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.
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.
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!
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.
Want to create professional videos without filming or hiring a team? Wondering how to use AI-generated video intros to grab attention on social media? In this article, you will learn how to create an AI avatar, produce catchy video hooks, edit existing material, and assemble long videos from 10-second clips using Google Gemini Omni. No more racking your brain over choosing a contractor or wasting nerves searching for specialists – all this can be done in one place, reducing costs and time.
Introduction: Revolutionizing Video Production with Google Gemini Omni
In today’s world, where visual content reigns supreme, creating high-quality videos is becoming a key success factor for brands and individual content creators. However, traditional video production often involves high costs and complex processes. This is where Google Gemini Omni comes in, offering a revolutionary solution. This tool allows you to create professional videos using advanced artificial intelligence technologies, making the process accessible, fast, and cost-effective.
What is Google Gemini Omni (OmniFlash)?
Google Gemini Omni, officially known as OmniFlash, is the successor to Veo3 – Google’s first AI video generator. Omni is built on Google’s “world models”, which were trained on an extensive YouTube video library. This training gave it a unique understanding of movement, environment, people, and speech intonations, which sets it apart from other AI video tools.
Thanks to this, you get unique and high-quality videos that are difficult to distinguish from those shot by professionals. OmniFlash opens new horizons for content creation, allowing users to embody the boldest ideas without the need for expensive equipment or specialized skills.
Access to Gemini Omni: Levels and Plans
Omni is available on several platforms, making AI video creation accessible at different levels. The easiest way is through the Gemini app, available on desktop and mobile devices. Within the Gemini chat interface, by clicking the “plus” button, you will see the “Video” option, which launches Omni in the background.
Gemini App: Ideal for beginners. Subscription starts at $20/month.
Google Labs: For advanced editing and modification features.
Third-party aggregators: Open Art and Higgs Field, which combine several AI models.
According to Eva Whitaker, a basic Gemini subscription for $20 a month is sufficient to start. The more expensive “ultra” tier at $200 a month includes access to all Google tools, but most users won’t need it right away. Credits are consumed for each generation, with higher resolution and longer clips requiring more credits.
Starting with the lowest tier and purchasing additional credits as needed will allow you to control costs.
“For those serious about AI video production, Eva recommends combining a direct Google Labs subscription with a third-party aggregator. The aggregator provides access to multiple AI models from a single dashboard, while Google Labs unlocks the full Omni editing capabilities.”
Creating an AI Avatar: Your Digital Twin
Unlike clones created by HeyGen, which generate talking-head videos from a long script, an avatar is an “AI twin”: a digital representation of a real person that can be placed into any scene or scenario using text prompts. This allows for unique content and makes it as personalized as possible.
Avatar Creation Process
The avatar creation process takes about five minutes. Open the Gemini app on your phone, tap the plus button, and scroll to “avatar.” The app will guide you through a capture sequence similar to Face ID: look left, right, up, down. Then it will display nonsensical sentences for you to read aloud to record your voice.
The intentionally strange text is designed to capture natural speech patterns without overthinking pronunciation.
Lighting: Natural light works best.
Audio: Minimize background noise for a clean voice recording.
Clothing: Choose a versatile outfit, as it will become the default outfit.
Hats and glasses: Avoid them during recording so that the AI does not distort the face.
Tone of voice: Speak naturally to maintain flexibility in future generations.
Once created and named, the avatar syncs across platforms. This means that a user who created their avatar on their phone can also summon it in Google Labs on the desktop by typing the “@” symbol followed by the avatar’s name.
Effective Prompts for AI Video: The Four-Element Formula
Effective prompts for AI video follow what Eva Whitaker calls the “four-element prompt formula”: subject, action, environment, and camera. Omni responds well to natural language, meaning the days of formatting prompts in JSON or coded syntax are over.
Subject: Who or what appears in the video (e.g., @avatar_name).
Action: What the subject is doing (dancing, talking to the camera).
Environment: The setting (a beach in Mexico, a kitchen).
Camera: The camera’s position and movement relative to the subject.
