The great AI hype correction of 2025

Some disillusionment was inevitable. When OpenAI released a free web app called ChatGPT in late 2022, it changed the course of an entire industry—and several world economies. Millions of people started talking to their computers, and their computers started talking back. We were enchanted, and we expected more. We got it. Technology companies scrambled to…

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Applying data loading best practices for ML training with Amazon S3 clients

Amazon Simple Storage Service (Amazon S3) is a highly elastic service that automatically scales with application demand, offering the high throughput performance required for modern ML workloads. High-performance client connectors such as the Amazon S3 Connector for PyTorch and Mountpoint for Amazon S3 provide native S3 integration in training pipelines without dealing directly with the…

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Checkpointless training on Amazon SageMaker HyperPod: Production-scale training with faster fault recovery

Foundation model training has reached an inflection point where traditional checkpoint-based recovery methods are becoming a bottleneck to efficiency and cost-effectiveness. As models grow to trillions of parameters and training clusters expand to thousands of AI accelerators, even minor disruptions can result in significant costs and delays. In this post, we introduce checkpointless training on…

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AI might not be coming for lawyers’ jobs anytime soon

But new benchmarks are aiming to better measure the models’ ability to do legal work in the real world. The Professional Reasoning Benchmark, published by ScaleAI in November, evaluated leading LLMs on legal and financial tasks designed by professionals in the field. The study found that the models have critical gaps in their reliability for…

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Nvidia Becomes a Major Model Maker With Nemotron 3

Nvidia has made a fortune supplying chips to companies working on artificial intelligence, but today the chipmaker took a step toward becoming a more serious model maker itself by releasing a series of cutting-edge open models, along with data and tools to help engineers use them. The move, which comes at a moment when AI…

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AI materials discovery now needs to move into the real world

The grand prize would be a room-temperature superconductor, a material that could transform computing and electricity but that has eluded scientists for decades. Periodic Labs, like Lila Sciences, has ambitions beyond designing and making new materials. It wants to “create an AI scientist”—specifically, one adept at the physical sciences. “LLMs have gotten quite good at…

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The fast and the future-focused are revolutionizing motorsport

When the ABB FIA Formula E World Championship launched its first race through Beijing’s Olympic Park in 2014, the idea of all-electric motorsport still bordered on experimental. Batteries couldn’t yet last a full race, and drivers had to switch cars mid-competition. Just over a decade later, Formula E has evolved into a global entertainment brand…

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Google’s AI Playbook for Sustainability Reporting

We’re sharing a practical playbook to help organizations streamline and enhance sustainability reporting with AI. Corporate transparency is essential, but navigating fragmented data and labor-intensive processes often leaves little bandwidth for the strategic work that actually drives progress forward. After two years of integrating AI into our own environmental reporting process, we’re open-sourcing our learnings…

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Operationalize generative AI workloads and scale to hundreds of use cases with Amazon Bedrock – Part 1: GenAIOps

Enterprise organizations are rapidly moving beyond generative AI experiments to production deployments and complex agentic AI solutions, facing new challenges in scaling, security, governance, and operational efficiency. This blog post series introduces generative AI operations (GenAIOps), the application of DevOps principles to generative AI solutions, and demonstrates how to implement it for applications powered by…

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