diff --git a/src/charts/narrative_dashboard.py b/src/charts/narrative_dashboard.py
new file mode 100644
index 0000000..a1de08d
--- /dev/null
+++ b/src/charts/narrative_dashboard.py
@@ -0,0 +1,293 @@
+"""Narrative Dashboard — 3×3 grid telling the AI bubble story
+
+FLAGSHIP chart: single figure combining all evidence streams
+into a cohesive visual narrative.
+"""
+import sys
+import os
+# Ensure the project root is on sys.path so `src.*` imports work
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+import matplotlib
+matplotlib.use("Agg")
+
+# Patch matplotlib Path.__deepcopy__ to break Python 3.14 recursion loop
+# Known bug: https://github.com/matplotlib/matplotlib/issues/29280
+try:
+    from matplotlib.path import Path
+    _original_path_deepcopy = Path.__deepcopy__
+
+    def _safe_path_deepcopy(self, memo):
+        if id(self) in memo:
+            return memo[id(self)]
+        memo[id(self)] = self
+        return self
+
+    Path.__deepcopy__ = _safe_path_deepcopy
+except Exception:
+    pass
+
+import matplotlib.pyplot as plt
+import numpy as np
+from src.data.market_bubbles import shiller_cape, buffett_indicator, sp500_pe
+from src.data.ai_infrastructure import hyperscaler_capex_annual, nvidia_revenue
+from src.data.agent_adoption import agent_survey_data, developer_ai_adoption
+from src.utils.styling import (
+    get_theme, EXPORT_DPI, BUBBLE_ZONE, WARNING_ZONE, NORMAL_ZONE,
+    GRAY_DARK, GRAY_MEDIUM, BLACK, WHITE,
+    AGENT_GROWTH, REVENUE, DEBT, AI_SPEND, PRODUCTIVITY,
+    get_company_colors,
+)
+
+
+def plot_narrative_dashboard() -> str:
+    """Generate the flagship 3×3 narrative dashboard.
+
+    Returns the output file path.
+    """
+    plt.rcParams.update(get_theme())
+
+    fig, axes = plt.subplots(3, 3, figsize=(20, 16))
+    fig.set_facecolor(WHITE)
+
+    # ------------------------------------------------------------------
+    # ROW 1: Market Bubble Evidence
+    # ------------------------------------------------------------------
+
+    # Panel (0,0): Shiller CAPE
+    ax = axes[0, 0]
+    years = [d["year"] for d in shiller_cape]
+    values = [d["value"] for d in shiller_cape]
+    ax.axhspan(0, 20, alpha=0.15, color=NORMAL_ZONE)
+    ax.axhspan(20, 30, alpha=0.15, color=WARNING_ZONE)
+    ax.axhspan(30, 50, alpha=0.15, color=BUBBLE_ZONE)
+    ax.plot(years, values, color=GRAY_DARK, linewidth=0.8)
+    ax.axhline(y=17.39, color="#333", linestyle="--", linewidth=0.6)
+    ax.text(2024, 18.5, "mean: 17.4", fontsize=7, color=GRAY_MEDIUM)
+    ax.annotate(
+        f"{values[-1]:.1f}",
+        xy=(2026, values[-1]), fontsize=7, fontweight="bold",
+        color=BUBBLE_ZONE, xytext=(2023, values[-1] - 5),
+        arrowprops=dict(arrowstyle="->", color=BUBBLE_ZONE, lw=0.6),
+    )
+    ax.set_title("Shiller CAPE (1880–2026)", fontsize=11, fontweight="bold")
+    ax.set_ylabel("CAPE")
+    ax.set_ylim(0, 50)
+    ax.tick_params(labelsize=7)
+    ax.grid(True, alpha=0.2)
+
+    # Panel (0,1): Buffett Indicator
+    ax = axes[0, 1]
+    b_years = [d["year"] for d in buffett_indicator]
+    b_vals = [d["value"] for d in buffett_indicator]
+    ax.axhspan(0, 100, alpha=0.15, color=NORMAL_ZONE)
+    ax.axhspan(100, 200, alpha=0.15, color=WARNING_ZONE)
+    ax.axhspan(200, 300, alpha=0.15, color=BUBBLE_ZONE)
+    ax.plot(b_years, b_vals, color=GRAY_DARK, linewidth=0.8)
+    ax.axhline(y=200, color=BUBBLE_ZONE, linestyle="--", linewidth=1)
+    ax.text(2000, 205, "Danger: 200%", fontsize=7, color=BUBBLE_ZONE)
+    ax.annotate(
+        f"{b_vals[-1]:.0f}%",
+        xy=(2026, b_vals[-1]), fontsize=7, fontweight="bold",
+        color=BUBBLE_ZONE, xytext=(2020, b_vals[-1] + 10),
+        arrowprops=dict(arrowstyle="->", color=BUBBLE_ZONE, lw=0.6),
+    )
+    ax.set_title("Buffett Indicator (1975–2026)", fontsize=11, fontweight="bold")
+    ax.set_ylabel("Mkt Cap / GDP %")
+    ax.tick_params(labelsize=7)
+    ax.grid(True, alpha=0.2)
+
+    # Panel (0,2): S&P 500 P/E
+    ax = axes[0, 2]
