Basar AI

Never shilled a single method and never shilled a single subnet and im getting spammed by people asking me what subnets to buy and havent answered anyone. It takes 2 minutes for your lazy fuck to go see what subnets pumped that day and if you really were in the ecosystem you woudlve known what happened that day. Anyways next time before giving a stupid statement and accusing people of something do your work. Have a nice day

Incentive Model Update and Strategic Direction 1. Current Position of the Subnet Video Subnet 85 has reached an important stage in its development. The subnet is actively processing workloads and building the infrastructure required to support significantly larger-scale demand. As the project prepares for upcoming partnerships and enterprise-scale integrations, the team has been evaluating whether the current incentive structure properly reflects both the present stage of the network and the future direction of the subnet. The current design distributes emissions across approximately 120 miners, while burning roughly 35% of emissions. In practice, however: ● The studio interface, which handles the majority of current organic usage, selects only the top-performing miners. ● A large number of miners receive identical scores, with minimal differentiation between roughly rank 13 and rank 100. ● This reduces competitive pressure and results in rewards being distributed more broadly than necessary at the current stage of network scaling. As Video prepares to support larger client workloads, it becomes important to reduce unnecessary emissions while strengthening competitive performance among miners. 2. Immediate Incentive Model Adjustment The first stage of this transition is a refinement of the emission distribution model. Current Model ● 33% emissions burned ● Remaining emissions distributed across approximately 120 miners Proposed Model The updated model concentrates rewards on the most performant miners while significantly increasing the burn rate. Top 20 miners ● Receive 10% of emissions collectively (currently around 6%) ● These rewards are shared across the top miners and represent the primary reward pool. Remaining 100 miners ● Receive 10% of emissions collectively ● These rewards are intentionally small but provide visibility into how close miners are to reaching the top tier. Emission burn ● 80% of emissions burned This structure ensures: ● Stronger incentives for high-performance miners ● A clear competitive pathway into the top reward tier ● Significantly reduced emission waste 3. Why This Change Is Necessary This adjustment improves the subnet in several important ways. Stronger Competitive Differentiation Currently, many miners receive identical scores, which limits meaningful competition. Concentrating rewards among the highest-performing miners encourages continuous improvement in models and infrastructure. Healthier Token Economics By increasing the burn rate to 80%, the subnet significantly reduces unnecessary emission distribution while strengthening long-term token dynamics. Preparing for Future Demand The subnet is preparing to support much larger workloads and enterprise integrations. Aligning the incentive structure now ensures the network is ready to scale efficiently when those workloads arrive. 4. Strategic Direction of Video Subnet 85 Video Subnet 85 is evolving toward supporting large-scale commercial video workloads. The project is currently engaged in discussions around partnerships that require: ● scalable video processing ● reliable throughput ● enterprise-grade security ● predictable compute environments To support these requirements, the architecture of the subnet will evolve beyond the current structure. 5. Hybrid Infrastructure Model Rather than executing all workloads directly on the open subnet, Video will utilize trusted execution environments provided by third-party infrastructure providers. These providers include: ● Targon ● Chutes ● Basilica These environments allow: ● encrypted and secure data processing ● predictable compute performance ● enterprise-compatible execution environments Importantly, Video itself is not building these environments, but leveraging specialized providers within the broader ecosystem. 6. Role of Miners in the Future Network Miners will compete to develop the best-performing solutions for specific video processing tasks required by clients. These tasks may include: ● video compression ● video upscaling ● encoding ● lip-syncing ● context understanding ● specialized transformation tasks ● model-based video processing The subnet therefore becomes a competitive innovation layer, where miners continuously improve algorithms and models that can be deployed into production workflows. 7. Revenue Feedback Into the Subnet The long-term economic model introduces real revenue flowing into the subnet. While third-party providers operate the trusted execution environments and cover their own operational costs, revenue generated from client workloads will contribute back into the subnet ecosystem. This has two important effects: 1. Offset miner emissions 2. Increase reward pools for valuable tasks From an investor perspective, this model aims to create a positive economic dynamic where: capital inflows from client usage exceed token outflows from miner rewards. When demand for video processing grows, this dynamic strengthens the economic value of the subnet. 8. Future Competition-Driven Mechanism The longer-term evolution of the subnet will introduce a competition-driven reward environment. Rather than static emissions tied to miner ranking, the network will increasingly reward: ● innovation within key video-processing tasks ● measurable performance improvements ● solutions that meet real client needs Miners will compete to develop the most effective models and techniques within these domains. 9. Two-Stage Upgrade Path This transition will occur in two stages. Stage 1 - Immediate Change ● Adjustment of emission distribution ● Increased burn rate to 80% ● Concentration of rewards among the top-performing miners This step is primarily an economic and efficiency improvement to reduce unnecessary emissions while the subnet prepares for larger throughput. Stage 2 - Future Upgrade ● Introduction of a competition-driven mechanism ● Expanded task categories ● Integration with enterprise execution environments This stage is currently under development and will roll out as client integrations and contracts mature. 10. Expected Outcome These changes aim to ensure that Video Subnet 85: ● becomes more competitive ● reduces wasted emissions ● strengthens token economics ● prepares for enterprise-scale workloads ● evolves into a high-performance innovation layer for video AI By aligning incentives with both current demand and future growth, the subnet is positioning itself for the next phase of its development.

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