In case you missed it:

• 💍 Oura: $0 to $1B Revenue in 10 Years

• ⚛️ Sierra: $0 to $165M ARR in 26 months

• 💳 Ramp: $0 → $1B+ Revenue in 6 Years

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Hello there!

Hope you managed to take some time off in August. Europe’s been pretty flat, but luckily managed to catch a few waves up at Scotland’s finest wave-pool:

We’re coming back with a deep-dive on the growth behind Supabase, which recently raised a $500M round at a $10B valuation, 6 years after their start:

For the non-technical you can think about Supabase like this:

Apps need a place to remember things like your login, messages, orders etc. plus a pile of “invisible plumbing” around it. They provide all of that “out-of-the-box”. If you’ve ever played around on Lovable chances you used Supabase.

Their growth story is especially interesting to me because its a great example of how a small startup displaces a big player in the space (Google Firebase).

What you’ll learn in this edition

• The 0 to 1 story (from New Zealand to the world)

• How they replicated YC conditions internally (“internal accelerator”)

• How they used memes + community as a growth lever

• What their salespeople get paid on (something different)

• How the rivals “built to replace them” end up running on Supabase

• Why they “obsess” over first database created as a key growth metric

And other growth mechanics, lets dive in.

From Firebase alternative → backend for agents

Here’s how the market evolved in a nutshell:

Where we come from

Over time different software waves have had their own database:

• Enterprise era: Oracle

• Web era: MongoDB

• Mobile era: Firebase (bought by Google in 2014).

In every cycle you see the same pattern where they win developers fast first then alienates them when they start going after bigger fish. Firebase for example was great to start, harder to scale, and then lived in Google’s own format which made leaving hard (lock-in). Database developers wanted Postgres (open source and 30 years old), but it was a pain to set up and hard to run and scale on your own.

Where we are

As we’ll see later, Supabase used this pain (and market timing) as a wedge to grab the market. Today they have almost 10 million registered devs and 250K+ paying customers, with database launches up 600% year over year (as of June 2026). Apparently over half of the most recent YC batch is building on it (full circle considering they went through the batch in 2020).

Where the market is going

Apparently 60% of new databases today are already being launched by AI agents (in the millions every month). Claude Code alone became on of the biggest contributors to new databases in 2026.

We’re already seeing how these “on-ramps” to intelligence (your LLM of choice, now new chokepoints for how humans use the internet) and their agents shop very differently than a human. Meaning they tend to pick from a small menu of tools (the CEO talks about 3 per job). Supabase is on that menu today for this job, but the game is moving and it is moving very fast.

Handing over a database is getting commoditised but keeping them running well at scale (fasta, reliable, safe etc) is still a hard problem especially with this incoming wave. Supabase seems to be placing bets with a few things to ride the momentum like Multigres (open-source layer for running big fleets of postgres databases) and OrioleDB (faster engine for postgres).

Now unto how they grew so fast.

Act 1: A side project

2019 → end 2021 · $0 → ~$1M ARR

The 0 to 1 story (from New Zealand to the world)

The founder, Paul Copplestone, grew up in a farm in New Zealand, left school at 18 + contracted his way through university. He briefly worked at a hedge fund in Australia before joining Accenture.

In 2015 a friend of his called with a startup job in Kuala Lumpur, he jumped on a plane and co-founded ServisHero as its CTO (a marketplace that connected homeowners with service providers in Southeast Asia). The company worked, but Paul wanted to start something new. He went to Singapore for the Entrepreneur First program, which he apparently didn’t stay for, but stayed there and met his now co-founder.

Ant Wilson is from Liverpool, did a masters in software engineering and spent around 10 years working in venture-backed startups.

The origin of the idea is linked to Paul solving his own problem. In his previous startup he had a chat feature (which needs the ability to update instantly for everyone’s screen when someone sends a message). Postgres (the databased he ran for it) couldn’t do it, so just for that feature he integrated Firebase (google’s database) which could. The problem is Firebase had issues under a lot of load and customers began receiving chats out of order (breaks conversaiton).

He tried to solve it with postgres but its built-in workaround didn’t work on messages that where beyond a very small size. So he decided to build the missing piece himself which was a small engine that watches the database and pushes every change to your screen when it happens, for free as open source.

The early months of their 0 → 1 went like this in a nutshell:

• January 2020: Ant joins Paul and together decide to “build the most Y Combinator-friendly team, one of those things that so obviously should exist in the world”

  • Picked the ICP: going after vc-backed startups to grow fast first (more on this in growth lever 1).

  • Got their first 100K pre-seed from angels.

• March 2020: the “platform” (mvp) is a form, where users submit their details and Paul launched every database by hand.

  • Employee #3: Steve Chavez who was a maintainer of PostgREST (open-source tool core to their stack) joins.

  • Firebase’s founder passes on investing in them: a YC partner connected them to James Tamplin, he’d apparently seen 40+ backend-as-a-service pitches in a previous wave and this looked like one more (investing is hard! In this podcast he mentions “I just didn't invest and didn't join you on the journey, which I now deeply regret.", fun fact also from the UK like Ant).

• Spring 2020: go accepted into YC’s summer batch.

Now unto how they grew from there:

Growth Lever 1: Launched against Firebase by name (8 →800 databases in 3 days)

They applied to YC before they had a product “worth launching” due to the customer list, which was the batch of startups itself (makes a lot of sense, and one of the clearest value-adds I’ve seen from a YC program, they took full advantage). These 100+ startups are basically their ideal early customers and by the end of their S20 batch they had around 80 alpha users + a launch planned for Demo Day.

For a while apparently the site said "Supabase, real-time Postgres" and according to the founders "nobody cared, it didn't explain what it is, what it does, who it's for."

In May they changed it for “the open source Firebase alternative”, and even though it didn’t fully account for the serendipity that came their way it certainly helped:

• An early user (not them) posted the site to Hacker News (see above)

• The moderator converted it into their official launch live and the second most upvoted launch HN has apparently ever seen (behind Stripe)

• Got them from 8 → 800 databases in 3 days

I thought this phrase from the founder summed it well:

"product-market fit often is just like a product-positioning fit." (first round pod)

Growth Lever 2: “Replicated” YC’s demo day intensity inside the company (every 3 months)

“Why don’t we just pretend that we’re starting the batch again, and do our best to recreate the conditions of an accelerator internally, complete with our very own Demo Day?” — Ant Wilson, “How we launch at Supabase,” Nov 2021

The batch pressure worked for them and they took what worked best and started implementing bits and pieces of it internally (to avoid the feared post-batch cliff).

The launch thread’s most “popular” demand was Auth, and Michael Seibel kept asking about it to them at his weekly office hours until it apparently got pretty uncomfortable:

"Paul, for like 4 weeks you've been telling me you're going to build auth, when the [!] are you going to ship auth?" (Accel pod)

So when the batch ended they took steps to build somewhat of an internal accelerator:

• An internal “Demo Day” every roughly 3 months: with kick-off events, inviting speakers, custom swag every cycle etc with a basically arbitrary deadline that forces everyone to ship (constraints improve creativity!).