Eva advises starting simple and iterating. For example, if the first generation places the subject too far from the camera, the next prompt adds: “she stands close to the camera” or “she approaches the camera.” Each generation shows how Omni handles different types of instructions.
Including a script in the prompt is crucial. Without it, Omni will generate its own dialogue, which sometimes leads to interesting coincidences, but more often to unusable audio.
Creating Engaging Video Hooks for Social Media
The primary use case Eva recommends for Gemini Omni is creating attention-grabbing hooks for short videos on TikTok, Instagram, YouTube Shorts, and other platforms. The concept is simple: shoot a standard talking-head video the traditional way, then use Omni to create a visually striking 2-3 second hook to open the video. This allows for maximum audience engagement as quickly and effectively as possible.
Hook Examples and Creation Process
Eva created hooks where her avatar is slimed Nickelodeon-style, has a cake shoved in her face while wearing an evening gown, stands in a park where tennis balls fall from the sky, rides a buffalo in front of a house for sale sign, skydives onto a lawn next to a “for sale” sign, and walks across a kitchen counter as a six-inch avatar, saying: “The only small thing in this kitchen is me.”
Speed of creation is part of the appeal. Eva created the buffalo-riding hook while waiting in line for coffee: she opened the Gemini app on her phone, summoned her avatar, asked it to ride a buffalo in front of a house for sale sign, using a Zillow photo as a reference image, and had the finished clip by the time she picked up her order. This mobile workflow allows for polished hooks to be created anywhere, without a desktop computer or editing software.
The “Captain Obvious” Method for Creative Ideas
Eva uses LLMs to generate AI video hook concepts rather than brainstorming them alone. The initial process starts with describing a topic and asking for creative visual hooks. The initial suggestions from any LLM are typically predictable, and this is where Eva’s “Captain Obvious” technique comes in.
This method allows for guiding the LLM away from its default, predictable suggestions to more creative and distinctive ideas. When the LLM suggests a predictable visual, such as a person sitting at a computer with too many tabs open to represent “AI overload,” Eva tells it:
“That’s too obvious. That’s something anyone could come up with. Give me something smarter, more creative, something that makes people stop and think, ‘What was that?’”
This redirection consistently leads to more creative and distinctive concepts. When combining an AI-generated hook with a traditionally filmed talking head video, visual continuity is important. If the avatar appears in different clothing than in the video, the transition feels jarring. Eva’s solution is to take a screenshot from the video and upload it as a reference image, then ask Omni to dress the avatar in the same clothing. The hook clips and talking head video are then edited together in CapCut, Instagram Edits, or a similar tool.
Editing Existing Videos with Gemini Omni
Gemini Omni can alter existing videos using natural language prompts, making editing that once required a professional editor accessible to anyone. These capabilities, available through Google Labs and aggregator platforms, go far beyond simple generation. Real footage, shot on a phone or AI-generated, can be uploaded and transformed with a text description of the desired change. This allows for budget savings on post-production and quick results.
Editing Capabilities
Changing the environment: A summer park can be transformed into a winter one with a single prompt.
Adding motion graphics: Animated text overlays with ingredient names can be added to a cooking video.
Changing the subject’s clothing: A character can change outfits at your request.
Removing objects from a scene: Remove unwanted elements from the frame.
Transforming a person into a cartoon version: Create a unique visual style.
Complete background change: A talking head video can be transported to a beach or a news studio.
Tip: Videos uploaded for editing should be trimmed to 10 seconds or less. Omni provides a built-in trimming tool, so longer footage can be cut to the appropriate segment before applying modifications.
Creating Long Videos from 10-Second Clips
The 10-second generation limit does not prevent the creation of longer AI video content. This requires prior planning and a third-party editor. For a 90-second video, the approach is to generate nine 10-second clips and edit them together. The key is to plan what the last frame of each clip looks like and how it transitions into the first frame of the next clip.
Here we have collected answers to the most frequent questions about Google Gemini Omni.
How to get started with Gemini Omni?
Start with the Gemini app, available on desktop and mobile devices. A subscription starting from $20/month will allow you to explore the basic features. For more advanced capabilities, consider Google Labs or third-party aggregators.
