AI year in review: Trends shaping 2026

2026-01-02 · Source: IBM Technology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Software Development & Engineering · Depth: Advanced, extended

Summary

The "Mixture of Experts" podcast reviews AI trends from 2025 and predicts developments for 2026, focusing on super agents, open-source AI, hardware, and multimodal models. Chris defends his "super agent" prediction, noting the evolution from specialized agents to orchestrators like ChatGPT, Claude, and Gemini, which combine reasoning and tools to perform complex tasks. Gabe highlights 2025 as the "year of open source," emphasizing the narrowing gap between proprietary and open-source models, but points out a "packaging problem" where integrating open-source components into cohesive, user-friendly systems remains a challenge. Kar discusses AI hardware, noting 2025's demand outstripping supply and predicting a 2026 focus on efficient models and specialized chips for agentic workloads. Aaron and Abe explore multimodal models, anticipating their role in creating "digital workers" and the shift towards modular, orchestrated multimodal capabilities for enterprise use cases, including complex tables, charts, computer vision, and audio speech recognition.

Key takeaway

For CTOs and VPs of Engineering planning their 2026 AI strategy, recognize the shift towards integrated "super agents" and the critical role of orchestration in both proprietary and open-source deployments. Your teams should prioritize developing or adopting systems that can seamlessly combine reasoning, tools, and diverse modalities, while also focusing on hardware-efficient models to navigate ongoing compute scarcity and optimize operational costs.

Key insights

AI is evolving towards integrated "super agents," robust open-source ecosystems, efficient hardware, and modular multimodal capabilities.

Principles

• AI demand outpaces hardware supply.
• Open-source AI needs better packaging.
• Orchestration is key for agentic systems.

Method

Super agents combine reasoning and tools to plan and execute multi-step tasks, moving beyond specialized, single-function agents. Modular multimodal systems orchestrate specialized models for diverse inputs and outputs.

In practice

• Explore orchestrator agents like ChatGPT, Claude, Gemini.
• Prioritize hardware-aware, efficient models for deployment.
• Investigate modular multimodal architectures for enterprise data.

Topics

• Super Agents
• Open-Source AI
• AI Hardware Trends
• Multimodal AI
• Agent Orchestration

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, AI Product Manager, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by IBM Technology.