Web3 and Generative AI: Exploring the Possibilities
Web3 architectures have not traditionally been built for artificial intelligence (AI), but according to Jesus Rodriguez, CEO of IntoTheBlock, they could be. The emergence of generative AI has had a transformative impact on technology, with applications ranging from conversational wallets to language exploration. The Web3 community has been speculating about the potential intersection of generative AI and Web3, and there are several key points to consider:
1. Open-source momentum vs. centralized control: Open-source alternatives to API-based tech like GPT-4 have been gaining traction, reducing the gap between API-based and open-source foundation models. Concerns about centralized control of foundation models have also led to the exploration of Web3 architectures.
2. Emergence of open-source foundation models: The release of Stable Diffusion and the leak of Meta AI Research’s LLaMA model have sparked an open-source innovation movement. Models like Alpaca, Dolly, and Koala have rapidly closed the gap with commercial alternatives in terms of quality.
3. Transparency and control risks: The lack of transparency and centralized control of foundation models is a significant concern. Web3 architectures, with their decentralized nature, offer potential solutions to achieve accountability, transparency, and interpretability in generative AI.
4. Building a generative AI foundation in Web3: Web3 platforms can adopt generative AI capabilities in two ways. They can incorporate generative AI into existing platforms as a consumer of AI capabilities, or they can construct new platforms designed with generative AI as a foundational component.
5. The need for a new blockchain: The complexity of running foundation models and the architectural mismatch with blockchain runtimes suggest the need for a new type of architecture to effectively support generative AI. A specialized blockchain for generative AI could enable core capabilities such as running nodes that execute foundation models, executing pretraining and fine-tuning workflows, and enabling transparency and interpretability.
Ignoring the potential of generative AI in Web3 comes with risks. The technological gap between Web2 and Web3 architectures will continue to widen without native generative AI capabilities in Web3. Furthermore, generative AI has the potential to transform the lower layers of the blockchain stack, making it essential to explore new architectures that can incorporate these changes.
In conclusion, Web3 architectures have the potential to embrace generative AI and address its challenges. The combination of open-source innovation, concerns about centralized control, and the unique properties of blockchain architectures create a window of opportunity for Web3 to incorporate generative AI capabilities and drive transformative changes in the industry.
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