AI is making your life more expensive. Here’s how

AI is raising prices for Americans – and not just electricity bills. Major technological innovations carry the potential to transform economies by creating opportunities, jobs and even new industries while supercharging productivity and growth. However, that promise of longer-term gains often is preceded by short- and medium-term pain. In the case of artificial intelligence, that has included job losses, slower wage growth, widening wealth inequity and, especially in recent months, higher inflation. Recent data shows that the gargantuan interest and investment in AI adoption (estimated to be around $750 billion for this year alone) have pushed a variety of prices higher, lifting overall inflation in the process. “The higher inflation means that households must spend just over $375 more to purchase the same goods and services as they did this time last year due to AI’s inflationary impact,” Mark Zandi, chief economist at Moody’s Analytics, wrote in an email to CNN. The good news: AI is still just a minor contributor (an estimated 0.2 percentage points) of overall inflation, and the impacts are currently limited to a handful of categories. But the not-so-good news: AI is pushing inflation higher and further compounding longstanding affordability concerns in the process. Plus, these price pressures aren’t expected to go away anytime soon, and they very well could broaden. That potential dynamic has Federal Reserve officials, including the central bank’s new chairman, on alert. Here’s a look at where AI has already shown up in inflation and where it could crop up next. Electricity AI data centers can have voracious appetites for energy (notably electricity and water), and the rapid expansion of these monoliths threaten to strain grids and drive prices up further. That’s largely because demand is outrunning supply. Data center facilities can be built or expanded at double or triple the pace of new electricity generations systems needed to serve them, PJM Interconnection, America’s largest grid operator, noted recently. Combine those needs with retiring coal plants, increased electrification needs, extreme weather and aging infrastructure, and it further widens the gap between supply and demand. “Data centers are demanding huge amounts of power, and that’s tending to crowd out the electricity available to distribute to residents; it’s also led to wholesale electricity prices being bid up, because data centers are willing to pay the price that providers ask them for, and that ends up also raising the prices for residential electricity costs,” Pooja Sriram, US economist at Barclays, told CNN. US Consumer Price Index data shows that residential electricity prices rose about twice as fast in 2025 as compared to the average seen in years prior, she noted. And through the first five months of this year, electricity prices were climbing even faster than in 2025, Bureau of Labor Statistics data shows. “I think that is one of the clearest imprints of AI data center demand driving up residential electricity costs,” she said. Electricity prices unexpectedly fell 1% in June but continue to outpace overall inflation and are up 4% from a year ago, the latest CPI data shows. Memory chips The data centers’ appetites, however, aren’t fully sated with power alone. The massive buildouts also led to a surge in demand for memory chips, which has benefited manufacturers handsomely, Sriram said. “The issue is not just the demand; the issue is the supply side for those memory chips has been very constrained,” she said. The trillions of dollars chasing AI-related components now have storage and memory suppliers prioritizing their wafer-manufacturing capabilities toward high-bandwidth and high-speed (and highly profitable) memory products commanded by data centers, she said. “What that does has basically diverted (the production of) the memory chips that you need for consumer products toward very specific high-performance memory chips that the data centers need,” she said. The pricing pressures of these and other components have been most evident at the producer level. The Producer Price Index chart semiconductor and other electronic component manufacturing industry looks like a hockey stick. As of June, that category’s wholesale prices were up 26% from a year ago – a stark shift from June 2025 when prices were falling 0.8% on an annual basis, PPI data shows. “Why this matters for the end consumer is, at the end of the day, our laptops, computers, iPhones, iPads all have some sort of memory chip embedded in that hardware, and those chips have become quite expensive,” she said. Computer hardware and software Late last month, Apple hiked the prices for some of its most popular products by roughly 20%. In a statement, the company noted that AI data centers created an “extraordinary surge” in demand for memory and storage. Sony upped the price of its PlayStation console earlier this year, and Microsoft last month raised the price of its Xbox consoles by about 25% for similar reasons. “The entire consumer electronics industry is struggling with the current components crisis, but the effects are particularly hard on consoles,” Microsoft wrote in a statement. Computers and related hardware have typically been a highly deflationary product category in the CPI: Because of technological advancements, consumers can get more bang for their buck. (For example, a $1,500 computer in 2025 was likely more powerful than a $1,500 year-ago model, and as such, the BLS treats this as a price drop) For the first half of 2026, however, computers and related products have experienced price inflation, BLS data shows. “We’re in the early innings of these consumer price pressures and the pass-through from higher producer prices, higher import prices and greater demand, especially for AI-led investment,” said Gregory Daco, chief economist at EY-Parthenon. Adding AI features in business applications also affects the price of software. For example, Microsoft raised personal Office 365 prices by 43% in February (30% for a family plan) after keeping them steady for a decade. The new feature: Copilot, Microsoft’s new AI tool. Construction costs, wages Data centers also are impacting the supply of other key construction inputs such as copper and electrical wiring, as well as workers. “If you’re looking for data that was conclusive (about AI’s effect on the economy and inflation), albeit a bit more subtle, you would look and see whether wages in construction were going up more than wages in the rest of the economy,” said Thierry Wizman, Global FX and rates strategist at Macquarie Group. “Because if in fact there is upward pressure, straining resources of the economy because of the AI data center buildout, you would see it in wages as well – specifically in the wages of labor that would be working these projects,” he added. So far, the available national-level wage data is showing a “robust divergence” between the construction sector and the aggregate, he noted. Regional data could prove even more telling, he said, noting the importance of tracking construction wages in areas with a high concentration of data centers. (That will take more patience, however, as that more localized data is lagged due to collection and modeling needs). “We’re having a problem with housing in the country these days; people talk about it as being unaffordable,” Wizman said. “It could be the case that the fact wages in construction have been rising a lot is putting upward pressure on houses as well.” By Alicia Wallace

