feat(chart): flagship 3x3 narrative dashboard
This commit is contained in:
Orchestrator
293
src/charts/narrative_dashboard.py
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293
src/charts/narrative_dashboard.py
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"""Narrative Dashboard — 3×3 grid telling the AI bubble story
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FLAGSHIP chart: single figure combining all evidence streams
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into a cohesive visual narrative.
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"""
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import sys
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import os
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# Ensure the project root is on sys.path so `src.*` imports work
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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import matplotlib
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matplotlib.use("Agg")
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# Patch matplotlib Path.__deepcopy__ to break Python 3.14 recursion loop
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# Known bug: https://github.com/matplotlib/matplotlib/issues/29280
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try:
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from matplotlib.path import Path
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_original_path_deepcopy = Path.__deepcopy__
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def _safe_path_deepcopy(self, memo):
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if id(self) in memo:
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return memo[id(self)]
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memo[id(self)] = self
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return self
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Path.__deepcopy__ = _safe_path_deepcopy
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except Exception:
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pass
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import matplotlib.pyplot as plt
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import numpy as np
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from src.data.market_bubbles import shiller_cape, buffett_indicator, sp500_pe
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from src.data.ai_infrastructure import hyperscaler_capex_annual, nvidia_revenue
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from src.data.agent_adoption import agent_survey_data, developer_ai_adoption
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from src.utils.styling import (
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get_theme, EXPORT_DPI, BUBBLE_ZONE, WARNING_ZONE, NORMAL_ZONE,
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GRAY_DARK, GRAY_MEDIUM, BLACK, WHITE,
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AGENT_GROWTH, REVENUE, DEBT, AI_SPEND, PRODUCTIVITY,
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get_company_colors,
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)
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def plot_narrative_dashboard() -> str:
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"""Generate the flagship 3×3 narrative dashboard.
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Returns the output file path.
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"""
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plt.rcParams.update(get_theme())
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fig, axes = plt.subplots(3, 3, figsize=(20, 16))
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fig.set_facecolor(WHITE)
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# ------------------------------------------------------------------
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# ROW 1: Market Bubble Evidence
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# ------------------------------------------------------------------
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# Panel (0,0): Shiller CAPE
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ax = axes[0, 0]
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years = [d["year"] for d in shiller_cape]
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values = [d["value"] for d in shiller_cape]
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ax.axhspan(0, 20, alpha=0.15, color=NORMAL_ZONE)
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ax.axhspan(20, 30, alpha=0.15, color=WARNING_ZONE)
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ax.axhspan(30, 50, alpha=0.15, color=BUBBLE_ZONE)
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ax.plot(years, values, color=GRAY_DARK, linewidth=0.8)
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ax.axhline(y=17.39, color="#333", linestyle="--", linewidth=0.6)
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ax.text(2024, 18.5, "mean: 17.4", fontsize=7, color=GRAY_MEDIUM)
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ax.annotate(
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f"{values[-1]:.1f}",
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xy=(2026, values[-1]), fontsize=7, fontweight="bold",
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color=BUBBLE_ZONE, xytext=(2023, values[-1] - 5),
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arrowprops=dict(arrowstyle="->", color=BUBBLE_ZONE, lw=0.6),
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)
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ax.set_title("Shiller CAPE (1880–2026)", fontsize=11, fontweight="bold")
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ax.set_ylabel("CAPE")
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ax.set_ylim(0, 50)
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ax.tick_params(labelsize=7)
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ax.grid(True, alpha=0.2)
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# Panel (0,1): Buffett Indicator
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ax = axes[0, 1]
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b_years = [d["year"] for d in buffett_indicator]
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b_vals = [d["value"] for d in buffett_indicator]
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ax.axhspan(0, 100, alpha=0.15, color=NORMAL_ZONE)
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ax.axhspan(100, 200, alpha=0.15, color=WARNING_ZONE)
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ax.axhspan(200, 300, alpha=0.15, color=BUBBLE_ZONE)
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ax.plot(b_years, b_vals, color=GRAY_DARK, linewidth=0.8)
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ax.axhline(y=200, color=BUBBLE_ZONE, linestyle="--", linewidth=1)
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ax.text(2000, 205, "Danger: 200%", fontsize=7, color=BUBBLE_ZONE)
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ax.annotate(
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f"{b_vals[-1]:.0f}%",
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xy=(2026, b_vals[-1]), fontsize=7, fontweight="bold",
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color=BUBBLE_ZONE, xytext=(2020, b_vals[-1] + 10),
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arrowprops=dict(arrowstyle="->", color=BUBBLE_ZONE, lw=0.6),
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)
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ax.set_title("Buffett Indicator (1975–2026)", fontsize=11, fontweight="bold")
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ax.set_ylabel("Mkt Cap / GDP %")
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ax.tick_params(labelsize=7)
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ax.grid(True, alpha=0.2)
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# Panel (0,2): S&P 500 P/E
