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AI Council 2026 San Francisco

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About this event

What to expect

AI Council 2026 brings the world's top AI infrastructure builders to San Francisco for three intensive days of technical talks, hands-on workshops, and meaningful networking. Taking place May 12-14, 2026 at the Marriott Marquis, this vendor-neutral conference is where the architects behind breakthrough AI systems share what actually works in production.

Why Attend AI Council 2026

Since 2015 (formerly Data Council), this conference has been the gathering point for people building the foundational infrastructure powering modern AI applications. You'll learn directly from engineers and technical leaders at OpenAI, NVIDIA, Snowflake, GitLab, Microsoft, Hugging Face, and dozens of other companies shipping AI systems at scale. This is not a marketing event — expect deep technical content on real-world architectures, tools, and best practices.

Featured Topics & Speakers

The 2026 agenda spans critical areas of AI infrastructure including agent systems, inference optimization, model serving, data pipelines, security, and MLOps. Confirmed speakers include Hannes Mühleisen (Co-Founder & CEO of DuckDB Labs) revealing "Super-Secret Next Big Thing for DuckDB" and Leonhard Spiegelberg (OpenAI) delivering a lightning talk on "The Modern Data Stack Lost the War: Stop Building more DataFrame APIs."

Session tracks cover:

  • Agent Infrastructure — building reliable autonomous systems
  • AI Engineering — patterns for production AI applications
  • Foundation Models — training, fine-tuning, and deployment at scale
  • Inference Systems — optimizing latency and throughput
  • Data Engineering & Databases — pipelines feeding AI workloads
  • ML Ops & Platforms — tooling for the full ML lifecycle
  • AI Security & Safety — protecting models and data
  • Coding Agents & Autonomous Dev — AI-assisted development workflows
  • Applied AI — real-world use cases and results

View the full event schedule to plan your experience.

Who Should Attend

This conference is designed for technical practitioners: AI engineers, ML engineers, data engineers, infrastructure architects, and technical leads building or scaling AI systems. Whether

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