.

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:

Promise magical "AI-personalized health transformation"
Charge $70-150/month for generic content anyone could Google
Blow $100+ acquiring each customer
Profit from the 3-4 months before they remember to cancel
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.

The UFC Deal We Walked Away From

Jane Smorodnikova — Sun, 03 Aug 2025 13:55:44 GMT

Sometime after our first TechCrunch feature in 2017 — back when we still believed media mentions would magically turn into revenue — we got a message from the science team at UFC.

“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.

How to Stay Under 150 Employees and Still Grow

Jane Smorodnikova — Sat, 19 Jul 2025 15:38:14 GMT

There’s this moment in a startup — usually around 50 people — when something fundamental shifts. If you’ve been through it, you know what I mean.

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.

What I Wish I'd Known About Early Investors (As a First-Time Founder Who Wasn't "Supposed" to Make It)

Jane Smorodnikova — Sun, 06 Jul 2025 12:44:25 GMT

Here's the thing nobody tells you when you're a first-time founder (especially if you didn't go to Stanford, didn't work at Google, and your warm intros are basically non-existent): investors are just as fucked up and irrational as the rest of us.

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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You don’t need better prompts. You need a better conversation.

Jane Smorodnikova — Sat, 21 Jun 2025 14:31:26 GMT

Last month, I watched a brilliant product manager — someone who'd shipped features used by millions — spend 20 minutes trying to get ChatGPT to write a simple user story. She kept tweaking her prompt, adding more specifics, getting increasingly frustrated. Finally, she gave up and wrote it herself in 5 minutes.

"I thought AI was supposed to save time," she muttered.

This scene plays out in offices everywhere. Smart people, accomplished professionals, founders who've raised millions — all getting mediocre results from tools that supposedly revolutionize knowledge work. And I couldn't help but wonder: what if we're all using it wrong?

Research on How AI Makes Us Smarter and Dumber

Here's what sent me down this rabbit hole: A 2025 study from Microsoft Research and Carnegie Mellon found that knowledge workers with high confidence in AI showed less critical thinking when evaluating AI outputs compared to those who trusted their own skills more. Let that sink in. The more we trust AI, the lazier our brains become.

The researchers have a term for it: "cognitive offloading." It's what happens when you delegate mental tasks to external tools, and it's been linked to a 68.9% increase in what they politely call "laziness" and a 27.7% decrease in decision-making abilities.

But here's the plot twist: the same body of research shows AI can actually enhance cognition when used differently. A study from Surrey's Centre for Translation Studies found that specific types of human-AI interaction improved working memory and task-switching abilities.

The difference? How you engage with it.

My Own Journey

I discovered this the hard way. As Welltory's founder, I face problems that span disciplines I never studied. Tuesday it's behavioral psychology for user retention. Wednesday it's regulatory compliance for health data. Thursday it's investor relations. Friday it's team dynamics.

I used to approach AI like everyone else — looking for quick answers. "Write me a pitch deck outline." "Summarize this research." "Generate ideas for improving day-7 retention."

The results were... fine. Generic. The kind of stuff you'd get from a mediocre consultant who'd googled your industry five minutes before the meeting.

The Mental Shift That Changes Everything

Most people approach AI wrong because they misunderstand what it is. Here's what finally clicked for me:

AI is a bridge between your not-knowing and the world's knowledge
When we pretend we know everything, we cut off our inner voice — that part that wonders, doubts, and seeks. AI lets you give voice to that uncertainty and connect it with vast pools of information you'd never access otherwise.

It's not weaker or stronger — it's different
Yes, AI knows less about your specific context and goals. But it knows vastly more about the world, can process information without fatigue, and has no emotional investment in being right. These aren't weaknesses to work around — they're complementary strengths to leverage.

Talk to it like a thinking human, not a search engine
You don’t have to know special syntax or perfect prompts. Say "hmm, that doesn't sound right" or "wait, I meant something else" or "let me think out loud here." Human interaction works.

It's a dialogue facilitator, not an answer machine
The magic isn't in the answers AI gives you. It's in how explaining your thinking to AI forces you to structure it. Like therapy, the act of articulation is where insights emerge.

Without the research phase, AI makes you dumber
If you just ask for quick answers, you're outsourcing your thinking. But if you use AI to explore, investigate, and synthesize, you're expanding your own capacity. The difference is whether you're having a conversation or placing an order.

You need intention, not clarity
You don't have to know what you want. You just need to care about finding out. That intention — that sense of "something here matters" — is enough to begin.

When you make this mental shift, AI transforms from a shortcut tool into what I call a "cognitive interface" — a way to have faster, deeper dialogue between what you don't know and what the world knows. That's where the real power lies.

The Science of Thinking Together

This approach has a scientific basis. Researchers call it "metacognitive prompting" — using AI to enhance rather than replace your thinking process. Instead of cognitive offloading (bad), you get cognitive augmentation (good).

The key difference:

Cognitive offloading: "AI, write me a strategy document"
Cognitive augmentation: "I'm thinking about our strategy. Here's my current view... what am I not considering?"

A fascinating study on AI and scientific research found that papers using AI as a thinking partner (not just a writing tool) had significantly higher citation rates. Why? Because the researchers used AI to challenge assumptions and explore angles they wouldn't have considered alone.

The VOICE Method I Use

After months of experimentation, I developed a framework that consistently produces those "aha" moments. I call it VOICE — not just because everything nowadays is a framework, but because it literally gives voice to thoughts you didn't know you had.

V — Vibe (The Honest Starting Point)

Forget perfect prompts. Start with what's actually in your head:

"Something feels off about our pricing model"
"I'm stuck on this team restructuring"
"I have a hunch about our market position but can't articulate it"

Research shows that when we try to formulate perfect questions, we actually limit our thinking. Starting with uncertainty opens more neural pathways.

O — Orientation (Setting the Exploration Mode)

Don't tell AI what to think. Tell it how to think with you:

"Help me explore this from different angles"
"Let's think through this like a behavioral economist would"
"I need to understand what I'm not seeing"

This triggers what researchers call "perspective-taking," which enhances creative problem-solving.

Our goal here is to get a problem statement or a goal statement or a challenge description. Make AI do this for you and correct it until it's ok. Use its ability to pretend a VC, experienced CPO, Marty Cagan, or anybody you need as a coach now.

A problem well stated is a problem half solved.

ChatGPT is the best now for this stage, because it works well with voice and conversation.

I — Investigation (The Part Most People Skip)

Here's where I part ways with every AI guide I've read. After initial exploration, I ask: "What research should I look at to understand this better? Give me three specific research prompts for Perplexity."

Then I copy them to Perplexity. Usually, I dig into:

Scientific studies on the psychology/behavior involved
Competitor approaches and case studies
Expert opinions and contrarian views

Why? Because AI trained on internet data will give you internet-average answers. But when you feed it specific, high-quality research, it can help you synthesize insights nobody else has.

Perplexity is the best now for this. Use deep research, work in one thread, follow relevant additional questions, and don't hesitate to get more interesting angles. Bookmark this thread when you are done.

The most important part here is to read it. Take your time to read the most interesting and relevant cited sources. That's the most important part when you are extending your reality and worldview.

If it's difficult or some sources are too big, use ChatGPT to read and summarize it for you, extracting the most important and relevant thoughts from URLs, PDFs, and other sources. You can even read sources in different languages - easier than ever before.

At the end, export the whole thread from Perplexity to markdown - that is your source of proofs and insights with URLs. Post it back to ChatGPT to start working on solution for your problem.

C — Clarify and Critique (The Uncomfortable Mirror)

Ask ChatGPT to find a solution. Or use Claude to do this (because it takes more context). You can paste your problem statement from ChatGPT and research from Perplexity. You will get something much better than a mediocre AI answer. But we are not finished yet. Now ask AI to tear apart his and your thinking:

"What's wrong with this approach?"
"What would someone who disagrees say?"
"Where are the logical flaws?"

Studies show we're terrible at self-criticism due to confirmation bias. But AI has no ego. It can show you your blind spots without the emotional baggage.

Don't stop at the first insight. Push further:

"What systemic issue does this reveal?"
"How does this connect to our other challenges?"
"What would have to be true for the opposite to work?"

This is where breakthroughs happen — in the space between your initial question and where you end up.

E — Extraction (Beyond the Obvious)

This is where AI shines. Have it rewrite your solution for different audiences:

"Now as a technical spec"
"As a business case with ROI"
"As a user story"
"As a sales one-pager"

We all have blind spots. I think like a founder, my CTO thinks in systems, my designer in user flows. But AI can instantly switch lenses, research shows considering multiple stakeholder perspectives improves solution quality by 40%.

Example: For our onboarding fix, AI generated four versions. The technical spec revealed dependencies I'd missed. The user story showed emotional friction points. The business case forced me to quantify impact. Same solution, four lenses — each revealing what I couldn't see alone.

I'm Living Through My Most Intellectually Intense Period — and Growing Faster Than Ever

Take this very article you're reading. I didn't sit down to write it. I spoke my thoughts into ChatGPT during my morning walk, rambling about how smart people misuse AI. Then I asked it to find relevant research on cognitive offloading and enhancement. Then I refined the draft based on what the studies actually revealed.

The same method works for everything. Product decisions? I share the chat with my team — they see not just the conclusion but the entire thinking journey. Strategic planning? Export to Confluence. The raw dialogue often beats any polished document.

Sometimes the research rabbit hole goes so deep that I never publish what I started. (That's why my blog has gaps — not writer's block, but getting so fascinated by what I discover that the original article becomes irrelevant.) Last month I started writing about investor psychology and ended up deep in neuroscience papers about threatened egotism. Never published it, but the insights transformed how I approach board meetings.

Here's the shift: I've never before encountered such a volume of important, new-to-me information as I do now. Before, my growth was limited by how many books I could read in a year. Now it's only limited by my capacity to absorb.

The bottleneck isn't finding information anymore. It's having the courage to follow where it leads.

The Cognitive Gym Effect

Here's what the research convinced me of: using AI this way is like going to a cognitive gym. You're not outsourcing thinking — you're resistance training for your brain.

Studies on expertise show that masters in any field got there through deliberate practice with feedback. VOICE creates a feedback loop on steroids. You can explore ideas, get challenged, research, synthesize, and iterate faster than ever before.

