I structure content for AI search by making every page clear, credible, and easy for answer engines to understand. That means I do not rely on keywords alone. I combine strong SEO fundamentals with topical authority, earned media, and answer-first formatting so AI systems can recognize what my brand knows, where it is trusted, and why it should be surfaced in relevant responses.
When I think about AI visibility, I focus on discovery from the start. I want my content to answer real questions directly, connect related topics naturally, and support each claim with signals that build confidence. This approach helps improve how my brand appears across AI search experiences, traditional search results, and emerging discovery platforms.
For me, the goal is simple: create content that is useful for people and understandable for machines. By organizing information around intent, authority, and clarity, I make it easier for AI tools to cite, summarize, and recommend my brand when users are looking for trusted answers.
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.
When I look at Similarweb’s findings, the message is clear: users who saw a brand recommended by ChatGPT were much more likely to visit that brand’s website within a week.
What happened. I found the biggest takeaway in the behavior shift. On average, users were 2.5 times more likely to visit an AI-recommended brand than a direct competitor, based on Similarweb’s study of U.S. desktop activity across finance, travel, and beauty.
Similarweb tracked users who asked ChatGPT industry-relevant questions, received a specific brand recommendation, and then visited either that recommended brand’s website or a competitor’s site within seven days.
To keep the data focused, the study excluded users who had visited the brand’s site in the prior four weeks or had named the brand directly in their prompt.
Recommendations shifted traffic. I saw the same pattern appear across all three industries Similarweb analyzed, which makes this more than a one-category trend.
In finance, after an American Express recommendation, 7.2% of users visited American Express, compared with 3.1% who visited Capital One. After a Capital One recommendation, 14.2% visited Capital One, compared with 3.8% who visited American Express.
In travel, after a Skyscanner recommendation, 9.5% visited Skyscanner, compared with 7.6% who visited Kayak. After a Kayak recommendation, 12% visited Kayak, compared with 3.4% who visited Skyscanner.
In beauty, after a Sephora recommendation, 7.9% visited Sephora, compared with 3.3% who visited Ulta. After an Ulta recommendation, 7.6% visited Ulta, compared with 4.6% who visited Sephora.
AI demand showed up in search. What stands out to me is that most AI-influenced visits did not appear as AI referral traffic. ChatGPT may shape the user’s brand choice, but the later website visit often shows up in analytics as search traffic instead.
Similarweb found that 55.9% of AI-influenced visits came through search, compared with 40.4% of non-AI-influenced visits.
Direct traffic told a different story. It accounted for 19.9% of AI-influenced visits, compared with 38.8% of standard visits.
Recommended users stayed longer. I also think the engagement data matters. AI-influenced visitors viewed 12 pages and spent 11.8 minutes on site, on average, compared with 6.5 pages and 5.6 minutes for non-AI-influenced visitors.
That deeper engagement suggests these users may have already narrowed their options during the AI conversation before they ever reached the brand’s website, Similarweb said.
Why I care. AI visibility can drive meaningful visits even when referral reports miss the original source of influence. I need to understand whether ChatGPT is creating demand for my brand or sending that demand to a competitor.
About the data. Similarweb used its opted-in U.S. desktop web panel to track user journeys from July through December 2025. The report focused on finance, travel, and beauty brand pairs with competitive overlap.
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.
What Google said. Google wrote, “Released the June 2026 spam update, which applies globally and to all languages. The rollout may take a few days to complete.”
Timing. I expect this update to move fairly quickly, since Google said the rollout may take only a few days to finish.
Why I care. Google releases search ranking updates several times each year, and spam updates are meant to target sites that use manipulative tactics to abuse the ranking system. If a site is not relying on those kinds of practices, I would not expect it to be the main target of this update.
More on spam updates. Google’s documentation explains that its automated systems are always working to detect search spam, but the company occasionally makes notable improvements to those systems and labels them as spam updates.
Google also points to SpamBrain, its AI-based spam-prevention system, as one example of how it improves its ability to identify spam and catch new types of abuse.
If I saw a ranking change after a spam update, my first step would be to review Google’s spam policies and make sure the site is complying with them. Sites that violate those policies may rank lower or disappear from results, while improvements can help over time if Google’s automated systems recognize that the site is now compliant.
