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

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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?​

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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

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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.

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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

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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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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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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?

CDSEO is unlocking the full potential of Shopify SEO

“I get pitched tools all the time. Most of the time, I pass. Not because they’re bad, but because I don’t have the time. I made an exception here, and I’m glad I did.

CDSEO is a must-have if you’re running on Shopify. It doesn’t just surface issues; it actually lets you fix them at scale. No more clicking into page after page to make the same update.

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

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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

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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

You might not realize you’ve been training your SEO team to do the impossible.

They come to you with a list of smart, high-impact ideas. The kind that requires actual inputs: budget, engineering time, subject-matter experts, and maybe even PR support.

And the meeting ends the same way. “Not this quarter.” “No headcount.” “We’ll revisit.” Pick the excuse.

But the annual SEO goals stay. Non-negotiable.

For a long time, your SEO team made it work anyway. They found loopholes. They squeezed wins out of scraps. They used shortcuts that appeared to show growth because the dashboard moved in the right direction.

Now it’s 2026. The game changed.

We’re in an AI/LLM-first world. SERP manipulation is table stakes. Measurement is messier. And the shortcuts you tolerated, link schemes, spun content, low-quality scale tactics, are not just less effective.

They’re a business risk.

Yet the expectation is the same: work magic and deliver.

That disconnect is on all of us.

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The uncomfortable truth: SEOs created this expectation

The uncomfortable truth is that we built this expectation together.

For years, SEO teams were forced to be scrappy because the inputs that drive durable organic growth, budget, engineering time, SMEs, and PR support, were always the hardest to secure. So we adapted. We hunted for loopholes, squeezed marginal gains from technical fixes and internal linking, and pushed content through whatever bottlenecks existed. Sometimes we leaned on shortcuts that moved metrics fast enough to hit a quarterly target.

And here’s what that trained everyone to believe: SEO is cheap, goals are always figureoutable, and “making it work” is just part of the job.

That belief is the problem in 2026. Those shortcuts aren’t just less effective now. They’re a business risk.

We didn’t just adapt to the constraint. We normalized it.

What changed in 2026

We’re in an AI/LLM-first reality now. The search experience isn’t just “rank a page and win a click.” It’s increasingly an answer layer that summarizes, compares, and recommends before anyone ever reaches your site. That means traditional SERP manipulation is table stakes, not a source of differentiation. The brands that show up consistently are the ones that have real trust signals, real authority, and real brand gravity across the web.

At the same time, the old playbook gets weaker by the month. Link buying, spun content, and thin progromatic “SEO pages” don’t scale (as much) in a world where quality systems are faster to detect patterns and users are faster to bounce. And when shortcuts fail now, they don’t fail quietly. They create volatility, technical debt, reputational risk, and a larger cleanup bill than the original investment would’ve ever covered.

Measurement is also worse. Zero-click behavior is up, attribution is messier, and the wins that matter take longer and look different.

If your expectations are still built around “publish, rank, profit,” you’re going to be disappointed.

The new rule: organic visibility is a business outcome

Here’s the hard reset: organic visibility is no longer something you “do to a website.” It’s a business outcome you earn.

In 2026, the brands that win organic are the ones that are actually worth recommending. That starts with product value and customer experience. If customers bounce, complain, return, or leave bad reviews, the internet records that story everywhere, and the machines read it too. Brand trust and reputation are no longer fluffy marketing concepts. They’re ranking inputs.

Distribution matters more than ever as well. Partnerships, affiliates, communities, PR, and real-world word-of-mouth create signals that algorithms and LLMs use to determine who is credible. And content has to prove expertise, not just target keywords. Anyone can publish a best or top page, but not everyone can publish something that feels like it came from people who actually know what they’re talking about.

This is why SEO’s job is shifting. It’s less “optimize pages” and more “influence decisions” across the business: what you build, how you explain it, how you earn trust, and how you show up everywhere customers research.

If your business isn’t worth trusting, your rankings won’t be either.

The ask: stop demanding magic, fund the inputs

Here’s the ask, and it’s not complicated:

Stop demanding magic and start funding the inputs.

If you want organic growth in 2026, you have to invest in the levers that actually create it. That means content operations that don’t rely on one SEO pushing drafts uphill all week. Real writers, real SMEs, real editorial standards. It means technical resources, not just “submit a ticket and pray.” Dev time, site quality, and the ability to ship improvements without waiting three quarters. It means PR and brand building that earn trust signals rather than rent them: earned media, partnerships, distribution.

But there’s a second layer most leaders ignore: incentives.

SEO is dependent on teams like Product, Engineering, PR, Brand, and Content. If those teams aren’t measured, rewarded, or resourced to support SEO priorities, they won’t. Not because they’re a part of the “mean girls club”, but because they’re rational. You can’t say you want “collaboration” and then reward internal competition.

So here’s the trade: if you want outcomes, give SEO inputs. Because SEO isn’t a vending machine.

