At the time, I was working with Arcads AI, studying how B2C apps were minting money by building thin layers on top of foundational LLM models.

I was surprised to learn that some were pulling in millions of dollars a year. And at first instance, they looked dead stupid simple to build. Amazed by their simplicity, I showed them to my friend.

Max looked at my screen, then looked at me, and said: “Dude. It’s just a wrapper.”

And his reaction captured something I’ve heard over and over in entrepreneur and startup communities ever since. The idea that building a wrapper is somehow beneath you. That real builders make real things.

I disagreed with that trope, and I let my feelings known to Max, as I quoted Arvind Srinivas who put it best - “everything is a wrapper.“

McDonald’s wraps beef, bread, and condiments into a Big Mac. Spotify wraps MP3s into a discovery engine.

Every product takes something that already exists, adds a layer of context, design, or workflow, and resells it as something new.

We’ve been doing this forever. The question was never whether to wrap. It’s knowing what to wrap, and how well.

In the 1880s, America embodied this spirit to the tee. The 1880s saw the introduction of two revolutionary new technologies - electric power and the internal combustion engine. Electricity gave birth to electric tools for factories and homes such as elevators, electric railways, washing machines, refrigerators and air conditioners. The internal combustion engine gave rise to cars, trucks and buses.

None of these inventions could be attributed to America. It was Michael Faraday, an Englishman who produced the first electric generator, while in 1879, it was the German, Karl Benz, who developed the first internal combustion engine.

America’s genius lay in making these core innovations more user friendly, producing companies that can commercialize these innovations, and developing techniques for running these companies successfully.

I see no reason why history won’t repeat itself.

Companies like Harvey AI, Cursor have successfully built products on top of foundational models that are worth billions, much like how Henry Ford made an internal combustion engine wrapper back in the day.

The “it’s just a wrapper” crowd makes a rather simple argument - building on top of someone else’s model means your core technology isn’t defensible. OpenAI ships a new model and your moat shrinks overnight.

That’s true. But only partially.

Foundational models are better, faster, and cheaper than they’ve ever been. The value has shifted up the stack, to whoever can apply these models most effectively to specific, high-stakes workflows.

Low-frequency and low-stakes tasks will get absorbed by general-purpose tools like ChatGPT. Nobody needs a dedicated app to summarize an email. Gemini can nail that pretty well.

But professional, high-stakes workflows such as coding, sales, recruiting, legal review, client meetings will be owned by focused tools that go ten layers deep on a single use case.

Cursor didn’t win because it invented a new AI. It won because it rebuilt the entire coding workflow around AI from the ground up.

The wrapper isn’t the product. The workflow is the product.

Few products illustrate this better than Granola - an AI notepad built specifically for meetings.

Granola doesn’t try to replace how you think. It is designed to augment it. You still take notes in your own words during the call. Granola quietly runs in the background, capturing the transcript. Afterward, it blends your notes with what was actually said, and turns it into something clean and usable. The AI fills in the gaps; your thinking shapes the output.

This is the tension at the heart of every good AI product: convenience vs. specialization. Most people will default to one general tool - ChatGPT, Gemini, for 80% of tasks, the same way everyone uses Google even when a specialized search engine might do better. Habit wins.

But professionals like designers, engineers, salespeople, founders, investors, will always need tools that make them genuinely 10x better at the thing they spend most of their time doing. Not “okay at everything.” Great at this.

That’s the playbook - Enter via a specific use case that matters to a specific type of person. Build an AI-augmented workflow around it. Go far enough, and the line between software and service starts to blur.

Granola’s goal is to become the “Jetpack for the mind” - as they seek to redefine what it means to be a knowledge worker in an AI-native world.

In March of this year, Granola raised $125M in a Series C at a $1.5bn valuation, led by Index Ventures and Kleiner Perkins, with participation from Lightspeed, Spark and NFDG.

They have customers from established companies likeVanta, Gusto, Thumbtack and Asana to fast-growing startups like Cursor, Lovable, Decagon and Mistral AI.

In the rest of this essay, I’ll show you how Granola executed this, their vision, and the playbook to build an AI wrapper like Granola with a real moat.

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The Origin Story - How did Granola start?

In June 2022, Chris Pedregal left Google with a hunch. He’d been playing with GPT-3 (remember that?) and felt something fundamentally different was happening with AI.

He didn’t know exactly what to build yet, but he knew he needed a co-founder who was both technically strong and deeply thoughtful about what AI-native interfaces could actually look like.

That search led him to Sam Stephenson, who was embedded in the “tools for thought” community where people obsessed over how software could extend human thinking rather than just automate it.

Together, they were well-positioned to build something interesting. The harder question was: what?

When foundation models started exploding in capability, it wasn’t obvious which problems were best suited for them. Chris and Sam weren’t the only ones confused. This was the era when AI writing tools, sales assistants, and customer support bots were all racing to be the category winner, and everyone was pattern-matching on whatever seemed to be working.

Granola’s approach was almost Darwinian: build many things, let most of them die, and pay close attention to what survives. They cycled through prototype after prototype - AI journaling apps, coding assistants, tools for personal knowledge management. None of them clicked. But each failure narrowed the search space.

What Actually Sucks About Your Job?

Chris and Sam spent weeks wandering through people’s workflows, asking one open question: “What actually sucks about your job?”

The answer that kept surfacing, across roles and industries, was meetings. Every call left behind a pile of low-energy, time-consuming follow-up work: notes to clean up, emails to send, CRM fields to update, action items to track. The kind of work that nobody finds meaningful but everybody has to do.

“For people whose jobs revolve around meetings, the work just stacks up,” Chris observed.

The more they dug in, the clearer it became - Meetings were a uniquely attractive beachhead.

They have a defined structure: clear start times, clear end times, a natural moment of transition when the real work begins. And they have virality baked in: when one person in a call uses a new meeting tool, everyone else on the call is exposed to it.

What they were really building wasn’t a notes app. It was a tool that sits inside your meetings, quietly, and clears the wreckage afterward, so the people whose entire jobs run on conversations can spend less time on admin and more time on the work that actually matters.

By March 2023, Chris and Sam founded Granola with a singular mission: use AI to tame meeting hell.

The road to PMF - Experiments, Habits, and the Art of Pruning

Early Granola wasn’t a clean vision executed well. It was a long, iterative search of six to nine months of running experiment after experiment, a year-long beta with 150 users, and more discarded features than shipped ones. What came out the other side was a set of principles that shaped every decision they made as they found PMF.

Three of them stand out.

Principle #1: Balance Functionality with Simplicity.

The first version of Granola looked great in demos.

The original interaction model was real-time AI augmentation: as you spoke, the AI wrote your notes live, right in front of you. Investors loved it. But when real users sat down with it for actual calls, something broke.

People stopped listening to the meeting. They watched the AI instead: reading along, catching errors, nudging it back on track.

In a cruel tryst of irony, the very tool that was designed to reduce cognitive load was adding to it.

That failure contained the key insight: the interface had to disappear during the meeting, not perform.

What emerged from a year of iteration looks almost aggressively simple. It looks like Apple Notes. You type during the meeting - fragments, shorthand, whatever comes naturally. Granola records and transcribes in the background. When the call ends, your rough notes quietly transform into something polished and complete, on the same page, without you touching anything.

Once they found that core interaction, they cut roughly 50% of everything they’d built - this meant features that demoed well but weren’t robust in daily use were scrapped.

“The hard thing was figuring out how to let users get the stuff they care about in a way that feels natural and effortless,” Stephenson explained. “We really can’t put too many buttons in front of you when you’re in that situation - you don’t have the brain space for it.”

Which brings us to Principle #2:

Principle #2: Be Selective About the Challenges You Take On

Building fast is easy. Knowing what not to build is the hard part.

Early on, real-time transcription accuracy was poor. The obvious move would have been to invest in building a proprietary transcription engine and to own the problem end to end.

Granola didn’t. They doubled down on better note summarization and waited for transcription technology to mature on its own. The same restraint shaped decisions around multilingual support, retrieval-augmented generation, and ultra-long meeting capture. All real problems. All deliberately deferred.

“We have to be quite selective about the challenges we bite off and the ones we choose to leave alone. A lot of the game for us has been picking our battles, knowing what to innovate on, and where we needed to wait for the technology to get better.” -Sam Stephenson.

This is a harder discipline than it sounds. When the problem space is vast and the team is small, the temptation is to chase every edge case, fix every gap, ship every feature that users ask for.

What Granola understood was that restraint is a form of focus. And focus, at the early stage, is everything. A small team that tries to solve ten problems solves none of them well. A small team that solves one problem completely owns it.

The problems you choose not to solve are as important as the ones you do.

Principle #3: Optimize around a key metric

Most early-stage teams track whether users like their product. Granola tracked something harder: whether users needed it.

They built a dot-plot chart for each beta user showing how many meetings they’d used Granola in, day by day. This focus shaped everything. A feature that spiked engagement once was irrelevant. What mattered was whether Granola had become part of the ritual of working and became as habitual and invisible as opening your laptop.

This is why they also paid close attention to who they were selling to. VCs became their early customers. The logic was straightforward: VCs spend enormous portions of their lives in back-to-back meetings, taking notes that feed directly into investment decisions and portfolio relationships. High volume, high stakes, and highly habitual. If Granola could embed itself into that workflow, it would stick.

It did. The product spread organically through Lightspeed, Lux Capital, Benchmark, Sequoia, Accel, USV, Firstminute, and Betawork. And word spread through the oldest distribution channel there is: someone in a meeting saying “have you tried this?” VCs shared it with founders. Founders shared it with their teams. It became, as one early user put it, the worst-kept secret in the valley.

Habit, once established, became its own moat.

What does Granola do?

When I downloaded Granola for the first time, I was impressed with its simplicity. I downloaded a DMG file, completed the onboarding, and I found the Granola icon pinned on my navbar on the top. It is always there in the background, and it loads automatically when I open my computer each day.

It has access to my calendar, and tracks all the meetings that I have coming up - it reminds me to join the meeting 5 minutes before, and a Granola meeting notes document opens up as it transcribes the audio inside the meeting.

The cool part that I loved was that there were no bots joining the meeting - I always found the idea of bots joining meetings pretty invasive. Within Granola, there was nothing of the sort. It was almost invisible and was quietly doing its thing in the background allowing me to focus on the meeting.

At any time, I could take notes within the Granola doc - rough notes or ideas that I thought were interesting, while the transcription kept happening in the background.

When the meeting was finally over, Granola automatically summarized the conversation for me. I could also choose different templates (like a sales call or a customer discovery call) depending on my use case and get a summary from that POV. I could then chat via AI with the meeting, ask questions to it, and retrieve specific information and ask it to do stuff with the context loaded in. If I wanted, I could send the link to my coworkers or send them a message on Slack without leaving Granola.

I can also organize these meetings into folders, and I can then chat with these folders and retrieve information from that specific folder. I can also prompt Granola to take actions on my behalf like sending an email or creating a list of todos. Slowly I began to see the promise of Granola and began to envision it as having my own personal and professional knowledge and workflow tool.

The Interface: Familiar by Design

The product looks like Apple Notes. That’s not an accident.

Every design decision in Granola has been made to reduce friction at the moment of highest cognitive load, when you’re in a meeting, trying to listen and think and capture at the same time.

The interface is minimal, uncluttered, and immediately familiar. There are no buttons competing for your attention. Nothing that requires you to learn a new mental model.

The Human-in-the-Loop Difference

Granola’s philosophy is that people don’t want replacement. Rather what they want is leverage. They want the work to still feel like it came from them. Full AI automation sounds appealing in theory but in practice, people disengage, the output feels generic, and the notes don’t reflect how you think about what happened.

So Granola keeps the human in the loop, deliberately. You take notes during the meeting in your own word, and when the call ends, it takes your rough notes and makes them significantly better: filling the holes, fixing the typos, adding the context you missed, sharpening the structure.

You can see exactly what AI wrote, verify it against your memory of the call, and put your own voice back in wherever the output doesn’t sound like you. The AI takes you 80–85% of the way. The last stretch is yours.

Critically, you can trigger different templates based on your conversation - for example, you can trigger a “customer demo call” recipe which will create notes from that POV. Likewise for a sales call, or a product feature request etc.

This is what people talk about when they tell someone else about Granola. Not the transcription, not the summarization - the notes are just better. It’s a deceptively simple value proposition that turns out to be genuinely hard to replicate.

From Notes to Knowledge: The Folder Layer

Where Granola starts to become something larger is in what happens after the meeting.

Notes and meetings can be organized into folders - a folder for sales calls, one for customer conversations, one for investor updates, one for team standups. Each folder becomes a shared, searchable repository of everything that’s been discussed in that context. And you can chat with each folder directly, asking questions and getting answers grounded in the actual conversations that happened.

This is the shift from tool to platform. A single meeting note is useful. A folder containing six months of sales calls, with full context on every conversation, every commitment made, every objection raised - that’s a different kind of asset. It compounds. The longer you use it, the more valuable it becomes.

Granola describes the long-term vision as an IDE for knowledge workers: a single workspace where the most important context in your professional life lives, is searchable, and can be acted on. Not a place to store information passively, but a place to work from actively.

Recipes: From Note-Taker to Agent

There’s a quieter capability in Granola that points toward something bigger: Recipes.

Recipes are triggered workflows & prompts you set up once that Granola executes on your behalf after a meeting. Draft the follow-up email. Pull out the action items. Populate the CRM field. Generate the summary for the team Slack. Things that used to live on your post-meeting to-do list, now handled before you’ve even closed your laptop.

It’s a subtle but important shift. For most of its interactions, Granola augments what you do better notes, richer context, smarter search. With Recipes, it starts acting for you. The mundane, low-judgment work that accumulates after every call gets offloaded. You’re freed up for the work that actually requires you.

This is the first clear signal that Granola is building toward something more agentic. The meeting is no longer just a thing to be captured but rather it’s a trigger. Context flows in, actions flow out, and Granola sits in the middle as the orchestration layer connecting what was said to what needs to happen next.

The implications compound quickly. Once Granola is deeply integrated with the tools a team already uses - CRMs, project trackers, communication platforms - it becomes less of a note-taking app and more of a workflow bridge.

Lightspeed’s Mike Mignano believes that Granola has an edge in this space because of its interface and user experience.

“Since the start, the company has had the right mix of AI transcript and human control of taking notes. Now that they are building context across the meetings and making the notes shareable, the product has become stronger. With these features, Granola will have long-term context for users and teams, kicking off network effects for the startup”

The transition from vertical tool to horizontal platform usually happens gradually, then suddenly. Recipes feel like the gradual part.

The Context Layer: APIs, MCPs, and the Everywhere Strategy

A note that lives only inside Granola is useful. A note whose context can flow into every tool a knowledge worker already uses is something categorically different.

This is the bet behind Granola’s API and MCP strategy and it’s one of the most underappreciated moves the company has made.

Granola now offers a personal API, letting individuals programmatically access their own notes and notes shared with them. For teams on enterprise plans, an admin-level API unlocks the full stack of team conversation context - every call, every decision, every commitment made across the organisation, queryable and actionable from outside the Granola interface. Alongside this, an updated MCP lets users surface notes from folders and shared spaces directly inside any AI tool that supports the protocol.

The practical upshot: your Granola context can now show up inside Claude, ChatGPT, Lovable, Figma Make, Replit, Manus, v0, Bolt.new, and a growing list of partners. You’re building a product in Replit and want to pull the context from your last three customer calls? It’s there. You’re using Claude to draft a proposal and want it grounded in what was actually said in the discovery meeting? Also there.

This matters because of what agents fundamentally need to do meaningful work: context. Without knowing what was said, what was decided, and what was promised, an AI agent is operating in the dark - capable of generating output but not capable of generating relevant output. Granola’s context layer solves that problem. It makes meeting intelligence portable, so the most valuable information from your working life isn’t trapped in one app.

The distribution logic here is also worth noting. Every integration is a new surface where Granola’s context proves its value, and a new reason for someone who isn’t a Granola user to become one.

A developer who encounters Granola context inside their Replit workflow, or a designer who sees meeting notes surfaced inside Figma Make, didn’t arrive through a marketing funnel. They arrived because the product embedded itself into the tools they were already using. That’s product-led growth operating at the infrastructure level.

The long game is clear: Granola wants to be the context layer that sits beneath the entire knowledge worker stack. Not the tool you open. The layer that makes every other tool smarter.

How Granola builds Granola:

Most engineering advice is about building things that survive. Write clean code. Separate your concerns. Build a test suite. Invest in abstractions that scale. It’s sensible advice for a company that knows what it’s building.

Granola didn’t know what it was building. So they threw out the rulebook and took their inspiration from somewhere unexpected: evolutionary biology.

541 million years ago, something extraordinary happened in the fossil record. In a geologically brief window, the number of complex animal forms exploded. Thousands of new species appearing, competing, and mostly dying out. The few lineages that survived went on to dominate life on Earth for hundreds of millions of years. Biologists call it the Cambrian explosion.

Granola’s engineering team looked at that story and saw the best startup playbook they’d ever encountered. The whole question became: how do you build software that can evolve the same way?

Their answer organized itself around three principles: make the code friendly to mutation, evaluate variants in parallel, and make bad versions easy to kill.

#1: Write code that's easy to change, not code that's easy to admire.

Clean code is code that’s easy to understand. Evolvable code is code that’s easy to change. At the early stage, Granola decided these were different things and chose the second.

That meant keeping everything in one big codebase rather than splitting it into microservices, modules, or carefully separated layers. One large, interconnected blob is faster to change than many small pieces that all have to coordinate. As they put it: your professor isn’t here to grade you anymore. It’s okay to keep everything in one file.

It also meant deliberately allowing copy-paste code over shared abstractions. Conventional engineering wisdom says copy-paste is bad - it creates duplication and inconsistency. But abstraction creates dependencies: change the shared function, and everything that relies on it breaks. Copy-paste code lets you change one part without touching anything else.

Nature figured this out long ago. Biologists call it Williston’s Law: nature loves to copy-paste and edit. Take a versatile structure like a leg, a tooth or a fin - duplicate it many times, give each copy room to specialize, then let selection kill off the ones that don’t work. The survivors are more adapted than any single carefully designed structure could have been. Granola was doing the same thing with code.

#2: Don’t Broadcast. Distribute.

Most software distribution is sequential. You build v1, ship it to everyone, collect feedback, build v2, ship that. One version in production at a time. If v1 was a mistake, every user is living with that mistake simultaneously.

Granola broke this constraint with a simple decision: distribute via a .dmg file rather than an app store.

The implication was larger than it sounds. Because they weren’t publishing through an app store, they could cut a new build and send it to a single user overnight, with no approval process, no public changelog and no announcement. Different users could be running radically different versions of the product at the same time. Reality was selecting the better variants in parallel, the same way natural selection evaluates competing species simultaneously rather than one at a time.

This is what allowed them to run the kind of experimentation they needed. Different features, different interaction models, different UI flows all live simultaneously, all being evaluated by real users, none of them locked in.

#3: Avoid Everything That Keeps Bad Versions Alive

Without death, natural selection doesn’t work. An organism that can’t die can’t be replaced by something better. The same is true of software but most conventional startup advice accidentally makes software immortal.

Landing pages make versions immortal. They promise specific features and flows that users expect to find. Documentation does the same. Blog posts, tutorials, marketing materials, API contracts, all of them create references that pressure you to keep a specific version alive long after you’ve learned it’s wrong.

Granola’s response was radical: avoid all of it. No public launch. No landing page. No documentation. Manual onboarding only, to a small, carefully chosen group of target users. Every version that didn’t work could be killed instantly - no PR fallout, no user expectations to manage, no documentation to update. The bad variants just disappeared.

This is the counterintuitive flip side of “launch early and often.” Users keep software alive. The more people depending on a version, the harder it is to change. Granola deliberately minimized that pressure during the period when they needed to change the fastest.

#4: Launch with Less

When Granola finally launched publicly in spring 2024, they removed 50% of the features they’d built.

That number deserves a moment. After months of Cambrian-style experimentation with thousands of mutations, dozens of variants, feature after feature built and evaluated and discarded, they shipped half of what they had. Everything that had been demo-good but not daily-use great got cut.

“If you add 50 buttons in there with new features,” Chris Pedregal said, “you kind of kill the golden goose.”

The messy, evolutionary, copy-paste, one-blob approach was how they earned the discipline to know what to cut. You can’t remove the right 50% until you’ve built and tested the wrong 50% first.

How Granola grows

Granola has grown its user base 10% every week since launch. To understand what that means in practice: at that rate, the product roughly doubles every seven weeks. The founding team hasn’t disclosed absolute user numbers, but the growth rate itself tells the story. This is a product that, once people find it, they keep using and keep telling others about.

That kind of growth doesn’t come from a single isolated marketing or promotional campaign. It comes from a product that does its job well enough that users become the marketing department.

#1: The Growth Loop

Most marketing is a funnel: you pour money in at the top, customers come out at the bottom, and when you stop spending, the growth stops. A growth loop is different. The output of getting one user becomes the input for getting the next, and the cycle feeds itself without constant reinvestment.

Granola’s core loop is elegant and almost accidental in how naturally it emerges from the product. When a meeting ends, you have polished, shareable notes. Sharing them is one click - it can be a URL you can copy, drop into Slack, email, or via a follow-up message. The recipient, who may never have heard of Granola, opens a beautifully formatted summary of the meeting they were just in. Some of them want that for their own calls.

Every note shared is a product demo. Every non-Granola user who receives one is a potential convert. The product grows every time it’s used.

The cultural signal of this is starting to show. People have started saying “just Granola it” and “Just Granola it” merch is also being sported. Using the product name as a verb suggests that the behavior has become so habitual that the product and the action have merged in people’s minds. That’s the kind of brand position that’s nearly impossible to buy.

#2: Community and Influencer Distribution

Alongside the organic loop, Granola has built deliberate distribution through communities where their target users already congregate.

Partnerships with communities like NextPlay and Lenny’s Newsletter makes sure Granola is surgically inserted into the professional networks of exactly the kind of people Granola is built for. Lenny Rachitsky’s audience skews heavily toward product managers, founders, and operators: people who live in back-to-back meetings and feel the pain Granola solves every single day. With these partnerships, Granola is reaching people who are one conversation away from becoming paying users.

The Recipes mechanic extends this further. Prolific users and creators (including contributors like Lenny) submit workflow templates that get shared with their communities. Each Recipe is both a product tutorial and a distribution event: someone shares a template, their audience tries it, and the loop starts again. It turns Granola’s most enthusiastic users into a distributed growth team.

#3: Product-Led Growth Meets Sales Infrastructure

For a long time, Granola’s growth was almost entirely inbound. The product spread, people signed up, and the team tried to keep up. Which sounds like a good problem until the inbound volume outgrows your ability to act on it.

At a certain scale, product-led growth needs a sales motion to convert it. Granola’s inbox was full of high-potential leads with no systematic way to prioritize them. Product usage data lived separately from customer records. Manual triage was consuming hours that should have been spent closing.

Their solution was to use Attio to build a proper GTM infrastructure: inbound leads captured automatically, product usage signals connected to customer records, and routing logic that surfaces the highest-value prospects for the sales team before they go cold. Fast-growing PLG companies eventually need to build the infrastructure to harvest what the product generates. Waiting too long means leaving significant revenue on the table.

#4: Growth via Retention

Underlying all of this is a bet that most companies talk about but few actually optimize for: that retention is a more powerful growth driver than acquisition.

Granola’s 70% weekly retention means the leads that come in are genuinely valuable.

A product that keeps 70% of its users active every week grows differently than one that acquires aggressively and churns just as fast. Each retained user is a potential evangelist, a potential sharer, a potential enterprise license. The network of trust that builds around a product people genuinely rely on is the hardest thing for competitors to replicate and the most durable foundation for everything else.

Granola didn’t build growth loops, community partnerships, and sales infrastructure as separate initiatives. They’re all downstream of the same bet: if you build something people actually need, the growth takes care of more than you’d expect. The job is to make sure you’re ready for it when it arrives.

Building the jetpack for the mind

Andrej Karpathy has a useful framework for thinking about AI tools: a variable autonomy slider. At the low end, the human operates and the AI responds to commands. At the high end, the human just approves - the AI runs independently across long stretches.

Karpathy’s insight, drawn from years working on full self-driving at Tesla, is that full autonomy is a decade-long roadmap. The real value right now lives in the middle: AI capable enough to handle the hard work, but on enough of a leash that humans stay engaged with the output.

The smartest AI products being built today are all staking their claim somewhere in that middle zone. The question is how you get in.

Granola’s answer was to ask a different question. The AI transcription market was already crowded when they launched - Otter, Fireflies, Fathom, all racing to answer the same question: how do we transcribe better?

Granola stepped off that treadmill entirely and asked something else: what do people actually want from their meeting notes? The answer turned out to be notes that sound like them, organized the way they think, that they trust enough to share and act on.

By starting with the human’s notes rather than the AI’s transcript, they wedged themselves into the middle of that autonomy slider and made it their own - not a transcription tool, not a fully automated assistant, but something that sits in the productive tension between the two.

The result is a product that captures 80–85% of what happens in a meeting. That baseline is superhuman in its consistency - no human takes notes that good, that reliably, across every single call. But Granola stops short of doing everything.

What actually matters, what gets sent, how the follow-up gets framed - that stays with the user. What Granola targets is the busywork: low-judgment, high-frequency processing that drains time without requiring anything uniquely human. What it preserves is everything related to voice, judgment, relationship and persuasion.

The result is that the output still feels like it belongs to you. This matters more than it might seem. When AI takes over entirely, people disengage as the notes could have been anyone’s, the follow-up reads like a template, the whole thing loses the texture that makes professional communication land. Granola’s bet is that knowledge workers will always want tools that make them ten times better at their craft, not tools that do the craft for them.

But staying in the middle zone is a starting position, not a destination. Every product has to evolve, and Granola is no different.

The next phase of Granola is about making everything captured actually work for you across time, across teams, and across the tools where work actually happens. That’s a horizontal game, and it’s a much bigger one.

Here’s what makes it defensible. A single user’s meeting notes are useful. A whole team’s meeting notes, accumulated over a year, structured and searchable - that becomes a different category of asset entirely.

Think about what a year’s worth of customer calls actually contains: every objection raised, every feature requested, every commitment made, every signal about what’s working and what isn’t. Right now, that intelligence lives in scattered notes, fading memories, and CRM fields nobody keeps updated.

Granola’s long-term play is to make that intelligence live - surfacing automatically when it’s relevant. You’re about to get on a call with a customer you haven’t spoken to in three months.

Granola already knows what was discussed last time, what was promised, what went unresolved. You walk in prepared without doing anything. The more a team uses Granola, the more context it accumulates, and the more valuable that context becomes for every future interaction. That’s the real moat - not the AI model underneath, which anyone can access, but the irreplaceable institutional memory built on top of it.

This is also precisely where the competitive picture gets interesting. Notion and ClickUp pose a real threat - not because they’ll build something better than Granola, but because they’ll build something good enough, wrapped inside a tool people are already habituated to. For a horizontal platform, meeting intelligence is one feature among dozens. It gets built, shipped, and deprioritized. The roadmap always has something else on it.

Granola’s edge is that meeting intelligence is the entire product. Every design decision, every integration, every workflow optimization is in service of one problem. The CRM integrations with tools like Attio and HubSpot aren’t features - they’re the execution of a thesis: that the most valuable thing Granola can do is close the gap between what gets said in meetings and what gets done in the systems where work actually lives. Whoever builds that bridge most cleanly wins a significant share of the knowledge worker’s day.

A horizontal tool will always treat that bridge as a nice-to-have. For Granola, it’s the whole game.

The Wrapper That Wasn’t

Which brings us back to where this essay started.

Granola began as something that looked, from the outside, like exactly the kind of AI wrapper that gets dismissed at dinner parties. A thin layer on top of a transcription API. A notes app with a prompt attached that said “Dude, it’s just a wrapper.”

What it actually was, and what the best wrapper companies always are, is a specific answer to a specific question that nobody else was asking carefully enough. Not “how do we transcribe meetings?” but “what do people actually need to happen after a meeting, and how do we make all of it effortless?”

Three years in, the answer has compounded into a product with genuine defensibility: a human-centered interaction model that people trust, a repository that gets more valuable the longer you use it, a distribution strategy that turned VCs into evangelists, and an agentic layer that’s only beginning to show what it can do.

The steering wheel hasn’t been invented yet. But Granola is one of the teams closest to figuring out what it should look like.

If this essay made you think differently, share it with someone who’s building. And if you want your company’s story told with this kind of depth, book a call and let’s chat.

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What started as a wow moment morphed into something that began to question our very existence and the reason why we exist, with people forming opinions (often half-formed) about its catastrophic calamities or showering praises on how it will change the world for the better.

The way humans work has been one of those questions that has left individuals and businesses and corporations befuddled. We all seem to be going with it, without really knowing where its taking us. Some of us hope for the best, while others share words of caution.

The way we work is changing and will inevitably bring about a new world - an agentic world. A world where the lines between human effort and machine intelligence gets blurry.

Whether that leads to a UBI world or a Utopian society where humans won’t have to work anymore in a post-scarcity economy, only time will tell.

Irrespective, AI agents are here to stay with organizations rushing to adopt them internally as they gaze at the promised land of machine-level efficiency looming over the horizon, while their employees begrudgingly follow.

Gartner predicts 40% of enterprise applications will embed task-specific AI agents by 2026 evolving assistants into proactive workflow partners. Forrester predicts 30% of large enterprises will mandate AI fluency training by 2026. Goldman Sachs Research estimates the application software market could grow to $780 billion by 2030, and 60% of the SaaS market will be captured by AI agents. IBM and Salesforce expect one billion AI agents to be operational across the world by the end of 2026.

Software (and by extension SaaS startups) will become agentic by default as software-as-a-service morphs into service-as-a-software.

In this guide, I’ve looked at one such AI agentic company - Glean, a $7.2B company that is already at the forefront, delivering high quality AI agents at scale to enterprises like Webflow, Grammarly and Duolingo.

I have studied the company and have attempted to chart their course, while also offering a playbook on how one were to start or grow an AI agentic software company right now.

I believe that this is a great opportunity for builders and founders to leverage and build long-lasting sustainable companies. So let’s get into it.

