Tech
AI, energy, and the new rules of cloud sustainability competition | Computer Weekly
The cloud industry has long promoted a reassuring sustainability narrative: Hyperscalers are more efficient and renewables for energy are growing, which means shifting workloads off‑premises and reduces emissions.
But how “green” is a specific cloud workload, in a specific region, at a specific time?
For most enterprise buyers, that comparison remains frustratingly hard. Amazon Web Services (AWS), Microsoft, and Google all publish sustainability data and customer-facing emissions tools, but they disclose different things, at different levels of granularity, using different methods. That makes direct comparisons difficult just as cloud becomes the default destination for AI workloads.
Similar metrics on the surface: Different workload details
Hyperscalers’ public disclosures reveal a shared narrative. All three talk about energy efficiency, carbon-free or renewable electricity, customer carbon-accounting tools, water usage, and embodied carbon.
AWS says Amazon matched 100% of the electricity it consumed with renewable energy in 2024, reported a global power usage effectiveness (PUE) of 1.15 and a water usage effectiveness (WUE) of 0.15 liters per kilowatt-hour (L/kWh), and now offers customer emissions visibility by scope, region, and service through its Customer Carbon Footprint Tool.
Microsoft’s reporting for fiscal year ’25 shows a global PUE of 1.17 and a WUE of 0.27 L/kWh for the data centres it fully owns and controls, and also offers customer emissions tracking through its Emissions Impact Dashboard for Azure and Microsoft 365.
Google provides both market-based and location-based emissions data across all three scopes through its Google Cloud Carbon Footprint and publishes regional carbon-free energy indicators and grid carbon intensity data to help customers evaluate location choices.
The industry, however, still lacks a standardised, apples‑to‑apples view of workload‑level sustainability across cloud providers. Native tools offered by cloud providers continue to improve, but they do not yet enable enterprises to easily compare the emissions profile of the same AI or cloud workload across Amazon Web Services, Microsoft Azure, and Google Cloud using a common framework.
Providers now give embodied carbon more attention, yet customers still rarely receive precise, comparable allocations of emissions from servers, GPUs, networking equipment, storage, and buildings.
Cloud providers also disclose water data unevenly. They may publish high‑level water usage effectiveness metrics but typically do not show the customer‑attributable water burden of workloads running in water‑stressed regions.
AI is making these gaps impossible to ignore. As AI receives unprecedented investments across industries, executive scrutiny around AI costs and outcomes is tightening, and the same scrutiny is emerging for environmental impact.
Microsoft says that as demand for AI and cloud services grows, it is redesigning data centers with direct-to-chip cooling and next-generation facilities that can avoid more than 125 million litres of water evaporation per facility each year.
AWS says it is building data centres for the next generation of AI workloads and highlights the efficiency benefits of its own silicon, including Graviton and Trainium.
Google’s 2025 Environmental Report is explicitly centered on energy, AI, and resilience, and its Carbon Footprint documentation now incorporates AI inference emissions for cloud AI services such as Vertex AI and Document AI.
What used to be a reputational issue is becoming an infrastructure accounting issue.
Community impact touted As improving, but not comparable either
There is a second, equally important dimension to this story: the community impact of data centres. AI data centres are deeply embedded in local contexts, drawing electricity from regional grids, occupying land in real communities, and often consuming water in areas already under stress.
Microsoft has increasingly linked its data centre expansion to community stewardship, citing initiatives such as the Quincy Water Reuse Utility, which it says reduced regional potable water use by 97% while supplying 1.5 million cubic meters of water annually for local drinking needs.
Amazon Web Services reports that its water replenishment programs are expected to return more than 18 billion litres per year to local communities, alongside renewable energy investments that support jobs and local economic development.
A 2025 investigation by The Guardian found that Amazon, Microsoft, and Google were operating or developing data centres in an expanding number of water‑scarce regions as AI and cloud demand rose. As a result, credible sustainability disclosures must now address not only carbon efficiency but also the local environmental and social consequences of digital infrastructure growth.
Sustainability transparency as a minimum purchasing standard
What should enterprise buyers do?
First, stop treating cloud sustainability as a secondary sustainability afterthought, and start treating it as part of core architecture and sourcing governance.
That means requiring market-based and location-based emissions data, requiring reporting by service, region, project, and month, and demanding visibility into embodied carbon, AI-specific energy use, and water intensity.
Second, don’t rely exclusively on native dashboards. Third-party comparison tools exist because enterprises need normalisation across clouds, not just better single-provider views.
Finally, write these requirements directly into cloud and AI RFPs, rather than leave them as optional side conversations after contracts are signed.
