Hey friends, TGIF! ☕️

Over the past few weeks, I’ve been thinking a lot about why some AI startups seem unstoppable while others quietly fade away.

And the answer, I’ve realized, rarely comes down to funding, team size, or even technical brilliance.

It comes down to focus.

The most resilient founders don’t try to do everything.

They pick one small, painful problem — one no one else is paying attention to — and solve it with such obsessive clarity that it becomes their unfair advantage.

That’s what I call the AI wedge — a narrow but defensible insight that lets you enter a crowded market through a door the giants never noticed.

And once you’re in, your wedge becomes a moat — sharpened by feedback, trust, and data only you can access.

Today, we’re exploring:

I’ll be honest — this week’s edition made me rethink what “starting small” really means.

If you’ve ever felt the pressure to do more, this one’s a reminder that precision often beats scale.

Let’s dive in.

— Naseema Perveen

With thousands of AI tools available, how do you know which ones are worth your money? Subscribe to Mindstream and get our expert guide comparing 40+ popular AI tools. Discover which free options rival paid versions and when upgrading is essential. Stop overspending on tools you don't need and find the perfect AI stack for your workflow.

Subscribe to Get Your Free Comparison

Let’s define it simply.

An AI wedge is the first micro-problem your product solves so effectively that it opens the door to everything else. It’s not your grand vision. It’s your proof of precision.

McKinsey’s latest State of AI 2025 report calls this the focus advantage — where narrow AI systems outperform general ones by up to 40 % on domain-specific tasks.

MIT Sloan researchers describe a similar dynamic in their 2024 study on The Narrow Learning Effect: teams that focus on one vertical or dataset hit market fit faster and retain customers longer than those chasing broad utility.

Think of your wedge as the spearhead — small in surface area, but with incredible penetrating force. It doesn’t need to conquer the market. It just needs to enter it cleanly.

Let’s look at a few examples:

Each of these companies found a door no one else saw.

Their wedges weren’t just products — they were early insights about what friction actually matters.

Before AI, building a wedge required years of product iteration.
Now, it can happen in weeks.

Here’s why the AI landscape makes wedge strategies even more powerful:

The iteration loop is 10× faster.
Small teams can retrain, fine-tune, or rebuild entire workflows overnight. MIT’s 2024 “Rapid AI Experimentation” study found that startups running 48-hour sprints achieved triple the learning velocity of traditional MVP cycles.

Distribution has been democratized.
AI products grow through curiosity loops — users sharing results, screenshots, and workflows. A single viral Loom demo or GPT showcase can attract thousands of users. No ad budget required.

Data is the new real estate.
Every interaction trains your model — giving you a proprietary edge no competitor can easily copy. Giants like Google and Meta have big data; you can have relevant data.

Incumbents move slowly.
Large organizations are burdened by risk management, governance, and brand protection. Startups move where lawyers haven’t yet drawn the lines.

That’s why the wedge era belongs to small builders.
The very constraints that used to hold you back — limited capital, small teams, narrow domains — now define your speed advantage.

As McKinsey’s report puts it:

“Domain-embedded intelligence, not general models, will drive the next wave of enterprise AI adoption.”

Translation: the smallest wedge often hides the biggest market.

Let’s turn this idea into something founders can actually use.

This playbook distills what dozens of early AI founders are doing to find their wedge, validate it fast, and expand naturally.

Don’t look for novelty. Look for repetition.
The best wedges live in boring workflows.

Ask yourself:

“What task do my users do so often it’s become invisible?”

For example:

If a task feels trivial but drains hours weekly, you’ve found a wedge candidate.

Great founders obsess over unscalable annoyances.

They notice the sighs, not the headlines.

Perfection kills momentum.
Your only goal at this stage is to test friction removal.

Can AI cut 70 % of the effort from this workflow?
If yes, build a rough demo. Use ChatGPT, Replit, Notion AI — whatever gets you something users can touch.

MIT’s “Rapid AI Experimentation” paper notes that fast-cycling prototypes increase customer learning loops by 180 % compared to teams chasing polished MVPs.

Speed doesn’t just validate the idea. It tells you how willing users are to forgive imperfection — which is a proxy for pain intensity.

Stop counting users. Start measuring impact per user.

Ask:

This is how you discover your unit of delight.

Founders who measure depth before breadth build wedges that last.
Every satisfied user becomes a reference point in your data and your brand story.

Your first users aren’t customers. They’re teachers.
Set up a tight loop where user behavior → model update → experience improvement happens in days.

This “Feedback Flywheel” looks like this:

Users talk → You listen → You tweak → They return.

According to McKinsey’s 2025 startup dataset, teams running weekly model updates based on user feedback achieve 2.3× higher retention than those updating quarterly.

In AI, your moat isn’t patents. It’s learning speed.

When your wedge works, adjacent needs reveal themselves.

Examples:

The pattern: expansion is an outcome, not a strategy.
You earn it through obsession with one pain until users beg you to solve the next.

Goal: Find a recurring task or emotion people quietly endure.
Ask:
→ What’s one workflow people tolerate but secretly hate?
→ Where do people create hacks or workarounds?
Example: Solace → emotional exhaustion at work; Enterpret → ignored support tickets; Runway → tedious manual editing.

✅ Deliverable: List 3 “pain loops” that happen weekly in your user’s life.

Goal: Test whether AI can reduce the friction significantly.
Ask:
→ Can automation or intelligence remove 70% of this pain?
→ Would users trust AI in this context (emotionally or operationally)?

