Democratic Illinois Gov. J.B. Pritzker ripped into Chicago Mayor Brandon Johnson for lacking any sort of plan to keep the Bears in the city, despite the mayors claims that he wants them to remain in

Speaking in an interview with CBSs Margaret Brennan on Face the News, Sen. Bill Cassidy (R-LA), the RINO who lost his seat in the primary process in a big way, becoming the first incumbent Republican

As I sip coffee in my Berlin apartment and fire a question at Google's AI chatbot Gemini, it's easy not to think about the energy it takes to generate a response. Once the signal reaches my router, it whizzes, I assume, through copper wires or fiber-optic cables to one of Google's data center hubs. Somewhere inside the data center's labyrinthine halls of stacked processors, my query gets converted into numbers and undergoes billions of computations to determine context and meaning. The answer, once assembled, races back, in the blink of an eye.Data centers the beating hearts of the internet, powering everything from email to web searches have existed for decades, but with the growing popularity of AI to generate text, images and video, they're using more energy than ever. According to Google's own estimates, processing a median-length text prompt with its AI assistant Gemini consumes around 0.24 watt-hours.These amounts, individually small 0.24 watt-hours is equivalent to watching TV for about nine seconds are adding up fast. In March 2026, OpenAI estimated that more than 900 million people use its AI chatbot, ChatGPT, every week, tallying billions of queries daily.The exact amount of electricity consumed by data centers, globally or in the United States, which hosts more than any other nation, isn't publicly reported by all tech companies, says Eric Masanet of the University of California, Santa Barbara, who researches data center sustainability. But according to the most recent estimates by the International Energy Agency, US data centers guzzled some 224 terawatt-hours of electricity in 2025 more than 5 percent of the country's electricity use. That's a significant uptick from an estimated 1.9 percent consumed in 2018, well before the mainstream surge of generative AI.This electricity use seems set to soar. In the race to secure market leadership for generative AI products, companies like Google, Meta, Amazon, OpenAI, Anthropic, Microsoft and Oracle are investing tens to hundreds of billions of dollars to build AI-focused data centers. Compared to data centers of the pre-AI days that consume, say, 100 megawatts of electricity enough to power 83,000 homes with average demand the newcomers are often "hyperscale" and can use a gigawatt or more, or roughly a tenth of the electrical capacity of Los Angeles.Masanet and other experts have been alarmed to see much of this demand met by plants powered by fossil fuels, such as gas, whose burning releases planet-warming carbon dioxide. A key reason is that data centers are often constructed in places without abundant renewable energy sources like hydropower, geothermal, solar or wind.Tech companies often offset emissions by investing in renewable energy elsewhere. But unless those clean energy plants make more energy than the data centers use, this strategy at best keeps CO2 emissions of centers in stasis rather than reducing them to a net of nothing, important for halting global warming. "For every megawatt for which we install fossil fuel power," Masanet says, "it sets us back on our progress."And that's not considering the resources spent on manufacturing the hardware that fills new data centers, or the impacts on communities living near them, which often suffer from air and noise pollution from gas plants and possible strain on local water resources, which are used to cool the data centers.Many data centers in the US are concentrated in the Virginia area, according to a non-exhaustive database from the International Energy Agency. (Image credit: IEA / ENERGY AND AI OBSERVATORY 2025. CC BY 4.0)Although forecasts for AI's energy impact remain devilishly tricky, especially since the size of payoffs from investments in AI are uncertain, it's clear to experts that energy-saving strategies are urgently needed. Without them, according to one 2025 estimate, US data centers could soon be releasing the equivalent of 24 to 44 megatons of CO2 annually, the latter equivalent to the annual emissions of Norway.And so computer scientists and engineers are rethinking some of the power-hungry hardware and software that fuel AI. They're working to develop energy-saving algorithms and processor designs, and carefully considering where, and how, data centers are constructed."AI's energy cost is not an accident: This is basically a product of how our systems are built," says Fengqi You, an expert in energy systems at Cornell University. But with the right mix of solutions, he says, "we could really reshape the trajectory."The roots of AI's energy problemTo comprehend AI's energy cost, it helps to understand large language models (LLMs) the lifeblood of AI text generation tools such as chatbots and AI assistants specifically, ones based on a design described in 2017 by the machine-learning laboratory Google Brain. This design, transformer architecture, can process text at lightning speed by simultaneously taking each word and weighing its relationship to every other word it sees. It "learns" which words go together by computing how strongly each word relates to all other words in a text, examining each word in many contexts. (A similar design