In this episode, Andrew Ng and Lawrence Moroni discuss why now is the "golden age" for building a career in AI, highlighting how the complexity of tasks AI can perform is doubling as rapidly as every seven months,,. The conversation explores the shifting landscape of "vibe coding" and the evolving ratio between engineers and product managers, emphasizing that the new bottleneck in software development is deciding what to build rather than the coding itself,,. Finally, the experts offer practical advice on navigating a competitive job market by focusing on business value, managing technical debt, and preparing for the rise of "small AI" and self-hosted models,,,.

Source: https://www.youtube.com/watch?v=AuZoDsNmG_s

This episode explores the critical July 2025 maturation point, where the initial generative AI hype gives way to the practical challenges of securing ROI and closing the "Data Readiness Gap". We break down the rise of Agentic AI and the strategic architectures essential for moving beyond disjointed pilot programs to translate initiatives into measurable financial value. Finally, we examine how leaders can navigate a divided global regulatory landscape to empower their workforce and unlock true "superagency".

This episode explores the rapid shift in enterprise AI adoption, highlighting how production-level agent deployment has surged as organizations move past initial experimental phases. Drawing on a study of thousands of use cases, we analyze the ROI of AI, distinguishing between common time savings and the high-impact, transformational benefits found in automation and risk reduction. We also examine why systematic, cross-organizational strategies are increasingly helping leaders realize significant returns on their investments within a one-to-three-year window.

Join us as we tackle the "last mile" of AI Agents series, exploring the rigorous operational discipline required to transform fragile prototypes into reliable, production-grade agents. We break down the essential infrastructure of trust—from evaluation gates to automated pipelines—that ensures your system is ready for the real world. This foundation paves the way for our series finale where we will move beyond single-agent operations to look at the future of autonomous collaboration within multi-agent ecosystems.

In this episode of our AI Agent series, we synthesize our previous discussions on evaluation frameworks and observability into a cohesive operational playbook known as the "Agent Quality Flywheel",. Join us as we explore how to transform raw telemetry into a continuous feedback loop that drives relentless improvement, ensuring your autonomous agents are not just capable, but truly trustworthy,. This episode bridges the critical gap between theory and production, providing the final principles needed to build enterprise-grade reliance in an agentic world,.

Moving beyond the temporary "workbench" of individual sessions, episode 3 of our AI Agents series unlocks the power of Memory—the mechanism that allows AI agents to persist knowledge and personalize interactions over time. We will explore how agents utilize an intelligent extraction and consolidation pipeline to curate a long-term "filing cabinet" of user insights, effectively turning them from generic assistants into experts on the specific users they serve.

The new Gemini interactions API unifies modern LLM requirements, moving past older stateless APIs to fully embrace agents and complex workflows. This major update simplifies multimodality and structured outputs, while offering optional server-side history to persist conversation memory. We explore using the API to call the Gemini research agent and leverage background execution for long-running inference loops.

In this episode in series AI Agents, we discuss the transformation of foundation models from static prediction engines into "Agentic AI" capable of using tools as their "eyes and hands" to interact with the world. We highlight the complexity of connecting these agents to diverse enterprise systems, setting the stage for Episode 1 to explain the "N x M" integration problem and the Model Context Protocol's role in solving it.

Join us for the premiere of our series on AI Agents, exploring the paradigm shift from passive predictive models to autonomous systems capable of reasoning and action. In this episode, we deconstruct the core anatomy of an agent—its "Brain" (Model), "Hands" (Tools), and "Nervous System" (Orchestration)—to explain the fundamental "Think, Act, Observe" loop that drives their behavior. Discover how this architecture enables developers to move beyond traditional coding to become "directors" of intelligent, goal-oriented applications.

Explore the emerging "year of agentic AI" through the first large-scale field study of Perplexity’s Comet browser, which analyzes millions of user interactions to understand how autonomous agents are reshaping daily workflows,. We discuss key findings revealing that early adoption is driven by educated, knowledge-intensive professionals who primarily use agents for productivity and learning tasks,. Tune in to learn how these "sticky" habits are evolving from simple queries to complex, cognitive actions that bridge the gap between information and execution,.