Morning guys, happy Saturday.

I’ve been thinking about how often we blame people for decisions that were mostly shaped before they got to make them.

A user chooses the wrong mode, but we gave them six modes. A patient misses an appointment, but the train was late and missing work would cost them their job. A founder keeps chasing the wrong things, but their environment is feeding them the same information every day. An investor makes a bad call, then explains it as an isolated mistake even though they have made some version of the same mistake three times.

We tend to focus on the visible decision because that is the part we can see. I think the more interesting stuff often happens one layer earlier, in the interface, environment, incentives and habits that made one choice much easier than another.

There were five good examples of that this week.

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1. Anthropic finally stopped making users understand Anthropic

Anthropic merged Claude Chat and Cowork into one interface last week. Cowork is not actually gone, despite some of the headlines. The capability still exists alongside Artifacts, Design and the new Docs and Slides features. What disappeared is the need to decide, before doing any work, which Anthropic product your work belongs inside. Claude can increasingly make that choice itself.

This sounds like a fairly boring UX change, but I think it points toward where AI software is going. Traditional software makes us translate what we want into the structure of the product. If I want to make a presentation, I first choose Slides. If I want to analyze something, maybe I open a spreadsheet. If I want to design something, I open Figma. We have spent years learning which tool corresponds to which type of intent because the software itself was not smart enough to make that connection.

Once the software understands the request, though, the whole thing starts to look backwards. Why should I know whether my request needs Cowork, Docs, Design or some internal Anthropic capability? I just know what I am trying to get done. Asking me to select the implementation is a little like asking someone ordering dinner which pan the chef should use.

This problem shows up everywhere because products tend to inherit the structure of the company that built them. You have a team responsible for analytics, another for automation and another for reporting, and eventually the customer gets three tabs called Analytics, Automation and Reporting. Internally, that makes perfect sense. Externally, you have handed the customer a small version of your org chart and asked them to learn it.

AI gives companies a chance to reverse that. Products can become much more complicated underneath while becoming simpler on top. I suspect this will be one of the better tests for whether something is genuinely AI-native. If every new capability produces another mode, tab and selector, you might just be putting AI inside the old software model.

The better product may be the one where the user says what they want and never has to care which product team built the answer.

2. Market research is getting cheaper. Real depth is not.

Rex Woodbury tells a story about asking Priscilla Chan how she thinks about depth versus breadth of impact. Chan talked about something she noticed while working at UCSF Medical Center: one of the things determining whether some patients actually received care was whether BART was running on time. If the train was delayed, the patient might skip the appointment rather than risk arriving late to work.

I like this example because I cannot imagine it appearing near the top of a normal healthcare market map. If you approached the problem from the outside, you would probably start with insurance, physician availability, wait times, hospital capacity and cost. All reasonable things. You could ask Claude to produce fifty pages on barriers to healthcare access and end up with a very intelligent document that never makes you care about one delayed train.

That is the difference between knowing more about a market and being inside it long enough to know what actually matters.

This matters more now because the first kind of knowledge is becoming ridiculously cheap. Anyone can generate a competitor matrix, summarize reviews, map an industry and produce a list of underserved niches before breakfast. That is useful, but it also means thousands of people can start with essentially the same map. The advantage moves toward whatever does not make it into the map.

Usually that is some small, annoying detail people inside the industry have stopped noticing. A spreadsheet everyone emails around because the official software is useless. A manual approval step everybody assumes has to exist. The thing customers complain about versus the thing that actually makes them leave. A train schedule that determines whether someone sees their doctor.

People talk about founder-market fit as though it means being passionate about an industry. I think the practical version is much more valuable: you have spent enough time there to rank the facts differently from everyone looking in from outside.

AI can help us understand what people already know. Depth is how you notice the thing nobody thought to write down.

3. The useful part of the 80/20 rule is choosing a side

Greg Isenberg has an 80/20 list for founders that keeps getting passed around: retention over acquisition, existing customers over new leads, listening over pitching, word of mouth over paid advertising, customer experience over branding and so on.

I would not take the numbers literally. There is no universal law saying that a company should spend exactly 80% of its effort on retention and 20% on acquisition, and this is not really the Pareto principle in any rigorous sense. But I think arguing over the percentages misses why lists like this are useful.

They force you to stop saying that everything matters.

Most business advice is annoyingly hard to disagree with. Product matters. Distribution matters. Customers matter. Brand matters. Growth matters. Quality matters. Everyone nods, then keeps doing everything they were already doing because nothing in that list forces a tradeoff.

The moment you say retention matters more than acquisition right now, something has to lose. If listening matters more than pitching, maybe you do not spend tomorrow writing another sales deck. If existing customers matter more than new leads, perhaps the founder joins three customer calls instead of opening another outbound channel. Strategy gets more useful once two good things are placed against each other and one of them wins.

There is also something interesting about which activities usually end up on the weaker side of these lists. They tend to be the visible ones. Getting a new logo feels like progress. Launching a campaign creates screenshots. Redesigning the website gives everybody something new to look at. Keeping an existing customer happy is much less theatrical. Nobody posts a celebratory Slack message because a customer quietly renewed for the fourth year.

Businesses naturally drift toward work that looks like work. I think a good 80/20 list is mostly a device for catching yourself doing that.

So I would not ask whether Greg’s percentages are correct. I would ask which pair makes you slightly uncomfortable because you know you have been spending most of your time on the easier side.

That is probably the useful one.

4. Sometimes you are not stuck. Your context is.

Zan Tafakari writes about something I keep noticing in my own work: we treat thoughts as though they come entirely from inside us, even though the environment around us is constantly deciding what gets into our heads in the first place.

This sounds obvious until you think about how knowledge work actually looks now. Same laptop, same desk, same Slack channels, same browser, same feeds, five days a week. Then you get stuck on a problem and the response is usually to stare at the same screen harder. Maybe open another tab. Maybe ask another model. Maybe make another coffee and sit back down in exactly the place where you have already failed to solve it for three hours.

I do this all the time, and I think there is a bad assumption underneath it. We assume the answer is already somewhere inside our heads and the only thing missing is more computation.

Sometimes the missing thing is new input.

Go sit with the person using the product. Take the call while walking. Visit the warehouse. Work somewhere else for an afternoon. Talk to someone who has nothing to do with your industry. Read something that has nothing to do with the problem. You are not guaranteed an epiphany, obviously, but at least you have changed the information entering the system.

There is a reason good field research can beat weeks of dashboard analysis. A dashboard shows you the variables someone already decided were worth measuring. Walking around the real thing gives you a chance to notice variables nobody put in the dashboard.

This is one area where AI might actually make us worse if we are not careful. Models make it possible to do more and more intellectual work without leaving the same interface. Research, brainstorming, writing, coding, analysis and planning can all happen in one chat window. That is incredibly useful, but it can also make the information environment narrower while making it feel broader.

You can have twenty conversations without encountering anything genuinely unexpected.

Sometimes the smartest thing you can do with a problem is stop giving your brain the same world.

5. You probably have recurring bugs too

Investor Chris Paik has a good essay about trying to outsmart yourself. His argument is that investing has an obvious outward-facing component, studying companies, markets and data, but also an inward-facing one that gets much less attention: understanding the person making the judgment.

That sounds soft compared with building a model or analyzing a market, which is probably why people avoid it. Markets can be researched from a comfortable distance. Looking at your own bad decisions tends to involve discovering that the problem was greed, ego, impatience, fear of missing out or some other explanation that is considerably less flattering than “the thesis changed.”

What I like about Paik’s approach is that he makes self-knowledge operational. He has noticed, for example, that doing his own work on an investment matters because his willingness to do that work is itself information. If he cannot get interested enough to properly investigate something, he should probably pay attention to that instead of finding a clever reason to invest anyway.

Software people would recognize the idea immediately if we changed the nouns. A program produces a bad result. You inspect the logs, reproduce the issue and eventually discover that what looked like five unrelated failures all came from the same bug.

People are very good at treating the equivalent failures in their own lives as unrelated events.

You hired too quickly because that candidate was unusually convincing. You invested too early because this market was different. You kept the product alive too long because customers really did love it. You abandoned another one too early because the market was obviously too small. Each story sounds reasonable on its own. Put ten years of them next to one another and you might discover the same person making the same decision for the same emotional reason.

This is why I like decision journals, even though the phrase makes them sound more serious than they need to be. Before an important decision, write down what you believe, what would prove you wrong and what is actually driving the decision. Then come back later and compare it with what happened.

The useful part is not predicting everything correctly. It is removing your ability to quietly rewrite history.

Maybe you repeatedly mistake urgency for importance. Maybe charismatic people make you abandon your skepticism. Maybe you keep projects alive because stopping feels like admitting you wasted time. Maybe every time somebody else starts succeeding in a category, you convince yourself you need to enter it too.

Those patterns are much more valuable to know than the details of any one mistake because they travel with you into the next decision.

We would never ship an important piece of software, watch it fail repeatedly and refuse to inspect the underlying code. Somehow we are much more comfortable doing that with ourselves.

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Morning guys, happy Saturday.

I’ve started noticing that a lot of work now arrives looking finished before I’m convinced anyone has really thought about it. The deck is clean, the memo has a neat structure, the launch post has exactly the right cadence, and the product somehow already has six features. None of those things are bad, obviously. The weird part is that they used to contain a small amount of evidence that somebody had spent time on the problem.

AI is removing that signal. You can now make an ordinary idea look like a polished idea almost instantly, which means polish tells us less than it used to. I increasingly find myself trusting the slightly rough memo with one interesting observation over the immaculate one that could have been written about any company in the world.

The same thing is happening to products. Building is cheaper, experiments are cheaper, new features are cheaper and launching something is cheaper. When execution gets easier, the interesting decisions move earlier: what should we build, what actually belongs together, what deserves a launch, which experiment is worth taking seriously, and which perfectly buildable thing should simply be left alone?

A few things I’ve been reading lately all seem to circle that same problem.

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1. Bundling only works when the products make each other better

I went back to an old conversation with Hiten Shah on Relay because his explanation of bundling is more useful than most of the current conversation around “compound startups.”

His basic point is that software companies keep rediscovering bundling under new names. Parker Conrad calls it the compound startup. HubSpot figured out a version of it years ago. Freshworks, Zoho and plenty of others have built broad suites too. But simply putting a lot of products under one logo is not what makes the model work.

The important thing is the core underneath them.

Hiten describes Salesforce as owning the customer record and HubSpot as owning the contact record. Rippling is trying to do something similar with employee data. Once one system becomes the place where an important object is created, updated and trusted, every adjacent product can use that same information without rebuilding the context from scratch.

That gives us a much better test for whether something is actually a bundle. Imagine removing one product from the suite. Does another product become worse because it loses data, workflow or context that only the combined system had? If yes, there is something compounding. If nothing changes and the main benefit was one invoice and a 20% discount, you have a Costco multipack.

This distinction matters because software companies naturally want to sell adjacent products. You already have the customer, so why not add payroll, expenses, recruiting, analytics or whatever sits nearby? But adjacency on a slide does not create product advantage. The new product needs to become better because the customer already uses the old one. Otherwise a great point solution can still arrive and beat you.

The system of record is what makes that possible because it gives every new product an unfair starting point. If Rippling already knows who every employee is, where they work, what role they have, what device they use and who manages them, another product built on top of that dataset does not begin at zero. It inherits the company.

I also like Hiten’s observation that these strategies usually sound much cleaner in hindsight than they did at the beginning. Early HubSpot was an inbound marketing company. I doubt somebody in 2006 had a perfectly drawn map showing how it would eventually compete across CRM, sales, marketing, service and commerce. Rippling probably did not begin with every detail of its current thesis either.

That might actually be the important bit. You probably cannot sit down on day one and declare that you will become the system of record for an entire category. Nobody awards you that status. You discover that the narrow product you built happens to sit upstream of something much larger, then you methodically earn the right to build outward.

Which is probably why Hiten says he would not talk much about it if he discovered the same thing at Nira. Not because somebody will copy the idea tomorrow, but because the idea is almost meaningless before the product proves it. Big strategic narratives sound intelligent after the compounding starts. Before that, they mostly sound delusional.

The clever part is noticing the core early enough to keep building around it.

2. Duolingo didn’t hijack Apple’s launch in nine minutes

When Apple unveiled its new iPhone Duo, Duolingo immediately started having fun with the name. Its account posted variations of “it’s called the iPhone WHAT???” and eventually landed on the obvious joke: an expensive iPhone Duo versus Duo on your iPhone for free. Samsung joined the pile-on too, and the whole exchange ended up getting covered well outside the original posts.

It is easy to file this under “brands should be fast on social.” I think that misses most of what happened. Plenty of companies could have posted within ten minutes and produced something painfully unfunny.

Duolingo’s reaction was fast because the company had already done years of work before Apple ever announced the phone. People know what the owl sounds like. The social team knows what it can joke about. The company has a fairly clear sense of how strange it is allowed to be. Whoever wrote the post was not inventing a personality during the keynote. They were operating one that already existed.

That is what a good brand voice actually buys you. It is not just consistency across website copy. It reduces decision time. When something happens in culture, the team can ask “What would we say?” instead of first asking “Who are we?”

I think companies underestimate the operational value of that. A weak brand voice creates a meeting every time you need to speak. Someone writes the first version, marketing makes it safer, legal removes the joke, leadership asks whether it aligns with positioning, somebody edits it again and two days later the internet has moved on.

A strong identity acts like a decision framework. It tells the team which jokes belong, which opinions sound natural and where the line is. That means you can move quickly without making every post a miniature governance exercise.

So the lesson I’d steal from Duolingo is not “roast Apple.” It is to decide what your company sounds like before you need to say something quickly.

Relevance looks spontaneous from the outside. Usually it was rehearsed for years.

3. Launch day is disappearing because release day already did

Kyle Poyar recently broke down how Notion, Rippling and Profound approach product launches, and one point stuck with me: the release and the launch no longer need to be the same event.

That sounds small, but it changes quite a lot. Software teams can now ship continuously. A feature can reach users quietly, collect feedback, improve for a few weeks and only then get a proper launch once the company understands what story is actually worth telling. You no longer need to attach all the emotional weight of “launch day” to the exact moment the code becomes available.

This matters because shipping has become much faster than customer attention. Internally, your team can produce meaningful changes every week. Externally, your customer did not suddenly gain another ten hours a week to understand all of them. Five launches in a month might feel like momentum to the company and noise to everyone else.

The job of a launch therefore changes. It is no longer enough to announce that you shipped something. Customers assume software companies ship things. A launch has to explain why this particular change deserves to enter somebody’s mental model of the product.

That is why I like the examples of founders and builders talking directly about what they made. A PM saying, “We watched customers do this ridiculous five-step process for six months, so I built this,” contains information. It tells me where the feature came from, why it exists and why somebody cared enough to build it.

Compare that with the usual “We are excited to announce our next-generation workflow experience.” The second sentence is cleaner. It is also nearly empty.

There is a connection back to the writing problem here. When beautifully polished output becomes cheap, polish loses some of its signaling value. That does not mean every launch should look amateur. It means evidence becomes more valuable. Show me the customer reaction. Show me the broken workflow. Let the person who built it explain what annoyed them enough to fix it.

The companies that are genuinely shipping useful things should probably become more comfortable showing the fingerprints of the people making them.

Maybe the future launch is less of a giant event and more of a running public record that your company keeps noticing real problems and fixing them.

That is harder to fake every two weeks.

4. AI made the $10,000 experiment cheaper. It did not make being wrong cheaper.

A guest essay on Annie Duke’s newsletter makes an interesting argument: big companies say they want more experimentation, but their internal incentives often make small experiments personally riskier than large projects.

A $10 million initiative usually comes with institutional cover. There is a committee, a strategy deck, a budget process, consultants, leadership approval and enough signatures that if the whole thing fails, everybody can reasonably say “we decided.” A $10,000 experiment might have one person’s name beside it.

That creates a strange situation where the smaller financial risk can carry the larger career risk.

The essay gives a good example of the mismatch AI is creating. A team used modern tools to make an enormous change to a legacy codebase over a weekend, work that previously would have taken months. The old security process then required a manual review that was expected to take even longer than the original development would have taken.

AI removed the production bottleneck and immediately exposed the permission bottleneck behind it.

We are going to see this everywhere. A prototype can now be built before the meeting where people decide whether someone should be allowed to build a prototype. A market test might cost less than the hours of senior management discussing whether the market test is worth running. At some point, deliberation becomes more expensive than experimentation.

The obvious response is to tell people to be more courageous. I think that is mostly useless. People respond to incentives, and if success is shared while failed experiments get attached to individual names, rational employees will avoid bets that can make them look stupid.

A better system evaluates the quality of the decision separately from the outcome. Was the hypothesis sensible? Was the downside bounded? Was the experiment cheap? Did we decide beforehand what result would make us stop? Did we learn something useful when it failed?

I especially like pre-committing to the kill condition. If nobody uses it by this date, stop. If conversion stays below this number, stop. If three customers do not ask for it, stop. Then when the result arrives, you are not negotiating with a team that has spent six weeks becoming emotionally attached to its own work.

Cheap experimentation only matters if stopping is cheap too.

Otherwise AI just helps us produce zombie projects faster.

5. Software’s biggest problem may be that almost nothing forces us to stop

There is a great essay called “Software Drives People Insane” that makes a fairly simple observation: software combines speed, abstraction, complexity and an almost unlimited ability to change your mind. Put those together for long enough and perfectly normal people start behaving as though everything is permanently one refactor away from being fixed.

The physical world gives you friction for free. If you decide halfway through building a house that the kitchen belongs on the other side, you can see the pipes in the wall, the timber already cut and the labor required to undo the decision. Changing your mind has a visible cost.

Software hides most of that debris.

A request might sound like one little button while touching six services. A rewrite creates no pile of demolished concrete for management to walk past. The cost appears later as regressions, context switching, abandoned assumptions and engineers spending another week relearning a part of the system they had finally started to understand.

Because the cost is hidden, “we could change this” slowly turns into “we should change this.” Growth slows, so redo onboarding. Customers are confused, so redesign. Development feels slow, so replace the process. Then replace the tools supporting the process. Then reorganize the team using the tools.

Eventually the company itself starts getting treated like software: permanently mutable and permanently one reorganization away from finally working.

AI makes this temptation worse because implementation is becoming less of a natural constraint. If a founder can ask an agent for another dashboard, another workflow, another pricing experiment and another microsite before lunch, then engineering capacity stops protecting the product from the founder’s imagination.

The thing standing between the company and enormous complexity becomes judgment.

Or, maybe more accurately, taste.