is used for AI image and video generators.)On a computational level, this happens by converting words or word fragments into numbers and performing additions and multiplications between them. Key to the speed is being able to do these calculations in parallel, made possible by graphic processor units (GPUs) mostly manufactured by the company NVIDIA originally invented for rapid 3D rendering of imagery during gaming.Manufacturers of the processing chips that fuel AI computations are working to make the chips more energy efficient; examples are the latest AI-specialized chips developed by NVIDIA. (Image credit: NVIDIA)The initial training of an LLM, required to learn all these relationships, consumes vast amounts of energy. Because each word it trains on must be weighed against all others in a given chunk of text, the number of computations the model performs hence the energy required increases quadratically relative to the length of text (i.e., doubling the length of text quadruples the number of computations). That adds up quickly given that most LLMs are trained on massive swaths of publicly available internet text. Some estimates suggest that training GPT-4 the iteration of ChatGPT that launched in 2023 guzzled between 50 and 60 gigawatt-hours of electricity, enough to power San Francisco for three to four days.But experts are more worried about the energy costs of using the models to generate data once they've been trained, a process called inference. "You train once, then you inference for a billion people in the world," says Mosharaf Chowdhury, an AI systems expert at the University of Michigan who has been measuring the electricity usage of a handful of large language models that have been made publicly available.This process is surprisingly inefficient: Each time transformer models generate a word by selecting the one with the highest probability of following the previous word, given context they put the query and partially written answer through the model. In doing so, they apply all of the parameters they've calculated during training to understand language patterns which number in the hundreds of billions or even trillions."The fact that you have to do a lot of calculations for a single word to be added thats a problematic thing," says Gnter Klambauer, an AI expert at Johannes Kepler University in Austria.Tweaking AI software to save energyThis recognition has triggered interest in smaller language models specialized to specific tasks. These are trained more narrowly, have fewer parameters say, tens or hundreds of millions and perform substantially less computation than larger models. In one 2025 paper published by UNESCO, computer scientist Ivana Drobnjak of University College London and colleagues compared energy consumption of Meta's language model Llama-3.1 with smaller AI models dedicated to particular tasks ones called DistilBART and t5-small-xsum for summarization, and others for translation or answering questions. When used for their respective tasks, the smaller models consumed more than 90 percent less energy than Llama 3.1 on the same job.And so computer scientists have been driven to build a similar kind of task specialization into LLMs themselves. In "mixture of expert" models, only particular parts of one big model are activated for certain tasks. These parts "learn to handle different patterns in language," Drobnjak says.This is thought to be one reason why R1, an LLM developed by the Chinese company DeepSeek, reportedly consumed significantly less energy than other models (independent experts have raised doubts about those figures). Udit Gupta, an expert in electrical and computer engineering at Cornell Tech, says that LLMs like Gemini or ChatGPT are similarly routing queries to more specialized sub-models. "There's a lot of work being done on how to assess the complexity of the query or task that's coming from users and then find the right model," Gupta says. (While Google spokesperson Ralf Bremer notes that the 0.24 watt-hours currently spent on processing median-length Gemini prompts is already 33 times more efficient than it was back in 2024, some experts suspect that processing queries with an LLM still consumes more energy than an equivalent web search.)Scientists are also exploring different kinds of LLMs, to break what Klambauer calls the "quadratic curse" of transformer models.One alternative, called a long short-term memory (LSTM) model, gets around this alarming energy increase by temporarily storing a kind of summary of the prompt that was inputted by the user plus the text generated so far, akin to recalling important plot points instead of an entire movie. That way, it only has to process the summary, rather than all the words in the full text to date, every time it generates a new word. This prevents LSTM's energy costs from skyrocketing as it responds to a query using about 50 percent less energy than transformer-type models to process texts of around 8,000 words in length, Klambauer says.LSTM models were developed in the 1990s but were abandoned because transformers could be trained much faster. But Klambauer says that recent advances have improved the performance of LSTM, now called xLSTM. He's working with the Austrian startup NXAI to further develop and optimize xLSTM, "because we think it's worth it for energy efficiency," he says.But major tech companies have invested so many years and resources into developing transformer-based models that switching to other models would be costly, says Wolfgang Maa, an AI and