In 2026, AI will move from hype to pragmatism

If 2025 was the year The AI ​​received a mood check2026 will be the year technology becomes practical. The focus is already on building ever-larger language models and doing the harder work of making AI usable. In practice, this means deploying smaller models where they fit, embedding intelligence into physical devices, and designing systems that integrate seamlessly with human workflows.

TechCrunch experts have talked about viewing 2026 as a year of transition, one that evolves from brute-force scaling to the pursuit of new architectures, from flashy demos to targeted deployments, and from agents that promise autonomy to those that actually augment the way people work.

The party isn’t over, but the industry is starting to sober up.

Scaling laws will not be enough

In 2012, Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton ImageNet article showed how AI systems could “learn” to recognize objects in images by examining millions of examples. The approach was computationally expensive, but made possible thanks to GPUs. The result? A decade of extensive AI research during which scientists worked to invent new architectures for different tasks.

This culminated around 2020 when OpenAI released GPT-3, which showed how simply making the model 100 times larger unlocks abilities like coding and reasoning without requiring explicit training. This marked the transition to what Kian Katanforoosh, CEO and founder of AI agent platform Workera, calls the “age of scaling”: a period defined by the belief that more compute, more data, and larger transformer models would inevitably drive the next major advances in AI.

Today, many researchers believe that the AI ​​industry is beginning to exhaust the limits of scaling laws and will once again enter the era of research.

Yann LeCun, Meta’s former chief AI scientisthas long opposed an over-reliance on scaling and emphasizes the need to develop better architectures. And Sutskever said in a recent interview that current models are plateauing and pre-training results have stabilized, indicating a need for new ideas.

“I think over the next five years we will most likely find a better architecture that will be a significant improvement over transformers,” Katanforoosh said. “And if we don’t, we can’t expect much improvement on the models.”

Sometimes less is more

Large language models are great for generalizing knowledge, but many experts say the next wave of enterprise AI adoption will be driven by smaller, more agile language models, which can be fine-tuned for domain-specific solutions.

“Refined SLMs will be the big trend and become a staple used by mature AI companies in 2026, as the cost and performance benefits will drive usage over off-the-shelf LLMs,” AT&T Chief Data Officer Andy Markus told TechCrunch. “We have already seen companies rely more and more on SLMs because, if tuned correctly, they match the larger, generalized models in terms of accuracy for enterprise business applications and are excellent in terms of cost and speed.

We have already seen this argument from the French AI start-up Mistral: it makes its small models actually work better than larger models on several benchmarks after fine-tuning.

“The efficiency, cost-effectiveness and adaptability of SLMs make them ideal for tailored applications where accuracy is paramount,” said Jon Knisley, AI strategist at ABBYY, an Austin-based enterprise AI company.

While Markus believes SLMs will be essential in the agentic era, Knisley says the nature of small models means they are more suited to deployment on local devices, “a trend accelerated by advances in edge computing”.

Learn from experience

Humans don’t just learn through language; we learn by experiencing how the world works. But LLMs don’t really understand the world; they simply predict the next word or idea. That’s why many researchers believe the next big step will come from global models: AI systems that learn how things move and interact in 3D spaces so they can make predictions and take actions.

The signs that 2026 will be a big year for the world’s models are growing. LeCun left Meta to create his own global modeling lab and reportedly seeking $5 billion valuation. Google’s DeepMind connected with Genie and launched its latest model in August that builds general-purpose, real-time interactive world models. Alongside startup demos like Deviation And Odyssey, Fei-Fei Li Global Laboratories launched its first global business model, Marble. Newcomers like General Intuition in October got a $134 million funding round to teach spatial reasoning to agents, and the video generation startup Runway published its world’s first model, GWM-1.

Although researchers see long-term potential in robotics and autonomy, the short-term impact will likely first manifest in video games. PitchBook predicts that the market for global game models could grow from $1.2 billion between 2022 and 2025 to $276 billion by 2030, driven by technology’s ability to generate more realistic interactive worlds and non-player characters.

Pim de Witte, founder of General Intuition, told TechCrunch that virtual environments could not only reshape gaming, but become a critical testing ground for the next generation of core models.

Agent Nation

Agents haven’t lived up to the hype of 2025, but that’s mainly because it’s difficult to connect them to the systems where the work actually happens. Without a way to access tools and context, most agents found themselves trapped in pilot workflows.

Anthropic’s Model Context Protocol (MCP), a “USB-C for AI” that allows AI agents to communicate with external tools such as databases, search engines, and APIs, proved the missing connective tissue and quickly became the standard. OpenAI and Microsoft have publicly adopted MCP, and Anthropic recently donated it to the The Linux Foundation’s new Agentic AI Foundationwhich aims to help standardize open source agentic tools. Google also started to stand out managed MCP servers to connect AI agents to its products and services.

With MCP reducing the friction of connecting agents to real systems, 2026 will likely be the year that agent workflows finally move from demos to daily practice.

Rajeev Dham, partner at Sapphire Ventures, says these advancements will lead to solutions focused on agents taking on “system of record roles” across industries.

“As voice agents handle more and more comprehensive tasks such as greeting and communicating with customers, they will also begin to train the underlying core systems,” Dham said. “We will see this across diverse industries such as home services, real estate technology and healthcare, as well as across horizontal functions such as sales, IT and support.”

Augmentation, not automation

While agentier workflows may raise fears of layoffs, Workera’s Katanforoosh isn’t sure that’s the message: “2026 will be the year of humans,” he said.

By 2024, all AI companies predicted they would automate tasks without the need for humans. But the technology isn’t there yet, and in an unstable economy, it’s not exactly popular talk. Katanforoosh says that next year we’ll realize that “AI hasn’t worked as autonomously as we thought” and that the conversation will be more about how AI is used to augment human workflows, rather than replace them.

“And I think a lot of companies are going to start hiring,” he added, noting that he expects new roles in AI governance, transparency, security and data management. “I am quite optimistic that average unemployment will be below 4% next year.”

“People want to be above the API, not below it, and I think 2026 is an important year for that,” de Witte added.

Getting physical

According to experts, technological advancements such as small models, global models and edge computing will enable more physical applications of machine learning.

“Physical AI will go mainstream in 2026, as new categories of AI-powered devices, including robotics, AVs, drones and wearables, begin to enter the market,” Vikram Taneja, head of AT&T Ventures, told TechCrunch.

While autonomous vehicles and robotics are obvious use cases for physical AI that will undoubtedly continue to grow in 2026, the training and deployment required remains expensive. Wearable devices, on the other hand, offer a lower cost advantage through consumer buy-in. Smart glasses like Ray-Ben’s Meat are starting to ship assistants that can answer questions about what you’re watching, as well as new form factors like AI-powered health rings And smart watches normalize inference always active and on the body.

“Connectivity providers will be working to optimize their network infrastructure to support this new wave of devices, and those with flexibility in how they can deliver connectivity will be best placed,” Taneja said.