• The first one produced the Supabase Beta: with an exit criteria of 3x Firestore’s performance, a pen test and a public uptime page.

• The second one invented the “Launch Week”: after someone apparently asked "why just have one launch?" (they shipped 7 features in 5 days on March 2021)

• They used the cap table to amplify: just like a yc demo day, but leveraging their angels (picked for developer-advocacy), they were part of the line items / schedule for launch

• Rules: they had a fixed timeline and a flexible scope, shipping early and re-launching loudly (many times over, which is an underrated tactic). They also had the engineer who built the feature be the one who wrote the marketing.

With most of the features we ship, the person who implemented the code will be the same person who writes the marketing content. This means that the content usually includes deep technical discussion. —Ant Wilson (CTO)

Apparenly the CEO sill calls each launch week “our next demo day”. During this time, they manage to grow databases up 47% month over month for 18 months. It took them about 1.5 years to earn their first dollar, then in 1 year they ended with c.1M ARR (founder-stated), followed by their Series A in September 2021 (30M led by Coatue, at a time where they had around 50K databases equivalent to 16x in 9 months).

Act 2: Building the “boring” moat

2022 → late 2024 · ~$1M → ~$30M ARR

Growth Lever 3: Cut database setup from 8.5 minutes to 5 seconds and removed any friction to leave (increased user freedom and trust)

Migrating databases is pretty rare, and as history showed for over 20 years the only new winners like Mongo and Firebase won by owning the moment a developer starts a project (day 0).

The founder timed the AWS way of getting to a first row of data and apparently it was around 8.5 minutes, with know-how required / not that easy. Supabase does it in about 5 seconds ad they don’t even advertise that number. Early on they also downplayed Postgres itself just in case the market wanted MySQL.

The second part of this leaver was to invert Firebase’s original sin (making you feel trapped or locked-in after an easy start):

"be there when people are getting started and then make sure that they never want to leave." - Paul

In practice:

• They (kind of) undersold the product (on purpose to earn extra trust): while everyone else kept calling everything “production-ready” they decided to keep the beta for 4 years (with thousands already running on it) and only claimed the “ready” label in April 2024 (earned trust first).

• Made leaving easy (understanding buyer psychology): data sits in standard Postgres so you can pack up and leave whenever (more trust).

• Made big companies wait their turn: they turned down big contracts that came with additional demands (distractions) to build custom features (only built what thousand customers want).

Growth Lever 4: Gave the software for free and only charged for keeping it running

Everything is open source except the platform and billing code, so you can create a database for free. They charge for operating it, keeping it fast, safe and always on. It’s also part of the reason why Amazon could struggle to “kill them” / an important part of their moat.

The usual suspect / fear with open source is that someone (i.e. AWS) copies your product and then sells it cheaper, but in this case Amazon already rents out Postgres through 2 of its own services (RDS + Aurora), so there’s technically nothing new to take. The hard part for them would be running five services as one including database, logins, file storage, live update and functions (the part they charge for)

Giving it aways has kept paying the back in a few ways:

• Recruiting: employee #3 maintained PostgREST and the first 50 hires came from the community (by Series B the team was 20% ex-founders!)

• R&D timing: in February 2023 a stranger emailed a PR adding pgvector (ai-search extension), they merged it, hired the guy and where there first when the AI wave hit.

• Free product strategy: because postgres has thousands of ready-made add-ons their “launches” are often switching one on + writing the guide for it.

Growth Lever 5: Focused marketing on free databases, community and memes

Digging into podcasts I found both the CEO and the head of growth confirm that they don’t spend money on paid ads. The reason is that at their scale paid users tend to arrive with worse intent.

Wilson (CTO) calls himself the Chief Meme Officer and runs it on the simple theory (but effective) that a developer “buys” a backend once (the moment a project starts) and its hard to catch that exact moment.

Here’s how they did it:

Read more

In case you missed it:

• ⚛️ Sierra: $0 to $165M ARR in 26 months

• 💳 Ramp: $0 → $1B+ Revenue in 6 Years

• 🤖 Replit: $2.5M to $250M ARR in 12 Months

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Welcome back!

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:

What you’ll learn in this edition

• Their 0 to 1 story, from a Finnish city to the world.

• How they built a wearable in a way so you’d never take it off

• How they let Stanford prove their claims for them

• How they got 60% of sales to come from users recommending the ring

• Why they sold to institutions in crisis and then let endorsements sell consumers

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

And other growth mechanics, so lets dive in!

From sleep tracker → to the S-1 pile

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

Where we come from

Wearables started on the wrist with Fitbit starting this wave in 2007, the Apple Watch arriving later in 2015, and for a decade or so the category was mostly a screen on your arm “counting” your activity (we all remember the old step counter).

The wrist won (the watch form-factor was already there) for the same reason that when you go to a hospital they clip a sensor to your finger, largely because the arteries sit close to the surface there and the signal is cleaner. Meanwhile sleep went mostly unmeasured, which is interesting considering it is the most information-dense 8 hours of your physiology, and tends to be the 80/20 to fix first.

Where we are today

Roughly 164 million smartwatches shipped in 2025. The ring is now basically its own category owned by Oura with 80%+ market share. Competitor pressure has also started to build up (as expected):

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 → a rumor that comes and goes.

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 likely going next

The fight is moving from tracking your body to “prevention”, and 3 things will likely shape the next few years:

• Platform owners set the price of software at 0: Samsung alredy did it and if Apple ships a ring bundled with an iPhone upsell it’ll build up more competitive pressure.

• Healthcare: the ring already flags signs of sleep apnea and then pushes Medicare members toward an actual doctor (also Eli Lilly invested in the company recently). In Europe healthcare represents c.10% of GDP and Hale (the current CEO) keeps saying out loud that he wants Oura to be “a virtual doctor in your pocket” (Slush, 2025).

• The IPO wave sets the price: Oura filed its S-1 recently and public markets will either read it as a hardware company or as a subscription company, we’ll see (big implications for how much they’d be able to reinvest in product + growth)

Act 1: A Kickstarter In Finland

2013 → 2019 · $0 → first 150K rings

Oura was founded in 2013 in Oulu which is a Finnish city 2 hours below the Arctic Circle. It is also a place that Nokia’s collapse filled with unemployed hardware engineers at the time 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 there and the tissue is uniform so the signal tends to be cleaner + accuracy claims get “easier” to defend.

• Put nothing on the ring → without a screen, LEDs, or an 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.”

These design decisions where important because they were intentionally building a product intended for you to forget you are wearing it (and this way you don’t take it off). So continuous wear tends to lead to continuous data and therefore insights that compound (i.e.cycle prediction needs months of temperature).

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

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

During their Kickstarter the Stanford Research Institute apparently bought 2 rings and ran them against polysomnography which is a sleep lab measurement. The study, published in March 2017 found that the ring was the most accurate wearable for sleep staging. And just like that Oura became the first independently validated wearable on the market.

