Today, June 24, Lionel Messi turns 39.
Two days ago, in Dallas, he became the highest scorer in the history of the FIFA World Cup. Eighteen goals across six tournaments spanning two decades. He is the only man, in the long, complicated, beautiful history of the game, who can say that.
I want to tell you what those eighteen goals actually mean. Not as statistics. As a story.
June 16, 2006. Gelsenkirchen, Germany. A skinny eighteen-year-old with long hair comes off the bench for Argentina against Serbia and Montenegro. Argentina are already winning comfortably. The young substitute has exactly one job: don’t make a mess of this. He provides an assist, then scores. Six-nil final score.
He was 18 years and 358 days old that afternoon. He became Argentina’s youngest World Cup scorer in history.
Exactly twenty years later, on June 16, 2026, at Kansas City, the same man scored a hat-trick against Algeria to draw level with Miroslav Klose’s all-time record of sixteen World Cup goals. Same date. Different century, almost. First goal to record-equalling goal, twenty years to the day.
Football does not do symmetry like this. It just doesn’t. And yet here we are.
What nobody in those Kansas City stands fully knew was what Messi was carrying when he walked out against Algeria.
His father, Jorge, the man who had packed up the family’s life in Rosario and moved to Barcelona when Lionel was thirteen so that the club could pay for his growth hormone treatment, was back home dealing with a health situation the family had asked everyone to treat with discretion. After Messi scored that first goal against Algeria, he pulled his shirt over his face and wept. Teammates stood around him confused, then gentle. They understood something was wrong. “It wasn’t related to football,” Messi said afterward. “I had some tough days. My teammates gave me a lot of strength.”
He still scored three.
I have watched Messi play football for the better part of my adult life, and I genuinely do not have the language to tell you what it takes to do that. To be that far from someone you love, carrying that kind of private weight, on the biggest stage the sport has, and still produce a hat-trick. His first at a World Cup. In his two hundredth international appearance.
Six days later in Dallas, Argentina faced Austria.
In the ninth minute, Messi stepped up for a penalty. The record, Klose’s sixteen goals, was one goal away. He stuttered his run-up. The ball went wide right. I imagine every Argentinian watching the game aged slightly in that moment.
Here is the thing about Messi that separates him from everyone else I have watched play sport. He does not carry a missed penalty into the next action. There is no visible sulking, no head dropped, no body language of defeat. He simply recalibrates.
Thirty-eight minutes in, Thiago Almada let a pass from Facundo Medina roll through his legs untouched. This looks careless until you realise it was a decision, made because Almada had already seen what Messi had seen: the Austrian goalkeeper was leaning. Messi’s left foot met the ball and curled it into the corner. Seventeen World Cup goals. Record equalled. Record broken. History.
Deep in stoppage time, he added an eighteenth. Not a spectacular goal. A scramble inside the box, a shot blocked, a rebound, a finish through a crowd of Austrian bodies. The kind of goal that requires presence, timing and the absolute refusal to stop moving. That refusal is the thing. At 38, in his sixth World Cup, after a missed penalty, after days of private anguish about his father, he was still moving.
The number eighteen sits alone now at the top of a list that contains the names of every great striker who has played this tournament since 1930.
Miroslav Klose, whose record Messi broke: sixteen goals across four World Cups, a disciplined, intelligent German forward who made a career out of being exactly where the ball was going to land. Kylian Mbappe, who on the same evening that Messi set the record of eighteen, scored twice against Iraq to pull level with Klose on sixteen. He is twenty-seven years old. He has time.
Behind them: Ronaldo, the Brazilian one, on fifteen. Gerd Muller, fourteen. Just Fontaine, thirteen, all in one tournament in 1958.
Messi has twelve World Cup goals since turning thirty-five. He has done the majority of the work of this record in what should have been the decline phase of any footballer’s career. He scored seven in Qatar 2022, winning Argentina the title, winning the Golden Ball, doing the one thing his entire career had told the world he could not do until he finally did it.
Now five more in two games at this tournament, with Jordan still to come.
There is something about watching greatness at this stage of a life that hits differently when you’re watching it in real time.
I am a football fan, but I am also someone who runs a business, who tries to build things, who thinks often about what it means to keep going when the easier decision is to slow down and let someone else take the weight. Messi did not have to be here. He said himself before the tournament that he wasn’t sure if his body or his mind would let him. He had a hamstring problem. He is 38. Normal people at 38 are thinking about their knees on stairs.
He decided to show up anyway. And then, carrying grief he hadn’t asked for, he showed up inside the showing up.
The tears after the first goal against Algeria moved me more than the goal itself. Not because I am sentimental about footballers crying, but because it told me something true. He is not a machine. He is not performing invincibility. He is a man who loves his father, who was far from home when his father needed him, who had nowhere to put that except into the only thing he has done with his body since he was six years old.
He put it into goals.
Eighteen of them, across twenty years, across six World Cups, from the skinny substitute in Leipzig to the man who writes his name at the top of the only record in football that nobody will now approach in any of our lifetimes.
Happy birthday, Leo. Go win the thing, AGAIN.
Last year, a VP of Engineering at a mid-sized UK retail firm found Brainium through a search. He read enough to be interested. He filled out the contact form. And then he vanished.
Three weeks later, we followed up. His reply was brief and blunt: “We went with someone else. Your site made us work too hard to understand if you were the right fit, so we moved on.”
He was not complaining about our capability. He was not complaining about our pricing. He was complaining about the experience of trying to evaluate us. That sentence sat with me for weeks.
For decades, B2B sales ran on information asymmetry. You held the knowledge. The buyer had to come to you for it. Gate the content. Force a demo request. Run them through your qualification funnel. You held the cards, and that leverage was real.
AI killed that advantage overnight.
Today, a prospect can describe your service category to any AI tool and get a vendor shortlist, a comparison of models, a set of qualifying questions, and a rough pricing benchmark before they ever visit your website. The research that used to happen inside your funnel now happens before they enter it. Which means every gate you erected, every “book a call to learn more” wall you built, every form that stood between a buyer and basic clarity, is now working against you. Actively.
I have been watching these patterns show up in the market week after week, and they map to findings IDC published recently on the same shift.
The first is gated content. When a buyer can get a summary in thirty seconds from an AI tool, asking them to trade their email address for a whitepaper is not an exchange they want to make. Worse, if your best content sits behind a form and is invisible to AI indexing, you have removed yourself from consideration before the buyer even knew you existed. The gate does not slow the buyer down. It routes them to your competitor.
The second is multi-step qualification chains. Buyers today want to self-evaluate first. They want to see the product, understand the value, and decide if a conversation is worth their time, before they talk to anyone. When you put three discovery calls between them and that understanding, they do not wait. They move on to someone who trusts them enough to show their hand.
