2025 Why You Should Relearn AI PMF? Open AI Product Manager's Four Steps to Reconstruct the Artificial Intelligence PMF Framework

OpenAI Product Lead Miqdad Jaffer pointed out in a personal blog that the market fit for traditional products in 2925 has already become obsolete, and the so-called AI PMF paradox means that while AI makes it easier to achieve product market fit, it also makes it harder to attain. 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 the market demand for a product. Miqdad Jaffer clearly states that product-market fit used to be simple: create what people want, validate the demand, and then scale up. However, in the age of AI, everything has changed. The speed of iteration, the complexity of user expectations, and the rapid pace of technological advancement have rendered the traditional product-market fit framework outdated.

PMF in artificial intelligence has fundamental differences in three key aspects:

As users interact with artificial intelligence and discover new workflows, the problems also evolve.

Due to the flexibility of models, prompts, and training data, the solution space is infinite.

With the emergence of top-tier artificial intelligence like ChatGPT, user expectations have grown exponentially.

These differences imply the need to adopt a new framework of rapid iteration, probabilistic behavior, and an evolving definition of success.

AI PMF Paradox: Artificial Intelligence Makes PMF Easier and Harder

He proposed the AI PMF paradox, where AI makes achieving PMF easier with faster iterations, more personalization, and stronger analytics, but also makes achieving PMF more difficult due to higher user expectations, the benchmark being ChatGPT, and lower tolerance for errors.

He stated in a class, "The biggest mistake I see AI founders making is treating PMF as a checkbox. In the world of AI, PMF is a constantly evolving target. As users experience other superior AI systems, their definition of what is smart changes every month." And this is what he calls the AI PMF paradox: you have to cater to a market with increasingly high demands for AI capabilities and ever-changing expectations.

Why is traditional PMF no longer applicable?

In the era of AI, problems continue to evolve with user learning. Traditional products address known issues, while artificial intelligence products typically solve problems that users are unaware of, or create entirely new workflows that they have never imagined.

Infinite Solution Space: The output of AI products is difficult to predict, while traditional software is constrained by development resources and technical complexity. The limitations of artificial intelligence, however, relate to training data, model capabilities, and rapid engineering. This means that your MVP may be very strong in certain areas while surprisingly limited in others, leading to unpredictable user experiences.

Users expect explosive growth: Once users experience artificial intelligence performing well in specific scenarios, they will expect it to be applicable in all scenarios. If ChatGPT can understand subtle requests, why can't your industry-specific AI tools? A groundbreaking product like ChatGPT sets a continuously rising standard for Product-Market Fit (PMF).

OpenAI's product chief restructures the AI product PMF framework, moving towards systematic success in four stages.

In this regard, Miqdad Jaffer proposed a new AI PMF framework, systematically outlining four stages of success.

Discover opportunities and identify the native pain points of artificial intelligence.

He believes that the biggest mistake of AI founders is adding AI on top of existing workflows. This is not innovation, but rather using AI to improve processes. A true AI project management framework )PMF( originates from identifying pain points that can only be addressed through the unique capabilities of AI.

He pointed out that the best artificial intelligence opportunities often look like problems that don't need to be solved. In the past, users developed complex solutions to problems that artificial intelligence can easily solve. These frictions are deeply embedded in current workflows to the point that users are no longer even aware that it is a problem. For example, in a startup, most developers spend 40% of their time on routine programming tasks, but they don't see it as a problem; they think of it as just part of the job.

The foundation of AI PMF is 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 question.

Scale: How many people face this pain point? AI Consideration: Does this pain point exist in various 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 serious is this pain point? AI Consideration: Does this pain point involve cognitive load, pattern recognition, or decision-making that AI excels at?

Competition: Who else is addressing this pain point? AI Consideration: Are current solutions limited by human capabilities, and can artificial intelligence surpass these limitations?

Comparison: Did your competitors receive negative reviews for their way of solving this pain point? AI Consideration: Are users complaining that the existing solutions lack personalization, speed, or intelligence?

