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Disney Just Bet $1 Billion That AI Video Is the Future

Hollywood's most powerful IP guardian partners with OpenAI. Plus three AI video generation tutorials to level up your AI filmmaking workflow

Author

Lawrence Jordan
December 11, 2025

Welcome to Today’s AIography!

The mouse just entered the chat. Disney, the company that guards Mickey more fiercely than Fort Knox guards gold, announced a $1 billion investment in OpenAI and is licensing over 200 characters for use in Sora and ChatGPT.

Let that sink in.

The studio that sued its way through the VCR era, fought tooth and nail over streaming rights, and has an army of attorneys dedicated solely to protecting its IP just gave the green light to AI-generated Star Wars, Marvel, and Pixar content. For an industry that's spent the last two years treating generative video with something between suspicion and outright hostility, this isn't just news, it's a seismic shift. Hollywood's biggest player just declared which side of history they're betting on.

But this issue isn't just about the suits making deals. We're loading you up with three hands-on tutorials (and yes, expect at least one every issue from now on): First, Jay from RoboNuggets shows us an n8n workflow that produces broadcast-quality ads for under $3! Tao Prompts seven prompt styles that actually work for AI video, and TechHalla's grid prompting technique for multi-character consistency. Plus, Runway dropped five world model announcements that signal where video AI might really be headed. Man, it is crazeballs out there. Let's get into it.

 In today’s AIography:

  • Disney Partners with OpenAI: Sora Can Now Generate Videos with Mickey and Marvel

  • $3.00 AI Ads That Look Like $30K Productions. RoboNuggets n8n Workflow Breakdown

  • Runway Just Declared War on Reality. 5 World Model Announcements That Change Everything

  • 7 AI Video Prompt Styles That Actually Work: Stop Overcomplicating It

  • Grid Prompting: The Nano Banana Pro Trick for Multi-Character Consistency

  • Essential Tools

  • Short Takes

  • One More Thing…

Read time: About 10 minutes

THE LATEST NEWS

TL;DR: 
Disney just signed a landmark deal allowing Sora to generate videos featuring its IP, from Mickey Mouse to Star Wars to Marvel characters. Disney will also become a major OpenAI customer, using its APIs to build new products for Disney+ and beyond.

Key Takeaways: 
✨ First major studio to license its IP library for AI video generation: massive validation for the tech
🎬 Creators could potentially use Sora to make content with Disney characters (licensing terms still unclear)
💡 Disney becoming a 'major customer' signals they're building AI tools internally, likely for content creation pipelines
⚠️ Deal happens amid Hollywood's ongoing AI anxiety. Disney betting on partnership over opposition
🔥 Opens door for other studios to follow & could transform what IP is available for AI video projects

Why It's Important: 
This isn't just a tech deal, it's a fundamental shift in how Hollywood's most powerful IP holder views AI video generation. For years, the conversation has been about AI threatening creative jobs and stealing copyrighted work. Disney just flipped the script by actively enabling AI to use its characters.

For creators, the implications are huge but still murky. Will indie filmmakers be able to license Disney characters through Sora for their projects? Or is this exclusive to Disney's internal use? Either way, it legitimizes AI video generation in a way no announcement from OpenAI alone could. When the company that guards Mickey Mouse more fiercely than Fort Knox says 'yes' to AI, every other studio is paying attention.

The timing is fascinating. This comes while Hollywood is still processing last year's strikes, where AI was a central concern. Disney's move suggests they believe the future is partnership, not resistance. They're also clearly building something: becoming a 'major customer' for APIs means they're integrating OpenAI's tools deep into their production and distribution infrastructure, possibly for Disney+. Expect announcements about AI-powered personalization, content creation tools, or entirely new formats.

My Take: 
Disney doesn't make bets like this lightly. This is them signaling AI video gen is production-ready, at least for some applications. The real question: will they open the IP floodgates to creators, or keep the magic kingdom walled off for internal use only?

TL;DR:
Jay from RoboNuggets built an n8n automation system that orchestrates Nano Banana Pro, Veo 3.1, Suno, and ElevenLabs to create broadcast-quality brand ads for under $3. And he walked through the entire production process.

Key Takeaways:
✨ Bypassing aggregators like Freepik and hitting models directly through KAI.ai cuts costs 2-3x (Veo 3.1 at 30 cents per 8-second clip vs subscription limitations)
🎬 Three inputs drive the whole system: a hero "money shot" image, an elements board showing character/setting/product angles, and written creative direction
🛠️ The n8n workflow has five streams: prompts → images → videos → music → voiceover, all feeding into an Airtable DB for organized review.

