MIT study reveals AI’s hidden potential to automate over 11% of US jobs

Research from MIT introduces Project Iceberg, measuring AI’s current capacity to automate 11.7% of US jobs, exposing regional, sectoral, and socio-economic vulnerabilities and highlighting the need for targeted reskilling strategies.

In a detailed and data-driven exploration of artificial intelligence’s current impact on the U.S. labour market, researchers at the Massachusetts Institute of Technology have developed Project Iceberg, which reveals that AI technologies today could automate 11.7% of American jobs, equating to approximately $1.2 trillion in annual wages. Unlike speculative future forecasts, this assessment measures the existing capabilities of AI by simulating a “digital twin” of the U.S. workforce across 171 million workers, 923 occupations, and more than 32,000 skills. The study represents a significant step in understanding AI’s subtle and widespread influence, notably in cognitive and administrative tasks across sectors such as finance, healthcare, and professional services.

Central to Project Iceberg is the Iceberg Index, a novel metric that quantifies the overlap between AI technical capacities and human occupational skills, weighted by wage values. While visible AI applications, such as chatbots deployed in tech-driven roles, account for a modest 2.2% of wage exposure, the hidden potential beneath the surface spans much broader job functions. Administrative, logistical, and knowledge work areas are particularly susceptible, revealing a labor market exposure far beyond the well-recognized tech hubs. For instance, states like Delaware, South Dakota, North Carolina, and Utah exhibit higher hidden exposure levels than even California, a notable centre for technological adoption. This wide geographic dispersion offers policymakers specific insights into regional vulnerabilities, facilitating targeted interventions.

The methodology behind Project Iceberg integrates diverse data sources, including occupational skill sets from O*NET, employment figures from the Bureau of Labor Statistics, and census demographics. Collaboration with Oak Ridge National Laboratory has provided the computational power necessary for simulating these complex labor scenarios. This simulation-based approach marks an innovative shift from traditional expert surveys, allowing researchers to evaluate AI’s ability to perform discrete tasks rather than entire job replacements. Notably, areas such as finance and healthcare show that AI can effectively automate meaningful proportions of tasks, 15% in financial data analysis and compliance, and many administrative healthcare duties like scheduling or record-keeping.

Importantly, the project underscores that the Iceberg Index does not predict inevitable job loss but rather measures “exposure”, the extent to which AI can feasibly execute tasks within occupations. Researchers advise that this index should be used as a strategic tool for workforce planning, highlighting the need for investments in training, skill development, and infrastructure to support displaced workers and maintain economic stability. Regions and sectors identified as high exposure can thus be prioritised for reskilling programmes and technological adaptation strategies. For example, Tennessee has already referenced the Iceberg Index in shaping its AI Workforce Action Plan, with other states like Utah following suit.

Project Iceberg also draws attention to socio-economic disparities embedded in AI’s reach. Lower-wage workers, disproportionately women and minorities engaged in administrative roles, face higher automation risks, raising urgent calls for equity-focused training initiatives. Moreover, the research points to a paradox where even high-paying, information-intensive jobs exhibit significant AI exposure due to the nature of their tasks, such as legal research or market analysis. This complexity reflects the nuanced ways AI alters the labor landscape, benefitting certain workers and employers, especially those who rapidly adopt AI technologies. Firms utilising AI extensively tend to be larger, more productive, and grow faster, making a case for proactive engagement with the technology to avoid competitive disadvantages.

Despite these challenges, early evidence suggests that AI adoption has not yet caused substantial net employment losses, as gains in productivity and new job creation within innovative firms have offset declines in highly exposed roles. However, this balance may shift with the rapid expansion of generative AI tools, which emerged after much of the study’s data collection. The ongoing evolution of AI applications hints at a future where workforce transformations might accelerate, underscoring the importance of Project Iceberg’s framework to anticipate trends and intervene wisely.

Beyond individual occupations, the study reveals AI’s cascading economic effects on supply chains and entire sectors. Automating routine processing tasks can enable professionals to focus on higher-value activities, potentially enhancing productivity by 10-15% in some industries. Yet the simulations also caution about widening inequality if gains disproportionately accrue to highly skilled workers, while others, especially in slower-adopting regions or firms, may be left behind.

