The collaboration demonstrates how sports organisations are utilising cloud infrastructure to drive tangible business outcomes, rather than just securing surface-level sponsorship. The partnership focuses on optimising business operations, ranging from race management to the fan experience.

Operational twins and carbon data to achieve net zero targets

While marketing visibility often drives sports partnerships, this agreement builds on a technical foundation first formalised in January 2025. The elevation to “Principal Partner” involves Formula E adopting Google Cloud technologies for business-critical functions.

The immediate application involves optimising the complex logistics of a global championship. Advanced AI modelling of the back office and the creation of race and event digital twins allow the organisation to simulate and optimise site builds virtually.

This application directly affects Scope 3 emissions. The capability to plan infrastructure virtually minimises the need for physical on-site reconnaissance and reduces the transport of heavy equipment.

For a championship that is the only sport-certified net zero carbon entity since inception, maintaining this status requires finding efficiencies in the supply chain. The digital twin approach delivers a quantifiable reduction in the operational carbon footprint while maintaining performance.

Beyond logistical modelling, the Google Cloud AI partnership extends into the workforce productivity layer. Formula E is deploying Google Workspace with Gemini AI to enable greater agility and efficiency across its organisation.

The organisation intends to use these tools to accelerate performance and deliver faster operations. This reflects a broader trend where generative AI tools are provisioned to reduce administrative latency in distributed workforces.

The viability of these implementations to achieve net zero targets is supported by previous collaborative projects. Formula E recently utilised Google’s AI Studio and Gemini models to execute the ‘Mountain Recharge’ initiative.

Engineers used the models to map an optimal route for the GENBETA car during a mountain descent. The AI identified and analysed specific braking zones, calculating the necessary regenerative braking required to harvest enough energy to complete a full lap of the Monaco circuit subsequently.

This specific use case demonstrates how high-dimensional data – including topography, friction, and energy consumption – can be processed to define physical execution.

Using Google Cloud AI to enhance Formula E’s data product

The partnership also addresses the commercial requirement to retain and grow a digital audience. Formula E has integrated a ‘Strategy Agent’ into its live broadcasts. This tool processes real-time data to provide viewers with tailored insights and predictions regarding race strategy and driver performance.

Millions of viewers have utilised these insights, which explain complex race dynamics as they unfold. This mirrors the enterprise challenge of observability (i.e. taking vast streams of real-time technical data and synthesising them into understandable narratives for stakeholders.)

Beyond helping to achieve net zero targets, the leadership at both organisations frames this expansion as a necessary evolution of their technical stack.

Jeff Dodds, CEO of Formula E, said: “Our expanded partnership with Google Cloud is a true game-changer for Formula E and for motorsport as a whole. We are already pushing the boundaries of technology in sport, and this Principal Partnership confirms our vision.

“The integration of Google Cloud’s AI capabilities will unlock a new dimension of real-time performance optimisation and strategic decision-making, both for the Championship and for our global broadcast audience. This collaboration will redefine how fans experience our races and set a new benchmark for technology integration in sport worldwide.”

Tara Brady, President of Google Cloud EMEA, added: “Formula E is a hub of innovation, where milliseconds can define success. This expanded partnership is a testament to the power of Google Cloud’s AI and data analytics, showing how our technology can deliver a competitive advantage in the most demanding scenarios.”

The progression from the initial partnership in January 2025 to this expanded scope suggests the pilot programs provided sufficient ROI to warrant a broader rollout. As organisations face pressure to balance performance with net zero targets, the use of virtual simulation to optimise physical deployment remains a high-value area for investment.

See also: Controlling AI agent sprawl: The CIO’s guide to governance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

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Disney’s agreement with OpenAI shows how a large, IP-heavy organisation is attempting to resolve that tension by putting AI inside its operating system rather than treating it as a side experiment.

Under the deal, Disney becomes both a licensing partner and a major enterprise customer. OpenAI’s video model Sora will be able to generate short, user-prompted videos using a defined set of Disney-owned characters and environments. Separately, Disney will use OpenAI’s APIs to build internal tools and new consumer experiences, including integrations tied to Disney+. The company will also deploy ChatGPT internally for employees.

The mechanics matter more than the spectacle. Disney is not opening its catalogue to unrestricted generation. The licence excludes actor likenesses and voices, limits which assets can be used, and applies safety and age-appropriate controls. In practice, this positions generative AI as a constrained production layer—capable of generating variation and volume, but bounded by governance.

AI inside existing workflows

A consistent failure mode in enterprise AI programmes is separation. Tools live outside the systems where work actually happens, adding steps instead of removing them. Disney’s approach mirrors a more pragmatic pattern: put AI where decisions are already made.

On the consumer side, AI-generated content will surface through Disney+, rather than through a standalone experiment. On the enterprise side, employees gain access to AI through APIs and a standardised assistant, rather than a patchwork of ad hoc tools. This reduces friction and makes AI usage observable and governable.

The implication is organisational. Disney is treating generative AI as a horizontal capability—closer to a platform service than a creative add-on. That framing makes it easier to scale usage across teams without multiplying risk.

Variation without expanding headcount

The Sora licence focuses on short-form content derived from pre-approved assets. That constraint is deliberate. In production environments, much of the cost sits not in ideation but in generating usable variations, reviewing them, and moving them through distribution pipelines.

By allowing prompt-driven generation inside a defined asset set, Disney can reduce the marginal cost of experimentation and fan engagement without increasing manual production or review load. The output is not a finished film. It is a controlled input into marketing, social, and engagement workflows.

This mirrors a broader enterprise pattern: AI earns its place when it shortens the path from intent to usable output, not when it creates standalone artefacts.

