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As demand for EVs declines, electric automaker Rivian is taking this time to adapt its business and expand its brand. Rivians founder and CEO RJ Scaringe joins Rapid Response to explore the companys recent $5.8 billion partnership with Volkswagen, the ongoing risk assessment for self-driving features, and how Rivian’s AI-enabled ‘technological plumbing’ can accelerate the brand beyond incumbent manufacturers. This is an abridged transcript of an interview from Rapid Response, hosted by the former editor-in-chief of Fast Company Bob Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with todays top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. I want to ask about AI because everyone’s focused on how it will impact car experiences. What are you doing at Rivian, and how far are we from a driverless world? Should we have one? I think it’s incredibly important. It arguably becomes the most important part of the businessthe vehicle’s ability to drive itself. Consumers start getting their time back. Even if you enjoy driving, the ability to leave and have the car take you is a nice feature. This is a big focus for us. When we launched our first products, we had a limited highway assist feature that allowed hands-on wheel and eyes on the road, but the vehicle drives itself. On our Gen 2 vehicle, we started working on designing an entire camera set, with 55 megapixels, more than any other vehicle sold in the U.S. We have five radars, including a front imaging radar. We control that entire stack and use emerging technologies to train the platform. Self-driving developed before 2021 was heavily rules-based. But now, we can use end-to-end training, using modern techniques akin to what’s used in large language models and transformers. It’s completely changing self-driving development and speeding it up. We announced a hands-free feature, where the vehicle will drive itself on highways with hands off the wheel, coming very soon. After that, we’ll expand it to other roads, then to hands-free, eyes-off. There are exciting features coming. I want to make sure I really understand this. So the car can essentially be driven without you doing anything, but it’s not safe enough to turn it entirely over to the car in all situations. But as it improves, you’ll update the software to allow that safely, without changing the vehicle. When we release or enable our self-driving features, we start in domains with extremely high confidence. It’s a bit of the Wild West because there’s no legal body arbitrating the level at which to expose these features. It’s your judgment about what you feel confident in and the risk you’re willing to take? Every brand makes this decision differently. We’ve really erred on hyper-focus on safety, making sure that before we expand the operating windows to, let’s say, neighborhood roads or school zones, we want to really be robust in the solution. We’re talking about autonomy, which is one slice of AI in the vehicle. We’ll see other AI elements emerge. Think of something as simple as navigating. Imagine if you don’t know where you want to go. You get in the car, and say, “I’m hungry.” And the car says, “Well, what do you feel like?” And you say, “I don’t know.” And it says, “Well, yesterday you had Italian. What do you feel about burritos today?” And, you know, so just the ability to be conversational and contextual. It’ll be one of those kinds of changes, I think, where it’ll happen, we won’t even fully realize it’s happening. And then we’ll look back and be like, how did we used to live? When you think about AI applications beyond self-driving, how important is it for Rivian to be at the forefront versus following along? Early on, we realized software was going to be important. At Rivian, we’re controlling the whole software stack, not using suppliers for all these computers. We’re making our own, with all our own computers. It requires fundamental shifts, not relying on third-party suppliers for software or computers. This architecture I’ve described underpins what we’ve achieved. We did a $5.8 billion joint venture and licensing deal with Volkswagen Group, the second-largest car company in the world. We’re providing software and electronics to enable what I just described, allowing them to step massively forward in network architecture and software topology. And, that’s of course what we architected. If you don’t have that, it’s hard to imagine integrating AI properly. Step one is getting the plumbing right. You’ve got to get the network architecture right. You’ve got to get the topology of computers right. You’ve got to get the right levels of compute. I mean, down into the basics of like, what level of memory do you have? What’s your graphics capabilities? And these are things that are going to be really hard without making a big break from the traditional model for existing manufacturers. So I say all this because I think we’re at this inflection point where the cars of the historical past in terms of architecture and the cars of the future in terms of architectureI put ourselves, I put Tesla in that category. And the features are sort of similar, like they’re mostly the same. And it’s easy to confuse features for capability, but the platforms are totally different. And so the growth potential of those two platforms in terms of adopting future technology is wildly different. So where they end up in, let’s say five to 10 years, is in very, very different places. And so I think we’re gonna see a lot of existing incumbent manufacturers work very hard either through partnerships like what was done with us, or through other means to move to these newer technology platforms. China has become a leading EV producer. I’m curious about the implications for the car industry. How do you view China’s developments and their impact on your business? Well, the world is electrifying. The U.S. market is slower than Europe or China. But probably the singular issue that I’d say there’s very clear alignment between both the Democratic side of the United States and the Republican side of the United States is that the United States needs to continue to lead in technology and to continue to really serve as an economic superpower. And in order for that to be true, we also need to continue to be great at the world’s future technologies. China benefited from government support for EVs. If America wants to stay at the forefront in this technological development, how important is government support? China has many electric car companies, a lot narrowly differentiated. Their regions provided much financing, leading to intense price competition to capture a growing market. However, there’s uncertainty about whether these products can be sold in the U.S. and the tariffs involved. Probably, in the short term, there’s going to continue to be not a lot of trade from the U. S. shipping products to Chinaand vice versa China shipping products and vehicles to the United States. But I think in the long term everyone should be thinking about this, to say, let’s imagine a world where we can all compete freely, meaning we’re competing head to head. And so we spend every day thinking about How do we make our products better? How do we look at what others are doing and embrace the competition and see it as an opportunity for us to run faster? That’s the mindset we built into the business. This interview is part of a Rapid Response partnership with Stripe.
