{"id":6597,"date":"2026-04-17T07:42:58","date_gmt":"2026-04-17T07:42:58","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/6597\/"},"modified":"2026-04-17T07:42:58","modified_gmt":"2026-04-17T07:42:58","slug":"this-simulation-startup-wants-to-be-the-cursor-for-physical-ai","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/6597\/","title":{"rendered":"This simulation startup wants to be the Cursor for physical AI"},"content":{"rendered":"

The promise of physical AI is that engineers will be able to program physical agents the same way they do digital ones.\u00a0<\/p>\n

We\u2019re 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.\u00a0<\/p>\n

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.\u00a0<\/p>\n

Antioch<\/a>, a startup building simulation tools for robot developers, wants to close what the industry calls the sim-to-real gap \u2014 the challenge of making virtual environments realistic enough that robots trained inside them can operate reliably in the physical world.<\/p>\n

\u201cHow 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?\u201d Antioch co-founder Harry Mellsop said.<\/p>\n

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.<\/p>\n

Mellsop started the New York-based company with four co-founders in May of last year. Two of the other founders, Alex Langshur and Michael Calvey, joined him to co-found Transpose, a security and intelligence startup, and sell it to Chainalysis<\/a> for an undisclosed amount. The other two \u2014 Collin Schlager and Colton Swingle \u2014 previously worked at Meta Reality Labs and Google DeepMind, respectively.<\/p>\n

Techcrunch event<\/p>\n

\n\t\t\t\t\t\t\t\t\tSan Francisco, CA
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\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 13-15, 2026\n\t\t\t\t\t\t\t<\/p>\n

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\u2019s 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.\u00a0<\/p>\n

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\u2019t have the capital to build physical testing arenas or drive sensor-studded cars for a few million miles.\u00a0<\/p>\n

\u201cThe vast majority of the industry doesn\u2019t use simulation whatsoever, and I think we\u2019re now just really understanding clearly that we need to move faster,\u201d Mellsop said.\u00a0<\/p>\n

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\u2019s software would receive in the real world. These environments allow developers to test edge cases, perform reinforcement learning, or generate new training data.<\/p>\n

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.\u00a0<\/p>\n

\u201cWhat happened with software engineering and LLMs is just starting to happen with physical AI,\u201d \u00c7a\u011fla Kaymaz, a partner at Category Ventures, told TechCrunch. \u201cWe do a lot of work on dev tools, and we love that vertical, but the challenges are different. With software, you can have these bad coding tools, and the risk is generally pretty contained to the digital world. In the physical world, the stakes are much higher.\u201d<\/p>\n

Antioch\u2019s focus now is mainly on sensor and perception systems, which account for the bulk of the need in automated cars and trucks, farm and construction machinery, or aerial drones. Aspirations for physical AI to power generalized robots to replicate human tasks are further away. While Antioch\u2019s pitch is to startups, some of its earliest engagements have been with huge multinationals that are already investing heavily in robotics.\u00a0<\/p>\n

Adrian Macneil has a solid understanding of this space. As an executive at the self-driving startup Cruise, he built the company\u2019s data infrastructure, and in 2021 founded Foxglove, a company that offers the same kind of data pipelines to physical AI startups. Macneil is backing Antioch as an angel investor.<\/p>\n

\u201cSimulation is really important when you\u2019re trying to build a safety case or dealing with very high-accuracy tasks,\u201d he said at the Ride AI conference in San Francisco on Wednesday. \u201cIt\u2019s not possible to drive enough miles in the real world.\u201d<\/p>\n

Macneil would like to see the same kind of tools that drove the SaaS revolution \u2014 platforms like GitHub, Stripe, and Twilio \u2014 emerging to support physical AI. \u201cWe need a lot more of the entire toolchain to be available off the shelf,\u201d he told TechCrunch.\u00a0<\/p>\n

\u201cWe genuinely all think that anyone building an autonomous system for the real world is going to do so in software primarily in two to three years,\u201d Mellsop said. \u201cIt\u2019s the first time you can have autonomous agents iterate on a physical autonomy system, and actually close the feedback loop.\u201d<\/p>\n

There are already experiments in this direction. David Mayo, a researcher at MIT\u2019s Computer Science and Artificial Intelligence Laboratory, is using Antioch\u2019s platform to evaluate LLMs. In one experiment, Mayo has AI models design robots, then use Antioch\u2019s simulator to test them. It can even pit the models against each other in simulated contests, like pushing a rival bot off a platform. Giving the LLMs a realistic sandbox could help provide a new paradigm for benchmarking them.<\/p>\n

Before a world of AI engineers arrives, however, there is still more work ahead to close the gap between the digital models and the real world. If it can be done, developers will be able to create the kind of data flywheel that Macneil believes is the key to the success of category leaders like Waymo, where engineers are increasingly confident that next month\u2019s model will be more capable than the last.<\/p>\n

If other companies want to replicate that success, they\u2019ll need to build those tools themselves \u2014 or buy them.<\/p>\n","protected":false},"excerpt":{"rendered":"The promise of physical AI is that engineers will be able to program physical agents the same way…\n","protected":false},"author":2,"featured_media":6598,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[1754,24,6055,25,1367],"class_list":{"0":"post-6597","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai","8":"tag-a","9":"tag-ai","10":"tag-antioch","11":"tag-artificial-intelligence","12":"tag-exclusive"},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/6597","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=6597"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/6597\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/6598"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=6597"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=6597"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=6597"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}