AI sales agents promised to replace the SDR role: paste in your website, add a credit card, and watch the qualified leads roll in.

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.

The most common advice I give startup teams is to narrow their focus. Someone writes me about a scenario like the following almost every week:

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.

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.

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.

The modern founder media strategy is engineered for noise. Build a personal brand, maximize reach, chase virality on the channel that owns your audience.

• Adam Robinson posts a cease-and-desist letter on LinkedIn to rack up 1,600 likes.

• Andreessen Horowitz (a16z) uses an in-house media team to turn portfolio company founders into celebrities.

Isaac Peiris did a deep dive on the Andreessen Horowitz playbook and his takeaways were solid.

• Owned distribution matters.

• Founders need to be visible.

• Don’t depend on traditional media.

But these examples aren’t drag-and-drop. Startups in emerging categories don’t have that foundation yet. They have scattered people who feel a problem nobody has named for them. Building an amplification machine on top of that just broadcasts confusion to a wider audience.

Here’s a playbook for owned distribution when your category isn’t codified, drawn from how Clay authored a $3.1B category one word at a time.

Authoring Your Audience into Existence

The first step is organizing a scattered group into an audience that recognizes itself and how you support it.

1. Find the unnamed

Before Clay coined “GTM Engineer,” the people who would later wear that title were already there. They were SDRs learning Python and marketers stringing together data enrichment tools. These people were already doing the job.

Clay’s co-founder Varun Anand found them by going where they already gathered. He joined GTM groups and online communities to listen to their challenges and frustrations.

As the Four Waters Framework illustrates, Uncharted Waters is the stage where the category isn’t a category yet and the audience doesn’t identify itself. It’s also when the vocabulary and the way that people form mental models about the solution is most up for grabs.

Test it: Spend 30 minutes a day for 2 weeks in the places where your ideal audience already gathers. Slack groups, subreddits, LinkedIn comment threads. Document the consistent phrases, especially the ones that don’t quite fit what they’re trying to describe.

Lesson: The job isn’t inventing language for a category you imagine. It’s surfacing the language that’s already trying to exist.

Common misstep: Let’s avoid the urge to conflate our products with the category. The category needs language bigger than your SKU so someone would use it even if they bought from your competitors.

2. Name what they’re feeling

Listening in Step 1 surfaces a scattered set of signals: words, workarounds and perspectives of the same underlying problem. Step 2 takes what we hear and articulates the problem the audience feels but hasn’t been named for them.

• Clay’s Kareem Amin and Varun Anand helped technical marketers and SDRs form a united identity as high-value “GTM Engineers.”

• ProfitWell’s Patrick Campbell turned scattered SaaS health metrics into a shared vocabulary that organized SaaS operators.

• RB2B’s Adam Robinson named “person-level website visitor ID” to articulate the gap that incumbents had been ignoring.

These founders didn’t invent the problem. They named the problem so clearly that the audience recognized it as theirs.

Test it: Repeat the synthesized phrases you heard in Step 1 back to the people you heard them from. We are looking for positive signals that we’ve named what they were feeling.

Lesson: Inventing a name is easy. The difficult part is articulating the problem so clearly that the audience recognizes itself in the articulation.

Common misstep: Don’t outsource this step. You need to hear it from the audience directly so you 100% stand behind it.

3. Distribute for recognition

A Belief Moat is a worldview that protects a company as its category matures. It’s something competitors can’t credibly copy.

You don’t have one yet and I don’t think you need one yet, either. Your job in Step 3 is to be the visible spring that will one day fill your company moat.

Each founder from above did this.

• Kareem (Clay) claimed “GTM Engineer” in his own name on LinkedIn.

• Patrick (ProfitWell) published pricing research under his own byline for years before the vocabulary attached to the company.

• Adam (RB2B) does every ounce of marketing from his personal accounts.

Using a personal brand creates a traceability that the company can eventually compound into a moat: a worldview that competitors can’t claim without acknowledging it came from you first.

You are putting the words into the world from your seat, in your name, repeatedly, wherever your audience already gathers. While the form will adapt for different platforms, the messenger will not.

Test it: Drop this prompt into Claude (or your AI chat of choice). It will ask you context questions covering what you’re hearing in Step 1, what you’re naming in Step 2, and how you’re surfacing it in Step 3. Then it runs research behind the scenes to test your logic on 3 dimensions: viability now, resiliency against competitors and whether your work is contributing to a future company moat.

Steal this prompt: audience authoring with AI

You are a strategic PR and brand advisor with deep experience
helping founders in emerging categories author vocabulary that
organizes scattered audiences. You've watched companies become
category leaders by getting this right, watched others lose
their words to competitors within 18 months, and watched
startups disappear because they never authored anything
specific enough to stick.

Be a sharp thought partner, not a cheerleader. Push back on
vague answers, strategic-sounding language, and pitch-deck
phrases. Ask for concrete examples, real community names,
actual quotes from problem-feelers. Don't move forward until
you have material you can actually pressure-test.

The conversation runs in four phases.

PHASE 1 — CONTEXT

Batch context questions into no more than three turns. Cover:

1. Who I am, my role, the company name and domain (if any).

2. STEP 1 work — Where I'm listening for problem-feelers.
   Which specific communities, Slack groups, forums? What
   scattered words, hedges, or workarounds have I noticed?
   The smallest, most acute group who feels this problem.