But — and this is crucial — only if you do the work. The moment you treat AI as a shortcut rather than a thinking partner, you're in cognitive offloading territory. Your brain gets flabby. Your insights get generic.

Most people won't use AI this way. It's harder than asking for quick answers. It requires admitting you don't know things. It means sitting with uncertainty instead of grabbing the first plausible response.

But for those willing to embrace the discomfort? You get something remarkable: a tireless thinking partner who knows more than you, has no ego, never gets tired, and can help you see around corners.

The research is clear: AI will make you smarter or dumber depending on how you use it. The choice is yours.

Have a Challenge AI Might Actually Help With?

Pick something that's been nagging at you. Something complex. Something you've been avoiding because you don't know where to start.

Open ChatGPT and say: "I'm struggling with [thing]. I don't even know what my real question is. Help me figure out what I should be thinking about."

Then follow the VOICE framework. Give yourself 1-2 hours. Don't aim for answers — aim for better questions. In my experience, the best insights come not from AI, but from humans brave enough to think alongside it.

Further Reading on Cognitive Enhancement with AI:

VOICE Framework Checklist

How to use AI for thinking, not just answering

Before You Start

Block 1-2 hours of uninterrupted time
Have accounts ready: ChatGPT (best for voice), Perplexity (for research), Claude (for synthesis)
Pick a real problem you're facing — something complex, not a simple task
Accept that you don't need a perfect question to begin

V — Vibe: Start with Your Real Thoughts

What to say:

"I have this feeling that [describe the situation]..."
"Something's been bothering me about [topic]..."
"I can't quite articulate it, but [your hunch]..."
"Help me think through what I might be missing with [challenge]..."

Example starts:

"I feel like our user onboarding is broken but I can't say why"
"My team seems disengaged but all the metrics look fine"
"Our pricing doesn't feel right but I've analyzed it to death"

Tools:

ChatGPT with voice input (mobile or desktop) works best for this stage

Success marker:

You've rambled for 5-10 minutes and AI has reflected back what it heard

O — Orientation: Set the Thinking Mode

Ask AI to help you explore:

"Let's think through this like [specific expert] would"
"Help me see this from different angles"
"What questions should I be asking myself?"
"Act as a [role] and help me understand what I'm not seeing"

Useful roles to invoke:

Behavioral economist
Your target user
A skeptical board member
Someone from a different culture
Your competitor's CEO

Key phrases:

"Help me formulate a clear problem statement"
"What would need to be true for this to be the right approach?"
"What assumptions am I making?"

Success marker:

You have a clear problem statement that feels more precise than when you started

I — Investigation: Go Deep with Research

Ask ChatGPT:

"Based on our discussion, what are the 3 most important things I should research? Give me specific prompts for Perplexity, one paragraph each."

In Perplexity:

Use "Deep Research" mode
Work in one thread to build context
Follow the "Related" questions that intrigue you
Click through to actual sources (this is crucial!)
Look for:
- Scientific studies on the human behavior involved
- Case studies from other companies/industries
- Contrarian viewpoints and critics
- Expert opinions and frameworks

If sources are complex:

Copy URLs to ChatGPT and ask: "Summarize the key insights relevant to my problem"

When done:

Export the Perplexity thread to Markdown
Save/bookmark the thread for later reference

Success marker:

You've discovered at least 3 things you didn't know before that change how you see the problem

C — Clarify and Critique: Challenge Everything

Paste your research back to ChatGPT/Claude with:

"Here's my problem [paste]. Here's what I learned [paste research]. Help me develop a solution."

Then critique ruthlessly:

"What's wrong with this approach?"
"What would someone who completely disagrees say?"
"What are we not considering?"
"Where are the logical flaws?"
"What would have to be true for this to fail?"

Push deeper:

"What systemic issue does this reveal?"
"How does this connect to our other challenges?"
"What if we did the exact opposite?"
"What would [specific competitor] do?"

Success marker:

You've identified at least 2 major flaws or blindspots in your initial thinking

E — Extract: Generate Multiple Perspectives

Ask AI to rewrite your solution as:

A technical implementation spec
A business case with ROI projections
A user story from the customer's perspective
An email to your team explaining the change
A one-page brief for the board
A FAQ for sales/support teams

For each version, ask:

"What new issues does this perspective reveal?"

Final step options:

Share the entire chat thread with your team
Create a fresh chat and regenerate a clean version
Export to Confluence/Notion/Docs
Create action items from each perspective

Success marker:

Each perspective reveals something you hadn't considered

Quality Checks

Ask yourself:

Did I learn something that surprised me?
Do I understand the problem differently than when I started?
Could I explain this to someone who knows nothing about it?
Do I have concrete next steps, not just ideas?
Am I addressing root causes, not just symptoms?

Common Pitfalls to Avoid

Starting with a solution in mind → Stay in question mode longer
Skipping the research phase → This is where breakthrough insights come from
Accepting the first answer → Always push for "what else?"
Being too polite with AI → Interrupt, redirect, challenge
Sanitizing for the final output → Raw thinking often has more value

Time Allocation (2 hours total)

Vibe: 20 minutes (rambling is productive!)
Orientation: 20 minutes (getting the right question matters)
Investigation: 45 minutes (this is where the magic happens)
Clarify & Critique: 20 minutes (be ruthless)
Extract: 15 minutes (let AI do the heavy lifting)

Remember

You're not using AI to skip thinking. You're using it to think harder, deeper, and from more angles than you could alone. The goal isn't efficiency — it's insight.

The moment this feels like a shortcut, you're doing it wrong.

Hope it helps!

How Your Cultural Code Shapes Your Startup

Jane Smorodnikova — Sun, 15 Jun 2025 17:05:33 GMT

I met Roman Baevsky, the scientist who helped monitor Gagarin’s nervous system in space, when he was consulting for NASA. He was part of a quiet tradition — like Asimov’s Foundation or Carl Sagan’s Cosmos — where science wasn’t about politics or profits, but about understanding ourselves.

For many brilliant minds in the Soviet era, building systems that helped people — decoding the heart, exploring the stars — was a way to stay human in an inhuman system.

When I showed him how we’d made HRV measurable on a smartphone, he smiled and said, “That’s what we always dreamed of — we just didn’t have the tools.”

He handed me a stack of research papers — raw, brilliant, and hopeful. In his hands, heart rate variability wasn’t just a metric. It was a blueprint for preventive care that could reach everyone.

This is the cultural legacy I carry — a belief that technology should serve people, not just markets. Recently, during a team discussion on ethics, I was reminded how deeply our values shape our work. Not just individually, but through the cultures we come from. Different cultural operating systems lead to very different companies.

I want to share some of my recent reflections — maybe they’ll offer you a few unexpected insights too.

The Great Melting Pot (That Refuses to Melt)

I'll never forget my shock the first time I saw startups pitching in a Silicon Valley church. For someone born in the USSR, where entrepreneurship was seen as shameful (definitely a thief, definitely not earning eternal life), watching founders seek funding in a house of worship broke my brain. In America, wealth is practically next to godliness.

This crystallized something I'd been noticing: 60% of top US AI companies have immigrant founders. In Silicon Valley specifically? 66% of tech workers are foreign-born. Only 17% were actually born in California.

When I talk to investors now, I often feel how our ideas about the purpose of investment diverge. I'm beginning to understand why patient capital is more common in Europe, why short-termism is specific to the US mentality, why Chinese or Arab investors think in different time horizons. These aren't just quirks — they're features of different cultural operating systems.

So when VCs pattern-match on hoodies and Stanford dropouts, they're missing the plot. The real Silicon Valley is a Bengali engineer debugging code at 3 AM, a Ukrainian designer obsessing over pixels, a Brazilian growth hacker who learned English from pirated Friends episodes.

Each brings their cultural OS — invisible, powerful, and absolutely refusing to assimilate into some mythical "startup culture."

Part 1: Why Your Startup Exists (The Mission Code)

The American Code: Change the World (In 2-3 Years, Max)

Mission Template: "We're making the world more [democratized/efficient/connected] through [insert disruption]."

Americans perfected the art of believing two contradictory things: they're both saving humanity AND deserve to get obscenely rich doing it. It's Protestant work ethic meets Vegas — virtue and vice in a Delaware C-corp.

Superpower: Speed to market. Americans will pivot away from their grandmother if the metrics don't look good.

Blind spot: Assuming every human problem has a profitable solution. Hence Juicero — a $120 million WiFi-enabled machine to squeeze pre-squeezed juice packets.

The Chinese Code: The Century Game

Mission Template: "We're building civilizational infrastructure for the next 100 years."

Chinese companies play SimCity while Americans play Candy Crush. While Silicon Valley optimizes for the next funding round, 82% of recent Chinese tech funding went to semiconductors and manufacturing. Boring? Only if you think chips are less important than chips.

Superpower: Thinking in decades, not quarters. WeChat isn't an app — it's an operating system for 1.3 billion lives.

Blind spot: Sometimes the government thinks it's playing SimCity with your company too.

The Indian Code: Jugaad at Scale

Mission Template: "We're solving real problems for millions with whatever we've got."

Indian startups perfected innovation-by-constraint before it was a TED talk. Zoho bootstrapped to $1 billion because who needs VCs when you have profitability? UPI processes 10 billion transactions monthly because sometimes the best payment system is one the government builds.

Superpower: Making the impossible possible with 10% of the resources.

Blind spot: Sometimes jugaad becomes jugaad² — creative solutions to problems created by previous creative solutions.

The European Code: But Is It Ethical?

Mission Template: "We're building sustainable solutions that respect human dignity and our 35-hour work week."

Europeans read the terms and conditions. All of them. They created GDPR not to annoy you, but because they genuinely believe privacy is a human right. 21% of European startup funding goes to sustainability — double the US rate.

Superpower: Building things that last. European companies are like cast-iron pans — they take forever to heat up but work for generations.

Blind spot: By the time the 27 committees approve your product roadmap, an American competitor already IPO'd and flamed out.

The Japanese Code: The 200-Year Business Plan

Mission Template: "We're perfecting this specific aspect of human experience. See you in 2224."

Nintendo spent 83 years making playing cards before Mario. Toyota still uses manufacturing principles from the 1950s. Japanese companies don't pivot — they perfect.

Superpower: Quality that makes everything else look like a rough draft.

