AI could soon surpass Bitcoin mining in energy consumption, according to a new analysis that concludes artificial intelligence could use close to half of all the electricity consumed by data centers globally by the end of 2025.
Technology
AI could consume more power than Bitcoin by the end of 2025
The estimates come from Alex de Vries-Gao, a PhD candidate at Vrije Universiteit Amsterdam Institute for Environmental Studies who has tracked cryptocurrencies’ electricity consumption and environmental impact in previous research and on his website Digiconomist. He published his latest commentary on AI’s growing electricity demand last week in the journal Joule.
AI already accounts for up to a fifth of the electricity that data centers use, according to de Vries-Gao. It’s a tricky number to pin down without big tech companies sharing data specifically on how much energy their AI models consume. De Vries-Gao had to make projections based on the supply chain for specialized computer chips used for AI. He and other researchers trying to understand AI’s energy consumption have found, however, that its appetite is growing despite efficiency gains — and at a fast enough clip to warrant more scrutiny.
“Oh boy, here we go.”
With alternative cryptocurrencies to Bitcoin — namely Ethereum — moving to less energy-intensive technologies, de Vries-Gao says he figured he was about to hang up his hat. And then “ChatGPT happened,” he tells The Verge. “I was like, Oh boy, here we go. This is another usually energy-intensive technology, especially in extremely competitive markets.”
There are a couple key parallels he sees. First is a mindset of “bigger is better.” “We see these big tech [companies] constantly boosting the size of their models, trying to have the very best model out there, but in the meanwhile, of course, also boosting the resource demands of those models,” he says.
That chase has led to a boom in new data centers for AI, particularly in the US, where there are more data centers than in any other country. Energy companies plan to build out new gas-fired power plants and nuclear reactors to meet growing electricity demand from AI. Sudden spikes in electricity demand can stress power grids and derail efforts to switch to cleaner sources of energy, problems similarly posed by new crypto mines that are essentially like data centers used to validate blockchain transactions.
The other parallel de Vries-Gao sees with his previous work on crypto mining is how hard it can be to suss out how much energy these technologies are actually using and their environmental impact. To be sure, many major tech companies developing AI tools have set climate goals and include their greenhouse gas emissions in annual sustainability reports. That’s how we know that both Google’s and Microsoft’s carbon footprints have grown in recent years as they focus on AI. But companies usually don’t break down the data to show what’s attributable to AI specifically.
To figure this out, de Vries-Gao used what he calls a “triangulation” technique. He turned to publicly available device details, analyst estimates, and companies’ earnings calls to estimate hardware production for AI and how much energy that hardware will likely use. Taiwan Semiconductor Manufacturing Company (TSMC), which fabricates AI chips for other companies including Nvidia and AMD, saw its production capacity for packaged chips used for AI more than double between 2023 and 2024.
After calculating how much specialized AI equipment can be produced, de Vries-Gao compared that to information about how much electricity these devices consume. Last year, they likely burned through as much electricity as de Vries-Gao’s home country of the Netherlands, he found. He expects that number to grow closer to a country as large as the UK by the end of 2025, with power demand for AI reaching 23GW.
Last week, a separate report from consulting firm ICF forecasts a 25 percent rise in electricity demand in the US by the end of the decade thanks in large part to AI, traditional data centers, and Bitcoin mining.
It’s still really hard to make blanket predictions about AI’s energy consumption and the resulting environmental impact — a point laid out clearly in a deeply reported article published in MIT Technology Review last week with support from the Tarbell Center for AI Journalism. A person using AI tools to promote a fundraiser might create nearly twice as much carbon pollution if their queries were answered by data centers in West Virginia than in California, as an example. Energy intensity and emissions depend on a range of factors including the types of queries made, the size of the models answering those queries, and the share of renewables and fossil fuels on the local power grid feeding the data center.
It’s a mystery that could be solved if tech companies were more transparent
It’s a mystery that could be solved if tech companies were more transparent about AI in their sustainability reporting. “The crazy amount of steps that you have to go through to be able to put any number at all on this, I think this is really absurd,” de Vries-Gao says. “It shouldn’t be this ridiculously hard. But sadly, it is.”