For link spam updates specifically, Google says recovery can work differently. If Google removes the value of spammy links, any ranking benefit those links once created is lost, and that benefit cannot be regained simply by cleaning up the links later.
I see Google Discover’s “Tailor Your Feed,” now showing up as “Add topics to your feed,” as a meaningful shift in how people can shape what appears in their feed. Instead of relying only on Google’s inferred signals, such as clicks, dwell time, follows, and engagement history, I can now type what I want to see in natural language and let Google translate that request into feed instructions.
That matters because it creates a third visibility path for small and niche publishers. Until now, a smaller site usually needed either strong implicit affinity from a user or an explicit follow. With prompt-based tuning, a user can simply ask for a topic, creator, source, or type of content, and Google can retrieve matching material even when that content has barely appeared in Discover before.
In my tracking, the feature turns prompts into actions such as SEE_MORE and SEE_LESS. Those actions are applied after the user refreshes or updates the feed. The experience feels conversational, but underneath it appears to create persistent instructions that can affect both the current feed and future Discover sessions.
I also see signs of an LLM-style system behind the workflow. A user prompt is interpreted, converted into a readable assistant response, and returned with a structured result. In one observed example, the prompt “show me more content on seroundtable.com” produced an actionable SEE_MORE response and a persistent thread key, suggesting that feed tuning is treated as an ongoing conversation rather than a single isolated command.
The feature first appeared in Search Labs for US English accounts in December 2025. At that stage, the impact was subtle: after several refreshes, I could see a few on-topic cards, but the feed did not radically transform. By early 2026, Google started adding attribution, including labels such as “resulting from natural language tuning” and later “You asked to see,” making it easier to identify which cards were influenced by a prompt.
By spring 2026, “Tailor Your Feed” had effectively become “Add topics to your feed.” The interface moved toward a chat-style entry point with prompt starters such as “Show me content from…,” “I want videos about…,” and “Keep me updated…”. The same underlying verbs remained, but Google made them easier for everyday users to trigger.
The most important technical clue is the pipeline behind the feature. Discover cards influenced by these prompts can be associated with naturallanguagetuningcontent.f for current tuning and historicalnaturallanguagetuningcontent.f for older prompts that continue shaping the feed. I read that “historical” pipeline as evidence that these preferences are meant to last over time, not disappear after one refresh.
From the observed cards, I see two ways this content is selected. The first and dominant mode is entity or interest expansion. A prompt is mapped to related people, topics, publishers, or concepts, and Discover expands around that meaning. This is why asking for one source or creator may also surface related sources, related subjects, or nearby entities rather than only the exact name typed into the prompt box.
The second and more interesting mode is query-intent fan-out. In this mode, a prompt is decomposed into natural-language retrieval queries. A broad request about SEO, for example, can become query intents such as “SEO strategies algorithm changes,” “Google ranking system updates,” or “tips for getting content into google discover.” Those query intents then retrieve articles based on semantic relevance.
This is where the connection to Generative Engine Optimization becomes clear to me. The Discover fan-out behaves like the retrieval pattern we see in generative search: one user prompt becomes several more specific sub-queries, and content is selected because it answers one of those sub-queries well. Popularity can still matter in some cases, but it is not the only gatekeeper.
That distinction is what gives niche publishers a real opening. In the observed data, prompts surfaced examples such as vegan recipe creators, Mississippi Today, a LinkedIn post, niche Japanese-property blogs, and a gardening site tied to a seed-starting query. Some mainstream publishers still appeared, including Reuters and VentureBeat in certain contexts, but the pattern was not limited to the usual high-volume Discover winners.
In the most striking cases, the pipeline surfaced articles with no detectable prior Discover distribution in the tracking dataset. I am not using “distribution” here as an audience number or a Search Console metric. I mean that the article did not appear to have circulated previously in the Discover tracking data available for analysis.
That makes this pipeline different from classic Discover distribution. Traditional Discover systems often re-serve articles that already have engagement momentum. Prompt-based tuning can retrieve content because it matches what a user explicitly asked for, even if the article has not already built a Discover track record.
I would not treat this as a mass traffic channel yet. Google appears to promote these cards cautiously, and the pipeline does not seem to snowball the way broader Discover pipelines can. It serves the user who asked. It does not automatically broadcast the content to a much larger audience.
