AI has left the engineering org. 💊 A real playbook for bringing AI to every team, GTM, marketing, post-sales, and beyond, has started to take shape. Come hear from: > @vercel > @cursor_ai > @Chime + more Spots are limited and filling up fast! ↓

AI is chronically under-leveraged in Crypto atm I give it <24 months before every protocol has an AI feature embedded in-app I think the reason why adoption is slow along existing apps is because there needs to be a philosophy shift Ppl haven’t adjusted to the new norm yet

AI deployment doesn’t have to mean reinventing your infrastructure. AMD’s Suresh Andani explains how AMD Instinct MI350P PCIe GPUs combine open software, enterprise-ready AI infrastructure, and scalable inference performance to help organizations accelerate AI adoption within existing data centers. Same racks. Same cooling. Dramatically more AI. Watch the full video on YouTube:

AI infrastructure is scaling globally. Hyperscalers (Microsoft, Amazon, Google, Meta and others) are on track for roughly $765 billion in annual AI-related CapEx in 2026 alone, with cumulative AI data center capital expenditures projected to reach $5.2 trillion by 2030 in the base case (and up to $7.9 trillion in accelerated scenarios), according to McKinsey (March 2026). The global AI data center market itself is expected to grow from $147 billion in 2025 to $811 billion by 2033 at a CAGR of 23.9%, per Grand View Research. Meanwhile, global data center electricity consumption hit ~485 TWh in 2025 (up 17% YoY) and is projected to roughly double to ~950 TWh by 2030, with AI-focused facilities growing even faster (IEA, April 2026 report). The financial layer around it is still early. Despite these trillions in required capital, the entire tokenized Real World Assets (RWA) market (excluding stablecoins) stands at only ~$30–37.5 billion as of May 2026 — still tiny relative to the physical buildout and overwhelmingly dominated by traditional assets like Treasuries and private credit rather than AI compute, energy, or data centers ( and market reports, May 2026). As more compute, energy, and data infrastructure come online, new models for access, coordination, and capital formation may emerge on-chain. Rax Finance is exploring this direction by building a full-stack on-chain registry, metering, and settlement layer that tokenizes GPU capacity, data center power, and energy resources into verifiable, insured, yield-bearing RWAs — turning physical AI infrastructure into globally accessible, programmable assets. What are your thoughts on the future of AI infrastructure RWAs, Rax Fam? Would love to hear your ideas below 👇

AI agents are becoming economic actors. The real labor question is not adaptation, it is ownership. If data contributors cannot claim, track, and monetize their contribution, AI productivity becomes value extraction.