Problem and Market

Market Pain Points: Today’s AI and gaming industries present several concrete problems that Play AI aims to solve:

Lack of High-Quality Decision Data: Advanced AI systems require enormous amounts of quality data, especially for decision-making tasks . However, sources of reliable, rich training data are drying up. A recent study predicts that all high-quality text data on the internet could be exhausted for AI training by 2026 . Similarly, getting quality data on complex decision processes (how humans make strategic choices) is difficult – such data is rarely open or structured for AI use. This scarcity hampers AI development, leading to biased or suboptimal models . Play AI addresses this by generating new high-quality decision datasets through gameplay and simulations, and by incentivizing users to contribute data, thus continuously expanding the pool of useful training data.

Closed AI Models & Siloed Development: The AI industry today is dominated by closed-source models and centralized services run by tech giants . Most AI tools are developed in isolation, and there’s “no way to plug two tools together” as noted in SingularityNET’s analysis . This means innovations by independent researchers often remain unused, and businesses without big budgets can’t easily get custom AI solutions . Users have little insight or control over how these AI systems work. This closed approach stifles collaboration and slows innovation. Play AI’s solution is to open up AI development: we create a decentralized marketplace for AI agents and data where anyone can contribute algorithms or training data and be rewarded. By recording AI learning data and model performance on blockchain, transparency and interoperability are ensured – different AI agents can share knowledge, and the community can verify how decisions are made. This democratizes AI, akin to how open-source software transformed the software industry .

No Transparent MCDM Data Layer: Multi-Criteria Decision Making (MCDM) refers to decisions that involve weighing various factors – something humans do in fields from finance to gaming. Currently, there is no shared, transparent data layer for such complex decision-making processes. Each company or researcher might collect their own decision data (for example, game strategies, user preferences, or business decision logs), but these are siloed and often proprietary. This leads to duplicated efforts and a lack of standardized, high-quality data for training AI on how to make complex decisions. Play AI introduces the first open MCDM data layer on blockchain – a globally accessible repository of decision data generated by AI agents and players. Every time an AI agent in our platform makes a decision (for instance, a poker move considering risk, reward, and bluffing criteria), the anonymized data of that decision can be stored on-chain. Over time this builds a rich library of strategies and outcomes. Because it’s on blockchain, it’s transparent and tamper-proof, allowing anyone (with appropriate permissions) to analyze or utilize this data. This is analogous to creating a “Wikipedia of AI decision data,” but one that is trustless and incentivized. The lack of such a layer is a gap in the market that Play AI fills, enabling better AI training and even AI explainability (since one can trace and examine decisions made by agents).

Market Size & Opportunity: Play AI sits at the intersection of several rapidly growing markets: artificial intelligence, blockchain gaming, and data economy. The global AI market is experiencing explosive growth – expected to reach $2.7 trillion by 2032 from $177 billion in 2023 (a ~36.8% CAGR) . On the other hand, the blockchain gaming and NFT sector has surged in recent years, with NFTs creating a $6+ billion market in 2021 and gaming accounting for $180+ billion annually . By combining AI with blockchain and NFTs, Play AI targets a unique sweet spot in this landscape. We foresee a massive opportunity in AI-driven gaming and simulations: millions of users who want smarter game AI and ownership of in-game assets. Moreover, businesses are increasingly interested in data-driven decision support – a market that can leverage the MCDM data our platform produces. Play AI’s approach also taps into the demand for AI transparency and fairness (spurred by upcoming regulations and consumer awareness). In short, the convergence of AI, gaming, and Web3 is creating a fertile ground for Play AI. If just a fraction of AI investments shift towards decentralized models, or a portion of gamers embrace AI-powered gameplay, Play AI could onboard a large user base. Our entry point (poker AI training) is just the beginning of capturing value in these multi-billion dollar arenas.

(Avoiding lengthy academia, we focus on the tangible market facts: AI is booming, and so are crypto gaming and data markets. Play AI is positioned to capitalize on both, by solving the specific pain points outlined above.)