I’m watching OpenAI discontinue ChatGPT Atlas, its standalone desktop browser, and move its browser-based AI features into the new ChatGPT desktop app. That app brings together ChatGPT Work, OpenAI’s work-focused agent, and ChatGPT Codex.
The end of Atlas. I’m taking note of an Aug. 9 retirement date after OpenAI’s James Sun confirmed the plan on X.
I’m also noting Sun’s exact wording: “The current targeted date for deprecation is 8/9, and we’ll share more information in the upcoming days both in-app and via email.”
One desktop app. I see the new ChatGPT desktop app becoming OpenAI’s primary desktop product, complete with built-in browser capabilities. Instead of maintaining a separate AI browser, OpenAI is combining browsing, work-agent features, and Codex in one place.
Chrome users can keep Chrome. If I prefer using Chrome, I can access ChatGPT and Codex through OpenAI’s Chrome extension without switching to a dedicated OpenAI browser.
As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.
Why I care. I see this as an important shift because OpenAI is moving AI browsing into the main ChatGPT experience, where more people can ask questions, research brands, and complete tasks. In my view, that gives ChatGPT another opportunity to influence discovery beyond traditional search results.
I first saw ChatGPT Atlas launch on Mac in October. OpenAI later released a dedicated Codex app and added an in-app browser in April. Now, I’m watching those capabilities move into the new unified ChatGPT desktop app.
I often get asked why I “only” run each prompt one time per day.
For me, the answer comes down to signal quality. Running a prompt once daily gives me enough consistent data to understand performance without overloading the process with unnecessary repetition.
The statistics show that a single daily run is plenty. It gives me a reliable view of how prompts behave over time, while keeping the workflow focused, efficient, and easier to interpret.
I see advanced architecture as much more than a technical framework now. It shapes whether my content can be found, understood, and surfaced by search engines and AI systems.
That is why I am paying close attention to the next SMX Now on July 15, featuring Shari Thurow, co-founder, information scientist, and search director at the Information Architecture Gateway. She will explain how advanced architecture really works and where many AI, SEO, and site development workflows tend to fall short.
In this session, I will explore a five-phase framework Thurow has tested through decades of client work with organizations including Microsoft, Google Cloud, Abbott Laboratories, CVS Pharmacy, WebMD, Sony Music, the Library of Congress, Best Buy, and Merriam-Webster. I will learn how architecture decisions influence labeling systems, wayfinding networks, taxonomy, wireframes, and AI access to valuable content.
I also expect the session to challenge some long-standing assumptions, including the three-click rule, the idea that taxonomy is only a hierarchy, and the belief that AI can create effective wireframes without a deeper architectural model behind them.
Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.
By the end, I will have a practical framework for building sites that communicate more clearly with users, search engines, and human-centered AI systems.
[Boston, MA, July 6, 2026] — I am sharing that Traffic Think Tank has officially joined the Search Engine Land family, creating more opportunities for search marketers like us to connect, collaborate, and keep learning through one of the industry’s most established professional communities.
I want members to know that Traffic Think Tank will continue operating as a private Slack community. It will remain a trusted place where we can exchange ideas, validate strategies, solve real marketing challenges, and stay current on search engine optimization, paid media, artificial intelligence, and related marketing topics.
As part of this relationship, I see Search Engine Land supporting the community’s continued growth by increasing visibility across its editorial and marketing channels while preserving the collaborative environment members already value.
“For years, Search Engine Land has represented the marketing community through its contributor network in a way few other sites have,” said Kyle Morley, Head of Sales and Marketing at Third Door Media, parent to Search Engine Land. “Launching a community like Traffic Think Tank feels like a natural extension of our identity, and I’m thrilled we now have more opportunity to connect with marketers in our space.”
I am also noting that David Broderick has been appointed Lead Community Manager and will oversee the day-to-day community experience. He will be supported by Liz Dougherty, who will take an active role in encouraging member engagement and helping guide the community’s continued growth.
Beyond ongoing peer-to-peer discussions, I expect members to benefit from expanded community programming and discussions, increased visibility through Search Engine Land and Third Door Media channels, exclusive discounts on Search Marketing Expo events and training, and new opportunities to connect with search marketers across the industry.
