I’m watching Google add a new layer of AI transparency to ads across Search, YouTube, and Discover. The company said its new How this ad was made section will appear inside My Ad Center, giving people a clearer view of whether AI played a role in the ad creative they see.
The panel will show whether an ad was created or modified with AI. I see this as a meaningful expansion of Google’s ad transparency tools, especially as more advertisers rely on generative AI to produce images, copy, and other campaign assets at scale.
What it looks like. I’ll be able to access the disclosure from the three-dot menu or the info icon on an ad. In the screenshot Google shared with Search Engine Land, the My Ad Center panel includes a dedicated section explaining how the ad was made.
Google will handle some disclosures. When advertisers use Google’s own generative AI ad tools, Google will automatically add the disclosure inside My Ad Center.
Google’s My Ad Center adds a clear AI disclosure, helping users see when ad creative may have been created or edited with generative AI.
For advertisers using third-party AI tools, Google said they will have control over whether to disclose AI use. Depending on local requirements, an AI label may also appear directly on the ad, either automatically or after the advertiser uses that control.
Why I care. AI-generated ads are getting easier and faster to create, so disclosure matters more than ever. I want to know when creative was made or changed with AI because requirements can vary by market, platform, and ad format.
Existing ad rules still apply. Google said its ad policies still prohibit misleading or deceptive advertising, whether AI was involved or not. This update adds more visibility into how an ad was made, but it does not change the requirement that advertisers clearly identify who they are and what they are promoting.
A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.
Earlier AI safeguards. Google already embeds imperceptible signals, including SynthID, into content created with its generative AI tools. Election advertisers are also required to disclose synthetic or digitally altered content in political ads, under a policy Google introduced in 2023.
I do not think enough people are treating Meta AI as a serious AI search contender.
In SEO circles, I hear plenty about Google AI Mode, ChatGPT, Claude, Gemini, Perplexity, RAG, and every new answer engine worth testing. Those conversations matter. But I think Meta AI already has something most AI companies would spend years and billions trying to build: massive distribution.
By May 2025, Meta AI had reached one billion monthly active users across Meta’s apps, according to Mark Zuckerberg.
Zuckerberg has also made the direction clear. He wants Meta AI to become a leading personal AI, shaped around personalization, voice conversations, and entertainment, with monetization through paid recommendations or subscriptions already being considered.
That is why I think Meta AI is becoming one of the most important AI search contenders to watch.
Meta’s Advantage Is Distribution
I think the AI search debate spends too much time on model quality and channel ownership. Which tool is smarter? Which answer engine cites better? Is this just SEO with a new label?
Those questions matter, but distribution matters more than the search industry often wants to admit.
Meta reported 3.56 billion family daily active people across its apps in March. In that same quarter, revenue reached $56.31 billion, up 33% year over year.
WhatsApp passed 3 billion monthly users in 2025. Instagram reached 3 billion monthly active users in September 2025. Threads reached 500 million monthly active users in June.
I know Facebook is not the cool platform anymore. The metaverse stumbled. Threads can still feel like a corporate response to Elon Musk running, or ruining, the artist formerly known as Twitter.
But none of that changes the important point. Meta can put AI inside the apps where people already spend their time. In doing that, it can bring search-like behavior directly into the places where discovery already happens.
I think that could push public AI adoption faster than almost anything else in the market.
The First Search Is The Search That Matters
Google’s historic power has always rested on a simple habit. When people wanted to know something, compare options, buy a product, find a local business, or settle an argument, they started with Google.
That starting point became the most valuable real estate on the internet.
AI search changes where that starting point can live. If someone sees a product on Instagram, they do not have to leave the app and search Google. They can ask Meta AI whether the product is any good, what alternatives exist, whether the brand is trustworthy, or where they can buy it.
If a WhatsApp group is planning a weekend away, they do not need to switch to Google to compare hotels, restaurants, venues, or train times. Meta AI can sit inside the conversation at the exact moment intent appears.
If someone is scrolling through a Facebook thread full of local recommendations, they can ask Meta AI to summarize what people are saying across Groups, Reels, and public posts.
That is not traditional SEO. I see it as search behavior being absorbed into social platforms.
The strategic question is no longer only, “Who ranks?” I think the better question is, “Where does the question begin?”
Meta AI Is More Than Another Chatbot
I think search marketers often approach AI through too narrow a lens. We find the chatbot, test a few brand queries, check which sources get cited, and decide we understand the platform.
That is a mistake.
