How AI is Changing Video Generation

🎥 AI Video in 2025: The Shift Has Already Happened. Here are some of my experiments in Sora. Video has always been powerful—but for the longest time, it’s been bound by constraints. Time. Budget. Production complexity. Now, those barriers are vanishing. In 2024, several prominent brands ventured into AI-generated video content, showcasing both the potential and challenges . Coca-Cola released an AI-generated Christmas advertisement, reimagining their classic 1995 "Holidays Are Coming" campaign. Toys "R" Us embraced AI to craft a nostalgic brand film detailing the origin story of its founder, Charles Lazarus. Collaborating with creative agency Native Foreign, they utilised OpenAI's Sora to produce a 66-second video that blended historical elements with imaginative visuals. 🔥 Video is Becoming a Two-Way Medium Videos that could adapt and interact. Soon, audiences won’t just watch—they’ll navigate, choose, and engage inside the frame. 📹 One Campaign, Infinite Variations AI will make it possible to generate a unique video for every viewer—changing tone, visuals, and language dynamically. A single ad could exist in a thousand personalised versions. 🎬 The End of Production as We Know It? The need for studios, cameras, and crews could fade out? AI tools like Runway, Veo 2, and Movie Gen are crafting high-quality, story-driven video without a single frame of original footage. 👥 Brand Presence Without Physical Presence AI-generated avatars aren’t a novelty. They will be handling customer engagement, hosting product explainers, and delivering training content—without fatigue, time zones, or scheduling conflicts. The Challenge: Creativity vs. Control Right now, AI video tools are powerful ideation engines—generating impressive results from simple text prompts. But that’s also the limitation. They provide speed, but not precision. For AI-generated video to truly become a filmmaker’s tool rather than just an assistant, we need more granular control—frame-by-frame editing, scene manipulation, and narrative structuring. Expect 2025 to bring features that bridge the gap between automation and artistry. 🛠 Tools to Check Out—and Tools to Watch in 2025 > Sora - From OpenAI and used in the video below. > Runway – Leading the way in AI-generated cinematic content. > Pika – Excels in real-time video transformation. > Luma Labs – Specialises in AI-generated lighting and cinematics. > Veo 2 (Google) & Movie Gen (Meta) > Comment below for other tools you have tried or see breaking through in 2025. > Emerging startups – Expect new names to shake up the space in 2025, bringing faster, more powerful video-generation capabilities. 🚀 The Real Question: Are You Adapting? This shift isn’t on the horizon—it’s already here. The challenge for Marketers isn’t whether to use AI-driven video. It’s how quickly they’ll figure out what to do with it. 📌 What’s your take? Which of these shifts excites you? What’s still a challenge?

A new research paper featuring collaborations from NVIDIA, Stanford University, UC San Diego, University of California, Berkeley, and The University of Texas at Austin introduces a breakthrough method that could redefine how we generate long-form videos from textual storyboards. 💡 The Challenge: While modern Transformers have excelled in producing short video clips, generating complex, multi-scene, one-minute videos has remained a hurdle due to the inefficiencies of handling long temporal contexts with traditional self-attention layers. 🔍 The Solution: Introducing Test-Time Training (TTT) layers! This innovative approach integrates neural networks within RNN hidden states, yielding more expressive video generation capabilities. By adding TTT layers to pre-trained Transformers, the team managed to create one-minute videos that maintain coherence across scenes and even complex storylines. 🎬 Proof of Concept: The research team showcased this by utilizing a dataset based on classic Tom and Jerry cartoons. The results highlighted TTT layers outperforming existing approaches like Mamba 2 and Gated DeltaNet, evidenced by a 34 Elo point lead in human evaluations. 🔗 Sample videos, code, and annotations: https://lnkd.in/g3D72gGH #AI #VideoGeneration #MachineLearning #Innovation #Research #TomAndJerry #ArtificialIntelligence #NVIDIA #Stanford #UCBerkeley #UCSD #UTAustin