They then decided to “industrialize” this happy accident with:

• A real science org → with c. 70 people including 30+ PhDs and a 100K person blood pressure study.

• Building a partner ecosystem doing distribution → 800+ partners by end of 2024.

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

“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 claim that c.60% of sales have come from existing users recommending the ring “and that has been the same for all the time” (Slush 2024).

The design goal was a product that produces a personal revelation (you learn you’ve been under-sleeping for x years or you see the night the wine impacted you etc), and that “revelation” is the thing those of us who’ve tried these sorts of wearables have screenshotted and shared with friends on whatsapp before.

The loop became something like this:

• The product produces “tellable” moments → it can flag certain things like you’re getting sick a couple days before you actually feel it. The CEO tells the story of a stranger stopping him in an airport to tell him “Oura got me pregnant” (Slush 2025).

• Celebrites started wearing it (without contracts in the background)→ Bill Gates, Bezos, Bono, Jennifer Aniston, and many others apparently. They didn’t sign their first paid ambassadors (Harry Kane, Declan Rice) until 2026.

• Prince Harry as an “extreme” case → he wore it on official tours in 2018 and the press went hunting for what that black band on his finger was. They apparently never paid him or met him. The whole thing went through his doctor and the note that came back was roughly to not talk about the ring but to talk about how this can help adults stay or become more 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 2 university studies said Oura could spot symptoms early and 65K users donated their data to prove it (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:

Read more

Over 30,000 subscribers turn to us for startup growth strategy deep-dives.

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

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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:

Growth Lever 4: They got users to train the model without noticing.

Ever wondered why Suno gives you 2 songs every time you hit Create?

Well it apparently started because as we’ve discussed at the beginning songs were usually not great, and 2 tries 2x’d the odds you’d like one. And it turned out to be a very good design decision.

The benchmark problem from Lever 1 applies to training too. You can’t improve a model if you can’t score its output, and it’s hard to “score a song”, so with this mechanism they let users do the scoring without telling them:

1. 2 songs is a blind taste test: model serves two outputs it rates as equal and whatever you do next (plays, likes, shares, remixes) becomes a training label. “You want to be very subtle with how you introduce this into your product, we don’t want this to look like a data collection platform.”

2. The signal is scary sensitive: add 3 seconds of artificial lag and users rate the same songs worse. If likes can pick up latency they can pick up anything.

3. Power users show them what to build: songwriters have always hummed nonsense syllables to find a melody before writing words and Suno users kind of reinvented this on their own (typing gibberish into the lyrics box to get the melody right first, then writing lyrics to match). Suno noticed and built the workflow into the product.

Those little votes become the moat, it becomes harder to copy something that has 3 years of users picking song A over song B millions of times a day (teaching a model taste).

Growth Lever 5: They treated the lawsuit as the price of the catalog.

There is no version of this story where Suno stumbled into infringement.

Antonio Rodriguez of Matrix, who backed them before the product existed, told Rolling Stone in early 2024 that a label lawsuit was “the risk we had to underwrite when we invested in the company, because we’re the fat wallet that will get sued right behind these guys.”

And in the same interview:

“Honestly, if we had deals with labels when this company got started, I probably wouldn’t have invested in it. I think that they needed to make this product without the constraints.”

So the investor is basically saying permission would have killed the company, so they priced the punishment instead.

What made the bet survivable was everything in Levers 1-4 aka:

1. Revenue + traction quality kept the burn low: ”revenues mean we burn it very slowly, and that is a real weapon”.

2. The product kept shipping + improving at full speed through the suit: mobile app (July 2024), v4 (November 2024), Covers, Personas, v4.5, the WavTool acquisition, v5 and the Suno Studio DAW (September 2025).

3. And the founder kept the temperature down in public: “It just seems silly to throw a bunch of venture dollars at lawyers instead of sitting down and talking about how you could work together.”

The uncomfortable part and worth being honest about is that the labels’ September 2025 amended complaint alleges Suno got its training data by stream-ripping YouTube through its encryption, a claim shaped by Anthropic’s $1.5B settlement over pirated books. That specific allegation is unresolved and it is the sharpest legal edge left in the case apparently.

Growth Lever 6: They made professionals the proof and amateurs the volume.

Suno’s user base is split in two, on one hand you have power users who sit for hours perfecting a track, on the other you have casuals “soundtracking their life”. The casuals bring the volume and the pros bring the permission.

The machine they built for the pro side:

1. Timbaland, October 2024: was already a heavy user before they ever met (”you can’t fake this stuff”), he became a strategic advisor, paid in equity, no cash. His "Love Again" remix contest put his stems on Suno with a $100K+ prize pool, and 100K people apparently remixed the song.

2. The VIP program: thousands of professional producers and songwriters with names you’d recognize, on-call support, early features, songwriting camps in LA, New York and Tokyo. Their retention is the highest of any cohort.

3. The retention numbers themselves became the pitch: from the deck Billboard obtained ~25% of subscribers still active after 30 days, 78% weekly retention for subscribers, and 350K lapsed users reactivating every week. “Numbers like that are unheard of for tools. People use tools weekly, not daily. Those look more like consumer social applications.”

Then the proof went mainstream when in September last year Xania Monet (an AI-voiced persona built on Suno by poet Telisha “Nikki” Jones!), became the first AI act to chart on Billboard radio airplay and signed a multimillion dollar deal with Hallwood.

By November 19, 2025, Suno announced a $250M Series C at $2.45B, led by Menlo with Nvidia’s venture arm, on $200M of revenue and 1M paid subscribers.

Act 3: The Table Flip

Nov 2025 → today · $200M → $300M+ ARR

On November 25, 2025, Warner Music Group settled. They announced a full partnership with licensed models built on opted-in Warner catalog, artist name-and-likeness controls, new revenue streams for songwriters.

And as part of the deal, Suno acquired Songkick, the concert discovery platform, from Warner. Quite the turn-around!

Growth Lever 7: They turned the settlement into an acquisition.

Everything Suno built from 2023 to 2025 converted into negotiating position in that room:

1. The paywall data walked in the door with them: 2 million people paying $8-24 a month specifically to make music is a behavior no label had ever monetized or even measured. Warner CEO Robert Kyncl said it plainly in the press release: “With Suno rapidly scaling, both in users and monetization, we’ve seized this opportunity to shape models that expand revenue.”

2. They got softer terms than the competition: Udio’s settlements with UMG and Warner apparently locked its users into a walled garden whereas Suno’s deal kept creation and sharing open, moved downloads behind the paid tier (a monetization lever disguised as a concession), and committed to licensed models replacing the current ones in 2026. Asked directly whether Warner took equity, Shulman went “stay tuned, later this year more will come.”

3. Songkick points at the next act: live music data plus fan behavior plus a platform where a quarter of all songs are remixes of other songs. Shulman’s stated vision is your favorite artist releasing a $19.99 interactive album, fans filling in verses, and the artist playing the best fan remix live at MSG.

