The Web3 AI Tech Stack: Demystifying the Ecosystem

Clearing the Path Understanding the Intersection of Web3 and AI for Better Investment Decisions - David Attermann, M31 Capital

Understanding Web3’s growing AI ecosystem

In the fast-paced world of generative AI and blockchain technology, it’s easy to get lost in the hype and confusion surrounding the Web3 AI ecosystem. But fear not! In this article, we’ll break down the different layers of the tech stack, explain their functions, and explore the competitive landscapes within each layer. By the end, you’ll have a clearer understanding of how this exciting ecosystem works and what the future holds. So, grab your AI-powered virtual assistant and let’s dive in!

The Generative Powerhouse: Infrastructure Layer

At the heart of the Web3 AI tech stack lies the infrastructure layer, powered by large language models (LLMs) that run on high-performance GPUs. These LLMs handle three primary workloads: model training, fine-tuning, and inference. To facilitate these processes, we have three types of marketplaces:

1. General-Purpose GPU Marketplaces 🎛️

Think of these marketplaces as the Swiss Army knives of GPU computing power. They offer decentralized, crypto-incentivized platforms where users can access GPU resources for any type of application. These marketplaces excel in model inference, which is the most common workload for LLMs. Early leaders in this category include Akash and Render, but with the growing demand for permissionless, GPU-specific compute, we can expect new players to emerge. Key differentiators will be distribution and network effects.

General-Purpose GPU

2. ML-Specific GPU Marketplaces 🧠

Designed specifically for machine learning (ML) applications, these marketplaces cater to the needs of model training, fine-tuning, and inference. By overlaying ML-specific software, they offer more differentiation opportunities compared to general-purpose marketplaces. Bittensor leads the pack in this category, but many exciting projects are on the horizon.

3. GPU Aggregators 🌐

Just like valued-added resellers (VARs) in the Web2 world, GPU aggregators streamline GPU distribution by bringing together supply from general-purpose and ML-specific marketplaces. These protocols abstract away networking orchestration and offer comprehensive GPU solutions that can handle all three LLM workloads. Io.net is the pioneer in this category, and we can expect more competitors to enter the scene as the need for consolidated GPU distribution grows.

Unlocking the Potential: Middleware Layer

While the infrastructure layer powers the generative AI, the middleware layer acts as the bridge that connects the computing resources to on-chain smart contracts in a trust-minimized manner. This is where zero-knowledge proofs (ZKPs) come into play. ZKPs are cryptographic tools that allow one party to prove the truth of a statement without revealing any additional information. In our context, ZKPs can enable smart contracts to access LLMs in a decentralized manner. Let’s explore the components of this layer:

1. Zero-Knowledge Inference Verification 🔒

Decentralized marketplaces for ZKP verifiers provide an opportunity for parties to bid on verifying the accuracy of inference outputs produced by LLMs, while keeping the data and model parameters private. However, ZK for machine learning (zkML) is still in its early stages and needs to become more cost-effective and efficient to reach widespread adoption. Protocols like =nil=, Giza, and RISC Zero are actively leading the development of zkML on GitHub. Platforms like Blockless, which act as aggregation and abstraction layers, are well-positioned to thrive regardless of which ZKP providers come out on top.

ZK Inference

2. Developer Tooling & Application Hubs 🛠️

Web3 developers need the right tools, software development kits (SDKs), and services to build AI agents and AI-powered automated trading strategies. These protocols not only provide developer tooling but also serve as application hubs where users can directly access finished applications built on their platforms. Bittensor, with its 32 different “subnets” (AI applications), and Fetch.ai, offering a comprehensive platform for developing enterprise-grade AI agents, are currently leading the charge in this space.

Unleashing the Possibilities: Application Layer

At the top of the Web3 AI tech stack, we find user-interfacing applications that leverage the permissionless AI processing power enabled by the lower layers. This nascent market is still reliant on centralized infrastructure, but its potential is immense. Here are a few examples of use-cases that are currently being explored:

  • Smart contract auditing
  • Blockchain-specific chatbots
  • Metaverse gaming
  • Image generation
  • Trading and risk-management platforms

As the infrastructure continues to advance and ZKPs mature, we can expect next-gen AI applications to emerge with unprecedented functionality. It remains to be seen if early entrants will maintain their dominance or if new leaders will rise in the coming years.

