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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:

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Hello there (for this analysis, salut!),

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

Which is roughly 20x in 12 months…

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

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

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

What you’ll learn:

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

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

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

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

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

• Why a chip-equipment maker led their biggest round

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

Let’s dive in:

The setup for a $400M run

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

Where we come from

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

So the moat was model quality.

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

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

Where we are

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

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

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

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

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

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

Where it’s going

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

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

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

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

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

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

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

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

Now, lets get into how they grow:

ACT 1 — The Memo

April 2023 → December 2023

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

8 months later they had:

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

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

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

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

The levers they pulled on:

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

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

• BNP Paribas, Europe’s largest bank

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ACT 2 — The First $100M

2024 → mid-2025

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

Read more

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Hello there!

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

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

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

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

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

What you’ll learn:

• Why 6 years of going wide nearly killed the company

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

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

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

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

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

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

Let’s dive in:

From rented lists to orchestrated data

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

Where we come from

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

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

specialization survives, systems survive, the tactics get rebuilt.

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

Where we are

Two things shifted between 2022 and 2025:

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

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

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

Where the market is going

3 dynamics will shape the next 24 months.

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

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

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

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

ACT 1 — The Wander

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

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

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

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

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

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

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

• Recruiters used it to source candidates.

• Salespeople used it for outbound.

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

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

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

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

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

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

• One customer: outbound sales.

• One problem: great data enrichment in a table.

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

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

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

There were 2 product principles underneath the discipline:

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

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

ACT 2 — The Machine

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

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

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

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

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

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

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

Then they built a social-listening stack:

• Slack keyword alerts on the words customers used.

• A tool for monitoring Reddit and the open web.

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

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

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

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

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

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

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

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

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

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Hello there!

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

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

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

What you’ll learn

• How Mercor charged before they raised

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

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

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

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

📐 Quick note on editorial and methodology: this report is me surfacing the 80/20 positive anomalies that explain their growth (not a comprehensive company profile). 14 founder interviews (Lenny’s Podcast, 20VC, No Priors, Stanford ETL, etc), plus reporting from TechCrunch, Bloomberg, Forbes, Wired, etc. All revenue figures sourced. Reported ARR figures throughout are gross GMV, assume 20-30% take rate. Treat directional estimates as directional.

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:

📐 Quick note on editorial and methodology: this report is my best attempt at surfacing the 80/20 positive anomalies that explain their growth (my opinion, not a comprehensive company profile). Sourced from 20+ podcasts and long-form interviews with Masad and team (My First Million, Sequoia Training Data, Masters of Scale, 20VC, Lenny’s, YC, Joe Rogan, and others), plus reporting from Forbes, TechCrunch, Semafor, Bloomberg, Inc, Sacra, and Replit’s own blog. All revenue figures and funding rounds sourced. Treat directional estimates as directional.

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

Eight years, 3 YC rejections and 1 master plan.

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

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

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

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

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

So, how did they get there?

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

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

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

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

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

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

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

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

The product mechanics they shipped to make this work:

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

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

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

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

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

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

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

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

The founder made 3 bets in the opposite direction:

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

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

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

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

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

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

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

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

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

And this was a massive advantage.

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

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

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

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

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

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

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

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

What that bought them by April 2023:

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

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

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

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

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

The desert

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

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

1. GitHub Copilot was free for every Microsoft customer.

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

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

A few specific things broke at once that hurt them:

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

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

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

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

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

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

The war room

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

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

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

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

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

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

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

Agent v1 ships

Agent v1 went live and in the first 7 days:

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

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

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

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

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

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

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

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

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

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

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

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

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

The compounding effect:

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

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

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

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

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

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

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

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

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

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

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

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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

📐 Quick note on methodology: 22 founder and operator interviews (Lenny’s Podcast, Acquired, Sequoia, Stripe’s Cheeky Pint, etc.), plus reporting from TechCrunch, Bloomberg, Fortune, The Information, and Sacra Research, plus Sierra’s own blog and 15+ named customer case studies. All revenue figures sourced. Treat directional estimates as directional.

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:

📐 Editorial and methodology note: Granola sponsors today’s edition but did not review or approve this Playbook before publication. The Growth Playbook format is mine, I’d write this breakdown whether they sponsored or not. Sourced from 18 podcasts and long-form interviews, plus TechCrunch, Bloomberg, Sifted, Sacra, and Granola’s blog. Treat directional estimates as directional.

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

From $0 to 5,000 weekly users.

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

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

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

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

Then they went quiet for a full year.

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

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

From the MAD Podcast:

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

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

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

Aaand it didn’t.

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

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

Pedregal on Invest Like the Best:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

VCs were the wedge and founders were the spreaders.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Sam Stephenson on Cognitive Revolution in April 2026:

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

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

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

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

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

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

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

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

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

The numbers at end of Act 1

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

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

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

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

The question shifted from “how do we get users” to “how do we keep them when notes themselves become a commodity?”

Growth Lever 5: Pricing the team plan below the personal plan to force expansion

Read more

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

Hello there!

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

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

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

What you’ll learn in this edition:

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

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

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

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

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

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

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

Lets dive in:

📐 Quick note on methodology: 10+ podcasts and long-form interviews (20VC x2, YC, HBS, EO, and others), plus press from the FT, Fortune, and TechCrunch, research from Sacra and Contrary, and Perplexity's own blog. All revenue figures sourced. Treat directional estimates as directional.

Act 1: The Answer Engine

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

The founding accident

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

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

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

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

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

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

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

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

But this lit up the idea that built the company.

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

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

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

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

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

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

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

Aravind ignored him and they were off to the races.

Growth Lever 1: Trust as a growth engine

First a quick primer on perplexity vs traditional search:

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

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

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

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

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

Why does trust matter so much for growth?

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

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

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

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

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

Growth Lever 2: The three-query activation threshold

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

how do you convert anonymous visitors into retained users?

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

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

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

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

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

Interesting trade-off:

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

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

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

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

• On the mobile app: nothing.

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

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

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

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

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

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

Growth Lever 3: The partnership distribution machine

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

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

The list tells the story:

• LinkedIn (free Pro for premium members)

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

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

• ’s newsletter (free Pro for paid subscribers)

• PayPal/Venmo (12-month free Pro)

• Snapchat (search integration reaching nearly a billion users)

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

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

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

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

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

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

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

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

Two tactics drove the biggest retention gains:

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

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

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

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

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

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

Growth Lever 5: The campus ground game

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

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

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

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

The numbers at end of Act 1

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

Act 2: The Platform

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

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

Read more

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

Hello friend,

This edition will be a little different.

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

It’ll be useful for those interested in either:

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

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

So let’s get into it:

Act I: The Facebook BJJ origin story

Finding a co-founder

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

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

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

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

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

Building something people want (on the right flywheel)

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

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

The criteria were:

1. Build something cool

2. That helps our tech ecosystem grow

And that’s how we landed on Revenue Squared:

Problem

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

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

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

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

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

Solution

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

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

• Mission: Elevate the sales profession.

Validating demand (through pre-selling)

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

So here’s what we did:

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

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

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

And it worked.

Act II: The Facebook Community Playbook

The Spark AR Community

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

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

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

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

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

Here are the top 10:

1. Build the MVP where they already are

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

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

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

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

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

Our R2 tech stack at launch was:

• Tally (forms)

• Webflow (website)

• Slack (community)

• Stripe (payments)

• Google Meets (workshops)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4. Charge from Day 1

We started at €20/month, then raised to €30/month as demand grew, and experimented with a few different payment plans.

Charging did three things surprisingly well:

1. It filtered for quality, especially in a geo less prone to pay for stuff. People who pay are people who are serious about getting value and contributing.

2. It raised engagement. When you pay for something, you tend to use it way more.

3. It validated demand continuously. Every renewal was a signal that the product was working.

Of the 400+ that applied through our Tally form, we interviewed 30+ in the first few weeks. Only those who cleared our talent bar got a payment link, which set a high bar and created scarcity.

The scarcity drove word-of-mouth and word-of-mouth fed the top of funnel.

The bottom line is that, even though it does require some courage, when you charge for access the people who get in are the people who care.

5. Measure everything (the Vader Score)

When the Spark AR community started growing fast, it became impossible to manually track what people wanted, what was broken, and how they felt about the product. So I built a system.

It analysed every comment and post from the community every day and overlaid a sentiment analysis algorithm (called the Vader Score). This let me build dashboards with word clouds showing what came up most often, a data-driven approach to counting and prioritizing feature requests, and a per-feature sentiment tracker so engineers could see exactly how creators felt about the specific features they were working on.

It was like assembling an NPS score from raw community chatter and tracking how it evolved over time. It was great because it allowed us to pull on different product, bugs and other leaves that moved creator sentiment from 3.8 to 4.5+ out of 5 stars over the course of a year.

I also built what I called the “Signal Triangulation Program,” bringing together Design, Research, Data Science and Product Marketing to cross-reference community signal with quantitative data and qualitative research, which became the foundation for how the Spark team made quality planning decisions.

At Revenue Squared, we applied a simpler version of the same idea. We paid close attention to which Slack channels had the most activity, what types of questions people were asking, and where the energy was. Even though Slack provides pretty meh analytics, we were also able to infer some insights to act on.

This is how we discovered that the #hiring channel was way more valuable than we expected, that the #anonymous channel was where the deepest value lived, and that people wanted in person events and workshops more than we thought.

6. The 150-member inflection point

There is a magic number in community building and in our experience with R2, the flywheel kicked in around 150 members.

Before 150, we were still pushing with seeding content, hosting everything ourselves, making sure every AMA had an audience etc. The community was getting warmer but hadn’t yet taken off.

After 150, the peer-to-peer value flywheel activated. People started answering each other’s questions before we could. The #anonymous channel became a place where someone could post a complex sales problem and get lots of thoughtful responses within hours.

