in this conversation, you’ll learn:
* why product market fit feels shakier even when growth looks strong.
* how ai changed the economics of building and copying software.
* what “habit gravity” is and why it replaced features as the real moat.
* how modern products become part of a user’s daily mental workflow.
where to find prayerson:
* x: https://x.com/iamprayerson
* linkedin: https://www.linkedin.com/in/prayersonchristian/
in this episode, we cover:
(00:00 - 02:33) the pmf paradox
* why products can look successful but still feel fragile inside.
* how ai made building easy but made staying hard.
(02:33 - 05:27) the old pmf model
* how scarcity, switching costs, and slow imitation created moats.
* why early winners like slack, dropbox, and google could compound trust over time.
(05:28 - 07:24) the collapse of feature advantage
* how ai shrank the gap between invention and imitation.
* why proving demand now instantly creates saturation.
(07:25 - 10:36) the ai native user
* how chatgpt and midjourney reset expectations for speed and simplicity.
* why context, responsiveness, and memory now define good software.
(10:36 - 12:52) why retention is the only truth
* how novelty creates fake pmf through vanity metrics.
* why real pmf only shows up when users return without being pushed.
(13:11 - 17:58) the four forces of habit gravity
* how frequency, switching pain, context lock in, and workflow depth create reliance.
* why aligning all four turns tools into dependencies.
(18:07 - 22:51) pmf case studies
* how notion, midjourney, chatgpt, and perplexity score on habit gravity.
* where each product gains strength or shows vulnerability.
(23:09 - 26:09) the new pmf playbook
* how pms must hunt for behavioral loops instead of features.
* why repetition, depth, and memory now drive pmf experiments.
(26:09 - 28:13) the future of pmf
* why pmf now lives inside human routines, not code.
* how anticipatory memory could become the next competitive moat.
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