The first version of the First to Market category database was 19 Excel workbooks and whatever my memory could spare that week while also trying to potty train a toddler. The mess was fine because I was the only person using it.

Version 2 pulled data from that folder of XLSX files with Claude and automated the intake with an MVP site on Bolt.new + Supabase. It at least looked legit enough for someone to spend two minutes using it (so thanks to the few dozen who tested it). But everything after that was still done manually, backed by data that was always at least a little bit out of date.

Version 3 launched this week.

The data now updates itself every night instead of whenever I have time, and it’s public at categories.firsttomarket.co instead of locked in a spreadsheet or my sleep-deprived brain. (More detail in this week’s “From the Workbench” section at the end.)

How much polish a new product launch needs depends more on its audience than on our company stage. A rough version of the Category Database was 100% fine when I was gauging interest. But a scaled public product needed more to be valuable.

That probably sounds familiar because startups go through the same shift as they grow. From the database, 79 of 161 startups now sell to a bigger customer than the one they launched to, and only 6 moved to a smaller one.

Figma launched with an invite-only preview for individual designers. But now 95% of the Fortune 500 use it. That’s why when Figma rushed an AI feature for its 2024 conference, it had to quickly pull it.

Different audiences expect different things from a launch, and likely tolerate fewer mistakes. The four questions below will help you decide how much polish your next launch needs, whether it’s your first product or your 50th feature. (At the end, I’ve added an AI prompt that you can copy/paste to run the exercise, too.)

1. Who will see it first?

Early adopters sign up knowing the product is unfinished so a bug is less likely to surprise them. (Talk to any Tesla owner about this.) But a mainstream buyer is more likely grading the experience against another option with less tolerance for blemishes.

Elizabeth McCluskey, managing director at TruStage Ventures, told me: “Early-stage teams get a handful of first impressions, so I don’t like to see them launch too early. When you put something in front of people before it works, you can easily lose their trust for good.”

Elizabeth’s advice is to start with a small group to “treat these first users as collaborators.” One of her examples is AVA, an AI assistant for parents. The founder shared the early product with a small beta group of parents first, then iterated with them, before ever opening it to the public. Elizabeth said those early testers became its advocates. (Again, think about Tesla.)

• Linear added about 10,000 emails to a waitlist then let in a few per week. Its founder told First Round: “We didn’t want to launch publicly and have a ton of people checking it out for a day, and then leaving, thinking the tool sucks.”

• Canva launched for social media managers. Then 11 years later it added an enterprise plan with large-org requirements: SSO, admin controls and security standards. New features now must meet those requirements, as well as move fast enough for its OG early adopters.

Common misstep: Don’t expect the future to always look like the past. Our first launch probably went to a small group that expected bugs. Our customer base now might include people with different expectations. (This is why I still drive a Rivian and my wife does not.)

2. What will they compare you to?

Buyers judge every launch against something, and what that is can depend on how mature your category is. The Four Waters Framework sorts categories into four stages of maturity.

I checked this against the database (startups that launched in 2023 or later) and a launch often gets compared to something else:

• An incumbent: About half the startups that launched into Crowded Waters named a specific incumbent at launch, like HubSpot or QuickBooks. Prospects in these categories compare options (feature vs feature) so it can pay to bring a more “fully cooked” product to the market. Attio entered CRM, one of the most crowded categories in the database, and spent about three years building before launching. Superhuman spent two years building an email client and still refused to launch. Its founder called the “launch and see what happens” approach “irresponsible and reckless.”

• The old way: In First Voyage, most of the startups positioned against the status quo: hiring SDRs, paying a research agency or doing traditional SEO. So an AI SDR is measured against the human SDR it would replace.

• Today’s price: Twenty is an open-source CRM and its CEO Félix Malfait told me that being open source means “you’re competing against the free version of yourself.” Every paid feature is measured against what customers can already get for free.

Test it: Look at your last ten lost deals and write down what each buyer chose instead. That’s what our next launch will be compared with so that’s the bar it has to clear.

Common misstep: Don’t confuse this with feature parity. Attio didn’t need every feature that Salesforce offered in CRM. It needed to win on the things about which its first customers cared most.

3. What could this launch break?

Once you have paying customers, every new launch is judged partly by what it does to the product they already use. The closer it sits to that product, the more polish it needs.

• A separate product can move faster. FigJam is a whiteboard product from Figma that launched quickly because they said, “We were playing catch up. Speed seemed of the essence.” Having a rough first version of FigJam didn’t change the core design product.

• An add-on is judged on completeness. The team at Gong started with call recording then added products for forecasting and engagement. Its co-founder explained that a second product is judged on completeness rather than product-market fit, “because at the end of the day, product-market fit is for a category.”

• A change to the core product needs the most care. When Lindy pivoted from a workflow product to an AI assistant, growth marketing leader Haneen Azhar told me it kept supporting the enterprise customers on its original product instead of disrupting those relationships.

Test it: Ask whether a customer who never uses the new feature could still be hurt by it. If so, it needs the same polish as your core product.

Common misstep: Every new idea doesn’t need to be part of the core product. Launching it adjacent to the core product, the way Figma did with FigJam, can grant you needed grace.

4. Are you polishing the right thing?

The other way to get a launch wrong is to spend months (or years) perfecting something built on the wrong assumptions. In the category database, 30% startups founded in 2013-2016 took two or more years to launch. Of the 23 founded in 2023, only 13% took that long. It’s directional at best, but fewer startups are spending years building before launch.

• Speed makes it easier to be wrong. Lindsay Liu, co-founder of Super and writer of The Headline, told me her team’s first beta took 10+ months, which “was considered fast back in that era.” But she warned that AI tools “bias towards too much execution. And if you don’t know the right brief, then you’ll get lots of polish on the wrong output.”

• The wrong buyer is costly. The team at Stripe said that its billing product was “naively launched” on the assumption that the developers who integrated payments would also evaluate recurring billing. But that decision lives in finance so Stripe missed its quota.

• Pre-selling tests your assumptions before you build. Kat Weaver, an exited founder and angel investor who has helped founders raise $100M+, told me that “there will never be a perfect or right time before going to market.” Her advice is to “start testing with potential customers before it feels fully viable,” and even pre-sell before an MVP exists: “The customer intention is what sets one up for success.” (She writes about raising capital in her newsletter.)

• Weak feedback doesn’t always mean wrong assumptions. The team at Mercury showed the MVP to 100 founders, and only two were enthusiastic. But they kept building for more than a year on the premise that the founders understood the problem and “just couldn’t imagine what a better banking experience could be.”

Test it: Ask a few target customers to explain the problem you’re solving. If their description doesn’t match yours, fix that before you fix the product.

Common misstep: Don’t consider something an MVP until you’ve talked to customers about it. As Kat put it, “Doesn’t count as a true MVP to me if you haven’t done customer interviews around it – just making something doesn’t mean a customer wants it.”

Before your next launch

To work through these four tests for your own launch, paste this into Claude, ChatGPT or your AI chat of choice:

Help me decide how much polish my next launch needs, using the four questions from Andrew Garberson's First to Market article "Is Your Next Launch Too Early or Too Polished?"

Step 1: Context. If you already know my company, product and customers from memory or earlier chats, summarize what you know in two or three sentences and ask me to confirm or correct it. If you don't, ask what I'm launching (a first product, a new product or a feature), who it's for and what category it's in.

Step 2: Ask me these questions one at a time, and wait for my answer before moving on. If an answer is vague, ask one follow-up, then move on.
1. Who will see it first? Are they early adopters who expect rough edges, or customers who expect it to work the first time?
2. What will they compare it to? An incumbent, the old way of doing the job (a spreadsheet, an agency, a new hire), or a free version, including my own? If I have notes on lost deals, what did those buyers choose instead?
3. What could this launch break? Could a customer who never uses it still be affected?
4. Am I polishing the right thing? Could a few target customers describe the problem back to me in their own words?

Step 3: Give me a short readout, under 200 words:
- Where my answers point toward launching now, and where they point toward waiting.
- The assumption I seem least sure of.
- Any answers that pull against each other.
Don't tell me what to do, and don't invent statistics or examples. Use only what I've told you and these four questions.

Step 4: Close with:
- A pre-mortem: ask me to imagine it's 30 days after launch and it went badly, and what most likely went wrong.
- One small step I could take this week to test the assumption I'm least sure of.
- If my category is at https://categories.firsttomarket.co/, point me to its page so I can see how other companies in it launched and positioned themselves. If you can't browse the web, just give me the link.

From the Workbench

I previewed v3 of the category database at the top. Here’s a little more on how it works, in case you’re curious. If you want specifics about the tech choices, reply and I’m happy to share.

It checks every company every month. Each night, automated research jobs run for several hours. They work through a slice of the companies in the database and check each one’s positioning statements, pricing, comparison pages, blog and open roles. Over the month, every company gets checked, and the database records only what changed, saving all of the historical data where it makes sense.

It re-researches what a crawler can’t see. A separate job works through every company on a rolling cycle for things like funding, headcount, customers and go-to-market motion.

It looks for what’s new. Every week, it scans accelerator batches (like YC), analyst coverage and funding news for categories and companies that weren’t on the map before. Nothing gets added without my approval, and once I approve one, the jobs build its profile the following night.

It’s starting to have a memory. Each company’s public history is getting reconstructed back to its launch year. That’s how this week’s article could compare what startups said at launch (whenever that was) with what they say today.

It tries to show its work. The 14,500+ data points are dated, sourced and given a confidence level that I can double check. Every week, I review a log of what changed, and the public category pages update when the data does.

All is free for subscribers at categories.firsttomarket.co so please enjoy it.

And have a beautiful weekend.

Early-stage teams often overlook their own milestone moments as opportunities. Since most of us aren’t announcing $1B valuations, why would a reporter at TechCrunch care to cover us?

TechCrunch probably doesn’t want to cover it, so you’re not wrong, but it misses the point: national bylines and Sunday editions don’t need to be the goal. Our product launch has to compete with people’s kids, their quarterly targets and whatever other international catastrophe happened that day. It’s going to lose, and that’s okay.

The goal for startups in new categories is establishing a simple narrative: what prospects, customers and investors believe about your category and your place in it.

The press is simply one part of that narrative distribution, and we’re fortunate it still covers these moments at all. Even when the startup where I’m head of marketing (Recurrent) was too small to mean anything to anyone, our early funding rounds were covered by Axios, TechCrunch, Business Insider and Forbes. (A TechCrunch reporter even shared why it writes so many funding stories: “If you want to chat with a CEO on the record for half an hour, the next time their startup raises is probably your best chance.”)

This is all top-of-mind for me right now. At Recurrent, we’re launching our first paid consumer product. It needs different messaging than anything we’ve done before, and the launch is a chance to reset how the market sees us and how all of our products work together.

This week I looked at how other category leaders use these moments by auditing 700+ press releases (thanks, Claude), coding 388 funding and launch announcements from 133 companies in the category database against 13 signals, then mapping each one to its category’s Four Waters stage at the time it went out.

Yes, it was a lot. But the lessons and insights are 100% worth the effort. Here’s what I learned and you should borrow.

1. Start with the narrative.

Roughly two thirds of the announcements (63%) named a category or intentionally talked about the bad/busted status quo.

The marketing team at Profound told me they treat every funding announcement as the best opportunity to evolve the company narrative. Dozens of AI startups announce funding every week so it is noisy and crowded. Profound chooses the story it wants stakeholders to remember and builds the messaging around what they plan to do with the money.

You can see it in three of the Profound funding rounds:

• Series A set the category by raising “$20M to pioneer Answer Engine Optimization.”

• Series C adapted the category “to build the marketing platform for the AI era.”

• Series D (announced last week) raised $180M “to build the AI platform for marketing teams.”

Brendan Hufford, who writes Growing Up, a newsletter with deep dives into how top companies get customers, told me that he read the latest announcement as a category repositioning: “Series D repositioned them from an AEO tool to a larger AI marketing tool, largely placing them against how we’re all using Claude right now.” (That’s good company to keep.)

Test it: If you’re planning to announce a funding round, cover up the dollar amount in your release draft to make sure the positioning and category statements can stand on their own without a big number propping them up.

Common misstep: Don’t focus on the past. The big number and investor names will be forgotten in a week. But your stakeholders will remember your description of what the future looks like.

2. Tie the category to the broader market.

More than a third (38%) of announcements addressed the essential “Why now?” question by connecting their news to the broader market trends and stories.

• Gorgias focused on ecommerce customers during its pandemic-era Series B.

• Tealium aligned its Series F with GDPR.

• Reevo tied a raise to the trending complaints about tool sprawl, even designating a “Why Now” section.

• Ramp turned its down round into a recession-era efficiency story and its recent raise into the creation of a “Token Spend Management” subcategory. Brendan also happened to do an exhaustive recap on Ramp this week (and it’s worth your time).

Test it: What are your stakeholders thinking about this month? Imagine the tabs open in their browser that still don’t have an answer. Those can help with your “Why now?” connection.

Common misstep: Don’t chase the trend (called “newsjacking”) just because it’s in the news. Tealium included GDPR because it was in the news AND also a top-of-mind threat to its customers and prospects.

3. Let someone else make the claim.

Early in a category taking shape, you can’t crown yourself the “Category Leader.” Investors made the claim for startups in 42% of funding announcements from early-stage categories (Uncharted Waters + First Voyage). In later stages, it dropped to 25%.

• HubSpot used a Sequoia investor to call it “the emerging category leader in the SaaS marketing sector.”

• PolyAI used Khosla Ventures to call it “a category creator for AI-powered, voice-based customer support.”

• Lightfield used a16z to say “Salesforce defined it for the cloud era, and Lightfield is defining it for the agent era.”

As the category matures, companies have the data to make the claim themselves. Superlative claims in the headline or opening paragraph roughly double from 11% of early-category announcements to more than 20% in later stages.

But those claims are harder to believe coming from the company. As Luca Fancello told me for the AEO piece: “an AI discounts you calling yourself the best.” People do, too.

Test it: Who outside your company could loan their credibility to make your leadership claim for you at your next milestone? (I’ve even used customers and new executive hires.)

Common misstep: Don’t think of the announcement as a one-to-many broadcast. The Profound marketing team told me that it gives employees, customers and the rest of its community a heads-up and a reminder the morning it goes out. We aren’t making the announcement alone.

4. Count other people’s decisions.

Every milestone can be proof that a stakeholder with options chose you: investors, employees, customers, partners. All of them can be proof points. 78% of funding announcements stacked two or more proof points, compared with 44% of product launches.

• Unify announced its Series B after "growing revenue 8x" and adding Cursor, Perplexity and others as customers.

• Agility Robotics launched a product with “more than $300 million in multi-year customer orders.”

• Pipedrive launched a product with early traction from “over 70,000 customers.”

• Firecrawl used “more than 350,000 developers already signed up.”

• Qualified cited “$200M in generated pipeline, 9,000+ meetings booked, and $6.8M in cost savings.”

Test it: What quantified examples of other people choosing you can you say publicly?

Common misstep: Don’t say “fastest growing” (or similar) without proof. More than half of the product launches audited did not stack multiple proof points. If you don’t have them, maybe “fastest growing” shouldn’t be your angle.

5. Stack the moment.

Once a category reached Charted Course, about half of funding announcements packaged the round with a launch, customer, partner or hire. In younger categories, it was about a third.

• Ramp paired a raise with product launches around bill pay and travel.

• Clarify paired its first acquisition with an exclusive waitlist for a new product.

• Outreach paired a raise with three new products.

• Profound paired a raise with a new agentic product.

Test it: Look at your roadmap for the next quarter. What other milestones could be paired with your next announcement?

Common misstep: Trying to be everything to everyone everywhere. The Profound marketing team told me it picks one social platform to win and designs all creative for that. Then it points everything back to the one post with a single call to action.

More Than a Day of Coverage

It’s easy to think of these announcements like a ticket on a kanban board: new → in progress → closed. But the finish line isn’t a published press release. We are trying to establish our narrative in the minds of stakeholders (prospects, customers, employees, investors, an LLM). It’s one part of our approach to saying something enough times, in enough different ways, that people we care about start to say it back.

