This story is a part of MIT Technology Review's series "Power Hungry: AI and our energy future," on the energy demands and carbon costs of the artificial-intelligence revolution. These somewhat unlikely partnerships could be a win for both the nuclear power industry and large tech companies. Tech giants need guaranteed sources of energy, and many are looking for low-emissions ones to hit their climate goals. For nuclear plant operators and nuclear technology developers, the financial support of massive established customers could help keep old nuclear power plants open and push new technologies forward. "There [are] a lot of advantages to nuclear," says Michael Terrell, senior director of clean energy and carbon reduction at Google.

"Dollars are being invested, GPUs are being burned, water is being evaporated--it's just absolutely the wrong direction," says Ali Farhadi, CEO of the Seattle-based nonprofit Allen Institute for AI. But sift through the talk of rocketing costs--and climate impact--and you'll find reasons to be hopeful. There are innovations underway that could improve the efficiency of the software behind AI models, the computer chips those models run on, and the data centers where those chips hum around the clock. Here's what you need to know about how energy use, and therefore carbon emissions, could be cut across all three of those domains, plus an added argument for cautious optimism: There are reasons to believe that the underlying business realities will ultimately bend toward more energy-efficient AI. The most obvious place to start is with the models themselves--the way they're created and the way they're run.

Despite the fact that billions of dollars are being poured into reshaping energy infrastructure around the needs of AI, no one has settled on a way to quantify AI's energy usage. Worse, companies are generally unwilling to disclose their own piece of the puzzle. There are also limitations to estimating the emissions associated with that energy demand, because the grid hosts a complicated, ever-changing mix of energy sources. So, that said, here are the many variables, assumptions, and caveats that we used to calculate the consequences of an AI query. Companies like OpenAI, dealing in "closed-source" models, generally offer access to their systems through an interface where you input a question and receive an answer.

AI's integration into our lives is the most significant shift in online life in more than a decade. Hundreds of millions of people now regularly turn to chatbots for help with homework, research, coding, or to create images and videos. Today, new analysis by MIT Technology Review provides an unprecedented and comprehensive look at how much energy the AI industry uses--down to a single query--to trace where its carbon footprint stands now, and where it's headed, as AI barrels towards billions of daily users. This story is a part of MIT Technology Review's series "Power Hungry: AI and our energy future," on the energy demands and carbon costs of the artificial-intelligence revolution. We spoke to two dozen experts measuring AI's energy demands, evaluated different AI models and prompts, pored over hundreds of pages of projections and reports, and questioned top AI model makers about their plans.

Innovation in IT got us to this point. Graphics processing units (GPUs) that power the computing behind AI have fallen in cost by 99% since 2006. There was similar concern about the energy use of data centers in the early 2010s, with wild projections of growth in electricity demand. But gains in computing power and energy efficiency not only proved these projections wrong but enabled a 550% increase in global computing capability from 2010 to 2018 with only minimal increases in energy use. In the late 2010s, however, the trends that had saved us began to break.

Recent advances in large language models (LLMs) have fueled the vision of automated scientific discovery, often called AI Co-Scientists. To date, prior work casts these systems as generative co-authors responsible for crafting hypotheses, synthesizing code, or drafting manuscripts. In this work, we explore a complementary application: using LLMs as verifiers to automate the \textbf{academic verification of scientific manuscripts}. To that end, we introduce SPOT, a dataset of 83 published papers paired with 91 errors significant enough to prompt errata or retraction, cross-validated with actual authors and human annotators. Evaluating state-of-the-art LLMs on SPOT, we find that none surpasses 21.1\% recall or 6.1\% precision (o3 achieves the best scores, with all others near zero). Furthermore, confidence estimates are uniformly low, and across eight independent runs, models rarely rediscover the same errors, undermining their reliability. Finally, qualitative analysis with domain experts reveals that even the strongest models make mistakes resembling student-level misconceptions derived from misunderstandings. These findings highlight the substantial gap between current LLM capabilities and the requirements for dependable AI-assisted academic verification.

Giant Deep Neural Networks (DNNs), have become indispensable for accurate and robust support of large-scale cloud based AI services. However, serving giant DNNs is prohibitively expensive from an energy consumption viewpoint easily exceeding that of training, due to the enormous scale of GPU clusters needed to hold giant DNN model partitions and replicas. Existing approaches can either optimize energy efficiency or inference accuracy but not both. To overcome this status quo, we propose HybridServe, a novel hybrid DNN model serving system that leverages multiple sized versions (small to giant) of the model to be served in tandem. Through a confidence based hybrid model serving dataflow, HybridServe prefers to serve inference requests with energy-efficient smaller models so long as accuracy is not compromised, thereby reducing the number of replicas needed for giant DNNs. HybridServe also features a dataflow planner for efficient partitioning and replication of candidate models to maximize serving system throughput. Experimental results using a prototype implementation of HybridServe show that it reduces energy footprint by up to 19.8x compared to the state-of-the-art DNN model serving systems while matching the accuracy of serving solely with giant DNNs.

