AI Dynamics

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@mattlynley

  • Waymo Safety Impact Data CSVs Available for Download

    The raw data CSVs can be found at the way bottom of this page waymo.com/safety/impact/

    → View original post on X — @mattlynley, 2025-09-17 18:35 UTC

  • Waymo’s Autonomous Vehicles Demonstrate Revolutionary Road Safety Achievement
    Waymo’s Autonomous Vehicles Demonstrate Revolutionary Road Safety Achievement

    As a neurosurgeon I care a lot about road safety. By now you’ve probably seen @Waymo’s stunning safety results (like 91% fewer serious crashes). But they didn’t just publish data headlines. They released the raw CSV files and data dictionaries. I did a much deeper analysis. A fascinating story emerges when you analyze how they’re achieving this. This isn’t incremental improvement – it’s categorical. We’re looking at the potential elimination of traffic deaths as a leading cause of mortality. The intersection breakthrough: Waymo has essentially solved intersection crashes, with 95% fewer injury incidents than human drivers in the same locations. That’s transforming the deadliest driving scenario. The national math: If every US vehicle performed like Waymo, we’d prevent 33,000-39,000 deaths annually and save $0.9-1.25 trillion in societal costs. Even partial adoption at 27% would save ~10,000 lives per year. In terms of magnitude, this would be the equivalent of eliminating every pedestrian death nationally in a year. The physics signature: Here’s what fascinates me: 47% of Waymo’s contacts involve less than 1 mph delta-V. They’re not just avoiding crashes; they’re converting unavoidable incidents into gentle bumps. It’s like having physics itself on your side. We’re not talking about marginal safety gains. The data represents a fundamental shift from harm reduction to harm prevention. The methodology matters: I used their dynamic geographic benchmarks (comparing like-for-like road conditions) and verified the findings hold across San Francisco, Phoenix, LA, and Austin. The safety advantage actually increases in more complex urban environments. Link to raw data below…. Notes on my approach: Analysis based on 96 million miles of Waymo Rider-Only (RO) data through June 2025, utilizing Waymo's dynamic geographic benchmarks to compare Waymo Driver performance against human drivers under similar road conditions and operational design domains. The projections for national impact (deaths prevented, societal costs) involve several assumptions. Given Waymo's zero reported fatalities, the direct serious injury reductions were mapped to national fatality statistics using established NHTSA-derived ratios that correlate serious injury crash rates with fatality rates. This extrapolation assumes that Waymo's observed serious injury prevention capability would translate proportionally to fatality prevention. Societal cost savings are estimated by applying average per-fatality and per-injury economic costs (e.g., medical, lost productivity, quality of life) as published by NHTSA, scaling these national averages to the projected number of avoided fatalities and injuries based on Waymo's safety performance. These figures represent the potential annual impact if the Waymo Driver's safety profile were widely integrated into the national fleet. @ethanteicher

    → View original post on X — @mattlynley, 2025-09-17 18:29 UTC

  • Matt Lynley Joins dMatrix AI as Technical Product Marketing Manager

    Excited to be starting at @dMatrix_AI as a Technical PMM! Efficient and high-performance inference will be a tremendously important part of the next phase of AI, from agents to ever-more powerful reasoning models. I'm thrilled to be working at the forefront of this next phase!

    → View original post on X — @mattlynley, 2025-07-22 18:04 UTC

  • Matt Lynley Joins Lux Capital as Investor and Partner

    Excited to share I have joined the eclectic team at @Lux_Capital. Grateful for my time at Cerebras with Andrew and a team who showed me the vision, grit, and adaptability it takes to build a generational AI semiconductor company. The same curiosity that drew me from the Senate to Silicon Valley to understand the bare metal powering AI, has now led me to join Lux to partner, invest, and explore how AI is transforming the physical world. Lux’s commitment to building technology at the frontier is special, and Josh and Peter have built a incredible team that I’m lucky to work with and learn from. To founders and friends, especially in the robotics, manufacturing, and energy space, I’d love to meet you. 🚀@wolfejosh, Peter, @breeves08, @Farshchi, @deenashakir, @graceisford, @velvetatom, @lanjiang653, Shaq, @davidkmyang

    → View original post on X — @mattlynley, 2025-06-30 15:33 UTC

  • Matt Lynley Ends Full-Time Work on Supervised Newsletter

    Full thing here (and why I had to end working full time on the newsletter) supervised.news/p/whats-next…

    → View original post on X — @mattlynley, 2025-05-17 19:35 UTC

  • Modern Data Stack Consolidation Through M&A Activity

    I’m back to (recreationally) writing while figuring out what’s next. First thing looking what’s happening with the modern data (and MLOps) stack. With the flurry of M&A (and M&A talks), the long-coming and frequent joke that the MDS consolidation may have finally arrived!

    → View original post on X — @mattlynley, 2025-05-17 18:58 UTC

  • Venture mania in data/ML space: overcrowded and overfunded market

    In the later 10s/early 20s there was practically a venture mania for companies in the orbit of Snowflake and Databricks, with some (like Dbt, Weights & Biases, and so on) reaching lofty valuations. But most you’d talk to would consider it massively overcrowded (and overfunded).

    → View original post on X — @mattlynley, 2025-05-17 18:58 UTC

  • Questioning Deep Research credibility due to widespread hallucinations
    Questioning Deep Research credibility due to widespread hallucinations

    I really have to question anyone who claims that Deep Research is fantastic, when I see so many reports like this. Paul Calcraft (@paul_cal) Worst hallucination I've seen from a sota LLM for a while Deep Research made up a bunch of stats & analysis, while claiming to compile a dataset of 1000s of articles, & supposedly gather birth year info for each author from reputable sources None of this is true — https://nitter.net/paul_cal/status/1891896917161906204#m

    → View original post on X — @mattlynley, 2025-02-20 03:09 UTC