Measuring AI impact like it’s 1995

Reflecting on the early days of the internet, the text draws parallels between the experimental approach to web development in the 1990s and the current landscape of artificial intelligence (AI). Just as the early web required a learning-first mindset rather than a focus on immediate ROI, the text argues that organizations today should adopt a similar approach with AI. The pressure to quickly showcase AI's value through traditional metrics is misguided, as these metrics often fail to capture AI's true potential, which lies in enabling new forms of collaboration and discovery. Instead of optimizing existing processes, companies should focus on how AI can help them learn and adapt, emphasizing the importance of experimentation and understanding customer needs. The text warns that while current economic conditions and VC subsidies make AI experimentation feasible, this window is closing, and only those who have invested in learning will thrive. It advocates for creating environments that encourage safe experimentation and knowledge sharing, focusing on building organizational capacity to learn and adapt rather than on traditional productivity metrics.

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