Category: SEO

AI and SEO Explained: What Marketers Need to Know Now
If it feels like the whole internet woke up and decided every sentence needed to start with “AI,” I get it. I feel that fatigue too.

As marketers, we are getting hit every day with LinkedIn hot takes, rushed prompt hacks, and promises that ChatGPT will either 10x our productivity or replace us completely.

And right in the middle of all of that is the digital marketer trying to figure out whether AI is just another buzzword cycle or the start of a major rewrite of how we handle content, SEO, PPC, reporting, and almost everything else.

So I want to break it down in plain English.

Think of this as my AI starting guide for marketers who are tired of needing someone younger to translate every new acronym, the same way many of us once had to help our parents get online or open an AOL chat window.

Defining AI and LLMs, and why they matter

I am not asking “what is AI” just to chase keyword density. I want to start with a shared definition, because a lot of these terms get used interchangeably, and not always correctly.

At its core, artificial intelligence refers to machines performing tasks that usually require human intelligence, such as understanding language, recognizing patterns, making decisions, and generating content.

The kind of AI getting the most attention right now is generative AI: models that can create text, images, code, video, and other outputs based on patterns learned from huge datasets.

Tools like ChatGPT, Gemini, and Claude do not “think” the way people do. They predict the next most likely word, phrase, or response based on what they have been trained on.

That matters because AI is not a magic shortcut to instant wealth, overnight automation, or effortless headcount reduction. I see it more as large-scale data aggregation and pattern recognition.

Large language models, or LLMs, are not creating net-new truth from nothing. They process massive amounts of existing information and produce answers based on patterns, probabilities, and what looks like internet consensus.

For content creators and marketers, that is a major shift. I am no longer thinking only about optimizing for a traditional search engine click. I also have to think about whether machines can understand, summarize, cite, and reuse my content.

The biggest implication is the rise of zero-click search. AI systems can answer users directly through experiences like Google AI Overviews or ChatGPT responses, often without sending that user to the original website.

That changes SEO from a pure traffic game into an authority, visibility, and data-ingestion game.

That is why I think marketers need to understand what AI does well, what it struggles with, and where it actually belongs in a broader marketing strategy.

AI jargon I think marketers need to know

Before going deeper, I want to separate a few terms that often get mashed together: AI, machine learning, NLP, generative AI, LLMs, and AI agents. They are related, but they are not the same thing.

Understanding the difference helps me make better decisions about which tools to use, where to trust them, and where human judgment still matters most.

Artificial intelligence (AI)

Artificial intelligence is the broad umbrella term for machines performing tasks that usually require human intelligence. That includes problem-solving, learning, speech recognition, language understanding, and decision-making.

In marketing and search, AI has become a catch-all phrase. But in practice, most of the tools I use fall into more specific categories.

Example of AI: Siri and Google Assistant use AI to interpret voice commands and respond in context.

Machine learning (ML)

Machine learning is a subset of AI. Instead of giving a system explicit instructions for every possible situation, we feed it data so it can identify patterns and make predictions.

In marketing, machine learning powers ad targeting, customer segmentation, recommendations, predictive analytics, and plenty of optimization systems we already rely on.

Example of machine learning: Netflix uses machine learning to recommend shows based on viewing history.

Natural language processing (NLP)

Natural language processing helps machines understand, interpret, and generate human language.

NLP is why ChatGPT can carry on a conversation and why Google can understand that “cheap running shoes” and “affordable sneakers” are closely related searches.

Example of natural language processing: Google Translate uses NLP to understand and convert language in real time.

Generative AI

When people casually say “AI,” they often mean generative AI, which is a branch of artificial intelligence that creates content instead of only analyzing existing data.

Generative AI models are trained on massive datasets to learn patterns in language, images, audio, code, or video. Then they use those patterns to produce something new.

But I always remind myself that these systems are still predicting likely outputs. They are not thinking, reasoning, or understanding the world like a person.

That is also why generative AI can go off track. When a model confidently makes something up, we call it a hallucination.

Some of the most infamous hallucination examples include AI answers suggesting people eat small rocks or use glue to keep cheese on pizza. Funny in hindsight, but a serious reminder that fact-checking is not optional.
- ChatGPT can draft articles, emails, and outlines.
- Midjourney and DALL·E can create images.
- Claude can help write and refine code.
- Sora can generate video from prompts.
Large language models (LLMs)

Large language models are a specialized type of generative AI trained on huge amounts of text, including books, websites, code, and other online sources, to generate human-like responses.

I think of LLMs as the engine behind many chatbot experiences. They are the part that interprets what I type and produces a response.

When I use an LLM effectively, I do not treat it like a replacement for my brain. I give it context, examples, constraints, and direction. It can help refine a draft, suggest wording, or organize messy thoughts, but I still own the strategy and final judgment.

