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YouTube is preparing a significant policy update aimed at tackling the flood of low-quality, AI-generated “mass-produced” videos on its platform. Starting July 15, 2025, the YouTube Partner Program (YPP) will enforce stricter guidelines, requiring content to be “significantly original and authentic” to remain eligible for monetization.
For creators relying on AI tools to generate content at scale, this change marks a clear signal: YouTube is taking steps to protect its ecosystem from what has been described across the industry as “AI slop” – low-quality, repetitive, and spam-like videos that have proliferated with the rise of generative AI tools.
While YouTube has always required “original and authentic” content for monetization, the new policy aims to better define what counts as “inauthentic” in the age of easily accessible AI. According to Rene Ritchie, YouTube’s Head of Editorial & Creator Liaison, the update is a “minor clarification” rather than a sweeping overhaul, but it will help the platform better identify mass-produced or repetitive content that fails to add human value.
Examples of the type of content at risk of demonetization include videos composed solely of text-to-video AI clips, stolen or recycled footage, automated voiceovers over stock images, and channels posting large quantities of low-effort AI-generated material.
The updated policy comes amid growing concern over AI-generated spam flooding YouTube and other platforms. As generative AI tools like Google Veo 3 make it easy to produce video content with minimal human input, the platform faces challenges in maintaining the visibility of higher-quality, human-created content.
Instances of fully AI-generated true crime series going viral, deepfake scams using the likeness of public figures, and fake AI-generated news videos drawing millions of views have underscored the need for clearer guidelines to prevent misuse of monetization systems.
Not necessarily. YouTube clarified that AI-assisted content can still be monetized if it is unique, significantly transformed, and provides added human value. The policy aims to filter out spam and low-quality repetitions, not to ban creators from using AI as a tool within their workflow, provided the final product meets YouTube’s originality standards.
While the exact language of the policy update has yet to be released, creators should evaluate their workflows if they rely heavily on automated content generation. Channels posting large volumes of AI-generated content without substantial human curation or transformation may see monetization revoked under the updated YPP guidelines.
In short: the update does not ban the use of AI but enforces that authentic, human-led creativity remains central to monetizable content on YouTube.
For filmmakers using YouTube as part of their distribution, marketing, or community-building strategy, this policy shift is unlikely to affect high-quality, original work. However, it serves as a reminder that even AI-assisted workflows – such as using generative tools for B-roll variations, script ideation, or quick captions – must result in content that is uniquely your own. If you’re experimenting with AI-generated explainer clips, stock replacements, or news updates for your channel, ensure that your voice, perspective, and editorial oversight shape the final product. YouTube’s updated enforcement is a clear signal that the platform intends to safeguard content that demonstrates human intention and craftsmanship – aligning with the standards most of us already uphold. I think it‘s a long overdue step in the right direction!
What do you think about YouTube’s move to curb AI-generated spam? Is this a necessary step to protect human creativity on the platform, or could it hamper creators who responsibly use AI in their workflows? Let us know in the comments below.


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Nino Leitner, AAC is Co-CEO of CineD and MZed. He co-owns CineD (alongside Johnnie Behiri), through his company Nino Film GmbH. Nino is a cinematographer and producer, well-traveled around the world for his productions and filmmaking workshops. He specializes in shooting documentaries and commercials, and at times a narrative piece. Nino is a studied Master of Arts. He lives with his wife and two sons in Vienna, Austria.
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Catch up on select AI news and developments from the past week or so (in no particular order):
DeepSeek releases upgraded V3 model, escalating competition with OpenAI. Chinese AI startup DeepSeek has launched an upgraded version of its V3 large language model, enhancing reasoning and coding abilities. Published on Hugging Face, the new model shows strong performance across technical benchmarks. This release follows earlier models, including V3 and R1, positioning DeepSeek as a serious contender to OpenAI and Anthropic. The company emphasizes lower operational costs, helping it compete globally. The release underscores China’s growing momentum in foundational AI research and deployment. Importance for marketers: DeepSeek’s advancements may broaden options for generative AI tools and reduce dependency on US models, especially for cost-conscious marketing programs.
Tencent unveils T1 reasoning model to advance AI capabilities in China. Tencent has launched the official version of its T1 reasoning model, aimed at improving speed and long-text comprehension. Powered by Tencent’s Turbo S foundational model, T1 reportedly outperforms DeepSeek’s R1 in reasoning benchmarks and maintains a low hallucination rate. The release follows a preview launch and comes amid intensified competition in China’s AI sector. Tencent plans to further increase AI investment in 2025, continuing its aggressive 2024 spending. The model also powers applications like Tencent’s AI assistant Yuanbao, with a focus on clarity, logic, and accuracy in generated content. Importance for marketers: Tencent’s AI advancements may influence content quality, personalization, and natural language processing (NLP) tools in China’s digital marketing ecosystem.
