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Moonvalley’s Marey is a state-of-the-art AI video model trained on FULLY LICENSED data

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A few years ago, there was no such thing as a “generative AI video model.” Today, there are dozens, including many capable of rendering ultra high-definition, ultra-realistic Hollywood-caliber video in seconds from text prompts or user-uploaded […]

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A few years ago, there was no such thing as a “generative AI video model.”

Today, there are dozens, including many capable of rendering ultra high-definition, ultra-realistic Hollywood-caliber video in seconds from text prompts or user-uploaded images and existing video clips. If you’ve read VentureBeat in the last few months, you’ve no doubt come across articles about these models and the companies behind them, from Runway’s Gen-3 to Google’s Veo 2 to OpenAI’s long-delayed but finally available Sora to Luma AI, Pika, and Chinese upstarts Kling and Hailuo. Even Alibaba and a startup called Genmo have offered open source versions.

Already, they’ve been used to make portions of major blockbusters from Everything, Everywhere All At Once to HBO’s True Detective: Night Country to music videos and TV commercials by Toys R’ Us and Coca Cola. But despite Hollywood and filmmakers’ relatively rapid embrace of AI, there’s still one big potential looming issue: copyright concerns.

As best as we can tell given that most of these AI video model startups don’t publicly share precise details of their training data, most are trained on vast swaths of videos uploaded to the web or collected from other archival sources, including those with copyrights who may or may not have actually granted express permission to the AI video companies to train on them. In fact, Runway is among the companies facing down a class action lawsuit (still working its way through the courts), over this very issue, and Nvidia reportedly scraped a huge swath of YouTube videos as well for this purpose. The dispute is ongoing — whether scraping data including videos constitutes fair and transformational use, or not.

But now there’s a new alternative for those concerned about copyright and aren’t wanting to use models where there is a question mark: a startup called Moonvalley — founded by former Google DeepMinders and researchers from Meta, Microsoft and TikTok, among others — has introduced Marey, a generative AI video model designed for Hollywood studios, filmmakers, and enterprise brands. Positioned as a “clean” state-of-the-art foundational AI video model, Marey is trained exclusively on owned and licensed data, offering an ethical alternative to AI models developed using scraped content.

“People said it wasn’t technically feasible to build a cutting-edge AI video model without using scraped data,” said Moonvalley CEO and co-founder Naeem Talukdar, in a recent video call interview with VentureBeat. “We proved otherwise.”

Marey, available now on an invitation-only waitlist basis, joins Adobe’s Firefly Video model, which that long established software vendor also says is also enterprise-grade — having been trained only on licensed data and Adobe Stock data (to the controversy of some contributors) — and provides enterprises indemnification for using. Moonvalley also provides indemnification on clause 7 of this document, saying it will defend its customers at its own expense.

Moonvalley is hoping these features will make Marey appealing to big studios — even as others such as Runway make deals with them — and filmmakers, over the countless and ever-growing array of new AI video creation options.

More ‘ethical’ AI video?

Marey is the result of a collaboration between Moonvalley and Asteria, an artist-led AI film and animation studio. The model is built to assist rather than replace creative professionals, providing filmmakers with new tools for AI-driven video production while maintaining traditional industry standards.

“Our conviction was that you’re not going to get mainstream adoption in this industry unless you do this with the industry,” Talukdar said. “The industry has been loud and clear that in order for them to actually use these models, we need to figure out how to build a clean model. And up until today, the top track was you couldn’t do it.”

Rather than scraping the internet for content, Moonvalley built direct relationships with these creators to license their footage. The company took several months to establish these partnerships, ensuring all data used for training was legally acquired and fully licensed.

Moonvalley’s licensing strategy is also designed to support content creators by compensating them for their contributions.

“Most of our relationships are actually coming inbound now that people have started to hear about what we’re doing,” Talukdar said. “For small-town creators, a lot of their footage is just sitting around. We want to help them monetize it, and we want to do artist-focused models. It ends up being a very good relationship.”

Talukdar told VentureBeat that while the company is still assessing and revising its compensation models, it generally compensates creators based on the duration of their footage, paying them an hourly or minutely rate under fixed-term licensing agreements (e.g., 12 or 4 months). This allows for potential recurring payments if the content continues to be used.

