The era of AI video generation on YouTube is dawning, and it seems creators are embracing it. Despite a wave of criticism related to the use of large language models (LLMs) for content scraping and flooding the platform with “junk,” a recent YouTube study reveals an unexpected turn. The platform is actively implementing Google Gemini products and other AI tools, opening new horizons for creators, and it turns out many of them are already actively experimenting with these technologies.
YouTube Creators: Is AI a Tool, Not a Threat?
The YouTube Culture and Trends team, in collaboration with NRG, surveyed American creators. The sample included 694 individuals who had uploaded videos in the past 12 months.
The results were surprising: 72% of respondents stated they already use AI for video and image work. Moreover, 61% expressed interest in using AI to integrate their digital twins into content.
How Creators Use Generative AI
Digital Avatars and Simulations: Creating “younger versions of themselves” for interactive scenarios, such as “hugging your younger self.” This opens up possibilities for animated AI characters and AI avatars with subtitles.
Emotion Visualization: Using AI to transform complex feelings into visually understandable and empathetic images.
Mass Video Generation: Although not emphasized in the report, creators’ interest in AI hints at the potential for mass video generation by neural networks and the creation of AI advertising videos. Imagine neuro-production by subscription, allowing hundreds of videos to be generated per day.
“For some experimenters, AI is a way to play with different aspects of their self-identity and imagine alternative versions of themselves. By using it to visualize their complex emotions, people transform confusing feelings into cathartic images that others can empathize with,” the YouTube report states.
Why Is YouTube So Actively Promoting AI?
It’s clear that YouTube has its own goals in actively promoting generative AI. The Culture and Trends team will naturally support the products the platform implements.
However, even with this in mind, the positive survey results show that not all creators are skeptical. Many see AI not as a threat, but as a powerful tool for creativity and scaling.
The Potential of AI for Content Creators
This research confirms that the future of content lies with AI. The opportunities opened up by neural networks are enormous: from creating AI advertising videos to neural networks for UGC advertising.
Now one can imagine an AI conveyor producing 500 videos per day for e-commerce or even AI UGC for a crypto project. The effectiveness of AI versus real UGC becomes a key indicator.
Frequently Asked Questions
How much does one AI creative cost?
The cost of 1 minute of AI video varies significantly depending on the platform and generation complexity, but in general, it is orders of magnitude lower than traditional production. Services like Synthesia offer various pricing plans.
What is AI content production?
This is the process of creating videos, images, texts, and other content using artificial intelligence. It includes generating video from text, AI voiceovers, creating AI clips with face swapping, and even automatic scriptwriting and editing.
YouTube’s research confirms that AI is not just “going to be a lot,” it is already actively used and positively perceived by a significant part of the creative community. This opens the door for incredible innovations in mass video generation by neural networks and the creation of personalized content.
If you are not yet using AI in your content production, now is the time to start experimenting. The possibilities of neuro-production by subscription and mass generation API can radically change your approach to video creation. Be at the forefront while competitors catch up!
Google released the Nano Banana 2.1 update on October 6, 2026, promising significant improvements in image generation. The new neural network is designed to better follow prompts, generate more accurate text, ensure character consistency, and work with wide formats. We checked how these promises align with reality by comparing both versions on five identical tasks. The short answer: for new projects, it’s worth choosing Nano Banana 2.1 right away, as it more often accurately fulfills minor requirements and less frequently adds unnecessary elements. However, the update hasn’t solved all problems, especially when transferring appearance between scenes, where the old version sometimes preserved features just as well. This is an important point for those planning to scale AI video and image generation.
Technical Features and Version Costs
Nano Banana 2 operates under the technical name gemini-3.1-flash-image, while Nano Banana 2.1 is based on Gemini 3.6 Flash and is named gemini-nano-banana-2.1. Google claims improvements in realism, instruction understanding, text rendering, and multi-step editing. The new model can accept up to 14 references: up to four characters and up to ten objects.
According to Google’s official pricing, a 2K image in Nano Banana 2.1 costs approximately half as much as in Nano Banana 2 ($0.0504 versus $0.101), making it attractive for AI content production. At the same time, the 0.5K mode has disappeared from 2.1, and Nano Banana 2 is already marked as an outdated model.
Testing Methodology: 5 Prompts for Comparison
For an objective comparison, we relied on the open Fuser test from October 6, 2026. In it, both models received identical English-language prompts and source data, and images were generated in 2K with one attempt without restarts. This allows us to evaluate the effectiveness of AI under controlled conditions. If you decide to repeat the test, use the same settings and do not select the best results to avoid distortions.
Prompt 1: Photorealistic Scene with Precise Requirements
“Photorealistic documentary photograph of an elderly fisherman mending a bright orange net on an old wooden pier at dawn. In the background, a foggy harbor. The fisherman’s hands are in sharp focus, the background is softly blurred. Natural light, 50mm lens effect, muted color palette. Do not add text or inscriptions to the scene.”
Nano Banana 2.1 kept the cool misty background, showed the fisherman in full, and left the frame free of extraneous text. Nano Banana 2 made the scene warmer, more saturated, and added a fictional inscription on the boat. Here, 2.1 won due to more precise adherence to the constraints, which is critical for AI video for advertising.
Prompt 2: Poster with exact text
“Create a minimalist concert poster on light paper. The top half is occupied by a large dark blue wave. Place four lines of text: “NIGHT TIDE” “Live at Harbour Hall” “Friday 14 November · Doors 8 PM” “harbourhall.com/nighttide” Do not add any other words. Make the first line a large narrow sans-serif, the second a lighter typeface.”
In this test, the difference between the versions was minimal. Both models correctly wrote the specified lines, including the date and address. This shows that in some tasks where Nano Banana 2 already performs well, an update may not provide a noticeable leap in quality, especially in text rendering.
Prompt 3: Product photography with space for ad text
“Studio photograph of a matte ceramic sage-green coffee funnel on a light oak board. Soft window light from the left, a light shadow should fall to the right. Clean, almost white background. Leave the right third of the frame free for ad text. Realistic product photography. No additional objects or inscriptions.”
Nano Banana 2.1 fulfilled all conditions: light from the left, shadow to the right, and a free area for text. Nano Banana 2 also left space, but divided the image with an empty light area, and the shadow direction was less pronounced. For creating AI commercials and banners, this is a significant advantage, saving designer time.
Prompt 4: Transferring a product to another location without alteration
“Place this same bottle on a wet, moss-covered stone. The scene is outdoors in the morning, with soft sunlight and a blurred forest stream in the background. Change only the environment and lighting. Do not change the bottle’s shape, amber glass, black dropper cap, or the inscription “LUMA 02”.”
