Analysis · 26 July 2026 · 7 min read

What the research actually agrees on about AI and jobs

We scored 423 occupations against 29 research sources. Only one clears the high risk threshold, and the studies disagree more than the headlines do.

We built this site to answer one question for individuals: how exposed is my job. Doing that meant reading 29 research sources on AI and work, extracting their occupation level estimates, and reconciling them into one number per role.

Reconciling them turned out to be the interesting part. Here is what the research base actually looks like once you line it up side by side, including the finding that surprised us most.

Almost nothing scores as high risk

Our bands are simple. Below 40 is lower risk, 40 to 69 is transitioning, 70 and above is high risk.

Across 423 occupations backed by at least two sources each, exactly one clears 70. The mean score is 26. The median is 22.

That is not the picture you get from headlines, and it is not the picture we expected when we started ingesting papers. The single highest scoring occupation in the whole index, Market Research Analysts and Marketing Specialists, sits at 70.

Read carefully, the aggregate research consensus is not saying that any specific occupation is close to fully automatable today. It is saying that a meaningful slice of tasks in a minority of occupations is now within reach of the tools, and that the slice is largest in office and text heavy work.

Two caveats keep this honest. Weighting is ours: we weight more recent and higher confidence estimates more heavily, and a different weighting scheme would move individual numbers. And these are measures of task overlap, not forecasts of employment. A score of 30 does not mean a job is 70 percent safe forever.

What the sources broadly agree on

Three things show up consistently once you compare estimates for the same occupation across different studies.

Direction. Sources disagree about magnitude far more than direction. Where one study calls an occupation more exposed than average, others usually agree it is above average, even when their numbers differ a lot. Administrative, clerical and text handling roles are consistently placed above the middle. Physical, hands on and unpredictable environment roles are consistently placed below it.

Task content beats industry. Every source that works at task level lands in a similar place: what matters is the mix of activities inside a role, not the sector it sits in. Our own data shows this starkly, with healthcare occupations appearing both near the top and near the bottom of the ranking.

Exposure is not displacement. Nearly every serious source in this base is careful to say it measures overlap between job tasks and model capability, not job losses. The gap between those two ideas is where most misleading coverage lives.

Where they disagree, and by how much

This is the part we think deserves more attention.

For every occupation with more than one source, we can measure the spread: the gap between the lowest and highest individual estimate before aggregation. Across our dataset, the median spread is 24 points on a 100 point scale. The mean is 25. And 151 of 423 occupations have a spread of 30 points or more.

The widest disagreements among well evidenced occupations:

Look at Computer Programmers. One source puts exposure at 25. Another puts it at 89. Both are published, both are recent, and they are describing the same occupation. Software Developers runs from 7 to 65.

If you have ever read two credible articles about AI and programming that seemed to describe different universes, this is why. They were not being careless. The underlying research genuinely disagrees this much.

There are legitimate reasons for the spread. Studies define occupations differently, measure different things (task overlap, wage weighted exposure, adoption rates), were written at different points in a fast moving field, and cover different countries and labour markets. A 2023 estimate and a 2026 estimate are not measuring the same technology.

What that means for reading any single number

Three practical conclusions, including one that is inconvenient for us.

Be suspicious of any single confident figure, including a single study that says your job is 85 percent automatable, and including a single number on a website. The honest version of this research is a range with disagreement inside it, which is exactly why every occupation page here lists the individual sources behind its score.

A consensus is more useful than a favourite study, but it is still a summary. Aggregating reduces the influence of any one outlier. It does not make the underlying uncertainty disappear, and averaging can flatten a genuine signal from the newest study.

The direction is more trustworthy than the magnitude. If your role is text and screen heavy, the sources agree you are more exposed than average, even though they will argue about whether that means 40 or 70. That directional signal is actionable. The precise number is not.

The sources

The 29 sources behind these scores range from peer reviewed academic work to central bank and consultancy analysis. By number of occupations covered, the largest contributors are:

Two sources cover most of the index, which is itself a limitation worth stating: broad coverage comes from a small number of large datasets, and depth of independent corroboration varies a lot by occupation. Occupations with two sources are held to a lower evidence standard than occupations with ten, and we publish the count on every page for that reason.

Occupations scored by only one source are excluded from the public index entirely. That is why the index covers 423 occupations rather than every role in our database.

You can see the full ranking, with the source list for each occupation, in the AI Job Risk Index, and read how the aggregation works in our methodology.

Figures come from the AI Job Risk Index and were current when this was published. Scores change as new research is ingested, so the index is always the live version. See how scoring works. Informational guidance based on published research, not professional career or financial advice.