If you believe search marketing enjoys some kind of protected status because the public cares who\u2014or what\u2014does the work, Harvard just published a number that demolishes that assumption. Assistant Professor James Riley asked the American public to score how morally objectionable it would be to hand each of 940 different occupations to a machine, using a scale from 1 to 7. Search marketing strategists scored 2.31. Of the 10 occupations Harvard charted, only file clerks scored lower. Clergy scored 5.91. Childcare workers scored 5.86. Whatever is currently standing between white-collar jobs and full automation, it isn\u2019t the public\u2019s conscience. The real shield is a competence gap, and that gap is closing faster than most in the industry want to acknowledge.<\/p>\n
I should reveal upfront that I love the old joke about the grocery store located between Harvard and MIT, where a student wheels a cart with 15 items into the 10-items-or-less lane. The cashier looks at the sign, looks at the student, and sighs: \u201cYou must either go to Harvard and can\u2019t count, or go to MIT and can\u2019t read.\u201d It\u2019s a joke about elite blind spots. It\u2019s also a pretty good description of what happens when SEOs read AI research\u2014and I nearly did it to myself with this very report. The numbers from Cambridge are unambiguous, but the interpretation requires a careful reading of what was actually measured.<\/p>\n
Some context first. \u201cAI in 2026: From Adoption to Agentic\u201d is a February 2026<\/a> roundup from HBS Working Knowledge that bundles five previously published pieces. The two that ran experiments are where the real news lives: \u201cPeople Are Mostly OK With AI Taking Over Many Jobs\u2014Up to a Point,\u201d which cites research from James Riley\u2019s October 2025 study, and \u201cWho Should Approve Bank Loans: People or Algorithms?\u201d which references Assistant Professor Elisabeth Paulson\u2019s research co-authored with Kirk Bansak from 2024.<\/p>\n Riley\u2019s occupation-scoring survey covered 940 jobs and 2,357 respondents. The 2.31 score for search marketing is only half of what he found. Based on AI\u2019s current capabilities, the public supports fully automating roughly 30% of the occupations he tested. When Riley\u2019s survey instead asked people to imagine a more advanced AI that outperforms humans at a lower cost, support for automation nearly doubled to 58%. A moral floor exists, but it\u2019s narrow. Only about 12% of occupations\u2014among them clergy, childcare workers, and athletes\u2014drew strong moral resistance regardless of how well AI could do the job. Another 42% left people ambivalent. Riley\u2019s own conclusion is that resistance to automation is mostly a story about whether the technology can do the job yet, not about principle. Search marketing already sits near the bottom of that moral floor. The thing standing between your job and a much higher automation number isn\u2019t sentiment. It\u2019s whether the tools are good enough\u2014and that\u2019s a much shakier position to be defending.<\/p>\n What is the public\u2019s moral objection to AI taking search marketing jobs?<\/strong> According to Harvard data, the public finds it only slightly objectionable\u2014a 2.31 on a 7-point scale. That places search marketing among the least morally protected occupations, directly behind file clerks. When respondents considered a hypothetical AI that outperforms humans, support for automating search marketing nearly doubled. The implication is clear: moral resistance is negligible and largely conditional on AI\u2019s perceived competence.<\/p>\n Paulson\u2019s research is a different experiment and doesn\u2019t have an \u201cadvanced algorithm\u201d condition the way Riley\u2019s does, so it\u2019s worth being precise about what it actually found. She and coauthor Kirk Bansak, an assistant professor at UC Berkeley<\/a>, ran a conjoint experiment with 9,000 participants, asking them to choose a human or an algorithm to approve a loan or decide on a defendant\u2019s pretrial release. On average, and even controlling for performance, people leaned human\u2014by 4.3 percentage points on the loan and 7.6 points on pretrial release. Fairness, meaning equal treatment across racial groups, turned out to be the least important factor in how anyone judged either kind of decision-maker.<\/p>\n The more interesting number is buried in a chart on page 13 of the report, and it\u2019s a belief split rather than a flat preference. Among respondents who already believed algorithms outperformed humans at these tasks, 56% chose the algorithm for pretrial release and 54% chose it for the loan. Among respondents who believed humans were better, 63% and 59% went with the human. Paulson said that if you can prove real accuracy gains without other metrics slipping, \u201cthat\u2019s probably sufficient.\u201d What her data shows is that the human preference isn\u2019t a fixed moral stance at all. It\u2019s downstream of a belief about who\u2019s currently better at the job\u2014which lines up almost exactly with Riley\u2019s technical-feasibility argument, even though the two studies were built to test different things.<\/p>\n Raffaella Sadun, Karim Lakhani, and their coauthors tracked 791 product developers at Procter & Gamble, some working alone, some in teams, some with an internal GPT-4 tool and some without. Ideas ranking in the top 10% of quality were three times more likely to come from AI-assisted teams than from unassisted individuals working without it. Employees using AI also reported higher enthusiasm and energy for the work, and less anxiety and frustration, than employees who worked alone without it. That\u2019s the exact kind of idea generation and content work search marketers get paid for, and the competence gap Riley\u2019s data says is the only thing currently protecting the job is closing on this front in real time.<\/p>\n Tsedal Neeley and Expedia Group\u2019s Ritcha Ranjan\u2019s technical note describes where that competence is headed next. Their vision has agentic AI acting as a chief of staff, a competitive intelligence analyst, and an executive coach, running with minimal human oversight once it\u2019s set up. Neeley\u2019s advice to leaders adopting it is to start with what she calls the \u201cno-joy\u201d work\u2014the repetitive tasks nobody wants\u2014before handing over anything higher stakes. That\u2019s a sensible on-ramp. It\u2019s also a description of exactly how automation tends to creep upward once the technology proves itself on the boring stuff first.<\/p>\nThe Belief Gap Behind Both Papers<\/h2>\n
The Competence Gap Is Closing Fast<\/h2>\n
Why This Matters For SEO: The Moral Cover Is a Mirage<\/h2>\n