People use “taste” as though it only means knowing what looks good. I think one of its more valuable forms is knowing what does not need to exist. Which feature should not be built. Which customer request should not be followed. Which process is good enough. Which slightly ugly thing should remain slightly ugly because fixing it would make three other things worse.

Leaving something alone sounds passive, which is why companies are bad at celebrating it. Nobody gives an award to the product manager who spent a quarter deciding not to redesign a working settings page. But restraint can preserve years of accumulated user understanding and engineering stability.

There is an old startup instinct that says speed wins. Often it does. But speed helps when you know roughly which direction deserves acceleration. Moving faster between a hundred unnecessary changes does not create clarity. It just shortens the interval between mistakes.

A useful question for almost any established product might be: what would actually happen if we did nothing to this part for six months?

Customers might have time to learn it. Engineers might finally understand it. The team might discover whether the original idea was actually good before replacing it with the next one.

In an industry built around making change cheap, restraint may become one of the expensive skills.

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Morning guys, happy Saturday.

There is a slightly annoying thing about finding product-market fit: eventually the thing that made you successful can start limiting what you are allowed to become.

You win because customers associate you with one problem. Then that association gets stronger. Sales gets easier, referrals get better and your brand becomes shorthand for the category. Great. Except now the market has a very specific idea of what you are, even if the product underneath has quietly become capable of much more.

The same thing happens with distribution. A company starts by using someone else’s channel, eventually owns enough demand that other companies want access to it, then discovers the channel might be more valuable than the thing it originally distributed. Netflix spent years being the content people subscribed to. Now it is at least considering becoming the place where people subscribe to everyone else.

I think the common thread is that the most valuable layer of a business keeps moving. Sometimes it starts in the product, moves into the data, then into distribution, brand or infrastructure. The difficult bit is noticing when your old advantage has become a constraint.

1. Netflix might be more valuable as a shelf than another thing on the shelf

Netflix has reportedly discussed bringing services such as Peacock and Fox One inside the Netflix app. There is no announced deal yet, and even the structure is unsettled. Netflix could potentially sell subscriptions to those services, surface their programming inside its interface, or do some variation of both. The Verge’s reporting on Netflix opening its app to rival streamers

It would not come completely out of nowhere. Netflix has already started experimenting with this idea in France, where it integrated TF1’s live channels and on-demand programming directly into Netflix. Co-CEO Greg Peters later described the early results as promising and said Netflix would consider similar arrangements when they benefit Netflix members and the partner.

What I find interesting is how different this ambition is from simply buying another catalogue. Another show or studio is valuable for as long as people want that content. Owning the place where people decide what to watch lets somebody else’s content create value for you too.

Ben Thompson has been thinking about this through the aggregation lens and recently argued that Netflix selling other streaming services could make strategic sense, even if it represents a shift from the company’s original ambitions. Ben Thompson on Netflix becoming a streaming aggregator

This is the strange end state of great distribution. Early on, you spend money desperately trying to acquire customers. Later, suppliers start paying to get in front of the customers you already acquired.

Amazon did this in commerce. Apple does it through the App Store. Google did it with search. The initial product attracts demand, but eventually the demand itself becomes something the company can sell.

Netflix historically understood this from the opposite side. It resisted being absorbed into other streaming bundles because it wanted the relationship with its own subscribers. The slightly funny outcome would be Netflix eventually becoming the aggregator it avoided becoming inventory for.

There is probably a useful question here even if you are nowhere near Netflix scale: if you have become unusually good at reaching one type of customer, is your own product still the only thing worth distributing through that relationship?

Sometimes the second business hiding inside the first one is distribution.

2. Going narrow works so well that it can become difficult to escape

Sara Du wrote a great retrospective on what happened at Alloy Automation after the company spent nearly three years becoming known for commerce infrastructure. Alloy’s original ambition had actually been much broader: integration infrastructure in general. Commerce became the wedge because there was obvious demand, particularly during the pandemic, and a tiny team could move quickly around a much more specific market. Sara Du on Alloy’s vertical GTM strategy

That part of verticalizing makes intuitive sense. “Integration infrastructure for everyone” is difficult to sell when you are tiny. “Integration infrastructure for commerce companies” tells marketing where to aim, sales who to call, partnerships who matters and product which problems deserve attention first.

Sara calls it something close to finding micro product-market fit. I like that framing because the point of the vertical is not necessarily to reduce the eventual ambition. It is to reduce the number of variables you need to solve at once.

Her framework for choosing the first vertical is also refreshingly practical. Look for pull, meaning a type of customer is already finding you. Look at timing, meaning the segment can become meaningful revenue quickly. Then look at your Rolodex, because knowing the important people and partners inside a small industry can be a much bigger advantage than it looks from outside.

The harder problem showed up later. Alloy’s customers increasingly wanted ERP, accounting and finance integrations. Companies outside commerce started approaching the team with problems Alloy could already solve. Sales found itself repeatedly explaining that, despite all the commerce messaging, the underlying product was much broader.

The wedge had worked so well that the brand became narrower than the product.

I think this is an underrated startup problem. Positioning does not just explain what you do. If it works, it changes what people notice you for. That is incredibly useful when you need focus, but every strong association also excludes other associations. Become “the commerce integration company” for long enough and even people who know you may stop thinking of you when a non-commerce integration problem appears.

So when has a vertical done its job? Sara’s experience suggests the answer probably is not “once we own X% of the market.” The better signal is when reality keeps pushing past the positioning. Customers ask for adjacent workflows. Referrals arrive from other industries. The product performs well somewhere the marketing says it should not. Sales starts spending time correcting the market’s idea of the company.

At that point, focus has started becoming friction.

The broader lesson is that early positioning should probably be allowed to be strategically incomplete. You are not writing your company’s biography. You are giving the market one simple reason to care about you now.

The mistake is forgetting that the sentence can change after it has done its job.

3. PostHog is building the company that might kill PostHog

PostHog recently asked a fairly uncomfortable question: if an AI-native team rebuilt our flagship product today, what would they build and what would they skip?

Instead of keeping that as an offsite exercise, they actually funded the answer. The work eventually became PostHog Desktop, which the company describes as an internal attempt to disrupt itself and find product-market fit again rather than assuming the current version lasts forever. PostHog’s case study on disrupting itself

I like this because companies usually talk about disruption in a very abstract way. Everybody agrees that AI might change the category. There are strategy decks about agents, model improvements and new interfaces. Then almost every engineer goes back to improving the product that already pays the bills.

That behavior is rational. Existing customers produce loud, specific feedback. Revenue tells you which requests look valuable. Your roadmap, team structure, analytics and sales process are all built around making the existing product a little better.

Unfortunately, that machinery can also be extremely good at making sure weird new ideas never survive.

PostHog’s premise is that product-market fit is not something a company permanently earns. A small AI-native team can arrive with none of your architectural baggage and ask which pieces of your product still need to exist at all.

One detail in the story is especially interesting. PostHog says a large customer had already combined its MCP capabilities with Claude and other tools to create something that looked suspiciously like a prototype of PostHog’s future. The customer was effectively showing them a different interface to the same underlying problem.

That feels like a useful signal to watch now. If customers start stitching together your API, an LLM and other tools to avoid interacting with your product in the way you intended, they may be designing your replacement for you.

The uncomfortable bit is that the replacement does not have to beat the old product on every feature. It just has to make the old way of doing the job feel unnecessary.

Product-market fit used to sound like a destination.

It increasingly feels like rent.

4. A laptop lid is interesting because nobody had priced the attention yet

There is now a marketplace called BrandMyLaptop where people sell advertising spots on the backs of their laptops. A creator lists areas of the lid, brands buy the spots, and the stickers stay there while that laptop travels through meetings, conferences, cafés, videos and whatever other places its owner works. Browse the BrandMyLaptop marketplace

The marketplace currently shows more than $13,000 worth of ad space sold, with individual placements ranging from tiny inexpensive stickers to much larger sponsorship packages. Some listings keep the sponsor on the laptop for as long as 24 months.

I have no idea whether laptop lids become a meaningful advertising category. Probably not. That is not really why I like the idea.

What is interesting is that it starts from attention rather than an existing ad format.

Most advertising begins with a container somebody already standardized. There is a feed, so we sell feed ads. There is a search result, so we sell sponsored results. There is a newsletter, so we sell a slot in the newsletter. Eventually everyone competes for the same inventory and the price climbs.

A laptop lid starts with a different question: where is my exact customer already looking that nobody has bothered to price?

A consultant sitting opposite executives all week may have a surprisingly useful rectangle of unused space on the back of a MacBook. A developer carrying a laptop through conferences has a different audience. Somebody whose laptop appears in every YouTube video has another.

The targeting is almost accidental. You do not need an ad platform to infer that someone staring at a consultant’s laptop during a client meeting probably overlaps with whatever industry that consultant works in.

That makes me think there are probably thousands of tiny pieces of unpriced attention hiding in ordinary workflows. A loading screen inside a niche piece of software. A shipping box that always gets opened by one profession. An internal report that gets forwarded around an industry. Something sitting on the table in every sales meeting.

None has enough scale to interest a giant ad network.

That might be exactly why some of them remain interesting.

The internet trained us to think distribution means finding the biggest possible pool of attention and targeting it more precisely.

Sometimes the better move is finding a tiny pool that nobody has priced yet.

5. Drone delivery gets interesting when the drone stops being the story

Uber and Zipline announced a partnership in August with a pretty aggressive target: one million drone deliveries per day by the end of 2029. Zipline deliveries will appear inside Uber Eats, starting in markets where Zipline already operates, while Uber is also making a strategic investment in the company. Uber and Zipline’s partnership announcement

Zipline says its system can deliver items in roughly five to ten minutes and that the initial Uber partnership will build from existing markets before expanding further across the US. Zipline’s explanation of the Uber rollout

DoorDash is approaching the same shift differently. In July, DoorDash Labs received FAA Part 135 air-carrier certification and launched DoorDash Air, allowing the company to commercially operate an in-house drone delivery service inside its existing marketplace. DoorDash’s announcement of DoorDash Air

The obvious story here is that drones are getting better. I think the more interesting story is that drones are becoming boring enough to turn into infrastructure.

Uber does not need to become the world’s best drone manufacturer. It already owns a giant demand surface where somebody taps a button and asks an object to appear. Zipline can become one method for moving that object. Another partner can handle sidewalk robots, another autonomous cars, another human couriers. The user can remain inside Uber while the logistics underneath become increasingly interchangeable.

That is basically how Uber has approached other autonomous technologies too: aggregate multiple operators behind the network rather than assuming Uber has to build every vehicle itself.

DoorDash is making a different bet. It has spent years working with drone partners and has now decided that enough of this layer matters strategically to justify building and certifying its own operation. DoorDash says the drones are being designed around local-commerce delivery and integrated directly into its existing marketplace infrastructure.

These sound like opposite strategies, but I think they are really different answers to the same question: where does our advantage actually live?

Uber is betting heavily on the demand layer. If the best drone comes from Zipline and the best robot comes from someone else, that is fine as long as the customer still opens Uber.

DoorDash seems to believe more of the delivery system itself can become part of the advantage. Merchant handoffs, routing, aircraft, landing constraints and the final interaction with the customer all affect the experience, so owning more of that system may eventually produce something a generic logistics partner cannot.

It reminds me of the Netflix question. Sometimes the valuable layer is the infrastructure. Sometimes it is the interface sitting above all the infrastructure. Sometimes it moves from one to the other as the market matures.

That is probably the larger thread here. Alloy used a vertical until the vertical started limiting it. PostHog is attacking the product that currently defines it. Netflix may turn competing services into inventory. Uber wants many delivery technologies underneath one demand layer, while DoorDash is pulling more of the delivery technology inside.

A good strategy creates an advantage.

A very good company notices when that same strategy has finished its job.

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Morning guys, happy Saturday.

There is a habit I’ve noticed in business writing where once something works, we immediately invent a clean explanation for why it worked. A startup gets its first ten customers and suddenly it has a “sales motion.” A product grows and we decide which feature caused it. A new technology changes hiring and the story becomes that AI is taking jobs. The explanation is usually much neater than the system underneath it.

That matters because the explanation determines what happens next. If you misunderstand why customers are buying, you hire around the wrong sales process. If you misunderstand why a product is sticky, you can optimize away the thing people actually liked. If you misunderstand how a labor market is adjusting, you solve for layoffs while the real change is happening at the hiring door.

A few things I’ve been reading recently are good reminders that outcomes and mechanisms are not the same thing. Getting the result once is useful. Understanding what produced it is what lets you do it again.

1. Founder-led sales is supposed to stop working

In 2023, Warmly went from zero to more than 100 paying customers. The more interesting part of Maximus Greenwald’s breakdown is how deliberately the founder removed himself from the process as the year went on. The first ten customers were founder-led, customers 10 to 30 added a sales leader, the next phase put sellers in front while the founder stayed involved, and by the time Warmly crossed 100 customers the goal was for the founder to stop closing altogether.

At first that sounds like a fairly normal sales hiring story, but I think the sequencing matters more than the hires. In Q1, Warmly was not really trying to scale sales. It was trying to understand whether anybody would buy, what they objected to, how the product should be described and which parts of the pitch survived contact with an actual customer. The founder was doing sales because the company still needed the information more than it needed efficiency.

This is why hiring a polished sales organization too early can be weirdly counterproductive. You are asking someone to repeat a playbook that does not exist yet, so the new hire either invents one themselves or executes whatever playbook worked at their last company. Ravi Parikh made a similar point when talking about building the early sales team at Heap: the first reps had to be part salesperson, part sales engineer and part product marketer because specialization creates overhead before there is enough repeatable work to specialize.

There is another thing happening in those first few deals that gets lost once people start drawing funnels. Early customers are not only evaluating the product. They are evaluating whether this tiny company will still exist next year and whether the founder will pick up the phone if something breaks. Shruti Kapoor described this as the early startup’s “trust deficit”, which is why founder-led sales can outperform a theoretically better salesperson when the company has almost no proof yet.

The handoff should therefore happen gradually because what you are transferring is not a list of scripts. You are turning all the strange little things the founder has learned into something another person can reproduce: who gets excited, which objection actually matters, which demo sequence works, when to push, when to stop talking, and which customers are politely saying no even though they sound interested.

Warmly’s Q4 is probably the most important part of the whole story because the GTM strategy barely changed. That was the point. Once the same process could be run by sellers, managed by a sales leader and produce roughly understandable inputs and outputs without the founder rescuing every deal, sales had started becoming a system rather than founder skill.

The first ten customers prove that you can sell something. The next hundred start proving that the company can.

2. Ozempic works. The explanation is getting stranger.

GLP-1 drugs are a good example of something becoming enormously useful before scientists have completely mapped the mechanism behind the result. One common explanation for drugs such as semaglutide has been that they reduce appetite partly by suppressing AgRP neurons, a set of neurons associated with hunger. But new research from Yale found something unexpected in mice: during chronic semaglutide treatment, those hunger-related neurons became more active, not less.

The researchers then removed or silenced those neurons and found that the animals could no longer sustain the same weight-loss effect. The proposed mechanism is more complicated than “drug turns hunger off.” The calorie deficit created by treatment appears to recruit these neurons as part of a broader metabolic adaptation that helps sustain fat loss. This was a mouse study, so it is not evidence that the same mechanism works identically in people, but it does complicate a very intuitive story about why these drugs work.

I like this example because companies do the same thing with products all the time. Something works, so we attach the easiest explanation to the visible behavior. People use the social feature, therefore they must want more social features. Customers stay on the paid plan, therefore the premium dashboard must be sticky. Users spend hours in the app, therefore engagement is obviously the thing to maximize.

Then somebody “improves” the product around that explanation and accidentally damages the mechanism that was producing the result.

This is why good product research can feel frustratingly slow. Analytics will tell you what people touched. Interviews will tell you what people remember touching. Neither automatically tells you which part created the value. Sometimes the useful variable is something nobody thought to measure because everybody already agreed on a cleaner explanation.

The commercial success of GLP-1 drugs came before a complete understanding of their biology. Products can work the same way. The dangerous moment is when success makes us overconfident that we understand why.

3. “Undruggable” often means “undruggable with the tools we have right now”

In August, the FDA approved Rasonque, or daraxonrasib, for certain adults with metastatic pancreatic adenocarcinoma. The drug targets multiple forms of RAS, a family of proteins that drives tumor growth in most pancreatic adenocarcinomas and has spent decades being one of oncology’s notoriously difficult targets.

The Phase 3 results are pretty remarkable. In the trial population, median overall survival was 13.2 months with daraxonrasib versus 6.7 months with standard chemotherapy, with a 60% reduction in the risk of death. This is not “cancer solved,” obviously, and the approval is for a specific population with metastatic pancreatic cancer, but it is a meaningful advance against a target scientists spent a very long time struggling to reach.

The part I keep coming back to is the word undruggable. It sounds like a property of the target itself, but often it is really a description of the available toolset at a particular moment. The biology did not suddenly become easier. Chemistry, structural understanding, screening techniques and the surrounding research stack improved enough that a problem which looked unreasonable in one decade became tractable in another.

That distinction matters outside science because markets accumulate abandoned problems too. There are ideas somebody tried in 2012 when mobile hardware was worse, payments were painful, models were dumb, cloud infrastructure was expensive or the necessary distribution channel did not exist yet. The startup failed, and over time “that company failed” quietly turns into “that idea does not work.”

Those are very different statements.

The interesting opportunities are sometimes hiding inside old failures where the constraint has since disappeared. A product requiring impossible computer vision ten years ago might be trivial now. A service that needed a call center might suddenly work with agents. A logistics idea that collapsed under delivery costs can change completely if autonomous delivery becomes cheap enough.

You still need to be careful because plenty of bad ideas remain bad when technology improves. But “people already tried this” is much less useful than asking exactly why they failed and whether that reason is still true.

Sometimes the market is wrong. More often, the market was right at the time.

4. AI may be removing the first rung before it removes the whole ladder

The cleanest AI employment story would be mass layoffs. It is also not what the best data currently shows. Stanford researchers using payroll records from millions of U.S. workers found no evidence of widespread economy-wide displacement, but they did find a sharp divergence among younger workers in occupations highly exposed to generative AI.

Employment among 22 to 25-year-olds in those occupations was about 19% below where it would have been if it had kept pace with similarly aged workers in less AI-exposed jobs. More importantly, Stanford found that the adjustment appears to be happening mainly through reduced hiring rather than increased separations. Companies are not necessarily replacing a room full of junior employees with Claude. They may simply be deciding they need fewer juniors the next time they open a role.

That is a much quieter change, which is probably why it matters. Layoffs make headlines and appear in official announcements. A role that never gets created leaves almost no trace. Ten companies deciding not to hire their next junior analyst can change a career market without any CEO ever saying “AI replaced these jobs.”

