Not “I checked the data and this is my thinking.” Not “I compared a few options and this one makes more sense for us.” Just: GPT suggested it.

I want to be honest from the start: I’m not against AI. I’ve been working in SEO for many years, mostly with a technical and research-heavy approach and I use AI tools every single day. They save me time, they improve my workflows, they give me ideas, and sometimes they remind me of things I would forget. In 2026, not using these tools also has a real cost, sometimes a big one.

But that sentence stayed in my mind. Because it was not the first time I heard something like it, and what bothered me was not the tool. It was the missing step between the tool’s answer and the final decision. The thinking step.

Around the same time, I came across a concept from psychology that gave a name to this exact feeling: cognitive surrender. I’m not a psychologist; I’m an SEO. But when I read about it, I felt it was describing our industry right now. So I did my own research on it, and I wrote this article for SEO people like me. Honestly, I also wrote it for myself.

What “cognitive surrender” means, in simple words

We have always used tools to do part of our thinking. A calculator for math, GPS for directions, a search engine for facts. Researchers call this cognitive offloading, and it’s healthy. You hand over the doing of a task, but you keep the goal, the question, and the final judgment. The decision stays with you.

Cognitive surrender is something else. The term comes from research by Steven Shaw and Gideon Nave at Wharton, and their idea is simple and a little scary: surrender happens when the tool stops being the worker and quietly becomes the decision-maker, and you adopt its decision as your own without noticing that the handover happened. Because you never built your own answer first, there is nothing in your head to compare the AI’s answer against. The machine’s output simply becomes your output.

One line from all this stuck with me:

Offloading is delegating the execution. Surrender is delegating the judgment.

What the research actually found

I’ll keep this short, because I’m an SEO writing for SEOs, not a scientist. But three studies, all summarized well in Addy Osmani’s piece on cognitive surrender, are worth two minutes of your time.

First, the Wharton experiments: more than 1,300 people, over 9,000 trials. When the AI gave correct answers, people’s accuracy jumped by about 25 points. Great. But when the AI was secretly set up to give faulty reasoning, people accepted the wrong answer around 73% of the time. And here is the worst part: people’s confidence went up even when the answer was a hallucination. The researchers call it “borrowed confidence”, the model sounds sure, so you feel sure. Time pressure and even money rewards did not break the pattern.

Second, an MIT Media Lab brain study tracked people writing essays over several months, with EEG. The heavy LLM users showed the weakest brain engagement, felt less ownership of their work, and often could not even quote their own writing afterwards. The researchers called the lasting effect “cognitive debt.” When the tool was taken away, the under-engagement stayed.

Third, a study by Anthropic with 52 engineers learning an unfamiliar programming library. With AI help they worked at about the same speed, but scored 17% lower on understanding, and the biggest drop was in debugging, which is exactly the skill you need to catch AI mistakes.

I read that last one and thought: replace “debugging code” with “diagnosing a traffic drop,” and this is us.

My simple rule: split the work by the kind of thinking it needs

Here is how I look at it in daily SEO work, and it’s the rule I push in my team.

Some tasks are simple and mechanical. For example: compare the last 7 days with the 7 days before. That’s basic math, a few numbers, percentages, the difference between them. If someone does this by hand, or exports the data to Google Sheets, or builds a small Looker Studio view just for this, while an AI tool is sitting right there and can do it in seconds, to me, that’s not smart work. Delegate it. No discussion. The same goes for cleaning a keyword list, structuring messy notes, summarizing a long document, drafting a quick regex or a formula. These tasks eat your time and teach you almost nothing new after the hundredth repetition.

And some tasks need judgment. Diagnosing why traffic dropped. Deciding a content or internal linking strategy. Choosing what to do after a core update. Prioritizing a roadmap. Approving an action that touches indexed pages. For this kind of work, AI can absolutely help, it gives ideas, it challenges you, it reminds you of factors you forgot. But its suggestions are a starting point, not an answer. You have to look at them carefully, compare them with your own data and experience, and then decide.

When I see someone copy an AI output into a report without really reading it, or build an action plan because “GPT suggested it,” this second category is where it worries me. And to be clear, I have seen this passive acceptance at every level, junior, senior and higher levels.

Why am I so strict about this? Two reasons.

Reason one: the loop that slowly turns off your thinking

You ask, the tool answers, the answer is good, you trust it a little more, and next time you check a little less. Repeat this for months. Each round feels like a win, and honestly, most rounds are a win. But there is a side effect that builds quietly: you think less, you use your own head less, and a kind of dependency forms.

The problem is that situations are not always the same. Maybe ten requests in a row go well. Then request number eleven arrives, a new situation, a context the tool has never seen, an input from you that is incomplete because you didn’t have the full picture either. The output is wrong. But now two things work against you. First, your mind says “it was right ten times, it’s probably right now.” Second, and this is the dangerous part, your critical and analytical muscles are weaker than before, because you haven’t been training them. The exact moment you need your judgment the most is the moment you have the least of it.