+    pe_years = [d["year"] for d in sp500_pe]
+    pe_vals = [d["value"] for d in sp500_pe]
+    ax.plot(pe_years, pe_vals, color=GRAY_DARK, linewidth=0.8)
+    ax.axhline(y=17.9, color="#333", linestyle="--", linewidth=0.6)
+    ax.text(2020, 19, "mean: 17.9", fontsize=7, color=GRAY_MEDIUM)
+    ax.annotate(
+        f"{pe_vals[-1]:.1f}",
+        xy=(2026, pe_vals[-1]), fontsize=7, fontweight="bold",
+        color=WARNING_ZONE, xytext=(2023, pe_vals[-1] - 3),
+        arrowprops=dict(arrowstyle="->", color=WARNING_ZONE, lw=0.6),
+    )
+    ax.set_title("S&P 500 P/E (1950–2026)", fontsize=11, fontweight="bold")
+    ax.set_ylabel("P/E")
+    ax.set_ylim(0, 75)
+    ax.tick_params(labelsize=7)
+    ax.grid(True, alpha=0.2)
+
+    # ------------------------------------------------------------------
+    # ROW 2: AI Infrastructure Buildout
+    # ------------------------------------------------------------------
+
+    # Panel (1,0): Hyperscaler Capex (stacked area, 2020–2026)
+    ax = axes[1, 0]
+    company_colors = get_company_colors()
+    companies = ["Microsoft", "Alphabet", "Meta", "Amazon"]
+    years_annual = list(range(2020, 2027))
+
+    data = {c: [0.0] * 7 for c in companies}
+    for entry in hyperscaler_capex_annual:
+        idx = entry["year"] - 2020
+        if 0 <= idx < 7:
+            data[entry["company"]][idx] = entry["capex_billions"]
+
+    y_off = np.zeros(7)
+    for c in companies:
+        vals = np.array(data[c], dtype=float)
+        ax.fill_between(
+            years_annual, y_off, y_off + vals,
+            alpha=0.7, color=company_colors[c], label=c,
+        )
+        y_off += vals
+
+    ax.set_title("Hyperscaler Capex (2020–2026)", fontsize=11, fontweight="bold")
+    ax.set_ylabel("Capex $B")
+    ax.tick_params(labelsize=7)
+    ax.legend(loc="upper left", fontsize=6, framealpha=0.8)
+    ax.grid(True, alpha=0.2, axis="y")
+
+    # Panel (1,1): Tech Debt Spike
+    ax = axes[1, 1]
+    debt_years = [2020, 2021, 2022, 2023, 2024, 2025, 2026]
+    debt_vals = [25, 30, 28, 25, 30, 121, 125]
+    colors_debt = [GRAY_DARK] * 5 + [BUBBLE_ZONE, WARNING_ZONE]
+    bars = ax.bar(debt_years, debt_vals, color=colors_debt, width=0.5)
+    avg5 = np.mean(debt_vals[:5])
+    ax.axhline(y=avg5, color="#333", linestyle="--", linewidth=1)
+    ax.text(2022, avg5 + 3, f"pre-2025 avg: ${avg5:.0f}B",
+            fontsize=7, color=GRAY_MEDIUM)
+    ax.text(2025.5, 125 + 5, "4× spike!", fontsize=8,
+            fontweight="bold", color=BUBBLE_ZONE, ha="right")
+    ax.set_title("Tech Debt: 2025 4× Spike", fontsize=11, fontweight="bold")
+    ax.set_ylabel("Debt $B")
+    ax.set_ylim(0, 150)
+    ax.tick_params(labelsize=7)
+
+    # Panel (1,2): NVIDIA Data Center Revenue
+    ax = axes[1, 2]
+    dc_rev = [d.get("data_center_billions",
+              d.get("compute_billions", 0) + d.get("networking_billions", 0))
+              for d in nvidia_revenue]
+    quarters = list(range(len(dc_rev)))
+    ax.fill_between(quarters, dc_rev, alpha=0.25, color=REVENUE)
+    ax.plot(quarters, dc_rev, color=REVENUE, linewidth=1)
+
+    # Mark the inflection and latest
+    nvidia_quarters_labels = [d["fiscal_quarter"] for d in nvidia_revenue]
+    # Highlight 2026-Q4 (index 27)
+    latest_idx = len(dc_rev) - 2  # before FY2027-Q1
+    ax.plot(latest_idx, dc_rev[latest_idx], "o", color=REVENUE,
+            markersize=5)
+    ax.annotate(
+        f"${dc_rev[latest_idx]:.1f}B",
+        xy=(latest_idx, dc_rev[latest_idx]),
+        xytext=(latest_idx - 3, dc_rev[latest_idx] - 8),
+        fontsize=7, fontweight="bold", color=REVENUE,
+        arrowprops=dict(arrowstyle="->", color=REVENUE, lw=0.5),
+    )
+    ax.set_title("NVIDIA DC Revenue (Quarterly)", fontsize=11, fontweight="bold")
+    ax.set_ylabel("Revenue $B")
+    ax.tick_params(labelsize=7)
+    ax.set_xticks(range(0, len(quarters), 4))
+    ax.set_xticklabels([nvidia_quarters_labels[i].replace("FY", "")
+                        for i in range(0, len(quarters), 4)],
+                       rotation=45, ha="right")
