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Precision agriculture uses tools and technologies such as GPS and sensors to monitor, measure, and respond to changes within a farm field in real time. This includes using artificial intelligence technologies for tasks such as helping farmers apply pesticides only where and when they are needed. However, precision agriculture has not been widely implemented in many rural areas of the United States. We study smart communities, environmental health sciences, and health policy and community health, and we participated in a research project on AI and pesticide use in a rural Georgia agricultural community. Our team, led by Georgia Southern University and the City of Millen, with support from University of Georgia Cooperative Extension, local high schools and agriculture technology company FarmSense, is piloting AI-powered sensors to help cotton farmers optimize pesticide use. Georgia is one of the top cotton-producing states in the U.S., with cotton contributing nearly US$1 billion to the states economy in 2024. But only 13% of Georgia farmers use precision agriculture practices. Public-private-academic partnership Innovation drives economic growth, but access to it often stops at major city limits. Smaller and rural communities are frequently left out, lacking the funding, partnerships and technical resources that fuel progress elsewhere. At the same time, 75% of generative AIs projected economic impact is concentrated in customer operations, marketing, software engineering and research and development, according to a 2023 McKinsey report. In contrast, applications of AI that improve infrastructure, food systems, safety and health remain underexplored. Yet smaller and rural communities are rich in potentialhome to anchor institutions like small businesses, civic groups and schools that are deeply invested in their communities. And that potential could be tapped to develop AI applications that fall outside of traditional corporate domains. The Partnership for Innovation, a coalition of people and organizations from academia, government and industry, helps bridge that gap. Since its launch almost five years ago, the Partnership for Innovation has supported 220 projects across Georgia, South Carolina, Kentucky, Tennessee, Virginia, Texas and Alabama, partnering with more than 300 communities on challenges from energy poverty to river safety. !function(){"use strict";window.addEventListener("message",function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data["datawrapper-height"][t]+"px";r.style.height=d}}})}(); One Partnership for Innovation program provides seed funding and technical support for community research teams. This support enables local problem-solving that strengthens both research scholarship and community outcomes. The program has recently focused on the role of civic artificial intelligence AI that supports communities and local governments. Our project on cotton field pesticide use is part of this program. Cotton pests and pesticides Our project in Jenkins County, Georgia, is testing that potential. Jenkins County, with a population of around 8,700, is among the top 25 cotton-growing counties in the state. In 2024, approximately 1.1 million acres of land in Georgia were planted with cotton, and based on the 2022 agricultural county profiles census, Jenkins County ranked 173rd out of the 765 counties producing cotton in the United States. The state benefits from fertile soils, a subtropical-to-temperate climate, and abundant natural resources, all of which support a thriving agricultural industry. But these same conditions also foster pests and diseases. Farmers in Jenkins County, like many farmers, face numerous insect infestations, including stink bugs, cotton bollworms, corn earworms, tarnished plant bugs and aphids. Farmers make heavy use of pesticides. Without precise data on the bugs, farmers end up using more pesticides than they likely need, risking residents health and adding costs. While there are some existing tools for integrated pest management, such as the Georgia Cotton Insect Advisor app, they are not widely adopted and are limited to certain bugs. Other methods, such as traditional manual scouting and using sticky traps, are labor-intensive and time-consuming, particularly in the hot summer climate. Our research team set out to combine AI-based early pest detection methods with existing integrated pest management practices and the insect advisor app. The goal was to significantly improve pest detection, decrease pesticide exposure levels and reduce insecticide use on cotton farms in Jenkins County. The work compares different insect monitoring methods and assesses pesticide levels in both the fields and nearby semi-urban areas. We selected eight large cotton fields operated by local farmers in Millen, four active and four control sites, to collect environmental samples before farmers began planting cotton and applying pesticides. The team was aided by a new AI-based insect monitoring system called the FlightSensor by FarmSense. The system uses a machine learning algorithm that was trained to recognize the unique wingbeats of each pest insect species. The specialized trap is equipped with infrared optical sensors that project an invisible infrared light beam called a light curtain across the entrance of a triangular tunnel. A sensor monitors the light curtain and uses the mahine learning algorithm to identify each pest species as insects fly into the trap. FlightSensor provides information on the prevalence of targeted insects, giving farmers an alternative to traditional manual insect scouting. The information enables the farmers to adjust their pesticide-spraying frequency to match the need. What weve learned Here are three things we have learned so far: 1. Predictive pest control potential AI tools can help farmers pinpoint exactly where pest outbreaks are likelybefore they happen. That means they can treat only the areas that need it, saving time, labor and pesticide costs. Its a shift from blanket spraying to precision farming and its a skill farmers can use season after season. 