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!
Creating videos with AI has become commonplace, but until now, the process of refining the first draft to perfection took hours. Not anymore. Lumen5 Agent is a revolutionary tool that not only generates videos but also efficiently processes feedback, making the iteration process as fast and intuitive as possible.
The Problem with AI Video: Not Generation, But Iteration
We’ve all seen this “trick”: you insert a link, blink, and the AI produces a video in a minute. But then hours of edits begin: the scene is too long, the title is unreadable on a light background, a beach clip in a video about a quarterly report.
Turning “some video” into “a video the team can send” is where precious time is lost. This is the gap Agent closes, and it’s already available in Lumen5.
How Agent Changes the Game in AI Production
Lumen5 Agent is a conversational way to create and edit videos. You describe what you want in simple language, watch it build the video scene by scene, and then make changes directly in the editor until the result is perfect.
Most AI video tools produce a first draft and “disappear.” Agent, however, stays for the second, third, and fourth drafts. It’s not “here’s your video, good luck,” but “okay, what are we changing?”
“Before, dealing with AI video was like this: you gave a link, and you got whatever came back. Like a vending machine, but for footage. Agent demands more, because that’s how you get something usable.”
Length, style, media: Specify the desired duration, style, and suitable media for your audience, and Agent will build the video according to this brief.
Smart questions: If your request leaves something important open, Agent doesn’t guess, but asks clarifying questions through a new questionnaire. Only those it cannot ascertain from the information already provided, so you don’t fill out a tax return for a 30-second promo.
New Opportunities for Startups and Teams
Now there are six ways to get started on the “Create” page. If your team works with PDFs and slides, this is for you. Agent understands page numbers, including those printed on the pages themselves.
When you ask for a diagram on page 12, you get a diagram on page 12, not on page 11 or “something that looks like a diagram from somewhere in the middle.”
Page snapshots: Agent can take an image of a page from a PDF, PowerPoint, or Word document and insert it directly into your video. This is useful when the document itself is a key element, such as a report cover or an important slide.
Acceleration: According to our product lead’s testing, the document workflow is now approximately 50% faster than before. This is scalable efficiency that saves time and resources.
Process Transparency and Instant Feedback
While Agent is working, the creation window now shows each scene as it’s being built and updated. Goodbye, loading bar and silent prayers. Hello, real-time understanding of what’s happening.
This sounds like a cosmetic improvement, but it’s not. If you see that the second scene is going wrong while the fifth is still being built, you can intervene immediately, instead of discovering the problem at the end and starting over with a heavy sigh.
New Recommendation Engine: Quality, Not Automation
Under the hood, a brand new recommendation engine is making choices. We are proud of its features:
Text and icon synchronization: Text and icons appear the moment they are spoken, so nothing appears too early or lingers after the phrase is uttered.
Contrast detection: Checks text readability against any background, not assuming every photo is conveniently dark.
Smart text wrapping: Headings break at sensible points and resize less awkwardly. No more lonely words left on a separate line.
Listening length: Requests to make a video longer or shorter now hit the target duration much more accurately.
On-demand clip replacement: “Replace this clip with something from the office” – and Agent understands.
These details collectively distinguish a video that looks automated from a video that looks made by a caring human. This is key to AI efficiency versus real UGC.
Agent for Teams: Democratizing Video Production
A new Agent tab has appeared in the editor. After creating the first draft, you can continue to make requests in plain language without leaving the editor. Your video is adjusted on demand, not by “hide-and-seek” with the right panel.
All manual controls remain in place, so experienced users can continue to work in their usual way. The point is not that you stop editing, but that the fastest way to a specific change is simply to describe it.
Brand Consistency with “Recipes”
For a team, this is a big shift. Instead of one person who knows the editor by heart (and gets all the “quick requests” in the office), everyone can release a video.
The Create page now starts with “Recipes,” each with a preview of what it produces. These are great for inspiration if you’re new. But their real superpower is consistency. When four people on the same team choose “Report Summary,” you get four videos that are clearly from the same brand, not four creative interpretations, plus a brand manager reaching for the paracetamol.
The Future of AI Video: Focus on Iterations
If you’ve been following AI video, you’ve noticed the conversation has changed this year. The interesting question stopped being “can a model create a clip?” and became “can it follow directions?”. Editing has always taken up most of the production time. Faster footage generation doesn’t fix that.
