Google vs. Everyone: How YouTube Ownership Gives Veo an Unfair Advantage in the AI Video War

April 14, 2026

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Jay Kim

Google vs. Everyone: How YouTube Ownership Gives Veo an Unfair Advantage in the AI Video War

Google confirmed it trains Veo on YouTube's 20 billion videos — 40x more data than any competitor. With custom TPUs, free consumer access, and YouTube integration, here's why the AI video race may already be over.

In AI, we talk endlessly about model architectures, parameter counts, and benchmark scores. We debate whether diffusion transformers are better than autoregressive approaches. We compare inference speeds and credit pricing. And we mostly miss the real story.

The real battle for AI video supremacy was not decided by who built the cleverest architecture. It was decided years ago in the most unlikely place: a simple video-sharing platform called YouTube.[10]

Google is using its expansive library of YouTube videos to train its AI models, including Gemini and the Veo 3 video and audio generator.[1] The company confirmed this directly to CNBC in June 2025. And when you understand the scale of what that means — not just the raw video, but the metadata, the engagement signals, the quality filtering, the creator labeling, and the distribution infrastructure that comes with it — you begin to understand why the AI video race might already be over.

On the model side, Google's Veo 3.1 has achieved near-total dominance at 96.4% market share among production users, with OpenAI's Sora 2 capturing just 2.0%.[5]

This is the story of how owning the world's largest video platform became the most decisive competitive advantage in AI history — why every competitor is structurally disadvantaged, what it means for creators caught in the middle, and what you should do about it.

The 20 Billion Video Moat: A Data Advantage No One Can Replicate

Let's start with the number that defines everything.

The tech company is turning to its catalog of 20 billion YouTube videos to train these new-age AI tools, according to a person who was not authorized to speak publicly about the matter.[1]

Twenty billion videos. The company confirmed to CNBC that it relies on its vault of YouTube videos to train its AI models, but Google said it only uses a subset of its videos for the training.[1]

But even a tiny subset is astronomically large by any competitor's standard. Given the platform's scale, training on just 1% of the catalog would amount to 2.3 billion minutes of content, which experts say is more than 40 times the training data used by competing AI models.[1]

Read that again. One percent of YouTube's library gives Google 40 times more training data than everyone else. Not 40 percent more. Forty times more.

And according to YouTube, an average of 20 million videos are uploaded to the platform each day by independent creators and nearly every major media company.[1] This means the dataset is not static. It grows by 20 million videos every single day. Google's training advantage gets wider with each passing hour, and every competitor falls further behind.

For creators who want to take advantage of the best available AI video right now — regardless of who trained on what — the Cinematic Video Generator inside Miraflow AI lets you generate professional-quality clips from text prompts, giving you access to multiple leading models from a single dashboard.

It's Not Just the Video — It's Everything Around the Video

Raw video footage is only part of the advantage. What makes YouTube's data uniquely powerful for training a video generation model is the extraordinary richness of the metadata and engagement signals that come attached to every single video.

The platform's built-in metadata — titles, descriptions, tags, timestamps, and subtitles — adds another layer of labeling that dramatically lowers the cost of training sophisticated multimodal models.[1]

Consider what Google has access to that no competitor does. Every YouTube video comes paired with a human-written title and description that explains what the video contains. It has tags and categories. It has auto-generated captions and often creator-written subtitles. It has chapters with timestamps that segment the video into meaningful sections. And critically, it has engagement data: view counts, watch time patterns, likes, comments, shares, audience retention curves, and click-through rates on thumbnails.

This richness is why video is considered the hardest AI problem; YouTube already possesses the massive, real-world footage required to solve it.[1]

YouTube provides Google with real-time insight into creative evolution. New visual trends, emerging editing techniques, and shifting audience preferences appear on the platform before they spread to traditional media. Gemini + VEO 3 continuously learns from this creative frontier, staying current with visual language that resonates with contemporary audiences.[10]

While competitors train models on static datasets that become outdated, Google's model evolves with culture itself, learning from creators who are actively experimenting with new forms of visual expression.[10]

This creates what analysts call a dynamic learning advantage. OpenAI's Sora was trained on a fixed dataset that immediately began aging. Runway's models face the same problem. Google's Veo, by contrast, has access to a living, breathing, constantly updated library of human creative expression — annotated by the creators themselves and quality-filtered by billions of viewer interactions.

