Elizabeth Warren seeks details on private equity data center deals

AI stock rally's boom phase suggests potential market concentration risks, echoing dot-com era, impacting broader investment strategies. The post Bank of America survey shows AI stock rally in boom phase, not euphoria appeared first on Crypto Briefing.

Although AI laggards are still racing to adopt the technology, for the heaviest users tokenmaxxing is out. Some, including Meta and Amazon, have scrapped their leaderboards.

A Silicon Valley software maker and an ecommerce company reveal to WIRED how they are navigating the emerging challenge of “tokenomics.”

Most organizations are struggling to use AI insights. Even as it’s been easier than ever to produce predictions, recommendations and scores, many data science and business teams end up with a stockpile of unused information that doesn’t drive meaningful transformation. Decision intelligence helps organizations bridge that gap by embedding insights [...] The post Decision intelligence: Why insights alone don’t create value appeared first on SAS Blogs.

Nvidia’s new RTX Spark is one of the most interesting personal computing announcements in years. That’s because it’s not just another PC platform, but tries to redefine the role of the personal computer in the age of AI. Announced at Computex 2026, RTX Spark is Nvidia’s new platform for slim Windows laptops and compact desktops, designed to combine an Arm-based CPU, Blackwell-based RTX graphics, and a large, unified memory architecture into a single AI-first computing system. We have all grown accustomed to a cloud-centric AI model over the past few years. We open an application, send a request over the network, and a hosted service in a distant data center provides the intelligence. ChatGPT, Grok, Gemini, and similar systems have trained the market to think of AI as something that lives elsewhere. RTX Spark proposes a different model. It asks a simple yet disruptive question: What if the model, the agent, the data, and the application could all live on your own machine? Nvidia is not

The acquisition adds about 490 megawatts of secured power in Spain as IREN expands beyond Bitcoin mining and builds its European AI cloud platform.

AIs struggle to understand documents designed for humans; the DocLang working group seeks to flip that imbalance with its specification for machine-readable business documents “built from the ground up for LLM tokenizers.” The working group, founded by IBM, Nvidia, and Red Hat and hosted by the Linux Foundation’s LF AI & Data project, aims to create an open, universal, AI-native document format designed to improve how enterprises prepare, exchange, and govern document data for AI systems. ABBYY and Human Signal will also be involved in its development, and other contributors are welcome. “Enterprises today work across a fragmented landscape of document formats, including PDFs, JPEGs, and other file types built primarily for human consumption rather than AI interpretation,” the group said in its launch announcement. “This disconnect can introduce complexity, raise costs, and reduce reliability when extracting meaning from business documents,” as organizations increasingly rely on genera

Hot AI companies can’t stop talking about forward-deployed engineers (FDEs), which are now very much in vogue.  FDEs, in case you haven’t heard, are hired by companies looking (hoping?) to successfully deploy AI tools and services. It’s one of the hotter professions in a world still trying to understand the impact of AI on careers. So, what exactly are FDEs — are they techy lone rangers like the ones OpenAI, Google and Microsoft are hiring? Turns out it’s not so much about individual engineers who swoop in to design and roll out AI deployments; it’s more about a team of engineers working together at customer sites. At least, that’s the view at Amazon Web Services (AWS). In fact, according to Taimur Rashid, managing director of the AWS Generative AI Innovation Center, the FDE concept pre-dates the current generative AI (genAI) gold rush. The same kinds of engineering teams were needed for the earlier machine-learning and cloud eras to help companies with deployments. Taimur Rashid, man