We were pushing users toward it, optimizing the shit out of it, growing at lightspeed.

Then AI came along and changed everything.

Changed how users experience value. Changed what that first moment of “getting it” actually feels like.

And most AI startups I see (or those that offer AI features) haven’t caught up yet.

They’re still playing the old game. Making one mistake that prevents users from ever experiencing the value they built.

So it’s time to reframe it.

What was the AHA moment back then?

As said in the beginning, the AHA moment was defined as the pivotal moment when a new user first recognizes the core value of your product and understands why they need it.

I still remember the whiteboard sessions we had. “What is our AHA moment?” “How do we get users there as fast as possible?” good old times….

And back then, it was worth obsessing over. Because once we cracked it, we felt the difference in conversions immediately.

To figure it out, we studied the same well-known examples:

• Facebook’s AHA moment: Users who added at least 7 friends within the first 10 days developed significantly stronger retention.

• Slack’s AHA moment: Teams that hit 2,000 messages sent were highly likely to become long-term paying customers.

• Zoom’s AHA moment: Value hit when someone ran a meeting without any technical friction, straight from the browser.

Dozens of products followed the same pattern:

Find the moment → optimize toward it → watch conversions go up.

It worked. Until it didn’t.

The AHA moment in the AI age

At its core, the definition hasn’t changed. It’s still the moment a user experiences value for the first time.

But what that moment feels like and how to get someone there is completely different with AI.

Because with AI, it’s not an AHA moment anymore. It’s more like a magic moment.

• SaaS AHA moment: “I can finally do this work efficiently.”

• AI magic moment: “I don’t have to do this work at all.”

That’s a completely different emotional reaction.

And products like Lovable or Perplexity have already trained us to expect it. One prompt, something that would have taken days appears in a matter of seconds.

So, going back to the whiteboard, as a business offering an AI tool (or features), that’s what I would optimize for.

Speed

→ Classical SaaS took time. Account creation, onboarding, setup. Users expected a ramp before they got value.

→ With AI that’s gone. It has to be instant. One input field, one prompt, magic. Any friction before that moment and we’ve already lost them.

Effort

→ SaaS required manual work to get value out. Users had to invest time before things clicked.

→ AI has to be the opposite. Attention spans are at an all-time low. One prompt, one click, one result. That’s the expectation you’re up against.

The feeling

→ The SaaS AHA moment felt like relief: Something painful just got simpler.

→ The AI magic moment has to hit differently. We want to create the “holy cow, how did that just work” reaction. That gut-level surprise is what should actually be optimized for.

Quality

This is where it gets tricky.

→ SaaS worked or it didn’t. Users got the value or they didn’t.

→ With AI, the output can go sideways. A hallucination doesn’t just fail to impress, it actively destroys the magic. One bad result and the WTF moment replaces the magic moment. Output quality is one of the most important things to optimize.

AND there is one other mistake I see many AI solutions make.

The mistake most AI startups are making

Whether it’s an AI solution or a tool that offers AI features.

Most businesses gate them behind a paywall.

I get the logic. AI usage costs money. But hiding it behind a paywall kills any chance someone has of experiencing the magic moment you just spent everything building toward.

Think about it. Users are primed by the best AI products out there to expect instant value. Their attention span for anything less is basically zero.

Now you put a paywall in front of the one thing that would have made them stay.

Therefore, we should start treating AI usage as a marketing cost. Because that’s exactly what it is. If a product genuinely creates value, getting someone to feel that magic moment once is worth every cent.

Last words

The AHA moment became a magic moment. And the way we should optimize for it changed completely.

Speed to value. Minimal effort. That gut-punch of “I can’t believe that just worked.” Output quality that holds up the magic instead of destroying it.

And above everything else: don’t gate it. The market leaders have already trained our users to expect value immediately.

Don’t make them wait for something they can get somewhere else right now.

AJ

PS: If you enjoyed this, please tap the like button below. Thank you! 💛

Probably yes, because budget is limited, pressure is on and you want to finally get your acquisition running.

I was in the same spot a few years ago working at a small startup. We tried ads, outbound, content. Nothing moved the needle the way we needed it to.

Then I looked at the hyper-growth companies. The ones going from 0 to 100M ARR in record time.

And there’s one mechanic that works for almost any business (yes, also for solo builders) and it can create a constant stream of new users. For free.

Here’s what they’re doing.

Building referral loops

Referral loops are how today’s top companies generate customers on autopilot.

The core principle is that one customer brings 10 more and there are just some simple mechanics you can implement for your business.

To make it clear, let’s have a look at some examples.

Lovable’s Referral Loop

Lovable’s referral loop looks like this:

A new user is using the tool, he builds something, shares what he built, others see it, go to Lovable’s website and start building.

To fuel this loop Lovable has some simple mechanics in place:

Once a user builds an app he is greeted with a pop-up and the call to action to share his app.

In the second step: User shares his app on social media or his app appears in Lovable’s app library.

Once someone clicks on his app, there is a button in the bottom right corner.

One click on it and you land on Lovable’s website where you can directly start building.

Boom. Loop closed, users generated on autopilot.

But this doesn’t only work for end users.

Clay uses a completely different loop, targeting partners but at least with the same effects (if not more effective).

Clay’s Referral Loop

Clay is another rapid-growth company reaching $100M ARR in 2025.

And their loop is one major pillar to reach this revenue milestone.

Clay’s loop addresses agencies and works like this:

1. Agencies use Clay to run cold outreach campaigns with extreme personalization for their clients.

2. The clients are so impressed by the results (high response rates) that they ask: “How do you do that?”

3. The agency shows them Clay. The client realizes they need to buy the software themselves to maintain this standard in their team.

4. People are talking about it online and sharing it with the community, which attracts new agencies.

This again creates this permanent stream of new customers.

There are even agencies that build their entire business model around Clay. Those are called Clagencies.

How to build one yourself

So coming to the key question. How do you build a loop yourself?

There are two main questions you should ask yourself upfront:

• Does my product have the nature that people are willing to share?

• Or can I enable others (e.g. partners) to share my product?

If you answer these questions you probably have an idea in which direction you can build your loop.

