A world model and GenAI hackathon, Worlds in Action Hack [02-LA]: SIGGRAPH Edition, runs July 18-19, 2026 at the Arizona State University California Center on Grand Avenue in Downtown Los Angeles, with a Friday warm-up day of workshops and team formation on July 17. Presented by SensAI Hackademy and co-hosted by Poeia XR and Machine Cinema, the event gives teams two days to build interactive, AI-driven 3D worlds, then puts the winning projects in front of SIGGRAPH week.

• Six challenge tracks span filmmaking and entertainment, gaming and UGC, interactive ads and brand activations, world models and 3D GenAI, agentic interfaces, and 3D reconstruction.

• Roughly $17,000 in total prize value, including a cash pool, World Labs API credits, and an XGRIDS PortalCam scanner.

• Judges and advisors from NVIDIA, Meta, ByteDance, Google, World Labs, and Roblox weigh the final builds.

Teams get two days to ship interactive AI worlds across six tracks

The hackathon frames itself around world models, the AI systems that generate navigable 3D scenes rather than flat images or clips. Participants build across six challenge categories: filmmaking, entertainment and simulation; gaming and UGC experiences; interactive or video ad, brand, or fan activations; world models and 3D GenAI implementation; agentic interfaces and systems; and 3D reconstruction projects.

The schedule starts with a July 17 keynote, workshops, networking, and team formation. Saturday opens with a kickoff briefing, ideation, two workshop blocks, and one-on-one project validation. Sunday runs a final sprint to a 1:00 PM submission deadline, followed by judging, a public showcase, and closing. One listed workshop, "Powered By Gaussian Splats: From World Models to 3D Interactive Worlds," ties the build tools directly to the Gaussian splatting techniques the tracks reward.

The prize stack pairs a cash pool with hardware and API credits

Organizers list a $10,000 prize pool alongside $2,000 in World Labs API credits and an XGRIDS PortalCam valued at $5,000, plus SIGGRAPH tickets and a showcase opportunity during the conference.

The PortalCam reward is hardware VP Land readers already know. We covered the $5,000 handheld scanner at launch as a Gaussian splatting capture device.

At the NAB Show, we took a closer look at how its LiDAR and four-camera array turn real-world locations into reconstructions.

The World Labs credits give teams spatial AI tooling from the company behind Marble, its multimodal 3D world generator.

World Labs also shipped Spark 2.0, which streams 3D Gaussian splatting worlds in a browser.

Partners and sponsors for the event include Pico, ByteDance, World Labs, XGRIDS, Unity, AWE, Beeble, Volinga, and Devpost.

Judges include developer advocates and engineers from major XR and AI teams

The advisor and judge roster pulls from across the spatial computing industry. Named judges include David Gene Oh from ByteDance's global developer advocacy team, Yiqi Zhao, a product design lead at Meta, and Ian Curtis, a product designer at World Labs. Neil Trevett, NVIDIA's VP of developer ecosystems and president of the Khronos Group, appears on the roster alongside Fasai Phuathavornskul, a senior software engineer on Google's Android XR, and Alberto Hojel, an AI engineer at Roblox.

Investor and studio voices round out the panel, including Tipatat Chennavasin, general partner and co-founder of The VR Fund, Alex Coulombe of Agile Lens, and Sonya Haskins, head of programming at AWE.

Application-based hacker passes are free, with spectator and mentor tickets open

Hacker access is free and application-based, and the event lists separate spectator, volunteer, mentor, and researcher registration types. The audience skews toward XR creators, AI filmmakers, developers, researchers, game designers, and world model builders, the same crowd converging on Los Angeles for SIGGRAPH. Teams that place get a showcase slot during the conference, turning a weekend build into visibility in front of the spatial AI research community. Registration and the full schedule are posted on the event page.

Epic Games has added native Model Context Protocol (MCP) support to Unreal Engine 5.8, letting external AI agents operate the Unreal Editor directly. According to Epic's documentation, the Unreal MCP plugin runs an MCP server inside the editor process, so MCP-compatible agents such as Claude Code, Cursor, or MCP Inspector can connect over a local HTTP link and call engine functions.

The plugin turns editor actions into agent-callable tools. Spawning actors, configuring lighting, creating material instances, inspecting Slate widgets, and running automation tests are all exposed as MCP Tools. Epic ships it as experimental. The documentation warns the feature is incomplete in places, that APIs and data formats may change, and that the plugin is not designed for remote use.

What "AI agents can touch the editor" actually means

The capability surfaced through a Reddit post in r/TopologyAI reacting to UE 5.8's MCP support, but the substance comes from Epic's own engine documentation.

The shift for 3D and virtual production teams is the move from AI that suggests steps to an agent that executes them. An MCP-connected agent can inspect a scene, create and edit actors, build material instances, and run tests against the project, rather than walking a user through those actions in a chat window. We previously covered how Aximmetry's MCP server lets agents build and adjust virtual production scenes from plain-language instructions; Epic's version brings that pattern inside the engine itself.

Setup runs through a local server and per-client config files

Turning it on means enabling the Unreal MCP plugin and configuring Model Context Protocol preferences in the editor. Per Epic's documentation, the plugin can auto-start a local server at 127.0.0.1:8000/mcp and generate AI client configuration files for a range of agents.

• Supported clients. The config generator targets Claude Code, Cursor, VSCode, Gemini, Codex, or all of them at once.

• Local by default. The server binds to localhost, and Epic states the plugin is not built for remote operation.

• HTTP connection. Agents reach the editor over the local HTTP endpoint rather than a cloud relay.

Studios can expose their own pipeline tools to agents

Unreal MCP is built on a Toolset Registry, an AllToolsets architecture that lets toolsets surface Python or C++ functions as AI-callable tools. The shipped toolsets include SceneTools, ActorTools, MaterialInstanceTools, and ObjectTools.

That extensibility is the part with the longest reach for production teams. A studio is not limited to the default editor functions; it can register proprietary pipeline operations as MCP tools and put them within an agent's reach. The approach echoes how Foundry's Griptape integration orchestrates AI agents and metadata across Nuke and other applications, and it extends Epic's broader AI push following its acquisition of Loci for 3D asset understanding.

Why this is infrastructure, not a production-ready autonomous TD

Epic's own labeling sets the expectations. The plugin is experimental and incomplete, it ships without an authentication layer, and it binds to local connections only. Tool calls also execute serially on the game thread, which limits how much an agent can run in parallel against a live project.

For virtual production and 3D teams, UE 5.8's MCP support is a formal bridge between outside AI agents and the Unreal Editor, with the toolset architecture pointing toward agents that operate custom pipeline functions over time. Epic's cautions make clear it is early-stage plumbing rather than a hands-off technical director, and teams evaluating it should treat it as something to test in a sandbox before trusting it near production work.

Sony Pictures Entertainment is investing $100 million in Cosm for a minority stake in the immersive venue company, according to The Hollywood Reporter. SPE chairman and CEO Ravi Ahuja will join Cosm's board as part of the deal.

The investment gives a major studio a direct financial and governance stake in a company building domed LED venues for live sports and theatrical content.

• $100 million for a minority stake. Sony does not take control, but the capital and board seat signal a long-term commitment to location-based exhibition.

• A studio chief on the board. Ahuja's seat puts Sony's top executive inside Cosm's strategic planning.

• Sony joins an established investor group. Existing Cosm backers include Fox, Kroenke Sports and Entertainment, Marc Lasry, and Bolt Ventures.

Cosm builds domed LED venues that recreate courtside and fieldside views

Cosm operates Shared Reality venues built around large LED dome environments, with locations in Los Angeles and other markets. The company has leaned heavily on live sports, presenting events including the FIFA World Cup, the NBA Finals, and UFC Freedom 250 using custom camera positions that approximate courtside and fieldside seats.

We previously broke down the technology behind these venues, which wrap audiences inside a curved LED dome rather than projecting onto a flat screen.

Cosm has expanded from live sports into immersive film programming

Beyond sports, Cosm has moved into entertainment programming with immersive versions of established film titles, including The Matrix, Willy Wonka and the Chocolate Factory, and Harry Potter and the Sorcerer's Stone. We covered the Harry Potter Shared Reality production when Cosm and Warner Bros. Pictures announced it.

The company has also widened its content network through immersive family-film partnerships, adding titles from Reef Distribution under a broader Premium Media Program. On the Denoised podcast, we shared our firsthand take on Cosm's dome screening of The Matrix, including what worked and what fell short.

The deal extends Sony's push into experience-led exhibition

Sony already owns Alamo Drafthouse, which it acquired in 2024 and placed under a new Sony Pictures Experiences division. The Cosm investment continues a pattern of Sony backing theatrical formats that sit outside the standard multiplex.

For a studio with one of Hollywood's largest libraries, Cosm offers a possible distribution path for premium, location-based versions of film, television, games, and live-event IP. Sony brings capital, a board seat, and access to that library; Cosm gains a studio partner with deep content holdings.

The move also fits Sony's broader cost-and-format calculus. SPE has publicly discussed leaning on AI to cut film costs, and we reported on Sony's stated plans to reduce production spending. Experience-led venues give the studio a higher-margin way to monetize existing IP without greenlighting new tentpoles.

What is confirmed, and what Sony has not announced

Sony has not announced specific IP plans for Cosm. The Hollywood Reporter notes that the partnership suggests Sony titles could reach Cosm venues, but no programming slate has been confirmed. The concrete news is the capital, the minority stake, and Ahuja's board seat.

