From 64ad357dce599b77b4fd1328018f8faf9e0f7a48 Mon Sep 17 00:00:00 2001 From: Daniel Miessler Date: Wed, 10 Dec 2025 14:26:55 -0800 Subject: [PATCH] feat: Add Alternative Estimates section to schema and summary --- Data/DATASET-TEMPLATE.md | 27 ++++++++++++------- .../SUMMARY.md | 24 +++++++++++++++++ 2 files changed, 42 insertions(+), 9 deletions(-) diff --git a/Data/DATASET-TEMPLATE.md b/Data/DATASET-TEMPLATE.md index 1786c7d..55ac01a 100644 --- a/Data/DATASET-TEMPLATE.md +++ b/Data/DATASET-TEMPLATE.md @@ -78,17 +78,25 @@ Use this template for all new Substrate datasets. The key principle: **put the a --- -## Commonly Confused Metrics +## Alternative Estimates & Why We Differ -[Optional section - use when the metric is often confused with similar-sounding metrics] +[Recommended section - use when other estimates exist that might seem to contradict yours] -| Metric | Value | What It Actually Measures | Source | -|--------|-------|---------------------------|--------| -| **This dataset** | [Value] | [Description] | This research | -| **Similar metric 1** | [Value] | [Description] | [Source] | -| **Similar metric 2** | [Value] | [Description] | [Source] | +| Estimate | Source | What It Actually Measures | Why It Differs | +|----------|--------|--------------------------|----------------| +| **[Alternative 1]** | [Source] | [What it measures] | [Why different from ours] | +| **[Alternative 2]** | [Source] | [What it measures] | [Why different from ours] | +| **[Our estimate]** | This research | [What we measure] | [Our approach] | -**Do not compare these - they measure different things.** +### Why Our Approach + +[2-4 bullet points explaining why you chose this measurement approach over alternatives: +- What makes it more appropriate for the question being answered +- Why it's more directly measurable or verifiable +- What constraints or sanity checks it passes +- Why apparent contradictions aren't actually contradictions] + +**Key insight:** [One sentence explaining that different estimates often measure different things, not that one is "wrong"] --- @@ -117,7 +125,8 @@ Use this template for all new Substrate datasets. The key principle: **put the a 4. **Quick Context** - 2-3 sentences max 5. **Methodology Summary** - How was this derived 6. **Sources** - Where did data come from -7. **Changelog** - Track all revisions +7. **Alternative Estimates & Why We Differ** - Recommended when other estimates exist +8. **Changelog** - Track all revisions ### Mandatory Fields in BEST ESTIMATE Table diff --git a/Data/Knowledge-Worker-Global-Salaries/SUMMARY.md b/Data/Knowledge-Worker-Global-Salaries/SUMMARY.md index 99d87c6..8f89004 100644 --- a/Data/Knowledge-Worker-Global-Salaries/SUMMARY.md +++ b/Data/Knowledge-Worker-Global-Salaries/SUMMARY.md @@ -95,6 +95,30 @@ The wide global range reflects genuine uncertainty in international data, not he --- +## Alternative Estimates & Why We Differ + +Various estimates for knowledge work value exist in the literature, but they often measure different things: + +| Estimate | Source | What It Actually Measures | Why It Differs | +|----------|--------|--------------------------|----------------| +| **$5-7 trillion** | McKinsey Global Institute | Economic value of *automatable* knowledge tasks | Measures AI productivity potential, not compensation | +| **$2-3 trillion** | Various tech industry | Professional services market revenue | Revenue ≠ compensation; excludes in-house knowledge workers | +| **$70+ trillion** | Some extrapolations | Knowledge worker share of all economic output | Confuses GDP contribution with compensation; exceeds labor share ceiling | +| **$35-50 trillion** | This research | Actual wages + benefits paid to knowledge workers | Direct compensation measurement | + +### Why Our Approach + +We chose to measure **actual compensation paid** rather than productivity value or market revenue because: + +1. **It's directly measurable** - BLS, ILO, and OECD track wages and benefits; productivity value requires modeling assumptions +2. **It's the right denominator for AI impact** - If you want to know what's at stake in the AI transition, you need to know what we actually pay people today +3. **It passes the math check** - Any estimate must fit within total global labor compensation (~$58T); productivity-value estimates often don't face this constraint +4. **It's definition-transparent** - We show exactly which occupational codes we include at each confidence level + +The key insight: estimates that seem wildly different often just measure different things. A $5T automation-value estimate and a $40T compensation estimate can both be correct—they're answering different questions. + +--- + ## Sources **Primary (High Weight):**