On the contrary, it can make things worse, because now you have ten versions to choose from instead of one, and no extra hours in the calendar. This aligns with what we see in marketing teams every day. The bottleneck isn’t creating the video, but the endless revisions after it’s made, and how many people on the team can actually manage that process.
So we built Agent around the iteration cycle, not just the first generation. Creating a video is easy now. Creating your video, the one that’s actually ready to send, that’s what we wanted to simplify.
Lumen5 Agent: Your New AI Production on Subscription
Agent is already in Lumen5, waiting on the Create page. If you’ve only used us by pasting a link and taking what came out, this is worth ten minutes of your team’s time. Give it a real brief, with length, style, and audience. Then tell it what to fix. Then change your mind and tell it again. It won’t take it personally.
And we genuinely want to hear where it falls short for your team. We’re still fine-tuning the recommendations, and the fastest way to improve them is to hear what you asked for and didn’t get. Tell us. We’re ready.
Want more ideas on how to turn existing content into more effective video? Subscribe to the Supercharge Marketing podcast, where marketing leaders share what really works for their teams.
Frequently Asked Questions
What is Lumen5 Agent?
Lumen5 Agent is a conversational tool for creating and editing AI videos within Lumen5. You describe the desired video, watch it build scene by scene, and then request changes in plain language until it’s ready.
How is Agent different from other AI video generators?
Most AI video tools focus on the first draft. Agent is built around the iteration cycle, so after creating the first version, you can continue to request edits (e.g., shorten a scene or replace a clip) directly in the editor.
What can I use to start a video with Agent?
You can start with a blog post, script, document, recording, screen recording, or an existing video similar to what you want.
Can Agent work with PDFs, PowerPoints, and Word files?
Yes. Agent understands page numbers, including those printed on the pages, and can turn a page from a PDF, PowerPoint, or Word file into an image in your video.
Do I still have manual editing controls?
Yes. All manual controls in the Lumen5 editor remain in place. Agent simply provides a faster way to make specific changes by describing them.
Does Agent change Lumen5 pricing or usage limits?
No. Your pricing and usage limits, as well as Blueprints and Script Composer, work exactly as before. This allows you to scale your AI content production without hidden costs.
How does Agent help teams stay on brand?
“Recipes” on the Create page, such as “Report Summary” or “Brand Story,” give everyone on your team the same starting point, so videos made by different people still look like they belong to the same brand. This is important for AI video for advertising and AI UGC for crypto projects, where consistency is critical.
Lumen5 helps marketing teams turn existing content into polished, on-brand video, faster and at a fraction of traditional production costs. Learn more or request a demo.
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.
Integrating an AI avatar into the existing voice AI administrator Aika is not just about adding a face; it involves a whole chain of technical challenges. We aimed to preserve the current architecture on Yandex Realtime so that the new service would only animate lips to already generated sound. During testing of Simli and Anam, nuances emerged with pauses before responding, video playback on iPhone, and mobile internet stability. And when creating the second character, Eva, it became clear that the persuasiveness of the neural network depends not only on the face itself but also on the context.
Our voice engine was already functional, so the task was solely to animate the face. Aika communicates with website visitors directly in the browser via WebSocket, which is connected to our FastAPI server, and that, in turn, maintains a session with Yandex Realtime. This ensures a “speech-to-speech” mode, eliminating the need for separate recognition chains, text models, and synthesis.
The server transmits uncompressed sound in PCM16 format at 24 kHz in chunks (pcm_chunk) to the browser, and it was precisely to this stream that the avatar’s facial expressions needed to be synchronized.
Choosing a platform for the AI Avatar: Simli vs Anam
Both tested services — Simli and Anam — offer a mode where the user provides their audio stream, and the service generates video with an animated face synchronized with the sound, returning media via WebRTC. Thus, Yandex continues to generate speech, and the avatar service is responsible for the visual component.
Technical aspects of sound integration
Sampling rate: Yandex outputs sound at 24 kHz. Simli requires 16 kHz, Anam was also configured for 16 kHz, although it supports other formats.
Processing audio chunks: Sound arrives in chunks. To avoid artifacts at the junctions, the fractional position and the last sample are carried over to the next chunk. This ensures smooth animation, although the quality of frequency recalculation without a full-fledged filter still requires improvement.
Security and session management
The service key does not reach the browser. The page requests a short-term session token from our server via POST /voice/avatar-session. The server checks the origin and limits token issuance: 6 per hour per IP and 30 per hour total.
