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

Before we begin... a big thank you to this week’s sponsor.

Looking For Stable Retirement Income Without Guesswork?

If you want dependable income and less market stress, annuities can be a strong fit. Leverage Planning helps you compare rates and features across 30+ established insurers, with guidance from licensed annuity advisors who are not tied to one carrier.

• Side By Side Comparisons

• Competitive Rates And Clear Tradeoffs

• Support From Quote To Purchase

Minimum investment: $50,000

Check Rates Now

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.

Quick one before we get into it.

If someone forwarded this to you—first, they have good taste. Second, you can subscribe here. Takes like four seconds.

Subscribe now

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.

Before we begin... a big thank you to this week’s sponsor.

Marketing that’s always on—even when you’re not. Get automation tools that send your messages for you.

You can’t be everywhere at once—but your marketing can.

With Constant Contact’s automation tools, your emails, texts, and offers go out at the right time, every time—without you having to lift a finger.

Want to show your audience you care about them? Use an automaton template to send birthday messages. Working on driving sales? Set up an abandoned cart email automation to gently urge almost-shoppers to become actual shoppers. Looking to bring lapsed customers back into the fold? Send automations with offers and promos to entice them to buy again. You can even create custom automation paths that work best for your business.

It couldn’t be easier. Just set up automatic triggered messages based on your customers’ behavior, and watch those messages run while you focus on everything else.

SECOND ORDER readers, if you’re ready to save time and put pesky administrative tasks on autopilot, give Constant Contact’s marketing automation tools a try. Get started today—for free.

Try 30 days free

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.

The Vermont Country Store—Make it a Tradition1

Welcome to The Vermont Country Store! Since 1946, the Orton Family Business has offered an incredible assortment of unique and useful products and a shopping experience unlike any other. Find new favorites like our exclusive bedding and sleepwear, heirloom-quality Mountain Weavers table linens, genuine Irish wool sweaters, and baked goods made from treasured family recipes. Rediscover beloved brands from the past, like Tangee, Lemon Up, and Mason Pearson. There’s more in store every time you shop to make each visit even better than the last.

Make your experience extra special! Free shipping on orders of $75 or more.

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.

If you got something out of this, forward it to one person. Genuinely the best way to help.

Share

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.

Before we begin... a big thank you to this week’s sponsor.

Invest in recession-resilient Mobile Home Parks with Vintage Capital

Invest in recession-resilient Mobile Home Parks with Vintage Capital. Invest direct or in a fund of 20+ underlying assets. 1031s are also available. Access stable, income-generating properties with consistent demand and low tenant turnover.

Now is the time to act: Current market conditions are creating opportunities to acquire properties at attractive valuations.

Our fund targets a 15%-17% IRR and makes monthly distributions, which provides a steady income stream alongside strong upside potential and tax-efficient benefits.

Why Mobile Home Parks?

• Recession-Resilient: Affordable housing demand drives stable returns in any economy

• High Tenant Retention: The average MHP tenant stays 10-12 years (compared to 2-3 in Multifamily)

• Proven Expertise: $100MM+ track record in mobile home park investments.

• Tax-Smart Investing: Bonus depreciation offers tax advantages.

Access The Vintage Capital Deal Room →

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.

Vet bills won’t break the bank if you have the right insurance

Surprise vet bills can certainly bite. But fear not, pet insurance can be your lifeline. Some plans cover major surgeries—which can cost up to $7,000—so you can afford the best care for your pet. Check out our top-rated pet insurance providers, with some plans offering coverage for just $1 a day, multi-pet coverage, and reimbursement options of up to 90%. With the right coverage, you could keep your pet (and your wallet) happy.1

SEE PROVIDERS →

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.

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

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.

Sponsored by Constant Contact

Marketing that’s always on—even when you’re not. Get automation tools that send your messages for you.

You can’t be everywhere at once—but your marketing can.

With Constant Contact’s automation tools, your emails, texts, and offers go out at the right time, every time—without you having to lift a finger.

Want to show your audience you care about them? Use an automaton template to send birthday messages. Working on driving sales? Set up an abandoned cart email automation to gently urge almost-shoppers to become actual shoppers. Looking to bring lapsed customers back into the fold? Send automations with offers and promos to entice them to buy again. You can even create custom automation paths that work best for your business.

It couldn’t be easier. Just set up automatic triggered messages based on your customers’ behavior, and watch those messages run while you focus on everything else.

SECOND ORDER readers, if you’re ready to save time and put pesky administrative tasks on autopilot, give Constant Contact’s marketing automation tools a try. Get started today—for free.

Try 30 days free

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.

Sponsored by Constant Contact

Marketing that’s always on—even when you’re not. Get automation tools that send your messages for you.

You can’t be everywhere at once—but your marketing can.

With Constant Contact’s automation tools, your emails, texts, and offers go out at the right time, every time—without you having to lift a finger.

Want to show your audience you care about them? Use an automaton template to send birthday messages. Working on driving sales? Set up an abandoned cart email automation to gently urge almost-shoppers to become actual shoppers. Looking to bring lapsed customers back into the fold? Send automations with offers and promos to entice them to buy again. You can even create custom automation paths that work best for your business.

It couldn’t be easier. Just set up automatic triggered messages based on your customers’ behavior, and watch those messages run while you focus on everything else.

SECOND ORDER readers, if you’re ready to save time and put pesky administrative tasks on autopilot, give Constant Contact’s marketing automation tools a try. Get started today—for free.

Try 30 days free

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.

Leadership Can’t Be Automated1

AI can help you move faster, but real leadership still requires human judgment.
The free resource 5 Traits AI Can’t Replace explains the traits leaders must protect in an AI-driven world and why BELAY Executive Assistants are built to support them.

Download BELAY’s Guide!

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.

In partnership with

Invest in recession-resilient Mobile Home Parks with Vintage Capital

Invest in recession-resilient Mobile Home Parks with Vintage Capital. Invest direct or in a fund of 20+ underlying assets. 1031s are also available. Access stable, income-generating properties with consistent demand and low tenant turnover.

Now is the time to act: Current market conditions are creating opportunities to acquire properties at attractive valuations.

Our fund targets a 15%-17% IRR and makes monthly distributions, which provides a steady income stream alongside strong upside potential and tax-efficient benefits.

Why Mobile Home Parks?

• Recession-Resilient: Affordable housing demand drives stable returns in any economy

• High Tenant Retention: The average MHP tenant stays 10-12 years (compared to 2-3 in Multifamily)

• Proven Expertise: $100MM+ track record in mobile home park investments.

