Artificial intelligence in 2025 there were fewer flashy demonstrations and more challenging questions. What actually works? What breaks in unexpected ways? And what are the environmental and economic costs of further scaling these systems?
This was the year in which generative AI went from novelty to routine use. Many people are accustomed to using artificial intelligence tools at work, receiving answers from AI searchand trusting chatbotsfor the better or for the worse. This was the year tech giants unveiled their AI agentsand the general public seemed generally uninterested in using them. It has also become impossible to ignore failures in the field of artificial intelligence – this was the case even at Merriam-Webster. word of the year.
Throughout it all, IEEE Spectrum'In the field of artificial intelligence, special attention is paid to separating signal from noise. Here are the stories that best reflect the current state of affairs.
Alami
AI programming assistants have gone from novelty to everyday infrastructure, but not all tools are equally effective or trustworthy. This is a practical guide To Spectrum Contributing Editor Matthew S. Smith evaluates today's leading artificial intelligence coding systems, examining where they significantly improve performance and where they still fall short. The result is a clear view of which tools are worth using now and which are better suited for experimentation.
Amanda Andrade-Rhoades/The Washington Post/Getty Images
Ace AI energy needs cause concern, water use has become a quieter but equally pressing issue. This article explains how data centers consume water for cooling, why the impacts vary dramatically by region, and what engineers and policymakers can do to reduce the load. Written by AI sustainability scientist Shaolei Ren and Microsoft Chief Sustainability Officer Amy Luers, the article set the stage for a vigorous public debate about data, context, and engineering reality.
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When AI systems fail, they fail in a different way than humans. This is an essaylegendary cybersecurity guru Bruce Schneier and his frequent collaborator Nathan E. Sanders explore how machine errors differ in structure, magnitude, and predictability from human errors. Understanding these differences, the researchers say, is essential to creating artificial intelligence systems that can be responsibly deployed in the real world.
Christy Hemm watch
In this insider accountJohn Dean, co-founder and CEO of the company Wind systemstells readers how his team created one of the most technically ambitious artificial intelligence forecasting systems to date. The company's approach combines long-range autonomous weather balloons that travel with the wind with a proprietary artificial intelligence model called WeatherMesh, which both sends high-level instructions to the balloons about where to go next and analyzes the atmospheric data they collect.
The WindBorne platform can make high-resolution forecasts faster, using much less computation and with greater accuracy than traditional physics-based methods. In the article Dean walks readers through the engineering trade-offs, design decisions, and real-world testing that shaped the system from concept to deployment.
Eddie Guy
This elegantly written article is my personal favorite for 2025. In it Spectrum Freelancer Matthew Hutson tackles one of the most important and controversial questions in artificial intelligence today: how to determine general artificial intelligence (AGI) and measure progress towards that elusive goal. Drawing on historical context, current test debates, and ideas from leading researchers, Hutson shows why traditional tests fall short and why creating meaningful tests for AGI is so fraught. Along the way, he explores deep conceptual problems in comparing machine and human intelligence.
Bonus: Try the test who use AI to see how smart they are!
IEEE spectrum
Every year I roll up my sleeves because Spectrum'AI editor and go through extensive Stanford An AI index to highlight the data that really matters to understand the progress and pitfalls of AI. Visual overview of 2025 condenses the more than 400-page report into a dozen charts that highlight key trends in the economics of artificial intelligence, energy use, geopolitical competition and public sentiment.
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