There is a second-order problem here that I think companies are going to run into later. Senior workers do not appear from nowhere. Most become senior by spending years doing the repetitive, slightly boring work that companies are now particularly tempted to automate. If you remove enough of that apprenticeship layer, you can save money today while accidentally shrinking the pool of experienced people you need five years from now.

This does not mean companies should preserve pointless work as a training program. It does mean the old path where a junior employee learned by producing the first draft, doing the basic analysis or writing the simple code may need a replacement. The interesting question is not whether a 23-year-old can beat an AI at the tasks we used to give new hires. They probably should not have to.

The question is how someone becomes excellent when the work that used to make them excellent is increasingly done by software.

We have spent a lot of time asking what AI does to jobs. I suspect the stranger question will be what it does to careers.

5. Spotify built the audiobook habit before asking people to pay more for it

Spotify entered audiobooks in a fairly normal way at first, selling individual books. Then in 2023 it made a much more interesting move: Premium subscribers started getting 15 hours of audiobook listening each month from a large catalogue without needing to buy a book separately.

That removed a surprisingly important decision. Audible asks you to decide that you want an audiobook and then spend a credit or money on one. Spotify could put a book beside the music and podcasts inside an app millions of people already opened every day. The user did not need a new subscription, new app or new habit before trying the format.

Three years later, Spotify says audiobook listening hours are up 60% year over year, while Audiobooks+ has crossed one million paying subscribers and is on track for roughly $100 million in annualized recurring revenue. Audiobooks+ is the clever part of the sequence because Spotify did not begin by asking people to pay extra. It gave Premium users enough listening to create the behavior first, then sold additional hours to the subset who repeatedly hit the limit.

That reverses the normal subscription funnel. Usually a company asks someone to pay, then hopes they develop the habit that justifies the purchase. Spotify already had the habit, attached a new behavior to it for almost no additional effort from the user, watched who became unusually engaged, and only then introduced another paid layer.

There is a useful product principle hiding in there. Before asking how to convince somebody to adopt a new product, ask whether you can attach the behavior to something they already do. A budgeting feature inside the banking app somebody already checks, a research workflow inside the browser they already use, or an AI capability inside the tool where the work already happens has a very different adoption problem from another icon asking to earn a place on the home screen.

Distribution is usually discussed as how people discover a product. Spotify is a reminder that distribution can also determine whether the behavior happens at all.

Sometimes the product is not missing a better feature. It is missing a place in somebody’s existing day.

The common thread across all five stories is that the visible result can arrive well before the system underneath it becomes obvious. Warmly could sell before it had repeatable sales. Semaglutide could produce weight loss while scientists were still uncovering important pieces of the mechanism. RAS could look impossible until the surrounding tools changed. AI can alter employment through jobs that never open rather than people being fired. Spotify could create audiobook demand simply by moving books into a habit that already existed.

The result tells you that something happened. The mechanism tells you what to do next.

That second part is usually where the interesting work starts.

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Morning guys, happy Saturday.

I think we still talk about startups as if building the product is the main event. That made sense when software was expensive to make, engineering teams were difficult to assemble, and a decent first version could take a year. It makes less sense when one person with good tools can now get surprisingly far in a weekend.

The product still matters, obviously, but more of the difficult work seems to be moving around it. Which market deserves your next five years? How do you get the first thousand people to care when you have no audience? Which tiny choice in the interface quietly determines what users do? What happens when AI can implement almost anything you ask for and the real question becomes whether you should have asked for it at all?

That shift makes some older startup stories more interesting, not less. Paperform launched before generative AI, but the way it used AppSumo looks almost tailor-made for the current environment. SavvyCal spent months choosing a market before building. And Kieran Klaassen’s work at Every shows what happens at the other end, when the implementation itself starts disappearing into the background.

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1. Paperform didn’t really sell 3,000 lifetime deals. It bought 3,000 chances to learn.

When Paperform launched in December 2016, founders Dean McPherson and Diony McPherson did something that SaaS people often have mixed feelings about: they ran an AppSumo lifetime deal. In two weeks they sold close to 3,000 licenses, and the cash helped bridge the gap between leaving their jobs and building enough recurring revenue to support themselves. Dean later described the money as a kind of seed funding, except there was no investor and no equity involved. Dean McPherson’s full Paperform retrospective

The more interesting number is 1,000. At least that many buyers redeemed their accounts on the first day and started trying to use Paperform for actual work. Suddenly two founders who had almost no owned distribution had a thousand people poking at the product, asking for features, finding bugs and discovering use cases the founders might not have thought of themselves. That is a very different thing from having 1,000 people on a waitlist telling you the landing page looks nice.

AppSumo buyers have a reputation for being demanding and price sensitive, which sounds like a terrible customer segment if your goal is maximizing revenue per user. It sounds much better if your goal is finding every weak point in a young product. Dean’s point is basically that the qualities founders complain about made those buyers useful early users, because when something was broken or missing, they were very happy to tell Paperform about it. When thirty people independently asked for the same thing, prioritization became much easier than sitting in a room guessing at the roadmap.

I think the interesting way to look at the deal is that Paperform traded future revenue from one cohort for three things it badly needed at that moment: cash, distribution and product feedback. The lifetime price looks cheap only if you evaluate it as a normal subscription sale. If you compare it with raising seed capital, buying thousands of users through ads, or spending a year building without enough feedback, the economics look different.

That does not mean lifetime deals are secretly a brilliant business model. Paperform stopped running them as the company matured because repeated discounting can change how the market perceives the product, and every lifetime customer creates ongoing support, infrastructure and product costs. The same channel that makes sense when nobody knows you can become actively unhelpful once your problem changes from “please try this” to “this is a premium product worth paying for every year.”

There is a bigger distribution lesson here. Early-stage companies often act as though they need to build an audience before they can launch, but borrowing somebody else’s audience can be perfectly rational when you have nothing to distribute to yet. AppSumo, Product Hunt, marketplaces, communities and partnerships are all versions of the same trade: you give up some economics or control in exchange for access to people you could not cheaply reach yourself.

The important thing is knowing when the trade has done its job. Paperform eventually found that repeated Product Hunt launches produced smaller and smaller bumps, because by then the company had customers, word of mouth and its own distribution. Borrowed distribution is useful precisely because it helps you survive long enough to build distribution you actually own.

2. SavvyCal chose the market before it chose the product

Derrick Reimer’s path to SavvyCal is almost the opposite of the usual “I had an idea in the shower and started coding that night” founder story. After selling Drip and later watching his Slack alternative Level fail, he spent months deliberately looking at markets before writing the first line of SavvyCal. His criteria were fairly boring: proven demand, recurring revenue and enough room for a meaningful point of differentiation. Derrick Reimer’s account of choosing the SavvyCal market

Scheduling passed the test because people were already paying for scheduling software, subscription economics were obvious, and Calendly had proven the market was real. Reimer was not trying to convince the world that scheduling software should exist. His insight was much smaller: sending someone your booking link can feel weirdly one-sided, so SavvyCal made the recipient feel more involved by letting them overlay their own calendar and compare availability more naturally.

That is a less romantic way to start a company, but maybe a healthier one. Founders spend enormous amounts of time thinking about product risk because product risk is fun. You can design around it, prototype it, argue about features and use new tools. Market risk is less satisfying because sometimes the answer is simply that people do not care enough, the budget is too small, or the incumbents already solve the problem well enough.

AI makes avoiding that question even easier because building the wrong thing has become much less painful. You can have a working prototype quickly enough that it feels wasteful not to keep going. A month later you are polishing onboarding for a market you never really established was worth entering.

Reimer’s story is useful because the market was part of the product decision. He was also choosing what kind of company he wanted to run: bootstrapped, small, profitable and durable rather than a giant venture bet. That changed which markets were attractive before he ever got to features, and six years later SavvyCal is still a small team with a five-figure monthly recurring revenue business.

There is probably more value in that constraint than founders admit. “What can I build?” now has almost infinite answers. “What market fits the company I actually want to spend ten years running?” cuts the list down very quickly.

3. One extra click can matter more than another page of persuasion

A field experiment by researchers from Harvard Business School and Boston University looked at cookie-consent interfaces and found something that anyone who has designed software probably suspects already: tiny changes in choice architecture can move behavior a lot. When cookie options were placed behind extra clicks, users shifted toward whatever choice was easier to reach, and many people later showed substantial confusion about what the defaults had actually been. Read the NBER paper on cookie consent and choice architecture

What I like about this study is that nobody’s underlying opinion about tracking needed to change. The interface changed, so the behavior changed. That seems obvious, but product teams still spend a lot of time treating every user action as evidence of preference rather than asking how much of it was produced by the path we put in front of them.

This is why defaults are such an underrated part of product strategy. The default onboarding path, notification setting, pricing option, dashboard view or sharing permission quietly becomes the way most people experience the product. If one route requires three decisions and another requires none, you should expect the second route to win even when users would tell you they prefer the first in a survey.

There is an ethical line here too, especially when the easier path benefits the company more than the user. The lesson from the research is not “hide undesirable choices behind extra clicks.” It is that interface design is doing more persuasion than we like to admit, which means defaults deserve the same scrutiny as copy, pricing and feature decisions.

For a good product, the useful question is probably whether the easiest path is also the path most users would choose if they fully understood the tradeoff. When those align, reducing friction is genuinely helpful. When they do not, you may have found a growth lever, but you have also found the sort of growth lever customers eventually resent.

4. Gumloop’s ad worked because the ad was a story about not buying ads

Gumloop CEO Max Brodeur once paid a trumpet player to perform outside the company’s office holding a Gumloop sign, then expanded the idea to more musicians around San Francisco during a conference. The post that travelled was not a detailed explanation of Gumloop’s workflow automation product. It was the much more retellable question: what if we spent our marketing budget on musicians instead? Tom Orbach’s breakdown of the anti-marketing idea

Tom Orbach calls this “anti-marketing,” and his examples include Sticker Mule spending a portion of its ad budget handing cash to strangers and Reddit buying only five seconds of Super Bowl airtime because it could not afford the conventional version. The common mechanic is not really that these companies stopped advertising. They spent money creating something people would voluntarily explain to somebody else, which gave the spend a second distribution layer.

That distinction matters because buying reach and creating a story are different jobs. A normal ad gets the impressions you purchased and hopefully converts some percentage of them. A strange enough marketing act can get the initial impressions and then produce screenshots, conversations, press, reposts and people like me writing about it later.

The obvious danger is that once every company starts “spending its marketing budget” on quirky stunts, the mechanism gets tired too. Anti-marketing only works while it feels like somebody broke the normal pattern, and copying the visible stunt without understanding why it travelled will produce the same problem as copying any other marketing tactic.

I think the more durable idea is to ask whether the spend itself can contain the story. Paying for an ad normally buys distribution after the creative is finished. Gumloop made the allocation of the budget part of the creative, which meant the media plan and the message were basically the same object.

That is a useful constraint for small companies because they are rarely going to outspend incumbents. They can sometimes make a small amount of money unusually interesting, and interesting money tends to travel further than ordinary money.

5. When AI builds the software, somebody still has to decide whether it feels good

Kieran Klaassen at Every has been writing about a software workflow where agents handle an increasingly large part of the engineering loop. His article on “polish” describes a surprisingly manual final step: put the working product in front of yourself, use it, notice what feels wrong, tell the agent, then keep repeating until the experience feels right. Kieran Klaassen on polishing software built by agents

What changes here is not that humans disappear from software creation. The human moves to a different part of the loop. If an agent can write the implementation, review a lot of the code and iterate quickly, spending your scarce attention inspecting every line becomes less useful than spending it deciding whether the finished thing behaves the way you actually want.

That sounds almost trivial until you try to automate taste. A test can tell you that a button works, but not necessarily that the animation feels cheap, that the information hierarchy is confusing, or that a technically correct interaction makes the whole product feel slightly worse. Those judgments are difficult because they are often built from hundreds of previous products you have used and a fuzzy internal model of what “right” feels like.

Klaassen’s interesting move is turning repeated feedback into reusable rules. If he keeps telling the agent that scrollbars should behave a certain way or animations should resolve toward the click, that preference can be codified so the next feature begins closer to what he wants. The human still judges the output, but the system slowly learns the boring parts of the judgment and stops asking for the same correction every time.

I suspect this is going to become a fairly normal way of working. A lot of knowledge work currently contains two jobs mixed together: producing the first version and deciding whether the first version is any good. AI is attacking the production part much faster than the judgment part, which makes the second job more visible.

That loops back to Paperform and SavvyCal in a way I like. Paperform’s scarce resource was not code, it was enough real users to tell the founders what mattered. SavvyCal’s scarce resource was not the ability to build scheduling software, it was choosing a market worth spending years inside. Gumloop’s scarce resource was not access to another ad unit, it was a story people wanted to repeat.

The product still matters, but it is increasingly surrounded by things that matter just as much: market selection, distribution, feedback, choice architecture and taste. As the cost of making software keeps falling, I think those surrounding systems will explain more of why one product wins and another perfectly functional one disappears.

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A useful way to understand a new technology is to ask what became cheap, then ask what became scarce as a result. The internet made publishing cheap, so attention became scarce. Cloud computing made servers easier to rent, so distribution and software became more important than owning hardware. AI is now making several kinds of production much cheaper at once: code, content, analysis and, increasingly, decision-making.

That does not remove bottlenecks. It moves them.

This week, the interesting stories are mostly about where those bottlenecks are going. Social feeds are deciding which kinds of stories humans learn to tell. Nvidia is trying to turn compute into something Wall Street can finance like infrastructure. Anthropic is heading toward the public markets, where the economics of frontier AI will become much harder to hide. Vibe coding is making software creation accessible faster than it is making software creation safe. And commerce is beginning to encounter customers that do not have eyes, fingers or much patience for a badly structured website.

1. The algorithm eventually teaches you what kind of life is worth reporting

Sociologist Kathryn Jezer-Morton’s new book, The Story of Your Life, examines a subtle consequence of spending two decades telling our lives through social platforms. In a recent conversation with The Atlantic, she argues that feeds tend to reward stories with recognizable movement: redemption, improvement, transformation. What struggles to travel is the “flat story,” where life continues without a satisfying before-and-after.

This sounds like a media observation, but it is really an incentives observation. Any system that rewards some behavior will eventually produce more of that behavior. The clever part about social media is that the reward does not stop at what we publish. Once people know which stories perform, they start noticing their lives through the same template. A holiday needs a highlight. A career setback needs a lesson. A bad year needs to become the year that changed everything. The feed starts as an editor and ends up as a co-author.

Companies do this too. Startup storytelling has converged on an oddly small number of plots: we were rejected, then we won; nobody believed us, then everyone did; we nearly died, then revenue exploded. These stories work because compressed narratives are easy to remember. But the danger is that founders start confusing what is narratively legible with what is strategically important. A business that quietly compounds for six years is less interesting on LinkedIn than one that almost collapses and recovers in six months, even though you would probably rather own the first one.

There is a useful marketing lesson here, but it is not simply “use anecdotes.” Anecdotes are powerful because human beings can simulate another person’s experience more easily than they can feel a percentage. That makes them good communication devices and terrible substitutes for evidence. The better move is to use the anecdote to make someone care, then use the evidence to determine whether the anecdote is actually representative.

The feed rewards the clean story. Reality rarely has one. Good operators should be able to tell the difference.

2. Nvidia has realized that financing can be a product feature

Nvidia announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to create financing platforms intended to mobilize more than $500 billion for AI infrastructure. Jensen Huang described compute as an “investable asset,” and Nvidia said the platforms are meant to give customers access to long-duration financing for what it calls AI factories. Huang also said Nvidia could backstop up to $125 billion of the potential deals.

At first glance this is a financing story. It may actually be a product strategy story.

Suppose you manufacture a machine that costs $10 million and your customer can clearly make $15 million with it. You still have a problem if the customer does not have $10 million. Lowering the price is one solution. Lending them the money is another. If capital becomes abundant enough, you can increase demand for the machine without making the machine cheaper.

That is roughly what Nvidia is helping build around compute.

For the first phase of the AI boom, access to GPUs itself was an advantage. Companies signed large contracts because capacity was scarce and possessing it created optionality. But if Nvidia and Wall Street succeed in making compute financeable, fungible and rentable, owning GPUs becomes less strategically interesting for most companies. The important question becomes what return you can produce from each dollar of compute.

This is what financialization often does to productive assets. It separates ownership from use. Airlines do not need to manufacture or necessarily own the plane to build an airline. A retailer does not need to own the building to operate a store. If compute follows the same path, the company with the largest GPU pile may not have the advantage. The company that can turn rented intelligence into the highest-value output might.

There is an obvious risk in a supplier helping finance demand for its own products. If financing becomes necessary to keep sales accelerating, it becomes harder to tell how much demand is natural and how much has been manufactured by cheap capital. But bubbles and useful infrastructure are not opposites. It is possible to overfinance something the world genuinely needs.

The more interesting change for everyone downstream is simpler: compute is slowly becoming a financing problem instead of a procurement problem. If that continues, buying lots of hardware because it feels strategically scarce may age badly. The financial system is being assembled specifically to make scarcity rentable.

3. Anthropic’s IPO may finally tell us what kind of business AI really is

Anthropic confidentially submitted a draft S-1 to the SEC in June. The filing itself is not public yet, so anyone claiming to know exactly what its prospectus contains is guessing. But the eventual public filing will matter for a reason much bigger than Anthropic’s valuation: it will give us one of the first detailed looks at the economics of a frontier AI lab.

People casually talk about AI companies as software companies. That comparison may turn out to be misleading.

Traditional software has wonderful economics because the millionth copy costs almost nothing to produce. AI is different. The millionth user creates inference cost. A customer who uses Claude twice as much actually consumes more resources. Better models can also require enormous continuing expenditure on training, chips, power and data centers. This does not make AI a bad business. It means the shape of the business matters.

The Anthropic prospectus should help answer whether frontier AI behaves more like SaaS, a cloud provider, a semiconductor company or some new mixture of all three. Gross margins will matter, but the really interesting numbers will be underneath them: how inference costs change as models improve, how much revenue comes from a small number of large customers, how long contracts last, how much compute has already been committed, and whether rapidly falling token prices are being offset by rapidly rising usage.

That last question matters enormously to anyone building on top of these models. Developers have spent the past few years benefiting from an extraordinary competition among labs. When companies are fighting to establish a platform, they have good reasons to make the platform attractive. Cheap API pricing can be part of customer acquisition.

Public companies eventually acquire another constituency: shareholders.

That does not automatically mean prices rise. Better chips and better models can keep lowering the cost of intelligence. But it does mean the economics become less philosophical. If serving a token costs more than the market thinks, the gap has to be paid by someone. Today that someone may be an investor financing growth. Tomorrow it may be the customer.

