The biggest innovation in AI won't come from new models, but from who owns the ones we build. When AI is decentralized, the value generated closes the loop: contributors → models → owners.

🚨 Cloudflare went down today, taking X and other major services offline, weeks after AWS caused widespread outages. Centralized infrastructure creates single points of failure. The next phase of AI isn’t just about access, it’s about who owns it and how resilient it is.

Decentralized AI is about to redefine the next web, because the future isn't just about access, it's about ownership. Web3 is moving beyond tokens. The real frontier is AI built on decentralized infrastructure. Centralized AI locks models, data, and value at the top.

Build once. Earn forever. 💸 The Action Model Marketplace is one of the world’s first AI marketplaces where creators get paid every time their automations run, by people or AI agents. Create workflows, publish them, and earn $LAM tokens with every use. This isn’t just another

Every token on the chain signals more than value, it signals participation. With $LAM you don't just hold a token - you earn it through training, automation and impact. That's how ownership becomes real.

The current AI ecosystem takes your data, your labor, and your value, and keeps it for themselves. Action Model flips that dynamic by letting anyone train, automate, or build - and earn $LAM for the intelligence they add. It's an AI network where value flows back to

We're live again diving into why DePIN is reshaping the entire tech landscape ⬇️ Who’s listening in? 4pm UTC We'll be joined by IOPn, JackGK, Jimi, wingbits and LazAI Network Trust us, this is one you’ll want to listen to ⚠️

Many people have asked me why Action Model is named the way it is. The best way to explain it is to start with what a Large Language Model actually is, and what it can’t do. It’s simple: LLMs generate text. LAMs take action. And that’s exactly why the name Action Model was

Most AI predicts; Action Model acts. It learns from real GUI actions - clicks, typing, navigation - taken by people in real workflows. That's how Large Action Models become truly capable: not by simulation, but by learning how humans actually get things done.