YouTube's engagement signals function as an implicit quality filter at a scale no competitor can match. Videos that hold viewer attention get recommended more. Videos with poor production quality, confusing content, or misleading thumbnails get buried. Google can use these signals to identify and prioritize the highest-quality training data — automatically, at massive scale, without manual curation.

The Legal Trap: Google Owns It, Everyone Else Scrapes It

Here is where the competitive advantage transforms from significant to potentially insurmountable.

Every major AI video competitor that used YouTube data to train their models did so without owning the platform — and many now face serious legal consequences for it.

A content creator responsible for the Ali Spagnola YouTube channel filed a complaint against Runway AI for alleged violations of the Digital Millennium Copyright Act, arising from allegations of unlawfully circumventing technological measures to access and scrape thousands of copyrighted videos from YouTube to train a large-scale GenAI model.[3]

Multiple lawsuits allege Runway trained on copyrighted YouTube videos and pirated films without permission.[1]

OpenAI faced the same problem with Sora. Its CTO could not even confirm whether YouTube videos were used in training — a vagueness that invited both legal scrutiny and public distrust.

By mid-September 2025, fifty copyright lawsuits, which include many proposed class actions of potentially millions of class members, have been filed against AI companies. More lawsuits are likely.[10]

Google, by contrast, sits in a fundamentally different legal position. It owns YouTube. YouTube's Terms of Service explicitly state that uploading content grants the platform a "worldwide, non-exclusive, royalty-free" license to use it for purposes including "operating and improving" services.[8]

Updated Terms of Service from September 2024 grant YouTube broad rights to use uploaded content, including for "machine learning and AI applications," but do not offer an opt-out for training by Google's own models, only for third parties like Apple or Anthropic.[4]

This creates a starkly asymmetric competitive landscape. While YouTube allows creators to opt out of providing their content for training to third-party AI companies, they cannot prevent Google from doing so.[6]

In other words: Google can legally train on YouTube. Everyone else does so at their own legal peril. Google can block competitors from accessing the data. Competitors cannot block Google. And Google includes an indemnification clause for its generative AI products, including Veo, which means that if a user faces a copyright challenge over AI-generated content, Google will take on legal responsibility and cover the associated costs.[1]

This indemnification clause is something no competitor can match, because no competitor has the legal standing to offer it — they don't own the data their models were trained on.

For creators who want to produce content without worrying about the legal uncertainty surrounding any particular model's training data, using a platform like Miraflow AI that aggregates tools through legitimate API partnerships is the safest approach.

The Compute Advantage: Google Builds the Chips Too

Data alone does not win the AI video race. You also need the compute infrastructure to train on it and serve it at scale. And here too, Google has a structural advantage that competitors cannot easily replicate.

Today's frontier models, including Google's Gemini, Veo, Imagen, and Anthropic's Claude train and serve on Tensor Processing Units (TPUs).[2]

Google has been designing and manufacturing custom AI chips since 2015. It is now on its seventh generation, called Ironwood. This new chip will come in two configurations: a 256-chip cluster and a 9,216-chip cluster.[8]

The significance for AI video generation is direct. Google's ability to press further into video generation while OpenAI backs off is a prime example of the company's edge in the AI market. While AI companies scrap for every morsel of compute they can get their hands on, Google sits atop a legacy. The company spent years building Google Cloud into one of the big three cloud hyperscalers, giving it a much-needed wealth of compute resources. Additionally, having cemented its billions in the digital advertising and search markets, Google doesn't have to worry about the revenue picture as much as rivals like OpenAI and Anthropic.[9]

Because of this, Google can take on the "side quests" as Simo called them, cornering a market while it's still nascent.[9]

Remember: OpenAI killed Sora because the compute costs were unsustainable at $15 million per day. Google is not only sustaining Veo — it is making it free. That gap in economic resilience reflects the difference between a company that rents its compute and one that builds it.

Firms that have an advantage in infrastructure will also have an advantage in the ability to deploy and scale applications with AI.[10]

The Distribution Moat: Veo Lives Inside Everything

Even if a competitor somehow matched Google's data quality, legal standing, and compute infrastructure, they would still face a distribution problem that Sora's failure illustrates perfectly.