APIs over point tools

Beyond content generation, the agreement positions OpenAI’s models as building blocks. Disney plans to use APIs to develop new products and internal tools, rather than relying solely on off-the-shelf interfaces.

This matters because enterprise AI programmes often stall on integration. Teams waste time copying outputs between systems or adapting generic tools to fit internal processes. API-level access allows Disney to embed AI directly into product logic, employee workflows, and existing systems of record.

In effect, AI becomes part of the connective tissue between tools, not another layer employees must learn to work around.

Aligning productivity with incentives

Disney’s $1 billion equity investment in OpenAI is less interesting as a valuation signal than as an operational one. It indicates an expectation that AI usage will be persistent and central, not optional or experimental.

For large organisations, AI investments fail when tooling remains disconnected from economic outcomes. Here, AI touches revenue-facing surfaces (Disney+ engagement), cost structures (content variation and internal productivity), and long-term platform strategy. That alignment increases the likelihood that AI becomes part of standard planning cycles rather than discretionary innovation spend.

Automation that makes scale less fragile

High-volume AI use amplifies small failures. Disney and OpenAI emphasise safeguards around IP, harmful content, and misuse, not as a values statement but as a scaling requirement.

Strong automation around safety and rights management reduces the need for manual intervention and supports consistent enforcement. As with fraud detection or content moderation in other industries, this kind of operational AI does not attract attention when it works—but it makes growth less brittle.

Lessons for enterprise leaders

1. Embed AI where work already happens. Disney targets product and employee workflows, not a separate AI sandbox.
2. Constrain before you scale. Defined asset sets and exclusions make deployment viable in high-liability environments.
3. Use APIs to reduce friction. Integration matters more than model novelty.
4. Tie AI to economics early. Productivity gains stick when they connect to revenue and cost structures.
5. Treat safety as infrastructure. Automation and controls are prerequisites for scale, not afterthoughts.

Disney’s specific assets are unique. The operating pattern is not. Enterprise AI delivers value when it is designed as part of the organisation’s core machinery—governed, integrated, and measured—rather than as a showcase for what models can generate.

(Photo by Héctor Vásquez)

See also: OpenAI targets AI skills gap with new certification standards

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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Roblox keeps AI where the work happens

Rather than pushing creators toward separate AI products, Roblox has embedded AI inside Roblox Studio, the environment where creators already build, test, and iterate. In its September 2025 RDC update, Roblox outlined “AI tools and an Assistant” designed to improve creator productivity, with an emphasis on small teams. Its annual economic impact report adds that Studio features such as Avatar Auto-Setup and Assistant already include “new AI capabilities” to “accelerate content creation”.

The language matters—Roblox frames AI in terms of cycle time and output, not abstract claims about transformation or innovation. That framing makes it easier to judge whether the tools are doing their job.

One of the more practical updates focuses on asset creation. Roblox described an AI capability that goes beyond static generation, allowing creators to produce “fully functional objects” from a prompt. The initial rollout covers selected vehicle and weapons categories, returning interactive assets that can be extended inside Studio.

This addresses a common bottleneck where drafting an idea is rarely the slow part; turning it into something that behaves correctly inside a live system is. By narrowing that gap, Roblox reduces the time spent translating concepts into working components.

The company also highlighted language tools delivered through APIs, including Text-to-Speech, Speech-to-Text, and real-time voice chat translation across multiple languages. These features lower the effort required to localise content and reach broader audiences. Similar tooling plays a role in training and support in other industries.

Roblox treats AI as connective tissue between tools

Roblox also put emphasis on how tools connect to one another. Its RDC post describes integrating the Model Context Protocol (MCP) into Studio’s Assistant, allowing creators to coordinate multi-step work across third-party tools that support MCP. Roblox points to practical examples, such as designing a UI in Figma or generating a skybox elsewhere, then importing the result directly into Studio.

This matters because many AI initiatives slow down at the workflow level. Teams spend time copying outputs, fixing formats, or reworking assets that do not quite fit. Orchestration reduces that overhead by turning AI into a bridge between tools, rather than another destination in the process.

Linking productivity to revenue

Roblox ties these workflow gains directly to economics. In its RDC post, the company reported that creators earned over $1 billion through its Developer Exchange programme over the past year, and it set a goal for 10% of gaming content revenue to flow through its ecosystem. It also announced an increased exchange rate so creators “earn 8.5% more” when converting Robux into cash.

The economic impact report makes the connection explicit. Alongside AI upgrades in Studio, Roblox highlights monetisation tools such as price optimisation and regional pricing. Even outside a marketplace model, the takeaway is clear: when AI productivity is paired with a financial lever, teams are more likely to treat new tooling as part of core operations rather than an experiment.

Roblox uses operational AI to scale safety systems

While creative tools attract attention, operational AI often determines whether growth is sustainable. In November 2025, Roblox published a technical post on its PII Classifier, an AI model used to detect attempts to share personal information in chat. Roblox reports handling an average of 6.1 billion chat messages per day, and says the classifier has been in production since late 2024, with a reported 98% recall on an internal test set at a 1% false positive rate.

This is a quieter form of efficiency. Automation at this level reduces the need for manual review and supports consistent policy enforcement, which helps prevent scale from becoming a liability.

What carries across, and what several patterns stand out:

• Put AI where decisions are already made. Roblox focuses on the build-and-review loop, rather than inserting a separate AI step.
• Reduce tool friction early. Orchestration matters because it cuts down on context switching and rework.
• Tie AI to something measurable. Creation speed is linked to monetisation and payout incentives.
• Keep adapting the system. Roblox describes ongoing updates to address new adversarial behaviour in safety models.

