The "crowding out" threat if AI doesn't deliver productivity gains soon: "The AI complex can go to markets and pay what it takes to get the capital they need, because they expect—and investors believe—that money poured into AI capex will deliver enormous returns in the future. Firms in other parts of the economy can’t promise those same returns, and so they are relinquishing their investment capacity to all things AI...This could be a problem for the economy. Sure, AI execs believe that future returns will be massive, but they could be wrong. And if AI doesn’t deliver on its promise then we face a double blow to growth: because AI didn’t expand our productive capacity as hoped, and because other investments which might have didn’t take place." As I dissect in my report on "The AI Trade" (sageroadresearch.com/product…), skilled construction labor exemplifies the "crowding out" impact of AI: "The US construction industry will need ~349k net new workers in 2026 and 456k additional workers in 2027—on top of normal hiring—just to keep supply and demand in balance. It’s unlikely that balance is achieved. ~30% of construction workers in the US are foreign born and net migration was cut in half comparing 2024 to 2025. Meanwhile, ~20% of the construction workforce is over the age of 55. Workforce aging is even more problematic in the more skilled trades. For one, nearly half of US engineers are now 50 or older. The skilled construction labor shortage is particularly problematic for data centers. According to Uptime survey results, ~60% of data center firms report that labor supply is a top reason for project delays. Data center builders face two core challenges: a limited supply of skilled labor and a mismatch between where data centers are being constructed and where that labor lives. As Bernstein noted in a report last month: "The US MEP labor pool is smaller than the 1.8m headline labor force suggests…Not all MEP workers are qualified, geographically accessible or available. Data center work requires a specialized set of skills, so even after narrowing this pool to [non-residential] construction (790,000), the pool needs further refinement given the high training bar. There is a geographic mismatch between where the data center projects will be built versus where the labor is—only 30% of the MEP labor resides where 70% of the projects are located." Even beyond the productivity implications of AI "crowding out", there are also rate implications. To quote Apollo: "AI's rate-insensitive boom is triggering "Dutch disease" in the US economy by draining capital, power and labor from vulnerable sectors like housing and autos. Trapped between inflation driven by the AI boom and weakness in rate-sensitive industries, the Fed will have to keep interest rates higher for longer." FT link: ft.com/content/bf40e0fb-9542… Apollo link: apollo.com/wealth/insights-n…

I'll say it again for the AI risk experts in the back: We've already run a real life, large scale experiment on what happens when a superintelligence is unleashed on a narrow objective. Look no further than U.S. capital markets. Millions of investors, analysts, traders and algorithms, backed by trillions of dollars and wired together through prices, all chasing one goal: the highest risk-adjusted return on capital. And it is spectacularly good at its job. It funds the best ideas, punishes the worst ones, and turns new information into prices faster than any of us can react. No individual can out-think it. 99+ percent of professionals can't beat it. But look at what else it's done along the way: - Optimized the measure instead of the intent. Quarterly earnings became the scoreboard, so companies learned to manage the scoreboard. - Treated anything without a price as free. Pollution, addiction, obesity, depression, political fragmentation, fragile supply chains, the long-term health of the communities it operates in. - Found every gap in the rules, then paid lobbyists to help write the next set and pull up the ladder behind incumbents. - Remade the world around its objective. Companies, pension funds and our retirement accounts now answer to the S&P 500. - Made itself impossible to switch off, because everything else now depends on it. None of this required malice. Nobody sat in a room and chose these outcomes. It's simply what a very capable optimizer does when its objective is a narrow proxy for what we actually care about. And note that we never solved this alignment problem. We've managed it, imperfectly, with regulation, taxes, disclosure rules and the occasional crisis to show us where the guardrails were missing. Over time, the market has captured the instutions designed to constrain it. We've been at it the better part of a century, and the misalignment is the worst it's ever been. So the next time someone tells you AI is only dangerous if it "wants" something bad, or if its goals are mis-specified, point them at the S&P 500. It doesn't want anything. It just optimizes. That's the whole problem.

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