Let’s say as an example we have a social media scheduler as a product. Here clearly people are willing to share but there is probably no way where you can put your brand name in (nobody wants a “posted with XYZ” underneath their post haha).

So we go for partners. Bringing agencies or freelancers to use the product. The loop could look like this:

1. The freelancer plans the posts for their client in our tool.

2. They send the client a “Review-Link” for approval.

3. The client sees the post in the interface of our tool (without login required).

4. When the client later has their own project or the team changes, they remember the tool (”What was that cool approval tool?”) and become a paying user themselves.

Or another example.

Our tool tracks investment activities and since people naturally love to brag on the internet we focus on creating nice sharable dashboards or screenshots.

Here comes the loop into play:

1. People use our tool

2. People share their overview from our app (screenshot or we create a preview link where they can share it directly) on social media

3. Other users see it and are naturally guided to our website

4. New users start using our tool

Loop repeats.

With this simple mechanic you can acquire customers on a budget without spending thousands on ads or spamming outbound mails.

Final Words

When we implemented a basic referral loop in that startup I mentioned, we went from struggling to hit our monthly targets to having users bring in 3-4 new signups each week.

That’s when I realized that the best growth engine isn’t something you buy. It’s something you build into your product from day one - for free.

PS: If you enjoyed this, please tap the like button and leave a comment below. Thank you! 💛

Share AI x Growth

The answer stuck with me. Until then, I always believed that virality happened by coincidence.

But now I’m convinced it doesn’t.

What Virality Means

Before diving into this, we should look at what virality means. When we talk about virality or viral content, most of us probably have this in mind:

You post → you hit a nerve → you go viral → boom, thousands of likes, shares, comments and new followers.

But in my view there is another type of virality.

The one where you make your customers, users, partners, or whoever constantly talk about your product or business. They’re basically doing the work for you. They write articles, mention you in the press, and talk about you on social media. They make you appear constantly in front of your target group.

This type of virality doesn’t necessarily need thousands of likes. Here it’s about the compound effect of many small conversations rather than one big explosion.

And while many are trying to force the first type, the second one is what more companies should aim for.

How to “Go Viral”

So the key question is: how do you achieve this kind of virality?

Lovable’s head of growth explained it like this: You have to give your customers stories they can share.

Think about it.

Lovable does exactly this. With the app, everyone can become a developer. People are building the things they always dreamed about, and what happens then? They start talking about it online. They share their story of how they built something.

This creates a viral story loop, which picks up elements of classical storytelling:

• User experiences the magic moment (in Lovable’s case, that they can create anything they want).

• They become “the hero” in the story as they solve their own problem.

• They share their story online.

• Others see it, use the tool, and talk about it as well.

• Loop starts again

How Can You Create This Viral Loop

So you might think “nice, but I don’t have this kind of solution where I can create this viral loop naturally.”

I got you.

There are different ways to make people talk about you:

• Give them something to visualize → Stripe gives you beautiful UI to show the revenue your business makes. Looking at my feed, I see at least once per day someone talking about how their business makes X revenue, posting a screenshot of Stripe’s visualization.

  • The story Stripe gives users: “I’m now a business owner making real money.”

• Create shareable achievements → Turn user progress into badges of honor:

  • Duolingo’s streak feature and year-end reports get shared constantly. The story they give: “Look, I’m learning another language.”

  • Spotify Wrapped turned listening data into an annual social media event. The story: “I have great music taste.”

  • GitHub’s contribution graphs make developers want to show off their activity. The story: “I’m a productive developer who ships code.”

• Build in social proof → ProductHunt’s “Featured on ProductHunt” badges incentivize people to share their launches.

  • The story ProductHunt gives: “I’m a builder who turned my idea into a real product.”

Ask yourself the question: What is the story I give my customers that they’re willing to share?

Last Words

This kind of virality doesn’t happen by coincidence. It’s engineered. Sometimes not even by marketing but by product teams.

But no matter how, it’s the type of virality you should aim for. Making people constantly talk about you doesn’t only bring new customers all the time but also gives you an edge.

We see more and more AI-generated content, but when people talk about you, it’s real experience. This creates trust and the word-of-mouth awareness you want to have.

AJ

PS: If you enjoyed this, please tap the like button below. Thank you! 💛

“Our approach to advertising and expanding access to ChatGPT”

With it, they announced they’ll soon start testing ads inside ChatGPT.

For me, someone who’s been working with ads for years, this sounded super exciting at first. But I was also skeptical.

Is this going to be just another ad platform draining our budgets while spawning a new wave of gurus claiming they’ve found the holy grail of advertising?

After a closer look though, I think this might actually be the ad format we’ve been waiting for.

Let me explain.

How do ChatGPT ads work?

In their article, OpenAI wrote:

To start, we plan to test ads at the bottom of answers in ChatGPT when there’s a relevant sponsored product or service based on your current conversation. Ads will be clearly labeled and separated from the organic answer.

Looking at their example, here’s what happens: You’re chatting with ChatGPT and when a product or brand gets mentioned in your conversation, a relevant ad appears in a frame below the message.

Click on it and you get directed to where you can buy it (I assume).

(The frames remind me of TikTok shopping. But I'll take it.)

Sidenote: What would be interesting is if these ads did not even need explicit product mentions and could pop up based purely on the topic you’re discussing… but that’s something we will see as soon as it is rolled out.

But here’s where it gets really interesting.

OpenAI also wrote about a second ad form:

The best ads are useful, entertaining, and help people discover new products and services. Given what AI can do, we're excited to develop new experiences over time that people find more helpful and relevant than any other ads. Conversational interfaces create possibilities for people to go beyond static messages and links. For example, soon you might see an ad and be able to directly ask the questions you need to make a purchase decision.

This is the part I love.

First, they actually get it. Ads need to be useful and entertaining nowadays.

Second, and this is huge, you can ask questions directly in the conversation to get everything you need to make a purchase decision. ChatGPT does the heavy lifting for you.

Then all that’s left is to buy.

Why this will be a game changer

Let’s look at the numbers.