The argument is straightforward: AI video generation isn't a destination product. Nobody opens a "video generation app" the way they open TikTok or YouTube. The value of AI video lives inside workflows — editing suites, social platforms, marketing tools, game engines. Sora tried to be a standalone experience when it should have been infrastructure.[7]

Google learned this lesson. Instead of building a standalone app, it embedded Veo everywhere.

On April 2, 2026, Google announced that Veo 3.1 would be available free of charge to all personal Google account holders through two distinct channels: Google Vids and Google Flow.[3]

YouTube integrated Veo 3.1 into its Ingredients to Video feature on January 13, 2026, allowing Shorts creators to combine three images into a single generated clip.[5]

Google Vids now lets anyone with a Google account generate high-quality video clips using Veo 3.1, with 10 free generations monthly.[7]

Google AI Ultra and Workspace AI Ultra accounts can now generate up to 1,000 Veo videos per month.[7]

The distribution strategy is comprehensive: Veo lives inside Google Vids (integrated with Drive, Docs, and the rest of Workspace), inside Google Flow (the dedicated filmmaking tool), inside YouTube Shorts (reaching creators directly), inside the Gemini app, and through the Gemini API and Vertex AI for developers. When the generator is embedded in a tool people already open for work, usage goes from experiment to habit.[6]

While competitors like Runway, Pika, and Kling charge per-second or per-generation fees, Google just made high-quality AI video generation a commodity feature bundled into its productivity suite.[10]

This is a classic platform strategy. Google is not trying to win the AI video race by having the best standalone product. It is trying to win by making AI video generation a feature of tools that billions of people already use — the same way Google Maps won by being embedded in Android, not by being a standalone app.

For creators who want to build their AI video workflow without being locked into any single platform, Miraflow AI offers an all-in-one dashboard where you can generate cinematic videos, create AI actor videos with 100+ avatars, produce complete YouTube Shorts from a single prompt, and design professional thumbnails — all without depending on Google or any single model provider.

What Veo 3.1 Actually Delivers: The Results Speak

All of these structural advantages would be theoretical if the output quality did not reflect them. But it does.

In May 2025, Google released Veo 3, which not only generates videos but also creates synchronized audio — including dialogue, sound effects, and ambient noise — to match the visuals.[3]

Google DeepMind CEO Demis Hassabis described the release as the moment when AI video generation left the era of the silent film.[3]

Released on January 13, 2026, Veo 3.1 brings professional-grade 4K upscaling, native 9:16 vertical video, and Scene Extension for 60+ second narratives.[6]

On benchmarks, the results are clear. Participants viewed 1,003 prompts and respective videos on MovieGenBench. Veo 3.1 performs best on its capability to follow prompts accurately.[2] Participants rate the visual quality of Veo's outputs more highly than other models.[2]

Google has also been aggressively reducing costs to expand access. Google introduced Veo 3.1 Lite, its most cost-effective video model. This model empowers developers to build high-volume video applications, at less than 50% of the cost of Veo 3.1 Fast, but with the same speed.[1]

Google is giving users a discount on Veo 3.1 Fast, its mid-range video generation model, starting April 7, cutting generation costs to 10 cents per second for 720p and 12 cents per second for 1080p.[9]

The Veo model family now spans four tiers — Lite, Fast, Standard, and Ultra — covering use cases from free consumer experimentation to enterprise-grade production. No competitor offers this breadth.

The Creator Dilemma: Powering Your Own Replacement

The most uncomfortable dimension of this story is what it means for the millions of creators who built YouTube into the platform that now fuels Google's AI advantage.

The move has sparked deep tensions between the world's biggest online video company and some of the creators who helped make it a behemoth. Google, creators say, is using their data to train something that could become their biggest competitor.[3]

CNBC spoke with multiple leading creators and IP professionals. None were aware or had been informed by YouTube that their content could be used to train Google's AI models.[1]

Cory Williams, creator of the popular Silly Crocodile animated character, pointed out: "They're training on things that we, the creators, are creating, but we're not getting anything in return for the help that we are providing."[3]

The detection data adds specificity to the concern. A video produced by YouTube creator Brodie Moss received a Trace ID score of 71 for visuals and over 90 for audio, indicating a high degree of similarity with content produced by Veo 3.[1]

Users who have uploaded content to the service have no way of opting out of letting Google train on their videos.[1]