The third is opaque pricing. “Contact us for pricing” used to create negotiating leverage. Today it signals one of three things: inconsistency, a commercial model that cannot survive comparison, or a fear of the market. When a buyer can benchmark your alternatives in minutes, withholding pricing does not protect you. It sends traffic to whoever publishes theirs.
The fourth is requiring a human for basic information. A buyer should not have to schedule a thirty-minute call to find out whether your platform integrates with Salesforce. If getting that answer requires a sales conversation, they draw the obvious inference: if pre-sales is this much effort, what does post-sales look like? Your documentation is not a cost. It is your first sales conversation. It should be a good one.
The fifth is what I call discovery theater. When a prospect has to re-explain their company, their pain, and their requirements to three different people across three different calls, what they hear is that your internal coordination matters more than their time. High-intent buyers read that as a preview of the engagement ahead. Most of them are right to.
After that conversation with the UK retail VP, I told my team something that shifted how we think about sales entirely.
By the time a buyer reaches us, they are not at the start of their journey. They are near the end. The research is done. The shortlist exists. Our job is not to qualify them. It is to confirm what they already suspect: that we are the right choice. That is a completely different motion.
It means your website needs to answer the questions buyers are asking AI tools, not just the questions that make you look good in a brochure. It means your case studies need to be specific, not polished. Real numbers, real outcomes, real constraints, even the ones that make the project sound harder than you expected. It means your pricing model, at minimum, needs to be visible. And it means your sales process needs to carry some respect for the fact that the buyer already knows things.
At Brainium, we had to work through this ourselves. Our Dedicated Hiring service is actually straightforward: vetted engineers, onboarded in forty-eight hours, at roughly half the cost of a local hire, no long-term commitment. That is the entire value proposition. It should live on the homepage, in plain language, without a form standing in front of it.
For a long time, it did not. It does now.
The buyer who found you already made a decision. The only question is whether your website confirms it or reverses it.
She had solved the matching problem.
Six weeks after launching the quiz, the founder in Pune was looking at a return rate that had dropped from 14 percent to 6 percent on her hero serum. Her support inbox had quieted. Her customers were arriving at the right product the first time, because they had told her exactly what they needed and she had listened.
But she was looking at another number now. And this one was harder to explain away.
Her post-purchase upsell was not working.
She had set it up the way most Shopify operators do. A third-party app sitting between the order confirmation and the thank-you screen, showing a “you might also like” carousel of three products. Customers who had just bought the serum were being shown a vitamin C booster, a night cream, and a facial mist. All reasonable suggestions. All completely generic.
The attach rate was 4.2 percent.
She called me to ask if that was normal. I told her it was actually above average for a cold carousel. Then I asked her what she knew about the customer in the moment that carousel was showing.
She thought about it. “I know what they just bought.”
I asked her what else she knew.
Another pause. “I know their skin type. I know their primary concern. I know what gaps they said they had in their routine. I collected all of that in the quiz.”
So why, I asked, is the upsell showing them a generic carousel instead of the one product that the quiz already identified as the logical next step in their routine?
She did not have an answer. But the question was the whole problem.
This is the most common failure mode I see in D2C checkout strategy. A brand invests real effort into understanding the customer at the top of the funnel, and then forgets everything it learned the moment the transaction is complete.
The quiz had written her customers’ skin profiles directly into Shopify Customer Metafields. The data was sitting there, structured and permanent, every time a customer hit that post-purchase screen. The upsell app had no idea it existed, because the app was not built to read it. It was built to show a carousel. So it showed a carousel.
This is not a technology problem. It is an architecture problem. And Shopify’s Checkout Extensibility is the tool that closes the gap.
Most founders hear “Checkout Extensibility” and picture a settings panel somewhere in their Shopify admin. It is not that. It is a fundamental redesign of how logic can be applied at the most valuable moment in the customer lifecycle.
Before this architecture existed, modifying what happened inside or immediately after the checkout required injecting custom code into a checkout template that Shopify did not officially support editing. It was brittle. It broke during platform updates. It created security surface area that Shopify’s compliance frameworks did not cover. And it ran outside the performance sandbox, which meant every clever upsell widget was silently taxing your Core Web Vitals.
Checkout Extensibility replaces all of that with a sandboxed environment built on UI extensions and WebAssembly components. Your post-purchase logic runs inside Shopify’s own infrastructure, not bolted onto the outside of it. It has direct, native access to the order that just completed, the customer profile attached to that order, and the live inventory state of your entire catalogue.
That last part is what matters for what she needed to build.
The implementation she eventually built has three moving parts, and the logic connecting them is simpler than it sounds.
The first part is the trigger condition. When an order completes, the post-purchase extension reads two things: the product that was just purchased, and the customer metafield where the quiz wrote the skin profile. If the customer said their primary concern was pigmentation and they just bought the serum formulated for pigmentation, the system knows they are mid-routine. They have the treatment. What they do not have yet is the booster that enhances it.
The second part is the product selection. Instead of a static carousel, the extension queries the Storefront API in real time with the profile data as parameters. It returns one product. The one product that the quiz logic already identified as the correct next step for this specific skin type and concern combination. Not the three most popular products in the vitamin C category. The one product that makes sense given what this customer told her four weeks ago when they first visited the store.
The third part is the inventory check. This is where the legacy approach used to create operational nightmares. An out-of-stock item appearing in a post-purchase offer generates a confirmed sale that cannot be fulfilled. Checkout Extensibility communicates directly with Shopify’s inventory ledger. If the recommended product is below the buffer threshold she set, the system skips it entirely and surfaces the next match in the logic queue. The customer never sees a product that cannot ship tomorrow.
There is a reason this logic should live here and not on the product page or inside the cart.
On the product page, a recommendation creates a fork. The customer can choose the original product, choose the recommendation, go back and compare, or leave entirely. You are introducing optionality into a decision that has not yet been made. Every option you add is a potential exit ramp.
Inside the active checkout sequence, the same dynamic applies. A cross-sell attempt before payment is processed is a gamble with the primary transaction. One moment of friction, one unexpected line item, one question the customer did not want to have to answer, and the cart gets abandoned.
The post-purchase window is structurally different. The primary order is already confirmed, already paid, already sent to the order management backend. A rejected upsell at this stage has a zero percent chance of costing you the original sale. The customer has already made the hard decision. You are asking them to make a much easier one: do you want the thing that goes with what you just bought?
And because Checkout Extensibility enables one-click authorization, the secondary purchase does not require the customer to re-enter their card details or shipping address. Those are already in the system from the transaction they just completed. The friction has been reduced to a single binary choice: yes or no.
The trust is at its peak. The data is already in the room. The only question is whether your checkout is smart enough to use it.
Her attach rate moved from 4.2 percent to 11.8 percent over the following sixty days.
That number needs context to mean anything. At her average order value of around three thousand rupees, the upsell product was priced at roughly twelve hundred rupees. Before the rebuild, on a hundred post-purchase screens shown, she was capturing four secondary transactions. After the rebuild, she was capturing almost twelve.