And no, “I’ll just review the AI’s output” does not save you, as much as we would like it to. Research on human-in-the-loop review shows that reviewers end up approving more than 94% of AI outputs, not because the outputs are all good, but because judging an already-written, polished, confident answer is much harder for the brain than forming your own answer from scratch. The AI has already framed the problem and chosen what matters before you even start reading. The Whartxon findings point the same way: under volume and time pressure, our brains pattern-match and approve.

This is exactly what I see when someone sends me a report they clearly didn’t read deeply. They were not lazy. Their brain did what brains do with polished, confident text: it surrendered.

Reason two: SEO is not linear, and wrong calls here are expensive

This is the part where my SEO experience makes me extra careful, more than any psychology paper.

SEO is genuinely complex. There is no clean “do X, get Y” in this field. Hundreds, probably thousands, of factors interact, things keep changing over time, and our data sources never get smaller, they only grow: Search Console, analytics, log files, crawl data, SERP features, competitors, and now visibility inside LLM answers and AI search results on top of everything. Without precise inputs, you simply cannot expect good outputs from any model.

And here is the uncomfortable truth: in real projects, we often don’t even have enough context to give the tool. The business history, the half-finished migration from two years ago, the reason a “useless-looking” page exists (a partner deal, a legal need, a seasonal pattern), the past experiments that failed, turning all of that into a prompt is not easy, and sometimes it is simply not possible. But we still ask, with incomplete context, and we still get a confident answer back. That gap between a confident output and an incomplete input is where business damage is born.

The worst part is the timing. A wrong SEO action usually doesn’t hurt you today. It shows up after three months, six months, a year. By then, debugging is painful, was it the migration? the content changes? the latest core update? that AI-suggested “cleanup” from last spring? and recovery is expensive. Sometimes you cannot even predict whether recovery is possible, let alone when. For businesses that depend on organic traffic, and now also on traffic and citations from LLM results, this is not a small operational risk. It can be a survival question. Engineering teams already have a name for this kind of delayed, invisible problem, comprehension debt: everything looks fast and green on the dashboard, until the day a real problem appears and nobody understands the system anymore. SEO has its own version of it.

What this looks like in real SEO work

Let me make this concrete, with situations I have seen myself or that the industry has documented well.

Scaled AI content, also known as “AI slop.” The clearest public example of cognitive surrender in our field is publishing large volumes of AI text that nobody truly read or improved. This content has a recognizable smell: templated phrases, vague claims with no real takeaway, zero first-hand experience, sometimes invented numbers. Google’s answer has been direct. Its guidance on AI-generated content was never “AI is banned”, it has always been about quality and who the content actually serves. But the March 2026 core and spam updates showed what enforcement looks like: sites built on scaled, thin AI content reportedly lost 60–80% of their traffic, AI-rewritten affiliate sites dropped 40–60%, and B2B blogs saw their AI-heavy pages fall 30–50%, while the human-written pages on the same domains stayed stable. Meanwhile, local sites with real first-hand content even moved up. The spam update finished rolling out in under 20 hours, the fastest on Google’s own status dashboard, which tells me Google knew its targets well in advance. The traffic pattern of these sites even has a name now: the crash and burn curve. Month one, indexing spike, everyone celebrates. Month two, users bounce and engagement signals collapse. Month three, the update lands.

E-E-A-T, and the thing AI cannot fake. Google’s quality framework starts with Experience, the first E. A model has no lived experience: it never ran your migration, never read your log files, never talked to your customers. When the core idea of a piece comes from a model, the content competes on the one axis where it can only score zero: adding something new. That is also why “information gain”, does this content add anything the internet didn’t already have? keeps coming up in every serious content strategy discussion this year.

Technical recommendations without context. This one is from my own daily life. AI audits love confident actions: “noindex these thin pages,” “merge these duplicates,” “remove this old content.” Sometimes that’s right. But the model never saw your log files, your conversion data, or the business reason a page exists. I have seen suggestions that, applied as-is, would have quietly removed pages that bring money, pages that look “thin” in a crawl but carry links, brand queries, or a partner agreement behind them. Accepting that output as an action, without checking it against your own data, is surrender with a release date.

Context bleeding into nonsense. And sometimes it gets absurd. In one case shared on the r/SEO community, an agency’s AI-assisted “optimizations” pushed references to the pancreas, blood clotting and diabetes into a B2B software article, the model was carrying context over from a medical client. Funny from a distance. Not funny when it ships under your brand and nobody in the loop reads carefully enough to catch it.

The part we don’t talk about enough: what it does to us

I’ll keep this short, because I’m an SEO, not a psychologist, but it matters, and it was a big reason I wanted to write this.

