I’m seeing Google roll out a new set of Demand Gen updates designed to help advertisers improve creative performance, reach more potential customers across YouTube, and measure campaign results with more clarity.
For me, the bigger story is that Demand Gen is becoming less about manually adapting assets and more about using AI-assisted tools to make creative work harder across Google’s most visual surfaces.
Demand Gen campaigns are built to drive discovery and conversions across Google’s visual placements. With these latest updates, I see Google trying to reduce creative friction while giving advertisers better visibility into what is actually moving performance.
Google says the enhancements arrive as YouTube continues to show value for customer acquisition. The company cited research from Measured showing that 72% of incremental conversions on YouTube come from new customers.
What’s new. I’m watching Demand Gen add expanded video resizing capabilities, giving advertisers the ability to automatically transform creative into more aspect ratios, including vertical-to-square, vertical-to-landscape, and square-to-landscape formats.
That matters because it should make it easier to adapt existing creative for different YouTube placements without having to produce every version manually from scratch.
Why I care. Expanded video resizing can help existing assets fit more YouTube inventory, Gemini can provide AI-powered recommendations before launch, and new web-to-app measurement can give marketers a clearer view of how Demand Gen campaigns influence app installs and return on ad spend.
Gemini joins the creative workflow. Google is also bringing Gemini-powered recommendations directly into the Demand Gen campaign creation process, which makes AI guidance part of the asset selection workflow instead of a separate optimization step.
When advertisers choose image and video assets, Gemini will offer automated suggestions for optimizing creative for YouTube. I see this as a way for marketers to improve asset choices before campaigns go live, rather than waiting for performance data after launch.
Better app measurement. Demand Gen now includes Web to App Acquisition Measurement, allowing advertisers to measure when web campaigns lead users to install an app.
The new reporting gives me a more complete way to evaluate campaign performance because it attributes app installs generated through Demand Gen campaigns. That should help advertisers better understand the full impact of their media spend.
The bottom line. I see Google’s latest Demand Gen updates as a practical combination of AI-powered creative guidance, more flexible video optimization, and broader measurement tools that can help advertisers improve performance while gaining clearer insight into customer acquisition.
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.
I see Google’s latest Google Ads API change as another clear move away from legacy automation and toward newer AI-driven campaign types, especially Performance Max.
Beginning August 3, 2026, Google says developers will no longer be able to create new Smart Campaigns through the Google Ads API. For me, the key detail is that this change is about new campaign creation only.
Existing Smart Campaigns are not being shut down. They can keep serving ads, and advertisers and developers will still be able to update and manage those campaigns through the API.
What changes is the ability to create brand-new Smart Campaigns through API workflows. If I depend on automated campaign setup, that is the part I would review now.
I care about this because it signals where Google wants advertisers to go next. Smart Campaigns may continue running, but the path for new API-based campaign creation is moving toward newer products such as Performance Max, Search campaigns, and Demand Gen campaigns.
Google is specifically pointing advertisers toward Performance Max as the primary alternative. Since Performance Max runs across Google’s advertising inventory and uses AI to automate more of the campaign process, it fits the broader direction Google has been taking for years.
I also see this as part of a wider consolidation around automated campaign formats. Google has increasingly emphasized systems that handle bidding, targeting, and creative optimization across channels, and limiting new Smart Campaign creation reinforces that shift.
For developers, the practical next step is to audit any application that creates Smart Campaigns before the August 3, 2026 deadline. The affected requests are campaign creation operations where advertising_channel_type is set to SMART and advertising_channel_sub_type is set to SMART_CAMPAIGN.
After August 3, attempts to create new Smart Campaigns through the API will fail. In version 24 of the Google Ads API, developers will receive a SmartCampaignError.CREATION_FAILED error.
In version 23 and earlier, the same type of request will return an OperationAccessDeniedError.CREATE_OPERATION_NOT_PERMITTED error.
My main takeaway is that advertisers, agencies, and software providers should not treat this as a last-minute technical cleanup. If campaign creation is built into an internal tool, onboarding flow, or platform integration, I would start mapping the replacement path now.
Google is not ending existing Smart Campaigns, but it is removing a key creation path for new ones. To me, that is a strong signal that future campaign planning should center on Performance Max and other AI-driven Google Ads campaign types.
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’m watching a small but meaningful Google Search ads experiment that could change how people notice paid results. Google is testing labels that call out the ads it believes are most relevant to a user’s search query, which could affect both user trust and advertiser performance.
What’s happening. Google has started testing new Search ads labels such as “Strongest match” and “Strong match” on select ads in search results. Google Ads Liaison Ginny Marvin confirmed the experiment and said the labels are meant to help users quickly spot ads that closely match their search intent.