The most interesting question in Anthropic’s S-1 will therefore not be “How fast is Claude growing?” We already know AI demand is growing quickly. It will be how much economic value Anthropic keeps each time Claude does another unit of work.

That number will tell us a lot about the businesses being built on top of it too.

4. Vibe coding removed the gatekeeper, not the responsibility

A recent preprint studying the security of vibe-coded applications assembled more than 10,000 real-world repositories and performed deeper audits on 200 deployed applications. The researchers found at least one exploitable vulnerability in 90% of those audited apps, with 76.7% of the identified vulnerabilities rated high or critical. Because this is a preprint and the detailed audit covered 200 applications, the numbers should not be treated as a census of every AI-built app. The failure patterns are still worth paying attention to: broken access control, cryptographic problems, injection, exposed secrets and temporary logic that quietly survived into production.

The obvious conclusion is that AI writes insecure code.

The more interesting conclusion is that we have changed who is allowed to create software without changing who is responsible when the software fails.

Traditional software development had an inefficient but useful feature: expertise acted as a gate. To put a database, authentication system and payment flow on the internet, somebody usually had to know enough about software to understand that these things contained danger. Vibe coding removes much of that gate. That is precisely why it is powerful. It also means people can now deploy systems containing security boundaries they do not know exist.

An AI agent can fix a requirement you give it. The harder problem is the requirement you never knew to give.

A founder can ask, “Add login.” An experienced engineer hears several unstated questions inside that sentence. How are sessions stored? What happens after password reset? Can one user access another user’s object by changing an ID? Are secrets exposed client-side? How long do tokens live? What happens after repeated failed attempts? The novice sees a login box. The engineer sees a collection of trust boundaries.

This is why the long-term security solution probably will not be telling millions of new builders to become security engineers. The platforms themselves will have to convert experienced engineers’ tacit knowledge into defaults. Lovable is already moving in this direction with security scanning and dedicated trust centers for published apps. That is the right abstraction. The user should not need to remember every dangerous thing the system could do.

Cars became mass-market products because drivers did not need to understand combustion, metallurgy and crash physics. The safety knowledge moved into the machine, the road and the rules around them.

Vibe coding will have to do the same thing.

The opportunity created by AI coding is not merely that more people can build software. It is that software platforms can eventually encode enough good judgment that people can safely build things they do not fully understand.

We are not there yet.

5. Your next customer may never see your website

On its first-quarter earnings call, Shopify said AI-driven traffic to its stores had grown eightfold year over year while orders from AI-powered searches had increased nearly thirteenfold. Shopify has also structured more than one billion products with attributes, current pricing and inventory data for AI systems, and said traffic from catalog-powered AI searches converts about twice as well as traffic from general AI searches built from scraped web information.

The temptation is to call this another acquisition channel.

It could become something more important: a change in who websites are designed for.

The web we know assumes a human is on the other side. Product photography builds desire. Navigation helps someone browse. Reviews create confidence. Checkout pages reassure a nervous buyer. Entire careers in conversion optimization exist because humans hesitate, become distracted and misunderstand things.

An agent has different problems. It wants an exact price. It wants to know whether the product is in stock. It wants machine-readable specifications. It wants to know whether shipping arrives before Tuesday. It wants a transaction path it can reliably complete.

In other words, much of the modern ecommerce site is a persuasive interface built over a database. Agents increasingly care about the database.

This creates an interesting inversion. For twenty years, online sellers optimized information so search engines would send them humans. In agentic commerce, software may increasingly consume the information and make part of the purchasing decision itself. SEO was about convincing an algorithm that a human should see your page. Agent commerce may partly be about convincing an algorithm that your product is safe to buy.

That makes structured data, accurate inventory, clean APIs and predictable checkout behavior more than technical housekeeping. They become merchandising.

There is also a bigger strategic risk. If people begin delegating product discovery and comparison to AI, the agent becomes a new intermediary between the merchant and the customer. We have seen this movie before. Google aggregated websites. Amazon aggregated sellers. App stores aggregated developers. Whoever controls discovery eventually acquires bargaining power over everyone supplying the thing being discovered.

So merchants should absolutely make themselves easy for agents to understand. But they should also notice what they are trading away.

The first stage of a new distribution channel feels like free growth. The second stage is usually somebody discovering they own the customer relationship.

That may be the more important agent-commerce story.

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Most companies spend an extraordinary amount of time thinking about what they sell and surprisingly little time thinking about the system around it. When do you ask someone to pay? What exactly is the team optimizing for? Who is allowed to modify the product? How does a customer justify paying you again next month? And once acquiring customers becomes predictable, should you even be using equity to pay for them?

These questions can look secondary because none of them are the product itself. But mature markets are often won in the layer surrounding the product. Two companies can sell roughly the same thing and produce very different businesses because one has better distribution, incentives, financing or timing. This week offered five unusually good examples.

1. ReelShort discovered that the paywall has a location

Short-form drama is becoming a real business. Media Partners Asia estimates that microdramas outside China will generate about $3.6 billion this year. ReelShort alone is projected to reach roughly $1.05 billion in 2026, up from $97 million in 2023 and $785 million last year. It is also expected to become meaningfully profitable for the first time.

The obvious comparison is Quibi, because both businesses tried to put professionally produced short episodes on phones. But saying ReelShort succeeded because Quibi was early or because people now have shorter attention spans misses a more interesting difference. ReelShort does not ask you to make the important economic decision before it has made you care.

The first episodes are cheap or free. The plot moves absurdly fast. Then someone discovers the billionaire is secretly her husband, the DNA results arrive, or the wedding gets interrupted, and the next episode sits behind a payment. The content is not merely entertainment. It is a machine for manufacturing intent immediately before the transaction.

Most software does nearly the opposite. A visitor lands on a homepage knowing almost nothing about the product and is immediately shown three pricing tiers. We ask for money at the moment when the customer has the least evidence that the product is useful. Then, after the user has imported their data, invited the team, generated the report or reached the useful result, we often stop selling because we assume the sale has already happened.

ReelShort suggests a broader rule: willingness to pay is not a stable property of a customer. It changes during the experience. The same person can value the same next episode at zero dollars when the story starts and several dollars ninety seconds later. The product changed their state.

This is why good monetization design is partly about finding moments of maximum accumulated value. Dropbox asking you to upgrade when storage fills is stronger than asking during signup. A research tool can let you discover the result before charging to export it. An accounting product can show the tax estimate before asking you to pay to file. The best paywall is often not the wall with the best copy. It is the wall placed where turning around feels least rational.

There is another part of the ReelShort story that is even more revealing. User acquisition has historically been its largest cost, and MPA expects marketing as a share of revenue to fall as franchises, owned distribution and other channels mature. That means the billion-dollar achievement is not proof that tiny soap operas are inherently wonderful businesses. It is evidence that once a company gets good enough at converting attention into transactions, the next problem becomes owning the attention rather than repeatedly buying it.

The cliffhanger gets the payment. Distribution gets the margin.

2. A roadmap can make a team less focused even when everyone is working on the same product

Researchers at UC Berkeley found something subtler than ordinary task switching. Participants repeatedly performed the same cognitive task, but researchers changed the objective between emphasizing speed and emphasizing accuracy. Performance deteriorated when people had to switch goals, particularly when they had little time to adjust. The work itself did not change. The definition of success did.

This is almost a description of startup planning.

A team can be working on one product while effectively changing jobs every few weeks. January is about activation. February is enterprise. March becomes retention because churn looks uncomfortable. Then an investor asks about AI and suddenly the roadmap acquires an agent.

Each objective can be reasonable independently. The mistake is assuming reasonable objectives add together into a reasonable strategy.

They often subtract from one another.

Suppose a product team is told to maximize activation. It might remove steps, simplify configuration and push users toward the fastest possible first success. Tell the same team to maximize enterprise readiness and the sensible choices reverse. Now permissions, administration, security and configurability matter. Neither approach is wrong. But a product cannot simultaneously become radically simpler and radically more configurable without someone deciding where the tradeoff should land.

A roadmap full of priorities is therefore not merely a scheduling problem. It can be evidence that nobody made the harder decision about which form of good matters most right now.

This is one reason small companies sometimes beat much larger ones despite having fewer engineers. Having ten times the people does not create ten times the progress if those people are moving toward different definitions of success. Coordination cost is not just meetings and Slack messages. It is the cost of maintaining a shared answer to the question, “What are we trying to make better?”

The useful unit of focus may not be the task. It may be the objective.

A team can work on twenty tasks and remain focused if they all push the same number in the same direction. It can also work on three tasks and be scattered if each exists for a different strategic reason.

The best roadmaps probably contain fewer verbs than most companies think. Grow this. Fix this. Prove this. One definition of better, held long enough for the organization to learn how to achieve it.

3. Spotify is trying to turn permission into a product

Spotify and Universal Music Group agreed in May to create a paid tool that will let users make AI-generated covers and remixes using music from participating artists and songwriters. The companies describe the model with three words: consent, credit and compensation. Participating rights holders choose whether to take part, and the resulting creations generate additional compensation on top of normal Spotify royalties.

Then this week Spotify expanded the initiative through a deal with Merlin, whose network represents tens of thousands of independent labels. The product has not launched yet, but the strategic move is already interesting.

The music industry’s first instinct toward generative AI was understandably defensive. If someone trains a system on your work and starts generating substitutes for it, litigation is a rational response. But lawsuits and products solve different problems. A lawsuit tells people what they cannot do. A product asks whether some version of what they want to do can be made legitimate and monetizable.

Spotify is betting that fans do not only want access to finished music. Some want permission to play with it.

That is an important change in what ownership means online. Historically, a valuable catalog was valuable partly because other people were excluded from modifying it. Generative AI makes modification inevitable enough that permission itself can become scarce. If millions of people can technically create a version of a song, then the official version of that capability, where the artist approves, gets credited and gets paid, becomes a product.

There is a larger pattern here for any company sitting on valuable intellectual property. AI lowers the cost of transforming existing assets. A publisher’s archive can become a research corpus. A game universe can become a creation environment. A design library can become a generator. Educational material can become a tutor. The instinctive response is often to protect the asset from transformation because transformation used to imply loss of control.

But there are two forms of control. One is preventing people from doing something. The other is building the place where they prefer to do it.

The second can be much more valuable.

This does not mean every rights holder should open everything. Some transformations damage the original asset, confuse provenance or destroy the economics that produced it. The interesting question is narrower: what are users already trying to do with your intellectual property that you currently classify only as misuse?

If the behavior has genuine demand, the unauthorized version may be a product specification in disguise.

4. Forgotten subscriptions were a business model. They are becoming a liability.

A lot of subscription businesses contain two kinds of customers. The first actively chooses the product every month. The second chose it once and then stopped choosing.

For years, the distinction barely mattered because both credit cards kept working.

Current subscription research collected by SubStop puts average American subscription spending around $219 per month and says 42% of consumers pay for subscriptions they have forgotten about. The exact consumer estimates vary substantially between surveys, which is why I would not over-index on one headline number. The durable fact is that recurring billing has historically benefited from inattention.

That is an unusual kind of moat because the customer becomes more valuable when they think about you less.

It is also unlikely to last.

Banks already identify recurring charges. Subscription-management apps collect them in one place. The obvious next step is an AI agent that looks at twelve months of transactions and asks a much less sentimental question: “You paid $240 for this product and used it twice. Should I cancel it?”

The customer no longer needs enough motivation to remember the service, find the account, recover the password, navigate to billing and click through a retention maze. They can delegate the audit.

This changes the economics of subscription products in a useful way. Companies will have to earn renewal closer to the way they earned the original purchase.

The products best prepared for this are the ones that can produce a value receipt. Not marketing claims about what the product could do, but evidence of what happened because the customer had it: 42 hours saved, nine invoices collected, 1,300 attacks blocked, $680 earned, 17 meetings summarized, 12 workouts completed.

Usage itself is not enough. Nobody wants an email saying they opened an app 19 times. The receipt has to translate usage into the reason the customer originally bought the product.

That creates an interesting product-design question: if an impartial agent reviewed your customer’s bank statement tomorrow and asked your software to justify its own charge, what evidence could it provide?

“We hope they forgot” used to be sufficient for a surprising amount of recurring revenue.

It will become a dangerous assumption once forgetting can be automated away.

5. General Catalyst is treating customers like factories

Function Health recently received $450 million through General Catalyst’s Customer Value Fund, only eight months after raising a $298 million Series B. Function sells a membership built around extensive lab testing and preventive-health services, and the new financing is designed to support further growth.

The interesting part is not the size. It is what General Catalyst thinks it is financing.

Traditional venture capital treats customer acquisition as one of the things a company does with equity. Raise $100 million, hire engineers, open offices, run ads, hire salespeople and hope the resulting company becomes worth much more than $100 million.

General Catalyst’s Customer Value Fund separates the customer-acquisition machine from the rest of the company. It can fund sales and marketing upfront and receive repayment only from the customer cohorts created by that spending, up to a capped return. If those customers underperform, the fund bears that downside rather than having a conventional claim on the entire company.

The idea sounds exotic until you compare it with almost every physical industry.

A manufacturer does not necessarily issue shares every time it wants another machine. An airline does not sell part of the company every time it adds an aircraft. Productive assets with sufficiently predictable cash flows can be financed separately because lenders know roughly what the asset will produce.

Technology companies have historically claimed to be “asset light,” but many of them spend enormous sums creating another kind of asset: customers.

If spending $100 reliably produces a customer who returns $180 of gross profit over a measurable period, then the acquisition process begins to resemble a machine. Put money in, receive a stream of cash out. General Catalyst has explicitly argued that CAC should be thought about more like capital expenditure once the underlying unit economics are predictable enough.

This reframes what maturity means for a startup.

Early on, almost everything is uncertain. You do not know whether the product works, whether anyone wants it, how much they will pay, how long they will stay or what it costs to acquire them. Equity is appropriate because equity absorbs uncertainty.

But eventually some parts of the company stop being uncertain. If the tenth million dollars of customer acquisition behaves a lot like the ninth million, financing it with the same expensive capital used to fund experimental R&D starts to look strange.

Equity should pay for uncertainty. Cheaper capital should increasingly pay for things you already understand.

That distinction could matter much more as companies become easier to start. AI is reducing the capital required to build products, but distribution can still be expensive. If customer acquisition itself becomes financeable, founders may eventually need less equity not only to create the product, but also to scale it.

The most valuable thing in that world is not merely good CAC or good LTV. It is predictability.

A business becomes easier to finance when its customers stop looking like a story and start looking like an asset.

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I have had the Jet2 jingle stuck in my head for about three weeks, and I have never once been on a Jet2 flight. I am not even sure I could find their terminal. And yet there it is, lodged between my own phone number and the chorus of a song I hate, this relentlessly cheerful little voice promising me that nothing beats a Jet2 holiday and that right now I can save fifty quid a person.

Sit with how strange that is for a second. A regional British package-holiday company, with a marketing budget I’d guess is smaller than a single Coca-Cola super-bowl spot, has colonized the brain of an American who will almost certainly never buy the product. And it did it because last summer roughly 6.6 million people voluntarily made videos using its ad, for free, on their own time, with their own footage. If you put a dollar value on that earned media you get a number that would get a CMO promoted twice.

So every founder and marketer looks at this and asks the same question, and it is the wrong one: how do we go viral too. Wrong question. The right one, the one almost nobody asks, is why this jingle and not the ten thousand others that just sit there being ignored. Because once you answer that, the whole thing stops looking like weather and starts looking like a thing you can build on purpose.

Here is the answer, and it is the only thing I really need you to take from this email: a viral asset is an unfinished joke. Jet2 wrote the setup and left the punchline blank, and the internet physically could not stop itself from finishing it.

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THE UNFINISHED JOKE

My working rule for whether anything spreads is dead simple. Can a stranger finish it?

Most jingles fail this instantly, because most jingles are statements. A brand name on a tune. A vibe. A warm feeling about laundry. You cannot finish a feeling. You cannot remix a mood. There is nothing for a human to do with a statement except nod at it, and nobody on earth has ever filmed themselves nodding.

A setup is the opposite. “Nothing beats a Jet2 holiday” makes a confident, falsifiable claim about reality and then just stops, leaving a blank where the punchline goes. And a confident claim with a blank after it is almost unbearable to leave alone. Every person who has had a flight delayed, a hotel flooded, or a seagull steal their chips already owns the punchline. It’s sitting in their camera roll. So they supply it. The chirpy voice insists nothing beats a Jet2 holiday, and underneath it a camel is chasing a screaming tourist down a beach. That gap is the joke, and the crowd writes it, millions of times, for nothing.

The asset isn’t the jingle. The asset is the blank.

THE SPECSAVERS PROOF

If Jet2 were the only example I’d call it a fluke and we’d all move on with our lives. It is not the only example. There is a brand that figured this out on purpose two decades ago and has been quietly compounding it ever since, and it is the single best proof that “leave the punchline blank” is a strategy and not a lightning strike: Specsavers.

In 2002 the British eyewear chain landed on “Should’ve gone to Specsavers.” The guy who wrote it has said the previous line got killed because it could have belonged to any brand. This one couldn’t, and not because it was witty. Because it’s a setup. Every ad shows somebody humiliating themselves because they can’t see, and the line lands like a verdict. Blunder is the setup. Slogan is the punchline. And because the joke is about making a dumb, universal mistake, the public never runs out of ways to finish it. Brits say it to each other in real life now. It’s in the dictionary of how the country talks. When a footballer bit an opponent at the World Cup, Specsavers fired off a one-liner about mixing a man up with a piece of pasta and got retweeted tens of thousands of times.

Twenty-plus years of free national media off one structural decision. Their own creatives have said the quiet part out loud: we’ve all gotten on the wrong bus or waved at a stranger, so the joke belongs to everyone. That is not a happy accident they noticed later. That is the spec they built to, on purpose, twenty years before Jet2 fell into the identical machine by accident. Same machine. The plan and the accident produced the same result, which is exactly how you know the result was never about the accident.

THE OCEAN SPRAY MIRAGE

Now the most expensive misunderstanding in the business, and the reason half of you are about to waste a quarter’s budget.

Everyone studies Ocean Spray and draws precisely the wrong lesson. In 2020 a guy in Idaho named Nathan Apodaca filmed himself longboarding to work, swigging Ocean Spray from the bottle, lip-syncing Fleetwood Mac’s “Dreams” because his car had died. Tens of millions of views. A 1977 song shoved back onto the charts. Ocean Spray gave him a truck. Gorgeous moment. Everyone wants one.