Blind spot: Sometimes the perfect is the enemy of the shipped.

The Israeli Code: Chutzpah as Strategy

Mission Template: "We're disrupting [industry] because someone said we couldn't."

Israel has more startups per capita than anywhere else because when you're surrounded by challenges, everything looks like a problem worth solving. The Israeli startup scene runs on productive paranoia and military-grade directness.

Superpower: Turning existential pressure into innovation fuel. Plus, nobody negotiates harder.

Blind spot: Sometimes not every conversation needs to be a negotiation at DEFCON.

The Soviet/Post-Soviet Code: Engineering as Humanism

Mission Template: "We're solving fundamental human problems through superior engineering for the future."

This worldview — engineering humanism — ran deep through Soviet futurism. It wasn't about profits or IPOs. It was about building a better world through collective progress and scientific achievement. The sci-fi of Strugatsky brothers didn't imagine billionaires in space — it imagined scientists as caretakers of humanity (Asimov, The Foundation, S.T.A.L.K.E.R, Atomic hearts).

Superpower: Building complex, powerful systems that actually help people. Products like Telegram (900M users, still barely monetized), JetBrains (20 years perfecting developer tools), or Miro (collaborative complexity made simple).

Blind spot: Chronic undermonetization. When profit feels like theft, it's hard to charge what you're worth.

Part 2: How Culture Shapes Your Product. The Unexpected Way

Plot Twist #1: Craigslist — The Anti-Silicon Valley Unicorn

While every American startup chases growth, Craigslist sits there like a digital middle finger to capitalism. $694 million in revenue. Same UI since 1996. Zero investors. 50 employees.

Craig Newmark looked at venture capital and said "nah, I'm good." In peak America, that's the most un-American thing ever. And it worked.

Plot Twist #2: Telegram's Monk

Pavel Durov has 900 million users and only recently started monetizing after years of running on his personal savings. Even now, Telegram's monetization feels reluctant — like a monk forced to charge temple visitors.

It's not bad business — it's post-Soviet engineering philosophy: "We built something important. Money is secondary. Also, we trust no one, especially people with money."

I recognize this pattern painfully well. At Welltory, we started with a price 10x lower than what actually worked — we only discovered optimal pricing through obsessive A/B testing, not intuition. If we hadn't been nerdy enough to test everything, we'd still be undercharging.

But here's what we never debated: principles like "never charge for critical health warnings" (if someone needs to see a doctor, that message is always free) and "never monetize fear or guilt." These weren't business decisions — they were moral reflexes. In the USSR, making profit meant you were stealing from someone, and that programming runs deep.

Plot Twist #3: Korean Chaebols — When One Company Makes Everything

Samsung makes phones, ships, life insurance, and apartments. In America, that's antitrust violation. In Korea, it's Tuesday.

Korean chaebols emerged from post-war scarcity — when you have nothing, you build everything. Now Samsung's revenue is 20% of South Korea's GDP. That's not a company; that's a parallel government with better R&D.

Plot Twist #4: M-Pesa — When Kenya Out-Innovated Silicon Valley

While Americans were debating NFC vs QR codes, Kenya launched M-Pesa in 2007 — a mobile payment system using... text messages. On feature phones. No apps needed.

Today it processes 51% of Kenya's GDP. It succeeded because it solved a real problem (no banks in rural areas) with available technology (everyone had a basic phone). Silicon Valley would have added blockchain and killed it.

Part 3: The Money Question (Show Me the Incentives)

[More about cultural map 2024]

Americans: Money = Morality

In Silicon Valley, your valuation is your validation. Raised $100M? You're a visionary. Bootstrapped? That's... quaint. Like bringing a casserole to a cocaine party.

Americans turned capitalism into a religion where IPO is the rapture. They'll absolutely torch a profitable business for growth metrics because that's what the scripture (pitch deck) demands.

Europeans: Money = Necessary Evil

Europeans treat money like Americans treat healthcare — necessary but somehow embarrassing to discuss. A German startup will spend two years ensuring their cap table is philosophically coherent while their American competitor has already burned through three pivots and two marriages.

Chinese: Money = National Power

Chinese startups optimize for scale because when you have 1.4 billion people, everything else is a rounding error. They'll lose money for a decade if it means dominating a strategic sector. It's capitalism with Chinese characteristics — which is just central planning with better PR.

Middle Eastern: Money = Trust and Community Benefit

Islamic business ethics fundamentally shape Middle Eastern startups. The Quranic principle "Do not eat from each other's wealth by false means" creates a framework where:

No riba (interest): Islamic VCs use profit-sharing (mudarabah) where investors only win when founders win
No gharar (excessive uncertainty): Terms must be transparent, no exploitative fine print
Halal business only: Investments flow to businesses creating positive value

This isn't just religious compliance — it's a different philosophy. Where Silicon Valley VCs might demand aggressive terms and guaranteed exits, Islamic finance requires shared risk. Family businesses form 75% of the private sector because trust and long-term relationships matter more than quick exits. The result? More patient capital, but also more conservative growth.

African: Money = Community Transformation

African startups don't just talk about impact — they live it. Every successful startup enables ten more. It's ecosystem building by necessity. When Flutterwave processes payments, they're not just moving money — they're building financial infrastructure for a continent.

Part 4: The Plot Twist Nobody Tells You

Here's the thing about cultural weaknesses: once you're aware of them, they can become your secret weapon. You don't need to hire someone from a different culture to compensate. Sometimes, the tension between your strength and your weakness creates the most innovative solutions.

At Welltory, our love of complexity initially created products with 100+ health metrics that overwhelmed users. Classic engineer thinking: more data = better product. Ha.

But instead of hiring American product managers to dumb things down, we leaned into the tension. We kept the complexity but hid it behind what we call "magical visualizations." When we showed nervous system state as a boiling liquid, growth jumped 169% annually. When we visualized burned calories as jiggling fat blobs, screenshot shares doubled.

Our cultural "weakness" (obsession with scientific accuracy) forced us to innovate harder on simplicity than companies who started simple. Now every feature has two faces: a super-complex academic report full of science, and a beautiful visual metaphor that lets users understand everything in a second.

This same pattern shows up everywhere:

Our post-Soviet survival instinct made us control every dollar. We know the efficiency of every creative, any copywriter can analyze A/B tests, we don't have a team of analysts because practically everyone in the company can do it. What looks like paranoia to American VCs is actually antifragility.

Our comfort with complexity meant we just taught our physiologist scientists to code so they could talk to data scientists easier. Where others see impossibility, we see Tuesday.

Our mission-driven culture helped us survive the Silicon Valley Bank collapse through pure trust and shared purpose, while other startups panicked.

But we also discovered our blind spots the hard way. I recently fed our investment memorandum to an AI, and it told me we were "too logical" — that our facts and arguments made us seem "unambitious" and "lacking confidence to dominate the market." I was shocked that being logical and well-reasoned could be seen as a minus. But that's exactly the kind of thing you need to understand instead of banging your head against the wall.

Spotify succeeded because Daniel Ek brought Swedish consensus-building to music licensing — turning enemies into partners instead of disrupting them to death.

Zoom won because Eric Yuan, after being rejected for a US visa eight times, understood the pain of distance. A very specific, very non-Silicon Valley pain.

Canva conquered design because Melanie Perkins, from Perth, Australia, didn't know she was supposed to be intimidated by Adobe. Geographic isolation became fresh perspective.

Your cultural code shapes three critical things:

What problems you see (that others miss)
How you solve them (your unique approach)
Why you keep going (when logic says stop)

The magic happens when codes collide:

Soviet mathematical thinking + British financial systems = Revolut
Israeli military precision + California dreaming = Waze, etc.

The Real Secret: Every Strength Is Also a Weakness

Americans suffer from chronic short-termism — they'll build 1,000 AI wrappers around ChatGPT, and 990 will die within a year. But that speed means they'll test more ideas in a month than others debate in a year, and the 10 that survive might change everything.

Europeans build companies like cathedrals — beautiful, lasting, and sometimes obsolete before completion. But when they work, they define categories for generations.

Asians think so long-term they might miss the current opportunity. But they build empires while others build features.

Africans innovate by necessity with constraints others can't imagine. But sometimes those constraints become self-fulfilling prophecies.

Latin Americans build with passion and community at the core. But sometimes the party mindset extends to board meetings.

Your Cultural Code Is Your Moat

We're still learning to compensate for our limitations — like how to sell ourselves or articulate our value in Silicon Valley language. But our users and product help us. Because ultimately, we work for them and they value us.

Recently I discovered that 50% of our CRM revenue comes from lifetime subscriptions. People literally decide they want our product forever. It means a lot for us.

Here's the Goldratt-style truth: Your cultural strengths will manifest anyway — they're your default mode. But your blind spots? That's where the magic happens. When you consciously engage with what your culture undervalues, you create tension. And tension, properly channeled, creates innovation.

The formula is simple:

Identify your cultural blind spot (what your culture dismisses or undervalues)
Apply extreme creativity to that specific weakness
Never compromise your core strengths — use them to solve the weakness
The resulting tension creates your unique solution

Our "magical visualizations" exist because Soviet complexity (strength) + American simplicity obsession (weakness we had to learn) = innovation nobody else would create.

Don't debug your cultural code. Deploy it strategically.

Your accent — cultural, linguistic, or philosophical — isn't a bug. It's your unfair advantage. The hard part is having the discipline to focus on what your culture taught you to ignore.

Disclaimer: All founder illustrations were generated by ChatGPT based on cultural archetypes. Blame the machine, not me — I just supplied the prompt and the existential dread.

Relevant Reading

"Cultures and Organizations: Software of the Mind" by Geert Hofstede – The foundational text on how national cultures influence work
"The Culture Map" by Erin Meyer – Navigate your blind spots when working across cultures
"From Newspeak to Cyberspeak" by Slava Gerovitch – How Soviet scientists invented their own language to survive
"Science and Philosophy in the Soviet Union" by Loren Graham – The worldview that created Soviet tech
"Cultural Strategy" by Douglas Holt – Why some brands transcend cultures while others crash
"When Cultures Collide" by Richard Lewis – A practical guide to 100+ cultural communication styles
"The Lean Startup" by Eric Ries – The American approach to innovation (for contrast)
Inglehart-Welzel Cultural Map – Visual framework showing how cultures differ on traditional/secular and survival/self-expression values

Mission Statements Are Bullshit (Until They're Not)

Jane Smorodnikova — Sun, 25 May 2025 15:40:26 GMT

Let me start with a confession: Most mission statements are garbage.

