How the Next Big Thing in Carbon Removal Sank Without a Trace

There's no shortage of generative AI benchmarks designed to measure the performance and accuracy of a given model on completing various helpful enterprise tasks — from coding to instruction following to agentic web browsing and tool use. But many of these benchmarks have one major shortcoming: they measure the AI's ability to complete specific problems and requests, not how factual the model is in its outputs — how well it generates objectively correct information tied to real-world

Presented by SAPWhen SAP ran a quiet internal experiment to gauge consultant attitudes toward AI, the results were striking. Five teams were asked to validate answers to more than 1,000 business requirements completed by SAP’s AI co-pilot, Joule for Consultants — a workload that would normally take several weeks.Four teams were told the analysis had been completed by junior interns fresh out of school. They reviewed the material, found it impressive, and rated the work about 95% accu

The tools are available to everyone. The subscription is company-wide. The training sessions have been held. And yet, in offices from Wall Street to Silicon Valley, a stark divide is opening between workers who have woven artificial intelligence into the fabric of their daily work and colleagues who have barely touched it.The gap is not small. According to a new report from OpenAI analyzing usage patterns across its more than one million business customers, workers at the 95th percentile of AI a

A Los Angeles-based startup has demonstrated what it calls a breakthrough in hardware development: an artificial intelligence system that designed a fully functional Linux computer in one week — a process that would typically consume nearly three months of skilled engineering labor.Quilter, which has raised more than $40 million from investors including Benchmark, Index Ventures, and Coatue, used its physics-driven AI to automate the design of a two-board computer system that booted succes

Engineering teams are generating more code with AI agents than ever before. But they're hitting a wall when that code reaches production.The problem isn't necessarily the AI-generated code itself. It's that traditional monitoring tools generally struggle to provide the granular, function-level data AI agents need to understand how code actually behaves in complex production environments. Without that context, agents can't detect issues or generate fixes that account for production reality.It's a