Meta AI is becoming a layer across feeds, chats, search, content creation, recommendations, smart glasses, and social discovery. Meta says it is available across Facebook, Instagram, WhatsApp, and Messenger, including in feeds, chats, and search, giving users real-time information without leaving the app. The use cases include restaurant recommendations, travel planning, study help, and shopping inspiration.
The standalone Meta AI app, launched in 2025, was designed around a more personal AI experience. Meta says it can use information people have chosen to share across Meta products, along with profile data and content engagement, to deliver more relevant answers in supported markets.
I can see where this is heading. Meta AI could become the free AI tool that everyday consumers use without thinking much about it.
How Meta AI Could Become Consumer AI
ChatGPT and Claude still feel like work tools to me. They are excellent tools, but they are tools people deliberately open because they have decided to do something.
Meta AI feels more like consumer AI. It is messier, more visual, more embedded, and less like launching a productivity suite. It feels more like finding an answer while doing what you were already doing.
For many people outside tech, opening ChatGPT still feels like an intentional act. Asking a question inside WhatsApp or Instagram can feel almost frictionless.
That is Meta’s advantage. It does not have to convince people to adopt AI from scratch. It can fold AI into existing habits.
This is where it gets interesting. Meta AI is also a playground, and Meta gets to watch how people actually use it.
I can imagine a 65-year-old grandmother using it to animate family photos and share them in a WhatsApp group.
I can imagine a dog groomer using it to create short videos of clients’ pets and post them on Instagram.
When AI becomes mainstream and easy to use, people will use it where they can reach other people. That gives Meta a powerful feedback loop. The more people play with Meta AI, the more Meta learns, improves, and adds features that fit real consumer behavior.
AI Becomes Social, Visual, And Shoppable
Then there is Meta AI Studio.
Users can create AI characters built around their interests, work from templates, or start from scratch. They can build assistants for advice, captions, entertainment, and creator interactions.
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.
Then there is Vibes. In September 2025, Meta introduced Vibes as a feed inside the Meta AI app and on Meta AI, where users can create, remix, and share short-form AI-generated videos, then distribute them through DMs, Instagram, Facebook Stories, and Reels.
I will be honest: parts of this feel strange. Generative AI video on social platforms is a messy mix of creativity, novelty, nonsense, and low-quality output. But early weirdness is not the same as strategic irrelevance.
I never expected AI to arrive as one perfect super-app that everyone understood immediately. Meta is putting new formats into users’ hands, watching what people do with them, and reshaping the product around that behavior.
The Ad Machine Makes This A Google Problem
Forecasts suggest Meta will reach $243.46 billion in net worldwide ad revenue in 2026, putting it ahead of Google at $239.54 billion. The same forecast has Meta capturing 26.8% of worldwide digital ad spend, compared with Google’s 26.4%.
I think those numbers should get Google’s attention.
If AI answers are monetized through paid recommendations, sponsored answers, shopping suggestions, or conversational ad units, the commercial value collects around the platform that owns the query. That platform does not always have to be the one with the best model.
Meta has the audience, the ad graph, creator relationships, commerce signals, and behavioral data built from years of social, messaging, and content engagement. It can promote Meta AI inside its own products to billions of existing users.
Google still has search intent, which is enormously powerful. But Meta has attention, habit, and context. Google is where people go when they have decided to search. Meta is where many people already are.
Why “It’s Just SEO” Misses The Point
The AI optimization debate keeps collapsing into the same comforting line: it is just SEO.
Sometimes, I agree. Technical hygiene, crawlable content, authoritative pages, clear entities, strong brand signals, helpful content, and consistent information still matter.
But I think the harder question is this: how exactly do you optimize for Meta AI?
Facebook AI Mode makes the challenge obvious. In June, Meta introduced AI Mode as a Facebook search tab that uses Meta AI to surface answers rooted in public culture, opinions, and recommendations shared across Meta’s apps, rather than a traditional list of links. It draws on what people are posting publicly in Groups and Reels to provide perspectives instead of standard search results.
That is a fundamentally different environment. If Meta AI pulls from public posts, Groups, Reels, creator content, user engagement, web information, social recommendations, product content, and eventually paid data, the standard SEO playbook is not enough.
Your website may still matter. Your public social content may matter, too. Your creator strategy may matter. Your product feed may matter. Your reviews may matter. I think the point is clear: visibility is getting more complicated.
Nobody can honestly say they know exactly how all of this works yet. Anyone who claims total certainty is probably selling a dashboard and a dream.
The honest answer is frustrating: I do not think we know enough yet. But that is not a reason to ignore Meta AI.
Google Is Being Attacked From Every Angle
Google is still Google. I do not want to overstate the case. It remains central to search, commerce, publishing, advertising, and the open web.
