In most factories, automation is rigid: robots and machines are programmed for one specific task and product. Changing a production line typically requires reprogramming and physical retooling, or swapping out equipment, a process that can take weeks or months. San Francisco-based Industrial Next asserts that it is changing this paradigm.
The company announced that it is partnering with the BMW Group Technology Office to evaluate Industrial Next’s AI-powered dexterous manipulation technology for manufacturing applications.
Industrial Next was established in 2021 by Lukas Pankau and Allen Pan, both of whom previously worked at Tesla. Pankau served as a lead electrical architect at Tesla before joining Waymo, while Pan led Tesla’s autonomous factory efforts and later became a chief architect at Neolix.
The founders conceived of Industrial Next while working together at Tesla. Pankau’s first encounter with Pan involved literally tripping over him as Pan was asleep on a factory catwalk after an all-nighter. Although they did not get along initially, they soon realized they shared an obsession with efficiency and possessed complementary skills.
After observing how cutting-edge vehicle and self-driving technology were often hindered by decades-old factory equipment, the founders determined that manufacturing required rebuilding. “We had all these fancy things that we could do on cars. I could run machine learning algorithms to design a car for me. But I couldn’t figure out how to program a robot, and this was pretty common for engineers at Tesla,” Pankau stated. “You had this massive divide, right? You had people who are really good at what they’re doing–designing cars and designing self-driving systems–and then you had them effectively stumped at the factory looking at this equipment from the 1980s. And so we started rethinking what this should look like using the technology that we have today and using everything that we know today.”
Even after moving to their subsequent roles at Waymo and Momenta, the duo remained focused on their frustrations with industrial technology. They stayed in touch, often reminiscing about machines that failed to meet their expectations. “I was at Waymo at the time, kind of bored out of my mind because I was missing that pace of Tesla,” Pankau noted. Pan, who was at Momenta, added, “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.”
Pankau explained that traditional industrial robots are typically programmed to perform highly specific, repeatable tasks. Consequently, even a minor change in the process or product can cause a robot to fail or trigger costly downtime, forcing manufacturers to simplify their products for the sake of repeatability.
Creating truly flexible robots presents a significant challenge, as they require enormous amounts of training data. Unlike AI models trained on the internet, robotics lacks the vast datasets needed to teach machines to perform complex physical tasks. “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,” Pankau explained. “This is where we’ve made a lot of progress on using simulated environments to do these high-precision and high-dexterity tasks. The secret sauce for the robots that we have is that we have given them an understanding of the process in general, but we build that understanding using an extremely high-fidelity simulation engine that is close to the real dynamics to where we can let the robot do these kinds of high precision tasks almost without seeing the parts at all.”
In essence, the robots practice in a highly detailed virtual world that closely mimics real-world physics. Industrial Next’s robotic stations can be reconfigured, combined, and redeployed across different product lines. New tasks are taught through simulation and demonstration, rather than through hard-coded scripts.
Pankau revealed that some of the startup’s workstations are already utilized in the production of one of the world’s most popular smartphones. They handle tasks that manufacturers had unsuccessfully attempted to automate for years, such as threading a delicate flex cable and snapping a tiny connector cap into place. He did not disclose the customer’s name. He also described a “film peeling” station, where their AI-powered robots strip 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 they have never encountered.
“We started this company because that is the kind of performance that we wish we had at Tesla, where we were bypassing robot stations because we couldn’t program them effectively enough using traditional technologies,” Pankau stated. “Now we want to take this to more complicated tasks than just building a phone. We ultimately want to take this to building a full car end-to-end without any human touching ever.”
The Editorial Staff at AIChief is a team of professional content writers with extensive experience in AI and marketing. Founded in 2025, AIChief has quickly grown into the largest free AI resource hub in the industry.