1) Predictive Maintenance Intelligence
Predict failures before they occur across infrastructure, energy, and industrial systems.
Studio Pod Thesis
Maintenance Intelligence
Build the intelligence layer—AI, platforms, and analytics—that shifts maintenance from reactive and manual to predictive and optimized across infrastructure, utilities, transport, and industry.
Total addressable base
$106T existing infrastructure
Spend mix
O&M 56.7% vs New 43.3%
Deferred maintenance
~7% annual compounding
PdM ROI
15–25% cost ↓ / 20–30% asset life ↑
01 Context & Summary
VCs chase growth CapEx; the overlooked opportunity is applying AI and platforms to maintain the world's built base. The "perception gap" (manual, low-margin) hides a platform-scale software play ("Salesforce for maintenance"). Focus regions—GCC, Southeast Asia, and Africa—face harsher climates, rapid urbanization, and fewer entrenched incumbents, creating leapfrog conditions.
Why now
Sensors are cheap, edge + AI are mature, mobile penetration is high, and regulation mandates non-discretionary work.
02 Core Beliefs
1) Non-discretionary spend
Physical assets degrade; deadlines and safety requirements force action.
2) The perception gap
From "wrenches" to "intelligence": arbitrage zone → pricing power.
3) The intelligence layer
Orchestrate sensors, predict failures, optimize workflows, consolidate tools.
03 Observations: Why this matters
A) Scale & shift
$4.4T global investment gap; O&M already the majority of spend.
B) CapEx imbalance
Growth CapEx is crowded; Maintenance CapEx is lower risk and under-served.
C) Compounding liability
Deferred items grow ~7% yearly; emergencies are cost multipliers.
D) Proven economics
Predictive programs cut cost 15–25% and extend life 20–30%.
04 The Maintenance Intelligence Stack
Layer 1 — Physical Assets
Infrastructure (roads, bridges), energy (grid, pipelines), industrial & transport.
Layer 2 — Data & Sensors
IoT, inspection (drones/vision), operational logs.
Layer 3 — Intelligence Platforms
Predictive analytics, digital twins, workflows, decision support.
Moat
More assets → better models → fewer failures → lower unit cost → more adoption.
05 Opportunity Areas
Thesis-Level Master Metrics
2) Inspection Intelligence
Automate defect detection, reporting, and prioritization with computer vision and anomaly models.
3) Asset Lifecycle Intelligence
Digital twins + predictive lifecycle models to optimize replacement timing and capital planning.
4) Grid & Energy Intelligence
Monitor grid health, predict faults, and optimize distributed energy resource performance.
5) Water Infrastructure Intelligence
Detect leaks, optimize networks, and automate PFAS/lead compliance tracking.
6) Maritime Hull Fouling Intelligence
Predict corrosion & fouling, schedule downtime, and optimize fleets with digital twins.
06 Regions & Tailwinds
Southeast Asia
'90s–'00s infra aging fast; flooding/typhoons accelerate deterioration.
Middle East / GCC
Heat, sand, humidity → 2–3× faster degradation; desal & cooling critical.
Africa
Outages cost 2–4% GDP; mobile money unlocks service models.
07 The Bottom Line
O&M is already the bigger half of infrastructure spend, and it's growing. Build the intelligence platforms that own non-discretionary maintenance budgets and compound via data network effects.