Medium: Rethinking product strategy in the age of AI

In this March 2026 article, Parth Chhaparwal — a product leader and writer — makes the case that the early failure pattern of AI startups has given way to a new kind of product thinking. Success now depends on how you architect the experience around AI workflows, not on which foundation model you use.

Context

Chhaparwal wrote this piece at a moment when most AI product discussions still defaulted to model comparisons. His argument is that model quality is table stakes — it is no longer where strategic differentiation comes from. The real question is how the product is structured around AI.

Early AI startups failed because they were dependent on model quality and faced rapid obsolescence as foundation models improved. The teams that survived built defensibility through experience design rather than technology.

Key method

The article introduces two high-level strategic options. The first is what Chhaparwal calls “Cursor for X”: rebuilding an entire workflow around an AI agent, eliminating legacy UX assumptions. The second is “Copilot for X”: layering AI assistance on top of an existing tool without changing the underlying structure.

He also identifies three interaction design choices that product teams face regardless of which direction they take: who initiates AI actions (user or system), what data and systems the AI can access, and how errors compound through multi-step workflows. These choices shape the risk surface and the user experience more than any model capability.

For teams building toward fully autonomous behavior, the article lays out five archetypes in sequence: bare API, chatbot, assistant, copilot, and agent. Each step expands both capability and the potential for compounding errors, which is why the author argues that governance structures need to scale alongside capability.

Takeaway

Cursor’s approach — rebuilding the developer environment from scratch around AI rather than extending an existing IDE — is the clearest example of the thesis. The author concludes that the winning move is often to rebuild the whole workflow rather than retrofit it.

Who it is useful for

This article is most relevant for PMs at companies building AI-native products or deciding whether to integrate AI into an existing product or build something new from scratch. It is a strategic read rather than a tactical guide, approximately 2,500 words, with decision frameworks that resonate with teams who have already shipped something and are rethinking their approach.