Two paragraphs in, I wrote, “Wes Anderson is an example of good taste.”

I stared at that sentence for a long time. Because if taste is fully subjective, that sentence means nothing. And if it means something, taste is not fully subjective. I had contradicted myself inside my own draft, and I wanted to know which half of me was right.

Paul Graham made this harder. His essay Taste for Makers argues taste is real, and his best punch is one I couldn’t shake: if taste is just preference, there’s no way to get better at making things, because everyone’s taste is already perfect. But anyone who makes things knows their taste improves. You look at old work and you don’t think “different,” you think “worse.” So taste can be wrong, so it isn’t just preference. I hate how well that lands. But then he derives universal rules from it, good design is simple, timeless, symmetrical, and that’s where I get off the train. I know maximalists whose work is loud, dense, ornamented to the ceiling, and unmistakably great, and minimalists whose restraint takes your breath away. If taste were a checklist, one of them would fail it. Neither does.

So taste is real, but it can’t be a ranking. Something else is going on, and I found it by thinking about what actually happens when a tastemaker gets hired.

Someone puts you in the decision-maker’s chair. Why you? Because they’ve seen your choices, recognized a pattern in them, and now trust that pattern to make a thousand small calls they’d endorse without checking each one. That’s the whole transaction. They’re not hiring correct taste, because correct taste doesn’t exist and everyone quietly knows it. They’re hiring a perspective they can recognize.

Look at what that requires. Consistent enough to be seen as a pattern. Legible enough that someone can point and say “that, I want more of that.” Predictive enough that your next hundred choices rhyme with your last hundred. None of that works if taste is random mush, and none of it needs taste to be objectively right.

Which gives me my answer: taste is a signature.

The easiest place to watch this is a museum. Walk through one with someone, say nothing, and just notice what they stop for. One person keeps stopping at the quiet Dutch interiors. Another blows past those and plants themselves in front of everything huge and violent. By the tenth stop you can predict the eleventh, and that’s the moment you’re seeing taste: not the stops, the pattern across the stops. And if there’s no pattern, if the stops are random, you’ve learned nothing about them at all. That’s the difference between having taste and having preferences.

Good taste is when your thousand small decisions all belong to the same person. Bad taste, the only bad taste there is, is incoherence: choices that don’t add up, a moodboard with no through-line, liking things one at a time instead of liking things in a way.

Software is failing this test at industry scale right now. Open ten startup websites and you’ll see the same site ten times: the same beige and cream palette, the same rounded sans-serif, the same component library everyone pulled from. And after ChatGPT, every chat product on earth shipped the same greyish shell, sidebar on the left, centered input box, as if the category came with a dress code. People defend this as taste because everyone converged on it. But convergence is the tell, not the proof. Each of these sites is internally consistent, sure, and yet none of them would survive the museum test, because their choices could belong to anyone, which means they belong to no one. Somewhere between the founder’s head and the homepage, nobody did the work of translating what they actually see into what they present. They let the aggregate decide, and the aggregate has no signature. A thousand teams making the identical safe choice is not a thousand teams with taste. It’s a thousand teams who skipped the question entirely.

Wes Anderson is the opposite case. He’s genuinely polarizing, half the world finds his films delightful and the other half finds them airless and twee, so if good taste meant universal approval, he’d fail. But nobody in either camp disputes the coherence. You can identify a single frame of his from across a room. There’s even research on why his ingredients work on so many of us, processing fluency, the finding that brains find easy-to-process patterns pleasant, which is partly why all that symmetry feels good. But the science only explains the ingredients. The taste is in how relentlessly he commits to them.

Once you see taste as a signature, inspiration makes sense too.

I used to think inspiration meant studying the winners until greatness rubbed off. What actually happens is recognition: you see someone’s work and something in you goes “that, that’s mine,” before you can explain why. The work is showing you a piece of your own signature you hadn’t articulated yet. Which is why the same reference hits one person like lightning and does nothing for the person next to them.

It also changes what you take. You don’t steal the output, you steal the move. There’s a research writer I love whose topics I’ll never write about, but the way they narrate, flowing first-person, walking you through what they saw as they saw it, that move is in my toolkit now. The difference between imitation and inspiration is whether your signature survives the theft.

And the oldest reference anyone steals from is nature. Every design tradition circles back to it, the symmetry of a face, the branching of a tree, patterns repeating inside themselves at every scale. Not because nature is objectively correct, but because it’s the one moodboard every human shares, so when your work echoes it, it reads as inevitable rather than styled. Even here, taste is in the sampling. The maximalist takes the density of a rainforest. The minimalist takes the emptiness of a desert. Same source, opposite signatures, both right.

So how do you develop one?

Volume first. You can’t find your signal in ten data points. Consume widely, mostly outside your field, because your field teaches you its conventions, and conventions are everyone’s signature, which means they’re no one’s.

Then notice your pulls. Not what you’re supposed to admire, what you actually return to: the saves you keep re-opening, the scene you’ve rewatched six times. The collection doesn’t matter. The pattern across it is everything.

Then name it, the step almost everyone skips. I wrote my own tone guide, an actual document describing what my writing does and what it refuses. It felt ridiculous. But the day I wrote it down, editing got ten times faster, because “does this sound like me” became a checkable question. A signature you can’t articulate is a signature you can’t protect. And it runs in both directions: I also built a small tool that detects AI writing patterns, fifteen categories of tells, and building it forced me to define exactly what I refuse. Knowing what you hate is half of taste.

Then cut. This is where taste actually grows. Your old work looks worse not because it broke universal rules but because it was diluted, full of choices that weren’t really yours. Learning taste is not accumulating better preferences. It’s removing everything that isn’t you until what’s left is unmistakable.

That’s the pursuit. Not outward toward some standard sitting above all of us. Inward, toward a signature only you can produce.

Taste can’t be wrong. It can only be incoherent. And coherence is learnable, one cut at a time.

Thank you for reading. More of my writings live here, and learn About Me.

If you found this interesting, I'd love to hear your thoughts. Share it on Twitter, LinkedIn or reach out at monalidambre710[at]gmail[dot]com.

And I don’t think it’s intelligence, or work ethic, or even experience.

It’s something more specific.

The ability to walk into a situation where nothing is structured yet, and start building the structure yourself, without waiting to be asked. (This post is written under the assumption that thinking with first principles is the baseline)

Most people are trained to operate inside systems that already exist. You get a role, you get a scope, you get processes to follow, and your job is to do them well. That's a perfectly valid way to work at companies that have already figured out what needs doing. But early-stage startups haven't figured that out yet. The processes don't exist. The role you were hired for will probably morph into something completely different within three months. And the founder is moving too fast to stop and build the operational backbone the company needs to function.

So someone has to do it.

The people who step into that gap, who see what’s missing and start building it before it’s assigned, are the ones who actually make these companies work.

This isn't about being a generalist who does a little of everything. I think the most useful framing is T-shaped: you go deep in one area where you build genuine expertise, and you develop enough range across other functions that you can build the first version of almost anything the company needs. The depth gives you credibility and judgment. The range is what lets you be useful in situations nobody planned for.

My depth is GTM and growth.

But across companies, I’ve had to build recruiting systems, product processes, community engines, expansion strategies, and internal tooling.

Not because I was the best person for all of it. But because nobody else was going to build it, and I love figuring things out!

And the barrier to doing this has basically collapsed. The technical threshold that used to stop non-engineers from building tools and automations basically doesn't exist anymore (love you, Claude Code :) & Cursor, you too, babe). What's left is just the willingness to do it.

The core of the operator mindset is thinking in systems rather than tasks.

Most people think in tasks. Answer the question → Close the loop → Move on.

Systems thinking is different.

Why does this keep happening?
What needs to exist so it stops repeating?

At an agentic AI infrastructure startup where I was employee number seven, developers were constantly sharing feedback across Discord, GitHub issues, and forums.

The signal was there. It just wasn’t going anywhere useful. The task would’ve been to stay active in the community.

The task-level response would have been to answer questions and keep the community active. What I actually built was a feedback infrastructure: categorised issues, recurring pattern analysis, feature signals routed to the people who could do something about them, all on a weekly cadence.

The community grew from ~9k to 33k GitHub stars.
The framework scaled to over a million monthly downloads.

But the real shift was this: Product decisions started being shaped by real user behaviour instead of internal assumptions.

At a B2B SaaS investment platform serving PE and VC firms, the problem looked different, but it was the same underlying gap.

The core product wasn’t structured for how users actually worked. I led the revamp of the core portfolio module, the product that investment professionals used daily to manage their deal flow and portfolio operations. That meant expanding into new geographies like the UK and Nordics, adapting the platform for regional use cases, and shipping strategic product components across three product lines: deals, sourcing, and portfolio management. (Pre ChatGPT era.)

Different industries, different problems, same approach every time. Find the gap where a system should exist but doesn’t. Build the first version. Lead it until it works. Then make it repeatable enough that it scales beyond you.

How does this work?

Find the gap → Build the first version → Stay with it until it works →Then make it repeatable.

Simple. Just make sure you're looking at where the company is headed, at the pace it's growing, at the commitments it's making, and you realise that a system needs to exist before the need becomes urgent. If you wait until it's a crisis, you're already behind.

The thing that actually separates this mindset from just being organised or resourceful is the willingness to lead what you build.

Basically, high agency!

