fix(battlecard): correct chart image references in cards 2-8 (add ../charts/ prefix)

output/battlecards/cards/card_02_ai_infrastructure.md

@@ -9,7 +9,7 @@
 - Tech debt spiked to $121B in 2025 — 4x the 5-year average — as companies rush to build AI infrastructure *(Source: tech debt tracking data, 2025)*
 - NVIDIA data center revenue grew from $1.57B (FY2020 Q1) to $75.2B (FY2027 Q1) — a 48x increase *(Source: NVIDIA earnings reports)*
 
-![](mini_capex_trajectory.png)
+![](../charts/mini_capex_trajectory.png)
 
 ## Impact
 

output/battlecards/cards/card_03_gpu_utilization.md

@@ -9,7 +9,7 @@
 - CPU utilization is at 8% and memory utilization at 20% — systemic over-provisioning across all resources *(Source: Cast AI 2026)*
 - 69% CPU over-provisioning (up from 40% YoY) and 79% memory over-provisioning *(Source: Cast AI 2026)*
 
-![](mini_gpu_utilization.png)
+![](../charts/mini_gpu_utilization.png)
 
 ## Impact
 

output/battlecards/cards/card_04_startup_valuations.md

@@ -9,7 +9,7 @@
 - Revenue multiples for AI startups range from 100x to 500x, far exceeding dot-com era peaks of 50-100x *(Source: PitchBook/CB Insights data)*
 - Burn rates are enormous: OpenAI alone has consumed over $7B in funding while pursuing path to profitability *(Source: public filings and media reports)*
 
-![](mini_startup_multiples.png)
+![](../charts/mini_startup_multiples.png)
 
 ## Impact
 

output/battlecards/cards/card_05_enterprise_deployment.md

@@ -9,7 +9,7 @@
 - ServiceNow partner SnowGeek achieved 73% midnight escalation reduction, 65% MTTR improvement, and $2.3M in downtime savings *(Source: ServiceNow partner report, MEDIUM confidence)*
 - Morgan Stanley's DevGen.AI reviewed 9M+ lines of legacy code, saving 280,000 developer hours *(Source: Morgan Stanley, 2025)*
 
-![](mini_enterprise_savings.png)
+![](../charts/mini_enterprise_savings.png)
 
 ## Impact
 

output/battlecards/cards/card_06_developer_adoption.md

@@ -9,7 +9,7 @@
 - 84% of developers use or plan to use AI coding tools, with 51% using them daily *(Source: JetBrains/Stack Overflow surveys)*
 - Code acceptance rate is ~30% initially, but code retention is 88% — suggesting AI-assisted code, once accepted, proves reliable *(Source: GitHub data)*
 
-![](mini_developer_adoption.png)
+![](../charts/mini_developer_adoption.png)
 
 ## Impact
 

output/battlecards/cards/card_07_code_quality_caveats.md

@@ -9,7 +9,7 @@
 - 7.2% drop in delivery stability from AI use, measured via DORA metrics *(Source: Google DORA report, 2024)*
 - 6.4% secret leakage rate in AI-generated code — credentials, API keys, and tokens embedded unintentionally *(Source: security analysis)*
 
-![](mini_code_vulnerabilities.png)
+![](../charts/mini_code_vulnerabilities.png)
 
 ## Impact
 

output/battlecards/cards/card_08_long_term_productivity.md

@@ -9,7 +9,7 @@
 - Google reports 21% of code in their codebase is now AI-assisted, with measurable quality improvements *(Source: Google internal research)*
 - Realistic productivity gain range: 20-67% across studies, with higher gains in tasks involving boilerplate and documentation *(Source: multiple academic and industry studies)*
 
-![](mini_productivity_trajectory.png)
+![](../charts/mini_productivity_trajectory.png)
 
 ## Impact