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I was part of the team that developed the very first self-driving car that won the…
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Adrian Kaehler, Ph.D. posted thisI am engaged in a rather surreal situation in which I am working on some code, and talking to THREE different AI's which are, themselves, proposing questions to one another. I am running a software development engineering meeting with a team of three engineers, none are human.
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Adrian Kaehler, Ph.D. posted thisThe venture world is scrambling right now - at least the smart ones are. But in the scramble, there is opportunity for those who can see it. For early-phase founders, or people thinking about starting something from scratch, the current economic chaos - and it is unadulterated chaos - is not entirely bad news. Because whenever cracks start to show in a system, it means there is an opportunity to fill them. And if you are a new founder looking at the venture space, that is as true as anywhere. Seed money isn’t the same as Series B money. It’s smaller, scrappier, and right now, there’s a good chance more of that capital will flow into the early stage - because later-stage rounds are drying up fast. That $100 million someone was going to put into a Series D may well get cut, or severely cut down, as we gaze into the turbulence ahead. Which means that money might get spread across dozens of seed rounds instead. Startups born in uncertainty also often turn out to be more resilient. If you can build something real with limited resources in a volatile market, you're building from a place of strength. So yeah - building a company right now might feel like stepping into the ring with Mike Tyson as your coach and your opponent. But if you can take a few hits and still move forward, then you know you’ve got what it takes to go the distance!
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Adrian Kaehler, Ph.D. posted thisStartups don’t need people who follow a job description. They need capable, smart people who can figure it out as they go, and who don’t accept defeat. If you are asking someone how much experience they have at something, if anyone has experience at the something you are trying to do, then what you are doing is probably trodden earth. Maybe neither of you has what it takes to do something truly new. You want the kind of team who, if you said “we need to build a rocket and get to the moon,” would nod, open a new tab, and start researching rocket fuel. Your leaders should be questioning you too. “Why the moon?” “Why a rocket?” “Space Elevator?” “Slingatron?” This is how elite generalists think. I have been privileged to work with many such people I would describe as ‘elite generalists’, and the difference it can make to a small company is unfathomable. They don’t need every answer upfront. They’re confident they can get there because they’ve found a way, in different forms, again and again. In the early days of a company’s life story, these are the people you need. These are the only people you need. Specialists are critical when you know exactly what needs to be done. But in the early stages of a company, you rarely do. Experience teaches that even if you think you do, you will soon discover that you don’t. What you need is range. You need people who can wear five hats, hold uncertainty, and still build something that works. And when the plan changes - which it will - they don’t flinch. They adapt, and the best of them share not only their new ideas, but the alternate paths their minds have already been exploring. And that’s the real value of hiring ‘elite generalists’ into your small team. Find them, invest in them, nurture their diversity, agility, and creativity. Never forget the Kobayashi Maru.
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Adrian Kaehler, Ph.D. posted thisPivot has become a bad word in Silicon Valley, and it’s a huge mistake. Somehow it’s come to mean that the leadership of a company didn’t make the right choices to begin with, a failure of planning, but it isn’t that at all. Pivoting a company isn’t about tweaking your pricing model or testing a new market. It’s a radical response to an emerging threat, or an extraordinary new opportunity. More often than not, it’s both. It is something start-ups can do, that bigger organizations cannot. It is their superpower. Truly pivoting on an idea is sitting in a room with your whole team - every single man and woman, and saying: “So you know everything we have been working on, scrap it. We have a new opportunity in front of us, which we are going after, and we’re starting from scratch.” You delete the roadmap. You clear the whiteboards. Something better has come into focus - a new opportunity, a customer need you didn’t see before, or a massive change to the landscape. And suddenly the old plan doesn’t make sense anymore. Most companies can’t even contemplate that. This isn’t idle speculation, tariffs, trade instability, and unstable foreign policy have sent countless companies, from the tiny to the titanic, scrambling for their footing. But the tiny ones, under good leadership, have the ability to adapt, and keep adapting, as the titan stumbles… and fall. You’re still nimble enough to throw out the rulebook on Monday and have your first working prototype of something better by Friday. It’s not chaos, it’s clarity. The courage to change direction - fully, decisively - is one of the most powerful tools you have as a founder. And it might be the only thing that keeps you alive.
