shift in how AI systems interact with physical infrastructure and real-world data. Unlike traditional AI development constrained by centralized data repositories controlled by large corporations, DePAI uses decentralized networks where ordinary users contribute to ML processes through everyday activities. Applications like Bittensor and Threefold demonstrate this potential, with Bittensor enabling decentralized AI model training and Threefold creating a sovereign digital identity system. Additionally, Morpheus incentivizes the first open-source peer-to-peer network for general- purpose AI, via its MOR token, and Gensyn offers an ethereum-based solution dedicated to ML, integrating off-chain execution, verification and communication frameworks. This democratization of AI training ensures models remain diverse and contextually relevant while compensating contributors through blockchain-based incentive systems. The middleware layer is rapidly maturing, with projects like IoTeX and Peaq providing critical components that bridge physical devices with blockchain networks, enabling integration and enhancing data processing capabilities and tokenomics design. As DePIN matures, it could reshape the future of omni computing by creating a more distributed computational fabric. The primary beneficiaries include AI computing infrastructure, with the top three revenue-generating DePIN projects (Aethir, Virtuals Protocol and IO.Net) all focused on providing decentralized computing resources for AI applications. Meanwhile, the wireless connectivity sector shows promising growth, with Helium Mobile surpassing 130,000 users and growing at 5.6% monthly. As the ecosystem evolves from token-incentive models to sustainable business models driven by genuine market demand, hybrid approaches are emerging, combining traditional infrastructure with decentralized components and reshaping how computational resources are built, accessed and governed in an increasingly connected world. 2.3 Engineering biology domain Engineering biology is transforming how biological systems integrate with physical and digital technologies, creating new capabilities across various sectors such as healthcare, manufacturing and environmental management. Advances in digital technology are further expanding the impact of engineering biology, reshaping its role in accelerating innovation. Notably, custom-built biosensors are improving biological data collection, while product stage biocomputing and commodity stage bioinformatics are advancing computational power for understanding biological systems. Custom-built bioprinting systems are innovating the construction of complex biological structures, and metabolic engineering is reprogramming cells to produce high-value materials. This layered maturity landscape creates the conditions for engineering biology for combinations and enables innovation. This convergence allows for integration with AI, omni compute, robotics and advanced materials. Technology Convergence Report 25

Technology Convergence Report 2025