Podcasts about selfdriving

The best piece of technology is often the one you don't even notice. When it works, everything runs smoothly and the tech itself vanishes – but how we achieve this is changing dramatically with the power of AI.This week on the show, we're following up our ‘Self-Driving Networks 1-0-1' episode with a ‘Self-Driving Networks 1-0-2': what do these networks look like in practice? Sunalini Sankhavaram, VP Product Management, HPE Networking, joins host Michael Bird to discuss:How machine learning works to help networks anticipate demand and respond accordinglyWhy proactive prevention is replacing traditional troubleshootingHow self driving networks can isolate and treat threats before they have a chance to cause damage

Stupid News 9-30-2026 8am …Want a Self-Driving Car? You can buy a DIY Kit for $1K …She hurled a Jar of Vegan Mayo into the car …High School Theater Students told they can't present the Musical, Flash Dance

The law in FL places liability on the driver OR the person in 'actual physical control of the vehicle.' https://www.lehtoslawcom

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss their Texas Field Work where they rode in Cybercab, Waymo, Avride, May Mobility robotaxis, Kodiak and Aurora autonomous trucks, and inspected depots across Austin and Dallas.Grayson's verdict on Tesla's Cybercab is simple. Once you go Cybercab, you don't go back. In Austin, wait times averaged two to five minutes across dozens of rides, and from a software perspective Grayson found no material difference between Cybercab and the Model Y Robotaxi in terms of performance.In Austin, Grayson counted roighly 200 to 300 vehicles at Avride's upfitting facility, a presence he believes the market is overlooking, and views Hyundai as a quiet winner of the robotaxi buildout with the IONIQ 5 powering Waymo, Avride and Motional. The Zoox depot in Dallas was under construction with no visible charging infrastructure, and Grayson estimates it is 12 to 18 months from being operational.Both Walt and Grayson rode in a Kodiak autonomous truck and Walt noted clear improvement ahead of Kodiak's driver-out target on the Dallas to Houston lane by year-end. Additionally, Walt rode in an unsupervised Aurora autonomous truck at their Investor Day.On the Foreign Autonomy Desk, Baidu is ramping supervised robotaxi testing in London with Lyft, Mercedes-Benz signed a licensing deal with Wayve, and Tensor Auto is preparing to deploy robotaxis with Volt Premium Taxi in Bulgaria.Episode Chapters0:00 Once You Go Cybercab, You Don't Go Back03:56 Robotaxi in Dallas09:01 Cybercab Wait Times, Pickups and Routing in Austin12:10 Austin Robotaxi Depots13:45 Avride's Austin Depot16:02 Zoox's Dallas Depot17:22 May Mobility in Arlington20:17 Kodiak Targets Driver-Out on Dallas to Houston by Year-End24:19 Avride in Dallas and the Uber Partner Math26:46 Hyundai Is the Quiet Winner in Robotaxis27:30 Walt Amends His View on Upfitting30:57 Tesla's Vertical Integration and Supercharger Advantage33:06 Aurora Investor Day and the 1,000 Truck Target40:22 President Xi's Visit and a Wake-Up Call for Detroit47:01 Foreign Autonomy Desk49:33 Next Week