Both versions successfully preserved the major features of the bottle (shape, glass color, text). However, both violated a minor condition by changing the smooth detail of the cap to ribbed. 2.1 chose a wider shot and kept the label clean, whereas Nano Banana 2 zoomed in on the object and added water droplets to the label. This demonstrates a limitation of image-to-image models: the phrase “change nothing” does not guarantee pixel-perfect identity of small details.
Prompt 5: Transferring a character to a new scene
«Use the woman from the reference portrait. Show her full-length riding a bicycle on a wet city street at night. Neon signs all around, she’s wearing a yellow raincoat with the hood down. Keep the same face, red curly hair, freckles, and round glasses. Photorealism.»
There is no clear winner here. Nano Banana 2 preserved the curls and glasses well but changed her facial expression to a wide smile. Nano Banana 2.1 framed the full-length shot more accurately but weakened the resemblance in details: her hair became less curly, the frames thinner, and freckles almost disappeared. The choice depends on priorities: either precise preservation of individual facial features or a natural composition of the new scene. This is especially relevant for animated AI characters and AI avatars.
Conclusions after five prompts: is it worth switching?
According to the test results, Nano Banana 2.1 looks like a careful refinement of the previous version, not a fundamentally new model. Its advantage is evident in tasks with many specific requirements, which aligns with Google’s official description of better prompt adherence and text rendering. For AI UGC for crypto projects, where detail accuracy and prompt following are critical, this is a significant plus.
Nano Banana 2.1 won two tasks and did not clearly lose to the old version in any.
When transferring appearance, the results remained ambiguous.
If you are starting a new project, it is more logical to use 2.1 immediately, especially given the reduced cost and Nano Banana 2‘s status as deprecated.
If you have a working pipeline on Nano Banana 2, test your typical prompts on both models to ensure the desired generation style is maintained.
Frequently Asked Questions
Is Nano Banana 2.1 really better than Nano Banana 2?
According to official specifications, 2.1 is the recommended version with improved instruction following, quality, text, and character stability. In our test, it showed better results in tasks with multiple constraints.
Why does the same prompt give different results?
Image generation in neural networks is not deterministic by default. Even within the same model, repeated runs can change the composition, face, lighting, or small objects. For a correct comparison, a series of runs with identical settings is required.
Why does the model ignore part of the prompt?
Often the problem arises when there are too many equally important instructions in one request. For critical constraints, it is better to use separate instructions, for example: «change only the background», «the text must be written verbatim», «do not add logos», «keep the face unchanged».
Should I always include high Thinking in Nano Banana 2.1?
No. 2.1 has minimal, medium, and high levels. A higher level can be useful for a complex layout or a large number of constraints, but it usually increases computation volume and execution time. For simple drafts, it is logical to start with minimal or the standard medium.
What Nano Banana 2 has that 2.1 doesn’t?
The most noticeable technical difference is the 0.5K generation mode. In 2.1, the minimum resolution starts at 1K. However, the new version is cheaper at matching resolutions and adds extremely wide and vertical formats.
How to improve results when working with a reference?
For the best results, use high-quality references. Make sure the reference images are clear and contain all necessary details. Experiment with the number of references (up to 14 in 2.1) to find the optimal balance.
How to access Nano Banana in Russia?
Nano Banana is available on special platforms that work without VPN and foreign cards. You can upload photos, change backgrounds and clothes, collect multiple references, and create images from text descriptions. Services usually work with ruble payments and provide a common balance for different models. Use promo code HABRSPESHUAI to increase your balance by 115%. This is a great way for crypto projects to order AI creatives or scale the generation of 100 videos per day.
Overall, Nano Banana 2.1 is a significant step forward, especially for tasks requiring high accuracy and detail. Its reduced cost and improved capabilities make it an attractive tool for subscription-based neuro-production and AI content production. Don’t miss the opportunity to scale the impossible – now it’s possible with new Google neural networks.
Claude for Editors: The Power of AI in Document Work and Beyond
The Claude AI, developed by Anthropic, is rapidly gaining momentum, offering unique capabilities for working with text data. From processing long documents to generating creative content, this AI, according to its developers, can change the approach to editorial tasks. But is this really true? We tested its functionality on real tasks to understand where Claude truly shines and where it still requires human oversight.
This platform, which has evolved from a simple chatbot to a powerful ecosystem, can now accept PDFs up to 1000 pages, work with Excel spreadsheets, and efficiently search for information. AI content production with Claude is becoming a reality, especially for those looking for AI production by subscription or striving to scale the impossible.
Claude’s Evolution: From Chatbot to Ecosystem
Anthropic, founded in 2021 by former OpenAI employees, introduced the first publicly available version of Claude in 2023. Since then, the AI has gone through several generations, reaching version Claude 5.5. The lineup includes models:
Claude Sonnet 5.5: for everyday tasks, document analysis, programming, information work, and mathematical calculations.
Claude Opus 5.5: for more complex and lengthy tasks.
Claude Fable 5.1: the most powerful for complex reasoning and autonomous assistant work.
Claude Haiku 5.5: the fastest and most affordable for mass tasks.
All current models support a context window of up to 1 million tokens and a response of up to 128 thousand tokens. However, it’s worth remembering that files, conversation history, and service data also take up space, reducing the actual limit.
Tariffs and Availability: How much does one AI creative cost?
The free version of Claude allows you to use chat, upload documents, and work on projects, but has a floating limit. Paid tariffs offer significantly more possibilities:
Pro: 15 euros per month (180 euros per year) plus VAT. Provides a larger volume of work, additional models, and tools.
Max: from 90 euros per month for intensive users.
For companies, Team and Enterprise tariffs are available with employee management functions, corporate search, and a single invoice. Russia is not yet on the list of officially supported countries, but Claude perfectly understands and generates texts in Russian, which makes it attractive for AI UGC for a crypto project and other Russian-language tasks.
How to work with Claude: interface and functionality
Registering with Claude.ai is simple: via Google or a one-time link to your email. The interface is intuitive, but to achieve the best results, it’s important to formulate requests as detailed as possible.
“Shorten the material to 3,000 characters. Preserve surnames, dates, amounts, and the author’s conclusion. Do not add facts not present in the original. After the text, list what had to be removed.”
This approach allows for more accurate and useful results than simply “shorten the text.”
Working with files and projects: scaling was impossible — now it’s possible
Claude supports uploading files in PDF, DOCX, CSV, TXT, HTML, ODT, RTF, EPUB, JSON formats, as well as JPEG, PNG, GIF, WebP images. Up to 20 files, each up to 500 MB, can be added to one chat. PDF documents up to 100 pages are analyzed considering text and images; longer ones are analyzed by text only. This is critical if you need to generate 100 videos or even an AI conveyor of 500 videos per day, as graphic processing in large documents is limited.
