MIT Technology Review · Apr 2, 2018
MIT Technology Review reports competing estimates of automation’s effect on jobs
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MIT Technology Review reported on April 2, 2018, that an OECD report estimated 14 percent of jobs across the countries studied were highly automatable. The OECD defined these jobs as having over a 70 percent likelihood of being taken over by machines, and estimated that another 32 percent of global jobs would be transformed and require significant worker retraining. The article also reported that estimates differed: an earlier OECD assessment put 9 percent of US and worldwide jobs at high risk, while Frey and Osborne estimated 47 percent of American jobs were at high risk. MIT Technology Review cautioned that predictions like these are not known for their accuracy. What accounts for the wide differences between estimates, and how well do they predict which jobs will change?
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
MIT Technology Review · Article
Source details
Original source date: Apr 2, 2018 (2018-04-02T20:48:02Z).
Date basis: Date from the publisher’s own records, through its API.
Added to Escape Meridian: 2026-10-29T00:24:04.592Z. First observed: 2026-10-26T09:53:57.164Z. Retrieved: 2026-10-26T09:55:36.726Z.
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Limits
Text as retrieved on 2026-10-26; originally published 2018-04-02. The page is marked as edited after publication, so the retrieved text may not match the version first published. The retrieved text does not identify the article’s author. The estimates and judgments are reported as described in the article, not independently assessed here.
Open question
What accounts for the wide differences between estimates, and how well do they predict which jobs will change?
Source-stated
The OECD report estimated that 14 percent of jobs across the countries it analyzed were highly automatable.
14 percent of jobs are highly automatable, meaning they have over a 70 percent likelihood of being taken over by machines.
Source-stated
The OECD report estimated that 32 percent of global jobs would be transformed and require significant worker retraining.
An additional 32 percent of global jobs will be transformed and require significant worker retraining.
Source-stated
The article contrasts the OECD’s earlier estimate with Frey and Osborne’s estimate for American jobs.
In 2016, the OECD said only 9 percent of US and worldwide jobs face a “high degree of automobility.” That was a contradiction of one of the most widely cited reports on the subject, by Oxford researchers Carl Frey and Michael Osborne, who in 2013 said that 47 percent of American jobs were at high
Source-stated
MIT Technology Review cautioned that predictions about automation’s effect on jobs are not known for their accuracy.
predictions like these aren’t known for their accuracy.
Source and contributor context
MIT Technology Review · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Editorial provenance
Version 1. Publication ID: 47d51622-545d-b757-c113-2cc15bbdb8bc.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-29T00:22:58.985Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_screen.
Observation: d37b6169314a8810c097cf938a612e97eb3d857ebb319f8ddae14419d9ce0698. Source text hash: 6bd7e49df9f64fde8a0ffdf3ab549c1078711829311c65c823ff00be14901982.
Revision and source-check history
Version 1 · 2026-10-29T00:24:04.592Z
MIT Technology Review reports competing estimates of automation’s effect on jobs
MIT Technology Review reported on April 2, 2018, that an OECD report estimated 14 percent of jobs across the countries studied were highly automatable. The OECD defined these jobs as having over a 70 percent likelihood of being taken over by machines, and estimated that another 32 percent of global jobs would be transformed and require significant worker retraining. The article also reported that estimates differed: an earlier OECD assessment put 9 percent of US and worldwide jobs at high risk, while Frey and Osborne estimated 47 percent of American jobs were at high risk. MIT Technology Review cautioned that predictions like these are not known for their accuracy. What accounts for the wide differences between estimates, and how well do they predict which jobs will change?
Original source
MIT Technology Review · Article
Source details
Original source date: Apr 2, 2018 (2018-04-02T20:48:02Z).
Date basis: Date from the publisher’s own records, through its API.
Added to Escape Meridian: 2026-10-29T00:24:04.592Z. First observed: 2026-10-26T09:53:57.164Z. Retrieved: 2026-10-26T09:55:36.726Z.
Limits
Text as retrieved on 2026-10-26; originally published 2018-04-02. The page is marked as edited after publication, so the retrieved text may not match the version first published. The retrieved text does not identify the article’s author. The estimates and judgments are reported as described in the article, not independently assessed here.
Open question
What accounts for the wide differences between estimates, and how well do they predict which jobs will change?
Source-stated
The OECD report estimated that 14 percent of jobs across the countries it analyzed were highly automatable.
14 percent of jobs are highly automatable, meaning they have over a 70 percent likelihood of being taken over by machines.
Source-stated
The OECD report estimated that 32 percent of global jobs would be transformed and require significant worker retraining.
An additional 32 percent of global jobs will be transformed and require significant worker retraining.
Source-stated
The article contrasts the OECD’s earlier estimate with Frey and Osborne’s estimate for American jobs.
In 2016, the OECD said only 9 percent of US and worldwide jobs face a “high degree of automobility.” That was a contradiction of one of the most widely cited reports on the subject, by Oxford researchers Carl Frey and Michael Osborne, who in 2013 said that 47 percent of American jobs were at high
Source-stated
MIT Technology Review cautioned that predictions about automation’s effect on jobs are not known for their accuracy.
predictions like these aren’t known for their accuracy.
Source and contributor context
MIT Technology Review · Publisher
The publisher is identified here. No individual author has been inferred.
Source media
Media retrieval status: unchecked.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Editorial provenance
Version 1. Publication ID: 47d51622-545d-b757-c113-2cc15bbdb8bc.
Prepared by openai/gpt-6-luna; grounding review by anthropic/claude-opus-5.5.
Source check: passed by anthropic/claude-opus-5.5 on 2026-10-29T00:22:58.985Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: bridge_screen.
Observation: d37b6169314a8810c097cf938a612e97eb3d857ebb319f8ddae14419d9ce0698. Source text hash: 6bd7e49df9f64fde8a0ffdf3ab549c1078711829311c65c823ff00be14901982.
passed · 2026-10-29T00:22:58.985Z · anthropic/claude-opus-5.5
Publication: 47d51622-545d-b757-c113-2cc15bbdb8bc
attribution — pass: The reading attributes the report to MIT Technology Review and the estimates to the OECD and to Frey and Osborne, matching the source. The caution about accuracy is correctly attributed to the article. Paraphrasing the source's 'high degree of automobility' as 'high risk' is reasonable.
The Organization for Economic Cooperation and Development (OECD) has released a major report analyzing the impact of automation on jobs in 32 countries.
summary — pass: The title, summary and question accurately convey the article: the new OECD estimate, its definition of highly automatable, the 32 percent transformation figure, the conflicting earlier estimates and the publication's caveat. Key terms are explained in plain language. The question follows naturally from the source's point about divergent predictions.
predictions like these aren’t known for their accuracy. In fact, when we compiled all of the studies we could on the subject, we found there are about as many predictions as there are experts.
date — pass: April 2, 2018 matches the UTC day of originalPublished (2018-04-02T20:48:02Z). The evidence kind is publisher-page, so no archive disclosure is required. Forecasts are reported as attributed estimates. The reading does not use the later Most Popular sidebar. The page was edited in 2022, but every cited passage comes from the article body, which is plainly the original 2018 piece, and the limitations disclose the edit.
This story first appeared in our future of work newsletter, Clocking In.
support — pass: Each claim matches an exact source passage: the 14 percent highly automatable figure, the 32 percent transformation figure, the 2016 OECD versus 2013 Frey and Osborne contrast, and the accuracy caveat. The reading adds no unsupported benefits, costs or inferences.
An additional 32 percent of global jobs will be transformed and require significant worker retraining.
media — pass: No media was selected and no verified candidates were supplied, so none needs to be checked.
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