Google has taken an important step towards content creators: users will now be able to remove the visible watermark from materials generated by its AI models. This applies to images, videos, and music created in Gemini, the Flow video editor, and other tools. Meanwhile, the invisible SynthID watermark and C2PA standard metadata will remain mandatory to preserve transparency of content origin. The decision is already being called revolutionary for AI content production and subscription-based neuro-production.
New Option in Gemini and Flow
Google’s Vice President of Gemini products, Josh Woodward, announced on social network X that the toggle will be available for the Nano Banana, Omni, and Lyria models. Users will be able to disable the visible watermark in Gemini and the Flow video editor, with support in Google Search coming later.
This change reflects the evolution of the approach to AI media labeling: visible watermarks often hinder professional and creative use of content, but the need to identify AI-generated content remains. Woodward explained: “We are balancing creative control and safety: visible watermarks are now optional, but invisible SynthID and C2PA metadata still ensure transparency. You can use Gemini or Search to check whether an image was created by AI.”
How It Will Work
The feature will be rolled out in the coming days. Once available, users will be able to go to “Settings” → “Media watermark” and enable or disable visible labeling. This makes mass generation of AI videos for advertising more flexible, as visible marks often reduce the conversion of AI creatives compared to real UGC videos.
Credentio: Local Validation for Developers
Google is also open-sourcing a new library called Credentio, which will allow developers to embed a local content authenticity verification mechanism into their applications. This is a step towards a decentralized trust system for AI media.
“We are balancing creative control and safety: visible watermarks are now optional, but invisible SynthID and C2PA metadata still ensure transparency” — Josh Woodward, Vice President at Google.
Context: Regulatory Pressure
Google’s decision follows a controversial move by Anthropic, which added a watermark to text and files created by Claude to comply with EU regulations. This highlights the growing importance of ethical labeling of AI content, especially when creating AI avatars with subtitles, AI voiceovers, and text-to-video generation for e-commerce and crypto projects.
What This Means for Business
For companies using an AI pipeline for mass generation of videos, for example, 500 videos per day, disabling the visible watermark simplifies the use of AI content in advertising without compromising trust. At the same time, the cost of one minute of AI video remains competitive compared to traditional production, and the mass generation API allows scaling production, which was previously impossible.
Frequently Asked Questions
Will it still be possible to check if content was created by AI?
Yes, the invisible SynthID watermark and C2PA metadata are preserved, so you can use Gemini or Search to verify the origin.
When will the feature become available?
The rollout will begin in the coming days, first in Gemini and Flow, then in Search.
Why is Google doing this?
To improve the usability of AI content for professional and creative tasks while maintaining transparency.
This decision opens new opportunities for subscription-based neuro-production and AI content production, making the generation of AI videos for advertising even more attractive. If you are looking for where to order AI video for crypto or e-commerce, the process has now become even more flexible.
Want to be the first to test new AI generation capabilities without visible watermarks? Subscribe to our updates to not miss the feature launch and get practical guides on using SynthID and C2PA in your projects.
An online school reduced DRR from 30% to 13.75% and reached stable revenue of over 3 million rubles per month — this is not a promise, but a real case study from the Academy of Modern Psychology. In this article, we break down how changing the approach to promotion and restructuring the funnel made it possible to achieve these numbers without relying on launches.
Initial data and task
The client is the Academy of Modern Psychology, specializing in online courses for beginners and practicing professionals. Average check — 60,000–130,000 rubles, deal cycle — from 1 to 3 months. The task was to increase budget and revenue while maintaining advertising profitability.
We worked with the project for almost two years. During this time, we encountered several challenges: moving to a new advertising account, seasonal demand fluctuations, and the need for structural changes in campaigns.
Key changes in strategy
Transition to separate campaigns for each course
Previously, all courses were promoted with a single strategy and a shared budget. When the number of courses increased, this approach stopped working: each program has its own audience and its own decision-making cycle.
We split the campaigns so that each course is now managed separately. This allows us to disable ineffective areas and scale successful ones.
Dividing the funnel into three levels
All campaigns were divided into three directions: brand traffic, traffic for each course, and general traffic. Each level solves its own task:
the top level fills the funnel with new people;
the middle level converts those who have made a choice;
the bottom level closes those ready to buy.
“Remove any level — the system will start to deplete,” notes Kristina Shev, managing partner of the Verga agency.
Results by direction
General traffic
From January to March, it attracted 16,063 clicks at 48 rubles each. This is not a direct sales channel, but it constantly brings new people into the funnel, who later return through brand search.
Traffic for courses
It consistently keeps DRR below 14% with budget and lead volume growth. Some March sales are not yet closed in CRM — we expect improved metrics.
Brand traffic
DRR has stayed below 14% for the entire quarter, and the conversion to leads grew from 3.4% in January to 5.2% in March. The best format is the extended snippet, which takes up the entire search results screen and leaves no room for competitors.
March: record indicators
In March, several factors came together: seasonal demand growth, budget redistribution after Telegram Ads restrictions, the start of new streams, and the maturation of leads with a long deal cycle.
The budget grew from 338,000 to 430,000 rubles, and the number of leads reached 136, which is 53% more than in February. The final DRR in March was 13.75% with revenue of 3.13 million rubles.