Only 18% of the releases audited (1) named the category, (2) let someone else vouch for the company, and (3) backed it up with proof points. Those are the companies using a milestone moment to make a claim on the category, and we can learn from them.

Lastly, a huge thanks to the team at Profound. I gave them an impossible deadline and they gave me some great insights to share with you all.

From the Workbench this Week

I mentioned earlier that Recurrent launched a new product recently. It gave us a reason to update how we describe the company, its products and where we add value to customers. It’s still a complex offering for such a small startup, meaning different things to different people, but I love this part of the learning phase.

• Ad tests are running.

• Value props are collecting feedback.

• Creators are activated.

• Valuable product data is streaming in.

Since this product is on the consumer side of the business, there are also more public signals available, like one of the many Reddit threads that have popped up this week.

The startup life is hard, and sometimes very unpleasant. But it definitely has its rewards. I hope you’re seeing the upside and affirmation once in a while, too.

Have a beautiful week.

Whether you’re building an AI-native product or not, we’re all selling in the AI era, and the way we talk to buyers has to keep up. This piece builds on the original research with some immediately useful insights on positioning and messaging.

Take these two ads from Fireflies, the AI meeting notetaker. The first promotes that “AI agents can now join your meetings.” The second attacks the underlying problem instead: “There’s a specific kind of tired that isn’t from doing work. It’s from holding work.”

While “AI” remains part of the Fireflies tagline and “AI meeting notetaker” category, it is being replaced in the ad copy and product positioning. And Fireflies isn’t alone. Some brands even position against AI.

• Lindy: “Most AI hands you a draft. Lindy hands you a finished task.”

• Replit: “Consultants take weeks. Chatbots give you instructions. Replit gives you the finished product.”

• Granola built its wedge on not being a “creepy AI bot.”

Here are five shifts showing up across brands in the category database and how we can learn from them.

AI Value in Plain Language

1. Don’t drop the category.

Otter still uses “AI Meeting Agent” on its home page and social profiles and has not walked away from its category to distance itself from AI. It’s changing the way that it talks about its value by focusing on what people get instead of how they get it:

• “Never miss what matters.”

• “Meetings shouldn’t create more work.”

• “Agentic chat handles the busy work so you can handle the eating of your g-damn lunch.”

Lindy also balances being an “AI teammate” with outcome-focused messaging for a customer base that ranges from prosumer to enterprise.

• “Lindy finishes the task, in the tools your team already uses.”

• “Lindy organizes your inbox, drafts replies, and preps every meeting.”

• “Ask Lindy. It’s done.”

Haneen Azhar, growth marketing manager at Lindy, told me about the thinking behind it: “By nature of being an emerging category, every piece of messaging has to do two things: educate on the category, then educate on the product.” Yes, Lindy is an AI assistant, but the value that they deliver is also tangible and more than an AI label.

Common misstep: Taking it too far could lose connective tissue to your category. At one point, Otter tried to call themselves “Ambient Voice Intelligence” and that isn’t a category or a solution to any problem.

2. Replace the tech.

Cursor ads from earlier in the year shifted from “code with agents” to “Build faster” and “Run in parallel.” The underlying tech moved slightly to the background while what the tech does for the buyer moved to the foreground.

Arielle Jackson at First Round traced the transition at Cursor. “AI-first” was enough at its 2023 seed announcement. Then Claude “reframed the question from ‘what editor do you code in?’ to ‘what agent codes with you?’” and Cursor repositioned around the agent instead of the editor.

It reinforces an original tenant of marketing: people care less about the thing than what the thing gets them. The thing is just a vehicle. “The AI is an engine, not the car, and buyers just care about where the car takes them,” Jonathan Bree, CEO at Fabric, told me.

Common misstep: Don’t simply replace one piece of jargon for another. “Agent” works for Cursor because of its developer audience. But it probably wouldn’t for Fabric or Lindy.

3. Sell the relief.

The best examples don’t stop at the task being done. The messaging empathizes with how they feel.

• Fireflies uses “At some point in the last few months I became ‘the person who remembers things’ at work.”

• Otter quotes “I feel so chill. That’s the feeling of winning when Otter handles your meetings for you.”

• Lindy tackles “The reports, the follow-ups, the small stuff that eats the week.”

• Granola promises “Walk in prepared, stay in the conversation, and leave with everything that mattered.”

At Lindy, Haneen told me about the relief that they want their customers to feel: “There’s so much you can delegate from a voice note, a text, a Slack DM. The work starts happening and you can close your devices and go to sleep.”

Common misstep: Relief is different than productivity. For a person who is already doing a million things in a day, doing more with AI may not be the result they are chasing.

4. Give it a number.

One of the findings from the original repositioning research was that AI-native companies promised a lot, and there was a big gap between the promise and the outcome. (And we all noticed.) “AI comes with some conceptual baggage around reliability and trustworthiness, as well as a lot of overselling of capability which users have learned to not take literally,” Jonathan at Fabric told me.

Christena Argo Reinhard at Pega told me she’s “noticed some of the sparkle being dulled as leaders realize there is no magic bullet.”

The claims get smaller, the proof gets bigger:

• Lindy uses ‘3x your output’ on purpose. “We’re very careful about saying this is an AI employee that will replace your department or person,” Haneen told me. “I don’t even think that’s the case.” The team is deliberate about a grounded claim rather than tossing around 10x or 20x.

• Replit ads went from “With Replit’s AI Agent, I typed exactly what I wanted” to “With Replit, he built a business that reached $180K in revenue in 6 weeks.”

• Synthesia said “the biggest AI tool for training videos” in July 2025 and by June 2026 adapted to “the tool used by 90% of Fortune 100 companies to make training videos.”

Common misstep: The number is not a measure of the tech. It should measure what the buyer gets, ideally in the form of a metric that they already measure, too. (Same logic as picking a pricing unit.)

5. Focus on the problem.

Haneen at Lindy does a lot of ad testing on Meta to learn and adapt messaging quickly: “You don’t say ‘this AI agent,’ you say: ‘you’ve got a pain point, that’s email,’ and then you show work getting done. You don’t show your product UI, you show Gmail being sorted, dashboards being created. Start from a use case they’d understand, from their world.”

Jonathan from Fabric made a similar comparison to me: “We don’t say ‘we invented a new kind of knowledge system,’ we say ‘it’s like Notion but it writes itself.’” Nobody buys something just because it is “AI-powered.”

Common misstep: Problem > product. Haneen’s team found that an ad of a Gmail inbox getting sorted outperformed a screenshot of the product workflow builder, as the first one is a picture of the buyer’s actual problem.

Not Everyone Is Moving

Some of the brands I audited are leaning harder into AI over the last year:

• Attio went from “a new kind of CRM” to “Ask Attio… with AI” to “AI-powered CRM.” For Attio, that’s probably still a differentiator from HubSpot and Salesforce, two brands that have publicly stumbled with AI.

• HeyGen uses “AI clone” constantly in its creator ads, where someone else is attaching their human brand.

• Granola has been doubling down on “AI notepad” over the past year, especially as it works to cement itself as the category leader.

I still see these as the exceptions more than the rule. As that First Round article said, “’AI-powered X’ stops being a position the second there are five credible ‘AI-powered X’ companies.” Most categories are well passed five.

I’ve got systems set up to keep tracking this into next year, when I’ll write an update. If you’ve noticed an example, hit reply or leave a comment. I’d like to see it.

From the Workbench this Week

I’ve been trying to make LinkedIn useful again. Somewhere in 15 years of accepting connection requests, my feed turned into strangers performing for an algorithm. It stopped being mutually beneficial: a place to learn from each other by sharing our experiences and lessons. (I actually think that’s part of why Substack is so popular right now.)

For LinkedIn, I had Claude sort through ~6,000 connections to unfollow 556 people who were adding noise (recruiters, ad vendors, etc.) and turned on notifications for the 100 I actually want more from. I shared the process earlier in the year and am working on it more this week. FYI: LinkedIn has been limiting how agentic tools can work in the browser so start small if you’re going to try it.

If you’re over there, connect with me. I promise not to unfollow you.

Have a beautiful weekend.

Search marketing has always been a venue where small companies could punch above their weight. At Recurrent, where I’m head of marketing, we outperform companies 500x our size with a simple formula: Proprietary data → compelling research articles → media coverage → SEO performance → more proprietary data.

But that’s SEO. AEO has been more of a slog.

Not only are we in an emerging category without standardized language, but we also support a variety of stakeholders in the auto industry. That means we are different things to different people. While SEO allows for that adaptability, AI models favor consistency that is difficult for us to offer: consumers on Reddit say one thing, industry publications say another, all while we try to shape the new category language with our website.

This newsletter is another example. Almost no citations despite some decent SEO. First to Market isn’t covering one TYPE of business. It’s a LENS on business: we’re all building in new categories that aren’t fully defined, where the tried-and-true playbooks for other companies don’t really work. We all get that as startup people, but it’s a tricky concept for an LLM.

I came up through SEO, and consider myself more adept than most at AEO, and it’s still difficult for me. Two experts and fellow startup leaders offered to help.

• Esteve Castells is cofounder of LLM Pulse.

• Luca Fancello is cofounder of Qwairy.

Before we get into ‘how,’ let’s start with ‘why’ AEO requires a different playbook for us.

What’s different in a new category

Being small is an advantage. No brand has built up an insurmountable lead in a new category so a few citations can have an immediate impact, as one study found.

You’re not filling a gap. In a mature category, you are looking for the places that incumbents missed or are not positioned to answer. A new category is more like creating boundaries because a lot of times the responses being returned today are sourced from incumbents in adjacent categories. That’s another example of the findability problem.

Volume is different than demand. Search platforms are designed for existing volume in defined categories. But we’re often starting from near zero here. “Keyword tools will tell you the market does not exist. They are measuring a name that has not been adopted yet, not demand,” said Esteve.

The language is in flux. One study found that Gymshark beat bigger incumbents (with better SEO authority) on athleisure topics by nimbly mirroring the phrases that consumers put in their AI prompt.

What doesn’t change

Buyers still describe problems. Whether someone is looking for a more affordable CRM or a formal shoe that fits wide feet, they are describing a problem. If you understand the customer, and can anticipate their language, then you can create content around the solution.

Traffic from AI chat still converts. LLM referral traffic converts as well as any other source. Lots of people have covered this, and I can validate it from personal experience. In a new category, where education is a critical part of the sales process, that could be even more impactful to us.

Third-party mentions still matter most. An LLM builds context by synthesizing across the web: every brand mention, Reddit comment, news story, product review. Your own site is just one of many, many, many signals.

Start here with AEO

Whether you’re deciding to revive an existing category or create a new category will ultimately influence your long-term AEO path. But that won’t matter until you’ve created a solid foundation. Either way, we all start with these steps.

1. Start from the buyer’s problem.

Our category name probably isn’t in anyone’s prompt yet. The prompts that people are using more likely describe the pain or ask for an alternative to their current attempted solution.

Esteve suggests: “Write down the 10 to 15 prompts a buyer would type. Those are your market until the category name exists.”

Test it: Try to draft the customer prompts that would have preceded your last 10 sales. Be SUPER specific with them then look for overlap at the end. Note: this exercise is even more revealing if you have call transcripts or long email threads.

2. Give the model a consistent summary.

“Update your one-sentence [company] description and repeat it identically everywhere,” said Esteve. “The model is going to paraphrase… [so] give it one sentence to paraphrase instead of five variants.”

This doesn’t need to replace your company slogan or homepage hero. It’s just the boilerplate summary of what you do for people that gets included in LinkedIn pages, media coverage, around the website, etc.

Test it: Go back to the prompts you wrote in Step 1 and use the common language + some aspirational positioning. That’s your first draft.

Common misstep: Don’t be cute. We all took a creative writing course and this isn’t that. It’s a mirror of the language your customers use.

3. Find the future mentions.

“In an emerging category the model has almost nothing to cite except your website, and it discounts your website because it reads as marketing,” Esteve told me.

Luca added: “A third-party mention beats self-praise every time [because] an AI discounts you calling yourself the best.”

Getting media coverage in a category that nobody covers is not fun. There’s no beat reporter or niche trade publication so it’s mostly pitching to people who don’t know or care why what you’re doing matters. (I’ve got hundreds of unanswered pitches in my Sent folder.)

But the other side is that nobody else has won it either. The bar is low and that makes it more likely for a no-name startup to clear it.

“Three or four placements can visibly change your share of voice in weeks,” Esteve confirmed.

Test it: Get an idea of the places that you need to show up by running the 10 customer prompts (from Step 1) and writing down the sources that get cited. You don’t need THOSE placements, just placements like those.

4. Give them something to cover.

Pitching a product to journalists is the worst PR sin. I do a lot of PR and meet with a lot of journalists and they often tell me about how they get (literally) hundreds of terrible cold email pitches per day.

Pitching your product is about you. But pitching exclusive data or information that they can’t find anywhere else is about them because you’re doing the first half of their job for them.

“Original data, a ‘state of the category’ study, the glossary that names the space,” Luca said. “Data and studies get cited constantly and can’t be copied.”

Test it: What can you see that nobody outside your company can? Aggregated data, consumption or usage patterns, customer surveys, etc. Those are the things that create a story that only you (and your company) can help to write.

Common misstep: This is not the time to look for a short cut. “New brands hunt for the trick that games the LLMs this week… when the thing that actually gets you cited is boring and slow,” Luca said.

5. Track your progress.

The main takeaway from Esteve: “When someone describes the problem you solve, who does the model name?”

That’s ultimately what we’re optimizing toward. You don’t need to spend $1,200/year on a platform starting today — there’s probably nothing to track yet. But once you’ve worked through Steps 1-4, you should have systems in place to track your ROI. Both contributors here build tools for exactly that (LLM Pulse and Qwairy) and there are others.

What comes next

Your AEO playbook after Step 5 depends on whether you’re attempting to win inside an existing category or attempting to build your own.

Category creation is a major positioning decision. AI search doesn’t really change that calculus.

But to help startup leaders going down both paths, I’m tracking companies in the category database against a fixed set of prompts. We’ll have a ton of great data in a month so expect an update once I have it.

In the meantime, a huge thanks to Esteve and Luca for contributing. They are super smart so we’re lucky to tap into their wisdom.

From the Workbench this Week

I’m running Steps 1-5 on this newsletter right now. As you can see, there’s some work to do.

The Category Signals page is half-baked right now. It’s a unique dataset and something that other newsletters don’t have or don’t give away for free. My goal this week is to continue to expand it and make it more helpful to both existing subscribers and future media mentions.

Ask me again in a quarter how it’s going.

Have a beautiful week.

Let’s see if we can improve on that.

A few weeks ago I talked to Fynn Glover for a post on pricing models. He had mentioned a B2B willingness-to-pay method that he uses so I called him back this week to talk me through it. I’m going through this right now as head of marketing at Recurrent so it was a (partially) selfish ask — more on that at the end.

Fynn is co-founder and CEO of pricing platform Schematic, before that led pricing at Automox, and wrote a book called The Pricing Playbook for AI & SaaS. (Get a free copy here.) His view is that pricing is the clearest statement of strategy a company makes, which is why he treats it as a research problem. He built his willingness-to-pay framework since he thinks that other popular methods (e.g. Van Westendorp) miss critical factors for B2B brands, like:

• Total cost of ownership

• Multiple decision makers

• Gaps in understanding about the product category

The format is an interview with 5 to 15 customers or prospects. This is his process and the company examples come from the category database to illustrate key points.

Step 1: Make Sure They Understand

“No willingness-to-pay exercise can be productive without ensuring the person you’re interviewing actually knows what the product does,” Fynn told me. “If that doesn’t happen, the interviewee is far less capable of giving you useful feedback.”

He gives them a few carefully crafted bullets or a recorded demo of 2-3 minutes that is identical across interviewees.

He also opens by telling them exactly what they signed up for: 8 questions over 30-45 minutes to help him understand how they perceive value.

Lesson: The quality of each answer (and the pricing test overall) depends on how well the interviewee understands what you offer, fix or improve.

Common misstep: The demo is not a sales pitch. We aren’t trying to get them hyped on the product or service.