But look closely, because it is not an unfinished joke. “Dreams” doesn’t claim anything you can prove wrong. There is no blank to fill. This spread on a completely different engine: a feeling people wanted to perform, a ritual simple enough to copy, and a stranger so likeable the whole thing felt true. A mood, a ritual, a human.

And this is the part that should change your roadmap: you cannot build that engine. Ocean Spray did not make it happen and could not have. It required one specific man’s car to die on one specific morning and for him to be exactly that charming on camera. No agency can summon that. You can react fast and warm when lightning hits, which Ocean Spray genuinely nailed. You cannot order the lightning. Anyone pitching you a plan to “engineer an Ocean Spray moment” is selling you a scratchcard and billing you for strategy.

The unfinished-joke engine is the opposite of a miracle. It is almost boring how buildable it is. Write a setup, make it universal, repeat it until everyone knows the words. No lightning. A decision and some patience.

THE EARWORM TRAP

Third category, and it’s where most brand sounds go to die. The earworm. The Go Compare opera guy. The We Buy Any Car loop. Engineered to be catchy, and they succeed at being catchy, and they generate roughly zero participation, because there’s no joke to finish and no feeling to perform, just a tune jackhammered into your skull.

This is the confusion that costs the most money: memorable and spreadable are not the same property. An earworm is a statement you can’t forget. It is still a statement. It still leaves no blank. You can spend seven figures making a sound everyone knows and nobody touches.

YOU DON’T OWN THE PUNCHLINE

One honest catch, because I’m not going to sell you the upside without the bill. When you leave the punchline blank, you don’t get to pick who fills it in.

Last summer the same audio kept getting bolted onto clips the brand would never have chosen in a hundred years, including political and other content miles away from anything about holidays. Jess Glynne, who wrote the song, publicly condemned one of the uglier uses of it. The same setup that sold cheap family trips got finished with stuff the brand hated, and there was nothing Jet2 could do but stay quiet, which they did, and which only worked because they had never claimed the joke as theirs. The thing that made it spread, that it belonged to everyone, is the same thing that made it impossible to steer. That’s the trade. Build a setup good enough to travel and you are signing up for the fact that some of the punchlines will be ones you can’t stand.

WHAT TO ACTUALLY DO

Seven steps. One of them matters ten times more than the other six.

1. Mine the claims

2. Run the Stranger Test (the gate)

3. Produce it too loud

4. Saturate, especially the boring places

5. Listen for the spark

6. Crack one door, then leave

7. Pre-write the guardrails

1. Mine the claims

Get the team in a room and bang out 20 to 30 candidate setups. One rule on the whiteboard → claims, not vibes. A setup is a confident, falsifiable claim about something everyone has lived through.

The prompt that unlocks it → what does our brand say with a straight face that reality keeps making look ridiculous?

• “The joy of travel” is a feeling. Nobody finishes a feeling.

• “Nothing beats a Jet2 holiday” is a setup. That’s the job.

2. Run the Stranger Test (the gate, and the whole ballgame)

Take your best eight → put each in front of real strangers → one instruction: finish this. If a stranger can’t finish it, the internet won’t either. Full stop.

Run it for almost nothing:

• a Tally or Typeform poll to your list

• a post in the right subreddit or Discord asking people to complete the line

• a few hundred dollars of TikTok Spark and Meta on the raw setup, then read the comments and stitches

Measuring one thing → do strangers instantly supply a punchline, and is it funny?

The trap that kills most brands → passing an idea because the room loves it. The room is not the signal. Strangers finishing it is. Blank stares → back to step 1.

3. Produce it too loud

Build the winner. Push the delivery past the point where it feels comfortable. The spread lives in the gap between your certainty and reality → a polite, reasonable read leaves no gap to play in.

You nailed it when a teammate who knows the plan still winces at how earnest it sounds. That wince is the asset working.

4. Saturate, especially the boring places

Run it everywhere, for years. Pay the most attention to the unglamorous surfaces nobody fights you for:

• hold music

• packaging

• the checkout screen

• the boarding announcement

• the confirmation email

Familiarity is the multiplier → people can only finish a setup they already know cold. Jet2 made it the boarding music. Specsavers ran the line for twenty years.

And for the love of God, stop refreshing the creative every quarter. Every refresh sets the clock back to zero. The boring consistency is the moat.

5. Listen for the spark

You can’t predict the moment, but you can catch it early. Put someone on the asset itself, not just your brand name:

• track the sound or phrase in TikTok Creative Center

• stand up Brand24 or Brandwatch, plus a Google Alert

• keep saved searches running on TikTok, Instagram, and X

The tell → strangers using it without tagging you, especially to dunk on your whole category. Climbing week over week → you’re live.

6. Crack one door, then leave

When it catches, the hardest thing you will do is resist the budget. Do exactly one small thing:

• a single post that nods at the joke and hands people a slightly better version to play with

• maybe a light contest

Then stop. The joke only spreads while the brand is the target, not the author → roll a giant campaign on top and you kill it. Jet2’s entire move was one employee lip-sync and a voucher. The restraint was the play, not the video.

7. Pre-write the guardrails

You don’t own the punchline, so decide before anything blows up:

• what you engage

• what you ignore

• what makes you go silent

The worst possible time to figure that out is live at 11pm. One page → an escalation path plus clear “we say nothing” criteria. The most valuable line in that doc is the one that tells your team when not to post.

If you take one thing from all of this, take the gate. Almost every brand falls in love with its own idea and ships it without ever checking whether a stranger can finish it, and that one test will save you more money than everything else here combined. The rest is patience and nerve. So stop trying to go viral and start trying to be finishable. Going viral is something other people do to your asset. Your only job is to build something worth finishing, and then get out of the way.

Tom & Al

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The campaign

In 2021, IKEA Norway partnered with Oslo-based agency TRY to launch The Trash Collection. The team spent two days driving around Oslo and surrounding neighborhoods with cameras, pulling recognizable IKEA furniture out of scrap heaps, street corners, and waste stations. They found 16 pieces in just a couple of days: chairs, shelves, tables, lamps. Each one still repairable with minimal effort.1

Every piece was photographed twice. First in the place it was found, exactly as it was: cracked, scuffed, sitting in the garbage. Then again after refurbishment, cleaned up and looking close to new. Each image was labeled with where the item had been discovered, what repairs it needed, and its secondhand resale price compared to the original retail cost.

The results ran as a hero film, out-of-home posters, and a dedicated section on IKEA’s website that linked directly to its buyback and spare-parts services. Following the campaign launch, IKEA Norway’s monthly average of buyback products doubled. In the first nine months, they collected 5,400 secondhand products through the program.2

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Making guilt concrete

The obvious lesson is about honesty in sustainability marketing. But I think there’s a more specific one about what happens when you show your customers a problem they didn’t know they had.

Norway throws away over three million pieces of furniture every year.3 IKEA, as the world’s largest furniture retailer, is responsible for a significant share of that waste. They knew this. Their customers probably knew it on some level too. But knowing something abstractly and seeing your KALLAX shelf sitting in a ditch are very different experiences.

The Trash Collection worked because it made a vague guilt concrete. It gave people a specific image they could react to and a specific action they could take in response. The campaign didn’t introduce any new services. IKEA’s buyback program had been running since November 2020 and the spare-parts program had existed for years. What the campaign did was make people aware these services existed, through images striking enough that they couldn’t ignore them.4

IKEA marketing manager Frode Skage Ullebust acknowledged the discomfort directly: “I must admit that it hurts a little to see our furniture presented in this way, but at the same time, I think it has become a very honest and beautiful way to get people to reflect.”5

That willingness to accept short-term brand discomfort for long-term brand trust is the core of what made this work. Most sustainability campaigns try to make the brand look good. IKEA let the brand look implicated.

From stunt to strategy

The Trash Collection fits into a larger strategic shift at IKEA. This wasn’t a one-off stunt. The buyback program has scaled globally since its 2019 announcement. By 2023, over 211,600 customers had used the service across markets, double the participation from the year before. IKEA sold over 263,000 Second-Chance items through online reservation alone in 2023, up from 70,000 the prior year. Around 430,000 items total were given a second life that year.6

By the end of 2024, the buyback service had processed over 495,000 used products globally and expanded to 33 U.S. stores. IKEA has since launched IKEA Preowned, a peer-to-peer marketplace currently being tested in Oslo and Madrid, and invested over $1 billion through Ingka Investments into recycling infrastructure companies.7

The numbers suggest the circular model is working financially too. Ingka Group’s sustainability report showed IKEA reduced its climate footprint by 24.3% while increasing revenue by 30.9%.8 The buyback program creates a purchase cycle: a customer who returns an old shelf and gets store credit is highly likely to spend more than that credit on something new. The return becomes the start of the next transaction, not the end.

This also positions IKEA in the growing furniture resale market, which was projected to reach $16.55 billion by end of 2025 for living room furniture alone.9 Rather than watch that market develop around them on platforms like Facebook Marketplace and Craigslist, IKEA is building the infrastructure to capture it directly.

Why the creative worked

Three things made the Trash Collection campaign specifically effective, beyond the broader strategic context.

• First, it was real. There were no staged photos or stock imagery. TRY’s team went out with cameras and filmed actual discarded IKEA products in actual locations. Caroline Riis, senior creative at TRY, said: “We knew that if we were doing this, we’d have to do it properly. We’d have to do it real and honest.”10 The production constraint, having just two weeks and a couple of days to source the items, actually helped. There was no time to overthink or polish. The roughness was the point.

• Second, the before-and-after format did something clever with brand perception. Showing the product in a trash pile demonstrated that furniture waste is a problem. Showing the same product refurbished demonstrated that IKEA products are durable enough to warrant repairing. The campaign simultaneously acknowledged the waste problem and made an argument for the quality of the product. One image did two jobs.

• Third, every element connected to an action. The posters linked to the website. The website linked to the buyback service and spare-parts ordering. The film told viewers what they could do right now. There was no gap between the emotional reaction the campaign provoked and the practical step it asked people to take. The distance from “that’s terrible” to “here’s what I can do about it” was one click.

The campaign was recognized globally by IKEA as best practice, and TRY followed it in 2022 with a successor campaign called The Life Collection, which explored the human reasons people let go of furniture, from divorces to estate clearances.11 Both campaigns performed strongly across brand awareness, sustainability metrics, and activation. The approach of radical honesty paired with functional infrastructure became a template.

Infrastructure first, campaign second

The most common mistake in sustainability marketing is talking about values without connecting them to behavior. IKEA avoided this by building the infrastructure first and the campaign second. The buyback program and spare-parts service already existed. The Trash Collection simply gave people a reason to use them.

Tobias Lien, Marketing Communications Manager at IKEA Norway, summarized it well: “One thing we have learnt is that Norwegians really want to recycle, buy secondhand and contribute to reaching our common 2030 goals. They just need to know where to start.”12

That might be the simplest and most useful insight in the whole case. People generally want to do the right thing. They just need to know where to start. Build the infrastructure, then show them the problem honestly enough that they feel compelled to use it.

What Doritos did in the summer of 2023 was different. Not because it was technologically complex or because they spent more money. It was different because they noticed something everyone else had ignored.

The invisible characters

Every game has non-playable characters. NPCs. They’re the background filler, the shopkeepers and bystanders who exist to make the game world feel populated. Nobody pays attention to them. They’re so unremarkable that “NPC” has become internet slang for a person who moves through life without independent thought.

Doritos, working with BBDO Canada, decided to make NPCs the centerpiece of a campaign. Instead of sponsoring the outfits that players wear, which is the standard playbook, they created a high-fashion collection exclusively for the characters nobody looks at.

The logic was counterintuitive but sound. Everyone sponsors main character skins. That space is crowded and expensive. NPCs are a blank canvas that nobody has touched. By dressing the characters players are trained to ignore, Doritos created something genuinely surprising in a category where surprise is increasingly rare.

Lisa Allie, senior marketing director at PepsiCo Foods Canada, told Campaign Canada: “The most unexpected thing we could do to support our limited-edition flavour this summer was to make the clothing drop limited for NPCs. This was our opportunity to pull them out of the background and help them boldly stand out.”

What they built

The campaign launched a limited-time Doritos flavor in Canada: Tangy All Dressed. To promote it, BBDO Canada partnered with Montreal skate-couture designer Markantoine Lynch-Boisvert (known as MRKNTN) to create five bold, Doritos-branded outfits. These weren’t slapped together. They were designed as a legitimate fashion collection, with the bold colors and triangular shapes of the Doritos brand translated into streetwear.

The outfits were placed on custom NPC models inside “Doritos Drip City,” a fully playable Fortnite map built by Pixel Hunters. Each NPC held a bag of Doritos Tangy All Dressed and had a challenge associated with it that players could unlock by interacting with the character. The map launched July 5, 2023 and ran through the end of August.

The reveal happened through a two-hour Twitch live stream hosted by Canadian Fortnite creator Nick Eh 30, who has 5.8 million followers. The choice of creator mattered. Nick Eh 30 has genuine authority in the Fortnite community. He wasn’t just reading a brand script. He was exploring the map live, reacting to the NPC outfits in real time, and his audience was reacting with him.

The campaign extended across TikTok, Instagram, YouTube, and a custom 3D billboard at Toronto’s Yonge Dundas Square. A dedicated microsite at DoritosNPCs.ca let fans explore the collection outside the game.

The unplanned second act

Here’s where it gets interesting. During the Twitch stream, someone in the chat said they were sad they couldn’t add the NPC outfits to their actual wardrobe. Doritos already had the designs. So they took that feedback and turned the digital fashion line into real, physical clothing.

This wasn’t part of the original plan. It was a response to a signal from the audience, executed quickly because the creative assets already existed. The pivot gave the campaign a second wave of attention and earned media that extended well beyond the gaming community.

It also demonstrated something important about the relationship between digital and physical products. The virtual collection functioned as a proof of concept. Doritos could test demand for the designs risk-free inside a game, then produce physical versions only after they’d confirmed people actually wanted them. That’s a product development loop that fashion brands are only beginning to figure out.

The numbers

The Shopper Innovation Awards case study and One Club submission lay out the results clearly.

Sales of the Tangy All Dressed flavor jumped 36%. Brand awareness hit 70% among the target demographic. The Twitch live stream smashed benchmarks across the board: chat engagement was up 325.9%, viewership exceeded benchmarks by 279%, and total minutes watched surpassed benchmarks by 619.8%, per the 2023 Media Agency Campaign Tracking Report.

Inside the game, players averaged 30 minutes of playtime on the Doritos Drip City map, which is five times higher than the average for branded Fortnite experiences. PR coverage garnered 18.8 million impressions across 178 outlets, more than double the brand’s internal goal of 7.4 to 10.3 million.

The campaign won at the Strategy Awards and Shopper Innovation Awards in 2024. BBDO Canada’s submission noted that the campaign “uncovered a new placement opportunity in gaming and made the first fashion drop the brand’s target couldn’t wear into something they couldn’t miss.”

Why this matters beyond Doritos

Brand activations inside Fortnite nearly doubled between 2023 and 2024, rising from 136 to 270, according to GEEIQ’s 2025 State of Brands in Gaming report. Branded Fortnite maps now account for 33% of all virtual-world brand activations. Companies typically spend between $300,000 and $500,000 on a custom map.

But there’s a growing problem: many of these activations don’t perform. Digiday reported that Fortnite creators are increasingly vocal about brands wasting money on custom maps that draw tiny player counts. The debate mirrors what happened on Roblox, where brands learned that simply existing inside a game isn’t enough. You have to give players a reason to care.

Doritos found that reason by inverting expectations. The $50 billion character customization industry is built around players dressing their own avatars. Doritos said: what if we dress the characters nobody dresses? The inversion is what made it noteworthy, shareable, and genuinely fun to engage with.

The in-game advertising market is projected to reach somewhere between $124 billion and $156 billion by the early 2030s depending on which research firm you ask (Statista, IMARC). Sixty-two percent of Gen Z gamers say they discover brands through video games, making gaming more influential than television for this demographic, according to research cited by Amra and Elma. And Bain & Company’s Gaming Report 2025 found that 46% of gamers often make purchases based on in-game ads, up from 40% the year before.

The opportunity is enormous and growing. But as more brands pile into game worlds with generic activations, the ones that will stand out are the ones that find the creative equivalent of what Doritos found: the thing everyone overlooks, the NPC of marketing tactics, that turns out to be the most interesting thing in the room.

What to take from this

Three things worth carrying forward.

• First, look for the thing nobody is doing, not a better version of what everyone is doing. Every brand was sponsoring player skins. Doritos sponsored NPCs. The creative gap between “slightly better execution of the standard approach” and “genuinely novel angle on a familiar tactic” is where the outsized results live.

• Second, choose creators who authenticate, not just amplify. Nick Eh 30 wasn’t a generic influencer reading talking points. He was a Fortnite native with real credibility in the community. His two-hour live stream wasn’t a commercial. It was content his audience would have watched regardless, which happened to feature a brand activation worth reacting to. The difference between a creator who authenticates your idea and one who merely distributes it is the difference between 30-minute average playtime and the forgettable branded map that nobody visits twice.

• Third, build campaigns that can respond to their audience. The physical clothing line wasn’t planned. It happened because someone in a Twitch chat expressed a desire, and the team was set up to act on it. Having the flexibility to extend a campaign based on real-time audience signals is increasingly what separates good activations from great ones. Plan the first move. Leave room for the second one to emerge from the response.

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Okay. Today’s issue:

Rovio made 51 games that nobody cared about. Then they made Angry Birds with €25,000 and twelve people left in the building. Three billion downloads later, here’s what actually happened.

2009. Helsinki. Twelve people left.

That’s where this story starts. Not with a brilliant eureka moment, not with VC money or a Stanford dorm room. It starts with a Finnish studio that was basically cooked.

Rovio Entertainment, or Rovio Mobile as it was called then, had been grinding since 2003. Three students from the Helsinki University of Technology, Niklas Hed, Jarno Väkeväinen, and Kim Dikert, won a Nokia and HP-sponsored mobile game competition and figured hey, let’s start a company. Made sense at the time. They were good at building games. Distribution though? That was another thing entirely.

For six years they shipped games. Sci-fi stuff, horror stuff, titles that were probably decent but went nowhere because nobody could find them. They made 51 games. Fifty-one. And by 2009 the money was basically gone. They’d had to let most of the team go. Twelve people left in the building. Maybe eight by some accounts. However you count it, tiny.

The board brought in Mikael Hed, Niklas’s cousin, as CEO. New blood, same problem: they had enough budget for maybe one more swing. One game. €25,000. That’s like $27,000 in today’s terms, which is not a lot of money to build anything, let alone a game that would eventually be downloaded three billion times.