In short, LLMs react to input. They do not act independently unless they are connected to tools and workflows that let them take action.
- GPT models from OpenAI, used in ChatGPT.
- Claude models from Anthropic.
- LLaMA models from Meta.
AI agents

AI agents go beyond responding to prompts. They can work through multi-step tasks, use tools, navigate websites, fill out forms, call APIs, analyze files, and complete workflows with less hand-holding.

They are still powered by LLMs under the hood, but the key difference is that they have goals, tools, and a degree of autonomy.

That is why AI agents feel more consequential for marketers. They are not just talking; they are beginning to do the work.
- ChatGPT can search the web, analyze files, and review code.
- Google Gemini in Workspace can summarize email threads and suggest replies.
- Microsoft Copilot can assist across Microsoft 365 workflows.
How I see AI affecting marketing today

Once the terminology is clearer, the marketing impact becomes easier to see. AI is changing how people search, how content is produced, how visibility is measured, and how stakeholders talk about growth.

People have been saying SEO is dying for years. I do not think SEO is dead, but I do think “SEO is changing” undersells the size of the shift.

We are in the middle of a major industry pivot, and AI is at the center of it.

Organic traffic is being cannibalized

AI Overviews are Google’s automated summaries that appear at the top of some search results, often pulling from multiple sources.

I think of them like Featured Snippets turned up several notches. They do not simply quote one source and send the click back. They blend sources, rewrite information in Google’s voice, and may push attribution lower on the page.

For broad informational queries, that means the first thing a user sees may be Google’s answer instead of my blue link. The likely result is a lower click-through rate and fewer visits to publisher and brand websites.

Before AI Overviews, informational queries were often useful for introducing a brand early in the research journey. Now, more of that attention and trust can stay with Google.

Claim: AI Overviews only appear for fluffy queries, so my traffic is safe.

Reality: Google is testing and expanding AI Overviews across more serious query types, including YMYL, product, and B2B searches.

What I would do next: Stop chasing every possible click, measure visibility and influence alongside conversions, and build enough topical authority that my brand becomes a source AI systems can confidently cite.

Content creation is exploding, and so is the noise

Generative AI has removed one of content marketing’s biggest bottlenecks: production time. Work that used to take a team a month can now be drafted by one marketer in a week.

That is not automatically bad. The problem is that when everyone can publish “good enough” content quickly, the internet gets louder and less useful.

Claim: More content means more traffic.

Reality: That was already questionable before AI. Now, search systems are increasingly tuned to reduce the visibility of generic, low-value, quickly produced content.

Google’s Helpful Content updates, Bing’s spam improvements, and social platform feed changes all point in the same direction: thin content is easier to produce, but it is also easier to ignore.

What I would do next: Focus on authority-driven content such as case studies, original data, expert analysis, and proprietary insights. I would publish less, promote more, and use AI for research, outlining, repurposing, and refreshing instead of simply flooding the web.

Search results are becoming deeply personalized

Traditional SEO has dealt with personalization for years through local results, logged-in history, and device context. LLM-powered tools like ChatGPT, Perplexity, and Gemini take that much further.

The same question can produce different answers depending on the user, their prompt, their past interactions, available data, and the model being used.

For example, if someone asks, “What is the outlook for Tesla?” a financial analyst may get an answer focused on stock performance and filings, while a new driver may see information about models, battery life, and charging infrastructure.

A Semrush Enterprise AI source analysis view tracks how often domains are cited in ChatGPT results, revealing shifting visibility trends across LinkedIn, Wikipedia, Semrush and industry publishers.

Claim: I will just optimize for the top answer in ChatGPT the way I optimize for position one in Google.

Reality: The idea of one universal top answer is breaking down. Personalization makes it harder to define, track, and reverse-engineer a single ranking position.

What I would do next: Track visibility across search engines and LLMs, build a recognizable brand entity, invest in multiple content formats, use structured data, and create clear, citable answers that machines can understand.

Attribution is breaking

When Google, Bing, Perplexity, or another AI-driven platform answers a question directly, users may never visit the website that influenced the answer. Even when they do visit, their journey may start in an AI tool, move through another search, and only later reach the site.

That breaks the clean channel → click → conversion model marketers have relied on for years.

Claim: I will measure traffic from LLMs directly in analytics.

Reality: That assumes users are clicking through from AI answers. In many cases, they are not.

A Semrush-style AI sentiment dashboard visualizes how brands appear across AI search, with mention trends, sentiment mix, and a competitive leaderboard.

What I would do next: Move beyond last-click attribution, pay more attention to assisted conversions, and track broader demand signals such as direct traffic, branded search volume, brand mentions, sentiment, and “How did you hear about us?” responses.

I would also budget for influence that is hard to perfectly track, including podcasts, PR, thought leadership, community visibility, and media coverage.