There is a deeper structural concern here. What YouTube's AI label policy never actually says is that every label a creator provides to Google is free information for Google about what AI video looks like and what it does not look like. Creators are labeling the exact data Google needs to improve Veo.[7]

In mid-2025, YouTube was discovered to secretly add AI enhancements, such as deblurring, denoising, and skin smoothing, to creators' Shorts videos without their consent. This came to light when prominent creators such as Rick Beato and Rhett Shull exposed this practice publicly. This forced YouTube to confess that they were conducting this "experiment".[7]

Google has taken some steps to address creator concerns. YouTube announced a partnership with Creative Artists Agency in December to develop access for top talent to identify and manage AI-generated content that features their likeness.[1] Creators can opt into a "digital twin" registry, allowing them to license their AI-generated likeness for specific campaigns while restricting unauthorized uses.[8]

But the fundamental tension remains: even if Veo 3's final output does not directly replicate existing work, the generated content fuels commercial tools that could compete with the creators who made the training data possible, all without credit, consent or compensation, experts said.[1]

If you are a creator navigating this landscape, diversifying your content creation tools is not just about efficiency — it is about reducing your dependency on any single platform. Our guide to what Wan 2.7 is and everything creators need to know covers the most feature-complete open-source video generation option available today.

What This Means for Every Competitor

Google's combination of advantages — data, legal standing, compute, distribution, and revenue resilience — creates an asymmetric landscape that different competitors experience in different ways.

For OpenAI, the story is already written. Sora is dead. The company has explicitly conceded the AI video category and redirected its resources toward enterprise AI and coding tools. OpenAI's video research team continues work on "world simulation" for robotics, but the consumer video product is gone.

For Runway, the path forward requires differentiation on control and creative tooling rather than raw output quality. Multiple lawsuits allege Runway trained on copyrighted YouTube videos and pirated films without permission.[1] The legal cloud over its training data is a persistent strategic vulnerability.

For Chinese competitors like Kuaishou (Kling) and ByteDance (Seedance), the advantage is different data access. Kuaishou owns its own short-video platform with hundreds of millions of users. ByteDance owns TikTok and Douyin. They have their own first-party video data moats, and they are less constrained by Western copyright frameworks. This is why Kling 3.0 generates synchronized audio natively[4] and why Seedance 2.0 is competitive on quality benchmarks.

For open-source models like Alibaba's Wan 2.7, the strategy is fundamentally different — democratize the technology so that anyone can run it locally, eliminating the per-generation cost problem entirely. This approach is structurally resilient against Google's platform advantage because it operates outside the platform ecosystem entirely.

Understanding Google's data advantage reveals why the video generation market is likely to consolidate around a few key players rather than remaining fragmented across numerous competitors.[10]

The Flywheel Effect: Why the Gap Gets Wider

What makes Google's position particularly durable is that it is not a static advantage. It is a flywheel.

More creators upload to YouTube → more training data for Veo → better Veo output → more Veo integration into YouTube tools → more creators using AI-assisted creation on YouTube → more uploads → more training data. Each turn of the cycle accelerates the next.

YouTube is now actively fueling this engine by investing in AI tools for creators. The platform's strategy is clear: empower creators to produce more content, faster, and in new formats. A key upcoming feature is the ability for creators to make Shorts using their own likeness. This directly increases the volume of high-quality, labeled data.[1]

At the same time, Shorts now averages 200 billion daily views.[1] That engagement data feeds back into the system, helping Google understand not just what videos look like, but which videos resonate with audiences — a signal that no competitor's training pipeline can replicate.

This leads to a growing and self-correcting dataset of AI video content generated by a platform that has 20 billion videos, policy-based, validated by millions of creators, and used directly in the next-generation Google AI video model.[7]

For creators building content businesses, the lesson here is to build workflows that are resilient to platform consolidation. The AI Image Generator and Background Music Generator inside Miraflow AI let you produce supporting assets for any video project — thumbnails, B-roll stills, and custom audio — without being locked into any single generation model.

The Google AI Video Stack: A Moat with Multiple Layers

To fully appreciate the depth of Google's advantage, consider the complete vertical stack it controls for AI video:

Data layer: YouTube's 20 billion videos with rich metadata, engagement signals, transcripts, and creator annotations — growing by 20 million videos per day.

Training infrastructure: Custom TPU chips (now in seventh generation), AI Hypercomputer architecture, and Google Cloud's global data center network.

