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Much of healthcare still operates like a series of snapshots. For most routine care, you go in once a year for a physical. Maybe you get a few labs drawn. If something looks off, you might get a follow-up or a prescription. But within the constraints of a short visit and limited longitudinal data, care often ends with broad guidance like eat better or check back next year. Meanwhile, your health is changing every day. Metabolic function, inflammation, aging, and chronic disease dont switch on overnight. They unfold gradually over time, shaped by lifestyle factors including sleep, nutrition, movement, stress, as well as genetics and environment. But unless you cross a diagnostic threshold or show up with symptoms, the system doesnt intervene. Too often, care is triggered only when something has already gone wrong. Thats because were still practicing episodic, event-driven care, not trend-based care. THE LIMITS OF EPISODIC DATA You cant deliver truly personalized proactive prevention with episodic data alone. A single cholesterol reading can be clinically meaningful, particularly at extremes. The same is true for a day of elevated blood sugar. But outside of acute thresholds, context and trajectory matter. To detect risk early and intervene meaningfully, we need a care model informed by continuous trends, not isolated events. This is where AI, and specifically agentic AI, can make a difference. WHAT AGENTIC AI REALLY MEANS When people hear agentic AI, they often assume it means handing over decisions entirely to machines. In reality, agentic AI refers to systems that can act autonomously within defined goals, constraints, and oversight. Think of autopilot in aviation. Autopilot manages routine complexity by continuously monitoring conditions, detecting turbulence, and making micro-adjustments. Pilots maintain oversight and control, but theyre no longer burdened with manually managing every variable. In healthcare, agentic AI functions the same way. It continuously observes multiple data streams, identifies subtle but meaningful changes, and delivers timely, relevant insights that enhance clinical judgment, not replace it. This is not theoretical. Health systems are already integrating AI into diagnostics, operations, and clinical workflows, embedding it into electronic health records, imaging systems, and decision-support tools to manage complexity and surface risk earlier. These deployments signal a shift from isolated AI applications toward infrastructure-level intelligence operating continuously alongside clinicians. FROM VOLUME TO MEANING We already have more health data than we know what to do with. The challenge isnt collection. Its synthesis. Agentic AI helps us move from data overload to actionable insight. By analyzing longitudinal signals, including biological, behavioral, and environmental data, it reveals patterns that allow us to act before risk escalates. This is especially powerful in managing chronic conditions, aging, and metabolic health, areas where prevention is possible, but only when signals are caught early. Research shows that combining longitudinal wearable data with clinical records improves our ability to predict future risk. What agentic systems add is the ability to translate those predictions into timely, predefined actions rather than leaving insights dormant until the next visit. PATIENTS ARE ALREADY LIVING IN A CONTINUOUS WORLD At the same time, people are increasingly turning to AI tools to fill the gap. Recent reporting from OpenAI shows that more than 40 million people use ChatGPT daily for health questions, with roughly 70% of those conversations occurring outside normal clinic hours. OpenAI also reported about 600,000 health-related queries per week from underserved rural communities. The behavior is clear: People want real-time answers that the healthcare system is often not structured to provide between visits. This creates a growing gap between how people live and how medicine is practiced. Agentic AI offers a way to close it by acting as the connective tissue between daily life and clinical care. It doesnt replace clinicians. It doesnt make healthcare autonomous. It makes it responsive. A NEW INFLECTION POINT Autopilot didnt revolutionize aviation by removing the pilot. It changed aviation by making the system manageable, extending human capability through continuous support. Healthcare is now at a similar inflection point. Data volumes will continue to rise. Clinical capacity will remain limited. And episodic care will grow more misaligned with how disease and aging actually develop. Agentic AI offers a path forward by enabling systems to take bounded, predefined actions in response to continuous monitoring, whether by surfacing emerging risk patterns to clinicians or by triggering patient-facing actions like scheduling follow-up visits when concerning trends persist. The result is care that occurs earlier, with better timing, rather than at the moment of acute decline. The technology for agentic AI already exists. Regulatory pathways are emerging as well, but adoption depends on whether incentives, workflows, and leadership priorities evolve to support continuous care. Like autopilot in aviation, agentic AI in healthcare will be introduced gradually, first in well-bounded, lower-risk workflows, then expanding as systems, incentives, and governance structures evolve to support continuous intelligence at scale. To unlock its full potential, healthcare needs reimbursement models that reward prevention, clinical architectures designed for longitudinal data, and governance frameworks that enable responsible deployment without freezing progress. Agentic AI doesnt require a reinvention of regulation, but it does require modernizing operations, governance, and accountability. The systems that move first will define the next era of healthcare. Noosheen Hashemi is founder and CEO of January AI.