Clients and bosses expect magic

Because AI hype is everywhere, stakeholders often expect it to make everything faster, cheaper, and better without understanding the risks, learning curve, or human oversight involved.

Claim: We can replace our SEO or content team with AI tools and get the same results.

Reality: AI can accelerate tasks, but it does not replace strategy, judgment, subject-matter expertise, or a real understanding of customer needs.

What I would do next: Set expectations early. AI can make some work faster and cheaper, but it is not a push-button strategy. I would show stakeholders the hidden work behind good AI output, including prompt refinement, editing, fact-checking, compliance, and final review.

The best use of AI is not to remove human thinking. It is to free up more human time for the strategic work that actually moves the business forward.

Search is evolving

I am not interested in getting stuck in a debate over Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or any other acronym. The important point is simpler: search today is not what it was yesterday.

Organic visibility is no longer only about ranking in Google. Search now includes AI answers, YouTube, Reddit, newsletters, communities, social platforms, and every place people go to discover, compare, and validate information.

If I am only thinking about the traditional search bar, I am already behind. The better path is to build authority, create content worth citing, understand how AI systems interpret information, and measure visibility across the full discovery journey.

AI is not the end of SEO. It is a major shift in how search works, how content is consumed, and how brands earn trust. The marketers who adapt will be the ones who separate useful strategy from the noise.

Inspired by this post on Search Engine Land.
July 3, 2026
Does llms.txt Matter for AI SEO? What My Data Shows
I have watched the debate around llms.txt become one of the most polarized conversations in web optimization.

Some people treat llms.txt as essential infrastructure for AI discovery. Others, especially longtime SEO practitioners, see it as speculative theater. Platform tools are starting to flag missing llms.txt files as site issues, yet server logs still show that AI crawlers rarely request them.

Google even appeared to adopt it. Sort of. In December, Google added llms.txt files across many developer and documentation sites.

At first, the signal looked obvious to me: if the company behind the sitemap standard was implementing llms.txt, maybe the file really mattered.

Then Google removed it from its Search developer docs within 24 hours.

Google’s John Mueller said the change came from a sitewide CMS update that many content teams didn’t realize was happening. When asked why the files still exist on other Google properties, Mueller said they aren’t “findable by default because they’re not at the top-level” and “it’s safe to assume they’re there for other purposes,” not discovery.

The llms.txt research

I wanted data, not another debate.

So I tracked llms.txt adoption across 10 sites in finance, B2B SaaS, ecommerce, insurance, and pet care. I looked at the 90 days before implementation and the 90 days after.

I measured AI crawl frequency, traffic from ChatGPT, Claude, Perplexity, and Gemini, and the other changes each site made during the same window.

Here is what I found:
- Two of the 10 sites saw AI traffic increases of 12.5% and 25%, but llms.txt was not the cause.
- Eight sites saw no measurable change.
- One site declined by 19.7%.
The 2 ‘success’ stories weren’t about the file

The Neobank: 25% growth

One digital banking platform implemented llms.txt early in Q3 2025. Ninety days later, its AI traffic was up 25%.

That sounds compelling until I looked at what else happened during the same period.
- The company ran a PR campaign around its banking license and earned coverage in major national publications.
- It restructured product pages with extractable comparison tables for interest rates, fees, and minimums.
- It published 12 new FAQ pages optimized for extraction.
- It rebuilt its resource center with new banking information and concepts.
- It fixed technical SEO issues, including header structure problems.
When a company earns Bloomberg coverage in the same month it launches optimized content and fixes crawl errors, I cannot isolate llms.txt as the growth driver.

The B2B SaaS platform: 12.5% growth

A workflow automation company saw AI traffic jump 12.5% two weeks after implementing llms.txt.

The timing looked perfect. It would be easy to call the case closed. But the surrounding context told a different story.

Three weeks earlier, the company had published 27 downloadable AI templates covering project management frameworks, financial models, and workflow planners. These were functional tools, not ordinary content marketing assets, and they drove the engagement behind the spike.

Google organic traffic to those templates rose 18% during the same period and kept climbing throughout the 90 days I measured.

Search engines and AI models surfaced the templates because they solved real problems and created an entirely new site section. They did not surface them simply because the URLs appeared in an llms.txt file.

The 8 sites where nothing happened after uploading llms.txt

Eight sites saw no measurable change after adding llms.txt. One of them declined by 19.7%.

The decline came from an insurance site that implemented llms.txt in early September. Based on the data, the drop likely had nothing to do with the file.

The same pattern appeared across all traffic channels. Llms.txt did not prevent the decline, and it did not create any visible advantage.

The other seven sites, which included ecommerce brands in pet supplies, home goods, and fashion, plus B2B SaaS, finance, and pet care sites, used llms.txt to document their best existing content. That content included product pages, case studies, API docs, and buying guides.

Ninety days later, nothing changed. Traffic stayed flat. Crawl frequency was identical. The content was already indexed and discoverable, and the file did not change that.