I would also be careful about false positives. In one Japanese-property cluster, relevant results such as guides to buying a home in Japan appeared alongside a video-game article about in-game home locations. That kind of loose match helps explain why Google may rank and distribute these cards conservatively.
For publishers, the practical implication is straightforward: I would optimize for both topical clarity and query-intent vocabulary. The entity-expansion mode rewards sites that are unmistakably about a topic users can name. The fan-out mode rewards titles, headings, and introductions that align with the natural-language questions and information needs Google derives from prompts.
That does not mean stuffing pages with raw keywords. The better move is to describe the content clearly in the language a real person would use when asking Discover for more of it. If a user might ask for “buying Japanese property guide,” “starting seeds indoors guide,” or “tips for getting content into google discover,” I want the page’s title, H1, and opening section to make that relevance obvious.
The strategic shift is that selection power moves closer to the user. In the classic feed, Google infers demand. In this model, the user declares it. Google then turns that declaration into entities, interests, and query intents that drive retrieval.
For small publishers, that is the opportunity. If the feature graduates from Search Labs and users adopt it at scale, a focused site with clear topical authority could appear because it directly satisfies declared demand, not because it already won the popularity contest inside Discover.
There are still real limits. The feature has been US English and Search Labs focused, with French feeds showing essentially no presence in the observed data. Adoption also appears early. A powerful prompt-based personalization system changes little if users do not actually use it.
What I am watching next is whether Google expands this beyond Search Labs, whether the current and historical tuning pipelines become more visible, and whether this behavior converges with broader generative retrieval systems. A nascent generativeretrieval.f pipeline has already appeared in tracking data, but that broader connection still needs confirmation.
My read is that Discover is moving from observed personalization toward declared personalization. Google still infers plenty, but users are beginning to write part of their own interest profile. If that model becomes mainstream, niche publishers with clear focus, strong entity signals, and natural-language relevance may gain a new route into Discover visibility.
Notes: In this analysis, a Discover pipeline means the selection circuit that chooses and serves cards. The .f suffix in identifiers such as historicalnaturallanguagetuningcontent.f is an observed internal marker attached to Discover card metadata. “Fan-out” refers to a mechanism where one prompt is broken into several retrieval sub-queries. “GEO” means Generative Engine Optimization, or the practice of optimizing content for visibility in generative search and answer systems. “AIO” refers to AI Overviews, and “AI Mode” refers to Google Search’s conversational interface.
Field tracking referenced here covers Google app Search Labs US English accounts from December 2025 through June 2026. Pipeline behavior is based on close observation of Discover feed cards and 1492.vision tracking data. The internal mechanisms described are my interpretation of observed data and public research, and approximate dates are treated as approximate.
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.
Every so often, I see a product launch turn into a marketing lesson bigger than the product itself. Selena Gomez’s Rare Beauty did that with a new fragrance, but it was not only the scent that drew attention. The bottle became the story. Its accessible, easy-to-use packaging sparked conversation, earned praise from accessibility advocates, and reminded me how powerful inclusive design can be when it is built into the product from the start.
For me, the lesson is clear: accessibility is not a side note. It can become the campaign. One thoughtful design choice created cultural impact that would be hard to buy with media spend alone. It also showed why accessibility can build loyalty, strengthen brand reputation, support compliance, and drive measurable growth.
Accessibility as a campaign strategy
I do not see Rare Beauty’s accessibility work as a one-off moment. From packaging to pricing to its ongoing mental health advocacy, the brand has consistently made inclusivity part of its identity. That matters because consumers can usually tell when a brand is chasing attention versus when it is acting from a real strategy. They reward brands that lead with values and follow through.
Rare Beauty is not alone. I see leading brands across industries using accessibility as a differentiator, not a footnote. Apple often frames accessibility features as part of product innovation. Microsoft has brought inclusive design into mainstream campaigns, including adaptive gaming products that positioned accessibility as a source of creativity and connection. In fashion and retail, brands like Tommy Hilfiger and Unilever have put adaptive design into product launches and brand identity instead of treating it as a niche offering.
Studies from Edelman and McKinsey show why this shift matters. According to those studies, 73% of Gen Z choose to buy from brands they believe in, and 70% say they try to purchase products from companies they consider ethical. I do not see those as fringe preferences. I see them as mainstream expectations that should change how marketers build trust and growth.
