Best of LinkedIn: Future Mobility & Market Evolution CW 39/ 40

00:00:00: Brought to you by Thomas Allgaier and Frennis, this edition highlights key LinkedIn posts on future mobility and market evolution in weeks thirty-nine and forty.

00:00:08: Frennis supports enterprises with market and competitive intelligence, decoding disruptive technologies, customer needs, regulatory change and competitive moves.

00:00:15: so product teams and strategy leaders don't just react but shape the future of mobility.

00:00:21: Welcome back to the Deep Dive.

00:00:22: If you're keeping an eye on the mobility industry, well, the last few weeks have felt like a real acceleration.

00:00:27: We're seeing policy shifts, big moves in automation, vehicle architecture.

00:00:31: It's a lot.

00:00:32: So our goal today is simple, give you that high value shortcut.

00:00:35: We've gone through the key insights shared on LinkedIn calendar weeks, thirty nine and forty.

00:00:39: And we've pulled out really three big storylines.

00:00:41: There's the robotaxi race and, you know, the economic disruption it's causing.

00:00:45: Then there's this new urgency around urban mobility and policy.

00:00:49: And finally, beneath it all, the technology stack war fought with AI and data.

00:00:53: And they're all connected, aren't they?

00:00:54: What we really saw consistently in the sources was, first, robotax is moving from just a concept to actual commercial reality.

00:01:02: maybe faster than people thought.

00:01:04: Second, the conversation around micro-mobility.

00:01:06: It's growing up.

00:01:07: It's less about logistics now and more about core city strategy.

00:01:11: And third, businesses, especially fleets, are getting really pragmatic.

00:01:15: They want integrated software, solid data foundations to, you know, manage everything effectively.

00:01:19: Okay, let's untack that, starting with maybe the most disruptive bit.

00:01:23: The RoboTaxi acceleration, everyone's talking market potential, sure, but what kind of disruption are we actually seeing on the ground?

00:01:30: Well, the numbers are.

00:01:31: Yeah.

00:01:32: they're potentially game-changing for global transport.

00:01:35: I mean, if you look at the projections, Lucas Neckerman's analysis, for instance, he's seeing autonomous ride services alone hitting maybe five hundred and fifty billion dollars in revenue.

00:01:45: Just

00:01:45: the rides?

00:01:46: Just

00:01:46: the rides.

00:01:47: And then if you broaden that out, include logistics, mining, other off-highway stuff, you're potentially talking over a trillion dollars within the decade.

00:01:55: That scale is just staggering.

00:01:59: But is that just, you know, wishful thinking or is there a real economic grounding for it right now?

00:02:03: Oh, it's deeply rooted in the economics.

00:02:05: The absolute core driver is cutting out the labor cost.

00:02:10: Alcatra pointed this out.

00:02:11: labor can be what almost seventy five percent of a typical ride hail company's costs.

00:02:16: right you take that away and suddenly robo taxes can undercut traditional services by a huge margin.

00:02:21: and that's where the different competitive approaches really start to bite isn't it?

00:02:25: we saw Phillip Pages highlighting Tesla's model very aggressive pricing

00:02:29: because they control the whole stack

00:02:31: exactly the software the vehicle itself the brand so they can potentially offer a ride for like a third of what an Uber might cost.

00:02:40: Yeah.

00:02:40: That pressure alone, it could force mass adoption pretty quickly, you'd think.

00:02:44: Absolutely.

00:02:44: And then when you look at how they're deploying, we're seeing sort of two main strategies playing out.

00:02:48: On one hand, you've got Waymo.

00:02:50: They're showing real operational maturity now.

00:02:53: Mark Byershooter shared data.

00:02:54: they did two point two million rides in just three months.

00:02:57: Wow.

00:02:57: And the safety numbers are looking good too.

00:02:59: Very compelling.

00:03:00: They're reporting something like eighty eight percent fewer crashes compared to human drivers.

00:03:05: And Henrik Langren added that in San Francisco, maybe twenty five percent a quarter of taxi rides are already fully autonomous.

00:03:12: So it's not just a tiny pilot anymore.

00:03:14: It's a real service.

00:03:15: It's becoming the benchmark for scaling up.

00:03:17: Yeah.

00:03:17: But then you have this other approach, more specialized.

00:03:20: Think Amazon backed Zooks.

00:03:22: They launched quietly in Las Vegas.

00:03:24: Gaurav Menderada highlighted this.

00:03:27: Their vehicle is purpose built, right?

00:03:30: As Michael Gansler described, no steering wheel seats facing each other.

00:03:33: It's designed purely for the ride.