Plans for the future
April is traditionally weaker, but the next peak is June-July. We plan to increase the budget to 500,000 rubles, launch auto-funnels, and test free webinars as an entry point.
We are also expanding the list of advertised courses — the client is preparing about 35 new programs.
Frequently asked questions
What DRR is considered profitable for online schools?
In this case, a DRR of 13-14% with an average check of 60,000+ rubles provides good margins. But there is no universal value — it all depends on the product’s margin and the share of variable costs.
What is cheaper: banner or targeting?
In this case, a banner on Yandex partner sites for narrow audiences showed a CPA of 1,681 rubles, which is cheaper than many other formats. But for broad topics, search works better — there is more targeted traffic.
How much does 1 million banner impressions cost?
In our case, CPM was about 48 rubles per 1,000 impressions — this is within the norm for Yandex Display Network. But the main thing is not the cost of impressions, but the cost of the result.
Should AI be used for creating videos?
If you need to quickly test hypotheses, AI generation can be cheaper than ordering from a contractor. But for sales videos where expertise matters, it is better to involve professionals.
Conclusion
March’s record is not a coincidence, but the result of a built system: funnel separation, managing each course individually, constant communication with the sales department, and optimization by revenue rather than leads.
If you want predictable revenue and lower DRR, start with an audit of your current campaign structure. Maybe you also need to divide your funnel into levels.
Want the same result? Contact us for a consultation.
ChatGPT chooses brands before searching: how to get on the list
ChatGPT inserts brand names into its search queries before it even loads pages. I read 60 conversations to understand when this happens and what helps a brand get mentioned. This is a key insight for anyone working on AI visibility: the decision on whether to recommend your brand is made long before ChatGPT touches your site.
I asked ChatGPT to name the best AI note-taking app. Seven words, no brand mentions. Before loading anything, it wrote itself a search query like this: Granola, Notion AI, Otter, Fireflies, Fathom, Mem, Limitless — seven products in one search. I didn’t name any of them, and nothing came from web search, so those names came from the model itself.
Then it ran nine more searches — that’s the “fan” of queries I wrote about in the first two parts. One search forms a shortlist, then one search per brand, each leading directly to the company’s website. The fan was never a candidate search — it’s ChatGPT going through a list it already has, one name at a time.
In the first two parts, I wrote that you need to survive the site:yourdomain.com check because ChatGPT runs such checks. But it only runs that check if you made it into the first search query. If you’re not on the shortlist, your site won’t be viewed at all, no matter how good it is. The decision is made before anything touches your server.
I spent two months reading this traffic for the first and second parts. This is the first thing that changes my recommendations to clients. Before reading further: all of this comes from a single account, so every percentage is a direction, not a measurement. The mechanism is another matter. You can reproduce it on your account in two minutes, and I’ll show you how at the end.
How it works
When you ask a question, ChatGPT rewrites it into its own search queries, runs them, reads the results, and then writes a response. These queries are in the response your browser loads, under the key search_queries. OpenAI renamed it from search_model_queries in early August 2026.
This isn’t a leak: your browser needs this JSON to render the page, and you can read it in DevTools on your account in about two minutes. Here’s an example from a question about chat software: I asked “best AI software for support chat,” and it added the year, “official,” “pricing,” and three names.
Everything below is based on reading several hundred such queries. Start with this: open ChatGPT, ask the question “best [your category]” that your customers ask, and read the query it writes. This line will show whether ChatGPT knows your brand exists—this is exactly what is usually sold as an AI visibility audit. The rest of the article is about what to do with what you find.
The Causality Test
The obvious objection to my note-taking apps example: perhaps ChatGPT first performed a search, saw those brands, and then wrote a smarter second query. That would make the names a result of the search, not a cause.
So I ran a test: for each dialogue, I took the first user message and the first search query, sorted by time. At that moment, nothing had been loaded, so there was nothing to learn from. In 21 out of 27 dialogues, the first query contained brands the user had not entered. Look at rows 2 and 3: the same question, slightly different wording—and the list grew from three to seven names. The list grows depending on the wording, not from a fixed table.
Then I ran 12 categories, completely unrelated to each other, to check that this was not a software quirk. 11 out of 13 showed the same thing. The robot vacuum row struck me: recalling that Roborock exists would be ordinary, but it recalled Saros, Dreame X50, and Eufy S1 Pro—current model numbers in the first query, without any prompt. This knowledge extends down to the product line.
The electric SUV row behaves differently. For accounting, therapy, and hosting, it named vendors and went to their pricing pages. For cars, it named magazines: Car and Driver, Edmunds, Top Gear. In one category, its instinct is to go to manufacturers; in another, to reviewers.
I was pleased with this and reran three categories to check stability. Language learning barely changed: five out of six names matched. Accounting shrank from six vendors to a single targeted query for QuickBooks. Web hosting completely switched sides: it dropped all vendors and went to a review site. So “vendors versus magazines” is a trend that can shift between runs, not a fixed property of the category.
Two things persist: the injection happened every time, and the category with the most obvious market leaders kept its names. I suspect that established categories have stable shortlists, while contested ones fluctuate, but three repetitions are not enough to assert this. Do not judge AI visibility by a single response. Run the question five times, because the list changes between runs.