Model development: Google DeepMind's research team, which has iterated from Veo 1 through Veo 3.1 in roughly two years and is reportedly working on Veo 4.

Distribution channels: Google Vids (free for all Google accounts), Google Flow (dedicated filmmaking tool), YouTube Shorts (integrated creator tools), Gemini App, Gemini API, and Vertex AI (enterprise).

Monetization engine: Google Ads integration, YouTube's $70 billion creator payout infrastructure, and Google Workspace's enterprise billing.

Legal framework: YouTube's Terms of Service provide broad data rights, and Google's indemnification clause protects commercial users.

No other company in the world controls all of these layers simultaneously. This is not a product advantage. It is a structural one.

Should You Be Worried? What Creators Should Actually Do

The strategic picture is clear: Google has an enormous structural advantage in AI video that is likely to persist and widen. But that does not mean creators are powerless. Here is a practical framework for navigating this landscape.

Accept that AI video generation is commoditizing, and plan accordingly. The value proposition can no longer be "we generate video from text" — that's now table stakes.[10] The tools are becoming cheaper and more accessible. Your competitive advantage as a creator is not the tool you use — it is the creative vision, audience relationship, and content strategy you bring to the tool.

Diversify your creation tools. Building your entire workflow around a single model from a single company is risky, as the Sora shutdown proved. Use multi-model platforms like Miraflow AI that give you access to multiple AI creation tools — images, videos, thumbnails, music — within a single dashboard, so your workflow survives if any single model disappears.

Understand the open-source alternative. Models like Wan 2.7 can be run locally with zero per-generation costs and no dependency on any cloud provider. For power users with access to GPU hardware, open-source is the most structurally resilient option. Our full breakdown of what Wan 2.7 is and everything creators need to know explains how to get started.

Take advantage of Google's free tier strategically. As of April 2026, any person with a standard Google account can generate high-quality video clips using Veo 3.1 — completely free, no subscription required.[1] Use it for prototyping, B-roll generation, and experimentation — but do not make it the only tool in your pipeline.

Focus on what AI cannot replicate. Personal brand, on-camera presence, community engagement, lived experience, authentic storytelling, and audience trust — these remain human competitive advantages. The creators who thrive will be those who use AI to multiply their human strengths, not replace them.

If you are building a YouTube channel and need to optimize your visual content strategy, these resources can help: 10 AI prompts for YouTube thumbnails that stop the scroll, YouTube thumbnail trends in 2026, and YouTube Shorts vs long-form: which grows your channel faster.

The Regulatory Wild Card

There is one force that could disrupt Google's advantage: regulation.

The stakes are high, and the consequences are often described in existential terms. Some warn that requiring AI companies to license copyrighted works would throttle a transformative technology, because it is not practically possible to obtain licenses for the volume and diversity of content necessary to power cutting-edge systems. Others fear that unlicensed training will corrode the creative ecosystem, with artists' entire bodies of works used against their will to produce content that competes with them in the marketplace.[4]

If courts rule that AI training on copyrighted content requires licensing, Google's advantage could actually increase — it already has a licensing relationship with YouTube's creators through its Terms of Service. But if regulations specifically target platforms that use first-party user data for AI training, Google could face unprecedented constraints.

The primary risk is regulatory and legal action over the use of user-generated content. The platform's 500 hours of video uploaded every minute create a massive, labeled dataset that AI companies are eager to consume. Yet creators uploaded for audience building and ad revenue, not to train competing AI tutors. This mismatch in expectation creates a trust gap that could erupt into a legal or regulatory storm.[1]

Several U.S. senators have already raised concerns about Google's use of YouTube data for AI training. The EU's AI Act includes provisions about training data transparency. And the U.S. Copyright Office has weighed in with a report that takes the position that the development and deployment of generative AI systems implicate several of the exclusive rights granted to copyright owners under the Copyright Act, including the rights to create copies and derivative works.[7]

The regulatory landscape is evolving fast, and it is the one variable that could meaningfully alter the competitive dynamics. But until regulators act, Google's advantage remains intact and growing.

Conclusion: The Race That Was Won Before It Started

The AI video war of 2026 has a clear leader, and the reasons for that leadership have less to do with model architecture than with strategic assets accumulated over two decades.