For working professionals, a studio committing $100 million and a board seat to a domed-venue company points to immersive, location-based exhibition becoming a planned distribution channel rather than a one-off experiment. The production implications follow from there: custom multi-position camera coverage, dome-mapped finishing, and venue-specific deliverables.

Seedance 2.0 Mini, a lighter variant of ByteDance's Seedance 2.0 video model, is now available, generating clips at roughly twice the speed and about half the cost of the standard model while holding characters, wardrobe, and visual style steady across multi-shot sequences.

• Roughly twice the speed of Seedance 2.0.

• About half the cost of the standard model.

• Multi-shot consistency for characters, wardrobe, and visual style.

Twitter tweet

Twice the speed and half the cost of standard Seedance 2.0

Seedance 2.0 Mini trades some of the full model's overhead for throughput. It runs at approximately twice the speed and roughly half the cost of standard Seedance 2.0, positioning it as the iteration tier in the family. Teams working a shot can run more generations for the same spend, then move heavier work to the standard model once the framing lands. The Mini variant joins the existing Seedance 2.0 options we covered at launch.

Multi-shot consistency carries over from the full model

The capability that defined Seedance 2.0 stays intact in Mini: it maintains consistent characters, wardrobe, and visual style across multi-shot sequences. For narrative work, that continuity is the difference between a usable sequence and a set of clips that drift between cuts. Keeping a character on-model across shots is one of the harder problems in AI video, and Mini holds that line at a lower price point. It is a problem competitors are chasing too, including Kuaishou's Kling 3.0, which built native multi-shot video and audio into its own release.

A fast-moving Seedance lineup from ByteDance

ByteDance has been widening the Seedance 2.0 family rather than replacing it. We covered the model's move to official 4K output alongside the company's tease of Seedance 2.5, which it says can generate 30-second clips in a single pass. Mini fits that pattern, adding a tier tuned for cost and speed next to options aimed at final quality. The result is a lineup creators can sort by what a given shot needs.

ElevenLabs has released a roughly 13-hour AI-generated audiobook of Homer's The Odyssey through its ElevenReader app, narrated by a licensed AI replica of Michael Caine's voice. Variety first reported the production.

A four-person team produced the full audiobook in six weeks. The cast runs to about 20 AI voice characters, backed by AI-generated sound and score. Caine licensed his voice through ElevenLabs' Iconic Marketplace and is paid each time it is used.

Caine licensed his voice through the Iconic Marketplace and signed off on this specific project

Michael Caine licensed his voice and likeness to ElevenLabs' Iconic Marketplace in 2025. For this audiobook, ElevenLabs says he was consulted on the project, approved the marketing materials, and receives compensation each time his voice runs in ElevenReader.

"By bridging classical storytelling with digital innovation, this timeless epic is reimagined for modern audiences, brought vividly to life through ElevenReader's cutting-edge technology," Caine said in a statement.

The consent structure speaks to the voice-licensing debate VP Land has tracked across AI audio work. We covered the Fortnite AI Darth Vader release, where SAG-AFTRA raised objections even after the rights were cleared, a sign that authorization and labor concerns can run on separate tracks.

A four-person team built the full production in six weeks

The audiobook features about 20 AI voice characters drawn from ElevenLabs' voice library, plus AI-generated sound and an original score. ElevenLabs also produced dubs across multiple languages.

According to Madeline Shue of the publisher partnerships team, that output was "previously not possible to do that type of work with a small team and a short timeline."

Dustin Blank, ElevenLabs' head of partnerships, framed the release as "another retelling" that he said "does justice" to the text.

The audiobook runs on ElevenReader, the consumer reading app where ElevenLabs has been expanding its synthetic-voice catalog. We previously reported on ElevenLabs' text-to-Voice Design tool, which generates custom voices from a text prompt.

The release is timed ahead of Christopher Nolan's 'Odyssey' film

ElevenLabs released the audiobook to precede Christopher Nolan's film adaptation of The Odyssey. We covered how Nolan pushed IMAX to develop new cameras for that production, which is being shot entirely on IMAX.

For ElevenLabs, the Caine project doubles as a reference case: a consent-based celebrity voice license, an IP tie-in around a major theatrical release, and a large-scale immersive audiobook produced quickly with a small crew. It also extends the company's move beyond speech into full audio production, alongside the custom music finetuning it rolled out for its creative suite.

What a licensed, AI-narrated audiobook signals for voice work

The Odyssey audiobook tests whether consent-based voice licensing can support a full commercial production rather than a demo. Caine's deal pairs an upfront license with per-use compensation, a structure that gives talent recurring payment while letting a four-person team produce at a scale that once required a full cast and studio. How widely that model spreads will depend on how SAG-AFTRA and individual performers weigh the terms against the production economics ElevenLabs is demonstrating.

ByteDance is advancing its Seedance video models on two tracks at once. Seedance 2.0 now generates 4K output, an upgrade the company confirmed at its Volcano Engine FORCE conference. Alongside it, ByteDance previewed Seedance 2.5, reported to produce 30-second videos in a single generation, plus a new platform built around licensing AI-generated content.

• Seedance 2.0 adds 4K. The resolution increase is an official conference update, not a leak.

• Seedance 2.5 remains reported, not documented. Its specs come from conference coverage, with no ByteDance product page published at this writing.

• A copyright commercialization platform pairs the higher-end generation with authorized IP, including licensed film-scene templates.

The confirmed piece is Seedance 2.0 reaching 4K

The clearest update is resolution. Seedance 2.0 now outputs 4K, an official step up for a model we covered at its multimodal launch with 15-second clips and dual-channel audio. The jump matters most for teams weighing AI-generated footage for finishing rather than previs, where 1080p ceilings have kept these models in early-stage roles.

ByteDance has consistently leaned on price as much as capability. Seedance 1.0 reached the top of text-to-video and image-to-video leaderboards while undercutting Western models on render cost; we tracked Seedance 1.0 topping the charts at roughly $0.50 for a 5-second full HD render through Volcano Engine.

Seedance 2.5, as reported: 30-second clips and up to 50 reference assets

ByteDance also previewed Seedance 2.5, though the details should be read as reported rather than documented. According to a TestingCatalog post and Chinese trade coverage from the conference, Volcano Engine president Tan Dai unveiled the model and said it is in global enterprise beta, with a launch expected in early July.

The reported specifications include:

• 30-second native generation in a single pass, rather than stitched extensions.

• Up to 50 multimodal reference assets to steer a generation.

No ByteDance-owned Seedance 2.5 product page or press release was available at capture time, so treat the 30-second and reference-asset figures as conference claims pending official documentation. The Seedance 2.0 4K update is the safer of the two announcements; multiple reports describe it as a formal conference release.

The copyright platform answers the IP fight that followed Seedance 2.0

ByteDance is also pairing the upgrades with a copyright commercialization platform that bundles the generation tools with authorized intellectual property. A reported early example is a set of officially licensed Stephen Chow film-scene templates, made available across Douyin, Jimeng, CapCut/Jianying, and Seedance-connected platforms.

That framing reads as a direct response to the legal pressure Seedance 2.0 drew. We covered the cease-and-desist wave from Disney and Paramount and ByteDance's pledge to tighten safeguards after Hollywood groups objected to the model's handling of protected characters and scenes. Pairing longer, higher-resolution generation with a licensing layer lets ByteDance court studios and rights holders instead of only absorbing their complaints.

What it means for production teams watching Chinese AI video

Seedance 2.0 at 4K widens the set of jobs where ByteDance's model can plausibly contribute finished frames, not just concept passes. The Seedance 2.5 claims, if they hold at launch, would push single-shot duration well past the short clips that define current models.

The licensing platform carries the longer-term signal. Chinese AI video has scaled fast on cost, with Kuaishou's Kling unit reportedly targeting a $20 billion valuation in a planned spinoff. If ByteDance can attach authorized IP to that price-and-capability advantage, it shifts the terms studios face when they decide whether to fight these tools or license into them. Early July, the reported Seedance 2.5 window, is the next checkpoint.

Krea has released the open weights for Krea 2, its in-house foundation image model, splitting the release into two checkpoints aimed at different jobs. Both are available to download now from Hugging Face.

• Krea 2 Raw is an undistilled checkpoint built as a base for fine-tuning and post-training.

• Krea 2 Turbo is a distilled, post-trained model that generates images at 2K native resolution and runs on consumer hardware.

• Krea says the license allows individuals and small companies to use both models commercially.

The company frames the two-model split with a simple instruction in its announcement: train on Raw, generate with Turbo.

Twitter tweet

Raw is an undistilled mid-training checkpoint meant to be fine-tuned

Krea 2 Raw is a mid-training checkpoint captured while the company was training Krea 2 Medium. Because it ships without distillation, fine-tuning, or post-training, Krea positions it as the model builders should start from when they want to fine-tune or post-train their own variant.

Krea's technical report describes K2 Raw as "an undistilled model without any post-training." That lack of post-training leaves the base behavior intact for teams that want to adapt the model rather than use it as shipped.

Turbo is distilled for fast 2K generation on consumer hardware

Krea 2 Turbo is the distilled and post-trained counterpart, built from Krea 2 Medium. Krea says it produces diverse aesthetics quickly at 2K native resolution and can run on consumer hardware, which puts local, high-resolution generation within reach of users without datacenter GPUs.

