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An AI model accurately predicted the trajectory of Hurricane Melissa, which wreaked havoc in the Caribbean last October.Credit: Gallo Images/Orbital Horizon/Copernicus Sentinel Data 2025/Getty
Technological progress, like scientific progress, is often incremental, driven in part by stochastic bursts of problem-solving and hurdle-clearing. Occasionally, an innovation can reach sufficient maturity to make a real impact: by coming into practical use at scale or finding a broad range of applications. Since 2018, Nature has drawn up a list of emerging technologies to watch in the coming year. Our latest edition is published this week.
Seven Technologies to watch in 2026
Artificial intelligence makes an appearance, as it often has in the past few years. This year’s main AI technology to watch is AI-powered meteorology, which is accelerating and improving local weather forecasting, storm tracking and global climate modelling. An AI model by researchers at Google DeepMind in London, for instance, anticipated that Hurricane Melissa, which wreaked havoc in the Caribbean last October, would become a category-5 event days in advance and also accurately predicted its trajectory. Another model, trained on weather data from several sources, was able to provide accurate forecasts up to ten days before a weather event1.
Quantum computing makes its second appearance on the list this year, following studies intended to improve the problem of error correction in quantum bits, or qubits, the fundamental units of quantum information2. The first time, in 2022, our writer noted the early but tantalizing progress made in manipulating individual atoms as qubits for a quantum processor. Since then, investment in this area has surged. In 2023, the United States, the United Kingdom, Germany and South Korea announced investments with a combined total of nearly US$10 billion in quantum technologies. And, in 2025, Japan alone invested some $7 billion.
This year, for the first time, the list also includes nuclear-energy technologies. Progress in nuclear fusion is now bringing the promise of abundant energy from this source closer. At the same time, small modular nuclear reactors are being developed rapidly to help nations to cope with a surge in energy demand from the data centres being built to power AI applications.
Decisions about which technologies to feature are informed by the recommendations and perspectives of editors of the Nature Portfolio journals, trends in the research and policy literature and current events. Looking back, developments in gene editing, microscopy and messenger-RNA technologies are ever-present. The shadow of the COVID-19 pandemic also looms. The first mRNA vaccine against the SARS-CoV-2 virus received emergency-use authorization in December 2020, less than a year after the start of the pandemic, and mRNA vaccines duly found their way onto the list in 2021.
AI promise — and peril
Perhaps unsurprisingly, AI has been one of the most notable recurring themes throughout the series. The words artificial intelligence were included in the first list, in 2018, in which AI is mentioned as a promising technology for integrating and analysing data from diverse sources, such as wearable devices, scientific instruments and the research literature. Two years later3, four Nature Portfolio journals published a series of articles that used machine learning to assess the world’s agricultural-science literature. These studies revealed a lack of research on smallholder farmers, who make up the majority of farmers worldwide4.
AI made its second appearance as a technology to watch in genomics. Protein-structure prediction was the AI technology to watch in 2022, on the back of the publication of the AlphaFold2 model5, which could extrapolate the shape of a folded protein from its amino-acid sequence. AI returned in 2024 as a technology to watch for the design of proteins with innovative functions for applications ranging from vaccine development to synthetic biology. That same year, we also drew attention to AI’s dark side — highlighting technologies that could be used to combat the proliferation of deepfake images, and to distinguish AI-generated audio, videos and photographs from the real thing.
Let 2026 be the year the world comes together for AI safety
Last year’s list had the most AI-related entries. The three technologies included were ‘self-driving’ laboratories in which robotics and AI algorithms can plan and interpret workflows in chemistry and materials research; models for classifying cell types and analysing gene networks; and a way to use AI to accelerate development of light-based or ‘photonic’ computers, in which photons are used instead of electrons to transmit and process data.
From the start, the writers of the series have recognized that AI’s transformative potential necessitates careful management of its associated risks. In January 2018, neuroscientist Vivienne Ming at Socos Labs in Berkeley, California, asked who would have control over data held on AI platforms and how new findings would be published, considering the dominance of large tech companies in the AI sector. Researchers at AI companies are publishing in the peer-reviewed literature6, albeit not nearly as much as they could, and should7. Ming’s words ring as true today as they did when this series began: “The amazing tradition that is science should not be obfuscated in the hands of just a few people.” We wholeheartedly agree, and we look forward to seeing more of the global scientific community’s biggest swings and most audacious bets in the years to come.