Stop treating SEO like the kid who gets handed a coloring book and told to “stay busy.” If organic growth matters, SEO belongs at the adults’ table with a real seat, a real budget, and real authority.

~Nick

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

In a recent campaign, we generated 4400 links in 20 months for Preply. If you want earned media that strengthens long-term discoverability, we can help.

Ready To Take SEO Serious? Let's Talk

Textbook implementation is always ideal. It’s not an all-or-nothing ultimatum.

I’ll never forget my first in-person client meeting.

I was prepared. I’d audited the hell out of their site and walked in with a full laundry list of recommendations.

Halfway through, the client asked a totally reasonable question:
“What if we can’t do all of this? What would you prioritize?”

And I, in all my early-career confidence, replied:
“If you’re not going to do it all, you probably shouldn’t bother with SEO.”

As soon as they walked out, the owner pulled me aside and absolutely lit me up. Fair. I deserved it. And I’ve never forgotten that moment.

That mistake is probably why, today, I still ask for the moon… but I plan for reality.

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

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Pitch Perfection and Take Steps Back

I swear I heard this from Tom Critchlow, but I can’t find the original source.

Either way, this line has guided my career:

“Pitch perfection, but be prepared to compromise by taking steps back. The goal isn’t perfect, it’s limiting the number of steps away from perfection.”

If it’s Tom’s, I want to credit it properly. If it’s not, someone please call me out so I can attribute it to the right person.

Regardless of who said it first, it’s one of the most accurate reminders I’ve ever heard in SEO, and it’s worth keeping top of mind.

Compromise is Prioritization

My early client mistake wasn’t the recommendations. Nine out of ten SEOs would agree with them.

The problem was me. I had no flexibility and no appreciation for budget, bandwidth, or how companies actually get work done.

Compromise in SEO is prioritization. It’s taking the “ideal” list, sorting by level of effort vs expected return, and fighting for the best outcome within real constraints.

The Audit Trap

This is where marketers get in trouble. We assume “more” means “better.”

Ever seen a 100-page SEO audit? It’s ugly, and it usually dies in a shared drive.

Executives don’t care how hard you worked. They care what to do next.

So the first pages should always answer:

• What’s wrong?

• What matters most?

• What’s the effort and payoff?

You still do the deep work. You just present it in layers:

• Leadership gets the 1,000-foot view fast

• Details are there if anyone wants to drill down

That clarity is what enables compromise.

Mini Exercise

Rank these from 1 (least effort / highest impact) to 5 (most effort / lowest impact):

• Update URLs to include hyphens and utilize keywords

• Address the indexation of a lower dev environment

• Move the blog from a subdomain to a folder

• Optimize Core Web Vitals to “pass” PageSpeed Insights

• Update pagination from lazy load to sequential pagination

Here’s my list:

1. Update pagination: lazy load → sequential
   Low effort / high impact

2. Fix dev environment indexation
   Low effort / medium impact

3. Move blog: subdomain → folder
   Medium effort / medium impact

4. CWV: “pass” PageSpeed
   Extremely high effort / low impact (in most cases)

5. URL rewrites for keywords/hyphens
   Medium effort / little to no impact

LOE varies by stack, but the bigger point is unavoidable: trade-offs exist.Reality + Impact > Best Practices

Reality + Impact Beats Best Practices

I’ve gotten pretty aggressive about calling out “checkbox SEO.”

You know the type: a giant best-practices list with zero thought given to impact, effort, or what the business is sacrificing to do it.

Look at the first two items above. Those aren’t nice-to-haves. Crawl and indexation issues can make content effectively invisible.

No discovery = no visibility = no ROI.

Everything after that gets murkier.

I’m generally team “folder,” but treating subdomain-to-folder like a universal law is lazy. Sometimes the blog is on a subdomain for legitimate reasons: platform constraints, ownership, technical debt, politics, or migration risk.

Same with CWV and URL rewrites. CWV can eat a disgusting amount of engineering time for marginal gains unless performance is truly broken. And URL “cleanup” often triggers redirects, extra work, and new ways to screw up signals across internal links, sitemaps, canonicals, and crawl directives.

None of that gets solved by yelling “best practices” louder.

Business Priorities over SEO Checkboxes

SEOs think:
Move content → redirects → sitemaps → ping Google.

Leaders think:
Opportunity cost → platform constraints → resourcing and risk → then the SEO checklist.

That’s why compromise is a skill.

Your job isn’t to “win” an SEO argument. It’s to help the business make the best trade-off with the time, money, and political capital it actually has.

So instead of: “We need to move the blog because best practices,” try:

• “We can do it, but it’s medium effort with real risk and opportunity cost.”

• “Before we do this, we should fix critical crawl/indexation blockers.”

• “If we still want the migration, here’s the lowest-risk version, plus what we’ll delay to make it happen.”

That’s compromise. Not lowering the bar, just negotiating reality.