The technical report calls K2 Turbo "a guidance- and timestep-distilled model capable of generating high-quality outputs within a few seconds." Those two distillation methods are what enable the few-second generation the report describes.

The license clears commercial use for individuals and small companies

Krea says its license is flexible enough for individuals and small companies to use the models commercially. Both Krea 2 Raw and Krea 2 Turbo can be downloaded from Krea's Hugging Face page, where the repositories are listed as Krea-2-Raw and Krea-2-Turbo.

Krea's second open release this year extends its hosted models to downloadable weights

The release moves Krea 2 from a hosted product into downloadable weights. We covered Krea 2 when the company launched it as an in-house foundation model built for aesthetic diversity and style control.

Krea 2 followed the company's first image model, Krea 1. Open releases are becoming a pattern for the company: we also covered Krea open-sourcing Krea Realtime, a 14-billion-parameter autoregressive video model, in October 2025.

Krea 2 lands in a stretch of downloadable image-model releases. Black Forest Labs released the FLUX.2 weights on Hugging Face for use on consumer GPUs.

Ideogram took a similar route, shipping downloadable weights for Ideogram 4.0 for self-hosting and fine-tuning.

What open Krea 2 weights mean for builders and small studios

For teams already building on hosted image APIs, the two-checkpoint split maps onto two distinct workflows: Raw for anyone training a custom model on their own data, and Turbo for fast generation that can run locally instead of through a paid endpoint. The commercial license terms for individuals and small companies lower the barrier for independent creators and small studios to put either model into production work. With both checkpoints live on Hugging Face, the open weights are available to test against existing pipelines now.

We got back from Google I/O with hands-on access to Omni, Google's new video world model, and we pitted it against Runway Aleph 2 on identical source footage. On the Denoised podcast, we break down what Omni is actually good at (hint: not Veo's job), test the avatar, and work through Flow's agentic editor, Google Pics, and Demis Hassabis on AGI timing.

Quick Take

Google dropped a stack of updates at I/O, and the framing that clicked was simple: Omni is not Veo 4. It is the video version of Nano Banana Pro, a world model built for modifying existing video rather than generating cinematic shots from scratch. That distinction changes how you use it, who it competes with, and why Runway Aleph 2 landed into a much harder comparison.

What We Tested: Omni as a Video-to-Video Model, Not a Veo Replacement

The first instinct on getting Omni access is to compare it to Kling, Seedance, and Veo for cinematic generation. That comparison falls flat. As Joey put it, "this is not Veo 4 and it's a completely different model. This is the video version of Nano Banana Pro."

What Omni is built for:

• Video-to-video edits (Addy's preferred term over "editing," which still means cutting in our world)

• Inpainting on video with object swaps that preserve motion and shadows

• World understanding prompts that reason about objects, physics, and continuity

• Sharper text rendering inside generated video

We ran a video-to-video test with a clip of someone walking a dog in Venice. Prompt: turn the dog into a robot. The dog became a matte-plastic robot dog while the leash, the hand grip, the pan, and the background transparency all held. As Addy noted, the model picked a quadruped robot rather than a biped: "that's reasoning. Like it's going through some sort of intelligence."

A second pass turned the dog into a chimpanzee and looked even more grounded once metals and reflections were off the table. A third test on a phone clip of a Google building, prompted "make the building take off like a spaceship," kept the building, added rocket exhaust, and busted out of the ground. We covered Omni's launch in the Gemini app separately for production pros.

The world understanding test: We prompted Omni to create single shots of vintage objects whose first letters spell DENOISED, with each letter visible on the object. Omni produced one clip stringing together a dial phone, newspaper, oil can, ink holder, stopwatch, and an Edison fan. Two letters drifted, but one prompt cut a coherent multi-object sequence with readable letterforms. That is what the "world model" label is pointing at.

What We Tested: The Avatar Feature That Buries Sora's

The avatar tool is buried in the Gemini app UI, and the first round of image-to-video tests with random photos was rough. Likeness fell apart. The actual workflow: calibrate on your phone like a Sora avatar, turn your head left and right, read off numbers. Google ties the avatar to your account.

Addy's take: "I think this is way better than Sora's avatar was." On the champagne shot and the Miami gray t-shirt shot, "that's 98% Joey."

Two caveats:

• The model amplifies. Sharper jawline, fuller hair, more jacked. Addy called it the "common denominator handsome" tendency. Google could dial it down if they wanted.

• Voice is the weak link. We calibrated in a hotel room on phone audio. The output didn't sound like him. Better mic input likely fixes it, but there is no Adobe Podcast Audio style cleanup happening here.

You can only build one avatar (yourself), and you can't grant permission for others to use it the way Sora characters work. We asked. The answer: maybe later, not on the roadmap. Addy's pitch for the real use case: faceless YouTube channels that want a synthetic talking head. For that audience, the current quality bar is already over the line.

What We Explored: Flow's Agentic Sidebar and a Vibe-Coded Tool Builder

Google Flow picked up the new Omni models, a character system, and a Gemini sidebar that operates as an agentic chat layer over the timeline.

Full breakdown in our coverage of Flow's agent update.

We tested it by asking for "10 different shots of the same person walking in the same field." Flow generated a reference image of the person, generated a reference image of the field, then used both as consistency inputs for the 10 shots. One vague prompt, three steps under the hood.

On the character system: We pressed Google on whether there is a special Omni variant powering Flow's character consistency. The answer was no. Same model the API will expose, with structuring under the hood. As Addy summarized, "there is no grand master plan to tie everything underneath with like a master workflow."

The tool builder: Flow added a vibe-coded mini-app builder inside the product. Describe a tool, get an applet. Examples included motion tracking. A public directory of community tools is already remixable. Open question: do creatives think in terms of building their own tools, or does this become a gateway to Unreal Engine or Google's Antigravity?

What We Explored: Google Pics Turns Nano Banana Into Editable Layers

Google Pics was the sleeper of the trip. Canva-shaped, built on Nano Banana. The trick: generated flyers and composites stay element-addressable after creation. Hover any piece of a generated design, click it, reprompt it, and only that element changes.

The demo: a generated "Rooted Future" flyer. We clicked the text and asked for it fancier. We clicked the left figure and put them in an astronaut spaceship suit. We clicked the right figure and put them in safari gear. The text restyled, both characters updated outfits while keeping likeness, the tape detail at the top stayed in place, the background held.

Addy's workflow: pair it with another image model for the first pass, then bring the result into Pics for layer-by-layer iteration. We could not confirm whether Pics accepts uploaded images for layered editing, but the inpainting and edge blending hold cohesion in a way we have not seen elsewhere.

What We Questioned: A 3-Year AGI Timeline and an AI Job Market Split

The Demis Hassabis fireside chat put AGI at three years out. Addy's read: the realistic window pushes further than the talking point suggests: "If they're saying AGI is now 3 years out, I'm guessing it's more like 5 to 6 years out, and ASI is probably decades away."

Demis spent significant time on AI for science, now its own Google division, with AlphaFold as the reference point. The pitch: if AI cures diseases, the conversation about job displacement looks different.

One Demis benchmark for AGI: give a model world knowledge up to roughly 1910 and see if it derives what Einstein and others did. Addy's connection: Yann LeCun's work abstracting away tokens in favor of vector-based, multimodal-by-default networks that keep absorbing inputs after training rather than freezing at the snapshot.

Gemini 3.5 Flash also dropped at I/O. It is the text model for coding and fast actions in the Gemini app, not a video model, and it beats benchmarks Google's own 3.1 Pro hit. The naming continues to confuse everyone, including us.

On the job market split, Addy's read: a hard fence between AI and non-AI roles: oversupply on the non-AI side with thousands of applicants per opening, undersupply on the AI side. ClickUp's CEO framed layoffs around expectations that remaining staff become 10x to 100x more effective. Meta reportedly cut roughly 10% while Zuck told staff the company is training on its own workforce.

What We Questioned: Waymo's Fringe Cases Got Real

Demis also talked about using Google's Genie world model to spin up simulated environments for Waymo fringe-case testing. His example: a Waymo surrounded by a forest fire. One-in-a-billion scenarios that never show up in real driving data.

Then the timing turned. Atlanta Waymos drove full-send into flooded streets, and the service has since paused. Joey: "they need to spin up some more Genie models to test out the fringe cases." Addy's stress test: drifting a car into a Waymo's lane in Santa Monica. The Waymo slowed, then honked when he got aggressive. More responsive than expected.

The broader Labs point: Google ships a lot because not every product survives. They know the kill-product meme. Trade-off is real velocity on creative tools with no guarantee of longevity.

What We Tested: Aleph 2 vs Omni on the Same Source Footage

Runway Aleph 2 shipped alongside Omni, which is brutal timing. We ran the same two source clips through both: the LACMA Metropolis sculpture pan and the dog-to-robot conversion from Venice.

What Aleph 2 added: a frame-picker slider. Pick any frame in the source video, edit that frame as guidance, propagate. Upload image references or generate a new first frame with Nano Banana Pro and feed it in. Real workflow improvement over text-only change descriptions.

What the head-to-head showed:

• Metropolis sculpture: Aleph 2 was softer, less detailed than Omni. Camera tracking held, but texture work lost specificity.

• Dog-to-robot: Aleph 2 produced a robot that still animated like a dog. Omni's robot dog had a gait rigged like a quadruped robot. As Addy described Aleph 2's approach: "it's like they're feeding it through OpenPose and just extracting the anchor points and then just attaching it to the new dog." Omni "fundamentally translated that animation into a completely robotic animation."