This week on Autonomy Signals presented by KPMG, Grayson Brulte and Rob Grant discuss May Mobility filing to go public through a SPAC merger with an estimated $93 million annual cash burn, Mercedes-Benz adding Wayve as its third automated driving software partner, and Figure AI's Helix 2.5 taking on household chores across 30 homes it had never seen before.May Mobility is merging with ACP Holdings Acquisition Corp at a $1.4 billion valuation and will trade under the ticker MAY upon completion of the transaction. Of the up to $337 million in proceeds, only the $120 million PIPE is committed.At the company's current burn rate, the PIPE covers about 1.3 years of operations against roughly $10 million in 2025 revenue. The remaining $217 million depends on the redemption rate.Mercedes-Benz signed with Wayve for point-to-point Level 2 driving assistance within two years. Wayve joins NVIDIA and Momenta as Mercedes retreats from L3 Drive Pilot and focuses on L2 driving assistance partnerships.Figure AI's Helix 2.5 raised zero-shot success from 9% to 56% by pre-training on human behavior data, using half the task-specific data. That still leaves a 44% failure rate in curated homes, with no independent replication and no commercial contracts. The result supports a learning strategy, not a dependable, unsupervised home humanoid.Episode Chapters0:00 KPMG Sponsor Introduction01:45 Signal 1: May Mobility's SPAC27:52 Signal 2: Mercedes Partners Wayve on Autonomous Driving47:07 Signal 3: Figure AI Introduces Helix 2.5Follow May Mobility on the Robotaxi IndexFollow Wayve on the Autonomous Driving Licensing Index --------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Earlier this month, world model company Runway introduced GWM Worlds 2, a research preview that “turns high-fidelity video and audio generation into real-time interactive simulation.” Runway calls this an “autoregressive diffusion” model; with autoregressive describing how it generates over time.One new feature in particular caught our eye: WorldPrompt, a proposed input format for specifying a generated world and the actions within it. It allows you to fix some aspects of a simulated environment — including the first frame — and then create a series of timestamped events. The events, or actions, can even be prompted in real-time.To understand the implications of WorldPrompt, we spoke to Kamil Sindi, Runway's CTO, and Robin Kahlow, its Principal Research Scientist for generative video and multimodal AI. We also have exclusive comments from Anastasis Germanidis, co-founder & co-CEO of Runway, courtesy of a podcast swyx and Vibhu did with him.Who's building real-time interactive world models?First, some context about world models that can generate interactive video and audio in real-time.Runway is reportedly valued at $5.3 billion, based on its most recent fund raise of $315 million in February. Its first release, GWM Worlds, was launched last December.Alongside Runway, there are several other notable projects in this domain: Google DeepMind's Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro, and World Labs' RTFM (Real-Time Frame Model). We've summarized their differences in the following table:Given the complexity and massive latency demands of real-time video and audio generation (which we'll get into below), all of the projects listed above have limitations. For instance, Google notes that Genie 3 “can currently support a few minutes of continuous interaction, rather than extended hours.”But as our interviews with Runway show, real progress is being made.The central idea of WorldPromptWorldPrompt, a new feature in GWM Worlds 2, helps differentiate Runway from its competition. You can think of it as a control layer for characters, cameras and the environment. As Kahlow put it, it's a way to “control all the different subjects in the world” — similar to a computer game.“Like, if there's an NPC [Non-Player Character] somewhere, the NPC might walk up to you and say something. So you could achieve the same thing with this kind of model, where you can have very detailed control over everything in the scene.”As the name suggests, WorldPrompt is a prompting mechanism — not a programming language. So, unlike virtual world games like Minecraft or Roblox, GWM Worlds 2 doesn't offer scripting capabilities or the ability to control state. But there's a power to that, as Sindi pointed out.“You can create promptable worlds on-demand with video and audio in sync, across all these different domains and environments. That's not a distant-future hypothetical thing,” he said.But there are also limitations to prompting a world model. We asked how reliably the model would follow an instruction to create, for example, a law of gravity or a certain ability in a character?“Yeah, so it's a research preview,” Kahlow replied. “So it's not perfect, of course, and there are still flaws. It really depends on how difficult the action is. I would say movement works quite reliably.”Sindi added that more training plus scaling the data and models is resulting in “better following.”How a video model becomes a real-time runtimeDespite the current limitations of GWM Worlds 2 — especially if you compare it to pre-designed and scriptable worlds like Minecraft or Roblox — the true promise of world models like Runway is that they'll eventually lead to fully self-generated, real-time games and experiences. Which is an extremely hard engineering problem, as Kahlow reminded us.“There are two challenges. One is making the model not generate a whole clip at once. So instead, you want it to generate frame by frame while you're looking at it. And the other challenge is actually making the generation fast, so you can play it in real time.”GWM Worlds 2 offers real-time interactive worlds streamed in continuous 720p video at 24 frames per second (fps) and audio at 48,000 Hz.Runway achieved this firstly by taking its foundational audio-video generation model and fine-tuning it to the new WorldPrompt format, so the model can follow that. It then post-trains the model to generate autoregressively.“And after that, we work on making it real-time through distillation methods,” Kahlow added.Co-CEO Anastasis Germanidis offered more technical details in our podcast with him. He told us that the process starts from “bidirectional diffusion that basically generates an entire video at once and [makes] it autoregressive.” This allows the model to “generate one frame or a few frames at a time.”Germanidis described two possible forms of distillation in order to make it real-time: distilling a larger model into a smaller one or reducing its diffusion steps. As a general example, he said a model might go from around 50 denoising steps to four, with some quality loss but potentially comparable results.The challenges of real-time generationGermanidis admitted that there were issues with how it generates real-time interactive video.“The biggest challenge with autoregressive models is error accumulation,” he said. “You're feeding generated frames back into the model to generate the next frames, and if there are any small errors, they accumulate over time.”Sindi told us there are also challenges dealing with “infinite generations” of content.“There's all these challenges around what context to keep, what to discard that's not important. And so there's all these optimizations we have to think about, so we're not blowing up our GPU memory.”Another current limitation is long-term memory. “The model does not have perfect memory,” Kahlow said. “That's still an open research problem.”Causality and correctnessWhile performance is the primary challenge for Runway at this time, its world model also has to produce plausible consequences when a user takes different actions.Germanidis used the example of simulating football; he pointed out that online video training data contains more successful goals than failed goal attempts, so a video model might render the first more convincingly.“If I take this action versus this action, you want it to generate equally realistic outcomes,” he told us. “That's, I think, the big gap between video models and world models: that idea of counterfactual generation.”Sindi told us that evaluation gets harder the more complex interactions get.“If you have this multi-prompt, multi-character, multi-scene [environment], how do you really understand what was causal and what was not?”To try and solve that, Runway has some automated verifiable tests. But since GWM Worlds 2 is a research preview, Kahlow noted that doing tests yourself is also advisable — “trying out your model to see what doesn't work is really important.”More than gaming — there are agent use cases tooGaming is the obvious use case for what Runway is building, but there are others. Kahlow mentioned robotics — for example using a simulated environment to test how a robot works.Another, more intriguing, use case is to use it to test agents at scale.“Having thousands of simulated environments is much less challenging if you have a suitable model like GWM Worlds,” Kahlow said.But how does an agent know what's changed in the world — is there a structured state that it can read, or is it just the generated video and audio that it's consuming and understanding?“So there's no structured state here,” Kahlow replied. “It's just observing the same thing you might observe in real life, just [in this case] from cameras.”Sindi noted that GWM Worlds can also be used for “synthetic data generation for agents.”Finally, Germanidis suggested there's potential to use these world models alongside reasoning models.“You're maybe using some reasoning [for] planning of the scene, and then you're passing it into the diffusion head that's actually generating the pixels.”Anastasis Germanidis* LinkedIn: https://www.linkedin.com/in/agermanidis/* X: https://x.com/agermanidisTimestamps00:00:00 Introduction00:05:17 Runway's Origins and the Bet on Generative Video00:12:23 The Stable Diffusion Story00:18:44 Gen-2, Controllability, and the Weekend Hack00:23:02 From Video Generation to World Models00:28:03 Learning From the World, Not Just Language00:35:04 Sora, Runway's Existential Crisis, and Gen-300:39:39 Why Real-Time Video Is Inevitable00:43:06 Interface World Models: Software Without Code00:50:25 The Fully Neural Operating System00:55:11 World Models for Robotics01:02:32 Robot Policies and World Action Models01:07:47 The Lucid Dream Test01:11:41 Video Agents and Omni Models01:23:12 Artists, AI, and Creative Workflows01:27:14 Physical AI and the Future of World ModelsTranscriptIntroduction: Runway, Creative AI, and the Early ThesisSwyx [00:00:00]: Okay, we're here with, Anastassios from Runway, with, me and Vibhu in the studio. Welcome.Anastasis [00:00:08]: Good to be here.Swyx [00:00:09]: Congrats on all your success and progress with Runway. You're opening offices all over the world. Did you envision this when you first started out?Anastasis [00:00:16]: Not quite. I think even when we started, we had this idea that, It was more a matter of when, not if, we were seeing the early generative models of 2016, 2017, and just extrapolating, assuming, we resolution, quality increases predictably over time. There's gonna be a point where most of content will be generated, and that was maybe the initial thesis of Runway was we will need, as a result of those generative models, rethink how creative tools are made. and as we built out the research behind, our generative models, it then became clear that they were useful far beyond that as well.Anastasis' Background: Art, Simulation, and Machine LearningSwyx [00:00:57]: And it is more obvious now with, like, the real-world stuff and the world models that we'll talk about later. I'm just kinda curious how you go from a background in, like, Zocdoc and, computer vision into Runway. Like, take us back to that early conversations with Chris and, whoever else is on your founding team.Anastasis [00:01:14]: I was always splitting through those two worlds. One was the I had my own art practice. I was making a lot of interactive art, I think for a long time. and then on the other side, I was working in startups, and I was working as a ML engineer, as a backend engineer at different companies. I've always been interested in, coding and computation, and especially interested in simulation and brought it back into my early artwork as well. And at the same time, I was interested inSwyx [00:01:43]: The personal site has a few, right?Anastasis [00:01:44]: Yeah.Swyx [00:01:45]: Is there one that we should pull up? Just in case there's something that's like. I just like to go down memory lane.Anastasis [00:01:50]: Yeah.Swyx [00:01:50]: Okay, what is this?Anastasis [00:01:51]: So this was, a project that I made, I think back in 2015, where I built this software that would give, voice instructions to people in a gallery space. So it would coordinate interactions between people. And so it will first give you an identity, like you're an, architect, you're 30 years old, and, you like sports. and then it would match you with another person, and you have this completely generated interaction. language models were not quite there at the time, and so it was it was a mix of some templates and some, like, some Markov chain-generated text, and it would just completely simulate these small talk conversations between, everyone in the gallery space. so was always very fascinated on the one hand with, generative models and, like, the early machine learning work that was being at that time. But at the same time, there was this separate thread of simulation and what it means. Like, what can we learn about humans by creating those very simple models of their interactions and their behavior?Early Generative Art: pix2pix, GANs, and Uncanny ValleyVibhu [00:02:56]: Did you generate the prompts or, the 30-year-old, whatever? Was it you generating them? How'd you, how'd youAnastasis [00:03:03]: Exactly. So the program would just generate- those, from. Yeah, a lot of it would be Mad Libs style of justVibhu [00:03:10]: YesAnastasis [00:03:10]: You have lists of different professions, lists of different,Vibhu [00:03:14]: HobbiesAnastasis [00:03:15]: Personality types, lists of different, ages, things like that. And then it would just combine those things together. And then maybe the next project we go is, Uncanny Valley, Uncanny Road, which wasSwyx [00:03:27]: GansAnastasis [00:03:27]: One of the first projects that, we built with, one of my two co-founders, Chris. This was taking, pix2pixHD, which was one of the early image-to-image models that NVIDIA released back in 2016 or 2017. and it was a model that would take a semantic map of a scene and then generate a photorealistic, let's call it, output. very early days, so it was not very high-fidelity outputs, but it w I think was the first image-generation model that could generate at 1K resolution. And it was all trained on self-driving datasets. So the semantic categories it would support were only, things you would encounter on the road. So it would be pedestrians, traffic signs,Vibhu [00:04:16]: StoplightsAnastasis [00:04:17]: Bikes, stoplights. And so that was one of our first indications that we built this and people were making all this, like, very surreal imagery of, yeah, a million plus a million pedestrians or a million traffic signs or, like, gigantic humans. And it was a indication that you could take a model that was trained on this very boring dataset, essentially, of, like, not that many interesting things happen when you're on the road, and then you can repurpose it and go very out of distribution and make something that was artistically compelling. And that was It's a summary of the thesis of Runway in some ways, that you can take the same generative models, and if you look at them from another direction, if you build interesting tools around them and you give them to artists, they're gonna do things that you don't expect.Vibhu [00:05:02]: Very cool. I like the, UX of it. You're just given an empty canvas, try whatever, do whatever. And then the other one, like, you see everyone with wired headphones? Like, that's, that's a sign that it's, it's veryAnastasis [00:05:16]: The AppleVibhu [00:05:17]: YeahAnastasis [00:05:17]: Apple, your version.Vibhu [00:05:17]: Original ads. Yeah. Take us to today. You've been doing this for seven years at Runway. How have we got to this? Like, how do we go from driving simulator data to all this? And you cover the whole stack of generative media?From Creative Tools to a Research LabAnastasis [00:05:33]: Interestingly, we're almost back in, we're, we're full circle. We're, we're now applying our models and beyond creative tools into real-world scenarios. But it was a, it was a long journey. It was very early on we realized the first version of Runway was a way to easily use the, all the open source model of the day, things like pix2pix to. and give them to artists. That was the initial idea, is those models are too difficult to use if you're not a machine learning engineer. Like, what happens when you give them to artists? Very quickly, we realized we needed to build a research org, inside of Runway, and that happened maybe on year one. And, a lot of the mandate there was. The image-generation models of the time, the video generation models of the time, or there were barely any video generations all the time, but they were not quite there where they could be productionized and brought into tools that would be part of creative workflows. so we need to push the frontier of the research. And so maybe the first four years of Runway, research was almost happening on the background until there was a moment in 2022, with latent diffusion, with, DALL-E 2, where, there was that step function change, and you guys maybe remember around the time.Swyx [00:06:49]: I started in this space because of latent diffusion and Stable Diffusion.Anastasis [00:06:54]: Yeah.Swyx [00:06:54]: Because I was like, “Wow, this is not only, like, feasible, it is doable on consumer hardware.”Anastasis [00:07:01]: Exactly, yeah.Vibhu [00:07:01]: I think the delta is also huge. Like, I learned pix2pix. Like, this was intro to ML, the TensorFlow, like, Jupyter, Google Colab notebooks were like this, and then you have a sudden step function change, with diffusion and whatnot. Any other ones since that. Like, there were clear examples of what early diffusion were to get to here. Any other changes in key technology research?Green Screen, Rotoscoping, and Early RunwayAnastasis [00:07:26]: Between, 2018 when we started and 2022?Vibhu [00:07:29]: Yeah.Anastasis [00:07:29]: So one of the early work that we did in Runway was solving segmentation, image and video segmentation. It was a very important problem because most VFX involves essentially separatingSwyx [00:07:42]: RotoscopeAnastasis [00:07:42]: Subjects. Yeah, rotoscoping. Extremely manual process. Nobody enjoys doing that. and so a lot of the early days of Runway was building this tool. It was called Green Screen, and it was for a long time the main thing that people were using Runway for. It ended up being used in, Everything Everywhere All at Once and a bunch of other high-visibility films and series. But that was essentially, Runway for a long time was a post-production tool until latent diffusion and generat- Gen-1, Gen-2, happened.Swyx [00:08:12]: Cool. let's, let's go past that moment. You've come a long way. Then you started releasing your own models. Maybe describe that journey as well.Scaling Video Models and the Bet on 1,000 A100sAnastasis [00:08:20]: Yeah, so we go to the other point, yeah, in mid-2022 when it became clear that we're doing research at a fairly small scale of compute, and it became clear that, like, scaling laws would apply to, image and video gen in the same way that we're applying to language generation. So we made a big bet, and I think at so at the time, we signed this deal to build a cluster of a thousand A100s, which at the time we were a Series B startup. That was a almost, slightly irrational decision maybe, but we really believed that if we trained a video model at a large scale, we would get, like, a great model at the end. And at the time, the goal or we set the goal around fall of 2022 of what is, what does the latent diffusion, Stable Diffusion moment look like for video? And at the time, the best model of the time was called CogVideo. it was one of the early video models. It was very 256 by 256 resolution, very not very high quality. and so we decided we're gonna build out this cluster, and we're gonna just invest in, like, in building out our own video model. it became clear as we're training Gen-1 that it was difficult to get to fully. we wanted to build text-to-video, but it became clear to us that an easier starting point would be to start from video to video. Because when you have a stronger conditioning, it's, it's an easier problem to restylize an existing video versus generate the video from scratch. And so we released Gen-1 first back in, it was January of, 2023. Yeah.Vibhu [00:10:04]: It's just a fun visual podcast, honestly. Like, if we can see February 2023, what was the state of stuff?Gen-1: Video-to-Video and Depth ConditioningAnastasis [00:10:10]: It's so interesting ‘cause at the time when you see those results, you think this is so incredible, and this is like, it's almost like image generation or video generation is solved. And then you look back a few years after, and it's like, it's It's just like you get used to the results very quickly, with those models. But at the time when we started seeing those results, it was, it felt quite incredible, and the level of, like, quality that you could get. And, so the Gen-1 was a depth-conditioned video model, so it would turn. it would take a input video, it would predict. it would it would first convert it into the depth map, and then we would generate, pixels with a latent diffusion model.Swyx [00:11:01]: Yeah, very effective.Vibhu [00:11:02]: Yeah. I didn't realize how distracting the blog post would be. Sorry.Anastasis [00:11:05]: Yeah, but, one of my favorite examples of on those, on Gen-1 was both, if you go up to mode three or mode two, there was this storyboard use case where