Why China’s New AI Model Has Silicon Valley and Washington on Edge

A little more than a year-and-a-half after China‘s DeepSeek put fear in the hearts of U.S. AI companies, another Chinese model has been released that further narrows the gap between Chinese and the most advanced U.S. systems. Chinese startup Moonshot on Friday unveiled Kimi ​K3. It’s an open-weight model, meaning developers can access, study, and build on top of its architecture and users can download, run, and customize the underlying systems, unlike ​closed-source models most commonly used in the U.S. Moonshot claims Kimi K3 surpasses top systems from both OpenAI and Anthropic in some benchmarks (though it falls short of Claude Fable 5 and GPT 5.6 Sol on overall performance). Kimi K3 is the largest AI model to come from China to date, with 2.8 trillion parameters, a figure that refers to the size of its neural network. And AI experts are saying it could signal a tipping point in the industry. “Kimi K3 may be an important inflection point for AI,” wrote Gavin Baker, managing partner at investment firm Atreides Management, in a social-media post. “A world where there are only [two to three] dominant frontier labs with 90 percent inference margins is net negative for every other layer while being awesome for those [two to three] labs.” It took just a few hours for Kimi K3 to jump to the leader position in the Frontend Code Arena, a live evaluation and benchmarking platform that tests how well artificial intelligence models generate user-facing web applications. That marked the first time a Chinese model had taken that spot, which sounded alarms among tech and political leaders. Some say this bolsters the argument many AI evangelists have been making against guardrails on American AI companies. “This is concerning,” wrote David Sacks, the general partner and co-founder of Craft Ventures who previously served as the White House AI and crypto czar. “America is tying itself in knots: politicians and bureaucrats are banning new data centers, piling on state regulations, and pushing for new federal agencies to pre-approve frontier models. This is how you lose the AI race. The rest of the world won’t play by our rules if we bog ourselves down.” Kimi K3’s launch came less than a month after Anthropic’s Fable and Mythos models were withdrawn by the U.S. government due to security concerns. Seeking dominance Kimi K3 represents a threat to the U.S. attempts to secure dominance in the AI world. Federal officials have laid out an AI Action Plan, which calls for accelerating innovation, building massive domestic data center infrastructure, and leveraging advanced AI for national security. Aside from national security concerns, there are financial factors at play. Three major AI companies are in various stages of going public. SpaceX (which contains Elon Musk’s AI company) made its Wall Street debut just over a month ago, with the largest IPO in Wall Street’s history. (Shares have since fallen 22 percent from their first trade price and are currently below the IPO price.) OpenAI and Anthropic, meanwhile, have both filed confidentially with the Securities and Exchange Commission as well, though neither has announced a fixed date yet. (Anthropic is currently scheduling investor meetings and is said to be targeting a possible October IPO, while OpenAI is said to be considering waiting until next year.) There have also been whispers of the government taking an equity stake in AI companies, though nothing has been decided. Chinese AI firms, though, have been releasing new models at an increasingly faster pace. Last month, for instance, Chinese AI startup Z.ai released its new flagship model, GLM-5.2, whose benchmarks narrowly trailed closed-source models. That model operated at roughly 17 percent of the cost of closed U.S. models, raising fears that U.S. companies could use a Chinese-created model, rather than a domestic one. Analysts at the time believed China’s AI models were four- to six-months behind the top models in the U.S. but were forced to reevaluate those thoughts. Kimi K3 appears to narrow the gap further and heightens fears that China may not only be catching up with U.S. AI companies, but could one day surpass them. “Despite persistent hardware/compute capacity constraints in China, K3 demonstrates that pre-training scaling, paired with architectural innovation, can still deliver step-change gains for flagship Chinese models,” said Bank of America in a note led by Alex Liu. BY CHRIS MORRIS @MORRISATLARGE

