US electric utilities are set to spend nearly $208 billion on the power grid in 2025 and more than $1.1 trillion over the next five years, according to the Edison Electric Institute. The surge in investment reflects rising demand from data centres, artificial intelligence, and wider electrification across the economy.
EEI data shows that investor-owned utilities spent $765 billion on capital projects in the five years to 2024. The new spending represents a significant increase and is aimed at upgrading and expanding infrastructure to keep pace with the accelerating demand for electricity.
The growing investment comes as demand from energy-intensive technologies continues to rise. Data centres and AI workloads are driving sustained growth in US power consumption, placing unprecedented pressure on existing infrastructure and prompting utilities to scale up their spending plans.
David Weeks, supply chain industry practice lead at Moody’s, warned that the escalating energy crisis could become a limiting factor across multiple industries. He said grid constraints and permitting delays must be factored into corporate supply chain strategies to avoid future disruptions.
As electrification spreads across the economy, grid reliability and capacity are becoming critical considerations for companies. The planned investment underscores the urgency of modernising the power grid to support economic growth while adapting to new technological demands.
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Researchers at MIT have developed a predictive model that could make fusion power plants more reliable and safe. The approach uses machine learning and physics-based simulations to predict plasma instabilities and prevent damage during tokamak shutdowns.
Experimental tokamaks use strong magnets to contain plasma hotter than the sun’s core. They often face challenges in safely ramping down plasma currents that circulate at extreme speeds and temperatures.
The model was trained and tested on data from the Swiss TCV tokamak. Combining neural networks with physics simulations, the team achieved accurate predictions using few plasma pulses, saving costs and overcoming limited experimental data.
The system can now generate practical ‘trajectories’ for controllers to adjust magnets and temperatures, helping to safely manage plasma during shutdowns.
Researchers say the method could be particularly important as fusion devices scale up to grid-level energy production. High-energy plasmas in larger reactors pose greater risks, and uncontrolled terminations could damage the machine.
The new model allows operators to carefully balance rampdowns, avoiding disruptions and ensuring safer, more efficient operation.
Work on the predictive model is part of wider collaboration with Commonwealth Fusion Systems and supported by the EUROfusion Consortium and Swiss research institutions. Scientists see it as a crucial step toward making fusion a practical, reliable, and sustainable energy source.
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MIT and McMaster researchers used AI to map how a narrow-spectrum antibiotic attacks harmful gut bacteria. Enterololin targets E. coli linked to Crohn’s flares while preserving most of the microbiome, providing a precise alternative to broad-spectrum antibiotics.
AI accelerated the process of identifying the drug’s mechanism of action, reducing a task that usually takes years to just months.
The team used DiffDock, a generative AI tool developed at MIT, to predict how enterololin binds to a protein complex called LolCDE in E. coli. Laboratory experiments, including mutant evolution, RNA sequencing, and CRISPR knockdowns, confirmed the AI predictions.
The method demonstrates how AI can provide mechanistic insights, guide experiments, and speed up early-stage antibiotic development.
Enterololin improved recovery and preserved the microbiome in mouse models compared with conventional treatments. Researchers aim to develop derivatives against resistant pathogens like Klebsiella pneumoniae, with early work underway at spinout company Stoked Bio.
The study highlights broader implications for precision antibiotics, which could treat infections without disrupting beneficial microbes. AI-driven mechanism mapping could speed up drug discovery, cut costs, and help tackle antimicrobial resistance.
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Google has expanded its AI Mode in Search, supporting over 35 new languages and 40 more countries and territories. The rollout expands access across Europe and other regions, reaching over 200 countries and territories worldwide.
The update aims to make AI-powered Search more accessible globally, allowing people to interact with Search in their native language. Expanding language support, Google will enable users to ask questions and access information in their preferred language.
AI Mode is powered by Google’s latest Gemini models, which deliver advanced reasoning and multimodal understanding. These capabilities help the system grasp the subtleties of local languages and provide relevant, context-aware answers, making AI Mode genuinely useful across diverse regions.
According to Google, people using AI Mode tend to explore topics in far greater depth, with queries nearly three times longer than traditional searches. The enhanced experience will continue to roll out globally over the coming week.
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Google DeepMind has launched the Gemini 2.5 Computer Use model, a specialised version of Gemini 2.5 Pro designed to let AI agents interact directly with digital user interfaces.
Available in preview through the Gemini API, developers can build agents capable of performing web and mobile tasks such as form-filling, navigation and interaction within apps.
Unlike models limited to structured APIs, Gemini 2.5 Computer Use can reason visually about what it sees on screen, making it possible to complete tasks requiring clicks, scrolls and text input.
While maintaining low latency, it outperforms rivals on several benchmarks, including Browserbase’s Online-Mind2Web and WebVoyager.
The model’s safety design includes per-step risk checks, built-in safeguards against misuse and developer-controlled restrictions on high-risk actions such as payments or security changes.
Google has already integrated it into systems like Project Mariner, Firebase Testing Agent and AI Mode in Search, while early testers report faster, more reliable automation.
Gemini 2.5 Computer Use is now available in public preview via Google AI Studio and Vertex AI, enabling developers to experiment with advanced interface-aware agents that can perform complex digital workflows securely and efficiently.
