887: Multi-Agent Teams, Quantum Computing and the Future of Work — with Dell’s Global CTO John Roese

Episode Overview

• John Roese of Dell discusses the practical, ROI-driven approach to implementing AI, emphasizing the need to move beyond speculative proofs of concept to projects with tangible business value.
• He makes a surprising prediction that the largest job creation from the AI cycle will be in construction due to the massive physical infrastructure build-out required to power AI systems.
• The conversation explores the evolution of AI from current "Reactive" systems that unlock proprietary data to future "Agentic AI" designed to digitize and automate unique skills.
• Roese envisions a future where "teams of agents" collaborate to solve complex problems, with humans transitioning from direct execution to supervisory roles like "software composers" and "AI explainers."

Key Concepts

• ROI-First AI Strategy: Prioritizing AI projects based on their potential for a clear and material Return on Investment (ROI) is crucial for building a successful program and escaping the "Proof of Concept prison."
• AI Infrastructure Build-out: The AI transformation requires a massive physical infrastructure development, comparable to a public works project, which is predicted to make construction a primary source of job creation.
• Enterprise Differentiation: A company's competitive advantage in the age of AI comes from two main sources: its proprietary data and the unique skills of its workforce.
• Reactive vs. Agentic AI: Current AI is largely "Reactive," with a human-in-the-loop using AI as a tool to unlock data. The next evolution is "Agentic AI," where autonomous systems digitize skills, and a human is "on the loop" as a supervisor.
• Teams of Agents: The most significant value from Agentic AI will not come from individual agents but from collaborative "teams of agents" that are orchestrated to solve complex problems, mirroring how human teams operate.
• The New Human Role: As AI automates execution, human roles will shift toward supervision, composition, and interpretation. New jobs like the "software composer" and "AI explainer" will emerge to design, direct, and translate the work of AI systems.

Quotes

• At 0:10 - "The single biggest job creation of the AI cycle is actually construction." - John Roese explains that the massive infrastructure build required to power the AI transformation will be a primary source of new jobs.
• At 2:15 - "ROI, return on investment, is the first and most important question before funding an AI project." - Host Jon Krohn quotes John Roese from a Fortune article, setting up a discussion on the business-first approach to AI.
• At 25:21 - "They are about the digitization of a skill. They are about saying, 'I'm not just interested in unlocking the data, I'm interested in distributing the work.'" - He defines Agentic AI's core purpose as automating tasks and skills, a step beyond merely accessing information.
• At 31:02 - "The real value of an agent is not an agent in isolation. It's just like the real value of a person is not an individual; it's a collective." - Roese explains that the future of agentic systems lies in creating collaborative teams of specialized agents to tackle complex tasks.
• At 39:37 - "Some human being has to be the composer of the software. They don't actually play the musical instrument, but they decide what this system should be, what is good." - Roese predicts the rise of new roles, like the "software composer," who will design and direct AI systems rather than writing the code.

Takeaways

• Prioritize AI projects with a clear and material Return on Investment (ROI) to create an innovation "flywheel" and move beyond experimental proofs of concept.
• Prepare for the shift from "Reactive AI" (unlocking data) to "Agentic AI" (digitizing skills), where autonomous agents will handle tasks under human supervision.
• The future of enterprise AI lies in orchestrating collaborative "teams of agents" to work together, making governance and inter-agent communication key challenges to solve.