The first effect is professional. People in our field describe sliding from being a strategist into being a cleanup worker for machine text, reviewing dozens of articles a month that they didn’t shape and don’t believe in. That is a fast road to burnout and to feeling that your skill has no value. The second effect is personal, and psychologists warn it doesn’t stay inside the chat window: when you get used to instant, effortless, always-agreeing answers, normal effort starts to feel heavy, and after a point, this passive mode can leak into how we handle problems, and even how we deal with other people. The brain strengthens what it uses and lets go of what it doesn’t. If we stop practicing judgment at work, we shouldn’t expect it to be waiting for us, fresh and strong, somewhere else.

How I try to keep the thinking

I don’t have a magic framework. But after a lot of trial and error and these are my working rules:

1. Delegate the execution work generously. Repetitive, time-eating tasks whose repetition teaches you nothing new, data pulls, comparisons, formatting, summarizing, first drafts of mechanical text. This is where AI shines and where you lose nothing.

2. Keep diagnosis, strategy, prioritization and final actions with humans. Not because AI is useless there, but because we hold the context: the product, the history, the past experiments, the stakeholders, and a hundred other factors. Turning all of that into a prompt is hard, often impossible, and even with great context the output still needs judgment.

3. Form your own view first. Even five minutes with the data before you ask. Then compare your take with the AI’s. If you never build your own answer, you have nothing to check the machine against, which is the literal definition of surrender.

4. Treat AI output like a draft from a smart junior. Useful, fast, sometimes brilliant, never auto-approved. You would not push a junior’s strategy to production without review; the bar should not drop just because the text is polished.

5. Be honest about your input. Weak or partial context in, low trust out. If you couldn’t give the tool the full picture, don’t bet the roadmap on its answer.

6. Make it show its work. Ask why. Ask for the assumptions behind a suggestion. Then check the claims against your numbers, Search Console, logs, analytics, not against how confident the answer sounds.

7. Keep some manual reps. Once in a while, run an analysis start to finish yourself. Not nostalgia, training. The skill you will need on “task eleven” is built on the boring days.

Where I land

I want to end where I started, so there is no confusion: these tools are some of the best things that have happened to our work. The creativity boost, the speed, the time and cost savings, all real. Refusing to use them has a cost too, and in 2026 that cost is high.

But a tool this powerful deserves awareness, the same way social media did, full of great features, and full of effects on our minds that we only understood later. The risk in front of us is not using AI. The risk is letting it think instead of us, one small unchecked answer at a time, in a field where mistakes surface months later and recovery is expensive, sometimes impossible.

So this is my reminder to every SEO reading this, the same one I give my own team and myself: use these tools every day. Delegate the execution without guilt. And never, ever delegate the judgment. Your experience, your context, and your critical thinking are not “nice to have” in this job. In an internet filling up with confident machine answers, they are the actual product.

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Six months ago I pulled 6,400 SEO job descriptions from LinkedIn and asked a simple question: was anything actually changing because of AI, or was it all conference-stage theatre?

The answer back in December 2025 was yes, but quietly. About one in six postings mentioned AI search or LLM-related terms. The US was running ahead of the rest of the world. Leadership roles were absorbing the new vocabulary first.

I just ran the same analysis again with a fresh pull — 5,931 postings across the same six countries (plus Japan this time).

The very short version: every signal that was emerging in December is now mainstream, and the part of the workforce that wasn’t asked for AI fluency six months ago, junior hires, is the segment that changed the most.

The six-month delta in one screen

1. The vocabulary moved from emerging to default

In December, GEO was the term that was clearly winning the naming war, 323 mentions versus 128 for AEO. Both terms have now roughly doubled, and so has LLM (210 → 539). ChatGPT, which was already the loudest specific tool, grew more modestly to 340.

Chart 1 — Every AI-search term in SEO postings roughly doubled in six months

Stack those together with Claude, Perplexity, Gemini, generative AI and the rest, and 25% of all SEO/marketing job postings now mention AI search or LLM-related terms in the six-country comparable sample. That’s up from ~16% in December.

One in four. That’s the line we crossed.

This is no longer a leading indicator. It’s a hiring norm.

2. The US still leads, but the rest of the world is catching up fast

The most striking pattern in December was how lopsided AI/GEO adoption was: 23.3% in the US, single digits everywhere else outside Germany. That gap has narrowed, not because the US slowed down, but because every other market roughly doubled.

Chart 2 — The US still leads, but every market doubled

• United States: 23.3% → 37.5% (+14.2pp)

• Germany: 11.4% → 25.4% (+14.0pp)

• United Kingdom: 10.5% → 22.6% (+12.1pp)

• Canada: 10.5% → 22.4% (+11.9pp)

• France: 9.5% → 22.8% (+13.3pp)

• UAE: 3.4% → 14.4% (+11.0pp)

And the new addition, Japan, comes in at 8.2%, roughly where the Western European markets were six months ago.

The US is still where the AI-SEO conversation is hottest, and where you’ll see the most aggressive job descriptions.

3. Junior roles are where the biggest shift happened

This was the most surprising finding when I re-ran the seniority cut.

