The easier it is to build, the harder it is to be heard

 

A few weeks ago, early morning on one of the weekends, I sat in a cafe in Dubai -- the kind with overpriced flat whites and Wi-Fi that works just well enough to make you feel productive -- staring at my laptop screen. I'd just asked an AI model to generate a product requirements document for a banking API. It spat out 20 pages in 12 seconds. Good structure. Decent language. Even included a section on regulatory considerations that would've taken a junior product manager a full day to research.

I felt two things simultaneously.

The first was awe. The second was a quiet, creeping unease.

This is the age of abundance. We have tools that write code, generate designs, draft strategy documents, synthesize customer feedback, and even simulate user interviews. We can spin up a full-stack application in minutes. We can prototype an MVP faster than we can order lunch (shameless plug: ogo1.com :)

But here's the thing I keep coming back to: AI changes everything, and it changes nothing.

Let me explain what I mean.

The Uncomfortable Truth About Speed

When I started my career, product development felt like carving stone. You'd spend weeks on requirements, months on development, and pray you'd guessed right. Feedback came slow — if it came at all. The cost of being wrong was astronomical. So we moved carefully. Too carefully, sometimes.

Today, I can build an MVP in days. Not months. Days. I've done it. Ogo1 (getting from 0-to-Go in sprint 1) exists precisely because of this shift. We take founders from "I have an idea" to "I have a working product in front of users" faster than most people can decide on a logo.

And yet.

I've seen founders (from DIFC Innovation & Founder Institute) with working products in hand, staring at analytics dashboards, completely lost. The product works. The code is clean. The features are all there.

But nobody wants it. 

Speed didn't fix that. AI didn't fix that. Because the problem was never about speed. The problem was about understanding.

What Abundance Really Means

Let's talk about what AI actually gives us.

First, speed. We can generate code, content, designs, and analysis at rates that were unthinkable a few years ago. Second, cost. The barrier to entry has collapsed. A solo founder with a laptop and an API key can build what once required a team of twenty. Third, scale. AI doesn't get tired. It can process billions of data points, analyze patterns, and surface insights across immense datasets.

This is abundance. It's real. It's transformative.

But abundance is not wisdom. It's not judgment. And it is most certainly not empathy.

The Thing That Doesn't Change

When I was leading product strategy at FAB, overseeing digital banking products generating over $300 million in revenue, I learned something that's stuck with me: products succeed or fail based on how well they solve real problems for real humans.

This sounds obvious. It's not. Because "solving a problem" isn't about the solution. It's about understanding the problem well enough to know which solution matters.

AI can generate a hundred solutions. It cannot tell you which one your users will actually care about. That requires something different — something that looks a lot like walking into a room, sitting down with someone, and actually listening.

Here's a concrete example.

At Bankableapi.com, we built a platform that enables banks and fintechs to connect via APIs. The technical architecture was solid. The security was enterprise-grade. We ticked all the boxes.

But the breakthrough moment came when we stopped talking to IT departments and started talking to product heads. We realized that the "problem" wasn't technical. It was organizational. Banks weren't struggling to implement APIs. They were struggling to justify their existence to skeptical leadership teams who didn't understand open banking.

So we shifted. We built better demos. We simplified the pitch. We made it easier for internal champions to sell the platform to their own stakeholders.

AI didn't help us see that. People did.

The Great Filter

I think we're entering a fascinating period — one I'm calling "The Great Filter."

For years, the limiting factor in product development was capability. Could we build this? Could we afford to? Could we scale it? AI removes a lot of that friction.

Now, the limiting factor becomes clarity.

Because when everyone can build everything fast and cheap, the only differentiator left is whether you're building the right thing. And that's never been a technical question. It's a human one.

What does your user actually need? What's the emotional core of their problem? What would make them love your product — not just use it, but genuinely feel grateful it exists?

These questions don't change with the arrival of new technology. They're the same questions product people have been asking for decades. The people who answer them well win. The people who don't, lose. No LLM changes that.

The Execution Trap

Here's another thing I've learned the hard way.

When you have tools that make execution easy, there's a temptation to fall in love with execution itself. To treat the build as the point. To mistake activity for progress.

I see this all the time now.

A founder comes with an idea. We build their MVP in days. They're thrilled. Then they spend weeks tweaking the UI, optimizing the load times, adding small features nobody asked for. They're "building." They're "iterating."

But they're not learning.

Because the product is in their hands, not their users'. They haven't gotten enough feedback to know what matters. They haven't made the painful, clarifying trade-offs that define great products.

The tragedy is that with AI, the temptation to keep building is stronger than ever. The feedback loop is instantaneous. You can generate, tweak, regenerate, and tweak again. It feels productive. It feels like progress.

But it's the sound of one hand clapping.

The Real Cost of "Free"

There's another layer to this conversation that I think about a lot.

AI is abundant. But attention isn't. Trust isn't. Time isn't.

When you can build products at near-zero marginal cost, the market floods. Every niche becomes crowded. Every user becomes skeptical. The noise drowns out the signal.