The $18 trillion market marketers overlook
More than 1.3 billion people globally live with a disability. Together with their friends and family, they control more than $18 trillion in spending power, according to the Return on Disability Group. I believe marketers should view this as more than a compliance issue. It is a growth opportunity, a reputation opportunity, and a trust-building opportunity with one of the world’s largest and most passionate consumer groups.
That passion often turns into advocacy. In discussions with AudioEye’s A11iance Team, a group of individuals with disabilities who regularly share feedback on real-world accessibility experiences, one member said, “If I find a website that works and works very well for me, I will always recommend it to friends and family because I want people to have the same experience that I have.”
Another A11iance Team member, Maxwell Ivey, put it this way: “The cheapest form of advertising is word of mouth, and people with disabilities can have some of the loudest voices when we find people willing to make the effort. Because it’s that sincere effort over time that really counts with us.”
When accessibility becomes part of the customer experience, I see it create something media budgets cannot easily buy: trust and loyalty that scale through advocacy. But the reverse is also true. In a survey of assistive technology users, 54% said they do not feel eCommerce companies care about earning their business.
That should get every marketer’s attention. Too many brands are still fighting for the same crowded audience segments while overlooking a major opportunity in plain sight. When they do, they leave loyalty, advocacy, and revenue on the table.
Here is where I see many brands stumble: accessibility often stops at the shelf. Marketers invest heavily in packaging, store displays, and product design, while digital experiences lag behind. Yet those digital experiences are often the first and most important touchpoints customers have with a brand.
As accessibility-led design earns more attention, loyalty, and earned media, the gap between physical product innovation and digital experience becomes harder to ignore.
AudioEye’s 2025 Digital Accessibility Index found an average of 297 accessibility issues per web page detectable by automation alone. Each issue can create friction in the customer journey, cost a conversion, or introduce compliance risk under frameworks such as the Americans with Disabilities Act (ADA) and the European Accessibility Act (EAA).
I would not launch a campaign without a brand review or a legal check. In the same way, I do not think any digital touchpoint should go live without an accessibility review.
Four moves marketing leaders can make
Too often, I see accessibility treated as a risk to manage instead of an advantage to use. The marketers who gain ground will be the ones who change that mindset. I would start with four practical moves.
1. Make accessibility your campaign hook
I would not hide accessibility in the fine print. I would lead with it. Brands like Rare Beauty have shown that inclusive design is the story. Build campaigns where accessibility is not an afterthought, but the differentiator that earns attention and loyalty.
2. Bake it into your brand system
Accessibility should not sit off to the side. I would make Web Content Accessibility Guidelines (WCAG) alignment part of the brand system, right alongside typography, logos, and tone of voice. When accessibility is documented and expected, it becomes easier to apply across every campaign.
3. Use data as your proof point
Marketers are storytellers, but numbers strengthen the story. I would track accessibility improvements such as fewer user-reported barriers, higher accessibility scores, stronger alt text, better color contrast, and more usable forms. Then I would connect those metrics to business outcomes like conversion, reach, and sentiment to show how accessibility drives ROI, not just compliance.
4. Protect accessibility like brand safety
I would treat accessibility with the same seriousness as brand safety. Every update, seasonal campaign, and product drop should be monitored for accessibility. Trust and reputation are too valuable to leave exposed.
The competitive advantage
Rare Beauty’s fragrance launch proved something important to me: when a brand leads with accessibility, the story can write itself. Loyalty builds more authentically, and momentum feels more natural because the value is real.
The larger opportunity is that many brands still do not see it. They continue to treat accessibility as a compliance checkbox when it can be a growth strategy.
For marketers, that is the wake-up call. Accessibility builds loyalty. It strengthens brand reputation. It supports compliance. And it can drive measurable growth across marketing efforts.
Rare Beauty showed how accessibility can capture attention at the shelf. Now I see the next opportunity clearly: making sure that same accessibility carries through online. When every touchpoint welcomes everyone, every campaign has a better chance to deliver its full impact.
I used to think of search as retrieval. I would open tabs, compare sources, read reviews, cross-check details, and then make the decision myself.
Now I see search becoming something different: delegation.