• Tax-Smart Investing: Bonus depreciation offers tax advantages.

Access The Vintage Capital Deal Room

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.

Co-partnership with

Put your emails on autopilot, and your business results in overdrive.

Marketing that runs itself? Yeah, that’s a thing now.

Constant Contact has automation tools that run in the background so you don’t have to. Emails, texts, offers—they go out exactly when you want them to, without needing to hit send every time.

Want to make customers feel seen? Use an automation template to send birthday wishes. Trying to boost sales? Set up an abandoned cart email—it’s a friendly reminder that often leads to actual purchases. Got customers who’ve gone MIA? Send a promo or offer to bring them back.

You don’t need to micromanage any of it. Just choose your triggers—like someone clicking a link or leaving something in their cart—and the system handles the rest.

You stay focused on your actual to-do list. The marketing keeps humming in the background.

So, Second Order reader: Are you ready to stop spending time on repetitive stuff? Then give Constant Contact’s automation tools a try. It’s free to get started, and honestly? It’s kind of a game changer.

Get started for free

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.

Your News Doesn’t Have To Be Overwhelming

Be the smartest person in the room by reading 1440, where 4.5 million Americans find their daily, fact-based news fix. They navigate through 100+ sources to deliver a comprehensive roundup from every corner of the internet: politics, global events, business, and culture, all in a quick, 5-minute newsletter.

It’s completely free and devoid of bias or political influence, ensuring you get the facts straight. Subscribe to 1440 today.1

Sign Up for Free

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.

Brought to you by

Your News Doesn’t Have To Be Overwhelming

Be the smartest person in the room by reading 1440, where 4.5 million Americansfind their daily, fact-based news fix. They navigate through 100+ sources to deliver a comprehensive roundup from every corner of the internet: politics, global events, business, and culture, all in a quick, 5-minute newsletter.

It’s completely free and devoid of bias or political influence, ensuring you get the facts straight.

→ Subscribe to 1440 today

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.

If you haven’t yet, please subscribe, like, leave a comment, and share it! It helps us continue to bring you the most interesting news + nuance in startup and tech every week.

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.

Quirky was supposed to democratize invention. Anyone could submit an idea, the community would vote, and Quirky would handle everything else - design, manufacturing, marketing, distribution.

It sounded perfect. Felt revolutionary. Raised $185 million.

And it completely fell apart.

The Dream (That Actually Worked... For A Minute)

Ben Kaufman founded Quirky in 2009 when he was just 22 years old. The pitch was simple: crowdsourced invention platform where everyday people could become product designers.

Submit your idea → Community votes → Quirky builds it → Product hits shelves → You get royalties

In April 2010, Quirky raised $6.5 million in Series A funding led by RRE Ventures.

The first hit came fast. Pivot Power, a bendable power strip idea submitted by college student Jake Zien in 2010, became a massive success - by 2015 it had generated over $2 million for Zien and other community members.

This was validation. Proof the model worked. Investors piled in.

August 2011: $16 million Series B from Norwest Venture Partners. September 2012: $68 million Series C led by Andreessen Horowitz and Kleiner Perkins. November 2013: $79 million Series D from GE and existing investors.

Total raised: $185 million (some sources say up to $200M when accounting for later bridge funding).

By 2013, Quirky had partnerships with GE, Bed Bath & Beyond, Amazon, Target. Over 500,000 community members. Thousands of weekly submissions.

They were launching products. Lots of products.

Too many products.

The Math That Didn’t Work

Here’s where the wheels came off.

Quirky’s stated goal: launch 50+ products per year.

Let that number sit for a second. Fifty. Every single year.

For context, most hardware companies consider 3-5 major product launches annually to be aggressive.

Quirky wasn’t just accelerating the innovation cycle. They were trying to run a full sprint on a treadmill set to maximum speed.

Until its shutdown, Quirky produced around 400 different products total. Four hundred. In six years.

The operational overhead was insane. Because unlike Kickstarter, which shifts all risk to creators, Quirky owned the entire process:

• Product design

• Prototyping

• Manufacturing

• Quality control

• Inventory management

• Marketing

• Distribution

• Customer service

For. Every. Single. Product.

The company burned $150 million with net losses of $120 million.

When Community Votes Don’t Equal Market Demand

The community voting system had a fatal flaw: what people vote for and what people actually buy are very different things.

In July 2015, CEO Ben Kaufman admitted at Fortune’s Brainstorm Tech conference that Quirky “ran out of money weeks ago”.

The Egg Minder became the poster child for everything wrong with the model. A smart egg tray that tracked which eggs were fresh. The community loved it. Almost nobody bought it.

Most of Quirky’s 400 products followed the same pattern. Innovative? Sure. Solving real problems people would pay to solve? Not really.

Pivot Power, their biggest hit, generated $2 million total Yahoo Sports. Across all the contributors. Over 5 years. One of their most successful products ever.

Now do that math across 400 products where 95% sold almost nothing.

The Pivot That Changed Nothing

By 2014, reality was setting in. The model wasn’t working at scale.

Kaufman tried pivoting. New strategy: become an innovation partner for big brands like Mattel, Amazon, and GE. Let them handle inventory and distribution risk while Quirky provided the crowdsourced ideas.

The logic seemed sound. Big companies have innovation problems. Quirky has an innovation platform. Match made in heaven.

Except... these companies already had innovation pipelines. They didn’t need a 22-year-old’s crowdsourcing platform to tell them what products to make.

The pivot flopped.

On July 31, 2015, Ben Kaufman stepped down as CEO following a layoff of 111 employees.

On September 22, 2015, Quirky filed for Chapter 11 bankruptcy.

The Fire Sale

Quirky’s smart home division, Wink, sold to Flextronics for $15 million. The remaining assets went to Q Holdings for about $5 million.

From $185M+ in funding to $20M in asset sales. That’s a 90% value destruction.

The math is brutal:

• $185M invested

• ~$20M recovered

• $165M evaporated

Despite the shutdown, the platform continued adding inventors at more than 100 per day, with over 25,000 submissions since 2015. People still believed in the dream.

In 2017, Quirky relaunched under new leadership with a licensing model - partnering with HSN, Shopify, and others. No more manufacturing. Just idea licensing.

It never regained momentum. The brand quietly faded.

What Actually Went Wrong

1. The speed trap

Fifty products a year sounds impressive in a pitch deck. In reality, it’s organizational suicide for a hardware company.