If the token is not received or the video does not start, the page switches to normal voice mode. 8 seconds are allotted for the token request, and 15 for SDK launch. It is important that a successful SDK launch does not guarantee the immediate appearance of the first frame; the face here is an addition, not a critical function.
Managing Avatar State in Anam
Anam provides two key commands:
endSequence(): called after sending the last audio segment of a replica.
interruptPersona(): stops the avatar’s current speech when interrupted, after which endSequence() is also called.
Comparison of Simli (old generation) and Anam Cara-4
We compared the versions available to us: Simli of the previous generation and Anam Cara-4. This is not an exhaustive comparison of all features, but a selection for a specific task.
Parameter
Simli (previous generation)
Anam Cara-4
Mimicry
Lips move, other mimicry is limited
Blinks, smiles, teeth visible, tilts head
Face creation
From photo via previous generation API
From a single photo, trial video available
Subjectively, Anam showed more lively and natural mimicry. For us, this was a decisive factor. Next, we encountered a number of specific problems.
Media Playback Features
Sound: In Simli, sound was played through a separate audio element; in Anam, it was played through the same video element. This means that the Anam video element cannot be left muted, otherwise the avatar will “speak” silently.
Resolution: Video often started at a low resolution (384×576) and only switched to high resolution (768×1152) after a few seconds. The sessionOptions.videoQuality: "high" option helped, but did not guarantee a high-quality start.
Frame generation delay: Anam needs to receive 0.8 seconds of audio before starting frame generation. This is not always equal to 0.8 seconds of real-time waiting, as audio can arrive faster than it is played.
Microphone and Session Management
Echo suppression: On the phone, while Aika is speaking, the microphone is muted to avoid picking up her voice from the speaker. On the PC, the microphone is active, and when the user starts speaking (barge_in event from Yandex Realtime), the avatar stops its current speech.
Busy session slot: On the free plan, one concurrent session is available. If the browser crashed, the slot could remain occupied. The GET /v1/sessions/concurrency request helps diagnose slot occupancy.
Connection cancellation: If the user clicks “stop” during video connection, it is necessary to correctly complete asynchronous operations so as not to leave an unnecessary session. For this, a generation counter videoGen was introduced.
Problems with Safari on iPhone
On iPhone in Safari, the video did not start. The hypothesis is related to autoplay rules. A preliminary call to play() on the video element with temporary stream feeding from canvas and unmuting helped in a test run, but is not a universal solution.
Optimizing the Response Pause
A long pause before an avatar’s response, where it silently stares at the user, feels much heavier than without it. Initially, the Voice Activity Detection (VAD) threshold was set to 2.2 seconds to prevent Aika from interrupting users dictating information with pauses.
We transferred the logic of the telephone bridge to the website: in a normal conversation, the threshold is 1500 ms, when dictating — 2200 ms. This allows dynamic adjustment of VAD sensitivity. Settings are applied via session.update without reconnection.
Creating a Second Character: Eva
For the studio administrator Eva, we faced the problem of a “stock blonde” against an empty background. The turning point came when an interior appeared behind her: warm lamps, shelves, plants, a blurred background. This made the image much more convincing, transforming a “catalog” into a “person at their workplace.”
Details of Background and Portrait Creation
Background: We used a ready-made interior in our style, removing people with a generator.
Character Cutout: Eva was cut out using macOS Vision (VNGeneratePersonSegmentationRequest with .accurate level). The mask edge was narrowed and softened to eliminate the halo.
Scale: It is important to maintain the scale of objects in the background to avoid comical situations (e.g., a giant mug behind her). The background was taken only above the countertop and blurred for a portrait lens effect.
Resolution: For Cara-4, a square source of 1152×1152 or higher is recommended. Pure Real-ESRGAN ×4 upscaling made the skin too smooth. A combination of neural network upscaling with regular scaling, sharpening, and grain addition looked better.
Eva’s Voice Acting
For Eva, we chose a voice from Yandex’s LiveTTS family. In SpeechKit API v3, the model is selected via "model": "livetts", and the voice and role are passed via hints. For long texts, the text was broken down into sentences and synthesized separately.
Out of seven options for voicing one phrase, we chose the vera voice in the casual role. We had to rewrite the phrase because the synthesis consistently made a mistake with the stress.