More users are realizing they do not need to compare 15 pages or jump between Google, Maps, reviews, forums, and videos before they act. They can ask AI to do much of that work for them.
In many ways, this is the closest most people have come to having a personal assistant. For a long time, delegation was a luxury. It usually meant having someone else research options, summarize information, and make recommendations. In practice, that kind of help was mostly available to people with money or support teams around them.
Now that capability is much more widely available. I believe that changes search behavior at a fundamental level. Users increasingly want synthesis instead of retrieval, recommendations instead of endless exploration, and reduced effort instead of exhaustive research. They want help evaluating options and making decisions.
This is a real behavioral shift. Where people once might have phoned a friend, they now ask an LLM.
Why I believe users are delegating more decisions
At the heart of this move from search to delegation is basic human psychology. Our brains are wired for cognitive ease. We naturally gravitate toward behaviors that reduce effort, simplify decisions, and save time.
AI tools fit that pattern perfectly. They remove friction from the decision-making process by helping users open fewer tabs, make fewer comparisons, carry less cognitive load, and reach outcomes faster.
I also see users becoming more comfortable with answers that are good enough and delivered quickly, rather than perfect answers that require a lot of effort to uncover.
For years, search behavior was built around gathering as much information as possible before making a decision. AI has changed that value exchange. Users do not always need every possible answer. They need confidence that the answer in front of them is sufficient.
Reflect Digital’s SearchPulse research found that up to 61% of AI users say they use these tools because of their speed and ease. Disclosure: I am Reflect Digital’s founder and CEO.
As technology has become part of everyday life, our expectations around convenience have evolved with it. We are already conditioned to optimize more of our lives than ever before, and AI is becoming another mechanism for doing exactly that.
Why delegation in search will not look the same for everyone
One of the biggest mistakes I think businesses can make right now is assuming this shift to delegation is happening evenly across all audiences and all search journeys. It is not.
People also delegate differently depending on the task they are trying to complete. Vacation planning is a useful example. Building an itinerary is an ideal delegation task because it traditionally requires maps, travel sites, timing decisions, logistics, and constant comparison.
Now, a user can ask AI something like: "Plan me a five-day itinerary around Tuscany with wine tasting, scenic towns, and minimal driving." That is decision outsourcing in action.
But choosing the vacation itself may still involve more exploration. A person may still want to browse destinations, look at imagery, watch videos, or validate ideas independently before narrowing the options.
The key point is that delegation is contextual. I believe businesses need to understand where delegation naturally fits within their audience’s decision-making process.
How I identify delegation opportunities in an audience
The important thing to understand is that delegation is rarely universal across an entire customer journey. AI adoption is not binary. People delegate specific types of decisions at specific moments.
I look for delegation opportunities in moments where users experience high cognitive load, too many variables, time pressure, repetitive comparison, decision fatigue, or information overload.
These are the moments where delegation becomes appealing. To understand what that means for a specific audience, I ask where they get overwhelmed, where they compare too many options, where they are trying to save time, and where they repeatedly ask for reassurance or recommendations.
I also look for the parts of the journey that feel effort-heavy rather than emotionally enjoyable. The more effort a task requires, the more likely delegation becomes.
Then I compare those answers with the areas where users may still want exploration, such as inspiration, entertainment, identity expression, aspirational browsing, and emotionally led decisions.
For example, a user may delegate the work of building a travel itinerary but still enjoy exploring vacation destinations on their own.
That distinction matters. The businesses that win in this new search environment will understand not only what their audience is searching for, but also what they are trying to offload.
Once I start looking for delegation-driven decisions, they become surprisingly easy to spot. They often appear when users ask AI to narrow down options, recommend the best fit, validate a choice, summarize information, compare alternatives, or reduce effort.
That means searches start to sound more like: "What’s best for me?" "What would you recommend?" "Compare these options." "Give me the top three." Or, "Summarize this for me."
Traditional search behavior, by contrast, is more exploration-heavy. It involves deeper comparisons, source checking, manual research, and detailed information gathering.
Most users will move between these two modes depending on what they are searching for and why. But I do not think businesses should rely only on internal assumptions or gut instinct to understand where those delegation moments exist.
Gut instinct only goes so far. To understand this shift properly, I believe businesses need to speak directly with their audience and combine behavioral observation with research such as surveys, customer interviews, roundtables, usability testing, journey analysis, search behavior analysis, and AI prompt analysis.