The “Projects” feature allows you to create a knowledge base from documents, text, and code, which Claude will refer to when performing tasks. For example, you can set general instructions:
Respond in Russian.
Do not change the spelling of names.
For each output, indicate the document and page.
If data is not available, state this directly.
This significantly increases the efficiency of AI against real UGC, as the neural network becomes more predictable and manageable.
Claude’s skills: automating routine
Skills in Claude are folders with instructions and scripts that the neural network applies when it deems them suitable for a task. This allows for automating repetitive processes and significantly speeding up work, for example, when mass generating videos with AI or creating AI commercial videos.
Testing Claude: what the neural network can do in practice
We tested Claude on several tasks typical for an editor:
Analysis of a voluminous PDF: Claude successfully compared economic scenarios from a 229-page Bank of Russia report, but clarification was needed for precise matching of indicators. This highlights the need to double-check answers, especially when dealing with financial data.
Navigation through applications: The neural network created a brief navigator for ten applications, but additional commands were required for accurate formatting.
Text editing: Claude shortened the text, preserving numbers, but lost an important caveat about the temporary nature of the economic slowdown. When working with an AI avatar with subtitles or AI voiceovers, such nuances can be critical.
Working with tables: The model correctly identified revenue drops and calculated order costs, pointing out missing data. It was important to request formulas and a list of omissions in advance.
Image recognition: Claude recognized pizza and created a recipe, and also recognized text on a cake with a resignation letter. However, for small inscriptions and curved surfaces, manual verification is recommended.
Argumentation: The neural network successfully objected to the author’s reasoning about screen adaptations but made a factual error, calling the 1968 film “The Brothers Karamazov” a TV series. This proves that deepfake-ethically only verified facts should be used.
API Cost: How much does 1 minute of AI video cost?
Separate tariffs are used for working with the Claude API. For example, Claude Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens. Fable 5.1 costs $10 and $50 respectively. Haiku 5.5 costs from $0.10 and $0.50. These costs are not included in the Max subscription and are paid separately. This is important to consider when planning video generation from text or using the mass generation API for AI video for advertising.
Frequently Asked Questions
What is Claude and how does it differ from ChatGPT?
Claude is a neural network from Anthropic, focused on deep analysis of long documents and complex reasoning. Unlike ChatGPT, Claude often demonstrates higher accuracy in working with large texts and has a more flexible system of projects and skills.
Can Claude be used to create AI commercials?
Yes, Claude can be useful at the stage of script creation, processing text information for animated AI characters and AI clips with face replacement. However, for full-fledged AI video for advertising, integration with other tools will be required.
How much does one AI creative or minute of AI video cost?
The cost depends on the Claude model used, the volume of tokens, and the complexity of the task. For accurate calculations, it is necessary to study the API tariffs. For example, the cost of 1 minute of AI video can vary depending on how much text needs to be processed and generated.
Where to order AI UGC for crypto?
Claude can be an excellent tool for creating AI UGC for a crypto project, helping to generate texts, analyze data, and even create scripts for UGC advertising. To order services for creating AI content for crypto projects, it is recommended to contact specialized agencies or freelancers working with neural networks.
Is Claude an analog of Synthesia?
Claude and Synthesia solve different problems. Claude focuses on text analysis and generation, while Synthesia specializes in creating videos with realistic AI avatars. Claude can be used to write scripts that will then be implemented in Synthesia.
Conclusion
Claude is a powerful tool for those who regularly work with large volumes of text information and strive to automate routine tasks. Its strengths include document analysis, project work, and the ability to engage in complex reasoning. However, like any neural network, Claude requires careful verification of results, especially where high accuracy is important. For editors aiming for AI efficiency in their work, Claude offers unique opportunities for subscription-based neuro-production. Contact us to discuss how Claude can transform your workflow and help scale the impossible.
Neural networks are changing the world, and the PR sphere is no exception. Imagine: you are communicating with a digital copy of a person who answers questions just like the original. This is exactly what happened when Alex Voica, Head of Corporate Affairs at Synthesia, introduced his interactive AI avatar. This is not just text generated by a neural network, but a real breakthrough that makes us think about the future of communications and how an interactive AI avatar can change our interaction with information.
Synthesia: from $100M ARR to creating digital twins
Synthesia is a major player in the AI avatar market, alongside companies like D-ID, HeyGen, and Colossyan. This year, the startup reached a valuation of $4 billion, and last year its annual recurring revenue (ARR) exceeded $100 million.
Their main task is to help businesses create interactive training videos using AI avatars. Synthesia recently launched Roleplay Sessions, a product that allows employees, for example, to practice sales presentations with an interactive AI avatar that provides feedback and evaluates their responses. This is no longer just generating 100 videos, but a full-fledged tool for learning and development.
How an AI journalist avatar is created: process and technologies
Creating a personal AI avatar is a fascinating process. I was offered to make my digital copy, and of course, I agreed. After all, it’s an opportunity to see how neuro-production by subscription works.
For this, I visited a mini-film studio in Synthesia’s office, where many photos were taken and a two-minute voice sample was recorded. Two types of avatars were created:
Personal avatars: simply reproduce any given script.
Interactive avatars: can listen and respond, trained on specific information.
“My mom called it ‘amazing,’ which is high praise from her. She and my father tried to ask questions ‘only they would know’ about me — but the model didn’t answer, redirecting them each time to a story about venture fraud. ‘I don’t remember giving birth to two of you,’ she joked after testing the model.”
My avatar’s technology stack includes Synthesia’s own video and voice models, but the company also allows clients to choose alternatives from other labs, such as Cartesia, ElevenLabs, Google, or OpenAI. Businesses can host their avatars on any cloud or pay Synthesia for hosting. This opens up endless possibilities for AI content production.
Technological Basis of the Interactive Avatar
The interactive avatar works thanks to a combination of several models:
Voice-to-text: converts user speech into text.
Agent language model: analyzes text and makes decisions based on it.
Text-to-voice: generates an audio response.
Video model (developed by Synthesia): animates the avatar during conversation.
This allows for the creation of an AI avatar with subtitles and AI voiceovers, making it ideal for AI video for advertising and mass video generation by neural network.
Synthesia Product Types: From Classic Avatars to API
Synthesia offers three main product types that make it possible to scale the impossible — now possible:
Video creation and distribution platform: classic avatars that voice a given script. Ideal for creating AI commercials.
Agent platform Sessions: interactive interaction with avatars in surveys or role-playing games.
API platform: allows combining Synthesia’s video and voice models with other technologies to create unique interactive avatars and products. This opens the way for API mass generation and the creation of AI UGC for a crypto project.
The Future of Journalism and AI Avatars: Ethical Questions
Creating my avatar took several days. First, I tested personal avatars to assess voice quality. It turned out to be quite accurate, and I was glad it didn’t convey the hoarseness I had during the recording.
