👋 Hey friends — it’s Friday, and a good time to step back and rethink how we’re building.

A founder said something to me last week that I can’t shake:

“We used to grow by getting louder.
Now we grow by getting smarter.”

That one sentence says everything about where growth is going next.

The old playbook — funnels, ads, and endless A/B tests — is breaking.
Not because it stopped working, but because the world has changed faster than the systems that run it.

Attention is maxed out.
Budgets are flat.
And most companies are realizing that “adding more AI” doesn’t magically fix broken growth engines.

So this week, I want to go deeper into what actually is working — and why.

We’ll unpack how AI is quietly rewriting the rules of product-led growth, and why the next era of scale won’t come from code or campaigns…

…it’ll come from learning.

Here’s what we’ll explore together:

Let’s dive in. 

— Naseema Perveen

AI has changed how consumers shop by speeding up research. But one thing hasn’t changed: shoppers still trust people more than AI.

Levanta’s new Affiliate 3.0 Consumer Report reveals a major shift in how shoppers blend AI tools with human influence. Consumers use AI to explore options, but when it comes time to buy, they still turn to creators, communities, and real experiences to validate their decisions.

The data shows:

The most effective brands are combining AI discovery with authentic human influence to drive measurable conversions.

Affiliate marketing isn’t being replaced by AI, it’s being amplified by it.

Download the full report to see what this means for your brand.

There’s no shortage of companies announcing “AI transformation.”
But behind the headlines, the reality looks very different.

Most large organizations are struggling to operationalize AI at scale.
Not because the models don’t work — but because the systems around them don’t learn.

Recent data makes this painfully clear:

This doesn’t mean AI failed. It means the context did.
Without feedback loops between data, decisions, and results, models stay clever — but not valuable.

And that’s the paradox of 2025.
AI is more powerful than ever — yet organizational growth is slower than expected.

Because scaling intelligence isn’t about deploying tools.
It’s about designing learning systems that improve with every use.

That’s why the real question isn’t, “How many AI tools are you rolling out?”
It’s, “How fast does your AI learn from its own mistakes?”

The companies leading this new era — Perplexity, Runway, Duolingo, Notion — are proof that small, feedback-driven intelligence beats big, directionless infrastructure.

The data doesn’t signal decline.
It signals discipline.

We’re moving past the “AI gold rush” phase into the Age of Maturity, where the winners will be those who can:

That’s the real growth story of this decade.
Not mass rollout.
Refined reinforcement.

In the 2010s, the most powerful concept in business was growth hacking — a mindset that turned marketing into engineering. You’d experiment fast, iterate faster, and double down on what worked.

In the 2020s, the next evolution has arrived: growth intelligence.

AI is changing how growth happens at a fundamental level.
Because for the first time, products can learn from every user interaction.

Traditional growth is mechanical.
AI-led growth is cognitive.

In the old world, you optimized funnels.
In the new world, you optimize feedback loops.

The old playbook grew audiences.
The new one grows awareness — both yours and your product’s.

It’s no longer about building a better ad funnel.
It’s about building a system that learns faster than it spends.

If you strip away the jargon, AI-led growth boils down to one elegant system — a living loop that learns, adapts, and improves with every interaction.

It’s not a funnel.
It’s not a campaign.
It’s a learning organism that gets smarter the more it’s used.

I call it the AI Growth Loop.

Think of it as a flywheel powered by intelligence instead of impressions.
Each spin makes the product sharper, faster, and more attuned to human behavior.
The companies mastering this aren’t just scaling users — they’re scaling understanding.

Here’s how it works in practice:

Observation is where intelligence begins.

Every click, hesitation, dwell time, or rephrase tells a story — if you’re listening.
The key is to capture not just what users do, but why they do it.

Traditional analytics tools like Mixpanel or GA4 can track events.
But AI lets you detect intent.

Example:
If a user types the same prompt three times, they’re not experimenting — they’re stuck.
If they abandon a form halfway, it’s not disinterest — it’s confusion or friction.

Observation powered by AI transforms behavior into meaningful signal.

To build this layer:

This subtle shift — from behavior tracking to behavior interpretation — is where the loop starts to feel alive.

Once you capture signals, the next step is to help your system learn from them.

This isn’t about training massive models.
It’s about teaching your product to recognize patterns faster than humans can.

AI here acts as an internal analyst — clustering data, surfacing hidden correlations, and suggesting hypotheses you might never see.

Example:
When Slack introduced Workflow Builder, they noticed users frequently renamed the same steps across teams.
That wasn’t randomness — it was emergent structure.
They used those insights to pre-fill workflows that mirrored how people actually worked.

