Best of LinkedIn: Autonomous & Mobility Markets CW 37/ 38

00:00:00: provided by Thomas Allgaier and Frennus, based on the most relevant LinkedIn posts about autonomous and mobility markets in calendar weeks thirty-seven and thirty eight.

00:00:09: Frenness supports tier one automotive suppliers with early stage market validation for their R&D efforts by combining secondary research direct OEM expert interviews and facilitated customer meetings.

00:00:21: You can find more info in the description.

00:00:22: Yeah, and we are really thrilled to dig into this today because if you're a mobility professional tuning-in... ...you know The Landscape is just completely shifting under our feet right

00:00:31: now!

00:00:31: Absolutely so.

00:00:32: Our mission for This Deep Dive Is To Cut Through All The Noise.

00:00:36: We've curated the absolute most critical insights directly from a stack of LinkedIn posts by the professionals who were actually building this future Right.

00:00:43: no fluff Just the mechanics Of what's Actually Happening Out There.

00:00:46: And We'll Cover Everything From Harsh Commercial Realities Running Robo- taxes to the really tricky policy issues around safety.

00:00:55: Yeah.

00:00:55: And then we'll wrap up by looking at how public transit and microability are physically reshaping our cities, yeah?

00:01:01: So to kick things off let's look at the biggest narrative shift in the industry right now

00:01:06: which is basically that The Tech demo phase is over

00:01:09: exactly.

00:01:10: We're no longer watching these engineering teams.

00:01:12: just try To prove a car can drive itself.

00:01:15: the tech Is you know largely assumed to work At this point like the new battleground is proving That You Can operate These fleets Profitably at A massive volume.

00:01:24: Yeah, and that shift of volume operations is happening fast.

00:01:28: Right?

00:01:28: Just look at Austin Texas Tesla's cyber cabs out there navigating downtown right now.

00:01:34: No steering wheel no pedals Which

00:01:35: is still a wild concept to see.

00:01:37: on public roads It

00:01:38: is!

00:01:38: And there was this really fascinating account from an industry observer Han Shen who actually took a ride in one.

00:01:44: Oh yeah I saw them.

00:01:45: What stood out for him?

00:01:46: wasn't it some thrilling futuristic roller coaster?

00:01:49: He said the ride felt remarkably normal just smooth and uneventful.

00:01:55: Which is exactly what you want from a taxi!

00:01:57: Exactly, but here's the critical detail.

00:02:00: for anyone tracking the unit economics The fare was roughly seventy-five percent less than a comparable Uber trip.

00:02:07: Seventy-five percent.

00:02:08: Wow, I mean that reduction is the exact inflection point where a robo taxi goes from being a novelty to just A basic urban utility.

00:02:15: right but you know focusing Just on hardware or the price point kind of misses The bigger strategic move happening behind the scenes.

00:02:23: how so

00:02:24: well?

00:02:24: You have to look at the deployment models.

00:02:26: AtlasBerry actually spotted something super revealing on the day that vehicle launched.

00:02:32: Tesla had quietly published a sign-up form on their site titled, Help Us Build Our Robotaxi Network.

00:02:37: Oh wait so they are crowdsourcing the fleet operations?

00:02:40: Basically yeah!

00:02:41: The forum has drop down menus asking third parties where they deploy fleet purchases or set up mobility hubs.

00:02:47: it's a massive pivot toward an asset light model

00:02:49: which completely flips the traditional automaker business model

00:02:53: Exactly.

00:02:53: I mean, holding tens of thousands of depreciating cars on your balance sheet is a terrible way to scale.

00:02:59: the goal Is clearly to sell hardware.

00:03:01: let local operators manage The physical burden Of the vehicles and then Tesla just skims A recurring software fee off every ride.

00:03:09: Wow so you Just offload all the operational friction Precisely And we are actually seeing The physical infrastructure for that Friction being built in real time.

00:03:19: Shari Mahajan pointed out that Tesla recently got zoning approval for a nearly one acre site near the San Antonio airport.

00:03:25: Oh, really?

00:03:26: Yeah

00:03:26: and The designation is literally A Robo Taxi Parking And Dispatch Hub.

00:03:32: Okay So let's unpack this because it totally reminds me of the airline industry.