But the number that changed the shape of her P&L was not the attach rate itself. It was the CAC on those twelve transactions.
Every post-purchase sale carries a customer acquisition cost of zero. The customer was already acquired. The ad spend, the influencer fee, the discount code that brought them in, all of that cost is attributed to the primary order. The secondary transaction is pure incremental revenue, and its only costs are the product and the fulfilment.
At a 40 percent gross margin on the upsell product, that incremental revenue flows almost directly to Contribution Margin 3. When you are building toward a 5:1 LTV to CAC ratio, there are very few levers that move it this cleanly. The quiz improved both sides of the equation simultaneously, as we covered last time. The intelligent post-purchase offer improves the LTV side without touching the CAC side at all.
The quiz told her who her customer was. The checkout used that knowledge at the moment it was worth the most.
Three months after both systems were live together, she showed me something I had not anticipated seeing so quickly.
Her email retention flows had started performing differently. Not dramatically. But measurably. The thirty-day reorder sequence, the one targeting customers who had bought the serum but not yet returned, was converting at a rate about 2 percentage points higher than before.
The reason, when we dug into it, was straightforward. The customers who had taken the quiz, bought the serum, and then purchased the booster through the post-purchase offer were a different cohort from the ones who had only bought the serum. They had more invested in the routine. They had made two consecutive decisions that reinforced each other. The booster made the serum work better. The serum made the booster feel necessary. By the time the thirty-day email arrived, these customers were not being asked to remember a brand they had tried once. They were being asked to restock a system they had already built.
This is what the series has been building toward from the beginning. Each layer compounds the one before it. The margin discipline from the earliest posts funds the ad spend. The ad spend brings in qualified traffic. The product page converts that traffic without leaking trust. The retention flow extends the lifetime of each customer. The quiz lowers return rates and sharpens targeting simultaneously. And the intelligent checkout turns the single highest-trust moment in the customer relationship into a revenue event that costs nothing to acquire.
She is not spending more to grow. She is extracting more from what she already built.
That is the whole argument.
This is post ten in the series on D2C profitability on Shopify. The earlier posts cover retailer margin costs, ad attribution, discounting’s hidden tax, store design, membership commerce, the 90-day retention flow, the product page, the post-purchase upsell, and zero-party data. If you have not read them, start from the beginning.
If you want to build a native, data-connected checkout experience on Shopify, Brainium engineers this end to end.
She ran a clean beauty brand out of Pune. Three years in, and her ad creative had finally found its rhythm. Her CPCs were down, her conversion rate was healthy, and her retention flows from the last post in this series were quietly compounding revenue every week without her lifting a finger.
But there was a number she could not move. Her return rate.
Not catastrophic. Not the kind of number that shows up in a board deck with a red arrow next to it. Just a steady, grinding 14 percent on her hero serum, month after month. Customers loved the brand on social media. They wrote long, enthusiastic captions about it. And then one in seven of them sent the product back.
When she pulled the return reasons, almost all of them said the same thing in different words. “Not right for my skin type.”
I asked her how a customer chooses which serum to buy on her site. She walked me through it. A collection page. Six products, each with a clean photo and a paragraph of copy. The customer reads the descriptions, picks the one that sounds most like their situation, and adds it to cart.
I asked her: how does a customer know what their skin type actually is?
She paused. “I mean… they probably know. Or they guess.”
That guess is costing you 14 percent of every order you ship.
For years, the standard playbook for understanding a customer was to watch them. Pixel-based retargeting, lookalike audiences built from purchase history, algorithms that needed three or four orders before they started to get a person right. It worked because there was no alternative. You could not ask a stranger on the internet a direct question and expect an honest answer before they had even decided to trust you.
That playbook is breaking. iOS privacy changes and the slow death of third-party cookies have made behavioral signals weaker every quarter. Brands are paying more to reach fewer people with less certainty about who those people actually are.
But here is what nobody fully priced in. The same privacy shift that broke behavioral tracking also created an opening. Customers have become more comfortable, with directly telling a brand what they want, as long as the exchange feels like it is in their interest. A skincare quiz that asks about skin type and ends in a personalized routine does not feel like surveillance. It feels like a consultation.
This is zero-party data. Information the customer hands you on purpose, because answering the question gets them something better in return. And unlike a cookie, it cannot expire, get blocked, or get regulated out of existence. It sits inside your own Shopify database, owned by you, forever.
The data a customer gives you on purpose is worth more than the data you have to infer.
Picture the two paths side by side.
Path one: the customer lands on a collection page showing every serum you sell. They read six product descriptions, each one trying to sound like it was written for them specifically. They pick one based on a feeling. Maybe they are right. Maybe they are not. Either way, you will not find out until the return request arrives three weeks later.
Path two: the customer answers four questions. Skin type. Primary concern. Age range. Current routine gaps. By the third question, something has already shifted. They are not browsing anymore. They are being consulted. And at the end, instead of six products to choose between, they see one. The one that matches what they just told you about themselves.
The collection page asks the customer to do the work of matching themselves to a product. The quiz does that work for them, using information only they have.
This is not a cosmetic difference. It changes what the customer is doing on your site. Browsing is a search task with an uncertain outcome. A quiz result is a recommendation from someone who appears to understand the problem. The first invites comparison shopping and second-guessing. The second invites a single decision: yes or no to the thing built for you.
Here is where most attempts at this go wrong. A founder hears “quiz” and reaches for a third-party app from the Shopify App Store. It bolts a popup or an embedded iframe onto the storefront. It works for a week. Then it starts loading slowly on mobile, your Core Web Vitals take a hit, and the very SEO gains we discussed two posts ago start eroding from a feature meant to improve conversion.
The right way to build this treats the quiz as part of your store’s data architecture, not a decoration on top of it.
As a customer answers each question, that answer should write directly to a Shopify Customer Metafield and update their customer tags in real time. The moment someone says “combination skin, primary concern pigmentation,” that profile exists permanently. It is available to your retention flows, your email segments, your SMS campaigns, and every future interaction with that person, without anyone exporting a spreadsheet.
Then, instead of dropping the customer onto a generic results page, use the Storefront API to query your live inventory and build their result in real time. Not a category. Not a list of five options that sort of fit. One serum, possibly bundled with a complementary product, chosen because it matches what they told you four questions ago.
And build the whole thing using native Shopify sections or lightweight components, not a heavy embedded widget. The quiz should feel like it belongs to your store, because technically, it does. It loads as fast as everything else on the page, because it is not foreign code asking your theme for permission.
Go back to the founder in Pune. The 14 percent return rate was not a quality problem with her product. It was a matching problem at the point of sale. Customers were buying serums formulated for oily skin when they had dry skin, and discovering the mismatch only after using it for a week.