And if you want to be taken seriously by leadership, this is the shift:
talk in trade-offs, not tactics.

If your recommendations can’t survive a budget meeting, they’re not real recommendations.

~ Nick

Please support this week's sponsor: North Star Inbound

I want to start with a big thank you to everyone who donated, shared, or engaged with my fundraiser supporting my local Minnesota community. I appreciate all of you so much. Together, we raised nearly $6000!

That fundraiser also highlighted one of the biggest measurement traps marketers will face this year: the idea that impressions are “valuable” and should be treated as a critical KPI, especially when discussing LLM visibility.

Let’s talk about why this could cause a big problem in 2026.

Thank you to this week’s sponsor: Airops.com

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Impressions: How Did We Get Here?

In an AI-first world, the user journey is moving away from a simple “Google → website” path and toward an ecosystem of off-site touchpoints. It’s no longer unreasonable to think someone watches your YouTube video, jumps on Reddit for feedback, runs a branded search, checks pricing on Amazon, and only then visits your site to purchase.

I haven’t been shy about how broken attribution is right now, especially for organic search. So the industry is getting creative, trying to prove ROI for work spanning dozens of surfaces. The solution I keep hearing over and over?

“Just waiting for impression data.”

The KPI Everyone’s Waiting For

Impressions didn’t become a default KPI because they’re meaningful. They became the default because they’re quantifiable and people assume they’ll be readily available (eventually for LLMs too?).

In the dinosaur ages, pre-internet, traditional media had a measurement problem. TV, billboards, radio, print. You couldn’t reliably connect exposure to action, so marketers leaned on reach-based proxies: how many people might have seen the message. Not great, but it was the best option.

Then digital showed up, and we kept the habit even when we didn’t have to.

Platforms love impressions because they’re a clean story to sell: spend X, get Y “visibility.” Whether the right people saw it, cared, or took action is usually left out of the conversation.

Impressions are frictionless to report, hard to challenge, and they look fantastic in slide decks. They give executives something to point at, teams something to celebrate, and agencies something to bill around.

The problem is simple: impressions are a proxy for exposure, not proof of impact.

Impressions are a proxy, not a performance KPI

Here’s a real example from last week.

I posted on LinkedIn about my fundraiser. It pulled 300+ likes, 60+ comments, and 30+ shares from a group within the SEO community of ridiculously high quality. Not random folks but actual people who matter. They influenced, they helped make a difference.

And it “only” got 9,000 impressions.

By impression logic, that’s not impressive. But the outcome was. That post helped drive nearly $6,000 in donations, sparked real conversation, and created a ton of goodwill and trust.

Impressions didn’t capture any of that. That’s the illusion.

If your reporting framework labels that as a “failed” post because the impression count wasn’t flashy, your framework is broken.

And this is exactly why LLM visibility reporting is about to get weird: we’re taking the noisiest, least truthful metric in marketing, slapping “AI” on it, and calling it measurement.

Apply it to LLM visibility (the 2026 spin)

Here’s the funniest part of this whole “LLM impressions” conversation.

Impressions aren’t even a real metric yet. Not in any standardized, trusted, cross-platform way.

What we actually have today is a patchwork of KPIs from third-party tools:

• Prompt rank tracking (did you show up for a specific query?)

• Citations and mentions (did the model reference you?)

• Share-of-voice visibility across a prompt set

• Referral clicks assuming source/medium is captured correctly (spoiler: it usually isn’t)

That’s it. No true reach. No frequency. No consistent definition of what counts as being “seen.”

But the industry is already chomping at the bit to crown impressions as the main KPI, because it’s familiar, easy to sell, and looks great on a dashboard.

Even IF we get “LLM impressions,” they won’t behave like the metrics marketers are used to. An LLM “impression” isn’t a page view. It’s a fleeting moment inside a private conversation where a user can get an answer, shortlist options, and make a decision without ever clicking to your site.

So when a vendor says, “You showed up 50,000 times,” the only real question is: did anything change? Trust, consideration, conversions? You usually can’t prove it.

And it’s getting worse. LLMs are already pushing paid placements, and the model is drifting toward impression-based billing. Add agentic shopping and Google’s UCP direction, and you’ve got a future where users stay in the interface and measurement gets even foggier.

So yes, track LLM visibility. You’d be dumb not to. But treat it like what it is:

LLM visibility is an early signal, not proof of impact.

OK, so if not impressions… how should we measure LLM impact?

In a perfect world, we’d have proper attribution. We’d know when an LLM mentioned your brand, what prompted it, what the user did next, and whether it drove incremental revenue.

That’s not the world we live in. And most SEO teams aren’t set up to run classic performance experiments anyway. Those require budgets, levers, and analytics support that SEO rarely gets.

So instead of begging for “LLM impressions” and calling it measurement, do what good SEOs already do with fuzzy channels: build an evidence stack.

