OpenAI’s Product Lead Reveals the New Playbook for Product-Market Fit in AI Startups

Why the old frameworks are failing in 2025, and how to build AI products that scale (free PRD template inside)

By Miqdad Jaffer, Product Lead at OpenAI

Product-Market Fit used to be straightforward. Build something people want, validate demand, scale up. But in the age of AI, everything has changed. The speed of iteration, the complexity of user expectations, and the sheer pace of technological advancement have rendered traditional PMF frameworks obsolete.

I've spent the last three years watching many AI startups attempt to achieve PMF. The ones that succeed aren't just building better technology—they're following an entirely new playbook. One that acknowledges a fundamental truth: AI doesn't just change how we build products; it changes what Product-Market Fit means entirely.

The AI PMF Paradox

Here's what most founders don't realize: achieving PMF in the AI era is both easier and harder than ever before.

It's easier because AI can help you iterate faster, understand users better, and build more personalized solutions than ever before. You can prototype in days, not months. You can analyze user behavior patterns that would have taken armies of analysts to uncover.

It's harder because user expectations have skyrocketed. Users now expect AI products to be intelligent, predictive, and almost magical in their capabilities. They compare every AI product to ChatGPT, regardless of the use case. The bar for "good enough" has never been higher.

"The biggest mistake I see AI founders make is treating PMF like a checkbox," I recently shared in our last cohort AI Product Management Certification. "In the AI world, PMF is a moving target. Your users' definition of 'intelligent enough' changes every month as they interact with better AI systems elsewhere."

This creates what I call the AI PMF Paradox: you need to achieve a fit with a market that's constantly evolving its expectations of what AI should do.

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The Traditional PMF Framework Is Broken for AI

Most PMF frameworks assume a relatively stable problem-solution relationship. You identify a pain point, build a solution, validate with users, and scale. But AI products break this linear progression in three critical ways:

1. The Problem Evolves as Users Learn

Traditional products solve known problems. AI products often solve problems users didn't know they had—or create entirely new workflows they never imagined possible. Your initial problem hypothesis might be completely wrong, not because you misunderstood the market, but because AI unlocked a more valuable use case.

2. The Solution Space Is Infinite

With traditional software, you're constrained by development resources and technical complexity. With AI, the constraints are different—it's about training data, model capabilities, and prompt engineering. This means your MVP might be incredibly powerful in some areas and surprisingly limited in others, creating unpredictable user experiences.

3. User Expectations Compound Exponentially

Once users experience AI that works well in one context, they expect it everywhere. If ChatGPT can understand nuanced requests, why can't your industry-specific AI tool? This creates a constantly rising bar for what constitutes PMF.

The New AI PMF Framework: 4 Phases to Systematic Success

After studying successful AI products and seeing a bunch of AI Capstone Projects from our AI Product Management certification, I've identified a new framework that actually works in the AI era. It's built around the reality that AI PMF is iterative, data-driven, and requires constant recalibration: