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Hello there!

If you are into health-tracking gadgets this one might be for you.

I’ve pretty much tested all wearables under the sun (i.e. whoop, fitbit etc). The one that lasted the longest for me was the Oura, so lets dive into their growth story.

Hardware plus subscription can be a very tough business model (especially if you talk to any vc’s). You pay to build the thing, you pay to ship the thing then you ask the customer to keep paying after they already bought it, tough.

On May 21 this year, our Finnish friends at Oura here filed confidentially for an IPO at an $11B last-round valuation, with revenue that went $100M → $165M → $250M → ~$500M → ~$1B in 5 years with this cap-table:

So I spent the past week pulling apart 10+ founder and executive interviews (the CEO, a co-founder, the CPO, the CMO), every funding round since the 2015 Kickstarter, the ITC patent rulings and the S-1 timeline.

What you’ll learn

• Why the new CEO cut marketing to 0 + the referral number they tracked

• The $6 subscription with no free tier and lapsed users who renew within 24 hours

• How Prince Harry’s doctor became a growth channel

• The $1,000 Gucci ring that sold out at 2.5x price and triggered the retail flip

• How “spend it or lose it” pre-tax money became a sales channel

And other growth mechanics, lets dive in!

From sleep tracker → the S-1 pile

A little about how this market evolved before we get to the growth mechanics:

Where we come from

Wearables grew up on the wrist with Fitbit putting a step counter there in 2007, the Apple Watch arriving in 2015, and for a decade or so the category meant mainly a screen on your arm “counting” your activity.

The wrist won by default (the watch form-factor was already there) for the same reason that when you go to a hospital they clip the sensor to your finger, because the arteries sit close to the surface there and the signal is clean. And sleep (most information-dense 8 hours of your physiology, and the absolute 80/20 to fix / step 0 to get anything health-wise done), went mostly unmeasured.

Where we are

Roughly 164M smartwatches shipped in 2025. The ring is now its own category basically owned by Oura with an estimated 80%+ market share. And of course the pressure arrived on schedule:

1. Samsung → launched the Galaxy Ring at $399 with no subscription.

2. The low-cost clones (Ultrahuman, RingConn) → attacked on price until the ITC banned their rings from the US in October 2025.

3. Apple → the rumor that never dies with analysts not expecting a ring before 2028.

Oura stopped being “just” a gadget company somewhere along the way with at least half of its story now running through glucose sensors, blood panels, a Medicare Advantage sleep-apnea pathway, plus thousands of rings across the US defense sector (US Department of Defense is now its largest enterprise customer!).

Where the market is going

The fight is shifting from tracking your body to standing between you and the healthcare system and 3 things will likely shape the next few years:

• Platform owners set the price of software at zero: Samsung already did and if Apple ships a ring bundled into an iPhone upsell it’ll shake up competitive dynamics. Oura’s answer is the subscription itself (more below).

• Healthcare: Oura routes sleep-apnea signals into physician referrals for Medicare members, and Eli Lilly took an equity stake this month. In Europe roughly 10% of GDP goes to healthcare, and Hale talks openly about being “a virtual doctor in your pocket” (Slush, 2025).

• The IPO wave decides the multiple. Oura filed its S-1 the same week as SpaceX. Whether public markets read it as a hardware company or a subscription company is worth billions either way, and that question runs through this whole piece.

Now to Oura’s origin and the 9 levers:

Act 1: The Kickstarter Ring From Oulu

2013 → 2019 · $0 → first 150K rings

Oura was born in 2013 in Oulu, a Finnish city 2 hours below the Arctic Circle that Nokia’s collapse had filled with unemployed hardware engineers and, usefully, world-class testing labs.

The 3 founders (Petteri Lahtela, Kari Kivelä, Markku Koskela) came out of the Polar and Nokia orbit, and Lahtela’s obsession came from years building IT systems for chronic disease management, where he watched health data reach patients years too late. The company was originally called Jouzen (from joutsen the Finnish word for swan) with the zen for calm and a deliberate phonetic nod to the English “chosen” (members have chosen to prioritize their health). Cash was so tight that salaries sometimes ran months late:

“We lived off personal credit cards, but everyone showed up to work every day, driven by the vision to bring Oura Ring to the world.” (Marjut Uusitalo, early employee, Oura’s history blog)

The 0 → 1: how the first rings actually got sold

They decided on day one that the US was the market to go after:

• March 2015 → came out of stealth on stage at the San Francisco Launch Festival, where Lahtela said “we all know the charge level of our mobile phones, but do you know the charge level of your body?” Gen1’s official job was a way to collect data to validate the science and hone the algorithms.

• The first believers → not easy to find. Lahtela says it was “really hard, really really hard,” until they landed on people who “spoke the same language,” first the Quantified Self crowd in San Francisco, then the biohacking and self-optimization scene. Those early users were “in resonance” with the approach so they understood what Oura was going for and according to the founders “helped us to form the product as well”.

• August 2015 → the Kickstarter hit its $100K target in 15 hours and closed at $650K from ~2,400 backers with apparently no PR and no paid ads. Those 2,400 backers were the first paying customers, recruited almost entirely through the communities above plus what Lahtela calls “lots of footwork in between and after.”

• Investors said no → “it was really hard to find investors who were also driven by something else other than money”. The early cap table filled up with angels instead including Will Smith, Shaquille O’Neal, YouTube’s Steve Chen and Twitch’s Kevin Lin (per Nordic Business Report, so treat as reported).

Then 2 outsiders found the company and both became levers:

• 2015-2017, Stanford Research Institute → secretly bought 2 rings off Kickstarter to test them (more in Growth Lever 2).

• 2016 a Whole Foods in New York → co-founder Kivelä spotted the first stranger he’d ever seen wearing the ring. It was Harpreet Singh Rai, a hedge fund PM who had lost ~50 pounds using it! Rai invested in September 2017, joined the board, moved to San Francisco and became CEO in 2018 (who later stepped down).

Gen2 debuted at Slush in November 2017 and sales jumped to ~10K rings then 100K+ rings across 100+ countries by August 2019, and roughly 150K total before 2020.

Now lets dive into how they grew:

Growth Lever 1: They built a wearable you never take off

Wearable decisions flow from 2 architectural choices they made in 2013:

• Measure from the finger → arteries sit closer to the skin than at the wrist, the tissue is uniform (no hair, bone, or tattoos in the way), so the signal is cleaner and the accuracy claims get easier to defend.

• Put nothing on the ring → no screen, no LEDs, no interface. You don’t feed it attention, you don’t charge it nightly (7-8 days of battery), and as Hale likes to put it, “it’s not another digital mouth to feed.”

It matters for growth because a sleep tracker only works if you wear it to sleep, every night, for years, and the device you forget you’re wearing is the device you never take off. Continuous wear → continuous data → insights that compound (illness prediction needs your baseline, cycle prediction needs months of temperature), so wearing comfort becomes the door to the data moat.

Lahtela claims retention runs 3 to 4x higher than any other wearable (Slush 2024).

Hold onto the ratio V over P (value over price) because it’s their mental model for the business and every lever below either pushes the V up or refuses to touch the P.

Growth Lever 2: They let Stanford prove their claims for them

In the middle of the Kickstarter chaos the Stanford Research Institute apparently bought 2 rings and ran them against polysomnography (the gold-standard sleep lab measurement). The study, published in March 2017, found Oura the most accurate wearable for sleep staging, which made a tiny Finnish startup the first independently validated wearable on the market.

Oura then “industrialized” the accident:

• A real science org → roughly 70 people including 30+ PhDs, about 7% of the company running validation across skin tones and health conditions, plus a 100,000-person blood pressure study.

• A partner ecosystem doing the distribution → 800+ ecosystem partners by the end of 2024 (company-reported), 1,000+ on Sacra’s current count, universities, hospitals, and health apps that run on Oura data.

• Clinical-grade as the internal bar → “we aspire to the level of accuracy and quality that would be appropriate for a medical device” (Hale, How Leaders Lead), without being regulated as one.

The growth lever here is clearly borrowing credibility (which beats bought credibility), since a peer-reviewed study converts skeptics (definitely better than ads). I liked Hale’s framing of the partner network:

“By enabling other partners they become your salespeople and your distribution channel and your referral network.” (Tom Hale, Masters of Scale 2025)

Growth Lever 3: They got 60% of sales from users recommending the ring

The founders’ number is that roughly 60% of all sales have come from existing users recommending the ring “and that has been the same for all the time” (Slush 2024). Hale’s version of the same number from the 2022 data is that 1 out of 4 customers showed up because someone recommended it.

The founders’ design goal was a product that produces a personal revelation (you learn you’ve been under-sleeping for 20 years, you see the exact night the wine wrecked you etc), because a “revelation” is the thing you tell your friends about.

The machinery in practice:

• The product produces tellable moments → it flags you’re getting sick 2 days before you feel it, and Hale tells the story of a stranger stopping him in an airport security line to say “Oura got me pregnant” (Slush 2025).

• The celebrity roster wore it without contracts → Bill Gates, Bezos, Bono, Jennifer Aniston, all organic per CMO Doug Sweeny. Oura didn’t sign its first paid ambassadors (Harry Kane, Declan Rice) until June 2026.

• Prince Harry was the extreme case → he wore it on official tours in 2018-2019 and the press noticed. Oura never paid him and never met him, the contact ran through his doctor, with one message, roughly, don’t talk about my ring, talk about how this product can help young adults stay healthy.

Act 2: The Bubble, the $6 Revolt, and the Missed Target

2020 → 2022 · 150K rings → $165M revenue

In 2020 COVID made body temperature front-page news for all of us. Then suddently 2 university studies said the ring could spot symptoms days early and 65,000 users donated their data to prove it (and everyone wanted one). But it didn’t last:

Growth Lever 4: They sold to institutions in crisis, then let the endorsements sell consumers

Who signed in 2020:

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In case you missed it:

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Hello there!

I’m a big music nerd, and this week’s deep-dive is about one of the companies that is truly breaking the frame for a lot of people in the industry.

Suno, the AI music generator app, recently raised $400M at a $5.4B valuation. Their ARR has also skyrocketed from $200M to $300M in roughly 90 days this winter, and the same week they announced it a group of artist groups published an open letter titled “Say No to Suno.”

In any case, this is the kind of company that leaves a mark. This is giving me flashbacks from when artists said Spotify would kill music, Napster and the MP3 drama before that, home taping was “killing music” in the 80s before that, radio before that… you see the pattern right?

I’m excited about this one, and trying the product is honestly pretty magical (I lost a few hours to it while writing this edition). No surprise they’ve attracted some of the sharpest money in tech over the past 3 years:

So as usual I spent the past week pulling apart everything I could get my hands on regarding their product and growth tactics.

What you’ll learn (from the 7 growth levers inside):

1. Charging before it was good: the day-1 paywall that ~50% of new users hit

2. Renting distribution: how 12 people got inside Microsoft Copilot

3. Shipping the shareable unit: why full fuzzy songs beat competitors

4. Users as the training team: the 2-songs-per-click trick

5. Pricing the lawsuit: why their first investor wanted no label deals

6. Pros as proof: the contest that pulled 100,000 remixers.

7. Flipping the table: how the defendant bought Songkick from the plaintiff.

Lets dive in.

Act 1: The Discord Bot

Mar 2023 → May 2024 · $0 → 10M users

SR30 Growth Index

In 2022 4 machine learning engineers leave Kensho (the fintech AI startup S&P Global bought for over $500M) to start an “audio” company (not a music company yet). The pitch is an audio understanding tool, because they think generating music is out of reach (for now).

“We said we would need a hundred times more compute to be able to produce good audio,” —Shulman

They close a seed round the same week Silicon Valley Bank collapses in March 2023.

“Most people didn’t know where their money was”

Weeks later they realize they were wrong about the compute, they see open-source tooling from the text world (Shulman name-checks Karpathy’s nanoGPT) had made small-scale generative audio possible, and they basically decide to throw away the company they pitched.

“We were building these speech things during the day and we would play with these things at night making music, and at some point it was just like…

what are we doing?”

Growth Lever 1: They made the paywall their quality benchmark.

One of Suno’s most important problems early on was measurement.

Whereas a coding model has benchmarks and a math model has right answers a song or music is “taste”, I would imagine it’s harder to run a test to objectively know whether your music model is getting better every week.

So their paywall (which started as “maybe a bit of a happy accident”, free tier capped at 10 songs a day then pay for more) became the company’s substitute for an eval:

1. Day-one paywall hits are the quality score: the number the founder watches most is how many new users burn all 10 free songs in their first session, because if you did, “you just enjoyed the last 10 or 12 minutes of your life.” Almost 50% of new users do (myself included). And when a model update ships that number is the verdict they pay attention to.

2. Early subscribers mark the magic moments: some users pay before ever hitting the wall, “Let’s go talk to them and figure out what was that magical moment.”, which also gives them a way to find and deeply understand those people / power users.

3. The money mattered too: since compute apparently runs a few times payroll.

An investor gave the founder a framing they now repeat internally:

“Let me write the obituary of the company for you. A bunch of brainiacs who think they’re smart enough to intuit what the best consumer experience is going to be, and they get their lunch eaten by a bunch of people who just iterate their way there faster.”

If your product’s quality is subjective then charging early might be a great measurement instrument (disguised as monetization).

Growth Lever 2: They rented distribution 3 times before building their own surface.

📌 Quick answer to something a reader asked after the Lovable edition:

👉 Where did the first users come from?

In Suno’s case it was an open source repo. In April 2023 they released Bark, a free text-to-speech model, the first very realistic open one at the time. It blew up (39K GitHub stars) and per Shulman “it put us on the map, people started to reach out: potential employees, investors, potential customers.”

The clever part is that, if you notice, the Bark README points everyone to Suno’s Discord. So when the music bot launched in August 2023, Shulman announced it with one line, “AI can now sing,” pointing at that same Discord.

The first product in August 2023 was a Discord bot with zero UI to build, community included, straight from the Midjourney playbook.

It grew to hundreds of thousands of members (today almost 400K).

Then in December 2023, Microsoft integrated Suno as a plugin inside Copilot, putting song generation in front of one of the largest distribution surfaces on earth (while they had about a dozen employees at the time).

In November 2023, co-founder Martin Camacho pushed a thin web app over Shulman’s objections (”I’m like no no no no”). It launched with less functionality than the Discord bot and within 5 days, 90% of traffic had moved to the web.

“There’s no world in which you can say that I got that right,” Shulman says.

Also, they didn’t shut the Discord down, it became a permanent free feedback firehose with hundreds of thousands of members. So distribution got rebuilt in-house but the community stayed rented. I also like Discord for this type of product / feedback.

Then in March 2024, Rolling Stone ran a cover-level feature and released "Soul of the Machine," a blues song generated from the prompt "Mississippi Delta blues song about a sad AI" and it went viral with 36,000+ plays in 4 days.

That same week they pushed v3 to the free tier, and 10 million people made a song in the first 8 months.

Growth Lever 3: They shipped whole songs when everyone else shipped clips (trade-off).

Early on, Suno’s model could only make 12 seconds of rough audio and the competition (and there were plenty of research demos) optimized for pristine 10-second loops and expected users to stitch them together.

But Suno went the other way and everybody could hear it, Shulman’s own words:

“Everybody could hear 1 second of a Suno song and know: oh, that sounds like crap, that’s a Suno song.”

Here’s why I think in this case the “worse-sounding” product won aka why the (hard) trade-off decision worked:

1. You can share a full song: a 10-second clip is a demo for other musicians, you need editing software and some skill to turn it into anything, but a two-and-a-half-minute song with your friend’s name in the chorus tends to go straight to the group chat (I’ve done it, multiple times).

2. That sharing was the growth engine: Shulman says all growth to date has been organic, “because music is really meant to be shared.” Every song made on Suno is an ad for Suno.

3. Bad audio was a problem that fixes itself: every new model version sounds better and v3 through v5 did exactly that. A clip never turns into a full song no matter how crisp it gets, whereas in their case they picked a flaw that time tends to erase (considering the past 2 years especially).

One technical detail here is that Suno generates music the way ChatGPT generates text (one token after another). Most audio research at the time used diffusion (the approach behind image models) which sounds cleaner but is worse at holding a song together over 3 minutes. They also refused to program music theory into the model, so it learns everything from raw audio:

“If you tell the model there are 12 tones, it will only ever produce those 12 tones. You will be forever limited.”

By May 2024 they announced $125M raised at a reported ~$500M valuation, led by Lightspeed with Nat Friedman, Daniel Gross, Matrix and Founder Collective.

Then 6 weeks after the announcement the music industry sued them…

Act 2: Speed & Craft

Jun 2024 → Nov 2025 · ~$40M → $200M ARR

On June 24, 2024, the RIAA filed suit on behalf of Sony, Universal and Warner. They now had billions in exposure, with roughly $40M in revenue and 12 employees.

17 months later they’d be worth $2.45B, here’s what they did next:

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Hello there friends!

One of my best friends spent a few years at Canva, and as a power-consumer of Powerpoint (pitch decks 😅) it’s a space I’ve always been curious about.

So I got interested in Gamma’s growth story. They recently crossed $100M in annualized revenue (2025), raised $68M from our friends at a16z at a $2.1B valuation, all with a team of around 50 people and profitable for over 2 years.

It’s an interesting example of a contrarian bet, considering it is a category most investors would call a “graveyard”. It was built by 3 first-time founders, one of whom heard “that is the worst idea I have ever heard” from a VC (always a good sign), who’ve managed to pull together a tier-1 cap-table:

So I spent the past week pulling apart a dozen founder interviews with the co-founders, every funding round, and all numbers they have let slip about how they actually did it.

What you'll learn (from the 8 growth levers inside):

1. Timing the AI wave: why Gamma’s real unlock was Stable Diffusion in mid-2022, months ahead of the ChatGPT boom.

2. Onboarding as the growth loop: how rebuilding the first 30 seconds of the product took them from a few hundred to 20K signups / day.

3. Charging the moment it clicks: the hand-typed Stripe link that proved people would pay and made Gamma profitable within 3 months of turning on pricing.

4. The micro-creator flywheel: why 1,000 small creators drove more than half of Gamma’s signups, and the 1-to-1.5 word-of-mouth math behind the compounding

5. Building a defensible moat: what a “thick wrapper” actually is and how a team that’s one-third designers with 0 ML engineers turned it into a real one.

6. Deferring enterprise to stay fast: why they turned down enterprise money for years to protect its speed and what changed in 2026.

Lets dive in.

From the 16:9 slide → the AI design partner

SR30 Growth Index

A little about the market before we get to the growth levers.

Where we come from

PowerPoint shipped in 1987 and for almost 40 years the category barely moved.

• They added color in the early 90s, clip art came and went but the core stayed pretty much the same. As Jon Noronha puts it Google Slides is “PowerPoint on the internet” and Keynote is “PowerPoint for Mac.” Which are different platforms but really the same product.

• Everything inherited the fixed 16:9 rectangle from physical film slides and a designer-canvas model where you resized boxes and aligned things by hand. But the problem is that most people are not designers so they spend their time decorating instead of thinking.

• Then Canva (2013) started winning by handing non-designers templates and by winning the same person across many asset types (turning a monthly user into a weekly one).

But for startups presentations have mostly been… difficult:

• The TAM was huge (PowerPoint and Google Slides each have hundreds of millions of users) but the use case was seen as episodic + not sticky.

• And most importantly (bad for startups), it was bundled free inside Office and Workspace (so nobody paid for the slides product directly).

Where we are

2 things really cracked the category open between late 2022 and 2023.

• Stable Diffusion (mid-2022) made instant image generation feel “magical” (despite weird hands and fingers and all sorts of hallucinations you’ve probably seen). Then ChatGPT (Nov 2022) created demand for “what can AI do for me.” and suddenly the most painful part of a deck (aka the formatting and the decorating) could be at least in theory automated.

• The new shape of the product is “come for the AI, stay for the product”, where the AI generates the first draft and drives the virality but it is the underlying tool that drives the retention.

A whole cohort of AI-native tools went after this wedge which you’ve probably seen:

• Gamma, Tome, Beautiful.ai, Canva’s Magic Studio and others all offering instant fancy decks, and here’s where Gamma is the one that grew fastest and stayed profitable (as we’ll see later 70M users and $100M+ ARR on a team of about 50).

• The incumbents are now bolting AI on too (i.e. Microsoft Copilot inside PowerPoint, Google Gemini inside Slides etc) aka the bundling threat is real and it is coming.

Where the market is going

3 dynamics will likely shape the next 24 months.

1. The suites bundle harder: as discussed Microsoft and Google each have millions of users / dominate distribution and can give AI slide features away for free. Gamma’s bet is that big incumbents cannot change their core product quickly because their buyers are paying for it to stay the same. But, every time the technology shifts that opens a window for a nimble player, Jon (founder) says: “You either die an obscure startup or live long enough to fight the big suites”

2. Creation goes agentic + multi-format: they already hit decks, docs, websites, social, and are apparently eyeing video next. As you’ve probably seen across these products the interface keeps flip-flopping between a tool you click and an agent you talk to (i.e. Claude plugin on Powerpoint) and pricing may shift from per-seat to usage-based as the agent does more of the work, we’ll see.

3. The wrapper question gets answered: as models commoditize text generation to a large extent the durable “value” or moat tends to move to everything around it (experience, clarity, layout, editing, export, brand, human control etc). Thin wrappers die but as we’ve seen with a few players on startup riders, thick wrappers can compound, and it goes without saying, the prize is huge (i.e. PowerPoint alone has a billion-plus users and is, as Grant says, “the language of business”).

That’s the market in a nutshell, now lets dive into Gamma’s origin + growth levers:

Act 1: The Modern Memo

Late 2020 → Feb 2023 · $0 → pre-revenue, signs of PMF

3 ex-Optimizely co-workers Grant Lee, Jon Noronha, and James Fox, start building at the end of 2020 (peak pandemic by the way). Grant is a first-time founder living in London pitching investors over Zoom (like half the world at the time) from the corner of a small flat between the kitchenette and the laundry room.

