EXPOSED
This is a residual BLS bucket — program coordinators, policy and research social workers, military and correctional social workers, community program staff — so the modal worker splits time between direct client contact and a heavy documentation load: intake forms, case notes, eligibility screening, resource referrals, grant reporting. The paperwork half is squarely in reach of current AI, which drafts case notes, summarizes records, and matches clients to benefit programs faster than a human. What holds is the in-person part: sitting with a person in crisis, home and facility visits, and being the named human accountable when a placement or safety call goes wrong.
Dipped in 2020, then grew past where it started.
Median pay $61,230 → $71,900 -6.1% 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
+3.9%
Percentage only. The projection counts a different population from the 62,930 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Growing, and only partly exposed
The BLS expects +3.9% more of these jobs by 2034, and at 57/100 the work is only partly exposed — some tasks are automatable, the core of the job is not. Nothing here is in tension.
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.
~7,000 openings a year on average, including replacing people who leave.
MonkNoviceReaderSisterOfficerDelegateSuperiorOrganizerPostulantSolicitorMissionarySupervisorCase WorkerHome WorkerCase ManagerFaith DoctorFaith HealerField WorkerGrand ScribeCase ReviewerDivine HealerGospel WorkerSocial WorkerYouth Teacher
The BLS uses Social Workers, 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: trust premium . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier An 11 reflects the split you already live: the eligibility screens, referral matching, case notes, and grant/outcome reporting that fill your afternoons are drafting-and-lookup work an LLM does adequately, while the crisis interview, the family meeting where three people want different things, and the facility walkthrough don't compress into a prompt — if you were a policy-research social worker with no caseload you'd score nearer 7, if you carried a full field caseload nearer 14.
Some physical or field component A 10 rather than a 4 because home visits, correctional units, barracks, shelters, and hospital bedsides are part of the standard week and you cannot assess a household's safety or a client's condition over the phone — but it isn't 15, because unlike a child protective investigator you are not routinely doing forcible removals or sustained physical intervention, and a real share of this bucket's hours are spent at a desk on program coordination.
Certification preferred, not legally required An 8 fits because LCSW/LMSW licensure is common but not universal across this residual bucket — many program coordinator, community outreach, and military/correctional social service posts hire at bachelor's level or accept unlicensed MSWs under supervision — so the credential that would make you personally answerable to a state board is a preference in many postings rather than the statutory gate it is for clinical social workers.
Meaningful discretion A 13 sits just under the top band because you make genuinely contestable calls — whether a home is safe enough, whether to escalate a suicide risk, which of eight equally needy families gets the last slot — but those calls usually route through a supervisor, a treatment team, or agency policy before they become final, so you own the assessment and the recommendation more often than you own the irreversible decision alone.
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 (11/20 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 (8/20) is whether the law requires a licensed human to sign. Trust premium (15/20) is whether buyers specifically pay for a person. Judgment and accountability (13/20) 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 36 of this occupation's 57 points (63%).
Embodiment (10/20) 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 71/100 — SAFE.
Task-mix shift: the occupation genuinely has two tiers. If note-writing, eligibility screening, referral matching and grant-report drafting are absorbed, the residual day is crisis contact, home/facility visits, mandated-reporter judgment and interagency negotiation — the harder tier becomes the whole job. Rises only if headcount is retained rather than cut with the paperwork
State licensure boards (e.g., ASWB-model rules already tightening telehealth and supervision requirements) or state Medicaid billing rules requiring that an LCSW personally sign and attest to any AI-drafted assessment, safety plan, or level-of-care determination — with the attestation naming the licensee as responsible for content, as CMS has done for physician attestation of scribe output
Child-welfare and placement litigation (post-Allegheny/Oregon algorithmic-screening controversies, and the 2024 DOJ inquiry into predictive screening tools) producing consent decrees or state rules that require a named human decision-maker for any removal, placement, or involuntary-hold recommendation and forbid citing a tool as the basis
Correctional and military settings adopting DoD/BOP policy barring automated risk-classification or suicide-risk determinations without a credentialed clinician of record; VA has already issued restrictions on AI in clinical decision paths
Grant and contract language — federal SAMHSA/HRSA notices of funding opportunity, or state block-grant scopes — specifying minimum in-person contact hours or caseload ratios per licensed worker, making the human hour the billable unit rather than the outcome
The limit. The trust premium here is paid by agencies and payers, not clients, so it tracks funding rules rather than consumer preference — and public-sector budget pressure pushes the other way. Embodiment has no upward route; visits are already counted.
| Minneapolis-St. Paul-Bloomington, MN-WI | 4,450 | $79,790 +11% |
| Phoenix-Mesa-Chandler, AZ | 2,040 | $62,150 -14% |
| Portland-Vancouver-Hillsboro, OR-WA | 1,810 | $67,820 -6% |
| Sacramento-Roseville-Folsom, CA | 1,620 | $36,160 -50% |
| New York-Newark-Jersey City, NY-NJ | 1,440 | $85,080 +18% |
| Los Angeles-Long Beach-Anaheim, CA | 1,410 | $68,630 -5% |
| Chicago-Naperville-Elgin, IL-IN | 1,190 | $71,740 +0% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 1,090 | $62,030 -14% |
| Reno, NV | 110 | $154,040 +114% |
| Stockton-Lodi, CA | 50 | $121,180 +69% |
| Las Vegas-Henderson-North Las Vegas, NV | 240 | $118,250 +64% |
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 57. 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.