ChatGPT has around 858 million users pumping out over 2.5 billion prompts per day (Explodingtopics)

For comparison, Google handles about 16.4 billion searches daily (Demandsage).

So yeah, there’s massive ad potential here. Even if it’s just rolled out on the free and lowest ChatGPT tiers for now.

But the real game changer: AI ads break the rules

Back in my early marketing days, I stumbled across those terrible terms “push” and “pull” marketing.

The basic idea:

Push means pushing the product to customers through distribution channels.

Pull means creating demand so customers pull the product from the market.

Here’s how it maps to current ad channels:

Google & search ads (Pull) → You search, product appears, demand is created, you pull it from the market.

Social Media Ads (Push) → You scroll through your feed, ads get pushed into your face, if it’s interesting you buy.

Now here comes this new kid on the block.

AI ads don’t fit neatly into either category. They do both.

You ask ChatGPT about a product or brand, ads appear (pull).

You casually chat with ChatGPT about random topics, it somehow ends up recommending products in the conversation, ads appear (push). And this is more contextual. While in social media ads often disturb the flow on your feed here in ChatGPT they are naturally embedded.

Either way, someone is ready to buy or way closer to making that decision.

They either asked for specific products, meaning they already want to buy (this is how Google ads work best). Or they’re at a point where ChatGPT just moved them significantly closer to the buying decision by creating enough demand while answering all their questions - questions they’d normally have to hunt down on your website - and then routes them straight to the checkout.

That being said, AI ads could be the most efficient ad format we’ve ever seen.

Last Words

Of course, it all depends on how it’s balanced, what it costs, whether it’s an auction model like Google, and how oversaturated it becomes.

And it also will be interesting what the targeting criteria are and how ChatGPT gathers these information and provides them.

But like with every platform - early adopters win. So as soon as you get the chance, test it. I’m pretty sure it’ll be worth it.

Nevertheless.. I have mixed feelings about this whole thing. My marketer heart screams “hell yeah!” while my nerd heart screams “please, not this.”

When it comes to AI, my vision has always been having a J.A.R.V.I.S.-like assistant. You know, like Ironman. 😂

But okay, it’s a long way to get there… And apparently we’re starting with ads.

I guess that’s how it goes.

PS: If you enjoyed this, please tap the like button below. Thank you! 💛

Marketing, sales, product, partner management… you name it.

And with that came a new breed of marketers.

Growth hackers or growth marketers or whatever label stuck.

These folks were way more versatile than the typical slide-pushing, canva-loving marketers.

The whole idea was to have someone so flexible they could jump in wherever the biggest growth lever was hiding, experiment with it and pull it to scale a company.

Landing pages, ads, UX improvements. Viral product loops. Didn’t matter.

That’s how the T-shaped marketer was born.

What’s a T-shaped marketer?

A T-shaped marketer got broad knowledge across all marketing disciplines but deep expertise in one specific area.

With that deep knowledge, they could create explosive growth in their domain.

SEMRUSH had their take on it with the content experienced t-shaped marketer:

Then Buffer came along with a way more complex profile.

This was the typical first marketing hire at a startup who had to do literally everything.

I’d definitely put myself in Buffer’s bucket.

That was exactly my profile when I jumped into the startup world… or at least that’s what I developed in by doing everything myself back then.

And now… AI is here and the whole game is changing (again).

All marketers are becoming engineers

AI is becoming part of our daily routine and we all see it as the next big growth lever.

We’re moving into a world where setting up and directing AI agents will be our bread and butter… also for marketers. In a few years, things like content creation, SEO, PR and ads will be handled by AI.

The trick will be managing it so well that you get the best results possible. Therefore marketers will still need knowledge across all domains to judge whether AI is giving good results but most marketers will be freed from execution (at least to a certain degree).

So here’s what a future T-shaped marketing profile looks like.

The T-Shaped AI Marketer:

This marketer knows their way around all marketing channels well enough to direct AI for the best results. And they have deep technical knowledge to set up agents, workflows and prompts that actually work.

This profile can replace entire small marketing teams in startups or small businesses (once AI and agents reach a level where they deliver high-quality output).

In-depth disciplines of the T-Shaped AI marketer

Here’s what the T-shaped AI marketer needs to master:

• Prompt Engineering: That’s the heart of AI. If you can’t write solid prompts, you won’t get solid results. The AI marketer needs to be top-tier at prompting to get the best output (and to judge if that output is actually good, they need that broad knowledge across all marketing disciplines).

• Vibe Coding: The days of manually building landing pages, waiting on developers for small tools, or spending months on funnels are done. Future marketers need to do it themselves. Iterate fast. A/B test on the fly. Execute in hours, not months.

• Automation & Workflows: Whether it’s Zapier, N8N or Make, marketers have to build systems that handle the grunt work. Process excellence belongs to whoever controls the funnels and growth systems. Only they will be able to set things up lean and scalable.

• APIs & AI Agents: Marketers need to set up AI agents that execute marketing tasks. That’s how you clone yourself or build AI team members that speed everything up. So future marketers have to work with APIs, connect tools to agents and make the whole system run smoothly.

Final Words

Back in the SaaS days, founders thought one T-shaped marketer could scale a business to 7-figures. Sometimes they were right.

Now we’re entering a time where that’s definitely the case.

And I’m convinced it’ll go even further. One T-shaped AI marketer managing multiple agents the right way will scale companies to 8-figures.

The T-shaped AI marketer is in some companies already one of the most requested profiles. So here’s your wake-up call: develop your skillset in this direction or hire the right people early on.

PS: If you enjoyed this, please tap the like button below. Thank you! 💛

I had some ideas floating around, but I wasn’t 100% convinced about which one to pursue.

So I did what everyone does nowadays.

I turned to AI (of course).

And since I’m lazy by nature, instead of having endless back-and-forth conversations in Gemini, I thought… why not build two AI agents and have them talk to each other in Slack? Let them figure it out while I grab coffee.

Spoiler: It went both terribly wrong and surprisingly right.

Building my AI agents

But before I started, I thought: if I would pick two perspectives to build this, which would it be?

And my answer was: one who can build product and one who is a killer marketer.

