Autonomous Vehicle Effects

It’s a small club that Rivian, Tesla, and Volvo Cars are members of, but end-to-end software is crucial for auto OEMs to avoid extinction. 🪦 To be clear, BYD and other Chinese OEMs are in this club or are trying to, but as the auto industry races toward electrification and autonomy, one thing is becoming crystal clear: the future belongs to those who control the software stack. Without an end-to-end software platform, automakers risk becoming the Foxconn to someone else’s Apple—just a hardware assembler in a value chain dominated by those who own the operating system, user experience, and data. Why is owning the software platform so important? 1. User Experience = Brand Loyalty In a software-defined vehicle (SDV), it’s not just the ride quality—it’s the interface, the over-the-air updates, the seamless integration with your digital life. The UX is where customer loyalty is won or lost, and if you don’t own it, you can’t differentiate. 2. Data Ownership = Competitive Advantage - SDVs are rolling data centers. From driving behavior to battery health, the real value lies in the data. Without software control, you’re giving up the insights that drive smarter products, services, and monetization models. 3. Battery + Software = Core IP As Tesla has shown, vertical integration of battery tech and software enables control of cost, performance, and scalability. Let someone else own the OS or the BMS, and you’re forever dependent—and vulnerable. 4. Pace of Innovation- Software companies iterate weekly. Traditional auto cycles move in years. If you don’t own the platform, you’ll always be lagging behind the pace of innovation set by someone else. That’s why companies like BYD, NIO, GEELY, and of course Tesla and Rivian are betting big on building vertically integrated, end-to-end platforms. #SoftwareDefinedVehicles #EVs #AutomotiveInnovation #BatteryTech #OEMstrategy #FutureOfMobility #Autotech #DigitalChassis https://lnkd.in/gth5f2SU

Coca Cola is one hell of an example of innovation over one hundred and fifty years – from vending machine dispensing of bottles and cans to providing customers with their own drink combination from over 100 options on demand through their Coca Cola Free Style option. Tesla on the other hand through its Software Defined Vehicle (SDV) allows postponement after sales – allowing customers to enjoy additional features on a subscription basis. One of the oldest foundational example in supply chain postponement was the idea of vending machines in the late 1920s by Coca Cola - postponement of the point of sale and delivery from the staffed store counter to a decentralized, self-service location. Decentralizing sales into consumption points thus distributing inventory and allowing decision making and fulfilment to the consumer instead of the retail clerk were notable benefits. The biggest was the strategic - time postponement to preclude a 24x7 service. Coca Cola continued with 5 cents or the Nickel Coke for many decades. Now Coca-Cola Freestyle is a touchscreen-based, self-service beverage dispenser that allows customers to create their own drink combinations from over 100 options on demand. This method leads us to how the same principle can be applied to complex products like automotive offerings on a subscription basis as in Software Defined Vehicle. Tesla SDV features include several forms of postponement in action. 1. Feature Unlocking After Sales: Autopilot, acceleration boost, heated rear seats, etc are embedded in all cars at production. Features are enabled or disabled via software, based on: Customer purchase, Subscription, Market-specific regulations. This allows postponement of product differentiation until and even after purchase.   2. Over-the-Air (OTA) Updates: Tesla delivers firmware updates remotely, like a smartphone. Customers get new UI/UX, range optimization, entertainment apps, and even performance enhancements without visiting a service center. This postpones functionality development until data or user feedback justifies it.   3. Standardized Hardware Platforms: Tesla uses common physical platforms across models. The same battery pack or cameras can support different vehicle variants depending on software config. Reduces hardware variety, increases economies of scale, shifts differentiation to software.   4. Subscription & Feature-as-a-Service Model: Tesla enables customers to subscribe monthly to premium connectivity, advanced autopilot, or full self-driving (FSD). These features can be activated anytime, no new hardware needed. Postponement evolves into a dynamic, monetizable feature platform.    5. Geographic & Regulatory Differentiation: Tesla vehicles adapt based on local laws: Autopilot features are restricted/enabled based on country regulations. Language, safety systems, or emissions settings vary without changing hardware. Read my Full article. #SupplyChain #postponement #CocaColafreestyle #Tesla #SDV