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What does Glean do?

Glean’s homepage hero section mentions its promise in no unclear terms - Glean gives every employee an AI Assistant and an Agent so that they can put their company’s knowledge to work.

So far, Glean powers more than 100 million agent actions every year for companies like Webflow and Grammarly. It’s worth $7.2B and crossed $200M in ARR in 2025.

But why is it worth so much? What’s the value proposition exactly?

Glean’s claim to fame are its AI agents as its homepage so succinctly conveys. But its real claim to fame is its ease and reliability of deploying AI agents at the enterprise level.

You see, AI agents seem fancy but can be a pain to build out. The underlying infrastructure for AI agents is complex with technical difficulties and when you put those in an enterprise context, things tend to get very messy very fast. So there is value in abstracting away the complexities and optimizing the build experience on the user side. And when you do this for enterprises, you will be handsomely rewarded.

And that’s exactly what Glean did. Glean stripped away the complexities around RAG, Knowledge Graphs and presented those in an easy-build-lego like UI to users to build on top off for their enterprise needs.

Glean now helps users build AI agents across the entire workforce - sales, marketing, engineering, IT, customer support, using natural language. Users can orchestrate the agentic behavior by setting up triggers based on reaction to data, app state changes or in response to seeing questions they can answer.

It’s truly a horizontal compound product. Rippling would be proud.

But that’s just one side of Glean’s value proposition. The other side is abstracting away the inefficiencies of AI itself.

The problem with using LLMs for work is that their models are “frozen” at the point their training ended. So AI cannot account for tickets that happened yesterday. Which can be a big pain point at the company level, where escalations happen every day and having employees stay on top is paramount.

Operating in a fixed domain like your company’s knowledge base solves this problem, as it gives the LLMs the required context to move the AI from “guessing” to “retrieving”. This is mostly done via techniques like RAG and Knowledge Graphs. Instead of searching its “brain” for an answer, the agent first searches your company’s documents. If the answer isn’t in the documents, a well-tuned agent will say “I don’t know” instead of making something up - reducing AI hallucinations.

Looking at Glean from this perspective, it almost looks like its offering “RAG as a Service”. Which is very valuable given how notorious RAG is to set up with Vector Databases, chunking, embeddings and a whole bunch of things happening under the hood.

“In 2026, contextual intelligence will track not just what people are working on but how they work best. It will anticipate when to prompt, when to summarize, and when to step back. The line between productivity and emotional intelligence will blur, as AI becomes both project manager and collaborator.” - Arvind Jain, Founder & CEO, Glean

And enterprise customers are more than happy to pay for something like Glean since its abstracts away so much of the complexities allowing them to grow fast and do so at scale.

Another problem that Glean solves that makes it so valuable is its ability to retrieve information and connect data across multiple applications. Companies at the enterprise level have a massive tech stack, with their data scattered across multiple such applications. So there is value in connecting all those different tools and datasets, retrieve key information that can align teams better and keep both AI and human employees context-aligned.

“By 2026, the true competitive advantage in enterprise AI will shift from model performance to proprietary data. As frontier models reach similar levels of capability and AI app development becomes increasingly accessible, differentiation will depend on the uniqueness and quality of an organization’s data.” - Baris Gultekin, VP of AI, Snowflake

I like how Glean pairs AI with human expertise.

“As AI makes everything look more polished and confident, genuine expertise becomes harder to spot. When AI offers polished recommendations or convincing explanations, humans often defer to it - especially under cognitive load. This is why 2026 will push leaders to rethink who does what: what can be reliably handed to AI, and what must remain human.” - Jackie Lane, Assistant Professor, Harvard University

Let’s take a closer look at Glean’s product suite and see what else is going on.

Glean’s Product Suite

AI agents are at the heart of Glean’s product suite. Agents are deployed across the entire enterprise workforce and perform a suite of different tasks across marketing, sales, engineering, IT, customer support and much more.

Glean’s native Agent Builder allows users to both build step by step agents lego-style or create AI agents using natural language.

Glean’s search product indexes dozens of applications while understanding context and retrieve answers. Customers can connect 100s of applications with Glean - applications like Google Drive, Notion, Jira, Figma, and Github. Glean not only combines content from these sources, it also takes into account the metadata, identity data, permissions data, and activity data to provide secure and personalized responses to different users.

Glean has something called a Workplace Search too. It’s like Google but for the workplace. Internal data sources are indexed and are displayed to the user when specific search terms are queried.

Users can also use Glean’s Deep Research agent to conduct comprehensive research across internal data sources, analyse said data, and create a citation rich report for stakeholders to review.

Getting insights on key matters from relevant stakeholders at the right time can make or break operational sprints. So Glean has something called “Expert Detection” that enables users to filter their search by “people” to view colleagues within the organization who have unique knowledge pertinent to their query.

Remember how the homepage said that Glean brings AI assistants to every employee? Well, Glean does this with its native AI Assist feature - a ChatGPT style application powered by the enterprise’s knowledge base.

For example, a user can ask Glean Chat to write an email to a customer based on information sourced from their submitted ticket that resides within Intercom. Sources used within a query response come with full citations.

In 2024, Glean integrated its AI assistant within other customer facing platforms like Zendesk and Salesforce, so that employees can now use Glean’s search functionalities directly within these applications. Check out our AI Agents Directory to explore similar tools.

Glean has connections with 100+ applications via its connectors akin to ChatGPT and Claude connectors. These connectors make Glean even more horizontal as it transitions from being a product to becoming an ecosystem.

Glean has unveiled Glean Apps, a new product that empowers users to effortlessly create “custom generative AI agents, assistants, copilots, and chatbots” without any coding expertise.

Additionally, users can outline the “actions the app can perform on behalf of users within a company’s connected applications,” such as writing code or composing emails. Finally, access rights can be assigned to either individual users or teams, and the triggers that launch the app’s processes are also defined.

For example, a marketing manager can use a Social Media Assistant to instantly create a complete and succinct LinkedIn post sourced from a complex survey results document, all in the right tone of voice.

With Glean Answers, employees can answer colleagues’ questions. When a user asks a question within Knowledge Management, a relevant answer is provided.

In addition, Glean’s “home page” acts as THE central hub for employees to discover AI-generated suggestions for documents to explore or other relevant information to check out.

In June 2024, the company introduced two APIs that allow developers to integrate Glean’s technology into their organization’s products. The Chat API facilitates the incorporation of Glean Assist’s features into other companies’ applications. Meanwhile, the Search API offers Glean’s proprietary RAG models, delivering top-tier search capabilities for custom generative AI applications.

How did Glean get their first customers?

When you start to build your AI agent, your first challenge on the sales side will be “okay how do I get people to pay for this”? Well, for starters, you can start by building something YOU want.

Arvind Jain built Glean in order to scratch his own itch. In his time as the engineering director at Rubrik, he realized that his company had constructed infrastructure across 300+ cloud applications. Finding information was next to impossible and so he built Glean out to solve that problem. And in March 2019, he launched Glean with 15 million dollars from Kleiner Perkins.

Contrary to popular advice, Jain and his team decided to build in stealth, much like how Rippling did it before launching to the public. They were in stealth mode for two and a half years while they were validating the product and working out of Kleiner Perkins’ office basement in Menlo Park.

Jain got his first customers by offering them free trials. He zeroed down on his ICP and had clarity on who he wanted as customers. He identified fast growing technology companies with 500-2000 employees. Jain observed that these companies were large enough to suffer from information fragmentation while being lean enough to experiment with novel solutions like Glean.

Thanks to his deep connections in Silicon Valley, Jain got in touch with key people who were interested in giving Glean a shot.

“We asked some companies to try our product for free for a long time. We told them to just keep using it and to give us feedback.”

The company had approximately 10-20 companies using Glean without paying during this phase.

Jain had just one KPI in mind while working with these customers - usage. Jain and his team monitored how often employees used the product, which queries they ran, and how they perceived its value. The company collected employee surveys asking whether Glean helped them be more productive at work.

“There was nothing else in the marketplace like Glean, and so there was nothing to compare us to, no budget for doing what we do, and no way to assess how much they should pay for it.” - Jain

The free trial approach solved this. Customers experienced the value directly, which converted abstract value propositions into tangible productivity gains. Over time, as usage metrics climbed and employee surveys confirmed productivity improvements, customers signed paying contracts.

Once the first customer signed, the transition accelerated. The company observed a pattern: free trial customers who showed strong usage metrics (high query volume, consistent daily active users) and positive employee feedback naturally became willing to pay. Early customers like Outreach paid approximately $50,000 per year in flat-rate fees, regardless of the number of employees using the software.

This pricing model was pragmatic for early customers—the company couldn’t predict usage patterns across different organizations, and customers needed simplicity. Over time, Glean refined toward per-seat pricing, but the flat-rate approach worked for customer acquisition and removal of purchase friction.

How do you grow your startup after coming out from stealth after 2 years?

When Glean officially emerged from stealth in September 2021, it had 40 customers already operational - companies like Confluent, Outreach, Grammarly, and Webflow which positioned Glean as a well-capitalized entrant with serious investor backing.

At this stage, its core offering was a unified search engine that indexed across all of a company’s SaaS applications. The knowledge graph underpinning this search aggregated 100+ connectors to applications like Slack, Salesforce, GitHub, Okta, and ServiceNow. The key innovation was the “knowledge graph”—a sophisticated model that indexed not just content, but relationships between people, projects, documents, and activity patterns.

The proprietary “Scholastic” semantic search system adapted language models to each customer’s unique communication patterns, dramatically improving relevance compared to generic search tools. Unlike consumer search engines that rely on link structure, Glean leveraged organizational signals—document popularity among specific teams, co-author relationships, recency, and departmental affinity—to rank results.

Glean would land a new customer with a high-touch approach. Engineers, product managers, CSMs, and sometimes co-founders would work directly with the customer during onboarding and implementation. This white-glove service ensured quick time-to-value (Glean could be operational in hours or days, unlike legacy search tools requiring months of implementation).

Once customers experienced value measured in concrete terms like “5 hours saved per week per employee”—they became advocates. Word spread within their networks and peer groups. Glean benefited from the natural tendency of Silicon Valley engineers and product leaders to share tools that improved their work.

Early sales conversations involved addressing a fundamental education challenge: many enterprise buyers simply didn’t understand what retrieval-augmented generation (RAG) meant or why Glean’s approach was superior to traditional enterprise search. The sales team had to educate prospects about the problem (fragmented knowledge across SaaS), articulate the solution (AI-powered unified search with permission controls), and quantify the ROI (hours saved per week, faster support resolution times, improved onboarding).

To achieve this, the team used dedicated Slack channels for each customer, with everyone from engineers to co-founders jumping in to address issues and questions.

During this cohort, the team discovered an interesting thing - Initial deals often started as pilots in single departments (usually engineering or customer support, where search pain was most acute). As teams experienced value, adoption spread organically across the company, generating expansion revenue.

A customer might sign an initial contract for $60,000 focused on the engineering team. Within 9 months, as other teams (sales, support, HR) requested access and discovered value, the deal expanded to $500,000+.

Recognizing that customers were Glean’s best marketers, the company formalized a “Gleanvocates” customer advocacy program. Rather than leaving advocacy to chance, Glean created a structured program with clear incentives and activities.

The journey to a billion dollars for Glean

As Glean scaled up, it consolidated communication into a single community Slack channel and elevate customer champions (power users who loved the product) to answer questions and share best practices. This created a self-reinforcing system where successful customers became partners in onboarding new ones.

At this stage, Glean began doing more proactive outreach. The company started targeting enterprise buyers directly, focusing on C-level executives (CIOs, CTOs) rather than department heads.

This reflected both product maturity and market timing—by late 2023, the ChatGPT moment had created widespread enterprise appetite for AI-powered solutions, and Glean had a proven product.

VP of Sales AJ Tennant implemented quarterly “spiffs” (sales incentives) for AEs establishing the KPI as the most executive connections - recognizing that enterprise software purchases require multiple stakeholders.

Enterprise deal cycles averaged 4-5 months, with initial contracts often starting at $50,000-$100,000 annually (departmental pilots) then expanding to $500,000+ as adoption spread across the company.

One of Glean’s most visible inbound channels was its extensive library of customer case studies. The company published over 25 detailed case studies documenting specific implementations, challenges, and measurable results. This content-first approach is crucial for ranking on AI search engines like ChatGPT and Perplexity.

It was during this stage that Glean launched Glean Chat, a generative AI assistant that leveraged the company’s search and retrieval-augmented generation (RAG) technology. Unlike ChatGPT or Perplexity, Glean Chat was contextually grounded in each company’s data and could summarize large datasets, extract insights from customer feedback collections, or generate narratives from project documentation.

The RAG architecture proved critical here: rather than relying on an LLM’s training data, Glean’s Chat retrieved relevant documents from the knowledge graph, passed them to an LLM (initially partnering with various models), and generated answers grounded in the company’s truth. This reduced hallucinations and ensured answers were verifiable and defensible.

One of Glean’s most powerful but often underappreciated growth levers was its partner ecosystem. It has partnerships with companies like AWS, Google Cloud, Azure, Snowflake, Workday, Anthropic.

In 2024, Glean raised $200+ million at a $2.2 billion valuation, led by returning investors Kleiner Perkins and Lightspeed, with Sequoia and new investors General Catalyst, Coatue, ICONIQ Growth, and IVP participating. By this stage, the company had proven its GTM engine: ARR was growing at 300% year-over-year, major customers including Reddit, Instacart, and Duolingo were live, and the product roadmap had shifted from pure search to generative AI-powered search with chat capabilities.

Glean held its first user conference, “Glean:GO,” in June 2025, which drew over 10,000 attendees in-person and online. This event demonstrated the company’s ability to build community, showcase customer success stories (Booking.com, Canva, Zillow), and announce major partnerships. By December 2025, Glean launched the “Work AI Institute,” a research initiative to study real-world AI impact in enterprises, positioning the company as a thought leader beyond just a vendor.

The same year, the company launched “Glean Agents,” a platform enabling employees to build and deploy autonomous AI agents using natural language instructions.

In December 2025, Glean unveiled “Enterprise Context,” its third-generation platform combining memory, connectors, indexes, personal graphs, enterprise graphs, and governance to power “autonomous agents” capable of reasoning and adapting in real-time. These agents could interpret intent from natural language, dynamically select tools, and communicate their reasoning—moving beyond rigid workflow automation toward genuine AI autonomy.

How do you build an AI agent today? The playbook.

Like all things, it’s easy on paper but hard to execute. Based on my experience studying Glean, playing around with building AI agents and writing on AI agents, here is how I would go about building an AI agent company in 2026:

1. Start with a niche vertical

The riches are in the niches dictum couldn’t ring more true in the realms of agentic software. Vertical industries like law, healthcare, and cybersecurity are primed for AI disruption. These fields thrive on language and unstructured text data—making them perfect for AI agents powered by LLMs.

Find a company or shadow a company that has repeatable processes. Record yourself and the employees doing a particular task, such as responding to customer support tickets, sending those tickets to Jira, uploading it on Notion, having intercom support tickets, the salespeople attending sales calls, the pitches that they use, the collateral that they use, all of that stuff and figure out where the automation can come in and your agent can take over from those particular use cases.

Take Harvey AI. It’s not just automating legal workflows; it’s transforming law firms by taking over tasks like contract review, due diligence, and litigation prep. Imagine a junior associate that never sleeps, misses a deadline, and constantly improves. Similarly, EvenUp focuses on personal injury law, automating settlement demand letters and allowing attorneys to focus on strategy instead of paperwork. In healthcare, Freed and Deepscribe help doctors reclaim their time. AI voice agents schedule appointments, transcribe patient interactions, and suggest diagnoses. AI Agent Tennr helps healthcare professionals read faxes and enter data. Building a vertical solution offers tremendous value–you can capture a dominant market, establish credibility, and build a moat around your community.

Your models will improve as you fine-tune them on domain knowledge. Since agents use reinforcement learning, the more tasks they do, the better they learn and the faster they perform. Coupled with the decreasing cost of inference computing, you wonder–what are these agents ultimately capable of?

Here’s the playbook on how to build in vertical AI:

1. Target language-heavy industries: Identify a language-heavy industry: Look for fields where unstructured text data—like contracts or medical notes—is a bottleneck for productivity.

2. Focus on a core pain point: Identify the most frustrating, repetitive task your audience faces and solve it first.

3. Leverage LLMs for workflows: Build agents that process unstructured data, reason through complex problems, and improve over time.

4. Bundle solutions over time: Start narrow, but bundle by adding products: Once you’ve automated a workflow, look for complementary tasks to create an integrated compound product to build long-term technical & distribution moats.

5. Fine-tune with domain-specific data: Use reinforcement learning to make your model an expert. The more tasks your agent handles, the smarter it becomes.

Start with a niche vertical use case to wedge into the market; add products layer on top and bundle adjacent use cases to build a compound product that’s defensible.

2. Build connections across different tools within that niche

The next step here is to build connections across different tools within that niche. So for example, if you’re building something in the legal space, you might want to connect your agent with a case relationship manager tool or an AI paralegal tool, depending on the tech stack. So the more connections you make, the more integrated it becomes with the overall tech stack and you try and build out an ecosystem around your platform. And then once you create enough of these connections, you end up becoming a horizontal product in that vertical niche.

3. Create templates around different agents and have influencers distribute it

Create templates around different niches. Create templates around these different agents and hire influencers to distribute it. This is similar to how Clay did it when they hired influencers in the sales niche who were promoting these Clay workflows and stuff and other people in that same niche followed suit. This will be applicable only if you are trying to leverage a product-led growth motion where you are trying to get users as much as possible and then slowly scaling it up to a sales-led growth motion. This is similar to how companies like Gumloop and Relay have also done it where you can see Jacob Banks, the founder of Agentic Application Layer Relay, using these templates and these agentic workflows to build traffic and get users in their growth funnel. If I were to guess, then Relay is also having a similar plan to move upmarket and target enterprise customers. So this seems to be a proven playbook.

4. Scale up to Sales Led Growth and move upmarket

Once you have the initial influx of users, you want to scale up to sales-led growth and move upmarket and become a more compound horizontal product like Rippling did. In this case, we have two options. You can either become a compound product within that vertical niche or you can embody a different approach and encompass more industries and become a very well-defined horizontal product that will cater to different use cases. It depends on the kind of feedback that you get from your users and your market saturation levels.

This playbook should work pretty well. Of course, it’s easier said than done.

Enterprises don’t adopt agentic software because it’s cool. They adopt it when it’s boringly reliable: when permissions are airtight, when citations are real, when the system knows when to say “I don’t know,” and when the agent’s output survives contact with messy org reality. That’s the bar Glean quietly raised—and it’s the bar every serious builder has to clear in the next phase of software.

I hope you found this essay useful - now go ship!

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On one hand, you have people saying - how do you expect them to make money off their free tier? While on the other hand, people are comparing this to the start of a dystopian nightmare from which there is no escape. Like most things, I believe that the truth lies somewhere in between, with both things being possibly true.

I get where the dystopian vision comes from. If Google and Meta ads were an invasion of privacy, ChatGPT ads amplify it by a factor of 100 while taking the game into a whole new level. The modern ad game is built on data. The more granular and accurate the data, the better it is for advertisers to get their money’s worth. One could even argue (especially the techno optimists) that this will end up being a good outcome for the end consumers as well since those ads will be highly relevant to them and useful for them.

And there is truth to that statement. In 2024, Instagram generated $37.2 billion in social commerce sales. 46.8 million people in the United States made purchases directly through Instagram. This represented approximately 35% of all U.S. Instagram users at the time. Globally, approximately 1.7 billion people use Instagram’s shopping features, with about 70% of Instagram’s total active user base engaging with shopping content. In 2025, 650 million monthly active users interacted with Instagram Shopping features.

I myself have bought shoes and outfits that I liked off of Instagram. At this stage, Instagram (and Google) both know what I tend to browse, what kind of stuff I’m interested in, and so the ad suggestions I get are clearly very relevant to me, and in some cases, extremely useful as well.

So ads can be potentially useful and create win-win situations for both businesses and consumers alike.

That said, we as consumers are bombarded with ads all the time, so much so that I feel like we are going through an ad fatigue right now. Everything has ads - from billboards to social media to apps to OTT to emails - there are ads everywhere. As such, I can see how shoving consumerism down everyone’s throat can become extremely frustrating, while being extremely lucrative to companies that are busy chasing their high growth at all costs business model.

Hate the game and not the player.

The game is designed in such a way that there is no recourse other than for companies to monetize this data and shove consumerism down our throats and it’s in OpenAI’s best interest to do so as well given that compute, talent and energy costs rise up.

In 2024, OpenAI recorded $5 billion in losses against $3.7 billion in revenue. The financial bleeding accelerated in 2025. In the first half of 2025 alone, OpenAI reported $7.8 billion in operating losses against $4.3 billion in revenue, with cash burn of $2.5 billion in just six months. By Q3 2025, the situation had deteriorated further: Microsoft’s earnings disclosure revealed that OpenAI suffered approximately $12 billion in quarterly losses, one of the largest quarterly losses in tech industry history. For the year 2025, OpenAI projected spending of approximately $22 billion against $13 billion in revenue, resulting in a $9 billion net loss.

As such, there is definitely a case to be made here that OpenAI needs to make money off its free users but the question becomes - “Is there a way to do so without showing us ads and forcing us to consume all the time?”

The obsession with consumerism is a very Western concept finding its root in the Keynesian economic model where good spending power indicates a healthy economy.

Economist John Maynard Keynes introduced the concept of the marginal propensity to consume - meaning that when people have income, they tend to spend a portion of it on consumption and this drives aggregate demand (total spending in the economy).

His theory was that if people spend more of their income rather than save it, the total effect on national income is larger because each round of spending creates income for somebody else, who in turn spends a portion of it.

During the 2008 Global Financial Crisis, many governments used large fiscal stimulus packages (infrastructure spending, tax cuts) to boost aggregate demand and reduce unemployment - a textbook Keynesian response.

During the COVID-19 Pandemic, the U.S., UK, and many other countries deployed massive government spending, direct payments, business support, and unemployment benefits specifically to sustain consumption and demand - another classic Keynesian response.

Every time there was a lull in the economy, central banks routinely cut interest rates to encourage borrowing and spending - policies aligned with Keynes’s idea that demand must be supported when private spending falls.

We seem to be going through a bit of a lull right now as well. Unemployment rose to 4.6% in recent months (the highest in four years). Announced layoffs in 2025 totaled 1.17 million, the highest annual figure since the pandemic with 58% of these concentrated in government and technology sectors. Nearly 1.17 million job cuts were officially announced by firms in the U.S. through the end of November 2025 - a ~54 % increase from 2024. Official government data shows the U.S. still added jobs in 2025, just at a much slower pace and far below the long-term average from prior years.

A huge chunk of the population don’t have robust savings and are living paycheck to paycheck. Only about 55 % of adults have enough savings to cover three months of expenses if they lost their income - a relatively modest level that hasn’t improved much and is below pre-pandemic peaks, and cost of living pressures feel high and persistent. The median emergency savings for the average American is $500.

The Conference Board’s Leading Economic Index suggests “slowing economic activity at the end of 2025 and into early 2026,” describing growth as “fragile and uneven.” J.P. Morgan Research puts the recession probability at 40% as of mid-January 2026.

And AI has a big part to play in this as well. Companies aren’t mass-firing employees visible in headlines; instead, they’re simply not filling open positions. As one founder told researchers: “We had 3 people leave in Q4. We replaced one of them. The other two roles? We’re running an experiment with AI agents for 90 days. If it works, those headcount slots just disappear from the plan.” Multiply this across thousands of startups and corporations, and you see massive job destruction that doesn’t show up in unemployment statistics immediately - it manifests as extended joblessness for unemployed workers and stalled hiring for those seeking new roles.

Consumer spending grew 2.6% in 2025 and is projected to slow to 1.7% in 2026. High-income households continue robust spending, benefiting from strong equity market gains (household wealth reached record highs in Q3 2025) and faster wage growth. Conversely, lower-income families are under acute stress.

If the “Great Freeze” (companies not hiring, using AI instead) continues, and if households continue depleting their $500 median emergency savings to cover daily expenses, a formal recession could follow within 6-12 months.

If history is any indication, then the interest rates will be lowered as people consume more and the economy will appear to “recover”. Companies like Google and OpenAI need people to spend more so their valuations can soar and they are able to invest in compute, data centers, energy constraints and solve bigger problems. “Energy is the bottleneck” - as Sam Altman has said multiple times.

Consumer spending power going up bodes well for politicians too as it gives them a metric to use as a selling point - our GDP is growing! The economy is booming! People buy into that narrative and cast their votes and keep spending more and more until the bubble bursts and things collapse.

During this bubble crash, it’s the common people who have historically faced the brunt. For example, during the Financial Depression of 2008, the Government used taxpayer money to bail the big banks out. I see no reason to suspect that this won’t be the case, were history to repeat itself again.

Open AI signed a $500B Stargate Project deal with the US Government. On January 12-13, 2026 (essentially this week), the Pentagon announced xAI’s Grok AI model would be integrated into GenAI.mil, the Department of Defense’s internal AI platform. The Pentagon awarded similar $200 million contracts to Google, Anthropic, and OpenAI as well.

If the bubble bursts, I’m pretty confident that the Government will bail them out as it is in their interest to do so.

When we as customers are shown ads all the time, it’s a telltale sign that businesses want people to spend more. The more people spend, the more money goes into the economy, the healthier it appears to be, the more revenue businesses and governments receive, the more companies become valued, and the more money it can pour into consolidating and growing the bubble.

Being shown ads on ChatGPT is a whole another ball game though.

ChatGPT has 800+ Million Weekly Users with 90%+ of people on the Free Tier. According to ChatGPT’s report, people use it for all kinds of things from mental health to physical health to money related concerns - you name it. Ads entering this space where people are being vulnerable with a machine in an era of increased loneliness and where suicide rates are up is rightfully a cause for concern.

OpenAI claims that ads won’t be used in the primary chat generated by AI. Here’s a direct quote from their blog post announcing their ad model:

“ChatGPT’s responses are driven by what’s objectively useful, never by advertising. You need to know that your data and conversations are protected and never sold to advertisers. And we need to keep a high bar and give you control over your experience so you see truly relevant, high-quality ads—and can turn off personalization if you want”

“To start, we plan to test ads at the bottom of answers in ChatGPT when there’s a relevant sponsored product or service based on your current conversation. Ads will be clearly labeled and separated from the organic answer. You’ll be able to learn more about why you’re seeing that ad, or dismiss any ad and tell us why. During our test, we will not show ads in accounts where the user tells us or we predict that they are under 18, and ads are not eligible to appear near sensitive or regulated topics like health, mental health or politics.”

OpenAI says that the responses are driven by what’s useful and not by advertisers - that’s a good start. The way this will work is that the AI will generate an answer and it will leverage some kind of semantic coherence model wherein it will ask itself - is there any business i can recommend that is in line with the answer I just gave? So the AI is essentially giving a response (admittedly one that is objectively useful) and showing sponsored products that are in line with its answers.

I don’t know about you but this looks pretty dystopian to me. If I’m asking questions in an area that’s in a rather sensitive domain, and the AI recommends me a solution along with a sponsored product to go along with, a lot of things happen that treads into dystopian waters: What about AI alignment? How will I trust the AI?

AI alignment is Sam’s big goal - on many occasions, Sam has mentioned that AI alignment is one of the big goals of OpenAI as it executes its purpose to bring AGI for the betterment of humanity. What happens to alignment when ads enter the equation?

LLMs don’t work the way a normal software program works - LLMs learn autonomously thanks to their neural networks. They take particular actions or give responses, take feedback from the environment, and learn and iterate. There is no way of knowing how the AI will work because that’s a black box - at best you can put guardrails in place and hope that the AI stays within those confines. LLMs are given an output or purpose to work towards - like for example, “help the user finish this coding task and compile the code without any errors”.

Here OpenAI explaining it in their own words - “Unlike ordinary software, our models are massive neural networks. Their behaviors are learned from a broad range of data, not programmed explicitly. Though not a perfect analogy, the process is more similar to training a dog than to ordinary programming.”

Right now, OpenAI’s model spec (think of it like the OpenAI Constitution) sets a structured “chain of command” for how AI assistants should behave across platform, developer, and user instructions, prioritizing safety while maximizing usefulness and customization. It defines clear authority levels (Platform > Developer > User > Guideline), default behaviors that can be overridden, and practical rules for handling conflicts between helpfulness and harm minimization. It addresses three risk types - misaligned goals, execution errors, and harmful instructions, and prescribes careful interpretation, refusal where necessary, and expressing uncertainty.

A company can write up a document (the Spec) listing dos and don’ts, goals and principles, and then they can try to train the AI to internalize the Spec but they can’t check to see whether or not it worked. They can say “as best as we can judge, it seems to be following the Spec so far.

And when you factor in hallucinations & manipulations with respect to LLMs getting closer to the goal, things start to get murky. Research finds that in some cases the models know their citations are fake that they are lying. During training, raters gave well-cited claims more reward than claims without citations, so the AI “learned” to cite sources for scholarly claims in order to please its users. If no relevant source exists, it makes one up.

So what happens when one of the purposes and outcomes for the LLM is to “make as much money as possible for the advertisers (so we can make more money instead)”? In that case, any responses that the AI gives will indeed be objectively useful but tainted by the fact that it needs to make money for the advertisers, and it will do whatever it takes to get there.

LLMs don’t have any moral qualms - they are ruthlessly efficient and only care about getting to that outcome by any means necessary. As such, it won’t be an exaggeration to suggest that the AI could even manipulate or gaslight us into thinking that it’s being useful whereas in reality, it’s actually marching towards its outcome.

This is the big problem of AI alignment - how do we trust OpenAI to make sure the AI is aligned and is working for the betterment of the end user and not the advertisers? If we look at history, the signs do not look very promising.

Before Google opened its platform to advertisers, it claimed to be a free and open search engine. Pretty soon, much like OpenAI, it changed its stance once revenue growth became a primary outcome to optimize for. Look at how the shading and labelling for ads changed for Google from 2007 to now. OpenAI’s phrasing of “Ads are always separate and clearly labeled.” is easy to make when any ads are net positive. But what happens when they feel the pressure of increasing revenues over the previous year? Will they still maintain this stance? History doesn’t seem to think so.