People were posting about whether to stay or leave their current job, sharing real compensation numbers for negotiation help, and working through difficult management conversations together. The #hiring channel turned into the community’s most active surface, with members finding jobs and startups filling roles organically.

This is what I mean by the boulder starting to roll downhill. Before 150, you’re pushing but after 150, you’re trying to keep up. Sort of like product-market fit:

Robin Dunbar’s research (which has held up for 30+ years) suggests 150 is in fact the natural group size where humans can maintain meaningful relationships, go figure! Gore-Tex discovered the same thing by accident apparently, they found that when more than 150 people worked in the same building, social problems started. So they capped every building at 150 employees with 150 parking spaces.

For building a community, you just need to be aware that there is an inflexion point at around that number. On one hand things get much easier (peer-to-peer), but on the other you have to pay attention and be ready to build process + features to help the community (and most importantly its vibe) scale appropriately.

7. In-person events as community fuel

In-person events became popular. We ran networking beers in Barcelona and Madrid quarterly, bigger events with keynote speakers like Gong. Small executive dinners, 8-12 people max, where members came prepared with specific GTM problems they needed help solving.

These dinners were always oversubscribed and generated some of the deepest relationships in the community, and were the primary source of organic referrals. Two of my closest friends in Barcelona today are people I met in the community. Members would tell colleagues about the experience, and the waitlist grew without us doing anything.

We eventually scaled this by creating Ambassadors: 2 per city to organize the local beer events. They were stoked to represent the community. It gave them status, made them feel like owners, and it took the operational burden off of us.

My lesson here is to not underestimate in-person. In a world drowning in digital noise, putting people in a room together really is a superpower.

8. Make members feel like owners and experiment (a lot)

At Spark AR, some of our best product decisions came directly from creator feedback. When creators felt heard, they invested more, they reported bugs because they wanted the product to improve and they suggested features because they cared about where it was going. We tried to lean into this hard.

We gave top creators early access to unreleased features, publicly showcased amazing effects, highlighted craftsmanship etc. It was cool because some of these effects were really amazing art: 3D meshes and textures and coded animations overlayed with emerging computer vision algorithms.

We ran Online Challenges that drove 150+ participants, and the success of the first one led to scaling the program 10x. We launched inspiration posts that caused a 130% spike in group engagement. We built a mentorship program that got 500+ signups. Basically running a ton of experiments to see what struck a chord.

At Revenue Squared, we applied the same principles with our own flavor. We built a diamond emoji culture: a Slack plugin tracked who received the most diamond emojis each month, and those top members got public recognition on our LinkedIn and Substack (which reached thousands of people). We ran annual sales voted “Top Performers” and gave trophies and certificates in public. We launched a bi-weekly peer to peer short 15’ networking call via Lunch Roulette. And a bunch of other experiments.

When we planned changes, we shared them with the community as proposals (not announcements), we asked for input and implemented suggestions. And then, we acknowledged the people who contributed ideas.

The bottom line is to make people feel like owners, test tons of ideas, kill them fast if they don’t work, ad them to the arsenal when they do.

9. Build the team around the mission

Community-as-a-product is deceptively hard to run because you are simultaneously a content producer, event organizer, moderator, recruiter, product manager and customer support agent. Across multiple surfaces, with paying members who have high expectations.

At R2, the team evolved in stages:

• Jorge (VP EMEA at Harvey) and I started everything ourselves. The first 50 members, the first dinners, the first Slack channels. But as the waitlist hit 400+ applications, we were drowning in interviews and community management on top of our day jobs.

• That’s when Alvaro (Chief Sales Officer at Vivla) joined. He had run events at Ironhack and brought edtech experience that was relevant for the workshops and training we wanted to build. He started helping with events and took over the interview pipeline.

• Then Luisen came in from Salesforce. He started on community management and eventually took on a broader founder and operational role, eventually leaving Salesforce to run R2 full-time in 2025.

Beyond the core team, we tested different profiles:

• A community manager for day-to-day Slack management

• A video editor for workshop content

• A professional content creator for infographics

• Julia Barrueco was great in helping us design and launch our web.

• Alejandro from NocodeHackers was super helpful in helping us build the private members area, the LMS on Podia, the CRM on Airtable, automations on Zapier, analytics dashboards and payment chasing (Stripe gives a lot of headaches, around 5% of subscribers experience card failures and you have to manually chase them)

• Event management vendors for our in-person events

• And we had to manage content across multiple surfaces: LinkedIn, a Substack newsletter, YouTube, Instagram and so on (plus multiple experiments).

A big lesson here is that the distinction between a community builder and community manager matters, and one is much harder to find than the other.

A community manager moderates, answers questions, sends emails (operational work). But a community builder creates the culture, designs the incentive structures, finds the first members, kills the cold-start problem, shapes the identity and so on. The builder is 10x harder to find (90% of the time, probably a good idea to hire within the community).

10. Build product to scale and retain

At Spark AR, I saw firsthand how the community outgrew its original surface. When we hit 40K+ members on Facebook Groups, basic things stopped working. People couldn’t find content, questions got buried, the format was constraining etc etc.

We started planning a dedicated forum (Oculus-style) and introduced new surfaces like in-app documentation, contextual help, and a creator certification program.

The same thing happened at Revenue Squared at around 200-300 members, we had a product backlog. The Slack community was great, but members wanted more.

So we started building:

• A private members area with perks, a member directory (”who’s in here”), and a member book portal so people could find and connect with each other based on their profile.

• A learning management system on Podia with all the workshop recordings organized by level: SDR, Account Executive, Advanced. Spanish-language sales training that simply didn’t exist anywhere else.

• Community management tools like a chat roulette / lunch roulette for peer-to-peer mentoring on autopilot. Members would get randomly paired for a 30-minute call every couple weeks.

• Event management on Luma for both online AMAs and in-person events.

• Slack channel management that grew from 8 channels to a more complex setup with specialized topics.

The lesson here is to keep your ear next to the ground at around 150-300 members because there is an inflexion point in volume.

The value prop that emerged organically

One of the most fun things running R2 was watching value show up that we didn’t plan for:

• Finding a job: a very active #hiring channel, with many members landing their next role from companies hiring within the community.

• Filling a job: startups posting open roles in the same channel.

• Sales insights: someone posts a complex problem in #anonymous. People navigating whether to stay or leave a company, sharing real numbers for salary negotiation, working through tough management conversations.

• Market intelligence: you get a high-fidelity sense of how companies are really performing by listening to their front-line troops. This helped members decide where to look for their next career move.

• Dealflow: many members were starting companies or connected to founders. This ended up being relevant for my day job as an investor, too.

LinkedIn as Top of Funnel

We knew salespeople live on LinkedIn. So we opened a LinkedIn account for Revenue Squared and started publishing content that would attract exactly the right people.

What worked:

1. Job listings from the community. We knew the #hiring channel was always on fire. People kept asking us to post their roles even without being members, and we said no. That became the pull.

2. Memes. Sales is tough. Humor builds connection.

3. GTM insights, reports and playbooks. Tactical stuff that the community was producing.

4. Community social proof. Photos and images from events, rankings, workshop clips.

5. Monthly top member recognition. Showcasing the best of the community to the outside world.

This fed the Tally waitlist, which kept growing. The waitlist gave us information about people’s level of interest and helped calibrate who would have the right profiles. We kept interviewing every prospective member until a few hundred members.

At that point we built a referral mechanism on the private area (i.e. share this link and you’ll move up the waitlist), and we started introducing more friction in the application form. This gave us more information about prospective members, and helped us play by ear if we needed interviews or not. The referral and deep profile information shared was usually enough to get a good sense of approval or rejection.

Act III: The Exit

I met Ramon from Nova over lunch in December 2023. They were building something aligned with our community’s mission at the time, and they saw in R2 what we’d built: an audience of 25K+ across Linkedin, Youtube and Substack, a highly curated and engaged paid membership community, with tier 1 sponsors, over 200+ learning modules sourced from real case-studies in our learning platform, and the number 1 GTM brand in the Spanish-speaking world.

The deal was modest and in equity, meaningful to me in a way that goes beyond the numbers. Many people found their next role through R2, some started companies together, and the mission and vision are still growing every day. I’m really stoked to have Nova give the community the resources it needs to keep growing.

That’s it for this week folks.

Thanks for reading,

Ivan

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

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If you enjoyed this, you might like:

→ How Harvey AI Hit $200M+ ARR in 36 Months

→ How Lovable Reached $400M ARR

→ How Freepik Hit 100M Users With €0 in Venture Capital

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Harvey AI’s Growth Playbook: Putting a lawyer in everyone’s pocket.

Hello there!

This week we’re deep diving into the growth playbook of one of the most important companies of this AI wave, Harvey.

This is a perfect storm story where you have a combination of extreme product-market fit (LLM’s usefulness on expensive, previously time-intensive, heavily text-based work), talent and investor hunger.

In July 2022, a securities litigator and a DeepMind researcher cold-emailed Sam Altman from their shared apartment in San Francisco.

The email subject line they used was on point:

Attached was a proof of concept where they’d pulled 100 questions from r/legaladvice (reddit), ran them through GPT-3’s API using chain-of-thought prompting before anyone was talking about chain-of-thought, and sent the outputs to practicing landlord-tenant attorneys. Turns out 86 out of 100 answers passed and nobody at OpenAI had tested this.

That email led to a July 4th pitch to OpenAI’s C-suite, a seed investment, and what became Harvey, going from $0 to $200M+ ARR in 36 months and an $11B valuation.

Two frameworks run through the whole story:

1. The first is prestige-led distribution: turns out that in professional services trust IS the product. Their hypothesis was that if you win the top firms first, even though it might be much harder to wedge yourself in initially, once you are embedded into those logos trust cascades downward through the entire industry.

2. The second is “expand and collapse”: Build specialized AI agents for narrow legal workflows. Then collapse them back into one interface that routes users intelligently. Which has become somewhat of a blueprint or operating system for how to build a compound vertical AI company in 2026.