The goal is to understand where users experience friction, feel overwhelmed, seek reassurance, want recommendations, and feel comfortable outsourcing decision-making.
The real competitive advantage comes from understanding what your audience no longer wants to do themselves.
This is where the shift becomes commercially important. I believe businesses now need both search-support content and decision-support content because both behaviors still exist.
Search-support content is designed for exploration. It is usually comprehensive, detailed, comparison-driven, educational, and deeply indexable. It helps users who still want to research extensively and validate decisions themselves.
Decision-support content serves a different purpose. It needs to be synthesized, recommendation-oriented, clearly structured, trust-heavy, and outcome-led.
This kind of content helps both users and AI systems quickly understand what a business offers, who it is for, when it is appropriate, and why it should be trusted.
For example, a traditional search-support page might compare every CRM platform feature in detail. A decision-support page might clearly explain the best CRM for a 50-person B2B sales team with limited implementation resources.
One page supports exploration. The other reduces decision-making effort.
Websites increasingly need to support two parallel journeys: humans who are exploring and humans who are delegating. Put another way, they need to support journeys for both people and AI agents.
How I audit content for delegation behavior
If delegation is becoming part of an audience’s decision-making process, the next question is simple: does the content support it?
I usually start by auditing existing content through two lenses: exploration support and decision support.
First, I ask whether the content helps someone explore. This is traditional search-support behavior. It includes detailed explanations, comparisons, educational depth, broad keyword coverage, manual research support, and multiple options without strong direction.
That type of content helps users gather information and evaluate independently.
Then I ask whether the content helps someone decide. Decision-support content reduces effort by offering clear recommendations, summarized takeaways, structured comparisons, strong trust signals, direct answers, contextual guidance, and outcome-focused language.
One of the easiest ways I spot gaps is by asking: "If an AI system landed on this page, would it clearly understand what we recommend, who this is for, and why it matters?"
Many businesses currently have a lot of exploration content but very little decision-support content. That creates a gap. Delegation is no longer only about being discoverable. It is about being usable within a decision-making process.
Some businesses are already making the mistake of abandoning traditional search behavior too early. I think that is a serious error because traditional search is not disappearing.
At the same time, delegation behavior cannot be ignored. Different audiences, moments, and decision types now require different search experiences.
The businesses that succeed will not be the ones chasing every AI trend. They will be the ones that deeply understand when users want exploration, when users want delegation, and how to support both effectively.
That matters because users increasingly seek help evaluating options and making decisions.
The brands that succeed in the future of search will be those that truly understand their audience and let that knowledge guide their strategy.
I’m looking at Yahoo! Scout as Yahoo’s most direct return to search and web discovery in years. The new AI-based answer engine is available at scout.yahoo.com, and Yahoo is also weaving it through its major properties, including Yahoo News, Yahoo Finance, Yahoo Mail and Yahoo Search. I think of it as a Yahoo-branded AI companion built to help people move through those familiar Yahoo experiences with more context and guidance.
What Yahoo Scout is. To me, Yahoo Scout is Yahoo’s version of an AI search engine and assistant, similar in broad idea to Google’s AI Mode or OpenAI’s ChatGPT, but with Yahoo’s own personality layered in. Yahoo told me it wanted Scout to feel fun, approachable and easy for people of all ages to understand.
When I first visited Yahoo Scout, the experience felt intentionally warm. The home page includes a search box, a playful slogan and an animated icon above it. Beneath the search box, Yahoo offers suggested searches that can be filtered by topics such as news, finance, sports, shopping and travel. On the left side, I could also see previous queries, making it easier to return to earlier searches and continue where I left off.
The home page also rotates through playful visual treatments. In one version I saw a cowboy hat, while other versions included a crystal ball, a gold medal, a walking cartoon brain and more.
Yahoo Scout’s advantage. The Yahoo Search team gave me early access to try Yahoo Scout. While the interface will feel familiar to anyone who has used other AI answer engines, the Yahoo-specific pieces are what stood out most to me.