✅ Deliverable: A low-fidelity prototype or concept demo that proves AI usefulness, not perfection.

Goal: Turn early users into co-designers.
Ask:
→ How can I get structured feedback from every user action?
→ What correction patterns keep repeating?

✅ Deliverable: A feedback capture system (e.g., in-app edits, journaling responses, or “why” prompts).

Goal: Identify adjacent workflows users naturally want you to fix next.
Ask:
→ What do users say right after “this is great, but…”?
→ Which part of their process still feels manual or disconnected?

✅ Deliverable: One logical adjacent feature or use case to test (not a full pivot).

Goal: Measure progress by speed of relief, not vanity metrics.
Ask:
→ Can users do this task 3x faster or feel 3x less frustrated?
→ Does this new loop bring them back without reminders?

✅ Deliverable: One quantifiable outcome — time saved, cost reduced, or confidence gained.

Run this loop every 30 days:

McKinsey’s AI Industry Barometer highlights five verticals where specialization is exploding — each filled with untapped micro-wedges.

Each sector hides a thousand entry points where humans still do robotic work.
As MIT’s Work of the Future report notes:

“AI’s productivity surge won’t come from replacing labor but from decomposing workflows into automatable fragments.”

That’s founder gold.
Find the fragment that frustrates professionals the most — and automate just that.

Once your wedge gains traction, the game shifts from discovery to compounding.

Picture this flywheel:

1️⃣ Solve one painful workflow better than anyone.
2️⃣ Capture proprietary data from every interaction.
3️⃣ Train your system using that feedback.
4️⃣ Improve accuracy and trust.
5️⃣ Use that trust to expand into adjacent workflows.

Each loop increases both data density and brand equity.
You’re not just scaling users — you’re scaling learning velocity.

Perplexity’s founder put it well:

“We didn’t out-spend Google. We just out-learned them.”

That’s the compounding magic of a wedge well-executed.

Solace — a one-person startup that helps professionals manage anxiety with AI journaling prompts.

Sarah Gwilliam began by solving her own problem: feeling mentally drained at work.

Her wedge? Emotional reflection through guided AI questions.

Within weeks, thousands joined her waitlist. She expanded slowly — adding daily check-ins, contextual mood tracking, and voice entries.
Her advantage wasn’t data — it was empathy loops.

Enterpret — founded to decode customer feedback.

They started with one unloved data source: support tickets.
That wedge evolved into a full customer intelligence platform.

Their insight: “Every company has feedback, but no one understands it holistically.”
By centralizing scattered signals, they built an unassailable data moat.

Runway — began with video masking tools for creators.

Its wedge? Time compression.

By turning hours of manual editing into minutes, it captured the imagination of small studios — and later Hollywood.

Common thread: each founder picked a small, overlooked pain and treated it as sacred.

Founders love ambition. But wedges demand humility.
Here are the traps I see most often:

Building demos, not solutions.
Cool doesn’t convert. Pain does. Focus on solving one repetitive problem people pay to eliminate.

Overengineering before evidence.
You don’t need a custom model to validate value. Use off-the-shelf tools until users pull the product from your hands.

Ignoring data defensibility.
If your model learns nothing unique from its users, it’s just a skin on OpenAI.

MIT Sloan’s 2024 analysis found that 70 % of AI startups without proprietary data loops were commoditized within 12 months.

So build feedback channels that generate exclusive insights — usage patterns, corrections, domain-specific labels.

Expanding too early.
Wedges die when founders chase breadth before depth. Nail one problem so deeply that expansion becomes inevitable.

Every wedge starts as a small fix, but the magic lies in how you listen.
→ Solace listened for emotion.
→ Enterpret listened for signal.
→ Runway listened for time.

AI may scale, but empathy compounds.
Start narrow. Learn fast. Expand later.

At a deeper level, the wedge mindset is about humility.

It says:

“I don’t need to predict the future. I just need to fix one broken thing right now — and learn from it faster than anyone.”

AI tempts us with limitless possibility, but success still comes from narrow focus.
Every billion-dollar company today started as a wedge:
Amazon → books.
Facebook → college networks.
OpenAI → text completion.

The same will be true for the next generation of AI startups.
As McKinsey notes:

“Winning founders in the AI era will master micro-scale transformation before chasing macro disruption.”

Focus is not limitation — it’s leverage.

In every technological revolution, there’s a moment before the noise — a quiet window when the field is open, the giants are still analyzing, and the underdogs are already moving.
That window is the wedge.

The wedge is not about resources — it’s about recognition.
It’s about seeing an inefficiency that’s been normalized for too long and asking,

When you start small, you gain speed.
When you focus deeply, you gain clarity.
And when you compound both, you gain leverage — the kind that even billion-dollar incumbents can’t replicate.

McKinsey calls it “focus advantage.” MIT calls it “niche learning.”
But founders know it as something more visceral — the art of spotting what others dismiss and turning it into something inevitable.

So if you’re building right now, remember:

AI has flattened the field. What remains is focus, persistence, and timing.
The wedge you choose today could be the moat everyone studies tomorrow.

The future doesn’t belong to the biggest players — it belongs to the fastest learners.

If this edition sparked something in you — an idea, a wedge, a direction — I’d love to hear it.

Reply to this issue and tell me what problem you’re solving next.

Stay curious, stay focused, and keep your wedge sharp.

— 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!

Join 130k+ AI and Data enthusiasts by subscribing to our LinkedIn page.

Become a sponsor of our next newsletter and connect with industry leaders and innovators.