AI Philosophy
AI Safety, Interpretability, and Societal Risk: What We Do Not Yet Understand
AI systems are deploying faster than our ability to understand or govern them. These three pieces map the gap between capability and accountability.
Mechanistic Interpretability: Breakthrough Technologies 2026
TLDR: Anthropic, OpenAI, and DeepMind are building tools to trace prompt-to-response paths inside models. The goal is moving from black-box testing to genuine understanding of how models arrive at outputs.
Key Insight: Mapping internal reasoning traces is the critical frontier for AI safety.
AI for Everything: Breakthrough Technologies 2024
TLDR: Generative AI reached consumers faster than almost any prior technology. This piece reflects on the societal implications of that speed — implications we have not yet fully reckoned with.
Key Insight: Speed of deployment without governance creates compounding problems.
AGI Will Not Happen in Your Lifetime. Or Will It?
TLDR: Gary Marcus and Grady Booch debate AGI timelines and conclude that large language models alone are insufficient. The core obstacle is architectural — integrating many individual capabilities into a coherent whole remains unsolved.
Key Insight: Evaluate AI on demonstrated capabilities, not promissory narratives about superintelligence.
What does this mean for how we think about AI?
The gap between deployment speed and interpretability is where real risk lives. Mechanistic interpretability offers a path toward accountability, but only if we resist the AGI hype cycle long enough to fund the slower, harder work of actually understanding these systems.