What Influences Brand Injection
All queries up to this point had the word “best,” so I tried to break it. I ran 24 additional queries without that word in seven different forms. It turned out that “best” has nothing to do with it. What matters is whether ChatGPT has to come up with products on its own.
When it doesn’t search at all, it doesn’t work. Ask how noise cancellation works or what a vector database is—it will answer from training without web search. The same goes for open complaints: “We spend too much on customer support tools”—also without search. Seven of my twenty-four queries didn’t touch the web at all, so there was no shortlist to get into.
If you name brands yourself, it accepts them. “Xero or QuickBooks for small business” immediately went to site:xero.com/uk. “Should I use HubSpot for a small agency”—to site:hubspot.com. If you leave candidates to its discretion, it turns to memory. This happened in ten out of eleven cases when I asked for a recommendation without naming names.
The third line is my favorite: I deliberately avoided the words “robot vacuum” and didn’t name anyone, and it went to the site of a specific Roborock model in the first search. Line 2 should worry you if you sell software: a complaint about meeting notes turned into “Granola official pricing” before the page even loaded.
There’s a version aimed at your competitors. All three of my queries about replacements led to new names: “Zendesk alternatives”—Help Scout; “what can I use instead of QuickBooks”—Zoho Books; “something like Duolingo but better for grammar”—Kwiziq and Babbel. When your customer is looking for a way out from your competitor, ChatGPT suggests those it already knows. That could be you, and on that day, you can’t influence it.
If ChatGPT searches for a product and you haven’t named any, it brings its own. Do this: run your category question five times and write down the names that appear in the query each time. The names that appear in every run are your real competitive set in ChatGPT’s mind. What appears and disappears is contested territory where you can shift things. If your brand doesn’t appear once in five runs, you have your answer, and it’s not technical.
Citation: a 3.1% chance
I divided all brands into two groups: those that appeared in the query ChatGPT wrote and those that were only loaded during search but not named. Then I checked how often each group made it into the final answer. The difference is about 33 times. I also found 86 cases where a brand was recommended but its site wasn’t loaded at all in that conversation. A mention doesn’t require a crawl.
This is uncomfortable for my own industry. Much of what is currently sold as GEO is extraction work, i.e., the 2.1% column. The 68.9% column is decided before all of this even runs. Split your budget according to the two columns.
If you’re not in the query, money should go toward getting written about, reviewed, compared, and included in lists: digital PR, category content, placements on review sites, analyst coverage, participation in roundups your buyers read. If you’re already in the query, those expenses are mostly done, and the technical work below remains.
If it all stopped there, the advice would be “build brand equity,” and we could all go home. But after the query, a second filter kicks in, and it’s ruthless. I created a labeled dataset of 57 dialogues: each loaded page as a row, with a label indicating whether it received a citation. 3,554 pages, 110 received citations—3.1%. ChatGPT reads about 600 pages to write a single answer and cites about 30. Almost everything gets read. The gap between reading and citing is where the work lies.
Three factors influencing citations
Position. ChatGPT groups results by domain, and your position within that group predicts almost everything. Below the top 2, citations are a rounding error. Being in the set isn’t a win if you’re ninth.
Cannibalization. Adding pages hurts. When multiple pages from the same domain appear in one group, per-page conversion drops sharply. Two closely related pages is optimal. More than six—you’re mostly competing with yourself. For a year, I’ve been telling clients to consolidate based on intuition. This is the first time I’ve seen it in data.
Relevance. Relevance qualifies but doesn’t select. I rated the cited page against all other pages retrieved for the same claim, based on how well its text matched the supported sentence. The cited page landed in the top 5% of the pool. But it was the single best match only 20% of the time, and its average overlap was significantly below the best available.
So, claim relevance shapes the shortlist, and something else picks the winner. Get into the top 10% for a specific claim—and you’re in the conversation. That part you control through text. The final selection involves things I can’t see.
Here’s what to do: take the questions your buyers ask and find all your pages answering the same question. Choose the page that best matches the intent, make it the answer, and consolidate or redirect the others to it. Ensure the sentence that actually answers the question is at the top of the page as plain HTML text with numbers.
Finding these overlaps manually on a real site is the most painful part. Cannibalization is usually assessed by keyword overlap, but that’s the wrong unit because ChatGPT groups by claims, not keywords. That’s exactly what Keyword Insights is built for: it clusters keywords by search intent, not string matching, so pages competing for one intent are visible as a group, even if they share no keywords. This grouping precisely matches what I see when ChatGPT compresses a domain to a single cited page. Full disclosure: it’s my company, so I know it handles this task.
The results divide AI visibility into two games that are often confused. If ChatGPT doesn’t associate your brand with your category, it won’t mention you in a query, and you have a 2% chance of being cited. A schema won’t fix this, nor will page speed. The llms.txt file has even less chance because your server isn’t contacted before the decision is made.
What seems to build the association is slow, unglamorous work: being written about, reviewed, compared, and debated across the open web for years until the association appears in training data. This is digital PR and category-defining content, which is inconvenient for those selling technical audits as an AI strategy.
The technical part is 3.1%, and it’s real: one precisely targeted page per intent, a proposition with a key claim at the top, facts and figures in plain HTML text, no cluster of near-duplicate pages competing with each other. Here’s the full sequence you can execute this week:
Check if ChatGPT knows your brand: ask a category question five times and see if your brand appears in the search results.
