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  • Nano Banana 2.1 vs 2.0: Comparing Neural Networks for Image Generation

    Nano Banana 2.1 vs 2.0: Comparing Neural Networks for Image Generation

    Google released the Nano Banana 2.1 update on October 6, 2026, promising significant improvements in image generation. The new neural network is designed to better follow prompts, generate more accurate text, ensure character consistency, and work with wide formats. We checked how these promises align with reality by comparing both versions on five identical tasks. The short answer: for new projects, it’s worth choosing Nano Banana 2.1 right away, as it more often accurately fulfills minor requirements and less frequently adds unnecessary elements. However, the update hasn’t solved all problems, especially when transferring appearance between scenes, where the old version sometimes preserved features just as well. This is an important point for those planning to scale AI video and image generation.

    Technical Features and Version Costs

    Nano Banana 2 operates under the technical name gemini-3.1-flash-image, while Nano Banana 2.1 is based on Gemini 3.6 Flash and is named gemini-nano-banana-2.1. Google claims improvements in realism, instruction understanding, text rendering, and multi-step editing. The new model can accept up to 14 references: up to four characters and up to ten objects.

    According to Google’s official pricing, a 2K image in Nano Banana 2.1 costs approximately half as much as in Nano Banana 2 ($0.0504 versus $0.101), making it attractive for AI content production. At the same time, the 0.5K mode has disappeared from 2.1, and Nano Banana 2 is already marked as an outdated model.

    Testing Methodology: 5 Prompts for Comparison

    For an objective comparison, we relied on the open Fuser test from October 6, 2026. In it, both models received identical English-language prompts and source data, and images were generated in 2K with one attempt without restarts. This allows us to evaluate the effectiveness of AI under controlled conditions. If you decide to repeat the test, use the same settings and do not select the best results to avoid distortions.

    Prompt 1: Photorealistic Scene with Precise Requirements

    “Photorealistic documentary photograph of an elderly fisherman mending a bright orange net on an old wooden pier at dawn. In the background, a foggy harbor. The fisherman’s hands are in sharp focus, the background is softly blurred. Natural light, 50mm lens effect, muted color palette. Do not add text or inscriptions to the scene.”

    Nano Banana 2.1 kept the cool misty background, showed the fisherman in full, and left the frame free of extraneous text. Nano Banana 2 made the scene warmer, more saturated, and added a fictional inscription on the boat. Here, 2.1 won due to more precise adherence to the constraints, which is critical for AI video for advertising.

    Prompt 2: Poster with exact text

    “Create a minimalist concert poster on light paper. The top half is occupied by a large dark blue wave. Place four lines of text: “NIGHT TIDE” “Live at Harbour Hall” “Friday 14 November · Doors 8 PM” “harbourhall.com/nighttide” Do not add any other words. Make the first line a large narrow sans-serif, the second a lighter typeface.”

    In this test, the difference between the versions was minimal. Both models correctly wrote the specified lines, including the date and address. This shows that in some tasks where Nano Banana 2 already performs well, an update may not provide a noticeable leap in quality, especially in text rendering.

    Prompt 3: Product photography with space for ad text

    “Studio photograph of a matte ceramic sage-green coffee funnel on a light oak board. Soft window light from the left, a light shadow should fall to the right. Clean, almost white background. Leave the right third of the frame free for ad text. Realistic product photography. No additional objects or inscriptions.”

    Nano Banana 2.1 fulfilled all conditions: light from the left, shadow to the right, and a free area for text. Nano Banana 2 also left space, but divided the image with an empty light area, and the shadow direction was less pronounced. For creating AI commercials and banners, this is a significant advantage, saving designer time.

    Prompt 4: Transferring a product to another location without alteration

    “Place this same bottle on a wet, moss-covered stone. The scene is outdoors in the morning, with soft sunlight and a blurred forest stream in the background. Change only the environment and lighting. Do not change the bottle’s shape, amber glass, black dropper cap, or the inscription “LUMA 02”.”

