The Fed faces a conundrum where its policy has uneven effects. While high rates are restrictive for the mortgage market, they are perceived as cheap financing for tech giants. These companies see borrowing as a low-cost call option on the massive potential of AI, fueling a CapEx boom that monetary policy struggles to contain.

Higher oil prices have limited direct impact on data center electricity, as only 0.6% of US power comes from petroleum. The real threat is macroeconomic: oil-driven inflation may force the Fed to raise rates, making the massive debt for data center construction significantly more expensive.

The AI build-out increases real interest rates by demanding vast amounts of capital, crowding out other investments. Simultaneously, it pushes up nominal rates by creating inflationary pressure on physical resources like labor, energy, and materials needed for data centers.

AI data center investments are viable because long-term leases cover initial costs, leaving developers with a valuable future asset. Rising rates increase lease costs and decrease the present value of that future asset, threatening the financial viability of the entire AI infrastructure buildout.

Despite rising borrowing costs from Fed rate hikes, AI infrastructure companies can maintain profitability. The intense, inelastic demand for their services allows them to command double their previous revenue per megawatt, offsetting increased interest expenses.

The AI industry's rapid expansion relies heavily on debt-funded data center construction. The Federal Reserve's recent interest rate hikes make this debt more expensive, potentially slowing down the physical hardware buildout that underpins all AI progress. This connects macroeconomic policy directly to the feasibility of AI's growth.

The macro trend of rising bond yields creates a specific, acute risk for the AI sector. Many AI startups are funded by floating-rate private credit, and their debt service costs will explode as rates rise. This is compounded by high CapEx and an inability to scale revenues proportionally, creating a potential crash.

The biggest risk to capital-intensive AI ventures isn't a lack of demand but losing access to cheap financing. The current boom is built on borrowing long-dated money at low rates (e.g., 6%). A shift to a higher yield environment (8-10%) would make funding massive, negative cash-flow projects untenable.

Tech giants are no longer funding AI capital expenditures solely with their massive free cash flow. They are increasingly turning to debt issuance, which fundamentally alters their risk profile. This introduces default risk and requires a repricing of their credit spreads and equity valuations.