What are the limitations of Gemini Omni?
The main limitation in the Gemini app is the generation limit (about 5-6 videos per hour). Google Labs and aggregators offer more generous limits. Avatars are limited to 10-second clips, which need to be edited to create longer videos.
Can I use my avatar on different platforms?
Yes, once created, the avatar is synchronized across platforms. You can create it on your phone and use it in Google Labs on your computer.
How to achieve high-quality avatar?
The recording environment plays an important role: natural lighting, minimal background noise, versatile clothing. Avoid hats and glasses during recording. Speak naturally to maintain flexibility in future generations.
How does Gemini Omni help save budget?
Using AI tools like OmniFlash allows you to replace a team with a service, reducing video production costs. You can order a full cycle of video production in one place, from avatar creation to editing and posting, which is significantly cheaper than hiring individual specialists.
Conclusion: Full-Cycle Turnkey Video Production
Google Gemini Omni (OmniFlash) is a powerful tool for creating professional videos using AI. It allows not only to generate unique avatars and captivating hooks but also to edit existing videos and assemble long videos from short segments. This is an ideal solution for those who want to get a full cycle of short video production, comprehensive promotion through videos, and content outsourcing for social networks, replacing an SMM team with one service.
Start using Gemini Omni today to save budget and nerves by creating high-quality video content that truly captures the audience’s attention. Order everything in one place and see how effective this approach is!
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.
Interested in how to create AI-generated video that looks cinematic, not cheap? Do you want to have a repeatable workflow for turning a simple concept into polished AI video content using tools like Seedance? In this article, you will learn a step-by-step process for creating professional-quality AI video, from concept development and building key visuals to generating clips with Seedance and assembling them into a finished product.
The biggest misconception about AI video is that it’s easy. Entering a prompt into a video model yields a result, but its quality is entirely dependent on the creator’s ability to communicate with the tool. Ross Symons notes that most people who dismiss AI video as “low quality” are seeing the result of vague prompts, not the limits of the technology itself.
Understanding Models: LLMs vs. Diffusion Models
The first thing to understand is the difference between how Large Language Models (LLMs) and diffusion models process instructions. LLMs, such as ChatGPT, Claude, or Gemini, understand conversational intent. A diffusion model, the underlying technology behind most AI image and video generators, including Midjourney and Seedance, does not.
Diffusion models extract visual keywords from a prompt and ignore conversational “fluff.”
The phrase “create me an image of a cat walking on a beach wearing a cowboy hat” works in Midjourney, but the model doesn’t parse that sentence the way a chatbot would. It looks for visual keywords: cat, beach, cowboy hat.
Prompting strategies that work well in chat do not directly translate to image and video generation. Each model has its own prompting structure, and learning that structure is what separates a polished result from an ordinary one. Ross notes that certain tools excel at specific tasks.
“Midjourney, for example, remains the preferred tool among creatives for art direction, ideation, and creative exploration,” — Ross Symons.
ChatGPT’s image generation has the advantage of a reasoning layer that interprets intent, but many professionals still prefer the aesthetic output of a specialized image model.
For those who don’t know how to write structured prompts for a tool like Midjourney, there’s a practical life hack. Instead of learning the syntax, tell ChatGPT what you want the desired image to look like and ask it to write a prompt in Midjourney format. ChatGPT converts conversational descriptions into a structured, keyword-based format that diffusion models respond to, leading to significantly better results.
Pro Tip:Image generation is the foundation for AI video. Mastering the creation and refinement of static images first leads to a much higher success rate when transitioning to video, because the same principles of prompting, composition, and visual direction apply.
1. Developing the AI Video Concept
The concept is the underlying idea of what the video is trying to convey. It doesn’t have to be complex or deeply artistic. It can be as simple as “I want to show my product in an unexpected environment” or “I want to explain this technique with a visual metaphor.” The point is to have an intention before touching a tool.