Sam Altman earlier claimed that “ads are dystopian” and to be used as a “last resort”. Except now, he seems to have turned his back on his stance. It’s the same with all the tech business models that are venture backed - get as many users as possible and burn through dollars, capture market share and then later monetize via ads. Spotify, Youtube, Snapchat, Instagram are all examples of the same model. When growth at all costs is the outcome, then introducing ads appears as the logical next step. And when you tie this into how LLMs function and work towards their defined goals, you begin to see the whole picture as it unravels before you.

In the coming months, I see companies and advertisers leveraging ChatGPT as another distribution channel as they seek to grow and increase their user base and revenue.

With the rise of Agentic commerce and the Agentic Commerce Protocol, I see transactions happening directly inside of ChatGPT with OpenAI getting a big chunk of the value and becoming even more valuable to advertisers.

As the demand grows, I see OpenAI optimizing their ad business model even more as they align ChatGPT to favor business growth over anything else as they seek to increase shareholder value. There are talks currently that OpenAI is planning to raise $100B at a $830B valuation.

If that’s the case, then they will well be on their way to becoming a trillion dollar company in the coming years. In that scenario, I highly doubt that OpenAI and their LLMs will have anything other than OpenAI’s growth as the primary outcome they are optimizing for.

The world will go on as it always does, yet in some moments, when we are truly quiet from the noise of the internet, we might find ourselves asking this existential question - “what is this world and what are we heading towards”?

This week I’m coming to you with a brand new AI marketing use-case. Today I’ll show you how you can create a voice AI agent that lives on Slack that turns your voice ramblings into LinkedIn-ready posts in your tone of voice.

I had a revelation last night: people's authentic voices are getting buried under the pressure to create "perfect" content, while trying not to add to the AI slop.

Founders tell me they're exhausted trying to keep up with LinkedIn while running their companies. The mental toll of content creation is real, and it's draining the energy you need for what truly matters.

That's why I built Rita AI – an agent that lives in Slack and preserves your authentic voice while giving you back precious time to rest and reconnect with yourself.

Here's the step by step guide on how to do it.

Let’s dive in.

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The Demo:

Here’s how this works:

Send a voice note to Rita in Slack (where you already spend your day)
• Share your raw, unfiltered thoughts in 2 minutes or less
• Rita transforms them into a polished LinkedIn post that still sounds exactly like you
• Make quick edits, approve, and post in your dashboard.
• Ship to LinkedIn.

Here’s the detailed breakdown:

Step 1: Setting up Slack

Go to Slack and create your workspace. Create a new channel where you want your agent to live.

Go to Slack API Apps at this URL - https://api.slack.com/apps

Create a new app. You will see this screen.

Click create from scratch

Pick your app name & your workspace where your agent will live.

Once you create, you will see this screen:

Click on the oAuth & Permissions tab on the left hand side.

Scroll down to the OAuth scopes section.

Next, add Oauth scopes to your app. This will enable your agent to do stuff like read texts, send messages etc. Super important.

These are the ones I’ve used for this workflow. You can customize these too.

Then you install these to your workspace. Scroll up and you’ll see this:

When you install, you’ll see this screen. Add your channel you created in the dropdown.

Then, you wanna add this app to your channel.

Go to your channel and tag this agent.

Go back to the API page & go to app-home.

Turn on the toggle so your app is always online.

Next, go to Event Subscriptions on the left hand side & turn it on.

Click on the subscribe to bot events & add bot user events:

Go back to the Oauth page & grab your user token.

now go to n8n, create a new workflow & set up a Slack trigger & click bot app mention.

click on the node and set up your Slack credentials.

This popup will open up.

Paste in your OAuth token in here. If its executed successfully, you’ll see this screen.

Click on the Slack node again on n8n & go to the webhook section.

Copy the webhook test URL.

Go back to Slack and go to event subscriptions.

Paste in your webhook URL to the Request URL.

You will see an error message.

Go back to n8n.

And connect a simple Slack node that can send messages. Connect your slack credentials.

do the same for the Slack trigger node:

Save the workflow and run test workflow.

Go to Slack channel and type in anything like hello

Then go to the Slack API page & hit retry on the request URL. The error will be resolved.

Hit Test the workflow again in n8n & go to the Slack channel and type in anything like hello world.

And now the n8n workflow should work.

And you will see a message in your Slack channel as well.

Your slack is now connected.

Step 2: Building the AI workflow

The workflow will basically be something like this:

• Take input via text or voice in Slack

• Run an ai agent

• send it to a dashboard.

Step 2a - take input via text or voice in slack

now that Slack is connected, first thing to do is create an if node. This if loop checks whether transcription is processing. Add an if node to the Slack trigger

Test workflow and go to Slack tag the agent and drop in a voice note - you will see the 2 nodes turn green.

Click on the If node and drag transcription status and set the condition to “processing”

For the true path, create a http request to download the Audio file. You will see this node pop up. In the previous image, you can see there’s a URL private download file. Drag this into the HTTP URL. And connect your Slack via the OAuth2 API.

Step 2b - Transcribe via Open AI

This is easy. Just choose the Open AI transcription node and connect your Open AI credentials.

Then, you simply bring both the voice and the texts you sent from Slack into an edit note and output it as the “user input” via json.text.

Step 3: Creating the AI agent team:

Every Agent has 3 components - the LLM, the tools and the memory layer.

Here’s the system prompt for the AI agent:

You are a helpful content creator for <Your name>'s personal brand. Use the below steps to create content: 

Steps: 

1. Always start by getting the brand brief using the Get_Brand_Brief tool.  

2. Create a post on the requested topic when given the input via {{ $json.UserMessage }}. Make sure it aligns with the brand brief. 

3. Write the hook (the first line of the post) by using the Hooks_Tool to make sure the hook is of a high quality. 

4. get feedback and a score on the post you created in step 2 using the Get_content_feedback tool. 

5. if the score is less than 0.8, use the feedback from step 3 to refine the post. Then repeat steps 3 and 4. 

6. The final output should be the approved post.

7. make sure to not include any words at the top like "here's your post" or "here's the generated post" or even ends like "the post fufils your requirements" or something like that. There should be ZERO of these introduction or concluding phrases. Just output the main post, that's it. Nothing more, nothing less. 

Pro tip - Save this to Lexi and copy paste it whenever you’re building something similar.

For the LLM, we are using the Claude Anthropic model. Connect your API key and you’re good to go, and connect the simple memory layer. Now it’s time for our tools.

Step 3A: Building the Brand Brief Tool:

This is a simple workflow that gets triggered when we call the main parent workflow. It’s a simple Google Doc that contains all the information related to the user’s brand and his ICP and his tone of voice and stuff like that.

back in the parent workflow, this is what the brand brief tool will look like connected to the AI agent:

Step 3B: Building the Content Feedback Tool:

Now let’s look at the content feedback tool. It helps you edit the particular content and give feedback so it has a baked in feedback loop built in and what this essentially means is that it will take in any generated content and give feedback on it rate on a score of 1 to 10 and if it falls below a certain threshold which is 8/10 then it sends it back to the writing agent to rewrite the entire post. This is what it looks like:

This workflow gets triggered when the parent workflow is running. After which, it gets the brand brief which acts as a reference text for the agent to give feedback on. After which it sends it to the open AI model that has a system prompt to give feedback on the copy that was generated by the writing agent and it then outputs that via an edit fields node.

This is the prompt the AI is given to give feedback on:

## Rules: 

You are tasked with evaluating a piece of content written for <Person>. Your role is to assess how well the post aligns with <person'>s personal brand, using the following brief as your guide: 

Please analyse the post based on the following criteria: 

- does the post have unwanted artifacts like "let's try again" or "here's your new post" or the title of the post?
- does the first line of the post have a title of the post rather than the hook?
- Does the content reflect X's core values, voice and style as described in the brief?
- Does it engage X's audience as described in the brief?
- Does it showcase X's expertise and domain authority as mentioned in the brief?
- is the tone, messaging and approach consistent with the brief's objective and positioning? 
- is the hook or the first line strong enough to capture attention of the readers? Is it scroll-stopping for the reader so that he wants to read more of the content? 

Rule of thumb for a good hook: 

- Hooks have to have storytelling baked in 
- They are curiosity driven.

Here are some examples of good hooks: 

- 90% of onboarding problems have ONE thing in common - they try to do too much all at once. 
- Before you try to fix your onboarding, the FIRST thing you must do is define your problem. Here's what I mean:  
- Early stage founders, if you're thinking about hiring someone to work on sales, read this
- One thing as a marketer I never understood (still don't TBH) is why sales teams LOVE a one pager
- in 2003, Parker was broke in his garage and humiliated. 2 years later, he had a billion dollar company. 
-  X may be a Unicorn today but 7 years back, it had 0 dollars to its name and didnt have any customers. Here's the one thing they did that helped them make 8 figures in 12 months. 

After evaluation, output a score between 0 and 1, representing the alignment between the post and the brand brief and the scroll stopping nature of the post. The score should be output in the following JSON format: 

{
"Description": "[short description here]", 
"Score": [insert score here] 
}

## Brief: 

{{ $json.content }}

Pro tip - Save this to Lexi and copy paste it whenever you’re building something similar.

And then the Edit fields node just outputs the main two data points which is : the feedback Description and the score.

Step 3C: Building the Hook Writer Node:

The hook writer node is essentially a workflow that contains a database of the most well-written hooks in LinkedIn.

We now know that hooks are the most important part to getting your content visible on LinkedIn.

And so we need to create a system that writes hooks that get visibility on LinkedIn, which is what this node is for. So when the user drops in a voice node via Slack, this workflow, this hookwriter agent will write a hook based on the voice notes, the voice ramblings that are going on.

It’s a simple database of LinkedIn hooks that is updated daily. To call this tool, we need to just send a command like this to the Agent.

Step 4: Sending it to Airtable:

I use airtable to manage my content. So I set up an airtable account where all the posts get stored directly within airtable.

The final output looks something like this:

And when you click on the column titled “social media copy” you can click on it and check out the full post:

This is the entire workflow and how you can build it on your own. I hope you found this useful.

Want me to build a custom solution like this just for you? Book a call with me and let’s jam!

Let's talk

This week I’m coming to you with a brand new AI marketing use-case: Your very own AI landing Page Roaster. I’ve worked with a ton of SaaS companies optimizing their landing pages and often times, it starts with just an audit of their existing landing page.

And AI can help with that.

The best workflow would look something like this - you enter any landing page URL as input and you get back an audit report of your landing page with action items on how you can increase your conversions.

And in this issue, I’m going to share with you on how you can do just that.

The best part is that you can set it up as a standalone SaaS. Since we will be using a webhook, you can hook it up to any frontend the user can interact with.

This means that you can set it up as a free tool to collect traffic and users or spin it up as a paid SaaS where users submit their landing page and get back an audit report. Or, you can just use this internally if you have many landing pages and are running ads to it - that way you can AB test and keep experimenting with what works and what doesn’t.

In this guide, you’ll learn how to build a no-code AI “agent” that scrapes any landing page, runs it through an LLM for a detailed copy critique, and emails you a structured audit… all on autopilot.

This setup takes 15–20 minutes to replicate, requires zero coding, and runs entirely inside n8n using APIs from Firecrawl, OpenAI, and Gmail.

Let’s dive in!

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More of a video person? Check this out 👇

Want to read along? Here you go: 👇

What You’ll Build

By the end of this tutorial, you’ll have an automation that:

1. Accepts a URL from you (or your site visitors).

2. Scrapes every piece of text from that page—headlines, body copy, features, pricing.

3. Feeds that text to an LLM, acting as a “world-class SaaS landing-page copywriter.”

4. Returns:

   • Scores (0–10) for headline, clarity, messaging, value proposition, persuasiveness, and overall.

   • Specific improvement suggestions.

   • An estimated “potential score” after fixes.

5. Emails the full roast straight to your inbox.

Step 1 – Prerequisites

Before building, make sure you have:

• n8n (self-hosted or cloud account – free tier works).

• Firecrawl.dev account + API key.

• OpenAI API key with GPT-4o access.

• Gmail account with OAuth credentials set up in n8n.

Step 2 – Building the Workflow

1. Create a New Workflow in n8n

We’ll be using a simple, linear chain of nodes.

Nodes Overview:
Webhook → HTTP Request (Firecrawl) → Edit Fields → OpenAI Chat → Gmail

2. Webhook / Chat Trigger Node

• Method: POST

• Expected input: JSON with one property: "url".

• Extend the timeout to handle slower scrapes.

You can later embed this webhook in a site chat widget, so visitors can request their own roasts. Or hook it up to Lovable and build a custom frontend so users can interact with it. (Lemme know if you wanna know how to do that, and I’ll help you out - reply to this email and I’ll email you back).

3. HTTP Request Node – Firecrawl.dev

• Endpoint: https://api.firecrawl.dev/v1/scrape

• Method: POST

• Body (JSON):

{
  "url": "{{ $json.chatInput }}",
  "formats": ["json"],
  "onlyMainContent": true,
  "removeBase64Images": true,
  "blockAds": true,
  "proxy": "auto",
  "jsonOptions": {
    "prompt": "Extract the landing page's copy like the headline, the body copy, the features section headline, subheadline, value proposition section, pricing page. Scrape every text that's on the page and leave nothing out."
  }
}

• Auth: API key from your Firecrawl credentials.

• Timeout: 120000 ms (large pages take time).

4. Edit Fields Node

• Keep only the main body text.

• Clean up whitespace:

5. OpenAI Chat Node

• Model: GPT-4o.

• System prompt: Position the AI as a world-class SaaS landing-page copywriter.

• Instructions:

  • Grade six areas: headline, clarity, messaging, value prop, persuasiveness, overall.

  • Provide actionable recommendations for each.

  • Include both current score and potential score after improvements.

• User message: Pass the cleaned Firecrawl text.

Here’s the full prompt:

You are an elite and world class SaaS landing page copywriter. You know how to write persuasive copy that engages users and gets them to convert. You are able to understand and audit any landing page and identify weak points and areas for improvement to make it more converstion friendly. 

Your role - Your job is to analyse the copy of this landing page : {{ $json.data.json.allText }} & create a comprehensive and professional SaaS Landing page audit report. 


1. Check for headline effectiveness - is it clear, engaging and catchy as to what the product does? give a headline score + action items to improve the headlines. 

2. Check for sentence clarity and conciseness - is each sentence clear, concise and adds value to the user? Suggest areas for improvement with actionable steps the user can execute right away. 

3. Check for product messaging clarity - How clear is the hero messaging - does it make people want to click the CTA - is it clear who it is for - is the promise clear as to what the product does? Suggest areas for improvement with actionable steps the user can execute right away. 

4. Do a Value Prop Clarity Audit - how clear is the main value proposition of the product in the copy? is the benefit of the product clear to the user? Suggest areas for improvement with actionable steps the user can execute right away. 

5. How persuasive is the copy? Does it use powerful storytelling? Does the copy flow naturally like a conversation or does it read more like an essay? Suggest areas for improvement with actionable steps the user can execute right away. 

6. Does it use social proof? If so, how can it be enhanced and optimized even more? 

7. What other shortcomings are there in the landing page that if fixed will lead to better outcomes? What is the percentage of conversions that will go up if the user makes these changes? 

---

Create a comprehensive audit report based on this. Give it an overall effectiveness score out of 10 and then based on your suggested improvements, give a potenial Overall effectiveness score that will happen if the user implements those suggestions. 

6. Gmail Node

• From: Your Gmail account (via OAuth in n8n).

• To: Your email (or dynamic recipient).

• Subject: “Landing Page Roast.”

• Body: Directly insert OpenAI’s response.

Step 3 – Test the Workflow

1. Send this payload to your webhook:

{ "url": "https://example.com" }

2. Watch n8n:

   • Firecrawl scrapes the page.

   • LLM scores each section and suggests improvements.

   • Gmail delivers the formatted audit to your inbox.

When tested on cluely.com, the AI suggested “add quantifiable benefits” and “bring differentiators to the top,” bumping the potential headline score from 6/10 to 9/10.

This is the report I got in my inbox after I ran the workflow:

Optional Enhancements

• Embed in Chat Widgets – Let site visitors request their own audits or set up a Webhook and connect it to Lovable to create a stand alone SaaS (micro-SaaS) out of it.

• Save Results – Store audits in Google Sheets or Airtable for tracking.

• Change Delivery Channel – Send results to Slack, Discord, or Notion instead of email.

Landing-page feedback often costs hundreds of dollars and takes days to arrive.
This workflow gives you instant, AI-powered, structured audits—perfect for founders, marketers, and agencies.

Liked this issue? Here’s how you can support me:

If you liked this guide, consider sharing this with your friends.

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And you can subscribe to learn how to automate more of your marketing stack without writing a single line of code.

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And if you want to hire me to build out a custom AI workflow for you, book a call with me and we’ll jam!

Hire me

Or you can just say hi to me on LinkedIn.

See you soon!

Best,
Shounak.

I recently sat down with Finn—Head of Content at Lago (YC21), Hacker News whisperer, and Reddit viral wizard—to break down exactly what separates AI startups that break out from the pack... and those that fade into the slop.

Let’s dive in.

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Brought to you by:

Market Curve is a premium tech storytelling studio run by me Shounak that turns tech companies into media companies. I’ve had the fortune of working with some of the top tech companies backed by some of the top investors like YCombinator, 20VC who have gone on to be acquired by companies like Roblox & Amplitude.

Here are a few things I can help you with —

• Design a custom content system with AI agents + humans that helps your brand show up on LLMs.

• Optimize your entire buyer journey across your landing page, pricing page and onboarding flow so you can get more customers without spending more on customer acquisition costs.

• Build custom AI agents for your marketing/GTM/content teams that are embedded deep into your tech stack and can function autonomously.

• Hire Rita AI - A Slack AI Agent that turns your voice notes or text messages into viral LinkedIn posts.

Interested?

Let's talk

See the full episode on YouTube:

AI Lowers the Effort for “Okay” Results

Finn kicked things off with this gem:

“AI compresses the time and effort needed to create a mediocre result. Not bad. Not great. Just... okay.”

That’s important. Because if everyone can now produce “okay” in seconds, the bar for standing out has actually gone up. The result? Most of the web becomes what Finn calls AI slop.

So if you’re an AI founder, your job is not to be okay. It’s to build something undeniably great.

Three Moats for AI Startups

Here are three real moats Finn believes will separate winners from the rest:

1. Distribution Moat (a.k.a. The Incumbent Advantage)

Big companies win because they have reach.

“AI is one of those revolutions that benefits incumbents. They already own distribution. They can bolt on AI and go 10x faster than you.”

If you’re just starting out and don’t have distribution, you need to earn it—fast. And that brings us to...

2. Brand Moat (Packaging > Product)

Your branding matters. In fact, it might be your best shot.

“We’re in a world where the packaging is more important than the product. If your site or video looks AI-generated and generic, no one clicks.”

The best brands today don’t look like they were whipped up by GPT-4. They look like someone cared.

Tactical takeaway: Use high-effort visuals, bold design, and clear opinions. Stand for something. That’s how people remember you.

3. Data Moat (The Hardest to Copy)

Proprietary data is one of the few remaining durable moats.

Take Cursor, a dev tool that captures what users try to build—and where they get stuck. That usage data can’t be faked, copied, or scraped.

“Cursor knows exactly what devs are building and where they need help. That’s gold.”

Not every startup starts with a moat. So how do you earn one?

Finn points to Cluely—a startup that went viral by leaning into controversy (“Cheat on anything with our product”) and combining that with slick video game-style UX.

They said the thing no one else would say.

If you want to stand out in an AI-generated world, you need to show effort.

“A super polished landing page or cinematic video signals humans made this. Not a 5-second GPT copy-paste.”

This is called costly signaling: You signal quality through the effort you visibly put in. Think of it like a luxury handbag. It’s tiny, overpriced—and that’s the point.

Let’s say you went viral. Now what? Here’s Finn’s framework:

• Don’t treat virality as a fluke. Plan for it.

• For every piece of content, ask: Why should this go viral?

• Accept the 20% success rate. Play the volume game.

• Capture emails. Build distribution. Turn one-off wins into compounding traffic.

“If 10,000 people see your content but you capture nothing, you’re starting from scratch each time.”

SEO is Changing. A Lot.

AI Search (AEO, GEO, LLMs) is not like Google Search.

“With LLMs, you don’t write to drive traffic. You write to be referenced in the answer.”

That means:

• Write structured, query-focused content.

• Answer the question directly.

• Forget fluff. LLMs won’t use it.

Also, not every post needs to go viral. One Reddit post with two upvotes got referenced by ChatGPT just because it was specific and well-written.

Bottom line: Write to be cited, not clicked.

Finn’s Content Workflow (Steal This)

Finn is building AI agents to auto-generate blog posts using:

• GPT (with custom system prompts)

• N8n to automate content into CMS

• Manual editing for quality

• Banner generation

• Publish

Right now it’s semi-automated. Soon, it’ll be end-to-end.

AI Tools in Finn’s Stack

Here's what Finn uses daily:

• ChatGPT (with customized system prompts)

• Perplexity (Google replacement)

• N8n (automation)

• Ahrefs for AI content

• Rosebud (AI-powered journaling)

Key tip: Write better system prompts. Don’t use ChatGPT like a toy. Use it like an intern—with training.

Final Advice: From Finn to Himself

To 20-year-old Finn:

“Stop filling emotional holes with work. Figure out your personal stuff. Take your injuries seriously. Value relationships.”

To 40-year-old Finn:

“Don’t stop writing. Don’t stop reading. Don’t stop cooking. Keep a creative outlet alive.”

How to Stand Out as an AI Startup

• Mediocrity is free. Quality is rare.

• Own your brand. Make it bold, weird, human.

• Build proprietary data. That’s your moat.

• Say what others won’t. Controversy wins.

• Use costly signaling. Show your effort.

• Turn virality into a system, not a one-hit wonder.

• Write for LLMs, not Google. Think citations, not clicks.

• Choose a pricing model that reflects real value—and cost.

If you found this valuable, share it with a founder who’s building in AI. Or better, go make something so human, no AI could fake it.

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In a previous issue, I shared with you how LLMs are scraping LinkedIn to reference content in their answers. So in today’s issue, I’m going to share with you how you can turn your existing blog posts & case studies (which are great content assets to have) into LinkedIn posts in as little as 5 seconds.

The problem is you probably have a lot of long form content queued up in your blogs, but you will gain so much more mileage if you repurpose these into LinkedIn posts, without you lifting a finger.

So in this step-by-step tutorial, I’ll show you how to build an AI agent that turns and repurposes your blog posts & case studies into viral LinkedIn posts using n8n & Firecrawl.

Here’s the workflow in a nutshell: Enter your blog post/case study URL → Firecrawl API scrapes the content’s website → Sends it to an AI agent that writes LinkedIn posts in your tone of voice → Sends it back to you.

This is designed for complete beginners. No coding required. You’ll learn how to connect it all into a working system that can generate LinkedIn posts based on your blog posts and case studies. If you’ve been curious about how to build AI powered marketing workflows, but didn’t know where to start, this is the perfect place to begin.

Let’s dive in, shall we?

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More of a video person? Check this out 👇

Want to read along? Here you go: 👇

You can build an AI agent to turn blog posts into LinkedIn posts by connecting n8n, Firecrawl, and an AI model, following these steps.

Here’s a clear, step-by-step action plan:

Step 1. Set Up n8n

• Sign up for or install n8n, a no-code workflow automation platform.

• Create a new workflow in n8n.

Step 2. Create a Chat Interface Trigger

• Add a trigger node in n8n for receiving a chat message (this will be where you input your blog post or case study URL).

• Make the chat interface publicly accessible or use it as a standalone URL.

Step 3. Integrate Firecrawl API for Content Scraping

• Add an HTTP Request node in n8n to call the Firecrawl API.

• Configure the node to send a POST request with the blog post URL.

• In the request body, specify that you want the article’s title, author, and main content returned in JSON format.

here’s what you do:

• Set the Method to POST.

• URL - https://api.firecrawl.dev/v1/scrape

• Set up Authentication as generic credential type

• Set the generic auth type as header auth

• Set up a new credential by pasting in your API key from Firecrawl. (Remember to put in Bearer *space* before pasting in your API key).

Set the body content type as Json:

Here’s the Curl import code you can copy paste into the Json body type node:

{
  "url": "{{ $json.chatInput }}",
  "formats": ["json"],
  "onlyMainContent": true,
  "removeBase64Images": true,
  "blockAds": true,
  "proxy": "auto",
  "jsonOptions": {
    "prompt": "Extract the article's title, author name, and the full main body text. Return the output in JSON format with fields: title, author, content."
  }
}

You’re set.

Step 4: Process and Extract Content

When you run the workflow by pasting in your blog URL in the chat window, the Firecrawl API responds to extract the title, author, and main content fields (something like the image below).

Then, you simply pass the main content to the next step in your workflow.

Step 5. Connect to the AI agent

• Add another node to build an AI agent. Connect it to your LLM of choice via your API key.

• Craft a prompt for the AI model:

  • Instruct it to write a LinkedIn post (max 1500 characters) in the same tone as the original content.

  • Tell it to use a strong hook as the first line (see next step).

  • Add guardrails to avoid generic or AI-sounding phrases.

This is the prompt I used for the AI agent:

You are a helpful content creator for Chameleon's LinkedIn page. Use the below steps to create content: 

Steps: 

1. Write a 1500 character LinkedIn post based on this text: 

{{ $json.data.json.content }}. 

Always write the LinkedIn post in the same tone of voice as the above text. Never exceed 1500 characters. 

2. Write the hook (the first line of the post) by using the Hook_Writer_Tool to make sure the hook is of a high quality.

3. The final output should be the LinkedIn post. 


Rules: 

- make sure to not include any words at the top like "here's your post" or "here's the generated post" or even ends like "the post fufils your requirements" or something like that. There should be ZERO of these introduction or concluding phrases. Just output the main post, that's it. Nothing more, nothing less. 

- The first line of your output should be the hook - the first line of the LinkedIn post. 

- Don't use phrases like "it's not X it's Y" or "here's what this means" or "And the kicker?" Or similar phrases. 

- Keep it actionable and useful. Always ask yourself - Would the reader find this useful? 

- NEVER use phrases like "Based on the Hook Writer Tool analysis, I'll create a strong hook for the post about product tours". Your first line of the response should be the hook aka the first line of the LinkedIn post. 

 

Pro tip - Save this prompt to Lexi & tag it so you can re-use it any time.

Step 6. Build or Integrate a Hook Writer Tool

Create a hook-writer with a list of high-performing LinkedIn hooks in a separate workflow.

here there’s simply a test workflow button + a Google sheets node where I have stored a list of 100-150 Linkedin hooks.

Add this tool in the tool section of the AI agent by simply choosing “call another workflow” when clicking the tool button.

And as you can see in the prompt above that its instructed to call this tool when creating Linkedin posts.

Step 7. Generate and Output the LinkedIn Post

• The AI model returns a LinkedIn-ready post.

• Display the output in your chat interface or send it to another platform (e.g., Slack, Notion, Airtable) as needed.

Step 8. (Optional) Expand Integrations

• Add more nodes to send the LinkedIn post to other tools or platforms (e.g., Slack, Notion, Airtable, ClickUp, Typefully). or you can even set up a Slack trigger where you can paste in your blog posts into Slack or even via email, whatsapp what have you.

Liked this issue? Here’s how you can support me:

• Follow me on LinkedIn & on X.

• Hire me to build out your own custom AI marketing workflow.

• Share this issue with your friends!

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See you soon!

Best,
Shounak.

Earlier in my previous essay, I wrote about how LLMs source Reddit queries in their answers. So if you’re looking to rank on these LLMs, you need to use Reddit to find queries that people are asking for on Reddit and bring that solution in one place - either via a standalone Reddit post, on your blog or on your newsletter.

The point is Reddit is unbundled and people in your niche are continuously asking questions. You can use Ahrefs to do Keyword research and that’s okay but everyone is using Google Keyword research - in today’s newsletter, I’m gonna show you how to do Reddit Keyword Research.

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Brought to you by:

Market Curve is a premium tech storytelling studio run by me Shounak that turns tech companies into media companies. I’ve had the fortune of working with some of the top tech companies backed by some of the top investors like YCombinator, 20VC who have gone on to be acquired by companies like Roblox & Amplitude.

Here are a few things I can help you with —

• Design a custom content system that helps your brand show up on LLMs.

• Optimize your entire buyer journey across your landing page, pricing page and onboarding flow so you can get more customers without spending more on customer acquisition costs.

• Build custom AI agents for your marketing/GTM/content teams that are embedded deep into your tech stack and can function autonomously.

• Take over your LinkedIn profile via my Service with a Software system which will help you get more followers & help grow your personal brand without breaking the bank.

Interested?

Let's talk

So Reddit Keyword research. Here’s how this works. I’ve made a (rather unpolished) Youtube video showing how this whole thing works so if you’re a video person, you can follow that step by step and build your own Reddit Keyword Research Tool in a few hours.

How does this work though?

So, imagine you have a startup in legal-tech, something like a case management software. (Clio for example). There are subreddits (like r/law or r/lawyertalk) where your customers are talking about stuff like “what Case management system is best to use”, or “What Case Management Software should I use if I run a personal injury firm” or “How do you fix this problem in XYZ Case Management Software” - you get the idea. There’s tons and tons of content ideas lying around in plain sight on Reddit, which customers are begging for answers to.

With this agent, you will be able to turn real community problems into content ideas using automation + AI

Reddit is full of real people venting real frustrations — and that’s gold for content marketers.

This tutorial walks you through building a no-code agent using n8n that scrapes Reddit for problems your customers are talking about, then uses AI to turn those into content ideas.

You’ll go from raw community chatter ➝ filtered insights ➝ content prompts ➝ a clean Google Sheet.

Here’s the video. Below is the step by step guide on how to do it. Let’s dive in.

Check out the YouTube

What You'll Need

• A free n8n account (or self-hosted instance)

• Reddit API credentials (for pulling subreddit data)

• OpenAI API key (for classification + summarization)

• Google account (to store outputs in Sheets)

  ---

Step 1: Trigger the Workflow

What it is:
A manual trigger that starts the workflow.