people would makeVibhu [00:11:18]: OohAnastasis [00:11:18]: WouldVibhu [00:11:20]: You can mess around with theAnastasis [00:11:20]: Make a city out of books or out of boxes, and then they would shoot a video with their phone and then translate it into a photo-photorealistic output. There was all these ways in which those models were starting to be used for storyboarding and also for really. and then if you go to mode four, like, of taking untextured 3D scenes and then turning them into photorealistic output. So we saw a lot of use cases early on where people that were familiar, were power VFX editors would just take a blender, render, and then they would get translated in with Gen-1 or create a scene in Unity and then take a capture a video of it and then translate into, restylize it. So I still think video to video is powerful. I think we had a recent video-to-video model as well, and it's one of my favorite ways of using those models is essentially using them to use ground truth video as, like, the initial inspiration and then translate into different styles or different outputs.Stable Diffusion, Stability AI, and Open SourceSwyx [00:12:23]: But I think we're gonna go into, like, the rest of Runway and catch people up to speed today. I did wanna cover the, let's call it the Stable Diffusion controversy, or, what happened with Stability AI, whatever. I think there was a two sides of the story. I think there's part of that is a normal thing of, like, people, join and leave companies, but what is the, retrospective now that, there's been some years behind it?Anastasis [00:12:49]: Yeah, it's a very, it's a very long story to go into. I think it wouldSwyx [00:12:53]: Which I remember you wrote a really long post about.Anastasis [00:12:56]: We would probably cover the whole hour to go into it in more detail. But, essentially, there was the latent diffusion paper that came in, I think that was at the end of, 2021. And then Patrick Esser, who was one of the researchers behind, latent diffusion, and he worked at Runway at the time, he built latent diffusion in collaboration with Robin Rumbach and a few other folks back, in the in, CompVis, which was, a labSwyx [00:13:26]: Like a research group, yeah.Anastasis [00:13:27]: And, after releasing the early latent diffusion model, they, essentially they were. the goal was to keep working on versions of the model, scale it up, incorporate new data, incorporate new tasks. And Stable Diffusion was the same model, but trained on more compute, and then with a few more tricks, like a classifier-free guidance paper came at some point, I think in the early 2022. And thatSwyx [00:13:52]: Which, like, was a big prompting improvement.Anastasis [00:13:55]: Yeah.Swyx [00:13:55]:?Anastasis [00:13:56]: That improved results. it was trained on better data, so like, the esthetic subset of LAION, but it was effectively, the same underlying architecture. And there was that big training run, that, happened on Stability's cluster. Stability financed that run. And looking back at that story, I think it was the work to build and train that model was done. It was a, it was a research project. It was done as part of, like, continuation of the latent diffusion work. It then, I think it the model became very successful, and it, I think there were the. And I think as a result of its success, other companies tried to, figure out the commercialization path for it. But for us, it was very important that we try to, we make sure that we. It was meant to be an open source research project, and so the we decided that we should continue releasing versions of it, since that was the original goal of Stable Diffusion, and that led to releasing Stable Diffusion 1.5. There was maybe a day of, a bit of, miscommunication there, but ultimately that was resolved very quickly within hours. so yeah, there wasSwyx [00:15:12]: OkayAnastasis [00:15:12]: Not a niceSwyx [00:15:13]: I just wanted to. you have toAnastasis [00:15:15]: Yeah.Swyx [00:15:15]: You're one of the main players in that journey, and so it's nice to hear from the source of, like, what happened. Yeah.Anastasis [00:15:22]: Yeah. I think it's all, it's all in the past nowSwyx [00:15:26]: YeahAnastasis [00:15:26]: I would say. and, like, both companies, Stability took its own path, Runway took its own path.Swyx [00:15:32]: Yeah. There's still. James Cameron is backing the new Stability, whatever they're doing with the Hollywood studios.Anastasis [00:15:38]: Right.Swyx [00:15:38]: I don't know what they are doing. I think one thing that impresses me, and I'm happy to move on, is that back in the that time, let's say, like 2021, 2022, there was this community of people that you were involved in that was researching all this stuff, right? And, like, from everyone I talked to who was active then, it seemed like it was fairly obvious that somebody would do the hero training run that would produce Stable Diffusion. So, like, I guess the question is, like, you had the you were you had made investments. You were you had the foresight. Is it accurate to say, like, that is reflective of, like, what people were thinking at the time? Or was it still very much like, “Well, we'll use it as, like, a post-production tool or something. I don't know.”? Like, where in the sentiment were we that maybe you can think back to, like, what the community was like back then?The Early Creative AI CommunityAnastasis [00:16:28]: I reminisce and I think very fondly those early years, from like 2018 to 2022, because it was a very small community that, as you said, were very convinced that this was gonna be a big thing. And at the time, anyone who. Because it was such a small circle and, everyone who would, like, be part of that circle and, like, make projects with it would, immediately get, go viral. so likeSwyx [00:16:55]: And you didn't know who they are, right? They're just some name on a, GitHub or Hugging Face somewhere.Anastasis [00:16:59]: Exactly, yeah. So I remember one of the first big viral moments of creative AI was, there was the neural style transfer paperSwyx [00:17:09]: HuhAnastasis [00:17:09]: ThatSwyx [00:17:10]: Something dreaming?Anastasis [00:17:11]: I think it was called neural style transfer.Swyx [00:17:14]: Okay.Anastasis [00:17:14]: There was also Deep Dream, the puppy sliceSwyx [00:17:16]: YesAnastasis [00:17:16]: Which was, also really cool. but, yeah, there was this project that, Jim Kogan, who was an early advisor of Runway and one of those,Swyx [00:17:25]: Marketing guysAnastasis [00:17:26]: Big, creative AI, folks, he literally just, like, showed a video of himself taking the New York Subway and going over the Williamsburg Bridge and then stylized it with, I think in the style of Van Gogh or, like, one, painter. And that was. Like, at the time, that was, like, so cool and it went viral and it was completely revelation to people that you could do this with generative models. And that was only, it was less than. It was maybe 10 years ago. So just, like, as an indication of, like, how quickly things have gone.Vibhu [00:18:02]: It's pretty crazy. Like, even since then, you've got people at every level of the stack. You've got devs, creatives, artists, hobbyists. You've got everyone using it. And for people that tried stuff early, they'll remember how hard it was to use regular diffusion, right? Like, nowadays, you can use your favorite ChatGPT image gen or whatever, give a sentence, get a beautiful output. But diffusion was like, the whole ultra HD, 4K, high resolution. Like, prompting these things was very different. anything you learned on the tooling side, like from the offerings you guys have now, so like creatives, devs, you really took the. Research and brought it to everyone to use. anything interesting there to share?From Gen-2 to Controllable Video GenerationAnastasis [00:18:44]: We had to build the entire model serving infrastructure for video diffusion models. There was nothing else, already, like, because we had Gen-2 was the first text-to-video model, I think, out in the market. So many things that we learn over time. I think the I think the biggest one was, like, we. it was very clear early on that text-to-video was not gonna be the answer. Like, you. Like, people wanted a lot more control than that, and so we invested in, like, control building on top of those models very quickly. how do you use the camera trajectory as control? How do you use an initial input frame as control? So that was a very early learning for us. With text-to-video was, like Gen-2 was an amazing, step function improvement in the quality of video models, but it was used much more in an exploratory way because there was nothing to ground it to. There was no reference that you could bring into it. There was no. You couldn't really control the camera motion. You couldn't control the object motion. And so the first year, in 2023, was really all about what are all the interesting ways in which we can condition those models? And it was a lot of just post-training rounds on top of the base model to figure out, like, what, -- how do people wanna control them? And so there was, like, this quick succession of the we it was called Motion Brush, which was you could, like, you could draw arrows and dictate where things should move in the scene.Vibhu [00:20:09]: That's so cool.Anastasis [00:20:09]: There was camera control that was you could just describe, like, how you want the camera to move in the scene. And because we work with filmmakers from the most of the history of Runway, we immediately got this feedback and got this, decided that this was worth investing in. And so control ability became a big theme, I think, very early on as we were building, as we were building those models. Something fun that I haven't really talked about too much was just how Gen-2 came to be out of Gen-1. So it was a bit strange because we announced Gen-2 two months after Gen-1 andHow Gen-2 Came From a Weekend HackVibhu [00:20:43]: We're accelerating.Anastasis [00:20:44]: It was before Gen-1 was even generally available. But Gen-1 was a depth-to-video model, so it would take a depth map and it would convert it into RGB. and we couldn't get, text or image-to-video to work directly, and that's why we started from depth to video. but, and we had discussions of like, okay, we need to spend the next six months investing in text-to-video, maybe increasing the compute scale or the model scale, like train a larger model. And I had this weekend project idea, which was, what if I take a model that, starts from text input and converts to depth maps and then use Gen-1 to convert the depth maps Into RGB?Vibhu [00:21:29]: It would probably work.Anastasis [00:21:30]: And so Gen-2 was that.Vibhu [00:21:32]: Oh. The hackathon pipeline.Swyx [00:21:35]: The weekend hackathon pipeline.Anastasis [00:21:36]: Yeah.Vibhu [00:21:37]: But it looks good.Anastasis [00:21:38]: And it worked pretty well. there were if you, with the knowledge that it has this, like, two-stage pipeline, you can tell in some cases that the structure of the video looks a bit off because you had to generate the depth first before you go into the output video. But it worked and it allowed us to bring this to our, to users very quickly. But it's now it's interesting because, like, people are coming back to this almost two-stage approach. Like, if you look at the Reve text-to-image model that came a few months ago, it had this planner model that would generate bounding boxes before it fed that into the diffusion transformer.Swyx [00:22:19]: Yeah, Ideogram also the same day.Anastasis [00:22:22]: Yeah.Swyx [00:22:22]: I remember that was very strange that both of them came out the same day with the same exact innovation.Anastasis [00:22:26]: It's a small community, I think.Swyx [00:22:28]: I'm like, this is like, this is completely coincidental, right?Anastasis [00:22:32]: People talk. So yeah, there's, there's definitely something into this approach. And, now, like every single like, video generation model in production uses a complex prompt completion pipeline under the hood. I think that's no secret that there is. ThatSwyx [00:22:48]: Humans are terrible at prompting.Prompt Rewriting, Camera Control, and the Seed of World ModelsVibhu [00:22:51]: I think across the board.Anastasis [00:22:51]: Yes.Vibhu [00:22:52]: But yeah, I think like the original Sora one blog post even told you that what happens after your input is rewriting your prompt. It's much more descriptive about what you would want.Anastasis [00:23:02]: Exactly. I, And there was the DALL-E 3 paper beforehand that, was the first public, description of the fact that synthetic captions and really detailed captions work really well. And then Sora built on that. Yeah, so it was 2023. We were releasing all these updates to Gen-2, like the camera control, Motion Brush. And there was something very interesting about camera control because it was the first time that you felt that instead of, like, you were creating video, you were creating a short video, you were navigating inside the world. And I think camera control was maybe the seed of some of the ideas that we had around world models and really opening up that research direction. We realized, it was this era and this series of, Gen-1 and Gen-2 models really proved to ourselves, yeah, this is theSwyx [00:23:56]: Cool.Anastasis [00:23:57]: So this is not the original camera control. This was the updated camera control on top of Gen-3. But yeah, I think it made those models usable to filmmakers, I would say. The so camera control was very popular. And so we realized, there is one way of seeing those models, which is, you're just as content creation machines, and there is the other way, which is you're. As you're predicting video in order to predict video well, you need to simulate the world in an increasing and increasing capacity. And if scaling laws apply on video, just like they apply on language models, then as we scale the compute that we put into those models, then they're gonna be able to simulate physics, they're gonna be able to simulate human actions and dynamics increasingly well and predictably well. That was the thesis about around our efforts on world models, and we spin up this research group to just focus on the world models and how do we turn the video generation models that we're building into something broader and something that would be useful beyond, also content creation as well.Swyx [00:25:04]: And that was roughly when?Anastasis [00:25:06]: Yeah, so that was inSwyx [00:25:06]: OhAnastasis [00:25:07]: In late 2023.Vibhu [00:25:08]: Interesting. like, I think, a lot of people have been saying a lot of video gen model companies have all pivoted to world models these days, but like, 2023, you're posting it. oneWorld Models: From Video Generation to SimulationSwyx [00:25:21]: It's, it's debatable whether it's a pivot.Vibhu [00:25:23]: Yeah.Swyx [00:25:23]: Like, arguablyVibhu [00:25:24]: YeahSwyx [00:25:24]: That's what you always had to do anyway, right?Anastasis [00:25:26]: It's in a way an expansionVibhu [00:25:28]: YeahAnastasis [00:25:28]: Of the applicationsVibhu [00:25:29]: YeahAnastasis [00:25:29]: Of the models as they become more capable.Vibhu [00:25:31]: The early signs, it seems like the original models you guy had, guys had, people would say it's very not bitter lesson pilled, right? You're adding, rewriting prompts, you're having all these one-off things, but that's just the state of the tech as it was versus the future of as you said, you can scale it up as, we can scale up to world models.Anastasis [00:25:50]: Yeah. So it just became. And if you looked at the outputs of Gen-2Vibhu [00:25:56]: YeahAnastasis [00:25:56]: It was not. I think it was not obvious to people that this would scale to become a general simulator of the world. Like, you had very limited movement, you had, very low fidelity or low resolution, like obvious mistakes in human anatomy, like all kinds of limitations. But it was just, the idea was that's just GPT-two, and GPT-two, it can barely generate, like, coherent sentences. Similar, Gen-2 can barely create coherent video, but if you scale it up, you're gonna. There is no reason why it shouldn't work in a way. It's, And I think that was. That's, that's always the mindset of Runway is like this extrapolation of, like, if, like, even when we started in 2018 and you looked at the results of the day, you need to look more at the trend of, like, where we were in 2018 versus when we were at the, when the first GAN came out in twenty, four 2014 or twenty, fifteen. And, you started from, like, thirty-two by thirty-two images of faces, and then by the time in 2018, you could generate, street images at the 1K resolution. And it was the same with world models, very early signs of something much bigger.Swyx [00:27:08]: Yeah. I was gonna say, like, it's diffusing into focus. Like, if you look at our visible output from year to year, it looks like a diffusion process itself.Anastasis [00:27:17]: Yeah.Vibhu [00:27:17]: Especially watching the early, like, old blog posts, you can really see the choppiness, the details.Anastasis [00:27:24]: Yeah. Like human civilization starting from random noise and thenVibhu [00:27:27]: YeahAnastasis [00:27:27]: Denoising intoSwyx [00:27:28]: Yeah. Just run it a hundred years.Anastasis [00:27:30]: Civilization.Swyx [00:27:30]: Yeah.Vibhu [00:27:31]: That's how you're on track, you're still noising, right?Swyx [00:27:34]: Yeah. I like the way that you guys phrased it when you, announced it in June, which is, oh, that you had a video essay. “The human mind is no longer the center of AI. Our world is.” Right? Which is, let's, let's call it the past five years of LLM-based AI is very much like trying to emulate human preferences and human speech. But now that's, like, mostly solved. I think that's, like, some of the context of your essay, which you also wrote around the time. And now it's like the focus is on modeling the world accurately.Scaling Laws for Video and Why Predicting Pixels MattersAnastasis [00:28:03]: Exactly, yeah. So the way we see it is, there is that, initial mission statement of DeepMind, which is, solve intelligence and then use it to solve everything else. But I think it's starting from everything else, could be valuable of, like, starting from. there is just so much complexity, and detail in the world that in order to. That it's, it's hard to learn directly from just human descriptions of the world. Like, we're assuming that, like, language models learn from everything that humans have written about the world, like our own understanding as of, the twenty twenties. And there is just so much that we don't know and so much that's not captured by existing text, about both the low level dynamics of the world, like we're not describing in detail. if I tell you to describe, like, how do you tie your shoes, that's a very difficult thing to describe in words, but it's very obvious thing to demonstrate. And so I think there's been. And there's, more of X paradox, like we're constantly underestimating all the complexity that goes into very, like, things that we do subconsciously as humans, and we don't even necessarily always have the words to describe them. And so in my mind, the simulating the world and simulating, physics, simulating the dynamics of the world has always been underestimated, compared to, we place too much emphasis on the things that are easy to talk about. but there is just all this complexity and richness of the world that if we just try and train directly on that observational data instead of training on how people describe the world, we would learn something new that we wouldn't otherwise know.Swyx [00:29:54]: You think that the present architectural paradigm is fine? You don't need, like, another layer, like JEPA, like another famous, New York AI leader would say?Anastasis [00:30:05]: We're a very pragmatic research lab. If, we have evidence that an approach works better than the approach that we're taking, then we have no qualms to taking it. We just have seen no indication that video prediction itself doesn't scale. And even if you look now, not just our work, but the work of others, you're seeing in robotics some of the most promising work, starts from video prediction models, and then you adapt them to also the action models, for example. so there is very little evidence that you need something else and that your time is better spent on a novel architectural change compared to improving data and improving the, and scaling the current approach. And so, We don't have any indication that. the, there is that counterargument that I think there was a tweet by Yann LeCun a few days ago that, understanding the dynamics of the world is very different than, generating, cute videos.Swyx [00:31:05]: And your answer is no, they're the same thing.Anastasis [00:31:07]: Yeah, they're the same thing.Swyx [00:31:08]: My cat videos are the same as understanding physics.Anastasis [00:31:11]: Right, because if you wanna generate. video models can cheat and, like, they could you could give, like, successive dif shots of the scene in a way that doesn't require you to simulate difficult physics. There is like, all these different ways in which you can hide the deficiencies of the model, and it's important not to be too tricked by the performance of the current video models. It's easy to, cherry-pick examples and think that video models are further advanced than they are. So there is a lot more work that we need to do to improve those models. But in my mind, very similar to language, and, like, we've. you go from barely coherent sentences to something that, could hold a conversation with a human to something that could can operate autonomously for a day and, like, create entire code bases. And the main difference, there is some architecture improvements along the way, but the main thing is scale. And so it's the same bet for video, and we have no indications that this is saturating. Like, we have benchmarks that we use for measuring the physics of those models, and we see those predictably improve as we scale those models. So there is. If you want to Google up, Physics-IQ, is one