Step 2: What It’s Worth vs What It Costs

After the introduction, Fynn asks three questions:

• What specific challenges or pain points do you think this product could address for you?

• Are there any features or aspects of the product that stand out to you as particularly valuable?

• How likely would you be to buy this product if it were fairly priced, on a scale of 1-5?

The last one assesses purchase intent with the price deliberately removed, which means a low score is more of a product or positioning problem.

Lesson: The buyer (or champion) must recognize value in order to test the price that they would be willing to pay.

Common misstep: Don’t fill their silence. Silence (or at least hesitation) is also an answer.

Step 3: Anchor Against What They Know

Ask them to help you build a 3-5 row table of adjacent things they already buy. The columns are simply: the category, the tool, what they pay for it annually, and our product’s relative value as a percentage: “We spend $100,000/year on Salesforce and you’re 50% as valuable as Salesforce” = $50,000/year.

For new categories, I think that software products may not be limited to grading themselves against only software products, either.

• 1mind prices its AI agents against employee headcount.

• Harvey ties its per-lawyer price to expected billable hours.

• Aurora quantifies in terms of autonomous trucking miles.

Lesson: Anchoring gets the interviewee thinking in dollars they already spend and understand.

Common misstep: Anchors aren’t limited to direct competitors. A range of tools and price points can be more revealing.

Step 4: Find the Price Metric

“Price metric is probably the most important part of the whole thing,” Fynn said. “It’s the fundamental element of your pricing strategy, and it helps establish how you will charge.”

The price metric is the unit you charge for: a seat, a ticket, a resolution, a mile. When it goes up, the customer invoice goes up.

His question: “Which price metric would be most intuitive for us to use or most aligned with the value you receive?”

He shows a list to guide them:

• Seats

• API calls

• Integrations

• SKUs

• Customer count

• Monthly active users

• Number of products

• Number of entitlements

• Number of features

• Other

If needed, sometimes Fynn redirects: “Imagine it’s Year 2, we doubled the price, and you are fine with it. What changed in your business?”

Asking what justifies 2x the price forces them to name the thing that actually drives value.

• Gorgias charges on ticket volume.

• Sierra charges on resolutions.

• Day AI charges for each AI assistant.

“I’m trying to listen for an answer across a composite of conversations,” Fynn said. “People don’t know how to talk about this stuff, so I’m trying to infer what’s actually sellable, not just take the first thing they reach for.”

Lesson: The best price metrics are what the answers keep returning to across conversations.

Common misstep: Don’t choose a price metric simply because it is easiest to track or bill.

Step 5: Test the Packaging

Show your latest packaging and give them 60 seconds to read it without talking over it. Fynn asks two questions after that.

1. What do you like and dislike?

2. Rank the tiers/options in order of preference, where most preferred means the tier that lines up best with the value you need.

Lesson: We’re not asking them to design our pricing page. We are listening for whether the tier we assumed they’d pick is the tier they pick.

Common misstep: Don’t jump in to explain the pricing to them. Just listen, document and answer questions as they come up.

Step 6: React to the Price

Give them a specific price and ask which of four statements fits best.

1. I would purchase this immediately, and I have the authority to do so.

2. I would advocate for it at this price, but I would need approval from others, which I believe is achievable.

3. I would consider it, but significant internal approval would be required and I am uncertain about the outcome.

4. At this price, the product is beyond our current budget or cost expectations.

The second option is where you’re aiming. It means the number is defensible without leaving too much money on the table.

If this all feels like an intimidating thing to commit to paper, just remember it’s not permanent:

• Tesla reprices the same car by thousands of dollars throughout the year.

• Lightfield made seats unlimited and moved to credits.

• Clay added a 2nd type of credits for its pricing tiers.

Lesson: You’re looking for the place where the price stops being defensible.

Common misstep: Don’t think of the price as some irreversible commitment. As long as we get a signal on the price metric, we have flexibility with the dollar amounts we assign to it.

Step 7: Find the Other Factors

At this point we have a direction on price metric, packaging and price tag. Fill in the gaps by asking for the other factors that influence what they’re willing to pay.

Giving them examples can help:

• Where does the budget come from?

• Who else needs to approve it?

• How often would you actually use this?

• What would you compare it against?

• Are there specific payment terms you require?

• Are there any current products or services you’d have to drop?

• Do you know other people/brands already using something like this?

Lesson: What people will pay isn’t 100% rational. Every org has budget pressure, internal politics, egos, personal aspirations and things that are difficult to address on pricing pages.

Common misstep: Don’t confuse a purchase hesitation with a price objection. Fynn didn’t say this but, from my experience, the overall price tag can be a much smaller factor than most of us think.

Step 8: Close It Out

Fynn ends interviews by asking whether there’s any other feedback on the product, pricing, packaging or the exercise itself.

In journalism school, we learned that the best question of any interview is “Is there anything I missed?”

Sitting through one of these is a lot for someone. Acknowledge that.

Fynn said that he gave discounts to early customers at Schematic. For similar types of customer interviews, I sent a gift card for more than expected because (1) I wanted it to be obvious that it is a gesture rather than a direct payment for time and (2) it still always paled in comparison to what I’m getting in aggregate, even if one or two of the calls were less valuable.

Lesson: You are very, very thankful. Make that clear.

Common misstep: Don’t miss the opportunity for a relationship- and retention-building moment with a customer.

Compile and Decide

“I think the business should bias towards speed,” Fynn said. “After 10, we’ve probably got what we need. I don’t think I need to do this 150 times to be statistically relevant.”

At his previous 3,000-customer company, about 30 interviews in a quarter was enough. Even 5 interviews can be enough to make confident decisions, according to Fynn.

Lesson: You’re looking for the trends across conversations. People won’t say the same thing, but they will use a lot of the same words and rationales.

Common misstep: Don’t wait for a definitive, statistically significant answer that is not likely at your scale. Make the decision and move forward as soon as you feel like you have just enough info to do it.

None of this requires a pricing consultant or product expert. After you convince a handful of customers to meet with you, it simply requires committing to the process: putting in the prep time up front, being disciplined in the interviews, (sometimes) chasing interviewees as they no-show and reschedule and life gets in the way.

You’ve got this. Hit reply or get Fynn’s free book if you need a pep talk.

From the Workbench This Week

I mentioned at the top that I’m doing price testing, too, which is why I tracked down Fynn.

We’re launching a new product in a very new category so it requires lots of up-front education for them to understand what we are doing and why it matters. It is always exhausting, sometimes discouraging, and occasionally super exciting, all in one day.

What Fynn’s process is giving me is permission to be wrong in the short term, hopefully in an effort to arrive at a place that is more “right” in the long term. I’ve long believed that customer interviews are the most valuable thing that a marketing leader can do, and this is only reinforcing it.

Next week I’m putting together an AEO strategy guide. If you’ve got questions you want answered or tactics to include, hit reply and tell me.

Have a beautiful weekend.

But they very intentionally didn’t market to “everyone” at launch. They spent months obsessing over one tiny audience.

Most of this post is originally from February, before I built the category audit database. It was also a piece I sent people often so I went back and checked it against the 202 companies across 19 categories.

The results reinforced the advice: The majority of successful brands in the database launched with a very narrow ideal customer (ICP), saturated it, then added the next one.

• Figma launched for individual designers inside a bigger team.

• Stripe launched for technical founders at early-stage startups.

• Deel launched for startups adding their first overseas contractor.

The Everyone Trap

Startup valuations (and investor interest) come from addressable market size. That means there’s often incentive to target bigger, wider, richer.

Canva co-founder Cameron Adams described the problem in an interview with First Round Review: when products are pushed out for “everyone,” the messaging becomes impossible. You can’t write a homepage, an onboarding email or an ad that resonates with a real estate agent and a YouTube creator and a nonprofit director. You end up kind of missing all of them.

We understood this at Recurrent and focused on the nerdiest and most passionate EV owners. These are the people who have all the charging apps, join all the forums, and talk your ear off about their car. By focusing on them, our message landed and early growth accelerated.

In addition to diluting the message, going broad loses the product signal, too: one segment loves you, one segment hates you, and one is indifferent. All you get is an average of them, which doesn’t really help much.

Here’s the first audience selection framework derived from Canva that any founding team in an emerging category can steal. (If you already launched and are looking to re-narrow your focus, I wrote the S.O.S. recovery version of it here.)

First Audience Framework

Ahead of Canva’s launch, the founding team chose social media managers as their core audience. That’s very specific.

• Not “small businesses” = tens of millions of potential customers ❌

• “Social media managers” = thousands of potential customers ✅

Here are the five criteria (or filters) for selecting your own specific audience.

1. Feedback Velocity

Social media managers needed a steady drip of high-quality graphics. Pinterest and Instagram were exploding when Canva launched in 2013 and the people creating that content were doing it dozens of times per day.

That frequency is feedback velocity. Daily users provide more nuanced product feedback in a week than monthly users do in a year. Canva could ship a feature by Monday and have an insight by Wednesday.

The same pattern shows up across the database:

• Granola launched for individuals in back to back to back meetings.

• Lululemon launched for yoga instructors teaching lots of classes.

Test it: Organize your pilot customers or waiting list by the frequency and variety of use. It’s a valuable view of your potential ICP that often gets overlooked.

Lesson: 500 daily users will teach you more in a week than 5,000 quarterly users will ever teach you.

Eliminate: For most startups, try to stay away from a first audience that uses your product/service monthly or less often. They just can’t help you iterate and grow fast enough.

2. Revenue Signal

A good first audience can (1) see what you’re worth and (2) approve the investment without asking for permission.

Social media managers had both qualities. Their work fed marketing metrics that execs already tracked and Canva was cheap enough to test on their own.

Test it: Go back to the list you sorted by frequency. For each group, name the tracked metric that goes up when they use you, along with its proximity to company revenue.

Lesson: Pick an audience that can prove your value and act on it. One that can only do the first will cap how fast you grow.

Eliminate: If you don’t influence one of the metrics that matter most to them, there’s no urgency and that audience is not a good place to start.

3. Organic Distribution

Social media managers post for a living. That’s a pretty incredible distribution opportunity for a first audience.

Cameron Adams referenced it in his interview with Lenny Rachitsky: these users loved being on social media, telling people about the tools they were using, and introducing Canva to others.

“[Early users] need to want to talk to other people about it, because in the early days of your startup you don’t have marketing dollars. You need to really foster the first people that are going to use your product. They’re going to be the ones that are going to spread it and they’re going to set the foundations for your growth.”

As a growth marketer, I tend to get caught up with channels. But it’s more about the mechanism: does the audience put them in front of the same or another potential audience?

• Superhuman targeted founders, and founders talk to other founders.

• Figma targeted individual designers who pull in other members of their team.

• Lululemon dressed instructors who stood in front of classes each day.

Test it: Find the communities where your potential first audiences already gather: Facebook groups, Slack communities, Reddit, Discord, industry forums. Are they actively making recommendations or do they treat what they do as secret sauce to hide from the world?

Lesson: The best first audiences don’t need incentives (e.g. referral programs) to spread the word. They share because that’s part of their identity, industry or path to success.

Eliminate: If your first audience doesn’t naturally share with peers, you’ll have to manufacture every single bit of awareness from scratch (or cash).

4. Acquisition Cost

Canva found that social media managers gathered in predictable and affordable places. Many subscribed to the same public Facebook groups, attended the same conferences, and read the same niche publications.

No part of Canva’s early acquisition strategy required a massive media budget. They knew where to find their target audience and designed every piece of messaging to speak to them.

• Clay answered questions in existing GTM communities.

• Product Hunt contacted the builders featured in their own daily email.

Test it: For each potential audience, list the 3 ways you would put your product in front of them along with the expected investment (time + money) of each one.

Lesson: Affordable and repeatable channels let you run more experiments. That’s the actual cost of an exclusive or expensive audience.

Eliminate: If you can’t reach an audience (at scale) on your budget, they shouldn’t be your first audience.

5. Market Bridge

After social media managers, Canva expanded to startup founders with lean teams by adding a ‘Presentation’ feature designed, in large part, specifically for them.

They also didn’t have to abandon their first audience to reach the second: Tech founders were early with social media so they were the ones hiring social media managers.

• Cursor launched for individual developers, whose output made them the case study for everyone else on the team.

• Figma launched for designers then bridged (through the designers) to the engineers and PMs who worked with them.

Test it: Create a relationship map for your first audience. Who do they naturally influence? Who do they share with outside their immediate peer group? We’re trying to identify at least two adjacent audiences that interact with your first audience.

Lesson: Your first audience should have somewhere to take you.

Eliminate: A highly engaged vertical audience that doesn’t interact with other verticals can trap you in a niche.

Choosing your first audience

Run each of your prospective audiences through the five filters. I bet you’ll walk away with two things:

1. One audience will look a lot better than the others after surviving this test.

2. That audience will probably feel uncomfortably narrow.

The founding team at Canva always intended to use its product to help billions of people. They’re well on their way with 250+ million monthly active users. But they had the restraint to target a few thousand first, and serve them so well that those thousands became the growth engine for everything and everyone else.

Hit reply if you need a pep talk. That’s what I’m here for.

I’ve been there and I get it: it’s a visible decision, it feels irreversible, and it’s the market’s first impression.

You’re not alone. To back it with data, I coded the pricing models of 160 companies in the category database then combed through 20 CRM startups for illustrative examples.

Four patterns emerged across both exercises.

1. Product “seats” are no longer the default. In CRM, Lightfield gives unlimited seats on every plan and prices credits.

2. But metering is usually AND rather than OR. In CRM, Attio kept its $35 seat and added two separate credit pools on top of it.

3. Companies add tiers to grow accounts. In CRM, Twenty lists $9 and $19 per seat on its pricing page, then an Enterprise tier starting at $50,000 a year.

4. Metered pricing is still confusing. In CRM, Clarify uses a table in its help docs to show the credit ranges of specific actions. (More on that later.)

CRM was an easy example because ‘seats’ had been the standard unit of measurement for 20 years. But the same trend is visible across the whole category library, partly outlined in last week’s Great AI Migration. Of all the audited companies founded since 2020 that publish a price, 56% price based on usage or outcomes instead of only ‘seats.’

That’s kind of a big deal for founders in emerging categories, where there isn’t usually an incumbent’s pricing model to copy.

I think that means we shouldn’t be worrying as much about the sticker price as what is being counted and how we are counting it. This should help you with that.

Questions to test pricing in new categories

1. What does your customer already count?

The best unit is usually a number your buyer tracks whether or not you exist.

• Fin (help desk) charges per resolution rather than users (like its predecessor Intercom).

• Gorgias (help desk) uses the monthly ticket volume that teams need to triage.

• Ashby (ATS) prices on company headcount instead of number of recruiters.

Fynn Glover, CEO at Schematic, a usage-based billing platform for companies with enterprise customers, told me that this step is a translation: You need to understand the intuitive value your customers already perceive then find the unit that captures it.

Common misstep: It’s easy to favor a unit that’s easiest to measure. For example, seats are easy because products have users.

2. Does the number go up when your customer wins?

The best unit of measurement makes your revenue a byproduct of your customer’s results: You get paid more because they got more.

• Ashby (ATS) grows with its best customers as they add new employees.

• Lightfield (CRM) meters credits against selling activity. The bill goes up when sellers are active (e.g. meetings are being booked, prospects are being contacted, etc.) instead of the company adding headcount.

Common misstep: Don’t assume more usage equals more value. It only counts if the value outpaces what the usage costs. Pylon (help desk) watched its annual AI bill go from ~$400,000 to $1.4 million without any change in value.

3. Can you say the price without explaining it?

Fynn said that most founders are better at pitching their product than pitching their pricing. If the pricing matrix or unit of measurement requires a long-winded justification, it’ll be even harder for the customer champion and procurement team and finance budgeters to justify you.

• Nutshell (CRM) prices by hours saved per month. ✅

• Fin (helpdesk) charges $0.99 per resolution. ✅

• Twenty (CRM) includes 50 workflow credits per year in the Pro tier for $9 per month. ❌

Clarify (CRM) publishes what each action costs in credits, which is more transparent, but I think that’s a symptom of the problem: If a buyer needs a detailed documentation to even remotely estimate their monthly bill, the model is too complicated.

