JobRiskAI

Physical Scientists, All Other

SOC 19-2099Life, Physical & Social ScienceData vintage 2026-07
Moderate exposure AI applicability score 0.170, higher than 60% of the 785 occupations measured · #33 most exposed of 47 in Life, Physical & Social Science

AI touches this job. It does not define it.

AI meaningfully touches parts of this work, yet the majority of its activities register little or no measured AI usage. That's balanced exposure: real efficiency gains are available without wholesale overlap.

Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.

What this job involves

All physical scientists not listed separately.

Where AI overlaps this work, activity by activity

The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.

Work activityShare of roleAI performance
Analyze environmental or geospatial data.26.7%Not observed 0.00
Maintain operational records.8.4%Not observed 0.00
Create visual designs or displays.7.9%Emerging 0.20
Direct scientific or technical activities.7.8%Not observed 0.00
Develop systems or practices to mitigate or resolve environmental problems.7.2%Not observed 0.00
Present research or technical information.7.1%High overlap 0.75
Compile records, documentation, or other data.6.4%Moderate 0.55
Design databases.6.3%Not observed 0.00
Develop research plans or methodologies.6.2%Not observed 0.00
Collect environmental or biological samples.5.8%Not observed 0.00
Train others on operational or work procedures.5.2%Not observed 0.00
Maintain current knowledge in area of expertise.5.1%High overlap 0.61

"Not observed" means the activity fell below the study's usage threshold, not that AI was tried and failed: how to read your score explains what a zero does and does not mean. AI performance = completion × scope × coverage (user-goal view), 0–1.

What drives this score: the biggest AI-overlapped activity in this role is present research or technical information; the activity with the lowest measured AI overlap is analyze environmental or geospatial data. The plan below is built around exactly that split.

Balanced exposure: take the gains, deepen the moat.

Next 30 days

  • Harvest the easy wins. Parts of this role, led by Present research or technical information, are already AI-assistable. Claim those hours back this month.
  • Map your own mix. Your split may differ from the occupation average. Run the six-question personalization above and read your range, not the headline.

Next 90 days

  • Invest saved time in the human core. The majority of this occupation's activities show little or no measured AI usage. Deepen exactly those, starting with Analyze environmental or geospatial data.
  • Stay current, lightly. A monthly hour trying new tools on your real tasks beats panic-learning later.

The longer game

  • Own a niche. Moderate exposure means AI will not define this occupation, but specialists who pair domain depth with tool fluency will out-earn generalists in it.
  • Watch the boundary activities. If the 'Emerging' rows in your table above turn 'High' in a future data update, revisit the plan. No countdown clocks, just recheck.

Where to put your learning time

AI reaches into parts of this work, above all the activity present research or technical information, while most of this occupation's measured activities sit outside it. That makes augmentation the realistic path: learn the tools well enough to direct them through the exposed slice, then reinvest the recovered hours in the lower-overlap half of your week, starting with the activity analyze environmental or geospatial data.

Partner terms checked in August 2026. Most Coursera courses can be audited free; payment covers graded work and certificates. edX works the same way through the audit track described above. Udemy sells its courses one at a time and keeps a free collection of its own, so look there before you spend anything. The partner links earn this site a commission if you buy or subscribe, at no extra cost to you, and they never change a score, a ranking or a plan here.

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Sources for this page:
  • AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
  • Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
  • Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.