Why We Founded Vivix

Beyond the Feed: Toward Generative, Interactive Media

For over a decade, content on the internet has been chosen for users — ranked, filtered, and pushed by recommendation systems. We call it the feed. It is passive and constrained. It relies on selecting from what already exists, rather than creating in response to the user. While remarkably effective, it was never designed for continuous interaction or real-time co-creation.

We believe interactive generative AI will become a foundational content for the next era. Rather than passively browsing static outputs, users will engage in dynamic, fluid interaction with systems that can see, hear, and respond in real time across modalities, including text, audio, video, and motion.

This evolution demands a fundamental rethinking of how digital experiences are designed and delivered, with content co-created with the user in the moment rather than pre-authored. Creation will feel intuitive, continuous, and alive.

We want to build the foundation for this paradigm shift: an AI-native platform that doesn’t recommend content — it creates personalized, multimodal experiences through continuous, real-time interaction.

Real-Time Infrastructure and the Economics of Latency

Today’s state-of-the-art generative models, especially video models, are optimized for quality, not for efficiency or interactivity. They often take seconds or minutes to produce an output, making them impractical for real-time use. And the brute-force solution (scaling up GPU parallelism) is cost-prohibitive: inference can cost a few dollars per second. In the realm of interactive AI, latency isn’t just a performance metric — it defines what’s possible. Cost-efficiency isn’t just an optimization — it determines what can scale. Responsiveness is the bedrock of imagination.

We took a different approach:

• A closed-loop architecture optimized for minimal latency and maximal throughput.
• ~230x cost efficiency over baseline approaches (as of April 2025), with a roadmap toward $0.000015/sec — bringing generative interaction into the realm of infrastructure-level marginality (like bandwidth and CDN costs for YouTube and TikTok).
• Sub-dollar-per-compute-hour inference costs (updated July 2025), achieved through tightly coupled model–infrastructure co-design.
• Real-time multimodal generation loop at 720p/24fps, combining perception and generation in a tight, closed interactive cycle.

We believe this is the inflection point: where real-time generative AI becomes economically viable for consumer applications at scale.

Native Multimodal Intelligence Beyond Language-Centered AI

Much of today’s work on LLM optimization — from retrieval-augmented generation (RAG) to chain-of-thought prompting, from test-time scaling to reinforcement learning for reasoning — reflects a deepening recognition of their structural limitations. Yet these methods largely work around the core constraint rather than resolving it. To go further, we must confront the foundations.

Current LLMs are built on a single training objective: next-token prediction. This is statistical mimicry, not understanding. It’s like an English speaker memorizing a Japanese song after thousands of listens — they might reproduce it fluently, yet remain oblivious to its meaning. The model completes forms, not thoughts.

The data these models consume is no less constraining. It’s human language, which is already a narrow-bandwidth encoding of human cognition, filtered by intent, culture, memory, and context. And the people whose words are written, stored, and sampled in training corpora represent a small, biased slice of humanity. We are training models not on reality but on a linguistic echo of it—filtered, compressed, and decontextualized.

This points to a deeper issue: even humans do not think through language alone. Our cognition is grounded in sight, sound, motion and memory — experiences shaped by our embodiment in the world. Thought emerges from interaction, not introspection. Current multimodal systems typically route everything — image, video and audio — through language. This introduces two structural problems:

• Information loss: Language discards visual rhythm, emotional tone, and spatial continuity.
• Cognitive bias: Language imposes its own priors, shaping how models interpret other modalities.

To build a more grounded, agentic intelligence, AI systems must move beyond token prediction over linguistic artifacts. They must perceive the world directly and synchronously across modalities, building native internal representations rather than translated ones. That means discarding the idea that language is the universal substrate of intelligence.

We’re building differently. At Vivix, we treat vision, audio, and motion as first-class citizens. We’ve developed a natively multimodal system — not stitched together from pretrained parts, but trained and designed to perceive and generate across modalities symmetrically. All modalities interact in a unified token space — not dominated by language, but shared across perception and generation.

Why Now & Who We Are

The timing is right, and the infrastructure is ready.

• On the user side, expectations have shifted. Generative AI is no longer a novelty — people seek agency, co-creation, and continuity. They want systems that are responsive, expressive, and personalized.
• On the technology side, the prevailing approach — stitching together LLMs, vision encoders, and diffusion models — has hit its limit. Too slow, too expensive and too language-dependent.

We believe the next chapter requires a clean break: a full-stack, real-time, and deeply multimodal approach. That’s the foundation we’re building.

Later this year, we’ll launch our first product: a live, consumer-facing experience built entirely on real-time, interactive generation.

We’re a small team (~20 people) of world-class researchers, engineers, designers, and builders. We move quickly, think rigorously, and care about long-term impact. We combine deep systems-level expertise with a sharp eye for product and user experience.

We’re fortunate to be supported by top-tier investors who share our conviction, led by IDG Capital and Sequoia Capital, from the first day of building.