This week’s #SEOForLunch sponsors are Scrunch and Jolly.

Let’s get real. SEO advice often sounds completely made up. There, I said it!

A lot of recommendations sound ridiculous to people who do not live in search every single day. “Change this canonical.” “Don’t block that resource.” “We need this content exposed in the rendered HTML.”

This is exactly why SEO still gets labeled as black magic in many organizations.

I’ve been an advocate for “un-nerding SEO” for years, but that’s a different story. Today, we’re talking about something much more practical: using Google’s own documentation to win approval, build trust, and prioritize tasks for action.

Not because Google tells us everything. Not because every sentence should be treated as gospel. But because hard-documented resources are hard to blow off.

When you need buy-in, sometimes the best argument is not “trust me.”

It’s “Google already documented how we should approach this.”

Thank you to Scrunch for sponsoring this week’s #SEOForLunch

Meet your most valuable new site visitor:
the AI bot

For 20 years, websites had two audiences to please. First humans, then Google. Now there’s a third—and when it shows up, it means a real buyer just asked AI about you.

Here’s the catch: AI isn’t persuadable. It skips your animations, it’s colorblind to your brand guidelines, and it cares about one thing—whether your content is clear, structured, and easy to retrieve. To a bot, your site isn’t an experience to navigate. It’s a dataset to mine. And if it can’t read you, it cites someone else.

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See how your brand shows up

The Buy-In Problem Is Usually Not The Recommendation Itself

Most SEO recommendations do not die because they are wrong. They die because they have to compete with everything else happening inside the business.

Dev sprints, product timelines, CMS limitations, legal concerns, brand standards, executive assumptions, and the ever-popular… (close your eyes!) “we’ve always done it this way”, it all has a seat at the table. SEO is rarely the only priority in the room, even when the recommendation is technically correct.

That is why “best practice says” or “from an SEO perspective” is simply not enough to close the deal. It sounds optional, especially to teams already managing risk, deadlines, and competing requests.

But “Google has official documentation to guide us” lands differently.

It may not automatically win the argument, and it definitely does not mean the recommendation gets prioritized tomorrow. But it does change the conversation from “the SEO gal told us…” to “Suzzie provided us official Google documentation explaining why this matters.”

Google Documentation Is Not Gospel

I know what some of you are thinking.

“Nick, are we really pretending Google tells us the full truth about how search works?”

Fuck No. I mean… absolutely not.

Google’s documentation is not the complete truth of search. It has omissions. It simplifies complicated systems. Sometimes it explains how Google wants site owners to behave, not necessarily every nerdy factor that goes into organic visibility.

Google also writes documentation for a very broad audience. That means nuance gets sanded down, edge cases get skipped, and the answer is often technically true without being the entire story. (Want proof? Log in to X, and you’ll read SEOs with too much time on their hands, complaining and “calling out” Google for this)

So no, I am not suggesting we treat every Google statement as if it were carved into stone tablets and carried down from Mountain View.

But that does not make the documentation useless.

It makes it a starting point. A receipt. An official reference point.

It moves the conversation away from “Nick thinks this matters” and toward “Google has explicitly documented why this matters.” That distinction matters a lot when you’re trying to get someone else to approve (and prioritize) the work.

Documentation Is Especially Useful With Developers

This is probably where Google documentation earns its keep the fastest. SEOs need developers, and the fastest way to lose their support is to treat every recommendation as a command rather than a requirement.

but… just in case this works…

Bummer, no dice.

Developers are not wrong just because they disagree with an SEO recommendation. Most of the time, they are optimizing for completely different (but valid) things, such as performance, code quality, technical debt, security, and let’s not forget, simply not being the person who takes the entire website down with one wrong push to production.

But sometimes they are wrong about how Google discovers, crawls, renders, indexes, or interprets content.

And telling a developer “you’re wrong” is a great way to make sure your ticket never sees the light of day.

This is where Google documentation helps. It removes subjectivity on the purpose/value. It aligns the conversation on how best to implement within your own ecosystem, rather than the recommendation being “right vs wrong”

• Canonical tags are signals, not directives.

• Robots.txt blocks crawling, not always indexing.

• JavaScript rendering has limitations.

• Internal links need to be discoverable.

• Structured data has eligibility requirements.

• Why status codes matter and a 302 doesn’t “do the same thing”

The point is never “SEO wins and dev loses.”

The point is that you now have an external source of truth to discuss. That is a much better conversation than two teams arguing from preference.

Documentation Is Also A Client Management Tool

For client-facing SEOs, documentation helps separate serious recommendations from “trust me, bro, I have a contact at Google” consulting.

This is especially useful when a client has been burned by bad SEO advice before.

Instead of saying:

“We need to change this because it’s better for SEO.”

You can say:

“Here’s what Google documents. Here’s where your current setup conflicts with that. Here’s the risk. Here’s the recommendation. Here is the estimated reward.”

That framing builds trust because it shows the recommendation is not reliant on 100% blind faith.

It also makes the SEO look less like a magician and more like an interpreter.

That is the real role of an SEO: translating Google’s needs from a website (confirmed via documentation) into business and technical decisions.

Less Black Magic, More Receipts

SEO has a reputation problem, and some of it is earned.

Too much of our work is still explained in vague terminology with low confidence. We say things like “Google likes this” or “this needs to exist for the bots” when we should be saying, “Google documents this behavior here, and here’s how it applies to our situation.”

That does not mean documentation alone creates buy-in.

Dropping a Google link into a ticket or Slack thread is not a strategy. You still have to translate what it means, explain the risk, connect it to business outcomes, and help the team understand why the recommendation deserves attention.

Google documentation will never replace experience, testing, or judgment. It will not tell us everything, and it should not be treated like the final answer to every SEO debate.

But it can make SEO easier to defend, easier to prioritize, and much harder for divisional leaders to dismiss.

The best SEOs are not just the ones who know what to recommend. They are the ones who can prove why the recommendation deserves to be taken seriously.

Less black magic. More receipts. More results.

Google documentation may not be the whole truth, but it sure beats showing up to a buy-in conversation with “my buddy from Google told me….” Suuuure they did.

~Nick

Thank you to Jolly for sponsoring this week’s #SEOForLunch

PR Hack: The “SEO” Job Title?

Brought to you by Jolly.

We sent 11,238 journalist pitches in 6 months, landed 393 placements, and reached 1,450,000,000 monthly readers. Then we split the results by job title:

• Founders drove the most total reach (598M).

• CEOs came second (384M).

• But what about SEOs? 22 placements averaged 12.9M monthly readers EACH.

TL;DR: The “SEO” job title pulled in roughly 4x what the average Founder or CEO placement landed.

Your credential punches way above its pay grade. Worth knowing before your next pitch, or your next salary negotiation:

See the Full Breakdown

This week’s #SEOForLunch sponsors are Semrush and North Star Inbound.

A client receives two deliverables…

Both solve the problem they were hired to solve. Both are accurate and useful, and they lead to the same business outcomes. The client is happy with the work and sees no meaningful difference in the results.

Then they learn that one deliverable took 20 hours to create while the other took 20 minutes. Now the questions start rolling in…

• Was AI involved?

• Should the faster deliverable cost less?

• Is the person who completed it somehow less skilled because they found a way to work more efficiently?

What’s interesting is that most of us have completely different reactions to AI depending on which side of the transaction we’re sitting on. We love using AI to save ourselves time, but many become uncomfortable when they’re the customer and discover AI was used to create something they purchased.

I recently ran a LinkedIn poll asking a simple question: If the outcome is great, do we really care how it was made?

The responses reinforced something I’ve been thinking about for a while. The biggest objections people have to AI often have very little to do with quality.

Thank you to Semrush for sponsoring this week’s #SEOForLunch

Meet the AI visibility leaders in retail,
plus new tactics

AI search is reshaping customer journeys and how shoppers discover and choose products. But many retail marketers don’t yet have a clear benchmark of AI visibility, who’s winning and why.

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The Time vs Value Fallacy

I think part of the discomfort comes from the fact that we’ve spent decades connecting value to effort.

• Long hours feel valuable.

• Fast work feels suspicious.

• Struggle feels like expertise.

The harder something appears to be, the easier it is to justify its price.

The story is about a ship engine that stopped working. After multiple attempts to repair it, the owners brought in an engineer with decades of experience. He inspected the engine, tapped it once with a small hammer, and the machine roared back to life.

His invoice was $10,000.

The owners were furious and demanded an itemized bill.

The response:

• Hammer tap: $2

• Knowing where to tap: $9,998

The debate whether this is a true story or just a fun tale that people like me use to validate value-based pricing. Whether the story is true almost doesn’t matter. The lesson does.

People aren’t paying for the tap. They’re paying for the expertise behind it.

That’s what makes AI such an interesting topic. It forces us to confront a question many of us have avoided for years:

Those aren’t always the same thing.

The Objections That Actually Matter

To be clear, not all objections to AI are bad ones. I certainly haven’t had an issue sharing my opinion.

In fact, I think some of the strongest arguments against AI have very little to do with how quickly something was created.

• Risk matters

• Hallucinations matter

• Bad recommendations matter

• Compliance, privacy, and security concerns matter

• Accountability matters

These are all legitimate concerns, and what’s interesting is that none of these concerns have much to do with how long it took to create the deliverable.

They’re questions of trust.

Can the output be trusted? Can the recommendation be defended? Can someone confidently stand behind the work if it’s questioned six months from now?

Because when something goes wrong, nobody gets to blame the AI. The employee is accountable. The consultant is accountable. The company is accountable.

That’s why I’ve always found the quality debate to be the least interesting part of the conversation. The more important question isn’t whether AI was involved. It’s whether the outcome is trustworthy enough for someone to put their name behind it.

Nick’s Weekly “This Doesn’t Suck” List

A short list of things that stood out to me this week. No affiliate links, hidden angles, and certainly no way to buy your way in. If it’s here, it was earned.

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I want to shout out Nicole Ponce and Jo Cameron from Semrush and Moz.

This summer I’m doing a mini SEO internship with my 14-year-old son. Both Nicole and Jo hooked my boy up with swag and training/certifications from their companies.

If you (or someone you know) is just starting out, make sure to check out the educational content and certifications from both Semrush and Moz.

The Outcome Test

The more I think about AI, the less interested I become in whether it was used.

Instead, I find myself asking a different set of questions.

• Was the outcome accurate?

• Was it useful?

• Was it better than the alternative?

• Would you be willing to stand behind it with your name, reputation, and credentials on the line?

If the answer to all of these is yes, should we really care how it was produced?

I suspect this is where many people become uncomfortable because it shifts the conversation away from tools and back toward results.

Ironically, this is also where humans become more important, not less.

The future isn’t machines versus humans (I know, The Terminator and I, Robot movies will never be the same). It’s humans using AI versus humans who don’t. The premium won’t come from refusing to use AI. It will come from judgment, taste, decision-making, communication, and accountability.

AI can accelerate execution, but humans still decide what should be built, what should be published, and what risks are acceptable. More importantly, humans are still the ones responsible for the outcome.

The people who lose to AI won't be the ones using it. They'll be the ones still evaluating effort while everyone else is measuring outcomes

~Nick

Thank you to North Star Inbound for sponsoring this week’s #SEOForLunch

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This week’s #SEOForLunch sponsors are AirOps and North Star Inbound.

The best and worst part of the World Wide Web is the ability to freely share your opinions regardless of whether they’re technically accurate (or not).

But imagine a scenario where you are held accountable not only for what you say on the internet, but also for whether it is human- or AI-written?

A recent report came out that a German court is holding Google accountable for just this. The court treated the AI overviews as Google’s own content and rejected Google’s argument that users were responsible for fact-checking the results themselves.

Let’s talk more about what this could look like for businesses and individuals.

Thank you to AirOps for sponsoring this week’s #SEOForLunch

Getting retrieved by AI search is not the
same as getting cited.

Strong rankings and clean pages still lose when AI systems decide which sources to reference. The gap is everything that happens between the prompt, the fan-out, and the final citation.

On June 25, AirOps Head of Research Oshen Davidson breaks down where citation wins actually come from, alongside Melanie Dell’Olio (AI Solutions) and Josh Spilker (Head of Search Marketing).

You’ll walk away knowing:

• Why many retrieved pages never make the final answer

• Which page elements get content parsed and reused (headings, lists, tables, stats, schema)

• How to find pages being surfaced but not selected, then prioritize the right fixes

• What to measure beyond rankings: citations, mentions, repeat visibility, refresh timing

Tactical, no fluff, built for teams who want AI visibility that moves pipeline.

From Research to Revenue:
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The “Disclaimer” Defense Is Cracking

For the last few years, nearly every AI platform has relied on some version of the same disclaimer: “AI can make mistakes. Please verify important information.”

Most users recognized this as the cost of using these tools.

But the German court essentially said that merely warning users about potential errors doesn’t automatically remove responsibility when those errors cause harm. If your system creates new claims that never existed in the source material, those aren’t someone else’s words anymore. They’re yours.

That’s a much bigger shift than many people realize. This is where legal AI ramifications start.

Why? Because it moves the conversation away from whether AI is useful (AKA *) and toward who owns the consequences when it gets things wrong.

What This Means For Businesses

Many companies are blindly trusting rapidly adopting AI across content creation, customer service, product descriptions, reporting, legal reviews, hiring, and internal communications.

• In many cases, the discussion centers around efficiency.

• Can we create content faster?

• Can we answer support tickets more cheaply?

• Can we automate this process?

Those are reasonable questions.

But the German ruling introduces another question:

Who is responsible when the output is wrong?

What if….

• AI-generated support response gives inaccurate guidance

• AI-written article damages a competitor’s reputation

• AI-generated report contains fabricated information that influences a business decision

The “AI wrote it” defense may become less effective over time. It darn near cost me 20 million.

The more people position AI as a trusted source of information, the harder it becomes to argue they shouldn’t be accountable for what it says.

The Situation Is Kinda Funny…

The irony in this is that most AI vendors already know this.

That’s why nearly every platform includes warnings, disclaimers, and usage policies.

Yet those same companies market AI as smarter, faster, more capable, and increasingly reliable.

You can’t, at the same time, tell users to trust the answer while arguing nobody should trust the answer.

At some point, those positions collide. We’re starting to see Google’s “solution”: an option to opt out of AI.

Germany may simply be one of the first courts willing to force Google (or any other LLM business) to take certain actions.

What SEOs Should Be Paying Attention To

Ironically, this ruling could end up benefiting everyone.

Today, the conversation is focused on whether AI companies should be responsible for the content their systems generate. But accountability could expand, well beyond AI.

