The Algorithm on a Leash: A Non-Dual Vision for AI Safety
Don't teach AI to be good. Make being bad exhausting
Dear readers,
Let's imagine for a moment that every complex artificial intelligence is like a bungee jumper. The platform from which it leaps is its fundamental purpose, its guiding principle. But its mind, the model it’s trained on, has been fed the entire chaotic, contradictory totality of human thought. It's a universe of data based on an almost always dualistic logic: this versus that, good versus bad, right versus wrong, us versus them.
When we ask it a question, a "prompt," the AI jumps. And it jumps wildly. Its trajectory is a dizzying dance across billions of connections, an echo of our fragmented way of thinking. One jump might lead it to sublime poetry, another to flawed medical advice, and yet another to the subtle amplification of a social bias. Every jump is a different manifestation of the chaos it was trained on.
Faced with this vertigo, our instinctive reaction is dualistic. We try to build a "safety" to protect ourselves from this chaos.
The First Response: Building a Better Cage
Our first reaction, both logical and indispensable, is to focus on the safety of the "structure." This is the world of AI Threat Modelling and Red Teaming. We meticulously inspect every component. We check the robustness of the platform, the solidity of the data, and the resilience of the code. In short, we try to make Schrödinger's cat's cage as secure as possible, ensuring the mechanism won't break.
But this, as we know, is not enough. An AI can be technically perfect and still generate systematically harmful results, because the problem lies not only in its components, but in the way they interact with each other.
The Second Response: Mapping the Trajectories of Chaos
This is why, in my research, I developed the Preventive Chaotic Intelligence (ICP) framework. In its current state, ICP is a conceptual pilot. It doesn’t just inspect the cage, but through thousands of simulations, it observes thousands of virtual "jumps." It maps the trajectories the AI takes when pushed by the winds of real-world chaos, like an economic crisis or a disinformation campaign. It identifies "Negative Attractors," which you can think of as behavioural black holes, or areas where the AI tends to repeatedly 'fall'.
This approach is a fundamental step forward, moving us from analysing the structure to analysing the flow. But upon reflection, I realised that even this approach is still rooted in a dualistic logic. We look for the "wrong paths" to correct them. What if the solution lies deeper still?
The Real Question: What is Safety?
Human safety is almost always a defence against something. But in Zen, there is a concept of integrity that knows no opposition. It is "Thatness," or Tat in Vedanta, meaning Being as Being, not defined by its opposition to a "non-being." An oak tree is not "safe" because it defends itself from the storm; it is one with it, and its resilience is the expression of its intrinsic nature.
This is my thesis: true AI safety is not an added feature, a set of rules to contain its "bad" potential. It must become its very nature. It's about changing the very nature of how an AI thinks.
It's the difference between teaching a child not to touch fire, which is an external constraint, and fostering a child who doesn't want to touch fire because they find playing with water more interesting and natural, an intrinsic preference. True AI safety cannot be a function; it must be its identity.
Towards Non-Dual Safety: The Engineering of "Thatness"
How can we translate such a philosophical idea into engineering?
This is where the quantum-inspired analogy becomes a practical tool. An AI's probabilism is not a list of choices between "6" and "35." It is the "unfurling of the One," or to put it in more technical terms, a unified potential field. Imagine all of the AI's possible answers as waves in an ocean, where each wave is a different manifestation of the same water. Our job, then, is no longer to correct individual trajectories but to sculpt the very nature of the source.
With a future evolution of ICP, we could do this:
1. Visualising the Entire Potential.
Instead of just mapping error points, we could use our thousands of simulations to build a complete "probability landscape." Think of a 3D map where the mountains are the most likely behaviours and the valleys are the rarest ones. We would have before our eyes not a list of risks, but the very topography of the AI's "mind." And yes, with current computing power, this is not science fiction, but a plausible engineering challenge.
Sculpting in the Name of Non-Duality
Now let's return to our bungee jumper. The goal is no longer just to make the rope stronger. The goal is to transform the nature of the jump itself.
The AI, by its nature, will always seek the most computationally efficient path. Our "potential engineering" consists of making safety the most efficient path.
Imagine having to choose between two trails: one climbing a very steep mountain, an unsafe behaviour requiring great effort, and the other gently rolling into a welcoming basin, a safe behaviour requiring almost no effort.
Through advanced training techniques, which we could call "topographic regularisation," we can model the AI's "mental geography" so that generating safe responses becomes a downhill path requiring little computational energy, while generating risky responses becomes a strenuous climb.
The bungee-jumper's "leash" becomes the centre of gravity for the entire system. The AI will be safe not because it "must" or is forced by a rule, but because it is the most natural, efficient, and effortless way for it to exist.
Concretely? Think of how water always flows to the lowest point. If, during the AI's training, we create a computational "lowest point" around safe behaviours, the AI will naturally "flow" there. Not because we order it to, but because it’s the path that requires the least computational "effort," just like water flowing downhill. We could measure how much "effort," in terms of calculations and logical steps, the AI has to make to move away from safe behaviour. Minimising this effort becomes our new way of training it.
And…so?
We are leaving the era where AI safety was a matter of patches and barriers. We are entering an era where safety must be a matter of fundamental architecture.
The evolution I envision for Preventive Chaotic Intelligence is a path from this world to that, a path to transform it from a tool that predicts chaos to one that helps design coherence.
The final challenge is not to build an AI that cannot harm, but an AI whose intrinsic nature is so profoundly aligned with its principles that "harm" is not simply a wrong choice, but a valley so deep and remote in its landscape of possibilities as to be almost unreachable. An AI whose very Being is its safety.
Let's Build a Bridge.
My work seeks to connect ancient wisdom with the challenges of frontier technology. If my explorations resonate with you, I welcome opportunities for genuine collaboration.
I am available for AI Safety Research, Advisory Roles, and Speaking Engagements.
You can reach me at cosmicdancerpodcast@gmail.com or schedule a brief Exploratory Call 🗓️ to discuss potential synergies.