Why It's Important:
This is the production pipeline a lot of us have been trying to piece together, except Jay actually built the thing and showed his work. The real insight isn't just "AI is cheap now," it's that the aggregator markup is where most people are bleeding money. Freepik, Higgsfield, and similar services are using the same underlying models but charging subscription premiums for the convenience layer.

His approach of using KAI to access Veo 3.1 directly at 30 cents per generation versus burning through a monthly Freepik allocation in just four videos is the kind of practical cost optimization that makes commercial AI work actually viable. Same models, fraction of the price, no subscription commitment.

The workflow architecture is smart too. Using Airtable as the hub means you can actually see your starting frames, ending frames, and prompts organized by scene instead of hunting through a feed-style interface. And the approval checkboxes let you selectively regenerate only the shots that didn't land without re-running everything.

But here's the part that matters most: Jay's adamant that human-in-the-loop isn't optional. His "garbage in, garbage out" point is dead-on. The system gives you a massive head start on prompts, but you're still the creative director deciding when a shot works and when it needs another pass. AI doesn't have taste yet. That's still your job.

The Gemini chat-based iteration for getting the hero image right is worth noting too. Instead of one-shot prompting, he's basically vibe-designing through conversation until the money shot lands. That iterative approach is way more reliable than trying to nail a complex image in a single prompt.

My Take:
This is the kind of end-to-end breakdown I wish more AI creators would do. Not just "look what I made" but "here's exactly how the sausage gets made, including the parts that didn't work the first time." If you're looking to learn any kind of AI video production workflow, Jay’s Skool community, RoboNuggets is chock full of all kinds of gen AI video workflows and IMHO, absolutely worth the price of admission.

TL;DR: 
Runway just released its first world model, a physics-aware AI that understands how reality works, not just how it looks. This powers their new native audio integration and opens doors to interactive agents, robotics simulations, and dynamic avatars beyond traditional video generation.

Key Takeaways: 
✨ World models simulate actual physics and causality, understanding why things happen instead of just predicting pixels
🎬 Native audio integration means Runway-generated video now comes with synchronized sound without separate tools
🤖 Beyond video: the same model can train AI agents, power robotics simulations, and create responsive avatars
💡 This shifts Runway from a video generator to a reality simulator, fundamentally different technology
🔥 Positions Runway ahead of competitors still focused solely on text-to-video without physics understanding

Why It's Important: 
This is a architectural leap, not just a feature update. While other video AI tools generate footage by predicting what comes next visually, world models simulate the underlying physics and rules of reality. Think of it as the difference between an artist painting fire versus a physics engine calculating combustion. For creators, this means more consistent object permanence, realistic interactions, and, critically, the ability to iterate and control outputs in ways that pure generative models can't match.

The native audio integration is the immediate practical win, eliminating the awkward workflow of generating video in Runway then adding sound in ElevenLabs or elsewhere. But the bigger story is where this technology leads: interactive characters that respond to prompts in real-time, virtual environments that obey actual physics for previz work, and eventually the ability to 'direct' AI agents within simulated worlds rather than just generating one-off clips.

Runway is clearly positioning for a future beyond stock video generation. They're building infrastructure for interactive media, game cinematics, and AI-powered production pipelines. If world models deliver on their promise, we're looking at the foundation for tools that don't just make content but simulate entire production environments. That's either visionary or overambitious, but either way, it signals where the serious money thinks video AI is heading.

My Take: 
World models are the real deal. This is the tech that'll separate lasting platforms from flashy demo tools. Runway just made a bet that understanding reality matters more than just faking it convincingly.

TL;DR:
This breakdown from AI video creator Tao Prompts covers the only prompt techniques you actually need for tools like Veo 3.1, Sora, and Kling, from camera control to timestamp sequencing to telling the AI what NOT to do.

Key Takeaways:
✨ Cinematic prompts control camera movement (zoom, pan, orbit, tilt). Same scene looks completely different depending on how you shoot it
🎬 Timestamp and cut-scene prompting let you sequence multiple actions and camera angles within a single generation
⚓ Anchor prompts remind the AI about details it can't see, like what's on your character's other shoulder when you're only showing one side

Why It's Important:
Most people overcomplicate AI video prompts. They write novels hoping more detail equals better results, then get frustrated when the output is a mess. This guide flips that to simple, clear, intentional prompts that the models actually respond to.

The timestamp prompting technique is particularly useful. Instead of hoping the AI figures out your sequence, you're literally telling it, at ":03 seconds: zoom in, :03-06 seconds: tilt down, :06-08 seconds: tilt back up." That's director-level control from a text prompt.

