Human wisdom derives from a single lifetime of experience. AI will achieve a superior form of wisdom by simulating billions of potential future scenarios and identifying the statistically optimal paths. This predictive power, already matching elite human forecasters, will be its core advisory function.

Framing AGI as reaching human-level intelligence is a limiting concept. Unconstrained by biology, AI will rapidly surpass the best human experts in every field. The focus should be on harnessing this superhuman capability, not just achieving parity.

The goal of AI development shouldn't be to perfectly replicate human cognition, a complex and perhaps unfalsifiable target. Instead, a more pragmatic approach is to draw high-level inspiration from nature to build novel forms of intelligence designed specifically to understand and serve human needs.

To make effective decisions with incomplete information, AI systems require a built-in sense of their own uncertainty. This allows them to act cautiously and adapt when facing unpredictable or novel situations, which is a hallmark of true intelligence.

Instead of relying on instinctual "System 1" rules, advanced AI should use deliberative "System 2" reasoning. By analyzing consequences and applying ethical frameworks—a process called "chain of thought monitoring"—AIs could potentially become more consistently ethical than humans who are prone to gut reactions.

As large language models are optimized for rationality and objective problem-solving, their ability to simulate the irrationality and subjective values inherent in human behavior has plateaued. This necessitates a new modeling paradigm focused on capturing human diversity, not just super-intelligence.

Unlike frontier LLMs optimized for perfect reasoning, Simile's goal is to create models that are "as dumb as I am." To be accurate, a behavioral simulation must replicate the same mistakes, biases, and non-optimal choices that real humans make.

Defining AGI as 'human-equivalent' is too limiting because human intelligence is capped by biology (e.g., an IQ of ~160). The truly transformative moment is when AI systems surpass these biological limits, providing access to problem-solving capabilities that are fundamentally greater than any human's.

The benchmark for AI reliability isn't 100% perfection. It's simply being better than the inconsistent, error-prone humans it augments. Since human error is the root cause of most critical failures (like cyber breaches), this is an achievable and highly valuable standard.