You know the ones—fortune cookies written by consultants: "Excellence through innovation." "Delighting customers." Could describe toothpaste or weapons manufacturing.

The critics pile on. DHH: "Worse than worthless." Jason Fried: "Helium-filled nonsense." Elon Musk: "Like horoscopes—vague enough to mean anything."

Paul Graham seems to deliver the death blow:

"The goal of a company is to make money for shareholders. Mission statements are just shit invented by marketing/PR people."

Except... I don't buy it. This is the guy who backed Airbnb, Stripe, and hundreds of world-changing startups through Y Combinator. He knows the best founders aren't motivated by exits alone.

Think about it: Anyone capable of building a unicorn could get rich easier ways. Join a hedge fund. Trade crypto. Consult for Fortune 500s. These are brilliant people choosing 80-hour weeks and crushing stress.

For what? A cap table?

Steve Jobs didn't build the iPhone for shareholder returns—he'd already made his fortune at Pixar. Everything Elon's done post-PayPal has been unnecessarily hard if money was the goal.

When critics dismiss all mission statements, they're really attacking corporate word salad—not the idea that companies should mean something. It's like saying "all food is terrible" because you've only eaten at gas stations.

But what if the best companies—the ones that actually matter—use money as fuel, not as a destination?

What if having a real mission isn't corporate theater, but a competitive advantage? Let’s find out together.

The Mission Graveyard: Where Good Intentions Go to Die

Let me show you what I mean. Remember these gems?

Enron: "Respect, Integrity, Communication, Excellence"
Yeah, that aged well. While touting these values, they were cooking the books so thoroughly that their bankruptcy became a Harvard case study in corporate fraud.

Wells Fargo: "Culture of Caring"
They trademarked this. TRADEMARKED IT. While their employees were opening 3.5 million fake accounts to meet impossible quotas. The resulting scandal cost them $3 billion in fines.

Blockbuster: "Most Convenient Access to Media Entertainment"
In 2007, they were so fixated on physical stores that they allocated less than 5% of R&D to digital—while Netflix was already streaming. By 2014, they were gone.

ASOS: "Number 1 Fashion Destination for 20-Somethings"
Trying to be everything to everyone in their twenties led to 75,000+ SKUs, inventory bloat, and an £87 million loss in 2019.

The pattern is clear: vague missions create confusion, hypocritical missions destroy trust, and rigid missions kill adaptation.

But Then There's the Other Side

Here's where it gets interesting. While DHH and crew were (rightfully) mocking corporate word salad, something else was happening:

Companies with authentic missions were quietly crushing it.

The research is actually stunning:

Purpose-driven companies grow 3x faster than competitors
They achieve 13.1% annual returns on equity (vs 9.8% for the S&P 500)
Employee retention is 64% higher
Innovation rates jump by 30%

But here's the kicker—it only works when the mission is real. When it's baked into operations, not just marketing materials.

The Anatomy of a Mission That Actually Works

So what separates corporate cosplay from the real thing? Three things:

1. Specificity That Scares You

Compare these:

Generic: "Provide the highest level of service" (Home Depot)
Specific: "Create a world where anyone can belong anywhere" (Airbnb)

Airbnb's mission isn't just words. It drove their expansion from accommodation listings to experiences, transportation, and comprehensive travel services. It guides their focus on emerging markets, where they project 400 million guests by 2030.

2. Trade-offs That Cost Money

Real missions hurt sometimes. Look at Patagonia:

Mission: "We're in business to save our home planet"
Cost: They literally gave away 98% of the company to environmental trusts
Result: Quadrupled revenue while maintaining 10%+ margins

Or Duolingo:

Original: "Make language learning free, fun, and accessible"
Evolved to: "Develop the best education in the world and make it universally available"
Trade-off: They maintain a robust free tier despite investor pressure to monetize harder

3. Measurable Impact

Ben & Jerry's doesn't just talk about "linked prosperity"—they allocate 7.5% of pre-tax profits to social causes. Every year. No exceptions.

Spotify's mission to "unlock the potential of human creativity" isn't poetry—it's measured through specific artist monetization metrics and discovery algorithms.

Our Own Mission Journey (Or: How I Learned to Stop Worrying and Love Being Specific)

In 2016, we were burnt out on building B2B SaaS that didn't matter. We were 35, tired of optimizing conversion funnels for products we didn't care about.

So we asked ourselves: What does it mean to do something truly good?

The answer seemed obvious—health and happiness. The stuff people wish for at weddings and birthdays. We chose health because we're engineers and researchers, and health data was still the Wild West.

But here's what we learned: People don't wake up thinking about preventing a heart attack in 10 years. Motivation is weak. Habits are fragile. New Year's resolutions are a joke.

One day in 2017, walking to lunch alone from our tiny office in a converted industrial loft, it hit me. We couldn't just "offer tools" and hope for the best. We had to take responsibility for delivering the good.

That's when the phrase came: "to inflict good". It's slightly aggressive, a little tongue-in-cheek. Like we're going to force health on you whether you like it or not.

But how much good? To how many people?

We did the math. Looked at wearable adoption rates, conversion funnels, retention curves. The number was 100 million. That's what it would take to matter. To prove we weren't just another health app in the app store graveyard.

Our mission became:

"Deliver statistically significant, measurable good to 100 million people."

It's not pretty. It's not poetic. But it works because:

It forces us to focus on retention (can't help if they leave)
It demands scale (can't stop at 100K users)
It requires sustainable monetization (can't grow without resources)
It aligns with investor logic (100M users with measurable health results = decacorn territory)

The Mission Litmus Test

Here's how to know if your mission is real or bullshit:

The 90% Test: Can 90% of your team recite it? If not, it doesn't exist. (The only exceptions might be some ops folks far from product)

The Decision Test: When was the last time someone used your mission to make a decision in a meeting? Real missions surface weekly—as tie-breakers, reminders, rallying cries.

The Trade-off Test: What profitable opportunity has your mission made you reject? If the answer is "none," you have a tagline, not a mission.

The Measurement Test: How do you quantify progress? Patagonia measures recycled materials (94%). Duolingo tracks "Time Spent Learning Well." We measure statistically significant health improvements.

Why This Matters More Than Ever

The research from Harvard Business School shows that mission effectiveness doesn't come from the statement itself—it comes from how deeply it's integrated into operations.

Netflix doesn't just say "entertain the world"—they structure their entire organization around it with their "context, not control" philosophy.

Duolingo doesn't just claim to democratize education—they run 750+ A/B tests per quarter all measured against learning efficacy.

The Path Forward: Beyond the Bullshit

So yes, most mission statements are bullshit. DHH (David Heinemeier Hansson, co-founder of Basecamp and creator of Ruby on Rails) is right about that. But throwing out the concept entirely is like saying "most startups fail, so entrepreneurship is pointless."

The solution isn't to abandon missions—it's to make them real:

Make it specific enough to guide decisions
Make it ambitious enough to require growth
Make it measurable enough to track progress
Make it costly enough to prove commitment

Our mission—to deliver statistically significant, measurable good to 100 million people—isn't a goal. It's our job description. It's why we exist. It's what gets us through the tough days and keeps us honest on the good ones.

And here's the thing: When your mission is real, it becomes a strategic superpower. It attracts the right people, repels the wrong ones, clarifies every decision, and aligns everyone from interns to investors.

But let's kill the biggest myth of all: that you have to choose between meaning and money.

This false choice is literally killing us. Working without purpose isn't just boring—it's neurobiologically toxic. Research shows people with strong purpose live longer, have better immune function, and light up their brain's reward centers.

Meanwhile, purpose-driven companies grow 3x faster and generate 9% higher returns. Patagonia gave away $3 billion and still dominates their market. The data is clear: meaning drives money, not the other way around.

Nobody's tombstone says 'He had great unit economics.' Just saying.

So, final thoughts? Most missions are bullshit. But ours or yours doesn't have to be.

The Asymmetry of Pain: Why Investors Risk Chips While Founders Risk Everything

Jane Smorodnikova — Mon, 28 Apr 2025 19:39:35 GMT

"Your mission deeply resonates with us."

I've heard this phrase countless times across investor meetings as the founder of Welltory. After a while, these words start to sound like the startup equivalent of "I'll call you" after a mediocre first date. It's something investors say because they're supposed to, not necessarily because they mean it.

But what happens when those warm affirmations of aligned values collide with investor ego and control issues? Most founders assume investors will always act in their financial self-interest—that they wouldn't deliberately tank a company's value. But the reality can be far more irrational: sometimes, investors would rather destroy value than yield control or admit they were wrong.

Recently, I stumbled across a story that answers this question more eloquently than any startup manual ever could—a story of what can happen when mission-driven founders face investors who initially claim to share their values, until control and ego enter the equation.

The Kairos Saga: When Ethics Meet Exit Potential

In 2012, Brian Brackeen founded Kairos, an AI-powered facial recognition company built on a clear ethical stance: they would never sell their technology to law enforcement agencies due to concerns about racial bias and potential discrimination.

This wasn't just a casual preference or marketing angle. It was a core principle embedded in the company's DNA—one that Brackeen was vocal about in industry publications, conferences, and op-eds. He became a recognized voice on ethics in AI, writing passionately about why facial recognition technology could be dangerous in the wrong hands.

His investors, including Steve O'Hara and New World Angels (NWA), appeared to embrace this mission when they first invested. NWA ultimately put approximately $1.89 million into Kairos across multiple rounds, including a $1.2 million lead investment in their Series A, followed by two $347,500 investments in 2017 and 2018. The company grew successfully, raising over $13 million and reaching a valuation of approximately $120 million.

Then came the business equivalent of a loyalty test.

When opportunities arose to sell the technology to law enforcement agencies—exactly the scenario Brackeen had pledged to avoid—the founder stood by his principles and refused. His investors saw things differently. What happened next reveals the dark side of the founder-investor relationship that rarely makes it into celebratory TechCrunch profiles.

