Rise score (0–100)

For each AI-native company in the Risers cohort:

rise_score =  0.47 · z(headcount_30d_%)
            + 0.33 · z(headcount_90d_%)
            + 0.20 · z(funding_events_90d, weighted_by_amount)

then min-max normalize to 0–100 across the cohort for the week.

Cut score (0–100)

For each legacy SaaS incumbent in the Cuts cohort:

cut_score  =  0.44 · z(layoff_severity_90d)   // pct × recency × ai_attribution
            + 0.28 · z(-headcount_30d_%)
            + 0.17 · z(-headcount_90d_%)
            + 0.11 · at_risk_category_bonus

then min-max normalize to 0–100 across the cohort for the week.

The pressure vs. efficiency split

A falling headcount at a legacy SaaS company isn't always the same story. Sometimes it's a company being disrupted by AI-native competitors. Sometimes it's a company using AI to run leaner — cutting roles while revenue grows. Both end up on the Cuts side of the index, but they mean very different things for investors, employees, and the market.

For every Cut, we classify the last 180 days of layoff announcements:

• Under pressure — layoffs announced with no AI-efficiency rationale in the majority of cases. Genuine AI disruption victims.
• AI efficiency — majority of recent layoffs explicitly attribute the cuts to AI productivity gains. Company restructuring around AI, not being displaced by it.
• Attrition only — contracting via hiring freezes or attrition, with no announced layoffs.

Next: weight the composite by revenue-per-employee trajectory (PDL inferred revenue, already ingested) and 30-day stock moves for public companies. A company shedding roles while revenue rises is a very different investment case from one where both are falling.

Guardrails

• Headcount % changes are capped at ±50% to limit single-data-error blast radius.
• Companies with <90 days of tracked history get a null score, not a forced zero.
• Companies with <50 employees are flagged "small base" and shown in a separate sub-bucket so noise doesn't dominate.

Data sources

• Headcount: People Data Labs Company Enrichment — monthlyemployee_count_by_monthseries, projected to weekly snapshots
• Layoffs: derived from PDLgross_departures_by_month— months where departures spike 2.5× above trailing 6-month median AND net headcount shrinks AND the excess exceeds 1.5% of workforce. Source URLs and AI-attribution filled in by news cross-reference when available.
• Funding: PDLfunding_details— amount, date, round type. Lead investor and source URL are intentionally not shown; PDL doesn't return a clean press-release URL or a reliable single-lead field, so rather than link to PDL's homepage or name a random angel as "lead," we show the round and nothing else. Press URLs will be filled in by the news classifier.

Refresh cadence: PDL ingest runs on the 1st of each month (PDL's underlying data moves monthly — weekly polling would be waste). Scoring re-runs every Monday to pick up funding events that land between PDL refreshes. The "updated weekly" line in the footer refers to the scoring pass.