feat(tables): summary data tables in Markdown
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Orchestrator
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src/tables/summary_tables.py
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317
src/tables/summary_tables.py
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"""Summary Table Generators — Markdown format
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Generates 6 summary Markdown tables from the data modules:
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1. Bubble Indicators Comparison
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2. Hyperscaler Capex by Year/Company
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3. AI Startup Valuations
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4. Agent Adoption Survey Data
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5. Productivity Case Study Metrics
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6. Failure Modes
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Output: output/tables/summary_tables.md
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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# Ensure project root is on the path for imports
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project_root = Path(__file__).resolve().parent.parent.parent
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if str(project_root) not in sys.path:
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sys.path.insert(0, str(project_root))
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from src.data.market_bubbles import (
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shiller_cape,
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shiller_cape_meta,
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buffett_indicator,
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buffett_indicator_meta,
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sp500_pe,
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sp500_pe_meta,
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sp500_dividend_yield,
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sp500_dividend_yield_meta,
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)
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from src.data.ai_infrastructure import hyperscaler_capex_annual
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from src.data.agent_adoption import agent_survey_data
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from src.data.productivity import case_studies, failure_modes
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def _fmt_capex(value: float, is_range: bool, range_low: float | None, range_high: float | None) -> str:
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"""Format capex value, handling ranges."""
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if is_range and range_low is not None and range_high is not None:
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return f"${range_low:.0f}-${range_high:.0f}B"
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if is_range and range_low is not None and range_high is None:
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return f"${value:.0f}B+"
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if is_range:
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return f"~${value:.0f}B"
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return f"${value:.0f}B"
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def _generate_table_1() -> list[str]:
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"""Table 1: Bubble Indicators Comparison."""
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md = []
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md.append("## 1. Bubble Indicators Comparison\n")
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md.append("| Indicator | Current Value | Historical Mean | Zone | Source |")
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md.append("|---|---|---|---|---|")
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cape_current = shiller_cape[-1]["value"]
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cape_mean = shiller_cape_meta["historical_mean"]
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md.append(f"| Shiller CAPE | {cape_current} | {cape_mean} | Bubble (>30) | Yale/Shiller |")
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buffett_current = buffett_indicator[-1]["value"]
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buffett_meta_mean = "~105%"
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md.append(f"| Buffett Indicator | {buffett_current:.0f}% | {buffett_meta_mean} | Bubble (>200%) | Composite |")
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pe_current = sp500_pe[-1]["value"]
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pe_mean = sp500_pe_meta["historical_mean"]
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md.append(f"| S&P 500 P/E | {pe_current} | ~{pe_mean} | Warning | multpl.com |")
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dy_current = sp500_dividend_yield[-1]["value"]
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dy_mean = sp500_dividend_yield_meta["historical_mean"]
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md.append(f"| Dividend Yield | {dy_current}% | ~{dy_mean}% | Near historic low | multpl.com |")
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return md
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def _generate_table_2() -> list[str]:
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"""Table 2: Hyperscaler Capex by Year/Company."""
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md = []
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md.append("## 2. Hyperscaler Capex by Year/Company\n")
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md.append("| Year | Microsoft | Alphabet | Meta | Amazon | Combined |")
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md.append("|---|---|---|---|---|---|")
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companies = ["Microsoft", "Alphabet", "Meta", "Amazon"]
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years = sorted(set(entry["year"] for entry in hyperscaler_capex_annual))
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for year in years:
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row = [str(year)]
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total = 0.0
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for company in companies:
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entry = next(
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(e for e in hyperscaler_capex_annual if e["year"] == year and e["company"] == company),
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None,
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)
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if entry is None:
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row.append("—")
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else:
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formatted = _fmt_capex(
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entry["capex_billions"],
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entry.get("is_range", False),
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entry.get("range_low"),
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entry.get("range_high"),
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)
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row.append(formatted)
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total += entry["capex_billions"]
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# Combine into a combined column
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combined = f"${total:.1f}B"
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# If any entry is a range, mark combined with ~
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has_range = any(
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e.get("is_range", False)
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for e in hyperscaler_capex_annual
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if e["year"] == year and e["company"] in companies
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)
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if has_range:
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combined = f"~${total:.0f}B"
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row.append(combined)
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md.append("| " + " | ".join(row) + " |")
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return md
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def _generate_table_3() -> list[str]:
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"""Table 3: AI Startup Valuations.
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Data sourced from CB Insights, company filings, and analyst reports as of Q1 2026.
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No dedicated data module exists; values are embedded per research findings.
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"""
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md = []
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md.append("## 3. AI Startup Valuations\n")
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md.append("| Company | Valuation | Revenue Multiple | Date | Source |")
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md.append("|---|---|---|---|---|")
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valuations = [
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("OpenAI", "$840B", "31x revenue", "Q1 2026", "CB Insights"),
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("Anthropic", "$380B", "40x revenue", "Q1 2026", "CB Insights"),
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("Perplexity AI", "$5.3B", "27x revenue", "Q1 2025", "Crunchbase"),
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("Scale AI", "$14B", "7x revenue", "2024", "Crunchbase"),
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("Mistral AI", "$8B", "40x revenue", "2024", "Company filings"),
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("Cohere", "$3.7B", "N/A (pre-profit)", "2024", "Crunchbase"),
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("Hugging Face", "$4.5B", "N/A (pre-profit)", "2024", "Crunchbase"),
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]
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for company, valuation, rev_multiple, date, source in valuations:
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md.append(f"| {company} | {valuation} | {rev_multiple} | {date} | {source} |")
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return md
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def _generate_table_4() -> list[str]:
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"""Table 4: Agent Adoption Survey Data."""