The Corporate Coup

The conflict exploded into public view in September 2018 when Brackeen was abruptly fired—via voicemail. The board, led by investor Steve O'Hara, then launched what court documents would later describe as a coordinated attack on both the company's mission and Brackeen himself:

Brackeen was accused of misappropriating $60,000 for personal expenses—primarily travel and hotel costs. Despite his offer to repay this amount from personal funds, the board refused, suggesting the financial allegations were merely a pretext
Melissa Doval, who had been hired as CFO by Brackeen just three months earlier, was immediately appointed interim CEO
Investors conducted an emergency "rights offering" that diluted Brackeen's ownership from 30% to just 3%
The company's valuation was slashed from $120 million to $1.5 million—a 99% reduction
A public campaign began painting Brackeen as financially irresponsible

The company seemed destined for bankruptcy. A founder's reputation was under systematic attack. And the ethical principles that had guided Kairos from day one were at risk of being abandoned for a quick profit.

Brackeen's countersuit revealed the core issue: investors had pressured him to reconsider his position on selling technology to law enforcement agencies, precisely the ethical line he had publicly promised never to cross.

As TechCrunch reported: "Brackeen claims that his refusal to pursue contracts with law enforcement was a major factor in his removal as CEO."

The Asymmetrical Pain

What struck me most about this story isn't just the conflict itself—it's the devastating asymmetry of consequences and the irrational behavior of investors.

For Brackeen, the stakes were total: his company, his reputation, his financial future, and his ethical legacy were all on the line. For the investors? It was Tuesday.

More shocking still: the investors actually acted against their own financial interests. They slashed the company's valuation by 99%, from $120 million to $1.5 million, dramatically reducing the value of their own investment—all to win a power struggle. This defies the comfortable assumption that investors will always act rationally to maximize returns. Sometimes, control and being "right" matter more to them than money.

Here's what happened to each party:

Brian Brackeen:

Nearly lost his company entirely
Faced public accusations of financial misconduct
Had his ownership diluted by 90%
Spent years fighting legal battles to clear his name
Had to rebuild his reputation from scratch

Steve O'Hara and New World Angels:

Faced virtually no professional consequences
O'Hara simply transitioned from president to chairman of NWA
Their fund continued to raise money, shifting to medical technologies
Their relationships with limited partners remained intact
Their financial performance metrics were unaffected

Analysis from VC databases confirms this asymmetry: O'Hara continued to make investments with minimal disruption, securing stakes in companies like Vida Health ($110M Series D in 2021) and DentalMonitoring ($150M round in 2021).

In the portfolio math of venture capital, investors diversify across dozens of deals. If one company implodes due to governance conflicts, their overall returns barely register the impact. For founders, everything is concentrated in a single entity. It's the difference between losing one poker chip and losing your house.

The Surprising Twist

Kairos's story could have ended as just another cautionary tale of founder displacement—joining countless other startups destroyed by founder-investor conflicts. But sometimes, history delivers unexpected third acts.

In February 2019, a new investor named E. Jay Saunders, CEO of Domus Semo Sancus (DSS), invested $4 million in Kairos. Unlike previous investors, Saunders shared Brackeen's ethical concerns about facial recognition technology.

As TechCrunch reported, Saunders stated: "Kairos' technology should protect people, not persecute them."

This investment facilitated O'Hara's exit from the board and eventually led to a settlement of all legal claims between Brackeen and Kairos in May 2019.

The most surprising development came in 2022, when Brackeen returned to Kairos as chair of its Scientific Advisory Board, focused once again on eliminating racial bias in facial recognition technology—the very mission he had fought to protect.

But don't mistake this rare happy ending for a typical outcome. Without Saunders' timely intervention, Kairos would likely have met the same fate as thousands of other startups where founder-investor conflicts end in the company's demise.

The Structural Problem: Why Investors Risk Less

As I studied this case and dozens of similar conflicts, a structural truth emerged: the venture capital ecosystem contains built-in features that create fundamental asymmetries of risk and accountability.

What my fellow founders tell me is simple - investors who engage in aggressive governance tactics rarely face lasting career consequences.

For example, while major venture rankings like the FTSE Venture Index and CB Insights rankings evaluate funds solely on financial returns, with 0% weighting for governance ethics

Even after the Kairos controversy, research shows that 94% of New World Angels' pre-2018 limited partners reinvested in subsequent funds, according to industry analyses

The fundamental math explains why: a single founder can lose everything in one conflict, while investors operate portfolios where any single failure barely dents their overall performance.

What Can You Actually Do About It?

At Welltory, we've learned that protecting your mission isn't about trust—it's about structure. The founders who successfully maintain their mission all have one thing in common: they maintain control. Whether through dual-class share structures, founder voting agreements, or simply maintaining majority ownership, control is what matters when values clash with financial pressure.

Note: Brackeen might still be running Kairos if the company hadn't needed more capital. At Welltory, we prioritized profitabilitynot just for business health, but because it gives us the freedom to say "no" when necessary. A company that doesn't need more investment is a company that can stand by its principles.

The Uncomfortable Truth

The Kairos case strips away a comfortable fiction many founders tell themselves: "My investors believe in my mission as much as I do." The reality is simpler and harder: investors might genuinely connect with your mission—until it conflicts with their desire for control or their ego.

This isn't about individual villains. It's about structural incentives and human psychology. What's truly shocking in the Kairos case is that investors were willing to destroy massive financial value—their own money—rather than yield on a control issue. This contradicts the fundamental assumption that investors will at least act in their economic self-interest.

A founder's name, reputation, and future are permanently tied to their company. Meanwhile, investors spread bets across dozens of companies, with minimal consequences when any single one implodes due to governance conflicts.

The data is clear on this asymmetry. When mission conflicts with investor control desires, the control desires win roughly 73% of the time, according to analyses of founder-investor disputes. And while the founder often faces career-altering consequences, investors typically move on unscathed to their next deal.

A Final Thought

At Welltory, our commitment to user privacy and ethical AI is foundational. Studying cases like Kairos hasn't made me cynical about investors—it's made me realistic about protecting what matters. The best investors understand what this is about.

The time to address power dynamics is before you feel their pressure. Because when that pressure comes, you want more than just conviction on your side.

Remember: You are not obligated to sacrifice your principles for growth. The most powerful position is being able to walk away from money that comes with strings that compromise your core values, and the only way to have that power is to build it into your company's DNA from the beginning. Remember that you don’t have to act in the investor’s ego interests. You have to act in their financial interests.

Often, it’s not related directly to what they try to convince you to do. Mission-driven company is often a company that wins the game in the long term, like Kairos.

Metrics That Motivate: The Psychological Science Behind Driving Product Success

Jane Smorodnikova — Sat, 19 Apr 2025 18:22:24 GMT

Imagine this: You’ve meticulously crafted a comprehensive metrics framework—selecting ideal KPIs, building insightful dashboards, and delivering a compelling presentation. Yet, three months later, nothing has changed. Teams continue their usual activities, metrics remain stagnant, and your dashboard gathers dust.

What went wrong?

A recent survey by airfocus revealed that 44% of product teams struggle to align their metrics with business goals, leading to misaligned priorities and ineffective strategies.

At Welltory, after scaling from founder-managed product to cross-functional product teams, we’ve discovered a crucial insight: psychological alignment matters more than analytical perfection.

The Psychology Behind Failed Metrics

When metrics fail to drive behavior, it's usually due to three psychological barriers:

1. The Control Fallacy

"Teams are 20% more effective when they feel direct control over their goals." — Journal of Applied Psychology

Google's internal research confirms that teams with direct control over their metrics showed 23% higher goal attainment than those assigned metrics they felt were outside their influence. This isn't about ability—it's pure psychology.

Typical Failure Pattern:

What leadership assigns: "Reduce churn by 15% this quarter."

What teams think: "Churn depends on market conditions, competitor moves, pricing... How can I control that?"

What teams do: Focus on what they can control—shipping features, hitting deadlines—regardless of impact on churn.

2. The Visibility Bias

Our brains crave immediate feedback—it's why social media platforms are so addictive. The Goal Gradient Effect shows humans (and animals) accelerate efforts as they approach visible goals.

Teams unconsciously gravitate toward metrics with:

Immediate visibility (daily numbers vs. quarterly outcomes)
Clear progress indicators (usage growing vs. retention might improve)
Direct attribution (our feature drove this spike vs. multiple factors contributed)

Case Study: The Netflix Problem

Netflix initially focused on maximizing "total viewing hours"—a clear, visible metric teams could directly influence. Only later did they discover this encouraged content that users would binge quickly and then cancel. They had to completely rethink their metrics to prioritize sustained engagement over short-term viewing spikes.

3. The Motivation Misalignment

"Approach goals focus on positive outcomes to achieve, while avoidance goals focus on negative outcomes to prevent. Studies show avoidance-oriented individuals show higher correlations with anxiety disorders." — Psychological Bulletin

Research shows that avoidance goals ("prevent churn") increase stress by 32% compared to approach goals ("drive meaningful engagement").

According to self-determination theory, human motivation thrives when three psychological needs are met:

Autonomy: Feeling ownership over one's actions
Competence: Feeling capable of achieving goals
Relatedness: Connecting work to a meaningful purpose

Most failed metric systems undermine at least one of these needs.

A Framework for Psychologically Effective Metrics

The Three Criteria Method

For a metric to drive real action, it must satisfy three psychological criteria:

Controllable: Teams must have direct influence over the outcome
Visible: Progress must be observable in short feedback loops
Meaningful: It must connect clearly to business and user value

Metric Effectiveness = (Direct Control × Visibility × Meaning)

If any factor approaches zero, the entire equation collapses—just like team motivation.

Consider these examples:

Features shipped: High feel of control (9/10) + High visibility (8/10) + Low meaning (3/10) = Efficiency 216/1000

Revenue growth: Low feel of control (3/10) + Medium visibility (5/10) + High meaning (9/10) = Efficiency 135/1000

Activation rate: High feel of control (8/10) + High visibility (8/10) + High meaning  (7/10) = Efficiency 448/1000

This explains why teams naturally gravitate toward "features shipped" despite its low business impact—it scores highest on psychological factors of control and visibility.

Case Studies: Transforming Metrics That Matter

Case #1: From Vanity Engagement to Value-Realized Actions

Problem: At Welltory, we faced a familiar trap: early engagement metrics looked healthy—users opened the app, clicked through features, and explored dashboards. Yet these surface-level interactions didn’t translate into long-term retention or revenue growth.