What you’ll learn in this edition:

• How Harvey went from a cold email to $200M+ ARR in 36 months with ~460 employees

• Why they targeted the hardest law firms first when every VC said start small

• The “expand and collapse” product strategy that is the actual playbook for vertical AI

• Why Harvey’s CEO says GRR is the metric most AI investors are dangerously ignoring

• How they’re shifting from selling software seats to selling legal work through revenue-share deals

📐 Quick note on methodology: 7 podcasts (20VC, Sequoia, Greylock, Kleiner Perkins, Biography Pod, This Week in Startups, Upstart Media), plus press from CNBC, TechCrunch, Bloomberg, Reuters, Sacra research, and Harvey’s own blog. All revenue figures sourced. Treat directional estimates as directional.

Act I: The Cold Email (July 2022 to Early 2024)

How it started

Winston was a first-year litigation associate at O’Melveny & Myers in Los Angeles and his cofounder and roommate Gabe had spent a couple years doing AI research at DeepMind and Meta.

Gabe had been brainstorming horizontal AI assistant ideas with friends from Google Brain, who ended up joining OpenAI and building ChatGPT.

But he went in a different direction.

“Winston showed me all his legal tech. Their document search to search maybe 10,000 documents would take 5 to 10 minutes. He was like, I do the search and then I go on Instagram for a bit. The client gets billed for this.”

Everything in law was text, the workflows were deeply manual and the economics were, as we all known too well when we’re billed for a couple hours of legal work, big.

How the legal market actually works

To understand Harvey’s growth, you have to understand the industry.

The global legal services market is roughly $900 billion (and that is the size of the market today, lets not fall into the classic market sizing trap).

The average US lawyer bills $352 per hour, and the top partners at elite firms bill $2,000+. The gap between a junior associate and the best partner on earth is only about 3-4x in price, which Winston thinks makes no sense and will change.

Law firms are organized around billable hours. Partners give tasks to associates, associates bill hours, and the firm collects. Typically a junior associate does the first draft, a second-year reviews it, a fifth-year reviews that, the partner reviews it one final time, and then it goes out.

This hierarchy matters for AI adoption because it means there’s a built-in human-in-the-loop review system. The minimum viable quality of an AI output can be lower for a law firm than for an in-house team, because everything gets reviewed anyway.

What excites me the most about this tech shift’s impact on Legal work is that the average price used to put legal services out of reach for most people, until now.

But the path runs through the premium market first:

Growth lever 1: The contrarian customer choice

Every VC and advisor told them to start with small firms, solo practitioners, the SMB market (not sure it is a VC’s job to tell a founder what market to go after).

Instead they went straight to Allen & Overy (now A&O Shearman), one of the world’s largest law firms and signed a 4,000-person enterprise rollout.

At that time Harvey had 4 people and were working out of an Airbnb.

“Some of our investors were like, this is a horrible idea. Don’t do this.”

But Winston understood something about professional services that pure technologists usually miss, which probably came from having worked and experienced how a law firm did things first hand.

“If you earn the trust of a few of those firms, the rest of them will trust you and the rest of the firms downstream will definitely trust you.

And their clients will trust you.”

The logic was both GTM and product:

• On the GTM side, prestige in professional services functions like a trust certificate. If A&O trusts you with their client work, every mid-market firm will trust you too.

• On the product side, the hardest work builds the most defensible systems. If you can handle a $68 billion Activision-Microsoft merger, an NDA review should be trivial. ChatGPT could already handle simple NDAs, and going down-market first means building something the models will eat in 18 months.

“The thing that makes me feel better is if you get to the point where GPT-7 can just do a simple lease analysis... yeah, we’re probably done. But I’d also say pretty much every other company on earth is done.”

I think their contrarian bet here is aligned with what we called the Systems of Action thesis a few months back. Let me refresh your memory:

The bet Harvey took here is that intelligence will become cheaper and cheaper.

And if that happens:

• “good enough” AI is everywhere

• the bottleneck is no longer having intelligence

The hard problems become:

• who is allowed to decide

• under which rules

• with which approvals

• and who is accountable when something breaks

Which are coordination problems, not intelligence problems. You can think of coordination as deciding who does what, in what order, under which rules, and who is responsible if it goes wrong. The intelligence is not choosing the goal but rather helping move work through a system of approvals, handoffs, and constraints without humans chasing each other. In this world:

• power shifts to systems that control workflows, rules, and decisions

• intelligence is a commodity

• whoever runs the work wins

This is similar to how Salesforce didn’t win by building better databases but by deciding how sales teams track leads, approve deals, forecast revenue, etc.

Harvey would have started as a Vertical AI tool, and if I’m right would be expanding to become a workflow builder climbing up the stack (platform).

Growth lever 2: Hyper-personalized demos that don’t scale

Before every pitch, Winston would look up the partner he was presenting to. He’d find a recent case they’d worked on (public court filings, merger agreements filed on EDGAR), and have Harvey analyze their own work.

For litigators apparently this move was pretty shocking.

He’d upload one of their own filed arguments and say:

“Tell me, make holes in these arguments. How would you argue against it? Lawyers are argumentative. Just let them fight with the model. They pay attention to every single word.”

Sometimes the model was wrong, especially in 2023 with early GPT-4, but that almost didn’t matter because the lawyers were reading every word, which no software demo had achieved before.

I’ve spoken on here before about talking to a lawyer friend of mine about using Harvey at work (before really knowing much about the company) and was shocked about how much value it drove for her (sharing the tool among multiple lawyers with one licence, saying it’d be incredibly painful to loose it).

Reminded me of Superhuman’s product-market fit engine and its core question:

“A lot of these products, it’s just populating text and how do you get someone to pay attention to that? Well, you tell a litigator that there might be holes in this argument and they go... but they want to prove you wrong.

And so they really pay attention.”

This is gold for a sales team.

They’ve since productized this approach, where partners can now click one button and run their own matter through specialized workflows without prompting. Which means the thing that didn’t scale became a deployment strategy, and when partners share those workflows with their clients, it becomes distribution.

Growth lever 3: The trust flywheel (external virality)

Harvey’s strongest growth loop is likely external, where law firms are bringing Harvey to their clients. Here’s how it works:

• A private equity fund’s law firm builds a fund formation workflow on Harvey.

• The firm shows it to the PE fund.

• The PE fund says “we want this too.”

• The PE fund is a happy customer, they start pulling in their other law firms.

A&O hit 70-80% daily usage during the initial pilot, so during the trial 3,500 lawyers asked 40,000 questions before the firm committed to a broader rollout.

Their first 50 enterprise customers were all referrals from law firm clients. By end of 2023, they had ~40 customers and were running at roughly $7M ARR (my estimate based on reported 4x growth to $50M by early 2025).

Act II: The Trust Ladder (2024 to Mid 2025)

This is where most AI companies stall or what we’ve been calling “vibe-revenue”. Now you have to actually retain, expand and build the machine that scales.

So how did they do it?

Growth lever 4: Expand the product, collapse the UI

Read more

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The engine behind every top 1% startup is a great CRM.

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The Lovable Company: Growth Playbook

One of the tools that fascinates me most is Lovable, both because of how fast their product ships (daily?) but also for how spectacular it is to enable creativity (i.e. meet my friend Arnold) and give you what genuinely feels like superpowers.

So I decided to deep dive into how this European outlier grows.

Here’s what actually worked, stage by stage:

📐 Quick note on methodology: 40+ sources including podcasts, company blogs, investor analyses, and third-party teardowns. All revenue figures from TechCrunch or public statements. This company moves so fast that data from 3 months ago is already outdated, I used the latest confirmed source in every case. Treat all numbers as directional.

Act 1, The Spark: $0–$10M

Anton Osika studied engineering physics at KTH, worked at CERN, was the first engineer at Sana Labs, and co-founded Depict.ai through YC. In 2023 he built GPT Engineer as a side project, an open-source experiment to see what LLMs could do with code.

It hit 50K GitHub stars which was the fastest-growing repository on GitHub that year. GitHub’s systems temporarily shut it down thinking it was a DDoS attack 😅, because it was spawning 15,000 new repositories a day.

He tried to productize it twice.

• Launch 1 (early 2024): user growth stalled almost immediately.

• Launch 2 (mid-2024): retention collapsed, the AI got stuck on anything complex, and the pricing model was bleeding money on power users.

Most people read that as a no, but Anton read it as a spec.

He and co-founder Fabian Hedin migrated the entire backend from Python to Go, built new scaling laws for AI self-debugging so the model could get itself unstuck, and made the call that changed everything: stop targeting developers.

Their hypothesis was that the real opportunity was giving non-technical people the ability to build software from scratch (superpowers).

“Lovable” as a rebrand was spot on, as it told you how it should feel. , who joined later as Head of Growth, has a name for this: the Minimum Lovable Product, software with personality, software you trust because it feels alive.

The name has also become somewhat of a cultural enforcement mechanism inside the company, when someone says “this is not lovable” in a meeting, everyone stops what they’re doing.

Third launch was in November 2024, which became #1 on Product Hunt, #1 on Hacker News and drove $10M ARR in 2 months with 15 people.

The levers that drove it:

1. Founder brand as distribution: Anton had already been cultivating an audience on socials built over years of posting raw numbers, failed experiments, hiring photos, half-formed ideas a-la Pieter Levels. When hey launched people already trusted the person before they trusted the product. This deserves a deep-dive on its own, worth having a trip down memory lane on Anton’s timeline.

2. GPT Engineer’s 50K GitHub stars were a pre-built trust base: Thousands of developers already knew the name and the credibility likely transferred directly to the new product, again part of a long-term founder brand investment.

