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No Priors Ep. 87 | With Co-CEO of Waymo Dmitri Dolgov

In this episode of No Priors, Dmitri Dolgov, Co-CEO of Waymo, joins Sarah and Elad to explore the evolution and advancements of Waymo's self-driving technology from its inception at Google to its current real-world deployment. Dmitri also shares insights into the technological breakthroughs and complexities of achieving full autonomy, the design innovations of Waymo’s sixth generation driverless cars, and the broader applications of Waymo’s advanced technology. They also discuss Waymo's strategic approach to scaling amidst regulation, deployment in cities like Phoenix and San Francisco, and the transformative potential of autonomous driving on car ownership and urban infrastructure. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Dmitri_Dolgov Shownotes: 00:00 Introduction 00:15 History of Self-Driving at Google 00:29 DARPA Challenges and Early Involvement 01:39 Formation of Waymo 01:53 Industry Lineage and Early Skepticism 03:05 Initial Goals and Milestones 4:33 Pivot to Full Autonomy 04:50 Scaling and Deployment 05:29 Generational Breakthroughs 06:59 Choosing Deployment Cities 09:26 Technological Advancements 11:01 Evaluating Safety 14:41 Regulatory Stance and Trust 16:52 Future of Autonomous Driving 23:19 Business Strategy and Partnerships 26:06 Changing Urban Mobility Trends 26:40 Challenges and Misconceptions in Self-Driving Timelines 28:43 The Role of Traditional OEMs in an Autonomous Future 30:54 Designing Cars for Autonomous Ride-Hailing 33:42 Scaling Responsibly 35:18 Generalizability and Future Applications of AI 37:10 The Complexity of Achieving Full Autonomy 42:58 The Importance of Data and Iteration in AI Development 46:13 Reflecting on the Journey and Future of Waymo

Sarah GuohostElad GilhostDmitri Dolgovguest
Oct 23, 202444mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Waymo’s Co-CEO Explains Cracking Full Autonomy And Scaling Robo‑taxis

  1. Dmitri Dolgov, co-CEO of Waymo, traces the company’s evolution from Google’s 2009 Chauffeur project to today’s large-scale robo‑taxi service delivering over 100,000 paid rides per week.
  2. He explains how successive “generations” of the Waymo Driver, driven largely by AI breakthroughs (ConvNets, transformers, larger models, VLMs) plus a robust simulation and evaluation stack, enabled a decisive jump to reliable full autonomy.
  3. Waymo now claims safety performance significantly better than human drivers, and Dolgov stresses that building and *evaluating* the driver—along with a cautious, trust‑earning rollout—are as critical as the core AI itself.
  4. Looking forward, Waymo’s primary near‑term business is ride hailing, but its generalized driver is intended to extend to deliveries, trucking, and other vehicle platforms in partnership with OEMs.

IDEAS WORTH REMEMBERING

5 ideas

Full autonomy required a pivot away from driver assistance toward solving the entire driving task.

Waymo initially considered an advanced driver‑assist product but decided around 2013 that incremental assistance wouldn’t deliver the safety and impact they wanted; the company refocused on removing the human from the loop entirely.

AI architectural leaps plus a strong evaluation machine enabled the ‘nut‑cracking’ moment.

ConvNets (post‑AlexNet) gave an early boost but plateaued; transformers, larger models, more compute, and a rigorous data/simulation‑driven evaluation pipeline together produced the performance jump that made broad deployment viable.

Evaluating an autonomous driver is as hard and important as building it.

Waymo relies on hundreds of safety and performance metrics, large‑scale simulation (open‑loop and closed‑loop), and a formal readiness and safety framework; this system underpins their confidence in deployment decisions and safety claims.

Waymo’s autonomous service now empirically outperforms human drivers on safety.

Based on 22 million+ fully driverless miles, Waymo reports roughly 2x fewer low‑severity collisions and 6x fewer airbag‑deployment crashes than human benchmarks, and a Swiss Re study found 4x fewer damage claims and zero bodily injury claims in its dataset.

Scaling safely is constrained more by trust and process than raw technology.

Despite exponential mileage growth, Dolgov emphasizes gradual rollouts, transparency with regulators and communities, and the fragility of public trust as the primary gating factors to faster deployment, not just capital or engineering capacity.

WORDS WORTH SAVING

5 quotes

The complexity is in the long tail of the many, many nines.

— Dmitri Dolgov

It’s all about AI. Full stop.

— Dmitri Dolgov

We are starting to actually earn the right to talk about realizing the mission of making roads safer.

— Dmitri Dolgov

Trust is this thing that’s hard to earn, but very easy to lose.

— Dmitri Dolgov

We think of what we’re doing as building the driver. You put the driver in, but you still need the car.

— Dmitri Dolgov

History of autonomous driving at Google and the DARPA Urban ChallengeWaymo’s generational hardware and software evolution (Firefly to Gen 5/6 Driver)AI breakthroughs: ConvNets, transformers, larger models, VLMs, and data flywheelsSafety measurement, simulation, and comparisons to human driving benchmarksRegulation, public trust, and the pace of geographic and fleet scale‑upSensor suite strategy, cost reduction, and end‑to‑end vs modular architecturesFuture business models: ride hailing focus, partnerships with OEMs, and impact on car ownership and cities

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