An Evaluation of Storage Alternatives for Service Interfaces Supporting a Decentralized AI Marketplace
Bibliographic record
Abstract
Given the exploding interest in generative AI and the concern that a few companies like Microsoft will monopolize access to such models, we address this centralization risk in the context of a DApp that matches buyers and sellers of various AI services. A key question for a decentralized marketplace is where and how to store the metadata that specifies the services’ properties in human and machine-readable formats. Having one or a few actors controlling access to that data constitutes undesirable centralization. We explore data storage alternatives to ensure decentralization, equitable match-making, and efficiency. Classifying decentralized storage alternatives as simple peer-to-peer replication, replication governed by a permissionless consensus, and replication governed by a private consensus, we select an exemplar for each category: IPFS, Tendermint Cosmos and Hyperledger Fabric. We conduct experiments on performance and find that read and write speeds are fastest for IPFS, about two times slower for Tendermint and slowest for Hyperledger. Writing using IPFS and Tendermint takes significantly longer than reading, and finally, specifically with IPFS, write speeds strongly depend on configuration. Given these results and the properties of the storage technologies, we conclude that simple peer-to-peer storage is the best option for the proposed AI marketplace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".