The company’s goal is to make high-end video production more accessible and cost-effective, allowing filmmakers, studios, and advertisers to explore AI-generated storytelling without legal or ethical concerns.

More cinematographic control beyond text prompts, images, and camera directions

Talukdar explained that Moonvalley took a different approach with its Marey AI video model from existing AI video models by focusing on professional-grade production rather than consumer applications.

“Most generative video companies today are more consumer-focused,” he said. “They build simple models where you prompt a chatbot, generate some clips, and add cool effects. Our focus is different—what’s the technology needed for Hollywood studios? What do major brands need to make Super Bowl commercials?”

Marey introduces several advancements in AI-generated video, including:

  • Native HD Generation – Generates high-definition video without relying on upscaling, reducing visual artifacts.
  • Extended Video Length – Unlike most AI video models, which generate only a few seconds of footage, Marey can create 30-second sequences in a single pass.
  • Layer-Based Editing – Unlike other generative video models, Marey allows users to separately edit the foreground, midground, and background, providing more precise control over video composition.
  • Storyboard & Sketch-Based Inputs – Instead of relying only on text prompts (which many AI models do), Marey enables filmmakers to create using storyboards, sketches, and even live-action references, making it more intuitive for professionals.
  • More Responsive to Conditioning Inputs – The model was designed to better interpret external inputs like drawings and motion references, making AI-generated video more controllable.
  • “Generative-Native” Video Editor – Moonvalley is developing companion software alongside Marey, which functions as a generative-native video editing tool that helps users manage projects and timelines more effectively.

“The model itself is just built very heavily around controllability,” Talukdar explained. “You need to have significantly more controls around the output—being able to change the characters. It’s the first model that allows you to do layer-based editing, so you can edit the foreground, mid-ground, and background separately. It’s also the first model built for Hollywood, purpose-built for production.”

In addition, he told VentureBeat that Marey relies on a Diffusion-Transformer Hybrid Model that combines diffusion and transformer-based architectures.

“The models are diffusion-transformer models, so it’s the transformer architecture, and then you have diffusion as part of the layers,” Talukdar said. “When you introduce controllability, it’s usually through those layers that you do it.”

Funded by big name VCs but not as much as other AI video startups (yet)

Moonvalley is also this week announcing a $70 million seed round led by Bessemer Venture Partners, Khosla Ventures, and General Catalyst. Investors Hemant Taneja, Samir Kaul, and Byron Deeter have also joined the company’s board of directors.

Talukdar noted that Moonvalley’s funding is substantially less than some of its competitors, so far — Runway is reported to have raised $270 million total across several rounds — but optimized its resources by assembling an elite team of AI researchers and engineers.

“We raised around $70 million, quite a bit less than our competitors, certainly,” he said. “But that really boils down to the team—having a team that can build that architecture significantly more efficiently, compute, and all those different things.”

Marey is currently in a limited-access phase, with select studios and filmmakers testing the model. Moonvalley plans to gradually expand access over the coming weeks.

“Right now, there’s a number of studios that are getting access to it, and we have an alpha group with a couple dozen filmmakers using it,” Talukdar confirmed. “The hope is that it’ll be fully available within a couple of weeks, worst case within a couple of months.”

With the launch of Marey, Moonvalley and Asteria aim to position themselves at the forefront of AI-assisted filmmaking, offering studios and brands a solution that integrates AI without compromising creative integrity. But with AI video startup rivals such as Runway, Pika, and Hedra continuing to add new features like character voice and movements.

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Peterhead’s Acorn CCS key to unlocking future of Grangemouth

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VergeIO enhances VergeFabric network virtualization offering

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Podcast: On the Frontier of Modular Edge AI Data Centers with Flexnode’s Andrew Lindsey

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Last Energy to Deploy 30 Microreactors in Texas for Data Centers

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Data Center Jobs: Engineering and Technician Jobs Available in Major Markets

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Amid Shifting Regional Data Center Policies, Iron Mountain and DC Blox Both Expand in Virginia’s Henrico County

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Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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One Turn After Another

While some games, like rock-paper-scissors, only work if all payers decide on their actions simultaneously, other games, like chess or Monopoly, expect the players to

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