00:03:35: oh

00:03:35: That's a good comparison

00:03:36: right like a Boeing seven thirty-seven Is this marvel of engineering.

00:03:40: but It's an absolute financial liability when its just sitting parked at a gate.

00:03:44: Plains only make money in the air Right.

00:03:47: The turnaround time is everything.

00:03:48: Exactly, yeah so it's a safe to say the competitive moat in mobility has shifted away from the AI and directly into depot management?

00:03:55: Oh absolutely that's the new reality!

00:03:57: The driving software is rapidly becoming table stakes...the company that wins Is the one that masters the pit stop.

00:04:03: Yeah Just look at Steven Snyder's analysis of Weymer's new Ojai vehicle That just launched in Phoenix.

00:04:10: The engineering priorities there speak volumes.

00:04:14: What are they prioritizing?

00:04:15: Well, the marquee features aren't just bigger screens for the passengers.

00:04:18: They're pushing faster charging architectures modular repair designs where tech can swap a broken sensor in minutes and interiors that are significantly easier to clean.

00:04:28: So The end user of this design isn't even passenger it's depot maintenance crew

00:04:33: Completely!

00:04:34: Because a robotaxi sitting at bay waiting for an interior detail is stranded asset.

00:04:39: Depo turnaround time is ultimate metric now.

00:04:42: And this operational scaling isn't just an American thing either, it's a global land grab.

00:04:46: Arini Zafferatu highlighted his major partnership between Bolt and Lucid.

00:04:51: Oh yeah the European deployment

00:04:53: Yeah.

00:04:53: they are planning to deploy at least twenty five thousand autonomous vehicles across Europe Scaling up to one hundred thousand by twenty thirty-five.

00:05:00: That is massive scale

00:05:02: It Is.

00:05:03: But what really compelling as mechanism?

00:05:05: They aren't just taking an American vehicle and shipping it over, they are defining the hardware and software requirements together from day one specifically to fit European infrastructure.

00:05:15: Which is absolutely essential!

00:05:17: I mean you can't just shoehorn a car that was optimized for a Texas highway into the center of Paris

00:05:22: Right The streets or narrower?

00:05:24: The pedestrian behavior's different Exactly...the

00:05:27: regulatory frameworks around data Are completely different too.

00:05:31: It requires a localized approach From the ground up.

00:05:34: It's just incredible to think about this scale, you know a hundred thousand vehicles planned for one network when This whole industry was basically A science experiment not that long ago.

00:05:43: Oh yeah Donnie sir Cigara had a brilliant historical take on this.

00:05:47: if You trace the talent roots of Waymo Aurora Zooks almost every major player The lineage goes back to one single event in the Mojave Desert.

00:06:00: Yes, which by traditional metrics was an absolute failure.

00:06:04: Fifteen driverless vehicles entered and zero finished.

00:06:07: Didn't the best one only go like seven miles?

00:06:09: Seven

00:06:09: miles from catching fire, yeah.

00:06:11: And today we're dissecting the unit economics of a massive continent-wide deployment.

00:06:16: That's wild

00:06:17: But it proves that talent concentration is the ultimate seed for innovation.

00:06:22: Putting brilliant people in a room to solve an impossible problem works.

00:06:26: Yeah.

00:06:26: so the talents solved the driving problem and now the operations teams are figuring out the depots but once you dispatch these vehicles they collide with the real

00:06:35: world.

00:06:36: Right, and the real-world isn't a test

00:06:37: track Not at all!

00:06:38: It's chaotic it's regulated And its full of unpredictable humans.

00:06:44: Which brings us to our next theme Policy safety in The Human Element.

00:06:48: This is where friction is highest right now.

00:06:50: Definitely.

00:06:51: Nicholas M. Shailen shared this genuinely wild incident from San Francisco.

00:06:55: that perfectly highlights this friction.

00:06:57: Oh...the Waymo Incident.

00:06:58: Yeah So four o'clock in the morning A waymo robot taxi abruptly pulled itself over dialed police and essentially turned into its own passengers.

00:07:06: The internal monitoring systems flag two miners in the backseat with a loaded, un-serialized ghost gun plus marijuana and mace.