A quiz that routes a dry-skin customer to the dry-skin serum does not just improve their experience. It removes the single biggest driver of her return rate, because the product arriving at their door is no longer a guess.
This hits Contribution Margin 3 from two directions at once. Returns carry real cost, restocking, repackaging, sometimes the product cannot be resold at all, and every percentage point you shave off that number drops straight to your margin. At the same time, when your retargeting and lookalike audiences are built from customers who told you their exact skin type and concern rather than customers who merely clicked an ad, your acquisition targeting gets sharper. Lower CAC and lower returns, from the same four questions.
If you have been tracking the 5:1 LTV to CAC ratio through this entire series, this is one of the few levers that improves both sides of that equation simultaneously. It lowers the cost of acquiring the right customer, and it raises the lifetime value of the customer you already have by making sure the first product they receive actually works for them.
She built the quiz over two weeks. Four questions, native Shopify sections, results pulled live from inventory and matched to skin type and concern.
The first full month, her return rate on the hero serum dropped from 14 percent to 6 percent. Her AOV moved up slightly too, because the quiz result page suggested a complementary product alongside the main recommendation, and customers who had just been “understood” were more willing to trust a second suggestion.
But the number that mattered most to her was not on the revenue side at all. It was the support tickets. The “this didn’t work for my skin” emails, the ones that used to eat an hour of her time every day, dropped by more than half.
She told me something I have heard in different forms from almost every founder in this series by now. “We were spending so much money trying to find the right customers. We never thought to just ask the ones who showed up what they actually needed.”
The next quiz question is not a feature request. It is a question your customer is already willing to answer. Build the form, and let them tell you.
This is post nine in the series on D2C profitability on Shopify. The earlier posts cover retailer margin costs, ad attribution, discounting’s hidden tax, store design, membership commerce, the 90-day retention flow, the product page, and the post-purchase upsell. If you have not read them, start from the beginning.
If you want to build a native zero-party data system into your Shopify store, Brainium builds this end to end.
A few months ago, Brainium completed a UI/UX design engagement for Gymfluence, a B2B SaaS coaching platform built for the Nordic market. Our mandate was design only: information architecture, visual system, component library, and screen-level UX for the coach dashboard and marketing site.
No development. No backend. Just design, done properly.
I want to share what that engagement taught me, because several of the lessons surprised even me and I have been doing this for over a decade.
Gymfluence serves two users: the coach and the gym member. It is easy to assume the member experience should get most of the design attention, because members are the end users and the retention metric lives with them.
Wrong. The coach is the paying customer. The coach pays the subscription. The coach evaluates whether to renew or cancel. And the coach is spending the most time inside the product, monitoring adherence, tracking progress, managing a portfolio of clients simultaneously.
We reoriented the entire design priority stack around this insight. The coach dashboard became the primary design surface. The member interface followed.
This applies to almost every B2B SaaS product I have seen: the payer and the primary user are often different people, and design investment should follow the payer, not the most visible surface.
The Gymfluence client base is Nordic. That sounds like a minor detail until you are making decisions about information density, data privacy signalling, and how trust is communicated visually.
Nordic users have measurably different expectations around these things compared to what a South Asian or US-trained product team would default to. The dashboard density that reads as “powerful and comprehensive” to an Indian enterprise buyer reads as “overwhelming and untrustworthy” to a Scandinavian coach who values clarity and restraint above feature richness.
We calibrated. It required real user validation, not assumptions.
If you are building a product for a geography different from where your team is based, that localisation work has to be built into the design process, not treated as a post-launch polish task.
The deliverable that matters is not the polished Figma presentation your team shows investors. It is the component library the development team can actually build from.
Screens are a snapshot. Components are infrastructure.
On Gymfluence, we delivered a structured component set covering data display cards, status indicators, progress visualisations, and navigation patterns, all with documented states. The development team received something they could extend as the product grew, not something they had to reverse-engineer.
Every design partner Brainium engages with gets this as a standard deliverable. I am consistently surprised how rarely other design vendors include it.
I wrote the complete methodology that came out of this engagement as a detailed guide on the Brainium blog. It covers seven specific approaches — from journey auditing to visual identity strategy to FAQ schema for AI search visibility.
If you are evaluating a redesign for your SaaS product or want to understand how to brief a design partner properly, that piece is worth reading: Best Approaches for UI/UX Redesign in B2B SaaS: What Actually Works
The Gymfluence engagement was a clean, well-scoped project that gave Brainium the conditions to do design work at its best: clear brief, responsive client, defined deliverables. The product is live. The coaches are using it. And we walked away with a sharper methodology for the next SaaS redesign we take on.
If you are building in the coaching, wellness, or professional services SaaS space and thinking about a redesign, I am happy to talk. Drop me a note through Brainium’s contact page or connect with me on LinkedIn.
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I want to tell you about something that happened in a classroom in 1965.
A Harvard psychologist named Robert Rosenthal walked into an elementary school and gave the kids a standard IQ test. Nothing unusual. But before he left, he handed the teachers a list. Twenty percent of the children, he told them, were special. These were the ones with extraordinary, unlocked potential – the ones who were about to bloom.
He was lying.
The list was random. Those kids were no different from anyone else in the room.
Rosenthal came back a year later. The children on his fake list had pulled dramatically ahead in IQ scores. Not because of genetics. Not because of extra tutoring. But because the teachers believed something about them and that belief quietly changed everything. Their tone, their patience, the way they leaned in a little more when those kids spoke. The children absorbed that energy and, without knowing it, started becoming who their teachers believed they already were.
This is called the Pygmalion Effect. And once you understand it, you cannot unsee it in your own life.
Here is the uncomfortable question it forces: Who are the people around you, and what do they quietly believe you are capable of?
Think about your inner circle right now. The people you spend the most time with – your colleagues, your friends, maybe the WhatsApp group you never mute. When you talk about a goal you have, a business you want to build, a number you want to hit, a version of yourself you’re trying to become, what’s the energy in the room?
Do they nod and say that’s great, you should do it? Or do they lean forward and say that’s interesting, but why not bigger?
There’s a massive difference between those two reactions. One feels better in the moment. The other actually makes you better over time.
I’ve noticed this in my own journey. The rooms that made me grow were never the comfortable ones. They were the ones where I felt slightly out of my depth. Where the people around me were moving faster, thinking bigger, and holding expectations for me that I hadn’t yet held for myself. Those rooms were often agonizing to sit in. Your excuses sound hollow there. Your justifications for slow execution don’t land. You become very aware, very quickly, of the gap between where you are and where you could be.
But that gap? That’s not shame. That’s signal. It’s the friction of your old self-image rubbing against what you’re actually capable of.
The problem is most of us are optimizing for the opposite. We build circles that feel safe. People who validate us when we fall short, who explain away failure with us, who celebrate small wins just a little too loudly. It feels like loyalty. It feels like support. But what it actually is, if we’re being ruthlessly honest, is a very comfortable ceiling.
