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This simulation startup wants to be the Cursor for physical AI Tim Fernholz 5:30 AM PDT · April 16, 2026 The promise of physical AI is that engineers will be able to program physical agents the same way they do digital ones.
We’re not there yet. Robotics is still held back by a paucity of data from physical spaces. To train their machines, companies need to build mock-up warehouses to test their machines, while an entire industry is springing up around surveilling factory lines and gig workers to train deep learning models to operate robots.
Another option is simulation; detailed virtual replicas of real-world environments could provide the data and workspaces that roboticists need to do this work in a scalable way.
Antioch, a startup building simulation tools for robot developers, wants to close what the industry calls the sim-to-real gap — the challenge of making virtual environments realistic enough that robots trained inside them can operate reliably in the physical world.
“How can we do the best possible job reducing that gap, to make simulation feel just like the real world from the perspective of your autonomous system?” Antioch CEO and cofounder Harry Mellsop said.
To do that, the company told TechCrunch today that it has raised an $8.5 million seed round that values it at $60 million, led by venture firm A* and Category Ventures, with additional participation from MaC Venture Capital, Abstract, Box Group, and Icehouse Ventures.
Mellsop started the New York-based company with four cofounders in May of last year. Two of the other founders, Alex Langshur and Michael Calvey, helped him found Transpose, a security and intelligence startup, and sell it to Chainalysis for an undisclosed amount. The other two — Collin Schlager and Colton Swingle — previously worked at Google DeepMind and Meta Reality Labs, respectively.
Techcrunch event Meet your next investor or portfolio startup at Disrupt Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. Register now to save up to $410. Meet your next investor or portfolio startup at Disrupt Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. Register now to save up to $410. San Francisco, CA | October 13-15, 2026 REGISTER NOW The need for better simulation is at the heart of what many major autonomy companies are doing. In the self-driving car space, for example, Waymo uses Google DeepMind’s world model to test and evaluate its driving model. In theory, that technique will make deploying Waymo vehicles in new areas require less data collection, a key cost in scaling up autonomous vehicle technology.
Building and using those models to test robots is arguably a different set of skills than creating a self-driving car, and Antioch wants to build the platform that solves that problem for newer companies without the capital to do it all themselves. Those smaller companies also don’t have the capital to build physical testing arenas or drive sensor-studded cars for a few million miles.
“The vast majority of the industry doesn’t use simulation whatsoever, and I think we’re now just really understanding clearly that we need to move faster,” Mellsop said.
Antioch executives compare their product to Cursor, the popular AI-powered software development tool. Antioch allows robot builders to spin up multiple digital instances of their hardware and connect them to simulated sensors that mimic the same data the robot’s software would receive in the real world. These environments allow developers to test edge cases, perform reinforcement learning, or generate new training data.
If, that is, the simulation is sufficiently high fidelity. The challenge here is making sure the physics in the simulation matches reality so that when the model is put in charge of a real machine, nothing goes wrong. The company starts with models built by Nvidia, World Labs, and others, and builds domain-specific libraries to make them easy to use. Working with multiple customers, executives say, gives Antioch a depth of context for refining its simulations that no single physical AI company could match on its own.
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