For me, Traffic Think Tank fits naturally with Search Engine Land’s mission of helping marketers stay informed and succeed in a rapidly evolving search landscape. Together, the publication and community give us access to trusted journalism, practical education, live events, and an active peer network for ongoing professional development.
Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.
I view Search Engine Land as a leading publication for news, insights, and education covering search engine optimization, paid media, artificial intelligence, and digital marketing. Through editorial coverage, events, training, and professional resources, Search Engine Land helps marketers stay ahead of industry change.
About Traffic Think Tank
I see Traffic Think Tank as a private community for search marketers that connects professionals through expert discussions, peer collaboration, and practical knowledge sharing. Members use the community to exchange ideas, solve challenges, validate strategies, and stay current on what’s working across search engine optimization, paid media, and artificial intelligence.
I’m measuring downstream web browsing after AI brand mentions, focusing on what happens once a brand shows up in an AI-generated answer or recommendation.
For me, the AI mention effect is about connecting visibility inside AI experiences with real user behavior afterward, especially whether those mentions lead people to search, click, browse, and engage beyond the original AI response.
When I think about AI deliverables, I keep coming back to a simple scenario: a client receives two pieces of work.
Both deliverables solve the problem they were hired to solve. Both are accurate, useful, and tied to the same business outcome. The client is happy, and from the outside, there is no meaningful difference in the results.
Then the client learns that one took 20 hours to create, while the other took 20 minutes. That is when the uncomfortable questions begin.
Was AI involved? Should the faster deliverable cost less? Is the person who completed it less skilled because they found a faster, more efficient way to reach the same result?
What I find most interesting is how differently many of us react to AI depending on which side of the transaction we are on. I love using AI when it saves me time, but I also understand why customers can feel uneasy when they discover AI helped create something they paid for.
I recently ran a LinkedIn poll asking a simple question: if the outcome is great, do we really care how it was made?
The responses reinforced something I have been thinking about for a while. Many of the strongest objections people have to AI are not really about quality at all.
The Time vs. Value Fallacy
I think part of the discomfort comes from the fact that we have spent decades tying value to effort.
Long hours feel valuable. Fast work feels suspicious. Struggle often gets mistaken for expertise.
The harder something appears to be, the easier it becomes to justify the price attached to it.
There is an old story about a ship engine that stopped working. After multiple failed attempts to repair it, the owners brought in an engineer with decades of experience. He inspected the engine, tapped it once with a small hammer, and the machine roared back to life.
His invoice was $10,000.
The owners were furious and demanded an itemized bill. The response was simple: hammer tap, $2. Knowing where to tap, $9,998.
People debate whether that story is true or just a useful tale for people like me who believe in value-based pricing. But whether it really happened almost does not matter. The lesson still holds.
People are not paying for the tap. They are paying for the expertise behind it.
That is what makes AI such an important topic for me. It forces us to confront a question many of us have avoided for years: are we paying for expertise, or are we paying for visible effort?
Those are not always the same thing.
The Objections That Actually Matter
To be clear, I do not think every objection to AI is unreasonable. I have shared plenty of my own concerns, and some of them are serious.
In fact, I think the strongest arguments against AI have very little to do with how quickly something was created.
Those are legitimate concerns. What stands out to me is that none of them has much to do with how long it took to create the deliverable.
They are questions of trust.
Can the output be trusted? Can the recommendation be defended? Can someone confidently stand behind the work if it is questioned six months from now?
Because when something goes wrong, nobody gets to blame the AI. The employee is accountable. The consultant is accountable. The company is accountable.
That is why I have always found the quality debate to be the least interesting part of the conversation. The more important question is not whether AI was involved. It is whether the outcome is trustworthy enough for someone to put their name behind it.
The Outcome Test
The more I think about AI, the less interested I become in whether it was used.
Instead, I find myself asking a different set of questions. Was the outcome accurate? Was it useful? Was it better than the alternative? Would I be willing to stand behind it with my name, reputation, and credentials on the line?
If the answer to all of those questions is yes, then I have a hard time arguing that the production method matters more than the result.
Ironically, this is also where humans become more important, not less.
The future is not machines versus humans. I know, "The Terminator" and "I, Robot" movies will never feel the same. The real shift is humans using AI versus humans who refuse to adapt.
