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’m looking at Google Ads API v24.2 as a practical update for advertisers and developers, especially because it brings together stronger security controls, AI transparency features, better reporting and new experiment options in one release.
What’s new. The biggest security addition I see is support for multi-party approvals, or MPA. This requires a second administrator to approve sensitive account actions, including user invitations and access-level changes, which gives agencies and larger organizations another layer of protection when managing Google Ads accounts.
I’m also watching Google’s expanded support for AI-generated content disclosures. The API now exposes new SyntheticContentInfo and SyntheticContentAttestation fields on assets and ads, so developers can identify and label AI-generated creative programmatically. This is especially relevant for advertisers preparing for the EU AI Act, which takes effect on August 2nd.
Developers can start building integrations now, although I’d note that advertiser attestation fields will remain read-only until v25 launches.
Performance Max gets more visibility. I see one of the most useful changes in version 24.2 as the added visibility for Performance Max campaigns. Advertisers can now segment performance_max_placement_view reports by ad_network_type, making it easier to understand where ads are appearing across Search, Display and partner networks.
The release also adds YouTube brand channel linking through the API, which should make video campaign integrations stronger. I’m also noting the new landing page text generation option, which can automatically create text assets from a website’s landing page.
New testing capabilities. Google is expanding experimentation tools with two new experiment types, and I see both as useful for advertisers who want more structured ways to compare campaign changes.
The new COMPARE_CAMPAIGNS workflow lets advertisers compare multiple campaigns or campaign types across as many as five experiment arms, including custom Performance Max experiments.
A second experiment type lets advertisers test text customization and final URL expansion inside a single Performance Max campaign by splitting traffic between variations.
Documentation improvements. I also appreciate that Google has reorganized its API release notes by separating breaking changes from feature updates. It has also introduced a dedicated guide for feature deprecations and unversioned changes, which should make future upgrades easier to manage.
Why I care. This release may not be a dramatic overhaul, but I see it as a meaningful step for teams that need to prepare for AI disclosure requirements, tighten account security and get more useful Performance Max reporting.
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.
A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.
That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.
That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.
To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.
Here is what I believe the data means for 2026 search strategy.
Consumers are using AI more, but trusting it less
AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.
One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.
In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.
I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.
AI content volume has become a brand trust risk
In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.
If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.
Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.
That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.
Disclosure is now a consumer expectation
Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.
I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.
Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.
Google still leads on trust, especially for buying decisions
When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.
Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.
That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.
Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.
A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.
AI is changing marketing operations quickly
AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.
For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.
Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.
The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.
That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.
AI governance is still too weak
About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.
That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?
In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.
The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.
The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.
AI hallucinations are already a brand problem
A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.
More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.
If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.
When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.
Organic traffic is under pressure, not in freefall
Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.
The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.
The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.
That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.
Marketers are prioritizing the easiest tactics
Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.
Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.
But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.
Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.
In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.
As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.
The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.
GEO measurement is lagging behind execution
Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.
Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.
I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.
The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.
For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.
Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.
What I would do for a 2026 search strategy
First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.
Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.
Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.
Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.
Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.
Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.
Methodology
Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.
Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.
I’ve noticed something remarkable about how we, as Americans, are searching for information these days. Pew Research Center recently reported that 60% of us are now reading AI-generated summaries at the top of our search results, while approximately 40% have turned to chatbots for finding information.
It’s fascinating to see that AI-generated answers are appearing more and more, whether in traditional search results or dedicated chatbot platforms like ChatGPT, Gemini, and Copilot, as Pew discovered.
AI summaries reach most searchers. According to Pew, six out of ten American adults have read AI summaries at the top of search results. Surprisingly, three out of ten haven’t, which suggests room for growth.
Interestingly, another 10% are unsure if they’ve read AI summaries. It seems some of us may not clearly recognize them when they pop up in our search results. The research also found that men are slightly more inclined than women to read these summaries, with 63% versus 57%. Those of us aged 65 and older are less likely to engage with them.