The cloud market is not short on climate ambition, glossy dashboards, or sustainability slogans. What it still lacks is anything buyers can reliably compare and use for procurement decisions.
AI has made cloud infrastructure core to enterprise architecture — more valuable, strategic, and resource-intensive. It has also made vague sustainability claims less defensible.
The next phase of competition among hyperscalers will not be won only on model access, who can secure enough GPUs, or price performance. Competition will expand beyond raw technical advantage and pricing into other dimensions such as sustainability transparency, infrastructure resilience, and credibility. It will also be shaped by the providers that can show what their platforms cost the communities around them.
For the fast-moving enterprise buyer juggling a multitude of variables in scaling AI, sustainability transparency is quickly becoming a minimum standard.
Tech
Do You Actually Need a Smart Bird Feeder With a Movable Camera?
Assembly was quick and tool-free, requiring only a handful of included knob screws. I also like that it included both fence- and pole-mounting options, the latter of which is critical for preventing squirrel damage.
ScreenshotCoolfly app via Kat Merck
Smart feeder companies continue to upgrade their cameras’ quality with each new model, but the general range still seems to be anywhere from 1080p photos and 2K video on the low end (as with the Birdfy Lite), all the way up to 32-MP photos and 4K video (as with Camojojo’s new Hibird Pro). The Aura falls somewhere in the middle of this range, with 4-MP photos and a respectable 2.5K Ultra HD video.
The camera’s 150-degree field of view is wider than that of a typical bird feeder camera, and it helps to capture all angles of what’s really the Aura’s signature feature—a wraparound perch with little platforms on the left and right sides, where you can position the camera upright (which shows pictures in a horizontal “landscape mode”) at the angle you prefer. If you want the camera to be on its side (vertical “portrait mode”), there’s a little adapter that connects to the back and screws into the platform. Do note, though, that despite some marketing photos showing the Aura with two cameras, it only comes with one camera, and when it’s on its side, it can only be mounted on the right side of the perch.
Portrait mode (the camera mounted on its side) allows for greater detail in photos, but it wasn’t always successful at capturing all the action, depending on where a bird stood. The biggest issue with this camera orientation, however, is that the app’s AI identification doesn’t work with it. I asked Coolfly if this was an error, but it turns out it’s how the camera was designed.
“To offer users ‘Limited Free AI’ without monthly subscription fees, our bird ID algorithm is hardcoded directly into the device’s hardware,” Coolfly’s rep told me. “Because this on-device neural network was trained exclusively on horizontal datasets, physically flipping the camera … disrupts the local algorithm’s spatial mapping.”
The solution? “If our users shoot vertically and spot an unknown bird, they can simply take a screenshot and send it to our in-app ChirpChat feature. Our interactive AI assistant will identify it perfectly from the image,” Coolfly’s rep said.
Though this step was cumbersome, it did correctly identify nearly all of the birds I proffered (as did the built-in AI ID). I liked seeing the birds slightly closer up with the side camera orientation, but it wasn’t a dramatic difference between the views. Certainly not dramatic enough to justify the hassle of losing the AI ID or of having to go out and fiddle with taking the camera on and off its little mount to switch modes. So for the majority of testing, I kept the camera in its default upright position.
Birds on Film
The Aura uses the Coolfly app, which isn’t as intuitive as some of the bigger brands’ apps, like Birdbuddy’s, but it was perfectly usable. There’s the ChirpChat, a bird search, and a Facebook-esque “social feed” where you can follow other Coolfly feeder users and see their posted videos and images. (Note that there were only about 10 users total at the time of my test.)
What I liked the most about the app was that it immediately IDs all the bird captures in the album with a little bird-head icon of that species. It helped me visually sort at a glance which visitors were new and noteworthy that day, and clicking the icon leads to an informational page on the bird, as well as a sound clip of the species’ typical call, so you can see if you’ve heard it around. What I liked the least, however, was the number of marketing push notifications the app would send, for sales and other irrelevant topics. It became so irritating, in fact, that I ended up turning off notifications altogether, which meant I was only aware of bird activity if I went into the app.
Tech
How Can Astronauts Tell How Fast They’re Going?
Let’s use our car again, but this time we’ll get real numbers from the accelerometer in our smartphone. Say we start at a red light and then accelerate at 2 m/s2 (meters per second squared) for five seconds. From the equation above, Δv1 would be 2 x 5 = 10 m/s, so that’s our velocity. Now, after cruising for a while, we accelerate again at 1 m/s2 for five more seconds. Δv2 is then 1 x 5 = 5 m/s. Adding these two changes, our velocity is now 15 m/s. And so on.