3. Short video demos as an ad: Nobody knew what vibe coding was in late 2024 but you watch 10 seconds of someone building a real app by typing a sentence and you think “I had no idea this was possible.” / what kind of sorcery is this. I think Twitter, Tiktok and YouTube drove real curiosity at scale due to the product’s inherent “magic moments”.

4. Co-marketing with Supabase, Replicate and Resend: gave them reach into audiences that were already product-curious and technically adjacent.

5. Product Hunt and Hacker News as coordinated launch amplifiers: as usual, but also with leveraged via points 1 and 2 above.

6. User-generated content from day one: Every post someone shared of their Lovable build was basically an organic proof point (PLG mechanics).

One mistake Anton calls out directly that caught my eye and is worth highlighting as it is a perpetual debate I had both working at Meta on product discussions but also with many of my founder friends (almost weekly):

On trying to keep the GPT Engineer open-source community alive alongside Lovable: “A bad idea to do two things that were like a bit too tangentially related.”

Community goodwill lost to radical focus every time. His advice now seems to be to find the bottleneck, solve for that, one thing at a time.

Act 2, The Machine: $10M–$100M

A founder’s personal brand can get you to $10M (as wild as that is). But what gets you to $100M is likely, as is usually the case, a system.

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Hello there!

This week I’m bringing my friend to co-write this edition. Jordi is also my colleague at JME Ventures, where we invest in startups from pre-seed to Series A. He’s been following Freepik closely for a while and put together the research behind this edition.

Jordi Noguera

Venture Capital | Startups Analysis

Why Freepik? Because it’s probably the best real-world case of a company that watched AI threaten to destroy its business, and instead of playing defense, ate its own product and came out growing faster than ever.

And it did it from Málaga, without a single euro of venture capital!

Three acts and three growth lessons, let’s get into it:

1. People were making logos in Word, and Freepik saw the gap. (2010–2020)

Few people know this, but Joaquín Cuenca had already sold 2 companies before Freepik. First Loquo, a classifieds portal sold to eBay in 2005. Then Panoramio, together with Eduardo Manchón (who later founded Mailtrack.io, now Mailsuite, with ~€8M in revenue and 110K paying customers), sold to Google in 2007. Joaquín stayed 3 years at Google Zurich as Tech Lead for Panoramio before starting Freepik in 2010.

The other co-founder, Alejandro Sánchez Blanes, was building BeSoccer (formerly Resultados de Fútbol) and Joaquín was helping them. They needed tons of illustrations for the app and were browsing stock pages one by one looking for them. It consumed so much time that he ended up building a search engine for himself.

He published it publicly and within just over a month, more than 100,000 users showed up.

At the time, people were making logos in Word using WordArt and there were few options for graphic design. Freepik was born in that gap.

How the stock image market works

To understand what Freepik did, you have to understand how this market evolved.

The industry starts in the 90s with Getty Images, digitizing photography. You bought unique photographs (”the pieces”) for $199–699 per image. Then Shutterstock appeared with a “microstock” model where they sold the same photograph for $3–69 (with a subscription) but to many more people. More marketplace but less exclusivity.

There was a massive market shift happening at the time. Everything was moving from TV ads and print to web, and that meant you needed way more items per project at a much lower price. Anyone could “create.” The market was going from low volume, high price to high volume, lower price.

Freepik’s key decision was to specialize in illustrations and icons. Getty and Shutterstock were only in photos. But illustrations have very different marketplace dynamics.

Airbnb would be a good comparable. The more fragmented supply and demand are, the more sense a marketplace makes. Illustrators weren’t good at positioning themselves, and there are so many that it’s impossible to reach them all.

Depending on the type of content, the dynamics and economics change a lot.

1. Photography. Maximum fragmentation. You need infinite capillarity to have every photo in the world (if you want a photo of X place, at X specific moment, in X style). This is the most suited for a marketplace for that reason.

2. Illustrations. A single illustrator can create many. Less fragmentation, but discovery is still hard.

3. Icons. Even less variety. One person can create many icons and cover a lot of ground.

A real case of my own. I had a company where we were Shutterstock and Getty users. We had €0 and were bypassing these platforms all day long. We bought photographs for commercial use (much higher price), and if you could find the author directly you’d get it cheaper. But finding them was nearly impossible. That explains why Shutterstock’s take rate is what it is.

The data moat nobody saw coming

What Freepik did differently was start as a mini Google for free illustrations, a search engine that scraped third-party pages. The problem was that the experience on those third-party pages was terrible. So after 3 years they started creating their own content.

Netflix would be a good comparison. They went from aggregating third-party content to producing their own. Same pattern.

Their access to search data from users became their “unfair advantage”.

How did they monetize at first? They “gave away” the content they created to users who had blogs, in exchange for referencing Freepik when they published it. Readers learned about Freepik and over time started coming in as users themselves. Pure organic growth.

Joaquín tells a case from Japan that captures the depth of this understanding well. When a user typed “mountain,” they actually wanted Mount Fuji. You had to show Mount Fuji at the very top. That level of user understanding, country by country, search by search, generated the flywheel.

Over time, many users who used the free illustrations were willing to pay. They moved to a freemium model and opened the platform for any designer to upload content and earn from downloads.

The model ended up at 50% revenue from own content and 50% from third parties with a 50% take rate. This matters because the economics are very different depending on the model.

Own content carries more risk because you’re investing in the content (an asset that depreciates, and photography depreciates faster than illustrations or icons), but it leaves you higher margins. Third-party content (marketplace) carries less risk because you’re just publishing on the platform, but also lower margins because of the take rate. Freepik had both. Smart diversification.

By 2020 the numbers spoke for themselves.

• 32M monthly users (from the EQT press release)

• 500,000+ paying subscribers with an average ticket of ~€7

• Revenue of €45M over 9 years from €200K in 2012

• With zero outside funding. Fully bootstrapped.

In May 2020, EQT Mid Market Europe buys 53% of Freepik for a valuation of ~€250M. Drake Star Partners advises the sale. Two of the three co-founders exit (Alejandro and Pablo Blanes). Joaquín Cuenca stays. Doesn’t sell a thing. In October of that same year, Atwater Capital enters with an additional minority stake and Vania Schlogel (founder of Atwater) joins the board.

Having a founder like Joaquín, with that level of ambition, is one of the reasons I like Freepik so much. In Spain, similar to Factorial or PLD Space.

2. “Everything we’ve built in 10 years is becoming obsolete.” (2022–2023)

In 2019–2020, DALL-E 1 appears, OpenAI’s image generator. First image diffusion model with any quality at all. The quality was terrible, but it showed something no previous system had achieved, which was real genericity. It could draw “a radish walking a dog.” Sounds absurd, but that’s exactly the point. No previous system could do that.

It might seem obvious now, but at the time, extrapolating that the technology would keep advancing, what was the point of Freepik? There were two possible stances.

The comfortable one was to think it wouldn’t change much.

The uncomfortable one was to assume everything changes and that AI improves in a matter of months.

With AI, there have always been three stages.

1. First you think it won’t be able to do the task, and it does.

2. Then you think it won’t do it as well as the top expert, and it does.

3. And finally you think it won’t surpass the expert, and it does. Cases like chess or Go (the Netflix documentary is incredible). The same pattern repeated with images.

Then DALL-E 2 arrived in April 2022. Joaquín was at home with COVID and spent entire days watching what people were generating. The quality jump was, in his words, “so dramatic” that the conclusion was inevitable. In one or two years, AI would match the quality of every image in Freepik’s catalog.

His reaction was honest. “Initially I’d say I felt some despair. I had no plan. I didn’t know what to do.”

The only thing he was sure of was that what Freepik had been doing for 12 years was no longer going to be the business of the future.

He had to tell more than 500 employees that everything they’d built over the last decade was becoming obsolete. Some employees needed psychological treatment. This is serious.

And yet, the business was better than ever. In 2021 they’d closed with €61.5M in revenue (+37% YoY), 380 employees (+26% YoY), and already exceeded 500K paying subscribers. By 2022 the headcount had grown to 567 (+49% in a single year) and revenue was around $87M. 70% of revenue was already recurring (subscriptions). Double-digit growth for years, “wildly profitable.” That’s the context that makes the decision harder, because they really weren’t in crisis but at the best moment in the company’s history.

What was happening in the market?

I always say that in a business there are two types of risk, technological and market. We’ve had many years of venture capital where almost every company has zero technological risk but does have market risk. The conversations are always about metrics, acquisition, positioning. You rarely talk about the technical approach at a deep level because you assume the team is capable of building the product.

Freepik had the opposite problem. Zero market risk with 100M users and more than proven demand, but suddenly a brutal technological risk.

Adobe’s position was understandable. They acknowledged the technology was incredible, but their stance was “this is illegal.” They had 400M images in their catalog and were in a comfortable position to defend this. Incentives explain everything.

Was it legal or illegal to train models with images? At the time, nothing was clear.

Freepik’s position was different. They thought ChatGPT and everything around it was too valuable for society, that no country would want to be left out of the race, and that if the law interpreted that all text and all images needed copyright clearance to train on them, ChatGPT couldn’t exist. Freepik didn’t see a future where states would impose that limitation.

They decided to take the aggressive position and assume the technology would exist and improve significantly over time.

How AI image generation works (for the curious)

Before generative AI there was an important intermediate step, GANs (generative adversarial networks). One neural network generates an image and another tries to determine if it’s real or not. Both improve by “fighting” each other. The result was decent quality but only for very narrow domains, like frontal human faces for example. Zero genericity. Only worked for a specific task.

Then diffusion models appeared (DALL-E, Stable Diffusion, Flux). The process works like this.

1. You take an image of a cat and add noise, which is random variations in the pixels, like grain or static.

2. You train a neural network to learn to remove that noise.

3. You repeat with more noise. The network keeps learning to “reconstruct” the original image.

4. You repeat this up to 100 times.

5. The magic step. Instead of starting with a noisy image, you start with pure noise. And the network is able to invent the cat.

