OpenAI Product Lead Miqdad Jaffer pointed out in his personal blog that the traditional Product-Market Fit (PMF) framework has become obsolete by 2925. The so-called AI PMF paradox is that while AI makes achieving PMF easier, it also makes it more difficult. He proposed a four-stage framework for achieving systematic success in AI PMF and included an AI product PRD template in the text.
There are three key differences between AI PMF and traditional frameworks
Product-Market Fit (PMF) is an industry term that refers to market demand for a product. Miqdad Jaffer bluntly states that PMF used to be simple: build what people want, validate the demand, and then scale. But in the AI era, everything has changed. The speed of iteration, the complexity of user expectations, and the rapid pace of technological advancement have rendered traditional PMF frameworks obsolete.
PMF in artificial intelligence fundamentally differs in three key areas:
As users interact with AI and discover new workflows, problems evolve.
Due to the flexibility of models, prompts, and training data, the solution space is infinite.
With the emergence of top AI tools like ChatGPT, user expectations have grown exponentially.
These differences mean that a new framework must be adopted that emphasizes rapid iteration, probabilistic behavior, and evolving definitions of success.
The AI PMF Paradox: AI makes achieving PMF easier and harder
He proposed the AI PMF paradox: AI makes achieving PMF easier (faster iteration, more personalization, stronger analysis) but also harder (increased user expectations, comparison benchmarks with ChatGPT, reduced tolerance for error).
In a lecture, he stated: 'The biggest mistake I see AI founders make is treating PMF as a checkbox. In the world of AI, PMF is a constantly changing target. As users experience other superior AI systems, their definitions of what is smart are changing every month.' This is what he calls the AI PMF paradox: you must cater to an increasingly demanding market with ever-changing expectations of AI capabilities.
Why traditional PMF is no longer applicable?
In the AI era, as users learn, problems continually evolve. Traditional products address known problems, whereas AI products often solve unknown user problems or create entirely new workflows they never imagined.
The solution space is infinitely large: AI product outputs are difficult to predict, while traditional software is limited by development resources and technical complexities. The limitations of artificial intelligence are related to training data, model capabilities, and rapid engineering. This means your MVP may be very strong in certain areas while surprisingly limited in others, resulting in unpredictable user experiences.
User expectations have exploded: Once users experience AI that performs well in specific scenarios, they expect it to be applicable in all scenarios. If ChatGPT can understand subtle requests, why can't your industry-specific AI tools? The PMF set by revolutionary products like ChatGPT sets a continuously rising standard.
OpenAI Product Lead reconstructs the AI product PMF framework, a four-stage path to systematic success.
In response, Miqdad Jaffer proposed a new AI PMF framework with four systematic success stages.
Discover opportunities and identify AI-native pain points.
He believes the biggest mistake AI founders make is adding AI on top of existing workflows. This is not innovation but rather improving processes with AI. A true AI project management framework (PMF) stems from identifying pain points that can only be resolved through AI's unique capabilities.
He points out that the best AI opportunities often appear to be problems that do not need solving. In the past, users developed complex solutions to address issues that AI can resolve simply. These frictions are so deeply embedded in current workflows that users may no longer even recognize them as problems. For example, in a startup, most developers spend 40% of their time on routine programming tasks, but they do not see this as a problem; they believe it's just part of the job.
The foundation of AI PMF is a rigorous pain point analysis. Use the following five questions to prioritize which pain points are worth solving, and apply an AI perspective to analyze each issue:
Scale: How many people face this pain point? AI Consideration: Does this pain point exist across industries where AI can be applied horizontally?
Frequency: How often do they encounter this pain point? AI Consideration: Is the frequency of this pain point sufficient to generate the data needed for AI learning and improvement?
Severity: How severe is this pain point? AI Consideration: Does this pain point involve cognitive load, pattern recognition, or decisions where AI excels?
Competition: Who else is solving this pain point? AI Consideration: Are current solutions limited by humans, while AI can surpass these limitations?
Comparison: Is the way your competitors solve this pain point receiving negative feedback? AI Consideration: Are users complaining that existing solutions lack personalization, speed, or intelligence?