Looking further into the future, there’s even more uncertainty when it comes to whether energy efficiency gains will eventually flatten out electricity demand. DeepSeek made a splash earlier this year when it said that its AI model could use a fraction of the electricity that Meta’s Llama 3.1 model does — raising questions about whether tech companies really need to be such energy hogs in order to make advances in AI. The question is whether they’ll prioritize building more efficient models and abandon the “bigger is better” approach of simply throwing more data and computing power at their AI ambitions.
When Ethereum transitioned to a far more energy efficient strategy for validating transactions than Bitcoin mining, its electricity consumption suddenly dropped by 99.988 percent. Environmental advocates have pressured other blockchain networks to follow suit. But others — namely Bitcoin miners — are reluctant to abandon investments they’ve already made in existing hardware (nor give up other ideological arguments for sticking with old habits).
There’s also the risk of Jevons paradox with AI, that more efficient models will still gobble up increasing amounts of electricity because people just start to use the technology more. Either way, it’ll be hard to manage the issue without measuring it first.
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You know that shopping moment when you can picture the exact item in your head, but you have no idea what to call it? Maybe you want a dining chair with a curved back. Maybe you are looking for a black dress with sheer sleeves, but you do not know the exact style name. So you type a few vague words, scroll through a wall of products and wonder why online shopping still feels like a guessing game.
Amazon now wants AI to help close that gap. Its newest search feature creates AI-generated images in real time as you type inside the Amazon Shopping app. The idea sounds simple: describe what you see in your head, watch the image change with your words and tap the version that looks closest to what you want.
From there, Amazon shows visually similar products you can actually shop. Here’s how the new search experience works and why it could change the way you browse for clothes, furniture and home finds.
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AI HOME SEARCH COULD CHANGE HOW YOU BUY A HOUSE
Amazon’s visual suggestions help shoppers narrow broad searches by showing style-based image filters as they type. (Serene Lee/SOPA Images/LightRocket)
Amazon brings AI images into the search bar
Amazon says the new feature appears in the search suggestions area of its Shopping app for U.S. customers. It is rolling out on iOS and Android, starting with apparel and home, where looks carry a lot of weight. Amazon says more categories will be added over time.
That makes sense. Visual details can make or break a purchase. A “blue chair” may give you thousands of results. A “blue velvet accent chair with gold legs” gets closer. Add “curved back” or “tufted seat,” and the AI image can shift as your description gets sharper.
Instead of forcing you to know the right design term, Amazon lets you describe the look. Then the app turns that description into a visual cue.
How the new Amazon AI search works
You start by typing into the Amazon search bar the way you normally would. However, this time, Amazon wants you to use more descriptive language.
For example, you might type: “green dress with puff sleeves” or “wood coffee table with rounded edges.”
As you add details, AI-generated images appear below the search bar. Those images update as you refine your wording. When one looks close to what you imagined, you can tap it and shop for products with a similar look.
That last part is important because the AI image itself may not represent a real product listing. It works more like a visual guide. Amazon uses it to understand the style you want, then matches that idea to items in its store.
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Amazon’s new AI search creates image suggestions in real time, helping shoppers refine a product idea with more descriptive words. (Amazon)
Here’s how to try Amazon’s AI image search
- Open the Amazon Shopping app on your iPhone or Android phone.
- Tap the search bar.
- Type a visual description of what you want. Include details like color, material, shape, pattern or size.
- Watch the AI-generated images appear in the suggestions area below the search bar.
- Keep adding details until one image looks close to what you had in mind.
- Tap the image to see visually similar products you can shop.
- From there, Amazon uses that visual cue to show products that look similar to your description.
How this could help you shop
The best use case here involves those hard-to-describe purchases. Furniture, clothing, accessories and decor often depend on texture, shape, pattern and color.
Search has always handled exact terms pretty well. Type a brand name or model number, and you usually get somewhere useful. The problem starts when you know the vibe but not the vocabulary.
Amazon’s AI search could help when you want:
- A coastal-looking couch that does not feel too beachy
- Dining chairs with a curved back
- A black dress with sheer sleeves
- Lighting with a woven shade
- Vintage-style rugs that do not look too formal
That could save time, especially for those of you who browse with a mental image instead of a shopping list.
The catch with AI-generated shopping images
There is just one big caution here: AI can create something that looks perfect but may not exist. That could lead to disappointment if the generated image looks better than the real products Amazon surfaces afterward. Shoppers may tap an image expecting an exact match and end up with close-enough results.
