Artificial intelligence has significantly improved weather forecasting in recent years, with global AI weather models now able to produce forecasts that match some of the world's best physics-based systems. This progress has been driven by three factors: vast amounts of weather data, advances in AI models, and unprecedented computing power. While much of the current discussion about improving AI for weather forecasting focuses on models or new hardware, the availability and quality of data are crucial. At the global scale, AI has benefited from decades of climate and weather records that cover the entire Earth. These datasets contain millions of examples of how atmospheric conditions evolve over time, allowing AI models to learn patterns in ways that would have been impossible a decade ago. However, when focusing on regional scales, forecasting becomes more challenging, especially in predicting hurricane intensity. Hurricane Polo rapidly intensified off Mexico's Pacific coast, growing from a tropical storm on Sept. 21, 2026, to a powerful Category 5 hurricane in just 24 hours. Polo quickly became one of the strongest Pacific storms in decades, with winds reaching 180 mph (290 kilometers per hour). Rapid intensification can surprise forecasters, as seen with Hurricane Michael in 2018, which grew into a destructive Category 5 hurricane before hitting Tyndall Air Force Base and Mexico Beach, Florida, leaving communities with limited time to evacuate and prepare. Unlike global weather forecasts, hurricane intensity forecasts are often considered a regional forecasting problem. Regional forecasts are often concerned with extreme events, such as heavy rainfall, squall lines, severe thunderstorms, or hurricanes. These extreme events often develop rapidly or move quickly over short periods of time. Capturing such behavior in AI models requires data in much greater detail than current global datasets can typically provide. When scientists train AI models to predict hurricane intensity, they usually rely on two sources of data. The first is observations, which include measurements of rainfall, near-surface temperature, wind speed, and other weather variables collected from weather stations, radars, buoys, and satellites. Such direct observations can be detailed, but they are often limited to near-coastal regions and unevenly distributed. Many of the most important stages of hurricane development occur over the open ocean, where direct observations are sparse. Modern satellites can help fill some of these gaps in the open ocean, but they can only estimate part of the rainfall, surface winds, or cloud-top temperatures due to limits in satellite coverage. In particular, they cannot simultaneously scan the complete three-dimensional structure of every hurricane around the globe. At present, even the best observational systems provide only a partial view of hurricanes at any point in time. The second source of training data comes from weather model simulations, which combine atmospheric conditions and knowledge of physics to provide the most complete three-dimensional picture of the atmosphere at high resolution. However, these simulated data are not perfect either, because all computer models contain approximations and uncertainties arising from incomplete knowledge of Earth's atmosphere. As such, there are always fine-scale processes that model simulations cannot capture. Thus, we simply don't have a good, full three-dimensional dataset to train an AI model for hurricane intensity prediction at present. Data is not the only issue for AI hurricane prediction. Even if scientists could measure every part of thousands of storms around the world, every second, that might not allow AI to predict hurricane intensity perfectly. The reason: chaos. Tiny differences in the initial state of a hurricane can quickly grow over time. Recent research suggests that hurricanes may contain some element of chaos that can prevent AI models from accurately predicting hurricane intensity at long forecast times. Once embedded in a favorable environment, a tropical storm can intensify toward a maximum possible strength. Scientists call this upper limit the potential intensity. It is determined primarily by the surrounding environment. For example, warm ocean water can fuel a hurricane's intensity, while wind shear can slow a hurricane's development. If the ocean temperature rises, the potential intensity of a hurricane increases, too. Any small disturbances will also cause hurricane intensity to fluctuate. The warmer the ocean surface, the greater the fluctuations. Recent studies have proposed that these fluctuations are not purely random but occur within what is known as a chaotic attractor—a set of possible storm states within which the hurricane can evolve unpredictably. Although the existence of such a chaotic intensity attractor has not yet been fully established, it presents a fundamental dilemma for training AI models to predict hurricane intensity. On one hand, scientists want AI models to make the most accurate predictions possible. Thus, during training, the goal is to minimize the difference between the forecast and what actually happens until an AI model achieves the smallest possible error. On the other hand, we also want the AI model to capture the hurricane's intrinsic chaos. But if an AI model can capture this chaos, then its error cannot be reduced indefinitely. An AI model trained to minimize forecast error may therefore learn the most likely evolution of a hurricane while smoothing out unpredictable fluctuations. In this regard, these two goals compete with one another. Because data always contain some uncertainty, the rules an AI model learns are only approximations. The accuracy of hurricane intensity forecasts will therefore get worse after just a few days. The challenge for AI models predicting hurricane intensity is not just about obtaining more data, building better neural networks, or deploying faster computers. It is also about understanding hurricane behavior and how chaos in intensity emerges. Both dictate whether AI models can learn what is predictable and what is unpredictable. That distinction not only puts a cap on the accuracy of our current hurricane intensity forecasts but also determines the next generation of weather forecasting and evaluation systems, which should focus on a range of possible hurricane intensities and their probabilities instead of a single intensity number.