business informatics researcher at the German Research Center for Artificial Intelligence. "We have to see whether this becomes as dominant, or whether it finds a niche in the whole market."Computing with wafers and lightThough experts say the fastest energy savings will come from software tweaks, some are also taking aim at the energy-hungry processing chips that fuel AI computations. Engineers have made chips increasingly efficient over time by packing more computing capacity into individual processors reducing the energy required to shuttle data between chips that are working together to perform AI computations. Engineers have done this by shrinking the size of transistors microscopic electrical switches that process data inside the chips.But because engineers are reaching the physical limits of how small transistors can be, "we need to think of alternate ideas to improve the designs," says computer architect Ajay Joshi of the Boston University Photonics Center.One strategy is to make the chips larger. Dinner-plate-sized "wafer-scale chips" can pack nearly 70 times as many transistors as a single, postage-stamp-sized GPU and consume 143 times less electricity for communication than comparable GPUs, says computer engineer Rakesh Kumar of the University of Illinois Urbana-Champaign. Commercially produced by the California company Cerebras, wafer-scale chips have drawbacks, including a greater risk of damage during manufacturing. But because of their energy-saving and other beneficial features, "they would be very attractive to many hyperscalers and AI companies," Kumar says.One strategy to make processors more efficient is to make them larger so they can contain more transistors, the building blocks of computers. "Wafer scale" chips, such as those developed by California-based manufacturer Cerebras, reduce the energy spent on shuttling information between individual chips. (Image credit: CEREBRAS SYSTEMS)Many tech companies have improved energy efficiency by fashioning their own processors that are tailor-made for AI computations such as Amazon Web Service's Trainium2 chip or Google's Ironwood Tensor Processing Units according to statements from those companies. As for NVIDIA, the company's head of sustainability Josh Parker says its AI-specialized GPUs have come a long way from the ones used for gaming and are now designed to run AI tasks as efficiently as possible; other innovations, such as making the interconnections between GPUs more efficient, have also helped. "Over the past eight years, NVIDIA GPUs have improved 45,000 [times] in energy efficiency for large language model workloads," he says.Engineers are also exploring alternative computing methods. Conventional AI processors calculate by encoding numbers in a binary system of ones and zeros, which is achieved by turning transistors on and off (representing the number 5, for instance, requires four transistors to represent the code 0101). But transistors can do more than function as binary switches allowing electron flow or not; they can also work as analog dials and hold intermediate voltages representing different numbers. That requires fewer transistors, and less energy, for computations. "People have known for decades that doing certain things in analog can be a lot more energy efficient," Kumar says.For example, electrical engineer Paul Manea of the German research institute Forschungszentrum Jlich and colleagues are working to develop devices called "gain cells" that are full of transistors working this way. Importantly, gain cells can both store the data required to process a query, and compute the answer. That overcomes another big energy bottleneck of conventional computing systems, where memory storage and computation occur on separate pieces of hardware.That's especially problematic for transformer-based LLMs, because each time they generate a word, they must shuttle the query and partially written answer from memory to a processor. Manea and colleagues estimate that gain cells in lieu of traditional GPUs can reduce the energy guzzled by one of the most energy-consuming parts of transformer-based LLMs by four orders of magnitude. But it will take more refining before they can be more widely used, Manea says.The notion of devices that both store and compute information is a key idea of "neuromorphic" computing, an up-and-coming field of computer engineering inspired by the human brain, which consumes orders of magnitude less energy than computers. Another brain-inspired invention is chips that encode information not in continuous data streams but like human nerve cells in the timing of voltage "spikes" propagating through the system. Allowing components to rest until they're needed "could potentially translate to less energy," says Eleni Vasilaki, an expert in bioinspired machine learning at the University of Sheffield in England.Maa, for example, is part of a team that received roughly $5.8 million from the German government to test neuromorphic chips, among other strategies, to reduce the energy required for AI models. Some brain-inspired chips are already commercially available, but the technology is still far from being attractive for mainstream computing, says nanoelectronics expert Tony Kenyon of University College London, whose team recently received $17 million from the UK government to develop neuromorphic computing.Other scientists are developing chips that process information not with electrons but through the interaction of photons particles of light with matter (fiber-optic