Artigo interessante explorando a mudança no PMF com o crescimento da AI.
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Understanding Your Level Of PMF Collapse Risk
- How directly do you own the customer relationship?
There are products that have tight ownership over the customer relationship (i.e. Github). There are others (i.e. Stack Overflow) where Google or some other channel tend to be the primary way users enter and return to the product. Product market fit will be easier to maintain and defend the more you own the customer relationship. A way to assess this is to measure the percentage of your users that come directly to your product vs through an intermediary.
- What is the frequency of your use case?
In Retention + Engagement we focus on defining your use cases and identifying your natural frequency of usage. There are low frequency products used 1 - 2X per year (i.e. Travel) and high frequency products used daily (i.e. Slack).
Low-frequency products are at higher risk in my opinion. The low-frequency nature provides easier opportunities for a user to switch the next time the need comes around. The habit is not as strong. High-frequency products have an established habit with users and habits can be hard to break even if there is a better alternative on the market. Learn how to determine your natural frequency here.
- Do you own the creation workflow?
AI’s “killer use case” often emerges exactly where the user creates something—in the coding environment (e.g., GitHub Copilot), the writing surface (e.g., Notion AI), or the design canvas (e.g., Figma). If your product sits “downstream” or outside of these creation surfaces rather than being the place where users do the core work, you’re more easily replaced. AI can directly integrate into the workflow and be a more seamless alternative vs leaving their primary work environment (like leaving a dev environment to Stack Overflow).
- Do you have proprietary data?
Data is the new oil (for real this time) in an AI world. Specifically proprietary data that the foundational LLM’s and models don’t have access to. If data (or content) is available and ingestible by the large language models then that is not defensible. The more proprietary data you have, the less risk of product market fit collapse.
- What would break your core growth loop?
You need to understand your growth model deeply. Don’t just map your growth loops, but understand why a user takes each step in their growth loop. If that “why” breaks then loops start to spin in a negative vs positive direction. For example, growth models that rely on user-generated content can unravel quickly if the incentives for contributors vanish. This is what we are seeing in the Stack Overflow case.
- How tech forward are your customers?
Fareed Mosavat had a good point:
“We’re seeing the real disruption today at the tip of the adoption curve (code, design, tech, students). Businesses that cater to less savvy customers are likely less susceptible.”
The earlier your audience tends to be on the adoption curve of new products, the quicker product market fit can break. They are willing to try new things, break old habits, and have low loyalty if a clearly better alternative emerges.