And of course the market’s verdict came fast with ARR going from $200M at the Series C to $300M by February 25, 2026, with paid subscribers doubling from 1M to 2M in roughly 90 days. And in June 2026, a $400M Series D at $5.4B led by Bond, with unnamed “leading artists, songwriters and producers” in the round.

Lets see where they go next!

5 Growth Lessons

My favorite take-aways:

1. Charged when there was no other way to measure quality: because music is harder to benchmark the day-one paywall hits / analytics became the score the model updates had to “beat”.

2. Rented distribution 3 times: discord for free UI, Copilot for Microsoft’s reach, then dropped them when their own thin web app won 90% of traffic in 5 days.

3. Shipped the shareable unit: whole fuzzy songs beat crisp clips because they realized a finished song is what people actually want to forward (growth loop).

4. Turned clicks into training data: two songs per Create built 3 years of preference data (moat).

5. Priced the lawsuit instead of asking permission: their first investor underwrote it before the product existed, and 17 months later the plaintiff chose a partnership.

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. 🙏

📚 Bibliography

Founder and operator interviews

• 20VC, Mikey Shulman, “The Future of Music, What is Gonna Happen?”

• Billboard On The Record, Mikey Shulman — March 11, 2026

• No Priors Ep. 64, Mikey Shulman with Sarah Guo and Elad Gil

• indie.vc podcast, “Riding an AI Rocketship with Mikey Shulman”

• Inspired with Alexa von Tobel, Mikey Shulman

• Build with Japan (Coral Capital), Mikey Shulman — December 11, 2025

• Sequoia Training Data Ep. 85, Mikey Shulman — June 2026

• Behind the Craft (Peter Yang), Mikey Shulman — January 26, 2025

• Redpoint Unsupervised Learning, Mikey Shulman — 2024

Suno primary sources

• Series B announcement, “Suno has raised $125 million” — May 21, 2024

• Series C announcement — November 19, 2025

• WMG partnership announcement, “A new chapter in music creation”

• Series D announcement, “The Next Chapter for Suno” — June 3, 2026

• Suno Studio launch release — September 25, 2025

• v5.5 announcement — March 2026

Coverage and analysis

• Rolling Stone: “A ChatGPT for Music Is Here. Inside Suno, the Startup Changing Everything”

• Rolling Stone: “Soul of the Machine” follow-up — March 2024

• Rolling Stone: Suno v4 — November 2024

• Rolling Stone: The Velvet Sundown investigation — July 2025

• Rolling Stone: Suno-Warner settlement — November 2025

• RIAA: press release and complaint — June 24, 2024 · complaint PDF

• Billboard: Suno pitch deck reporting — November 2025

• Billboard: 2M paid subscribers, $300M ARR — February 2026

• TechCrunch: Series C at $2.45B on $200M revenue — November 19, 2025

• TechCrunch: Warner settles with Suno — November 25, 2025

• TechCrunch: Suno hits 2M paid subscribers and $300M ARR — February 27, 2026

• TechCrunch: Microsoft Copilot gets Suno integration — December 19, 2023

• Music Business Worldwide: Series B at $500M valuation — May 2024

• Music Business Worldwide: v5 launch + stream-ripping allegations

• Music Business Worldwide: Timbaland’s Stage Zero and TaTa — June 2025

• Music Business Worldwide: Series D reports + distributor blocks — May 2026

• Music Business Worldwide: 2M subscribers, $300M ARR — February 2026

• Forbes: 2M subscribers as backlash grows — February 26, 2026

• Forbes: Suno eyes $5B valuation — May 4, 2026

• Warner Music Group press release — November 25, 2025

• Variety: Series D at $5.4B — June 2026

• Fortune: Suno and the future of music — June 4, 2026

• Hollywood Reporter: Series D coverage — June 2026

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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:

Read more

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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.

The “easy” 2023 move (the we saw many SaaS incumbents execute on), was to add AI features. n8n already had an “OpenAI node” you could call over the REST API, like everyone else, but Jan did the math out loud (quote above) and refused that path.

Instead of one node that calls a model they started letting people assemble real agents:

• Swap in any model

• Attach a vector store

• Add memory

• Chain multiple agents together

• Wire all of it to the hundreds of integrations n8n already had

The plumbing AI needs (connect to data, transform it, send it somewhere, trigger something) was the plumbing they had been building for 4 years. From decision to release was about 6 weeks done by Jan plus “one and a half” engineers, a tiger team on purpose (“smaller teams are actually much more helpful”).

Jan, who calls himself “not the best coder in the world,” went back to full-time coding and called it one of the most joyful stretches in years. The result was revenue quadrupled in the 8 months.

Apparently the launch was “underwhelming,” and the community Jan had spent 4 years building got a bit nervous, “concerned that the product they loved was being replaced instead of enhanced.” It took a year of listening and steady building to win them back but in his words: “It’s almost like we launched again.”

Growth Lever 5: They refused to bet on any model, so every launch helps them.

n8n connects to every model from every provider and bets on none of them, here’s why that’s a growth lever:

1. The labs do their R&D for them: burning billions to ship the next model, makes n8n better with every upgrade.

2. Neutrality is usually fatal (except here): supporting every model means paying for every model’s inference which sinks most companies but n8n sidesteps the bill entirely because customers plug in their own API keys, so n8n keeps the subscription and doesn’t pay for a token (that’s how they hold 75%+ gross margins).

3. Swapping models is easy but leaving n8n isn't so easy: a cheaper or better model comes out, the customer changes one setting and every workflow they built keeps working. So they switch models whenever they want, and n8n is the thing that stays underneath all of it. So while the labs fight each other on price n8n keeps the subscription either way.

4. Fragmentation is a tailwind: because more models, more providers, more tools means more stitching, and stitching is the job. Over 80% of workflows on the platform already run AI agents and the messier AI gets, the more you need the neutral layer holding it together.

Growth Lever 6: They deleted their lead-generation goal and let the community pull in the enterprise.

n8n’s marketing team apparently had always been measured on leads but the problem was that the team was great at adoption but always fell short on leads. So they kept pulling effort out of community growth to hit a lead target, which killed the long-term compounding. They decided to remove the goal entirely.

“It doesn’t matter for me if we have 10 or 20 million less ARR end of this year. What matters is we keep to the market.” (Jan, Slush fireside)

The logic underneath is a sequencing claim about how you actually reach the enterprise:

“Through the smallest builders, we get into the largest organizations. If you focus very heavily on enterprise only, you can never go downwards again. Google started the same way. Microsoft as well.”

So they fed the top of the funnel with events, content, and community programs. The flywheel that did the real work was creators, as the Felicis profile puts it, community members started making YouTube and LinkedIn content about building AI on n8n, and:

“the more content people create, the more other people want to create content, the better it gets ranked, and then everything started to explode.”

Act 3: The Excel of AI

Jan 2025 → today · 10x revenue, $270M → $5.2B valuation

In 2025 users grew 6x and revenue 10x in 1 year. Jan told Sequoia the company added 4 times the revenue in 8 months that the first 5 or 6 years had built. Sacra estimated ARR around $40M by mid-year, and it has since crossed €100M+ post-SAP.

