Science has named the seemingly unstoppable growth of renewable energy worldwide as the 2025 Breakthrough of the Year. Since the Industrial Revolution, humanity has relied on fossil fuels like coal, oil, and gas for energy. Carbon emissions from these finite resources have greatly contributed to accelerated climate warming. However, 2025 marked a significant shift in this paradigm as renewable energy generated from the Sun and wind began to surpass conventional fossil fuel-based energy production in several domains. This year, global renewable energy, led by solar and wind, grew fast enough to cover all the world’s new electricity demand in the first half of the year, and now supplies more electricity than coal worldwide. This transition is being led by China, whose efforts to scale up solar panels, wind turbines, and lithium battery storage have cemented the nation as a global leader in renewable energy production and technology. Elsewhere, small-scale rooftop solar systems – made affordable and widely accessible by China’s manufacturing dominance – are spreading rapidly, particularly across Europe, South Asia, and the Global South, and provide reliable, low-cost energy security for millions. Already, existing renewables have demonstrably slowed the growth of greenhouse emissions in China, hinting at a global turning point in addressing ongoing climate warming. What’s more, further technological innovations in this space, such as more efficient solar cells and battery chemistries, for example, promise to extend the reach and effectiveness of renewable energy. Many obstacles remain, however, including continued widespread coal use, infrastructure bottlenecks, and political resistance in some regions (including the United States). Yet, despite these challenges, this year’s breakthrough suggests that the transition from fossil fuels to clean, renewable energy is not just possible – it’s accelerating – and rapidly becoming the most practical and cost-effective choice.

 

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LLM-assisted manuscripts exhibit more complexity of the written word but are lower in research quality, according to a Policy Article by Keigo Kusumegi, Paul Ginsparg, and colleagues that sought to evaluate the impacts of widespread use of generative artificial intelligence (AI) technologies on scientific production. “As AI systems advance, they will challenge our fundamental assumptions about research quality, scholarly communication, and the nature of intellectual labor,” write the authors. “Science policymakers must consider how to evolve our scientific institutions to accommodate the rapidly changing scientific production process.” Despite enormous enthusiasm and growing concern surrounding the use of generative AI and large language models (LLMS) across research and academia, there has been little systemic evidence about how these technologies are reshaping scientific production. To address this gap, Kusumegi et al. assembled five large datasets spanning 2.1 million preprints, 28,000 peer-reviewed reports, and 246 million online views and downloads of scientific documents. Then, using text-based detectors to identify first-time LLM use, they conducted difference-in-differences analyses to compare researchers’ work before and after LLM adoption. They found that LLM adoption increases a researcher’s scientific output by 23.7 – 89.3%, with especially large boosts for authors facing higher writing and language barriers. Kusumegi et al. also discovered that LLM-assisted manuscripts show a reversal of the traditional positive relationship between writing complexity and research quality. After LLM adoption, more sophisticated language was used, but in substantively weak manuscripts. Lastly, the authors show that LLM adopters read and cite more diverse literature, referencing more books, younger works, and less-cited documents. “Our findings show that LLMs have begun to reshape scientific production. These changes portend an evolving research landscape in which the value of English fluency will recede, but the importance of robust quality-assessment frameworks and deep methodological scrutiny is paramount,” write the authors. “For peer reviewers and journal editors, and the community, more broadly, who create, consume, and apply this work, this represents a major issue."

Researchers present LightGen – the first all-optical chip capable of performing challenging advanced generative artificial intelligence (AI) tasks at speeds and energy efficiencies orders of magnitude beyond today’s traditional electronic hardware. Large-scale generative AI models can now create text, images, and video with remarkable fidelity. However, these sophisticated tasks require enormous computing power, time, and energy; existing hardware struggles to meet the demands of today’s large models. Photonic computing, which processes information using pulses of laser light instead of electricity, offers a promising path toward dramatically faster and more energy-efficient AI. Although early photonic systems already outperform conventional chipsets in speed and efficiency, fundamental limitations in their design and function have hindered their use in advanced AI applications. According to Yitong Chen and colleagues, solving these issues is crucial to enabling photonic hardware that can drive ultrafast, energy-efficient generative AI. Here, Chen et al. present LightGen, an entirely optical generative AI chip that could overcome the challenges of traditional photonic systems. The authors developed a so-called optical latent space and Bayes-based training algorithms, that allow varying the optical network dimensionality using pure light-based processes. What’s more, the chip hosts more than two million photonic “neurons,” enabling it to perform complex generative tasks, including high-resolution image synthesis, video manipulation, style transfer, and denoising. Chen et al. show that LightGen could achieve these tasks with exceptional speed and energy efficiency, exceeding current state-of-the-art electronic chips by orders of magnitude.