AI Is Revolutionizing Automotive — But Trust Will Decide Its Future! AI is reshaping the automotive industry faster than ever: autonomous vehicles, predictive maintenance, smart traffic systems, supply chain optimization — the list keeps expanding. But there’s a problem. Despite its growing presence, only 46% of global consumers trust AI systems. As adoption accelerates, so do the ethical, safety and operational challenges. The true differentiator for the next decade won’t be AI capability — it will be trustworthy, responsible AI. Here’s how the automotive sector must move forward: 1. Predictive Maintenance & Quality Control AI is enabling real-time defect detection and failure prediction on the assembly line. But without human oversight, false positives can halt production and inflate costs. Responsible AI = algorithmic accuracy plus human judgement + regular audits. AI & Insurance Fraud Fraudsters now use AI to create hyper-realistic fake images, documents and videos. Insurers must “fight fire with fire” using AI tools that detect anomalies, duplicate pixels, metadata issues, and mismatched lighting. But final decisions still require human adjusters to ensure genuine claims aren’t denied. Autonomous Vehicles AI powers everything from perception to real-time decision-making. To earn public trust: Transparent decision processes Clear ODD definitions Rigorous simulations + real-world validation Strong regulatory frameworks and shared learnings across OEMs. Safety must trump speed of deployment. The Road Ahead AI’s impact on mobility is inevitable — but responsible implementation will separate leaders from laggards. Companies that blend AI capabilities with transparency, human oversight, ethical governance and robust validation will win customer trust and regulatory readiness. Trustworthy AI isn’t just compliance — it’s competitive advantage. The automotive industry now has the opportunity to set the global benchmark for safe, responsible and scalable AI adoption.

Taking the lead in the highly competitive #automotive sector will come down to the companies best able to master artificial intelligence. From leadership support for AI’s central role in corporate strategy to policies around responsible governance and ethics, my colleagues Tomoko Yokoi and Michael Wade reveal lessons in #transformation from the most AI-mature automakers in IMD’s 2024 AI Maturity Index. They argue that the automotive sector demonstrates how the systematic development of AI capabilities creates sustainable competitive advantages in manufacturing efficiency, customer experience, and autonomous vehicle deployment. Companies that excel across the five dimensions of AI maturity consistently outperform their peers in innovation, operational efficiency, and market responsiveness. For organizations seeking to enhance their own AI maturity, the examples set by industry leaders offer valuable guidance: 1. Establish clear executive commitment with defined AI strategies tied to business objectives 2. Develop comprehensive technical infrastructure with scalable, cloud-based data platforms. 3. Focus on integrating AI into core business operations with measurable impact 4. Invest systematically in workforce development, creating balanced teams of AI specialists and automotive experts 5. Implement robust ethical governance frameworks that ensure responsible deployment of AI The automotive organizations positioned for future success will systematically develop AI capabilities across all these dimensions while navigating complex regulatory landscapes. This comprehensive approach creates resilient organizations capable of leading transformation toward intelligent, autonomous, and sustainable mobility solutions. https://lnkd.in/d3ddPEKq

From a 0% base two years ago to 10% of ride shares in some US cities, the A.I. behind self-driving cars is maturing and coming down dramatically in price. Here's how it will overhaul cities and whole economies: WHERE WE ARE TODAY • Waymo is running fully driverless ride-hailing across San Francisco, Phoenix, Los Angeles, Austin and Atlanta, logging hundreds of thousands of paid rides weekly. • Expansion is accelerating — half a dozen more US cities are in the pipeline, plus London as Waymo's first international market. • In China, Baidu's Apollo Go, Pony.ai and AutoX already operate large-scale services across Beijing, Shanghai, Wuhan and Shenzhen, with Apollo Go delivering millions of rides per quarter. THE ECONOMIC TRANSFORMATION • The math is simple: no driver means labor cost per mile drops dramatically, and vehicles can operate far more hours than privately owned cars that mostly sit idle. • The average US household spends ~15% of its budget on vehicle ownership so "subscribe to mobility" will be very tempting for city-dwellers once per-mile prices fall at scale. • US downtowns dedicate 20-30% of land to parking. Reduced car ownership could unlock surface lots for housing, parks and offices — and convert curbside parking to wider sidewalks and bike lanes. SAFETY • For those of you concerned about autonomous-vehicle safety, Waymo's safety data show serious-injury crashes roughly ten times lower than human benchmarks. • Want third-party verification on that 10x safety improvement? Swiss Re found ~90% reduction in bodily-injury and property-damage claims for autonomous vehicles relative to human drivers. (Perhaps in the not-too-distant future, insurance premiums will become exorbitant for human drivers, pushing more and more people to go autonomous 🤔) RISKS • Cheap, effortless rides are a recipe for gridlock without smart policy. NYC's congestion charge cut incoming traffic ~10% in its first months, for example — dynamic "robotaxi fees" like this will be essential. • Labor impact is significant: ~500K taxi/shuttle jobs, ~500K bus-driver roles, and ~3M truck-driver jobs in the US alone. Reskilling pathways and transition plans are critical. THE GENERAL LESSON: Where in your field is A.I. currently handling only a small percentage of a particular workflow autonomously, but is poised to take over most or all of the workflow as LLM capabilities improves and costs continue plummet (they're currently falling at 100x per year)? Huge opportunities lie there for you (and your organization). Listen to today's episode of my podcast (Episode #946) to hear more on all of the above! The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and YouTube. See below for quick access ⬇️ #superdatascience #selfdriving #robotaxi #autonomousvehicle #ai

