In case you missed it:

• 🧠 Cognition: $0 to $1B ARR in under 3 years

• 🎆 Fireworks AI: $0 to $1B ARR in under 4 years

• 🤗 Hugging Face: $0 to a $12.9B exit in 10 years

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Hello there!

This week I'm diving into Linear, a tool software teams use to plan and track their work, which says it passed $100M in yearly recurring revenue earlier in 2026 (7 years after its founding):

In a nutshell:

• Product: started as an issue tracker which is the shared list of bugs + tasks and projects dev team works from (something they open many times a day). For the last 20 years the default has been Atlassian's Jira (painful). 40K+ companies pay for it today including OpenAI, Ramp, Coinbase, Figma etc.

• Money: free for a team of any size up to 250 active tasks then $10 or $16 per person per month when paid yearly, with custom deals for big companies (+ since 2026 a usage charge for work done by Linear's own AI agent). Profitable since 2021 with about 120 people.

8 growth mechanics I found in this drop:

1. A waitlist used as a filter

2. Charging from day one

3. A changelog instead of ads

4. Built for the people doing the work

5. Free until the work piles up

6. Deployed sales teams only where they already had fans

7. Easy to try next to the old tool

8. AI agents as free users

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Zero To One

• Who: 3 Finnish friends fed up with the tool their company tracked its work in:

  • Karri Saarinen (CEO): is a designer who co-founded a bookmarking app called Kippt that got into Y Combinator in 2012 (first Finnish one I believe), then became Coinbase's founding designer (12 people to 100+) and a designer at Airbnb.

  • Jori Lallo (CPO): was Karri's Kippt co-founder and also joined Coinbase as an early engineer when it bought Kippt in 2014.

  • Tuomas Artman (CTO): spent 5 years at Uber where the mobile engineering team went from about 15 people to 400+.

• Where they started: at Airbnb Karri disliked Jira so much that he wrote a browser add-on to restyle it and about 100 colleagues installed it. Through 2018 the 3 met every Wednesday at a bar for about a year asking colleagues what they hated about their tools (Karri and Tuomas still had their day jobs).

• Market wedge: the number one complaint was that these tools are slow. The pain the founders felt most was that they are built for managers who buy them and not for people doing the work. They bet on a tracker that felt more instant and that engineers would potentially love using. They also kept it narrow on purpose for eng + designers at small startups, and one job only (tracking tasks and bugs).

• The MVP: went full time in March 2019 and had a prototype in about 1 month which they gave to 10 friends (one friend's whole 10-person team switched). Tuomas says they spent about half a year on the plumbing to make clicks feel fast.

  

• First users: after the 10 friends everyone came off a waitlist and they announced the company in April 2019 with a blog post before the product was open. Stephanie from Sequoia got in touch 2 days later and led a $4.2M seed.

Growth Mechanics

Lever 1: Only let in people the product could retain (then widened 1 group at a time)

What happened: 10k+ people joined the waitlist, product stayed invite-only for 1 year. Everyone who signed up answered a survey and then the team would use answers to decide who got in. First it was only founders of small startups whose complaint about their current tool was something Linear already fixed. When that group stopped asking for new things they let in a slightly wider group and did it again, until they opened to everyone in June 2020.

The details:

• The survey asked 6 things: company size, role, current tool, where the team keeps its code, why they wanted Linear and their pet peeve with what they used. Anyone whose code wasn't on GitHub was skipped because that was the only one Linear connected to, keeping problem space narrower.

• They picked founders first because founders will get on a call and will forgive missing features if they like where it's going. Tuomas says this gave them a first group that didn't leave.

• The pace started at about 10 people a week (more later), picked by hand and emailed by Karri himself. His logic is that if everyone arrives at once they all hit the same bug and tell you the same thing (Lenny's Podcast).

  

• When they let a few bigger companies in those left quickly so the team asked them why and kept the answers as the list of what bigger teams would need (Slush 2023).

• Karri's mentions that about 1 in 10 became users that year and says people stop caring after a few months of waiting.

So what: Tuomas' argument is that a typical MVP doesn't work when you walk into a crowded market like this one because everyone already uses a tool (job is being solved) and yours has to be better on the first try (meaningfully so). They decided to use the waitlist to choose their users instead and I'd guess that's why it mattered for growth, because every group was already sticking around before the next one came in so by launch day they knew people stayed (which likely helped give them the right type of word of mouth).

Lever 2: Charged from launch day (after testing that people would pay for “nothing”)

What happened: the beta was free but at some point they added a page in the settings where a team could choose to pay if they felt like it 🤯. They called it “Friends of Linear” and it came with nothing except the team's thanks, and people paid anyway! So in June 2020 they opened to everyone with real prices from day 1.

The details:

• This is a super interesting approach to initial pricing, Karri remembers the optional plan as a slider for how much to pay per person with people picking anything from $1 to about $20 (Lenny's Podcast).

• When “real” pricing went live the vast majority of beta users became paying customers.

• Revenue from launch day + slow hiring made June 2021 their first profitable month a year after launch. Tuomas says it wasn't planned and that they only hired people they were very sure about so the team grew slower than revenue.

• Accel wrote in 2025 that Linear had not touched its Series A or B money.

So what: this was a willingness-to-pay test that cost them a settings page (and worked!). Paying for “nothing extra” is a pretty seriously honest signal (vs a waitlist which only tells you people are curious).

Lever 3: Published what they shipped every week or two (instead of buying ads)

What happened: since April 2019 (before the product was open) they have posted a changelog every week for the first 3 years. It started as a way to keep themselves honest and turned into a form of cool marketing since users shared it and job candidates and investors read it. They also "launched" 4 separate times in 20 months.