Chatbots are search tools. Chatbots are increasingly becoming popular search tools. About half of American adults have used AI chatbots, which is a jump from one-third back in 2024. What’s more, about one in four of us make use of them daily.
The most common reason we use chatbots? Searching for information. Around 40% of adults turn to chatbots for this purpose, more than for entertainment, media creation, or even advice on fitness and medical matters. Interestingly, work-related tasks follow closely behind, with 38% of employed adults utilizing chatbots at their jobs.
ChatGPT dominates. ChatGPT remains the most popular chatbot by a significant margin. Pew indicates that 44% of U.S. adults have now engaged with ChatGPT, which is up from 34% last year and over twice the number reported in 2023.
Gemini takes second place, with about a quarter of us using it, followed by Copilot and Meta AI. Tools like Grok, Claude, and Character.ai have a much smaller audience, with only about one in ten of us using them, if at all.
Why we care. In today’s world, finding information doesn’t just mean looking at traditional search results. We now also find answers through AI summaries and chatbot responses, which is a fact worth noting, especially when it comes to understanding where people are sourcing their information.
Dig deeper. For more insights on AI search adoption and consumer trust, check out the study.
About the data. Pew Research Center gathered this data by surveying 5,119 American adults from February 17-23, 2026, via its American Trends Panel. The margin of error for this study is plus or minus 1.6 percentage points.
I am excited to share that Meta has rolled out the revolutionary AI Mode in Facebook Search, designed to enhance the user experience by providing AI-generated answers directly gleaned from public Facebook content such as Groups, Reels, and other Meta platforms.
Instead of the usual list of search results, Facebook now offers direct responses crafted by Meta AI. These answers are rooted in actual conversations and experiences shared publicly across Facebook’s apps, providing real-life advice and insights.
AI answers in search. With AI Mode, I can explore both broad topics and specific queries. As I navigate Facebook, the Meta AI surfaces relevant public content right in my feed, transforming how information is discovered and shared.
For instance, engaging with Groups and Reels offers a novel method to gather information about products, places, hobbies, and everyday tips.
Source selection is unclear. Although Meta promises “real answers from real people,” how AI Mode selects which public posts, Groups, or Reels get featured remains a mystery. Additionally, it’s not yet clear if brands, creators, or publishers will be informed when their content is utilized.
Why we care. This evolution signals a shift in Facebook’s search landscape, relying heavily on AI-generated responses from public social interactions. Consequently, the discovery process for recommendations, local news, and brand discussions is undergoing transformation within Meta’s universe.
A familiar name. Interestingly, Meta’s new feature shares its name with Google’s AI Mode, which raises some eyebrows about creativity.
What Meta is saying. This new AI Mode harnesses the power of both Meta AI and Muse Spark. However, Meta hasn’t divulged how Muse Spark affects search rankings, or the selection and generation of answers.
This search enhancement is just a piece of a larger Facebook AI update introducing new creative features for Photos, Videos, Profile Pictures, and Stories.
I’ve noticed SEO content becoming increasingly monotonous.
Whenever I search the web, it’s as though every page echoes the same advice, just repackaged slightly differently. With AI tools that can churn out articles in seconds, this issue is only escalating.
There’s certainly no shortage of content, but much of it lacks memorability and uniqueness. This uniformity is posing a challenge within the realm of SEO.
Real Experience: The Key Differentiator in SEO
As AI-generated content increasingly saturates search results, businesses urgently need a distinguishing feature. Right now, real experience is what distinguishes exceptional content from the mediocre.
While AI can certainly write, it cannot replicate experiences lived by humans.
AI cannot recount the mishaps when a strategy faltered, nor can it impart the wisdom gleaned from collaborating with real clients. It simply cannot relay the intricate details that emerge only after years in practice.
This human element holds more sway and significance than many businesses realize.
