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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”.

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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 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.

Read more

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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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Hello there!

Here’s the question I’ve been geeking out about:

When intelligence becomes abundant, where does durable power move next?

The answer might be Systems of Action.

Lets take a look at where capital is flowing to help answer this:

1. The “river of AI capital” is huge, but very concentrated in a handful of companies.

The details:

Capital is showing up all at once (kingmaking), and only in a handful of “the chosen one” companies.

When a company is perceived as “the winner in a category” a virtuous loop forms:

Saam Motamedi from Greylock described this as getting into a “river of capital”. Once you’re in the river everything downstream becomes easier(ish). But if you miss it, even strong companies can find themselves swimming upstream / ignored.

This also connects to what we discussed recently under the idea of AI Dutch disease, where certain sectors, geographies, and companies are being massively overfunded, while others are fighting for oxygen.

So what:

At this point in the AI hype-cycle I’m seeing a lot of investors shifting from:

“Is this a good company?” → “does this company make it into the river early enough?”.

Purely from an investor psychology perspective (not saying this is positive at all), fundamentals still matter, but momentum matters more than most people like to admit.

On the other hand, don’t let this “king-making” narrative discourage you because:

1. As with all cycles, the river (or music) will eventually stop and

2. Many of the most successful companies ever were built in the shadow and/or outside “hyped” narratives at the time (i.e. Nvidia).

2. AI investing returns look like a barbell: gains sit at the start and the end (mostly unrealised).

The details:

Despite the explosion in AI deal activity, returns (mostly on paper) are becoming more concentrated. Saam from Greylock frames today’s opportunity set as a barbell:

1. One end is first money: backing founders before there is traction, consensus, or a clear narrative. When this works, you don’t just invest in a company you help create the conditions for it to later enter the “capital river”.

2. The other end is last money: large private rounds where very few investors can write the check, and alpha comes from price-setting and ownership rather than discovery.

What seems to be hollowed out (and hyper-competitive) is the middle. Seed and early Series A rounds where traction exists, competition is super intense, and pricing increasingly reflects consensus rather than insight.

So what:

This is not a startup quality problem. Its a VC crowding problem where too many funds (mostly undifferentiated) are chasing mostly consensus bets in the middle of that barbell. If you’re building in the middle you can expect noise, price pressure, and probably less conviction than in other points of the hype-cycle.

3. After models and infra, vertical AI grows fast by replacing labor.

The details:

Vertical AI companies are scaling extremely fast, especially in professions built on expensive human labor and strong LLM–market fit (text-heavy work, as discussed in the Agentic Revolution).

This also reflects a shift in where AI investment has gone. The first wave flowed into models and infrastructure. What’s growing fastest now is the application layer where intelligence is turned into direct labor replacement and immediate ROI.

Legal AI is a good example (i.e. Harvey in the US, Legora in Europe). These systems replace large chunks of junior legal work at a fraction of the cost creating immediate and often pretty undeniable ROI.

I recently spoke with a lawyer friend of mine, and it was one of the clearest conversations on product–market fit I’ve had where she was telling me that if her company removed the tool her hours of work would expand dramatically (and she’d be extremely unhappy about that).

This pattern repeats across customer support, coding, content, sales prep, slide creation, transcription, and parts of medicine as we’ve been discussing here.

Wherever LLMs are unusually good at the underlying work and the human cost base is high, vertical AI pulls demand forward very aggressively, which explains the growth.

But, this is unlikely to be the end state and it is interesting to think about what happens when competition moves in (or have multiple “chosen ones” in a river of AI).

Once multiple comparable systems exist, models converge, and switching costs remain low, competition will likely push prices down. The surplus that initially accrues to the vendor starts flowing to the customer instead.

There may be real moats here like workflow depth, firm-specific knowledge, accumulated documents, cultural context etc. But history suggests these moats tend to be narrower than expected.

We’ve seen this before i.e. cloud infrastructure followed the same arc with huge early margins, followed by price pressure as capabilities converged.