For now, I see this as a limited test. Google says it is only appearing for a small percentage of users in the U.S., so most advertisers may not notice it in the wild yet.
Why I care. This kind of visual signal could influence which ads users view as the most relevant and trustworthy. If Google expands the experiment, advertisers with stronger relevance and quality signals may gain more attention, while weaker or less aligned ads could become easier to ignore.
How it works. According to Google, these labels rely on the same ad quality and relevance signals already used inside its advertising systems. In other words, Google is not introducing a new ranking factor here. It is making its relevance assessment more visible directly in the Search results interface.
I see the goal as fairly straightforward: help users identify the ads most likely to answer what they were searching for, without making them interpret relevance entirely on their own.
Why Google is testing it. Google says the experiment is designed to improve the Search ads experience for both consumers and advertisers.
For users, the label could act as another cue that a paid result may be especially useful for their query.
For advertisers, it could help highly relevant ads stand out in front of high-intent audiences, which may lead to stronger engagement and higher click-through rates if the feature performs well.
Reading between the lines. I view this test as part of Google’s broader push to make ad relevance more visible and more understandable to searchers.
Historically, relevance signals have mostly worked behind the scenes through auctions, quality systems, and ranking logic. By showing those signals more clearly, Google may be trying to build more trust in sponsored results while also rewarding advertisers that closely match their ads to search intent.
The timing also matters. Search platforms are under ongoing pressure to prove that their ad experiences are useful, high quality, and worth users’ attention. A label like this gives Google another way to frame certain ads as more helpful, not just more prominent.
What I’m watching next. Google has emphasized that this is an early-stage experiment and has not said whether “Strongest match” or “Strong match” labels will become permanent. For now, I would treat this as another reminder that ad relevance, landing page quality, and alignment with user intent remain central to Google’s direction for Search advertising.
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.
I have watched the debate around llms.txt become one of the most polarized conversations in web optimization.
Some people treat llms.txt as essential infrastructure for AI discovery. Others, especially longtime SEO practitioners, see it as speculative theater. Platform tools are starting to flag missing llms.txt files as site issues, yet server logs still show that AI crawlers rarely request them.
Google even appeared to adopt it. Sort of. In December, Google added llms.txt files across many developer and documentation sites.
At first, the signal looked obvious to me: if the company behind the sitemap standard was implementing llms.txt, maybe the file really mattered.
Then Google removed it from its Search developer docs within 24 hours.
Google’s John Mueller said the change came from a sitewide CMS update that many content teams didn’t realize was happening. When asked why the files still exist on other Google properties, Mueller said they aren’t “findable by default because they’re not at the top-level” and “it’s safe to assume they’re there for other purposes,” not discovery.
The llms.txt research
I wanted data, not another debate.
So I tracked llms.txt adoption across 10 sites in finance, B2B SaaS, ecommerce, insurance, and pet care. I looked at the 90 days before implementation and the 90 days after.
I measured AI crawl frequency, traffic from ChatGPT, Claude, Perplexity, and Gemini, and the other changes each site made during the same window.
Here is what I found:
Two of the 10 sites saw AI traffic increases of 12.5% and 25%, but llms.txt was not the cause.
Eight sites saw no measurable change.
One site declined by 19.7%.
The 2 ‘success’ stories weren’t about the file
The Neobank: 25% growth
One digital banking platform implemented llms.txt early in Q3 2025. Ninety days later, its AI traffic was up 25%.
That sounds compelling until I looked at what else happened during the same period.
The company ran a PR campaign around its banking license and earned coverage in major national publications.
It restructured product pages with extractable comparison tables for interest rates, fees, and minimums.
It published 12 new FAQ pages optimized for extraction.
It rebuilt its resource center with new banking information and concepts.
It fixed technical SEO issues, including header structure problems.
When a company earns Bloomberg coverage in the same month it launches optimized content and fixes crawl errors, I cannot isolate llms.txt as the growth driver.
The B2B SaaS platform: 12.5% growth
A workflow automation company saw AI traffic jump 12.5% two weeks after implementing llms.txt.
The timing looked perfect. It would be easy to call the case closed. But the surrounding context told a different story.
Three weeks earlier, the company had published 27 downloadable AI templates covering project management frameworks, financial models, and workflow planners. These were functional tools, not ordinary content marketing assets, and they drove the engagement behind the spike.
Google organic traffic to those templates rose 18% during the same period and kept climbing throughout the 90 days I measured.
Search engines and AI models surfaced the templates because they solved real problems and created an entirely new site section. They did not surface them simply because the URLs appeared in an llms.txt file.
The 8 sites where nothing happened after uploading llms.txt
Eight sites saw no measurable change after adding llms.txt. One of them declined by 19.7%.
