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I shopped around, but nothing came close to the Model Y.

My wife Deb and I had been thinking forever about what electric car to get.
We followed the news about electric models from Hyundai, and Kia, and Cadillac, and Polestar, and all the others. We noted every ad on TV and every story about upcoming releases. We asked Uber and Lyft drivers who had electric cars what they liked and disliked. One time a friend let us test drive his Polestar for a few days. (It was great.)
And we decided on Tesla.
We made our choice even though I consider Elon Musk’s role at Twitter to be wholly destructive, and even though I have followed the cautionary stories about Tesla as a company and about its “self-driving” technology and other systems.
So why the Tesla Model Y? Here was our check list:
1. Scale. Tesla is for now the market leader in the U.S. and some other parts of the world. When we were in the Bay Area of California earlier this summer, a huge share of the cars we saw on highways 101, 80, and 280 were Teslas. During the first quarter of this year, Tesla’s Model Y, the same kind we got, was California’s best selling vehicle of any sort — cars, trucks, hybrids, gas-powered, whatever. In second place was another Tesla, the Model 3.
I have a long track record of going against the grain in consumer selections. Time and again I’ve chosen the “interesting,” “elegant,” “actually, better value” alternative to the market leader. When first the Apple II and then the early IBM PC were coming onto the computer market, I proudly stuck with gorgeous-but-doomed outliers like the Processor Technology SOL-20 and the Victor 9000. I used the “technically superior” operating system OS/2 when the world was switching to Windows. I wrote several books with DeScribe as a word processor, rather than Word. Now I use Scrivener.
This is to say, I’ve explored the road less traveled.
This time we decided to go with the herd. There are real advantages of industry-standard scale. The most obvious is Tesla’s nationwide network of charging stations, which other companies are now joining.
2. Availability. Many articles about electric cars talk about multi-month waiting lists, or models that will come onto the market in a year or two.
We decided: The future is now. For better or worse, Tesla has a lot of inventory on hand for fast delivery. We brought ours home less than two weeks after we took a test drive and listed the specs for the model we wanted.
3. Price. This was a surprise. Usually I have terrible timing as a consumer. For instance, we bought our house when mortgage rates were around 20%. But we happened to be shopping just after Tesla had cut prices significantly to build sales volume, and to stay below a price ceiling that qualifies for federal tax credits. Prices went up again slightly a few weeks after we’d placed our order, but not nearly as much as they’d previously gone down.
This leads to…
4. Taxes. According to Tesla, our car was manufactured in Austin and shipped for delivery to where I live in Washington, D.C. This and other factors qualify the car for the current $7,500 federal tax credit for domestically produced electric vehicles.
This provision does not apply to cars from Hyundai, Kia, Mercedes, Polestar, and so on. Therefore it gives Tesla (plus GM, Ford, Jeep, etc.) a big edge.
After tax credits, the final cost to us was well under the average price for a new car in the U.S. And, inflation-adjusted, it was actually less than the price of my last beloved car, an Audi A4 I purchased back in 1999.
5. Vibe. This will mainly be my wife’s vehicle — I’m sticking with the Audi as long as D.C. inspection allows. For her it has just the layout, size, and space she had been looking for.
More importantly, she likes it — the look, the feel, the ride. I like it too.
There you have it! But then…
6. The Elon Factor. I believe that Elon Musk has damaged public discourse with his dismantling of Twitter and his related forays into politics. Naturally this makes me wonder about everything else he has been associated with.
What if there had been a comparably attractive choice — on price, availability, scale — from a company he didn’t run? We would have taken that. But there wasn’t.
The reality is that Tesla has transformed the electric-vehicle market in the U.S. We’re aware of the man but chose the car.
Some day, the beloved A4 will no longer pass D.C. inspections, or no longer get by on its aging, hard-to-replace parts. When that day comes, I’ll see what new EV models have appeared.
For now, we’re Tesla people.
Note: A version of this article originally appeared in the author’s newsletter, Breaking the News, and has been repurposed for Heatmap.
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The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation. What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.