AI accelerates this dynamic because once multiple “Harveys” exist, intelligence stops being the differentiator and the fight shifts to price, bundling, and distribution.

Much of the value created shows up as cheaper services and higher output per professional, not necessarily higher vendor margins.

So what:

Vertical AI monetises intelligence by replacing humans, but that same dynamic makes long-term pricing power potentially more fragile once intelligence itself becomes abundant.

Which leads to the deeper very interesting question: if intelligence is no longer scarce, where does durable power move next?

4. Where AI power goes depends on one thing: does intelligence stay scarce or become cheap?

The details:

At a high level, the entire AI market’s direction comes down to one question:

Does economically useful intelligence remain scarce, or does it become cheap and widely available?

By “intelligence” here I don’t mean understanding or judgment. I mean the new kind of machine intelligence we have today where systems that are extremely good at pattern matching, recall, and fluent output, but have no taste, no goals, and no real understanding of what matters.

As Naval puts it, AI predicts text (it doesn’t reason like humans). It kills memorization, lowers the cost of trying things, and acts as a force multiplier for people with good judgment BUT it does not replace judgment itself.

This matters because power flows to whoever is trusted with decisions (trust comes from proven good judgement), not just whoever provides the technology.

In my mind there are really two competing theses about where AI power ends up:

Thesis 1: Intelligence as infrastructure (“AI = Electricity”)

Think OpenAI, Anthropic, Google, Meta.

If intelligence stays scarce and trusted:

• building and running the best models remains expensive (compute, energy, safety)

• top models keep pulling ahead through scale and feedback loops

• companies increasingly defer decisions to the smartest system available

In this world:

• AI behaves like a utility (similar to cloud or electricity)

• apps become thin wrappers

• routing, prioritisation, and escalation happen inside the model

• value concentrates at the model layer

Thesis 2: Intelligence as coordination (“AI runs work”)

If intelligence becomes cheap and widely available:

• “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

Those are coordination problems, not intelligence problems. 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.

So what:

As intelligence becomes cheap, judgment does not. Deciding what matters, when to act, and who is accountable still sits with humans. This is why models, vertical tools, and incumbents will probably (?) start racing to move up into “coordination”.

5. This wave might unlock new horizontal winners

The details:

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Hello there!

It has been a massive quarter for AI, with a record breaking number of >$100M deals (the list of 40+ deals + 3 dominant categories here). So massive that I keep thinking about historical parallels to what might be (really) going on in our economy.

So, on this very topic, I ran into this must listen interview between Paul Krugman (Nobel-winning economist, NYT) and Paul Kedrosky (investor, MIT research fellow, long time tech + markets nerd).

It is one of the most contrarian and interesting conversations I’ve come across.

Here are the top 10 insights + my 2 cents:

1. The AI boom looks a lot like Dutch disease

What happened:

Krugman and Kedrosky frame AI as a classic “Dutch disease” story.

The details:

Dutch disease is what happens when one sector gets so hot that it quietly starves everything else of capital and attention. In the 70s, Dutch gas exports pushed wages up, strengthened the currency, and made every other industry less competitive. The gas boom was real but it hollowed out manufacturing because capital, talent, and policy chased the “easy returns”.

We see capital, talent, and political focus are being sucked into this one AI vertical today. I see it every time I talk to founders, LPs, VCs, banks, you name it (to the detriment of those building “outside” of being a “pure” ai company, for now).

So what:

When an economy gets Dutch disease founders don’t feel it as a “macro phenomenon” but they do feel it as harder fundraising, more hype expectations (as you’ve probably listened to in a most vc podcasts…) and less (perceived) room to build outside the narrative.

2. We already burned through the Saudi Arabia of data

What happened:

Transformers worked so well because we discovered a giant free data reservoir: the open internet.

