Adam Sadovsky

🩷 AI First, Human Always — a moment from Waymo in my hometown of Phoenix, birthplace of Waymo. 🚗 What is Waymo? Waymo is an AI autonomous ride-hailing service that began in Phoenix and uses AI to safely navigate without a human driver. ✨ Human Always In 2025, Waymo launched Teen Accounts in Phoenix, becoming the first autonomous ride service in the U.S. to let riders aged 14–17 travel solo while keeping parents connected. Parents can track rides in real time, get receipts, and know teens have access to specially trained support staff. 💬 Local families brought this to life: • A mother shared how it gave her daughter independence while easing her own routine, especially important because epilepsy limits her driving. • Another parent called Waymo a partner in keeping a busy family moving. 💡 So What? 1️⃣ AI earns trust when it solves real human problems 2️⃣ Safety and empathy can be growth strategies, not just nice-to-haves 3️⃣ Listening to local voices builds loyalty and credibility 4️⃣ Human storytelling makes advanced tech feel approachable and personal When AI meets genuine community needs, it moves from a novelty to a necessity.

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🚀 Can Waymo become a $1T company by 2035? Waymo’s recent $16 billion investment round at a $126 billion valuation suggests the market thinks so. But to reach a $1T valuation at a 6–8× revenue multiple (Apple/Google territory), Waymo would need ~$150B in annual revenue. I stress-tested this in my Autonomy Scale ROI model. Here’s what the math says: 1️⃣ The “Uber Plus” Trap 🛑 Uber reported ~211M trips/week in 2023. For Waymo to hit $150B, they need to facilitate 500M trips/week. The Insight: You can’t get there just by stealing ride-hailing market share. Waymo must convert a significant % of the 10B weekly trips currently taken in private vehicles. This requires a shift from "luxury service" to "utility infrastructure." 2️⃣ The Operational "Lights-Out" Lever 🛠️ Supporting 500M trips/week requires ~1.3M vehicles (~400× today). At that scale, the binding constraint isn’t hardware—it’s labor per vehicle-hour. Today, Waymo operates at roughly 42 vehicles per remote operator. To reach $1T territory, we need a “lights-out” depot model where total labor cost (remote assist + depot ops) drops toward ~$0.50 per vehicle operating hour. Why this matters: At 50% utilization, that’s ~$0.15 labor cost per productive mile. Once labor is decoupled from mileage, density effects compound faster than incremental hardware savings. 3️⃣ Destination Commerce: Beyond the Fare 💸 This is the biggest unlock. If Waymo only charges for the miles, it’s a commodity. The Opportunity: The real $1T play is becoming the platform that links transportation to commerce. Think: enterprise mobility contracts, bundled subscriptions, or "sponsored trips" where the destination (a retailer or restaurant) subsidizes the fare. The trip economics must extend beyond the fare itself. Airports monetize retail. Airlines monetize loyalty. Amazon monetizes Prime beyond shipping. Mobility will likely follow the same pattern. The Verdict: $150B revenue is mathematically possible, but only if Waymo stops being a "car company" and starts being a "spatial commerce platform." Try the simulation yourself: I’ve updated the Autonomy Scale ROI tool to allow for these "Hyper-Scale" scenarios, including inputs for long-term labor costs and platform-based revenue. 👉 https://lnkd.in/gYQDWprB Is $1/mile margin sustainable at million-vehicle scale? Or does competition compress it to pennies? Curious to hear operator views. #AutonomousVehicles #Robotaxi #UnitEconomics #Waymo #Tesla #Uber #Wayve #PhysicalAI #Mobility #Robotaxi #ProductManagement #ScalingAI

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Our Huxley-Gödel Machine learns to rewrite its own code, estimating its own long-term self-improvement potential. It generalizes on new tasks (SWE-Bench Lite), matching the best officially checked human-engineered agents. With Wenyi Wang, Piotr Piękos, Li Nanbo, Firas Laakom, Yimeng Chen, Mateusz Ostaszewski, Mingchen Zhuge. ArXiv: https://lnkd.in/e3zbgJQe Github: https://lnkd.in/e5_UW2MK

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Waymo is absolutely eating Tesla's lunch on autonomous driving and I'm here for it. Highlighted by their $16B raise this week and continued expansion, Waymo very much represents the right way to build, test, and scale such important and impactful technology. For one, Waymo has always stuck with a multi-sensory system using Lidar, computer vision and radar, knowing it was more expensive and would slow immediate scaling but fundamentally safer. Whereas Tesla relies solely on computer vision in order to keep costs down and push to market sooner. More than this, these two companies represent a fundamental difference in how they test and launch products. Waymo treats autonomous driving as requiring a ton of validation before deployment, while Tesla has been comfortable deploying it's technology before full validation, relying on customer experiences to guide future testing. This is fine if you are building an e-commerce platform, but is not a strong enough standard for autonomous driving. The result? According to numbers from the National Highway Traffic Safety Commission, Waymo has a 60% lower crash rate compared to Tesla's robotaxis. Waymo is a strong example of building technology responsibly, prioritizing safety not just speed. It's good to see the market rewarding this type of philosophy, because it's exactly what we need more of in the tech industry writ larger. Meanwhile, Tesla already seems to shifting more and more emphasis on it's Optimus humanoid robots. I'm sure nothing could go wrong with those.

