THE AI TIMES

In this exclusive subscriber-only ebook, you’ll learn about a new method that scientists have uncovered to look at the ways our bodies are aging. by  Jessica Hamzelou October 14, 2025 Table of contents: Related Content: Access all subscriber-only eBooks:

Presented by SAP When 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% accurate. The fifth team was told the very same answers had come from AI. They rejected almost everything. Only when asked to validate each answer one by one did they discover that the AI was, in fact, highly accurate — surfacing detailed insights the consultants had initially dismissed. The overall accuracy? Again, about 95%. “The lesson learned here is that we need to be very cautious as we introduce AI — especially in how we communicate with senior consultants about its possibilities and how to integrate

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 adoption are sending six times as many messages to ChatGPT as the median employee at the same companies. For specific tasks, the divide is even more dramatic: frontier workers send 17 times as many coding-related messages as their typical peers, and among data analysts, the heaviest users engage the data analysis tool 16 times more frequently than the median. This is not a story about access. It is a story about a new form of workplace stratification emerging in real time — one that may be resha

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 challenge that startup Hud is looking to help solve with the launch of its runtime code sensor on Wednesday. The company's eponymous sensor runs alongside production code, automatically tracking how every function behaves, giving developers a heads-up on what's actually occurring in deployment. "Every software team building at scale faces the same fundamental challenge: building high-quality products that work well in the real world," Roee Adler, CEO and founder of Hud, told VentureBeat in an

A San Francisco-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 successfully on its first attempt, requiring no costly revisions. The project, internally dubbed "Project Speedrun," required just 38.5 hours of human labor compared to the 428 hours that professional PCB designers quoted for the same task. The announcement also marks the first public disclosure that Tony Fadell, the engineer who led development of the iPod and iPhone at Apple and later founded Nest, has invested in the company and serves as an advisor. "We didn't teach Quilter to draw; we taught it to t

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How one controversial startup hopes to cool the planet Stardust Solutions believes that it can solve climate change—for a price.  The Israel-based geoengineering startup has said it expects nations will soon pay it…

At a regional hospital, a cardiac patient’s lab results sit behind layers of encryption, accessible to his surgeon but shielded from those without strictly need-to-know status. Across the street at a credit union, a small business owner anxiously awaits the all-clear for a wire transfer, unaware that fraud detection systems have flagged it for further…

French AI startup Mistral has weathered a rocky period of public questioning over the last year to emerge, now here in December 2025, with new, crowd-pleasing models for enterprise and indie developers. Just days after releasing its powerful open source, general purpose Mistral 3 LLM family for edge devices and local hardware, the company returned today to debut Devstral 2. The release includes a new pair of models optimized for software engineering tasks — again, with one small enough to run on a single laptop, offline and privately — alongside Mistral Vibe, a command-line interface (CLI) agent designed to allow developers to call the models up directly within their terminal environments. The models are fast, lean, and open—at least in theory. But the real story lies not just in the benchmarks, but in how Mistral is packaging this capability: one model fully free, another conditionally so, and a terminal interface built to scale with either. It’s an attempt not just to match propriet

Four sources close to the situation claim OpenAI has become hesitant to publish research on the negative impact of AI. The company says it has only expanded the economic research team’s scope.

American AI giants are backing a new effort to establish open standards for building agentic software and tools.