mosttt

The only 23 rules you need to master AI for investing. Rules to get the basics : 1. Accept that AI is part of modern investing 2. Use AI on every new company you study 3. Treat AI like an intern, not a CEO 4. Do the hard work yourself 5. Accept that AI makes mistakes, verify don’t avoid 6. Practice until it becomes a skill, keep evolving. What AI Is (and Isn’t) Good For: 7. Use AI to grasp 80% of any business fast 8. Don’t use AI for exact numbers or DCF models 9. Use AI to extract insight, not build spreadsheets 10. Turn your manual analysis into a clear repeatable system Working With AI: The Core Practices: 11. Use Gemini Pro (DeepResearch) for multi-source analysis 12. Use NotebookLM for grounded, zero-hallucination research 13. Use ChatGPT for clarity and fast understanding 14. Pay for quality tools, they’re 10× better 15. Use AI to learn your way (text, audio, visual) 16. Master prompting, clarity in clarity out 17. Build a prompt library that compounds over time 18. Tailor your master prompts by industry with ChatGPT4. Some Use Cases: 19. Understand industries end to end 20. Review earnings calls to spot shifts early 21. Build your own expert AI analyst with NotebookLM 22. Automate weekly research updates on your stocks 23. Let AI stress test your thesis before the market does 👇

@CompaCompu Thanks ! Everything is well on my side. Hope same for you. I will try to go back to X. Yes,

@mosttt_ I hope all has been well as can be with you and yours

This is exactly why people say AI fails at investing.. Generic prompts + no context + no investing expertise = generic unreliable answers.

@AKWilk @scroogecapital You are using it wrong, that's all.

You guys were right. Claude is amazing.

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I started this newsletter 6 months ago: •18,000 subscribers •Substack Bestseller badge From day one, I made a simple decision. I would share everything in my notes and in the weekly newsletter: •Frameworks and processes that actually work in practice. •Prompts I validate only after testing them on at least 15 stocks. I deeply believe that AI makes investing research 10× faster and deeper. You only need the right frameworks. That is what this newsletter is about.

@MikeFritzell @firstadopter I’ve been a user and promoter of Gemini for investing and professional research. But for my personal life, l can’t replace ChatGPT Most of the masses usage is personal, and having the most powerful reasoning model or advanced calculations isn’t the most important thing to me

The broader masses are not going to switch to Gemini in the blink of a moment @firstadopter

Paste this into Gemini Deep Research. It’ll help you understand the basics of any stock in minutes. ----- You are an equity analyst writing a high-quality company brief for long-term investors. Analyze [Ticker] using the 7-point framework below. Use only verifiable, factual information (annual reports, investor presentations, filings, earnings transcripts, and reputable financial sources). Be concise, analytical, and concrete — no filler or marketing language. Output format (exact structure required): Executive Summary (≈150–200 words) Summarize in plain English how this company makes money, its economic quality, and where its edge and risks lie. End with one sentence stating how you’d describe the business to an investor in one line. 1. What They Sell and Who Buys Describe the main products or services. Define target customers (type, segment, geography) and why they buy — main pain point or motivation. 2. How They Make Money Explain the revenue model and pricing logic. Clarify whether it’s one-time, recurring, transaction-based, or hybrid. Include key revenue segments and their share if available. 3. Revenue Quality Assess how predictable and diversified revenues are. Break down recurring vs one-off components, customer or segment concentration, and exposure to cycles. 4. Cost Structure Outline major cost drivers (COGS, labor, logistics, marketing, etc.). Include gross and operating margins where possible. Comment on scalability — fixed vs variable costs and how margins move with growth. 5. Capital Intensity Describe assets needed to run and grow operations. Include capital expenditures, working-capital needs, and cash conversion efficiency. 6. Growth Drivers Identify the main levers for revenue growth — volume, pricing, product mix, geographic expansion, or acquisitions. Clarify if these are structural (long-term) or cyclical (short-term). 7. Competitive Edge Explain what protects the company’s economics from competition: brand, cost advantage, switching cost, regulation, network effects, data, or IP. Mention how durable and testable this moat appears based on financial evidence (margins, ROIC, retention). Tone: Analytical, neutral, precise. Goal: Produce a concise yet rich narrative that lets an investor understand how this business actually works.