1) The ideal situation (if your org can support it)

If you have solid analytics and cross-channel buy-in, aim for incrementality. Just don’t expect a neat UI with paid-style testing toggles. You’ll be approximating:

• Staggered rollouts: launch LLM-focused changes on a subset of pages or products, then expand

• Prompt set + outcomes: track a fixed prompt list and compare movement against branded demand, conversion quality, and downstream behavior

Imperfect? Yep. Still better than “we got impressions.”

2) The SEO-realistic situation (directional metrics you can defend)

Most of us should track consistent signals that indicate rising consideration and trust:

Visibility

• Citations/mentions trending up across a consistent prompt set

• Share-of-voice improving vs real competitors

Demand and intent

• Lift in direct traffic and homepage traffic

• Growth in branded search and “brand + product” queries

Downstream impact

• Better customer service efficiency (fewer basic questions, faster resolution)

• Fewer returns or “wrong expectations” tickets

One metric is a story. A stack of aligned signals is evidence.

Because even if platforms give us “LLM impressions” tomorrow, impressions will still be what they’ve always been: a proxy. An educated guess, not proof.

The next wave of marketing BS is going to be impression-shaped. Don’t fall for it.

~Nick

P.S.  Please show love for our sponsor below, North Star Inbound.  👇

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I’m putting my ethics into action.

Last week, I spoke about drawing a line in the sand regarding what we tolerate professionally. Between the ICE surge in Minnesota and the tragic shooting of Renee Good, every one of my lines has been crossed.

I even shared my frustration on social as I struggled to figure out how I could help.

But I’ve now figured it out…

I will trade my SEO/AI services for your donation to GiveMN.org. This fund is distributed to individuals and families impacted by ongoing events.

Support GiveMN.org

Full details on how to participate and how we can potentially work together are below.

This week’s #SEOForLunch is sponsored by Semrush Enterprise AI Optimization
Note: Semrush is not affiliated with the initiative outlined in this post.

Get your free AI search blueprint for 2026

Start 2026 with a strategy that’s based on the best.

Semrush’s AI Visibility Index explores the brands leading AI search, by tracking thousands of prompts across 5 key industries. Discover insights like:

• Which brands lead in each industry

• Who gets mentioned in AI vs. who it cites

• Why brands Patagonia, Logitech, and others are scoring AI search wins

The AI Visibility Index is completely free and gives you the definitive blueprint for thriving in AI search.

Explore the AI Visibility Index

SEO For Ethical Treatment of People

TL;DR: I want to raise money for those affected by the recent (and ongoing) ICE raids here in Minnesota. If you’d like to help me out, please consider making a donation.

Trade Offer: My SEO/AI Consulting For Your Donation

I’m happy to use this time to hop on a call or spend the hour reviewing your site and emailing you everything I would do to improve visibility as if I owned it.

Want to avoid politics and industry talk? Great! I would love to chat about how miserable it is to be a Vikings fan, cars, fishing in sub-zero temps, parenting, freelancing, or anything else that is on your mind.

Trust me, I also understand that asking for $300 is steep, especially in this economy.

For that reason, I have decided to also link out to websites that make a $50.00+ donation in this very post.

Make a Donation

But Nick, I Have Questions! I Have Answers…

• Yes, the link will be “followed.”

• No, I won’t noindex this post or remove it after this initiative.

• No, I don’t care if Google “penalizes”, “de-indexes,” or any other “rankings threat” that happens to this domain (you signed up via email, and you’ll keep getting the posts regardless of what status this domain has within Google’s index)

• This is the only time you can buy your way into a #SEOForLunch post without being an official sponsor.

All you have to do is make your $50+ donation and take a screenshot of your confirmation screen.

Email me directly at nick@nickleroy.com with your confirmation screenshot, and I’ll work with you to schedule the call and/or link your website in this post.

Get Your Link

Note: If you choose to leverage this opportunity as an unethical play to gain links to your porn, pills, poker, or affiliate “money page”, I won’t publish your link nor refund your donation amount. We are doing good things here, not taking advantage or trying to gain “linkz juicez” on the back of people suffering.

Official Supporters List

Many times, we feel powerless in situations like these. Not everyone can support in person, and it can make us feel like we’re not supporting at all. This is my attempt to leverage my skills within SEO/AI and broader business to help others in need.

A special THANK YOU to the folks listed below for supporting the Minnesota community. I’ve officially kicked it off with an additional donation on behalf of the SEO and PPC job boards.