Check which pages are cited: use FanoutFox to track citations and view queries.
Eliminate cannibalization: find pages answering the same question and merge them.
Optimize content: ensure the key claim and figures are at the top of the page.
In my data, one brand was loaded 66 times and never cited. That’s not an awareness problem. The engine kept returning and decided there was nothing to cite. Step 4 exists to catch this.
Steps 1 and 4 are what I built FanoutFox for, because doing them manually for every response quickly becomes tedious. It’s a free Chrome extension; everything stays in your browser, and it reads your own
Cross-posting: state, idempotency, and Cyrillic issues
Automating cross-posting articles to social networks is a task that seems simple at first glance, but in practice turns into a complex pipeline with many points of failure. In this article, we’ll break down how state, idempotency, and encoding issues affect the reliability of mass posting, and how to build a workflow without surprises.
Pipeline architecture: splitting into stages
The key idea is to split the process into independent stages: source → content package → media resolution → transport → verification → report. This allows you to localize errors and formalize the responsibility of each stage.
Package, transport, and QC
The package handles materials, transport handles delivery, and QC handles proof of execution. This separation helps avoid chaos when an agent “does everything itself” and it’s impossible to understand where exactly the error occurred.
The Telegram problem: timeouts and duplicates
First incident: the CLI returned gateway timeout after 10000ms, even though the post was already published. Resending led to a duplicate. Conclusion: an unsuccessful transport response is not a reason to repeat an operation with side effects. First, you need to check the fact of publication.
Message format: Markdown vs HTML
The Markdown link visually blended with the text. The solution was to fix the message format as a set of verifiable conditions: short announcement, HTML anchor, paragraph separation.
Browser as fallback: VK, OK, and others
Browser scenarios (noVNC) showed instability: VK anti-bot checks, image upload issues, loss of the preview card in OK. The browser was left only as an emergency path, with the main transport being APIs of scheduled posting services.
API responses: not a guarantee of success
HTTP 201 and scheduled do not prove that the publication will be correct. For each platform, you need a verifiable final state: post ID, status. Otherwise, the run cannot be considered successful.
Images: resolver and validation rules
Problems: WebP is not supported everywhere, Google Drive breaks preview, HEAD requests are misleading. The solution is a separate image resolver with rules: public URL, suitable format, size within limits, MIME check based on actual download.
Cyrillic and U+FFFD: broken characters
Unicode replacement characters (U+FFFD) ended up in the Google Doc, indicating data loss. This cannot be fixed automatically. Therefore, a byte-level check (EF BF BD) and a hard gate were introduced: if encoding corruption is detected, transport does not run.
JSON from the API turned out to be a poor source: CTAs and banners got in. The solution was to use the full public HTML followed by cleaning out irrelevant blocks. Structure, volume, and absence of CTAs are checked.
Limiting scope of responsibility: one article per run
Processing multiple articles at once leads to error multiplication. The skill enforces a limit: per automatic run — only one fresh unpublished article.
Final report: rendering from state
The report should be built from facts, not from the model’s memory. Store the run state: article URL, package statuses, media, each channel, blockers. This avoids false claims.
Stop factors: turning errors into rules
Each error became a stop factor: fixed caption format, publication fact check, card check before URL deletion, MIME rules, byte-level check, HTML cleaning, channel state check. This made the process reliable.
Working procedure
Select one new article.
Get HTML and clean out irrelevant content.
Assemble the package: announcement, two rewrites.
Check structure, volume, links.
Run encoding QC.
Resolve and check media.
Generate Google Doc.
Publish to Telegram directly.
Schedule other channels via LiveDune.
Check the state of each platform.
Fail if state is not proven.
Frequently asked questions
How to avoid duplicates on timeouts?
Check the fact of publication before resending. If the post is already published, do not repeat the operation.
Why is WebP not suitable for all platforms?
Some social networks do not support WebP or have size limits. Use conversion to PNG/JPEG with size verification.
How to verify that a publication is actually scheduled?
Use the API to get the post ID and status. Only the presence of a confirmed state counts as success.
Conclusion
Cross-posting is not just an action, but a system with many points of failure. Implementing stop factors, state checks, and limits turns chaos into a manageable process. Start small: split the pipeline, add QC, and ensure that every error becomes a rule.
Trust is the main KPI in the creator economy: insights from YouTube and Adam W
At a closed meeting of The Made You Look Tour in Los Angeles, senior marketers heard from a top creator and the head of YouTube’s ecosystem about why trust is becoming a key performance indicator in the creator economy. 25 participants, no recordings — only a live conversation about how to build partnerships that actually work.
In an era when consumers are tired of intrusive advertising, it is authentic creator voices that become the bridge between brand and audience. But how do you measure what cannot be expressed in numbers? The answers are in this material.
The creator economy ecosystem is fragile, and that’s okay
Scott Sutton, CEO of Later, opened the session with an important message: the industry is still young, and all parties — brands, creators, platforms — are building it simultaneously. “This is truly a community, an ecosystem, and it is quite fragile,” he emphasized.
That is why the format of a meeting with a limited number of participants and no recording allows for discussing problems rather than just extracting profit. In such an atmosphere, honest conversations are born about what really matters.