OpenAI integrates AI image generation directly into ChatGPT. OpenAI now allows users to generate images directly within ChatGPT using its 4o model, eliminating the need for DALL-E. Free and paid users can access the feature, with enterprise and education rollout expected soon. The new capability focuses on business-relevant visuals like infographics and diagrams, offering improved text rendering. Though editing limitations remain, OpenAI is working on updates. Images lack visible watermarks but include C2PA metadata. Safety policies mirror those in the 4o model. Importance for marketers: This integration streamlines visual content creation inside a familiar interface, making image generation more accessible for campaign design and presentations.
Meta used pirated books to train AI, report reveals. A report by The Atlantic confirms Meta trained its AI using pirated books from shadow library LibGen, which holds millions of copyrighted titles. Internal emails show Meta prioritized these datasets, viewing them as crucial to model development. Authors have found their work used without consent. The report raises ethical concerns about IP rights and reveals tensions between AI development and traditional content industries. Importance for marketers: Trust and transparency issues in AI training may influence how marketers evaluate and deploy AI tools, especially in content creation and brand safety contexts.
xAI launches image generation API to enter visual AI space. Elon Musk’s xAI has released an image generation API that allows users to create images from text prompts using its “grok-2-image-1212” model. The tool generates up to 10 JPG images per request and charges $0.07 per image. Although limited in customization for now, xAI plans further updates. This launch aligns with xAI’s broader AI expansion strategy, including video generation, data center growth, and a $10 billion funding round that could raise its valuation to $75 billion. The move positions xAI to compete with image tools like Ideogram and Black Forest Labs. Importance for marketers: Marketers may gain another competitive tool for producing visual content efficiently across platforms.
Mistral CEO predicts AI will raise GDP of every country by double digits. Arthur Mensch, CEO of French AI startup Mistral, said AI will increase the GDP of every country by double digits in coming years. Speaking on the A16z podcast, he urged nations to develop sovereign AI infrastructure, warning of dependence on foreign systems. Mensch compared AI’s economic impact to electricity and emphasized the cultural and strategic significance of controlling national AI systems. He also reaffirmed Mistral’s commitment to open-source models, arguing they accelerate innovation. Mistral is competing with OpenAI, DeepSeek, and Anthropic, and was recently valued at $6.2 billion. Importance for marketers: The push for sovereign AI and open-source models may influence regional data control, tool availability, and marketing strategy localization.
Media agencies expand AI use but remain skeptical of AI-generated creative. Media agencies are increasingly using AI to automate tasks like media planning and campaign analysis, but they remain disappointed with AI-generated creative. At Digiday’s Media Buying Summit, Wpromote and Media By Mother executives shared how AI improves reporting and budget allocation yet falls short in producing quality creative assets. Agencies are also exploring new tools like, Scope3’s agentic media platform, which promotes transparency in AI-based buying. The shift is freeing teams for more strategic work, though concerns over creative quality and platform transparency persist. Importance for marketers: Marketers should monitor evolving AI tools in media buying while maintaining creative oversight for brand integrity and differentiation.
Perplexity makes public bid to acquire and rebuild TikTok with open AI. AI search startup Perplexity has proposed acquiring TikTok’s US operations and rebuilding its recommendation algorithm transparently and under US oversight. The plan includes open-sourcing the algorithm, enhancing personalization, and integrating Perplexity’s search tech. Perplexity suggests upgrades such as multilingual translation and video citation. The bid comes as TikTok faces legal uncertainty in the US and amid competition from Oracle and Microsoft. Analysts question the bid’s seriousness, noting Perplexity’s prior publicity stunts and its $18 billion valuation compared with TikTok’s estimated $30–50 billion. Importance for marketers: If taken seriously, this bid could shift content discovery and transparency in social video platforms, impacting campaign targeting and brand engagement.
China narrows AI gap with US to three months, says 01.AI’s Lee Kai-fu. Lee Kai-fu, founder of 01.AI and former Google China chief, said Chinese firms like DeepSeek have reduced the AI technology gap with the US to about three months. Using more efficient algorithms and less advanced chips, DeepSeek’s innovations show China is catching up or even leading in specific areas like infrastructure software. Lee argues that US semiconductor sanctions pushed Chinese companies to innovate under constraints. 01.AI is focusing on AI applications over foundational models to gain market traction. Importance for marketers: A narrowing AI gap may lead to faster regional AI innovation cycles, affecting the availability and cost of marketing technologies globally.
