AI’s nextphase will be shaped by learning from real-world experience. In industry, however, such experience is embedded in proprietary workflows, physical experiments, and decisions with real consequences. Turning such experience into a reliable source of learning requires more than deploying capable models. In this talk, I will describe our efforts to build the industrial learning loop through four connected foundations. 1) Access to training. Frontier model development remains concentrated in a handful of AI companies. To enable others to train their own models, we released an open, end-to-end, frontier-grade pipeline—from cluster setup to inference, including code, weights, corpus, environments, and logs. 2) The hands to build. Industrial data often cannot leave the building, and domain experts should not have to become AI engineers. Our agentic R&D system is designed for in-house development: experts define tasks, constraints, and acceptance criteria in natural language, while agents coordinate experimentation, model post-training, and workflow development. In one deployment, 300 AI agents worked together to post-train a roughly 400-billion-parameter model in two months. 3) Readiness on both sides. General-purpose AI often falls short of industrial requirements, while industrial infrastructure is rarely designed to support iterative learning. To address these challenges, we developed specialized models—from quantum chemistry at the scale of 100,000 atoms to a DNA model 200 times faster than before—alongside autonomous laboratories for high-throughput experimentation with AI models in the loop. 4) Training grounds built on trust. Industrial environments cannot simply be scraped. We therefore designed institutional arrangements in which enterprises define problems and partners share risks, creating incentives to open the loop. Across six enterprise groups and nine joint programs, individual engagements began to form shared infrastructure. Together, these pieces set the industrial learning loop in motion on a materials problem in the semiconductor sector that had resisted progress for decades, delivering a result that met industrial standards and was accepted by domain experts.
Creative AI Session 1
Affinity Support Pilot Program
Barbara Engelhardt
Creative AI Session 2
Science Communication Training Session 1
Effective Science Communication: Crafting an Elevator Pitch for Your Research
Whether explaining your work to collaborators, pitching for funding, talking to the media or interviewing for a top job, clarity will make your work stand out. This hands-on session will give you the tools to translate complex ideas into a clear and concise story. You will walk away with a tailored elevator pitch ready for any occasion.
This session will provide you with:
Exercises to find your core message and focus on what matters most for different audiences. Key concepts to make complex ideas memorable and persuasive. Storytelling techniques to turn complex data into engaging narratives. Individualized advice on public speaking to make the most out of every opportunity.
Generative AI and the Creator Economy
AI Infra Builders Social: Bridging Open Source, Research, and Production
AI Infra Builders Social Lillian Shao Alison Shao Frank Chen
An evening of meaningful conversations about AI, food, drinks, and new connections! AI infrastructure is built at the intersection of research, open source, and production. Researchers push the boundaries of training, inference, and efficiency. Open-source maintainers turn new ideas into reusable tools and frameworks. Production engineers bring these systems to life at scale. Yet these communities don’t always have the opportunity to exchange perspectives, share practical experiences, and learn from one another. No product pitches. No formal research presentations. Just the people building AI infrastructure, sharing perspectives, and connecting across communities. Easy-to-grab meals and drinks will be available throughout the evening.
Schedule 7:00 PM – 7:35 PM — Welcome, Easy-to-Grab Meal & Cross-Community Networking 7:35 PM – 8:15 PM — Lightning Talks “What is one thing you wish the other AI infrastructure communities better understood about your work?” 8:15 PM – 9:00 PM — Food, Drinks & Open Networking
Hosts SGLang is a high-performance, open-source serving framework for large language and multimodal models, advancing efficient and scalable AI inference. Oracle Cloud Infrastructure (OCI) provides high-performance AI infrastructure and cloud services designed to support demanding AI training and inference workloads at scale. Together, SGLang and OCI are bringing the AI infrastructure community together to foster open exchange, practical learning, and connections across research, open source, and production.
Building Multilingual and Multiregional Safety Datasets: A Roundtable on Data Collection Methodology
Lost in Translation, Found in Interdisciplinarity
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