Industrial Next, a San Francisco-based startup, presented at TechCrunch Disrupt 2026’s Startup Battlefield to make the case that factory automation doesn’t have to mean fixed, single-purpose production lines. The company builds AI-driven, self-contained robotic stations powered by proprietary foundation models that connect vision, task context and motion. According to the company, this lets a production line be retooled in minutes rather than weeks without traditional programming, opening up complex and delicate tasks that have historically required human labor. On stage, Industrial Next also announced a partnership with BMW Group Technology Office to evaluate its AI-powered dexterous manipulation for manufacturing.

The founders know the pain they’re attacking from the inside. Lukas Pankau and Allen Pan met at Tesla, where they watched cutting-edge vehicle and self-driving technology get held back by factory equipment from the 1980s. Pankau, who served as a lead electrical architect at Tesla before moving to Waymo, described the gap: he could run machine learning algorithms to design a car, but couldn’t figure out how to program a robot, and that was common for engineers at Tesla. Pan led Tesla’s autonomous factory efforts and later became a chief architect at autonomous driving company Neolix. The pair didn’t get along at first — Pankau’s first encounter with Pan was literally tripping over him as Pan slept on a factory catwalk after an all-nighter — but they shared an obsession with getting things done and complementary skills.

The technical challenge is steep. Traditional industrial robots are programmed for highly specific, repeatable tasks, so even a small change in process or product can trigger failures or costly downtime; manufacturers often simplify their products for repeatability as a result. Making robots truly flexible requires enormous amounts of training data, and unlike AI models trained on the internet, robotics lacks the vast datasets needed to teach complex physical tasks. Pankau’s point: ‘We can make robots do all these cool things like dance around, because there’s data for that, but we can’t make them assemble high-precision things.’

Industrial Next‘s answer is high-fidelity simulation. The robots practice in a virtual world that closely mimics real-world physics, gaining an understanding of the process in general rather than memorizing one task. That approach lets them perform high-precision tasks ‘almost without seeing the parts at all,’ Pankau said. New tasks are taught through simulation and demonstration rather than hard-coded scripts, and the robotic stations can be reconfigured, combined, and redeployed across product lines.

The startup says some workstations are already running in production at one of the world’s most popular smartphone makers, though it wouldn’t name the customer. The robots perform tasks manufacturers had tried and failed to automate for years, such as threading a delicate flex cable and snapping a tiny connector cap into place. Pankau also described a ‘film peeling’ station that strips protective film off camera modules and buttons at or above human speed with 100% accuracy, even when moved to a new phone model and production line the robots had never seen.

The company was founded in 2021 after Pankau and Pan kept complaining about industrial technology long after leaving Tesla. Pankau was at Waymo, ‘kind of bored out of my mind because I was missing that pace of Tesla’; Pan was in a similar position at Momenta, a developer of self-driving systems in China, Pankau added. As Pan recalled their conclusion: ‘We’re like, we’re not getting any younger, and we’re still obviously passionate about it. We still keep complaining about industrial technology. Like, this is three years later, we should start the company.’ The stated endgame is bigger than phones: ‘We ultimately want to take this to building a full car end-to-end without any human touching ever.’