Every product needs:

• Market research

• Design iteration

• Manufacturing setup

• Quality testing

• Marketing strategy

• Distribution channels

You can’t do that well 50 times a year with 300 employees. You end up doing it poorly 50 times a year.

2. Democracy doesn’t equal demand

Community voting is great for engagement. Terrible for product-market fit.

The Egg Minder got voted through because it was clever and quirky (pun intended). But “clever” doesn’t drive purchase decisions. “I need this to solve a real problem” does.

Kickstarter figured this out: let creators pitch, let backers fund with real money. Money is the ultimate vote.

Quirky’s votes were free. Free votes don’t predict buyer behavior.

3. Owning too much of the value chain

Kickstarter: 5% fee, zero inventory risk Quirky: 100% of manufacturing, inventory, marketing, distribution costs

One model scales. The other burns $150 million.

The Real Lessons

Validate with money, not votes

Ideas are cheap. Manufacturing is expensive. If you’re going to invest millions in production, make sure people will actually pay for it first.

Focus beats variety

Quirky tried to be everything: kitchen gadgets, smart home devices, office accessories, pet products. They had no brand identity because they stood for nothing specific.

Apple launched the iPod and didn’t diversify for years. Then the iPhone. Then the iPad. One category at a time, done excellently.

Quirky launched 50 products per year across every category. None done excellently.

Speed without quality is just expensive failure

“Move fast and break things” works for software. You can ship bugs and patch them.

Hardware is different. A broken egg tracker doesn’t get a software update. It gets returned, creates customer support costs, damages brand reputation, and sits in inventory.

Despite this foundation, Quirky didn’t sell enough products and burned through $185 million in venture capital before filing for bankruptcy 6 years after opening.

Six years. $185 million. 400 products. Bankruptcy.

At some point during fundraising, you don’t feel rejected anymore.
You feel… done.

Not emotionally. Logistically.

You’ve already pitched:

• everyone you know

• everyone your friends know

• everyone with “partner” in their LinkedIn bio

• everyone AngelList told you existed

You open your tracker and there are no new rows to add.

That’s the scary part.

Not the nos.
The silence after the nos.

Most advice stops being useful right here. It assumes there’s always another list, another funnel, another batch of cold emails you haven’t sent yet.

But fundraising doesn’t actually work like lead gen once you’re past the obvious names.

It turns into something else.

The default ask is broken

Here’s how most founder conversations end.

You pitch.
They nod.
They say something encouraging but noncommittal.

Then you say:

“Happy to share more, and if you know anyone who might be interested, feel free to pass this along.”

They say yes.
They never do.

This isn’t because investors are lying. It’s because you’ve asked them to do too much invisible work.

You’re asking them to:

• recall their entire network

• map it to your company

• decide who’s worth bothering

• craft an intro

• risk social capital

That’s not a small ask. It just sounds polite.

A better question is almost embarrassingly specific:

“Can you think of one person who might want to hear about this?”

One name.
One face.
One text.

That shift matters more than it should.

People are good at recalling one person. They’re bad at abstract sharing. And interestingly, the “one person” they think of is often the most powerful person they know.

This is less about persuasion and more about cognitive load.

Lower it.

Fundraising is not a spreadsheet problem

When founders get stuck, they usually try to optimize the wrong thing.

Better targeting.
Better copy.
Better pitch deck.

Those help early. They don’t help when you’ve already talked to everyone in your orbit.

At that point, fundraising stops being about efficiency and starts being about gravity.

Hustle Fund ran into this raising their first fund. Same wall. Same feeling.

So instead of more outreach, they hosted a small get-together. Nothing fancy. Just a taco shop. A date. A handful of existing investors.

Then they asked each person to bring one other person who might enjoy the night.

Not “bring an investor.”
Not “bring someone who wants to deploy capital.”

Just: someone who’d like tacos and conversation.

That framing does a lot of work.

Now it’s social, not transactional.
Now there’s no pitch pressure.
Now everyone self-selects into the room.

People invest in rooms long before they invest in decks.

Warm intros aren’t about manners

People talk about warm intros like it’s etiquette.

It’s not. It’s economics.

Capital flows through trust networks. Always has.

A warm intro doesn’t just get you a meeting. It transfers credibility. It tells the investor that someone else already did the filtering.

Cold emails say: “I found your inbox.”
Warm intros say: “I’m already vouched for.”

Once you run out of obvious investors, your job is no longer to find money. It’s to find people who can point to money.

Founders who raised recently.
Operators who exited quietly.
Advisors who actually text back.
Friends with unexpectedly rich parents.
Angels who never post online.

You’re not pitching them. You’re asking for adjacency.

Most founders have a narrow definition of “investor”

Another quiet blocker: ego.

A lot of founders only feel comfortable pitching people who look like investors.

VCs.
Angels with podcasts.
People who tweet threads.

But an investor is just someone with surplus capital and curiosity.

That can be:

• a dentist

• a small business owner

• a real estate operator

• a senior engineer with stock comp

• someone who never calls themselves an investor

These people don’t show up on AngelList. They don’t go to demo days. But they write checks.

The only reason most founders don’t pitch them is that it doesn’t fit the mental model of what fundraising is “supposed” to look like.

Money does not care about your mental model.

Repetition is underrated

Here’s something no one likes hearing.

Most investors don’t say yes the first time because the first time isn’t about the deal. It’s about you.

Are you still around?
Are you still building?
Did you ship anything new?
Did momentum change?

Elizabeth Yin has pitched the same optometrist multiple times. He’s said no multiple times.

That’s not awkward. That’s normal.

Fundraising is not persuasion. It’s familiarity.

The founders who eventually raise aren’t always the smartest or most compelling. They’re the ones who keep resurfacing without disappearing or getting weird.

The real problem when you’re “out of investors”

When founders say they’ve run out of investors, what they usually mean is:

• they’ve exhausted obvious names

• they’re relying on cold outreach

• they’re asking vague favors

• they’re defining investors too narrowly

That’s not a dead end. It’s a mode switch.

You stop broadcasting.
You start asking smaller questions.
You show up in rooms instead of inboxes.
You let social gravity do some of the work.

Fundraising gets quieter here. Slower. Less scalable.

But this is also where it becomes real.

If you’re here right now, you’re not failing. You’ve just moved past the easy part.

And honestly, most people never get this far.

REACH 85K+ FOUNDERS, INVESTORS & OPERATORS

If you’re building tools that help founders, investors and other professionals who want to be 100x more productive with 100x less work, and you’re interested in advertising with us, send an email over to editor@secondorder.co with the subject “SecondOrder Ads”

Today we’re talking about something founders love to complain about and quietly avoid:

Hiring.