Aleph 2's inpainting on background and crowd held up. Shadows on the dog swam slightly. Addy's closing point: a small company hanging with Google and OpenAI on a release cadence Cristóbal Valenzuela's team worked on for six months to a year before shipping the same week as Omni. Direction is right. Timing is unforgiving.

We covered the prior Runway and Google update cycle for context on Runway's release cadence.

For context on Google's earlier cinematic model, see Google Veo 3.

Bottom Line: Omni Is a New Category, Not a Veo Upgrade

Five stories from I/O, one frame:

• Omni is the video-to-video world model in the Gemini lineup. Stop comparing it to Veo. Compare it to Aleph 2, and on identical source footage Omni handled object reasoning more cleanly.

• The avatar feature beats Sora's on visual likeness from a phone calibration but ships with weak voice synthesis and no sharing. Faceless YouTube is the obvious wedge.

• Flow got an agentic chat layer and a remixable mini-tool builder. No secret model under it, just smarter scaffolding around the same API.

• Google Pics turned Nano Banana into a layered, addressable design surface. Element-level inpainting holds cohesion in a way Canva-style AI editors have not.

• AGI in three years, Waymos in floodwater, layoffs as 10x bets: the macro story is Google leaning into AI-for-science legitimacy while the industry sorts which jobs survive.

Omni and Aleph 2 landed together. The model that understands the world wins on physics. The one that ships better workflow scaffolding might still win on day-to-day use.

Links from This Episode

Tools & Platforms:

• Gemini Omni in the Gemini app

• Google Flow agent update

• Google Pics layered editing

• Runway Aleph 2

• Gemini 3.5 Flash

Projects:

• Project Genie public access, the world-model engine Demis cited for Waymo fringe-case testing

News & Analysis:

• Nano Banana Pro coverage, the image-model framing for Omni

• Comfy Cloud, AI camera control, Nano Banana 2 roundup

• Google Flow original launch

• Google Veo 3 launch

Companies:

• Google DeepMind (Demis Hassabis, AGI timeline)

• Runway (Cristóbal Valenzuela, Aleph 2)

• Meta (layoffs, leaked training audio)

• Waymo (Atlanta flood incidents)

This episode of Denoised pulls together two threads from AI on the Lot: studios committing real money to AI production pipelines, and a box office weekend where a $750,000 horror film outgrossed The Mandalorian. We recap Amazon MGM's Gen AI Creators Fund and its new Project Nara platform, the online firestorm that pushed Jorge Gutierrez out of the program, the agentic tools Dreamina and Luma showed off, and what Obsession and Backrooms suggest about the YouTube generation of directors.

Quick Take

What does AI in production actually look like once studios start writing checks? This episode walks through Amazon MGM's first slate of AI-assisted shorts, the online backlash that drove a director out of the program, and a box office weekend that handed the chart to two YouTube-trained filmmakers working at micro budgets. The thread: studios and creators alike are betting on more shots at smaller budgets, and the tools and talent pipelines that get them there are still being assembled in public.

What We Saw: AI on the Lot Got Bigger Without Losing the Room

The AI on the Lot team scaled the event into three or four studio lots, including Studio 15 at Amazon, with around 2,000 people at peak and the whole thing sold out. Technical sessions stayed small enough to ask questions, and the hallway track was where most of the connections happened. We covered the prior event, and the contrast was less about scale than about who was showing up. Multiple people walked in cold, including a Netflix friend who told us he came specifically because he knew nothing about AI and wanted a place to start.

What we noticed: more projects are in production using AI. The skeptics-on-the-fence crowd has shifted to the figure-it-out crowd. People who left media and entertainment a decade ago for consumer tech jobs at Meta, Instagram, and TikTok are boomeranging back into AI roles inside studios.

What We Debated: Amazon MGM's Creator Fund and the Jorge Gutierrez Backlash

Amazon MGM hosted AI on the Lot and opened with the Gen AI Creators Fund, an undisclosed pool backing three animated projects plus a live-action short. The slate:

• Punky Duck from Jorge Gutierrez

• Love Diana Music Hunters from Albie Hecht at Pocketwatch

• Cupcake and Friends from BuzzFeed Studios

• An unnamed live-action short with a creator Amazon declined to identify

We previously reported on Amazon MGM's AI production push when the closed beta was announced, and the studio's track record on hybrid AI work includes the 253 AI-generated shots in House of David Season 2. The Moses series with Ben Kingsley used a similar hybrid approach, combining LED volume cinematography with Gen AI backgrounds and motion transfer.

The Jorge Gutierrez fallout: Gutierrez, a well-known animator, drew significant online backlash for partnering with Amazon on an AI-assisted short. He ultimately exited the program. Reports surfaced of death threats and family doxxing.

Addy's take: Part of the heat came from Gutierrez having been publicly anti-AI before joining the project. Changing your stance after seeing the tools in action is allowed, and the nuance gets lost in the online pile-on.

Joey's take: The misconception driving the anger assumes AI animation means typing a prompt and getting a finished film. What's actually happening is using AI as in-between work alongside an animator, their team, and their developed characters, so a project that could not get funded at full traditional cost can still get made.

The structure of the fund: Amazon is treating these as proof-of-concept pilots and will greenlight what works into full projects. Animation is the starting point because the quality gap to a tier-two animated feature is much smaller than the gap to a live-action tentpole.

What We Explored: Project Nara and Amazon's Pipeline Play

Amazon described Project Nara as a collaborative workspace that integrates AI production agents with tools creators already use: Maya, Blender, Nuke, Unreal, and the Adobe suite. The architecture is model-agnostic. AWS is officially a partner on the platform.

Addy's take: This reads as the evolution of AWS Nimble Studio, the cloud-based studio offering. Whatever Amazon MGM builds will lean heavily on AWS infrastructure.

The portability question: Studios guard IP closely and want AI running inside their own walls. A pipeline built for Amazon may not translate cleanly to other studios that want self-contained deployments. If Amazon ever spins Project Nara into a hosted product on AWS Bedrock or similar, the bespoke nature of studio pipelines makes a self-serve version a tough sell.

What We Tested: Dreamina Octo and Luma Agents

Two agentic workflow tools landed during the event. Dreamina Octo is ByteDance's chatbot-driven canvas agent on its consumer-facing platform, tied into Seedance for video generation. Octo handles ideation, character sheets, and shot breakdowns, then calls the underlying models to produce images and video. Seedance already supports multi-shot generation from a single prompt, so layering an agent on top opens up faster sequence-building.

Luma Agents takes a different angle. It is model-agnostic, calling out to whichever model fits the task rather than locking users into Luma's own stack. We covered the Luma Agents launch and its underlying Uni-1 unified intelligence model when it shipped. That decision lets Luma sell the agent as a product on its own merits rather than as a funnel for Dream Machine.

Where these agents help:

• Generating quantity and variations fast: ten versions of a shot, ten angles on a scene

• Deconstructing a prompt into the steps a model needs to stay consistent (reference images for character and location before generating shots)

• Lowering the floor for users who do not already know how to chain models together

Where they fall short: Turning a spark into a fleshed-out script, finding the emotional tone of an act, or making genuinely original creative calls. An LLM does well as a knowledge base or a variation generator. Ideation that requires taste is still a human job.

Addy's take: "The more I look into the pre-generation part of the pipeline, the more I'm convinced that we're not gonna lose our jobs."

What We Questioned: Studios Want Volume, Not Cheaper Shoots

The common refrain from studio conversations at the event was not about cutting budgets. It was about spreading the same budget across more bets. Eighty percent of the people in the room described it the same way: spend $100 million, get ten films out of it instead of one.

The blackjack logic: A single $100 million tentpole is no longer a reliable hit. Ten $10 million projects give the studio ten shots at landing a breakout. If one hits $200 million, it pays for the others. The IP library a successful project produces is the long-tail revenue that justifies the bet.

The commercial side: Lots of hallway talk about commercials going fully AI-generated. The counter-take: as automated content saturates feeds, the appetite for handmade, bespoke work goes up. Super Bowl commercials still draw attention because they are recognizably built by humans. That dynamic likely sustains a market for craft commercials even as the volume tier gets automated.

What We Explored: Obsession, Backrooms, and the YouTube Generation

The box office weekend turned the studio strategy conversation into a live experiment. Two indie horror films took the top of the chart:

• Backrooms: $10 million budget, $81 million domestic

• Obsession: $750,000 budget, $26 million in its third weekend

• The Mandalorian and Grogu: came in third with $25 million

Obsession was acquired by Blumhouse at the Toronto Film Festival for $14 million. Parsons did the viral Blender-animated Backrooms short in 2022, and as we previously reported, A24 signed him on the strength of that work. Parsons was 16 when the short broke, signed at 18, shot the film at 19, and the movie released when he was 20.

AMC Entertainment reported its highest May attendance since 2019. Three movies opening the same weekend drove people back to theaters regardless of which one they came for.

Joey's take: This is not the MrBeast-to-Amazon model where a creator builds an audience on a channel and then transfers it to a streamer. Parsons and Curry Barker (the director of Obsession) did not run channels with built-in followings that translated to ticket sales. They posted work on YouTube, got noticed, got signed, and made movies. YouTube is functioning as a replacement for the Sundance circuit of the '90s and 2000s. Cuarón, Soderbergh, and that generation came up on film festivals. This generation comes up by posting work where any exec can watch it.