AI can accelerate execution, but people still decide what should be built, what should be published, and what risks are acceptable. More importantly, people are still responsible for the outcome.
The people who lose to AI will not be the ones using it. They will be the ones still evaluating effort while everyone else is measuring outcomes.
This post first appeared on the author’s website and is republished here with permission.
I think every PPC professional has at least one mistake they wish they could erase. For Danny Gavin, founder of Optidge, it was not a failed bidding strategy, a blown budget, or a campaign that never found its footing. It was something much simpler, and in many ways, much more painful.
When Danny joined me on PPC Live The Podcast, he shared the story of a technical issue that kept landing page leads from reaching the client. For one to two months, the campaigns were still generating qualified prospects, but the client believed nothing was working because those enquiries never appeared in their inbox.
The mistake that no one spotted
At the time, Danny’s agency was still small, with only a handful of people managing client accounts. One client, an autism therapy provider, appeared to be getting strong results inside Google Ads.
Clicks were rising. Cost per lead looked healthy. From inside the ad platform, everything pointed to success.
But the client was growing more frustrated because no enquiries were coming through.
The problem was not Google Ads.
It was not the landing page.
It was the email notification system.
Every form submission was being stored correctly in the database, but a technical failure stopped the notification emails from reaching the client. Because neither side realized those emails had failed, the issue went unnoticed for weeks.
By the time the problem was found, dozens of leads had already gone cold.
Why the emotional impact was worse than the technical problem
What stood out to me was that the financial loss was not the part Danny remembered most. The harder part was the feeling that his agency had let the client down. Because he knew the client personally, the mistake felt even more personal.
His team had spent weeks reporting positive campaign performance while the client saw no return from their investment. That disconnect created guilt, regret, and a real sense of helplessness.
As Danny explained it, the agency felt as if it had taken the client’s money without delivering value, even though the campaigns themselves were actually working.
Honesty became the first step
Once the problem became clear, Danny did not try to hide it. His view is straightforward: when mistakes happen, honesty is the only response that gives you any chance of repairing trust.
Instead of making excuses, the agency investigated immediately, exported every lead stored in the database, and gave the client everything they could recover. Many of those opportunities had already gone cold, but at least the client had access to the data that still existed.
From there, the focus had to move from blame to prevention.
Building systems that stop the same mistake happening twice
That experience changed the agency’s processes in a lasting way.
Instead of relying on one notification email, Danny’s team introduced multiple safeguards:
CC’ing the agency on every lead notification.
Automatically logging every lead into a shared Google Sheet.
Testing forms regularly to confirm submissions and notifications both work.
Checking with clients routinely to confirm leads are actually being received.
Those checks are now part of the agency’s standard operating procedures. They are no longer assumptions about technology working in the background.
Why communication matters as much as optimisation
Looking back, Danny sees the technical failure as only part of the issue. Communication failed too. No one had asked the simple question: “Are you actually receiving the leads?”
Today, communication is one of Optidge’s core values.
Rather than expecting PPC specialists to manage constant client communication while also running campaigns, the agency brought in dedicated account managers whose primary role is to keep clients informed.
The lesson I took from this is simple: campaign metrics alone do not define success.
Success only happens when the client experiences the results you are reporting.
Sometimes clients remember how you responded
At first, the relationship with the client ended. Danny assumed the mistake had permanently damaged the trust they had built.
Years later, though, that same client reached out again about potentially working together. In her email, she described Optidge as the most professional agency she had worked with. For Danny, it was a reminder that clients do not forget mistakes, but they also remember how agencies respond to them.
Transparency, professionalism, and a genuine effort to improve can leave a stronger impression than perfection.
Common PPC mistakes Danny still sees today
Although this happened years ago, Danny still sees agencies making similar mistakes today.
One of the biggest is focusing only on traffic instead of business outcomes. Sending visitors to a page is no longer enough.
Strong lead generation requires understanding what happens after someone clicks.
When Danny audits accounts, he often finds agencies failing to:
Feed qualified lead data back into advertising platforms.
Review search terms thoroughly and maintain negative keywords.
Build landing pages that match campaign intent.
Measure lead quality instead of simply counting conversions.