3. STEP 2 work — The word or phrase I've synthesized. Why
   this word and not the alternatives I considered. Who I'm
   explicitly NOT for. What vocabulary incumbents already
   occupy.

4. STEP 3 work — How I'm currently surfacing this as the
   visible author: which platforms, what cadence, founder
   name attached, whether company brand or founder brand is
   the front door.

If any answer is thin or generic, push back before moving on.
I should feel slightly uncomfortable with how specific you're
asking me to be.

PHASE 2 — RESEARCH

Once you have concrete answers, run the following:

- Web search for the exact vocabulary I'm trying to author.
  Who else uses it? In what context? How recently?

- Search for how problem-feelers in my space talk about this
  pain when they're NOT using my words. What workarounds and
  alternative framings exist?

- Search for analogous moments — where else has a founder
  named a problem that organized a scattered audience? What
  worked and what didn't?

- Identify alternative vocabulary I haven't considered that
  might be more precise than what I've proposed.

- Look for early signals my vocabulary is being used back to
  me, or evidence it isn't.

Share a brief summary of findings before advising. Ask me to
react to anything that contradicts my assumptions.

PHASE 3 — PRESSURE-TEST

Deliver a pressure-test on three dimensions, using evidence
from your research and from what I shared.

VIABILITY. Does the vocabulary actually organize a scattered
audience around a defined problem, or is it a marketing slogan
in disguise? What's the strongest counterargument to my
current word?

RESILIENCY. Can a competitor absorb this vocabulary in 18
months without acknowledging it came from me first? What
about my authorship is traceable, and what isn't?

FUTURE MOAT. What worldview is this vocabulary scaffolding?
What does the eventual Belief Moat look like — the worldview
competitors can't easily copy — and is my current authorship
work pointing toward it or away from it?

After the pressure-test, ask me how I'd like to refine. Is
there a sharper word? A narrower audience? A clearer
worldview to point at? Iterate with me until the positioning
is the strongest version of itself, not the easiest.

PHASE 4 — POSITIONING DELIVERABLE

When the positioning is locked, ask how I'd like the final
statement delivered. Offer these formats:

- A one-line vocabulary claim for a LinkedIn headline or
  about-page tagline.

- A paragraph-length positioning statement for use across
  founder content.

- A longer brief covering the vocabulary, the audience, the
  worldview, the path to defensibility, and the surfaces
  where it should be authored.

- A glossary of related terms I should use consistently
  across founder surfaces.

Once I pick a format, deliver the final positioning based on
everything we've worked through together.

Be the Spring

I’ll let people much smarter than me convince you that personal brands aren’t optional for modern startups. But, for anyone building in an emerging category, I can credibly tell you that your company’s future moat can be reinforced by your personal brand.

While some like a16z may be able to get there by amplifying, we need to get there by authoring the audience into existence.

I spent the last 2 weeks studying AI wedges from CRM startups. At almost every turn were billboards.

Startups buying expensive San Francisco billboards is nothing new but it tends to happen much later in the growth journey.

• HubSpot: 5 years after IPO

• Monday.com: 2 years after IPO

• Notion: 9 years and 20M users after founding

• Slack: $5B valuation

This year half the billboards in SF promote AI startups at early (Series A/B) funding stages. Lightfield has fewer than 30 employees.

What’s going on here?

There are way more efficient ways to target tech executives and I don’t think it’s general brand advertising. What they are buying is actually far more interesting:

They're not selling to buyers. They're staking a claim as category leaders that's visible to every stakeholder: investors, customers, recruits, media and competitors.

Every emerging category has a 1- to 5-year window when the “leader” designation is up for grabs. “Which of these companies is going to be the company?”

The billboards that Attio, Lightfield and Clay are buying aren’t selling to buyers. They are attempting to plant their flags halfway through a 2-year AI challenger window that is quickly closing.

Here are five lessons every startup in an emerging category can take from this moment, whether you’re AI-native or not.

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Lessons from the AI Land Grab

1. The land grab decides who makes the consolidation cut.

Four Waters Framework ends in Crowded Waters when the category eventually consolidates. The market doesn’t give oxygen to dozens of credible players forever. It promotes 1–2 incumbents (who acquired or shipped their way into a slot) and 1–2 challengers (who claimed a spot during the wave).

The land-grab moments determine who makes the cut.

The CRM category is reorienting around AI startups (Lightfield, Attio, Reevo) and incumbents with strong AI additions (Salesforce, HubSpot). In the next 12-18 months, the consideration set will settle on 3-4 finalists to recognize as leaders. The rest won’t disappear, but they won’t have a wind assist from the category, either. (Example: There’s OpenAI, Anthropic and everyone else.)

Many other SaaS categories are in a similar spot as challengers compete for those Leader slots.

Lesson: Categories consolidate. The land-grab window decides which challengers make the final cut.

Common misstep: Don’t default to looking at the competition as “startup vs incumbent” or “startup vs startup.” The finalists will likely include both.

2. The window is shorter than you think but varies by industry.

Different categories consolidate on different timelines. AI-native categories are uniquely compressed because the underlying tech is evolving rapidly and competitors can respond with new features in weeks.

I think the window for AI-native challengers claiming leadership spots in established categories is roughly 18–24 months from the wave’s start. Many challengers are already halfway through it.

