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Yahoo
6 minutes ago
- Yahoo
Your AI use could have a hidden environmental cost
Sign up for CNN's Life, But Greener newsletter. Our limited newsletter series guides you on how to minimize your personal role in the climate crisis — and reduce your eco-anxiety. Whether it's answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people's lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up. Each word in an AI prompt is broken down into clusters of numbers called 'token IDs' and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations. The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute. So, for each prompt you give AI, what's the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers. In addition, 'smarter' LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported. 'This shows us the tradeoff between energy consumption and the accuracy of model performance,' said Maximilian Dauner, a doctoral student at Hochschule München University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday. Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models. 'You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,' Dauner said. Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said. 'AI expends a lot of energy being polite, especially if the user is polite, saying 'please' and 'thank you,'' Dauner explained. 'But this just makes their responses even longer, expending more energy to generate each word.' For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don't need an explanation at all. Most important, Dauner's study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task. 'Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,' Luccioni explained. If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator. Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said. When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks. Putting a number on the environmental impact of AI has proved challenging. The study noted that energy consumption can vary based on the user's proximity to local energy grids and the hardware used to run AI partly why the researchers chose to represent carbon emissions within a range, Dauner said. Furthermore, many AI companies don't share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI's water consumption. 'You can't really say AI consumes this much energy or water on average — that's just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,' Ren said. One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested. 'Generally, if people were more informed about the average (environmental) cost of generating a response, people would maybe start thinking, 'Is it really necessary to turn myself into an action figure just because I'm bored?' Or 'do I have to tell ChatGPT jokes because I have nothing to do?'' Dauner said. Additionally, as more companies push to add generative AI tools to their systems, people may not have much choice how or when they use the technology, Luccioni said. 'We don't need generative AI in web search. Nobody asked for AI chatbots in (messaging apps) or on social media,' Luccioni said. 'This race to stuff them into every single existing technology is truly infuriating, since it comes with real consequences to our planet.' With less available information about AI's resource usage, consumers have less choice, Ren said, adding that regulatory pressures for more transparency are unlikely to the United States anytime soon. Instead, the best hope for more energy-efficient AI may lie in the cost efficacy of using less energy. 'Overall, I'm still positive about (the future). There are many software engineers working hard to improve resource efficiency,' Ren said. 'Other industries consume a lot of energy too, but it's not a reason to suggest AI's environmental impact is not a problem. We should definitely pay attention.'
Business Insider
17 minutes ago
- Business Insider
Flying Taxi Stocks Surge Into Focus after Trump Order – Is JOBY or ACHR the Better Buy?
Shares of flying taxi makers got a boost this month after President Trump signed an executive order to speed up the certification process for electric vertical take-off and landing aircraft (eVTOLs). The move aims to help the U.S. lead in this technology and opens the door for companies like Joby Aviation (JOBY) and Archer Aviation (ACHR) to bring their aircraft to market more quickly. An accelerated approval will no doubt enhance both stocks' reputation and their market position. Confident Investing Starts Here: The order directs the FAA to fast-track approvals and set up pilot programs for real-world use cases like cargo transport and emergency medical flights. It also includes a global certification plan with Canada, the UK, Australia, and New Zealand. Archer and Joby Are in Prime Position Both Joby and Archer are already working with the U.S. Air Force through the Agility Prime program. Joby has $131 million in military contracts, and Archer has up to $142 million. These contracts not only generate revenue but also validate the technology in real operational settings. Beta Technologies, another player in the space, has completed thousands of takeoffs and landings with the military and reports a strong dispatch rate. Its CEO said the company has visited around 10 bases and secured hundreds of millions of dollars in defense funding. While eVTOLs are often associated with urban air taxis, the first use cases are likely to be in defense, healthcare, and cargo. These areas are less sensitive to pricing and more focused on reliability, which plays to the strengths of early eVTOL platforms. Both Main Street Data's charts below show the slight differences between the two companies. Archer's income statement shows limited revenue and steady losses, suggesting Archer is still in early development. Joby's chart, by contrast, shows some revenue spikes and variable losses, pointing to early-stage operations and partial monetization, likely from defense or pilot programs. What's Next for Both Companies Joby and Archer could also qualify for the new federal pilot programs. Both companies have logged test flights and are well into the FAA's certification process. That gives them a head start over newer or less capitalized competitors. With clear government support, a path to commercialization, and proven military partnerships, JOBY and ACHR stand out. Investors are closely watching as the sector transitions from concept to execution. Flying taxis are still in their early stages, but the timeline has just gotten shorter. Investors looking at advanced air mobility now have a clearer idea of which companies could be first to generate real revenue.