The details:

Large language models got good by training on public text at internet scale. Once we found a way to turn raw text into predictions, scaling laws kicked in: more data, more compute, better models. That trick got us from “this is a cat?” to GPT 5 level systems today. The problem is that most of the high quality public data is now used and new gains need way more work for smaller jumps. There’s a must watch interview on this topic by Ilya (co-founder of OpenAI) on this topic which I summarised here.

So what:

The easy part of the curve is behind us and it looks like the future progress is less “just add more data” and more “we need new sources, new architectures, and smarter training”. Which (might be?) slower and potentially more expensive.

3. LLMs are great at code, less great at the rest

What happened:

Kedrosky is very skeptical that current LLMs are on a straight road to AGI.

The details:

Models are incredibly good at software because code gives a sharp learning signal. Natural language is fuzzy: you and I can disagree on which sentence is better. So the model learns much faster from code than from prose. Benchmarks that show models “crushing coding tasks” can mislead people into thinking we are close to general intelligence. Kedrosky argues that we are not and that we are close to “very good stochastic programmer”.

So what:

Betting on AI to keep eating software workflows (and in some sectors, parts of labour) is far more realistic than betting on near-term “god-level intelligence.” LLMs have clear strengths (i.e. code, text, structured tasks) and clear ceilings. The architecture is biased toward certain domains and plateaus in others.

4. GPUs age like fruit, not factories

What happened:

The chips inside AI data centers do not behave like normal industrial capital.

The details:

Training runs keep GPUs at full throttle, 24/7, under heavy thermal stress. That is not like your laptop but more like running a race car flat out all season. What ends up happening is high early failure rates, slowdowns, constant replacement etc etc.

Which means that in a 10,000 GPU data center you should expect regular failures every few hours. Which means that long before “the next generation chip” arrives, a lot of your fleet has already worn out.

So what:

AI infra is not laying railroads you can use for 50 years (there’s been a lot of analogies on this lately). It is closer to a warehouse full of bananas, which matters for depreciation, returns, and how fragile the whole buildout might be.

5. Power (not compute) is the real bottleneck

What happened:

We are hitting the limits of the electrical grid faster than the limits of model size.

The details:

Data centers want hundreds of megawatts and even gigawatts of power. These need the kind of power normally reserved for aluminum smelters or small cities. The problem is that the grid was never built for this. Substations are full, transmission lines take years to approve and local communities push back when they hear “your bills might go up.” So utilities do strange things:

• speculate on power for future AI sites

• cancel projects when the demand slips

• push hyperscalers into “bring your own power” deals

• slow-roll approvals because the grid can’t move at startup speed

Which ties into the broader idea of fragility in this boom, because the physical world moves slower than the hype cycle.

So what:

You can raise a billion dollars in a quarter but you can’t build a gigawatt in a quarter and if your AI forecast assumes infinite, instant electricity, you are not modelling reality. It is basically modelling a wish, and this makes the economy more fragile.

6. The financing stack looks like the “sum of all bubbles”

What happened:

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Hello there!

Atomico just dropped the State of European Tech 2025.

It’s 280+ pages. I read the whole thing so you don’t have to.

Here are the 10 insights that matter most:

1. Europe’s tech economy is now a $4T asset class

What happened:

In a decade, Europe’s tech ecosystem went from under $1T to around $4T in combined public + private value, and now represents ~15% of European GDP (up from 4% in 2016).

The details:

• The total ecosystem value growing almost linearly post-2016, with only a small dip after the 2021 peak.

• Tech is now one of Europe’s biggest “industries” by value creation, even if it still isn’t treated that way politically.

• This is happening with far less capital intensity than the US, basically building a $4T asset class on “hard mode”.

So what:

European tech has enough depth (talent, capital, buyers) to build something big from Europe, even if you sell globally from day one. Wrote about Sweden here a few weeks ago.

2. Optimism is back, but confidence is still fragile

What happened:

50% of people in the ecosystem feel more optimistic about European tech than 12 months ago (vs 34% last year), one of the highest readings of the decade.

The details:

• Sentiment steadily recovering after the 2022–23 hangover.

