Overview
Ordinals is a numbering scheme created by Casey Rodarmor that assigns a unique ordinal number to every individual satoshi (the smallest unit of bitcoin) based on the order in which it was created through mining. Combined with the ability to attach arbitrary data (inscriptions) to specific satoshis using witness data, Ordinals enables NFT-like digital artifacts natively on Bitcoin without requiring any protocol changes.
How Ordinal Numbers Work
Block 0 (Genesis): Satoshis 0 to 4,999,999,999
Block 1: Satoshis 5,000,000,000 to 9,999,999,999
Block 2: Satoshis 10,000,000,000 to 14,999,999,999
...and so on
Each sat has a unique ordinal number based on
the order it was mined into existence.
Tracking: When sats move in transactions, ordinals
follow a first-in-first-out (FIFO) rule.
Inscriptions
Inscriptions are arbitrary data (images, text, HTML, audio) embedded in the witness section of a Bitcoin transaction and associated with a specific satoshi via its ordinal number. The data is stored on-chain permanently, leveraging the witness discount introduced by SegWit that makes witness data cheaper than regular transaction data.
Transaction with inscription:
Input: [signature + inscription data in witness]
Output: [satoshi carrying the inscription]
Inscription data:
├── Content-type (e.g., "image/png")
└── Content (the actual data bytes)
Controversy
Ordinals have been controversial in the Bitcoin community. Supporters argue they bring new users, fee revenue for miners, and creative use of existing protocol features. Critics contend that inscriptions consume block space intended for financial transactions, increase UTXO set bloat, and raise the cost of running nodes.
Common Misconceptions
Ordinals do not require any changes to the Bitcoin protocol — they use existing features (witness data, Taproot script paths) in novel ways. The ordinal numbering is a social convention enforced by Ordinals-aware software, not by the Bitcoin consensus rules. Bitcoin nodes do not natively track or enforce ordinal numbers; they are an overlay interpretation of existing data.