Developers are Shifting from DeFi to On-Chain AI
Recent months have seen a noticeable migration of blockchain developers from decentralized finance (DeFi) to AI-centric projects. Ram Kumar, a core contributor and co-founder at Openledger, attributes this shift to the fresh technical puzzles, sustainable business models, and cultural significance offered by on-chain artificial intelligence (AI). Kumar advises aspiring builders to approach AI as a research project aimed at solving open challenges.
Beyond the Initial Hype
The AI sector has captured significant mindshare in the tech world, with AI token prices surging and drawing attention away from DeFi. This shift signifies a deeper evolution within Web3, driven by fresh technical challenges and AI’s cultural relevance.
Kumar highlights that developers are drawn to on-chain AI for its innovative technical puzzles, sustainable business models, and increased cultural significance in the current era of AI. While DeFi remains a foundational pillar of Web3, it has faced challenges in terms of continued breakthroughs and thinning margins, leading developers to explore new opportunities in AI.
The Challenges of Mainstream Adoption
Despite the buzz surrounding AI and Web3, their fusion remains largely theoretical, with tangible implementations lagging behind expectations. Kumar identifies several key obstacles hindering mainstream adoption, including the prohibitive expense of running large models on-chain and the lack of high-quality data on public blockchains.
He remains optimistic about breakthrough solutions on the horizon, such as rollups and zero-knowledge coprocessors that will significantly reduce costs. However, he acknowledges that frictions like cumbersome user experiences and regulatory concerns are keeping most deployments in the lab for now.
Regulatory Challenges in the AI Space
Growing concerns about the societal impact of AI have led to the enactment of laws and regulations in various countries. Kumar views regulation as a reactive force that follows technology to ensure safety and fairness, but notes that it can sometimes have unintended consequences.
He advocates for lighter regulations to incentivize open-source, safety-critical AI models with evaluation suites, promoting transparency and allowing stakeholders to address concerns without compromising consumer protection.