00:07:15: See on one hand you look at that and say the system function flawlessly.

00:07:19: it identified a severe public safety threat And neutralized

00:07:24: right without risking to human driver's life.

00:07:26: Exactly, but this raises an important question about surveillance.

00:07:30: I mean A private company algorithm recorded.

00:07:32: The inside of a vehicle You paid for flagged a threat and initiated a police response Without a warrant without a warrant and without a Human officer making a judgment call.

00:07:42: That camera rides with every passenger on every trip, so we really have to ask where is the threshold?

00:07:48: Where an algorithm decides your behavior.

00:07:51: Is a police matter?

00:07:52: well okay I have to challenge you on that framing.

00:07:54: Okay because here's what it gets really interesting.

00:07:57: Emily Yates framed this entirely differently through The lens of mobility equity.

00:08:02: she pointed To A twenty-twenty two World Bank study On public transit in Delhi.

00:08:06: oh

00:08:06: the harassment statistics.

00:08:08: yes the numbers are staggering.

00:08:10: Eighty-eight percent of women's surveyed reported experiencing harassment on public transport and even worse, fifty two percent turned down education or jobs just because the commute was unsafe.

00:08:22: That is a devastating loss of human potential!

00:08:25: Exactly so.

00:08:26: while we debate algorithmic privacy for a massive demographic, the RoboTaxi removes that stranger danger dynamic completely.

00:08:34: I see what you mean.

00:08:35: Yeah an enclosed monitored environment isn't a dystopian surveillance pod for them.

00:08:39: it's a sanctuary.

00:08:41: It completely changes the calculus of who gets to move safely through city.

00:08:44: That is very fair counterpoint...it does expand secure mobility.

00:08:48: but we also have look at systemic mismatch on who actually get this technology right now.

00:08:53: What do ya mean?

00:08:54: Well, David Zipper highlighted a really glaring geographic disparity.

00:08:58: If the promise of AVs is saving lives by eliminating human error The data shows they're operating in the wrong places.

00:09:03: Oh so

00:09:04: A Bloomberg analysis showed that rural areas like Mississippi have crashed death rates.

00:09:09: That dwarf dense urban centers Like New York or Boston.

00:09:12: Yet you don't see robotexies driving around rural dirt roads

00:09:15: Exactly!

00:09:16: They are exclusively geofenced In profitable urban centers.

00:09:20: Because of the commercial realities we talked about earlier, a RoboTaxi can't turn a profit if it only gets three rides per day in rural town.

00:09:28: So technology systematically ignores geography where it could mathematically save most lives?

00:09:34: Because societal need and commercial incentive are fundamentally misaligned here.

00:09:39: That's fascinating point.

00:09:40: By the way If you're finding this breakdown of mobility industry valuable Make sure to hit subscribe.

00:09:46: so catch our future deep dives

00:09:48: Definitely.

00:09:49: And getting back to those market dynamics, regulators are really stepping up to handle this influx.

00:09:54: Roger C Langtaught noted that the Nevada Transportation Authority just approved up to seventy-one hundred robo taxi permits.

00:10:01: Wait!

00:10:01: Seventy one hundred permits in one state?

00:10:04: In a single sweep Pesla got five thousand and the rest went to Waymo, Zooks & Uber.

00:10:09: That is a staggering amount of software hitting physical infrastructure all at once.

00:10:13: It Is and it shifts a massive operational burden to the local governments.

00:10:18: Imagine thousands of robotaxes trying to navigate airport drop-offs or casino valets without human driver make eye contact with?

00:10:26: It's an instant curb management crisis

00:10:28: Exactly!

00:10:29: Meanwhile, across the pond The UK is taking a vastly different approach.

00:10:33: Lawrence Penn highlighted their draft standard for self driving vehicles.

00:10:37: Instead demanding rigid numerical target they are setting a standard of careful & competent.

00:10:44: That sounds incredibly subjective for a piece of software.

00:10:47: It does sound subjective, but it actually mimics how a human driving examiner evaluates the student.

00:10:52: It looks at overall behavioral competence rather than isolated metrics—it's much more nuanced way to regulate AI.

00:10:59: Interesting!