I find it really cool how you can tell they care about the design / artifacts in these changelog notes, it feels pretty special and fun to read.

The details:

• I count about 250 entries on the public changelog up to September 2026.

• Total marketing spend from founding to September 2023 was $35K. Karri's own figure is about $30K on ads and all of it on 2 podcast sponsorships (to 20vc and another one).

• The small budget didn't last though since they now spend a lot more because big buyers need to see you're “serious” and since then they have sponsored at least 14 podcasts and newsletters for engineers and put up a billboard in SF.

• Designers have started imitating their style from their launch pages (dark, glassy) and since early 2023 the look even has a name “the Linear effect”.

• They also attack bugs in a similar way, a few years in they paused everything for about 3 weeks to fix 100+ or so open bugs (we used to do something similar at Facebook on my team, Fixathons) and since then every bug gets fixed within a week or openly declined (2K+ fixed in 2024). Karri tells new hires this is a commercial thing too since it gets people talking.

So what: this is building in public where the product roadmap is kind of a content calendar so in a way shipping gives them something fun / true to say every week (we saw Lovable do something similar in our growth deep-dive, levers 2 and 4).

Lever 4: Built it for the people doing the work, who signed up alone and pulled their company in

What happened: most work software is designed for whoever signs the contract, tends to fill up with reports + mandatory boxes that managers ask for. Linear decided its user is the engineer who opens it many times a day and refuses requests that make that person's day worse just to make a manager's reporting easier. Eng then started signing up by themselves and asking their own companies to switch 👍.

The details:

• There is one kind of request they always turn down = custom options asked for by middle managers to make reporting a bit easier at the expense of eng (according to Nan Yu, the then head of product).

• Users kept asking for “custom fields” (a box you add to every task, like “which customer wants this?”). When the team asked them why about 40% said they wanted to keep track of which customers had asked for what. In December 2024 instead of the box they built Customer Requests were requests from support chats or the sales CRM get attached to the task automatically. Custom fields still don’t exist in Linear. I loved how they framed this in one of their notes, a bit of a masterclass in product-sense:

• OpenAI started with a trial of about 100 people in November 2023 and has grown to 3,000 users, and Scale AI planned to move 50 people over and 200 more signed up on their own (both are Linear's own case studies).

So what: this is product-led growth aimed at the engineer who can't sign a contract but is the one complaining (or raving) about the tool all day, and it puts Linear in a different fight to Jira (which sells to the buyer). They’ve also managed to cultivate an aura of great product-sense + design which attracts top talent. I'd guess the trade-off here is it costs them some deals but it's why a lot of their big sales start with people already asking for it (lever 6).

Lever 5: Made it free for the whole team until the work piled up

What happened: most tools limit the free version by number of people (Jira's is free for up to 10 users). Linear did the opposite with unlimited people for free but only 250 active tasks. The team is in from the first day but the bill only starts once they are clearly doing real work in it (that limit hasn't moved since launch day apparently).

The details:

• On launch day Jori explained that small teams shouldn't have to worry about who to invite so only active tasks count (wrote it on Hacker News)

• They also went after companies while they were tiny. YC startups for example got a free year in 2021 (similar play to Supabase, although linear wasn’t in a batch) and as of October 2026 startups under 50 people can get the paid plan free for up to 6 months through about 490 partner investors, accelerators and startup communities.

• A lot of those startups grew for example Ramp (also covered their growth trajectory here a few weeks ago) has been on Linear since 2019 and Tuomas says that was the plan from the start.

• By September 2023 most of their monthly revenue growth came from existing customers adding employees.

So what: this is land + expand where the landing costs 0 since limiting work vs people lets the product spread through the company before someone asks for budget. Of course, downstream from building something people love using.

Lever 6: Waited 3 years to hire sales and then sold to companies where staff already used it

Read more

In case you missed it:

• 🎆 Fireworks AI: $0 to $1B ARR in under 4 years

• 🤗 Hugging Face: $0 to a $12.9B exit in 10 years

• ⚡️ Supabase: $1M to $170M ARR in 5 years

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Hello there!

This week I’m diving into Cognition, the startup behind Devin, an AI that writes and ships code autonomously which just crossed $1B in annualized revenue a few weeks after raising $2B at a $48B valuation (barely 3 years after its founding):

In a nutshell:

• Product: “Devin” kind of works like a junior engineer you message where you hand it a task (like fixing a bug or upgrading a library) and it works on its own in the cloud + comes back with a pull request (a proposed code change a human reviews and approves). It also owns Devin Desktop which is the AI code editor that used to be called Windsurf (which they acquired).

• Money: self-serve plans from Free to $200 a month but the real business is enterprises that get billed by how much work Devin does (c.75% of revenue)

8 growth mechanics in this drop:

1. Claimed a new category with a demo (before the product worked!)

2. Sent the whole team to make their first big customer work

3. Sold the boring jobs engineers hate (+ a before-and-after number)

4. Priced the AI like a worker vs like software

5. Bought a ready-made sales machine (in a weekend!)

6. Set the product to start a job every time something broke

7. Took the risk off the buyer with a money-back guarantee

8. Bet on staying neutral between the AI labs (vs rivals picking sides)

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Zero To One

• Who: the founding team hold a bunch of gold medals from the International Olympiad in Informatics between them and the team (definitely an edge in this AI wave both in terms of competitiveness and speed):

  • Scott Wu (CEO): three-time IOI gold medalist. Before this he co-founded Lunchclub (AI networking app) as CTO.

  • Steven Hao (CTO): one of the first engineers at Scale AI, MIT, IOI gold in 2014.

  • Walden Yan (CPO): early engineer at Cursor’s parent company who left Harvard in 2023.

