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Majority of Jobs Still Beyond AI Replacement: MIT Report
Majority of Jobs Still Beyond AI Replacement: MIT Report
Job Security, For Now

Majority of Jobs Still Beyond AI Replacement: MIT Report

Only 23% of jobs are replaceable in a cost-effective way

Published Updated

Key takeaways

  • MIT study reveals only 23% of jobs are cost-effectively replaceable by AI.
  • Only 3% of AI-exposed tasks are currently automatable, with potential growth to 40% by 2030.
  • Increased adoption of AI has led to heightened anxiety over job displacement.
  • Research analyzed 1,000 visually-assisted tasks across 800 occupations in the US.
  • Cost and accuracy improvements are critical for future AI job displacement.
5 min

In a recent study, the Massachusetts Institute of Technology (MIT) found that AI can still not replace most jobs cost-efficiently.

Over the last year, the discourse on AI eating away human jobs has gained widespread attention. This is because the adoption of AI has increased significantly after OpenAI released ChatGPT.

“Machines will steal our jobs’ is a sentiment frequently expressed during times of rapid technological change. Such anxiety has re-emerged with the creation of large language models,” the researchers said in the report titled ‘Beyond AI Exposure’.

In one of the most comprehensive examinations assessing the feasibility of AI replacing human labour, the researchers at MIT conducted a detailed analysis of the cost-effectiveness of automating diverse tasks in the US.

The focus was particularly on occupations utilising computer vision, a subset of AI enabling computers to derive information from images, videos and other inputs.

In the report, the researchers also said, “We find that at today’s costs, US businesses would choose not to automate most vision tasks that have ‘AI Exposure,’ and that only 23% of worker wages being paid for vision tasks would be attractive to automate.”

They further revealed that only 3% of these tasks can be automated in a cost-effective manner. However, the researchers indicated that this figure could potentially increase to 40% by 2030, assuming a decline in data costs and an enhancement in accuracy.

The research employed online surveys to gather data on approximately 1,000 visually-assisted tasks spanning 800 occupations.

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