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When it comes to autonomous driving, Tesla and Waymo couldn’t be more different in how they approach the problem, and what they believe it will take to solve it. Waymo is all-in on the “hardware-heavy” school of thought: LiDAR, radar, HD maps, redundant compute, and highly curated geo-fenced environments. It’s a masterpiece of engineering, no doubt. And it works, but only in specific cities, under tightly controlled conditions, and with a very high operational cost. Tesla, on the other hand, is betting on vision and scale. No LiDAR. No HD maps. Just neural networks trained on billions of real-world miles, constantly updated from a global fleet of cars. Less hardware. Lower cost. More scale. And most importantly, a direct path to consumer vehicles, not just robotaxi pilots. From a tech perspective, the contrast is clear: • Waymo builds autonomy like it’s building a #NASA mission. • Tesla builds it like it’s scaling a #smartphone feature. I’m leaning toward Tesla’s approach, not because it’s perfect, but because it’s data-rich, less dependent on expensive hardware, and more #economically viable in the long run. Of course, the race isn’t over. #Regulations, edge cases, and safety validation will shape the outcome. But if autonomy is ultimately a software problem at scale, I think Tesla may be closer than we think. HITEC Angeles Investors The Tech Series Porsche AG Pirelli NASA - National Aeronautics and Space Administration General Motors Rivian BYD U.S. Department of Transportation OpenAI

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Stock vesting schedules have been morphing, as we all know. And Waymo just slipped a new experiment into the mix. Two recent Waymo offers shared on Levels.fyi tell the story: - An L3 software-engineer package that vests RSUs over two years, 50 percent each year. - An L5 package that keeps the classic four-year, evenly split design. That contrast hints at a deliberate split strategy: shorter horizons for early-career hires, longer runways for senior talent. The logic tracks. New grads often treat their first role as a springboard, so a two-year vest ensures most of their grant actually pays out before they move on. For seasoned engineers, who hold bigger grants and deeper domain knowledge, Waymo still wants the retention pull of a four-year schedule. Waymo isn’t alone. Across tech, equity timelines are turning into a tuning knob: - Lyft issues single-year new-hire grants, then refreshes annually. - Uber front-loads at 35 / 30 / 20 / 15. - NVIDIA moved to 40 / 30 / 20 / 10. - Pinterest compresses to roughly 50 / 33 / 17 over three years. - A handful of high-growth startups grant two-year blocks by default. What’s driving the shift? 1. Pay-for-performance. Shorter cycles let companies re-price grants and reward top performers without being locked into four-year promises. 2. Dilution math. Fewer unvested shares lower equity overhang, especially valuable when stock prices spike. 3. Flexibility. Refreshers become the main retention lever, not year-three and year-four vest cliffs. 4. Talent liquidity. For early-career hires, faster access to equity can feel more tangible than a promise that’s years away. If you’re negotiating, remember that earlier liquidity doesn’t always mean bigger upside. Always compare total grant size, ask how refreshers are determined, and run the tax math on concentrated vesting events. Have you seen a two-year or front-loaded schedule in your offer recently? Comment below with your thoughts and I’ll send over a vesting report we put together here at Levels.fyi. #vestingschedule

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The Code Review reported on NVIDIA and Jensen Huang's announcement of the Drive AGX/Alpamayo autonomous technology, and what it means for industry players. With a special Fetii mention! "The announcement arrives at a moment when mobility players like Uber, Lyft, Waymo, and newer models such as high-capacity group transportation platform Fetii are defining increasingly distinct approaches to how people move." As NVIDIA and others standardize and open up autonomous vehicle technology, the cost curve will continue to compress and adoption will accelerate across OEMs. Autonomy won’t remain a novelty or a premium feature, it will become table stakes. The real advantage will belong to platforms that can turn that technology into sustained, high-utilization demand. That’s where Fetii is uniquely positioned. By focusing on high-capacity, repeatable group movement, Fetii and its partners are built to extract real economic value from autonomous fleets as they scale.