The product is a “modern memo”, basically a doc-and-deck hybrid built on a new primitive they call a card (any height), holds anything, instead of the fixed 16:9 rectangle, and the tagline is write like a doc, present like a deck.

By late 2022 they win Product Hunt’s product of the day, week, and month. Even though it feels like a win it’s not fully, active users are in the low thousands, “maybe the hundreds if we’re being really strict” according to Jon, with no word of mouth and about 1 year of runway.

The pitch itself was a proof of concept they built as a Gamma prototype (instead of slides) opening with Edward Tufte, PowerPoint’s most famous hater:

A16z’s Alex Danco reading it during Series B diligence 5 years later:

“I’m not sure I’ve ever seen a company’s Seed Round pitch deck include a quote from its subject medium’s greatest hater.”

The seed was led by Amy Saper at Accel (now Uncork capital), who had been a product marketer at Stripe and Twitter who had lived this pain, with Eric Yuan, Jeff Weiner, and founders at Airtable, Patreon and Segment behind her.

Growth Lever 1: They tested every idea on 20 strangers in under 24 hours.

Considering the founders came from an A/B testing company, it makes sense that Gamma had a testing machine before it had users, but how did they build it?

They learned the hard way that friends make useless testers (everyone said the product looked great, family is even worse) then Grant checked usage and most had 1 session of about 5 minutes and never came back.

“Friends are going to lie to you.”

They rebuilt the loop around strangers with “nothing to gain”:

• They recruited around 20 people per test who fit the user profile but had “0 skin in the game,” dropped them in with almost no context and had them narrate everything in their head out loud, and you feel exactly where they get lost.

• The stack was Voice Panel and UserTesting to source testers on demand (Grant discloses he angel-invested in Voice Panel), plus a private Slack of power users they call Gammbassadors (love it), over 1,000 strong today, where every rough prototype landed first.

• The cadence velocity was important, where an idea would drop in the morning, functional prototype by midday, live study in the afternoon, and a verdict the next day. This apparently happened with everything (landing pages, onboarding, exports, every feature basically).

2 things fell out of this loop before the AI launch:

• Testers kept repeating the same words unprompted, “I suck at PowerPoint,” “when it looks ugly, I look bad”, which told them that the pain was real.

• And interestingly it showed them exactly where users died which was usually the first screen, blank page, unfamiliar tool, gone. Which explains why Product Hunt signups spiked and then flattened, new users were not bringing new users.

“We could have fooled ourselves into thinking we had product market fit” (Grant).

Growth Lever 2: They interviewed ~100 people and built for the one pain all of them shared.

Every investor told them to niche down (salespeople, consultants, founders, pick one) but before deciding they talked to about 100 people across completely different jobs and asked each one to walk through the last presentation they made.

Turns out the doctor and the student wanted the same thing:

1. struggled to make it look good

2. feared being judged for it

3. lost the room.

So they organized the company around the shared pain rather than a persona. But going horizontal can often be a great way to kill a startup (so does lack of focus, interestingly Gamma also had 2 different products running in parallel when they started for 6 months!). But Grant has 2 answers for why it didn’t kill them:

1. They went wide on the audience but stayed narrow on the value prop. His framing at Stanford is that entering a market is like trying to penetrate a balloon, “be laser focused on just one value prop, don’t try to spread yourself thin across 10 different things”. Their one thing was the wedge most shared which is many of us are visual learners and almost none of us are visual designers.

2. They wanted Gamma to become the new standard, the tool everyone defaults to, and you can’t become the default for everyone by building for “salespeople only” for example.

Act 2: Flip It On

Mar 2023 → mid 2024 · $0 → ~$10M+ ARR

The standard story is Gamma added AI and took off but what actually happened was very different and took some hard product trade-off decisions.

Growth Lever 3: They bet the whole company on the first 30 seconds + launched it with one “spicy” tweet.

The AI trigger came with Stable Diffusion in mid-2022 and watching people generate an image out of nothing made them ask what actually hurts in a deck, and the answer was the formatting and the decorating.

So in early 2023 a dozen people and with runway draining they gave themselves a 4-month sprint with a target to fix the first 30 seconds:

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🌊 How N8N grew to $100M ARR

Hello there!

If you have ever played around with “no-code tools” like Webflow, Zappier, Make etc, you’ll enjoy this one on n8n, the ai agent + workflow automation platform of this “no-code” tool 2.0. wave.

There’s a few positive anomalies that make this company special. In May of this year SAP (europe’s largest software firm) took a stake in n8n at a $5.2 billion valuation and agreed to embed it inside its own AI product.

Which is double the $2.5 billion the company was worth in October 2025, which was already close to 9x the ~$270 million it was worth in March 2025:

The tool is pronounced “n-eight-n” (I miss-pronounced it like 7x on this podcast). If you’ve built anything with AI agents in the last year you’ve probably used it. But when I ask around almost nobody knows how it grew (the founder Jan Oberhauser barely does interviews).

For its first 5 years n8n was in Jan’s own words “growing okay. Not outstanding. But it felt solid.” Then it rebuilt itself in 6 weeks and 4x’d in 8 months. I spent the past week pulling apart every founder interview, funding round, blog post, and filings I could find on how they grew.

What you’ll learn

• How they grew from a side project to $100M+ ARR and a $5.2B valuation

• The 6-week rebuild that 4x’d their revenue in 8 months

• Why they stayed neutral on every AI model and how that became a growth engine

• How giving the product away for free is what won the enterprise

• Why they refused to trade power for simplicity (the 80% trap)

• The “fair-code” license fight that kept developers building on it

Turning n8n into “the Excel for AI”

SR30 Growth Index

A little about how this market evolved before we get to the 8 growth levers:

Where we come from

For most of the last decade automating work between apps meant one of 2 things:

1. You paid Zapier by the task to connect your SaaS tools with simple if-this-then-that rules

2. You paid an enterprise integration platform like Workato or UiPath a lot more to wire your systems together with the help of a consultant.

Both worked but they both also had a ceiling because simple tools were easy to start with and fell apart the moment your workflow got complicated. The enterprise tools were powerful and priced for a procurement cycle (left curious devs out).

n8n started in the gap between them. Jan’s framing of why those simpler tools kept disappointing people is interesting:

“The problem with these no-code low-code tools was always that they seem amazing in the beginning, you get a lot of value out of them, but when you want to bring it in production, you’re at 80% and you think ‘I need one or two more things’ and then you’re suddenly stuck. And then all the time you saved, you pay back 5 times to actually get it to production.”

Where we are

Then ChatGPT happened and the market split:

1. On one side are the model labs and agent builders that want to own the intelligence.

2. On the other side a fast-growing pile of vertical AI apps (each solving typically one narrow job)

And sitting between them is a question nobody had a clean answer for in 2023 which is, once you have 10 AI tools and 5 models and a dozen data sources:

what actually connects them and runs the thing in production?

Which is the “connective layer” where n8n positioned itself. According to Sacra, the company today in 2026 serves 1,400+ enterprise customers and supports a community of 1.7 million monthly active builders, with a revenue mix estimated at roughly 55% cloud subscriptions, 30% enterprise licenses, and 15% embedded partnerships. More than 80% of workflows built on the platform now involve AI agents.

Where the market is going

3 things will shape the next couple of years:

1. The work splits in 2: Jan’s distinction is that some enterprise work is deterministic where there is one correct outcome and anything else is a mistake (compliance checks, billing, data updates). The rest is non-deterministic where judgment and context shape the answer (triaging a ticket, drafting copy, deciding what to do with an anomaly etc). Most AI tools handle one or the other but n8n is supposedly built to mix code, rules, and agents in the same workflow.

2. Protocols standardize the plumbing: they call Model Context Protocol “the HTTP of AI workflows,” and sees n8n as the orchestration layer between MCP-connected services, agents, and tools.

3. The hyperscalers circle: Sacra flags the risks here that I tend to agree as AWS, Google, and Microsoft can bundle workflow automation into their AI services and subsidize it. The counter-move as we’ll see is the SAP deal and a self-hosting story the hyperscalers might struggle to copy.

Now lets dive into how they grew.

Act 1: The Lego Box

Oct 2019 → 2022 · $0 → “growing okay, not outstanding”

n8n started with a visual effects artist who was bored of doing the same thing twice.

Jan was a compositor (the person who combines the computer-generated 3D with the filmed footage and makes it look real). He got bored, moved into a more technical role as a pipeline technical director and spent his days building tools to make the artists around him faster.

Which is the job-to-be-done the company seems to comes from:

“Those people [were] very smart, very well paid and quite technical, but they were always reliant on me or other people like myself to actually do the things they wanted to be done. They could have had so much more impact if they would have been empowered, and they weren’t. I think n8n is doing exactly that.”

At a later startup he realized he was spending most of his own time rebuilding things that already existed:

“I spent probably 90% of my time reimplementing things that have been implemented before. Get something on GitHub, send a message to Slack. Each of those pieces have been implemented millions of times before by almost every developer out there, and this is never the most fun thing to do.”

So he set out to build a box of reusable Lego blocks for that plumbing, and free himself to spend time on the part that was actually specific to his problem.

He built n8n nights and weekends across roughly a year while working at a second startup to pay rent because as he puts it plainly, “I already had wife and one child back then, so I obviously needed some food and some shelter as well.”

He’d started coding at the beginning of 2018, a soft launch came in June 2019, the proper one in October. The name is actually a numeronym for “nodemation” (node plus automation), the 8 standing in for the 8 letters between the two n’s.

He’d been invited to Y Combinator and turned it down because he didn’t want to leave Berlin or incorporate in Delaware. Sequoia and firstminute co-led a $1.5M seed in March 2020 anyway, with Felicis leading a $12M Series A a year later. By that point the community was around 16,000 developers.

Growth Lever 1: They gave the product away and treated support as the product.

The product was free and self-hostable from the start.

One of the first people Jan he hired was a community person, before it made any obvious financial sense, purely to write documentation, run the forum, answer questions fast etc. He chose a forum over a Slack or Discord on purpose:

“These people have a problem, I answer that question once, maybe 3 times. At least I don’t have to answer it 50 times. That allowed us to scale.”

The bet was that a free product with rough edges plus genuinely fast, genuinely caring support produces users who leave a problem feeling better than if they’d never hit it because they now trust that someone will unblock them, and those users tell other people, they write tutorials, build integrations etc (similar flywheel to the one we ran at Facebook, explained in this playbook).

The clearest example is a developer named Ricardo, based in the US, who found n8n on Product Hunt at launch, and built one integration for fun. Jan reviewed it and gave feedback, he built another, and another. In the end he contributed 56 integrations before n8n had the money to pay him. As soon as it did, Jan hired him and he’s still there, and was for a long time the only n8n engineer in the US.

Growth Lever 2: Refused to trade power for simplicity

Everyone else in the last automation wave chased the same goal which was to get a beginner to their first working automation in 5 minutes. But n8n optimized for the other end which is the moment a workflow gets complicated and wouldn’t sacrifice that power to smooth out the first 5 minutes.

An example of this, under the drag-and-drop canvas there’s a real code editor, so when you hit something the buttons can’t do, you write a line of JavaScript or Python and keep going, whereas Zapier and Make lock the code away and cap you at what their menus allow (so n8n leaves the door open lets say).

It matters for growth because every easy tool tends to have the same leak, which is it wins you in week one, you build something real, you hit its ceiling (the 80% trap), and you move to a “proper” platform, so the tool that got you loses you right when you technically became “valuable”.

I think their goal was to set the ceiling is high enough that you never need to leave, so:

• A solo builder and a 500-person enterprise can technically use the same product.

• The people outgrowing Zapier and Make now move to n8n because there’s nothing left to “outgrow”.

• And the same canvas that sends for example your first Slack alerts later can run your company’s AI agents (or at least that’s their plan now).

As an example imagine a developer finds n8n on GitHub, runs it for free, builds something real, then pushes their company to pay as usage grows. The steeper curve is a side effect of keeping that power available and it self-selects for these people (aka technical enough to build serious things and the ones who get a tool into a Fortune 500).

Growth Lever 3: They never changed the rules on the community.

n8n is one of the most-starred projects on GitHub (194,000 stars, roughly a top-40 project globally), and Jan is careful to never call it open source.

It didn’t start clean though as n8n launched under Apache 2.0 with a Commons Clause modifier, which broke the technical definition of open source and made some people unhappy. But instead of fudging it, the founder dropped the words “open source” and coined his own term, “fair code.”

What fair code actually means:

• The code is visible and free to use, even in production, even inside a Fortune 500.

• The one thing you can’t do is take it and sell a hosted version.

It is a growth lever because a developer won’t pour months into building on a free tool, contributing integrations, and getting their company to standardize on it unless they trust the rules won’t change once they’re locked in.

And by 2023 developers had every reason to expect the opposite because a run of open-source companies (i.e. MongoDB, HashiCorp, Redis and others) got big then quietly changed their license to block cloud providers from reselling them and their communities turned on them.

“People didn’t hate it because of the license the companies chose. People were mainly angry because the company changed the rules. So I thought, I’m just very honest and upfront about it from the very beginning.” (Jan, Sequoia)

I imagine that honesty was an unlock because the founder said out loud that n8n was a business and would stay free under clear terms, so developers built, contributed, and standardized without watching their backs. So the free tier stayed genuinely free, the commercial license funded everything, and there was no betrayal in between.

Act 2: Six Weeks

2023 → Dec 2024 · the rebuild that changed everything

By early 2023 n8n was a solid, growing, community-loved dev tool.

Then Jan got scared:

“When AI came up the first time, I honestly was a bit scared, because what was clear is it’s changing the whole game. Things I just months before thought would be impossible suddenly became very easy.” (Jan, Accel)

What tipped him off was seeing Pinecone raise $30M, which made them ask why a vector database was suddenly so interesting.

“What they were in the past, they were the vector database. What they became, they became a deep database for AI. That’s where I said we have to do something very similar within n8n.”

Growth Lever 4: They spent 6 weeks turning n8n into the place you build AI.

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🌊 Synthesia’s Growth Playbook

Hello there!

In January Synthesia (ai video platform) announced a $200M Series E at a $4 billion valuation led by Google Ventures, capping a run from roughly 100 investor rejections to $150M ARR, (aiming to get past $200M this year).

What’s really interesting to me is that they started pre-ChatGPT, before the AI wave made any of this obvious. Building a video-AI company in 2017 was a true contrarian bet and the timing is a big part of why they’re so far ahead now.

Also, apparently >90% of the Fortune 100 now pays for their technology, surprising after all the drama surrounding “deepfake tech” over the past few years.

They have a 140%+ net revenue retention, meaning their average customer spends 40% more every year after they’ve bought. Their cap-table is equally impressive:

So I spent the past week pulling apart 18 founder and operator interviews, every funding round since 2017 and every leaked detail about how they’ve grown.

What you’ll learn

• The cold email inside the Sony hack that turned into $1M at 5am

• Why they killed a product with revenue after 18 months

• The pricing ladder that runs from a free demo video to $1M+ contracts

• How “we moderate in the Word document” became a key feature

• The 140% NRR engine hiding inside translation

• Why the CEO says they won the enterprise with the non-AI things

From deepfake panic → enterprise utility

A little about how this market evolved before we get to the growth levers:

Where we come from

In the fall of 2016 a research paper called Face2Face showed a neural network puppeteering a real person’s face in real time, work that would have taken (in the CEO’s estimate) 3 months and 10 people in a Hollywood VFX studio.

2 Danish operators, Victor Riparbelli (World of Warcraft player as a youngster, wild how this pattern repeats across outlier founders) and Steffen Tjerrild, saw it, quit their jobs, and recruited the professor behind the paper (Matthias Niessner, then Stanford, later TUM) plus UCL’s Lourdes Agapito.

Back then the world looked at the technology and reached a different conclusion:

• The public saw deepfakes: consumer face-swap apps like Reface racked up 100M+ downloads, journalists wrote panic pieces, and Victor recalls investors asking him “why the hell he would start a deepfake company”.

• Hollywood saw a toy: they saw de-aging actors and resurrecting Princess Leia as useful for a handful of $200M productions a year, meh.

• Investors saw a lawsuit generator, roughly 100 of them in Europe and the US.

So the panic is what probably made the market cheap to enter, nobody “serious” wanted to build a business on the most distrusted technology in AI (contrarian).

Where we are

AI video split in 2, on one side the spectacle models (Sora, Veo) that generate anything and live in your feed, and on the other the workflow products companies actually run on, where Synthesia owns the second category with 65,000+ customers and 90%+ of the Fortune 100. Their neighbors:

• HeyGen is the closest pure rival at ~$95M ARR, skewed to creators and SMBs, now moving upmarket.

• Colossyan ($22M Series A) and Hour One (~$20M raised, acquired by Wix) are an order of magnitude smaller.

• Adobe and Vimeo are bolting AI onto camera-era tools, and Adobe took a strategic stake in Synthesia the same day the $100M ARR announcement went out (more on Adobe in Act 3).

What also makes Synthesia a positive anomaly in this AI wave is their margins. The Google Ventures partner who led the Series E describes them as an AI company with 2021-SaaS economics:

“Really great growth, but really great margins and really solid retention.”

Where the market is going

The fight is shifting from video you watch to video you “talk to” and 3 things will shape the next couple of years I think:

• The frontier labs commoditize the pixels: rather than fight Sora 2 and Veo 3.1, Synthesia embeds them inside its own editor for B-roll and keeps the workflow.

• The interface shifts from playback to conversation: their new Skills product puts an agent at the end of a training video that role-plays an objecting customer with your sales rep and scores the exchange (funnily enough I actually started building that product a couple years back). It’s apparently in beta.

• Trust becomes regulated table stakes: provenance standards and AI acts favor whoever already built the compliance stack, and (as we’ll see in Lever 6) Synthesia started building theirs in 2017 (again, vision!).

Victor’s bet on formats is worth keeping in mind throughout. He thinks Hollywood adopts AI video last, the same way drum machines were built to replace drummers and instead invented house music.

Now to Synthesia’s origin and the 8 growth levers to a $4B valuation:

Act 1: The Wilderness

Apr 2017 → mid 2020 · $0 → <$1M revenue

Their initial pitch was that in 10 years you’ll make a Hollywood film from your laptop. Victor was 25 (not an AI researcher), not even a software developer, and they ran on personal savings and maxed credit cards.

After ~100 rejections, one name was left on their list. Mark Cuban, who had built broadcast.com in the first internet-radio wave and (it turned out) had personally implemented the Face2Face paper at home.

Steffen found Cuban’s private Gmail inside the Sony Pictures hack data dump (which anyone could download at the time, wild), and sent a short cold email with a video of the tech:

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Hello there!

It’s been an interesting week to be a corporate card company. This week Ramp announced a $750M Series F at a $44 billion valuation, capping a run from $0 to over $1B in annualized revenue in 6 years. Just a couple months earlier Brex, the rival that beat them to market by 2 years closed its sale to Capital One for $5.15 billion (a 58% discount to its peak valuation).

Two companies fought over the same customers with the same product for 6 years and one of them is now worth 8.5x what the other sold for, with quite the cap table:

There’s also a poetic detail in here that almost nobody mentions which is that the founders sold their first startup to Capital One, worked there, and left in 2019 to build Ramp.

So I spent the past week pulling apart 20 founder and operator interviews, every funding round since 2019, every blog post and leaked detail about how they’ve grown.

What you’ll learn

• The interchange math that pays for software Ramp gives away for free

• How a team of 8 engineers built an Amex competitor in 3 months

• The down round they took on purpose while revenue 3x’d

• The internal panic when they finally started charging for software

• Why direct mail (of all things) became one of their biggest channels

• The honest math under $44B (take rates, float, regulation, and the $700B government contract fight)

From expense reports → autonomous finance

A little about how this market evolved before we get to the growth levers:

Where we come from

For decades business spend ran on 2 tools that barely spoke to each other, a corporate card from a bank like Amex or Chase that made more money the more you spent + expense software like Concur (still gives me nightmares) or Expensify that you paid for by the seat. On one hand the issuer wanted volume and on the other the software vendor wanted seats, so the finance team was left stitching the 2 together by hand every month.

Then a wave of startups tried to merge the card and the software into one platform, and for a while it looked like one of them would run the table:

• Brex launched in 2017 and led with instant-approval cards for startups and became the defining spend story (reaching $12.3B valuation in January 2022, and yes later slashing it to 5B).

• Bill, public since 2019, bought Divvy for $2.5B in 2021 to bolt spend management onto its bill-pay network.

• Navan came at it from the travel-and-expense side and raised at a $9.2B valuation in 2022, and pushed into corporate cards and spend from there.

The category had a name (spend management) and a clear leader in Brex.

Where we are

2 things broke that order between 2022 and 2026. The first was rates going up, and once money stopped being free the fintechs that had raised at 2021 marks could no longer grow into them. 2 different things happened depending on which side of the Atlantic you were on:

The US cohort played for winner-take-all:

• Brex defended its $12.3B valuation and then sold to Capital One for $5.15B in January 2026 (58% discount to its peak).

• Navan IPO’d in October 2025 at a $6.21B implied valuation (below private mark).

• Divvy disappeared inside Bill (market cap $3.5B).

The European cohort played smaller and mostly survived:

• Pleo hit $4.7B in 2021 before its own investor marked it to around $2.3B by 2024.

• Payhawk (Sofia) is the one still climbing, $1B in 2021 and reportedly raising toward $2B in 2026.

• Spendesk (Paris) reached profitability rather than chasing the next mark.

The second thing that happened was Ramp going the other way from everyone, taking a 28% markdown on purpose in August 2023, compounding all the way through the downturn, and re-rating from $5.8B to $44B.

Where the market is going

The fight is shifting from selling a card with some software attached to owning the system of record for every dollar a company spends, and 3 things will shape the next couple of years:

• The incumbents are awake: Capital One paid $5.15B to get a modern spend platform it can aim at this exact shift and Amex and the big banks are the slower better-funded version of the same threat.

• AI spend is the new battleground: Tokens are a cost line that barely existed 3 years ago and they are growing fast. They sit invisible to every legacy finance tool so whoever builds the layer that sees them has a real claim on the next decade of corporate spend.

