Tag: Money

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-time scaling techniques to increase the accuracy of model responses, such as drawing multiple reasoning samples from a model at deployment.To bridge this gap, researchers at University of Wisconsin-Madison and Stanford University have introduced Train-to-Test (T2) scaling laws, a framework that jointly optimizes a model’s parameter size, its training data volume, and the number of test-time inference samples.In practice, their approach proves that it is compute-optimal to train substantially smaller models on vastly more data than traditional rules prescribe, and then use the saved computational overhead to generate multiple repeated samples at inference.For enterprise AI application developers who are train

A rogue AI agent at Meta passed every identity check and still exposed sensitive data to unauthorized employees in March. Two weeks later, Mercor, a $10 billion AI startup, confirmed a supply-chain breach through LiteLLM. Both are traced to the same structural gap. Monitoring without enforcement, enforcement without isolation. A VentureBeat three-wave survey of 108 qualified enterprises found that the gap is not an edge case. It is the most common security architecture in production today.Gravitee’s State of AI Agent Security 2026 survey of 919 executives and practitioners quantifies the disconnect. 82% of executives say their policies protect them from unauthorized agent actions. Eighty-eight percent repor

Anthropic today launched Claude Design, a new product from its Anthropic Labs division that allows users to create polished visual work — designs, interactive prototypes, slide decks, one-pagers, and marketing collateral — through conversational prompts and fine-grained editing controls. The release, available immediately in research preview to all paid Claude subscribers, is the company's most aggressive expansion beyond its core language model business and into the application layer that has historically belonged to companies like Figma, Adobe, and Canva.Claude Design is powered by Claud

For the past year, early adopters of autonomous AI agents have been forced to play a murky game of chance: keep the agent in a useless sandbox or give it the keys to the kingdom and hope it doesn't hallucinate a catastrophic "delete all" command.To unlock the true utility of an agent—scheduling meetings, triaging emails, or managing cloud infrastructure—users have had to grant these models raw API keys and broad permissions, raising the risk of their systems being disrupted by an accidental agent mistake. That tradeoff ends today. The creators of the open source sandboxed NanoClaw agent framework — now known under their new private startup named NanoCo — have announced a landmark partnership with Vercel and OneCLI to introduce a standardized, infrastructure-level approval system. By integrating Vercel’s Chat SDK and OneCLI

Salesforce on Wednesday unveiled the most ambitious architectural transformation in its 27-year history, introducing "Headless 360" — a sweeping initiative that exposes every capability in its platform as an API, MCP tool, or CLI command so AI agents can operate the entire system without ever opening a browser.The announcement, made at the company's annual TDX developer conference in San Francisco, ships more than 100 new tools and skills immediately available to developers. It marks a decisive response to the existential question hanging over enterprise software: In a world where AI agents can reason, plan, and execute, does a company still need a CRM with a graphical interface?Salesforce's answer: No — and that's exactly the point."We made a decision

Enterprise AI is entering a new phase — one where the central question is no longer what can be built, but how to make the most of our AI investment.At VentureBeat’s latest AI Impact Tour session, Brian Gracely, director of portfolio strategy at Red Hat, described the operational reality inside large organizations: AI sprawl, rising inference costs, and limited visibility into what those investments are actually returning.It’s the “Day 2” moment — when pilots give way to production, and cost, governance, and sustainability become harder than building the system in the first place."We've seen customers who say, 'I have 50,000 licenses of Copilot. I don't really know what people are getting out of that. But I do know that I'm paying for the most expensive computing in the world, because it's GPUs,'" Gracely said. "'How am I going to get that under control?'"Why enterprise AI costs are

The journey from a laboratory hypothesis to a pharmacy shelf is one of the most grueling marathons in modern industry, typically spanning 10 to 15 years and billions of dollars in investment. Progress is often stymied not just by the inherent mysteries of biology, but by the "fragmented and difficult to scale" workflows that force researchers to manually pivot between the actual experimental design equipment, software, and databases.But OpenAI is releasing a new specialized model GPT-Rosalind specifically to speed up this process and make it more efficient, easier, and ideally, more productive. Named after the pioneering chemist Rosalind Franklin, whose work was vital to the discovery of DNA’s structure (and was often overlooked for her male colleagues James Watson and Francis Crick), this new frontier reasoning model is purpose-built to act as a specialized intelligence layer for life sciences research.By shifting AI’s role from a general-p