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82% of all internet traffic in 2025 was video content.
Every gigabyte stored comes with a cost. Every piece of data delivered across the internet carries another. The companies controlling both, including AWS, Google Cloud, and Azure, have little incentive to change the system that profits from it.
As a result, the entire industry compromises.
Security operators compress surveillance footage until faces blur, while platforms lower resolution to cut storage costs. Medical systems often sacrifice diagnostic detail in exchange for smaller file sizes. Behind all of this sits the same underlying infrastructure.
Video codecs such as H.264 and H.265 have not fundamentally changed in nearly twenty years, and they still rely on fixed mathematical rules that treat every frame the same, regardless of what is actually happening in the image.
The problem is the architecture, comprising one provider and one pricing structure. Furthermore, pricing power sits with whoever controls the compute, and no single company in the supply chain has the incentive to build something different.
That is the problem Vidaio (Subnet 85) is building for.
Three forces make the problem structural. Storage costs rise linearly as content volume grows, meaning every additional hour of video permanently expands the infrastructure bill. Quality also degrades at the edges because users farther from data centers receive lower-quality streams, while pricing power remains concentrated with the companies that own the servers.
Clients have few viable alternatives, and with high switching costs and steep barriers to entry, every additional hour of stored video deepens the lock-in.
Vidaio is building an alternative. Storage and bandwidth represent the largest infrastructure expenses for any video platform, so reducing file size directly lowers both costs at once. When this improvement is applied at scale, the financial impact becomes significant, which is why Vidaio is targeting reductions of up to 80% in video file sizes.
AI Compression That Adapts to the Video
Vidaio is a decentralized video processing network built on Bittensor.
The project has core functions:
- AI-powered video upscaling
- AI-driven compression.
Both functions run through a distributed network of miners and validators, not a centralized server stack.
Vidaio Upscaling
The upscaling product converts low-resolution footage, including standard HD, degraded surveillance video, and archival content, to high resolution using deep learning. Traditional interpolation guesses at missing pixels, but Vidaio's models analyze patterns, textures, and edges to reconstruct accurate frames.
The output optimizes for perceptual quality: how footage looks to the human eye, not mathematical accuracy alone.
In a published benchmark using Vidaio's base mining code against competitors at standard settings, Vidaio's upscaling model scored 0.4697 on ClipIQA+, outperforming Topaz Video AI, a professional-grade tool widely used by filmmakers and content creators, which scored 0.4658.
ClipIQA+ is a no-reference perceptual quality metric scoring how natural and visually accurate enhanced footage looks to a human viewer. Miners on the network are already improving on that base code, pushing scores higher.
Vidaio
Vidaio Compression
The compression product analyzes each video before encoding. The model adapts to static or dynamic scenes, motion density, and visual complexity. H.264 and H.265 apply uniform rules regardless of content. Vidaio's model treats every file differently.
To understand why the 80% target matters: H.264 reduces raw video by 75 to 80% compared to uncompressed footage, and H.265 achieves roughly 40 to 50% additional compression on top of that.
To understand why Vidaio’s 80% target is significant, it helps to look at the baseline. H.264 already reduces raw video by roughly 75 to 80% compared with uncompressed footage, while H.265 can deliver an additional 40 to 50% reduction on top of that.
Vidaio’s target is not measured against raw, uncompressed video. It is measured against the already-compressed files that platforms actually store and deliver today.
That makes the benchmark materially harder to achieve. The target is currently being tested, and early results already show measurable improvements over H.264 and H.265 when applied to the same source material.
Arushi Gupta
A media company storing 1 petabyte of already-compressed video could, if Vidaio hits its target, store the same content in 200 terabytes.
AWS Hires Engineers. It Cannot Build This.
Vidaio runs on Bittensor because Bittensor solves the core economic problem that centralized providers cannot.
In the traditional model, the provider owns the hardware, sets the price, and captures the margin. As demand increases, so does their leverage over customers. Bittensor changes that structure entirely by turning AI infrastructure into an open marketplace.
On Subnet 85, miners compete to produce the highest-quality video processing outputs, while validators evaluate those outputs using objective, open-source metrics. Synthetic tasks are assessed using VMAF, while organic jobs rely on metrics such as PieAPP and ClipIQA+. Emissions flow to the miners delivering the best results, which means performance is directly tied to economic rewards. As miners improve their models to earn more TAO, the network’s capabilities advance continuously without requiring a central operator to coordinate development.
AWS can hire engineers, build internal teams, and iterate on products, but it cannot replicate a permissionless marketplace that constantly discovers and rewards the world's best AI video models. Every improvement a miner releases is immediately tested against competing models across the network, and the outputs that persist are the ones the market values most.