Industry leaders, including major tech firms, are beginning to align their strategies with these insights, investing not only in AI development but also in workforce training and ethical deployment initiatives. Additionally, there is growing recognition of the importance of infrastructure investments, such as broadband access and educational resources, to mitigate exposure by fostering more equitable reskilling opportunities.

Reflecting on historical patterns of technological disruption, researchers compare the current AI moment to past fears sparked by the microchip revolution of the 1970s, which ultimately did not cause the widespread displacement once anticipated. Project Iceberg thus positions itself as more than just an index; it is a forward-looking tool meant to demystify AI’s true capabilities and guide society towards balanced, inclusive growth in the face of profound change.

📌 Reference Map:

• ^[1] (WebProNews) – Paragraphs 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
• ^[2] (WebProNews) – Paragraphs 2, 5, 8
• ^[3] (PC Gamer) – Paragraphs 1, 2, 4, 11
• ^[4] (MIT Sloan) – Paragraphs 7, 8
• ^[5] (MIT Media Lab) – Paragraphs 2, 3, 5
• ^[6] (Fortune) – Paragraphs 1, 5
• ^[7] (NDTV) – Paragraph 1

Source: Noah Wire Services

Noah Fact Check Pro

The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.

Freshness check

Score:
8

Notes:
The narrative is recent, with the earliest known publication date being 3 days ago. The study has been reported by multiple reputable outlets, including Tom’s Hardware ([tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/mit-simulation-shows-ai-can-replace-11-7-percent-of-u-s-workers-worth-usd1-2-trillion-in-salaries-iceberg-index-tool-shows-jobs-are-affected-in-every-state-across-the-country?utm_source=openai)), TechRadar ([techradar.com](https://www.techradar.com/pro/mit-study-claims-ai-could-currently-replace-12-percent-of-total-us-jobs-market?utm_source=openai)), and PC Gamer ([pcgamer.com](https://www.pcgamer.com/software/ai/mits-new-iceberg-index-study-claims-ai-already-has-the-cognitive-and-administrative-capability-to-replace-11-7-percent-of-the-us-workforce/?utm_source=openai)). The presence of the same content across multiple sources suggests potential recycling. The report is based on a press release from MIT, which typically warrants a high freshness score. However, the widespread coverage may indicate a lack of original reporting.

Quotes check

Score:
7

Notes:
Direct quotes from the study are consistent across multiple reports, indicating potential reuse. For example, the description of the Iceberg Index as a “digital twin” of the U.S. labor market appears in several sources. Variations in wording are minimal, suggesting limited originality.

Source reliability

Score:
6

Notes:
The narrative originates from a press release by MIT, a reputable institution. However, the dissemination through WebProNews, a site with a mixed reputation, and the lack of direct access to the original study raise concerns about the reliability of the information presented.

Plausability check

Score:
9

Notes:
The claims align with existing research on AI’s impact on the workforce. The Iceberg Index methodology, which analyzes 151 million workers and over 32,000 skills, is detailed in the study. The findings are consistent with reports from other reputable outlets, such as Tom’s Hardware ([tomshardware.com](https://www.tomshardware.com/tech-industry/artificial-intelligence/mit-simulation-shows-ai-can-replace-11-7-percent-of-u-s-workers-worth-usd1-2-trillion-in-salaries-iceberg-index-tool-shows-jobs-are-affected-in-every-state-across-the-country?utm_source=openai)) and TechRadar ([techradar.com](https://www.techradar.com/pro/mit-study-claims-ai-could-currently-replace-12-percent-of-total-us-jobs-market?utm_source=openai)). The study’s focus on cognitive and administrative tasks across various sectors is plausible and supported by existing literature.

Overall assessment

Verdict (FAIL, OPEN, PASS): OPEN

Confidence (LOW, MEDIUM, HIGH): MEDIUM

Summary:
The narrative presents plausible claims supported by existing research. However, the reliance on a press release and the recycling of content across multiple sources raise concerns about originality and source reliability. The lack of direct access to the original study further complicates verification.