Addy's take: A festival showcases work to maybe 500 people in the room. YouTube is a democracy. Any exec scouting talent can pull up the work and gauge quality without sitting through a Park City lineup.

Where AI fits in two years: Neither Backrooms nor Obsession used AI in any meaningful way. Their VFX needs were modest because they leaned on confined locations and practical builds. The next cohort of directors coming up the same pipeline will have AI in the pre-vis, storyboarding, and concepting stages, plus the ability to self-finance and self-build at higher fidelity.

The Star Wars footnote: Joey noted that Dave Filoni reportedly did not enjoy Andor, which was shot without virtual production, and Addy added that Andor was all physically shot. Addy called Andor a standout against the rest of recent Disney output, a contrast worth noting against the Mandalorian numbers.

Bottom Line: Studios and Creators Are Both Hunting for Repeatable Hits

The two halves of this episode connect on the same problem from opposite ends: a $100 million bet on a known IP is no longer a sure thing, and the next generation of directors is being discovered on the same platform their audiences already live on. AI is the tool both sides reach for to make more bets affordable.

• Both halves of the episode point at the same math. Amazon MGM is structuring its Creator Fund as a portfolio of small pilots, and the box office is rewarding $750,000 horror over a Star Wars feature. The tentpole-or-bust era is yielding to volume betting on both sides of the studio gate.

• The tooling and the talent pipeline are moving on parallel tracks. Project Nara and agentic tools like Dreamina Octo and Luma Agents lower the floor for studios; YouTube lowers the floor for directors. Both sides converge on more shots at lower cost per shot.

• The discourse has not caught up to the work. The death threats and doxxing aimed at Gutierrez are a signal that the public conversation about AI in creative fields lags the tools and the staffing decisions already in motion inside studios.

The studios betting on volume and the creators emerging from YouTube are converging on the same answer: more shots on goal at lower budgets, with AI doing more of the work that used to require a full crew.

Links from This Episode

Events:

• What We Saw at AI on the Lot: Our prior coverage of the event

Studios & Programs:

• Amazon MGM AI Production Tools: Prior coverage of Amazon MGM's AI program

• House of David's 253 AI-Generated Shots: Amazon MGM's hybrid AI track record

Tools & Platforms:

• Luma Agents and Uni-1: Prior coverage of Luma's unified intelligence model

Projects:

• Backrooms (A24 Deal): Prior coverage of Kane Parsons' feature deal

Sim-Plates founder Alex Pearce has unveiled Sim-Plates.ai, an in-development AI plate-generation app that pairs the studio's existing CG driving-plate library with a guided five-step workflow for creating photoreal background plates for virtual production and VFX. Pearce shared the early look in a LinkedIn post describing the tool as still internal and gauging interest in a public release.

The hook: Sim-Plates is a CG-plate vendor moving into AI generation while keeping its own pre-rendered plates as the structural anchor. Composition, camera motion, perspective, geometry, and driving direction stay locked. Lighting, weather, atmosphere, texture, and "overall cinematic quality" become the variables.

The framing: Pearce positions Sim-Plates.ai as a companion to existing CG and live-captured plates, not a replacement. We previously covered how Sim-Plates supplies custom CG-rendered environments for car process shots on LED volumes, which is the workflow this new tool is meant to augment.

A guided five-workspace pipeline replaces the blank prompt box and the ComfyUI node graph

Pearce frames the design choice directly in the post: artists shape plates "using environment choices, source videos, style references, and AI generation controls instead of relying only on a blank prompt box or node spaghetti in the case of Comfyui." The interface is split into five sequential workspaces:

• Environment picks the base location and auto-customizes the prompt plus animation references.

• Settings sets camera animation type, speed, time of day, weather, and time period, all of which feed downstream prompts.

• Style auto-generates a style prompt from the prior selections; users can import their own style image or pick a built-in one, with a reference image or video loaded by default.

• Generate produces the video plate and auto-recommends a video model based on the inputs, though the user can swap to any supported model and edit the auto-generated prompt.

• AI Upscale is the final pass, with multiple upscalers available and a recommended one preloaded.

Each step inherits choices from the previous one, so a "1950s London, overcast, dolly move" selection in the early workspaces propagates into the style prompt and model recommendation rather than asking the artist to retype it.

The production problem is shoot-day plate failures, not eliminating CG or live capture

The pitch is shaped around a specific pain point Pearce has hit on set. "What if the plate doesn't exist? You need London in the 1950s or New York in 2032," he writes. "We can create these with CG, but most productions don't have the time or budget for us to do it properly, and shooting practically? Forget it." He adds a second use case: when a production needs a specific plate on shoot day and "for whatever reason they don't have it," citing an instance where the internet was too slow to download a plate from the vendor and another where the vendor sent the wrong file.

Driving plates remain one of the more workflow-sensitive elements in virtual production. Our iPhone car-chase experiment walked through the practical pain points of capturing and sequencing driving footage, and the same sensitivities carry over once those plates land on an LED volume.

Sim-Plates' own seamless 12K-24K looping plates showed up in our NAB virtual projection preview as a category of asset that LED-volume operators are actively buying. Sim-Plates.ai targets the gap in between: when neither the prerendered library nor a live shoot covers what the production actually needs.

Pearce is explicit that the goal is augmentation. "The larger goal is not to replace CG Plates (Sim-Plates) or even live-captured plates," he writes. The AI generation step inherits the camera move and driving direction from a Sim-Plates source plate, then layers atmosphere and era on top.

Access is gated to expression-of-interest, with no public release date

Sim-Plates.ai is not publicly available. Pearce describes the tool as having been built "in stealth mode" as an internal tool for the company's own Gen-AI production work, with a stated future state where "anyone to generate their own plates, using our extensive library and procedural toolset under the hood." For now, the LinkedIn post functions as an interest-gauge: "Sim-Plates.ai began as a tool for our own workflow. We're now exploring whether it should become something more, let me know if you're interested in exploring this when we make it public!"

Martin Scorsese has signed on as an advisor to Black Forest Labs, the AI lab behind the FLUX image model family, with the company framing his role around using its tools for pre-production storyboarding. The announcement, posted on BFL's site, pairs a written statement from Scorsese with a two-minute video of the director working through a storyboard session using FLUX.

Scorsese's stated use case is storyboarding, not finished image generation. BFL positions his involvement around speeding up the visual handoff between a director and their department heads. According to the announcement, Scorsese tested the tool on a scene during pre-production and described the result as "creatively freeing."

A director who has hand-drawn his own storyboards for 70 years is using FLUX to share visualizations with department heads

Scorsese's quote in the announcement frames the tool as a communication problem-solver rather than a generative shortcut. "For 70 years, I've been creating my own storyboards," he said. "There's always been this problem of how do you communicate what you see in your head to your cast and crew. There are some things you have to see and feel."

The director points specifically to the production designer, art designer, and cinematographer as the audience for the FLUX-assisted boards. He also draws a line back to earlier technology adoption in his own filmography:

I utilized 3D with Hugo and de-aging technology for The Irishman. Now, with this tool, I can share what I'm visualizing more clearly and efficiently to my creative team, the production designer, art designer, and cinematographer, for them to build on to enrich cinematic intelligence.

Scorsese adds that during pre-production, "time costs money, and this allowed us to move faster without sacrificing quality or craft." BFL's working storyboarding session video, posted to YouTube, shows the director working through that process with the model.

The advisor role extends BFL's filmmaker-facing positioning that began with the FLUX.2 launch

Black Forest Labs frames the partnership around "visual intelligence," which the company defines as "models that can reason in the physical and digital worlds." The same foundation, BFL writes, "can support everyone from storytellers to architects, designers, and engineers, and power tools from animation to robotics."

That filmmaker positioning is not new for the company. We covered the FLUX.2 family launch, when BFL released FLUX.2, FLUX.2 Max, and FLUX.2 Klein with NVIDIA RTX optimization and open weights for some variants. FLUX.2 Klein also surfaced in our Denoised episode notes as part of the open-weights conversation among filmmakers experimenting with local image pipelines. FLUX.2 also showed up in our end-of-year image model roundup alongside Kling, Seedream, and Z-image, where it stood out as one of the few releases shipping both an API and open weights.

BFL describes the role as advisor only, with no equity, term, or production commitment disclosed

The bfl.ai announcement page does not specify the structure of the relationship. There is no mention of equity, compensation, a multi-film commitment, or a fixed term. BFL describes the arrangement only as Scorsese "helping us shape visual intelligence as an advisor."

The company frames its end of the relationship around keeping "human taste, values, and judgment at the center" of how FLUX evolves, and says Scorsese "wants to use FLUX to help bring his ideas to life." Beyond the storyboarding scene Scorsese mentions, BFL does not name a specific production the tool is being used on.

Scorsese's own statement closes by pointing back to the youth of cinema itself. "Remember, cinema is a young medium, only around 125 years old, so we have to be open to how it can evolve," he said.

HeyGen has introduced frame.md, a brand-spec format aimed at letting AI agents generate branded video and motion content that stays on-brand. The company is positioning it as a video-focused counterpart to design.md, the spec format already used to keep visual identity consistent across static screens.

design.md works for webpages and decks, but agents that try to use it for video output keep collapsing back into webpage and deck layouts. frame.md is the proposed fix for that translation gap.