The decline came from an insurance site that implemented llms.txt in early September. Based on the data, the drop likely had nothing to do with the file.
The same pattern appeared across all traffic channels. Llms.txt did not prevent the decline, and it did not create any visible advantage.
The other seven sites, which included ecommerce brands in pet supplies, home goods, and fashion, plus B2B SaaS, finance, and pet care sites, used llms.txt to document their best existing content. That content included product pages, case studies, API docs, and buying guides.
Ninety days later, nothing changed. Traffic stayed flat. Crawl frequency was identical. The content was already indexed and discoverable, and the file did not change that.
The pattern was clear: sites that launched new, functional content saw gains. Sites that only documented existing content saw no gains.
Why the disconnect?
No major LLM provider has officially committed to parsing llms.txt. Not OpenAI. Not Anthropic. Not Google. Not Meta.
“None of the AI services have said they’re using llms.txt, and you can tell when you look at your server logs that they don’t even check for it.”
That is the reality I saw in the data. The file exists. The advocacy exists. But platform adoption does not show meaningful use yet.
The token efficiency argument and its limits
The strongest case for llms.txt is efficiency. Markdown can save time and tokens when AI agents parse documentation. It gives agents clean structure instead of forcing them through complex HTML, navigation, ads, and JavaScript.
That matters, but mostly for developer tools and API documentation. If your audience uses AI coding assistants like Cursor or GitHub Copilot to interact with your product, token efficiency can improve integration.
For ecommerce brands selling pet supplies, insurance companies explaining coverage, or B2B SaaS companies targeting nontechnical buyers, token efficiency does not automatically translate into traffic.
llms.txt is a sitemap, not a strategy
The closest comparison I can make is a sitemap.
Sitemaps are useful infrastructure. They help search engines discover and index content more efficiently. But I would not credit traffic growth to simply adding a sitemap. The sitemap documents what exists; the content drives discovery.
Llms.txt works in a similar way. It may help AI models parse a site more efficiently if they choose to use it, but it does not make the content more useful, authoritative, or likely to answer user queries.
In my analysis, the sites that grew did so because they:
Created functional assets such as downloadable templates, comparison tables, and structured data.
Earned external visibility through press and backlinks.
Fixed technical barriers such as crawl and indexing issues.
Published content optimized for extraction, including FAQs and structured comparisons.
Llms.txt documented those efforts. It did not drive them.
What actually works
The two successful sites showed me what actually matters.
Create functional, extractable assets. The SaaS platform built 27 downloadable templates that users could deploy immediately. AI models surfaced them because they solved real problems, not because they appeared in a markdown file.
Structure content for extraction. The neobank rebuilt product pages with comparison tables for interest rates, fees, and account minimums. That is data AI models can pull directly into answers without heavy interpretation.
Fix technical barriers first. The neobank fixed crawl errors that had blocked content for months. If AI models cannot access your content, no amount of documentation will help.
Earn external validation. Coverage from Bloomberg and other major publications drove referral traffic, branded searches, and likely influenced how AI models assessed authority.
Optimize for user intent. Both sites answered specific queries, such as “best project management templates” and “how do [brand] interest rates compare?” Models surface content that maps to what users ask, not content that is merely well documented.
None of this requires llms.txt. All of it can drive results.
Should you implement an llms.txt file?
If you run a developer tool and AI coding assistants are a primary distribution channel, I would implement llms.txt. In that context, token efficiency matters because your audience is already using agents to work with documentation.
For everyone else, I would treat llms.txt like a sitemap: useful infrastructure, not a growth lever.
It is good practice to have. It likely will not hurt. But the hour spent implementing llms.txt is often better spent restructuring product pages with extractable data, publishing functional assets, fixing technical SEO issues, creating FAQ content, or earning press coverage.
Those tactics have shown real ROI in AI discovery. Llms.txt has not, at least not yet.
The lesson I take from this is not that llms.txt is bad. It is that we are reaching for control in a system where the rules are still being written. Llms.txt offers comfort because it is concrete, actionable, and familiar. It looks like the web standards we already understand.
But looking like infrastructure is not the same as functioning like infrastructure.
My focus would stay on what is already working:
Create useful content.
Structure it for extraction.
Make it technically accessible.
Earn external validation.
Platforms and formats will change. The fundamentals will not.
Shopify has just launched Campaign Autopilot, an innovative tool powered by AI designed to streamline marketing efforts. By taking the reins of campaign creation, management, and optimization across various channels, it’s set to significantly ease my workload as a merchant.
Imagine having the power of Campaign Autopilot directly within the Shopify admin. This feature is in its early access stage but is already offering tremendous support in marketing automation.