The money followed. A €55M Series B (Highland Europe, ~€270M), then a $180M Series C (Accel, $2.5B, with Nvidia’s venture arm joining), then SAP at $5.2B.

“What was Excel 15 years ago for spreadsheets, n8n should become the same for AI. Empowering everybody to use AI to the fullest, from private use cases, to SMBs, to the largest enterprises, up to government organizations.” (Jan, Accel)

Growth Lever 7: The community pre-built 10K workflows, became the onboarding + top of funnel.

They also built a library of 10,245 ready-made workflows (sourced from the community of course) to deal with the “blank canvas” problem, pretty cool how each one is importable in one click.

Brought several advantages / fuelled their growth in a few ways:

1. It gets a new user to a win in minutes: search the thing you want, import a working version, plug in your keys, done.

2. Every template is a page that ranks on Google: 10K+ each one targeting a specific search like “n8n Notion sync” or “build an AI agent that does X.” So the community writes them, n8n’s search footprint grows without n8n paying for content. Whole third-party sites now exist just to re-host n8n templates for the traffic.

3. They pay people to feed the library: they run a creator and affiliate program so contributors get featured and earn for submitting templates. The library grows faster than any in-house team could write it, and the people building templates become the people selling n8n for them.

4. It caught the AI-agent search wave at the moment of intent: when everyone started searching “how do I build an agent that does X” they already had a template for X.

Also interesting, today over 80% of workflows on the platform now run AI agents.

Growth Lever 8: They let companies keep it fully in-house, which won the buyers who won't touch the cloud (including SAP)

Because n8n is fair-code and self-hostable a customer can run the thing on its own servers. For the most regulated buyers that’s a critical purchasing factor:

• Pure SaaS: the vendor can switch you off and your data lives somewhere you don’t control.

• Self-hosted n8n: you own it, you run it, nobody outside can touch it.

Jan calls that control “the difference between running AI in production and not running it at all.” Its a growth lever because it wins customers cloud-only rival can’t:

• Ministries of defense, the United Nations, banks, Vodafone, Delivery Hero, Microsoft etc.

• The biggest budgets with the lowest tolerance for risk.

SAP made the same call, at scale:

• It’s the back office for 99 of the 100 biggest companies on earth, all needing AI they can run on mission-critical paths with full control and audit trails.

• A cloud-only AI tool can’t sit there.

• So SAP is embedding n8n inside Joule Studio (GA Q3 2026) and invested at $5.2B, around €60M for just under 1.3%.

For n8n this is enterprise distribution (SAP hands it to the world’s largest companies), and n8n hands SAP an orchestration layer it didn’t want to build.

Lets see where the company goes next, excited to see them grow (from Europe!).

5 Growth Lessons

My favorite take-aways:

1. Built for the pain they lived: it was born out of one VFX engineer’s frustration (”smart people, always reliant on me”) + going deep + iterating fast.

2. Let the product build AI: adding an AI feature was the 30% move but becoming the place people build AI was the 10x move, they rebuilt for it in 6 weeks.

3. Stayed neutral on the bet everyone else was making: nobody knows which model wins so they bet on none of them and every launch makes it stronger now.

4. Deleted the metric that was eating or distracting them from their best channel: killed the lead-gen target and ate probably a bunch of ARR (hard trade-off) to keep feeding the community which was the thing actually compounding.

5. Sold ownership: “You own it and nobody can switch it off” was a big accelerant.

n8n now joins the $100M ARR Breakout Club, see the full Startup Riders Growth Index of the fastest-growing private tech companies past $100M ARR.

SR30 Growth Index

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. 🙏

📚 Bibliography

• Sequoia Training Data, Jan Oberhauser with George Robson & Pat Grady

• Accel Spotlight On, “Building the Excel of AI,” Jan Oberhauser, Oct 2025 (link)

• EU-Startups Podcast, Jan Oberhauser, Mar 2026 (link)

• Slush fireside, Jan Oberhauser with Ben Fletcher (Accel), Nov 2025

• n8n, “We just raised $180m” (Series C), Oct 9 2025 (link)

• n8n, “Announcing SAP’s strategic investment,” May 12 2026 (link)

• n8n GitHub repository (name origin, stars) (link)

• Felicis, “Becoming Essential to the AI Revolution,” Apr 7 2026 (link)

• Bloomberg, “SAP Invests in AI Automation Startup n8n at $5.2 Billion Value,” (link)

• Tech.eu, “n8n’s valuation doubles to $5.2bn,” May 2026 (link)

• ARR Club, “n8n ARR hit $100M, valued at $5.2B,” May 13 2026 (link)

• TechCrunch, “Fair-code pioneer n8n raises $60M,” Mar 24 2025 (link)

• TechCrunch, n8n raises $12M Series A, Apr 26 2021 (link)

• TechCrunch, n8n raises seed from Sequoia, Mar 13 2020 (link)

• trendingtopics.eu, SAP deal mechanics, May 2026 (link)

• Sacra, n8n revenue, valuation & funding (link)

• The Hacker News, npm supply-chain attack on n8n, Jan 2026 (link)

• The Hacker News, n8n webhooks abused for phishing, Apr 2026 (link)

• The Hacker News, critical n8n RCE flaws (Pillar Security), Mar 2026 (link)

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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”:

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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:

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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.

Growth Lever 6: Stacked consumption pricing on top of subscriptions

By early 2025 the math on Replit’s pricing was breaking because the base subscription was ~$20/mo but Agent users were running heavy AI workloads.

Most SaaS companies in this spot raise the subscription tier but they decided to keep the $20 subscription as the front door and stack usage-based pricing on top of it.

The pricing rebuild:

• Kept the entry subscription unchanged: $20/mo Replit Core (billed annually) stayed the front door.

• Bundled a credit allotment with the subscription: $25 of monthly credits, roughly 100 Agent checkpoints. Light users never run out while heavy users blow through it.

• Charged pay-as-you-go for everything above the allotment: Compute-heavy surfaces (AI model calls, deployments, autoscaling) got metered. Editor, public/private apps, and basic collaboration stayed unmetered.

• Repriced Teams around credits per seat: $40 of monthly credits per user, centralized billing, pay-as-you-go on top. Enterprise buyers compared the per-seat number to competitors and saw a manageable one, heavy-usage teams paid more total.

Growth Lever 7: Turned the boring infrastructure into the thing enterprise buyers paid for

They spent 9 years building the unglamorous parts that made them enterprise ready, giving them a good head start vs competitors.

4 things that made enterprise adoption work:

• They’d already passed the enterprise security audit: normally audits can take 6-12 months while they had passed this years before the landmine.

• Buyable through existing cloud contracts: Enterprises don’t sign new vendor contracts every time (can tend to buy through others, like Microsoft or Google Cloud which they’re likely already paying). The July 2025 Microsoft partnership put them on the Azure Marketplace (the App Store for enterprise software) and Google Cloud added them too.