of those benchmarks that measures how well does the model perform at solid mechanics or fluid dynamics or optics.Vibhu [00:32:32]: I'm curious if you've seen any emergence, any scaling law around this.Swyx [00:32:37]: Yeah, he's saying there is a scaling law, right?Anastasis [00:32:39]: Exactly.Vibhu [00:32:40]: Yeah,Anastasis [00:32:40]: So the way those models, those benchmarks work is you. the researchers have gone and, like, captured, a few videos that are representative of different physical phenomena, and then you can take the first frame and then pass it through an image-to-video model and then generate a rollout that shows what should happen next. So you have, a ball hanging from the ceiling, and then you use that as input, and then you the model predicts how the ball should fall on the ground. and this measures. we have an intuitive understanding of physics. I know, you can imagine what will happen next if I drop this bottle. So it's measuring that same intuitive physics understanding of those models, and we've measured that at different model scales, and we see, and compute scales, and we see that the score on physics IQ predictably improves. There's other, tricks and techniques that you can make to improve the score even further, but even scale alone helps, in the model learning better physics.Swyx [00:33:40]: My main sympathy with Yann LeCun is the, Plato's cave allegory, right? Like, you're, you're, like, learning on the output of a thing, not the internal process of a thing, and it's very noisy. And, if only you could observe the internals of a thing. It's hard to observe the internals of a human mind, but you can very much observe, or at least we have a whole branch of science and physics that we're ignoring on how to model Physics and movement and, gravity and, other interactions. and we're just, like, throwing away all of that and just saying just scale data, which is very much the lesson of unsupervised learning, but it feels wrong. that's the main idea.Anastasis [00:34:21]: I think the history of machine learning is, at large, it feels wrong.Swyx [00:34:25]: Yeah. It's a bitter lesson, right? Yeah. It's, it's, it's the simple answer to that.Vibhu [00:34:29]: I guess, how much can you scale? So, like, even on, let's say, the video generation side, like, there's one side of video understanding. Video generation, are we still gonna have tools where it's like, I wanna generate two hours, twenty hours? there's a infra way to do it in batches and stitch it together, but, like, do we just keep scaling? Do we just continue long generation consistency, all that at scale? And, like, tying it into where we're at now from we looked at Runway two to four point fiveGen-3, Sora, and Runway's Scaling InflectionAnastasis [00:34:58]: Yeah.Vibhu [00:34:58]: Like, technically, what advancements have we made to today, and then where do you see things still going?Anastasis [00:35:04]: So part of the answer is definitely scale. and that was. We learned that lesson in a big way for with Gen-3. So Gen-3 was the model we released the year after, like in 2024. That was a few months after Sora was released. so yeah, there's an interesting story of that came to be as well. Gen-3 for us was, the first time that we really needed to build. we had to learn all the lessons that the language model world learned in two in three years in the span of a few months. one of the biggest changes of Sora was using diffusion transformers instead of convnets. So a lot of the early, latent diffusion models were all, convnets for the diffusion model part. And the diffusion transformer paper came at some point in 2023, and it showed scaling laws for image, diffusion transformers. And we realized at that point that we needed to invest in infrastructure for model parallelism, for really scaling training to larger than, a few billion parameter models. And we spent maybe the, most of the fall of 2023 building out our infrastructure for distributed training. And we had a lot of false starts and a lot of failure in trying to scale, image and video diffusion transformers. And at that point, February 2024, Sora comes out, and the results areAnastasis [00:36:35]: Very much superior to what Gen-2 could produce. There were a lot of, a lot of chatter on Twitter about Runway. Runway's done. like, there is no way Runway will catch up. And if you remember, also OpenAI in the early twenty-It felt very, like it's aSwyx [00:36:56]: To the moonAnastasis [00:36:57]: It's a formidable opponent now, but at that point, it, they were on the top of their game. nobody could even get close to them. There was maybe Gemini was just the first version of Gemini had just released. So when OpenAI came with Sora and it was such a big jump of like quality, it gave me, there was like an existential crisis for a few hours. But that, I think the amazing thing about Runway and like I think the, we've been around eight years now, which is almost we're dinosaur in AI, and we had to like, we had there was a lot of those moments we had to learn, adapt very quickly and build out skill set in the team that we didn't have. And so, if you ask anyone what is their favorite time at Runway that was there during that time, it was that push in like three months to get to a model better than Sora. and it, we scaled 10x the model scale, the model size and the, compute that we were training on. we figured out model parallelism. We had zero expertise in that. And then we came out with Gen-3 during that summer. So that was a big turning point, I think, for the company where the research org grew very quickly, and we really started pursuing this vision of the general world model, in earnest, I think after Gen-3 was out.Swyx [00:38:12]: Yeah. that's the amazing thing about building when you're building. There's no stack to. You have to invent everything yourself. You have to be completely full stack. Now I think like there are inference specialists like Fal or whatever that can help with like, model serving, and I think you guys work with them as well. but yeah, like it's, it. But at the time, it was just. It's very interesting to think about what you do when Sora comes out and people are questioning whether your company should still exist.Distillation, Turbo Models, and Real-Time VideoAnastasis [00:38:41]: Yeah. And yeah, there was no, there was no VLM of diffusion models. Like, we had to build the whole model serving infrastructure and make things efficient. And a few months after we released Gen-3, we released the Turbo version, which I think was the first step-distilled model in production.Swyx [00:38:56]: That was a whole trend that we covered as well. Yeah.Anastasis [00:38:59]: So that allowed us, to serve those models at the larger scale, ‘cause I think the first version of Gen-3 was quite, expensive to serve.Swyx [00:39:09]: I think the whole like trend in like consistency models, Lightning and, Turbo and all these things somehow didn't really stick around. I don't know if you have any reflections on this. Because at the time, I was like, “Well, everything should start with a distilled model first, and then you can upscale,” right? It. your bigger models just turn into fancy upscalers, but like you should always draft with a smaller model and faster model, right? Because you can get it so quickly, like near real-time.Anastasis [00:39:39]: Yeah. I would not be so sure to say that didn't stick around. I think that, it's, it's likely to. that there is a lot of step-distilled models that are actively used in production. there is still a gap in quality compared to the, non-distilled model. but in my mind, we're still. there is a two to three year offset from language models. So the things that, So it's just a matter of time before there is better distillation techniques. we use. Right now we have a real-time model core character that I think is the largest deployment of real-time video models, that's a step-distilled model, and it's actively being used. It's a very specific use case compared to a general video model. So this is aSwyx [00:40:27]: Very cool, by the way.Anastasis [00:40:27]: This is avatars stuff, right?Swyx [00:40:28]: Consistency, character.Anastasis [00:40:30]: Yeah. So this is a talking avatar, model. we were able to. we optimized the hell out of it, and it generates at 24 FPS, and it's a, it's a step-distilled autoregressive video model. So if we look at our world model direction, a big component of it is starting from the bidirectional diffusion that generates entire video at once and making autoregressive shows. So you generate one frame or a few frames at a time. so there's a lot that goes into that pipeline of getting to a real-time model. It's first you need to make it into a causal autoregressive model, and then you just turn it into. You need to do some additional step distillation to get it to be real-time. and I think that part is just starting. I'll be very surprised if we're, two years from now, we don't primarily use real-time models. To me, real-time video generation is just inevitable that, it has much better user experience, it's much cheaper to serve, and, the quality gap between the base model and the real-time model is only gonna close as we figure out better, distillation techniques. And we made a lot of progress there internally on maintaining the quality of the base model when we distill them.Swyx [00:41:49]: How much of this is transferable? So is it the same base model? Like if you're doing diffusion across the whole sequence and you're converting it to step autoregressive distillation, is this like distillation where you still need to train both, you can use the same base and converter? What's that process like to go from regular model to something that's real-time on a technical level?Anastasis [00:42:11]: So the nice thing about diffusion models is you have, two axes of distillation. So there is the. You can distill to a smaller model, which resembles what you do in LLMs, or you can distill in terms of taking less steps, less diffusion steps. So you could take a model that generates in fifty steps and generate in four steps and get to, You have some performance, degradation, but very often you get comparable outputs. So you can even take the large frontier model and distill it with step distillation and get to a real-time performance, and that's what we've seen. So, depending on the use case, in some cases we might also serve with a smaller model, but in a lot of use cases, we just use theSwyx [00:42:56]: Step distillationAnastasis [00:42:56]: The frontier model, and we're able to make it work in real-time.Swyx [00:42:59]: I think this might be a good time to cut over to his laptop to show off some of the real-time stuff that you're doing.Interface World Models and Neural SoftwareAnastasis [00:43:06]: This is one of the research updates that we did recently. so we've been working and f in getting our general world models to, different applications. one of them that we think is very compelling is using general world models as essentially, an interface, a universal interface to software. This is a version of our world model that's called an interface world model. and the idea is that it essentially, replaces, the, front end of a software application. It renders the pixels directly of an interface and is trained to predict what happens next as a result of, a click or another interaction you have with the interface. So this is all pixels. it's there is no HTML, CSS, React that's powering this interface. This is directly at the output of our real-time, video generation model, and it takes clicks directly as input.Swyx [00:44:09]: And drags, click and drag.Anastasis [00:44:12]: Right. So it supportsSwyx [00:44:13]: Ooh.Anastasis [00:44:14]: Yeah, clicks. It supports drags. it also supports scrolling. and the amazing thing about this is that you can effectively describe in the prompt how you want different elements, like what do you want the behavior of different elements to be. So it's almost you're you can turn, an interface from, markup language description of, like, an HTML interface, and instead you can just describe the interface. if I press this button, I expect this to happen. If I press this button, this should happen. And it's useful, we believe, both for prototyping, for, like, just testing, like, what different interactions would feel like. you can also add audio to it. So it's a video audio generation model. So you get you essentially can describe both what the visual outcome should be of your click and also what the if there is a sound effect that comes out of it. So we believe that's gonna be a much more flexible way of building software. Just render. It just, in why generate the code that generates the pixels? Just generate the pixels directly.Anastasis [00:45:18]: It's the end-to-end philosophy applying applied to front ends.Anastasis [00:45:25]: So we think there is a few interesting use case. So you can build creative tools on top of it.Anastasis [00:45:32]: We think that, for any use case that involves a lot of exploration or, like, educational use case where you wanna learn about a new concept and you want some visualization and like, and open-ended exploration, we think those this is a very powerful, approach. you can imagine new forms of, design, industrial design software that could emerge as a result of those models. And this is all, generated in real-time as well. So, you can build a lot of interesting camera transitions and forms of interaction that are very difficult to build otherwise. And one way in which we evaluate this is what if you try to generate the same interface with Claude by just, prompting Claude, “Here's an image reference of my interface that I made in Figma or that I created somewhere else. create this particular interaction,” which in this case it's, drag that object, upwards. and beyond it being slower, it's also very difficult to capture some interactions by just fully, with just LLMs. So we think that this is likely to be the way that a lot of the future, like, software in the future will be created. and one of the additional benefits is personalization might be a lot easier done with those models. Like, you can essentially try out different prompts based on who is visiting the interface. You can, more easily, prompt engineer the interface to have larger size, text for more accessibility reasons, or you can make this or, like, if you have a particular aesthetic preferences. So we're very excited about this approach. It's early days, and I think we'll need to, make it more cost-effective as well to serve those models ‘cause, running a real-time video model versus just purely rendering HTML, there's -- the computational needs are much higher. but we do see a lot of potential in this approach to building front-end interfaces.Swyx [00:47:47]: So we covered this similar thing with Flipbook before with our, Ethan Hara episode with Groq, video. And yeah, I think it's very engaging visually. I think it's maybe very good for education, but it's it does sound expensive. I think there's an upper bound to how expensive it will be, though, right? Like, the inference cost will go down over time. You'll figure out ways to optimize it. Effectively, when it pauses, you don't you're not receiving human input. You don't have to generate anything, right? So.Anastasis [00:48:14]: Yeah, you could also. Like, in this case, you have ambient motion, so there is parts of the screen that might. if you're let's say you wanna, visit Paris and then you get this interface that allows you to explore.Swyx [00:48:29]: People walking. Yeah.Anastasis [00:48:29]: You have people walking or, like, things happening. But, it's, it's a no Yeah, it makes it more expensive because you need to run the model all the time. Maybe you have some looping mechanism so you don't need to do that. But all those things, I think, is stuff we'll need to figure out.Toward a Fully Neural Operating SystemSwyx [00:48:44]: Yeah.Anastasis [00:48:44]: I think our first consideration is let's make this clearly find some use cases where it's clearly a much more compelling interaction compared to traditional interfaces. And then it's a matter of time before it becomes more cost-effective to serve.Swyx [00:48:58]: Yeah. When it comes to the people walking, I think the approach that makes the most sense to me is Nick.Anastasis [00:49:04]: Nick.Swyx [00:49:04]: Oh, God. I keep messing up their name. With Chris Manning and Fanny Yan. I don't know if you've come across them, where they. Mapped to some game engine. I think it's Unity or something, or Godot. And they you can script some NPC behavior behind that and train on that. Whereas here, you can really imagine whatever you want. Like, that is a UI, right? Like, and it feels, like, more tractable, I guess, to, create a world model of software that is interactable because we have many of examples of that, and you can, do your fancy RL environment stuff on that than it is scaling up to embodied and real-world physical use cases. But this is a nice first step.Vibhu [00:49:43]: Or, there's the opposite of you have, like, one B models, three 50 million parameter language models. It just gets so small that they're just predicting, like, fishes moving.Swyx [00:49:53]: Small models are now 120 B, so.Vibhu [00:49:57]: Ultra mini on device.Vibhu [00:49:58]: But, no, I think it, like, it puts it into perspective, at least the car one for me, like, the applications, right? The amount of work to do that, sure, you only make one model year car per year, but applying this, it's also a cost-saving to have to manually make all this, right? So it opens up a lot of possibilities, too. I'm curious if you extend this out two, three years, so where do you see things going even further?Anastasis [00:50:25]: Effectively, the end game of something like interface world models is you have, a fully neural operating system. So I think, Andrej Karpathy has written about that quite a while back. But it's, You, I think to me it's, it's a bit, it's a bit odd that, we have, for example, with an interaction with an LLM of today, you have this LLM that can talk to you about anything. It can You can take the conversation in any direction. You can It's very general, so it can solve all those different tasks, but you interact with it through a very rigid interface. And so to me, it's just a matter of time before the interface itself becomes learnable and becomes, part of the whole loop of, like, you're not just delivering. You're delivering an application end-to-end, and that means you're delivering the language model, but you're also delivering the render and the pixels and that's also a learnable component. And the concept of applications might not necessarily. I think we'll need to figure out new abstractions for software. the concept of application comes from this idea that you need, separate code bases to describe, to, for, to power each individual, tool and each individual application. But you might think of something a lot more unified if you're. if you have, a video model that's generating the interface as you go. so it can take context from an LLM and allow you to combine different functionalities that traditionally would live in different applications. So it's a, it's a way to solve, software end-to-end, effectively. We also see this as a powerful way to train computer use agents as well. so this is, one way to see this as. And in general, with world models, there is those two directions. One is world models for humans and world models forSwyx [00:52:24]: AgentsAnastasis [00:52:24]: To train agents.Swyx [00:52:25]: Yeah.Anastasis [00:52:25]: And so for every new work of, world models that we do, we have this both uses become possible. So this is a powerful synthetic data generator for training computer use models. It could become, a live, RL environment that you could use to do online RL with a computer use agent, and you can get wide diversity of different interactions, kinds of interfaces, just generated on the fly that, to improve the how robust the, your agent, becomes. So that's the same also with the world models that we're working on for a robotics use case as well.Long Context, Error Accumulation, and Autoregressive VideoSwyx [00:53:02]: Is there a research breakthrough that you're Waiting for that would unlock the next set of use cases that you really wanna pursue?Anastasis [00:53:10]: Long context is a very important one, so being able to maintain consistency for long periods of time, and that depends on the use case. So for our characters model, for example, or for the interface world model, it's easier to maintain long sessions of interaction. If you go into more open-ended worlds that you navigate and you take arbitrary actions in, we, like, there is more the context at which you can and duration which you can generate becomes limited much more quickly.Swyx [00:53:40]: Yeah.Anastasis [00:53:40]: So we see more degradation and error accumulation happening. so the biggest challenge with autoregressive models is error accumulation, is you're feeding generative frames back into the model to generate the next The next frames. And if there is any small errors, they accumulate over time. That's not a new problem. It's a problem that LLMs also have, and we've seen the ability to generate now really long outputs. So it's a solved problem, but it's definitely still a challenge.Swyx [00:54:08]: Yeah. And what is the state of the art? so for Grok, it would be like 10 to 20 seconds of context going in there for video.Anastasis [00:54:16]: With our characters models, we're able to generate up to 30 minutes of video autoregressively.Swyx [00:54:21]: Yeah. But that's just for the avatars.Anastasis [00:54:24]: Yeah. So if we look at, GWM Worlds, which is more our open-ended world exploration model, it's, it's on the order of a few minutes, which is Yeah, soSwyx [00:54:35]: Probably enough for people because you have to cut to the next scene anyway, right?Anastasis [00:54:40]: Yeah, it's not, it's not the ideal game experience if you have to restart every few minutes. So I think. But, I think it's. Yeah, for certain kinds of game experiences, you can work around it.

