How we calculate this

We identify 3–5 publicly traded comparable companies for each unicorn. Peer choice matters enormously: anchoring a 200%-growth private AI lab to 15%-growth mega-caps under-prices it badly. We use sector-appropriate, growth-matched peer baskets — high-growth (NVDA, PLTR, CRWD, MDB, NET) for AI labs; data/infra (SNOW, DDOG, MDB, NET) for Databricks; payments (ADYEN, V, MA, MELI, PYPL) for Stripe; defense-tech (PLTR, KTOS, RTX, NOC) for Anduril; and so on.

For each peer we use the EV/Revenue multiple, weighted by that peer's revenue growth rate so the fastest-growing peers carry more influence. We then apply a per-company illiquidity discount (10–25%) to reflect lack of liquidity, audited financials, and preference-stack risk. Category leaders with deep secondary markets and a clear IPO path get a smaller discount (~10–13%); mid-tier names get 18–20%; smaller / less-liquid names get 22%+.

We multiply the adjusted multiple by the company's estimated annual revenue. For pre-revenue or opaque companies we substitute EV/Employee as a proxy.

Weight: 35%. Confidence: 30–75%, depending on peer fit and revenue-estimate freshness.

Secondary markets (Hiive, Forge, Caplight) provide live bid/ask prices for private company shares. These prices already incorporate a discount to primary — they reflect what real investors are paying today.

We collect the 3–5 most recent indications per company and apply exponential recency weighting: a transaction from 30 days ago has roughly 5× the weight of one from 180 days ago (e⁻ᵈ/¹⁸⁰ decay function). This prevents stale data from dominating the estimate.

Weight: 25%. Confidence: high when recent trades exist (up to 80%); drops quickly if data is older than 6 months.

The simplest anchor: take the last disclosed primary-round valuation and adjust forward using the company's estimated revenue growth and a small multiple-decay tax. Three corrections apply:

1. Per-company revenue growth rate — set explicitly from reported or estimated ARR trajectory (e.g. ~120% YoY for OpenAI, ~50% for SpaceX, ~27% for Stripe). Compounded for elapsed time since the primary. No more headcount proxy: for AI/SaaS companies, revenue decouples from headcount thanks to compute leverage.
2. Multiple decay — a 5%/year haircut on the multiple, floored at 0.85×, to reflect that markets re-rate downward by default unless the company grows into its valuation.
3. Secondary cap — if a secondary trade exists in the last 120 days, Method C's output is capped at 1.3× that secondary-implied valuation. Stops runaway extrapolation when modeled revenue growth outpaces what the market is actually paying.

Weight: 30%. Most reliable for recently funded companies (within 12 months). Confidence decays 20 pp/year from the primary date, flooring at 20%.

Method D uses the performance of a sector ETF as a market-proxy forward adjustment to the last primary round. It answers the question: "if this sector kept pace with its public benchmarks since the last funding round, what would that imply?"

Each company is mapped to the most relevant sector ETF:

We compound the ETF's annualised return from the last primary date to today to derive an adjusted valuation. Confidence starts at 60% and declines 15 pp/year (floor 10%).

Weight: 10%. Used as a tiebreaker and for companies with no secondary market data.

We compute a confidence-weighted average of the four implied valuations using weights A=35%, B=25%, C=30%, D=10%.

Outlier auto-downweight. Any method whose output strays more than 50% from the median of the other three has its confidence weight halved before the blend. This is a robust-statistics safety net — it prevents a single bad extrapolation (most often Method C overshooting for high-growth companies) from dragging the headline figure.

Spread guardrail. Even after downweighting, if the ratio between the highest and lowest method output exceeds 3×, the result is flagged as "wide spread". The displayed range widens (±1.8 σ instead of ±1.2 σ), overall confidence is capped at 35%, and the UI surfaces an explicit "methods disagree" notice. This is the single most important credibility safeguard on the site — a 13× spread shown without warning would tank user trust.

The spread (low–high range) is set to ±1.2 σ of the four method outputs (±1.8 σ when wide), with a minimum floor of ±15%.

Overall confidence = weighted average of the four method confidence levels. Above 65% = high; 40–65% = medium; below 40% = low.

The three scenarios apply fixed shocks to the mid-point valuation and model dilution:

The calculator correctly handles five equity types:

• We don't model liquidation preferences in detail — this requires cap table access.
• We don't predict IPO timing or probability.
• We don't account for clawbacks, transfer restrictions, or company-specific plan rules.
• We don't provide state tax estimates — consult a local tax advisor.
• We don't incorporate bid-ask spreads as separate secondary inputs yet.
• PPU holders: we model economic value only, not the profit-sharing mechanics of OpenAI's PPA. Consult your plan document.