Real-Time AI Video: From Hours to Milliseconds

Artificial intelligence has transformed how we create images, write text, and process data. But video generation has remained frustratingly slow, often taking 10 to 20 seconds to produce just one second of footage. That bottleneck is about to disappear entirely.

Decart, an Israeli AI startup, has achieved something that seemed impossible just months ago: real-time AI video generation running at 20 frames per second with latency under 100 milliseconds. Their breakthrough represents a fundamental shift from static AI content creation to dynamic, interactive experiences that respond instantly to user input.

The company's recent $100 million funding round at a $3.1 billion valuation signals that investors believe real-time AI video will reshape entire industries, from gaming and entertainment to education and virtual collaboration. But understanding why this matters requires looking at both the technical hurdles they've overcome and the new possibilities their solution unlocks.

The Speed Problem That Plagued AI Video

Traditional AI video generation follows a compute-intensive process that prioritizes quality over speed. Models like OpenAI's Sora, Google's Veo, or Runway's generators process each frame through multiple neural network layers, applying diffusion techniques that gradually refine random noise into coherent video content.

This approach produces stunning results, but at a crushing computational cost. Generating a 10-second clip typically requires processing hundreds of frames through billions of parameters, consuming massive GPU resources and taking minutes or hours to complete. For interactive applications, this latency makes real-time experiences impossible.

The mathematical reality is stark: interactive video needs new frames every 16-33 milliseconds to feel responsive, but existing AI models need 500-20,000 milliseconds per frame. That thousand-fold speed gap represented an seemingly insurmountable engineering challenge.

Most AI video companies accepted this limitation, focusing on offline content creation for filmmakers, marketers, and social media creators who could wait for high-quality results. Real-time applications remained the domain of traditional graphics engines and pre-rendered content.

Decart's Technical Breakthrough

Decart's solution centers on a proprietary GPU optimization stack that fundamentally reimagines how AI video models utilize computing resources. Instead of processing each frame independently through the full model, their system maintains persistent computational states and shares processing across frames.

Their Oasis model demonstrates this approach by generating playable video game environments entirely through AI, with no traditional graphics engine underneath. Players can move, jump, break blocks, and interact with objects while the AI generates each frame in real-time based on their actions[18][19].

The technical achievement becomes clear when comparing specifications. State-of-the-art models like Sora require multiple H100 GPUs and 10-20 seconds to generate one second of video. Decart's system generates video at 20 frames per second on similar hardware, representing a 400-fold improvement in throughput per dollar[12].

This performance improvement stems from architectural innovations including custom memory management, optimized attention mechanisms, and novel ways of sharing computations across temporal sequences. The company has also developed specialized inference frameworks that maximize GPU utilization specifically for transformer-based video models.

Historical Context and Competition

AI video generation emerged as a research area in the early 2020s, with early models producing short, low-resolution clips that often contained obvious artifacts. Google's ImageN Video and Meta's Make-A-Video represented early attempts at text-to-video generation, but remained primarily research demonstrations.

The field gained mainstream attention when OpenAI announced Sora in February 2024, showcasing minute-long videos with cinematic quality that stunned industry observers. However, Sora's commercial release has been delayed, partly due to computational costs and safety concerns about deepfake potential.

Other companies followed with their own approaches. Runway launched Gen-3 Alpha, focusing on creative professional tools. Stability AI released Stable Video Diffusion for open-source applications. Chinese company Kuaishou developed Kling, while Luma AI created Dream Machine for consumer content creation[13].

Despite impressive visual quality, all these models shared the same fundamental limitation: they were designed for offline content creation rather than interactive experiences. Real-time generation remained unexplored territory until Decart's breakthrough.

The competitive landscape has now shifted dramatically. While established players focused on improving quality and reducing costs for batch processing, Decart opened an entirely new market category by solving the latency problem that made interactive applications impossible.