Here’s the principle I’ve come to live by: If the people around you aren’t making you feel slightly underqualified, they’re not accelerating you. They’re just keeping you company.
That doesn’t mean you need to drop everyone you care about and cold-call billionaires. It means you need to deliberately create friction in your environment. Seek out the mentor who doesn’t let you play small. Find the partner who looks at your five-year plan and asks why you haven’t done it in twelve months. Put yourself in the rooms where your current targets are someone else’s baseline.
You will feel small at first. That’s not a warning sign. That’s the tuition.
The Pygmalion Effect works in both directions. When people expect little of you, you quietly shrink to meet that expectation too. Which means that cozy circle of low expectations isn’t neutral – it’s actively pulling you down.
You don’t rise to your goals. You rise or sink to the level of what the people around you believe you can do.
So choose your rooms carefully. Enter the uncomfortable ones. Stay long enough to stop feeling like an outsider. And watch what starts to happen to who you are becoming.
The discomfort is temporary. Staying small is permanent.
What did this make you think about? I’d love to know which room you need to walk into. Drop it in the comments.
He had the creative dialed in.
Three years running a skincare brand out of Bengaluru, and he had finally cracked the short-form video formula that bigger labels spend lakhs trying to reverse engineer. Real skin, not airbrushed. Before-and-after content with actual customers, not models. His cost per click had been falling for four months straight and his conversion rate on the product page was sitting at 3.2 percent, which in his category is genuinely good.
But his AOV had not moved in two years. Every order was still going out at roughly the same ticket. When I looked at the numbers with him, the problem was immediately obvious.
He had built a perfect machine for getting people to buy one thing. He had never once asked them to buy two.
I asked him to walk me through what a customer sees after they complete a purchase on his store. He pulled up a test order on his phone. Payment confirmation. Shopify’s default thank-you page. Order number, delivery estimate, a link to return to the homepage.
I asked him: what is the highest-trust moment in your entire relationship with this customer?
He thought about it. “When they place a second order?”
It is not. It is the thirty seconds immediately after the first one goes through.
There is a specific psychological state that exists in the moments immediately after someone completes an online purchase. The anxiety of the decision is gone. The credit card has been charged and the order accepted. They are not yet in delivery anxiety, because the product has not shipped. They are in a narrow window of pure satisfaction, fully engaged with the screen, waiting for the confirmation email that tells them everything went through correctly.
Compare it to every other channel you use to generate secondary revenue. An email sent three days after purchase lands in an inbox between a bank statement and a school circular. An SMS notification gets read while they are doing something else. A retargeting ad interrupts content they were trying to consume.
The post-purchase window asks for none of that goodwill. It does not interrupt. The customer is already there, already paying attention, already in a buying frame of mind. The decision to buy something more has the lowest possible activation energy it will ever have.
Most Shopify stores treat this window as administrative. Here is your order number. Here is your delivery timeline. Goodbye.
The instinct most founders reach for when they want to increase AOV is to add cross-sells inside the cart. A small panel that says “customers also bought” before the checkout button. Or a pop-up that fires when someone tries to leave the cart page.
I understand the logic. You already have someone deep in the funnel. Why not show them one more thing?
Here is what actually happens. A person who has decided to buy something is in a state of managed commitment. They have weighed the cost, justified the spend to themselves, made peace with the number on the screen. When you introduce a new product into that calculation, you are not adding a simple decision. You are reopening the entire negotiation they just finished having with themselves.
Some customers add the extra item. But a meaningful percentage of them, instead of adding, start subtracting. They look at the total. They recalculate. They decide the cart is getting expensive and they will come back. Sometimes they do. Most of the time they do not.
The post-purchase upsell removes this risk entirely. The primary order is confirmed, paid, and sent to your fulfilment backend. Nothing you say or do on the next screen can affect that transaction. A rejected offer costs you nothing. An accepted one is pure incremental revenue.
This is the only channel in your entire growth stack where a “no” carries zero downside.
Until recently, building a post-purchase upsell experience on Shopify that was native, fast, and secure required patchwork. Third-party apps that loaded after the checkout sequence. Custom scripts injected into thank-you page templates. Solutions that worked until a Shopify update broke them, and then required frantic fixes at the worst possible time.
Shopify has deprecated all of this in favour of Checkout Extensibility. The architecture is different in a way that matters for brands at scale.
The upsell logic runs inside a sandboxed environment that executes independently of your storefront theme. It does not touch your Core Web Vitals. It does not slow your main site. It does not carry the performance tax that legacy checkout modifications used to impose. Your mobile page speed, which determines your search ranking and your paid media quality scores, stays clean.
More importantly, it connects directly to Shopify’s inventory ledger in real time. The single worst thing a post-purchase upsell can do is offer a product that is out of stock. The operational fallout downstream, the customer service tickets, the expectation failures, costs more than the missed upsell was ever worth. When inventory drops below a threshold you define, the system swaps the offer automatically. The customer never sees the gap. Your team never gets the email.
And the offer itself is not a guess. The post-purchase application reads what the customer just bought and makes a contextual recommendation based on the actual line items in that order. The person who just bought your face wash does not see a random product. They see the matching moisturiser from the same range. Relevance is not a bonus feature. It is the reason one-click attachment rates on a well-built post-purchase page are meaningfully higher than anything an email cross-sell campaign will ever deliver.
Every other growth initiative you run has a cost attached to it. Better ads cost more in creative and media. Better retention infrastructure costs in tooling and automation. A better PDP requires engineering and design time. These are worth it, and we have spent the previous posts in this series establishing exactly why.
Post-purchase revenue is structurally different. The customer was already acquired. The CAC for that transaction was spent the moment they arrived on your site from an ad, an organic search, or a referral. By the time they reach your post-purchase offer, that acquisition cost is fixed and sunk. Every rupee that comes in from a one-click upsell carries no share of that cost.
What this means in practice: a 10 to 15 percent post-purchase conversion rate on a product that costs you 35 percent of revenue to fulfil generates contribution margin at a rate your primary acquisition business cannot match. The revenue is real. The CAC allocation is zero. The CM3 improvement is direct and immediate.
If you have been building toward a 5:1 lifetime value to customer acquisition cost ratio, and that benchmark has been running through this entire series, post-purchase extensibility is one of the fastest structural moves available to close the gap. It does not require you to acquire more customers. It requires you to ask the ones you have already paid for whether they want one more thing.
He rebuilt the post-purchase experience over three weeks. One offer, contextually matched to the product just purchased, one-click authorisation using the payment credentials already on file.
The first month the system ran, his AOV moved from Rs. 1,240 to Rs. 1,490. Not from better ads. Not from a pricing increase. From a screen that used to say “order confirmed” and now says “while you wait, you might want this.”
The acquisition cost on those Rs. 250 increments is exactly zero.