cables, which encode and transmit data as light pulses, are used around the world). With photons, more information can be transmitted at the same time, and signals can be altered much faster, says Elena Goi, a photonic computing researcher at Friedrich Schiller University Jena in Germany.Several companies have developed chips that can perform some AI computations with optical methods, says Joshi; he recently estimated that manufacturing optical chips could consume up to an order of magnitude less energy than conventional ones of the same size. Joshi hopes that, "in 10 years, we would have a practical solution that can be deployed pervasively across the data centers."Reshaping AI's energy trajectoryEven without reinventing how computers work, much can be done to reduce AI's impact not just on energy but also on water resources used for cooling data centers. Importantly, tech companies should reconsider where they build those centers, says energy systems expert You. Right now, existing US ones are concentrated in northern Virginia, which has limited water resources and renewable energy capacity compared with the Midwest, for instance. You recently estimated that better siting along with energy-efficient hardware and software could reduce future carbon and water footprints of US data centers by 73 percent and 86 percent, respectively.Data centers and the gas plants often built to power them can cause air and noise pollution and add further strain on local water resources, leading many communities to oppose their construction. (Image credit: SARA DIGGINS / THE AUSTIN AMERICAN-STATESMAN VIA GETTY IMAGES)Masanet adds that tech companies already with data centers across the country could at least train their models in strategic places. "Some companies like Google have been doing this: They shift their loads to follow renewables," he says. They also should address the electricity and resources spent on manufacturing processors for new data centers, as well as electronic waste as outdated tech is replaced every few years, he adds.Minimizing e-waste by using hardware for longer periods and recovering old electronics is one of Amazon's sustainability strategies, according to a statement to Knowable Magazine; so is designing data centers in energy- and water-saving ways and investing in a slew of renewable and nuclear energy projects. "We'll continue to implement solutions that benefit our customers and the communities we operate in," says Brandon Oyer, Amazon Web Services' head of energy and water in the Americas.Meanwhile, a press representative at Microsoft points to a number of sustainability initiatives the company has taken, including new cooling technologies, renewable energy investments and waste reduction. Google spokesperson Ralf Bremer emphasized the company's goal of reaching net-zero emissions across its operations by 2030 and replenishing 120 percent of the fresh water consumed by its offices and data centers by 2030. An OpenAI representative points to a press release outlining efforts to minimize water use and plans for solar energy generation at one of its campuses. Anthropic, Meta and Oracle did not respond to requests for comment by deadline.Though tech companies are taking sustainability into consideration, their main objective is to rapidly build out data center capacity, says computer engineer Benjamin Lee of the University of Pennsylvania. He predicts that, eventually, they'll need to step up efforts to improve energy efficiency to reduce costs. Governments should help to accelerate this shift, Masanet says. So far, he and his team have counted nearly 220 policies introduced to address data center sustainability at the US state level, 18 at the federal level, and more from other countries, though not all were ultimately adopted."It's clear that governments around the world are beginning to take action," he says. However, he adds, "we also see some state and local governments with proposed policies that mostly aim to incentivize and accelerate data center builds."The Industrial Sustainability Analysis Laboratory at the University of California, Santa Barbara has been tracking state and federal policies related to data centers. The vast majority of these policies relate to data center sustainability in some way, although they also include some tax incentives. This dataset may not be exhaustive. (Image credit: Knowable Magazine)Related storiesWhat's the biggest bottleneck to building better AI? It's no longer the lack of computing resources it's generating enough energy to feed itMIT's chip stacking breakthrough could cut energy use in power-hungry AI processesScientists build specialist 'AGI processor' that they believe will power the next wave of AI agentsAI's energy cost will ultimately be a balancing act: Will it save more resources through its problem-solving abilities deployed toward everything from finding cancer cures to improving logistics, than it demands? But though building a more frugal, energy-saving AI is important, so is carefully considering where AI is needed, Kenyon says. Is the world truly a better place, for example, with nonhuman "AI agents" providing customer support?"I think its a common mistake, when a new technology comes in, to suddenly think, 'Well, everything has to adopt that new technology,'" he says. "That approach really isn't doing us any favors."This article originally appeared in Knowable Magazine, a nonprofit publication dedicated to making scientific knowledge accessible to all. Sign up for Knowable Magazine's newsletter.