So treat the AI image as a sketch, not a product promise. Before you buy, check the actual listing photos, dimensions, materials, reviews and return policy. That extra minute can save you from ordering a “close match” that misses the detail you cared about most.
Amazon is expanding visual search in other ways, too
The new real-time AI image search fits into a larger push by Amazon to make shopping more visual. Amazon Lens already lets you point your phone camera at an item and search for similar products. Lens Live takes that further by scanning items in real time and showing matching products in a swipeable carousel.
You can also add text to an image search. So, if you upload a photo of a beige sofa, you can add a note like “in white” or “smaller size” to narrow the results.
Amazon also offers a “More like this” option on product images. That can help when you like one product’s look but want a different sleeve, length, color or style.
For iPhone users, Amazon Lens can also launch from the lock screen through a widget. That means you can spot something in the real world and search for it faster.
Shop by style adds outfit inspiration
Amazon is also using AI-generated style images in apparel search results. When you search for clothing, you may see “Shop by style” collages tied to looks such as “Urban luxe” or “Soft elegance.”
Tap a collage, and Amazon takes you to a page with shoppable items, similar products and style options you can browse. That makes the experience feel closer to a digital stylist than a basic product search.
It could help those of you who want outfit ideas rather than a single item. However, the same caution applies. Use the AI styling as inspiration, then judge the actual products on their own.
5 E-COMMERCE TECH TERMS EVERY SHOPPER SHOULD KNOW
Amazon’s “Shop by style” feature shows AI-generated outfit ideas in search results, making it easier to browse curated looks. (Amazon)
What this means for you
Amazon wants to make search feel less like typing keywords and more like describing a picture. That could make it easier to find products when you lack the exact name for a style, material or shape. It may also make browsing feel more personal and less frustrating.
Still, AI shopping tools can nudge you toward impulse buys. A polished image may make a product idea feel more appealing before you compare prices or check quality. So use the feature as a starting point, not the final word.
The smartest approach is simple: describe what you want, use the AI image to narrow your search and then slow down before checkout. Look at the real listing, read recent reviews and confirm the details that matter to you.
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Kurt’s key takeaways
Amazon’s new AI search could make online shopping feel more natural for those of you who think visually. Instead of guessing the right product term, you can type what you imagine and let the app build a picture from your words. That could be genuinely useful for home decor and fashion, where small details often decide whether something feels right. At the same time, shoppers should remember that AI images can create expectations that real products may not match. So yes, Amazon’s search bar may soon feel more creative. The bigger question is whether that creativity helps you buy smarter or simply makes you want more.
Would you trust an AI-generated shopping image to guide your next purchase, or would it make you more skeptical before clicking buy? Let us know by writing to us at Cyberguy.com
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According to the Wall Street Journal, the export control directive that led to Anthropic cutting off access to Fable 5 and Mythos 5 was triggered in part by cybersecurity research from Amazon and conversations between CEO Andy Jassy and the White House. According to the report, the paper from Amazon claims that, through a series of prompts, it was able to get Fable 5 to serve up information that could be used in cyberattacks. Amazon has yet to respond to a request for comment.
Shortly after Jassy shared the company’s findings with the government, it made the call to block its use by foreign nationals. Complicating this issue is that many of Anthropic’s researchers are foreign-born, meaning they were barred from accessing their own product.
In a statement, Anthropic disputed the government’s characterization of the issue as a “jailbreak.” It argued that many of the same vulnerabilities could be discovered using other publicly available models, including GPT 5.5. Some security researchers appear to back the company’s interpretation. Katie Moussouris, the founder and CEO of LutaSecurity posted on BlueSky that “I’ve seen the paper. It’s not a jailbreak.” Former Commerce Department official Kate Koren speculated to the WSJ that the White House’s dislike of Anthropic may have influenced the decision.
Anthropic and the Trump administration have been at odds for some time over the company’s refusal to allow its AI to be used for mass surveillance of Americans or to power lethal autonomous weapons. In February, Trump instructed federal agencies to stop using Anthropic’s AI. And just hours later, Secretary of Defense Pete Hegseth designated the company a supply chain risk.
The government and the company seemed to have made amends, and the two had worked together to expand access to Mythos. However, now the two seem destined to clash again.
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