Background

The Problem

1) AI Blind Spot → AI and robotics lack spatiotemporal presence, reducing accuracy.

Most models train on static or incomplete datasets, missing spatiotemporal patterns. Without knowing when and where people exist, navigation, prediction, and context-awareness remain error-prone.

Source: nature.com

2) Data Unrewarded → People generate valuable presence data but rarely receive compensation.

Studies show users attach real economic value to their location data; people expect rewards or compensation if their location is used commercially. Currently, most platforms monetize this data without sharing value with those who generate it.

Source: toch.tau.ac.il

3) Privacy Risks → Location tracking is invasive, often enabling profiling and surveillance.

Apps and services often collect more location/time data than users expect; anonymization is weak or bypassed via correlation with external data. Users’ movement patterns become traceable or usable for profiling. Public concern, lawsuits, and regulatory pressure around geodata misuse are increasing.

Sources: themarkup.org, pmc.ncbi.nlm.nih.gov

4) Hardware Barriers → Sensor-heavy networks need costly hardware, slowing scale and adoption.

Several DePIN-type and sensor-based networks require cameras, specialized hardware, or deployment in physical locations, which limits geographic and socioeconomic reach. This restricts coverage, slows adoption, and excludes users who can’t deploy or access hardware.

Sources: epjdatascience, pmc.ncbi.nlm.nih.gov

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The Opportunity

1) Rising Demand → AI & robotics need richer time-location data to improve predictions and safety.

The location intelligence market is growing fast, projected CAGR ~14.7% in North America through 2030, driven by demand for real-time analytics, IoT, and AI/ML applications.

Source: grandviewresearch.com

2) Incentive Innovation → Decentralized data platforms are designing reward systems to compensate data contributors.

Research on “FedToken” and similar tokenized incentive schemes shows viable models for fairly valuing contributions in federated data training. Such approaches reduce friction for participation and increase data quality.

Source: arxiv.org

3) Infrastructure Build-Out → AI infrastructure (data centers, hyperscalers) is expanding rapidly, forcing investment in data pipelines.

Reports show that demand for AI-ready data center capacity may rise ~33% annually between 2023-2030; existing capacities are under pressure to scale. This creates openings for decentralized, efficient data layers like Presens.

Source: mckinsey.com

4) Market Growth → Businesses are increasingly using location intelligence, opening commercial applications.

Businesses in sectors like retail, logistics, smart cities are already spending more on location intelligence tools; software segments of the market dominate revenue share. The boost in IoT + edge computing further accelerates adoption.

Source: grandviewresearch.com

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Why Now

1) AI Boom → Global AI adoption & investment are accelerating faster than ever.

In 2024, 78% of organizations reported using AI in at least one function (vs ~55% a year earlier), and private AI investment hit $109.1B in the U.S. alone. This means there’s huge demand for better, more nuanced data now, not five years down the line.

Sources: hai.stanford.edu, ff.co, mckinsey.com

2) Privacy Regulation Surge → Laws tightening around geolocation & data tracking.

Regulators in the U.S. (California etc.) and globally are pushing new bills demanding stricter consent & limitations on collecting location/tracking data. This creates an environment where privacy-first, transparent data layers (like what Presens does) are not just nice to have but necessary.

Sources: ey.com, workplaceprivacyreport.com, bakerdonelson.com

3) Market Growth Curve → The AI/data infrastructure market is growing rapidly—scale & capability expanding.

The global AI market is projected to grow massively (~CAGR 25-35%) by 2030; this growth isn’t just hype, it’s pushing infrastructure, data, compute, and demand forward. With AI’s spread, tools & systems that provide robust spatiotemporal intelligence will be essential.

Sources: ff.co, mckinsey.com, procurri.com