#MIT's new "Radial Attention" makes Generative Video 4.4x cheaper to train and 3.7x faster to run. Here's why: The problem with current AI video? It's BRUTALLY expensive. Every frame must "pay attention" to every other frame. With thousands of frames, costs explode exponentially. Training one model? $100K+ Running it? Painfully slow. Massachusetts Institute of Technology, NVIDIA, Princeton, UC Berkeley, Stanford, and First Intelligence just changed the game. Their breakthrough insight: Video attention works like physics. - Sound gets quieter with distance - Light dims as it travels - Heat dissipates over space Turns out, AI video tokens follow the same rules. Why waste compute power on distant, irrelevant connections? Enter Radial Attention: Instead of checking EVERY connection: • Nearby frames → full attention • Distant frames → sparse attention • Computation scales logarithmically, not quadratically Technical result: O(n log n) vs O(n²) Translation: MASSIVE efficiency gains Real-world results on production models: 📊 HunyuanVideo (Tencent): • 2.78x training speedup • 2.35x inference speedup 📊 Mochi 1: • 1.78x training speedup • 1.63x inference speedup Quality? Maintained or IMPROVED. What this unlocks: 4x longer videos, same resources 4.4x cheaper training costs 3.7x faster generation Works with existing models (no retraining!) And, MIT open-sourced everything: https://lnkd.in/gETYw8eT The bigger picture: The internet is transforming. BEFORE: A place to store videos from the real world NOW: A machine that generates synthetic content on demand Think about it: • TikTok filled with AI-generated content • YouTube creators using AI for entire videos • Streaming services producing personalized shows • Educational content generated for each student This changes everything. Remember when only big tech could afford image AI? 2020: GPT-3 → Only OpenAI 2022: Stable Diffusion → Everyone 2024: Midjourney everywhere Video AI is next. Radial Attention probably just accelerated the timeline. The future isn't coming. It's here. And it's more accessible than ever. Want to ride this wave? → Follow me for weekly AI breakthroughs → Share if this opened your eyes → Try the code: https://lnkd.in/gETYw8eT What will YOU create when video AI costs 4x less? #AI #VideoGeneration #MachineLearning #TechInnovation #FutureOfContent

The most meaningful AI shift for us at The Wise Idiot has not been in writing. It has been in video. Over the last few months, we have delivered multiple projects where AI-enabled video creation unlocked output that would have been difficult, slow, or uneconomical with traditional workflows. The turnaround time was nearly 20 percent of what conventional video production would require. Not just about scripts or static visuals. This is about full-fledged videos being generated and integrated into business narratives. Explainer content, contextual inserts, and subject-aligned clips that actually support communication goals. From a cost perspective, AI video is already more efficient than manual motion graphics and heavy editing. From a speed perspective, it is dramatically faster. That said, the tooling is still expensive. Regeneration errors can cost real money, and the margin for experimentation is not unlimited. This makes governance, clarity, and process critical. Despite this, the long-term potential is unmistakable. Video was historically a bottleneck for us. Today, it is becoming a capability. What we have built, delivered, and billed in recent months has materially changed how we think about scalable content and communication. For CXOs evaluating AI not as a trend but as an operating advantage, video is one of the most underutilized levers right now. If you are interested in understanding what we built, the tools we are using, and how we manage quality, cost, and outcomes, let me know in the comments. I am happy to share more.

🤩 ByteDance has released Seedance 2.0, the latest iteration of its generative video model, and it’s a notable step forward in how multimodal video generation is being packaged and deployed. Seedance 2.0 supports text, image, audio, and video inputs, and can generate multi-shot video sequences with synchronized audio in a single pass. Rather than focusing on short clips, the model is clearly aimed at structured sequences—handling shot transitions, camera motion, and basic narrative continuity without manual assembly. Key updates in Seedance 2.0: ✅ Multimodal conditioning (text + image + audio + video) ✅Multi-shot video generation with temporal coherence ✅Automatic shot planning from high-level prompts ✅Integrated audio generation instead of post-sync ✅Improved character and scene consistency across shots What’s interesting here isn’t novelty, but convergence. Models like this are starting to bundle tasks that previously required separate tools: scripting, storyboarding, shot layout, animation, and audio alignment. That has implications for creative workflows, especially in early-stage ideation, previsualization, and rapid content iteration. For teams working in advertising, social content, or internal prototyping, Seedance 2.0 looks positioned as a workflow accelerator, not a replacement for production—reducing setup time and manual stitching rather than eliminating creative or editorial decisions. As more companies (including ByteDance) push toward end-to-end video generation systems, the practical questions shift from “can it generate video?” to: How controllable is it? How repeatable are results? How well does it integrate into existing pipelines? Those answers will matter more than demos. #AI #GenerativeVideo #MultimodalAI #CreativeTechnology #Seedance

