The AI Trust Crisis
AI is no longer just answering questions. It's deciding truth. It determines what we read, where money flows, who gets hired, and how societies are governed.
But there is one problem no one is solving: AI is unverifiable.
A handful of corporations control intelligence itself. We are expected to trust their black-box models, no transparency, no auditability, and no public accountability.
IAN (Intelligent Agent Network) is building the first decentralized trust layer for AI, a protocol that makes intelligence transparent, provable, and collectively owned.
Just like HTTPS secured the internet, IAN secures AI.
When Google Bard hallucinated a false fact about exoplanets, Alphabet lost $100B in market cap. LLMs generate confidently wrong outputs every day, with no recourse. These hallucinations are difficult to catch and even harder to audit at scale. In enterprise settings, hallucinations can result in legal liability or strategic misdirection.
AI systems reinforce corporate, regional, and political biases. Ask the same question in different countries and get different "truths." Bias isn't just a problem of training data; it is embedded in model architectures and feedback loops. Without oversight, these biases propagate in critical areas like hiring, sentencing, lending, and policy.
Trained on the internet’s knowledge, owned by a few firms. Public knowledge becomes private profit—without attribution or economic alignment. The creators of foundational LLMs claim ownership of outputs derived from collective, publicly accessible inputs, creating a new kind of intellectual enclosure.
There is no record of how an AI reached a decision, what influenced it, or whether its output was challenged or verified. This breaks trust in enterprise contexts and undermines democratic values in public discourse.
AI today is a knowledge monopoly. IAN transforms it into collective infrastructure.