Curved displays in cars are increasingly common, adding a bit of shape to the increasing pixel overload we face behind the steering wheel. The designers of Porsche's upcoming Cayenne Electric, though, decided to do something more dramatic with the touchscreen that commands the center of the forthcoming SUV's dashboard.

Its shape flows between dashboard and center console in a smooth, organic way, with a pronounced bend in the middle. The first time I dragged my finger from top to bottom, traversing that kink, I decided I was going to hate this layout. But a day behind the wheel of a prototype version of Porsche's electric SUV changed my mind. I was left wondering why nobody has used this shape.

That was just one feature out of dozens that impressed in this SUV that promises somewhere north of 1,000 horsepower (745 kW) and something in excess of 300 miles (482 km) of range on a charge. It won't hit dealerships until sometime next year, but early impressions are that it will be worth the wait.

Intro to Cayenne

Porsche has camouflaged the EV prototype; don't expect the headlights or taillights to look like this. Credit: Tim Stevens

Porsche introduced the Cayenne in 2002 to global controversy. It was an SUV from a sports car company at a time when premium SUVs were still a rarity. In the decades since, it's earned respect thanks to its on-road performance and off-road capability.

The upcoming Cayenne Electric, which won't hit the market until next year, looks set to raise both bars in a big way. There will be three flavors of electric Cayenne, with the base SUV joined by the sportier Cayenne S and sportiest Cayenne Turbo.

Porsche hasn't finalized power output from those models, but engineers at the event told me to expect that the top-shelf trim will offer something north of 1,000 hp. That could make this Porsche's most powerful production machine yet, depending on how it compares to the Taycan Turbo GT.

The electric Cayenne Turbo's power comes from a new rear motor design on the Turbo that uses cooling technology borrowed from the company's Formula E efforts. Yes, in a rare example of tech transfer from the quietest of motorsports to the road, the electric Cayenne Turbo's rear power unit features a novel design with a coolant system slotting in between the motor's stator and rotor. Engineers told me this design not only means that it operates at a ridiculous 98 percent efficiency, but that it can output maximum power for longer.

This fin extends to add a little range. But only if you have the Cayenne Turbo. Credit: Tim Stevens

That means bombing down the Autobahn at top speed for as long as traffic and the SUV's 113 kWh (gross) battery will allow. Porsche said that, when driven more moderately, the electric Cayenne will do more than 373 miles (600 km) on the European WLTP cycle. On the more difficult American EPA cycle, that should equate to a range of roughly 300 miles. Charging will be done over a NACS port.

That range is further helped by a new and curious form of active aerodynamics. At speeds of over 37 mph (60 km/h), the Cayenne Turbo extends a pair of small fins from the rear bumper. These extend the car's side profile slightly, enough to deliver an extra seven miles of range. Sadly, lesser trims will have to do without the deployable gills.

Behind the wheel

The performance of the Cayenne Turbo is eye-opening, to say the least. After a stab of the push-to-pass button on the steering wheel, which sets the SUV to maximum output, a quick stomp on the accelerator was enough to make my peripheral vision narrow as the 22-inch tires beneath me struggled for grip.

The stability control system on the Cayenne doesn't dramatically cut power when torque exceeds traction. It simply reduces output somewhat until the big SUV has overcome its reluctant momentum. As the speed increased, so too did the power. This is an SUV designed for the derestricted sections of the Autobahn, after all, and despite not using a two-speed transmission like the Taycan, its acceleration did not abate, even at high speed.

But I spent much of my time behind the wheel at more moderate velocities, winding around the narrow, blind roads that work their way around the Catalan region of Spain. Porsche hasn't yet quoted a curb weight for any of the Cayenne Electric flavors, but however far it tips the scales, it still feels light and nimble. Steering is firm but sharp with decent feedback, and this big SUV dives into and screams out of corners with perfect poise.

It was only really over big, unsettling movements, speed bumps and the like, that I could feel how much mass was beneath me in the Cayenne Electric. When summiting asphalt imperfections like that, the curious shape of that central OLED really shone.

That display is bent at roughly a 45-degree angle, a profile that allows it to perfectly conform to both the angle of the dashboard and that of the center console. Porsche placed a padded wrist rest right beneath that and then designed the user interface to position the most important controls along the lower portion of the display, the part that's in line with your hand.

The result is you can rest your wrist there comfortably, queue up your favorite playlist, and crank the ventilated seats, all without making any accidental taps on bumpy roads. And despite this car not entering production until next year, that software was snappy and responsive. It didn't lock up on me once during a full day behind the wheel.

You'll need a low-grip surface if you want to go sliding around. Credit: Porsche

Yes, next year is a long time to wait for the Cayenne Electric to enter production. It's hard to know what the American EV scene will look like in three months, never mind 12, but for now, at least, Porsche's next SUV is shaping up extremely well. When it does hit the market, it will sit in dealerships alongside the existing Cayenne, which will continue to be available. Choice is good, and if you're in the market but not in a hurry, I'd suggest waiting for this. If the price is right, it will be a clear-cut winner.

