## The Deep Dive _Practical insights for evaluating opportunities and avoiding mistakes._ --- ## 1. WHY NOW? Infrastructure has been falling apart for decades. What changed in 2023-2024? **Five things converged:** 1. **Edge AI works now** - GPT-4 and edge models make real-time predictions viable. Cost dropped from dollars per inference to <$0.01. Can run AI on $50 edge devices. 2. **Sensors got cheap** - Industrial IoT sensors went from $100+ (2017) to <$10 (2023) at scale. LIDAR costs down 90%. Deployment now pays back in months, not years. 3. **Mobile penetration crossed 70%** in target markets - Inspectors use smartphones instead of clipboards. No legacy IT needed. 4. **Regulations kicked in** - US PFAS standards (2024), lead pipe deadlines (2024-27), infrastructure cybersecurity rules (2023), climate mandates across APAC/MENA. Created budgets and urgency. 5. **Climate forcing the issue** - Insurance costs up 30-40% annually. Extreme weather accelerating degradation 2-3x in hot/humid regions. **The window is 2024-2026.** Couldn't do this in 2020 (too expensive). Shouldn't wait until 2027 (competition will arrive). --- ## 2. THE WEDGE STRATEGY Don't try to do everything. Pick one thing, dominate it, expand systematically. ### Phase 1: Beachhead (12-18 months) - **One** specific pain point - **One** customer type - **One** geography - Prove 10x better, not 10% better **Good wedges:** - Water leak detection for Malaysian utilities - Desalination monitoring in UAE - Mining equipment in Zambian copper operations - Bridge inspection in ASEAN corridors **Bad wedges:** - "Infrastructure maintenance in Asia" (too broad) - "AI for utilities" (too vague) ### Phase 2: Expand within customer (18-30 months) Same customer, adjacent assets. Water utility goes from pipes → wastewater → quality sensors. ACV increases 3-5x. ### Phase 3: Horizontal scale (30-48 months) Prove it works across geography. Malaysia → Thailand → Indonesia. Use case studies and partnerships. ### Phase 4: Platform (36+ months) Become the infrastructure OS. APIs, marketplace, data monetization. **Example progression:** ``` Year 1: Water pipes in Malaysia - $100K ACV Year 2: + Wastewater + Quality - $250K ACV Year 3: + Treatment + Pumps - $400K ACV ``` --- ## 3. UNIT ECONOMICS THAT WORK **Revenue model:** Hardware + SaaS hybrid Year 1: $250-400K (hardware $150-250K + software $100-150K) Year 2+: $150-250K (software only + expansion) **Key metrics:** - Gross margin: 65-75% blended (40% Year 1, 85% Year 2+) - CAC payback: <12 months (target 6-9) - LTV/CAC: >5:1 (target 8-12:1) - Logo retention: 95-100% (utilities don't churn) - Net retention: 110-130% (they buy more over time) **Quick example:** ``` Water utility customer: - Pays $300K Year 1, $175K Years 2-6 - Total LTV: $1.2M - Your CAC: $75K - LTV/CAC: 16:1 ✓ - They save $500K/year in water loss - Their ROI: 2.5x what they pay you ``` **Path to scale:** - Year 1: $0.5M ARR (10 customers) - Year 3: $5M ARR (50 customers) - Break even here - Year 5: $40M ARR (200 customers) --- ## 4. DEFENSIBLE MOATS Technology commoditizes. Here's what actually defends your position: ### 1. Data network effects (the main one) Every sensor you deploy trains your AI. After 1,000 deployments, you have patterns competitors need 3-5 years to replicate. Your accuracy improves → customers stay → more data → better AI. This compounds. ### 2. Regulatory capture Work with government to write the standards. Join committees. Provide reference implementations. Get your methodology written into procurement specs. Suddenly competitors need to be "XYZ-compliant" and you have an 18-month head start. ### 3. Operational lock-in Embedded in daily workflows. 3-5 years of historical data. Staff trained. API integrations everywhere. Switching costs are $500K-2M and 12-18 months of pain. ### 4. Geographic density Own 70-80% of utilities in Malaysia before competitors arrive. Local partnerships, language localization, regulatory knowledge. Hard to displace. ### 5. Channel control Exclusive partnerships with infrastructure contractors. They sell your tech, you leverage their relationships. Takes time to replicate. **Timeline:** Moats take 24-36 months to build, not Day 1. Plan for this. --- ## 5. HOW TO PICK OPPORTUNITIES Score each on 7 criteria (2 points = strong, 1 = okay, 0 = weak): **1. Non-discretionary?