Discover Core Update, AI Mode Ads, and Scanning Policy

Recent updates in Google’s ecosystem demonstrate systemic changes in approaches to content ranking, search monetization, and interaction with AI services.

In February 2026, Google launched a separate core update for Discover — the first officially announced update targeting this recommendation feed specifically rather than traditional search. The update is aimed at improving content quality in Discover, particularly by increasing the visibility of locally relevant materials, reducing sensational and clickbait content, and strengthening the role of deep, original, and expert publications. The rollout began for English-speaking users in the United States, with further expansion to other regions planned.

For SEO specialists, this means the need to monitor Discover traffic separately, as changes in the feed may occur independently of search rankings. This increases the importance of topical expertise, content depth, and local relevance.

At the same time, Alphabet revealed details about the development of AI Mode in search. According to company leadership, queries in AI Mode are already approximately three times longer than traditional search queries, opening new opportunities to monetize more complex and previously “hard-to-advertise” scenarios. The company is testing ad placements directly below AI-generated answers, which may change the structure of ad inventory and user behavior in search.

Another important area of discussion is content optimization for AI bots. Google representatives expressed skepticism about creating separate content formats (such as Markdown versions) specifically for LLM crawlers, pointing out the technical and strategic risks of such an approach.

Overall, these changes indicate Google’s transition toward a more AI-oriented model of search and content distribution. For businesses, this means the need to adapt SEO and content marketing strategies to new logic: focusing on expertise, content usage context, and the full user journey rather than only traditional search signals.

AI Mode monetization and new advertising opportunities

The details of AI Mode monetization matter more than the revenue headlines themselves. Google treats AI Mode as additive ad inventory rather than a replacement for traditional search ads. Longer, conversational-style queries create new ad surfaces that did not exist during the era of short, multi-word search queries. For paid search specialists, this means new opportunities to build campaigns around conversational search.

Google is also focusing heavily on behavioral metrics. According to company data, users spend more time within the Google ecosystem when using AI Mode. Longer session duration is considered a growth driver, and the advertising infrastructure is evolving accordingly. At the same time, a potential risk remains — reduced referral traffic to external websites, since AI Mode creates a seamless user journey without requiring visits to third-party resources.

Google’s position on content optimization for LLM bots

Google has also clarified its position on optimizing content for LLM bots. Google Search representative John Mueller critically evaluated the idea of serving Markdown versions of pages to AI crawlers instead of standard HTML. The main concern is that Markdown may strip pages of structural elements that help search engines understand relationships between pages, navigation, and content hierarchy. This approach is viewed as a technical risk rather than an effective AI optimization strategy.

WooCommerce crawling issues and Google’s response

Another important signal is Google’s involvement in resolving crawling issues at the plugin level. The Search Relations team reported submitting bug reports to WordPress and WooCommerce plugin developers whose tools generate unnecessary URLs through action parameters such as add-to-cart links. These pages consume crawl budget without creating search value.

For e-commerce sites using WooCommerce, this means the need to audit plugins and review crawl statistics in Google Search Console. Special attention should be paid to URLs containing cart or checkout parameters that should not be indexed.

AI search visibility: LinkedIn research findings

LinkedIn published internal testing results on factors influencing content visibility in AI-generated search results. According to the company, in some B2B topics, non-brand awareness-driven traffic has declined by up to 60% across the market.

The research showed that structured content is cited more frequently in AI-generated answers. The best-performing pages include clearly identified authors, verified professional credentials, and transparent publication dates. LinkedIn is also developing new analytics tools to track traffic from LLM systems and monitor AI bot behavior in CMS logs.

Implications for SEO specialists

LinkedIn’s findings align with recommendations from AI platforms themselves. Independent conclusions from both content sources and answer-generating platforms suggest that stable visibility principles are forming in AI search. This reduces uncertainty and provides clearer optimization guidelines for the market.

Main trend of the week: fragmentation of Google’s ecosystem

Recent updates show that Google is no longer a single system to monitor. The company is separating Discover and Search updates, developing standalone ad formats in AI Mode, defining policies for how bots interact with content, and intervening in crawling issues at the plugin level.

At the same time, the market is receiving new traffic measurement channels, including those for LLM sources. Previously, one Search Console traffic graph could provide a reasonable performance overview. Today, analytics is fragmented across Discover, Search, AI Mode, and AI-generated traffic sources.

Conclusion

For marketers, these changes signal a gradual shift from the traditional SEO and paid search model to a multi-channel search visibility ecosystem. AI search is already reshaping traffic structures: the share of non-branded informational traffic may decline, especially in B2B, increasing the importance of brand awareness, expertise, and source trust.

Content quality and structure are becoming critically important. Materials with clearly defined authorship, verified expertise, publication dates, and logical structure are significantly more likely to appear in AI-generated answers. At the same time, new traffic sources are emerging that must be analyzed separately — traffic from LLMs, AI Mode, Discover, and traditional search are increasingly diverging in user behavior and conversion impact.

SEO can no longer be treated as a single channel. Digital presence strategies must simultaneously account for search, Discover, AI answers, and conversational search. As a result, analytics is becoming more complex: one Search Console graph is no longer sufficient — traffic must be analyzed across environments and user interaction scenarios.

In the new reality, depth of expertise, originality, and practical content value become key assets. Search and AI systems increasingly prioritize materials that fully solve user problems rather than content optimized only for specific keywords. Overall, marketing is shifting from query optimization toward intent, context, and real user needs.

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