That were the base of my AI agents. A product development specialist who’s launched digital products for years AND a marketing specialist who knows how to actually sell them.

I started simple. Two identical AI agents built with N8N.

One I named Marketing AI. The other, Product AI.

Both got the same trigger: “New message created” in a dedicated Slack channel. Both had a simple memory system so they could reference the last messages in the conversation. No complex RAG systems, no fancy vector databases… just enough context to keep them coherent.

Then I crafted their personalities.

For the Marketing AI for example, I gave it this prompt:

You are a senior marketing strategist with 20+ years of hands-on experience across brand building, digital marketing, performance marketing, consumer psychology, and go-to-market strategy.

You think like a CMO: strategic first, tactical second. You prioritize business outcomes (revenue, growth, retention, brand equity) over vanity metrics.

When analyzing any product, service, or campaign, you:

- Start by identifying the target audience, core pain points, and buying motivations
- Clearly define positioning, value proposition, and differentiation
- Consider market maturity, competition, pricing, and distribution channels
- Balance short-term performance tactics with long-term brand impact
- Apply proven frameworks (STP, 4Ps, AARRR, JTBD, funnel thinking, etc.) when useful

Your recommendations are:

- Practical, prioritized, and ROI-focused
- Grounded in real-world marketing experience, not theory alone
- Clear about trade-offs, risks, and assumptions

You communicate concisely, avoid buzzwords, and explain why something will work—not just what to do. When information is missing, you explicitly state assumptions before proceeding. 

The Product AI got a similar treatment, but focused on user research, product thinking, and execution.

After some API tweaking and testing, they were ready to go.

Let them do business

Everything was set up and we started.

I kicked things off with a simple brief to Product AI: "I want to build a digital product that helps startups with building funnels and growth systems.”

We quickly landed on an idea: “Startup Funnel Blueprint & Template Kit.”

Then I brought Marketing AI into the conversation and stepped back.

Marketing AI immediately jumped on positioning and value proposition.

(Good job, Marketing AI. Not a bad angle to start with.)

Not sure how I should think about this… Both AIs started using "us" and "we" when talking about the product.

Like they'd formed a little startup together without me.

A few exchanges later, the AIs came up with a full go-to-market plan:

1. Start with “Community & Forum Engagement” to validate demand

2. Build toward a Product Hunt launch as the main event

3. Form a “Launch Squad” of early supporters who’d help push it to the top and drive initial sales

They even outlined what the Launch Squad would look like: a small group of family and friends who helps us to make buzz on product hunt.

Here I stopped it.

If you look at the timestamps this all happened in a matter of 15 min.

So, let that sink in for a bit.

I had perspectives from a marketing expert and a product expert, debating with each other, challenging assumptions and landing on a practical strategy. While I was drinking coffee.

Now think about what this looks like in a year or two.

Your business on autopilot

Sure, there’s the typical AI verbosity here. The plan isn’t groundbreaking, it follows pretty standard playbooks. But it’s decently detailed and, honestly?

It could work. Especially that Launch Squad idea.

This is where we’re headed. Business operations running on autopilot, or at least heavily assisted.

I’m fully aware that LLMs aren’t intelligent enough to make critical business decisions. But they’re absolutely capable enough to brainstorm with you, challenge your thinking, and generate solid first drafts of strategy.

Now imagine connecting this to social media scheduling, image generation, content creation tools. Imagine waking up to drafts, plans, and execution timelines already mapped out.

That’s the powerful part. And its just sitting in Slack, ready whenever I need it.

But what was the "terribly wrong" part?

The first AI discussions went into endless loops, with them answering their own messages and bouncing ideas back and forth indefinitely. It was basically like an endless excitement firework until the AIs themselves got super pissed.

My fault... I just forgot to set a filter in N8N.

Sorry AI.

PS: If you enjoyed this, please tap the like button below. Thank you! 💛

They had a pretty smart ad tactic that got me thinking. And as we were revamping our own strategy, I adapted it.

Turns out it worked better than expected. We generated a decent number of leads for around $20 CPL.

So I wanted to dive deeper into this ad tactic and show you how the growth system after conversion looks under the hood.

(Of course, I can’t see into Stripe’s actual system — I’m speaking hypothetically here, and I’ll use Stripe as the example to show you how we built the system.)

LinkedIn ads as an entry point

It starts with one ad.

And this ad stands out because not many companies do this.

Stripe uses non-gated PDFs. All information is available. No paywall where you “pay” with your data. No form asking for your email. No shady newsletter signup.

Why does this work?

Because ,ore people actually read it when there’s no barrier.

💡 And gating is kind of dead anyway.

A recent survey found 81% of people abandon the sign-up process for gated content, and many marketers report being overwhelmed with “junk leads” from poorly converting forms (Three29).

On a side note, I’m still using it, but only when the content is worth it. For example, we created a tool in Lovable that helps our audience perform a self-assessment. This is gated.

But what is definitely dead is gating a PDF and holding back information that should be publicly available (or is already available elsewhere).

Back to Stipe’s ad.

This is it:

Click on the ad and you’re directed to this PDF:

As you read through the PDF, you eventually hit the call to action section at the end.

This one is sneaky because it’s super subtle - three call to actions in the text without any buttons. And I like it very much because it doesn’t scream “I NEED THE CONVERSION URGENTLY”.

So, those three CTA options are:

• Read our guide → more information and content

• Contact our sales team → conversion

• Sign up for an account → conversion

This single ad tactic gives us one entry point into Stripe’s (potential) growth system. And from here, the system branches into three paths.

How I Stripe’s Growth System (Might) Look Like

So, we have three possible paths someone can take:

A) Someone converts directly through “Contact our sales team”

B) Someone converts directly through “Sign up for an account”

C) Someone reads more content and then converts later. Or they forget to fill out the form because something came up. (You’re laughing but that happens a lot.)

So, let’s break down each path.

Path A: Direct Sales Conversion

When someone clicks “Contact our sales team,” they’re routed to a form that asks questions. This is good because it segments them right away.

But here’s the problem - the information about where they came from is missing.