  • Russell Kaplan (President): worked on Tesla Autopilot, then co-founded a computer vision startup that Scale AI bought in 2020 (ran machine learning).

• Where they started: the team came together in 2023. Kaplan says that from where he was at Scale (saw big AI labs buying training data), he could see each new model getting much better at code and bet software engineering would change first.

• Market wedge: coding assistants like GitHub Copilot already suggested the next line at the time but nobody yet sold an AI that took a whole task and finished it completely on its own. Scott mentions they were “late” (what a time to be in tech) to AI coding so they went after what was around the corner.

• The MVP: was a demo and in March-24 they launched Devin as “the first AI software engineer” with their $21M round led by Founders Fund and about 6 weeks later they raised $175M at a $2B valuation.

• First users: a wave of inbound pilots that in Scotts words “were all just failing.” Their first enterprise success was Nubank.

Growth Mechanics

Lever 1: Claimed a new category with a demo (even before the product fully worked)

What happened: their launch videos showed it doing planning, coding, testing and fixing of software on its own (even though it wasn’t really ready for real work just yet apparently). But it made Cognition the name for a new category while most of the other tools at the time were still talking about “AI coding assistants”. It got them attention + pilots, hires and money.

The details:

• They said Devin solved 13.86% of SWE-bench (test set of bugs from open-source projects) on its own against a previous best of 1.96% at the time, and it was tested on a quarter of the set (take it as a company benchmark).

• Their launch post on X has now over 30 million views.

• 2 years later Kaplan (one of the founders) mentioned that “it wasn’t even really useful for us for another 3 months after we built the prototype we shared with the world” (ChinaTalk)

• Critics at the time “attacked” / really criticized the demo but Wu doesn’t argue with it either, he says “I think we were wrong on a lot of things, to be clear.”

• A year later they did it something similar again with a free tool called DeepWiki which turned any public code project into a readable guide, with every page inviting you to “ask Devin”. It launched with 50K+ projects already covered and the CTO mentions it was built “more as a kind of like a marketing play, kind of as a public service”.

So what: naming a category before the product is ready is a way of borrowing attention against something you still have to build (which is risky) but what it got them was engineers who wanted in + a queue of big companies willing to try it. The pilots failing was probably the most useful part since it showed them where it broke, though obviously this only works if you can catch up fast (which I suspect is exactly why they hired IOI gold medalists, de-risking with proven talent execution speed + competitiveness).

Lever 2: Sent the full team to make the first big customer work

What happened: as discussed the pilots from the launch kept failing because agents weren’t good enough just yet so instead of spreading a small team across multiple trials they went all-in on one, a big code migration at Nubank. They flew the team over to Brazil and worked to really make Devin perform on that one job well. This is now apparently standard operating practice for them (sending Eng to live inside customers).

The details:

• They say the Nubank job was an 18-month migration spread across 1K+ eng done “with 1/8 the human time at a 20x cost saving” (AWS re:Invent)

• Goldman Sachs was next and also moved very fast, Wu says big companies usually take “like a 12 to 18 month cycle,” and “we did the whole thing start to finish in like 3 months” (Lux, May 2026).

• They also used the same logic to get their first government contracts with the U.S. Army, Navy, Treasury and NASA’s Jet Propulsion Laboratory, selling Windsurf as “the only FedRAMP High AI IDE” (which is the US government’s strictest cloud security clearance)

• They also skipped individual developers, they mentioned “We don’t focus at all on individual hobbyists who are just trying to make a cool thing,”

• And these Eng who work inside customers (FDE’s we’ve talked about these a bunch in past editions here on this ai wave), are now according to the founders “maybe the fastest growing of all the roles”.

So what: going from many pilots to focus on one is a hard trade-off, and it was especially tough probably for them considering the state of the product at the time. But one bank with a painful very specific job probably gave them a better product spec than any roadmap would have (building for a real migration instead of an “imagined user”), and it also gave them a very credible logo.

Lever 3: Sold the “boring jobs” engineers tend to not like, with a before-and-after number

Read more

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Hello there!

This week I’m diving into Fireworks AI, a company running open AI models for other companies (i.e. Cursor, Uber, Notion) that recently raised $1.5B at a $17.5B valuation (4 years after its founding, another AI-wave rocket-ship):

In a nutshell:

• Product: think about it like an “engine room” for AI. Companies that don’t want to depend on OpenAI / Anthropic take a free open model (i.e DeepSeek or Llama) and Fireworks runs it for them (fast, cheap and inside their product). Today it apparently already handles 40T+ tokens a day (tokens are the chunks of words AI reads + writes) for 10K+ companies.

• Money: the model is what’s quickly becoming a classic = pay per use (i.e. $0.30 per million tokens in, $1.20 per million out on a model like DeepSeek), renting chips by the hour (i.e. $8/hr for an Nvidia H100), or pay to train your own custom model. They’ve already passed $1B in annualized revenue as of July this year with around ~200 people ($5M per employee!).

• Driver: loved how they frame it here: “Companies are no longer renting general intelligence. They’re building their own.”

8 growth levers in this drop:

1. Sold Meta’s internal speed tricks to everyone

2. Made switching painless at the moment costs typically start to hurt

3. Had every hot new free model running on launch day

4. Built custom for the fastest-growing customer + then sold it to everyone

5. Made engineers part of the “unofficial” sales team (inside the customer’s Slack)

6. Made a custom model cost the same as a generic one

7. Turned big clouds into both suppliers and a sales channel

8. Bet on deciding which model runs for each task

Zero to one

• Who: 7 engineers who used to run the AI plumbing at my good old employer Meta + Google (6 from Meta and four of them actually from the PyTorch team aka the free toolkit most of the world uses to build AI models today).

