Trust score vs reputation vs credit score
Your FICO score predicts default. Your LinkedIn endorsements predict nothing. Your Manav Trust Score predicts whether your agents will ship the work. Three different scores, three different jobs.
The categories, sharply
Credit score. Bureau-calculated, regulated, narrowly predictive: will this person default on debt within 24 months? Drawn from a small set of authoritative sources. The privacy and bias trade-offs are well-known and partially regulated.
Online reputation (LinkedIn endorsements, marketplace stars, GitHub follower counts). Self-reported or thinly-validated; predictive of nothing reliable; useful as social signal. The system is not adversarial-resilient — anyone can pad endorsements, buy stars, or game the platform's discovery feed.
Manav Trust Score. Derived from cryptographic Layer-3 work attestations, peer co-signatures, and the human's track record of agent management. Predicts: will deliverables under this human's authority arrive, on time, with the claimed quality?
Why prediction targets matter
A score is only as useful as the question it predicts. FICO predicts a binary financial outcome over a defined window, with decades of validation. The Trust Score predicts a different binary: the integrity of attested deliverables. LinkedIn endorsements were never trying to predict anything — they were a social ritual.
The mistake most "reputation systems" make is conflating ritual with prediction. The Trust Score is structurally adversarial-resilient because gaming it requires producing actual attested work, which has Sybil costs above its reward.
Comparison
| FICO | LinkedIn endorsements | Manav Trust Score | |
|---|---|---|---|
| Source | Credit bureaus | Self / peer-claim | Cryptographic attestations |
| Adversarial-resilient | Partially | No | Yes |
| Predicts | Default risk | Vibe | Deliverable integrity |
| Portable | Within country | Within platform | Across employers and jurisdictions |
| Privacy | Regulated | Public | Selective disclosure |
| Used for | Lending | Discovery | Hiring, agent delegation, marketplaces |
What the Trust Score does for hiring
91% of US hiring managers encountered AI-generated interview answers. The hiring funnel desperately needs a score that predicts work delivery rather than a credential everyone can fabricate. Trust Score does that — derived from years of cryptographically-stamped work, weighted by peer co-signatures, with selective disclosure that respects the candidate's privacy.
What it does for agent delegation
An employer issuing delegation tokens to a knowledge worker can size the magnitude cap by Trust Score. A 1.0× scorer gets a $100 token. A 2.5× scorer gets a $5,000 token. The protocol does this dynamically, not by manager fiat. The result: trust accumulates with proven delivery and decays without it.
What it does for marketplaces
Talent marketplaces today have a Sybil problem. Identity-verified profiles are easy. Verified-work profiles are not. Marketplaces that integrate Trust Score discover that the highest-scoring contributors are not the loudest — they're the ones whose attestation history compounds quietly. Pricing follows.
What it does not do
The Trust Score is not a personality assessment. It does not measure niceness, leadership style, or culture fit. It measures one thing: do attested deliverables under this human's authority arrive as claimed. That single dimension is enough to fix the hiring fraud problem and almost none of the other things the HR-tech industry has been selling.
Common objections
Two objections come up across every conversation. Will the platform vendors ship this themselves? Some will, inside their boundary; none can ship the cross-platform shape, by their own architectural choice. Is the category too narrow to matter? It's the layer beneath every agent action — narrow looks broad once the wire bends.
Frequently asked questions
Why does this category not already exist? Because the failure mode it addresses is recent. The pre-agent enterprise could pretend the service account was the human; the agentic enterprise cannot. The category becomes named when the failure becomes regulator-visible, which is now.
Where does this end up in the standards stack? As a layer above OAuth and below the application. OAuth carried scoped delegation between services; this layer carries scoped delegation from a verified human to an agent. The IETF and W3C working groups are converging on the shape; the protocol that ships first sets the verbs.
What does adoption look like in practice? Quietly. The integrations are middleware, not platforms. Each vertical sees its specific compliance pain solved — healthcare gets Article 14, finance gets SOC 2 evidence, hiring gets continuous identity — and treats the underlying primitive as plumbing once it ships.
Where to start
Read proof of human work next for the deeper architecture. Then resume fraud ai era for the closest practical anchor. The mental model that holds those two together holds the rest of the site as well.
Why reputation systems collapse under AI
Yelp, eBay, Glassdoor, Uber. Every reputation system that worked before AI is being silently overwhelmed by AI-generated reviews, AI-generated transactions, and AI-generated profile activity. The defense pattern is the same in each — fraud detection, machine-learning classifiers, manual moderation — and the defense is losing because the signal-to-noise ratio is collapsing faster than the defenders can iterate. The Trust Score lives at a different layer because it is rooted in cryptographic identity binding, not behavioral pattern matching. An AI cannot fabricate a Manav-bound history because the history is signed at issuance by parties whose signatures the AI cannot forge. Reputation systems that retrofit cryptographic identity onto behavioral data inherit the substrate's durability. Reputation systems that try to keep the legacy architecture and out-iterate the attackers are betting on a race they cannot win. The collapse is gradual until it is sudden. The platforms that integrate first survive the collapse. The platforms that wait do not.
Endorsements are vibes. Trust Score is the receipt.