What’s happening? With AI technology, Campaign Autopilot orchestrates marketing campaigns on my behalf across channels like Meta, Shop Campaigns, and email, enhancing my marketing strategy effortlessly.
Additional support is in the pipeline for platforms such as ChatGPT Ads, Microsoft Advertising, and Snapchat—making it a versatile tool for future needs.
What makes this system stand out is its ability to autonomously handle campaign setup, financial planning, and constant adjustments based on real-time performance, leaving me time to focus on other aspects of my business.
Why I care. By simplifying the complex world of multi-channel marketing, Campaign Autopilot provides me with a user-friendly platform that traditionally relied on the expertise of agencies or specialized teams. Now, I can set my budget and objectives while Shopify’s AI takes care of the intricate details.
How it works. I decide on a monthly budget, select channels to collaborate with, and set guidelines. From there, Campaign Autopilot executes:
Creating and launching campaigns.
Distributing my budget across channels.
Adjusting expenditures based on feedback.
Suggesting automated email initiatives.
Evaluating and refining campaign effectiveness on an ongoing basis.
I have full control—approving or tweaking campaigns, modifying budgets, or halting actions whenever necessary.
How it stands out. Campaign Autopilot redefines contemporary campaign management by sidestepping traditional, more labor-intensive methods.
Its unique approach taps into performance insights gleaned from millions of Shopify stores, offering data-driven enhancements and budget allocations.
Moreover, it functions separately from existing Meta or Shop ads, ensuring previously planned campaigns remain unaffected.
The bigger picture. Shopify is not just about ecommerce anymore. It’s now moving into the realm of growth and customer acquisition by embedding AI deeper within its merchants’ operations.
Industry trends show a shift towards autonomous marketing systems, which can run campaigns with minimal human intervention, constantly optimizing performance along the way.
What to keep an eye on. Shopify will be expanding its channel support further, potentially integrating with platforms like ChatGPT Ads, Microsoft Advertising, and Snapchat.
There’s also the AI assistant, Sidekick, which I can use for reviewing recommendations, triggering actions, and keeping a close watch on campaign outcomes.
First spotted. This update came to my attention courtesy of Digital Marketing Consultant Susan Richards-Benson via a LinkedIn post, where she recommended it as a game-changer for smaller eCommerce brands.
I recently discovered that Cloudflare and beehiiv have teamed up to enhance how I control AI crawlers on my content, particularly newsletters. This latest addition to beehiiv’s platform provides me with the ability to effortlessly monitor, permit, or restrict AI bots directly from my dashboard as AI search evolves as a critical content discovery method.
The partnership integrates Cloudflare’s Crawl Control technology into beehiiv, announced just this past Tuesday. With this integration, I can decide how AI search engines and agents interact with my content. Whether I want broader exposure by allowing crawlers or aim to safeguard my archives for future monetization, the choice is entirely mine.
AI Bot Insights Made Easy. As a beehiiv user, I now have access to an intuitive on-platform dashboard. It displays which AI crawlers attempted to access my content, those that got blocked, and the amount of referral traffic they generated back to me. I love how it provides a clear overview of crawler activities, my blocking decisions, and any referral traffic resulting from AI interactions.
Simpler Publisher Controls. The system empowers me to either permit or block specific AI models with simple, one-click permissions. Plus, Cloudflare is committed to updating the system as new AI crawlers emerge, meaning I don’t need to fiddle with robots.txt files, firewalls, or code adjustments on my own.
What Industry Leaders Are Saying. According to Cloudflare CEO Matthew Prince, this partnership offers “transparency and control” for newsletter operators amid an ever-evolving internet landscape. Meanwhile, beehiiv CEO Tyler Denk emphasized the pressing need for publishers like me to have “real leverage” as AI transforms content discovery and consumption. Cloudflare’s announcement summarized:
“As AI models evolve to offer new forms of search and discovery, independent creators are looking for flexible ways to understand and manage how their content is accessed. This integration simplifies the process by letting beehiiv users manage their digital footprint through two clear choices: publishers can either opt-in to maximum discovery to allow AI search engines and agents to crawl their work freely for broader distribution, or choose content protection, blocking AI scraping to preserve their archives for future monetization and licensing opportunities.”
The Impact on Us. It remains to be seen if these controls will be widely adopted by publishers like myself once they are fully available. The rapid pace at which AI crawling is advancing has surpassed many content creators’ current management capabilities. The real test will be if these simplified controls are potent enough to alter my publishing strategies.
Rollout Begins. The rollout of these innovative controls begins through beehiiv’s standard dashboard settings. Every beehiiv user, myself included, will have beta access to AI Crawl Control, offering insights into AI crawler activity and traffic patterns. For beehiiv Max subscribers, the option to block AI crawlers will also be available.