A lot of people can identify a gap, and some of them will even build a first version of something to address it. But most people stop there. They hand it off, or they move on before anyone knows whether the thing actually worked. The operator mindset doesn’t let you do that. You build the feedback system and then you own it until the product team is actively changing roadmap priorities based on what it surfaces. You build the recruiting operation and then you run it until it’s generating consistent results and the team can sustain it without you in every decision. You don’t get to say “I built it” unless you can also say “and here’s what happened because of it.”

That’s a high bar, and it’s uncomfortable because it means you’re accountable for outcomes in areas where you might not be an expert. The community feedback infrastructure I built wasn’t my area of deep expertise; that was GTM and growth. But the company needed it, nobody else was building it, and I was willing to stay with it long enough to make it work and iterate when the early versions were too detailed or too shallow for anyone to actually use. That willingness to build outside your comfort zone and stay accountable for the result is what makes the T-shape actually valuable. The depth gives you confidence that you know how to build well, and the range permits you to try things you haven’t done before.

I want to push back a little on the idea that you’re either born with this instinct or you’re not, because I think the operator mindset is something you develop by doing it repeatedly across different contexts.

Every company I’ve worked at has been a different version of the same fundamental puzzle: here is a situation with no structure, figure out what needs to exist and build it. And each time, the pattern recognition gets faster. You start seeing the shape of the problem before anyone describes it to you. You know that the first version of any system you build will be wrong, and that’s fine because the skill isn’t getting it right immediately, it’s iterating fast enough that you converge on something useful before the company runs out of patience.

There’s this assumption that a career that moves across functions and industries is scattered or unfocused, and I understand why it looks that way from the outside. But from the inside, every context taught me a different version of the same skill, and each one made me faster and more confident at the next. Non-linear careers have the most advantages. The ability to walk into any mess and know where to start is the most important.

If you’re reading this and it resonates with how you already work, here’s what I’d say: stop waiting for your scope to be defined and start looking for the thing that everyone on your team knows is broken but nobody is building the fix for. The feedback loop that doesn’t exist. The process that costs the team hours every week because nobody has systematised it. The decision that keeps getting re-made because nobody documented it the first time.

Build the first version. It doesn’t need to be perfect; it just needs to be better than what’s there, which is probably nothing. Then lead it long enough to see whether it works, and if it doesn’t, figure out why and rebuild.

And if you’re a founder, know what this looks like when you’re hiring. The person you want should simply be high agency, invested in what you are building and a first principal thinker. These folks can figure stuff out!

Thank you for reading. More of my writings live here, and learn About Me.

If you found this interesting, I'd love to hear your thoughts. Share it on Twitter, LinkedIn or reach out at monalidambre710[at]gmail[dot]com.

I spend a lot of time thinking about what’s being built at the intersection of AI, healthcare, and hardware.

And the more I look at it, the more I think the consumer wellness stack that everyone’s currently spending on is actually the least interesting part of the story.

The $40 Pilates class is real, the Oura Ring is real, friends are paying for Whoop bands, creatine, AG1, and monthly therapy, and none of it is going away.

But underneath all of that, a completely different category is being built. One that isn’t about optimising your morning routine but about fundamentally changing how disease gets detected, treated, and prevented.

CES 2026 gave a pretty good preview of where this is heading. Withings showed its Body Scan 2, a smart scale that measures 60 health metrics, including arterial stiffness, cardiac efficiency, and metabolic health, in 90 seconds. A company called Hormometer debuted a saliva-based cortisol test you can take at home and analyze through an AI app.

NAOX unveiled headphones with built-in EEG, designed to make brain activity monitoring something you can do from your couch. Lenovo showed Qira, an AI health assistant that pulls data from your wearables and actually tells you what to do with it. And Vivoo introduced a smart menstrual pad that measures FSH levels to track fertility and hormonal health.

None of these is just tracking for tracking’s sake.

They’re all reaching toward the same idea: turning the body into a continuous data source and then using AI to make that data actually mean something.

The place I keep coming back to is the gap between what people are spending on wellness and what they’re actually getting back in clinical value. And to be fair, people are working on this. It’s not an empty space.

But the progress is uneven, and the pieces haven’t connected in the way they need to.

Take the data fragmentation problem.

Your Oura ring doesn’t talk to your Whoop, your Whoop doesn’t talk to your doctor, and your doctor has no idea what your HRV looked like the week before you got sick.

(Do you know: you should not do any intense physical activties until 2 days before any blood work)

Companies like Terra, Junction, and Metriport are building APIs to unify wearable data, and Apple HealthKit and Google Health Connect are doing it at the platform level. Epic, the biggest EHR system in the U.S., already supports connecting Fitbit and HealthKit data to clinical records at thousands of hospitals.

So the plumbing is being built.

But the end-to-end experience, where your wearable data actually flows into your doctor’s decision-making in a way that changes your care, that still doesn’t exist for most people.

The infrastructure is there in pieces. The killer product that ties it all together hasn’t arrived yet.

Then there’s personalisation. This one has moved further than people give it credit for.

Zoe is doing genuinely interesting work on personalised nutrition based on gut microbiome and blood sugar responses. Function Health is running comprehensive blood panels with AI-driven analysis. Oura itself keeps getting smarter about connecting sleep data to readiness and recovery.

But most of this personalisation is still siloed by category. Your sleep app knows about your sleep, your nutrition app knows about your food, your fitness tracker knows about your workouts. None of them talk to each other in a way that gives you an integrated picture of your health.

The real opportunity isn’t personalisation within a single category. It’s personalisation across your whole health picture. And that requires solving the data problem first.

There’s also a more fundamental issue underneath all of this.

Most wellness apps simply don’t have enough history on any individual user to say anything genuinely meaningful. Personalisation requires longitudinal data. Months and years of tracking, not weeks.

The companies that crack retention long enough to build that depth of data on their users are going to have a serious structural advantage that’s very hard for anyone else to replicate.

The third gap, and the one I think is genuinely the most underbuilt, is access.

The wellness industry as it currently exists is overwhelmingly an affluent consumer category.

Planet Fitness has figured out the $10/month gym. But for the premium wellness stack (wearables, supplements, boutique fitness, preventive testing) there isn’t really an equivalent affordable tier.

Most of the innovation is still designed for people who can comfortably spend $200+ a month on health optimization. The B2B2C model, where wellness platforms get sold through employers and health insurers, is growing as a workaround.

And the unit economics are better. But direct-to-consumer affordable wellness that reaches the people who arguably need preventive health tools the most?

That’s still largely missing. And it’s not just a social good argument. It’s a market size argument too.

What’s actually being built underneath all of this

The thing that excites me most isn’t any of the consumer-facing stuff. It’s the convergence happening at the clinical layer, where AI, healthcare, and hardware are intersecting in ways that weren’t possible even a few years ago.

AI-powered healthcare and biotech startups raised $10.7 billion in 2025. Already 24% ahead of 2024’s full-year total before the year was even over. And AI-enabled startups are raising 83% more per deal on average than non-AI healthcare companies.

That funding premium tells you everything about where investor conviction is sitting right now.

On the brain, Beacon Biosignals raised $86 million to build an FDA-cleared wearable EEG device that gives you clinical-grade brain data at home. The same quality as an overnight lab sleep study, without the lab. The brain has been the hardest organ to get real data on outside of hospital settings. And they’re using it to find new biomarkers for neurological and psychiatric conditions. The hardware captures the data continuously. And the AI is what turns it into something clinically useful.

That’s basically the pattern you see across this whole space.

On the heart, cardiovascular disease still kills 18 million people a year globally. And the data problem is fundamentally the same. Your smartwatch tracks heart rate. But that information sits in an app nobody looks at.

What’s being built now is different.

Researchers are running randomised controlled trials using consumer wearables to evaluate atrial fibrillation treatments. And AI models are being trained to detect cardiomyopathies from point-of-care ultrasound. The innovation isn’t the device itself.

It’s the AI layer that turns continuous passive data into an early warning system.

On cancer, the pace of change is genuinely hard to follow. PathAI got FDA clearance in June 2025 for a digital pathology platform. Letting pathologists make primary diagnoses from digital slides instead of shipping physical glass.

Tempus AI, which went public in 2024, processes genomic and clinical data to help oncologists make real-time personalised treatment decisions. Freenome is building a liquid biopsy platform that uses AI to detect colorectal cancer from a simple blood draw. And Xaira Therapeutics launched with $1 billion in committed funding.

Using AI for molecule design and compressing a drug discovery timeline that used to run 10–15 years.

And then there’s the hardware piece, which I personally find most exciting and most underappreciated.

The next generation of devices isn’t another ring or another watch. It’s sensors that go places watches can’t. Under the skin, into the bloodstream, embedded in fabric.

Biolinq just got FDA clearance in September 2025 for the first needle-free glucose sensor. A skin-applied patch that measures glucose from just beneath the surface without a single needle.

Ceribell has made a device that a nurse can apply in under five minutes to get brain activity data in an ICU. These aren’t consumer wellness products. They’re medical instruments being miniaturised and made accessible.

The thread running through all of it is the same insight: The data has always been in the body. We just didn’t have the hardware to capture it continuously. Or the AI to make it mean something.

Both of those problems are being solved at the same time.

Which is why this convergence feels like a genuine inflection point rather than incremental progress.

And here’s the part that connects back to everything in Part 1.

The consumer wellness stack that Gen Z is currently spending on: the rings, the classes, the supplements, is creating a generation that is already comfortable with biometric data.

Already paying for health tracking. Already thinking about their body as something to actively manage rather than just treat when something goes wrong.