Common misstep: Resistance doesn’t necessarily mean it’s too expensive. Understanding the unit of measurement or being able to translate it into ROI could be much bigger blockers.

4. Will you be able to tell whether it’s working?

Manish Choudhary, CEO at Flexprice, an open-source usage-based billing infrastructure for AI and SaaS companies, told me that he recommends two metrics to assess if pricing is working for or against you.

1. Check gross margin on the top 10% of accounts. The blended average hides the tail until it’s too late. (Kyle Poyar similarly highlights the dangerous token consumption of the top 10% of AI users too.) Just raising prices doesn’t work if the top accounts are systematically unprofitable.

2. Split expansion revenue into (1) making more because customers used more and (2) making more because you decided to charge them more. The first one is sustainable growth.

Common misstep: Don’t wait until you need this data to start collecting them. Like my dad (still) tells me about planting a tree, yesterday was the best time to start and today is the next best.

Get the Price Wrong

This should all read like a long permission to be ‘wrong’ with your initial pricing as long as you’re right about the unit of measurement.

• You can raise, lower, discount, grandfather or do whatever you need to do with prices without customers blowing up Reddit.

• Getting the unit right is a more difficult thing to adjust: terms resets, account margins shift, customers refactor the value of the relationship.

Intentionally select a unit that your buyer already counts and make sure it grows when they win. You’ll still probably want to recast your rates by the end of the year, but at least the underlying framework will be stable and sustainable.

We’ll figure out the rest as we go.

Thanks for reading this week. I’ll be back next Friday with a big analysis on time to value and exceeding customer expectations. If you have experience or tips, hit reply and let me know.

Have an unforgettable week.

Luna.ai was one of them, launching in 2023 on that premise. Today the domain is for sale, and I had to use Wayback Machine to find the old homepage.

They couldn’t deliver on the promise to replace people with agents, and they aren’t alone.

• Regie went from replacing the rep to being a “force multiplier” for one.

• Artisan went from “stop hiring humans” to working alongside your team.

• Day went from the “Waymo of CRM” to working in collaboration with reps.

Those are three of many AI-native companies that spent this year walking back their claims toward something more tangible and realistic: saving time, delivering value, not necessarily automating anyone out of a job.

At the same time, SaaS-era companies are racing to meet the same AI-era expectations from the opposite end of the spectrum, adding automation and pre-emptive action that frees customers from the (now-clunky) experience of navigating a user interface to find the right dashboard.

One widely publicized example is Clay, which now allows customers to skip its (once-indispensable) spreadsheet UI by connecting it to other tools through an API. Brendan Short covered it well in The Signal, if you haven’t read it.

Together, it’s two groups of companies moving in opposite directions, arriving at the same place. That’s a Great AI Migration, and it’s easier to see plotted on a single axis here.

To test if these are anecdotes or an actual pattern, I coded 57 companies across seven categories in the category database: help desk, CRM, legal tech, AI SDR, coding assistants, meeting assistants, and AEO.

It’s definitely a pattern.

It also produced a diagnostic that is helpful whether or not you’re building an AI-native product, as we’re all building in the AI era at this point.

To illustrate the insights from the audit, I interviewed Jim Watson, an executive at AEO pioneer Gumshoe. They’re an AI-native company in an analytics category still shaped by dashboards, and they’re further along in understanding this shift than most companies, so Jim’s wisdom is shared throughout the article.

Five questions to test your AI-era positioning:

1. Do you name the labor you remove?

It’s tempting to focus on the technology or the layer your company automates. But buyers want to know what you’re actually doing for them.

• Granola calls itself the AI notepad for people in back-to-back meetings. The labor they are removing is organizing notes while customers are already onto the next call.

• Regie talks about removing the thousands of micro-decisions that slow down sales reps.

• Codex says “drive the work you own and delegate the rest.”

For Gumshoe, Jim puts it as the difference between “what” and “why.” A tracking dashboard tells you your AI visibility is 32%. The labor they remove for their customers is figuring out why it’s 32% and what to do to improve it.

Common misstep: Peec AI sells “AI search analytics for marketing teams,” and it’s an example of a feature set that’s stuck in a dashboard era. There’s nothing that reveals what the customer gets to stop doing.

2. Can your customer do something different tomorrow morning?

This is what the Actionable Middle (from the chart) promises and neither end can deliver. A dashboard gave you a number that you had to research. Full autonomy did something, but the customer wasn’t in the loop.

• Zendesk moved from routing tickets to resolving them.

• Alta turned its dashboard into an “AI System of Actions.”

For Gumshoe, one customer was positioning itself around expertise. Gumshoe showed AI models were describing them through their loyalty program instead. They were winning an attribute they didn’t want and losing the one they’d invested in, a finding that easily translates to action.

Common misstep: When unsure of what’s actionable, it’s tempting to throw quantity of information at customers rather than quality. Being truly actionable can mean giving customers less. Gumshoe surfaces the single highest-leverage action in an audit, then checks back on whether it worked.

3. What gets your buyer promoted?

Someone inside the company has to champion your product, defend the expense, and explain the results next year. In B2B, the best products help both the client company and the client buyer.

• Day heard that a self driving CRM was “too scary” for customers so they pivoted to be a control and configurability layer.

• Harvey calls itself a co-pilot since the lawyer still has their name on the filing or brief.

For Gumshoe, marketers use it to turn AI search from a blind spot into a grade that moves “up and to the right.”

Common misstep: We’re selling to humans, and non-sociopath humans are unlikely to champion a product that promises to make their team redundant.

4. Does your revenue survive your own success?

Pricing models are a bet on behavior. If your pitch is that customers don’t need to log in to use a “seat” like a traditional SaaS product, then winning can break the model that pays for you.

• Clay is leaning into usage-based billing as customers stop logging in for that “seat” experience.

• Fin charges per ticket resolution. Zendesk is pivoting toward the same model, stuck in a hybrid limbo.

For Gumshoe, the unit is a project rather than a person (seat). What separates the tiers is how often the tools run and which ones you get.

Common misstep: Getting stuck in the middle ground between seats and usage is awkward for customers. Just ask a Salesforce user.

5. What can you afford to give away?

Freemium worked in the SaaS-era because additive product usage was super cheap or free. That’s no longer the case for AI-native companies, which have to pay token costs, but buyers still expect a cheap entry point that over-delivers on value.

• Zendesk uses ticketing as a table stakes entry point that moves people to per-resolution agentic features.

• Canva and Notion added tiers for their AI features.

For Gumshoe, it’s a product that needs tokens to run each day for each account. A “Free Tier” is never going to be sustainable, so they use a one-time audit as a “Free Trial” to cap the up-front investment, which speaks to me as a growth marketer.

Common misstep: Buyers expect to try something before they pay, and in an AI-native product that trial has a direct cost. Cap it so it’s predictable, then factor it into CAC as part of the growth marketing budget.

It’s a new era all over again

Business intelligence products have promised Answers > Dashboards for a decade, but they keep delivering fresh takes on old dashboards. This AI migration is different.

The startup audits I’ve done this week show lots of prominent brands actually converging on the same place, even if they are coming at it from different directions.

• The measurement companies are moving because parts of their product are becoming a commodity.

• The autonomy companies are moving because buyers stopped buying their promises.

The times are changing and now is when you need to be adapting, especially if you’re an early stage company with an opportunity to get there first like Gumshoe.

Let’s learn from AI startups like Luna.ai, even if that means we have to use Wayback Machine to do it.

1. A founding team launches its product.

2. They get an initial pop of interest.

3. Those early signals encourage the team to expand its audience focus, trying to replicate the initial pop on a larger overall audience size.

4. They can’t replicate it.

5. All growth stalls.

That timeline could be 2 weeks or 2 years, but it almost always follows that sequence and my advice is almost always the same: “You need to focus narrower rather than casting broader.” It takes the shape of:

• A landing page that tries to speak to everybody ends up resonating with nobody.

• An onboarding flow for all users never hooks any of them.

• A nurture sequence for three personas never motivates any of them.

While my advice is typically to get hyper-focused on one ICP or segment, then restart growth from there, I’ve never scrutinized it with historical data.

This week I coded the companies in our category database on how they described themselves at launch vs at 2 years vs today. For example:

• Cartesia launched as “real-time multimodal intelligence for every device” and narrowed to developers building voice agents.

• Framer launched for designers making prototypes and narrowed to teams shipping websites.

• m3ter launched for anyone running pricing operations and narrowed to companies billing on high-volume usage.

Some of the strongest performers in the database did show a narrowing after launch and, against their peers, a narrower focus in the early years was 2-3x more efficient with revenue growth. (Note: I used median revenue-per-dollar-raised to try to standardize an objective definition of “success.” It’s not perfect.)

That means I stand by my advice for a lot of teams: retreating to a narrower focus can actually accelerate growth rather than limit it.

If any part of this sounds familiar, here’s my playbook for addressing it.

Post-launch Narrowing

1. Narrow the Audience

“Narrow” doesn’t always mean what people expect. There are lots of ways to narrow your target market.

• Clay narrowed on the task to be done.

• Brex narrowed on the type of small business.

• HubSpot narrowed on the company size.

Ideally, your narrowed selection should have two qualities:

1. It represents your best customers already.

2. You have a tangible and defensible wedge to keep winning with that segment.

If your narrower audience is simply “the same pitch, fewer people,” you’ve cut your market without creating an advantage or potential flywheel.

Test it: Craft a one-sentence tailored pitch for a segment that shares the attributes of your 10 best customers and doesn’t share the attributes of your 10 worst customers.

Lesson: The goal is identifying your best customers then putting your business in a position to replicate them.

Common misstep: It’s tempting to choose the segment you want over the one you have. The shortest path to growth often goes through your existing customers.

2. Narrow the Product Messaging

“Narrow” doesn’t necessarily mean changes to the product or product roadmap. We’re really just talking about honing the way that the product is presented to its audience.

• Framer found that early customers kept using its broad prototyping tool to build simple sites. They now are an “AI website builder.”

• Clay found that people used its automatable spreadsheet to run outbound campaigns. They now sell “systems to grow revenue.”

Test it: Document the way that the segment from Step 1 talks about your product or service and the specific problems that you solve. Then compare that to your home page for messaging gaps or omissions.

Lesson: Your ideal customers can help you hone your product messaging if you take the time to capture it and use it.

Common misstep: These updates are more than a home page headline. The updated message needs to be pulled through the entire experience from the first advertisement to the final onboarding step.

3. Solidify the Wedge

Your narrower focus should also make you uniquely positioned and defensible against competitors.

• Linear chose “issue tracking” over “project management” to resonate with technical teams.

• Pipedrive spoke to the salesperson by emphasizing pipeline management rather than abstract revenue goals.

• Hightouch used technical jargon on purpose to speak to the data engineers it wanted and push away the marketers it didn’t.

Test it: Go back to your one-sentence pitch from Step 1 and your gap analysis from Step 2. Look for opportunities to tailor both to your target audience in a way that no other competitor could claim, acknowledging it might mean pushing some of the not-ideal people away.

Lesson: Positioning that is most likely to resonate with the right buyer may also push the wrong buyer away, and that’s a risk we’re willing to take.

Narrowing Isn’t Permanent

None of this means you have to stay small. The whole goal is demonstrating traction with a small base so you have the foundation to expand from there.

• Canva positioned itself exclusively for social media managers, then expanded to startup founders, then expanded from there.

• Deel went from one contractor workflow to “The Global People Platform.”

• HubSpot went from a single marketing tool to an entire customer platform for sales, service, marketing and product teams.

• Brex went from startup corporate card to enterprise spend platform.

Building has never been cheaper or easier, which means a feature list doesn’t make our companies unique any longer. While it may be tempting to go as broad as possible with your product, a bootstrapped competitor can copy a meaningful portion of most of our products in a weekend. Your best defense is a good offense, fueled by owning the small segment that you are uniquely positioned to win. Make whatever adjustments you need to go get them.

• We don’t acknowledge their size because it’d make us look small.

• We don’t accept their feature breadth because it’d make us look behind.

• We don’t admit to their market dominance because it’d make us look weak.

Instead, we position away from the Goliath: a different feature, an unbundled price, some angle they won’t match. Maybe if we can find ways to avoid talking about them, people won’t think about them.

But buyers are already thinking about the incumbent.

The category leader is the default choice. Pretending they don’t exist doesn’t eliminate the comparison, it just makes our relation to them fuzzier.

Supabase could have called itself “a modern backend platform for developers.” Instead, the top of their homepage read “The open source Firebase alternative” for its first five years and something north of $100M in annual revenue.

Then they moved away from it. As they became a leader, the alternative positioning no longer made sense.

I originally published this post in February, but a more complete arc is visible now. Supabase shared new information with its $500M Series F in June and I got some great data from comparing their playbook to 202 category leaders in the First to Market audit database.

Here’s the updated playbook.

The Alternative Positioning Framework

Step 1: Name the Giant

When Supabase said “open source Firebase alternative,” they answered three buyer questions in four words:

• What is it? A backend-as-a-service, like Firebase.

• Compared to who? Firebase, the industry standard.

• Why is it different? Open source.

How they arrived at that is equal parts interesting and lucky, based on an interview with CEO Paul Copplestone:

“Originally, we weren’t really thinking of building an open source Firebase alternative. It’s just that we changed the tagline on the website one day, and it went straight to the top of Hacker News that day… And we kind of had to run with it.”

My audit database backs him up. RudderStack at launch called themselves “an open-source platform for collecting and routing your customer event data.” No mention of Segment. People in Hacker News threads quickly made the comparison and within 6 months there are brand and founder mentions of RudderStack as an “open-source CDI (a.k.a. open-source Segment).”

In both cases the market helped to write the positioning and the founding teams listened.

Test it: Ask 10 prospects to describe what you do in one sentence. If they use a competitor’s name to explain you, it opens an intriguing door that you get to choose to walk through or not.

Lesson: Naming the incumbent can be a shortcut to product clarity and market confidence. It says “Yeah, we know they exist, and here’s why you should still pick us.”

Common misstep: Keep in mind that the comparison is not really in your control. Your buyers are already making associations.

Step 2: Claim the Wedge

Supabase designed its whole offering around something a loud minority of Firebase users already wanted: ownership and control.

They weren’t trying to resonate with Firebase’s whole customer base. They gave one specific segment permission to leave.

As the Supabase CEO acknowledged: “Firebase is the tool that developers love to hate, because it’s Google now.”

Freshworks is a similar case study. Its founders kept reading Zendesk customer complaints about the pricing model, revealing a clear wedge into the category and customer list: “Whenever I read something like this I can’t help but think ‘Here’s a potential customer to whomever can get this right and at the right price’. Here was an opportunity sitting right in front of me.”

Both wedge examples were responses to documented and quantifiable complaints from incumbent customers.

Test it: Let incumbent customers shape your wedge opportunities by reading Reddit, Hacker News, support forums.

Lesson: A good wedge needs to fuel a fire that’s already burning.

Common misstep: It’s easy to confuse preferences with wedge opportunities. “Faster” or “cheaper” or “easier” are common complaints but not always strong enough to be wedges.

Step 3: Prove the Difference

Lots of startups break down on the offramp of Open Source Alternative. It isn’t novel or durable by itself.

Supabase made it a structural piece of their product that was visible in the codebase, the community, the GitHub responses, and throughout the marketing site. That let skeptics verify the claim without talking to a salesperson.

Amplifying the voices of early customers can be a good way to do this, too. Cursor used its testimonial wall to make the point with quotes like: “Cursor has been a pleasant surprise. One of the best use of AI. Better than Copilot? I think so.”

Test it: Can a skeptical prospect verify your wedge without a demo or a sales call? An unverifiable wedge is just a claim that easily crumbles under pressure.

Lesson: Make the wedge easily checkable with comparisons, docs, benchmarks or testimonials.

Common misstep: The standard of ‘proof’ is different to different people. One customer quote on a landing page probably won’t cut it.

