EXPOSED
Roughly half the job is screen work AI now does well or better: literature synthesis, sequence and structure analysis, statistical modeling, figure generation, manuscript and grant drafting — and AlphaFold-class models erased much of the structure-prediction workload outright. What holds is the bench: designing and troubleshooting assays, purifying proteins, running cell culture and instrumentation, and deciding which result is real versus artifact. There is no license or signature requirement, so nothing regulatory protects the analytical tier, and lab automation plus AI compresses how many bodies a project needs.
Mixed — a routine tier and a judgment tier. At 10 the split is real: the dry-lab half — homology searches, docking runs, kinetics curve-fitting in Prism, RNA-seq pipelines, methods sections — is already model-assisted, while the wet half (cloning a construct that won't ligate, chasing a Western that keeps ghosting, tuning an HPLC gradient for a protein that aggregates at pH 7) still needs a person at the hood, which is why it sits mid-band rather than at 5 or at 15.
Some physical or field component. 12 reflects hands that matter but in a controlled room: BSL-2 hoods, -80 freezers, ultracentrifuges, mass spec sample prep, plate readers and cryostats are physical and dexterity-dependent, but it's all bench-top in a climate-controlled facility with fixed protocols, not the uncontrolled field conditions that push a score past 13.
No licence, no signature requirement. 3 is nearly floor because no state licenses a biochemist — no PE stamp, no board certification, no signature on a regulatory filing; a PhD and a PI's approval get you to the bench, and GLP or IRB requirements attach to the institution and the study director, not to your personal credential.
Some relationship component. 6 is the low end of relationship value: your reviewers are anonymous, your papers are judged on data not on you, and reagent vendors are fungible — the only durable relationships are with the program officer funding the grant and long-term collaborators who know your assay works, which is enough to lift you off the anonymous-output floor but not much further.
Meaningful discretion. 13 sits at the top of the discretion band because you decide whether an outlier is biology or a pipetting error, whether an off-target effect kills a candidate compound, and which mechanism the data actually supports — high-consequence calls, but ones that go through PI review, lab meeting, and peer review before anything irreversible happens, unlike a clinician's bedside decision.
Anesthesiologists SAFE
Has AI actually changed your work?