This allowed you to take much larger image datasets (potentially every image on the internet) and generate new images from them.

But it had limitations. Small models got confused with details, so if you asked for “green cat on blue table” the cat came out blue. They couldn’t count well. Hands were a disaster because of too many possible finger combinations. And text inside images was unreadable.

Now with autoregressive models (like Nano Banana Pro, similar to how ChatGPT works but for images) the image is divided into parts and each piece is like a word predicting the next one. This has massively increased comprehension of what you’re asking for. Hands come out right, text is readable, fine details work.

Freepik’s decision was to build while walking

Joaquín’s decision framework was simple.

We don’t have AI researchers, so we can’t create models ourselves. We don’t yet know what value we can add because there’s a machine that does everything, so what do we do? If the answer is “nothing,” fine, that option leads nowhere, discard it. But if the answer is “something,” what happens if we act now and shape how this technology reaches our users?

The decision was to grab this technology as soon as it was available and discover the value along the way.

Joaquín believes companies should build while walking. It’s very ambitious to think you’ll know what’s going to happen in 5 years, and the only way to move forward is step by step.

They started hiring and building an AI team without knowing exactly what to do, but with the stance that doing things leads to understanding things.

The first use case was improving the search engine. Before, it worked with a tag system where the illustrator uploaded content, added tags, and that’s how it was found. With AI, the search engine could understand the image directly. They used CLIP (Contrastive Language-Image Pre-training, from OpenAI). They integrated it in less than an hour after OpenAI’s release. That execution speed says a lot about the team’s mindset.

The second use case was generation. Stable Diffusion arrives as open-source in September 2022 and Freepik (through Wepik) integrates it into the platform, becoming the first non-professional editor to do so. They positioned themselves as “the simple way to use the model.” And just as they had the moat of most-searched keywords in the past, they now started getting real data on what users wanted to create.

And something counterintuitive happened. The business accelerated. Freepik grew faster after the pivot than in the five years before. AI generated new demand, it didn’t cannibalize stock downloads. By the end of 2023, Freepik reported 150M+ monthly users (vs. 41M in 2021), 2M MAU on AI tools alone (300% YoY growth), and 120M+ assets created with AI on the platform that year. 60% of new users came through AI tools.

In parallel, 2022 was the year of expansion through acquisition. They bought Videvo (UK, stock video), Iconfinder (Denmark, icon marketplace, whose CEO Martin LeBlanc Eigtved became Chief Product/Experience Officer at Freepik), Original Mockups (Colombia, 3D resources), and Check My Presets (photo filters). Four acquisitions in one year, each adding a new content vertical to the ecosystem.

Why? Because the 100M monthly visitors became instant distribution for AI tools. Volume from day one, which meant better unit economics on AI generation than any pure AI competitor. Lower inference costs that get passed on to the user, which brings more users. The flywheel again:

3. Models are the chips, and the platform is the computer. (2023–present)

The last time we had a technology that developed exponentially was chips. And what happened was that every improvement in the CPU enabled new use cases that were previously impossible, and each new use case created a bottleneck that in turn created the next use case.

It’s an analogy Joaquín uses. The technology is incredible (the CPU), but what you need is a computer, not to solder things together. Just like the iPod was born as a wrapper around a Samsung hard drive, but no, it was something else. It was the experience layer on top of the hardware.

Freepik’s reflection was that, just as it used to be very hard to publish a text and now it’s as easy as one click, the same thing can happen with visual content creation. There’s a new technology that enables anyone to have quality, infinite visual resources. That creates a bigger market, with much more volume. And to work with that, you need a platform that helps you manage it.

That’s where the orchestrator concept was born. Models are like CPUs and Freepik is the computer. Very specific models started coming out for specific tasks, and they realized the biggest help to the user was orchestrating those models, just like a computer orchestrates different components, and letting you do different things from a single place.

Today Freepik is ranked #11 in a16z’s ranking of the 50 most-visited generative AI products in the world. Above Remove.bg (#16), Grammarly (#18), CapCut (#17), and Midjourney (#43). A bootstrapped company from Málaga.

The acquisition of Magnific in May 2024 was a turning point. Javi López and Emilio Nicolás (Murcia) created an AI upscaler that went viral, 30,000 signups on day one, and Freepik bought it, bringing 700K+ professionals into the ecosystem. The co-founders were integrated into Freepik Labs under Omar Pera (CPO, and a JME Ventures portfolio founder!).

When you don’t have heavy fixed assets, the ability to move fast is enormous. You can pivot because you don’t have as much sunk cost. It’s the innovator’s dilemma in reverse. The one who had nothing to protect was the one who could attack fastest.

I look at Freepik and see something similar to what happened with Kodak and digital cameras, but with a different ending. We’re watching Kodak adapt to digital cameras. I’m surprised by the ability of 500 employees, with tens of millions in revenue, to turn the ship and say “I’m going to cannibalize myself.”

And the entire market has been moving from the low volume, high price model (exclusive photos at €300) to high volume, lower price. With the rise of social media everyone needs to publish more, and tools have appeared that give access to many more people. Freepik has surfed each of those waves.

Conclusion

The Freepik story is about what happens when a company that knows its users better than anyone encounters a technology that threatens to destroy it, and chooses to attack.

Three times the market changed and three times the data moat turned out to be what mattered. First for SEO, then for understanding what users wanted to create, and now for training AI models with clean data.

Bibliography

Podcasts and interviews with Joaquín Cuenca

• Drill Down Insider Ep. #22: Freepik CEO Joaquin Cuenca (March 2026). Cory Johnson.

• Freepik CEO: “The Day I Realized AI Would Destroy Us” (2026). A Company That Refused To Die.

• Joaquín Cuenca at Upscale Conf 2025 (San Francisco, 2025).

• La IA era la única opción para no morir como empresa (Podcast IA, 2025).

• Freepik en Itnig (2021). Podcast Itnig.

• Spicy4tuna: Freepik (December 2025).

Data and research

• a16z. Top 100 Gen AI Consumer Apps, 6th edition. Top 50 Gen AI Web Products by Unique Monthly Visits (Similarweb + Sensor Tower, January 2026).

• Reforge. The 8 Most Important Metrics for Marketplace Growth.

• Getty Images + Shutterstock merger ($3,700M, January 2025). Press releases. Getty Full Year 2025 Results (GlobeNewsWire, March 2026). Shutterstock Full Year 2025 Results.

• Canva. $4B ARR (SaaStr, Feb 2026). Valuation $42B (tender offer, August 2025).

• Adobe. FY2024 Earnings. Q1 2026: $70M shortfall in stock photography.

• Freepik. F Lite: Open-source 10B parameter model (April 2025). Official Freepik blog. GitHub / Hugging Face.

• Freepik. Magnific acquisition (May 2024). Official press release. Tech.eu.

• Freepik. Freepik Spaces launch (November 2025). BusinessWire.

• Freepik. Pikaso launch and metrics (December 2023). BusinessWire.

• Freepik. Nano Banana Pro. Official Freepik blog.

• Freepik. Ideogram 3.0 integrations (March 2025), Google Veo 2/3, Kling O1 (December 2025).

• EQT. Freepik acquisition announcement (May 2020).

• Atwater Capital. Minority stake (October 2020). Private Equity Wire.

• Mordor Intelligence / SkyQuest. Stock photography market size $4.5–7.2B (2024).

• Kaptur. The Silent Collapse of Photo Licensing Revenue. AI impact on photographers (Sep 2024, Feb 2025).

• El Español. Freepik closes 2021 with record revenue (Feb 2022). Freepik reaches 567 employees (Dec 2022).

• Tech.eu. Europe’s quiet AI giant (Jun 2025).

That’s it for this week folks.

Cheers,

Ivan

P.s. send me your questions about startup growth by replying to this email, I’ll try covering them in future editions 🤙

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Hello there!

This week I’ve been thinking a lot about one question:

Where is the next AI startup opportunity pocket moving?

3 things landed in my inbox that, once your layer them on top of each-other, tell a pretty compelling answer to that question (or at least a thesis). Today’s tl;dr:

1. 🤖 Where AI works today.

2. 🟧 Where 198 YC founders placed bets.

3. 📈 The $1T+ in services TAM almost nobody is touching (yet).

📐 Quick note on methodology: AI coverage data comes from Anthropic’s Economic Index (one model’s usage, not all AI). Employment data from BLS (2024). YC sector mapping is my own analysis. Services TAM from Sequoia. Treat all numbers as directional.

1. We’re barely scratching the surface.

The details

Anthropic dropped a new measure called “observed exposure” that combines what AI could theoretically do with what it’s actually doing today in the real world.

The numbers that jumped out:

• Computer & math occupations: AI could theoretically cover 94% of tasks. But the actual coverage today is 33% (although moving fast).

• Computer programmers are the single most exposed occupation at 75% coverage, followed by customer service reps (70%) and data entry keyers (67%).

• Office & admin is 90% theoretically feasible but only 14% observed. Business & finance is 87% possible, 18% real.

• Everything below that, education, healthcare, social services, construction, is in single digits.

This is what (CEO of Luzia, 65M users) called the jagged frontier, where AI is shockingly good at some tasks and pretty bad at others, but the boundary is unpredictable and moving fast:

So what

The playbook that worked for code is likely coming to every profession where the work is more “intelligence” (rules, patterns, process) than “judgement” (taste, experience, context), more on that in a moment.

The bottom line for me here is that even though we have yet to find any measurable increase in unemployment for exposed workers since 2022, young workers are starting to feel the pain. Hiring for 22-25 year olds has slowed 14% in exposed occupations, which means that entry points to the labour market are narrowing, and we should be thinking more deeply about:

1. How do we get ahead of this trend, which is likely to accelerate.

2. Education reform (would love to see more startups working on this problem).

2. Where 198 YC founders are placing bets

The details

This YC’s W26 batch is the largest yet with 198 companies. Here’s how they break down:

• Engineering, dev tools & infra: 41 companies (21%). The most crowded single category. This is where AI adoption is already highest (per Anthropic’s data), and where the most founders are piling in.