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Adrian Kaehler, Ph.D. posted thisFoundational academic research of AI is in an awkward place at the moment. And my hope is that over time it can correct itself. With the boom that is coming with AI, there is not a single industry that won’t want and need AI professionals - the people who can really maximize the technology for the benefit of that specific company or sector. But at the moment, the demand for these people still far outstrips the supply. The number of professionals who really understand the deep concepts of AI is still relatively small. This means that academics within the space - the people who we rely on to be looking inwards, questioning the foundations and futures of what AI really is - are being swept up by industries before they have a chance to ask the deeper academic questions. And you can’t blame them. If you are a professor here in California, having to fight tooth and nail to get a small grant proposal funded by the Department of Defence is a nightmare - but you can walk down the road to SandHill and get 10-100x that amount in startup funding just because they know you're smart… It’s no surprise which one more people would choose. AI is still such an evolving technology, and while we definitely need to be using it to make all of our lives easier and more efficient - there still needs to be incentive for someone to sit down in a lab somewhere and ask: ‘What are we actually doing?’
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Adrian Kaehler, Ph.D. shared thisAs we all know, modern LLM AI is literally defined by language. But is the AI now creating its own language without us noticing? It’s becoming more and more apparent to me that in order to get the best results out of AI systems, you should feed it other AI-generated prompts. But, why? Consider travelling to a foreign country where the local language is similar to, but not exactly, the one you speak. For example, you live in Eastern Canada and travel to France, or in Mexico and travel to Spain. It is tempting to simply speak your language directly to someone and accept that they seem to understand. But do they, really? On the other hand, if you took your time to explain your meaning very carefully to a local friend and then tasked him to relay your message to some third party, would they likely use the same words you would have? Of course not. The funny thing is, you could have spent months in that country and everyone would seem to understand you and you would seem to understand them. There would only be an occasional oddity when, for example, someone asks for a “tortilla” in a restaurant. Just as a non-native speaker might be using the same words as someone who is a native speaker - but the meaning can come out differently. Then it hits you. It wasn’t just the tortilla. There is something between you and these people, something that you might have interpreted as them not paying attention to you, or maybe even them being a little dumb. It wasn’t that at all. It was that they just didn’t understand you as well as you thought they did. I’ve begun to notice this pattern when talking to AI agents. They seem to speak my human language, but their natural language is something else… some “AI speak”. That’s why when I give Dall E a prompt, it’s generally not great. But if I get ChatGPT to create a Dall-E prompt for me, the result is often exponentially better! They speak the same vernacular of their own language: a language that I, apparently, do not speak.
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Adrian Kaehler, Ph.D. posted thisYou want to know the secret for where all this inflation is coming from? Cryptocurrency. You hear about inflation all the time - prices rising, dollars buying less, and no one able to say exactly why. But what if I told you that a major source of inflation isn’t just government spending or supply chain issues, but something else entirely? Cryptocurrency Consider this - when a central bank prints money, it increases the total amount of money in circulation. Well, cryptocurrency has done something similar. Over the last decade, crypto assets like Bitcoin, Ethereum, and others have added about $3 trillion worth of new "money" into the global economy at their peak. To put that in perspective, the total U.S. M2 money supply - which includes all cash, checking, and savings accounts - is around $21 trillion. That means crypto has effectively increased the available money pool by nearly 15%. It’s just a basic rule of money: when the supply of money increases, the value of each dollar decreases. If you add 15% more money to an economy, everything else being equal, the value of the existing money drops by a similar amount. It’s actually quite straightforward. So if you're wondering why your dollars don’t go as far as they used to, look beyond the usual suspects. Since the AI boom of the last few years, crypto has gone somewhat under the radar. But, while AI is certainly in a bubbly phase, the hope is that it can become more accessible and ultimately more beneficial than the crypto-sphere… People made billions on crypto, but they didn't get that money from nowhere. They got it from your pocket and from your bank account. Not because you used it to buy cryptocurrency, but through inflation. Your dollars are still there, they are just worth 15% less. AI puts value back on the other side of the ledger, doing real work and creating real value.
