In a recent LinkedIn post, Sachin Rekhi explores how the next wave of successful product managers (PMs) will differentiate themselves not by their ability to leverage AI for building, but by their skill in using AI to accelerate customer discovery. Rekhi argues that while AI is rapidly automating the ‘build’ phase of product development, the true bottleneck for innovation lies in understanding customer needs.
As Sachin Rekhi notes, the current AI revolution has equipped engineering teams with powerful tools that significantly speed up the delivery process. He highlights the impact of technologies like Cursor, Codex CLI, and Claude Code, stating:
“AI has handed engineering teams a jetpack. Cursor. Codex CLI. Claude Code. The delivery side of product development — build, specify, launch — is being automated at a breathtaking pace.”
However, Rekhi pivots to a critical observation, echoing sentiments from figures like Andrew Ng, that the primary challenge in product development has shifted from coding to discovery. He explains that the ability to build rapidly is only advantageous if directed by accurate insights.
The Shifting Bottleneck: From Building to Discovery
Sachin Rekhi emphasizes that the rapid acceleration in development capabilities, driven by AI, has outpaced our ability to learn from customers. This creates a risk of building products based on flawed assumptions, leading to costly mistakes made more quickly.
“While everyone raced to accelerate shipping, the question mark moved upstream. We now have the ability to build faster than we’ve ever been able to learn. And building fast on the wrong insight isn’t speed — it’s just expensive mistakes, sooner,” Rekhi writes.
Fortunately, Rekhi points out, the same AI advancements are also enhancing the discovery process. He outlines several emerging use cases that PMs can leverage:
- Analyzing Feedback at Scale: AI can now process vast amounts of customer feedback (NPS verbatims, support tickets, app reviews) to identify themes and patterns, a task that previously required significant researcher time.
- Automating Feedback Streams: Tools are emerging that continuously monitor customer feedback across multiple channels, surfacing actionable insights without manual intervention.
- AI-Moderated User Interviews: Platforms are enabling a much larger volume of user interviews than previously feasible with human moderators, allowing for broader qualitative data collection.
- Discovery via Prototypes: Tools that facilitate rapid prototyping allow PMs to gather behavioral data from users before investing in full production code.
- Natural Language Metric Analysis: PMs can now query databases using natural language, bypassing the need for SQL or data analyst support, thereby drastically shortening the feedback loop between hypothesis and data-driven answers.
Cultivating Product Intuition with AI
Rekhi posits that integrating these AI-driven discovery workflows will empower teams not only to be better informed but also to develop a sharper product intuition. He references David Lieb’s description of product intuition as “the world’s most sophisticated machine learning model ever created,” suggesting that AI can help hone this crucial human skill.
The post concludes with an invitation to a Lean Product Meetup where Rekhi plans to share his specific AI discovery workflows, underscoring his commitment to helping PMs navigate this new landscape effectively.
📝 About This Content
This article is based on insights shared by Sachin Rekhi on LinkedIn.
📅 Originally posted on March 3, 2026 | View original post on LinkedIn →