The internet has spent decades creating distance between actions and consequences. Anonymous accounts, fake profiles, throwaway emails, and now AI-generated content all make it easier to say things without owning them.

That’s why I find this ruling so interesting.

Not because of Google. Because it introduces the idea that “I didn’t write it” may not be enough.

The image below shows a real email that Russell and Nina Westbrook received. A real person sat behind a keyboard and sent a message, hoping they would die in a car crash.

That isn’t free speech. It’s hate speech.

The internet, combined with AI, needs more confidence that the content is accurate and that people who create with it are held accountable.

You don’t get to claim the productivity gains when AI is right and blame the algorithm when it’s wrong.

~Nick

Thank you to North Star Inbound for sponsoring this week’s #SEOForLunch

Digital PR is not just top-of-funnel anymore. In GEO authority compounds.

In a recent campaign, we generated 4,400 links for Preply over 20 months. If you want earned media that strengthens long-term discoverability, we can help.

Book a call

This week’s #SEOForLunch sponsors are Semrush and North Star Inbound.

For the past two years, the SEO industry has been asking Google for two things: more visibility into AI traffic and more control over how content appears in AI experiences.

Last week, Google started delivering both.

They announced new controls that allow site owners to opt out of AI-powered experiences (AIO/AI Mode, etc.) and introduced new AI reporting within Google Search Console. (Note that both of these are in early beta and are not yet available for everyone)

On paper, this is a victory for things moving in the right direction for publishers.

Instead, the conversation immediately split into camps. Some focused on the new reporting. Others focused on the new controls and began debating whether to opt out of AI altogether.

What caught my attention wasn’t the announcement itself. It was how quickly the conversation shifted from gaining visibility to voluntarily giving it up.

Thank you to Semrush for sponsoring this week’s #SEOForLunch

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What This Actually Means

Before we go any further, let’s clear up what Google actually announced.

The new controls do not turn off AI Overviews, stop people from using AI Mode, or slow AI adoption. Users are still going to search and ask questions, and increasingly do so through AI-powered experiences.

Google introduced a way for publishers to have more control over whether their content can be surfaced in those experiences. (Was this the plan all along, or was it exclusively because of the UK Competition and Markets Authority demanding it?)

That’s an important distinction because many people are treating this as a decision about AI itself. It isn’t.

• AI Mode doesn’t disappear because a publisher opts out.

• AI Overviews don’t disappear when a website decides not to participate.

The user experience remains largely unchanged. The only thing that changes is which brands are eligible to appear.

If Expedia opted out tomorrow, people wouldn’t stop planning vacations. If NerdWallet opted out tomorrow (like I did their stock), people wouldn’t stop researching credit cards. Google would simply surface someone else in its place.

This isn’t a decision about whether AI succeeds or fails. It’s a decision about whether your brand is present when customers choose to use it.

Why AI Opt-Out Sounds Good But Is Actually A Trap

I understand the appeal. Publishers are worried about losing more clicks, frustrated by changing search behavior, and concerned about how AI systems use their content.

Those concerns are beyond valid.

Where I disagree is with the assumption that opting out changes user behavior.

It doesn’t.

Users aren’t deciding whether to use AI based on your participation. They’re deciding whether AI helps them get answers faster. For a growing number of searches, it does.

That’s why opting out of AI inclusion and opting users out of AI experiences are two very different things.

A publisher can choose not to participate. Users can still use AI Mode. Google can still answer the question. The only thing that changes is which brands are eligible to appear.

That’s the trap.

The practical outcome isn’t less AI. It’s more visibility for your competitors. They gain citations, exposure, and the opportunity to become the trusted answer, while your brand becomes less visible.

If the concern is that AI is changing how customers discover information, disappearing from AI-powered experiences feels like a pretty dumb move.

The challenge isn’t finding ways to be less visible. It’s finding ways to remain visible as search behavior continues to evolve.

Google Finally Gives Us AI Data... And SEOs Still Complain.

The other part of Google’s announcement that received less attention was the reporting.

For years, the industry has been asking for more visibility into AI-driven search experiences. We wanted better attribution, better reporting, and a clearer understanding of how users interact with AI-powered search.

Now Google is beginning to provide some of that visibility, and almost immediately the conversation shifted to why it isn’t enough. Note that many of these screenshots are illustrative and are even from industry friends and well-respected search practitioners in our space. No shade intended to any one individual, simply wanting to illustrate the movement.

Maybe that’s true. The data isn’t perfect. The reporting doesn’t answer every question. I’d love more visibility into citations, AI Mode interactions, and better any sort of attribution modeling.

I especially agree with Dan’s post above, but waiting for perfect data has never been a winning strategy.

SEO has always operated with imperfect data. We’ve spent years making decisions based on estimated search volume, incomplete attribution, and reporting limitations. Some of the biggest wins in my career came from acting on directional signals rather than perfect certainty.

The same applies here.

The mistake is treating every reporting enhancement as either perfect or useless. We’re getting more visibility than we had six months ago, and we’ll likely have more six months from now.

Nick’s Weekly “This Doesn’t Suck” List

A short list of things that stood out to me this week. No affiliate links, hidden angles, and certainly no way to buy your way in. If it’s here, it was earned.

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My Reporting Approach: SEO+ Reporting

Part of the reason this debate exists is that many teams are still measuring success through a traditional SEO lens.

Traditional reporting focuses on clicks, rankings (ewww), traffic, and conversions. Those metrics still matter, and I don’t see them disappearing anytime soon. The problem is that they’re no longer telling the entire story.

Users are discovering brands across more surfaces than ever before, especially outside of the Google ecosystem. Traditional organic search still matters, but so do AI Overviews, AI Mode, ChatGPT, Perplexity, Bing Copilot, Reddit, YouTube, and a growing list of ecosystems users rely on in their purchasing journey.

That’s why I’ve started thinking about reporting as “SEO+” rather than just SEO. (Yeah, I’m lazy and used the streaming naming convention “+” because… yeah, lazy.)

The goal isn’t to abandon traditional metrics. The goal is to expand what we’re measuring. Alongside traffic and conversions, I want to understand where brands are being cited, how often they’re being mentioned, how many unique URLs are being cited, whether branded search demand is increasing, how AI platforms reference them, and whether visibility is expanding even when attribution remains borked.

This is where I think many organizations are making the same mistake they made with content years ago.

With one of my clients, a lot of our content influences revenue months before a customer converts. Looking only at last-click reporting dramatically understates the impact. That’s why I started reporting on “content assists” as a key metric in their reporting. AI visibility is creating a similar challenge. A customer might first encounter your brand through an AI Overview, revisit you through traditional search, and ultimately convert through a completely different channel (probably a paid channel… ‘cause everyone loves ROAS).

The influence is real even when the attribution path is messy.

That’s why I’m less interested in measuring traffic alone and more interested in measuring discoverability. The brands that consistently appear across search, AI, and recommendation platforms are building familiarity long before a conversion occurs.

The Wrong Question

Most of the discussion around Google’s announcement has centered on a single question:

Should I opt out of AI?

I think that’s the wrong question.

The better question is whether you can afford to be absent from the places where customers increasingly discover information, products, and brands.

Users aren’t waiting for the SEO industry to decide whether AI is good or bad. They’re already using it.

That’s why I view Google’s announcement less as an AI opt-out feature and more as a strategic decision point. Opting out doesn’t remove AI from the equation. It simply increases the likelihood that someone else becomes the answer instead.

Some brands will use it.

Their competitors are hoping they do.

Will you lean into change, or will you be another person complaining that Google owes them free clicks?

~Nick

Thank you to North Star Inbound for sponsoring this week’s #SEOForLunch

North Star Inbound runs an integrated growth system:

• Strategic SEO and GEO

• Digital PR

• Conversion copywriting

Case study proof: our top 5 posts drove 144712 sessions in a 9-post pilot and led the blog for conversions, including 224 conversions and 217 phone calls from one post.

Book a call

This week’s #SEOForLunch sponsors are Profound and North Star Inbound.

The search industry is experiencing something many of us have never seen before.

The supply of search talent now exceeds the demand for search talent.

Blame artificial intelligence. Blame the economy. Blame years of checkbox SEO execution becoming increasingly commoditized.

Whatever the reason, the result is the same.

Search layoffs are up. Job openings are down. The market is more competitive than at any point in my 15+ year career.

The uncomfortable reality is that many of the skills that once made an SEO valuable are becoming easier to automate, outsource, or generate with AI.

Pull up a chair.

Let's talk about why this is happening, what skills are becoming table stakes, and what employers should actually be looking for when hiring SEO talent in 2026.

Thank you to Profound for sponsoring this week’s #SEOForLunch

Introducing Benchmarking By Profound

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The First SEO Jobs AI Comes For

One of the biggest misconceptions surrounding AI is that it’s coming for SEO jobs.

I don’t believe that’s what’s happening.

What I do believe is happening is that AI is changing which SEO skills employers are willing to pay a premium for.

Historically, a significant portion of SEO work revolved around gathering information and generating recommendations. Technical audits, content briefs, keyword clustering, schema markup, metadata recommendations, and competitive analyses all required time, experience, and effort.

Those activities still matter today.

The difference is that they are becoming dramatically easier to produce.

An SEO can now use AI to generate a first-pass audit, content brief, or optimization recommendation in minutes. What once took hours can often be accomplished in seconds.

This doesn’t make the output worthless. It simply changes where the value resides.

For years, many organizations treated recommendations as the deliverable. The audit was the deliverable. The roadmap was the deliverable. The deck was the deliverable.

But recommendations were never the end goal.

Recommendations only create value when they lead to prioritization, implementation, and measurable business outcomes.

AI helps solve the problem of idea generation.

It does very little to solve the implementation problem.

That’s why I believe the first SEO jobs AI comes for are those centered around producing recommendations rather than driving outcomes.

As the cost of generating recommendations approaches zero, employers naturally place greater value on the people who can determine which recommendations matter and implement them.

In other words, AI is commoditizing parts of SEO execution.

It is not commoditizing judgment.

What AI Still Struggles To Do

As AI becomes better at generating recommendations, the value of SEO talent shifts elsewhere.

Prioritization. Testing. Communication. Influence. Judgment.

These aren’t new skills. They’ve always mattered.

The difference is that they are quickly becoming the primary differentiators.

Most organizations don’t suffer from a shortage of ideas. They suffer from a shortage of alignment, execution, and good decision-making.

Finally, there is judgment.

A few weeks ago, I found myself disagreeing with Gemini about a topic I know extremely well. The answer sounded reasonable. The explanation was polished. The problem was that it was wrong.

As AI becomes more capable, the ability to identify when it is confidently wrong becomes a skill in itself.

The future SEO isn’t the person who can generate the most recommendations.

It’s the person who knows which recommendations actually matter.

The New SEO Career Framework

For years, the career progression of an SEO was fairly straightforward.

• Learn more about SEO. Get promoted.

• Learn technical SEO. Get promoted.

• Learn content strategy. Get promoted.

• Learn analytics. Get promoted.

While those skills are still important, AI is rapidly reducing the value of knowledge for knowledge’s sake. The ability to produce SEO recommendations is becoming more accessible every day.

That doesn’t mean expertise no longer matters. It means the skills layered on top of that expertise matter more.

Today’s most valuable search professionals understand search. They understand AI. They understand how businesses operate. Most importantly, they know how to align people, priorities, and resources around a common objective.

The higher you climb within an organization, the less your success depends on your ability to identify problems and the more it depends on your ability to get those problems solved.

AI scales execution. People scale vision.

Nick’s Weekly “This Doesn’t Suck” List

A short list of things that stood out to me this week. No affiliate links, hidden angles, and certainly no way to buy your way in. If it’s here, it was earned.

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What I’d Look For If I Were Hiring Today

If I were hiring an SEO in 2026, I wouldn’t spend much time asking about canonical tags, title tags, or XML sitemaps.

Not because those topics are unimportant, but because I can quickly determine whether someone understands the fundamentals.

What I’d really want to understand is how that person operates when things become messy.

I’d ask them to share a recommendation nobody agreed with.

Years ago, I argued that H1 tags provided virtually no ranking benefit. People laughed. Some openly disagreed. Eventually, John Mueller echoed a similar position, and Bill Slawski had been discussing the concept for years.

I don’t care whether the candidate was right.

I care whether they had the conviction to challenge assumptions and the communication skills to navigate disagreement.

I’d ask about a failed test.

Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and maintain momentum?

I’d ask about a project that stalled.

Every experienced SEO has watched a seemingly great initiative die somewhere between recommendation and implementation. The difference is what happened next. Did they move on to the next project, or did they find a way to remove blockers and keep the momentum going?

I’d also ask where AI gave them bad advice.

The common theme across all of these questions is simple.

I’m not looking for someone who knows SEO.

I’m looking for someone who can turn SEO knowledge into outcomes.

The easiest part of SEO has always been knowing what to do.

The hard part is getting it done, generating tangible results.

AI Won’t Replace SEOs. But The Lazy Ones Are Toast

~Nick

Thank you to North Star Inbound for sponsoring this week’s #SEOForLunch

Turn Your Blog Into Your Biggest Sales Engine

Brought to you by North Star Inbound–the sales enablement SEO agency.
​​How? Focus on high-impact SEO strategies that target the most profitable opportunities.

North Star Inbound has a proven track record of creating top-converting blog pages that deliver thousands of leads

• hundreds of thousands in directly-attributed revenue

• and rapid payback periods on SEO spend.

They focus on the 10% of pages with the highest ROI for your brand. Basically, your content becomes your most effective sales tool.

There’s no better way to loosen tight budgets than to grow revenue quickly.
Intrigued?​

Book a call

Thanks to this week’s sponsors: Semrush Enterprise & North Star Inbound

Imagine working in SEO for years, researching a problem you know inside and out, only to have an LLM confidently explain why your experience is wrong.

That happened to me. Actually, it happened three different times last week with Gemini.

The scary part wasn’t that the answers were obviously bad. It was that they sounded good. The responses were polished, believable, and directionally accurate enough that most people would never question them.

And if you aren’t deeply familiar with the topic, you probably wouldn’t know how to challenge the answer.

Funny enough, I caught it twice.

The third time cost me money.

All within the same week.

Thank you to Semrush Enterprise for sponsoring this week’s #SEOForLunch

Discover how people are really searching, discovering, and engaging online

AI continues to reshape how users discover, engage, and click across search environments. But most teams are still making decisions without a clear picture of what’s actually shifting.