    Both versions successfully preserved the major features of the bottle (shape, glass color, text). However, both violated a minor condition by changing the smooth detail of the cap to ribbed. 2.1 chose a wider shot and kept the label clean, whereas Nano Banana 2 zoomed in on the object and added water droplets to the label. This demonstrates a limitation of image-to-image models: the phrase “change nothing” does not guarantee pixel-perfect identity of small details.

    Nano Banana 2.1 vs 2.0: Comparing AI image generation models — illustration 2

    Prompt 5: Transferring a character to a new scene

    «Use the woman from the reference portrait. Show her full-length riding a bicycle on a wet city street at night. Neon signs all around, she’s wearing a yellow raincoat with the hood down. Keep the same face, red curly hair, freckles, and round glasses. Photorealism.»

    There is no clear winner here. Nano Banana 2 preserved the curls and glasses well but changed her facial expression to a wide smile. Nano Banana 2.1 framed the full-length shot more accurately but weakened the resemblance in details: her hair became less curly, the frames thinner, and freckles almost disappeared. The choice depends on priorities: either precise preservation of individual facial features or a natural composition of the new scene. This is especially relevant for animated AI characters and AI avatars.

    Conclusions after five prompts: is it worth switching?

    According to the test results, Nano Banana 2.1 looks like a careful refinement of the previous version, not a fundamentally new model. Its advantage is evident in tasks with many specific requirements, which aligns with Google’s official description of better prompt adherence and text rendering. For AI UGC for crypto projects, where detail accuracy and prompt following are critical, this is a significant plus.

    • Nano Banana 2.1 won two tasks and did not clearly lose to the old version in any.
    • When transferring appearance, the results remained ambiguous.
    • If you are starting a new project, it is more logical to use 2.1 immediately, especially given the reduced cost and Nano Banana 2‘s status as deprecated.
    • If you have a working pipeline on Nano Banana 2, test your typical prompts on both models to ensure the desired generation style is maintained.

    Frequently Asked Questions

    Is Nano Banana 2.1 really better than Nano Banana 2?

    According to official specifications, 2.1 is the recommended version with improved instruction following, quality, text, and character stability. In our test, it showed better results in tasks with multiple constraints.

    Why does the same prompt give different results?

    Image generation in neural networks is not deterministic by default. Even within the same model, repeated runs can change the composition, face, lighting, or small objects. For a correct comparison, a series of runs with identical settings is required.

    Why does the model ignore part of the prompt?

    Often the problem arises when there are too many equally important instructions in one request. For critical constraints, it is better to use separate instructions, for example: «change only the background», «the text must be written verbatim», «do not add logos», «keep the face unchanged».

    Should I always include high Thinking in Nano Banana 2.1?

    No. 2.1 has minimal, medium, and high levels. A higher level can be useful for a complex layout or a large number of constraints, but it usually increases computation volume and execution time. For simple drafts, it is logical to start with minimal or the standard medium.

    What Nano Banana 2 has that 2.1 doesn’t?

    The most noticeable technical difference is the 0.5K generation mode. In 2.1, the minimum resolution starts at 1K. However, the new version is cheaper at matching resolutions and adds extremely wide and vertical formats.

    How to improve results when working with a reference?

    For the best results, use high-quality references. Make sure the reference images are clear and contain all necessary details. Experiment with the number of references (up to 14 in 2.1) to find the optimal balance.

    How to access Nano Banana in Russia?

    Nano Banana is available on special platforms that work without VPN and foreign cards. You can upload photos, change backgrounds and clothes, collect multiple references, and create images from text descriptions. Services usually work with ruble payments and provide a common balance for different models. Use promo code HABRSPESHUAI to increase your balance by 115%. This is a great way for crypto projects to order AI creatives or scale the generation of 100 videos per day.