Friday September 25, 2026 Record Month for Tesla Self-Driving Crashes

Would you trust a driverless car that just merged onto Mopac beside you?Story We Love: Waymo starts gradual freeway trips through Uber in Austin, JB clocks one on Mopac, and the table digs into whether highways are actually easier than downtown stare-downs. Tricia's friend has Tesla self-driving on a ninety-nine-a-month subscription with three strikes before it pulls you over like your mom. Sandy still thinks that Waymo tried to race her home from H-E-B.If you love The JB and Sandy Show, subscribe, leave a review, and share it with a Waymo watcher.

Today on AirTalk: Senate race predictions (0:30) Paid political influencers (18:43) Autonomous driving (34:43) Building local housing (52:04) LA CD-3 candidate Barri Worth Girvan (1:08:13) TV Talk (1:26:32) Visit www.preppi.com/LAist to receive a FREE Preppi Emergency Kit (with any purchase over $100) and be prepared for the next wildfire, earthquake or emergency.

Lee Redden, Founder of LastFeet joined Grayson Brulte on The Road to Autonomy podcast to discuss solving the last 50 feet of package delivery with a custom-built legged robot designed to ride in the jump seat of a delivery van and carry packages from the curb to the doorstep.After co-founding Blue River Technology and selling it to Deere, Lee returned to building because he believes this is one of the best times ever to build robots. LastFeet's robot walks on four legs with wheels on its feet, so it can climb the high step out of a box truck and then roll quickly and steadily to the door. It learns to walk entirely in simulation, a big change from the hand-calculated kinematics Lee learned in grad school.The inspiration came from delivery drivers. Lee interviewed dozens of drivers and shadowed their routes, then modeled his robot on the runner, the second person who rides in the passenger seat on busy days and carries packages to the door while the driver keeps driving.On day one, the robot sits in the jump seat with no change to how the back of the van is loaded. The driver loads a package, the robot bows at the door to set it on the step, and if it runs into something it can't handle, it asks a remote human for help.The first robot carries 15 pounds, which covers most packages, and runs on an NVIDIA Orin AGX with two-hour hot-swappable batteries. When it's unsure what to do, its safety response is to freeze in place and turn into a table.Episode Chapters0:00 Why Lee Redden Is Building Again02:41 How the LastFeet Robot Works04:56 How to Build a Robot in 202607:27 Testing in Snow and Ice10:00 How the Robot Delivers to Every Doorstep12:43 Riding in the Jump Seat of a Delivery Van15:00 Manufacturing at Scale18:46 Building for Rugged Delivery Vans22:54 LastFeet Business Model29:32 Battery Life and Charging in the Van32:11 What Lee Learned Shadowing 50 Delivery Drivers37:28 Safety and Building Trust With the Public40:18 The Future of LastFeet43:11 AUTNMY AIFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

CNBC's Phil LeBeau joins from the highway in Texas as he reaches his final destination on an autonomous trucking trip with the CEO of Aurora. Then, Schneider CEO Jim Filter breaks down the impact of record diesel prices on the business. Plus, Bernstein analyst Stacy Rasgon joins to discuss what's behind the rally in chip stocks today.Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss Waymo's expansion to Tokyo and Singapore, NHTSA's ongoing audit of Tesla's Cybercab in Austin and Bolt's plan to deploy up to 25,000 Lucid robotaxis in Europe.Today, Tesla has roughly 40 Cybercabs operating in Austin, and Grayson believes Tesla is respectfully holding at its home market while NHTSA completes a fact-finding audit. If Tesla passes, it is free to deploy in any state that permits fully autonomous operations. Walt expects the process to wrap by year-end, and Grayson reads Administrator Morrison's Pittsburgh speech on updating outdated federal motor vehicle safety standards (FMVSS) as a signal that a pathway is opening.As Waymo continues their global expansion, Grayson predicts Singapore will be an IONIQ 5 market and Tokyo a Toyota market, with the vehicle constraint that has defined Waymo now dissipating, and he reads the Allianz partnership as the insurance piece falling into place for Waymo's grand European expansion.In Las Vegas, riders can now go anywhere in the ODD, and in Nashville, The Road to Autonomy Special Field correspondent David Moss got banned from the Lyft app playing autonomy roulette.May Mobility announced a SPAC with $10 million of 2025 revenue and a $270,000 bill of materials, and Wayve hired Waymo's former CFO, which Grayson views as a signal Wayve may be eyeing the public markets.On the Foreign Autonomy Desk, Grayson and Walt analyzed Einride's deployment in Germany, Pony AI, Uber and Verne's driver-out launch in Zagreb on a 13.67-mile fixed loop. Spain issued its first SAE Level 4 permit to WeRide and Uber, with Grayson predicting a Waymo expansion to Madrid by Q2 of next year. In Singapore, Grab is expanding their WeRide fleet 5X to 50 robotaxis.Episode Chapters0:00 Reviewing the Jason Calacanis Episode03:17 Tesla Cybercab NHTSA Audit06:23 NHTSA Administrator Morrison's Pittsburgh Speech08:14 Waymo on Lyft in Nashville10:56 Waymo in Las Vegas13:17 Waymo in Tokyo and Singapore16:20 Waymo / Allianz Partnership17:25 Bolt's Grand Robotaxi Ambitions20:45 May Mobility SPAC23:42 Wayve's Hires Waymo's Fmr. CFO24:45 Hyundai IONIQ 5 Robotaxis28:11 Autonomous Trucking30:34 Foreign Autonomy Desk35:35 Next WeekFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week on Autonomy Signals presented by KPMG, Grayson Brulte and Rob Grant discuss NHTSA ordering Tesla to answer 21 technical and legal questions under oath by September 30 on Cybercab self-certification, Waymo hitting local political friction in San Diego after CPUC approval, and GMO Air unveiling a robot EMS vehicle in Tokyo built to service humanoids in the field.NHTSA's Office of Vehicle Safety Compliance, by special order signed by the Chief Counsel, required Tesla to explain how a steering-wheel-free Cybercab meets FMVSS 101, 102, 108, 111, 126, and 135 standards written around human controls such as mirrors, brake pedals, turn-signal self-cancel, and shift displays, and why Tesla did not seek a Part 555 exemption as Zoox did.The inquiry also asks whether some test results depended on temporary manual controls later removed. Public Cybercab service began September 3, 2026 in Austin. Nothing in the order grounds the fleet or caps production; September 30 is an evidentiary deadline, not a shutdown date.While Tesla answers Washington, San Diego City Council voted 8–0 to request local regulatory authority over AI and autonomous vehicles, a follow-up to CPUC approval that gives the city no power to stop Waymo. California has preempted local autonomous vehicles rules for more than a decade.The San Diego City Council resolution is toothless on paper, but it flags the real friction points: emergency-response incidents, airport access outside state preemption, stricter local ticketing after a July 1 law, and depot or charging permits that still sit with the city. Waymo called the move extraordinarily premature and cited a 100,000-person local waitlist; it already has a field-verified operational depot in San Diego.In Tokyo, GMO Air unveiled a robot EMS vehicle equipped with diagnostic tools, spare parts, a backup humanoid, and a purple roof light distinct from red human-ambulance lighting. The demo still used human-in-the-loop controls because of latency. The signal is adjacent infrastructure, field maintenance and uptime for deployed humanoids, not a driverless medical ambulance for people.Episode Chapters0:00 KPMG Sponsor Introduction01:41 Signal 1: Will NHTSA Ground Cybercab?26:59 Signal 2: San Diego City Council Wants Local Control48:39 Signal 3: Japan's Ambulance for HumanoidsFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The smart glasses war heats up as Snap releases their new glasses These are augmented reality glasses, so they will effectively put a screen in front of you. You can interact with the apps using your hands, so you can just reach out and tap an icon or a button. There's a social element too, so you can play dominos with a friend, each wearing your glasses and seeing the same thing, just from different views. Another experience helps people improve their golf swing by blending virtual lessons and challenges with a user's real-world view. What makes them different is that you don't need a phone – they connect to the mobile network via a data plan. They last only about four hours so aren't designed to be worn all day. They look a bit bulky. One reviewer said they were more awkward than the Meta versions. Meta's reportedly making some changes to their glasses too They were dubbed "pervert glasses" with their cameras able to record anyone and anything. Maybe that feedback got to them because reports say they're working on glasses now that won't have a camera at all. They're codenamed "Luna" and will be audio focused, so they'll still be listening with their six built in microphones! They'll be connected to their new Muse agent which is like a personal assistant. Without the camera tech they'll be slimmer too, so there can be more designs. Meta has a conference next week, so we're expecting them to be announced then. A self-driving car dobbed in its passengers' illegal activity Two people were riding in a Waymo self-driving car in San Francisco and had a semi-automatic AR weapon. Somehow, it was detected and the police were alerted, ending with the car pulling over and the passengers being arrested. It's still unclear if the car's cameras auto detected the gun or a human reviewer saw it, but either way we are being watched. What's the next step here? Should the car automatically lock the passengers inside until the police arrive? LISTEN ABOVE See omnystudio.com/listener for privacy information.

In hour three, a video of a self-driving Zamboni sparks a debate about the Zamboni-driving profession. Crowder argues he can drive a Zamboni. Rob Pizzola shares his picks for the Fins-Niners game and tonight's TNF matchup.