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The Sketch That Changed Everything

The iPhone had come out in 2007 and the Rovio team, to their credit, were paying attention. They spent time thinking about what kind of person uses an iPhone. Their answer was basically: everyone. That sounds obvious now, but it was actually a pivot from how they’d been thinking. Their previous games were niche, made for specific audiences. This time they wanted something universal.

So they put Jaakko Iisalo, their senior game designer, on concepts. He pitched a bunch of ideas, nothing was clicking. Then at some point in early 2009, he shows up with a screenshot. Just a static image. Round, angry-looking birds. No legs. No wings visible. Just these little furious creatures staring out of the screen.

Mikael Hed’s description of what happened next: “People saw this picture and it was just magical.” Which is kind of funny because on paper, angry legless birds are not a business plan. But there was something there. The characters had personality before the game even existed.

They built a game around the image. Physics games were popular at the time, specifically something called Crush the Castle, and they basically took that mechanic, birds launching from a slingshot to knock structures down, and wrapped it around these characters. The pigs became the villains because swine flu was everywhere in the news in 2009, which is maybe the most random product decision in startup history, but it worked.

They built the thing in about six months. The whole time, they were also doing contract work for other companies to keep the lights on. So the game that would eventually save the company was made on the side while they were building other people’s games.

The “we knew we had something” moment: every time a developer shot a bird to test a feature, five other people in the office would end up watching. And then Niklas’s mother, who doesn’t play games, played a pre-release build and burned her Christmas turkey because she got too into it. If your non-gamer parent can’t put your game down, you’re onto something.

Angry Birds launched on iOS in December 2009.

It went nowhere.

The Distribution Play

The US and UK markets basically ignored it. And here’s where the story gets actually interesting, because what Rovio did next is the part that gets left out of the usual “persistence pays off” version of this story.

Instead of trying to force their way into the big markets, they went small. Finland first. A few hundred downloads and they hit the top of the Finnish charts. Then Sweden. Then Denmark. Then Greece and Czech Republic.

This wasn’t random. Building traction in smaller markets where competition is lower is genuinely smart. You’re not fighting for attention against every major game studio on the planet, you’re building a base. Real downloads, real reviews, real signals.

When Angry Birds got to 40,000 downloads, something shifted. Apple noticed. On February 11, 2010, Apple featured the game as the UK App Store’s game of the week. From there the thing went vertical. Angry Birds went from #600 on the UK App Store charts to #1. By April 2010, it was #1 in the US too.

That’s the part worth sitting with. The game didn’t win because it got a big marketing push or raised a bunch of money. It won because Rovio built enough real traction in places where traction was achievable, and eventually a platform gatekeeper took notice. The small market strategy was basically a proof-of-concept loop that fed into the distribution break they needed.

Two Revenue Models, Running at the Same Time

Once the game was actually hitting, Rovio had to figure out money. And they did something smart here too.

On iOS, Angry Birds was paid. $0.99. On Android, it was free with ads. The iOS downloads were a clean revenue stream. The Android ads were netting them £600,000 a month at peak. Different platforms, different user behaviors, different monetization models to match.

They also had an in-app purchase: the Mighty Eagle, which let you skip any level for 89 pence. It got downloaded over two million times. That’s a $3.5 million-ish revenue line from one optional feature.

And then the physical stuff. They launched plush toys almost immediately. First order: 12,000 units. They sold 60,000. That’s not a demand forecast problem, that’s a signal. They kept going. By 2013, they had over 30,000 Angry Birds products in more than 500 locations worldwide. Merchandise alone accounted for close to half of their $216 million in revenue that year.

Read that again. A mobile game company made $216 million in revenue in 2013 and half of it came from physical stuff. Plush toys, board games, shirts, whatever. The game was the entry point. The brand was the business.

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The Overextension Problem

Okay, here’s where the story gets honest.

By 2014, things started breaking. Merchandise revenue fell 43% that year. Rovio laid off 260 people in 2015. They went from this company that had turned down a $2.25 billion acquisition offer (yes, they actually turned that down) to a company closing studios and restructuring.

What happened?

A few things, and they kind of compound each other.

One: they over-reliant on licensing revenue, which is inherently cyclical. Angry Birds merchandise was hot because Angry Birds was culturally relevant. When the cultural moment passed, the merchandise business went with it. You can’t sell 30,000 different products forever when the thing they’re based on stops being what everyone’s talking about.

Two: they tried to stretch the IP in every direction at once. Angry Birds Epic was an RPG. Angry Birds Transformers was a side-scroller. Angry Birds Go was a racing game. Angry Birds POP was a bubble shooter. Some of these were fine. But each one diluted what Angry Birds actually was. The slingshot mechanic was the product. When they abandoned the mechanic and kept the characters, they basically made generic mobile games with Angry Birds paint on them.

Three: the original game got retroactively ruined. The $0.99 paid game with no ads, the one that people loved, got converted to free-to-play with ads and microtransactions. So even the flagship product that built the whole thing stopped being what people remembered.

By 2016, Rovio was in a rough spot. Then The Angry Birds Movie came out, opened at number one in 50 countries, and basically saved them. Revenue hit $201 million that year. They went public on the Helsinki Stock Exchange in 2017 at about a $1 billion valuation.

Then in August 2023, Sega bought them for $776 million. Which is either a good outcome or a sad one depending on how you look at a company that turned down $2.25 billion and sold for $776 million eleven years later.

What Actually Matters Here

This story gets told as a persistence narrative, and that’s not wrong, but it’s not the whole thing. A few things actually worth taking:

• Distribution is the actual moat. The game that became the biggest mobile game of its era was not an instant hit. It became one because Rovio built real traction in small markets instead of trying to win everywhere at once. You can have a genuinely good product and still fail if you’re fighting for attention in a market that’s too crowded to notice you.

• The iPhone changed the rules and Rovio saw it. They explicitly reoriented their product strategy around a new platform and new user behavior. “The iPhone user is everybody” sounds obvious, but acting on that meant building differently than they’d built before. The people who don’t adapt when platforms shift are usually the ones you don’t hear about.

• Characters > mechanics. The birds and pigs had personality before the game was designed. That’s backwards from how most games are built, and it’s probably why the brand extended as far as it did. If you’re building something that might have brand potential, the characters matter more than you think.

• One-trick companies eventually pay for it. Rovio’s peak was built on one IP. When they tried to diversify they did it wrong, by expanding formats instead of building new properties. The stuff that worked, the movie, the original game, was almost always a return to the core thing that people actually cared about.

And maybe the most uncomfortable one: turning down $2.25 billion when you’re at your peak is usually a mistake.That’s not a lesson in having confidence in your vision. That’s a reminder that the best time to take the money is when you have the most leverage, and leverage disappears faster than you expect it to.

Rovio is still around. Angry Birds is still being played. And a Finnish studio with €25,000, a sketch of some angry birds, and twelve employees standing between them and nothing built something that 3 billion people downloaded. That’s real, whatever happened after.

The thing they got right wasn’t persistence for its own sake. It was knowing what they had the moment Jaakko showed up with that image, being willing to stay small long enough to build real proof, and then executing fast when the opening showed up.

Everything after that is just what happens when companies get big.

That’s it for today.

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See you Thursday.
Tom

Some startups feel inevitable while they’re happening. The product works, the growth looks real, and the story makes intuitive sense to everyone watching from the outside. Investors see a huge market, the media amplifies the narrative, and the company begins to feel less like an experiment and more like a foregone conclusion.

23andMe once felt like that kind of company. Millions of people willingly mailed their DNA to a lab, helping the company build one of the largest consumer genetic databases in the world. The science was credible, regulators approved the tests, and pharma companies were interested in what the data might unlock.

But interesting technology doesn’t always turn into a durable business. Sometimes the product works exactly as intended—and the business model still breaks.

That’s the story this week.

Estimated read time: 3 minutes, 20 seconds.

In 2015, 23andMe hit a milestone: one million DNA samples genotyped.

At the time, that felt like proof of concept. A consumer genetics company had convinced a million people to spit in a tube, mail it to a lab, and wait for a report on their ancestry and health risks. That was genuinely novel. The science was real. The product worked.

By 2018, they had 5 million customers. By the time they went public in 2021, they had 10 million. The pitch to investors was obvious: this is the largest crowdsourced genetic database in history, it’s proprietary, it takes years and hundreds of millions of dollars to replicate, and we’re just getting started on what we can do with it.

The stock hit $17.65. The company was worth $6 billion. Anne Wojcicki, founder and CEO, became a billionaire.

Four years later, all outstanding common stock was canceled and discharged in bankruptcy court. Shareholders got nothing.

Here’s what actually happened.

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The backdrop

To understand why 23andMe failed, you have to understand what it was trying to be.

It launched in 2006 as a direct-to-consumer genetics company. The original product was simple: pay around $100, get a kit, send back your saliva, receive a report. Ancestry. Traits. Health risk flags. For a lot of people, it was fun. Oprah put the kit on her “Favorite Things” list. Celebrities talked about it. The category exploded.

The FDA slowed them down in 2013, sending a warning letter over health-related claims in the testing. The company took its health results products off the market and spent two years navigating regulatory affairs before winning FDA approval for consumer-focused genomic health tests. That was actually a meaningful moment. No one had done that before. First FDA-approved direct-to-consumer genetic health test. Real milestone.

But the business had a structural problem baked in from day one, and Wojcicki knew it. The kit was a one-time purchase. You do it once, you get your results, you move on. There’s no natural reason to come back. No consumable. No ongoing service that requires re-engagement. Just a report you check a few times and mostly forget about.

By 2019, kit sales had dropped 46% compared to 2018. One year, nearly half the revenue, gone. That’s not a bad quarter. That’s the market telling you the TAM has limits.

23andMe had two options at that point. Fix the consumer business. Or bet on the database.

They tried both, and succeeded at neither.

The subscription that didn’t convert

The obvious fix for a one-time purchase problem is a recurring model. 23andMe introduced a premium subscription product in 2020 that it hoped would make up for the lack of recurring revenue. The strategy failed to pan out.

In retrospect, this was almost inevitable. The product front-loaded all its value. You got your DNA results. You understood your ancestry. You saw your health risk flags. The report was comprehensive and, once read, mostly static. What exactly were you renewing for?

The company pursued other ventures, launching premium features such as matching a person’s DNA to historical figures and offering tests to calculate a person’s “biological age.” These are interesting parlor tricks. They’re not $20/month reasons to stay subscribed.

The subscription thesis required 23andMe to become a continuous health platform, not a one-time insight product. That’s a completely different company, with different product infrastructure, different clinical partnerships, different regulatory relationships. They never made that transition.

The pharma bet

So they went bigger.

If the consumer business had a ceiling, the real play was licensing the database to pharma companies for drug discovery. 15 million genotyped customers, health histories, longitudinal research opt-ins. That’s genuinely valuable to a pharmaceutical company trying to identify drug targets faster.

In 2018, 23andMe struck a deal with GlaxoSmithKline that included a $300M upfront investment, giving GSK exclusive rights to use 23andMe’s anonymized genetic database to identify novel drug targets.

That was a real deal. Real money. Real validation. GSK believed in the database enough to write a nine-figure check.

The problem: customers were “surprised and angry, unaware of what they had already signed and spat away.” The deal was legal. 23andMe’s privacy policy disclosed data could be used for research. But most customers had no idea that’s what they were consenting to when they checked the box. The trust erosion started here, years before the data breach.

GSK extended for a fifth year. Then the partnership ended. After the GSK deal ended, 23andMe didn’t find another pharmaceutical partner and its revenue took a hit.

That’s the whole pharma thesis in one sentence. They got one deal. When it ended, no one else stepped up at the same scale. The database that was supposed to power a platform of pharma relationships turned out to produce one anchor customer that eventually moved on.

The drug discovery gamble

Rather than doubling down on licensing, Wojcicki decided to go even further upstream: 23andMe would develop drugs itself.

Wojcicki later recounted how she was warned against doing drug research, which can cost hundreds of millions of dollars, require several years, and doesn’t guarantee success. She did it anyway.

R&D expenses consistently exceeded $150M annually from FY2022 to FY2024. The drug pipeline produced two candidates that reached early-stage clinical trials. Out of thousands of compounds. That’s actually not terrible by biotech standards. By 23andMe’s financial standards, it was a disaster. They were spending like a pharma company while generating revenue like a struggling consumer startup.

23andMe was “always very optimistic about the business and strategy,” said a former analyst, but “typically you don’t have investors who like hybrid companies.”

That’s the crux of it. Public markets want a story they can underwrite. A consumer brand is one story. A drug discovery company is a different story. 23andMe was asking investors to hold both simultaneously, while neither was working.

The data breach

Then came October 2023.

Hackers accessed the information of nearly 7 million customer accounts. Names, birth years, ancestry reports, health data.

For most companies, a breach is a bad news cycle and a settlement. For a company whose entire value proposition was “trust us with your most sensitive biological data,” it was a different category of problem. The thing customers had always been vaguely nervous about had actually happened.

By September 2023, 23andMe’s share price slid below $1. The company reported a $312M net loss in fiscal 2023.

Subscriptions weren’t growing. The pharma pipeline had stalled. The drug discovery was burning cash without returns. And now the trust angle was gone too.

The governance collapse

What followed was as messy as anything in recent startup history.

In September 2024, all seven independent directors resigned en masse, citing frustration with Wojcicki’s strategic direction and her efforts to take the company private. She remained the sole board member. A few months later, the company engineered a reverse stock split, consolidating every 20 shares into one, to avoid being delisted from Nasdaq.

When 23andMe reported earnings in November 2024, there were no Wall Street analysts on the call.

That detail says everything. Not the stock price. Not the layoffs. No analysts. The financial community had already moved on.

In February 2025, Wojcicki submitted a proposal to buy 23andMe that valued the company at $75 million—down from a $6B peak. The board rejected it. A month later, she filed for bankruptcy.

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The strange ending

In an unexpected turn, the winning bidder for 23andMe’s data was a nonprofit created and controlled by 23andMe’s own founder. TTAM Research Institute acquired the genetic database for $305M.

The same person who ran the company into the ground bought it back, at a fraction of the peak valuation, through a newly formed nonprofit entity. The bankruptcy court approved it over a competing bid from Regeneron.

All 27.5 million shares of existing common stock were canceled and discharged. Public shareholders got essentially nothing. Wojcicki kept the database.

What actually went wrong

The easy answer is “no recurring revenue.” That’s true but incomplete.

The deeper answer is that 23andMe was always two or three businesses at once, none of which fully worked, and the company spent a decade burning cash trying to make all of them work simultaneously.

The consumer business had a natural ceiling. The subscription product never found a compelling enough reason to renew. The pharma licensing produced one major deal that ended. The drug discovery was expensive and slow and required a different company than the one they had. And the data breach destroyed the trust that was the foundation of everything.

23andMe was still operating like a startup long after the runway ran out.

They had 15 million customers. They had a database nobody could replicate. They had real scientific credibility and an FDA-approved product. None of it translated into a durable business because the unit economics never worked, the retention was structurally broken, and every attempt to fix it added cost without adding revenue.

The DNA was real. The business model never was.

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See you tomorrow (Monday)—Tom

He didn’t speak Japanese. His co-founder Simon Last didn’t either. They’d just laid off their entire team. His mother had lent him $150,000 to keep the company alive because there was nobody else to ask. They ate noodles every day, reading kanji well enough to tell beef from chicken, came home, and coded for another eighteen hours.

They were building Notion. For the second time. The first version hadn’t worked. So they scrapped everything... the codebase, the architecture, the product decisions, all of it... and started over from nothing.

Ten years later the company does $600 million in revenue, has 100 million users, and has never had a VC on its board.

Here’s how that actually happened.

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The Kyoto reset

By 2015, Ivan Zhao had burned through most of his $2 million seed round and had nothing to show for it. The product was built on the wrong technical stack... CouchDB and WebKit, optimized for offline-first functionality that turned out to be the wrong bet entirely. Sync was broken. The interface was a mess. He laid off the entire team. That left two people: Zhao and co-founder Simon Last.

They closed the San Francisco office and moved to Kyoto. Not a calculated strategic relocation. More like controlled desperation. Japan was cheaper. They didn’t know anyone there. Neither of them spoke Japanese. They survived by reading menus well enough to tell beef from chicken. Zhao’s mother lent him $150,000 to keep the lights on.

They spent 18 hours a day rebuilding the product from scratch. New codebase, new architecture, new everything. Coded in their underwear. Went out for noodles. Came back and coded more. For a year.

What came out of that year was Notion 1.0. Launched March 2016. Hit #1 product of the day, week, and month on Product Hunt.

The business that almost didn’t raise

Here’s what’s strange about Notion’s funding history.

2019 Series A: $10 million at $800 million valuation. Team still under ten people. Revenue: $3 million. That’s a 267x revenue multiple, which is either visionary investor conviction or peak Silicon Valley madness depending on who you ask.

2020 Series B: $50 million at $2 billion valuation. Index Ventures wired the money 36 hours after Zhao started looking. Sequoia made their decision after reviewing the numbers for thirty minutes.

2021 Series C: $275 million at $10 billion valuation. Led by Coatue and Sequoia.

Total raised across twelve years: $343 million. Not a single investor got a board seat. Zhao still owns at least 30% of the company. That almost never happens at this scale. Most founders are diluted into irrelevance long before $10 billion. Zhao structured every round to sell 2-3% equity at most... which is why seed investors are sitting on roughly 370x returns on paper.

The reason he could do this: he didn’t need the money badly enough to give anything away. Which is a function of running lean for so long that he had leverage in every negotiation.

No sales team. No marketing budget. Just templates.

Notion’s entire distribution strategy for the first five years was product-led growth and community. No outbound sales. No paid acquisition. No growth team running experiments on the signup funnel.

What they had was a template ecosystem. Notion shipped 30 templates when they launched. The community built thousands more. Every template was a use case... project management, personal CRM, reading lists, startup operating systems, study trackers. Each one was a demonstration of what Notion could do without Notion having to explain it. Users became recruiters. The average Notion power user has introduced it to at least three other people. That’s not marketing. That’s product.

Revenue numbers tell the story of what happens when a product grows through genuine utility rather than spend:

2019: $3M → 2020: $13M → 2021: $31M → 2022: $67M → 2023: $250M → 2024: $400M → 2025: $600M

The jump from $67M to $250M in a single year is not a sales motion. That’s a product that hit an inflection point... partly the return-to-office wave, partly enterprise adoption, partly a TikTok moment in early 2021 that crashed their servers and brought in a wave of users who’d never heard of productivity software before.

80% of Notion’s 100 million users are outside the United States. They never ran a localization campaign. People in Korea and Japan and Brazil just started using it and telling other people.

Why no board seats matters more than people realize

The single most underrated decision in Notion’s history is the board structure.