Roblox’s tools will not translate directly to every sector. The underlying approach will. AI tends to pay for itself when it shortens the path from intent to usable output, and when that output is clearly connected to real economic value.

(Photo by Oberon Copeland @veryinformed.com)

See also: Mining business learnings for AI deployment

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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The main bottleneck in creating immersive spatial computing experiences – whether for consumer gaming, industrial digital twins, or employee training simulations – has long been the labour-intensive nature of 3D modelling. The production of an interactive environment typically requires teams of specialised artists working for weeks.

WorldGen, according to a new technical report from Meta’s Reality Labs, is capable of generating traversable and interactive 3D worlds from a single text prompt in approximately five minutes.

While the technology is currently research-grade, the WorldGen architecture addresses specific pain points that have prevented generative AI from being useful in professional workflows: functional interactivity, engine compatibility, and editorial control.

Generative AI environments become truly interactive 3D worlds

The primary failing of many existing text-to-3D models is that they prioritise visual fidelity over function. Approaches such as gaussian splatting create photorealistic scenes that look impressive in a video but often lack the underlying physical structure required for a user to interact with the environment. Assets lacking collision data or ramp physics hold little-to-no value for simulation or gaming.

WorldGen diverges from this path by prioritising “traversability”. The system generates a navigation mesh (navmesh) – a simplified polygon mesh that defines walkable surfaces – alongside the visual geometry. This ensures that a prompt such as “medieval village” produces not just a collection of houses, but a spatially-coherent layout where streets are clear of obstructions and open spaces are accessible.

For enterprises, this distinction is vital. A digital twin of a factory floor or a safety training simulation for hazardous environments requires valid physics and navigation data.

Meta’s approach ensures the output is “game engine-ready,” meaning the assets can be exported directly into standard platforms like Unity or Unreal Engine. This compatibility allows technical teams to integrate generative workflows into existing pipelines without needing specialised rendering hardware that other methods, such as radiance fields, often demand.

The four-stage production line of WorldGen

Meta’s researchers have structured WorldGen as a modular AI pipeline that mirrors traditional development workflows for creating 3D worlds.

The process begins with scene planning. A LLM acts as a structural engineer, parsing the user’s text prompt to generate a logical layout. It determines the placement of key structures and terrain features, producing a “blockout” – a rough 3D sketch – that guarantees the scene makes physical sense.

The subsequent “scene reconstruction” phase builds the initial geometry. The system conditions the generation on the navmesh, ensuring that as the AI “hallucinates” details, it does not inadvertently place a boulder in a doorway or block a fire exit path.

“Scene decomposition,” the third stage, is perhaps the most relevant for operational flexibility. The system uses a method called AutoPartGen to identify and separate individual objects within the scene—distinguishing a tree from the ground, or a crate from a warehouse floor.

In many “single-shot” generative models, the scene is a single fused lump of geometry. By separating components, WorldGen allows human editors to move, delete, or modify specific assets post-generation without breaking the entire world.

For the last step, “scene enhancement” polishes the assets. The system generates high-resolution textures and refines the geometry of individual objects to ensure visual quality holds up when close.

Operational realism of using generative AI to create 3D worlds

Implementing such technology requires an assessment of current infrastructure. WorldGen’s outputs are standard textured meshes. This choice avoids the vendor lock-in associated with proprietary rendering techniques. It means that a logistics firm building a VR training module could theoretically use this tool to prototype layouts rapidly, then hand them over to human developers for refinement.

Creating a fully textured, navigable scene takes roughly five minutes on sufficient hardware. For studios or departments accustomed to multi-day turnaround times for basic environment blocking, this efficiency gain is quite literally world-changing.

However, the technology does have limitations. The current iteration relies on generating a single reference view, which restricts the scale of the worlds it can produce. It cannot yet natively generate sprawling open worlds spanning kilometres without stitching multiple regions together, which risks visual inconsistencies.

The system also currently represents each object independently without reuse, which could lead to memory inefficiencies in very large scenes compared to hand-optimised assets where a single chair model is repeated fifty times. Future iterations aim to address larger world sizes and lower latency.

Comparing WorldGen against other emerging technologies

Evaluating this approach against other emerging AI technologies for creating 3D worlds offers clarity. World Labs, a competitor in the space, employs a system called Marble that uses Gaussian splats to achieve high photorealism. While visually striking, these splat-based scenes often degrade in quality when the camera moves away from the centre and can drop in fidelity just 3-5 metres from the viewpoint.

Meta’s choice to output mesh-based geometry positions WorldGen as a tool for functional application development rather than just visual content creation. It supports physics, collisions, and navigation natively—features that are non-negotiable for interactive software. Consequently, WorldGen can generate scenes spanning 50×50 metres that maintain geometric integrity throughout.

For leaders in the technology and creative sectors, the arrival of systems like WorldGen brings exciting new possibilities. Organisations should audit their current 3D workflows to identify where “blockout” and prototyping absorb the most resources. Generative tools are best deployed here to accelerate iteration, rather than attempting to replace final-quality production immediately.

Concurrently, technical artists and level designers will need to transition from placing every vertex manually to prompting and curating AI outputs. Training programmes should focus on “prompt engineering for spatial layout” and editing AI-generated assets for 3D worlds. Finally, while the output is standard, the generation process requires plenty of compute. Assessing on-premise versus cloud rendering capabilities will be necessary for adoption.

Generative 3D serves best as a force multiplier for structural layout and asset population rather than a total replacement for human creativity. By automating the foundational work of building a world, enterprise teams can focus their budgets on the interactions and logic that drive business value.