  • Lin Qiao (CEO): led the PyTorch there growing the team from 5 to ~300 people, before that IBM and LinkedIn. She started Fireworks at age 48 (after shelving a 2015 startup plan to learn how to lead people at Facebook). She meant to stay “one year or two” and stayed 7.

  • Dmytro Dzhulgakov (CTO): from Ukraine who’s a PyTorch core maintainer after 11 years at Meta, before intern at Google.

  • Benny Chen: a new Zealand-born engineer who ran ads infra also at Meta.

  • Plus Dmytro Ivchenko, James Reed, Pawel Garbacki (all ex-Meta) and Chenyu Zhao (ex-Google).

• Where they started (fall 2022): they all apparently left Meta about 2 months before ChatGPT came out and the first plan was a platform to help companies use PyTorch. Benny, in 2024 said: “Exactly how, we didn’t really figure out completely... ChatGPT wasn’t out yet, so we had to pivot somewhere in the middle.” They raised $25M from Benchmark and Sequoia before having let alone launching a product. Apparently, fun detail, they started in a building literally called the “Sequoia building” before Sequoia invested.

• The wedge: PyTorch was of course free and open so companies like Walmart, Disney, Tesla, Netflix etc used it too and they kept coming back to Lin’s team asking “can you build this training platform for us? Can you build this serving platform for us?” Lin took it as a signal the industry was ready / market timing was right, “and then more importantly, we have the key.”

• The MVP: a platform that launched in August 2023 (11 months in). Run and customize free models through a simple web connection with a free tier for devs:

• First users: “a lot of ex-coworkers from Meta,” says Benny plus teams who trusted the PyTorch name (this is a classic Silicon Valley dense network kindling effect), and by mid-2024 the list already included Cursor, DoorDash and Quora.

Growth Mechanics

Lever 1: Sold Meta’s internal speed tricks to everyone

Read more

In case you missed it:

• ⚡️ Supabase: $1M to $170M ARR in 5 years

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

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

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Hello there!

This week we’re diving into 🤗 Hugging Face, the open-source AI platform that Nvidia just agreed to buy for $12.9B, 10 years after its founding.

In a nutshell:

• Product: a website where the world’s open AI models live, a bit like GitHub but for AI instead of code (today 3M models, 18M devs, and 50% of Fortune 500).

• Money: free for everyone, companies pay for private hosting, security and admin features + the compute power to run models (from $9/month up to enterprise contracts). Roughly $150M a year at the sale from what I could find.

• Driver: best way to describe it is using Clem Delangue’s (founder) words: “Our main goal is not so much to build a big company or to make money. I’m most excited about the potential for change and reinventing new rules.”

Also trying something new with this one. We’re going straight to growth mechanics that could be used by you (or at least help you to think different).

If you prefer it to the usual long version, hit reply and tell me 🙏.

7 growth levers this drop:

1. Made a giant’s breakthrough work where users already were

2. Became the place where a whole field stores its work

3. Made their research and team a marketing engine

4. Stayed neutral so everyone’s customers could use them

5. Gave the software away and charged for running it

6. Made leaving the expensive option a one-line change

7. Entered their next market by making it cheap to start

Zero to one

• Who: 3 French guys (none of whom were AI researchers btw).

  • Clem Delangue (CEO): a business school grad, teenage eBay power-seller, then product at an image-recognition startup Google later bought.

  • Julien Chaumond (CTO): Polytechnique + Stanford engineer, ex-adviser on digital at France’s Ministry of Economy.

  • Thomas Wolf (science): he is a quantum physics PhD, spent 6 years as a patent lawyer.

• Where they started (2016): an “AI best-friend” chatbot for teenagers named after the 🤗 emoji. To power it they built their own language tools and shared them online for free (which is how researchers eventually noticed them).

• The wedge (Nov 2018): Google released BERT (program that understood written language better) and gave it away. The catch was it only worked with Google’s own software kit for building AI (most researchers used Facebook’s kit at the time, the two didn’t mix, think of this like an Android app on an iPhone).

• The MVP: Hugging Face’s team used Facebook’s kit and wanted BERT for their chatbot so Wolf and 2 teammates rebuilt it for Facebook’s kit (in 1 weekend!), checked it gave the same answers and gave it away free.

• First users: researchers found it on their own and 5 months later over 5000 developers had bookmarked it (“we were certainly not expecting that”). 1 year later they were at 1M installs…

• The call: after 6 months of watching the free side project do better than the “original” product they decided to kill the chatbot and raise $15M from Lux.

Growth Mechanics

Lever 1: Made a giant's breakthrough work (Google’s BERT) where users already were

What happened: Wolf’s rewrite of BERT made Hugging Face the easiest way to use Google’s best language AI at that time, which grew into transformers (still one of the most-used free AI libraries in the world).

The details:

• Their library worked with both kits (both Google’s and Facebook’s). A lab could publish a model once and reach everyone and a developer could try a model with basically 3 lines of code (made it very easy).

• They kept doing it, for example OpenAI’s early models were in the library very quickly so researchers stopped checking each lab vs waited for the HF’s version.

• Today it has been downloaded over 100 million times a month!

• That weekend experiment was sort of instrumentalized into a “policy” apparently with the founder Clem keeping 30-40% of the company focused on side bets (increasing the surface area for luck).

So what: often some of the best things in a field is stuck in a form most people can’t use (too complicated, obscure, expensive, hard etc), and whoever puts it where users already do their work can become the way in / build a wedge. In their case what was beautiful about this serendipity is that researchers who adopted it became salespersons for it (i.e. papers + tutorials sent the next researcher to that place).

