Trust at Scale

Trust is the foundation of commerce. When you buy from a stranger, you're trusting that they'll deliver what they promised. When you share your credit card, you're trusting the payment system to protect it. When you tell a business your preferences, you're trusting them to use that information responsibly.

In AI-mediated commerce, trust becomes more complex. Now there are three parties: the user, the business, and the AI systems that intermediate between them. Each needs to trust the others. Each can be trusted—or not.

The Trust Triangle

Consider a simple transaction: a user asks their AI assistant to book a restaurant reservation.

The user needs to trust:

• That their AI assistant is acting in their interest, not someone else's
• That the business will honor the reservation and provide good service
• That their personal information (preferences, payment details) will be handled appropriately

The business needs to trust:

• That the reservation request is genuine (not a bot filling slots maliciously)
• That the customer will actually show up
• That the information provided about the customer is accurate

The AI systems need to trust:

• That the business's stated capabilities are accurate
• That the business will fulfill its commitments
• That interactions will be fair and non-adversarial

This is the trust triangle. Each edge needs to be strong for the system to work. Weakness anywhere creates failure modes that can cascade through the entire network.

Trust Score

We've developed a Trust Score system to quantify and track trustworthiness across the network. It's not a single number—it's a multidimensional assessment that captures different aspects of reliability.

For businesses:

• Fulfillment rate: How often do they deliver what they promised?
• Information accuracy: Is their stated availability, pricing, and capability correct?
• Response reliability: Do their AI systems respond consistently and accurately?
• Dispute resolution: When problems occur, are they handled fairly?

For users:

• Commitment reliability: Do they show up for reservations? Complete purchases?
• Payment reliability: Are transactions processed successfully?
• Feedback quality: Is their feedback honest and useful?

Trust Scores are earned through behavior, not claimed through assertion. They accumulate slowly through consistent good behavior and can erode quickly through failures or bad faith actions.

Privacy-First Design

Trust requires privacy. Users won't share preferences if they fear that information will be misused. Businesses won't share operational data if it might be used against them.

Our approach to privacy is built on several principles:

Data Minimization

We collect only what's necessary for the transaction at hand. If a restaurant reservation doesn't require dietary preferences, we don't ask for them. If a user hasn't opted into preference sharing, we don't share.

User Control

Users decide what to share and with whom. Every piece of personal information has an associated permission. Users can grant, revoke, or modify these permissions at any time.

Transparency

When data is shared, both parties know what's being shared and why. There are no hidden data flows. The user's AI assistant can explain exactly what information went to the business and what came back.

Minimal Surface Area

We don't store what we don't need to store. Transaction details needed for immediate processing are held temporarily. Long-term storage is limited to what's necessary for trust scoring and dispute resolution.

LMIF: Look Ma, I'm Famous

One particular trust challenge deserves special attention: intellectual property protection. When AI systems can easily generate content, how do creators protect their work?

LMIF (Look Ma, I'm Famous) is our approach to this problem. It's a registration system that allows creators to establish provenance for their work—whether that's recipes, designs, training materials, or other intellectual property.

LMIF provides several capabilities:

• Timestamped registration: Prove that you created something before a certain date
• Attribution tracking: When your work is referenced, you receive credit
• Usage notification: Know when and how your registered content is being used
• Licensing frameworks: Define terms under which others can use your work

LMIF isn't a DRM system that tries to prevent copying—that approach has consistently failed. Instead, it focuses on establishing clear provenance and enabling fair attribution. When a business's recipe or methodology appears in another context, LMIF can demonstrate the original source.

Learn more at lookmainfamous.com.

Handling Trust Failures

No system is perfect. Trust failures will occur—businesses will miss commitments, users will no-show, AI systems will make mistakes. The question is how to handle these failures gracefully.

Our approach has several components:

Graduated Consequences

First failures get warnings and opportunities to correct. Repeated failures lead to Trust Score degradation. Severe or deliberate violations can result in removal from the network.

Dispute Resolution

When parties disagree about what happened, there needs to be a fair process for resolution. We maintain interaction logs that can be reviewed. Both parties can present their perspective. Resolutions are tracked and used to improve future interactions.

Recovery Paths

Trust Scores can be rebuilt. A business that had a bad period can demonstrate improved behavior and regain standing. The system isn't designed to permanently punish—it's designed to incentivize good behavior.

Trust and Ecosystem Health

Individual trust relationships are important, but so is overall ecosystem health. A network where most participants are trustworthy is more valuable than one where bad actors are common.

We invest in ecosystem health through:

• Fraud detection: Identifying and removing bad actors before they cause widespread harm
• Pattern recognition: Spotting systemic issues before they become critical
• Incentive alignment: Ensuring that good behavior is consistently rewarded
• Transparency reporting: Publishing aggregate trust metrics so participants can make informed decisions

The Role of Humans

AI systems can track trust signals at scale, but humans remain essential to the trust infrastructure. Certain decisions—especially those involving judgment calls about fairness or intent—require human oversight.

Our approach keeps humans in the loop for:

• Disputes that can't be resolved automatically
• Trust Score appeals
• Policy decisions about what constitutes acceptable behavior
• Edge cases where automated systems are uncertain

This isn't about distrust of AI. It's about appropriate allocation of decision-making. AI systems are good at pattern recognition and consistent application of rules. Humans are good at judgment, context, and fairness considerations that resist algorithmic treatment.

Building Trust Takes Time

Trust can't be rushed. It accumulates through repeated positive interactions over time. There are no shortcuts—no way to buy a high Trust Score or manufacture reputation.

This is a feature, not a bug. Systems where trust can be purchased or gamed quickly become worthless. By making trust hard to acquire and easy to lose, we create incentives for genuine good behavior rather than sophisticated gaming.

For businesses joining the network, this means patience. Trust Scores start neutral and build through demonstrated reliability. The businesses that will thrive are those that invest in actually being trustworthy—not just appearing so.

Looking Forward

Trust infrastructure is foundational to AI-mediated commerce. Without it, users won't share preferences. Businesses won't share capabilities. AI systems won't be able to make reliable recommendations.

We're still early in developing these systems. The Trust Score methodology will evolve as we learn from real interactions. Privacy frameworks will adapt to new threats and opportunities. LMIF will expand to cover more types of intellectual property.

What won't change is the fundamental insight: trust is earned, not claimed. The businesses that succeed will be those that are genuinely trustworthy. The AI-mediated future rewards substance over appearance.