With all the AI tools out there for publishers — including those within our own Mirabel Technologies software — I’m inclined to focus on how professionals are using the technology to improve work and create efficiencies rather than seeing it as some looming behemoth that will ultimately replace workers.
Perhaps that’s being overly optimistic about how people will best wield AI’s power, especially in light of new MIT and Oak Ridge National Laboratory research that’s casting a harsher, more blunt light on standpoints such as mine.
As The Neuron summed up about those findings, AI technology could “technically replace 11.7% of the American workforce right now.”
“That’s not a prediction for 2030,” The Neuron’s Grant Harvey clarifies regarding the sobering statistics from the Project Iceberg research. “That’s what’s possible with current technology.”
By simulating the labor market — representing, specifically, 151 million workers “executing over 32,000 skills across 3,000 counties and interacting with thousands of AI tools” — Project Iceberg aims to analyze the overlap of AI and human capabilities “before adoption crystallizes” to better see the wage value of skills.
“AI adoption concentrated in computing and technology (2.2% of wage value, approximately $211 billion) represents only the tip of the iceberg,” the report says. “Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approximately $1.2 trillion).”
(Source: “The Iceberg Index”)
The analysis shows the “exposure difference” to be five times larger in fields that have yet to fully embrace AI automation as well as more largely distributed across the United States, where the “industrial heartland” like the Rust Belt states saw the biggest gaps, indicating that “workforce preparation strategies based on visible technology-sector signals may substantially undercount transformation potential.”
“[The Iceberg Index] reveals not only visible disruption in technology sectors but the larger transformation beneath the surface,” the report says. “By measuring exposure before adoption reshapes work, the Index enables states to prepare rather than react — turning AI into a navigable transition.”
That transition, as The Neuron helps spell out, requires self-reflection in a number of ways, from asking what are the fastest ways to retrain millions of professionals to evaluating what skills will actually matter down the line.
A checklist like that may feel like a wake-up call to states who perhaps felt insulated or shielded from such sudden changes, but for entities large or small. — and for standpoints optimistic or otherwise — it’s important to remember that rigorous, challenging work doesn’t have to be reluctant, impossible-to-swallow acceptance. With AI especially, there are rewards in the now in addition to the benefits of being ready for what might lie ahead.
“Now we need to decide if we’re building the infrastructure and retraining systems to handle what’s coming,” writes Harvey, “or just watching the ‘berg drift closer while we debate whether there’s room for Jack on the door with Rose.”
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