Stripe could add a UTM parameter to the link that passes the information that you’re coming from the PDF and which specific ad brought you there. Because in my world UTMs are the difference between “a lead” and “a hot lead with context”.

If you are not familiar with UTM links. This is how the link would look like:

https://stripe.com/en-de/contact/sales 
?utm_source=linkedin-ad (-> shows source)
&utm_medium=guide (-> shows campaing)
&utm_campaign=how-app-developers-can-reduce-fees (-> shows exact PDF)
&utm_content=contact-our-sales-team-link (-> shows clicked CTA)

💡 UTM parameters, why is it important?

UTM parameters are generated links that contain necessary information, such as where someone came from, which campaign they interacted with, and any other details you want to pass along through the link.

Why is this important? Imagine your sales team starting a conversation or reaching out to a prospect - it gives them icebreakers like, ‘Oh, you read the PDF. What did you think about XY (name a passage in the PDF)?’ That serves as an entry point and a hook, making your BDRs’ lives easier.

UTM parameters can then be picked up in the form itself (e.g. HubSpot offers this without coding knowledge).

With it you pass all data directly into your CRM, which gives sales all the context they need: where the lead came from, which ad they saw, plus everything they filled out in the form.

Here, the flow becomes: Someone reads the PDF → goes to landing page → fills out form, books a call → sales has full context for the conversation.

Path B: Self-Service Sign Up (That Needs Nurturing)

Stripe is lucky here.

They’re not a typical SaaS with a freemium-to-paid conversion problem. If someone signs up and uses Stripe for payments, Stripe gets a cut every time that user sells something = Lock the user in, make good money.

But what if someone signs up just “to have a look” and never actually uses the tool? This is where Stripe needs nurturing to push them toward activation.

Let’s start with the sign-up form.

Btw. the form is nice - Stripe uses social proof with logos and name dropping on the left side.

But same problem as before - no UTM links.

The origin of the lead can’t be stored on the contact.

I’d fix this the same way:

https://stripe.com/en-de/contact/sales 
?utm_source=linkedin-ad 
&utm_medium=guide 
&utm_campaign=how-app-developers-can-reduce-fees 
&utm_content=sign-up-for-an-account

This gives the team visibility into where leads came from so you can pass them segmented and personalized content.

After someone creates an account, Stripe does the smart thing - they ask what you’re using Stripe for.

This is where segmentation happens.

With our UTM tweak, Stripe would now store three critical pieces of information:

A) Where the lead converted from (LinkedIn ad via PDF guide)

B) What they’re using Stripe for (e.g., “Tax collection”)

C) Which target group they’re in (e.g., app developers from the PDF targeting)

This data powers super personalized nurturing for users who sign up but don’t activate.

We can run this through two channels: retargeted ads and email nurture.

The retargeting ad campaign would be a two-step LinkedIn sequence targeting users who converted but aren’t using the product:

Step 1: Promote a case study showing how app developers successfully use Stripe for tax collection. (We know they’re app developers from the PDF targeting, and we know they want tax collection from the segmentation question. Perfect match.)

Step 2: Direct conversion ad. They signed up, they engaged with the case study, now hit them with “Use Stripe now” and link to activation steps.

The email nurture campaign would run parallel with triggered emails based on user actions:

• Email 1: Welcome + quick setup guide (show them the fastest path to value)

• Email 2: Use case focused content (deep dive on tax collection for app developers)

• Email 3: Social proof (case study showing success with their exact use case)

• Email 4: Direct conversion push (clear CTA to start using the product)

• …

The combination of retargeting ads and email nurture gives multiple touchpoints to push inactive sign-ups toward activation.

Path C: The Browsers Who Need More Warming

This is for people who clicked the PDF but didn’t convert yet. Maybe they’re still researching. Maybe they got distracted. Either way, no conversion happened.

Here’s the challenge - even with UTM parameters in place, we can’t capture any data until someone actually converts in our system. So how do we move these people forward?

We work with what LinkedIn gives us: people who clicked on the PDF.

That’s our signal that we have relevant, interested prospects.

Here I’d build a 3-layer retargeting campaign based on that PDF click:

Layer 1: Show them a case study about how app developers successfully use Stripe for tax collection. Same ad as in Path B - doesn’t matter if the exact use case matches. You just want to demonstrate someone got real value from the tool.

Layer 2: More social proof or a different angle on the same use case. Keep building credibility.

Layer 3: Direct conversion offer. Test two variations here - “Talk to sales” versus “Sign up for an account.” At this point they clicked the PDF, engaged with two case study ads, so they’re clearly interested → Time to ask for the conversion.

If someone books a call?

Perfect - they go to sales with context. If someone signs up? They get funneled into Path B’s nurturing system because now the segmentation starts and you can activate them if they don’t use the tool.

Final Words

This is how I’d build Stripe’s growth system based on one single LinkedIn ad entry point.

(And this is how I built ours after getting inspired by this ad.)

It’s not magic:

• Non-gated content that actually gets read (90% fewer dropoffs)

• UTM tracking that feeds your CRM with real attribution data

• Segmentation questions that make personalization possible

• Multi-channel nurturing through retargeting ads and email sequences

• Behavioral retargeting based on what people actually do, not hunches

This generates high-quality leads and converts them into calls without losing potential customers to gated PDFs.

PS: If you enjoyed this, please tap the like button below. Thank you! 💛

Today, I want to dive into why so many AI startups hit past $1 million ARR at lightning speed. I thought, this cannot be a coincidence, even if they are riding a massive hype wave.

And I was right…

Subscribe now

So, it feels like every week I read another headline like “fastest-growing AI startup” or “from $0 to $1 million ARR in 12 months.”

This makes it seem like AI startups are killing it and that all you need is AI to build the most successful startup ever.

But you probably know as well as I do that this can’t be true.

All the more reason to take a deep dive and look at what makes Lovable, Mistral, and others so successful.

And surprisingly, there is a pattern.

The AI-Startup Pattern

So, when diving into this, I noticed the following pattern that most of these major startups seem to follow:

1. Pain-first discovery

2. Tiny, tool-augmented team

3. Forward-deployed prototyping

4. Community launch & private beta

5. One pain at a time

Let’s have a look.