• B2B horizontal / uncategorized: 49 (25%). A grab bag, but many of these are AI wrappers, workflow tools, and agent scaffolding.

• Finance & fintech: 23 (12%). Accounting, payments, lending, insurance.

• Healthcare: 16 (8%). Drug discovery, healthcare IT, and a handful doing actual patient-facing work.

• Legal: 7 (3.5%). Small but punching above its weight given Harvey and Legora just hit unicorn status.

So what

The batch confirms what Anthropic’s data shows which is that founders are building where AI already works, which is totally rational because the near-term PMF is obvious in software, finance, and legal (crossed the adoption threshold).

But it also means 198 of the smartest founders in tech are mostly fighting over the same territory, and this is an opportunity for you. The sectors where AI’s theoretical capability is high but actual usage is low (education, healthcare operations, social services, office admin) are almost empty.

Which brings us to the gap.

3. The $1T blind spot.

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Hello there!

This has been another week filled with OpenAI drama.

And following the news of a 295% uninstall spike after they swooped-in to fill Anthropic’s U.S. Department of War deal, it prompted me to ask:

Who is actually winning the AI race?

Ramp’s latest AI Index data came in handy to showcase a few insights on this following a couple viral tweets on the topic. One claimed Claude hit ~70% market share while another said OpenAI still dominates, and both cited the same data source.

Both were kind of right, but also both kind of wrong.

Today’s tl;dr:

1. 🏁 Who’s winning the AI race (it depends which chart you read).

2. 📚 Top 10 research reports this week.

3. 🌊 43 European startups that raised €10M+ in February.

📐 Quick note on methodology: Ramp tracks 50K+ US businesses via corporate card and bill pay. It skews US, mid-market, tech-forward. The index measures three things separately (adoption, chat spend, API spend) and most viral takes conflated them into one “market share” number. Treat all numbers as directional.

1. OpenAI leads adoption (36%) but Anthropic leads spend (>50% chat, ~80% API).

The details

Ramp’s latest drop tracks 3 different things, and people keep confusing them:

• Adoption rate (who has more paying customers): OpenAI leads at 35.9% of businesses vs Anthropic at 19.5%. Google trails at 4.5%.

• Chat subscription spend (where seat revenue goes): Anthropic leads with >50% of enterprise AI chat spend. Claude Team, Max, and Enterprise seats cost more than ChatGPT Plus.

• API spend (what developers build on): Anthropic dominates at ~80%. In July 2025, OpenAI had ~95% of this market. Eight months later, it completely flipped!! Claude 4.5 Sonnet and Opus have eaten the developer stack. There’s a fantastic interview on ‘s podcast with Claude Code’s creator well worth your time.

So what

OpenAI wins breadth, Anthropic wins value and the developer API market has picked a side (and if you have been playing with Opus 4.6 you probably have experienced why). The race looks very different depending on which metric you read, which is why “who’s winning” is kind of the wrong question altogether, but still fun to explore.

2. 16% of US businesses now pay for both ChatGPT and Claude. Double from a year ago.

The details

This is what I think is the most interesting number in the whole Ramp report:

• 79% of businesses paying for Anthropic also pay for OpenAI.

• Churn rates are nearly identical at ~4% per month for both. If businesses were replacing one with the other, you’d see it in cancellations, but you don’t (so far).

• The share of businesses paying for both doubled from 8% to 16% in 12 months

• Anthropic went from 1 in 25 to 1 in 5 businesses in a year (4x growth) while OpenAI stayed roughly flat around 36%.

• Total AI adoption hit 46.8%, an all-time high. Nearly half of US businesses now pay for AI, whereas two years ago this was close to zero.

So what

Businesses are actually stacking providers, where different teams with different use cases at same company use different models at different times. If you’re building, designing for a multi-model world likely makes sense since customers seem to already be doing it.

3. Anthropic's run rate: $9B (Dec) → $14B (Feb) → $19B (Mar).

The details

Bloomberg reported this week that Anthropic’s annualised revenue run rate just hit $19 billion:

• $9B at end of 2025

• $14B in February 2026

• $19B in early March 2026

So what

This rate of adoption is driven mostly by Claude Code and enterprise API adoption. There’s also a good question (for another time) to dig on here which is the speed at which people switch models (for the same use-case) and how enterprise sales cycles will affect their stickiness (and vice-versa).

4. 295% spike in ChatGPT uninstalls. Claude #1 on App Store for the first time.

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Hello there!

I’m moderating a panel at 4YFN Barcelona next week called “How Agents Are Prompting the Business Playbook” with (CEO, Luzia, 65M users), Daniel (Chief AI Officer, WPP), and Elisenda (CEO, Cala AI).

To prep I went deep on every framework I could find about what’s actually happening to SaaS (and it’s been quite a week to do so) to answer one question:

If AI agents eat the business logic of software applications…

what happens next?

Today’s tl;dr:

1. The stack is inverting: Microsoft’s framework.

2. We’ve seen this movie before: OpenAI Chairman’s analogies.

3. Builders aren’t scared: Naval Ravikant’s motorcycle for the mind.

4. The flywheel was designed this way: the ultimate flywheel.

5. Keynes predicted this in 1930: economic possibilities for our grandchildren.

6. Judgment is the scarce resource: 5 skills for anti-fragility.

1. The stack is inverting

A year ago or so Satya was already talking about this shift.

Before LLMs, you had a human interact with the UI of an app, which ran business logic, which ran on top of a database.

But today, as Naval coined recently, “AI is eating UX.” A new surface area of interaction is emerging: agents. A human-like presence in a box that reads context, chooses actions, and calls tools for us (what LLMs enabled).

Which raised questions about where power is shifting, and why we’re seeing big tech splashing cash and new startups popping up everywhere to surf this wave - because there is a platform shift (and therefore, opportunity).

In this context, and as we know from our friends Benjamin Graham’s Mr. Market definition (irrational, often contradictory) and John Maynard Keyne’s “animal spirits”, the stock market got fearful. All of a sudden.

Here’s my best attempt at representing Satya’s framework for going from SaaS → Agents, which has been helpful to keep in mind lately:

2. We’ve seen this movie before

Came across this pod from Bret Taylor (Sierra founder, chairman at OpenAI) with tons of insight and a few super sticky analogies.

I noticed great leaders are often amazing at this, remember Jim Barksdale’s aphorisms.

The one that stuck with me most: Systems of record are like the sun in a solar system. They hold gravitational pull and everything orbits around them (think Salesforce).

The question is: does that gravity shift to the agents in this new “era”?

3. Builders aren’t scared

This opportunity window (SaaS → Agents) is also being turbo-charged with all these emerging tools, making high-agency entrepreneurs-to-be much more leveraged.

I found some gems in Naval’s recent podcast on this:

4. The flywheel was designed this way

Matt Shumer’s essay “Something Big Is Happening” hit 80 million views recently, because it touched a social fiber that is really tender: agents are already here.

He openly talks about how recent this change has been (Opus 4.6 on Claude released earlier this month, and GPT-5.3 Codex idem), and I couldn’t agree more with him that you really don’t know what you are talking about until you feel the difference using these models and how they’ve dramatically improved in a matter of weeks.

To the point where he openly says:

I am no longer needed for the actual technical work of my job

His most important insight is that AI labs made a deliberate choice to make AI great at code first because building AI requires code. And if AI writes code, it helps build the next version of itself. Smarter version writes better code. Better code builds smarter AI. This is basically the flywheel of flywheels.

Which explains the acceleration we’re all feeling:

5. Keynes predicted this in 1930

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One of my favourite things to do is track early, underrated data sources that give us a glimpse into the future.

Imagine my delight when I found Ramp’s software vendor data.

They process billions in business expenses across 50,000+ companies on their corporate card and bill pay platform. That means we’re not looking at what founders claim or what VCs fund but at what finance teams actually approve (real PMF).

I enriched Ramp’s trending vendor list with funding data, revenue estimates, and investor intel to answer:

Which AI startups are actually winning customers (not just raising money)?

Today’s tl;dr:

1. 📈 Who’s gaining customers fastest.

2. 🔎 Their efficiency numbers are outstanding.

3. 🌊 Three patterns the spend data reveals.

4. 💰 Five VC funds keep showing up.

5. 🔐 The breakout list + full dataset.

Let’s get into it:

📐 Quick note on methodology: Ramp’s “fastest growing software vendors” ranks companies by net-new customer additions on their platform. It skews US, mid-market, and tech-forward. I suspect it doesn’t measure total revenue or usage depth just adoption velocity. I enriched the top companies with funding, valuation, team size, and ARR data from public sources. Treat all numbers as directional.

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While everyone’s obsessing over AI, there’s a quieter wave forming in preventive health, one that AI is actually accelerating.

I got curious about this space for a personal reason: I wanted to find an all-in-one preventive health service for my parents. Something simple that doesn’t require them to navigate the healthcare maze.

Here’s what I found instead: chaos.

In this issue:

1. 🏥 Why preventive health is broken (and who's fixing it)

2. 🏆 The 3 leaders: Function ($2.5B), Neko ($1.8B), Prenuvo

3. 💀 The $350M failure: What killed Forward Health

4. 📊 18 startups mapped (dealflow)

Let's dive in:

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1. The problem

• Information lives in silos. Your blood work is here, your imaging is there, your genetic data is somewhere else. Nothing talks to each other.

• Nobody knows what tests to actually get. Beyond whatever your GP orders (usually the bare minimum), you’re on your own. Good luck finding out what the 80/20 of preventative testing is (I had to ask my friend , check out his newsletter!).

• It’s expensive, confusing, and fragmented. Want comprehensive screening? You’ll need to:

  • Schedule 4-5 different appointments

  • Pay 4-5 different providers

  • Collect paperwork from 4-5 different systems

  • Make sense of it yourself

  • No reminders. No proactive alerts. No app that ties it together.