In December, AI/GEO adoption was concentrated at the top: Leadership (20.7%), Manager (20.1%), Junior (4.7%). That looked like classic strategy-rolls-down behaviour: experienced people figure out the new thing, then it works its way into entry roles.

What actually happened is that every tier moved up sharply, but junior roles moved fastest in relative terms.

Chart 3 — AI requirements are no longer just a leadership thing

• Leadership: 20.7% → 41.0% — roughly double. Senior people are now expected to be conversant in AI search at almost half of openings.

• Manager: 20.1% → 34.4%.

• Senior / Lead (new cut this round): 30.2%.

• Junior / Entry-level: 4.7% → 18.9% — four-fold increase.

I read this two ways. The optimistic read: employers have decided AI tooling is a baseline competency, and they want it in the door from day one. The cynical read: with so few entry-level SEO openings being posted in absolute terms, employers are using AI/GEO requirements as a filter to thin the applicant pile.

Both are probably partially true. Either way, “junior” no longer means “tool-free.”

4. The tool stack: ChatGPT is now top-3, and Claude is the dark horse

The classic toolkit hasn’t gone anywhere. Google Analytics is still mentioned in 1,116 postings (essentially unchanged from December’s ~1,100). Search Console, Ahrefs, Screaming Frog and Semrush are all firmly entrenched.

But two interesting things happened:

1. Semrush slipped — 697 → 589 mentions. Still huge, but the only classic tool to lose ground in absolute mentions.

2. ChatGPT is now the #3 most-named tool at 340 mentions, ahead of Ahrefs and within striking distance of Semrush.

Chart 4 — ChatGPT is closing the gap with the classic SEO toolkit

The bigger story underneath the ChatGPT line: Claude has appeared from nowhere with 278 mentions, Perplexity is at 165, Gemini at 142. None of those were named tools in the December analysis in any meaningful volume.

That shift matters because it kills the narrative that “ChatGPT is shorthand for AI.” Recruiters are increasingly naming specific models, which means they’re starting to have an opinion about which AI tools matter for the role. Claude in particular shows up most often in technical and analytical postings; Perplexity in research-oriented ones.

Specialized AI writing tools (Jasper, Surfer SEO) are still niche, 26 and 19 mentions respectively. The market hasn’t fragmented at the writing layer. It has fragmented at the model layer.

5. The “AI jobs need coders” finding still holds, but at a lower altitude

In December I reported that 29.7% of AI-focused SEO jobs required Python/SQL/similar, vs. 14.2% for traditional SEO jobs, a roughly 2.1× gap.

That gap is still there. But both numbers came down.

Chart 5 — AI-focused SEO jobs still demand code roughly 2.3× more often

• AI-focused SEO jobs requiring Python/SQL: 29.7% → 10.9%

• Traditional SEO jobs requiring Python/SQL: 14.2% → 4.7%

• Relative gap: 2.3× (essentially unchanged)

What’s going on? My best read: when AI search optimization was new, employers wanted humans who could build the analysis pipelines themselves. Six months on, a lot of that has been packaged into off-the-shelf products and AI agents. You no longer need to write the Python to talk to an LLM about your sitemap, there are five startups that will do it.

The AI tools, in other words, are doing the coding. The job description is downgrading accordingly.

That doesn’t make technical fluency obsolete. 52% of AI-focused SEO postings still ask for Technical SEO skills vs. 36% of traditional postings, a +16pp gap that’s almost identical to the +19pp I measured in December. Schema, crawl architecture, and site speed have not gotten any less important. They’ve gotten more important relative to traditional SEO. They just don’t always come with a “must write Python” clause anymore.

6. What didn’t change

Worth flagging the steady state, because it’s easy to overstate the AI story:

• Google Analytics is still the most-mentioned tool, by a wide margin.

• Keyword research still appears more often than GEO.

• Content / editorial skills remain the second-most-mentioned cluster after analytics.

• Full-time is still 89% of SEO postings; freelance/contract didn’t surge the way some predicted.

• The fundamentals haven’t been replaced. They’ve just acquired a new layer on top.

If anyone tells you to forget Search Console and learn prompt engineering, they’re selling a course.

What this means for your career (updated)

In December I said: if you’re an SEO professional planning to keep doing exactly what you’ve been doing, you’ll probably be fine for now.

I’d revise that downwards. The window is closing. The “for now” was the operative phrase, and six months later “now” looks shorter than I assumed.

Three things I’d do this quarter if I were job-hunting:

3. Pick one AI tool and go deep. Not “I’ve tried ChatGPT” deep, “I’ve shipped three workflows with it” deep. Claude or ChatGPT is fine. Recruiters are starting to name specific models; specificity now beats breadth.

4. Learn to talk about GEO and AEO without sounding like a LinkedIn post. What changes about your sitemap when an LLM is the visitor? What does internal linking do for citation rates? If you can answer those questions concretely, you are now in the top 25% of applicants.

5. If you’re entry-level, treat AI fluency as table stakes, not a differentiator. Six months ago AI on your CV got you noticed. Now its absence gets you skipped.