Step 4: Lower Switching Costs

Selling someone on a solution to a problem that they’ve already solved (with a competitor) means you are partly responsible for overcoming the switching costs. If you’re a marketer, think about migrating a CRM: there’s the technical migration, the downtime, the ramp up and time to value, and the risk of picking the wrong vendor. That’s a lot to overcome.

• Supabase used generous free tiers to start new projects rather than migrate old ones. They also made resources for partial migrations or running both in parallel, then used the community to help solve the weird edge cases in public.

• Lightfield created a CRM migration agent to ingest exports from HubSpot and other legacy CRMs.

• Gorgias tried to pre-empt the switch by getting in front of prospects the moment that a competitor product was installed.

Test it: List every friction point between an unhappy incumbent customer and the complete adoption of your product or service. Approval, training, migration, downtime, the risk of looking dumb if it fails. The best at alternative positioning help their prospects with each one.

Lesson: Status quo bias means doing nothing is always easiest. Every piece of switching friction compounds against you.

What the Data Says About Alternative Positioning

In my audit of 202 company profiles across 19 categories in the First to Market database, I found only two that positioned themselves against an incumbent at launch.

1. It is more common to see startups position against incumbents after a couple years when the new-kid-at-school buzz fades or the hyper-niche focus needs to expand to meet growth goals:

• tldraw started naming Miro.

• Otter started naming Fireflies.

• Rippling started naming ADP and Gusto.

2. Companies that named an incumbent tended to be self-serve or PLG led. That makes sense. If your product requires a six-month enterprise cycle, the incumbent comparison is a more tailored response to a specific question.

3. For B2B sellers, that could mean that alternative positioning has an expiration date when you decide to sell upmarket.

For Supabase and Freshworks, who stopped naming the former incumbent as they approached incumbent status, helps to cement a key lesson: a startup should view the incumbent’s angry customers as the beachhead or a wedge into the market. It’s not an evergreen brand strategy.

Should You Run This Play?

If you’re still nodding along with me, here are the four tests I’d use to decide if this strategy is for you.

1. Is there one dominant incumbent?

This should take less than a second to answer. The strategy only works when there’s a single default choice your buyers already know.

Fragmented markets with heavy feature overlap aren’t a fit. An easy example of that is marketing automation with too many leaders, lots of product parity, and many serving their own niche. If you can’t name a clear incumbent, the market is either too fragmented or you’re creating a new category.

2. What’s the wedge?

Complete this: “We’re like [incumbent] but [meaningful difference].”

Then: Is the difference tangible? Does it solve real pain for a segment you can actually target? How hard is it for the incumbent to copy?

You’re biased here. Get someone to check your work.

3. Can you prove it without a sales call?

Ideally you become the wedge, the way Supabase became synonymous with open source. A skeptic should be able to verify it alone.

The Takeaway

Positioning as the alternative means tying yourself to someone else’s narrative. You boom when they boom. You stumble when they stumble. If they pivot, you must adapt.

In exchange, you inherit their existing demand and budget line. Supabase never had to convince developers that backend-as-a-service was valuable because Firebase already did that. They only had to convince one segment that open source and SQL were worth the switch.

But the data suggests that this is a phase. See it as the opportunity that it is without confusing it for the evergreen business plan that it isn’t.

Gartner’s 12 for 2026:

• DxM not LLM is domain-specific AI models.

• AI use-case optimization for agentic is (a mouthful for) frameworks to govern AI agents.

• AI-disaggregated applications is an array of agentic micro systems or services.

• Physical AI is AI inside robots, machines and industrial systems.

• Autonomous drone is an aircraft without human oversight.

• Intelligent simulation is AI-driven digital twins used to simulate scenarios.

• Hyper-synthetic data is artificially generated training data.

• Low-power AI is models that are light enough to run on-device.

• Earth intelligence is AI applied to satellite and geospatial data.

• Wicked intelligence is AI aimed at stubborn problems like global warming.

• Cyberkinetic security is focused on defending physical and autonomous systems.

• Authenticity platforms is largely deepfake detection right now.

The audit begins by placing each category in one of four stages from the Four Waters Framework then uses its maturity signals to plot the next steps and potential missteps:

• Uncharted Waters is where people don’t think the problem is worth solving yet. You’re selling belief and your top competitor is doubt.

• First Voyage is where early adopters validate the product, and there’s a race to define the category before rivals set the terms.

• Charted Course is when the category is real and mainstream buyers enter. It’s a land grab.

• Crowded Waters is when everyone gets it, so you fight for market share on execution, price and unit economics.

Comparing Gartner’s 2026 disruptors against the library of 10+ year-old categories identified lots of trends, but one big takeaway was that the competitor that beats you usually isn’t the one selling what you sell.

• It’s a budget that doesn’t exist.

• It’s a platform that turns you into a feature.

• It’s an analyst rewriting your category thesis.

• It’s your own GTM motion fighting your buyer.

Here are six early-category competitors that you’re probably not watching.

1. The budget that doesn’t exist yet

Deepfake detection has some basic ingredients for a category gold rush: real threat, scary headlines, complicated technical problem. Reality Defender, Truepic, and GetReal have all raised money on that, but no pure-play unicorns have emerged.

Compare those to legal or healthcare AI, born during the same years and built on the same models.

• Harvey is valued at over $11 billion.

• OpenEvidence is $12 billion.

• Abridge is $5 billion.

A big difference between them and deepfake detection is how they are funded. A hospital already paid for the hours a doctor spends writing notes. A law firm already tracked and optimized billable hours. That meant Abridge and Harvey got to tap into a budget line item that already existed, and the ROI math was basic arithmetic.

That’s not the case for deepfake risk.

The same is mostly true with answer engine optimization (AEO). Marketing teams already have budgets for SEO and content tools, and every “What’s your AEO plan?” headline makes it easier to siphon from them. AEO startup Profound reached a billion dollar valuation in only a few years.

The pattern holds as I look back through the mature categories, too. Applicant tracking (Greenhouse, iCIMS) pulled from a recruiting budget that predated ATS by decades. Help desk software (Zendesk) pulled from a support budget line. None of them asked their buyers to totally invent a new line in the budget.

Lesson: If your prospects aren’t already paying to solve that or a related problem, you’re selling a behavior change in addition to selling your product. That takes extra time.

Test it: Ask your early customers how they are funding your product or service. Where does the money come from and who signs for it?

Common misstep: Don’t worry about potential competitors or differentiation until you help customers find the budget authority to pay for you.

2. The platform that turns you into a feature

“Synthetic data” is doing two things at once that usually happen years apart: It’s maturing and consolidating. Gartner just named it as a 2026 disruptor, but the startups that pioneered it are mostly gone.

• NVIDIA bought Gretel.

• SAS bought Hazy.

Synthetic data is a horizontal capability that makes a bigger platform stickier: better training data sells more NVIDIA compute and more Databricks contracts. The platforms either built or acquired early and the category largely landed as a feature inside existing tools.

From what I can tell, the remaining independents mostly niched down or shut down:

• Tonic refocused on data for engineering teams.

• Parallel Domain refocused on data for self-driving.

• Mostly AI and Datagen faded away.

Lesson: If your product and emerging category are horizontal and make a bigger platform stickier, the familiar land-grab window may not open before the platforms absorb it.

Common misstep: Don’t be so committed to creating the category that you miss the early exit opportunity. See: Gretel.

3. Your buyer’s calendar

How fast your category matures comes down to two things with outsized influence:

1. How long your product or service takes to implement

2. How long it takes to deliver value

When a single user can adopt in an afternoon and experience value immediately, the whole category matures faster. Granola reached a $1.5 billion valuation in “AI note-taking” in about three years.

When the product depends on long implementation, the pace is slow but the moat is deeper. A hospital’s procurement schedule, a regulator’s sign-off, an OEM’s manufacturing calendar are all mostly out of your control.

But the time it takes to deliver value is often more in your control and it’s getting shorter. As Santosh Sankar of Dynamo Ventures explained to me about the emerging physical AI category, “AI is compressing that implementation cycle, shortening time to ROI.” For a startup burning through venture and debt funding, compressing time-to-value can be the difference between sinking and swimming.

Lesson: You can’t fight your customer’s buying cycle, but you can cut time-to-ROI to accelerate your momentum and category maturity.

Common misstep: If you’re new to your customer’s industry, don’t convince yourself that you can shift their timelines. Put in the time up front to understand and align with them.

4. The fight for naming rights

In a young category, the market hasn’t settled on what it’s called or where its edges are. That means everyone with a stake starts pushing their version of it.

Incumbents want to fold the new thing into a category they already lead. When agentic tooling got hot, Datadog and New Relic started promoting “agent observability” and “AI observability,” stretching “observability,” the word for the monitoring category they already lead, to cover the new potential one.

Startup competitors rush to get their labels adopted, solidifying their position as thought leaders, if not new category leaders. Answer engine optimization (AEO) also gets packaged and sold as “GEO,” “AI SEO,” “LLMO,” and “AI visibility,” each vendor hoping it can influence the direction.

Analysts may look like neutral referees, but writing a new category name, definition, and the company shortlist is an attractive product. (Who do you think licenses Gartner’s Magic Quadrant?) Gartner pushes to define “CTEM” and “intelligent simulation” and “wicked intelligence” for a reason.

The point isn’t that the analyst or incumbent or loudest competitor takes your category. It’s that all of them are trying to, and the only defense is guiding your buyer’s language before someone else does.

Lesson: You don’t name your category alone. Incumbents, competitors, and analysts are all authoring it with you, so the goal is to rally them around a label that still tilts the category your way. Start with your buyer’s language because that’s often the strongest current.

Common misstep: Think through the implications before dropping an incumbent’s or an analyst’s category label on your own homepage.

5. Your go-to-market motion

Your buyer controls your GTM motion more than you do. If a single person can start using your product without asking anyone’s permission, bottom-up growth tends to work. If purchases must go through procurement, security reviews or regulators, the motion skews top-down whether you like it or not.

• Of Gartner’s 12 disruptors, two-thirds sell top-down: drones, industrial AI, edge silicon, Earth intelligence, simulation, cyber-physical security, deepfake detection, and domain LLMs. Prompt-to-app (Replit, Lovable, Vercel) is the one that’s truly product-led.

• In the audit library, every pure product-led winner could be adopted by a single user: AI coding (Cursor), AI meeting notes (Granola), collaborative whiteboards (Figma, Miro). Every category with a committee or a regulator in the loop is sales-led or hybrid (CRM, applicant tracking, legal, authenticity).

• Freemium is similar because it’s almost universal where the user is the buyer, but rare where a committee signs.

Lesson: Your buyer picks your GTM motion.

Test it: If you have a B2B product, map out the chronological steps for a customer to fully adopt your product. I’ve surprised founders by doing this on the back of a napkin because it’s often more complex in practice than it is in their head.

Common misstep: Don’t assume PLG or freemium is on the menu. For many categories and buyers, it isn’t.

6. The missing flywheel

After 202 audits, the companies started to blur together and look similar. One stark trend that kept appearing was that some companies compounded, seemingly getting stronger with each new customer, while others did not.

That compounding can take many shapes.

• Figma and Miro grow on a multiplayer loop where every user drags in collaborators, and the shared files create lock-in.

• Clay grew on a community flywheel where users are naturally incentivized to share their own tactics and every shared playbook pulls in the next wave of users.

• Gong and Granola use every conversation to get more useful, and harder to rip out, with every customer they land.

Different compounding loops, but the same trait: customer #20 makes customer #21 cheaper and easier to win.

Lesson: A compounding advantage is one of the strongest indicators of future success in an emerging category. As your competitors focus on who they want to be when they grow up, compounding loops help you get better each day.

Test it: What mechanism makes you noticeably better (faster, cheaper, smarter) to each customer as you grow?

Common misstep: Don’t confuse compounding with incrementality. Moats are built from exponential compounding.

The competitor you’re not watching

The rival with a similar product is often the least of your problems in an emerging category.

If you want to see which of these you’re facing, run your own company and category through the Four Waters audit. It’s the same tool I used on the 12 from Gartner, backed by data from 202 companies across 19 categories.

Analysts will never stop filling your inbox with hot takes on the latest disruptors. This audit will tell you who your real competitors are.

That’s all for this week. Thanks to Santosh Sankar from Dynamo Ventures for sharing his wisdom with us.

A phrase only becomes a category when people with no reason to help you start using it: competitors, analysts, LLMs, buyers on a sales call. That’s why most category creators end up retreating to the comfort of an established space.

• Greenhouse tried “recruiting optimization,” then went back to applicant tracking system (ATS).

• Marketo tried “revenue performance management,” then back to marketing automation.

• Folk tried “xRM.” Today it’s a CRM.

• Otter tried “Ambient Voice Intelligence.” The market (understandably) shrugged and called it an AI notetaker.

But some do stick. Gong made “revenue intelligence“ work. Hightouch made “reverse ETL.” Hubspot won with “inbound.”

When a name sticks, the author owns the language buyers use to describe the problem, the checklist they paste into the RFP, and how they grade vendors.

I studied how 96 companies across 13 categories positioned themselves at launch, then how they think of themselves today. The category retreats outnumber the wins, and clear patterns emerge.

Here are the five reasons category names stall, along with companies that broke each rule and got away with it.

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1. It focuses on the tech

When a category phrase focuses on how the product works rather than what the buyer is trying to accomplish, it is deprioritizing the buyer in favor of the builder.

Gong walked away from “conversation intelligence,” a term that describes what the software does to your call recordings, and declared “revenue intelligence” instead. In my breakdown of Gong’s shaper content, their team shares that conversation intelligence was easy for a CRO to delegate, but revenue intelligence had their job title in it.

Otter tried “Ambient Voice Intelligence,” a phrase about the technology. Nobody needs that the way they need meeting notes. The market settled on “AI meeting notetaker” without Otter’s input or influence. As Otter still tries to push “conversation AI,” at least they have “AI Notetaker” in the home page title tag.

Counter Example: “Reverse ETL” is a description of the technical plumbing, but Hightouch got it to stick. Their buyer is a data engineer, and for that role the medium is the thing that matters.

Lesson: A good name reflects the job to be done rather than how the job is done.

2. It needs a translator

People don’t repeat what isn’t legible or is awkward to say.

Folk launched as “xRM“ short for Extended Relationship Manager. The ‘x’ wasn’t self-explanatory, so it never caught on. Day AI tried the same with “CRMx“ where the ‘x’ stood for context. Attio launched with the word buyers already used, and never looked back.

Marketo tried “revenue performance management.” Three abstract words with no clear picture in the buyer’s head. Back to marketing automation.

Counter Example: This rule was broken by Datadog. “Observability” was a term nobody in DevOps used, and buyers needed it explained before they’d buy it. Datadog had the scale and budget to teach the market anyway, and the term ended up with its own Magic Quadrant with Datadog as the leader.

Lesson: A name shouldn’t need a translator unless you’re prepared to fund the translation.

3. It fights what buyers already use

A stumbling incumbent is not necessarily a stumbling category. When the market leader looks slow or dated, it’s tempting to treat the whole category as up for grabs and rename it. But the leader can lose momentum while the term buyers use stays entrenched.

Greenhouse tried to differentiate from outdated tech by making “recruiting optimization” happen. Recruiters just kept saying “ATS.” Marketo making the same bet against “marketing automation” is another example. Nothing was broken about the incumbent term, so the challenger had nothing to be the opposite of.

Counter Example: Using “inbound” worked for Hubspot because the old methods were limited. Marketers were tired of chasing customers who didn’t want to be chased, and “inbound” named the alternative: getting customers to come to them. When the old approach is failing, a new name has an opportunity.

Lesson: Replace a category word only when the old one lies to your buyer. Boring is not the same as broken.

4. It reaches for the platform

Some names are aspirations by the builder that the buyer can immediately see through: platform, operating system, suite. Adobe and Microsoft earned these designations by building individually successful products. It’s uncomfortably obvious (to everyone) when startups reach for them too quickly.

• Sanity tried to call itself “content operating system.”

• Contentstack tried “agentic experience platform.”

• Treasure Data tried to drop CDP for “agentic experience platform” too.

Nobody types “content operating system” into a search bar. All of that aspirational naming crumbles when the buying starts, regardless of how good it looks in a press release.

