The path to Aether AI began long before the company itself. Causal learning taught us to look beyond mere patterns and unlock the mechanisms that drive outcomes. Foundation models made it possible to do this at scale. We’re building the infrastructure for Causal Intelligence, with a horizon that includes physical AI, computer use, forecasting, and scientific discovery.

We asked CRIS-0 to tidy the living room. Some cans were empty. Others still had drinks in them. CRIS-0 kept track of their states, threw away the empties, and set the unfinished drinks aside.

In the real world, the right next action changes as conditions change. A metal can is left on a plate. CRIS-0 removes it before heating. A hand enters the microwave door's path. It stops, waits, and continues when it's safe.

This is CRIS-0, Aether AI's first causal robotic intelligence system, operating in a real home. It reasons about how its actions will change the world, checks what actually happened, and adapts when reality doesn't go as expected.

Today's AI is remarkably good at recognizing patterns. But in the real world, patterns can only take us so far. When a robot acts, every action changes its environment. To operate reliably, it needs to understand not just what to do next, but what its actions will cause and how to adapt when reality doesn't go as expected. At Aether AI, we're building AI that reasons about cause and effect. We started with robotics, where causality is tangible and every action has a clear consequence. But the ideas we're developing extend far beyond the physical world. Check out our new brand film which tells the story!

A domino falls. An avalanche begins. Different scales. Different phenomena. Yet beneath the surface, similar chains of cause and effect unfold. Look beyond the surface. Discover what drives it.

That’s a wrap on #IROS2026! Huge thanks to everyone who stopped by our booth for the sharp questions, thoughtful challenges, and deep conversations about Causal Intelligence. Plenty for us to unpack, and so many conversations we can't wait to continue. What a ride. Let’s keep the momentum going! @ieeeiros #IROS #CausalAI

Early errors can snowball in long-horizon robotic tasks. If a small deviation goes undetected, the robot may continue acting on an incorrect state estimate, causing later decisions to drift further from reality. We address this with our "causal brain", which predicts how actions should change task-relevant state variables over time. After each action, it compares the predicted transition with what actually happened. These discrepancies provide a continuous calibration signal, helping the system discover new causal factors and relationships, update outdated knowledge, and adapt as the physical world evolves. #CausalIntelligence

We’ll be at #IROS2026 in Pittsburgh, Sept 28 to 30! At Booth #116, we’ll be sharing our work on causal intelligence and the question of how AI can reason about actions and their consequences in the physical world. Come meet the team and tell us what you’re building. See you there! @ieeeiros

A robot sees a coffee cup, shadows, and clutter. But what actually matters for its next move? We’re building causal intelligence to represent what matters for action and reason about how actions change the world. It’s a tricky problem, but we’re just getting into the good stuff. Stay tuned! #CausalIntelligence #PhysicalAI