• Consolidation keeps running: With Brex gone and Divvy absorbed the independent field is thin and the survivors will keep buying whatever is left.

Now to Ramp’s origin and the 9 levers:

Act 1: The Anti-Points Card

March 2019 → March 2022 · $0 → $100M+ annualized revenue

In 2016, Capital One acquired Paribus, a price-tracker built by 2 Harvard classmates, Eric and Karim, that had saved consumers over $100M by automatically claiming refunds when prices dropped. They stayed long enough to make the acquisition work and then left in March 2019 with one of their Paribus engineers, Gene Lee.

Before writing a line of code they set themselves a goal that sounded like a joke at the time, they wanted to build a company worth $1 billion in 18 months and they reverse engineered the plan from there. Glyman confirmed the story on My First Million (well worth your time listening to the episode), and their founding engineer later looked it up and found that no New York company had ever hit $1B inside 3 years, let alone 18 months.

They got there in roughly 2 years, which is close enough, with the aim to “build a technology-driven financial services company” (email from Karim to Founders Fund in 2019):

The founding idea came from asking what would happen if your credit card could help your business spend less money. They interviewed around 100 entrepreneurs and finance leaders before launch and found something better than the original idea which is that people hated expense reports (I can vouch for that) even more than they hated wasted spend.

Lever 1: They made money when clients spent, and won them by helping clients spend less

Every card company on earth earns interchange, the small cut taken every time a card gets swiped, which means every card company on earth gets richer when you spend more. The whole industry built points, rewards, lounges and steak dinners on top of that incentive, anything to make you swipe. Glyman on Stripe’s Cheeky Pint said:

“We entered into this industry where it was very profitable, but not only was it misaligned, but people were fighting over basis points.”

Ramp kept the revenue model (interchange) BUT inverted the promise:

• No points, no rewards theater, just 1.5% flat cashback and a pitch that they will supposedly help you spend less

• Savings insights as the product, flagging duplicate SaaS subscriptions and unused seats (i.e. you’re paying for 200 Asana seats but only 100 people log in, which they detect from Okta data, etc).

• A public number to anchor it (claim to save the average business about 5% per year).

Spending less sounds like revenue suicide for a company paid per dollar spent but the bet was that it works the other way around. Every dollar Ramp saves a customer buys trust, and trust is what gets a finance team to route more of its total spend through you. You lose basis points per dollar and win all the dollars.

Hold onto the word subsidy (give away the thing your rivals charge for, get paid on the flow underneath it). In Act 3 we’ll look at what that trade actually costs though.

Lever 2: They gave the software away and let the card pay for it

Every time someone pays with a Ramp card the store pays a small fee (usually 2-3%) Ramp keeps a slice. That slice supposedly pays for everything else, which is why the software is free:

• Expense reports, receipt scanning, bill pay, approvals, accounting that updates itself

• No per-user fees, contracts, interest, or late fees

• The tools Expensify, Bill.com, and Concur charge for, they give away to anyone who spends on the card

The more spending crosses the card the more Ramp earns, so its job is to capture as much of a company’s card spending as it can. As Glyman put it:

“if you want to grow interchange, grow purchase volume.”

That sounds like it contradicts Lever 1, but the two measure different things:

1. Spending less is about the total bill, the duplicate subscriptions and unused seats Ramp cuts.

2. Growing volume is about how much of the spending you were doing anyway runs across Ramp’s card instead of an Amex or a bank wire. Ramp pitches shrinking your total spend and grows its own volume at once, because the volume comes from taking share off the incumbents (not making you buy more).

As you can imagine this is probably pretty brutal to compete with, because a rival charging monthly for expense software now faces the same thing for free, basically funded by a card fee they can’t touch.

By March 2022, 2 years after launch, they were doing $100M a year with 100 to 200 employees, and Founders Fund valued it at $8.1B just before the market turned.

Lever 3: They shipped products faster than competitors shipped features

One of Ramp’s most important edges is shipping product faster than most in finance, their numbers are almost hard to believe:

• It reached $100M in annual revenue with fewer than 50 engineers and fewer than 5 product managers

• One small team built a competitor to Bill.com in 3 months (pre vibe coding)

• It cloned the core products of AmEx, Expensify, Concur, and Bill.com and gave them away free, each built by a pod of 3-5 engineers

• Keith Rabois (backed Stripe, DoorDash, etc), called the pace “absolutely unprecedented in my 21 years working with technology businesses”

Some of the 80/20 choices that probably help shaped the culture:

• 2 kinds of engineers: After a Capital One reprimand for shipping too fast inside a regulated bank the founders split the team in 2, careful builders on the things that can’t break like underwriting and payments, and fast movers on a long leash for new products, in 3-to-5-person pods (I love this).

• Hire for slope (not résumé): 1 in 3 of the first 60 hires was a former founder, and the rule was to bet on people for 10x upside rather than screen for 0 defects.

• An org built to force cooperation: Product, design, engineering, and data all report to the CTO, and every spec and decision is published in the open, so teams hunt for dissent rather than sign-off and nothing waits on a gatekeeper.

• Sequence new products on rails you already have: the VP Product’s rule is to pick the next product that reuses the last one’s plumbing (cards, expense, and reimbursements all share the same employee identity).

• Organize teams around outcomes (not features): Each pod owns one big business result rather than a product surface (drive half of qualified leads, raise bill-pay attach rate), and has to publish an open contract laying out its goal, strategy, roadmap, and metrics so the whole company can see what it is accountable for.

One gem worth stealing is the product strategy template their VP Product shared on ’s newsletter, which makes every team answer the same 7 Q’s before building (goal, hypothesis, right to win, metric, initiatives, risks, compounding).

By early 2022 the flywheel looked unstoppable, but then the market broke, opening the most interesting growth levers of the story:

Act 2: The Down Round

March 2022 → December 2024 · $100M → ~$500M annualized revenue

Between March 2022 and August 2023, interest rates shot up, and that resets how startups are priced. When money is cheap to borrow, investors chase fast-growing companies and (more?) happily pay a premium on their revenue. When rates jump, safer bets suddenly pay well, that premium shrinks, and the same company is worth far less without anything about it changing.

Every startup that had raised at 2021 prices now had to decide whether to defend that number. Rates hit Ramp’s actual business too, lifting the interest it earns on customer cash while raising the cost of the debt that funds card spending. They chose to reprice in public:

• In August 2023 it raised $300M at a $5.8B valuation, a 28% cut.

• The framing missed that revenue had 3x across those rounds, from $100M to over $300M, so the multiple compressed from 81x to roughly 18x and the price reset to something defensible.

• Brex defended its $12.3B mark for 3 years and sold at $5.15B.

• By April 2024 the valuation followed reality back up to $7.65B, with revenue nearing ~$500M annualized by mid-year.

Lever 4: Every free product became a new front door

They kept adding products (bill pay in 2021, financing in 2022, procurement and travel in 2024, a treasury account in January 2025). You would expect them to land a customer with the card and then upsell the rest but each new product turned into its own front door, pulling in customers who never touched the card:

• Thousands of businesses showed up for bill pay first (no card involved)

• Procurement, the software that lets a company request, approve, and buy from its vendors, is now its fastest-growing line

• Accounting firms started running their clients on Ramp and bringing 100-200 businesses each onto the platform, a sales team Ramp doesn’t pay for (it turned this into a product called Stack in 2026, per the Series F release)

Every product door leads into the same building and they earn a small cut of whatever money flows through it, a fee on payments, a margin on financing, a commission on travel, and interest on the cash companies park with it ($1.5B+ within a year of launching treasury). Basically this is the same trade as Lever 1 (give the software away and earn on the flow underneath it) but run on every new surface.

It shows up in two numbers, with 50% of customers now using two or more products, and Ramp apparently having more than 30% of its profit come from outside the card in 2025 (estimate plan according to Sacra for that year).

Lever 5: They started charging only when customers were already hooked

As they moved upmarket the free model hit a wall, since interchange on a big company’s card spend gets nowhere near covering what that company needs:

• A 5,000-person enterprise wants deep integrations, audit controls, and custom workflows, and as Atiyeh put it, “we’re not able to capture any of that value for really, really large businesses because our revenue mechanism is not scaling”

• So they launched Ramp Plus, a paid tier ($15 per user per month) for the advanced features, and braced for impact:

• Paying customers got more demanding, leaned on the sales team, and started steering the roadmap, and growth sped up rather than slowing

Lever 6: They hunted growth where nobody was looking, or nobody believed

Ramp’s growth team runs on finding alpha (stolen from investing). Saturated channels price in their own returns, so the yield is in channels your competitors either haven’t found or have already dismissed.

The VP Growth’s (George Bonaci) favorite example is direct mail:

“It was like, why would direct mail work, junk mail to people’s homes... and it became one of our most successful, biggest channels after a few iterations.”

It worked because probably few in B2B fintech was in their clients mailbox at the time, and because the channel is absurdly scalable for experimentation (“very few channels where you can go, hey, tomorrow let’s go reach 200,000 people”).

A few more pieces of the operating system:

• Sequencing doctrine (from Sri Batchu, who ran growth before Bonaci, on Lenny’s): founder-led sales first, then your first reps plus cheap targeted marketing (content, community, small events, PR), and only after all that paid and brand. Channels get more expensive down the list, and you get better at buying them as you learn your customer.

• Fail conclusively (Batchu): around 30% of growth experiments work, and the sin isn’t failure but a sloppy test that leaves the question open, so the same bad idea returns every year with a new executive attached.

• Events are binary (Bonaci): either take over the conference (the billboard outside, the hotel key cards, the speaker slot) or don’t go, because the lonely booth in the corner is money on fire.

• Saturate winners fast (Bonaci): when a channel works, push it to its ceiling quickly, since “most things saturate probably more slowly than people expect.”

Act 3: The Token Land Grab

January 2025 → today · ~$700M → $1B+ annualized revenue

In 2025 the compounding became hard to miss:

• 5 valuation marks in 15 months, $13B → $16B → $22.5B → $32B → $44B

• $1B+ revenue, cash-flow positive, on $200B+ of volume growing ~170% a year

• Capital One bought Brex for $5.15B, ending the 6-year war

Lever 7: They gave away their data and became the news

A fact about card data that most people don’t know is that hedge funds used to pay serious money for it. Aggregators bought anonymized transaction data from card companies, cleaned it, and sold it to funds trading on spend trends.

Ramp sits on a feed like that, across tens of thousands of businesses, in near real time. I’ve done a few analysis using data from Ramp’s Economics Lab (image above).

They could have sold it but instead they publish it for free on a fixed monthly schedule:

• AI adoption rates by company size and industry

• Spend categories rising and falling in near real time

• What businesses actually buy versus what’s being marketed to them

The return on giving it away beats the return on selling it. Every release gets picked up because it’s primary data nobody else has (i.e. CNBC quotes the AI-spend numbers in their Series F coverage), and the company becomes the reference source on business spending, which is exactly the authority you want when your product is seeing and managing business spending.

As someone who writes a data newsletter, I’ll say this is the cheapest credibility engine in B2B. If your product generates proprietary data as exhaust, publishing it on a schedule can turn your ops into free media / distribution.

Lever 8: They chased the new cost line before tools existed for it

So in April Ramp shipped token spend management, pulling usage data straight from Anthropic, OpenAI and OpenRouter, joining it with billing data, and attributing it to teams and projects.

Their own example of the resolution they’re after reads like an APM dashboard for money (”the search team spent $340K on Claude Sonnet for the recommendation engine, up 40% since the last deploy, driven by a prompt change that doubled average token count”).

Interestingly this is kind of their 2019 playbook rerun where they find a cost line that’s growing, painful and invisible, build the visibility layer before the category has a name, give it away to capture the flow. In 2019 the cost line was card spend and the incumbent was Amex’s points machine, while in 2026 the cost line is tokens and the incumbent is a spreadsheet.

Whether token visibility monetizes like card interchange is an open question (there’s no swipe fee on an Anthropic invoice).

The fine print under $44B

Its also interesting to dig a little into the open questions that came up when deep-diving into this. 5 questions the bull case tends to gloss over:

1. What does Ramp actually keep per dollar?

Nobody outside the company discloses it precisely. Sacra notes the take rate is compressing for 2 reasons, more spend moving onto cheaper rails like bill pay and ACH that carry almost no fee, and bigger enterprise customers paying lower rates than startups. The slice per dollar keeps thinning but volume grows faster, which is how revenue crossed $1B. The risk sits underneath that where a business whose margin per dollar falls every year has to keep winning new volume just to hold revenue flat, and the thinner the blended rate gets, the more a software valuation starts to look like a payments one.

2. Is the revenue real “ARR”?

The skeptic's case is that Ramp is a payments company being valued on software multiples, and interchange is not the contracted, recurring revenue those multiples assume. The counterargument is that over 30% of contribution profit now comes from software and services, the revenue converts to cash unusually well, and the business throws off free cash flow at $1B scale, which most "real" SaaS companies that size don't. Both can be true at once.

3. What happens to the charge book in a real recession?

Ramp underwrites 30-day charge cards against customer cash balances, with receivables funded by Goldman and Citi warehouses. That’s elegant in good times (no revolving credit risk, no consumer-style losses) and untested at scale in bad ones, because SMB cash balances are usually what evaporates in a downturn. Loss rates are not disclosed (I’d love to see them).

4. How much of the profitability is rate float?

Treasury launched January 2025, crossed $1.5B in balances within the year, and earns interest in a high-rate world. Free cash flow positive and “underlying profitability up 153%” are real claims, and some unknown slice of them is the rate cycle rather than the product. When rates fall things will change.

5. The regulation risk.

2 exposures, both about the swipe fee that funds everything:

• Ramp’s whole model runs on the 2-3% fee merchants pay on card spend. In the US there’s no legal cap on that fee for business cards, but Europe caps it near 0.3%. If US regulators ever did the same, Ramp’s main revenue source would mostly disappear overnight. It’s unlikely, but it would be fatal if it happened.

• Ramp is reportedly chasing a slice of the US government’s own card program, a ~$700B pot of spend, and the way that deal was being handled has already drawn a congressional inquiry. A win would be a huge new revenue line, but revenue won through politics can be taken away by politics just as fast.

None of this is disqualifying and all of it is the price of the subsidy trade from Lever 1. When you give the product away and monetize the flow, your risks live in the flow (take rates, credit, rates, regulation), and the $44B question is whether the flow keeps compounding faster than its risks do.

20 quarters of evidence say yes for now.

5 Growth Lessons

My favorite take-aways:

1. They sold against their own industry’s incentive: Every card company earns more when you spend more, so the whole industry pushes you to spend more. They promised the opposite, to help you spend less, and the trust that earned is what pulled companies onto the platform.

2. They funded a free product from a real revenue stream rather than from runway: The software is given away, but the card’s swipe fee pays for it the same day, so free was a business model and not a countdown to the next raise (not without its trade-offs).

3. They waited to charge until customers were already dependent on them: Ramp Plus launched only after enterprises were running on its rails, and rather than churning, paying customers got more demanding and started shaping the roadmap, which made the product better and growth faster.

4. They spent their experiment budget where others had given up: example with direct mail, it looked like junk mail, which is why it was cheap and uncontested, and after a few iterations it became one of their biggest channels.

5. They gave away the data their product sits on: Spend data that hedge funds used to pay for (published free every month) and the press picks it up each time, which makes them the company quoted on how businesses spend.

That’s it for this week friends!

Cheers,

Ivan

P.s. If you want to help me out, the best thing you can do is share my work. 🙏

📈 You might also like / In case you missed it:

• ⚛ Sierra’s Growth Playbook: $0 to $200M ARR in 26 months

• 💻 Mercor: $1M to $2B GMV in 24 months

• 🖼️ Gamma AI: 0 to $100M ARR in 6 years

• 🤖 n8n’s Growth Playbook: 0 to $100M ARR

• 🎬 Synthesia’s Growth Playbook: $0 → $150M ARR

📚 Bibliography

Founder and operator interviews

• My First Million ft. Eric Glyman, “I built a billion dollar company in 18 months,” 2025

• Invest Like the Best ft. Karim Atiyeh, “The Anatomy of Ramp’s Hyper-Growth,” 2025

• Cheeky Pint (Stripe) ft. Eric Glyman, with John Collison and Alex Rampell, February 17, 2026

• 20VC ft. Keith Rabois and Eric Glyman, E1148, 2024

• 20VC ft. George Bonaci (VP Growth), E1264, February 28, 2025

• 20VC ft. Geoff Charles (CPO), E1091, 2023

• Lenny’s Podcast ft. Geoff Charles, 2024

• Lenny’s Podcast ft. Sri Batchu (ex-Growth), 2023

• This Week in Startups ft. Eric Glyman, E2192, 2025

• ICONIQ Podcast ft. Eric Glyman, “The Uncommon Path,” 2025

• The Biography Podcast, “How He Grew Ramp To A $32 Billion Business In 6 Years,” 2025

• Geoffrey Woo Podcast ft. Eric Glyman, 2024

• Mad Podcast ft. Eric Glyman, “AI at Ramp,” 2024

• Peter Yang Podcast ft. Geoff Charles, 2025

• Pavilion Podcast ft. Nate Follen (ex-Ramp), 2025

• Invest Like the Best / Colossus ft. Eric Glyman, “Reimagining Corporate Finance” (transcript)

Primary sources

• Ramp Series F announcement, PRNewswire, June 4, 2026

• Ramp $32B announcement, PRNewswire, November 17, 2025

• Capital One completes Brex acquisition, April 7, 2026

• Capital One to acquire Brex, January 22, 2026

• House Oversight: Connolly letter to GSA, May 9, 2025

Coverage and analysis

• Not Boring: “Ramp at $1 Billion,” September 9, 2025

• Not Boring: “Ramping Up,” 2021

• Sacra Research: Ramp company profile

• Contrary Research: Ramp business breakdown

• CNBC: $13 billion secondary deal, March 3, 2025

• TechCrunch: down round coverage, August 22, 2023

• TechCrunch: Oversight investigation, May 12, 2025

• TechCrunch: Goldman debt facility, February 10, 2021

• TechCrunch: Paribus acquisition, October 6, 2016

• Yahoo/Reuters: $750M raise at $8.1B, March 2022

• AOL/Reuters: $150M at $7.65B, April 2024

• ProPublica: “Trump Team Eyes Politically Connected Startup,” April 17, 2025

• Bloomberg: How Eric Glyman built Ramp (profile)

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Hello there (for this analysis, salut!),

Apparently our french friends at Mistral have achieved an annualized revenue north of $400M in January 2026 (up from $20M a year earlier).

Which is roughly 20x in 12 months…

It was founded in Paris in April 2023 and its the only European company building frontier AI anywhere near the level of OpenAI and Google. They’ve also raised a near-$2B Series C at a ~$14B valuation led by ASML, and is targeting more $1B+ in revenue this year, with quite the cap-table:

The most interesting question that came to mind during this analysis is, how do you build something durable when the product you sell might become a commodity (with enormous competitive pressure)?

Which is the exact (contrarian at the time) bet they made from day one. So I spent the past week pulling apart 11 founder interviews, every funding round, and Mistral research I could find.

What you’ll learn:

• Why they raised the biggest seed in European history on a 7-page memo

• The model they dropped as a bare torrent link at 5am on Twitter

• How they took Microsoft’s money and shipped a closed model the same day, then survived the backlash

• Why “you don’t want anyone to turn off your systems” became their pitch

• How and why they put their own engineers inside their customers

• Why a chip-equipment maker led their biggest round

And as usual, a few growth lessons to steal at the bottom.

Let’s dive in:

The setup for a $400M run

Quick look at the market’s evolution before we dive into growth levers:

Where we come from

From 2020 to 2023 the scarce resource in AI was the handful of people who knew how to train a frontier model. The big dogs (OpenAi etc) turned that scarcity into closed APIs where you sent a prompt to a black box in a US cloud and got an answer back but you never saw the weights and your data sat on their servers.

So the moat was model quality.

Mistral bet on the opposite, with a founding thesis that frontier models commoditize:

“This tech is inherently going to get commoditized. It’s not hard to build. You have around 10 labs in the world that know how to build it, that get access to similar data, that follow the same recipes and algorithms.”

Where we are

The gap between the best open and best closed model shrank from ~6 months in 2024 to ~3 months in 2025. This mainly happened because throwing more raw compute at pre-training stopped working, and every lab now hits the same ceiling in a couple of months. So the labs behind caught up, and the labs ahead ran out of room to pull away.

DeepSeek’s cheap open model in January 2025 was proof of this, one of Mistral’s founder called it “a great moment for open-source models”.

If the model commoditization assumption holds, then the hundreds of billions going into bigger models might be capex on a depreciating asset:

Mistral’s strategy seems to be a bet that the value won’t sit there, and instead might sit in the 2 things a potential commodity can’t give you:

1. Being open (so you own and control the thing), not open then closed (wink wink).

2. Being European (so your data and compute stay in your jurisdiction).

Where it’s going

3 dynamics to keep an eye on over the next 24 months:

• The gap keeps closing: 6 months, then 3 months, etc so “best model” as a moat keeps eroding, and value keeps sliding downstream to applications, services and infrastructure. This is tends to be good for Mistral’s positioning and bad for anyone whose pitch is “we’re the smartest”. The counter is that “best model” could be the wrong benchmark now because the gap is closing on raw IQ but opening on agentic reliability, distribution, and inference-time compute etc., and those tend to favor the labs with more users and more capital.

• Sovereignty becomes a budget (vs a slogan): the founders’ claim is that Europe depends on US providers for the bulk of its digital services, and US decoupling fears turned that into spend. France announced €109B of AI investment around the Paris AI Action Summit, so in a way defense, banking and government now have a reason to buy local that has nothing to do with benchmarks. Then again, the cap table tells a different story which I’m struggling to reconcile... Sovereignty, but funded by a16z, General Catalyst and Microsoft?

