Thirty SEO professionals on AI: no panic, a verification tax, and a move up to strategy

The public story about AI and search is simple: the machines write the content, the specialists lose their jobs. I have worked in SEO since 2008 and I build AI systems for a living, so I wanted to know whether the people doing the work actually see it that way.

Between March and May 2026 I asked them. The result was my bachelor’s thesis at the Faculty of Political, Administrative and Communication Sciences (FSPAC), Babeș-Bolyai University, supervised by Dr. Dorin Spoaller: The Perception of Artificial Intelligence among SEO Professionals in Romania — a qualitative study of adaptation and the redefinition of professional identity.

The short version: the people who do this work are not panicking. They are paying a tax nobody talks about, and they are quietly moving their value up the ladder.

Who I asked

Thirty practitioners in total:

• 8 in-depth interviews, about 244 minutes of recorded conversation, with agency staff, freelancers, founders and an in-house specialist at a multinational;
• 21 survey responses, of which 20 form the statistical cohort (collected between 30 March and 21 May 2026);
• one long e-mail exchange with a practitioner who rejects AI on principle, and one extended chat with a veteran who uses AI heavily but rejects the hype.

This is a qualitative-first study. Thirty people are not the Romanian SEO industry, and the people most anxious about AI may simply not have answered. I read the numbers below as a picture of a community, not a census.

1. High adoption, no panic

Among the 20 survey respondents, 55% use AI every day and 90% use it at least weekly. Only one rejects it outright.

Asked to rate their optimism about AI’s effect on their career from 1 to 10, they gave a mean of 7.55 and a median of 8. Twelve of twenty (60%) scored 8 or higher; six gave a flat 10.

What I did not find was severe anxiety — not even among juniors, the group the literature expects to worry most. A respondent with two or three years in an agency put it plainly:

“No sense of worry. I think AI will keep being used as a tool, not a direct replacement. Human competence is still needed, and will be, for decision-making in SEO.”

There are honest reasons to be careful with that finding: self-selection, a feeling of control that comes from using the tools daily, benefits that are concrete while threats stay abstract, and timing — AI has not yet visibly cut SEO hiring in Romania. But the absence of fear was consistent across every source.

2. The verification tax

The most interesting emotion was not fear. It was fatigue with a specific shape.

AI promises speed. Part of the time it saves on generating output is then spent checking, correcting and re-prompting that same output. One respondent described it better than I could:

“The content lacks emotion, and the more technical tasks it often gets wrong. Sometimes you lose more time telling the AI it is wrong while it gives you excuses and essays.”

I call this the verification tax. It does not push people away from AI; it changes the relationship. The anxiety is no longer will this replace me but can I manage this competently. The experienced practitioners in the sample had already turned it into a method — one of them does the delegated task by hand once a month and compares:

“Once a month, set aside time to do yourself what you gave the AI in that prompt, by hand, and compare the output. If what you did is much worse, that should worry you.”

3. What is exposed, and what is not

I asked which parts of the job AI will hit hardest. Eighteen respondents answered that question:

Task	Seen as exposed	Perceived risk
Generic content and blog articles	14 of 18 (78%)	High
Repetitive on-page work (meta, alt text)	9 of 18 (50%)	High
Automated link outreach	7 of 18 (39%)	High
Entry-level tasks	5 of 18 (28%)	High
Keyword research and clustering	6 of 18 (33%)	Medium
Standard reporting	5 of 18 (28%)	Medium

And what they see as protected: strategy (named by 11 of 18), client relationships (9 of 18) and interpreting data in context (7 of 18).

The line runs exactly where labour economists would draw it: tasks with clear inputs, clear outputs and no need for judgement are exposed; tasks that combine messy information and end in a decision are not. One veteran added a useful correction — the business of mass-produced generic content was already declining around 2019, before generative AI. AI is accelerating that decline, not starting it.

4. Moving up, not out

The strongest pattern in the whole study was a shift in where practitioners locate their own value: from execution to strategy. An agency specialist with six to eight years of experience:

“Now that AI optimises many tasks, a good SEO specialist brings value through strategy and consultancy, not through the one-off tasks a junior can now do just as efficiently.”

A founder with the same seniority, more bluntly:

“AI can’t be a strategist. However well trained, it won’t personalise a business’s strategy the way a person does.”

In the thesis I describe this as ascending identity work. The textbook response to a threatening technology is defensive: people protect the identity they have. What I saw instead was people redefining the job one level up and treating the lower level as something the tools now handle. Professional identity came out intact — in several cases stronger.

5. Ten ways to adapt

From the ten richest voices I built a typology of ten adaptation archetypes. Seven of them do not fit the classic adopter categories of the diffusion-of-innovations model, which is why I think the typology is worth publishing on its own:

• the enthusiastic integrator, who builds AI into a method of their own;
• the niche specialist, who uses AI selectively and invests in depth in one vertical;
• the senior consultant-evaluator, who uses AI heavily but treats every output as raw material to be checked;
• the enterprise specialist, working under corporate rules on what may and may not be sent to a model;
• the AI-native entrepreneur, who builds a business with AI in the offer from day one;
• the anti-hype high adopter, who uses AI all day and rejects every claim of revolution;
• the reflective one, who deliberately keeps doing some work by hand to stay sharp;
• the all-round freelance veteran, who absorbs each new tool into a long practice;
• the pragmatist in training, for whom constant learning is the whole strategy;
• the traditional sceptic, who holds that AI is for people without experience.

The advice they would give colleagues ranges from “don’t be repelled by AI, but don’t get lost in its maze” to “stop the lies: AI brings almost nothing new to optimising a site for search.” Both came from people who use it every day.

What I take from it

Three things changed how I build.

Design for the verification tax. If the people using AI spend real time checking it, the system should make checking cheap. At Profilmedic, where I run AI and automation, the AI reception answers from a knowledge base generated from the site’s own text, and a person can take over any conversation. That is not caution for its own sake; it is what practitioners told me they need.

Juniors need the fundamentals before the delegation. Tasks that a junior used to learn on are exactly the ones AI now does. Someone who never did them by hand cannot tell when the output is wrong. Training should put understanding first and delegation second.

Sell the decision, not the execution. The practitioners who were calmest were the ones already charging for judgement rather than for hours of routine work.

The full thesis is in Romanian. If you are researching the same question — or you disagree with any of this — write to me at [email protected].

Participants are anonymised in this article. The quotes are translated from Romanian.