Yahoo’s biggest advantage is its existing reach. The company already has a large audience across Yahoo Mail, Yahoo News, Yahoo Finance and Yahoo Search. Yahoo told me it has more than 500 million user profiles, stores signals such as queries, usage and intent, has more than one billion entities in its knowledge graph and processes 18 trillion consumer events and signals across its properties. That gives Yahoo a lot of context it can use to personalize AI search and better categorize queries.
Yahoo also told me it is the second-largest email company and the third-largest search engine.
Because Scout is connected to Yahoo’s own properties, it can bring Yahoo Finance widgets, financial data, tables, citations, weather results, news results and other rich content directly into answers.
“Search is fundamentally changing, and our team has been inspired to use our decades of experience and extremely rare assets to create something uniquely useful for Yahoo’s hundreds of millions of monthly users,” said Jim Lanzone, CEO of Yahoo. “This beta launch is just the starting point. From search to our industry-leading verticals, Yahoo Scout will help our users accomplish their goals online faster and better than ever before.”
Sending traffic to publishers. Jim Lanzone told me Scout is closely tied to Yahoo’s original mission of being a trusted guide to the internet. Because of that, Yahoo says it designed Scout with the open web in mind, including ways to send traffic downstream to content creators and publishers.
In Yahoo Scout responses, I saw large blue highlights over portions of the answer text. When I hovered over those highlights, I could click through to the source. Each response also includes a visible “featured source” area, along with tables, imagery, related news articles and other source-driven elements meant to make publisher links more prominent.
Lanzone told me early AI answer engines have not done enough to send traffic back to the sources behind their answers. Yahoo wants Scout to be an example of how that relationship can work better. Since there is not enough licensing revenue for every publisher to make deals with AI companies, Yahoo is leaning into the historical search model: give users answers, but also send meaningful traffic to the sites that produced the underlying content.
CTR expectations. I asked Yahoo what click-through rate it expects from Yahoo Scout to publishers. The honest answer was that it does not know yet. Yahoo expects to learn from real user data after launch and then iterate to improve downstream clicks.
Yahoo expects queries in Scout to be longer than queries in Yahoo Search. It also expects ad loads to be lighter, and the team hopes click-through rates will be higher than the industry average.
Yahoo also told me it plans to build a way for publishers to see impression and click data in the future. I see that as something like a Yahoo Webmaster Tools-style reporting experience, though crawling and indexing data would still be tied to Microsoft Bing because Bing powers the underlying search index.
Yahoo Scout across Yahoo properties. I expect Scout to show up throughout Yahoo’s ecosystem. Yahoo Search will use Scout-powered AI summaries. Yahoo News will provide article highlights and may include daily digest audio summaries. Yahoo Finance will add an Analyze button powered by Scout. Yahoo Mail will summarize emails and extract action items, such as adding events to a calendar.
Examples of Yahoo Scout in action. Yahoo Scout is not perfect, but for something Yahoo says was built in about six months, I came away impressed.
When I asked Yahoo Scout for help understanding how SEO works, it returned a useful response with citations throughout the summary. SEO is complex, and not everyone would agree with every part of the answer, but the citation structure made the experience more transparent.
I then asked it for sources I could use to find more content on the topic. There were clearly missed opportunities to link out more often, and I shared that feedback with Yahoo. The team agreed there was room to improve.
When I followed up by asking how I could navigate to the sources it had mentioned, Scout did provide links at that point. I also saw citation previews appear when hovering over linked highlights.
I tried several other types of searches as well. For entertainment queries, Scout pulled in news articles with larger graphics and clickable card-style formats. For finance queries, Yahoo brought in Yahoo Finance, though I was not able to generate stock charts during my own testing, even though I saw that capability in a demo. It may still have been in progress at the time.
For weather, I tested Scout on a Sunday morning as a major snowstorm was touching down in New York. I was able to get a Yahoo Weather chart, along with practical tips on how to stay warm.
For sports, I asked about Super Bowl predictions. As a lifelong Jets fan, I also asked whether the Jets had any chance of winning the Super Bowl in the next 10 years. The answer was not especially encouraging, but I was glad to see a chart embedded directly in the response.
For shopping, Scout gave me advice on how to dress for the weather. That is where Yahoo’s commerce strategy becomes more visible.
Ads and commissions. Yahoo Scout will show ads at the bottom of some responses. Commerce-related queries will also be monetized through affiliate commissions, which is already a common revenue model across the web.