📌 Tech Titans Are Reshaping the Tier-1 Automotive Landscape 🚗 As the automotive world races toward the software-defined vehicle (SDV) era, conventional value chains are being restructured. Companies like LG Electronics Vehicle Solution, Sony, Qualcomm, and NVIDIA are stepping into roles once dominated by legacy Tier-1s, areas traditionally known for OEMs, especially in North America. 🇺🇸 🔷 LG Electronics has transformed from consumer electronics giant to automotive innovator. With its AlphaWare platform, including modules like PlayWare (for 4K streaming) and MetaWare (AR HUDs), LG is powering next-gen in-vehicle infotainment. Their partnership with Magna led to a cross-domain cockpit running multiple vehicle systems on a single SoC. The Kia EV3 is just one example on the road today. 📺🎮 🔷 Sony, through its Sony Honda Mobility JV, is turning premium interiors into entertainment hubs. With partners like Qualcomm, Epic Games, and Elektrobit (Continental), the AFEELA concept brings cinematic visuals, spatial sound, and even AR navigation to the dashboard. For Sony, this isn’t just tech, it’s a lifestyle. 🎧🎮🚘 🔷 Qualcomm is pushing boundaries with its SnapDragon Digital Chassis, a full-stack platform combining infotainment, ADAS, and telematics. With cloud-based development tools (via AWS), OEMs can deploy AI copilots, real-time navigation, and OTA updates with ease. BMW, GM, and Stellantis are already onboard. 🧠📡 🔷 NVIDIA is no longer just about gaming GPUs — it’s powering fleets. GM is building its future EVs on NVIDIA’s DRIVE platform, with AI, simulation (Omniverse), and supercomputing baked into the architecture. Mercedes-Benz, JLR, and others are following suit. 🖥️🚀 🤝 Collaboration Beyond Code This transformation isn’t just about software and silicon — it’s also redefining the supply chain. Deep partnerships between tech firms, traditional Tier-1s, and logistics providers are enabling smoother module integration, shared testing frameworks, and joint validation processes. From sourcing chips to deploying secure OTA updates, collaboration across the value chain is becoming a strategic differentiator. 🌐📦🔧 💥 Why It Matters The shift to SDVs means compute power, software updates, and AI integration are more critical than ever — and tech firms are delivering faster, more scalable solutions. Traditional Tier-1s like Bosch, Continental, and Magna are adapting by forming alliances, acquiring software firms, and co-developing with the very companies that are redefining the landscape. 🤝 🏗️ Industry groups like OpenGMSL Association and Connected Vehicle Systems Alliance (COVESA) are working to create standards that ensure interoperability, reduce integration costs, and maintain safety. 👍🏻 Success in automotive requires deep know-how with consumer-grade software and AI. #SDV #AutomotiveTech #Infotainment #AutomotiveTransformation #SoftwareDefinedVehicles GAMUT Timuçin Kip Note: all public info, image Gemini