Ross illustrates this with a project he originally created as a stop-motion animation many years ago. The concept: a can of Red Bull sits on a table. A piece of paper slides, folds into an origami bull, bumps into the can, opens it, drinks the liquid, grows wings, and flies away. The whole work is a play on the words “Red Bull gives you wings.” This concept has transitioned across different mediums. He recently recreated it using Seedance, feeding the sequence into an AI video model with a couple of image references, and the concept held up because the idea was strong regardless of the production tool.
Ross recommends using LLMs to further develop the concept. Even a basic idea can be expanded by asking ChatGPT to help extrapolate the narrative, suggest visual sequences, or variations. The goal is to have a clear understanding of the story before moving on to visual development.
2. Creating Key Visual Elements
Once the concept exists, the next step is to develop the key visual elements that will anchor the video. Ross divides this into three components:
Subject (or hero): This is the focus of the story. It can be a product, a person, or any object that drives the narrative. For the perfume ad Ross created, the hero was the bottle itself. He generated a product image mockup with Midjourney, establishing the look of the bottle before doing anything else. Creating product mockups with ChatGPT, Midjourney, or Gemini is a valid starting point when professional product photography is not available.
Environment: This is the setting where the story takes place. Ross built a jungle environment in Midjourney for a perfume ad. An environment can be created in any image generation tool or sourced from reference images on Pinterest, Shutterstock, or a personal photo library. It’s important to be specific about concrete visual qualities. Instead of telling the model to make something “cool,” Ross recommends describing what is appealing about the reference: the time of day, how the light hits surfaces, color temperature, and depth of field. The more specific the direction, the better the result.
Secondary Character or Element: This is anything else that appears in the scene to support the narrative. In the perfume example, Ross added a black panther entering the frame and eventually looking at the camera, then leaping toward it. This secondary element created tension and movement in what would otherwise be a static product shot.
Pro Tip: When using a personal photo as a character, isolate the subject on a solid background. Do not use a photo with a busy environment or other people. Provide multiple photos taken from different angles, all showing the subject in the same clothing. This gives the model clear data to work with when placing the character in new environments, poses, and scenarios.
Using Camera Angles for Visual Storytelling
Camera perspective is one of the quickest ways to elevate AI-generated visuals beyond the flat, centered composition most models produce. A few basic cinematography principles go a long way.
Low angles make a character powerful and dominant.
High angles can make a character appear submissive or vulnerable.
Close-ups create intensity.
Wide, pulled-back shots with empty space around the subject suggest isolation or vulnerability.
A practical tip for those without a cinematography background: use ChatGPT to analyze a film still. Upload an image and ask the model to explain what creates a certain emotional effect. Is it the camera angle? Dramatic lighting? Soft bokeh? Contrast? ChatGPT will break it down using the correct cinematic terminology, which can then be directly used in image and video prompts.
Ross tested this approach by asking ChatGPT to generate “Guy Ritchie’s six most commonly used shots” in a single image. The result included close-ups, high-angle shots, and several techniques Ross hadn’t encountered before, each usable as a reference for future prompts. Creators can reference specific directors or films without knowing the technical vocabulary. Phrases like “make this character look like Aladdin in this scene” or “show me a Guy Ritchie-style shot with this character in this environment” yield results that differ from the generic, centered output most people receive.
3. Creating an AI Video Storyboard Using Keyframes
A keyframe in the context of AI video is an image used as a reference point for the model. A storyboard is a sequence of 6-12 keyframes that outline the main moments of the video, establishing the angle, lighting, and composition for each moment.
The storyboard serves the creator more than the model. It provides structure, prevents overloading a single clip with too much action, and establishes a clear sequence of events before video generation begins. Ross emphasizes that the storyboard doesn’t need to be professional. It’s a planning tool, and rough images are perfectly adequate if they convey how each moment should look and feel.
There are two main approaches to using keyframes with AI video models:
Start Frame + End Frame + Prompt: This method provides the model with an image to start the video, an image for its end, and a text prompt describing what happens in between. For example, the start frame is an empty surface. The end frame shows a Red Bull can centered in the shot. The prompt reads: “A Red Bull can slides in from the right at a slow speed and stops directly in the center.” The combination of visual anchors and text instruction gives the model clear constraints.