Google unveils Gemini 2.5, its most advanced AI model to date. Google has introduced Gemini 2.5 Pro, its most advanced AI model focused on enhanced reasoning, coding, and multimodal capabilities. The model outperforms competitors in benchmarks like LMArena, AIME 2025, and SWE-Bench Verified. It features a one-million-token context window and can process diverse data types. Available in Google AI Studio and the Gemini app, Gemini 2.5 Pro is positioned for high-complexity tasks such as scientific analysis and agent development. Enterprise-grade deployment on Vertex AI is expected soon. Importance for marketers: Gemini 2.5 could improve marketing automation, customer segmentation, and complex analytics across multimodal content formats.
Gemini gains real-time video and screen-reading capabilities on mobile. Google has begun rolling out Astra-powered features to Gemini Live, enabling the assistant to interpret live smartphone camera feeds and on-screen content. These updates are part of the Google One AI Premium plan for Gemini Advanced subscribers. Demonstrations show users querying the AI about their surroundings or phone screens in real-time. This feature set marks a step ahead of Amazon’s Alexa Plus and Apple’s delayed Siri upgrade. Gemini remains the default AI assistant on Samsung phones, offering wide access. Importance for marketers: Real-time camera and screen interaction could create opportunities for immersive product experiences and real-world engagement through AI assistants.
Google’s Gemini Gems now available to free users on mobile. Google has expanded access to Gemini Gems, its customizable AI personas, to free users on Android and iOS. Five prebuilt Gems are available, including tools for writing, coding, and learning. Users can also create their own Gems with specific instructions, behavior rules, and reference files. Creation remains limited to the Web, but usage is now possible on mobile apps. This follows broader feature expansions, such as document uploads and deep research. Importance for marketers: Custom Gems can be configured to support branded AI interactions or task automation, offering potential efficiency boosts in content and campaign workflows.
Microsoft 365 Copilot adds Researcher and Analyst AI agents. Microsoft has introduced two AI agents, Researcher and Analyst, within Microsoft 365 Copilot. Researcher delivers insights by combining internal work data with external sources to support strategy and reporting. Analyst uses OpenAI’s o3-mini model and Python to analyze complex data, generate visualizations, and deliver forecasts. These agents will roll out to Frontier program users in April. Microsoft is also enhancing Copilot Studio with agent flows and autonomous agent development features. Importance for marketers: These agents may help marketers analyze campaign data, discover trends, and develop data-driven strategies faster within familiar productivity tools.
Anthropic wins early round in music publishers’ AI copyright lawsuit. A US judge denied music publishers’ request to stop Anthropic from using copyrighted lyrics to train its Claude chatbot. Universal Music Group and others alleged unauthorized use of lyrics from hundreds of songs. The court found the injunction request too broad and unsupported by proof of irreparable harm. The broader lawsuit continues and hinges on fair use. This case is part of a larger wave of AI-related copyright litigation. Importance for marketers: The outcome may shape how AI models access and use copyrighted material, influencing content safety and compliance in AI-generated marketing outputs.
Deloitte opens global AI simulation center in India to support enterprise innovation. Deloitte has launched a Global AI Simulation Centre of Excellence in Bengaluru, part of its $3 billion investment in generative AI. The center supports real-time simulations, strategic modeling, and decision insights across sectors like IoT, robotics, and the metaverse. It specializes in physical, process, people, and strategic simulations. Deloitte aims to help clients improve efficiency and reduce risk through advanced visualizations and multi-agent systems. The center reflects India’s growing AI footprint and Deloitte’s global expansion strategy. Importance for marketers: Simulation capabilities could enable more precise campaign modeling, customer journey planning, and market forecasting across enterprise marketing environments.
Judge allows New York Times copyright lawsuit against OpenAI to proceed. A federal judge ruled that The New York Times’s copyright lawsuit against OpenAI and Microsoft can move forward. Though some claims were dismissed, the core allegations—that millions of articles were misused to train AI—remain intact. The court did not yet address the question of fair use. The publishers argue that AI companies are stealing their work and harming the news business. OpenAI maintains its models were trained using publicly available data under fair use. Importance for marketers: The case may shape legal boundaries for AI training, impacting data sourcing practices and content usage in marketing applications.