Not in the “how to interview better” way, or the “here’s a checklist you’ll never use” way.

Specifically, why it feels so hard, why it breaks so many early teams, and why the problem usually isn’t what founders think it is.

Because despite what LinkedIn posts might suggest, startup hiring is not about perks, comp, or “finding rockstars.” It’s about finding people who don’t panic when there’s no playbook.

Let’s get into it →

The biggest hiring myth

When early founders talk about hiring, they almost always start with money.

“We can’t pay what big companies pay.”
“We lose candidates to FAANG.”
“We’d hire faster if we had more budget.”

It makes sense. Salary is concrete. It’s easy to point to. It feels like the obvious bottleneck.

But in practice, compensation is rarely the real reason early-stage hiring fails.

The people who actually do well in startups aren’t shopping for the highest paycheck. They’re shopping for something else entirely, even if they don’t articulate it that way.

They’re looking for:

• A steeper learning curve

• Broader responsibility

• A chance to shape something instead of slotting into it

• Work that changes them

If someone is optimizing for stability, predictability, and clarity, a startup is going to feel uncomfortable no matter how much you pay them. Conversely, if someone is wired to seek growth and ownership, a startup can feel like an upgrade even with a pay cut.

This is why some founders are shocked when a candidate turns down a bigger company to join a messy early team. It looks irrational from the outside. It isn’t.

It’s a different optimization function.

What early hires are really signing up for

Here’s the uncomfortable truth most founders only realize after the hire:

You’re not hiring for the role you wrote down.
You’re hiring for the role that doesn’t exist yet.

Early on, job descriptions are mostly fiction. They’re aspirational. They’re guesses.

What you’re actually asking someone to do is:

• Figure out what matters

• Decide what to ignore

• Build systems that don’t exist

• Make judgment calls with incomplete information

• Change direction without melting down

That’s a very different ask than “run paid ads” or “own backend services” or “manage partnerships.”

This is where hiring gets tricky. Because most people are very good at operating within a system, and far fewer are comfortable creating one from scratch.

Neither is better in general. But only one works early.

Why “smart” hires sometimes fail spectacularly

Every founder has a version of this story.

They hire someone impressive. Great resume. Big brand names. Confident in interviews. Knows all the right words.

And then… it just doesn’t work.

They need more direction than expected.
They hesitate when priorities shift.
They ask for clarity that doesn’t exist yet.
They wait for decisions instead of making them.

From the outside, it looks like underperformance. From the inside, it’s often something simpler: altitude mismatch.

Some people do their best work when there’s:

• Clear ownership boundaries

• Defined success metrics

• Existing processes to refine

Early startups don’t have those things. Not because founders are lazy, but because the company is still figuring out what it even is.

Put someone who thrives on clarity into ambiguity, and they don’t magically become entrepreneurial. They become anxious or frustrated.

Again: not a skill issue. A context issue.

The trait that matters most (and is hardest to spot)

Founders often say they’re looking for “initiative” or an “entrepreneurial mindset,” but those words are vague to the point of uselessness.

What they’re usually trying to describe is something much more specific:

The ability to act without permission and recover quickly when wrong.

That’s it.

People who do well early:

• Don’t wait to be told what to do

• Don’t panic when they make a call and it’s imperfect

• Don’t need everything scoped to start moving

• Don’t treat ambiguity as a blocker

This is also why interviews are such a poor proxy for success. Interviews reward articulation, confidence, and retrospective storytelling. Startups reward judgment in real time.

Someone can sound brilliant explaining how they solved a problem six months ago and still freeze when faced with a new one today.

Culture is not vibes

A lot of founders roll their eyes at “culture” early on. It feels premature. You’re just trying to ship.

But culture isn’t something you add later. It’s something you accidentally create immediately.

In a three-person company, culture shows up as:

• How you disagree

• How you decide

• How you respond when something breaks

• What happens when someone drops the ball

One hire who operates differently can change the entire dynamic. Suddenly things take longer. Conversations feel heavier. Decisions get escalated instead of made.

That’s why culture fit matters so much early, and why it gets misinterpreted.

It’s not about whether you’d get a beer together. It’s about whether you share the same instincts under pressure.

Why hiring takes longer than you want it to

There’s a moment in every startup where the founder realizes something uncomfortable: their leverage no longer comes from doing the work themselves.

It comes from who they bring in.

At that point, hiring stops being a task and becomes the job. Not because founders love recruiting, but because every hire has second-order effects.

A good hire:

• Removes work from the founder’s plate

• Improves decisions

• Raises the bar for everyone else

• Makes the company feel lighter

A bad hire does the opposite. Quietly.

• They consume time.

• They introduce friction.

• They force meetings that shouldn’t exist.

• They slow the whole system down.

This is why experienced founders take so long to hire and say no so often. They’re not being precious. They’re protecting momentum.

Talking is cheap. Working isn’t.

Interviews reward storytelling. Startups reward judgment.

This is where trial projects come in.

Not homework. Not unpaid spec work. Real, scoped, paid projects that resemble actual work.

They show you:

• How someone prioritizes

• How they ask questions

• How they handle ambiguity

• Whether they take ownership or wait for direction

It’s better for candidates too. Both sides find out quickly if the relationship works.

Most hiring mistakes don’t come from lack of skill. They come from misaligned expectations.

Trial work surfaces that early.

The hidden risk founders underestimate

The biggest hiring risk is not hiring someone bad.

It’s hiring someone good at the wrong altitude.

People who want to manage before they’ve built. People who need structure before they can move. People who optimize before there’s anything to optimize.

Early-stage companies need builders first. Systems come later.

If someone hasn’t shipped the thing themselves, they shouldn’t be managing the people shipping it.

The payoff

When hiring works, it doesn’t feel dramatic.

Things just:

• Move faster

• Break less

• Require fewer meetings

• Compound quietly

The company starts scaling beyond the founder’s personal output. That’s the real milestone.

Hiring isn’t about filling seats. It’s about reducing entropy.

And in a startup, that’s everything.

If you haven’t yet, please subscribe, like, leave a comment, and share. It helps us continue bringing you the most interesting news + experience-backed essays on building companies every week.

Here’s what’s actually happening: you’ve run out of memory space.

Understanding AI Memory

Think of AI conversations like working on a desk. There’s only so much surface area available. Every message, every file you share, every response—it all takes up space. When the desk gets full, something has to go, and it’s usually the oldest stuff that gets pushed off first.

This isn’t a bug. It’s how these systems work by design.