Twitter tweet

design.md held the screen, but agents couldn't carry the brand into motion

In its announcement, HeyGen describes design.md as the format that "kept your brand consistent across screens." That worked when the agent's output was a webpage, a slide, or a static layout. The brand spec told the agent what to honor, and the rendered surface obeyed.

The breakdown, according to HeyGen's post, came when agents were pointed at video. Given a design.md, agents "translated it back into webpages and decks," defaulting to the static surfaces the spec was originally written for. The brand stayed intact, but the output stopped being video.

That is the gap HeyGen is naming. A brand spec written for screens does not carry timing, motion, scene transitions, on-screen text behavior, or any of the other choices that make a piece of content read as video rather than a slide deck on autoplay. Agents reading design.md had no instructions for those choices, so they reached for the surface they did know how to render.

frame.md is positioned as the video-and-motion counterpart, not a replacement

HeyGen describes frame.md as "a spec built for videos & motion" that "teaches your agents how to make branded video." The company's call-to-action in the post is to "turn your design.md into frame.md," which suggests frame.md is meant to extend an existing brand spec rather than replace it.

The announcement itself stops there. HeyGen has not, in the post, published a schema, a sample document, or a property-by-property breakdown of what frame.md contains. The post links out to a HeyGen URL for more detail, but the on-tweet description is the only surface verified here. Anything beyond "video and motion brand spec for agents" is implication, not stated fact.

frame.md extends HeyGen's agent-orchestration push for videohttps://www.vp-land.com/p…

HeyGen has been framing itself less as an avatar generator and more as an agent-orchestration layer for video. We covered the HeyGen Video Agent launch, where CEO Joshua Xu described the product as a "Creative Operating System" that runs script-to-final-cut through agents rather than a single generation model.

A brand-spec format is the logical next layer for that stack. An agent that can write, generate, and assemble a video still needs an instruction set for what the finished piece is supposed to look and feel like for a specific company. frame.md is HeyGen's pitch for what that instruction set should look like, sitting one level above the model and one level below the brief.

Ideogram released Ideogram 4.0 with downloadable model weights, available on every Ideogram plan and through the company's API on launch day. The company announced the release on X, positioning 4.0 as "the best open image model in the world" and pitching the workflow as "Think it. Make it. Own it."

The open-weights distribution is the headline change. Ideogram has run as a hosted product and API surface through its prior generations, including the Ideogram 3.0 release we covered alongside ChatGPT 4o image and Reve. A downloadable checkpoint for the new model puts Ideogram in a different distribution posture than that earlier closed surface.

Weights are downloadable, fine-tunable, and runnable on user hardware

Ideogram's launch post lays out three things the new release enables for anyone who pulls the weights. Users can download the model, fine-tune it on their own data, and run inference on their own hardware. The company ties each step to its "Think it. Make it. Own it." pitch, with the ownership line attached to the local-runtime option.

The verified launch post does not specify a license, a parameter count, a hardware floor, or any benchmark numbers. Teams evaluating 4.0 for a production pipeline will want to pull the official model card before committing to a workflow that depends on a specific license posture or VRAM budget.

Live availability across every Ideogram plan and the API on day one means the hosted product and the open release ship together, rather than the open weights trailing the hosted launch by weeks or months. Subscribers continue to get the hosted surface; anyone who wants a local pipeline gets a checkpoint to work from.

The release lands in a year of open-weight image-model launches

Ideogram joins a stretch of image-model releases that have leaned on open weights as part of the launch. Black Forest Labs took a similar route with the FLUX.2 family, shipping open weights for some variants.

Alibaba's Z-Image release put another open-source image model into the same conversation.

Ideogram's prior identity has centered on text rendering and graphic-design use cases, including the editing surface we wrote about in our Ideogram Canvas coverage. The 4.0 checkpoint pushes that text-and-design lineage into a configuration teams can host themselves.

What 4.0 changes for teams already on the API or the app

For a production team running on the API, the launch-day availability means there is no waiting period before 4.0 outputs are accessible through existing integrations. For teams that have wanted to keep image generation inside their own infrastructure, the downloadable weights are an option that did not exist on Ideogram's previous generations.

Ideogram's "the best open image model in the world" line is the company's own positioning, not an independent assessment.

Reve has launched Reve 2.0, a new image model that the company is pitching on two claims: that it is "the best 4K image model in the world," and that it introduces "a new way to generate and edit any image using precise layouts." Both are Reve's framing rather than independently tested results, and the layout-control pitch, not the resolution, is what the company is leading with.

The launch arrives roughly fifteen months after Reve first surfaced with its debut text-to-image model, which we covered as a free preview from a new entrant that scored well on prompt adherence and visual quality. Reve 2.0 is the company's first major model update since that debut.

Layout control is the headline change, not resolution

Reve's announcement frames the model around what it calls "precise layouts" as the mechanism for both generating and editing any image. The company has not published, in the announcement itself, an interface walkthrough, schema, or set of node-level details, so the practical shape of the layout system is still to be seen.

In the announcement post, Reve adds the line, "For the first time, it's possible to create images you can touch." That is the company's metaphor for layout-driven editing rather than a literal claim about physical or haptic output. Read in context, it points at the same idea as the rest of the post: image components can be directly manipulated through the layout system instead of regenerated by rewriting a prompt.

Reve has not shared specifications, pricing, or availability terms in the tweet itself, or said whether the layout primitives persist across an image series, batch generations, or edits.

A 4K positioning in a category Google has already moved into

The "best 4K image model in the world" line places Reve 2.0 in a category that has been actively contested at the high end. We previously covered Google's Nano Banana Pro, which pushed 4K image output paired with real-world knowledge grounding, as a marker of where the resolution conversation in image models had moved.

Reve's framing is similar on the resolution axis, but the differentiator the company is leading with is the layout system rather than pixel count. The announcement does not include comparative benchmarks, model size, training details, license terms, or pricing tiers, and the post does not specify whether Reve 2.0 ships as an API, hosted product, downloadable model, or some combination. Until the company publishes specifications or opens access, the layout-control claim and the 4K claim both sit as Reve's own positioning rather than verified capability.

Reve 2.0 is the company's second public model, following its text-to-image debut.

Miso Labs has released the open weights for Miso One, an 8-billion-parameter text-to-speech model the company pitches as "the most emotive voice model in the world." Co-founder Aoden Teo announced the model on X, citing 110 milliseconds of latency and saying API access is "coming soon."

Miso Labs has not published a license type, training-data disclosure, supported-language list, or voice count alongside the announcement. The tweet links to a sample thread VP Land has not independently evaluated. Open weights are available now, but the hosted API is not.

An 8B-parameter open-weights model claiming 110ms latency

The eye-catching specification in Miso Labs' announcement is the 110-millisecond latency figure. Paired with an 8-billion-parameter backbone and open model weights at launch, the package is unusual in a TTS market where the fastest, most expressive models tend to ship as closed APIs.

Per Teo, Miso One "emotes like a human and responds faster than a human." That framing belongs to Miso Labs; the company has not published comparative benchmarks against other speech models, and no third-party evaluations are available at announcement time.

The "most emotive voice model in the world" claim is similarly Miso Labs' own. Expressiveness in modern TTS has become a moving target as competing teams push prosody, breath, and emotional range, and any ranking depends on which test set and which listeners are doing the rating.

Open weights ship before the API

Most production-grade speech models have launched API-first, with self-hostable weights either never appearing or arriving long after the hosted product. ElevenLabs, for example, runs entirely through a managed API; we covered the company's Voice Design tool for generating custom voices from text prompts, a feature available only through the hosted service.

Miso Labs is reversing that order. By publishing weights before opening the API, the company gives researchers and self-hosting teams a usable artifact on day one, with the hosted product trailing. The trade-off: anyone running Miso One today has to stand up their own inference stack and reach the 110ms latency number on their own hardware.

The release fits a broader pattern of low-latency, developer-facing voice infrastructure. Deepgram's Saga voice operating system similarly leans on self-hostable, low-latency design as its core sell, though Saga targets voice-controlled developer workflows rather than expressive synthesis.

What Miso Labs has not disclosed

The announcement leaves several specifications open, and Miso Labs has not published a model card or technical report alongside the tweet. Items not addressed:

• License terms for the open weights, including any commercial-use restrictions

• Supported languages and the number of distinct voices shipped with the model

• Audio output format, sample rate, and streaming behavior

• Hardware requirements to hit the cited 110ms latency

• Training data sources and any consent or licensing disclosures

• Fine-tuning support for cloning or custom voice creation

• Pricing for the forthcoming API tier

Miso Labs lists Teo as a co-founder on his X profile under the handle @MisoLabsAI. The company has not named additional team members in the launch tweet, and the announcement points readers to an audio sample thread for listening tests.

Google is putting a reported $75 million into A24 and pairing the studio with Google DeepMind to develop AI filmmaking tools, according to the companies' research partnership announcement. Google's own post confirms the partnership and the investment in A24, but does not name the dollar figure.

The $75 million figure comes from Wall Street Journal reporting, relayed by Variety and other outlets. The size matters, but the structure matters more: the next generation of filmmaking tools will be shaped inside a working studio, with directors in the room while the tools are still being built, rather than handed down as a finished model.