• Margins compress toward utility economics: the founder says the 70% gross-margin software era is ending and you should run AI “as an infrastructure business.” So this should reshape who wins (aka not the lab with the best demo but the one that owns cheap compute + sticky deployments).

Also, the same logic that helps Mistral can hurt it with (among other threats):

1. Open weights commoditizing fast cutting both ways (Llama, DeepSeek, Qwen)

2. Hyperscalers building down into the sovereign on-prem layer.

3. The $830M GPU debt only pays off if those GPUs run hot (inference prices fall)

Now, lets get into how they grow:

ACT 1 — The Memo

April 2023 → December 2023

This all started when 3 researchers leave Google DeepMind and Meta and start a company in Paris on 28 April 2023. Arthur (CEO) co-authored DeepMind’s Chinchilla paper on compute-efficient training, Guillaume (chief scientist) and Timothée (CTO) came out of Meta’s FAIR lab and the original LLaMA project.

8 months later they had:

• Raised a €105M seed led by Lightspeed at a ~€240M valuation (4 weeks after launch, not bad). largest seed in European history at the time.

• Open-sourced Mistral 7B in September as a bare torrent link on X.

• Open-sourced Mixtral 8x7B in December, beat Llama 2 70B and GPT-3.5.

• Closed a $415M Series A at a $2B valuation, led by Andreessen Horowitz.

The levers they pulled on:

Growth Lever 1: They turned their cap table into their first customers.

Their Series A round had some interesting / unconventional names in it:

• BNP Paribas, Europe’s largest bank

• CMA CGM, one of the world’s biggest shipping groups

BNP became Mistral’s first paying customer the same year (at ~15 employees) and CMA CGM later signed a €100M, 5-year deal with 6 Mistral staff embedded inside its Marseille HQ (more on this in later). 2 things they did:

• The pitch was built for these buyers from week 1: their day 0 memo talked about Mistral as a “European champion with a global vocation” which for a French bank and a shipping group, backing that is almost a form of industrial policy.

• They sold before they had a product: months between seed and Series A were spent in enterprise conversations with nothing to sell but prepping the ground.

“We did start the go-to-market motion at the time where we had absolutely nothing to sell. It did create some brand awareness despite the absence of anything.” — Arthur Mensch, 20VC

Growth Lever 2: They gave their first model away as a torrent link.

On 27 September 2023 they shipped its first model (Mistral 7B) as a bare magnet link on a then-inactive Twitter account and it beat Meta’s Llama 2 13B on the benchmarks while being smaller

They sized the model on purpose so hobbyists could run it on a laptop or a gaming PC. The founder posted the link himself at 5am from a then-inactive account and it did over a million views.

This looks like a generosity play but it was really a distribution play:

• Free distribution: developers who ran it locally became a user with 0 sales cost and the deliberately small size maximised how many machines could run it.

• Recruiting magnet: Shipping the best small open model can be a signal flare to the exact engineers you want to hire (worked).

• Political goodwill: “European open champion” became an angle (fed Lever 5).

• A de facto standard: They repeated it with Mixtral 8x7B in December, which beat Llama 2 70B and GPT-3.5, deliberately the opposite of Google’s heavily-orchestrated Gemini launch.

Growth Lever 3: They bet on efficiency while everyone else bought more chips.

While US labs raised billions to buy bigger and bigger clusters they bet on getting more out of less (remember, Mensch co-wrote Chinchilla, so compute efficiency is in the founding DNA):

“If you look at the way we train models 3 years ago and the way we train models today, I think we have probably made something around 100 times algorithmic improvement. So that’s probably where most of the gains were actually made.”

The efficiency bet shows up in 3 places (and they compound):

• Cheaper to train: They started at zero GPUs and trained 7B on about 500 GPUs, shipping it in 4 months, against rivals burning multiples of that. Compute cost also falls ~30% every 2 years on hardware alone, so algorithmic gains stack on top of a falling base.

• Cheaper talent: A junior European ML engineer can “produce as much as a million-dollar Bay Area engineer for 10x less” (I’m all for raising wages and fixing / increasing ESOP in European tech, but the point still holds).

• Cheaper to run: Small specialised models can punch above their size. Their Arabic model M-Saaba (24B) outperforms models 5 times larger. That matters a lot later because cheap-to-run is what makes on-prem and sovereign deployments economically viable.

The most interesting part of this bet is how they reframed their constraint as a strategy, which in turn is what makes the entire downstream business model possible (running it yourself, on-prem, cheaply etc).

ACT 2 — The First $100M

2024 → mid-2025

Act 1 mostly built a brand and a developer base but it did not build a business. Act 2 is how the open-source darling had to do the uncomfortable thing and pick a way to make money (+ take the heat for it):

Read more

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Hello there!

If you work in tech you’ve heard of the fancy term “Go-to-market Engineering”.

Today’s growth playbook is all about the pioneer of this category: Clay.

In January 2026 it ran an employee tender at a $5 billion valuation:

A “spreadsheet anyone could rebuild” (for the skeptics at the time) grew from near 0 revenue to a $100M+ in about 3 years.

So I spent the past week pulling apart 18 founder and operator interviews, every Clay blog post I could find, and the pricing memos Clay published themselves.

What you’ll learn:

• Why 6 years of going wide nearly killed the company

• The one tiny customer they bet on that helped them take-off

• The “reverse demo” run 8 times a day, and how it killed an 8-call sales cycle

• Why they deleted their support widget and forced everyone into one Slack room

• The pricing bet every rival got wrong, and the rollout they had to walk back

• How they invented a job title, then hired their own sales team into it

And as usual, a few growth lessons for you to steal at the bottom.

Let’s dive in:

From rented lists to orchestrated data

A quick overview of how this market evolved before we get into Clay’s growth levers:

Where we come from

To understand the market Clay grew into we need to talk about ZoomInfo first. For two decades the center of gravity in sales was a single proprietary contact database you rented. Then Apollo, Cognism and others ran the same model where you license the list, load the sequencer, and dial.

The playbook on top was Aaron Ross’s Predictable Revenue (specialize the roles, label the lead types, build systems for repeatable lead gen).

specialization survives, systems survive, the tactics get rebuilt.

The bottleneck buyers cared about was raw pipeline, and pipeline needed contacts in bulk. So the category became contact data, profitable but commoditized, and the moat belonged to whoever owned the biggest list (ZoomInfo peaked as a public company worth more than $20B on this thesis).

Where we are

Two things shifted between 2022 and 2025:

1. AI collapsed the cost of outbound volume to near zero, so everyone got volume and a resource everyone has stops being an edge, where the scarce resource moved from the contact to the context around it.

2. My friend Sunny, who had been an early employee at Gong, put it best when I interviewed him after his team analyzed 600,000 sales calls: the sales world is starving for data. Not the list but the accurate, current context on the specific buyer in front of you. Roughly 75% of a rep’s time goes to non-selling work, most of it the research that turns a name into a reason to call, and reps who cite two or more specific buyer facts convert about 30% more.

Enter Clay as a startup well positioned to solve this increasingly important problem.

Where the market is going

3 dynamics will shape the next 24 months.

1. Consolidation: teams are drowning in 20 single-purpose AI tools bought on experimental budgets. Clay wants to be that platform for the data layer.

2. The agentic shift: enrichment stops returning data and starts acting on it.

3. The threat: if the foundation labs or a revived ZoomInfo decide orchestration is core, they build down into Clay’s layer, and Clay’s defense has to be the ecosystem rather than the software.

ZoomInfo and Apollo still sell database access, Outreach and Salesloft own sequencing one layer up, but Clay built something structurally different underneath, and that structural choice is where the story starts today:

ACT 1 — The Wander

2017 to 2022. From an abstract mission to near-zero revenue, and a 6-year search for who Clay was for.

If you read the Replit Playbook two weeks ago, the shape here will feel familiar. Replit spent 8 years grinding before the curve went vertical, Clay spent 6.

The company was founded in 2017 by Kareem Amin and Nicolae Rusan who were McGill classmates and repeat founders. They believed “normal people” should be able to do things that only programmers could do.

But, they just didn’t know what to build. They tried making the computer terminal easier to use, then a tool to help people use APIs without code, but neither caught on. Then they looked at the spreadsheet (hundreds of millions of people live in them) and most would never call themselves “programmers”. So they asked a simple question:

What if a spreadsheet could pull live data from the internet?

Growth Lever 1: They picked one customer and cut everything else.

For about 5 years, Clay had too many customer problems they were solving for:

• Recruiters used it to source candidates.

• Salespeople used it for outbound.

• Front-end engineers used it as a low-code backend.

This looks like a good problem at first but its typically a trap because a product 5 different people use for 5 different things has no clear pitch, no clear feature set, and no clear language on the website. The early spark kept dying because people would try Clay, say “wow, this is so powerful,” and never come back.

Kareem calls this stretch “the spiral” where they’d commit to salespeople, a customer would describe an exciting adjacent use case, and a few months later the team would land back at the same decision with slightly more information. Recruiting felt safe to chase because “recruiting is just sales, so it’s not really losing focus, is it?”

The fix was not a product but a positioning change. Kareem on Langchain’s pod:

“We took our positioning and marketing and narrowed that. We removed the workflow product and the function product. We kept the table. We narrowed down our go-to-market and kept the product flexible.”

He compares Clay to a guitar vs a microwave, where a microwave has one button and one job, a guitar has six strings and a lifetime of mastery in it. They kept the guitar and narrowed the story they told about it:

• One customer: outbound sales.

• One problem: great data enrichment in a table.

• One page, one set of docs, one vocabulary.

This is one of those tough / brave product trade-off decisions where cost is real (apparently most of their original customers churned). The founder is honest that narrowing felt “claustrophobic” (letting go was a personal block). What got him out was reframing the chain:

“How do we get customers as quickly as possible, so we can learn as quickly as possible, so we can improve as quickly as possible?” — Kareem on First Round

There were 2 product principles underneath the discipline:

1. Aggregate every data source but own none: they hold no proprietary database. It orchestrates 150+ third-party providers, trying one after another until it has the best answer. Every rival builds and defends its own database but Clay’s bet was the opposite.

2. Flexibility beats ease of use (for their use-case): Everyone told them to make the product one-click simple but they refused, which again was a tough product trade-off, but helped them become a power-user tool vs a toy.

ACT 2 — The Machine

2022 to 2024. Clay finds its wedge, then builds a repeatable engine on top of it. Roughly $0 to $30M ARR.

In 2021 Varun Anand joined. He had been running a business unit at an insurance tech company, where a lack of engineers pushed him deep into no-code tools, and that is how he got obsessed with the space. He was in a Slack group about no-code products when someone posted a Clay webinar. He signed up, and he was the only person who showed up. So he cold-emailed Kareem, who ignored him because he was on a silent meditation retreat 😅.

Varun joined anyway (at a low point apparently as some of the team was leaving, again echoes of the Replit story).

Growth Lever 2: They grew inside other people’s communities before building their own.

This was the first real growth motion and it cost close to nothing.

Varun went into Modern Sales Pros (a sales community) and searched its history for “data enrichment” and “outbound.” He found about 30 people who had said something intelligent on the topic and emailed all of them. Almost all took a call and on those calls, exactly one type of person actually understood Clay, which were lead-generation agency owners.

These were small, technical, scrappy, running low-margin services across many clients, feeling the pain the most (it felt they’d found their ICP).

Then they built a social-listening stack:

• Slack keyword alerts on the words customers used.

• A tool for monitoring Reddit and the open web.

• A dozen-plus “dark social” WhatsApp group.

When someone in those rooms described a problem Clay could solve, Varun direct-messaged them. Kareem’s description of why:

“One of our earliest employees was an agency owner himself and a power user himself. He has a ton of credibility in the space and is a community builder in the space.”

He bought instant credibility in the rooms their customers lived in. Varun on whether seeding a community you don’t own feels like spam made me laugh a little:

“Do I care if I get kicked out of a community? Not really. I’m trying to help these people, I’m trying to solve their problem.”

Having built and sold a GTM community myself, doing this in a spammy way would get you kicked out pretty fast, but there’s a way to do it adding value to all (some of our best members at Revenue Squared did it regularly, including about Clay, adding value first + data + pov then soft “pitch”, lets call it intelligently).

By mid-2022 they had a customer, a community, and a wedge. What it did not have was a way to turn that into a machine that ran without them in the room. The next 4 levers are how a founder doing 8 onboarding calls a day built himself out of a job.

Growth Lever 3: They threw out the demo and let the customer drive.

Clay wanted a self-serve product, but the path to self-serve ran through a year of brutal manual work. They ran roughly 8 onboarding calls a day for about a year, but without demoing Clay. Instead they did the opposite, or call it “the reverse demo”:

Read more

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Hello there!

It’s been a wild year for Mercor. They crossed $1B in annualized revenue in early 2026, hit a $10B valuation at their Series C last October, and the 3 founders (22 years old at the time) became the youngest self-made billionaires in history.

Their ARR went from $1M → $1B+ in 20 months, and have been backed by our friends at Benchmark, Felicis and General Catalyst:

So I spent the past week pulling apart 14 founder interviews, every Mercor blog post, every funding round and every leaked detail about how they did it.

What you’ll learn

• How Mercor charged before they raised

• The matching engine that hires Indian devs and Goldman associates the same

• Why paying supply 3x the going rate was a strategic move

• How they turned Scale’s June 2025 collapse into 4x growth in two quarters

• The benchmark trick that made every AI model launch an implicit Mercor ad

From data labeling → agentic data

A little about the evolution of the market before we dive into the growth levers:

Where we come from

To understand the market we need to talk about Scale AI first, which was founded in 2016 by Alexandr Wang (MIT dropout who was 19 at the time).

The first product would route low-skill human labor to label training data for autonomous vehicle companies (i.e. workers drawing bounding boxes around stop signs, pedestrians, etc), and Waymo, Toyota, and GM were all early customers.

For 6 years that was the category where Scale, Surge, Appen, Sama, and a few others ran sort of a global call center for AI training data. The work was crowdsourced and the pay was around $30 an hour at the high end (often a lot less).

The bottleneck the labs cared about was volume because pre-training was the constraint that mattered and pre-training mostly needed bulk. And the category’s name became data labeling (profitable but commoditized). By 2022, Scale had raised over $600M at a $7.3B valuation.

Where we are

Two things shifted between late 2022 and mid-2024:

1. Pre-training stopped being the bottleneck: By 2023 the labs had crawled most of the public internet and the marginal return on more pre-training data was approaching 0, which moved the bottleneck to post-training (reinforcement learning, instruction tuning, expert-built evals, etc), which needs different labor (i.e. an undergrad cannot write a rubric for what a Goldman associate’s investment memo should look like).

2. The work itself changed: if the model is the product then the eval is the product requirement doc (PRD), and building the eval requires the same kind of person who’d write the PRD. So a $30/hour generalist typically can’t do it, you need a $200/hour FAANG engineer or a $500/hour M&A lawyer etc.

And it was our friends at Mercor who capitalised on this shift.

They started in January 2023 (before most of the market understood what was changing) and started paying 3x the going rate from day 1. They built a matching engine that worked for any role and sat in front of the right anchor customer at exactly the right moment.

In June 2025, Meta acquired 49% of Scale for $14.3B. The founder left to run Meta’s superintelligence team and the category leader from the previous era got removed from the board in 90 days, while Mercor (along with Surge and Handshake) absorbed the demand.

The 4 companies on the chart represent the full competitive set right now, with Mercor’s curve being the steepest.

Where the market is going

Mercor’s founder says that soon every job will be taught to AI the way pilots learn in flight simulators (i.e. give it a task, let it try, score the attempt).

But the hard part is the scoring, because grading whether an AI did banking work right takes a real banker (which is what Mercor sells right now for banking, law, consulting, medicine, and engineering).

3 dynamics will shape the next 24 months.

1. The labs going in-house: xAI laid off 500 generalist annotators in September 2025 and shifted to internal teams, same with OpenAI that has apparently been quietly building in-house data operations. So the labs that can vertically integrate this layer eventually will, the question is whether they do it before Mercor builds enough expert supply and benchmark gravity to be too painful to displace.

2. The category consolidating: Handshake’s acquisition of Cleanlab in January 2026 and Taro in February 2026 is the first real M&A in this market and Mercor will likely follow. Let’s see what happens.

3. The work changing shape: The fear is this market shrinks once the models have learned everything but I don’t think it does, or at least not for a good while. The frontier keeps moving so the AI is always being graded against a harder task than last year. The world keeps changing (i.e. new laws, new jobs, new etc), so rubrics written today are stale in 2 years and as AI does more of the work, someone still has to verify it did the work right.

The TAM is hard to size cleanly because nobody knows how much labs will spend on training data when models are doing 10x or 100x the work humans do today, but it’s very, very big.

That’s the market in a nutshell, now to Mercor’s origin + growth levers:

Act 1: The Bootstrap

Jan 2023 → Sept 2024 · $0 → $2M ARR

Three high-school debate teammates start a recruiting company on January 1st, 2023. The original premise is to match American startups with Indian software engineers, charge $500 a week, and take 20%. They are 19 years old, running this out of a Palo Alto office that Surya Midha later described as “barely larger than a conference room.”

Nine months later they hit $1M in annualised revenue, still with no funding and no team beyond the 3 of them and a handful of engineers in India.

Growth Lever 1: They charged before they raised.

They sold a paid service for 9 months before raising, which gave them leverage. By the time General Catalyst led the seed in late 2023, the matching engine was running and the unit economics were already mostly proven.

A lot of founders raise at the proof-of-concept stage when they still need the money to find out if the idea works (pre-seed / seed). Its especially impressive for Mercor to jump straight to seed with a good chunk of revenue considering their age and experience.

Not all business models allow for this, but in this case the leverage they got allows them to hit less dilution and most importantly a cap table they could hand-pick (Thiel, Dorsey, D’Angelo, and Summers all got in early).

It also bought them a position most startups never get which is that by Series C the business was profitable enough that Foody could say “we don’t really need to go out for financing” (20VC, September 2025). And apparently every round since has been an offer they didn’t need rather than one they had to chase.

Growth Lever 2: They built one engine that worked for every role.

The obvious play in a hiring marketplace is to specialise (i.e. one vertical for engineers, one for lawyers, one for doctors etc). The verticalisation plays well in pitch decks because it’s specific and defensible.

But these guys went the other way.

The matching engine they built in 2023 to hire Indian software engineers at $500 a week is the same one that hires Goldman associates at $500 an hour today. They have the same intake (resume + portfolio + GitHub crawl), same AI interviewer (custom 20-minute conversation, spun up in under 10 seconds for any role) and same matching model underneath:

“Whenever the models improve, the experience for applicants on our platform also improves pretty significantly. Can we ride that wave to make our product better and better and better.”

This is the lever that scaled supply from roughly 2,000 contractors in early 2024 to 30,000+ today without a corresponding ops headcount blowup. Scale and Surge built workforce ops infrastructure for one type of labor while Mercor built infrastructure that absorbs new labor types as a feature.

The product compounding is multi-directional because each new role on the platform improves matching for adjacent roles (a lawyer’s interview rubric teaches the engine something about how to interview an investment banker).

And each lab Mercor works with feeds back signal on which experts actually drive model improvement. So the matching engine keeps learning even when the supply mix and customer mix change.

Growth Lever 3: They got one lab deep instead of ten labs shallow.

In mid-2023 a customer introduced Mercor to the xAI co-founding team and 2 days later the trio (still college students btw) meets the xAI team at the Tesla office. xAI wasn’t ready for human data yet, but the meeting told Foody the market was about to shift.

A few months later, after spamming OpenAI’s head of human data operations with emails (never underestimate the power of cold outbound) Foody landed a meeting with Shaun VanWeelden.

OpenAI wanted Mercor to find Math Olympiad winners to help train OpenAI’s models and within 9 months Mercor was OpenAI’s largest data vendor.

What’s interesting here is that they apparently didn’t chase 10 frontier labs at once but decided to get one lab deep, and that one relationship opened every other door.

Going deep on OpenAI also dragged product quality up. Foody calls Mercor “a deep research partner to our customers.” Serving OpenAI meant building for the hardest customer first with investment-grade rubrics, FAANG-level talent vetting, latency tight enough for hourly delivery and so on. So once they could serve OpenAI every other lab was a downgrade in difficulty.

That anchor relationship is also why Mercor was positioned to win when the category leader stumbled a year later:

Act 2: The Eval Era

Sept 2024 → Sept 2025 · $2M → $500M ARR

They then went on to grow 54% month-over-month, sustained, for 12 months.

Most companies that scale at that rate either burn cash to do it (Uber, DoorDash) or hire furiously to keep up (Scale itself did this from 2019-2022).

But Mercor managed to scale supply without scaling ops and keeping the business profitable. The 3 levers in this act are how:

Read more

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🌊 Replit’s Growth Playbook

Hello there!

Every operator I know has now heard the story of Replit hitting $250M ARR last year but most have no idea what’s actually under the hood.

I’m excited about this one because I’ve been using Replit for a few years now, and always loved the product (i.e. built a Pitch Deck GPT to 50K+ conversations on OpenAI and used Replit to use it outside their wall-garden):

Their revenue and funding velocity has made them quite the outlier:

They call themselves the “agentic software creation platform”. I interpret as an ambition to own the whole stack from “type an idea” to “your app is live with users and revenue” with one AI agent driving it. We’ve covered Lovable’s growth curve before, but Replit’s Agent was the first breakthrough product of its kind, credit where credit is due.

So I spent the past week pulling apart 20+ founder interviews, every Replit blog post since 2018, every press release of their 8 funding rounds, and all customer case studies I could find.

What you’ll learn in this edition:

• How Replit went from $2.8M to $250M ARR in 12 months

• The Tesla-style master plan Amjad Masad wrote in 2015 (before ChatGPT)

• The 6 months in 2024 when employees were quitting daily

• The 10-person stealth team that built Replit Agent through the desert

• The 1-week pivot that rebuilt every team for AI users instead of human ones

• The pricing change that took ARR-per-employee to $2.5M

• Why Anthropic, Google, and Microsoft ship Replit their newest models on day one

And as usual, a few growth lessons for you to steal at the bottom.

