EXPOSED
This catch-all covers technicians who prep samples, run and calibrate instruments, collect field data, and log results for scientists — the modal worker spends real hours with hands on equipment and specimens, which robotics and AI cannot yet take. But the screen half of the job — transcribing readings, spreadsheet calculations, chart generation, QC flagging, drafting summary reports — is already automatable, and lab automation platforms are eating routine pipetting and plate handling. There is no licensure and no client relationship to defend the role, so the surviving tier is the person who can troubleshoot a misbehaving instrument and vouch for whether a result is real.
Dipped in 2020, then grew past where it started.
Median pay $50,550 → $62,280 -1.4% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
+3.5% 83,200 → 86,200 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +3.5% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~10,600 openings a year on average, including replacing people who leave.
TesterScannerColoristObserverExcavatorDrone PilotLab AnalystMap PlotterHydrographerRadiographerRemote PilotSmoke TesterWater TesterDrone OperatorRemote MonitorImage ScientistImagery AnalystQuality AnalystQuality AuditorScientific AideDrone TechnicianLaser TechnicianWeather ObserverLaboratory Worker
The BLS uses Life, Physical, and Social Science Technicians, All Other for work that doesn't fit any named occupation, so it covers roles that have little in common with each other. Two consequences worth knowing before you read anything below:
Holding it up: embodiment . Weakest point: trust premium .
Mixed — a routine tier and a judgment tier At 9 the split is roughly even: mounting specimens, swapping columns on a chromatograph, re-seating a leaking fitting and hauling a sampling kit into the field are still hand work, but the run logs, unit conversions, calibration-curve math, control-chart plotting and the boilerplate 'results within spec' memo are the other half of your week and LIMS-plus-liquid-handler setups already do them, which is why this sits below the 14+ band held by trades that never touch a screen.
Hands-on in uncontrolled environments 13 reflects that a large share of these technicians work outside a controlled bench — surface-water and soil sampling, wildlife or vegetation plots, stack and ambient air monitoring, crop or greenhouse trials — where terrain, weather and uncooperative specimens defeat fixed automation; it is not higher because plenty in this catch-all spend their days at a fume hood or a bench instrument in a room with stable temperature and power.
No licence, no signature requirement 4 is the ceiling for a role with no state licence and no personal signature: your data goes out under the PI's, lab director's, or certifying officer's name, and even in NELAP/ISO 17025 shops the accreditation attaches to the laboratory and its technical manager, not to you, so employer-specific training records are the only credential standing between you and replacement.
Meaningful discretion 7 acknowledges genuine discretion inside written limits — deciding a calibration has drifted enough to rerun, rejecting a sample for broken chain-of-custody or hold-time exceedance, judging a peak from baseline noise — but SOPs, method manuals and a supervising scientist define the boundaries, and the consequential call on whether a result is reported or a study conclusion drawn is escalated, not yours.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (9/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (4/20) is whether the law requires a licensed human to sign. Trust premium (4/20) is whether buyers specifically pay for a person. Judgment and accountability (7/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 15 of this occupation's 37 points (41%).
Embodiment (13/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 51/100, still EXPOSED.
Task-mix shift: as automated liquid handlers and LIMS absorb routine pipetting, transcription and chart generation, the residual role concentrates in non-routine instrument troubleshooting, method transfer/validation, novel sample matrices, and chain-of-custody field collection. Recognisable signal: job postings for the same title shifting from 'data entry/sample prep' language to 'instrument qualification (IQ/OQ/PQ)', 'deviation investigation', 'method validation' — already visible in pharma QC and environmental lab ads.
Accreditation bodies tightening the named-analyst requirement: TNI/NELAP and ISO/IEC 17025 already require an identified, competency-demonstrated analyst to sign data packages; an explicit clause requiring a named human technician to attest to any AI- or software-generated QC flag, integration, or result recalculation (analogous to FDA 21 CFR Part 11 electronic-signature and ALCOA+ data-integrity enforcement in warning letters) would harden this. Watch for FDA data-integrity guidance naming AI-assisted chromatographic peak reintegration.
Formalizing the technician as the owner of out-of-specification and deviation calls: an OOS investigation SOP, or a state environmental lab rule, that makes the named analyst — not an automated flag — responsible for rejecting a run, invalidating a batch, or declaring a result reportable. Forensic-lab reform after crime-lab scandals (e.g. Massachusetts drug-lab cases) pushed exactly this kind of individual analyst accountability.
Growth in the field-sampling and hazardous-matrix segments (EPA/state PFAS and lead-service-line sampling programs, well and stormwater monitoring, wildlife and soil collection) where sampling must be done on-site under unpredictable conditions with documented custody. If the occupation's center of mass moves toward field collection rather than benchtop prep, physical irreducibility rises.
The limit. Trust premium has no realistic route: buyers of lab and field data are institutions purchasing accredited numbers, not a named human, and no client relationship exists to defend. Even with full liability and accountability gains this occupation stays capability-exposed on the screen half, so the realistic ceiling is mid-50s and only for the accredited/regulated subset — unaccredited academic and industrial research technicians gain little from any of these levers.
| New York-Newark-Jersey City, NY-NJ | 6,470 | $64,730 +4% |
| Houston-Pasadena-The Woodlands, TX | 2,520 | $48,680 -22% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 2,110 | $80,600 +29% |
| Dallas-Fort Worth-Arlington, TX | 2,100 | $48,960 -21% |
| San Diego-Chula Vista-Carlsbad, CA | 1,900 | $75,970 +22% |
| San Francisco-Oakland-Fremont, CA | 1,880 | $77,670 +25% |
| Atlanta-Sandy Springs-Roswell, GA | 1,570 | $87,180 +40% |
| Baltimore-Columbia-Towson, MD | 1,520 | $63,560 +2% |
| Oklahoma City, OK | 290 | $100,190 +61% |
| Atlanta-Sandy Springs-Roswell, GA | 1,570 | $87,180 +40% |
| Bakersfield-Delano, CA | 70 | $87,180 +40% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 37. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.