Recording Videos with the Avatar
Eva’s video was recorded using the same animation engine as a live call, using MediaRecorder directly from video.srcObject (video and audio together, WebM with VP9 and Opus). For correct recording start, the stream was pre-“woken up” with a second of silence so that the video would not start with a black screen.
Frequently Asked Questions
What is an AI avatar and how does it work?
An AI avatar is a digital character capable of animating facial expressions and lip movements in accordance with provided audio. It uses neural networks to create a realistic visual image that synchronizes with speech, making interaction more natural and engaging.
Can I use my own voice for an AI avatar?
Yes, most platforms, such as Anam and Simli, allow you to use your own voice engine or pre-recorded audio. The avatar will animate its face to this audio stream, maintaining your brand or persona’s recognition.
What are the main challenges when integrating an AI avatar?
The main challenges include audio and video synchronization, processing audio chunks, optimizing latency, ensuring stable operation on various devices (especially mobile), and creating a convincing visual image that matches the context and brand.
How does the background affect the perception of an AI avatar?
The background plays a critical role in creating a convincing image. An empty or inappropriate background can make the avatar appear unnatural. An interior that matches the character’s or brand’s style significantly increases the avatar’s realism and trustworthiness, creating the feeling of a “person at work.”
How much does it cost to create an AI avatar?
The cost of creating an AI avatar can vary greatly depending on the chosen platform, the level of customization, the detail of facial expressions, and the need for unique functions. Some services offer subscription models, while others may charge per minute of generated video or for the development of a unique character.
Conclusion
Integrating an AI avatar is a powerful tool for increasing user engagement, but it requires deep technical development. From platform selection to fine-tuning backgrounds and pauses, every step affects the final result. We have seen that neural networks can breathe new life into voice interfaces, but only with careful attention to detail.
If you want to scale your content and create AI video for advertising, consider the possibilities of AI production by subscription. This allows you to generate hundreds of videos, whether it’s AI UGC for a crypto project or an AI video factory for e-commerce. We are ready to help you with this! Contact us to find out how AI content production can transform your business.
Anthropic, the developer of the Claude neural network, is significantly tightening its platform usage rules, effective November 12. It is now explicitly forbidden to use Claude to “seed” search engine and AI system sources with misleading content. This applies to materials that misrepresent their origin, authorship, or independence. Imagine a network of websites that pretend to be independent but promote the same claims – this is what Anthropic will fight against, striving to ensure the purity of the information space.
Anthropic tightens rules: fighting fake sources for AI answers
New section “Prohibition of misleading campaigns”
The key change in Anthropic’s policy is the introduction of a new section titled “Do not engage in deceptive campaigns or artificial activity.” This section clearly states the unacceptability of manipulating sources from which search engines or AI systems draw answers. For example, creating networks of websites that pretend to be independent sources but confirm the same claims is now strictly prohibited. This is critical for maintaining trust in information generated by neural networks.
Prohibition of using fake personas, publications, and reviews.
Dissemination of content through “fake or supposedly independent publications, websites, or accounts” with the aim of concealing a common source.
Prohibition of creating tools for deceptive campaigns and selling such services.
Previously, similar rules were scattered across various sections of the policy concerning elections, fraud, privacy, and disinformation. Now everything is collected in one place, which significantly simplifies the understanding and application of the new norms.
Lessons from September: real cases of deception
Anthropic is not just introducing new rules, but also reinforcing them with real examples from its September threat analysis report. The company identified about 70 websites masquerading as independent local news outlets. All of them were linked to one advertising agency from France.
“The operation involved using Claude to create articles in a uniform format, each containing three to four internal links, for automatic publication.”
These articles, of which there were at least 8913 in approximately 20 languages, were specifically designed to increase the authority of the sites in search engines. Although most of them did not receive significant audience response, Anthropic promptly responded by blocking the Claude account and the organization associated with this activity.
An interesting change has affected automated content publishing. Previously, “automated generation and publication of content for external consumption” was considered a high-risk scenario. This point has now been removed from the list. This does not mean complete permissiveness, but indicates that Anthropic sees potential in mass AI video generation and other content formats, provided they do not violate other rules, especially regarding misinformation.
The list of high-risk areas requiring review and disclosure now includes 11 items, such as legal, medical, and financial advice, as well as decisions about credit, housing, and employment. The requirements for reviewing recommendations in these areas remain unchanged: a qualified professional must review the findings, and users must be notified that the content was created with AI involvement. This opens new horizons for AI video for ads and AI UGC for crypto projects, where AI-powered subscription production could become the standard.