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Adrian Kaehler, Ph.D. posted thisIs AI just the ‘next’ big thing, or is it something completely different? Every time a transformative technology emerges, people try to understand it through the lens of what came before. But true innovation is disruptive by definition - it isn’t “the next step” in the line of technology, it’s a break from it. - Facebook came after GeoCities, but it wasn’t “the next” GeoCities. - Similarly, Amazon wasn’t “the next” Sears Catalog. And AI won’t be the ‘next’ of anything. It’s something entirely different. The biggest technological breakthroughs throughout history - fire, writing, electricity, computers, the internet - didn’t simply iterate on existing models, they redefined the landscape. And AI is doing the same. People assume power will consolidate, but where? Who will be the dominant players? What will be the prevailing business model? No one really knows yet. And yet, much of the conversation around AI remains trapped in outdated frameworks. On the one hand, AI is just “a new tool”, but like great innovations before it, it brings with it an entirely new perspective from which the whole of our reality is reimaged and reimagined. Those who recognize this have a role to play in shaping this new future. Those who don’t, will be left behind.
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Adrian Kaehler, Ph.D. shared thisElon Musk has issued a sweeping mandate: every U.S. federal worker was required to submit an email listing five things they did in the last week. “Failure to comply will be treated as a resignation”, he stated. The sheer scale of this directive (we’re talking about millions of workers here) means there is no feasible way this can be reviewed manually. AI is the only way to process and judge this massive volume of text. This is a pivotal moment. AI has been integrated into workplaces before, but not like this, not as a filter determining who stays and who goes. We’re looking at one of the first mass uses of AI as a weapon of control, assessing human labour at an unprecedented scale. Or is it assessing human labor? Anyone could make up anything in those emails. Would an AI have the context to judge if they are true? Probably not without a lot of data integration work that clearly hasn’t happened (yet). But can it judge if these responses are plausible? …If the employee took the task seriously? …Who is pushing back and who is, at least giving the appearance of, complying? Yes, even one or two years ago’s LLMs could have done this with near-perfect precision. It can’t tell who is working hard, it can only tell who is loyal or afraid enough to pretend to be. This isn’t just about bureaucratic oversight or productivity tracking. It’s about control, and the massive scale of action that AI enables is the tool of control that those who disdain democracy have sought for countless generations. Maybe, in time, a system could be devised that would at least attempt to make meaningful judgements of productivity, but is that really much better? is that how we want to be living our lives? Do we want to be constantly audited, judged, and - inevitably - punished by algorithmic systems designed for that purpose? AI can be a great gift to and for humanity, but it can also be a weapon. This is the first time that I am aware that AI has been used as a weapon against a million or more people in a single stroke.