Without those fundamentals, campaign optimisation is based on incomplete information.
Where AI is genuinely helping lead generation
Danny believes AI has real potential in lead generation, but not always in the way marketers expect.
One of the most useful opportunities is phone call analysis.
Instead of manually listening to every conversation, AI can now help agencies:
Generate call transcripts.
Categorise calls by quality.
Identify whether a call became a genuine sales opportunity.
Feed qualified conversion data back into Google Ads.
That makes it possible to optimise around real business outcomes instead of surface-level metrics.
Why AI still needs human oversight
Even though Danny is using AI, he does not treat it as an infallible system.
Like automation inside advertising platforms, AI can make mistakes, miss context, and confidently reach the wrong conclusion.
For industries with strict privacy requirements, such as healthcare, AI may not be appropriate for handling sensitive customer information at all.
His advice is to trust AI enough to improve efficiency, but always verify the work.
Human expertise still matters.
The biggest lesson
I do not think any PPC professional can avoid mistakes completely.
What defines a strong agency is how it responds when something goes wrong.
That means being honest, fixing the immediate problem, building safeguards, and making sure the same issue does not happen again.
As Danny puts it, a mistake only becomes valuable when you have genuinely learned from it.
The best and worst part of the web, in my view, is that I can share an opinion freely even when that opinion is not technically accurate.
But I keep wondering what happens when that freedom comes with real accountability, not only for what I say online, but also for whether the words came from me or from AI.
A recent report makes that question feel a lot less theoretical. A German court held Google accountable for AI Overview content, treating those AI-generated summaries as Google’s own content and rejecting the idea that users alone were responsible for fact-checking the results.
I want to unpack what that could mean for businesses, SEOs, and individuals who are leaning harder on AI every day.
The ‘disclaimer’ defense is cracking
For the last few years, I have seen nearly every AI platform rely on some version of the same warning: AI can make mistakes, so users should verify important information.
Most of us accepted that as the price of using these tools.
But the German court essentially said that a warning about possible errors does not automatically erase responsibility when those errors cause harm. If a system creates new claims that were never in the source material, those claims are no longer just someone else’s words. They become the platform’s words.
Why? Because the conversation moves away from whether AI is useful and toward who owns the consequences when AI gets something wrong.
What this means for businesses
I see many companies rapidly adopting AI across content creation, customer service, product descriptions, reporting, legal reviews, hiring, and internal communications. In many cases, they are blindly trusting the output because the efficiency gains are so tempting.
Most of the conversation still centers on speed and cost. Can we create content faster? Can we answer support tickets more cheaply? Can we automate this process?
Those are fair questions. I ask them too.
But this ruling adds a more important question: Who is responsible when the output is wrong?
What happens if an AI-generated support response gives a customer inaccurate guidance? What happens if an AI-written article damages a competitor’s reputation? What happens if an AI-generated report includes fabricated information that influences a business decision?
The more we position AI as a trusted source of information, the harder it becomes to argue that we should not be accountable for what it says.
The situation is kinda funny…
The irony is that most AI vendors already know this.
That is why nearly every platform includes warnings, disclaimers, and usage policies.
At the same time, those same companies market AI as smarter, faster, more capable, and increasingly reliable.
I do not think you can tell users to trust the answer while also arguing that nobody should trust the answer.
At some point, those positions collide. We are already starting to see Google’s solution: an option to opt out of AI.
Germany may simply be one of the first courts willing to force Google, or any other LLM business, to take clearer responsibility for the systems it puts in front of users.
What SEOs should be paying attention to
Ironically, I think this ruling could end up benefiting everyone.
Right now, the debate is focused on whether AI companies should be responsible for the content their systems generate. But I can see accountability expanding well beyond AI.
The internet has spent decades creating distance between actions and consequences. Anonymous accounts, fake profiles, throwaway emails, and now AI-generated content all make it easier for people to say things without owning them.
That is why I find this ruling so interesting.
It is not just about Google. It is about the idea that “I did not write it” may no longer be enough.
The image below shows a real email that Russell and Nina Westbrook received. A real person sat behind a keyboard and sent a message hoping they would die in a car crash.
That is not free speech. It is hate speech.