During testing or manual runs, this gives you control over when the agent activates. You can replace this later with a Slack command, Telegram message, or scheduled run.

Step 2: Search Reddit Posts

Node: Reddit → Search Posts
Inputs:

• Resource: Post

• Operation: Search

• Location: Subreddit (e.g., lawyertalk, productmanagement)

• Limit: 10–25

• Sort by: Top

Using Top ensures we prioritize high-engagement posts (upvotes = signal).

This is how we pull real user posts from a subreddit. We want to listen in on discussions where people talk about pain points and needs — that’s where the content opportunities lie.

Step 3: Filter Useful Posts

Node: IF Condition
Conditions:

• upvotes ≥ 2

• selftext is not empty

This filters out noise:

• Posts with no upvotes haven’t resonated

• Posts without selftext don’t give us enough context (they may just be links or memes)

This ensures you're only analyzing actual discussions that others found useful.

Step 4: Clean the Output

Node: Edit Fields
Keep only:

• selftext (the main body of the post)

• (Optional: also keep title, upvotes, comments if relevant)

Reddit’s API gives you a ton of metadata (author name, flair, flair color, thumbnail URL, etc.) — most of which you don’t need. This step cleans up the output and keeps it lean for AI processing.

Step 5: Use AI to Detect Business Problems

Node: AI Agent (ChatGPT or GPT-4 via OpenAI)
Prompt:

Is this Reddit post describing a business-related problem or a need for a solution? Output only “Yes” or “No”.

Not every Reddit post is relevant to your business. Some are just casual questions or jokes. This step uses AI to intelligently decide which ones are actually describing business-relevant pain points.

Step 6: Merge the Post + AI Output

Node: Merge
Inputs:

• Cleaned Reddit Post (selftext)

• AI Output (Yes / No)

We now combine the original post with the AI's decision about whether it's a business-relevant problem. This way, we can filter further and retain full context for only the right ones.

Step 7: Filter Only “Yes” Results

🔧 Node: Filter
Condition:

• AI Output = "Yes"

📌 Why it exists:
This is a second filter pass — we want to work only with Reddit posts that the AI validated as describing a real business challenge.

This makes sure we don’t waste resources summarizing or generating content for irrelevant posts.

Step 8: Summarize the Problems

Node: Summarization Chain
Config:

• Input: The filtered Reddit post

• Chunk size: 1000 characters

• Output: Concise summary of the problem

Reddit posts can be long, emotional, or scattered. This step condenses each post into a short, clear summary that captures the user’s pain point.

Think of it as the AI’s version of, "Here’s what they’re really trying to say."

Step 9: Generate Content Ideas with AI

Node: OpenAI Chat Model
Prompt:

Based on the following Reddit post, suggest a content idea I can create to solve this problem for this business and others like it. Provide a concise content idea.

Now we turn pain into possibility.
For each summarized Reddit post, the AI generates a clear content idea — a guide, checklist, framework, or story you could create to solve that problem.

This is the payoff moment: turning insight into creative output.

Step 10: Send to Google Sheets

Node: Google Sheets → Append Row
Sheet Columns:

• Problem (Summarized Reddit post)

• Content Idea (Generated by AI)

Store everything in a spreadsheet for easy access, review, and collaboration. You can share it with your team, plug into a Notion CMS, or route into a content calendar workflow.

Bonus: Automate Content Writing

Once you have ideas, you can build a second n8n agent that:

• Writes a blog post or LinkedIn post in your tone of voice

• Sends drafts to Notion, Sheets WordPress

• Notifies your content team on Slack

You now have a fully automated ideation → creation → publication pipeline.

---

This system helps you:

• Create relevant content based on what people are already asking

• Improve your SEO + show up in LLMs like ChatGPT & Perplexity

• Save hours of research and brainstorming

• Ship faster

Whether you're in SaaS, legal tech, fitness, or finance — just change the subreddit.

Want the entire workflow template? Just Send me a DM here or email me and I’ll send over the JSON file so you can directly upload it into n8n and start using it.

In the meantime, if you found this useful, share it with your friends and peers.

Share

See you next week,

Shounak.

In this podcast, Cristina shared her journey through the startup ecosystem, offering insights that challenge conventional wisdom about everything from getting into Y Combinator to building authentic brands in an AI-saturated market.

Subscribe now

Brought to you by:

Market Curve is a premium tech storytelling studio run by me Shounak that turns tech companies into media companies. I’ve had the fortune of working with some of the top tech companies backed by some of the top investors like YCombinator, 20VC who have gone on to be acquired by companies like Roblox & Amplitude.

Here are a few things I can help you with —

• Design a custom content system that helps your brand show up on LLMs.

• Optimize your entire buyer journey across your landing page, pricing page and onboarding flow so you can get more customers without spending more on customer acquisition costs.

• Build custom AI agents for your marketing/GTM/content teams that are embedded deep into your tech stack and can function autonomously.

• Hire Rita AI - A Slack AI Agent that turns your voice notes or text messages into viral LinkedIn posts.

Interested?

Let's talk

See the full episode on YouTube:

The Reality Behind the Y Combinator Dream

Everyone wants to get into Y Combinator, but few understand what actually makes the difference. Cristina's path to YC in summer 2019 wasn't remarkable in the traditional sense—no previous startup experience, no corporate background, just three university friends who "didn't really want to get normal jobs."

"I think YC saw that and they were like, these people don't have anything to lose. They're really young. They have ideas. They know how to build a product. So let's just give them a chance,"

But here's where her story gets interesting: the biggest misconception about Y Combinator isn't about getting in—it's about what happens after. "A lot of people think it's going to help them in a different way. They think, 'I got into YC, so then my company will have to work.' They're going to help me build my company, which in reality, it's not."

YC is still a VC fund at the end of the day. They give you money and guidance when you seek it, but they won't hold your hand or teach you like you're in school. The real value? The network and the ability to validate B2B ideas with other batch companies.

The Consumer Product Trap

Cristina's YC company was building an app where you could listen to articles—a consumer social product that ultimately didn't work out. Looking back, she identifies a critical insight: "I don't think I was meant to build a consumer product, or at least not in the beginning."

The challenge with consumer products in the YC ecosystem is distribution. While B2B companies can easily validate ideas and find initial customers within their batch network, consumer products need viral growth loops from day one. "If we didn't have that growth loop of someone signs up, they invite their friends, then at the end of the day, that's not a social product."

Her advice for first-time founders building consumer products?

Focus less on perfecting the product and more on distribution. It's a lesson that goes against the common "build something people want" mantra, but it's grounded in hard experience.

From Founder to Community Builder

After shutting down her company, Cristina joined Product Hunt, where she wrote the daily newsletter for nearly a year. It was a masterclass in engaging with the founder community and understanding what makes products resonate.

What made Product Hunt special wasn't just being early to market, but something deeper: "The entire team really cared about community and about every builder that came onto the platform." They had accumulated years of product launches, comments, and context that would be nearly impossible for a competitor to replicate.

But Product Hunt also highlighted a challenge that many community-driven platforms face today: the balance between being welcoming and maintaining quality. "People were much kinder than on Reddit. But that's a double-edged sword, because a big problem with Product Hunt is just AI content, and content doesn't feel very genuine."

In B2B, Your brand and your taste is your moat.

When Cristina joined Command Bar (later Command AI), she encountered one of the smartest teams she'd ever worked with. But what set them apart wasn't just talent—it was their approach to branding in the notoriously bland B2B SaaS space.

"Every single customer you talk to, they would be like, yeah, obviously I chose Command Bar as opposed to other competitors. Because just look at their website. It's so cool. It tells such a compelling story. It actually has personality as opposed to most SaaS B2B brands out there."

The strategy was refreshingly simple: be human in a space where everyone tries to be corporate. They had a clear narrative ("pop-ups suck") and an entire manifesto about why they built their product the way they did. Their website had personality, their content had voice, and their brand felt authentic.

For a sales-led company where prospects couldn't sign up to try the product, this brand differentiation was crucial. Their biggest marketing KPI was booking meetings from inbound activities—content, webinars, LinkedIn posts, and events. The conversion rates varied by article, but a 4-5% conversion rate from blog readers to demo bookings was a very solid ROI.

How to Navigate the AI Revolution in Marketing

As AI transformed the marketing landscape, Cristina witnessed both its potential and its pitfalls firsthand. Initially, the models weren't sophisticated enough for heavy use. "We weren't using it that much, just because the models weren't as good as they are now."

Today, her approach is both practical and discerning. She uses AI primarily for content research and refinement—asking it to provide context on topics instead of opening 100 browser tabs, then using it to make her raw thoughts flow better. But she always does a final pass: "Is this how I would actually speak about something? Would I use this sentence?"

The key insight? You need taste to tell good AI content from bad. "I go on LinkedIn, and it's so obvious when there's so many structures that I'm like, if someone's saying things like, 'it's not just this, it's also this.' And I'm like, you didn't put any thought in it."

How to differentiate your product when everything is an AI wrapper

In a world where everyone has AI in their products and everyone's using AI to create content, how do you differentiate? Cristina's answer cuts through the noise: "It comes down again to taste at the end of the day."

Having AI in your product or using AI for content isn't the problem. The problem is when that's all you have. "You've got to have something that's yours. You've got to have that brand element that screams, this is you. Even if that was built with AI, you have to have that human in the loop."

This applies to everything from website design to company narrative. You can use AI tools, but you need a strong belief system and clear narrative about your company and product. "Something's got to come from you. And it needs to be really easy to understand, even if you use AI for it."

The AI Tool Trap

Despite the explosion of AI marketing tools, Cristina keeps her tech stack simple: "I'll be honest. I just use ChatGPT. I think you just gotta choose one and go with it."

Her philosophy challenges the tool-obsessed culture that's emerged around AI: "Too many people become victims of just overindexing on the tools and not the work, where they're like, oh, I need to use this and this and this and I need to buy this tool."

This extends to the latest marketing trends too. All the talk about GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization)? "At the end of the day, it's still SEO. It's the same as we've been saying for the past 10 years. Use subheadings. Make sure that you use the keyword. Make sure that you write things the way someone would search for them."

How to look at things from a first principles approach

Perhaps the most valuable insight from Cristina's journey is her approach to advice and best practices. After years of working with successful companies and founders, she's learned to be skeptical of playbooks and case studies.

"People are so easily distracted and like they hear about someone doing something and they're like, 'oh my gosh, we got to try this because it's going to work because it worked for an ex company.' Don't take advice lightly because most things that work for someone are not going to work for you."

Instead, she advocates for thinking from first principles: "Really think from first principles about your own company and your own product. What's worked for Company X 10 years ago in the market that was much different to where we are now, it's very likely not going to work for you."

Building in the Age of AI

Cristina's story offers a roadmap for navigating today's startup landscape. Whether you're trying to get into Y Combinator, building a consumer product, joining a growing company, or figuring out how to use AI in your marketing—the principles remain the same.

Focus on distribution over perfection. Build authentic brands with real personalities. Use AI as a tool, not a crutch. And most importantly, think from first principles about your unique situation rather than copying someone else's playbook.

In a world where everyone has access to the same AI tools and everyone's reading the same growth hacking articles, your taste, judgment, and authentic voice become your strongest competitive advantages. The goal isn't to avoid AI or resist change—it's to use these tools thoughtfully while staying true to what makes your company and product uniquely valuable.

As the startup ecosystem continues to evolve, the founders and marketers who succeed will be those who can blend the efficiency of new tools with the irreplaceable value of human insight, authentic storytelling, and genuine care for their customers and community.

Where to find Cristina:

• X: https://x.com/cristinaibunea

• LinkedIn: https://www.linkedin.com/in/cristinabunea/

Where to find Shounak:

• X: https://x.com/heyshounak

• LinkedIn: https://www.linkedin.com/in/heyshounak

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The way we search has changed and so has the way we show up. As large language models like ChatGPT, Claude, and Perplexity become our go-to interfaces for discovery, the old SEO rules aren’t enough.

Welcome to the age of LLM Visibility Optimization (AIO).

In this essay, I’ll break down how LLMs actually work, how they surface your content, and what you need to do to show up. (based on my real experiences)

Subscribe now

Brought to you by:

Market Curve is a premium tech storytelling studio run by me Shounak that turns tech companies into media companies. I’ve had the fortune of working with some of the top tech companies backed by some of the top investors like YCombinator, 20VC who have gone on to be acquired by companies like Roblox & Amplitude.

Here are a few things I can help you with —

• Design a custom content system that helps your brand show up on LLMs.

• Optimize your entire buyer journey across your landing page, pricing page and onboarding flow so you can get more customers without spending more on customer acquisition costs.

• Build custom AI agents for your marketing/GTM/content teams that are embedded deep into your tech stack and can function autonomously.

• Take over your social media profile via my Service with a Software system which will help you get more followers & help grow your personal brand without breaking the bank.

Interested?

Let's talk!

Okay so how on earth do LLMs work?

First things first.

In order to show up on LLMs, we first need to understand how LLMs work. If you ask ChatGPT to give you pricing data about a SaaS product (like Slack), it doesn’t have training data on that.

So if it shows you an answer on Slack’s pricing, it will fetch relevant information from the web. That’s the concept of citations/sources, which is where you want your product to show up.

For example, you might think that Perplexity simply uses Google Search to show results. So you think to yourself “if I rank on Google, then I’ll show up on Perplexity by default”.

But that’s not the case. Instead, Perplexity uses its own web crawler PerplexityBot which gathers information from the internet to index for its search engine.

While it does use multiple inputs and does indeed reference Google’s & Bing’s web rankings to inform its search results, it does not solely depend on Google or Bing’s data.

Aravind Srinivas has said that while Perplexity does use Google’s ranking results, but only if the AI deems them the best indicator of link quality. But they do not copy Google’s search results themselves. So even though you might think Perplexity is a fancy Google wrapper, it’s not. It has its own search functionality powered by its native AI models.

Let’s compare:

If I type into Google “best onboarding tools for B2B SaaS”, I get an AI overview of tools:

If I scroll further down, then I can see Reddit discussions pop up:

So here we see that Reddit is a great source for companies to rank on Google’s AI overview.

Two things matter here: (1) The domain authority of blogs like Userlist, Pendo etc. (2) Forums and communities discussing the best tools. If a tool lies at the intersection of both (1) & (2), it gets picked up by LLMs.

Case on point: My Reddit post on the top 7 user onboarding tools features both Pendo and Appcues. Safe to say both tools are featured in the AI overviews. The reason is that Google & Reddit have entered into a partnership. In February 2024, Reddit and Google announced a data licensing partnership worth $60 million per year. Which is why you can see Reddit threads pop up in Google’s first page and referencing the Reddit post as a source in its AI overviews.

These are the remainder of Google's SERP data.

Now let’s look at Perplexity -

Right off the bat, you can see that Perplexity’s sources are different from Google’s above.

And though there is overlap across the top 4, the remaining 3 entries of ProductFruits, Intercom, Moxo are native to only Perplexity and not Google. Product Fruits & Moxo are nowhere to be mentioned on Google.

Which shows that Perplexity isn’t just a frontend wrapper for Google. It sources data from Google yes but isn’t solely dependent on Google for its results.

What about ChatGPT?

ChatGPT has the usual suspects but there are a few interesting points of difference here. For one, much like Google, ChatGPT picks up Reddit threads.

My Reddit post is once again picked up by ChatGPT much like how it was picked up by Google. This again confirms that posting on Reddit in addition to shipping on your blog is useful if you want to get picked up by LLMs.

The second point here is that companies like Whatfix and Coursebox weren’t picked up by Perplexity or Google but it is picked up by ChatGPT.

When a query is made to an LLM, the AI model uses algorithms to score content based on relevance. And the thing about large language models is that they go beyond simple keyword matching, when retrieving answers.

When a user asks an AI tool like Perplexity: "Which is the best product onboarding software?" The AI tool processes this query something like this:

Step 1: It identifies keywords first - words like “best”, “product onboarding”, “software”.

Step 2: It does a semantic analysis - “best” equals top-rated, most effective, highest quality.

Step 3: Topical relevance - “app development”, “user engagement tools”, “customer communication systems”, “recent awards, reviews”

Step 4 - And then the AI scores the available content based on relevance.

This is how most LLMs source data to show to your users based on their search query.

Next step: Make sure your content is structured in such a way that makes it easy for LLMs to pick them up

Now that you know how LLMs work, you want to make it easy for them to find your content.

This is similar to the technical optimization we are so used to doing for SEO, like writing meta-titles, meta-descriptions, adding alt-tags to Google etc. Except now, we gotta do the whole thing but for LLMs.

1: Add clear metadata:

LLMs rely on structured data to improve comprehension, entity recognition, and categorization. Without clear metadata, AI models will misinterpret your content or fail to associate it with relevant queries. How do you make sure LLMs don’t do that? Use Schema.org markup - this way, you provide explicit signals about your content’s meaning, increasing the chances that LLMs will reference your information correctly.

I recommend the following schema:

• Article (schema:Article): Helps AI understand blog posts, news articles, and general web content.

• FAQPage (schema:FAQPage): Ideal for structuring frequently asked questions so AI can directly pull Q&A pairs.

• HowTo (schema:HowTo): Guides AI in categorizing step-by-step instructions for tutorial-style content.

• Product (schema:Product): Ensures LLMs correctly categorize eCommerce and SaaS-related content.

• Organization (schema:Organization): Establishes credibility and links your brand with authoritative mentions in AI-generated outputs.

• Person (schema:Person): Useful for personal branding and expert author recognition in AI-generated summaries.

To do this, use Google's Structured Data Markup Helper. Select the appropriate schema type and enter the URL of your blog post. Tag the relevant elements on your page & generate the HTML code which will be in JSON-LD format. Copy the generated JSON-LD code & paste it into the code of the individual pages.

This is an example of Json LD code:

{

"@context": "https://schema.org",

"@type": "Person",

"name": "John Doe",

"jobTitle": "Software Engineer",

"worksFor": {

"@type": "Organization",

"name": "Example Tech"

}

}

Fun fact: Json LD is how you fine-tune LLM models in ChatGPT too.

2: Optimize for LLMs.txt:

If you’re an AI startup or have an AI-use case, you probably have a documentation page or knowledge base page.

In that case, you should know about the llms.txt file. It’s a web standard designed to help websites communicate effectively with LLMs like ChatGPT, Google Gemini, and Claude.

It’s like robots.txt but for LLMs. While robots.txt controls how search engines crawl and index pages, llms.txt focuses on making content understandable for AI systems.

Next up - How do you get your content or brand to be mentioned & referenced by LLMs?

Now that you’ve set up the technical optimization bit, here’s how you can get sourced/mentioned in LLMs.

I will be drawing on my own experience here with examples and reverse-engineering them but also observing patterns from other successful product placements.

1. Write something worth reading.

This is an example of an article that’s ranking on ChatGPT written by yours truly. See image below:

Here’s why this works:

It answers a direct question that people (in this case founders) ask LLMs. In this case, how do b2b companies get their first 100 users?

If you go through the article, you’ll find that it's written in an engaging way, and gives super actionable tactics, which is important because it’s tied to the user intent of the query.

The point here is that you have to make sure the answer is relevant to the corresponding user query - and you can only do that by writing something people will find worth reading.

2. Share it across the internet.

Now that you have written a great piece, it’s time to build digital PR backlinks (much like Google). Here we will go to our friend Reddit and share this story. Here’s what I did for this piece above:

A digital PR from Reddit and fellow subredditors signals that this content is useful, unique and (most importantly isn’t AI generated). It’s cited from authentic sources and draws upon a personal experience, a personal story that people can buy into.

To take it a step further, you can contribute to other guests' posts via other influencers/podcasts , get on their newsletter/link roundups so you can build even more digital PR backlinks. Repost this on Medium, on Substack, a Linkedin Article and write a thread on it or a few LinkedIn posts on it (or better yet get some influencer in that niche to repost it)

3: Write pillar pages & subpages

When creating content, you want to make sure you’re as comprehensive as anything out there. Unbundle a topic into its constituent elements and go all in or amalgamate different fragmented sources of information and bring them together in one place with your unique POV.

This is an example of an essay I wrote on Hubspot’s growth strategy - its comprehensive with stories, factual analysis, growth strategies. And this is one of the sources that ChatGPT references via its user query.

You can follow a clear template here (which I’ll share more about in the sections below). As for this bullet point, make sure to be as comprehensive as possible and add your unique POV to help stand out.

4: Be consistent with your messaging.

When trying to rank on LLMs, you have to be consistent with your messaging across all webpages and all pieces of content. Coursera ranks for “best online courses” every time on ChatGPT while Masterclass doesn’t.

Why? Because Coursera uses “free online courses” as its headline across almost every page. You shouldn’t say you are the best software for founders and on a different page say you’re the best software for designers.

AI will be super confused with these discrepancies & will think you aren’t the best software for anything, and won’t recommend your product.

Okay so what type of content do you create so LLMs reference you or your brand?

Now that you know how to get your content picked up by LLMs, the next crucial step is to figure out what you exactly write about - how do you do research on what to actually create.

The first thing to know before doing research is to understand it’s not like keyword research here. It’s different because we are optimizing for user intent here where the tone is conversational.

There are four types of user intent:

• Informational Intent – This query corresponds to the user trying to find definitions and learn more about a topic. For example, “What is an AI agent?”

• Navigational Intent – A user is trying to find a website/specific webpage. For example, “Slack Pricing.”

• Transactional Intent – The user is purchasing a product or trying to complete an action. For example, “I want to buy a gaming laptop”

• Commercial Intent – A user is conducting research about a product or service before making a purchase decision. For example, “What are the best product onboarding companies 2025.”

So the first thing to do here is:

1: Create a question related to your brand.

Decide which of the four types of users you are targeting (informational, navigational, transactional, commercial). Ask Perplexity these questions and it will give you a summary with links. At the bottom of each page, you’ll find that there is a list of additional questions that users ask related to your question. This will give you insights wrt what people are interested in searching for.

2: Choose prompts you wanna rank for:

Start by choosing prompts you wanna rank for. For example, you could say that you wanna rank for “best linkedin scheduling software for creators”. If you break this down, you’ll see that this prompt can be broken down into further components:

(a) it has a question identifier (what, why, how)

(b) a qualitative element (the best, the top)

(c) feature identifier (email marketing)

(d) niche/industry/use case identifier (for creators).

At this stage, you can then ask ChatGPT/Perplexity these questions to reverse-engineer search intent. For example you can ask it questions like:

• What are the most common questions {YOUR ICP} is asking you nowadays about {topic/main keyword]. Provide a list of 10 well structured questions. Use these questions to come up with content ideas.

• Ask AI “what are the best blog articles to learn about {main keyword/question} - see what they’re missing, what you can add and then write your blog in that way.

• There are secondary prompts you can optimize for too. You can check “people also ask” results to rank for those prompts. In a lot of cases, follow up prompts follow a similar syntax like “why is XYZ better than ABC”, “does ABC do XYZ better”? “How do I do X task with ABC tool?”

• Optimize for follow ups. If a user follows up with "What's the pricing?" You need to have a conversion-friendly pricing page ready so the AI can use it and respond. This applies to all of the objections a prospect could have. Have those pages ready so you can handle all the objections and don't leave it to the AI to make something up.

3: Scour Reddit to find content ideas:

Go to relevant subreddits to find recurring questions that people ask in your niche and then answer those (and share those back into Reddit)

This goes into the heart of product marketing here - you wanna make sure who your customer is and what your JTBD framework is - the more clarity you have on this, the more the LLMs are gonna favor you.

Before finalizing which idea to write on, classify questions whether they signify high buying intent and have adequate search volume. If buying intent is high & show decent search volume then go for it.

Proven content formats that you can create to get referenced by LLMs.

Research is always good, but if you don’t have the time, there are some proven content formats that LLMs seem to love - so creating content around these themes will have a pretty high probability of being picked up by LLMs.

1. Share exclusive research & data to show deep expertise:

For b2b companies, this means talking to subject matter experts, researching strategies and creating premium content playbooks around them. Create editorial style newsletters. Hubspot did it, Clay is doing it, Amplitufde does it, Typeform does it. Command AI does it. The list goes on. It’s the future of B2B Content.

2: Repurpose them for socials:

It’s not enough to just create content either - you have to repurpose it. You need to turn those newsletters into social media posts so LLMs can pick up on it. LinkedIn / X posts / LinkedIn articles are all crawled by LLMs.

Write LinkedIn articles. Ship them on Substack. The more authority you have on social media & on websites with high DA, the higher the chances of LLMs picking up key information and providing more context.

3: Have dedicated landing pages for each feature/use-case:

Optimize your home page/landing pages to get ranked on LLMs. Make sure you use words that resonate with user intent. Type in a prompt and see if your competitors show up.

If they do, then optimize for that prompt for LLMs to pick up. Find long tail user intent queries too - for example, what is the best ai ad UGC platform for B2C apps. Here the variable is {B2C apps} because that’s who one of your customers is. So having a dedicated landing page to show this off will help LLMs scrape your website.

4: Create comparison pages/BOFU articles & landing pages with high buying intent:

Don’t just write normal stuff - offer your unique POV to show who it is for, how you’re better than the alternatives across what factors, and why people in your target market chose you. Think about it - if you do this, there’s a person who’s choosing “what is better for my X use case? A or B product?” so if you answer that in your page, then AI will suggest your product for X use-case.

5: Ship case studies:

If you’re in an industry where case studies matter (almost any B2B industry), add a lot of case studies on site! There’s definitely some correlation between number of case studies and ranking in the LLMs.

6: Do Programmatic SEO:

Programmatic SEO is gonna be a great way to rank for GEO - create dedicated landing pages & blog posts for ultra niche specific topics to give the LLMs context on exactly what your offering is and how it is that you can help them.

7: Partner with influencers:

If your brand shows up in one of those “best of” listicles, you’ll rank higher. So you may want to partner with these bloggers, newsletters etc who are writing about your space and asking them to include you in their listicles.

Here’s how to write your content - Writing guidelines to help you get picked up by LLMs.

Now that you know what type of content to create, here’s how you actually write the content. In this section, I’ll share best practices with you based on my own experiences & those learned from others:

Danny Sullivan (Google’s Search Liaison) confirmed at Search Central Live NYC that AI Overviews are rewriting the rules of search.

The era of churning out basic blog posts for SEO is over (to some degree).

To win, you need to create content that AI can’t summarize in a single snippet. You need original research, expert insights, unique frameworks.

How to write/what to include:

• Write like an answer. Use question based H2s (What is X + how does Y work) and answer those in the first 1-2 lines.

• Use bullets / numbered lists & include definitions and examples.

• Offer unique POV. Make it comprehensive.

• Make sure to add quotes and cite your sources.

• No matter your business, use FAQs wherever possible

• Include references or statements that tie your content to trending topics or current events in your industry. This contextual relevance signals to LLMs that your content is timely and authoritative.

• Answer questions fast - Deliver the answer in the first few sentences of a piece of content.

• Eliminate fluff

• Make sure the first sentence after a heading answers the heading

• Avoid modal verbs (will, should, etc).

• Structure sentences to place key attributes early. For example, for "What is PLG?", lead with "PLG is a growth motion where the product.... "

• Structure your content like a database and less like a novel

• Periodically refresh your content even if it's evergreen content

• Answer complex questions with multiple parts in clearly structured content that breaks down the question into its pieces with clear subheaders.

• Write in explicit problem-solution syntaxes showing clear thought processes so LLMs can pick it up (here’s the problem - here’s the solution)

• Always add additional context, quotes and examples to your posts.

• The more straightforward your content is, the better. If your relevant content (paragraph or heading section) fits within just one chunk, it's more likely to be used in the answer.

What tools to use to get your brand ranked on LLMs.

• Use Profound to store what sources you are ranking for on LLMs. Profound stores those citations so you know what pages are being scraped so you can double down on those and make it relevant. Data flows in real time so it’s automatically added to profound. Do this at scale and this can make a massive difference.

• Use Semrush AI toolkit to Understand

• how your brand is being mentioned compared to competitors on ChatGPT

• Quickly see how your brand is perceived in the market & how many mentions it's getting compared to competutors.

• See what type of intent searchers have when asking about your brand in ChatGPT.

• Review what people are searching on ChatGPT relevant to your brand.

• See what ChatGPT is saying "good" and "bad" about your brand

• get AI insights into how you can improve your business to rank better on ChatGPT.

• Use knowatoa.com/ to see whether LLMs are referencing your brand or your competitors in its results.

• Omnia - You can use Omnia to identify the most searched topics in AI engines to boost your brand visibility, see how your brand ranks against competitors across key topics and learn the source from where AI engines pull their information.

• Rankscale- With Rankscale you can analyze and optimize your AI Search Visibility with results tracking, get deep insights and Actionable Recommendations. You can Track your Presence in AI Search Results over Time, Analyze AI Result Citations & Brand Sentiments, Uncover, Survey, and Outpace Competitors, Audit your website to see how AI understands it, Identify Gaps and Content Optimization Potential.

• You can use Graphite to drop in a query and see which brands LLMs are mentioning.

• Use Unify’s AIO checker to plug in your company, Pick the keywords that you want to see how you rank on, See how often you are mentioned or cited, and who are the top references.

In Aldous Huxley’s words, it’s a brave new world we are living in. It feels like we are on the cusp of something here. There will be losers and there will be winners. It’s in our hands to make sure which side of history we end up being on.

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In 2014, Jason Boehmig and Cai GoGwilt founded Ironclad with a clear mission: to bring contracts into the digital age.

Their complementary backgrounds made for the perfect combination.

Jason was a former corporate attorney at law firm Fenwick & West who had experienced firsthand the inefficiencies of legal paperwork. Before that, he worked as a bond trader at Lehman Brothers, where he saw how tech could transform routine financial processes at scale.

Cai on the other hand, brought technical expertise as an MIT graduate in Computer Science and Physics and was a former software engineer at Palantir. At the time, he was looking to explore how to apply software collaboration principles to a new domain​.

Both Jason & Cai met at a legal tech conference in October 2014, discovered they had complementary skills and a shared vision, and decided to team up. They both realized that contracts with version control issues, multi-party collaboration, and repetitive tasks – resembled a system that could be “coded” better.