Let’s dive in:

Act 1: The Grind (April 2016 → April 2024)

Eight years, 3 YC rejections and 1 master plan.

In 2015, Amjad Masad sat down and wrote a 3-step plan for the company he hadn’t started yet. The deck looked a bit like Tesla’s master plan, bear in mind this is 2016:

Amjad quit Facebook in 2016 and co-founded Replit with his wife Haya Odeh and his brother Faris. The first investor was Roy Bahat at Bloomberg Beta, who Masad had met during his Codecademy days, on a ~$500K pre-seed round (with 100K monthly active users! let’s just say the fundraising environment is… different today).

By late 2017 they had 3 Y Combinator rejections and weren’t even getting invited to interviews. Then Sam Altman DM’d Masad on Twitter and said come meet me at OpenAI’s mission office. They talked, and Altman told him it was Paul Graham who’d actually found Replit on Hacker News, but PG was retired and living abroad, so they’d have to talk by email.

What followed was a 2-month email exchange between Masad and PG, during the holidays, that Masad later described as feeling like “talking to a prophet”. On the night before YC W18 was about to start, Sam told them to submit a late application “even if you don’t have time to answer all the questions” and that the interview would be the next morning. They got in + a $120K check.

By April 2023 they had raised $97.4M at $1.16B, with 22.5 million users, but “only” ~$2.8M ARR. A unicorn that wasn’t quite a unicorn just yet.

So, how did they get there?

Growth Lever 1: Picked the smallest possible buyer, in countries Silicon Valley wasn’t even pitching yet

Most YC-backed dev tools target professional developers because that’s where the budget is but they ignored this and targeted 13-to-17-year-olds learning to code.

The economics were not ideal (school and education customers don’t tend to pay much), the procurement cycle is awful, and most of the users are minors who can’t transact.

Apparently every investor they pitched in 2016-2018 told him to go after pro devs for this reason, but just like we saw in the Granola growth playbook, outlier founders tend to dance to their own drumbeat, and Amjad fits in this bucket.

Their play was different (contrarian and right they say). The kid playing with Replit at 15 becomes the founder at 25 and the buyer at 30.

And, more importantly, kids in countries Silicon Valley doesn’t ship to could use Replit because nothing had to be installed (the product ran in a browser). Every laptop, including the second-hand Chromebook in a school in Lagos or Jakarta, suddenly had a full development environment in it.

“Originally the US was dominating by a lot. We see certain countries light up at the same time. The strongest growth right now is coming from more developing parts of the world. India is the strongest one, but we’re seeing really strong growth in the Middle East and Africa as well.”

By 2021 Replit was the dev tool of choice in India, Pakistan, the Middle East, and several African markets. While Cursor and Lovable hadn’t been founded, while GitHub was mostly invisible to anyone without a credit card, Replit had already built distribution into the markets every western tool wanted to be in 5 years later.

The product mechanics they shipped to make this work:

• No-install dev environment, browser-based, runs in any tab. Crucial for users without GitHub-quality dev machines.

• Multiplayer (2018), real-time collab on code, like Google Docs for programmers. Made pair-programming free across timezones.

• Free tier with real compute. Half a virtual CPU and 500MB of memory free, forever (heavy enough to build with).

This added up to a 10-year cohort of users who became Replit-native before they were technical and by 2023, the platform had 22.5M registered users and you could see the geographic distribution in the user logos.

By the time the AI wave broke they didn’t need to acquire its non-technical buyer (PMs, designers, ops people, lawyers etc) because they were already kind of there.

Worth flagging here that this was the move competing tools structurally can’t copy because going after kids and emerging markets in 2017 looked like an obvious negative-NPV decision (you need 10+ year horizon).

Growth Lever 2: Made platform bets when everyone else made narrow bets

In 2016 most venture-backed dev tools were picking a single lane (i.e. Glitch bet on JavaScript, Cloud Nine on browser-based execution, etc).

The founder made 3 bets in the opposite direction:

1. Multi-language from day one: Python, Ruby, C, Java, Go, Rust. Glitch’s seed round was many times Replit’s pre-seed (they’d raised a 30M Series A).

2. Cloud over browser: The original Repl.it ran code inside the browser tab and that “capped” what users could build. So they moved execution to containers on real servers and kept the interface in the browser (not an easy trade-off).

3. Open it in the browser, not as an app you install: Every other IDE (the software developers use to write code) in 2016 was a desktop app but Replit worked like Figma or Google Docs (open link, share it, 2 people in the same project at once). Figma had proved the model for designers but nobody had built it for coders.

Each bet cost speed but paid out from year 5 onwards (again, parallels here on other great teams we’ve reviewed, willing to take hard product trade-offs).

By December 2021 they had 10M users (nearest competitor had under 1M), but zero paid and in a market that wasn’t quite fully cooked yet.

The bigger payoff came later because when AI hit (current wave I mean) they didn’t have to rebuild for it as much as others because:

1. Cloud-native meant Agent could spin up real infrastructure on demand.

2. Web-native meant non-technical users could share a working app with a URL.

3. Multi-language meant Agent could pick Streamlit for one prompt, React for another, without forcing a constraint.

And this was a massive advantage.

Growth Lever 3: Shipped on every new AI model within weeks of access

Remember Step 2 of the 2016 master plan (the AI layer)?

The founders treated it as a real product layer from 2017. They’d built 1 throwaway ML feature every year and apparently killed each one before it shipped because pre-transformer models couldn’t compile code or hold context past a few lines.

They built it anyway because the point was keeping a small group close enough to the research that when something crossed the “usefulness threshold” they’d immediately recognize it and jump on it.

That moment came in February 2019 when GPT-2 dropped, and they shipped:

1. June 2020, Explain Code: Replit got into OpenAI’s GPT-3 private beta (a small group with early access) and shipped a feature that let developers highlight any code and get a plain-English explanation. One of the first real GPT-3 products in market where each query cost more than they charged the user (ran it anyway).

2. June 2022, Ghostwriter: Microsoft had locked up GPT-3 with an exclusive license in late 2020 blocking everyone else, so Replit trained their own AI on top of a free model from Salesforce and shipped Ghostwriter (AI that writes code as you type) 3 months before GitHub Copilot was public.

3. May 2023, their own model: Michele Catasta, who ran Google’s PaLM AI team, quit after Google refused to ship a code version. He joined as VP of AI and 3 months later a team of ~30 engineers trained Replit’s own AI and beat every free model in the world at writing code.

What that bought them by April 2023:

• More AI features in production than any developer tool company.

• Internal tooling, evaluation infrastructure, and fine-tuning workflows battle tested.

• Day-one access to new models from every major lab.

The Series B Extension closed in April 2023 at $1.16B with a16z Growth leading (2.8M ARR…).

Act 2: The Landmine (May 2024 → Sept. 2024)

The desert

By early 2024 Replit had ~$2.8M ARR and were likely burning through a lot of cash (thesis hadn’t yet converted into revenue).

Then the AI wave broke and the company was caught (a little?) flat-footed:

1. GitHub Copilot was free for every Microsoft customer.

2. Cursor launched in March 2023 and was growing faster than anyone.

3. v0, Bolt, and every week another AI coding product launched on Twitter.

A few specific things broke at once that hurt them:

• Pro devs wouldn’t pay: they’d spent 2 years building features for professional devs (advanced deployments, team collab, custom domains) but pro devs looked at Cursor and bought Cursor.

• Education revenue capped out: Teachers and schools were the original buyer but as we talked about earlier this is a tough market (unit economics never funded the AI infrastructure spend).

• Free tier got expensive: ~30M users were running compute on their infra for free and per-user cost went up every time the team shipped a new AI feature.

• Investor patience was thin: Cursor raised at $400M in October 2023 with better revenue than Replit had at $1.16B (a16z were in both for example).

I heard Amjad talk about this part of the story and credit to him for opening up about a part of startup life that is not often talked about and yet very common. The team started leaving and it was a very hard moment for them, he described it as watching the company bleed out one Slack departure at a time.

Worth flagging that the trap wasn’t a bad product (the product was amazing), but that the user Replit had built for (the professional developer) was getting eaten by Cursor faster than Replit could compete, and the user Replit could win (the non-technical builder) didn’t yet exist as a category anyone paid for.

The war room

In May 2024 they made a series of moves (some very hard):

• Cut the team to ~30 people: Engineers, designers, support, founding-era hires.

• Pulled 10 engineers into a stealth team: with an assignment to rebuild the editor, runtime, and deploy stack for an AI agent.

• Gave them 90 days: The rest of the company kept the existing product running.

• Bet on Claude over their own model: Reversed the 2022-2023 “own the model” strategy and built an Agent on top of Anthropic instead of fine-tuning internally.

• Threw out every pro-dev feature: Team collab, advanced deployments, custom domains etc.

By August they soft-launched the way game studios do to PMs at portfolio companies, designers at YC startups, solo founders Masad knew etc, and the success rate was high enough to commit to a September public launch.

Agent v1 ships

Agent v1 went live and in the first 7 days:

• Day 1 = $1M in new ARR: More than any single month of the prior 18.

• Day 2 = $2M: ARR line went near-vertical.

• Week 1 = ~$10M ARR added: More than the entire base built in 8 years.

• 500K+ apps built: Most by non-technical users.

By December they were at $50M ARR, by September 2025 they raised the Series C at $3B on $150M ARR and by March 2026, the Series D at $9B on $250M ARR.

Act 3: The Land Grab (Oct 2024 → today)

Growth Lever 4: Rebuilt every team for AI users instead of human ones

In the week after launch they had to rebuild every team:

• Killed the enterprise sales pipeline: Cut outbound to Fortune 500 CTOs and redirected the reps to inbound from PMs, designers, and ops people already building on Agent.

• Rebuilt support around non-technical tickets: new team was generalists handling “what is a database” and “how do I share my app with my boss.”

• Reshot the marketing site in 5 days: the new homepage was the same one-button entry as the product: “what do you want to build today?” which apparently led to signup conversion going from low single digits to 12%+.

• Renamed every product surface in plain English: “Repls” became “Apps.” “Deployments” became “Publish.” “Environment variables” became “Secrets.”

• Built a new growth team from scratch: Hired 4 generalists from consumer software companies. New mandate was acquisition through consumer channels (Twitter, TikTok, LinkedIn, creator partnerships).

The compounding effect:

• Activation rate (signup → first deployed app) roughly tripled in 90 days.

• ARR-per-employee went from ~$50K to over $2.5M within a year.

Growth Lever 5: Publicly pointed their biggest user segment at the competitor

By late 2024 professional developers were a large share of Replit’s usage and a small share of revenue, but the non-technical builder was converting at a higher rate.

They spent late 2024 and early 2025 publicly drawing the line that Replit was for non-developers and that the company had stopped competing for the professional developer market.

Why the public handoff worked vs a “typical” quiet deprioritization:

• Cleared the roadmap publicly: Team had air cover to kill every pro-dev feature on the backlog = no internal debate about whether to keep building = speed.

• Hard-positioned out of the dev tools category: Market stopped comparing them to tools built for engineers, they competed in the non-technical wedge.

• Surfaced the right ICP faster: Made it easier for non-technical buyers to self-identify and convert (pipeline velocity).

• Made the next pricing change defensible: When they repriced the non-technical tier (Lever 6 below) nobody was confused about who it was for.

Next, they pulled on a lever that got them to 80-90% enterprise margins while most AI coding companies were still unprofitable.

Read more

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• Systems thinking for every stage of the customer journey

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• Reference material built for seed to Series A teams, free to use and keep

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🌊 Sierra’s Growth Playbook

Hello there!

It’s been a huge week for Sierra, who just announced a fresh $950M round at a $15B valuation in an attempt to win the enterprise segment for customer experience.

It's also a unique growth story because Bret Taylor is running it (ex Meta CTO, Salesforce co-CEO, OpenAI chairman).

ARR has gone from $20M → $165M ARR in a little over 1 year, and they serve 40% of the Fortune 50, 2 years and one month after public launch, which is the fastest enterprise software scaling curve I’ve ever charted.

So I spent the past week pulling apart 22 founder interviews, every Sierra blog post, every customer case study, and every funding round.

What you’ll learn

• The 4 customers Sierra picked to break and build the product

• Why they played for the Fortune 500 quadrant

• The product and pricing choices they made to accelerate time-to-live

• The land-and-expand play that turns customer service into a $5M+ ACV

• How they named the category before Salesforce woke up

Act 1: The Stealth Build

The same week ChatGPT launches in November 2022, Bret Taylor walks out of the Salesforce co-CEO office for the last time. In his words:

“Nerd sniped me a little bit, I just couldn’t stop thinking about it.”

He then starts building again (an app for his daughter), a “choose-your-own-adventure” game, but the AI writes the next chapter based on her decision. He had written technology that wrote a book in real time, which turned his model of what computers were upside down.

A few months later he calls Clay Bavor.

The two had been trying to work together for 15 years. Apparently they had “a monthly poker game that happens roughly twice a year.”

Clay was a year ahead at Google in the APM program, they worked on Maps and Gmail and Drive in adjacent buildings during the early 2000s, but Clay stayed at Google for 18 years.

We won’t spent time on Bret’s trajectory (well covered), but in a nutshell, he left for Friendfeed, then Facebook, then Quip, then Salesforce, then OpenAI’s board chair seat. Every 2 or 3 years one of them would float the idea of building something together but it never aligned.

Until early 2023, when they started Sierra. At that time most AI startups were going horizontal (remember all the chatgpt API and everyone wrapping it in a UI?). But these two went vertical and they went narrow (at first), and picked customer service.

Lever 1: They sold to people who already trusted them.

By mid-2023 every Fortune 500 CIO was getting pitched by 20+ AI vendors every week because product readiness and model capability had stopped being the bottleneck.

Sierra went around the new war for attention by going “around it” and selling first to people who trusted them (cashing-in their “trust piggybank”).

Between both founders they had 30+ plus years across 3 of the companies that literally built modern enterprise SaaS, in jobs where buying decisions came across the desk every week. So, they knew some people.

They recruited 4 design partners to build the product in stealth for 11 months.

The four were:

• WeightWatchers: was inside a corporate restructure and needed customer service costs to come down fast.

• SiriusXM: had 34 million subscribers and a legacy IVR that customers hated, which meant any AI improvement showed up in NPS the same week.

• Sonos: has Black Friday surges that hit 10x normal volume.

• OluKai: was the smallest (on purpose). A sandals brand with low ticket volume and a customer base obsessed with the brand voice, which forced Sierra to build agents that could hold a personality at production scale.

What’s smart and interesting in their decision on who they picked for this phase, is that they intentionally chose customers who’d force the hardest constraints on the product.

And because they had chosen the intersection of that type of customer + the fact that they already knew and trusted them as partners, gave them room to ask for things (i.e. full IVR data, a senior CX exec assigned full time, etc) that no cold pitch usually gets.

Most competitors in 2023 had bigger models, more engineers, faster shipping, etc but lost the first 18 months of the enterprise AI race because they were still trying to get on the calendar of people while Sierra had been building with for almost 1 year.

Hold onto the word alignment here because the first lever is alignment between Sierra and the 4 customers who would shape the product, and every lever after this one is the same architecture pointed at a different stakeholder.

Lever 2: They let unit economics pick the customer list

Bret has a quote on Lenny’s Podcast that I think explains the customer selection:

“The price of a phone call is $10, maybe $20 depending how long the conversation is. If you think about the average revenue per user of Google, they literally just can’t afford to do it.”

A human-handled customer service call costs roughly $10 in the US (load in agent salary, benefits, supervision, software, and the six to twelve minutes the call takes). Sierra’s per-resolution cost sits somewhere between 10 cents and $1 depending on conversation length.

Once customer service is 100x cheaper, it stops being a back-office cost and starts being something companies can actually compete on (faster resolutions, better experience, cheaper than competitors).

The trick is that every Sierra customer gets roughly the same percentage savings (~90%), so the % doesn’t really tell you anything about which customers are good targets. What matters is the absolute dollar number, and that depends entirely on company size. A small company saving 90% of a small bill is still a small number. A massive company saving 90% of a massive bill is a number that lands on the CEO’s desk. Here’s a quick example with 3 companies + 3 scales:

• Regional retailer, 500K customers → ~$10M of contact-center spend → AI saves ~$9M. Real money for a small business but not really enough to move a board or accelerate a sales-cycle + integration.

• Mid-market subscription, 5M customers → ~$75M of spend → ~$70M saved. Significant, but the deal still has to fight for attention against 20 other cost-cutting initiatives every quarter, similar to above.

• Fortune 500 telecom, 50M customers → ~$1.2B of spend → ~$1.1B saved. This is a strategic priority that lands on the CEO’s desk and gets fast-tracked.

The threshold probably sits around $100M in annual contact-center spend, because below that the deal might still close but on a slow enterprise cycle alongside other vendor evaluations.

Each of Sierra's 4 design partners turned into a unit-economics laboratory for proving the model to the next wave of Fortune 500 buyers:

• WeightWatchers evaluated on containment rate

• Sonos on time-to-music-playing

• SiriusXM on subscription saves and upgrades

• OluKai on customer satisfaction during the brand’s signature aloha moments

With this early liquidity quality, they raised $110M from Sequoia and Benchmark at just under a $1 billion valuation. Eight months later (October 2024) they raised a Series B at a $4.5B valuation, with production volume scaled into the next wave (i.e. Cigna, ADT, SoFi, Casper, etc), and ARR tracking toward $20M.

Act 2: The Outcome Era

In December 2024 they published a pricing manifesto explaining how Sierra would charge customers going forward, and the answer was that they would only get paid when their AI agent successfully resolved a customer service case.

Lever 3: They only got paid when the AI worked.

The model itself is simple:

• Sierra’s AI agent resolves the case without human intervention → Sierra gets paid a pre-negotiated rate

• The case escalates to a human → Sierra gets nothing

Bret has talked about it on every podcast since:

“For a customer service context, that means if the AI agent resolves the case, no human intervention, there’s a pre-negotiated rate for that. If we do have to escalate to a person, that’s free. For sales, it would be a sales commission. Wherever possible, there’s a way to align our interests with our clients’, we choose it.”

The closest precedent for what Sierra is doing is the shift from impression-based ads to cost-per-click:

“I don’t think any ad platform thinks ‘man, think of all the impressions we’re giving away for free.’ When you charge for something closer to a business value, it’s actually more valuable. It’s more efficient.”

CPC took over because once one platform proved the alignment worked (Google), and Sierra is making the same bet on enterprise software.

Every incumbent in customer service software runs on per-seat subscription revenue (i.e. Salesforce Service Cloud, Zendesk, Intercom, etc).

But to match Sierra’s pricing model every one of them would have to cannibalise the revenue base their public market cap is priced on:

Business model transitions are harder than technology transitions. The revenue dips for a period as they come back out. And any public company CEO will tell you that's easier said than done.

Outcome pricing is the lever everyone sees, but the lever underneath it (the one that actually decides whether Sierra wins) is speed-to-live.

Read more

🌊 Granola’s Growth Playbook

Hello there!

Every operator I know has now heard the story of Granola hitting unicorn status but most have no idea how it actually happened.

Their usage growth and financing rounds speed has made them quite the outlier:

Granola calls itself a “steering wheel for LLMs”. I interpret it as, in a world where ChatGPT and Claude can answer almost any question, their product is the thing that steers those models with the context of what was actually said in your meetings.

Every move in the growth playbook below is basically a brick in that steering wheel.

So I spent the past week pulling apart 10+ founder interviews, every analyst report I could find, and a few profile + metrics floating around the internet.

What you’ll learn in this edition:

• The “year in stealth” playbook that cut 50% of features before launch

• Why they refused to build a meeting bot when every investor told to

• The frontier-model rule that turned a 12.5x cost collapse into a moat

• Their “5 Hidden Rules for Building AI Products”

• The Recipes launch that turned the angel cap table into a distribution amplifier

• The MCP/API/Spaces pivot that turned a notepad into the context layer

And as usual, a few growth lessons for you to steal at the bottom.

Let’s dive in:

Act 1: The Wedge (May 2023 → October 2024)

From $0 to 5,000 weekly users.

Chris Pedregal had just sold his last startup, Socratic (ai-powered tutor for high-schoolers), to Google. He quit Google because he wanted to build something on top of GPT-3.

“Within a week of quitting Google I started playing with the instruct version of GPT-3 that had just come out. I was blown away. I was like, okay, this is new. This is different. I don’t know what it is exactly.”

He started looking for a co-founder and stumbled into Sam Stephenson’s profile. Sam was a designer who’d been working on the same idea space from the design side, also based in London, at the note-taking startup Ideaflow. Pedregal sent him a cold email asking to grab a beer and they were off to the races.

They co-founded Granola in March 2023 and within two months they’d closed a $4.25M seed round led by Lightspeed (Mike Mignano was there at the time).

Then they went quiet for a full year.

Growth Lever 1: A stealth year to cut half the product before launch

Before hitting product-market fit, they were onboarding users manually until they got to about 150 by launch day.

From the MAD Podcast:

“What is the fastest way for me to learn? Will I learn faster if I launch publicly or will I learn faster if I don’t? For about a year, the answer was we’d learn faster if we didn’t launch publicly”

The cost of a public launch in phase 0 is very high because once you have public users, you can’t pivot the core interaction without breaking trust, and they were most likely going to need to pivot.

The first 6 months they built a real-time interaction where you’d type a keyword, hit tab, and Granola would autocomplete the note in real time during a meeting, which made for an incredible demo, they spent 6 months trying to make it work.

Aaand it didn’t.

The notes were good but if a computer is writing notes for you in real time, you can’t help but read them and if you’re reading them. Which kind of went against the whole point of Granola making you more present.

They scrapped the interaction pattern and rebuilt it as a calm text editor that does the magic at the end of the meeting, which was likely a less impressive demo but a way better product.

Pedregal on Invest Like the Best:

“If we had launched that publicly, we never would have been able to switch it. There’s no way. Users would have learned a new behavior. We would have not retained that many users, and that would have been it.”

The stealth year ended with one more move that I think is the single most underrated growth decision they made which was to cut 50% of the features.