“If you focus on value, it doesn’t matter who we were ten years ago or who we will be in five years” — Andrew Peterson, head of the creator economy ecosystem at YouTube.
Value matters more than format: a lesson for brands
Andrew advised brands to choose value for the audience rather than chase trends. This is what allowed YouTube to grow in connected TV, on-demand, and short-form without losing focus.
For marketers, this means: first determine what you give viewers, then choose the format. Not the other way around.
Reconsider what “good” view counts mean
Many marketers underestimate scale. Andrew gave an example: 180 views for a beginner is not a failure, while 20 million is the population of Oregon.
“What room have you been in with 180 people where that seemed small?” he asked. Every view is a person, and the real reach of creators is almost always higher than it seems at first glance.
Subscribers ≠ community: the difference between Instagram and YouTube
Adam W, a creator with 21K+ subscribers, clearly distinguished platform audiences. On Instagram and TikTok — passive scrollers; on YouTube — a community that notices details and genuinely takes an interest in the personality.
“There is a difference between a million views from random people and a million from those who are truly engaged with you,” Adam explained. This directly affects conversion: a community is more willing to click and buy.
Adam emphasized that audience growth requires both consistency and content value. “It’s quality and quantity: publishing content consistently, but making it useful at the same time,” he said.
Scott criticized the habit of equating a creator’s view with a banner impression. “A view is not an annoyed consumer with a blocked experience, but a fan who opens something interesting from someone they love,” he noted.
Mixing these metrics means undervaluing creator content and making wrong strategic decisions.
Brand at the End of the Joke: Adam’s Method
Adam shared his technique: he looks for the most relatable conflict around the product, builds a story or joke, and only then “lets in” the brand as the solution.
Example with Old Spice: a video built on the problem of odor gained 255 million views without advertising. Meanwhile, product placement at the beginning often fails.
Choice: Bring the Creator into Your World or Enter Theirs
Andrew proposed a key strategic question: “Are you trying to bring the creator into your world or enter theirs?” Both approaches work but require different implementations.
The Old Spice example is entering the creator’s world, which can be uncomfortable for brands used to controlling the message.
The Truth About the Brand from Creators
Scott noted that brands do not control perception: “You think you own the brand, but the consumer owns the perception.” Creators, being closer to the audience, often see the truth better.
Example with New Balance: the brand became desirable by listening to external opinion rather than fighting it.
Optimizing for AI Search: Trust Decides
Andrew suggested replacing the word “algorithm” with “audience.” YouTube recommends content that brings maximum value, and 78% of viewers trust creators in product recommendations.
LLMs also choose reliable sources, so authentic creator voices are already appearing in AI outputs — brands should invest in such partners.
Meet the Audience Everywhere
Andrew described his day: Shorts on the go, a podcast in the evening, a documentary on TV. Brands should think about the audience and story, then adapt the format to each moment.
Scott added that creators now participate in brand strategic planning, not just in one-off campaigns.
Frequently Asked Questions
What is The Made You Look Tour?
It is a series of closed meetings for senior marketers where the future of the creator economy is discussed. Participation is by invitation only, with no recording.
Why is trust the main KPI in creator marketing?
Because 78% of viewers trust creator recommendations, and this trust converts into purchases better than traditional advertising.
How should a brand choose between its own world and the creator’s world?
You need to consciously decide: bring the creator into your context or integrate into theirs. Both approaches work but require different creative strategies.
Conclusion
Trust is the currency of the creator economy, and it must be built through value, consistency, and a willingness to let go of control. Brands that understand this are already gaining a competitive advantage today.
Want to create partnerships that truly work? Request an invitation to the next tour or discuss a strategy with our team.
The streaming platform Kick, associated with Stake.com, has officially launched advertising in live streams. Almost four years after Kick’s start, it introduces a new revenue stream that could benefit both creators and brands. Previously, Kick avoided advertising: monetization for creators was based on a rewards program tied to watch time and small prize pools. However, with the platform’s growth, the introduction of ads became inevitable.
As early as December, Kick CEO Ed Craven confirmed that the company would introduce advertising “at some point” in the future. “Enjoy the absence of ads while you can,” said Craven. Now, ads on Kick have become a reality, and they arrive at a pivotal moment. The platform attracts millions of concurrent viewers on its biggest streams, with monthly viewing volume exceeding 300 million hours.
Scale and audience
In the ad launch announcement, it is noted that Kick now has over 100 million active users. This is a massive audience, and 81% of them are aged 18 to 34. Kick claims its advertising can connect brands with the hard-to-reach Generation Z, echoing arguments heard before.
“The viewer is everything on KICK. They’re not just watching in the background; they’re in the chat, they’re part of the stream,” said Ryan Webb, Kick’s head of growth and revenue. “We’ve spent over three years earning this, building a platform where creators want to stream and communities want to spend time.”
Challenges and solutions
Now that Kick has ads, the platform will have to face the same issues that cause headaches on other streaming services. For example, Twitch is forced to give streamers more control over ad breaks and manage their duration. YouTube, in turn, experiments with less intrusive ad formats.
For now, Kick responds to potential obstacles with a “wait and see” strategy. The platform asks creators to report inappropriate ads but also warns that the length of breaks may vary. For poorly timed interruptions, there is a stream rewind feature launched last year.