OpenAI and Meta explore AI partnerships with India’s Reliance. OpenAI and Meta have discussed AI partnerships with India’s Reliance Industries, including options for distributing ChatGPT through Reliance Jio and selling AI models via APIs. Talks also included hosting OpenAI models in a massive Jamnagar data center to keep Indian customer data localized. OpenAI may lower ChatGPT subscription pricing, and both firms are exploring enterprise use cases. The partnership could make advanced AI tools more accessible across India’s enterprise landscape. Still under discussion, the potential collaborations signal deeper AI integration into Indian digital infrastructure. Importance for marketers: This move could open new channels for AI-powered marketing, especially in India’s vast and rapidly digitizing market.
 
You can find the previous issue of AI Update here.
Editor’s note: GPT-4o was used to help compile this week’s AI Update.
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AI Update, March 28, 2025: AI News and Views From the Past Week
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Download your cheat sheet and checklist to start building content that works harder.
Your Competitors Have Already Started.
You’ll learn why traditional SEO tactics still matter, how query fanout shapes which documents are selected, and what makes content truly quotable.
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Google’s AI Mode is reshaping SERP rankings. Discover how embedding vectors, not keywords, now determine visibility.
This post was sponsored by MarketBrew. The opinions expressed in this article are the sponsor’s own.
Is Google using AI to censor thousands of independent websites?
Wondering why your traffic has suddenly dropped, even though you’re doing SEO properly?
Between letters to the FTC describing a systematic dismantling of the open web by Google to SEO professionals who may be unaware that their strategies no longer make an impact, these changes represent a definite re-architecting of the web’s entire incentive structure.
It’s time to adapt.
While some were warning about AI passage retrieval and vector scoring, the industry largely stuck to legacy thinking. SEOs continued to focus on E-E-A-T, backlinks, and content refresh cycles, assuming that if they simply improved quality, recovery would come.
But the rules had changed.
In late 2023 and early 2024, Google began rolling out what it now refers to as AI Mode.
AI Mode breaks content into passages, embeds those passages into a multi-dimensional vector space, and compares them directly to queries using cosine similarity.
In this new model, relevance is determined geometrically rather than lexically. Instead of ranking entire pages, Google evaluates individual passages. The most relevant passages are then surfaced in a ChatGPT-like interface, often without any need for users to click through to the source.
Beneath this visible change is a deeper shift: content scoring has become embedding-first.
Embedding vectors are mathematical representations of meaning. When Google processes a passage of content, it converts that passage into a vector, a list of numbers that captures the semantic context of the text. These vectors exist in a multi-dimensional space where the distance between vectors reflects how similar the meanings are.
Instead of relying on exact keywords or matching phrases, Google compares the embedding vector of a search query to the embedding vectors of individual passages. This allows it to identify relevance based on deeper context, implied meaning, and overall intent.
Traditional SEO practices like keyword targeting and topical coverage do not carry the same weight in this system. A passage does not need to use specific words to be considered relevant. What matters is whether its vector lands close to the query vector in this semantic space.

Keywords focus on exact matches. Embedding vectors focus on meaning.
Traditional SEO relied on placing target terms throughout a page. But Google’s AI Mode now compares the semantic meaning of a query and a passage using embedding vectors. A passage can rank well even if it doesn’t use the same words, as long as its meaning aligns closely with the query.
This shift has made many SEO strategies outdated. Pages may be well-written and keyword-rich, yet still underperform if their embedded meaning doesn’t match search intent.
The story isn’t just about Google changing the game, it’s also about how the SEO industry failed to notice the rules had already shifted.
As rankings dropped, many teams assumed they’d been hit by a quality update or core algorithm tweak. They doubled down on familiar tactics: improving E-E-A-T signals, updating titles, and refreshing content. They pruned thin pages, boosted internal links, and ran audits.
But these efforts were based on outdated models. They treated the symptom, visibility loss, not the cause: semantic drift.
Semantic drift happens when your content’s vector no longer aligns with the evolving vector of search intent. It’s invisible to traditional SEO tools because it occurs in latent space, not your HTML.
No amount of backlinks or content tweaks can fix that.
This wasn’t just platform abuse. It was also a strategic oversight.
SEO teams:
Many believed that doing what Google said, improving helpfulness, pruning content, and writing for humans, would be enough.
That promise collapsed under AI scrutiny.
But we’re not powerless.
Google told the industry to “focus on helpful content,” and SEOs listened, through a lexical lens. They optimized for tone, readability, and FAQs.