One interesting pattern I’m finding in the last few editions analysing growth playbooks of Harvey, Lovable and Perplexity is their willingness to take on hard product trade-offs, almost “courageous”, as long as the iteration cycles were fast enough. I often see startups dying due to a combination of both these factors, hiding behind a false sense of security, not choosing a path with conviction.

“We were in stealth for a year and we kept adding things and adding features and adding views. By the end there was this version of Granola where you could swipe and there were all these panels, your transcript, your notes, your private notes, your notes in another language. It was really like that. We looked at it all and we cut out 50%.”

My take-away here is that the speed at which you can iterate the core interaction is the most valuable asset of an early-stage AI product, because public launches lock you in while stealth can potentially buy you the right to be wrong many times.

Growth Lever 2: Targeting VCs to win the founders behind them

Granola launched publicly on May 22, 2024 with a team of 4 people and a simple pricing of free for the first 25 meetings, and then $10 a month (more on what happened to that price in Lever 5).

Pedregal decided to go after the most specific user persona he could think of, which were venture capitalists. From the MAD Podcast:

“We needed a user type that has a lot of meetings, relatively formulaic, with a relatively formulaic note style, and that we have easy access to. VCs.”

VCs are the smallest market you could pick because they don’t pay much for software, there aren’t that many of them and investors normally see “building for VCs” as a red flag because “the TAM is so small”.

But Pedregal was building for the VC distribution (smart).

Investors are loud on Twitter, talk to founders all day and meet other investors at every dinner. And if a VC starts using Granola, every founder in their portfolio likely sees it, every co-investor sees it and most importantly, VCs were the people Pedregal could get coffee with in London on a week’s notice.

Then, on launch day, he told the team “now we stop building for VCs”.

“As soon as we launched, we said okay great, now we’re done with VCs. We’re going to focus on a different user type. And we chose founders, just because we thought they’d be the hardest. If we could build a great product for founders, then by default it would be a decent product for everyone else.”

VCs were the wedge and founders were the spreaders.

This is similar to the Superhuman strategy and it’s my favorite kind of Trojan horse because you pick the user with the highest signal density per user (not the user with the highest market size per user). If you get them to use it intimately you’ve recruited every person they meet to evangelize on your behalf.

By Series A in October 2024 57% of Granola users were in leadership positions. Which is interesting because Granola didn’t target executives but apparently many of those who were picking up the tool became leaders shortly after.

Growth Lever 3: Refusing the meeting bot to win the meetings that mattered

When they launched, every other meeting note-taker in the market joined the call as a visible bot because the bot was their distribution (and it was very annoying).

Granola’s investors told them they were crazy to give that up, but as is usually the case, outlier founders tend to dance to their own beat:

“Bots make you feel kind of weird. A big black box on the screen, sometimes shows up before you’ve joined. But beautiful from a growth distribution standpoint. Every user is exposing everybody they’re meeting with to your product. So everyone thought we were kind of crazy not to do that.”

The cost of the no-bot decision was real and immediate because Granola had no built-in viral loop, no free billboards, no “what’s that bot in the meeting?” recruitment moment and so on.

In return they got something that ended up mattering more which is the right to be in sensitive meetings.

Bots get banned from confidential calls like board meetings, M&A discussions, and executive 1:1s. They usually get rejected by lawyers, doctors, therapists you name it.

So the visible bot maximizes top-of-funnel but minimizes ceiling and they understood that early.

Granola’s experience was to seat on your computer, never announced itself to other participants (someone in a GDPR office right now is having a moment), never recorded audio (transcript only, that decision saved them from many enterprise blockers later), and worked across Zoom, Meet, Teams, Slack Huddles, and in-person calls without any per-platform configuration.

What they discovered is that giving up the viral bot didn’t kill virality but did change the loop:

“If you have a Zoom call and your AI bot shows up, people are now telling each other, ‘Hey, what are you doing with an AI bot? Why aren’t you on Granola yet?”

What’s really cool is that now the competitor’s bot became Granola’s growth loop (this has happened to me, multiple times).

Growth Lever 4: Running frontier models so the moat would be quality

In 2023, every meeting note that Granola generated probably cost a few cents in inference, but a heavy user doing 6 meetings a day on the free plan was unprofitable. Which meant every other notetaker in the market used cheaper models or self-hosted to protect margins.

Pedregal told Peter Yang on Behind the Craft why this was actually an advantage:

“AI is different because these models are still expensive to run. Our costs scale linearly with users. This creates an opportunity: as a small startup with fewer users, we can use cutting-edge models that would be financially impossible for big companies to deploy at scale.”

Granola was small enough to run frontier models on every user. But competitors couldn’t, like Otter which likely had $100M ARR and millions of users at the time, couldn’t switch to GPT-4-class models on every transcript without blowing margins.

Then frontier costs collapsed, with the price of transcription alone going from $0.25 per minute in 2021 to $0.02 per minute today, which is a 12.5x cost compression in 5 years, on the single most expensive line in Granola’s stack.

Sam Stephenson on Cognitive Revolution in April 2026:

“There was a time where half of our burn rate as a company was going on transcription. That’s a lot better and more under control now.”

The bet was that running expensive products today is a temporary disadvantage that becomes a permanent advantage. They have apparently been running on frontier-model output since launch, with internal eval tooling that lets the team route across OpenAI, Anthropic, and Google models and swap them overnight without breaking what they call “the Granola voice.”

By the time the costs collapsed users had been trained on a quality of output that nobody else could probably match.

📐 Sidebar: the CEO’s 5 Hidden Rules for Building AI Products

• Rule #1: Don’t solve problems that won’t be problems soon: The current wave of AI startups tend to have 2 types of product problems, those the next model release will solve, and those that remain regardless. The mistake tends to be solving the first kind. For example, Granola refused to build chunking for long meetings (context windows expanded), and refused to build multi-language tooling (newer models handled it natively). “As a product person, it goes against every instinct to deny users something they’re actively requesting. But, in AI, sometimes the best strategy is to focus on problems that will still matter even as the tech evolves.”

• Rule #2: Go narrow, go deep: “General-purpose tools like Claude and ChatGPT are surprisingly good at many tasks, so if you are building a startup, it needs to solve a problem 10x better. The only way to achieve that is by choosing a narrow use case and making that experience exceptional.” The 10x often comes from non-AI work. For example they built an echo cancellation system for users with and without headphones, which as you can imagine has little to do with note-taking.

• Rule #3: Context is king: Pedregal treats the LLM like a smart intern on their first day. The product's job is feeding the intern enough context to figure it out, whereas most AI products out there fail this by writing system prompts that try to anticipate every output.

• Rule #4: Your marginal cost is my opportunity: The lever we just discussed above. Frontier models are too expensive for incumbents to deploy at scale, small startups have a temporary cost disadvantage that becomes a permanent quality advantage as costs collapse.

• Rule #5: Build products that have a soul. Cohesion comes from intuition, and if you like product as a discipline you’ve probably felt “delight” using Granola. Pedregal does a user call daily and has screens in the office showing real-time feedback, but designs from first principles. “When you’re constantly immersed in user feedback, you develop an emotional sense of what matters rather than just analyzing metrics.”

The numbers at end of Act 1

October 2024: 5,000 weekly active users, a $20M Series A from Spark Capital at undisclosed valuation, which closed in roughly a week after a single day of about 12 investor meetings.

Act 2: The Ladder (October 2024 → March 2026)

By January 2025, Granola was becoming more of a habit than a product with VCs evangelising to founders, founders adopting it for their leadership teams and leadership teams asking for company licenses and so on.

But the AI notetaker market itself was getting brutal (Otter was at $100M ARR, Fireflies hit $1B in a tender offer, Read AI raised $50M, Fathom raised $17M, Plaud was selling AI hardware pendants at $250M annualized, etc etc).

The question shifted from “how do we get users” to “how do we keep them when notes themselves become a commodity?”

Growth Lever 5: Pricing the team plan below the personal plan to force expansion

Read more

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Perplexity’s Growth Playbook: 0 to $450M ARR in 3 Years

Hello there!

Every investor I know has the same debate about Perplexity on whether it’s a real $20B company or if Google is about to eat them alive.

Then February happened and they added $150M of annualised revenue in 30 days, and blew past their 2026 internal target 9 months early.

So I spent the past week pulling apart every founder interview, growth-team podcast and leaked revenue number I could find.

What you’ll learn in this edition:

• The Slack bot accident that became a multi-billion dollar product idea

• Why the VP Growth told the company to stop worrying about acquisition

• The specific activation metric that predicts whether a new user will stay or leave

• Why they killed their own advertising business after making $20,000 from it

• The trust architecture that earned them the right to charge $271/mo

• And the pricing shift that just pushed them past half a billion ARR

And as usual, a few growth lessons for you to steal ;)

Lets dive in:

Act 1: The Answer Engine

$0 to $80M ARR. August 2022 to December 2024.

The founding accident

Perplexity almost didn’t build the product that made them famous.

The company was founded in August 2022 by Aravind (CEO, ex-OpenAI), Denis (CTO, ex-Meta AI), Johnny (CSO, ex-Quora), and Andy (co-founder of Databricks).

These are four people with world-class AI research backgrounds who, by Aravind’s own admission, “started the company without actually having a clear idea of what to build”. Which is interesting considering how many times I’ve heard from “tier 1 VC’s” that they only back founders that are completing their “life’s work”:

Their first product was called Bird SQL, which basically let you search Twitter using plain English. You could type something like “Who is Lex Fridman following that Elon Musk is also following?” and the system would translate that into a database query and return the answer.

In February 2023, Twitter killed free API access and that product died overnight.

But something else had been happening inside the company. They’d built a Slack bot that plugged into GPT-3.5 and let the team ask it questions.

The first question was about health insurance (their first employee needed a plan) and nobody on the founding team knew anything about it. The problem was that the Slack bot made things up, a problem we are all too familiar with in this AI era.

It (confidently) gave wrong answers about insurance plans, providers, coverage details, etc. Which was the known failure mode of every AI chatbot at that time. You probably remember how the internet was full of screenshots mocking AI mistakes.

But this lit up the idea that built the company.

Aravind and his co-founder Denis had both been academics. And in academic publishing, there is one hard rule, and that’s that every sentence you write must be backed by a citation from a peer-reviewed paper or your own experimental results.

They decided to force the AI to follow that rule. So not a chatbot that happens to cite sources, but instead a system architecturally designed so the AI can only say things it can find evidence for on the internet.

They also made a decision at founding which was NOT to try to build their own AI model. As Aravind told Fortune:

“It was a decision driven through conviction and pragmatism. Pragmatism because we were broke. Conviction in that it was already clear that AI models would become increasingly commoditized.”

They used OpenAI’s GPT-3.5, available on a pay-per-query basis, and focused all their energy on the search, retrieval, and the orchestration layer.

One of their early seed investors, a former Googler, told Aravind directly:

“The reason [AI] is going viral right now is because people want to laugh at AI’s mistakes. Your product is designed to not make mistakes, you are always pulling sources. I don’t think this will work.”

Aravind ignored him and they were off to the races.

Growth Lever 1: Trust as a growth engine

First a quick primer on perplexity vs traditional search:

• “Traditional” Search: You ask Google a question, you get ten blue links, you click three of them, you read two and piece together an answer yourself.

• ChatGPT Search: generates answers from its training data, which means it is drawing from memory (and as mentioned above, hallucinations happen).

• Perplexity Search: they do that work for you. It searches the web, reads the relevant pages, and a clean answer with footnotes to every claim. It generates answers from live web search results (drawing from evidence), and when the evidence conflicts or doesn’t exist, the system says so instead of making something up. Basically a research assistant who shows their homework.

They also built a feature they originally called “Copilot” (now Pro Search) that asks clarifying questions before answering.

This matters for growth because it means even a vague question produces a great answer, which means more positive first experiences, which means more word of mouth. As you’ll see further down, their growth team runs a bunch of these 1% experiments, which staked together explains a lot of their growth story.

Why does trust matter so much for growth?

Because their citation architecture made the product more accurate and therefore made it recommendable. We’ve all been through that fear of double, tripple checking AI generated answers. In this case they figure out part of their growth loop went through making users share a Perplexity answer with a colleague and say “look, here are the sources” without risk(ish).

The retention data seems to back this up with c.85% of users who try Perplexity returning, and 90% of users coming back within 30 days of their first visit. Direct traffic (people typing perplexity.ai into their browser or using a bookmark) accounts for 82% of all visits as of mid-2025, and the vast majority of usage comes from people who already know and trust the product.

One more data point I loved is that the average Perplexity query is 9 words long, versus 3-4 words on Google. So users write longer, more specific questions because they’ve learned the system can handle complexity, same pull you probably feel when you decide to go to OpenAI vs asking Google.

This in itself is an interesting data flywheel, because longer queries give Perplexity more context, which produces better answers, which drives retention.

Hold onto the word “trust” because its basically the architecture that earned Perplexity the right to charge $271/mo and hold your credit card, and every lever below is a brick in that architecture.

Growth Lever 2: The three-query activation threshold

Another of Perplexity’s growth levers is that you don’t have to sign up to use it. Which creates a specific challenge that mapped out well:

how do you convert anonymous visitors into retained users?

Raman Malik, Perplexity’s Head of Growth (ex-Lyft) says the growth team worked backwards from retained users. They looked at everyone who was still active at day 30 and day 60, then examined what those users did differently in their very first session.

The pattern they found is that users who hit 3 queries in their first session retained at dramatically higher rates. 3 queries is enough time in the product to understand the value and became the team’s north star: get new users to three queries in their first session.

Raman’s target was 30% of all logged-out visitors hitting this milestone.

The obvious way to force engagement is to add a signup gate at query 5 in the common style of “You’ve used your free queries, create an account to continue.”, which usually works for conversion, activations go up, but it also kills query volume.

People hit the gate, get annoyed, and leave, so you’ve juiced one metric (activations) while hurting another (total searches, which drive the data flywheel).

Interesting trade-off:

“Those are the trickiest ones to navigate. How do I value those two things down the road? Will that activated user retain and over time make up for the initial loss in query volume?”

Their answer was progressive, device-specific friction (instead of a hard gate):

• On mobile browsers: a full-screen signup takeover blocks further searches

• On desktop browsers: a softer half-screen prompt appears but users can keep searching. It only escalates to a full takeover by search #5

• On the mobile app: nothing.

They figured out each surface has a different user intent. Mobile web is transactional, so a hard prompt converts. Desktop users tab-hop, so a soft prompt keeps them searching. And App users already committed by installing, so interrupting them hurts more than it helps.

The friction only works because Perplexity has already hooked you 2x before the prompt shows up:

1. Rotating homepage queries: Instead of a blank search box, the homepage cycles through trending, engineered suggestions. It kills blank-page paralysis and hand-delivers new users into their first good experience.

2. Related searches at the bottom of every answer: Same trick Google uses, but tuned to pull you deeper into the rabbit hole. By the time you’ve clicked through two or three of these, you’ve hit the 3-query threshold without realizing you were being activated.

So by the time the signup prompt appears, the user has already felt the “aha.”

The biggest takeaway here is that activation shouldn’t be thought of as a gate but rather as a tiered system.

Growth Lever 3: The partnership distribution machine

About 80% of Perplexity’s growth is organic word of mouth, but the remaining 20% comes from a partnership strategy that their CBO Dmitry Shevelenko built into a machine. He joined from Uber (4 years), and before that was at Facebook when it had 500 employees and 80 million users. The man knows distribution.

Their playbook was to give away a year of Perplexity Pro ($20/month subscription) through partners who already have your target audience.

The list tells the story:

• LinkedIn (free Pro for premium members)

• Samsung (pre-installed on all 2025 TV models with 12-month free Pro)

• Airtel (India's largest telecom, with 360M+ Indian subscribers)

• ’s newsletter (free Pro for paid subscribers)

• PayPal/Venmo (12-month free Pro)

• Snapchat (search integration reaching nearly a billion users)

The unit economics are elegant because in theory if a partner gives away 100K free Pro accounts, and 80% of those people never actually use the product, Perplexity’s variable cost on those 80,000 dormant accounts is close to zero. They only incur inference costs when someone sends a query, and the 20,000 who do use it enter the conversion funnel.

“We have a phenomenal Partnerships team. They are absolute sharks.”

Not everything in this era was smooth though as they’ve had their fair share of hiccups with Forbes, Condé Nast, The New York Times, and Dow Jones all suing or issued cease-and-desists over Perplexity reproducing their content without licensing.

The tension they create for them is clear because an answer engine reduces clicks to the publishers who wrote the content in the first place. So they responded in 2024 with a $42.5M publisher revenue-share program, splitting ad revenue with sources whose content appears in answers.

Apparently the lawsuits haven’t fully resolved but growth hasn’t slowed either.

Growth Lever 4: Retention over acquisition (and the Lyft lesson)

The first thing the VP Growth did when he joined was to map the entire funnel, and attention went straight to retention. It was “pretty healthy consumer retention.” But he double-clicked on something often overlooked:

a 10% improvement in retention raises the entire water level of your active user base, and it compounds.

Two tactics drove the biggest retention gains:

1. Cross-device conversion: Users on both desktop and mobile retained at significantly higher rates. Desktop during work hours, and mobile on the couch.

2. Audience mix-shifting: Casual “weekender” users asking novelty questions have lower retention than knowledge workers solving real problems. Students using Perplexity to find sources for papers are extremely sticky, so the growth team targeted acquisition toward high-retention segments, changing the composition of incoming cohorts.

What they explicitly did NOT do is spend money on paid acquisition.

Interesting story Raman tells on the 20vc pod, that he learned this lesson at Lyft, where he started his career spending millions on paid channels. One day, Lyft’s head of growth told them to turn off every single paid channel for new passengers, and signups barely moved (maybe 5-10% down), and all low-quality users dropped. Which means paid spend had been almost entirely cannibalizing organic traffic.

Also interesting his honest admission about a weakness, which can therefore be turned into a strength (plenty of evidence of sharing happening on Twitter for example):

“I don’t think sharing is inherent to the Perplexity user journey. I don’t think we have an inherent viral loop.”

Growth Lever 5: The campus ground game

Raman identified students as a high-retention, high-potential audience that Perplexity was under-indexed in compared to ChatGPT, and had an obvious fit (remember citations!).

Perplexity launched a formal Campus Strategist Program where students get a marketing budget, mentorship from the growth team, Pro accounts, and early access to features. In return, they host events, run workshops, and drive referrals through affiliate links. Large universities can have up to 100 partners.

At CMU, a campus strategist hosted “Boba & Perplexity Power Hour” where 120 students explored the product over free boba tea. At Penn/Wharton, a strategist organized a speaker event featuring Raman himself, drawing 150+ students from undergrad to MBA. Great tactics for step 0 of building a community.

“These are not big campuses. You can build density very very quickly. And as soon as you have that initial density, then you see word of mouth start to come alive.”

The numbers at end of Act 1

By December 2024: roughly $80M ARR, $900M+ raised, a $9B valuation and 22 million MAU, with every major AI company copying their citations feature.

Act 2: The Platform

From $80M to $300M ARR, in 12 months:

By January 2025, Perplexity's product was no longer differentiated because every chatbot cited sources. The question shifted to “how do we win before Google kills us with distribution”.

Read more

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🌊 What 3 Years at Facebook Taught Me About Building Revenue Squared

Hello friend,

This edition will be a little different.

I just sold my €10K MRR side-hustle to Nova.

It’ll be useful for those interested in either:

• Building a community-first product, private community or vertical social network.

• Or learning battle-tested tactics to build a successful community for your startup.

So let’s get into it:

Act I: The Facebook BJJ origin story

Finding a co-founder

Back in 2017 I was walking around the third floor of our new Soho office when I stumbled upon a dude with a big smile and a Spanish-American accent, with a bunch of tatami mats stacked next to his standing desk.

We soon realised we were both obsessed with surfing, tech, and mixed martial arts.

We started the Facebook Brazilian Jiu Jitsu club at the London office (which spread across other offices across the world). Looking at the social club budget, we also figured out there was a “glitch in the system”, which made it possible for us to start other clubs. Needless to say, we ended up with one martial art per day of the week including boxing, MMA, BJJ, wrestling and so on.

Some say that Zuck started training BJJ because of the trend we started 😜.

This was the beginning of my friendship and founding story with Jorge Bestard.

Building something people want (on the right flywheel)

We tried a bunch of things including building a training app, an ecommerce, a marketplace and a bunch of other ideas. But none of them took off, and we both felt something was missing.

Time went by and we kept throwing things at the wall. But this time it had to tick a few boxes, since we had both switched roles and felt strongly about helping our home turf.

The criteria were:

1. Build something cool

2. That helps our tech ecosystem grow

And that’s how we landed on Revenue Squared:

Problem

• Tech sales is underrated: especially in the spanish-speaking world, where it is typically misunderstood (people think “car salesman”).

• Top talent often doesn’t even know its an option: and that it can not only be very attractive in terms of income potential, but also in terms of career fulfilment.

• Cleaning up your information diet and continuous learning is hard: Keeping up to date with the latest developments in sales - tooling, plabooks, market trends - is challenging, especially in a space mined with questionable “sales gurus”.

• Sales can be lonely: And finding a curated peer group to bounce ideas off of when you are stuck, and that helps you guide your career choices, is hard.

• Linkedin is bad at community: People have spent the better part of the past 10 years being “harvested”, as passive consumers of information.

Solution

• A highly curated paid membership community for the best tech sales professionals in the Spanish-speaking world.

• Vision: The sales profession is understood as a great career path - attracting more talent into the tech scene in the Spanish-speaking world, both in terms of its earning potential and how interesting / prestigious the job can be.

• Mission: Elevate the sales profession.

Validating demand (through pre-selling)

We both knew we didn’t want to re-invent the wheel in terms of business model, and that we wanted to get paid first before building anything to validate demand.

So here’s what we did:

1. Throw our top 50 smart GTM friends in a slack channel. Mostly spanish-speaking sales leaders from unicorns and high-growth startups.