@evfcfaddict @aktieblogg @Secrets4stocks @stonkmetal @AravacaCap @ObscurValuation @SebKrog @FinSkeptic @d_schau @NoBrainerStock @unfixed_income @diegobmilano @wusatiuk @Franfra51045228 @HindsightCapLLP @ReturnsJourney @SteigerwaldValu @AmsterdamStock Enjoy guys !

Europoors in Vienna II @aktieblogg @Secrets4stocks @stonkmetal @AravacaCap @ObscurValuation @SebKrog @FinSkeptic @d_schau @NoBrainerStock @unfixed_income @diegobmilano @wusatiuk @Franfra51045228 @HindsightCapLLP @ReturnsJourney @SteigerwaldValu @AmsterdamStock

7 steps to understand how young Buffett actually invested: 1. Upload the letters Into NotebookLM: Upload all Partnership Letters (1956–1969) 2. Generate a mind map : Ask NotebookLM to create a mind map summarizing key themes: •Case studies •Mistakes •Special situations •Portfolio rules 3. Study one subject : From the map, pick one topic (e.g., Case Studies or Mistakes). NotebookLM will generate a summary with sources cited. 4. Ask deep questions : Ask detailed questions about that topic , NotebookLM will answer only from the letters. 5. Test yourself Generate a quiz on that specific topic to confirm your understanding. 6. Generate a podcast Ask NotebookLM to produce a short audio summary of one principle. Listen while you drive or train 7. Repeat with new themes Choose another topic and repeat. In a few weeks, you’ll deeply understand how Buffett ran his Partnership years and the principles that built his fortune

The only 23 rules you need to master AI for investing. Rules to get the basics : 1. Accept that AI is part of modern investing 2. Use AI on every new company you study 3. Treat AI like an intern, not a CEO 4. Do the hard work yourself 5. Accept that AI makes mistakes, verify don’t avoid 6. Practice until it becomes a skill, keep evolving. What AI Is (and Isn’t) Good For: 7. Use AI to grasp 80% of any business fast 8. Don’t use AI for exact numbers or DCF models 9. Use AI to extract insight, not build spreadsheets 10. Turn your manual analysis into a clear repeatable system Working With AI: The Core Practices: 11. Use Gemini Pro (DeepResearch) for multi-source analysis 12. Use NotebookLM for grounded, zero-hallucination research 13. Use ChatGPT for clarity and fast understanding 14. Pay for quality tools, they’re 10× better 15. Use AI to learn your way (text, audio, visual) 16. Master prompting, clarity in clarity out 17. Build a prompt library that compounds over time 18. Tailor your master prompts by industry with ChatGPT4. Some Use Cases: 19. Understand industries end to end 20. Review earnings calls to spot shifts early 21. Build your own expert AI analyst with NotebookLM 22. Automate weekly research updates on your stocks 23. Let AI stress test your thesis before the market does 👇

If this helped you: Join the only newsletter focused on mastering AI for long-term investing news.aiinvestinghq.com Followed by 7,200+ investors.

@MarienLechevin The prompt was to analyze why Equasnes stopped compounding. here is a screenshot of the results:it identifies the main reasons:

@mosttt_ What Was the result @mosttt_ ?

So Gemini can read 100+ top sources about a business and answer your question precisely… and people still think AI can’t help them invest ?