• The experience is stuck in 1995. Sterile clinics. Anxiety-inducing environments. Reactive instead of proactive.

What’s missing is an Apple-like preventive health product that’s non-invasive, non-scary, integrated with your phone and wearables, and priced for the mass market.

2. The solution

A few companies are racing to build exactly this. Three stand out:

• Neko Health → Europe’s bet (Daniel Ek’s company)

• Function Health → The US lab-first approach

• Prenuvo → The premium MRI play

3. The numbers

Neko built custom hardware: 70+ sensors that scan your body in 15 minutes. No MRI. No radiation. They’re vertically integrated: own the device, own the clinic, own the AI. European expansion is slow but deliberate.

Function went asset-light on hardware but deep on data. 160+ lab biomarkers, AI-powered insights, and they acquired Ezra (MRI scanning startup) to add imaging. Celebrity backers include Matt Damon and Magic Johnson. Fastest to unicorn status.

Prenuvo is the premium play. Full-body MRI at $2,499. They’re the only profitable one, reportedly hit $100M revenue in 2024. Kim Kardashian and Cindy Crawford are investors and evangelists.

4. The funding race

Forward Health raised almost as much as Function Health, $350M from Founders Fund, Khosla, and SoftBank with a peak valuation of $1B, yet they unfortunately had to shut down in November 2024.

From what I could find in research they tried to do many things like primary care, AI-powered “CarePods” and tech-forward clinics at a premium pricing of $149/month.

But unfortunately a mix of expensive real estate, technical glitches, a model that was harder to scale and prioritising tech over healthcare delivery likely impacted them.

Today Neko and Function are the two leading the pack across 🇪🇺 and 🇺🇸:

5. Why Europe has so few Neko-like companies

It is a pity and my hope is that it is changing, but largely due to:

1. Regulation: Medical imaging and screening moves slow in the EU where compliance is likely heavier.

2. Reimbursement: Preventive care isn’t well-paid by public health systems and consumer-pay models are harder to scale when people expect healthcare to be “free”.

3. Capital risk appetite: Full-body scanning requires hardware, clinics, operations, compliance etc, etc. EU VCs historically have a lower appetite for this type of risk. Neko likely only exists because Daniel Ek (spotify’s founder) can take long-term, founder-led risk.

This is a rare case where Europe is under-built in a category that:

• Has real consumer demand

• Has proven US traction

• Has willingness to pay (even in Europe)

• Has massive upside if cracked (and this is going to exist, one way or another)

But things are changing, and a new wave of startups is leading the charge!

🔐 The Preventive Health Deal-flow List

The 18 companies defining this category, from $2.5B leaders to early-stage picks:

• Full funding data, investors, and status

• 18 early-stage startups to watch (Seed–Series B)

• Downloadable Google Sheet

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Hello there!

Anthropic’s revenue has exploded from $1B to $10B in 2025:

Which begs the question:

What is AI actually doing all day in the real world?

I dove into Anthropic’s Economic Index Report, which is the cleanest answer I’ve seen so far because it looks at anonymised Claude usage in November 2025 across both their consumer chat (which I’ve been using a lot lately) and their first-party enterprise API.

So as usual, here are my top key takeaways:

1. AI usage is still shockingly concentrated

The details

Anthropic sees over 3,000 unique work tasks on Claude.ai, but the top 10 tasks account for 24% of sampled conversations. That concentration also shows up even harder in enterprise API usage, where the top 10 tasks are 32% of traffic.

This is the opposite of the “AI does everything now” vibe. It’s more like early SaaS where lots of possible use cases popped-up everywhere, but only a few pulled $$.

Also worth noting that the single most common task is still basically “fix the bug”.

So what

This is how real tech diffusion looks where it typically starts narrow where the ROI is more obvious and then it fans out. The money today seems to still be in a small set of painful frequent jobs where AI already works reliably enough.

2. Coding is still the core “AI job”

The details

Across Claude usage the biggest block is still Computer & Mathematical work. On Claude.ai it’s roughly a third of usage, and in the first-party enterprise API it’s closer to 50%.

Another interesting trend to follow is "Office and Adminstrative Support”, which is super interesting considering what is happening this very week with the parallel rise of ClawdBot / MoltBot / OpenClaw (stay tuned).

So what

The first wave of AI adoption is the economy paying to speed up people who already work with systems. If you are building, this translates to build where outcomes can be proved (i.e. “We saved engineers 30%” > “we improved thinking”).

3. Most people use AI like a co-worker, not an autopilot

The details

On Claude.ai, Anthropic classifies usage as either “automation” or “augmentation”. In November 2025, augmentation is 52%, while automation is 45%.

Augmentation” is back-and-forth: edit, refine, rewrite, sanity check, iterate. Not “do the whole thing and I disappear”.

And the trend is interesting because Anthropic shows automation had risen earlier, then came back down, with augmentation becoming dominant again. They mention product changes like file creation and workflow skills as possible drivers.

So what

This is why AI feels like productivity and not replacement (mostly, and for now). Most value right now comes from people who stay in the loop and use AI as a force multiplier like yours truly over here.

4. AI saves the most time on hard thinking work

The details

Anthropic estimates “speedup” by comparing how long a task would take a human alone vs human plus AI. When they plot that against task complexity (proxied by years of schooling needed to understand the prompt), the curve goes up.

In Claude.ai conversations, tasks around high school level show about 9x speedup, while college-level prompts show about 12x speedup.

So what

This is one of the most founder-relevant facts in the report because it suggests that AI is not only “junior labor” but also clearly “senior leverage”, because it can collapse time for high-skill tasks like analysis plans, architecture thinking, and complex writing.

That pushes a lot of advantage to teams that already have taste and good judgment because they can run more cycles per week.

5. But harder work also fails more often

The details

As task complexity rises, success rates fall.

Simple tasks succeed roughly in the high-60s to low-70s percent range.

More complex tasks dip below that.

This is the gap behind most Christmas-table conversations that start with
“AI is amazing, but…”. The model can move fast, but it is not always right. And the cost of being wrong rises quickly as the work gets harder.

So what

This is why the next wave is not just “bigger models” and you see big rivers of capital going into tooling, evals, monitoring, guardrails, workflows, and product design that makes failure cheap and detectable.

6. AI breaks when a job runs for too long

The Details

They also look at how often AI succeeds as a job gets longer (which is different from our previous point, hard ≠ long, not always):

• Short jobs usually work.

• As jobs run longer, success drops.

• For API-based automation, success falls below 50% after about 3.5 hours.

So what

If you ask AI to run one long process from start to finish, it will often break, and to make this work, you either:

• keep the job very small, or

• build a system that can stop and recover.

7. Breaking work into steps makes AI work much better

The details

The same chart tells a second story. On Claude.ai, success rate declines much more slowly with task duration. Anthropic extrapolates that Claude.ai would hit 50% success at about 19 hours.

Their explanation is that multi-turn chat breaks complex tasks into steps and each turn becomes a feedback checkpoint.

So what

The agentic future is not “more autonomy” but better scaffolding with memory, checkpoints, validation, retries, and human review at the right moments.

Stepping back, the picture is pretty consistent: we are seeing AI doing real work, but mostly in narrow, repeatable, high-skill tasks. It tends to work best with humans in the loop, breaks on long horizons, and creates the most value where problems are clearly defined.

🌊 Top Research Reports

• CB Insights - Physical AI Models Market Map

• McKinsey & Company - Age of AI report

• CB Insights - Top 14 2026 Tech Trends

• Dealroom - European Spinouts Report

• Anthropic - Technology Adolescence

• Anthropic - Economic Index

• A16Z - State of Markets

• KKR - 2026 Outlook

🌊 VC Wave Tracker:

A list of 30 startups that just raised Series B, all likely hiring:

1. Upwind Security - cloud runtime security (US) $250M

2. Harmattan AI - industrial vision AI (FR) $200M

3. JetZero - all wing aircraft (US) $175M

4. Defense Unicorns - defense software (US) $136M

5. Railway - app hosting platform (US) $100M

6. Cambium - defense materials (US) $100M

7. Tandem - AI healthcare ops (US) $100M

8. Ethernovia - vehicle networking (US) $90M

9. Superstate - onchain funds (US) $82.5M

10. Apella - AI clinical copilot (US) $80M

11. Emergent - AI agent platform (US) $70M

12. Listen Labs - AI user research (US) $60M

13. Accelsius - data center cooling (US) $55M

14. Aikido Security - app security scans (BE) $60M

15. Hydrosat - water satellite data (US) $55M

16. Orbital - legal due diligence (UK) $60M

17. Ivo - AI contract review (US) $55M

18. Zocks | AI for Advisors - advisor meeting AI (US) $45M

19. Benepass - employee benefits (US) $40M

20. AnswersNow - autism telehealth (US) $40M

21. Articul8 AI - enterprise genAI (US) $35M

22. hashtag#Compa - pay benchmark data (US) $35M

23. GovDash - gov contract tools (US) $30M

24. Unbox Robotics- warehouse robots (IN) $25M

25. Visitt - property ops tools (IL) $20M

26. VelaFi - nonprofit banking (US) $20M

27. MontyCloud - cloud governance (US) $18M

28. CloudSEK - cyber threat intel (SG) $20M

29. Klir - water utility software (US) $20M

30. Eat App - restaurant ops software (US) $16M

31. AppliedAI - enterprise automation (DE) €12M

3 clear waves show up in this list:

1. AI for real work: Security, contracts, research, healthcare, ops.

2. Infrastructure for AI scale: Cooling, networking, chips, cloud control.

3. Regulated markets modernizing: Defense, water, government, finance.

That’s it!

If you want to help me out, the best thing you can do is share this newsletter with someone who’d find it useful. 🙏🤙

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Hello there!

