In 2026, the artificial intelligence sector is expected to shift its primary focus from developing increasingly large language models to implementing practical AI applications, according to experts interviewed by TechCrunch. This transition involves deploying smaller, more specialized AI models, integrating AI into physical devices, and creating systems designed to seamlessly integrate with existing human workflows.

The shift marks a move away from the brute-force scaling of AI models that characterized the previous years. Instead, the industry will concentrate on researching new AI architectures and moving from flashy demonstrations to targeted deployments. The goal is to develop AI agents that augment human capabilities rather than promising full autonomy.

This change in direction follows a period of rapid growth in AI, spurred by breakthroughs like the AlexNet paper in 2012, which demonstrated the ability of AI systems to learn object recognition through massive datasets. This research, driven by computational power from GPUs, led to the development of increasingly sophisticated models, culminating in OpenAI's GPT-3 around 2020.

However, experts now believe that simply scaling models is not sufficient. The focus is now on making AI more usable and applicable in real-world scenarios. This includes embedding intelligence into everyday devices and creating AI systems that can work alongside humans in various industries. The transition suggests a maturing of the AI field, moving beyond theoretical possibilities to practical implementations.