• But, the old continent hasn’t yet convinced its own stakeholders that this is the best place in the world to build.

• Europe’s tech share of GDP has quietly crept to 15%, but culturally we still talk like underdogs.

So what:

Founders can be early to take Europe seriously while others hedge (and others just flood twitter with anti-eu memes). The founders who act as if Europe will win, and structure their company accordingly, likely get compounding advantages in talent, policy access, and capital.

3. Funding has bottomed out in Europe, but the US is playing a different game

What happened:

European startup funding is stabilising around $44B for 2025, up from the 2024 bottom but far from 2021 highs. Meanwhile, the US just had a new surge: $177B into tech in the first nine months of 2025, almost double last year, largely driven by a few AI giants.

The details:

Europe’s problem isn’t no capital but rather a combination of: 1. less mature ecosystem (culture, what excellence looks like) with a flywheel that is starting to work (and “less” late-stage capital than the US as a consequence) 2. an urgent need to reform incentives (see EU Inc.) 3. an urgent need to improve access to liquidity and local patient capital capital markets (again good examples here on what Sweden has built).

So what:

If you’re raising Seed or Series A in Europe, the market is “normal hard” again, not existential. But if you’re aiming for global category-winner scale, you need a conscious strategy for the growth stage: either tap US capital early, or be very deliberate about becoming one of the few European companies that local growth funds will stack capital behind.

4. Deep tech & AI are now Europe’s core bet, but ticket sizes trail the US by an order of magnitude

What happened:

36% of European VC dollars in 2025 went into deep tech (up from 19% in 2021). But where the US stacks tens of billions into a handful of AI giants, Europe spreads $16B across many themes: compute, quantum, defence, mobility, climate.

The details:

• The deep-tech chart shows:

  • Europe: $16B in deep tech (+10% vs 2024).

  • US: $130B (+91% vs 2024), excluding the monster OpenAI round.

• Europe sees big moments: Helsing’s $660M Series D, Isomorphic Labs’ $600M, Proxima Fusion’s €200M, NScale’s $1.1B strategic Series B, but we don’t yet have the same concentration of capital behind a few national champions.

So what:

If you’re building deep tech in Europe, you’re in the right place but don’t assume US-style firepower. The bar is higher on capital efficiency and strategic partnerships.

5. Europe’s founder engine is running hot

What happened:

More than 27k founders started companies in Europe in 2025, ~60% more than in 2023, and the highest number on record. Europe still contributes the largest share of global founders, but Asia has now caught up at ~28%.

The details:

• Europe’s share of global new founders staying slightly ahead of the US, but Asia (driven by India + UAE) now matching Europe’s share.

• Low-code/no-code, cheaper infra, and stronger networks are lowering the barrier to start something everywhere.

So what:

The idea that “there aren’t enough founders in Europe” is now objectively false.

6. Europe keeps most AI founders at home, but seasoned founders quietly incorporate in the US

What happened:

Around 4 in 5 European founders are still building on home soil, and among AI founders that share actually increased from 74% to 81% since 2016. But among “seasoned” European founders, the share incorporating their companies in the US has nearly doubled from 10% to 18%.

The details:

• First-time European founders overwhelmingly base and HQ their companies in Europe.

• Seasoned founders (previous exits / C-suite / heavy fundraises) are much more likely to pick a US HQ or dual structure.

• The report reads this as a proxy for: US incorporation is faster, cheaper, and better plugged into growth capital and public markets.

So what:

Regulators should pay attention at the structural friction that’s making repeat founders move their HQ to the US (and fix it fast, since the eu has now exported over 100 unicorn founders there).

7. Europe’s capital stack is underweight at the top: pensions + late-stage create a $200B+ opportunity

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Hello there!

Ben Evans just published one of the clearest analyses of the “AI platform shift”.

I went through the deck + pod.

Here’s what matter’s most:

1. AI adoption is early and mostly additive, but has already cracked the “Google habit”

What happened

Despite the hype, most people still use GenAI intermittently, folding it into existing habits rather than replacing them.