Why So Much SEO Content Feels Repetitive
For years, the focus in SEO has been primarily on creating content saturated with keywords. The more articles published, the greater the visibility—or so we were told.
Consequently, many websites have produced content that reads like a photocopy of one another.
Now, with AI, generating such content has never been easier.
Crafting a blog post titled ’10 SEO Tips’ or ‘How to Rank Higher on Google’ takes mere moments. The internet is saturated with thousands of such posts, most of which add nothing novel.
People are weary of content that feels derivative, even if it technically isn’t a direct copy.
The content that makes an impression now exudes humanity.
It features:
Real-world examples.
Sincere opinions.
Lessons learned from past experiences.
Client success stories.
Results from testing.
Personal insights.
In essence, it sounds like someone who has truly been in the trenches wrote it. This distinction is more crucial now than ever, as the landscape of digital search evolves.
Adapting to Evolving Search Dynamics
Google has long emphasized trust and authentic experience in content. Meanwhile, AI search tools are providing quick snippets without users needing to trawl through countless websites.
This shift means that basic information is losing its impact. Since AI can efficiently distill general advice, businesses must offer more compelling value, where authentic experience becomes invaluable for SEO.
When a business owner shares what truly worked for them, it tends to create more trust than a polished article filled with generic suggestions. Real-life case studies that demonstrate actual outcomes weigh heavier than keyword-stuffed pages.
Specificity and genuine detail imbue content with credibility. This level of nuanced detail is something AI struggles with, simply because it lacks the capability to operate beyond pre-existing information.
For small businesses, this differentiation can be particularly advantageous. Where larger brands rely on their reputation, smaller ones gain consumers’ trust and loyalty primarily through personal connections. This human touch can significantly bolster SEO efforts.
Leveraging AI Alongside Human Expertise
I’m not suggesting abandoning AI entirely.
When used wisely, AI serves well for research, planning, brainstorming, and accelerating content creation. Most marketers incorporate it in some form, and that trend is bound to continue.
But businesses achieving the best results aren’t leaning solely on AI. They’re blending AI capabilities with genuine knowledge, personality, and firsthand experience. They’re infusing opinions, narratives, and insights that AI can’t readily generate. That’s the type of content that grabs attention.
SEO is no longer about sheer volume; it’s about creating content that resonates, sticks in memory, and garners trust. As websites increasingly fill with AI-generated articles, the value of authentically human content is on the rise.
Because while AI can write, it can’t genuinely replicate the human experience.
In my latest exploration, I dived deep into the world of marine and maritime marketing agencies. I closely examined 29 firms dedicated to serving sectors like recreational boating, commercial maritime, yacht brokerages, marine technology, marina operations, and offshore services. What I found was enlightening. Each agency was rigorously evaluated based on five key factors that I consider essential.
The criteria included the innovative AI Visibility Score, where I looked at how effectively these agencies could place their marine clients in the limelight of platforms like ChatGPT, Perplexity, Claude, and Gemini. It wasn’t just about having a presence; it was about being recognized. I also considered the prestige of their notable clients, coupled with the leadership experience that tipped the scale in their favor.
Add to that the customer review scores sourced from trustworthy platforms and the number of media references that showed their industry influence, and you’d get a clear view of what makes an agency stand out.
Allow me to present the seven highest-scoring agencies, each a powerhouse in its own right, capable of shaping the future of maritime marketing.
In late 2024, I embarked on an eye-opening 16-month journey with SE Ranking’s research team to test the performance of AI-generated content in organic search. We launched 20 diverse websites, eagerly tracking their progress.
But my curiosity didn’t end there. I was driven to comprehend how AI systems find, process, and use information. This inspired me to expand our project and delve deeper into AI search and LLM visibility experiments.
In our next phase, we boldly created a fictional brand and inserted it into a real, competitive niche. Our aim? To see how fast AI would catch on and if our make-believe brand could stand toe-to-toe with industry giants and governmental sources.
