Solana-based decentralised infrastructure provider io.net, which allows users to rent out GPU power for money, replaced its CEO two days before the project’s token launch.
Ahmad Shadid, a co-founder of io.net, resigned immediately and was replaced by fellow co-founder and former COO Tory Green.
“While there have been allegations regarding my past, I want to emphasise that I am stepping down as CEO to allow io.net to move forward without distraction and to focus on its growth and success,” Shadid posted on X on June 9.
Shadid did not directly address these “allegations,” though critics believe he misled the community regarding the actual number of GPU chips io.net offers.
Additionally, on April 28, the network’s GPU metadata was attacked, temporarily reducing the number of active GPU connections from 600,000 to 10,000.
io.net Token Launch
The io.net token- IO – will launch on Binance’s Launchpool on June 11 at 12:00 am UTC. At launch, 95 million IO tokens will be released, with a maximum supply of 800 million tokens.
Shadid’s departure raised concerns that he might “dump” his IO coins at launch and “disappear.”
“[It’s] as shady as DePIN gets,” industry analyst badenglishtea told their 15,300 X followers.
Shadid refuted the accusation, asserting that his IO tokens are subject to a 4-year lockup and that no investor, adviser, or team member can sell their vested tokens until June 2025.
The former CEO also announced a personal contribution of one million IO tokens to the firm’s Internet of GPUs Foundation to “help grow the ecosystem.”
Shadid did not clarify whether he would remain involved with the io.net ecosystem.
Further leadership changes at io.net would be announced in the “coming days,” according to new CEO Green.
Green described the token launch as “ushers in a new phase of growth for the network.”
“We remain unwavering in our mission to build the world’s largest decentralised AI compute network and expect to focus heavily on acquiring and retaining suppliers and onboarding new customers.”
io.net has onboarded around 20,000 cluster-ready GPUs and serves AI inference and model training workloads for several AI-focused companies, Green explained.