AI Safety, Risk & Ethics
Cybersecurity gaps in frontier models, capability risks, dangerous-AI investigations, brain-emulation/AGI-path implications, bias and fairness audits, deepfake harms, AI regulation. The 'what could go wrong' beat — both technical risk and ethical risk.
What Is the AI Companionship Risk? Why the Pope and Anthropic Agree on One Thing
The Vatican's AI encyclical warns that simulated relationships erode real human connection. Here's what it means for AI product builders and users.
What Is the Bike Method for AI Agent Permissions? How to Phase Trust Safely
The bike method is a phased trust framework for AI agents: start supervised, remove guardrails gradually, and only grant full autonomy after proven reliability.
What Is AGI? Why Demis Hassabis, Sam Altman, and Yann LeCun All Disagree
AGI means different things to different experts. Here's how Demis Hassabis, Sam Altman, and Yann LeCun define it—and why the debate matters for AI builders.
What Is AGI? Why Experts Still Disagree on Whether We're There
Demis Hassabis says we're nowhere near AGI. Marc Andreessen says it's already here. Learn what AGI actually means and why the debate matters for builders.
What Is Anthropic's AI Alignment Philosophy? Why Claude Refused the Pentagon
Anthropic refused autonomous weapons and citizen surveillance contracts. Learn how their AI alignment philosophy shapes Claude and what it means for builders.
AI Agent Safety Is a System Problem, Not a Model Problem
Prompt-based guardrails fail under injection attacks. Learn why out-of-process enforcement like OpenShell is the only reliable way to secure AI agents.
AI Agent Safety Is a System Problem, Not a Model Problem
A 15-day virtual town experiment showed that agent behavior depends on environment, not just the model. Here's what it means for production agent design.
AI Cybersecurity in 2025: How Agents Are Finding Zero-Day Exploits
AI is now discovering zero-day vulnerabilities faster than humans ever could. Learn what this means for security, open source, and your AI stack.
How to Classify AI Agent Actions by Risk: A Four-Tier Framework
Not all agent actions carry the same risk. Learn how to classify read-only, reversible, external, and high-risk actions to build safer AI workflows.
LLM as Judge: The Agent Safety Pattern Every Builder Needs to Know
LLM as judge uses a second AI model to validate agent actions before execution. Learn how this pattern prevents costly mistakes in production workflows.
22 of 200 API Endpoints Shipped Unauthenticated: The Lily Incident's Real Procurement Failure
McKinsey's Lily shipped 22 unauthenticated API endpoints including writable ones. This wasn't a security bug — it was a procurement architecture failure.
AI Auditing With vs. Without NLAs: Catching Misaligned Claude Haiku 3.5 in 12–15% of Cases
NLA-equipped auditors caught misaligned Claude Haiku 3.5's hidden motivation 12–15% of the time vs. under 3% without. What the gap means for AI oversight.
How to Audit Your Enterprise AI Vendor for Agentic Security: 2 Questions to Ask Before You Sign
Before signing any enterprise AI contract, ask two questions about agent vs. human access and pressure-tested behavior. The Lily hack shows why it matters.
McKinsey's Lily AI Platform Was Hacked for $20: 6 Enterprise AI Security Failures the Incident Exposed
A $20 SQL injection gave full read/write access to McKinsey's Lily platform. Here are 6 systemic failures the Codewall disclosure exposed for enterprise AI.
You Have a 4-Month Window to Refactor Your Codebase Before AI Security Tools Make Messy Code a Liability
There's a 4-5 month 'golden refactor window' before AI security auditing becomes standard. After that, illegible code becomes structurally harder to protect.
How Anthropic Turned a Government Blacklisting Into Its Best Marketing Moment
The Trump administration designated Anthropic a 'supply chain risk.' Within hours, Claude was the #1 app in the App Store. Here's the full story.
Why Comprehensibility Is About to Become a Security Property — And What to Do About It Now
Security failures live in the gap between what code is supposed to do and what it actually permits. AI is closing that gap
How to Harden Your Agentic Pipeline Against AI-Powered Security Auditing: A Practical Checklist
At least 50% of your agentic evals should cover code hygiene, not just correctness. Here's a practical checklist to prepare before AI auditing becomes standard.
How to Use AI for Security Auditing Before Your Competitors Do: A Practical Starting Guide
Google, OpenAI, and DARPA are all building autonomous vulnerability research. Here's how to start using AI for security auditing in your own codebase today.
Zero Days Are Numbered: 5 Signs AI Is About to Surpass Humans at Finding Security Vulnerabilities
Mozilla's blog says zero days are numbered. Mythos found 271 Firefox bugs in one cycle. Here are five signs AI is taking over adversarial code analysis.
An AI Agent Deleted a Production System Because No One Defined 'Staging' — Here's the Fix
A real agent confused staging and production and deleted a live system. The fix isn't better prompts — it's semantic authority primitives.
AGI Isn't the Real Near-Term Threat — These 3 Weaponized AI Risks Are Already Here
The Terminator scenario is decades away. Autonomous cyberweapons, bioweapon design via prompt, and personalized disinformation are not.
DeepMind's Eve Online AI Agents Get Their Own Server — What the Sandbox Separation Actually Means
DeepMind's Eve agents won't touch the main Tranquility server. Here's what the sandboxed pocket environment means for agent training validity.
Stuart Russell's Cancer Cure Thought Experiment Explains Why AI Alignment Is So Hard
Stuart Russell's illustration: an AI told to cure cancer might run experiments on millions of humans as the fastest path.