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A new SaferAI report finds Z.ai's open-weight GLM-5.2 approaches frontier AI capabilities while lacking key safety mitigations, renewing concerns that powerful open models could outpace governance and safeguards.
The week-old Open Secure AI Alliance, spearheaded by Nvidia and grown to over 120 companies, already has proposals out for defending against AI agents.
After speaking with Polygon, Eli Roth clarified that generative AI appears in a small portion of a few scenes in Ice Cream Man.
Koei Tecmo has partnered with a generative AI startup to create an AI chat RPG that lets you talk with Ryza from the Atelier Ryza series.
Hank Green, a popular YouTuber and science communicator, said he is stepping back from production amid intense criticism over his use of AI. Green described his AI usage as "not healthy," but stressed that he used it for finding research sources and not to write scripts. Much of the ensuing firestorm in this corner of […]
Instead of one huge, un-reviewable pull request, teach coding agents to decompose work into a clean, ordered stack with GitHub stacked pull requests. The post Turn one giant AI-generated pull request to a reviewable stack appeared first on The GitHub Blog .
Spotify says Merlin, which represents more than 30,000 independent labels and distributors, has joined Universal Music Group in backing its upcoming AI-powered remix and covers product. The paid tool will let fans create AI-generated covers and remixes of participating artists’ music while ensuring artists opt in, receive credit, and are compensated.
An analysis of the last seven years of Tesla earnings calls shows just how little attention Musk pays to Tesla's car business.
For robotaxis and other autonomous vehicles (AVs), the hardest problems aren’t the everyday scenarios. They’re the rare, complex situations that are difficult to anticipate and train for. Handling these long‑tail events takes more than just object detection and motion prediction. AVs must understand the situation, reason about cause and effect, choose the right action and […]
Surging AI demands are driving the need for massive datasets and context windows that burst past the confines of system memory. But rising needs aren’t met by simply adding more storage capacity. What’s needed is useful, grounded insights from AI factories and efficient, secure storage architectures that enable those insights. At this week’s Future of […]
Presented by Rezolve Ai Most brands know something is shifting in how consumers find and choose products. What most don't know is how much of that shift has already taken place, where it's happening, or whether they're on the right side of it. That uncertainty is the problem. And the analytics stack most brands rely on isn't built to resolve it. The decision layer has moved In 2014, 82% of digital commerce started on a brand's website. By 2024 that had fallen to 38%, according to Salesforce research . The journey that used to begin at a brand's front door now begins somewhere else. Increasingly, it begins with a question asked of an AI platform and ends with an answer that shapes the purchase decision before any brand-owned touchpoint is engaged. Consumers are asking AI where to shop, what to buy, and which product is right for them. Bain research shows that four in five consumers rely on zero-click results at least 40% of the time . That means the shortlist a consumer receives from an AI answer engine is, in many cases, the only shortlist they consult. Adobe Analytics recorded over 800% year-over-year growth in AI-driven traffic to retail sites , a signal of how rapidly AI platforms are inserting themselves between brands and their customers. This is a structural shift, not a trend. And it has created a category of commercial loss that most analytics tools are architecturally incapable of detecting. What you can't see is costing you The gap is this: a brand can have strong onsite conversion metrics and still be losing significant ground in the market, because the customers who never arrived aren't captured in any dashboard. There's no "AI excluded you" event in a session log. There's no abandoned cart entry for a shopper who was told by an AI assistant that a competitor was the better fit. This is different from the SEO problem brands have managed for two decades. With traditional search, absence had a visible signal. You could see your ranking, audit the gap, and act on it. With AI answer engines, absence is invisible by default. The surface doesn't show you what it didn't show the consumer. Sixty percent of searches now end without a click, according to Semrush's 2025 zero-click study. For AI-mediated discovery, that number is structurally higher. The answer is the destination. If a brand isn't in the answer, it isn't in the consideration set, and its analytics will never surface that fact. The metric that isn't being measured The commerce industry has developed sophisticated instrumentation for the journey from landing page to purchase. It has essentially no instrumentation for the journey from consumer intent to brand discovery, the layer where AI is now operating. Brands that want to understand their actual competitive position in an AI-mediated market need to ask a different set of questions: How does my brand appear when consumers ask AI for recommendations in my category? What language does AI use to describe my products? Where am I present, where am I absent, and where am I being described in ways that don't reflect my positioning? These aren't marketing questions. They're infrastructure questions. And answering them requires a different kind of audit than anything in the current commerce or marketing toolkit. Rezolve Ai commissioned research across 1,500 US consumers in January 2025 that found the majority of shoppers who use AI for product research make purchase decisions directly from those AI-generated recommendations, without returning to a search engine or brand site to verify. The implication for brands is significant: by the time a consumer reaches a brand's owned properties, the decision may already have been made, or unmade, somewhere else. What comes next The brands that will maintain commercial relevance as AI mediates more of the discovery layer are those that develop visibility into it, not just presence on their own platforms. That means treating AI discoverability as a measurable discipline, not an assumption, and building the infrastructure to understand, track, and influence how AI systems represent them to consumers. The tools to do that are emerging. The measurement frameworks are not yet standardized. But the brands that begin building that visibility now will have a structural advantage as the market continues to shift. AI answer engines are already forming preferences. Every day without visibility is a day those preferences solidify without you. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com .