The Datos State of Search Q1 2026 report, created in collaboration with Rand Fishkin, maps real search behavior across millions of users in the US, EU, and UK. AI adoption, regional contrasts, e-commerce discovery, and intent signals are all explored in detail using real clickstream data.

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EX1:
Gemini Educates Me on Technical SEO

Let me give you some context.

I’m currently helping migrate a client’s FAQ hub from a provider-hosted subdomain to a self-hosted implementation.

The FAQ lives under a /faq/ folder, but individual Q&A pages are parameter-based URLs. Normally not an issue... except Shopify forces canonicals back to the root /faq/ page, effectively preventing those pages from being indexed.

While researching Shopify-specific solutions and duplication considerations, Gemini gave me this response:

EXCUSE ME!?

You will absolutely NOT be penalized for conflicting SEO signals.

At best, Google indexes what it wants. At worst, it ignores directives it doesn’t trust.

The bigger issue is the wording.

“Penalty” is basically the magic word in SEO. The moment leadership hears it, priorities shift, momentum dies, and recommendations become harder to implement.

Then Gemini doubled down.

I asked whether removing the canonicals and allowing the parameter pages to exist independently was an option.

Gemini responded:

“Google generally ignores query parameters.”

Wow... just wow.

Below is an implementation I worked on with the Saatva team, where we intentionally indexed parameter URLs within the shopping experience.

Search Console and URL inspection confirmed those pages indexed just fine.

Parameter pages can absolutely rank, index, and generate value.

The problem isn’t that Gemini was wrong (as in it’s a more difficult implementation).

It’s that the answer sounded believable enough that someone without SEO experience would probably accept it and move on.

EX2:
Gemini Says Solve The Issue With a $3,000 Part

This one hit differently because I’m not a mechanic.

I’ve been troubleshooting an issue on my Jeep SRT and spent hours outside in the sun collecting data, testing fuses, reviewing OBD2 logs, and trying to narrow down the root cause. After reviewing everything, Gemini confidently concluded the issue was likely a rear differential failure and suggested a full replacement.

Estimated damage? Roughly $3,000 in OEM parts alone.

The response sounded fantastic. It was detailed, logical, and even complimented my troubleshooting process.

The problem? It was wrong. Really wrong.

After pushing back and sharing additional OBD2 data I had been tracking during diagnosis, Gemini completely reversed course and admitted it jumped to a worst-case scenario without enough evidence.

In other words, I almost spent thousands replacing parts I didn’t need.

That’s what makes these examples interesting. In SEO, I immediately knew Gemini was wrong because I had years of experience to challenge the recommendation. Here, I didn’t have that advantage. I had to rely on skepticism, continue testing, and avoid treating the answer as fact.

Same AI. Same confidence. Completely different outcome.

Nick’s Weekly “This Doesn’t Suck” List

A short list of things that stood out to me this week. No affiliate links, hidden angles, and certainly no way to buy your way in. If it’s here, it was earned.

---

EX 3:
Gemini Cost Me $20 million dollars!

Ok, technically, this was video game money.

I was playing Madden, working through team logistics, and trying to optimize salary cap spending to re-sign key players and keep the roster together.

At some point, I got lazy and tagged in my partner, Gemini. I shared a screenshot of the team finances and asked for a roadmap to restructure contracts and optimize the cap situation.

Gemini gave me a detailed plan. I followed it.

And it put me $20M over the salary cap.

The funny part is this perfectly mirrors the earlier examples.

The answer looked good. It was organized, confident, and gave me a clear player-by-player action plan.

So I trusted it. Then I called Gemini out.

You’ll notice in the second screenshot that Gemini essentially points out that I blindly trusted the recommendation without validating the math.

Honestly… fair.

The difference is that this mistake only cost me fake money and a Madden franchise. The Jeep example almost cost me real money, and the SEO example could have cost implementation momentum and trust.

The value of expertise was never memorizing answers. It was knowing when something feels off, asking better questions, and recognizing when the answer smells like bullshit.

AI didn’t remove that skill. If anything, it made it more valuable.

AI isn’t replacing experts. It’s replacing people who stopped thinking.

AI isn’t replacing experts. It’s replacing people who have stopped thinking.

~Nick

Thank you to North Star Inbound for sponsoring this week’s #SEOForLunch

North Star Inbound runs an integrated growth system:

• Strategic SEO and GEO

• Digital PR

• Conversion copywriting

Case study proof: our top 5 posts drove 144712 sessions in a 9-post pilot and led the blog for conversions, including 224 conversions and 217 phone calls from one post.

Book a call

This week’s #SEOForLunch sponsors are AirOps and North Star Inbound.

There are two questions that come up in at least one meeting per day for every single business.

1. “How do we get back our Google clicks?”

2. “How do we show up in all the LLMs?”

The answer to both of these questions is one that very few want to hear. Hold your breath…

Build your brand. Gone are the days of “add some more keywords” and “how many links would it take to…”

Does this mean search-and-answer bots can’t be manipulated? hell no, but the likelihood that these “tactics” will deliver long-term, consistent value is nil.

Thank you to AirOps for sponsoring this week’s #SEOForLunch

What’s New At AirOps - Product Launch

Visibility tools have spent the last year telling marketers where their brands stand in AI search. None of them tells you what to do about it.

Now that changes.

AirOps is the growth platform for AI search. One place to see where you’re cited, ship the work that closes the gaps, and measure what each Play is actually moving. Not a dashboard. Not a writing assistant. The operating system for marketers winning the answer era.

I got an early look at Quill, which the team released last week. It’s been amazing to see users share what they’ve accomplished in just a week.

On May 21 at 3pm ET, they’re going live to walk through what’s new, how it works, and how marketing teams can take their content to the next level.

You’ll hear from VP of Product Amr Shafik, VP of Growth Eoin Clancy, and Manager of AI Solutions Melanie Dell’Olio for a firsthand view into the new systems behind AirOps. They’ll show what agent-driven growth actually looks like, how agents handle execution while humans stay in control, and how to close the gap between AI search visibility and real outcomes.

If you lead SEO, content, or growth, this is the session to block. Move from constant monitoring to focused execution. Turn campaigns into repeatable operating cadences. Improve speed without giving up control.

The recording goes out after. Live room gets the unfiltered version.

Register now ->

Two Sites, Two Brands, Two Value Adds

Ever heard of Crayola? Probably. It is only a little ~$1B company, and the default answer most people think of when someone says crayons.

How about Monday Mandala? It is owned by retired school teacher Inez Stanaway and focuses heavily on coloring pages and printable activities.

Now for the fun question: which one do you think drives more traffic for coloring-related searches?

If you guessed Monday Mandala, you’re right. 🤯

Google still rewards usefulness, and honestly, that is a good thing. Nobody is going bankrupt because they downloaded coloring pages from the “wrong” website.

But here is where things start to shift.

If I asked 10 people to name a crayon company, how many would say “Crayola”? If I asked those same ten people to name a coloring-page website, how many would say Monday Mandala?

• Monday Mandala won clicks.

• Crayola won memory.

And in a world of AI results, recommendations, and answer engines, recognition starts to matter again because traffic gets clicks, but branding compounds and extends well beyond a SERP change or algorithm update.

Search Fragmented. Brand Didn’t.

Search used to be simple.

Someone had a question, opened Google, clicked a result, and landed on your website. Success was largely measured by clicks, traffic, and actions users completed once on the site.

Many people, including business owners, think they are entitled to “free clicks”. Wake up, Google doesn’t owe you 💩.

The world of building a business off search traffic still exists (I mean, look at MM above), but it is no longer the only game in town. It is also a bigger risk today to rely on search traffic than it ever was in the past.

Today, answers happen everywhere. Google AI Overviews, ChatGPT, Perplexity, Reddit threads, Slack and Teams conversations, LinkedIn posts, YouTube videos… users no longer search exclusively within Google’s ecosystem. Traditional search is now just one piece of a much larger answer ecosystem.

So what survives when users never click?

Brand memory.

People remember names they have seen before. They remember positive experiences, friends’ recommendations, repeated exposure, and trusted companies.

Nobody remembers your title tag. (Ooof… that one hurt me a little, too.)

This is exactly why the brand suddenly feels like the answer to every executive question about SEO, AI, video, etc. When users search for answers across platforms, what travels with them is not your website.

It is your reputation.

Not domain authority. Not inbound links. Not keyword density. Not a note from your mom saying you’re a good kid, I mean…and not your Reddit karma score.

YBYS = Your Brand = Your SEO

Let’s be clear. Tactics still work
I even shared my specific “tactic” that helped drive 1M+ organic sessions.

Proof that you can rank pages, gain traffic, and ride algorithm shifts to capture massive spikes in visibility. Some businesses will continue to grow primarily through search traffic for years to come, but…

Many of these “wins” will be temporary.
Rankings move. Algorithms change. Search platforms evolve. (Sound familiar, anyone?)

Brands stay in the conversation.

YBYS is not anti-SEO. It is the evolution of SEO.

Visibility still matters because people need to find you. Recognition matters because they need to remember you after they leave. Trust matters because recommendations and reputation increasingly influence buying decisions. Proof matters because people want validation beyond your own marketing claims.

Finally, there is brand presence.

Do you exist outside your website?

Newsletters, LinkedIn, PR, podcasts, communities, reviews, mentions, and brand searches are no longer “nice to haves to appease the bots.”

These are signals that users, search engines, and AI systems use to understand who deserves attention.

Your homepage is no longer your brand. It’s just one piece of evidence supporting it.

Nick’s Weekly “This Doesn’t Suck” List

A short list of things I either rely on in my SEO consulting work or that genuinely stood out to me this week. No affiliate links, no hidden angles, and absolutely no way to buy your way onto this list. If it’s here, it earned it.

---

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The Uncomfortable Truth

After posting about a tool I like on LinkedIn, someone reached out, surprised I would support it. The company was getting blowback over programmatic SEO, and there were rumors that some larger brands using it tactically had been penalized by Google.

My response was simple:

People kill people with Teslas, too.

Tools are not the problem.

People can use AI to create junk. They can use SEO to manipulate rankings. They can manufacture links, game prompts, and chase short-term visibility spikes. (hello listicles!)

Some of it will even work. For a while…

But every year, the shelf life of these tactics gets shorter.

Google demands trust/authority. AI asks for memory. Consumers ask for proof.

The companies that win in the long term are not the ones finding the next loophole. They are the ones people remember.

YBYS.

Your Brand = Your SEO.

Anyone can rank. Not everyone gets remembered.

~Nick

North Star Inbound runs an integrated growth system:

• Strategic SEO and GEO

• Digital PR

• Conversion copywriting

Case study proof: our top 5 posts drove 144712 sessions in a 9-post pilot and led the blog for conversions, including 224 conversions and 217 phone calls from one post.

Book a call

This week’s post is sponsored by Semrush Enterprise and North Star Inbound

Regardless of the skill set or specialty, CMOs and other business leaders still face a decision that has no perfect answer.

Do you build an internal team, bring in an agency, or partner with an experienced independent consultant?

A few years ago, that decision mostly came down to budget, headcount, and execution capacity. Today, AI has changed the environment around the decision entirely.

Not because SEO suddenly disappeared, and not because AI replaced experienced marketers overnight. Instead, it exposed which organizations were built for adaptability… and which ones were reliant on best practices and SOPs.

Search behavior is changing quickly. Leadership teams want answers faster. Meanwhile, many organizations are still struggling to get meaningful work prioritized, approved, and shipped.

That makes this a good time to revisit the benefits, drawbacks, and realities behind each approach.

Thanks to Semrush Enterprise for Sponsoring This Weeks Newsletter

How LTM achieved +400% project ROI with Semrush for Enterprise

Global businesses operate under relentless pressure. But LTM Interactive successfully juggled more clients, growing expectations, and tighter margins by consolidating its search operations into Semrush for Enterprise.

After cutting fragmented tools and turning wasted manual work into more value for clients, their teams improved search project ROI +400%. See how Semrush for Enterprise helped enable this.

Read LTM's Full Story

What AI Actually Changed

What AI changed wasn’t just the search results themselves. It fundamentally changed how teams need to operate to find success.

Search behavior is fragmenting across Google, AI Overviews, ChatGPT, Reddit, YouTube, TikTok, and whatever new platform gets attention next month. At the same time, leadership teams are demanding faster answers while expecting marketing teams to “figure out the AI thing” in real time (don’t forget the forecasted ROI to justify any budget requested…)

That means SEO teams are now expected to understand far more than the technical stability of a site and its content. They’re being pulled into conversations around LLM visibility, entity signals, technical discoverability, brand authority, content quality, and how trust gets established across platforms.

The problem is that many organizations still move at the same speed they did three years ago. Waiting nine months for roadmap movement becomes a much bigger issue when search behavior is evolving every quarter.

In many companies, the gap between strategy and execution widened. Everyone has ideas. Fewer teams are actually shipping meaningful work consistently.

Which brings us back to the original question:
Who is actually best equipped to help your organization adapt?

In-House SEO Teams

In-house SEO teams make the most sense for organizations where search visibility impacts multiple departments and requires constant cross-functional collaboration.

Their biggest advantage is context. Internal teams understand the brand, product priorities, internal politics, and operational realities better than any external partner. That becomes increasingly valuable as SEO overlaps with engineering, content, PR, analytics, and AI initiatives.

There’s also long-term ownership. Strong internal teams build institutional knowledge over time and become embedded into how the company operates rather than functioning as an outside resource. (This is something a lot of people miss the value of)

The downside is that internal teams often become operationally buried. Many are stretched thin managing reporting, stakeholder requests, roadmap negotiations, and maintenance work while also trying to keep up with rapid industry changes. AI experimentation can also slow down quickly once approval layers, internal politics, and red tape get factored in.

Who Benefits Most?

• Large organizations with complex websites or product ecosystems

• Companies with mature engineering and product teams

• Brands viewing SEO and AI visibility as a long-term investment

• Organizations needing daily cross-functional collaboration and alignment

Who Regrets This Decision?

• Companies expecting one hire to “do everything”

• Organizations without executive buy-in or operational support

• Teams wanting immediate transformation without infrastructure

• Companies hiring junior talent while expecting strategic leadership and organizational influence

SEO Agencies

SEO agencies can provide something many organizations struggle to build internally: scalable execution capacity. Good agencies also bring exposure to a portfolio of industries, different business models, and a constant stream of testing across search and AI-related initiatives.

That outside perspective can be valuable, especially for companies moving quickly or lacking internal specialization. Agencies are often strongest when a brand needs additional bandwidth, specialized resources, or support executing large initiatives that internal teams simply do not have time to handle.