That’s a massive behavioural shift. And it creates the demand surface that makes the clinical hardware layer commercially viable in a way it hasn’t been before.

The most interesting white space right now is probably the bridge between consumer and clinical. Products that are consumer-grade in their experience but clinical-grade in their data. Sitting at the exact point where your Oura ring stops being useful and your doctor’s visit begins.

That gap is narrowing.

But it’s still enormous.

And worth a lot to whoever closes it first.

Thank you for reading. More of my writings live here, and learn About Me.

If you found this interesting, I'd love to hear your thoughts. Share it on Twitter, LinkedIn or reach out at monalidambre710[at]gmail[dot]com.

I haven’t stopped thinking about it since.

Gen Z and Millennials make up about 36% of the U.S. adult population, but they’re driving 41% of all annual wellness spending. Oura rings, red light therapy, preventative Botox at 24, $40 fitness classes on a Tuesday morning, IV drips, collagen gummies. And it’s not slowing down. 53% of Gen Z increased their spending on cosmetic procedures in 2024 alone, and “better appearance” went from the 6th most important health priority to the 3rd for Gen Z consumers in a single year.

Which raises an obvious question. Where is this money coming from?

Because this same generation has a homeownership rate of 26.1%, essentially flat from the year before, and the gap with previous generations at the same age is real. About 33% of today’s 27-year-olds own a home, compared to roughly 40% of Baby Boomers when they were 27. The median house price is now nearly 6 times the median household income. In 1984 it was 3.57 times. Mortgage rates sat around 6.5-7% through most of 2024-2025, and the typical monthly payment for a new home hit a record $2,800 last year.

A 2024 Harris Poll found that 46% of Gen Z agreed with this: “No matter how hard I work, I will never be able to afford a home I really love.” Nearly half. Just quietly decided it’s not happening.

Two doctoral candidates at Northwestern and the University of Chicago, Seung Hyeong Lee and Younggeun Yoo, actually studied what happens when an entire generation crosses that threshold. They built a mathematical model of consumer behaviour, tested it against real data, and published a working paper called “Giving Up.” The pattern they found was pretty consistent: when homeownership stops feeling delayed and starts feeling impossible, people reduce work effort, take on riskier bets like crypto, and increase lifestyle spending. When the long game feels broken, you play the short one.

And so the money goes somewhere. It goes into the body.

The global wellness economy hit $6.8 trillion in 2024 according to the Global Wellness Institute. Bigger than global tourism, bigger than the entire IT sector, growing at nearly 8% a year and on track for $9.8 trillion by 2029. Fitness tech alone doubled in five years to reach $86 billion.

When you can’t build equity in a home, you build it somewhere you actually have control. Your sleep score, your skin, your VO2 max, your nervous system. No landlord, no mortgage, no 20% down payment you’ll spend a decade saving for. The body becomes the asset, and the reallocation starts to make a kind of sense that’s hard to argue with.

But here’s the thing that sits a little uncomfortably with me. A house eventually gets paid off. Self-optimization doesn’t. There’s no finish line. Your Oura ring will never tell you you’re done. There will always be a new metric, a new supplement, a new treatment promising to extend your healthspan, and the same thing that makes wellness feel empowering is exactly what makes it a perfect business model. It’s designed to surface new inadequacies at the same rate you resolve the old ones.

McKinsey’s latest wellness research actually names this as the retention strategy: get your product into someone’s daily ritual, and you’ve sold them an identity. That’s not a customer relationship, that’s something stickier.

I want to be careful here, though, because the “Gen Z is doom-spending instead of saving” narrative is a bit too clean. They’re also saving for retirement earlier than previous generations, growing their 401(k) balances, and inflation has been forcing cutbacks across the board. The picture is messier than one clean trade-off.

The real story might be more uncomfortable than that. Kyla Scanlon, a financial creator and author who’s become one of the sharpest voices on generational economics, writes about something she calls “financial nihilism.” Not as an insult, but as an observed pattern. When the traditional markers of progress feel structurally out of reach, people stop optimising for them, not because they’re lazy or short-sighted, but because the math stopped making sense. And when that happens, the wellness industry is right there, offering something you can actually control, actually see progress in, actually feel.

It’s rational. And it’s also exactly what a $6.8 trillion industry is counting on.

So I keep coming back to that line. The body is the last investable asset class. There’s something both true and a little sad about it. Taking care of yourself is genuinely good, and having a domain you control when everything else feels uncertain is genuinely valuable. But “invest in yourself” is also an instruction with no endpoint, by design.

Worth thinking about whose interests that serves.

Thank you for reading. More of my writings live here, and learn About Me.

If you found this interesting, I'd love to hear your thoughts. Share it on Twitter, LinkedIn or reach out at monalidambre710[at]gmail[dot]com.

And most of them wanted to help. One thing to always remember is that people who are at the top of their game always want to help others; they just want you to make it easy for them.

Two years ago, my cold emails were garbage. Generic, forgettable, and just really bad. When I quit my last job, I had a couple months to sort out what was next - should I start something, join an early-stage startup again, build one of the ideas bouncing around in my head about agentic security, memory frameworks, context management, evals, Consumer products, B2B SaaS (end of road is always B2B SaaS, ofcourse)?, or if I wanted to go the growth agency route. I was confused, so I decided to just talk to people who'd been there.

The response rate got better as I learned what actually works. I figured out what busy people need to see in an email to decide if they want to reply.

Here’s what I learned.

Every email that worked followed the same pattern: Signal → Context → Ask. That’s it.

Three paragraphs about your life story before you get to the point? They’ve already moved on. The people you’re reaching out to want to help, but you’re making them work to figure out what you want. Don’t do that. Your email should take a 10-second skim to grasp.

Signal means you’ve actually done your homework. You’re not mass-emailing; you actually engaged with their work. It doesn’t have to be elaborate - “Used the platform today, the <features> are really well done and this <feature>, really makes deployment secure.” works better than “I’ve been following your work for a while,” which could mean anything or nothing.

Context is who you are, but tight. Two or three sentences max, with specifics that matter to them. Not "I'm passionate about growth and learning" - that's meaningless. "I was founding ops & growth at an agentic infrastructure startup, scaled our open source by 100x, built partnerships & community, and led Growth and GTM at multiple early stage startups." gives them enough to understand why you're reaching out without making them read your whole resume.

The ask has to be concrete. Make it easy to say yes to. "Would love to connect sometime" is not an ask; it's a placeholder for an ask. "Would you have 15 minutes in the next couple of weeks to chat about <> ?" - now they know what you want and can decide if they have time for it.

When I was looking for career advice, my best emails looked like this: "I recently did <> using your product <>. I've been following your career path, and your transition to growth is really unconventional. I'm looking to make a similar move. Would you have 15 minutes in the next couple of weeks for a quick call? Happy to work around your schedule."

Results: I got on a call in the same week, also an insight that they are opening a new location in Germany and an opportunity to interview with them.

That worked because it had a specific signal (I actually took time to go through their product and noticed something specific about it), clear context (career transition), and a concrete ask (15 minutes, specific topic). The person reading it knows exactly what I want and how long it'll take.

When I was exploring roles, I'd write something like: <About the product>, <Feature implementation or Bug>, <context/about me tailored to how I am positioning myself> would love to chat about your developer reach and how you are thinking of expanding it to enterprises.

That worked because I'd actually used their product, provided value (bug report), showed relevant experience, and the ask was conversational rather than desperate. It didn't scream "give me a job," it said, "I'm interested in what you're building."

The conversation went so well with the founder that I got an offer to join them in their London Office.

The weakest email I sent was when I reached out to a founder for advice: "I've been following you from the early days of [Company] and what an insane journey it's been. I'm exploring the founder route. You've scaled [Company] to $16B - would love to hear how it's been for you and what you learned along the way. Would love to catch up sometime for a quick chat." The founder was kind enough to still respond to me and have a chat with me.

Looking back, that one's too generic. The signal isn't specific enough - "following from the early days" could mean I read one TechCrunch article two years ago. A better version would reference a specific decision they made or challenge they overcame that's relevant to what I'm figuring out.

Here’s what not to do.

Don’t let AI write the whole email. AI emails are obvious - the tone is wrong, they sound polished but soulless. If you use AI to draft something, rewrite it completely. Make it sound like you’re talking to a person, not submitting a college application. Basics to keep in mind: Be respectful and humble. You are there to learn.

Don’t be vague with your ask. “Pick your brain” and “learn from you” aren’t asks; they’re placeholders. What specifically do you want to know? What decision are you trying to make? What problem are you solving? Be concrete.

Don’t write a novel. If your email is longer than your phone screen, it’s too long. Busy people scan. If they can’t understand what you want in 10 seconds, they’ll skip it.

Don’t offer things you can’t deliver. “I’d love to help with X in return” - unless you have something specific and valuable to offer, don’t say this. It sounds transactional. Most people genuinely want to help if you just ask. But always make sure you present yourself as a person who would always be up to help them.

Your first 10-20 emails will be bad. Mine were. Two years ago, I was writing the kind of messages I’d delete without reading now. But you learn by doing - what tone gets responses, how much context is too much, which questions are too vague, what makes someone actually want to reply. Give yourself space to iterate. Not everyone will respond. Maybe they’re busy, maybe the timing was off. It doesn’t matter.

And the most important, Cold outreach works when it’s not actually cold. You warm it up by doing your homework, showing genuine interest, and making it easy to say yes. The people who replied didn’t do it because my emails were perfectly crafted. They replied because I made it clear I’d actually engaged with their work and had a specific reason for reaching out.