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Lever 2: Became the place where an entire (new) field stores its work

What happened: after the library they built a website where people could upload an AI model and anyone else could download it, kind of like a public Dropbox for AI.

The details:

• They won the researchers first who were putting their models there and the developers who wanted to use those models had to follow.

• Users went from 5M in 2024 to 18M at the sale.

• Someone uploads something new every 7 seconds (in 2024 it was every 10).

• They made leaving easy on purpose (we saw this also with our Supabase deep-dive). Meaning you can copy your files out any time (nobody feels trapped), which had the effect of more people moving in.

So what: this is in a way a two-sided network effect where every upload makes the place more useful to the next visitor and every visitor makes it more worth uploading to (eventually becoming “the place to be”).

Lever 3: Made their research and team the marketing engine

What happened: they’ve had no marketing team, PR, community manager etc. They have everyone at the company talk to users in public, make their own research visible.

The details:

• Got 1K+ researchers from 70+ countries to build a big AI model together publicly.

• They ran a public ranking of which AI models are best with 2M+ visitors in 10 months + every new model gets measured on it (retired in 2025 though).

• They also gave small builders $10M of free computing power to spin this wheel.

• Good example of this is Clem tweeting that he was in San Francisco and suddenly a 400-person meetup became 5,000 attendees and 3 llamas.

So what: they leveraged the fact that their buyers read research and rankings (not ads necessarily in its strictest sense) and leaned in hard on it.

Lever 4: Stayed neutral so everyone’s customers could use them

What happened: in 2023 they raised $235M from 8 big boys that compete with each-other to some extent on this front (on purpose). With all of them on the cap-table no single one could steer the platform, and therefore they could keep working with everybody.

The details:

• One round with Google, Amazon, Nvidia, Salesforce, Intel, AMD, Qualcomm, IBM.

• Clem called this “a very intentional decision”

• Several of them plugged their own products into Hugging Face anyway (see here for example with Nvidia in 2023 and Google in 2024).

• Nvidia became the biggest contributor of free models

So what: picking a side costs you every customer on the other sides. Belonging to nobody made them usable by everybody.

Lever 5: Let employees use it free and charged companies for control

Read more

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

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In case you missed it:

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

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

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

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.

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Hello there friends!

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

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

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

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

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

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

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

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

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

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

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

Lets dive in.

From the 16:9 slide → the AI design partner

SR30 Growth Index

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

Where we come from

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

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

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

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

But for startups presentations have mostly been… difficult:

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

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

Where we are

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

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

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

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

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

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

Where the market is going

3 dynamics will likely shape the next 24 months.

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

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

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

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

Act 1: The Modern Memo

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

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

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

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

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

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

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

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

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

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

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

“Friends are going to lie to you.”

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

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

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

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

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

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

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

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

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

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

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

1. struggled to make it look good

2. feared being judged for it

3. lost the room.

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

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

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

Act 2: Flip It On

Mar 2023 → mid 2024 · $0 → ~$10M+ ARR

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

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

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

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

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🌊 How N8N grew to $100M ARR

Hello there!

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

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

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

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

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

What you’ll learn

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

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

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

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

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

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

Turning n8n into “the Excel for AI”

SR30 Growth Index

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

Where we come from

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

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

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

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

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

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

Where we are

Then ChatGPT happened and the market split:

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

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

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

what actually connects them and runs the thing in production?

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

Where the market is going

3 things will shape the next couple of years:

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

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

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

Now lets dive into how they grew.

Act 1: The Lego Box

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

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

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

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

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

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

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

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

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

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

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

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

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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

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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):

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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:

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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

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Perplexity’s Growth Playbook: 0 to $450M ARR in 3 Years

Hello there!

Every investor I know has the same debate about Perplexity on whether it’s a real $20B company or if Google is about to eat them alive.

Then February happened and they added $150M of annualised revenue in 30 days, and blew past their 2026 internal target 9 months early.

So I spent the past week pulling apart every founder interview, growth-team podcast and leaked revenue number I could find.

What you’ll learn in this edition:

• The Slack bot accident that became a multi-billion dollar product idea

• Why the VP Growth told the company to stop worrying about acquisition

• The specific activation metric that predicts whether a new user will stay or leave

• Why they killed their own advertising business after making $20,000 from it

• The trust architecture that earned them the right to charge $271/mo

• And the pricing shift that just pushed them past half a billion ARR

And as usual, a few growth lessons for you to steal ;)

Lets dive in:

Act 1: The Answer Engine

$0 to $80M ARR. August 2022 to December 2024.

The founding accident

Perplexity almost didn’t build the product that made them famous.

The company was founded in August 2022 by Aravind (CEO, ex-OpenAI), Denis (CTO, ex-Meta AI), Johnny (CSO, ex-Quora), and Andy (co-founder of Databricks).

These are four people with world-class AI research backgrounds who, by Aravind’s own admission, “started the company without actually having a clear idea of what to build”. Which is interesting considering how many times I’ve heard from “tier 1 VC’s” that they only back founders that are completing their “life’s work”:

Their first product was called Bird SQL, which basically let you search Twitter using plain English. You could type something like “Who is Lex Fridman following that Elon Musk is also following?” and the system would translate that into a database query and return the answer.

In February 2023, Twitter killed free API access and that product died overnight.

But something else had been happening inside the company. They’d built a Slack bot that plugged into GPT-3.5 and let the team ask it questions.

The first question was about health insurance (their first employee needed a plan) and nobody on the founding team knew anything about it. The problem was that the Slack bot made things up, a problem we are all too familiar with in this AI era.

It (confidently) gave wrong answers about insurance plans, providers, coverage details, etc. Which was the known failure mode of every AI chatbot at that time. You probably remember how the internet was full of screenshots mocking AI mistakes.