Google and Anthropic: A United Front Against Spam
Google is also actively fighting the manipulation of generative AI responses in search, calling it spam. An updated document from May 15 clearly stated that these policies apply to generative AI responses. While Google applies its rules to websites, which can lead to their ranking being lowered or removal from search results, Anthropic acts at the user level for Claude, restricting or revoking access. This demonstrates a unified approach by tech giants to ensure the integrity of the information space.
Frequently Asked Questions
What has changed in Claude’s usage policy?
The main change is the prohibition of using Claude to create and disseminate misleading content that manipulates information sources for search engines and AI. A new section on “Prohibition of Misleading Campaigns or Artificial Activity” has been added.
Why did Anthropic introduce these changes?
The changes are aimed at combating misinformation and manipulation that can affect search engine and AI responses. This is a response to identified cases where Claude was used to create networks of fake news websites.
How will this affect AI content creation?
This does not prohibit AI content creation as such, but establishes strict frameworks for its use. For example, mass video generation by neural networks or the creation of AI avatars with subtitles are still possible if the content does not mislead about its origin or authorship. It is important to be transparent and ethical.
What are the “high-risk” areas of Claude’s use?
High-risk areas include legal, medical, financial advice, as well as decisions affecting credit, housing, and employment. In these cases, mandatory verification by a qualified specialist and user notification that the content was created using AI are required.
Is automatic content publishing now prohibited?
No, automatic content publishing is no longer considered “high-risk.” This opens up opportunities for an AI pipeline of 500 videos per day or an AI video factory for e-commerce, provided all other rules are followed, especially those concerning misinformation and manipulation.
Conclusion
Anthropic’s updated policy is an important step towards more ethical and responsible use of generative AI. This is not only about protecting users from misinformation but also about creating a more transparent environment for technological development. For businesses that use AI video for advertising or neural networks for UGC advertising, this means being even more attentive to the origin and presentation of content. We see that the effectiveness of AI versus real UGC continues to grow, but with it, so does responsibility. It’s time to adapt and build a future where technology serves for good, not for deception. If you are looking for where to order AI UGC for crypto or other projects, make sure your partner adheres to high standards of ethics and transparency.
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.
In a world where speed and efficiency play a key role, Melius, a platform for creating advertising campaigns, images, and videos using artificial intelligence, is making a real breakthrough. The company announced that it has raised $25 million in investments, which underscores the enormous potential of AI video generation in the modern industry. This significant financial influx includes a $20 million Series A round led by CRV and an initial $5 million round from General Catalyst. Founded by former Ramp engineers, Melius demonstrates impressive growth, achieving over $1 million in annual recurring revenue just two months after emerging from stealth mode in July.
From Idea to Scaling: How Melius Reinvented Itself
Melius co-founder Juwon Kim admits that the company’s journey was not smooth. Initially, over a year ago, the team, consisting of Juwon Kim, Yang Kim, and Arnav Ramu (all three former Ramp engineers), focused on creating a tool to optimize advertising spending.
However, as Kim notes, after six months, it became clear that the initial idea “had no prospects.”
“We completely destroyed the entire codebase; we burned it all down,” Kim told TechCrunch.
This decisive step allowed Melius to reorient itself to a much larger task: developing tools for generating creative assets themselves and advertising campaigns. This marked the beginning of a new era for the company, creating an “agent lab for creative work.”
Melius in the Competitive Landscape of AI Production
The market for AI creative generation is rapidly evolving. Among Melius’s competitors are projects such as Higgsfield, which was valued at $5.4 billion in August and demonstrates annual revenue of over $700 million. There are also smaller players, such as Krea and Flora AI. Juwon Kim sees no problem with this, emphasizing the enormous potential of the market.
Higgsfield: Valuation of $5.4 billion, annual revenue of $700+ million.
Krea: A young startup in the field of AI generation.
Flora AI: Another player creating creatives with AI.
“There are a lot of players out there, and it’s pretty exciting,” Kim noted. “It means there are customers to win, and there’s demand in this space.” This confirms our position that the cost of one AI creative today is significantly lower than traditional production, which opens up huge opportunities for mass video generation by neural networks.
