What is this? AIStackHub pulled live data from government databases (BLS, SEC EDGAR), open-source repositories (GitHub), industry surveys (McKinsey Q1 2026, Stanford HAI 2026), and financial disclosures to create research that combines sources no existing report cross-references. Each theme answers: what companies say they're doing, what they're actually spending, who they're hiring, and where the gaps are. Data pulled May 2026.
5 Research Themes
Each page includes headline stats, data tables, methodology, and full source citations.
Theme 1 · Adoption Gap
AI Adoption Reality vs. Hype
73%Fortune 500 mention AI in 10-K filings (SEC EDGAR, 2025)
What companies report in earnings vs. what BLS hiring data and actual capex reveal — the gap is wider than any survey admits.
Sources: SEC EDGAR, McKinsey Q1 2026, BLS JOLTS, LinkedIn Workforce Report
Theme 2 · ROI Analysis
AI Investment Efficiency Index
$4.2BAvg Big Tech AI capex per company, Q1 2026 earnings (MSFT, GOOG, AMZN, META)
Patent filings per $1B AI spend, job creation vs. displacement ratios, and which industries get the most output per AI dollar.
Sources: USPTO, SEC 10-K/10-Q, BLS, Gartner $2.52T forecast
Theme 3 · Developer Signals
The AI Stack Shift
+340%LangChain GitHub stars growth, 2024→2026 (GitHub public data)
GitHub framework adoption trends vs. enterprise software spending — are enterprises buying what developers are actually using?
Sources: GitHub Stars/Forks API, Stack Overflow Dev Survey 2025, IDC Enterprise AI Spend
Theme 4 · Readiness Gap
AI Readiness Gap by Industry
61%of healthcare orgs report "infrastructure ready" but lack AI-fluent talent (NIST + BLS cross)
NIST AI RMF adoption rates vs. actual implementation vs. talent availability — who has the infrastructure but not the people?
Sources: NIST AI RMF, BLS Occupational Outlook, McKinsey Talent Survey 2026, Stanford HAI
Theme 5 · Capital Flows
Money Follows Proof
$23.4BAI startup VC funding in Q1 2026, down 18% from Q4 2025 peak (Crunchbase public data)
VC funding trends by vertical vs. enterprise adoption rates vs. reported ROI — where investor money is going is not where the ROI is.
Sources: Crunchbase, PitchBook (public data), McKinsey GenAI ROI Survey, Stanford HAI Index
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Methodology
Each research theme cross-references at least three independent primary sources. Government databases (BLS, SEC EDGAR, USPTO) provide verifiable, timestamped data. Analyst reports (McKinsey, Stanford HAI, Gartner) provide survey-based adoption signals. Open-source signals (GitHub stars, Stack Overflow tags) provide real-time developer behavior. The cross-source synthesis — comparing stated intent vs. actual spending vs. hiring vs. open-source activity — produces insights that exist nowhere else because no single report spans all of these sources.
Data pulled: May 2026. All figures cited with source, URL, and access date on individual theme pages. AIStackHub does not estimate or extrapolate data — if a number isn't verifiable, it isn't included.