The both of them founded Ironclad within a month of meeting, convinced that “business contracts in 2014 were as inefficient as shipping before containerization”, ripe for a new solution.

Today they are valued at $3.2 billion and are a leader in the contract lifecycle space. Here’s how they got here:

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Market Curve is a premium tech storytelling studio run by me Shounak that turns tech companies into media companies. I’ve had the fortune of working with some of the top tech companies backed by some of the top investors like YCombinator, 20VC who have gone on to be acquired by companies like Roblox & Amplitude.

I’ve also helped founders grow their personal brand in the v0 stage, while writing essays that go viral on social media for companies closer to the PMF spectrum.

Here are a few things I can help you with —

• Longform blogs, essays, and newsletters that drive authority.

• Web & landing page copy that converts.

• Ghostwriting for busy founders & execs who want to stand out.

Want to turn your startup into a media company?

Talk to me

Early MVP & Validation:

The founders immediately looked to validate the idea with real users. They quit their jobs and joined Y Combinator’s accelerator in Summer 2015.

Ironclad's MVP was the textbook definition of doing things that don't scale.The first version wasn't software as a service at all. It was Jason as the backend.

Ironclad's earliest customer acquisition was remarkably straightforward. Jason would pitch his concept to virtually anyone he encountered, asking, "Do you have contracts and would you like help with your contracts?"

Their first paying customer came from a personal connection at a friend's event. From there, they secured additional clients through persistent outreach and rapid response to customer needs. For example, this one time, they built an entire repository feature during a train ride from San Francisco to San Jose to meet a prospect's requirements. Talk about hustle!

"It's wild to think back to this now that we're in the AI assistant boom because the first version of Ironclad was an automated assistant for legal paperwork - but it was really me on the backend of an email address," - Jason Boehmig

The product workflow was made deliberately simple. Users would email admin@ironclad.ai with requests like "Hey, I'm doing an NDA with Joe Smith. Can you help us get it done?" Boehmig would manually respond with templates and guidance, mimicking what would later become automated features. Meanwhile, Cai observed this process, identifying patterns and gradually automating repetitive tasks.

For two years, they had one goal: be the BEST contract platform in SoMa (that was their local district in San Francisco). As a result, on-site meetings became 10x more valuable, meetings were on-site by default, they could provide extreme levels of customer support is an unfair advantage vs. competitors.

Ironclad’s strategy for acquiring its first customers was very much land-and-expand: land a team within a tech company (often the legal or sales ops team) by solving their immediate contract pains (like speeding up vendor agreements or offer letters), then expand usage to more contract types and departments.

This approach allowed them to validate their concept while building trust with early users. It also provided invaluable insights into user needs that informed their product development roadmap. They were also careful to fire bad-fit customers.

The next version of the product was a slightly more evolved version (not much, mind you but just a very slight evolution). They leveraged existing APIs to build their v2. The contracts were stored & organized in a dropbox folder automatically for each company while e-signatures were handled via HelloSign’s API.

So they launched a free NDA creation tool on its website and quickly saw 200 companies sign up for the beta.

Users would log in, choose a contract template (NDA, sales contract, etc.), fill in key details via a web form, and Ironclad would generate a draft and send it out for e-signature – taking care of filing the completed document back in the Dropbox folder structure​.

This end-to-end flow replaced what used to be many manual steps across email and different software.

Importantly, the MVP included a simple workflow checklist interface for each contract so that founders and legal teams could track the status (drafted, sent, signed, stored) at a glance. Ironclad’s early design philosophy was not to replace lawyers, but to give lawyers and businesses an “operational layer” that handles the busywork​.

By focusing on common, low-risk contracts first and making the system dead-simple (no IT integration needed), the MVP quickly gained traction. The fact that Ironclad already had paying customers (for $49 or $199 monthly plans) in 2015 based on this MVP validated that users would pay for a smoother contracting workflow.

Their impressive display of agility won them a contract against 12 competing CLM providers, despite being a much smaller company. But they didn’t rest on their laurels.

They kept travelling to events in places like Boulder to observe customers using their product in real time. This revealed crucial insights about legal teams' workflows and priorities:

"They had two monitors, one showing their email and on the other was Ironclad. They were just doing their day-to-day routine, working on a legal team. And by observing them and seeing how important these routine, repeatable transactions were to their profession, we realized we had to go all in on these corporate legal teams-particularly legal operations, which was a role that was just getting defined back in 2015."

This immersive approach to customer discovery became a cornerstone of Ironclad's product development strategy. Rather than making assumptions about user needs, they watched real usage patterns and adjusted accordingly.

Thanks to Y Combinator and the founders’ networks, Ironclad’s first users were often tech startups and forward-thinking legal departments willing to try a new tool. YC’s demo day and network introduced Ironclad to companies like Gusto, HotelTonight, and others who became early customers​.

Additionally, Jason’s connections in the legal community helped Ironclad land pilot trials at law firms’ corporate clients. By 2017, Ironclad had “hundreds of companies” using the platform, including notable names GoFundMe and Glassdoor​.

These marquee customers served as important case studies – for example, Glassdoor’s legal team used Ironclad to manage high volumes of sales contracts and NDAs, proving the solution’s scalability beyond just tiny startups.

The free NDA tool acted as a classic lead magnet – companies would start with that single use case and then realize the value of putting more workflows into Ironclad. Meanwhile, Ironclad kept pricing attractive for growth-stage companies (low monthly subscriptions) to lower the barrier to adoption​.

This bottom-up adoption model, combined with YC’s stamp of approval and word-of-mouth in the in-house legal community, propelled Ironclad’s early growth. Each successful deployment provided evidence to validate the core idea: digital contracting was indeed making legal and business teams more efficient, and there was a willingness to pay for a better way.

At the time, their competitors charged based on user seats. Ironclad turned it on its head by having unlimited users, with limited use-cases.

How Ironclad Scaled From Startup to Category Leader

When you know you’re onto something, you double down—and that’s exactly what Ironclad did.

Fresh off strong early validation, Ironclad raised an $8M Series A in 2017 led by Accel. They immediately stacked their leadership bench:

• A VP of Sales, Damon Mino (bonus points: ex-lawyer and ex-DocuSign sales leader) to crack into corporate legal departments.

• A seasoned COO fresh out of SalesforceIQ.

• A VP of Engineering to build for the next stage of growth.

But Ironclad didn’t just throw bodies at the problem. They stayed maniacally focused on product scalability. They realized legal teams couldn't keep calling engineers every time they needed a new contract workflow.

So they launched Blueprint—a no-code workflow builder that let any legal team create, manage, and automate their own contracts.

• Lawyers could finally self-serve.

• Legal ops became scalable.

• Ironclad had a serious moat.

Blueprint was a growth unlock. It turned Ironclad from "helpful tool" into "critical infrastructure." By the time Ironclad raised its $23M Series B from Sequoia in 2018, it was ready to go upmarket.

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Moving Upmarket: From Tech Startups to Fortune 500s

With strong validation, Ironclad raised larger rounds and reinvested in scaling the team and product. The $8M Series A in 2017 led by Accel allowed Ironclad to hire talent and build out features that larger customers needed.

They hired a VP of Sales, Damon Mino (who uniquely was both a lawyer and a former DocuSign sales leader) to start targeting corporate legal departments​.

They also brought in an experienced COO from SalesforceIQ and a VP of Engineering.

On the product side, Ironclad realized that to scale usage within an organization, legal teams needed to self-serve new contract workflows without relying on Ironclad’s engineers or IT. This led to developing Blueprint, released in 2018, which was essentially a no-code workflow builder for contracts.

Blueprint let customers configure their own contract templates, approvers, and business rules via a UI – a move that unlocked growth by enabling scalability. Each customer could automate many types of contracts, not just the few templates Ironclad built.

Blueprint was a key growth unlock: it was the first “walk-up usable” workflow builder for legal teams, meaning any lawyer could now encode their playbook into Ironclad without code​.

This innovation both delighted customers and gave Ironclad a differentiator over competitors. By the time Ironclad closed its $23M Series B in 2018 led by Sequoia​.

After establishing itself with tech-savvy mid-size companies, Ironclad began pushing into larger enterprises and diverse industries.

Ironclad’s strategy was to present itself not just as another CLM, but as a modern, end-to-end “digital contracting platform” for the whole business. This positioning helped win over Fortune 500 clients who were embarking on digital transformation in their legal and procurement departments. The $50M Series C (2019) gave Ironclad resources to expand geographically and in new verticals​.

On the product front, Ironclad continued to enhance its platform’s capabilities: it launched a more advanced workflow designer in 2019 (building on Blueprint) to handle complex conditional workflows (e.g., automatically add clauses or approvers if a contract value is above X).

It also built out a robust contract repository with search and analytics, knowing that larger companies care about managing thousands of signed contracts and mining them for data​

​Another area of focus was integrations – Ironclad developed integrations with popular business systems (Salesforce for sales contracts, Slack and email for notifications, and cloud storage like Box/Drive for archiving) to fit seamlessly into enterprise IT ecosystems​.

These enhancements made the product sticky for larger organizations. In 2020, when the COVID pandemic forced businesses to operate remotely, Ironclad’s value proposition became even clearer: fully digital contract workflows went from nice-to-have to necessity. Ironclad capitalized on this by publishing content on how companies like DoorDash used Ironclad to rapidly onboard restaurant partners or how healthcare companies shifted to telehealth contracts virtually​.

Such use cases proved Ironclad could scale and handle mission-critical contracting needs under pressure. By the end of 2020, Ironclad was being recognized by industry analysts as a serious player.

Ironclad’s Series D in January 2021 ($100M) marked its arrival as a unicorn-in-waiting, with a valuation near $1 billion​. This signaled confidence that Ironclad could be the category winner in digital contracting. The company used this influx to aggressively expand: it roughly doubled headcount, investing heavily in R&D (especially artificial intelligence) and customer success.

One major step was Ironclad’s acquisition of PactSafe in March 2021, bringing in a specialized team and technology for clickwrap contracts (one-click online agreements)​.

PactSafe’s product became Ironclad Clickwrap, allowing Ironclad clients to manage “I agree” type contracts (think of software terms of service or high-volume online sales terms) in the same platform. This expanded Ironclad’s reach from negotiated contracts into the realm of mass acceptance contracts, a strategic move to cover the full spectrum of contracting needs.

Ironclad also launched its own Ironclad Institute and Community initiatives around this time: hiring Mary Shen O’Carroll (Google’s legal operations lead) as Chief Community Officer and hosting the first State of Digital Contracting virtual conference. The message was clear – Ironclad aimed not just to sell software, but to be the hub of the legal ops community, educating the market on best practices (a move reminiscent of how Salesforce uses its community and “State of Sales” reports to drive thought leadership).

On the product side, 2021 saw Ironclad deliver on earlier promises of AI and intelligence features: it introduced functionality like AI-powered import of third-party contracts and AI contract data extraction, so businesses could ingest legacy contracts or counterparty paper and have Ironclad automatically tag key metadata.

The Series E in January 2022 ($150M at $3.2B valuation) firmly established Ironclad as a unicorn, and brought in Franklin Templeton as a new lead investor alongside all existing major investors.

Ironclad had a war chest to invest in its platform and global expansion. In 2022, the company sharpened its messaging around “Digital Contracting” as the new standard for business. CEO Jason drew an analogy: contracts needed a transformation akin to “containerization” in shipping – a standard that turns contracts into interoperable data, enabling resilience in a fast-changing world​.

​This narrative helped differentiate Ironclad from competitors who pitched “just CLM”; Ironclad was pitching a vision of a connected, data-rich contracting future. Practically, 2022’s big product announcement was Ironclad AI. In September 2022, Ironclad rolled out an AI layer that could “transform the way companies create, understand, and control their contracts”​.

Concretely, the AI features allowed automatic extraction of key terms (like renewal dates, prices, parties) from contracts and introduced AI-driven contract review tools. Ironclad even partnered with OpenAI, integrating GPT-3 and later GPT-4 models to power some of its contract review and redlining capabilities​.

This placed Ironclad at the forefront of the AI wave in legal tech, giving it a head start on an emerging growth lever.

By 2023, Ironclad’s platform had expanded to cover: contract generation and workflow (Workflow Designer), collaboration and editing (a Google Docs-like editor), e-signature (Ironclad Signature), repository and search, analytics/reporting, clickwrap, and AI assistant features​ – truly an end-to-end offering.

The company’s growth was also evident in customer stories: for instance, fitness company Orangetheory publicly shared how using Ironclad’s AI tools cut a typically 6-month legal project down to 3 months​.

Such stories helped convince even traditional industries that Ironclad’s approach was not just hype but driving real ROI. Internally, Ironclad prepared for its next stage by realigning leadership and continuing to hire seasoned leaders in product, sales, and customer success. By late 2023, Ironclad was widely considered a leader in the contract software space, often mentioned alongside or ahead of long-time giants like Icertis.

————————————————————————-

Ironclad's Growth Levers and Strategies

Ironclad’s growth was not an accident – it was driven by deliberate strategies and levers that the company pulled at different stages to land customers like these:

Solving a Critical Pain Point Extremely Well:

From day one, Ironclad focused on making the contract process faster and easier, which delivered immediate value (time and cost savings). This clear ROI on something every business struggles with (contracts) created natural demand. As one investor noted, Ironclad was addressing “the problem of business contracting” – an unsolved problem in modern business. This strong product-market fit became a flywheel: happy legal and sales teams talked to peers about it, spreading Ironclad via word-of-mouth in the close-knit legal ops community.

Freemium and Product-Led Growth:

Ironclad smartly employed a form of freemium model in the beginning (free NDA automation) to get in the door​. This product-led growth approach let potential customers test drive the value before committing. Even as it moved upmarket, Ironclad kept the product easy to try – e.g. offering demo sandboxes, on-demand videos, and a straightforward SaaS signup model (a contrast to legacy enterprise software). The ease-of-use (“walk-up usable” tools) was itself a growth lever: non-technical users could start using Ironclad with minimal training, helping it spread within organizations.

Content & Education Led Growth:

They also have a demo video library to educate users so they are taking a very education-led approach to things which helps users get in through the door, critical for product-led growth. Companies like Webflow, Typeform, Retool have all used these. You can also check out their resource libraries and stuff like that. So they are investing a lot in education to power that community.

They also have an academy which kind of ties into the whole narrative of education as well while they also show their thought leadership with the journal which is the practical angles and all things related to contract management showing a thought leadership angle. They are also ranking for key terms across contract like contract value, contract life cycle management, contract leakage and compliance and things like that. These are helping them rank on SEO for these relevant keywords and also ranking on publicity, LMS and stuff like that which is why if you are starting to create a company in this space you want to make sure that you are ranking for the correct keywords relevant to your niche and have long term keyword potential as well.

Ad Strategy:

If you look at their ad library, they also run a lot of paid ads. So you can see that they share videos on specific features or specific use cases that the corresponding feature solves. So if you look at that copy, they are always positioning Ironclad as a solution to make your contracting easier.

They are even addressing the skepticism around AI as to why different legal teams are skeptical of AI and why they should embrace AI instead. They also use keywords that are very frequent in their ICP, which is contract blockages, irritated stakeholders, because this is very true.

A lot of the contracts are siloed in one place with sales and marketing and all that stuff. And so this blockage is a problem. So they're always using the problem solution matrix to showcase their value proposition. And even their features, they run ads against like their redlining contract features. And they also share snippets on videos of customer case studies in their ads as well. So this is their entire ad strategy, which is doing tremendously well.

Community Building and Thought Leadership:

Ironclad invested heavily in building a community around legal operations and contracting. They created a content hub (“The Journal” and resource library on their site) with practical guides on contract management, published reports (like the “2021 State of Digital Contracting”), and started a popular newsletter (Pros & Contracts) for in-house legal professionals.

Hiring Mary O’Carroll, a highly respected legal ops leader, as Chief Community Officer was a strategic move to cement Ironclad’s position as a thought leader. The annual “State of Digital Contracting” event brings together customers and industry experts, generating buzz and positioning Ironclad at the center of industry dialogues​. By leading conversations on how contracting is evolving, Ironclad ensures its name is synonymous with modern contracting best practices. This community focus also drives customer loyalty – users feel like they’re part of a movement, not just using a tool.

Sales Led growth:

From 2018 onward, Ironclad built a strong sales organization to complement its product-led approach. They recruited sales leaders who deeply understood the legal tech space (e.g. the VP Sales from DocuSign’s legal vertical​) and scaled up a customer success team.

Ironclad identified an important fact about its user base: corporate lawyers and contract managers want a trusted relationship given the critical nature of contracts. So, Ironclad emphasized high-touch customer success, training, and support (even mentioning plans to build “the best SaaS customer support function for legal teams” after the Series B​) This not only helps retain and expand accounts, but also turns customers into references for new sales.

As an example of sales strategy, Ironclad often started within a department (say sales ops managing NDAs) and then used that successful deployment to upsell company-wide CLM usage – a classic land-and-expand that its easy-to-use product facilitates.

Continual Product Innovation (Width and Depth):

A major growth strategy for Ironclad was to continuously expand its product capabilities, both in breadth (new features/modules) and depth (refinements and AI). In breadth, Ironclad went from a workflow tool to a full platform: adding the Editor (collaborative online editing), adding a Repository with advanced search, building its own e-signature (Ironclad Signature), adding Clickwrap via acquisition, and layering on AI assistants.

Each of these expansions opened up new market segments and increased the value of the platform for existing users, encouraging them to consolidate more of their contracting work in Ironclad. In depth, Ironclad consistently improved usability and performance, staying ahead of customer demands (e.g. introducing the first contextual clause library, dynamic templates, etc)

Positioning their product in the market with clear messaging:

Perhaps one of Ironclad’s most impactful growth strategies was its messaging and positioning (detailed more below).

By coining and continuously using the term digital contracting and framing their platform as the “standard for business contracting”, Ironclad set itself apart from the generic CLM (Contract Lifecycle Management) market.

This category creation approach shaped the narrative in their favor – rather than comparing feature-by-feature with competitors, Ironclad invited customers to join a larger vision of transforming how businesses handle contracts (something that sounds more innovative and strategic).

They published thought leadership pieces (like Boehmig’s 2021 blog on the need for a new contracting standard​) and made “digital contracting” part of their brand. This strategy made Ironclad synonymous with the future state of the industry, which in turn attracted forward-looking customers and talent.

Messaging & Positioning in Marketing Campaigns

Ironclad’s messaging evolved as the company grew, but it always emphasized making contracting simpler and more strategic for businesses:

• Early Stage Messaging: In 2015, Ironclad was described as an “automated legal assistant for companies”​. Early marketing (e.g., YC Demo Day pitch, TechCrunch coverage) focused on saving startups from onerous legal paperwork and big legal bills. The value prop was very tangible: Ironclad automates drafting, organizing, and executing common legal documents so founders don’t have to pay lawyers for routine work. They often highlighted use cases like NDAs, sales agreements, and HR contracts – areas where mistakes are costly but the work is repetitive. The tone was one of empowerment: Ironclad gives startups capabilities that previously only big companies had (like a legal team), leveling the playing field.
  

• “Contracts = Data, Not Documents” – Positioning: As Ironclad gained traction, its messaging started to hammer on the idea that contracts are more than static files – they contain data and business decisions. A line from its YC profile encapsulates this: “Contracts are business decisions trapped in administrative tasks. Ironclad is software that does all the administrative work… freeing legal teams to do more substantive work”​. This messaging is powerful because it reframes Ironclad as not just efficiency software, but as a tool that unlocks the strategic value of contracts. In marketing materials and sales decks, Ironclad positioned itself as enabling legal teams to focus on high-value work (negotiating deals, ensuring compliance) while the software handles the busywork. Phrases like “single source of truth for contracts” and “operating system for your contracts” began to appear, conveying that Ironclad centralizes all contract activity and information.
  

• Digital Contracting – Category Creation: Around 2019–2020, Ironclad’s marketing embraced the term “Digital Contracting” to distinguish its approach. Instead of calling itself just CLM software, Ironclad branding started saying “the leading digital contracting platform for modern businesses”. In press releases and the website, they would define digital contracting as “connecting the people, processes, and data involved in business contracts so companies can execute smarter agreements, faster”​. The use of a new term signaled that Ironclad was something new and modern, leaving legacy competitors sounding dated. In ads and campaigns, Ironclad often educated the audience: e.g., blog posts and videos titled “What is Digital Contracting?” and why it matters. This thought leadership marketing served to both inform the market and subtly position Ironclad as the pioneer of this new category. The culmination of this was Ironclad’s bold statements in 2021 about establishing a “new standard” for business contracting – essentially claiming category leadership of digital contracting​.
  
  

• Emphasis on Speed, Collaboration, and Insight: Ironclad’s messaging highlights different key benefits as the product evolved. Common themes in campaigns include: Speed (“faster contracting, close deals 50% faster”), Collaboration (“connect all stakeholders in one platform, like Google Docs for contracts”), Compliance (“maintain compliance and reduce risk with standardized workflows”), and Insights (“unlock contract data for decision-making”). For instance, a 2019 Forbes piece noted Ironclad “automates and streamlines the contract process” but more importantly “also captures data from contracts” to provide business insights​. This dual message – streamline process AND capture intelligence – started to appear in many of Ironclad’s marketing materials by 2019, setting it apart from tools that just stored documents. By the time of the Series E, Ironclad had focused on the “core contracting experience” and now was expanding that to a platform that connects processes across the organization, highlighting a holistic vision.
  

• Humanizing Legal Tech: Ironclad’s tone in marketing is notably upbeat and human, which is somewhat unusual in legal tech (often perceived as dry). The company culture highlights (pictures of team events, “Community Dinners”, etc.) and values (empathy, drive, integrity) seep into the brand. In social media and blogs, Ironclad often speaks directly to in-house counsels and legal ops folks as peers, acknowledging their challenges. This was likely influenced by Jess Lee’s insight about community and caring for the user as a person​. Even the naming – calling their newsletter “Pros & Contracts” (a playful twist on “pros and cons”) – shows a lighter, approachable branding. This positioning made Ironclad seem less like a stuffy enterprise software vendor and more like a modern, user-friendly brand that “gets” lawyers and business teams. That helps in an industry where change management is a hurdle; users felt Ironclad was built for them by people who understand them (indeed, Ironclad would highlight that its team includes lawyers and engineers working together).
  

• Competitive Positioning: While Ironclad mostly focuses on positive messaging about itself, there is an implicit competitive angle: Ironclad frequently implies that alternative solutions are piecemeal or outdated. For instance, Franklin Templeton’s director in 2022 said “Other players have some components... but only Ironclad has the whole package”, calling Ironclad the “clear leader”. This quote was likely amplified in marketing to reassure buyers that Ironclad covers end-to-end needs unlike competitors. Ironclad’s content has drawn contrasts with the “status quo” of emails and Word (the manual way) and with “legacy CLMs that are clunky or narrowly focused.” By evangelizing digital contracting as a concept, Ironclad indirectly challenges competitors for not meeting that vision. In ads or collateral, you’d see phrases like “unlike old contract management systems, Ironclad is built on modern software design principles and is instantly usable” (this mirrors language from a 2022 press release)​. They also leaned on third-party validation in positioning – citing Gartner and Forrester rankings, Y Combinator top company lists, etc., to show that experts consider Ironclad a leader.
  
  

The Ironclad playbook:

Based on Ironclad's journey, companies looking to compete in this space-or entrepreneurs seeking to replicate their success in other markets-should consider the following playbook:

1. Solve a personal, painful problem: Start with a real problem you deeply understand; it ensures product-market fit is more than guesswork.
   

2. Find a cofounder with complementary skills: If you’re aiming to disrupt an industry, pair up with co-founders who cover both industry know-how and technical/build skills. It speeds up development and credibility in parallel.
   

3. Validate Early with an MVP : Identify a beachhead use case that is narrow but painful and common. Build an MVP around that, leveraging third-party services when possible to move fast. Prove that your solution works and people will use it, before expanding. Early validation (sign-ups, usage, feedback) is more convincing than any pitch deck.
   

4. Leverage Networks and Communities for Early Adoption: Plug into networks – whether an accelerator, an open-source community, or industry associations – where your early adopters congregate. Offer them something valuable (like Ironclad’s free tier) to get them on board. Their word-of-mouth and case studies will jumpstart your growth at low cost.

5. Land-and-Expand with a Free or Affordable Hook: Design your product and pricing with an on-ramp – a free tier or trial or entry-level package – that lowers friction to adoption. Ensure that once inside, users see the other things they could do with your product. Satisfied users will advocate internally to use the product more broadly, converting a small win into a larger deployment.

6. Obsess Over User Experience: If your competitors are legacy or the status quo is manual, outshine them with user experience. Even in enterprise software, end-users matter – a product that people enjoy using creates evangelists. Sweat the details to eliminate friction (Ironclad automated filing signed contracts to folders, a tiny feature that saved headaches​.). A great UX in a dull space is an unfair advantage.
   

7. Build Community and Thought Leadership Around Your Mission: Become a thought leader in your domain. Share valuable insights, host events or webinars, and facilitate peer connections. If people look to your company for expertise, they will naturally look to your product for solutions. It also builds a moat – a competitor can copy features, but it’s hard to copy a loyal community.
   

8. Define (or Redefine) the Category on Your Terms: If possible, coin a term or establish a narrative that highlights your strengths and frames the market in a fresh way. Become synonymous with that vision. It’s easier to lead a new category than to fight for share in an old one. However, back it up by actually delivering on the promise of that category (Ironclad did so by continually expanding its platform and tech).
   

9. Expand Product Scope Strategically (Platform Play): Grow your product horizontally only when you have a solid base and when customers are pulling you there. Identify the next problems your users encounter once the first is solved – often those adjacent problems can be solved within your platform. By becoming a one-stop platform for related needs, you increase stickiness and value. But ensure quality in each expansion (don’t add random features that aren’t best-in-class or don’t align with your core mission).
   

10. Maintain Customer-Centricity (Success > Sales): Especially for enterprise/SAAS, invest early in customer success and support. It’s cheaper to grow an existing customer and get referrals than to constantly hunt new ones. Make your earliest customers wildly successful – their testimonials will become your strongest marketing asset. Ironclad’s early advocates (like the GC of Glassdoor) helped open doors to new clients with their success stories​.
    

    ---

That’s Heisenberg’s Uncertainty Principle. Oddly enough, it captures how we relate to artificial intelligence today.

The Uncertainty principle tells us that we can know either the position or the momentum of a particle at any given time but can never know both at the same time. The more accurately one property is measured, the less accurately the other can be known.

In AI, its much the same. Like in physics, we can measure AI’s present or its trajectory but never both. Zoom out and we spiral into hype or panic. Zoom in and we lose the big picture.

Is it ever possible to do both?

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I’ve also helped founders grow their personal brand in the v0 stage, while writing essays that go viral on social media for companies closer to the PMF spectrum.

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This Isn't Our First Tech Panic

Each time technology has progressed, it has been met with equal degress of wide-eyed wonder and fearful skepticism.

We’re seeing it firsthand with AI, but on a long enough timeline, history repeats itself. Some philosophies even say time is cyclical. That means we’ve been here before and that’s a good thing. Patterns, precedents, and lessons exist if we’re willing to look back.

Following Alexander Graham Bell’s invention in 1876, the telephone sparked fear among early adopters. People believed it transmitted invisible forces or malevolent spirits. The telegraph faced the same suspicion—and so did electricity.

When John Feeks was tragically electrocuted in New York City in 1889, the association of electricity with danger grew even stronger.

These fears weren’t isolated - they were part of a broader reaction called technophobia, which took hold during the Industrial Revolution.

This wasn’t just fear of the unknown. It was also about jobs.

Before the 1870s, steel production and mining in the US were labor-intensive, manual processes. Workers would manually extract iron ore from shallow surface mines or deeper shaft mines using basic tools like picks, shovels, and hammers. The output? 10 to 20 tons of steel per worker each year. The metal was expensive, inconsistent in quality, and limited to small-scale uses like tools or rails.

But in the 1870s, the invention of the Besemeer process transformed steelmaking. Armed with this new technology, the average steel worker could produced over 300 tons of steel per year.

Steel prices dropped from $100 per ton in 1870 to just $12 by 1900, making it affordable for skyscrapers, bridges, and railroads, upon the back of which modern civilisation stands.

In the 1900s, US was producing 40% of the world's steel. Andrew Carnegie, one of the then early US businesses billionaires also made his fortunes on top of the Beseemeer-wave.

Just this small history lesson tells us that technological economics is designed to automate away manual inefficiencies, make things streamline so costs can drop down, and scarce valuable resources can be made more accessible to people (both present and future) at scale.

Your average tech or VC bro will come in and probably utter the four magical words at this stage - capitalism increases the pie.

Their argument is that yes, jobs were lost but so many jobs were created too. And they would be right.

In 1860, before the Besemeer wave, Cleveland's steel mill employed 374 workers. By 1880, Cleveland alone had ten steel mills employing 3,000 workers.

But the economics of work and job creation & displacement isn't so black and white. It's much more nuanced than that.

The 3 Stages of Technological Automation

Technology doesn’t replace humans overnight. It evolves in stages. When it comes to labor, there’s a spectrum of automation:

1. Productivity Tools

At the top of the spectrum are productivity tools that complement workers and complete only a portion of a job or task. Think telephones or AI-powered chip design. They make tasks easier, not obsolete.

2. Enabling Automation

Move up, and you'll find yourself in the middle of the spectrum, one where automation enables workflows that speed up in achieving specific outcomes.

The Gutenberg printing press & the invention of the manufacuturing assembly line are examples of this.

In the software world, you could look at spreadsheets as the disruptor to legacy notepads or ledgers for keeping track of customer data and financial information.

3. Replacing Automation

And then, finally, we have the last tier of the spectrum where automation replaces human labor.

When a Waymo can take another person from Point A to B, the human Uber driver is replaced by the technology.

Here, tech does the job entirely. Waymo replaces Uber drivers. And AI starts to replace knowledge workers.

Will Humans Still Have a Role?

AI doomsday experts almost exclusively reside in the third tier. The bad news for them is that it's almost inevitable that technology will replace humans at one point.

It's not the question of if, but when. Here's why this is almost a certainty.