    Overall, Nano Banana 2.1 is a significant step forward, especially for tasks requiring high accuracy and detail. Its reduced cost and improved capabilities make it an attractive tool for subscription-based neuro-production and AI content production. Don’t miss the opportunity to scale the impossible – now it’s possible with new Google neural networks.

  • Is CPM profitable? Should Perplexity be excluded from AI visibility tracking?

    Is CPM profitable? Should Perplexity be excluded from AI visibility tracking?

    In the world of AI search, leaders are constantly changing, and the question of which platforms to track becomes critical for SEO specialists. Recent discussions have revolved around the Perplexity platform: should we continue monitoring it or focus exclusively on giants like ChatGPT, Gemini, and Claude? We will examine how changes in market share affect visibility assessment and how much money each spent ruble will bring to understand if CPM is profitable.

    Is Perplexity really losing ground?

    Ross Hudgens, CEO of Siege Media, claims that Perplexity’s market share is shrinking. Including it in general metrics alongside ChatGPT, Gemini, Claude, and Google AI products can distort the real picture of visibility. Hudgens urges: “Everyone should remove Perplexity from their LLM trackers today.”

    However, as experience shows, hasty conclusions can be erroneous. In March 2002, at the Search Engine Strategies conference, the reduction of tracked search engines from 15 to 5 was discussed. Google was then excluded from the list. This turned out to be a serious oversight, as Google was rapidly gaining momentum. A similar situation could repeat itself today.

    Data confirms Perplexity’s decline

    • According to StatCounter, in June 2026, Perplexity held 7.91% of AI chatbot referral share, almost on par with Gemini (7.94%).
    • By August 2026, Perplexity’s share had fallen to 4.31%, while Gemini grew to 10.9%.

    “A small market share does not always mean small strategic importance. It’s like investing in stocks: you shouldn’t always look only at current capitalization, but also at growth potential.”

    The market is consolidating, but not around a single leader

    The latest Similarweb data for May 2026 shows the dominance of ChatGPT (53.9% of global web visits among seven major AI assistants). Gemini accounts for 27.9%, Claude – 9.2%, DeepSeek – 4.1%, Grok – 2.4%, and Perplexity and Copilot – 1.3% each.

    Significant changes occurred not in the disappearance of Perplexity, but in the emergence of a strong second player. ChatGPT’s share of AI chatbot web traffic decreased from 76.4% a year ago to 52.7% in May 2026. Gemini grew from 9% to 27.3%, and Claude – from 1.6% to 8.9%.

    Key players and their advantages

    • ChatGPT: over 900 million weekly active users (February), over 1 billion active users across all products (end of July). This is a huge direct consumer base.
  • Gemini: The Gemini app exceeded 1 billion monthly users (August). It has Google distribution through Search, Android, and the entire product ecosystem.
  • Claude: Has gained a strong position in the enterprise segment and among developers. In April, over 100,000 customers used Claude on Amazon Bedrock. Annual revenue exceeded $65 billion (August).

These are not three versions of the same business, but three different distribution advantages. The market looks like an emerging oligopoly, not a “winner takes all” situation.

What SEOs Should Track

Google AI Overviews and AI Mode should not be viewed as ordinary LLMs. They are much more important. In June, Google reported that AI Overviews cover more than 2.5 billion users per month, and AI Mode exceeded 1 billion monthly users. AI Mode queries have doubled every quarter since launch.

According to Similarweb, AI Overviews appeared in 43% of Google search queries in the US by May 2026, compared to 15% a year earlier. AI Mode visits grew from 126 million in June 2025 to 279 million in May 2026.

This means that Google AI is a separate layer in the world’s dominant search ecosystem. For B2B companies, Claude is also of immense importance, despite relatively low consumer traffic. PwC, TCS, and Cognizant are actively integrating Claude into their workflows, training tens of thousands of employees. Over 1,000 business customers spend more than $1 million annually on Claude.