• Real production infrastructure: Live apps need a real database, servers that scale with traffic, and uptime guarantees and they had been running all of it for paying developers since 2021.

• 1 vendor instead of 5: A Zillow marketer went from prompt to live app inside the product, whereas the alternative was v0 for the design, Cursor for the code, Supabase for the database, Vercel for hosting, a separate security review for each etc.

By early 2026, 85% of Fortune 500 companies had employees on Replit.

Growth Lever 8: Turned users into the growth engine

A few growth levers they pulled to turn non-technical users into the growth engine:

• Built a Bounties marketplace (then sunset it): From 2022-25 or so users posted coding jobs and other users built them. The loop worked while the buyer was peer-2-peer but then they cut it and partnered with Contra (growth mechanics have seasons).

• DevRel rebuilt around teaching: usually companies in this space would hire engineers to convince other engineers but in this case they hired teachers to convince non-engineers. A few outputs include the 100 Days of Code, tutorials, 1:1 calls with first-time builders etc.

• Surfaced unexpected builders as the proof: A Korean physical therapist built a 3D fascia tracking app, a pool maintenance founder built vertical SaaS for his industry, etc you get the idea. And they used these stories as free proof to every other non-technical buyer that someone like them had already done it.

• Made every app forkable by default: public apps could be cloned with one click and customized.

Growth Lever 9: Turned model labs into a distribution partner

In October 2025 the a16z AI Spending Report ranked Replit #3 across all AI applications startups paid for, behind only OpenAI and Anthropic, ahead of every other developer tool.

Most application companies running on third-party models try to hide the dependency (there’s been a tendency by some investors to read it as “weakness”), but they did the opposite. Some of the levers they pulled on:

• Pitted Anthropic, OpenAI, and Google against each other inside the product: Replit Agent has run Claude for agentic reasoning, GPT-4 for code review, and Gemini for cost-sensitive operations. The founder said publicly that they “pit multiple LLMs against each other.” And each lab got benchmarked in real workloads, giving replit early access to stay in the rotation.

• Shipped product upgrades on the labs’ launch days: for example Replit Agent v2 launched in February 2025 announced as “in partnership with Anthropic’s Claude 3.7 Sonnet launch”, which gave them a distribution beat on publications covering the Claude release.

• Got placed on every cloud procurement marketplace: Google Cloud partnership (Vertex AI + Cloud Run, 100K+ apps deployed) and Microsoft Azure partnership (July 2025, Azure Marketplace listing). They had a commercial reason to push Replit through their enterprise channels because Replit was proof their AI infrastructure worked.

5 Growth Lessons

My favorite take-aways:

1. Bought their buyers 10 years before those buyers had money: the master plan slide is pretty mind blowing because it took courage, execution quality, persistence on that plan, and timing luck (which they almost ran out of).

2. Made the pivot impossible to walk back: going on the record with “we don’t care about professional coders anymore” cleared the roadmap and ended the debate on whether to keep building pro-dev features (execution speed).

3. Kept the front door unchanged and metered the back end: kept the $20 entry, kept new-user conversion flat, and stacked metered consumption on top for compute-heavy work (inflection point).

4. Spent 9 years on infrastructure that looked uneconomic at the time: browser runtime, cloud containers, multi-language support, SOC 2 compliance, cloud marketplace listings etc helped win enterprise deals when the time came.

5. Made the supplier dependency public: shipped Agent v2 on Anthropic’s Claude 3.7 Sonnet launch day, ran 3 labs against each other in production, and turned supplier press cycle into a Replit press cycle.

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. 🙏

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📚 Bibliography

Founder voice, primary sources:

• My First Million ft. Amjad Masad (Sam Parr + Shaan Puri), May 2026 and December 2024

• Sequoia Capital Training Data ft. Amjad Masad, 2025

• Lenny’s Podcast ft. Amjad Masad, November 2024

• 20VC ft. Amjad Masad (Harry Stebbings), April 2026

• Masters of Scale ft. Amjad Masad (Reid Hoffman), January 2026

• YC Podcast ft. Amjad Masad, April 2026 and July 2025

• The Joe Rogan Experience ft. Amjad Masad, late 2025

• Social Radar Podcast ft. Amjad and Haya Masad, 2025

• Step Podcast ft. Amjad Masad, 2025

• Peter Diamandis ft. Amjad Masad, September 2025

• Silicon Valley Girl ft. Amjad Masad, August 2025

• Coder Kids Podcast ft. Amjad Masad, February 2021

• David Lee Podcast ft. Amjad Masad, 2021

• South Park Commons ft. Amjad Masad, April 2026

• Chris Vasquez Podcast ft. Amjad Masad, 2025

• First Round Capital ft. Amjad Masad, 2024

• Berkeley University talk ft. Amjad Masad, 2023

• Replit Pod ft. CTO Luis Hector Chavez, April 2026

• Nooks ft. Founding SDR Leader Jacob Fernow, April 2026

• Harlem Capital ft. Founding Agent PM Aman Mathur, May 2026

• Scaling DevTools ft. DevRel Lead Matt Palmer, August 2025

• Google ft. Head of AI Michele Catasta, April 2026

• Replit Team Documentary, March 2026

Press and analyst coverage:

• Forbes “Meet The $9 Billion AI Company Reimagining Vibe Coding”

• Semafor “We don’t care about professional coders anymore”

• TechCrunch “Replit snags $9B valuation 6 months after hitting $3B”,

• TechCrunch “After nine years of grinding, Replit finally found its market”

• Sacra company profile for Replit

• Not Boring “Replit: Remix the Internet”

• a16z + Mercury “The AI Application Spending Report”

• Contrary Research Replit business breakdown

• CB Insights, Tracxn, Crunchbase funding history

Replit’s own communications:

• Replit Blog: “Rejected Then Recruited: Our Journey into Y Combinator”

• Replit Blog: “Introducing Replit Agent 4”, March 11, 2026

• “Georgian Leads $400M Series D Investment in Replit”

• Replit Blog: “What’s changed from Replit Agent 3 to Agent 4”, March 19, 2026

• Replit Series C announcement (Prysm-led), September 10, 2025

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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

As you can see there’s no personal paid tier and if you want unlimited history, integrations, MCP access, or the better thinking models, your only path is the team plan. This was a recent change as far as I can tell.

The exec champion who used to pay $18/month for themselves now has two options, either stay on free with a 30-day note window, or bring teammates (growth loop).

The price card got there in 3 moves over 22 months (free → cheap → asymmetric → personal plan deleted):

1. May 2024: Launch with one plan free for the first 25 meetings, then $10/month. Same price through the Series A.

2. May 2025 (Series B and Granola 2.0): Add Business at $14/user and raise Individual from $10 to $18. Note the asymmetry where the team plan is now $4 cheaper than the personal plan. They kept that structure for 10 months or so. The exec champion already paying $18 sees Business at $14 and does the math, which is a pull to bring on more teammates (cheaper than staying solo).