Start Frame + Prompt Only: This method uses only a starting image and allows the prompt to guide the action without a fixed endpoint. It offers more creative freedom. For a 10-second clip, the start frame could be an empty surface, and the prompt could read: “A Red Bull can slides in from the left, a paper airplane flies in from above, lands next to the can, unfolds into a flat sheet of paper.” The model fills in the motion and timing.
Pro Tip: To create a longer video, stitch clips together by extracting the last frame from the previous clip and using it as the start frame for the next. This creates visual continuity across a multi-clip sequence without having to regenerate consistent elements from scratch.
Common Mistakes When Creating Keyframe Sequences
The most common mistake Ross sees is people cramming too much action into a short clip. A 5-second clip can only accommodate one or two actions. Writing a long paragraph describing a sliding can, flying paper, a bull forming, and an explosion, all within five seconds, overloads the model. The result is distorted limbs, warped perspectives, and visual artifacts. The solution is to match the complexity of the prompt to the duration of the clip.
Ross also distinguishes between using images as keyframes and using them as references. Keyframes require precise reproduction at specific points in the video, often leading to awkward transitions and unnatural camera movements as the model is forced to move between two rigid endpoints. References give the model an idea of the desired look without requiring pixel-perfect accuracy, and they tend to produce smoother, more natural results. Ross recommends using references instead of keyframes in most cases.
4. Video Generation with Seedance and Assembling the Final Product
Seedance, developed by ByteDance (the company behind TikTok), stands out for its adherence to prompts and the accuracy of reference images. Ross describes it as almost flawless in both areas, which is a significant step forward compared to video models even two years ago.
Seedance is not a standalone application. It is a model accessed through aggregator platforms. Ross recommends Luma AI (lumalabs.ai) as the preferred platform, along with Flora, Figma Weave, Artlist, Imagine Art, Open Art, and Krea. These platforms provide access to Seedance alongside other video models such as Kling 3.0, Veo 3, and others, all connected via API.
Google Veo 3 (available via Gemini) offers free video generation and was considered cutting-edge before the advent of Seedance and Kling 3.0. Ross notes that while Veo is useful, skills learned on one model do not automatically transfer to another. Each video model has its own prompting structure. Practice with Veo builds general familiarity with AI video, but getting the best results on Seedance requires learning how Seedance specifically responds to prompts, timing instructions, and reference images.
How to Effectively Use Prompts in Seedance
Seedance supports time-segmented prompting, which allows creators to specify what happens at certain intervals within a single clip. For a 15-second clip, the prompt might be structured as: “Between 0 and 4 seconds, this happens. Between 4 and 8 seconds, this happens. Between 8 and 12 seconds, this happens.” The model adheres to these timing instructions, allowing for the choreography of multi-step sequences in a single generation rather than stitching together multiple short clips.
Seedance Cost and Scaling Workaround
Seedance offers a choice of durations from 5 to 30 seconds per clip. Ross advises beginners to start with shorter, low-resolution clips to get familiar with the workflow. Generating a 30-second high-resolution clip without experience often leads to wasted money and unsatisfactory results. A 30-second Seedance clip at 720p resolution costs approximately $14. Increasing the resolution to 1080p roughly doubles the cost to $28-32. These prices are significantly higher than earlier AI video models, where 5-second clips cost less than $0.20, but the quality difference is also significant.
A cost-effective alternative is to generate at a lower resolution and then upscale. Ross generated a 480p clip for approximately $6, then upscaled it twice using the platform’s built-in tools to near-4K quality for an additional $3. The total cost of $9 yielded quality comparable to a native high-resolution render, at about a third of the price. The two leading upscaling tools are Topaz Labs and Magnific, both of which are integrated into most aggregator platforms.
Conclusion
Mastering AI video isn’t just about mastering tools; it’s about developing a director’s mindset. By applying principles of concept, visual development, and storyboarding, you can create high-quality AI video content that looks professional and engaging. Start with a clear idea, use LLMs to develop it, create strong visuals, and meticulously plan your sequences. Then you can scale your video productions to levels previously impossible, and now possible.