The only problem is that inertial measurement isn’t as accurate as the Doppler method over long periods, because small errors will keep accumulating. That means you need to recalibrate your system periodically using some other method.
Optical Navigation
On Earth, people have long navigated by the stars. In the northern hemisphere, just find Polaris. It’s called the North Star because Earth’s axis of rotation points right at it. That’s why it appears stationary, while the other stars seem to revolve around it. If you point a finger at Polaris you’ll be pointing north, and you can use that orientation to go in whatever direction you want.
Now, if you can measure the angle of Polaris above the horizon, you’ll also know your latitude. If the angle is 30 degrees, you’re at latitude 30 degrees. See, it’s easy. And once you can measure position, you just need to do it twice and record the time interval to find your velocity.
But celestial navigation works because we know how the Earth rotates, and that doesn’t help in a spacecraft. Oh well, can we just use the stars like you would use the cows on the side of the road? Nope. The stars are so far away, astronauts would need to travel for many, many generations to detect any shift in their position. Like the airplane flying over the sea, you’d seem to be stationary, even while traveling 25,000 mph.
But we can still use the basic idea. For optical navigation in space, a spacecraft can locate other objects in the solar system. By knowing the precise location of these objects (which change over time) and where they appear relative to the viewer, it’s possible to triangulate a position. And again, by taking multiple position measurements over time, you can calculate a velocity.
In the end, even though spaceships lack speedometers, it’s possible to track their speed indirectly with a little physics. But it’s just another example of how flying in space is really, totally different—and way more complicated—than driving or flying on Earth.
Tech
The Shocking Secrets of Madison Square Garden’s Surveillance Machine
If those posts could be interpreted in any way as threats, Eversole would contact their hometown police, multiple security team sources say. “He would take it upon himself to reach out to someone somewhere and introduce himself as the CSO of Madison Square Garden and demand that the local PD take action,” the security veteran adds.
One teenager posted a tweet, and MSG security asked local law enforcement to visit him. “They scared the crap [poop emoji] out of some 14 year old kid in Colorado,” one MSG security staffer texted in a message we reviewed. Cops would at times ignore Eversole’s demands. He and his deputies would then “freak the fuck out when a PD somewhere would not play ball,” the second veteran continues.
Eversole would also allegedly push his subordinates to act more like municipal cops. He’d urge them to patrol the streets surrounding MSG, which is located in one of Manhattan’s more derelict neighborhoods, functionally acting as a second, ersatz police force—without formal permission of New York’s real one. “On many occasions, I was ordered to stop traffic, close sidewalks, and unlawfully detain individuals in the venue and demand identification,” Munn, the former security worker, wrote in his filing. Munn added that these orders were “against NY State/City laws without proper permits or NYPD’s authorization, which MSG did not maintain.” An NYPD spokesperson confirms that such authorization was never given.
Eversole would tell his teams to bust the guys selling knockoff merchandise and “remove scalpers and drug dealers daily, in areas outside and around MSG properties, without back up, communication, or assistance from MSG venue security or NYPD paid detail,” Ingrasselino alleged in his lawsuit.
Ingrasselino’s former colleagues emphasize that the work could be dangerous, possibly illegal, and in no way a normal task for a private security force. Ingrasselino, among others, claimed that a former NYPD assistant chief now working for MSG was once attacked by scalpers and sent to the hospital. In his filing, Munn claimed that during his time “overseeing all security aspects” of several Dolan properties, he had been “ordered to do many things I felt were unsafe, unethical, and illegal, all at the direction” of Eversole.
Ingrasselino also alleges in his suit that he was ordered to embed “in the middle of pro-Palestine or anti-Israel protests” that happened to be passing a Dolan venue. Other security sources say that they were not ordered to insert themselves into any demonstrations. But they confirm that they were asked to observe protests that went anywhere near a Dolan venue. Given those venues’ central location, it happened a lot.
Some protests would get special scrutiny. When the Professional Bull Riders tour came to the Garden, animal rights activists would at times gather outside, or in front of the MSG president’s apartment building. The leaders felt they were being singled out and surveilled.
Even people working for the state government found themselves in MSG’s sights.
In late 2022 and early 2023, when word about the lawyer bans began to spread and uproar over the face-recognition program was hitting a peak, the State Liquor Authority decided to look into it; per state law, according to the SLA, you’re not allowed to both serve booze and arbitrarily lock people out of your place. Dolan’s response may have been a touch over-the-top. He went on TV, held up a photograph of the then head of the liquor authority with the man’s phone number and email underneath, and told the audience to reach out to him, and “tell him to stick to his knitting.”
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