Digital Matter Theory

A new era of digital substance

What is Digital Matter Theory

Basic Premise

Digital Matter Theory proposes that it is possible to create a form of digital substance by leveraging the inherent patterns present in data.

Fundamental Concepts

Patterns in Data: The theory suggests that data, when analyzed and interpreted, reveals underlying patterns. These patterns could be harnessed to generate digital substances.

Digital Substance: In this context, "digital substance" refers to a computational or information-based material that exhibits properties akin to physical matter. This substance may not have a physical presence but can be manipulated and interacted with in a digital environment.

Applying DMT to Bitcoin

Bitcoin blocks and transactions are rich with many fields of information forever logged and stored on-chain in a decentralized and secure public ledger. From this, we can apply the principles of DMT to create a new class of digital value through a unified process that harnesses the innate properties of non-arbitrary patterns. The usage of at least a single data point in a generative process is the qualifying threshold for DMT application.


We've identified 3 different applications of DMT within the Bitcoin ecosystem. Starting with Ordinal Theory proposed by Casey Rodarmor. Ordinal Theory assigns sats with a numismatic value, allowing them to be collected and traded as digital artifacts. This assignment is a recognition of a pattern that assigns a unique ID to every satoshi that enters the ecosystem that anyone can leverage to create digital assets to be inscribed on top of a satoshi.

The second application are known as Rare Sats. Based on Ordinal Theory, sats that are in a unique position on Bitcoin's blockchain are considered rare sats. An example is the first sat of every block is known as an "uncommon" sat. This form of pattern recognition attributes value to satoshis in rare positions, in the same way we attribute value to low inscription numbers or rare commodities.

Finally, we have the third known application of DMT called Bitmap Theory by Bitoshi Blockamoto. Bitmap Theory identifies every block on Bitcoin's blockchain as digital real estate called a district. The transactions that make up a Bitcoin block partition the district into individual parcels. This mechanism enables the first non-arbitrary metaverse design.

These examples are the first digital archeologists that ignited a momentum of pattern discovery on top of Bitcoin's blockchain. We hope to provide a systematic way to build non-arbitrary digital assets from the data of Bitcoin's blockchain to further the proliferation of creation that leverages the principles of DMT.

Why Non-Arbitrary Creation Matters

Authenticity and Provenance: Utilizing patterns from blockchain data can enhance the authenticity and provenance of generated digital products. The transparency and immutability of blockchain records provide a trustworthy source for verifying the origin and history of these products.

Unique and Novel Outputs: Pattern-driven generation can lead to the creation of unique and novel digital products that are inherently tied to the blockchain's historical data. This uniqueness can increase the perceived value of the products.

Efficient Creation: Using established patterns can streamline the creative process by eliminating the need to manually design parameters. This can lead to faster creation and deployment of new digital content.

Reduced Subjectivity: Relying on objective patterns reduces the subjective influence of creators, leading to more diverse and unbiased digital outputs.


The following draft intends to introduce standard specifications and methodology to identify and register through the act of inscribing, discoverable patterns within stored Bitcoin block data. These registered elements will serve as reference IDs to be called upon when generating non-arbitrary token parameters that can then be applied in the digital token creation process.

This is an open source experimental framework. Use at your own risk.

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