I WAS THINKING:  TACOMA BUSINESS OWNERS AND PHILANTHRIPISTS UNJUSTLY CANCELED. Tacoma brewery caught in controversy as owners’ political donations spark “anti-trans” allegations // The Road to a Universal Basic Income. John on humanity’s misguided for socialism // Kids arrested after Waymo reports a gun in self-driving car 

Ben Seidl, Co-Founder and CEO of Autolane joined Grayson Brulte on The Road to Autonomy podcast to discuss building Plaid for the autonomy economy, an independent, OEM-agnostic orchestration platform that connects autonomous vehicles to businesses for tip-free delivery.The inspiration for Autolane was born in August 2024 when Ben turned on FSD in his Tesla for the first time and worked backwards from a simple question. When personally owned autonomous vehicles become a reality, what has to exist for a car to drive to a store, park in the right stall, pop its trunk, and complete a transaction without a human in the loop?The answer, a neutral platform that no OEM, marketplace, or retailer controls. Studying the Argo AI and Cruise OEM control of AV 1.0 convinced Ben that taking capital from a Ford or a Walmart limits the market and muddies decision making.Autolane entered the market through ride-hail orchestration, deploying first at Stanford Shopping Center with Simon Property Group in September 2025 to give landlords control over where Waymo and Tesla robotaxis arrive on private property.Over the summer, Autolane went live with So Chinese Takeout in Austin, operating a fleet of 13 Tesla Model Ys equipped with temperature-controlled smart lockers that batch two to three orders per trip as they expand the business beyond ride-hail orchestration.Episode Chapters0:00 Inspiration for Autolane4:34 Autolane's Quest to Remain Independent11:06 Deploying at Stanford Shopping Center13:02 Chapter One: Orchestration16:46 Autolane Zones26:41 Fleet Ownership28:36 Underwriting Autonomous Delivery33:53 Matching Supply and Demand37:29 French Fry Test39:38 QSR Delivery46:59 Sidewalk Dots, Drones, and Humanoids54:35 Plaid for the Autonomy Economy57:42 AUTNMY AIFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The show-ending “Smoking Gun” segment on the Tuesday Bob Rose 9-15-26

Federal legislation aimed at setting national safety standards for autonomous vehicles seems to be sending mixed signals. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk are joined by special guest Jason Calacanis, one of the first investors in Uber, to debate his bull case on Uber, who wins the robotaxi endgame and whether local governments will cap the deployment of autonomous vehicles.Jason earns the first clappy hat of the episode for saying what few in the market will, Tesla is obsessed with safety. The incidents Waymo has absorbed would be showstoppers under Tesla, and Tesla knows it. Jason, who drives FSD daily on Hardware 4, puts the system at 98 percent of the way to Level 4 and wants to buy a Cybercab fleet.The robotaxi endgame, in Jason's view, comes down to manufacturing. A shift from 2 percent of global miles on rideshare to 30 percent requires more than 100 million autonomous vehicles and a $5 trillion fleet build-out larger than the AI data center build-out.Tesla wins the gold because it owns the factory. In Jason's opinion Waymo will have to buy a car company, likely Rivian or Lucid, after it spins out of Google and IPOs. Uber owns the demand and will take the rest of the market with its 20 partners, several of which Jason predicts will pivot to building only robotaxis. His market share call is 40 percent Tesla, 35 percent Uber and 15 percent Waymo.Walt pushes back on whether there is enough at the table for Uber's twelfth partner and questions whether any of them are close to Tesla or Waymo today, with 300 Nuro powered Lucid Gravity vehicles by June being a win. Jason counters that Nuro and Avride will be driver-out within 24 months and that no one else can afford the $100 to $300 per customer required to build another Uber-scale network.The sharpest debate of the episode is regulatory capture. Jason argues local governments will cap and license autonomous vehicles the way they did Airbnb, that drivers will be the first mass unemployment from AI and that Uber is right to negotiate a soft landing with unions.Grayson argues federal preemption is coming and that caps will disappear when a national framework arrives. The two settle it with a steak dinner bet on whether local communities or the federal government win the fight in the United States.Jason closes with his theory on Uber and his dream scenario of Travis Kalanick returning as CEO of Uber.Episode Chapters0:00 Jason Calacanis joins Autonomy Markets02:19 Cybercab Fleets04:04 Tesla's obsession with Safety07:27 Everywhere-All-at-Once Fallacy09:33 Uber Shutting Down ATG Was a Mistake12:51 The Gap Between Engineers and the Market16:37 Jason Calacanis on Why Waymo has to Buy a Car Company18:15 Uber's Surge Pricing Strategy19:16 Jason Calacanis' 40/35/15 Robotaxi Prediction25:58 Are Any of Uber's Partners Technically Close to Tesla or Waymo?30:23 Uber Eats is Half of Uber's business32:18 100 Unsupervised Robotaxi Benchmark33:37 Robotaxis in China35:31 Regulatory Capture Debate begins41:31 Steak Dinner Bet49:02 Personally Owned Autonomous Vehicles Impact on Shared Rides52:27 Waymo and Toyota moment to Watch in Q1 2755:30 Puke in Uber, Get a Bonus56:28 Travis Kalanick, CloudKitchens and Atoms58:08 What Travis Kalanick Would do Differently59:09 PIF, Lucid, Nuro and a Full-Stack Uber with-in 36 months1:06:16 Atoms Robotaxi Depot Play1:12:32 If and When Waymo Goes Public1:17:36 Why Japan needs autonomy1:20:46 The Next 12 months1:22:20 The Next Tesla Robotaxi format1:27:42 Autonomous Regional Travel1:29:47 Amazon, Google or Tesla should buy Uber1:31:39 Consolidation, First Dominos, Zipline, Wayve and Nuro1:34:52 Framing the BetFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this sponsored episode, Kyle Baxter and Sridhar Katere from HPE join Drew to talk about automating enterprise data center networks. They discuss how HPE integrates its Apstra and Mist products to bring AI ops and self-driving capabilities into data center operations. They also break down how HPE leverages the different strengths of each platform,... Read more »

In this sponsored episode, Kyle Baxter and Sridhar Katere from HPE join Drew to talk about automating enterprise data center networks. They discuss how HPE integrates its Apstra and Mist products to bring AI ops and self-driving capabilities into data center operations. They also break down how HPE leverages the different strengths of each platform,... Read more »

In this sponsored episode, Kyle Baxter and Sridhar Katere from HPE join Drew to talk about automating enterprise data center networks. They discuss how HPE integrates its Apstra and Mist products to bring AI ops and self-driving capabilities into data center operations. They also break down how HPE leverages the different strengths of each platform,... Read more »

This week on Autonomy Signals presented by KPMG, Grayson Brulte and Rob Grant discuss NHTSA opening an audit query into Tesla's Cybercab self-certification hours after commercial launch, Waymo launching in Nashville on both the Waymo and Lyft apps with Flexdrive managing fleet operations, and XPENG commissioning a dedicated humanoid production facility in Guangzhou, China.On September 3rd, NHTSA's Office of Vehicle Safety Compliance opened audit query AQ26002 covering an estimated 1,000 Cybercabs, triggered by Tesla deploying a bespoke vehicle with no manual controls on public roads with paying members of the public.The question is not whether the Cybercab is unsafe but whether Tesla unilaterally decided that steering wheel, pedal, and mirror provisions do not apply to a vehicle with no manual controls rather than seeking a Part 555 exemption as Zoox did. Nothing in NHTSA's query halts operations or caps Tesla's Cybercab production.While NHTSA probes Tesla, Waymo went live in Nashville as the first market where its vehicles are bookable through both the Waymo app and the Lyft app with Flexdrive managing the fleet.As robotaxis scale in America, over in China, XPENG commissioned its humanoid production facility in Guangzhou with an 80% automated build, backed by a $900 million raise at a $6.3 billion post-money valuation, the largest single-round private raise in China's embodied AI sector.The Iron humanoid has 76 degrees of freedom and three Turing chips delivering 2,250 TOPS running XPENG's physical AI model on device, with mass production beginning by the end of 2026. Episode Chapters0:00 KPMG Sponsor Introduction01:02 Signal 1: NHTSA Opens Audit Query into Tesla Cybercab32:00 Signal 2: Waymo Launches on Lyft in Nashville47:11 Signal 3: XPENG Commissions Humanoid Production FacilityFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If you've ever watched a skilled driver thread through chaotic city traffic, you already understand what most businesses feel like on the inside: constant merging, sudden stops, and near-misses that burn time and focus. We take that real-world chaos and use it to explain a better operating model for AI in business, sparked by a striking image: a self-driving Tesla calmly navigating conditions that would stress out most humans.Our core claim is simple: your company needs a supervised self-driving mode. When leaders and top performers spend their days drafting routine emails, hunting for files, reformatting decks, and cleaning up systems, they are “texting and driving” with your revenue in the passenger seat. We lay out a four-layer framework that shifts your team from doing the driving to deciding the destination: human experts define the vision and what winning looks like, high-value employees handle navigation and judgment calls, AI takes on repeatable execution, and automation becomes the pit crew for admin tasks like CRM hygiene and status updates.Then we get practical with Gemini Notebook as a model for implementation. We walk through building a closed, approved knowledge base as your source of truth, using chat as an internal boardroom for supervised thinking, and turning inputs into polished outputs in the studio area. If you want AI agents that actually reduce stress, speed up execution, and create competitive advantage without losing control, this is the blueprint. Subscribe, share this with a teammate who's overloaded, and leave a review with the first task you want to put into self-driving mode.Send us Fan MailSupport the show

Creating effective AI models takes more than just huge amounts of compute. It requires the high-performance networks needed to connect increasingly powerful GPU clusters and move vast amounts of data. With the speed of AI adoption showing no signs of slowing, our entire infrastructure is having to change to keep up, but can the AI being trained also be used to increase the efficiency of the networks supporting it?This week, Technology Now revisits the concepts of networks for AI and AI for networking, to see how much has changed in the last six months. Praveen Jain, SVP & GM Data Center and AI Networking, HPE, joins the show to explain:The difference between a traditional data center and one optimized for AIHow to maximize the efficiency of expensive GPUsWhy infrastructure is becoming self-healingThe vision of an autonomous data center in the future

Happy Labor Day! This week we touch on many aspects of the international gig work industry as well as the hidden labor behind "autonomous vehicles". Other stories we cover include repression of immigrants in Canada, how AI isn't replacing workers at all, tech and video game workers, and the continued union fight at Amazon. We get to celebrate the Seattle Teachers winning a TA and wrap up with a story about internal union efforts in IUPAT to make sure they are really fighting for workers. It also may come as no surprise but people in the US support unions at high levels, and new data shows that its a majority across the US political spectrum.    Join the discord: discord.gg/tDvmNzX  Follow the pod at instagram.com/workstoppage, @WorkStoppagePod on Twitter,  John @facebookvillain, and Lina @solidaritybee

George Kalligeros, Co-Founder and CEO of Aseon Labs joined Grayson Brulte on The Road to Autonomy podcast to discuss building, deploying and scaling autonomous robotaxi infrastructure.After visiting depots across the country, George noticed that the current large-scale depot model needs an update as he estimates that roughly 40% of the cost of a robotaxi depot visit is tied to relocating the vehicle, both in the cost of the trip and in lost demand.Today a vehicle can leave service for two hours at a time, three times a day, to travel to a centralized depot. Aseon's answer is to shrink the depot footprint and disperse it directly into the operating zones where vehicles already work.Each Aseon pod occupies a single parking bay, arrives on a flatbed truck, rolls off on its own wheels, and expands into position. Inside, a robotic system plugs in the charger, inspects the interior and exterior, removes lost items and trash, vacuums and wipes down the cabin, syncs data, and safely stores the vehicle. Aseon calls it the core reset, and it takes roughly 30 minutes, with a target of 95% fully autonomous uptime and 20 to 30 robotaxi charge/cleaning sessions per pod per day.Because the pods are classified as temporary infrastructure, Aseon can deploy on existing charge point operator sites or EV-permitted parking lots with a conditional use authorization in eight to 12 weeks and relocate a pod on a few days' notice.Fresh off a $10 million seed round, the company is targeting Dallas, Austin, and Phoenix for its first pilots with robotaxi operators, with a long-term view that infrastructure is the key to scaling robotaxis.Episode Chapters0:00 Why Robotaxis Need Autonomous Infrastructure00:47 The 40% Relocation Cost Insight04:28 Lost Utilization and Where Pods Get Placed05:55 The Core Reset Services07:23 Commercialization and Deployment Models10:14 Depots, Satellites, and Nodes13:54 Portability and Permitting15:22 Lessons from Pushme and Tier Mobility18:11 Inside the Robotic Reset18:54 Deploying the $10 Million Seed24:36 Vandalism and Security26:21 Infrastructure Over the Next Decade29:51 AUTNMY AIFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss Tesla's Cybercab commercial launch in Austin, Uber's continued regulatory capture campaign and Waymo's first ever debt raise.Tesla officially opened Cybercab to the public in Austin on September 3rd, with paid rides across the full operational design domain (ODD) in a vehicle with no steering wheel and no pedals. Grayson and Walt call it a major milestone for the Autonomy Economy, despite a coordinated effort to make the launch look unimportant.The bigger story surrounding the launch, in Grayson's view, is Tesla's new form inviting individuals and companies to purchase Cybercab fleets and build and own robotaxi infrastructure. Grayson sees this as a potential franchise model with Tesla's ability to finance the fleet as a structural advantage.The Financial Times is reporting on Uber's continued regulatory capture strategy, requiring human drivers to handle the majority of rides as part of a hybrid network strategy. Grayson concludes Uber is under siege from autonomy and reaching for regulatory capture, while Walt notes it also kneecaps Uber's own autonomy partners.Waymo opened public rides in Denver, San Diego and Tampa, reaching 14 U.S. cities, and is in the final stage of raising roughly $3 billion in debt, its first borrowing.Zoox earns its first clappy hat for curbside pickup and drop-off at Harry Reid International Airport and announced expanded testing to Houston and San Diego. Wayve and Uber launched supervised robotaxi service in London, with Wayve stating plainly that a safety driver is on board while Uber framed it as the first autonomous rides in the UK.On the foreign autonomy desk, Applied Intuition partnered with Humain to build an autonomous freight corridor in Saudi Arabia, DiDi began fully driverless trials of its purpose-built R2 robotaxi with the UAE next, Waymo will deploy in Munich next year against Momenta and Uber, and WeRide's robobus is operating in Singapore.Episode Chapters0:00 Tesla's Cybercab Launch in Austin08:38 NHTSA's Probe into Tesla's Self-Certification15:49 LiDAR Debate21:42 Tesla's Fleet Ownership Model Questions26:21 Tempering Expectations on Cybercab Rollout27:54 Uber's Regulatory Capture Campaign34:44 Waymo Expands to Denver, San Diego and Tampa36:33 Waymo's $3 Billion Debt Raise38:45 Robotaxi Depot Infrastructure41:08 Zoox Launches Service at Harry Reid International Airport (LAS)48:40 Wayve and Uber Launch Supervised Rides in London52:32 Autonomous Trucking55:22 Foreign Autonomy Desk1:00:33 Next WeekFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week on Autonomy Signals presented by KPMG, Grayson Brulte and Rob Grant discuss Waymo launching commercial robotaxi service in Denver, San Diego, and Tampa on the same day, UBTECH's humanoid revenue surging 1,445% on industrial demand, and Sandvik unveiling SAMI, a cabin-less autonomous surface drill that removes humans from the blast zone.On September 1st, Waymo opened service to the public in Denver, San Diego, and Tampa, bringing the service to 14 cities with a fleet exceeding 4,000 vehicles, roughly 500,000 paid weekly trips, and 220 million fully autonomous miles.Launching three major markets simultaneously confirms Waymo has moved from single-city validation into a repeatable multi-market playbook, timed on the eve of Tesla's Cybercab launch to keep the market focused on verified commercial metrics.Denver is the signal inside the signal as it's Waymo's first cold-weather market, and Tampa becomes the first true head-to-head market where Waymo and Tesla launched within five weeks of each other. Waymo's constraint is vehicle supply, Tesla's is regulatory.While Waymo expanded, UBTECH reported roughly $190 million in first-half revenue, with humanoid sales surging nearly 1,500% year over year to 46% of group sales on 921 full-size Walker S units delivered at a 66.8% gross margin to automotive and smart factory customers such as Foxconn. The caveat is that 96% of that revenue came from bespoke customization contracts rather than repeatable product sales, making UBTECH a high-margin systems integrator wearing a humanoid robotics label, while China's near monopoly on motors, actuators, sensors, and batteries raises the risk that the West is pushed into a software IP licensing role.As humanoids scale on Chinese factory floors, Sandvik introduced SAMI, a fully autonomous, battery-electric, cabin-less concept surface drill that self-replaces its own bits and hammers via an onboard robotic arm, coordinated by a mine-wide AI agent. Removing the operator from the blast zone is immediately quantifiable ROI, and while SAMI remains a concept with unconfirmed timelines, it confirms industrial autonomy is shifting from single-vehicle automation to multi-asset orchestration, where value accrues to whoever owns the digital twin layer rather than the hardware.Episode Chapters0:00 KPMG Sponsor Introduction02:07 Signal 1: Waymo Launches service in Denver, San Diego, and Tampa31:29 Signal 2: UBTECH Humanoid Revenue Surges 1,445%51:10 Signal 3: Sandvik SAMI Takes Humans Out of the Blast ZoneFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Professor Byron Boots is the co-founder and CEO of Overland AI and a leading expert in machine learning, robotics, and autonomous systems. A full professor at the University of Washington with a PhD from Carnegie Mellon, he previously held research roles at NVIDIA and Google and led the University of Washington's DARPA RACER team to victory. Through Overland AI, Byron is developing autonomous ground vehicles for the U.S. military, helping modernize battlefield logistics, improve operational effectiveness, and reduce risk to service members. Privacy Isn't Paranoia. It's Protection. Download Glacier - https://srs.site/glacierapp Website - https://theglacierapp.com Shawn Ryan Show Sponsors: Free trial at https://shopify.com/srs Go to https://helixsleep.com/SRS for up to 30% off. If you have an iPhone, go to https://ladder.fit/SRS to take a quick quiz, get matched with your coach, and get a free 7-day trial (no credit card needed) plus $10 off your first month if you join. For a limited time, our listeners get 50% off FOR LIFE, Free Shipping, AND 3 Free Gifts at Mars Men at https://Mengotomars.com Byron Boots Links: X - https://x.com/Overland_AI_X Youtube - https://www.youtube.com/@OverlandAI Website - https://www.overland.ai Learn more about your ad choices. Visit podcastchoices.com/adchoices

