aidisruption.fyi tracks public labor-market data and published AI-exposure research to provide a real-time, evidence-based view of how AI is affecting the US workforce. I have taken pains to not avoid commentary, bias or prediction. Instead, my hope is to provide an accurate and useful monitoring tool, built to show where and how quickly labor-market patterns are changing, which published research suggests those changes may be AI-related, and which careers/metropolitan areas are being most impacted.
Many different sites and better funded research exists, much of it confusing and contradictory: (case in point. Better minds are on this problem, but I figured that creating a meta-analysis that draws upon the best practices of measuring economic impact but tracked over time would be more useful to the general population and policy makers than point-in-time snapshots using the same methodology.
In order to accomplish this goal, the site aggregates a range of public data sources into three composite indices: labor-market stress, structural AI exposure, and community adaptive capacity. I also built an excellent research library that uses a local AI that is tuned to detect sentiment to read and determine the latest released AI papers and determine if the paper paints a rosy, neutral or negative picture of AI's impact on the United States' economy.
One caveat is worth stating plainly: nothing on this site establishes causation. No single layoff, hiring change, or shift in unemployment can be attributed to AI alone; the economy has too many simultaneous drivers for that kind of clean attribution. What the site's indices can show is that certain patterns are consistent with AI-driven displacement — not proof of it. That distinction is treated carefully throughout the methodology and in how the scores themselves are presented.
I am Dan Rohan, and I built and maintain this site independently. I do it on my own time and the work here is completely unaffiliated with my employer, IBM.
I spent the first half of my career as a network and systems engineer, then transitioned into product management about a decade ago, working on infrastructure and security tools. I hold a Master's degree in Public Policy from Carnegie Mellon's Heinz College.
Professionally, I lead a product team building secure access tools for both human users and AI agents. My work puts me close to how AI agents are actually being deployed inside large organizations, and it has shaped how I think about this project.
While I attempt to present unbiased data, I think it's important to admit my own real biases instead of pretending that they don't exist: I am both fascinated and terrified by AI. I use AI every single day of my life and am amazed by the quality and speed that the technology is advancing. And at the same time, I lay awake at night thinking about the future of my career and how I will provide for my family in the coming future if my industry and career were to be impacted. During the daytime, I hear people close to me question whether their jobs, or their children's future jobs, will exist in five or ten years. I began looking into the data and quickly discovered that building a data-based opinion was impossible by reading studies alone.
This site is my attempt to answer that question to the best of my ability. I want to ground my own opinion in real data. And I want to base that opinion on facts describing where labor-market stress is emerging, which industries and regions appear structurally exposed to AI, and which communities have greater capacity to adapt.