Why AI-Powered Investing Matters Right Now

As we head toward year-end 2025, investors are rebalancing portfolios and searching for an edge in a market defined by fast-moving tech cycles and persistent uncertainty. An accessible AI investing tool can help you cut through noise, surface repeatable patterns, and act with more discipline—without hiring a quant team.

This guide shows you how to build a no-code AI investing tool using Google Sheets and a lightweight Replit app. Inspired by a simple strategy that favored founder-led, network-effect companies and backtested at 22.4% vs. the market's 9% in a sample period, you'll learn the core logic, the workflow, and the exact prompts to replicate and extend the approach. We'll keep it practical, transparent, and mindful of risks. This is not financial advice, but a blueprint for research rigor you can own.

The goal isn't to predict the future—it's to consistently apply a focused edge with guardrails.

The Edge: Founder-Led + Network Effects

Founder-led, network-effect businesses have historically produced outsized outcomes because they compound cultural focus and competitive advantages.

Why founder-led can win

• Skin in the game: Founders often retain meaningful ownership and think in decades, not quarters.
• Capital allocation: Willingness to invest through cycles and place asymmetric bets.
• Culture and speed: Clear mission, faster decision loops, and tighter product feedback cycles.

Signals to track:

• Is the CEO a founder or co-founder?
• Insider ownership above a threshold (e.g., >3-5%)
• Consistency of product vision across shareholder letters and earnings calls

Why network effects matter

Network effects create increasing returns—each new user makes the network more valuable for all users.

Signals to track:

• Multi-sided marketplaces (buyers/sellers), platforms with developers, or social graphs
• Engagement and retention stability through different market regimes
• Unit economics that improve with scale (lower CAC, higher LTV, margin expansion)

Practical screening takeaway

You don't need perfect data to start. Use AI to compress qualitative research into a structured scorecard, then layer in simple fundamentals like revenue growth, margins, and leverage. The combination helps you avoid hype while still capturing compounding advantages.

Research 500 Companies in Minutes with AI

You can direct an LLM to process large company lists efficiently and consistently.

Workflow overview

1. Create a ticker list: Start with the constituents of a broad index or a sector you know.
2. Process in batches: Feed 50–100 tickers per prompt to avoid context overflow.
3. Ask for structured output: Request a compact table or JSON with specific fields and short justifications.
4. Reconcile and review: Merge outputs in Sheets, spot-check edge cases, and revise prompts to improve precision.

Example structured fields

• Ticker, Company
• Founder_CEO (Yes/No + name)
• Insider_Ownership_Est (Low/Med/High)
• Network_Effect_Type (Marketplace/Platform/Social/None)
• Network_Effect_Strength (0–5)
• Revenue_Growth_Trajectory (Declining/Stable/Improving)
• Profitability_Signal (Path to FCF? Yes/No)
• Risks (1–2 bullets)

Prompt pattern (for batches)

"Analyze the following tickers. For each, return a single-row JSON object with: Ticker, Company, Founder_CEO (Yes/No + name), Insider_Ownership_Est (Low/Med/High), Network_Effect_Type, Network_Effect_Strength (0–5), Revenue_Growth_Trajectory, Profitability_Signal (Yes/No), and a 12-word max Risk note. Keep responses terse, factual, and consistent. Limit to one line per ticker."

Build a Live Stock Database in Google Sheets

You can stand up a living research hub in under an hour.

Step 1: Structure your sheet

Create columns:

• Ticker, Company, Founder_CEO, Insider_Ownership_Est, Network_Effect_Type, Network_Effect_Strength
• Rev_Growth_%, Gross_Margin_%, FCF_Margin_%, Debt_to_Equity
• Price, Market_Cap, Composite_Score, Notes

Step 2: Pull live data

• Use =GOOGLEFINANCE("TICKER","price") for price and market cap where supported.
• For fundamentals, either paste AI-sourced summaries or use a custom Apps Script to fetch metrics from your chosen data provider. Keep the same schema to ensure your formulas don't break.

Step 3: Score the edge

Create a composite to rank ideas. Example:

=ROUND(
  0.30*VALUE(Network_Effect_Strength) +
  0.20*IF(Founder_CEO="Yes",5,0) +
  0.20*MIN(MAX(Rev_Growth_%/10,0),5) +
  0.15*MIN(MAX(Gross_Margin_%/20,0),5) +
  0.15*IF(FCF_Margin_%>0,5,0)
,2)

Adjust weights to your conviction. The goal is not perfection; it's consistency.

Step 4: Filter and watchlist

Use filters to view only:

• Founder-led = Yes
• Network_Effect_Strength ≥ 3
• Positive or improving FCF trend

Promote the top decile to a "watchlist" tab for deeper dives and alerts.

A Simple No-Code Web App with Replit

If you want a cleaner interface, a minimal Replit app can expose your screener to any device.