That’s the whole thing.

Thank you for reading. You can find more of my writings here and more About Me.

I notice communication. All of it. The good, the bad, and the “what did you even mean by this” kind.

I remember almost every time I’ve personally messed up an exchange. Every time I’ve walked away from a conversation thinking that whole thing could have been five minutes if we’d just started with the right information. Maybe this comes from having way too much respect for time. Mine, yours, everybody’s. Is it not the only asset that, once gone, is just gone?

I also remember sitting across from someone, or staring at a message on my screen, realizing I had absolutely no idea what they were asking me because they’d handed me maybe 30% of the picture. So I’d ask a follow-up. They’d answer. I’d ask another. They’d answer again, slightly impatient now. And somewhere in the middle of that back-and-forth, the energy just got off. Such conversations drain the energy out of me.

I’ve watched this happen around me constantly. One incomplete message, three people pulled into a thread, 15 minutes gone. All of it would have been avoidable if the first message had just included the full picture.

I think it bothers me more than most because of how I’m wired. I can’t speak about something I don’t fully understand yet. I need all the parts first. So naturally, I started expecting the same from my own messages and over time, it just became how I communicate, in writing and in person.

I didn’t expect it to be something people noticed. But my team has mentioned it, my manager has mentioned it, and even the founders I’ve worked with have said it. That my communication is unusually clear. Those things move faster because of it. I’ve spent most of my career in people-facing roles, and there have been moments where people have explicitly said: Your communication is so clear, we love how you speak. (Had to include that, sorry not sorry.)

So I just wanted to come here and share what that actually looks like.

The one thing most messages are missing: context.

A complete message should give the other person everything they need to respond, decide, or act without having to come back to you for the basics. Think of it like handing someone a file. You wouldn’t give them half the pages and say, “Let me know your thoughts.” You’d hand them the whole thing.

That’s all this is.

So what goes into a complete message?

Four things, usually.

1. What is it?

Name the thing. Don’t assume the other person remembers a conversation from two weeks ago or was on that email chain. Just assume they don’t know. One sentence of context costs you ten seconds and saves everyone twenty minutes.

Instead of: “Can you look at the deck?”

Try: “This is the Q2 GTM deck for the board meeting on Friday. It covers our pipeline health, campaign performance, and the revised ICP we landed on last month.”

2. What do you actually need from them?

“Thoughts?” is not an ask. Be specific. Do you need a yes or no? A review? A decision by a certain date? Say the thing.

Instead of: “Are we good for the launch?”

Try: “Can you confirm by Wednesday if the integration docs are ready to go live? We’re announcing on Product Hunt Thursday morning, and I need to know if we’re pushing with or without them.”

3. The details they need to actually respond.

This is the part people skip most often. If your ask requires context to answer, that context belongs in the same message. Not in a follow-up, not in a separate thread.

Real example. Your SDR team has been running an outbound sequence for six weeks. Open rates are fine, but reply rates are tanking. You need your manager to make a call.

Instead of: “Should we pause the outbound sequence?”

Try: “The outbound sequence we launched six weeks ago has a 42% open rate but only a 1.2% reply rate, well below our 3% benchmark. I think the messaging isn’t resonating with our mid-market ICP. Two options: rework the copy and test for two more weeks, or kill it and reallocate those touchpoints to accounts already showing intent. What’s your call?”

The second version gets answered in one reply. The first opens a back-and-forth that never needed to happen.

4. A timeline, or the lack of one.

If there’s a deadline, say it. If there isn’t, say that too. “Whenever you get a chance” is useful information. “Need this by EOD” is also useful information. Silence just makes people guess, and they’ll almost always guess wrong.

Why do people skip this stuff?

We send quick messages because it feels faster in the moment. But you’re not saving time, you’re just shifting the work onto the other person. They now have to ask, wait, process, and ask again. The total time spent on the exchange is almost always longer than if you’d just written a complete message to begin with.

There’s also an assumption problem. We assume people remember things we told them weeks ago. We assume a shared context that doesn’t actually exist. We assume “they’ll figure it out.” They usually don’t. Or they do, but it takes way more effort than it should.

A small note on async communication specifically.

Most people know how to be clear in person. You can read the room, course-correct, and add detail when you see confusion on someone’s face. But in writing — Slack, email, a voice note — you don’t get that feedback loop. What you send is what they get. There’s no “oh wait, let me add one thing.” So the bar for completeness has to be higher, not lower. If anything, slow down a little before you hit send on a written message. You only get one shot at the first impression of it.

And this isn’t just a work thing, by the way.

“Want to grab coffee?” could mean this week, next month, or genuinely never. We’ve all sent that message with zero intention of following through.

But “Hey, free Saturday morning? There’s this new place in Indiranagar I’ve been wanting to try”, that’s a plan. Your friend can say yes or no right there. No “when were you thinking”, no “where”, no three-day thread to land on a simple plan.

Same principle. Any context, any relationship.

I follow this when I write and when I speak. It’s not a formula I consciously run through anymore; it’s just how I think about communication now. And the biggest thing it’s given me is time. Fewer follow-ups, faster decisions, less friction all around.

Give people the full picture. The first time.

Credits: Thank you to Ashpreet (Founder, Agno) and Anika (Head of Ops, Agno and my manager) for sharpening how I speak, the words I choose, and the tone I carry. They've put so much into making this feel polished. And to my dad, who would never reply to a half-baked message and would simply say: "Take a minute, reframe it, then let's chat."

Happy Reading!

You can find more of my writings here.

Here are the ones that stayed with me.

The Palace of Illusions -- Chitra Banerjee Divakaruni

The Mahabharata from Draupadi’s POV. I finished it in two days; I was that hooked.

This was also the first book I ever read with a female protagonist. (Yes, I know.) It cracked something open in me -- the realisation that so many stories we accept as “just the way things are” were built without ever asking the women inside them. I have always rebelled against being put in a box. This book gave that feeling a name. After this, I went straight through The Forest of Enchantments and The Last Queen. Each one gripping. Chitra Banerjee does not miss.

Lessons in Chemistry -- Bonnie Garmus

A female chemist in 1960s America becomes the reluctant host of a cooking show and quietly changes everything around her.

Elizabeth Zott is who I want to be. Not dramatic about it, not trying to prove anything, just completely, immovably herself. This book gives me strength every time I think about it. And yes, it is set in the 60s, but honestly? Not that much has changed. Women building careers still walk into rooms full of people waiting to take credit or question their competence. The geography is different. The dynamic is not.

Remarkably Bright Creatures -- Shelby Van Pelt

A grieving old woman, a lost young man, and an octopus who decides to fix everything.

This is the softest book I have read in a long time. The comfort in it is so real. It is a story about grief and about people finding their way back to each other through the most unexpected routes. Just pure warmth. I needed this one.

Anxious People -- Fredrik Backman

A failed bank robber accidentally takes strangers hostage at an apartment viewing. What follows is the most beautiful book I have ever read. Please just read this.

What I took from it: everyone is carrying something. The person who seems cold is probably terrified. The person who seems fine is probably not. Backman writes with this enormous patience toward ordinary human failure, and it wrecked me in the best way. Also, there is a moment where you realise you made a gender assumption about a character the entire book without being told to. You just did it automatically. I had to sit with that for a while. (I am still sitting with it.)

The Vegetarian -- Han Kang

A woman in South Korea stops eating meat. Her family, her husband, her entire world responds like she has declared war.

This is not about vegetarianism. It is about what happens when a woman makes a quiet personal decision about her own body and the violence that follows. Strange, dark, deeply uncomfortable, but it asked me a question I kept thinking about for weeks: how much of ourselves do we quietly erase just to avoid making other people uncomfortable?

Kim Jiyoung, Born 1982 -- Cho Nam-joo

A woman in South Korea starts speaking in the voices of other women she has known. Her husband takes her to a psychiatrist. The psychiatrist unfolds her whole life.

And what a life it is. Completely ordinary. That is the entire point. This book gave me language for things I had felt but never quite named: the small diminishments, the exhausting math women do before speaking, the way ambition looks different on a woman than it does on a man. I finished it and felt seen in a way I did not expect.

How to Kill Men and Get Away With It -- Katy Brent

Kitty Collins is a vegan Instagram influencer in Chelsea. She is also quietly working through a list of men who hurt women. She is getting away with it.

Do not let anyone in the world mess with you. You are the force, the power. The world needs people like these. Kitty is reckless and sometimes infuriating, but the rage underneath this book is real. It was written in the wake of #MeToo, and you feel that on every page. Dark, funny, and genuinely cathartic. I raced through it.

Butter -- Asako Yuzuki

A Tokyo journalist tries to interview a convicted female serial killer who seduced wealthy men with her cooking. She goes in to understand the killer. The killer starts changing her instead.

I am a foodie, so this was an absolute joy ride. The food descriptions alone are worth it. But underneath all of that is a sharp feminist novel about how women are policed in their appetites, for food, for pleasure, for space. Rika, the journalist, gains weight over the course of the story and watches every single person in her life react as if something has gone wrong. The book uses that to say something true: so much of how women move through the world is just the management of other people’s comfort.

Almond -- Won-pyung Sohn

A teenage boy in South Korea was born unable to feel emotions. On his sixteenth birthday, everything he had was taken from him.