The internet, especially now that AI is layered into it, needs more confidence that content is accurate and that the people and companies creating it can be held accountable.
I do not believe we get to claim the productivity gains when AI is right and then blame the algorithm when it is wrong.
This post first appeared on the author’s website and is republished here with permission.
I think one of the biggest mistakes in AI marketing is positioning a product as a replacement for people. That message can win attention in the short term, but I believe it quietly drains trust over time.
This is a little different from what I usually write about, but it matters. The way we talk about AI shapes how customers, employees, executives, and markets respond to it.
In this memo, I want to focus on three things: why “substitution positioning” feels powerful at first but weakens a brand later, what the data says about whether AI is actually replacing people, and how I think companies should position AI instead.
The cardinal sin of positioning in the AI era is replacement. I call it substitution positioning. It is tempting because it sounds bold, efficient, and disruptive. But over time, it creates anxiety, skepticism, and credibility problems.
We have seen this pattern already. Anthropic CEO Dario Amodei predicted that software engineering jobs could disappear within 6 to 12 months as models began doing most or all of what software engineers do end to end. Yet demand for software engineers has continued to look strong.
OpenAI CEO Sam Altman also predicted that many customer support jobs would go away because AI could handle that work better. Soon after, customer service hiring began outpacing the broader job market.
I understand why fear works as a marketing tool. The fear of being replaced gets attention fast. It got me, too. When powerful AI models gained traction, I worried about my own future. But when I still see AI companies hiring copywriters, SEOs, engineers, and support teams, I sleep better.
Fear sells because it taps into fight-or-flight. Layoffs make that story even louder. They let companies frame cost-cutting as innovation and make the replacement narrative feel more real than it may actually be.
But I do not think the facts support the clean replacement story. In New York, companies can indicate when mass layoffs are caused by technological innovation or automation. In one reported period, more than 160 companies filed mass layoffs affecting roughly 28,300 workers, and not one chose AI as the reason. That list included companies such as Amazon and Goldman Sachs.
Researchers at Yale also studied employment data from the Current Population Survey over 33 months and found no evidence of job displacement from AI. To me, the pattern looks less like instant replacement and more like the earlier waves of computers and the internet changing how work gets done.
That is why I keep coming back to this point: stop trying to make replacement happen. It is not happening in the simple, dramatic way many AI narratives suggest.
AI is powerful, but it is also inconsistent. In its current form, it can do some tasks better than humans and fail badly at others. That paradox is often called the Jagged Frontier.
The Jagged Frontier idea matters because it explains why some people see AI as transformative while others remain lukewarm. A BCG and Harvard study of 758 knowledge workers found that people get the most value from AI when they understand what it is good at and where it breaks down.
Microsoft reached a similar conclusion in its 2026 Work Trend Index Annual Report. The company found that a small group of advanced AI users, described as Frontier Professionals, were not simply using AI more often. They also knew which mode of AI use fit each task.
That distinction is important. The best AI users are not handing everything over blindly. They are applying judgment. They know when to use AI as a helper, when to use it as a collaborator, when to use agents for multi-step workflows, and when to keep a human firmly in control.
I still do not trust most AI workflows enough to leave them running with no maintenance, review, or quality assurance. The question I ask is simple: would I bet my brand, customer experience, or revenue on a fully automated workflow with no human oversight?
Klarna is a useful warning here. The company publicly promoted the idea that AI was doing the work of hundreds of agents and helping reduce headcount. Later, it reversed course and rehired humans after leadership acknowledged that aggressive cost-cutting had lowered quality and that customers still wanted a human option.
That is the tradeoff I see with substitution positioning. It creates immediate attention, but it can damage long-term credibility. The words often do not match the operational reality.
Replacement positioning could work if customers truly wanted full replacement and if the technology were consistently ready for it. I do not think either condition is true.
Cost reduction is a strong AI argument because it shows up quickly on the P&L. Productivity gains usually take longer. They build inside companies over time and often take even longer to appear across the broader economy.
But when replacement positioning goes beyond cost-cutting and becomes people-cutting, I believe it starts to antagonize the very people companies need to win over.