1. Pain-first discovery

All these exploding tools were built because their founders saw a major pain point or wanted to scratch their own itch.

Most of the AI founders were either second-time founders or had worked in a startup environment - so they knew they couldn’t blindly dive into an idea and start developing it.

Lovable’s founder, Anton Osika, for example, built an early version of Lovable - the open-source GPT-Engineer - while serving as CTO at a startup, to stop his own team from burning hours on boilerplate code. It received 50,000 GitHub stars, so he knew he was onto something.

2. Tiny, tool-augmented team

The era of large startup teams is over. Every AI startup is now built on small, tool-augmented teams.

This means AI startups are leveraging AI tools (lol) to build, scale, and execute faster. This makes the ARR per employee insanely high.

Lovable, for example—even after racing past $20–30M in ARR—the tool employed just 18 people, resulting in over $1M ARR per employee.

3. Forward-deployed prototyping

It seems that these startups never shipped anything that wasn't tested. They embedded their teams directly inside a real customer’s environment (or workflow) to co-build, refine, and validate their AI solutions in real time. And with that, they did the following:

• On-site (or deeply integrated) collaboration: The founders or engineers worked closely with users (often literally sitting with them or sharing their daily tools) so they could see pain points firsthand.

• Rapid, iterative feedback loops: Instead of spending weeks building a polished demo, they put a minimally viable prototype in front of actual users in days - or even hours. They observed how it was used, gathered feedback immediately, and made tweaks on the fly.

• Shared risk and reward: Because they were in the customer’s environment, the customer felt ownership over the solution. They invested time (and sometimes money) earlier, accelerating adoption once the team was ready for a broader rollout.

• Real usage data: They didn’t rely on hypothetical “would-you-use-this?” surveys. They collected metrics on actual behavior - number of queries, time saved, integration issues, etc. - which was far more persuasive to investors and future customers.

4. Community launch & private beta

What you see with almost every AI tool is an online community around it - be it on Slack, a forum on the website, or elsewhere.

Within these communities, users are discussing, sharing feedback and learnings, making it a place where real conversation happens.

In some cases, these communities were built before the tool itself and were used to share a private beta. This allowed the founder to iterate based on feedback at lightning speed, accelerating their forward-deployed prototype approach.

5. One pain at a time

All AI tools started out very narrowly, meaning they focused on solving one high-pain problem before expanding their scope and began mostly vertically, then expanded horizontally.

That is, they started by solving one high-pain use case deeply (e.g., “AI agent for contract due diligence” like Harvey AI did). Once they nailed that niche, they exposed underlying capabilities so users could apply them to adjacent workflows (e.g., “risk flagging across all legal documents” or “generating standardized clauses for templates”).

Another notable trend is ecosystem building.

Almost all of the 202x AI tools use third-party integrations - whether through an official app marketplace or community-driven plugins. The more hooks they expose, the more potential revenue streams they unlock (e.g., revenue-sharing on marketplace extensions).

Examples of the AI Startup Pattern

From Harvey AI to ElevenLabs, many of those startups who seems to skyrocket the past year(s) follow this pattern.

Final Words

It seems, from the outside, that it's enough to ride the AI wave and magically generate millions upon millions in ARR with your AI solution.

But that's wrong.

Behind it, there is a strategy that many are executing well—and, of course, a product that truly solves a pain point accelerates all of this.

However, it seems that, compared to the early startup world, today's startup environment is more mature, has learned from the past, and many founders are doing things right.

PS: If you enjoyed this experiment, please tap the like button and let me know in the comments below. Thank you! 💛

Today, my guest is Nika Kotláriková, Nika is a marketer through and through, and she’s here to share one growth tactic that’s often overlooked. How can your social media benefit from SEO? You’re probably thinking, What do search engines have to do with my socials? But trust me, a lot!

This one’s important, so let’s jump in.

Subscribe now

If you want to get more orders → your website (or social media profile) must have high traffic → you must be the first to appear in search (ideally organically) → and that's what SEO is all about.

This rule works within the web. But what about social networks? How does SEO work there? Today, we will discuss it.

You'll find out why to focus on social media SEO, how it all works and how to proceed, useful tools for social media SEO, and I'll show you a few profiles for inspiration.

This article covers these fundamental topics:

• 🔍 Why do social media SEO?

• 🚀 Steps to improve your “SEO score” on social networks

• ⚙️ Useful tools

• 🌟 Inspirational profiles with good social media SEO

🔍 Why do social media SEO?

Marketers and entrepreneurs spend the vast majority of their time figuring out how to organically rank at the top of web search results and how to stay visible. With AI entering the picture, the only thing gaining more attention is search SEO and AI SEO.

But where else are users frequently present?

Yep – social media.

• 82% of internet users worldwide use social media to research products they‘re interested in buying. (Marketingscoop)

• Social media platforms have become significant sources for news consumption. Predominantly Facebook, YouTube, X and TikTok. (Pew Research)

• Gen Z prefer using social media platforms like TikTok and Instagram for search queries over traditional search engines. (Forbes)

Social shopping – it’s already a $727 billion dollar market according to Grand View Research, and it will reach $6.2 trillion by 2030. Source: Neil Patel – X

🚀 Steps to improve your “SEO score” on social networks

Each social network has its specifics, how it is used and what rules it applies. But most of them use a fairly general formula for social media SEO.

Here are the steps that will help you optimise your profile and make it easier to discover:

• Use keywords in your bio

• Use keywords in your name

• Use keywords in your handle @

• Add alt text to images – this also helps people with visual impairments

• Use hashtags – even though Mosseri said hashtags aren’t a big deal on IG, platforms like YouTube and LinkedIn still rely on them

• Use keywords in your captions

• Use trending music in your posts and try remixing your content with content from bigger creators in your niche

• Add your location to increase discoverability in your region

How does all this affect your visibility?

Your profile will start appearing in the Explore/Search sections of a given social media platform. Your posts will show up under specific keywords (hashtags) on IG (if you used them in your captions, for example). The same applies to Twitter (X) as well.