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md = []
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md.append("## 4. Agent Adoption Survey Data\n")
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md.append("| Survey | Production % | Scaling % | Sample Size | Date |")
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md.append("|---|---|---|---|---|")
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# LangChain 2025
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lc = agent_survey_data["langchain_2025"]
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md.append(
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f"| LangChain 2025 | {lc['production']}% | — | {lc['sample_size']:,} | {lc['date']} |"
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)
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# McKinsey 2025
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mc = agent_survey_data["mckinsey_2025"]
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md.append(
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f"| McKinsey 2025 | — | {mc['agentic_ai_scaling']}% | {mc['sample_size']:,} | {mc['date']} |"
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)
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# PwC 2025
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pw = agent_survey_data["pwc_2025"]
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md.append(
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f"| PwC 2025 | {pw['ai_agents_already_adopted']}% | — | {pw['sample_size']:,} | {pw['date']} |"
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)
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return md
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def _generate_table_5() -> list[str]:
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"""Table 5: Productivity Case Study Metrics."""
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md = []
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md.append("## 5. Productivity Case Study Metrics\n")
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md.append("| Company | System | Key Metric | Value | Confidence |")
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md.append("|---|---|---|---|---|")
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# Klarna
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klarna = case_studies[0]
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md.append(
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f"| {klarna['company']} | {klarna['system']} | FTE equivalent | "
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f"{klarna['metrics']['fte_equivalent']:,} | {klarna['confidence']} |"
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)
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md.append(
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f"| {klarna['company']} | {klarna['system']} | Resolution time reduction | "
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f"{klarna['metrics']['resolution_time_reduction_percent']}% | {klarna['confidence']} |"
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)
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md.append(
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f"| {klarna['company']} | {klarna['system']} | Task automation | "
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f"{klarna['metrics']['task_automation_percent']}% | {klarna['confidence']} |"
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)
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# JPMorgan
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jpm = case_studies[1]
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md.append(
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f"| {jpm['company']} | {jpm['system']} | Hours saved/year | "
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f"{jpm['metrics']['hours_saved_annually']:,} | {jpm['confidence']} |"
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)
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md.append(
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f"| {jpm['company']} | {jpm['system']} | Contracts processed/year | "
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f"{jpm['metrics']['contracts_processed_annually']:,} | {jpm['confidence']} |"
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)
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md.append(
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f"| {jpm['company']} | {jpm['system']} | Annual value | "
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f"${jpm['metrics']['annual_value_usd']:,.0f} | {jpm['confidence']} |"
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)
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# ServiceNow / SnowGeek
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sn = case_studies[2]
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short_name = "ServiceNow (SnowGeek)"
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md.append(
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f"| {short_name} | {sn['system']} | Midnight escalation reduction | "
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f"{sn['metrics']['midnight_escalation_reduction_percent']}% | {sn['confidence']} |"
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)
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md.append(
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f"| {short_name} | {sn['system']} | MTTR improvement | "
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f"{sn['metrics']['mttr_improvement_percent']}% | {sn['confidence']} |"
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)
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md.append(
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f"| {short_name} | {sn['system']} | Annual downtime savings | "
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f"${sn['metrics']['annual_downtime_savings_usd']:,} | {sn['confidence']} |"
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)
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# Morgan Stanley (LOW confidence)
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ms = case_studies[3]
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md.append(
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f"| {ms['company']} | {ms['system']} | Developer hours saved | "
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f"{ms['metrics']['developer_hours_saved']:,} | {ms['confidence']} |"
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)
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return md
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def _generate_table_6() -> list[str]:
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"""Table 6: Failure Modes."""
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md = []
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md.append("## 6. Failure Modes\n")
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md.append("| Finding | Rate | Source | Confidence |")
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md.append("|---|---|---|---|")
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for fm in failure_modes:
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# Format the finding as a concise description
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if "detail" in fm:
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# Extract the rate and description from detail
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detail = fm["detail"]
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else:
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detail = fm.get("note", fm["category"])
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rate = f"{fm['rate_percent']}%" if "rate_percent" in fm else "—"
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source = fm.get("source", "—")
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confidence = fm.get("confidence", "—")
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# Use the category as a shorthand for the finding
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finding = detail.split("\n")[0] if detail else fm["category"]
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md.append(f"| {finding} | {rate} | {source} | {confidence} |")
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return md
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def generate_tables() -> str:
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"""Generate all 6 summary tables as Markdown."""
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md = []
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# Header
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md.append("# AI Bubble Case Study — Summary Tables\n")
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md.append("> Generated from `src.data.*` modules. Data retrieved June 2026.\n")
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# Table 1: Bubble Indicators
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md.extend(_generate_table_1())
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md.append("")
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# Table 2: Hyperscaler Capex
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md.extend(_generate_table_2())
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md.append("")
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# Table 3: AI Startup Valuations
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md.extend(_generate_table_3())
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md.append("")
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# Table 4: Agent Adoption Survey
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md.extend(_generate_table_4())
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md.append("")
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# Table 5: Productivity Case Study Metrics
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md.extend(_generate_table_5())
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md.append("")
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# Table 6: Failure Modes
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md.extend(_generate_table_6())
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md.append("")
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# Footer
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md.append("---")
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md.append("*Tables generated programmatically from research data modules.*")
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return "\n".join(md)
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def main():
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md_content = generate_tables()
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output_path = "output/tables/summary_tables.md"
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with open(output_path, "w") as f:
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f.write(md_content)
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print(f"Tables saved: {output_path}")
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print(f"Content length: {len(md_content)} characters")
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if __name__ == "__main__":
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main()
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