Updated: Feb 7, 2026
Investing in AI infrastructure category leaders—the picks and shovels of the AI era.
Personal investment documentation for educational purposes. Not investment advice. See full disclosure in footer.
Disciplined evaluation when exceptional companies become publicly available.
While private equity dominates access to high-growth category leaders, individual investors can apply the same rigorous analysis when exceptional companies become available through IPOs. This portfolio uses a systematic 4-filter framework that typically surfaces just 1 exceptional investment every 1-2 years—patience becomes competitive advantage.
Additionally evaluates management quality through public communications—strong leadership with clear strategic vision and track record of execution against stated goals.
Current Focus: AI infrastructure layer—the picks and shovels that every AI application requires. While mega-funds deploy billions into $50B+ positions, our size allows access to category leaders in the $10-30B range where institutional minimums exclude larger capital. We're not betting on which AI models win, but on the infrastructure all of them need.
The goal: Build concentrated positions in 3-5 infrastructure category leaders, allowing time for these businesses to compound and deliver transformational returns. Every decision is documented—wins, losses, and lessons learned.
Four-filter system requiring strong performance across all criteria. Subjective assessments, not objective ratings or recommendations.
| Filter | Weight | Key Criteria |
|---|---|---|
| Category Creator/Leader | 30% | Defining new problem/space, 40%+ market share, owns the language |
| Strong Fundamentals | 25% | 30%+ revenue growth, 60%+ gross margins, path to profitability |
| Durable Moat | 25% | Network effects, switching costs, data advantages, regulatory barriers |
| Long Runway | 20% | $50B+ TAM, <10% penetration, multiple expansion paths |
Category leader creating and dominating Zero Trust Data Security.
The picks and shovels advantage—selling tools to gold miners, not betting on which miner strikes gold.
During the 1850s California Gold Rush, most prospectors lost money chasing gold. But the merchants selling picks, shovels, jeans (Levi Strauss), and supplies made fortunes—regardless of which miners succeeded. The same pattern repeated in the 1990s dot-com boom: infrastructure companies (Cisco, Oracle, Microsoft) delivered sustained returns while most dot-com applications flamed out.
Today's AI Gold Rush: We're not betting on which AI model wins (ChatGPT vs. Claude vs. Gemini) or which AI applications succeed. We're investing in the infrastructure layer that every AI company must buy to operate—the picks and shovels of 2026.
The Size Advantage: Mega-funds like Coatue, Tiger Global, and Berkshire Hathaway are constrained by institutional minimums—they must deploy into $50B+ positions (NVDA, MSFT, GOOGL). Our portfolio size creates competitive advantage: access to category-leading infrastructure plays in the $10-30B range that are too small for large institutional allocations but large enough to have proven category leadership.
Circle of Competence: This approach leverages 10+ years as a Software Product Manager at enterprise companies. Direct experience evaluating enterprise software purchases, understanding technical architecture, and recognizing authentic category creation vs. marketing narratives. This domain expertise is the primary competitive advantage—not superior market timing or financial analysis.
Every AI application generates massive data that must be protected and recoverable
Why This Layer Matters: As AI systems proliferate, organizations need zero-trust data security for training data, model outputs, and customer information. Every AI company—from OpenAI to Anthropic to enterprise AI deployments—requires cyber resilience infrastructure. This is infrastructure software, not AI software—it benefits from AI tailwinds without competing in AI applications.
Monitoring, observability, data platforms, and operational intelligence
Additional infrastructure positions disclosed after entry per portfolio transparency policy. Target: 3-5 concentrated positions in non-competing infrastructure categories. Each must independently score 8.0+ on framework criteria.
Why This Isn't Sector Concentration: These are category leaders in distinct, non-competing infrastructure layers (data security, observability, data platforms, etc.) that happen to benefit from AI tailwinds. Each position must independently meet framework criteria—the AI infrastructure thesis is confirmatory, not primary. We're investing in publicly-traded category leaders in enterprise software infrastructure during their 3-7 year maturity window when category leadership is proven but massive scale hasn't been reached. This is not early-stage venture capital or AI application speculation.
Companies that meet framework criteria but currently fail on valuation discipline.
✅ TOP WATCHLIST
Highest Conviction
Valuation: $100B+ (private)
Category: Data Lakehouse
Created and dominates unified analytics platform. $2.4B+ ARR growing 50%+, strong fundamentals. Framework criteria met at $30-50B post-IPO valuation. Monitoring for typical 30-50% correction pattern post-IPO (same as SNOW).
Action Required: IPO filing announcement + post-IPO correction to $30-50B market cap range.
