This episode reveals how a BitTensor subnet is building a real-world, revenue-generating business by targeting the sports analytics market, proving the tangible economic power of decentralized AI.
Introduction: The Optic Nerve of AI
- Host Mark Jeffrey welcomes Max Sebi of Score Computer Vision (Subnet 44), a project focused on solving computer vision challenges.
- Mark highlights Score's unique go-to-market strategy: instead of tackling the broad, abstract challenge of computer vision, they are "crossing the chasm" by focusing on a specific, revenue-generating niche in sports intelligence.
The Core Technology: A Three-Step Process for Video Analysis
- Max outlines Score's three-step technical process for analyzing video footage, a workflow applicable to any industry leveraging video data.
- Step 1: Annotation (Tracking): This is the foundational layer. Max defines annotation as the process of assigning specific coordinates to objects within a video. For example, tracking a player's movement from one side of the field to the other or pinpointing the exact location of the ball frame by frame.
- Step 2: Event Understanding: After tracking, neural networks are used to interpret the tracked data and identify significant events, such as a player scoring a goal or sustaining an injury. This moves beyond simple coordinates to contextual understanding.
- Step 3: Predictive Inference: By analyzing historical annotated data, the system learns to predict future frames in real-time. Max explains, "Every time an object is moving frame by frame, you can deduct what's what's likely to happen." This capability is critical for applications in sports, robotics, and autonomous vehicles.
The Business Case: Scouting, Hedge Funds, and New Data Sets
- Max details Score's primary customer segments, explaining how the technology provides distinct value to each.
- Sports Teams: The main use case is scouting. The system automates the analysis of vast amounts of video, making player recruitment far more efficient. It allows teams to score players, systems, and formations, providing a powerful management tool both for scouting and in-game analysis.
- Sports Hedge Funds & Betting Syndicates: These clients are seeking a competitive edge. Score provides this by creating entirely new, proprietary data sets derived from its deep video analysis. The ability to process real-time data also opens up opportunities for sophisticated in-game betting strategies.
Democratizing Sports Analytics for the Long Tail
- Max presents a compelling analysis of the current market asymmetry, where only top-tier teams with massive budgets can afford advanced analytics, which are often manual and human-intensive.
- Top clubs spend between $1-3 million annually on scouting data. Max states that Score can provide a superior, automated solution at a fraction of the cost: "If you think about something like score, we would do it at like a hundredth of the price."
- This cost advantage is because Score is automating a human-centric process, not just competing with other automated solutions.
- Strategic Implication: Score's core thesis is to address the "long tail"—the vast market of smaller clubs, semi-pro teams, and even individuals who lack the resources for expensive computer vision tools. This strategy could be a blueprint for other subnets seeking product-market fit.
Revenue, Market Size, and Go-to-Market Focus
- While not disclosing specific figures, Max confirms Score is generating revenue. Their initial deal with a sports hedge fund involves a 20% share of the upside generated, with revenue to be demonstrated to the community via token buybacks.
- He clarifies their Total Addressable Market (TAM) is not the entire $600 billion football industry, but a more focused $1.5 billion market for sports analytics and data.
- Max emphasizes a disciplined approach, focusing on the sports market for the next six months to build a solid revenue foundation before exploring other industries like the broader data annotation market, where they could compete with giants like Scale AI.
Addressing Enterprise Concerns: Data Privacy and Centralization
- Mark raises a critical concern for enterprise adoption of BitTensor: the anonymity of miners who process the data.
- Max explains that Score proactively solved this by partnering with a key, trusted data provider, 6TV. This ensures all data is sourced cleanly and ethically, providing customers with confidence and sidestepping the "anonymous miner" problem.
- While this approach is less decentralized initially, it was a necessary strategic trade-off to build trust and secure enterprise partners.
The Economic Moat: Why Subnets Won't Abandon BitTensor
- Mark challenges Max on a common critique: what prevents a successful subnet from abandoning its token and miners to operate as a centralized company?
- Max provides a powerful, business-driven rebuttal, arguing it would be strategically foolish.
- Cost Savings: Score saves approximately $500,000 per month in compute and salary costs by leveraging the BitTensor network. Abandoning this would be financially crippling.
- Continuous Innovation: The 256 miners on the subnet are a global, decentralized R&D team constantly working to improve the models. Losing this competitive edge would leave the company vulnerable to disruption from centralized players like OpenAI.
- Max's perspective is grounded in pragmatism, not just ideology: "We would be crazy not to think about it this way because how could you like kickstart something like score literally save half a million a month in compute and you know salaries... and then just move away?"
Analyzing BitTensor's DTO System
- The conversation shifts to DTO (Dynamic TAO Allocation), BitTensor's mechanism where TAO is staked for subnet tokens, directing network rewards.
- Max offers a pragmatic view:
- Positive: The system is a powerful engine for attracting attention and liquidity, creating an exciting, competitive environment that draws in users from other ecosystems.
- Negative: It adds a significant layer of complexity to an already complex system, making it harder to explain BitTensor to newcomers.
- Mark adds that the DTO system cleverly drives value to the core TAO token and creates a level playing field, as the liquidity mechanism prevents insiders from having an unfair advantage.
Subnet Composability: A Collaborative Ecosystem
- Max reveals that Score actively uses other subnets, partnering with Macrocosmos (Subnet 13) for data and planning to share revenue back with them.
- He champions the idea of "subnet composability," where subnets collaborate and build on each other's services, creating an integrated and more powerful ecosystem.
- Actionable Insight: This trend of inter-subnet collaboration is a key indicator of the network's maturing health. Investors should watch for these partnerships as they create positive-sum dynamics that strengthen the entire BitTensor ecosystem.
Conclusion
- Score's journey illustrates a powerful blueprint for success on BitTensor: target a niche market, build a real business, and leverage the network's unique economic incentives for an unbeatable competitive advantage. For investors, this highlights the immense value in subnets that combine technical innovation with a clear, pragmatic go-to-market strategy.