What the deal reportedly includes: a multiyear, nonexclusive R&D relationship, not a product

Google describes the arrangement as "a deep research and development collaboration between A24 and Google DeepMind spanning multiple projects over time." Reports add that the deal is multiyear and nonexclusive. This is a research relationship, not a model release or a film slate. A24's filmmakers get hands-on input into tools in development, and DeepMind gets feedback from working artists before those tools reach a wider market. Google says the specific goals, technical outputs, and creative milestones will evolve as the work continues.

The first project reportedly underway runs through A24 Labs, the studio's technology group, and uses AI to generate storyboards, the rough visual sketches directors use to block out scenes before a shoot. Scott Belsky, who leads A24 Labs, framed the aim as tools that "preserve creative control and support risk-taking" and that "won't look anything like the prompted generation type of AI that people feel uncomfortable with," according to Variety's reporting. That points the early work at assistive pre-production for directors rather than one-prompt scene generation.

Reported terms keep A24's movie library and data out of Google's reach

One reported detail matters for anyone tracking the fight over AI training material: the deal does not give Google access to A24's film and television library or its underlying content data. Studio catalogs are exactly the kind of high-value material AI companies have pursued, and the reporting frames this as funding and research alignment around tools, not a content-licensing arrangement.

Google has not addressed the data question in its public announcement, so treat that carve-out as reported rather than officially stated. It is one of the few concrete boundaries the coverage has established so far.

DeepMind tested its tools with individual filmmakers before backing a whole studio

The A24 deal extends a run of filmmaker collaborations DeepMind has built around its generative tools. We covered the lab's work with Darren Aronofsky and his Primordial Soup banner on the AI-hybrid film Ancestra, which gave DeepMind a visible creative partner for testing generative tools inside a real production.

That experimentation has spanned formats. We also covered DeepMind's Sundance animated short, where the emphasis fell on workflow and creative control rather than raw model output. The A24 partnership scales that approach from a single filmmaker or one-off project to a standing relationship with a studio brand.

A24 brings a director-forward reputation and a habit of betting on new pipelines

A24 is not a random partner for this. We reported on the studio's feature deal with Backrooms creator Kane Parsons, a sign of how far A24 will reach into internet-native creator culture and younger production pipelines.

Tying that brand to DeepMind gives Google tools co-developed with a studio filmmakers respect, which is part of what the reported $75 million buys.

What the partnership means for filmmakers right now

For crews, editors, and production teams, there is nothing to adopt today. What the partnership offers instead is a statement of intent about how the next wave of filmmaking tools gets made and who gets a say in shaping them. Google's broader push already spans research demos, filmmaker collaborations, and shipping products such as Flow, and the A24 deal wires artist feedback into that pipeline at the studio level.

"We believe the best way to develop tools that empower artists is to work directly with them," Google DeepMind CEO Demis Hassabis said in announcing the partnership.

The Academy Software Foundation released the full schedule for Open Source Days, its annual open source program for motion picture and media technology. The event runs July 19-20, 2026 at the J.W. Marriott L.A. Live in Los Angeles.

DreamWorks Animation CTO Bill Ballew keynotes on MoonRay's move from in-house renderer to community-run open source project. July 19 holds the main program, and July 20 runs a full day of open source Birds of a Feather sessions inside SIGGRAPH. Main program registration is $50, with complimentary passes for ASWF member company employees who register before July 1, 2026.

Bill Ballew keynotes on MoonRay's path from proprietary DreamWorks tool to ASWF project

The keynote tracks MoonRay's transition from a proprietary DreamWorks rendering tool to an open source project and ASWF community effort. We covered MoonRay joining the ASWF as a hosted project, and the Open Source Days keynote puts the studio's CTO on stage to walk through that shift.

Ballew's talk covers the work of moving a renderer that shipped DreamWorks features into a project that outside contributors can build on.

The two-day structure splits a main program and a SIGGRAPH Birds of a Feather day

The program runs across two distinct days:

• July 19: Main Program. The full slate of talks at the J.W. Marriott L.A. Live, anchored by the MoonRay keynote.

• July 20: Birds of a Feather sessions. A full day of open source working sessions held within SIGGRAPH, the computer graphics conference.

Co-locating with SIGGRAPH keeps the working sessions next to the broader graphics research community. We previously covered the ASWF open source track at NAB, where engineers and tech leads work through the same open standards in a trade-show setting.

Talks span OpenPBR, OpenUSD, MaterialX, color standards, and studio-safe AI

Beyond the keynote, the main program covers a range of open standards and production workflows:

• Blender on OpenPBR. A session on the OpenPBR shading model.

• ASC StEM3-VP assets joining DPEL. The American Society of Cinematographers' StEM3-VP virtual production evaluation assets are coming to DPEL.

• Color fidelity standards for 3D artwork. A talk on standards for color reproduction of 3D artwork, the same open color management territory ASWF expanded when ACES joined the foundation.

• Studio-safe AI open source workflows from Griptape by Foundry. Foundry's Griptape presents open source approaches to running AI inside studio pipelines, building on the Griptape AI agents Foundry brought into Nuke, Blender, and Maya.

• MaterialX BSDF networks in RenderMan XPU. A Pixar and Lucasfilm session on running MaterialX BSDF networks in RenderMan XPU.

• OpenUSD proposals from NVIDIA and Pixar. Proposed additions to OpenUSD from the two companies.

Registration is $50, free for ASWF member companies before July 1

Main program registration costs $50. Employees of ASWF member companies can register at no cost if they sign up before July 1, 2026. The Birds of a Feather day on July 20 sits within SIGGRAPH.

What Open Source Days signals for production technology

Open Source Days gives the studios and vendors building the open standards behind modern production a single venue to compare notes. The 2026 program puts DreamWorks, Pixar, Lucasfilm, NVIDIA, Foundry, and the Blender community in the same room around rendering, color, virtual production assets, and the open question of how to run AI safely inside studio pipelines.

For technical leads and pipeline engineers, the schedule doubles as a map of where the open source production stack is heading over the next year. The registration window for free member passes closes July 1, 2026.

Aximmetry showed an upcoming MCP server at NAB that lets any AI agent operate its virtual production software through natural language. The company is targeting a summer release alongside the next version of Aximmetry, and the install is a single configuration file that drops into any MCP-compatible agent client.

Once installed, the agent understands the Aximmetry control board. Operators can ask it to build a basic virtual production scene, move talent inside the scene, draw a virtual path, or change other control board settings without opening the panel. The agent also goes beyond the manual UI, generating components like a transition effect between two clips on request. The result is a standard Aximmetry project file, so any change the agent makes can be opened and refined the way an operator would edit it by hand.

How the MCP Server Works

The Model Context Protocol is the standard Anthropic introduced for letting AI agents call external software through a documented tool interface. Aximmetry's implementation ships as a server file that any MCP-compatible agent client can register. After that, the agent treats Aximmetry as a tool it knows how to drive.

The scope of what the agent can do mirrors the application itself. Anything an operator can adjust on the Aximmetry control board is exposed as an instruction the agent can issue. That includes:

• Scene creation. Building a basic virtual production from a prompt.

• Talent positioning. Moving subjects inside the scene.

• Virtual paths. Drawing motion paths through the volume.

• Component creation. Building elements like a transition effect between two source clips.

Because Aximmetry projects remain standard project files after the agent finishes, a producer can hand a chat-built scene to a virtual production operator for refinement, or use the agent's output as a starting point and finish manually.

MCP Adoption Spreads Across Production Tools

Aximmetry's server lands as the broader VFX and post toolchain begins exposing itself to AI agents through the same protocol. We previously covered Foundry's Griptape acquisition, which brings agent orchestration into Nuke and Nuke Stage. The pattern is consistent: vendors skip bolt-on AI features and expose their existing software interfaces to whatever agent the user already trusts.

For virtual production crews not yet running a dedicated technical director, an MCP server changes the practical entry point. A chat interface can stand up a workable scene before someone with deep Aximmetry experience does the polish pass. That fits the company's broader accessibility push, which previously brought Aximmetry Instant to NAB as a streamlined version of the platform aimed at users without prior virtual production experience.

Image Gen Lives Inside Aximmetry Today, 3D Generative Backgrounds Still Under Review

The MCP server is one piece of a longer roadmap. Aximmetry has already integrated 2D image generation into the platform: operators can ask the AI to generate a still or video for placement on a virtual screen, or have it format incoming data and render it inside the scene. A future build is expected to embed an agent inside Aximmetry itself, so users can interact conversationally without bouncing to an external client.

In the interview, Aximmetry said it is continuing to evaluate generative 3D systems for backgrounds and environments, including World Labs and NVIDIA's generative models, but has not integrated any yet. The company plans to add a 3D system once it identifies one capable of meeting its requirements.

The MCP server is targeted for release alongside Aximmetry's next major version this summer.

DreamWorks Animation's MoonRay renderer — the path-tracing engine behind every DreamWorks feature film since 2019 — has joined the Academy Software Foundation as an official hosted project. For VFX and animation professionals, this moves a battle-tested studio renderer into neutral, community-governed territory, opening the door to serious adoption well beyond DreamWorks' own walls.

MoonRay has been open source for a while. The meaningful shift here is the step up to ASWF hosting — the same governance model that underpins tools like OpenColorIO, OpenEXR, and MaterialX, all now standard fixtures across the industry's pipeline.

What's in the Vault: MoonRay has real production credentials — the renderer has a direct line to The Wild Robot, The Bad Guys 2, and every DreamWorks animated feature for the past six-plus years.