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During Robert F. Kennedy Jr.s Senate confirmation hearing for secretary of the U.S. Department of Health and Human Services (HHS), Democratic Senator Tina Smith of Minnesota asked him about his stance on people who take antidepressants. “I know people, including members of my family, who’ve had a much worse time getting off of SSRIs than they have getting off of heroin,” Kennedy responded. While many of Kennedys beliefs are questionable, hes voicing a common misconception around SSRIs, or selective serotonin reuptake inhibitors. Approximately 13% of Americans take SSRIs, which are a type of antidepressant that work by increasing serotonin levels in the brain. To begin with, Dr. Sarah Hartz, a professor of psychiatry at Washington University School of Medicine in St. Louis, points out that there is a difference between being addicted to a substance and taking a medication for a chronic condition. With the latter, your symptoms may come back if you stop taking medicationwhich can be the case for people who have severe anxiety or depression, or high blood pressure. In addition, several medications require patients taper off of them slowly, or theyll experience unpleasant side effects. These include blood-pressure and heart medications, and, yes, in some cases SSRIs. A 2024 analysis of 79 studies encompassing 21,000 patients found that approximately one in 30 patients have severe symptoms when they stop using antidepressants. With SSRIs, people have different tolerances for how quickly they can get off of them. Some people have to taper, some dont, Hartz says. Furthermore, addiction itself is a tricky term to unpack. In the most general sense, addiction means a person cant quit a substance even if they want to. Technically, sugar and caffeine are addictive. So is alcohol. Yet, using them regularly is widely accepted. Likely, when Kennedy compared heroin to SSRIs, he was referring to severe addiction, or what psychiatrists call substance use disorder. Substance use disorder has 11 different criteria, which can be grouped into four categories: Physical dependence: Developing a tolerance for increased amounts of the substance and experiencing withdrawal symptoms when you stop. Risky use: For example, using the substance while driving or continuing to use it despite experiencing mental or physical problems. Social problems: For example, neglecting responsibilities or continuing to use the substance despite it causing problems in relationships. Impaired control: Taking the substance longer than your meant to, having cravings, experiencing an inability to stop, or spending significant amounts of time obtaining, using, or recovering from the substance. Hartz points out that SSRIs dont cause these four categories of problems. While, some people do experience withdrawal symptoms and others need to increase their dose, usually taking SSRIs improves someones ability to functional socially. Furthermore, it takes a few weeks for SSRIs to kick in, so they are less likely to be abused or cause impaired control. You cant get high from SSRIs, says Hartz. You can take five times the recommended dose and you wont get high. Theres no instantaneous mood change so people are less likely to misuse them, unlike opioids or stimulants for ADHD. SSRIs are also accompanied by side effects such as decreased libido, inability to climax, headaches, and nausea. I dont see people taking them when they dont need them, Hartz says. Most people dont want to take a pill they arent getting a benefit from. Hartz notes that Kennedys statements echo existing stigmas around mental health. Psychiatric medications are singled out in a way they shouldnt be, Hartz says. People think mental health conditions such as anxiety and depression arent medical problems. They think its about self-control and behavior so they feel guilty about seeking help. But depression and anxiety can be debilitating and we have treatments for them.”
