YouTube introduces artificial intelligence-powered remixing tool that allows creators to generate new video segments for existing Shorts content.
YouTube announced on September 26, 2025, the launch of Extend with AI, a new feature that enables creators to add artificial intelligence-generated segments to existing Shorts videos. According to Meaghan from TeamYouTube, the feature provides creative suggestions to help users add new segments to eligible Shorts, expanding the platform’s remix capabilities beyond traditional editing tools.
The new capability allows creators to select moments from existing Shorts and choose from AI-generated prompts that describe potential continuations. Once a creator picks a video segment up to 5 seconds in length and selects a suggested prompt, the system generates a new segment that can be incorporated into their own Short. The remixed creation becomes a new Short while preserving the original creator’s video unchanged.
Most Shorts that have opted into video remix features qualify for the Extend with AI functionality. Creators who prefer to exclude their content from AI remixing can opt out following existing instructions within YouTube’s creator settings. The feature builds upon YouTube’s established remix tools, including the Cut feature that already allows creators to add their own endings to Shorts.
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The Extend with AI feature generates video segments typically lasting 8 seconds or less. Creators can trim these generated segments directly within Shorts Creation tools to achieve their desired length. During the preview stage after draft generation, users can add music from YouTube’s curated audio track library or return to the camera interface to record additional video content.
The system generates prompts based on the specific video content selected for remixing. This tailored approach ensures that suggested continuations align with the original video’s context and style. Creators can reorder clips within the editing interface and combine AI-generated segments with traditionally recorded content.
Generated videos receive an AI label in their description, consistent with YouTube’s labeling practices for other artificial intelligence tools within the Shorts ecosystem. Every Short created using Extend with AI links back to the original creator’s video from the Shorts player, providing discovery opportunities for both the original creator and the remixing creator.
Extend with AI will roll out gradually to all countries outside the European Union and United Kingdom over the coming months. Initially, the feature operates exclusively in English, with YouTube planning to expand both language and country availability in future updates. The phased rollout approach mirrors YouTube’s deployment strategy for other Shorts features, allowing for technical optimization and user feedback integration.
The feature’s exclusion from EU and UK markets aligns with recent regulatory considerations affecting AI-powered content generation tools. YouTube has faced scrutiny over AI enhancement practices that modify creator content without explicit consent, prompting more careful implementation approaches in regulated markets.
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YouTube’s introduction of Extend with AI represents the latest advancement in the platform’s artificial intelligence integration strategy. The company previously launched Dream Track, which generates instrumental soundtracks for Shorts using AI technology, and Dream Screen, enabling AI-generated video backgrounds through text prompts.
The timing coincides with YouTube’s extension of Shorts duration from 60 seconds to 3 minutes in October 2024, providing creators with expanded creative possibilities. These duration changes affected videos uploaded after October 15, 2024, with a gradual rollout process spanning several weeks.
Recent updates to Shorts editing capabilities have included layout modification features for converting long-form clips and enhanced remix options. The platform has also introduced template functionality, allowing creators to recreate popular Shorts formats while adding personal elements.
The Extend with AI feature complements YouTube’s existing remix toolset, which includes audio remixing, video cutting, green screen backgrounds, and collaboration features. Creators can already remix content from music videos, extracting audio tracks, using green screen backgrounds, or creating collaborative content alongside original videos.
YouTube’s remix capabilities have extended to official music videos from major artists, enabling creators to use snippets as soundtracks or visual elements in their own content. The platform’s integration with Official Artist Channels provides access to popular sounds sections where creators can sample audio for their Shorts.
All remixed content maintains attribution to original creators through direct linking in the Shorts player. This attribution system ensures that popular remixes can drive viewership back to source videos, creating mutual benefits for both original creators and remixing users.
The introduction of AI-generated content continuation presents new opportunities for overcoming creative blocks while maintaining personal creative expression. The feature specifically targets moments when creators experience inspiration from existing content but struggle to develop continuation concepts independently.
YouTube’s implementation emphasizes collaboration between human creativity and artificial intelligence assistance rather than complete automation of content creation. Creators retain control over prompt selection, segment timing, and final editing decisions while benefiting from AI-generated visual elements.
The feature’s impact on content discovery patterns remains to be observed as creators begin incorporating AI-generated segments into their content strategies. YouTube’s algorithm recommendations for AI-enhanced content may influence which remixed Shorts gain prominent placement within the platform’s discovery systems.