+    ax.grid(True, alpha=0.2)
+
+    # ------------------------------------------------------------------
+    # ROW 3: Agent Revolution and Reality
+    # ------------------------------------------------------------------
+
+    # Panel (2,0): GPU Utilization Paradox
+    ax = axes[2, 0]
+    cats = ["AI Spend", "GPU Util.", "Target", "Human"]
+    vals = [100, 5, 65, 85]
+    colors_util = [AI_SPEND, BUBBLE_ZONE, NORMAL_ZONE, WARNING_ZONE]
+    bars = ax.barh(cats, vals, color=colors_util, height=0.5)
+    for bar, v in zip(bars, vals):
+        ax.text(v + 2, bar.get_y() + bar.get_height() / 2,
+                f"{v}%", va="center", fontsize=8, fontweight="bold",
+                color=BLACK)
+    ax.set_title("GPU Utilization Paradox", fontsize=11, fontweight="bold")
+    ax.set_xlim(0, 115)
+    ax.tick_params(labelsize=8)
+    ax.grid(True, alpha=0.2, axis="x")
+
+    # Panel (2,1): Developer AI Reality
+    ax = axes[2, 1]
+    dev_cats = ["Use AI tools", "Daily AI use", "AI code merged", "AI PR issues"]
+    dev_vals = [84, 51, 22, 70]  # 70 ≈ 1.7× more issues (scaled to %)
+    dev_colors = [AGENT_GROWTH, AGENT_GROWTH, NORMAL_ZONE, BUBBLE_ZONE]
+    bars = ax.barh(dev_cats, dev_vals, color=dev_colors, height=0.5)
+    for bar, v in zip(bars, dev_vals):
+        ax.text(v + 2, bar.get_y() + bar.get_height() / 2,
+                f"{v}%", va="center", fontsize=8, fontweight="bold",
+                color=BLACK)
+    ax.set_title("Developer AI Reality", fontsize=11, fontweight="bold")
+    ax.set_xlim(0, 100)
+    ax.tick_params(labelsize=8)
+    ax.grid(True, alpha=0.2, axis="x")
+
+    # Panel (2,2): Enterprise Agent Adoption
+    ax = axes[2, 2]
+    surveys = ["LangChain", "McKinsey", "PwC"]
+    labels = ["In production", "Scaling agents", "Measurable value"]
+    adoption = [
+        agent_survey_data["langchain_2025"]["production"],
+        agent_survey_data["mckinsey_2025"]["agentic_ai_scaling"],
+        agent_survey_data["pwc_2025"]["measurable_productivity_value"],
+    ]
+    bars = ax.barh(surveys, adoption, color=AGENT_GROWTH, height=0.5)
+    for bar, v, label in zip(bars, adoption, labels):
+        ax.text(v + 1, bar.get_y() + bar.get_height() / 2,
+                f"{v:.0f}%  ({label})", va="center", fontsize=7,
+                fontweight="bold", color=BLACK)
+    ax.set_title("Enterprise Agent Adoption", fontsize=11, fontweight="bold")
+    ax.set_xlim(0, 100)
+    ax.tick_params(labelsize=8)
+    ax.grid(True, alpha=0.2, axis="x")
+
+    # ------------------------------------------------------------------
+    # Row labels (vertical text on the left)
+    # ------------------------------------------------------------------
+    fig.text(0.02, 0.78, "MARKET BUBBLE EVIDENCE", fontsize=10,
+             fontweight="bold", color=GRAY_MEDIUM, rotation=90,
+             va="center")
+    fig.text(0.02, 0.50, "AI INFRASTRUCTURE BUILDOUT", fontsize=10,
+             fontweight="bold", color=GRAY_MEDIUM, rotation=90,
+             va="center")
+    fig.text(0.02, 0.22, "AGENT REVOLUTION & REALITY", fontsize=10,
+             fontweight="bold", color=GRAY_MEDIUM, rotation=90,
+             va="center")
+
+    # ------------------------------------------------------------------
+    # Overall title
+    # ------------------------------------------------------------------
+    fig.suptitle(
+        "The AI Bubble and the Fundamental Value of LLMs — June 2026",
+        fontsize=20, fontweight="bold", y=0.98,
+    )
+
+    fig.subplots_adjust(
+        hspace=0.35, wspace=0.25,
+        left=0.06, right=0.98,
+        top=0.95, bottom=0.04,
+    )
+
+    out_path = "output/combined/narrative_dashboard.png"
+    fig.savefig(
+        out_path, dpi=EXPORT_DPI,
+        facecolor=fig.get_facecolor(), edgecolor="none",
+    )
+    plt.close(fig)
+    return out_path
+
+
+def main():
+    path = plot_narrative_dashboard()
+    print(f"Dashboard saved: {path}")
+
+
+if __name__ == "__main__":
+    main()