The $100 Million Bet on Interactive AI

Venture capital investment in AI startups reached record levels in 2025, but Decart's funding round stands out for both its size and strategic implications. The $100 million Series B, led by Benchmark and Sequoia Capital, values the company at $3.1 billion despite being founded less than two years ago[3][12].

This valuation reflects investor confidence that real-time AI video will create entirely new market categories. Gaming, currently a $200 billion industry, could be fundamentally transformed by AI-generated content that adapts dynamically to player actions. Entertainment platforms might offer personalized, interactive experiences that blend traditional media with real-time generation.

Remarkably, Decart has achieved this while maintaining unusual capital efficiency. The company reports spending less than $10 million of its total $153 million in funding, with revenue from GPU acceleration services and video licensing covering operational costs. This contrasts sharply with typical AI startups that burn hundreds of millions before generating meaningful revenue[10].

The funding will accelerate research into larger models, expand infrastructure to support millions of concurrent users, and establish partnerships with gaming and entertainment companies. Decart is also opening a new R&D center in San Francisco, led by Dr. Kfir Aberman, formerly of Snap and Google.

Applications Across Industries

Real-time AI video generation enables applications that were previously impossible due to latency constraints. Interactive gaming represents the most obvious use case, allowing developers to create procedurally generated worlds that respond to player actions without requiring massive asset libraries or complex physics engines.

Educational technology could leverage real-time generation for immersive learning experiences, where historical events, scientific concepts, or complex systems are visualized dynamically based on student questions and interactions. Medical training simulations could generate realistic patient scenarios that adapt to trainee responses in real-time.

Virtual collaboration platforms might integrate real-time AI video to create more engaging remote meetings, where backgrounds, avatars, and visual aids are generated and modified instantly based on conversation context. This could make video conferencing more interactive and visually rich without requiring expensive production equipment.

The entertainment industry sees potential for new forms of interactive media that blur the lines between games, movies, and social experiences. Viewers might influence narrative direction through voice commands or gestures, with AI generating appropriate visual content in response.

Marketing and e-commerce applications could enable customers to see products in customized environments or scenarios generated in real-time. Fashion retailers might let customers visualize clothing in different settings, while automotive companies could show cars in various driving conditions based on customer preferences.

Technical Challenges and Solutions

Achieving real-time performance required solving multiple interconnected technical challenges. Memory bandwidth limitations forced Decart to develop novel approaches to data loading and caching, ensuring that model parameters and intermediate computations remain accessible without causing GPU stalls.

Maintaining visual consistency across frames presented another hurdle. Traditional video models process each frame independently, leading to temporal flickering or discontinuities. Decart's approach maintains state information across frames while ensuring that new frames remain coherent with previous outputs[19].

Energy efficiency became critical for commercial viability. Running AI models continuously at interactive framerates consumes significant power, making cost-per-hour calculations essential for sustainable business models. Decart reports reducing operational costs to approximately $0.25 per hour compared to $10-1000 for comparable models[12].

Quality control in real-time systems requires different approaches than batch processing. Traditional AI video models can retry generation or apply post-processing corrections, but interactive applications must produce acceptable results on the first attempt. Decart developed specialized training techniques that improve first-attempt success rates.

Scaling to support millions of concurrent users presents infrastructure challenges that batch-oriented models never face. The company built custom load balancing and resource allocation systems that can dynamically adjust computational resources based on user demand and content complexity.

Market Landscape and Competition

The real-time AI video market remains nascent, with most established players still focused on offline content creation. However, recognition of interactive applications' potential is driving increased research investment across the industry.

Google has demonstrated early real-time capabilities with Genie 3, showing that major AI labs are pursuing similar goals. However, their approach appears more research-focused rather than commercially ready[13]. Meta's investment in metaverse technologies suggests they may also explore real-time AI video for virtual environments.

Gaming companies represent both potential partners and competitors. Unity and Unreal Engine dominate game development tools, but their traditional graphics pipelines might be disrupted by AI-generated content that requires different development approaches. Some gaming companies are exploring partnerships with AI video providers rather than developing competing technology internally.

