Sep 18, 2026

Databricks CEO: Stop Scaring People About AI

Databricks CEO Ali Ghodsi discusses the current state of AI, arguing against existential risk alarmism and advocating for a focus on immediate threats like AI-powered cyberattacks. He provides a rigorous definition for recursive self-improvement and explains how building an organizational 'ontology' can unlock massive productivity gains in enterprises. Ghodsi also covers the shift to multi-model AI strategies and cost management, highlighting how infrastructure is evolving to support agentic systems.

Sep 16, 2026

AI Tech Stack | The Founders and Builders Architecting Product Market Fit | Creator's Cut | 2026 |4K

This panel discussion explores the evolving AI tech stack and its impact on product-market fit, ethical governance, and business transformation. Experts from Humana, Catenate, In-Telecom, and Spacer AI explain how AI-powered prompts are replacing static dashboards, detail multi-model API strategies for cost efficiency, and delve into the critical role of human-in-the-loop oversight for autonomous agents to ensure data security and responsible AI deployment. They emphasize that true value lies in domain expertise and solving real-world pain points, not just building 'shiny toys'.

Sep 15, 2026

Box's Aaron Levie: On Reinventing Yourself in the AI Age and Enterprise Diffusion

Aaron Levie, CEO of Box, details his company's successful pivot to AI, emphasizing the critical role of application companies in bridging raw model capabilities to complex enterprise workflows. He argues that significant value will accrue to "applied AI" solutions that understand domain-specific needs, can handle intricate data permissions, and build specialized agents, rather than relying solely on foundational models. Levie shares insights on the economics of tokens, the rise of open-weight models, and why AI diffusion in coding differs from other knowledge work, predicting that 90% of enterprise tokens will soon be consumed by non-human initiated tasks.

Sep 14, 2026

No Memory, No Harness: Why the Database Is the Last Line of Defense — Kay Malcolm, Oracle

Kay Malcolm discusses how AI, while boosting individual performance, often hinders overall team productivity due to fragmented context. She introduces the concept of an "agent harness" with "memory" as its central nervous system, vital for sharing context. Drawing on personal experiences with database sprawl, she highlights the inefficiencies of storing diverse memory types across multiple data stores. Malcolm then presents Oracle's AI database (26AI) as a unified solution, capable of natively managing all memory types in a single platform, thereby enhancing team collaboration and solving the context problem for enterprise AI agents.

Sep 09, 2026

Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked

Brandon Waselnuk of Unblocked discusses the critical need for robust context engines in AI-driven software development. He explains how AI agents' lack of company-specific understanding leads to compounding costs like wasted tokens and rework. Waselnuk outlines common pitfalls in providing context and details six essential characteristics of an effective context engine, demonstrating its impact with a 50% token reduction and significant time savings. He also introduces three open-source tools from Unblocked, including a workshop to build a relational context engine, emphasizing that "access to information is not understanding" and that context, not intelligence, is the current bottleneck for AI effectiveness.

Sep 09, 2026

Inside the Race to Measure Frontier Intelligence

Independent model evaluation is crucial for AI progress, as public benchmarks are saturated and models optimize for them. Rayan Krishnan discusses VALS's role in providing continuously evolving, third-party evaluations, highlighting the Llama 4 'disaster' and the need to measure increasingly agentic systems. The conversation covers the complexities of evaluation beyond capability to include cost and latency, the looming financial impact of token spend on enterprises, and the critical role of evaluations in informing AI policy and international coordination for aligning AI with human intent and managing geopolitical risks, especially concerning recursive self-improvement.