My friends, not involved in technology, found it interesting and a little scary. My interactive avatar turned out to be deterministic, meaning it only answered questions related to the topic it was trained on — my article on venture fraud. This raises important questions about the future of journalism.
Will people be willing to watch news presented by avatars?
Can avatars complement or even replace journalists?
Will company executives want to communicate with an AI journalist avatar rather than a human?
The main aspect of journalism is trust, and it cannot be conveyed by AI. However, outside of journalism, the idea of cloning oneself can be very appealing. Imagine: you are resting, and your digital copy continues to work, answering questions. This is no longer fantasy, but a reality that allows for the creation of AI clips with face replacement and where a neural network writes scripts and edits. This is an AI video factory for e-commerce that can produce 500 videos per day.
Frequently Asked Questions
How much does 1 minute of AI video cost?
The cost of 1 minute of AI video can vary greatly depending on the chosen platform, the complexity of the avatar, the quality of the voiceover, and additional features. Synthesia offers various pricing plans that may include subscription-based neuro-production, making it accessible for different budgets. On average, the cost can start from a few dollars per minute and reach tens or even hundreds of dollars for customized solutions.
What is AI content production?
AI content production is the process of creating media content (video, audio, text, images) using artificial intelligence. This includes generating video from text, creating animated AI characters, AI voiceovers, and even scriptwriting. The goal is to automate and accelerate content creation, increasing its efficiency and reducing costs. This allows for achieving AI efficiency versus real UGC.
Where to order AI video for a crypto project?
For a crypto project, where speed and scalability are important, AI video production is an ideal solution. You can turn to platforms such as Synthesia, HeyGen, or Colossyan, which offer services for creating AI UGC for a crypto project. These companies provide tools for mass video generation by neural networks, creating AI avatars with subtitles, and quickly adapting content for various marketing campaigns. You can also use the mass generation API to integrate AI video into your own systems.
Conclusion
The world is changing, and neural networks are becoming an integral part of our lives. The creation of interactive AI avatars is not just a technological novelty, but a step into the future, where rendering and deepfake ethics become key concepts. Although I have mixed feelings about my digital twin, I see huge potential in this technology for business and communications.
We are on the verge of a new era, where the cost of one AI creative will be determined not by hours of work, but by the efficiency of algorithms. Join us to learn more about how AI is changing the world! Subscribe to our newsletter so you don’t miss new articles about future technologies.
The era of mass video generation by neural networks is already here. If you’ve ever considered running ads on TikTok, but the Ads Manager interface seemed too complex and the entry barrier too high, then this article is for you. We will tell you how new AI videos for advertising and AI-powered tools like Agentic Hub, Symphony Creative Studio, and Content Suite simplify campaign creation, management, and optimization, allowing you to generate AI UGC for a crypto project or any other business without deep expert knowledge of the platform.
TikTok AI: New Horizons for Advertisers
TikTok is actively implementing AI tools to democratize the process of creating and managing ad campaigns. According to Melissa Lorie, a short-form video expert, if TikTok Ads Manager is a restaurant, then MCP (Marketing Campaign Platform) is the waiter, and AI tools (like Claude or ChatGPT) are the menu.
Thanks to this integration, marketers can interact with platforms in a normal way, asking questions about campaign performance, requesting reports, and generating ideas.
MCP: Two-Way Data Exchange with AI
Before MCP, connecting AI tools to ad platforms required complex API integrations implemented by developers. MCP provides two-way communication between AI and the platform, allowing not only data extraction but also actions to be taken.
Some connections are read-only (for reports), while others are read-write (for creating campaigns or adjusting settings directly from an AI tool).
Pro Tip: AI recommendations should not be taken as gospel. If a new creative has only been running for a few days, TikTok’s AI might suggest pausing it because it’s performing worse than older creatives. However, this ignores the fact that the platform prioritizes ads that have been running longer.
Marketers need to rely on their own judgment and challenge AI suggestions by asking questions like: “Is there another way to do this?” or “Can I do this better?” Often, AI revises its recommendations when challenged.
AI Skills: Shortening the Path to Success
Available skills include Viral Video Creator, Ad Group Optimizer, Account Diagnosis Co-pilots, and ByteDance’s C-Dance video creation tool. These skills can be found by searching for “TikTok AI skills” online.
Viral Video Creator: This skill, a favorite of Melissa Laurie, analyzes millions of videos processed daily by TikTok to identify the most effective ads. Marketers can use this data to model their own videos, from the catchy beginning to the call to action. Melissa estimates that the skill brings creative work to 70% completion; the remaining 30% requires human refinement, such as emphasizing specific product benefits. This allows the AI for UGC advertising to operate at peak efficiency.
Skill Flexibility: Since skills are files, they can be modified, copied, and improved. Marketers familiar with AI can add additional instructions on top of official TikTok skills, customize recurring tasks, and create their own automations.
Cross-Platform Compatibility: An unexpected benefit is that skills are not tied to the platform on which they were created. A marketer can use a TikTok copywriting skill to create ads for Meta or find that Meta’s Instagram copywriting skill works better for their TikTok campaigns. This interoperability represents a complete paradigm shift.
Campaign Management Automation
For campaign management automation, Melissa recommends starting with the following basic tasks:
Automating daily performance reports.
Querying AI about which creative has the strongest “hooks.”
Monitoring creative fatigue.
When setting up a campaign, marketers can tell the AI their budget and goals (e.g., “I have $5,000, and I want to achieve X”) and request specific recommendations for campaign setup. This allows mass video generation by AI to become truly manageable.
Symphony Creative Studio: AI-powered Production by Subscription
Symphony Creative Studio is TikTok’s hub for creating AI-powered commercials. The updated Symphony Agent uses TikTok trends, signals from top-performing ads, business goals, and brand guidelines to create creatives at scale.
The tool can take a campaign from brief to finished video, generating product briefs, insights, storyboards, scripts, avatars, and videos. Marketers then intervene to revise scenes, add voiceovers, and refine calls to action. This is true AI content production.
Instant Video Generation from Text
The interface is simple and resembles the query window of any other AI tool. Marketers add their prompts, brand guidelines, and other details, and Symphony generates videos almost instantly. This allows for generating video from text with unprecedented speed.
Product Videos: Where AI Shines Brightest
Product videos are an area where Symphony is currently delivering the strongest results. Melissa gives the example of a furniture company that uses this tool to create 100 videos a week, each showcasing a separate piece of furniture.
Since these videos don’t require people on camera, the company can produce content at a scale that would be impossible with traditional video production. This is an AI pipeline of 500 videos per day for e-commerce.