00:10:59: So we've got tech scaling and policies evolving... ...but autonomous vehicles are just one layer of bigger puzzle To truly fix cities.

00:11:07: professionals look at optimizing public transit and micro mobility too

00:11:11: Absolutely.

00:11:12: Sometimes optimizing a city means you have to physically reclaim street space.

00:11:16: Yeah, and Glasgow is doing exactly that.

00:11:19: John Pinkard brought up this fascinating infrastructure choice.

00:11:22: The Glasgow City Council Is considering completely removing the M-AIDS Woodside Viaducts.

00:11:28: Really?

00:11:28: Just tearing them down

00:11:29: just tearing them Down.

00:11:30: historically they'd either repair it for two hundred million pounds or replace It For half A billion but total removal is actually the cheapest option at Roughly one hundred and twenty five million Pounds.

00:11:40: That's a huge shift in urban planning.

00:11:42: It makes me think of deleting legacy code and software engineering, like sometimes the best upgrade is just removing what's no longer serving you.

00:11:51: tearing down that concrete shifts the focus for managing traffic to actual urban placemaking.

00:11:57: I love that analogy.

00:11:58: And if we connect this to the bigger picture, optimizing the city also means optimizing the data that runs it right?

00:12:05: Breed Daman shared this incredible example involving Sweden's Snelltoget rail network.

00:12:10: They wanted to launch a new international route into Norway

00:12:14: and usually cross-border integration is in nightmare

00:12:16: Exactly.

00:12:17: Custom API's constant breaking, but because they used a standardized European data format called NetEx it was basically frictionless.

00:12:26: They uploaded a new timetable on a Monday and by Tuesday tickets were selling across international platforms.

00:12:33: Wait with zero bespoke integration.

00:12:36: Zero Bespoke Integration.

00:12:37: This is how public transit actually scales.

00:12:40: A universal datagrammer eliminates all that manual configuration.

00:12:45: That is wild.

00:12:46: And we're seeing that same kind of scale hit the micro level of our city streets, too.

00:12:50: Micro-mobility's just exploding.

00:12:52: Sean Madison noted that Chicago's Divi Bike Share topped one million rides in both July and August.

00:12:58: Two Million Rides In two months?

00:13:00: Thats not a fringe alternative.

00:13:01: thats foundational transit Exactly!

00:13:03: The hardware getting smarter to match this scale.

00:13:05: Dimitri Skian has pointed out.

00:13:07: Segway third generation shared eScooter.

00:13:12: It's got advanced writer assistance systems baked in now.

00:13:15: We're talking active blind spot detection, traction control and embedded cameras that detect accident risks in real

00:13:20: time.".

00:13:21: It is incredible how much computational power they can pack into a standing scooter!

00:13:26: And speaking of form factors... Andrew Firmstone Williams shared great insight on cargo bikes.

00:13:32: The industry standard was around one point five cubic meters But operators are pushing to two point five cubic meters now,

00:13:39: which fundamentally changes last mile delivery economics.

00:13:42: Right

00:13:42: because you add all that cargo space without increasing the vehicle's footprint much.

00:13:46: It chips away at the need for delivery vans and congested areas reducing both curb friction And emissions.

00:13:53: it all comes back to relentless efficiency whether.

00:13:56: It's a larger cargo bike or standardized rail data Or a Robo taxi depot?

00:14:01: The entire industry is just optimizing for scale

00:14:04: definitely

00:14:05: But as we process all these shifts, I want to leave you with one final profound paradigm shift that Venkatesh Sankaran brought up.

00:14:12: Was

00:14:13: it?

00:14:13: We spend a lot of time debating if robotexes will kill short-haul airline flights but he argues this is the wrong question.

00:14:20: Why is it the wrong question?

00:14:22: Because we keep judging autonomous tech as just a replacement for human driver.

00:14:27: But imagine a vehicle with real comfortable bed.

00:14:30: If you can safely achieve deep sleep while system navigates highway You completely collapse boundary between transportation and rest.

00:14:39: A sleeping robot taxi turns an eight hour drive into zero perceived hours.

00:14:43: Fall asleep in Austin, wake up New Orleans.

00:14:45: The ultimate product isn't driving software.

00:14:48: the night's sleep you get

00:15:10: back.