2. Wrote and published a sign-up form on Tally, with the value-prop and explicitly calling out that is is a paid membership community.

3. Scheduled 30+ interviews selecting from that list of 400+ submissions, and sent payment links to those who cleared our talent bar.

And it worked.

Act II: The Facebook Community Playbook

The Spark AR Community

There is a surprising amount of work that goes into building a successful community, especially when its a community-as-a-product.

I know because I was a Product Specialist on the Spark AR team at Facebook, working hand-in-hand with engineering and design to ship Instagram's AR camera effects. We built the community as a product lever because we saw the opportunity.

My team launched these filters in 2017, and went from zero to hundreds of millions of daily active users, over 1 billion unique camera effects used, and suddenly thousands of creators making a living off of building these filters.

The Spark AR Community grew to over 100,000 members. It was considered internally as a source of competitive advantage for the platform. And we proved with data that community creators were significantly more likely to publish effects and retain as active developers.

When we started Revenue Squared, we took those learnings and applied them.

Here are the top 10:

1. Build the MVP where they already are

At Facebook, we built the Spark community on Facebook Groups. We looked at a bunch of options (custom platform, standalone app, stack overflow, etc). We chose Groups because creators were already logged in, already had notifications turned on, already had the muscle memory of checking their feed every day.

Whenever someone asks me about this I use a freeway analogy.

Your community is a town. The platforms your audience already uses every day are freeways. For salespeople, the two biggest freeways are LinkedIn and Slack. They're already on both, every single day, with notifications on. So we built our town right at that intersection. The Slack community was where members lived. LinkedIn was how new people found us.

The point is, don't make people take a detour to reach you. Build where the traffic already is.

The temptation is always to build something custom. Resist it for as long as you can.

Our R2 tech stack at launch was:

• Tally (forms)

• Webflow (website)

• Slack (community)

• Stripe (payments)

• Google Meets (workshops)

That’s it. We didn’t add Podia, Luma, etc until we had 150+ members.

2. Find your first 50 “kindling members” + MVP

calls this the “atomic network” in The Cold Start Problem. Which means the smallest possible group of users that can sustain the network on its own.

Tinder for example did it by throwing a campus party where 500 students downloaded the app in one night. LinkedIn did it by having employees and investors invite their professional contacts. And Reddit literally faked early activity with multiple accounts to make the community feel alive.

At Facebook, the first Spark AR creators were hand-picked. My team identified the most talented AR creators we could find (create + technical usually), gave them early beta access, and brought them in one by one. These first creators set the tone for everything that came after. They built the best effects, answered other creators’ questions, and gave feedback that shaped the product roadmap.

It was also super cool to see how excited our own Engineers got when they saw real people building amazing stuff in real time, collaborating, commenting and so on.

With R2, we ran the same playbook. Jorge and I had deep networks across tech sales from Facebook, Bloomreach, Canva, Miro and so on. Our first 50 members came from personal relationships. We literally threw our smartest GTM friends in a Slack channel. Then we started hosting dinners in Madrid and Barcelona, bringing together talented revenue professionals we thought we’d enjoy hanging out with. Every dinner was oversubscribed and word-of-mouth did the rest.

You cannot crowdsource your founding members, you need to hand-pick them very carefully because they are your kindling. And without kindling, there’s no fire.

This is also super important for any startup thinking of bolting on a community for “support purposes”. As we discussed in our recent Growth Playbook about Lovable’s journey to 400M in ARR, most communities become dumping grounds for negative sentiment if you don’t get this step right.

3. Kill the cold-start problem with curated seeding and setting the tone for culture

The thing nobody talks about but is crucial in the early days of any community, and also the most anxiety inducing, is that the biggest risk is silence. People join, see nothing happening, and leave.

At Facebook, we seeded the Spark AR group with content every day. Tutorials, creator spotlights, product updates, detailed comments to work showcases, announcing bug fixes.

I remember some of our most amazing content came from our Engineers or Designers directly pushing a demo of what they were releasing on the group. Some of them were almost “superstars” in the eyes of these talented creators.

My teammates and I were in there constantly, posting, commenting, surfacing great work, from all across our org including PMs, Data Science, Research, Eng, etc.

With R2, we did the same thing. Before we had organic discussion, we seeded the Slack with data, articles, sales insights, and questions worth debating. One of our most popular channels ever is #Memes. We asked questions we actually wanted answers to and shared stuff we’d found genuinely useful.

Here’s where we found Luisen through Twitter. He was at Salesforce at the time. He joined the team to run community management, and having someone dedicated to keeping the conversation alive made all the difference. But most importantly, he had a great pulse on culture, and maintaining that tone across all our channels.

4. Charge from Day 1

We started at €20/month, then raised to €30/month as demand grew, and experimented with a few different payment plans.

Charging did three things surprisingly well:

1. It filtered for quality, especially in a geo less prone to pay for stuff. People who pay are people who are serious about getting value and contributing.

2. It raised engagement. When you pay for something, you tend to use it way more.

3. It validated demand continuously. Every renewal was a signal that the product was working.

Of the 400+ that applied through our Tally form, we interviewed 30+ in the first few weeks. Only those who cleared our talent bar got a payment link, which set a high bar and created scarcity.

The scarcity drove word-of-mouth and word-of-mouth fed the top of funnel.

The bottom line is that, even though it does require some courage, when you charge for access the people who get in are the people who care.

5. Measure everything (the Vader Score)

When the Spark AR community started growing fast, it became impossible to manually track what people wanted, what was broken, and how they felt about the product. So I built a system.

It analysed every comment and post from the community every day and overlaid a sentiment analysis algorithm (called the Vader Score). This let me build dashboards with word clouds showing what came up most often, a data-driven approach to counting and prioritizing feature requests, and a per-feature sentiment tracker so engineers could see exactly how creators felt about the specific features they were working on.

It was like assembling an NPS score from raw community chatter and tracking how it evolved over time. It was great because it allowed us to pull on different product, bugs and other leaves that moved creator sentiment from 3.8 to 4.5+ out of 5 stars over the course of a year.

I also built what I called the “Signal Triangulation Program,” bringing together Design, Research, Data Science and Product Marketing to cross-reference community signal with quantitative data and qualitative research, which became the foundation for how the Spark team made quality planning decisions.

At Revenue Squared, we applied a simpler version of the same idea. We paid close attention to which Slack channels had the most activity, what types of questions people were asking, and where the energy was. Even though Slack provides pretty meh analytics, we were also able to infer some insights to act on.

This is how we discovered that the #hiring channel was way more valuable than we expected, that the #anonymous channel was where the deepest value lived, and that people wanted in person events and workshops more than we thought.

6. The 150-member inflection point

There is a magic number in community building and in our experience with R2, the flywheel kicked in around 150 members.

Before 150, we were still pushing with seeding content, hosting everything ourselves, making sure every AMA had an audience etc. The community was getting warmer but hadn’t yet taken off.

After 150, the peer-to-peer value flywheel activated. People started answering each other’s questions before we could. The #anonymous channel became a place where someone could post a complex sales problem and get lots of thoughtful responses within hours.

People were posting about whether to stay or leave their current job, sharing real compensation numbers for negotiation help, and working through difficult management conversations together. The #hiring channel turned into the community’s most active surface, with members finding jobs and startups filling roles organically.

This is what I mean by the boulder starting to roll downhill. Before 150, you’re pushing but after 150, you’re trying to keep up. Sort of like product-market fit:

Robin Dunbar’s research (which has held up for 30+ years) suggests 150 is in fact the natural group size where humans can maintain meaningful relationships, go figure! Gore-Tex discovered the same thing by accident apparently, they found that when more than 150 people worked in the same building, social problems started. So they capped every building at 150 employees with 150 parking spaces.

For building a community, you just need to be aware that there is an inflexion point at around that number. On one hand things get much easier (peer-to-peer), but on the other you have to pay attention and be ready to build process + features to help the community (and most importantly its vibe) scale appropriately.

7. In-person events as community fuel

In-person events became popular. We ran networking beers in Barcelona and Madrid quarterly, bigger events with keynote speakers like Gong. Small executive dinners, 8-12 people max, where members came prepared with specific GTM problems they needed help solving.

These dinners were always oversubscribed and generated some of the deepest relationships in the community, and were the primary source of organic referrals. Two of my closest friends in Barcelona today are people I met in the community. Members would tell colleagues about the experience, and the waitlist grew without us doing anything.

We eventually scaled this by creating Ambassadors: 2 per city to organize the local beer events. They were stoked to represent the community. It gave them status, made them feel like owners, and it took the operational burden off of us.

My lesson here is to not underestimate in-person. In a world drowning in digital noise, putting people in a room together really is a superpower.

8. Make members feel like owners and experiment (a lot)

At Spark AR, some of our best product decisions came directly from creator feedback. When creators felt heard, they invested more, they reported bugs because they wanted the product to improve and they suggested features because they cared about where it was going. We tried to lean into this hard.

We gave top creators early access to unreleased features, publicly showcased amazing effects, highlighted craftsmanship etc. It was cool because some of these effects were really amazing art: 3D meshes and textures and coded animations overlayed with emerging computer vision algorithms.

We ran Online Challenges that drove 150+ participants, and the success of the first one led to scaling the program 10x. We launched inspiration posts that caused a 130% spike in group engagement. We built a mentorship program that got 500+ signups. Basically running a ton of experiments to see what struck a chord.

At Revenue Squared, we applied the same principles with our own flavor. We built a diamond emoji culture: a Slack plugin tracked who received the most diamond emojis each month, and those top members got public recognition on our LinkedIn and Substack (which reached thousands of people). We ran annual sales voted “Top Performers” and gave trophies and certificates in public. We launched a bi-weekly peer to peer short 15’ networking call via Lunch Roulette. And a bunch of other experiments.

When we planned changes, we shared them with the community as proposals (not announcements), we asked for input and implemented suggestions. And then, we acknowledged the people who contributed ideas.

The bottom line is to make people feel like owners, test tons of ideas, kill them fast if they don’t work, ad them to the arsenal when they do.

9. Build the team around the mission

Community-as-a-product is deceptively hard to run because you are simultaneously a content producer, event organizer, moderator, recruiter, product manager and customer support agent. Across multiple surfaces, with paying members who have high expectations.

At R2, the team evolved in stages:

• Jorge (VP EMEA at Harvey) and I started everything ourselves. The first 50 members, the first dinners, the first Slack channels. But as the waitlist hit 400+ applications, we were drowning in interviews and community management on top of our day jobs.

• That’s when Alvaro (Chief Sales Officer at Vivla) joined. He had run events at Ironhack and brought edtech experience that was relevant for the workshops and training we wanted to build. He started helping with events and took over the interview pipeline.

• Then Luisen came in from Salesforce. He started on community management and eventually took on a broader founder and operational role, eventually leaving Salesforce to run R2 full-time in 2025.

Beyond the core team, we tested different profiles:

• A community manager for day-to-day Slack management

• A video editor for workshop content

• A professional content creator for infographics

• Julia Barrueco was great in helping us design and launch our web.

• Alejandro from NocodeHackers was super helpful in helping us build the private members area, the LMS on Podia, the CRM on Airtable, automations on Zapier, analytics dashboards and payment chasing (Stripe gives a lot of headaches, around 5% of subscribers experience card failures and you have to manually chase them)

• Event management vendors for our in-person events

• And we had to manage content across multiple surfaces: LinkedIn, a Substack newsletter, YouTube, Instagram and so on (plus multiple experiments).

A big lesson here is that the distinction between a community builder and community manager matters, and one is much harder to find than the other.

A community manager moderates, answers questions, sends emails (operational work). But a community builder creates the culture, designs the incentive structures, finds the first members, kills the cold-start problem, shapes the identity and so on. The builder is 10x harder to find (90% of the time, probably a good idea to hire within the community).

10. Build product to scale and retain

At Spark AR, I saw firsthand how the community outgrew its original surface. When we hit 40K+ members on Facebook Groups, basic things stopped working. People couldn’t find content, questions got buried, the format was constraining etc etc.

We started planning a dedicated forum (Oculus-style) and introduced new surfaces like in-app documentation, contextual help, and a creator certification program.

The same thing happened at Revenue Squared at around 200-300 members, we had a product backlog. The Slack community was great, but members wanted more.

So we started building:

• A private members area with perks, a member directory (”who’s in here”), and a member book portal so people could find and connect with each other based on their profile.

• A learning management system on Podia with all the workshop recordings organized by level: SDR, Account Executive, Advanced. Spanish-language sales training that simply didn’t exist anywhere else.

• Community management tools like a chat roulette / lunch roulette for peer-to-peer mentoring on autopilot. Members would get randomly paired for a 30-minute call every couple weeks.

• Event management on Luma for both online AMAs and in-person events.

• Slack channel management that grew from 8 channels to a more complex setup with specialized topics.

The lesson here is to keep your ear next to the ground at around 150-300 members because there is an inflexion point in volume.

The value prop that emerged organically

One of the most fun things running R2 was watching value show up that we didn’t plan for:

• Finding a job: a very active #hiring channel, with many members landing their next role from companies hiring within the community.

• Filling a job: startups posting open roles in the same channel.

• Sales insights: someone posts a complex problem in #anonymous. People navigating whether to stay or leave a company, sharing real numbers for salary negotiation, working through tough management conversations.

• Market intelligence: you get a high-fidelity sense of how companies are really performing by listening to their front-line troops. This helped members decide where to look for their next career move.

• Dealflow: many members were starting companies or connected to founders. This ended up being relevant for my day job as an investor, too.

LinkedIn as Top of Funnel

We knew salespeople live on LinkedIn. So we opened a LinkedIn account for Revenue Squared and started publishing content that would attract exactly the right people.

What worked:

1. Job listings from the community. We knew the #hiring channel was always on fire. People kept asking us to post their roles even without being members, and we said no. That became the pull.

2. Memes. Sales is tough. Humor builds connection.

3. GTM insights, reports and playbooks. Tactical stuff that the community was producing.

4. Community social proof. Photos and images from events, rankings, workshop clips.

5. Monthly top member recognition. Showcasing the best of the community to the outside world.

This fed the Tally waitlist, which kept growing. The waitlist gave us information about people’s level of interest and helped calibrate who would have the right profiles. We kept interviewing every prospective member until a few hundred members.

At that point we built a referral mechanism on the private area (i.e. share this link and you’ll move up the waitlist), and we started introducing more friction in the application form. This gave us more information about prospective members, and helped us play by ear if we needed interviews or not. The referral and deep profile information shared was usually enough to get a good sense of approval or rejection.

Act III: The Exit

I met Ramon from Nova over lunch in December 2023. They were building something aligned with our community’s mission at the time, and they saw in R2 what we’d built: an audience of 25K+ across Linkedin, Youtube and Substack, a highly curated and engaged paid membership community, with tier 1 sponsors, over 200+ learning modules sourced from real case-studies in our learning platform, and the number 1 GTM brand in the Spanish-speaking world.

The deal was modest and in equity, meaningful to me in a way that goes beyond the numbers. Many people found their next role through R2, some started companies together, and the mission and vision are still growing every day. I’m really stoked to have Nova give the community the resources it needs to keep growing.

That’s it for this week folks.

Thanks for reading,

Ivan

P.s. if you want to help me out, the best thing you can do is to share my work! 🙏

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If you enjoyed this, you might like:

→ How Harvey AI Hit $200M+ ARR in 36 Months

→ How Lovable Reached $400M ARR

→ How Freepik Hit 100M Users With €0 in Venture Capital

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Harvey AI’s Growth Playbook: Putting a lawyer in everyone’s pocket.

Hello there!

This week we’re deep diving into the growth playbook of one of the most important companies of this AI wave, Harvey.

This is a perfect storm story where you have a combination of extreme product-market fit (LLM’s usefulness on expensive, previously time-intensive, heavily text-based work), talent and investor hunger.

In July 2022, a securities litigator and a DeepMind researcher cold-emailed Sam Altman from their shared apartment in San Francisco.

The email subject line they used was on point:

Attached was a proof of concept where they’d pulled 100 questions from r/legaladvice (reddit), ran them through GPT-3’s API using chain-of-thought prompting before anyone was talking about chain-of-thought, and sent the outputs to practicing landlord-tenant attorneys. Turns out 86 out of 100 answers passed and nobody at OpenAI had tested this.

That email led to a July 4th pitch to OpenAI’s C-suite, a seed investment, and what became Harvey, going from $0 to $200M+ ARR in 36 months and an $11B valuation.

Two frameworks run through the whole story:

1. The first is prestige-led distribution: turns out that in professional services trust IS the product. Their hypothesis was that if you win the top firms first, even though it might be much harder to wedge yourself in initially, once you are embedded into those logos trust cascades downward through the entire industry.

2. The second is “expand and collapse”: Build specialized AI agents for narrow legal workflows. Then collapse them back into one interface that routes users intelligently. Which has become somewhat of a blueprint or operating system for how to build a compound vertical AI company in 2026.

What you’ll learn in this edition:

• How Harvey went from a cold email to $200M+ ARR in 36 months with ~460 employees

• Why they targeted the hardest law firms first when every VC said start small

• The “expand and collapse” product strategy that is the actual playbook for vertical AI

• Why Harvey’s CEO says GRR is the metric most AI investors are dangerously ignoring

• How they’re shifting from selling software seats to selling legal work through revenue-share deals

Act I: The Cold Email (July 2022 to Early 2024)

How it started

Winston was a first-year litigation associate at O’Melveny & Myers in Los Angeles and his cofounder and roommate Gabe had spent a couple years doing AI research at DeepMind and Meta.

Gabe had been brainstorming horizontal AI assistant ideas with friends from Google Brain, who ended up joining OpenAI and building ChatGPT.

But he went in a different direction.

“Winston showed me all his legal tech. Their document search to search maybe 10,000 documents would take 5 to 10 minutes. He was like, I do the search and then I go on Instagram for a bit. The client gets billed for this.”

Everything in law was text, the workflows were deeply manual and the economics were, as we all known too well when we’re billed for a couple hours of legal work, big.

How the legal market actually works

To understand Harvey’s growth, you have to understand the industry.

The global legal services market is roughly $900 billion (and that is the size of the market today, lets not fall into the classic market sizing trap).

The average US lawyer bills $352 per hour, and the top partners at elite firms bill $2,000+. The gap between a junior associate and the best partner on earth is only about 3-4x in price, which Winston thinks makes no sense and will change.

Law firms are organized around billable hours. Partners give tasks to associates, associates bill hours, and the firm collects. Typically a junior associate does the first draft, a second-year reviews it, a fifth-year reviews that, the partner reviews it one final time, and then it goes out.

This hierarchy matters for AI adoption because it means there’s a built-in human-in-the-loop review system. The minimum viable quality of an AI output can be lower for a law firm than for an in-house team, because everything gets reviewed anyway.

What excites me the most about this tech shift’s impact on Legal work is that the average price used to put legal services out of reach for most people, until now.

But the path runs through the premium market first:

Growth lever 1: The contrarian customer choice

Every VC and advisor told them to start with small firms, solo practitioners, the SMB market (not sure it is a VC’s job to tell a founder what market to go after).

Instead they went straight to Allen & Overy (now A&O Shearman), one of the world’s largest law firms and signed a 4,000-person enterprise rollout.

At that time Harvey had 4 people and were working out of an Airbnb.

“Some of our investors were like, this is a horrible idea. Don’t do this.”

But Winston understood something about professional services that pure technologists usually miss, which probably came from having worked and experienced how a law firm did things first hand.

“If you earn the trust of a few of those firms, the rest of them will trust you and the rest of the firms downstream will definitely trust you.

And their clients will trust you.”

The logic was both GTM and product:

• On the GTM side, prestige in professional services functions like a trust certificate. If A&O trusts you with their client work, every mid-market firm will trust you too.

• On the product side, the hardest work builds the most defensible systems. If you can handle a $68 billion Activision-Microsoft merger, an NDA review should be trivial. ChatGPT could already handle simple NDAs, and going down-market first means building something the models will eat in 18 months.

“The thing that makes me feel better is if you get to the point where GPT-7 can just do a simple lease analysis... yeah, we’re probably done. But I’d also say pretty much every other company on earth is done.”

I think their contrarian bet here is aligned with what we called the Systems of Action thesis a few months back. Let me refresh your memory:

The bet Harvey took here is that intelligence will become cheaper and cheaper.

And if that happens:

• “good enough” AI is everywhere

• the bottleneck is no longer having intelligence

The hard problems become:

• who is allowed to decide

• under which rules

• with which approvals

• and who is accountable when something breaks

Which are coordination problems, not intelligence problems. You can think of coordination as deciding who does what, in what order, under which rules, and who is responsible if it goes wrong. The intelligence is not choosing the goal but rather helping move work through a system of approvals, handoffs, and constraints without humans chasing each other. In this world:

• power shifts to systems that control workflows, rules, and decisions

• intelligence is a commodity

• whoever runs the work wins

This is similar to how Salesforce didn’t win by building better databases but by deciding how sales teams track leads, approve deals, forecast revenue, etc.

Harvey would have started as a Vertical AI tool, and if I’m right would be expanding to become a workflow builder climbing up the stack (platform).

Growth lever 2: Hyper-personalized demos that don’t scale

Before every pitch, Winston would look up the partner he was presenting to. He’d find a recent case they’d worked on (public court filings, merger agreements filed on EDGAR), and have Harvey analyze their own work.

For litigators apparently this move was pretty shocking.

He’d upload one of their own filed arguments and say:

“Tell me, make holes in these arguments. How would you argue against it? Lawyers are argumentative. Just let them fight with the model. They pay attention to every single word.”

Sometimes the model was wrong, especially in 2023 with early GPT-4, but that almost didn’t matter because the lawyers were reading every word, which no software demo had achieved before.

I’ve spoken on here before about talking to a lawyer friend of mine about using Harvey at work (before really knowing much about the company) and was shocked about how much value it drove for her (sharing the tool among multiple lawyers with one licence, saying it’d be incredibly painful to loose it).