We have already seen backlash. Duolingo’s AI-first memo drew heavy criticism before the company reframed AI as a tool to accelerate work rather than replace contractors. Surveys have found that some workers refuse to use AI tools because they fear job loss. Pew has reported that many U.S. adults are more concerned than excited about AI in daily life. Reuters/Ipsos polling has shown widespread fear that AI will permanently displace workers.
There is also a quality problem. When employees believe the purpose of AI is to replace them, they may disengage or produce lower-quality work. In my view, that is not just an adoption issue. It is a positioning failure.
Executives often feel more excited about AI than the employees asked to use it every day. That gap matters. If leadership talks about AI as a replacement engine, employees hear a threat. If leadership talks about AI as leverage, employees have a reason to learn.
Token economics also complicate the replacement story. Some companies have bragged about massive AI usage, but token costs are still a real business variable. As those costs normalize, the math may make junior employees look interesting again, especially when human judgment, context, and accountability are part of the output.
So what should replace replacement? I think the answer is enhancement. Instead of positioning AI as a way to remove people, I would position it as a way to make capable people more effective.
AI can be used in two broad ways. A company can try to reduce the number of people, or it can grow output with the same number of people. The data I have seen suggests that productivity gains often create the stronger return.
A National Bureau of Economic Research paper surveyed 750 executives about AI’s impact on productivity and labor markets. Larger firms showed more interest in replacing labor costs, but the highest ROI came from productivity growth.
That is the lesson I take from the research: doing more with the talent you already have is often stronger than trying to remove the talent that knows what good work looks like.
Building products has become easier, but distribution has not. When supply explodes, the scarce thing is not output. The scarce thing is being the product, brand, or service that actually gets chosen.
That is why positioning matters more than ever. Product quality still matters, but the way I frame AI use can determine whether people see it as empowering or threatening.
My takeaway is simple: I would stop selling AI as a people replacement. I would sell it as judgment leverage, workflow acceleration, and creative expansion. Fear can get attention, but empowerment is a better long-term strategy.
This post first appeared on the author’s website and is republished here with permission.
If it feels like the whole internet woke up and decided every sentence needed to start with “AI,” I get it. I feel that fatigue too.
As marketers, we are getting hit every day with LinkedIn hot takes, rushed prompt hacks, and promises that ChatGPT will either 10x our productivity or replace us completely.
And right in the middle of all of that is the digital marketer trying to figure out whether AI is just another buzzword cycle or the start of a major rewrite of how we handle content, SEO, PPC, reporting, and almost everything else.
So I want to break it down in plain English.
Think of this as my AI starting guide for marketers who are tired of needing someone younger to translate every new acronym, the same way many of us once had to help our parents get online or open an AOL chat window.
Defining AI and LLMs, and why they matter
I am not asking “what is AI” just to chase keyword density. I want to start with a shared definition, because a lot of these terms get used interchangeably, and not always correctly.
At its core, artificial intelligence refers to machines performing tasks that usually require human intelligence, such as understanding language, recognizing patterns, making decisions, and generating content.
The kind of AI getting the most attention right now is generative AI: models that can create text, images, code, video, and other outputs based on patterns learned from huge datasets.
Tools like ChatGPT, Gemini, and Claude do not “think” the way people do. They predict the next most likely word, phrase, or response based on what they have been trained on.
That matters because AI is not a magic shortcut to instant wealth, overnight automation, or effortless headcount reduction. I see it more as large-scale data aggregation and pattern recognition.
Large language models, or LLMs, are not creating net-new truth from nothing. They process massive amounts of existing information and produce answers based on patterns, probabilities, and what looks like internet consensus.
For content creators and marketers, that is a major shift. I am no longer thinking only about optimizing for a traditional search engine click. I also have to think about whether machines can understand, summarize, cite, and reuse my content.
The biggest implication is the rise of zero-click search. AI systems can answer users directly through experiences like Google AI Overviews or ChatGPT responses, often without sending that user to the original website.
That changes SEO from a pure traffic game into an authority, visibility, and data-ingestion game.
That is why I think marketers need to understand what AI does well, what it struggles with, and where it actually belongs in a broader marketing strategy.
AI jargon I think marketers need to know
Before going deeper, I want to separate a few terms that often get mashed together: AI, machine learning, NLP, generative AI, LLMs, and AI agents. They are related, but they are not the same thing.
