Tag: WebDev

Every frontier AI lab right now is rationing two things: electricity and compute. Most of them buy their compute for model training from the same supplier, at the steep gross margins that have turned Nvidia into one of the most valuable companies in the world. Google does not.On Tuesday night, inside a private gathering at F1 Plaza in Las Vegas, Google previewed its eighth-generation Tensor Processing Units. The pitch: two custom silicon designs shipping later this year, each purpose-built for a different half of the modern AI workload. TPU 8t targets training for frontier models, and TPU 8i targets the low-latency, memory-hungry world of agentic inference and real-time sampling.Amin Vahdat, Google's SVP and chief technologist for AI and infrastructure (pictured above left), used his time onstage to make a point that matters more to enterprise buyers than any individual spec: Google designs every layer of its AI stack end-to-end, and that vertical integration is s

When startup fundraising platform VentureCrowd began deploying AI coding agents, they saw the same gains as other enterprises: they cut the front-end development cycle by 90% in some projects.However, it didn’t come easy or without a lot of trial and error. VentureCrowd’s first challenge revolved around data and context quality, since Diego Mogollon, chief product officer at VentureCrowd, told VentureBeat that “agents reason against whatever data they can access at runtime” and would then be confidently “wrong” because they’re only basing their knowledge on the context given to them.Their other roadblock, like many others, was messy data and unclear processes. Similar to context, Mogollon said coding agents would amplify bad data, so the company had to build a well-structured codebase first.  “The challenges are rarely about the coding agents themselves; they are about everything around them,” said Mogollon. “It’s a context probl

Enterprise data stacks were built for humans running scheduled queries. As AI agents increasingly act autonomously on behalf of businesses around the clock, that architecture is breaking down — and vendors are racing to rebuild it. Google's answer, announced at Cloud Next on Wednesday, is the Agentic Data Cloud.The architecture has three pillars:Knowledge Catalog. Automates semantic metadata curation, inferring business logic from query logs without manual data steward interventionCross-cloud lakehouse. Lets BigQuery query Iceberg tables on AWS S3 via private network with no egress feesData Agent Kit. Drops MCP tools into VS Code, Claude Code and Gemini CLI so data engineers describe outcomes rather than write pipelines"The data architecture has to change now," Andi Gutmans, VP and GM of Data Cloud at Google Cloud, told VentureBeat. "We're moving from human scale to

Cirrascale Cloud Services today announced it has expanded its partnership with Google Cloud to deliver the Gemini model on-premises through Google Distributed Cloud, making it the first neocloud provider to offer Google's most advanced AI model as a fully private, disconnected appliance. The announcement, timed to coincide with Google Cloud Next 2026 in Las Vegas, addresses a stubborn problem that has plagued regulated industries since the generative AI boom began: how to access frontier-class AI models without surrendering control of your data.

Google on Monday unveiled the most significant upgrade to its autonomous research agent capabilities since the product's debut, launching two new agents — Deep Research and Deep Research Max — that for the first time allow developers to fuse open web data with proprietary enterprise information through a single API call, produce native charts and infographics inside research reports, and connect to arbitrary third-party data sources through the Model Context Protocol (MCP).The release

One employee at Vercel adopted an AI tool. One employee at that AI vendor got hit with an infostealer. That combination created a walk-in path to Vercel’s production environments through an OAuth grant that nobody had reviewed.Vercel, the cloud platform behind Next.js and its millions of weekly npm downloads, confirmed on Sunday that attackers gained unauthorized access to internal systems. Mandiant was brought in. Law enforcement was notified. Investigations remain active. An update on Monday confirmed that Vercel collaborated with GitHub, Microsoft, npm, and Socket to verify that no Vercel npm packages were compromised. Vercel also announced it is now defaulting environment variable creation to “sensitive.” Next.js, Turbopack, AI SDK, and all Vercel-published npm packages remain uncompromised after a coordinated audit with GitHub, Microsoft, npm, and Socket.Context.ai was the entry

Decision makers at 72% of organizations claim to have two or more AI platforms that they identify as their "primary" layer, according to a survey of 40 enterprise companies conducted by VentureBeat last month, revealing real gaps in security and control. For enterprise management and technical leaders, and especially security leaders, these multiple AI platforms extend the attack surfaces of most enterprises at a time when AI-driven attacks have become increasingly potent.The multiple platforms — which include offerings from hyperscaler or AI labs like Microsoft Azure, Google, OpenAI or Anthropic, or big application companies like Epic, Workday or ServiceNow — reflect a state of sprawl that has emerged as these big software providers rush to offer their own AI to their enterprise customers. Those customers, in their own rush to scale AI, are finding they aren’t building a singular strategy — in fact they may be building a collection o