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YouTube’s expansion of AI-powered creation tools occurs alongside developments in Shorts advertising capabilities. The platform has integrated Shorts ads into efficient reach line items within Display & Video 360, automatically including Shorts inventory in advertiser campaigns seeking maximum reach.
Recent changes to Shorts view counting methodology implemented on March 31, 2025, removed minimum watch time requirements, potentially affecting how AI-enhanced content performs within YouTube’s metrics systems. However, monetization eligibility continues to rely on engaged views rather than total view counts.
The platform’s Creator Fund and revenue sharing programs for Shorts creators remain based on traditional engagement metrics rather than AI enhancement utilization. This approach maintains focus on audience interaction quality over technological tool adoption.
YouTube’s Extend with AI launch positions the platform competitively against TikTok’s AI-powered creation features and Instagram’s Reels enhancement tools. The integration of Google DeepMind’s technology provides YouTube with advanced AI capabilities that distinguish its offering from competitors relying on third-party AI providers.
The feature’s attribution requirements and opt-out mechanisms address creator concerns about unauthorized content usage that have emerged across social media platforms implementing AI features. YouTube’s approach emphasizes transparency and creator control over content utilization, potentially setting industry standards for responsible AI implementation.
The gradual rollout strategy allows YouTube to monitor creator adoption patterns and technical performance before full global deployment. This measured approach reflects lessons learned from previous feature launches that experienced scaling challenges or creator resistance.
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Who: YouTube creators and the TeamYouTube team, specifically Meaghan from TeamYouTube who announced the feature
What: Launch of Extend with AI, a new feature allowing creators to add AI-generated video segments to existing Shorts using creative prompts and machine learning technology
When: Announced on September 26, 2025, with gradual rollout beginning immediately over the coming months
Where: Available globally except in European Union and United Kingdom markets, initially in English language only
Why: To help creators overcome creative blocks, expand remix capabilities, and provide new tools for content creation while maintaining attribution to original creators and enabling new audience discovery opportunities

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Search Engine Land » Guides » What the AI Visibility Index tells us about LLMs & search
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Generative AI is reshaping how people search and discover information, making traditional SEO metrics like rankings and click-through rates incomplete. 
To address this, Semrush’s AI Visibility Index provides a holistic view of marketing visibility, layering LLM visibility data alongside traditional search. It reports on both global insights and industry-specific breakdowns, helping marketers see the bigger picture of where their brands stand.
The numbers speak for themselves: ChatGPT alone sees over 800 million active users weekly and more than 2.5 billion prompts daily, much of which is invisible to conventional analytics. That’s a lot of potential audience engagement. 
With AI-powered search experiences—from Google’s Search Generative Experience to ChatGPT, Claude, and Perplexity—brands now need to think about “LLM visibility,” or how often they surface in AI-generated responses. Understanding and optimizing for this new dimension is quickly becoming a core part of brand tracking and digital strategy.
It’s tempting to think that if you’re crushing it at E-E-A-T, you’ll see killer rankings in the SERPs and you’ll be frequently mentioned in LLMs as well as cited as a source.
Think again. You’ll need a more refined approach.
We already know that AI frequently cites lower-ranking search results. But the way AI handles specific brands is even more nuanced. That same Index found that fewer than 25% of the most mentioned brands were also the most sourced.
Take B2B SaaS leader Zapier. The Index found that it’s the #1 cited search in digital technology and software, but only #44 in brand mentions.
What are LLMs picking up on? The answer lies in the types of assets associated with the brand’s presence. Zapier maintains a large library of integration guides and tutorials, giving it strong authority for AI training since it’s effectively a repository of facts. But when people talk in reviews and discussions, competing brands tend to come up more often.
Publishing a ton of content on your site alone is not enough. LLMs also rely on specific platforms as sources they’ve identified as authorities, providing a knowledge foundation. New information is compared to and understood in terms of this foundation. Your presence on these platforms can also help your visibility.
Why does this matter? A single mention in an AI response might carry more weight than a traditional search result because it’s presented as a curated, authoritative recommendation rather than one of many options. It also might reach a more motivated audience that’s closer to conversion. 
Data also shows that the weighting of sources varies by LLM as well as by industry. For example, while ChatGPT tends to rely on Wikipedia and Reddit as major sources, Google AI Mode shows way more variation.