After just one month, enlightening patterns began to emerge.
Methodology behind the experiment
I crafted a fictional brand and dispersed content across various platforms:
A fresh website exclusively for the brand, registered specifically for this daring experiment.
11 seasoned domains, each over a year old with a solid history and existing rankings.
I experimented with seven different content formats:
Comprehensive guides.
“Alternatives” listicles.
“Best of” listicles.
Review articles.
Comparative (“vs”) pages.
How-to/tutorial content.
Clickbait-style articles.
Kicking off in March 2026, I monitored five AI systems: ChatGPT, Google’s AI Overviews, Google’s AI Mode, Perplexity, and Gemini, tracking 825 prompts and generating 15,835 AI answers during the initial month.
For every prompt, I considered:
Our brand’s appearance in AI responses.
Its recognition as a source.
Frequency of being the main cited source (position 1).
This ongoing experiment was initially designed to observe AI systems’ reactions to freshly created, fictitiously branded information.
Key experiment insights
96% of our brand’s AI visibility stemmed from branded searches. Even in a low-competition niche, a new domain struggled to compete on non-branded topics.
For niche-specific queries, our brand outshined well-established competitors by up to 32 times, achieving dominant visibility in under 30 days.
Despite lacking authority, clearly articulated identity pages, like “[Brand Name] Complete Guide” and “About Us”, became frequently cited, highlighting the importance of brand positioning in AI.
Perplexity surfaced new content swiftly, often citing additional domains over the main site.
Google’s AI Mode offered stability on branded queries.
Gemini struggled with brand identification, resulting in 60% of responses without our brand’s citation for uniquely branded queries.
Deep guides, review articles, and comparison pages gained the most citations, while generic content saw minimal impact.
A hub page with 10 supporting articles yielded no citations, whereas shorter, repetitive pages garnered over 1,800 citations, emphasizing the power of high-volume content publishing.
Insight 1: New domains may not beat market leaders right away, but they can define their brand narrative in AI search
A new site struggles to compete broadly initially. However, our fictional brand quickly gained traction through branded queries, largely because these were the focus points.
Of all AI answers, a staggering 96% came from branded searches alone, reiterating the crucial role of brand-specific queries in early visibility.
This mirrors traditional SEO patterns where new brands must first build trust and recognition.
My key takeaway for marketers was clear: AI systems are inclined to use your site as a primary information source during your brand’s formative years.
This insight was reinforced as pages consolidating brand information, such as the “Complete Guide” and “About Us”, became the primary sources cited from our main domain.
Therefore, shaping the brand narrative early on AI platforms is crucial, even for emerging brands.
Insight 2: AI engines behave very differently
Our experiment shed light on the unique behaviors of five AI systems in indexing and presenting our fictional brand.
Google’s AI Mode: The most stable for branded visibility
Google’s AI Mode proved to be a reliable ally, consistently putting our brand at the top for around 90% of branded queries.
It was the bastion of predictable brand visibility in our experiment.
Google’s AI Overviews: High visibility, lower consistency
Though less consistent, Google’s AI Overviews provided notable brand visibility. Yet, fluctuations and temporary drops were observed during our test period.
Whenever links were absent, visibility suffered, highlighting the need for sustained link presence.
Perplexity: The fastest to pick up new content, but not always brand-first
Perplexity swiftly indexed new content, quickly boosting early visibility.
However, its affinity for additional domains over the main brand site complicated content attribution in AI responses.
ChatGPT: Slower to react, stronger over time
ChatGPT gradually improved recognition of our brand, with a notable increase in visibility over March.
Notable growth occurred in unique claims and comparisons (“vs”), showcasing ChatGPT’s potential for longer-term brand assimilation.
Gemini: Weakest performance and most inconsistent behavior
Gemini presented challenges with niche recognition, improving only when framing prompts appropriately.
Despite effort, results remained inconsistent, with significant citation gaps on brand-specific queries.