Gold rush
Perhaps in the future Kick will develop more refined advertising policies, but first there will be a gold rush. Creators have attracted millions of hours of attention on Kick. Now we’ll find out how much that attention is really worth.
Frequently asked questions
How will Kick deal with inappropriate ads?
Kick asks creators to report inappropriate ads but has not yet introduced strict moderation algorithms.
Will ads affect the viewing experience?
Variable breaks are expected, but the stream rewind feature will help viewers not miss important moments.
What ad formats will be used?
For now, Kick has launched standard in-stream ad inserts, similar to other platforms.
If you are a content creator or brand, now is the time to explore advertising opportunities on Kick. Contact the platform to learn the terms and start earning from audience attention.
Spotify has announced the introduction of “AI Persona” labels for profiles created by artificial intelligence and the exclusion of their music from editorial and algorithmic recommendations. The innovation, announced on Tuesday, aims to combat the proliferation of low-quality AI content and protect user experience. This is an important step for the platform, which seeks to maintain a balance between innovation and content quality.
How it works
From mid-September, Spotify users will see “AI Persona” badges in the profiles of artists whose public identity is entirely generated by neural networks. The company will not rely solely on self-identification: moderators will review profiles, identifying those where the name and images look like photorealistic AI avatars. The review will start with artists who have exceeded set listening thresholds to cover the most popular ones.
After labeling, badges will appear in the profile banner, in the “About the artist” section, in search, and in playlist tracks. By default, music from AI personas will not be included in editorial selections or personal recommendations — an exception is made only for those the user has explicitly followed. Following is a conscious choice, so Spotify considers it a clear signal of interest.
Spotify’s AI policy
This is an extension of the service’s existing AI rules, first introduced in September 2025. They already prohibit unauthorized voice clones and deepfakes, and use industry methods to identify AI music. Spotify balances between innovation — its own AI playlists, AI DJ, and future remixes — and the need to curb the flow of “AI junk” that has become too easy to produce.
Allowing it to spread would lead to a deterioration of user experience and subscriber churn. Therefore, the company is implementing clear rules that distinguish the creativity of real people from fully generated personas.
Right to appeal and feedback
Artists will be able to dispute the “AI Persona” label if they believe it has been applied incorrectly. Spotify emphasizes that the label refers to public identity, not the way the music was created: “While there is a wide spectrum of AI use as a creative tool, the question of whether a profile represents a real person is where Spotify can give a clear answer. This badge is about identity, not process.”
Information about how the music was made will remain available through the AI Credits and SongDNA features. In the coming months, a tool will be introduced for reporting profiles that look like AI personas but are not yet labeled. This will also help distinguish them from future AI remixes and covers allowed by recent licensing agreements with labels UMG and Merlin. The latter will allow fan remixes and covers with royalties to artists.
Implementation timeline
Self-labeling through Spotify for Artists will become available on August 11, 2026, and the badges themselves will appear the following month. Users will see the interface changes almost immediately after launch.
Frequently asked questions
What is an “AI Persona” on Spotify?
It is a label indicating that an artist’s profile represents an AI-generated persona, not a real person. It helps users distinguish such content from music by live performers.
Will the label affect streams?
Yes, music from AI personas is excluded from recommendations and playlists but remains available to followers. If a user has followed such an artist themselves, they can continue listening to their tracks.
Can the label be appealed?
Yes, artists can file an appeal if they believe the label has been applied incorrectly. Spotify will review each case individually.
How does this relate to AI remixes?
The labels help distinguish AI personas from permitted AI remixes and covers that will soon appear thanks to licensing deals. Remixes will be labeled differently and will not be excluded from recommendations.
Conclusion
Spotify is betting on transparency and quality control to maintain the trust of users and artists. If you create music with AI, it’s important to keep up with policy updates and properly label your profiles. And if you’re a user — now you have more information about who is behind the tracks in your playlists.
Stay tuned for updates and share your opinion about the new labels in the comments — your feedback will help make the platform better for everyone.
Starting February 1, 2027, YouTube is raising the entry thresholds for the Partner Program. To earn ad revenue from Shorts, creators will need twice as many views, and for access to overall ad revenue and Premium, more watch hours. This is a step toward making short videos generate “meaningful” income, but only for a select few.
The new Shorts monetization rules will affect everyone planning to earn on the platform. If you’re just starting your YouTube journey, it’s important to understand how the landscape will change in the coming years.
New requirements for the Partner Program
Currently, joining the YouTube Partner Program requires 1,000 subscribers and either 4,000 watch hours per year or 10 million Shorts views in 90 days. Starting February 2027, new applicants will need to reach 8,000 watch hours over 365 days or 20 million Shorts views in 90 days.
Current program participants retain their terms—thresholds remain unchanged for them. This grandfather clause protects already-earning creators from sudden changes.
Restrictions on Shorts ads
Separately, requirements for accessing Shorts ad revenue are being tightened. Regardless of how you enter the Partner Program, to earn a share of ad revenue from short videos, you’ll need at least 10 million Shorts views in 90 days.
If a creator drops below this threshold, their Shorts ads are automatically disabled but resume once views exceed the mark again. This makes Shorts monetization more exclusive and geared toward large channels.