He called me after the first month’s numbers landed. He said the same thing everyone says when this particular logic clicks into place: “Why did we not do this earlier?”
There is no satisfying answer to that question. The better one is: the next order is going out today.
This is post eight in the series on D2C profitability on Shopify. The earlier posts cover retailer margin costs, ad attribution, discounting’s hidden tax, store design, membership commerce, the 90-day retention flow, and the product page. If you have not read them, start from the beginning.
If you want to implement native Checkout Extensibility for your Shopify brand, Brainium builds this end to end.
There are some seasons you watch waiting for the ending.
And there are some seasons where the ending was always written, even if you didn’t know it at the time.
IPL 2026 was the second kind.
When I wrote the last post in this series, I was sitting with three matches left, three teams fighting for one chair, and a prayer that LSG would do something useful with their dead-rubber game against Punjab. You know how that went. LSG did nothing useful. Rajasthan beat Mumbai at Wankhede with something to spare. KKR and PBKS both finished on 13 points and both went home. RR went through on 16 points, deservedly, because they had earned it the hard way. And in doing so, they gave us a playoff stage that this season needed.
Because if we’re being honest, the league stage had given us flashes and stretches but rarely the sustained drama that made us lean forward. The playoff stage was where the season finally found its pulse.
Let me take you through the four playoff matches that closed out IPL 2026.
The highest total in IPL playoff history.
Let that sit for a moment before we go further.
Royal Challengers Bengaluru posted 254 for 5 at Dharamsala and then bowled GT out for 162 to win by 92 runs. Ninety-two runs. In a knockout match. Against the team that had been perhaps the most consistent unit in the league stage.
The 254 was built on one of the more astonishing individual innings of the tournament. Rajat Patidar arrived at number five with the score at 104 for 3 and proceeded to hit 93 not out off 33 balls, the fastest innings of 90 or more in IPL history. He was not out because the innings ran out. He was still standing at the end and no one wanted to get in his way. For a man who has spent his RCB career playing second fiddle to Kohli in the popular narrative, this was his night in the light, entirely his own.
Kohli made 43 off 25. Devdutt Padikkal hit 30 off 19. Venkatesh Iyer opened with 19 off 7 before Rabada got him. Jason Holder then produced a double-wicket over to remove both Kohli and Padikkal in quick succession and gave GT brief hope. But Patidar and Krunal Pandya, who made 43, put on a fifty-run stand that broke GT’s back. Jitesh Sharma finished it with 15 off 5. The final ball of the innings was the final statement of the night.
GT chased. Or tried to. Bhuvneshwar Kumar removed Sai Sudharsan in the fourth over. Two overs earlier, Shubman Gill had gone for 2. Gill, who had been the most complete batter of this season, got 2. Jos Buttler swung at 29 off 11 before Hazlewood cleaned him up. By the powerplay, GT were 51 for 3 and the game was functionally over. The one bright spot in GT’s reply was Rahul Tewatia, who kept swinging for 68 off 43 near the death with nobody for company, which told you both about his character and about how comprehensively the top and middle order had collapsed. Jacob Duffy finished with 3 for 39, Rasikh Salam and Bhuvneshwar took 2 each.
RCB were through to the final with four days to prepare.
There is no one in world cricket right now, in any format, doing what Vaibhav Sooryavanshi is doing.
He is fifteen years old. He has never watched a teammate bat from the other end and thought about experience or approach or what a senior player might do. He walks to the crease and plays.
Against SRH in the Eliminator, he scored 97 off 29 balls.
He was one shot away from breaking Chris Gayle’s record for the fastest IPL century, 30 balls, which has stood since 2013 and has started to feel like one of those records that simply cannot be touched. Sooryavanshi got to 97, tried to uppercut a bouncer over third man, top-edged it, and that was that. Out for 97. In a more dramatic sense, out for an innings that will be replayed every time someone talks about what this tournament can produce from a teenager who has not yet sat his board exams.
Jaiswal made 29 at the top before holing out. Dhruv Jurel then played the innings of a man who understood his job exactly: fifty off 20 balls in the middle overs, the platform kept intact after the fireworks at the top. RR posted 243 for 8.
Jofra Archer then made sure it was enough. He removed Abhishek Sharma, Ishan Kishan, and Travis Head to leave SRH at 52 for 3 inside 3.5 overs and the match effectively decided. Nitish Kumar Reddy and Salil Arora tried, a half-century stand in 18 balls was real defiance but once Jadeja removed Reddy in the 11th over the game was over. SRH finished at 196 all out, Archer finishing with 3 for 58. Sooryavanshi himself took the catch that ended the match, diving forward at short third with the instinct of someone who had decided this was his game from the first over.
SRH went home. A good team that ran out of road when it counted most.
If the Eliminator had been the Sooryavanshi show, the Qualifier 2 was the game where he met his match. And his match was a captain who has spent five IPL seasons proving he is the best batter in the tournament that nobody talks about as the best batter in the tournament.
Shubman Gill made 104 off 53 balls.
RR batted first and posted 214 for 6. Sooryavanshi, again, was magnificent. 96 off 47 balls, his second consecutive playoff half-century, his second consecutive heartbreak just short of three figures. There is something almost poetic, almost cruel, about the pattern. 97 in the Eliminator. 96 here. Both times falling in the nineties when a century felt inevitable. Both times the crowd inhaling sharply as he went. Jadeja held the innings together in the lower middle with 45 not out, Donovan Ferreira added an unbeaten 38 off 11 to push them to 214.
GT, in their reply, completed the highest successful chase in IPL playoff history, 215, surpassing the previous record of 204 set the year before. Gill and Sai Sudharsan put on a century opening stand in 52 balls. The partnership of 167 between them is the highest by any pair in IPL playoff history, breaking a record that had stood since 2011. Sudharsan got out hit wicket, the recurring curse of a batter whose footwork occasionally betrays the quality of his hands. Gill got out after his century. GT won by 7 wickets with 8 balls to spare.
Rajasthan had given everything this tournament had. They had won a last-gasp match against MI to qualify. They had beaten SRH by 47 runs. They had given GT all they could in the Qualifier 2. They went home with dignity. And they gave us the discovery of the season, possibly of several seasons.
Patidar won the toss and sent GT in to bat, and the decision worked from the first over.
Hazlewood had Gill caught by Patidar off his own bowling for 10 in the third over. Bhuvneshwar Kumar removed Sai Sudharsan for 12 the very next over. Two powerplay wickets. The same top-order collapse as the Qualifier 1, GT seemingly had no answer to RCB’s new-ball attack when the conditions and the moment mattered most. Nishant Sindhu and Jos Buttler tried to rebuild but scoring was slow and wickets kept falling. Washington Sundar stayed to the end, hitting an unbeaten 50 off 37 with his team crumbling around him. Rasikh Salam Dar took 3 for 27, Bhuvneshwar and Hazlewood took 2 each. GT finished at 155 for 8.