Most companies at $100M ARR have four or five VCs sitting on their board telling them to expand into enterprise, build a sales team, launch a second product line, grow headcount faster. The board meeting becomes a quarterly negotiation between the founder’s instincts and investors’ pattern-matching from other companies.

Zhao never had that conversation. Every product decision was his. When users complained about the mobile app for three years straight, he shipped it when it was right, not when the board got uncomfortable. When the market was pushing everyone toward feature bloat in 2018 and 2019, Notion stayed narrow. When the AI wave hit, they moved fast... Notion AI launched November 2022, two weeks before ChatGPT. Now more than 50% of customers pay for AI features. That timing and that speed comes from a company where one person can make the call.

You cannot maintain that kind of decision velocity with five investors in the room.

The actual numbers

$600M ARR in 2025. 100 million users. 4 million paying customers. Over 50% of Fortune 500 companies using it. ~800 employees total, which means roughly $750,000 in revenue per employee. Profitable, or close to it. More cash on the balance sheet than they’ve raised in their entire history.

IPO likely 2026. At current trajectory, $1 billion ARR by then. Public market comp probably $15-20 billion.

From two people coding in their underwear in Kyoto on a $150,000 loan from a founder’s mother.

The thing that’s easy to miss in Notion’s story is that none of the outcomes were inevitable.

Zhao could have pivoted the thesis when the first product failed. He could have taken the board seats when investors wanted them. He could have hired a sales team and chased enterprise earlier. He could have launched five products instead of one. Every one of those decisions would have been defensible. Rational even. Most founders in his position would have made at least one of them.

He didn’t. Not because he had better information. Because he had a specific kind of stubbornness about the problem he was trying to solve... and enough self-awareness to know that the itch to do something different was almost never a signal about strategy. It was usually just impatience with the difficulty of the thing he was already doing.

That’s not a business insight. It’s a psychological one.

And it’s probably the hardest thing to teach.

See you Friday—bring a problem worth staying with.

Tom

Every week I sit down and try to write my best newsletter yet.

Maybe today I did it.

Estimated read time: 2 minute 17 seconds.

Everyone in tech is looking at the wrong number.

They’re watching SaaS revenues, cloud margins, API pricing wars. They’re debating whether OpenAI can justify a $730 billion valuation based on what developers will pay per token. Smart people, running serious models, all pointed at the same thing: software eating software.

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But the real play here isn’t software at all. It’s the roughly $61 trillion that the global economy spends every year on human labor. That’s the number that matters. That’s the ocean these companies are sailing toward and most people are still arguing about the dock.

Think about what actually costs money inside a company. Not the Salesforce license. Not the AWS bill. It’s people. It has always been people. Payroll is typically the single biggest line item in most companies’ operating budgets. An accountant reviewing contracts. A paralegal doing document review. A junior analyst building decks nobody asked for. A customer service rep explaining a return policy for the four hundredth time that week. A radiologist reading scans. These are not edge cases. This is the whole economy.

Software has been chipping away at this for forty years and it barely made a dent. Spreadsheets didn’t replace finance teams. CRMs didn’t replace sales teams. ERPs created entire new categories of consultants to implement and maintain them. Software made workers more productive but it didn’t replace the work itself because software, until very recently, could only follow rules. And most valuable human work isn’t rule-following. It’s judgment.

What changed is that AI actually has judgment now. Or something close enough to judgment that the distinction stops mattering in most business contexts. It can read a messy contract and find the problematic clause. It can look at an x-ray and grade the severity. It can write a passable first draft of almost anything and a genuinely good fifth draft if you push it. It can answer customer questions with context and nuance. Not perfectly. But good enough. And good enough at a tenth of the cost is a completely different market than perfect at the same cost.

This is the shift that is being underpriced by most of the conversation happening right now.

The software market, giant as it is, was always a margin game on top of labor. You buy the tool so your people can do more. The new game is different. The new game is what happens when the tool starts doing what the people do. You are not selling productivity software anymore. You are competing with the salary line.

Marc Andreessen famously said software is eating the world. That was true and it was a big deal. But labor is the world. Global GDP sits at roughly $117 trillion as of 2025. The ILO puts labor’s share at about 52 percent of that, which gets you to somewhere around $61 trillion flowing to workers every year. Software was eating the world but it was eating the edges, the distribution, the interface layers. AI is going after the core.

And the core is enormous in ways that the software comparison doesn’t capture. The global software market is around $750 to $800 billion. The global market for knowledge work alone, white collar wages in industries that look automatable, is somewhere north of $15 to $20 trillion just in the developed world. That’s a 20x difference. The addressable market for AI if it can genuinely replace, or more likely redistribute, even a fraction of that labor is not a software story. It’s something that doesn’t have a clean historical analogy.

There’s a version of this that sounds dystopian and a version that sounds like the biggest economic unlock in human history. Both are probably partially right, which is the uncomfortable thing nobody wants to sit with. The economy will create new jobs, it usually does, but the transition is real and the people most exposed are not the people with the most political power, which makes the policy response slow and inadequate. But that’s a different essay.

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The point for now is simpler. If you are trying to understand where the value in AI is going to land, stop thinking about software multiples and start thinking about what things cost when a smart capable person does them versus what they’ll cost when AI does them just as well. The spread between those two numbers, multiplied across the entire global economy, is the actual market. And it is not a software company number. It is something larger than most people have a comfortable mental model for.

The software industry spent fifty years building picks and shovels for human workers. The next twenty years are about what happens when the picks and shovels learn to mine.

If you like what you read please do “add to address book” or reply (it helps with deliverability). If you don’t you can unsubscribe below.

See you tomorrow (Monday)—Tom

Let me tell you about a jar of almond butter.

It’s sitting in my pantry right now. Creamy, no added sugar, dry-roasted. The label is clean—simple font, earthy color palette, nothing flashy. It tastes, genuinely, excellent. I’ve been buying it for three months straight.

It’s Trader Joe’s house brand. It costs $4.99. The “real” version I used to buy—same ingredients, nearly identical taste, much cooler branding—runs $13 at Whole Foods.

I made my peace with this a while ago. Apparently, so did everyone else.

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We are living through the golden age of the dupe. And I don’t mean the TikTok kind, where a fast fashion brand knocks off a designer bag and a 22-year-old gleefully holds both up to the camera. I mean something quieter and more structurally significant: the systematic, sophisticated, and increasingly unapologetic rise of private-label CPG.

Store brands used to be the walk of shame of the grocery aisle. The Great Value ketchup. The off-brand cereal in the bag instead of the box. The thing you bought when you were broke and pretended you didn’t. The implicit message was: you couldn’t afford the real thing, so here’s this.

That stigma is gone. And it’s not coming back.

In 2024, private-label products reached a record share of total U.S. grocery sales — crossing 25% of units sold in many categories. The Private Label Manufacturers Association reported that store brand dollar sales grew faster than national brands for the third consecutive year. And perhaps more telling than any statistic: consumers who traded down to store brands during the inflation crunch of 2022 and 2023 largely haven’t traded back up.

They tried the dupe. They liked it. They stayed.

What changed, exactly?

A few things converged at once, and it’s worth pulling them apart.

First, the quality gap actually closed. This is the foundational shift that makes everything else possible. Retailers—especially the ones who take food seriously—spent years investing in their house brands. Trader Joe’s built an entire identity around the concept. Costco’s Kirkland Signature became a punchline in the best possible way: people genuinely brag about it. Target’s Good & Gather line launched in 2019 with a real food-first philosophy and has expanded to over 2,000 products. Whole Foods’ 365 brand was acquired and amplified.

These aren’t “close enough” products anymore. Many of them are made in the same facilities as the national brands, sometimes literally on the same production lines, with tweaked formulations and unbranded packaging. The gap between a $14 olive oil and a $7 house brand olive oil, in terms of actual taste and quality, is—in many cases—negligible.

Second, inflation broke the spell of premium pricing. When grocery bills spiked 20%+ between 2021 and 2023, consumers became ferocious label-readers—and not just for ingredients. They started comparing prices with real scrutiny for the first time in years. What they found, in category after category, was that the premium they were paying for name brands wasn’t necessarily buying them better product. It was buying them marketing. Distribution. The familiarity of a logo they’d grown up with.

For a lot of people, that was a genuinely clarifying moment.

Third—and this one is underappreciated—the cultural cachet of “finding a good dupe” became a whole thing.TikTok didn’t invent dupe culture in CPG, but it turbo-charged it. There are entire accounts dedicated to blind taste tests between store brands and their name-brand equivalents. Costco haul videos. Aldi finds. “I switched to this and never looked back” content that performs extremely well because it feels like insider knowledge—like you’re being let in on a secret the brands don’t want you to know.

Frugality used to be embarrassing. Now it’s a flex.

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So what does this mean for emerging and premium brands?

Here’s where it gets complicated. Because the story isn’t simply “store brands win, everyone else loses.” It’s messier and more interesting than that.

The brands getting hurt worst are the ones in the middle—established national brands with significant legacy overhead, mature distribution, and no particularly compelling reason to exist beyond decades of habit and shelf placement. Think: the third-ranked salad dressing, the second-most-popular pasta sauce, the private equity-owned snack brand coasting on a name from the ‘90s. These are the products getting squeezed from both sides—by premium indie brands on one end and private label on the other.

If you don’t have a genuinely differentiated product or a price advantage, you’re in trouble.

For emerging premium brands, the calculus is different—but the pressure is real. Here’s the core tension: the consumer who is willing to pay $12 for your hot sauce is increasingly asking why. Not in a hostile way. In a legitimate, show-me way. They’ve been burned by enough pretty packaging over enough years that the aesthetics alone don’t move them anymore. The bar for justifying a premium has gone up.

What justifies it now?

A few things actually work. True differentiation — a genuinely novel ingredient, process, or flavor profile that can’t be easily replicated at scale — still commands a premium. Provenance and transparency—brands where the sourcing story is real and verifiable, not just a font choice—still hold power with a meaningful slice of consumers. Community and identity—the brands where buying the product is a statement of belonging to something—still convert.

What doesn’t work anymore is vibes alone. Clean branding used to be enough to signal quality. Now it signals that you hired a good designer. That’s not nothing, but it’s not sufficient.

The most interesting response I’ve seen from premium brands is a kind of radical honesty—leaning into what makes them expensive rather than obscuring it. Less “crafted with care” vagueness, more “we pay our farmers 40% above market rate and here’s exactly what that does to our margins.” Transparency as differentiation. It’s not a mass strategy, but for the right brand with the right audience, it’s working.

A word on the retailers

It would be a mistake to discuss the dupe economy without acknowledging that the incentives here are not neutral.

Retailers love private label. The margin structure is significantly better than selling national brands. They control the supply chain. They control the placement. They can put their house brand at eye level and nudge the name brand to the bottom shelf, and there’s not a lot the brand can do about it. As private-label quality has improved, retailers have become increasingly aggressive about expanding their house offerings into new categories—including ones that were, until recently, considered “safe” premium territory.

This creates a genuinely uncomfortable dynamic for brands that rely heavily on any single retail channel. You’re essentially helping your landlord understand which categories are profitable, and then watching them compete with you in those categories.

The brands navigating this best are the ones diversifying their retail footprint, investing in DTC, and building direct consumer relationships that exist outside of the retailer’s ecosystem. The brand that lives entirely on grocery shelves, with no meaningful community or DTC presence, is increasingly vulnerable.

I want to end with something that I think gets lost in these conversations.

The dupe economy is, in many ways, a good thing for consumers. More access to quality products at lower price points is not a problem. The fact that a family that’s watching their grocery budget can buy excellent olive oil and almond butter and pasta without paying a premium for a logo—that’s genuinely good.

The challenge for the industry is figuring out what role branded products play in a world where quality is no longer a reliable differentiator at the premium tier. And I think the honest answer is: the brands that survive this era will be the ones that offer something a store brand structurally cannot—a real point of view, a community, a story that is verifiably true and meaningfully different.

That’s a higher bar than it used to be.

But for the brands that can clear it, the dupe economy might actually be clarifying. It’s forcing the question every brand should be able to answer anyway:

Why do you exist?

Thoughts? Brands you’ve switched to or away from? Reply and let me know—best responses might end up in a future issue.

— Tom

If you found this useful, forward it to someone in CPG, retail, or anyone who has ever felt smug about their Kirkland Signature find. (That’s all of us.)

The AI wrapper class is collapsing. This is not a controversial statement anymore. A Google VP said it on TechCrunch last week. A Forbes Council post said it the week before. What both pieces got wrong, and what actually matters, is what happens to the one or two companies in this category that won’t collapse. Because those companies are going to be worth more than anyone currently thinks.

The MySQL Rebuttal + Mowry Pivot

There is a popular defense of the wrapper startup that goes like this: calling a company an AI wrapper is like calling Salesforce a MySQL wrapper. Technically true. Completely meaningless. The YC partners made this argument on their Lightcone podcast, and they are not wrong in the abstract.

But the analogy only holds if the company actually built something on top of the infrastructure. Salesforce did not wrap MySQL. It built an entirely new category of organizational software that happened to use a relational database underneath. Most AI wrappers did not do that equivalent. They built a UI and called it distribution. They built a prompt and called it IP. They built a landing page with a gradient and called it a company.

Google’s VP of global startups made a sharper analogy last week. He compared today’s AI aggregators to the AWS resellers of 2010, middlemen who marketed themselves as easier entry points until Amazon built enterprise tools directly and squeezed them out. The only survivors were the ones that had added real services before Amazon noticed them: security, migration, DevOps consulting.

He is right about aggregators. The more interesting question is which wrappers are the Rackspace of this cycle, the ones that built real services before the platform noticed them, and which ones are just reselling compute with a gradient. That distinction is worth a few billion dollars and almost nobody is drawing it cleanly.

The test is simple: if OpenAI ships a feature and your product evaporates, you were never a company. You were a gap in someone else’s roadmap. And OpenAI closes gaps for sport.

Dozens of “chat with your PDF” startups learned this when ChatGPT added document uploads natively. Dozens of AI meeting summary products learned it when Zoom, Teams, and Google Meet built the feature themselves. The wrapper model assumed the gap was permanent. It was not. It was just early.

The Survivor Numbers

Here is something the “wrappers are all dead” crowd is not adequately pricing: Cursor went from $1 million to $1 billion in annualized revenue in under two years. It is now valued at $29.3 billion. Half the Fortune 500 uses it. Replit grew 1,556% year over year, going from $16 million ARR at the end of 2024 to an estimated $265 million through 2025. Lovable hit $70 million ARR by June 2025, having launched in late 2024, making it among the fastest-growing startups in European history.

These companies were all dismissed at some point as AI wrappers. Cursor was “just a GitHub Copilot clone.” Replit was “just a browser IDE.” And yet OpenAI reportedly tried to acquire Cursor before the company declined. It then bid $3 billion for Windsurf, another player in the space, only to have that deal fall apart when Microsoft blocked it over IP concerns. At which point Google stepped in and paid $2.4 billion for Windsurf’s talent and licensing rights. Two of the biggest AI companies on the planet engaged in a bidding war over companies that a certain strain of VC conventional wisdom had written off as undifferentiated wrappers.

It also helped that Cursor launched into a category tailwind where every developer on earth simultaneously decided AI coding tools mattered, timing that cannot be engineered backward into a clean framework. But timing explains the speed, not the destination. The question is what they built that made the destination worth $29 billion.

The Behavior Loop

The thing that separates Cursor from a dead AI coding assistant is not its model. Its model is, by design, someone else’s model. What Cursor built is a proprietary understanding of how software engineers actually work: the keystrokes, the context-switching patterns, the way developers structure files, navigate repos, write comments, recover from errors. Every session is a training signal. Every user interaction compounds into something increasingly hard to replicate from scratch, even with a better underlying model.

This is the flywheel the dead wrappers never built. They captured user sessions as revenue events. The survivors captured them as data assets. One produces a subscription. The other produces a moat.

Replit took a different path to the same destination. It did not just build AI coding assistance on top of a browser IDE. It built the full development environment: deploying, hosting, managing infrastructure, running agents, and made that environment the home base for a new class of builder who had never opened a terminal before. The switching cost is not “I would miss the autocomplete.” It is “my entire deployment stack lives here and my team has three months of project history on this platform.” That is not a feature. That is a workspace. Workspaces are sticky in ways features never are.

Lovable went further still, targeting the non-developer entirely and building toward an outcome: a shipped product, not just written code. When your product is defined by what gets created rather than what gets typed, you are playing a different game than the AI coding assistant commodity market.

Foundation Models Coming

The foundation models will eventually ship a version of your product. This is not a theory. Jasper AI saw its valuation cut by 20% after ChatGPT launched competing content features. OpenAI eliminated four major wrapper categories within 18 months of identifying market demand. There is a credible argument that platform providers deliberately encourage wrapper proliferation as market research: letting thousands of companies experiment, then integrating the successful patterns natively and capturing the value while eliminating the intermediaries.

OpenAI acquiring Windsurf and trying to buy Cursor were not the actions of a company that believes application-layer startups are irrelevant. They were the actions of a company that realized the application layer creates distribution, behavioral data, and user habituation that pure model capability cannot replicate from the API layer. Google paying $2.4 billion for Windsurf’s talent and licensing rights confirmed the same fear from the other side.

The window for wrappers to build something defensible before the foundation model companies arrive is short. Short does not mean zero. It means you need to know what you are racing toward before the platform notices you are winning.

The Three Rules + The Named Category

The pattern in the survivors is consistent enough to be a framework.

They own the environment, not just a feature within it. Cursor owns the editor. Replit owns the deployment stack and the project. Lovable owns the creation context from idea to shipped app. None of them are plug-ins to a larger workflow. They are the workflow. Owning the environment means accumulating context that is genuinely costly to migrate, not just annoying to migrate.

They built a data asset the foundation models do not have. Cursor knows how professional software engineers work in production codebases. Replit knows how non-technical builders describe and iterate on products in natural language. This behavioral data makes their products better in ways that cannot be closed by OpenAI dropping a new model weight. It is not the intelligence that is proprietary. It is the understanding of the human on the other end.

They expanded the addressable market rather than fighting for existing users. Cursor did not try to take enterprise developers away from GitHub Copilot. It created a new kind of power user. Replit did not fight for the professional developer. It went after the twenty million people who had an app idea and no computer science degree.

The coding vertical got to these answers first because the feedback loop is fast. The category that has not sorted itself out yet is legal.

Harvey AI is the strongest candidate for the Cursor of legal: deep workflow integration, behavioral data on how lawyers actually research and draft, enterprise contracts with switching costs baked into the product. But the category is not settled. There are still dozens of “GPT for lawyers” wrappers that will die exactly the way the PDF chat companies died, the moment a foundation model ships a legal reasoning feature natively and their entire value proposition lives in a single JSON payload.

