Crypto is not just a ledger of transactions---it’s a ledger of truth and trust. That’s what makes Bitcoin valuable. For Bitcoin this requires burning energy on random number guessing. What if we could instead leverage massive, real-world computation to achieve the same level of security? This has been an outstanding open problem for more than 30 years in academia, and since the emergence of blockchains in industry. Last year, we proposed the first solution (eprint.iacr.org/2025/685). Our mathematical breakthrough suggests piggy-backing on matrix multiplications, the native operation of GPUs that power the AI revolution, from pre-training, post-training, to inference. The potential applications are endless: improving the unit-economics of LLMs, shifting AI-generated wealth back to users, and enabling new primitives such as settlement and even UBI systems for AI agents. Since then, we've worked hard turning the math into a fully operational system. From the algebra and CUDA kernels to a working L1 blockchain and a production LLM inference pipeline implementing this “2-for-1” technology. Today, we’re excited to share that the @prlnet is ready, and will soon enable serving SoTA LLMs while mining the blockchain at negligible additional cost. Along the way, we encountered many fascinating challenges. We’re now publishing them as a collaborative Polymath challenge, spanning open questions in math, systems and economics. If you’re interested, take a look and feel free to reach out: pearlpolymath.com. #PRL #AIMoney