Ekaterina Gorshkova, author of "Kafka for Architects", discusses her journey with Apache Kafka, from its early adoption in enterprise integration to its modern role as a foundational layer for AI and ML systems. The conversation covers key architectural decisions, the limitations of Kafka, and why the real challenges are often organizational rather than technical.
SAP CTO Philipp Herzig discusses the company's AI-driven transformation, focusing on three core pillars: generative UI, AI-native business processes, and a unified data layer. He explores the primary challenges to enterprise AI adoption—scale, data fragmentation, and security—while emphasizing the critical role of verifiability and "agent mining" in creating reliable, compounding value. Herzig also details the limitations of LLMs for predictive analytics on tabular data and introduces SAP's alternative, Relational Pre-trained Transformers (RPT1).
Changan Chen, co-founder of Rhoda AI, discusses their vision-first approach to building foundation models for robotics. The conversation covers their unique training pipeline, the distinction between policy and world models, and the path to deploying data-efficient, reliable robots in real-world industrial settings.
Shopify CTO Mikhail Parakhin details their aggressive internal AI adoption, revealing the internal stack—Tangle, Tangent, and SimGym—that powers reproducible ML, auto-research, and customer simulation. He also discusses how AI shifts the software bottleneck to PR review and CI/CD and explains their use of Liquid AI for low-latency, non-transformer workloads.
Marc Andreessen discusses the launch of "Monitoring the Situation" (MTS), a new media network on X. He explores the concept of the “current thing,” how the internet has reinvented media consumption through viral outrage cycles, and why today's internet-native media landscape is reshaping politics, culture, and our collective attention, drawing parallels from the history of CNN to the theories of Marshall McLuhan.
Jacob Lauritzen, CTO of Legora, argues that as AI agents tackle more complex work, the bottleneck shifts from task execution to planning and review. He proposes a framework for human-agent collaboration based on increasing 'trust' and 'control', and advocates for moving beyond simple chat interfaces to high-bandwidth, domain-specific artifacts like documents and structured reviews for more effective collaboration.