Original research judgment
92Deciding which questions are worth years of work — before evidence exists to train on — is the profession's defining act, and generative systems built on the existing literature are structurally weakest exactly there.
Professor · The scholar paid to profess a subject in public — twenty-five centuries of teaching and discovery, from Plato's grove to the tenure track and the AI tutor.
Darker cells mean a higher score for this topic on that metric.
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At research universities, almost always: the doctorate is the profession's baseline credential in nearly every country. The main exceptions are professors of practice — distinguished judges, architects, artists or executives appointed to teach their craft — and some conservatoire and fine-arts traditions where a professional reputation substitutes for a doctorate. German-speaking systems historically demanded even more: a second, post-doctoral thesis called the Habilitation.
Typically ten to fifteen years after secondary school: a bachelor's degree of three to four years, a doctorate of four to seven, then two to six years of postdoctoral or fixed-term posts before a first permanent appointment. Tenure or an equivalent permanent status usually arrives in a candidate's mid-to-late thirties, and in German-speaking and some Asian systems often later still.
Tenure is an indefinite appointment that can be revoked only for serious cause, designed to protect scholars who publish unpopular findings. The American standard was set by the AAUP's 1940 Statement of Principles, typically after a six-year probation and an up-or-out review. Other systems protect professors differently: German professors are civil servants, while the UK abolished formal tenure in its 1988 Education Reform Act.
In the North American system they are career ranks: assistant professors are on probation for tenure, associate professors have usually just earned it, and full professors have passed a second promotion review. In the UK and much of the Commonwealth, the ladder runs lecturer, senior lecturer, reader, professor — so "professor" there means only the top rank, not any university teacher.
It varies more by country and rank than most people expect. US full professors averaged roughly $150,000–$170,000 in 2024, with Swiss chairs among the world's best paid at over CHF 200,000; UK professors typically earn £75,000–£100,000, and Indian government-university professors around ₹25–35 lakh. At the other pole, US adjuncts teaching per course commonly receive only $3,000–$4,000 per class.
It depends entirely on the institution. A research-university post is nominally split roughly 40 percent research, 40 percent teaching and 20 percent service, though grant deadlines routinely bend that. Teaching-focused institutions can assign four or more courses per term, leaving research to evenings. Faculty time-use surveys in several countries consistently report total working weeks of fifty hours or more across both kinds of job.
The professor's most visible product — the lecture — is precisely the part of the job machines copy best. A recorded course scales to millions, an AI tutor answers at 3 a.m. without office hours, and automated graders already mark code and short answers at many universities. If the job were only content delivery, it would be in serious trouble.
But universities do not pay professors mainly to deliver content; they pay them to produce knowledge, to certify competence, and to reproduce the profession by training researchers. Those functions rest on judgment about what is true and what matters — exactly where current AI, which synthesizes the existing literature rather than extending it, remains a tool in the hands of the person accountable for the answer.
Roughly the content-delivery and first-pass-assessment layer of the job is automatable now or soon: recorded and AI-assisted instruction, routine grading, literature triage, administrative reporting. The core — choosing research questions, supervising apprentice researchers, refereeing what enters the canon, and answering for the certification of students — requires accountable expert judgment that no current system can carry. The realistic near-term future is fewer purely teaching roles and more machine leverage per professor, not the disappearance of the chair.
Scored from the tasks, not the job title. Lower is safer.
Jobs AI cannot take →Deciding which questions are worth years of work — before evidence exists to train on — is the profession's defining act, and generative systems built on the existing literature are structurally weakest exactly there.
Forming a researcher takes years of individually calibrated challenge, encouragement and honest correction inside a relationship of trust; it is apprenticeship, and apprenticeship has never been deliverable by tool.
Degrees are trusted because named, credentialed humans answer for what they attest. Delegating that judgment wholesale to systems that can be gamed would spend the very trust universities sell.
Reading a room, pressing the student who coasts, drawing out the one who hides — the improvised, social craft of live teaching resists automation long after content delivery falls.
Editing journals, refereeing disputes, setting curricula and standards — the profession governs itself, and self-governance requires members whose judgment peers accept as legitimate.
Recorded courses, MOOCs and AI tutors already carry introductory instruction at scale; once content is standardized, the marginal lecture is the most replaceable hour of a professor's week.
AI systems now draft literature reviews, screen thousands of abstracts and flag relevant citations in minutes — work that consumed weeks of every research project and most of every doctoral student's first year.
Autograders have marked programming assignments for a decade; essay-scoring and feedback systems increasingly handle routine assessment, leaving instructors the contested and high-stakes cases.
