Let’s be honest about what’s happening in the market: Public cloud has become the easy button for AI. It offers immediate access to compute, storage, managed services, foundation model ecosystems, automation tools, and global reach. For enterprises that want to launch quickly, it is hard to argue against it. You do not need to spend years standing up infrastructure, hiring specialized operations teams, or engineering your own scalable environment before you can test your first use case.
This is exactly why adoption continues even as confidence in cloud resilience becomes more complicated. This article about the expanding cloud market makes the point clearly. Enterprises are not pulling back from hyperscale clouds despite numerous outages. They continue to move forward because the benefits of agility, scalability, and rapid deployment are too valuable to ignore. The cloud remains deeply embedded in business operations, and for many organizations, stepping away would undo years, often de
Teradata has launched its Autonomous Knowledge Platform, a new flagship offering that brings together data, analytics, AI development, agent orchestration, and governance across cloud, on-premises, and hybrid environments.
The target customer is an enterprise that has moved beyond testing AI assistants and is now asking harder questions: which data agents can use, what actions they can take, how much they will cost to run, and who is accountable when something goes wrong.
The company said the platform builds on its existing database engine and governance infrastructure, while adding new capabilities and more tightly integrating existing ones, including AI Studio, the Tera natural-language workspace, Tera Agents, Elastic Compute on Teradata Cloud, and the upcoming Teradata Factory for on-premises AI workloads.
Teradata is entering a competitive market with this. Snowflake, Databricks, Microsoft, Oracle, and Salesforce are all trying to persuade customers that their platforms should beco
Parloa leverages OpenAI models to power scalable, voice-driven AI customer service agents, enabling enterprises to design, simulate, and deploy reliable, real-time interactions.
Google Chrome may be taking up more of your storage than expected thanks to a large on-device AI model file that, in some cases, is being automatically downloaded to the browser's system folders. Users who have noticed unexplained drops in their available desktop device storage are now discovering that Chrome is installing a 4GB weights.bin file inside their browser directory when certain AI features are enabled.
The weights.bin file in question is connected to Google's Gemini Nano AI model, which powers Chrome AI tools like scam detection, writing assistance, autofill, and suggestion features. As the Gemini Nano model is designed to run lo …
Read the full story at The Verge.
Enterprises are experimenting with AI agents internally first, using smaller testing teams and strict governance before deploying customer-facing applications.
Insider Brief PRESS RELEASE — Applied Digital Corporation (NASDAQ: APLD), a designer, builder, and operator of high-performance, sustainably engineered data centers and colocation services for artificial intelligence, cloud, networking, and blockchain workloads, has announced the closing of a $300 million senior secured bridge facility led by Goldman Sachs. The facility is intended to fund the continued development […]
Microsoft and Google are adding new controls for AI agents, as enterprise IT teams try to keep up with tools that can access corporate data and act across business applications.
Microsoft’s Agent 365, made generally available for commercial customers on May 1, is designed to help organizations discover, govern, and secure AI agents, including those operating across Microsoft, third-party SaaS, cloud, and local environments.
Google’s new AI control center for Workspace, announced this week, focuses more specifically on giving administrators a centralized view of AI usage, security settings, data protection controls, and privacy safeguards within Workspace.
The timing reflects a shift in enterprise AI use. Many companies are no longer just testing chatbots, but are beginning to use agents that can reach corporate systems and carry out tasks on behalf of users.
Analysts said the shift changes how CIOs and CISOs should think about AI agents inside the enterprise.
“By placing agent controls
In 2021, I was developing software for an aerospace manufacturer and met with our machine learning team to discuss innovative approaches for tracking FOD (free-orbiting debris), a major security and operational concern in the industry. What struck me wasn’t the algorithms or tracking equipment, but the terabytes of data (up to petabytes) that were being produced.
Old-school problems of limited hardware resources and inefficient data compression were bottlenecking cutting-edge visual learning models and traditional tracking solutions alike. The team was smart and could fine-tune quickly, but the real challenge was making sure our infrastructure could scale with them.
In aerospace, performance hinges on how fast systems can absorb and interpret massive telemetry streams, and storage is often the silent limiter. When you’re generating terabytes to petabytes of data in a single test cycle, even a brief stall in the storage layer becomes a bottleneck. A few milliseconds of delay between wha