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Adrian Kaehler, Ph.D. liked thisBison Partner Caleb Appleton spoke on a panel at Biocom Converge's summit last week on funding trends and the impact of AI on the venture ecosystem in biotech. Read some of his takeaways below:Adrian Kaehler, Ph.D. liked thisCapital in both AI and biotech is consolidating into fewer, larger rounds. The reasons couldn't be more different. In AI, investors believe they're funding the largest technology and market-reshaping moment in history. They're optimizing for unbounded upside. In biotech, the belief is that more capital deployed per program reduces risk and lifts success rates above the industry baseline. Investors are optimizing for downside protection. At Bison Ventures I invest across both, and last week at Biocom Converge I shared some thoughts on what it takes for companies that straddle the two to win. A few themes kept surfacing: 𝗪𝗲 𝗱𝗼𝗻'𝘁 𝘆𝗲𝘁 𝗸𝗻𝗼𝘄 𝘁𝗵𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗶𝗻 𝗯𝗶𝗼. In LLMs, we have a relatively clear picture of how performance moves with data, parameters, and compute. In biology, we don't. Neither the scaling relationships nor the specific datasets that would unlock them are well understood. What we do see consistently: when generalist models are given high-quality, narrowly scoped data to reason over, they perform remarkably well. Peyton Greenside at BigHat Biosciences talked about where they've seen this play out in antibody design. The frontier in bio isn't necessarily bigger pretraining corpora. It's figuring out which data, at what fidelity, against which questions. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝘂𝗻𝗹𝗼𝗰𝗸 𝗺𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘀𝘁𝗶𝗹𝗹 𝘂𝗻𝗱𝗲𝗿𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗻𝗴. RL is what took LLMs from impressive autocomplete to systems that can actually reason: the jump from GPT-4 to o1, R1, and the current generation of reasoning models came largely from RL on verifiable outcomes, not more pretraining. Bio needs its own version of this loop: tightly coupled hypothesis generation and experimental validation, where the model learns from real wet-lab signal. The winners won't be do-it-all AI scientists or fully lights-out labs. They'll be systems purpose-built to answer specific, high-value questions in drug discovery, with the right data, automation, and agentic control wrapped around them. Vivodyne in my portfolio is building exactly this. 𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗔𝗜 𝗶𝗺𝗽𝗮𝗰𝘁 𝗶𝗻 𝗯𝗶𝗼𝗽𝗵𝗮𝗿𝗺𝗮 𝗺𝗮𝘆 𝗻𝗼𝘁 𝗯𝗲 𝗶𝗻 𝗥&𝗗. Clinical operations, commercial, and post-launch are wide open, and almost no one is building there. R&D will get the headlines, but the nearest-term value (and some of the most defensible companies of the next decade) will come from the parts of the industry everyone overlooks. Proud to back Sleuth on this front. The companies that will define the next decade of biopharma won't do so by seeking to avoid risk and if the industry isn't careful, Anthropic, Deepmind, and OpenAI might win the race to using AI impact human health at scale.
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Adrian Kaehler, Ph.D. reacted on thisAdrian Kaehler, Ph.D. reacted on thisBuilding a platform is never a solo journey — it’s shaped by the wisdom, honesty, and guidance of brilliant advisors who challenge my thinking and help refine our direction. Truly grateful for your support, Adrian Kaehler, Ph.D. and Erik Norden — These conversations go beyond strategy. They’re about clarity, resilience, and making the right decisions when it matters most. #Gratitude #Leadership #Advisors #StartupJourney #Robotics #smAIT
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Education
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Columbia University in the City of New York
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As part of my Ph.D. research at Columbia, I was part of the team that built the Teraflops QCDSP machine. We used this machine for theoretical explorations of Strong Interactions and Lattice Gauge Theory. We later won the Gordon Bell Prize for our work in building this machine.
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Publications
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Learning OpenCV 3
O'Reilly
Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to "see" and make decisions based on that data.
With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such…Get started in the rapidly expanding field of computer vision with this practical guide. Written by Adrian Kaehler and Gary Bradski, creator of the open source OpenCV library, this book provides a thorough introduction for developers, academics, roboticists, and hobbyists. You’ll learn what it takes to build applications that enable computers to "see" and make decisions based on that data.
With over 500 functions that span many areas in vision, OpenCV is used for commercial applications such as security, medical imaging, pattern and face recognition, robotics, and factory product inspection. This book gives you a firm grounding in computer vision and OpenCV for building simple or sophisticated vision applications. Hands-on exercises in each chapter help you apply what you’ve learned.
This volume covers the entire library, in its modern C++ implementation, including machine learning tools for computer vision.
Learn OpenCV data types, array types, and array operations
Capture and store still and video images with HighGUI
Transform images to stretch, shrink, warp, remap, and repair
Explore pattern recognition, including face detection
Track objects and motion through the visual field
Reconstruct 3D images from stereo vision
Discover basic and advanced machine learning techniques in OpenCV
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Learning OpenCV
O'Reilly Media
The best and best selling book on computer vision today. This book was written partially while I was at Intel, and completed after moving to Applied Minds Inc. This book is utilized as both a text book and a primary reference for computer vision researchers and practitioners world wide. It has been translated into a dozen or more languages, sold many tens of thousands of copies, and at last count had over two thousand citations in academic papers.
