In this episode of Excess Returns, we sit down with Matt Russell of Business Breakdowns to explore how AI is actually being used in investing today. We go beyond the hype and break down practical use cases for AI in portfolio management, stock research, due diligence, monitoring, and idea generation. From deep research models and agentic AI to prompt engineering and workflow design, this conversation walks through how professional investors can use AI tools to increase productivity, improve decision-making, and reduce blind spots without losing their edge. If you are an asset manager, analyst, allocator, or DIY investor wondering how AI will impact investing and stock picking, this episode offers a clear, practical roadmap.Main topics covered:The evolution from early large language models to deep research and agentic AI for investorsLLMs vs agent-based AI and why the distinction matters for investment researchHow AI fits into an investor’s workflow, from due diligence to portfolio monitoringUsing AI to monitor KPIs, earnings calls, and cross-industry signals in real timeHow AI can help kill bad ideas faster and surface deal breakers earlyPrompt engineering for investors, including mindset framing, audience targeting, and output designBuilding mental models into AI systems to reflect your investment philosophyAI tech stacks for investors, including writing tools, deep research models, and browser-based AIIteration, experimentation, and standardized testing of prompts across model upgradesThe impact of AI on alpha generation, active management, and generalist vs specialist investorsOrganizational adoption strategies for investment firms considering AICustomization, agentic workflows, and what AI in investing could look like five years from nowTimestamps:00:00 How AI tools increase investor productivity01:16 Why early ChatGPT was a head fake for investors03:07 The inflection point with deep research and agentic AI05:00 LLMs vs agents explained in plain English07:01 Where AI fits inside an investment workflow09:28 Replacing manual earnings transcript work11:40 Real-time monitoring and AI alerts19:24 Using AI to kill bad investment ideas faster22:01 Trust but verify, hallucinations and safeguards25:29 Matt’s AI tech stack for investing30:00 Prompt engineering breakthroughs33:00 Standardized experimentation across new AI models36:07 Building idea generation prompts step by step40:15 Using AI as an editor and critical reviewer43:50 Does AI compress investor skill differences46:10 How funds should adopt AI internally50:40 Fear of falling behind in asset management53:05 Generalists vs specialists in an AI world55:18 AI and the pursuit of alpha57:00 Customization, agents and the future of investing01:01:10 Coding agents and building tools with AI