From YouTube Dream to AI Avatars: The Founder’s Personal Motivation
Interestingly, behind the decision of the former Ramp engineers to engage in advertising generation is Juwon Kim’s personal story. He describes himself as a social media influencer and dreamed of creating short videos since childhood. “I tried my best to make vlogs,” Kim recalls. “My father was great at FinalCut Pro, and I wasn’t.”
These early attempts were unsuccessful, but his passion for digital media remained. Now, with Melius, Kim is building the tool he always dreamed of: a platform where anyone, from an experienced creative director to a novice, can bring their ideas to life using video generation from text.
This will allow content creation to be scaled in ways previously impossible, making subscription-based neuro-production accessible to everyone looking for AI efficiency versus real UGC.
Frequently Asked Questions
Question
Answer
What is Melius and what services does it offer?
Melius is an AI platform for generating advertising campaigns, images, and videos. It allows users to create creative assets using artificial intelligence, which significantly simplifies and speeds up the content production process.
How much investment has Melius attracted?
Melius has attracted a total of $25 million: $20 million in a Series A round and $5 million in an initial round.
Who founded Melius?
Melius was founded by former Ramp engineers: Juwon Kim, Yang Kim, and Arnav Ramu.
What is Melius’s annual revenue?
Melius achieved over $1 million in annual recurring revenue just two months after emerging from stealth mode in July.
Does Melius have competitors in the AI ad generation market?
Yes, Melius has competitors such as Higgsfield, Krea, and Flora AI. However, Melius founders are confident that the market is large enough for multiple players.
Conclusion
The story of Melius is a vivid example of how flexibility and foresight can lead to success in the rapidly changing world of technology. By abandoning its initial idea, the team created a platform that not only attracts significant investment but also promises to revolutionize AI video for advertising. We see how neural networks are transforming content creation, making AI conveyor 500 videos per day possible and offering new opportunities for scaling creative processes.
If you want to know where to order AI UGC for a crypto project or how to generate 100 videos using neural networks, Melius demonstrates that the future is already here. Join this neuro-revolution and contact us to find out how AI can scale your content production!
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.
In the world of artificial intelligence, a serious discussion is brewing: should we slow down the pace of development or continue to move forward without looking back? Against the backdrop of Anthropic CEO Dario Amodei’s plans to “pace the frontier” and Nvidia CEO Jensen Huang’s statements about the unnecessary nature of regulation, the question arises: how serious are the intentions of industry leaders like Amodei and OpenAI’s Sam Altman regarding a slowdown?
Slowing Down AI Progress: Myth or Reality?
On the latest TechCrunch Equity podcast, Kirsten Korosec, Sean O’Kane, and Anthony Ha discussed these conflicting opinions. Anthony Ha was surprised by how many industry leaders supported Amodei’s plan.
However, Sean O’Kane noted that the plan looks vague and lacks specifics, which raises doubts about its feasibility.
Lack of Regulation and Consumer Choice
“The federal government right now is certainly not eager to broadly apply regulation, let alone in specific cases,” Sean O’Kane stated, emphasizing that existing laws are not always effective in a rapidly changing technological environment.
He also noted that in the market for AI solutions, there is virtually no consumer choice that could serve as leverage over companies.
“It doesn’t feel like consumer choice is heavily influencing this market in the sense that if one of these companies does something really bad, they’re going to see the consequences in terms of cancellations or anything,” O’Kane added.
This means that even serious missteps by major players may not lead to significant financial losses due to the loyalty of corporate clients and huge investments.
Jensen Huang and Nvidia’s Position
Nvidia CEO Jensen Huang publicly spoke out against a slowdown, supporting Donald Trump’s view that fears about AI are a “hoax” and regulation is unnecessary.
His position raises questions, given that Nvidia is one of the main beneficiaries of the rapid development of AI technologies, supplying the necessary equipment for rendering and training neural networks.
Why is Nvidia Against a Slowdown?
Economic interests: The rapid growth of AI directly correlates with the increased demand for Nvidia’s graphics processors. A slowdown could negatively impact their revenues.
Role of the “adult in the room”: Sean O’Kane believes that Huang positions himself as the “adult in the room,” aiming to be a useful liaison between the industry and the administration. This allows him to influence decisions and shape the agenda.
Lack of direct competition: Nvidia does not develop end-user AI models, but provides infrastructure, so the direct risks from “unsafe” AI are lower for them.
Specifics of “moderate development” proposals
Kirsten Coroset mentioned proposals for “moderate development,” which include:
Attracting independent third-party evaluators to monitor AI safety within companies such as Anthropic and OpenAI.
