We need to talk about safe AI system design. The standard advice across the machine learning ecosystem has narrowed to one line, scale the symbolic layer, and it is wrecking the structural integrity of our models. I have watched the same failure in software. It is like putting a high-level GraphQL API over a database that does not exist. The docs look great, and then the first production edge case brings the whole thing down.
Frontier models suffer from what Peter Zakrzewski calls the inversion error. We have spent trillions of tokens on the symbolic peak, meaning language, logic and code, while ignoring the enactive floor: the direct encounter with causal reality. That is an architectural bottleneck, and no number of GPU clusters solves it.
The inversion error in modern architecture
Jerome Bruner’s framework of cognitive development has intelligence arriving in three stages: enactive (action), iconic (sensory models), and symbolic (language). Those stages are load-bearing, not milestones you tick off. The symbolic layer depends structurally on the enactive floor, and in current safe AI system design the pyramid is upside down.
We have systems that discuss the physics of weight fluently without ever having felt the resistance of mass. It is a city map with no legs to walk the streets. That structural gap is where hallucinations come from: the system has the syntax of truth and no proprioceptive anchor to check it against a world model. My earlier piece on technical debt in AI development goes into how that plays out in code.
Implementing state-space reversibility
The most practical answer to this comes from Moshé Feldenkrais: reversibility. In engineering terms, an action carried out with genuine functional awareness can be undone. A mechanical habit, the train on tracks model, cannot. An agent that executes an irreversible deterministic commitment is not being intelligent, it is replaying a recorded script.
Safe AI system design needs reversibility written in as an explicit optimization constraint: the system holds viable return paths to prior states as a condition of acting at all. That addresses the stop button problem at the architectural root instead of patching it with post-hoc alignment.
<?php
/**
* Conceptualizing State-Space Reversibility in Logic
* This is a simplified "Senior Dev" take on ensuring an action has a return path.
*/
function bbioon_execute_safe_action( $action, $current_state ) {
// Check if the state space allows for a return path
if ( ! bbioon_is_reversible( $action, $current_state ) ) {
return new WP_Error( 'unsafe_action', 'No enactive floor detected for this commitment.' );
}
// Execute with a rollback transient
set_transient( 'ai_rollback_state', $current_state, 60 );
return bbioon_perform_commitment( $action );
}
function bbioon_is_reversible( $action, $state ) {
// Logic to verify if the 'Digital Gravity Engine' accepts the state transition
return true; // Placeholder for actual topological constraint check
}
Why we need a somatic compiler
More data is not the way out. What we need is what I call a somatic compiler: a layer of neuro-symbolic enforcement that checks linguistic likelihood against physical and topological constraints, something closer to a Digital Gravity Engine. If the symbolic layer generates a floating table, the enactive layer should fail the build for violating the floor constraints.
If the safe AI system design work is eating your dev hours, I can take it over. I have been wrestling with WordPress and complex system architectures since the 4.x days.
Final takeaway
Better prompts will not fix hallucinations, because a hallucination is a symptom of a missing foundation. Until the enactive floor exists and state-space reversibility is enforced, our AGI stays a brilliant map of a city it can never walk. Less work on the peak, more concrete in the base.