See also: How the Royal Navy is using AI to cut its recruitment workload

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security Expo. Click here for more information.

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Sora first arrived in the US and Canada in early September, and many users there have already shared clips. The app has since passed one million downloads in under five days, according to a social media post by Sora head Bill Peebles, who noted that it reached that milestone even faster than ChatGPT did at launch, despite requiring users to be invited to use the app at launch.

People in Thailand can download the app for free on iOS with no invite code. For now, use limits are relatively generous, though those limits may change.

The app is powered by Sora 2, a video generation model that can produce ‘original’ clips, remix existing creations, and suggest content through a personal feed. Users can also appear directly inside scenes through a feature called Cameos, which requires a one-time check to confirm identity and likeness. The app supports Thai language input.

Cameos have quickly become a popular feature among early testers as they offer a playful way to interact and connect with friends. Thai creator Woody Milintachinda said, “Sora allows me to bring ideas to life in a way that immediately resonates with audiences. They can see and feel the story unfold. It has also given me a unique platform to share my experiences with a wide community of creators and storytellers not just in Thailand but the world, inspiring new forms of connection and creativity. With Sora, the creative possibilities truly feel limitless.”

To go with this release, the app now includes Character Cameos, with which users can turn nearly anything into a reusable character, such as a pet, drawing, personal item, or original design created inside Sora. After uploading a video of the character, users can assign permissions that are separate from their personal likeness. That character can stay private, be shared only with followers, or be opened to everyone on the platform. Once named, the character can appear in any future video.

To mark the Halloween season, the app launches with a starter pack that includes classic characters like Dracula, Frankenstein’s monster, Ghost, Witch, and Jack-O-Lantern.

The company says it plans to bring Sora to Thailand with responsibility in mind. The feed is designed to encourage creation rather than passive viewing, aimed at accounts users follow. The aim is not to increase screen time but to spark creative output, the company states.

Users can keep control of their likeness when using Cameos, deciding who can use it, and the account holder can remove access or take down any video that includes their likeness at any time. Videos made with a cameo of the user created by someone else remain visible to the user.

Videos produced in Sora include a visible, animated watermark and an invisible C2PA digital watermark. The hidden version cannot be added to content that was not created in Sora, helping confirm which clips were created on the platform.

For teens, the app applies daily limits on how many generated videos appear in their feed. Cameos also come with stricter rules for this demographic. Safety systems exist, and human moderators can review bullying cases. Parents can use ChatGPT-based controls to adjust feed limits, turn off personalisation, and manage direct message settings.

(Photo by Mariia Shalabaieva)

See also: OpenAI unveils open-weight AI safety models for developers

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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Huawei brought the international media to witness this implementation firsthand, offering many of us in the press corps our first experience of Chinese football culture. As supporters cheered in waves of blue and white, capturing moments on their phones and sharing videos (despite the crushing crowd density), China Mobile Shanghai’s newly deployed 5G-A network monetisation strategy was being tested in real-time, powered by Huawei’s GainLeap solution and intelligent wireless infrastructure.

From the media section, the scale of the technical challenge became apparent – ensuring 80,000 simultaneous users could stream, upload, and transact without network degradation. This was something that required more than additional bandwidth.

China Mobile Shanghai has become the first carrier in China to launch a differentiated 5G-A experience package, marking what industry observers see as a shift in how telecom operators might address revenue growth in saturated markets.

The “5G-A Exclusive Package for Shenhua Football Fans” is a way to transform the elastic capabilities of 5G-Advanced networks into tangible value that users can perceive and are willing to pay for.

The technical foundation for this 5G-A network monetisation strategy relies heavily on Huawei’s technology portfolio, from the GainLeap solution that identifies premium subscribers, to the AI-powered intelligent wireless boards that optimise network performance.

The business model innovation

The partnership between China Mobile Shanghai and Shanghai Shenhua Football Club offers approximately 200,000 football fans an annual package that combines network performance guarantees with fan-specific benefits.

Subscribers receive network acceleration on 5G-A, access to all matches via the Migu streaming service, unlimited video ringback tone downloads, and Shanghai Shenhua Football Club merchandise.

This approach to 5G-A network monetisation addresses what China Mobile Shanghai identifies as an important pain point for the telecommunications industry: how to drive quality growth when user acquisition has reached its ceiling. Rather than competing solely on price or basic connectivity, the package creates value through enhanced experiences in specific use-cases.

The technical infrastructure behind the experience

For Shanghai Stadium, China Mobile Shanghai implemented an elastic, scalable network capable of handling massive concurrent demand. During the match, with 80,000 users accessing the network simultaneously, 5G-A package subscribers can achieve download speeds of up to 600 Mbps.

The necessary technical foundation relies on Huawei’s GainLeap solution, which lets the network identify 5G-A subscribers and allocates them a high-speed 3CC (three-component carrier) channel. The differentiation is key to the 5G-A network monetisation model – creating measurable performance differences between standard and premium subscribers.

Behind the scenes, Huawei’s AI-powered intelligent wireless boards play a central role. They integrate on-board communications capabilities with artificial intelligence to perceive network service types, user experience goals, device characteristics, and resource status, in milliseconds.

According to test data provided by China Mobile Shanghai, they have helped result in QR code scanning latency reduced by 47%, WeChat uploading time shortened by 25%, live streaming speeds increased by 27%, and high-definition video ratios increased by 11%.

Infrastructure deployment scale

To support the high concurrent demand during events, China Mobile Shanghai and Huawei conducted comprehensive network upgrades at the stadium. The lower stands received 32 new 2.6 GHz and 4.9 GHz pRRUs (passive remote radio units), more than doubling overall network capacity. Seven escalator entrances each received a 4.9 GHz EM device to eliminate coverage dead spots.