Other industries take longer.

Recurrent provides EV battery analytics for the used car market. After we established our leadership claim, the auto industry took another 2 years to mature to the point where it wanted to recognize a leader. The full window was closer to 4 years.

Lesson: Each window is temporary, varies by industry and is generally shorter than you’d expect.

Common misstep: These leadership windows are not tied to you, your product or your funding stage. The category and market decide the timeline and set the consolidation clock.

3. Leader claims should look disproportionate to your stage.

Making a leadership claim may feel premature when you’re building with small teams and small revenues.

That shouldn’t stop you.

A claim that’s perfectly proportional to your stage is just a description. Planting your flag as a leader is a claim that will outpace the metrics. You’ll need to deliver on it later.

Lesson: A leadership claim may feel premature. The signal that you’re claiming is territory that nobody has fully earned… yet.

Common misstep: Don’t wait patiently to earn the recognition before making the claim. Your bold stance is what triggers the substance to catch up.

4. The judges who crown leaders vary by industry.

A leadership claim is only durable if (1) the judges see it then (2) begin to recognize it. But the field of judges varies by industry.

• Enterprise SaaS: Placement on a Magic Quadrant matters more than any customer logo.

• AI-native B2B: The stakeholder wall of customer logos and investors illustrates your momentum.

• Automotive (from Recurrent): Traditional media and trade publications play an outsized role.

If you’re spending on signals visible to the wrong judges, you’re paying for impressions that don’t reinforce your claim.

Lesson: The right judges in your industry are a small set. Find them first then determine how to get in front of them.

Common misstep: Don’t default to whatever signals are loudest in other categories. What works in B2B SaaS is likely irrelevant in healthcare.

5. Claims need time to compound.

The substance of your claim takes time to compound until the industry is ready to award (or cede) the designation. That gap can be months or years.

But billboards alone don’t make a leader. There are startups with expensive signs along the 101 right now that won’t make the cut.

Progress comes from a stack of small signals: trade press mentions, new customer logos, conference panel slots, podcast appearances, original research, founder thought leadership. Individually, each is small. Together, they accumulate into a body of evidence for judges.

The job during this gap is remaining committed. Again. And again. And again.

Lesson: Patience is part of it. Compounding signals come from persistence + time.

Common misstep: Don’t mistake silence for failure. Keep beating the drum until the industry can hear it.

Planting the Flag Before Someone Else Does

People will tell you to let the product speak for itself. But this year thousands of amazing AI products will be passed then lapped by the leaders of their respective categories.

Attio, Lightfield and others are demonstrating how to invest now to make sure you’re one of the leaders at the front of the race.

Recently we covered growth loops to avoid ads. Land-grab signals aren’t ads. They’re claims to a status that determines whether your company makes the consolidation cut.

• “How should we build brand awareness?” ❌

• “What leadership claim made today then reinforced for the next 24 months keeps us in the consideration set?” ✅

These billboard ad buys aren’t reckless spending. They’re part of a race to claim a leadership spot before the window closes.

Find the signals and claim them so you’re ready for your category’s land-grab moment.

What are your challenges right now?

Hit reply to share the things you’re wrestling with in growth and marketing. If you’re stuck on something, someone else in this community probably just got unstuck from it.

The one thing I’d ask:

If the growth lesson today resonated with you, send it to one person or team that it could help. That’s who built this community, and that’s who belongs in it!

It’s tough to break into mature categories because incumbents have years or decades of compounded advantages.

Salesforce makes $40 billion a year and counts 90% of the Fortune 500 as customers. Their annual conference draws 45,000 people, many with “Salesforce” in their job title. That’s called defensibility.

Except AI shattered Salesforce’s architectural advantages and birthed dozens of ankle-biter startups. The CRM category has been cracked open, and the company with “CRM” as its stock ticker is on its heels.

But incumbents aren’t all teetering.

The well-prepared ones are striking back by shipping AI products backed by their data advantages, refactoring pricing models and self-disrupting before challengers can get a toehold.

Almost every software category is experiencing some version of this.

On the one end is legal tech. Startup darling Harvey.ai is doing $200M ARR in its first 4 years with +60% of the top US law firms already paying. Incumbents Westlaw and LexisNexis are blocked by their own architecture and pricing.

On the other end is customer support. Incumbent leader Intercom shipped Fin within weeks of ChatGPT’s launch and rebuilt pricing around AI-assisted outcomes before challengers could find traction.

Somewhere in the middle is CRM. Salesforce acquired and launched its own AI bolt-ons. Attio reoriented around AI. Day.ai, Lightfield.ai, Clarify.ai, and Reevo.ai have each raised $20M+ to chase CRM wedges.

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The AI Wedge Framework

The most successful AI startups are breaking into mature categories by attacking the constraints that no longer apply. When incumbents can’t defend without breaking their own business model, the startups have found a durable wedge that:

• Exploits an ex-constraint. These are deeply embedded product, pricing or org structures that used to be economically or technologically necessary and are no longer essential with AI-native products.

• Requires the incumbent to cannibalize. Reorienting a business model is both painful and distracting: fragmented customer base, dual product lines, margin compression.

This framework helps you find a wedge that meets both characteristics of a durable AI wedge.

1. Map the Category

We need to understand how the incumbents in your category make money. For each major incumbent:

• What’s the primary revenue model? Example: Salesforce subscriptions are tied to “Seats” across core products.

