Google’s Grid Warning: Why Transmission Is the Real Threat to U.S. Grid Reliability

AI data centers are exposing the quiet bottleneck utilities can’t outrun: the wires, the queues, and the legacy systems behind them.

Transmission Explained: The Grid’s Highways

Most people talk about the grid like it’s one thing. It isn’t. It’s three big layers that do three different jobs. Generation is where electricity is made (power plants, wind, solar, batteries).

Transmission is how that electricity moves long distances (the high-voltage “backbone”).

And lastly, distribution is how it gets from your local substation to your neighborhood (the lower-voltage lines on poles and underground).

This article focuses on transmission—the part that gets misunderstood because you don’t “see” it unless something goes wrong. Basically, it's the interstate highway system for electricity. Big, high-voltage lines that move power hundreds of miles from where it’s produced to where it’s needed, through substations and interregional corridors.

You can have enough electricity overall and still be short where it matters. That’s not a generation problem—that’s a transmission problem.

Think of it like this: a city can have plenty of food in warehouses across the state, but if the highways into town are clogged, grocery shelves still go empty. The U.S. grid is running into that same dynamic. Load growth (especially from AI-driven data centers) is arriving fast in specific locations, but the “highways” that would move bulk power into those areas aren’t expanding or upgrading at the same pace.

Google’s Warning Should Scare Utilities (For the Right Reason)

That’s why Google’s warning landed so hard. In January 2026, Google told Reuters that the U.S. transmission system is the biggest challenge for connecting data centers, with grid connection wait times stretching beyond a decade in some regions.

When Google publicly flags grid constraints, it’s easy to dismiss it as a big tech complaint—another hyperscaler wanting special treatment. But, that’s not what this is. Because when interconnection stretches into 10+ year territory, the grid loses flexibility in three ways.

First, congestion becomes chronic, not occasional. Second, utilities end up operating closer to system limits for longer periods so minor disturbances have less margin before they cascade. Third, every delayed upgrade pushes more load onto aging infrastructure that wasn’t built for today’s demand.

Translation: the grid doesn’t fail all at once. It gets tight—then brittle.

AI Exposed Planning Assumptions That No Longer Fit

Traditional utility planning assumed load growth would look like a gentle ramp: predictable, seasonal, and measured in small increments. In contrast, AI data centers show up like a stadium event—except it’s every day.

And the forecasts aren’t subtle. The U.S. Department of Energy notes EPRI’s estimate that data centers could grow to up to ~9% of U.S. electricity consumption by 2030. Meanwhile, the range of projections is huge (because the pace is still volatile). For instance, one summary of modeling work highlights forecasts spanning ~200 TWh/year to more than ~1,050 TWh/year by 2030.

Utilities can handle growth. What’s harder is lumpy growth—hundreds of megawatts landing in one region, on one timeline, with one customer class that doesn’t negotiate with “maybe in seven years.” And that collides head-on with the uncomfortable truth: transmission timelines are measured in years, not quarters.

Transmission Is Slow by Design—and the Queue Proves It

This is where people outside the industry get frustrated: “Why can’t you just build more lines?” Because transmission is slow on purpose—and not always in a bad way. There are real reasons: multi-state routing, land use, environmental review, and regulatory coordination. But the math is getting ugly.

• DOE’s own permitting program notes that federal permitting for a new transmission line averages about four years—before you even account for all the non-federal complexity.

• Some analyses cite ~10 years on average for completing new high-voltage line projects end-to-end.

Now layer on the backlog. Lawrence Berkeley Lab’s interconnection queue tracking shows that as of the end of 2024, nearly 2,300 GW of generation and storage capacity were actively seeking interconnection. And the 2025 edition of the same research notes ~10,300 projects in queues, representing ~1,400 GW of generation and ~890 GW of storage.

Even if only a fraction gets built, the queues tell you something important: the grid is now a timing problem as much as it is a supply problem.

The “Who Pays?” Debate Is Real—But It’s Not the Main Constraint

Anytime big new loads appear, the cost allocation question pops up: should the companies driving extraordinary demand contribute more to upgrades? It’s a legitimate debate. But it’s also a distraction if it becomes the only debate.

It’s important not to pretend cost allocation alone is the bottleneck. Even if you decide exactly who pays tomorrow (ratepayers, big tech, “beneficiaries,” some hybrid tariff), you can’t purchase time off the construction schedule. Transmission and substation upgrades still require studies, siting, equipment lead times, outage windows, and coordination across regulators and grid operators. Money helps—but it doesn’t erase timelines.

So yes—who pays is a real fight, and it’s getting louder. But the reliability outcome will hinge just as much on whether utilities can execute upgrades with speed and precision, using modern planning data, clear governance, and cross-functional coordination—not just in terms of where the invoice is addressed.

The Hidden Reliability Risk: Legacy Planning, Data, and IT Systems

Here's the reality—transmission decisions are only as good as the systems behind them. Many utilities still operate with fragmented planning tools, disconnected asset data, and manual scenario workflows. When load forecasts change, scenario modeling can become slow, inconsistent, and hard to trust.

That’s how you end up in the most dangerous failure mode in reliability: “Everything looked fine.” Reliability erosion is gradual. Early warning signs get buried in delayed, simplified, or stale data. The system looks stable—until stress reveals the cracks you didn’t see growing.

Managing AI-era grid risk with legacy IT is like predicting hurricanes with last year’s forecast.

Closing the Execution Gap

Awareness won’t save the grid. Execution does. We may need thousands of miles of new high-capacity transmission per year to keep up with reliability and growth needs—and recent buildout has lagged far behind that scale. At the same time, queue pressure and new large loads mean utilities can’t wait for a perfect future-state grid before they modernize how decisions get made.

This is exactly where Tamazari focuses: helping utilities turn strategy into delivery—especially when the constraints are real. Because the grid rarely fails at the moment the lights go out. It fails earlier—when outdated processes can’t keep up with reality.

Explore all our Energy IT Modernization services.

Footnotes

1. Google warning on transmission as biggest obstacle; decade-plus waits in some regions (Reuters, Jan 14, 2026).

2. Interconnection queue scale: ~2,300 GW seeking connection by end of 2024 (LBNL).

3. Queue details: ~10,300 projects; ~1,400 GW generation and ~890 GW storage (LBNL, “Queued Up: 2025 Edition”).

4. Data center demand: EPRI estimate up to ~9% of U.S. electricity by 2030 (DOE GDO page citing EPRI).

5. Modeled 2030 demand range: ~200 to >1,050 TWh/year (WRI synthesis).

6. Federal permitting average ~4 years for new transmission lines (DOE CITAP).

7. High-voltage transmission projects can take ~10 years on average (Pew; RMI).

8. Transmission need scale and buildout lag (analysis referencing DOE planning study needs).

9. Reliability pressure: NERC long-term warnGrid Reliability Blog Post #2r peak demand +245 GW over 10 years; shrinking reserve margins.