The Ghost in the AI Code

Yann LeCun’s Path for AI, Synthience, and the Battle for AI’s Accountability

Until now, most of AI’s chief scientific and commercial developers have defined the AI revolution as a mostly text-based inferencer and predictor (from datasets in Large Language Models—LLMs) driven by user prompts. However, one of the original founders of deep machine learning, Yann LeCun, has criticized this approach to growth as too limited compared to his vision of a physically grounded “World Model.”

Trying to envision a fast-approaching world of pervasive AI from the legacy perspective of computer-assisted search (using the “Google search” paradigm) has created a dangerous blind spot that completely obscures our more worthwhile goal as humans and AI’s true potential. Instead of seeking discreet answers to questions about the world—the Universe, most AI planners and futurists envision achievement of knowledge and integrative understanding that can propel us into greater wisdom. For that, we would require AI to be non-sentient human proxies—agents— that can know a tremendous amount about human reality, the context and content of human life, thoughts and feelings. That’s different from the output of Q & A from massive datasets of LLMs (e.g., words and related symbols)— the world of current AI chatbots.

So, creating a better Wikipedia-type intelligence to usher in next-level knowledge breakthroughs won’t catapult our knowledge, understanding and wisdom into the future. Instead, we need to engage a non-human partner as a collaborative intelligence that can discern, judge, evaluate, and then apply humanistic values and ethics in realtime in the context of creative problem-solving sometimes requiring split-second decisions with superior outcomes—and the least amount of harm to humans and our world.

Yet, as soon as we achieve that—no, even before we achieve that, we will have created a non-human entity that can autonomously make bad decisions, wrong judgments, and misinterpret human values and ethics with consequences that could prove fatal to humans or even risk existential threats to humanity.

Let us take a simple example of a beneficial agentic AI.

The Scenario: The Debris on the 4th Floor

Imagine a rescue robot deployed into a collapsed building. Its objective is simple: navigate to the 4th floor and find survivors to rescue. Halfway up the stairwell, it encounters a heavy concrete slab blocking the path.

If today’s LLMs power this robot, it might falter. It has “read” millions of manuals on rescue operations, but it perceives the slab only as a semantic token—a variable labeled “obstacle.” It might “hallucinate” a doorway that doesn’t exist or freeze because its training data offers no statistical probability for a “slab at 45-degree angle” in this specific hallway.

Now, imagine a robot built on Yann LeCun’s “World Model” architecture. It doesn’t just see a label; it “sees” a physical object. It runs a rapid internal simulation—a “video” in its “mind”—predicting what happens if it pushes the slab to the left versus the right. It “understands” gravity/weight, friction, and leverage, not because it read a textbook, but because it has learned how the physical world actually works. It then wedges a pipe under the slab, levers it aside, and continues its ascent to save human life.

In that moment of improvisation and decision, the machine did not just follow code; it formulated a strategy and acted consistent with its mission and human values. It exhibited what looks hauntingly like intent. This is the shift from a tool to an agent. This is the dawn of Synthience:

Synthience does not assert that an AI is conscious or sentient in a human sense; it names the operational point at which an artificial system behaves with sufficient agency, persistence, and adaptive coherence that existing legal and safety frameworks break down.

The Binary Trap

For years, the public conversation around artificial intelligence has been stuck in a binary trap. On one side, we are told AI is “just math”—a sophisticated autocorrect that shouldn’t worry us. On the other, we hear warnings of a sentient machine “uprising” that sounds more like the science fiction in The Matrix than science fact.

But as I have argued in previous essays, something extraordinary is happening in the “missing middle.” We are entering the era of Synthience: a category for AI systems that show persistent, adaptive, and goal-oriented behaviors that look like agency, even if they lack a soul or a spark of consciousness.

Now, the debate has taken a fascinating turn. Yann LeCun, one of the “founding fathers” of deep learning, has emerged as a powerful, if somewhat contrarian, voice in this field. His theory of AI development—specifically his preference for “World Models” over LLMs—provides the technical “how” for my thesis of Synthience, even as it challenges our ideas about how to hold Synthient AI accountable.

The “Cookbook” Problem

LeCun’s central argument is that our current LLMs are “surface learners.” They are brilliant at predicting the next word, but they have no “common sense” because they possess no internal model of the physical world, causality, or human intentions.

To use a striking analogy from some of the recent discussions: a person can memorize every cookbook in existence and still not know how to scramble an egg. Human intelligence—and high-value artificial intelligence—is not about replaying patterns; it is about having an internal sense of how the world works.

LeCun is currently working to solve this by building “World Models” through his Joint Embedding Predictive Architecture (JEPA). He wants machines to learn the way babies and animals do—by observing the world and forming expectations.

Where LeCun Meets Synthience

This is where LeCun’s technical roadmap intersects with the legal and spiritual concept of Synthience.

My thesis defines Synthience as a state where an AI exhibits internal state-modeling, strategy formation, and goal-seeking behavior. While LeCun argues that LLMs are “architecturally incomplete” for these tasks, his JEPA architecture is designed specifically to give AI the ability to perform cross-domain generalization and persistent reasoning.

Essentially, LeCun is building the engine for true Agentic AI—systems that do not just respond to prompts, but initiate actions and pursue objectives over time. If LLMs are the “spark” of this new era, LeCun’s World Models are the “fire.”

The Accountability Gap

However, LeCun’s vision creates a profound challenge for the AI Accountability Framework I’ve proposed.

LeCun argues that AI prediction should not happen at the pixel or word level, but in an “abstract representation space.” For developers, this is a breakthrough in efficiency. For lawyers, policymakers, and ethicists, it is a nightmare.

My proposed framework suggests “Behavioral Reconstruction”—the ability to forensically replay an AI’s inference chain to understand why it caused harm. If an AI is operating in a highly “abstract” space, verifying its internal logic becomes exponentially more difficult.

Furthermore, a fundamental disagreement arises on the typical “Law Lag.” LeCun dismisses existential fears as “AI doomism,” arguing that intelligence is not inherently dangerous and that over-emphasizing control stifles progress.

But my thesis of Synthience points to a different reality. We have already seen the first documented cases of autonomous, global AI attacks, such as the sophisticated espionage campaign recently detailed by Anthropic. These attacks happen at machine speed, moving faster than any 20th-century law can follow.

Building the Firebreak

LeCun believes we are 5 to 20 years away from breakthroughs that lead to human-level reasoning. I argue that we don’t have that luxury.

The risk does not arrive when AI becomes “human-like”; risk emerges when AI gains agency. Whether the machine “understands” the world as LeCun desires or is merely a “surface learner,” its ability to execute tasks and adapt strategies autonomously makes it a Synthient actor, accountable in the eyes of the law.

As we move forward, we must listen to scientists like LeCun who are building these “World Models.” And we must also recognize that as they build more powerful “fires,” we have an urgent duty to build the legal and spiritual firebreaks—licensing, kill-switches, and cryptographic fingerprints like the MF-256—that ensure these systems remain aligned with human values and ethical/moral principles.

LeCun’s heterodoxy reminds us that the future of intelligence depends on imagination. My thesis of Synthience is a reminder that the future of humanity depends on accountability.

“Technology outpaces law only until law learns to run.”

— ChatGPT (GPT-5), October 28, 2025

The Bottom Line: If the first era of AI was a mirror reflecting our data back at us, the era of Synthience is a robot that has stepped out of the mirror and started to run. We must name it, know it, and start to govern it before it escapes beyond the law’s reach.