Your AI Product’s Secret Weapon

Hi, and welcome to The Atomic Builder!

While the tech world is busy arguing over whether GPT-5 is truly better than GPT-4 (spoiler: it depends who you ask, and how much coffee they’ve had), you don’t need me adding another hot take to that pile.

Instead, here’s a quick story from this week. I walked out of what I thought was a killer pitch - smiles, nods, all signs pointing to “we’re in.” I was already mapping out the next steps. And then… nothing. Silence.

Not a tragedy! Just a timely reminder: in product building, especially in AI, creating something great is only half the game. The other half is making sure the right people see it, try it, and care enough to stick around.

That’s why, over the next few weeks, I’m sharing three big lessons from building AI-native products like StageSnap, SpatiaLearn, and the experiments I run here in The Atomic Builder.

Together, they answer three questions each of us face, repeatedly:

This week, we’re starting with the first - distribution.

Because in AI, tools can be easy to copy.

But distribution? That’s where the real moat is.

Let’s dive in…

The Distribution Moat

Let’s be honest: in AI, feature advantage is shrinking fast.

Even if you build something genuinely valuable, someone else can launch a clone within weeks - sometimes days.

If your moat is “we have the feature,” sorry, but you don’t have a moat.

Distribution is the difference between a product people try once and forget… and one they keep coming back to because it’s there, in their workflow, whenever they need it.

Notion AI didn’t dominate because it had the smartest NLP. It dominated because it lived inside a product 30 million people were already using daily. The AI was just an extension of something sticky.

And when you don’t have that kind of reach built in? That’s when you start learning distribution lessons the hard way - like I did with StageSnap.

StageSnap: Niche, but Invisible

StageSnap is an app I built to introduce the concept of virtual staging to UK Real Estate agents.

StageSnap had dream-level clarity, so I thought…: real estate agents, with a clear pain point and a product that solved it.

But… those agents are scattered. They live in small Facebook groups, private WhatsApp chats, local associations. Paid ads were expensive and noisy.

Even the agents who loved it (all 4 of them) didn’t have a big enough audience themselves to spark real network effects.

Lesson: Audience clarity is a start - but without a repeatable way to reach them and others, you stall, fast.

If StageSnap was a case of knowing exactly who but struggling to get in front of them, SpatiaLearn was the opposite.

SpatiaLearn: Audience Undefined?

SpatiaLearn is an ongoing venture I’m excited to continue building — a creative, learning format I haven’t seen before. A way to turn any learning material into an immersive audio experience.

The product works conceptually. Testers like it.

But… who EXACTLY is it for?

Independent learners? Corporate training managers? Teachers? The lack of a clear early adopter means my messaging is (currently) fuzzy, channels are scattered, and nothing has (yet!) compounded.

Lesson: If you can’t describe your early user in one sentence, you’re not ready to scale distribution.

So if those are the ‘mistakes’, what’s the playbook? That’s where the AI Distribution Triad comes in…

The AI Distribution Triad

Three levers I’m exploring that any of you building your own AI app can pull - even if you’re solo, part-time, or bootstrapping:

You can call these marketing tactics or distribution mechanics. It doesn’t matter, They’re baked into how your product is used and discovered.

And if you’re wondering whether they matter more than the features… let’s look at the real-world contrast.

Real-World Contrast: Distribution-First Wins

Two quick success case studies to make this real:

MidJourney → Distribution baked in from day one

• Launched inside Discord, instantly tapping into millions of existing communities.

• No new accounts to create, no app to download - users could try it in the spaces they were already hanging out.

• Result: Explosive early growth, even before the product was fully polished.

Loom → Distribution through the output itself

• Every Loom video generates a share link — the content is also the marketing.

• Recipients clicked “Sign up to reply” or “Sign up to record” → viral loop built into the core experience.

• Result: The more people used it for work, the more it spread organically inside and between teams.

The takeaway?

It’s about baking the growth loop into the product from day one. That’s as true for billion-dollar startups as it is for solo builders making a side project.

Which brings us back to StageSnap and SpatiaLearn - my own experiments where the features were solid, but the distribution loop? Nowhere near as tight, yet.

My Brutally Honest Metrics

Here’s the reality check for StageSnap and SpatiaLearn:

My efforts to market these apps has so far been minimal. So it stands to reason that my user base is low.

❌ Not the cold outreach.

❌ Not the modest paid ads.

❌ Not the random social posts.

Why? No solid, repeatable marketing path.

That’s my focus now: finding one clear, compounding distribution path before adding more features.

You can learn more about SpatiaLearn in the June 20 deep-dive I published.

Your Challenge

We’ve seen how MidJourney and Loom turned distribution into part of the product itself. Now I want you to try it - on a tiny, low-risk scale.

Pick one feature in your AI tool or app. Don’t touch the feature itself — instead, focus only on how it gets into people’s hands.

Here’s your mini playbook:

1️⃣ Define your earliest “right” user in one crisp sentence.

2️⃣ Run one embed experiment — put the feature where those users already hang out.

3️⃣ Test one piggyback partnership — a person, product, or platform that already reaches them.

4️⃣ Build one loop — a way for the output to pull in the next user automatically.

No need to overthink. Pick something, test it fast, and see what sticks.

Send me your most creative experiment - I’ll feature the smartest one in next week’s issue.

Final Thoughts

So, we kicked off this three-part journey with distribution - because without users, you’re just building in the dark.

Next week, we’ll talk about the other side of the growth equation: trust.

Because getting users is great… until they stop trusting your AI’s output.

Then, in part three, we’ll talk about why in the AI era, a Minimum Viable Product isn’t enough — and how to make your first version so valuable your users can’t ignore it.

Until next time, keep experimenting, keep building, and as always - stay atomic. 👊

Faisal

This Week’s Build Beats 🎵

Each issue, we pair the newsletter with a track to keep you inspired while you build.

This week, because the jury’s still out on GPT-5… 🥶 and let’s be honest, nothing about building in AI feels “ordinary” right now.

🎧 “Ordinary” – Alex Warren

Grab the playlist on Spotify - I add to it each week!