That competitive feedback loop is what allows the system to evolve in real time, and it is something centralized providers cannot easily reproduce.
Hundreds of Billions of Infrastructure Waiting for a Better Solution
Surveillance and security are a $60 billion market.
Security footage is often stored at lower resolutions to keep storage costs manageable. The consequences can be significant. For example, faces could appear blurred in criminal investigations, critical details degrade in insurance disputes, and footage that was meant to provide answers ultimately fails to deliver them.
Vidaio’s upscaling technology allows operators to store highly compressed, lower-resolution footage while reconstructing high-quality frames when they are needed. This approach delivers immediate cost savings on storage, while preserving the long-term forensic and compliance value of the original footage.
Cloud storage and CDN providers together represent an addressable market exceeding $140 billion. Platforms such as AWS S3, Google Cloud Storage, and Azure generate substantial revenue from storing and delivering video at scale. A compression layer that significantly reduces storage requirements quickly moves beyond a product feature and into the realm of board-level procurement decisions.
Video streaming platforms represent a market exceeding $100 billion. Bandwidth, not storage, is the primary infrastructure expense. Lower file sizes mean lower delivery costs, better playback in low-connectivity environments, and improved quality at the edge. Any durable cost advantage compounds across hundreds of millions of hours of content.
Medical and industrial imaging represent a market valued at more than $40 billion in 2024, according to Grand View Research and IMARC Group. High-resolution diagnostic scans cannot afford to lose perceptual detail during compression, which makes efficient storage and transmission particularly challenging. Vidaio’s adaptive compression addresses this by analyzing scene complexity before encoding, allowing files to become significantly smaller while preserving the clinical detail required for diagnosis.
The AI-powered video analytics market was valued at $12.71 billion in 2024 and is projected to grow at an annual rate of 19.5%, according to Grand View Research. As AI-generated video scales, the volume of synthetic content requiring processing, storage, and delivery is expected to surpass anything produced by human creators.
The API Is Where the Subnet Becomes a Business
Vidaio's roadmap extends into live streaming, where real-time AI upscaling and compression can improve video quality while reducing bandwidth requirements. The platform is also developing adaptive bitrate streaming for playback across variable network conditions, along with a RESTful API that allows external platforms to integrate Vidaio’s processing directly into their existing video pipelines.
Today, Vidaio operates as a subnet, with jobs coming from synthetic workloads and early adopters. Emissions are distributed based on internal validator scoring, and the incentive mechanism largely reflects activity generated within the network itself.
The API changes that dynamic. A streaming platform integrating Vidaio’s compression API does not need to understand Bittensor, hold TAO, or run its own node. It simply submits a video processing job, the subnet handles the computation, and the platform receives the processed output. Behind the scenes, that organic workload is broken into tasks, distributed across miners, and evaluated by validators, allowing real-world demand to flow directly into the subnet’s emission calculations.
This is the point where Bittensor subnets transition from infrastructure experiments into revenue-generating products. As organic job volume grows, validators gain stronger incentives to participate. That demand increases staking activity, and staking pressure supports the value of the subnet’s alpha token. When real-world usage begins to anchor the incentive mechanism, the entire economic stack starts to reinforce itself.
For investors watching Subnet 85, the API launch marks a critical transition point. It signals that external demand is beginning to connect with the subnet’s internal incentive structure, creating a feedback loop that is far more durable than either system operating on its own.
— Vidaio (@vidaio_) February 9, 2026
The Cloud Video Infrastructure Challenger
The incumbents in video infrastructure, including cloud providers, CDN operators, and centralized processing platforms, hold the traditional advantages: long-standing contracts, global distribution, and high switching costs that keep customers embedded in their ecosystems.
What they lack is a permissionless network of specialists continuously competing to build better AI video models, where every improvement is immediately tested against competing approaches in real time.
Vidaio is building that network on Bittensor. Its published upscaling benchmark already surpasses the performance of Topaz Video AI, and miners on the subnet are continuing to push beyond those initial results. If Vidaio’s compression targets are achieved at the scale outlined in its roadmap, the resulting efficiency gains could meaningfully reshape the storage economics of video platforms worldwide.
The incumbents are unlikely to lose ground to a competitor with more funding or larger infrastructure. The greater risk comes from a system designed to continuously produce better output.
Subnet 85 is building that system.
Disclaimer: This article is for informational purposes only and does not constitute financial, investment, or trading advice. The information provided should not be interpreted as an endorsement of any digital asset, security, or investment strategy. Readers should conduct their own research and consult with a licensed financial professional before making any investment decisions. The publisher and its contributors are not responsible for any losses that may arise from reliance on the information presented.