Awesome People and Companies:

(Last updated 1/31 09:40 AM CST)

Total Confirmed Funds Raised: $6,410.00

• SEOJobs.com/PPCJobs.com

• Profound (Nick Lafferty)

• Gray Dot Group (Tory Gray)

• TheHotBot.ai (Erik Hinds)

• Trevorfox.com (Trevor Fox)

• SEOTesting.com (Ryan Jones)

• Celiac Self Care newsletter (Christina Heiser)

• Meta Monster AI (AndrewAskins)

• SEO for Doctors (Dorin B)

• Jess does marketing (Jessica Baker)

• Whitespark (Darren Shaw)

• Augarian (Micah Perzichilli)

• Redirects.net (Tony Castillo)

• Inbound Scope (Jess Joyce)

• Zyppy (Cyrus Shepard)

• Stashclicks (Anthony Lavall)

• FisherSEO (Joe Fisher)

• Previsible (Jordan Koene

• Oleg Korneitchouk

• Uproer (Griffin Roer)

• Nick LeRoy Consulting (N. LeRoy)

• Gray Dot Group (Tory Gray) TWO DONATIONS!!!

• lovebrand.com (Sam Pennington)

• Sam Thillen

• Overlap Interactive (Derrick Castro and team)

• Judd Lyon

• Momentic + MKE DMC (Tyler Einberger)

• Physics Forums (Greg Bernhardt)

• BoutiqueSEO (Jane Nriapia)

• Hill Web Creations (Jeannie Hill)

• OSFsolutions (Tarik Najeddine)

• Adamvala.cz (Adam Vala)

• KathyAliceBrown.com (Kathy Alice Brown)

• Samuellavoie.com (Samuel Lovoie)

• Hook Agency (Bea Bonte + Tim Brown)

• Sandy Eggo SEO (Pablo Villalpando)

SPECIAL THANK YOU to Seer Interactive, who provided a $1,000 donation, taking our efforts over the $5000 mark.

Also, a big thank you to North Star Inbound for sponsoring the #SEOForLunch.
Please note that sponsors are not tied to the contents of this week’s newsletter.

Let's Book A Call Today

As a friendly reminder, this is NOT about politics.

This is about treating all people as humans. Donations to the Minnesota Immigrant Rights Action Committee will support local efforts to do just this.

Thank you in advance for your support. Our industry is like no other, and I couldn’t be more proud to be a part of it.

~ Nick

P.S. Regular sass and hot takes resume next week. I’m cooking up something good!

Whoa, What’s Happening? Give Me More Details.

The United States federal government initiated Operation “Metro Surge” in December 2025, deploying approximately 3,000 agents from Immigration and Customs Enforcement (ICE) and Border Patrol to the Twin Cities (Minneapolis/St Paul).

The public-facing goal was to remove “the worst of the worst” criminals who are also illegal immigrants, including those convicted of murder and drug trafficking.

The reality is that “Metro Surge” has led to unlawful racial profiling and straight-up terrorizing people (especially our Samali communities) about their citizenship status.

As protests started, ICE fatally shot 37-year-old Renee Nicole Good in downtown Minneapolis, MN. This has sparked mass protests (The George Floyd murder occurred 1 mile away from this event), which has further not only the anger of Minnesotans but brought on more extreme (illegal) actions from ICE. This includes detaining US citizens regardless of whether they have provided legal immigration paperwork.

Relevant resources worth reading (local and global coverage)

• ICE officer kills a Minneapolis driver over an immigration operation

• ICE is going into Minnesota schools, taking students into custody.

• ICE is breaking into privately owned homes without proper warrants

• ICE is detaining legal immigrants at their job location.

• ICE is forcing restaurants to close due to fear
  

Learn more about MIRAC

MIRAC Mission:

MIRAC is fundraising to support our beloved Latino and Somali immigrant communities during this time of fear and intimidation from federal officials. We are also leading a movement to say No More Deportations and to say No More Collaboration between local public safety officials and federal ICE officers.

MIRAC Education Fund is a 501(c)(3) non-profit organization, and so all donations are tax-exempt.

A few weeks back, I came across a post on LinkedIn by Matt Tutt that made me sit up, re-read, and actually think. He shared how Jeffrey Epstein was paying for SEO services to “optimize” search results for his name.

Most people would bucket that under “reputation management,” where the goal is to push negative content down and make it harder to find.

I sat with that for a minute, and my ADHD brain sprinted straight to the real question: Should ethics play a role in SEO and AI?

Because, as Uncle Ben told Peter Parker, with great power comes great responsibility…

A special THANK YOU to this week’s sponsor: Profound

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Define the real question

SEO ethics gets messy because we’re really arguing about two different things.

Capability debate: Can SEO and AI influence perception? Obviously. Rankings, headlines, Knowledge Panels, AI answers, and everything that shows up on page one shape what people believe is “true.”

Ethics debate: Should we help anyone influence perception? That’s where it stops being a technical conversation and becomes a values one.

Here’s the simple truth: SEO isn’t morally neutral in practice. It changes what people see first, what they trust, and what they decide to do next.

Why ethics matters even more now with AI

This matters more now because AI changes the output and the stakes. Search results are one thing. You still get a list of links, a mix of sources, and the user can decide what to click. LLM answers are another. They don’t just surface information. They compress it into a single narrative, delivered with confidence and little to no uncertainty.