Cloud computing providers including AWS, Google Cloud, and Microsoft Azure are investing heavily in GPU infrastructure optimized for AI workloads. This creates potential partnerships for companies like Decart that need massive computational resources to serve consumer applications at scale.

Semiconductor companies are developing specialized chips for AI inference, including Etched's Sohu transformer ASIC that Decart has optimized for. These hardware improvements could further reduce costs and latency for real-time AI video applications[18].

Technical Infrastructure Requirements

Real-time AI video generation demands fundamentally different infrastructure than batch processing models. Traditional AI video services can queue requests and process them during off-peak hours, but interactive applications require guaranteed response times regardless of system load.

GPU utilization patterns differ significantly between batch and real-time workloads. Batch processing can achieve high efficiency by processing multiple requests simultaneously, while real-time applications require dedicated resources for each user session. This changes the economics of AI service provision and requires new optimization approaches.

Network latency becomes critical for cloud-based real-time AI video services. Even millisecond delays in network transmission can break the illusion of real-time responsiveness, forcing companies to deploy compute resources closer to users through edge computing strategies.

Storage requirements also shift from batch to real-time scenarios. Batch services can store intermediate results and final outputs for later retrieval, while real-time applications must generate and transmit content simultaneously without persistent storage overhead.

The infrastructure requirements help explain why breakthrough companies like Decart require significant funding despite efficient operations. Building systems capable of serving millions of concurrent real-time AI video users requires massive capital investment in specialized hardware and global network infrastructure.

Future Implications and Research Directions

The success of real-time AI video generation opens research questions that extend far beyond entertainment applications. Scientific visualization could benefit from AI-generated models that respond to researcher queries in real-time, enabling more intuitive exploration of complex data sets.

Robotics applications might integrate real-time AI video for enhanced human-robot interaction, where robots generate visual explanations of their decision-making processes or create predictive visualizations of intended actions.

The convergence of real-time AI video with augmented reality technologies could create entirely new categories of mixed-reality experiences. Instead of pre-rendered AR objects, users might interact with AI-generated content that adapts dynamically to their environment and actions.

Privacy and safety considerations will become increasingly important as real-time AI video becomes more accessible. The ability to generate convincing video content in real-time raises new concerns about deepfakes, misinformation, and identity theft that require both technical and regulatory responses.

Research into multimodal integration could enable real-time AI video systems that respond to voice, text, gesture, and environmental inputs simultaneously. This could create more natural and intuitive interfaces for human-AI interaction across various applications.

The development of real-time AI video also highlights the growing importance of specialized AI hardware. As models become more complex and applications more demanding, the traditional approach of using general-purpose GPUs may give way to custom silicon designed specifically for AI inference workloads.

The Path Forward

Decart's breakthrough in real-time AI video generation represents more than a technical achievement; it signals the beginning of a new era in human-computer interaction. By eliminating the latency barrier that prevented interactive AI video applications, they have opened possibilities that seemed like science fiction just years ago.

The implications extend far beyond gaming and entertainment. Education, healthcare, business communication, and scientific research could all be transformed by AI systems that generate relevant visual content in response to human actions and queries.

However, realizing this potential requires continued innovation in both software and hardware. The computational demands of real-time AI video will drive development of more efficient algorithms, specialized processors, and distributed computing architectures.

As the technology matures, questions around content quality, user safety, and economic sustainability will need answers. The companies that successfully navigate these challenges while maintaining the technical performance advantages demonstrated by pioneers like Decart will shape how we interact with AI systems for decades to come.

The transformation from hours to milliseconds in AI video generation represents more than a speed improvement. It represents the emergence of AI as a truly interactive medium, capable of participating in real-time human experiences rather than simply producing content for later consumption. That shift may prove to be one of the most significant developments in the history of artificial intelligence.