Gensyn AI Network Reports Surging Transaction Volume, Nearing 300,000 Daily

London, UK – Gensyn AI, a prominent decentralized machine learning compute protocol, is reporting a significant surge in its network activity. Jeff Amico, a figure associated with Gensyn AI, announced on social media that the Gensyn network is "hitting ATHs [all-time highs] in transaction volume, now over 300k per day." Amico's tweet, which included the enthusiastic remark "Just getting started," signals robust growth for the platform.

".@gensynai network hitting ATHs in transaction volume, now over 300k per day. Just getting started," Jeff Amico stated in a recent tweet.

Gensyn AI, founded in 2020 by Ben Fielding and Harry Grieve, aims to democratize access to machine learning compute power. The company's protocol allows developers to train AI models by leveraging a global network of distributed computing resources, including idle GPUs from personal computers and data centers. This decentralized approach seeks to provide a cost-efficient and scalable alternative to traditional centralized cloud computing services.

The company secured $43 million in Series A funding in June 2023, led by a16z crypto, with participation from CoinFund, Canonical Crypto, Protocol Labs, and Eden Block. This investment was earmarked to accelerate the protocol's launch and expand its team of machine learning engineers. Gensyn's core innovation lies in its cryptographic verification network, which ensures the integrity of machine learning tasks completed across its decentralized network without relying on intermediaries.

While the specific details of the network's transaction metrics are not independently verified through official reports at this time, the reported volume underscores the increasing demand for decentralized AI compute solutions. The Gensyn testnet, which operates as a custom Ethereum Rollup dedicated to machine learning, tracks participation and coordinates remote execution and payments for AI tasks. This infrastructure is designed to handle the complex, state-dependent nature of deep learning computations efficiently.

The broader landscape of decentralized AI is gaining traction as a solution to the high computational costs and centralization concerns associated with frontier AI model training. Projects like Gensyn are positioned to capitalize on the growing need for accessible and verifiable compute resources, potentially enabling a more diverse and open ecosystem for AI development.