5-Minute AI Investing Playbook: Watchlist, Prompt, Setup & Risks

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🚨 5-Minute AI Playbook: 3 Bottlenecks Driving the Next Winners

Quick market take, focused watchlist, a copy-paste research prompt (with verification), and a simple trade template you can use today.

📝 Editor’s Note
This issue is built for speed. Skim the headers, steal the prompt, and use the verification checklist to keep your research clean. Nothing here is financial advice - just a toolkit to help you think clearly about AI-driven stocks.

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⚡ Quick Market Take (AI Edition)

AI leaders keep pulling attention, but capital is quietly rotating among three “picks-and-shovels” lanes: compute (GPUs/CPUs), bandwidth (switches/optics), and power/real estate (data-center REITs and utilities). Expect choppier leadership as investors recalibrate around unit economics - cost per inference, utilization, and power availability.

🧠 Featured Thesis: The “Boring Is Beautiful” Phase

Narratives cool off; plumbing gets funded. As models scale, the bottlenecks shift from model quality to throughput, power, and delivery. That favors:

• Foundry & lithography (capacity and yields)

• Networking & storage (east-west traffic and low-latency access)

• Power + cooling + real estate (MW, heat, and zoning are the new moats)

Translation: Don’t just chase model owners. Track who enables the next 10× of usage.

📊 Watchlist (1-liners you can act on)

• NVDA - Core compute; watch supply signals and new product cycles.

• ASML - EUV/High-NA as a capacity gate; follow tool lead times.

• AVGO - Accelerators + networking; look for hyperscaler design wins.

• MSFT - Model + infra + distribution; track AI attach to enterprise seats.

• EQIX / DLR - Data-center REITs; focus on power commitments and pre-leasing.

Idea flow tip: For each stock, keep a tiny card with: “Growth driver → Bottleneck → Telltale metric.”

🧪 Backtest Idea to Try (DIY, no hype)

Question: Do AI-heavy leaders that reclaim their 50-day moving average on rising volume tend to trend for 20 trading days?
How-to:

1. Build a universe of your AI stocks.

2. Signal = Close > 50-DMA and 20-day volume > 20-day average volume.

3. Track the next 20 trading days; log max drawdown and close-to-close return.

4. Segment by market regime (e.g., above/below 200-DMA).
   Deliverable: % winners, average return, and worst drawdown. No opinions - just numbers.

🎯 Trade Setup Template (Use with any AI stock)

• Trigger: Break above a well-tested level (prior swing high or anchored VWAP).

• Entry: First close above the level or a limit order on a shallow pullback.

• Invalidation: Last higher low (or ATR-based stop: 1.5–2.0× ATR).

• Sizing: Risk ≤ 0.5-1.0% of account per idea.

• Exits:

  • Take partial at +1R, trail the rest with a higher-low stop, or

  • Use a time-based exit (10-20 trading days) if momentum stalls.
    Discipline beats prediction. Log every trade, even the passes.

🤖 Copy-Paste Research Prompt (Company AI Reality Check)

1. Map current AI products/features, stating the revenue stream and whether it’s material (estimate or management commentary).

2. Break down AI capex/opex drivers (compute, networking, data center, power) and link to official sources (10-K/10-Q, shareholder letters, investor day decks, earnings transcripts).

3. Identify bottlenecks (talent, supply chain, regulation, power) and how management plans to solve them.

4. Estimate unit economics where possible (e.g., cost per inference/user) and compare to peers.

5. Provide three falsifiable risks that would invalidate the bullish case.

6. Output a bullet summary with inline citations to source docs (exact page/slide). Keep it under 300 words.”

✅ How to Verify the Prompt’s Output

• Match citations: Open each cited 10-K/10-Q/investor deck and confirm the quoted line and page/slide number.

• Numbers audit: Recalculate ratios (capex/revenue, growth %) from the original tables.

• Transcript sanity check: Cross-read earnings call remarks for context and date.

• Peer compare: Spot-check at least one peer filing to see if claims are industry-standard or outliers.

• Consistency: Ensure historical figures tie across quarters (no sudden restatements without explanation).
  If any claim lacks a primary-source doc, tag it as Unverified in your notes.

🛠️ Tool Corner (lightweight workflow)

• Filings first: Start at the company’s Investor Relations page → SEC filings → 10-K/10-Q.

• Layer transcripts: Use any transcript source, but always tie quotes to date/time.

• Chart the tell: Track one simple metric that reflects the thesis (e.g., R&D %, capex trend, or power commitments for data centers). Keep a single-chart dashboard.

⚠️ Risks & Reality Check

• Power is the moat: MW availability and time-to-energize can trump chip supply.

• Unit economics: If inference costs don’t fall with scale, adoption stalls.

• Regulatory drag: Data use, safety, and export controls can shift timelines quickly.

🎓 60-Second Explainer: Inference Cost vs. Training Cost

• Training = one-time, large spend to create a model.

• Inference = recurring cost every time the model serves a user/task.
  Investors should track how fast inference costs fall - that’s the lever for margins and broad adoption.

🗓️ What to Watch Next

• Hyperscaler updates on AI attach rates to enterprise suites.

• Supply-chain breadcrumbs: lead times for advanced chips, optics, and power gear.

• Data-center power deals and pre-leasing commentary.

• Example: On July 30, 2025 (MSFT FY25 Q4), management highlighted the largest add of Microsoft 365 Copilot seats since launch; and on July 3, 2025, CoreWeave announced first hyperscaler deployments of new Blackwell-class racks - signals to watch for demand and infrastructure readiness.

👉 Next Step

• Pick one ticker from the Watchlist.

• Paste the research prompt into your AI tool and run it.

• Use the Verification checklist to validate every claim; mark items as Verified or Unverified.

• Write a 5-line decision note: thesis, catalyst, risk, invalidation, checklist status.

• Set a 14-day reminder to reassess and track one metric (e.g., capex trend or power commitments).

Vaulting Your Wealth Forward,
– T. D. Thompson

AI Investing Vault

The content above is for educational and informational purposes only and does not constitute financial advice or a solicitation to buy or sell any financial instruments. Trading and investing involve significant risk of loss, and past performance is not indicative of future results. Always consult with a licensed financial advisor or conduct your own research before making any investment decisions. Use of AI tools and strategies mentioned above is at your own discretion and risk. AI Investing Vault may receive compensation if you purchase tools or services mentioned in this email, at no additional cost to you.