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Honors & Awards
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Gordon Bell Prize
Gordon Bell Foundation
In 1998, my Columbia University team was awarded the Gordon Bell prize in computing for building the QCDSP supercomputer, what was then the fastest computer in the world. My own work on this computer involved not only the design and implementation of they physical architecture of the machine, but also operating system and, most importantly, high performance parallel implementations of algorithms designed to simulate, and solve, complex problems in theoretical physics.
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Allan Sachs Award
Columbia University
Taught theoretical physics for the Rabi Scholars program at Columbia University and won the Allan Sachs award for teaching work at Columbia University.
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DARPA Grand Challenge
DARPA
I wrote the code for the very first self-driving car, the DARPA-sponsored competition that later became Google Waymo and inspired the creation of countless other self-driving companies. The prize money that our Stanford team won for the DARPA Grand Challenge was donated to Stanford for the "Stanley Chair of Computer Science" in the CS AI lab (called "CSAIL"), the same lab in which I was a visiting scholar.
Organizations
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OpenCV
Board Member
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Philip Koopman
NTSB has a preliminary description of the Waymo robotaxi colliding with a student pedestrian in Santa Monica. The diagram provides useful insight into how it happened. The student seems to have crossed mid-block instead of using the available marked crosswalk at the end of the block. But then again, this was a 9-year-old student and it is an elementary school, so such activity should not be a complete surprise. We also do not know if that is a customary behavior for other students in this scenario, which would be something accounted for by human drivers participating in the cultural experience that is "drop-off line". The student was crossing from a vehicle in the opposite-direction travel lane as the robotaxi. It is worth paying particular attention to the boundaries of the school speed limit, as well as the proximity to the school. The collision occurred just after the transition from 15 mph to 25 mph, with the robotaxi going 17 mph. Based on this analysis that is below the legal speed limit, but speed appropriate for context is an additional determination that is still pending. One fact of interest is that the robotaxi was there because it had just "completed a passenger drop-off ... at the stop sign in front of an elementary school". (Wondering how old that passenger riding solo was arriving at an elementary school .... but that potential issue is unrelated to the collision.) Thanks as always to NTSB for their rigorous approach to investigations. Worth noting as well that Waymo, as a party to the investigation, would not be expected to comment in an ongoing investigation. So a lack of comment from them is neither a positive nor negative indication. NTSB Investigation web page: https://lnkd.in/epCe6Qmc
83 Comments -
P MOHAN
I recently rode in a fully driverless Waymo in San Francisco — and honestly, it felt smarter than most human drivers. Waymo’s robotaxis (Jaguar I-Pace EVs) are now operating in the city, available to anyone through an app. Using a combination of lidar, cameras, and advanced sensors, these vehicles navigate city traffic, obey signals, and safely maneuver around pedestrians and cyclists. It does raise an important question: could such technology be feasible in Indian cities? While India’s traffic conditions, road infrastructure, and regulatory frameworks are very different, the opportunity is huge. With controlled pilots in IT campuses, airports, industrial corridors, and smart cities, autonomous mobility could gradually find its place. P. Mohan
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Keith Gipson
