Techin' Geek

Sheryl Sandberg has spearheaded a $10 million investment in Self Inspection, a startup located in San Diego, which also receives support from Jon McNeill, former president of Tesla, through DVx Ventures.

Founded in 2021, the startup has been working over the past few years to revolutionize the vehicle inspection process by enabling an accurate assessment of body damage using nothing more than a smartphone camera. Self Inspection informed TechCrunch that it has already conducted over 1 million vehicle inspections for rental fleets, automotive finance institutions, auctions, and marketplaces, with Stellantis’ financial arm utilizing the platform for inspections of corporate-owned vehicles and lease terminations.

“The most significant technology companies emerge by transforming large, critical industries that are poised for change,” Sandberg stated in a comment to TechCrunch. “Vehicle condition impacts billions of dollars in automotive decisions annually, yet the data is still scattered. That is about to change. We are confident that Self Inspection will create the essential system of record for the automotive sector.”

The funding round was predominantly led by her family office, Sandberg Bernthal Venture Partners, alongside strategic investments from U.S. AutoForce, a tire distributor, and Westlake Financial, an automotive lender. Early-stage investors such as Costanoa Ventures, Rebellion Ventures, and BrightCap Ventures were also participants.

Self Inspection is among several startups aiming to utilize AI to modernize the automotive landscape. Toma and Flai are working on enhancing dealership communications through voice agents, while BidBus enables dealerships to bid on privately owned vehicles competitively.

Other companies like UVeye have adopted a more comprehensive, infrastructure-level strategy to upgrade vehicle inspections.

However, a core aspect of Self Inspection’s appeal is its straightforwardness. The company markets its software to clients like Stellantis, which allows them to send a link to anyone with a smartphone to upload car photos. Self Inspection’s platform directs the user through the process to ensure comprehensive coverage of the vehicle.

The business is essentially capitalizing on the fact that “everyone has access to a capable camera” and “can take pictures,” CEO Constantine Yaremtso stated to TechCrunch last year.

Subsequently, the photos are matched against what Self Inspection refers to as “one of the largest datasets of damaged vehicles” to identify the presence and extent of any harm. Following this, the startup’s software generates a cost estimation along with a detailed inspection report.

“What we provide is actually a thoroughly detailed PDF report that you would typically only receive from a body shop, outlining what labor is necessary for the damage, how much repairs will cost, how many parts are needed, etc.,” Yaremtso remarked. He further noted that Self Inspection can also gather data from an OBD2 computer for even more comprehensive insights.

Self Inspection informed TechCrunch that its platform has already assisted clients in cutting costs by over $80 million and saving more than 300,000 operational hours. The startup intends to utilize the new funding to develop more products, reach additional enterprise customers, and expand into Europe.

Amidst the AI sector’s rush to label their developments as “AGI” or “superintelligence,” Alexandre LeBrun, the CEO of AMI Labs, a venture under Yann LeCun’s world model initiative, does not employ these terms at all. LeBrun shared during a conversation with TechCrunch that their company completely steers clear of phrases like “AGI” or “superintelligence.”

“We’ve never used the term AGI. And I’ve observed that nobody seems to be using it anymore; they’ve turned to superintelligence,” he mentioned. “Next time we might adopt yet another term.” He remains skeptical about the new terminology too. “There’s no strong definition. What does superintelligence even mean? I’m uncertain. It’s not particularly helpful.”

This is a deliberate position from a founder positioned at the forefront of AI’s latest competition.

LeBrun spoke with TechCrunch during his visit to Seoul last week for The International Conference on Machine Learning, where he was searching for local industrial partners, global enterprises, and researchers. Although AMI Labs has not yet released a product, it is already engaging with sectors like robotics, manufacturing, and electronics. LeBrun clarified that a world model, which integrates physics to anticipate and interact with reality, must demonstrate its capabilities beyond laboratory settings.

One domain where world models are anticipated to significantly impact is robotics. Currently, robots merely execute fixed sequences, remaining “entirely static,” and AI still seems “quite limited in physical contexts,” LeBrun remarked.

Even the capability for AI to at least make robots “aware of their environment” would represent “a substantial change for the world.” Such contextually aware AI could have, for instance, prevented a robot performing dance and kung fu at an event from approaching and injuring a child. “The hardware has progressed immensely; the advancements in hardware over recent months are astounding, yet there’s no intelligence.”

A large language model (LLM) predicts subsequent words or text, while a world model forecasts the next state. If a glass is nudged off a table, you know it will tilt and spill; that’s the insight a world model aims to encapsulate: foreseeing the subsequent condition of reality, LeBrun elucidated.

He does not assert that world models surpass LLMs, which are “complementary, not substitutable” within AI frameworks that comprehend the physical realm, LeBrun stated. Drawing a comparison to the human brain’s separate language and reasoning functionalities, he mentioned that LLMs will continue to be the most effective tools for language processing while world models will offer context and real-world insights.

Virtually every industry that interacts with the “real world” could ultimately harness robotics founded on world models, LeBrun asserted, contending that physical environments are still where LLMs tend to falter.

A factory robot performing repetitive actions functions adequately today, he noted. The challenges arise when “you introduce your robot to an open environment, whether at home or on the street,” where it needs to comprehend its surroundings and act safely. “Currently, robots are not safe,” he stated. “There’s no solution to that at present.”

Healthcare provides a more personal illustration for LeBrun, whose former company was Nabla, an AI health startup. He compared the current AI systems to a doctor trained solely in textbooks without any practical experience. LLMs may have utility in healthcare, he pointed out, but they only address “1% of the healthcare landscape.” The remaining 99% relies on real-world experience. 

However, according to LeBrun, a world model cannot be developed in a laboratory setting. To train on practical realities, AMI requires genuine environments and committed partners, as per the CEO. “We need access to the real world,” and forming partnerships is “more efficient for us.” This is part of what draws him to Asia, where the necessary robots, chips, and factories reside.

LeBrun is not ready to outline a comprehensive strategy for Asia just yet. “It’s premature,” he remarked. However, the attraction to South Korea is rooted in two main factors. First, Korea boasts advanced industries in robotics, semiconductors, and manufacturing—the sectors that the initial phase of AI barely engaged.

The second draw is speed. LeBrun highlighted Korea’s national initiative to invest in AI and its history of early adoption. “Korea was the swiftest adopter of the internet 25 years ago,” he recalled. This combination of a solid industrial foundation alongside a propensity to rapidly embrace AI is what he considers “distinctive,” and the reason “we aim to establish ourselves here from the outset.”

“I’ve been advising Alex and the team to visit Korea,” JP Lee, the CEO of SBVA and one of AMI’s investors in Asia, conveyed to TechCrunch.

The government has been “remarkably effective” in sponsoring local sovereign LLM models, Lee remarked, and those already function “adequately” for general applications, but he is advocating for Korea to continue investing in physical AI as well. He referred to Seoul’s June plan to allocate around $880 billion for chips, AI data centers, and physical AI, as one of its three designated pillars: “They ought to coexist.”

Lee argued that Korea’s significance to foreign enterprises lies not just in hardware. Local developers rapidly adopt and tailor new tools, a phenomenon that has fostered homegrown internet entities like Naver and Kakao.

Despite its immense backing and financial support, AMI has yet to offer any products. The startup, co-founded by Turing Award laureate Yann LeCun after his tenure at Meta, secured $1.03 billion in March at a pre-money valuation of $3.5 billion. There is no product available yet, nor any timeline he is prepared to confirm. “We’ll make an announcement when we’re ready,” LeBrun stated.