One case is the AI assistant launched by Klarna. They initially did not try to 'improve customer service with AI.' Instead, they identified an invisible pain point: customers typically had to wait 11 minutes to resolve simple payment issues that did not require human intervention, only access to account information and following standard procedures. Now their AI assistant can complete all tasks within 2 minutes, handling 2.3 million conversations per month, equivalent to 700 full-time customer service representatives; this is the opportunity discovery inherent in AI.
Using AI product requirement documents (PRD) to build an MVP
When you find pain points that AI can solve, traditional product requirement documents become out of place. The most common mistake is linear application of traditional frameworks to AI. AI products are fundamentally based on probabilistic model operations, where the same input can yield different outputs probabilistically. We cannot precisely anticipate AI's behavior in every scenario, but we can create frameworks to achieve consistent and valuable outputs.
Miqdad Jaffer co-created an AI product requirement document with Product Professor. As mentioned earlier, traditional product requirement documents assume behavior is deterministic. In contrast, AI product requirement documents assume behavior is probabilistic. Therefore, an AI product requirement document is not just a document but a mandatory function for thinking about all the ways AI might fail.
The key is that AI products require dual success metrics: traditional user metrics (such as engagement, retention, and conversion rates) and AI-specific metrics (such as accuracy, hallucination rate, and response quality). Both are essential to truly achieve Product-Market Fit (PMF).
Using strategic frameworks to scale
Most AI startups encounter bottlenecks when trying to scale. Their MVPs perform exceptionally well in the eyes of early adopters, but broader market applications stagnate. This is because they have not comprehensively considered the readiness for product launch from a strategic perspective. Scaling AI products is not just about handling more users; it involves maintaining large-scale AI performance, managing data quality across different use cases, and ensuring a consistent experience when models encounter edge cases. Miqdad Jaffer uses four dimensions to assess scaling readiness:
Customers
The size and growth rate of the target market segment
Customer retention rates and organic usage frequency
The severity of the pain points being addressed and users' willingness to pay
Product
The strength of your unique advantages (data, models)
The coverage and viral potential of the product
The uniqueness of AI capabilities compared to competitors
Company
Technical feasibility of scaling AI infrastructure
Market entry feasibility and sales process validation
The team's ability to respond to rapid growth and the complexities of artificial intelligence
Competition
The number and strength of competitors in your field
Barriers to entry for new AI competitors
Supplier power (reliance on model providers like OpenAI)
He points out that the biggest challenge in scaling AI products is not technical but maintaining quality when faced with diverse use cases. Your AI system may perform perfectly for initial users, but when new users bring different contexts, vocabulary, or expectations, serious performance issues can arise.
Establish a sustainable growth loop
Miqdad Jaffer believes traditional products focus on optimizing conversion funnels and user engagement. In contrast, AI products must optimize model performance, data quality, and user trust. This creates a unique opportunity: AI products can attract new users while simultaneously improving the user experience of existing users.
He proposed the AI growth framework:
Data network effects: Every user interaction allows AI to learn, making the model smarter. Implement feedback loops to improve model performance and fine-tune responses based on user corrections to create a system that learns from successful user outcomes.
Intelligent moats: Competitive advantages of products are the AI's performance itself, trying to develop proprietary datasets that competitors cannot replicate, creating AI workflows that have unique value in specific domains, and establishing user interfaces that make it easier for users to engage.
The trust compounding effect: When users trust your AI, it fosters organic growth for the AI. Therefore, during the scaling process, it is crucial to maintain consistent quality standards and not lower quality for expansion, as this will reduce user trust.
He often tells founders: 'The most successful AI products I've seen not only solve problems but their ability to solve problems improves over time. This is your ultimate competitive moat.' Truly achieving PMF with AI products can create complex advantages that traditional software cannot match.
Every user interaction allows the model to learn. Every edge case you handle makes your AI more robust. Every successful outcome enhances user trust and drives organic growth. This is why AI PMF, if done well, can create an almost unshakeable competitive position.
This article 'Why You Should Rethink AI PMF in 2025? OpenAI Product Lead’s Four-Step Reconstruction of the AI PMF Framework' first appeared in Chain News ABMedia.