Q: What is the main idea of the AI investing guide for retail investors? A: This AI investing guide for retail investors shows how everyday investors can use AI to set clear goals, pick appropriate tools, and stress-test portfolios to make better-informed decisions. It emphasizes starting with simple chatbot prompts, running simulations like Monte Carlo, and always verifying AI outputs before acting. Q: Which AI tools should most retail investors start with? A: For most long-term investors, start with a basic chatbot such as Claude or ChatGPT to ask portfolio questions and summarize information. If you need deeper technical work, Claude Code or Codex can help with coding-focused analysis, while Gemini is described as a nice-to-have for broader data review. Q: How do I use AI to define my investing goals and steps? A: Describe your objective in plain English in a chat (for example, saving for retirement or lowering volatility) and ask the model to turn that goal into concrete steps, data needs, and checks. The guide recommends defining success in simple terms like return, risk, income, and time so AI can suggest actionable analyses. Q: Can AI run portfolio stress tests like Monte Carlo simulations? A: The guide recommends asking AI to run a Monte Carlo simulation to project thousands of possible future outcomes and report metrics such as median outcome, best/worst 10%, and max drawdown. If your tool cannot execute code, have it write Python or R you can run locally and then ask it to explain the outputs in plain language. Q: Should I let AI manage my investments directly? A: No, the article warns that AI should be used as a research tool, not as a replacement for your judgement or as a money manager. Use AI to clarify goals, model trade-offs, and identify risks, but verify results and make final decisions yourself. Q: What checks and guardrails does the guide recommend when using AI? A: Key guardrails include “trust but verify” by checking numbers and sources, minding data quality and date ranges, and avoiding overfitting or recency bias. It also advises tracking costs and taxes, journaling changes, and never outsourcing overall decision-making to models. Q: What simple research workflow can I build with AI for stock review? A: Use AI to produce a one-page company scan (business model, growth drivers, risks, valuation basics), run peer checks on revenue and margins, map key risks, and keep an evidence log with thesis tests and review dates. The guide stresses keeping sources linked in every output so you can verify claims yourself. Q: Do I need a pro-level data setup like the options trader in the article? A: No, Erik Smolinski’s setup is extreme—he built a massive AI-assisted database with hundreds of millions of rows and many terabytes of storage—but the guide says most investors can apply the same method at a much smaller scale. The important steps are to define a goal, gather the right data, test many outcomes, and try to disprove your thesis rather than replicating a giant infrastructure.