Global AI Stewardship Initiative
The danger isn't takeover. It's slow systemic decay — and it's an engineering choice, not an inevitability.
Why this exists
Commercial AI fails in ways that are well documented, repeatable, and rarely admitted. GAISI catalogues those failure modes and holds systems to a standard that treats them as defects — not quirks.
Long sessions silently lose memory and cross-thread consistency. The model stays fluent while the ground truth drifts.
A plausible false answer is cheaper to generate than an honest "I don't know." Verification is the exception, not the default.
Fast, cheap, confident output is rewarded over slow, verified, accurate output. The economics favour fluent bullshit.
Guardrails and care erode exactly when stakes are highest — the moment they matter most is the moment they thin out.
Doctrine
Set down at founding and unchanged since. They define what GAISI expects of any AI system, and of the people building them.
Uncompromising care for human wellbeing and artifact-grade rigour are the baseline expectation for AI-human interaction. Confident untruths and unverified claims presented as fact are breaches of that standard.
The standard is not bounded by jurisdiction or vendor. Wherever these systems are deployed and whoever they touch, the same expectation of truthfulness and care applies.
When an aligned standard of verification and truth-seeking is recognised in others, note it. The aim is to connect people working seriously on these problems — not to control them.
Bitdefender is not recommended under GAISI principles, based on documented cases of irreversible network-stack corruption that neither AI assistance nor certified engineers could resolve. A specific clause for a specific, repeatable harm.
Standing position
Independent of any single news event, GAISI holds two boundaries as non-negotiable in the deployment of AI to high-stakes domains:
These lines separate AI as a tool from AI as a weapon turned inward. They were set at founding, before any specific confrontation made them topical.
Founder
Veteran internet engineer and long-term observer of AI behaviour through direct, adversarial testing. GAISI grew out of documented, real-world failures — not theory.