The AGI-Ops Index (Europe)
The macro thermometer tracking process optimization, operational leverage, and automation efficiency across 28 liquid European technology and digital scale firms.
Beats SX8P by +58.04% over 4Y
Equal-weighted across all 28 analyzed tech firms
Legacy operators with elevated SG&A overhead
Leaders vs. Laggards Spread: +110.04%
Macro Benchmark: The AGI-Ops Index (Europe) vs. SX8P (4Y)
Point-in-time Equal-Weight performance with 55-day operational filing lag and Revenue Stability Guardrail.
Sector Performance & AGI-Ops Score Breakdown
Toggle operational sectors (`Hardware & Semiconductors`, `AI-Native`, `Cloud & Infra`, `Enterprise SaaS`) and switch timeframes to inspect their relative efficiency curves and average 0–100 sub-scores (`agi-ops-sectors.json`).
Data Analysis: What Does The AGI-Ops Index Really Tell Us?
A fundamental breakdown of the 2022–2026 technology cycle: why public markets stopped rewarding headcount growth at all costs and now concentrate capital in firms demonstrating structural AI-driven operating leverage.
Over the past four years, the Top 10 AGI-Ops Leaders basket—equal-weighted at 10.0% per constituent across META, PLTR, NVDA, DUOL, GTLB, IOT, SPOT, GOOGL, MSFT, and AVGO—generated a cumulative return of +510.87% (reaching an index level of 610.87 pts from a Base 100 start). This performance substantially outpaced both the broader 54-stock Equal-Weight General Index (+219.68%) and the Nasdaq-100 Benchmark (QQQ, +159.03%), delivering +351.84 percentage points of net alpha over the benchmark.
At the opposite end of the distribution, the Bottom 10 Laggards basket (DOCU, ADBE, INTC, PAYC, SAP, CRM, PCTY, IBM, ASAN, and U) returned just +88.32% over the same 4-year window. The empirical takeaway is unmistakable: equity markets no longer reward vague AI product announcements—they reward verifiable income-statement proof of expanding revenue per employee paired with aggressive SG&A overhead compression.
Disaggregating the 54-company universe across our four operational sectors exposes a structural fault line in modern technology unit economics:
- Hardware & Semiconductors (+402.07% 4Y · Avg AGI-Ops Score: 62.2/100): Anchored by NVDA (85.1) and AVGO (72.1), this sector captures the highest operating margin expansion and revenue-per-engineer scaling due to inelastic hyperscaler compute demand and unmatched pricing power.
- AI-Native (+269.41% 4Y · Avg AGI-Ops Score: 57.2/100): Operators such as PLTR (85.7), DUOL (82.0), IOT (78.8), and SPOT (76.1) compound top-line revenue while keeping headcount nearly flat—automating customer support, code generation, and enterprise deployment from the ground up.
- Cloud & Infra (+204.22% 4Y · Avg AGI-Ops Score: 49.7/100): Propelled by the aggressive operational restructuring of META (87.8, #1 overall), GOOGL (74.1), and MSFT (73.4), which systematically reallocated billions from middle-management SG&A into direct AI infrastructure CapEx.
- Enterprise SaaS (+91.15% 4Y · Avg AGI-Ops Score: 39.7/100): The primary laggard of the cycle. Legacy seat-based software vendors face a dual headwind: corporate customers trimming seat licenses as they automate workflows, and bloated internal sales-and-marketing structures (35%–55% SG&A-to-Revenue) that compress operating margins.
How the index separates true AI automation from distressed layoffs
Naive layoff trackers mistakenly reward struggling companies that slash headcount because their core business is shrinking. The AGI-Ops Index enforces three institutional guardrails:
If a company's YoY revenue contracts by more than -3.0%, its composite AGI-Ops Score receives an automatic 0.78× penalty multiplier. Cutting staff amidst demand collapse never qualifies as AI process optimization.
No quarterly accounting statement enters the backtest until 55 calendar days after the fiscal period-end date, strictly eliminating Look-Ahead Bias across all 16 quarterly rebalance snapshots (Q1–Q4).
The AI Investment pillar (R&D + CapEx, 20% weight) requires an operating margin of ≥ 8% alongside a liquidity floor of $2.0B Market Cap and $25M ADDV.
Inspect the mathematical specification, Python script logic, and all 4 JSON endpoints in our technical subpage.
The Extremes: Top 10 AGI-Ops Leaders vs. Bottom 10 Laggards
Process Optimization Leaders
Highest-scoring companies combining rapid Revenue/Employee scaling, Operating Margin expansion, SG&A compression, and aggressive AI R&D/CapEx.
Bloated Legacy Operators
Lowest-scoring companies weighed down by high cash SG&A-to-Revenue overhead, stagnant employee productivity, or contracting operating margins.
Compare Individual Company Trajectories & 0–100 AGI-Ops Scores
Search and toggle up to 8 companies below (or click any row in the #1–#28 Leaderboard) to plot their normalized equity curves alongside `QQQ`.
Full 0–100 AGI-Ops Leaderboard (#1 to #28)
API & Quantitative Methodology Docs
Explore the full mathematical breakdown of the 4 accounting pillars, the 55-day SEC filing lag architecture, the Python backtest engine, and interactive JSON response previews with ready-to-copy cURL and Python snippets.
/indices/agi-ops/docs