Coordination between major AI companies in democratic countries to develop common safety standards and restrictions.
International coordination of efforts to regulate AI.
However, as Coroset noted, these proposals suffer from a lack of specifics. It is unclear who will act as independent evaluators, what standards will be applied, and how international cooperation will be carried out.
This creates the impression that statements about a “slowdown” may be more declarative than practical.
AI pipeline: from text to video in seconds
While discussions about regulation continue, the AI video generation industry is demonstrating unprecedented growth rates. Companies like Synthesia and other market players offer solutions for mass video generation by neural networks, opening new horizons for business.
We see how neural networks allow the creation of AI commercials, animated AI characters, and even AI avatars with subtitles and AI voiceovers.
Advantages of AI production:
Speed: An AI pipeline can produce hundreds of videos per day, which was impossible to scale – now possible.
Cost savings: The cost of 1 minute of AI video is significantly lower than traditional production. This makes neuro-production by subscription accessible even for small businesses.
Personalization: The ability to generate video from text and use mass generation API allows for the creation of unique content for each client.
Efficiency: The efficiency of AI versus real UGC (User-Generated Content) can in some cases be higher, especially when dealing with large volumes of content.
These technologies are actively used in e-commerce, for AI UGC advertising, and even in crypto projects, where fast and effective communication with the audience is required.
The ability of neural networks to write scripts and edit videos significantly reduces the time and cost of content creation.
Frequently Asked Questions
What is AI content production?
AI content production is the process of creating video, audio, and text content using artificial intelligence technologies. This includes generating video from text, creating animated AI characters, AI voiceovers, and even neural networks writing scripts and editing.
How much does one AI creative cost?
The cost of an AI creative can vary depending on complexity, duration, and features used. However, in general, the cost of 1 minute of AI video is significantly lower than traditional production methods, making it accessible to a wide range of companies, including startups and projects in the cryptocurrency sector.
For AI UGC for a crypto project, you can turn to specialized platforms and studios that offer services for mass video generation by neural networks. Many of them provide AI production by subscription and use mass generation API to create content tailored to the specific needs of the crypto industry.
Conclusion
The discussion about slowing down the development of the AI industry, while important for ensuring safety, faces pragmatic interests and a lack of clear implementation mechanisms. At the same time, AI video generation continues to develop rapidly, offering businesses unprecedented opportunities for content creation.
Instead of slowing down, we are likely to see increased competition and the emergence of new, even more effective solutions for AI production. Want to know how AI video for advertising can scale your business? Contact us to discuss the possibilities of an AI video factory for e-commerce and generating 100 videos for your marketing campaigns.
In the era of mass AI video generation and other types of content, many companies face a problem: their new pages, created specifically to improve visibility in AI search, are not being indexed and are not bringing the expected results. Why does this happen, and how can one build an effective workflow for AI video for advertising and other formats? The answer lies in understanding not just content gaps, but solution gaps.
The Problem of “Content Parity” in AI Search
One client found that their new pages, created for AI search, were not being indexed by Google. The team followed all recommendations: analyzed AI responses, studied competitors, identified gaps, and created more comprehensive content. However, in Google Search Console, these pages had the status “Crawled – currently not indexed.”
“Indexing takes time,” said the SEO consultant, but a month had already passed.
The problem lay in what is called “content parity.” The content was high-quality and well-organized, but it merely repeated existing information without adding anything new. This did not lead to information gain, which AI values so much.
Differences Between Information Gain and Competitive Gaps
Competitive gaps: This is what you lack but your competitors have. Filling these gaps is important, but not always sufficient for information gain.
Information gain: This is new, unique information that helps the user or AI make a decision.
Solution Gaps: The Key to Effective Content
Instead of simply filling competitive gaps, it is necessary to focus on solution gaps. This is information that the user or LLM needs to make a decision, but which neither you nor your competitors have.
Example: “Best Family Resort in Cancun”
The query “What is the best all-inclusive family resort on the beach in Cancun?” is not just a set of keywords. It is a request for a decision. The AI system must evaluate criteria: “all-inclusive,” “family,” “on the beach.”
“All-inclusive”: Not as Simple as It Seems
The definition of “all-inclusive” is often vague. Marketing is great at selling a promise but poor at defining boundaries. For example, one resort states in its FAQ that the package includes accommodation, meals, drinks, sports, entertainment, and children’s programs, but excursions and spa treatments may be at an additional cost.