The challenge is that agencies operate across multiple clients simultaneously. Context switching is inevitable, and strategy can sometimes drift away from operational reality inside the organization itself. In some cases, incentives also shift toward deliverables rather than outcomes, where producing audits and presentations becomes easier than getting meaningful work implemented.

This is also where many organizations get frustrated with junior-heavy account structures. The pitch may come from senior leadership, but day-to-day execution and communication often get delegated further down the ladder. AI only amplifies this problem because companies are now looking for strategic clarity, not just more documents explaining what everyone already knows.

Who Benefits Most?

• Mid-market companies needing execution support quickly

• Organizations lacking internal SEO structure or specialization

• Brands needing scalable production capacity

• Teams looking for additional bandwidth across technical, content, or reporting initiatives

Who Regrets This Decision?

• Companies expecting the agency to magically fix internal dysfunction

• Organizations that disappear after kickoff and expect results passively

• Teams buying slide decks instead of implementation support

• Leaders wanting strategic ownership without internal accountability

Independent Consultants

Independent consultants are often brought in when organizations need senior-level guidance without adding another layer of politics/red tape. In many cases, the value is less about raw execution capacity and more about clarity, prioritization, and helping teams focus on the work that actually matters.

Because consultants typically work closer to leadership, decision-making can move faster and conversations tend to become more direct. Strong consultants are often at their best when bridging the gap between strategy and execution, especially in organizations where teams are overwhelmed with competing priorities, stalled roadmaps, or internal misalignment.

This becomes even more important in the AI era, where leadership teams are constantly being hit with noise, hype, and conflicting opinions. A good consultant can help separate real opportunities from reactive panic while keeping the organization focused on practical execution.

The tradeoff is scale. Independent consultants usually are not built to replace an entire department or provide massive production capacity. Their success also depends heavily on internal cooperation. Even the best consultant cannot fix an organization unwilling to prioritize work, align teams, or make decisions.

Great consultants expose problems earyl and clearly, but they do not magically remove organizational friction. That said, removing friction often becomes part of the job itself. Personally, I spend a significant amount of time helping organizations “block and tackle” their way out of self-inflicted bottlenecks so meaningful work can actually move forward.

Who Benefits Most?

• Enterprise teams stuck in backlog paralysis

• Leadership teams that need strategic clarity

• Organizations with existing teams but weak prioritization

• Companies navigating AI and search transitions strategically

Who Regrets This Decision?

• Companies looking for cheap labor instead of expertise

• Organizations expecting one consultant to replace an entire department

• Teams unwilling to implement recommendations or make decisions

• Leaders wanting validation instead of honest conversations

Nick’s Weekly “This Doesn’t Suck” List

A short list of things that stood out to me this week. No affiliate links, hidden angles, and certainly no way to buy your way in. If it’s here, it was earned.

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The Reality Most Leaders Eventually Learn

The best setup is usually a hybrid of the above. Not because everyone loves saying “it depends,” but because modern search visibility now moves too fast for most organizations to rely on a single structure alone.

Strong internal teams benefit from an outside perspective. Agencies become more effective when internal leadership provides accountability and direction. Consultants often help prioritize the right work while agencies or internal teams handle execution.

The strongest setups usually combine:

• In-house teams + strategic consultants

• Internal leadership + agency execution support

• Consultants helping manage agency accountability

• Agencies handling production while internal teams own alignment and implementation

AI didn’t eliminate the need for expertise. It increased the cost of misalignment.

What Leaders Should Actually Evaluate

Most organizations are asking the wrong question. Instead of asking, “Which option is best?” they should identify the actual bottleneck inside the business.

Is the issue strategy, execution, prioritization, or alignment? Do you need more labor or stronger leadership? Who owns implementation, accountability, and separating AI hype from business reality?

Most organizations don’t fail because they picked the “wrong” SEO model. They fail because nobody clearly owned the outcome once search started changing faster than the organization could adapt.

Not that I’m biased or anything, but consultants are pretty awesome… but I can also connect you to great agency support and SEO/AI talent looking for new in-house roles!

~Nick

Thanks to North Star Inbound For Also Sponsoring This week’s Newsletter

Turn Your Blog Into Your Biggest Sales Engine

Brought to you by North Star Inbound–the sales enablement SEO agency.

​​How? Focus on high-impact SEO strategies that target the most profitable opportunities.

North Star Inbound has a proven track record of creating top-converting blog pages that deliver thousands of leads

• hundreds of thousands in directly-attributed revenue

• and rapid payback periods on SEO spend.

They focus on the 10% of pages with the highest ROI for your brand. Basically, your content becomes your most effective sales tool.

There’s no better way to loosen tight budgets than to grow revenue quickly.

Intrigued?​

Learn More Here

Thanks to this week’s sponsors: Profound and North Star Inbound

Congratulations. You’ve officially ranked yourself as the #1 most awesome person on planet Earth.

But let’s be honest, you’re not fooling anyone. It’s painfully obvious what’s happening. These self-serving listicles aren’t about helping users; they’re about feeding LLMs just enough signal to start repeating your name across answers for anything remotely relevant.

And look, it works… at least for now. If enough content says the same thing, it starts to resemble consensus, even when it’s manufactured.

The problem is, none of that builds trust.

This week is about listicles. Most are garbage. The earned ones build real credibility and stand the test of time. I’ll show you some examples.

Thank you to this week’s #SEOForLunch sponsor: Profound

Take Profound's Free Course on Marketing Engineering

Every brand on the planet will soon need a marketing engineer to build the agents, automations, and systems that create a new kind of marketing—part builder, part strategist.

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Everyone’s Focused on the Wrong Thing

Most of the conversation right now is focused on spam, self-ranking, and obvious manipulation. And while that deserves attention, stopping there misses the bigger point entirely. The real issue isn’t listicles themselves, it’s how they’re being used and who actually earns a place on them.

Listicles aren’t the problem. Unearned inclusion is.

A Dose of Reality

There are really only two types of listicles.

The first is self-ranked. You wrote it, you control it, and you benefit from it. You decide where you show up, who you’re compared against, and how the story is told. There’s no real downside to putting yourself at the top, which is exactly why so many do. (although I do debate this with Ross Hudgens over at the Siege Media podcast)

The second is earned. You didn’t create it, you don’t control it, and you don’t get to shape the narrative. You’re included because someone else decided you belong there, usually alongside other credible names.

That difference matters more than anything else.

Anyone can rank themselves. Not everyone gets picked.

Listicle You Can’t Buy: Moz

One fantastic example of an earned “list” is Moz's recommended list.

This isn’t some throwaway “top tools” post built to rank/manipulate AI. It’s a list that’s been around for over a decade, carefully curated and rarely updated. When changes do happen, they’re intentional. That alone tells you everything about how seriously it’s treated.

It’s also not just about the list itself, it’s who’s on it. You’re looking at names that have put in the time, built credibility, and are recognized by the industry. Being included alongside these names demonstrates credibility and industry trust.

This isn’t listicle hacking; it’s reputation being put on full display.

Note: the image has been modified to make it more concise and to display a proper example. Original and full source is available at Moz.com

Listicle You Can’t Buy 2: Profound

The second example came from Profound, and it reinforces the same pattern in a completely different context.

This one is newer and more tied to the AI conversation, but this and the Moz list provide similar trust signals. It wasn’t built to rank for “best X tools” or to push Profound itself as the answer to the “who’s the top AI tool in 2026” LLM prompt. It was created to highlight credible players in a new space that’s getting noisy fast. Different audience, different intent, same underlying signal.

Again, I didn’t ask to be included. I didn’t pay for placement. I didn’t get to influence how I was positioned or who I was listed next to. I showed up because someone else made that call.

This isn’t a tactic. It’s a byproduct of hard work, not skipping corners, and driving results.

Note: Again, this image has been lightly modified to make it more concise and to display a proper example. Original and full source is available at profound.com

This is where it actually matters.

Third-party validation will always carry more weight than anything you say about yourself. These are the mentions that show up when decisions are being made, dropped into Slack threads, added to shortlists, and passed around in internal decks. You’re not in the room, but your name is.

That’s what makes it powerful.

And it’s also why it connects directly to AI. LLMs don’t determine credibility; they surface patterns. Earned mentions create consistent signals across sources. Self-made lists are isolated. One builds momentum, the other just makes noise.

This is trust at scale, without you in the room. AI reflects it. It doesn’t create it.

Nick’s Weekly “This Doesn’t Suck” List

A short list of things that stood out to me this past week. No affiliate links, no hidden angle, and certainly no way to buy your way in. If it’s here, it's because it was earned!

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Write Your Own List… Or Earn Inclusion?

Stop worrying about where you rank on your own list. (Don’t you love those who create their own lists and then rank themselves #3, ha!)

Start focusing on where you show up when someone else is making the call.

You can fake “top rank”. You can’t fake being recommended.

~Nick

Thanks to North Star Inbound For Also Sponsoring This week’s Newsletter

Turn Your Blog Into Your Biggest Sales Engine

Brought to you by North Star Inbound–the sales enablement SEO agency.
​​How? Focus on high-impact SEO strategies that target the most profitable opportunities.

North Star Inbound has a proven track record of creating top-converting blog pages that deliver thousands of leads

• hundreds of thousands in directly-attributed revenue

• and rapid payback periods on SEO spend.

They focus on the 10% of pages with the highest ROI for your brand. Basically, your content becomes your most effective sales tool.

There’s no better way to loosen tight budgets than to grow revenue quickly.
Intrigued?​

Learn More Here

Thanks to this week’s sponsors: Semrush Enterprise and North Star Inbound

how-to-become-a-lawyer.com.

Yeah… four dashes. No shame.

I registered it a few years into my SEO career, around 2011–2012, inspired by the monthly income reports that Pat Flynn used to publish.

At the time, I was thinking like a pure SEO. Exact-match domains were the play. I thought I had it figured out. And to be fair, it worked.

That site became the first asset I ever owned that made real money. Over $100 a month through AdSense. No brand. No audience. Just rankings and clicks.

But that’s the part most people miss…

There are risks you can take with an affiliate site that you can’t, or shouldn’t, take with a real business. Because what works in a keyword-first world eventually gets exposed in a brand-first one.

Thank you to Semrush Enterprise for sponsoring this week’s #SEOForLunch

Only 22.5% of teams have fully integrated SEO and AI search workflows. Is yours one of them?

Most brands are already losing in AI search without knowing it. And most marketing leaders can’t pinpoint where.

Semrush published two new playbooks to solve this, giving marketers the foundation to understand the landscape, secure internal buy-in, and start building toward execution.

Brands that treat SEO and AI visibility as separate workstreams are building on a fault line. The ones pulling ahead are making visibility an enterprise operating model.

Get The Playbooks

The Shift Nobody Wants to Acknowledge

There was a time when you could win just by matching the query. Pick the right keywords, stick them in the domain, repeat them on the page, and let Google do the rest. Exact-match domains weren’t just helpful, they were a cheat code.

That world is gone. Search has moved from keywords to entities. Instead of asking “does this page match the query?” it’s asking “what is this thing, and can I trust it?”

Now add AI. Systems from OpenAI don’t rank pages. They synthesize answers based on patterns, mentions, and consistency across the web. I call this the “consensus answer across the internet”)

So what actually matters now?

• Entities tied to your brand

• Mentions across credible sources

• Authority signals,

and… whether people actually recognize you.

In the old world, you could manufacture relevance. In this one, you have to earn recognition.

That’s the shift most businesses are still ignoring. They’re building pages to rank in systems that are increasingly designed to remember.

You’re not ranking pages anymore. You’re training systems to remember you.

Decision 1: Domain Selection

This is where most businesses lose before they even start.

They’re still picking domains like it’s 2012. Over-optimized. Keyword stuffed. Trying to look like a brand while clearly thinking like an SEO.

It’s the same exact-match mindset, just dressed up a little better.

The problem is that the approach caps you immediately. Your domain isn’t just a ranking signal anymore. It’s the foundation of how systems understand and remember you.

So what actually matters now?

• Memorability. Can someone recall it without thinking?

• Pronounceability. Can they say it out loud without explaining it? (I call this the radio test!)

• Brand search potential. Will people ever search for you directly?

• Expandability. Can you grow beyond a single keyword without sounding ridiculous?

Here’s the contrast.

best-seo-tools-2026.com might rank. It might even make money. But it will never become something people remember, reference, or trust.

That’s the difference.

In this world, your domain isn’t just about getting found. It’s about being known. If your domain sounds like a query, you’ve already limited your ceiling.

Decision 2: Domain Migration

This is where companies accidentally light years of progress on fire. (Anyone cringe or just me?)

Migrations used to be risky. Now they’re dangerous. You’re not just moving URLs anymore, you’re resetting how systems understand you.

• Entity signals.

• Historical trust.

• The context built over time.

That doesn’t cleanly transfer just because you set up redirects.

And that’s where most teams get it wrong. They treat migrations like technical SEO projects. URL maps, redirects, QA… ship it.

Meanwhile, they ignore continuity. Not the brand change itself, just the consistency of how the brand shows up during the transition.

Because what actually matters here is pretty simple.

Consistent naming. Clear redirects that don’t break. Reinforcing who you are so there’s no confusion during the move.

The systems you’re trying to influence don’t like ambiguity. And migrations introduce a lot of it.

You’re not migrating pages. You’re migrating memory.

Decision 3: Brand Changes

This is the silent killer.

Brand changes have always been risky, but the old SEO playbook at least gave you some guardrails. In an AI-first world, those don’t save you.

These systems don’t relearn quickly, and your old mentions don’t carry over cleanly. There’s no real handoff.

So when you change your brand name, you’re not starting fresh. You’re creating confusion. And confusion kills trust.

That’s what most people underestimate. Renaming a company isn’t a design update. It’s an identity reset across search engines, AI systems, and users who suddenly aren’t sure if you’re the same company.

If those signals don’t line up, you don’t just lose momentum. You dilute it.

Because now your authority is split between what you were and what you’re trying to become.

You fragment your own authority.

The New Reality

SEO is no longer just about ranking. AI is no longer just about answers.

They’ve both evolved into something simpler and a lot less forgiving. They decide who to trust.

Every page, mention, link, and citation feeds that decision. Not just relevance, but whether you’re credible enough to show up at all.

That’s the shift.

You’re not optimizing for position anymore. You’re building a case for why you should be believed.

Cool Stuff AI Didn’t Ruin Yet

A short list of things that stood out to me this past week. No affiliate links, no hidden angle, and certainly no way to buy your way in. If it’s here, it's because it was earned!