Why YouTube is doing this
According to YouTube’s Vice President of Creator Products, Amjad Hanif, the changes are based on “the growth of the creator ecosystem over recent years.” In a video on the Creator Insider channel, he emphasized that the goal is to make Shorts ads a “meaningful” source of income.
To earn real money from Shorts, you need millions of views—hence the new requirements. YouTube is deliberately restricting access to ad revenue, pushing smaller creators toward alternative monetization methods.
What doesn’t change: Fan Funding and new incentives
Requirements for Fan Funding remain the same: 500 subscribers, three uploads in 90 days, and either 3,000 watch hours per year or 3 million Shorts views in 90 days. This allows smaller channels to receive direct fan support.
YouTube’s blog states: “We’re expanding earning opportunities by introducing new incentives rather than relying solely on ads.” For channels below the 10 million view threshold, bonuses for achieving goals will be introduced, such as for YouTube Shopping, brand deals, and participating in trends. Details are promised later.
Impact on creators
YouTube’s Partner Program has existed for nearly 20 years and continues to generate income for millions of creators. However, the revenue structure is shifting: more creators are earning through direct funding methods.
The new rules acknowledge this trend but don’t abandon those who rely on ads. Current partners retain their terms, while newcomers will have to work harder to get into the program.
Frequently asked questions
When do the new requirements take effect?
Starting February 1, 2027.
What are the new thresholds for joining the Partner Program?
You need 8,000 watch hours over 365 days or 20 million Shorts views in 90 days.
What happens to current Partner Program participants?
Their terms won’t change—they retain the old thresholds.
How do I now earn ad revenue from Shorts?
You need to reach 10 million Shorts views in 90 days, regardless of your Partner Program status.
What alternatives exist for smaller channels?
Fan Funding (500 subscribers, 3 uploads, 3,000 watch hours or 3 million Shorts views) and new bonus programs that will be introduced later.
Bottom line: YouTube is betting on quality and scale. If you plan to grow a channel, focus on a long-term strategy: combine Shorts with long-form videos, use Fan Funding, and keep an eye on new bonus programs. Adapt to changes early—and your channel will remain profitable.
Have you ever wondered why your short videos don’t bring results, even if you ordered them from professionals? Perhaps the problem isn’t the quality of the videos, but that your target audience simply doesn’t see them. In the era of artificial intelligence and algorithms that decide what to show users, it’s important not only to create content but also to ensure its visibility. This is where comprehensive promotion through short videos comes to the rescue, which includes not only production but also clipping, posting, and analysis. In this article, we’ll break down how to order everything in one place, save your budget and nerves, and why replacing your SMM team with a service could become your competitive advantage.
Why Short Videos Require a Comprehensive Approach
Short videos have become the main tool for capturing attention on social media. But simply shooting a video isn’t enough. You need to think through a strategy: how often to post, at what time, which formats to use, and how to adapt content for different platforms. That’s why outsourcing content for social media is becoming increasingly popular. You delegate not only shooting but also all the routine: editing, clipping, music selection, subtitles, publishing, and analytics.
How to Order Everything in One Place: Saving Budget and Nerves
Many entrepreneurs make the same mistake: they hire a separate operator, editor, targeting specialist, and SMM expert. The result is a headache from coordination, missed deadlines, and unexpected expenses. Ordering everything in one place means handing your project to a unified team that handles all stages. This not only saves your budget but also frees you from the need to control each contractor.
Replacing a Team with a Service: When It’s Beneficial
Replacing an SMM team with a service is justified when you need a steady stream of content without hiring in-house staff. Services offer a fixed price for service packages, allowing you to plan expenses. Additionally, you get access to analytics tools that show the effectiveness of each video.
With the development of technology, many wonder: what’s cheaper—AI or real clipping? Artificial intelligence can quickly cut videos, but it doesn’t understand context and emotions. A real editor sees details that AI misses. Therefore, the best option is to combine: use AI for rough cuts and a human for final processing.
Comparison of Repurposing and Shooting from Scratch
Repurposing is the processing of existing content (e.g., a webinar) into short videos. This is significantly cheaper than shooting from scratch but requires high-quality source material. If you have recordings of streams, webinars, or interviews—repurposing will be an excellent solution. If not, you’ll need to invest in shooting.
How to Choose a Contractor for Video Clipping
Choosing a contractor is a responsible step. Pay attention to their portfolio, reviews, timelines, and cost. Ask how they work with algorithms and promotion. A good contractor doesn’t just do clipping but also considers trends and the specifics of each platform.
What to Pay Attention to When Ordering AI Advertising
If you decide to use AI for creating ads, make sure the service provides a full cycle: from idea generation to publication. It’s important that the AI is tuned to your niche and target audience. Request examples of their work and clarify what metrics you’ll receive in reports.
Quality Criteria for Mass Posting
Mass posting is publishing content on multiple platforms simultaneously. A quality service should ensure stability, consider time zones, and provide reports. Make sure it supports all the social networks you need and allows you to schedule posts.
Frequently Asked Questions
What’s Cheaper: AI or Real Clipping?
AI is faster and cheaper at the rough-cut stage, but for a quality result, you need a human. The optimal approach is a combination of AI and manual refinement.
How to Order Everything in One Place?