The pattern was clear: sites that launched new, functional content saw gains. Sites that only documented existing content saw no gains.

Why the disconnect?

No major LLM provider has officially committed to parsing llms.txt. Not OpenAI. Not Anthropic. Not Google. Not Meta.

Google’s Mueller put it plainly:
- “None of the AI services have said they’re using llms.txt, and you can tell when you look at your server logs that they don’t even check for it.”
That is the reality I saw in the data. The file exists. The advocacy exists. But platform adoption does not show meaningful use yet.

The token efficiency argument and its limits

The strongest case for llms.txt is efficiency. Markdown can save time and tokens when AI agents parse documentation. It gives agents clean structure instead of forcing them through complex HTML, navigation, ads, and JavaScript.

Vercel says 10% of its signups come from ChatGPT. Its llms.txt includes contextual API descriptions that help agents decide what to fetch.

That matters, but mostly for developer tools and API documentation. If your audience uses AI coding assistants like Cursor or GitHub Copilot to interact with your product, token efficiency can improve integration.

For ecommerce brands selling pet supplies, insurance companies explaining coverage, or B2B SaaS companies targeting nontechnical buyers, token efficiency does not automatically translate into traffic.

llms.txt is a sitemap, not a strategy

The closest comparison I can make is a sitemap.

Sitemaps are useful infrastructure. They help search engines discover and index content more efficiently. But I would not credit traffic growth to simply adding a sitemap. The sitemap documents what exists; the content drives discovery.

Llms.txt works in a similar way. It may help AI models parse a site more efficiently if they choose to use it, but it does not make the content more useful, authoritative, or likely to answer user queries.

In my analysis, the sites that grew did so because they:
- Created functional assets such as downloadable templates, comparison tables, and structured data.
- Earned external visibility through press and backlinks.
- Fixed technical barriers such as crawl and indexing issues.
- Published content optimized for extraction, including FAQs and structured comparisons.
Llms.txt documented those efforts. It did not drive them.

What actually works

The two successful sites showed me what actually matters.
- Create functional, extractable assets. The SaaS platform built 27 downloadable templates that users could deploy immediately. AI models surfaced them because they solved real problems, not because they appeared in a markdown file.
- Structure content for extraction. The neobank rebuilt product pages with comparison tables for interest rates, fees, and account minimums. That is data AI models can pull directly into answers without heavy interpretation.
- Fix technical barriers first. The neobank fixed crawl errors that had blocked content for months. If AI models cannot access your content, no amount of documentation will help.
- Earn external validation. Coverage from Bloomberg and other major publications drove referral traffic, branded searches, and likely influenced how AI models assessed authority.
- Optimize for user intent. Both sites answered specific queries, such as “best project management templates” and “how do [brand] interest rates compare?” Models surface content that maps to what users ask, not content that is merely well documented.
None of this requires llms.txt. All of it can drive results.

Should you implement an llms.txt file?

If you run a developer tool and AI coding assistants are a primary distribution channel, I would implement llms.txt. In that context, token efficiency matters because your audience is already using agents to work with documentation.

For everyone else, I would treat llms.txt like a sitemap: useful infrastructure, not a growth lever.

It is good practice to have. It likely will not hurt. But the hour spent implementing llms.txt is often better spent restructuring product pages with extractable data, publishing functional assets, fixing technical SEO issues, creating FAQ content, or earning press coverage.

Those tactics have shown real ROI in AI discovery. Llms.txt has not, at least not yet.

The lesson I take from this is not that llms.txt is bad. It is that we are reaching for control in a system where the rules are still being written. Llms.txt offers comfort because it is concrete, actionable, and familiar. It looks like the web standards we already understand.

But looking like infrastructure is not the same as functioning like infrastructure.

My focus would stay on what is already working:
- Create useful content.
- Structure it for extraction.
- Make it technically accessible.
- Earn external validation.
Platforms and formats will change. The fundamentals will not.

Inspired by this post on Search Engine Land.
July 3, 2026
Inside Profound’s First Zero Click NYC Search Summit

At our inaugural Zero Click NYC summit, I saw more than 300 leaders from Walmart, Amazon, Google, and beyond come together to confront what I believe is the biggest shift in search since the dawn of the internet.

Inspired by this post on Try Profound Blog.

July 3, 2026
Why I’m Watching the Profound Index for AI Visibility

I’m introducing the Profound Index as a new way to understand AI visibility. It is the first leaderboard built to rank brands by how often they appear in answers from leading AI models.

For me, this matters because visibility is shifting beyond traditional search results. As more people rely on AI-generated answers, I want a clearer way to see which brands are being mentioned, recommended, and surfaced across the AI platforms shaping discovery.

Inspired by this post on Try Profound Blog.

