The Ethereum Foundation has launched a new distributed AI team led by Davide Crapis. In the case of Gilrosen, co-founder of the Blockchain Builders Fund, the move represents the shift in Ethereum from a neutral settlement to a more “opinional” layer-1.
Ethereum Foundation ventures into AI with new teams
The Ethereum Foundation recently launched a distributed artificial intelligence (AI) team led by Davide Crapis, deploying the Ethereum blockchain as the basic settlement and coordination layer for autonomous AI agents. The move reflects Ethereum’s ambition to play a central role in shaping the future of AI.
As part of that mission, the team will develop a fully distributed AI stack to ensure that the evolution of AI technology does not remain under the control of several dominant entities. By integrating AI with Ethereum’s distributed architecture, the team aims to unlock new possibilities for autonomous systems, such as over-chain decision-making and untrustworthy coordination between intelligent agents. The launch is widely seen as an important step towards democratizing AI development and embedding it into the spirit of Web3.
Ethereum’s entry into the AI space is expected to have a broad impact on the crypto industry, particularly for chains that focus on AI. Gilrosen, co-founder of the Blockchain Builders Fund, explained that the development is welcome and worth noting.
“The announcement of the AI team will shift Ethereum from a relatively neutral payment layer of Layer 2 and less performance critical Layer 1 applications, targeting specific sectors with infrastructure and targeting specific sectors to support them,” Rosen said.
The distributed AI team is expected to affect AI-focused Layer 2, indicating the emergence of basic layer functionality tailored to needs.
A large number of projects compete across the blockchain ecosystem, building decentralized, censor-resistant AI infrastructure and taking charge of the foundations of a transparent AI economy without centralized management. These efforts aim to ensure that the future of artificial intelligence is dominated by unauthorized innovations rather than gatekeeping by a few powerful entities.
While Ethereum faces technical limitations that could hinder competitiveness against new protocols, Rosen believes it is appropriate to act as a global verifiable and payments layer due to its widespread adoption and interoperability.
To date, the most successful AI blockchain projects focus on Web2 use cases, but agent infrastructure chains such as virtual and Sahara are said to have struggled to gain traction. Rosen has a limited impact on the relatively small market size of Web3 AI compared to Web2 AI. However, Ethereum is seen as a potential success.
“From a market perspective, Ethereum’s biggest value proposition here is to start as a layer of truth verifiability, a layer of truth that Vitalik (Vitalin) has long been promoting through Ethereum’s proof capabilities,” Rosen told Bitcoin.com News.
Technical challenges and future possibilities
Meanwhile, experts argue that if Ethereum succeeds in becoming the verifiable and payment layer of Web2’s blockchain, its meaning could be far-reaching. As Ethereum expands its basechain performance, it could potentially compete as an “long-tailed AI stack of open source and interoperable models.” This is important for nation-states that are aware of excessive reliance on high-tech giants such as Openai, Google, and Anthropic. In such a scenario, Ethereum could potentially act as an AI infrastructure stack in a market as large as its current total valuation.
“AI agents can be an immeasurable source of demand,” Rosen added.
Still, distributed AI teams face technical challenges. The two were identified by Cartesi’s solutions architect Carlo Fragni. He emphasized the importance of determinism.
“If we don’t quadrature determinism, there are no reproducible models or inference/classifications, making consensus difficult,” says Fragni.
In a written response to Bitcoin.com news, Fragni explained that training AI models requires large datasets and intensive calculations, making distributed storage and difficult to execute. In particular, large-scale language models (LLMS) exceed the capabilities of Ethereum and current Zero Knowledge (ZK) solutions, Fragni added. He also said rebuilding existing AI libraries from scratch is resource-intensive and slow, and is essential to leveraging existing frameworks.
Some experts speculate that if Ethereum succeeds in becoming a settlement and adjustment tier in the AI economy, the value of ETH could increase. Rosen believes that such transformations will ultimately position ETH as a priority settlement currency.
“If Ethereum becomes a layer of a reliable, near-real-time, digital world where agents can coordinate and trade, then Rosen concludes that it goes beyond the scenario where all humans use ETH for all trades.