I have cried so much over this book. Yunjae cannot feel fear or grief the way the rest of us do, and yet his story is one of the most moving things I have read about human connection. Into his life comes Gon, an angry and volatile kid who makes everything harder at first. This book is about kindness, about teenage and a lot more. Heartwarming and emotional rollercoaster. The kid really goes through a lot.

I know the list looks very women focused. But trust me, it does not matter who you are; these books will teach you something. They are just great reads. Pick one up.

If you want to see more of what I have been reading, my full reading list is on Fable -- link.

Happy Reading!

Ben and I have been friends for about five years now. IIT grad, started in startups, very similar career track to mine. He recently joined a VC fund, and this was the first time I was seeing him since the switch.

I’ve seen him in all kinds of zones and phases. But this version of Ben? More self-reflective. More aware. More mature. And most importantly, the desire to be better at his craft. (I love these kinds of people!)

We spoke for six hours straight. Human behaviour, startups, how to get obsessively good at what you do, we covered everything. I loved it. We have decided to make an effort to meet each other more frequently. (We have done this before and it took us 6 months to meet again, so let’s see.)

But somewhere in the middle of all that, he asked me something I haven’t been able to shake.

“Don’t you ever feel like you’ve outgrown the people around you?”

And yeah. I do. Every other day.

I wrote an essay on “Why we outgrow the people we once loved” near new years and never published it. I think it’s a good time now.

This is not an essay of emotions, but about how we change and how our brain changes to and how our identification of self changes every few months

We often assume relationships last because of emotional depth. But they last only as long as two people’s mental models, habits, and values evolve in compatible directions.

Every experience rewires our brain through neuroplasticity. The brain is constantly forming new connections and weakening old ones. So the version of you that formed a relationship two years ago is literally not the same version sitting here today. I personally don’t relate to my six-month-old self a lot of the time.

Psychologists call these internal settings schemas, the frameworks through which we understand life. Which is just a fancy way of saying how you see the world. When we were younger, my friends and I had the same world: the same school stress, the same jokes, the same lens for everything. But as we grew up, our worlds split. And that’s where the gap began.

And it’s not just how you see the world that changes. What excites you changes too. The kind of conversations you want, the energy you’re looking for, what feels stimulating vs what feels stale. Your brain is quietly updating all of this in the background. You start craving different things, often before you even realise it consciously.

Over the last two years, something shifted in me. I got obsessive about learning. Not just work stuff. Everything. Human behaviour, how systems work, why people make the decisions they make. I became a sponge. And that changed who I wanted to be around.

I started gravitating toward people who challenged me. People who showed up sharper every time I met them. People who had this glitter in their eyes when they spoke about their work. And the people where conversation had gone stale, I just naturally spent less time with them. Not because they’re bad people but different internal schemas/frameworks, or what we like to call Vibes.

But then there’s Kejri.

Kejri has somehow survived every single version of me. And we have absolutely nothing in common. I still cannot explain what he does for a living. Something with experimental physics and quantum computers at Caltech. Basically Leonard from Big Bang Theory. I always get it wrong.

And yet we still make sense. Probably because our core childish energy matches, and because our careers are so wildly different that we only ever talk about life. Just life. He is so away from the bubble I live in.

(P.S. Thank you for existing, love you Kejri.)

Kejri is the reason I think the answer to Ben’s question is more nuanced than a simple yes.

Because maybe it’s not about outgrowing people. Maybe it’s about your brain seeking new alignment. Love can remain, but resonance changes. Some relationships survive because they’re built on shared principles: curiosity, honesty, the way you treat people. Others fade because they were built on shared phases: same job, same city, same struggle.

Ben and I connected this weekend because we’re in a similar phase and share similar principles. Kejri and I have been connected for years because our principles match, even though our phases never have.

So all I wanted to say was, Outgrowing people isn't a loss. It's evolution. Its a sign of growth. Your brain is learning, updating, and expanding. The heart catches up eventually. Change doesn't erase connection; it just rewrites it.

Happy Reading!

You can find more of my writings here.

For the past few years, if you wanted a smarter AI system, the answer was simple: throw more compute at the problem.

But a recent research paper from DeepSeek suggests that this approach may be reaching its limits. Instead of relying purely on scale, the paper explores a different idea:

What if we could make AI models smarter by redesigning how information moves inside them — without dramatically increasing cost?

To understand why this matters, we first need to understand how modern AI models communicate internally.

How information moves inside an AI model

Most modern AI models are built as stacks of layers.

Each layer:

• receives information

• modifies it slightly

• passes it to the next layer

At small scales, this works well. But as models grow deeper, a problem starts to appear. Important signals weaken, distort, or disappear as they pass through dozens of layers. By the time information reaches the end of the model, some of it has simply faded away.

The breakthrough that made deep models possible: shortcuts

Researchers solved this years ago using residual connections, often called shortcuts.

Instead of forcing information to travel through every layer one by one, residual connections allow important signals to skip layers entirely. The original information stays intact as it moves forward.

A useful analogy here is a highway.

Residual connections add an express lane, allowing critical information to bypass traffic and move quickly across the model. This breakthrough made very deep models trainable and is now standard in systems GPT models.

When one express lane becomes a bottleneck

As AI models grew larger and more complex, researchers ran into a new limitation.

When all important information is forced through a single express lane, different ideas begin to interfere with one another. The shortcut itself becomes a bottleneck. Internal communication is limited.

This led to a natural question:

What if we added more express lanes instead of just one?

Hyperconnections: more lanes, more flexibility

In 2024, researchers (notably from ByteDance) introduced Hyperconnections.

Instead of a single shortcut, hyperconnections allow models to run multiple parallel information streams at the same time. You can think of this as a multi-lane highway, where different “thoughts” travel in parallel.

This had clear benefits:

• different types of information could move separately

• ideas could combine more flexibly

• performance improved

But it also introduced a serious problem.

The problem with too much freedom

When many information streams mix freely, things can spiral out of control.

Signals may:

• become too strong and explode

• become too weak and vanish

Training becomes unstable. Memory usage grows. Compute costs rise sharply.

Using the highway analogy again:

Hyperconnections created a multi-lane highway with no speed limits, no lane dividers, and no traffic rules. Cars could change lanes unpredictably, collide, or cause massive pileups.

Hyperconnections worked in theory — but they didn’t scale cleanly.

DeepSeek’s idea: add rules, not more compute

DeepSeek’s contribution is a new architecture called Manifold-Constrained Hyperconnections, or mHC.

Instead of removing hyperconnections, DeepSeek keeps the multi-lane highway — but adds guardrails.

Their key insight is simple:

Information should be allowed to mix, but only in controlled, predictable ways.

The highway analogy (the core intuition)

• Hyperconnections give you multiple lanes

• But without rules → chaos

• DeepSeek adds:

  • speed limits

  • lane dividers

  • traffic rules

Cars (information) can still:

• change lanes

• share space

• move freely

But they can’t crash into each other or spiral out of control.

Why this design stays stable, even at scale

One of the most important parts of the paper is that this stability isn’t accidental.

DeepSeek designs the system so that:

• information can’t get louder than it started

• information can’t disappear entirely

• these rules stay true no matter how many layers you stack

Every time information is shared, it must behave like a fair blend, not a wild remix. Layers can redistribute information, but they can’t exaggerate it.

This means the model:

• doesn’t blow up

• doesn’t collapse

• remains trainable at very large scales

DeepSeek designed the system so that it stays stable by construction.

How they made it practical on GPUs

Architectural ideas only matter if they can run efficiently on GPUs.

Hyperconnections are usually expensive because they increase memory usage and require a lot of data movement — something GPUs are particularly bad at.

DeepSeek realized that the main cost wasn’t computation.

It was moving data around.

So instead of changing the core idea, they focused on how it runs efficiently:

• multiple steps are fused into single GPU operations

• cheap values are recomputed instead of stored

• computation and communication are carefully overlapped

The result is surprisingly efficient. Despite much richer internal communication, mHC adds only about 6.7% overhead compared to standard models.

What this achieves

DeepSeek tested this approach on small, medium, and very large models and found:

• better internal communication

• stable training

• improved downstream performance, including reasoning-heavy tasks

• minimal additional compute cost

And most importantly, it worked consistently across small, medium, and large dense models (up to 27B parameters).

Architecture choices matter enormously.

You can squeeze more intelligence out of the same hardware by designing better internal communication — not just by buying more GPUs.

For a company like DeepSeek, competing with OpenAI and Google DeepMind without matching their compute budgets, this is strategically huge.

God bless you if your team is remote or global or both or even has multiple offices. That’s usually the moment you realise the team doesn’t need more energy.

It needs a nervous system.

When I say “nervous system,” I mean the way a startup actually thinks and moves.

• How information travels across the team.

• How work moves from idea to execution.

• How decisions are made and remembered.

• How customer signals reach the right people.

• How feedback shapes the next cycle.

A good nervous system reduces cognitive load for everyone. People don’t go hunting for information or ask the same question twice. Ownership stays clear. Context lives somewhere real. And the company moves without constant friction.

A weak one? You’ll know it instantly — confusion, repeated conversations, Slack archaeology, and three versions of the truth floating around.

Your startup’s nervous system has five layers:

Information, Execution, Decisions, Customer Signals, and Feedback/Rhythm.

This part focuses on the first layer, because if the information layer is broken, every other layer ends up compensating badly.

The Information Layer

This is the bloodstream of the company. If information moves cleanly, the team moves cleanly. If it doesn’t, the company drags. Simple.