But this lit up the idea that built the company.

Aravind and his co-founder Denis had both been academics. And in academic publishing, there is one hard rule, and that’s that every sentence you write must be backed by a citation from a peer-reviewed paper or your own experimental results.

They decided to force the AI to follow that rule. So not a chatbot that happens to cite sources, but instead a system architecturally designed so the AI can only say things it can find evidence for on the internet.

They also made a decision at founding which was NOT to try to build their own AI model. As Aravind told Fortune:

“It was a decision driven through conviction and pragmatism. Pragmatism because we were broke. Conviction in that it was already clear that AI models would become increasingly commoditized.”

They used OpenAI’s GPT-3.5, available on a pay-per-query basis, and focused all their energy on the search, retrieval, and the orchestration layer.

One of their early seed investors, a former Googler, told Aravind directly:

“The reason [AI] is going viral right now is because people want to laugh at AI’s mistakes. Your product is designed to not make mistakes, you are always pulling sources. I don’t think this will work.”

Aravind ignored him and they were off to the races.

Growth Lever 1: Trust as a growth engine

First a quick primer on perplexity vs traditional search:

• “Traditional” Search: You ask Google a question, you get ten blue links, you click three of them, you read two and piece together an answer yourself.

• ChatGPT Search: generates answers from its training data, which means it is drawing from memory (and as mentioned above, hallucinations happen).

• Perplexity Search: they do that work for you. It searches the web, reads the relevant pages, and a clean answer with footnotes to every claim. It generates answers from live web search results (drawing from evidence), and when the evidence conflicts or doesn’t exist, the system says so instead of making something up. Basically a research assistant who shows their homework.

They also built a feature they originally called “Copilot” (now Pro Search) that asks clarifying questions before answering.

This matters for growth because it means even a vague question produces a great answer, which means more positive first experiences, which means more word of mouth. As you’ll see further down, their growth team runs a bunch of these 1% experiments, which staked together explains a lot of their growth story.

Why does trust matter so much for growth?

Because their citation architecture made the product more accurate and therefore made it recommendable. We’ve all been through that fear of double, tripple checking AI generated answers. In this case they figure out part of their growth loop went through making users share a Perplexity answer with a colleague and say “look, here are the sources” without risk(ish).

The retention data seems to back this up with c.85% of users who try Perplexity returning, and 90% of users coming back within 30 days of their first visit. Direct traffic (people typing perplexity.ai into their browser or using a bookmark) accounts for 82% of all visits as of mid-2025, and the vast majority of usage comes from people who already know and trust the product.

One more data point I loved is that the average Perplexity query is 9 words long, versus 3-4 words on Google. So users write longer, more specific questions because they’ve learned the system can handle complexity, same pull you probably feel when you decide to go to OpenAI vs asking Google.

This in itself is an interesting data flywheel, because longer queries give Perplexity more context, which produces better answers, which drives retention.

Hold onto the word “trust” because its basically the architecture that earned Perplexity the right to charge $271/mo and hold your credit card, and every lever below is a brick in that architecture.

Growth Lever 2: The three-query activation threshold

Another of Perplexity’s growth levers is that you don’t have to sign up to use it. Which creates a specific challenge that mapped out well:

how do you convert anonymous visitors into retained users?

Raman Malik, Perplexity’s Head of Growth (ex-Lyft) says the growth team worked backwards from retained users. They looked at everyone who was still active at day 30 and day 60, then examined what those users did differently in their very first session.

The pattern they found is that users who hit 3 queries in their first session retained at dramatically higher rates. 3 queries is enough time in the product to understand the value and became the team’s north star: get new users to three queries in their first session.

Raman’s target was 30% of all logged-out visitors hitting this milestone.

The obvious way to force engagement is to add a signup gate at query 5 in the common style of “You’ve used your free queries, create an account to continue.”, which usually works for conversion, activations go up, but it also kills query volume.

People hit the gate, get annoyed, and leave, so you’ve juiced one metric (activations) while hurting another (total searches, which drive the data flywheel).

Interesting trade-off:

“Those are the trickiest ones to navigate. How do I value those two things down the road? Will that activated user retain and over time make up for the initial loss in query volume?”

Their answer was progressive, device-specific friction (instead of a hard gate):

• On mobile browsers: a full-screen signup takeover blocks further searches

• On desktop browsers: a softer half-screen prompt appears but users can keep searching. It only escalates to a full takeover by search #5

• On the mobile app: nothing.

They figured out each surface has a different user intent. Mobile web is transactional, so a hard prompt converts. Desktop users tab-hop, so a soft prompt keeps them searching. And App users already committed by installing, so interrupting them hurts more than it helps.

The friction only works because Perplexity has already hooked you 2x before the prompt shows up:

1. Rotating homepage queries: Instead of a blank search box, the homepage cycles through trending, engineered suggestions. It kills blank-page paralysis and hand-delivers new users into their first good experience.

2. Related searches at the bottom of every answer: Same trick Google uses, but tuned to pull you deeper into the rabbit hole. By the time you’ve clicked through two or three of these, you’ve hit the 3-query threshold without realizing you were being activated.

So by the time the signup prompt appears, the user has already felt the “aha.”

The biggest takeaway here is that activation shouldn’t be thought of as a gate but rather as a tiered system.

Growth Lever 3: The partnership distribution machine

About 80% of Perplexity’s growth is organic word of mouth, but the remaining 20% comes from a partnership strategy that their CBO Dmitry Shevelenko built into a machine. He joined from Uber (4 years), and before that was at Facebook when it had 500 employees and 80 million users. The man knows distribution.

Their playbook was to give away a year of Perplexity Pro ($20/month subscription) through partners who already have your target audience.

