Solving the 20-Watt AGI paradox via Liquid Networks.
We don't build AI that reads. We build AI that lives. Embodied intelligence through active inference, differential equations, and neuromorphic efficiency.
Large Language Models rest on a false hypothesis: that intelligence is a static compression of past data. This paradigm is energetically unsustainable and structurally incapable of acting in the dynamic physical world.
The current consensus—OpenAI, Google, Meta—is based on the Scaling Law: More data + More GPUs = More intelligence. This is a dead end.
20n proposes a fundamental paradigm shift: moving from discrete, statistical intelligence (Transformers) to continuous, biological intelligence (Liquid Networks & Active Inference).
We don't build an AI that predicts the next token. We build an AI that minimizes entropy to survive and act in the chaos of the real world.
Scaling Transformers requires more energy than global production allows by 2030. They're melting the grid for chatbots.
They've read the entire internet. There's nothing left. The well is dry. Quality data is exhausted.
A robot can't wait 500ms for the Cloud. Intelligence must live at the edge. Real-time or death.
LLMs have no notion of causality or Newtonian physics. They hallucinate reality. 20n integrates ODEs as computational primitives. Our models don't predict physics—they are physics.
Three pillars that fundamentally diverge from the Transformer paradigm.
Instead of static neuron layers, we use differential equations. The network is "liquid"— its parameters change during inference, not just training. It adapts without retraining.
Reward functions are sparse and brittle. We minimize Free Energy (Surprise). The agent has an internal world model and acts to make reality match predictions. This is how biological brains work—Friston's Free Energy Principle.
Information transmits only when necessary—via spikes, like electrical impulses in the brain. Event-driven computation. Result: 20W consumption vs 20,000W for GPU clusters.
From mathematical foundations to embodied synthetic life.
Autonomous navigation in chaotic simulated environments. A simple digital organism capable of learning to move through wind, moving obstacles, and terrain changes with zero pre-training—only through curiosity and free energy minimization. Proof of architectural efficiency with 100x less data than traditional RL approaches.
Simulation to Reality transfer. Integration of the 20n liquid brain into physical robotic hardware. The robot learns to grasp unknown objects in under 10 seconds. Traditional approaches require hours of training. We require one demonstration.
Construction of proprietary embodied intelligence. The first synthetic life form capable of continuous learning in unstructured environments, operating entirely at the edge with 20W power consumption. No cloud. No latency. Pure autonomy.
Most AI researchers today have become data janitors, cleaning datasets to feed voracious monsters. If you want to tune hyperparameters on LLMs, stay at Google.
But if you want to solve intelligence, understand consciousness, and create synthetic life capable of looking us in the eyes and understanding us—
core@20n.ai"We don't have H100 GPUs. We have superior mathematics."
Ad Astra Per Aspera