Most teams use Slack for conversations and Notion (or something similar) for documentation. The goal is not to add more tools — please don’t do that — but to make sure the tools you already use actually talk to each other. One unified system is the way!

Most of the tools we already use come with AI built in. Slack has Slack AI, which can summarise any channel or thread in seconds. It won’t let you set a custom date range on the summary, but it will give you a clean, high-signal recap of what happened — which is honestly all most people need.

Notion has Notion AI and a Slack integration that lets you send Slack messages straight into Notion databases. You can then use AI-powered search across your notes, pages, and the connected Slack context. It’s not a full, magical merge of the two products, but it’s a really solid start toward having one memory for the whole team.

The real point is this: your information layer is not just “have a knowledge base.” It is also “make your tools talk to each other” so that all your information actually forms a layer. If someone has to open three different apps just to answer a simple question, then your operations are broken. Efficiency is broken. People start depending on Slack archaeology and oral storytelling, and the whole company slows down.

This is where an agent layer becomes powerful. You can build (or use) a simple agent that sits above Slack + Notion + Drive + whatever else you use, and pulls everything together into one unified view. Then you can ask, “What did we decide about onboarding in the last 60 days?” and actually get one clear answer instead of twenty tabs and five DMs.

That’s the point. One system. One memory.

When this works well, information stops being a scavenger hunt and starts being something you can actually trust.

And to make this work cleanly, you need one more foundational piece:

your own internal knowledge base.

Your Internal Knowledge Base

A knowledge base is simply the place where truth lives. Nothing more, nothing less.

It matters because relying on memory is fun until the fifth time someone asks, “Wait, why did we do this again?”

A few reasons this layer matters so much:

• New hires shouldn’t spend two weeks asking the same questions

• People joining mid-project shouldn’t need a tour of old screenshots and lost DM threads

• Decisions shouldn’t become folklore

• Remote teams shouldn’t depend on whoever was awake when the conversation happened

• And the team shouldn’t collapse the moment one person takes a vacation

I once watched a team spend three days trying to “re-decide” something they had already decided a month earlier — because the original decision was buried in a Slack thread nobody could find. That was the moment I realised how important it is to have an internal knowledge base and to keep it updated (!)

Here’s the simplest way to keep yours alive:

1. Capture decisions immediately

   When a decision is made in Slack, tag it with something like a :decision: emoji. That tiny action makes all the difference.

   Your agent should lift that tagged message, create a small Notion page, fill in the context, the final decision, the owner, and the date, and link the original thread. One tap from the team, and the memory is captured forever. You can also do the same with meeting notes.

   Next time someone asks, “Why did we remove the second onboarding step?” the answer won’t depend on who remembers.

2. Do a tiny clean-up ritual

   Notion doesn’t need a big ceremony.

   Just a five-minute reset:

   • archive old things

   • update anything clearly outdated

   • fix missing owners or dates

   It’s the same feeling as clearing your desk before the weekend — small effort, huge mental relief.

3. Let everything be searchable

   This is where everything comes together.

   If your decisions, documents, and important conversations are all captured and searchable, the entire company becomes quieter and faster. You stop relying on “ask this person, they’ll know.” The system remembers for you.

   This is when your information layer starts feeling like a real nervous system — you ask a question, you get the answer, and you move on.

4. Push updates to people instead of making them go digging

   Let’s be honest: nobody wakes up excited to open Notion. So don’t make them.

   Let your agent drop a small daily update in Slack — a couple of decisions, a blocker, something a customer mentioned, one thing happening tomorrow. The entire team stays aligned almost passively.

Visibility Rituals That Support the Information Layer

If your team is remote, global, or just very async, visibility rituals matter more than you realise. They reduce the “What is everyone doing?” anxiety without forcing meetings.

Individual daily update channels

Some teams give each person their own channel — a small thread where they write what they shipped, what’s next, and where they’re stuck. It’s not reporting. It’s simply leaving footprints so the team can see the shape of the work. This is part of Cursor’s culture.

The “Today I Shipped” channel

Everyone posts one small update at the end of the day — a feature, a fix, a small improvement, even a stubborn blocker that finally moved. It builds a feeling of momentum across the team. We did this at “Agno” and it genuinely made the whole week feel faster.

Async standups

If your team spans time zones, synchronous standups are a nightmare.

Go async. Everyone posts by a certain time and Slack AI compiles the summary: who shipped what, what decisions landed, what’s stuck, and what needs review. You eliminate the meeting while keeping every ounce of alignment.

No private DMs

One of the easiest ways to slow a company down is letting half the work happen inside private DMs. The more conversations stay in public channels, the faster things move. Everyone gets context. Everyone sees what’s happening. And problems stop hiding in someone’s inbox.

A small housekeeping habit helps a lot here: whenever someone starts a thread or drops an important update in Slack, they should tag the right people. It doesn’t need to be complicated — just make sure the message isn’t floating alone in a channel with zero owners. Tagging keeps work moving instead of disappearing into the feed.

When information flows well, the whole team moves better. People stop repeating work, decisions stay visible, and the company finally feels like it’s moving with intention instead of chaos.

This is just Part One.

In the next part, I’ll go deeper into the other layers of the nervous system — execution, decisions, customer signals, and rhythm. I’m also writing a few smaller notes on hiring, early growth, tools I use to scale Growth and Ops, and how to turn customer feedback into real product movement.

Thank you for reading. See you back!

Now it’s time for the action plan. Here’s what you can actually do to improve your visibility in answer engines.

Research & Inspiration

The insights in this guide come from three sources:

Lenny’s Podcast with Ethan Smith (Graphite CEO): Ethan’s framework on experimentation and measuring what works in AEO was invaluable. His approach to control groups vs test groups shaped our thinking on validation. Watch the full episode

Our own research building Orion: Months of testing queries across platforms, analysing citation patterns, and understanding what gets mentioned vs ignored.

Manual validation: Hundreds of queries tested across ChatGPT, Perplexity, and Gemini to identify what actually works.

Step 1: Question Research

You need to know which questions people are actually asking. This is harder than traditional keyword research because answer engines don’t publish search volumes.

Three methods to find questions:

Method A: Convert existing keywords into questions

Start with your current keyword list and reframe them as questions:

• “project management software” → “What is the best project management software?”

• “CRM pricing” → “How much does a CRM cost?”

• “email marketing tools” → “Which email marketing tool should I use?”

Method B: Mine your customer conversations

Your customers are already asking the questions that matter:

• Sales calls: What do prospects ask repeatedly? Record common objections and questions

• Customer support tickets: What confusion points come up? These reveal gaps in understanding

• Reddit threads in your category: What are people genuinely curious about? Browse subreddits where your audience hangs out

Method C: Use your help desk to surface follow-up questions

Look for patterns in support queries. These often reveal the sub-questions users have after their initial research.

If customers repeatedly ask “How do I integrate X with Y?”, that’s a question worth targeting.

Step 2: Track Your Keywords in Answer Engines

Once you have your target questions, you need to track them. Remember from Part 1: automated tracking is broken. Here’s the approach that actually works.

The racking Process:

• Pick 10 to 20 critical queries in your category that you KNOW matter to buyers

• Test them weekly across ChatGPT, Perplexity, and Gemini

• Document everything:

  • Which brands get mentioned?

  • In what order?

  • With citations or just mentions?

  • What sources are cited?

• Look for patterns over 4 to 8 weeks

• Create a simple tracking spreadsheet:

Step 3: Identify Who’s Getting Cited (And Why)

• Study your competition systematically:

Questions to answer:

• • Who shows up consistently? Are the same 3 to 5 brands dominating every query?

  • What content formats are being cited? Blog posts? YouTube videos? Reddit threads? Review sites?

  • What’s the common thread? Do cited sources have certain characteristics (length, structure, data)?

  • Where do citations come from? Their own sites? Third-party reviews? Community discussions?

Develop a strategy for each citation type:

• Easy wins: Check the most cited threads and figure out a way to add your product to them.

  Your own site:

  • Optimize landing pages (see Step 4)

  • Add comprehensive FAQs

  • Include data and statistics

  YouTube/Vimeo:

  • Create video content for high-value keywords

  • Include full transcripts

  • Optimize descriptions with Q&A format

  Reddit:

  • Deploy authentic participation strategy (see below)

  • Focus on helpful, non-promotional contributions

  Review sites and affiliates:

  • Build relationships with authoritative review platforms

  • Provide accurate information and resources

  • Make it easy for them to cover you accurately

Step 4: Optimize Your Landing Pages (Answer EVERYTHING)

• Your landing page optimization should focus on comprehensiveness.

The golden rule: Answer all the follow-up questions a user might have.

• Example: If you’re targeting “website builders”

  Your page should answer:

  • What is a website builder?

  • How much do website builders cost?

  • Which website builder is best for beginners?

  • What’s the difference between Builder A and Builder B?

  • Can I use a website builder for e-commerce?

  • How long does it take to build a website?

  • Do I need coding skills?

  • Can I switch builders later?

Structure your content for citation:

• Use clear Q&A formatting:

  Q: What is [topic]?

  A: [Direct, factual answer in 2 to 3 sentences]

  Include these elements:

  • Clean, factual statements without fluff

  • Statistics, numbers, and data-backed claims

  • Proper schema markup (FAQPage, HowTo, Article)

  • Clear heading hierarchy (H2, H3 for questions)

  • Short paragraphs (2 to 4 sentences max)

  What to avoid:

  • Marketing fluff and vague claims

  • Long-winded introductions

  • Burying answers deep in content

  • Walls of text without structure

Step 5: Off-Site Strategy (Build Your Citation Network)

• You can’t only optimize your own site. You need citations from external sources.