Monday.com has shown us this a few times. It spent years selling the idea of a “Work OS” and eventually walked away to relaunch itself as an “AI work platform.” Even a public company with a real multi-product suite couldn’t get its own OS name to stick. On a related note, I’m not convinced that buyers are searching for an “AI work platform” either.

Counter Example: “Customer Data Platform” is a platform name that stuck, and it stuck because it came from an analyst rather than a brand. Then Gartner picked it up, and the vendors followed along. That’s the difference.

Lesson: Name the wedge you’re selling now and the enterprise platform name later once buyers want one.

5. It blocks others from using it

A category is a term that the whole market shares. Making it uniquely yours by sticking a ™ at the end of it actually limits its potential. If a competitor can’t use it, it stays a branded name and never becomes the category for you to lead.

Otter AI trademarked “Ambient Voice Intelligence” so that’s where it dead-ended: Gartner isn’t making a Magic Quadrant with a protected term. If anyone else had wanted to use it — a big if there — that didn’t matter.

Counter Example: Clay turned “GTM engineer” into a job title rather than a product name so the entire market could use it. Getting strangers to repeat a term is a discipline of its own, which I broke down in the shaper content piece. A name you guard never has that opportunity.

Lesson: If you’d sue a competitor for using your term, it isn’t going to define the category.

Name it so they’ll say it back

Think of the name as a lever. The goal is not cleverness or brandability.

Whatever your candidate, test it against the five common missteps.

1. Does it describe your buyer’s job or your own technology?

2. Can they quickly decode it?

3. Does it give them something an incumbent term couldn’t?

4. Are you naming the wedge or the fantasy?

5. Would you be ok with a competitor using it?

Clear all five and you might have something worth owning. Miss them and it could just be a clever term you’ll quietly retire in a year or let it fade with you, while the market names the category without you.

Name it so they’ll say it back.

This newsletter is where I hand off the finished frameworks. But most of my week is messier than that, running marketing and growth at a VC-backed Series A startup.

• Build vs buy mar-tech decisions

• Weighing PR risks and rewards

• The outbound template we use each week with ZERO personalization

I’ve started posting that stuff on LinkedIn. If you want the operating decisions in real time, connect with me on LinkedIn (and mention ‘First to Market’ so I know to accept).

I also set up a First to Market page so I can tag contributors and keep the archive in one place. Follow it if you want, but my profile is where I actually post.

Ok, enough housekeeping. Let’s get into the good stuff.

Cold outbound gets a bad rap, and most of it is deserved. Anyone with a senior-ish job title on LinkedIn can tell you that. Now that every seller in the world can be supercharged by AI for only $20/month, I get at least 2 dozen terrible cold emails per day.

But I’d argue that being great at engaging people who don’t know they need you yet is an essential skill for startups in new and emerging categories.

This week’s newsletter is about that.

In my years and years of managing campaigns, they often look broken right up until they work because the wrong expectations lead to measuring the wrong metrics.

• Click rate says one thing.

• Reply rate says another.

• Open rate still doesn’t tell you anything.

So I asked three people who spend their days thinking about startup outbound strategy.

• Brendan Short writes The Signal, a newsletter on GTM automation, with operator experience at Apollo.io, Zoom and Wiser.

• Alon Even is a fractional CMO and writes Alon’s GTM Take, where he shares go-to-market systems that accelerate B2B pipeline.

• Jasper Vanuytrecht is a GTM leader and writes Founder’s GTM, which shares Claude Code skills that founding teams use to win more clients without hiring.

Their answers overlapped more than they clashed so I adapted those conversations into a portable framework I’m thinking of as The Outbound Recognition Ladder.

Use it to spark ideas and inspire your own approach.

The Outbound Recognition Ladder

The tactic-du-jour for outbound right now is leading with a valuable insight and asking nothing in return. Brendan told me that’s his most common advice: “Give something that is genuinely valuable to your prospect. Even something they’d be willing to pay to receive.”

He’s right. It’s the best version of outbound. But it also assumes two things your buyer might not have yet:

1. the context to make sense of the insight

2. the belief that the problem is real and/or urgent

When the problem is genuinely new to them, they likely have neither.

This framework is built on the premise that recognition comes before intent. That means the first touch gets a smaller job: earn a click by naming a problem the person already feels.

Jasper tries to lead with a problem they already feel but haven’t put words to yet. “‘You’re probably still doing X by hand every week’ beats a clever category insight,” he told me.

Alon agrees: don’t lead with new category language yet. “Borrow the buyer’s current language: the workaround, broken workflow, manual process, or hidden cost they already recognize.” The job of the first touch “isn’t to sell the category, but to create problem recognition.”

Rung 1: The Click

The first sign of recognition is not a “I’d like to learn more…” reply.

“Recognition shows up as a click before it shows up as words,” Jasper told me. Someone who opens the page you built is telling you the symptom landed, even if they don’t write back right away. Alon reminds us that we really just want to see “the right accounts engaging with the narrative.”

Test it: Direct people to a landing page that uses the same problem language from your outreach. Match the symptom you named (word for word) before introducing a product or category. The goal here is simply to teach and label a single problem that you are (eventually) positioned to help them solve.

Rung 2: The Question

The second rung is the start of a conversation. The click comes first. The question usually comes later, sometimes from a follow-up.

From Jasper: someone asking “wait, how would this even work?” can be worth more than a booked meeting because they’re processing a problem they hadn’t named an hour ago. Alon likes to see a short confirmation that says “yes, this is happening.” Same signal: the prospect is now inside the problem with you.

Test it: Count questions, not just meetings. Then answer each one by naming the problem in your language again, not by pitching. You’re feeding them the exact phrase you want to hear come back in Rung 3.

Rung 3: The Echo

The sign that the system is working is when your words are repeated.

“The early tell is not just reply rate. It’s whether your language starts coming back to you,” Alon told me. When prospects start “describing the problem in the language we introduced,” you have created demand that didn’t exist before the email thread.

This has been my lived experience as head of marketing at Recurrent. Electric cars were a small portion of the overall US auto market and the batteries were all new enough that it was easy for the industry to prioritize other pressing issues. It wasn’t until we helped them with the context and language that they were able to design business cases around how we could help them.

Test it: Pull recent replies and call transcripts. Highlight every place a prospect describes the problem in words you seeded. That count is your rung-three baseline, and you can measure it this week.

Revenue Is Still the Destination

None of this replaces sales this quarter. “Revenue is still the destination,” as Alon put it. The ladder is simply the leading indicator that tells you pipeline is coming before it arrives so you know you’re on the right path.

It also tells you when you’ve earned something bigger. As Jasper put it:

“The category is yours to teach later, once they’ve raised their hand.” By leading with the problem they feel, and letting your language come back to you, you’ll be the one in position to name the category. That’s part of the way to write the category into existence.

A big thanks to Brendan (The Signal), Alon (Alon’s GTM Take), and Jasper (Founder’s GTM). Subscribe to their newsletters. I promise that what they publish each week will make you better at this.

That’s all for this week. Hit reply with questions or ideas for future posts.

A journalism professor once told our class that it’s almost impossible to be right on time. You need to decide if you’re going to be early or late, and we couldn’t be late.

Most of us can’t afford to be late here, either.

Last week we tested to make sure that demand exists in the market. We know it’s there but something is sitting between the buyer and the purchase:

1. Belief blocker: buyers don’t fully understand or trust the category yet

2. Enablement blocker: something outside our control is in the way

While proof points like case studies and testimonials and references can break a belief blocker, data isn’t going to crack an enablement blocker.

This post is about surviving an enablement blocker, backed by an uncomfortable amount of first-hand experience.

1. Position for the future

You can see the wave forming but there’s still time before it breaks. While you wait, that time goes into authoring the category’s vocabulary and future standards. When buyers and practitioners finally show up, the terms they use to think about the problem are the ones you wrote.

dbt launched as data warehouses moved to the cloud. But a warehouse is just infrastructure and nobody had worked out the discipline to operate one at a high level. As a founder put it, the industry “treated data analysts as low-level, non-technical people.” That needed to change for dbt to be successful so the team spent the slow ramp championing an analytics engineer role and defining how the job should be done. As the cloud data stack went mainstream and every company suddenly needed that function, dbt was the pioneer behind the definition.

We had a similar scenario at Recurrent. The used EV market was too small in the US for most to care about but we could see a wave coming: years of new-EV sales meant a flood of vehicles on short leases that would be returned as used cars. We spent the gap codifying the language and success metrics that the industry would one day want to use. When buyers and sellers finally arrived, the playbooks they wanted were already written.

Test it: Write the sentence your buyer will use to describe this problem two years from now. Then work backwards to find the missing pieces: tech, titles, terms, etc. At the very least, you’ve just outlined your content calendar. The goal is hearing those terms repeated by prospects and competitors.

Lesson: A visible gap between today and demand on the horizon can give you just enough time to author the category’s language and success criteria. Whoever defines how buyers judge the category is the obvious default when the judging starts.

Common misstep: Don’t mistake your conviction for future demand. Name the external signal that demand is actually forming (lease-return volume, search queries, a regulation’s effective date) before you fund a year of positioning activities.

2. Relocate to the current opportunity

A chokepoint that blocks demand is only one point. Sometimes you need to go to where it doesn’t exist, even if that somewhere is not the ideal customer or region or deal size. Start there, get momentum, then use that to expand.

Ramp wanted to replace the goliaths of corporate spending with something better and more modern. Although a great idea, it required an even greater switching cost for their prospects.

For a large enterprise, that meant procurement cycles, migration downtime, uncomfortable risk. Switching costs were an incredible hurdle, but only for established companies. A startup in its infancy had nothing to change or break. Ramp shifted its focus down-market (to startups) and only moved upmarket once it had built the controls and integrations to lower enterprise switching costs.

Waymo ran the same logic on geography. It didn’t launch where revenue potential would have been the highest. It launched in suburban Phoenix where flat roads and blue skies and lax state regulations flattened likely blockers. It was years before it took on hilly, foggy, dense, regulated (and lucrative) places like San Francisco.

Test it: Instead of asking who your ideal customer is, ask who has nothing in the way: nothing to rip out, no one to convince, no missing piece they’re waiting on. That may not be the customer you set out to win, but that could be why the segment is still available while you wait on other blockers to clear.

Lesson: You don’t have to clear the blocker everywhere. You need the one place it doesn’t exist, then a route from there to the rest of the market.

Common misstep: Don’t lose sight of the desired destination. You’re relocating to fund survival but the risk is getting stuck there so go in with that in mind.

3. Build or partner to unlock demand

Some blockers can be built around, either independently or by partnering with someone who also benefits from removing that blocker.

Cursor is an AI coding editor that sits on top of incredibly expensive models. When its suppliers, OpenAI and Anthropic, launched their own coding tools, Cursor’s vendors became its competitors. It couldn’t out-spend them to build a rival model, so it announced an acquisition by SpaceX, which now (somehow) owns the Grok models. Cursor’s bridge got it across the blocker, but it wasn’t a moat.

Common misstep: Be careful not to rent a core piece of your product (or moat) from a company whose roadmap runs through you, too.

4. Create momentum with competitors

The Belief Blocker scenario from last week was about one buyer’s doubt, which you can often clear yourself with a pilot project or case study data.

Industry-wide doubt exists too, held by analysts, investors and incumbents. Sometimes the best solution is making the space so obviously real and investable that bigger players enter to help you establish the market. Big names attract analyst coverage, drive awareness with marketing budgets you could never match, and de-risk the decision for early adopters.

Harvey’s AI-native legal product is a helpful example. When the trusted legacy legal-tech brands (Thomson Reuters and LexisNexis) started shipping their own AI products, they helped to alleviate the natural concern of every firm’s managing partner: “Is AI actually safe to use on legal work?”

Incumbent marketing budgets and reputations cleared the industry-wide doubt that Harvey could not have done on its own, and suddenly every firm was looking for a legal AI tool. Harvey was positioned to win the ones who wanted the best product because the AI-native option had better UX and value to users. The incumbents brought the demand so Harvey could capture it.

(The thing I’m watching now is if Harvey can protect itself against the inevitable bundling from incumbents. Think: Slack vs Teams)

Test it: You often can’t control which incumbents show up. Before you make it attractive for them to enter your market, identify the advantage (moat) you’ll still have when they show up with a good-enough version and a bigger budget. That’s where your energy should be going while you wait.

Lesson: A slice of a big, validated market beats owning a sliver of a tiny, unproven one. Let incumbents build the market for you.

Common misstep: Be careful of welcoming giants before you’re defensible. Leadership roles are tough for pioneers to recover once they cede them.

A way across the demand gap

The four moves are more of a diagnosis than a menu.

• If the blocker will clear on its own with some time, position for it.

• If it’s already gone in some corner of the market, relocate there.

• If it won’t clear and you can afford the fix, build it.

• If it’s too big for any startup to move, enlist the giants who can.

Under all of them sits one constraint: stay cheap enough to still be standing when the wave arrives. It won’t matter how incredible the market demand has become if you can’t survive long enough to harvest it.

Arriving too early is simply a gap to cross. As that journalism professor reminded us, early was the only option for us. Make the most of it.

A study of 500 brands across 50 categories found that half of pioneers fail. Many of the companies that ended up leading those categories showed up about a decade later.

That’s the bad news.

The good news is that the other half didn’t fail. I find that 1993 study even more motivating today as AI shrinks the cost and timelines necessary to bring an idea to market.

The catch is timing. You can have the right idea and still be too early to make it. As a founder put it to me recently: “How do you tell the difference between being early and being wrong?”

In real time, it’s difficult. Early and Wrong give off the same signal: People say they like the concept then don’t follow through. (Keep in mind I’m living some of this right now in the EV sector.)

This post is the diagnosis of being early vs wrong. Next week is the prescription: How to cross the gap once you’ve determined that you’re not wrong.

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The Blocked Demand Test

Demand that hasn’t arrived is either blocked or absent.

• Blocked demand has something sitting between the buyer and the purchase.

• Absent demand isn’t there and may never be.

We’ll start by going through the two common types of blocked demand, followed by what absent demand looks like from a seat inside the company.

1. The Belief Blocker

In this scenario, the demand is real but buyers don’t fully understand or trust the category yet. That’s often the blessing and curse of being early.

Slack is an easy example. When it launched, no one thought of team chat as something worthy of a budget line item. Instead of convincing a CFO that it deserved budget, Slack got the people who’d actually use it hooked first, one department at a time. By the time the invoice reached finance, the belief was already undercut by hundreds of employees who loved the product. Slack’s CEO said the growth was “almost entirely on word of mouth.” Their belief blocker just needed exposure.

Harvey broke through the belief blocker with client logos. It courted the most prestigious firms first, betting that these clients would also be the most persuasive references. Harvey now counts the majority of the Am Law 100 as customers and reached $11B in valuation.

Test it: Review recent deals or customer surveys for recurring doubts, which is how a belief blocker presents itself in the buying process. What happened when you put proof in front of that doubt? (Client reference call, case study, pilot program, testimonials) Did the blockers give way? Belief blockers tend to budge with reassuring evidence, whereas other blockers do not.

Lesson: A belief blocker is something that education and proof can unblock. (We’ll go through how to package it in part 2 next week.)

Common misstep: Resist the urge to treat every objection as a belief problem.

2. The Enablement Blocker

Here the demand is real, and buyers get it, but something outside your control is in the way.

• A missing piece of infrastructure

• A partner who won’t play their part

• A behavior the rest of the chain hasn’t adopted yet

Grocery delivery is a good example. Webvan was the original Instacart. It raised nearly $1B to build its own warehouses and truck fleet, then caved under the costs. A decade later, after smartphones enabled gig driving, Instacart rebooted the idea and it worked. People wanted what Webvan originally offered but the infrastructure didn’t exist to support it in a cost effective way yet.

Michelin built run-flat tires that drivers wanted and carmakers backed, but the product stalled because service garages wouldn’t support it. The blocker was a critical ecosystem partner rather than a buyer issue.

Test it: Go back to those same sales notes to look for conditional language: “once we have,” “we can do X when Y is ready,” “we’re waiting on...” Enablement-blocked buyers are postponing vs rejecting.