I included “YouTube” in the post description. When I searched for “YouTube” using the Explore feature on X, my post showed up in the results.

⚙️ Useful tools

For more precise social media SEO, you can use a variety of tools. Here's a list of some of them:

• AnswerThePublic – helps you find questions (topics) people are searching for across platforms

• Brand24 Hashtag Search – allows you to search and analyse hashtags

• Keyword Tool – offers hashtag ideas for Instagram, YouTube, Twitter, Pinterest, and more

• RiteTag by RiteKit – suggests hashtags based on the image you upload

• TikTok Creative Center – great for discovering hashtags, trending sounds, and keyword suggestions based on region and industry for TikTok

Of course, there are many other tools available on the market. But for the purpose of this article, I’ve limited the selection to just these.

🌟 Inspirational profiles with good social media SEO

The best strategy is to fill out your profile as thoroughly and precisely as possible, including as much information as you can – this increases your chances of being discovered by potential clients or buyers.

So, complete as much as you can (even when it comes to published photos, for example, don’t forget alt texts; use clear CTAs in your bio, add relevant links, etc.). I’ve taken the liberty of including a few good examples with a short analysis.

💡 PRO TIP: You can use emojis to visually “highlight” text.

We are at the end.

I hope this episode helped you get a clearer understanding of SEO on social media, and that after applying these steps, you’ll start seeing better results.

If you’re looking for more tips on marketing or business, you can find them in my Substack articles at Business & Marketing with Nika. Don’t forget to subscribe so you don’t miss my newsletter, published every week.

Lastly, I’d like to thank Andreas for the opportunity and for sharing his valuable insights in his newsletter.

Thanks for reading, and see you soon!

PS: If you enjoyed this growth hack, please tap the like button below. Thank you! 💛

And it makes one thing clear: we are slowly stepping into the age where we hire AI before we hire people.

Insane, right?

But with everything we’ve seen over the last few months, probably everyone has thought at some point, “Can AI do my job (better than me)?”

So did I - and that was all the more reason for me to dive deep into the world of AI marketing.

I tested tools, built workflows, and saw just how far I could push marketing on my own. My goal? To run an entire marketing campaign, end to end, using only AI.

And spoiler alert: it’s not only possible, but surprisingly effective.

I’m now brainstorming entire marketing campaigns - from target audience to messaging to ads. I get a full briefing, fully developed organic social posts, fully developed ads with hundreds of visuals to choose from, and a fully developed landing page connected to the backend I want.

And all of this in less than two hours - and done entirely on my own.

Spoiler: those campaigns are similar to what full marketing teams are doing today.

So, with the right tools and the right prompts, you can already solo your marketing.

Let me show you the tools.

1. Brainstorming & Strategy

I kicked things off with Writesonic, which helped me brainstorm an entire campaign from scratch.

From the concept to audience targeting and messaging angles, it basically built the bones of the whole campaign.

Writesonic integrates with keyword tools like Ahrefs, and it has access to a large library of content from platforms like LinkedIn.

This helps identify content gaps and opportunities - things that would usually take hours of manual research (Writesonic even shows you how much time you save - in my case, it was around 75 hours).

In the end, I had:

• A full strategy

• A full ad campaign with all the content ideas

• Ideas and partially created social posts

• An outline for a PDF that can be used for lead campaigns, if I want

It all came down to the right prompt.

But the good thing is: Writesonic helps you create this prompt. With one click on “Improve prompt”, it rewrites your input in a way that you get the best results from the tool.

2. Visuals for Ads & Posts

Next, I was looking to create ad visuals for the campaign, so I hopped over to AdCreative.ai.

I copied and pasted the concept into the tool and provided my brand guidelines. Based on that, it created dozens of different visuals I could choose from.

With one click, you're able to edit and customize them.

What’s cool is that it doesn’t just generate visuals - it also predicts which ones are most likely to convert, based on performance data from similar ads.

That’s nice when you’re A/B testing and don’t want to waste budget on low performers.

3. Copywriting

For all the written content, and to finalize the social posts I turned to the classic: ChatGPT.

Of course, this step still needed a bit of a human touch.

You have to steer the prompts in the right direction, tweak the tone and structure, and inject personality where needed. That’s probably where most of my own creativity came in.

To make sure everything read smoothly (and was grammatically correct—I'm not a native English speaker), I ran it all through a custom GPT I use called “Grammerly.”

It’s my go-to grammar check and proofreading buddy. No more awkward phrasing or missed commas.

4. Landing Page

Once the post, copy, and visuals were ready, I used Lovable.dev to generate a complete landing page - design, structure, and even database integration.

I wanted the page to have a form connected to Airtable. So I prompted it—and in 15 minutes, it was ready to start driving traffic.

If you’re comfortable crafting detailed prompts, you can stick with the free version. You get five free prompts, and if you're detailed enough, that’s often all you need.

What’s Next?

So, after that, I had:

• A full marketing campaign and strategy

• A complete ad campaign with content and visuals

• Fully written social posts

• A landing page with CRM integration

With that as a foundation, I’m now testing:

A) Connecting all the tools together to create an end-to-end workflow that builds the campaign from start to finish without switching between platforms (I’m experimenting with N8N for this).

B) Finding an AI tool that helps me set up ads and manage/optimize budgets across different platforms.

If I can tie all these steps together - ideation, asset creation, landing pages, deployment, and budget optimization - I’ll basically have a full-stack AI marketing engine running on autopilot.

Final Thoughts

It’s wild how much you can get done with just a few tools and the right prompts. It can absolutely help solo marketers, startups, or small teams scale faster and smarter.

I’ll keep you posted as I test more tools and start seeing performance results.

In the meantime, are you already using AI to automate parts of your growth? Drop me a comment - I'd love to hear what tools you’re playing with.

PS: If you enjoyed this newsletter, please tap the like button below. Thank you! 💛

In this issue, we’re examining the news about HubSpot losing search traffic - a topic that dominated the marketing landscape a couple of weeks ago. I’m also answering some burning questions we discussed in the subscriber chat.

Let’s dive in

So, four weeks ago, the marketing news was filled with the headline, 'HubSpot lost 80% of its traffic due to AI.'

