Cues' initial product was a specialized AI design agent. However, they observed that users were more frequently uploading files to use it as a knowledge base. Recognizing this emergent behavior, they pivoted to a more horizontal product, which was key to their rapid growth and product-market fit.

SaaStr's initial AI, a clone of founder Jason Lemkin for giving advice, unexpectedly received many questions about events and sales. This user behavior revealed a clear need for dedicated go-to-market AI agents, pivoting their AI strategy from a simple experiment to a core business function.

Major product opportunities are revealed by observing how customers use your product in unintended ways or "jump through hoops" to achieve a goal. For example, Anthropic noticed non-engineers struggling to use their coding tool, revealing the latent demand for CoWork, a knowledge-work assistant.

To overcome the slow pace of its 26-year-old legacy platform, Alibaba.com ran its AI agent, Axio, as an internal startup. This model allowed the Axio team to ship new versions daily, fostering rapid innovation without disrupting the core business, which released updates bi-weekly or monthly.

Zapier evolved from a simple trigger-action tool by observing how advanced users "hacked" it for unintended purposes. When users treated Google Sheets like databases, Zapier built Tables and Interfaces, proving that emergent behavior is the clearest signal for your product roadmap.

Uber observed users unofficially using its Courier service for personal shopping. Instead of stopping it, they recognized this "hack" as a clear market signal and built a dedicated "Shop for Me" feature, turning user ingenuity into a new product.

Unlike the Western focus on general AI assistants, Chinese firms like Alibaba and Tencent deploy agentic AI within their 'super apps.' The goal is not just user convenience but to direct consumers to their own vast networks of goods and services, boosting retention and monetization within a closed loop.

A new product development principle for AI is to observe the model's "latent demand"—what it attempts to do on its own. Instead of just reacting to user hacks, Anthropic builds tools to facilitate the model's innate tendencies, inverting the traditional user-centric approach.

Shopping agent Glance's model is to first generate new product ideas tailored to a user, then find the closest match in retail inventory. This contrasts with tools that start with existing items. This 'idea-first' approach prioritizes optimal user discovery over becoming a simple reseller for brands' existing stock.