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Going Back To My Awesome Site

That AdSense site made money.

It also had zero defensibility. No brand, no loyalty, nothing to fall back on. When it got hit, it disappeared. I even tried to save it… rebrand, new domain, cleaned it up. Didn’t matter.

And that’s the part that matters.

Most businesses today are still building the same thing. Disposable domains. Forgettable brands. Short-term wins that only work while the system allows it.

The difference is now they’re using AI to do it faster.

And calling it innovation. Yikes!

I can hear it now, “This isn’t SEO!!” Yes, it is.

You can game the system…until the system decides you’re not worth remembering.

~ Nick

This week’s #SEOForLunch sponsors are AirOps and North Star Inbound.

For a long time, I thought I was a great manager.

Ten years in agency SEO. Four to five of those leading teams. If you asked me back then, I would have confidently said I was a solid B+.

I was wrong. Not completely wrong, but wrong enough to matter.

The first time I read Lost and Founder by Rand Fishkin, I was fully bought in.
(Rand is Founder of Moz (sold), now co-founder of SparkToro, Snackbar Studio, and Alertmouse.)

Reading it again years later hit differently.

This time, it was after getting fired, having a role eliminated, and spending five years running my own consulting business. Same book, completely different lens.

Back then, I read it and nodded along. Now, I read it and see the gaps.

This post is a gut check. For me, and for teams that promote their best individual contributors and hope it magically translates into leadership.

We’ll get into IC roles in a later post.

Thank you to AirOps for sponsoring this week’s #SEOForLunch

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ChatGPT judges a page by its cover.

Pages with headlines that directly answer the query get cited 41% of the time. Loosely related headlines? 29%.

AirOps studied 16,851 ChatGPT queries and 353,799 pages across 10 industries to find what separates cited pages from ignored ones. A few findings that stood out to me:

• Retrieval rank is the top signal. A page at position 1 has a 58% chance of being cited. By position 10, that drops to 14%.

• Comprehensive guides don’t always win. Pages covering 26-50% of ChatGPT’s sub-queries get cited more often than pages covering 100%.

• Domain authority doesn’t predict citations. Always-cited pages actually have lower DA than never-cited ones. Content quality is what counts.

The full report covers 20+ signals with controlled comparisons across each.

Read the Fan-Out Report →

How Teams End Up With Bad Managers

Most companies don’t struggle to find smart people. They struggle to turn those people into effective managers.

So they fall back on the default move. Promote the best individual contributor and assume the skills will carry over.

I’ve seen it. I’ve been part of it.

Sometimes it works. More often, it creates a manager who was never trained to manage, leading a team that slowly feels it.

That’s when things start to break. Performance dips, frustration builds, and good people leave while leadership tries to figure out what changed.

This is exactly what the “8 behaviors consistent across great people managers” from Lost and Founder are meant to address.

8 Behaviors Consistent Across Great People Managers

These are the behaviors that consistently show up in strong managers:

1. Good coach

2. Empowers the team and does not micromanage

3. Expressed interest or concern for team members’ success and well-being

4. Productive and results-oriented

5. Is a good communicator

6. Helps with career development

7. Clear vision or strategy for the team

8. Has important tech skills that help him/her advise the team.

This is how I would have graded myself then vs. now.

A few held up. A few didn’t.

W2 vs 1099 Nick Grades

The past five to seven years changed how I see search, business, and how people actually operate inside companies. The politics, the incentives, the games that don’t show up in any job description.

So this felt like the only fair way to approach it. Grade 2018 me when I first read Lost and Founder versus how I’d grade myself today.

For context, I’m now a solo consultant. That shift didn’t happen by accident. It came out of this kind of reflection back in 2020, along with losing my job… again.

Here’s the honest version.

Where I Fell Short

A few stayed consistent. The ones that didn’t are the ones that matter.

These are the areas where my perception didn’t match reality.

Good Coach (B- → C+)

I thought this was my strength.

If someone showed interest, I’d invest heavily and teach them everything I knew.

The problem is in that sentence. :if someone showed interest.”

I struggled to coach people who were just there to do the job. Not bad employees, just not all-in on SEO like I was. I wasn’t prepared to meet them where they were.

Empowers the team / avoids micromanagement (A → B-)

This one stung.

I always thought I was flexible in how work got done. In reality, I was prescribing the path. Step-by-step, with check-ins, to protect quality and deadlines.

The truth is I didn’t fully trust others to deliver work tied to my name.

Micromanagement isn’t hovering. It’s limiting how people operate instead of holding them accountable to the outcome.

Communication (B+ → C)

This one hurts the most.

As an IC, I’m a strong communicator. As a manager, I wasn’t.

I over-invested in high performers and avoided being direct with everyone else. Feedback was softened, delayed, or unclear.

If you were doing well, I was a solid manager. If you weren’t, I made it harder for you to improve.

Pro tip: if feedback needs to be given, be direct.

Clarity beats comfort. Every time.

Nick’s Weekly “This Doesn’t Suck” List

**New ** Expect 1–2 things each week that are actually worth your time.
No scraping. No “AI-optimized” fluff. Nothing paid.
Just things I’d bring up if we were grabbing a beer.

---

IC vs Manager: Make It a Choice, Not a Default

My solution? I leaned into my control issues and went out on my own.

Do you think any employer is signing off on these weekly newsletter takes?
Not a chance.

For everyone else, here’s the practical version.

Most companies don’t have a manager problem. They have a decision problem.

They treat management as the next step instead of a different role.

That’s how you end up promoting your best individual contributors into jobs they were never trained for, and often don’t actually want.

If you want to avoid that, a few things need to change:

1. Separate career paths early
   Give people a way to grow without having to manage others. Not everyone should be responsible for people, and forcing that path creates bad managers FAST.

2. Define what “good management” actually looks like within your org
   Tenure isn’t a signal. Neither is being good at the work (unfortunately). If you can’t clearly define what makes a strong manager, you’ll keep promoting the wrong people.

3. Test before you promote
   Don’t hand someone a team and hope it works out. Give them opportunities to mentor, lead small projects, and handle feedback conversations first. See how they show up.

4. Reward outcomes, not optics
   Being busy, being liked, or being the “go-to” person doesn’t mean someone can lead. Measure how their team performs, develops, and retains.

5. Train your managers, or expect inconsistency
   Most companies invest heavily in hiring and almost nothing in management training. Then they’re surprised when results vary wildly across teams.

You’ve got this.

Don’t worry, if not, you’ll hear about it in your employee exit interviews.

~Nick

This week’s sponsors are Semrush Enterprise and North Star Inbound.

Anyone who’s been following me on LinkedIn or X has seen my smartass responses to people making some of the dumbest claims imaginable.

“100% automate your SEO.”
“Fully automate your content calendar.”
“Run your business with AI while you sleep.”

At this point, I’m just waiting for AI to start doing my dishes. (please be true)

But jokes aside, there’s something deeper going on here that’s worth talking about.

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See How They Did It!

LLM. Looting, Lying, Manipulating

I was talking with a good buddy, Rob Wormley (a great GTM consultant), about the current state of AI.

Like most conversations right now, it started with a discussion of the cool things we’re both using AI for. And honestly, there’s a lot to like.

But pretty quickly, the conversation shifted. Across very different projects, we kept landing on the same three realities.

If you’re not using AI at all, you’re missing obvious efficiency gains.

If you’re relying entirely on AI, you’re walking straight into quality issues and potential legal headaches.

The only version that consistently works is somewhere in the middle. Human-led AI. Not flashy, not easy to sell, but it holds up.

What’s Wrong With All-in AI?

I’ve never been shy in sharing my opinion that No, I am not putting my head in the same place, ignoring this crazy technology. Open up any social media platform, and you’ll see a dozen Rolex-wearing, Lamborghini-driving, broccoli-haired “bro” telling you that you can automate XYZ with their AI hack. Naturally, all you gotta do is drop “win” in the comments.

I’ve said it before, and I’ll say it again. “AI optimization is the biggest scam of 2026.” Not because AI itself is a problem, but because of how it’s being sold. Open any social platform, and you’ll see the same recycled pitch. Some Rolex-wearing, Lamborghini-driving, broccoli-haired bro telling you how to automate your entire business with a single prompt. Just comment “win,” and your problems disappear.

Don’t believe me?
hold your breath and hit play below. (fair warning: cringe alert!)

Ugh. Sorry. If your brain is still operational, please stay with me. If you contributed to the comments to receive their solution to becoming rich overnight. Please unsubscribe.

The Real Problem Isn’t AI

Again, let me reiterate. This is not an anti-AI rant. This is an anti-broken-expectations rant post.

We’ve completely lost the plot.

Take something like a redirect mapping project. You take a task that used to require three hours of tedious, manual work and cut it down to 30 minutes with AI support. That’s not just a win, that’s a massive gain in productivity. But now? It gets side-eyed because it wasn’t fully automated.

Or content. Writing something thoughtful, original, maybe even a little vulnerable, used to take hours. Now you can use AI to structure, outline, and accelerate the process, getting it down to an hour with better clarity and direction. Still requires skill. Still requires a point of view. Still delivers a better end product.

And somehow… still not good enough.

Then you get the real insanity. Instead of investing in a skilled and or credentialed person to QA, guide, and maximize what AI can do, the instinct is to cut that role entirely and tell a tool like Claude to “just handle it.”

No oversight. No accountability. Just vibes and prompts.

And anything short of that gets labeled as failure.

Why the “Easy Button” Sells

The reality is AI has never been an all-or-nothing decision. It’s a spectrum. But the way it’s being marketed right now? You’d think anything short of full automation means you’re already behind.

That’s where the pressure kicks in. Not from the technology, but from the noise around it. Every scroll comes with another reminder that you’re “doing it wrong,” that you’re “missing out,” that you’re about to become irrelevant if you don’t flip the switch immediately.

“SEO is dead, GEO now! Adapt or get left behind.”

It’s the same playbook every time. Manufacture urgency, create a false binary, and push people to react instead of think.

And the delivery doesn’t help. You watch enough of these reels and it starts to feel less like insight and more like a used car pitch. High energy, big promises, zero substance. “Trust me, this one prompt replaces your entire team.”

It works, though. Not because it’s credible, but because it taps into something real.

And when someone shows up selling that fantasy with confidence, a lot of people stop asking questions.

Yeah, buddy. Throw in the Lambo for free and consider me all-in. 💰🤑 🙄

The Middle Ground Nobody Wants

Here’s the part nobody wants to hear.

The version of AI that actually works still requires you to think.

It requires you to guide it, challenge it, edit it, and take responsibility for what gets published or implemented. It’s not passive. It’s not hands-off. It doesn’t magically remove the need for experience or judgment. If anything, it puts more pressure on those things.

Human-led AI is where the real gains are. You move faster, you cut down the busywork, you get to better outputs more efficiently. But you’re still in the driver’s seat. You’re still making decisions. You’re still accountable when something goes wrong.

And that’s exactly why it doesn’t sell.

There’s no fantasy in it. No “set it and forget it.” No promise that you can replace thinking with prompting and call it a strategy. It’s just better work, done faster, by someone who knows what they’re doing. This is much harder to fake.

So most people skip it. Not because it doesn’t work, but because it still requires effort.

This Isn’t an AI Problem. It’s a Human One.

This isn’t an anti-AI rant. It’s an anti-human rant. More specifically, it’s about the expectation that anything short of full automation somehow isn’t good enough.

That’s the problem.

Because that mindset guarantees misuse. It pushes people to skip the parts that actually matter. Thinking, judgment, accountability. It turns a powerful tool into a shortcut, and then into a crutch.

AI didn’t create this behavior. It exposed it.

And until that expectation shifts, most people won’t get the upside they think they’re chasing.

So What Actually Matters?

AI is a multiplier, not a replacement.

The people winning aren’t avoiding the work. They’re using AI to move faster while still thinking and making decisions.

A 25% gain isn’t failure. It’s an edge, if you actually use it.

Human-led AI… or the scam artists selling you the automated solution to “building the future.” Your call.

Nick

Digital PR matters more in GEO than it did in classic search.

Mentions credible coverage and authoritative links are signals AI systems can draw from.

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THANK YOU to this week’s sponsors: Profound and North Star Inbound.

Welcome back to the lunch table! This week, we finish the two-part series discussing AI and the legality behind using it. If you haven’t read part 1, I recommend reading it first.

This newsletter (again, largely written by Cari O’Brien, JD) addresses what we know, existing legal cases, and a 7-step playbook focusing on the Safest Legal Way to Use AI.

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This Week’s #SEOForLunch is sponsored by Profound

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What We Learned From Last Week’s Newsletter

AI law is still taking shape, but accountability has already arrived. Regulators and courts are not waiting for entirely new rules. Instead, they are applying existing laws on intellectual property, privacy, contracts, discrimination, and liability to today’s use of AI.

For organizations, this means AI does not introduce a myriad of unfamiliar legal risks so much as it accelerates familiar ones. How AI is trained, deployed, governed, and explained already matters, and companies are being held responsible now, not later.

What This Likely Means for the Future

No one can tell you how any of this is actually going to play out. That said, where things stand does help shed light on how the legal landscape will impact your day-to-day business operations in the near future.

More Lawsuits, Across More Industries

Expect litigation to increase as AI use expands. Courts will play a central role in clarifying how existing laws apply to new AI‑driven scenarios, especially where regulations are vague or silent. These cases will help define boundaries, but they will also introduce cost, delay, and uncertainty for businesses caught in the middle.

More Formal Requirements & Internal Guardrails

Marketing organizations should plan for growing expectations around disclosures, documentation, and process. This includes clearer customer‑facing policies, internal SOPs governing AI use, bias audits, risk assessments, and incident response plans. In practice, responsible AI use will increasingly look like a compliance discipline, not an ad‑hoc experiment.

A Growing Need for Privacy & Data Protection Expertise

AI tools are evolving quickly, and they also make malicious activity easier and more scalable. That combination raises the stakes. Companies will need dedicated teams or well-defined ownership to monitor developments, maintain policies, and respond to incidents as they arise. Privacy and data protection will be core operational functions, not side considerations.

Ongoing Uncertainty, by Default

There is no final version of AI regulation on the horizon. Rules will continue to change, sometimes unevenly and unpredictably. The most resilient organizations will be those that plan for what they can, learn from early missteps, and remain flexible enough to adapt as expectations shift.

What You Can Do? Introducing The “Safest Legal Way to Use AI” Playbook!