Reminded me of Superhuman’s product-market fit engine and its core question:

“A lot of these products, it’s just populating text and how do you get someone to pay attention to that? Well, you tell a litigator that there might be holes in this argument and they go... but they want to prove you wrong.

And so they really pay attention.”

This is gold for a sales team.

They’ve since productized this approach, where partners can now click one button and run their own matter through specialized workflows without prompting. Which means the thing that didn’t scale became a deployment strategy, and when partners share those workflows with their clients, it becomes distribution.

Growth lever 3: The trust flywheel (external virality)

Harvey’s strongest growth loop is likely external, where law firms are bringing Harvey to their clients. Here’s how it works:

• A private equity fund’s law firm builds a fund formation workflow on Harvey.

• The firm shows it to the PE fund.

• The PE fund says “we want this too.”

• The PE fund is a happy customer, they start pulling in their other law firms.

A&O hit 70-80% daily usage during the initial pilot, so during the trial 3,500 lawyers asked 40,000 questions before the firm committed to a broader rollout.

Their first 50 enterprise customers were all referrals from law firm clients. By end of 2023, they had ~40 customers and were running at roughly $7M ARR (my estimate based on reported 4x growth to $50M by early 2025).

Act II: The Trust Ladder (2024 to Mid 2025)

This is where most AI companies stall or what we’ve been calling “vibe-revenue”. Now you have to actually retain, expand and build the machine that scales.

So how did they do it?

Growth lever 4: Expand the product, collapse the UI

Read more

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The engine behind every top 1% startup is a great CRM.

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The Lovable Company: Growth Playbook

One of the tools that fascinates me most is Lovable, both because of how fast their product ships (daily?) but also for how spectacular it is to enable creativity (i.e. meet my friend Arnold) and give you what genuinely feels like superpowers.

So I decided to deep dive into how this European outlier grows.

Here’s what actually worked, stage by stage:

📐 Quick note on methodology: this analysis focuses on the 80/20 mechanics that help explain Lovable’s growth (not a comprehensive profile, and not an endorsement or investment advice). Based on 40+ sources including podcasts, company blogs, investor analyses, and third-party teardowns. All revenue figures from TechCrunch or public statements. This company moves so fast that data from 3 months ago is already outdated, I used the latest confirmed source in every case. Treat all numbers as directional.

Act 1, The Spark: $0–$10M

Anton Osika studied engineering physics at KTH, worked at CERN, was the first engineer at Sana Labs, and co-founded Depict.ai through YC. In 2023 he built GPT Engineer as a side project, an open-source experiment to see what LLMs could do with code.

It hit 50K GitHub stars which was the fastest-growing repository on GitHub that year. GitHub’s systems temporarily shut it down thinking it was a DDoS attack 😅, because it was spawning 15,000 new repositories a day.

He tried to productize it twice.

• Launch 1 (early 2024): user growth stalled almost immediately.

• Launch 2 (mid-2024): retention collapsed, the AI got stuck on anything complex, and the pricing model was bleeding money on power users.

Most people read that as a no, but Anton read it as a spec.

He and co-founder Fabian Hedin migrated the entire backend from Python to Go, built new scaling laws for AI self-debugging so the model could get itself unstuck, and made the call that changed everything: stop targeting developers.

Their hypothesis was that the real opportunity was giving non-technical people the ability to build software from scratch (superpowers).

“Lovable” as a rebrand was spot on, as it told you how it should feel. , who joined later as Head of Growth, has a name for this: the Minimum Lovable Product, software with personality, software you trust because it feels alive.

The name has also become somewhat of a cultural enforcement mechanism inside the company, when someone says “this is not lovable” in a meeting, everyone stops what they’re doing.

Third launch was in November 2024, which became #1 on Product Hunt, #1 on Hacker News and drove $10M ARR in 2 months with 15 people.

The levers that drove it:

1. Founder brand as distribution: Anton had already been cultivating an audience on socials built over years of posting raw numbers, failed experiments, hiring photos, half-formed ideas a-la Pieter Levels. When hey launched people already trusted the person before they trusted the product. This deserves a deep-dive on its own, worth having a trip down memory lane on Anton’s timeline.

2. GPT Engineer’s 50K GitHub stars were a pre-built trust base: Thousands of developers already knew the name and the credibility likely transferred directly to the new product, again part of a long-term founder brand investment.

3. Short video demos as an ad: Nobody knew what vibe coding was in late 2024 but you watch 10 seconds of someone building a real app by typing a sentence and you think “I had no idea this was possible.” / what kind of sorcery is this. I think Twitter, Tiktok and YouTube drove real curiosity at scale due to the product’s inherent “magic moments”.

4. Co-marketing with Supabase, Replicate and Resend: gave them reach into audiences that were already product-curious and technically adjacent.

5. Product Hunt and Hacker News as coordinated launch amplifiers: as usual, but also with leveraged via points 1 and 2 above.

6. User-generated content from day one: Every post someone shared of their Lovable build was basically an organic proof point (PLG mechanics).

One mistake Anton calls out directly that caught my eye and is worth highlighting as it is a perpetual debate I had both working at Meta on product discussions but also with many of my founder friends (almost weekly):

On trying to keep the GPT Engineer open-source community alive alongside Lovable: “A bad idea to do two things that were like a bit too tangentially related.”

Community goodwill lost to radical focus every time. His advice now seems to be to find the bottleneck, solve for that, one thing at a time.

Act 2, The Machine: $10M–$100M

A founder’s personal brand can get you to $10M (as wild as that is). But what gets you to $100M is likely, as is usually the case, a system.

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Hello there!

This week I’m bringing my friend to co-write this edition. Jordi is also my colleague at JME Ventures, where we invest in startups from pre-seed to Series A. He’s been following Freepik closely for a while and put together the research behind this edition.

Jordi Noguera

Venture Capital | Startups Analysis

Why Freepik? Because it’s probably the best real-world case of a company that watched AI threaten to destroy its business, and instead of playing defense, ate its own product and came out growing faster than ever.

And it did it from Málaga, without a single euro of venture capital!

Three acts and three growth lessons, let’s get into it:

1. People were making logos in Word, and Freepik saw the gap. (2010–2020)

Few people know this, but Joaquín Cuenca had already sold 2 companies before Freepik. First Loquo, a classifieds portal sold to eBay in 2005. Then Panoramio, together with Eduardo Manchón (who later founded Mailtrack.io, now Mailsuite, with ~€8M in revenue and 110K paying customers), sold to Google in 2007. Joaquín stayed 3 years at Google Zurich as Tech Lead for Panoramio before starting Freepik in 2010.

The other co-founder, Alejandro Sánchez Blanes, was building BeSoccer (formerly Resultados de Fútbol) and Joaquín was helping them. They needed tons of illustrations for the app and were browsing stock pages one by one looking for them. It consumed so much time that he ended up building a search engine for himself.

He published it publicly and within just over a month, more than 100,000 users showed up.

At the time, people were making logos in Word using WordArt and there were few options for graphic design. Freepik was born in that gap.

How the stock image market works

To understand what Freepik did, you have to understand how this market evolved.

The industry starts in the 90s with Getty Images, digitizing photography. You bought unique photographs (”the pieces”) for $199–699 per image. Then Shutterstock appeared with a “microstock” model where they sold the same photograph for $3–69 (with a subscription) but to many more people. More marketplace but less exclusivity.

There was a massive market shift happening at the time. Everything was moving from TV ads and print to web, and that meant you needed way more items per project at a much lower price. Anyone could “create.” The market was going from low volume, high price to high volume, lower price.

Freepik’s key decision was to specialize in illustrations and icons. Getty and Shutterstock were only in photos. But illustrations have very different marketplace dynamics.

Airbnb would be a good comparable. The more fragmented supply and demand are, the more sense a marketplace makes. Illustrators weren’t good at positioning themselves, and there are so many that it’s impossible to reach them all.

Depending on the type of content, the dynamics and economics change a lot.

1. Photography. Maximum fragmentation. You need infinite capillarity to have every photo in the world (if you want a photo of X place, at X specific moment, in X style). This is the most suited for a marketplace for that reason.

2. Illustrations. A single illustrator can create many. Less fragmentation, but discovery is still hard.

3. Icons. Even less variety. One person can create many icons and cover a lot of ground.

A real case of my own. I had a company where we were Shutterstock and Getty users. We had €0 and were bypassing these platforms all day long. We bought photographs for commercial use (much higher price), and if you could find the author directly you’d get it cheaper. But finding them was nearly impossible. That explains why Shutterstock’s take rate is what it is.

The data moat nobody saw coming

What Freepik did differently was start as a mini Google for free illustrations, a search engine that scraped third-party pages. The problem was that the experience on those third-party pages was terrible. So after 3 years they started creating their own content.

Netflix would be a good comparison. They went from aggregating third-party content to producing their own. Same pattern.

Their access to search data from users became their “unfair advantage”.

How did they monetize at first? They “gave away” the content they created to users who had blogs, in exchange for referencing Freepik when they published it. Readers learned about Freepik and over time started coming in as users themselves. Pure organic growth.

Joaquín tells a case from Japan that captures the depth of this understanding well. When a user typed “mountain,” they actually wanted Mount Fuji. You had to show Mount Fuji at the very top. That level of user understanding, country by country, search by search, generated the flywheel.

Over time, many users who used the free illustrations were willing to pay. They moved to a freemium model and opened the platform for any designer to upload content and earn from downloads.

The model ended up at 50% revenue from own content and 50% from third parties with a 50% take rate. This matters because the economics are very different depending on the model.

Own content carries more risk because you’re investing in the content (an asset that depreciates, and photography depreciates faster than illustrations or icons), but it leaves you higher margins. Third-party content (marketplace) carries less risk because you’re just publishing on the platform, but also lower margins because of the take rate. Freepik had both. Smart diversification.

By 2020 the numbers spoke for themselves.

• 32M monthly users (from the EQT press release)

• 500,000+ paying subscribers with an average ticket of ~€7

• Revenue of €45M over 9 years from €200K in 2012

• With zero outside funding. Fully bootstrapped.

In May 2020, EQT Mid Market Europe buys 53% of Freepik for a valuation of ~€250M. Drake Star Partners advises the sale. Two of the three co-founders exit (Alejandro and Pablo Blanes). Joaquín Cuenca stays. Doesn’t sell a thing. In October of that same year, Atwater Capital enters with an additional minority stake and Vania Schlogel (founder of Atwater) joins the board.

Having a founder like Joaquín, with that level of ambition, is one of the reasons I like Freepik so much. In Spain, similar to Factorial or PLD Space.

2. “Everything we’ve built in 10 years is becoming obsolete.” (2022–2023)

In 2019–2020, DALL-E 1 appears, OpenAI’s image generator. First image diffusion model with any quality at all. The quality was terrible, but it showed something no previous system had achieved, which was real genericity. It could draw “a radish walking a dog.” Sounds absurd, but that’s exactly the point. No previous system could do that.

It might seem obvious now, but at the time, extrapolating that the technology would keep advancing, what was the point of Freepik? There were two possible stances.

The comfortable one was to think it wouldn’t change much.

The uncomfortable one was to assume everything changes and that AI improves in a matter of months.

With AI, there have always been three stages.

1. First you think it won’t be able to do the task, and it does.

2. Then you think it won’t do it as well as the top expert, and it does.

3. And finally you think it won’t surpass the expert, and it does. Cases like chess or Go (the Netflix documentary is incredible). The same pattern repeated with images.

Then DALL-E 2 arrived in April 2022. Joaquín was at home with COVID and spent entire days watching what people were generating. The quality jump was, in his words, “so dramatic” that the conclusion was inevitable. In one or two years, AI would match the quality of every image in Freepik’s catalog.

His reaction was honest. “Initially I’d say I felt some despair. I had no plan. I didn’t know what to do.”

The only thing he was sure of was that what Freepik had been doing for 12 years was no longer going to be the business of the future.

He had to tell more than 500 employees that everything they’d built over the last decade was becoming obsolete. Some employees needed psychological treatment. This is serious.

And yet, the business was better than ever. In 2021 they’d closed with €61.5M in revenue (+37% YoY), 380 employees (+26% YoY), and already exceeded 500K paying subscribers. By 2022 the headcount had grown to 567 (+49% in a single year) and revenue was around $87M. 70% of revenue was already recurring (subscriptions). Double-digit growth for years, “wildly profitable.” That’s the context that makes the decision harder, because they really weren’t in crisis but at the best moment in the company’s history.

What was happening in the market?

I always say that in a business there are two types of risk, technological and market. We’ve had many years of venture capital where almost every company has zero technological risk but does have market risk. The conversations are always about metrics, acquisition, positioning. You rarely talk about the technical approach at a deep level because you assume the team is capable of building the product.

Freepik had the opposite problem. Zero market risk with 100M users and more than proven demand, but suddenly a brutal technological risk.

Adobe’s position was understandable. They acknowledged the technology was incredible, but their stance was “this is illegal.” They had 400M images in their catalog and were in a comfortable position to defend this. Incentives explain everything.

Was it legal or illegal to train models with images? At the time, nothing was clear.

Freepik’s position was different. They thought ChatGPT and everything around it was too valuable for society, that no country would want to be left out of the race, and that if the law interpreted that all text and all images needed copyright clearance to train on them, ChatGPT couldn’t exist. Freepik didn’t see a future where states would impose that limitation.

They decided to take the aggressive position and assume the technology would exist and improve significantly over time.

How AI image generation works (for the curious)

Before generative AI there was an important intermediate step, GANs (generative adversarial networks). One neural network generates an image and another tries to determine if it’s real or not. Both improve by “fighting” each other. The result was decent quality but only for very narrow domains, like frontal human faces for example. Zero genericity. Only worked for a specific task.

Then diffusion models appeared (DALL-E, Stable Diffusion, Flux). The process works like this.

1. You take an image of a cat and add noise, which is random variations in the pixels, like grain or static.

2. You train a neural network to learn to remove that noise.

3. You repeat with more noise. The network keeps learning to “reconstruct” the original image.

4. You repeat this up to 100 times.

5. The magic step. Instead of starting with a noisy image, you start with pure noise. And the network is able to invent the cat.

This allowed you to take much larger image datasets (potentially every image on the internet) and generate new images from them.

But it had limitations. Small models got confused with details, so if you asked for “green cat on blue table” the cat came out blue. They couldn’t count well. Hands were a disaster because of too many possible finger combinations. And text inside images was unreadable.

Now with autoregressive models (like Nano Banana Pro, similar to how ChatGPT works but for images) the image is divided into parts and each piece is like a word predicting the next one. This has massively increased comprehension of what you’re asking for. Hands come out right, text is readable, fine details work.

Freepik’s decision was to build while walking

Joaquín’s decision framework was simple.

We don’t have AI researchers, so we can’t create models ourselves. We don’t yet know what value we can add because there’s a machine that does everything, so what do we do? If the answer is “nothing,” fine, that option leads nowhere, discard it. But if the answer is “something,” what happens if we act now and shape how this technology reaches our users?

The decision was to grab this technology as soon as it was available and discover the value along the way.

Joaquín believes companies should build while walking. It’s very ambitious to think you’ll know what’s going to happen in 5 years, and the only way to move forward is step by step.

They started hiring and building an AI team without knowing exactly what to do, but with the stance that doing things leads to understanding things.

The first use case was improving the search engine. Before, it worked with a tag system where the illustrator uploaded content, added tags, and that’s how it was found. With AI, the search engine could understand the image directly. They used CLIP (Contrastive Language-Image Pre-training, from OpenAI). They integrated it in less than an hour after OpenAI’s release. That execution speed says a lot about the team’s mindset.

The second use case was generation. Stable Diffusion arrives as open-source in September 2022 and Freepik (through Wepik) integrates it into the platform, becoming the first non-professional editor to do so. They positioned themselves as “the simple way to use the model.” And just as they had the moat of most-searched keywords in the past, they now started getting real data on what users wanted to create.

And something counterintuitive happened. The business accelerated. Freepik grew faster after the pivot than in the five years before. AI generated new demand, it didn’t cannibalize stock downloads. By the end of 2023, Freepik reported 150M+ monthly users (vs. 41M in 2021), 2M MAU on AI tools alone (300% YoY growth), and 120M+ assets created with AI on the platform that year. 60% of new users came through AI tools.

In parallel, 2022 was the year of expansion through acquisition. They bought Videvo (UK, stock video), Iconfinder (Denmark, icon marketplace, whose CEO Martin LeBlanc Eigtved became Chief Product/Experience Officer at Freepik), Original Mockups (Colombia, 3D resources), and Check My Presets (photo filters). Four acquisitions in one year, each adding a new content vertical to the ecosystem.

Why? Because the 100M monthly visitors became instant distribution for AI tools. Volume from day one, which meant better unit economics on AI generation than any pure AI competitor. Lower inference costs that get passed on to the user, which brings more users. The flywheel again:

3. Models are the chips, and the platform is the computer. (2023–present)

The last time we had a technology that developed exponentially was chips. And what happened was that every improvement in the CPU enabled new use cases that were previously impossible, and each new use case created a bottleneck that in turn created the next use case.

It’s an analogy Joaquín uses. The technology is incredible (the CPU), but what you need is a computer, not to solder things together. Just like the iPod was born as a wrapper around a Samsung hard drive, but no, it was something else. It was the experience layer on top of the hardware.

Freepik’s reflection was that, just as it used to be very hard to publish a text and now it’s as easy as one click, the same thing can happen with visual content creation. There’s a new technology that enables anyone to have quality, infinite visual resources. That creates a bigger market, with much more volume. And to work with that, you need a platform that helps you manage it.

That’s where the orchestrator concept was born. Models are like CPUs and Freepik is the computer. Very specific models started coming out for specific tasks, and they realized the biggest help to the user was orchestrating those models, just like a computer orchestrates different components, and letting you do different things from a single place.

Today Freepik is ranked #11 in a16z’s ranking of the 50 most-visited generative AI products in the world. Above Remove.bg (#16), Grammarly (#18), CapCut (#17), and Midjourney (#43). A bootstrapped company from Málaga.

The acquisition of Magnific in May 2024 was a turning point. Javi López and Emilio Nicolás (Murcia) created an AI upscaler that went viral, 30,000 signups on day one, and Freepik bought it, bringing 700K+ professionals into the ecosystem. The co-founders were integrated into Freepik Labs under Omar Pera (CPO, and a JME Ventures portfolio founder!).

When you don’t have heavy fixed assets, the ability to move fast is enormous. You can pivot because you don’t have as much sunk cost. It’s the innovator’s dilemma in reverse. The one who had nothing to protect was the one who could attack fastest.

I look at Freepik and see something similar to what happened with Kodak and digital cameras, but with a different ending. We’re watching Kodak adapt to digital cameras. I’m surprised by the ability of 500 employees, with tens of millions in revenue, to turn the ship and say “I’m going to cannibalize myself.”

And the entire market has been moving from the low volume, high price model (exclusive photos at €300) to high volume, lower price. With the rise of social media everyone needs to publish more, and tools have appeared that give access to many more people. Freepik has surfed each of those waves.

Conclusion

The Freepik story is about what happens when a company that knows its users better than anyone encounters a technology that threatens to destroy it, and chooses to attack.

Three times the market changed and three times the data moat turned out to be what mattered. First for SEO, then for understanding what users wanted to create, and now for training AI models with clean data.

Bibliography

Podcasts and interviews with Joaquín Cuenca

• Drill Down Insider Ep. #22: Freepik CEO Joaquin Cuenca (March 2026). Cory Johnson.

• Freepik CEO: “The Day I Realized AI Would Destroy Us” (2026). A Company That Refused To Die.

• Joaquín Cuenca at Upscale Conf 2025 (San Francisco, 2025).

• La IA era la única opción para no morir como empresa (Podcast IA, 2025).

• Freepik en Itnig (2021). Podcast Itnig.

• Spicy4tuna: Freepik (December 2025).

Data and research

• a16z. Top 100 Gen AI Consumer Apps, 6th edition. Top 50 Gen AI Web Products by Unique Monthly Visits (Similarweb + Sensor Tower, January 2026).

• Reforge. The 8 Most Important Metrics for Marketplace Growth.

• Getty Images + Shutterstock merger ($3,700M, January 2025). Press releases. Getty Full Year 2025 Results (GlobeNewsWire, March 2026). Shutterstock Full Year 2025 Results.

• Canva. $4B ARR (SaaStr, Feb 2026). Valuation $42B (tender offer, August 2025).

• Adobe. FY2024 Earnings. Q1 2026: $70M shortfall in stock photography.

• Freepik. F Lite: Open-source 10B parameter model (April 2025). Official Freepik blog. GitHub / Hugging Face.

• Freepik. Magnific acquisition (May 2024). Official press release. Tech.eu.

• Freepik. Freepik Spaces launch (November 2025). BusinessWire.

• Freepik. Pikaso launch and metrics (December 2023). BusinessWire.

• Freepik. Nano Banana Pro. Official Freepik blog.

• Freepik. Ideogram 3.0 integrations (March 2025), Google Veo 2/3, Kling O1 (December 2025).

• EQT. Freepik acquisition announcement (May 2020).

• Atwater Capital. Minority stake (October 2020). Private Equity Wire.

• Mordor Intelligence / SkyQuest. Stock photography market size $4.5–7.2B (2024).

• Kaptur. The Silent Collapse of Photo Licensing Revenue. AI impact on photographers (Sep 2024, Feb 2025).

• El Español. Freepik closes 2021 with record revenue (Feb 2022). Freepik reaches 567 employees (Dec 2022).

• Tech.eu. Europe’s quiet AI giant (Jun 2025).

That’s it for this week folks.

Cheers,

Ivan

P.s. send me your questions about startup growth by replying to this email, I’ll try covering them in future editions 🤙

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Hello there!

This week I’ve been thinking a lot about one question:

Where is the next AI startup opportunity pocket moving?

3 things landed in my inbox that, once your layer them on top of each-other, tell a pretty compelling answer to that question (or at least a thesis). Today’s tl;dr:

1. 🤖 Where AI works today.

2. 🟧 Where 198 YC founders placed bets.

3. 📈 The $1T+ in services TAM almost nobody is touching (yet).