Yibiao Zhao

Uber is pushing out Waymo Uber just announced a multibillion-dollar push into robotaxis in partnership with Lucid and Nuro. The era of self-driving at scale is arriving, and Uber wants to be the operating system of urban mobility. What struck me about this story wasn’t just the deal - but the evolution behind it. I had the chance to speak with Lesya Hendrix, a former Technical Program Manager at Nuro, who worked directly on the robotic fleet involved in this transition. Her story offers a rare behind-the-scenes look at what it takes to pivot a robotics company into a scalable AI platform. → From robots to revenue models “In 2023, Nuro pivoted from being a hardware company to a software company,” Lesya shared. “We had built a fleet of 30 delivery robots across #MountainView, #PaloAlto, and #Houston. But ultimately, the economics didn’t support scale. #Hardware was costly, and the unit economics of last-mile delivery didn’t justify the infrastructure.” Instead, Nuro focused on what it did best: its autonomy stack. They shifted from building robots to licensing their self-driving software - partnering with Lucid who had the vehicles, and Uber who could go to market. → A pivot born of pragmatism This isn’t uncommon in robotics. Many #hardware-heavy startups face the harsh math of scaling physical infrastructure. But what Nuro did well - according to Lesya - was preserve the integrity of the team and the culture during the shift. “In all my time working in #SiliconValley, Nuro had the best culture. The CTO became my mentor. And even during the pivot they kept open communication and offered healthy terms of transition” → Why Uber, not Waymo? “Because Uber is better positioned to tap into global markets,” Lesya told me. “And whenever they won’t have have enough autonomous rides in your area - they can offer you the good old human driven alternative and keep customers. This is a stronger business proposition and I am so glad that Nuro’s technology is a part of it” Uber’s strength lies in scale and logistics. What they needed was a cutting-edge autonomy stack that could plug into their massive rider base. Nuro and Lucid complete the trifecta: #AI brains, electric muscle, and consumer reach. → What this means for investors Nuro remains private. But public market participants are already reacting - Uber’s stock jumped on the news, and Lucid may follow. For those looking to get exposure to autonomy, Uber and Alphabet (which owns Waymo) remain key players. But the real story is in how traditional business models are being rebuilt around software, data, and partnerships. Lesya shared - "Today, I’m building Spacer Robotics - a company focused on autonomous, non-humanoid robots for construction on Earth and in Space. Because if robotaxis are transforming how we move within the cities, then construction is next. And someone has to build the builders."

81

54 Comments

The Bitter Lesson is wrong. General methods that leverage massive computation will only outperform human-designed, domain-specific solutions if we have access to high-quality data. While we can probably use LLMs to generate synthetic data to improve model performance, we will still need human data to engender new capabilities and to improve user experiences. But existing tools/systems for collecting human data are falling short. Data labelling with unskilled labor and annotation with domain experts are fundamentally different and will require different approaches. Annotation requires us to find and screen experts who can perform increasingly challenging work. Collecting high-quality annotations faster will require us to design and develop new approaches to empower amateurs and enable experts to increase their throughput. Techniques from scalable oversight research can be applied to improve the throughput and quality of annotation but we also need to think through how to streamline annotation processes. I've discussed these themes and others in the first blogpost I've written in about a decade. Link to post in comments!

77

14 Comments

Generative AI’s greatest potential lies in the physical world, where the ability to simulate, design, and iterate faster can reshape trillion-dollar sectors. That’s why we’re thrilled to announce that N47 is leading Luminary Cloud’s $72M Series B. The Luminary team is pioneering the Physics AI Model Factory, underpinned by a GPU-native physics simulation platform. This technology is designed from the ground up to unleash the performance of modern GPUs and generate the high-value datasets that power next-generation AI. Now, engineers can run hundreds of design iterations overnight instead of waiting days. AI models trained on physics-grounded data can suggest new aerodynamic designs or optimize energy systems in ways previously impossible, and companies across industries can accelerate their innovation cycles while reducing cost. With co-founders Juan Alonso and Pete Schlampp, Luminary has the technical depth and operational experience to define the new era of Physics AI. We’re proud to partner with them as they create the future of simulation and redefine what’s possible in the built world. Learn more about Luminary Cloud’s vision here: https://lnkd.in/gbjeWrvy 

119

6 Comments

In today's Unsupervised Learning I dive deep into self-driving cars and everything AI x hardware with Vincent Vanhoucke, Distinguished Engineer at Waymo and former Head of Robotics at DeepMind. Vincent has spent years at the intersection of AI and robotics, shaping how machines perceive, plan, and act in the physical world. From self-driving cars navigating complex cityscapes to the future of generalist robots, Vincent breaks down the real challenges — and unexpected breakthroughs — in bringing AI out of the cloud and onto the streets. Some highlights: 1️⃣ The milestones that matter in AI x robotics In self-driving the challenge has shifted from getting cars to drive autonomously to handling the long tail of rare, unpredictable edge cases that emerge over millions of miles driven. Vincent highlighted that the development of physically realistic world models, enabling robots and autonomous vehicles to simulate and train for countless real-world scenarios with high fidelity would be game-changing. Ultimately, scaling and real-world deployment, rather than isolated lab successes, are the true markers of progress in AI-driven robotics. 2️⃣ The Impact of LLMs on robotics Vincent shared how LLMs and VLMs have had a transformative impact on robotics by introducing world knowledge into AI systems, significantly enhancing their perception and reasoning capabilities. Unlike traditional models that rely solely on sensor data from specific environments, LLMs can provide contextual understanding, allowing robots to recognize objects or situations they've never directly encountered — like identifying unfamiliar police cars in a new city or recognizing rare accident scenarios. This semantic awareness helps self-driving cars and robots better interpret complex, real-world environments. By scaling up and leveraging LLMs, robotics can now bridge the gap between raw data perception and higher-level reasoning, pushing machines closer to human-like understanding. 3️⃣ How Waymo enters new cities When Waymo enters a new city, their focus is on ensuring the system can handle local nuances. The core models are designed to be highly portable across different environments, but specific elements—like recognizing unique emergency vehicle designs or adapting to new traffic patterns—require validation to maintain safety and reliability. A significant part of the process involves extensive evaluation and testing, often using simulations to explore edge cases rather than simply gathering more real-world data. Additionally, Waymo works closely with regulators and local communities to ensure compliance and public trust. Vincent emphasizes that the biggest hurdle isn’t always technical but about gaining social acceptance. A truly fascinating conversation on topics I've wanted to cover for awhile check out the full discussion below: YouTube: https://lnkd.in/gEFNDDR6 Spotify: https://bit.ly/4gXP8gK Apple: https://bit.ly/4gU2HNX

91

4 Comments