Can we Trust AI? No – But Eventually We Must

Businesses risk overreliance on large language models because they are probabilistic, ungrounded, and prone to hallucinations, bias, sycophancy, and model collapse—weaknesses attackers and misuse can exploit. A growing AI security industry (e.g., DeepKeep, AI Sequrity, Kamiwaza) is building provenance, guardrails, drift detection and agent-level controls to mitigate operational, reputational and adversarial risks, but robust defenses remain difficult and incomplete. #ModelCollapse #DeepKeep

Keypoints

LLMs operate on token probabilities rather than objective facts, which leads to unavoidable uncertainty and errors.
Hallucinations (confabulations), bias from training data, and sycophancy pose operational and social risks to users.
Model collapse—training models on AI-generated data—causes compounding errors that degrade models over generations.
Rapid business adoption often outpaces security, exposing organizations to adversarial exploitation, data leakage, and compliance failures.
Specialized firms like DeepKeep, AI Sequrity, and Kamiwaza are developing guardrails, provenance tracking, and agent-level security to reduce AI risk.