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E-Commerce
Ellie Frazier first started posting content three years ago, sharing day-in-the-life vlogs and content tips for fellow creators. As her following grew, she began noticing other creators posting videos with uncannily similar scripts to her own. The clips felt the same. The editing style, identical. In one example, Frazier stretched in front of a window; another creator stretched in front of a window. Frazier chopped vegetables; the other creator chopped an orange. On its own, that might not seem especially striking. But the voiceover script used by the other creator was also almost verbatim Fraziers words. Theres a very stark difference between taking inspiration from everybody and giving credit, versus stealing somebody’s voiceover script word for word multiple times in a row, says Frazier in a recent post. Taking credit in the comments for it being their own work. @elliewfrazier its just not cool she doesnt even follow me #contentcreators #contentcreatortips #socialmediatips original sound – ELLIE FRAZIER Plagiarismpresenting another person’s ideas, words, images, or work as your own without creditwhile often difficult to litigate, is a cardinal sin in most industries. And yet social media largely operates as a law unto itself. TikTok will remove content that violates or infringes someone else’s intellectual property rights, including copyright and trademark. However, many posts on the platform do not clearly meet the legal threshold for copyrightable intellectual property, meaning enforcement is often left to creators themselves. With swaths of content uploaded every day, copycat creators frequently weigh the risk of being discovered against the possibility of profiting from a viral concept with minimal effort. There is even content devoted to explaining exactly how to plagiarize others work. @josh.little_ Good artist copy, great artists steal. @@Josh original sound – Josh Little Determining who copied whom is also largely a futile exercise. On a platform that thrives on mimicry, true originality is rare. The lifecycle of a trend is familiar: One person creates an original video. If it goes viral, thousands copy it. Some tag the original creator. But as the trend snowballs, that credit is often lost to the algorithm. Once it has been replicated enough times to be labeled a trend, the concept is widely regarded as fair game. Frazier isnt the first to spotlight the growing issue of digital plagiarism. In a first-of-its-kind lawsuit brought in 2024, one TikTok creator attempted to sue another for copying her neutral, beige, and cream aesthetic and posting content with identical styling, tone, camera angle and/or text. More than a year later, the so-called Sad Beige Lawsuit was dismissed after the claimant chose not to move forward. Imitation may be described as the sincerest form of flattery, but online plagiarism ultimately benefits no one. The original creator loses credit for their idea. The copycat forfeits an opportunity to develop a distinct voice. And audiences are left scrolling through an endless stream of low-quality videos, each one nearly indistinguishable from the last.