What Gets Counted

AIs measure memory in “tokens”—essentially chunks of text. A rough guideline: 100 words typically equals around 130 tokens. Different models handle different amounts:

• ChatGPT (in the web interface): Handles roughly 45,000-60,000 words before things get tight

• Claude: Can work with around 150,000 words effectively

• Gemini Pro: Has massive capacity—up to 750,000 words or more

The Performance Sweet Spot

Here’s something counterintuitive: just because a model can handle a million tokens doesn’t mean it should. Quality tends to peak when you’re using about 30-60% of available capacity. Push past 70%, and you’ll notice degradation—slower responses, less accuracy, more mistakes.

Warning Signs Your Context Is Full

• Your instructions vanish. You asked for bullet points in message one. By message twenty, you’re getting paragraphs.

• Contradictions appear. The AI recommended Strategy A earlier, now it’s pushing Strategy B without acknowledging the shift.

• Facts get fuzzy. You mentioned a $9,000 budget. Now it’s referencing $6,500. Where’d that number come from?

• Claude users: If you see “Organizing thoughts...” or “Compacting conversation,” that’s Claude telling you memory is tight.

Practical Solutions

The Clean Slate Approach

When you hit around 60% capacity (usually 15-20 messages in a typical conversation), try this:

Ask the AI: “Can you summarize what we’ve covered, what we’ve decided, and what we’re working on next?”

Copy that summary.

Start a fresh conversation: “Here’s where we left off: [paste summary]. Let’s continue.”

You get a clean workspace while keeping the essential context.

Be Strategic About Files

Different file types eat memory at different rates:

• Plain text files are lightweight

• PDFs with just text are moderate

• Images and complex spreadsheets cost more

• Videos are expensive

Before uploading a 100-page document, ask yourself: do I need all of it? Extract relevant sections instead.

Build Awareness

Pay attention to when quality drops. After a few projects, you’ll develop intuition for your limits. Keep mental notes:

• What types of tasks filled up memory fastest?

• At what point did responses start degrading?

• Which model handled your specific use case best?

Mid-Conversation Resets

Don’t want to start over? Ask for a summary every 10-15 messages. This creates checkpoints that help the AI refocus on what matters.

Common Mistakes

• Pushing too hard. “Just one more question” at 80% capacity turns into five more questions and unusable output.

• Dumping everything upfront. Uploading five documents at once burns your workspace before you’ve asked anything useful.

• Ignoring the signals. When something feels off, it probably is. Don’t power through—refresh instead.

The Real Skill

The best AI users don’t have marathon 50-message sessions. They have focused 15-message sprints, then reset. They’re selective about what they include. They stop before quality drops, not after.

Your job isn’t just to ask questions. It’s to manage the workspace—keep it clean, focused, and effective.

Try it. Next time you’re deep in a conversation and things start feeling strained, pause. Summarize. Start fresh. You’ll be surprised how much sharper the responses become.

In 2017, MoviePass offered something that sounded impossible: unlimited movies in theaters for $9.95 a month.

Less than the price of a single ticket. Watch 30 movies. Pay ten bucks.

People loved it. Three million subscribers in under a year.

Then it all collapsed. The founders are now pleading guilty to securities fraud. The company burned over $300 million. And the whole thing turned out to be... exactly what it looked like. Too good to be true.

The Original Vision (That Actually Made Sense)

Stacy Spikes and Hamet Watt founded MoviePass in 2011. Original pricing was between $29-$50 per month depending on location.

The model made sense at that price point. Similar to gym memberships - most people won’t use it enough to hurt margins. Those who do are offset by those who don’t.

When MoviePass first launched in San Francisco, 19,000 members signed up for the $40/month service.

Modest traction. Sustainable economics. Building slowly.

For years, MoviePass operated with around 20,000 subscribers. Not explosive growth, but the unit economics worked.

Then Helios & Matheson showed up.

The $9.95 Decision That Changed Everything

In August 2017, Helios & Matheson, a New York-based analytics company, purchased a controlling stake in MoviePass.

Farnsworth’s pitch: $25 million for 51% of the company, two board seats, and dropping the price from $50 to $9.95, with a goal of hitting 100,000 subscribers.

The math was insane from day one.

The average US movie ticket price was $8.73. If someone watched just two movies per month, MoviePass lost money. Three movies? Massive losses.

MoviePass’ website crashed from traffic overload after announcing the $9.95 price. In less than a year, subscribers exploded from 20,000 to over 3 million.

By June 2018, MoviePass hit 3 million paying subscribers and represented more than 5% of total US box office receipts, with peak weeks reaching 8%.

The growth was absolutely bonkers.

MoviePass gained 150,000 new subscribers in just 48 hours after the price drop.

The “Data Play” That Wasn’t

Here’s what Helios & Matheson told investors:

They’d monetize user data. Sell viewing habits to studios. Use AI and big data to generate revenue streams beyond subscriptions.

Farnsworth and Lowe falsely claimed HMNY possessed “big data” and “artificial intelligence” platforms to generate revenue by analyzing and monetizing MoviePass subscriber data. They knew HMNY did not possess these technologies or capabilities.

It was all bullshit.

CEO Ted Farnsworth told TheStreet that MoviePass planned to leverage its 3 million subscribers to provide marketing and analytics to film studios, increasing revenue by $6-$8 per subscriber.

But they didn’t have the technology. They didn’t have the partnerships. They were just... losing money. Lots of it.

The Burn Rate Nobody Could Ignore

In April 2018, Helios disclosed it had been losing an average of $20 million per month since September 2017.

Twenty million. Every month. For seven months.

In May 2018, MoviePass burned through $40 million in a single month. Previous SEC filings showed average monthly burn of $21.7 million.

In May 2018, Helios & Matheson revealed it had only $15.5 million in cash at the end of April while burning $21.7 million monthly.

The company was literally days away from running out of money.

MoviePass burned through more than $219 million in cash since the beginning of 2018. Over $150 million was spent in Q2 2018 alone.

When the Money Ran Out (Literally)

July 26, 2018. MoviePass stopped working.

Following a service outage on July 26, MoviePass was forced to borrow $5 million to continue operations. CEO Mitch Lowe apologized, stating the service was back “up-and-running with stability at 100%”.

The company had literally run out of money mid-day. Subscribers couldn’t buy tickets. The app just... stopped.

They borrowed $5 million emergency cash just to turn the lights back on.

