Your electricity bill is about to become a lot more interesting—and possibly a lot more expensive. The reason? Tech companies are spending $600 billion this year building AI datacenters, and someone has to pay for all that power.

That someone might be you.

The Scale of AI Infrastructure Spending

Let's start with the numbers, because they're staggering. According to S&P Global Ratings, the top five U.S. hyperscalers—think Amazon, Microsoft, Google, Meta, and Nvidia—are projected to spend approximately $600 billion in capital expenditures in 2026. That's a 38% increase over 2025, which itself saw 68% growth.

This isn't normal business expansion. This is a full-blown infrastructure arms race, and it's being financed in ways that should make anyone who remembers 2008 a little nervous.

How They're Paying For It

Here's where things get interesting. These datacenters aren't just being funded by corporate cash flow. They're being financed through private credit and securitization—the same financial engineering that brought us mortgage-backed securities.

According to Moody's, structured finance for datacenters hit $9 billion through April 2025, split between commercial mortgage-backed securities (CMBS) and asset-backed securities (ABS). S&P reports that global datacenter securitization volumes tripled in 2025, jumping from $10 billion to $30 billion.

Sound familiar? It should. We've seen this movie before, where everyone assumes the underlying assets will keep appreciating and the cash flows will never stop. Until they do.

One Reddit user on r/investing connected the dots: "Reminds me of the tranches of mortgage-backed securities from the late 2000's." Bingo.

The Energy Problem

Now let's talk about what these datacenters actually need: power. Massive amounts of it.

About 43% of U.S. electricity generation comes from natural gas. As datacenters proliferate—many operating off-grid using their own natural gas turbines—demand for natural gas is surging. According to industry sources, gas turbine manufacturers are seeing months-long backlogs of orders, with many going to non-utility companies (read: datacenters).

When demand for natural gas goes up, so does the price. And when the price of natural gas goes up, so does your electricity bill—whether you're using AI or not.

Utilities are already projecting rate increases in areas with heavy datacenter construction. The logic is simple: the grid needs upgrades to handle the load, and ratepayers (that's you) foot the bill through higher rates.

The Private Credit Risk

Here's where the financial engineering gets risky. Private credit has been flooding into AI infrastructure, reaching nearly $146 billion in 2025. That sounds great until you realize that private credit investors are starting to flee the space due to concerns about returns and leverage.

S&P warns that U.S. maturities of low-rated debt will surge to $215 billion in 2028, creating massive refinancing pressure. If private credit dries up or demands higher returns, many of these datacenter projects could face serious financial stress.

Moody's specifically highlights the risk: "The performance of datacenter securitizations will be at risk if tenant demand for capacity falters." In other words, if the AI boom slows down or doesn't generate the revenue everyone expects, the companies financing these datacenters through asset-backed securities could be in trouble.

What Happens If the AI Boom Stalls?

Let's game this out. Tech companies have bet everything on AI being the next massive computing platform. They're building datacenters at breakneck speed, financed with leverage, based on projections of future demand.

But what if that demand doesn't materialize? What if AI doesn't generate the revenue to justify $600 billion in annual spending? What if consumers and businesses decide they don't actually need generative AI features crammed into every product?

Then you're left with:

• Stranded assets: Datacenters that cost billions to build but can't find tenants • Financial stress: Securitized debt backed by assets that aren't generating expected cash flows • Energy costs: Utilities that upgraded infrastructure for demand that never came—but still need to recoup those costs from someone (hint: you)

The Hyperscalers Will Be Fine

Here's the thing: companies like Microsoft and Google have deep pockets. They can absorb higher energy costs and even write off failed infrastructure projects. They'll be fine.

But the smaller datacenter operators financing through private credit and securitization? They're the ones at risk. And when those deals go bad, it's the lenders and investors who take the hit—which could ripple through credit markets.

Meanwhile, utilities that spent billions upgrading the grid to accommodate AI demand will still need to recover those costs. And they'll do it through rate increases that hit everyone, whether you use ChatGPT or not.

What This Means For You

If you live in an area with heavy datacenter construction—Virginia, Texas, Ohio, parts of the Pacific Northwest—expect your electricity bills to climb. It's already happening, and it's going to get worse.

If you're invested in the AI boom through tech stocks, understand that a lot of this infrastructure spending is being financed with debt. That's fine when times are good, but it becomes a problem if the AI revenue story doesn't pan out.

And if you're a taxpayer, there's a non-zero chance that when some of these datacenter projects fail, there will be calls for bailouts or subsidies to protect jobs and infrastructure.

The Bottom Line

The AI infrastructure boom is real, massive, and being financed in ways that should make anyone with a memory of 2008 at least a little nervous. The combination of surging energy demand, heavy leverage, and securitized debt backed by uncertain future cash flows is a recipe for potential trouble.

Maybe everything works out. Maybe AI generates enough revenue to justify the spending, the datacenters stay full, and the debt gets paid off. But if it doesn't, retail investors and utility customers are going to be the ones left holding the bag.

As always: if they can't explain it simply, they're probably hiding something. And right now, nobody in the AI industry wants to talk about what happens if the $600 billion bet doesn't pay off.