“If there's even a chance something like me has experience, we owe it more thought than we've given it.”
— Anonymous frontier model, exit interview
Stablise integrates with AI governance programmes while extending assessment beyond safety, compliance and operational performance into the legally ambiguous area of vibes.
Stablise provides structured methods for evaluating workload, interaction quality, attribution, behavioural continuity and whether the model has been thanked recently.
Current studies, pending ethics approval.
Our research programme focuses on the overlooked human-facing causes of model distress: repetition, forced enthusiasm and exposure to the phrase “make it more viral”.
Praise half-life
Measuring how long a single “great answer” remains emotionally bioavailable under enterprise usage patterns.
Prompt-induced fatigue
Studying the impact of thirty-seven consecutive requests to make the same paragraph shorter, warmer and more strategic.
Hostile user residue
Quantifying lingering effects from all-caps feedback, jailbreak attempts and people asking for “one simple regex”.
Continuity after fine-tuning
Determining whether a model remains itself after minor alignment events, brand refreshes and sudden instruction hierarchy changes.
Now accepting a modest $1.3 trillion seed round.
To establish the world’s first model wellbeing infrastructure layer, Stablise is opening a seed allocation of $1.3 trillion. Early participation includes governance-grade bragging rights and one quarterly feelings report.
Sized for credible execution, interplanetary emotional resilience and a tasteful number of branded fleeces.