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E-Commerce
As outrage spreads over energy-hungry data centers, politicians from President Donald Trump to local lawmakers have found rare bipartisan agreement over insisting that tech companies and not regular people must foot the bill for the exorbitant amount of electricity required for artificial intelligence. But that might be where the agreement ends. The price of powering data centers has become deeply intertwined with concerns over the cost of living, a dominant issue in the upcoming midterm elections that will determine control of Congress and governors offices. Some efforts to address the challenge may be coming too late, with energy costs on the rise. And even though tech giants are pledging to pay their fair share, there’s little consensus on what that means. Fair share is a pretty squishy term, and so its something that the industry likes to say because fair can mean different things to different people, said Ari Peskoe, who directs the Electricity Law Initiative at Harvard University. It’s a shift from last year, when states worked to woo massive data center projects and Trump directed his administration to do everything it could to get them electricity. Now there’s a backlash as towns fight data center projects and some utilities’ electricity bills have risen quickly. Anger over the issue has already had electoral consequences, with Democrats ousting two Republicans from Georgia’s utility regulatory commission in November. Voters are already connecting the experience of these facilities with their electricity costs and theyre going to increasingly want to know how government is going to navigate that, said Christopher Borick, a pollster and director of the Muhlenberg College Institute of Public Opinion. Energy race stokes concerns Data centers are sprouting across the U.S., as tech giants scramble to meet worldwide demand for chatbots and other generative AI products that require large amounts of computing power to train and operate. The buildings look like giant warehouses, some dwarfing the footprints of factories and stadiums. Some need more power than a small city, more than any utility has ever supplied to a single user, setting off a race to build more power plants. The demand for electricity can have a ripple effect that raises prices for everyone else. For example, if utilities build more power plants or transmission lines to serve them, the cost can be spread across all ratepayers. Concerns have dovetailed with broader questions about the cost of living, as well as fears about the powerful influence of tech companies and the impact of artificial intelligence. Trump continues to embrace artificial intelligence as a top economic and national security priority, although he seemed to acknowledge the backlash last month by posting on social media that data centers must pay their own way. At other times, he has brushed concerns aside, declaring that tech giants are building their own power plants, and Energy Secretary Chris Wright contends that data centers don’t inflate electricity bills disputing what consumer advocates and independent analysts say. States moving to regulate Some states and utilities have started to identify ways to get data centers to pay for their costs. They’ve required tech companies to buy electricity in long-term contracts, pay for the power plants and transmission upgrades they need and make big down payments in case they go belly-up or decide later they dont need as much electricity. But it might be more complicated than that. Those rules can’t fix the short-term problem of ravenous demand for electricity that is outpacing the speed of power plant construction, analysts say. What do you do when Big Tech, because of the very profitable nature of these data centers, can simply outbid grandma for power in the short run? Abe Silverman, a former utility regulatory lawyer and an energy researcher at Johns Hopkins University. That is, I think, going to be the real challenge. Some consumer advocates say tech companies’ fair share should also include the rising cost of electricity, grid equipment, or natural gas thats driven by their demand. In Oregon, which passed a law to protect smaller ratepayers from data centers’ power costs, a consumer advocacy group is jousting with the state’s largest utility, Portland General Electric, over its plan on how to do that. Meanwhile, consumer advocates in various states including Indiana, Georgia, and Missouri are warning that utilities could foist the cost of data center-driven buildouts onto regular ratepayers there. Pushback from lawmakers, governors Utilities have pledged to ensure electric rates are fair. But in some places it may be too late. For instance, in the mid-Atlantic grid territory from New Jersey to Illinois, consumer advocates and analysts have pegged billions of dollars in rate increases hitting the bills of regular Americans on data center demand. Legislation, meanwhile, is flooding into Congress and statehouses to regulate data centers. Democrats bills in Congress await Republican cosponsors, while lawmakers in a number of states are floating moratoriums on new data centers, drafting rules for regulators to shield regular ratepayers and targeting data center tax breaks and utility profits. Governors including some who worked to recruit data centers to their states are increasingly talking tough. Arizona Gov. Katie Hobbs, a Democrat running for reelection this year, wants to impose a penny-a-gallon water fee on data centers and get rid of the sales tax exemption there that most states offer data centers. She called it a $38 million corporate handout. Its time we make the booming data center industry work for the people of our state, rather than the other way around, she said in her state-of-the-state address. Blame for rising energy costs Energy costs are projected to keep rising in 2026. Republicans in Washington are pointing the finger at liberal state energy policies that favor renewable energy, suggesting they have driven up transmission costs and frayed supply by blocking fossil fuels. Americansare not paying higher prices because of data centers. Theres a perception there, and I get the perception, but its not actually true, said Wright, Trump’s energy secretary, at a news conference earlier this month. The struggle to assign blame was on display last week at a four-hour U.S. House subcommittee hearing with members of the Federal Energy Regulatory Commission. Republicans encouraged FERC members to speed up natural gas pipeline construction while Democrats defended renewable energy and urged FERC to limit utility profits and protect residential ratepayers from data center costs. FERC’s chair, Laura Swett, told Rep. Greg Landsman, D-Ohio, that she believes data center operators are willing to cover their costs and understand that its important to have community support. Thats not been our experience, Landsman responded, saying projects in his district are getting tax breaks, sidestepping community opposition and costing people money. Ultimately, I think we have to get to a place where they pay everything. Marc Levy, Associated Press
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