3. March 2026 (post-Series C): Kill the Individual plan entirely, 3 tiers only: free, Business, Enterprise. Long-term Individual users get migrated to Basic with a 30-day history window unless they upgrade to a team subscription.

Granola already won the individual through Levers 2 and 3 and the bottleneck was acquiring seat number two+. So they priced seat two below seat one, then deleted seat one when the company was infrastructure-ready and individual revenue was a rounding error against enterprise contracts.

Growth Lever 6: Shipping Granola 2.0 to turn the notepad into a workspace

In May 2025, Granola raised a $43M Series B at a $250M valuation, led by NFDG (Nat Friedman + Daniel Gross), and the same week, they shipped Granola 2.0.

Up until that point, Granola was a personal tool, but now they shifted their positioning to become a workspace (“a second brain for your team”). What shipped:

• Shared Team Folders: Sales calls, customer feedback, hiring loops, weekly syncs which anyone on the team can access (even without an account)

• Chat with Folders: Query an entire folder of meetings using best-in-class reasoning models, with citations to specific meetings and transcripts

• Enterprise Collaboration: Business and Enterprise users can explore any public folder inside their domain. Useful for competitive intel, customer success, onboarding new hires etc

Stephenson summarized the user-facing thesis:

“With every call in one place, sales leaders can ask ‘Why are we losing deals this quarter?,’ product managers can investigate ‘Which UX issues come up most often?,’ and recruiters can understand ‘Where do our interviews keep stalling?,’ all answered instantly with source-linked citations.”

Kind of reminds me of a cross-functional + ai-native Gong.io.

This new iteration sold you the unified record of every conversation your company has ever had and by Series B, they were processing millions of minutes of conversation per day (remember they don’t store audio, just transcripts, which is a smart product trade-off discussion in itself), with 19 employees.

The angel cap table (or: how the Superhuman strategy compounded into the Series B)

NFDG led the series B but the angel roster on that round is a who’s-who of distribution-rich operators:

What’s cool is that these people also have audiences, and they recommend tools.

So we see the Superhuman strategy going one level higher here where you have VCs as the initial wedge, founders as spreaders, leadership teams converting, and Angels pre-loaded with audiences.

As a little example of the power of leveraging your cap-table, in September 2025 they shipped Recipes (prompts paired with the context of your conversations, written by people who know what great output looks like).

And the launch contributors were:

• : “Write PRD”

• Matt Mochary: “Coach Me”

• Ridd: design feedback Recipes

• Peter Yang: “Gather product feedback”

• Nikita Bier: consumer growth Recipes

A second-order effect of 2.0 that the founder talks about often is that the viral loop changed. Before 2.0, virality was social, but after, virality was structural:

“So often I’ll be talking to a user and I’ll ask how they found out about Granola. And they’ll say, I was in this meeting, and 30 seconds after the meeting, these beautiful notes just popped up in Slack and I was like, how did you do that? And then they dig and they find out it’s Granola.”

Growth Lever 7: Anchoring the habit to the calendar to never be forgotten

By Series A Granola reported a week-1 retention of 70%+, which is exceptional for consumer AI, and by mid-2025, millions of minutes of transcript were flowing through the product’s data layer every day.

The reason this matters is that meetings are calendar events and calendar events repeat:

“The beautiful thing about meetings is that they’re on a calendar. There’s a very specific moment where we know you’re going to do a meeting. The combination that Granola is useful and we can send notifications at the right moment, that’s what leads to retention.”

Most consumer AI products lose users to forgetfulness due to the classic “Oh, I just forgot to use it.”, but Granola fixed it by anchoring to a habit that already existed in the user’s calendar (and by the way they do this super smoothly, I like the detail of the time running down on the notification, makes me want to “run to click on it”).

Growth Lever 8: Building from London to win Silicon Valley

Granola is headquartered in London, but Pedregal is American (and Stephenson sounds pretty British to me). They built the company in London for personal reasons (Pedregal’s wife is English, and both have families there). But they made a decision early on to frame Granola as a Silicon Valley company that happens to be in London.

All copy is American English, all early users were US-based founders and VCs and the first office expansion (Q1 2026) was San Francisco, with 5 sales reps shipped over to start it. The Series B and Series C investor lists are entirely US-based and the product launches time to US news cycles.

“If you’re in London, it’s too easy to think about the UK and Europe. My general view is that in this AI space, you need to be competitive in the US. Otherwise someone will win the US, and then you’re going to have to fight them in Europe. There’s no reason why you can’t go after the US market from here.”

The notetaker wave: where Granola sits in the race

Act 3: The Context Layer (February 2026 → today)

Days before the Series C, Granola encrypted their local cache (the file on your Mac where meeting notes live).

It was a security upgrade but it broke something users had built on top of (their own AI agents wired up to Claude Desktop and Cursor, reading directly from that file).

When the cache went encrypted, those agents stopped working and users got mad on the internet, including an a16z partner who posted that the app now had “zero value” in an agent-managed world.

The founder apologized and promised proper APIs were coming and just days later, they shipped them.

Growth Lever 9: Shipping MCP to become the memory layer for AI agents

When Granola launched their MCP server they gave away their data layer. Now you could connect it to Claude, ChatGPT, Cursor, Lovable, Replit, etc and any of those tools could pull from your Granola meetings on demand.

Every AI agent that wants to be useful at work needs to know what was said in your meetings and Granola became the only company that could serve that context.

“Conversation transcripts are the richest source of context for what’s happening across your company. When paired with powerful AI models, they can unlock workflows that wouldn’t have been possible before.”

This means that meetings are the densest unstructured data your company produces, which is the trojan horse the founders had been talking about since 2023.

Growth Lever 10: Opening the data layer with APIs to flip the price ceiling

Three weeks after MCP, they announced the Series C and shipped 3 more pieces:

• Personal API: for individual users on Business and Enterprise plans, programmatic access to your own meeting context

• Enterprise API: for admins, programmatic access to the whole team’s context, with SSO, SCIM, and access controls

• Spaces: team workspaces with granular permissions

Spaces apparently took about a year to build:

“We’ve made the product secure enough and compliant enough. The fact that we were able to work with DoorDash, that’s testament to all the work the team’s done.”

The APIs change the price ceiling and as notes commoditize, the value moves from the notes to the structured context layer that powers AI agents. And while you can’t charge $200/user/month for better notes, you can charge that for the layer.

Similar playbook Perplexity ran with Computer in February 2026, which is an economic flip wrapped in a product launch.

But the real question in all our minds is still, is Granola defensible against foundation models?

You’ve heard the bear case a million times. Transcription is a commodity, summarization is a free model call, ChatGPT and Claude both shipped persistent memory, distribution is uphill, the category has failed, Granola is a feature not a company etc, etc. Heard it, moving on.

Best thing about being a VC is that its made for optimists because you gotta think about what could go right (otherwise you’d never invest in anything in a field surrounded by constant startup mortality and outliers).

