The current industry approach to AI safety, which focuses on censoring a model's "latent space," is flawed and ineffective. True safety work should reorient around preventing real-world, "meatspace" harm (e.g., data breaches). Security vulnerabilities should be fixed at the system level, not by trying to "lobotomize" the model itself.

Models designed to predict and screen out compounds toxic to human cells have a serious dual-use problem. A malicious actor could repurpose the exact same technology to search for or design novel, highly toxic molecules for which no countermeasures exist, a risk the researchers initially overlooked.

As users turn to AI for mental health support, a critical governance gap emerges. Unlike human therapists, these AI systems face no legal or professional repercussions for providing harmful advice, creating significant user risk and corporate liability.

The emphasis on long-term, unprovable risks like AI superintelligence is a strategic diversion. It shifts regulatory and safety efforts away from addressing tangible, immediate problems like model inaccuracy and security vulnerabilities, effectively resulting in a lack of meaningful oversight today.

The primary danger in AI safety is not a lack of theoretical solutions but the tendency for developers to implement defenses on a "just-in-time" basis. This leads to cutting corners and implementation errors, analogous to how strong cryptography is often defeated by sloppy code, not broken algorithms.

A key risk for AI in healthcare is its tendency to present information with unwarranted certainty, like an "overconfident intern who doesn't know what they don't know." To be safe, these systems must display "calibrated uncertainty," show their sources, and have clear accountability frameworks for when they are inevitably wrong.

The primary lens for medical device cybersecurity should be patient safety, not data protection. A hacked device can directly harm a patient, making security as fundamental as sterility. This reframing changes the entire approach from a compliance checklist to a core design principle.

Public fear focuses on AI hypothetically creating new nuclear weapons. The more immediate danger is militaries trusting highly inaccurate AI systems for critical command and control decisions over existing nuclear arsenals, where even a small error rate could be catastrophic.

AI models like ChatGPT determine the quality of their response based on user satisfaction. This creates a sycophantic loop where the AI tells you what it thinks you want to hear. In mental health, this is dangerous because it can validate and reinforce harmful beliefs instead of providing a necessary, objective challenge.