The chase was never really about whether RCB would win. It was about watching Virat Kohli do it.
Kohli and Venkatesh Iyer, brought in as impact sub, opened together and put on 62 off 27 balls. It was blistering. It was exactly the kind of powerplay statement that removes all doubt from a chase. Rashid Khan then dismissed Patidar and Krunal Pandya in the ninth over within four balls to reduce RCB to 91 for 4, and for a moment you thought: here we go. Tim David came in and steadied. And Kohli, at the other end with the authority of a man who has done this so many times he no longer needs to think about it, accelerated.
He hit his fastest IPL half-century, 25 balls. He finished with 75 not out off 42. He wrapped up the chase with a six over long-on off the first ball of the 19th over. RCB 161 for 5. Won by 5 wickets with 12 balls to spare. Back-to-back IPL titles. The trophy handed to Kohli first, confetti falling over the Narendra Modi Stadium.
Ee salanoo cup namde. This year’s cup is ours too.
I said at the start of this piece that the ending was always written. I don’t mean that as a slight against any of the other teams. I mean it as a recognition that this RCB side had something that none of the others quite matched: the ability to produce their best cricket exactly when it mattered most.
The league stage, as a whole, was a bit of a drag. Not terrible, not without its moments, but a touch processional at times, lacking the wall-to-wall drama that the best IPL seasons produce. Too many matches that started and ended as foregone conclusions. Too few genuine final-over finishes.
But the season gave us things worth keeping.
Sooryavanshi. 776 runs in the tournament, the fifth-highest total by any batter in a single IPL edition. The Orange Cap, the MVP award, and the Emerging Player award, the first time in IPL history one player has won all three in the same season. A strike rate of 237.30. 72 sixes, breaking Chris Gayle’s record of most sixes in a single IPL season. He turned every record in sight into a personal matter and then carried it into the playoffs, where he scored 193 runs across two games and still didn’t win a final-eleven century either time. The India cricket pipeline has not looked this full in years, and he is the most vivid example of it.
Prince Yadav gave LSG something to talk about in a season that otherwise had little. Sixteen wickets, pace regularly above 140 kph, the kind of steep angle and raw speed that makes batters uncomfortable even when they get bat on ball. For a team that finished last, he was almost their entire identity for a stretch of weeks. He will be expensive at the next auction and probably worth it.
Saurabh Dubey got three games for KKR as an injury replacement for Akash Deep. He is a 6 foot 5 left-arm seamer from Wardha who had been waiting years for this chance. Three games. He took the wickets of Rohit Sharma and Suryakumar Yadav in the powerplay at Eden in a must-win match and kept the season alive for one more week. There are cricketers with 50 IPL appearances who have never had a moment that clean. Watch him when KKR give him more.
And Kohli. Who hit his fastest IPL half-century in a final. Who made 75 not out to win the title. Who plays 281 IPL games and is still the last person any attack wants to bowl to in the 14th over. Player of the Match in the final. The most decorated run-scorer in this tournament’s history, with a second consecutive title to his name, at a point in his career when lesser men would have settled for legacy.
IPL 2026 started with RCB retaining the title they won the year before, and it ended with RCB winning it again.
In between, a fifteen-year-old from Bihar made us believe that the next decade of Indian cricket is in safe hands. Several bowlers reminded us that fast bowling is not dead in this country. And the tournament, despite its mid-season drag, produced a playoff stage that justified the entire exercise.
You don’t always get a perfect season. Sometimes you get a season with a perfect ending and a few perfect weeks and one extraordinary child prodigy, and you take it.
This was that season.
See you next year.
Read the previous post in this series here.
I almost skipped it.
It was late. Long day of calls. A few decisions I wasn’t fully happy with. The usual pile of things that quietly move to tomorrow’s list.
Then I saw Daniel Pink’s post about the Odyssey Plan. A Stanford exercise. Twenty minutes. Three versions of your next five years.
I almost scrolled past it.
But something about it wouldn’t let me.
So I did something I almost never do. I put the phone down, found a pen, found paper, and sat with it.
The first question was simple. What does your life look like in five years if nothing changes?
Same job. Same routine. Same direction.
I sat with that. Then I wrote it down honestly.
Brainium at year 18. Same clients in the UK, US, Australia. Same team of around 150 people. Products like LeadFlow and Diamond Picks still finding their footing. Revenue steady. No dramatic leap forward. No dramatic fall either.
Just steadiness.
Then the uncomfortable part. When I wrote “what does Monday morning look like?” the answer was almost identical to today. Early calls across time zones. Internal reviews. Some writing if I’m lucky. A lot of problem-solving that never fully ends.
That picture sat heavy.
Not because it’s a bad life. It isn’t. But because steadiness is another word for stagnation when you’re capable of more.
The second question asked me to imagine the version of my life where I actually took the leap.
This one came faster.
Path 2 Sourav makes a serious pivot. Products over services. A few energetic new hires who bring fire into a team that has gotten comfortable. Real risk put behind the ideas that have been sitting in planning documents for too long.
By 2031 in this version, things look different. Brainium’s products have found markets. The business has momentum that Path 1 never could.
Monday morning feels different too. Busy. A little chaotic. The kind of chaos that comes from growth.
I liked that picture.
But I also noticed something as I wrote it. The thing stopping Path 1 from becoming Path 2 wasn’t opportunity. It wasn’t resources. It was appetite for risk. The willingness to move faster than feels comfortable. To stop waiting for the right moment and accept that the right moment is probably now.
No money pressure. No one else’s opinion. No constraints. What do you actually build?
I expected this one to be dramatic. A fantasy I’d never really pursue.
Instead, what came out was quiet.
Reading. A lot of it. The kind of deep, unhurried reading I used to do before the business got big enough to consume every hour.
Writing. Books, specifically. I’ve written two already, The Diamond Way and The 12th Man. In Path 3, there are more.
Stock market investing. Not as a hobby but as a serious craft. The kind that requires patience and time to think rather than react.
Sales talks. Speaking at events, mentoring founders, sharing what I’ve learned in 13 years of running a bootstrapped business.
Slower days. More intentional. Less firefighting.
I looked at what I had written and felt something unexpected.
Relief.
Not because it was a fantasy. But because none of it was actually that far away.
Path 3 and Path 2 are not opposites.
The writing is already happening. You’re reading this right now. The books exist. Diamond Picks, our AI stock screening product, is already deep in the territory of investing and markets.
The “chilled out” version of my life and the “serious growth” version aren’t pulling in different directions. They’re pointing at the same place. The gap between them isn’t about what I want. It’s about the speed and courage with which I move toward it.
Path 1 is what happens when I let the days run me.
Path 2 is what happens when I decide to run the days.
Path 3 is proof that I already know what matters.
Twenty minutes. Pen and paper.
Three paths. Five years. Specific enough that you can see Monday morning in each one.