For employers: the data says you should stop hiring “an SEO” and start hiring for a hybrid you may have to define yourself. There is no clean job spec that does both AI search optimization and traditional SEO well, because the field is still inventing one. The companies that win are the ones that figure it out before the spec stabilises and salaries reset.

What I’d watch in the next six months

A few things I’ll be tracking when I run this analysis again at the end of 2026:

• Does junior AI-fluency hit 30%? If the slope holds, yes.

• Does Semrush keep losing share, or does it ship the AI feature that wins it back?

• Does any single AI tool consolidate to >50% of the AI-tool mentions, or does it stay fragmented?

• Does Japan’s adoption curve look more like the US or more like France? That tells us whether AI search adoption is a language-model-coverage story or a market-maturity story.

If any of you have a hypothesis on those, I’d love to hear it in the comments.

Methodology

• Source: LinkedIn job postings collected, 7 countries, United States, United Kingdom, Canada, Germany, France, UAE, and (new this round) Japan.

• Sample size: 5,931 total postings; 5,306 in the 6-country sample comparable to the December 2025 analysis.

• Term matching: Same regex-based pattern matching as the December article, exact word boundaries on “GEO”, “AEO”, “LLM”, “ChatGPT”, etc. applied to the full description text.

• “AI-focused” definition: A posting that explicitly names GEO, AEO, LLM, or ChatGPT in its description. The broader “any AI mention” category (used for the 25% headline) also includes Claude, Perplexity, Gemini, generative AI, and AI Overview references.

• Seniority: Inferred from job title using the same rules as the original article (Head/Director/VP = Leadership; Manager; Senior/Lead; Junior/Entry).

• Caveats: LinkedIn job descriptions are noisy. Some mentions are recruiter buzzword padding rather than real requirements. The directional signal is reliable; treat any single percentage with appropriate skepticism.

I wanted to know what’s actually happening. Not what we think should happen, not what the conference circuit is selling. What are companies really asking for when they hire SEO professionals in 2026?

So I pulled over 6,400 recent job descriptions from LinkedIn across the US, UK, Canada, Germany, France, and UAE. All SEO-related roles, from junior specialists to VP-level positions. I ran them through an analysis to find out: has the rise of LLMs actually changed what organizations expect from us?

The short answer: yes, but not in the way most people assume.

The “GEO” Question Is Settled

Let’s address the terminology debate first. I was curious whether any of these new terms had real traction, or if it was all LinkedIn performance art.

Turns out “GEO” won. It appears in 323 job descriptions, significantly ahead of “AEO” (Answer Engine Optimization) at 128 mentions. ChatGPT as a specific skill requirement shows up in 279 postings. LLM knowledge is mentioned 210 times.

That’s roughly 16% of all postings explicitly mentioning AI search or LLM-related terms. Not a majority, but substantial enough that you can’t ignore it.

But here’s the context that matters: Google Analytics still appears in over 1,100 job descriptions. Semrush shows up 697 times. The traditional toolkit isn’t going anywhere.

The new skills are additive, not substitutive. You still need to know your way around Search Console and Ahrefs. You just also need to understand how LLMs work now.

The US Is Playing a Different Game

Here’s where it gets interesting. The adoption of AI-related requirements isn’t even close to uniform globally.

In the United States, 23.3% of SEO job postings mention AI/GEO terms. That’s nearly one in four jobs. In Germany, it’s 11.4%. Canada and UK hover around 10.5%. France sits at 9.5%. The UAE? Just 3.4%.

The gap between the US and everywhere else is striking. American companies are either ahead of the curve or overreacting to the hype. Probably both, depending on the company.

It’s a Leadership Problem, Not an Execution Task

One of the clearest patterns in the data: AI search optimization is being treated as a strategic challenge, not a tactical one.

Leadership roles (Head, Director, VP) show a 20.7% adoption rate for these new skills. Managers are at 20.1%. Junior roles? Just 4.7%.

Companies aren’t hiring entry-level employees to “do the AI stuff.” They’re bringing in experienced strategists to figure out what the AI stuff even means for their business.

This tracks with reality. There’s no established playbook for GEO yet. No checklist to follow. When your traffic from AI Overviews starts mattering, you need someone who can think through the implications, not just execute a process that doesn’t exist.

The Technical Escalation

Here’s an interesting finding: jobs requiring AI/GEO skills are twice as likely to also require coding skills.

29.7% of AI-focused SEO jobs require Python, SQL, or similar technical abilities. Traditional SEO jobs? Just 14.2%.

This isn’t about prompting ChatGPT better. The new “GEO” role is becoming a hybrid part marketer, part data scientist. You’re expected to analyze, build, and automate, not just optimize.

Technical SEO requirements also jump significantly in AI-focused roles (+19.1% compared to traditional postings). Schema markup, site architecture, crawlability, these foundational skills become more important, not less, when you’re trying to get cited by language models.

