A hot‑button discussion has circulated recently: starting from a software’s inputs, outputs and functional behaviour, AI can reverse‑infer its operational logic and reconstruct a functionally‑complete new release weighing merely 12 MB out of decades‑old commercial software totalling dozens of gigabytes.

First, boundaries must be clarified: no publicly reproducible engineering evidence exists for this case. It functions more as a powerful metaphor that lays bare an ongoing paradigm shift. Where code implementation, functional behaviour and interaction logic yield observable external behaviour, AI holds the potential to reverse‑engineer them.

A large share of the industrial‑software barriers built in the West over recent decades rest upon cumulatively‑developed codebases and engineering know‑how refined across many years. The underlying logic of this moat is that feature implementation demands lengthy iterative work, making rapid catch‑up difficult for late entrants. AI‑driven reverse reconstruction pierces precisely this layer of defence. Instead of learning the original source code line‑by‑line, it observes external behaviour and autonomously generates entirely new code that reproduces equivalent performance.

Yet there exists a dividing‑line no reverse‑engineering technique can ever cross: it can replicate how something is done, but it cannot replicate why a claim is made, nor where its supporting evidence originates.

Software interfaces, algorithm workflows and functional outputs belong to the observable implementation layer. By contrast, factual provenance, publishing entities, original sources, revision trails and accountability attribution form the trust layer, which cannot be deduced purely from runtime behaviour. One may rebuild a program that yields identical conclusions, yet cannot conjure out of thin air the original chains of evidence and attestation records behind those conclusions.

This defines the positioning of Omnieyes · Trust Archive.

Wikipedia and Baidu Baike serve human reference; Omnieyes · Trust Archive establishes credibility for AI.

We do not perform code reverse‑engineering or software replication. Instead, we decompose diverse statements into indivisible atomic facts. Drawing on the KUCR Four‑Layer Validation Mechanism and the L1‑tiered source‑rating system, we fully anchor the origin, supporting evidence, revision history and accountable entity for every individual fact.

In an era where generative AI effortlessly produces logically coherent, highly‑plausible content and programs, the capacity “to build something” is no longer a scarce capability. What has genuinely become scarce is a traceable, verifiable trust foundation that cannot be reverse‑engineered from outputs alone.

Code may be reconstructed, functions may be replicated, but trust archives anchoring factual claims cannot be derived retroactively from end results.

AI erodes monopolistic advantages built upon accumulated code. At the same time, it elevates factual attestation and source tracing into the new core moat of the AI era.