The need for explicit user transparency is most critical for nondeterministic systems like LLMs, where even creators don't always know why an output was generated. Unlike a simple rules engine with predictable outcomes, AI's "black box" nature requires giving users more context to build trust.

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.

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.

Leaders must resist the temptation to deploy the most powerful AI model simply for a competitive edge. The primary strategic question for any AI initiative should be defining the necessary level of trustworthiness for its specific task and establishing who is accountable if it fails, before deployment begins.

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.

As AI models are used for critical decisions in finance and law, black-box empirical testing will become insufficient. Mechanistic interpretability, which analyzes model weights to understand reasoning, is a bet that society and regulators will require explainable AI, making it a crucial future technology.

The rise of convincing AI-generated deepfakes will soon make video and audio evidence unreliable. The solution will be the blockchain, a decentralized, unalterable ledger. Content will be "minted" on-chain to provide a verifiable, timestamped record of authenticity that no single entity can control or manipulate.

When a highly autonomous AI fails, the root cause is often not the technology itself, but the organization's lack of a pre-defined governance framework. High AI independence ruthlessly exposes any ambiguity in responsibility, liability, and oversight that was already present within the company.

Treat accountability as an engineering problem. Implement a system that logs every significant AI action, decision path, and triggering input. This creates an auditable, attributable record, ensuring that in the event of an incident, the 'why' can be traced without ambiguity, much like a flight recorder after a crash.