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Joining the broader trend, Denmark plans to ban children under 15 from using social media as Prime Minister Mette Frederiksen announced during her address to parliament on Tuesday.
Describing platforms as having ‘stolen our children’s childhood’, she said the government must act to protect young people from the growing harms of digital dependency.
Frederiksen urged lawmakers to ‘tighten the law’ to ensure greater child safety online, adding that parents could still grant consent for children aged 13 and above to have social media accounts.
Although the proposal is not yet part of the government’s legislative agenda, it builds on a 2024 citizen initiative that called for banning platforms such as TikTok, Snapchat and Instagram.
The prime minister’s comments reflect Denmark’s broader push within the EU to require age verification systems for online platforms.
Her statement follows a broader debate across Europe over children’s digital well-being and the responsibilities of tech companies in verifying user age and safeguarding minors.
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The 2025 Nobel Prize in Physics has been awarded to John Clarke, Michel Devoret and John Martinis for their experiments that brought quantum mechanical effects into macroscopic systems.
The Royal Swedish Academy of Sciences cited their ‘discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit’.
In classic quantum mechanics, particles can sometimes cross through energy barriers via a process known as tunnelling, behaviour that typically occurs at atomic or subatomic scales.
The laureates’ work showed that such quantum phenomena can appear in larger electrical circuits using superconductors and Josephson junctions, systems that were thought to be firmly in the domain of classical physics.
Their experiments (conducted in the mid-1980s) involved circuits made of superconducting materials separated by a thin insulating barrier. By finely tuning currents and electromagnetic stimuli, they were able to force a system to switch between zero-voltage and finite voltage states, essentially demonstrating that the circuit could ‘tunnel’ from one state to another.
In addition, they demonstrated energy quantisation in these systems, that the circuits absorb and emit energy in discrete packets, consistent with quantum theory.
This work is widely viewed as a foundational bridge between theoretical quantum mechanics and practical quantum technology. Superconducting circuits (such as qubits in quantum computers) rely on precisely these kinds of effects, and the laureates’ results helped validate the notion that quantum engineering is possible in engineered devices.
As the Nobel announcement puts it, their experiments ‘revealed quantum physics in action’ in a device small enough to hold in hand.
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Facebook enhances how users find and share Reels, focusing on personalisation and social interaction.
The platform’s new recommendation engine learns user interests faster, presenting more relevant and up-to-date content. Video viewing time in the US has risen over 20% year-on-year, reflecting the growing appeal of both short and long-form clips.
The update introduces new ‘friend bubbles’ showing which Reels or posts friends have liked, allowing users to start private chats instantly.
A feature that encourages more spontaneous conversation and discovery through shared interests. Facebook’s ‘Save’ option has also been simplified, letting users collect favourite posts and Reels in one place, while improving future recommendations.
AI now plays a larger role in content exploration, offering suggested searches on certain Reels to help users find related topics without leaving the player. By combining smarter algorithms with stronger social cues, Facebook aims to make video discovery more meaningful and community-driven.
Further personalisation tools are expected to follow as the platform continues refining its Reels experience.
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Researchers in China have used AI to reveal how different species independently develop similar traits when adapting to shared environments. The study focuses on echolocation in bats and toothed whales, two distant groups that created this ability separately despite their evolutionary differences.
The Institute of Zoology, Chinese Academy of Sciences team found that high-order protein features are crucial to adaptive convergence. Convergent evolution is the independent emergence of similar traits across species, often under similar ecological pressures.
Led by Zou Zhengting, the researchers developed a framework called ACEP, which utilises a pre-trained protein language model to analyse amino acid sequences. This method reveals hidden structural and functional information in proteins, shedding light on how traits are formed at the molecular level.
The findings, published in the Proceedings of the National Academy of Sciences, reveal how AI can detect deep biological patterns behind convergent evolution. The study demonstrates how combining AI with protein analysis provides powerful tools for understanding complex evolutionary mechanisms.
Zou said the work deepens the understanding of life’s evolutionary laws and highlights the growing role of AI in biology. The team in China hopes this approach can be applied to other evolutionary questions, broadening the use of AI in decoding life’s hidden patterns.
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AI could help design buildings that are resilient to both climate extremes and infectious disease threats, according to new research. The study, conducted in collaboration with Charles Darwin University, examines the application of AI in smart buildings, with a focus on energy efficiency and management.
Buildings account for over two-thirds of global carbon emissions and energy consumption, but reducing consumption remains challenging and costly. The study highlights how AI can enhance ventilation and thermal comfort, overcoming the limitations of static HVAC systems that impact sustainability and health.
Researchers propose adaptive thermal control systems that respond in real-time to occupancy, outdoor conditions, and internal heat. Machine learning can optimise temperature and airflow to balance comfort, energy efficiency, and infection control.
A new framework enables designers and facility managers to simulate thermal scenarios and assess their impact on the risk of airborne transmission. It is modular and adaptable to different building types, offering a quantitative basis for future regulatory standards.
The study was conducted with lead author Mohammadreza Haghighat from the University of Tehran and CDU’s Ehsan Mohammadi Savadkoohi. Future work will integrate real-time sensor data to strengthen building resilience against future climate and health threats.
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