Yahoo told me the ads are still powered by Microsoft Advertising, but Yahoo controls how those ads appear inside the Scout experience.
Those ads will be charged on a CPC basis, not on an impression basis like some other AI engines have announced. I also saw product results labeled with “Yahoo may earn commission from these links.”
How Yahoo Scout came together. Yahoo has been hinting for about three years that it wanted to return to the search game. In 2009, Yahoo made a deal with Microsoft to have Microsoft power Yahoo Search, which effectively ended Yahoo’s work on its own search technology. Since then, Yahoo has outsourced search technology until this new Scout effort.
About six months ago, Yahoo acquired Eric Feng’s company to lead consumer search at Yahoo. Feng co-founded the online video platform Mojiti, which Hulu acquired in 2007. He then became Hulu’s founding CTO and head of product. Before that, he worked in Microsoft Research on search-related problems.
“Yahoo’s deep knowledge base, 30 years in the making, allows us to deliver guidance that our users can trust and easily understand, and will become even more personalized over the coming months,” said Eric Feng, Senior Vice President and General Manager of Yahoo Research Group, the creators of Yahoo Scout. “Yahoo Scout now powers a new generation of intelligence experiences across Yahoo, seamlessly integrated into the products people use every day.”
Lanzone, who also has a long history in search from his years as CEO of Ask.com, told me Feng has been instrumental in building Yahoo Scout over the past six months. Yahoo says this first public release is only the beginning, and more iterations and improvements are expected.
Anthropic and Claude. Yahoo Scout is not built on Yahoo’s own LLM. Yahoo partnered with Anthropic and uses Claude as Scout’s primary foundational AI model. Anthropic, founded in 2021 by former OpenAI employees including Daniela Amodei and Dario Amodei, has become one of the leading AI companies. Amazon announced an investment of up to $4 billion in September 2023, Google committed $2 billion the following month, and as of November 2025 Anthropic had an estimated value of $350 billion.
Even though Scout uses Anthropic’s foundational AI models, Yahoo has customized the experience and combined it with proprietary Yahoo data. Running the same searches directly on Anthropic’s tools would not produce the same Yahoo Scout experience.
“When you’re serving hundreds of millions of users, you need AI that can do more than retrieve information – it has to reason, synthesize, and explain. Yahoo is building toward a more personalized, trustworthy kind of search, and Claude’s ability to deliver that quality of guidance at scale is at the heart of Yahoo Scout,” said Ami Vora, Head of Product at Anthropic.
Microsoft Bing. Microsoft Bing data is also part of Yahoo Scout. Bing provides the underlying search index, but Yahoo says the responses, ranking and overall experience are Yahoo’s. Yahoo wrote that Scout builds on its long-standing Microsoft relationship by using Microsoft Bing’s grounding API, combining that API with Yahoo’s trusted data and content ecosystem so answers are informed by authoritative sources across the open web.
Yahoo is also joining Microsoft’s Publisher Content Marketplace pilot. Microsoft says that marketplace can help support publisher revenue, and Yahoo described the move as “reflecting a shared commitment to expanding publisher reach, connecting original work with new audiences, and supporting sustainable revenue opportunities for publishers.”
Hallucinations. I asked Yahoo about hallucinations, and the company told me it has added many guardrails to reduce them as much as possible. Yahoo says its entity graph, news content and other Yahoo-specific data help ground the answers. The team believes Scout’s hallucination rate should be “very low” compared with other AI engines.
Yahoo Scout blends AI search with commerce, surfacing winter parka recommendations, affiliate shopping cards and trusted weather sources in one answer-style interface.
Agents. Many AI engines are moving toward agentic experiences that can complete tasks for users. Google, OpenAI and Microsoft are all investing heavily in this area.
Yahoo Scout already includes some agent-like elements, especially inside Yahoo Mail, where it can help add calendar events, support smart compose features and surface action items. Yahoo says more is coming on that front.
Why I care. Search is changing quickly, and I find it exciting to see Yahoo step back into the space in a meaningful way. As someone who has followed search for more than 20 years, I appreciate seeing Yahoo try to make search feel fresh again.
Seeing people such as Jim Lanzone, Eric Feng and Brian Provost work on AI search at Yahoo makes this feel like more than just another answer engine launch. I’m interested to see what Yahoo does next.
