Burn Multiple
Burn multiple = net burn / net new ARR · sub-1× is great · AI startups regularly run 3-5× due to compute cost.
Burn multiple = net burn / net new ARR · sub-1× is great · AI startups regularly run 3-5× due to compute cost.
Basic
David Sacks popularized the burn multiple: money burned per dollar of new ARR. A 1× burn multiple means $1 spent per $1 of net new ARR. Best-in-class SaaS: 0.5-1×. Average: 1.5-2×. AI startups often run 3-5× because training runs and inference compute burn cash before revenue scales.
Deep
AI burn multiples decompose into: R&D (model training, salaries), inference COGS, sales, and GTM spend. Training runs can be $10M+ each; a startup doing monthly checkpoints may burn $120M/year on training alone. Inference COGS grows with usage but has less up-front cost. Fundraising dynamics: $500M+ Series B rounds are now common at 3× burn multiples because investors price in the training expense.
Expert
Perplexity's leaked numbers (2024): burn multiple ~4× at $50M ARR. Anthropic: ~2× after Series E at $1B ARR · training dominates but revenue scales fast. OpenAI burn multiple is positive but opaque · ChatGPT consumer losses offset by API gains. Burn multiple compresses as revenue crosses $100M ARR for well-run AI companies · training cost becomes a smaller % of total.
Depending on why you're here
- ·Burn multiple = net burn / net new ARR
- ·AI startups: 3-5× pre-scale
- ·Compresses as revenue scales past $100M ARR
- ·Track your startup's burn multiple quarterly
- ·Watch training + inference cost as % of total burn
- ·Healthy: trending down QoQ
- ·Primary capital efficiency metric for Series B+ AI
- ·< 2× at $50M+ ARR = efficient · > 5× = concerning
- ·AI makes burn multiples worse early, better late
- ·How much money a company loses to grow $1 of new sales
- ·AI companies spend a lot early to build models
- ·Gets better as they get big
AI burn multiples are 2-3× higher than SaaS at same revenue · investors accept it because the trajectory flips fast at scale.