AI can do the same for any product:

This transforms static data into dynamic understanding.
The goal isn’t more dashboards — it’s fewer decisions left in the dark.

Learning without adaptation is just storage.
The real magic happens when your product starts changing because it learned.

This is where you translate understanding into action.
Micro-adjustments in copy, flow, and logic make the product feel alive — responsive, not reactive.

Example:
When Notion AI detects that users repeatedly format text the same way, it now suggests ready-made templates for that use case.
No one “told” it to — it observed a pattern, generalized it, and acted.

Adaptation can happen at many levels:

And because AI handles these micro-decisions continuously, you don’t need massive redesigns.
Your system evolves organically, guided by evidence, not intuition.

In the past, growth teams ran quarterly tests.
Now the product itself runs thousands of micro-tests daily — silently learning from every interaction.

Here’s where the loop moves from smart to magical.

Personalization isn’t new — recommendation engines have existed for decades.
But AI allows real-time, multi-dimensional personalization that feels human.

It’s not “users who bought X also liked Y.”
It’s “this specific user, right now, in this mood, prefers this flow.”

Each new user benefits from collective learning — yet experiences the product in a way that feels uniquely theirs.

Examples:

This level of adaptive personalization deepens engagement because it mirrors how human relationships evolve — mutual recognition, memory, and adjustment.

When your product can say, “I know how you like to work,” you’ve moved beyond growth. You’ve built attachment.

And attachment is the new retention.

The final stage — and the most misunderstood — is compounding.

In traditional funnels, you “optimize” performance.
In AI loops, you accelerate performance.
Each turn of the loop strengthens the next.

Observation improves learning.
Learning sharpens adaptation.
Adaptation enriches personalization.
Personalization drives engagement.
Engagement fuels more observation.

It’s not linear. It’s exponential.

This compounding creates what I call Cognitive Network Effects.
Instead of “more users = more exposure,” it’s more users = smarter system.

Example:
When Perplexity answers millions of queries, it’s not just serving users — it’s fine-tuning how it reasons.
Every new interaction upgrades everyone’s experience.

The more people use it, the more useful it becomes — without traditional marketing.

That’s not growth by reach.
That’s growth by refinement.

Over time, this loop builds an intelligence moat — one competitors can’t copy without your exact data, logic, and learning speed.

Funnels were designed for conversion.
Loops are designed for evolution.

Funnels end when users convert.
Loops never end — they evolve as long as someone interacts.

Funnels measure success in numbers.
Loops measure success in understanding.

In the age of AI, the best companies won’t be the ones that market hardest.
They’ll be the ones that learn fastest.

If you’re building your own AI Growth Loop, start here:
✅ Observation: Map your top 5 user behaviors and ask, “What’s the intent behind this?”
✅ Learning: Use an AI summarizer weekly to cluster support feedback or user transcripts.
✅ Adaptation: Automate one small product tweak per week based on those insights.
✅ Personalization: Add one variable that changes per user (content, order, tone, etc.).
✅ Compounding: Track how feedback velocity accelerates over time.

You’ll realize that growth doesn’t come from “doing more.”
It comes from learning better.

The AI Growth Loop isn’t just a framework — it’s a mindset.

It shifts your focus from “How do we acquire users?”
to “How do we accelerate understanding?”

Because in this new era, data is no longer your asset.
Learning is.

Every click, hesitation, and correction is your competitive advantage — if you can turn it into clarity.
And clarity compounds.

That’s the future of growth.
Not more funnels.
Smarter loops.

Let’s look at how today’s most successful AI-first companies are quietly applying this new playbook.

Perplexity didn’t win users by marketing itself as “Google but with AI.”
It built a product that rewards curiosity.

Every time you ask a question, the model refines its retrieval strategy. Every user interaction improves results for the next one.

Their growth loop is simple but powerful:
Curiosity → Query → Feedback → Better Answers → More Curiosity.

Perplexity doesn’t just scale usage.
It scales understanding — one search at a time.

Runway noticed creators were spending hours tweaking generated video styles.
So, they started training models on those exact edits.

Each time a user corrected the output, the system learned stylistic intent.
Eventually, the product could anticipate edits — delivering videos closer to what users envisioned.

That feedback loop became a moat.
Creators felt like Runway “got them,” so they stuck around.

Notion’s AI success didn’t come from slapping an “AI” label on top of notes.
They observed that users struggled with rewriting, summarizing, and structuring text.
Then they embedded AI into those workflows.

Every note edited became training data.
The more people wrote, the better Notion got at predicting structure.
The better it predicted, the more people wrote.