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The cylinder reminds me of an internet router. And when I press the button on the back, a gentle whir of a fan begins, while a soft blue glow emits from the top. But it’s not sending out Wi-Fi waves. The Airea is launching microscopic drops into the air that should bind to and eliminate viruses, volatile organic compounds (VOCs), and molds for up to 800 square feet. All without swapping a high-efficiency particulate air (HEPA) filter. In fact, Airea can run for two years straight, and all you need to do is change a light bulb.

Airea, which is on the market now for $350, was created and funded in-house by the design firm MNML. Developed by Scott Wilson—the former global creative director at Nike, who has built everything from smart watches to Theraguns and CBD inhalers—he was first inspired through his work with Marriott. 

The hotel chain was interested in pursuing a pillow chocolate that might help someone sleep, but along the way, someone on the team showed Wilson a giant machine that could sterilize rooms—eliminating cigar smoke and SARS alike. It pumped H202 molecules (also known as hydrogen peroxide) into the air, which trapped particles and also sterilized surfaces. 

“I was like, why is this not in the consumer segment?” recalls Wilson. 

A month later, COVID-19 would reshape the world. And Wilson became obsessed with how H202 technology might be translated from expensive, large industrial machinery to a more practical domestic gadget. 

Hydrogen peroxide is what many of us know as a strong bleaching agent, used in everything from sanitizers to teeth whitening strips. Pumping that into the air is an inherently unsettling idea. But as Wilson learned, our body naturally produces some H202 as part of our immune process, and given its ability to neutralize viruses, it’s been researched for use in the development in vaccines. 

In significant amounts, H202 is unhealthy, but it’s still used today in places like food factories to sterilize machinery. Occupational Safety and Health Administration (OSHA) standards regulate that air should not have more than 1 part per billion of H202. What Wilson developed over the following four years with Airea operates at levels 60 times lower than that, saturating the air with H202 levels at 15 parts per billion to 20 parts per billion. And while it’s not yet Food and Drug Administration (FDA) approved, independent lab testing has demonstrated that Airea should eliminate more than 99% of viruses in the air.

Building a quieter, no fuss air purifier 

When Wilson’s team began working on Airea, he put a significant restraint on the design. He wanted the product to be so simple that it could be manufactured in the U.S. rather than China. (And he got close. Airea’s major components are molded in the U.S., with its final assembly in the Dominican Republic.)

He still can’t say exactly what drove the impulse, which now seems prophetic given global supply chain issues and tariffs. “Our job is to predict things. Part of it was, [the world] seemed unpredictable,” says Wilson. “And honestly, I didn’t want it to get knocked off.”

The ensuing architecture of Airea is quite simple. It’s essentially an extruded aluminum base, a computer fan, and a light bulb. The real ingenuity behind the design is that the aluminum is treated with a special coating so that when the light hits it, the surface generates H202 molecules. (Wilson has patents on the geometries of these aluminum fins, which maximize the surface area for light to hit and generate H202.)

“People might be able to reverse-engineer the coating, but there are only so many ways to create that much surface area in a small footprint,” says Wilson.

As for the design itself—with its router-like aesthetic—it’s not exactly an object of extreme beauty, but it’s also relatively innocuous. With a footprint somewhere between a tallboy and a small table lamp, it can be stuck just about anywhere to make a room’s air that much safer to breathe, and its surfaces that much more sterile to touch.

For now, just 5,000 units have been produced in Airea’s first run, as Wilson plans FDA testing and talks to partners who might expand their reach into various industries like healthcare.

In my own experience living with Airea, I did find the understated operation strange. At first, my mind oscillated between, “Is this even working?” and, “What am I breathing?” It’s substantially quieter than my HEPA unit that sits nearby, and you don’t really feel any breeze kicking out unless you hold your hand close. Airea’s protection is almost completely imperceptible, and I found myself craving some sort of extra proof of function before allowing myself to appreciate its minimal operation.

In this quasi-post-COVID-19 world, it’s easy to forget the amount of comfort air filters provided  just a few years ago—and that clean air is something we should still be prioritizing in buildings today. When my wife walked into the kitchen one recent morning with a cough, it was an excellent reminder: I subtly turned the Airea back on and breathed a bit easier. 

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So far this year, electricity use in the US is up nearly 4 percent compared to the same period the year prior. That comes after decades of essentially flat use, a change that has been associated with a rapid expansion of data centers. And a lot of those data centers are being built to serve the boom in AI usage. Given that some of this rising demand is being met by increased coal use (as of May, coal's share of generation is up about 20 percent compared to the year prior), the environmental impact of AI is looking pretty bad.

But it's difficult to know for certain without access to the sorts of details that you'd only get by running a data center, such as how often the hardware is in use, and how often it's serving AI queries. So, while academics can test the power needs of individual AI models, it's hard to extrapolate that to real-world use cases.