What it does

• Displays your Sheet data
• Lets you filter by founder-led and network-effect strength
• Ranks by your composite score

Minimal example (one-file approach)

# app.py (Python / Flask)
from flask import Flask, request, render_template_string
import csv

app = Flask(__name__)

TEMPLATE = """

<html> <body style='font-family: system-ui; max-width:960px; margin:40px auto;'> <h2>AI Investing Screener</h2> <form> Founder-led: <select name="founder"><option value="any">Any</option><option value="Yes">Yes</option><option value="No">No</option></select> Min Network Effect: <input name="minne" value="3" size="2" /> <button type="submit">Filter</button> </form> <table border="1" cellpadding="6" cellspacing="0"> <tr><th>Ticker</th><th>Founder_CEO</th><th>NE_Strength</th><th>Score</th></tr> {% for r in rows %} <tr><td>{{r['Ticker']}}</td><td>{{r['Founder_CEO']}}</td><td>{{r['Network_Effect_Strength']}}</td><td>{{r['Composite_Score']}}</td></tr> {% endfor %} </table> </body> </html> """

Replace with a local CSV export of your Sheet

DATA_PATH = 'screener.csv'

@app.route('/') def home(): founder = request.args.get('founder', 'any') minne = float(request.args.get('minne', '3')) rows = [] with open(DATA_PATH) as f: reader = csv.DictReader(f) for r in reader: if founder != 'any' and r['Founder_CEO'] != founder: continue if float(r['Network_Effect_Strength']) < minne: continue rows.append(r) rows.sort(key=lambda x: float(x['Composite_Score']), reverse=True) return render_template_string(TEMPLATE, rows=rows)

if name == 'main': app.run(host='0.0.0.0', port=8000)

This is optional, but it turns your Sheet into a portable tool you can share with collaborators. Keep credentials and API keys secure if you extend it.

## Backtesting, Guardrails, and What 22.4% Really Means
The reference backtest showed a 22.4% return versus about 9% for the broad market in its sample period. That's encouraging—but only if you understand its limits.

### Test with discipline
- Define rules: Entry/exit logic, rebalance frequency (e.g., quarterly), position sizing.
- Use rolling windows: Don't cherry-pick start/end dates; test multiple periods.
- Include costs: Slippage, commissions, and taxes can erode returns.
- Track drawdowns: A high CAGR with painful drawdowns may be unlivable.

### Avoid common pitfalls
- Survivorship bias: Include delisted names in historical universes when possible.
- Overfitting: If too many knobs create the win, it's probably luck.
- Non-stationarity: Market regimes change—retest annually and adapt weights.

### From backtest to practice
- Start with a paper portfolio for 4–12 weeks.
- Size small, add on proof: Begin with a core index holding and a satellite sleeve for the strategy.
- Journal decisions: Note why you bought/sold; compare to your rules.

## 10 Deep AI Prompts to Find Stock and Crypto Ideas
Copy, paste, and adapt these to your universe and risk tolerance.

1. Founder-led detection: "For each ticker, state whether the current CEO is a founder/co-founder and provide a one-line justification. Output Yes/No + name."
2. Network effect classification: "Classify the network effect type (Marketplace/Platform/Social/Utility) and rate strength 0–5 based on user interactions, switching costs, and third-party participation."
3. Moat durability: "Summarize the primary moat (network effects, scale economies, brand, IP) and the top 2 threats that could erode it over 3–5 years."
4. Unit economics trend: "Assess whether unit economics are improving or deteriorating using gross margin, CAC/LTV, and FCF trends. Label Improving/Stable/Deteriorating with a 15-word rationale."
5. Founder-market fit: "Evaluate the leadership team's experience vs. the company's core market. Score 0–5 and cite one credential."
6. Catalysts calendar: "List the next 3 potential catalysts (product launches, regulatory events, partnerships) and estimate likelihood Low/Med/High."
7. Risk matrix: "Identify the top 3 risks across execution, competition, and regulation. Rate impact 1–5 and mitigation clarity 1–5."
8. Regime resilience: "Would this business model historically perform better in low-rate vs. high-rate regimes? Provide a 2-sentence explanation."
9. Valuation sanity check: "Compare valuation multiples to peers and the firm's growth/margin profile. Flag 'Overheat', 'Fair', or 'Undervalued' with a short note."
10. Crypto filter: "For these tokens, assess network usage, developer activity, token utility vs. speculation, and supply schedule dynamics. Output a 0–5 health score."

Feed outputs into your Sheet and recompute the composite score. Over time, refine weights that best reflect your edge.

## Putting It All Together
- Identify your universe and paste tickers into Sheets.
- Use the prompts to generate structured insights at scale.
- Pull prices and basics, then compute your composite score.
- Filter for founder-led, network-effect strength ≥ 3, and improving fundamentals.
- Backtest with realistic assumptions, then paper trade.
- If desired, deploy a simple Replit app to browse your screener on the go.

The power of an AI investing tool isn't in a magic model—it's in consistent questions, clean structure, and disciplined iteration. As you approach 2026, set a cadence: refresh data monthly, retest quarterly, and adjust weights thoughtfully.

> Build a repeatable process you can trust, not a one-off stock pick.

If you'd like the full prompt pack, a ready-to-copy Sheet schema, and a step-by-step workflow, reach out or subscribe to our free daily insights. Stay curious, stay skeptical, and let your AI investing tool do the heavy lifting while you make the final call.