Growing AI Ecosystem in Web3: Exploring Protocols, Integration, and Supply Chain

Published On: January 11, 2024753 words4 min readBy:

The Web3 landscape, with its promise of decentralized digital utopias, is rapidly expanding to embrace the emergent capabilities of artificial intelligence. As AI continues to make inroads into this technologically advanced space, we find ourselves amidst a burgeoning AI ecosystem that intertwines with the decentralization principles of blockchain technology. Nevertheless, amid this growth, there’s a palpable sense of bewilderment concerning the array of protocols and their various functionalities. This exposition aims to demystify these intricacies, offering an insightful exploration of the Web3 AI supply chain and highlighting key competitive landscapes you should monitor.

Within this sphere, one can’t understate the gravity of Generative AI, essentially driven by Large Language Models (LLMs) that function on the back of high-performance GPUs. The utility of these models spans across three core workloads: the creation of the model itself (training), its refinement for specific subjects (fine-tuning), and finally, its application (inference), which is particularly demanding. When dissecting this segment, we categorize it into generalized GPUs, ML-specific GPUs, and GPU aggregators—all with distinct capabilities and target use cases.

In the realm of general-purpose GPUs, the spotlight falls on crypto-incentivized marketplaces. Their computing aptitude is primed primarily for inference, the most frequently utilized LLM workload. Platforms such as Akash and Render have carved out early leadership positions, but it remains an open field as many new contenders join the fray. While compute resources may technically be a commodity, the rising demands of Web3 for permissionless, GPU-specific compute promises significant growth. Long-term success here may well pivot on strategic distribution and network effects, ensuring that users have optimal access to these decentralized computing powerhouses.

On the other hand, ML-specific GPUs are tailored towards specialized machine learning applications. They can tackle training, fine-tuning, and inference, setting them apart from their general-purpose counterparts. Protocols in this niche have the opportunity to enhance their offering through ML-specific software overlays. Projects like Bittensor have secured an early advantage, yet with more launches on the horizon, competition is set to intensify.

Drilling down further, GPU Aggregators emerge as pivotal players in the ecosystem. These entities consolidate GPU supply from both general and ML-specific categories, simplifying networking arrangements and adding a layer of crucial ML software. Their role in the market is analogous to that of Value-Added Resellers (VARs) in the Web2 domain. They deliver comprehensive GPU solutions for a full spectrum of LLM workloads. As Io.net pioneers this category, we anticipate a surge of competitors, given the escalating demand for more aggregated GPU solutions.

Underpinning the effective utilization of this raw AI processing power is critical middleware which bridges the GPUs and on-chain smart contracts. This is where zero-knowledge proofs (ZKPs) come into play. This cryptographic innovation enables a prover to validate the truth of a claim to a verifier without disclosing any additional information. In the context of Web3 AI, ZKPs ensure that LLM outputs are trustworthy while maintaining data privacy. Protocols like =nil;, Giza, and RISC Zero are in the vanguard of ZKP development, hoping to advance the technology to a stage where it is faster and more cost-effective.

Web3 developers require not only sophisticated computational resources but equally vital are developer tooling, SDKs, and services for building AI-driven applications. This sector is also home to platforms that double as hubs where users can access a plethora of applications developed in-house. Bittensor is a trailblazer in this domain, hosting numerous AI applications (or subnets), whereas platforms like Fetch.ai offer robust infrastructures for creating enterprise-level AI agents.

At the zenith of the tech stack reside user-centered applications that harness this evolved AI processing muscle to perform specific tasks. Current applications range from blockchain-specific chatbots to metaverse experiences and from image generation to analytical trading systems. However, the most groundbreaking applications of AI in Web3, enhanced by ZKPs and other developing tech, likely remain beyond our current conceptions.

Investor Perspective: The scope for investment within the AI web stack is promising, with infrastructure and middleware protocols presenting especially compelling opportunities given the inherent uncertainties associated with end-user applications.

As we consider the intersection of AI and blockchain technologies viable and vital for a Crypto education platform or for those seeking How to make a Crypto Investment blockchain, we at GIE Crypto remain dedicated to advancing both the knowledge and practical applications of these two transformative technologies. Whether you’re looking to Download digital wallet or find the best Crypto Exchange service United States has to offer, we are here to guide and inform your journey in this innovative convergence of Web3 and AI.

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