Georgia Tech's "Jill Watson" answered students' routine forum questions in 2016 without most noticing she was software; scheduling, syllabus queries and deadline logistics are exactly this kind of automatable traffic.
With content delivery cheap — video, adaptive tutors, AI explainers — class time shifts toward what only presence provides: discussion, problem-solving, peer instruction. The lecture survives, but as one tool among several rather than the job's definition.
Following Georgia Tech's 2016 Jill Watson experiment, universities now deploy AI assistants for routine questions, drafting feedback and tutoring — while the professor's new task is quality control: auditing the machine's answers and teaching students to interrogate rather than trust generated text.
Online mega-providers already split the professor's bundle — course design, delivery, assessment, mentoring — across specialists and software. The research professorship persists at one end while a growing family of teaching, design and verification roles emerges from the pieces.
DeepMind's AlphaFold effectively solved protein-structure prediction in 2020, collapsing a decades-old research program and redirecting a whole field's professors overnight. As AI absorbs more routine analysis, the scarce faculty skill becomes posing the next question — and verifying machine-generated answers.
The specialist who turns subject expertise into structured online and blended courses — one of the fastest-growing roles in education, staffed heavily by PhDs who chose design over the tenure lottery.
Building and tuning the adaptive platforms, AI tutors and analytics pipelines universities increasingly teach through; a hybrid of pedagogy and software engineering that barely existed in 2010.
Academically trained explainers now reach audiences no lecture hall ever held — mathematics and science channels run by ex-academics count subscribers in the tens of millions — turning the professor's explaining craft into a standalone career.
Corporate AI and biotech labs recruit professors and their doctoral students at multiples of academic pay — Uber hired some forty researchers from Carnegie Mellon's robotics center in one 2015 raid — making industry research the profession's largest adjacent employer.
Three reversible lenses: augment the work, replace a slice, or open a niche. Teaching marks — not forecasts.
Keep the role; AI speeds drafts, triage, or research while judgement and accountability stay human.
A narrow task stack may compress first (templates, first drafts, routine scoring) while adjacent craft grows.
Oversight, integration, and domain QA roles can appear where AI output must be trusted in regulated settings.
The tasks most exposed are those that can be fully specified in advance: the standardized lecture, the routine grade, the literature summary. The tasks most protected are the ones that cannot — the unformulated research question, the struggling doctoral student, the judgment call about what a field should count as true.
The likelier disruption is economic rather than technological: automation gives universities new reasons to divide the professor's bundle into cheaper parts, accelerating a shift toward teaching-only and course-facilitator contracts that began decades before AI. The research professorship endures; the question is how many of the surrounding roles keep the title.
Every previous technology aimed at this profession — print, broadcast, the MOOC — was announced as its end and absorbed as its tool. AI is the most capable yet, and the first to reach into assessment and synthesis. But as long as societies want new knowledge produced and credentials worth trusting, someone accountable must profess — and be answerable for it.
Closest neighbours on the six-score profile — not the same field only.
From Babylonian scribes to Fields medalists and AI-assisted proof: the profession that turns hard questions into permanent certainty, one theorem at a time.
AI-resistant 70 🔭The scientist who measures the universe, from Babylonian clay tablets to space telescopes, still deciding which flicker in the data is a discovery.
AI-resistant 70 🎬Turns a screenplay into a finished film by deciding every shot, performance and cut, then persuades a producer, a studio and an audience the vision was worth the budget.
AI-resistant 73 ⚛️Derives and tests the mathematical laws governing matter, energy, space and time, from a lone chalkboard to a 3,000-author particle-collider paper.
AI-resistant 65 ⚖️The person societies trust to decide: from Hammurabi's stele to AI-scored bail hearings, the hardest disputes still end in front of one accountable human.
AI-resistant 90 🧬The scientist who studies life itself, from Linnaeus naming species by hand to editing genomes with CRISPR, still testing every idea against a living organism.
AI-resistant 64The world's largest profession: from Sumerian tablet houses to AI-era classrooms, one adult tasked with turning thirty strangers into a room that learns.
AI-resistant 78 📜The trained interrogator of the past — from Herodotus and Sima Qian to the digital archive, turning fragile documents into checkable accounts of what happened.
AI-resistant 70 🧩The scientist of mind and behavior, from Wundt's 1879 Leipzig laboratory to the therapy room — trained to hear what a person cannot yet say themselves.
AI-resistant 82 🌐The profession that carries meaning between languages — from the Septuagint and Jerome's Vulgate to Toledo, Nuremberg's booths and the machine-translation age.
AI-resistant 28 📖The keeper and guide of recorded knowledge, from Nineveh's clay tablets to the e-lending fight — organizing what humanity knows so that anyone can find it.
AI-resistant 55