One case is the AI assistant launched by Klarna. They initially did not attempt to "improve customer service with AI." Instead, they discovered an invisible pain point: customers had to wait an average of 11 minutes to resolve simple payment issues, which actually did not require human intervention and could be solved by accessing account information and following standard procedures. Now their AI assistant can complete all tasks within 2 minutes, handling 2.3 million conversations each month, which is equivalent to the efficiency of 700 full-time customer service representatives. This is the opportunity for AI native discovery.

Use AI product requirement document )PRD( to establish MVP

When you find pain points that AI can solve, traditional product requirement documents seem out of place. The most common mistake is to linearly apply traditional frameworks to AI. AI products are fundamentally based on probabilistic models, which means the same input can yield different outputs with certain probabilities. We cannot precisely predict AI's behavior patterns in every situation, but we can create frameworks to obtain consistent and valuable outputs.

Miqdad Jaffer and Product Professor jointly created an AI product requirements document. As mentioned earlier, traditional product requirement documents assume behavior is deterministic. In contrast, AI product requirement documents assume behavior is probabilistic. Therefore, the AI product requirements document is not just a document, but a mandatory function for thinking about all the potential failure modes that AI might exhibit.

The key lies in: AI products require dual success metrics, traditional user metrics like engagement, retention, and conversion rates, as well as AI-specific metrics such as accuracy, hallucination rate, and response quality. Both are essential to truly achieve product-market fit (PMF).

Leverage strategic frameworks to scale up.

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 haven't comprehensively considered the readiness for product launch from a strategic perspective. Scaling AI products is not just about handling more users, but also about maintaining large-scale AI performance, managing data quality across different use cases, and ensuring a consistent experience when models encounter edge cases. Miqdad Jaffer assesses scaling readiness using four dimensions:

Customer

Market segmentation size and growth rate

Customer retention rate and organic usage frequency

The extent of the pain points being addressed and the user's willingness to pay.

Product

Your unequal advantage ) data, model ( strength

The coverage and viral dissemination potential of the product

The uniqueness of AI capabilities compared to competitors.

Company

Technical feasibility of expanding AI infrastructure

Market feasibility and sales process verification

The team's ability to cope with rapid growth and the complexity of artificial intelligence.

competition

The number and strength of competitors in your field

The entry barriers for new artificial intelligence competitors

Supplier power ) relies on model providers like OpenAI (

He pointed out that the biggest challenge in expanding AI products is not at the technical level, but how to maintain quality when facing more diverse use cases. Your artificial intelligence system may perform perfectly for initial users, but when new users bring different contexts, vocabularies, or expectations, serious performance issues can arise.

Establish a sustainable growth cycle

Miqdad Jaffer believes that 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: while AI products attract new users, they also improve the user experience for existing users.

He proposed the AI growth framework:

Data network effects: Each user interaction allows the AI to learn from it, making the model smarter. Implement feedback loops to enhance model performance and fine-tune responses based on user corrections, building a system that learns from successful user outcomes.

Smart Moat: The competitive advantage of the product lies in the AI performance itself, attempting to develop proprietary datasets that competitors cannot replicate, creating AI workflows with unique value in specific fields, and establishing user interfaces that make it easier for users to access.

The compound effect of trust: When users develop trust in your AI, it promotes the organic growth of the AI. Therefore, it is essential to maintain consistent quality standards during the expansion process, and do not lower quality for the sake of expansion, as this will decrease users' trust.

He often told the founder: "The most successful artificial intelligence products I have seen not only solve problems, but their ability to solve problems becomes stronger over time. This is your ultimate competitive moat." Artificial intelligence products that truly achieve PMF can create complex advantages that traditional software cannot match.

Every user interaction allows the model to learn. Each edge case you handle makes your artificial intelligence more robust. Every successful outcome enhances user trust and drives organic growth. This is why a well-executed AI PMF can create an almost unshakeable competitive position.

This article 2025 Why You Should Relearn AI PMF? Open AI Product Director's Four Steps to Restructure the Artificial Intelligence PMF Framework first appeared in Chain News ABMedia.