On match days, more than 40 engineers are stationed onsite for real-time network monitoring and dynamic optimisation. Outside of the stadium, China Mobile Shanghai has achieved continuous 5G-A coverage in the area inside Shanghai’s Outer Ring Road, the five new towns further out, and 21 metro lines in the city.

The practical user experience

For fans at the match, the differentiated service manifested practically. The high bandwidth and business-level assurance capabilities enabled quick mobile payments for drinks, snacks, and souvenirs onsite. Users could share video highlights in real time without lag, even during peak moments when thousands of fans uploaded data simultaneously.

The ability to instantly see likes and comments from friends while still in the stadium represents the kind of enhanced experience that China Mobile Shanghai is betting users will value enough to pay a premium for. Whether this bet pays off commercially remains to be seen, but the technical execution at the September 21 match demonstrated that the infrastructure delivers on its promises.

Industry implications

The initiative raises questions about the future of 5G-A network monetisation strategies in the telecommunications industry. Traditional models have struggled to justify the massive infrastructure investments required for 5G and latterly, 5G-Advanced networks. By creating tiered experiences tied to specific user communities – in this case, football fans – carriers may have found a way to differentiate services beyond simple speed tiers.

The approach also represents a test case for how deeply integrated AI capabilities in network infrastructure can enable new business models. The intelligent wireless boards’ ability to make millisecond-level decisions about resource allocation is what makes the performance differentiation technically feasible at scale.

China Mobile Shanghai’s target of serving 200,000 Shenhua fans provides a measurable benchmark for assessing commercial viability.

As telecommunications companies globally grapple with how to monetise increasingly expensive network upgrades, China Mobile Shanghai’s experiment with community-specific, experience-based packages may offer insights for the industry’s evolution beyond traditional connectivity provision.

(Image source: Smart Shanghai )

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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Ever watched an AI-generated video and felt like something was missing? The visuals might be stunning, but they often have an eerie silence that breaks the spell. In the film industry, the sound that fills that silence – the rustle of leaves, the clap of thunder, the clink of a glass – is called Foley art, and it’s a painstaking craft performed by experts.

Matching that level of detail is a huge challenge for AI. For years, automated systems have struggled to create believable sounds for videos.

How is Tencent solving the AI-generated audio for video problem?

One of the biggest reasons video-to-audio (V2A) models often fell short in the sound department was what the researchers call “modality imbalance”. Essentially, the AI was listening more to the text prompts it was given than it was watching the actual video.

For instance, if you gave a model a video of a busy beach with people walking and seagulls flying, but the text prompt only said “the sound of ocean waves,” you’d likely just get the sound of waves. The AI would completely ignore the footsteps in the sand and the calls of the birds, making the scene feel lifeless.

On top of that, the quality of the audio was often subpar, and there simply wasn’t enough high-quality video with sound to train the models effectively.

Tencent’s Hunyuan team tackled these problems from three different angles:

1. Tencent realised the AI needed a better education, so they built a massive, 100,000-hour library of video, audio, and text descriptions for it to learn from. They created an automated pipeline that filtered out low-quality content from the internet, getting rid of clips with long silences or compressed, fuzzy audio, ensuring the AI learned from the best possible material.

2. They designed a smarter architecture for the AI. Think of it like teaching the model to properly multitask. The system first pays incredibly close attention to the visual-audio link to get the timing just right—like matching the thump of a footstep to the exact moment a shoe hits the pavement. Once it has that timing locked down, it then incorporates the text prompt to understand the overall mood and context of the scene. This dual approach ensures the specific details of the video are never overlooked.

3. To guarantee the sound was high-quality, they used a training strategy called Representation Alignment (REPA). This is like having an expert audio engineer constantly looking over the AI’s shoulder during its training. It compares the AI’s work to features from a pre-trained, professional-grade audio model to guide it towards producing cleaner, richer, and more stable sound.

The results speak sound for themselves

When Tencent tested Hunyuan Video-Foley against other leading AI models, the audio results were clear. It wasn’t just that the computer-based metrics were better; human listeners consistently rated its output as higher quality, better matched to the video, and more accurately timed.

Across the board, the AI delivered improvements in making the sound match the on-screen action, both in terms of content and timing. The results across multiple evaluation datasets support this:

Tencent’s work helps to close the gap between silent AI videos and an immersive viewing experience with quality audio. It’s bringing the magic of Foley art to the world of automated content creation, which could be a powerful capability for filmmakers, animators, and creators everywhere.

See also: Google Vids gets AI avatars and image-to-video tools

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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Between wrestling with complicated software, finding someone willing to be on camera, and then spending hours editing out all the “ums” and “ahs,” video production often feels more trouble than it’s worth. Google is aiming to change that narrative with Vids.

So far, it seems to be finding its audience. Google announced that Vids has already rocketed past one million monthly active users, a clear sign that teams are crying out for simpler ways to bring their ideas to life with video.

Your photos now move, and avatars do the talking

Among the latest additions is the ability to turn static images into motion pictures. Imagine you’ve got a great photo of a new product but need something more engaging for a social media post or presentation. You can now upload that picture to Vids, type a quick prompt describing what you want to happen, and Google’s Veo AI will turn it into an eight-second animated clip, complete with sound. It’s a simple way to create eye-catching, brand-aligned content in minutes.

For anyone who dreads being on camera, the new AI avatars will be a welcome relief. This feature lets you produce a polished video without ever stepping in front of a lens. You write your script, choose from a selection of digital presenters, and the AI handles the delivery. It’s perfect for creating consistent training guides, product demos, or team updates without worrying about lighting, background noise, or re-recording twenty takes to get it right.

