EXPOSED
Astronomy is largely screen work — pipeline code, photometry and spectral fitting, catalog cross-matching, literature review, and manuscript drafting — and AI already does credible versions of most of it, with ML classifiers outperforming humans on transient and morphology sorting. What resists is deciding which questions are worth years of telescope time, designing and commissioning instruments, defending survey systematics, and owning the claim when a result is extraordinary. There is no licensure and no signature requirement; the field's real constraint is that only ~2,000 US positions exist and they are gated by grants and faculty lines, not by automation.
Mixed — a routine tier and a judgment tier. Reduction pipelines, aperture photometry, redshift fitting and cross-matching against Gaia/SDSS catalogs are already scripted and increasingly ML-driven, but writing a competitive TAC or NASA ADAP proposal, choosing the observing strategy that survives moonlight and weather constraints, and diagnosing an unexpected instrumental systematic in the data still take a person who knows the hardware — that mix of automatable analysis over irreplaceable framing is why this lands at 10 rather than 5 or 15.
Some physical or field component. Most astronomers work from an office and a queue-scheduled interface, with the physical component limited to observing runs at Kitt Peak or Mauna Kea, cryostat and detector work for instrument builders, and occasional lab alignment — real but episodic and confined to controlled domes, which is why it sits at 5 rather than the 12+ of someone whose week is spent on a mountain.
No licence, no signature requirement. There is no state licence, no board certification, and no stamped deliverable in astronomy; the only formal gates are a PhD and peer review, and a retraction damages a reputation rather than triggering legal exposure — hence 1 rather than 0, since institutional review boards and export-control rules touch some instrument and defense-adjacent work.
Some relationship component. Papers are judged on data and method by anonymous referees, and citation counts do not care who you are, but proposal panels, long-running collaborations like LSST or ALMA working groups, and graduate advising all run on named reputation and personal standing — enough relational weight for 8, well short of a practice where clients hire you specifically.
Meaningful discretion. When you claim a planet in the habitable zone, a 5-sigma detection at the edge of your noise floor, or a cosmological parameter that disagrees with Planck, you personally decide whether the systematics are understood well enough to publish, and that call is unfalsifiable in the moment and career-defining afterward — 13 rather than higher only because no one's safety or money rides on the answer.
Has AI actually changed your work?