Three-Tier AI Visibility Measurement System

  1. First Tier: Platforms with scale and strategic importance. Track ChatGPT and Gemini separately. Add Claude for B2B, corporate, and professional audiences.
  2. Second Tier: AI search embedded in existing ecosystems. Track Google AI Overviews and AI Mode separately from ChatGPT, Gemini, and Claude. The goal is to measure how AI changes the search journey. Microsoft Copilot can also be included in this tier if the company has a significant presence in Microsoft 365.
  3. Third Tier: Emerging or specialized platforms. This includes Perplexity, Grok, DeepSeek. Do not ignore them, but do not give them equal weight. Monitor them for unusual visibility, referral traffic, citations, and growth.

Don’t Let Averages Mask Real Data

An aggregated LLM visibility metric can create a false sense of accuracy. For example, if a brand has 40% citation rate in ChatGPT, 35% in Gemini, 30% in Claude, and 90% in Perplexity, a simple average would be 48.75%. This number says little if Perplexity accounts for only a small fraction of the traffic important to the business.

CPM выгодный? Стоит ли исключать Perplexity из отслеживания ИИ-видимости? — illustration 2

Instead of asking, “What is our average LLM visibility?” you should ask, “Where do the people important to this business actually encounter our brand?”

This requires combining three data sets:

  • Audience Reach: usage, visits, platform distribution.
  • Visibility: mentions, citations, referring URLs, queries that generate them.
  • Business Impact: linking AI referrals to engagement, leads, sales, and other conversions.

My Verdict on Perplexity

Ross Hudgens is right about the problem, but not entirely right about the solution. Don’t remove Perplexity from your LLM tracker, but reduce its weight. If Perplexity generates 1% of your AI traffic, giving it 25% or 33% weight in your visibility score is unjustified. However, if it accounts for more than 5% of referrals, as Hudgens himself suggests, the argument for it becomes stronger.

Perplexity’s trajectory is worth watching. Similarweb’s August 2026 analysis still describes Perplexity as an active player in the AI search market, noting its ad-free strategy and focus on subscriptions and enterprise deals. This shows why “small” and “insignificant” are not synonyms.

Frequently Asked Questions

What CPM should be considered profitable for AI platforms?

Profitable CPM in AI platforms depends on your target audience and ROI. If a platform generates high-quality leads or sales, even with a relatively high CPM, it can be profitable. The main thing is to consider not only the cost per impression but also the conversion rate.

Is it worth testing hypotheses cheaply on new AI platforms?

Yes, cheap hypothesis testing on new AI platforms is essential. This is how you can discover the next Google. You shouldn’t spend huge budgets on them, but monitoring and minimal testing will allow you not to miss a potential breakthrough.

What is cheaper: a banner or a blogger, when compared to AI platforms?

The question of what is cheaper, a banner or a blogger, is incorrect in the context of AI. AI platforms offer a different type of interaction. Price comparison should be done through efficiency metrics: how much does 1 million impressions via a banner cost versus the cost of generating 100 AI videos or the cost of publishing 500 videos via AI tools. Each channel has its own unit economics.

How to estimate the cost of uniqueizing one video using AI?

The cost of uniqueizing one video using AI depends on the complexity of the task and the tool used. It is important to evaluate not only direct costs but also the time that AI will save. The price per minute of AI video with an avatar also varies, but it often turns out to be more profitable than manual labor, especially for large volumes.

Conclusion

The AI search market is dynamic. Ignoring Perplexity or other niche platforms entirely means risking missing important signals. However, blindly giving them equal weight with giants distorts the picture. A mathematical approach to analysis, weighing platforms by their real business impact, and constant monitoring of new players are key to success in SEO and a profitable CPM strategy.

We count cash, not just the number of platforms in the tracker. Don’t overlook the outsiders who are gaining momentum. Perhaps among them lies the next market leader. Start analyzing your data today so you don’t miss new opportunities!