Understanding the difference helps me make better decisions about which tools to use, where to trust them, and where human judgment still matters most.
Artificial intelligence (AI)
Artificial intelligence is the broad umbrella term for machines performing tasks that usually require human intelligence. That includes problem-solving, learning, speech recognition, language understanding, and decision-making.
In marketing and search, AI has become a catch-all phrase. But in practice, most of the tools I use fall into more specific categories.
Example of AI: Siri and Google Assistant use AI to interpret voice commands and respond in context.
Machine learning (ML)
Machine learning is a subset of AI. Instead of giving a system explicit instructions for every possible situation, we feed it data so it can identify patterns and make predictions.
In marketing, machine learning powers ad targeting, customer segmentation, recommendations, predictive analytics, and plenty of optimization systems we already rely on.
Example of machine learning: Netflix uses machine learning to recommend shows based on viewing history.
Natural language processing (NLP)
Natural language processing helps machines understand, interpret, and generate human language.
NLP is why ChatGPT can carry on a conversation and why Google can understand that “cheap running shoes” and “affordable sneakers” are closely related searches.
Example of natural language processing: Google Translate uses NLP to understand and convert language in real time.
Generative AI
When people casually say “AI,” they often mean generative AI, which is a branch of artificial intelligence that creates content instead of only analyzing existing data.
Generative AI models are trained on massive datasets to learn patterns in language, images, audio, code, or video. Then they use those patterns to produce something new.
But I always remind myself that these systems are still predicting likely outputs. They are not thinking, reasoning, or understanding the world like a person.
That is also why generative AI can go off track. When a model confidently makes something up, we call it a hallucination.
Some of the most infamous hallucination examples include AI answers suggesting people eat small rocks or use glue to keep cheese on pizza. Funny in hindsight, but a serious reminder that fact-checking is not optional.
Large language models are a specialized type of generative AI trained on huge amounts of text, including books, websites, code, and other online sources, to generate human-like responses.
I think of LLMs as the engine behind many chatbot experiences. They are the part that interprets what I type and produces a response.
When I use an LLM effectively, I do not treat it like a replacement for my brain. I give it context, examples, constraints, and direction. It can help refine a draft, suggest wording, or organize messy thoughts, but I still own the strategy and final judgment.
In short, LLMs react to input. They do not act independently unless they are connected to tools and workflows that let them take action.
GPT models from OpenAI, used in ChatGPT.
Claude models from Anthropic.
LLaMA models from Meta.
AI agents
AI agents go beyond responding to prompts. They can work through multi-step tasks, use tools, navigate websites, fill out forms, call APIs, analyze files, and complete workflows with less hand-holding.
They are still powered by LLMs under the hood, but the key difference is that they have goals, tools, and a degree of autonomy.
That is why AI agents feel more consequential for marketers. They are not just talking; they are beginning to do the work.
ChatGPT can search the web, analyze files, and review code.
Google Gemini in Workspace can summarize email threads and suggest replies.
Microsoft Copilot can assist across Microsoft 365 workflows.
How I see AI affecting marketing today
Once the terminology is clearer, the marketing impact becomes easier to see. AI is changing how people search, how content is produced, how visibility is measured, and how stakeholders talk about growth.
People have been saying SEO is dying for years. I do not think SEO is dead, but I do think “SEO is changing” undersells the size of the shift.
We are in the middle of a major industry pivot, and AI is at the center of it.
Organic traffic is being cannibalized
AI Overviews are Google’s automated summaries that appear at the top of some search results, often pulling from multiple sources.
I think of them like Featured Snippets turned up several notches. They do not simply quote one source and send the click back. They blend sources, rewrite information in Google’s voice, and may push attribution lower on the page.
For broad informational queries, that means the first thing a user sees may be Google’s answer instead of my blue link. The likely result is a lower click-through rate and fewer visits to publisher and brand websites.
Before AI Overviews, informational queries were often useful for introducing a brand early in the research journey. Now, more of that attention and trust can stay with Google.
Claim: AI Overviews only appear for fluffy queries, so my traffic is safe.
Reality: Google is testing and expanding AI Overviews across more serious query types, including YMYL, product, and B2B searches.
