Paul Winters, University of Notre Dame and Amir Jina, University of Chicago

For farmers, every planting decision carries risks, and many of those risks are increasing with climate change. One of the most consequential is weather, which can damage crop yields and livelihoods. A delayed monsoon, for example, can force a rice farmer in South Asia to replant or switch crops altogether, losing both time and income.

Access to reliable, timely weather forecasts can help farmers prepare for the weeks ahead, find the best time to plant or determine how much fertilizer will be needed, resulting in better crop yields and lower costs.

Yet, in many low- and middle-income countries, accurate weather forecasts remain out of reach, limited by the high technology costs and infrastructure demands of traditional forecasting models.

A new wave of AI-powered weather forecasting models has the potential to change that.

By using artificial intelligence, these models can deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models. This makes it possible for national meteorological agencies in developing countries to provide farmers with the timely, localized information about changing rainfall patterns that the farmers need.

The challenge is getting this technology where it’s needed.

Why AI forecasting matters now

The physics-based weather prediction models used by major meteorological centers around the world are powerful but costly. They simulate atmospheric physics to forecast weather conditions ahead, but they require expensive computing infrastructure. The cost puts them out of reach for most developing countries.

Moreover, these models have mainly been developed by and optimized for northern countries. They tend to focus on temperate, high-income regions and pay less attention to the tropics, where many low- and middle-income countries are located.

A major shift in weather models began in 2022 as industry and university researchers developed deep learning models that could generate accurate short- and medium-range forecasts for locations around the globe up to two weeks ahead.

These models worked at speeds several orders of magnitude faster than physics-based models, and they could run on laptops instead of supercomputers. Newer models, such as Pangu-Weather and GraphCast, have matched or even outperformed leading physics-based systems for some predictions, such as temperature.

AI-driven models require dramatically less computing power than the traditional systems.

While physics-based systems may need thousands of CPU hours to run a single forecast cycle, modern AI models can do so using a single GPU in minutes once the model has been trained. This is because the intensive part of the AI model training, which learns relationships in the climate from data, can use those learned relationships to produce a forecast without further extensive computation – that’s a major shortcut. In contrast, the physics-based models need to calculate the physics for each variable in each place and time for every forecast produced.

While training these models from physics-based model data does require significant upfront investment, once the AI is trained, the model can generate large ensemble forecasts — sets of multiple forecast runs — at a fraction of the computational cost of physics-based models.

Even the expensive step of training an AI weather model shows considerable computational savings. One study found the early model FourCastNet could be trained in about an hour on a supercomputer. That made its time to presenting a forecast thousands of times faster than state-of-the-art, physics-based models.

The result of all these advances: high-resolution forecasts globally within seconds on a single laptop or desktop computer.

Research is also rapidly advancing to expand the use of AI for forecasts weeks to months ahead, which helps farmers in making planting choices. AI models are already being tested for improving extreme weather prediction, such as for extratropical cyclones and abnormal rainfall.

Tailoring forecasts for real-world decisions

While AI weather models offer impressive technical capabilities, they are not plug-and-play solutions. Their impact depends on how well they are calibrated to local weather, benchmarked against real-world agricultural conditions, and aligned with the actual decisions farmers need to make, such as what and when to plant, or when drought is likely.

To unlock its full potential, AI forecasting must be connected to the people whose decisions it’s meant to guide.

That’s why groups such as AIM for Scale, a collaboration we work with as researchers in public policy and sustainability, are helping governments to develop AI tools that meet real-world needs, including training users and tailoring forecasts to farmers’ needs. International development institutions and the World Meteorological Organization are also working to expand access to AI forecasting models in low- and middle-income countries.

AI forecasts can be tailored to context-specific agricultural needs, such as identifying optimal planting windows, predicting dry spells or planning pest management. Disseminating those forecasts through text messages, radio, extension agents or mobile apps can then help reach farmers who can benefit. This is especially true when the messages themselves are constantly tested and improved to ensure they meet the farmers’ needs.

A recent study in India found that when farmers there received more accurate monsoon forecasts, they made more informed decisions about what and how much to plant – or whether to plant at all – resulting in better investment outcomes and reduced risk.

A new era in climate adaptation

AI weather forecasting has reached a pivotal moment. Tools that were experimental just five years ago are now being integrated into government weather forecasting systems. But technology alone won’t change lives.

With support, low- and middle-income countries can build the capacity to generate, evaluate and act on their own forecasts, providing valuable information to farmers that has long been missing in weather services.

Paul Winters, Professor of Sustainable Development, University of Notre Dame and Amir Jina, Assistant Professor of Public Policy, University of Chicago

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Adam Nemeroff, Quinnipiac University

You’ve probably encountered images in your social media feeds that look like a cross between photographs and computer-generated graphics. Some are fantastical – think Shrimp Jesus – and some are believable at a quick glance – remember the little girl clutching a puppy in a boat during a flood?

These are examples of AI slop, low- to mid-quality content – video, images, audio, text or a mix – created with AI tools, often with little regard for accuracy. It’s fast, easy and inexpensive to make this content. AI slop producers typically place it on social media to exploit the economics of attention on the internet, displacing higher-quality material that could be more helpful.

AI slop has been increasing over the past few years. As the term “slop” indicates, that’s generally not good for people using the internet.

AI slop’s many forms

The Guardian published an analysis in July 2025 examining how AI slop is taking over YouTube’s fastest-growing channels. The journalists found that nine out of the top 100 fastest-growing channels feature AI-generated content like zombie football and cat soap operas.

Listening to Spotify? Be skeptical of that new band, The Velvet Sundown, that appeared on the streaming service with a creative backstory and derivative tracks. It’s AI-generated.

In many cases, people submit AI slop that’s just good enough to attract and keep users’ attention, allowing the submitter to profit from platforms that monetize streaming and view-based content.

The ease of generating content with AI enables people to submit low-quality articles to publications. Clarkesworld, an online science fiction magazine that accepts user submissions and pays contributors, stopped taking new submissions in 2024 because of the flood of AI-generated writing it was getting.

These aren’t the only places where this happens — even Wikipedia is dealing with AI-generated low-quality content that strains its entire community moderation system. If the organization is not successful in removing it, a key information resource people depend on is at risk.

Harms of AI slop

AI-driven slop is making its way upstream into people’s media diets as well. During Hurricane Helene, opponents of President Joe Biden cited AI-generated images of a displaced child clutching a puppy as evidence of the administration’s purported mishandling of the disaster response. Even when it’s apparent that content is AI-generated, it can still be used to spread misinformation by fooling some people who briefly glance at it.

AI slop also harms artists by causing job and financial losses and crowding out content made by real creators. The placement of this lower-quality AI-generated content is often not distinguished by the algorithms that drive social media consumption, and it displace entire classes of creators who previously made their livelihood from online content.

Wherever it’s enabled, you can flag content that’s harmful or problematic. On some platforms, you can add community notes to the content to provide context. For harmful content, you can try to report it.

Along with forcing us to be on guard for deepfakes and “inauthentic” social media accounts, AI is now leading to piles of dreck degrading our media environment. At least there’s a catchy name for it.

Adam Nemeroff, Assistant Provost for Innovations in Learning, Teaching, and Technology, Quinnipiac University

This article is republished from The Conversation under a Creative Commons license. Read the original article.