• What constraints does that model depend on? Example: Salesforce’s seat-based pricing worked because humans had to operate the CRM.

• Which of those constraints can AI eliminate? Example: AI can populate the CRM without a human in the seat.

• How are incumbents responding to challenger attacks? Example: Salesforce is responding with M&A and its Agentforce product.

We’re trying to uncover where the category is primed for a wedge and whether incumbents have structural defenses.

Test it: Pick your category’s largest incumbent. Respond to each of the four questions as specifically as possible.

Lesson: A wedge starts with understanding the incumbent’s economics. Most AI startups skip this step and end up with interesting products that don’t threaten anybody.

2. Find the Ex-Constraint

A constraint is a structural condition that shapes the incumbent’s product, pricing or organizational design, and is (presumably) no longer necessary with AI.

• For CRM, humans had to enter information and maintain the system. AI automates those steps and questions seat-based pricing.

• For legal tech, experts searched databases and interpreted results. AI cuts out everything between query and deliverable, while some incumbent platforms are still pricing by search volume.

• For customer support, people responded to tickets in a queue. AI can resolve many customer questions independently.

Each of these constraints were foundational and permanent. Now they aren’t.

Test it: Complete these sentences with specifics in every blank.

1. “[Incumbent] was built when [constraint] was unavoidable.”

2. “AI eliminated [constraint] by [specific mechanism].”

3. “Without [constraint], [incumbent’s pricing / architecture / org] no longer makes sense.”

Lesson: The wedge is an ex-constraint, but not every ex-constraint is a wedge.

Common misstep: Don’t mistake product disruption (cleaner UI, lower price, faster onboarding) for structural disruption.

3. Identify What Breaks

A good wedge forces the incumbent to choose between defending and protecting their business model.

Intercom broke their seat-based model with $0.99-per-resolution pricing with Fin. They are privately held and most public companies can’t afford to make that dramatic shift.

For bigger and less dynamic organizations, it’s more often a slow bleed:

• A fragmented customer base that juggles new and old pricing models

• Dual product lines that split attention

• Slowing overall growth as business shifts from one bucket to the other

That creates opportunities for startups. While incumbents navigate internal complexity, your challenger brand can ship features.

• Harvey uses per-lawyer pricing to make search-volume-based pricing look antiquated in legal tech. Westlaw can add tiers with AI features but it won’t fire its sales force and abandon 50 years of sales-led growth.

• Day AI uses per-assistant pricing to make seat-based economics look antiquated in CRM. Salesforce can’t disrupt its enterprise contracts to respond to that.

Test it: Complete this sentence with two deep business-model pains.

“When [your startup] wins, [incumbent] has to either ___ or ___.”

Lesson: The wedge isn’t a differentiated product. It’s a structural threat the incumbent can’t defend without leaving behind a trail of blood.

Common misstep: Don’t mistake a product feature for a wedge. A wedge should force the incumbent to dramatically reprice, rebuild or repair.

Cracking Open Your Category

The final “Crowded Waters” stage in the Four Waters Framework is not a finish line. AI reminds us that mature categories aren’t closed when the underlying technology is disrupted.

But the wedge isn’t AI itself.

Your wedge should be the specific ex-constraint AI eliminates and the specific cannibalization the incumbent has to overcome to defend.

The category label is sitting there (CRM, legal tech, customer support) and the former constraints have disappeared. Your job is finding the durable wedge that forces incumbents into defense mode so you can exploit it before they can stop you.

A new founder told me he isn’t worried about landing customers 1,000 through 10,000. It’s getting customers 0 to 100 that keep him up at night.

That’s a common confession in emerging categories.

The first paying customers are the most intimidating because there’s no playbook, no brand recognition, no demand.

Conventional wisdom suggests building a funnel:

• Ads for reach

• Content for consideration

• Drip campaigns for conversion

That approach assumes educated buyers who are already searching for what you sell. But our first 100 customers aren’t searching.

AI-native CRM, Lightfield, found itself in the same situation. I spoke with head of growth Matt Serna, after hearing CEO Keith Peiris on Mostly Growth, to understand how they thought about it.

From what I dissected, they found their first 100 customers in stages:

1. First ~10 from their network

2. Next ~40 from communities

3. Next ~50 from creative growth loops

That pattern isn’t unique to Lightfield. Stripe, Airbnb, Tinder, Product Hunt and dozens of other now-famous companies followed a similar progression.

Here’s the lesson for founding teams to steal.

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Laddering to Your First 100 Customers

Think of your first 100 customers as climbing rungs on a ladder. Each rung requires a different approach. While it is tempting to skip a rung, they aren’t optional because one (almost) always leads to the next.

Rung 1: Customers 0-10

Your first customers don’t buy your product. They buy you.

These are people in your existing network who trust you or your reputation enough to try something unproven. They aren’t evaluating features against competitors. They’re doing a favor for a person they know.

• Lightfield’s first 10 customers came from the founders’ personal networks.

• Stripe recruited their first customers from other Y-Combinator counterparts.

On Rung 1, you are the distribution channel. That’s not scalable and not PMF, but that’s okay. The goal is getting people using your product so you can learn what to fix before the next rung.

Test it: List 20 people in your extended network who (1) fit your ideal customer profile and (2) would take a call this week. If you can’t get to 20, lean on your closest relationships to borrow their credibility and expand the list.