Google is also tackling the tedious task of editing. A new automatic transcript trimming tool listens to your recordings and, with a few clicks, snips out all the filler words and awkward silences. Speaking from plenty of experience, that will be a huge time-saver.

Building on this, the company confirmed that familiar tools from Google Meet – like noise cancellation, custom backgrounds, and appearance filters – are set to arrive next month. Google Vids will also soon support portrait and square formats, making it much easier to create content for different platforms.

Getting started with Google Vids

With these new tools, Google is trying to make video creation as routine as building a slide deck.

The company is broadening access to Google Vids, making it available to more Workspace customers on business and education plans. Better yet, a basic version of the Vids editor is now completely free for all consumers, offering a range of templates to help you create anything from a tutorial to a party invitation.

To get everyone up to speed, Google has also launched a new “Vids on Vids” instructional series. The video guides walk you through the entire process, demonstrates the best features, and offers practical tips to help you create professional-looking content quickly.

Real businesses are seeing the benefit

Companies are already putting Vids to work. At Mercer International, a global manufacturing firm, it’s being used for employee safety training.

Alistair Skey, CIO of Mercer International, said: “Google Vids has given us the ability to create safety content, developed and curated by our organisation rather than having to go to market to hire very expensive resources to produce that for us.”

It’s also a story of speed and scale. Forest Donovan from the data platform Fullstory was impressed by the efficiency gains. “The amount of [high gloss] content we can create in a matter of hours versus what would normally take weeks has been astounding,” he said.

By embedding these powerful yet simple AI tools directly into its Workspace suite, Google is making the case that video is no longer the exclusive domain of specialist creative teams. It’s becoming a fundamental tool for everyday communication, and these updates just made it accessible to everyone.

See also: Google Cloud unveils AI ally for security teams

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In less than a couple of years, the Beijing-based newcomer has accomplished what many thought impossible: creating AI models that compete with industry giants while spending only a fraction of their competitors’ budgets teaching models and inferring responses.

The impact at the time of the public launch was immediate and measurable. According to the South China Morning Post, major tech stocks, including Nvidia, Microsoft, and Meta, experienced significant declines as investors grappled with the implications of DeepSeek’s existence.

The startup’s free AI assistant application for iOS and Android, launched on January 10, quickly climbed to the top spot on Apple’s US App Store, displacing OpenAI’s ChatGPT and marking a historic first for a Chinese AI product in the American market.

What makes this particularly significant is DeepSeek’s technological approach. The Algorithmic Bridge reports the company has implemented several innovative solutions, including Multi-head Latent Attention (MLA) to reduce memory bottlenecks and Group Relative Policy Optimisation (GRPO) to streamline reinforcement learning.

The advances allow DeepSeek to achieve comparable or superior results to US competitors while using significantly fewer resources. The company’s resource efficiency is striking: DeepSeek operates with less than 100,000 H100 GPUs, while Meta will deploy 1.3 million GPUs by late 2025.

The efficiency extends beyond hardware. The Algorithmic Bridge suggests that DeepSeek’s approach represents a tenfold improvement in resource utilisation when considering factors like development time and infrastructure costs.

However, the rapid rise into Western users’ consciousness wasn’t without challenges. The South China Morning Post reported that DeepSeek’s sudden popularity led to significant infrastructure stress, resulting in server crashes and cybersecurity concerns that forced temporary registration limits. The growing pains highlight the real-world challenges of scaling AI services, regardless of architectural efficiency.

The company’s commitment to open-source development and research transparency starkly contrasts the secretive approaches of major US tech companies. To many industry observers, open and locally-hosted AI may be the preferred deployment blueprint.

The company earned praise from prominent figures in the tech industry, including venture capitalist Marc Andreessen, who described DeepSeek’s developments as “one of the most amazing and impressive breakthroughs.”

The political implications of the events are significant. US President Donald Trump characterised DeepSeek’s emergence as a “wake-up call” for American industry, reflecting broader concerns about technological competition between the United States and China. He continues to battle Chinese competition in technology, imposing restrictive tariffs that have affected all corners of the globe.

However, the situation transcends simple national rivalry, representing a fundamental challenge to established thinking about AI development.

Looking ahead, several key questions remain. Can DeepSeek’s efficient approach scale to meet growing demand? Have established players adapted their strategies in effective response? The Chinese company has demonstrated that algorithmic efficiency and open collaboration can replace raw computational power and secrecy as the primary drivers of AI advancement.

The AI market disruption may ultimately benefit the entire field by forcing a re-evaluation of established practices and could potentially lead to more efficient, accessible AI development methods.

While DeepSeek’s achievements are remarkable since springing into the public’s consciousness, it’s important to note that major US tech companies have released advances of their own, and market volatility in the tech sector remains high.

What’s clear is that DeepSeek introduced a viable alternative to the capital-intensive approach that has dominated AI development. Whether this becomes the new industry standard or simply one of many successful strategies remains to be seen, but the company’s impact on the industry already significant.

Photo by Markus Spiske)

See also: DeepSeek restricts sign-ups amid ‘large-scale malicious attacks’

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The new Hunyuan3D-PolyGen model is Tencent’s first attempt at delivering what they’re calling “art-grade” 3D generation, specifically built for the professionals who craft the digital worlds we play in.

Creating high-quality 3D assets has always been a bottleneck for game developers, with artists spending countless hours perfecting wireframes and wrestling with complex geometry. Tencent reckons they’ve found a way to tackle these headaches head-on, potentially transforming how studios approach asset creation entirely.