00:03:35: Okay, so way most scaling with retrofits, proving the operations versus zoops building a dedicated vehicle and experience from scratch, that's a really interesting contrast in strategy.

00:03:45: And it's not just a U.S.

00:03:46: race anymore, is it?

00:03:47: Carrington Mellon mentioned trials kicking off in Qatar with Pony.ai.

00:03:51: And back in the

00:03:52: U.S.,

00:03:52: more competition, main mobility working with Lyft in Atlanta.

00:03:55: Daniel Abreu Marques noted that.

00:03:56: It's heating up.

00:03:57: And that global race really throws the core technology battle into sharper leaf.

00:04:03: NeuroJS detailed as well.

00:04:04: It's that philosophical fight, isn't it?

00:04:06: Tesla's vision-only system versus, you know, the sensor redundancy path that Waymo, Zooks, and others are taking.

00:04:13: Is there a clear winner emerging there yet?

00:04:15: Not obviously, no.

00:04:17: But it seems the real advantage, the true moat, is just the sheer scale of real-world data you can gather and learn from, regardless of the exact sensor setup, which interestingly ties back to something quite profound.

00:04:29: Dr.

00:04:30: Gabriel Cyberth raised.

00:04:31: He basically said that solving generalized self-driving, well, it's like solving a big chunk of artificial general intelligence.

00:04:37: You're teaching a machine to handle the sheer unpredictability of the real world.

00:04:41: That jump from complex automation to basically AGI.

00:04:45: It does give you a bit of whiplash, but while the tech guys are chasing AGI, the human side, the governance side, it feels like it's lagging way behind.

00:04:52: Way behind.

00:04:53: John DeWald brought up some really serious immediate worries.

00:04:56: You've got maybe three to four million professional drivers whose jobs are directly on the line.

00:05:01: He used a personal story.

00:05:03: about his driver, Mike, to really ground this huge shift in real human impact.

00:05:09: We can't just brush past the workforce disruption.

00:05:11: That

00:05:11: human cost, yeah, it forces a pause.

00:05:13: And the legal side seems just as messy.

00:05:15: Kevin, Irvin, Kelly asked that killer question.

00:05:17: A driverless car breaks a rule who gets the ticket.

00:05:20: Right now, it seems like the answer is often just a software update.

00:05:24: Not an actual fine.

00:05:26: That just doesn't seem sustainable as these things scale up, does it?

00:05:29: We need clearer rules for automation.

00:05:31: Definitely.

00:05:32: Governing a technology that learns and operates outside our current legal boxes, that's the massive challenge right there.

00:05:38: Okay, so regulation is clearly playing catch up with robotexes, but that governance gap feels just as important when we shift to our next big theme.

00:05:46: Managing transport in cities, specifically micromobility and city policy.

00:05:51: Yeah, and maybe the first step for better governance is just changing how we talk about it.

00:05:57: Rina Mahajan argued pretty convincingly that we should normalize things like bikes, scooters, walking, make them the baseline for getting around town.

00:06:05: Her idea was maybe we should call big private cars macromobility.

00:06:11: Put the burden of justification on the mode that takes up the most space.

00:06:15: I like that reframing.

00:06:15: It's clever.

00:06:16: Yeah Frank Eldorf seems to agree suggesting we could call the sector hyper mobility.

00:06:21: He sees it as really spearheading urban change by twenty-thirty, but only if it's integrated systemically.

00:06:27: Integration, that's the word.

00:06:28: Which brings us straight to Maas, mobility as a service.

00:06:32: Now, that initial idea of one app bundling everything, it kind of fizzled out financially, didn't it?

00:06:36: Yeah.

00:06:37: Lars Christian Grudmulsen pointed to tier data showing people just weren't really using those dedicated Maas apps much.

00:06:43: So the bundled app failed.

00:06:44: The consumer-facing bundled app?

00:06:46: Yeah, seems like it.

00:06:47: People tend to use transit apps for transit, shared mobility apps for shared mobility.

00:06:51: But the goal of integration?

00:06:53: That hasn't gone away.

00:06:54: Actually, we saw a big step forward in Germany recently.

00:06:57: Berlin's BVG, Hamburg's Hosea HBOD on Munich's MVG.

00:07:02: They signed a strategic partnership to push integrated platforms together.

00:07:05: So the focus shifts from the front end app to the back end coordination.

00:07:09: Okay, makes sense.

00:07:10: And for that coordination to work, cities need some levers, right?

00:07:13: Policy levers.

00:07:14: This is where Tomasca Borre's analysis on pricing public space comes in.

00:07:18: He talked about a potential movement fee.

00:07:20: Right.

00:07:21: And this is fascinating.