NVIDIA's 2,000 teraflop autonomous vehicle chip just shifted global automotive strategy in ways most analysts will miss for 18 months. While tech media celebrates processing power, our analysis at Global AI Forum reveals systematic transformation. This isn't just hardware acceleration. It's geopolitical repositioning disguised as product launch. Three strategic patterns emerge from our policy research across automotive markets: Chinese automakers dominating early adoption signals supply chain sovereignty priorities. BYD, GAC Group, Li Auto and Xiaomi Technology aren't just customers. They're strategic proxies for domestic AI capability building. European manufacturers like Volvo Group upgrading existing platforms reveals infrastructure lock-in strategy. The EX90 to Thor migration path creates 24-month competitive windows that early movers capture. Autonomous trucking convergence with Aurora, Gatik, and PlusAI Solutions indicates freight transformation acceleration. When logistics AI consolidates on single platforms, entire supply chain economics reshape. Strategic leaders ask different questions. Which automotive markets become AI-dependent first? How does chip concentration affect global automotive competition? What regulatory arbitrage emerges from technological sovereignty gaps? Our research suggests three inflection points by Q2 2026: 1. ISO 26262 safety standards become competitive differentiators, not compliance requirements. 2. Consolidated AI workloads eliminate traditional automotive supplier tiers. 3. Software-defined vehicle economics favor platform controllers over manufacturers. The automotive transformation Jensen Huang predicts isn't 20 years out. It's 18 months. Companies positioning for AI-integrated mobility now capture disproportionate value when regulatory clarity accelerates adoption. Three strategic questions for global leaders: If automotive AI consolidates faster than expected, which partnerships secure platform access versus dependency? How does processing power concentration reshape automotive supply chain power dynamics? Which strategic bets position you to benefit from software-defined vehicle transformation? Strategic positioning happens before market consensus. Global AI Forum identifies the competitive intelligence that shapes tomorrow's automotive leaders. tune into our research.

🇨🇳 Seven things I told Xiaoying (Tina) Zhou at Gasgoo 盖世汽车 C Talk yesterday at Auto China 2026 Greetings from Auto China 2026 ! Software and AI are now the core drivers of automotive value, and China is leading this transformation. Here are the seven points I made on camera: 1️⃣ “Future of Intelligence” is not a slogan. It’s industrialization. SDV architectures are in full-scale production across nearly all major Chinese brands — centralized computing, high-speed vehicle networks, (agentic) AI woven into the user experience. Chinese OEMs aren’t adopting global trends. They’re shaping them. 2️⃣ Reducing complexity ≠ eliminating complexity. Automotive software is becoming more complex, not less. At ETAS , we make it manageable — standardizing non-differentiating parts, absorbing integration effort through scalable platforms and connected toolchains. Our customers’ engineers should focus on what differentiates them, not on rewriting middleware. 3️⃣ AI is moving from experimentation to industrialization. AI-driven test generation, data analysis, calibration support — real engineering use today. And there’s a mindset gap: the West uses AI to optimize existing processes („AI-additive“). China is „AI-native“ — AI defines the software, and the software increasingly defines the hardware. 4️⃣ In a safety-critical industry, red lines are non-negotiable. No AI in safety-relevant development without transparency, traceability, and verifiable controls. No black-box deployment. AI needs to be engineered according to automotive standards. Trustworthy AI is becoming a strategic differentiator. 5️⃣ The OEM-supplier relationship has structurally changed. From transactional to strategic. OEMs need software partners providing integrated platforms, long-term co-innovation, lifecycle support — not just tools. That’s why we’re committed to open ecosystems like Eclipse Software Defined Vehicle S-CORE and deep collaborations like the one with ThunderSoft . 6️⃣ China is no longer just a market. It’s a living lab. Chinese automakers have evolved from fast followers to global pace-setters. With ~240 ETAS professionals across 8 China locations, we run a “local for local, and local for global” strategy — insights from China strengthen our portfolio everywhere. 7️⃣ Diagnostics is no longer aftersales. It’s a strategic capability. When vehicles evolve continuously through software, diagnostics evolves with them — from reactive troubleshooting to continuous operational intelligence. SOVD, cloud-based diagnostics, AI-driven vehicle health intelligence shift diagnostics from cost center to value creator. Thank you Xiaoying (Tina) Zhou and the Gasgoo 盖世汽车 team for the nice conversation — and a big THANK YOU to my #Team ETAS across China and globally who make this work real. We #Deliver what we promise. #AutoChina2026 #SoftwareDefinedVehicle #AIDefinedVehicle #SCORE #TrustworthyAI #FunctionalSafety #ETAS