Frequently Asked Questions
What is Seedance and how does it work?
Seedance is an AI model for video generation developed by ByteDance. It allows you to create videos based on text prompts and reference images, while ensuring high accuracy and quality. It is accessed through aggregator platforms such as Luma AI.
How much does it cost to create a video using Seedance?
The cost of a 30-second clip at 720p resolution is about $14, and at 1080p it’s $28-32. However, you can save money by
Generative AI promises a revolution, but the real cost of content production often remains hidden. We calculated how much it costs to create a 25-second commercial using neural networks, including script, images, video, voiceover, and music. Spoiler: the 25-second video cost 2,126 ₽, or 85 ₽ per second. The lion’s share of the budget, almost 90%, was “eaten up” by video generation.
How much does AI advertising cost: a 25-second video for 2,126 ₽
In a world where technology is developing at an incredible pace, artificial intelligence is becoming an integral part of many industries, including content creation. But what is the real price of this innovation? We conducted an experiment to find out how much it costs to create a 25-second commercial entirely with neural networks. The results may surprise you, because the cost of unique content for one video and profitable CPM for advertising materials are becoming key factors.
The experiment was conducted through BotHub using the following models:
We advertised a herbarium, without commercial implications. Important: only the marginal cost was taken into account — the cost of successful generations included in the final video. Actual costs may vary depending on prompting skills and perfectionism level, increasing the number of regenerations and, accordingly, the budget.
Methodology for calculating the cost of AI production
For an objective assessment, it is important to distinguish between two types of costs:
Cost of result generation: direct computational costs for content creation.
Cost of infrastructure access: costs for subscriptions or minimum credit packages for using services.
In the case of a single video, the second amount can significantly exceed the first. Of course, open-source models can be deployed locally, but this requires a powerful PC, which is not always available.
«Don’t replace professionals with browser tabs. AI content has nuances: rights, licenses, character stability, result control, and brand requirements. AI slop is easy to get, and you’ll get flamed for it».
We started by creating a concept. GPT-6 Astra acted as a creative producer and scriptwriter. The prompt was formulated so that the model immediately took into account the limitations of AI generation and formed prompts for each element.
Prompt to GPT-6 Astra:
«Role: You are an experienced creative producer and scriptwriter specializing in creating advertisements using neural networks. Task: Develop a detailed script for a 25-second commercial for a Herbarium (an album with a collection of dried plants/herbs). Important condition: All content (video, photo, audio) will be AI-generated. Therefore, your task is not just to provide a script with a description of the picture, but a script supplemented with prompts for each element. Atmosphere: warm, autumn, cozy».
The script development and adjustments took ₽102.09.
Step 2: Generating images for the ad
Nano Banana 2 was used to create eight base images that set the composition, atmosphere, and style. The total cost of this stage was ₽128.29, which is an average of ₽16 per image.
The images served as an intermediate state: they fixed the spatial structure of the frame, which simplifies the task for the video model. This allows for consistency between scenes, which is critical for a commercial.
Image-to-video: precise control over the result
Unlike text-to-video, where the model simultaneously interprets the description, chooses the composition, and adds movement, image-to-video fixes most variables. This gives greater control over the result and ensures stylistic unity across all frames, which reduces the cost of uniqueizing one video and provides a favorable CPM for advertising materials.
Step 3: Video generation: the most expensive part of AI advertising
Converting static images into video scenes is the most resource-intensive stage. Seven video scenes were generated using Kling v3 Video, preserving the original composition and adding natural dynamics.
The cost of one video scene was about ₽268, and the total cost of video generation was ₽1,878.17. This is 88% of the total budget. The high cost is due to the need to ensure temporal consistency: objects, camera, lighting, and scene geometry must be stable throughout the video.
Step 4: Ad voiceover with ElevenLabs: cheap and tasteful
ElevenLabs Flash v3 was used for the voiceover. This stage turned out to be one of the cheapest — only 6.04 ₽. TTS technologies significantly simplify the process compared to traditional voice actor recording, providing fast generation and low costs. This is an excellent option for cheap hypothesis testing and prototypes.