Cut-scene prompting is the sleeper technique here. Using "cut to" in your prompt lets you generate multiple camera angles in a single video. Wide shot of astronaut walking, cut to close-up of boots on terrain. You're basically storyboarding inside the prompt itself. Fair warning though: if your cuts are too different from the original scene, you'll get style inconsistency. The AI has to imagine too much and things get weird.

The anchor prompt concept is the one most people miss entirely. AI video generators don't remember what they can't see. If your character has a tattoo on their right shoulder but you're only showing their left side, the AI will just make something up. You have to anchor those details in the prompt: "no armor on right shoulder, blue tribal tattoos visible." It's not about describing everything, it's about protecting the details that matter to you.

The GPT prompt helper idea is clever but comes with a huge caveat: official documentation never tells you what the models are bad at. Training a GPT on incomplete information means it'll confidently write prompts that ask for things like "angry crowd surrounding her,” and crowds are exactly what these models butcher. You still need to learn and know the limitations of the model yourself.

Negative prompting is underrated. Sometimes the fastest path to what you want is telling the AI what you don't want. "No windows" is way easier than trying to describe exactly what the wall should look like.

My Take: 
Print this list out. Cinematic, timestamp, cut-scene, GPT-assisted, anchor, image, and negative prompting. That's the toolkit. Everything else is just variations on these seven.

TL;DR:
TechHalla's workflow uses layered 2x2 grids as "consistency anchors" to maintain character and scene coherence across multiple shots, solving one of AI image generation's biggest headaches.

Key Takeaways:
✨ Generate a character grid first (multiple angles/poses) to establish your consistency reference
🎬 Combine character grids with background images to create scene-locked 2x2 composites
🔗 Merge multiple character grids together, then extract individual frames for final shots

Why It's Important:
Character consistency is still the thing that kills most AI filmmaking projects. You generate a perfect hero shot, then try to get the same character from a different angle or in a different scene, and suddenly they've got a different nose, different outfit, different everything. This workflow attacks that problem head-on.

The insight here is using grids as anchors rather than single images. When you generate a 2x2 grid showing your character from multiple angles, you're giving the model a richer understanding of who this person is. Not just one view, but a spatial relationship. Then when you use that grid as a reference for subsequent generations, the model has more to work with.

The layering approach is smart: start with character grid, add background to create scene grid, then combine multiple character grids to get multi-character scenes. Each layer builds on the last, and because you're always referencing the grids, consistency compounds rather than degrades.

Combining grids with the prompt "keep the 2x2 grid but now both characters are in" is the money move. You're essentially telling the model to maintain the grid structure while merging the character references. The results show two completely different characters (a Lakers mascot guy and a Starlight-type superhero) maintaining their distinct looks across multiple compositions.

The extraction step matters too. Once you've got a 2x2 grid you like, you can pull individual frames with "extract frame number X" rather than regenerating and risking drift.

TechHalla uses Higgsfield AI running Nano Banana Pro at 2K resolution, 16:9 aspect ratio in his demo, but the workflow should translate to other platforms that support image references.

My Take: 
This is the kind of systematic approach that separates people getting lucky shots from people building actual scenes. If you're doing anything with recurring characters, this grid-anchoring method is worth adding to your toolkit. BONUS: I’ve put the entire workflow together as a PDF download for easy reference. If you’d like a copy, head on over to our free Skool community to get it.

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SHORT TAKES

ONE MORE THING…

Video of the Week

This week’s VOTW is from Turkish filmmaker and AI creator Nuri Yıldız (@nouryyildiz), who wowed crowds on X and Instagram with an incredibly slick video titled "Hollywood Selfie Part 2." The video features the Nuri running from classic Hollywood set to set, palling around with everyone from Clint Eastwood on "The Good, The Bad and The Ugly" to Harrison Ford on one of the Indy sagas, Jack Nicholson on "The Shining," Leonardo DiCaprio and Kate Winslet on "Titanic," and. Wait for it. Marlon Brando on "The Godfather." He's both a time traveler and superfan as he deftly navigates classic modern film history.

Yes, the ethics and legality will be debated, but I'm fairly confident it's not putting a dent in any of these celebrities' bank accounts. His one-minute video, which has racked up over 2 million views on Instagram, bears out an indisputable fact: the social media world loves this because it's just plain fun.

His techniques are top-notch and, being an editor, his transitions are seamless 😉. He states in the comments that he did everything with Nano Banana and Kling, but that's about as specific as he got.

Enjoy!

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