If 2025 marked a vibe check for AI, then 2026 is poised to be the year when the technology becomes truly functional. The attention is already turning from constructing larger language models to the more challenging task of making AI practical. This means implementing smaller models in suitable areas, integrating intelligence into physical devices, and creating systems that blend seamlessly into human processes. 

The specialists consulted by TechCrunch view 2026 as a period of evolution, transitioning from brute-force scaling to exploring new architectures, from impressive demonstrations to focused applications, and from agents that claim autonomy to those that genuinely enhance human work. 

The celebration isn’t finished, but the sector is beginning to come down to earth.

Scaling laws won’t suffice

In 2012, the AlexNet paper by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton illustrated how AI systems can “learn” to identify objects in images by analyzing millions of examples. The method was computationally intensive but feasible thanks to GPUs. The outcome? A decade of intense AI research as scholars endeavored to create new architectures for varied tasks.

This peak occurred around 2020 with OpenAI’s launch of GPT-3, demonstrating that merely enlarging the model by 100 times unlocked capabilities like coding and reasoning without needing explicit training. This signified the shift into what Kian Katanforoosh, CEO and founder of AI agent platform Workera, describes as the “era of scaling”: a time characterized by the belief that increased compute, more data, and larger transformer models would essentially lead to the next significant advances in AI.

Currently, many researchers believe the AI industry is reaching the limits of scaling laws and will revert to a phase of renewed research.

Yann LeCun, former chief AI scientist at Meta, has consistently argued against a heavy dependence on scaling, emphasizing the necessity of developing improved architectures. Meanwhile, Sutskever mentioned in a recent interview that contemporary models are plateauing and that pre-training outcomes have stagnated, suggesting a demand for new concepts.  

“It is likely that in the next five years, we will discover a superior architecture that significantly outperforms transformers,” Katanforoosh stated. “If we don’t, we can’t anticipate substantial enhancements in the models.”

Occasionally, less is more

Large language models excel in generalizing knowledge; however, many experts believe the upcoming wave of enterprise AI adoption will hinge on smaller, more nimble language models that can be customized for specific solutions. 

“Fine-tuned SLMs will become the prominent trend, becoming standard among established AI companies in 2026, as the cost and performance benefits will favor their use over generic LLMs,” stated Andy Markus, AT&T’s chief data officer, to TechCrunch. “Businesses are increasingly depending on SLMs since, when properly fine-tuned, they can match the accuracy of the larger, generalized models for enterprise applications while also being significantly more cost-effective and faster.”

This perspective has been previously advocated by French open-weight AI firm Mistral, which claims its smaller models outperform larger counterparts on various benchmarks post fine-tuning. 

“The efficiency, affordability, and flexibility of SLMs make them ideal for specific applications requiring high precision,” remarked Jon Knisley, an AI strategist at ABBYY, based in Austin. 

While Markus believes SLMs will play a crucial role in the agentic age, Knisley suggests that the characteristics of small models make them more suitable for deployment on local devices, “a trend hastened by improvements in edge computing.”

Learning from experience

Humans acquire knowledge not just through language but also by experiencing how the world operates. However, LLMs don’t truly comprehend the world; they simply predict the subsequent word or concept. This is why numerous researchers assert that the next substantial advancement will originate from world models: AI systems that learn how objects move and interact in three-dimensional spaces, allowing them to make forecasts and take actions. 

Signs are pointing to 2026 being significant for world models. LeCun departed Meta to establish his own lab focused on world models and is reportedly aiming for a $5 billion valuation. Google’s DeepMind has been diligently working on Genie, and in August rolled out its most recent model that creates real-time interactive general-purpose world models. In addition to startups like Decart and Odyssey showcasing their developments, Fei-Fei Li’s World Labs has unveiled its first commercial world model, Marble. New entities like General Intuition secured a $134 million seed round in October to educate agents in spatial reasoning, and video generation pioneer Runway released its inaugural world model, GWM-1, in December. 

While researchers see significant promise in robotics and autonomy over the long run, the immediate impact is likely to manifest first in the gaming industry. PitchBook forecasts that the market for world models in gaming could surge from $1.2 billion between 2022 and 2025 to $276 billion by 2030, propelled by the technology’s capability to create interactive environments and more realistic non-player characters. 

Pim de Witte, founder of General Intuition, mentioned to TechCrunch that virtual settings may not only revolutionize gaming but also serve as essential testing environments for the next generation of foundational models.

Agentic nation

Agents fell short of the expectations in 2025, largely because linking them to the systems where actual work occurs is challenging. Without a means to access tools and context, most agents remained confined to pilot workflows. 

Anthropic’s Model Context Protocol (MCP), referred to as a “USB-C for AI,” enables AI agents to interface with external tools such as databases, search engines, and APIs, filling the gap and is rapidly becoming the standard. OpenAI and Microsoft have publicly adopted MCP, and Anthropic recently contributed it to the Linux Foundation’s newly formed Agentic AI Foundation, which aims to standardize open-source agentic tools. Google has also initiated its own managed MCP servers to connect AI agents with its products and services. 

With MCP alleviating the challenges of linking agents to real systems, 2026 is expected to be the year when agentic workflows transition from demonstrations to everyday implementation. 

Rajeev Dham, a partner at Sapphire Ventures, states that these innovations will enable agent-first solutions to assume “system-of-record roles” across various sectors. 

“As voice agents take on more comprehensive tasks like intake and customer communication, they will also begin to establish the underlying core systems,” Dham observed. “We will see this across diverse fields such as home services, proptech, and healthcare, in addition to horizontal functions like sales, IT, and support.” 

Augmentation, not automation

While increased agentic workflows may raise concerns about potential layoffs, Katanforoosh from Workera isn’t convinced that narrative holds true. 

“2026 will be the year for humans,” he asserted. 

In 2024, every AI company predicted they would eliminate jobs due to a reduced need for humans. However, the technology isn’t ready for that yet, and such rhetoric isn’t popular in an uncertain economy. Katanforoosh believes that next year, we will realize that “AI has not functioned as independently as we believed,” and the discourse will shift towards how AI enhances human workflows instead of replacing them. 

“And I anticipate that many companies will begin hiring,” he continued, pointing out that he expects new roles to emerge in AI governance, transparency, safety, and data management. “I am optimistic about unemployment averaging below 4% next year.”

“People prefer to operate above the API, not beneath it, and I believe 2026 will be significant for this,” de Witte remarked.

Becoming physical

Experts suggest that technological advancements such as small models, world models, and edge computing will facilitate more physical applications of machine learning. 

“Physical AI will go mainstream in 2026 as new categories of AI-enhanced devices, including robotics, autonomous vehicles, drones, and wearables begin entering the market,” stated Vikram Taneja, head of AT&T Ventures, to TechCrunch. 

Although autonomous vehicles and robotics are evident use cases for physical AI that will undoubtedly thrive in 2026, the necessary training and implementation remain costly. On the other hand, wearables offer a more affordable gateway with consumer acceptance. Smart glasses like Meta’s Ray Bans are beginning to ship with assistants that can respond to inquiries about what the user is observing, and emerging formats such as AI-infused health rings and smartwatches are normalizing continuous, on-body inference.

“Connectivity providers will strive to enhance their network infrastructure to support this new wave of devices, and those adaptable in their connectivity offerings will be best aligned for success,” Taneja noted.