The list tells the story:

• LinkedIn (free Pro for premium members)

• Samsung (pre-installed on all 2025 TV models with 12-month free Pro)

• Airtel (India's largest telecom, with 360M+ Indian subscribers)

• ’s newsletter (free Pro for paid subscribers)

• PayPal/Venmo (12-month free Pro)

• Snapchat (search integration reaching nearly a billion users)

The unit economics are elegant because in theory if a partner gives away 100K free Pro accounts, and 80% of those people never actually use the product, Perplexity’s variable cost on those 80,000 dormant accounts is close to zero. They only incur inference costs when someone sends a query, and the 20,000 who do use it enter the conversion funnel.

“We have a phenomenal Partnerships team. They are absolute sharks.”

Not everything in this era was smooth though as they’ve had their fair share of hiccups with Forbes, Condé Nast, The New York Times, and Dow Jones all suing or issued cease-and-desists over Perplexity reproducing their content without licensing.

The tension they create for them is clear because an answer engine reduces clicks to the publishers who wrote the content in the first place. So they responded in 2024 with a $42.5M publisher revenue-share program, splitting ad revenue with sources whose content appears in answers.

Apparently the lawsuits haven’t fully resolved but growth hasn’t slowed either.

Growth Lever 4: Retention over acquisition (and the Lyft lesson)

The first thing the VP Growth did when he joined was to map the entire funnel, and attention went straight to retention. It was “pretty healthy consumer retention.” But he double-clicked on something often overlooked:

a 10% improvement in retention raises the entire water level of your active user base, and it compounds.

Two tactics drove the biggest retention gains:

1. Cross-device conversion: Users on both desktop and mobile retained at significantly higher rates. Desktop during work hours, and mobile on the couch.

2. Audience mix-shifting: Casual “weekender” users asking novelty questions have lower retention than knowledge workers solving real problems. Students using Perplexity to find sources for papers are extremely sticky, so the growth team targeted acquisition toward high-retention segments, changing the composition of incoming cohorts.

What they explicitly did NOT do is spend money on paid acquisition.

Interesting story Raman tells on the 20vc pod, that he learned this lesson at Lyft, where he started his career spending millions on paid channels. One day, Lyft’s head of growth told them to turn off every single paid channel for new passengers, and signups barely moved (maybe 5-10% down), and all low-quality users dropped. Which means paid spend had been almost entirely cannibalizing organic traffic.

Also interesting his honest admission about a weakness, which can therefore be turned into a strength (plenty of evidence of sharing happening on Twitter for example):

“I don’t think sharing is inherent to the Perplexity user journey. I don’t think we have an inherent viral loop.”

Growth Lever 5: The campus ground game

Raman identified students as a high-retention, high-potential audience that Perplexity was under-indexed in compared to ChatGPT, and had an obvious fit (remember citations!).

Perplexity launched a formal Campus Strategist Program where students get a marketing budget, mentorship from the growth team, Pro accounts, and early access to features. In return, they host events, run workshops, and drive referrals through affiliate links. Large universities can have up to 100 partners.

At CMU, a campus strategist hosted “Boba & Perplexity Power Hour” where 120 students explored the product over free boba tea. At Penn/Wharton, a strategist organized a speaker event featuring Raman himself, drawing 150+ students from undergrad to MBA. Great tactics for step 0 of building a community.

“These are not big campuses. You can build density very very quickly. And as soon as you have that initial density, then you see word of mouth start to come alive.”

The numbers at end of Act 1

By December 2024: roughly $80M ARR, $900M+ raised, a $9B valuation and 22 million MAU, with every major AI company copying their citations feature.

Act 2: The Platform

From $80M to $300M ARR, in 12 months:

By January 2025, Perplexity's product was no longer differentiated because every chatbot cited sources. The question shifted to “how do we win before Google kills us with distribution”.

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🌊 What 3 Years at Facebook Taught Me About Building Revenue Squared

Hello friend,

This edition will be a little different.

I just sold my €10K MRR side-hustle to Nova.

It’ll be useful for those interested in either:

• Building a community-first product, private community or vertical social network.

• Or learning battle-tested tactics to build a successful community for your startup.

So let’s get into it:

Act I: The Facebook BJJ origin story

Finding a co-founder

Back in 2017 I was walking around the third floor of our new Soho office when I stumbled upon a dude with a big smile and a Spanish-American accent, with a bunch of tatami mats stacked next to his standing desk.

We soon realised we were both obsessed with surfing, tech, and mixed martial arts.

We started the Facebook Brazilian Jiu Jitsu club at the London office (which spread across other offices across the world). Looking at the social club budget, we also figured out there was a “glitch in the system”, which made it possible for us to start other clubs. Needless to say, we ended up with one martial art per day of the week including boxing, MMA, BJJ, wrestling and so on.

Some say that Zuck started training BJJ because of the trend we started 😜.

This was the beginning of my friendship and founding story with Jorge Bestard.

Building something people want (on the right flywheel)

We tried a bunch of things including building a training app, an ecommerce, a marketplace and a bunch of other ideas. But none of them took off, and we both felt something was missing.

Time went by and we kept throwing things at the wall. But this time it had to tick a few boxes, since we had both switched roles and felt strongly about helping our home turf.

The criteria were:

1. Build something cool

2. That helps our tech ecosystem grow

And that’s how we landed on Revenue Squared:

Problem

• Tech sales is underrated: especially in the spanish-speaking world, where it is typically misunderstood (people think “car salesman”).

• Top talent often doesn’t even know its an option: and that it can not only be very attractive in terms of income potential, but also in terms of career fulfilment.

