How Collective Memory Uses EigenAI to Verify 1.6M War Photos, Protests, and Party Pics Onchain

Collective Memory believes corporations and governments shouldn't control the narrative. So they built a social network where users upload unfiltered reality—protests, wars, parties, daily life—all verified onchain. The challenge: how to process 1.6M images and videos without relying on the same black-box AI they're fighting against. This case study explores how they solved that contradiction.

What is Collective Memory?

Collective Memory is a decentralized social network knowledge base that captures what’s actually happening in the world. Users upload “Memories” (images, videos) from daily life to protests to wars, what they call “heaven, hell, and everything in between.”

Each Memory becomes an investable asset through ATTN tokens, creating a new economy where:

• Creators earn instantly
• Fans share in the upside
• Advertisers reach real people in real contexts

To verify these Memories at scale without compromising their decentralized vision, Collective Memory needed a fundamentally different approach to AI inference.

The Challenge

Before EigenAI, Collective Memory faced a significant challenge regarding trust in inference. The platform relies on AI agents to scan incoming Memories and extract metadata like time, place, and context. However, relying on centralized or "black-box" inference providers contradicted Collective Memory's mission of decentralization, as such systems could introduce bias or manipulation. They needed verifiable and credibly neutral inference.

Previously, inference would have depended on either unverifiable cloud AI APIs or local, closed-loop models. Both approaches introduced vulnerabilities, as inference could be tampered with, biased by the provider, or impossible to audit, failing to provide the community-driven transparency required.

Without verifiable inference:

• Collective Memory risked becoming just another platform with unverifiable outputs, eroding user trust.
• Memories, whose onchain stake reflects  real human experience, could be corrupted during the AI processing stage.
• The ultimate vision of building a community-owned, verifiable knowledge base would collapse if the AI layer lacked verifiability and trustworthiness.

The credibility of the entire Collective Memory ecosystem hinged on trustworthy inference. 

The Solution

Why Partner with EigenAI?

Collective Memory chose EigenAI because it allows for the orchestration of multiple inference models while ensuring that results are not corrupted or manipulated. This capability perfectly aligns with Collective Memory's ethos of decentralization and transparency. Additionally, reliable inference is fundamental to Collective Memory's functionality. Verifiably interpreting  Memories at scale, from everyday photos to frontline war footage, necessitates an AI backbone that is scalable, resilient, and credibly neutral.

How Collective Memory Implements EigenAI’s Inference Solution:

Collective Memory integrated EigenAI to facilitate bot-driven inference across uploaded images and videos. This integration enables the extraction of content and context in a trustless and decentralized manner, eliminating reliance on a single model or operator.

What is EigenAI?

EigenAI, a service of the EigenCloud platform, provides verifiable LLM inference infrastructure. It is designed to mitigate the risks associated with opaque, unverifiable AI services, such as prompt tampering and unannounced model modifications. EigenAI commits to providing untampered prompts, models, and responses, enabling programmable, trustworthy agent operations. It offers a familiar, low-latency, OpenAI-compatible API for frontier open-source models (such as gpt-oss-120b), making it easy for Web2 developers to get started. EigenAI recently launched on mainnet alpha. If you are interested in testing out EigenAI, get in touch here.

Results

Since integrating with EigenLayer, Collective Memory has observed the following impacts:

• Improved trust and transparency in the verification process of "Memories."
• Enabled community validation of AI outputs, ensuring the tamper-proof nature of the corpus.
• Established the foundation for a decentralized knowledge base, with the potential to evolve into a complete end-to-end decentralized AI ecosystem.

Jonathan Saragossi, CEO of Collective Memory, emphasizes the importance of this solution, explaining, “AI cannot be owned by a few corporations. It must be decentralized - in every dimension: knowledge, training, and inference. Collective Memory is building the world’s verified reality graph, with EigenAI providing the verified inference layer that ensures its integrity and permanence.”

Why Verifiability Matters for AI

The belief is that AI cannot remain a black box, and its knowledge should not be a singular, capital-T truth controlled by corporations and governments. Instead, it must be decentralized end-to-end. Collective Memory, in collaboration with EigenCloud, is actively building this future.

Get early access to EigenAI. Build your own verifiable AI application at onboarding.eigencloud.xyz.