AI Avatars: Challenges and Prospects
Animated AI characters in videos are not yet ready for most brands. The consensus among many leading companies is that the technology is not yet mature enough to create convincing human presenters. The trust issue is real: an AI avatar saying “all my friends are talking about this” raises obvious concerns. However, AI avatars with subtitles and AI voiceovers are already actively used for other purposes.
Hybrid Approach: AI-Enhanced Real Videos
Melissa sees potential in using AI to enhance real videos, rather than completely replacing them. Her agency, Oysterly Media, works with 3M on content for a mop. Creators film themselves demonstrating the mop, and then AI adds visual elements, such as animated “angry germs,” to dramatize the before-and-after effect.
This hybrid approach—real creator plus AI enhancement—is one of two main ways that prominent brands are using AI video now. The other is product demonstration videos without people, especially in the beauty industry, where AI can render product materials, textures, and effects, such as how lip gloss looks when applied.
Content Suite: Market Research with AI
Content Suite is a library of UGC-style videos and ads running on the platform. The new AI search update significantly simplifies finding relevant content for research and ideas, solving one of the biggest challenges marketers face: coming up with new content ideas. This is a powerful tool for AI UGC for crypto projects and other niches.
Smart Search and Competitive Analysis
Marketers can search Content Suite by keywords, industry categories (e.g., airlines or beauty), time filters (e.g., last 30 days), campaign goal (sales, lead generation, or brand awareness), and geography. The tool functions similarly to Facebook Ads Library, but is easier to navigate, especially with AI search.
Melissa recommends starting by searching for the company’s own brand, then expanding to competitor products and the broader industry category. The real value lies in seeing what creatives other brands in the same niche are launching and using that insight to create original content.
Content Suite can also be used for direct competitive research, identifying what types of ads competitors are investing in and what creative approaches they are testing. For those who have not yet used TikTok advertising, Content Suite is the best starting point. Before spending money, browse the library to see what videos are being created in the relevant industry. This research expands creative thinking and provides concrete models to work with.
Anatomy of a High-Performing TikTok Video
As AI tools make ad creation more accessible, the quality bar on the platform is rising. Melissa argues that the way to stay above this rising bar is to understand what makes a video high-performing. The first frame matters most, not the first three seconds.
Key elements of a captivating video:
First Frame: The initial image of the video that a viewer sees before deciding to scroll past. This single frame determines whether someone stops.
Billboard Text: A large text overlay on the screen. Melissa calls it “billboard text” because it works like a roadside billboard: the viewer has one moment to glance at it while scrolling, so it must instantly grab attention and convey the video’s value.
Opening Words: If there’s a voiceover or someone speaking, the first few spoken words must immediately hook the viewer after the visuals have stopped the scroll.
Connecting Tissue: Melissa’s term for the element that connects the beginning of the video to its middle section. Without this connecting tissue, viewers drop off between the “hook” and the main body.
Middle Content: When sharing tips, Melissa recommends grouping them in sets of 3 or 5. Three is the ideal number to keep content concise.
Abrupt End: The video should end with a sharp cut so that viewers don’t realize it’s about to end. A common mistake is slowing down and speaking more slowly towards the end, which signals the video’s conclusion and gives viewers a reason to scroll past. A clean cut tells the algorithm that viewers watched until the end, which helps with distribution.
Authenticity vs. Polish
Melissa’s agency preaches creating videos that feel like content from a family member or friend. Polished, staged videos make viewers scroll past because they are immediately perceived as ads. Naturalness is what drives performance.
Tips for creating natural content:
Don’t stand still and talk directly into the camera. Instead, creators should be doing something while talking: picking up a water bottle, applying makeup, or walking through a door.
Even raw videos benefit from light editing. Melissa recommends setting a clear start and end point, adding subtitles (as many viewers scroll without sound), and using occasional jump cuts. These jump cuts signal to the brain that something interesting is happening, helping to retain viewers.
Pro Tip: Don’t give away the ending at the beginning of the video. Build anticipation by first hinting at the implications, then deliver the reveal in the middle or at the end. For example, instead of starting with “Instagram just launched feature X,” start with “Instagram just made a very big update. This will change how you do X, Y, and Z.” Then explain the implications before finally revealing the specific update. When the answer is given first, viewers have no reason to keep watching.
Conclusion: The Future of AI Production
Melissa Laurie, founder of Oysterly Media, believes that marketing agencies still have an important place in this new landscape. She compares it to flying: economy, business class, and first class all get passengers to the same destination, but some companies prefer the full range of agency services.
What has changed is that the economy option – doing it yourself with AI – is now a viable path for businesses willing to invest time in learning. Want to learn more about how AI writes scripts and edits, and how to use mass generation APIs? Explore new TikTok tools, browse Content Suite, and start experimenting. The future of AI clips with face swapping and AI video factories for e-commerce is already here, and it’s accessible to anyone willing to master these technologies. Start scaling your content production today!
Frequently Asked Questions
What is Agentic Hub and how does it help with TikTok advertising?
Agentic Hub is a TikTok platform that allows the integration of AI tools for creating, managing, and optimizing advertising campaigns. It simplifies marketers’ interaction with Ads Manager, enabling them to ask questions and receive reports in natural language, and automate routine tasks.
Can TikTok AI tools be used for other platforms?
Yes, one of the key advantages of TikTok’s AI skills is their cross-platform compatibility. For example, TikTok’s copywriting skill can be used to create ads for Meta, opening up new opportunities for marketers and changing the paradigm of working with different ecosystems.
How effective is Symphony Creative Studio for video creation?
Symphony Creative Studio allows you to generate videos from text, using TikTok trends, effective ad signals, and brand books. The best results are achieved when creating product videos without people in the frame. Some companies generate up to 100 videos per week, which would be impossible with traditional production. This is an excellent Synthesia alternative for mass production.
Should you use AI avatars in advertising videos?
Currently, AI avatars are not ready for most brands due to issues with persuasiveness and trust. Experts recommend using AI to enhance real videos, for example, by adding visual effects or animated elements, rather than completely replacing human presenters. However, AI avatars with subtitles and AI voiceovers can be useful in other contexts.
How to get started with AI advertising on TikTok?
Melissa Laurie recommends starting by exploring new TikTok tools and reviewing the Content Suite to understand what content other brands in your niche are creating. This will help form the basis for developing original creatives without initial advertising costs.
In a world where neural networks and AI avatars are becoming the norm, tech giants are actively restructuring their strategies. Recent events show how AI transformation and mass video generation by neural networks are changing the market landscape, forcing companies to optimize processes and cut costs. A Reuters report sheds light on Meta’s large-scale “Organizational Transformation” (Project OT), aimed at significantly reducing staff by transferring functions to agency technologies.
