Macro investor Raoul Pal expects AI, robotics and rapidly expanding computing infrastructure to drive a major economic transformation by 2029, potentially making intelligence and labor dramatically cheaper.
On Tuesday, in a post on X, Pal argued that people are underestimating how quickly the infrastructure needed to support AI will be built, despite current constraints involving electricity, data centers and advanced chips.
"Everyone is looking at the roadblocks to AI … and assuming this is all going to be slow," Pal wrote, predicting that much of the computing infrastructure would be built by 2027.
He expects electricity capacity, robotics and next-generation chips to become available at scale by 2028 and 2029, potentially transforming how businesses operate and produce goods.
"At that point the price of everything will collapse," Pal said.
He described this potential shift as "The Economic Singularity," defining it as a point when intelligence and labor become so cheap and abundant that traditional economic rules stop working.
Pal also argued that the transition could create a narrow investment window, calling it "the best opportunity most of us will ever get."
He pointed to The Everything Code as a roadmap for understanding liquidity flows, assets that could outpace currency debasement and the economic changes that may follow.
On Monday, CNBC’s Jim Cramer called the rebound in AI data center stocks "a good start" but said sustained gains and continued AI spending by Microsoft, Alphabet and Amazon were needed to restore investor confidence.
Last week, San Francisco Mayor Daniel Lurie signed a 45-day moratorium on data center development to assess the facilities’ impact on electricity costs, water resources and local communities, saying the city would prioritize "community needs, keeping electricity rates low, and protecting the clean water and air."
Elon Musk said demand for AI supercomputer data centers remained strong but warned that "getting money to build complex hardware systems with massive power, cooling & networking is not the same as doing it," highlighting the challenges of executing large-scale projects despite strong demand.
Disclaimer: This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors.
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