The demand for high-quality video processing has never been greater. From streaming platforms and content creators to media production companies and AI-powered applications, the world is producing (and consuming) video at unprecedented scale.
But behind the scenes, processing that video remains expensive, centralized, and difficult to scale efficiently: That is the problem Vidaio is solving.
Built on Bittensor Subnet 85, Vidaio is developing a decentralized infrastructure for AI-driven video enhancement. Its mission is straightforward yet ambitious: to democratize video processing through decentralized compute, artificial intelligence, and blockchain incentives.
Now, as the subnet matures and prepares for significantly larger workloads, the Vidaio team has announced a major update to its incentive model which is designed to strengthen competition among miners, reduce emission waste, and position the network for enterprise-scale demand.
Building a Decentralized Engine for Video Enhancement
Vidaio, Bittensor Subnet 85, is an open-source video processing subnet focused initially on AI-powered video upscaling, with plans to expand into compression and streaming.
Unlike traditional centralized platforms, Vidaio operates within the Bittensor ecosystem, a decentralized network where contributors provide computational resources and are rewarded based on the quality of their outputs.
This structure allows the subnet to tap into a distributed global pool of GPUs while maintaining a competitive environment that continuously improves the network’s AI models.
The approach delivers several key advantages:
a. Decentralized compute capacity sourced from global miners,
b. Lower costs compared to centralized providers,
c. Continuous model improvement through competition, and
d. Resilience against censorship and single points of failure.
Because the project is fully open-source and built around Bittensor’s merit-based incentive system, performance (not ownership) determines success within the network.
A Critical Stage in the Subnet’s Development
Video (Subnet 85) has now reached an important milestone, and the network is actively processing workloads while simultaneously building the infrastructure necessary to support far larger volumes of demand.
However, as the subnet grows, the Vidaio team has identified inefficiencies within the current incentive structure.
At present:
a. Emissions are distributed across approximately 120 miners,
b. Roughly 33–35% of emissions are burned, and
c. Real workloads (particularly those executed through the studio interface) are handled primarily by top-performing miners.
This has created an imbalance where many miners receive similar rewards despite substantial differences in output quality.
In fact, miners ranked roughly 13 through 100 often receive identical scores, limiting meaningful competition and spreading emissions more broadly than necessary.
To address this issue, the subnet is introducing a more performance-focused reward model.
A New Emission Model Focused on Performance
The updated incentive structure significantly increases the burn rate while concentrating rewards among the most effective miners.
The current model burns 33% of emissions generated and the remaining of it is distributed across roughly 120 miners.
However, under the updated model, the top 20 miners (which represent the primary reward pool) collectively receive 10% of emissions, with the rewards distributed based on performance.
The remaining 100 miners collectively receive 10% of emissions which are smaller rewards that provide feedback on proximity to the top tier.
Also, the remaining 80% of the emissions are burned.
This model dramatically reduces unnecessary emission distribution while sharpening competitive incentives across the network.
Why the Incentive Change Matters
The update addresses several structural challenges within the subnet while preparing it for the next phase of growth.
a. Stronger Competitive Differentiation
By concentrating rewards among top performers, the new model encourages miners to continually improve their models, infrastructure, and processing techniques.
Performance becomes the clear driver of rewards.
b. Improved Token Economics
Increasing the burn rate to 80% significantly reduces excess emissions, strengthening the subnet’s long-term economic sustainability.
This approach aligns reward distribution more closely with real network value.
c. Preparing for Enterprise Demand
Perhaps most importantly, the incentive adjustment prepares the subnet for much larger workloads and enterprise integrations expected in future phases of development.
Aligning incentives early ensures the network can scale efficiently as demand grows.
Expanding Toward Enterprise-Grade Infrastructure
As Vidaio explores partnerships and commercial integrations, the subnet’s architecture is also evolving. Enterprise clients often require capabilities that go beyond traditional decentralized execution environments, including:
a. Secure processing of sensitive data,
b. Predictable compute performance,
c. High-throughput for large workloads, and
d. Enterprise-level reliability standards.
To meet these requirements, Vidaio is introducing a hybrid infrastructure model.
The Hybrid Infrastructure Approach
Under this model, not all workloads will execute directly on the open subnet. Instead, the network will also leverage trusted execution environments (TEEs) operated by specialized infrastructure providers.
These providers include:
b. Chutes (Subnet 64), and
These TEEs offer several advantages for enterprise workloads:
a. Encrypted and secure data processing,
b. Predictable compute environments, and
c. Enterprise-compatible compliance and security standards.
Importantly, Vidaio itself is not building these environments, instead, it is integrating with infrastructure providers already operating within the broader ecosystem.
The Evolving Role of Miners
While execution environments evolve, miners remain central to the network’s long-term design. Their role will increasingly revolve around innovation and algorithm development, rather than simply processing workloads.
Miners will compete to produce the most effective solutions for tasks such as:
a. AI-driven video compression,
b. Video upscaling and enhancement,
c. Encoding pipelines,
d. Lip-syncing models,
e. Context-aware video transformations, and
d. Specialized AI video-processing techniques.
In this structure, the subnet becomes a competitive innovation layer where breakthroughs developed by miners can ultimately power production workloads.
Introducing Real Revenue Flows
Vidaio’s long-term economic vision goes beyond token emissions. The subnet is designed to eventually integrate real revenue generated by client workloads.
Under this model:
a. Infrastructure providers operate execution environments and cover operational costs,
b. Clients pay for video processing services, and
c. A portion of that value flows back into the subnet ecosystem.
This structure produces two key economic effects:
a. Offsetting miner emissions, and
b. Expanding reward pools tied to real demand.
From an investor perspective, the aim is to create a positive dynamic where incoming revenue from client usage exceeds token emissions distributed to miners.
As demand for video processing grows, the economic strength of the subnet grows with it.
A Future Driven by Competition and Innovation
Beyond the immediate incentive update, Vidaio is also preparing a broader evolution of its reward system. Future upgrades will introduce a competition-driven reward mechanism where incentives are tied directly to innovation and measurable improvements.
Miners will be rewarded for developing superior models, achieving measurable performance gains, and solving real-world client challenges
In this framework, the subnet becomes more than just a distributed compute network, it becomes an open marketplace for video AI innovation.
A Two-Stage Transition
The roadmap toward achieving this new model will unfold in two phases.
a. Stage One: Immediate Changes
This stage involves:
1. Updated emission distribution model,
2. Burn rate increase up to 80%, and
3. Stronger incentives for top-performing miners.
This phase improves efficiency while preparing the network for larger throughput.
b. Stage Two: Future Upgrade
Under this stage, the team would:
1. Introduce competition-driven rewards
2. Expand supported processing tasks
3. Look into deeper integration with enterprise execution environments
This stage will roll out gradually as partnerships and enterprise integrations mature.
Positioning Vidaio for the Next Era of Video AI
The evolution of Vidaio (Bittensor Subnet 85) reflects a broader shift happening across the decentralized AI ecosystem. Early experimentation is giving way to production-grade infrastructure, systems designed not just for research but for real-world deployment.
By tightening incentives, reducing emission waste, and preparing its architecture for enterprise-scale workloads, Vidaio is positioning itself as a high-performance decentralized engine for video AI.
If steady on this “progress pace,” the subnet could demonstrate how decentralized networks can compete with, and potentially outperform, centralized video processing platforms.
In a world where video continues to dominate digital communication, the race to build scalable, open infrastructure for video AI may only be beginning.
Be the first to comment