Causal Labs
We build safe, steerable physics foundation models to understand, predict, and shape weather conditions.
| Date | Investors | Amount | Round |
|---|---|---|---|
* | $6.0m | Seed | |
| Total Funding | 000k | ||
Causal Labs is a technology company using artificial intelligence to create physics-based models for addressing large-scale challenges, with an initial focus on weather prediction and control. The company was founded in 2024 by Kelsie Zhao and Dar Mehta and is headquartered in San Francisco.
The founders bring extensive experience in safety-critical AI and robotics to the firm. Kelsie Zhao is a veteran of the self-driving car industry, where she developed foundational elements of Cruise's autonomous driving technology. CEO Dar Mehta has a background that includes work at Google Research, Meta, Cruise, and a YC-backed robotics startup. Their shared experience in deploying autonomous systems that struggle to generalize beyond their training data led them to focus on creating AI that understands cause and effect from first principles.
Causal Labs is developing an AI-driven system that simulates atmospheric behavior to generate more accurate weather forecasts in minutes, a significant departure from traditional supercomputer models that can be slow and expensive. The platform aims to provide hyper-local, real-time weather forecasting and decision-making tools. Beyond just forecasting, the model is designed to redefine how weather-related decisions are made, including potential atmospheric interventions and operational responses to climate risks. The company's business model involves providing these AI-powered physics models to transform complex data into actionable insights for businesses and governments. It serves industries such as agriculture, renewable energy, aviation, and emergency management that are significantly impacted by weather and climate conditions.
In March 2025, Causal Labs announced it had raised $6 million in a seed funding round led by Kindred Ventures, with participation from other firms including Refactor, BoxGroup, and Factorial. The company intends to use the capital to expand its team, advance the development of its initial models, and launch pilot programs. The long-term vision extends beyond meteorology to create a general, large-scale physics foundation model applicable to other domains like space, climate change, and government simulations.
Keywords: causal AI, physics-based models, weather prediction, climate tech, atmospheric science, AI forecasting, environmental technology, decision intelligence, real-time forecasting, autonomous systems, climate risk, renewable energy, agriculture technology, aviation weather, physics simulation, large-scale AI, foundation models, machine learning, robotics, climate adaptation