If traditional SEO was moving blue links around, AI visibility is shaping the story people walk away with.

That’s why ethics gets messier. You’re no longer optimizing what ranks. You’re influencing what gets believed, even when the user never sees the sources behind it.

A practical ethics framework

Earlier in my career, I treated “ethics in SEO” the same way many treat celebrity political takes: optional, noisy, and mostly irrelevant to the job.

My goal was simple. Rank. Bank. Move on.

A wife, three kids, and about ten years later, I’ve changed my tune.

Here’s the simplest way I’ve found to think about ethics in SEO and AI without turning this into a philosophy class. Before you take the work, run it through a four-question filter.

1. Intent: Are we helping someone be understood, or helping them be forgotten? There’s a massive difference between adding context and erasing consequences.

2. Harm: Who would be harmed if this worked? Not just the client’s “reputation.” Think victims, customers, voters, patients, and the public. If your work makes it harder for people to make informed decisions, that’s a problem. (And yeah, the Epstein example lands here like a brick.)

3. Truth: Are we elevating accurate, verifiable context, or burying accurate reporting? “It’s negative” doesn’t mean it’s false. And “it’s old” doesn’t mean it’s irrelevant.

4. Accountability: Would I be comfortable explaining this project publicly? Not in vague case-study speak. Like, could I explain what I did and why, out loud, with my name attached?

Here’s my line in the sand: if the work primarily exists to reduce accountability for real harm, I’m out. Not “let’s reframe it.” Not “let’s make it more positive.” Out.

Because at that point, it’s no longer marketing.

And even if someone else takes the engagement, reality tends to win. SEO isn’t a magic eraser anymore. If you refuse to address the root issue (the behavior, the product, the service), the business will go under anyway… no matter how perfect page one looks.

Three common scenarios

I find that 99% of engagements fall into these three buckets. The line in the sand you draw might be different than mine, and that’s fine. But let’s talk about each.

Scenario A: Legit reputation repair

A company has outdated or flat-out inaccurate coverage ranking for their name. Maybe the issue was resolved, ownership changed, or the original claim was corrected. The ethical path here isn’t “make it disappear.” It’s to publish transparent updates, improve documentation, and earn coverage that reflects what’s true today. You’re helping people find accurate context, not hiding history.

Scenario B: Gray area

A company has valid criticism ranking because customers are mad… and for good reason. The tell is when they refuse to change anything but still want “better SERPs.” If the strategy is purely to muzzle feedback rather than fix the root problem, that’s an easy nope.

Now, there is nuance here.

If you’ve got that one customer from hell who’s determined to burn your business down over a refund they don’t deserve (after eating the entire meal, of course), I’m not going to pretend that’s “accountability.” That’s extortion with extra steps.

In that case, I’m fine helping a brand put the record straight, document what actually happened, and make sure the truth is easy to find. But if you’ve got 200 angry customers saying the same thing and your only plan is to “SEO it away”? That’s not a reputation problem. That’s a business problem.

Scenario C: The hard “no.”

If there’s serious harm, fraud, abuse, or violent history and the goal is to make that harder to discover, I’m out. And honestly, forget the ethics debate for a second. I don’t want my name associated with that. Ever. Never ever. Ever, ever. No-way. 😅

“But SEO is just a service…” (counterargument + rebuttal)

I can already hear the pushback: “We’re not judges,” and “If I don’t do it, someone else will.”

And look, let’s be real. Not everyone gets to choose their projects. If you’re in-house, you’re assigned work. If you’re at an agency, leadership might sell something before you ever hear about it. You might not be in a position to quit on principle, and I’m not going to sit here and pretend that’s an easy call when you’ve got bills to pay.

But “I was just following orders” can’t be the end of the conversation.

That logic generates excuses. It turns every decision into a shrug and a paycheck. No standards. No spine. Just another cog within the “internal politics” engine.

You don’t need to be the morality police to have boundaries. You need a line you won’t cross and the willingness to raise your hand when the work smells off. Have the conversation with leadership. Push for a policy. Document concerns. Ask the uncomfortable questions.

Because the second you tell yourself, “it’s not my problem,” you’ve volunteered to be the name attached to it when it inevitably blows up.

What ethical SEO/AI looks like in practice

What does ethical SEO and AI look like in practice? It’s boring in the best way: standards, documentation, and honesty.

Write down your boundaries. Literally. “Here’s what we won’t take on.” Ask harder intake questions before you say yes. Put “truth + transparency” in the brief, not as a vibe, as a requirement. And if a client wants a reputation lift, make it conditional on real-world fixes: product changes, policy updates, refunds, better support, public clarification. If nothing changes in reality, don’t pretend a new narrative will save them.

Ethical SEO is simple: don’t be a consultant for villains.

~Nick

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Right before the holidays, I was catching up with Kevin Indig (Growth Memo). Within minutes, AI came up. After comparing client examples, we both agreed on the obvious truth.