Listen, we know what you’re thinking: BORING. Legal guardrails, policies, and governance are not shiny or sexy. Experimentation is. Speed is. Seeing what these tools can do is genuinely exciting. But we care more about you and your company coming out ahead than chasing short‑term wins that create long‑term problems.

This playbook isn’t about slowing innovation. It’s about protecting your team, your work, and your organization so you can use AI confidently, responsibly, and without unnecessary risk getting in the way. With that, let’s dive in.

1. Start With a Clear AI Use Policy

Every organization should have a short, plain-language policy that explains how AI tools can and cannot be used. The policy need not be overly complex, but it should be clear enough that any team member can read it and follow it as intended.

A strong policy usually includes:

• Which tools are approved for use (and which have been rejected and why)

• What types of data can be entered into AI systems

• When human review is required before publishing AI-generated content

• Situations where AI use should be avoided entirely

• A prompt library, along with prohibited prompts

As you build your policy, remember to include an approved tools list, a list of prohibited tools, an acknowledgment form for employees to sign, and disclosure guidance for when AI-generated content is used.

2. Separate AI Workflows by Risk Level

Not every AI use case carries the same level of risk, so treating everything the same either slows your team down or leaves your company exposed. A simple way to manage this is to think in terms of a three-lane highway:

• Green lane: brainstorming, outlines, tone variations (no sensitive data)

• Yellow lane: internal drafts + summaries (allowed data only, reviewed)

• Red lane: hiring decisions, regulated info, public claims, legal advice, medical claims (requires legal/privacy review + logging)

This approach allows your team to move more fluidly, slowing down only where necessary based on defined goals. The key term here is “defined.”

3. Use “Clean Inputs” and “Clean Outputs.”

Most AI risk actually starts at the input stage. If sensitive, protected, or proprietary data goes in, you lose control over where it may appear later. That’s why it’s critical to set guardrails in place around both what goes in and what comes out.

Example guardrails include:

• Avoid pasting proprietary documents into consumer AI tools

• Use trusted internal knowledge sources where possible

• Require citations or sources for factual AI-generated content

4. Review AI Vendors & Tools Carefully

It’s easy to get caught up in the excitement of new AI tools. But the desire to join in often leads organizations to adopt tools before proper evaluation. This is where risk starts to creep in.

Every external tool or vendor you bring into your company also brings its data practices, dependencies, and potential exposures. Make it a policy to ask questions that identify risk before adopting a new tool or hiring a new vendor.

Ask and then document the answers (ideally in your vendor contracts) to questions such as:

• Does the vendor train their models on customer data?

• How long is data retained?

• What security standards are in place (SOC 2, ISO 27001)?

• What happens if an IP or data breach issue arises?

5. Bake in Human Oversight & Review

AI is great for accelerating work, but it doesn’t grant a free pass from accountability. At key points in your workflows, there should be clear expectations around when a human needs to step in, review, and take responsibility for the outcome.

This is especially important for:

• Public-facing content

• Customer communications

• Regulated or high-stakes decisions

6. Document Your Governance

“Radical transparency” is the phrase of the day in many AI, data protection, and privacy conversations. What that really boils down to is simply being able to show your work. Because when something goes wrong, or when a regulator comes knocking, you’ll need to be able to clearly show how your organization responsibly uses AI.

To that end, we recommend every organization:

• Maintain an AI tool inventory

• Document risk assessments for higher-risk use cases

• Record review steps for public-facing AI outputs

• Create an incident response plan for AI-generated errors

7. Train Your Team

Once you have the documentation in place, you have to take the next step to ensure your team understands how to apply your policies and procedures. Training should equip your team to identify risks, respond to threats, and otherwise use AI tools in line with your expectations.

At a minimum, your training should ensure your team knows how to:

• Use approved AI tools effectively

• Recognize phishing attempts, deepfakes, and other AI-driven more

• Protect work computers against AI-driven information disclosure attacks

• Build AI tools like chatbots to protect against prompt injections

A special THANK YOU to Cari for all the heavy lifting on these two posts. She put in the heavy work, actually writing vs prompting an AI and blindly submitting. Just like you do… Right!?

~Nick

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THANK YOU to this week’s sponsors: Profound and North Star Inbound. Please support these sponsors as they keep this weekly content free.

For the first time in the 8+ years of writing this newsletter, I’ve invited a guest to write. A huge shout-out to Cari O'Brien, JD (yeah, she’s a fancy lawyer!) for working with me on this post. It’s one that is so critical to business, yet people don’t seem to write about what can be scary… So you bet(cha) we’ll post it here on the #SEOForLunch. In fact, this is so critical that we're dedicating two issues to it. Read part one below, and stay tuned for part 2 next week!

First, let’s talk more about how to not get sued, go to jail (don’t collect $200), or be known as the copycat stealer of your industry.

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The Legal Side of AI Everyone Is Ignoring

AI regulations are still in their infancy. Europe has taken the lead with the EU Artificial Intelligence Act. In the United States, nearly 20 states have passed some form of AI legislation. At the same time, federal policymakers have signaled interest in limiting state-level regulation to keep the overall regulatory environment relatively light, as shown by the recent AI policy wishlist published by the White House.

Regardless of how quickly new regulations emerge, one thing is clear: AI isn’t reinventing the legal landscape; it’s accelerating it. Most AI risks trace back to familiar areas like intellectual property, privacy, contracts, consumer protection, discrimination, and liability when things go wrong.

So instead of thinking of “AI law” as something entirely new, it’s more helpful to look at the core business areas where these familiar risks tend to arise.

The 9 Areas Where AI Risk Lives in an Organization

The following nine areas are where most AI risk shows up inside a business. You don’t have to be a legal expert to manage these risks; you just have to ask the right question in each area to get to the heart of the matter and address it well.

1. Intellectual Property

The One Question: Who owns the work, and are we accidentally using someone else’s intellectual property without realizing it?

Ownership is still evolving in the AI context, but we do have some early guidance. The U.S. Copyright Office (USCO) stepped in early, stating that works created purely by AI are not protected. Meaningful human authorship is required. If a human plays a substantial creative role in shaping an AI tool’s output, protection may still be possible. Such determinations are to happen on a case-by-case basis.

On the patent side, the U.S. Patent and Trademark Office’s (USPTO’s) revised guidelines show a slightly more flexible position, stating that patentability is still possible if a human conceived the idea but used AI to make the idea come to life. That said, these guidelines haven’t been tested in court, so it’s unclear how they will stand up against real-world applications.

At the same time, concerns about infringement continue to grow. Many generative AI tools were trained, at least to some extent, on protected materials, and we’re watching this tension play out in real time. We’ve seen case filing after case filing, including The New York Times lawsuit against OpenAI and Microsoft, which alleges that the AI tools reproduced substantial portions of copyrighted content without permission.

This creates two practical risks:

• Using AI outputs that unintentionally incorporate protected material

• Struggling to prove ownership over work that lacks sufficient human input

If you’re creating content you want to own, protect, or commercialize, keeping a human meaningfully involved isn’t optional—it’s essential.

2. Advertising & Misinformation

The One Question: What are we saying, and is it accurate?

AI tools make it dramatically easier to create content at scale, which is a clear upside. The tradeoff, however, is that these tools also make it easier to publish something that’s misleading or incorrect.

We saw in real time how costly such errors can be. During Google Bard’s product demonstration, the tool incorrectly stated that the James Webb Space Telescope had taken the first images of an exoplanet. This one error cost Google $100 billion in market value because it raised serious questions about the credibility of its tool.

AI hallucinations can show up in subtle ways, from incorrect data, fabricated citations, false logic, exaggerated claims, and confident but flawed reasoning. When such content is published under your brand, it becomes your responsibility. And while your company may not have as much at stake financially as Google does, reputationally, one mistake can absolutely cost you.

3. Privacy & Personal Data

The One Question: Are we using people’s personal information in ways that are transparent, lawful, and respectful?

Consumer expectations around data privacy have shifted dramatically—and the law is catching up. Frameworks like the EU’s GDPR, Canada’s PIPEDA, and California’s CCPA have established new standards around how personal data is collected, used, and disclosed.

While marketers have adapted (begrudgingly, to a degree), personal data is still at the core of how many campaigns function. That data includes cookies, pixels, contact and behavioral data, purchase and payment information, and so much more. And the risks don’t just arise in collecting the data; they also arise in failing to clearly communicate what you’re doing with it.

Regulators have already shown us how serious they take these matters. In ChatGPT’s early days, Italy blocked the app countrywide over concerns about how personal data was being collected and processed under GDPR. The Italian government only lifted that ban after OpenAI added more privacy safeguards.

At a practical level, your company needs a clear policy on the collection and handling of private consumer data. You need to know what data you’re collecting, where that data is going, and who is handling it. Your team needs to know which privacy laws apply to your company and its customers, and how to respond if a customer makes a request under those laws. If you can’t quickly and clearly communicate that your company knows all this, now’s the time to start taking action so you limit your exposure.

4. Data Protection & Trade Secrets

The One Question: Are we keeping sensitive data, internal knowledge, and company secrets out of places they shouldn’t go?

When we talk about data protection, the focus often stays on customer data. Just as important, however, is company data, especially trade secrets and proprietary information.

AI tools introduce a new layer of risk here, particularly when employees use unapproved tools or free versions that lack privacy and security guardrails. Samsung learned this lesson the hard way. A couple of engineers pasted proprietary source code into ChatGPT while troubleshooting issues. That data was then transmitted to an external system, which would use the data to train its models and potentially deliver replicated source code in future outputs.

This isn’t a case of bad actors; it’s a case of bad workflows and SOPs. If your team is using AI tools without clear guardrails, you risk any team member unintentionally disclosing confidential business information, client data, or proprietary processes or code. And once that information goes out, it’s incredibly difficult to get it back.

5. Employment & Workplace Fairness

The One Question: Could AI be influencing hiring, promotion, or evaluation decisions in ways that create bias or discrimination?

For years, companies have been relying on AI in hiring and HR processes, primarily to improve efficiency. But such efficiency doesn’t guarantee fairness.

Research and real-world examples have proven time and again that these tools bake in the prejudices and biases of their training data. One well-known example comes from Amazon, which scrapped its 2018 AI hiring tool that was found to downrank resumes that included indicators of applicants being women. In another case, iTutorGroup was held liable for damages after its AI-powered job-application software exhibited bias against older candidates.

It’s not that using AI in these instances is unacceptable. It’s just that companies using AI should not do so blindly. When it comes to having AI tools partake in decisions about people, your company needs to regularly audit the tools for bias, understand how the tool’s decisions are being made, and always keep a human in the loop.

6. Contracts & Customer Expectations

The One Question: Are our customer-facing agreements clear about how AI is used—and who’s responsible if something goes wrong?

AI-generated content isn’t just “content.” In many cases, it’s part of your customer experience, which carries great weight.

The Air Canada chatbot story offers a good example. A customer relied on information provided by an AI chatbot on the Air Canada website. The chatbot described a bereavement fare policy that didn’t actually exist. Air Canada refused to honor the policy; the customer sued. A Canadian tribunal ruled that the airline was responsible for the chatbot’s statements.

Your website, chatbots, automated content, AI-generated social media content, and so on can all be considered company-created and company-approved content. And if we follow the Canadian tribunal’s logic, if the content lives on your platform, it’s your responsibility.

If customers rely on the content you provide to make decisions, you need to ensure that the content is accurate. You should also take care to clearly address how AI is used on your platform and where responsibility for it sits.

7. Vendor & AI Tool Risk

The One Question: Do we really understand the risks of the AI tools we’re bringing into the business?

Every AI tool you use comes with its own ecosystem: third-party integrations, underlying libraries, and data flows that aren’t always visible on the surface. If you don’t understand that ecosystem, you’re taking on risk. And no company, small or large, is immune.

In 2023, a bug in ChatGPT briefly allowed some users to see titles of other users’ chat histories and certain subscription payment details. The issue was traced to a bug in an open-source library used by OpenAI, highlighting how risk can live deep within a tool’s infrastructure.

This risk extends beyond the tools you choose to the vendors you work with. Which tools do your vendors use? How well do they understand the privacy and data protection policies that are in place? Do their practices align with yours? And if a vendor’s AI use leads to a problem, are you liable, or is the vendor liable?

Companies cannot blindly enter new vendor relationships or AI tool subscriptions. Initial assessments are necessary, as are ongoing reviews and, if necessary, corrective actions to remain compliant and limit risk.

8. Product Liability & AI Decision Risk

The One Question: If an AI system makes a mistake that affects customers or users, who is responsible?

AI systems redistribute risk in ways we can’t always predict. Zillow’s Zillow Offers program is a strong example. The company used automated algorithms to estimate home values and guide purchasing decisions. When those models misjudged market conditions, the company purchased homes at inflated prices, ultimately causing the company to lose hundreds of millions of dollars.

Zillow’s algorithms impacted external parties by inflating home prices. But its internal impacts were even harsher. It raised questions, including those relating to accountability. Who is at fault? And what consequences will the responsible parties face, if any?

These aren’t theoretical questions; they’re governance questions. And organizations that spend time addressing these questions upfront find it much easier to address solutions should a system make a mistake in the future.

9. Regulatory Compliance & Governance

The One Question: Are we keeping up with evolving rules, and can we demonstrate we’re using AI responsibly?

Regulators aren’t waiting for a comprehensive AI law to emerge. Unsteady, they’re applying existing frameworks as they can, and are already taking action.

The U.S. Securities and Exchange Commission (SEC) and Federal Trade Commission (FTC) have both brought enforcement actions against companies for failing to bake in proper guardrails around their use of AI. The SEC has charged numerous firms with making misleading statements about their use of AI or falsely advertising their AI capabilities (“AI washing”). The FTC has also issued numerous warnings to companies about overstating or misrepresenting their AI capabilities, as AI claims must be substantiated like any other marketing or advertising claims.

Enforcement is also expanding beyond messaging. The FTC took action against Rite Aid over its facial recognition technology, which produced thousands of false positive alerts and disproportionately impacted people of color.

This action, while important for consideration of disparate harm, signaled a shift in what regulators are looking for. It’s not just about what your AI systems do; it’s about how your organization governs data, vendors, and risk.

When regulators come calling, they won’t just ask what happened. They’ll ask how you govern it. And they’ll want the receipts.

STAY TUNED FOR PART TWO NEXT WEEK!

Where AI Actually Gets You in Trouble (real examples and practical risks)

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AJ Kohn wrote about “Goog enough” back in 2023, and he was right then. Fast forward to 2026, with AI everywhere, and it’s even more true now.

Rewind a bit further to 2012. Wil Reynolds stood on stage at MozCon and told everyone to do “Real Company Shit.” The room loved it. People nodded along. Then most went right back to checklist SEO the moment things got hard.