Welcome to a new era where artificial intelligence is not just a tool, but a driving force behind global corporate changes. From Meta, re-evaluating its priorities after ambitious projects, to The Trade Desk, implementing intelligent assistants for advertising campaigns, and USA Today, adapting content for AI bots — each company is finding its way in this rapidly evolving reality. Get ready to learn how AI is changing the rules of the game, optimizing processes, cutting costs, and opening new horizons for business.
Meta: From Metaverse to AI Optimization
After an ambitious but not very successful dive into the metaverse, Meta has focused on staff optimization and automation. Project OT is an internal project to reduce the number of employees and transfer services to agency technologies.
These are not just cutbacks, but a strategic redistribution of resources, where AI efficiency versus real UGC becomes a key factor.
The Trade Desk: “Ask Koa” — A New Level of AI Interaction
Intelligent Assistant for Advertising Campaigns
The Trade Desk (TTD) introduces “Ask Koa” — a centralized AI interface that serves as a “single point of entry for various AI agents.” These chatbots will manage various aspects of the programmatic workflow, including audience creation, campaign optimization, and insight generation.
This significantly increases AI production efficiency, allowing for scaling the impossible.
Expanding Functionality and Combating “Ad Fatigue”
Transition to closed beta: “Ask Koa” is transitioning from alpha to closed beta this week, with plans for further expansion.
Frequency Capping Tool: A new tool automatically adjusts ad frequency based on brand performance data and campaign pace. This reduces “ad fatigue” and increases AI creative conversion.
“Much of the input for our product development process comes from customer feedback,” notes Eric Bosco, VP of Product Management at TTD. This confirms a customer-centric approach to AI solution development.
USA Today Co. joins publishers like Time and The Economist in restructuring its content for AI bots. The new format will make it easier for AI systems to process and cite USA Today content.
This is part of the publisher’s broader GEO strategy aimed at optimizing for search engines using AI.
Invisible Changes for Users
Kara Chiles, Senior Vice President of Product Management, emphasizes that these changes will not affect how people perceive content. “Most of the modifications will not be visible to people. This concerns infrastructure, metadata, how we structure our formats and templates to be as discoverable as possible for AEO and GEO,” she says.
The goal is to conclude more licensing agreements with AI companies and increase the value of current deals, which opens up new opportunities for AI UGC for crypto projects and other niche markets.
Other AI Industry News
Meta agrees to pay up to $18 billion to 48 states as part of a settlement of a federal lawsuit over the harm of social media to children and adolescents.
Attaching a celebrity name to a brand does not replace good marketing.
Reddit is no longer a leading source of citations in ChatGPT, but remains important for marketers.
AI-generated content is so prevalent that even images of cute animals can be fake.
X (formerly Twitter) demands that companies that allow content to be viewed without accessing the X site cease operations.
Frequently Asked Questions
What are “agent technologies” in the context of Meta?
“Agent technologies” at Meta are AI systems capable of performing tasks and providing services that previously required human involvement. This allows for significant staff reductions and scaling operations, for example, in subscription neuro-production.
How will The Trade Desk’s “Ask Koa” improve advertising campaigns?
“Ask Koa” integrates various AI agents to optimize advertising campaigns. It assists in audience creation, improving efficiency, and generating insights, leading to more effective AI advertising and a reduction in the cost per AI creative.
Why are publishers restructuring content for AI bots?
Publishers, such as USA Today, are restructuring content for AI bots to improve its discoverability in AI systems, facilitate citation, and secure more licensing agreements with AI companies. This also aids in SEO optimization and text-to-video generation.
Conclusion
The era of AI video generation and automation has arrived. Companies like Meta and The Trade Desk are actively investing in neural networks and agent technologies to remain competitive. This opens up unprecedented opportunities for mass video generation by neural networks, creating AI commercials, and even AI video factories for e-commerce.
If you want to know how much one AI creative costs or how a neural network writes scripts and edits, follow our updates. The future of content production is in AI. Don’t miss your chance to be at the forefront of this revolution! Learn more about AI content production.
Google’s recent August spam update appears to have targeted mass-generated AI content for SEO. Reports from online sources indicate that part of this update involved the removal of materials created by neural networks solely to manipulate search rankings. This aligns with Google’s recent research on identifying such spam, which is an important clue for anyone involved in AI video for advertising or mass video generation by neural network.
It’s important to understand: using AI itself doesn’t make content spam. However, any material created for the purpose of ranking for keywords often balances on the line between regular content and spam. It seems Google has implemented new mechanisms to detect AI-generated content that crosses this line, which may explain the decline in rankings for many sites actively using neural networks for UGC advertising.
Impact on AI-Generated Content
Experts are actively discussing the consequences of the update. For example,
Oka Takuma (@OkaTakuma1) wrote: “Regarding this Google spam update, it seems that sites automatically publishing content using Claude Code, Codec, etc., are being universally filtered and losing rankings. Google may automatically identify such content by attaching some kind of ‘AI credit,’ similar to generated images or videos.”
He also noted that sites that started with manual publishing and then switched to LLM automation have survived in some cases. This is explained by accumulated domain trust. Sites entirely created with automation from scratch do not have “trust signals” from Google.
Manual Review as a Buffer
Oka Takuma provided an interesting example, mentioning a Japanese magazine that publishes AI-generated content and was not affected by the update. The reason is simple:
“…but so far it has not received any penalties (naturally, we conduct a manual visual review by humans before publishing articles).”
This highlights the importance of human control, even when using technologies such as AI avatar with subtitles or AI voiceovers. Manual review can be a lifeline for AI production by subscription.
Not AI, but the Purpose of Use
Seiichi Satoweb (@seiichi_satoweb) believes that the problem is not with AI content itself, but with its mass production to manipulate search results. He noted:
“Companies managing media should check their rankings from August 18th to 21st. …If it dropped, I think the first thing to suspect is not the quality of the articles, but the ‘mass production method’.”
Google defines malicious use of mass-generated content as creating a large number of pages primarily to manipulate search rankings, rather than to support users. This is critically important for those looking for AI content production or where to order for crypto similar services.
Mass video generation by neural network for SEO purposes can be risky.
AI clips with face swap or AI avatar with subtitles should serve a real purpose.
Focusing on quality, not quantity, is key to survival in Google’s new reality.
“AI Junk” and its Consequences
On forums like Blackhat World, users complain about the prevalence of “AI junk” in search results. One participant wrote:
“They finally realized that AI junk is taking over the SERPs. I recently searched for a quick tutorial on how to change settings in an app and saw 4 sites in a row, clearly AI-generated to create articles answering that question — all looked absolutely identical, with the same AI formatting of subheadings and bullet points, poor spacing between sections, 3 sentences inflated to 500 words. AI junk pages are the new doorways, and Google is struggling to deal with it.”
This confirms that the effectiveness of AI versus real UGC is still questionable when it comes to low-quality content. Generating 100 videos or an AI conveyor of 500 videos per day must be supported by a quality strategy, not just volume.