  • CPM profitable: Google and AI — people use both, not replace

    CPM profitable: Google and AI — people use both, not replace

    In the modern digital landscape, where technology is evolving at an incredible pace, the question of how artificial intelligence (AI) affects traditional traffic generation methods is becoming increasingly relevant. This is especially true for a metric like profitable CPM. Information categories are losing clicks, Google is losing queries, but retaining its audience. AI does not replace search, but complements it. How much cache will this bring? Let’s calculate.

    95% of ChatGPT users are still on Google: What does this mean?

    According to Similarweb, 95% of ChatGPT users continue to use Google. This figure remained at 95% from September 2025 to May 2026, even though traffic to generative AI platforms grew by 70% year-over-year. Similarweb interprets this as AI complementing search, not replacing it.

    However, a study by Bocconi University showed a decrease in traditional search queries by 9.4% among households that gained access to ChatGPT Search. The discrepancy in data is explained by Similarweb counting people (overlap), while Bocconi counts queries per household. The same user might use Google for maps or login, and redirect specific queries to ChatGPT.

    How does AI affect search behavior?

    • Reduced queries: Households with access to ChatGPT Search reduced the number of traditional search queries by an average of 3.14 per week, which is 9.4% less than the pre-expansion level (33.51 queries). The reduction reached 17.0% after 20 weeks.
    • Drop in referrals: The largest drop in referrals was observed in information categories: academic sites (-32.8%), reference sites (-26.5%), developer sites (-15.1%), news sites (-13.4%).
    • Increased ChatGPT reach: Google’s household reach remained stable (around 49%), while ChatGPT grew from 4.6% to 7.9%.

    “When people use our AI-powered features in Search, they use Search more often,” — Sundar Pichai, CEO of Google.

    However, Google does not provide baseline data or user-level data to independently verify these claims. We are seeing an increase in the use of AI features in Google, which may replace the traditional results page.

    Experiment with AI Mode: What happens to clicks?

    A study by Stephanie T. Wang and her team (August 2024) involving 1100 Chrome users showed that when AI Mode was forced (94.7% of queries via AI), the number of clicks to external sites decreased by 18.8 percentage points.

    • Search sessions decreased by approximately 0.92 per day.
    • The average session duration increased by 0.43 minutes.
    • Clicks to news sites fell by 12.5 points, Reddit by 21.2 points, Wikipedia by 9.9 points.
  • The share of participants using Bing, DuckDuckGo, or Yahoo increased by 11.2 points.
  • Trust, satisfaction, and usefulness ratings decreased.

    • At the same time, 15.3% of AI Mode users experienced difficulties accessing specific websites. It is important to note that in March 2024, AI Mode did not display ads. Since May 20, Google has been testing new ad formats created with Gemini in AI Mode, which may change satisfaction and click metrics.

    What do current data not account for?

    None of the current datasets track individual users across different platforms (Google Search, AI Mode, ChatGPT, Gemini) at a task level over the long term.

    Google does not disclose user-level data to support its claims. It is unclear what types of queries transition to AI and which remain in traditional search. Bocconi’s data on the division into informational and transactional categories is only descriptive.

    CPM выгодный: Google и AI — люди используют оба, не заменяют — illustration 2

    Conclusion: CPM is profitable, but where does the cash go?

    We see that Google retains users but loses some queries and clicks. Optimization for voice search and AI functionality are becoming critically important. If you are paying for a profitable CPM in traditional search, but users are switching to AI, your unit economics may suffer.

    Cheap hypothesis testing using AI tools for content creation (generating 100 AI videos, video uniqueization) can be more effective than traditional methods. Is AI worth investing in? Facts show that it changes user behavior. The question is how to convert this into cash.

    For an arbitrageur, this means: a CPM cheaper than 50 rubles for a banner may be useless if clicks go to AI. Strategies need to be adapted. Analyze your metrics, compare price vs result, to understand what CPM to consider profitable for your niche.

    Frequently Asked Questions

    Q: What is cheaper: banner or target?

    A: This depends on your target audience and campaign goals. Data shows that AI changes user behavior, reducing the clickability of traditional ads. It may be worth considering a budget alternative to seeding through AI content.

