When AI Disruption Never Ends
AI has turned disruption into a permanent condition rather than a process with an endpoint, and leaders who optimize only for speed will burn out their organizations chasing a finish line that no longer exists.
Summary
Darden's Rory McDonald (extending his earlier disruption research with Clayton Christensen and Michael Raynor) and TCU's Will Drover argue AI disruption differs qualitatively from past technology waves because it never resolves into a stable new equilibrium, unlike the shift from video rental to streaming, which eventually hardened into a settled arrangement. They warn that the standard leadership response — move faster, push harder, treat adoption as an execution problem, as in the cautionary tale of Chegg's collapse — creates organizational fatigue when applied to a disruption with no finish line, and call for leaders to build organizational endurance alongside speed.
Editor's opinion
A genuinely new conceptual contribution rather than a repackaging of known ideas: "steady-state disruption" is carefully distinguished from VUCA and dynamic-capabilities vocabulary specifically because those assume turbulence eventually resolves, while this argues the calm never comes since AI's self-improving nature keeps shrinking the distance between waves rather than letting it settle. Well-evidenced (Stanford's AI Index, workload-intensification research, Deloitte's burnout data) and translated into three specific, well-sourced organizational practices -- Microsoft's AI Center of Excellence, Airtable's Kahneman-inspired fast/slow team split, Salesforce's flow-of-work learning -- each concrete enough to adapt directly rather than serving as illustrative flavor text.
Key concepts
Disruption as permanent condition vs. process with an endpoint · organizational fatigue