Fringe Untapped Data

• Problem: Wearables, industrial monitors, and real time sensors flood legacy data pipelines, but these systems can’t sift through the noise to deliver precise, high impact insights. Protocols collecting and tokenizing Fringe Untapped Data (FUD) focus on why events happen, mining deeper signals such as intent, emotional cues, and localized conditions. Rather than depending on broad, internet only datasets, these FUD protocols create high fidelity data assets customized for fields like healthcare, robotics, finance, and energy. The real alpha is found beyond the internet, hidden in rare books~~,~~ specialized medical files, sensor outputs (eg, in flight diagnostics, orbital data) that AI has not yet consumed.
• Tailwinds
  • From General Bulk to Domain Precision — The idea that ‘bigger is better’ in AI is losing steam. Large internet-scraped datasets have led to breakthroughs, but they often lack specific insights needed for complex tasks, like rare disease diagnosis or specialized robotics. It’s no longer just about volume. The quality and relevance of data matter more for breakthroughs in these emerging, offline domains.
  • Sensorfication of Everything — From smartwatches to industrial sensors, devices are continuously generating real time data. However, legacy systems are ill equipped to authenticate or interpret effectively.
  • Decentralized Coordination — Massive networks (e.g., Facebook, Uber) excel at global scale but struggle with local nuance. Decentralized systems bridge these gaps, allowing context rich signals to flow while preserving trust and transparency.
• Example Companies (Existing and Potential): PuffPaw, PrismaX, Skymapper, ScreenPipe, Peregrine, Palette Labs
• Further Reading:
  • The Use of AI in the Treatment of Rare Diseases
  • Improving Scaling Laws of Synthetic Data with Deliberate Practice
  • The AI Services Wave

Fleet Coherence

• Problem: As we build the aerospace internet, drones and satellites have evolved into active network nodes capable of autonomous decision making and real time coordination. Tasks like terrain mapping or cargo delivery require ultra fast, low latency connections. However, current centralized networks are prone to congestion and not optimized for machine to machine communication, limiting scalability.

  A key factor in coordinating large fleets of drones or robots is Fleet Coherence where each unit constantly updates others about its tasks, progress, and any obstacles. This coordination enhances efficiency but demands significant bandwidth, leading to unpredictable costs. Traditional subscription models can't accommodate these fluctuations economically, making pay as you go data models crucial.

• Tailwinds

  • Drone Adoption Surging — Drones are increasingly utilized in military, logistics, and commercial sectors. As fleets expand, effective Fleet Coherence becomes vital for managing intricate tasks in real time.
  • The Sky is Getting Crowded — The proliferation of drone swarms and satellite constellations is straining existing networks, highlighting the need for robust, machine centric communication solutions.
  • Security & Risk Mitigation is a Growing Market — There's a growing market for airspace monitoring, secure communications, and coordinated operations, creating opportunities for decentralized, cost effective infrastructures supporting real time Fleet Coherence.

• Example Companies (Existing and Potential): SkyGrid, Skydio, Spexi (Mapping), Raad (Mapping), Fleet Space, Enhanced Radar, Geodnet

• Further Reading

  • Energy Flow Optimizations at Harmonized Aerial Intersections
  • Key Takeaways for the Drone Industry

Bitcoin’s Strategy

• Problem: Bitcoin, while being the largest cryptocurrency by market value, offers limited core network features, restricting development on its platform. Existing second layer solutions often rely on trust heavy mechanisms, compromising Bitcoin's foundational immutability and deterring widespread adoption. As a result:

  • No True Programmability — Developers can’t easily create advanced apps (like lending or trading) on Bitcoin’s main network.
  • Trust Concerns — Early Bitcoin L2s haven’t matched Bitcoin’s strong security model, causing users to hesitate in moving large amounts of BTC from mainnet.
  • Missed Opportunity — Despite Bitcoin’s huge market cap, the base layer lacks robust programmability. This opens the door for a new ‘execution layer’ that can introduce scalable and secure features to Bitcoin.

• Tailwinds

  • Trust Minimization SoonTM — New approaches (e.g., BitVM proofs, advanced sidechains, bridging solutions) aim to bring complex contract capabilities to Bitcoin without undermining security. These innovations may also help sustain miner incentives once block rewards diminish.
  • Growing Institutional Adoption — Companies like MicroStrategy have adopted Bitcoin as a treasury reserve, holding 471,107 BTC—about 2.2% of the total supply. This level of commitment from large entities shows substantial capital is ready to move into secure, scalable Bitcoin solutions.
  • Governments Are Here — Abu Dhabi added IBIT shares, reserve bills in 20+ US states, and the Strategic Bitcoin Reserve highlight that governments are increasingly interested in BTC, creating opportunities for venture scalable service based businesses.

• Example Companies (Existing and Potential): Alpen Labs, Citrea, Starkware, Lava

• Further Reading:

  • Bitcoin L2s: A Modular Future

From NFT to NFA

• Problem: NFTs were supposed to enable inclusive ownership and verifiable authenticity in the digital age but we haven’t achieved this at scale yet. The market’s overemphasis on static JPEGs and speculative hype led to oversaturation and plummeting sales. Meanwhile, the proliferation of generative AI makes proof of ownership more critical than ever. Additionally, builders focusing on real world impact struggle to bridge physical assets with onchain governance, so NFTs have yet to realize their potential as both a digital and offline conduit for trust and value.