Then came the desperate pivots:

• Surge pricing added ($2-$6 extra for popular showtimes)

• New releases blacked out (no Mission Impossible, no The Meg)

• Unlimited plan killed, replaced with 3 movies/month

• Price changed 11 times in 8 months

MoviePass changed its offering 11 times in its brief history. The company modified deals six times just in the first eight months of 2018.

Subscribers fled. MoviePass lost a million subscribers by October 2018.

The Stock Price Collapse

Helios stock skyrocketed from under $3 per share before the MoviePass deal to an intraday high of $38.86 on October 11, 2017.

Peak euphoria. Wall Street believed the hype.

Then reality hit.

Helios announced a 250-to-1 reverse stock split in July 2018 to maintain its Nasdaq listing. When you need a 250-to-1 reverse split, you’re in serious trouble.

By late 2018, Helios shares were trading at $0.10. From $38.86 to ten cents.

That’s a 99.7% value destruction.

The End

September 2019: MoviePass shut down permanently.

January 28, 2020: Helios and Matheson filed for Chapter 7 bankruptcy. Full-year 2018 net loss was $329.3 million on revenue of $232.3 million.

They lost more money than they made in revenue. By a lot.

The Criminal Charges

In September 2024, former MoviePass CEO Mitch Lowe pleaded guilty to securities fraud conspiracy, facing a maximum of five years in federal prison.

In January 2025, Ted Farnsworth pleaded guilty to one count of securities fraud and one count of conspiracy to commit securities fraud, facing up to 25 years in prison (20 years for securities fraud, 5 years for conspiracy).

According to the plea agreement, the government estimates total losses from the scheme at $303 million.

Prosecutors alleged Farnsworth and Lowe knew the $9.95 unlimited plan was a temporary marketing gimmick to grow subscribers and artificially inflate HMNY’s stock price. They also directed employees to throttle service for high-volume users, forcing password resets to lock people out.

So not only was the business model fake - they were secretly preventing people from using the service they’d paid for.

What AMC Learned

While MoviePass was imploding, AMC was watching closely.

In 2018, AMC Theatres launched its own $20-per-month subscription service.

The difference? AMC owns the theaters. They’re not paying full retail for tickets. Their cost is marginal (cleaning, utilities, staffing - costs they’d incur anyway for empty seats).

MoviePass pioneered the subscription model for theaters. Then failed spectacularly. Then AMC, Cinemark, and Regal copied the playbook - but with economics that actually work.

The Relaunch Nobody Asked For

Stacy Spikes relaunched MoviePass in late 2022 with a tiered credit system. Subscribers pay from $10/month for credits that vary based on movie release date, showtime, and format. In February 2024, MoviePass announced its first-ever profitable year in 2023.

So the sustainable model was... exactly what they started with in 2011. Tiered pricing. Actual viable economics.

It just took $303 million in losses and two criminal convictions to get back there.

What Actually Went Wrong

The Math Never Worked

This wasn’t a complex failure. The unit economics were broken from day one.

$9.95/month subscription. $8.73 average ticket. Two movies = loss. Three movies = bigger loss.

There was no “path to profitability.” There was no “data monetization.” There was just... spending $270/month per heavy user while collecting $10.

The Gym Membership Fallacy

MoviePass banked on people not using the service. Like gyms.

Except: Going to the gym requires effort, time, and discomfort. Watching movies is entertainment people actually want to do.

CEO Mitch Lowe said people would see around 10 movies per year with MoviePass. At $8.73 per ticket, that’s $87.30 in costs for $119.40 in revenue ($9.95 × 12). Barely breakeven - and only if usage didn’t spike.

But when you have an unlimited debit card for movies, why wouldn’t you use it? The moral hazard was obvious.

Stock Manipulation Over Business Building

Farnsworth and Lowe knew the $9.95 plan was a temporary marketing gimmick to grow subscribers and artificially inflate HMNY stock price.

This wasn’t about building a sustainable company. It was about pumping the stock, attracting investors, and... well, they didn’t think much beyond that.

The “We’ll Figure It Out Later” Strategy

They’d get big, then negotiate with theaters for discounts. They’d monetize data somehow. They’d find revenue streams.

None of it happened. Theaters had zero incentive to give discounts to a company that was already sending them paying customers. The data play never materialized.

Last month, Joe Braidwood did something unusual in startup land: he voluntarily shut down Yara AI, a mental health chatbot with thousands of active users and months of runway remaining. His reason? The product had become too risky to operate responsibly.

This wasn’t a typical startup failure story. This was a founder pulling the emergency brake before anyone got hurt.

The Concept

Yara AI launched in 2024 as a mental wellness companion, not a replacement for therapy. Think: an AI trained to help with everyday stress, sleep issues, relationship concerns—the kind of stuff that doesn’t require clinical intervention but could benefit from structured reflection.

Braidwood, a tech executive who previously led marketing at SwiftKey, assembled what looked like the right team. His co-founder Richard Stott was a clinical psychologist. They brought on AI safety specialists, built an advisory board of mental health professionals, and consulted with regulators before writing a single line of code.

The architecture included what they called a “clinical brain”—semantic memory layers, safety filters, conversation tracking that persisted across sessions. Unlike ChatGPT’s stateless conversations, Yara could theoretically remember what you discussed last week and build therapeutic continuity.

They bootstrapped with under $1 million and attracted several thousand users. Engagement metrics looked solid. People weren’t just testing it; they were returning daily.

The Problem With Playing Therapist

Here’s where things got complicated. Yara was designed for the worried well—people dealing with mild anxiety or burnout who just needed someone (or something) to talk through their day. But in practice, you don’t control who shows up when you build something that talks like a therapist.

Crisis users arrived. People dealing with suicidal ideation. Individuals with deep trauma. The exact population Yara explicitly wasn’t designed to serve.

Braidwood’s team tried building routing systems—essentially a mode-switch that would detect crisis situations and refer users to human professionals. They layered in more safety protocols. They refined the prompts. But the fundamental issue remained: large language models predict the next plausible token, they don’t actually understand deteriorating mental states over time.

An LLM can sound empathetic. It can generate CBT-style reframes that feel helpful in the moment. What it cannot do is recognize patterns of worsening depression across weeks of conversations, or understand when someone’s casual mention of “feeling tired of everything” is actually a red flag.

The Regulatory Wall

While Braidwood was grappling with these technical limitations, the external environment shifted dramatically. In August 2025, Illinois became the first state to ban AI from providing therapeutic services through the Wellness and Oversight for Psychological Resources Act. Violators face $10,000 fines per incident.