The Tool Stack Is Evolving (Slowly)

Despite all the AI buzz, the actual tooling requirements tell a more grounded story. Semrush and Ahrefs remain the undisputed kings of the SEO toolkit. But ChatGPT has firmly planted itself as the third most requested tool proficiency.

Specialized AI writing tools like Jasper and Surfer SEO? Still niche. Claude and Perplexity are showing up, but in small numbers. The market hasn’t fragmented into a dozen AI tools yet—it’s consolidated around ChatGPT as the default.

I believe companies don’t care which AI tool you use. They care that you understand how to work with LLMs at all. ChatGPT is shorthand for “can work with AI,” not a specific technical requirement.

What This Actually Means for Your Career

Let me be direct about the implications.

If you’re a SEO professional and you’re planning to keep doing exactly what you’ve been doing, you’ll probably be fine for now. “Google Analytics” still appears in over 1,000 job descriptions. “Keyword Research” is still mentioned more often than “GEO.” The fundamentals haven’t been replaced.

But if you want to advance into leadership roles, the math is changing. You need to be conversational in LLM concepts. You definitely need to be able to think strategically about problems that don’t have established solutions yet.

The Real Question

Here’s what I keep coming back to: is any of this actually about skill, or is it about signaling?

Some percentage of those 323 GEO mentions are probably just recruiters adding buzzwords. Some companies asking for “LLM knowledge” couldn’t tell you what they want someone to do with it.

But the pattern is consistent enough across regions, seniority levels, and company types that I don’t think it’s all noise. Something real is shifting in how organizations think about search optimization.

The debate over whether GEO is “real” or just “SEO with extra steps” misses the point. Companies believe it’s different enough to change their hiring criteria. That makes it real in the only way that matters for your career.

My suggestion: don’t overthink the terminology. Focus on the underlying capabilities. Get comfortable with technical analysis and develop your strategic thinking.

The specific acronym might change. The direction won’t.

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What I’m against is relying on AI to actually create your content.

The Google Reality Check

Let’s look back at 2023 when Google introduced the Helpful Content Update. Search engines exist to provide the best results to users. Google, as the biggest search engine, spends massive amounts on crawling, analyzing, and improving its AI to assess content quality. When AI-generated content started flooding the web, it raised Google’s costs to crawl and analyze all of it.

That’s why they introduced the Helpful Content Update—one of the most important updates in recent years. The core idea is simple: content should create value and be helpful. But why is helpfulness so important now? Because we already have thousands of articles on general topics. The real question is whether new content is necessary, or if it brings a new point of view or experience that didn’t exist before.

Think about it this way: if I write about the calories in eggs, is it really needed? Will it change anything or bring something new? Usually not. Value comes when what we publish brings a new perspective or experience. Google wants to know if the writer has the needed experience, because a real expert will have a unique viewpoint. That’s when new value is created, and that’s when content has a better chance to be seen.

Why AI Content Fails

Here’s the fundamental problem: large language models are trained on existing content and can’t create something truly new. They rearrange what already exists based on prompts. Creation is what matters now, and AI can’t go beyond its training to do real research or trial and error, especially in areas like YMYL (Your Money or Your Life) topics and specialized fields.

As we move through 2025, concepts from E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) and the Search Quality Rater Guidelines are more important than ever.

The Hype vs. Reality

There’s a hype among company managers and stakeholders who fear they’re falling behind if they don’t use AI. They get excited thinking they can publish lots of content and believe they’re being smart. Some inexperienced people even switch to “AI Content Engineer” roles, thinking they can generate impressive results. But in reality, they’re just producing repetitive content with different wording.

My question to them is: how much new value are you really creating? Can you be honest with your audience and tell them this was AI-generated? Can you claim a real expert wrote it? These are the exact questions you’ll find in Google’s helpful content guidelines.

Real-World Failures

We’re seeing the consequences play out in real time. Let me share some concrete examples:

1- The “SEO Heist”

That Backfired In late 2023, a startup called Casual tried something that seemed clever at first. They looked at their competitor Exceljet—a site with thousands of well-structured articles about Excel formulas—and decided to copy their approach using AI.

Here’s what they did:

• Exported Exceljet’s complete sitemap

• Converted the URLs into AI prompts

• Generated about 1,800 articles in hours

• Published everything immediately

Initially, it seemed to work. Traffic surged from near-zero to 490,000 monthly visits. But then the operator bragged about it on Twitter, calling it “stealing” traffic. That got Google’s attention.

The site was hit with a “Pure Spam” manual action. Traffic plummeted from 610,000 visits to under 200,000, then eventually to almost nothing. The deep pages they’d generated were completely deindexed—dropping to just a single page in Google’s index.

2- The 22,000-Page Disaster

Another example is TailRide (formerly GetInvoice). They tried using programmatic SEO with AI to shortcut their growth. They generated over 22,000 pages targeting random long-tail keywords like “forever 21 return policy” and “dual digital option”—topics completely unrelated to their invoicing software.