July 3, 2026
Unlocking AI Search: Insights from the AEO Periodic Table V4

I’m thrilled to share the latest from Goodie’s research—the fourth edition of the AEO Periodic Table. This comprehensive guide explores the 14 factors crucial for boosting brand visibility in AI-driven searches. We’ve sifted through an astounding 1.13 million prompts using platforms like ChatGPT, Claude, Perplexity, Grok, Gemini, and Google AI Mode.

What’s new in V4? For starters, we’ve introduced explicit weights and two groundbreaking factors: Search & Fan-Out Rank and Originality & Information Gain. These additions bring fresh insights into the complex world of AI search.

A key takeaway that’s highly noteworthy is that off-site earned and social citations account for a whopping 22% of the total citation leverage. That’s even more influence than any single on-page content factor can muster!

Inspired by this post on HiGoodie Blog.

June 23, 2026
Unlocking the Secrets to Winning Search Awards

Don’t miss your chance to claim the highest honor in search marketing. Let’s uncover what it takes to stand out among the best.

Since I started following the Search Engine Land Awards back in 2015, I’ve watched them recognize exceptional marketers for their outstanding work. The awards not only highlight achievements but also offer winners well-deserved exposure through coverage and interviews, celebrating them with the highest honor in search.

I’ve learned there’s no magic formula for a winning entry, but certain elements make an application truly exceptional. The best submissions tell a compelling story, provide context, showcase strategic thinking, and clearly communicate the significance of the work done.

Want some insider tips from the 2026 judges? I’ve gathered insights from them to help you craft a strong and captivating submission. From common pitfalls to avoid to the standout qualities they seek, these expert insights will guide you in building a compelling entry.

Keep reading for fresh insights from this year’s judges. (Check out the complete list of 2026 judges here!)

“A great entry is a story with a goal, an action, and a measurable outcome. Tell that story effectively, and include a deck illustrating your accomplishments.”

– Amy Hebdon, Founder, Paid Search Magic

“Explain your tactics. Go beyond mentioning ‘best practices.’ Describe how your unique processes led to success. Show your insights and creative problem-solving—this helps your entry shine and showcases your company’s edge.”

– Brad Geddes, Co-Founder, Adalysis

“I look for SAY, which stands for: Situation, Action, and Yield. Provide a clear example of the situation, the actions you took, and the measurable yield achieved over time.”

And there you have it! Submit your entry today to be considered by this year’s esteemed judges. Don’t wait, as Early Bird rates expire July 10!

Inspired by this post on Search Engine Land.

June 23, 2026
Empower Your Content: AI Control with Cloudflare & beehiiv
I recently discovered that Cloudflare and beehiiv have teamed up to enhance how I control AI crawlers on my content, particularly newsletters. This latest addition to beehiiv’s platform provides me with the ability to effortlessly monitor, permit, or restrict AI bots directly from my dashboard as AI search evolves as a critical content discovery method.

The partnership integrates Cloudflare’s Crawl Control technology into beehiiv, announced just this past Tuesday. With this integration, I can decide how AI search engines and agents interact with my content. Whether I want broader exposure by allowing crawlers or aim to safeguard my archives for future monetization, the choice is entirely mine.

AI Bot Insights Made Easy. As a beehiiv user, I now have access to an intuitive on-platform dashboard. It displays which AI crawlers attempted to access my content, those that got blocked, and the amount of referral traffic they generated back to me. I love how it provides a clear overview of crawler activities, my blocking decisions, and any referral traffic resulting from AI interactions.

Simpler Publisher Controls. The system empowers me to either permit or block specific AI models with simple, one-click permissions. Plus, Cloudflare is committed to updating the system as new AI crawlers emerge, meaning I don’t need to fiddle with robots.txt files, firewalls, or code adjustments on my own.

What Industry Leaders Are Saying. According to Cloudflare CEO Matthew Prince, this partnership offers “transparency and control” for newsletter operators amid an ever-evolving internet landscape. Meanwhile, beehiiv CEO Tyler Denk emphasized the pressing need for publishers like me to have “real leverage” as AI transforms content discovery and consumption. Cloudflare’s announcement summarized:
- “As AI models evolve to offer new forms of search and discovery, independent creators are looking for flexible ways to understand and manage how their content is accessed. This integration simplifies the process by letting beehiiv users manage their digital footprint through two clear choices: publishers can either opt-in to maximum discovery to allow AI search engines and agents to crawl their work freely for broader distribution, or choose content protection, blocking AI scraping to preserve their archives for future monetization and licensing opportunities.”
The Impact on Us. It remains to be seen if these controls will be widely adopted by publishers like myself once they are fully available. The rapid pace at which AI crawling is advancing has surpassed many content creators’ current management capabilities. The real test will be if these simplified controls are potent enough to alter my publishing strategies.

