The artificial intelligence landscape is poised for a significant shift in 2026, moving away from the pursuit of ever-larger models towards practical applications and usability. Experts predict the focus will be on deploying smaller, more efficient AI systems, integrating intelligence into physical devices, and designing workflows that seamlessly augment human capabilities. This transition marks a move from groundbreaking research to tangible implementation, a necessary step for broader industry adoption.
After a period defined by scaling up model size, the industry is beginning to sober up and address the challenges of making AI genuinely useful in real-world scenarios. The relentless pursuit of larger language models will give way to a more nuanced approach, prioritizing architectural innovation and targeted deployments over sheer computational power. This recalibration is already evident in industry discussions and research priorities.
Scaling Laws Won’t Cut It
Early advancements in artificial intelligence, like the 2012 ImageNet breakthrough, demonstrated the power of applying greater computing power to complex problems. This fueled a decade of research centered on new architectures and improved performance. Around 2020, OpenAI’s GPT-3 showcased that increasing model size dramatically unlocked capabilities like coding and reasoning without explicit training, initiating what has been called the “age of scaling.”
However, many researchers now believe the industry is approaching the limits of this strategy. Meta’s former chief AI scientist, Yann LeCun, has consistently argued for prioritizing architectural improvements over simply increasing model size. Recent statements from Ilya Sutskever suggest that the gains from pretraining are beginning to plateau, reinforcing the need for new ideas and approaches.
Kian Katanforoosh, CEO and founder of the AI agent platform Workera, posits that the next major breakthroughs will require fundamentally better architectures. “I think most likely in the next five years, we are going to find a better architecture that is a significant improvement on transformers,” he said. “And if we don’t, we can’t expect much improvement on the models.”
Sometimes Less is More: The Rise of Small Language Models
While large language models excel at general knowledge, the implementation of smaller, more focused language models (SLMs) is gaining traction for enterprise applications. These SLMs can be fine-tuned for specific domains, offering a cost-effective and efficient alternative to their larger counterparts.
Andy Markus, Chief Data Officer at AT&T, believes fine-tuned SLMs will become a mainstay in mature AI enterprises. “Fine-tuned SLMs will be the big trend and become a staple used by mature AI enterprises in 2026, as the cost and performance advantages will drive usage over out-of-the-box LLMs,” Markus stated. “If fine-tuned properly, they match the larger, generalized models in accuracy for enterprise business applications, and are superb in terms of cost and speed.”
This effectiveness is supported by companies like Mistral, which argue that their smaller models achieve comparable, and sometimes superior, performance to larger models after targeted fine-tuning. Jon Knisley, an AI strategist at ABBYY, echoes this sentiment, emphasizing the adaptability and cost-effectiveness of SLMs. Furthermore, he highlights their suitability for deployment on local devices through advancements in edge computing.
Learning Through Experience: The Potential of World Models
Current large language models demonstrate impressive linguistic abilities but lack a true understanding of the physical world. The development of world models – AI systems that learn and predict how things interact in 3D spaces – represents a crucial next step. These models promise to enable more robust and adaptable AI capable of navigating and manipulating the environment.
Investment and development in world models are rapidly accelerating. Fei-Fei Li’s World Labs recently launched Marble, its first commercial world model. Google’s DeepMind has been actively researching this area, and newcomers like General Intuition have secured significant funding to advance spatial reasoning in AI agents. The initial applications of world models are anticipated to be substantial in the video game industry, potentially growing to a $276 billion market by 2030, according to PitchBook.
Agentic Nation and Augmentation, Not Automation
The promise of AI agents has yet to be fully realized, largely due to difficulties integrating them into existing systems and workflows. However, Anthropic’s Model Context Protocol (MCP) is emerging as a critical standard, enabling seamless communication between AI agents and external tools like databases and APIs. The widespread adoption of MCP by companies like OpenAI, Microsoft and Google could unlock new possibilities for agentic workflows.
Looking ahead, Kian Katanforoosh predicts a shift in focus towards augmenting human capabilities rather than aiming for full automation. “2026 will be the year of the humans,” he said. “AI has not worked as autonomously as we thought… and the conversation will focus more on how AI is being used to augment human workflows.”
Getting Physical: AI Beyond the Cloud
The convergence of smaller models, world models, and edge computing is paving the way for the proliferation of AI-powered physical devices. This includes advancements in robotics, autonomous vehicles, drones, and wearable technology. Vikram Taneja, head of AT&T Ventures, forecasts a surge in these devices entering mainstream markets in 2026.
While robotics and autonomous vehicles continue to be key areas, the growing popularity of wearable devices like smart glasses and health rings presents a more accessible entry point for physical AI. These devices are normalizing on-body inference, setting the stage for increasingly sophisticated and integrated AI experiences.
The integration of physical AI will likely necessitate optimized network infrastructure to support the bandwidth and low-latency requirements of these devices. Connectivity providers who can adapt and offer flexible solutions will be best positioned to capitalize on this emerging trend.
As we move into 2026, the focus will likely center on refining these agentic workflows and demonstrating tangible value in real-world applications. Further developments in world models and edge computing will be critical to watch, as will the ongoing debate surrounding architectural improvements for foundational models. The success of these initiatives—and the ultimate impact of AI—will hinge on the industry’s ability to move beyond hype and deliver practical solutions.
