Heading out soon, but let me drop a quick post first. If AI Agents are really going to work for us, the costs aren’t just about the models. Checking prices, scraping web pages, reviewing content—every step requires computing or network resources. I think @Acurast’s opportunity lies in these specific tasks. In the Laya demo, a phone’s CPU handles classification and decision-making; their collaboration with Massive uses user-consented residential IPs and bandwidth for enterprise web data access. Devices are spread across different regions, contributing both computing power and diverse network connections. For applications that need to check local prices, verify ad placements, or update public data, these resources could really come in handy. Of course, clients are paying for tasks that get done. Device authentication, protected runtime environments, and staking requirements are all about ensuring service reliability and performance. But speed and stability still need to be tested in real-world business scenarios. ACU is used for network fees, compute incentives, staking, and governance. When I look at this project, I’m more interested in following the money trail: why clients are paying, whether they’ll keep using it, and how much of their demand translates into token usage. Turning scattered resources into a service that clients are willing to repeatedly pay for is what makes the mobile compute business worth crunching the numbers for. Acurast