Nvidia’s Jensen Huang Just Declared the ‘Next Frontier’ of AI—and It’s Not Chatbots

They may not have been delivered with the sonorous tones of Star Trek’s William Shatner or Patrick Stewart speaking over the show’s iconic intro, but when Nvidia’s CEO Jensen Huang talked about the “frontier” of AI this week, his words still carried significant weight. That’s because AI is seemingly everywhere and in everything at the moment, and Nvidia‘s chips are on a (much longer than five year) mission to power as much of the revolution as possible. Huang was technically unveiling a new AI model—which you may think is not really news in a time when new AI models are popping up like weeds through cracked pavement. But the new Cosmos 3 Edge model Nvidia revealed is something different. It’s a “world model.” Speaking in Japan, Huang’s comments were aimed partly at the nation where Nvidia is creating a coalition with some of the biggest tech manufacturing names, but they’re also a significant sign about where AI is going in the near future: into robots. “The next frontier of AI is in the physical world,” Huang said in a statement, adding that the coalition being created to tap physical AI is a “once-in-a-generation opportunity for Japan.” He noted that, in many ways, “Japan invented modern manufacturing,” and as he sees it, the country now has an “opportunity to reinvent it for the age of intelligent industries.” But Huang’s words will echo far beyond Japan’s borders. While OpenAI, Anthropic, and other players in the game release large language models to power their linguistic-centered AI tools, Cosmos 3 Edge is designed to help systems perceive and then navigate and manipulate physical environments in real time, CNBC reported. This kind of “world model” takes in very different types of data to the often text-centric material used in AI chatbot training, technically teaching the AI physical details about things in our human world. In a press release, Nvidia claims Cosmos “combines vision reasoning, world generation, and action prediction.” It’s all about understanding and generating “text, images, video, ambient sound and actions with leading physics accuracy.” The goal is to allow robots to maneuver in the real world and manipulate items they encounter. Think of a world model as a kind of enhancement of the AI that gives Teslas their real-time self-driving powers. Robots—be they humanoid or more purpose-designed, like welding bots in car factories—will need this kind of understanding if they’re to move into our workplaces and homes in a safe way, and if they’re to have the ability to take on different jobs they’re assigned in real time. This is where the Cosmos 3 Edge gets its name, because it’s designed to do “real-time inference at the edge.” This means that significant AI processing will happen inside robots, at the “edge” of the digital network they’re connected to, allowing the robot to, for example, predict which way something that it throws will go. Why should you care about this? Robotics company 1X recently revealed a spectacularly dexterous robot manipulator it had created for its humanoid bots. The new hand is capable of unsettlingly human-like movements, and is both delicate and precise enough to pick grapes from a bunch or to speedily manipulate a gaming controller. In its press release, 1X suggested that human-like hands were one of the last barriers to allowing robots to work in human workspaces, and that the only limit left was the sophistication of the AI code. Huang proclaiming physical AI as the “next frontier” his tech giant company is going to conquer is another giant sign that the robotic revolution may be arriving much sooner than you think. Remember, ChatGPT only really arrived in late 2022… and now sophisticated AI tools are everywhere online. If your company’s plan for the next couple of years doesn’t involve robots, that’s probably fine, but you may want to get your planning team thinking about how robots will be working for you inside five to 10 years. BY KIT EATON @KITEATON