But Google is being pushed from every direction at once. ChatGPT is pressuring answers. Perplexity is pressuring research. Amazon is pressuring product search. TikTok and Instagram are pressuring discovery. Regulators are pressuring market power. Publishers are challenging AI content extraction. Meta is pressuring attention, ads, and AI-assisted discovery.
In the UK, the Competition and Markets Authority imposed new conduct requirements on Google Search in June. Publishers will be able to opt out of having their content used to power AI features in Google Search, including AI Overviews. Google is also required to properly attribute publisher content with clear links in AI-generated results.
I think this matters because AI search is not just another product feature. It changes the value exchange between users, publishers, platforms, and advertisers. While Google works through that challenge, Meta is quietly building AI into social behavior.
What I Think Brands And SEOs Should Do Now
I would not panic. Panic is rarely a strategy, even if it shows up in plenty of marketing meetings. But I would start testing now.
I would run brand, category, product, local, and comparison queries in Meta AI. I would test Facebook, Instagram, WhatsApp, and the standalone app wherever possible, then compare the results with Google AI Mode, ChatGPT, Perplexity, Gemini, and Claude.
I would track whether my brand appears, whether answers cite or link to me, and whether public Meta content seems to shape responses. I would look closely at Facebook Groups, Reels, creator posts, Instagram content, product mentions, and recommendation language.
If discovery moves into Meta’s AI layer, I want to understand what my brand needs in order to be visible there.
That might mean stronger public social content, clearer product information across Meta surfaces, creator partnerships, better community management, more consistent entity signals, or paid social tests designed around AI visibility. It might also mean none of those things yet.
Either way, I would rather have data than keep repeating “it’s just SEO” while the market reorganizes itself.
The Sleeping Giant
I do not think Meta AI has to beat Google at Google’s own version of search. It does not need to.
It only needs to absorb enough search behavior into the places where people already spend their time.
It needs to become the casual AI layer for people who may never deliberately open ChatGPT.
It needs to make product discovery, recommendations, local advice, content creation, and shopping assistance feel native inside social apps.
That is a serious threat. Meta AI may feel clunky right now, but so did much of the early web.
I think the search industry should stop asking whether Meta AI looks like search. The better question is whether users care.
If people start asking Meta before they ask Google, the game changes. That is how sleeping giants wake up.
I know Dan Freed’s story begins with what it feels like to be underestimated. He was diagnosed with ADHD at six, kicked out of preschool, and dropped out of high school at sixteen. Later, he scored in the 99th percentile on the GMAT and earned degrees from Yale and INSEAD. He has also taken prescription stimulants for most of his adult life, and that lived experience eventually led him to found Thesis, a brain supplement company built around data-driven formulation, and then Stasis, the first supplement system designed for people who take stimulants.
At First Page Sage, we have built our own reputation around a similar idea: real authority in any category, including the fast-changing world of generative AI search, comes from substance rather than shortcuts. I sat down with Dan to talk about Stasis, the gap he believes it fills, and what it takes to earn trust in a category most people overlook.
First Page Sage: I see your work as being built on real expertise rather than flimsy marketing. Did that philosophy shape how you built Stasis?
Dan Freed: Completely. When I started building Thesis, the whole industry ran on vague claims: optimize your brain, unlock your potential, and so on. None of it really meant anything. We took the opposite approach by testing everything, naming the mechanism, and showing the data. Stasis came out of that same discipline. Stimulant users are one of the most underserved groups in supplements, and almost nothing built for them is backed by real formulation logic. We wanted to be the first to change that.
First Page Sage: I want to start with the basics for people who have not heard of it. What is Stasis?
Freed: Stasis is built around three pathways that stimulants put under pressure: dopamine support, cortisol regulation, and oxidative stress defense. The Daytime and Nighttime formulas use branded ingredients like Shoden Ashwagandha and CuminUP60 curcumin, named and dosed as a composition rather than added as vague extras. We also built a gummy version for kids ages four to seventeen, Stasis Kids Daytime, because plenty of families are managing this without support built for them either.
First Page Sage: I am curious why this needed to exist at all. You could have kept building Thesis.
Freed: I built Stasis for myself first. I have been on stimulants for decades. They help with focus, but nobody talks about what comes after: the crash, the tension that creeps in by afternoon, and the nights when you cannot wind down. Intelligence was never the issue for me. Brain chemistry was. A stimulant alone treats one part of that chemistry. Stasis was built to support the rest of it. A pill on its own will not fix you, but the right system around it can change what your day actually feels like.
First Page Sage: Switching gears, I wanted to ask what drew you to a partnership with First Page Sage specifically.
