38 Products Tested, 6 Scaled: Portfolio Discipline and the Power Law in Practice

The Setup

The power law governs venture capital returns: a small number of investments generate the majority of portfolio value. What is less discussed is how the same dynamic applies at the product level inside operating businesses. Most operators either over-commit to a single product (concentration risk) or spread resources evenly across a portfolio (dilution risk). The discipline that separates durable operations from both failure modes is systematic testing with fast kill decisions.

According to CB Insights' 2024 research, 35% of startups fail because there is no market need for their product. A 2023 McKinsey study on product portfolio management found that top-quartile consumer companies test 3-5x more product concepts than bottom-quartile peers — but allocate 80%+ of scale capital to only the top 10-15% of performers. The test-and-learn discipline is well-documented at the enterprise level. It is rarely documented at the solo operator level with transaction data.

The Stealth Labz operation, run by Michael George Keating, launched 38 distinct product SKUs through Konnektive CRM over 28 months (October 2023 through January 2026), generating $1,075,946 in gross revenue. Six products reached meaningful scale. Thirty-two did not. That ratio is not a failure rate — it is the power law operating exactly as expected.

What the Data Shows

The 38-product portfolio segments into three tiers with clean separation. Tier 1 — six products exceeding $25K in net revenue — drove 94.5% of all net revenue (approximately $888K). Tier 2 — nine products generating $1K to $25K — contributed 5.0% (approximately $47K). Tier 3 — twenty-three micro-tests generating under $1K each — produced 0.5% ($4.3K), averaging $56 per product before being killed.

The Tier 1 products tell the portfolio story. PRD-01 generated $509,821 in net revenue — 54% of the entire portfolio from a single product. PRD-02 contributed $128,794. PRD-03 added $100,909. PRD-04 delivered $71,792. PRD-06 produced $48,280. PRD-05 rounded out the tier at $28,574. The top product alone generated more than 10x the combined output of the 32 products outside Tier 1.

According to a 2024 analysis by Andreessen Horowitz on power law dynamics in consumer products, the typical distribution in DTC portfolios shows the top 10-20% of SKUs generating 70-85% of revenue. At 94.5% concentration in the top 15.8% of SKUs (6 of 38), the Stealth Labz portfolio exhibits a steeper power law than industry benchmarks — meaning the testing discipline was effective at finding extreme outliers, not just moderate performers.

The 23 micro-tests are the most misunderstood part of the portfolio. At $56 average revenue each, they appear to be waste. They are the opposite. Each micro-test ran on shared infrastructure — the same Konnektive CRM, the same payment processing, the same affiliate tracking, the same fulfillment pipeline operated through PRJ-12. The marginal cost of testing a new product on existing infrastructure was near zero. The testing layer generated the signal that informed every scale decision.

Three distinct product lifecycle patterns emerged from the data. Flash-scale products (PRD-01, PRD-02) peaked fast on affiliate traffic then declined — PRD-01 went from $0 to $173K/month in three months, then declined 88% in one month when affiliate traffic stopped. Steady performers (PRD-06, PRD-05) showed consistent monthly revenue across longer periods. Late-stage breakouts (PRD-03 — launched August 2025, $100,909 net in five months) demonstrated that the testing pipeline continued generating winners even 20+ months into the portfolio.

How It Works

The launch discipline operated in structured waves. The pattern: deploy 2-4 products in a testing window, measure results within 30-60 days using the revenue attribution system (15 data views across 7 dimensions — detailed in CS21), scale what shows traction, kill the rest, repeat.

The critical infrastructure decision was shared infrastructure from day one. Every product — from PRD-01 at $509K to the micro-tests averaging $56 — ran on the same stack. This means the cost of testing was not "build a product and see if it works." The cost of testing was "add a SKU to an existing operational pipeline and see if it generates orders." The former costs months and significant capital. The latter costs days and nearly nothing.

This is what portfolio discipline looks like at the operational level: the willingness to kill 32 products that did not perform, combined with the infrastructure that makes testing them essentially free. The operator who tests 38 products and scales 6 has a fundamentally different risk profile than the operator who builds one product and hopes.

What This Means for Decision-Makers

The 6-of-38 hit rate maps directly to power law portfolio theory that PE operators already apply at the fund level. The difference is seeing it applied at the product level inside a single operation, with transaction-level evidence for every tier.

For portfolio evaluation, the infrastructure is the durable asset — not any individual product. PRD-01 generated $509K and then wound down. PRD-03 generated $101K starting 20 months later, on the same infrastructure. The product lifecycle is finite. The testing engine is persistent. An operation that can launch, test, and kill 38 products in 28 months at near-zero marginal test cost has a structurally different compounding trajectory than one that depends on a single product finding market fit. The power law requires volume at the testing layer and discipline at the scale layer. This portfolio demonstrates both.

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Related: [C7_S150 — Complete Product Lifecycle: From $0 to $173K/Month] | [C7_S152 — Revenue Distribution Across 18 Business Lines] | [C7_S155 — 15 Attribution Views Across 7 Dimensions]

References

1. CB Insights (2024). "Startup Failure Analysis." Research on market need as a failure driver and product-market fit rates.
2. McKinsey & Company (2023). "Product Portfolio Management." Top-quartile versus bottom-quartile product testing volume and scale capital allocation patterns.
3. Andreessen Horowitz (2024). "Power Law in Consumer Products." Revenue concentration analysis across DTC SKU portfolios.
4. Keating, M.G. (2026). "Case Study: The Product Launch Engine." Stealth Labz. Read case study