🎶 Two very different AIs are shaping the future of video. Think of it like composing original music vs. producing a remix with the best instruments and tracks. 1️⃣ Generative AI video This is like composing a song note by note. Every frame, every detail is created from scratch. You might start with a text description (“a man walking through a neon-lit Tokyo street in the rain”), or an image prompt, and the model generates a video sequence that has never existed before. This approach relies on massive datasets of training images and videos, combined with diffusion or transformer-based architectures, to predict and synthesize each frame. The promise here is limitless creativity: you can conjure up scenes, characters, and effects that don’t exist in real life or would be too costly to film. That’s why OpenAI’s Sora, Google’s Veo, or Kuaishou’s Kling have captured so much attention: They showcase how far we’ve come in realism, physics, and motion continuity. Avatar generation tools like Synthesia or Captions are also part of this paradigm, producing fully synthetic talking-head presenters from text input. The tradeoff? Just like composing, it’s time- and resource-intensive. These models are compute-heavy, often expensive, and results can vary; you may need several iterations before landing the right look and feel. They’re great for short clips or creating raw material you can later assemble into a story. 2️⃣ AI in Generating Videos This is more like producing a remix using the best existing tracks, instruments, and beats. Instead of starting from silence, you’re orchestrating different parts to create something polished and impactful. The process often starts with AI models that understand your input: summarizing text, transcribing video, or interpreting documents. From there, other AI models: Search licensed stock or custom video libraries Convert text to natural-sounding narration Match music tracks to mood and pacing Choose layouts, scene transitions, and sequencing All of these components are then combined into a finished video. Tools like Pictory or Veed are great examples. Instead of starting from a blank canvas, you’re repurposing existing assets, documents, blogs, slide decks, or long-form videos, into short, engaging stories. Large stock libraries provide instant visual variety, while AI does the heavy lifting in matching the right clips, music, and narration to your message. The result? ✅ Faster production ✅ Lower cost ✅ High-quality storytelling that’s grounded in your brand and message Both paradigms are valuable. 🎼 One lets you compose original symphonies. 🎛 The other produces remixes that are ready to publish quickly and reliably. Over time, they’ll even blend—where AI video assembly tools plug in generative clips or avatars seamlessly. 💡 The key isn’t choosing “better” or “worse.” It’s knowing which AI you’re using and what it’s built for. 👉 Which paradigm do you think will dominate the next 2–3 years?

I've been wrong about AI video for months. Thought it was mostly hype. Then I spent a week actually using these tools. Here's what I discovered: 🧵 The obvious insight I missed: Most AI video tools are designed by engineers for engineers. They optimize for technical capability, not user workflow. Result: Tools that can do amazing things... that nobody can actually use. The real problem isn't the AI. It's the interface. Current state: • Write perfect prompt • Generate random video • Hope it works • Start over when it doesn't That's not an effiecent way to create great content. Here's what works instead: Conversational editing. You iterate through conversation, not through prompt engineering. The technical breakthrough that changes everything: Character consistency across scenes. Sounds boring. It's not. This solves the "uncanny valley" problem that makes AI videos feel fake. Here's what the workflow actually looks like: Notice: No complex prompts. No technical knowledge. Just normal conversation. What this means for the next 12 months: • Conversational interfaces will become standard • Tools that require "prompt engineering" will lose • Creative professionals will adopt AI faster than expected The question isn't whether AI will change video creation. It's whether the tools will work for day-to-day creative tasks, not just viral experiments.