WIRED
19 minutes ago
- WIRED
Tesla's Robotaxi Service Hits the Road in Texas
The company debuted its autonomous ride-hailing service Sunday. The limited program is invite-only and uses around 20 cars—signs that Tesla has a long way to go to catch up to its robotaxi rivals. Photograph: DavidAfter nearly a decade of waiting, Tesla has launched a limited self-driving car service in the Austin, Texas, area. Company executives, including Musk, have said the autonomous vehicle technology debuting today is critical to Tesla's future. The limited service, which for now is only open to early users invited by Tesla, includes some 20 2025 Model Y sedans available for rides through a Tesla-made app between 6 and 12 am. Terms of service posted on X by invited riders indicate that the service will be paused or limited for bad weather. Rides during this invite-only phase are available for a flat $4.20 fee, Musk posted on X Sunday. People who scored one of the limited invitations—several of whom traveled to Texas this weekend to participate in the launch—were able to start taking rides around 2 pm local time on Sunday. The company has said that its purpose-built Cybercab will go into production next year; for now, Model Ys will be the only Teslas driving autonomously as part of the program. According to screenshots posted on X, the service appears to only pick up and drop off in an area of Austin limited to part of the south side of the city, just across the Colorado River from downtown. The service area appears to include the bustling thoroughfares of South Congress Avenue and South Lamar Boulevard. The service cannot go to the local airport, Austin-Bergstrom International, which is about five miles from downtown. Those invited to try the service can bring one guest on the ride, as long as they are 18 or older. In an email to invitees posted on X this week, Tesla said that a company employee would sit in the front passenger seat of each robotaxi. Launching an autonomous vehicle service with a 'safety driver' is not unusual. Alphabet subsidiary Waymo launched its service with a safety driver in 2018, as did General Motors' Cruise in 2020. The Michigan company May Mobility says it will do the same when it starts service in Atlanta this year. But a Tesla safety monitor in the passenger seat—not the driver's seat—likely won't be able to grab the steering wheel or hit the brakes in the case of a road incident. Tesla's robotaxi service will also likely be augmented by teleoperators: drivers who can, when needed, advise or perhaps even pilot the car remotely to get it around an unorthodox obstacle or out of a sticky situation. Musk has been promising Tesla robotaxi technology since October 2016, when he told investors every vehicle his company produced from then on had all the hardware needed to become self-driving. That wasn't true; Tesla has since updated the hardware on its vehicles. In 2019, Musk said Tesla would have 1 million robotaxis on the road by the next year. (It didn't.) Musk said earlier this year that the company will have hundreds of thousands of robotaxis on public roads next year. The company has said that Tesla owners will eventually be able to transform their own cars into self-driving taxis that can collect fares while they're not being used. But the company released no timeline Sunday for that plan. Tesla's driver assistance technology has been the subject of federal safety probes, two recalls, and customer complaints related to reports that the vehicles suddenly brake for no apparent reason and can collide with stationary objects—including emergency vehicles. That tech, which includes the older Autopilot feature and the newer Full Self-Driving (Supervised) feature, is distinct from Tesla's autonomous features. With the assistance features, the drivers are required to stay behind the wheel and keep their eyes on the road at all times. Autonomous features don't require any driver action or attention. Issues with those older technologies raise questions about the safety of Tesla's new autonomous tech, says Sam Abuelsamid, an auto analyst who focuses on autonomous technology at Telemetry Insight. Full Self-Driving (Supervised) 'will work fine for perhaps hours at a time and then randomly make very serious mistakes in ways that are not necessarily repeatable,' he says. Unlike other autonomous technology developers, which use a number of pricier sensors to detect obstacles around their vehicles, Tesla depends only on cameras. Some experts have cast doubt on that choice, which could potentially lead to issues with sun glare and has been blamed for previous Tesla collisions with emergency vehicles. But financial experts say the approach could give Tesla an advantage in getting its less expensive tech in the hands of consumers more quickly. Tesla did not respond to questions about robotaxi safety. Musk said earlier this month that the company is 'being super paranoid about safety.' Heavy Traffic Tesla enters a suddenly busy American autonomous vehicle space. Waymo first launched a driverless service in metro Phoenix, Arizona in 2020, and now operates in parts of the San Francisco Bay Area, Los Angeles, and Austin. It is slated to soon open service in Atlanta, Georgia, and Miami, Florida, where customers can order a Waymo using the Uber app. Amazon-owned Zoox says it will launch its own autonomous service in Las Vegas later this year. May Mobility is aiming to offer rides around Atlanta through the Lyft app this year. VW's Moia subsidiary announced this spring that it would launch a self-driving service in Los Angeles in 2026, also on the Uber app. The experiences of those companies show that Tesla has several logistical hurdles to jump before its robotaxi service expands widely. There are the human roles: Remote assistance workers might be on hand to help confused riders remotely; maintenance workers might repair cars during their downtime; cleaners might clear away trash, lost items, or anything worse left behind by riders. There are infrastructure needs, too. VW's Moia has operated an electric ride-sharing service in Hamburg, Germany since 2019, using that experience to prep for eventual driverless cars. The firm has determined that it will need a well-developed and decentralized footprint across any city it services. Scattered depots will 'host the vehicles and provide charging and maintenance infrastructure, and also the opportunity to do constant safety checks for the vehicle,' says Sascha Meyer, the company's CEO. In other words: There's a big difference between a handful of self-driving cars and a self-driving service.