These snapshots of industry trends are powerful starting points for your work and can help illuminate next steps for the next 90 days. But keeping close tabs on your competitors in both the SERPs and LLMs will help you not only directly outpace them, but could also give you insights into trends and headwinds.
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This checklist highlights practical steps you can start on now to make your content and brand more discoverable in AI-powered environments. From technical SEO adjustments to content rewrites and community engagement, these actions help ensure your entity is understood, cited, and trusted by both search engines and generative AI platforms.
AI-driven crawlers such as GPTBot (OpenAI), CCBot (Common Crawl), and Claude-We (Anthropic) function similarly to traditional search engine bots, but their role is to feed information into large language models. 
If your robots.txt file accidentally blocks them, whether through overbroad disallow rules or inherited directives, you’re effectively shutting the door on your content being indexed for use in generative AI platforms. 
This doesn’t just affect training data; it also limits your chances of being surfaced in real-time responses, summaries, and AI-powered search features. 
Regularly auditing your robots.txt file, testing crawler access, and aligning permissions with your visibility goals helps ensure that your brand’s knowledge remains accessible where discovery increasingly happens: inside AI-driven engines and assistants.
Many AI systems, like traditional crawlers, still have difficulty parsing client-side rendered content that depends heavily on JavaScript. If your critical text, product information, or structured data only appears after scripts execute, there’s a strong chance it won’t be fully captured or indexed by AI crawlers. Server-side rendering (SSR) solves this by delivering pre-rendered HTML directly from the server, ensuring that the essential content is visible at the time of crawl. 
This approach not only improves accessibility for AI systems like ChatGPT or Perplexity but also enhances performance for users on slower connections. By implementing SSR for high-value pages—such as product detail pages, core service offerings, and FAQ hubs—you create a consistent, crawlable foundation that boosts visibility in both traditional search results and generative AI outputs.
Using elements like <article>, <section>, <nav>, and <header> provides explicit signals about the role and relationship of different blocks of content. For example, wrapping a blog post in <article> tells crawlers this is a standalone piece of information, while <aside> can mark supporting context.
Heading hierarchies (H1 through H6) work the same way: they create a logical outline of your content. An H1 defines the page’s core topic, H2s break it into key themes, and H3s or H4s drill into supporting points. AI crawlers use these patterns to parse and extract information accurately, which directly influences whether your content can be cited in generative responses. Pages that rely only on visual styling (bold fonts, larger text) without semantic tags risk being misinterpreted or overlooked entirely.
By consistently applying semantic HTML and clear heading structures, you make your site more machine-readable, improve content extractability, and increase the odds that both search engines and AI platforms pull your information into summaries, snippets, and conversational answers.
When you test your priority topics in AI platforms such as ChatGPT, Claude, or Perplexity, you’re essentially auditing the sources those systems deem trustworthy and authoritative. Pay close attention to which competitors, publications, or data sets are consistently cited in responses. This reveals the knowledge graph you’re competing against—the ecosystem of entities, sources, and relationships that AI models use to assemble answers.
If your competitors’ blogs, research reports, or even forum contributions appear but your brand does not, that’s a signal of a content or authority gap. It may mean you need to produce more in-depth resources, pursue stronger backlinks and citations, or distribute expertise across third-party sites that AI systems favor. Over time, this type of monitoring shows patterns: which types of sources (academic studies, media outlets, niche blogs, Q&A forums) carry the most weight and where you can strategically position your brand to earn visibility.
In practice, this exercise turns AI outputs into a form of competitive intelligence, helping you benchmark your brand’s presence in generative ecosystems and prioritize the content formats and authority signals that AI models actually reward.
AI optimization (AIO) tools like the AI SEO Toolkit extend beyond traditional SEO analytics by tracking how your brand shows up inside generative platforms. Instead of just measuring keyword rankings or backlinks, these tools reveal your presence across AI assistants and AI-powered search, showing how often your brand is cited compared to competitors, what topics are working for you, and what prompt opportunities are out there. They also surface mentions in AI-generated outputs, letting you see whether platforms like ChatGPT, Claude, or Perplexity are pulling your brand into responses, and in what context.
Equally important is sentiment analysis. AIO tools can detect whether your brand is described positively, negatively, or neutrally within these generative answers. This helps you understand not only how visible you are, but also how you’re being framed in the conversations that influence decision-making. 
For example, if competitors are cited more frequently or framed more favorably, it highlights opportunities to strengthen your authority signals through better content, reviews, or media coverage.