The details

Even among under-30s, “Always GenAI” is a minority behaviour. Most consumers alternate between traditional search and AI tools.

The threat to Google is likely not replacement but erosion. Once the reflexive habit of typing a query into Google weakens, the moat might begin to crack. That being said, Google is still the only player fully vertically integrated in this wave, and moving fast on all fronts (see Gemini’s 3.0. performance here).

So what

Search as we know it will likely not collapse suddenly. It will decay and change slowly. It’ll be interesting to see how founders build for hybrid behaviour.

2. We have lived through this panic before, and the pattern always repeats

What happened

Automation fear cycles are not new. They follow a predictable psychological script.

The details

The IBM “150 extra engineers” advertisement above is almost identical to the conversations happening now about LLM job displacement. The technology shocks of the 1950s, 1980s, and 2000s followed similar curves including a period of panic, adaptation, productivity gains, and then new categories of work.

As Mr. Bill Gates once said, we “tend to overestimate destruction in the short term and underestimate long-term transformation”.

So what

Every platform shift starts with a change in how people behave, and AI is no different. We’re already seeing new defaults emerge:

1. Expressing complex intent in natural language (i.e. Lovable)

2. Delegating repetitive work to “infinite interns” (i.e. Harvey)

3. Feeling comfortable talking to a robot (i.e. HappyRobot)

4. Wanting fewer screens / our attention back (i.e. Balance Phone)

5. Expecting tools to remember us (i.e. OpenAI memory)

6. And many more that are emerging fast.

These micro-behaviours always arrive before the category shifts. They show you where workflows are about to break and where new ones want to form.

And the pattern is the same as every previous cycle:

• Google followed the behaviour of link-based discovery

• Instagram followed the rise of mobile photo culture (after pivoting from Burbn).

• Shopify followed the self-serve ecommerce wave

• TikTok followed swipe-first video.

Basically it seems like winners didn’t predict the platform shift but rather noticed the behaviour it unlocked and rebuilt the workflow around it.

3. Adoption tends to move in 3 phases, and we’re still in the first one

What happened

Every platform shift starts by absorbing the new tech into old workflows. Only later do the new bundles and new categories appear.

Despite the hype, we’re still very early in this first phase.

The details

LLMs today mostly sit on top of legacy processes like coding assistants inside IDEs, copilots layered onto Zendesk/Salesforce, meeting-note bots, marketing-draft generators, knowledge copilots etc.

These accelerate the workflow but don’t change its structure. Many workflows only exist because humans had to read, route, check, and summarise information manually.

So what

I like this question to stress our thinking about what could come next:

“If the human constraints disappear, does this workflow still make sense?”

Some examples:

• support that resolves itself without tickets

• FP&A that updates continuously without analysts

• sales pipelines maintained by agents not reps

• compliance that monitors in real time instead of via PDFs.

4. The central strategic question: what does this wave unbundle?

What happened

The internet unbundled physical things (newspapers → articles, stores → SKUs). AI now unbundles office work, the long, messy processes that only existed because humans had to read and decide things.

The details

Workflows like compliance checks, claims processing, RFPs, legal review, customer ops, and financial modelling are not “designed systems.”

These are chains of people passing information because no software could understand the content. Now models can understand it, which means these chains don’t need to stay glued together.

So what

The biggest opportunities lie in spotting these “fake workflows”, processes that exist only because humans had to push the work forward. Companies unbundling these are starting to create new categories (some experiencing early explosive growth).

5. Enterprises are reorganising around it

What happened

Enterprise AI adoption is both driven by economics and by vibe-revenue / novelty (despite interesting arguments on both sides of the debate). When reasoning becomes cheap, the rational response is to restructure work around it.

The details

For the first time, companies can delegate reading, summarising, drafting, checking, or routing tasks to software and not people. This is why adoption happens even when the outputs aren’t perfect.

So what

The CFO doesn’t care about “magic AI moments”. They care about turning 50-person workflows into 5-person workflows.

