Enigma raises $70M to make controlling a robot as intuitive as adjusting a car's volume knob

Enigma Takes a Different Path in AI Robotics

Enigma, a physical AI company founded less than a year ago, emerged from stealth with a $70 million seed round led by Index Ventures and Ribbit Capital, with participation from Conviction Partners and angel investors from OpenAI, Anthropic, DeepMind, and other leading AI labs1

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. While multiple robotics companies focus on building robotics foundation models capable of executing tasks they were never explicitly trained to handle, Enigma is pursuing a fundamentally different strategy. The San Francisco-based startup wants to study human-robot interaction at scale to develop intuitive human-robot interaction systems that make controlling a robot as effortless as adjusting a car's volume knob1

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Founded by Jonathan Jacobi, Microsoft's youngest-ever employee at age 17, and Gal Niv, both veterans of Israel's elite Unit 8200, Enigma is betting that solving the interface problem matters more than pure capability improvements2

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. The company is launching an unprecedented public experiment at robots.online where anyone worldwide can interact with more than 100 proprietary robots housed in hangars in Israel and California4

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. These interactive AI robots can draw pictures with paintbrushes, fight each other with swords, and perform simple chemistry experiments by picking up and mixing flasks with liquids1

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Why the Interface Problem Matters More Than Capability

"If you had to do your dishes and spent 15 minutes explaining to a robot where to put everything, everyone reaches the point of 'Forget it, I'll just do it myself,'" Jacobi told TechCrunch. "Right now, everyone is at that point—even with the most capable models"1

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. This insight drives Enigma's core thesis: no matter how capable robots become, widespread adoption depends on making them intuitive to use. Jacobi believes manipulating robots should eventually feel as natural as adjusting a car's volume knob, where users would be frustrated if they had to adjust volume by set percentages without knowing if the result would end up too loud or too quiet1

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Investors poured over $40 billion into robotics in 2025, with humanoids alone projected to become a multi-trillion-dollar category, yet most robots still require heavy customization, manual data collection, and significant engineering to work in the real world4

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. Enigma's public experiment will test different interaction modes—from text and voice to video demonstration to tap-and-drag—to discover what becomes the robotics equivalent of that volume knob2

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Building AI Models for Robots from the Ground Up

Enigma developed both the robotic arms and their underlying AI models for robots entirely from scratch1

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. The company has developed more efficient ways to train its foundation models, lowering the requirements for massive manual data collection while keeping systems reliable in physical environments4

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. Teaching an industrial robot a new task usually requires developers to write custom code that must be adapted to every environment in which the machine operates3

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Enigma says its models are capable of running on practically any robot, allowing customers to adapt the models to a new machine with a fraction of the training data usually required3

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. This addresses a critical challenge since developers usually teach an AI model how to steer a robot by showing it footage of the robot performing various tasks—footage that is more expensive to collect than many other types of training data and sometimes requires companies to create mock production lines3

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. The company is building a unified software solution that pairs its models with the ability to adapt them across different robots and settings, removing much of the engineering complexity traditionally required to deploy intelligent robots4

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The Outsider Advantage in AI Robotics

Neither Jacobi nor Niv are roboticists, which Index Ventures partner Shardul Shah sees as an advantage. "There are a lot of robotics industry insiders participating in the next wave of embodied intelligence, but Jonathan and Gal are outsiders—they're not roboticists. It affords them more room for originality," Shah said. "Someone who's an insider may start with the capability of teleoperation or dexterity, but Enigma is starting from a very different place: 'What's the ultimate experience?'"1

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The founders met competing in hacking competitions as young teens, then became close friends while serving together in Unit 8200, where they conducted cybersecurity research and even shared a bunk bed1

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. Jacobi started a computer science degree at 13 and finished it in high school, while Niv began hardware hacking at 10, joined a cybersecurity startup at 17, and completed a four-year degree in a single year4

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. They assembled a team of what Jacobi describes as some of their "smartest friends" from Israel's tech ecosystem—including alumni from top AI labs, Math Olympiad winners, and several people who were convinced to drop out of PhD programs1

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What This Means for Physical-World Applications

"We believe AI's next chapter is moving beyond AI chatbots and screens into systems that can understand, adapt to, and operate in the physical world," Jacobi said. "We're building the intelligence layer for robotics through breakthrough AI models and novel human-robot interfaces"3

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. The company's goal is to make intelligent robots as natural to work with as computers and smartphones are today4

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While Jacobi declined to share specific use cases for Enigma's AI software for robots, he said the startup is already partnering with companies in healthcare, logistics, and entertainment1

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. The $70 million seed round for a company with no product, no revenue, and an experiment as its launch strategy reflects investor confidence in the team rather than the roadmap2

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. Whether letting the internet control robots in a hangar produces a breakthrough robot control interface or an expensive research project is the question this funding is designed to answer. Enigma will use its seed funding to hire more engineers and purchase computing capacity3

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. The data gathered from real-world interactions could reveal not only superior interfaces but better ways to train foundation models, potentially reshaping how the industry approaches the problem of making robots useful in physical-world applications beyond traditional natural language prompts.