    Q: Is it worth investing in AI for content?

    A: Yes, data indicates a growing use of AI for information retrieval. The cost per finished clip created by AI, or the cost of making a video unique, can be significantly lower than manual production, while still providing effective reach.

    Q: What CPM should be considered profitable?

    A: A profitable CPM is determined not only by the cost per thousand impressions but also by conversion. If users use AI to get answers without visiting websites, then even a very low CPM will not yield the desired result. It is important to evaluate ROI.

    Q: How does AI affect SEO?

    A: AI can reduce the number of direct clicks to your site, especially for informational queries. Optimizing content for AI Overviews and creating FAQ sections for voice search becomes critically important.

    Call to action: Analyze your data, test new hypotheses, and adapt strategies to changing user behavior. AI is not the future; it is already the present. How much cash are you willing to lose by ignoring this?

  • Google AI: Judge doubts spam update hits AI content

    Google AI: Judge doubts spam update hits AI content

    This week, the SEO world is buzzing with activity: a judge questions Google’s monopoly on AI content, a spam update pulls the rug out from under automated publications, and the old debate about geotargeting flares up again. Let’s break down what this means for your cache, especially in the context of Google AI.

    Legal Battles: Google AI and “Unfair” Content Usage

    A federal judge questioned Google’s practice of using web content for its AI overviews, calling it “grossly unfair.” This jeopardizes the current “traffic in exchange for content” model.

    Details of Penske Media vs. Google Case

    • During the antitrust hearing for Penske Media, Judge Amit Mehta questioned whether Google’s AI overviews could be considered a “product improvement.”
    • Digital Content Next CEO Jason Kint quoted the judge, who called the situation “really unfair” and noted that the improvement is being developed “at the expense of publishers.”
    • Judge Mehta has not yet ruled on Google’s motion to dismiss the lawsuit.

    “Publishers have no control over how Google uses their content,” Kint noted, emphasizing that opting out of Google indexing is not a realistic choice for publishers dependent on search traffic.

    Google Spam Update: A Blow to AI Publications

    Although Google has not officially stated that it is targeting AI-generated content in the August spam update, reports from website owners indicate problems for automated publications. Is cheap AI content worth such risks?

    Initial Observations and Conclusions

    • Roger Montti collected examples where fully automated sites lost visibility, while resources using AI with human refinement suffered less.
    • These are currently anecdotal reports, not official confirmation of changes in Google’s algorithms.
    • Google released the S-CTS system to detect coordinated synthetic abuse on video platforms, but this is not related to Google Search.
    • The mere fact of AI authorship is not a reason for a drop in rankings. Google’s policy concerns large-scale content created to manipulate rankings, regardless of the author.

    • SEO consultant Takuma Oka warns that his sample is small and the conclusions are not definitive. What matters is not the AI itself, but the method and purpose of mass production.

    SEO vs. GEO: What John Mueller Says

    John Mueller from Google once again commented on the “SEO vs. GEO” discussion, clarifying that there are no special requirements for generative AI answers in search. That is, testing hypotheses cheaply does not mean that basic principles can be forgotten.

    Nuances for AI Answers

    Dan Akedju summarized: “The fundamentals are the same. The extraction level is different.” This is a key point: favorable CPM only when the platform’s features are taken into account.

    AI Recommendations: Brands are Recommended, but Others are Cited

    New data shows that a brand recommendation in an AI answer does not guarantee a direct link to your site. This affects ROI and requires a review of metrics.

    Shero Commerce Analysis

    Aleyda Solis emphasized: “Your brand may be recommended in the answer… while the citation (and click) goes to a magazine or marketplace.” This means that the cost of uniqueizing one video or the price of generating 100 AI videos should pay off not only with mentions but also with direct links.

    ChatGPT and Reddit: Complex Citation Dynamics

    Promptwatch data showed a sharp drop in Reddit’s citation share in ChatGPT in mid-August, but new tests show that Reddit still plays an important role in the information extraction process.