Step 5: Music for advertising in Suno: budget and limitations
The musical accompaniment was created using Suno v5.5. The model allows describing the mood and genre, rather than specific notes, which is convenient for advertising tasks. The cost of music generation was 11.82 ₽.
Although AI music saves budget, full control over the result is not guaranteed. However, for small projects, it is an effective alternative to stock tracks or ordering original music.
Total cost and comparison with direct subscriptions
The total cost of a 25-second advertising video created with AI was 2,126.41 ₽.
Tool
Cost
Script and prompts (GPT-6 Astra)
102.09 ₽
Base images (Nano Banana 2)
128.29 ₽
Video generation (Kling v3 Video)
1,878.17 ₽
Voiceover (ElevenLabs Flash v3)
6.04 ₽
Music (Suno v5.5)
11.82 ₽
TOTAL
2,126.41 ₽
If access to each service were paid for separately, minimum monthly subscriptions would be around 3,591 ₽ (at an exchange rate of $1 = 86.4 ₽):
ChatGPT (Plus): $20 (~1,729 ₽)
Nano Banana 2: ~$0.54 (~47 ₽)
Kling v3 Video: $6.99 (~604 ₽)
ElevenLabs Flash: $6 (~519 ₽)
Suno v5.5: $8 (~692 ₽)
These 3,591 ₽ are more of a starting capital than the cost of a single video, as tariffs usually include many more credits than required for one project. This highlights the benefit of using aggregators like BotHub for one-off tasks or cheap hypothesis testing.
Conclusions and prospects for AI production
AI production is not yet a seamless process. Each model has its own features and data formats. Cohesion between stages is ensured by an external workflow: prompts, references, manual editing. This distinguishes the demonstration of individual model capabilities from a full-fledged production system, where all elements must work in unison. The price per finished clip with AI can be significantly lower than traditional production, but requires competent pipeline management.
Overall, AI tools significantly lower the barrier to entry for video content creation, offering a budget-friendly alternative to seeding and traditional production studios. However, obtaining a high-quality result requires skills and an understanding of each model’s limitations. This makes CPM profitable for testing various advertising creatives.
Frequently asked questions
What CPM should be considered profitable for AI advertising?
A profitable CPM depends on the niche and campaign goals. If the cost of producing a video is low, as in our case, then even with an average CPM, the overall return on investment (ROI) can be high. The main advantage is the ability to quickly and cheaply test hypotheses.
What is cheaper: a banner, a blogger, or an AI video?
A direct comparison is incorrect, as these are different formats with different goals. However, AI video offers cheap hypothesis testing and can be significantly cheaper than producing a video with a blogger or even some types of banners, especially if complex animations are required. The cost of creating one AI video at 2,126 ₽ makes it competitive for mass content production.
Is it worth investing in AI for content production?
Yes, it is. AI significantly reduces the time and cost of production, allowing for quick creation and testing of advertising materials. This is especially relevant for small and medium-sized businesses, as well as for arbitragers who value the cash and unit economics of each creative. However, it is necessary to consider that the price vs. result strongly depends on the skills of working with prompts and post-processing.
How much does it cost to cut 1 hour of stream using AI?
The cost of cutting an hour of stream using AI will depend on the services used and their rates for video processing. If it’s about creating short clips, it can be significantly cheaper than manual work and comparable to the price of generating one video, as the main costs will be for transcription and content analysis, not for generating video from scratch.
What is the price per minute of AI video with an avatar?
The price per minute of AI video with an avatar varies depending on the platform and the complexity of the avatar (2D, 3D, customization). In our experiment, 25 seconds of video cost 1,878 ₽, which is equivalent to approximately 4,500 ₽ per minute if only the video generation cost is extrapolated. However, avatar platforms have their own rates, which can be both higher and lower, depending on the features and quality provided.
AI advertising opens up new horizons for content creation, making it more accessible and faster. If you want to delve deeper into the world of AI and learn how it can transform your business, visit our blog. Discover new opportunities and start creating unique content today!
