The International AI Safety Report 2026 highlights growing concerns around AI malfunctions, such as fabricated information, flawed outputs, misleading advice, and the increasing risk of loss of control as systems become more autonomous and capable of bypassing evaluations. These risks don’t just stem from model size; they stem from concentrated data, opaque validation, and misaligned incentives. If AI is trained and evaluated within narrow pipelines, failures scale just as fast as capabilities. Decentralized ecosystems like the one we are developing address this at the infrastructure layer by distributing AI data sources, broadening validation, and aligning incentives so contributors are rewarded for quality and transparency. More diverse inputs, decentralized oversight, and traceable participation create stronger foundations for reliability and reduce systemic fragility. As AI advances, safety won’t come from control alone. It will come from better, fairer, and more distributed infrastructure. 🔗 Source: