Looking for AI Use-Cases

This analysis by Benedict Evans explores the challenge of finding practical use cases for generative AI tools like ChatGPT, despite their impressive technical capabilities. Evans draws parallels to early personal computers in the 1980s, where the technology existed but clear applications weren't immediately obvious until breakthrough products like VisiCalc (the first spreadsheet) demonstrated transformative value for specific user groups like accountants.

The core tension Evans identifies is between AI's promise as a universal, general-purpose tool versus the reality that users struggle to identify concrete applications for their specific needs. While some use cases have emerged (coding, brainstorming, generic writing), many professionals haven't found AI tools that match their particular workflows. This creates a "cognitive dissonance" where companies like OpenAI promote general-purpose autonomous agents, while simultaneously spawning countless startups that wrap AI APIs into single-purpose, problem-specific applications with dedicated UIs and sales teams.

The key insight for product managers is that breakthrough adoption requires more than impressive technology—it demands clear problem identification and user education. Even revolutionary tools need entrepreneurs to recognize specific use cases, build appropriate interfaces, and actively sell the solution to users who may not realize they have the problem. Evans suggests that like previous technology waves, AI will likely follow the pattern of starting with obvious applications before gradually transforming how we work, but this transition requires deliberate product development and market education rather than expecting users to discover applications independently.