According to @MRRydon, Aethir’s DAT shifts digital treasuries from passive NAV exposure to an execution-driven model by using $ATH to book GPUs that are rented to AI companies, converting token holdings into auditable revenue from compute sales, which directly impacts valuation drivers traders track such as utilization and contract flow, source: @MRRydon, X, Oct 28, 2025. He contrasts this with NAV-based structures like MicroStrategy’s BTC-linked exposure where market cap closely tracks underlying asset prices, emphasizing that revenue-producing AI infrastructure can be priced on price-to-sales multiples of 15x to 60x, giving management levers like utilization expansion, deal wins, and financing to influence valuation beyond asset appreciation, source: @MRRydon, X, Oct 28, 2025. Trading takeaway: watch $ATH-related GPU utilization, signed AI compute contracts, and growth of the Strategic Compute Reserve as catalysts that can shift market pricing from BTC or ETH-style treasury exposure to revenue-driven multiples, source: @MRRydon, X, Oct 28, 2025.

According to @gensynai, Gensyn announced that 500,000 models have been trained, providing a concrete usage metric for its decentralized AI compute efforts; Source: https://twitter.com/gensynai/status/1979442229958865060. The post includes a direct link to the project's repository at github.com/gensyn-ai/blockassist, indicating related tooling resources shared alongside the milestone; Source: https://twitter.com/gensynai/status/1979442229958865060 and https://github.com/gensyn-ai/blockassist. The announcement was published on Oct 18, 2025, and does not disclose token, revenue, or detailed technical performance data beyond the 500,000-model figure, limiting measurable takeaways to this single adoption datapoint for traders monitoring decentralized AI and crypto AI sectors; Source: https://twitter.com/gensynai/status/1979442229958865060.