Artificial intelligence has become deeply embedded in everyday life, driving advancements in everything from voice assistants to autonomous vehicles. As AI models grow increasingly complex and powerful, the demand for greater computational capacity and energy efficiency continues to outpace the capabilities of conventional silicon-based systems. Traditional computing architectures struggle to keep up, consuming vast amounts of electricity and generating significant heat, which limits scalability and sustainability. In response, researchers are exploring alternative approaches to AI hardware, with a growing focus on physical neural networks that leverage light instead of electricity for computation. These systems use photonic components—such as waveguides and optical modulators—to process information at the speed of light, dramatically reducing energy consumption and latency. Unlike traditional electronic circuits, where data is transmitted through electrical signals that generate heat and resistance, photonic systems manipulate light to encode and transmit data. This allows for parallel processing across multiple wavelengths, enabling massive throughput with minimal energy use. When applied to neural networks, light-based systems can perform matrix multiplications—the core operation in AI training and inference—far more efficiently than electronic chips. Recent breakthroughs have demonstrated physical neural networks that can be trained using light itself, adapting their optical properties in response to input signals. These systems can learn from data through feedback mechanisms that adjust the intensity, phase, or direction of light, mimicking how biological neurons adapt during learning. The potential benefits are substantial. Light-based AI hardware could drastically reduce the carbon footprint of large-scale AI training, which currently consumes as much energy as a small country annually. By operating at near-zero heat and with lower power demands, these systems could enable AI deployment in remote or energy-constrained environments, from space missions to edge devices. While still in the early stages, the development of photonic neural networks marks a promising step toward sustainable AI. As the world seeks to balance innovation with environmental responsibility, light-powered computing offers a path to more efficient, scalable, and eco-friendly artificial intelligence.