You also see Share of Voice alongside sentiment. In traditional marketing and SEO, Share of Voice measures what percentage of the overall conversation or visibility your brand commands compared to competitors. In the world of AI optimization tools, it works the same way, but the “conversation” is happening inside generative platforms like ChatGPT, Claude, Gemini, or Perplexity.
For example, if you ask an AI assistant 100 queries in your category (say, “best project management software” or “how to reduce cloud costs”), and your brand is cited or mentioned in 20 of those answers, your Share of Voice would be 20%. The tools automate this at scale: They track how often your brand appears, how it’s positioned (top citation vs passing mention), and how that compares to key competitors.
This metric is powerful because it tells you how much space your brand occupies in AI-driven discovery ecosystems, where traditional ranking reports can’t reach. A rising Share of Voice means AI platforms increasingly trust and surface your content, while a declining one is an early warning that competitors are capturing attention in generative results. In the AI SEO Toolkit, you can dive deeper into what’s driving positive or negative sentiment and figure out what’s needed to put your brand in the best light, authentically.
In short, these tools give you a new layer of competitive intelligence—one that reflects the realities of AI search. By monitoring visibility, mentions, and sentiment, you can benchmark your standing, identify gaps, and adapt your strategy to stay discoverable and credible in the spaces where users are increasingly finding answers.
LLMs  are designed to surface clear, factual, and verifiable information. When content leans too heavily on buzzwords or vague promises—“industry-leading,” “cutting-edge,” “best-in-class”—AI systems have little substance to work with and are less likely to extract or cite it. What resonates instead are specifics: quantifiable data, well-attributed statistics, expert commentary, and case studies that provide evidence.
For example, instead of writing “Our platform improves efficiency,” you might say “Our platform reduces average processing time by 37%, based on a study of 500 enterprise clients.” Similarly, weaving in expert quotes from industry leaders or referencing reputable sources signals to AI that your content is trustworthy and grounded in fact. This not only increases your odds of being cited in AI-generated responses but also builds credibility with human readers who demand more than marketing spin.
Ultimately, the more concrete, evidence-backed language you embed in your content, the more LLMs will view it as useful material to pull into conversational answers, summaries, and overviews—directly boosting your visibility in generative search.
Adding FAQ sections to your most important pages—such as product, service, and topic hubs—bridges the gap between how people ask questions and how AI systems deliver answers. Users often type or speak their queries in a natural, conversational way (“How much does this cost?” “Is this safe for beginners?”), and AI platforms are built to recognize and respond to this Q&A structure. By anticipating these questions and embedding them into your content, you make it easier for generative models to parse, extract, and cite your information.
FAQs also help you capture long-tail queries that may not justify standalone pages but are still valuable for traffic and visibility. When optimized with clear, concise answers—and, where relevant, enriched with structured data markup—they can feed directly into AI Overviews, featured snippets, and conversational responses. This not only boosts your chances of being quoted but also positions your site as a reliable resource when users are in research or decision-making mode.
Over time, well-crafted FAQs strengthen your site’s role as a conversational authority, ensuring that both search engines and AI-driven platforms recognize your content as aligned with the way people actually ask questions.
Active participation in niche online communities like Reddit, Quora, and GitHub not only builds trust with human audiences but also increases your footprint in the sources that AI systems frequently draw from. These platforms are rich with Q&A-style content, practical solutions, and peer-to-peer insights, which are exactly the kind of material large language models index and surface in generative answers.
The key is to engage authentically. Instead of dropping links or promotional soundbites, focus on answering questions thoroughly, sharing unique perspectives, or contributing code snippets and documentation where relevant. Over time, your contributions can gain upvotes, visibility, and citations, signaling authority both to users and to AI models trained on that content.
By embedding your expertise into these high-signal communities, you effectively plant seeds of authority across the web. These contributions not only drive referral traffic and brand recognition but also increase the likelihood that your insights will be quoted, paraphrased, or referenced in AI-generated responses, extending your reach well beyond your owned channels.
Reviews act as social proof not only for people but also for AI systems. Generative models scan sentiment signals from platforms like Google, G2, Trustpilot, Yelp, or industry-specific directories, and they often incorporate those perspectives into summaries and recommendations. If most of the visible reviews about your brand are negative or if you lack reviews altogether, AI may interpret your entity as less credible, reducing the likelihood of being favorably cited in conversational search.