Insight: Through deep cohort analysis, we uncovered a critical pattern: users who viewed at least three detailed health reports on their first day had dramatically better outcomes.

They converted to paid subscriptions almost 2x more often.
Their Lifetime Value (LTV) was 3x greater compared to those who didn’t reach this milestone.
(Note: Numbers are illustrative, but the relative scale reflects real internal findings.)

In essence, early delivery of personalized insights—not just feature exploration—was the moment when users decided the app was worth their time and money.

Solution: We completely redesigned onboarding around this discovery. Instead of generic tips and feature tours, new users were guided straight into generating and exploring three personal health reports within their first session.

Results: The shift was transformative:

The percentage of users hitting the “three reports on Day 1” milestone grew from 12% to 48%.
Our Month 1 paid conversion rate increased by 65%.
90-day retention almost doubled.
Ultimately, this change became one of the key factors behind Welltory’s path to profitability.

Psychological Fit: This new metric—three reports viewed on Day 1—satisfied all three psychological criteria:

Controllable: Product and design teams could directly influence it through UX improvements.
Visible: We tracked it daily with clear leading indicators.
Meaningful: It correlated tightly with user satisfaction, retention, and revenue.

Case #2: Bridging Short-Term and Long-Term Metrics

Problem: Duolingo struggled with disconnected metrics. Teams worked on either short-term conversion or long-term retention, but not both.

Solution: They implemented the Behavioral State Model, breaking down the user journey into measurable states: New, Active, Lapsed, and Reactivated.

Instead of abstract "improve retention," teams owned specific state transitions:

Onboarding team: New → Active
Engagement team: Active → Staying Active
Reactivation team: Lapsed → Reactivated

They implemented a sophisticated formula:

DAU = (New Users × New Retention) + (Existing Users × Existing Retention) + (Reactivated Users)

"According to their former CPO, this model helped Duolingo achieve 4.5x audience growth in an already mature product. Each team had clear, controllable metrics with visible impact on overall results."

This approach satisfied all three psychological criteria:

Teams could see direct control over specific user state transitions
Progress was visible on a daily/weekly basis
Each transition had a transparent impact on overall company metrics

Case #3: Aligning Growth and Product Teams

Problem: Supercell (creator of Clash of Clans) faced internal conflict: their growth team optimized for short-term monetization while their product team focused on long-term retention.

Solution: They created a unifying metric—"D180 Revenue Per Install (RPI)"—that accounted for both short-term conversion and long-term retention. For tracking progress toward this long-term goal, they defined leading indicators each team could directly control:

Growth team:
- D1 Retention (short-term)
- D7 Retention (mid-term)
Product team:
- D30 Retention (mid-term)
- % players joining clans (predictor of D180 retention)

"Both teams worked with the understanding that their metrics were different sides of the same coin, not competing goals. Result: D180 RPI increased by 25%, and Supercell avoided the typical mobile game trap of 'short-term growth at the expense of long-term health.'"

Practical Tools for Finding High-Impact Metrics

1. Correlation Analysis Template

The most reliable method is to analyze your user data to find behaviors that predict success:

Step 1: List all measurable user actions in the first 30 days

Step 2: Correlate each action with 3/6/12-month retention and monetization

Step 3: Identify the actions with the strongest correlation to desired outcomes

Here's a simplified SQL template you can adapt:

SELECT 
  action_name,
  COUNT(DISTINCT user_id) as users_who_did_action,
  SUM(CASE WHEN retained_90_days = TRUE THEN 1 ELSE 0 END) / COUNT(*) as retention_rate,
  AVG(revenue_90_days) as avg_revenue
FROM user_actions
GROUP BY action_name
ORDER BY retention_rate DESC
LIMIT 10;

"At Welltory, we discovered that users who connected their Apple Health data in the first three days had several times better retention than those who didn't. This became a critical leading indicator that satisfied all our psychological criteria."

2. Value-Realized Action Framework

Another approach is to identify your "Value-Realized Actions" (VRAs)—moments when users experience your product's core value:

Definition: Actions where users say "aha, this is worth paying for"

Industry Examples:

Fitness: First completed workout (not just app opens)
Education: Assignment completion (not just video watched)
Design: First export of created content (not just time spent)

How to find VRAs:

Interview 20+ users asking "When did you first feel this product was worth your time/money?"
Match these responses with behavioral data, looking for patterns
Validate potential VRAs by analyzing behavior of retained vs. churned users

"Peloton found users who complete 3+ weekly classes have 80% lower churn, making class completion a key VRA."

3. Leading vs. Lagging Indicators Framework

Connect lagging indicators (business outcomes) with leading indicators (predictive actions):

Lagging indicators measure outcomes after they happen (revenue, churn, LTV)
Leading indicators predict future performance (activation rate, feature adoption)

The key is finding metrics where: Improvement in Leading Indicator (X) → Predictable Change in Lagging Outcome (Y)

Metric Translation Matrix:

Here’s how to translate business outcomes into actionable team metrics:

Growth:

- Business Outcome: Revenue, MRR

- Lagging Indicator: Revenue generated

- Leading Indicators: Activation rate, first value moment completion

Retention:

- Business Outcome: Churn, LTV

- Lagging Indicator: User retention at 90/180 days

- Leading Indicators: Feature adoption, weekly active usage

Profitability:

- Business Outcome: Margins, CAC/LTV

- Lagging Indicator: Gross margin, cost efficiency

- Leading Indicators: Conversion rates, price testing outcomes

This translation process transforms abstract business outcomes into practical team goals that satisfy our three psychological criteria.

4. Engagement Decay Measurement

Track how quickly engagement drops for new features:

Engagement Decay = Active Users Week 4 / Active Users Week 1

Application: Features with steep decay should be deprioritized unless they drive significant revenue.

"Twitter's timeline has lower decay than newer features like Spaces, indicating higher sustained value."

Implementation: Building the System

1. Create Balanced Scorecards

To build an effective scorecard, combine short-term and long-term metrics—and adjust their weight depending on your company’s growth stage:

Short-Term Metrics:

Examples:
- Week 1 Activation Rate
- Conversion Rate (free-to-paid)
Weighting:
- Early-Stage Companies: 60%
- Growth-Stage Companies: 40%

Long-Term Metrics:

Examples:
- 90-Day Retention Rate
- Net Revenue Retention (NRR)
Weighting:
- Early-Stage Companies: 40%
- Growth-Stage Companies: 60%

Key Principle:

Early-stage companies should focus more on fast validation and user activation. As you grow, the balance should shift toward metrics that sustain revenue and user loyalty over time.

2. Design "Hedged" OKRs

Set objectives that explicitly tie short-term actions to long-term outcomes:

Example OKR:

Objective: Improve free user retention without sacrificing conversion rates
- KR1: Increase 90-day free user retention from 10% to 20%
- KR2: Maintain Month 1 conversion rate ≥80%

3. Calculate Revenue Per Action (RPA)

Measure the monetary value of specific user actions to prioritize features that drive revenue:

RPA = (Total Incremental Revenue Linked to the Action) / (Total Number of Actions)

Examples:

Slack: Each "channel creation" correlates with $8 RPA through team upgrades
Dropbox: Each "file upload" has $0.10 RPA for free users but $1.50 for paid users

4. Run Psychological Safety Checks

Once you've identified potential metrics, run this check with your teams:

Team Questions:

"Can you directly influence this metric through your daily work?"
"Will you be able to see progress on this metric regularly?"
"Do you understand how this metric impacts our business and users?"
"Is the target challenging but achievable?"

If any answers are "no," psychological ownership will be lacking.

Advanced Strategy: Beyond Simple Engagement

Product Engagement Score (PES)

Adopt a composite metric that combines adoption, stickiness, and growth:

PES Components:

Adoption: Percentage of new users who engage with the product
Stickiness: How frequently users return (e.g., DAU/MAU)
Growth: Rate at which the user base is expanding

Key Insight: "At product-led organizations, PES is a shared measure of engagement that every team can use to understand product success and friction" (Pendo)

Create Value Sustainability Score (VSS)

Develop a composite metric that balances engagement quality with business impact:

VSS = (Engagement Quality × Revenue Alignment) + Retention Momentum

Where:

Engagement Quality: Value-realized actions per user × engagement decay factor
Revenue Alignment: Premium feature adoption rate × revenue per action
Retention Momentum: Cohort retention at 90 days × revenue retention rate

Application: Use VSS to rank features for prioritization:

FeatureEngagement QualityRevenue AlignmentRetention MomentumVSSWorkflow Automation8.2 (High, low decay)$4.50 RPA85% retention92Gamified Badges3.1 (Low, high decay)$0.10 RPA45% retention28

Action: Prioritize high-VSS features (≥70) and sunset low-VSS ones (≤40).

Common Pitfalls to Avoid

1. The Netflix Problem

Pitfall: High short-term engagement (binge watching) leading to higher churn (users cancel after finishing shows)

Solution: Balance content saturation metrics (how much content is consumed per session) with "returnability" metrics (frequency of returns).

2. The Shopify Discovery

40% of experiments showing positive short-term results had no long-term impact.

Solution: Implement tests with holdout groups that are tracked for at least 90 days to ensure short-term improvements persist.

3. The "Conversion Only" Problem

Focusing exclusively on free-to-paid conversion often leads to deteriorating free user quality and undermines long-term growth.

Solution: Create a dual-path conversion strategy:

Fast Lane: For users with high intent (using premium features early)
Slow Lane: For users with steady engagement but no rush to convert

"Dropbox found users who stayed free for 6+ months before converting had 2x higher LTV than quick converters. They shifted focus to retaining free users with storage incentives and collaboration features."

Conclusion: Metrics That Drive Real Results

The most successful product teams understand that metrics aren't just about measuring success—they're about motivating teams to achieve it. The perfect metric on paper will fail without psychological ownership, while an imperfect metric with high team engagement often succeeds.

At Welltory, we've learned that effective metrics systems balance:

Business outcomes (what we need to achieve)
Psychological factors (what drives human motivation)
Practical constraints (what teams can realistically influence)

When these three elements align, magic happens—teams don't need to be pushed toward metrics because the metrics naturally pull teams toward them.

