AI model compression techniques optimise large language models for faster, cheaper predictions whilst maintaining performance. Key methods include model pruning, quantization, and knowledge distillation. These enable deployment in resource-constrained environments, reduce latency, lower costs, and improve energy efficiency, particularly for real-time AI applications and edge devices.

**For AI-driven companies:** You can now deploy powerful AI models in resource-constrained environments, opening up new market opportunities. This allows you to offer AI-powered products on edge devices or in situations where computing resources are limited. Consider revising your product roadmaps to include these new possibilities. **For all businesses:** Expect AI integration costs to decrease. The ability to run efficient AI models on existing hardware means you can enhance your products and services without significant infrastructure investments. Start identifying areas where AI could add value to your offerings without breaking the bank. **For cloud service providers:** Prepare for potential shifts in demand. As businesses can run AI models more efficiently on-premise or on edge devices, you may see changes in cloud usage patterns. Consider developing services that support this trend, such as tools for model compression or edge AI deployment.

Anticipate a proliferation of AI-enhanced devices and applications. As barriers to AI deployment lower, expect to see AI capabilities integrated into a wider range of products and services. This could lead to increased competition and innovation across various sectors. Prepare for potential disruption in industries reliant on centralized computing. The ability to run powerful AI models on edge devices could shift paradigms in areas like autonomous vehicles, smart home devices, and industrial IoT. Expect advancements in AI model design to focus increasingly on efficiency. As the benefits of model compression become more apparent, research and development efforts may shift towards creating inherently more efficient AI architectures. Be ready for potential changes in AI-related energy consumption patterns. While individual models become more efficient, the overall proliferation of AI could lead to increased total energy use. This might spark discussions and innovations around sustainable AI practices.