2. Stronger decision-making for farmers The preliminary results indicate that the proposed sensors can effectively monitor insect populations specific to cotton farms. Even after the sensors are gone, farmers who used them get better at spotting pests. Thats because the AI dashboards and mobile apps help them see how pest populations grow over time and respond to different field conditions. Researchers also have the ability to access this data remotely through satellite-based monitoring platforms on their computers, further enhancing the collaboration and learning. 3. Building local agtech talent Training students and farmers on AI pest detection is doing more than protecting cotton crops. Its building digital literacy, opening doors to agtech careers and preparing communities for future innovation. The same tools could help local governments manage mosquitoes and ticks and open up more agtech innovations. Blueprint for rural innovation By using AI to detect pests early and reduce pesticide use, the project aims to lower harmful residues in local soil and air while supporting more sustainable farming. This pilot project could be a blueprint for how rural communities use AI generally to boost agriculture, reduce public health risks, and build local expertise. Just as important, this work encourages more civic AI applications grounded in real community needs that others can adopt and adapt elsewhere. AI and innovation do not need to be urban or corporate to have a significant effect, nor do you need advanced technology degrees to be innovative. With the right partnerships, small towns, too, can harness innovations for economic and community growth. Debra Lam is a founding director of the Partnership for Inclusive Innovation and the Enterprise Innovation Institute at Georgia Institute of Technology. Atin Adhikari is a professor of biostatistics, epidemiology & environmental health sciences at Georgia Southern University. James E. Thomas is a senior lecturer in health policy & community health at Georgia Southern University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
Theres no doubt we are witnessing a quiet shift in labor: artificial intelligence is no longer confined to experimental labs or consumer chatbots, it is now eroding the foundation of human labor in ways that are less visible, but potentially more consequential, than the headlines about AI assistants or superintelligence. Last week, Google abruptly terminated 200 AI contractors, many of them involved in annotation and evaluation work. Officially, the company described this as part of a ramp-down, but workers pointed mainly to low pay and job insecurity. What matters is that the roles being cut are precisely those that ensure human oversight of AI systems: the raters, annotators, and evaluators who form the invisible scaffolding of smart or intelligent products. In parallel, at an Axios event, Anthropic CEO Dario Amodei warned that AI is on track to displace many white-collar jobs within five years. Not decades. Not in some speculative future. Within the next cycle of corporate planning, the world of professional work, from law, finance, consulting, or even management, may look very different. From invisible work to invisible loss For years, the human labor that powers AI has been hidden behind the curtain: underpaid annotators in developing countries, moderators exposed to traumatic content, contractors who quietly clean and structure data so models can be trained. These jobs were rarely acknowledged, let alone respected. Now they are being erased altogether, as companies shift from human-in-the-loop to automation-in-the-loop. The question is not only about employment. It is about what disappears when we remove human judgment from the system. Annotators catch ambiguities, flag dangerous edge cases, and apply moral reasoning that models cannot replicate. Raters provide cultural and linguistic nuance. When those roles are automated away, the systems may still functionbut blind spots deepen, errors multiply, and biases are amplified. Efficiency rises, but resilience declines. White-collar work on the clock Amodeis warning points to a broader reality: AI is moving up the value chain: it is no longer confined to support tasks, it is encroaching on analysis, writing, design, and even decision-making. The professional classes that once considered themselves insulated from automation are now squarely in the crosshairs. If blue-collar workers were the first wave of technological displacement in the 20th century, white-collar workers may be the second in the 21st. The rhetoric from tech leaders often frames this as an opportunity: liberation from drudgery, new roles created, productivity unleashed. But the record of previous technological shifts is sobering. Yes, new roles emerge, but not necessarily for the same people, in the same places, or at the same wages. The painful transition costs are borne not by shareholders but by workers. Regulation in fragments Governments are beginning to notice. Italy has just introduced an AI legislative package that tries to target harmful deepfakes, set workplace standards, and enhance child protections. It is among the first attempts to go beyond reactive guardrails and impose preemptive controls on how AI can