Joe Cirincione explains Trump's illegal, unauthorized, unnecessary war is the worst strategic defeat in U.S. history - and - The main national security threat we face is not in Russia, China, North Korea or even Canada, but in the White House. Geeky Science. Researchers are Alarmed as GPS Readings Suddenly Veer Off by 33 Feet, Enough to Crash Self-Driving Cars. Oh oh. “Cyclospora research shelved“ by Marcia Brown and Rachel Shin. Whistleblower claims thousands of mail-in ballots could be rejected with new system“ by Ken Dixon. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AP correspondent Charles de Ledesma reports Uber and British tech company Wayve have launched a robotaxi service in London.

Dr. Eliot explores how AI self-driving cars cope with manhole covers. See his Forbes column for further info: https://www.forbes.com/sites/lanceeliot/

Self-driving taxi services are becoming more widely available, as Waymo rolls out service in three new cities and Tesla prepares to launch the Cybercab. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Frank Reig, CEO of Voltera joined Grayson Brulte on The Road to Autonomy podcast to discuss how Voltera is powering the growth of robotaxis and what goes into building a robotaxi depot.As the robotaxi market grows, infrastructure developers are racing to meet the demand for large-scale fleet depots. Following the strategic merger between Voltera and Revel, the newly combined Voltera has doubled its development and real estate capacity to build, own, and operate high-power charging depots tailored specifically for robotaxis.Voltera's robotaxi infrastructure strategy centers on a hub-and-spoke model, combining large-scale central facilities capable of heavy maintenance with strategically dispersed charging hubs designed for high utilization and quick vehicle turnaround.To support the rapid growth of robotaxis, Voltera standardizes site layouts while maintaining the flexibility to adapt to local constraints, future-proofing its locations with high-capacity underground conduits, fiber connectivity, and high-voltage power architectures to meet escalating power demands.Episode Chapters0:00 AUTNMY AI0:36 Growth of Robotaxis1:34 The Voltera and Revel merger3:31 Permitting and Powering Depots7:49 Airport Depots11:37 Standardizing Depot Design16:11 Single-Tenant vs Multi-Tenant Depots18:18 Automation Inside the Depot20:49 Future-Proofing for 800-Volt Architecture24:38 Will Voltera Expand Internationally?30:48 Hub-and-Spoke Depot Model35:39 The Future of VolteraFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

As agentic AI pushes investment management toward greater autonomy, research points to credible risks that lie ahead. So, what happens when AI moves from assisting portfolio managers to actually making and governing investment decisions? Kevin Max, editor of Enterprising Investor, sits down with Irina Bevza, PhD, CFA, Head of Quantitative Solutions at Fineco Asset Management, Dublin, to explore the promise and faults of the self-driving portfolio. This episode explores what self-driving portfolios could mean for institutional asset management—and asks the bigger question for investment professionals: If AI can increasingly perform the work of the portfolio manager, where does human judgment remain essential?

Like the show? Show your support by using our sponsors.Need to update your shop systems and software? Try Tekmetric HERELaunch your tool game to the next level with Launch Tech USA! HEREIn this episode, Jeff Compton talks with Tanner Waters, a field and shop technician in rural Montana, about his path into the trade and what life is like working on tractors, combines, and hay equipment. Tanner shares his apprenticeship experience, field diagnostics, long drives, and major repairs while comparing hourly efficiency pay with flat rate.The conversation also explores why younger technicians leave the industry, the importance of shop culture and leadership, technician communication, diagnostic time, training apprentices, and the future of agricultural equipment with autonomy, EVs, emissions, and right to repair.Timestamps:00:00 Shop Culture Wins 00:34 Podcast Intro and Guest Setup 01:45 Montana Life and Fishing Talk 02:43 How Tanner Got Wrenching 03:24 Corvette and Hemi Memories 05:43 Wrangler Ownership and Oil Cooler 08:41 Life as a Field Tech 11:55 Winter Regens and Emissions 13:36 From Kenworth to John Deere 14:32 Old-School Trucks and Cabovers 18:17 Apprenticeship Tools and Toolboxes 22:50 Princess Auto and Canada Road Trip 26:14 MMA Stories 31:02 The Missing Age Group in the Trade 32:00 The Flat Rate Gap 34:48 Recalls and PIPs 36:08 Why Technicians Leave 40:54 Hourly Efficiency Pay 51:18 Workflow Support Roles 55:03 Technicians Selling the Job 59:54 Pre-Authorizing Repairs 01:04:18 Why Diagnostics Take Time 01:09:50 Transmission Service Expectations 01:11:05 Free Diagnostics vs. Evaluation 01:13:08 Asking Better Questions 01:14:07 Intermittent Turbo Faults 01:15:19 Parts Quality Reality Check 01:18:39 Build Quality and Technician Pay 01:20:54 Training Apprentices the Right Way 01:23:27 Motivation and Career Paths 01:27:13 Turnover and Leadership 01:32:56 Ag Tech Levels and Certifications 01:34:08 Autonomy and Electric Agriculture 01:38:29 EVs in Rural Life 01:41:32 Muscle Cars and Collectors 01:44:49 Jeep Ducks Culture 01:45:53 The Top-Off Jeep Debate 01:47:13 Wrangler Tops and Leaks 01:48:26 Refrigerant Changes and 1234yf 01:49:58 AC Repair Customer Headaches 01:52:57 DIY Refrigerant Cans and Regulations 01:57:29 Right to Repair Deere 02:01:26 Technician Shortage and OTA Updates 02:04:36 Self-Driving and Subscriptions 02:08:54 Shop Culture and Career 02:13:35 Events, Sponsors, and Farewell Follow/Subscribe to the show on social media! TikTok - https://www.tiktok.com/@jeffcompton7YouTube - https://www.youtube.com/@TheJadedMechanicFacebook - https://www.facebook.com/profile.php?id=100091347564232

There's no cash prize for getting them all right, because there's no way of knowing whether you cheated or not. Facebook PatreonSee omnystudio.com/listener for privacy information.

Did Austin just put a shotgun turret on a self-driving Army truck? The Army is testing an F-150 with a 50-pound turret from Austin's Allen Control Systems that can knock down fast-moving drones. JB's theory: a redneck welder who had been building these for ranchers. America, one attack at a time.Tricia's Care/Don't Care has a Prince vault album of unreleased tracks, caftans coming back, and baby Bigfoots in Pennsylvania, three to four feet tall. Stephen Presley from Underpop TV talks Bastrop tunnels and The Boring Company. H-E-B is teaming up with the Dallas Cowboys. Scottie Scheffler, a Longhorn, won the PGA Tour Championship. JB scored Marine Layer pants at Lyle's Garage Sale for a dollar. They sell for a hundred and forty-eight. Minute to Win It had a technical issue, so five pair of Tim McGraw tickets roll to tomorrow.Subscribe, leave a review, and share this with someone who still thinks Bigfoot does not have kids.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss Grayson's field work across the Bay Area, Tesla's September 3rd Cybercab launch and Nevada's authorization of 8,000 autonomous vehicles in Clark County.Fresh off a week of field work in California, Grayson shares firsthand impressions from his first rides in Waymo's Zeekr-built Ojai, including a visit to Waymo's Toland depot in San Francisco. While the Ojai rides lighter and less luxurious than the Jaguar I-PACE, its elevated sensor placement meaningfully reduces repair costs and insurance payouts in the event of a rear-end collision.Grayson views the Ojai as a stopgap vehicle on the path to the Hyundai IONIQ 5, which he believes will be Waymo's massive production ramp vehicle. After riding in the Zoox, the Ojai and the Jaguar back-to-back, and observing Waymo's infrastructure buildout from San Francisco down to San Jose, Grayson makes a prediction. When Waymo adds roughly 1,000 to 1,200 additional vehicles in the Bay Area, the company will capture a significant portion of the rideshare market share.Grayson also rode with Wayve with his AUTNMY AI co-founder Rob Grant in a fully dynamic, no-fixed-route supervised ride, noting the technology has accelerated meaningfully since his last ride in London as Wayve gears up for unsupervised operations.Turning to Tesla, the Cybercab is scheduled to commercially launch in Austin on September 3rd, with Grayson expecting a measured rollout of sub-30 Cybercabs in week one, driven by an internal safety metric and infrastructure that is still being built out.Meanwhile, Nevada's Transportation Authority unanimously authorized 8,000 autonomous vehicles in Clark County over 12 months. But the growth comes with a catch, as autonomy drop-off fees and exclusive resort partnerships threaten to bring Vegas-style nickel and diming to robotaxis in Sin City.On the foreign autonomy desk, China's Autonomous Belt and Road Initiative continues to take hold in Europe.Episode Chapters0:00 Grayson's Latest Bay Area Field Report19:30 How Many Cars Will Actually Be on the Road when Cybercab Launches?24:25 Tesla's Self-Certification Strategy for Cybercab25:31 Tesla Semi30:40 Waymo's 10 AI Lessons36:36 The Robotaxi Infrastructure, Energy and Manufacturing Layers37:38 Nevada Authorizes 8,000 Autonomous Vehicles in Clark County39:55 Uber's Robotaxi Playbook Raises Doubts42:08 Waymo Expands to Munich, Germany45:06 NVIDIA Earnings. Where is Automotive?49:53 Autonomous Food Delivery52:33 Who is Cheaper? Uber or Lyft?56:39 SFO Robotaxi Access58:10 Foreign Autonomy Desk59:53 Next WeekFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

We're talking driving on this episode of the Roz & Mocha Retro Show! First, we jump back to 2013 and hear what we thought about self-driving cars long before they became a reality. Then, it's 2014 and a hilarious look at the surprising list of celebrities who don't drive. Plus, a trip back to the days when Maurie used to drive Roz to work, complete with a soundtrack of questionable '90s songs that made those early morning rides unforgettable Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Mike and Mark dip into our future behind the wheel, examining full self-driving capability in cars, the proliferation of robo-taxis, and the effect of all of this technology on the economy and safety. ----M and M EXTRA: Two iconic talk radio hosts. One unfiltered daily conversation. No scripts. No spin. Just Mike Gallagher and Mark Davis breaking down the news the way it should be — with decades of experience and zero apologies. If you love smart unscripted talk show chemistry, you’re in the right place. -----Thank you to our amazing sponsors for supporting the podcast! PreBorn saves babies and souls, by providing free ultrasounds to women. When a young woman SEES her baby and HEARS her baby’s heartbeat, it doubles the chance she’ll choose LIFE!  Whether you want to save one baby or hundreds…you are just a call or a click away. Call 833-850-BABY that’s 833-850-2229 OR go to preborn.com/MMExtra! PHD Weightloss:If you're ready to finally take control of your health, PHD Weightloss has the plan that works. Real science. Real support. Real results. Visit PHDWeightloss.com or call 864-644-1900 and mention Mike and Mark. Relief FactorAre you in pain? When it comes to supplements, two things matter most: that it works, and that you can trust it. That's why we love Relief Factor. Get relief from pain at relieffactor.com or call 1-800-4-RELIEF to get your 3-Week Starter for $17.76. EKKL StreamingThe stars of Duck Dynasty, Willie & Korie Robertson, teamed up with EKKL to create a brand new, one-of-a-kind streaming platform built around faith and family with movies, TV series and podcasts. The best part? You get all of this for only $5.83 a month. Sign up for EKKL at ekkl.com today!See omnystudio.com/listener for privacy information.