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E-Commerce
A new technology can pinpoint victims of intimate partner violence four years earlier than other detection systems and with 80% accuracy. The Automated Intimate Partner Violence Risk Support System (AIRS) utilizes clinical history and radiologic data to pinpoint patients seen in the emergency room who may be at a risk for intimate partner violence (IPV). Developed over the past five years, AIRS has been rolled out to the Brigham and Womens Hospitals Emergency Rooms in Boston as well as surrounding primary care sites. Currently, the tool has been validated at the University of California-San Francisco Medical Center and is being evaluated by the Alameda Health System for its role in clinical workflow. Data labeling quality is a huge concernnot just with intimate partner violence care, but in machine learning for healthcare and machine learning, broadly speaking, says cofounder Irene Chen. Our hope is that with training, clinicians can be taught how to spot intimate partner violencewe are hoping to find a set of cleaner labels. AIRS is an AI-based program that is run on the Electronic Health Record data. It takes an individual patients EHR dataincluding past radiographic imaging results and patient clinical historyand uses an algorithm to evaluate a patients risk for and severity of IPV. This is followed by a “silent study” where the algorithm makes recommendations for patient care for patients who have been labeled as victims. Both the radiological data and the patient clinical histories have been revealing for Chen and her cofounder Bharti Khurana. For instance, Chen shared that many victims of intimate partner violence who were detected through AIRS had experienced a broken ulna (a bone in the forearm), a defensive injury. When questioned, they had lied that they had fallen down but did not have the instinct to catch themselves, which is more likely to lead to a broken wrist rather than a broken ulna. Ulna fractures signaled an attack by someone from above and were a strong indicator of IPV. AIRS clinical data comes from three sources: patient diagnosis codes (usually intended for billing purposes), whether patients opt-in to hospital resources correlated with IPV (including social workers or legal assistance), and direct interviewing of clinicians to incorporate into the algorithm for AIRS. That latter data source can often prove to be most challenging as many physiciansdespite their years of trainingare not taught to spot IPV. This gap in training is significant: A May 2024 study published in Nature found that only 25% of IPV cases are correctly diagnosed, underscoring the need for more systematic detection methods like AIRS. Suzanne Freitag, director of the Ophthalmic Plastic Surgery Service at Massachusetts Eye and Ear, who has decades of experience treating victims of IPV, cautions against treating AI as a magic font of knowledge that can replace a clinician’s training. While she believes in the pattern recognizing power of AIRS that is a hallmark of radiology, Freitag remains cautious about using patient clinical history as a ground truth for IPV diagnosis. I try to be careful not to stereotype because domestic violence happens to people of all ethnicities, socioeconomic statuses, sexualities, and education levels, Freitag says. Chen and Khurana, for their part, believe that AI can eliminate implicit biases to provide a clearer diagnosis for IPV victimization. The two first connected in February 2020, Khurana saw Chen (then a PhD candidate in electrical engineering and computer science at MIT) deliver a talk on algorithmic bias in medicine at Harvard; shortly afterward, Khuranaan radiology professor at Harvard Medical Schoolapproached Chen to discuss collaborating to apply machine learning to intimate partner violence detection. Five years and one $3.2 million National Institutes of Health grant later, Chen and Khurana have not only built and validated AIRS but are now working to expand its implementation across multiple hospital networks. Social work staff have also been crucial to the implementation of AIRS at Brigham and Womens Hospital, where Chen and Khurana have tapped into records from an existing program called Passageway. A free, voluntary, and confidential resource, Passageway allows patients afflicted by IPV to gain access to social workers and legal representation to seek help. Chenwhile not blinded by imperfections of implementing machine learning in clinical settingsfeels optimistic about AIRS implementation. She points to a 2023 study by the Pew Research Center which found that 38% of a sample of 10,000 Americans believed that AI would improve patient outcomes. While skepticism of AI is alive, Chen feels that it is important not to surrender to it. Annie Lewis OConnor, a nurse practitioner and founder of the Coordinated Approach to Resilience and Empowerment Clinic at Brigham, believes that clinicians and technology must work in tandem to care for patients experiencing intimate partner violence. OConnor, who assists in AIRS prediction model, appreciates its help in diagnosing IPV risk and severity as well as in assisting with clinical decision support. To understand the usability, feasibility, and application of AI and machine learning tools, we must be diligent in gathering evidence on outcomes from the use of such tools, OConnor writes in an email. [AI] is something that compliments and assists the clinician in the care and treatment of patients experiencing IPV.
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