For a long time, the common reassurance was that AI would mostly affect tech jobs. Developers, data scientists, maybe a few analysts — everyone else felt relatively safe. But that narrative is starting to crack, and MIT’s Project Iceberg makes that very clear. What we were looking at before wasn’t the whole picture. It was just the tip.
MIT, together with Oak Ridge National Laboratory, ran an enormous simulation tracking 151 million U.S. workers across more than 32,000 skills and 923 occupations. The goal wasn’t to predict the future in 2035 or 2040 — it was to answer a much more uncomfortable question: what could AI automate right now, using technology that already exists?
The answer is sobering. According to Project Iceberg, AI can technically replace about 11.7% of the current U.S. workforce today. That translates to roughly $1.2 trillion in wages. This isn’t a theoretical risk or a distant timeline. From a purely technical standpoint, the capability is already here.
What makes this even more interesting is the discrepancy between what AI can do and what it’s actually doing. When MIT looked only at real-world deployment — where AI is currently used day to day — they found that just 2.2% of jobs appear affected. They call this the “Surface Index.” Above the surface, things seem manageable. Below it, there’s a vast layer of cognitive work that could be automated but hasn’t been fully touched yet.
That hidden layer includes roles many people still consider “safe”: finance, healthcare administration, operations, coordination, professional services. These jobs rely heavily on analysis, documentation, scheduling, and structured decision-making — exactly the kind of work modern AI systems are starting to handle well.
So what changed? The short answer is access.
Until recently, AI assistants lived outside our actual work environments. They could chat, summarize, and generate text, but they couldn’t see your calendar, your project tools, your internal databases, or your workflows. That barrier started to fall in late 2024 with the introduction of the Model Context Protocol, or MCP.
MCP allows AI models to plug directly into tools and data sources through standardized connections. That single shift unlocked something new: AI agents that don’t just advise, but act. As of March 2025, there are over 7,900 MCP servers live. AI can now check calendars, book rooms, send meeting invites, update project plans, reconcile data, and generate reports — autonomously.
Project Iceberg tracks all of this in real time, mapping these capabilities directly onto workforce skills. And this is where the data takes an unexpected turn.
The biggest vulnerability isn’t concentrated in Silicon Valley. It’s showing up strongly in Rust Belt states like Ohio, Michigan, and Tennessee. Not because factory floors are full of robots, but because the cognitive support roles around manufacturing — financial analysis, administrative coordination, compliance, planning — are highly automatable. These are jobs that look stable on the surface but sit squarely below the iceberg.
Experts aren’t dismissing these findings as alarmist. A separate study of 339 superforecasters and AI experts suggests that by 2030, about 18% of work hours will be AI-assisted. That lines up surprisingly well with MIT’s current 11.7% technical exposure, making Project Iceberg feel less speculative and more directionally accurate.
What really stands out is how this information is being used. Project Iceberg isn’t just a research report — it’s an early warning system. States are already using it to identify at-risk skills and invest in retraining programs before displacement happens. The focus is shifting from job titles to skill clusters: what parts of a role are automatable, and what parts still require human judgment, creativity, empathy, or relational work.
The bigger question now isn’t whether AI will change work. That part is already settled. The real question is whether systems, institutions, and governments are building the infrastructure fast enough to support an estimated 21 million potentially displaced workers. The iceberg is already there. What matters is whether we’re steering — or waiting to hit it.