EXPOSED
The modal biological technician runs defined protocols at a bench — sample prep, pipetting, cell culture, PCR, running instruments, logging results — plus a meaningful share of screen work (data tabulation, QC checks, drafting run summaries) that AI already handles. The hands-on portion is real protection, but it happens in a controlled lab, which is exactly where liquid handlers, automated colony pickers, and integrated plate-reader pipelines are already displacing manual steps. There's no license, no signature requirement, and little client relationship, so nothing outside the tasks themselves shields the role.
Most of this decline happened after 2021 — it is not the pandemic dip.
Median pay $45,860 → $57,510 +0.3% 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%
Percentage only. The projection counts a different population from the 69,620 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
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.
~9,100 openings a year on average, including replacing people who leave.
Seed AnalystBiotechnicianBabcock TesterBiologist AideBiological AideBiotechnologistGame TechnicianHerbarium WorkerFowl Blood TesterLaboratory WorkerDairy TechnologistFeed Research AideFiber TechnologistResearch AssistantResearch AssociateAquatics TechnicianPoultry InseminatorResearch SpecialistResearch TechnicianSpecimen TechnicianWildlife TechnicianBioprocess AssociateLaboratory AssistantBiological Technician
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Plating cells, maintaining cultures, feeding animals, calibrating a microscope and troubleshooting a clogged HPLC line still require hands at the bench, but the other half of the day — Excel tabulation, plate-reader output cleanup, ELISA curve fitting, drafting the section of the methods writeup the PI edits — is already being done by software, which is what pins this at 10 rather than the 15+ of a field ecologist.
Some physical or field component The work is physical but the environment is engineered to be predictable — a BSC hood, a fixed 96-well footprint, an incubator at 37°C — which is precisely the geometry Hamilton and Opentrons liquid handlers were built for; the 12 reflects the genuinely awkward residue (dissecting tissue, coaxing a temperamental centrifuge, animal handling), not the routine pipetting.
No licence, no signature requirement Nothing you produce carries your name in a legally meaningful way: the PI signs the grant report, the study director signs the GLP protocol, the CLIA lab director signs out clinical results, and your role in the audit trail is an initial in a notebook margin that the 3 accounts for.
Executes defined procedures on defined inputs You work from SOPs, IACUC-approved protocols and validated assay procedures where deviation requires a supervisor's sign-off, so the discretion is narrow — deciding a plate is contaminated and rerunning it, or flagging that a standard curve failed acceptance criteria — which is a 6, not the 10+ of someone choosing the experimental design.
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 (10/20 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 (3/20) 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 (6/20) 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 13 of this occupation's 35 points (37%).
Embodiment (12/20) 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 liquid handlers absorb pipetting and plate prep: the residual role becomes method troubleshooting, non-standard sample handling (primary tissue, difficult organisms, contamination forensics), instrument qualification and assay validation. Occupations in GLP/GMP labs already show this — technicians who own deviation investigation and out-of-specification (OOS) root-cause work under 21 CFR 211.192 retain a genuinely hard tier.
Formal ownership of data-integrity calls: FDA ALCOA+ and Part 11 enforcement, plus EU Annex 11, push toward a named human accountable for whether a run is valid, whether an outlier is excluded, and whether a deviation is reported. A warning-letter pattern penalizing labs that let software auto-adjudicate OOS results would convert routine logging into a signed judgment.
Named-analyst accountability regimes: CLIA high-complexity testing personnel requirements (42 CFR 493.1489) already require qualified individuals to perform and document tests, and forensic labs under ISO/IEC 17025 plus post-2009 NAS reform require a named analyst attestable and cross-examinable in court. If clinical/forensic labs are barred from letting automated pipelines produce unattested results — e.g. a CAP checklist item requiring a qualified human to attest to any AI-assisted result review — the shield moves from near-zero to real for that segment.
Growth in work that resists caging: field/environmental sampling, necropsy and animal facility handling under USDA/AWA oversight, BSL-3 containment work where robot decontamination and validation costs exceed human labor, and primary-tissue dissection. If technician headcount concentrates in these settings rather than plate-based screening, measured embodiment rises even with no change to any individual job.
The limit. Trust premium has no plausible route — the buyer of a PCR result is an internal PI or a sponsor who wants the number, not a human's hand on it, and no purchasing pattern anywhere pays extra for manual bench work. Also note the liability and judgment levers only reach the clinical/forensic/GMP-regulated slice of this SOC; academic and R&D technicians, likely the majority, sit outside every one of these regimes and stay unshielded.
| Boston-Cambridge-Newton, MA-NH | 7,320 | $61,610 +7% |
| New York-Newark-Jersey City, NY-NJ | 3,270 | $62,780 +9% |
| San Francisco-Oakland-Fremont, CA | 2,690 | $95,220 +66% |
| Seattle-Tacoma-Bellevue, WA | 2,020 | $63,770 +11% |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 1,950 | $60,630 +5% |
| Los Angeles-Long Beach-Anaheim, CA | 1,830 | $66,610 +16% |
| San Diego-Chula Vista-Carlsbad, CA | 1,770 | $67,060 +17% |
| Houston-Pasadena-The Woodlands, TX | 1,660 | $50,140 -13% |
| San Francisco-Oakland-Fremont, CA | 2,690 | $95,220 +66% |
| Trenton-Princeton, NJ | 180 | $77,170 +34% |
| Tyler, TX | 50 | $77,020 +34% |
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 35. 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.