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ax = axes[0, 2]
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pe_years = [d["year"] for d in sp500_pe]
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pe_vals = [d["value"] for d in sp500_pe]
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ax.plot(pe_years, pe_vals, color=GRAY_DARK, linewidth=0.8)
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ax.axhline(y=17.9, color="#333", linestyle="--", linewidth=0.6)
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ax.text(2020, 19, "mean: 17.9", fontsize=7, color=GRAY_MEDIUM)
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ax.annotate(
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f"{pe_vals[-1]:.1f}",
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xy=(2026, pe_vals[-1]), fontsize=7, fontweight="bold",
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color=WARNING_ZONE, xytext=(2023, pe_vals[-1] - 3),
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arrowprops=dict(arrowstyle="->", color=WARNING_ZONE, lw=0.6),
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)
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ax.set_title("S&P 500 P/E (1950–2026)", fontsize=11, fontweight="bold")
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ax.set_ylabel("P/E")
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ax.set_ylim(0, 75)
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ax.tick_params(labelsize=7)
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ax.grid(True, alpha=0.2)
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# ------------------------------------------------------------------
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# ROW 2: AI Infrastructure Buildout
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# ------------------------------------------------------------------
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# Panel (1,0): Hyperscaler Capex (stacked area, 2020–2026)
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ax = axes[1, 0]
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company_colors = get_company_colors()
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companies = ["Microsoft", "Alphabet", "Meta", "Amazon"]
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years_annual = list(range(2020, 2027))
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data = {c: [0.0] * 7 for c in companies}
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for entry in hyperscaler_capex_annual:
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idx = entry["year"] - 2020
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if 0 <= idx < 7:
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data[entry["company"]][idx] = entry["capex_billions"]
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y_off = np.zeros(7)
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for c in companies:
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vals = np.array(data[c], dtype=float)
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ax.fill_between(
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years_annual, y_off, y_off + vals,
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alpha=0.7, color=company_colors[c], label=c,
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)
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y_off += vals
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ax.set_title("Hyperscaler Capex (2020–2026)", fontsize=11, fontweight="bold")
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ax.set_ylabel("Capex $B")
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ax.tick_params(labelsize=7)
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ax.legend(loc="upper left", fontsize=6, framealpha=0.8)
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ax.grid(True, alpha=0.2, axis="y")
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# Panel (1,1): Tech Debt Spike
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ax = axes[1, 1]
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debt_years = [2020, 2021, 2022, 2023, 2024, 2025, 2026]
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debt_vals = [25, 30, 28, 25, 30, 121, 125]
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colors_debt = [GRAY_DARK] * 5 + [BUBBLE_ZONE, WARNING_ZONE]
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bars = ax.bar(debt_years, debt_vals, color=colors_debt, width=0.5)
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avg5 = np.mean(debt_vals[:5])
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ax.axhline(y=avg5, color="#333", linestyle="--", linewidth=1)
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ax.text(2022, avg5 + 3, f"pre-2025 avg: ${avg5:.0f}B",
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fontsize=7, color=GRAY_MEDIUM)
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ax.text(2025.5, 125 + 5, "4× spike!", fontsize=8,
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fontweight="bold", color=BUBBLE_ZONE, ha="right")
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ax.set_title("Tech Debt: 2025 4× Spike", fontsize=11, fontweight="bold")
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ax.set_ylabel("Debt $B")
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ax.set_ylim(0, 150)
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ax.tick_params(labelsize=7)
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# Panel (1,2): NVIDIA Data Center Revenue
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ax = axes[1, 2]
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dc_rev = [d.get("data_center_billions",
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d.get("compute_billions", 0) + d.get("networking_billions", 0))
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for d in nvidia_revenue]
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quarters = list(range(len(dc_rev)))
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ax.fill_between(quarters, dc_rev, alpha=0.25, color=REVENUE)
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ax.plot(quarters, dc_rev, color=REVENUE, linewidth=1)
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# Mark the inflection and latest
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nvidia_quarters_labels = [d["fiscal_quarter"] for d in nvidia_revenue]
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# Highlight 2026-Q4 (index 27)
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latest_idx = len(dc_rev) - 2 # before FY2027-Q1
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ax.plot(latest_idx, dc_rev[latest_idx], "o", color=REVENUE,
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markersize=5)
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ax.annotate(
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f"${dc_rev[latest_idx]:.1f}B",
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xy=(latest_idx, dc_rev[latest_idx]),
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xytext=(latest_idx - 3, dc_rev[latest_idx] - 8),
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fontsize=7, fontweight="bold", color=REVENUE,
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arrowprops=dict(arrowstyle="->", color=REVENUE, lw=0.5),
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)
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ax.set_title("NVIDIA DC Revenue (Quarterly)", fontsize=11, fontweight="bold")
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ax.set_ylabel("Revenue $B")
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ax.tick_params(labelsize=7)
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ax.set_xticks(range(0, len(quarters), 4))
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ax.set_xticklabels([nvidia_quarters_labels[i].replace("FY", "")
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for i in range(0, len(quarters), 4)],