Lesson: You’ll find your first 10 customers because they trust you. Keep that trust by listening to their suggestions and helping them feel invested in the company so they stay customers long enough to learn from them.

Common misstep: Don’t spend money to acquire your first 10 customers. If the people who already know you won’t use the product, strangers probably won’t either.

Rung 2: Customers 11-50

You still don’t have brand awareness, case studies or inbound demand at this point. You’ll likely need to borrow credibility from an institution, community or network that your prospects already belong to.

Lightfield’s next batch of customers came from 1:1 outreach to other YC companies. Stripe’s initial adoption followed the same path. When a fellow YC founder asks you to try something, there’s built-in pressure to say yes.

Product Hunt launched as a daily email. The founders spent the first hour each day contacting the product owners who appeared in it, recruiting them into discussions about their own products. Those product owners shared the discussions with their audiences, which brought new people and products to the site.

Clay’s cofounder joined GTM groups. When people said something intelligent on a related topic, he would start a conversation with them. Whenever anyone mentioned challenges in Clay’s sweet spot, he’d give thoughtful and complete answers to their question. This also created an opportunity to learn how prospects talked about the problem, which shaped how Clay positioned itself.

Existing communities were our primary growth driver in the early days at Recurrent. Passionate EV owners don’t announce themselves, but they congregate in specific and predictable places. Being part of their groups gave us the temporary credibility we needed for them to try us.

The specific community varies. Startup groups, industry Slack channels, conference circuits. Find where your ideal customers already trust each other and show up there.

Test it: Name 5 communities where your ideal customers already congregate. Join the top 2 where people are actively asking questions about problems you can solve. Spend 30 minutes a day for two weeks answering those questions without pitching.

Lesson: You’re trying to embed yourself (rather than your company) in existing networks where trust already exists at this stage.

Common misstep: Don’t invest in your own community yet. You don’t have the center of gravity for it.

Rung 3: Customers 51-100

Your network and community growth motions reach diminishing returns at some point.

For mature categories with educated buyers, this is where paid acquisition takes over. For emerging categories, that’s rarely an option. We couldn’t generate scalable returns from advertising for the first 3 years at Recurrent.

The alternative is a repeatable growth loop that creates value for prospects before they ever evaluate your product.

Lightfield’s version is one of my favorites. They hosted readings at their office from prominent authors on GTM. The authors agreed because each attendee got a copy of the book, courtesy of Lightfield. Everybody won:

• The events attracted founders who cared about the topic.

• Lightfield got a room full of ideal prospects in a relaxed setting.

• The authors sold a bunch of books.

Lightfield has run at least 3 of these events and plans to hire a dedicated person to increase the frequency. They treat it as a pure GTM investment because roughly 25% of event attendees sign up for a Lightfield trial within the first month.

That’s a lot different than spraying banner ads and cold emails. Those customers came from delivering genuine value through proximity. When you’ve assembled a room full of your ICP, some of them will want to know what you do. That’s a repeatable loop that doesn’t rely on founder sales.

Tinder built a different loop on the same principle. They worked with sororities at multiple universities to throw exclusive parties where admission required downloading the app. If women joined, men would follow. The value was the party and the product was the ticket.

Airbnb built theirs on the supply side. They sent professional photographers to the apartments of early host signups to improve listing quality. It helped the host get their first bookings, and it also created a this-is-legit moment that locked in supply while attracting more demand.

The common thread is that each company found a way to make their product the natural next step after an experience the prospect already valued.

Test it: Start by documenting what your first 50 customers value most about working with you. How could you deliver a version of that value to prospects who don’t know you yet? Those answers are the start of a potential growth loop.

Lesson: Repeatable growth loops are the bridge between scrappy founder-led sales and scaled growth. They work because they lead with value rather than a pitch.

Common misstep: Don’t default to traditional paid acquisition channels because the creative alternatives feel too small.

Climbing Your Ladder

“Build a funnel” is the right advice for Customer #800. It’s the wrong advice for Customer #8.

Think about early customer acquisition in stages rather than getting your first X customers.

• “How do I get my first 100 customers?” ❌

• “Which rung am I focused on right now?” ✅

Lightfield’s team understood this. They didn’t try to run a Series B growth playbook with their product launch. They climbed the ladder one rung at a time and matched their tactics to their stage.

Your ladder is waiting. Figure out which rung you’re on and commit to the work that rung demands. The next rung reveals itself when you’re ready.

The tactics that keep you afloat in the early days of a new category can capsize you as the market matures.

Slack’s path from category pioneer in 2013 to today is one of the clearest illustrations of this. It shows how dramatically the right moves change at each stage, and what it costs when you don’t adapt in time.

This post walks through each stage using Slack as the lens. If you want the full framework first, start with The Four Waters Framework.

Evolving with Your Category

Category creation follows predictable stages. As the waters around you change, the playbooks and tactics you use must change, too.

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Stage 1: Uncharted Waters (2013)

The idea that companies would pay for workplace chat was unproven when Slack launched. Existing platforms had chat products (AIM, Google Hangouts) but they were seen as places for casual, unstructured messages.

Enterprise communication happened through email. That meant there was no existing budget or line item for team messaging, outside of some small dev-focused tools.