    Google AI: The judge doubts, the spam update hits AI content — illustration 2

    Variability of ChatGPT Behavior

    Mohanadasan summarized: “Reddit citations were not simply ‘turned off.’ They seem to be query-dependent.” This demonstrates that price comparison of various sources and their citability is a dynamic process and requires constant monitoring.

    Conclusion: Visibility vs. Value – New Metrics

    This week, it became clear that visibility and real value are beginning to diverge. Publishers share information, but don’t always get traffic. Brands receive recommendations, but links go to others. Reddit may be a data source for AI, but not appear in final citations. “AI visibility” is becoming too broad a concept, requiring more precise metrics to assess ROI.

    It’s important not only to be indexed, but also to get a mention, a citation, a click, and ultimately, a conversion. How much money will each dollar spent bring – that’s the main question. Re-evaluate your strategies and metrics to avoid wasting your budget.

    Frequently Asked Questions

    What is “unfair” use of content by Google AI?

    Judge Amit Mehta opined that Google, by using publishers’ content for its AI overviews without adequate compensation or control from publishers, creates an unfair advantage, as publishers cannot opt out of Google indexing without losing traffic.

    Is Google’s spam update really targeting AI-generated content?

    Google has not officially confirmed this. However, according to reports from website owners, resources that use fully automated content generation have lost visibility. At the same time, sites where AI content was refined by humans suffered less. The focus is on large-scale content created to manipulate rankings, regardless of the generation method.

    What does John Mueller advise regarding SEO for AI answers?

    Mueller states that there are no special requirements for generative AI answers in Google Search. If you are already optimizing your site for Google, no additional technical checklists are needed for AI overviews. However, it is important to remember that different AI systems extract and select sources differently.

    Why might a brand be recommended by AI, but the link goes to another site?

    Shero Commerce research showed that AI systems often recommend brands by name, but cite third-party resources (e.g., magazines or marketplaces) instead of the brand’s official pages. This means that brand visibility does not always convert into direct clicks and traffic to your resource.

    How has ChatGPT’s behavior regarding Reddit citations changed?

    Data shows that Reddit citations in ChatGPT have become more complex and depend on the specific query. In some cases, Reddit is actively used and cited, in others, a lot of data is extracted, but there are no citations. This indicates the dynamism of algorithms and the need for constant analysis of sources that AI systems prefer for specific topics.

  • AI Changes Search: Why Your Content Needs to Provide an Immediate Answer

    AI Changes Search: Why Your Content Needs to Provide an Immediate Answer

    The era of fragmented queries is fading. Data from a year of Google’s AI search mode shows: users are asking complete questions, not fragments of phrases. This is critically important for stream highlights and any other content: now the main information should be at the beginning so that AI algorithms can instantly extract it and present it in their reviews. If you bury the essence in long introductions, your content risks going unnoticed.

    Evolution of Search: From Keywords to Dialogue

    Since the launch of AI mode, user queries have fundamentally changed. According to Shivani Mohan, Google’s VP of Data Science, AI mode has reached 1 billion active users per month. But it’s not just about the increase in the number of queries.

    • Query length has tripled: The average query in AI mode in the US is three times longer than traditional queries.
    • Multimodal search: More than 1 in 6 queries now include images, voice input, or interactive dialogue.
    • Growth of images: Image-based queries are growing by 40% monthly.
    • Follow-up questions: Users ask clarifying questions, indicating the depth of their search.

    The most common first words in AI mode queries are “what,” “how,” “I,” “is,” and “can.” This demonstrates a shift from searching by individual words to task- and question-oriented search, similar to communicating with a human.