Mike and Mark dip into our future behind the wheel, examining full self-driving capability in cars, the proliferation of robo-taxis, and the effect of all of this technology on the economy and safety. ----M and M EXTRA: Two iconic talk radio hosts. One unfiltered daily conversation. No scripts. No spin. Just Mike Gallagher and Mark Davis breaking down the news the way it should be — with decades of experience and zero apologies. If you love smart unscripted talk show chemistry, you’re in the right place. -----Thank you to our amazing sponsors for supporting the podcast! PreBorn saves babies and souls, by providing free ultrasounds to women. When a young woman SEES her baby and HEARS her baby’s heartbeat, it doubles the chance she’ll choose LIFE!  Whether you want to save one baby or hundreds…you are just a call or a click away. Call 833-850-BABY that’s 833-850-2229 OR go to preborn.com/MMExtra! PHD Weightloss:If you're ready to finally take control of your health, PHD Weightloss has the plan that works. Real science. Real support. Real results. Visit PHDWeightloss.com or call 864-644-1900 and mention Mike and Mark. Relief FactorAre you in pain? When it comes to supplements, two things matter most: that it works, and that you can trust it. That's why we love Relief Factor. Get relief from pain at relieffactor.com or call 1-800-4-RELIEF to get your 3-Week Starter for $17.76. EKKL StreamingThe stars of Duck Dynasty, Willie & Korie Robertson, teamed up with EKKL to create a brand new, one-of-a-kind streaming platform built around faith and family with movies, TV series and podcasts. The best part? You get all of this for only $5.83 a month. Sign up for EKKL at ekkl.com today!See omnystudio.com/listener for privacy information.

Mike and Mark examine the depths of leftist rhetoric in celebrating violence toward their opponents; What is driving the empathy and outright support for Lindsay Clancy in her murder trial?; Self-driving technology is winning over skeptics. ----M and M EXTRA: Two iconic talk radio hosts. One unfiltered daily conversation. No scripts. No spin. Just Mike Gallagher and Mark Davis breaking down the news the way it should be — with decades of experience and zero apologies. If you love smart unscripted talk show chemistry, you’re in the right place. -----Thank you to our amazing sponsors for supporting the podcast! PreBorn saves babies and souls, by providing free ultrasounds to women. When a young woman SEES her baby and HEARS her baby’s heartbeat, it doubles the chance she’ll choose LIFE!  Whether you want to save one baby or hundreds…you are just a call or a click away. Call 833-850-BABY that’s 833-850-2229 OR go to preborn.com/MMExtra! PHD Weightloss:If you're ready to finally take control of your health, PHD Weightloss has the plan that works. Real science. Real support. Real results. Visit PHDWeightloss.com or call 864-644-1900 and mention Mike and Mark. Relief FactorAre you in pain? When it comes to supplements, two things matter most: that it works, and that you can trust it. That's why we love Relief Factor. Get relief from pain at relieffactor.com or call 1-800-4-RELIEF to get your 3-Week Starter for $17.76. EKKL StreamingThe stars of Duck Dynasty, Willie & Korie Robertson, teamed up with EKKL to create a brand new, one-of-a-kind streaming platform built around faith and family with movies, TV series and podcasts. The best part? You get all of this for only $5.83 a month. Sign up for EKKL at ekkl.com today!See omnystudio.com/listener for privacy information.

Mike and Mark examine the depths of leftist rhetoric in celebrating violence toward their opponents; What is driving the empathy and outright support for Lindsay Clancy in her murder trial?; Self-driving technology is winning over skeptics. ----M and M EXTRA: Two iconic talk radio hosts. One unfiltered daily conversation. No scripts. No spin. Just Mike Gallagher and Mark Davis breaking down the news the way it should be — with decades of experience and zero apologies. If you love smart unscripted talk show chemistry, you’re in the right place. -----Thank you to our amazing sponsors for supporting the podcast! PreBorn saves babies and souls, by providing free ultrasounds to women. When a young woman SEES her baby and HEARS her baby’s heartbeat, it doubles the chance she’ll choose LIFE!  Whether you want to save one baby or hundreds…you are just a call or a click away. Call 833-850-BABY that’s 833-850-2229 OR go to preborn.com/MMExtra! PHD Weightloss:If you're ready to finally take control of your health, PHD Weightloss has the plan that works. Real science. Real support. Real results. Visit PHDWeightloss.com or call 864-644-1900 and mention Mike and Mark. Relief FactorAre you in pain? When it comes to supplements, two things matter most: that it works, and that you can trust it. That's why we love Relief Factor. Get relief from pain at relieffactor.com or call 1-800-4-RELIEF to get your 3-Week Starter for $17.76. EKKL StreamingThe stars of Duck Dynasty, Willie & Korie Robertson, teamed up with EKKL to create a brand new, one-of-a-kind streaming platform built around faith and family with movies, TV series and podcasts. The best part? You get all of this for only $5.83 a month. Sign up for EKKL at ekkl.com today!See omnystudio.com/listener for privacy information.

This week on Autonomy Markets, Grayson Brulte and Walter Piecyk discuss Waymo's California regulatory moat, Cybercab's pending commercial launch and Uber's series of supervised launches in Europe and Dubai.In California, Waymo is completing 1.4 million fully autonomous rides per month, a 10X growth rate over two years. Additionally, the CPUC (California Public Utilities Commission) granted Waymo commercial driverless expansion authority across 40,000 square miles, covering roughly 25 million California residents and giving Waymo an estimated two-year regulatory moat over competitors.Waymo also opened service to all riders in Houston, its 10th market, with San Antonio likely to follow, and launched the Zeekr-built Ojai to all riders across San Francisco, Los Angeles and Phoenix while introducing its own custom silicon chip to boost compute performance and drive down vehicle costs.While Waymo continues to scale, Tesla is preparing for a Cybercab commercial launch in Austin following first-responder training sessions with local fire departments. Then there is Uber, which is focused on expanding globally.In Dubai, Baidu's Apollo Go went live on the Uber app, allowing riders to explicitly select an autonomous ride or be assigned one when booking Comfort or UberX.In London, Wayve and Uber launched early trip access using supervised Ford Mach-E vehicles, though debate remains over who will secure the critical Autonomous Passenger Service (APS) permit. Pony AI also touted its growing Uber partnership during earnings, though its planned European expansion faced scrutiny over ODD limitations and safety driver supervision.In sidewalk delivery, Avride is expanding its autonomous campus delivery fleet to 1,000 bots across 25 universities this fall. Following its split from Uber, Serve Robotics quickly pivoted by cutting deals with DoorDash and Grubhub, expanding into San Jose and Washington, D.C., while promoting its Beacon platform to potentially disintermediate traditional delivery platforms for local restaurants.Episode Chapters00:00 Waymo Racks Up the Miles in California07:02 Airports Matter, In California Permits Matter More09:50 Waymo Opens Houston to All Riders, San Antonio Likely Next13:34 Waymo Opens Ojai to All Riders16:21 Waymo's Declining Costs19:28 New York City Does Not Want Robotaxis21:28 The 100 Car Benchmark25:05 Pony AI's European Expansion 28:09 Baidu Apollo Go Goes Live in Dubai30:31 Wayve and Uber Launch Supervised in London. Who Gets the APS Permit?36:17 Avride Expands Campus Delivery Bots37:07 Serve Robotics Expands with DoorDash, Grubhub and Beacon41:27 Next WeekFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

This week on Autonomy Signals presented by KPMG, Grayson Brulte and Rob Grant discuss Tesla preparing to launch the steering wheel-free and pedal-free Cybercab in Austin, Waymo unlocking commercial driverless service across 18 California counties, and SoftBank leading a $200 million Series A in Gravis Robotics that values the company at $1 billion.As Tesla prepares for the commercial launch of Cybercab in Austin, the company will most likely self-certify the Cybercab to the Federal Motor Vehicle Safety Standards, a path that carries no caps on production volume and, if NHTSA does not intervene, clears the way for Gigafactory Texas to produce more than 125,000 Cybercabs a year, creating a manufacturing moat no competitor can currently match.While Tesla readies the Cybercab, the CPUC (California Public Utilities Commission) approved Waymo's Tier 2 advice letter authorizing paid driverless rides across 18 California counties from Sacramento to San Diego, covering roughly two-thirds of the state's population.With Waymo currently operating 100% of the 1.4 million monthly driverless rides in the state and no other operator holding a driverless deployment permit, Waymo's regulatory lead in California is measured in years, though the CPUC's phased rollout framework may open a door for Zoox or Tesla to apply for the entire state at once.As Waymo consolidates the Golden State, SoftBank led a $200 million Series A in ETH Zurich spin-out Gravis Robotics, the largest Series A in construction robotics history, valuing the company at approximately $1 billion. Gravis' retrofit system bolts onto legacy excavators from Caterpillar, Komatsu, Volvo and John Deere, and SoftBank is betting that monetizing the existing $1 trillion install base of heavy machinery through rental companies, rather than requiring fleet replacement, is a software-multiple opportunity and not an industrial hardware play.Episode Chapters 0:00 KPMG Sponsor Introduction 01:50 Signal 1: Cybercab Launch is on the Horizon 39:06 Signal 2: Waymo's Golden State Robotaxi Monopoly1:11:11 Signal 3: SoftBank's $200M Bet on Gravis RoboticsFollow The Road to Autonomy Indices--------About The Road to AutonomyThe Road to Autonomy is the leading applied intelligence platform covering the convergence of automation, autonomy, and the Autonomy Economy.™.Through our podcasts, newsletter, and proprietary applied intelligence, we set the narrative for institutional investors, industry executives, and policymakers navigating the convergence of automation, autonomy, and economic growth.Join institutional investors and industry leaders who read This Week in The Autonomy Economy every Sunday. Each edition delivers exclusive insight and commentary on the autonomy economy, helping you stay ahead of what's next.Sign up for This Week in The Autonomy Economy newsletterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Andrew Macdonald (Mac) is the longest-serving employee at Uber. Today, he is the President and COO. No one on the planet has spent more time mastering ride-sharing than Mac. Uber now does 300M rides per week, has 200M users, and is one of the most recognised brands on the planet. Mac never does interviews and so this was a rare look behind the scenes at the Uber machine.  AGENDA: 05:39 – How was Mac the only survivor from the Travis era? 10:39 – How does Uber decide what to include in Uber One membership? 11:45 – How does Uber decide which new products to pursue? 16:08 – What does Uber need to do to reach 500 million users? 20:37 – Why Uber was right to focus on its core business and divest autonomy? 24:08 – Why India and Brazil will delay Uber's autonomous future? 28:34 – The craziest story from Uber's battle in China 29:27 – Why Travis Kalanick believed money was the moat? 31:17 – Was Uber structurally disadvantaged in China from day one? 35:48 – Inside Uber's SWAT team of its 30 best AI engineers 37:55 – How companies need to extract real efficiency from AI? 42:00 – Will Uber have more or fewer employees in five years? 43:52 – Will companies that do not work with frontier models be disaggregated? 46:02 – Will AI agents disaggregate Uber's interface and customer relationship? 48:26 – Why Brian Chesky was right: chat is not the best interface for everything 55:52 – Why it is bullshit to say DoorDash would not be number one if Travis were still CEO 59:13 – The single biggest lesson from Travis Kalanick 1:00:07 – The second biggest lesson from Travis Kalanick  

Amazon's autonomous vehicle company, Zoox, can now roll out 2,500 of its vehicles annually in the next two years. Are more robotic cars coming, or will there be more speed bumps? But first, we'll look at the market impact of the Fed's decision to keep interest rates steady in our Weekly Wrap. Plus, the effect of rising health insurance costs for employers, a retiree returning to the workforce for a family business, and a look at how Boulder, Colorado is making room for the Sundance Film Festival.Every story has an economic angle. Want some in your inbox? Subscribe to our daily or weekly newsletter.Marketplace is more than a radio show. Check out our original reporting and financial literacy content at marketplace.org — and consider making an investment in our future.Read the stories in today's episode:Weekly Wrap: Kevin Warsh's dilemma on interest ratesRising health insurance costs may take a bite out of your paycheckLeaving retirement for a new family businessAmazon's self-driving Zoox taxis are about to hit the roadsWhen a legendary film festival comes to Colorado, will homeowners open their doors?