The solution is to proactively encourage authentic reviews from satisfied customers and manage feedback consistently. Prompting clients at the right moment in their journey, simplifying the review process, and addressing complaints constructively all help shift sentiment in your favor. Positive patterns of feedback create a reputation signal that both users and AI can trust, while thoughtful responses to negative reviews demonstrate accountability.
Over time, this builds a reputation moat: a body of credible, third-party validation that AI systems draw from when generating answers. In practice, that means your brand isn’t just trusted by human audiences; it becomes algorithmically trusted, increasing your chances of being surfaced, recommended, and cited across AI-driven platforms.
Media coverage functions as a high-value trust signal in both traditional SEO and AI-driven discovery. Mentions in reputable outlets—whether through press articles, interviews, podcast appearances, or industry features—are disproportionately influential because AI models weigh these sources more heavily than self-published content. When your brand is cited by journalists or discussed in respected media, that reference becomes part of the authoritative data pool models use to generate responses.
Strategically, this means pursuing earned media partnerships should go beyond PR vanity—it’s a visibility play in the era of generative AI. Pitching thought leadership pieces, offering expert commentary, or collaborating on podcast discussions not only introduces your brand to new audiences but also creates durable citations that AI systems are more likely to trust and replicate.
By cultivating these relationships, you build a citation footprint that extends across different formats (articles, transcripts, audio summaries). Each mention increases the probability that your brand surfaces in AI overviews, conversational answers, and knowledge graph associations, reinforcing your authority in the spaces that matter most.
Entity-based clustering takes the traditional SEO concept of topic clusters and adapts it for the AI-first search environment. Instead of treating each page as a standalone asset, you organize content around well-defined entities—people, products, industries, or concepts—that map directly to how AI systems structure knowledge. A strong cluster typically includes a pillar page that defines the entity, supported by subpages, FAQs, and related resources that cover its attributes, use cases, and connections to other entities.
This structure makes it easier for AI systems to understand relationships and context. For example, a cybersecurity brand that builds a cluster around “Zero Trust Security” would include subtopics like authentication methods, case studies, regulatory standards, and common FAQs. By interlinking these assets, you give both search engines and LLMs a clear semantic map, reinforcing your expertise in that domain.
The payoff is twofold: Your brand is more likely to be recognized as an authoritative source within that knowledge space, and your content becomes more extractable, meaning AI platforms can easily pull accurate, well-structured snippets into summaries, answers, and generative search results. In short, entity-based clusters teach AI systems exactly who you are and why you’re credible.
The shift toward AI-mediated information discovery represents the most significant change in search behavior since the advent of Google. Developing competency in LLM visibility tracking and optimization will ensure your brand remains discoverable and authoritative in the new landscape of AI-powered information consumption.
As these tools and methodologies continue to mature, the brands that invest early in understanding and optimizing for LLM visibility will have significant competitive advantages in the AI-first world that’s rapidly approaching. Dive deeper into the AI Visibility Index or go straight into exploring your LLM visibility.
Andrea Pretorian
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A sneak peek of Google’s new Google Home app design reveals that Gemini will be front and center as the company transforms its new products with artificial intelligence. Smart House, you will always be famous.
Google Home v3.41.50.3, a forthcoming app update, includes a significant UI overhaul, according to an exclusive by Android Authority. The app will now feature Gemini and Gemini Live (not Google Assistant) front and center, which allows users to fully automate their Google Home settings using the AI assistant. Simply “Ask Home.”
Users will be able to use the new Ask Home search bar to do just that, and can oversee their preferred devices, automations, and environmental feedback on a revived “favorites” page. Google’s also added tiles for expected video and thermometer add-ons. The app comes with fresh icons, cleaner pages, and important privacy disclosures for Gemini Home’s data collection, too — keep your eyes peeled.
Google is set to announce a new line of smart products in October, including a new Nest speaker with built-in Gemini, a new Nest doorbell, and a series of Nest cams for a fully rounded smart home setup. In April, Google announced it would be ending support for first—and second-generation Nest Thermostats, which was explained as a necessary move to focus on supporting newer hardware.
In addition to the hardware upgrades, Google Home users can expect new AI-powered functions and features, such as daily summaries and AI event descriptions, first demoed at the company’s Made by Google event in August.
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Chase joined Mashable’s Social Good team in 2020, covering online stories about digital activism, climate justice, accessibility, and media representation. Her work also captures how these conversations manifest in politics, popular culture, and fandom. Sometimes she’s very funny.

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