6. AI is stuck in pilots because it isn’t reliable enough (yet)

What happened

Enterprises are running hundreds of AI pilots, but very few make it into real workflows.

The details

Two things break when you try to automate real work:

• Plumbing: security reviews, data access, permissions, compliance, audit trails.

• Outputs: the model is powerful, but it sometimes thinks wrong and systems can’t yet catch those errors cheaply.

Until both issues are solved, automation can’t run unattended.

So what

There’s opportunity in infrastructure that makes automation safe including verification, monitoring, auditability, fallback logic, secure execution, and workflow-level dependability (i.e. Galtea).

7. AI can figure out what you’re trying to do, not just what you clicked

What happened

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Hello there!

Accel just dropped an important research report on the Race to Compute in 2025.

I read the whole thing. Here are the 10 insights that matter most:

1. The AI boom is a platform shift

What happened:

AI isn’t a short-term hype cycle. It’s likely the start of a new computing era. The NASDAQ is up +114% since 2020, almost entirely driven by AI companies. You can read my take on why the bubble vs no bubble debate is the wrong question to ask.

The details:

Every tech wave changed how humans and computers talk.

• The PC let us read code.

• The Internet let us read and share.

• The Cloud let us read and write in real time.

• Now AI lets software read, write, and think.

The Transformer architecture (the “T” in GPT) broke software out of its rule-based box. Before, computers could only follow exact commands. Now they can understand context and respond in natural language. For the first time, code understands us and can even write or execute code itself.

So what:

We’ve moved from software that follows instructions to software that collaborates. It’s not fully autonomous yet, but it’s already semi-autonomous enough to reason, plan, and act. And that unlocks a new kind of economy built on agents, automation, and intelligence at scale.

2. Compute is the new utility

What happened:

We’re entering a $4.1T buildout of AI data centers, the factories of the AI age.

The details:

Every major wave had its physical backbone. Railroads for the Industrial Revolution. Power plants for electricity. Now, data centers are the backbone of AI. That $4.1T is CapEx, basically money spent on things you build once and use for years: land, servers, chips, cooling systems, fiber, and power infrastructure.

The chart shows that more than half of all the data-center capacity expected by 2030 is already committed led by OpenAI, Meta, and Microsoft. They’ve locked in about 65 gigawatts of power, which is HUGE.

Why spend this much? Because both phases are power hungry:

• Training is when you teach a model, like sending it to school. It takes massive compute upfront, done once per generation.

• Inference is when you use the model, like asking it a question. It happens billions of times a day, and it’s what drives recurring energy demand (will grow fastest).

So what:

This is the real infrastructure behind the Agentic Revolution. Instead of generating electricity, these plants generate intelligence. They are the power grid of the next economy, one that runs on reasoning.

3. The bottleneck isn’t money, it’s electricity

What happened:

Even with trillions in funding, the real constraint is power, not capital.

The details:

The US grid will face a 36 gigawatt shortfall by 2028, the equivalent of 35 nuclear reactors. Data centers built for the AI boom are colliding with power systems designed for another century. Every hyperscaler now talks less about chips and more about megawatts.

So what:

AI has outgrown the digital world and hit the limits of the physical one. The next big unlock won’t come from code but from kilowatts. And if, as Stratechery notes, this is a bubble, it might still be a productive one, because when it pops, the world will be left with abundant, cleaner power that fuels the next wave.

4. AI must pay for itself, and it can

What happened:

AI infrastructure has to prove it can pay for itself, and for now, the math roughly works.

The details:

To recoup $4.1T in CapEx, global data centers need to generate about $3.1T in new annual revenue by 2030. That’s roughly 1–2% of global GDP, which puts AI’s economic impact on par with past industrial revolutions. In practical terms, it means every dollar invested in chips, cooling, and power has to translate into new productivity, not just speculation. Inference costs are falling, adoption curves are steepening, and companies are starting to measure AI in ROI, not just R&D.