If you want SEO work approved right now, tie it to AI and LLM outcomes. That’s the AI Robe Strategy.

Leadership is obsessed with “future-proofing.” The second your SEO initiative also sounds like an AI initiative, it gets treated like a priority. Same recommendation. Faster yes.

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Why This Works Right Now

Someone might be thinking, “Great Nick, now we’re resorting to lying to keep SEO a priority.”

Nope. This isn’t deception. It’s incentive alignment. No false promises, no bait-and-switch. The work supports AI and LLM visibility, and you still get SEO wins as the byproduct.

Most SEO backlogs are full of high-ROI work that gets ignored because it’s expensive, slow, and not sexy. The irony is that “LLM optimization” overlaps with SEO fundamentals:

• Clarity (entities and structure)

• Credibility (citations and brand signals)

• Accessibility (crawlability and discoverability)

Same work. Better packaging. Faster approvals.

The overlap: SEO wins that also help LLM visibility

If you want “AI results” without the bullshit gimmicks, start here.

Entity Clarity and Consistency

Make it obvious what you do, who you serve, and why you’re credible. Strong About and author pages + consistent naming helps search engines and gives LLMs clean context to describe your brand.

Content Consolidation and Pruning

If five pages compete for the same intent, you don’t have five assets, you have one problem. Merge duplicates, reduce cannibalization, and build a single best-answer page LLMs can confidently pull from.

Structured Data and Page Structure

Clean templates, headings, and schema where it truly fits make content easier to parse. This helps crawlability and snippets, and improves accurate extraction and citation in LLM answers.

The AI Robe Framework (how to pitch it)

For every SEO recommendation, add three lines.
Not three slides. Not a 40-tab spreadsheet. Three lines.

1. LLM outcome: retrievability, citation likelihood, brand associations, answer accuracy

2. SEO outcome: topic coverage, crawl efficiency, CTR, topical authority

3. Business outcome: pipeline, revenue, retention, support deflection, CAC

This forces your roadmap to connect to dollars, not “best practices.”

This forces your roadmap to connect to dollars, not “best practices.”

One rule: if you can’t tie it to business impact, don’t pitch it.

One Recommendation, Two Rewards

I have a client in the mattress space who treated SEO like publishing, not “words on a page.” That approach drove 1M+ blog visits.

Then AI Overviews hit, and traffic got cut in half.

SEO isn’t dead, but the playbook has to evolve. For them, that meant “SEO+”: ship work that pays off now in search and also holds up in LLMs.

One simple example: “Is [Brand] sold in stores?” They started online-only, but recently opened physical locations. Customers were already asking, yet the web still reflected the old narrative. And LLMs will repeat outdated info if you let them.

So we framed it as brand narrative control, not “another blog post.” Easy yes.

Result: LLMs picked it up fast, and we took over the answer for a high-intent query.

Google AIO

Gemini

ChatGPT (note this client owns all citation sources)

Can This Be Reproduced?

Yes. I ran the same play with a CRM client, where “Brand A vs Brand B” comparison queries are common and convert well.

Google AIO

ChatGPT

“Clicks” and Secondary SEO Value

LLM tracking is still messy, but you don’t need perfect attribution to see impact. A basic referral source report showed direct LLM traffic landing on the page.

And here’s the part nobody wants to admit out loud: it wasn’t just an “AI visibility” win. It drove meaningful organic traffic, too.

Two birds, one stone. We got approval by speaking to the AI priority, delivered the AI outcome we pitched, and captured classic SEO upside as a byproduct.

Oh, and that “SEO is dead” traffic? It drove $100K in incremental sales.

If you’re still fighting the “LLM vs SEO” battle, you’re losing.
Use the AI hype to get real SEO work shipped, and win everywhere your customers search.

~ Nick

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2025 was a bizarre year full of dread and excitement: LLMs showed up everywhere, AI sped up the chaos, Google kept rewriting the rules, and lawsuits became part of the norm.

And I don’t think we’ve seen the worst (or best) of it yet.

So this week, I’m sharing 5 spicy SEO/AI predictions for 2026. Drop yours in the comments, too. I want to see what you’re all calling!

Happy holidays, and a quick thank you to everyone who took my 3-question survey the other week. In 2026, I’m going to keep shipping thought-provoking takes that (politely, maybe) punch the status quo in the face.

Thanks again to this week’s sponsor, Junction Jack.

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Prediction 1:
AI-Mode Doesn’t Become Default Until Q4 26’+

Google has made it crystal clear that AI is the direction of travel. AI Overviews and AI Mode keep users on Google longer and answer more questions without needing ten blue links. (or so Google wants us to think, ha!)

But wanting it and defaulting it are two very different things.

Google is sitting on a golden goose. Alphabet did roughly $307B in revenue in 2023, and ads and clicks still power the machine.