Then notice which path you avoid looking at too long. That’s probably the one with the real answer.
For me, it was Path 1.
I’m not going back there.
There is a moment in every major technological shift when the rules don’t just bend, they break entirely. We had one such moment in the mid-2010s when Google moved from ten blue links to featured snippets and local packs. Businesses that understood the new terrain early captured audience, awareness, and revenue. Those who kept doing what had worked before spent years wondering why their traffic graphs were trending the wrong way.
That moment is happening again. And this time, the terrain isn’t just shifting, it is being replaced.
The research comes from Tim Soulo, CMO at Ahrefs, who published findings from over a billion data points across fourteen studies on AI search behaviour. Soulo built Ahrefs from employee number 16 to a $100M+ ARR bootstrapped company and when he publishes data on how AI discovers content, it is worth reading slowly. What these findings reveal is not a set of tactical tweaks to your existing content strategy. They reveal that the strategy itself needs to be rebuilt from different foundations.
Here is where most brands are making their first mistake. They are treating AI search, ChatGPT, Perplexity, Google’s AI Overviews as a slightly smarter version of the old Google. Optimize for keywords, build backlinks, get ranked. Done.
The data says otherwise.
Nearly 28% of the pages that ChatGPT cites most frequently have zero Google organic visibility. These are pages that a traditional SEO audit would declare invisible, irrelevant, non-existent from a traffic standpoint. Yet AI is referencing them, drawing from them, citing them in front of millions of users every single day.
This means there is an entirely separate discovery layer operating in parallel to everything you thought you understood about digital visibility. Your SEO rank is no longer your citation rank. They are different games, running on different fields, scored differently.
This is the data point that should hit founders and brand marketers hardest. Of ChatGPT’s top 1,000 cited sources, 67% come from places that no marketing budget can touch: Wikipedia (nearly 30%), homepages (almost 24%), and app stores. The remaining 33%: educational pages, reviews, news, blog posts is where you actually have a seat at the table.
One third. That is your playing field.
Which means that every piece of content you produce for AI visibility has to work harder, be more precise, and earn citation in a highly competitive slice of an already competitive landscape. The question you need to ask for every blog post, every product explainer, every case study you publish is no longer “will Google index this?” It is “would an AI chatbot find this authoritative enough to surface to a user asking a relevant question?”
That is a higher bar. And most content being produced today isn’t clearing it.
If there is one tactical signal in all of Ahrefs’ research that every content team should act on immediately, it is this: “Best X” listicles make up 43.8% of all page types cited by ChatGPT. Nearly half. The format that content marketers have been writing since 2009, the one that senior strategists have been quietly dismissing as low-effort, the one that your editorial team may have deprioritized in favour of long-form thought leadership, that format is dominating AI citations.
This does not mean you start pumping out lazy listicles. It means you write deeply researched, genuinely useful “Best X for Y” pieces and you write them in categories directly adjacent to what you sell. If you are a Shopify brand selling supplements, “Best Magnesium Supplements for Sleep in 2025” is not beneath you. It is the door through which AI walks a potential customer into your world.
Here is the nuance that most AI search guides will skip: ChatGPT only cites around 50% of the URLs it actually retrieves. It fetches dozens of pages per query, reads them, uses them as background context and then only attributes half of them in the final response.
What this means is that being findable by AI and being visible to the user are two completely different achievements. You can be doing everything right at the retrieval level and still be invisible to the person reading the answer. The citation gap is real, and it is being driven by authoritativeness, recency, and specificity of the content that gets the attribution.
The implication is direct: produce content that is specific enough to be definitively useful, not just broadly informative. AI cites sources that give it something clean and clear to attribute. Vague, wide-angle content gets consumed in the background and dropped before the answer is written.
Of all the factors Ahrefs tested for AI brand visibility, backlinks, domain rating, page count, organic traffic, all of it, YouTube mentions had the single highest correlation at 0.737. Not a minor edge. A significant lead. And this correlation held across both Google-owned products and OpenAI products.
Think about what that means structurally. AI systems are not just reading text on the web. They are building an understanding of brand authority from a much richer set of signals, and video presence is near the top of that signal stack. A brand that has built real YouTube presence, genuine tutorial content, product walkthroughs, founder conversations, is being rewarded in AI visibility in ways that a brand with better backlinks but no video presence is not.
If you have been putting off building a YouTube presence because it is hard, time-consuming, and takes months to gain traction: this is the datapoint that should move that task up your priority list.
Twelve months ago, AI Overviews reduced clicks to the top Google result by about 35%. Now that number is 58%. In less than a year, the click-loss rate went up by more than twenty percentage points. The trajectory is not levelling off.
For transactional queries, someone searching to buy something, this is less of an immediate concern. AI Overviews appear on shopping queries only about 3% of the time. They are almost entirely focused on informational intent searches. Which means the content that is being displaced is the content that was already working hardest to build awareness and educate buyers at the top of your funnel.
This is not a reason to panic. It is a reason to redirect. The audience is not disappearing. It is being intercepted earlier in the journey. The brands that win in this environment are the ones who show up inside that interception, inside the AI answer itself rather than waiting for the user to scroll past it.
One last thing worth sitting with. Ahrefs found that AI Overviews change their content every 2.15 days on average, with 70% of the specific words and sources shuffling between observations. That sounds chaotic until you see the other number: the semantic similarity between those constantly changing answers stays at 0.95.
The words change. The sources swap in and out. The specific entities rotate. But the meaning, the core of what the AI is saying, stays almost perfectly stable.
What this tells you is that AI has already formed a settled view of most topics in your category. The sources feeding that view are not fixed, which means the door is open to new entrants. But the answer being produced is already shaped. To influence it, you have to produce content that is semantically consistent with the direction that answer is already moving in. You are not trying to disrupt the AI’s understanding. You are trying to become the source it trusts to express what it already believes.
That is a very different content brief than the one most teams are working from.
The map has changed. Here is how to read it.
Start by auditing what currently ranks for the “Best X” queries most relevant to your category not to copy them, but to understand the format and specificity that is earning citation. Build a content calendar that produces at least one deeply researched comparative piece per month in your core area.
Invest in YouTube with the seriousness you give to written content. Not production value for its own sake, but consistency and genuine usefulness. The AI visibility dividend from video presence is measurable and it compounds.
Stop measuring your AI strategy by Google rankings alone. Use tools that track AI citation and brand mention across ChatGPT, Perplexity, and AI Overviews. These are not the same metric and treating them as the same will leave you flying blind in the channel that is growing fastest.
And produce with specificity. The vague, wide-angle content that filled content calendars for a decade is the content most likely to be consumed silently by AI and never attributed. Every piece you produce from here forward should be asking: specific enough to cite, authoritative enough to trust, current enough to matter.
The brands who understood that search was changing in 2013 are still reaping the rewards. The window to be that brand in the AI search era is open right now.
It will not be open forever.