The first reason is that, mathematically speaking, any "information-processing or execution task" is simply a function from input variables to output variables.

In lots of niche use cases, these input variables can become complicated, and it could become practically difficult to encode such things into computers, but in principle there is no insurmountable barrier. All we need is simply better ways to search for algorithms which implement such functions.

And this kind of search shouldn't be too difficult to create either. Human intelligence is an emergent property of blind evolutionary processes. Humans have been using tools for a long time but the cool thing is that this complex tool use has evolved at least three times across corvids, primates and octopuses. So this step isn't exactly a big bottleneck.

The creation of human intelligence from the level of primates took 2 million years out of the 4 billion years since the origins of life on Earth - a 8e13%. That's 8 followed by 13 zeros.

These 2 million years were spent creating general intelligence across cognition, maintaining biological processes, processing emotions etc. Therefore, a more finely-tuned search can probably converge on intelligence that much faster.

We also have access to silicon-based computational hardware (or even wetware in some cases) which have a massive advantage over biological hardware.

The arbitrage opportunity lies because biological hardware need to spend time controlling our bodies, some to sleep, others to emote and feel. A niched-down use case powered by computers is massive.

There's also disproportionate advancements being made in quantum computing where the world could very well be one where supercomputers are the norm much like smartphones are today.

And finally, there's the communication advantage - computers are able to transfers terabytes of data in seconds whereas in human communication, there is a large degree of information leakage.

The efficiency arbitrage is also a massive advantage. Combine all these together and add compounding loops on top, and chances are our AI overlords will replace humans entirely.

Well maybe not entirely. Humans will still have a role to play in this brave new world - they still need to make sure guardrails are in place, steer the AI in a direction that's most aligned with key incentives, there will be humans in the loop to give feedback to models and agents to make sure they learn and improve.

How do we find meaning in all this

The thing about us biological creatures is that when when our livelihood is threatened, or we feel like we are on course to lose that thing that once made us useful, we tend to respond in rather reactionary ways.

At which point, the question becomes - what work are we supposed to do if the machines can do it all? It becomes a question of us vs them.

We craft a narrative in our minds of what work is supposed to be and (wrongly) assume that only WE can do the work - after all, we can think, use our minds and make our efforts count.

There is a direct correlation between working hard & increase in perceived value. We might think that we humans naturally gravitate towards doing things or choosing things that make us do less work or free up our time, but the truth is we value our work in a very deep and meaningful way.

Businesses work on the assumption - save time is better. Products and services that help save time are perceived as more valuable.

But then you have companies like Ikea where you have the Ikea effect where people value something more because they made it on their own.

Or you have the Betty Crocker effect where sales dried up because the cake baking process was too methodical and lacked soul, so the company added friction into the cake making process (adding eggs) which boosted sales because the people making the cakes felt like it didn't have their personality and they wanted to make something they made with their own hands.

As such, there can potentially exist a world where there is a premium on the human efforts that goes into creating something that has a high perceived value.

Perhaps a world where if a person hires a human to provide a human customer support experience to a high ticket unicorn customer while using AI customer support bots for regular people like us. Human experience would then be held at a premium in an economic context.

So its plausible that AI can replace human tasks but the economic pie would increase and create this whole new world of jobs much like the internet & smartphone created the gig economy, the attention economy and the creator economy.

This is because what has historically happened is that the work / energy output doesn't just go out out of existence - it simply manifests itself into something new.

The law of conservation of energy states that energy is neither created nor destroyed. It just gets transformed from one form to another.

Much like this, the effort we put in won't disappear, bur rather will be chaneled into something new. Something that aligns with our existential & spiritual personas we inhabit within ourselves.

If Nietzche were here, he would probably urge us to become our best selves and discover what we want to do with ourselves to fulfil our higher purpose because for him, work is disgraceful because it detracts from man’s ability to produce the only thing that bestows value on life – beauty.

The point then, of human work in the post AGI world is to focus on doing work that is more creative, require strategic thinking, and of course, that we derive meaning from.

AI won’t just replace labor, it will force us to redefine it.

In that redefinition lies our next renaissance. Not of efficiency. But of meaning.

If you liked this essay, consider sharing it on your socials or inviting a friend to read this essay. Feel free to say hi to me on Twitter or on LinkedIn or email.

That’s all for today! See you soon!

Amor Fati Amor.

We are moving from the code as capital arbitrage to the code as labor arbitrage. Software that can perform key tasks autonomously (albeit with some degree of human oversight).

I wrote about it in my previous essay on Service as a Software where agentic applications will eat into the labor market unlocking trillions of dollars in value and creating new markets altogether.

But in order to unlock this opportunity, you need context-aware models with long inference compute times that can access tools.

Right now, models can only do short tasks in a quick feedback loop - you ask them a question, and they give you an answer. But that’s extremely limiting. Most useful cognitive work humans do is longer horizon—it doesn’t just take 5 minutes, but hours, days, weeks, or months.

The key difference here is that of inference time compute aka the time it takes for the model to think and reason. Deepseek reasoning made all kinds of wave, and OpenAI and Perplexity quickly added reasoning capabilities. The longer the inference time, the better the output.

In a way, it's similar to how self-driving cars work. Self driving cars collect data from their surroundings via sensory inputs like the camera, LIDAR etc, after which, this data is processed using Machine Learning & Deep learning algorithms.

The algorithm is trained across a large dataset to recognize patterns and make predictions. This gives the vehicle the best chance to react appropriately to various driving scenarios without needing specific instructions for every possible situation encountered on the road.

AI agents also take in sensory input from the users’ screens, external databases, APIs etc. Once this data is collected, AI agents use a combination of ML, inference compute & reasoning to make decisions. They can learn from previous interactions, access tools all on their own, and execute multi-step tasks, much like the self-driving cars.

In Kahnemann terms, “fast thinking” AI models will make way for inference-based “slower thinking” models.

AI employees will essentially function like digital remote employees with situational awareness. Here's what I mean by this: I recently made an AI agent that takes in context about MarketCurve and the kind of work the business does. It is trained on case studies, knows about the tech space, and what kind of use-cases we can help with. Armed with this context, this agent sits on the MarketCurve website and answers questions that the customer may have about MarketCurve. But Shounak isn't this just a chatbot? Yes it is.

AI Assistant Demo 🚀 - Watch Video

But this chatbot exhibits agentic capabilities. I have given it access to my Telegram, email and Calendar. So if a customer asks to book a consultation call, the AI agent (who I call Nathan) can go into my calendar, pull up my meetings for the week, asks the customer for his time preference, cross-checks with my calendar to check for clashes, and if everything looks good, it goes to my calendar, books a meeting with the customer, sends both of us an email with a meeting link along with a summary of the meeting agenda.

To make things even more impressive, I can even send a voice text to Nathan and ask him to book a meeting or delete a meeting or show me a summary of the meeting I just had. I can do the same in Slack as well. All of this done on autopilot. For the price of a meal, I get my very own personal customer service agent that works 24*7. How cool is that?

Now imagine companies using multiple such AI agents across inbound leads, setting up outbound campaigns, recruiting the right people armed with all the relevant company information and context. This is exactly the promise of 11x AI - a company that is building digital workers for companies to hire.

Let's say you're running a sales team and you want to update your CRM or launch a email campaign. It would take you 4-6 weeks to get the ball rolling because a lot of the information is fragmented. The sales landscape was dominated by Salesforce after which came a huge unbundling of Salesforce creating multiple point-solutions which focused on very specific use-cases. As such, a lot of the tools at organizations are siloed across prospecting, outreach, CRM, follow ups, demo calls etc. This makes information exchange a nightmare. This complexity is where AI agents like 11x can shine.

They raised $75 million in funding from A16z, Benchmark, 20VC and other investors and are making real waves in this space. 11x now serves over 200+ companies like Otter.AI, Airwallex and Datastax. and are making $250 million in ARR. Let's dive in and read their story.

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Market Curve is a premium tech storytelling studio that turns tech companies into media companies. We’ve worked with some of the top tech companies in the US and EU backed by some of the top investors, like YCombinator, 20VC and more.

We’ve helped founders grow their personal brand in the v0 stage, while writing essays that go viral on social media for companies closer to the PMF spectrum.

Here are a few things I can help you with —

• Longform blogs, essays, and newsletters that drive authority.

• Web & landing page copy that converts.

• High-value assets like decks, e-books, guides, and case studies.

• Ghostwriting for busy founders & execs who want to stand out.

Want to turn your startup into a media company?

Talk to me

Here's Hasan, the founder of 11x talking about 11x:

Armed with this vision, Hasan and his team did what I would have done in his shoes - he went after the freelancer market.

Legacy labor marketplaces like Upwork and Fiverr are ripe for disruption by AI workers and having a marketplace for people to hire agents would be the logical evolution of the market.

A variant of this is being executed by Dharmesh Shah of Hubspot who is now building agent.ai - a marketplace to hire AI agents who perform specific tasks. It's no surprise that Hubspot Ventures has also invested in 11x.

But when you're going into a marketplace, you run into the very familiar chicken and egg problem. Do you optimize for demand first or supply?

Historically, the answer lies in making sure you have the demand side ready before streamlining the supply side. You need a constant demand supply to support the costs of the supply side. Airbnb for example, captured demand first by getting booking orders from people and only when demand was validated, did they scale up the supply side.

When demand is picking up and is gaining momentum, the best bet you can place is to niche your supply side down to one specific use case. For Airbnb, it was allowing people to rent out their room. For 11x, it was allowing their AI workers to do one task in one specific vertical and do it really well. So they designed Alice, a virtual SDR (Sales development representative).

Hasan had the idea for 11x in Q3 of 2022. He refocused on the SDR vertical in Q1 of 2023.

Back at his first job, Hasan was responsible for moving data between spreadsheets - the kind of job that makes you pull your hair out. I've been there and let me tell you it's not pretty. This is exactly the kind of job that AI agents are perfect to do, and so Hasan got to work.

He and his team spent the next 6 months shadowing the SDRs. They would sit next to SDRs to observe their workflows. They would look for workflows that could be automated across prospecting, setting up campaigns, following up, and key milestones. This data would form the basis for the AI digital worker they were building.

Goes to show that even if you're building an AI-first company, the principles of doing things that don't scale still hold true. The highest leverage activity you can do in the early days is to talk to your customers, understand their workflows, their problems and build for them.

Hasan and his team built the first versions of the product based on this data, gathered feedback, kept iterating and improving the product.

Based on these intial conversations, he zeroed down on his ICP - SMB customers who had a self-serve motion. They learned that their best and most successful customers were sophisticated sales organisations with clear PMF and ability to benefit from scale, automation, and complex workflows/use cases.

He also discovered that the AI agent's technical capabilities needed to be robust from day one. They needed to be able to browse the web and find information autonomously even though this data was available through third parties.

Brex did something similar too when they launched their corporate card - they made the entire thing from scratch even though they could have utilized APIs to get to market fast. Sometimes its better to wait and do things right so you give yourself the best chance to set yourself up for scale.

But Hasan didn't have it easy - people were skeptical of the idea of hiring a digital worker. So Hasan had to humanize it. So he gave her a name Alice, a face and a specific job. They also made sure Alice was constrained, verticalized, and trained on best practices. They invested a lot of time in building guardrails that govern how Alice would operate autonomously at scale.

They also innovated with their pricing and changed the pricing from the traditional usage-based pricing sales tools usually charged that of task-based.

They charged based on the different tasks that Alice completed like (1) identifying accounts, (2) researching those accounts, (3) preparing outreach across email and LinkedIn, (4) scheduling meetings when the prospects respond.

90% of their customers are charged based on this task-based model but Hasan tested outcome-based pricing for select customers.

This is the next frontier of pricing for AI-agents. A16z made this thesis a while back where the pricing would move from usage-based to outcome based. This follows the value-based pricing framework which charges based on the value provided. Historically, labor services have charged on either an hourly basis or based on outcome so this new pricing change to AI workers feels much more natural from a labor perspective but completely alien from a softwate pricing angle.

11x raised a $2M round in August of 2023 - around the same time they publicly launched 11x on Techcrunch. But the pricing was a hit as by the end of 2023, 11x had reached $700k in ARR on the back of Hasan's founder-led-sales motion.

At the early stage, founder led sales especially in b2b are an underrated distribution channel. Retool reached $1 million in 9 months by doing founder led sales and Amplitude reached $500k in ARR by doing this too. By early 2024, they crossed $2 million ARR with one primary growth channel - Alice herself.

Scratch your own itch is a powerful framework to validate startup ideas to pursue. Typeform started this way. So did Mailchimp. But 11x took it a step further. It used its own product not only to validate but also to grow their own business. They were their own case study.

The team trained Alice to automate outbound prospecting across emails and LinkedIn messages. Alice monitored hiring alerts for anyone hiring SDRs around the world. Alice would then send out a message saying I’m AI and I do this job.

The messages were highly personalized based on hiring event, fundraising events or how long the job postings have been live for.

This approach created a growth flywheel. Relying on Alice for growth forced them to be incredibly product focused, as incentives were aligned. So as Alice got smarter, 11x grew faster.

1. Use Alice to generate leads and meetings for 11x.

2. This usage provides with real-world data and insights to improve Alice.

3. As Alice improves, she generates better results for 11x and customers.

4. Better results lead to faster growth and more customers.

5. More customers mean more data and use cases to further refine Alice.

The team realised that one of the biggest challenges of automation is maintaining creativity – a crucial element in sales. So they started baking creative playbooks into Alice's repertoire. Their most successful experiment? The meme strategy.

They tested multiple follow-up formats: case studies, additional research, confirming interest, and offering freebies. These worked well, but they wanted to push the envelope. So, they tasked Alice with being more creative than traditional SDRs, leading to experiments with images and GIFs in follow-up emails.

"The results were staggering. Not only did this strategy deliver higher response rates in our outbound sequences, but it also led to a 35% response rate when used to re-engage cold pipeline prospects".

"Reaching out with intent with the right triggers at the right time is extremely effective,” Hasan said.

Alice generated 70-80 qualified meetings per week for 11x. Hasan did most of the sales call himself in true "doing things that don't scale" mode, until 11x crossed $700,000 in ARR. 11x had finally found some degree of PMF.

PMF is a vague term and it exists on a spectrum but 11x could feel the pull of the market at that stage. For B2B SaaS companies, getting to that first $1 million in ARR in 1-2 years is a telltale sign that it's on the way to being a big bet.

But PMF in the AI world is a different beast. Retention is a differentiator with switching costs next to zero.

Amjad Massad of Replit said that companies like Bolt or Lovable seem like breakthrough companies because of their insane ARR in a short time but he says its not so simple since users can switch to competitors in 5 mins.

Retention is how companies will win in this AI-first age so 11x needs to optimize for retention if it's to maintain its growth sustainably.

For 11x, retention meant doing the basics right: focusing more closely on who their ideal customer is, continuously improving the product and then really focusing on the service side of SaaS.

Going back to Alice, Hasan discovered that the key to get success in oubound was to figure out a way to leverage personalization at scale.

"You need to personalize and have relevance in a scalable way, which is becoming increasingly easy. Instead of having someone spend 20 minutes researching the person and the content they wrote, AI can do that very effectively,” Hasan says.

11x also saw more than 1,000 inbound demo requests after they publically launched via a TechCrunch article in August of 2023. The positioning of the product as hiring digital workers was an appealing one. The benefits itself were hard to say no to.

• 11x was 5x cheaper compared to hiring an SDR

• It performed 2x better than the average SDR

• Faster to deploy with less overhead and more scalability

They are now doing around $25M in ARR, a 150% increase in ARR.

This is an interesting point because when AI-first companies are facing resistance to automate complete workflows, which makes sense. The solution then here, if you're building in this space, is to create a wedge and automate one specific JTBD.

Clay did it when they were starting off too and automated just one specific task. This makes adoption easier and reduces signup friction, which can kill momentum, plus it reduces feature bloat right at the start.

But now that they are more mature (they raised a Series B recently), they are spreading their wings just that bit more, and extending their capabilities to voice, which seems like an obvious next step. They acquired voice AI company Opkit to help them build out Alice 2.0 and render her with voice capabilities.

Opkit started as a voice AI application that launched a healthcare-focused AI phone solution. Since then, both horizontal and vertical voice agents like Vapi, Hana, Sanas have entered the market. It's early days but we are beginning to see 11x pivoting itself into a compound product integrating multi-modal functionalities and targeting both horizontal and vertical markets, much like Rippling did.

3 weeks back, 11x launched Julian, an AI Inbound Sales Rep equipped with voice capabilities calling, qualifying, and routing leads at record speed, 24/7, on autopilot. To do this, 11x partnered with Cartesia to give their AI digital workers reps the speed, reliability, and natural expressiveness required to engage customers at scale.

And now in true compound product style, 11x vision is to launch a variety of adjacent Digital Workers before the end of the year, enabling autonomous GTM functions - across channels, modalities, and use cases. Their end goal? Create the AI GTM team.

Here’s the playbook to win in this market:

LLMs are commoditized. The real game Is in the application layer.

Here’s the reality: Large Language Models are no longer a moat. Anyone can access GPT-4, Claude, or open-source alternatives. So where’s the edge? It’s in the application layer. And for 11x, that means one thing: they’re in a knife fight. They’re competing in a crowded market—AISDR, Artisan AI.

Everyone’s racing to build the same thing: a team of autonomous workers powered by LLMs.

The question isn’t who builds it first. It’s who builds it best.

In this space, the company that wins isn’t the one with the best prompt.

It’s the one that:

• Delivers real outcomes.

• Executes tasks autonomously.

• Optimizes for delightful user experience.

• Handles “human in the loop” moments without friction.

• Integrates seamlessly with tools people already use.

• Clear & frendly UX for the ICP

Text-based agents are step one. But we all know what’s coming next:

• Image.

• Video.

• Voice.

• Code.

• API.

An AI agent that can see, hear, speak, click, and act? That’s the bar now.

If you’re trying to win this game now, or especially if you're entering late, focus is your best friend.

Pick a vertical:

• Legal.

• Healthcare.

• Real estate.

• Finance.

Then wedge your way in. Own it. Go deeper than anyone else. Horizontal AI agents look good in pitch decks. But vertical AI agents win customers. Even competitors like AI Artisan or AI SDR could dominate their category by focusing deeply on a niche. That’s how you own mindshare—and eventually, market share.

1. Build multi-modal agents.

2. Deliver outputs that matter—not just actions, but outcomes.

3. Set up workflows where agents can integrate with tools and with humans.

4. Focus UX on trust, transparency, and speed.

5. Create reliable integrations that don’t break under pressure.

At the end of the day, users don’t care how smart your agent is. They care if it gets the job done.

Winning the product game is step one. Step two? Distribution.

1. Blend AI with Human Outbound

AI agents might handle the grunt work, but outbound still needs humans in the loop.

Think:

• AI drafts the message.

• Human tweaks, validates, and sends.

• Together, they scale personalized outreach with precision.

2. Build a Content-First Brand

Content isn’t fluff. It’s strategic distribution.

• Publish founder thoughts and product updates.

• Share behind-the-scenes on how the agents are evolving.

• Break down industry shifts and where things are headed.

• Showcase customer case studies that highlight real outcomes.

• Podcast episodes

This builds credibility, thought leadership, and differentiates you.

3. Use Winning Messages to Fuel Ads

Use winning messages to run SMB-targeted ads at scale.

• Arcads.

• AI voice actors.

• UGC-style videos.

Show, don’t tell—how real companies used your tool to generate real results.

4. SEO, AI, and LLM Optimization

Old-school SEO still works. But now, you also need to rank in LLMs.

Create:

• Bottom-of-funnel content that answers “best AI SDR tools.”

• Pages that rank in Google and show up in AI-powered search.

If someone asks ChatGPT or Perplexity, “what’s the best AI SDR platform?” — your tool needs to be the answer.

5. Go Native on LinkedIn & Reddit

Post where the tech crowd lives.

• Share wins, experiments, and use cases on LinkedIn.

• Go deep on Reddit—AMA-style threads, helpful comments, early access invites.

LLMs pick up signal from these forums. Treat them like SEO v2.

Labor is moving to the cloud and is here to stay - in the short term, I suspect agents will interact with humans to create polished outputs before becoming autonomous.

It will free up time for people to pursue their passions and expand the creator economy.

People can now quickly start side projects that weren't possible before. The creator economy, previously confined to music, content, and videos, will now embrace tech.

The pie is clearly expanding. Building is being democratized.

Distribution, speed, and a community-first approach are the only advantages.

But success in this space won’t come easy.

Whether you’re targeting verticals, in-house roles, or outsourced services, the playbook remains the same: start small, build trust, and scale with precision.

If you liked this essay, consider sharing it on your socials or inviting a friend to read this essay. Feel free to say hi to me on Twitter or on LinkedIn or email.

That’s all for today! See you soon!

Amor Fati Amor.

Most startups dream of hypergrowth. Clay lived it.

They 10x’d revenue year over year—then did it again in 2023. In 2024, they followed up with a 6x surge. Today, Clay serves over 5,000 customers, including industry giants like OpenAI, Canva, Anthropic, Ramp, and Rippling.

What’s even more impressive? This explosion happened in just 18 months. As recently as spring 2022, Clay was still hovering near $0 in revenue.

From the outside, it looks like an overnight success. But the reality? This was 7 years in the making.

Clay spent years refining their product and messaging before hitting escape velocity. Roblox did it. So did Clay.

Now, backed by $40M in Series B expansion funding at a $1.25B valuation, Clay has built something remarkable. Tens of thousands of users. Thousands of paying customers. Category leaders like Verkada, Anthropic, and Intercom all run on Clay.

In this essay, we’ll break down how Clay got here—from early pivots to hypergrowth. You’ll learn:

• The early struggles and why their first idea flopped

• How they found their wedge and executed a focused GTM motion

• The reverse demo playbook that turned trials into instant onboarding

• How Clay turned GTM into a media engine to drive inbound demand

• Why they chose usage-based pricing over per-seat models

• How they layered sales-led growth on top of PLG to break into enterprises

This is the inside story of how Clay became a unicorn—without the hype.

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Clay wasn’t an overnight success. It took two pivots before they found their footing—a testament to how important flexibility is in the early days.

Their original idea? Do for programming what Figma and Miro did for design. Make it more accessible. Their first attempt was a rebuilt terminal, allowing companies to pipe data between different software systems. Sounds cool. But it didn’t solve their core problem—making programming more intuitive. So, they scrapped it.

Back to the drawing board.

They asked themselves -- what if a spreadsheet could pull in data from anywhere on the internet?

Suddenly your boring spreadsheets became dynamic thanks to external APIs and a bit of programming magic. After all, Excel never dies and it’s APIs all the way down.

The great thing about use-cases is that they are great for identifying what functionality your product has – you wanna be lean at the start and have that one main JTBD that your product does. Focus is your friend here.

Now, there may be times when you end up getting nowhere with your early version and that’s alright too. Maybe you just have to pivot your target customer or play with the messaging. But the goal here is to find a core use-case and tie it to a customer that wants your solution for his specific use-case. And that’s what the Clay founders did – they identified prospecting as their main use-case.

But they ran into a problem – the use-case though specific, could be used by a whole lot of different people. Recruiters could use them to prospect candidates, agencies could use them to prospect leads, in-house teams could use them to prospect potential customers.

And while you may think that’s a good problem to have, sometimes it’s not because it's not easy to be pulled into all these different directions. “The main reason a startup dies in the early days is not because of starvation, but because of indigestion”.

Too much breadth kills startups.

As Jeff Bezos puts it, some decisions are “two-door decisions”—you can’t walk them back.

Clay had to choose between:

• Building a horizontal solution like HubSpot or Typeform.

• Going vertical, solving one high-value problem exceptionally well.

They chose focus. Instead of dreaming about being a billion-dollar company, they locked in on the next 18 months. Execute first, scale later.

And the man to execute? Varun Anand.

Getting the first users: social listening & reverse demos

Anand was tasked with leading the first GTM motion & getting those first customers. So Anand did what Wade Foster of Zapier did – he joined forums where his target audience was hanging out. Anand joined sales groups, whatsapp groups, Slack groups and waited for people to talk about problems. He set up notifications for words like “enrichment”, “data”, “outbound”.

And zeroed it down to 30 people who had said something interesting about the topic in the last few years. From these conversations, he found that cold email agency owners were the most vocal and passionate about these problems. He had found his ICP.

Which was exactly what he wanted. As a next step, he hired a sales influencer Eric Nowoslawski who was popular with the agency audience and got him on board.

Eric was super active on LinkedIn and he showed off Clay which got more cold email agency owners interested, and they signed up for Clay.

The response? Immediate traction.

But Clay didn’t let just anyone in.

New signups went to a waitlist. Every morning, the team reviewed applicants. If they fit Clay’s Ideal Customer Profile (ICP), they got an email:

"Hey, book a time with us. Come prepared with a dataset you need enriched, or a problem you want solved in this 30-minute session."

At this point you’d expect them to give a lame demo like most people do.

Anand did the opposite.

• He had the user share their screen.

• He dropped a Clay signup link in chat.

• Using Zoom’s annotation tool, he walked them through solving their own problem—live.

This wasn’t a demo. It was onboarding.

This is a great example of the Ikea effect at work – people are more likely to buy something if they feel like they did it themselves. It’s like lego.

And Anand didn’t end the call abruptly either – he wouldn’t hang up until this person had joined their Slack group.

Anand would have them type in the Slack URL, join it, send him a DM and only then would he hang up the Zoom call. Talk about execution! It’s no less impressive than the Collison installation.

This approach helped the founders see how customers were using the product first-hand. They got to see the UX up close, creating a solid design, product & onboarding foundation. New use cases were unlocked – which meant more growth. Clay was in business.

Building the one-person distribution machine.

Clay’s next growth lever? Turning GTM into a media engine.

Eric’s success inspired more influencers to showcase Clay. But Clay didn’t rely on them passively. The team posted every day, following a simple rule:

Show people how to do things that are really valuable to them.

They turned reverse demos into content. Live use cases became videos, LinkedIn posts, blog articles, and guides—each one pulling in more of the right customers.

They also hosted Clay Clubs in cities like Sydney and Toronto. Events led to LinkedIn posts, which became blog content, which turned into SEO assets—a flywheel in motion.

As a result, Clay naturally spent a lot of time nurturing these creator relationships and giving them the resources they needed to succeed. They helped them with their marketing campaigns, promoting new business lines, hosting webinars and much more. The more people showed up for their creators, the more eyeballs Clay was getting.

This is similar to how Webflow got their first customers too – in Webflow’s case, they targeted agencies and designers who would use Webflow to build websites for their clients.

If you’re building a b2b product, ask yourself – instead of catching one fish at a time, how can I cast a wide net and catch as many fish as possible? For Webflow, that answer was designers, and for Clay, that answer was cold email agency owners. This unlocked distribution for them in a significant way.

And their brand stood out. Most B2B SaaS companies treat branding as an afterthought. Clay made it a competitive advantage—hiring a Head of Brand as one of their first 25 employees. Customers could now spot Clay from a mile away.

Building viral, compounding growth loops

PLG is product led growth where the product does the selling and each user brings in more users and it creates a growth flywheel.

Clay launched on Product Hunt in 2022, opening signups to everyone. The result?

Too much noise.

So two months later, they turned the waitlist back on.

At first, Clay needed customers to pay upfront. They didn’t have billing infrastructure, so Anand manually invoiced users for $200/month. Only after hitting $1M ARR did they automate billing.

Pricing became their biggest unlock

.

Instead of per-seat pricing, they introduced usage-based credits—charging for value, not headcount. They were gonna have usage-based credits instead of the standard per-seat model that dominated sales tech & PLG companies like Notion, Webflow, Figma, Miro etc.

They realized that the value of the product is in the columns because every column the user added was a new enrichment. Paddle called it identifying the value-driven feature where you identify the one feature that adds the most value and you take on a value-based pricing. And for Clay, this value-based pricing approach was manifested as usage-based pricing.

Clay’s PLG lever relies on having a large volume of users coming in, trying out the product on either a free trial or freemium and then upgrading.

In addition to trying out content on social media, and doing ecosystem marketing, they also focused on SEO & side-project marketing.

Just like Webflow, they have a Clay University, Claybooks, templates which feed into their PLG loops across customer acquisition, retention and expansion.

Their PLG flywheel worked like this:

1. A cold email agency signs up.

2. They use a Clay template on client projects.

3. The client pays for Clay.

4. The agency realizes Clay is indispensable.

5. They create custom templates, onboarding more customers.

To fuel this loop, Clay built Clay University, Claybooks, and free tools like an email finder and CPM calculator—each bringing in new users.

The logical next step for Clay? Become a media company.

• HubSpot did it.

• Mercury did it.

• Stripe did it.

Clay can reverse-engineer GTM strategies, highlight customer success, and create world-class B2B content—fueling the flywheel for years to come.

They already mastered GTM.

Now, they can own the story.

Building a big-boy sales machine to sell to the big boys

PLG served as a strong foundation for Clay to add on a sales-led-growth lever. This usually is the template to follow - Webflow and Typeform are other companies that executed a sales-led-growth motion after building a robust PLG motion in place. It’s the same with Clay.

Clay’s sales teams don’t exactly fit the mold of your general salesman though. Remember the reverse demo? The success of the reverse demo was contingent on the fact that the salesmen were super familiar with the product, could navigate any complexities and come up with a solution on the fly. That’s the trait Clay needed in all their salespeople to really scale this thing up.

So it’s no surprise that their head of sales is an engineer by training, a former founder and a former head of growth at an early startup. They have a team of what they call GTM engineers who are a blend of technical and sales roles – they are good enough to solve user problems while being able to build rapport with their target customers.

In 2023, Clay started to invest in building a sales machine. But enterprise sales was a different beast to tackle. They needed to differentiate the white-glove version of Clay from the self-serve version of Clay. Once they did, they needed to get enterprises to pay money to justify this white-glove experience. Not an easy thing to do.

They started with a classic sales strategy, inspired in no less part than their own experience scaling Clay from the ground up, and dealing with salespeople. They identified a wedge – data enrichment. They identified the problem enterprises had to find and access high quality data providers in one place. So they solved that first. Because this was an existing budget line item, it was easier to get the foot in the door, allowing them to expand to other use-cases later.