Google owns the world's largest video platform with 20 billion videos. It has legal access to that data through its Terms of Service. It builds custom AI chips. It operates one of the three largest cloud infrastructures on earth. It has integrated Veo into YouTube, Google Vids, Google Flow, Gemini, and Vertex AI. It is funded by a digital advertising business that generates over $300 billion annually. And it just made its best video model free for billions of users.

Google Veo leads the generative video market with superior 4K photorealism and integrated audio, an advantage derived from its YouTube training data.[9]

The competitors that survive will be those that find different strategic positions: open-source accessibility (Wan), platform-specific data advantages (Kling, Seedance), creative control and filmmaker tooling (Runway), or multi-model aggregation and workflow integration (platforms like Miraflow AI).

For creators, the message is not doom and gloom. The tools available right now are better, cheaper, and more accessible than anything that existed even six months ago. The creators who thrive in 2026 will be the ones who build resilient, diversified workflows and focus on the irreplaceable human elements of content creation — voice, vision, authenticity, and audience connection.

Start building your AI content pipeline today with Miraflow AI. The technology is here, it is powerful, and the creators who master it now will have a compounding advantage for years to come.

Frequently Asked Questions

Does Google use YouTube videos to train Veo?
Yes. Google confirmed to CNBC in June 2025 that it uses YouTube videos to train its AI models, including Veo 3. Google says it only uses a "subset" of YouTube's 20 billion videos and honors specific agreements with creators and media companies, but experts estimate even 1% of YouTube's library represents 40 times more data than competing models use.

Can YouTube creators opt out of Google using their videos for AI training?
No. YouTube allows creators to opt out of sharing their content with third-party AI companies like Apple, Anthropic, or Amazon, but there is no mechanism to prevent Google itself from using creator videos for AI training. This is governed by YouTube's Terms of Service, which grant broad rights to use uploaded content for "machine learning and AI applications."

How much more training data does Google have compared to competitors?
Experts cited by CNBC estimate that even 1% of YouTube's catalog — which YouTube has not confirmed as the amount used — would equal approximately 2.3 billion minutes of content, more than 40 times the training data used by competing AI models. The full library contains over 20 billion videos and grows by 20 million new uploads per day.

Why is YouTube's data better than other video datasets?
YouTube's data advantage extends beyond raw video footage. Each video includes human-written titles, descriptions, tags, timestamps, auto-generated and creator-written captions, chapter markers, and rich engagement data (views, watch time, likes, comments, audience retention). This metadata acts as a massive implicit labeling system that dramatically lowers the cost and improves the quality of training.

Is Veo 3.1 really free?
Yes, as of April 2026, any person with a Google account can generate video clips using Veo 3.1 at no cost — 10 generations per month through Google Vids and additional daily credits through Google Flow. The free tier is capped at 720p resolution and 8 seconds per clip. Paid tiers (AI Pro at ~$20/month and AI Ultra at ~$250/month) offer higher resolution, more generations, and additional features.

What happened to Sora after Google's advantage became clear?
OpenAI shut down Sora on March 24, 2026, citing unsustainable economics ($15 million/day in inference costs against $2.1 million in total lifetime revenue) and the need to reallocate compute to enterprise products. The shutdown demonstrated the fundamental challenge of competing in AI video without first-party data and compute infrastructure.

What is Veo's market share in AI video generation?
According to Vivideo platform data from early 2026, Veo 3.1 commands approximately 96.4% of model share among production users on their platform, compared to just 2.0% for Sora 2. While this is one platform's data and not an industry-wide figure, it illustrates the scale of Veo's dominance.

Can Google's advantage be disrupted?
The most likely disruption would come from regulation. If courts or legislatures require explicit creator consent or licensing for AI training on user-generated content, Google's Terms of Service defense could be challenged. The EU's AI Act, pending U.S. legislation, and ongoing copyright lawsuits all represent potential disruptions. Open-source models like Wan 2.7, which anyone can run locally, also offer an alternative path that operates outside the platform ecosystem entirely.

What should creators use instead of (or alongside) Veo?
The best approach in 2026 is a multi-model workflow. Use Veo 3.1 (free tier) for prototyping and YouTube Shorts, Wan 2.7 (open-source) for local generation without per-clip costs, Kling 3.0 for competitive quality at lower prices, and a platform like Miraflow AI for an all-in-one dashboard covering video, images, thumbnails, music, and AI actors.