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This InfoQ report is a practitioner-oriented survey of AI and ML trends, not a primary research result. Its main signals are a shift toward small language models and reasoning models, agents moving from individual use to team and organization-wide deployment, growing attention to inference and evaluation, and early progress in physical AI. Readers should treat these as editorial judgments about adoption rather than measured outcomes, and the trends graph is most useful for seeing which ideas are still pre-chasm.
Article: InfoQ AI, ML and Data Engineering Trends Report - 2026
This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in AI and ML technologies and what to look out for in the next 12 months. By Srini Penchikala, Chip Huyen, Daniel Dominguez, Hien Luu, Arthur Casals
Language models have shifted from large-sized models to the small language models. We are also seeing the rise of reasoning models that can think through math and logic problems before responding. AI Agents are moving from personal agents to the agents working with teams to companies using agents across entire organizations. System One models like TypeSafe AI’s Jev will see more adoption because of their deterministic and faster inference, in addition to their lower costs compared to the LLMs. AI Inference continues to be a useful and powerful trend and more and more companies will start doing this moving forward. AI evaluations have been getting a lot of attention this year. Evaluations are a way to help us to make sure that the agents do the right thing for the right tasks. Physical AI has seen significant developments in 2026. We are starting to connect the software intelligence to machines that can perceive and act in the physical world. The InfoQ AI ML Annual Trends Reports offer InfoQ readers a comprehensive overview of emerging trends and technologies in the areas of AI, ML, and Data Engineering. This report summarizes the InfoQ editorial team’s podcast with external guests to discuss the…
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