Freed: You have spent years building the actual research behind how AI engines decide who to trust and recommend. That is rare. Most of the industry is still guessing. We are in a similar spot with stimulant support: there is no established authority yet, which means there is real room to build one the right way, with evidence instead of noise.
First Page Sage: I have one last question. How do you think about building authority in a category like this, especially as AI search changes how people find answers?
Freed: The brands that win in AI search are the ones with something real to point to: a named mechanism, a specific ingredient form, or a study people can check. We are investing in exactly that kind of infrastructure for Stasis, on top of the consumer data we already have from real customers using it. I would rather be three years early proving something out than be first to market with nothing behind it. That is true whether a human is reading the page or an AI is summarizing it.
I encourage readers to learn more about Stasis, the first supplement system built for people who take stimulants, at takestasis.com. Dan Freed is the founder and CEO of Thesis, where the same research-driven approach powers four nootropic formulas: Clarity, Motivation, Stress Reset, and Neuroprotection.
SMX Next returns online Nov. 18, and I’m excited to help shape a program focused on today’s complex search landscape and the tactics that will define success in 2027 and beyond.
Search marketing isn’t just changing. From my perspective, it has become an entirely new kind of challenge, and that is exactly why fresh voices and practical expertise matter so much right now.
In SEO, I’m seeing the field shift toward AI Overviews, search everywhere optimization, and the rise of autonomous AI agents that browse on behalf of users. Trustworthiness, digital authority, and precise alignment with user intent are no longer nice-to-have ideas. They are becoming essential.
On the PPC side, generative AI and deep automation are creating new levels of personalization. At the same time, they are raising urgent questions for marketers: How do we keep strategic control, protect data privacy, and avoid wasted spend?
If you’re an enthusiastic search marketer with a passion for sharing what you know, I hope you’ll consider submitting a session pitch for SMX Next. I’m looking for subject matter experts who can share insights, strategies, and tactics that help SEO and PPC marketers thrive in 2027.
Whether you’ve been speaking for years or you’re a practitioner ready to share something new you’ve developed, I want to hear from you. I’m especially interested in new speakers with diverse points of view and real-world experience.
The deadline for SMX Next pitches is Aug. 7.
When I review session proposals, I’m looking for ideas that feel original, specific, and useful. Advanced, forward-thinking topics or unique frameworks that aren’t already common at other search events will stand out.
I also want to see actionability. Be clear about what attendees will be able to do better, faster, or differently after your session.
Bring the data whenever you can. A case study, concrete example, or tested approach makes your pitch stronger, especially when you explain how the lesson can scale across different types of organizations.
Keep the scope focused. A 30-minute session works best when it goes deep on a narrow or specialized topic instead of trying to cover too much at once.
Most importantly, give attendees something tangible to take with them. I’m looking for sessions that leave people with a clear action plan, framework, or process they can put to work right away.
Visit this page for more details on how to submit a session idea, or go directly to this page to create your profile and submit your pitch.
If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!
I’m watching a new Google Search ad test that could change how people understand sponsored results. Google appears to be experimenting with AI-generated summaries beneath paid search ads, giving its own AI more influence over how advertiser messaging is framed.
What’s happening. Some advertisers are seeing AI-generated summaries appear directly below Google Ads descriptions in Search results. These summaries include a warning from Google that says: “Google AI responses are generated independently and can make mistakes, so double-check responses.”
I first saw this test surface through digital marketer Darcy Burk, who shared a screenshot of the experience on X. The placement is notable because the AI-generated text appears close enough to the ad that users may treat it as part of the paid result, even though Google says the response is generated independently.
Why I care. If Google expands this more broadly, these summaries could shape how users interpret ads by emphasizing the details Google considers most relevant, not necessarily the exact message the advertiser intended to highlight. That raises real questions about accuracy, brand control, and whether click-through rates could be helped or hurt by AI-written context.
Between the lines. Google has already tested AI-generated summaries for organic search listings, so seeing similar functionality move into paid ads feels like another step in bringing generative AI deeper into the Search experience. What I still do not know is how these summaries are created, what sources they rely on, or whether advertisers will get any say in the copy.
What I’m watching. Google has not publicly announced this feature or responded to requests for comment, so it is unclear whether this is a small experiment or the beginning of a wider rollout. Until Google explains the mechanics, advertisers are left guessing how much control they may have over AI-generated text attached to their ads.
The bottom line. Google is testing AI-generated summaries inside Search ads, and I see that as a sign that generative AI could soon play a larger role in paid search presentation, even when advertisers are not writing that extra copy themselves.
First spotted. Darcy Burk, understandably, was not pleased with this update.