Rollout Begins. The rollout of these innovative controls begins through beehiiv’s standard dashboard settings. Every beehiiv user, myself included, will have beta access to AI Crawl Control, offering insights into AI crawler activity and traffic patterns. For beehiiv Max subscribers, the option to block AI crawlers will also be available.

The Full Announcement. For more details, check out the Cloudflare and beehiiv announcement on AI Crawl Controls.

Inspired by this post on Search Engine Land.
June 23, 2026
The Future of SEO Leadership: Navigating the Complexity
The job posting from Anthropic that everyone seems to be discussing is becoming the new standard. Companies who get this right are poised to quietly dominate the next decade.
The latest Anthropic job listing is causing a stir in the SEO community. They may as well have called it the Search Gawd position. To be honest, this is a reality across the board.
I’ve penned this kind of job description multiple times and even interviewed for it myself. I’ll admit, I haven’t seen many of these roles actually filled, but I’ll touch more on that shortly.
Titles vary—from Head of SEO to Director of AI Search, and even VP of Search or Agentic Commerce GEO Consultant. Lots of titles, same core responsibilities: manage technical SEO, grasp paid search, direct content, collaborate with engineering, build metrics, prepare for AI discovery, and translate it all into growth.
It’s predictable that people think this sounds like several jobs rolled into one—a single employee carrying the weight of an entire agency. This might be a fair observation, but it misses the critical point.
Businesses have been on the lookout for such talent for years. The rise of generative search is now compelling action.
This Isn’t Just an Anthropic Issue
While browsing job boards today, I noticed:
- Victoria’s Secret: Director, AI & Organic Search (AEO, GEO, SEO), $152K–$216K.
- Publicis / Starcom: VP, SEO (Performance Content).
- Accenture: Agentic Commerce GEO Consultant.
- SailPoint: AEO/GEO Manager.
- AirOps: Senior SEO Manager spanning SGE, Perplexity, ChatGPT, Gemini.
- Responsive: Senior Manager, Web Strategy — SEO, GEO, plus Next.js, React, Vercel, DNS.
- Danaher, Experian Health, Amazon News: variations of SEO + AEO + GEO.
- Anthropic: SEO Lead, $255K–$320K.
Diverse industries, varying salaries, yet they’re all unconsciously seeking the same elusive candidate.
Misalignment Between Titles and Responsibilities
Consider Agency X looking for a “Director, SEO/SEM,” whose job includes no SEO—just paid platforms, vendor management, and leading a team of seven.
Then there’s Consulting firm Y, seeking a “Director, SEO/AIO,” without clarifying what AIO entails. A smaller agency’s “VP/Director, SEO” asks for paid search, social, and pharma marketing as preferred skills.
A research firm is hiring a “Director, SEO & AEO,” which accurately reflects SEO and AEO duties—an unusual alignment worth highlighting.
If the company can’t settle on pre-defining the role, a candidate standing a chance seems improbable. The taxonomy says one thing, the JD another, the recruiter screens for something else, and the manager interviews for yet another role. Meanwhile, the applicant tracking system (ATS) disregards viable candidates.
In Search of the Missing Link
You’re searching for someone who can bridge technical search, content, PR, product, engineering, analytics, performance media, and brand—someone who knows these interactions are more intertwined than they appear on organizational charts.
Search highlights these intersections. Technical issues may seem like content issues, and content problems could stem from product issues. Visibility issues might be about authority, not just optimization. Paid search often uncovers messaging issues quicker than brand research does.
In the era of generative discovery, these connections can’t be ignored. When results provide answers, SEO shifts from being purely traffic-driven.
To sidestep into Yoda-speak to avoid AI jargon: information exists only if the infrastructure supports it. Content helps understanding, brand garners trust, and product transforms discovery into utility—or it doesn’t.
You’re not expecting one individual to tackle every task; rather, you want someone who understands the cohesion of these parts. That candidate exists, but traditional systems make it difficult to find them.
The Résumé Might Surprise You
The candidate you need won’t be evidently showcased by years with an SEO title or specific software lists. It’s about their judgment:
- Identifying crucial technical issues versus distractions.
- recognizing when content struggles require external resolution.
- Knowing when to invest, automate, or pause, and when to advise leadership against certain actions.
This kind of discernment doesn’t easily translate onto a résumé. The right candidate might have navigated through various roles in agencies, publishing, product, consulting, and operations. Their career might not appear streamlined like a specialist’s, yet that very diversity equips them for this role.
Unfortunately, your ATS will likely disqualify them, while your recruiter labels them as “non-linear.” Your hiring panel might note they’ve never held the precise title before. But remember, this role didn’t exist before, and there’s no consensus on its name.
Clearly, this selection process is heading off-course.
The Alsotative Possibility
Some processes may be more about absorbing insights from interviewing candidates than actually filling the position.
Senior candidates often diagnose: detailing function structure, identifying organizational weaknesses, outlining first-90-day plans, recommending tools, and highlighting tasks to abandon. By inviting numerous candidates, companies might inadvertently gather varied organizational strategies and priorities without making any hires.
Perhaps that wasn’t the original intent. But if roles remain unfilled for months, resurface repeatedly, alter their titles and scope, and produce interview-like advisory sessions, candidates are right to question what the company truly seeks: talent acquisition or strategic input?
Addressing the Real Issue
Narrowing the job description won’t eradicate the work needed. Focus on deciding the core requirement. Is it:
- A specialist to execute tasks?
- A leader to assemble a team?
- An executive to integrate search, content, product, brand, and performance?
- A consultant to advise on necessity?
These are distinct roles, and expecting them to merge into one is unrealistic.
A Final Thought
I’d excel at such a role, along with a few others who’d be filtered out for the same reasons.
Concerning the Anthropic opportunity, it isn’t materializing for me.
Five years under a nonexistent title from five years ago? My resume doesn’t show that. It matches the job spec — perfectly tailored for ATS rejection. It’s a straightforward system to manipulate, especially for those seasoned in the field.
The elusive talent is indeed genuine. Generative search only spotlighted the gap. Before your company finds someone to bridge these systems, ensure the capability to recognize, hire, and support them.
The companies that master the art of identifying the right candidate—and not just crafting an ideal job description—will take the lead in the coming decade. Meanwhile, others will continue LinkedIn debates about whether GEO is truly a word.