⏸️ MONITORING
Strong Candidate
Status: Pre-IPO
Category: Agentic AI Development Platform
Category creator in AI agent development tools. Framework criteria potentially align at <$3B IPO valuation. Requires deeper due diligence on fundamentals and competitive moat.
Action Required: IPO filing + fundamentals verification + valuation at <$3B.
⏸️ VALUATION WATCH
Meets Framework
Current: $160 (34x sales)
Category: Edge Computing Network
Clear category leader. Strong fundamentals but trading at 34x sales with decelerating growth (30% → 25% → 20%). No margin of safety at current valuation.
Action Required: Pullback to $140 or below (25x sales threshold) + growth stabilization.
⏸️ VALUATION WATCH
Meets Framework, Marginal Moat
Current: ~$430 (14x sales, $35B cap)
Category: NoSQL Database Leader
Strong NoSQL position but moat concerns (AWS DocumentDB threat) and runway limitations (needs $350B for 10x from $35B). Trading at 14x sales for decelerating growth.
Action Required: Significant pullback + AWS competitive risk clarification.
Watchlist Strategy: These companies qualify on framework criteria but fail on valuation discipline. Databricks is highest conviction—would be immediate buy at $30-50B post-IPO. Others require significant price corrections or thesis improvements. Patience and discipline remain competitive advantage.
Why most exceptional companies don't make the cut—learning from rejection patterns.
❌ PERMANENT PASS
Excellent Quality, Wrong Size
Market Cap: $42B
Pattern: The $50B Problem
Clear category leader in Cloud Data Platform. Strong fundamentals (26% growth, 72% margins), durable moat. Quality company at impossible valuation—would need $420B market cap for 10x (larger than Oracle). Already too mature for 10-year 10x thesis.
Lesson: Quality ≠ Opportunity. Amazing companies at $40B+ market caps mathematically can't deliver 10x returns. The window for category kings closes—timing matters as much as quality. Would have been perfect at $4-8B market cap (2020 IPO window).
The Reality of Disciplined Investing: 95% rejection rate across companies evaluated monthly. Common failures include: weak fundamentals, commoditizing markets, too expensive (>$50B can't mathematically 10x), valuation discipline failures, or wrong timing (too early/too late in lifecycle).
The competitive advantage is saying "no" to 95% of opportunities—including excellent companies like Snowflake—and holding cash at 3.69% until companies meeting all criteria appear at reasonable valuations. Roughly 1 exceptional opportunity every 1-2 years.
Transparent documentation of personal portfolio returns. Updated monthly.
Performance Disclosure: This represents one individual's personal account only. Not a track record of managing others' money. Performance data documented for personal accountability, not to solicit others to invest similarly.
| Month | Portfolio | S&P 500 | Notes |
|---|---|---|---|
| Jan 2026 | -19.3% | +1.4% | First full month |
| Dec 2025 | +11.8%* | +2.4%* | Partial month (Dec 23-31) |
| *Partial month only (8 trading days). Track record begins December 23, 2025. | |||
Performance calculated using time-weighted returns. Updated first Friday of each month. Current position: RBRK purchased at $67.51 (December 23, 2025), currently at $54.50 (-19.3% as of February 7, 2026). Thesis unchanged—volatility expected for category-creating companies in early public markets.
⚠️ New Track Record: Performance tracking begins December 2025. Professional investors require minimum 3-5 year track records to evaluate skill versus luck. This data is published for personal accountability only—creating a permanent record that cannot be revised with hindsight. Judge results over 10 years (target: 2035), not 1-2 months.
Includes methodology, calculations, and full disclosure
Quarterly portfolio updates documenting positions, analysis process, and lessons learned. Educational commentary, not investment advice.
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The principles guiding every investment decision.
Index funds provide market returns (~10% annually) through broad diversification. This approach seeks transformational outcomes through concentrated positions in 3-5 category leaders, accepting higher volatility for potential 10x+ returns over 10-year horizons. Quality compounds, diversification dilutes.
Companies that create new categories capture 76% of market value and maintain 50%+ market share. This approach invests in category designers, not category participants—businesses that define entirely new markets rather than compete in existing ones.
Exceptional returns require 7-10 years of patient holding through market volatility. This approach typically surfaces 1 investment every 1-2 years—patience and saying "no" to 95% of opportunities becomes competitive advantage.
This approach combines Graham's margin of safety with Buffett's quality focus and circle of competence principle. Investing exclusively in publicly-traded enterprise software infrastructure where 10+ years as a Software Product Manager provides competitive advantage: direct experience evaluating enterprise purchases, understanding technical architecture, and recognizing authentic category creation vs. marketing narratives. Currently focused on AI infrastructure layer—not because AI is trendy, but because that's where category-creating software companies are emerging in public markets today.