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DeepDive 

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If you’re a founder in 2026, you have access to world-class foundation models. Development tools are improving by the month. In theory, this should be the easiest time in history to launch a tech company.  

But while AI startups can now build almost anything, they face a very real problem: they just don’t know whether anyone actually needs what they’re building.  

As AI development becomes faster and more accessible, product-market fit is becoming one of the few advantages that technology alone can't solve. A sophisticated model is one thing – but it won’t succeed if you don’t understand your customers better than anyone else.  

The temptation to start with the tech 

The AI industry loves capability. Every week brings a new benchmark, or a new demonstration of what machines can do. Founders become captivated by the possibilities – and start building before they've fully understood the problem they're trying to solve. 

But the most successful AI products tend to emerge from the opposite approach. 

Rather than asking what the tech can do, they start by asking what problem is causing people the most pain.  

When we interviewed Aadil Jaleel Choudhry (Co-Founder and CEO of vResolv.io) for an earlier edition of the newsletter, he explained how that principle sits at the centre of product development. 

"Every product we build begins with an acute focus on solving real-world problems, whether for our clients or under our own brand." 

It's a simple idea. Yet in a market filled with excitement around AI capabilities, it can be surprisingly difficult to follow. 

Product-market fit starts long before launch 

When Choudhry's team began developing Qazi.ai, an AI-powered judicial assistant, they didn't begin by training models or designing interfaces. 

They started by listening. 

"We collaborated with a Supreme Court legal advisor to deeply understand the judicial landscape, identifying pain points that technology could alleviate." 

And that decision heavily influenced the development of their product. Instead of making assumptions about what the legal sector needed, the team immersed themselves in the realities of the people they hoped to serve. They explored where inefficiencies existed, which processes created friction, and where technology could deliver genuine value. 

Only then did they begin building.  

Because you don’t find product-market fit in the boardroom. You uncover it through conversations with the people experiencing the problem firsthand. 

Customers don't want AI 

We know this might sound strange coming from us – but most customers don't actually want AI. 

They want outcomes. 

A lawyer wants faster research. A doctor wants better diagnostic support. A business leader wants more efficient operations. AI might be part of the solution, but it’s not the reason someone decides to adopt a product. 

So founders should stop focusing on the novelty of AI, and turn their attention to the jobs that people are trying to accomplish.  

When you’re too focused on the tech, you risk building features that impress investors and peers without solving a meaningful customer problem. When you focus on outcomes, you create products that naturally fit into people’s workflows and priorities.  

Validation is more than positive feedback 

One of the biggest misconceptions about product-market fit is that it can be measured through enthusiasm. 

People saying an idea sounds good is encouraging – but it isn’t validation.  

Real validation happens when stakeholders invest time, resources, attention or money into solving the problem. 

For Qazi.ai, that meant engaging with decision-makers who could assess whether the solution had relevance beyond a single use case. 

"To validate the concept and explore scalability, we engaged in discussions with key policymakers, including Pakistan's IT&T Minister, focusing on modernising judicial processes." 

Those conversations helped test whether the problem was significant enough to drive broader adoption. 

This is so important for AI founders. The goal, far beyond just proving that a product can work, is to prove that people care enough about the problem to change their behaviour. 

Listening is the competitive advantage 

The irony of the AI era is that while technology is advancing at extraordinary speed, one of the most important lessons for founders hasn't changed at all. 

Listen first. 

Build after. 

As Choudhry said: 

"Our methodology – rooted in collaboration, problem-solving, and market validation – has been instrumental in building products that resonate with end users and achieve lasting impact." 

The tools available to entrepreneurs will continue to evolve. But product-market fit remains stubbornly human. 

What’s your perspective? 

What’s the biggest mistake AI startups make when trying to find product-market fit? Open this newsletter on LinkedIn and tell us what you think in the comments. 

We’ll see you back here next week.