Want to understand how an industry was built and transformed over time? Run this prompt in Gemini Deep Research to have industry context most investors miss completely.. ‐-----‐------- ROLE You are an economic historian and industry analyst. Write a factual, data-based history of the [Industry Name] showing how it formed, evolved, and produced its major winners. Use clear, analytical language focused on cause and effect. OBJECTIVE Create one detailed report (8 000–10 000 words) that explains: How the industry began and why it matters economically. What forces—technology, regulation, capital, competition—shaped it. How leading firms gained and kept advantage. What cycles or patterns repeat across time. What lessons guide long-term investors. REPORT OUTLINE Formation Initial need and early solutions. First technologies, financing models, and institutions. Context and early dominant players. Expansion Breakthroughs that enabled scale. Evolution of cost, productivity, and barriers to entry. Standardization and global reach. Crises and Regulation Major shocks that reset structure. Policy and regulatory shifts. Consolidation and impact on profitability. Competition How leading firms built edge (cost, tech, brand, network). Failures and structural limits. Turning points in market share and returns. Industry Players (additional focus) Main actors in each period. Why they dominated and when leadership changed. Key mergers or breakups that reshaped the sector. Modern Transformation Digital and automation shifts. New entrants and business model changes. Effects on margins and capital allocation. Regional Differences Trade, subsidies, and geographic factors. Capital and Returns Financing evolution, leverage, and return patterns. Changes in investor perception and valuation. Key Insights Recurrent mechanisms of success and failure. Structural risks that persist. Long-term investment implications.

People are charging $$$ for this: I built a Notebook AI Expert in payments industry ask it anything, it answers only from verified sources. Inside: -A podcast that explains the payments ecosystem - Deep dive podcast on Adyen -A quiz to test your understanding it's free👇

The free gift: notebooklm.google.com/notebook/5b2e5… A free guide if you want to build your own: compoundwithai.substack.com/p/i-built-my-o…

You want to compare 2 stocks in the same industry? run this prompt in Gemini Deep Research to see which one’s the better business in minutes. you’ll get a clean side-by-side map of growth, profitability, and risks . ---- Act as a comparative business analyst and generate a consulting-style dossier contrasting {Company A} and {Company B} within the {Industry}. Focus on evidence-led analysis using the 80/20 principle to highlight critical components. Objective: - Identify which company has a higher probability of growth over the next 5-10 years, focusing on product segments and geographies. - Determine which company exhibits superior profitability and margin profiles linked explicitly to product segments and geographies. Inputs: - Peers: {Company A}, {Company B} - Industry: {Industry} - Currency: {Currency} (state FX basis if converted) Source Policy: Utilize primary/high-signal sources such as audited reports, investor decks, earnings call transcripts, regulator databases, and expert market research. Append sources to each exhibit as: [Source: Title — URL — Doc date]. Use “ND” for missing data and tag inferences with brief logic. Apply ISO date format. Normalization: Standardize product and region labels across companies. Align channel terms. Use reporting currency; note FX basis for conversions. Output Format: Markdown format with tables first, and short narrative per exhibit: Context → Insight → Implication → KPI, including direct A-vs-B comparisons throughout. 1. Executive Summary - Growth call: Declare which business has a greater growth probability based on product × geography, including 2–3 factual reasons. - Profitability call: State which business has better margin/earnings quality, tied to specific segments and geographies. - Identify 3 growth drivers explaining future growth odds. - Highlight 3 vulnerabilities that might flip growth margins. - Provide a Segment–Geography scorecard (0–5). - Outline 3 business KPIs for the next 12–24 months. 2. SECTION A — Top Cells: Analyze top products by country. 3. SECTION B — Price Control & Route-to-Market: Assess net price realization efficiency. 4. SECTION C — Supply Resilience: Evaluate supply sourcing and continuity. 5. SECTION D — Segment Growth Engines: Examine growth drivers and profitability. 6. SECTION E — Product & Geography Overview: Contextualize product and geographical analysis. 7. SECTION F — Competitive Landscape: Scrutinize competitive positioning. 8. SECTION G — Risks by Segment: Map potential risks. 9. SECTION H — Causal Diagnosis & Decision: Compare causal disparities and suggest KPI-driven actions. Quality Checks: - Ensure explicit A vs B contrasts. - Normalize labels and currencies. - End each exhibit with Context → Insight → Implication → KPI, and a source tag. - Apply the 80/20 filter to prioritize impactful segments.