debt_leverage_profile
Net debt ~$20B+; debt/EBITDA ~6-7x; heavily leveraged utility/IPP; 20bp rate rise increases annual interest expense ~$40-80M given floating-rate exposure
Inferred
Agent_Inference
interest_rate_sensitivity
Significant sensitivity; ~30-40% of debt is floating or near-term refinancing; 20bp rise compresses equity value ~2-4% given high leverage multiples
Inferred
Agent_Inference
geopolitical_supply_exposure
High intensity; European gas dependency on Russia exposed structural energy security vulnerabilities.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
Panama Canal (LNG/coal transit for Latin America assets) and Strait of Hormuz (natural gas supply chains for Middle East-adjacent markets)
Inferred
Agent_Inference
international_expansion_readiness
High currency devaluation exposure: Chilean peso (~15% of revenue), Colombian peso (~8%), Brazilian real (~7%); limited natural hedges increase earnings volatility
Inferred
Agent_Inference
geographic_footprint
Operations in 15+ countries; top markets Chile, Colombia, Brazil carry chronic currency devaluation risk; USD-denominated contracts partially mitigate but not eliminate exposure
Inferred
Agent_Inference
commodity_exposure_profile
High intensity; commodities: Natural Gas, Coal, Uranium, Crude Oil, Copper (grid), Lithium (storage); geopolitical: European gas dependency on Russia exposed structural energy security vulnerabilities.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
No single vendor exceeds 30% of input costs; fuel supply diversified across LNG, coal, renewables; EPC contractors fragmented—moderate substitutability
Inferred
Agent_Inference
business_model_type_primary
Physical infrastructure/energy utility; cloud termination is immaterial to core operations; grid-connected power plants are not cloud-dependent for revenue generation
Inferred
Agent_Inference
business_model_type_secondary
Some digital/operational software (e.g., Fluence energy storage JV) has cloud dependencies; disruption would affect optimization but not electricity delivery
Inferred
Agent_Inference
switching_cost_profile
Low API coupling risk; AES is a physical-asset energy company; operational technology (SCADA/EMS) is proprietary hardware-based, not heavily API-coupled to third-party platforms
Inferred
Agent_Inference
howey_test_risk_index
Very low Howey risk; AES sells electricity under regulated/contracted agreements—clearly a commodity/service, not a security or investment contract
Inferred
Agent_Inference
regulatory_burden_tier
Very High
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Moderate GDPR/CCPA exposure; AES collects limited consumer data; primary risk in EU renewable operations and smart-grid customer data; not a primary data-processing business
Inferred
Agent_Inference
antitrust_exposure_flag
Low-moderate; AES competes in regulated utility markets; no dominant market share in any single jurisdiction; FERC oversight provides structural antitrust guardrails
Inferred
Agent_Inference
regulatory_exposure_profile
Very High burden; regimes: FERC, NERC, EPA, NRC, State PUCs, DOE; Rate-case lag and clean-energy mandates compress returns on regulated asset base.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
~75-80% recurring via long-term PPAs (10-25 year contracts) and regulated tariffs; ~20-25% merchant/spot market exposure—highly contracted revenue base
Inferred
Agent_Inference
monetization_vector
Long-term power purchase agreements (PPAs) and regulated utility tariffs; secondary monetization via capacity payments and ancillary grid services
Inferred
Agent_Inference
pricing_architecture
Largely cost-of-service or PPA-fixed pricing; stress scenario: commodity cost spikes compress margins on merchant exposure (~20-25% of revenue) with limited pass-through ability
Inferred
Agent_Inference
pricing_power_rating
Moderate; regulated segments have formulaic rate recovery; renewable PPAs lock in pricing, limiting upside but providing downside protection—pricing power rating 6/10
Inferred
Agent_Inference
target_gross_margin_bracket
18.1% Gross Margin (Thin (<20%))
High
SEC-XBRL
churn_vulnerability_index
Minimal free-rider risk; electricity is metered and billed; churn risk low given long-term PPA structures; utility customers have near-zero switching in regulated territories
Inferred
Agent_Inference
headcount_cost_structure
Revenue growth is sublinear to headcount; new generation capacity adds minimal incremental staff; capital-intensive not labor-intensive—doubling revenue requires ~10-20% headcount growth
Inferred
Agent_Inference
marginal_cost_of_growth
Marginal cost of growth is capital-intensive (CapEx ~$3-4B/year) but operationally sublinear; once built, incremental power MWh has near-zero marginal labor cost
Inferred
Agent_Inference
franchise_compliance_risk
Not a franchise model; AES operates directly owned subsidiaries and JVs; compliance drift risk is regulatory (FERC, NERC, local regulators) not franchise-network in nature
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale, CAC irrelevant—AES wins customers via PPA/RFP processes; unit economics strengthen with scale due to lower LCOE from larger renewable procurement
Inferred
Agent_Inference
network_effect_present
No meaningful network effects in traditional utility/IPP model; Fluence (battery storage JV) has weak data network effects from fleet optimization—not a core AES advantage
Inferred
Agent_Inference
asset_efficiency_ratio
0.2% Return on Assets (Excellent)
High
SEC-XBRL
recession_resistance_tier
Tier 1 recession-resistant; electricity demand is essential and inelastic; regulated revenues contractually protected; ~75%+ of revenue insulated from economic cycles
Inferred
Agent_Inference
customer_segment_primary
Utilities, municipalities, and large industrial offtakers under long-term PPAs; top customers (e.g., large US utilities) may represent 10-20% of segment revenue—moderate concentration
Inferred
Agent_Inference
customer_segment_secondary
Retail/residential customers via regulated utility subsidiary (AES Indiana/Ohio); diversified base of millions of customers—low individual concentration risk
Inferred
Agent_Inference
characteristic_occupations
["11-0000 Management Occupations", "13-0000 Business and Financial Operations Occupations", "15-0000 Computer and Mathematical Occupations", "17-0000 Architecture and Engineering Occupations", "23-0000 Legal Occupations", "41-0000 Sales and Related Occupations", "43-0000 Office and Administrative Support Occupations", "47-0000 Construction and Extraction Occupations", "49-0000 Installation, Maintenance, and Repair Occupations"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.34 (HIL — ~34% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
48.5% CapEx / Revenue (High-CapEx Infrastructure)
High
SEC-XBRL
sec_cik
0000874761
High
SEC-EDGAR
ticker
AES
High
SEC-EDGAR