Operations | Monitoring | ITSM | DevOps | Cloud

In this episode, Andrew Hillier and Bijit Ghosh discuss the evolving landscape of AI, discussing the growing prominence of inference over training, hybrid cloud strategies, balancing cost with performance, and the orchestration of complex hardware environments. The conversation also touches on emerging concepts like AI factories, the challenges of sovereign cloud, and how enterprises are navigating data gravity and regulatory constraints. It's a deep dive into optimizing AI infrastructure, managing costs, and the disruptive changes that are transforming both technology and business outcomes.

See how Selector’s AI Copilot accelerates issue diagnosis in real time. In this demo, watch how natural language queries and AI-driven insights help teams quickly analyze incidents, surface root cause, and understand impact - without digging through multiple tools. Instead of manual investigation, Selector guides operators to answers faster, reducing noise and speeding up resolution. Built for network and operations teams who need clarity, speed, and smarter troubleshooting.

Lightrun’s Dan Putman demonstrates the power of the latest Lightrun MCP skill. Watch how your AI code agent can now debug live applications directly in production. By connecting OpenAI's Codex to real-time runtime data via the Lightrun MCP, engineers can now generate and validate hypotheses using live telemetry and snapshots, without breaking flow. Ready to bring runtime context to your AI agents?

ValueOps Vaia: AI Driven Planning and Execution AI is moving fast, but without control, it creates chaos. Discover how Vaia helps you manage AI economics, reduce waste, and deliver measurable business value across strategy, planning, and execution.

Learn more: speedscale.com.

AI adoption is nearly universal. So why are most teams still struggling? In this session from GitKon, Nathen Harvey, head of DORA at Google Cloud, shares findings from the 2025 DORA State of AI-Assisted Software Development report, drawing on data from nearly 5,000 developers worldwide. The answer isn't more AI. It's what surrounds it.

LLMs have sucked all the oxygen out of the AI conversation — but AI is much more than just LLMs, and network engineers have been using AI techniques (machine learning, statistics, fuzzy logic, expert systems, neural networks) for decades. So what should LLMs be doing in network operations, what shouldn't they be doing, and how do agentic AI architectures fit in?