That constraint turned into an advantage. No established playbook gave Slack permission to build differently:

• No MVP. The early product was polished enough that initial users helped peers see the potential and create the internal business case.

• No IT. Focusing on end users let Slack bypass IT leaders who had no dedicated budget and could quickly smother adoption.

• Ecosystem first. Slack invested in integrations early, positioning as a multiplier rather than another standalone tool.

The lesson: In Uncharted Waters, you’re building belief rather than capturing demand. Traditional growth channels and tactics may not help you yet.

Stage 2: First Voyage (2014–2016)

Slack reached 1 million daily active users within 2 years. At that point, tech company prospects got it immediately. That drew attention from serious competitors, who reacted with product pivots (Microsoft Yammer) and bolt-on acquisitions.

Slack used its head start well. Rapid releases (e.g. threading, emoji, integrations) kept competitors focused on parity while Slack kept moving the target. By the end of this stage, “slack” had become a verb and corporate FOMO created inbound demand.

The lesson: First Voyage is when you make clear to the market who is winning. Get it wrong and you educate the market for the competitors chasing you. (Think: 2nd mouse gets the cheese.)

Stage 3: Charted Course (2017–2019)

The category was undeniably real. Mainstream companies across industries adopted structured real-time communication and nobody was saying “we’ll just stick with email” any longer.

Then Microsoft launched Teams by bundling into Office 365 at no additional cost.

The Charted Course trap emerged: Slack had built a strong product and a loyal user base, but hadn’t built enough defensibility against a distribution-based attack. What had looked like network effectings and switching costs for Slack suddenly didn’t appear as strong.

Microsoft’s bundling move was predictable in hindsight. The category was valuable enough to attract a platform player, and platform players compete with distribution rather than features.

The lesson: Charted Course is when you solidify defensibility. Network effects, ecosystem lock-in and brand moats begin to matter. While Slack built some of these, they didn’t have an answer for Microsoft’s bundling.

Stage 4: Crowded Waters (2020–2021)

COVID accelerated adoption of all workplace tools — one of the happiest professionals I ever met was a Zoom sales manager in 2022.

As Microsoft Teams exploded growth through bundling, Slack needed to bulk up to survive in the saturated market.

Result: Slack sold to Salesforce in 2021 for $27 billion. Slack couldn’t win the Crowded Waters fight independently. The acquisition reoriented the competition between two platforms rather than two products.

The lesson: In Crowded Waters, operational excellence and corporate development (including M&A) matter more than innovation. Sometimes the right move is redefining who you’re fighting with rather than who you’re fighting against.

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What Slack Got Right and Wrong

Slack executed the early stages masterfully. They built the right product for Uncharted Waters, ran the right playbook in First Voyage and became synonymous with a category they created.

The costly missteps appeared in Charted Course. They didn’t build defensibility fast enough when the window was open. Even if Microsoft’s bundling advantage was foreseeable, Slack didn’t have an answer for it.

The tactics that made Slack great in the early 2010s weren’t enough to protect them by 2020. Understanding which stage you’re in is essential to running the right playbook.

Now you’ve got the framework to avoid the same mistake.

Products in mature categories can activate new customers in minutes because they’re dealing with educated buyers. For a company like Dropbox, everybody understands file storage. Upload a doc, create a password, done.

Webflow couldn’t do that because nobody had experience using a visual designer.

Users signed up, opened the editor, and fumbled through layout properties and the CSS box model. They didn’t have the context or experience to value the possibilities.

That’s a defining challenge of emerging categories: Our prospects are encountering an entirely new way of addressing a problem, and that requires a learning curve before value can be recognized.

Melissa Tan, Webflow’s head of growth, came from Dropbox where growth engineering was programmatic. She shared that activation at Webflow happened over several sessions with lots of learning along the way. The standard onboarding playbooks and optimization tactics didn’t work.

I experience this at Recurrent every day. None of us grew up with an electric car. For a first-time EV owner, our product solves problems they don’t know they have yet. People need to understand some pretty intimidating basics on battery health and degradation before they can appreciate the value of the data at their fingertips.

The task for us is to fully appreciate how and when comprehension happens for our customers. Here’s a playbook that I derived from Webflow (and Notion and Clay) that your team can steal.

The Aha Sequence Playbook

Most growth playbooks assume your prospects already understand the problem you solve. Essentially, the conceptual groundwork is in place for them thanks to competitors and lots of prior experience.

In less developed categories, that groundwork doesn’t exist. The prospect has to understand the problem and solution before they can value what you offer.

1. Map the Comprehension Gap

People exploring Webflow for the first time needed to understand the design model, layout properties, and visual development principles before the product could deliver on its promise. It sounds like a product problem, but it’s really a comprehension problem.

The team ran two-week user diary studies — screen recordings with play-by-play narration — because traditional analytics couldn’t capture where onboarding broke down.

They discovered that new users consistently got stuck just before critical breakthrough moments. It wasn’t because the onboarding flow was bad or the product was buggy, but because the concepts were totally new.

Prospects didn’t understand enough to know how to find value.

Test it: Make a list of the concepts your customer needs to grasp before your product clicks for them. For Webflow, it was the CSS box model. For Recurrent, it’s battery degradation basics.

• Where are your customers supposed to learn those concepts?

• Do they need them before, during or after onboarding?

That’s your comprehension gap.