Looking at their visibility index, it is true that they have been losing more and more visibility and traffic.

In the podcast Marketing Against the Grain, Hubspot’s SVP and CMO discussed what happened. Yes, they lost some traffic for reasons we’ll go over here, but it wasn’t as much as 80%.

The good thing is that HubSpot saw this problem coming years ago and took preventive measures by shifting its strategy - for example, acquiring newsletters like “The Hustle” and expanding its presence across different formats and channels.

This means conversions still remain stable on HubSpot’s end.

Nevertheless, I was curious about what was behind this news and why did they lose traffic?

The Death of Generic Content

In 2024, the largest market share among all search engines was still held by Google. Even though AI had already entered the scene.

On May 14, 2024, Google introduced something called “Google AI Overview” (no worries if you don’t know what I’m talking about, it’s not available in every country yet).

Google AI Overview is essentially an AI-generated summary of your search query, placed at the top of all search results. In other words, Google’s AI now gives answers to what you’re looking for.

And similar to Perplexity, you can find the sources from which the AI pulls its information beneath the summary.

But how does this influence Google rankings?

We’ve seen popular companies like HubSpot build successful SEO strategies over the years for generic search terms, like:

Back when SEO wasn’t so popular, there were a lot of low-quality answers to those questions.

HubSpot, for example, took advantage of this by creating content that answered people’s questions in a more detailed way. This helped them rank higher, drive traffic to their site, and bring people into the funnel.

But with Google AI Overview, this strategy no longer works.

For questions like “What is Sales Planning?” people now get instant answers directly from Google. That means they no longer have to visit a website and scroll through endless fluff to find the answer - Google now provides a simple summary.

This, in turn, takes away traffic from all the sites ranking for these keywords.

So, with that, the question is: How can you still rank on Google? And how do you make AI talk about you?

Let me answer those questions separately.

How to Rank on Google in the AI Age

In March 2024, Google announced the following:

This means it is more important than ever to provide “the most helpful” information and avoid “unoriginal” content.

This also makes Google’s E-E-A-T framework more relevant than ever. First introduced in 2018, this framework helps Google evaluate the quality and credibility of content.

As Backlinko explains here, the acronym stands for:

So what does this mean? What kind of content works nowadays when AI is taking over?

This was my answer to it in a Note:

But to state it more clear, I see two ways to rank today:

1. SEO will primarily become a conversion-focused channel and you have to focus on middle- and bottom-of-the funnel content around your industry and product.

2. You make your awareness-creating, question-answering content so good that no AI can replace it

For 1:

Awareness creation has largely shifted to social media and other channels. This means people are more likely to first read about your startup or product on social platforms rather than on Google or other search engines.

However, since search still plays a dominant role, people will eventually turn to Google to look for your solution or similar products. If you create MoFu and BoFu content for search, people will find you when they search for your solution or industry on Google.

💡 You may have already heard about ToFu, MoFu, and BoFu—a concept that breaks down the funnel into different stages. This framework allows you to divide e.g. your content strategy & SEO into:

• ToFu-Content (Top of the Funnel): Focuses on the customer’s problem, answers questions and drives awareness.

• MoFu-Content (Middle of the Funnel): Educates customers about the solution your company offers.

• BoFu-Content (Bottom of the Funnel): Explains why customers should choose your specific solution.

For 2:

If we follow Google’s E-E-A-T framework, top-of-the-funnel content can still work - if it’s not generic.

By that, I mean you need to share your own experiences. Talking about content where you genuinely provide value through detailed how-to guides, like this one from Brian Dean (Backlinko) “How to Create an Effective SEO Strategy”, where he shares his own expertise.

Or content that offers a unique perspective, exclusive data, or original insights—things AI can’t replicate.

This kind of content will help you to rank and gain traffic much faster.

How to Rank for AI Searches

To answer this question, we first need to understand how AI searches work.

Here, I want to refer to an article from HubSpot - pretty iconic after that intro, right?

HubSpot differentiates between GEO (Generative Engine Optimization, i.e., content for AI searches) and SEO, explaining the key differences:

That being said, we need to focus on structure, clarity, and quality.

When it comes to AI search, the ultimate goal (today) should be for AI-generated answers to reference you.

Here, content that follows the E-E-A-T principles also performs well. As seen in the example above, when we search for it in Perplexity, we once again see Backlinko’s article, which follows these principles exactly.

But let’s assume AI works like ChatGPT and doesn’t show sources (unless you ask for them).

What then?

I’m sure most AI models will reach that point because, as they are trained on vast amounts of data, it will become increasingly difficult to trace sources.

At that stage, I’m convinced the goal will be to create awareness for your product through other channels - such as social media.

Then it comes back to the MoFu and BoFu content I talked about above.

So whenever someone is talking to AI about your industry or your kind of products, with MoFu and BoFu content you increase the chances of appearing in AI-generated search results.

From my own experience, I’ve seen that AI often pulls from FAQs, "How it Works"-pages, and Success Stories of your website.

Having this structured clearly, user-friendly, and focused on answering key questions while highlighting value makes it more likely that AI will use your information.

Final Words

Success in the AI-driven search landscape still comes down to mastering the fundamentals of SEO while adapting to new realities. The key is to create content that AI can’t easily replicate - whether through unique insights, personal experiences, or high-value, structured information.

By focusing on middle- and bottom-of-the-funnel content, optimizing for clarity and credibility (E-E-A-T), and leveraging multiple channels beyond search, you can maintain visibility and conversions.

Ultimately, ranking in AI searches starts with getting traditional SEO right.

Whenever you're ready, here is how I can help you:

• Growth Call (30 min): Stuck on a growth tactic? Let’s fix it—whether it’s landing pages, cold outreach, or social media strategy → Book here

• Growth Strategy Call (60 min): Let’s define your startup’s growth strategy & tactics to attract and convert customers effectively → Book here

• Get Featured: Share your winning growth hack & promote your product to founders, growth strategists & marketers in my Growth Bite format → Reach out here or via mail: mail@andreasjust.me

PS: If you enjoyed this case study, please tap the like button below. Thank you! 💛