Find a service that offers a full cycle: from strategy to analytics. Usually, these are agencies or studios specializing in short videos.
Should You Use Repurposing or Create New Content?
If you have high-quality source material—use repurposing. If not, it’s better to order new shooting so the content is unique and aligns with your goals.
How to Choose a Contractor for Video Clipping?
Look at portfolios, read reviews, and clarify how they work with promotion. Request a test assignment.
Conclusion
A comprehensive approach to short video production is not just a trend but a necessity in modern marketing. Ordering everything in one place means saving time, money, and nerves while getting a quality result. Remember that visibility in search and social media depends not only on content but also on technical setup. Use the checklists and tips from this article to choose a reliable contractor and achieve success. If you’d like to discuss your project, contact us—we’ll help you create an effective promotion strategy.
YouTube continues to tighten rules for creators: starting February 1, 2027, new monetization requirements take effect, doubling the thresholds for watch hours and Shorts views. This decision will affect thousands of content creators who are just planning to join the partner program. In this article, we’ll break down all the changes, who they affect, and how to prepare for the new conditions.
New Requirements for Joining YPP
The subscriber requirement remains at 1,000. However, activity metrics for new applicants are doubled. For full access to ad revenue and YouTube Premium, you must meet one of two conditions: either 8,000 watch hours over 12 months, or 20 million Shorts views over 90 days.
Before January 31, 2027: 4,000 hours or 10 million Shorts views.
From February 1, 2027: 8,000 hours or 20 million Shorts views.
It’s important to understand: for scale, 8,000 hours per year is roughly 22 hours of viewing per day, and 20 million Shorts views is about 222,222 views per day. These numbers only grant the right to submit an application; YouTube still reviews the channel for compliance with monetization policies.
The reaction from creators was expected. For example, @NigelsTravels commented on vidIQ’s breakdown: “Doubling the watch hours requirement from 4,000 to 8,000 is a huge barrier for small channels.”
Who Is Affected by the New Rules?
New Creators
If you’re not yet in YPP and want to earn ad revenue, compare your metrics in Studio against the new thresholds. If you applied before February 1, 2027, but your application is still under review, transitional rules haven’t been published—check with support.
Existing Partners
If you’re already in YPP, review the new terms and ensure your channel meets the activity requirements. From February 1, 2027, a channel is considered active if it meets at least one of the following: publishing at least one long-form video or Short in the last 90 days, or 1,000 watch hours over 365 days, or 1 million Shorts views over 90 days. If a channel loses activity, you get 90 days to recover.
Tier with 500 Subscribers
Early access for channels with 500 subscribers remains unchanged: same thresholds for fan funding and Shopping features. This tier does not grant access to ads or Premium.
Shorts Creators
From February 1, 2027, for income from the Shorts Creator Pool, you need 10 million qualified views in the last 90 days. If views are lower, payouts are paused, but the channel remains in YPP, and long-form video revenue is unaffected.
What Counts as Qualified Views?
Qualified watch hours include only long-form videos that are publicly available. Not counted: views from Shorts, from ads, from private or unlisted videos, and repeat views. For Shorts, only public Shorts in the Shorts feed are counted; views from ads or unlisted videos are not included.
What to Do Before February 1, 2027?
If you’re close to the threshold: submit your application as soon as Studio shows you meet the current requirements.
If you’re already in YPP: accept the new terms by January 31, otherwise payouts will be paused.
If you’re far from the threshold: choose one path—long-form or Shorts—and focus on it. You can also grow income through sponsorships, affiliate programs, and merchandise.
Why Is YouTube Raising Requirements?
YouTube explains this as a desire to encourage active creators and reflect changes in viewer behavior. The company notes over 200 billion daily Shorts views and over a billion hours of daily TV watch time.
New Earning Opportunities
YouTube is expanding Premium Lite to all countries where Premium is available. Premium Lite uses a creator pool comprising 60% of net subscription revenue, while standard Premium uses 30%. Creators receive 55% from long-form and 45% from Shorts. Future Shorts incentives related to shopping, brand deals, and trends were also announced, but details will come later.
Frequently Asked Questions
Will there be changes for existing partners?
No, the 8,000-hour threshold applies only to new creators. Existing partners must monitor activity and Shorts rules.
Do I need 1,000 subscribers?
Yes, subscribers remain mandatory, plus one of the metrics: 8,000 hours or 20 million Shorts views.
Does the 500-subscriber tier change?
No, its requirements remain unchanged, but it doesn’t grant access to ads.
What if I’m in YPP but don’t reach 10 million Shorts views?
Payouts from the Shorts Creator Pool are paused, but the channel stays in YPP, and long-form revenue is unaffected.
What if my application is under review on February 1?
There’s no official answer. It’s recommended to apply as early as possible and contact support.
Conclusion
Raising requirements is a significant step, but creators have time to prepare. Check your metrics in Studio, choose the right format, and keep creating quality content. If you’re not yet in YPP, don’t despair—thresholds are achievable with systematic work. Start optimizing your videos today to meet the new rules by February 2027.
The key is not to panic but to act. Analyze your strategy, choose a direction, and move toward your goal. And if you need help optimizing your channel for the new requirements—subscribe to blog updates; we’ll continue to monitor changes and share practical tips.