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I could not have said it better, and in fact am annoyed I didn't think to say it this way in the first place. He's absolutely right. If instead of agentic AI coders, we think of them as physical robots in our homes: do we think robots will go invent a new cuisinart? or just read the manual and use the one we have? Invent a new microwave? or use the one we have? What is happening in the public markets is a reaction akin to "if you get a physical robot, it will rebuild your home, each appliance you have ever used, a generator to power your home, and and and." It simply doesn't make sense. Not everything has to be redone with AI, just because it can be. AI should be pointed at solving new problems that couldn't be tackled before -- *that's* exciting innovation. For months, we've been reassuring our investors that in each innovation cycle (on prem -> cloud, desktop -> mobile, and now Saas -> AI native), the companies who have a deep command of the problems their customers face, who have held their customers' experience at the heart of what they do, and who don't stick their heads in the sand and act like nothing is changing, well, they have the advantage. They already have customers who love them. And so long as they continue to unlock new ways to add value as they appreciate the power that AI brings to the problems they're seeking to solve for customers, they'll be just fine. This is why there's never been a better time to be a software investor, and why we continue to caution our portfolio companies about recklessly pushing AI initiatives without tying them back to customer value and experience. The transition will happen, probably faster than on prem -> cloud did, but most companies will get there and this is not the end of software. Not to mention that part about how nice those SaaS 85% GMs are... https://lnkd.in/eMeyjyh8

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Big news that Waymo is expanding to more cities including Seattle and Denver 🚗🤖 It’s easy to forget that Waymo is an early example of an advanced AI agents in production, operating at scale, in a high-risk environments and it works reliably! Why? Because of countless hours of training and hardening in simulation before hitting real roads. Enterprise AI agents face the same reality. If they’re expected to operate in risky or high-stakes environments, they need robust simulation to prepare. At Veris AI, we build high-fidelity simulations so AI agents can learn and expand safely, like a Waymo.

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  Lessons from the ’80s (and a power outage in SF) Over the last week, my feed has been full of people dunking on Waymo after a power outage temporarily froze its robotaxi fleet in San Francisco. Fair criticism of reliability matters. But obsessing over moments like this misses the bigger pattern. Every foundational technology looks fragile when you evaluate it through today’s use cases. That clicked for me after reading a piece about AT&T in the early 1980s. Back then, AT&T asked McKinsey & Company to forecast the future of mobile phones. The conclusion: cellular would be niche, limited, and never scale beyond a small fraction of landline usage. The reasoning wasn’t irrational. Phones were bulky. Batteries were bad. Coverage was spotty. Costs were high. What they missed wasn’t execution. It was imagination: They modeled the future as an extension of the present, instead of asking what entirely new behaviors mobility might unlock. That same trap shows up again and again. When you evaluate autonomy only as “Uber without a driver,” you underestimate it. When you see robotaxis only through today’s reliability gaps, you miss what happens when infrastructure hardens, costs collapse, and new usage patterns emerge that don’t exist yet. Even Uber made this mistake early on. The market they created ended up far larger than the taxi market analysts originally modeled, because it enabled behavior people weren’t previously able to express. The takeaway for me is simple: If you measure new technology only by what it can replace today, you will systematically underestimate what it becomes tomorrow. Growing pains are real. They’re also the least interesting part of the story.  

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Watching Jensen Huang on the NVIDIA GTC stage highlight the critical need for codebase awareness and understanding wasn't just a proud moment for our team; it was a powerful validation for every enterprise development leader navigating the complexities of AI. For too long, the AI for coding conversation has focused almost solely on code generation. But the real enterprise challenge isn't just creating new code; it's managing the vast, complex systems they already have - it's the daily reality for developers navigating a web of interconnected repositories, all while adhering to specific best practices and piecing together information from siloed teams and documents. This is where the paradigm is shifting. We're moving from AI as a simple assistant to AI as a core intelligence layer woven into the entire SDLC. High-value tasks like ensuring code integrity, preventing cross-repo breakages, and enforcing organizational standards require true contextual awareness. This has been our mission at Qodo from day one. We are building an integrated platform not just to help developers write code faster, but to help enterprises ship better software with confidence and control. The GTC mention is a fantastic milestone, and it energizes us to keep pushing the boundaries of what's possible with agentic software development.

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Just told my 7 year old nephew that Meta poached like 10 people who worked on GPT-4o, Gemini, ChatGPT, Waymo perception, and god knows what else She was quiet for a second and then said “Given the level of model convergence, talent is the last real differentiator. If you can’t win on distribution or data moats, you hire the people who actually built the thing.”

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Waymo collected ~20M miles over 15 years before launching L4 in SF. That’s years of data collection, billions in capital, and a scale most companies simply can’t afford. And in many industries — especially defense — you don’t even have the luxury of commercial-scale deployment in the first place. The reality? Autonomy doesn’t fail because of models. It fails because of missing edge cases, weak coverage, and slow data iteration cycles. That’s exactly the problem we’re solving at DiffuseDrive. In this Forbes piece, Sabbir Rangwala breaks this topic down. Synthetic data isn’t about replacing reality. It’s about completing it. https://lnkd.in/dqNSA4t7 Roland PinterJordan KretchmerOutlander VCPresto Tech Horizonse2vc #PhysicalAI #Autonomy #DefenseTech #SyntheticData #Robotics #GenerativeAI

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