• Cleaning up your information diet and continuous learning is hard: Keeping up to date with the latest developments in sales - tooling, plabooks, market trends - is challenging, especially in a space mined with questionable “sales gurus”.

• Sales can be lonely: And finding a curated peer group to bounce ideas off of when you are stuck, and that helps you guide your career choices, is hard.

• Linkedin is bad at community: People have spent the better part of the past 10 years being “harvested”, as passive consumers of information.

Solution

• A highly curated paid membership community for the best tech sales professionals in the Spanish-speaking world.

• Vision: The sales profession is understood as a great career path - attracting more talent into the tech scene in the Spanish-speaking world, both in terms of its earning potential and how interesting / prestigious the job can be.

• Mission: Elevate the sales profession.

Validating demand (through pre-selling)

We both knew we didn’t want to re-invent the wheel in terms of business model, and that we wanted to get paid first before building anything to validate demand.

So here’s what we did:

1. Throw our top 50 smart GTM friends in a slack channel. Mostly spanish-speaking sales leaders from unicorns and high-growth startups.

2. Wrote and published a sign-up form on Tally, with the value-prop and explicitly calling out that is is a paid membership community.

3. Scheduled 30+ interviews selecting from that list of 400+ submissions, and sent payment links to those who cleared our talent bar.

And it worked.

Act II: The Facebook Community Playbook

The Spark AR Community

There is a surprising amount of work that goes into building a successful community, especially when its a community-as-a-product.

I know because I was a Product Specialist on the Spark AR team at Facebook, working hand-in-hand with engineering and design to ship Instagram's AR camera effects. We built the community as a product lever because we saw the opportunity.

My team launched these filters in 2017, and went from zero to hundreds of millions of daily active users, over 1 billion unique camera effects used, and suddenly thousands of creators making a living off of building these filters.

The Spark AR Community grew to over 100,000 members. It was considered internally as a source of competitive advantage for the platform. And we proved with data that community creators were significantly more likely to publish effects and retain as active developers.

When we started Revenue Squared, we took those learnings and applied them.

Here are the top 10:

1. Build the MVP where they already are

At Facebook, we built the Spark community on Facebook Groups. We looked at a bunch of options (custom platform, standalone app, stack overflow, etc). We chose Groups because creators were already logged in, already had notifications turned on, already had the muscle memory of checking their feed every day.

Whenever someone asks me about this I use a freeway analogy.

Your community is a town. The platforms your audience already uses every day are freeways. For salespeople, the two biggest freeways are LinkedIn and Slack. They're already on both, every single day, with notifications on. So we built our town right at that intersection. The Slack community was where members lived. LinkedIn was how new people found us.

The point is, don't make people take a detour to reach you. Build where the traffic already is.

The temptation is always to build something custom. Resist it for as long as you can.

Our R2 tech stack at launch was:

• Tally (forms)

• Webflow (website)

• Slack (community)

• Stripe (payments)

• Google Meets (workshops)

That’s it. We didn’t add Podia, Luma, etc until we had 150+ members.

2. Find your first 50 “kindling members” + MVP

calls this the “atomic network” in The Cold Start Problem. Which means the smallest possible group of users that can sustain the network on its own.

Tinder for example did it by throwing a campus party where 500 students downloaded the app in one night. LinkedIn did it by having employees and investors invite their professional contacts. And Reddit literally faked early activity with multiple accounts to make the community feel alive.

At Facebook, the first Spark AR creators were hand-picked. My team identified the most talented AR creators we could find (create + technical usually), gave them early beta access, and brought them in one by one. These first creators set the tone for everything that came after. They built the best effects, answered other creators’ questions, and gave feedback that shaped the product roadmap.

It was also super cool to see how excited our own Engineers got when they saw real people building amazing stuff in real time, collaborating, commenting and so on.

With R2, we ran the same playbook. Jorge and I had deep networks across tech sales from Facebook, Bloomreach, Canva, Miro and so on. Our first 50 members came from personal relationships. We literally threw our smartest GTM friends in a Slack channel. Then we started hosting dinners in Madrid and Barcelona, bringing together talented revenue professionals we thought we’d enjoy hanging out with. Every dinner was oversubscribed and word-of-mouth did the rest.

You cannot crowdsource your founding members, you need to hand-pick them very carefully because they are your kindling. And without kindling, there’s no fire.

This is also super important for any startup thinking of bolting on a community for “support purposes”. As we discussed in our recent Growth Playbook about Lovable’s journey to 400M in ARR, most communities become dumping grounds for negative sentiment if you don’t get this step right.

3. Kill the cold-start problem with curated seeding and setting the tone for culture

The thing nobody talks about but is crucial in the early days of any community, and also the most anxiety inducing, is that the biggest risk is silence. People join, see nothing happening, and leave.

At Facebook, we seeded the Spark AR group with content every day. Tutorials, creator spotlights, product updates, detailed comments to work showcases, announcing bug fixes.

I remember some of our most amazing content came from our Engineers or Designers directly pushing a demo of what they were releasing on the group. Some of them were almost “superstars” in the eyes of these talented creators.

My teammates and I were in there constantly, posting, commenting, surfacing great work, from all across our org including PMs, Data Science, Research, Eng, etc.

With R2, we did the same thing. Before we had organic discussion, we seeded the Slack with data, articles, sales insights, and questions worth debating. One of our most popular channels ever is #Memes. We asked questions we actually wanted answers to and shared stuff we’d found genuinely useful.

Here’s where we found Luisen through Twitter. He was at Salesforce at the time. He joined the team to run community management, and having someone dedicated to keeping the conversation alive made all the difference. But most importantly, he had a great pulse on culture, and maintaining that tone across all our channels.

4. Charge from Day 1

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