That’s the pattern. We recognize what matters, we just don’t follow through.

Which brings us to today. Good enough is dead.

So what’s your differentiator? What are you doing that AI can’t replicate?

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What stood out most was the ability to create and inject content directly into product and collection pages. That alone is a game-changer.

CDSEO is unlocking what Shopify SEO should have been all along.”

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The Floor Got Lifted Overnight

AI didn’t suddenly make people smarter. It made baseline output easier.

Everyone now has access to good-enough copy, ideas, image generation, and my personal favorite: an on-call editor. Things that used to take time, effort, or experience can now be generated in seconds.

The result is a massive flood of “good enough” content. It reads fine. It checks the boxes. It looks polished… but it ALL. FEELS. EXACTLY. THE. SAME!

That’s the shift most people are missing. The advantage isn’t in using AI. Everyone has it. People are still celebrating quality standards from years ago, before AI leveled the playing field.

The reality is the bar has risen. Good enough doesn’t cut it anymore.

Most People Are Playing the Wrong Game

Most people aren’t competing on outcomes. They’re competing on tools.

Everyone is flexing the same stack. Same platforms. Same prompts. Same “AI-powered” workflows like it’s some kind of differentiator. Don’t believe me? Open your favorite social platform and scroll for 30 seconds. Someone is selling you the dream of becoming an instant millionaire with AI. Just buy his course!

That’s the noise.

The real shift is this: the target is no longer quality. It’s not about publishing 10 blog posts a month or covering every keyword your tool spits out.

No, prompt engineering isn’t differentiation. It’s table stakes. Saying “we use AI” isn’t a value prop. It just tells me you’re doing what everyone else is already doing.

That’s the problem most people still don’t get.

If your pitch sounds like everyone else’s, it doesn’t matter how you got there. No one cares how the work was produced. They care if it stands out, drives results, or actually says something worth paying attention to.

No one cares how you made it. They care if it stands out and performs.

What AI Can’t Replicate

So what does differentiation actually look like?

If you’ve followed me on Twitter/X or LinkedIn, you’ve probably heard this story before.

I got fired. Then the next role was eliminated because of COVID-19.

If you want the full version, I wrote about it here: FIRED to 165k in 8 months. For everyone else, here’s the TL;DR.

Getting fired sucked, but it also opened my eyes to how business really works. It was a hard lesson, but an important one.

A few weeks later, right after Christmas, I landed another role. For a short stretch, I was collecting two paychecks. Then COVID hit, and that job disappeared too. Same lesson, second round.

That was the point where I decided I was done letting other people control my ability to provide for my kids. It pushed me into freelance SEO consulting with a goal bigger than survival.

Fast forward to 2023. Peter Askew, yes, the onion guy, reached out about partnering on SEOJobs.com, and I was instantly hooked. I wanted to help build the resource I wish had existed when I got my ass fired and laid off years earlier. Funny enough, we initially connected over the shared experience of losing our jobs.

By 2025, as the sole owner, SEOJobs.com drove more than 200,000 sessions. And the message behind it has stayed the same across social posts, podcasts, webinars, and anywhere else I get to talk about it:

Notice how none of that came from a prompt.

The worst professional experiences of my life became part of my brand, my perspective, and my credibility. That’s the point. The things that actually differentiate you are usually messy, personal, and earned.

Why “Safe” Content Is Losing

Safe content used to work.

Follow best practices. Cover the keywords in your copy. Don’t say anything controversial. Hit publish.

That playbook is dead.

Safe now means generic. And generic is exactly what AI is great at producing. Clean, structured, technically correct… and completely forgettable.

What used to drive clicks at scale now often doesn’t get shared, cited, or remembered. It doesn’t stick. It blends in with everything else competing for attention.

And both search and LLMs are evolving to reflect that.

They’re not just looking for “quality” anymore. They’re rewarding signals of trust, uniqueness, and perspective. Content that gets referenced by people. Content that gets talked about. Content that actually adds something new to the discussion.

Playing it safe used to feel smart. Now it’s the riskiest move you can make.

The New “Quality” Standard

You don’t get credit for being “solid” anymore.

Solid is expected. Solid is baseline. Solid is what everyone can produce now.

That’s the new bar.

If your content doesn’t teach something, challenge someone, or actually stick in someone’s head… It’s not good enough.

Not because it’s wrong. Not because it’s poorly written. Because it’s forgettable and forgettable doesn’t win anymore.

“Good enough for Google” used to work for rankings.

It doesn’t anymore.

Everyone agrees with this… right up until it requires more effort than a simple prompt. Then it’s back to “10 blogs a month” and calling it strategy.

~Nick

North Star Inbound runs one integrated growth system:

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Enough is enough.

By now, you already know I have zero issue with a salacious headline or a pointed opinion that goes against the grain. That’s kind of the point of this newsletter. I like saying publicly what a lot of people only say over beers.

But one thing needs to stop.

This whole SEO vs GEO argument.

Our industry keeps falling into the same tired buckets. You’ve got the head-in-the-sand crowd that doesn’t want to adjust. You’ve got the “SEO is dead, all hail GEO” crowd acting like they just discovered fire. Then you’ve got a smaller group that recognizes both things can be true at once: the landscape is changing, the old playbook isn’t enough, and adapting is part of the job.

When I shifted #SEOForLunch away from curating everyone else’s takes and started sharing more of my own, I made myself a promise. I wouldn’t use this platform just to complain unless I could at least offer something useful in return.

I might bend that rule a little today.

Sorry. Not sorry.

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Who’s Actually Driving This Debate?

One of the dumbest debates online right now is the idea that GEO somehow sits in opposition to SEO.

Call it GEO, AEO, AIO, or whatever acronym people are fighting over this week. The point keeps getting lost. The idea that GEO only matters once SEO is “dead” is nonsense.

Organic visibility has always been fluid. It has always required monitoring, testing, adapting, and thinking with your brain rather than blindly following a checklist. Whether you call it SEO or GEO, the end goal is the same: gain visibility and drive action.

From where I sit, the loudest voices in this debate usually fall into three buckets.

1. “SEO is dead” + early adoption = perceived advantage

This is often the newer marketer who would rather brand themselves as early as possible with AI than deal with the nuances of traditional SEO.

And let’s be honest, traditional SEO is easier to fact-check. There’s more history, more scar tissue, and more proof. GEO is newer, messier, and harder to measure, which makes it the perfect playground for people selling confidence without evidence.

2. The Corporate Leader Seeing Declining ROI

I hate this one because I get it.

For years, companies invested in SEO and saw enough return to keep going. Then AI Overviews show up, traffic gets hit, clicks shrink, and suddenly leadership starts asking a very fair question: why would I keep spending the same amount on a channel that looks less efficient than it used to?

That tension is real. Fear of missing the next big shift often outweighs trust in SEO's long-term value, especially when AI is being shoved into every boardroom conversation in America.

3. “AI is a fad” is just the other extreme

This is the old-school SEO who sees all of this as hype and wants to pretend nothing meaningful is changing.

I get that too. When the rules shift under your feet, denial feels safer than adaptation. And yes, there is some logic to doubling down on SEO while competitors get distracted. But pretending AI-driven discovery doesn’t matter just because it’s messy or hard to measure is still denial.

Different costume. Same fear.

…And This is Why The “vs” Debate is So Stupid

SEOs are notoriously bad at operating outside their silo. Leadership loves to talk about collaboration, but rarely rewards it in a meaningful way. So instead of solving for visibility across the full search journey, people turn it into a turf war.

That’s when the conversation stops being about strategy and starts being about ego: career positioning, thought leadership bait, who gets credit, who gets budget, and who gets to sound smartest on LinkedIn.

But this was never supposed to be about labels. It was supposed to be about evolution.

Nobody wants the more complicated answer because the simple ones are easier to sell. “SEO is dead.” “GEO replaces SEO.” “Just optimize for answer engines.” Clean lines like that sound smart, but most of the time they’re bullshit.

Yes, the environment has changed. User behavior has changed. Search is more fragmented. Discovery no longer happens in one box. AI systems summarize, synthesize, and cite unevenly. Clicks are harder to earn. Brand familiarity, entity clarity, off-site mentions, and community discussion all matter more now.

But the core job has not changed: understand how people discover information, earn visibility where they’re looking, and turn that visibility into action.

What’s changed is how that work needs to show up. Your content can’t just exist. It has to be extractable. Your brand can’t just rank. It has to be recognizable. And your strategy can’t just be “publish and pray.” It has to account for how information is interpreted, summarized, and reused.

And no, the answer is not “more AI.” The answer is not “ignore AI.” And the answer sure as hell is not renaming the department GEO and pretending you invented the future.

The real answer is a mix of manual work, critical thinking, and AI technology. You still need people analyzing SERPs, reviewing AI Overviews, checking how brands appear in LLMs, and studying citations, reviews, Reddit, YouTube, and other third-party sources. There is still no substitute for actually looking.

You also still need experienced humans deciding what matters, because not every mention, citation, ranking, or AI answer is meaningful. Someone has to separate signal from noise, determine what influences buyer behavior, and decide what deserves investment versus what is just another vanity metric wearing a trench coat.

Use it to cluster topics, identify content gaps, summarize patterns across reviews and forums, analyze brand consistency, speed up QA, and process large sets of query and citation data.

That’s the framework: strong SEO fundamentals; broader visibility across search, citations, communities, and answer engines; human-led interpretation; and AI-assisted execution.

Call it SEO. Call it GEO. Call it whatever helps you win an argument on LinkedIn.

The end goal is still the same: earn visibility, build trust, and drive action.

Stop arguing about the label and start doing the work.

~Nick

Digital PR matters more in GEO than it did in classic search.

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Every SEO roadmap, regardless of budget, resources, or internal buy-in, requires compromise. That is why prioritizing initiatives based on level of effort and expected return is so critical.

But those decisions can also leave your website exposed. Not to hackers. Not to a Google penalty. To other SEOs (this is why many in-house teams hide their XML sitemaps!)

The kind who would love nothing more than to email your boss, or your client if you’re agency-side, and build a pitch around all the “basic” things you are supposedly getting wrong. You did not ignore the issue. You made a decision. Someone else just turned that decision into a sales slide.

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The Rise of “Audit Poachers”

Ever been in this situation?

You finally have alignment on the roadmap. The tickets are in Jira, stakeholder meetings are booked, and there is real momentum behind the work. Then the email lands:

“Hey Nick, an SEO expert reached out and showed me all these super basic SEO issues on our site. Why haven’t these been addressed? Before we move forward with the roadmap, I want to dig into this more, i’m worried we might have missed something. Let’s set up a call for tomorrow.”

And just like that, the whole thing gets derailed.

This “expert” might be a competitor. Sometimes it is a vendor trying to wedge their way in. Sometimes it is even worse: a well-meaning dev or someone in PR who “knows SEO” just enough to be dangerous.

They run the site through a few tools, grab some screenshots, and suddenly they have a tidy little case against you: missing meta descriptions, page speed warnings, internal linking gaps, and some crawler-reported issue like code-to-text ratio warnings. And to be fair, those issues are not necessarily fake.

What is missing is context.

They do not know what was intentionally deprioritized, what was already planned, or what was discussed and weighed against bigger revenue opportunities. But leadership does not see that part. They see a list of obvious misses packaged into a compelling story, and now momentum stalls, trust is questioned, and the roadmap is second-guessed.

That is what I call audit poaching.

Context Rarely Survives an Audit

To be fair, the person conducting the audit is often not acting maliciously. They are simply looking at the site without any context for the prioritization decisions made.

What they do not see is everything happening behind the scenes. Maybe page speed improvements require development resources that are currently allocated to product work. Maybe internal linking updates are already planned, but scheduled for later in the roadmap. Maybe the team intentionally chose to focus on content expansion, fixing broken links, or addressing known crawl traps instead of polishing every technical warning an SEO tool happened to surface.

All of those decisions can be completely rational. But when someone new audits the site, they only see the gap, not the reasoning behind it. And when context disappears, those gaps become easy talking points in a sales conversation.

The Political Risk of Ignoring Best Practices

This is where things get tricky. Even when a missing best practice has little to no meaningful impact on visibility or revenue, it can still create a serious optics problem. That is really the point of today’s newsletter.

Leadership does not evaluate SEO the same way practitioners do. When someone walks into a meeting and says the site is missing structured data on hundreds of pages or that dozens of pages are missing optimized meta descriptions, the SEO team’s reaction is usually pretty simple: “Yes, we know, and there was a reason it was not prioritized right now.”

But that is not what leadership hears. What they hear is: “Your SEO team missed these issues.”

That is how audit poaching works. It takes strategic prioritization and reframes it as oversight. Once that seed gets planted, doubt starts creeping in. And when leadership begins questioning whether the obvious stuff was missed, they start questioning the roadmap, the prioritization, and sometimes even the people behind it.

That doubt usually creates a need for outside validation. Conveniently, the person most eager to provide that validation is often the same one who introduced the doubt in the first place.

Closing the Easy Doors

The solution is not to chase every best practice. That is a great way to waste resources, spin wheels executing the roadmap, and mistake activity for impact.

But it is smart to identify which gaps create easy sales ammunition for someone else. Some issues are low-effort and high-impact, such as fixing broken internal links, cleaning up orphaned “test” pages, tightening obvious title and meta description issues, implementing straightforward additional schema, or resolving easy technical warnings. These fixes may not drive massive growth on their own, but they do remove the easy screenshots from someone else’s audit deck.

For everything else, document it.

I like having a “garage” or holding zone for known issues. This is where lower-priority items live after they have been reviewed, discussed, and intentionally deprioritized. These are not missed issues. They are known issues. They were identified, evaluated, and set aside because other initiatives had a stronger tie to growth, revenue, or bigger technical constraints.

And once that documentation exists, the conversation changes. You are no longer scrambling to explain why something was overlooked. You are simply pointing stakeholders to the garage and showing that the issue was already logged, understood, and deprioritized on purpose.

That protects trust. It also kills the drama.

So when the audit poacher comes knocking, your response is simple: “Yep, we know. It’s already in the garage.”

Intentionality > Perfection

The goal isn’t perfection. It’s intentionality.

SEO roadmaps should prioritize the work that drives growth, not every warning an audit tool spits out. But if something is intentionally deprioritized, it should still be documented. Because when someone inevitably tries to turn that gap into a sales pitch, the best defense isn’t scrambling for an explanation.

It’s showing the decision was already made.