This creates a paradoxical effect: the easier it is to build, the harder it is to be heard. Let me put that in quotes: 

the easier it is to build, the harder it is to be heard

We're already seeing this. SaaS products proliferate at terrifying rates. AI-generated content clogs every channel. The signal-to-noise ratio is collapsing.

So the real question isn't "Can I build this?" It's "Why should anyone care?"

That's not an engineering question. That's a human question. And it's more urgent now than ever.

What I've Learned Building

I've built a few things. Some succeeded. Some didn't. All taught me the same lesson, repeated across contexts.

Technology is leverage. Vision is direction. Execution is craft. But empathy is the foundation.

Without empathy, you're building for yourself. Or worse, for what you assume others want. And assumptions, I've found, are almost always wrong.

Here's what I mean.

When we built the e-commerce marketplace in Pakistan, we thought the biggest challenge would be logistics. Or payment integration. Or supply chain.

It wasn't any of those things.

It was trust. Sellers didn't trust the platform. Buyers didn't trust the sellers. The entire system was held together by fragile relationships and cash-on-delivery — a workaround that worked but was inefficient at scale.

We had to earn trust. Not with technology — though that helped. With consistency, transparency, and relentless follow-through. With proving, over time, that we were who we said we were and that we would do what we promised.

AI couldn't give us that. It had to come from us.
The Unchanged Equation

Here's the equation I've come to believe in:

Great Product = Deep Understanding × Right Solution × Excellent Execution

AI changes the third term. It amplifies execution. It makes it faster, cheaper, and more scalable. That's real. It's valuable.

But it doesn't touch the first term. And it barely grazes the second.

Deep understanding requires curiosity, humility, and patience. It requires stepping away from the keyboard and into someone else's world. It requires listening to what people say, noticing what they don't say, and piecing together a picture of the problem they might not even know they have.

Right solution requires judgment. It requires deciding what matters and what doesn't. It requires saying no to a thousand good ideas so you can say yes to one great one. It requires trade-offs that are painful and clear.

AI can help with execution. It cannot help with judgment. At least not yet.

And maybe that's fine. Maybe that's the point.

The Builder's Calling

I help founders build MVPs fast. But what we're really selling is not speed. It's clarity. It's the ability to test a hypothesis in days rather than months, to learn what doesn't work before spending a year on it, to find the kernel of truth at the center of an idea and build from there.

That's the promise. Not the code. The learning.

Because in the end, the most important thing we build is not the product. It's the understanding.

It's the insight that emerges when you put something in front of users and watch them use it. It's the recognition that you were wrong about something important, and the pivot that follows. It's the strange, beautiful moment when a product stops being "yours" and starts being "theirs" — when it becomes a tool they use to solve their own problems, not the solution you assumed they wanted.

That can't be automated. It shouldn't be.

Back to the Cafe

Sitting in that cafe in Dubai, staring at the AI-generated PRD, I could feel the weight of the moment.

Here was a tool that could do in seconds what once took weeks. It was incredible. It was humbling. It was a little scary.

But then I remembered something.

The PRD was well-structured. It was comprehensive. It was technically correct.

It was also, in a deeper sense, irrelevant. Because it was built on assumptions — about who the users were, about what they needed, about the context in which they would use the product. And those assumptions, no matter how sophisticated the AI, had not been tested.

The document was, in its own way, a pause.

A moment to step back and ask: what am I really trying to build here? And why? And for whom?

Those questions aren't answered by speed. They're answered by reflection. By conversation. By the uncomfortable, messy, deeply human process of figuring out what actually matters.

What I'll Keep Building

So here's what I'm taking away from all of this.

I'll keep using AI. It's powerful. It makes me faster. It helps me see patterns I might have missed. It's a tool like any other — useful, but not a substitute for thinking.

But I'll also keep doing the things that don't scale. I'll keep talking to users. Keep asking the same question in five different ways to get past the first answer. Keep walking through workflows, sitting in silence, watching people use things and noting where they hesitate.

I'll keep building, but I'll build more slowly in the places that matter most.

Because the age of abundance isn't about how much you can make. It's about what you choose to make, and why.

And if you get that right — if you build something that genuinely fits into someone's life, solves a problem they actually have, and does it with integrity — then speed becomes less important.

You already won. The rest is just code.

The Pause We Never Take

We don't pause enough (read previous blog post). We rush, we build, we optimize, we ship. We're so busy doing that we forget to think about whether we're doing the right things at all.

AI accelerates this tendency. It makes it easier to do more, faster. But more isn't better. It's just more.

The pause is where clarity lives. It's where we ask the hard questions we've been avoiding. It's where we admit that maybe we don't know, and that's okay, because knowing is a process, not a destination.

So here's my invitation to you, whoever you are reading this.

Pause.

Ask yourself: what are you building, and why? Who are you building it for? And how will you know if it's working?

Then build. Build fast, build lean, build with AI if you want. But don't build blindly. Don't mistake activity for progress. Don't confuse the tool with the craft.

The age of abundance is here. It's exhilarating. It's unsettling. It's full of possibility.

But the fundamentals — the questions that have always defined great product work — remain unchanged.

Answer those, and everything else follows.