At first glance, AI and blockchain might seem like entirely different technologies. Blockchain focuses on decentralization but struggles with limited memory and throughput rates. In contrast, AI thrives on large datasets and requires high-performance computing. Machine learning models, especially deep learning networks, need vast amounts of data and powerful GPUs or specialized hardware to process this information efficiently.

According to the International Energy Agency (IEA), global electricity demand for AI is expected to reach 800 TWh by 2026, a 75% increase from 460 TWh in 2022. Similar forecasts from Morgan Stanley and Wells Fargo suggest that by 2030, AI-related energy consumption could account for 16% of the USA's current electricity demand.

Despite their differences, AI and blockchain are increasingly converging, with innovative projects emerging. For example, Ocean Protocol offers a decentralized data exchange, enabling AI to access data while maintaining privacy and security. ThoughtAI integrates AI and blockchain directly into data, eliminating traditional application layers and creating more adaptive AI solutions.

However, scalability remains a significant challenge for AI on blockchain. Traditional blockchain architectures need to overcome limitations in data availability and throughput. ZeroGravity (0G) is a platform addressing these issues. 0G is the world's first data availability system with a built-in general-purpose storage layer, offering high scalability and decentralization. It separates data availability into publishing and storage lanes, minimizing data transfer volume and enabling unprecedented data availability and transaction throughput.

0G's performance is impressive, achieving about 50 gigabytes per second compared to competitors like Celestia's 1.4 to 1.5 megabytes per second. Additionally, 0G is approximately 100 times cheaper than its closest competitors.

This performance opens up numerous AI/blockchain use cases previously deemed impractical. In finance, 0G's scalability could enable sophisticated AI-powered trading algorithms to operate directly on-chain. Large-scale federated learning systems on the blockchain could lead to breakthroughs in privacy-preserving AI, allowing multiple parties to train AI models collaboratively without sharing sensitive data. This has significant implications for fields like healthcare, where data privacy is crucial but collaborative research is essential.

The convergence of AI and blockchain represents a massive economic opportunity. The AI industry is projected to be worth $1.3 trillion by 2030, while the blockchain market is expected to reach $248.8 billion by 2029. Companies and platforms that successfully navigate this convergence, solving technical challenges and unlocking new value propositions, will be well-positioned to capture a significant share of this trillion-dollar opportunity.