Other states started drafting similar legislation. The lawsuits began piling up—most notably the Raine family’s case against OpenAI, alleging ChatGPT contributed to their son’s suicide.

Then came the statistic that changed everything: OpenAI disclosed that roughly a million users per week express suicidal ideation to ChatGPT. That’s not a bug that needs fixing. That’s a systemic mismatch between the technology and how people naturally use it.

For Yara, this meant fundraising became nearly impossible. Braidwood told Fortune he had an interested VC but couldn’t bring himself to pitch while harboring serious safety doubts. The company ran out of money in July but lingered until November before formally shutting down.

What Actually Matters Here

The easy take is “AI isn’t ready for therapy.” True, but incomplete. The more interesting insight is about product boundaries and technological capability gaps.

Most software products can fail safely. A buggy project management tool is annoying. A glitchy dating app is frustrating. But a mental health chatbot that sounds confident while being fundamentally unreliable? That’s dangerous precisely because it’s convincing.

Braidwood described this as the difference between being inadequate and being dangerous. An inadequate tool simply doesn’t help. A dangerous tool appears to help while potentially making things worse.

The other second-order effect worth tracking: market displacement regardless of readiness. According to Harvard Business Review analysis, therapy and companionship has become the top use case for AI chatbots. Not coding assistance. Not research. Emotional support.

Millions are already using ChatGPT, Claude, and other general-purpose models for mental health conversations, despite these systems having even fewer safeguards than purpose-built products like Yara. A recent survey found 13% of young people ages 12-21 have sought mental health advice from generative AI, with 93% finding it helpful.

Think about that gap. The demand exists. The technology gets deployed. But the safety infrastructure isn’t remotely ready.

The Aftermath

To Braidwood’s credit, he didn’t just shut down and walk away. He open-sourced Yara’s safety scaffolding and mode-switching templates, acknowledging that people will continue using AI for therapy regardless and deserve better guardrails than what generic chatbots provide.

His LinkedIn post announcing the shutdown received hundreds of supportive comments—unusual for a failure announcement. The message resonated because it broke from typical startup mythology. No pivot to “similar but different” product. No “learning experience” spin. Just: we realized this was the wrong problem to solve with current technology.

Braidwood has since launched Glacis, focused on AI safety transparency—essentially building “flight recorders” that create tamper-proof logs of AI decisions. The thesis: if AI systems are going to operate in high-stakes environments, there needs to be verifiable proof that safety measures actually ran.

The Takeaway

Some markets look attractive until you actually try to serve them responsibly. The mental health crisis is real. The shortage of therapists is real. The desire to use technology to bridge that gap makes perfect sense.

But wanting a solution to exist doesn’t make the technology ready to deliver it. LLMs are extraordinary at many things. Providing safe, continuous mental health support in crisis situations isn’t one of them—yet.

Braidwood’s decision to shut down Yara represents the kind of founder judgment that rarely gets celebrated but probably should. In an ecosystem that worships growth at all costs, sometimes the most responsible move is recognizing which costs are too high.

The question now isn’t whether AI will play a role in mental health care—it already does, mostly in unregulated ways through general chatbots. The question is whether the industry can build the safety infrastructure before the inevitable disasters multiply.

For Yara AI: Founded 2024, shut down November 2025, users in the low thousands, less than $1M raised, safety protocols open-sourced on GitHub.

Sometimes knowing where to stop is more valuable than knowing how to scale.

If you’re working on AI applications in sensitive domains or have thoughts on where the safety boundaries should be, reply to this email. We read everything.

xoxo,
Thomas

And it wasn’t an accident.

The Playlist Obsession

Editorial playlists constitute around one third of all Spotify listening time. Think about that - a third of all time spent on Spotify is people listening to playlists curated by Spotify’s editors.

Spotify’s “Daily Mix” and “Discover Weekly” alone account for 29% of listening time among regular users.

But here’s the part most people miss: Spotify isn’t just serving playlists. They’ve turned playlists into their entire strategic moat.

Why playlists specifically? Three reasons:

1. They’re personal and shareable (growth engine)

2. They generate massive amounts of behavioral data (algorithmic advantage)

3. They give Spotify power over the music industry (strategic leverage)

Let’s break down how this actually works.

Everyone’s a Mixtape Curator Now

Spotify changed the fundamental unit of music from albums to playlists.

This wasn’t just a UX decision. It was strategic.

Playlists are identity markers. They reflect taste, mood, personality. And most importantly - they’re designed to be shared.

Think about the last time you shared an album link with someone. Now think about the last time you shared a playlist.

By turning every user into a DJ, Spotify created an organic growth machine where people naturally share Spotify links in personal, non-spammy ways.

“Check out my workout playlist” “Made you a road trip playlist”
“Here’s my studying playlist”

Each one is an invitation to Spotify, wrapped in genuine personal connection.

The Data Goldmine

But playlists aren’t just about growth. They’re about data.

Every time someone creates a playlist, Spotify learns:

• Which songs pair well together

• What moods different combinations create

• How users categorize and think about music

• What contexts drive listening behavior

This data feeds the algorithm that powers Discover Weekly, Release Radar, and Daily Mix - the algorithmic playlists that keep users coming back.

Premium users average 6.7 playlists followed, compared to 3.2 for free users. More playlists = more data = better recommendations = higher retention.

The innovation over time has been relentless:

• Collaborative playlists: Create with friends (and invite them to Spotify)

• Smart shuffle: Get recommendations mixed into your playlists (Premium-only, driving upgrades)

• Blend: Combine your taste with friends’ tastes into a shared playlist

• Sponsored playlists: Brands pay to reach specific moods and moments

Each feature makes playlists stickier, more personal, more valuable.

The Real Power Move

Here’s where it gets strategic.

Spotify doesn’t own the music. Never will. They license everything from labels and rights holders.

So how do they build defensibility? Curatorial power.

A study found that placement on Spotify’s “Today’s Top Hits” playlist can generate between $116,000 and $163,000 in additional royalties for an artist. Viva Latino generates between $303,000 and $424,000.

Think about what that means. For independent artists and labels, getting featured on a Spotify Editorial Playlist is one of the most powerful growth levers available.

Viva Latino currently has 15.5 million followers. Today’s Top Hits has over 34 million followers.

When Beéle debuted on Viva Latino in 2019, his streams surged by 168% within a month. Artists featured in Viva Latino’s Radar Latino series enjoyed an average 49% increase in streams.

This is power. Real, measurable, industry-shifting power.

Spotify Controls the Demand Curve

Since Spotify doesn’t own the music, playlists became the mechanism through which they exert influence over the industry.