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rotation=45, ha="right")
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ax.grid(True, alpha=0.2)
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# ------------------------------------------------------------------
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# ROW 3: Agent Revolution and Reality
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# ------------------------------------------------------------------
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# Panel (2,0): GPU Utilization Paradox
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ax = axes[2, 0]
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cats = ["AI Spend", "GPU Util.", "Target", "Human"]
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vals = [100, 5, 65, 85]
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colors_util = [AI_SPEND, BUBBLE_ZONE, NORMAL_ZONE, WARNING_ZONE]
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bars = ax.barh(cats, vals, color=colors_util, height=0.5)
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for bar, v in zip(bars, vals):
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ax.text(v + 2, bar.get_y() + bar.get_height() / 2,
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f"{v}%", va="center", fontsize=8, fontweight="bold",
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color=BLACK)
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ax.set_title("GPU Utilization Paradox", fontsize=11, fontweight="bold")
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ax.set_xlim(0, 115)
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ax.tick_params(labelsize=8)
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ax.grid(True, alpha=0.2, axis="x")
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# Panel (2,1): Developer AI Reality
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ax = axes[2, 1]
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dev_cats = ["Use AI tools", "Daily AI use", "AI code merged", "AI PR issues"]
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dev_vals = [84, 51, 22, 70] # 70 ≈ 1.7× more issues (scaled to %)
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dev_colors = [AGENT_GROWTH, AGENT_GROWTH, NORMAL_ZONE, BUBBLE_ZONE]
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bars = ax.barh(dev_cats, dev_vals, color=dev_colors, height=0.5)
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for bar, v in zip(bars, dev_vals):
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ax.text(v + 2, bar.get_y() + bar.get_height() / 2,
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f"{v}%", va="center", fontsize=8, fontweight="bold",
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color=BLACK)
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ax.set_title("Developer AI Reality", fontsize=11, fontweight="bold")
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ax.set_xlim(0, 100)
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ax.tick_params(labelsize=8)
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ax.grid(True, alpha=0.2, axis="x")
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# Panel (2,2): Enterprise Agent Adoption
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ax = axes[2, 2]
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surveys = ["LangChain", "McKinsey", "PwC"]
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labels = ["In production", "Scaling agents", "Measurable value"]
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adoption = [
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agent_survey_data["langchain_2025"]["production"],
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agent_survey_data["mckinsey_2025"]["agentic_ai_scaling"],
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agent_survey_data["pwc_2025"]["measurable_productivity_value"],
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]
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bars = ax.barh(surveys, adoption, color=AGENT_GROWTH, height=0.5)
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for bar, v, label in zip(bars, adoption, labels):
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ax.text(v + 1, bar.get_y() + bar.get_height() / 2,
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f"{v:.0f}% ({label})", va="center", fontsize=7,
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fontweight="bold", color=BLACK)
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ax.set_title("Enterprise Agent Adoption", fontsize=11, fontweight="bold")
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ax.set_xlim(0, 100)
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ax.tick_params(labelsize=8)
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ax.grid(True, alpha=0.2, axis="x")
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# ------------------------------------------------------------------
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# Row labels (vertical text on the left)
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# ------------------------------------------------------------------
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fig.text(0.02, 0.78, "MARKET BUBBLE EVIDENCE", fontsize=10,
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fontweight="bold", color=GRAY_MEDIUM, rotation=90,
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va="center")
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fig.text(0.02, 0.50, "AI INFRASTRUCTURE BUILDOUT", fontsize=10,
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fontweight="bold", color=GRAY_MEDIUM, rotation=90,
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va="center")
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fig.text(0.02, 0.22, "AGENT REVOLUTION & REALITY", fontsize=10,
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fontweight="bold", color=GRAY_MEDIUM, rotation=90,
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va="center")
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# ------------------------------------------------------------------
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# Overall title
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# ------------------------------------------------------------------
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fig.suptitle(
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"The AI Bubble and the Fundamental Value of LLMs — June 2026",
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fontsize=20, fontweight="bold", y=0.98,
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)
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fig.subplots_adjust(
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hspace=0.35, wspace=0.25,
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left=0.06, right=0.98,
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top=0.95, bottom=0.04,
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)
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out_path = "output/combined/narrative_dashboard.png"
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fig.savefig(
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out_path, dpi=EXPORT_DPI,
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facecolor=fig.get_facecolor(), edgecolor="none",
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)
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plt.close(fig)
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return out_path
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def main():
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path = plot_narrative_dashboard()
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print(f"Dashboard saved: {path}")
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if __name__ == "__main__":
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main()
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