So what:

The first model race was about intelligence. The next race is both about intelligence and economics, who can make increasingly better reasoning profitable.

5. The US dominates models, but Europe is accelerating across the stack

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Hello there! This week I had lots of vc meetups and almost got Tarikoplata’d in bjj.

The main thing on everyone’s mind is (still…) the bubble vs no bubble debate. What most people miss is that sure, there will be a pricing reset, but the “ask AI” default habit is out of the bottle.

And once you start outsourcing intelligence like that, and you realize most of the world is still mostly oblivious to this change, watch what happens in the mid-run.

Great for innovation, but a hell of a lot of creative destruction incoming.

So, here’s what went down in startup-land this week:

🤖 AI

I published a list of the top 40 European AI startups that raised over $50M so far in 2025. So much talent coming out of the old continent in this wave. Interesting to see an important subset of 10 of those making a big splash in Healthcare AI.

We’re witnessing the fastest scaling, and highest-valuation-per-employee AI startups in history this year in 2025. Startups leading the charge include Figure, Cognition, Anthropic, OpenEvidence, Decart, Sierra and many others on that list.

OpenAI restructured to become the richest charity in history. Not a surprise considering ChatGPT’s retention curves.

Replit (vibe-coding app we used here a couple years ago to demo Pitch Deck GPT remember?) shared revenue numbers that went from $2M → $250M in 2 years.

Google demoed a new chip called Willow that cracked a complex problem 13,000x faster than today’s top supercomputer, thanks to a breakthrough technique called Quantum Echo.

Jeff Bezo’s explanation of the ai bubble and why it is going to be transformative regardless of price adjustments is pretty spot on and worth listening to.

Sam Altman and Satya where on this pod together sparring on the future of ai agents and how, because OpenAI’s most advanced models stay tethered to Azure, Microsoft owns the on-ramps for enterprise AI.

Found this great visual on why you should never underestimate Google, showing how they are the only player that is truly vertically integrated in AI.

🌊 VC

I wrote about why I’m bullish about european tech startups and Swedish unicorns (20+, latest being Legora), with 5 key ecosystem lessons we should export to the rest of the old continent.

I shared a list of the top 50 EU startups that raised a series A this month. Interesting to see 19 took place in the UK, seeing strong biotech and industrial AI presence, deeptech > saas and B2B heavy. Also published 20 spanish startups that have raised a round in October 2025.

Seed VC funds are having an existential crisis. Rob from Nextview breaks down 4 main reasons, which I found fascinating:

• The industry matured into a barbell: megafunds a-la Squoia and scaled “crafty” seed-specialized funds. Everything in between is hollowing-out, and there are now just too many Seed funds.

• YC and megafunds: YC represents 40 seed funds worth of competition and makes up at least 10% of the overall institutional seed market. Together with other growing megafund programs like a16z speedrun these likely make up c.25% of the seed market, and growing. Their models are orthogonal to the institutional seed model in most cases.

• The power law is now consensus thinking: made “worse” by super-outliers being larger than ever and exit markets supporting a “go-for-broke” approach (500M+ to IPO vs 100M).

• AI Platform shift: all 3 previous points turned all the way up + most of the value being accrued deeper in the tech stack, what Bill Gurley calls “a sport of kings”, meaning deep pockets needed.

• My 2 cents: I agree consensus seed deals are crowded. But real non-consensus isn’t actually being bid up, because true non-consensus still feels uncomfortable or way early or “weird”. And because mimetic desire seems to send everyone to the same signals (YC / a16z etc), which I’ve been seeing a lot of lately.

Also on a positive note, Adam from True Ventures argues the opposite, that there’s never been a better time for innovation, that we need more of everything (founders, programs a-la yc, seed managers etc), but with a clearer angle / differentiation.

Carta published data on how crazy the valuation expansion at Seed has gotten, from 10M median post-money valuation in 2016 (when Seed wasn’t yet a “thing”) to a whopping 20M today, and 80M (!) at the top of the range (98th percentile).