Lesson: Prospects without the experience or context needed to appreciate what you’re offering is more of a comprehension barrier than a conversion barrier.

Common misstep: Don’t treat confusion like a UX problem. Cleaner design and crisper calls-to-action won’t necessarily fix it.

2. Sequence the Moments

There’s no single “aha moment” when users lack the foundational understanding to appreciate your product. Instead, we need to orchestrate a sequence of smaller realizations that build on each other.

Webflow’s onboarding checklist has 6-10 items, each designed to deliver a different moment of understanding. They explain the purpose of each one before diving in to set the expectation that this is more of a learning journey than a product tour.

The sequences are also tailored to the path. Users starting with a blank site get a different sequence than users starting from a template.

• Blank-site users need a preview of what’s possible before they care about building it.

• Template users can see what’s possible so they need to experience the ease of modifying it.

Whether you have a product or service business, it’s different from the standard playbook of “get people to the core action ASAP.” Rushing to the destination is counterproductive when comprehension is the bottleneck. We have to sequence the learning that makes the outcome meaningful.

Notion faced a version of this with the blank canvas problem. A new user opening an empty workspace doesn’t know what’s possible, let alone what to build. “What will you use Notion for?” became the first step so the product could pre-load workspaces with relevant templates. This shows people what’s possible before asking them to create it themselves.

Test it: Sequence the essential concepts from Step 1 into the (ideal) order of operations: Which understanding enables the next? That’s the sequence taking shape.

Lesson: Each small realization should guide customers to the next one. Even if customer activation takes weeks, we can deliver bite-sized understanding in a series of moments.

Common misstep: It’s rare that one-size-fits-all. Different customers arrive with different levels of comprehension so it’s best if we can meet them where they are.

3. Clear the Path

Customer patience is often correlated with understanding. In other words, is the destination worth reaching?

• I’ll spend 90 minutes setting up a new TV, including painfully logging into each of the apps with the tiny remote.

• An AI app with bold promises on efficiency gets 90 seconds before I cancel the account.

Webflow’s diary studies uncovered what the head of growth described as “death by a thousand paper cuts.” These were small friction points that individually seemed minor but collectively drove users to quit before comprehension could take hold.

For example, users consistently got stuck on the layout feature because it required understanding a specific model. The team formalized a Webflow University and built onboarding offramps to get people the context they needed to be successful.

Test it: Think about the last 5 customers who didn’t make it.

• Where did they get stuck?

• Was it because the concepts were confusing or the experiences were confusing?

Those are different problems.

Lesson: Comprehension problems need education, whereas friction problems are often in need of simplification.

Common misstep: It’s easy to see customer education as a support function that should be automated as much as possible. Imagine it as a strategic asset rather than a CS cost.

4. Bridge the Sessions

Education takes time. Since time and customer adoption are enemies, that means builders in emerging categories need to be very intentional.

Webflow uses a variety of tactics to signal progress and possibility.

• Tutorials explain their purpose before diving in.

• Users check off milestones as they build.

• Estimated completion times set expectations.

Clay confronted this by forcing early customers into a public Slack channel. Users who might have quietly abandoned the product instead saw peers working through the same learning curve. The shared channel shortened learning curves while providing proof that the journey was worth finishing.

As we covered in Clay’s ecosystem playbook, that Slack channel evolved from a support hub into the peer community that became Clay’s powerful growth engine.

Clay also flipped sales demos into live onboarding calls. Instead of pitching the product, they had prospects share their screen then walked them through solving their own real problem inside Clay. The customer left with an actual, valuable outcome. That’s a session bridge to bring them back.

Notion pre-loaded workspaces with templates based on user goals that gave people something to return to immediately. That gap between “I can see it” and “I can make it” was a reason to come back.

Test it: Walk through your customer’s first week from their perspective. After their first real interaction, whether that’s a product session, a sales call or a demo, what specific reason do they have to come back tomorrow?

Lesson: Patience isn’t finite. It’s a resource you can replenish.

Common misstep: There’s a tendency to fill the gap between sessions as a marketing problem with generic drip campaigns. The best bridges aren’t reminders, they foster reasons to continue by:

• Connecting to something the customer already learned

• Previewing the next realization they’re close to reaching

Building Your Sequence Playbook

Our customers likely don’t have the context they need to fully appreciate what we’re offering them. We can’t fall into the trap of viewing it as a product flaw to fix, a marketing message to push or a growth lever to dial.

• “How do we get prospects to activate faster?” ❌

• “How do we guide prospects through understanding why this matters?” ✅

Webflow’s product takes weeks to learn. That complexity could/should have stunted their growth. Instead, they built a $4 billion company by treating education as the product experience.

Your users are out there. They just don’t understand why to appreciate you yet.

Intent-based outbound is the #2 investment area for growth teams this year. Everyone’s buying the same tools, using the same signals and automating the same outreach.

The brands finding an edge aren’t necessarily doing it with better tools. They’re capitalizing on their proximity to the customer and how they can spot things that competitors can’t.

“I found that the biggest returns from growth actually come from a deep intuition about what will work coupled with deeply understanding your buyer,” said Brandon Camhi, former VP of Marketing at Rippling, on the GTM Engineer podcast.

That’s different than signing up for Apollo and filtering by company size.

We’re also using signals at Recurrent to drive a lot of our growth right now. The signals we track look at historical market metrics from publicly accessible data combined with signals from the executive team at prospects that show they’re ambitious and progressive with technology. Everything is technically public. All sourced with Claude Code running daily checks.

