Web3 and Generative AI: Unraveling the Unspoken Mismatch

• Generative AI and Web3: A Peculiar Intersection in the Digital Assets Space
• Challenges in Integrating Generative AI with Web3
• The Role of Autonomous Agents
• Exploring the Potential Integration between Generative AI and Web3
• Semi-Autonomous Agents: Bridging the Gap
• The Role of Semi-Autonomous Agents in Blockchain Integration
• Why Semi-Autonomous Agents?

Generative AI and Web3: A Peculiar Intersection in the Digital Assets Space

Generative artificial intelligence (AI) intersecting with Web3 is a captivating trend within the digital assets industry. Although many concur that generative AI will likely be a component of the next phase of Web3 technologies, the specifics are far from straightforward. After all, AI was never identified as a significant feature of Web3 architectures, and varying generations of L1s and L2s were not created to handle AI workloads.

Challenges in Integrating Generative AI with Web3

Web3 technologists face a significant mismatch in data and computation requirements when contemplating the incorporation of Web3 runtimes with generative AI technologies. Generative AI workloads, designed to be computationally intensive, function on highly parallelizable GPUs. In contrast, blockchain runtimes have limited data and computation capabilities. Nevertheless, Web3 is in dire need of integrating generative AI capabilities to compete with Web 2 alternatives. The critical question is: how will the integration of generative AI and Web3 transpire?

The Role of Autonomous Agents

Among various trends in generative AI, one seems fitting for blockchain integration and has garnered mainstream attention through recent announcements by OpenAI at its Developer Days conference: autonomous agents. It is essential to investigate two main points:

1. Web3 capabilities currently provide only marginal benefits to the existing wave of generative AI solutions.
2. Autonomous agents are the one trend in generative AI that can incorporate Web3 capabilities.

Publications often exaggerate how blockchain and generative AI technologies pair perfectly. While such declarations make for interesting headlines, they often lack in-depth technical understanding of both technologies' current state. Delving into the potential integration paths between Web3 and generative AI exposes a challenging landscape.

Exploring the Potential Integration between Generative AI and Web3

Considering the potential integration of generative AI and Web3, we must acknowledge two fundamental dimensions:

1. A new generation of Web3 technologies leveraging generative AI capabilities.
2. Generative AI solutions incorporating blockchain technologies.

The first dimension is somewhat puzzling. We can envision a new generation of DeFi protocols or blockchain runtimes incorporating intelligent capabilities powered by generative AI. However, these use cases are in their infancy or almost non-existent.

The second category offers a broader set of opportunities, yet it remains equally challenging. For instance, we are witnessing an increasing number of zero-knowledge or decentralized stacks for generative AI, which might appear mismatched with the current wave of generative AI stacks.

Semi-Autonomous Agents: Bridging the Gap

If you follow the generative AI space, you've likely encountered projects such as AutoGPT, BabyAGI, or the recently announced OpenAI GPTs, which are based on semi-autonomous agent capabilities. These intelligent models reason through abstract tasks, formulate, and execute plans in a given environment.

Semi-autonomous agents enhance foundation models with capabilities such as memory, tool integration, security guardrails, among others. These agents have evolved from an obscure research topic to one of the hottest trends in generative AI. More importantly, these semi-autonomous agents may be key to aligning generative AI and blockchains.

The Role of Semi-Autonomous Agents in Blockchain Integration

In the current state of semi-autonomous agent technologies, there are four dominant use cases that align well with blockchain runtimes:

1. Transparency: Agents' plans and decisions can be recorded on blockchain runtimes.
2. Decentralized Coordination: Semi-autonomous agents can cooperate to achieve specific goals, requiring decentralized coordination perfectly suited for blockchains.
3. Guardrails: Smart contracts can establish immutable guardrails around agents, ensuring the potential impact of those actions is within set boundaries.
4. Economic Incentives: Crypto assets can serve as a significant economic layer for semi-autonomous agents, providing them with a means to receive payment for their services.

Why Semi-Autonomous Agents?

Generative AI is evolving without the need for blockchain runtimes. Finding a common ground between these two technology trends is no easy task. The challenge is to identify genuine problems in generative AI that can be solved with blockchain technologies.

Semi-autonomous agents seem to have the right blend of technological and timing fit for blockchain runtimes. This trend is gaining acceptance as the next wave in generative AI

Edited by Jonathan Stoker