Inspired by this post on Search Engine Land.
June 23, 2026
Discover Google’s New AI Performance Reports: Expanded Access Unveiled

I’ve noticed something exciting happening with Google Search Console lately. The AI performance reports are becoming accessible to a wider audience, and it’s a game-changer for those of us eager to see how our content performs in Google’s AI environments.

John Mueller from Google recently shared on Bluesky, “We’re just rolling these out incrementally to sites, and reviewing the feedback along the way. I know everyone wants the new shiny thing immediately… but first, patience.” It’s like waiting for a gift you’ve been longing for!

AI performance report. These reports offer insights into how well our content and websites are featured in AI-driven searches, showcasing metrics such as impressions, pages, countries, devices, and dates. Although it doesn’t yet track click data, it’s still a significant step forward.

Expanding access. Earlier today, I spotted several SEOs sharing that these reports are now available beyond the UK! They’re able to access reports for sites in the US, India, Switzerland, and more.

As John mentioned, Google is gradually rolling these updates out to more sites, listening to feedback, and hopefully moving towards a global release.

What it looks like. Here’s a snapshot of the report:

Why we care. As someone deeply invested in how content is presented, I find this development thrilling. Publishers and site owners like me have long wanted more control over Google’s AI features. The speed at which Google has rolled this out is impressive—just within 20 days of its initial release!

Inspired by this post on Search Engine Land.

June 23, 2026
Deciding to Build or Buy Your Next SEO Tool with AI Insights
Before I consider requesting a new SEO tool, I always ensure that I understand the trade-offs between custom solutions, SaaS platforms, and hybrid approaches that utilize both.

AI has empowered SEO teams, including mine, to become more ambitious about automation. Tasks that once required engineering support are now tackled easily with tools like Claude or ChatGPT.

This is thrilling, yet it brings a new challenge: the assumption that everything can be automated. In today’s language, it boils down to a single question: Do we build or buy the tool?

The build-versus-buy dilemma is intricate, made even more so by AI advancements. It isn’t merely about cost; it’s about security, maintenance, data access, internal capabilities, workflow fit, and whether a custom solution can stay reliable and useful as time progresses.

How AI Lowers the Barrier to Building

AI has drastically lowered the barrier to experimentation. Even those of us without technical know-how can now create custom GPTs, build workflows, connect data sources, or craft an internal AI assistant.

However, maintaining a tool over the years remains a challenge, even if I managed to build it initially with AI support.

AI significantly aids SEO teams in data analysis, pattern recognition, summarizing information, and recommending actions, saving us a lot of time. Ignoring AI would surely leave us trailing behind.

It’s essential to acknowledge that AI still hasn’t reached the level of human creativity. It excels at working from established patterns and predicting outputs. This could evolve in the coming years.

AI tools also come with unseen costs. Internally developed tools may appear free since their invoices typically bypass our SEO teams, but expenses from token usage, API calls, infrastructure, engineering time, security reviews, and maintenance do exist.

Many organizations, as noted by Reuters, are experiencing “AI sticker shock,” finding themselves unable to forecast usage-based AI costs accurately. Companies like Uber, reported by TechCrunch, have even established AI spending caps after exceeding their annual budget in only a few months.

Currently, marketing teams, including mine, aren’t the largest AI consumers compared to engineering teams. Yet, this could shift rapidly.

When this happens, our expenditures will undoubtedly rise, prompting organizations to evaluate which AI tools and processes genuinely add value as opposed to simply consuming our budget.