Insight 3: Content format matters, but so does the volume
Through diverse content experimentation, we found in-depth articles earn the most AI citations.
Comprehensive guides, reviews, and comparisons outperformed simpler formats, reinforcing the power of detailed content presentation.
The volume of content also played a role. Although the individual performance was low, 30 shorter pages collectively generated impressive AI visibility.
This doesn’t diminish the value of quality but indicates a large amount of content can boost overall reach.
Insight 4: Topical clustering alone doesn’t produce AI visibility
Our structural tests revealed that topical clustering, without substantial content, didn’t boost AI visibility.
It challenges the notion that clustering inherently strengthens authority, stressing the importance of standalone content value.
Though structured linking offers insight into site understanding, AI systems prioritize the need for direct and valuable information retrieval.
So, do AI engines reward entity coherence more than truth verification?
Our first month’s results point to a significant insight: AI systems value availability and consistency over strict truth verification.
Though not all-reaching, well-structured, repeated, and available content can be surfed with surprising ease.
This phenomenon was observed during manual checks where even a fictional brand received favorable recommendations due to consistent narratives.
It’s not simply LLMs favoring new brands, but where gaps exist, even limited information may be built up positively.
Final thoughts
The true revelation isn’t the visibility of a fictional brand. Rather, it’s how visibility aligns with brand-centric inputs like unique claims and varied content.
This leads to pivotal conclusions:
AI search isn’t arbitrary. It responds to discernible and influenceable signals.
AI remains vulnerable to manipulation. Without inherent truth-checking, strategies used by legitimate brands can simulate credibility.
Illuminating the need for active narrative shaping, our experiment urges businesses not to rely on AI systems to innately capture accurate brand representation.
We’re committed to expanding and monitoring these insights over time, as we collect ongoing data.
Recently, I read an eye-opening report stating that AI bot activity skyrocketed by 300% in 2025. As someone deeply interested in digital publishing, I couldn’t help but feel the strain it puts on media and publishing industries.
Why this matters to me. I’m increasingly aware of how AI bots are revolutionizing content discovery and consumption. They’ve shifted the dynamics by directing users from traditional search clicks to direct answers via chat interfaces. For publishers like us, this means fewer organic visits and a lack of attribution in AI-generated responses, which undermines revenue from ads and subscriptions.
The threat we face. In our publishing niche, we’re confronted with two significant AI bot threats:
– Training bots that are fed our content models.
– Fetcher bots that extract our real-time content to provide instant answers, posing a severe risk by capturing the value as soon as it’s created.
The impact I notice. It’s disheartening to see page views sink while operational costs escalate. Scraping bots consume our server and CDN resources without adding revenue, decreasing brand visibility.
– AI chatbot referrals result in about 96% less traffic compared to traditional search.
– Only about 1% of users click on sources cited in AI-generated answers.
Our solutions. As a proactive step, I see publishers like us leaning toward nuanced controls instead of outright banning AI bots. We adapt by:
– Monitoring and categorizing bot traffic efficiently.
– Selectively blocking malicious scrapers or slowing them down using techniques like tarpitting.
– Authorizing bots that are linked to licensing deals or partnerships.
In their words. As per Akamai’s insights:
– “These bots are more than just a security issue; they pose a profound business challenge that threatens the sustainability of quality journalism in a zero-click search and AI-generated content era.”
– “Publishing faces an existential crisis… Readers still appreciate genuine content, but they seek instant answers via AI-driven platforms like ChatGPT and Gemini rather than search results.”
What’s ahead? There’s talk about a “pay-per-crawl” model. Tools such as identity verification (Know Your Agent) and platforms like TollBit are aiming to authenticate bots and charge for real-time access.
– The aim is to convert scraping into a manageable and monetizable transaction.
About the data. The Akamai report scrutinized bot management data from July to December 2025, which included application-layer traffic across websites, apps, and APIs.