be used. Whether this becomes a model for others remains uncertain. Spain, by contrast, is coming up with a mixed model: on one hand, it has enacted laws requiring labeling of all AI-generated content with heavy fines and formed the AESIA (Spanish AI Supervisory Agency) to oversee compliance; on the other, it is also heavily subsidizing AI development and innovation. The tension is real: measures meant to protect truth and transparency may impose burdens on small startups; enforcement capacity is far from guaranteed; and legislative clarity lags behind technological change. The Spanish case exemplifies a border zone: regulation and innovation both encouraged, but not always reconciled. The irony is that regulation is moving fastest on visible harms that generate social alarm such as deepfakes, disinformation, and child safety, while the invisible erosion of labor goes largely unaddressed. It is easier to ban a fake video than to confront a business model that treats human judgment as a disposable cost. Efficiency is not ethics This moment forces a deeper question: just because AI can replace a human ole, does it mean it should? Not every gain in efficiency is a gain in ethics. Removing moderators may cut costs, but at what price to safety? Automating evaluation may accelerate deployment, but at what risk of error? Displacing white-collar workers could improve the margin, but the costs to social stability are pretty clear. Are we all now behaving like Meta, moving fast and breaking things, focusing on profitability without paying attention to other potential consequences? We should all exert some caution from a future in which AI not only mediates our information but also dictates our labor markets, silently restructuring what it means to be useful. Companies should not outsource that responsibility to regulators. They must recognize that the invisible revolution they are driving has significant human consequences, and those consequences will eventually come back to shape their own legitimacy. The real invisible hand The invisible hand in todays AI economy is not Adam Smiths market. It is the invisible labor that has powered machine learning, and the invisible losses that come when that labor is discarded. The layoffs at Google and the warnings from Anthropic are signals, not outliers. We are watching the early stages of a transformation that could redefine not just how we work, but what kinds of work society still values. If companies want AI to be sustainable, they need to treat human judgment not as a temporary scaffold to be eliminated, but as a core component of systems that aspire to interact with the world. Without that, we risk building an economy where jobs are interchangeable, oversight is optional, and the human cost of efficiency is hidden until it is too late.
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E-Commerce
NBCUniversal may soon be pulling its programming from Google’s YouTube TV. The news comes as a dispute between the companies over carriage fees and terms is ramping up. NBC began warning customers on Thursday evening that its programming would leave the streaming platform if the companies don’t reach an agreement by Sept. 30, the date its contract is set to renew. If a blackout were to occur, popular programs such as Sunday Night Football, The Voice, NBA games, and the Oct. 4 premiere of Saturday Night Live, wouldn’t be viewable on the platform. However, a separate spat between YouTube TV and TelevisaUnivision comes at the same time, as both companies’ contracts are set for renewal on Sept. 30. If both networks pulled their programming, two major hispanic networks, Univision and NBCU-owned Telemundo, would no longer be accessible on the platform. TelevisaUnivision alleged that YouTube TV was being discriminatory in an open letter published on Sept. 24. “YouTube TV will force millions of Hispanic viewers to pay an 18% premium a Hispanic tax to maintain access to trusted Spanish-language news, sports, and entertainment,” it said. NBC issued its own statement on the dispute. “Google, with its $3 trillion market cap, already controls what Americans see online through search and adsnow it wants to control what we watch, NBC said, per Axios. YouTube TV has refused the best rates and terms in the market, demanding preferential treatment and seeking an unfair advantage over competitors to dominate the video marketplaceall under the false pretense of fighting for the consumer. NBCUniversal has never before pulled its programming from a streaming platform. YouTube says it’s committed to negotiating with the network, but the price is too high. “NBCUniversal is asking us to pay more than what they charge consumers for the same content on Peacock, which would mean less flexibility and higher prices for our subscribers,” it said in a Sept. 25 statement. The company announced it would reimburse customers $10 if the blackout occurs for an extended time. While the dispute could have big implications, it seems to mark a growing trend, as digital live TV providers, like YouTube TV which has more than 10 million subscribers, continue to grow. And it’s not the first time YouTube TV has dug its heels in. In August, the company said it would drop Fox Corp. channels if an agreement wasn’t reached ahead of football season. The companies reached an agreement days later. “We have reached a short-term extension with Fox to prevent disruption to YouTube TV subscribers as we continue to work on a new agreement,” YouTube TV said in an Aug. 25 blog post. “We are committed to advocating on behalf of our subscribers as we work toward a fair deal and will keep you updated on our progress.”
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