Large Language Models

Today's Stories

1. 1

   Google Cloud, Verizon Partner to Scale Enterprise AI

   Google Cloud announced a strategic partnership with Verizon to deploy its full-stack AI, including Gemini Enterprise, across Verizon's customer experience and enterprise operations. Verizon's AI transformation includes using Gemini for customer service automation, network anomaly prediction, marketing automation, and employee productivity, potentially reshaping telecom operations and customer experience.

   Verizon's multi-year data consolidation to Google's Agentic Data Cloud underpins this AI acceleration, enabling the building and deployment of AI agents across all business units.

2. 2

   Verizon taps Google Cloud for enterprise AI

   Verizon and Google Cloud announced a strategic partnership Monday. Verizon will deploy Google Cloud's full-stack AI, including its data infrastructure and Gemini Enterprise, to modernize customer experiences and scale AI. Verizon has been focusing on customer experience and retention after net losses in 2024 and 2025. Despite increased churn, its mobile and broadband service revenue reached about $23.4 billion in Q2 2026, a 2.8% increase year-over-year.

   The partnership targets AI implementation areas including Gemini's conversational capabilities for customer support, an autonomous network intelligence framework, cloud security, and modernized marketing. Google's Agentic Data Cloud will help unify data for AI agents.

3. 3

   No-Code AI Shows Promise for Bone Fracture Detection

   Researchers used Google Cloud Vertex AI to build a no-code AutoML model that classifies bone X-rays as fractured or non-fractured, achieving initial results on a mixed public dataset. This proof-of-concept demonstrates that non-experts can potentially create AI diagnostic tools, which could help expand access to medical imaging analysis. However, it is a preliminary step, not a ready-to-use clinical tool.

   Further validation on larger, more diverse datasets is needed before such models can be considered for real-world clinical use.

4. 4

   BMS taps Chai Discovery's AI for antibody design

   Bristol Myers Squibb (BMS) is partnering with Chai Discovery to use its generative AI for antibody design across BMS's portfolio. The collaboration is announced, though specific financial terms are not disclosed. This marks a significant adoption of generative AI in drug discovery, potentially accelerating the design of antibodies, which are key components in many modern therapies. It underscores the growing role of AI in pharmaceutical R&D.

   How this partnership may influence BMS's pipeline and whether it signals broader industry adoption of AI-driven drug design. The specific antibodies or therapeutic areas targeted are not yet specified.

5. 5

   IBM dual-ISA mainframe chip runs Arm natively

   IBM detailed its future IBM Z and LinuxONE processors at Hot Chips 2026. Each core natively runs both z/Architecture and Arm AArch64, with 11 cores at 5.7+ GHz on a 2nm process and 2,792 implemented AArch64 instructions. This merges mainframe reliability with the Arm software ecosystem, letting customers run unmodified Arm software on a mainframe. It also adds a second-generation AI inference accelerator with 96GB of HBM3e, targeting larger enterprise workloads.

   How much of the surrounding Arm software stack runs unmodified on the core. IBM claims Arm SystemReady compliance, which could determine how much off-the-shelf software the platform can absorb.

6. 6

   SpaceXAI adopts NVIDIA Vera CPU for agentic AI

   NVIDIA announced SpaceXAI will deploy Vera CPUs to speed up its next-generation agentic AI applications. SpaceXAI also plans to expand its Grok AI infrastructure on the NVIDIA Vera Rubin platform and use an optimized Vera Rubin NVL72 system for its first Starmind AI satellite. Agentic AI relies on CPUs for tasks like tool orchestration, code execution, and data processing between model calls. Vera is the first CPU built for AI agents, offering up to 1.8x faster task completion than x86 CPUs, which could help AI agents act faster and keep GPUs fully utilized.

   SpaceXAI is scaling toward gigawatts of computing capacity with Vera Rubin, and plans to bring NVIDIA accelerated computing into orbit with the Starmind satellite. The platform aims to drive down cost per token while maintaining high performance and energy efficiency.