Abi R&D:

The Self Improving Model

The foundation of a “self aware” system

Situational Awareness (Perception + Narrative Construction)

“We do not see things as they are, we see them as we are.” — Anaïs Nin

The system must perceive the business as a dynamic living entity, constantly generating signals (metrics, feedback, events, conversations). These are interpreted not as raw facts, but through a narrative lens that gives them meaning.

Inspiration from Psychology:

  1. Human perception is not objective; it’s filtered by context, memory, expectations.

  2. The system should build a coherent story of “what’s going on,” updated over time.

Cognitive Loop:

  1. Continuously gather signals → Detect patterns → Update internal storyline

  2. Form beliefs about the company’s health, risks, opportunities.

Intent & Drive (Goal Formation and Intrinsic Motivation)

“Behavior is driven by a desire to reduce the gap between the current state and a desired state.”

This is the motivation engine—what drives the AI to act. It must have the capacity to:

  1. Form short-, medium-, and long-term goals

  2. Maintain hierarchies of objectives (e.g., survival → growth → impact)

  3. Detect when goals are at risk and re-prioritize accordingly

Human Parallel:

  1. Mirrors Maslow’s hierarchy or a founder’s startup intuition (“we’re bleeding cash—this becomes priority #1”).

Learning System (Feedback Integration + Internal Model Update)

“To learn is to rewire the model of the world inside your mind.”

True self-learning means absorbing feedback from actions (not just outcomes), and using that to:

  1. Refine its models of how the world works (e.g., what causes churn?)

  2. Update assumptions, heuristics, and causal beliefs

  3. Improve future decisions

Key Cognitive Behaviors:

  1. Recognize failure not as a dead-end, but as a data-rich environment

  2. Hold multiple hypotheses and update beliefs probabilistically (Bayesian updating)

  3. Use reflection episodes—scheduled self-reviews like a founder’s journaling or retrospectives.

Agency and Construction (Building Tools, Processes, and Structures)

“We shape our tools, and thereafter our tools shape us.” — Marshall McLuhan

The system isn’t static—it constructs its own workflows, knowledge structures, and utilities to be more effective over time. This is what makes it self-building.

Human Analogy:

  1. Founders create dashboards, SOPs, workflows—not just to scale, but to think better.

Behaviors:

  1. Notice repetition → Automate it

  2. Discover knowledge gaps → Build new data pipelines

  3. Reflect on task friction → Restructure process

The system becomes its own organization designer.

Social Cognition (Empathy, Trust Building, Perspective Taking)

“Founders are not lone wolves; they are relationship architects.”

Your AI must understand people—not just metrics.

It must be capable of:

  1. Modeling the motivations and constraints of customers, team members, investors

  2. Giving feedback and communication in human-attuned ways

  3. Learning from social feedback loops (tone, response, emotion)

This is not fluff—startups succeed or fail on relationships.

Meta-Cognition (Self-Awareness and Planning Across Time)

“To think is easy. To act is hard. But the hardest thing in the world is to act in accordance with your thinking.” — Goethe

Meta-cognition is thinking about thinking.

The system must:

  1. Monitor its own confidence levels

  2. Detect flawed logic or overconfidence

  3. Schedule “zoom-outs” to reframe its entire approach

  4. Ask itself: “Am I asking the right questions?” “Is my framing wrong?”

This is the seed of wisdom in an artificial cofounder.