Sheryl Sandberg has led a $10 million investment into Self Inspection, a San Diego-based startup that is also backed by former Tesla president Jon McNeill’s DVx Ventures.

The startup, founded in 2021, has spent the last few years trying to disrupt the vehicle inspection process by making it possible to properly asses body damage on a car with as little tech as a smartphone camera. Self Inspection told TechCrunch that it has already completed more than 1 million vehicle inspections for rental fleets, automotive finance companies, auctions, and marketplaces, with Stellantis’ financial services arm using the platform for corporate-owned vehicles and lease-end inspections.

“The biggest technology companies are built by transforming industries that are massive, essential, and ready for change,” Sandberg said in a statement to TechCrunch. “Vehicle condition touches billions of dollars in automotive decisions every year, yet the data remains fragmented. That is changing. We believe Self Inspection will build the system of record that the automotive industry needs.”

The funding round was led by her family office, Sandberg Bernthal Venture Partners, with strategic investment from tire distributor U.S. AutoForce and automotive lender Westlake Financial. Early stage funds Costanoa Ventures, Rebellion Ventures, and BrightCap Ventures also invested.

Self Inspection is one of a number of startups trying to use AI to modernize the automotive industry. Toma and Flai are trying to improve dealership communication with voice agents. BidBus is making it possible for dealerships to competitively bid on privately-owned cars.

Other startups like UVeye have taken a bigger, infrastructure-level approach to modernizing vehicle inspections.

But a big part of Self Inspection’s pitch is simplicity. The company sells its software to customers like Stellantis, and that software allows the customer to send a link to anyone with a smartphone so they can upload photos of a car. Self Inspection’s software guides the user through the process, making sure the whole car is covered.

The company is basically leveraging the fact that “everyone has a good camera” and “knows how to capture photos,” CEO Constantine Yaremtso told TechCrunch last year.

From there, the photos get compared to what Self Inspection has described as “one of the largest datasets of damaged vehicles” to detect the presence and severity of any damage. After that, the startup’s software spits out a cost estimate and a detailed inspection report.

“What we deliver is actually a fully detailed PDF report that you would normally only get from a body shop, which will tell you what labor needs to be done on the damage, how much it costs to repair, how many parts do you need, and so on,” Yaremtso said. He added that Self Inspection can also pull data from an OBD2 computer for even more detailed information.

Self Inspection told TechCrunch that its platform has already helped its customers reduce their costs by over $80 million and save more than 300,000 operational hours. The startup plans to use the new funding to build more products, reach more enterprise customers, and expand to Europe.

While the rest of the AI industry races to label its work as “AGI” or “superintelligence,” Alexandre LeBrun, the CEO of Yann LeCun’s world model startup, AMI Labs, avoids the terms altogether.  Lebrun said in an interview with TechCrunch that the company doesn’t use terms like “AGI” or “superintelligence” at all.

“We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence,” he said. “Next time we’ll switch to something else.” He isn’t sold on the new label either. “There’s no good definition. What is superintelligence? I don’t know. It’s not a very useful word.”

It’s a pointed stance from a founder sitting at the center of AI’s newest race.

TechCrunch talked to LeBrun while he was in Seoul last week for The International Conference on Machine Learning, where he was scouting for local industrial partners, global companies and researchers. AMI Labs is still pre-product, but it’s already courting robotics, manufacturing and electronics players. A world model, which incorporates physics to predict and work with the real world, needs to prove itself outside the lab, LeBrun explained.

One area where world models are expected to have a large impact is robotics. For now, robots are just running fixed routines, “completely static”, and AI remains “really dumb in the physical world,” LeBrun said.

Even when AI can merely make robots “aware of the context” that would mark “a very big difference for the world.” Such context-aware AI would have been useful, for example, in preventing a robot that was dancing and doing kung fu at a public event from approaching and kicking a child. “The hardware is very advanced; progress in hardware in the last few months is incredible, but there’s no brain.”

A large language model (LLM) predicts the next word or text, and a world model predicts the next state. Nudge a glass off the table, and you already know it will tip and spill; that’s the intuition a world model is meant to capture: predicting the next state of the world, LeBrun explained.

He isn’t claiming world models are better than LLMs, which are “complementary, not replaceable” when it comes to AI systems that understand the physical world, LeBrun said. Drawing a parallel to the human brain’s distinct language and reasoning functions, he added that LLMs will remain the most efficient tools for processing language while world models will provide context and real-world understanding.

Almost every industry that “touches the real world” could eventually make use of robotics based on world models, LeBrun said, arguing that physical environments remain where LLMs are weakest.

A factory robot repeating the same motion works well enough today, he said. The challenge begins when “you take your robot outside into a more open environment, in your household, or in the street”, where it must understand its surroundings and operate safely. “Robots are not safe right now,” he said. “There’s no solution for that today.”

Healthcare offers a more personal example for LeBrun, whose previous company was Nabla, an AI health startup. He likened today’s AI systems to a doctor trained only on textbooks and without a residency. LLMs may be useful in medicine, he said, but they cover “only 1% of healthcare.” The rest depends on real-world experience.  

But a world model, LeBrun said, can’t be built inside a lab. To train on reality, AMI needs real environments and close partners, according to the CEO. “We need access to the real world,” and it’s “easier for us to do that with partners.” That is part of what pulls him toward Asia, where the robots, chips, and factories actually are.

LeBrun won’t spell out a full Asia strategy yet. “It’s too early,” he said. But the pull toward South Korea comes down to two things. First, Korea has advanced industries in robotics, semiconductors and manufacturing; the hardware-heavy sectors that the first wave of AI barely touched.

The second attraction is speed. LeBrun pointed to Korea’s national plan to pour money into AI and its track record as an early adopter.  “Korea was the fastest adopter of the internet 25 years ago,” he said. It’s that combination, a deep industrial base plus a willingness to embrace AI fast, that he calls “unique,” and the reason “we want to be here from day one.”

“I’ve been telling Alex and the team to come to Korea,” JP Lee, the CEO of SBVA and one of AMI’s backers in Asia, told TechCrunch.

The government has done “a tremendous job” funding local sovereign LLM models, Lee said, and those already work “well enough” for general-purpose tasks, but he’s pushing for Korea to keep investing in physical AI, too. He points to Seoul’s June plan to mobilize some $880 billion for chips, AI data centers and physical AI, as one of its three declared pillars. “They should coexist.”

Korea’s value to foreign firms, Lee argued, isn’t only in hardware. Local developers are quick to adopt and adapt new tools, a pattern that has produced homegrown internet players like Naver and Kakao.

For all the star power and the billion-dollar check, AMI has nothing to sell yet. The startup, co-founded by Turing Award winner Yann LeCun after he left Meta, raised $1.03 billion in March at a $3.5 billion pre-money valuation. There’s no product yet, and no timeline he’ll commit to. “We’ll make a surprise when we’re ready,” LeBrun said.