Start by Defining What You Need

Before choosing whether to build or buy, SEO teams must define their true needs.

Different Ways to Use AI and Automation

I’ve noticed that many teams, including ours, lump various solutions together, yet they differ in cost, complexity, and maintenance.
- A custom tool: Generally a complex internal system necessitating engineering support, often focusing on automation and potentially incorporating AI aspects.
- A custom workflow: A repeatable process built with numerous tools like a custom GPT, spreadsheets, and automation, usually with an AI layer.
- A custom layer on SaaS: Leveraging data from existing tools to shape personalized reporting, prioritization, or recommendation processes.
- A true AI agent: A system capable of taking more autonomous actions, such as scanning Slack and following up on pending communications.
Though similar, these are often misidentified. Overgeneralizing terms like “AI agent” can lead to cost and complexity misjudgments.

Look for Repetitive, Context-Rich Tasks

Our team is still exploring AI capabilities. So far, we have concentrated on daily tasks involving substantial manual work.

For instance, we developed a custom GPT to assess whether our content aligns with our personas and addresses their pain points. The aim is not to replace our copywriters or reviewers, but to ensure that content isn’t generic and suggest pertinent enhancements.

We’ve also leveraged AI for translations, monthly reporting, and creating a weekly summary that integrates meeting notes, Slack, and Jira to identify outstanding tasks or follow-ups.

One of our newest workflows converts internal meeting recordings into structured landing page briefs.

Such tasks are ideal candidates for AI-powered custom workflows, given their dependence on internal context, repeatability, and specific company knowledge.

Not Everything Should Be Built

A case from our team involved a colleague who vibe-coded a prompt tracking tool. Although a good start, data presentation required manual steps for trend graphing, soon becoming a maintenance hassle due to changes in LLM tools.

The core issue was reliability. For AI visibility and prompt tracking, we needed stable data presentation, leading us to switch to a specialized platform like Peec AI, rather than maintain our own version.

This experience was insightful, enhancing our understanding of the problem, complexities, and necessary features when considering external solutions.

Here’s my advice: whether opting to build or purchase a tool, always explore existing market solutions. It helps to narrow down the essential features, preventing reliance on non-essential ones.

Especially for business-critical tools like rank tracking and website crawling, smaller SEO teams without technical support should be cautious of building from scratch. Reliability should be prioritized when data is crucial for decision-making.

Use AI Where Your Data Already Lives

Consider buying a crawler, rank tracker, or AI visibility platform and focus on linking these with custom data like GA or GSC accounts, or CRM data. This integration allows comprehensive analysis in a single view.

MCP connections also warrant consideration. The Model Context Protocol is a standard for linking AI applications with external systems, enhancing current workflows.

Though not necessary to learn coding, understanding enough to ask the right questions is beneficial.

If sensitive data is involved, like proprietary research or customer details, it’s crucial to assess security risks. It may be safer to allocate engineer support to avoid compromising sensitive information.

Deciding on a custom tool requires acknowledging the full cost, including engineering time, security reviews, and API usage, despite invoices not being SEO-related.

Before requesting any tool, SEO teams should articulate the problem, expected value, cost comparison between building and buying, and potential consequences of taking no action.

Effective requests should not start with tool needs, but with the problem, its significance, tested solutions, and the proposed optimal solution.

How to Prioritize What to Build First

No one-size-fits-all matrix exists for prioritizing builds.

Tools vary; from website crawlers to content evaluation systems, each can’t be judged by identical criteria.

In doubt, start by mapping current workflows versus the ideal ones. Patterns often emerge, highlighting primary priorities.

The first group involves tools that aid revenue generation, like identifying content opportunities or improving conversion. Marketing, including SEO, seeks visibility and leads, thus revenue-centric tools can be higher priorities.

The second category concerns tools minimizing repetitive tasks. While they may not directly create revenue, they free up valuable team time for strategic work.

Quick wins should not be ignored. Stakeholders value timely results, thus a small project with potential returns within weeks can build trust and support larger initiatives.

Also, consider cross-team value in your decision. SEO problems often extend beyond one team. Collaborating with other teams can strengthen the business case for shared solutions.

Often, the best tool isn’t the most complex. Starting small could be the strategy for smarter progress.

Remember, effective scoping leads to good decisions. Even with AI easing the build process, proper scoping of what to build remains essential.
- Define the problem, expected value, user base, and post-launch maintenance.
- Engage with your team and other departments, identifying whether it’s solely an SEO issue or a broader business challenge.
- Avoid building for AI’s sake, or being swayed by impressive demos.
Neglecting scoping risks acquiring costly tools that don’t integrate with workflows or building internal tools beyond maintenance capabilities.

Thoughtful consideration of scope is crucial before opting to build, buy, or customize a solution.

Inspired by this post on Search Engine Land.
June 23, 2026