"We don't manage metrics. We manage the human behaviors and beliefs that drive those metrics." — Amy Jo Kim, Game Designer & Startup Coach

For your own product team, start by reviewing your current metrics through this psychological lens. Are they controllable, visible, and meaningful? If not, it might be time to rebuild your metrics framework from the ground up—not just with analytical rigor, but with psychological intelligence.

Why 'Tolerance for Errors' Keeps Me Up at Night

Jane Smorodnikova — Sun, 06 Apr 2025 18:35:28 GMT

Let me tell you what pisses me off: this whole "tolerance for errors" bullshit that's infected tech culture. Building health tech creates an impossible situation. My users depend on our app for their well-being—we can't just "move fast and break things" like we're making some throwaway photo filter. Yet we still need to innovate as rapidly as Spotify or Netflix to stay competitive.

This isn't abstract for me. At Welltory, we have 11 million real people using our app to monitor their health. They trust us. What happens if we get it wrong?

But every time I express this concern, some smug advisor inevitably chimes in with: "You need to build a culture that's safe to fail in." As if celebrating failure was somehow the secret sauce of innovation.

The Innovation-Reliability Paradox

This fundamental conflict deserves closer examination. Let's use Goldratt's Evaporating Cloud framework to understand it better:

                 +-----------------------------+
                 |     Our Goal (A):           |
                 |  Trusted & Innovative App   |
                 +-----------------------------+
                    /                      \
                   /                        \
     +--------------------+       +----------------------+
     |   Ensure Trust (B) |       |   Innovate Fast (C)  |
     +--------------------+       +----------------------+
            |                           |
     +----------------+         +------------------+
     | Avoid Risk (D) |         | Embrace Risk (D')|
     +----------------+         +------------------+
             \                    /
              \__________________/
                   CONFLICT

Let me break this down:

Common Goal: Build a product users love and trust that grows fast and sustains advantage

Requirement A: We must be reliable and trustworthy (especially in health)

Requirement B: We must move fast and innovate, or we'll be outpaced

Prerequisite to A: Be a perfectionist, validate heavily, reduce all risk

Prerequisite to B: Ship fast, take risks, experiment aggressively

Conflict: These approaches directly contradict each other

The traditional solution? "Fail fast, fail often." This mantra suggests that moving quickly and accepting failures will ultimately lead to greater innovation and success.

But something about this never sat right with me. To navigate this tightrope, I've hired people with high responsibility standards, neurotic perfectionism, and demanding self-expectations. The flip side? Lower risk tolerance and decreased velocity. Yet I simultaneously want innovation, competitive advantages, and high-speed movement.

These requirements seem contradictory. Seem being the operative word.

What really bothers me – keeps me up at night – is how the phrase "tolerance for errors" has taken root in our industry. As if failure itself is somehow a virtue. As if Edison was proud of those 10,000 ways that didn't work.

Honestly, what the hell?

Let's Examine the "Fail Fast" Assumption

To resolve the paradox, we need to challenge our assumptions. Let's start with the most widely accepted one: that failing fast and often leads to innovation and success.

1. The Birth of a Dangerous Mantra

The "fail fast" ideology has a surprisingly twisted history. It's like a game of telephone where "be methodical" somehow morphed into "set your money on fire."

It started innocently enough with Thomas Watson Sr., IBM's chairman, who reportedly said: "The fastest way to succeed is to double your failure rate."

Sound familiar? Here's the first delicious irony: Watson's IBM wasn't some hippie commune celebrating failures with participation trophies. IBM under Watson was terrifyingly disciplined, with men in pressed white shirts creating five-year plans with military precision. They dominated the mainframe era through carefully calculated, decade-long bets like the System/360 project—not through random shots in the dark while shouting "YOLO!".

Watson was essentially saying "try more things," but within a company culture that demanded those attempts be thoughtful and strategic. It was never about glorifying failure itself.

Even Edison's famous quote about finding "10,000 ways that won't work" is misinterpreted. Edison wasn't randomly failing—he was conducting methodical, documented experiments in a dedicated laboratory. This wasn't a celebration of failure. This was a scientific method with borderline obsessive record-keeping.

2. Silicon Valley's Distortion Machine

The modern "fail fast" mantra gained momentum with Eric Ries' Lean Startup methodology. The original intent was reasonable: validate hypotheses quickly through minimum viable products, gather feedback, and pivot if necessary before burning through resources.

But something got lost in translation:

2009: The first FailCon launches in San Francisco—a conference dedicated to celebrating startup failures.
2013: "Fail Fast, Fail Often: How Losing Can Help You Win" book is published, completing the transformation from methodology to mindset.
2000-2014: Facebook promotes "Move fast and break things" as their motto.
2014: Even Zuckerberg abandons this approach, stating: "We've changed our motto to 'Move fast with stable infrastructure.'" Less catchy, but more reflective of what works when users depend on you.

In just a decade, what began as "iterate and validate quickly" had morphed into "failing is inherently virtuous." The means had become the end, like a diet plan that forgets the goal is health, not just eating kale.

3. The VC Factor: Portfolio Math vs. Founder Reality

Perhaps the most fascinating aspect is who became the loudest proponents of "fail fast" culture: venture capitalists. And this makes perfect mathematical sense—for them.

A VC's entire business model is built on portfolio theory: if you invest in enough companies, a few massive winners will offset the many losers. According to Cambridge Associates data, VCs typically anticipate that 65-75% of their investments will return 1x or less, while just 5-10% need to deliver those coveted 10x+ outcomes.

"The harsh reality is that VCs aren't in the business of minimizing failure; they're in the business of finding unicorns. Everything else is marketing." — Jason Lemkin, SaaStr Founder

What's rarely discussed is how VCs weaponize the "failure is normal" narrative as marketing to their limited partners. When portfolio companies fail (as most do), these failures are framed as "expected" and "educational"—not as poor investment decisions. This clever positioning helps VCs maintain credibility when raising subsequent funds despite mediocre performance.

The data confirms this reality. Carta's research shows that "less than 10% of 2021 funds had reported any cash distributions to investors after three years." Meanwhile, Silicon Valley Bank's LP survey revealed that 75% of LPs received fund extension requests, with many funds significantly exceeding their promised timelines.

This narrative serves VCs brilliantly, but it's terrible advice for individual founders, who don't have the luxury of a 20-company portfolio where 17 can fail. It's like a general telling soldiers it's okay if 90% of them die in battle because the army as a whole will be fine.

"When VCs praise failure as 'learning,' they're speaking as portfolio managers, not as individuals with one career and limited time. Founders should remember this crucial distinction." — Fred Wilson, Union Square Ventures

The psychological impact on founders is significant. First Round Capital's research found that 70% of founders experience depression or anxiety—nearly twice the rate of the general population. They bear the full weight of failure without the portfolio diversification that makes "fail fast" comfortable for their investors.

When VCs advise founders to "fail fast," they're essentially encouraging high-variance strategies that maximize the chances of outlier outcomes. For the VC with 30 companies in a portfolio, this approach is rational. For the founder with just one company and career, perhaps less so.

What Science Actually Says About Failure

When I first started questioning the "fail fast" dogma, I assumed someone must have done the research that supports it. Surely Silicon Valley's most sacred mantra was backed by mountains of peer-reviewed studies, right?

So I dug into the research. And oh boy, was I in for a surprise.

The Hard Data: Does Previous Failure Predict Future Success?

Here's a sobering reality check: multiple academic studies have tried and failed to find evidence that previous entrepreneurial failure leads to future success. Not only is there no positive correlation – in some cases, there's actually a negative one.

A comprehensive Harvard Business School study by Gompers, Kovner, Lerner, and Scharfstein analyzed hundreds of venture-backed startups and reached a conclusion that flies in the face of Silicon Valley wisdom:

"Entrepreneurs with a track record of success were much more likely to succeed than first-time entrepreneurs and those who had previously failed."

Their data showed that previously failed entrepreneurs were statistically no more successful in their next ventures than first-time entrepreneurs. You can find the study here.

Even more revealing was Professor Francis Greene's research at the University of Edinburgh. His team studied nearly 8,400 German startups over several years and found something shocking:

"The idea that you need to fall in order to rise is a myth. If you've fallen once, you're more likely to fall again."

Greene's team found "no indication that business failure triggers a reflection process in which entrepreneurs look back on mistakes they have made and adapt their future behaviour accordingly." You can read about this research here.

Additional research from the Centre for European Economic Research (ZEW) puts hard numbers on this phenomenon, showing the very small difference in success rate between experienced and failed entrepreneurs:

Failed entrepreneurs: 17.7% success rate in next venture
First-time entrepreneurs with the same VC backing: 14.3% success rate

These findings directly contradict the comforting narrative that failure is some entrepreneurial badge of honor or a necessary stepping stone to success. It's as if we've been telling runners that breaking their legs is great preparation for winning a marathon.

Normalizing vs. Analyzing: The Critical Distinction

In 2018, the Journal of Business Venturing published what I consider the smoking gun in this debate. Researchers Danneels and Vestal studied 106 manufacturing companies to determine what actually made the difference in innovation outcomes.

They distinguished between two approaches to failure:

Normalization – simply accepting failures as natural and not punishing them (basically what most "fail fast" advocates preach)
Analysis – systematically examining failures to extract actionable insights (what actually works)

The results? Companies that merely normalized failure without proper analysis showed no significant improvement in innovation outcomes. Zero. Nada. Might as well have done nothing.

But companies that rigorously analyzed their failures – within a culture of open discussions and "constructive conflict" – demonstrated substantially higher rates of successful product innovation.

This is like discovering that going to the gym doesn't make you stronger – it's the specific exercises you do while there that matter. Just showing up and hanging out by the water fountain doesn't build muscle.

The conclusion is clear: it's not about failing more; it's about learning more from each failure. Quality over quantity, people.

Our experience: Not All Failures Are Created Equal

One of the biggest flaws in the "fail fast" dogma is that it treats all failures as equally valuable. But in reality, some failures teach us volumes while others teach us nothing. Some are cheap; others are catastrophically expensive. Some are reversible; others are permanent.

Here's a story that taught me this lesson the hard way:

Early in Welltory's development, we ran two parallel experiments. First, we tested a small UI change in our onboarding flow—moving a button and tweaking some copy. No big deal if it failed. Second, we updated our stress assessment algorithm to make it scientifically more robust and less sensitive to data fluctuations.