By contrast, Google has all sorts of data available from real-world use cases. As such, its release of a new analysis of AI's environmental impact is a rare opportunity to peer a tiny bit under the hood. But the new analysis suggests that energy estimates are currently a moving target, as the company says its data shows the energy drain of a search has dropped by a factor of 33 in just the past year.

What’s in, what’s out

One of the big questions when doing these analyses is what to include. There's obviously the energy consumed by the processors when handling a request. But there's also the energy required for memory, storage, cooling, and more needed to support those processors. Beyond that, there's the energy used to manufacture all that hardware and build the facilities that house them. AIs also require a lot of energy during training, a fraction of which might be counted against any single request made to the model post-training.

Any analysis of energy use needs to make decisions about which of these factors to consider. For many of the ones that have been done in the past, various factors have been skipped largely because the people performing the analysis don't have access to the relevant data. They probably don't know how many processors need to be dedicated to a given task, much less the carbon emissions associated with producing them.

But Google has access to pretty much everything: the energy used to service a request, the hardware needed to do so, the cooling requirements, and more. And, since it's becoming standard practice to follow both Scope 2 and Scope 3 emissions that are produced due to the company's activities (either directly, through things like power generation, or indirectly through a supply chain), the company likely has access to those, as well.

For the new analysis, Google tracks the energy of CPUs, dedicated AI accelerators, and memory, both when active on handling queries and while idling in between queries. It also follows the energy and water use of the data center as a whole and knows what else is in that data center so it can estimate the fraction that's given over to serving AI queries. It's also tracking the carbon emissions associated with the electricity supply, as well as the emissions that resulted from the production of all the hardware it's using.

Three major factors don't make the cut. One is the environmental cost of the networking capacity used to receive requests and deliver results, which will vary considerably depending on the request. The same applies to the computational load on the end-user hardware; that's going to see vast differences between someone using a gaming desktop and someone using a smartphone. The one thing that Google could have made a reasonable estimate of, but didn't, is the impact of training its models. At this point, it will clearly know the energy costs there and can probably make reasonable estimates of a trained model's useful lifetime and number of requests handled during that period. But it didn't include that in the current estimates.

To come up with typical numbers, the team that did the analysis tracked requests and the hardware that served them for a 24 hour period, as well as the idle time for that hardware. This gives them an energy per request estimate, which differs based on the model being used. For each day, they identify the median prompt and use that to calculate the environmental impact.

Going down

Using those estimates, they find that the impact of an individual text request is pretty small. "We estimate the median Gemini Apps text prompt uses 0.24 watt-hours of energy, emits 0.03 grams of carbon dioxide equivalent (gCO2e), and consumes 0.26 milliliters (or about five drops) of water," they conclude. To put that in context, they estimate that the energy use is similar to about nine seconds of TV viewing.

The bad news is that the volume of requests is undoubtedly very high. The company has chosen to execute an AI operation with every single search request, a compute demand that simply didn't exist a couple of years ago. So, while the individual impact is small, the cumulative cost is likely to be considerable.

The good news? Just a year ago, it would have been far, far worse.

Some of this is just down to circumstances. With the boom in solar power in the US and elsewhere, it has gotten easier for Google to arrange for renewable power. As a result, the carbon emissions per unit of energy consumed saw a 1.4x reduction over the past year. But the biggest wins have been on the software side, where different approaches have led to a 33x reduction in energy consumed per prompt.

Most of the energy use in serving AI requests comes from time spent in the custom accelerator chips. Credit: Elsworth, et. al.

The Google team describes a number of optimizations the company has made that contribute to this. One is an approach termed Mixture-of-Experts, which involves figuring out how to only activate the portion of an AI model needed to handle specific requests, which can drop computational needs by a factor of 10 to 100. They've developed a number of compact versions of their main model, which also reduce the computational load. Data center management also plays a role, as the company can make sure that any active hardware is fully utilized, while allowing the rest to stay in a low-power state.

The other thing is that Google designs its own custom AI accelerators, and it architects the software that runs on them, allowing it to optimize both sides of the hardware/software divide to operate well with each other. That's especially critical given that activity on the AI accelerators accounts for over half of the total energy use of a query. Google also has lots of experience running efficient data centers that carries over to the experience with AI.

The result of all this is that it estimates that the energy consumption of a typical text query has gone down by 33x in the last year alone. That has knock-on effects, since things like the carbon emissions associated with, say, building the hardware gets diluted out by the fact that the hardware can handle far more queries over the course of its useful lifetime.

Given these efficiency gains, it would have been easy for Google to simply use the results as a PR exercise; instead, the company has detailed its methodology and considerations in something that reads very much like an academic publication. It's taking that approach because the people behind this work would like to see others in the field adopt its approach. "We advocate for the widespread adoption of this or similarly comprehensive measurement frameworks to ensure that as the capabilities of AI advance, their environmental efficiency does as well," they conclude.

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