Google’s Head of Search Shares 3 Rules Every Business Needs to Win in the AI Era

Google Search has undergone some serious changes that will likely have big implications for how businesses present themselves online—and how people find them. Google VP of Search Liz Reid says there are no quick tips to hack the system, but she does explain there are techniques that entrepreneurs and business-owners can use to optimize their digital footprint for AI search. Hint: it’s all about quality. When Reid took stage at the tech company’s annual developer’s conference in May, she spelled out her vision for a brand new world of Google Search. She touted the biggest change to Google’s iconic search bar in 25 years. That included baking AI into the search experience through conversational language search interface, contextual search history, and even search with video and images. She also teased agentic features that are still only slowly rolling out to select users. Given the fact that AI Mode rolled out in 2025, these changes are hardly the first to rock Google search in recent years. Entrepreneurs may be understandably confused about how best to optimize their online presence, as AI changes the game for keywords and SEO. Despite all these changes and perhaps a bit of confusion, there are still some best practices to keep in mind. Reid sat down with Inc. to share her advice for getting the best results out of Google search for your company. Go deep and niche Reid says the recent changes to Google Search have resulted in a higher overall volume of queries and a change in their nature. The incorporation of generative AI into Google Search means people are ditching keywords in favor of questions that are phrased the way they might actually ask them. They are getting specific, providing context, and asking follow-up questions. Because users are able to get more detailed about their questions and preferences, Reid says this new behavior offers a real opportunity for companies to seize upon what makes their product or expertise unique. She gives the example of searching for shoes, which in the era of keyword search may have weighted many shoes equally. Now users can specify that they want “eco and sustainable shoes,” made by a founder with certain specifications, Reid notes. “Now this opportunity for people to differentiate themselves and shine becomes much more possible than if you could have done it before with keywords,” she says. Of course, that means business owners have to work that individuality into content on their own websites, online storefronts, and social media. Make content with value Reid encourages entrepreneurs not only to identify what makes them unique, but create value around that. In e-commerce, that might mean uploading more detailed product information to Google’s Merchant Center, and including nuanced descriptions of products on a company’s website. For a service-based business, that can mean focusing on thought leadership through mediums like podcasts, articles, and blogs that dig deeper than surface level information people may already be able to find in a Google AI overview. “Sometimes you’ll have a bunch of sites in which they’re basically saying the same thing as 500 other sites. That’s not going to go,” she says. “People would rather go and see a site that doesn’t say this exact same thing as AI overviews, but now takes it down to a much deeper level.” And, of course, Reid says, content has to be interesting: “It’s not going to rank well if when people click on it, they hate it, and they leave right away. So the first thing you should figure out is, would anyone want to read it?” Be wary of GEO claims SEO, or search engine optimization, has quickly given way to what’s being called GEO, or generative engine optimization. And a number of businesses have already been built on the claim that they’ve unlocked the secret to GEO. Reid says entrepreneurs should be skeptical of such claims and check out Google’s own resource with best practices to help people optimize for generative AI. The site notes that SEO rules still apply, and offers more detail on what high quality content actually means. It also includes “myths” about AI search, including that websites must have machine readable .txt files, content written especially for AI, or specially structured data. “I would definitely encourage people to use the tips and to express some skepticism in what they’re reading from some folks, especially if there’s business incentives for them to claim they can solve all your problems,” she says. BY CHLOE AIELLO @CHLOBO_ILO