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.

Reinforcement learning incentivizes AIs to find the right answer, not just mimic human text. This leads to them developing their own internal "dialect" for reasoning—a chain of thought that is effective but increasingly incomprehensible and alien to human observers.

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.

Goodfire frames interpretability as the core of the AI-human interface. One direction is intentional design, allowing human control. The other, especially with superhuman scientific models, is extracting novel knowledge (e.g., new Alzheimer's biomarkers) that the AI discovers.

Research suggests a formal equivalence between modifying a model's internal activations (steering) and providing prompt examples (in-context learning). This framework could potentially create a formula to convert between the two techniques, even for complex behaviors like jailbreaks.

Just as biology deciphers the complex systems created by evolution, mechanistic interpretability seeks to understand the "how" inside neural networks. Instead of treating models as black boxes, it examines their internal parameters and activations to reverse-engineer how they work, moving beyond just measuring their external behavior.

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.

For AI systems to be adopted in scientific labs, they must be interpretable. Researchers need to understand the 'why' behind an AI's experimental plan to validate and trust the process, making interpretability a more critical feature than raw predictive power.

AI development has evolved to where models can be directed using human-like language. Instead of complex prompt engineering or fine-tuning, developers can provide instructions, documentation, and context in plain English to guide the AI's behavior, democratizing access to sophisticated outcomes.