This paper presents the design, development, and application of a novel space simulation environment for rapidly prototyping and testing flight software for distributed space systems. The environment combines the flexibility, determinism, and observability of software-only simulation with the fidelity and depth normally attained only by real-time hardware-in-the-loop testing. Ultimately, this work enables an engineering process in which flight software is continuously improved and delivered in its final, flight-ready form, and which reduces the cost of design changes and software revisions with respect to a traditional linear development process. Three key methods not found in existing tools enable this environment's novel capabilities: first, a hybrid event-driven simulation architecture that combines continuous-time and discrete-event simulation paradigms; second, a lightweight application-layer software virtualization design that allows executing compiled flight software binaries while modeling process scheduling, input/output, and memory use; and third, high-fidelity models for the multi-spacecraft space environment, including for wireless communication, relative sensing such as differential GPS and cameras, and flight computer health metrics like heap exhaustion and fragmentation. The simulation environment's capabilities are applied to the iterative development and testing of two flight-ready software packages: the guidance, navigation, and control software for the VISORS mission, and the Stanford Space Rendezvous Laboratory software kit for rendezvous and proximity operations. Results from 33 months of flight software development demonstrate the use of this simulation environment to rapidly and reliably identify and resolve defects, characterize navigation and control performance, and scrutinize implementation details like memory allocation and inter-spacecraft network protocols.

--Our brain has an inner global positioning system which enables us to sense and navigate 3D spaces in real time. Can mobile robots replicate such a biological feat in a dynamic environment? We introduce the first spatial reasoning framework for real-time surface reconstruction and navigation that is designed for outdoor LiDAR scanning data captured by ground mobile robots and capable of handling moving objects such as pedestrians. Our reconstruction-based approach is well aligned with the critical cellular functions performed by the border vector cells (BVCs) over all layers of the medial entorhinal cortex (MEC) for surface sensing and tracking. T o address the challenges arising from blurred boundaries resulting from sparse single-frame LiDAR points and outdated data due to object movements, we integrate real-time single-frame mesh reconstruction, via visibility reasoning, with robot navigation assistance through on-the-fly 3D free space determination. This enables continuous and incremental updates of the scene and free space across multiple frames. Key to our method is the utilization of line-of-sight (LoS) vectors from LiDAR, which enable real-time surface normal estimation, as well as robust and instantaneous per-voxel free space updates. Comprehensive experiments on both synthetic and real scenes highlight our method's superiority in speed and quality over existing real-time LiDAR processing approaches. UMANS and most animals all possess the innate ability to sense and navigate through spatial environments around them in real time. While the complete and precise mechanisms behind such capabilities are not yet fully understood, the Nobel-winning work conducted by John O'Keefe in the 1970s on place cells [1], [2] has paved the way for understanding the brain's "inner global positioning system (GPS)". Pengdi Huang, Mingyang Wang, Huan Tian, and Hui Huang are with College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China (email: alualu628628@gmail.com; Minglun Gong is with School of Computer Science, University of Guelph, Delft N1G 2W1, Canada (email: minglun@uoguelph.ca) Hao Zhang is with School of Computing Science, Simon Fraser University, Burnaby V3J 1A1, Canada (email: haoz@sfu.ca)

As modern software systems expand in scale and complexity, the challenges associated with their modeling and formulation grow increasingly intricate. Traditional approaches often fall short in effectively addressing these complexities, particularly in tasks such as design pattern detection for maintenance and assessment, as well as code refactoring for optimization and long-term sustainability. This growing inadequacy underscores the need for a paradigm shift in how such challenges are approached and resolved. This paper presents Analytical Software Engineering (ASE), a novel design paradigm aimed at balancing abstraction, tool accessibility, compatibility, and scalability. ASE enables effective modeling and resolution of complex software engineering problems. The paradigm is evaluated through two frameworks Behavioral-Structural Sequences (BSS) and Optimized Design Refactoring (ODR), both developed in accordance with ASE principles. BSS offers a compact, language-agnostic representation of codebases to facilitate precise design pattern detection. ODR unifies artifact and solution representations to optimize code refactoring via heuristic algorithms while eliminating iterative computational overhead. By providing a structured approach to software design challenges, ASE lays the groundwork for future research in encoding and analyzing complex software metrics.