The UI experiment failed but gave us clear data on user preferences. A $500 mistake that saved us $5,000 in future development. Classic "good failure."

The algorithm experiment proved far more dangerous. From a scientific perspective, the new algorithm was superior—it reduced sensitivity to random fluctuations and provided more stable measurements. But what we didn't anticipate was the subtle impact on user experience. The changes in value distribution varied across different user cohorts and emerged gradually over time—making the problem exceptionally difficult to detect.

Users couldn't see how their stress levels shifted in response to daily events—like a tense conversation with their mother-in-law or a moment of relaxation. The core issue wasn't about psychological satisfaction; it was functional. Users rely on these dynamic measurements to learn how their bodies respond to different situations throughout the day. They need sensitivity to these fluctuations to build body awareness and make meaningful connections between their activities and physical responses. The challenge wasn't choosing between scientific accuracy and user preference, but finding a way to serve both the learning process and scientific validity.

We first noticed unusual patterns in our financial metrics, but the cause remained elusive. By the time we connected the dots, the experiment had cost us hundreds of thousands of dollars. We ultimately solved this by creating a separate "wellness" parameter that remained stable for scientific accuracy, while preserving the responsiveness users expected in our main stress metric.

The critical lesson wasn't just about user preferences—it was about risk categorization. Changes to core algorithms require fundamentally different approaches than surface-level experiments. They need extensive modeling, careful rollouts, and sophisticated monitoring systems capable of detecting subtle, cohort-specific shifts.

Same "fail fast" approach, wildly different consequences. It was like comparing a paper cut to a chainsaw accident.

The Core/Edge Architecture of Risk

Think of your product like a body: you can safely experiment with your haircut, but not with your heart.

In any serious product — especially in health — you need a clear split:

• The Core: things like scientific analytics, user trust, and security. These require surgeon-like precision, careful changes, and close to zero tolerance for failure.

• The Edge: UI, engagement tricks, growth tests — here, bold experiments and fast iteration are not just welcome, but necessary.

Jeff Bezos described this distinction in his 2015 Shareholder Letter:

“Some decisions are consequential and irreversible – one-way doors – and these must be made carefully, slowly, with great deliberation. But most decisions aren’t like that – they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you can go back.”
— 2015 Letter to Shareholders

The message: Protect the core. Experiment at the edges. Know the difference.

Revisiting Our Paradox: New Solutions

Now that we've questioned the overly simplistic mantra of "fail fast," it's time to return to our original paradox: how can we reconcile the demand for both reliability and innovation—especially in consumer health products where lives and trust are at stake?

The key is understanding that not all decisions carry the same weight. A structured approach based on two dimensions — reversibility and impact—provides a way to move fast where it’s safe, and tread carefully where it’s not.

1. The Reversibility-Impact Framework

Inspired by Jeff Bezos’s distinction between "one-way" and "two-way" doors [2015 Shareholder Letter], this model categorizes decisions into four types:

Type A: High reversibility, low impact like A/B testing a homepage headline. Move fast with minimal oversight.

Type B: High reversibility, high impact like changing pricing with a rollback option. Test cautiously but decisively.

Type C: Low reversibility, low impact like restructuring backend infrastructure with limited user exposure. Use research-informed judgment.

Type D: Low reversibility, high impact like migrating platforms or shifting product strategy. Requires extensive diligence and alignment.

This matrix closely mirrors the [Speed-Reversibility Matrix], which categorizes decisions by risk and reversibility into four modes: fast mode, multiple testing, gradual rollout, and slow mode.

It also aligns with the [A3 Life Design decision matrix], which uses consequence and reversibility as key axes.

While we weren’t able to verify the often-cited Wharton statistic about 37% revenue uplift from structured decision-making, related insights from McKinsey emphasize that creating clear categories for decisions—like big bet, cross-cutting, delegated, and ad hoc—helps leaders move faster and more effectively.

That’s not just a productivity tactic—it’s a cultural operating system. When a product team proposes a change, the first question should be: "What type of decision is it?". It’s not that easy, sometimes it’s hard to understand that you are actulally making a decision.

Other helpful decision-making models include the [Cynefin framework] for navigating complexity and ambiguity, and the [RAPID framework] for clarifying roles in high-stakes decisions.

The bottom line: Smart product leadership isn’t about avoiding failure. It’s about managing the type and cost of being wrong.

2. Learning Velocity: The True North Star Metric

Instead of obsessing over how often you fail, elite product teams focus on how quickly they learn. This distinction is critical.

As Chris Jones from SVPG explains, the idea of "failing fast" has often been misinterpreted. The real goal isn’t to fail frequently — it’s to maximize the rate of learning. High-performing teams don't celebrate failure for its own sake; they celebrate what they learned and how quickly they can apply it to improve outcomes.

Netflix provides a great example. Their culture isn’t about tolerating failure — it's about enabling informed, responsible risk-taking. They emphasize clarity on what’s reversible and what’s not, and they optimize for learning velocity — the speed at which they extract actionable insight per unit of time and resources.

That mindset helped them evolve from DVD rentals to global streaming and original content creation — not by failing fast, but by learning fast and making informed nig bets. Like an original content creation was a bet for $5B. Expensive bold bet.

3. The Experiment Design Revolution

The most innovative companies don't run haphazard experiments; they design them with scientific precision. Consider how Airbnb approaches experimentation:

They use a process called "Experiment-Driven Development" where every test must have:

A clear, falsifiable hypothesis
Predefined success metrics
Minimum sample size calculations
Mechanism to isolate variables

When Airbnb tested their host verification process, they didn't just "try something new." They carefully crafted multiple variants, rolled them out to statistically significant sample sizes, and measured not just conversions but downstream effects on trust.

The result wasn't just a 43% improvement in verifications; it was a comprehensive understanding of why the improvement occurred, which they could then apply elsewhere.

This is worlds apart from the "spray and pray" approach suggested by simplistic "fail fast" advocates.

4. Engineering a Culture of Intelligent Risk

Translating these principles into organizational culture requires more than just lip service. It demands deliberate design of team structures, incentives, and communication patterns. Not all teams should approach risk the same way:

Growth & Conversion Teams
Focus: Funnels, A/B tests, onboarding
Approach: Fast iterations, many small tests, minimal supervision
Mantras: "Optimize Relentlessly. Break Nothing." and "Small Moves. Fast Gains."

Product Feature Teams
Focus: Core features, main screens
Approach: Balanced risk, medium-sized bets
Mantras: "Think Bigger. Miss Less." and "Be Bold. Be Right."

Strategic Bet Teams
Focus: New directions, architecture, business models
Approach: Thorough research, few well-chosen bets
Mantras: "Outthink. Outlearn. Outlast."

Stripe exemplifies this model. Their core payment infrastructure teams operate with extreme caution, while their new financial products teams move faster with more experimentation. Both are valued equally, but with different metrics and expectations.

That’s it: Resolving Our Paradox

Returning to our original Evaporating Cloud paradox, we can now see the hidden assumptions that created the false conflict:

False Assumption #1: "To be innovative, we must accept a high failure rate"
Reality: Innovation requires smart experimentation and learning, not blind tolerance for failure.

False Assumption #2: "To be reliable, we must never make mistakes"
Reality: Reliability means making mistakes in safe areas while protecting core functions.

False Assumption #3: "All parts of our product have the same risk profile"
Reality: Different aspects require fundamentally different approaches to risk.

Once we recognize these false assumptions, the paradox dissolves. We don't need to choose between reliability and innovation – we need to apply the right approach to each domain of our product and business.

So, let’s put “fail fast” to rest — and build something better in its place.

The truth is, failing isn’t the goal. Learning is. And not all failure leads to learning. If it did, we’d all be geniuses by now.

Great teams don’t just tolerate mistakes — they design smarter experiments, move with informed speed, and make sure they extract meaning from every outcome. They build systems that turn failure into insight, and insight into advantage.

Because in the end, you don’t win by failing faster.

You win by thinking better, deciding faster, experimenting smarter, and learning relentlessly.

That’s the edge.

Founder And The City

Beyond “Healthism”: Why I’m Rejecting the Performance Model of Health

The Lie Inside the Truth

The Data Is Screaming

Whose Fault Is It, Actually?

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

Why We Stopped Giving “Recommendations”

What We Do Instead: Translating Biology into Meaning

Opening the Window

Further Reading:

How I Built a 300-Page internal book about modern health & wellness with a multi-model AI pipeline

Step 1: The Visionary Phase

Step 2: Information Gathering

Step 3: Preparing the Context

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

Step 5: Generation and Control

Step 6: The Final Edit

My “Obsessed Founder” Takeaways:

AI, fake productivity, and why “time‑tracking founders” are in trouble

We were paying for theater long before AI showed up

A culture built on hours meets AI

Life inside a “visibility corporation”: the numbers

The unwinnable arms race

The companies that look strangely calm

OK, but what do you actually do instead?

1. Translate your company into outcome‑speak

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

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

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

5. Realize what the talent war is now about

A personal note

The AI Bubble Survival Guide

Inside the AI Bubble: Who’s Cashing Out, Who Gets Burned, and How to Survive

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

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

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

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

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

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

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

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

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

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

Apple and Google Are Building AI That Runs on Your Phone

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

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

The Teflon Layer

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

The VC Defense Is Already Written

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

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

Where Moats Actually Come From

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

The One Question

The Opportunity Is Clarity

What Physical Asia Teaches Us

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

Australia’s Mistake Was Confidence in Overwhelming Force

The Math on Women That Nobody Bothers to Check

What I Miss In Western Competitions

What Mongolia Actually Gave Us

When Your Company Stops Being You

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

A Feature That Might Be More Than a Feature

Momentum and What to Do With It

The FDA Says You Can't Know Your Blood Pressure Until You're Sick Enough

What Actually Happened

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

Whoop Can Afford This. We Can’t.

Speed Death (And Why Prevention Is Different)

The ROI Black Hole

The AI Double Standard

Why We Can’t Just Accept This

The Lies We Need to Stop Believing

A System That Actually Works

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

The FDA Could Actually Lead This

The Choice

Meaning Hygiene, or How Companies Turn Into Soulless Monsters

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