Artificial intelligence depends on access to massive datasets and the ability to search them efficiently. Current approaches rely on large compute clusters and expensive memory systems. DNA provides an alternative storage and compute layer where embeddings and datasets can be preserved permanently and searched at molecular scale.Documentation Index
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Embedding Archives
Modern AI workflows transform data into embeddings high-dimensional vectors representing semantic meaning. Millions or billions of embeddings are generated for language, images, proteins, and blockchain transactions. Storing these in DNA creates a permanent semantic index.In-DNA Similarity Search
Select and quotient provide the primitives for approximate nearest-neighbor search:- A query embedding is converted into a codeword
- Select operations enrich identifiers that overlap with its one-bit positions
- Quotient aggregates signals from related items
- Molecular signal strength correlates with similarity to the query
- Only a fraction of molecules need to be sequenced reducing digital compute by orders of magnitude
Hybrid Workflows
DNA search is not a replacement for GPU training or fine-grained ranking. It acts as a first-stage filter:Stage 1 Molecular recall
Biochemical select and quotient narrow the candidate pool from billions to
thousands. Energy and compute cost: near zero.
Stage 2 Digital ranking
Sequence only the enriched subset. Run final ranking and inference digitally
on a compact candidate set.
Model Permanence
AI models are increasingly valuable intellectual property yet weights, often hundreds of gigabytes, are stored on fragile media. Encoding weights into DNA provides century-scale preservation. On-chain anchoring guarantees model versions remain auditable and verifiable across time.A model trained today can be reproduced or audited decades into the future
with no dependence on any specific hardware, format, or cloud provider.
Applications
| Domain | DNA enables |
|---|---|
| Language models | Store embeddings of corpora; retrieve documents by semantic similarity |
| Drug discovery | Store embeddings of chemical libraries and proteins; retrieve candidates by molecular similarity |
| Blockchain analytics | Store embeddings of transactions or contracts; run similarity queries across historical ledgers |
| Multi-modal AI | Preserve image, video, and genomic embeddings together in a unified molecular archive |