A. YouTube and Vimeo Videos

• Video content is increasingly parsed and cited by AI systems.

  When to prioritize video:

  • High-LTV products or services

  • Complex topics that benefit from visual explanation

  • Topics where competitors lack video content

  Best practices:

  • Include full transcripts

  • Write detailed descriptions with Q&A format

  • Use clear chapter markers

  • Optimize titles for question format

B. Affiliate and Review Site Partnerships

• Strategy:

  • Identify authoritative review sites in your category (G2, Capterra, Wirecutter-style sites)

  • Provide them with accurate information, statistics, and comparison data

  • Make it easy to review you fairly

  • Consider paying for sponsored placements on high-authority review sites

  Focus on quality over quantity. One citation from a major review site beats 100 mentions on low-authority blogs.

C. Reddit Strategy (The Underrated Channel)

• Reddit is increasingly cited by answer engines because it contains authentic, unfiltered opinions.

  Why Reddit matters:

  • ChatGPT cites Reddit frequently

  • Users trust Reddit for “real” opinions

  • Threads rank well in search and get cited

  How to use Reddit for AEO:

  1. Find threads that are already being cited

     • Use tools like Gummy Search (https://gummysearch.com/) to identify relevant subreddits and threads

     • Search for your category keywords in answer engines and see which Reddit threads get cited

  2. Participate authentically

     • Say who you are and where you work

     • Share genuinely useful information

     • Don’t hide your affiliation

     • Focus on being helpful, not promotional

  3. Focus on quality over quantity

     • One thoughtful, detailed comment beats ten promotional ones

     • Answer questions thoroughly

     • Provide context and nuance

  4. Target the right subreddits

     • Where does your audience ask questions?

     • Examples for marketing tools: r/marketing, r/entrepreneur, r/smallbusiness

     • Examples for dev tools: r/webdev, r/programming, r/devopsExample of good Reddit participation:

     “Hey, I work at [Company]. Here’s what I’ve seen work for this use case: [detailed, helpful answer]. Happy to answer any specific questions about our approach vs alternatives.”

     • Example of bad Reddit participation:

     “Check out [Company]! We’re the best solution for this.”

Step 6: Run Experiments and Measure Results

• Create a testing framework to identify what actually works: Framework inspiration from Ethan Smith

Set up your experiment:

• Control group:

  • 5 to 10 keywords where you make NO changes

  • Track baseline visibility

  Test group:

  • 5 to 10 keywords where you implement optimizations

  • Make one change at a time when possible

  Track both groups over 30, 60, and 90 days

What to measure:

• Mention frequency (are you cited more often?)

• Citation quality (with link vs without?)

• Position in responses (first mention vs buried?)

• Platform differences (works on Perplexity but not ChatGPT?)

Document everything:

• What you changed

• When you changed it

• What happened after

• Which platforms showed impact

This helps you build your own dataset of what works for YOUR category.

The Reality: AEO Is a Long Game

• Here’s what you need to know about timelines:

Why patience is required:

• AI models retrain on cycles

  • Training happens weeks to months apart

  • Your changes need to be crawled and incorporated

  • Citation patterns take time to establish

  Content validation takes time

  • Your content needs to be crawled

  • It needs to appear across multiple indexes

  • Authority signals need to build

  Building authority in new channels requires consistency

  • Reddit participation needs sustained engagement

  • YouTube presence needs multiple videos

  • Review site citations need relationship building

Typical timeline:

• Months 1 to 2: Setup and optimization

  • Question research

  • Landing page optimization

  • Begin tracking

  • Start Reddit participation

  Months 3 to 4: Early signals

  • First mentions on some platforms

  • Pattern identification

  • Refinement of approach

  Months 6+: Consistent visibility

  • Regular citations if you’ve done the work

  • Established patterns

  • Measurable impact

  Think of it like traditional SEO in 2010. You’re building for the future, not just this quarter.

  ---

  Don’t Abandon SEO Fundamentals

  While focusing on AEO, remember that SEO fundamentals still matter.

  Google’s E-E-A-T principles apply to answer engines:

  • Experience: Is the author or website experienced in the topic they’re writing about?

  • Expertise: How much expertise, skill, or knowledge does the creator have on the subject?

  • Authoritativeness: Is this recognized as a go-to source in the industry?

  • Trustworthiness: Is the content authentic and trustworthy?

  These signals matter for both traditional search and answer engines. Strong SEO fundamentals create the foundation for AEO visibility.

  ---

Final Thoughts: Start Simple

We’re in the early days of AEO. Most brands haven’t started optimizing yet.

That’s your opportunity.

The brands that establish themselves as authoritative, frequently-cited sources now will have a massive advantage as answer engine usage continues to grow.

Your action plan:

1. Audit where you stand (manually test your top 10 queries)

2. Pick your top questions (what matters most to your buyers?)

3. Optimize comprehensively (answer everything on one page)

4. Track your progress (manual, weekly checks)

5. Build citations systematically (your site, video, Reddit, review sites)

6. Give it time (6+ months for real results)

AEO isn’t about gaming a system. It’s about becoming the most helpful, authoritative, and comprehensive source in your category.

Do that, and the citations will follow.

The Series:

• Part 1: We Built an AEO Tool and Here’s Why It Failed

• Part 2: How Answer Engines Actually Work (And Which Sources They Trust)

• Part 3: The 6-Step AEO Action Plan (you are here)

To optimize effectively, you need to understand the mechanics. Here’s how answer engines actually work and which sources they trust.

The Mechanics: LLM + RAG Explained

Answer engines use a combination of LLM (Large Language Model) and RAG (Retrieval-Augmented Generation).

Here’s what happens when someone asks a question:

1. User asks a question

   Example: “What’s the best project management tool for remote teams?”

2. The system searches in real-time

   It queries web pages, databases, documents, and other indexed sources for relevant information

3. The LLM synthesizes the information

   It takes multiple sources and generates a coherent answer, combining information from different places

4. Citations are provided (sometimes)

   Depending on the platform, sources may be linked or just referenced

The key insight: Unlike traditional search where you click through to one result, answer engines are reading and combining multiple sources to create a single response.

It’s About Answering ALL the Questions

Here’s what most people get wrong: they think of optimization as targeting one keyword.

In reality, your landing page is competing to answer hundreds or thousands of related questions.

Example: If you’re targeting “website builders”

Your content needs to answer:

• What is a website builder?

• How much do website builders cost?

• Which website builder is best for beginners?

• What’s the difference between Wix and Squarespace?

• Can I use a website builder for e-commerce?

• How long does it take to build a website?

• Do I need coding skills?

• Can I migrate from one builder to another?

The more comprehensively you answer related questions, the more likely you are to get cited.

If you don’t answer a question that competitors do, you won’t show up. If you answer questions others don’t, you become the authoritative source.

Which Sources Do Answer Engines Trust?

Different platforms have different citation patterns. Here’s what we’ve observed through testing:

ChatGPT (OpenAI)

Primary sources:

• High-authority websites (established brands, major publications)

• Major news outlets for current events

• GitHub for technical and developer content

• Reddit for authentic opinions and user experiences

What matters:

• Content depth and comprehensiveness

• Clear, factual statements

• Recent content for time-sensitive topics

• Community validation (especially on Reddit)

Perplexity

Primary sources:

• Real-time web results (your live content matters most)

• Academic sources and research papers

• Recent, authoritative content

• Industry-specific publications

What matters:

• Freshness (recent content gets priority)

• Source authority and credibility

• Direct answers to specific questions

• Structured, scannable content

Gemini (Google)

Primary sources:

• Inherits Google’s search signals and Knowledge Graph

• Websites with strong E-E-A-T signals

• Content with proper schema markup

• Established, authoritative domains

What matters:

• Experience, Expertise, Authoritativeness, Trustworthiness (E-E-A-T)

• Structured data implementation

• Traditional SEO fundamentals

• Author credibility and credentials

General Patterns Across All Platforms

We’ve noticed these sources appear consistently:

High-priority sources (Tier 1):

• Your own website (if it’s authoritative)

• YouTube and Vimeo videos (increasingly parsed for content)

• User-generated content platforms (Reddit, Quora)

• Established review sites (Wirecutter, G2, Capterra)

• Academic and research papers

Secondary sources (Tier 2):

• Affiliate blogs and comparison sites

• Medium-authority publications

• Niche community forums

• Industry blogs

The citation hierarchy isn’t about gaming the system. It’s about being where authoritative information lives.

What Makes Content “Citation-Worthy”?

Through our research and testing, we’ve identified patterns in what gets cited:

1. Direct, Factual Answers

Gets cited:

“The average cost of project management software ranges from $7 to $45 per user per month. Entry-level plans typically start at $10 per user.”

Doesn’t get cited:

“Project management software pricing varies depending on many factors including team size, features needed, and integration requirements...”

Why it matters: Answer engines want to extract clear facts, not wade through hedging.

2. Structured Q&A Format

Gets cited:

What is the difference between Asana and Monday.com?

Asana focuses on task management with a simple interface, while Monday.com emphasizes workflow automation and customization.

Doesn’t get cited:

“When comparing different project management tools, there are several considerations to keep in mind...”

Why it matters: Q&A format is easy for LLMs to parse and extract.

