#utilityrates

💡 Electricity Bills Up 7.2%? How AI Data Centers Are Hitting Your Wallet

You haven't changed your habits. But your bill keeps climbing. Why?

The answer: AI data centers.

The AI revolution is quietly driving up your electricity costs. Here's how 👇

By the numbers:

A single AI data center = electricity for 50,000 homes

Global data centers consumed 460 TWh in 2025 (more than France)

Projected to reach 1,000 TWh by 2027 (nearly double)

Training one large AI model (like GPT-4) = electricity for 130 homes for a year

Why so much energy?

1️⃣ Specialized AI chips (GPUs): 3-5x more electricity than traditional chips 2️⃣ 24/7 operation: No "off" switch – training runs for weeks at a time 3️⃣ Massive cooling: GPUs generate intense heat – cooling systems use enormous power 4️⃣ Redundancy: Backup systems that also consume electricity

How this hits your wallet:

The grid is shared – costs are spread across ALL customers

Utilities build new power plants/transmission lines for data centers – and pass the cost to you

Baseline demand rises → efficiency drops → per-kWh cost goes up

The hidden cost per query:

Standard Google search: 0.3 watt-hours

ChatGPT query: 2.9 watt-hours (nearly 10x more)

What you can do:

✅ Switch to time-of-use plans (shift usage to off-peak hours)

✅ Reduce your own usage (thermostat, LEDs, unplug devices)

✅ Attend public utility hearings – challenge rate hikes

✅ Support efficiency standards for data centers

Full breakdown + action steps in the link in bio 👆

💡 Is Your Electric Bill Too High? How to Fight AI-Driven Rate Hikes

You haven't changed your habits. You haven't bought new appliances. But your bill is climbing. What's happening?

AI data centers.

They consume massive electricity, and utilities are passing the infrastructure costs to you. Residential rates are up 7.2% nationwide (12-15% in some states).

But you're not powerless. Here's how to fight back 👇

Step 1: Audit your current bill

Decode supply vs. delivery charges

Calculate your effective rate per kWh (national avg: ~16.5¢)

Check for billing errors (meter reading vs. bill)

Ensure you're on the correct rate schedule

Step 2: Reduce usage immediately

AC: Set to 78°F (home) / 85°F (away). Each degree below 78 adds 3-5% to costs.

Water heater: Lower to 120°F (save 5-10% on water heating).

Wash clothes: Use cold water (90% of washing machine energy goes to heating water).

Kill energy vampires: Unplug chargers, coffee makers, and electronics (phantom load = 5-10% of use).

Switch to LEDs: Replace 5 most-used bulbs to save ~$75/year.

Step 3: Switch to a cheaper rate plan

Time-of-use (TOU) plans: Shift usage to off-peak hours (after 8 PM) to save 10-30%.

Run dishwasher, washing machine, dryer after 8 PM.

Pre-cool home before peak hours (2-8 PM).

Charge EVs overnight.

Call your utility: Ask if you're on the best plan for your usage.

Step 4: Challenge rate hikes

Attend public utility commission hearings (yes, you can speak).

Submit written comments opposing unjustified rate increases.

Join local consumer advocacy groups fighting AI-driven cost shifts.

Your action plan:

Audit your bill this week

Implement 2-3 usage changes

Call your utility to compare plans

Check if you can take legal action (utility advocacy groups)

Full breakdown + resources for fighting rate hikes in the link in bio 👆

In response to a recent twitter thread, I dug up an idea I proposed more than a decade ago about requiring utilities to release their rate structures in a standardized structured electronic format. I originally proposed this in my testimony to the Joint Economic Committee of the United States Congress on July 30, 2008 (Archived webcast).

Here is the key paragraph (in the context of ways information technology (IT) can promote efficiency):

IT also helps users manage data more effectively, particularly when data are released in a standardized format. For example, electric utility rates, which are now almost exclusively printed on paper, are difficult to manage for large companies with facilities in many states. The rates are complicated and they vary state-by-state and over time in unpredictable ways. If the federal government were to promote the development of a standardized electronic format for utility rates it would allow greater efficiencies in the design and energy management of facilities owned by multi-state and multi-national companies. The Lawrence Berkeley National Laboratory tariff analysis project made a first pass at creating a database of such tariffs manually <https://energyanalysis.lbl.gov/publications/tariff-analysis-project-database-and>, but that’s a far cry from having such data released and updated automatically by each utility. A nice side effect of such standardization would be that web-based energy analysis tools could more easily evaluate utility bills for residential and smaller commercial customers as well.

And here’s the bullet point recommending action:

Second, the U.S. Department of Energy and the Federal Energy Regulatory Commission should be asked to assess the benefits and costs of promoting standardized electronic formats for utility rates.

Here’s how I described the benefits of this idea in a summary I wrote after the hearing:

The benefits of enabling such customer comparisons would be substantial.  Utility customers would save billions by choosing the tariffs most beneficial to them.  Utilities would face new pressure to rationalize their tariffs and align them with actual costs. The implications of discriminatory tariffs that inhibit adoption of new electricity technologies would become immediately apparent, and thus adoption of alternative generation and efficiency technologies would be accelerated. Finally, non-utility companies devoted to offering energy services at lowest total cost would gain a powerful new tool to help their customers.

Senator Jeff Bingaman approached me after the hearing about this idea and put me in touch with his staff person in charge of these issues (Alicia Jackson). I wrote up a few pages describing the idea and how to move forward. Ms. Jackson eventually drafted legislative language that made its way into one of the big energy policy bills then being considered. 

Senator Bingaman is not in the Senate anymore and the big bill was never passed, but I remain convinced that this is an idea that could make a real difference. Key links are below.

My short writeup of the idea: “A proposal for standardized metadata formats for retail utility tariffs that will promote economic productivity, energy efficiency, and technological innovation”. PDF.  Microsoft Word 2016.

The 1 page draft legislative language in its most advanced form.  PDF.  Microsoft Word 2016.

My 2017 book, Turning Numbers into Knowledge: Mastering the Art of Problem Solving, which talks about the uses and limitations of structured data for analysis and sharing of data (Chapter 39).

The Open EI project has an updated database of utility rates, but like all efforts before it, this database was created by scraping rate data from utility PDFs.