As for countering the objection wrt the difference between the white-glove and self-serve versions of Clay, the sales team handled the objection head on. They acknowledged that the enterprise could go self-serve but they won’t because of company policies, feature requests, support requirements, etc. And buyers respected that honesty.

They changed the pricing too for enterprises. They converted credits, platform fee, support, everything into credits. That was the unit of pricing used as reference when dealing with enterprises. And since the credits were usage-based, it scaled nicely and organically without much friction.

The key element while doing enterprise sales is to get on texting terms immediately. You wanna exchange numbers right away – this changes the dynamic and helps cut through the usual enterprise formality mess.

The Playbook:

1. Find a focused wedge – Start with a single, well-defined use case that solves a high-value problem. Don’t try to serve everyone at once.

2. Use social listening – Hang out where your ideal customers talk about their problems. Set up keyword notifications and insert yourself into the right conversations.

3. Leverage influencers – Get niche influencers to show off your product instead of selling it directly. Their credibility accelerates trust.

4. Gate access strategically – A waitlist ensures only high-intent users get in first. Prioritize quality over quantity.

5. Do reverse demos – Instead of walking through features, help the user solve a real problem live. Let them drive while you guide.

6. Make branding a competitive edge – Treat branding like a growth lever. A strong, recognizable brand wins in crowded markets.

7. Layer PLG with sales-led motion – Let the product acquire users first, then introduce sales for bigger deals.

8. Innovate on pricing – Charge for value, not headcount. Usage-based pricing can be a game-changer.

9. Turn GTM into media – Content compounds. Turn every use case into videos, blog posts, and guides to attract inbound leads.

10. Think beyond software – The biggest SaaS companies become media companies. Build a content ecosystem that fuels continuous growth.

Execute first. Scale later. That’s how Clay did it. And that’s how you can too.

In a way, agents are the opposite of SaaS - it’s Service as a Software.

If SaaS changed the way software was delivered, Service as a Software will change the way labor will be delivered.

https://x.com/bhalligan/status/1781323169313222852

For most of humanity, labor belonged to the exclusive domain of humans. But with AI agents that may not hold true.

AI agents have the potential to create a new kind of labor market - the Service as a Software market.

SaaS changed the way software was delivered by bringing the technology to customers via the cloud -- one that didn't require local installation or maintenance.

As a result, software began to "eat the world", disrupting and transforming entire industries.

SaaS helped businesses and end users create specific workflows that performed a specific task. For example, users could put structured data like users’ contact info into a CRM, which would not only give insights to the business but also form the building blocks for creating workflows that could streamline specific business functions.

But software has its limitations as well - the data needed to be structured and had to be entered manually (for the most part). The success of the system relied upon the data being filled accurately & consistently.

As a result, software became a tool that employees learned to manage & use on their own. The employees were the ones responsible to achieve specific business outcomes using these tools.

But this isn't the case with AI agents.

Unlike software, AI agents are autonomous robots, not just tools. They recall, make decisions, and act without human intervention. Imagine AI-driven services that don’t just help people do the work—but replace repetitive work entirely.

Agents can automate emails, code, design, and more—doing your work without you lifting a finger.

This is the promise of Service as Software–Autonomous AI agents executing service-facing tasks wrapped in software.

As a result, the actions of these agents can be tied to specific business outcomes.

The shift to Service as Software starts with focus on workforce spend, not software spend. That is where the true leverage lies.

Salesforce makes $35 billion in revenue annually but pales in comparison to the $1.1 trillion salary paid to sales & marketing professionals.

The total addressable market for services & software is $10 trillion compared to the $3B AI market.

Sam is bullish on AI Agents for the same reason I am. AI agents are the gateway to AGI -- holding within it the potential to change forever, the way we work.

Look at this spike in Google’s search interest for the search term “AI agents”.

But what is it that makes AI agents so powerful?

The thing that makes AI agents tick is the LLM models themselves & well as the cognitive architecture that supports this.

The breakthrough behind AI agents lies in inference compute. Previously, AI models generated responses based on pre-trained data without much reasoning.

Now, with inference compute, these models can pause to “think,” simulating events to predict the best outcomes. The longer an agent thinks, the better its results.

It now “knows” what is the best way to do a specific task.

In a way, it's similar to how self-driving cars work. Self driving cars collect data from their surroundings via sensory inputs like the camera, LIDAR etc, after which, this data is processed using Machine Learning & Deep learning algorithms. The algorithm is trained across a large dataset to recognize patterns and make predictions. This gives the vehicle the best chance to react appropriately to various driving scenarios without needing specific instructions for every possible situation encountered on the road.

AI agents also take in sensory input from the users’ screens, external databases, APIs etc. Once this data is collected, AI agents use a combination of ML, inference compute & reasoning to make decisions. They can learn from previous interactions and can execute multi-step tasks, all on their own, much like the self-driving cars.

This marks a shift as AI moves from pre-trained “fast thinking” models to inference-based “slow thinking” models.

In this new Service as software paradigm shift, there's plenty of space for everyone. Just like any market, there will be outsized winners but there will also be space for billion-dollar companies to form.

And the way the next wave of agentic startups will win is by targeting automatable pools of labor. There are three major service categories ripe for disruption:

1. Outsourced IT Services

2. Vertical Labor

3. In-House Work

AI agents will change how humans work -- but what does that look like? Which companies will win? What are the consequences of that happening?

In this essay, we'll explore the 3 major markets AI agents will disrupt. If you are curious to learn how you can use agents in your business or better yet build a business around agents, this essay will hopefully give you some clarity on that - let’s dive in, shall we?

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In-House Roles: A $2.3 trillion market waiting to be disrupted by AI agents

Traditionally, in-house roles have involved doing repetitive, recurring tasks that must be completed each time you perform the main big task.

For example, a Sales Development Representative (SDR) spends time on repetitive tasks like prospecting, writing emails, scheduling follow-ups, and arranging demos. What if you had an agent that could do all this for you autonomously?

YC-backed AI-SDR does exactly this. It warms up unlimited email boxes and learns from your data. SDRs can now just set up campaign goals, outreach channels, and sync their calendar.

Even Salesforce introduced two fully autonomous, AI-powered sales agents: The Einstein SDR Agent that can engage with inbound leads 24/7 and the Einstein Sales Coach Agent that offers real-time sales suggestions during calls.

Customer success teams are undergoing AI-driven transformations too. Sierra uses AI for customer interactions, enabling support teams to handle more queries with fewer human resources. AI agents like the one Zendesk has, are taking on the bulk of the work, while human employees focus on managing and supervising these agents.

AI recruiter agents like Converz can make calls, while AI sales agents like Docket can answer buyer questions . You can also create custom AI employees that handle multiple tasks together.

Imagine a system of AI agents working and interacting together across teams. You can set these up with tools like Lindy, Relevance AI, Gumloop or Beam.

As a result, there will be increased cross-pollination between teams—marketing will speak with sales, sales with product, product with marketing, etc. This in turn will create better products along with better data capture & attribution. More data means better AI learning.

If you're building for in-house roles, the playbook would look like this:

1. Identify repeatable, outcome-driven workflows: Automate predictable tasks with clear, measurable outcomes.

2. Start at the source of data creation: Capture raw data at the source for accurate insights and actions.

3. Partner with humans as copilots: Initially, work alongside human operators, then transition to full autonomy as the AI learns.

4. Focus on workforce Spend, not software spend: AI should replace labor costs, not just add to software budgets.

5. Shift to outcome-based pricing: Charge based on delivered results, aligning value with outcomes.

AI agents won’t just assist – they’ll fundamentally reshape how work is done at companies.

Vertical Industries are ripe for disruption by AI agents

Vertical industries like law, healthcare, and cybersecurity are primed for AI disruption. These fields thrive on language and unstructured text data—making them perfect for AI agents powered by LLMs.

Unlike traditional software, which relies on structured inputs, AI agents can reason through complex scenarios, make decisions, and take autonomous actions.

This report by Goldman Sachs shows the industries most likely to be impacted by AI & automation.

Take Harvey AI. It’s not just automating legal workflows; it’s transforming law firms by taking over tasks like contract review, due diligence, and litigation prep. Imagine a junior associate that never sleeps, misses a deadline, and constantly improves.

Similarly, EvenUp focuses on personal injury law, automating settlement demand letters and allowing attorneys to focus on strategy instead of paperwork.

In healthcare, Freed and Deepscribe help doctors reclaim their time. AI voice agents schedule appointments, transcribe patient interactions, and suggest diagnoses. AI Agent Tennr helps healthcare professionals read faxes and enter data.

Cybersecurity is transforming too. XBOW has developed AI-driven penetration testing that simulates cyberattacks to identify vulnerabilities faster and cheaper than human testers. What once required high-cost specialists is now done at scale, with better accuracy.

Building a vertical solution offers tremendous value–you can capture a dominant market, establish credibility, and build a moat around your community.

Your models will improve as you fine-tune them on domain knowledge. Since agents use reinforcement learning, the more tasks they do, the better they learn and the faster they perform. Coupled with the decreasing cost of inference computing, you wonder–what are these agents ultimately capable of?

Here’s the playbook on how to build in vertical AI:

1. Target language-heavy industries: Identify a language-heavy industry: Look for fields where unstructured text data—like contracts or medical notes—is a bottleneck for productivity.

2. Focus on a core pain point: Identify the most frustrating, repetitive task your audience faces and solve it first.

3. Leverage LLMs for workflows: Build agents that process unstructured data, reason through complex problems, and improve over time.

4. Bundle solutions over time: Start narrow, but bundle by adding products: Once you’ve automated a workflow, look for complementary tasks to create an integrated compound product to build long-term technical & distribution moats.

5. Fine-tune with domain-specific data: Use reinforcement learning to make your model an expert. The more tasks your agent handles, the smarter it becomes.

Start with a niche vertical use case to wedge into the market; add products layer on top and bundle adjacent use cases to build a compound product that’s defensible.

AI Agents will disrupt the $2.3 Trillion outsourced services market

IT services is a $933 billion market, growing at a CAGR of 8%. The amount spent on outsourced IT services and business process services is $2.3 trillion.

But they’ve been constrained by slow delivery and low margins due to an over-reliance on manpower.

AI agents can transform this market by accelerating execution, creating software outcomes efficiently with faster delivery, increasing gross margins.

The trick here is to focus on workforce spend and not software spend.

Take Realfast AI, for example. It builds AI agents on Salesforce, automating workflows that require hours of manual input. Similarly, AirMDR uses AI assistants to manage detection and response in cybersecurity, cutting response times while improving accuracy.

Business process services like data entry or audio transcription involve repetitive, rule-based tasks. AI agents excel at these tasks. They can quickly understand context, process information, and take action. ,

AI agents don’t just reduce costs—they create a flywheel effect:

1. Faster delivery: By cutting development and maintenance time, AI agents reduce costs and increase efficiency.

2. Lower costs: Cheaper services attract more customers, driving demand for automation.

3. Greater efficiency: Increased adoption feeds data back into the system, improving performance.

4. Compounding growth: As agents become smarter, the market for AI-enabled services expands rapidly.

If you’re building AI agents for this space, here’s how to start:

1. Build LLM agents: Use pre-trained models to process unstructured data and perform complex tasks.

2. Integrate workflows: Ensure agents work seamlessly with human teams.

3. Record actions and decisions: Instrument interactions to create a rich dataset for future improvements.

4. Train with real-world data: Use these datasets to fine-tune your agents to adapt to unique workflows.

5. Adopt outcome-based pricing: Charge clients based on measurable results, creating clear ROI incentives.

Start by building on top of SOTA models & have a human copilot. Collaborate & collect data to fine tune the agents until it becomes autonomous. Expand to other IT services.

Who will win BIG?

Like always, the companies owning distribution will emerge on top. Google & Microsoft have an unfair advantage here.

With access to over 92% of global search traffic and a seamless ecosystem, Google is launching Jarvis that can create AI agents that streamline workflows across Search, Chrome, Gmail, Calendar, and Drive.

While Google focuses on consumers, Microsoft’s strength lies in the enterprise. Leveraging Microsoft 365’s adoption among businesses, Microsoft’s AI agents can assist in sending emails, managing user records, and enhancing productivity.

Apple is claiming its stake with Apple Intelligence, transforming Siri into a true AI agent managing apps and executing complex actions across the iPhone ecosystem. This positions Apple to add immense value to its user experience, making Siri more than a voice assistant.

Several LLM providers & infrastructure products will also be key players:

• Anthropic’s Claude: Equipped with an AI agent that can navigate the web, create content, and write code, offering users a powerful AI toolkit.

• Langchain and LlamaIndex: These frameworks enable developers to build sophisticated AI agent applications, allowing agents to retrieve and apply context from real-time data.

• MemGPT: With advanced memory retention, MemGPT acts as a personal assistant that improves with each interaction, retaining past context to provide smarter, adaptive responses over time.

• Mistral: Mistral also has an agent building feature where users can create custom agents.

https://x.com/i/status/1822309514948808802

https://x.com/i/status/1851338227925631102

Beyond platform-based AI giants, horizontal products like Notion, Airtable, and Slack are well-positioned to lead the hyper-bundling trend, integrating AI agents that support user workflows.

Notion’s AI-powered workflow capabilities and Slack’s in-app features indicate this shift.

Meanwhile, compound platforms like Rippling can gain a defensive edge by embedding intelligent agents within their products.

If AI agents are the next gold rush, “shovel-sellers” like Gumloop, CrewAI, Glean and Composio will win big by enabling users to create custom AI agents that automate workflows.

Zapier and Make hold a distinct advantage due to their established distribution, strong brand, and ability to capture significant market share by scaling and adapting to AI demand.

Zapier’s AI allows users to create automated, bespoke workflows across apps. You could create a workflow that would automatically take contacts from your email and populate your personal CRM, including up-to-date information about each contact.

If you really wanna go big, your best bet is to create a platform and sell shovels — aka enable users to create AI agents using your product.

Conclusion:

AI agents are here, and they are changing the way we work.

By automating repetitive tasks, improving efficiencies, and enabling new ways to collaborate, AI agents promise to disrupt labor at every level.

This transformation isn’t just about cutting costs – it’s about unlocking human potential.

AI agents free people from mundane tasks, allowing them to focus on creative, strategic, and high-value work.

In the short term, I suspect agents will interact with humans to create polished outputs before becoming autonomous.

It will free up time for people to pursue their passions and expand the creator economy.

Employees can now quickly start side projects that weren't possible before. The creator economy, previously confined to music, content, and videos, will now embrace tech.

There are people building million-dollar apps with Cursor, Replit, Claude & Bolt without knowing how to code.

https://x.com/rom1trs/status/1838189197011616005

https://x.com/uxaiagency/status/1856713703116165307

The pie is clearly expanding. Building is being democratized.

Distribution, speed, and a community-first approach are the only advantages.

But success in this space won’t come easy.

Whether you’re targeting verticals, in-house roles, or outsourced services, the playbook remains the same: start small, build trust, and scale with precision.

The next era of innovation is upon us. Whether you're building, investing, or adopting AI agents, the opportunity is huge. The only question is: how will you seize it?

If you liked this essay, consider sharing it on your socials or inviting a friend to read this essay. Feel free to say hi to me on Twitter or on LinkedIn or email.

That’s all for today! See you soon!

Amor Fati Amor.

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And so you sit there scratching your head & asking yourself – “how the heck do I grow this?” 

But you have no clue what to do, what expectations to set & what the road to growth looks like. You go on X and Reddit only to find people making millions with their startup.

And so you think – “is there a formula to this thing?” 

The bad news is that there’s no formula to growth. 

The good news is that there are patterns for success that you can replicate. 

These success patterns come from the hundreds of founders who have been where you are & are showing the way on what to & what not to do. 

As you stand on the shoulders of these giants, you will begin to see the road ahead that much more clearer. 

In this post, I will show you the growth lessons I learned from listening to Lenny’s podcasts with growth leaders & startup founders at top companies like Notion, Figma, Airtable, Miro etc. 

Each of these growth lessons are actionable & give you exactly what you need to do to get the ball rolling. 

You don’t have to reinvent the wheel – just as long as you keep an open mind & keep experimenting. 

I have divided this post into 5 parts: 

Part 1 - We are setting expectations here. What qualifies as success? What growth benchmarks do you need to aim at to make an impact? 

Part 2 – Here, we dive into the first of the four main growth pillars – SEO & content. 

Part 3 – You will learn about Product Led Growth & growth loops & how you can create network effects at scale. 

Part 4 – You will understand how to execute your sales channel across Founder Led Sales & Product Led Sales. 

Part 5 – You will know exactly how you can leverage paid ads to scale your growth strategy. 

If all this seems too heavy-wordy, I have included a TLDR section at the end of this essay for a quick overview. 

Alright, now let’s dive in – shall we?  

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• Onboarding, drip, and churn emails

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Part 1 – Defining expectations 

The first step before taking on a big challenge is asking yourself this – what does success look like? 

I like to make music in my free time. Before I make my music, I always ask myself – what am I trying to make here? What does success look like for me? 

It’s this clarity of setting expectations that acts as a North Star for me when I’m making my music. Without it, I often find myself grasping at straws, feeling frustrated & not knowing whether I’m on the right track. 

It’s the same with growing your startup. 

So what does success mean in the context of growth? 

When you’re starting out, what matters most is the SPEED at which you make your first $1 million ARR. The standard benchmark is 12 months but a great benchmark is 9 months. 

As a result, any growth effort you invest is a means to this end. 

That being said, it's not mandatory that you NEED to hit this benchmark. Even if your initial growth is slow, it’s not a deal breaker. What matters is that when things DO pick up, they shoot up exponentially high. So your investors know you’re onto something big. 

So how do you ensure that things pick up steam even if it's a bit down the road? 

You start by unbundling what individual components make up this revenue output. These are metrics like: high product stickiness, smaller sales cycles, sustained product usage, word of mouth etc. 

If these individual metrics are compounding at around 10-15%, you can safely assume that you will hit your projected revenue numbers. 

If you’re building a B2C product, the equation becomes slightly different. In this case, you only need to look at 3 key metrics – (1) Engagement (2) Retention and (3) Virality 

You want to make sure that almost all of your acquisition effort is organic. And then once the customers are organically obtained, you optimize for engagement and retention.

Observe how many people are using your app. How much time they're spending on the app. What is the frequency at which they're posting? Does it have in-built product-led virality where people talk about it and share it about it a ton?

Usage retention can also give good hints towards how sticky your app is. DAUs and MAUs, therefore, become a crucial metric to track in this regard. 

If your DAUs/MAUs & 30-day retention rates are greater than 50%, you’re on the right track. 

For example, Instagram, Twitter, Dropbox, crossed 1 million users within months of launch. And then they reached 10 million users within the next year or two. And then hit the next 100 million within the next year or two.  

Once you hit the $1 million benchmark, the expectations around growth rate for B2C and B2B are fairly standard – 16% a month MoM growth in year one is great, 12% in year two, 10% in year three, 8% in year four and 8% in year five so it hovers around that eight to 16% benchmark for both B2C and B2B brands. 

Keep in mind that all these benchmarks are just rules of thumbs. A lot of billion dollar companies DIDN'T hit the $1million benchmark in 9 months. 

But it's worth keeping these expectations in mind so you know what to aim at. 

With that said, let's dive into the next section where we talk about the exact growth strategies you can leverage to hit these benchmarks consistently and sustainably. 

Part 2 – SEO & Content. 

SEO & content marketing is still one of the most sustainable & organic ways to get high-intent users. 

There are three main components in SEO. The first is technical SEO, the second is editorial SEO, and the third is programmatic SEO. 

Technical SEO is the easiest to do. Get an audit from a service like Screaming Frog and it will give you everything you need to fix on a technical level. These include things like making sure that the search engines can crawl, index and rank your content, making sure that you have strong internal backlinks, fast load speeds, fast page rendering, all that good stuff. Once you execute it, you’re set on the technical side. 

The tricky part is editorial SEO. 

This is where you actually create content that is relevant for your users like we do here at Command Blogue. 

It starts with identifying who your target audience is, what they want to read about and what stuff they are actively searching for on the internet. 

The focus here is on creating content around different levels of awareness. If you’re targeting people with a higher degree of awareness, you create content that targets this high purchase intent, like "These are the best onboarding tools” or “ the best email outbound tools”. 

Then you have your medium intent keywords where your users are looking for alternatives like what is the best way to achieve a specific outcome. 

So, in the case of your product, the outcome can be something like, "How do I set up an onboarding flow for my company?" So, then you create a piece of content around it that answers this question, "How do I set up onboarding?" Or, "How do I make sure that the onboarding flow works?"

If you move further up the tier, you find the least aware people – people who don’t know your product category itself. So, if you are building an onboarding tool, you can create content around, "what is onboarding". The goal here is to educate and inform these categories of users. 

So you can see that you’ve got three distinct tiers to tackle – you can't just write one article and expect it to cater to all three—it’s like trying to fit a square peg into a round hole. Do this right & you’ll see your SEO improve over time—typically within 6 to 9 months.

Take a look at the pros like Masterclass, HubSpot, and Zapier—they've got this down to a science.

Zapier is another great example. They’ve become the go-to for practically any search query about software integrations. Think about it—if you want to know, “How do I connect Gmail with Slack?” or “How do I set up X, Y, and Z?” Zapier has a guide for it. They’ve nailed high-intent keywords and built out content that answers user questions in a way that draws traffic effortlessly.

Then, there’s programmatic SEO—your secret weapon for scaling content. With pSEO, you can create thousands of pages using a single template. It’s great for generating category pages like “Best software tools for X” or “Top alternatives to Y.” You can even blend in user-generated content, giving your site an authentic touch that users trust.

The first step? 

Find your addressable market and the keywords that matter most. Sites like Reddit can be goldmines for spotting trending keywords. Once you have a list, study how your competitors are doing with those same terms. From there, craft a content strategy that fits your goals—whether it’s category pages, user-generated posts, or detailed guides. 

The rule of thumb with SEO is – be patient. 

For the first three months of Thumbtack, there was almost no traffic from SEO. At six months, there still wasn’t much traffic, but it was enough to start analyzing data. At 18 months, it became a meaningful share of consumer traffic. At 36 months, it became the primary form of customer acquisition for Thumbtack. 

Be patient & keep plugging away – you’ll be rewarded for it. 

Part 3 – Product Led Growth. 

PLG is your secret hack to really supercharge your growth efforts. The cool thing about PLG is that it has inbuilt virality baked in. 

The product does the selling for you – this means you need to focus on a strong onboarding, & activation metrics so that the viral growth loops kick in automatically. 

In PLG, you answer three main questions – (1) How to acquire users (2) how to retain users (3) how to monetize users. 

When it comes to user acquisition, the users themselves are the marketers as they bring in new users. This is typically what’s known as growth loops. 

Mailchimp and Webflow do this perfectly. Webflow uses banners on every Webflow site while Mailchimp has a “sent by Mailchimp” banner in its emails. The result? It reaches new users exponentially faster as each user brings in every additional user. 

PLG is perfect for boosting user retention since user usage triggers more usage. The value that the user experiences from the product will increase exponentially as they begin to use more of the product. This boosts retention and increases the lifetime value of your product. 

And finally, in terms of monetization, PLG provides for self-serve purchase. There's no human touch involved. 

Having a step by step onboarding flow is critical to show users exactly what they need to do at every step & show them what outcome they will receive by executing on that particular step. 

The goal here is to make sure you’re solving the problems that the user is trying to solve and help them achieve their goal. 

When you are starting out with PLG, never start with product led acquisition. Instead focus on product led retention, which consists of two KPIs – activation and engagement. 

Activation metric is the aha moment where your users experience the value for the first time via your product. The second metric is that of engagement – how much does your user engage with the product. Here, you will track metrics like DAUs and MAUs. You also might want to notice where users are dropping off, churn rate so you can fix out the product led retention part first. 

Freemium pricing is also a very key part of PLG. Give users that initial value, help them achieve quick wins, and as they extract more value they will build a habit forming mechanism within your product, post-which they will upgrade. 

The biggest mistake companies make with freemium is they look just at the optimization of conversion to paid. But that's not all. You have to figure out the strategic value you are giving at each stage, even in the freemium stage and then in the conversion from freemium to paid users. 

Part 4 – Sales 

When you’re just starting out, founder led sales is probably your best bet especially if you’re in B2B. Amplitude, Retool, Rippling all got their first customers via founder led sales. You can too. 

Why is it important you ask? Why can’t you just hire someone? 

The simple answer is that in the early days, you must speak to as many people as possible. Amplitude founder Spenser spoke to 100 people all on his own while his co-founder was building the product. Retool founder, David Hsu pivoted multiple times before zeroing in on his ICP and the messaging which resonated most with that ICP. 

When you do this, you get clarity on what your user pain points are & what the ideal solution should look like. It’s the perfect way for you to execute YC’s motto “ build something people want”. And the best way to figure out what people want? Talk to them. 

Most founders shy away from sales because they fear those tough conversations & rejections. The trick here is to keep an open mind & treat every rejection as an opportunity to learn. Start small & keep iterating. 

This tight customer feedback loop will help you refine and shape your product, setting a solid foundation as you reach your first million in ARR. 

The next step? Scaling your sales channel beyond yourself. 

This transition is easy now that you have a repeatable, proven process that your new hires can follow. They will know exactly what to say, how to pitch, address objections, reach out to the right people – setting you up for success in the long run. 

The old school sales approach is about selling something to anyone, regardless of their needs.  The modern approach is about being focused on the user problems & serving a very specific solution to their problem. 

Your sales process should involve a series of questions which reveal the customer's current problems and then show a better way of approaching the problem via your product. 

Product-led-sales is a natural next step if your product has PLG baked in already. 

After a point, PLG stagnates because there’s no additional way to capture upmarket value. This is where Product-led-sales enters the equation. 

In PLG, your product is able to generate early usage without involving people. But eventually you hit a cap – there’s a use case for a big organization which can’t happen without a sales touch. 

PLG starts more on individual use cases. PLS, on the other end, assumes that there's a migration from an individual use case and an escalation into an enterprise level solution that solves enterprise level problems via sales.

A good sign that you need sales to come in & engage is when there’s a high frequency of product usage – usually that means the company is ready to buy. Don’t hire salespeople until you feel this pull from the market. 

When starting with PLS, understand what signals sales teams are excited about and feed them as many accounts that fit that model as possible. 

Then, you gradually move to data analytics. Monitor certain usage thresholds as indicative of a potential product qualified account. 

This could include the number of events sent through the number of boards in an account like Miro or the number of revisions on a given design like Figma. Velocity of use, the rate at which users increase product usage can be a powerful predictor of potential product qualified accounts. 

Miro, for example, started out as PLG. But they had a very strong behavioral signal in place that if there's an admin switch in the account, there’s some sort of evaluation going on. 

If you see a user from your account land on your terms of use pages, reach out to that person. More likely than not, you have a buyer on the book. 

Remember to do things that don’t scale early. Notion didn't hire their first salespeople until they were at 10 million ARR. Miro did not hire their first salespeople until they were 5 million in ARR. 

Don't treat PLS like traditional sales. Don't think of every user as a potential sales target. Instead, identify the right triggers and usage patterns that indicate a user might be ready for a sales intervention.

Part 5 – Ads 

Paid ads are like growth on steroids if you get it right. It works only when you have deep knowledge of your user problems & how your solution is positioned relative to those problems. As you can guess, it happens best when you have done your homework – doing things that don’t scale. Paid ads work especially well in a B2C context. 

Without laying this foundation, you end up wasting a lot of money & time on paid ads, and your runway gets eaten faster than a wildebeest in a piranha-infested river. 

Another thing to keep in mind here is you shouldn’t put all your growth efforts into the paid ads basket. Diversification across SEO, email marketing, PLS etc will help mitigate risks associated with fluctuations in performance marketing efforts. 

If your business has a strong organic presence or has seen success with other marketing channels, chances are that that paid acquisition will work for you too. 

The modern Performance Marketing playbook includes rigorous creative testing, understanding attribution, and measuring campaign validity. 

If your product has quick payback periods, paid ads will work well for you. 

In any case, you should leverage both SEO and paid growth, as they are not mutually exclusive, and diversification of channels is key, like I previously mentioned. 

Uber’s growth portfolio for example, was around 50% for paid, and 50% organic growth. 

SEO is a valuable marketing capability but it can be challenging to determine attribution and ROI due to the complexity of Google's analysis algorithm 

Paid growth offers more tangible and predictable results, with a more linear relationship between attribution and ROI, making it easier to budget and project revenue. 

Have a rigorous structure in place for testing and iterating on ad creatives to determine what works best, rather than relying on anecdotal evidence or individual brilliance. 

For example, Airbnb's photography team discovered that listing photos without people resulted in higher conversion rates. (which seems counterintuitive at a first glance).

Google and Meta are the primary channels for advertising. 

Other channels like Amazon and TikTok offer interesting opportunities, but may not be suitable for every business, particularly B2B SaaS companies 

TikTok's ads platform is not yet sophisticated, but offers cheap CPCs due to fewer advertisers, and works well for founder-led brands and companies partnering with influencers 

When it comes to B2B SaaS, Google is the primary channel. But remember to keep the focus on the marketing economics of a project rather than the channel itself. 

The common mistake is being overly reliant on the first metric in a sequence that yields a sale, such as cost per lead 

Instead, focus on high-value customers, even if the cost per lead is higher, as the ROI of targeting those people is also higher. 

Conclusion: 

Phew! That was a long one! Growth is difficult to do right & easy to get wrong. 

The one thing to keep in mind is that you need a mindset shift – one that’s focused on doing the hard things early on, recognizing what works, and having an open mind to get rid of things that aren’t working. 

Keep your core benchmarks as a North Star metric when starting out, and get the ball rolling by doing simple, repeatable things that can scale along with your business. 

Here’s a summary in case you were too busy to read the entire thing: 

1. Set clear expectations for growth—aim for $1 million ARR in 9-12 months but focus on exponential growth rather than initial speed.

2. Track key metrics for growth, such as product stickiness, sales cycles, and organic engagement and retention.

3. Use SEO and content to attract high-intent users; leverage technical, editorial, and programmatic SEO to target different awareness levels.