Under New Management (Sort Of)

The practical change is how MoonRay will be developed and governed going forward.

According to the announcement, MoonRay will now be maintained and further developed under ASWF with contributions from the global open source community. DreamWorks Animation — already an ASWF member — will continue providing ongoing support and dedicated engineering resources.

That last part matters. Rendering tools that lose their core maintainer team tend to stagnate quickly. Knowing DreamWorks remains actively involved while ASWF provides neutral stewardship addresses the biggest concern that comes with depending on a studio-originated tool.

For production teams evaluating external dependencies, ASWF hosting means:

• Open development processes with transparent roadmaps and community-driven priorities

• Vendor- and studio-neutral governance, reducing the risk of one organization's internal priorities derailing the tool

• Lower friction for contributions — pipeline engineers can upstream patches, tool vendors can target the renderer without negotiating direct relationships with DreamWorks

• Educational alignment — academic programs building curriculum around real production tools now have a clearly sanctioned, neutral home to point students to

What MoonRay Actually Is

MoonRay is a production path-tracing renderer — a CPU and GPU-capable system that handles both stylized and photorealistic output, with the full set of features production teams expect: AOVs, LPEs, distributed rendering, and a Hydra Render Delegate (hdMoonRay) that plugs into any DCC tool with USD Hydra support for interactive preview rendering.

That last point is particularly relevant for studios already invested in USD-based pipelines — Hydra compatibility means MoonRay can slot into existing DCC workflows without requiring custom integrations from scratch.

What separates MoonRay from other open source render options is simply where it came from: it was built inside a major Hollywood animation studio and refined over years of demanding production use, not assembled by committee or maintained primarily as a demo tool.

Where This Fits in Your Pipeline

The honest answer is: it depends heavily on your setup.

For large facilities and studios:

• MoonRay's multi-year feature film track record gives it credibility most open source renderers lack at this scale

• The realistic adoption path is probably a secondary renderer — test it on a single sequence, evaluate pipeline compatibility, then scale from there

• Studios that want to own their rendering stack without building from scratch now have a strong starting point, with an active core team still in place

For mid-sized studios and indie pipelines:

• No per-core licensing is a meaningful cost advantage for smaller operations

• Integration, however, is non-trivial — you'll need pipeline engineering time and rendering expertise to get meaningful results

• MoonRay is most likely to click immediately for teams already running Linux-heavy, open source stacks

For tool vendors and developers:

• ASWF hosting makes MoonRay a viable rendering backend to support without the dependency risk of a single-studio project

• The Hydra delegate path provides a clean integration surface for DCC and pipeline tools

For specifics on supported platforms, licensing terms, and documentation depth, the openmoonray.org project site is the authoritative starting point.

How to Train Your Renderer

The second part of the news may ultimately be more useful for practitioners than the press announcement itself.

DreamWorks Animation CTO Bill Ballew will keynote ASWF's Open Source Days in Los Angeles on July 19–20, with a talk titled "How to Train Your Renderer: MoonRay's Journey from DreamWorks' Dragons to the ASWF."

According to the announcement, Ballew will cover:

• The technical challenges of "breaking out of the studio" — decoupling a proprietary renderer from internal build systems and dependencies

• Strategy and lessons learned in building a sustainable open source community around a production tool

• Why ASWF's role is considered "vital" for MoonRay's path forward

For anyone dealing with rendering pipelines, tool development, or open source strategy in animation and VFX, this session has the potential to offer concrete, hard-won insight that a press release can't. It's also likely to reveal roadmap clarity and community-building plans that will determine how fast MoonRay picks up non-DreamWorks contributors.

Reading the Credits

The real measure of this announcement won't come from the press release — it'll come from job postings and show credits over the next few years.

ASWF hosting makes serious, broad adoption of MoonRay possible in a way that ad-hoc open sourcing does not. Whether that potential converts to actual adoption depends on how quickly the documentation matures, how actively DreamWorks and new contributors maintain the roadmap, and whether mid-sized facilities start testing it in production.

The tools to watch it happen are all public: ASWF project communications, the MoonRay GitHub repository, and Ballew's Open Source Days keynote in July. If you're in Los Angeles that week, it's worth the room.

Canon announced the EOS R6 V, the first full-frame body in its video-focused EOS V series. The camera pairs a 32.5MP sensor with 7K Open Gate capture, built-in active cooling, and a flat, rig-friendly body that drops the EVF and mechanical shutter in exchange for longer recording times and a lower price.

Key specs:

• 7K Open Gate (3:2) up to 30p in 12-bit RAW and MP4, plus 7K DCI 60p in RAW Light

• Uncropped 4K 120p for slow motion, with oversampled 4K Fine up to 60p from the full 7K readout

• Active cooling fan rated by Canon for roughly 120 minutes or more of continuous 7K 60p RAW Light, limited by power or card capacity rather than overheating

• Body-only price: USD $2,499, with shipping in late June 2026

A Video-First Body in the EOS Lineup

The EOS V series began with the smaller PowerShot V1 and EOS R50 V in 2025, aimed at creators stepping up from phones and entry-level cameras. The R6 V pushes that line into full-frame territory and positions itself below Canon's dedicated C80 6K full-frame cinema camera as a hybrid option for solo shooters, event videographers, wedding crews, and streamers who need cinema-style capture in a smaller package.

The design choices reflect that video focus. There is no electronic viewfinder. The shutter is electronic only. The body is flat with minimal protrusions, built around a large vari-angle rear LCD, an integrated zoom lever, an in-body tally lamp, and vertical shooting support with an optional vertical grip. The result is a camera that drops onto a gimbal or into a cage without fighting the rig.

7K Sensor, RAW Pipeline, and Internal Capture

The 32.5MP full-frame CMOS sensor reads out at roughly 6960 x 4640 for Open Gate work, with a latest-generation DIGIC-class processor handling the data. Internal recording covers 7K Open Gate up to 30p in 12-bit Cinema RAW Light and MP4, and 7K DCI (17:9) up to 60p in 12-bit RAW Light. Oversampled 4K DCI and UHD "Fine" record up to 60p from the full 7K readout without a crop, and uncropped 4K runs up to 120p for slow motion.

Canon Log 2 and Log 3 are both onboard, alongside Custom Picture controls and user LUT support. The camera includes dual base ISO in Log, around 800 and 6400 for RAW C-Log2/3, plus an HDR Movie Mode up to 4K DCI/UHD 60p, Slow & Fast Motion recording, and 4-channel audio.

For external work, the full-size HDMI Type-A output supports uncropped 7K 30p ProRes RAW or cropped ~4.3K 60p ProRes RAW with compatible Atomos recorders. Third-party coverage from CineD reports dual card slots: CFexpress Type B and SD UHS-II.

Active Cooling Built for Long Takes

The headline change for long-form shooters is the integrated cooling fan. Canon rates the camera for roughly 120 minutes or more of continuous 7K 60p RAW Light, or uncropped 4K DCI/UHD Fine 60p, with the limit being card capacity and battery rather than thermal cutoff. That puts the R6 V into territory typically reserved for boxier cinema bodies, while staying in a mirrorless form factor.

Stabilization is handled by a 5-axis in-body system rated up to 7.5 stops at center and around 7 stops at the periphery with compatible lenses, with coordinated control across IS-equipped optics. Autofocus has been tuned for video work, with smooth focus transitions and tracking aimed at handheld and gimbal use.

Stills Toolkit Keeps 40fps Burst but Loses Mechanical Shutter

Despite the video orientation, the R6 V keeps a hybrid stills toolkit. The electronic shutter runs up to roughly 40 fps with AF/AE tracking, with a buffer of around 330 JPEGs or 150 RAW frames at that speed. A pre-continuous shooting mode captures up to 20 frames in RAW, HEIF, or JPEG before the shutter is fully pressed.

The tradeoffs are real, though. Dropping the mechanical shutter means relying on the electronic shutter's rolling-shutter characteristics for fast action and flash sync, and the missing EVF takes bright outdoor stills work into rear-LCD-only territory.

RF 20-50mm Power Zoom and Accessories

Canon paired the camera with the RF 20-50mm F4 L IS USM PZ, the first full-frame L-series RF lens with a built-in Power Zoom. The lens offers a constant f/4 aperture, optical image stabilization, and motorized zoom designed for smooth ramps and remote control during video shoots.

The launch also includes a Tripod Grip HG-200TBR, the Wireless Remote Control BR-E2, and the Macro Lite Adapter Set AD-M1.

What This Means for Hybrid Shooters

At $2,499, the R6 V slots in as a lower-cost, video-optimized alternative to the EOS R6 Mark III, trading the EVF and mechanical shutter for 7K Open Gate, active cooling, rig-friendly ergonomics, and long-form capture rated past two hours. Canon already covers high-end full-frame video with the C80 in the Cinema EOS line; the R6 V offers a different bet, that solo operators and small crews want most of those capabilities in a mirrorless body they can hand-hold, mount on a gimbal, or drop into a cage without adapting a cinema chassis.

For working professionals, the practical question is workflow. The internal Cinema RAW Light pipeline, full-size HDMI for ProRes RAW out, and Canon Log 2/3 support put the R6 V into the same post pipeline as Canon's cinema cameras. With body shipping in late June 2026, the next signals to watch are real-world rolling-shutter performance on the electronic shutter, sustained-recording behavior in hot environments, and how the RF 20-50mm PZ holds up as the first full-frame Power Zoom L-series lens.