3. Data-Backed Claims

Gets cited:

“According to a 2024 Gartner study, 73% of remote teams using dedicated project management software reported improved collaboration.”

Doesn’t get cited:

“Most teams find that project management software improves collaboration.”

Why it matters: Specific numbers and sources signal credibility.

4. Comprehensive Coverage

The best-cited content doesn’t just answer one question. It anticipates and answers all the follow-up questions a user might have.

If someone asks about “best CRM software,” they probably also want to know:

• Pricing

• Key features

• Comparison with alternatives

• Implementation difficulty

• Integration options

• Use cases

Answer ALL of these on the same page.

Platform-Specific Citation Quirks

Google AI Overviews

• Heavily favors content with schema markup (FAQPage, HowTo, Article)

• Prefers established domains with strong E-E-A-T

• Often pulls from featured snippets

• Shows preference for content with clear heading structure

ChatGPT

• Cites Reddit surprisingly often (values authentic discussion)

• Tends to synthesize from multiple sources rather than citing one

• Favors comprehensive guides over short articles

• Training data cutoff means older established content has advantage

Perplexity

• Real-time search means fresh content has equal shot

• Heavy preference for academic and research sources

• Will cite smaller, authoritative niche sites

• Provides clickable citations more consistently

Claude (Anthropic)

• Tends to cite fewer sources overall

• Strong preference for authoritative, well-structured content

• Values nuance and balanced perspectives

• Less likely to cite social platforms like Reddit

The Reality: You Can’t Optimize for Everything

Here’s the truth: you can’t perfectly optimize for every platform’s quirks.

What you CAN do:

1. Focus on fundamentals that work across platforms:

   • Clear, factual content

   • Comprehensive answers

   • Proper structure and formatting

   • Credible sources and data

2. Prioritize the platforms where your buyers actually are:

   • B2B SaaS? Focus on ChatGPT and Perplexity

   • Consumer products? Google AI Overviews matters most

   • Developer tools? Reddit and GitHub citations are gold

3. Build citations systematically:

   • Start with your own site

   • Expand to video content

   • Participate authentically in community spaces

   • Build relationships with review sites

You’re not gaming an algorithm. You’re becoming the most comprehensive, trustworthy source in your category.

What’s Next

Now you understand how answer engines work and which sources they trust.

In Part 3, we’ll share the action plan:

• Question research (how to find what people actually ask)

• Tracking your visibility

• Off-site strategy

and a lot more. Plus: realistic timelines, what to measure, and why patience matters.

That question led us to build Orion, an AI SEO tool that tracks brand visibility across ChatGPT, Claude, Gemini, and Perplexity. In the process, we realised we weren’t just building a tracking tool. We were navigating an entirely new optimization landscape where the old SEO playbook only gets you halfway there.

That question led us down a rabbit hole that changed how we think about visibility, search, and the future of marketing. This is part one of everything we learned.

The Terminology Mess: AEO, GEO, AI SEO, and everything in between

This field is so new that there’s no common taxonomy yet. Agencies, publishers, marketers, and SEO specialists have adopted different acronyms to describe the same trend. AEO, GEO, and AI SEO, for starters, all describe the same thing: optimizing for AI-powered answer systems. Here’s the only distinction that matters: SEO vs. AEO.

Here’s how we think about it:

SEO (Search Engine Optimization)

• The oldest and broadest concept

• Goal: Make your website rank higher on Google, Bing, etc.

• Focus: Keywords, backlinks, technical site health, mobile performance, metadata

• Audience: Traditional search engines

AEO (Answer Engine Optimization)

• Goal: Optimize for generative AI engines (ChatGPT, Claude, Perplexity, Gemini)

• Focus:

  • Make content LLM-friendly (clear, scannable, factual, well-sourced)

  • Encourage citations and retrieval by generative models

  • Adapt to multi-turn conversational AI, not just one-shot answers

• Audience: Pure generative AI systems, not just search engines

The Fundamental Question: does SEO help AEO?

Partly, yes.

• SEO best practices like structured content, FAQs, and backlinks make your site easier for AI crawlers (OpenAI, Anthropic, Perplexity, Google Gemini) to ingest

• If you’re ranking high in Google, chances are you’re also on the “first layer” of training data that AI models reference

But it’s not enough.

• AEO cares less about click volume or link juice and more about direct factual mentions and clear Q&A style text that models can reuse in answers

• Traditional SEO signals (domain authority, backlinks) matter less than content structure and factual accuracy

Bottom line: Good SEO creates a foundation for AEO, but you need additional work. Structured answers, citations from trusted sources, and brand mentions across platforms to actually show up when users ask ChatGPT or Perplexity about your category.

Think of SEO as necessary but not sufficient. You can’t skip it, but you can’t stop there either.

Where you probably stand right now (spoiler: it’s not great)

Here’s the sobering reality we discovered: most companies have zero visibility in answer engines.

Even brands with strong SEO rankings often get zero mentions when users ask ChatGPT or Perplexity about their category.

How to figure out where you stand today:

Ask yourself these questions:

• When I type “best [category tool]” into ChatGPT, Claude, or Perplexity, does my brand show up?

• How often am I mentioned compared to competitors?

• Am I cited with a clickable link, or just referenced without attribution?

If you’re not being mentioned, you’re invisible in the fastest-growing search channel.

The Answer Engine Landscape: know your audience

Not all answer engines are created equal, and not all have the same audience size. Here’s where the users actually are:

AI Platform Active Users (2025):

• Google AI Overview: 2 billion monthly users

• Meta AI: 1 billion monthly users

• ChatGPT: 800 million weekly active users

• Gemini App: 450 million monthly users

• Perplexity: 22 million monthly users

What this means for your strategy:

Know your brand and category. Where does your target audience actually search?

• B2B SaaS buyers might heavily use ChatGPT and Perplexity for research

• Consumer products might see more volume from Google AI Overviews

• Mobile-first audiences might engage more with Meta AI on WhatsApp and Instagram

The platform you optimize for should match where your buyers are asking questions.

About Orion and why we shelved it:

We started building Orion, an AEO tracking tool that would monitor brand visibility across ChatGPT, Perplexity, Gemini, and Claude. The idea was simple: help brands understand if they’re showing up when buyers ask questions about their category.

We hit a fundamental problem…

The Data Problem: Why AEO tracking is broken

Unlike traditional SEO, there’s no ground truth data for answer engines.

Google gives you:

• Search Console with real impressions

• Google Ads API with actual search volumes

• Clear metrics you can validate

With ChatGPT, Perplexity, and other answer engines? Nothing.

There’s no public API. No query volume data. No “truth set” to validate against.

What this means for every AEO tool (including what we were building)

Every AEO tracking tool in the market—including what we were building with Orion—faces the same fundamental limitations:

1. Expensive (API Costs)

• Running thousands of synthetic queries costs real money

• ChatGPT API: ~$0.002-0.03 per query

• Multiply by hundreds of keywords × multiple platforms × daily checks

2. Inconsistent (Results Vary by Prompt Phrasing)

• “Best project management tools” vs “What’s the best project management software” can yield completely different citations

• The same query asked 10 minutes apart can produce different results

• Prompt engineering becomes a guessing game

• No way to know if you’re tracking queries that users actually ask

3. Incomplete (Can’t Track Every Possible Query)

• Users ask questions in infinite variations

• You’re sampling, not measuring

• Long-tail queries (where most traffic lives) are nearly impossible to track

• Your “visibility score” is based on your best guess of what people ask

4. Delayed (Manual Checking Takes Time)

• Automated tracking requires careful parsing of responses

• Citation formats differ across platforms

• Some platforms rate-limit requests

• By the time you identify a trend, it may have already changed

The Bigger Problem: synthetic queries ≠ real usage

Here’s what kept us up at night: We had no way to validate if the queries we were tracking actually represented real user behaviour.

With Google, you can verify:

• “This keyword gets 10,000 searches/month” (Google Ads tells you)

• “You ranked #3 and got 847 clicks” (Search Console confirms it)

With answer engines, you’re flying blind:

• You don’t know which questions users actually ask

• You don’t know how often they ask them

• You can’t verify if the citations in your test queries match what real users see

• You’re building a dashboard based on assumptions

What this means for you

Does this mean AEO doesn’t matter? Absolutely not.

But it means:

1. Be skeptical of “visibility scores” from any tool claiming to track your AEO performance comprehensively

2. Manual spot-checking matters more than automated tracking (for now)

3. Focus on fundamentals rather than chasing metrics you can’t validate

4. Understand that AEO optimization is part art, part science—you’re optimizing for an invisible algorithm

The one thing that still works: manual validation

What you CAN do:

• Manually test 10-20 critical queries in your category

• Check them weekly across key platforms

• Track who gets cited and why

• Document patterns over time

• Focus on queries you KNOW matter to your buyers (talk to your customers, EVERY DAY)

It’s not scalable. It’s not automated. But it’s a good start.

What’s Next

We shelved Orion because we couldn’t solve the data problem. But we didn’t stop researching.

In Part 2, we’ll dive deep into:

• How answer engines actually work (LLM + RAG mechanics)

• Which sources does each platform trust and why

• The citation hierarchy that determines who gets mentioned

• Platform-specific patterns we’ve discovered through manual testing

In Part 3, we’ll share the action plan:

• The 6 steps to improve your AEO visibility

• From question research to Reddit strategy

• What actually moves the needle (based on what we’ve seen work)

The fundamentals still matter. The tactics still work. You just need to understand the limitations of what you can measure.

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