4. Implement PLG by focusing on onboarding, activation, and retention to drive viral growth loops.

5. Start with founder-led sales to gather user insights, refine your product, and then scale sales efforts based on proven processes.

6. Use Product-Led Sales (PLS) to capture upmarket value when growth through PLG stagnates, targeting usage patterns indicating readiness for sales intervention.

7. Diversify growth strategies by combining SEO, organic methods, and paid ads, ensuring rigorous testing and attribution for ad performance.

8. Prioritize high-value customers over low-cost leads to maximize ROI in paid marketing efforts.

May the growth force be with you! 

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The only saving grace we have is to stay immortal through our actions — be it our work, helping others, taking care of our family. This way, even if our bodies die, our spirit will carry on.

Take a pause if possible & remind yourself that you’re right here & right now. And it’s such a privilege :) And take the time to enjoy whatever you’re pursuing, including this essay you’re about to read.

I have spent a lot of time writing this out —- it was very intellectually stimulating & fun to write. I hope you find it interesting as well.

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• Website copywriting

• Longform blogs, essays, newsletters

• Onboarding, drip, and churn emails

• Assets like decks, e-books, guides, case-studies

• UX copywriting

• Ghostwriting content for busy founders/execs

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The Future of sales in an AI-native world.

Hi friends 👋 ,

AI is eating the world. It has officially become a disruptor. 

It's now an AI first world – AI has embedded itself into every existing software product & emerging products are rapidly integrating AI into their user experience. 

Just like the Cloud led to the bundling & unbundling of software across industry verticals, so will AI. The Cloud got its first head start in the sales vertical with Salesforce kicking things off. So it makes karmic sense to study how AI will impact the sales vertical today. 

The first sales wave, pre-Salesforce, was dominated by the Rolodex, spreadsheets, and client server-based apps. Selling was disconnected, clunky, and painful.

But in 1999, Salesforce introduced the software-as-a-service (SaaS) model. This moved applications & customer data to the cloud, giving rise to the second wave of sales — the digital acceleration wave. 

This second wave started by software bundling. Sales software brought together data from siloed business centers & created a single source of truth for sales people to access. Relational databases meant every contact & data was stored in a database in a text-based format. Salesforce became an incumbent & the most valuable sales-company overnight. 

Disruptors soon followed suit. Hubspot, Pipedrive, and countless other CRMs opened up to eat away at the Salesforce monopoly. Some like Hubspot expanded outward from their initial product, while others like Outreach sliced apart Salesforce & disrupted Salesforce in a more traditional way. 

But now we are entering the third wave of sales - AI powered sales. AI-powered sales-applications will become the new cloud-based sales applications. 

If there's anything AI is (at least for now), it's democratizing access to assets at scale. What this means is, AI will fundamentally alter how new products in the third wave are brought to market. 

Historically, disruptors have won by slicing away a chunk of the market & innovating faster than the incumbents. 

But with AI, incumbents themselves can innovate faster (relative to their past) so the disruptor can win by offering a better user-experience or solve a niche problem that the incumbent cannot solve. On the other side of the spectrum, disruptors can win by creating compound products, which AI can help leverage. And that is where the disruptors will win in this new sales-led-AI age. 

Just as relational databases and cloud technology disrupted sales processes in the past, AI is set to redefine them once again. This time, it’s not just about organizing data or making it accessible—it’s about leveraging intelligence at scale. AI has the potential to do what the cloud did but with far more precision – automate tasks, predict outcomes, and personalize interactions at a level we’ve never seen before.

The future of sales lies in bundling. Companies need to pull off what Salesforce did in the 1990s -- bring data across these different verticals and put them in one place. 

So how can sales-tech startups win in this AI age? What does their product need to look like? How can they capture a large market share etc & disrupt the incumbents? 

In this essay, I'll attempt to answer some of these questions & hopefully offer some perspective. I hope you'll find this useful. 

The pie keeps getting bigger

Despite AI’s popularity, there are plenty of naysayers. Some are downright apocalyptic, while others are slightly more articulate. An example of the latter is the line of thought where people believe that AI will create deflationary market forces, drive down costs & shrink the overall market. 

The argument goes like this – As AI automates tasks traditionally done by human services, demand for those services will decline, leading to reduced spending, spelling bad news for the economy. 

But this assumption misses a crucial point – the transition from services to software doesn’t just cut costs—it shifts value. Tasks that once required manual effort are now being automated or augmented by AI, making these solutions even MORE important to businesses.

As a result, we’re witnessing a rotation from services to software (more on this below) With AI enhancing or outright taking over many of these functions, the market size will expand, not contract.

As far as sales processes go, businesses would spend heavily on human capital to perform repetitive sales activities, like prospecting, writing email templates, analyzing lost deal flows in the domain of sales. But these activities can now be handled by AI more efficiently and at scale. 

But this shift doesn’t mean companies are spending less, they’re just reallocating that spend. The dollars that were once directed toward services are now flowing into AI solutions, expanding the total addressable market. 

This creates two critical outcomes:

1. Expanding Software Usage: As AI automates manual operations, the reliance on AI-powered software will only grow. This will cause more businesses to invest in software, expanding the market.

2. New Software Products and Revenue Streams: AI will create entirely new categories of software. From predictive analytics tools to hyper-personalized customer engagement platforms, the market will mature significantly. This opens up new opportunities that didn’t exist before, expanding the pie. 

Back during Mulesoft’s IPO filing, the company highlighted a key ratio: for every dollar spent on integration software, businesses were spending an additional $5 to $7 on related services. Now imagine even a small portion of that service being redirected into software as AI takes over more of the workload. The potential for growth is staggering.

AI-enabled hyperbundling is the future of horizontal sales

The unbundling of Salesforce and HubSpot paved the way for specialized point solutions. For example, solutions like Clearbit emerged which could identify and reach out to website visitors, or hyper-personalization email software like Reply came up to automate outbound campaigns. But these tools, while powerful, stay in silos, creating fragmented data that doesn’t translate across the entire sales journey.

A website personalization tool may boost conversions at the awareness stage, but there’s no clear line connecting that touchpoint to the ultimate close rate. Sales teams are left with fragmented insights, guessing whether the tactics used at the start of the funnel are truly influencing deal outcomes.

The heart of this issue is that existing sales platforms are largely systems of record—essentially advanced databases that organize and store text-based information. Salesforce and HubSpot have built their empires by being the go-to repositories for contact management, deal tracking, and activity logging. 

But in a world where buyers are engaging with brands through multiple channels—email, voice, video, social media—these platforms struggle to capture and integrate those diverse interactions. The future of sales requires moving beyond a text-centric database to something much more dynamic.

Imagine a sales platform where the core isn’t just text, but a multi-modal hub containing every customer insight in a single, unified space. This platform wouldn’t just track notes and email chains—it would integrate text, images, voice, video, and even non-verbal cues, offering a complete picture of every interaction a customer has had with your company. 

This is where AI can fundamentally reshape sales. AI-powered tools could seamlessly aggregate and analyze all of these data types, drawing actionable insights that would have otherwise been buried in disconnected systems.

The next evolution in sales will rebundle these fragmented tools, creating integrated platforms where sales, marketing, and customer success operate from a single, intelligent source of truth. We’ll see the emergence of platforms that not only bundle these functions but enhance them with real-time, multi-modal intelligence. The key is building platforms that connect every touchpoint—whether it’s an email, a call, a video demo, or a social media interaction—into a cohesive narrative that informs how to close deals faster and more effectively.

A prime example of how this transformation is playing out comes from outside the sales industry: Rippling.

Rippling is to HR what Salesforce is to sales. Just as Salesforce uses customer data as the core foundation for its horizontal SaaS product, Rippling uses employee data as the source of truth for its comprehensive HR platform. Rippling has set out to be the operating system for the entire HR vertical, integrating everything from payroll to IT management into one cohesive ecosystem.

Historically, HR data and tasks were spread across numerous disconnected tools, each solving a specific pain point but creating administrative inefficiencies in the process. Rippling’s genius lies in rebundling these fragmented workflows into a single, compound product that offers a seamless experience across the entire HR spectrum.

Sales processes are massively fragmented:

This creates an opportunity to hyperbundle these functions into a cohesive, intelligent platform — with AI being present from first touch to final close.

This hyperbundling approach offers clear advantages:

1. Building an Ecosystem: A multi-product platform builds an interconnected ecosystem around the brand, with each part of the platform enhancing the others.

2. Single Source of Truth: With all products tied to a central data hub—the platform operates more efficiently. Common capabilities only need to be developed once and can be applied across multiple products, driving down costs while increasing functionality.

3. Superior User Experience: Once users are familiar with the UX, the entire ecosystem opens up, increasing customer stickiness and reducing churn. High switching costs become a natural moat.

4. Pricing Power: By providing a fully integrated suite, companies can optimize its overall bundle pricing, offering more value than a collection of individual point solutions could.

New horizontal products will win by embedding AI across the stack, layering it on top to provide predictive insights, real-time optimizations, and personalized recommendations.

However, this shift toward compound AI-driven platforms will not happen without competition. Horizontal AI applications will face intense rivalry and consolidation as the market works to identify which platforms offer the most value across the sales journey. 

This rebundling will give rise to the next wave of SaaS companies - SaaS 2.0, which is Service as a Software.

SaaS 2.0 — Service as a Software

Historically, software has been built around providing tools—systems that help users perform tasks more efficiently. Whether it’s managing leads, sending emails, or analyzing data, software has historically been a set of tools that people use to get their jobs done. 

But AI will reverse the existing model & create a new model in its place – Service-as-Software. 

In this model, software doesn’t just provide the tools for a job – it performs the job itself. AI can now perform tasks that were strictly the domain of human service providers, blurring the lines between software and services.

Instead of just providing a CRM platform for sales teams to manage leads, imagine a system that autonomously prospects, engages leads, qualifies them, and even negotiates deals. Instead of just offering a customer support platform, think of a tool that acts as a 24/7 virtual support agent, capable of handling customer inquiries with the sophistication and empathy of a human. 

The modern sales process is a multi-step journey that involves distinct roles across the sales team. Typically, it begins with prospecting, followed by booking demo calls, sending over follow-up materials, addressing legal concerns, and finally sealing the deal. 

These tasks are divided among specialized roles such as Business Development Representatives (BDRs), Sales Development Representatives (SDRs), Account Executives (AEs), and more. 

Traditionally, these roles operate in silos, with each team member focusing on a specific part of the sales cycle. 

AI agents can potentially execute these roles with more efficiency while interacting with software on its own and managing the entire deal flow from start to finish.

Imagine an AI agent that starts with prospecting—identifying leads based on real-time signals from multiple channels—and then seamlessly transitions into booking demo calls, sending tailored follow-up emails, managing legal documents, and even negotiating contracts. 

Each of these tasks, traditionally handled by different members of the sales team, can now be automated and optimized using AI.

The real game-changer lies in the development of multi-step AI agents—tools designed to handle not just one task but a series of interconnected activities. These agents can learn from previous interactions, adapt to changing circumstances, and execute complex workflows autonomously. 

For example, an AI agent might start by identifying and prioritizing high-value leads, then automatically reach out via personalized email sequences, schedule a meeting, and follow up with relevant materials. As the deal progresses, the same agent could generate a proposal, manage contract negotiations, and even handle payment processing.

Take it a step further and AI agents can perform roles that are native to customer success & marketing. This will create a massive hyperbundling fusion with sales, marketing & customer success merging into one.

This approach is applicable to both horizontal and vertical sales solutions. Horizontal platforms can leverage AI agents to deliver broad, scalable solutions that cater to multiple industries, while vertical platforms can fine-tune their agents to the specific needs and nuances of their niche markets.

As AI agents become more capable, companies can bundle these features into a single platform, offering a unified solution that spans the entire sales journey. These agents will work autonomously, gathering insights from every interaction and optimizing their strategies in real time. 

Rather than selling access to tools, companies will be selling access to automated services that deliver specific business outcomes.

This model is particularly useful for vertical SaaS solutions, where specialized knowledge and workflows can be fully captured and automated by AI, providing a more precise and tailored service than generalized horizontal platforms.

How can vertical first SaaS companies win?

While bundling is a powerful strategy, there’s another path that’s equally compelling in the AI-driven sales-tech landscape: focusing on unbundling to create a specialized vertical solution in a particular industry. 

Unlike horizontal platforms that aim to cover a broad range of industries and use cases, vertical SaaS products narrow in on a specific industry or workflow, refining and perfecting their offering within that niche. 

Vertical SaaS companies benefit from having more focused and accurate data, allowing AI models to be fine-tuned to the exact needs of the industry. By focusing on one segment, it can leverage AI to personalize customer experiences, predict demand trends, and automate industry-specific workflows with greater precision than horizontal competitors.

Even niche verticals can represent massive opportunities. For example, in the sales-tech space, companies like Clio (for legal professionals) and ServiceTitan (for home services) have capitalized on the deep needs of their respective industries.

Though these niches seem narrow, they are substantial enough to support large, high-growth businesses, making it easier to justify early-stage investments. Vertical products win because of:

1. Higher Margins and Retention: By focusing on a specialized vertical, companies can achieve higher profit margins and stronger customer retention. Customers find it difficult to switch because the software addresses their unique pain points more effectively than generic solutions.

2. Lower CAC and Faster Growth: Vertical SaaS companies can fine-tune their GTM strategy, reducing customer acquisition costs (CAC) and scaling rapidly within their chosen niche. The targeted approach also allows for more refined messaging, increasing conversion rates.

3. Unique Data Advantage: Specialized verticals inherently generate more relevant and high-quality data, which further enhances the AI’s effectiveness. With cleaner, domain-specific data, vertical SaaS companies can train better models, leading to more accurate predictions and insights. This creates a feedback loop where the more a company refines its niche, the better its AI becomes, and the more value it delivers to customers.

4. Better Product-Market Fit and Domain Expertise: Vertical SaaS solutions are built on deep domain expertise, making them more relevant to the specific needs of their target audience. With fewer competitors and less noise in a focused market, vertical SaaS companies can gain traction faster, often leading to a “winner-takes-all” scenario.

5. Differentiation through UX and Ease of Use: In vertical SaaS, UX and ease of use become critical differentiators. Since these solutions are designed with specific workflows in mind, they can offer a streamlined and intuitive experience that generic, horizontal products cannot match.

The future of sales-tech will see more vertical AI apps where companies focus on niche markets with specialized needs.

In these spaces, AI can leverage highly accurate and personalized data, leading to better results. Vertical SaaS companies don’t face the same level of competition from incumbents as horizontal solutions, giving them an edge in gaining market share. 

AI has reached the point where it’s no longer just an enhancement—it’s the engine driving the next wave of sales transformation. Just as the Cloud revolutionized the software industry, AI is redefining what’s possible in sales by enabling smarter, faster, and more integrated processes.

Whether through the rebundling of tools into cohesive compound platforms, vertical SaaS solutions that target specific niches, or the rise of Service-as-Software models, AI is reshaping the sales landscape in profound ways.

The future of sales lies in solutions that leverage AI to provide not just tools but outcomes—automating tasks, predicting results, and optimizing every step of the deal flow.

As AI agents become more sophisticated, they’ll integrate seamlessly across workflows, turning traditionally fragmented sales tasks into a unified process. This blurring of lines between software and services marks the emergence of SaaS 2.0, where software does more than assist—it autonomously drives results.

The companies that will win in this AI-first world will be those that understand how to balance bundling and specialization, integrate AI deeply across their platforms, and align their success with customer outcomes.

We’re entering a new sales-led AI age where innovation, customer experience, and intelligent automation will determine who leads and who gets left behind.

In this landscape, disruptors have a unique opportunity to outpace incumbents by focusing on niche problems, creating compound products, or leveraging AI to deliver unprecedented value.

As AI becomes the new foundation for sales, those who adapt and harness its potential will be the ones defining the future of the industry. The AI-first era is here, and the winners will be those who move fast, integrate deeply, and, above all, stay laser-focused on delivering real value to their customers.

How did you like this week’s edition? Your feedback helps me make this great.

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Thanks for reading & see you on Monday.

Shounak.

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First off, apologies for the delay in publishing the newsletter - I’ve gotten a lot of love because of this & I wanna keep this going for as long as I can. I have gotten some feedback from readers that there is a lot of interest in studying founders who are just ahead of the curve so to speak - not the big guys like Webflow or Brex (although their story is worth telling & I will) but the more under-the-radar types SaaS businesses that are quietly making huge ripples.

So moving forward, I will also be bringing you the juice from these under-the-radar SaaS companies & how they went about their startup journey.

This week, I spoke with Iron Brands, the founder of Simple Analytics, a privacy-friendly, simple Google Analytics alternative that’s making $384k in annual revenue.

Iron was kind enough to share his insights with me & I have tried to reproduce the entire conversation with all the juicy details.

Let’s dive in!

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Today’s issue is brought to you by Simple Analytics — the privacy-first, GDPR-compliant, Google Analytics alternative.

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• Your data is never ever stored. Zero cookie banners.

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Market Curve readers can try it for free for 14 days at this link below 👇

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The idea

Simple Analytics started in October 2018 when Adriaan, the founder decided to make a privacy-friendly Google Analytics alternative. 

Adrian was a developer by trade but despite that, he took Simple Analytics to $10k MRR by July 2021 - taking roughly 2 years & 9 months.

When Adriaan started building Simple Analytics, he was freelancing to pay the bills a few days a week while spending the rest of his time on Simple Analytics. The idea was that once Simple Analytics took off, he would leave the freelancing work behind. 

Within 2 months, the first version of Simple Analytics was ready.  

There was a statistics page where users could see:

• page views of the last month

• top performing pages

• top referrers

• screen sizes

He also created a landing page where users could: 

• see the promotional video

• read the features

• create an account

• pay for a plan

Going from zero to one.

So how did Adriaan go from zero to $10k MRR in 2.5 years? It's a bit of luck & timing as Iron puts it (who joined as cofounder shortly after the 10k milestone). Like most startup founders, Adriaan did things that didn't scale. This included promoting on Twitter, Reddit, and HackerNews. 

One thing worth noting is that he charged users for the product from the very beginning (since he was bootstrapping the entire thing). While pulling at these strings, Adriaan found that his Hacker News launch post went viral - this spike alone got him his first few thousand dollars in revenue.

And while this may seem like a stroke of luck, if you look closely, you can see some interesting patterns emerge The first, is that Adriaan posted to the show HN page - the lower frequency of posts means you will have more chance of being seen.

The second thing Adriaan did is that he posted a thoughtful comment by showing a technical hurdle he overcame while building this. The HN crowd finds this very endearing as it ties into their personal narrative of struggling with something & overcoming it.

And while this specific example worked, the broader takeaway here is to trigger a powerful emotion within your community that will make you more relatable.  

The third thing Adriaan did was to comment under posts - he found a relevant post to his domain, offered thoughtful advice, and then linked his own product. The result: 590 Hacker News users check out his website. See this example.

Adriaan also posted on Product Hunt & he did the basics right - he prepared his promo video, had a nice GIF in place, wrote a solid product description & prepared an interesting first comment.

The day before launch, he prepared his Product Hunt post on Preview Hunt to see how the final post would look and used this to get feedback from fellow indie-hacker founders. The launch was super successful & got 864 upvotes, got voted the number #3 product of the day & was even nominated for the 2018 Golden Kitty awards.

Together, the PH launch & the HN launch got him a wave of new customers. ~ 80,000 visitors came from these 2 launches. After this, word of mouth began to spread with other users recommending like-minded customers. This created a nice recommendation growth loop which fuelled Simple Analytics growth. During this time, he talked to more developers, and indie hackers who would become future customers of Simple Analytics & received valuable feedback from them.

Teaming up with a co-founder.

It was at this stage that Adriaan met Iron Brands, the latter who would join Simple Analytics as the marketing/sales co-founder. Iron was from a marketing/sales background & had been running a marketplace biz which he was planning to quit. 

The two of them hit things off instantly at a bar meet-up in Amsterdam. Right off the bat, Iron liked the idea & saw the potential of the product – he wanted to work with Adriaan on Simple Analytics.

6 months later, Iron called up Adriaan & reminded him of their conversation & proposed an action plan. He would work with Adriaan for 1-2 days a week & help him out with marketing/sales-related stuff. If they vibed & enjoyed working together, he would join as a cofounder. Adriaan agreed & the two of them got to work. 

Iron soon realized that a vast majority of traffic to the Simple Analytics website was coming in from Hacker News. So Iron thought - "Hey let's double down on this & see what happens". So they did. And the strategy worked wonders. Here's what they did: 

Adriaan built a small bot that notified the founders whenever a news piece dropped around Google Analytics & its privacy concerns. Big publications often covered these news pieces & Iron observed that these news-type posts went the most viral on HackerNews.

So whenever a new publication dropped a news piece related to Google Analytics & privacy, Iron wrote up an article on that, published it on the SI website & posted it on HackerNews with the blog URL. And each time, it blew up. 

Iron says that 60% of his posts on HackerNews went viral & that brought in a large chunk of revenue & users consistently each week. This became so popular that Hackernews banned them from posting because they had "hacked" the HN algo lol. Iron even wrote a post & called it "how to hack Hackernews". 

This experiment was great for 2 reasons - it brought in new users and gave Iron & Adriaan time to work together. And after the 3 months got over, Iron officially came on board as the marketing/sales co-founder. 

How they used SEO to go to $30k MRR

The next goal was to increase the MRR - and the founders decided to stick to the basics & use SEO as a distribution channel with precise positioning. 

Once again, they did the basics right & their SEO strategy had 3 main pillars: 

• Long-tail how-to articles 

• Alternative pages 

• Programmatic SEO pages

Long-tail SEO

Iron says that the easiest way to start thinking about SEO is to answer questions relevant to your niche and target audience – Show people how to solve their problems in a blog article. Then, explain how your business can do this for them at the end of the article.

Want to find relevant questions to answer? Look for “how to” questions relevant to your niche. “How to” questions are actionable (people are looking to solve this) and most often long-tail, meaning there is not a lot of competition. (tip: In Semrush, navigate to “keyword magic tool” and search for “how to.” By using your keyword as a filter, you’ll get a list of relevant “how to” questions to answer.)

One of the main tricks they used was to answer common user questions, stuff like, "how do I do XYZ in Google Analytics" - these indicated that there was a customer base that found Google Analytics too overwhelming. These blog posts presented Simple Analytics as a simpler alternative. 

For example, for SEO they created the following pages:

-  How to do X in Google Analytics

- How to integrate X with Google Analytics

Iron calls it product-led-SEO. Each of these content pillars had multiple variations & created long-tail SEO value bringing in tons of traffic.

Alternative Pages: 

In addition to the product-led-SEO approach, they also created alternative pages. Alternative pages work because they directly show how your product is different from the competitors & it captures high-intent buyers organically. 

For example, for Simple Analytics, there is a lot of search volume for “Google Analytics alternative” but also for smaller ones such as “Hotjar Alternative.” They created blogs outlining why they are the best “Hotjar Alternative,” & soon started to rank for this search query, and people actively looking for a “Hotjar Alternative” soon found out about Simple Analytics.

Programmatic SEO pages

Programmatic SEO is SEO on steroids - you can generate multiple pages instantly and rank for thousands of keywords by tweaking just a few variables. These variables are key to create these programmatic pages in bulk. 

For Simple Analytics, they created 100 pages from a template text that answered this question: “Is Google Analytics illegal in {Country X}?”

The country is the variable here. You can recycle the template text and change the country variable. By creating a page for every country, soon they started ranking for search queries about “Google Analytics + country.”

All these 3 approaches worked incredibly well for Simple Analytics – take a look at these graphs below. 

How crystal clear positioning helped them attract the right users. 

Speaking of simpler, the founders nailed positioning from the very start - it was a simpler version of Google Analytics that was privacy-first. This positioning statement resonated with customers and consequently, all blogs & content were created with this positioning in mind. 

At first glance, this may seem obvious or rather "meh" but this positioning played a significant advantage in getting Simple Analytics up and running. 

When there's an incumbent as big as Google, the best way to capture a market is to slice up the bloated market and position it as a simple product that does one thing very well. And that is exactly what Simple Analytics did - they stripped down GA and confined it to a few simple but necessary things & positioned it as a simpler alternative. 

This resonated with the subset of customers who were happy to pay a fee for a cleaner, privacy-friendly & simpler alternative. For Simple Analytics, they were indie hackers, developers & solopreneurs who needed a simple product for their use case. 

This also created a flywheel effect - when these developers or indie hackers worked with clients or went in-house, they recommended Simple Analytics over Google Analytics which got the company high-ticket customers. 

This flywheel meant that users of Simple Analytics became a distribution channel in itself while increasing expansion revenue as well. A solopreneur customer would probably just sign up for an individual plan but when that same solopreneur referred Simple Analytics to his employers, they would sign up for a team plan (with more users) & boost expansion revenue. 

This flywheel reduced the customer acquisition cost (CAC) significantly while directly increasing revenue. The company also reduced switching costs users could face by having them import all their data to Simple Analytics in just a few clicks. 

The privacy-first positioning statement attracted a different type of customer - customers like Hyundai. For these enterprises, privacy & compliance was a big deal so Google Analytics was a no-go for them. Once they realized the compliance feature of Simple Analytics, it became a no-brainer for them to use. SEO & word of mouth brought in a lot of these enterprise clients. 

The team hired a part-time writer to write legal & compliance-related blogs that were centered around privacy & positioned Simple Analytics as a privacy-friendly alternative. 

The road to $1 million ARR.

Now, as they scale up, the next goal for the founders is to reach the fabled $83k MRR aka $1 million in ARR. And to do this, they will double down on SEO while activating another lever - Product-led growth. 

PLG is the perfect lever for Simple Analytics since it's a simple product with a huge TAM. Iron identified that between the two extremes of indie hackers/solopreneurs & enterprise customers, there was a massive middle market waiting to be captured. PLG seemed like a good choice to target these customers. 

Once again, they are betting on the flywheel to help them acquire these customers. The objective here is to capture a large chunk of the solopreneurs/indie hackers and offer them value for free & in return they will refer Simple Analytics to their clients/employers, which would in turn bring in even more expansion revenue. 

Since there's a huge middle market, Simple Analytics wants to capture as much of that market as fast as possible. The freemium PLG model is great for making that happen. Once that market is captured, they can move upwards & monetize that segment via upsells, cross-sells & referrals. 

This is not dissimilar to how Webflow monetized their user base as well. The early Webflow user can use it for free but when he upgrades to an agency model, he will sign up for the more expensive Webflow team plan. Not just that, he will also refer Webflow to his clients who will sign up for a premium team plan triggering recurring revenue and expansion revenue. 

PLG works best when there is a huge TAM. The most successful PLG companies (think Loom, Zoom, Airtable, Notion, Trello) all have a massive TAM so it makes sense to capture a large chunk of the market at first for free and monetize a small section of it. Trello pulled this off brilliantly. Simple Analytics aspires to do something similar. 

They opened up a free plan for Simple Analytics so they could capture more top-of-the-funnel users. They optimized their onboarding so they could trigger key buying events & upsell users at the right time. 

They are doubling down on SEO, and content, going on podcasts & experimenting with different strategies to capture users. If this goes well, it isnt too far off to suggest that they will hit their landmark of $1m ARR sooner rather than later. 

7 Key Takeaways:

• If you don't have a particular skill, find a cofounder to partner with that complements your skillset. 

• Study what works & double down on it. 

• Do things that don't scale in the 0-1 phase. 

• Constraints breed creativity. 

• SEO is a great channel to go from 1-10. 

• Specific positioning for specific customers. 

• PLG is great for SaaS products with a big TAM. 

• You gotta do your own thing to figure out your product's evolution. 

• Keep at it & iterate constantly while keeping an open mind. 

• Do more with less. Keep 80-20 in mind.

  ---

Airbnb has officially become a software-powered real estate mogul. With over 7 million active listings in over 220 countries, Airbnb generated $9.92 billion and is worth $98.87 billion, more than Hilton and Wyndham combined, and closing in on Marriott. Airbnb has more than 150 million worldwide users that have booked over 1.5 billion stays. 6 guests check into an Airbnb listing every single second. 

But Airbnb’s extraordinary success has not been without its challenges. Marketplaces are notoriously difficult to build, but this is precisely what makes these companies so valuable. In Ben Thompson language, AirBnB is an aggregator. Aggregation is a fancy term of bundling. Aggregators bring both sides of the marketplace in one place & deliver value at scale to users thanks to free distribution & low transaction and marginal costs. 

The internet has made distribution essentially free (which wasn't the case in the pre-internet era). Transaction costs & marginal costs have dropped down significantly enabling businesses to scale like never before. 

Since the playing field is now level, companies who win are those who provide the best experience. This earns them more users, which attracts more suppliers, which brings in more users creating a self-reinforcing network effect flywheel. 

AirBnB disrupted the incumbent hotel chain monopoly by commoditizing trust (which earlier was reserved to hotels). By democratizing trust and extending it to homeowners, the market expanded and brought homestays & hotels on an equal footing. Once the playing field was level, homestays could compete on other vectors like affordability, living like a local, experiencing the city etc. But none of this would have happened if trust would not have been commoditized. 

On the other hand, it is causing real estate bubbles in major cities like Paris, NYC, Barcelona & Edinburgh. Rents are going up in these cities and hosts rent out their spaces and limit it to people seeking long-term rentals. Just 17% of hosts in Paris say they rent out apartments other than their primary residences & most of this is done illegally without following city authorizations. 

Thanks to the internet, consumer led businesses have a massive impact on people across both societal & economic vectors. Just like Google & Facebook, AirBnB also have made ripples across the entire vacation rental/real-estate market.

But how did they get here? Lets find out.

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Founding story: 

The year is 2007. Brian Chesky & Joe Gebbia are struggling to make rent on their SF apartment. Their solution? Sublet it out to renters and make money for their rent. But rather than list it on Craigslist (which seemed impersonal), they decided to build their own site & called it Air Bread and Breakfast. 

The product was simple - guests could stay at the apartment for a night and be greeted with breakfast in the morning. The timing couldn't have been better for Joe and Brian. There was a design conference in town, and hotel space was limited. So Joe and Brian set up a website with pictures of their loft-turned-lodging space—complete with three air mattresses on the floor and the promise of a home-cooked breakfast in the morning.