00:07:22: It lets cities use prices as a signal.

00:07:24: In Vienna, for example, they estimated a floor cost might be around, say, thirty cents a mile.

00:07:29: That fee tells users the real cost of using that road space.

00:07:33: And crucially, you can use it to penalize inefficiency, like deadhead miles, vehicles driving empty.

00:07:38: Ah, okay.

00:07:39: And you can use it to positively incentivize things you do want, like pooling rides, or using robotaxis to connect to public transport.

00:07:45: Using economics to shape behavior, essentially.

00:07:48: Because without that kind of strategic thinking, you could end up accidentally killing off useful services.

00:07:53: Martin LaFerronk flagged this contradiction happening in Brussels.

00:07:56: Exactly.

00:07:56: Shared micro-mobility is seen as vital there, but it's also facing potential extinction.

00:08:01: because of pushback, new taxes like a per trip tax in one area, Etterbeek, and policies that just focus on the negatives, the nuisances instead of the overall benefits.

00:08:11: So policy needs to be smarter, more strategic, not just reactive.

00:08:16: Okay, let's pivot to our third and final theme.

00:08:19: We're shifting focus now to the back end, the enterprise side.

00:08:23: fleet digitization, AI, and vehicle architecture.

00:08:26: This is about operational efficiency.

00:08:29: Yeah, and the biggest trend here for fleets seems to be integration.

00:08:32: They're moving past relying on lots of separate dashboards.

00:08:35: The real priority now is building integrated data pipelines, things that directly help with, you know, smarter dispatch, better vehicle uptime, smoother driver workflows.

00:08:43: We're seeing early wins, particularly in last mile delivery, where they're linking route planning directly with EV charging needs.

00:08:50: And AI's role in operations is becoming much clearer, much more measurable.

00:08:55: Giuliano Legore mentioned its proven value in predictive maintenance catching problems before they cause breakdowns.

00:09:00: Definitely.

00:09:01: But probably the most striking, really specific example of AI in action came from a perva mancat.

00:09:07: He talked about WebExpress, a logistics firm in India.

00:09:10: They've deployed an AI agent that actually makes phone calls to drivers.

00:09:14: in Hindi.

00:09:15: It calls them to confirm where they are and why they might be delayed.

00:09:17: Wait, and AI is calling drivers and having a conversation about schedules.

00:09:21: Exactly.

00:09:22: It's handling tasks twenty-four seven that human teams just don't have a bandwidth for.

00:09:26: That's a perfect example of using AI for real practical operational value today, not just some far off future vision.

00:09:34: That level of software capability, it must be changing how the vehicle manufacturers, the OEMs, see their own role.

00:09:41: Augustine Friedl observed something interesting here that OEMs are focusing hard on things like OTA updates and modular middleware, because maybe their bigger dreams of owning entire mobility ecosystems are sort of fading.

00:09:52: I think that's a key shift, yeah.

00:09:54: It feels like they're potentially being pushed down the value chain a bit.

00:09:58: Instead of running the whole show, maybe their future for many is being a vehicle partner, an integrator for the tech players.

00:10:05: They face this choice.

00:10:07: Do they own the core software architecture, or do they end up conceding that crucial value to others?

00:10:14: And of course, as everything gets more connected, more software driven, the risks go up too, right?

00:10:18: Security risks.

00:10:20: S.D.

00:10:20: Peschen highlighted that Renault UK data breach.

00:10:24: It didn't even happen directly, it was through a third-party supplier.

00:10:27: It's a huge wake-up call for the whole industry, the whole supply chain.

00:10:31: You build these complex interconnected systems and suddenly your attack surface is massive.

00:10:35: Digital resilience isn't just a feature anymore, it's fundamental to even staying in business.

00:10:41: If you enjoyed this deep dive, new additions drop every two weeks.

00:10:44: Also check out our other additions on electrification and battery technology, next-gen vehicle intelligence, and commercial fleet insights.

00:10:51: And just to leave you with one final thought to chew on, let's circle back to robotaxes and that's safety data.

00:10:58: We mentioned Waymo's numbers looking significantly better than human drivers, fewer crashes, fewer injuries.

00:11:03: It really forces a tough question on regulators and on the industry itself.

00:11:07: Should the priority be net progress, adopting tech that's already statistically safer, even if imperfect?

00:11:14: Or should we hold out for perfect safety, knowing that delay might cost lives that could have been saved by the safer than human tech we have now?

00:11:20: That really is the core dilemma for governing AI, isn't it?

00:11:24: Balancing those proven statistical gains against our societal desire for absolute perfection.

00:11:29: Thank you for joining us for this deep dive into the latest market intelligence.

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