Levelling up generating 3D assets

The secret sauce behind Hunyuan3D-PolyGen lies in what Tencent calls BPT technology. In layman’s terms, it means they’ve figured out how to compress massive amounts of 3D data without losing the detail that matters. In practice, that means it’s possible to generate 3D assets with tens of thousands of polygons that actually look professional enough to ship in a commercial game.

What’s particularly clever is how the system handles both triangular and quadrilateral faces. If you’ve ever tried to move 3D assets between different software packages, you’ll know why this matters. Different engines and tools have their preferences, and compatibility issues have historically been a nightmare for studios trying to streamline their workflows.

According to technical documentation, the system utilises an autoregressive mesh generation framework that performs spatial inference through explicit and discrete vertex and patch modelling. This approach ensures the production of high-quality 3D models that meet stringent artistic specifications demanded by commercial game development.

Hunyuan3D-PolyGen works through what’s essentially a three-step dance. First, it takes existing 3D meshes and converts them into a language the AI can understand.

Using point cloud data as a starting point, the system then generates new mesh instructions using techniques borrowed from natural language processing. It’s like teaching the AI to speak in 3D geometry, predicting what should come next based on what it’s already created.

Finally, the system translates these instructions back into actual 3D meshes, complete with all the vertices and faces that make up the final model. The whole process maintains geometric integrity while producing results that would make any technical artist nod in approval.

Tencent isn’t just talking about theoretical improvements that fall apart when tested in real studios; they’ve put this technology to work in their own game development studios. The results? Artists claim to report efficiency gains of over 70 percent.

The system has been baked into Tencent’s Hunyuan 3D AI creation engine and is already running across multiple game development pipelines. This means it’s being used to create actual 3D game assets that players will eventually interact with.

Teaching AI to think like an artist

One of the most impressive aspects of Hunyuan3D-PolyGen is how Tencent has approached quality control. They’ve developed a reinforcement learning system that essentially teaches the AI to recognise good work from bad work, much like how a mentor might guide a junior artist.

The system learns from feedback, gradually improving its ability to generate 3D assets that meet professional standards. This means fewer duds and more usable results straight out of the box. For studios already stretched thin on resources, this kind of reliability could be transformative.

The gaming industry has been grappling with a fundamental problem for years. While AI has made impressive strides in generating 3D models, most of the output has been, quite frankly, not good enough for commercial use. The gap between “looks impressive in a demo” and “ready for a AAA game” has been enormous.

Tencent’s approach with Hunyuan3D-PolyGen feels different because it’s clearly been designed by people who understand what actual game development looks like. Instead of chasing flashy demonstrations, they’ve focused on solving real workflow problems that have been frustrating developers for decades.

As development costs continue to spiral and timelines get ever more compressed, tools that can accelerate asset creation without compromising quality become incredibly valuable.

The release of Hunyuan3D-PolyGen hints at a future where the relationship between human creativity and AI assistance becomes far more nuanced. Rather than replacing artists, this technology appears designed to handle the grunt work of creating 3D assets, freeing up talented creators to focus on the conceptual and creative challenges that humans excel at.

This represents a mature approach to AI integration in creative industries. Instead of the usual “AI will replace everyone” narrative, we’re seeing tools that augment human capabilities rather than attempt to replicate them entirely. The 70 percent efficiency improvement reported by Tencent’s teams suggests this philosophy is working in practice.

The broader implications are fascinating to consider. As these systems become more sophisticated and reliable, we might see a fundamental shift in how game development studios are structured and how projects are scoped. The technology could democratise high-quality asset creation, potentially allowing smaller studios to compete with larger operations that traditionally had resource advantages.

The success of Hunyuan3D-PolyGen could well encourage other major players to accelerate their own AI-assisted creative tools beyond generating 3D assets, potentially leading to a new wave of productivity improvements across the industry. For game developers who’ve been watching AI developments with a mixture of excitement and scepticism, this might be the moment when the technology finally delivers on its promises.

See also: UK and Singapore form alliance to guide AI in finance

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The interactive video generated by Odyssey’s AI model responds to inputs in real-time. You can interact with it using your keyboard, phone, controller, or eventually even voice commands. The folks at Odyssey are billing it as an “early version of the Holodeck.”

The underlying AI can generate realistic-looking video frames every 40 milliseconds. That means when you press a button or make a gesture, the video responds almost instantly—creating the illusion that you’re actually influencing this digital world.

“The experience today feels like exploring a glitchy dream—raw, unstable, but undeniably new,” according to Odyssey. We’re not talking about polished, AAA-game quality visuals here, at least not yet.

Not your standard video tech

Let’s get a bit technical for a moment. What makes this AI-generated interactive video tech different from, say, a standard video game or CGI? It all comes down to something Odyssey calls a “world model.”

Unlike traditional video models that generate entire clips in one go, world models work frame-by-frame to predict what should come next based on the current state and any user inputs. It’s similar to how large language models predict the next word in a sequence, but infinitely more complex because we’re talking about high-resolution video frames rather than words.

“A world model is, at its core, an action-conditioned dynamics model,” as Odyssey puts it. Each time you interact, the model takes the current state, your action, and the history of what’s happened, then generates the next video frame accordingly.

The result is something that feels more organic and unpredictable than a traditional game. There’s no pre-programmed logic saying “if a player does X, then Y happens”—instead, the AI is making its best guess at what should happen next based on what it’s learned from watching countless videos.