Loved this visual by a16z on how they’re turning VC into a network effect biz:

📈 Founders

I had a great conversation with Rodri founder at Crossmint (recently raised >$20M).

His trajectory bends every rule in the traditional playbook: sleeping on buses in London → Stanford GSB dropout → months in a Zen monastery → emerging with the conviction to build programmable money infrastructure for the internet:

Watch on Youtube

🌱 Favourite startups from YC’s fall 2025 batch:

From those published so far:

• 🇺🇸 Kalpa Labs: Building frontier speech models + agent voice-infrastructure for long-form convo flows.

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Hello there - back from a tech event at a longevity blue zone, class.

Here’s what went down in startup-land this week:

🤖 AI

• Karpathy, one of OpenAI’s founders, just dropped the most important AI talk of the year. He says we don’t need true intelligence for impact because most work is pattern matching and AI already 2–3x’s that. What we call agents are still scripted assistants, not autonomous. Says the real unlock will be memory, reasoning and reflection (really worth a listen, but be ready to pay attention 😉).

• Data from Ramp AI index + an analysis by points out that AI adoption flattened in September, with startups moving from hype to proof (positive retention signs in the enterprise + bigger and bigger contract sizes though).

• Found a great list of 160+ YC summer startups, surprise surprise mostly AI. Mostly vertical focus, real-world use, and infrastructure use-cases. Fewer chatbots, more back offices, factories, and devices getting smart.

• The infamous Chammath shared some concerning data on the decrease in usage of several vibe coding tools or as he calls them “Slopware App-crappers” 😅.

• Anthropic launched Claude Code on the web, letting developers run and track multiple coding tasks directly from their browser.

• Anthropic also launched Agent Skills, which basically lets Claude “learn on demand.” Think of them like plug-in brains, folders of instructions or scripts Claude loads only when needed.

• Google dropped Veo 3.1, its upgraded AI video model built for filmmakers who want more control. It can now keep characters consistent across scenes, blend start and end frames smoothly, and extend clips up to a full minute.

• OpenAi dropped a new browser called Atlas.

• Perplexity shipped a new experience for learning languages.

🌊 VC

• Menlo ventures published a market analysis on healthcare AI. A $4.9T sector (20% of the US economy) now deploying ai 2.2x faster than the broader economy

• (investor in 40+ unicorns) was on the podcast dropping gold for founders and investors.

• Great pod with Sequoia’s Roelof Botha on how venture capital is broken. I summarised the main insights here.

• I keep running into what has now become a meme on twitter, Silicon Valley’s scene on a founder that killed his startup by raising too much money.

• Legendary Benchmark investor Bill Gurley dropped a new book (worth it).

• Coatue dropped its “Fantastic 40”, basically a curated index of the 40 companies best positioned to lead in an AI-driven world. Think of it as the new FAANG benchmark for the next decade.

• YC reminder that the real edge isn’t spotting what’s hot but having the conviction to build what looks stupid right before it becomes inevitable. Basically by the time something feels safe it’s already saturated (this is always a good reminder).

• Great piece by Dan Gray on how venture was meant to be adventure capital, how investors are outsourcing decisions to “herd behaviour signal” + broken incentives. On the flip-side, this also means there’s a big opportunity to build better VC firms (and a good reminder that it is not the firm, it is the person).

• wrote about how “surrendering to chaos” is how you stay ahead. Founders look for PMF frameworks because they “want permission to stop being scared”, but maps only show where others have already been.

• You can now fundraise directly from Stripe Atlas.

• Great fundraising data room template by .

🧩 Founder Frameworks

• Bezos on the most important question nobody asks.

• Sam Altman on how to increase your surface area for luck.

• Michael Dell on how to make mistakes (small and fast).

🤖 How to sell with AI

We ran a workshop with my friend Sunny at Gong (>$7.3B valuation), it was a blast.

He analysed 600K+ calls to find what works best when humans and AI sell together.

Startup Riders Pro members get the full 20-slide deck (dm me) + full video workshop:

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