Tag: digital production

  • Serhiy Tokarev Backs Zibra AI’s ZibraGDS: A Real-Time 3D Graphics Breakthrough

    Serhiy Tokarev Backs Zibra AI’s ZibraGDS: A Real-Time 3D Graphics Breakthrough

    Ukrainian technology company Zibra AI has unveiled ZibraGDS, a solution that could transform the way complex 3D graphics are created. The technology makes it possible to work with animated geometry directly in real time, whereas similar effects previously often required lengthy offline rendering.

    The development has already been recognized as a promising innovation in computer graphics: on July 21, Zibra AI presented ZibraGDS at ACM SIGGRAPH 2026 Real-Time Live!, an international program showcasing the latest solutions for working with digital imagery. This year, only eight projects were selected from hundreds of submissions.

    Zibra AI’s technological development is supported by Serhiy Tokarev, founder of Roosh and an investor in Zibra AI. He focuses on building products that shape a new infrastructure for artificial intelligence and digital production.

    ZibraGDS: How the Technology Accelerates Complex Graphics Workflows

    ZibraGDS is primarily designed for highly detailed animated 3D scenes. These include object destruction, water simulations, particles, complex visual effects, and other elements traditionally created through resource-intensive offline processes.

    The drawback of such approaches is the lengthy processing time. Even minor changes to a scene could require rerendering, slowing down teams in game development, film production, and other areas of digital production.

    According to Zibra AI, the new technology can:

    • Reduce the volume of animated data by up to 92%
    • Make rendering 5-10 times faster than traditional solutions
    • Handle scenes containing millions of polygons
    • Process complex geometry directly on the GPU

    According to Serhiy Tokarev, investing in technological solutions of this caliber plays a major role in advancing new fields, from AI to digital content creation tools.

    The Technology Behind ZibraGDS

    One of ZibraGDS’s key features is its approach to handling large volumes of geometric data. During compression, 3D objects are automatically divided into small structural units called meshlets.

    The system then decompresses the data directly on the GPU using amplification shaders and mesh shaders. This architecture makes it possible to process large data arrays in parallel and render complex scenes without a significant drop in performance.

    Zibra AI says the new development builds on its proprietary technology platform. In 2024, the company introduced ZibraVDB, a solution for running volumetric effects, including smoke, fire, and liquids, in real time within Unreal Engine.

    ZibraGDS builds on this work, shifting the focus from volumetric effects to the rapid processing of complex animated geometry.

    New Infrastructure Is Needed to Advance AI and Digital Content

    Serhiy Tokarev, founder of the Roosh investment group and an investor in Zibra AI, says the future of digital production will depend on technologies that make it possible to create and scale complex products faster:

    “The next generation of AI and digital content requires new infrastructure. We invest specifically in foundational technologies that define what the entire industry can achieve. ZibraGDS is another important step toward making high-quality digital content production faster, more accessible, and more scalable.”

    From Games to Film: How ZibraGDS Could Transform Production Workflows

    Zibra AI explains that the new technology emerged from a practical need faced by teams working with large-scale 3D scenes.

    “ZibraGDS solves a problem our team encountered in nearly every project. Animated geometry almost always meant offline rendering and hours of waiting. Now these scenes can run directly inside the engine in real time. This changes not only the speed of work but also the way studios approach content production,” said Alex Petrenko, CEO of Zibra AI.

    Technologies of this kind could reshape how video games, films, virtual spaces, and interactive products are created. The ability to see results in real time allows teams to test solutions faster, shorten production cycles, and use resources more efficiently.

    Zibra AI’s development is part of a broader technology focus supported by Serhiy Tokarev in his role as an investor. These projects focus on creating solutions capable of competing in the global market and setting new standards for digital production.

  • AI’s Quiet Cost Revolution: How Digital Production Is Becoming Nearly Free

    AI’s Quiet Cost Revolution: How Digital Production Is Becoming Nearly Free

    There’s a version of the AI story that grabs headlines: frontier models, billion-parameter training runs, and the race between labs. But the version that matters to most businesses is far more consequential — and far less flashy. It’s the story of specific, boring production costs collapsing toward zero, and of the workflows, budgets, and org charts built around those costs quietly becoming obsolete.

    This isn’t about intelligence. It’s about cost structure. And cost structure, historically, is what actually reorganizes industries.

    The Death of the Resolution Constraint

    For twenty years, image resolution dictated real decisions. A 600-pixel product photo couldn’t be used on a billboard, a hero banner, or a modern retina display. You either reshot the asset, licensed a new one, or accepted a visibly worse result. That constraint dictated photography budgets, campaign timelines, and asset management policy — and created a graveyard of unusable assets.

    Machine learning didn’t remove that constraint by making cameras better. It removed it by making missing information reconstructable. Modern neural upscaling doesn’t just stretch pixels; it infers what detail would have been there, based on training on millions of high-resolution originals. The result: a low-resolution asset from a decade ago can be brought to usable modern size without a reshoot or compromise.

    The economic consequence is not that images look nicer. It’s that an entire category of work stopped needing to happen. Every archived asset a company owns just became potentially reusable. The reshoot budget line item became optional. And the capability has already commoditized — free browser-based implementations put it in anyone’s hands.

    The Death of the Build Constraint

    The same compression is happening to software production. Building a native mobile app once required platform-specific engineers, build pipelines, store submission expertise, and ongoing maintenance — a gate that filtered out all but well-funded organizations. That gate is now being dismantled from two directions: no-code platforms abstracted away the mechanics, and generative models absorbed parts that required judgment.

    The marginal cost of putting a functioning native app in front of customers has fallen roughly 100x in under a decade. Of course, the output doesn’t match a bespoke engineering team — but that’s the wrong comparison. The real counterfactual for most businesses was no app at all. When price drops 100x while quality drops only 20%, the market floods with new entrants who were previously priced out.

    The Pattern Beneath Both

    Strip away the specifics, and a four-step structure appears:

    1. A production capability was expensive because it required scarce human expertise.
    2. A model learned the statistical structure of that expertise well enough to approximate it.
    3. The capability became a commodity at near-zero marginal cost.
    4. The bottleneck moved upstream — into judgment.

    When image upscaling was hard, owning good source assets was a competitive advantage. Now upscaling is free, it’s not. When app development was expensive, having an app signaled seriousness. Now anyone can ship one in an afternoon, it signals nothing.

    What This Means in Practice

    Capabilities that become universal stop being advantages. If a tool is free and browser-based, your competitor has it too. ‘We use AI’ is not a strategy — it’s like saying ‘we use electricity.’ The window where AI adoption itself feels like a differentiator is closing fast.

    Value is captured downstream of the tools. The operator who identifies a category that nobody served because it cost $60,000 — and serves it for $200 — captures the value. That identification is genuinely hard work.

    Quality thresholds are contextual. A 20% quality degradation is catastrophic for a medical device, irrelevant for a neighborhood gym’s booking app. Most evaluations compare AI output against the best human alternative, but the real counterfactual is usually nothing at all.

    The compression is not finished. Every production task with large training data and a tolerant quality threshold is on the list. Copywriting went first. Design is going. Video is going now. Voice has largely gone.

    For businesses, the strategic move is not to adopt AI tools faster — everyone will have them soon. The move is to notice which constraint just disappeared, and to reorganize around a world where it’s gone, before the rest of the market updates its assumptions. The tools are already free. Working out what’s now possible that was impossible 18 months ago — and acting on it — is the part that still requires a human.