AI Shifts from Hype to Practical Use by 2026

If 2025 served as a reality check for AI, 2026 will be the year the technology becomes genuinely useful. The focus is shifting from creating increasingly large language models to the more challenging task of making AI practical. This means deploying smaller models where they fit best, embedding intelligence into physical devices, and designing systems that seamlessly integrate into human workflows.

Experts interviewed by TechCrunch view 2026 as a transitional period—moving from brute-force scaling to exploring new architectures, from impressive demos to targeted implementations, and from agents that claim autonomy to those that genuinely enhance human productivity.

The celebration isn't over, but the industry is beginning to get serious.

Scaling laws won’t cut it

In 2012, the AlexNet paper by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton demonstrated how AI systems could learn to recognize objects in images by analyzing millions of examples. This approach was computationally intensive but became feasible with GPUs, sparking a decade of dedicated AI research as scientists developed new architectures for various tasks.

This culminated around 2020 with the launch of OpenAI's GPT-3, which showed that simply scaling a model 100 times larger could unlock capabilities like coding and reasoning without explicit training. This marked the beginning of what Kian Katanforoosh, CEO and founder of the AI agent platform Workera, calls the "age of scaling"—a period defined by the belief that more computing power, more data, and larger transformer models would inevitably lead to the next major AI breakthroughs.

Today, many researchers believe the AI industry is approaching the limits of scaling laws and is poised to enter a new era focused on research and innovation.

Yann LeCun, Meta's former chief AI scientist, has long criticized the over-reliance on scaling and emphasized the need for better architectures. Similarly, Sutskever noted in a recent interview that current models are reaching a plateau, with pre-training results leveling off, signaling a need for fresh ideas.

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Join the Disrupt 2026 waitlist to get early access when Early Bird tickets become available. Previous Disrupt events have featured leaders from Google Cloud, Netflix, Microsoft, Box, Phia, a16z, ElevenLabs, Wayve, Hugging Face, Elad Gil, and Vinod Khosla—part of over 250 industry experts leading 200+ sessions designed to accelerate your growth and sharpen your competitive edge. You'll also have the chance to connect with hundreds of startups driving innovation across various sectors.

"I believe that within the next five years, we are likely to discover a better architecture that represents a significant improvement over transformers," Katanforoosh said. "If we don't, we shouldn't expect substantial progress in model capabilities."

Sometimes less is more

While large language models excel at general knowledge, many experts predict the next wave of enterprise AI adoption will be driven by smaller, more agile language models that can be fine-tuned for specific domain applications.

"Fine-tuned SLMs will become a major trend and a staple for mature AI enterprises in 2026, as their cost and performance advantages make them more appealing than off-the-shelf LLMs," Andy Markus, AT&T's chief data officer, told TechCrunch. "We're already seeing businesses increasingly rely on SLMs because, when properly fine-tuned, they match the accuracy of larger, general models for enterprise applications while offering superior cost-efficiency and speed."

This argument has been made before by French open-weight AI startup Mistral, which claims its small models outperform larger ones on several benchmarks after fine-tuning.

"The efficiency, cost-effectiveness, and adaptability of SLMs make them ideal for tailored applications where precision is critical," said Jon Knisley, an AI strategist at ABBYY, an enterprise AI company based in Austin.

While Markus believes SLMs will play a key role in the agentic era, Knisley points out that their compact nature makes them better suited for deployment on local devices—a trend accelerated by advancements in edge computing.

Learning through experience

Humans learn not only through language but also by interacting with the world. LLMs, however, don't truly understand the world; they simply predict the next word or concept. This is why many researchers believe the next major advancement will come from world models—AI systems that learn how objects move and interact in 3D spaces, enabling them to make predictions and take actions.

There are growing indications that 2026 will be a pivotal year for world models. LeCun left Meta to start his own world model lab and is reportedly seeking a $5 billion valuation. Google's DeepMind has been developing Genie and, in August, launched its latest model capable of building real-time interactive general-purpose world models. Alongside demos from startups like Decart and Odyssey, Fei-Fei Li's World Labs has released its first commercial world model, Marble. New entrants like General Intuition secured a $134 million seed round in October to teach agents spatial reasoning, and video generation startup Runway released its first world model, GWM-1, in December.

Although researchers see long-term potential in robotics and autonomy, the near-term impact is expected to be most visible in video games. PitchBook forecasts that the market for world models in gaming could grow from $1.2 billion between 2022 and 2025 to $276 billion by 2030, driven by the technology's ability to generate interactive environments and more realistic non-player characters.

Pim de Witte, founder of General Intuition, told TechCrunch that virtual environments could not only transform gaming but also serve as crucial testing grounds for the next generation of foundation models.

Agentic nation

AI agents fell short of expectations in 2025, largely because integrating them into actual work systems proved difficult. Without access to tools and context, most agents were confined to pilot workflows.

Anthropic's Model Context Protocol (MCP), described as a "USB-C for AI," allows AI agents to communicate with external tools like databases, search engines, and APIs. This missing link is quickly becoming the standard, with OpenAI and Microsoft publicly endorsing MCP. Anthropic recently donated the protocol to the Linux Foundation's new Agentic AI Foundation, which aims to standardize open-source agentic tools. Google has also begun setting up its own managed MCP servers to connect AI agents to its products and services.

With MCP reducing the friction of connecting agents to real systems, 2026 is likely to be the year when agentic workflows move from demonstrations to everyday practice.

Rajeev Dham, a partner at Sapphire Ventures, believes these advancements will lead to agent-first solutions taking on "system-of-record roles" across various industries.

"As voice agents handle more end-to-end tasks such as intake and customer communication, they will also begin to form the core underlying systems," Dham said. "We expect to see this trend in sectors like home services, property technology, and healthcare, as well as in horizontal functions such as sales, IT, and support."

Augmentation, not automation

While the rise of agentic workflows may raise concerns about job losses, Katanforoosh of Workera isn't convinced that's the message for 2026.

"2026 will be the year of the humans," he said.

In 2024, many AI companies predicted they would automate jobs to eliminate the need for human workers. However, the technology isn't yet capable of that, and in an unstable economy, such rhetoric isn't popular. Katanforoosh suggests that next year, we'll realize that "AI hasn't worked as autonomously as we expected," and the conversation will shift toward how AI can augment human workflows rather than replace them.

"I also think many companies will start hiring again," he added, noting that he anticipates new roles in AI governance, transparency, safety, and data management. "I'm fairly optimistic that unemployment will average below 4% next year."

"People want to be above the API, not below it, and I believe 2026 will be a crucial year for this shift," de Witte added.

Getting physical

Advancements in small models, world models, and edge computing will enable more physical applications of machine learning, according to experts.

"Physical AI will go mainstream in 2026 as new categories of AI-powered devices—including robotics, autonomous vehicles, drones, and wearables—begin entering the market," Vikram Taneja, head of AT&T Ventures, told TechCrunch.

While autonomous vehicles and robotics are obvious use cases for physical AI and will continue to grow in 2026, the required training and deployment remain expensive. Wearables, on the other hand, offer a more affordable entry point with consumer appeal. Smart glasses like Meta's Ray-Bans are starting to include assistants that can answer questions about what you're looking at, and new form factors like AI-powered health rings and smart watches are making always-on, on-body inference more common.

"Connectivity providers will work to optimize their network infrastructure to support this new wave of devices, and those with flexible connectivity options will be best positioned to succeed," Taneja said.