But “helpfulness” was being determined mathematically by whether your vectors aligned with the AI’s interpretation of the query.
Thousands of reworked sites still dropped in visibility. Why? Because while polishing copy, they never asked: Does this content geometrically align with search intent?
The new SEO playbook begins with a simple truth: you are optimizing for math, not words.
Here’s what we now know:
It depends on your goals; for success in SERPs, you need to focus on tools that not only show you visibility data, but also how to get there.
Profound was one of the first tools to measure whether content appeared inside large language models, essentially offering a visibility check for LLM inclusion. It gave SEOs early signals that AI systems were beginning to treat search results differently, sometimes surfacing pages that never ranked traditionally. Profound made it clear: LLMs were not relying on the same scoring systems that SEOs had spent decades trying to influence.
But Profound stopped short of offering explanations. It told you if your content was chosen, but not why. It didn’t simulate the algorithmic behavior of AI Mode or reveal what changes would lead to better inclusion.
That’s where simulation-based platforms came in.
Market Brew approached the challenge differently. Instead of auditing what was visible inside an AI system, they reconstructed the inner logic of those systems, building search engine models that mirrored Google’s evolution toward embeddings and vector-based scoring. These platforms didn’t just observe the effects of AI Mode, they recreated its mechanisms.
As early as 2023, Market Brew had already implemented:
🔍 Market Brew Tutorial: Mastering the Top Cluster Similarity Ranking Factor | First Principles SEO
This meant users could test a set of prompts against their content and watch the algorithm think, block by block, similarity score by score.
Where Profound offered visibility, Market Brew offered agency.
Instead of asking “Did I show up in an AI overview?”, simulation tools helped SEOs ask, “Why didn’t I?” and more importantly, “What can I change to improve my chances?”
By visualizing AI Mode behavior before Google ever acknowledged it publicly, these platforms gave early adopters a critical edge. The SEOs using them didn’t wait for traffic to drop before acting, they were already optimizing for vector alignment and semantic coverage long before most of the industry knew it mattered.
And in an era where rankings hinge on how well your embeddings match a user’s intent, that head start has made all the difference.
SEO didn’t die. It transformed, from art into applied geometry.
AI Mode Visualizer Tutorial
To help SEOs adapt to this AI-driven landscape, Market Brew has just announced the AI Mode Visualizer, a free tool that simulates how Google’s AI Overviews evaluate your content:
🔗 Try the AI Mode Visualizer
This is the only tool that lets you watch AI Mode think.
Nate Hake is right: Google restructured the game. The data reflects an industry still catching up to the new playbook.
Because two things can be true:
It’s time to move beyond guesses.
If AI Mode is the new architecture of search, we need tools that expose how it works, not just theories about what changed.
We were bringing you this story back in early 2024, before AI Overviews had a name, explaining how embeddings and vector scoring would reshape SEO.
Tools like the AI Mode Visualizer offer a rare chance to see behind the curtain.
Use it. Test your assumptions. Map the space between your content and modern relevance.
Search didn’t end.
But the way forward demands new eyes.
________________________________________________________________________________________________
Image Credits
Featured Image: Image by MarketBrew. Used with permission.
Market Brew is a predictive SEO platform that models search engine behavior to help teams understand, test, and adapt to …
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Fabrice Canel, long time Microsoft employee and the Bing Search Product Manager, said the way you optimize for AI Search is by following SEO guidelines. He echoed Google’s statement that doing normal SEO works for AI Search.
Fabrice Cancel and his team wrote up this blog post named Keeping Content Discoverable with Sitemaps in AI Powered Search. In short, it goes over the importance of making sure you do what you can to ensure your content is fresh in Bing’s search index.
It recommends using:
(1) XML sitemaps
(2) Lastmod field in that sitemap, which Bing told us is critical previously
(3) IndexNow
I asked Fabrice, does this mean that “normal SEO for AI search,” like Google said and he replied, “Absolutely at 💯. Optimize for AI Search by following SEO guidelines.”
Here are those posts:

Absolutely at 💯. Optimize for AI Search by following SEO guidelines

— Fabrice Canel (@facan) July 31, 2025

Absolutely at 💯. Optimize for AI Search by following SEO guidelines
Again, when you ask a search engine – they will tell you that normal SEO works for AI Search because the way AI Search gets your content is through crawling and indexing. Being clicked on from AI Search is a different story, but making sure AI Search is aware of your content requires crawling and indexing.
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