At first, they saw millions of impressions and hundreds of clicks. But on February 28, 2024, their traffic dropped to zero overnight. The penalty was so severe they had to abandon their entire brand and domain, rebranding from GetInvoice to TailRide.

Read their Blog Post

3- The “325 Posts Per Day” Case

One of the most extreme examples is ZacJohnson.com. Between September 2023 and March 2024, this site published over 60,000 articles—averaging about 325 posts per day. Let that sink in: 325 articles every single day.

The site shifted from legitimate marketing advice to covering everything celebrity gossip, “best-of” lists, trending news basically anything with search volume. They used AI to mass-produce content on topics completely outside their expertise, hoping to capture broad traffic for ad revenue.

The result? Traffic collapsed from approximately 8.2 million monthly visits to zero. The entire site was deindexed.

Other Examples

FreshersLive.com:

Lost approximately 10 million monthly visits when deindexed

EquityAtlas.org:

Dropped from 4 million monthly visitors to near zero

Beingselfish.in:

Fell from 1 million+ visits to zero

Google’s New Definition of Spam

In March 2024, Google fundamentally changed its spam policies. They introduced the concept of “Scaled Content Abuse,” defining it as “generating many pages for the primary purpose of manipulating Search rankings and not helping users”—regardless of whether it’s done by AI, humans, or a combination.

This definition focuses on intent and value rather than method. Even if a human edits every AI article, if the intent is to flood Google with low-value content to capture traffic, it’s spam.

The Trust Problem

Here’s what many people miss: Google’s “Helpful Content” system creates a site-wide signal. If you have 100 excellent human-written articles and 5,000 AI-generated filler pages, Google identifies your entire domain as “mostly unhelpful.” This negative signal suppresses the rankings of all your pages, including the good ones.

This is why simply deleting bad content (called “pruning”) is often required for recovery—the bad content actively drags down the good content.

My Bottom Line

That’s the main reason I’m against AI content creation. We’re in a game where if you don’t create real value, you shouldn’t be seen. AI should be a helper in your process for research, analysis, and organization. But the actual creation needs to come from real experience and expertise.

When you publish AI-generated content at scale, you’re not just risking a ranking drop. You’re risking:

• Complete deindexing from Google

• Manual penalties that can permanently damage your domain

• Loss of trust that spreads across your entire website

• Having to abandon your brand and start over

The sites that are succeeding now are the ones that can prove a human expert actually created the content, brought real experience to the topic, and added something new to the conversation.

If you can’t honestly tell your audience that a real expert with genuine experience wrote your content, then you shouldn’t publish it. It’s that simple.

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Technical Related Tips

1. Carefully Read the Job Description:
   Pay attention to the requirements and expectations. If there’s a skill or topic you’re unfamiliar with, do some basic research beforehand.

2. Adopt a Business-Oriented Approach:
   Don’t let your technical answers drift away from business objectives. The ultimate goal of SEO is to drive relevant traffic that helps grow a business. Always consider the business impact of your strategies.

3. Show Awareness of SEO Trends:
   One of the most important skills in SEO is continuous learning. With constant changes in search engines and the rise of platforms that act as search engines (e.g., Amazon, TikTok, YouTube, ChatGPT), it’s crucial to show that you’re up-to-date with trends and have perspectives on them.

4. Master Crawling and Indexing Concepts:
   These may seem basic, but trust me, I’ve worked with senior and managerial-level SEOs who lacked a deep understanding of these concepts. Google has extensive documentation on crawling and indexing—use it to enhance your expertise.

5. Understand Page Speed Optimization:
   While there’s debate about how much page speed directly affects rankings, improving it enhances user experience, which indirectly benefits SEO. Master the concepts of page speed optimization thoroughly.

6. Deeply Understand Rendering Concepts:
   One of the most challenging topics in SEO is how Googlebot renders your pages. Strengthen your knowledge of various rendering solutions and their impact on search engines. Also, learn about testing methods and the tools available for rendering analysis.

7. Think and Respond Data-Driven:
   By 2025, SEO will no longer be a field where you can succeed without solid data analysis. Be serious about being data-driven and reflect this approach in your interview responses.

8. Highlight Related Skills:
   SEO requires a mix of hard and soft skills. During the interview, mention any additional skills you have that are relevant to SEO—these can give you an edge.

9. Adopt a Problem-Solving Mindset:
   SEO projects often come with significant challenges. Demonstrate that you can handle these problems effectively. If the interviewer presents a scenario you haven’t encountered before, offer the most logical solution you can think of and explain your approach. Emphasize that your method involves reviewing available data and testing multiple hypotheses.

General Tips

1. Stay Calm and Confident:
   Above all, you need to stay calm and show up with confidence. Many times, as an interviewer, I’ve seen candidates with strong technical knowledge underperform simply due to anxiety or low confidence.

2. Prepare for the Interview Setting:

   • For in-person interviews: Plan your travel so you arrive on time. Aim to be there at least 15 minutes early to familiarize yourself with the company environment.