What I would do next: Stop chasing every possible click, measure visibility and influence alongside conversions, and build enough topical authority that my brand becomes a source AI systems can confidently cite.
Content creation is exploding, and so is the noise
Generative AI has removed one of content marketing’s biggest bottlenecks: production time. Work that used to take a team a month can now be drafted by one marketer in a week.
That is not automatically bad. The problem is that when everyone can publish “good enough” content quickly, the internet gets louder and less useful.
Claim: More content means more traffic.
Reality: That was already questionable before AI. Now, search systems are increasingly tuned to reduce the visibility of generic, low-value, quickly produced content.
Google’s Helpful Content updates, Bing’s spam improvements, and social platform feed changes all point in the same direction: thin content is easier to produce, but it is also easier to ignore.
What I would do next: Focus on authority-driven content such as case studies, original data, expert analysis, and proprietary insights. I would publish less, promote more, and use AI for research, outlining, repurposing, and refreshing instead of simply flooding the web.
Search results are becoming deeply personalized
Traditional SEO has dealt with personalization for years through local results, logged-in history, and device context. LLM-powered tools like ChatGPT, Perplexity, and Gemini take that much further.
The same question can produce different answers depending on the user, their prompt, their past interactions, available data, and the model being used.
For example, if someone asks, “What is the outlook for Tesla?” a financial analyst may get an answer focused on stock performance and filings, while a new driver may see information about models, battery life, and charging infrastructure.
A Semrush Enterprise AI source analysis view tracks how often domains are cited in ChatGPT results, revealing shifting visibility trends across LinkedIn, Wikipedia, Semrush and industry publishers.
Claim: I will just optimize for the top answer in ChatGPT the way I optimize for position one in Google.
Reality: The idea of one universal top answer is breaking down. Personalization makes it harder to define, track, and reverse-engineer a single ranking position.
What I would do next: Track visibility across search engines and LLMs, build a recognizable brand entity, invest in multiple content formats, use structured data, and create clear, citable answers that machines can understand.
Attribution is breaking
When Google, Bing, Perplexity, or another AI-driven platform answers a question directly, users may never visit the website that influenced the answer. Even when they do visit, their journey may start in an AI tool, move through another search, and only later reach the site.
That breaks the clean channel → click → conversion model marketers have relied on for years.
Claim: I will measure traffic from LLMs directly in analytics.
Reality: That assumes users are clicking through from AI answers. In many cases, they are not.
A Semrush-style AI sentiment dashboard visualizes how brands appear across AI search, with mention trends, sentiment mix, and a competitive leaderboard.
What I would do next: Move beyond last-click attribution, pay more attention to assisted conversions, and track broader demand signals such as direct traffic, branded search volume, brand mentions, sentiment, and “How did you hear about us?” responses.
I would also budget for influence that is hard to perfectly track, including podcasts, PR, thought leadership, community visibility, and media coverage.
Clients and bosses expect magic
Because AI hype is everywhere, stakeholders often expect it to make everything faster, cheaper, and better without understanding the risks, learning curve, or human oversight involved.
Claim: We can replace our SEO or content team with AI tools and get the same results.
Reality: AI can accelerate tasks, but it does not replace strategy, judgment, subject-matter expertise, or a real understanding of customer needs.
What I would do next: Set expectations early. AI can make some work faster and cheaper, but it is not a push-button strategy. I would show stakeholders the hidden work behind good AI output, including prompt refinement, editing, fact-checking, compliance, and final review.
The best use of AI is not to remove human thinking. It is to free up more human time for the strategic work that actually moves the business forward.
Search is evolving
I am not interested in getting stuck in a debate over Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or any other acronym. The important point is simpler: search today is not what it was yesterday.
Organic visibility is no longer only about ranking in Google. Search now includes AI answers, YouTube, Reddit, newsletters, communities, social platforms, and every place people go to discover, compare, and validate information.
If I am only thinking about the traditional search bar, I am already behind. The better path is to build authority, create content worth citing, understand how AI systems interpret information, and measure visibility across the full discovery journey.
AI is not the end of SEO. It is a major shift in how search works, how content is consumed, and how brands earn trust. The marketers who adapt will be the ones who separate useful strategy from the noise.