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Delicious Crock Pot Chili Cheese Tater Tot Casserole is an easy and flavorful dinner thats loaded with beef, cheese, and crispy tater tots.A Filing and Hearty Dinner RecipeI dont care how old I get, I love tater tots! This Crock Pot Chili Cheese Tater Tot Casserole is an easy, family-friendly dinner that comes together with very little prep. If you know me, you know I love this kind of meal! It really is a whole meal in one or you can serve it with some steamed veggies or a salad to round it out.Frequently Asked Questions:Why are the tots not cooking evenly? Its all about how theyre laid in the Crock Pot to cook. Layer tots as evenly as possible so they cook consistently. You dont want them overlapping each other (if possible.) Can I cut this recipe in half? Absolutely. This recipe can easily be halved and cooked in a 4-quart slow cooker. The cooking time should stay fairly close to the original recipe, but start checking a little earlier. Can I make this in the oven instead? Yep! Prepare the beef mixture on the stovetop, then spread it into a greased 913-inch baking dish. Top with the tater tots and bake at 375F for about 40-45 minutes until hot and bubbly. Sprinkle the cheese on during the last 10 minutes of baking. How do I keep the tater tots from getting soggy? Placing a layer of paper towels underneath the slow cooker lid helps absorb excess moisture while the casserole cooks. Also, avoid lifting the lid too often during cooking. What else can I add? Black beans, kidney beans, or pinto beans can easily be added for extra protein and fiber and to stretch the meal a bit further. I am at that age where I gotta think about the fiber yall. Ha! You can bring some heat by adding in some jalapeos. Bring some creaminess to this dish by adding a bit of sour cream. What sides go well with this Chili Cheese Tater Tot Casserole? This casserole pairs well with a simple green salad, coleslaw, roasted vegetables, corn on the cob, garlic bread, dinner rolls, or fresh fruit. Is this spicy? Most people would not consider this recipe spicy at all. The tomatoes with green chiles add flavor more than heat. If you are super sensitive to spice, you can use regular diced tomatoes instead. How to store leftovers? Store leftovers in an airtight container in the refrigerator for up to 4 days. Reheat in the microwave or oven until heated through. You can also freeze leftovers for up to 3 months. Thaw overnight in the refrigerator before reheating. Ingredients Needed: (See Recipe Card Below For The Full Recipe)ground beef Ground turkey or ground chicken work great in this recipe and can sometimes be a more budget-friendly option than ground beef. You may want to add a little extra seasoning since they are milder in flavor. I find in my area, that its cheaper to buy frozen ground turkey in a tube than fresh ground turkey, but this will vary depending on store and region. I also know that buying in bulk can bring the price per pound down as well, so that can help if needing to cut overall prices for groceries down. You can freeze any leftover ground meat as well for later. onion fir you dont like onion, just leave it out. A mild sweet onion works well here.mild chili seasoninggarlic you can use freshly minced or the jarred stuff.diced tomatoes with green chiles like a can of Rotel. If you dont want to use Rotel, you can use a can of diced tomatoes. tomato saucefrozen tater tots theres no need to thaw them first.shredded cheddar cheese Pepper Jack, Colby Jack, Monterey Jack, or a Mexican cheese blend all work well in this recipe. I