Every breakthrough follows the same cycle: fear → casualties → standards. Vaccines, aviation, nuclear energy — all went through it. Today, AI is racing through Stage 2: mass adoption has happened, but mature standards are still catching up.
The Technology Cycle
Stage 1. Early chaos. Experiments, failures, frightening cases. For AI, that’s deceptive model behavior in lab tests and easy jailbreaks.
Stage 2. Mass adoption. AI is embedded in global industries, yet “guardrails” are fragile. Incident databases grow daily — just like aviation before ICAO and FAA standards.
Stage 3. Mature standards. Shared tests, independent evaluations, mandatory certification and reporting. We’re moving there — through the EU AI Act, NIST risk profiles, new safety institutes, and “responsible scaling” pledges from companies.
- 1Early chaos
blind spots and vulnerabilities exposed - 2Mass adoption
products everywhere, but risk practices immature - 3Mature standards
metrics, audits, mandatory incident reporting
The real question: will we learn fast, or wait for a catastrophic lesson — as other industries once did?
Research, Standards, Incident Repositories
Curated list for deeper reading — no inline clutter, just clean clickable cards.
Hidden deceptive behaviors resistant to fine-tuning
Independent evaluations on model deception
Multi-model tests on insider-style deception
Implementation steps for GPAI & high-risk systems
Risk framework with Generative AI profile (2024)
Approaches to evals, red teaming, “inability” arguments
International principles for generative AI
1200+ recorded cases of harm and near-misses
154M lives saved estimate over 50 years
How standards drove aviation safety
Technology never stops. Society adapts through standards. AI is no exception. The only question is speed and discipline.