Barcelona surfing has been on fire this week with a big swell hitting our coast.

And speaking about swells, in 2025 we finally saw a big wave of $131B in venture capital-backed M&A.

Dealroom shared a great report that helps answers an important question on this:

What is likely to happen to liquidity in 2026?

So as usual, my top 10 key take-aways:

1. Capital came back in 2025, but only for the winners.

The details

In 2025 we saw venture capital’s usual power law asserting itself a little more violently than usual at the top.

Funding grew about 27 percent and exits reopened (and we have a big queue at the gates of IPO as we’ll cover later), but capital skipped the usual middle phase.

Instead of a gradual re-risking from seed to growth money jumped straight to companies that already looked inevitable (we covered this in point 2 of Systems of Action, where AI investment returns start looking like a barbell, on paper!).

There was a big jump in the number of 1B+ rounds, and a record 18 rounds that were $2B+. Which is obviously dominated by AI infrastructure and frontier model companies (OpenAI, xAI, Anthropic, Safe Superintelligence, Mistral etc.)

We’ve also seen the $100M-revenue companies pulling forward liquidity and optionality, while Series B and C stayed structurally hard.

That is not the normal shape of a recovery. In past cycles capital re-entered more broadly and sorted winners later.

So what

Basically the power law compressed due to both market sentiment AND real market impact generated by many of these companies (wether it persists or not is a different question).

As we discussed previously when talking about “Rivers of Capital”, the market seems to no longer be using capital to discover winners but rather to reinforce the ones it already believes in.

2. Tech is bigger than ever, and a third of it is still private.

The details

Global tech enterprise value hit $55T, up 30% percent in one year. This should raise some suspicious eyebrows especially considering that $18T is still private.

So what

This is a historical abnormality with an enormous amount of value creation that has happened without public price discovery (and associated concerns).

3. 2026 could be the biggest IPO year since 2021. But only for giants.

The details

The companies lining up for IPOs are already worth $3T+ combined (which means that less than 20% of private value is actually liquid-ready):

• SpaceX is the outlier. Internal trades and secondaries price it north of $1T, with Starlink doing most of the heavy lifting.

• OpenAI sits in the $830B–$1T range after its latest tender offers. Revenue is real at around $20B but with margins and structure still as open questions.

• ByteDance is valued around $480–500B in private markets. The business is ready but likely regulation decides if it ever lists.

• Anthropic is now priced at $230–300B in late-stage rounds. One of the fastest valuation ramps ever and gaining ground fast in the enterprise segment (check this out)

• Databricks trades privately around $134–160B with strong revenue and enterprise-heavy. Might be the cleanest IPO setup on the board.

• Stripe is back in the $90–120B range. Cash flow positive and likely waiting for multiples.

• Revolut sits around $75–90B, driven by profitability and global scale. One of the few fintechs markets still like.

• Canva is valued at $50–56B with strong profits and simple story. Exactly what public markets reward right now.

So what

What’s really happening is that the IPO window keeps moving later and later in a company’s life. Companies now go public only once most uncertainty is gone which is a shift that worries our friend Bill Gurley (one of my favorite investors) for many reasons.

But one of the most important reasons is that this reflects that public markets no longer fund innovation (or do so less, because they can’t access it early enough due to how much private capital is available) and just validate outcomes already decided in private which can have second-order consequences like founders optimising less for building enduring public companies and more for late-stage private rounds or strategic exits (generalising here, obviously not true across the board).

Which is a change that matters more than whether 2026 has “many IPOs” or not.

4. $100M in revenue is now more important than the unicorn badge.

The details

The US produced 27 new $100M-revenue companies in 2025.

Europe looks fragmented country by country. But if you sum Europe, you get roughly 21 new $100M-revenue companies which puts Europe much closer to the US than the chart suggests at first glance. The US creates revenue winners at scale in fewer companies whereas Europe creates them across many countries, sectors, and markets, with smaller individual outcomes but a similar aggregate.

So what

Europe does not have a startup creation problem but a scale and aggregation problem. Talent is there and revenue is showing up but it compounds more slowly because markets, buyers, late-stage capital and access to capital markets are more scarce / fragmented. That matters for exits but it also explains why European companies are more likely to sell earlier often below their technical potential.

But things are moving in the right direction ;).

5. Unicorns came back. Decacorns really came back.

The details

This is where North America is the undisputed MVP, producing 36 new decacorns in 2025 whereas Europe and Asia added only 8 each.

So what

Europe is clearly able to create companies that hit escape velocity, and this AI wave is making that happen faster and more often. But the US still compounds those companies into global scale like nowhere else. I’ve written before about ecosystems like Sweden that show what works, and those lessons are probably the most valuable thing Europe can export to itself right now.

6. The Bay Area captured the AI scale-up cycle

The details

In 2025 Bay Area companies raised $131B in $100M+ rounds, which is more than EMEA ($34B) and Asia Pacific ($24B) combined. Zooming out, 46% of global scale-up capital went to one region (74% went to one country, which is wild).

So what

This tells you where late-stage AI companies end up raising when they need very large checks. When companies crossed into big scale-up territory almost half of global capital landed in the Bay, which means late-stage investors, large buyers, and acquirers are still concentrating risk and ownership there.

7. Capital is flowing into these 5 key areas:

(This is where capital is concentrating, not where experiments are happening.)

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Hello there!

Battery Ventures published their “State of AI report”, and it answers a great question:

After training, demos, and hype, where does AI revenue come from?

The answer might be inference.

Here are my top 10 take-aways:

1. AI explains most of the market’s gains since 2022

The Details

Since ChatGPT launched in Nov 2022 we’ve seen AI-exposed companies adding around $18T in market cap... That is about 75% of all S&P 500 gains in the same period and are now close to 50% of total S&P 500 value.

This is not one sector winning though as it cuts across chips, cloud, power, infra, and software. Capital is basically betting on a system shift.

So what

This doesn’t scream rational investment behaviour, especially considering that the real bottleneck in AI today is power, and timelines to build this out don’t quite align with how the market has already priced this in imho (but hey, no crystal ball here).

Markets are now pricing non-AI exposure as structural risk.

Translation: if you do not benefit from AI demand your multiple compresses. If you do, you get rewarded even before margins fully show up.

2. Cloud growth is now driven by AI workloads (not SaaS demand)

The details

After years of a little deceleration we are now seeing cloud growth accelerating to a $285B run rate (a 20% growth range).

This is basically compute-driven demand where stuff like training runs, inference calls, and agent workloads consume raw capacity. What’s interesting and different relative to the previous wave is that usage scales independently of headcount or licenses.

You see revenue grow even when customer counts do not (yet?), which again can be pointing to more fragility in the economy. That’s why you could argue that cloud itself is starting to behave less like a software platform and more like a utility again.

So what

In SaaS 1.0 we saw that growth was constrained by sales cycles and budgets but in this AI cloud wave growth is constrained by power, chips and physical infra (which explains the massive CapEx investments we explored in Accel’s research report, section 3).

3. AI revenue is shifting from training → inference

The details

Up to now, most AI cloud revenue has come from training, which requires big bursts of compute, happens infrequently, and is tightly concentrated among a few frontier labs (what the left side of the slide above shows).

But the more interesting question is: where does revenue accumulate over time?.

As AI moves from demos into products fed into the economy the dominant workload will change because models stop being trained occasionally and start being used constantly aka inference.

Inference is every single time a model does work in the real world (i.e. answering a question, classifying a document, running an agent step, making a decision inside a workflow etc.). Each of those consumes compute and happen millions or billions of times once software is deployed.

That is why the right side of the slide looks so different, which shows that when revenue contribution broadens it spreads from a handful of model builders to a much larger surface area of companies sitting closer to usage (i.e. applications, agent platforms, orchestration tools, workflow software etc.).

There is an important piece of context here from outside this report:

In his recent conversation with Dwarkesh Patel, Ilya Sutskever (co-founder of OpenAI) made a simple but important point:

Today’s AI models already look very smart in tests but they still make basic mistakes when used in real products.

They repeat themselves, break workflows, or fail in edge cases.

Making models bigger helps a bit but it no longer fixes this reliably.

In simple terms, we may be approaching the limits of what this current training recipe can deliver quickly.

The hard part is no longer teaching models more facts but getting existing models to work reliably, cheaply, and continuously inside real systems. That includes better runtimes, better routing, better monitoring, better integration with software, and better economics.

That work happens during inference.

And even if a new architecture eventually replaces today’s models, this doesn’t really change the picture. Any future system still has to be deployed, it still has to run inside the economy, and every time it runs it is doing inference.

So what

The companies that win in an inference-led world are not just the ones that build the smartest models but the ones that make AI usable at scale. The ones that control how models are called, combined, monitored, priced, and embedded into workflows.

This is what I was trying to get at last week with the Systems of Action wave.
Once AI systems are expected to run real work, value shifts to whoever controls how actions are triggered, routed, constrained, and audited inside software.

4. Early AI leaders are no longer staying neatly in one layer of the stack.

The Details

Model companies push upward into applications and application companies pull infra-like capabilities closer to themselves.

Once AI systems run in production the hardest problems are no longer about model quality but about operating the system day to day like controlling costs, handling failures, routing work, enforcing rules, etc.

Those problems tend to sit between classic infra and classic apps.

This is where incumbents and newcomers start to collide. Incumbents like Salesforce, ServiceNow, Microsoft, or SAP already sit inside workflows and have trust, distribution, and enterprise relationships. But their stacks were built for humans in the loop and not for autonomous systems acting continuously.

Newcomers are more willing to redesign this layer from scratch building products around inference-first assumptions, usage-based economics, and AI running without constant human supervision.

So what

This creates a narrow but important battleground:

1/ Incumbents have position but architectural baggage.
2/ New entrants have cleaner designs but must earn trust and distribution.

5. Pre Vs Post-AI GTM metrics have changed

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