That’s what I call symbiotic growth — both user and product evolve together.

People think Duolingo’s success is about streaks or memes.
It’s not. It’s about emotional calibration.

The app measures frustration, confidence, and boredom by tracking how fast and how often users answer correctly.
It adjusts difficulty, pacing, and tone automatically.

That’s not gamification — it’s empathy at scale.
AI here doesn’t just learn your skill level. It learns your mood.

Harvey, the legal AI startup, didn’t chase scale early.
They picked one workflow — contract review — and mastered the reasoning pattern behind it.

Their model learned how lawyers think: clause by clause, not keyword by keyword.
That cognitive accuracy built trust, which became the ultimate growth engine.

Their product didn’t just automate tasks.
It mirrored professional judgment — something no ad campaign could replicate.

So how do you build this kind of loop?
Here’s a practical roadmap you can use today.

Every scalable product starts with a repetitive pain.
Look for moments where users pause, retry, or complain.
Those aren’t bugs — they’re learning opportunities.

Ask:

Friction is feedback in disguise.

Most analytics tell you what users did, not why.
Add layers of context — session recordings, conversation transcripts, sentiment tags.

Feed that context into an AI summarizer.
The goal is to spot patterns of confusion.

Because behind every hesitation is an insight.

Set up a recurring AI analysis loop:
“Summarize all user frustrations from this week’s logs and cluster them by theme.”

You can do this right inside Notion, Airtable, or a tool like Relevance AI.
This turns raw activity into structured learning.

Start small.
If users hesitate on a setup step, show contextual tooltips.
If they ignore a feature, trigger an educational micro-demo.

Each improvement closes a feedback loop.
You’re not adding features — you’re teaching your product to listen.

Invite early adopters to annotate or correct outputs.
Every correction they make is free training data.

The best founders don’t “survey” users.
They observe them.
Because behavior is the most honest feedback there is.

In AI-led growth, the key metric isn’t MAU or ARR.
It’s Learning Velocity — how quickly your product converts feedback into improvement.

Ask:

A product that learns faster compounds faster.

Your best growth lever isn’t acquisition. It’s curiosity.
Design experiences that encourage users to explore, test, and create.

Every curious action feeds your loop.
Every loop fuels your learning.
And every learning compounds into trust.

Most people still see AI as logical — rational, mechanical, precise.
But the best growth loops aren’t just intelligent. They’re empathetic.

Think about why users return to certain tools every day:

That’s emotional adaptation — the product senses your rhythm.
And AI can learn that rhythm faster than any human team.

Growth now isn’t about hooking users.
It’s about harmonizing with them.

Because the market is saturated with products that “work.”
The only differentiator left is products that learn.

We’ve hit peak efficiency — what matters now is responsiveness.
How quickly can you translate observation into adaptation?

AI makes that real.

And it’s not just startups.
Enterprises are shifting too:

This is the rise of adaptive business systems — organizations that don’t just operate, they self-correct.

Here’s something few people will say out loud:
Funnels are dead.

Not because they don’t work — but because they assume linear behavior.
People don’t move in lines anymore. They move in loops.

AI understands those loops — how emotion, intent, and context intertwine.

Growth isn’t about pushing people down a pipeline.
It’s about pulling insight out of interaction.

In a world of intelligent systems, learning velocity replaces lead velocity.

If you’re building today, this is your real job:
You’re not the CEO of a product.
You’re the architect of a learning organism.

That means you need to:
✅ Design feedback capture at every layer.
✅ Incentivize curiosity, not clicks.
✅ Replace reports with reflection.
✅ See friction as training data.

Your best growth team might not be people — it might be your product itself.

Because the companies that master AI-led feedback loops will grow smarter than their competition, not just faster.

Growth used to mean “getting bigger.”
Now it means “getting smarter.”

When AI enters the loop, growth stops being a race for attention and becomes a rhythm of understanding.

Every click becomes a clue.
Every error becomes education.
Every user becomes a co-pilot.

The best founders won’t chase virality — they’ll design learning velocity.
Because in this era, the product that learns fastest wins longest.

The truth is, AI isn’t rewriting your growth playbook.
It’s reminding you what growth really was all along:
A process of learning, adapting, and compounding clarity over time.

So, stop obsessing over your next campaign.
Start asking:
“How fast is my system learning?”

Because in the new economy of intelligence, clarity compounds faster than capital.

— Naseema

That’s all for now. And, thanks for staying with us. If you have specific feedback, please let us know by leaving a comment or emailing us. We are here to serve you!

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