Oct 08, 2026

NVIDIA Vera CPU: The Foundation for Agentic AI

NVIDIA presents Vera, a CPU designed for agentic AI workloads such as tool calls, code execution, sandboxes and reinforcement learning loops. The talk traces how cloud economics shaped CPU design toward high core counts at the expense of per-core speed, then explains how Vera's Olympus core, monolithic fabric and LPDDR5X memory deliver high single-threaded performance at scale. CoreWeave then shares its early results running Vera for sandboxes and agent workloads.

Oct 08, 2026

AI Tokenomics Explained

A product and go-to-market lead at NVIDIA examines the economics of AI inference as agentic AI multiplies token demand. The talk covers matching premium versus efficient models to use cases, model routing, open models, sizing token demand, and extreme co-design across mixture-of-experts models, NVLink systems, KV caching, software stacks, and CPUs to maximize tokens per watt and minimize cost per token.

Oct 07, 2026

Every AI Company Is Accidentally Building a Bank — Dor Sasson, Stigg

Dor Sasson argues that the 2026 AI pricing crises at Anthropic, OpenAI, and GitHub were infrastructure failures: usage was checked after invoicing rather than before inference. He proposes synchronous entitlement checks with asynchronous settlement, borrows banking concepts such as holds, double-entry bookkeeping, and credit sources, and outlines four patterns for selling AI at scale.

Oct 06, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville discusses her research into "surprising failures" in AI systems, where models, particularly LLMs, struggle with complexity in realistic user workflows. Her team focuses on practical evaluation for multi-turn conversations and agentic knowledge work, emphasizing the need for robust AI that understands context and user intent over time. She offers insights into bridging the gap between user expectations and AI capabilities, advocating for detailed user feedback and outlining the future of human-AI collaboration.

Oct 05, 2026

How a Logistics Giant Keeps AI Data Locked Down

Jason Ward, FinOps for AI lead at C.H. Robinson, shares insights on managing AI costs for enterprises and startups. He details a FinOps strategy centered on comprehensive tagging, leveraging AI for observability (e.g., detecting prompt bloat and context starvation), and pioneering advanced metrics like "cost per thought" and "reasoning ratio" to optimize complex AI workloads beyond simple discrete tasks.

Sep 30, 2026

The Chief AI Officer: Scientist, Architect, Coach — Rania Khalaf, WSO2

Rania Khalaf, Chief AI Officer at WSO2 and formerly a long-time leader at IBM Research, shares a framework for a role that is still being defined. She covers the Scientist, Architect and Coach focus areas, how the balance shifts by company type and AI maturity, and why she measures AI adoption, agent readiness and market signals instead of token counts. She also addresses the need for CEO backing and the question of who should own AI.