Odyssey tackles historic challenges with AI-generated video

Building something like this isn’t exactly a walk in the park. One of the biggest hurdles with AI-generated interactive video is keeping it stable over time. When you’re generating each frame based on previous ones, small errors can compound quickly (a phenomenon AI researchers call “drift.”)

To tackle this, Odyssey has used what they term a “narrow distribution model”—essentially pre-training their AI on general video footage, then fine-tuning it on a smaller set of environments. This trade-off means less variety but better stability so everything doesn’t become a bizarre mess.

The company says they’re already making “fast progress” on their next-gen model, which apparently shows “a richer range of pixels, dynamics, and actions.”

Running all this fancy AI tech in real-time isn’t cheap. Currently, the infrastructure powering this experience costs between £0.80-£1.60 (1-2) per user-hour, relying on clusters of H100 GPUs scattered across the US and EU.

That might sound expensive for streaming video, but it’s remarkably cheap compared to producing traditional game or film content. And Odyssey expects these costs to tumble further as models become more efficient.

Interactive video: The next storytelling medium?

Throughout history, new technologies have given birth to new forms of storytelling—from cave paintings to books, photography, radio, film, and video games. Odyssey believes AI-generated interactive video is the next step in this evolution.

If they’re right, we might be looking at the prototype of something that will transform entertainment, education, advertising, and more. Imagine training videos where you can practice the skills being taught, or travel experiences where you can explore destinations from your sofa.

The research preview available now is obviously just a small step towards this vision and more of a proof of concept than a finished product. However, it’s an intriguing glimpse at what might be possible when AI-generated worlds become interactive playgrounds rather than just passive experiences.

You can give the research preview a try here.

See also: Telegram and xAI forge Grok AI deal

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Over the past year, the tech giant says it has prevented $4 billion in fraud attempts, blocking approximately 1.6 million bot sign-up attempts every hour – showing the scale of this growing threat.

The ninth edition of Microsoft’s Cyber Signals report, titled “AI-powered deception: Emerging fraud threats and countermeasures,” reveals how artificial intelligence has lowered the technical barriers for cybercriminals, enabling even low-skilled actors to generate sophisticated scams with minimal effort.

What previously took scammers days or weeks to create can now be accomplished in minutes.

The democratisation of fraud capabilities represents a shift in the criminal landscape that affects consumers and businesses worldwide.

The evolution of AI-enhanced cyber scams

Microsoft’s report highlights how AI tools can now scan and scrape the web for company information, helping cybercriminals build detailed profiles of potential targets for highly-convincing social engineering attacks.

Bad actors can lure victims into complex fraud schemes using fake AI-enhanced product reviews and AI-generated storefronts, which come complete with fabricated business histories and customer testimonials.

According to Kelly Bissell, Corporate Vice President of Anti-Fraud and Product Abuse at Microsoft Security, the threat numbers continue to increase. “Cybercrime is a trillion-dollar problem, and it’s been going up every year for the past 30 years,” per the report.

“I think we have an opportunity today to adopt AI faster so we can detect and close the gap of exposure quickly. Now we have AI that can make a difference at scale and help us build security and fraud protections into our products much faster.”

The Microsoft anti-fraud team reports that AI-powered fraud attacks happen globally, with significant activity originating from China and Europe – particularly Germany, due to its status as one of the largest e-commerce markets in the European Union.

The report notes that the larger a digital marketplace is, the more likely a proportional degree of attempted fraud will occur.

E-commerce and employment scams leading

Two particularly concerning areas of AI-enhanced fraud include e-commerce and job recruitment scams.In the ecommerce space, fraudulent websites can now be created in minutes using AI tools with minimal technical knowledge.

Sites often mimic legitimate businesses, using AI-generated product descriptions, images, and customer reviews to fool consumers into believing they’re interacting with genuine merchants.

Adding another layer of deception, AI-powered customer service chatbots can interact convincingly with customers, delay chargebacks by stalling with scripted excuses, and manipulate complaints with AI-generated responses that make scam sites appear professional.

Job seekers are equally at risk. According to the report, generative AI has made it significantly easier for scammers to create fake listings on various employment platforms. Criminals generate fake profiles with stolen credentials, fake job postings with auto-generated descriptions, and AI-powered email campaigns to phish job seekers.

AI-powered interviews and automated emails enhance the credibility of these scams, making them harder to identify. “Fraudsters often ask for personal information, like resumes or even bank account details, under the guise of verifying the applicant’s information,” the report says.

Red flags include unsolicited job offers, requests for payment and communication through informal platforms like text messages or WhatsApp.

Microsoft’s countermeasures to AI fraud

To combat emerging threats, Microsoft says it has implemented a multi-pronged approach across its products and services. Microsoft Defender for Cloud provides threat protection for Azure resources, while Microsoft Edge, like many browsers, features website typo protection and domain impersonation protection. Edge is noted by the Microsoft report as using deep learning technology to help users avoid fraudulent websites.

The company has also enhanced Windows Quick Assist with warning messages to alert users about possible tech support scams before they grant access to someone claiming to be from IT support. Microsoft now blocks an average of 4,415 suspicious Quick Assist connection attempts daily.

Microsoft has also introduced a new fraud prevention policy as part of its Secure Future Initiative (SFI). As of January 2025, Microsoft product teams must perform fraud prevention assessments and implement fraud controls as part of their design process, ensuring products are “fraud-resistant by design.”

As AI-powered scams continue to evolve, consumer awareness remains important. Microsoft advises users to be cautious of urgency tactics, verify website legitimacy before making purchases, and never provide personal or financial information to unverified sources.

For enterprises, implementing multi-factor authentication and deploying deepfake-detection algorithms can help mitigate risk.

See also: Wozniak warns AI will power next-gen scams

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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