The future of AI research is proactive discovery. The goal is a system that not only monitors a portfolio but also recognizes what it doesn't know, then autonomously tasks its AI interviewer to conduct expert calls to generate the missing insights and deliver the new analysis to the user.

As AI evolves from single-task tools to autonomous agents, the human role transforms. Instead of simply using AI, professionals will need to manage and oversee multiple AI agents, ensuring their actions are safe, ethical, and aligned with business goals, acting as a critical control layer.

True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.

While current AI tools focus on individual productivity (e.g., coding faster), the real breakthrough will come from systems that improve organizational productivity. The next wave of AI will focus on how large teams of humans and AI agents coordinate on complex projects, a fundamentally different challenge than simply making one person faster.

The durable investment opportunities in agentic AI tooling fall into three categories that will persist across model generations. These are: 1) connecting agents to data for better context, 2) orchestrating and coordinating parallel agents, and 3) providing observability and monitoring to debug inevitable failures.

Agentic AI will evolve into a 'multi-agent ecosystem.' This means AI agents from different companies—like an airline and a hotel—will interact directly with each other to autonomously solve a customer's complex problem, freeing humans from multi-party coordination tasks.

Elias Torres argues that the current AI paradigm, which focuses on tools that assist humans (e.g., summarizers, drafters), is fundamentally limited. He believes true value is unlocked when you can instruct an AI to perform a task *infinitely* on its own, without requiring a human to type into a chat box for every action.

AI will make the production of investment memos and rote analysis functionally free. The role of an investment analyst will therefore evolve from creating this content to prompting, steering, and quality-assuring the output of AI agents. The job becomes about evaluation and verification, not initial generation.

Block's CTO believes the key to building complex applications with AI isn't a single, powerful model. Instead, he predicts a future of "swarm intelligence"—where hundreds of smaller, cheaper, open-source agents work collaboratively, with their collective capability surpassing any individual large model.