Google’s New System: S-CTS
Google recently published research on a new system called S-CTS (Scalable Cluster Termination System). This system is designed to identify and terminate networks of AI-generated spam. This means that an AI video factory for e-commerce or video generation from text via mass generation API are now under close scrutiny.
How much does one AI creative cost or the cost of 1 minute of AI video — these metrics now need to be evaluated not only from a production perspective but also from the perspective of potential SEO risks.
Conclusion: The Future of AI Content
Google’s August spam update clearly showed that the era of mindless mass generation of AI content for SEO is coming to an end. Now that AI UGC for crypto projects or creating AI commercials are becoming increasingly accessible, it is critically important to focus on quality, uniqueness, and real value for the user. It’s not just about a neural network writing scripts and editing, but about creating something meaningful and useful.
For those who want to scale their content production, solutions like Synthesia alternatives should be used wisely and supported by a strategy that does not contradict Google’s principles. Only then can sustainable success be achieved in a world where scaling was impossible — now it’s possible, but with an eye on quality.
Frequently Asked Questions
What is “AI junk” in the context of Google?
“AI junk” is low-quality, mass-generated artificial intelligence content created primarily to manipulate search rankings, rather than to provide valuable information to users. It is often characterized by repetitive phrases, poor formatting, and a lack of originality, as in the case of AI video for advertising created without proper oversight.
How does Google distinguish quality AI content from spam?
Google does not penalize the use of AI per se. The main focus is on the purpose of content creation and its value to the user. If an AI avatar with subtitles or AI voiceovers are used to create useful, unique material that has undergone human review, it is not considered spam. The problem arises with mass video generation by neural networks solely for SEO without regard for quality.
What is Google’s S-CTS system?
S-CTS (Scalable Cluster Termination System) is a new Google system designed to identify and terminate networks engaged in AI-generated spam. It is a tool to combat large-scale operations producing low-quality content that use mass generation APIs to create thousands of pages or videos.
Can AI be used to create UGC content?
Yes, neural networks can be used for UGC advertising, but with caution. The key to success is creating valuable and unique content that is perceived as authentic. The effectiveness of AI versus real UGC will depend on how well AI integrates with the creative process and how thoroughly the content is human-reviewed to avoid “AI junk.”
Harvard Business School (HBS) is launching an innovative Foundry bootcamp where AI avatars of instructors will provide personalized feedback. This is a new step in scaling educational programs, significantly increasing learning efficiency. The cost of the eight-week course for entrepreneurs is only $699.
The Foundry bootcamp, developed by HBS, offers weekly live sessions with instructors. However, a key feature is the use of animated AI characters for interactive engagement. These neural networks, created by the startup HeyGen, provide personalized feedback during practical presentations and simulated board meetings.
This approach significantly increases the volume of individual support that would be impossible to provide through traditional methods.
AI Avatars in Action: Personalized Feedback
New York Times reporter Sarah Kessler personally tested the system, presenting her “Uber for bananas” project to the AI avatar of Flybridge Capital co-founder Jeff Bussgang. Although neither the real Bussgang nor his digital copy were impressed with the idea, Kessler noted that the virtual version displayed a “noticeably frozen smile” during her pitch.
This highlights the potential of AI avatars with subtitles and facial expressions to create more realistic interaction.
From Chatbot to Mentor: The Evolution of AI in Learning
Project director Katharina Rings initially envisioned the AI component as a regular chatbot. However, after a pilot launch, students expressed a desire for a more structured and guided experience.
This feedback led to the development of more sophisticated AI avatars, capable of not only answering questions but also actively participating in the learning process. This demonstrates the adaptability of neural production by subscription to user needs.
“My students are thrilled with the digital copy,” said Jeff Bussgang, admitting that his AI avatar is a bit “creepy” but extremely useful in the learning process.
The Effectiveness of AI vs. Real UGC: New Horizons
Foundry bootcamp participants, unlike some students who express negative attitudes towards AI, positively evaluated the use of animated AI characters. This confirms that, with proper application, mass video generation by neural network and creation of AI commercials can be not only effective but also in demand.
How much does one AI creative cost compared to traditional methods? Obviously, significantly less, which opens up new opportunities for AI UGC for a crypto project and other niches.
Advantages of AI in Education and Marketing
Scalability: Allows providing individualized feedback to thousands of students simultaneously, making what was impossible to scale — now possible.
Cost-effectiveness: Reduces the cost of training and content creation. The cost of 1 minute of AI video is significantly lower than traditional production methods.
Personalization:AI avatar with subtitles and an individual approach improves the quality of learning.
Innovation: The use of neural networks for UGC advertising and an AI conveyor of 500 videos per day open up new opportunities for marketing and education.
Frequently Asked Questions
What is an AI avatar in the context of education?
An AI avatar is a digital copy of a person, created using artificial intelligence, capable of mimicking their appearance, voice, and mannerisms for interactive engagement, such as providing feedback or conducting lectures. This allows for generating 100 videos or more with the same “instructor.”
How do AI avatars help scale educational programs?
By using AI avatars, educational institutions can provide personalized learning and feedback to a significantly larger number of students than is possible with live instructors. This reduces operational costs and increases access to quality education. It’s like an AI video factory for e-commerce, but for education.
Which companies develop technologies for AI avatars?
One of the key players in this field is the startup HeyGen, which created AI avatars for Harvard Business School. There are other companies offering similar solutions, for example, a Synthesia alternative, specializing in video generation from text and mass generation API.
Can AI avatars be used to create advertising content?
Absolutely. AI avatars and AI video for advertising are actively used to create AI face-swap clips, AI voiceovers, and AI writes scripts and edits videos. This significantly reduces the cost and speeds up the production of advertising content, making the effectiveness of AI versus real UGC very attractive.
Conclusion
The integration of AI avatars into Harvard Business School’s educational programs demonstrates a significant breakthrough in EdTech and AI content production. This not only increases the accessibility and quality of education but also opens new horizons for the application of artificial intelligence in various fields, from education to marketing.
If you are looking for where to order mass video generation for crypto or other projects — the technology is already here. The future of education and content production lies with neural networks, and Harvard is already setting the pace. Learn more about creating AI commercials on our website.
92.8% of dialogues in ChatGPT end without a transition to the website — this is Profound data that Kevin Indig analyzed in the digest on July 21. Measured on live traffic of a real product, the figure can be trusted. All search promotion relied on a single deal: you get to the top of search results, a person clicks the link to your site, and converting that visit into a lead is your concern. The link was the point where a business first touches a customer. In the AI response, this deal breaks at the very first step: the click most often simply doesn’t happen.