The wrapper that survives in legal will be the one that owns the workflow environment, not the one with the best document summarizer. It will know how partners at a specific firm actually structure arguments, where deals stall in diligence, which clause language their clients reject. That knowledge does not come from a better model. It comes from thousands of sessions inside the actual work. The markdown file is a snapshot. The environment is the asset.

The companies that escape the graveyard will not look different from the ones that do not, not at first. Same API calls. Same gradient backgrounds. Same pitch about transforming workflows.

The difference is invisible until it is not. One of them is building a behavioral dataset their users cannot migrate. One of them owns an environment their users live inside. One of them is expanding a market that did not exist before they arrived.

By the time it is obvious which is which, the acquisition offers are already on the table and the window is closed. The question is not whether to build a wrapper. It is whether you are building the right thing inside it.

The companies that made usage-based pricing famous — Twilio, Stripe, AWS — are now held up as proof it works. And it does work. For them. The problem is the lesson most startups draw from this is the wrong one. They see the trophy and copy it, not realizing the trophy came from a completely different sport.

Usage-based pricing is a growth story that only works once you’ve already won. For most startups, it’s not a pricing model. It’s a slow bleed with good PR.

The Survivors Bias is Enormous

Let’s actually look at who made usage-based pricing famous. Twilio charges per text message or API call. Stripe charges per successful transaction. AWS charges per compute second. Notice something? These are infrastructure plays. The unit cost is essentially constant and predictable — sending one million texts costs Twilio roughly the same per text as sending a hundred. Their marginal cost curve is flat. Their pricing axis is bulletproof.

Twilio grew from $277 million in revenue in 2016 to over $3.8 billion in 2022 on this model. But here’s what people forget: the product was mission-critical and nearly impossible to throttle without degrading the core service. If you want your text messages delivered, you use Twilio. There’s no “use fewer messages to save money” option that doesn’t also mean “your product works worse.” That’s not a pricing model. That’s a structural lock-in with flexible billing attached.

Application-layer software — your CRM integration, your analytics tool, your AI-powered workflow product — doesn’t work that way. Users absolutely can throttle their usage without reducing core value. And when a CFO sees an unpredictable line item creeping up, they will.

A 2023 KeyBanc Capital Markets survey found that 68% of SaaS executives cite forecasting difficulties as their primary concern with pure usage-based models. That’s not a minority opinion. That’s a supermajority of the people who have actually tried to run finance inside a company using this model, telling you it’s hard.

Salesforce Just Ran This Experiment So You Don’t Have To

The cleanest case study on why pure usage-based pricing fails isn’t some obscure Series B startup. It’s Salesforce, a company with 150,000 customers, a decade of enterprise sales infrastructure, and Marc Benioff’s entire personality on the line.

When Salesforce launched Agentforce in late 2024, they priced it at $2 per conversation. Clean. Simple. Democratic. Within months, the backlash was severe. Customers couldn’t define what a “conversation” even was when a single query triggered eight backend processes. One support team leader calculated that five agents handling 70 conversations per day would cost roughly $900 daily. Budget teams panicked. Procurement stalled. Of the first 5,000 Agentforce deals, only 3,000 were paid.

Salesforce pivoted to Flex Credits in May 2025 — $0.10 per action. More granular. But as one product officer put it, it still “felt like a black box — hard to predict, hard to explain to stakeholders.” By late 2025, Salesforce introduced per-user licenses at $125 per month, the model they’d spent two years trying to move away from. CFOs got a number they could budget. The chaos subsided.

Salesforce changed its pricing model three times in eighteen months. If the company with the deepest enterprise sales relationships in the world couldn’t make pure usage-based pricing stick for AI, you should have a very good reason why you think your seed-stage company can.

The Revenue EKG Problem

Here is what usage-based pricing does to a startup’s financials: it makes your revenue look like a cardiac monitor. Customers spike in January, go quiet in February, have a big project in March, do nothing in Q2.

This is a problem for three distinct people, and all three of them matter.

The first is your VC. Investors price predictability. Annual Recurring Revenue, the metric that made SaaS companies worth 20x revenue, works because it’s recurring — you can underwrite it. Usage-based revenue is closer to a service business than a software business, and service businesses get service multiples. Hybrid pricing models report the highest median growth rates at 21%, according to Bain, outperforming both pure subscription and pure usage. But the market punishes companies that can’t tell a clean ARR story.

The second is your sales team. Commission structures for usage-based models are genuinely difficult. Do you pay on first contract? On annualized run-rate? On actual consumption over 90 days? Each option creates different incentives, and most of them are wrong. You end up with reps who close deals and immediately disengage, because there’s no comp mechanism that rewards ongoing usage growth.

The third is your customer’s CFO. A 2025 Salesforce study found that 90% of CIOs believe managing AI costs limits their ability to maximize value. The reaction isn’t to use more and spend more. The reaction is to set caps. To throttle. To approve a small pilot and refuse to expand it until the line item becomes predictable. This is not customer success. This is customer sandbagging, and it destroys your expansion revenue.

The Cost Variance Problem Nobody Talks About

There’s a dimension specific to AI-powered products that makes usage-based pricing even harder than it was for Twilio in 2010: your own costs aren’t flat either.

Traditional software had 80%+ gross margins because the marginal cost of serving an additional user was essentially zero. Build it once, distribute forever. In AI-powered products, every action has a compute cost, and that cost varies wildly based on complexity. One customer service platform found that simple queries cost them $0.04 to serve while complex ones ran to $2.80. If you price at average, you either lose money on heavy users or gouge light ones. Neither is a business.

65% of SaaS vendors are now layering usage-based components on top of seat pricing to manage exactly this problem, according to Bain. The market is already voting. Pure usage-based is retreating to hybrid. The companies loudly proclaiming they’ve “moved beyond the seat” are mostly companies that haven’t yet had to file a quarterly earnings call.

When It Actually Works

This isn’t an argument that usage-based pricing is wrong in all situations. It’s an argument that it’s misapplied in most.

It works when your cost per unit is genuinely flat and predictable. Infrastructure, communications, transactions. It works when your buyers are technical and comfortable reasoning about variable costs — developers are fine with it, procurement committees are not. It works when you’re large enough that the variance in any individual customer’s usage averages out across a portfolio. A thousand customers’ EKGs, combined, start to look like a flat line.

It works, most of all, when your product is genuinely impossible to throttle without degrading value. That’s the Twilio test. Apply it before you decide your startup passes.

Usage-based pricing is not a business model innovation. It’s a go-to-market tactic borrowed from infrastructure companies and reapplied, mostly unsuccessfully, to application-layer software that doesn’t share the same cost structure, buyer profile, or lock-in mechanics.

Twilio and Stripe weren’t startups when usage-based pricing made them famous. They were already large, already entrenched, already operating at a scale where variance smooths out. The lesson from their success isn’t “usage-based pricing is good.” It’s “infrastructure with commodity unit economics and irreplaceable switching costs can support usage-based pricing.”

For the vast majority of startups — the AI-native product, the vertical workflow tool, the enterprise automation play — the model sounds like freedom and operates like a trap. You’re handing your customers a dial to turn down your revenue, giving your VCs a forecast they can’t underwrite, and building a sales motion that rewards closing over expanding.

Democratic pricing is a beautiful idea. It just turns out that in software, the market keeps voting for certainty.

Thanks for reading. If you found this useful, the subscription button is right there and it costs a flat, predictable, non-usage-based amount per month.

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In 2010, Google offered to buy Groupon for $6 billion.

Andrew Mason said no.

A year later, Groupon went public at a $12.6 billion valuation. Mason looked like a genius. The fastest-growing company in history had bet on itself and won.

Except it hadn’t.

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The Meteoric Rise Nobody Saw Coming

Groupon launched in Chicago in November 2008, right in the teeth of the financial crisis. Timing was everything - people wanted deals, businesses needed customers.

The model was brilliantly simple: buy discounted vouchers for local experiences, but only if enough people signed up. Group buying power. Groupon.

Just two years after launch, Groupon’s revenue was expected to exceed $500 million in 2010. The company filed documents showing $713.4 million in revenue for 2010, making it the first company to surpass $500 million in its third year.

Groupon reached unicorn status ($ billion valuation) in just 1 year and 5 months after founding - faster than any company before it.

For context: That beat Amazon, Google, Facebook. Every single one.

By October 2010, Groupon was available in 150 cities in North America and 100 cities in Europe, Asia and South America, with 35 million registered users.

The growth was absolutely insane. From January 2010 to January 2011, Groupon’s U.S. monthly revenue grew from $11 million to $89 million.

That’s 8x revenue growth. In one year.

The Offers Nobody Could Refuse

First, Yahoo offered over $3 billion. Mason declined.

Then in November 2010, Google offered $5.3 billion with a $700 million earnout (some sources cite $6 billion).

Mason said no to that too.

His logic? Groupon was worth more. They’d prove it with an IPO.

On November 4, 2011, Groupon went public at $12.65 billion valuation, raising $700 million. It was the biggest Internet IPO since Google in 2004.

Mason’s bet looked brilliant. He’d turned down $6 billion and doubled the valuation in 12 months.

Wall Street celebrated. Tech media fawned. Mason was a visionary.

For about five minutes.

The Cracks That Were Always There

In its first earnings release as a public company, Groupon reported a fourth-quarter 2011 loss of $9.8 million on an adjusted basis, disappointing investors.

Then it got worse.

Additional investor concerns arose after the company restated 2011 revenues downward in March 2012.

The stock that debuted at $20 started plummeting. Eventually hit $5.

What happened?

The business model that looked amazing at scale... didn’t actually work at scale.

Why Deep Discounts Don’t Build Loyalty

Here’s the fatal flaw nobody wanted to talk about during the hypergrowth phase:

Groupon’s deals attracted bargain hunters, not loyal customers.

A restaurant offers 50% off through Groupon. Great! New customers flood in. The restaurant loses money on every transaction (paying Groupon’s cut plus the discount), but figures they’ll make it back through repeat visits.

Except... they don’t.

The Groupon customers come once for the deal, then never return. They’re not restaurant loyalists. They’re discount chasers. As soon as the next deal pops up elsewhere, they’re gone.

Merchants quickly realized Groupon wasn’t customer acquisition. It was expensive customer rental.

So they churned. Stopped offering deals. Moved on.

This created Groupon’s death spiral: To keep the daily deal emails fresh, they needed constant new merchant supply. But satisfied merchants didn’t come back. So Groupon had to spend heavily acquiring new merchants to replace the ones who churned.

The unit economics didn’t work. Growth masked the problem. Until it couldn’t anymore.

The Distribution Channel That Disappeared

Groupon’s entire distribution strategy relied on email.

They started with a 500-person email list in 2008 - friends, family, anyone they knew. First deal was 2-for-1 pizza at a bar downstairs from their Chicago office.

That email list grew to tens of millions. It was Groupon’s superpower. Daily deals landing in inbox. Open rates were phenomenal. Conversion was strong.

Then Google changed Gmail’s algorithm.

Groupon’s emails started landing in spam folders. The primary distribution channel - the entire growth engine - got kneecapped overnight.

They tried pivoting. Launched Groupon Goods to compete with Amazon on merchandise. Didn’t work. The magic was local deals, not e-commerce.

Meanwhile, Facebook and Google Ads matured. Small businesses could target customers directly, with better data, for less money. Why use Groupon?

The Accounting Tricks Didn’t Help

Groupon initially used a non-standard accounting metric called ACSOI (Adjusted Consolidated Segment Operating Income). Critics argued it was used to present misleading profitability.

Groupon’s original IPO filing with ACSOI showed positive operating income of $60.6 million for 2010. After replacing ACSOI with standard metrics, the filing showed operating losses.

The SEC forced them to restate everything properly. The numbers looked way worse.

This destroyed investor confidence. Not only was the business model broken, but management had been playing accounting games to hide it.

On July 31, 2015, Ben Kaufman stepped down as CEO, Andrew Mason was replaced by Eric Lefkofsky in 2013.

Wait, that’s Quirky. Let me search for the correct Groupon CEO transition.

Actually, from the sources: Mason was CEO from founding until 2013 when he was replaced. The company continued struggling through multiple leadership changes.

Where They Are Now

Groupon’s current market capitalization is $4.71 billion, down from its $12.65 billion IPO in 2011 (note: this was from 2015, it’s declined further since).

The company still exists, but operates on a much smaller scale. They’ve shifted focus to experiences and travel deals rather than daily discounts.

Multiple rounds of layoffs. Office closures. Strategic pivots. The typical death-by-a-thousand-cuts decline of a company that grew too fast on a fundamentally flawed model.

What Actually Went Wrong

The Two-Sided Marketplace That Only Worked on One Side

Customers loved Groupon. Merchants? Not so much. When only one side of your marketplace is happy, you don’t have a sustainable business. You have a slow-motion implosion.

Email Dependency

Building your entire distribution strategy on a channel you don’t control (someone else’s email algorithms) is organizational suicide. When Google changed Gmail, Groupon had no backup plan.

Discounts Don’t Build Brands

Groupon helped merchants attract bargain hunters, not build loyal customer bases. The merchants figured this out. Groupon’s response was to find new merchants, which worked until it didn’t.

Saying No to $6 Billion

This is the big one. Sometimes the best decision is to take the exit.

Mason turned down $6 billion from Google thinking Groupon was worth more. He was right - for about 12 months. Then the stock crashed and never recovered.

If your business model has fundamental flaws (and Groupon’s did), growing bigger just makes the eventual collapse more spectacular.

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For founders and investors, nuclear isn’t an abstract “energy” story anymore. It’s a supply-chain, talent, and capital formation story with geopolitical leverage.

1. The new atomic stack

Nuclear used to be monolithic—slow, overregulated, and state-controlled. What’s emerging now looks more like a startup ecosystem than a utility monopoly. You have:

• Fusion firms like Commonwealth Fusion Systems (CFS), Helion Energy, and Type One Energy, building modular power systems using high-temperature superconductors, compact tokamaks, or pulsed magnets.

• Advanced fission players like Oklo, Last Energy, and Kairos, redesigning reactors for scalability, safety, and grid flexibility.

• Component and materials startups focused on tritium breeding, heat-resistant alloys, vacuum seals, and neutron-resilient magnets.

• Infrastructure and finance layers—insurance, offtake, power-purchase agreements, and regulatory software—building the connective tissue to make these plants fundable.

This is what the atomic ecosystem looks like in 2025: distributed, privately capitalized, and unrecognizable to the generation that built Three Mile Island.

2. Why now: the AI energy paradox

The AI boom quietly rewrote the U.S. energy map. Data centers already account for ~4.5% of U.S. electricity and could exceed 10% by 2030, according to EIA projections. Each new GPU cluster needs a dedicated power plant—and no grid built for intermittent renewables can handle that on its own.

That’s why Virginia, Oregon, and Washington—all dense with data infrastructure—are becoming early fusion sites.

CFS’s ARC plant is being built near Dominion Energy’s Chesterfield site, the same region hosting hundreds of hyperscale centers. Helion’s plant in Washington is designed to feed Microsoft’s cloud stack directly.

Nuclear has gone from political liability to AI infrastructure. For the first time in decades, there’s clear offtake demand before a single watt is generated.

3. The capital architecture: from labs to term sheets

Between 2020 and 2025, private fusion funding in the U.S. jumped from under $2B to nearly $14B, per PitchBook. The source of that capital is telling:

• Generalist VCs (Andreessen Horowitz, Khosla, Lowercarbon) see nuclear as a rare alignment of software-adjacent infrastructure and industrial scale.

• Corporate strategics (Google, Chevron, Eni, Alphabet) are backing early projects to hedge their own energy exposure.

• Sovereign and climate funds view fusion as a way to re-industrialize the U.S. cleanly, not just decarbonize.

What’s changed is the structure of capital: milestone-based DOE programs now co-fund private progress; regulatory risk is clearer; and fusion components could soon qualify for the 45X Advanced Manufacturing Tax Credit. That trifecta—predictable regulation, cost-share from government, and future tax credits—finally gives institutional capital a reason to model ROI.

For operators, that means a new playbook: fusion startups can be treated like infrastructure SaaS—long lead times, yes, but with compounding moats once deployed.

4. The new industrial policy loop

Every fusion plant is effectively a hardware startup with a sovereign footprint. Tritium, magnets, vacuum chambers, high-temperature ceramics — all require supply chains that currently run through China, South Korea, or the EU.
That’s why the policy debate isn’t about fusion science anymore; it’s about manufacturing parity and strategic control.

The Fusion Advanced Manufacturing Parity Act introduced in 2025 is one of the first signs of this shift. It’s not about R&D—it’s about keeping the economic capture domestic.
If the U.S. loses the race to scale, it loses leverage over one of the few post-carbon energy sources that can anchor AI, chip fabrication, and industrial reshoring.

Fusion is becoming the new semiconductor policy: whoever controls the supply chain controls the innovation curve.

5. Founders and operators: where opportunity lives

For founders and builders, nuclear’s revival is not about reactors. It’s about everything around them.

Each technical bottleneck is a startup category in disguise:

The real play is to modularize the nuclear stack—turn what used to be single-vendor, multi-decade systems into interoperable, venture-fundable components.

For investors, this looks a lot like space pre-SpaceX: physics-hard, policy-driven, but ultimately inevitable once cost curves bend. The reward isn’t one company succeeding—it’s a new asset class being born.

6. The geopolitical layer

While the U.S. ecosystem is driven by private capital, China is running a state-led race. Its fusion program, backed by heavy subsidies and centralized manufacturing, is already scaling tritium production and reactor materials.
American founders like Shine Technologies’ Greg Piefer have been blunt: “China is investing far more than the U.S. government in fusion. We can’t afford to lose that race.”

In other words, the race isn’t just about clean power—it’s about strategic resilience.

A fusion economy that runs on hydrogen isotopes instead of uranium redefines energy independence. It collapses the geopolitical map that shaped the oil and gas era.

7. The next decade

Helion says it will deliver electricity to Microsoft in 2028. CFS targets its first grid connection in the early 2030s. These timelines are aggressive, but not implausible.
The NRC has already defined fusion’s regulatory category; DOE milestone programs are live; and capital formation is scaling faster than any prior energy technology at this stage of maturity.

If even one of these firms delivers commercial output, the energy curve breaks open—and everything from battery economics to AI scaling laws changes with it.

For boomers, nuclear was a fear word. For zoomers, it might become an API—clean, programmable, on-demand power priced by the megawatt-hour.

Startups aren’t just racing to make fusion work; they’re rebuilding America’s industrial DNA around it.

And if they succeed, nuclear won’t be a legacy system anymore—it’ll be the operating system of the 21st-century economy.