    Five User Behavior Modes in AI Search

    Mohan identifies five main user behavior modes, each requiring a specific approach to content:

    • Explore: Open-ended queries for brainstorming. Growing 30% faster than overall AI mode traffic.
    • Decide: Queries with comparison words (“which of,” “which one”). Growing 40% faster.
    • Learn: Understanding new concepts and professional development.
    • Create: Queries for image creation have tripled since the beginning of the year.
    • Do: Planning (workouts, travel, budgets). Growing 80% faster in the last six months.

    “This is not just a change in ranking factors; it’s a transformation of user behavior. If you’re still building content around keywords, that model is rapidly becoming obsolete,” says Greg Jarboe.

    How to adapt your content strategy for AI search

    To make your content effective in the new reality of AI search, follow these five steps:

    1. Start with the main point

    Place the main definition, key metrics, or main conclusions directly in the first sentence of each main section. This allows AI algorithms to quickly extract the necessary information.

    2. Use specifics

    Formulate sentences with specific brand names, geographical markers, exact dates, and verified numerical values. Avoid vague generalizations so that your text is “mathematically readable” for search engines and reduces the risk of AI “hallucinations.”

    3. Structure for scanning

    Organize the main text into short paragraphs (2-3 sentences). After section headings, use brief summaries, ordered lists, or structured tables. This facilitates segment extraction for AI and ensures readability for humans. This is especially important for mass cutting of 100+ clips, where each fragment must be self-contained and informative.

    AI is changing search: Why your content should provide an answer immediately — illustration 2

    4. Prepare answers to follow-up questions

    If multi-turn dialogues are growing by 40% per month, your content should answer the second and third questions a reader will ask after the first. Structure long content so that each section can serve as an answer to a specific follow-up question. This increases the likelihood that this particular “chunk” will be provided by the AI mode. For cutting game streams, this means creating clips that answer specific viewer questions.

    5. Optimize multimodal content

    With the growth of image queries (40% monthly) and a threefold increase in image creation requests, alt-text, image context, and visual content quality are becoming key ranking factors. They are no longer secondary but are part of what the AI mode reads.

    Conclusion: From “assembly line” to “smart content production”

    Success in modern SEO is not about flooding the internet with templated texts, but about mastering structural clarity that provides immediate and verified benefits to both algorithms and readers. Just as 19th-century journalists adapted to the telegraph, we must rethink our approach to content creation for the AI era. Cutting streams without losing quality, creating highlights, and cropping 9:16 from horizontal — all this requires not just technical skills, but also a strategic approach to information delivery. Remember that the person who cuts streams must now think like an SEO specialist so that every Twitch clipping or podcast video cutting works as effectively as possible to attract an audience. Contact us to find out how we can help you with this.

    Frequently Asked Questions

    How long does it take to cut a 2-hour stream?

    The cutting time depends on the complexity of the content and the desired number of highlights. On average, professional cutting without loss of quality of a 2-hour stream into 5-10 clips can take from 1 to 3 hours, including adding subtitles for cuts and optimization. If you need bulk cutting of 100+ clips, automated tools such as Opus Clip analog service are used, which significantly speed up the process but require subsequent manual refinement.

    What moments are usually cut from streams for highlights?

    For highlight editing, the most emotional moments, key game events (e.g., “kills”, “win rates”), funny jokes, important announcements, Q&A sessions, and fragments that evoke a strong audience reaction are usually cut. The goal is to create cuts for TikTok from Twitch or YouTube that quickly grab attention.

    Are subtitles needed for stream cuts?

    Yes, subtitles for cuts are extremely important. They improve content accessibility, allow videos to be watched without sound (e.g., in public places), and increase engagement. In addition, subtitles contribute to better indexing of content by search engines, which is important for stream monetization through cuts.

    What is the price for 1 hour of stream for cutting 20 clips?

    The price for cutting 20 clips from a stream for 1 hour can vary depending on the specialist’s qualifications, the complexity of the editing, and the need to add graphics and subtitles. Approximately, the cost can range from 1000 to 3000 rubles per hour of source material, if it is a high-quality work taking into account SEO optimization and 9:16 cropping from horizontal for different platforms.