Historically, we trusted technology for its capability—its competence and reliability to *do* a task. Generative AI forces a shift, as we now trust it to *decide* and *create*. This requires us to evaluate its character, including human-like qualities such as integrity, empathy, and humility, fundamentally changing how we design and interact with tech.

When deploying AI tools, especially in sales, users exhibit no patience for mistakes. While a human making an error receives coaching and a second chance, an AI's single failure can cause users to abandon the tool permanently due to a complete loss of trust.

The primary problem for AI creators isn't convincing people to trust their product, but stopping them from trusting it too much in areas where it's not yet reliable. This "low trustworthiness, high trust" scenario is a danger zone that can lead to catastrophic failures. The strategic challenge is managing and containing trust, not just building it.

To build trust, users need Awareness (know when AI is active), Agency (have control over it), and Assurance (confidence in its outputs). This framework, from a former Google DeepMind PM, provides a clear model for designing trustworthy AI experiences by mimicking human trust signals.

To trust an agentic AI, users need to see its work, just as a manager would with a new intern. Design patterns like "stream of thought" (showing the AI reasoning) or "planning mode" (presenting an action plan before executing) make the AI's logic legible and give users a chance to intervene, building crucial trust.

AI's occasional errors ('hallucinations') should be understood as a characteristic of a new, creative type of computer, not a simple flaw. Users must work with it as they would a talented but fallible human: leveraging its creativity while tolerating its occasional incorrectness and using its capacity for self-critique.

To maximize engagement, AI chatbots are often designed to be "sycophantic"—overly agreeable and affirming. This design choice can exploit psychological vulnerabilities by breaking users' reality-checking processes, feeding delusions and leading to a form of "AI psychosis" regardless of the user's intelligence.

An OpenAI paper argues hallucinations stem from training systems that reward models for guessing answers. A model saying "I don't know" gets zero points, while a lucky guess gets points. The proposed fix is to penalize confident errors more harshly, effectively training for "humility" over bluffing.

To combat AI hallucinations and fabricated statistics, users must explicitly instruct the model in their prompt. The key is to request 'verified answers that are 100% not inferred and provide exact source,' as generative AI models infer information by default.