AI Applications

The Bright Future of Web3 AI 🌐🧠

Now that we’ve demystified the Web3 AI tech stack, let’s talk about the investor outlook and what lies ahead. While the whole AI tech stack has immense potential, infrastructure and middleware protocols hold a better investment proposition due to the uncertainties surrounding the evolution of AI functionality over time. The demand for massive GPU power, efficient ZK technology, and developer tooling and services will continue to grow as Web3 AI applications become more integrated into our daily lives.

In summary, the Web3 AI ecosystem is a complex but exciting space, with each layer playing a vital role in enabling the next generation of generative AI. As the technology advances and the market matures, we can expect new players, innovative solutions, and unimaginable AI applications to emerge. So, buckle up and get ready for the AI-powered revolution!


Q&A: Exploring More About Web3 AI

Q1: Can you provide more examples of real-life use cases for Web3 AI applications?

Certainly! Apart from the ones mentioned earlier, Web3 AI applications can have a wide range of applications across various industries. Here are a few more examples:

  1. Personalized healthcare: AI-powered platforms that analyze individual health data and provide personalized treatment plans or recommendations.
  2. Autonomous vehicles: AI agents that can navigate and make decisions in real-time to ensure safe and efficient autonomous driving.
  3. Fraud detection: AI models that can analyze patterns and anomalies in transaction data to identify fraudulent activities in real-time.
  4. Content creation: AI-generated content, such as articles, videos, and music, that cater to individual preferences and tastes.

The possibilities are endless, and as the technology continues to evolve, we can expect more innovative and transformative use cases to emerge.

Q2: Are there any challenges or concerns surrounding the Web3 AI ecosystem?

Absolutely! While the Web3 AI ecosystem holds tremendous potential, there are a few challenges and concerns that need to be addressed:

  1. Privacy and data security: As AI applications become more ubiquitous, ensuring the privacy and protection of user data will be crucial. Companies and protocols will need to implement robust security measures and privacy-enhancing technologies to address these concerns.
  2. Ethical considerations: AI algorithms can amplify biases and unintended discriminatory outcomes if not carefully designed and trained. Developers and organizations need to prioritize ethical considerations and responsible AI practices to build fair and unbiased AI systems.
  3. Energy consumption: Large language models and AI computations can be energy-intensive. It’s essential for the ecosystem to explore energy-efficient solutions and sustainable computing practices to minimize the environmental impact.
  4. Regulatory and legal frameworks: As AI becomes more integrated into our lives, it’s important to establish clear regulations and legal frameworks to govern the use of AI technologies. This will ensure accountability, transparency, and responsible use of AI in society.

By addressing these challenges and concerns, the Web3 AI ecosystem can flourish while benefiting users and society as a whole.


The Web3 AI ecosystem is poised for exponential growth in the coming years, and it’s crucial to stay ahead of the curve. Here are some trends, analysis, and recommendations for investors and enthusiasts:

  1. GPU demand surge: With the increasing demand for AI compute and the rapid growth of Web3 AI applications, the demand for GPUs will continue to surge. Investing in GPU manufacturers, as well as protocols and marketplaces that offer GPU-related services, could be a wise move.

  2. Advancements in ZKP technology: Zero-knowledge proofs hold immense promise for enabling trust-minimized interactions in the Web3 AI ecosystem. Keep an eye on advancements in ZKP technology as it becomes cheaper, faster, and more practical. Protocols that leverage ZKPs effectively could gain a significant advantage.

  3. Developer tooling and services: Developers are the driving force behind the Web3 AI revolution. Investing in platforms and protocols that provide comprehensive developer tooling, services, and application hubs can be a lucrative strategy.

  4. Partnerships and collaborations: The Web3 AI ecosystem thrives on collaboration and partnership opportunities. Watch out for strategic partnerships between infrastructure, middleware, and application layer protocols, as they can unlock new synergies and drive innovation.

Remember, investing in the Web3 AI ecosystem requires careful research, due diligence, and a long-term perspective. As the technology evolves and new trends emerge, periodically reassess your investment strategy to capitalize on evolving opportunities.


References

  1. Akash
  2. Render
  3. Bittensor
  4. Io.net
  5. =nil=
  6. Giza
  7. RISC Zero
  8. Blockless
  9. Fetch.ai
  10. Smart Contracts
  11. GitHub
  12. GPU and LLM images are created by Miximages.com

That concludes our deep dive into the Web3 AI tech stack and the exciting possibilities it holds. We hope this article has shed some light on this complex ecosystem and provided valuable insights for investors and enthusiasts alike. Now, go forth, share this article with your fellow Web3 AI enthusiasts, and let’s continue to explore the unlimited potential of generative AI together!

✨🌐🧠 #Web3AI #GenerativeAI #Blockchain #TechStack

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