Lesson: Every business depends on the health and collaboration of its ecosystem. If the blocker does not live with the product or buyer, other parties may be limiting the free flow of demand. (We’ll get into a whole targeted playbook for dislodging these in part 2.)

3. Absent Demand

If it’s not one of the other two blockers, that’s when I start to look for absent demand.

Quibi is the case study. They raised almost $2 billion, launched in April 2020, and threw in the towel by October. The founders blamed timing: “I attribute everything that has gone wrong to coronavirus. Everything.”

Except they weren’t just early and it wasn’t just COVID.

Short form video exploded on TikTok over that same period and streaming hit all-time highs as screens became our closest friends. People simply didn’t want Quibi’s version of it.

Amazon’s Fire Phone has similar lessons. The smartphone market was mature in 2014 — iPhone was already on #6 at that point? — and Amazon had the brand + distribution. But the market didn’t want it.

Test it: Take your most engaged prospect (the one closest to ‘yes’) and remove the blocker yourself: eat the cost, build the one-off integration, hand them the thing they said they’re waiting on. If they still don’t move, you may have found the reason.

Do the people who do buy keep coming back on their own or only when you nudge them? Read “What not having product-market fit feels like” by Melissa Kwan to double check your gut here.

Common misstep: Being “early” can be easier to stomach than “wrong” so be careful not to find a convenient false positive.

Unblocking the blockers

Each scenario leaves a different constraint on your pipeline.

1. Belief-blocked buyers are hesitating.

2. Enablement-blocked buyers are delaying.

3. Absent demand is curious but needs continuous prodding to maintain interest.

They can look similar at first glance: initial interest that won’t convert. The difference is how they respond when you intervene. Put targeted proof in front of a belief blocker and it moves. Whereas absent demand stays stuck even when you remove the blocker entirely.

I’ve experienced all of them and none are fun. Being early is actually the harder job because now you have to survive the gap while working to break through the blockers.

Next week is the prescription.

We’ll go through the four ways I’ve found to cross the gap when you’re early, including what to do when the blocker is yours to remove, and how to avoid handing the win to whoever shows up after you.

The waitlist did exactly what a waitlist does. It measured curiosity.

For Tesla, the refundable $100 deposit was only 0.1% of the price tag, which ended up nearly 2x over the original estimate. Tesla and auto analysts both confused that waitlist for demand.

But waitlists aren’t the problem.

We’ve used them at Recurrent to launch the company and accelerate cold starts for several new products, and I still recommend them to many of the startups I advise.

• Linear turned a 10,000-person waitlist into a filter.

• Robinhood turned one into a competition.

• Superhuman turned one into a badge of exclusivity.

Using one successfully comes down to what you make people do to get on it and what you do with them while they wait.

Since most teams do neither, here is a new way to think about it so you don’t make their mistakes.

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The Waitlist Matrix

A waitlist has two key variables:

1. What it costs someone to get on. All of them cost something: an email address, refundable deposit, intro call, referral.

2. What you do for people once they’re waiting. You can let them sit while you work or you can work the list while they sit.

Plot the two and you get four kinds of waitlists.

It’s rare that startup teams consider this before publishing their landing page. They end up chasing total signups and find the bottom left without intention. It’s optimized for size so it’s cheap to join and generally put on a shelf until launch.

That’s the Vanity Corner. It makes sense on paper because a big number is hard to argue with. “Hundreds of people want our product so it must be a great idea.”

Two moves help you avoid that misstep.

1. Move right so it costs something.

2. Move up to engage the audience while they wait.

You don’t need both to have a valuable waitlist. But I’d encourage you to pick one thoughtfully. Here’s how you can make that decision.

Move 1: Make the signal cost something

The signal you get is aligned with the cost you charge.

• Quick and free = curiosity but not intent

• Steep investment = intent but limited sample size

But keep in mind that the cost doesn’t have to be money. It can be effort, and sometimes that’s a better currency.

GitHub gated its Copilot preview to people already on a paid plan. The list would be shorter, but everyone on it had already had a credit card on file.

Superhuman charged in effort. Originally you had to book an onboarding call to even get an opportunity to get access. That sounds insane for a prosumer product. But it also got people to invest half an hour with the founders. We value what we work for.

Cognition’s waitlist was a form that asked what you’d want to build so the team could review before sending onboarding invites. The application was the filter, similar to what Linear did with its waitlist.

Waymo limited early access with a Trusted Tester program that included an NDA and request for detailed feedback. The cost of entry was a willingness to do research alongside them.

• “Join the waitlist.” ❌

• “Tell us what you’d use this for.” ✅

Test it: We should be deciding what we want from the waitlist before we build the form. Finish this sentence: “A signup tells me this person ______.” Pick what you actually need to learn and make joining cost that.

Lesson: Build the waitlist signup backward. Start from the signal you want, and the right cost becomes obvious.

Common misstep: Friction for its own sake isn’t a signal. The cost has to select for the customer you’re after rather than just having a hurdle to have one.

Move 2: Work the wait

Intent has a half-life. I can’t find the podcast to attribute it, but I heard Attio founder Nicolas Sharp say that the half life of a waitlist is much shorter than startups expect. He suggests that it is probably measured in days, even though people sit on the list for months.

We have two ways to counter that.

1. Shorten the wait

2. Keep them interested

The mechanics are well documented in academic studies.

1. People work harder the closer a reward feels so a waitlist that shows someone progressing is motivating.

2. Another study shows visible progress nearly doubled completion rate. In the trial, a loyalty card needing eight stamps from zero got redeemed 19% of the time, while a card needing ten stamps with two pre-filled delivered 34%.

That’s what fueled the Robinhood waitlist. You signed up, got your spot in line, and moved up by referring friends. A million people joined before launch. Even as regulatory approval delayed launch, the list stayed warm the whole way.

Folk CRM added a quick 10,000 signups but worked the waitlist by treating it as a conversation. The founders thanked them, asked what they expected the product to do, then granted access slowly. For them, a waitlist was user research that shaped what they built.

• “We’ll email you when we launch.” ❌

• “You’re #840. Here’s what we shipped this week for your use case.” ✅

Test it: Start by getting honest about how long the wait actually is because Attio guessed one year and it was three years. Then map the time between waitlist signup and launch as a journey. How much will they trust you in week #2, week #20 and the week before you launch? The goal is guiding them from a curious stranger to a ready buyer.

Lesson: The wait isn’t dead time. It’s a finite window where attention is high and expectations are low. It has a short half life.

Common misstep: Don’t waste people’s time with shallow updates on progress. A series of “We’re so close!” updates only trains people to ignore you.

Finding your spot on the waitlist matrix

The matrix has four corners so consider where you want to end up.

• Vanity Corner (e.g. Cybertruck) is where lots of startups land by accident.

• Warm Corner (e.g. Robinhood, Folk) keeps a low bar to entry but engineers the wait. It’s right when your bottleneck is awareness and the product explains itself.

• Qualified Corner (e.g. Superhuman, Attio) asks people to invest up-front then keeps them close until you can serve them. This is right when your bottleneck is comprehension or capacity. It trades scale for intent. Clay kept its waitlist until it had millions in ARR for this reason.

• Squandered Corner makes people earn their way in, then goes silent. But as a lot of hardware startups that picked this corner can tell you, a higher cost of entry only raises the price of getting it wrong.

“Grow the waitlist” sounds like a good use of energy but it isn’t a strategy. Choosing the Vanity Corner gives you a number that climbs while the signal underneath it gets weaker.

Before picking that path without intention, ask yourself what you really want from the waitlist.

Curiosity is cheap. It’s better to ask for something or earn the right to keep them. Ideally both. Do neither and you only get a nice number for a press release.

• Add an LLMs.txt file.

• Actually don’t bother because the platforms ignore it.

• Rewrite everything on your blog in Q&A format.

• But content on your site doesn’t matter so focus on Reddit.

I came up through SEO in the early 2010s so this is familiar. When the platform is a black box, the community gets one result from one test and extrapolates it into the must-do tactic of the year.

I watched that cycle repeat for a decade and the results got predictable. The brands that won were guided by a clear thesis, making small adjustments as the market evolved. The brands that lost ended up like ClickUp, chasing tactics like scaling its blog to 7,000+ programmatically generated posts then losing 98% of traffic a year later.

Today I’m sharing a general approach to the AI search era and, more importantly, a framework for adapting it to fit your business, using Gong and a few other successful brands as guideposts along the way. So let’s start with how I think about content.

Shaper vs Chaser Content

Somewhere along the way “content marketing” became synonymous with a blog. Write a post for your ICP, sprinkle some keywords for visibility, and post it on yourdomain.com/blog.

AI has killed that.

But content marketing has always been much bigger than that. Don’t forget that Red Bull dropped someone from space as part of a larger content marketing engine.

In the startup world, content marketing also helps us define our emerging categories. We name a problem people feel but haven’t defined, then hand them the vocabulary they’ll use to evaluate every solution (including ours).

AI isn’t changing that.

Think about the content you produce as two types:

1. Chaser content captures existing demand. It chases the topics and phrases people are already using (”best CRM for startups”) and competes to be the answer. This is what SEO optimized for and what AI now does better than any single source.

2. Shaper content creates the demand that chasers compete for. It shapes a problem your buyer feels and teaches prospects, search engines and LLMs the language they’ll use to evaluate solutions.

Note: Content does other jobs — converting, retaining, etc. — but we are focusing on content’s go-to-market role in this piece.

AI search doesn’t erode shaper content. It actually amplifies it. The models answering your buyer’s questions in 2028 will be trained on whatever language your category settles on between now and then.

That means the play is the same one it’s always been, with much higher stakes.

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The Shaper Content Playbook

1. Diagnose the Job

Do you own the language you’re using or are you renting it?

The answer probably depends on your category’s maturity. Most brands operate in Crowded Waters, where the language settled years ago. They have to compete for and pay to capture existing demand with words some other brand authored.

New categories carry the opposite burden and opportunity: you have to teach your audience the language first, but every term you seed becomes an unfair advantage later. When the category matures, you own the vocabulary and everyone else is bidding on it.

Gong got there after four years of a narrative war in Conversation Intelligence. So Gong ceded the category to its competitor and declared a new one: Revenue Intelligence, a term with zero search volume.

Then they put every ounce of content and budget into establishing it. (More on that later.)

Test it: Pull up your last ten content titles. For each, ask if a competitor could publish it verbatim with their logo on it? Every ‘yes’ is a piece competing for traffic that AI is quickly eroding. As the organic traffic shrinks, that’s traffic you’ll have to pay for in the future. And, arguably, if you have to pay for people to see your content, that’s not content marketing. It’s labor-intensive advertising.

Lesson: A category you don’t guide with language is one you’ll eventually have to pay to participate in. Unique perspectives differentiate, and language is where that starts.

Common misstep: SEO and AEO tools can only see questions that already exist so they will always steer you toward ‘renting’ vs owning the language.

2. Name the Question

The language you shape doesn’t have to be a category name. Moz shaped a metric, HubSpot shaped an “inbound” methodology, and Salesforce shaped an identity. Each of those, regardless of form, installs a question your buyer will be answering.

• A category = “Which of these do we need?”

• A metric = “What’s our number?”

• A methodology = “Are we doing this right?”

In every case, the author of the language is the default source of the answer.

This matters 10x in AI search. A word is something a model can swap for a synonym. A question is something a buyer asks their assistant. When you participate in creating the question, you’re writing the prompts your future buyers will use.

That’s in part why “revenue intelligence” was successful. As Gong exec Udi Ledergor has shared in podcast interviews, “conversation intelligence” was easy for a CRO to delegate, but revenue intelligence had their job title in it. The phrase aligned with a question sales leaders couldn’t ignore: “What’s actually happening in our revenue engine?”

Test it: Write down several questions you want your buyers asking in two years. Then answer each one honestly. If a competitor could give the same answer today, cross it out. Whatever survives is an example of your first shaper content.

Lesson: Identify the key (future) questions before you pick the words to answer them. Gong, Moz, and HubSpot each own different kinds of language, but all three own the question underneath it.

3. Amplify Until They Say It Back

New language becomes a category standard when people with no incentive to help you start repeating it. That usage is what influences the human buyer and the LLM model. Competitor pages, analyst reports, podcast transcripts, Reddit threads… that’s the material AI answers are built from, and you want them written in your language.

But that doesn’t mean “build it and they will come.”

Gong didn’t hit publish on its “revenue intelligence” press release and wait for the market to adopt it. They treated it like a product launch then kept pushing it.

HubSpot relied on similar repetition. “Inbound marketing” is a conference, a certification, an academy and a book, all amplifying the same two words until the market adopted it.

One mention is a data point. Hundreds of consistent mentions across channels and sources you’ve seeded is a definition that people and LLMs can’t ignore.

Test it: Ask a popular AI chatbot “what is [your category term]?” and look at whose framing comes back. If it answers in a competitor’s words, or generic ones, you can see one of the amplification gaps to fill.

Lesson: The SEO era measured traffic. The agentic era measures influence: how often the answer comes back in your words.

Common misstep: Don’t lock your language so tightly with trademarks and branding that nobody else can use it. The point is broad adoption, which means language bigger than you, your product and your company.

Which Half Are You Holding?

AI is eating a lot of what we used to view as content marketing. That’s the bad news.

But if you’re building in an emerging category, your opportunity is the half that AI can’t erode. In fact, you win when AI learns from it. That’s the good news.

AI can synthesize 20 answers to a known question. It can’t name a question nobody has asked.

Whether that question is a category name, a metric, or a method, the naming job can still be yours. Every piece of shaper content you publish and amplify becomes the material that others (humans + bots) use when the category matures.

• “How will AI cite our page?” ❌

• “How will AI say it back in our words?” ✅

The first is a chaser question, and the organic version of that is going away. The second is a shaper question, and in your category, right now, it is wide open.

Last week we looked at the case for staying in an established category. Greenhouse tried to create “recruiting optimization” then retreated back to ATS. Marketo retreated back to marketing automation. Domo retreated back to business intelligence.

But startups do break out and create categories that stick:

• Datadog unified IT monitoring into observability.

• Beyond Meat added plant-based meat to the meat aisle.

We have data on why these creators were successful. Elizabeth Pontikes at UC Davis analyzed 4,500+ software companies over 12 years.

She found that new categories are most likely to stick when the legacy category is constrained, meaning buyers can consistently describe it in a sentence. For example, in the late 1990s buyers could easily define “videoconferencing.” When document collaboration emerged, the contrast was simple to spot. “Collaboration software” had the freedom to create its own category (and charge more).

New categories don’t stick when the old one is lenient, meaning nobody can agree on what the label means. For example, partner relationship management (PRM) tried to break out of CRM. CRM was not defined enough in the late 90s so buyers couldn’t tell what CRM was or what it wasn’t. PRM had nothing to be the opposite of so it faded away.

Here’s a three-part diagnostic based on her research (and work by other academics) that you can use if you’re considering breaking out of an established category.

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1. Is the old category well-defined?

Buyers can describe a well-defined category in short, simple ways that mostly match what other buyers would say. That’s what gives a new category something to be the opposite of.

Datadog had both when it broke out of IT monitoring. Engineers tracked application performance, system logs, and infrastructure health in separate, well-defined tools. Datadog’s pitch was to unify all three under what would become known as “observability.” That pitch worked because each bucket already had clear boundaries to build on.

Beyond Meat had the same advantage in veggie burgers. Shoppers had the decades of experience with veggie burgers: frozen patties for vegetarians made from beans and grain. The consistency from incumbents like Boca and Gardenburger created space to be not-that.

Compare those to “AI assistants” in 2026. Siri, Glean, Gemini’s morning brief, Microsoft Copilot and hundreds of vertical tools all go to market as AI assistants. But ask 5 buyers what makes a product an AI assistant and you’ll get 5 answers. That lacks consistency so a brand trying to break away from AI assistants will struggle to differentiate.

Test it: Google a few variants of “what is [old category]?” to look for trends. Take note of how closely the top results agree on the definition. If they don’t seem to, it may not be well defined yet.

Lesson: The clearer the old category’s edges, the cleaner your break-out can be noticed by buyers.

