AI in 2026: What to expect from the next wave

The Rise of Multimodal and Action-Oriented AI

One of the defining trends of 2026 is the transition toward multimodal AI systems. Earlier generations of AI focused primarily on processing text (large language models, or LLMs) or combining text and images (vision-language models, or VLMs).

The next stage introduces Vision-Language-Action (VLA) models — systems capable of not only understanding text and visual input but also taking action based on that understanding.

These models can:

Interpret natural language instructions
Analyze visual environments
Perform physical tasks such as picking up objects, opening drawers, or moving items

Instead of merely offering recommendations, AI systems are increasingly able to interact with their surroundings. This development represents a shift from passive analysis to embodied intelligence — AI that understands context and responds dynamically without highly specific instructions.

Robotics Moves Into Commercial and Industrial Spaces

This transition is already visible in robotics.

One example is Covariant, a startup founded by researchers from the University of California, Berkeley. In 2024, the company introduced its RFM-1 robotics foundation model, designed to serve as a “brain” for industrial robots. Later that year, Amazon acquired a non-exclusive license to Covariant’s technology and brought its founders into its robotics division — signaling growing corporate interest in AI-powered automation.

Similarly, Ambi Robotics has been developing robotic systems capable of autonomously sorting and packing parcels using cameras and neural networks. Its warehouse-focused solutions, including automated stacking and industrial robotic platforms, are already being deployed by major logistics operators.

These developments illustrate how AI is expanding beyond research labs into practical industrial use — particularly in logistics, warehousing, and manufacturing.

The Emergence of Home Humanoid Robots

The next frontier lies in household robotics.

In 2025, Norwegian startup 1X introduced a home humanoid robot known as NEO. Designed to assist with everyday tasks, NEO can clean rooms, organize shelves, fold laundry, adjust lighting, welcome guests, and interact conversationally with users.

Such robots also demonstrate contextual awareness. For example, by analyzing the contents of a refrigerator and learning from a user’s habits, they can suggest meal options. Subscription-based consumer models are expected to accelerate adoption as hardware and AI capabilities mature.

While widespread household adoption may take time, the direction is clear: AI-powered physical assistants are gradually moving into daily life.

AI in Energy: Slow but Strategic Adoption

In contrast to robotics, the energy sector has been slower to integrate AI solutions. The primary reason lies in infrastructure. Many energy companies operate legacy systems that are reliable but difficult to integrate with modern AI platforms. Updating these systems often requires significant financial investment and structural redesign.

Currently, AI is mainly used in:

Demand forecasting
Consumption pattern analysis
Predictive maintenance

However, more advanced applications — such as grid optimization, smart charging station management, and leak detection — are expected to gain momentum over time. As energy systems modernize, automation and AI integration may become strategic priorities.

Healthcare and Pharmaceuticals: Regulation Meets Innovation

Healthcare adoption of AI progresses cautiously due to strict regulatory requirements. Clinical applications require validation, transparency, and compliance with safety standards.

However, pharmaceuticals and biotechnology are advancing more rapidly. Neural networks are increasingly used to:

Analyze molecular structures
Model chemical interactions
Accelerate drug discovery
Predict compound properties

AI-driven drug design is becoming a significant innovation area, potentially reducing development timelines and costs. At the intersection of biotechnology and machine learning, new professional roles are emerging.

The Labor Market in 2026: Augmentation, Not Replacement

Concerns that artificial intelligence will displace workers entirely appear overstated. In practice, AI is proving to be a tool that enhances human productivity rather than replaces it outright.

The emerging competition is less about humans versus machines and more about:

Professionals who can effectively use AI tools
Professionals who lack AI integration skills

Research suggests that workers proficient in AI-assisted workflows demonstrate significantly higher productivity — in some cases around one-third more efficient than their peers.

As a result, the labor market is evolving to favor AI literacy across sectors.

Emerging Professions in the AI Era

As technologies develop, new professional specializations are forming. Among the most prominent:

AI Safety Engineers

These specialists focus on ensuring reliability, robustness, and ethical alignment of AI systems, particularly as models become more autonomous.

Vision-Language-Action Engineers

Working at the intersection of robotics and machine learning, these professionals design training environments, simulators, and behavioral frameworks for action-capable AI systems.

AI-Driven Drug Design Experts

In pharmaceuticals and biotech, specialists combine chemistry expertise with machine learning tools to model molecules and predict biological interactions.

A Defining Shift Toward Real-World Intelligence

By 2026, artificial intelligence is expected to move decisively beyond content generation. The central transformation lies in its growing ability to perceive environments, interpret context, and act within the physical world.

From industrial robotics to household assistants, from predictive energy management to AI-assisted drug discovery, the technology is gradually embedding itself into core economic and social systems.

Rather than replacing human expertise, AI is redefining it — creating new skill demands, new professions, and new standards of productivity. The coming years are likely to mark not just incremental improvements in algorithms, but a structural shift in how machines participate in everyday life.