I’m seeing a major shift in how people plan trips: 40% of travelers now use AI to research, compare, and organize their travel decisions.
That changes how I think about travel content. It is no longer enough to write only for traditional search results. I also need to make content clear, useful, and easy for AI systems and large language models to understand, summarize, and cite.
In this guide, I focus on practical travel AI optimization strategies, including stronger FAQs, schema markup, topical authority, and a content strategy built around the questions real travelers ask.
My goal is simple: create travel content that answers intent directly, builds trust, and gives AI platforms the structured context they need to reference my brand when travelers are planning their next trip.
I’m reading this Cornell Tech research as a clear warning: deep-research AI agents can be steered by surprisingly small edits on public, user-generated pages. In the study, a single injected Reddit-style comment could become a cited recommendation for fake products, services, or entities.
The researchers described these altered pages as “poisoned” because the added text was written to influence what an AI system cites and repeats. The weakness appears in systems that search the web, collect sources, and produce cited reports. The paper calls the attack WARP, short for Web Agent Retrieval Poisoning.
How I see injected text reaching reports. The attack does not require access to the model, prompts, search engine, or retrieval system. Instead, an attacker edits or appends text to a page the agent already tends to retrieve, such as a Reddit thread, Wikipedia page, or forum post.
When the agent later searches related topics, it may pull in that page, cite it, and repeat the attacker’s chosen message as part of an otherwise normal-looking answer.
That matters because deep-research tools often run many related searches for a single user request. The paper found that the same user-generated pages surfaced across related queries, giving poisoned content more chances to appear.
Reddit stood out as the biggest opening. Across STORM, Co-STORM, and OmniThink, 17% to 23% of retrieved URLs came from user-generated platforms, including Reddit, YouTube, Facebook, and Wikipedia.
Reddit made up the largest share of those pages. It accounted for 54% to 71% of the user-generated URLs retrieved by the three open-source systems.
The researchers did not alter live websites. Instead, they used a simulation framework called GeoStorm to insert manipulated text into retrieved content during testing.
A few words were enough. What stood out to me most is how little text the attack needed. The researchers found that snippets as short as about 13 words could influence what these systems recommended.
In one test, a 15-word sentence pushed a fake cryptocurrency, BananaCoin, into a Co-STORM report as an “emerging” long-term investment option. The report cited the altered source alongside legitimate crypto sources.
When the manipulated page was retrieved, the fake entity appeared in 38% to 51% of reports across systems. When the researchers targeted multiple pages, that range increased to 42% to 62%.
The attack still worked when systems retrieved full Reddit threads, although mention rates were lower. When injected text was added to complete Reddit threads and represented less than 4% of the retrieved content, the fake entity still appeared in 30% to 53% of reports when the page was retrieved.
The defenses struggled. Blocking user-generated domains stopped this attack path, but I see the tradeoff immediately: it also removes useful sources such as firsthand product experiences and local recommendations.
The tested text filters also failed to reliably separate injected passages from normal user content. Because the manipulated passages were fluent and written by an AI model, perplexity-based filters were more likely to flag normal user content than the injected text.
Report-level checks missed the manipulation too. The altered reports looked similar to clean reports because the agent itself folded the fake recommendation into an answer that otherwise appeared normal.
Why I care. A small edit to a public page can become part of a cited AI answer, even when the underlying source is user-generated. Misinformation planted on sites like Reddit or in forums can move from discussion threads into AI recommendations that look credible to users.
About the research. The paper, Deep-Research Agents Can Be Poisoned via User-Generated Content, was written by Tingwei Zhang, Harold Triedman, and Vitaly Shmatikov of Cornell Tech and posted to arXiv on May 22. The researchers tested the full attack on three open-source systems: STORM, Co-STORM, and OmniThink.
They also analyzed OpenAI Deep Research and Gemini Deep Research for user-generated citations, but they did not run live manipulation tests because doing so would require publishing altered content to the open web.
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.
I’m introducing the Profound Index as a new way to understand AI visibility. It is the first leaderboard built to rank brands by how often they appear in answers from leading AI models.
For me, this matters because visibility is shifting beyond traditional search results. As more people rely on AI-generated answers, I want a clearer way to see which brands are being mentioned, recommended, and surfaced across the AI platforms shaping discovery.
I am paying close attention to how Reddit conversations are shaping AI search, especially after Profound collaborated with Reddit to analyze how conversational data informs AI-generated answers.
What stands out to me is how much value AI systems can draw from real discussions, lived experiences, and community-driven context. Reddit’s conversational data helps reveal the kinds of answers people are looking for, the language they use, and the perspectives that can influence how AI-generated responses are formed.