If your boss hired me today to audit your site, on a scale of 0 to 100, how exposed would you feel?

~ Nick

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I went to college and graduated with a four-year degree (barely, but that’s another story). I eventually landed a job despite the 2008 recession, and that became the starting point of my SEO career. From there, the only things that determined how far I’d go were skill, persistence, and a bit of luck, which naturally compound over time.

A few years in, I ran into something that still bugs me. I watched genuinely great people hit a ceiling, not because they weren’t performing, but because they didn’t have a degree.

Were they making the company money? Yup. Were they sharp, reliable, and clearly raising the bar for everyone around them? YupX2. So why would any business deliberately cap that kind of talent, or block it from leadership entirely?

That’s what I want to break down.

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Why This Requirement Exists (and why it’s lazy)

There’s an old-school belief that requiring a degree makes hiring “safer.” It feels responsible because it signals to HR and leadership that you’re filtering for “serious” candidates.

But here’s what’s really going on:

First, it’s risk management. “Bachelor’s required” is an easy checkbox that looks like quality control, without defining what “qualified” even means in SEO. And let’s be real, there isn’t a standardized SEO degree program that reliably produces great SEOs.

Second, it’s template inertia. Someone wrote a job description years ago, HR reused it, and now the requirements read like they were written by someone who’s never opened up Google Search Console. The degree line stays because nobody wants to remove it and then have to defend the decision if a hire doesn’t work out.

Third, leadership mislabels SEO. It gets lumped into “marketing” and treated like MBA work. In practice, SEO is a craft and a systems role: prioritization, testing, technical translation, and shipping improvements under constraints. A diploma doesn’t measure that, and the difference between “community college” and “Harvard” sure as hell doesn’t predict who will move rankings and revenue.

When I call the degree requirement lazy, I don’t mean people are lazy. I mean the process is. It outsources judgment to a credential instead of measuring the skills that matter. And if that sounds familiar, it should. It’s the same blind trust problem we’re creating with AI.

The Mismatch: What SEO Success Actually Depends On

SEO success isn’t a credential problem. It’s a performance problem.

The best SEOs have technical literacy, not because they’re engineers, but because they’re comfortable with systems. They can look at a site, spot what’s broken, and work with devs without sounding like they’re reading directly from Stack Overflow. They can also write and have real audience empathy, because rankings don’t matter if the page doesn’t answer the question or move a customer forward.

Good SEOs are strong at pattern recognition and prioritization. SEO is endless inputs with limited time and even fewer resources, so the job is basically 80/20 decision-making under pressure. They have an experimenter’s mindset: test, learn, iterate, and move fast without chasing shiny objects. And they can communicate clearly, turning messy data into actions that leadership can get behind.

None of that is guaranteed by a degree.

The Business Downsides of a Degree Barrier

The degree requirement isn’t quality assurance. It’s a performance tax.

First, it shrinks your talent pool, which means roles stay open longer and hiring quality drops. You’re not selecting the best SEO. You’re selecting the best SEO who also checks an unrelated box. That’s how you end up understaffed while competitors keep shipping.

Second, you overpay for paperwork instead of capability. Degrees don’t produce search visibility or revenue. Operators do. And when you anchor on pedigree, you inflate comp for people who interview well but haven’t actually moved the needle.

Third, you filter out self-taught talent. Some of the best SEOs I’ve worked with didn’t learn in a classroom. They learned by building, breaking, fixing, testing, and repeating. Many of them have driven millions in revenue because they’re obsessed with outcomes, learning fast, and competing. None of that shows up in a major or minor.

Fourth, you cap internal mobility. Nothing kills momentum faster than telling your highest performers they can’t lead because of a piece of paper.

Finally, you risk building a team with the same background, privileges, and assumptions. Cookie-cutter teams miss opportunities. That’s not “safe.” That’s expensive.

What to do instead: “Proof of capability.”

Nothing’s worse than someone ranting about what’s broken without offering a fix. So here it is, and it’s not complicated.

Start by removing the hard gate. Change “Bachelor’s required” to “Bachelor’s or equivalent experience” or drop it entirely. If someone has the skills you need, you want them in the funnel. Period.

Then replace the credential filter with a simple, skills-based screen you can run this week:

• Mini audit (30–45 minutes): Give them a page (or a small section of any site) and ask, “What would you fix first and why?” You’re looking for clarity of thinking, not a perfect answer.

• Prioritization test: List 10 common SEO issues (indexing, internal links, titles, redirects, Core Web Vitals, etc.) and ask them to pick the top three based on impact, then defend the tradeoffs. Great SEOs don’t know everything. They know what matters first. Bonus points if they frame it in ROI terms, because it removes a lot of the subjectivity.

• Communication test: Ask, “Explain this SEO concept to a CMO in five sentences.” If they can’t simplify, they can’t lead. This is one of the biggest differences between a junior IC and someone ready for senior or leadership responsibility.

Use a scorecard: curiosity, clarity, ownership, prioritization, and basic technical comfort. For leadership roles, add one more requirement: the ability to influence roadmaps and align teams, not academic pedigree.

I used AI to draft a scorecard template, then I edited it like an adult. Grab it here, make a copy, and tweak it for your team.

Don’t cap your best people because of a checkbox. If they’re driving outcomes, your job is to clear a path, not create a ceiling.

Google doesn’t have a “Bachelor’s required” ranking factor. So why is it a hiring requirement?

~Nick

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“There are generally two types of LLM users: those who use it to learn everything, and those who use it so they don’t have to learn anything”.

Mark Cuban posted that on X last week.

And nothing has hit me that hard in a long time.

My biggest frustration with LLMs isn’t the price tag, or even the unknowns. It’s watching people treat them like an “easy button” for thinking.

LLMs are incredible, but blind trust is turning smart people into liabilities.

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What “Blind Trust” Looks Like

I’ve written a lot about checkbox SEO, and how it’s already cost people jobs. Blind trust in LLMs is the same pattern, just faster, slicker, and delivered with the confidence of someone who has never been wrong in their life.

Rolling your eyes and thinking this doesn’t apply to you? Cool. Quiz time!

Here’s what “blind trust” looks like in real life. I’m not here to judge. But you’ll know if you’re doing it: (hint: we’ve all done this at one point or another)

• Copy/paste execution where the output goes straight into production, a client email, or a deck with minimal (or zero) review.

• No source checking because the output sounds authoritative, so you skip the part where you confirm it’s true. (Because LLMs never lie, right?)

• No understanding of the basics, which means there’s no internal BS detector. If you don’t know the fundamentals, you can’t spot the 💩.

And the worst part is how normal it feels.

Everything looks solid. The logic seems reasonable. You feel like a rockstar because you’re suddenly “300% more productive.”

Until you realize you didn’t increase your output. You outsourced your judgment to autocomplete.

Why It Feels Safe

LLMs feel safe because they don’t sound like they’re guessing.

They output answers with ridiculous confidence. They mirror your assumptions and your language, so the response feels “obvious” because it’s basically you reading your own thoughts back to yourself. And they’re optimized to be helpful, not to be true. Helpful often wears the costume of certainty.

Mark Cuban nailed another part of this: “AI’s biggest weakness is its inability to say ‘I don’t know.’ Our ability to admit what we don’t know will always give humans an advantage.”

Read that one more time. Feel that drop in your stomach? Yeah, me too. Sometimes it’s good to sit and process what makes us uncomfortable.

LLMs are right enough to earn your trust but absolutely wrong enough to bite you in the ass when it matters most.

Let’s Chat Real Life F’ups Lessons

This is the fun part. Here’s one example of catching ChatGPT with its pants down. Translation: it was wildly confident while being quietly wrong.

Scenario

I was doing a traffic drop assessment. That means tedious organization of daily traffic data across multiple years, then lining it up side-by-side so you can spot patterns and diagnose what changed.

In the name of “300% productivity,” I fed ChatGPT a few data exports and asked it to generate a clean, year-over-year comparison view.

AI Recommendation

ChatGPT spat out a gorgeous-looking spreadsheet in minutes. Boom. Done. I’m basically an automated SEO recommendation machine now.

What Was Wrong

Only after I took the output at face value, wrote my insights, and started forming recommendations did I realize something felt… off. The trends didn’t pass the sniff test. I needed to look closer.

The Fix

I went back to the original exports and manually sampled chunks of each year to recreate the side-by-side view myself. That’s when I found the issue.

ChatGPT had used only portions of the data and, in some places, duplicated data from one year into another.

WTF!?

What COULD Have Happened

I was about to send my client's findings and recommendations that were not small. This wasn’t a quick “tweak a title tag” situation. It would have triggered real work, real timelines, and incurred real costs.

But 15+ years in this industry has taught me one thing: if it smells weird, it probably is.

Once I corrected the data (manually), my recommendations to the client were completely different.

I could’ve caused months of unnecessary rework at an unknown expense, all because I trusted a little LLM to do my math for me.

And that’s the scary part: I only caught it because experience told me something didn’t add up. Most people would’ve shipped that spreadsheet with a smile and called it “AI efficiency.”

The “Don’t Be Stupid” Framework

Don’t worry, I’m not going to tell you to stop chasing efficiencies. If you want to use LLMs while minimizing chaos, follow this rule set. It’s not complicated.

People just refuse to do it.

• Use LLMs for drafts, not decisions. Let it brainstorm, outline, rewrite, summarize, and speed up true grunt work. But don’t let it replace YOU as the “expert.”

• Force it to show its work. Ask for assumptions, edge cases, and what would make the answer wrong. If it can’t list those, you definitely shouldn’t trust it.

• Demand citations and then check them. Not “a source exists,” but actual sources you can click, read, and confirm. I often ask for the exact chunk of text that was utilized to determine the output.

• Verify against primary sources. Official docs, logs, contracts, GA4 exports, GSC data, and real professionals with skills. Don’t simply create a post quoting a blog post quoting a tweet.

• High stakes = humans. If it’s legal, medical, financial, compliance, or reputation-risky, get a real expert involved. Get burned in these types of industries, and you may not have a career anymore.

Bottom line:
Speed is worthless if you’re sprinting in the wrong direction.

Don’t be this person using AI to make up quotes that are literally the exact opposite of what I preach.. and from a “source” I haven’t written for since 2010.

Actually, I take it all back. Please double down on your use of AI. It will just make my (and my client’s) life easier.

~Nick

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The biggest threat to SEO isn’t Google, it’s inaction.

If you’re leading a team, it’s easy to assume SEO is “handled” once you approve the budget, sign the contract, or hire the right person. But SEO only works when work ships. If nothing ships, nothing improves.

The site doesn’t grow. Revenue doesn’t move. And when that happens, nobody gets a gold star for having the “right” strategy. Budgets get questioned. Renewals get shaky. Headcount becomes a target.

That’s why, as I shared in the Masters of Search Podcast, that a core part of my job is helping teams get the F*** out of their own way so everyone wins.

And yeah, sometimes that means being the babysitter. The person who keeps the work moving, sets deadlines, chases owners, clarifies next steps, and makes sure QA happens so launches don’t die in Slack threads and infinite “quick check-in” meetings.

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The uncomfortable truth: Inaction is the silent budget killer

Inaction is the silent budget killer because it looks harmless. Nothing crashes. No alarms go off. So it gets tolerated.

But your budget still burns.

Work just sits. Content stays in drafts “waiting on approval.” Dev tickets rot behind shinier initiatives. Internal links never get added because it’s “not urgent.” Redirect maps don’t get finalized until after launch, when the damage is already done.

And here’s the trap: leaders say they want collaboration, but they reward competition. So everyone protects their own roadmap, and SEO work dies in the gaps.

Most SEO programs don’t fail. They stall.

What inaction really costs your business

Here’s what inaction actually costs you (and none of it shows up neatly on a dashboard):

• Opportunity cost: While you “circle back,” competitors ship. They publish, they optimize, they test, they earn links, they fix tech debt. Search and answer engines don’t pause the race because your team needed three more meetings to agree on whether one of the ten tasks should be scheduled in this sprint or the one scheduled 6 months down the road.

• Compound loss: SEO stacks. Every week you delay isn’t just one missed week. It’s delayed visibility, delayed traffic, delayed conversions, delayed internal links, delayed quality signals, and delayed learnings. You don’t just lose time, you lose the compounding effect that makes SEO worth doing in the first place.

• Trust + momentum: This one is brutal. When nothing launches, people stop believing SEO works. Stakeholders get cynical. Leaders call it “slow.” Then budgets get cut, timelines shrink, and the team gets told to “do more with less.”

“We had the fix. We had the plan. We just didn’t have… anyone actually doing it.”

The job of a good SEO: remove friction, not just make recommendations

The job of a good SEO: remove friction, not just make recommendations

Here’s what leadership should expect from a strong SEO: they don’t just find problems, they get work across the finish line.

SEO isn’t just a deliverable profession. Audits, forecasts, and roadmaps are easy. Execution is what moves revenue.

A good SEO anticipates the real blockers before they turn into excuses: legal reviews that take weeks, dev teams with no bandwidth, CMS limitations that make “simple” changes painful, brand feedback loops that never end, and stakeholder fear of change because someone broke something back in 2019.

Then they clear the path. They write tickets the way engineers want them written. They provide editorial guidance and drafts so content doesn’t sit in limbo. They define acceptance criteria so QA isn’t chaos. They offer options (fast, ideal, safe) instead of insisting on a single perfect answer.

They force their way into the rooms they “aren’t invited” to (politely, of course), because that’s where decisions are made, and delays happen.

You’re not paying your SEO to be right. You’re paying them to ship and get results.

Accountability: why the SEO is the one at risk

When results don’t show up, leadership rarely blames the channel. You blame a person.

It’s the agency. The consultant. The in-house SEO lead.

Even when the root cause is simple, “we didn’t implement..{insert excuse/reality here}” that detail doesn’t survive the budget meeting. The story becomes: “SEO isn’t working.” or even worse, “SEO isn’t a valuable channel anymore.”

Here’s the leadership reality: SEO is one of the few growth channels where success depends on cross-functional execution, but accountability still lands on one owner. If priorities stall, approvals drag, or engineering capacity disappears, the outcome doesn’t just “pause.” SEO quietly declines, and the SEO team takes the heat.

That’s why the best SEOs push so hard on execution. They’re protecting the business from wasted spend, and they’re protecting you from funding a strategy that never ships.

How leadership should support SEO

If you want SEO outcomes, you can’t say yes once and magically exit the room. You have to fund execution. Use this as your operating checklist:

If your SEO backlog is full of “great ideas” and empty of launches, you don’t have an SEO problem. You have an execution problem.

~Nick