diff --git a/src/charts/spending_debt.py b/src/charts/spending_debt.py
new file mode 100644
index 0000000..d7c5c55
--- /dev/null
+++ b/src/charts/spending_debt.py
@@ -0,0 +1,74 @@
+"""Spending and Debt Charts — Hyperscaler Capex, Tech Debt, NVIDIA Revenue"""
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+from src.data.ai_infrastructure import hyperscaler_capex_annual, hyperscaler_capex_meta
+from src.utils.styling import get_theme, EXPORT_DPI, get_company_colors, GRAY_DARK
+from src.utils.export import save_chart_tight
+
+
+def plot_hyperscaler_capex() -> str:
+    plt.rcParams.update(get_theme())
+    fig, ax = plt.subplots(figsize=(14, 8))
+
+    company_colors = get_company_colors()
+    companies = ["Microsoft", "Alphabet", "Meta", "Amazon"]
+    years = list(range(2020, 2027))
+
+    # Organize data by company
+    data = {c: [] for c in companies}
+    for entry in hyperscaler_capex_annual:
+        data[entry["company"]].append(entry["capex_billions"])
+
+    # Fill missing years with 0
+    for c in companies:
+        while len(data[c]) < len(years):
+            data[c].append(0)
+
+    # Stacked area
+    y_offset = np.zeros(len(years))
+    totals = []
+    for c in companies:
+        values = np.array(data[c])
+        ax.fill_between(years, y_offset, y_offset + values,
+                        alpha=0.8, color=company_colors.get(c, "#666"), label=c)
+        ax.plot(years, y_offset + values, color=company_colors.get(c, "#666"), linewidth=1)
+        y_offset += values
+        totals.append(sum(values))
+
+    # Total annotations
+    for i, (year, total) in enumerate(zip(years, y_offset)):
+        label = f"${total:.0f}B"
+        if year >= 2026:
+            label += "*"
+        ax.text(year, total + 5, label, ha="center", fontsize=9, fontweight="bold")
+
+    # Dashed vertical line at 2026 to mark guided/projected boundary
+    ax.axvline(x=2025.5, color=GRAY_DARK, linestyle="--", alpha=0.4, linewidth=1)
+
+    ax.set_title("Hyperscaler AI Infrastructure Capex — 2020 to 2026", fontsize=16, fontweight="bold")
+    ax.set_xlabel("Year", fontsize=12)
+    ax.set_ylabel("Capex (Billions USD)", fontsize=12)
+    ax.legend(loc="upper left", fontsize=10)
+    ax.grid(True, alpha=0.3)
+    ax.text(0.02, 0.97, "*2026 = guided/projected", transform=ax.transAxes,
+            fontsize=9, style="italic", color="gray", va="top")
+
+    # AI-related capex share note
+    ax.text(0.98, 0.03, "80-90% of 2025/2026 capex is AI-related",
+            transform=ax.transAxes, fontsize=9, style="italic",
+            color="gray", ha="right", va="bottom")
+
+    path = save_chart_tight(fig, "05_hyperscaler_capex.png")
+    plt.close(fig)
+    return path
+
+
+def main():
+    path = plot_hyperscaler_capex()
+    print(f"Chart saved: {path}")
+
+
+if __name__ == "__main__":
+    main()