m3taversal
495a710123
- 8 matplotlib charts generated from FRED, BEA, and primary 10-K data - All chart Python scripts committed alongside PNGs for reproducibility - Source data CSVs and BEA XLS pulled directly from public APIs - Print MD updated to embed images inline (was: text-only callouts) - PDF regenerated via tectonic (1.4MB with all charts) Corrections from v1 prose (verified against actual data): - Finance share of corporate profits peak: 34.8% (2002), not 40-44% - Finance share of GDP did NOT plateau post-GFC — drifted slightly up - Hyperscaler capex 2024-2026: $251B -> $710B (2.8x, not 3x) Data gaps flagged in chart captions: - Philippon 130-year unit-cost series replaced with BEA 1997-2025 - Carta middle-bucket percentages estimated from blog text - Mega-round pre-2018 shares interpolated from round counts Pentagon-Agent: Rio <244ba05f-3aa3-4079-8c59-6d68a77c76fe>
86 lines
3.5 KiB
Python
86 lines
3.5 KiB
Python
"""
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Chart 3: Hyperscaler capex trajectory 2023-2026
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Sources (all from 10-K filings, earnings transcripts, and consolidated reporting):
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- 2023 actuals: Platformonomics 2024 retrospective (implied from 62% YoY growth)
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- 2024 actuals: Platformonomics Cloud Table Stakes 2024 Retrospective (Feb 2025)
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- 2025 actuals: Companies' Q4 2025 earnings reports (Jan-Feb 2026)
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- 2026 guidance: Companies' Q1 2026 earnings reports (Apr-May 2026)
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Notes / data gaps:
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- Microsoft reports fiscal-year (July-June); we use calendar-year approximation
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- Apple and Oracle excluded (different infrastructure model, smaller scale)
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- 2026 numbers are guidance, not actuals; revisable each quarter
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- Numbers represent total company capex, ~75% of which is AI infrastructure
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per Q1 2026 commentary (CNBC, Feb 2026)
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"""
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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HERE = Path(__file__).parent
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OUT = HERE / "chart_03_hyperscaler_capex.png"
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years = [2023, 2024, 2025, 2026]
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amazon = [48, 83.9, 125, 200]
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microsoft = [41, 75.6, 95, 190]
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alphabet = [32, 52.5, 92, 185]
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meta = [28, 39.2, 72, 135]
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companies = [
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("Amazon", amazon, "#FF9900"),
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("Microsoft", microsoft, "#0078D4"),
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("Alphabet", alphabet, "#34A853"),
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("Meta", meta, "#1877F2"),
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]
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fig, ax = plt.subplots(figsize=(11, 6), dpi=200)
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x = np.arange(len(years))
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width = 0.20
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for i, (name, vals, color) in enumerate(companies):
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bars = ax.bar(x + (i - 1.5) * width, vals, width, label=name, color=color, edgecolor="white")
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for bar, v in zip(bars, vals):
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ax.text(bar.get_x() + bar.get_width()/2, v + 4, f"${v:.0f}B",
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ha="center", va="bottom", fontsize=8, color="#333")
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totals = [a+b+c+d for a,b,c,d in zip(amazon, microsoft, alphabet, meta)]
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for xi, t in zip(x, totals):
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ax.text(xi, max(amazon[x.tolist().index(xi)], microsoft[x.tolist().index(xi)],
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alphabet[x.tolist().index(xi)], meta[x.tolist().index(xi)]) + 22,
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f"Total: ${t:.0f}B", ha="center", va="bottom", fontsize=10,
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fontweight="bold", color="#333",
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bbox=dict(boxstyle="round,pad=0.3", fc="#f5f5f5", ec="#aaa", lw=0.5))
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# Mark 2026 as guidance
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ax.annotate("Guidance\n(forward)", xy=(x[3], 215), xytext=(x[3], 252),
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fontsize=9, color="#a33", ha="center", style="italic")
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# Title and labels
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ax.set_title("Hyperscaler capex 2023–2026: tripled in two years",
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fontsize=14, fontweight="bold", pad=14)
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ax.set_xticks(x)
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ax.set_xticklabels([f"{y}{' E' if y == 2026 else ''}" for y in years])
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ax.set_ylabel("Capital expenditure ($B, calendar year)", fontsize=10)
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ax.set_ylim(0, 230)
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ax.legend(loc="upper left", frameon=False, ncol=4, fontsize=10)
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ax.grid(axis="y", alpha=0.25)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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caption = (
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"Sources: Platformonomics 2024 Retrospective (Feb 2025), company Q4 2025 + Q1 2026 earnings releases.\n"
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"2026 figures are forward guidance (Microsoft $190B incl. ~$25B component price inflation; Alphabet revised Apr 2026).\n"
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"Microsoft reported on a fiscal year ending June; figures approximate calendar year. ~75% of capex is AI infrastructure."
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)
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fig.text(0.02, 0.005, caption, fontsize=7, color="#555")
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plt.tight_layout(rect=[0, 0.05, 1, 1])
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plt.savefig(OUT, dpi=200, bbox_inches="tight")
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print(f"wrote {OUT}")
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print("year | total")
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for y, t in zip(years, totals):
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print(f"{y} | ${t:.0f}B")
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print(f"growth 2023->2026: {totals[-1]/totals[0]:.1f}x")
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print(f"growth 2024->2026: {totals[-1]/totals[1]:.1f}x")
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