Here’s the problem: early data suggests the AI experience is bad for the click economy. Seer’s research found that for informational queries with AI Overviews, organic CTR dropped 61% and paid CTR dropped 68% vs mid-2024.

There’s no chance Google makes AI Mode the default experience at scale until monetization is solved in a way that actually holds up.

So my bet: 2026 stays “optional tab / toggle,” with any true default push happening late in the year at best, and only after a meaningfully different ad model shows up.

Prediction 2:
Many SEO agencies will go out of business.

Here’s the uncomfortable truth: you never had to be good at SEO to run an agency.

For years, many shops built a decent standard operating procedure (SOP), slapped it on a retainer, then hired the cheapest possible labor (overseas, contractors, entry-level) to “execute.” The deliverables factory looked productive. The business impact was… meh at best.

Checkbox SEO is finally getting exposed. Business owners are going to realize their $1k, $2k, or $5k+/month retainer isn’t moving the needle, and that money can be spent somewhere else that actually drives revenue.

Some agencies will pivot and evolve. Many won’t.

A few will try to rebrand the same old work as “AI optimization” and sprinkle in buzzwords like it’s magic fairy dust. But a lot of the rest? They’ll close up shop and move on. Digital marketing was easy money for a long time. Those days are over. And it’s getting clearer by the month that SEO is no longer a fit for every business.

Usually, this gets framed as “bad for the industry,” and I’m not cheering for layoffs (trust me, I spend way too much time/money running seojobs.com!) But the barrier to entry has been so low for so long that a bit of house cleaning might actually raise the bar for everyone who’s left.

Prediction 3:
LLMs Start Providing Paid Ads

I was talking to Dan “Dejan” Patrovic on Twitter/X /the other day about this. Unlike Google, OpenAI, Perplexity, Claude, and the rest do not have a decades-old, ad-click money printer keeping the lights on. They’re building the plane while flying it, and $20 monthly subscriptions alone won't fund this arms race forever.

Perplexity already started experimenting with ads, and their first formats were basically “sponsored follow-up questions” sitting next to answers. That’s the “tell.” The market is training users (and advertisers) to treat LLM attention as monetizable, even when packaged as “helpful suggestions.”

The bigger question is what the trade looks like: do we get obvious sponsored placements (fine), or do we get “sponsored influence” inside the answer (not acceptable)? Either way, ads are coming because the incentives are inevitable.

Dan’s CAPS idea is one of the few publisher-friendly paths I see that at least tries to solve the “AI uses your content, you get paid” problem. But until something like that is real and scalable, the easiest lever to pull is still the same one it’s always been: paid placements.

Prediction 4:
LLMs Measurement Businesses Start To Fail

AI visibility tooling is having its “gold rush” moment. Everyone wants a dashboard that shows where they appear in ChatGPT, AI Mode, Perplexity, etc.

The problem is we still haven’t agreed on the fundamentals:

• What are the KPIs?

• What does “doing good” even look like?

• How do you prove ROI when the journey is mostly off-platform and clickless?

So a lot of these standalone “LLM reporting” tools are going to run into the same wall in 2026: renewal conversations. When budgets tighten, pretty charts without a clear financial return are the first to get cut.

The companies I’m less worried about are the ones where AI/LLM measurement is an extension of an existing, sticky product (think Semrush and Ahrefs).

The standalone players will need to evolve fast. Tracking alone will get commoditized. The winners will pair measurement with action: automation, workflows, content ops, refresh programs, and execution that actually moves visibility. AirOps and Profound feel closer to that direction than most.

The rest? I’d bet a bunch of them are gone by the end of 2026.

Prediction 5:
C-Suite Eats Crow: SEO Job Market Returns

I’ve seen the drop in SEO job listings firsthand. You’ve seen it too: layoffs, hiring freezes, and companies announcing “replacing” teams with AI automation.

Here’s the twist: “SEO” didn’t die. It got promoted.

In a world where visibility now includes LLM answers, AI Mode, and recommendation engines, companies still need someone who understands discoverability, technical foundations, content strategy, and brand signals. The difference is that the job title might change, but the skill set is about to be back in demand.

I’ve written before about leaders chasing the easy button. In 2026, more of them are going to regret using AI as an excuse to cut payroll to make a single year’s P&L look prettier. The Salesforce example is a prime example: cut thousands, tout AI efficiency, then reality shows up when quality, trust, and outcomes start wobbling.

I’ve been yelling this for a while: AI is an incredible tool. But it’s supposed to augment strong humans, not replace them.

So yes, I think a meaningful chunk of these roles come back in 26’. The frustrating part is that compensation is likely to drop in the short term because companies will try to get “senior experience” at a discount, leveraging the AI fear/reality impacting jobs in 2025. But that won’t last. Once enough teams feel the pain of cheap execution and expensive mistakes, the market will correct, and the best talent will win again.

Cheers to 2026. May your AI outputs be accurate and your humans still employed.

~Nick