   • For online interviews: Choose a quiet and suitable space. Test your internet connection and ensure everything is set up properly. Avoid joining interviews from noisy places, cars, or public areas—this sends the message that you’re not taking the interview seriously.

3. Be Honest with the Interviewer:
   If you don’t know the answer to a question, don’t waste time with vague or false responses. Instead, clearly state that you haven’t encountered that specific topic yet but emphasize your growth mindset—you’re willing and capable of learning it quickly.

4. Be a Good Listener:
   Active listening is one of your most important skills. Focus on understanding the exact question being asked. If you don’t understand the question, don’t rush to answer. Politely ask the interviewer to repeat or clarify the question.

5. Research the Company:
   The more familiar you are with the company, its business goals, products or services, and market position, the better you can conduct yourself during the interview.

Final Note

Remember, SEO is one of the most dynamic and fast-changing areas of digital marketing. Pay attention to details, stay updated, analyze case studies, and never make decisions without data. By doing this, you’ll not only grow as an SEO professional but also set yourself apart in interviews.

Understanding SEO: The Basics

SEO involves optimizing your website to rank higher on search engine results pages (SERPs). Higher rankings lead to increased visibility, which can drive more traffic to your site. SEO is a long-term strategy that requires consistent effort, but the payoff can be substantial in terms of organic growth.

Why SEO Matters for Startups

1. Cost-Effective Marketing: Unlike paid advertising, which requires ongoing investment, SEO can provide sustained organic traffic with relatively low ongoing costs.

2. Building Credibility: High search rankings can enhance your brand's credibility. Users tend to trust websites that appear at the top of search results.

3. Competitive Advantage: Many startups neglect SEO, focusing instead on paid ads or social media. Investing in SEO can give you a competitive edge in your industry.

Key SEO Strategies for Startups

1. Keyword Research

   Identifying the right keywords is the foundation of any successful SEO strategy. Use tools like Google Keyword Planner, Ahrefs, or SEMrush to find relevant keywords with high search volume and low competition. Focus on long-tail keywords that are more specific and less competitive.

2. Quality Content Creation

   Content is king in SEO. Create high-quality, valuable content that addresses the needs and questions of your target audience. Blog posts, how-to guides, infographics, and videos can all help attract and engage visitors. Ensure your content is original, well-researched, and optimized with relevant keywords.

3. On-Page SEO

   On-page SEO involves optimizing individual pages on your website. Key elements include:

   • Title Tags: Ensure each page has a unique, keyword-rich title tag.

   • Meta Descriptions: Write compelling meta descriptions that encourage users to click through to your site.

   • Header Tags: Use H1, H2, and H3 tags to structure your content and include keywords.

   • URL Structure: Use clean, descriptive URLs that include keywords.

   • Internal Linking: Link to other relevant pages on your site to keep users engaged and help search engines understand your site structure.

4. Technical SEO

   Technical SEO focuses on the backend of your site to ensure it is crawlable and indexable by search engines. Key aspects include:

   • Site Speed: Ensure your website loads quickly. Use tools like Google PageSpeed Insights to identify and fix speed issues.

   • Mobile-Friendliness: Make sure your site is optimized for mobile devices. Google uses mobile-first indexing, meaning it primarily uses the mobile version of your site for ranking.

   • Sitemap: Submit a sitemap to Google Search Console to help search engines discover your pages.

   • Robots.txt: Ensure your robots.txt file is correctly configured to guide search engines on which pages to crawl.

5. Off-Page SEO

   Off-page SEO involves activities outside your website that can improve your search rankings. Key strategies include:

   • Backlink Building: Acquire high-quality backlinks from reputable sites. Guest blogging, partnerships, and creating shareable content can help attract backlinks.

   • Social Media Engagement: Active social media profiles can drive traffic to your site and increase your brand's visibility.

   • Local SEO: If your startup has a physical presence, optimize for local search by claiming your Google My Business listing and encouraging customer reviews.

6. Analytics and Continuous Improvement

   SEO is an ongoing process. Use tools like Google Analytics and Google Search Console to monitor your traffic, track keyword rankings, and identify areas for improvement. Regularly update your content and SEO strategies based on performance data.

Common SEO Mistakes to Avoid

1. Keyword Stuffing: Overloading your content with keywords can lead to penalties from search engines. Focus on natural, meaningful use of keywords.

2. Duplicate Content: Ensure all content on your site is unique. Duplicate content can confuse search engines and harm your rankings.

3. Ignoring User Experience: A well-optimized site should also provide a great user experience. Ensure your site is easy to navigate and user-friendly.

Conclusion

SEO is a powerful tool for startups looking to build their online presence and attract organic traffic. By understanding the basics, implementing key strategies, and avoiding common pitfalls, you can set your startup on the path to SEO success. Remember, SEO is a marathon, not a sprint. Consistent effort and continuous improvement are key to achieving and maintaining high search rankings.

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