Common misstep: Don’t confuse your own clarity for the buyer’s. You live in this space and they may only think about it once a year.

2. Can buyers tell you what it’s NOT?

A more reliable signal can actually be what buyers say the category isn’t.

When Beyond Meat launched, they designed it to be easy to see what it wasn’t: a frozen Gardenburger disk. They put it in the meat aisle, gave it beef-section pricing and packaged it to look nothing like a freezer-aisle veggie burger. Having a confident “not-that” helped plant-based meat land as a category.

Datadog could pass a similar test with its niche audience. By the late 2010s, engineers could tell you observability wasn’t just application performance or system logs or infrastructure health. Each piece had clear edges, which is what let observability sit between them.

Run the same test on AI assistants. Ask buyers what isn’t an AI assistant. Is a copilot one? Is an LLM chatbot one? Is a workflow tool one? The boundaries don’t exist to push against for most users.

Test it: Ask prospects what else they are considering. Their answer will tell you how well-defined the old category is.

Lesson: A new category lands when buyers can confidently tell you what it isn’t.

Common misstep: Don’t take “we don’t know what to compare you to” as a compliment. It can mean the old category is too blurry to anchor a new one.

3. Where will the money come from?

A new category doesn’t invent a budget from thin air. Successful creators identify a specific funded pool to siphon from, and position themselves so drawing from it feels obvious to the buyer.

In B2B, those pools are lines in the budget. In B2C, they’re stable household buckets and retailer shelf space. Fuzzy categories are the ones with no obvious place to point, which is why your new category can’t pull money from them.

Datadog’s pitch wasn’t “fund a new observability budget.” It was “consolidate 3 budgets you already have.” Datadog used the gap between those funded buckets to pull money from all of them. Today, “observability” is its own line item, but only because the older categories it absorbed were funded first.

For Beyond Meat, the veggie burger share-of-wallet was tiny when nested in the frozen food section. Especially compared to the amount that the average household spends on meat each visit. Beyond Meat’s pitch to grocery stores was about getting placement in the meat aisle, which meant accessing a much larger share of the grocery receipt. That shelf placement became the budget signal. Now other large players have launched their own plant-based meat lines to codify the placement and category.

AI startups are seeing this right now. Vendors are bidding on “AI assistant” keywords, but the ads are positioned different ways for different buyers. The category hasn’t settled.

Test it: Ask prospects what they are replacing with your product. Their answer will tell you if your siphoning hypothesis is resonating. If they can’t, your product cost may not find a budget to support it.

Lesson: New categories that stick pull money from well-defined categories that already had funding.

Variables You Can’t Control

Pontikes’ direct advice to founders: “No matter how new your technology is, if the categories around you aren’t well-defined, it’s going to be hard to position that product as something new. You’re probably better off saying that you’re exactly like what the other guys are doing.”

Most founders evaluate this decision by looking inward at their product, team or underlying tech. But it’s the shape of the old category (a variable you can’t control) that influences whether your new positioning lands.

Together with last week’s salvageable test, the two-part question is structural before it’s creative:

• “Should we try to create a new category?” ❌

• “Is the old category settled enough for a new label to make sense?” ✅

The good news is that fuzzy categories don’t stay fuzzy forever. They clarify as buyers form expectations and labels become consistent. Until then, patience and language discipline may be the best move.

For now simply figure out which side of that line you’re on.

Greenhouse originally tried to build a new category called “recruiting optimization.” Former marketing VP Barbra Gago shared the story several years ago on Lenny’s Podcast:

• They tried it.

• It didn’t stick.

• They quickly retreated to an established applicant tracking software (ATS) category and became a leader.

Greenhouse isn’t unique. Marketo tried to make “revenue performance management” a category until going back to marketing automation. Amplitude pushed “digital optimization system” then quietly went back to being an analytics platform. Domo pitched itself as a “business cloud,” but never made it out of the business-intelligence umbrella.

For B2B products in particular, it’s a structural problem: it’s easier to tap into an existing budget line item than ask customers to add a new one. Plus, when you abandon a category to invent one, the competitor who stays often absorbs the budget that you left behind.

The penalty for not fitting into a category is well documented by economic sociologists. Markets discount companies that don’t cleanly classify, and buyers devalue products that straddle two categories instead of picking a lane. More on that later.

This is a two-part series on the decision to revive or abandon an existing category. In this first part, we’ll use Greenhouse (and a few others) as examples for grading the potential of an existing category. Next week, we’ll flip it: the startups that ran the same test, got to “no,” and built a new category instead. Both parts share practical lessons from brands you know and what you can borrow.

The Salvageable Category

Under-served categories tend to reward modernizers: a startup that adopts the existing language then sets a new bar for customer expectations. Here are three ways to test if your category is salvageable with questions on whether its buyers can recognize it, fund it and find it.

1. Do buyers describe their problem in the category’s words?

You already know this from your last five sales calls. Hearing prospects say “we need a better ATS” or “our CRM should be smarter” demonstrates that the category’s language is functioning and helping buyers align solutions to their problems.

Greenhouse heard recruiters say they needed a better ATS and could not get them to disassociate that need with the established category.

HubSpot heard the opposite. Marketers told them that outbound was broken and lead generation didn’t work. Hubspot’s approach of using content + SEO to drive demand didn’t have a clean name, and the category label actively misled prospects. That mismatch made their inbound marketing label defensible.

The lesson: It’s difficult to replace the language that your buyers actively use.

The misstep: It’s possible to confuse ‘tired’ category language for misleading language. One is a copy problem and the other is a category problem.

2. Where does the money come from?

The money for products and services has to come from somewhere. For a company buyer, that’s a line in a budget, or even a specific RFP template or procurement code. For an individual buyer, it’s a mental bucket for “groceries” or “vacations” or whatever.

Those budgets are organized by category so a new category asks the buyer to remodel their budget. That’s a big ask:

1. Understand the value of this new product.

2. Assess my current budget.

3. Create a new line item.

4. Move money from something else to fund it.

While that exercise may take a split second for a high-income family in the checkout line, it could take years for a large organization.

Greenhouse found that customers and prospects had an ATS budget and it was growing over time. Decision makers and procurement teams could write the check from it without going to finance. That was the single strongest signal to stay.

Test it: Ask prospects how they are funding the purchase. You’re listening for whether you are slotting into a line item they already have or they’ll have to create one.

The lesson: If your product can fall into a budget line that already exists, the category is doing the funding work for you. If it doesn’t, you’ll need to sell both your product and their budget reorganization.

The misstep: Don’t get a positive response to the vocabulary you test in the previous step then automatically expect the budget to follow. Money moves slower than words.

3. Can buyers find you?

Our product research and purchase decisions are shaped by existing categories more than most people realize.

• Google serves us Wirecutter listicles on product categories.

• ChatGPT builds its answers from analyst reports and category Magic Quadrants.

• Amazon’s menu is organized by categories.

• The App Store sorts by categories.

The systems that we trust to help us shop are organized into categories that already exist. If you don’t fit into one, you’re invisible to prospective buyers and have to generate each ounce of awareness by yourself.

Economic sociologists even have names for it. Ezra Zuckerman calls it the categorical imperative: firms that don’t fit a recognized category trade at a discount because confusion about what they offer depresses demand.

Greenhouse found that the infrastructure for discovery in ATS was too valuable to ignore. There was a Gartner quadrant, a G2 grid, listicles, reviews, subreddits. Building their brand in ATS would not be easy, but it concentrated all of their marketing efforts in familiar places.

It’s more complicated when I talk to AI startups that don’t cleanly align to an existing category. That tempts founding teams toward “we have to create the category.” By the end of our discussions, we often align on a playbook that is more similar to Greenhouse: Picking an established category to modernize as an awareness and distribution shortcut.

The lesson: Existing category maps aren’t the only way buyers find products, but they are the easiest. Slot into one and the category does the discovery work for you. Skip it and we’ll be doing that work ourselves.

The misstep: Don’t grade findability from a biased sample of customers that already know you. We want to know how new customers will discover our solution to their problem… at scale.

One Thing This Doesn’t Measure

This test doesn’t measure whether you can win. You could breeze through all three questions and still abandon the category because an entrenched incumbent already owns the modernized version.

Salvageable is not the same as winnable.

Winnable is a separate question with its own signals (e.g. defensibility, distribution, switching costs) and it’s big enough that I’ve earmarked several posts for it later this year. For now, this salvageable test answers the prerequisite: Is the category even a viable home for what I’m offering the market?

It’s Only the Beginning

A lot of category creation advice treats the decision as largely creative: the right name, the right positioning, the right story. From my experience, it is more of a structural decision: existing language, budget, discoverability.

• “Should we try to create a new category?” ❌

• “Is an existing category serviceable?” ✅

Everybody knows the big winners like Salesforce and Snowflake so we need to be careful making decisions based on survivorship bias. Most of the startups that tried to mint a category got buried and lost to time.

Hubspot left and Greenhouse stayed. Both made the right decision.

I also ended that post with a promise to let people smarter than me on personal branding guide you on packaging and distribution.

This is me delivering on that promise.

I asked three experts on founder brands the same set of questions, then used their answers to triangulate the best takeaways.

• Dianne Wilson writes The Boundless Brand Club and turns founder ambiguity into positioning clarity.

• Alicia Teltz writes The Hype Department and spent years inside LinkedIn before becoming one of its distinct voices.

• Shreya Vaidya writes Grow-th and is a LinkedIn Content Strategist who is also building a community for AI-forward legal professionals at Spryngbase.

Three different vantage points with a mostly unanimous answer: a founder brand isn’t built by simply being louder. It’s built by distilling your unique view of the world into simple, portable ideas.

I’ve witnessed this at Recurrent. It wasn’t the concepts that we pushed the hardest that the industry adopted. It was the ones that distilled complex topics or emotions into things that could be delivered through the CEO that stuck around and put our team on people’s radars.

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Putting Your Ideas to the Test

In last week’s post, we worked on discovering and framing our unique point of view as a leader in a new category.

1. Find the unnamed language that’s already trying to exist.

2. Name what they’re feeling by articulating the problem so clearly that the audience recognizes itself in it.

But a brilliant point of view in your head isn’t a brand. It becomes one when it survives contact with the market and lodges itself into people’s memories. Use these six criteria from our guest experts to pressure-test those ideas.

1. It’s unique.

A point of view everyone agrees with is one nobody remembers.

“Good branding repels as much as it attracts,” says Dianne. It’s more important that “your identity is clear enough that the right people recognize themselves in it and the wrong ones opt out early.”

That doesn’t mean you need to be an agitator, simply to agitate. It’s more that neutrality isn’t memorable. “The market does not remember generality; it remembers distinct interpretations,” Dianne says.

Shreya names why leaders settle for forgettable: They are “so scared of being viewed as ‘unprofessional’ that they veer towards being ultra-safe (read: boring).” Her fix is to gravitate toward the edges on purpose: “What makes them interesting, unique and even (slightly) odd? This is the part that nobody else can imitate.“

That uncopyable part is the personal-brand version of the Belief Moat: the worldview competitors can’t copy without recognizing you.

Test it: Read your last 10 posts on the channel you’re most active on. If no one could disagree with any of them, there’s a chance the view you’re sharing isn’t uniquely brandable.

2. It’s borrowable.

Think back to a meeting where someone told you something that you originally told them. Regardless of whether it was your original thought, it’s an example of a phrase or concept that could be borrowed and used by a different owner.

In other words, it wasn’t limited to you.

Dianne reminds that raw thinking only compounds “once it becomes transferable.” A founder “stops being merely interesting and starts becoming influential when people begin borrowing their language to explain their own experiences.“

The goal here is to compress a complex belief into a reusable mental model that’s light enough for someone else to carry.

She’s specific about what makes language portable: “Compression, specificity, asymmetry, and uncompromising clarity.” A sharp, specific idea is one that can be borrowed and travel without you.

Test it: Take one of your core ideas and shrink it to one sentence or phrase to use in meetings this week. People’s initial reactions will tell you a lot. Maybe you’ll even have someone say it back to you.

3. It’s repeatable.

‘Consistency’ is a common suggestion for branding and social media. Alicia thinks that’s the wrong target.

“Consistency alone does not create compounding. Otherwise every founder posting 5 lessons from building a startup three times a week would be internet famous by now.”

What compounds is recognizable thinking: Returning to the same handful of ideas, questions, and frustrations — “packaged in different ways over time” — until your audience starts associating them with you.

Shreya is specific: “Begin with a rough idea of your signature style and positioning, and once you learn from experimentation… begin applying your learnings to recurring formats.” It’s how you find the few formats worth repeating.

A better way to think of consistency is stamina. The thing that quietly kills compounding, Alicia says, is “building a content system that makes you miserable.” You’re not going to succeed by suffering your way through years of low feedback in a format or position that you quietly hate.

Test it: Take one of your core ideas and see how many distinct angles you can pull from it: a contrarian take, a prediction, a customer story, a hard-won frustration. Finding 10 is a theme that is repeatable.

4. It’s matched to the messenger.

The right format is the one that best complements you as a messenger

Alicia again: “Some founders are incredible writers. Some are awful writers but brilliant speakers. Some need conversation to unlock their best ideas.”

It’s easier than it used to be because the platforms have all converged. LinkedIn, Twitter, Instagram all offer the same menu of short posts, long-form, comments, vertical and horizontal video. The format isn’t trapped on one platform anymore.

Set yourself up for success by choosing a format you’re comfortable in, especially if publishing isn’t a natural part of your personality.

Test it: Name the format where your best ideas actually come out (writing, scripted, live conversation, etc.) and you feel like you have a comparable advantage.

5. It’s findable.

The right channel is wherever the specific people who need your idea already pay attention. If that sounds overly simple, it is.

Dianne’s caution: “Founders often over-index on platform selection before they have developed message–audience fit. But a platform cannot compensate for unclear positioning.” Avoid that misstep by starting with who needs your perspective and where they already gather, then aim narrow for “resonance with the specific person who most needs the message.”

Then commit. Alicia’s rule is to “go all in on one first” and expand to other channels later, once you have a team and content worth recycling. Spreading across five channels on day one is how you stay invisible on all of them.

Shreya’s approach is “adding your ideal customer to your network” one person at a time, with your point of view in every post, so the clear idea is what does the pulling.

That’s the thing all three keep returning to: clarity before channel. “Clear thinking and a strong point of view create pull long before scale does,” as Dianne puts it.

Test it: Name the one person, role or persona who most needs your idea and the single channel where they already spend time.

6. It’s a story.

Stories travel better than facts. When a category has no shared language yet, it’s your stories that carry the idea.

Alicia reminds that stories don’t sound like a tidy case study. It’s more like you’re noticing something out loud, in real time. “The founders who build momentum early are usually the ones publicly noticing tensions before everyone else has words for them.” She’ll open a post with “Something weird is happening here,” or “Everyone thinks AI will replace X, but we’re actually seeing Y.” As she puts it, “nobody has ever remembered a human being for saying ‘unlocking efficiency.’“

Dianne adds the strongest stories often aren’t about you at all. It’s stronger when “the right people recognize themselves in it.” It’s working when people apply your idea to their own situation or problem.

Test it: Try exercising this muscle by writing your next post as a real-time observation rather than a conclusion: “Here’s something I keep seeing…” Then hold it to Dianne’s bar: Does it give the reader language for their own situation?

Common Missteps

Across all three conversations, the same three missteps kept surfacing:

• Don’t reshape your worldview to fit the audience until there’s nothing unique. As Dianne puts it, trying to be likeable is what makes you blend in.

• Don’t hide behind the company brand. People attach to a person with a point of view so put yourself forward, knowing it’s best for the company, too.

• Don’t outsource your thinking to AI. Once you’ve defined your perspective and tone, you can use AI tools to brainstorm new angles or adapt your copy for different mediums. But that’s much later.

Be the Spring

Last week I called your personal brand the spring that one day fills your company’s moat. Authoring the point of view is the spring. These six tests are how the water actually reaches the moat.

A massive thanks to Dianne Wilson (The Boundless Brand Club), Alicia Teltz (The Hype Department) and Shreya Vaidya (Grow-th). Subscribe to their newsletters for more brilliant perspectives from them.