Why 92.8% of dialogues in ChatGPT end without a transition to the website
For several months, I’ve been measuring how ChatGPT, Perplexity, Alisa AQI, and GigaChat name brands in their responses: I run control prompts and see which model mentioned whom and which didn’t. Behind these runs, a figure surfaced that breaks the usual SEO logic — and made me change what I track for clients altogether. Let me break it down in order.
Three weeks ago, we ran prompts for a client in the food delivery niche through ChatGPT. Usual routine: you type a question like “which delivery service to choose in Moscow,” and look at the response. At that point, the client had far more links from external sites than the nearest competitor. In Ahrefs — a service that calculates a site’s link weight — this is immediately visible: both the overall authority score is higher, and the number of different sites linking to it is one and a half times greater. ChatGPT named the competitor. The client — no. Didn’t mention it at all, not once in twenty prompts.
I double-checked three times, changed the wording of the question, the date of the dialogue, and the region. The result was the same. For an hour and a half, I honestly couldn’t understand what was going on. Twenty years of website promotion taught a simple rule: the more links point to you, the higher you rank in Google and Yandex results, and therefore the more readily neural networks should name you. Here, the rule didn’t work. Only there was no search results page in the usual sense. There was a model that decided on its own whom to name and whom not to. And it decided not by a link counter.
A brand mention cannot be bought with links — it accumulates like reputation
At the same time, AI traffic itself is tiny in volume — Ahrefs reports that ChatGPT accounts for about 0.19% of all link transitions on the internet, compared to 42% for Google. Ridiculously small if you compare head-on. But here’s what’s more interesting: those few who do click from ChatGPT convert far more readily — conversion is about 7.1%, and the visit itself is valued 4.4 times higher than a regular one. Fewer people reach the site, but those who do — came ready to make a purchase or any other target action.
And here’s an important twist. If the decision is made BEFORE the click, inside the model’s response itself, then you need to fight not for the click. You need to fight for how you’re named at the moment when the click hasn’t happened yet and, most likely, won’t happen at all. A mention in a ChatGPT response is not structured like a spot in search results. A spot can be earned by working on the site — links, loading speed, technical cleanup. A mention can’t be bought that way. The model gathers it from two sources: from the texts it was trained on and from pages it pulls up right during the response. And what decides is not the number of links, but how the brand is generally written about — in reviews, on forums, in industry articles, in comparisons.
Search promotion used to be about taking top positions, because a position led to a click on the site. Promotion in AI answer engines works differently: you have to fight for the mention itself, because a click may not happen at all, and the person will still make a decision — based on how the model named the brand. A link is a one-time asset. It works as long as the page holds in the search results, as long as the site is alive, as long as the algorithm values it. A mention accumulates differently: it is reputation that has already settled into the texts on which the model was trained and which it reads now. Remove one article — the mention will not go anywhere; it is spread across dozens of sources.
This is where my mistake with that food delivery client was. His link profile was strong, but almost all of it came from aggregators and directories, where the brand is simply listed in a list. The competitor had fewer links, but had three detailed editorial reviews describing how the service differs from others. The model was trained on them, among other things. It did not count links — it read context. The first place in Google, by the way, does not at all guarantee a mention in ChatGPT. We checked this on several clients separately: search and AI engines have different selection mechanics, and the first place in regular search results does not by itself turn into a line inside the model’s answer.
Where a link still brings a client, and where the question is closed right in the answer
The link has not died entirely — it is important not to confuse this. In regular Google, 68% of searches in the US by early 2026 also end without a click, according to SparkToro citing Search Engine Land. An answer without a click to the site has also come to good old search, it is just that it was visible there earlier — through blocks with ready answers, knowledge cards, and direct answers at the top of the results.
The link still works where the decision is made AFTER the first touch: price comparison, reading reviews, technical documentation, everything that requires going into details that simply do not fit in the model’s short answer. It works in niches with a long decision-making cycle — there, a person will still reach the site, just later. The link does not work where the question is closed with one phrase in the answer. “Which delivery service to choose” — closed with a phrase. “How much does delivery from a specific restaurant in a specific area cost tomorrow evening” — not closed, a click is needed there. The same business lives on both sides of this boundary — it all depends on which specific query the user asked.
What I now measure for clients instead of search positions
I stopped looking only at positions in the search results and added a separate measurement: how often and in what context models mention the client’s brand directly, without a click, without a transition. We run control prompts — real audience questions — through ChatGPT, Perplexity, Alisa AI, and GigaChat, and see whether the model names the brand, in what order relative to competitors, and with what wording. ChatGPT is not the only one here. Perplexity builds its answer almost entirely from cited sources and shows them as a list — the model readily takes from articles on third-party platforms. Alisa AI mixes in fresh Yandex results, GigaChat answers in its own way. A brand that sounds in one model may be silent in another — so one ChatGPT is not enough for the measurement.
From this, three things emerged that I now advise any marketer to track:
Share of mentions among top competitors for your own key questions: a specific percentage for a specific set of prompts, without general ratings.
Tone of phrasing: the model may mention the brand neutrally, or it may add an evaluative word like “trusted” or “expensive” — this influences choice more than the mere fact of mention.
Sources from which the model apparently draws this phrasing: often it’s one or two articles — these should be prioritized for strengthening, instead of spreading the budget across dozens of platforms.
With that food delivery client, we ultimately didn’t chase new links. We wrote several detailed materials about the product on platforms that the model clearly reads and cites — and after a month and a half, the mention appeared, and visibility in AI responses grew. The client had more links, but ChatGPT named a competitor. Now we know why.
Frequently Asked Questions
How to measure brand mentions in ChatGPT?
Run control prompts — real audience questions — through ChatGPT, Perplexity, Alice AI, and GigaChat. Calculate the share of mentions among top competitors, record the tone and the sources from which the model draws information.
Can you buy a mention in neural networks with links?
No. Links work in classic search, but neural networks don’t count them directly. A mention depends on how the brand is written about in reviews, articles, and forums — it’s reputation that accumulates in the texts on which the model is trained.
What to do if the brand is not mentioned in ChatGPT?
Find the platforms that the model cites most often and place detailed materials about the product there. Strengthen editorial reviews, comparisons, and expert articles — they influence mentions more than link mass.
Should you abandon SEO in favor of neural networks?
No. Links still work in niches with a long decision-making cycle and for transactional queries. The optimal strategy is to combine classic SEO with work on mentions in AI responses.
Conclusion
The bottom line is simple: 92.8% of dialogues in ChatGPT end without a click, but the decision to choose a brand has already been made. A link is a one-time asset; a mention is cumulative reputation. To win in AI search results, stop chasing links and start managing how you are written about in the texts that models read. Run control prompts, track the share of mentions and tone — and you’ll see where you’re really losing customers. Start with an audit of your mentions today.