Another resort dedicates an entire page to “all-inclusive” services, but a precise definition of what exactly is included is hard to find.
The problem is not the lack of content, but that it doesn’t fully resolve the criteria. The user or AI has to piece together the definition to understand where “all-inclusive” ends and “additional costs” begin.
“Family-friendly”: what lies behind the beautiful words
A similar situation applies to “family-friendly.” A resort may have an excellent section for children and families, but the description of the program for teenagers aged 14-17 might be too general. A parent wants to know specifics: is it supervised childcare or optional activities? What are the operating hours? What activities are offered? Do teenagers leave the premises? What level of supervision is provided? These questions remain unanswered.
Content analysis tools might show that the resort has “strong thematic coverage” for teen programs. But a parent doesn’t evaluate content architecture; they make a decision that will meet their family’s needs.
A new approach to content creation: from criteria to knowledge
To create content that is truly indexed and useful, we need to change our approach. Instead of simply generating content, we must:
Define the client’s solution: What is the client or LLM trying to solve?
Identify solution criteria: What criteria are important for making this decision?
Find existing evidence: Where is the information that supports these criteria?
Discover gaps in evidence: What information is missing?
Qualify the gaps: Is it a knowledge gap, information fragmentation, or intentional omission?
Acquire knowledge: If information is missing, how can it be obtained?
Validate: Check the accuracy and completeness of the information.
Create content: Using AI, transform knowledge into content.
Where to get new knowledge?
AI can analyze thousands of pages, but it cannot “produce” an organizational fact. If the policy for supervising a teen program is not documented, the model will not know it. If no one has measured the actual distance from the room to the beach, AI will not create that knowledge.
Answers can be found in:
Support tickets
Call center transcripts
Internal site search queries
CRM records
Product documentation
Surveys
Training materials
The minds of expert employees
Sometimes even Reddit, reviews, forums, and other communities where people discuss details that companies miss are useful. For example, a resort describes itself as “family-friendly,” and a frustrated parent explains what happened when they tried to enroll a three-year-old in the kids’ club.
AI and the Future of Content
AI has significantly reduced the cost of converting knowledge into content. Now, subscription-based neuro-production or an AI pipeline generating 500 videos per day have become a reality. However, this does not negate the need for knowledge acquisition. Text-to-video generation or mass generation APIs do not create facts; they merely process existing data.
The advantage shifts up the chain: to a better understanding of customer decisions, identifying criteria that others have missed, uncovering operational knowledge hidden within the organization, connecting it to your products and experience, and validating the answer before publication. This is not just about creating AI commercials or AI UGC for a crypto project; it’s about creating value.
Sometimes this means analyzing queries, sometimes listening to support calls, sometimes discovering that Reddit users are answering a question the company never addressed. And sometimes it means something that seems old-fashioned: talking to an expert and gaining new knowledge.
AI can help us identify gaps, organize evidence, and transform acquired knowledge into useful content. But for true informational gain, there must always be something new to acquire.
Content parity is the creation of content that almost completely duplicates information already available from competitors or other sources. It is harmful for SEO because search engines, especially AI search, look for unique, valuable, and new information (informational gain) that helps the user make a decision, rather than simply copying what has already been said. Such content is often not indexed or ranks poorly.
How can AI help in creating content that brings informational gain?
AI can significantly speed up many stages of content creation: from competitor analysis and identifying existing gaps to organizing evidence and transforming knowledge into content. However, AI cannot independently create new, unique knowledge. It is a powerful tool for processing and structuring information, but human input in the form of research, expert knowledge, and understanding user needs remains critically important for achieving informational gain.
In what way do competitive gaps and solution gaps differ?
Competitive gaps are areas of content where your competitors have information, but you don’t. Filling these gaps helps you catch up with competitors. Solution gaps are information that a user (or AI) needs to make an important decision, but which neither you nor your competitors have, or it is fragmented and incomplete. Focusing on solution gaps allows you to create truly valuable and unique content.
Master new approaches to content creation so that your AI avatar with subtitles or AI face-swap clips not only generate videos but also provide real value. Discover how neural networks write scripts and edit, while you focus on the main thing – knowledge. Find out how much one AI creative costs or the cost of 1 minute of AI video, and how to maximize ROI by creating content that truly solves customer problems.
