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Beyond being an inflation hedge, infrastructure represents a key constraint on AI's growth. Investing in areas like power capacity and data compute allows investors to "own the constraint on AI," providing a diversified way to gain exposure to the dominant technology theme.
While AI chips represent the bulk of a data center's cost ($20-25M/MW), the remaining $10 million per megawatt for essentials like powered land, construction, and capital goods is where real bottlenecks lie. This 'picks and shovels' segment faces significant supply shortages and is considered a less speculative investment area with no bubble.
A safer way to play the AI boom is to invest in companies selling the underlying compute infrastructure rather than the hyperscalers buying it. This strategy captures the upside of the secular trend while avoiding direct exposure to how the massive capital expenditure is funded, which may involve risky credit.
Amid uncertainty about which AI applications will win, Blackstone's strategy is to invest in the essential infrastructure all AI companies need. This "picks and shovels" approach targets data centers and electricity, guaranteeing exposure to the boom without betting on specific, high-risk application companies.
The primary bottleneck for scaling AI over the next decade may be the difficulty of bringing gigawatt-scale power online to support data centers. Smart money is already focused on this challenge, which is more complex than silicon supply.
During the dot-com crash, application-layer companies like Pets.com went to zero, while infrastructure providers like Intel and Cisco survived. The lesson for AI investors is to focus on the underlying "picks and shovels"—compute, chips, and data centers—rather than consumer-facing apps that may become obsolete.
Instead of betting on which AI models or applications will win, Karmel Capital focuses on the infrastructure layer (neocloud companies). This "pick and shovel" strategy provides exposure to the entire ecosystem's growth with lower valuations and less risk, as infrastructure is essential regardless of who wins at the top layers.
Before AI delivers long-term deflationary productivity, it requires a massive, inflationary build-out of physical infrastructure. This makes sectors like utilities, pipelines, and energy infrastructure a timely hedge against inflation and a diversifier away from concentrated tech bets.
Concerned about AI's potential to displace white-collar jobs, Wilkinson views investing in the underlying infrastructure as a key strategy. He specifically invested in a Bitcoin mining company pivoting to AI data centers, effectively buying into the "toll bridge" of the future to protect his capital.
Rather than picking a winning AI or crypto, the smarter investment is in the 'picks and shovels.' This means focusing on the infrastructure every autonomous agent will require to transact—such as wallets, custody services, and blockchain rails—regardless of which specific application succeeds.
To capitalize on the AI boom while mitigating risk, investors should focus on 'enablers'—companies providing essential infrastructure like semiconductors, data centers, and cloud services. This 'picks and shovels' strategy avoids betting on specific application-level winners, which was a losing strategy for many dot-com investors.