So here’s my bull case:

• Beautiful products own mind share, and mind share is the cheapest CAC there is: Granola’s whole product is kind of “a feeling”, you see it because people don’t say “the AI notetaker”, they say “Granola”, it’s already a verb in tech.

• Owning the workflow could beat owning the model: The valuable position is “we own the daily ritual.” The same way Notion isn’t a better word processor, they just own where work happens. Granola aims to own the moment a meeting starts (a workflow you do 5x+ a day).

• Cross-platform persistence is structurally different from any platform’s native feature: Microsoft can win Teams meetings but they can’t make your Zoom call show up in your Microsoft notes (for now, afaik).

• Meeting context could be the highest-quality memory substrate: OpenAI can ship “memory” but they can’t ship a year of your team’s actual conversations (yet).

• The “wrapper” critique aged badly: People said the same thing about Cursor (wrapper around VS Code + Claude), Perplexity (wrapper around search + LLMs), Harvey (wrapper around GPT-4 + legal docs), all at $100M+ ARR…

• The whole space is still in exploration mode (not exploitation mode): I like this frame from the founder. Nobody knows yet what the persistent memory substrate for AI agents looks like.

The net net is that if the meeting context becomes the dominant memory substrate for AI agents over the next 24 months, whoever owns ingestion of that substrate has a chance at a (very) big outcome.

We’ll see!

5 Growth Lessons You Can Steal

My biggest takeaways:

1. Stayed in stealth long (for our times): Granola spent a year onboarding 150 users one by one before they launched and used that year to scrap their core interaction pattern and cut 50% of features.

2. Picked their wedge by signal density, and kept compounding the status ladder: Granola targeted VCs first knowing the TAM was tiny because each VC user produces an outsized amount of evangelism, network spillover, and product feedback.

3. Easy trade-offs, hard life. Hard trade-offs, easy life: Granola took a bunch of hard product trade-off decisions (i.e. walked away from the meeting bot), which was the entire growth engine of their category at the time. Doing it would have locked them out of the most valuable meetings, and the competitor’s bot ended up being Granola’s growth loop anyway.

4. Ran frontier models even when the unit economics didn’t work yet: They ran on the most expensive available LLMs since 2023 because they bet costs would collapse before competitors caught up on quality, and they were right.

5. Used pricing strategically: Once an exec champion is using your product, the cheapest path to seven more users is to make the team plan obviously cheaper than the personal plan they’re already paying for.

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. 🙏

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📚 Bibliography

Founder voice, primary sources:

1. Behind the Craft (Peter Yang), The 5 Hidden Rules Behind Successful AI Products: Chris Pedregal (Granola), Jan 19, 2025

2. Every / AI & I (Dan Shipper), The Secret to Building Sticky AI Products, Dec 2024

3. Invest Like the Best (Patrick O’Shaughnessy), Chris Pedregal: Building Granola, EP.412, Feb 25, 2025

4. The MAD Podcast (Matt Turck, FirstMark), How to Build a Beloved AI Product: Granola CEO Chris Pedregal, Aug 21, 2025

5. Generative Now (Lightspeed, Mike Mignano), Chris Pedregal + Sam Stephenson: Making Meetings More Effective with Granola, May 15, 2025

6. Lightspeed (written), Generative London: How to Win AI’s App Layer, with Granola, May 15, 2025

7. The Cognitive Revolution (Nathan Labenz), Sam Stephenson on Granola’s design philosophy, April 2026

8. South Park Commons, Granola, March 2026

9. Pathfounders, Sam Stephenson on the Series C, Feb 2026

10. Scaling Europe, Granola, March 2026

11. EO Studio, Chris Pedregal on Granola, January 2026

12. Human in the Loop, Granola, September 2025

13. Dive Club (Ridd), Sam Stephenson: The journey of designing an AI product, Mar 28, 2025

14. Zapier, Granola, October 2025

15. Product Talk, Chris Pedregal, August 2025

16. Speed Podcast, Sam Stephenson: From Designer to $250M Founder in Two Years, Sep 11, 2025

Long-form journalism and analysis:

21. The Information, How Granola, and AI Note Taking, Grabbed Silicon Valley’s Attention, Jul 12, 2025

22. Upstarts Media (Alex Konrad), Can Granola’s AI meeting notes eat bigger apps for breakfast?, Apr 2, 2025

23. Upstarts Media (Alex Konrad), ‘I Had To Close My Laptop’: Granola Adds Coaching, Recipes To Its AI Meeting Notes, Sep 30, 2025

24. Fast Company, This AI note-taking startup thinks it’s building the ‘steering wheel’ for chatbots, Feb 25, 2026

25. Reworked, Granola Lands $125M to Turn Meetings Into AI Memory, Mar 25, 2026

26. Over the Anthill, Granola: The AI Note-Taker with Big Plans, Jun 22, 2025

Funding and milestone announcements:

28. TechCrunch, Granola raises $125M, hits $1.5B valuation as it expands from meeting notetaker to enterprise AI app, Mar 25, 2026

29. Bloomberg, AI Notetaker Granola Hits $1.5 Billion Value in $125 Million Funding, Mar 25, 2026

30. Sifted, AI notetaking startup Granola hits unicorn status, Mar 25, 2026

31. SiliconANGLE, Granola raises $125M at $1.5B valuation for its AI note-taking app, Mar 25, 2026

32. BusinessWire, Granola Launches AI Workspace for Teams and Raises $43M Series B, May 14, 2025

33. TechCrunch, Granola debuts an AI notepad for meetings, May 22, 2024

Granola’s own blog:

36. Granola raises $125M to put your company’s context to work, Mar 25, 2026

37. Introducing Granola MCP, Feb 4, 2026

38. Granola integrations: complete guide to connecting your meeting tools, Mar 20, 2026

39. Introducing Recipes, in the all-new Granola Chat, Sep 30, 2025

40. Granola raises $20M (Series A blog), Oct 2024. Has the 10-week retention stat.

41. Granola Updates page. Feature changelog and timeline source of truth.

Analyst and market data:

42. Sacra, Granola company profile (the Superhuman strategy frame, the 57% leadership user breakdown)

43. Sacra, Granola vs Zoom (the $0.25/min → $0.02/min transcription cost-collapse data)

44. a16z, The Top 100 Gen AI Consumer Apps, 6th Edition (Olivia Moore + Anish Acharya), Mar 9, 2026

45. Crunchbase, Granola company profile

Notetaker wave context:

50. Halisi, Fireflies Hits $1 Billion Valuation, Jun 2025

51. Otter.ai blog/BusinessWire, $100M ARR Milestone, Dec 22, 2025

52. TechCrunch, AI notetaker Fathom raises $17M, Sep 19, 2024

53. TechCrunch, Read AI raises $50M, Oct 28, 2024

54. TechCrunch, Plaud launches a new AI pin and a desktop meeting notetaker, Jan 4, 2026

If you enjoyed this, you might like:

→ Perplexity’s Growth Playbook: 0 → $450M ARR in 3.5 years

→ 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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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.