Google is just about to drop Veo, a video generation model that can create high-quality 1080p footage from text, image, and video prompts. Announced at Google I/O, Veo outputs cinematic shots with accurate physics, realistic motion, and a surprising grasp of visual storytelling — all from a short prompt. It joins a fairly substantial list of competitors; see my lists of the top 10 global and top 5 Chinese text-to-video models below. Veo supports advanced controls like masking and camera movement, and it even understands cinematic terms like “timelapse” or “aerial shot of a landscape.” Google says it worked closely with artists and filmmakers to align Veo’s capabilities with creative workflows, including Oscar-winning director Donald Glover and his creative studio Gilga. The rollout is limited for now. Veo is available to select creators through VideoFX, and Google says broader access is coming “soon.” The model will also integrate with YouTube Shorts and other Google products down the line — an advantage OpenAI doesn’t have. This isn’t a research paper or a teaser trailer — it’s a real product. I’ve had access to Veo for about a month, and I can tell you first-hand: it’s pretty spectacular. Magical, really. Google’s ecosystem integration will make Veo even more interesting. It’s likely to have a major impact on video creation because everyone will be a description and a tap away from producing and distributing all the video they can think of. Some people will call this new genre of vibe-video creation “AI slop.” That’s about as useless a description — and as egotistically pejorative — as saying YouTube and UGC were only good for videos of cats on skateboards. We’re on the cusp of something incredibly new and incredibly exciting. Global Leaders in AI Video Generation 1. OpenAI – Sora: Advanced text-to-video model producing high-resolution, realistic videos from textual prompts. 2. Google – Veo: Generates cinematic 1080p videos with accurate physics and motion, integrated with Google’s ecosystem. 3. Runway – Gen-3 Alpha: Offers real-time video generation with tools for editing and cinematic production. 4. Pika Labs – Pika 1.0: Enables collaborative editing and supports various styles, including anime and 3D animation. 5. Luma AI – Dream Machine: Focuses on high-speed rendering and realistic video generation from text and images. 6. Adobe – Firefly Generate Video: Integrates with Adobe Creative Cloud, allowing text-to-video and image-to-video generation. 7. Meta – Movie Gen: Generates personalized videos up to 16 seconds long, supporting video, image, and audio inputs. 8. Kling AI: Provides high-quality, realistic video generation with advanced motion control features. 9. Hailuo Minmax: Specializes in generating complex and long-duration videos from text and images. 10. Magic Hour: Offers tools for video manipulation, animation, and free AI-powered video editing.

We’re at a tipping point in avatar generation — especially for education and training! Over the years at SkildLabs, I’ve experimented with a variety of avatar generation tools to transform and enhance training materials. Every 4–6 months, the underlying models powering these tools have undergone exponential improvements. The video below was produced with HeyGen, using studio-grade video footage as the source. Why does this matter? 1. Photo-based avatars have limits. Many avatar tools rely on still images. No matter how high-quality the photos are, the generated motion often looks slightly off (eg. unnatural teeth color, mouth movement, or gestures). This “AI look” happens because the model has to guess much of the motion. 2. Video-based avatars capture both voice and motion. When using a video source, you can clone the voice simultaneously, so the final output includes both natural video and audio (although the pitch can occasionally sound high — a problem that’s improving rapidly). With HeyGen, you can even integrate the ElevenLabs API to connect a custom voice clone directly to the video — a great way to keep everything unified. This is how I normally do it. 3. Greater control and refinement. Custom avatars created from video sources allow for detailed motion and gesture adjustments without looking awkward. Imperfections can also be refined using complementary models like Nanobanana or Kling. --------------------- Before AI, most training video production required studio setups — costly, time-consuming, and hard to update (this was probably the toughest one) due to logistics and equipment consistency issues. Now, AI avatar generation has reached a level where a short, high-quality studio recording (just 2–5 minutes) can serve as the foundation for hours of engaging training content. Of course, poor input quality can undermine the learning experience, so intentional production still matters. That said, instructional design has already benefited immensely from these technologies, and I’m excited to see the creative ways they’ll continue to shape learning and development.

Think like a cinematographer. AI video creation is moving beyond imitation and outpacing its limitations, blending traditional techniques with in-model innovations that mimic practical effects. My last post focused on effects that happen in-frame, but there are two elements critical to great cinematography that happen out-of-frame: lensing and lighting. One of my more interesting recent experiments is how camera lensing and lighting—usually the domain of on-set tools—can now be orchestrated directly within AI video, producing strikingly cinematic results. Dynamic Lighting Animation Prompt lights as if they're off-camera, using terms like "flood" for soft illumination or "barn doors" to shape and direct beams. For drama, try "Venetian blinds" to cast patterned shadows or simulate spillover light. These choices guide focus and emotion while interacting naturally with subjects. Use prompt traveling to move the lights with terms like "sweep" or "strobe". Lensing for Depth Add lens details to your prompts to define the field of view and depth. 14mm: Ultra-wide for landscapes and dramatic perspectives. 24mm: Wide-angle for balanced realism. 35mm: A storyteller's go-to, offering a natural view. 50mm: Intimate and human, mimicking the eye's perspective. Pair lenses with aperture tweaks like "f/1.8" for dreamy focus or "f/16" for sharp, detailed scenes. As these AI video models advance, integrating these techniques will redefine "practical effects" for creators and filmmakers. Give it a try for yourself.