have found that cheese that is freshly shredded from the block melts better than the pre-shredded stuff in a bag. But, if you only have that stuff, it should work, it just may need more time to melt. sliced green onions this is an optional ingredient that I used for garnish.How To Make Crock pot Chili Cheese Tater Tot CasseroleCook and crumble the ground beefIn a large skillet, brown and crumble the ground beef until fully cooked. Drain any excess grease. Note: If you like your onions to be super soft, cook the onions along with the ground beef to soften.To a 6 quart slow cooker add the ground beef and onion.Add everything to the crock potTo the slow cooker add salt, pepper, chili seasoning, garlic, diced tomatoes with green chilies, tomato sauce and half cup of cheese. Spread tater tots in a even layer on top of ground beef mixture.Cover and cookCook on low for 45 hours or on high for 2-3 hours (note: it is recommended to cook on low.) To help keep the tater tots from getting too soggy from the steam, put a layer of about 2-3 paper towels under the lid of the slow cooker.Sprinkle cheese on topDuring the last 15 minutes of cooking, sprinkle the remaining cheese on top of the tater tots and cover until melted.Craving More Recipes?Crock Pot Chicken Tater Tot CasseroleChili Cheese Dog Tater Tot CasseroleTater Tot CasseroleTater Tot Chicken Pot PieTater Tot Enchilada Bake PrintCrock Pot Chili Cheese Tater Tot CasseroleThis Crock Pot Chili Cheese Tater Tot Casserole is a simple dinner layered with seasoned beef, cheesy chili sauce, crispy tater tots, and melted cheddar cheese.Course Dinner, Main CourseCuisine AmericanPrep Time 15 minutes minutesCook Time 5 hours hoursTotal Time 5 hours hours 15 minutes minutesServings 8Calories 635kcalAuthor Brandie SkibinskiIngredients2 pounds ground beef1 small onion, diced1 teaspoon salt teaspoon black pepper2 (1 ounce) packets mild chili seasoning (can use no or low sodium)2 cloves garlic, minced10.5 ounces can diced tomatoes with green chiles, drained (like Rotel)15 ounces tomato sauce28 ounces frozen tater tots (no need to thaw first)2 cups shredded cheddar cheese (divided use)sliced green onions (for garnish, optional)InstructionsIn a large skillet, brown and crumble the 2 pounds ground beef until fully cooked. Drain any excess grease. Note: If you like your onions to be super soft, cook the 1 small onion, diced along with the ground beef to soften.To a 6 quart slow cooker add the ground beef and 1 small onion, diced.To the slow cooker add 1 teaspoon salt, teaspoon black pepper, 2 (1 ounce) packets mild chili seasoning, 2 cloves garlic, minced, 10.5 ounces can diced tomatoes with green chiles, drained, 15 ounces tomato sauce and half cup of cheese. Spread 28 ounces frozen tater tots in a even layer on top of ground beef mixture. Cook on low for 45 hours or on high for 2-3 hours (note: it is recommended to cook on low.) To help keep the tater tots from getting too soggy from the steam, put a layer of about 2-3 paper towels under the lid of the slow cooker. During the last 15 minutes of cooking, sprinkle the remaining cheese on top of the tater tots and cover until melted.Then serve! NotesPlease refer to my FAQs (Frequently Asked Questions) and ingredient list above for other substitutions or for the answers to the most common questions.If you are concerned about this being too salty or you, use low or no sodium chili seasoning. You can always season with salt later but you cant take it out once it is in there.NutritionCalories: 635kcal | Carbohydrates: 36g | Protein: 30g | Fat: 43g | Sodium: 1260mg | Fiber: 6g | Sugar: 4g