If the past decade was about making software easier to build, the next one will be about making sure it’s worth building at all.

AI has completely transformed the speed and cost of product creation. Just a few years ago, building and launching new software required months of development, expensive engineering resources, and a team of specialists. Today, AI-powered tools are shattering those constraints. With a single prompt, an entrepreneur can generate product ideas, spin up a working prototype in hours, and launch it to the world in days. The barriers to creation are disappearing.

That should be good news. But it’s exposing a new reality: the biggest challenge is no longer building software; it’s building the right software.

The Illusion of AI-Driven Innovation

The ability to create something quickly and cheaply can feel like a superpower. AI enables anyone—startup founders, enterprise teams, even hobbyists—to bring ideas to life with unprecedented ease. Tools like Lovable generate 80% of an MVP in hours. Platforms like Cursor, Supabase, and Vercel automate coding, authentication, and deployment, putting fully functional apps in users’ hands almost instantly.

But with infinite possibilities comes a trap: building for the sake of building.

History tells us that when the cost of creation plummets, the volume of bad ideas skyrockets. The rise of no-code tools a few years ago gave birth to thousands of new apps, but how many of them truly addressed meaningful customer problems? How many became essential products, rather than just another entry in an overcrowded marketplace?

Take the wave of AI-powered writing assistants. Tools like Jasper, Copy.ai, and ChatGPT make it easier than ever to generate content. But how many have jumped in without first asking: What kind of content does my audience actually need? The result is an explosion of AI-generated articles that no one reads because they fail to address real questions or offer original insights.

Or consider the flood of AI-generated productivity apps. The market is filled with tools claiming to streamline workflow, automate tasks, and enhance collaboration. But without understanding the specific pain points of a team—whether it’s managing asynchronous communication or reducing meeting fatigue—these tools become just another layer of digital clutter.

AI is making that problem exponentially worse. The market is being flooded with new products, but most of them are solutions in search of a problem. The illusion of effortless innovation leads to wasted resources, bloated feature sets, and an overwhelming amount of noise. More software. More products. More failures.

The Real Problem: What Should We Build?

The fundamental question has changed. It’s no longer can we build this?—because AI increasingly makes that trivially easy. The real question is: Should we build this?

To answer this question, AI-innovators need a compass for where to point AI’s incredible power: this compass is a deep understanding of a customer problem worth solving. While there are many methods for developing such an understanding, one of the most effective is the ‘Jobs to be Done’ (JTBD) approach.

There are many variations of JTBD floating around, but at a high level it is a simple model for explaining why customers make the choices they do. The model asserts that the root cause underlying purchase decisions is the existence of important, unsatisfied jobs customers want to get done - not the product itself, its features, or customer demographics. A job can be a problem a customer wants to solve (e.g., get out of debt, have more energy, keep track of work commitments) or a goal to achieve (e.g., get into college, run a marathon, go on a vacation). In either case, a job represents the progress customers aspire to make in their lives - and it is this desire for progress that provides the motivation and energy to go out and search for a solution to ‘hire’ to make that progress.

The success of any product, therefore —AI-generated or not—depends entirely on how well it helps the customer make such progress - i.e., to solve the job to be done. Without that connection, even the most sophisticated AI-generated software is destined to fail. Because no matter how fast you build it, no one will care if it doesn’t address a real need.

Customer Jobs as the Ultimate AI Filter

If AI has made it easier to generate products, then understanding customer jobs is what ensures we’re generating the right ones. It acts as a strategic filter, helping teams avoid the trap of building for the sake of building. Before writing a single line of code, teams should be asking:
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• What critical problem does this solve for a customer? Instead of starting with "What can AI build?" start with "What problem do customers struggle with today?" If you’ve already built a product, you can ask, “if our product disappeared tomorrow, what real pain would it leave behind?”
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• Who is currently solving this problem, and why do customers use (or abandon) their solutions? Understanding competitive alternatives, why customers do/do not choose them, and how they could be better provides insight into whether your AI-generated product is actually an improvement or just another option in an overcrowded space.
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• Would a customer be willing to pay for this, switch to it, or recommend it? If the answer is no, AI-powered efficiency won’t make it any more valuable.
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• If they did pay for it, how would it help them better solve for their JTBD? Are they completing tasks faster? Reducing frustration? Achieving something that was previously impossible?
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Perfectly solving a customer’s JTBD is the difference between successful AI-powered innovation and a wasteful feature factory. The companies that will thrive in the AI era aren’t the ones that can build the most things the fastest—they’re the ones that build only the things that truly matter.

The Future: AI + JTBD = Smarter Innovation

In a world where everyone can build anything, the companies that win will be those that combine AI’s raw creative power with deep customer insights. AI is a powerful accelerant, but it accelerates both success and failure—the difference is knowing what customers truly need.

AI may have removed the barriers to building, but it hasn’t changed the fundamental truth at the heart of every great product: customers don’t want software. They want to make progress in their lives by solving important problems and achieving important goals. And in a world of infinite possibilities, understanding these is the only way to ensure we’re building what truly matters.

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