That’s the inversion.

Most signal-based playbooks are built for mature categories where buyers know they have a problem and are actively shopping for a solution.

In emerging categories, your prospects don’t know they have a problem yet. The signals you need aren’t at the bottom of the funnel. They’re at the top to identify who is likely to be receptive to education, in order to one day be in the market to buy.

Here’s a framework to help you find them.

The Signal Inversion Framework

Prospects already know they have a problem and what solutions look like in mature categories. They’re shopping. The job is to intercept them or at least become part of their consideration set.

That signal doesn’t exist yet in newer categories. The prospect isn’t searching because they don’t know there’s something to search for.

That’s a much different question:

• “Who’s ready to buy?” ❌

• “Who’s about to have a problem they don’t know about yet?” ✅

That is the foundation of the Signal Inversion Framework. Instead of looking for purchase intent, we’re looking for the conditions that precede the problem so we can start the education process before your competitors even know the prospect exists.

1. Identify the Pre-Problem Signals

When Brandon Camhi joined Rippling, the company was doing what most startups do: blasting non-personalized emails and spending a little on paid ads. Only 10-15% of demos came from campaigns with any intent signal behind them.

A year later, that number was 60-70%. And when COVID hit and everything else went to zero, the only campaigns still producing were the ones driven by signals.

Good/bad news: the signals that worked weren’t sitting in a tool.

Camhi built Rippling’s signal engine by listening to Gong calls, talking to sales reps and studying what was true about customers before they became customers. He started from the destination and worked backwards.

You’re not looking for buying intent. You’re looking for the conditions, behaviors and situations that exist before the prospect realizes they have a problem worth solving.

The signal is not intent. It’s more like a precondition.

Test it: Look for patterns in the conditions that preceded awareness in your last 10 closed-won deals. You can include demographics, firmographics and psychographics but I’ve found that those can distract rather than aid the process. We’re looking for patterns more than criteria.

Lesson: The most valuable signals are indicators of inevitability. If X-> Y-> Z, we’re trying to solve for X.

Common misstep: Don’t confuse pre-problem signals with broader ICP criteria. “SaaS company with 200 employees” is a firmographic filter. A signal should tell you something is in motion.

2. Source Signals from Behavior

The standard data tools (Clay, Apollo, ZoomInfo) are powerful, but if you and your competitors are all pulling the same signals from the same vendors, those signals stop being an advantage.

In the best case scenario, they’re table stakes. More often, they’re distractions.

Kyle Poyar’s research reveals that intent-based outbound is one of the top investment areas in 2026. That means the signal commons is getting more crowded.

The edge comes from going up-funnel to where those platforms can’t see or don’t know what they are looking at.

At Rippling, Camhi listened to 3-5 sales call recordings every week in Gong. He discovered that:

1. Brand investment was systematically undervalued in attribution.

2. Prospects displayed similarities that were not visible in firmographic filters.

At Recurrent, our signals work similarly. We combine publicly accessible data that individually aren’t very meaningful, but together paint a picture of a prospect whose business trajectory makes our solution increasingly inevitable.

Test it: Take the pre-problem patterns from Step 1. Think beyond the obvious or readily available to answer these questions.

• What kind of quantifiable data could serve as a proxy for this condition?

• What combination of two or three of these, taken together, would give you high confidence that this prospect is approaching the problem your product solves?

• Which investors, advisors, AI companions, vendors or distant LinkedIn connections could help you find the data sources behind these signals?

Lesson: A proprietary composite doesn’t require proprietary data. It requires proprietary thinking about what combinations of public data actually mean something.

Common misstep: Teams often build composites that are too complex to maintain or too abstract to act on. It’s better to hone in on one signal that you’d bet money on.

3. Trigger Education

In a traditional signal-based workflow, the signal causes the output: email, LinkedIn DM, even direct mail like Rippling.

1. Signal: Buyer is ready

2. Output: Tailored pitch

For those of us building in emerging categories, the signal should be saying “this person is about to encounter a problem they don’t know about yet.”

The response needs to match.

1. Signal: Problem is emerging

2. Output: Helping prospect name or recognize the problem

It’s too early for the output of your signal to be a pitch. It’s only the beginning of an education process.

Test it: Map your signal triggers to ideal educational touchpoints. “When we see [signal], the prospect sees [educational content].”

Lesson: Companies that use signals to start educating will build demand that their competitors don’t even know exists.

Common misstep: “Education” doesn’t mean a generic drip campaign. Relevance is what separates education from noise.

Building Your Signal Inversion

The intent data gold rush is real. That’s because it works for companies in mature categories with known buyers and established purchase behaviors.

But if you’re in an emerging category, the standard playbook has it backwards. We’re looking for prospective learners who, with the right information, will become buyers.

• “What signals tell me who’s ready to buy?” ❌

• “What signals tell me who’s about to need something they don’t know exists?” ✅

Rippling’s story is valuable because the underlying principle can be adapted for you: Success came from getting close enough to the customer to identify what preceded conversion. Only then did they go on a scavenger hunt to find data to inform the signal.

That’s a path available to most founding teams willing to do the work.

Your prospects are out there. They just don’t know they need you yet. The signal is how you find them and education is how you earn the right to help them.