In 2026, AI will move from hype to pragmatism
If 2025 was the year when AI underwent a critical evaluation, then 2026 is expected to be the period when the technology becomes genuinely practical. The emphasis is shifting away from creating ever-larger language models and focusing instead on the more challenging task of enabling AI to be truly usable. This means deploying more appropriately sized models, embedding intelligence into tangible devices, and developing systems that seamlessly fit into human workflows.
The experts consulted foresee 2026 as a transitional phase that moves away from sheer scaling towards exploring novel architectures, evolving from visually impressive demonstrations to precise deployments, and shifting from autonomous agent promises to genuinely augmenting human work. While the excitement around AI continues, the industry is adopting a more mature outlook.
The era of relying solely on scaling is reaching its limitations. The breakthrough in 2012 with object recognition using millions of examples set a precedent for AI research focused on inventing new architectures for specific tasks. This period culminated around 2020 when a much larger model demonstrated capabilities like coding and reasoning without explicit training, ushering in what some call the “age of scaling.” The belief was that increasing computational power, datasets, and transformer model size would guarantee future breakthroughs.
Now, many researchers agree that this scaling strategy is hitting a plateau. Influential voices have cautioned against depending too much on scaling, urging the development of superior architectures. There’s consensus that current model improvements have leveled off, signaling a necessary pivot to fresh ideas.
Smaller models are gaining traction, particularly in enterprise settings where they can be fine-tuned to specific domains and offer advantages in cost, speed, and accuracy compared to large general-purpose models. Industry leaders believe that these specialized models will become standard practice as they combine efficiency with adaptability, making them ideal for tailored applications. This trend is further accelerated by advancements in edge computing, which facilitates deployment on local devices.
Another anticipated breakthrough revolves around world models—AI systems that understand and simulate the physical world in three dimensions. Unlike language models that predict text, world models learn how entities move and interact, enabling predictions and actions. Investments and developments from major players and startups indicate that these models will flourish, especially in fields like gaming, which may see gaming worlds and characters becoming dramatically more interactive and realistic. Virtual environments could also become significant testing grounds for future foundational AI models.
Agent technology, which fell short of expectations last year due to difficulties integrating with actual workflows, is poised for advancement. New protocols that enable seamless communication between AI agents and external tools are gaining widespread acceptance and becoming industry standards. This improved connectivity is expected to transition agentic workflows from experimental demos to routine applications across diverse sectors including home services, real estate, healthcare, and various operational functions.
Despite concerns about automation displacing jobs, the prevailing viewpoint is shifting toward augmentation rather than replacement. AI is proving less autonomous than anticipated, and there’s growing recognition that it will enhance human work instead of supplanting it. This shift is likely to foster job creation in areas like AI governance, transparency, safety, and data management, supporting a positive outlook for employment rates.
Finally, physical AI—integrating machine learning into tangible devices—is expected to enter mainstream adoption. Innovations in smaller models, world models, and edge computing will pave the way for AI-powered robotics, autonomous vehicles, drones, and wearables. While some applications remain costly, others, like smart glasses and health-monitoring wearables, are becoming affordable and socially accepted. Network providers are preparing infrastructure improvements to support this burgeoning category of intelligent connected devices, with flexible connectivity offerings positioned for success.
