Industrial Next promises factory retooling in minutes, with BMW as first test

Industrial Next, a San Francisco startup founded by two former Tesla engineers, says its AI-powered robotic stations can retool a production line in minutes rather than weeks — and BMW has agreed to put that claim to the test. The company announced the BMW Group Technology Office partnership on stage at TechCrunch Disrupt 2026's Startup Battlefield, where it presented its self-contained robotic workstations driven by proprietary foundation models linking vision, task context and motion.

The stakes are straightforward. In most factories today, automation is fixed: robots run one programmed task for one product, and any change forces weeks of reprogramming, physical retooling or equipment swaps. Pankau argues this rigidity even shapes what gets built, since manufacturers simplify products so robots can handle them.

The claim, however, rests on claims the company has not independently verified. Industrial Next says a film-peeling station strips protective film off camera modules and buttons at or above human speed with 100% accuracy — even after being moved to a phone model and production line the robots had never seen. That figure comes from the company itself, and no third-party audit is mentioned.

Who is behind Industrial Next?

Lukas Pankau and Allen Pan founded the company in 2021. Both are Tesla alumni. Pankau was a lead electrical architect at Tesla before moving to Waymo; Pan led Tesla's autonomous factory efforts and later became a chief architect at the self-driving company Neolix.

The two met at Tesla under less-than-diplomatic circumstances. Pankau's first encounter with Pan was tripping over him asleep on a factory catwalk after an all-nighter. They did not get along at first, but found complementary skills and a shared frustration: cutting-edge vehicle software held back by aging factory equipment.

"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 said. "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."

The idea survived two job changes. Pankau was at Waymo — "kind of bored out of my mind because I was missing that pace of Tesla," he said — while Pan worked at Momenta, a self-driving developer in China. "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," Pan recalled.

Takeaway: the founders' credibility comes from Tesla's factory trenches, not from academic robotics.

How can robots learn new tasks without reprogramming?

The technical hurdle is data. Robotics lacks the vast datasets that power internet-trained AI models, which is why demo robots can dance but struggle with precision assembly.

"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 said. "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 practice, the robots train in a virtual environment tuned to mimic real-world physics. New tasks are taught through simulation and demonstration rather than hard-coded scripts, and the stations themselves can be reconfigured, combined and redeployed across product lines. The company has not disclosed how much real-world fine-tuning the simulated training still requires.

Takeaway: the pitch depends on simulation fidelity bridging a gap that real-world data cannot yet fill.

What is already running in production?

Pankau said some of the company's workstations already operate on the production line of one of the world's most popular smartphones. He declined to name the customer. The stations handle tasks that manufacturers tried and failed to automate for years, including threading a delicate flex cable and snapping a tiny connector cap into place.

The film-peeling station is the company's showpiece claim: full accuracy, human-or-better speed, and transfer to an unseen phone model and production line. If accurate, that transfer capability is the commercially significant part — it is what converts a robot from a single-purpose machine into a reusable asset.

What remains unsaid is scale. The company offered no production volume figures, no pricing, and no details on deployment breadth beyond the unnamed smartphone customer and the BMW evaluation.

What does the BMW partnership involve?

BMW Group Technology Office will evaluate Industrial Next's AI-powered dexterous manipulation for manufacturing. That is an evaluation, not a deployment — the announcement says nothing about production lines, timelines or volumes, and no BMW statement appears in the announcement.

For a startup founded in 2021, a BMW evaluation is a meaningful validator. For BMW, dexterous manipulation addresses one of the most stubborn gaps in automotive automation: tasks like wiring harness routing and flexible-part handling that still require human hands.

Takeaway: the partnership is a trial with a major automaker, and its results are not yet public.

Why the founding story matters

The Tesla origin is not incidental to the product. Pankau framed the entire company as a response to problems he witnessed firsthand.

"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," he said.

The end goal is larger than phones. "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," Pankau said.

That is an ambition, not a roadmap, and full lights-out automotive assembly (factories with no human labor on the line) has eluded every major automaker including Tesla. Pankau's claim carries the caveat of distance: the company's demonstrated work so far involves film peeling and small connector tasks on a phone, several orders of magnitude simpler than end-to-end car assembly.

Takeaway: the vision is total factory automation; the evidence so far covers small, delicate, repetitive tasks.

The BMW evaluation will be the first external test of whether Industrial Next's simulated dexterity holds up outside the demo stage — and its results will say more than any Startup Battlefield pitch.