** Regulatory mandate or prevents asset failure (not "nice to have") **2. ROI <12 months?** Can they measure savings clearly and quickly? **3. Budget exists?** Dedicated line item or creating new category? **4. Fast validation?** Prove it in 12-18 months with 10 customers, or 5-year development cycle? **5. Expansion path?** Can you 3-5x revenue per customer over time? **6. Moat possible?** Data network effects or regulatory capture achievable? **7. Founder-market fit?** Domain expertise + technical chops + sales ability? **Scoring:** - 12-14 points: Do it - 9-11 points: Maybe - <9 points: Probably not --- ## 6. WHAT WILL GO WRONG **Long sales cycles:** Utilities take 18-24 months to buy. → Fix: $25K pilot programs (90 days), partner channels, target smaller customers first **Hardware fails in harsh conditions:** Sensors die in extreme heat/humidity. → Fix: Industrial-grade components, redundancy, performance-based contracts, 3-year warranty **AI misses failures:** Miss a prediction → pipe bursts → liability. → Fix: Position as "decision support" not autonomous, humans review recommendations, insurance **Well-funded competitor:** Someone raises $50M and undercuts pricing. → Fix: Focus on outcomes not price, lock-in through data, move fast, partnership model harder to copy **Regulatory changes:** New rules favor incumbents. → Fix: Engage regulators early, join standards committees, get certified, partner with certified contractors **Budget cuts:** Economic downturn freezes spending. → Fix: ROI so good they can't cut it, compliance budget more stable, diversify customers --- ## 7. COMPETITION **vs. Status quo (70-80% of market):** Manual processes, Excel tracking → You win: 10x faster, cheaper at scale, better accuracy **vs. Legacy tech (15-20%):** Old monitoring companies → You win: Modern AI vs rules, mobile-first, faster iteration **vs. Other startups (5%):** Well-funded new entrants → You win: Geographic focus, data head start, better economics **vs. Big tech (5%):** GE, Siemens adding IoT → You win: Faster, better business model, they sell equipment not outcomes **vs. Tech giants (negligible):** Oracle, Microsoft, Google → You win: Not worth their time until $1B+ market (you have years) **Strategy by phase:** - Year 1-2: Beat status quo (prove ROI) - Year 2-3: Defend vs startups (build data moat) - Year 3-5: Fend off big tech (platform effects) - Year 5+: Exit to them or IPO --- ## 8. TEAM REQUIREMENTS **You need three capabilities:** **Domain expert (CEO):** 7-10 years in the industry. Knows customers, has relationships, credible with buyers. Buyers don't trust tech outsiders. **Technical leader (CTO):** Sensors/IoT + AI/ML, shipped hardware before. Reliability matters more than innovation in infrastructure. **GTM expert (CRO):** Sold to utilities/government/industrial before. Understands procurement, RFPs, budget cycles. Can close first 10 customers. **Red flags:** - All tech team learning industry from scratch - No enterprise sales experience - No hardware experience building IoT - Remote team selling into foreign markets **If you have gaps:** - Missing domain: Recruit advisor with equity, hire from industry, spend 6 months in customer discovery - Missing sales: Hire experienced VP Sales early, don't DIY - Missing tech: Partner for hardware, focus on software - Missing local knowledge: Establish local subsidiary, hire local team, co-locate for 6-12 months --- ## BOTTOM LINE **Winners will:** - Pick narrow wedges and dominate (12-18 months) - Prove 10x better, not incremental (speed, cost, or accuracy) - Build data moats (24-36 month barrier) - Target emerging markets (higher growth, less competition) - Show ROI <12 months (measurable outcomes) - Have founder-market fit (domain + tech + sales) - Expand systematically (wedge → platform) - Hit strong unit economics (LTV/CAC >5:1, margins >65%) **That's it. Everything else is noise.**