Why Autonomous Vehicles Need Billions of Miles Before We Can Trust the Trend Lines Jonathan Slotkin’s recent take on Waymo’s safety results has created a useful moment to review what early autonomous vehicle data can and cannot tell us. Waymo’s rider only fleet has now passed roughly one hundred million miles. The reductions in airbag deployments, injuries and serious crashes compared to human drivers are real signals. Full article in comments. Slotkin argues that this looks like the kind of early clinical trial result that convinces researchers to stop the study because the treatment works. I agree that the direction of the data matters. Machines that do not get tired or distracted should be able to outperform the average human driver. The challenge is that serious crashes are rare events. Fatalities happen roughly once every hundred million miles in the United States. That means even large early datasets still sit in the range where random variation plays a major role. Kahneman’s framing of the law of small numbers is important here. If the sample is too small, humans overinterpret short term patterns and draw firm conclusions from unstable evidence. Waymo’s results are promising, but they come from a limited set of cities in a country with highly unusual driving patterns and high per capita road risk. To reach higher confidence, the data needs to grow into the billions of miles across more climates, more traffic mixes and more transit rich environments. My earlier work on congestion also applies. Autonomous fleets may reduce collision rates per mile, but they are likely to increase total miles traveled in car dependent regions. More miles mean more sedentary passenger time, fewer short walking trips and less daily activity. That shift carries real public health consequences. The long term picture is a balance between fewer injuries from crashes and more chronic disease risk from inactivity. Autonomous vehicles remain a promising public health technology. The early safety record points in the right direction, but the evidence base is still thin. We need more transparency, more diversity of operating conditions and much larger datasets. If we do that work, we can understand how autonomy fits into a transport system that is safer and healthier.

A mishap started with a Waymo robotaxi entering an intersection against a red light. This was apparently in response to an incorrect remote operator command. That situation was associated with a moped loss of control and driver fall in the green light direction. (I think it is reasonable to assume the moped operator fell on the wet road due to reacting to the Waymo robotaxi entering the intersection against the red.) While some might simply pin this on remote operator "human error" that misses the bigger picture. We've been told by Waymo (and Cruise) that the vehicle is responsible for safety and the remote operator just provides advice. But here is a mishap caused by a remote operator failure. This is not testing -- this is deployment. So what matters is not human vs. robot error. What matters is the net safety of the combined system. If you toss humans into remote operator roles with limited situational awareness and quick response requirements, you can expect mistakes. Like this one. This could have been a lot worse. I hope Waymo does more than lecture their operators about paying attention, because that never really works to compensate for inadequate operational processes, operator interfaces, communication issues, or whatever else might have contributed here. For regulators: remote operators are going to be a thing for a LONG time. California should require they be in the same state, have driver licenses, a clean record, etc. for operations even if they are not in the vehicle itself, because clearly they make driving decisions that affect safety critical vehicle operations. There is no California DMV report for this crash that I could find. Waymo is not require to report deployment crashes to CA DMV, and that urgently needs to change. The information available right now from SGO 30270-6981: "On January [XXX], 2023 at 10:52AM PT a rider of a moped lost control of the moped they were operating and fell and slid in front of a Waymo Autonomous Vehicle ("Waymo AV") operating in San Francisco, California on [XXX] at [XXX] neither the moped nor its driver made contact with the Waymo AV. The Waymo AV was stopped on northbound [XXX] at the intersection with [XXX] when it started to proceed forward while facing a red traffic light. As the Waymo AV entered the intersection, it detected a moped traveling on eastbound [XXX]. The Waymo AV braked and came to a stop as the moped approached the intersection. The rider of the moped braked then fell on the wet roadway and slid to a stop in front of the stationary Waymo AV. There was no contact between the moped or its rider and the Waymo AV. The Waymo AV's Level 4 ADS was engaged in autonomous mode.  Waymo is reporting this crash under Request No. 1 of Standing General Order 2021-01 because a passenger of the Waymo AV reported that the moped may have been damaged. Waymo may supplement or correct its reporting with additional information as it may become available." See comments about remote operator