Making Sense of Web3’s Burgeoning AI Ecosystem

In just over a year since ChatGPT’s debut release, generative AI has arguably become the most influential küresel narrative today. OpenAI’s early success drove a surge in investor interest for large language models (LLMs) and AI applications, attracting $25 billion in funding in 2023 (up 5x YoY!), in pursuit of the potential multi-trillion-dollar market opportunity.

As I’ve previously written, AI and crypto technologies complement each other well, so it’s not surprising to see a growing AI ecosystem emerging within Web3. Despite all the attention, I’ve noticed a lot of confusion about what these protocols do, what’s hype vs. real, and how they all fit together. This report will map out the Web3 AI supply chain, define each layer in the tech stack, and explore the various competitive landscapes. By the end you should have a basic understanding of how the ecosystem works and what to look out for next.

Web3’s AI tech stack

Infrastructure Layer

Generative AI is powered by LLMs, which run on high-performance GPUs. LLMs have three main workloads: training (model creation), fine-tuning (sector/topic specialization) and inference (running the model). I’ve segmented this layer into general-purpose GPU, ML-specific GPU, and GPU aggregators, which are characterized by their different workload capabilities and use-cases. These P2P marketplaces are crypto-incentivized to ensure secure decentralization,but it’s important to note the actual GPU processing occurs off-chain.

Middleware Layer

The previous layer enables permissionless access to GPUs, but middleware is needed to connect this computing resource to on-chain smart contracts in a trust-minimized manner (i.e., for use by Web3 applications). Enter zero-knowledge proofs (ZKPs), a cryptographic method by which one party (prover) can prove to another party (verifier) that a given statement is true, while avoiding conveying to the verifier any information beyond the fact of the statement’s truth. In our case, the “statement” is the LLMs output given specific input.

Application Layer

And finally, at the top of the tech stack, we have user-interfacing applications that leverage Web3’s permissionless AI processing power (enabled by the previous two layers) to complete specific tasks for a variety of use-cases. This portion of the market is still nascent, and still relies on centralized infrastructure, but early examples include smart contract auditing, blockchain-specific chatbots, metaverse gaming, image generation, and trading and risk-management platforms. As the underlying infrastructure continues to advance, and ZKPs mature, next-gen AI applications will emerge with functionality that’s difficult to imagine today. It’s unclear if early entrants will be able to keep up or if new leaders will emerge in 2024 and beyond.

Investor outlook: While I’m bullish on the whole AI tech stack, I believe infrastructure and middleware protocols are better investments today given the uncertainty in how AI functionality will evolve over time. However it does evolve, Web3 AI applications will no doubt require massive GPU power, ZKP technology, and developer tooling and services (i.e. infrastructure & middleware).

Disclosure: M31 Capital has positions in several tokens mentioned in this article.