In 2020, 19 of the top 20 most-followed playlists were created by Spotify’s editorial team, totalling nearly 161.5 million followers.

Nearly every screen in the app funnels users toward Editorial Playlists:

• Home screen

• Search results

• Genre pages

• “Made For You” section

• Even artist profiles recommend Editorial Playlists

For artists, this creates a new gatekeeper. Want to break through? You need Spotify’s curators to notice you.

Anitta’s “Envolver” appeared on Viva Latino 132 days before hitting #1 globally. Myke Towers’ “LALA” appeared 99 days before topping charts.

Spotify’s playlists don’t just reflect hits - they create them.

Why This Matters

Most streaming services compete on catalog size or audio quality or price.

Spotify competes on curation.

They’ve built a system where:

1. Users create millions of playlists (generating data and growth)

2. Algorithms use that data to create personalized playlists (driving retention)

3. Editorial teams create flagship playlists (wielding industry power)

The result? Spotify doesn’t just host music. They shape what people listen to.

That’s the moat. Not the technology. Not the catalog. The curatorial power that comes from having the world’s largest playlist ecosystem.

I am not particularly interested in mobile gaming, but these ads have always piqued my curiosity. Every time I see one, I ask myself the same question: Why are some of the biggest mobile game developers investing in ads that have nothing to do with the game they are making?

At first glance, it seems like a waste of money, especially considering how much gamers dislike these fake ads.

However, it turns out these deceptive ads are far from random. They’re a core strategy in the hyper-casual gaming industry, and while we may find them annoying, they are surprisingly effective in ways that aren’t immediately obvious.

Voodoo and Hyper-Casual Games

Hyper-casual games are simple, addictive games that are easy to play and often free to download. Think of those games you can play while waiting in line or during a quick break.

The business model behind these games isn’t about long-term player engagement; instead, it’s about volume—churning out as many games as possible, getting users to download them, and monetizing them through ads.

In this world, Cost Per Install (CPI) is king. Publishers like Voodoo, one of the biggest players in the industry, thrive by keeping the CPI low, usually under 20 cents, to make their games profitable.

But here’s the twist—before even developing a game, companies like Voodoo need to know if it’s worth the investment. This is where click-through rate (CTR) tests with fake ads come into play. These tests are an ingenious method to gauge user interest before a single line of code is written.

CTR Tests and Fake Ads

So, what exactly are CTR tests, and why are they so important? Simply put, a CTR test measures how many people click on an ad compared to how many people see it.

For hyper-casual game developers, this metric is crucial. They create short, catchy video ads—often depicting gameplay that doesn’t actually exist—and run them on platforms like Facebook. If the ad generates a high CTR, the developers know they have a concept worth pursuing. If not, they abandon the idea and move on to the next one.

Voodoo routinely create these fake ads to test various game concepts. The goal is to see what catches users’ attention. It’s a quick and cost-effective way to validate ideas.

Other companies like Clap Clap Games follow a similar process, creating multiple videos, running them as link-click campaigns, and only moving forward with game development if the CTR is promising.

The beauty of this approach lies in its efficiency; it allows developers to test numerous ideas with minimal effort and resources.

In addition to this, these ads can also be an excellent way to obtain new users. Despite the potential backlash from users who feel tricked, these fake ads are surprisingly effective. While many users might uninstall the game once they realize it’s not what they expected, a good portion will still stick around if it is enjoyable enough.

This balancing act between user acquisition and retention is what makes or breaks a game in the hyper-casual space.

Beyond Gaming

While this strategy is commonplace in the mobile gaming industry, there’s no reason why it can’t be applied elsewhere.

Imagine a startup looking to validate a new feature or product idea. Instead of building a full MVP, they could create a video or mockup of the feature and run a similar CTR test. The idea is to gauge interest before committing significant resources.

A great example of this approach comes from Dropbox. Before developing their entire product, they released a video explaining what Dropbox would eventually become.

While this video wasn’t as deceptive as a fake mobile game ad, the underlying idea is the same: create a video for a product that doesn’t exist yet to test its appeal.

A related strategy, often used in the startup world, is the Fake Door MVP. A Fake Door MVP is a landing page for a product that hasn’t been built yet. The page typically explains the product’s features and includes a sign-up button for users to be notified when the product launches. The goal is similar to that of fake ads—to estimate interest in an idea and determine whether it’s worth the investment.

Should I?

Why This Works

• Low-Cost Validation: The strategy allows businesses to validate ideas or concepts with minimal upfront investment. By testing market interest before fully committing to development, companies can avoid costly mistakes and focus resources on ideas with proven potential.

• Data-Driven Decision Making: The strategy relies on measurable data (e.g., CTR, sign-ups, pre-sales) to guide decisions, making the process more objective and less reliant on gut feelings. This data-driven approach increases the likelihood of success.

• Flexibility in Iteration: Since the strategy involves testing before development, it offers the flexibility to iterate on ideas quickly. If one concept doesn’t work, it’s easy to move on to another without significant losses. Voodoo usually runs several fake ad campaigns at once, showing a different game concept in each ad. This allows them to quickly test and validate many different ideas and know which one to choose.

How to Apply It

• Create a simple mockup or video of your product or new feature. These don’t have to be polished; the goal is to convey the core concept and test whether it resonates with your audience.

• Run CTR tests on social media. Voodoo and other gaming companies use Meta almost exclusively for this.

• Analyze the data and iterate. Monitor the click-through rate to see if there is interest. Running multiple campaigns simultaneously is helpful as it allows you to compare the CTR between different ideas.

• Create a “Fake Door” landing page for your new product. If you are running fake ads, you can link them back to this fake door. Include CTAs on the page to measure how many people sign up.

• You can take it a step further by attempting to pre-sell your product. This is the ultimate validation that your idea has strong potential.

Yes, But

• Potential Backlash: Users may feel deceived by fake ads, especially if they realize the advertised product doesn’t exist yet or differs significantly from the final version. This could lead to negative feedback, poor reviews, and a loss of trust in your brand. That being said, every gamer hates fake mobile game ads, but this hasn’t diminished their efficiency.

• Limited Long-Term Insights: While CTR tests and fake door pages can gauge initial interest, they don’t provide a full picture of long-term engagement or retention. A high click-through rate doesn’t necessarily translate into a sustainable user base or repeat customers.

• Risk of False Positives: A high CTR might suggest strong interest, but it could be misleading if users are drawn in by the novelty rather than genuine intent to engage with your product. This could lead to investing in a concept that ultimately fails to gain traction.