【Bloomberg Asia Centric Podcast】 China has long relied on massive infrastructure spending and an unstoppable export engine, leading to a record $1.2 trillion trade surplus last year. However, this investment-heavy strategy is testing its limits as global trading partners increasingly push back, making Beijing's transition toward a consumption-based economy more critical than ever. But how achievable is this transition, and how long will it take? Hao Hong, Chief Economist and Chief Investment Officer at Lotus Asset Management, joins John Lee on the Asia Centric podcast to weigh in. He also breaks down the current regime shift in raw materials, explaining why the global economy is entering a new commodity supercycle driven by Western supply chain investments, AI infrastructure demands and a decade of severe industry underinvestment. 长期以来，中国去年创纪录的人类历史上最大的1.2万亿美元贸易顺差。然而，随着全球贸易摩擦升温，之前以投资为重的战略正在考验其极限，向以消费为基础的经济的转型比以往任何时候都更加重要。但这种转型何时实现？ 我还讨论了当前原材料行业的模式转变，解释了为什么全球经济正在进入由供应链投重构、人工智能基础设施需求和十年严重的大宗商品原材料行业投资不足而驱动的大宗商品超级周期。 我还讨论了从一个“全球最受关注之一的经济学家”（彭博社主持人原话）到“一个成功的对冲基金经理”（彭博社主持人原话）的转变。

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 👇