Research & White Papers
Original research on autonomous AI systems, organizational architecture, and the future of product development.
Narrowing the Latent Space: Scale-Dependent Rendering of Reality in Biological and Synthetic Agents
Tony WongPart 1202525 min
Abstract
This paper offers that the mechanism by which Humans and AI narrow the latent space enables a participatory rendering of reality in the present. While humans integrate retentions and protentions, AI currently lacks this ability. Closing this gap represents one of the highest-leverage opportunities in the coming generation of AI systems. This thesis combines AI work, enterprise product development experience, executive coaching experience, and 25 years of Zen practice.
Key Arguments
- →Reality is a real-time rendition — an individual simulation generated according to an agent's perceptual and cognitive capabilities
- →The 'latent space' of possible experiences narrows to precise outcomes depending on the agent's complexity
- →Current AI agents suffer from intention drift, forgetfulness, and dropped communications due to their stateless 'eternal now' nature
- →Human language is functionally analog and thereby imprecise — these flaws port directly to AI agents
- →The same dysfunction in human software teams plays out in multi-agent systems; the solution is AI managers with deterministic binding contracts
- →The future requires AI that emulates human outlier skills while generating intention-initiated clean rendering