Happy Friday! 🖼️ A picture is worth a thousand words! ☺️ This is the latest data from our Central Plant AI optimization solution running at California State University-Dominguez Hills Kenny Seeton. Pretty exciting that we've confirmed that it takes our AI/ML/RL platform a total of TEN (10) days, from “scratch, to completely RE-discover the data points needed, classify them, "map" them, build its OWN model, train itself, and INCREASE the Central Plant efficiency by FORTY-EIGHT PERECNT! From 0.81 kW/ton down to 0.39 kW/ton! 🚷 NO human intervention! Below is a snapshot of the CP. Restart 5/5/25. We deleted ALL of the learnings from the last 2 1/2 years. About 2GB. 1 week baseline. 10 days RL. Notable observations: 1. Even though the Plant Efficiency increased by 48%, the Chiller is STILL "holding" the CHW-S temp set-point of 47 DEG F (46.5 DEG F Actual). 2. The one-thousand TON (rated) JCI/York YMC2 Chiller is "putting out" 668 TONS of cooling (67% of its RATED capacity), YET the Chiller itself is only "loaded up" 39.4%! This means the Chillers CAPACITY (output) INCREASED significantly. 3. The original BMS algorithm was programmed to set the CW Temp to Outside Air Wet-Bulb (OA-WB) PLUS five (5) degrees. Which is standard practice, and has been, for decades. However, our AI trained itself and controlled the CW Temp everywhere from +4 degrees above OA-WB to TWENTY-FOUR (+24) degrees above OA-WB. And everywhere in between. Every five (5) minutes. 288 cycles (solutions) per day. The new set-point is computed and sent from the cloud. Takes 4-5 secs. Let's talk about "guardrails". 😉 YES! 😁 They're there. Low: 60.0 DEG F to a High: 90.0 DEG F. In fact, these same "guardrails" are typically already IN PLACE, with your existing BMS logic. AND there's mechanical safeties, ON the Chiller(s) and equipment. We take the safety and security of your organization VERY seriously. But here's the thing about facil.ai: How can you TRUST our AI? Because you can TRUST the people who built it! And install it. Our partners are the same BMS service providers, consultants, mechanical contractors, ESCOs, Property Managers, Peers and friends: Who are serving you right now! Ready to learn more? Contact us: https://lnkd.in/gVutYEiX Want to explore being a partner? https://lnkd.in/gigtx2vx Want to invest in the future of AI Digital Technology? We're putting together a crowdfunding growth round with StartEngine https://lnkd.in/gwcSRR3b Please sign up on the list if interested: https://lnkd.in/gJ_2yCr3 Let's start a conversation! I'm always ready to "talk shop" 🤗 Shoot me a note on LinkedIn or e-mail: keith@facil-ai.com 🦄 We’re thrilled to be in conversations with several large infrastructure companies as partners or acquirers. Inquiries to our interim CTO John Majeski BUILD🛠️GROW📈FUND💰EXIT🦄 ❤️ Our customers love us! 🚀 facil.ai is proud to be a Portola Valley Partners portfolio company. #innovation #technology #management #creativity #future
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Farrukh Mahboob
The internet is arguing about LiDAR vs Vision after Waymo's fleet went offline during the SF power outage while Tesla's vision-only robotaxis kept running. Here's my take: You need both. But vision alone can do wonders. LiDAR is precise. It's great in controlled environments, but it doesn't scale without infrastructure, maps, and significant investment at every location. Vision adapts. It learns from the real world and works anywhere — no special conditions required. We see the exact same debate in logistics: Vision vs RFID. RFID needs a tag on every item, a reader at every checkpoint, and a controlled environment to function. It works, but it's expensive and doesn't scale easily. Vision just needs a camera. That doesn't mean sensors, LiDAR, or RFID don't have value; they absolutely do. But if I had to pick one to bet on, Vision. Every single time. It solves most of the problems at a fraction of the cost and complexity. And it doesn't require perfect conditions to work; it adapts to imperfect ones. That's how AI scales in the real world. #Vision #AI #Lidar #RFID #OCR #Barcode #Tesla
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Spencer Penn
If you've been to San Francisco recently, you'll notice something unusual.... Thousands of Waymo autonomous vehicle roaming the streets. When I started at Waymo back in 2019 it was more an R&D Project, than a bonafide commercial product. That's changed. Reports are indicating that Waymo now officially surpassed Lyft in ride-hailing market share in SF, with Uber potentially next in its sights. This isn't just about one company winning; it's a powerful signal about the shifting landscape of disruption, particularly how technology is now decisively trumping incumbency. For years, when we talked about competitive moats in tech, the "network effect" was king. Think Facebook: "People are on Facebook because other people are on Facebook." It created an almost impenetrable barrier. For rideshare companies like Uber and Lyft, their network effect was built on a two-sided marketplace: riders <-> platform <-> drivers. The more riders, the more drivers; the more drivers, the better service for riders, creating a virtuous cycle. But what Waymo is demonstrating is a profound disruption to this established idea, especially when AI and end-to-end automation enter the picture. Here's why it's different for self-driving: Waymo has fundamentally removed the marketplace element from the equation. Consider the traditional rideshare model: - Uber/Lyft: Riders <--- Platform ---> Drivers (a complex marketplace to manage, balance, and incentivize) Now look at Waymo: - Waymo: Riders <--- Platform ---> .... Nothing (no drivers to manage, no human supply/demand dynamics, just pure autonomous service delivery) They've replaced one side of the network with pure technology. The network effect, in its traditional sense, relied on the human element, the supply side of the marketplace. When you automate that supply side entirely with AI-driven vehicles, the competitive advantage shifts dramatically from a "people network" to a "technology and operational efficiency network." This isn't just a niche observation; it's a blueprint for disruption across industries. When you can automate an entire end-to-end process – removing the complex, variable human element from one side of a traditional marketplace – you redefine what a competitive moat looks like. It's no longer just about who has the most users or suppliers; it's about who has the most robust, scalable, and efficient autonomous system. For those of us in procurement and supply chain, this is a profound case study. Are we truly leveraging AI to remove complexity and build end-to-end automation, or are we just optimizing existing marketplaces? The Waymo story highlights that AI isn't just an efficiency tool; it's a fundamental re-architecting force that can dismantle long-held competitive advantages. What are your thoughts on this? Where else do you see AI dismantling traditional network effects? #AI #Automation #Waymo #NetworkEffect #Disruption #Procurement #Innovation
5 Comments -
Dar Patel
Think cutting-edge means owning every asset? Waymo’s latest deal says otherwise. Yesterday, Waymo signed a deal that shows scaling robo-taxis is more about ops partnerships than flashy lidar. Here’s the story: - Waymo taps Avis Budget Group to manage, charge, clean, and service its entire robotaxi fleet in Dallas. - Fleet: 200+ all-electric JLR (Jaguar) I-PACE SUVs ready to roll by year-end. - Avis staff will handle 24/7 turnarounds, while Waymo keeps its focus on autonomy, routing, and rider UX. - This mirrors Waymo’s Phoenix model, where an external partner slashed downtime by >20%. Why it matters: Waymo stays asset-light by outsourcing fleet logistics to Avis, focusing on core AI while turning a legacy rental giant into the backbone of tomorrow’s robotaxi network. What about Avis? Avis lands a foothold in next-gen mobility, future-proofing a rental business that’s been squeezed by ride-hail for a decade. For founders, the message is clear: 1️⃣ Partnerships can accelerate scale faster than capital. Asset-light beats asset-heavy when the tech is your edge. 2️⃣ Focus on your differentiator. Waymo owns autonomy IP; Avis owns fleet ops. Everyone stays in their genius zone. 3️⃣ Legacy incumbents are leverage, not obstacles. The right deal turns yesterday’s infrastructure into tomorrow’s distribution. The next wave of mobility, and many other industries, will be won by orchestrating partnerships that let each player compound what they do best. 🔗 Read more → https://lnkd.in/dkMYgyFj 💬 Founders: if you outsourced your lowest-margin activity tomorrow, how much faster could you ship your core value? #Waymo #Avis #MobilityTech #FounderStrategy #MyLittlePursuits ________ ♻️ Repost this to share it with others. I’m Dar, founder of Little Pursuits. I help founders turn bold ideas into traction through strategic and leadership coaching. Follow for more insights like this.
2 Comments -
Ken Kuang
Weather 🆚 Tech: The Autonomous Battleground 🌧️ Waymo recently paused operations in LA due to heavy rain—just days after an upgrade meant to improve "sensor fusion" for foul weather. It brings Elon Musk’s long-standing critique of LiDAR back to the forefront: "LiDAR does not work well in snow, rain, or dust due to reflection scatter." He’s not saying the tech is bad—he used it for SpaceX Dragon—he’s saying it’s the wrong tool for the "all-weather" job of driving. The Question: Can software solve a hardware physics problem (reflection)? Or is Vision-only the ultimate winner?
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