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Speaker 1: Technology. Hey there, and welcome to Tech Stuff. I am

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Speaker 1: Jonathan Strickland and dig today together again at last, Scott Benjamin. Hey,

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Speaker 1: I'm back joining the show and and I actually have

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Speaker 1: you here talking about cars as usual, but thank you.

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Speaker 1: You know what, I always appreciate the invite. And uh

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Speaker 1: and this one is again right in my wheelhouse here,

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Speaker 1: so yeah, I'm excited about It's where I keep my

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Speaker 1: wheels in the house. Yeah. So we we're talking today

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Speaker 1: about a peculiar event, something that happened in February, and

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Speaker 1: it's just taken me this long to finally get around

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Speaker 1: and addressing it. You might have heard in previous episodes

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Speaker 1: of Tech Stuff about how I would champion the fact

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Speaker 1: that Google with their self driving cars had had enormous

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Speaker 1: success flawless. You might argue success something like one point

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Speaker 1: four five million miles driven without a single accident caused

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Speaker 1: by the autonomous system. That there had been about fourteen

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Speaker 1: or so accidents, but all of those were either the

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Speaker 1: fault of a person manually driving the car in manual

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Speaker 1: mode or another driver colliding with the autonomous car, but

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Speaker 1: never the fall of the autonomous car itself. It was

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Speaker 1: a perfect system until February, and that is the day

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Speaker 1: of what what I'll cahol And I really don't mean

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Speaker 1: to over dramaticize this at all, So maybe I'm titling

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Speaker 1: this episode. I think maybe we should call this the St.

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Speaker 1: Valentine's Day Google self driving Car Massacre. That is an

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Speaker 1: excellent title and definitely not overly dramatic in any way. Well,

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Speaker 1: I mean it's it's also funny because we're recording this, uh,

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Speaker 1: the week after I've gotten back from south By Southwest,

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Speaker 1: and this was a topic that was discussed heavily at

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Speaker 1: south By Southwest because until this incident, it was a

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Speaker 1: very easy sell to say autonomous cars are the way

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Speaker 1: to go. And then this little accident happened, and and

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Speaker 1: it it wasn't terrible. We'll get into the details of

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Speaker 1: the accident. But this little accident happened suddenly it sounded

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Speaker 1: like Google's autonomous car had caused an enormous pile up

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Speaker 1: on the highway. Everyone was much more cautious. So you're

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Speaker 1: maybe not buying my alternate title, then, is that what

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Speaker 1: you're saying? What's your alternate No, No, that I'm totally no.

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Speaker 1: I think it is a massacre, but I think it's

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Speaker 1: a massacre in the sense of the public perception of

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Speaker 1: autonomous cars. I see, okay, yeah, I'm thinking of from

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Speaker 1: a pr stuff. I could I could take this opportunity

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Speaker 1: to gloat and say, ah, they're not as infallible as

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Speaker 1: you thought they're They're not perfect. But but I'm going

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Speaker 1: to take a different stance here in this in this podcast,

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Speaker 1: And and I think that as we as we talk

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Speaker 1: through this, h we're gonna realize that they've been held

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Speaker 1: to a much higher standard than they probably need to

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Speaker 1: be right. And I know that's that's tough to to take,

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Speaker 1: you know, when you when you just hear it that way,

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Speaker 1: But listen to our argument back and forth about this,

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Speaker 1: and and understand that they're being held to perfection when

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Speaker 1: they probably shouldn't be when humans, I mean, we're not perfect.

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Speaker 1: Of course, there's a vast um a chasm between what

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Speaker 1: the standards that they're held to versus the standard that

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Speaker 1: human test drivers are held to. Sure, yeah, if you

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Speaker 1: look at if you look at the standard driving test

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Speaker 1: that you have to pass before you get a license, uh,

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Speaker 1: I would argue most autonomous cars could likely pass such

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Speaker 1: a test as close to flawlessly as you can get,

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Speaker 1: but you don't have to be flawless when you take

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Speaker 1: a driver's test. There's room for you to not completely

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Speaker 1: do something perfectly, like if your parallel parking isn't exactly right,

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Speaker 1: you're gonna get points to die did from your total,

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Speaker 1: but you may still be you know, high enough, score

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Speaker 1: high enough so that you could pass the full driver's test. Exactly.

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Speaker 1: Knock over cone. It's not really a big deal, but

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Speaker 1: an autonomous car knocks over a cone, everybody points at

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Speaker 1: it and says, look at that thing, it's it's pilot junk. Yeah, exactly.

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Speaker 1: It's interesting that you point that out too, because that

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Speaker 1: ties into a different discussion. I saw it south by

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Speaker 1: Southwest that wasn't specifically about autonomous cars. It was about robots.

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Speaker 1: So this is a little bit of a tangent, but

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Speaker 1: it it goes to illustrate the point you just made.

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Speaker 1: So this this UH panel about robots. There was a woman,

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Speaker 1: Layla taka Yama, who used to work for Google X

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Speaker 1: but not in the autonomous car division. She talked about

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Speaker 1: how she ran a a an experiment. She got a

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Speaker 1: guy from Pixar to do a series of very simple

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Speaker 1: animations to show people, the interactions between a robot and

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Speaker 1: a person, and then to judge which robot is considered

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Speaker 1: UH in elligent versus not intelligent. And the whole point

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Speaker 1: of this was to show the differences between succeeding and failing,

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Speaker 1: but giving no indication that the robot understands it succeeded

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Speaker 1: or failed, or building in expressions for the robot to

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Speaker 1: follow a success or failure UH to indicate it quote

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Speaker 1: unquote understands what happened. And it was fascinating because they

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Speaker 1: showed a very simple UH experiment with a robot trying

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Speaker 1: to open the door. And again this is an animation,

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Speaker 1: so there was a different scenarios. There's one where the

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Speaker 1: robot opens the door and the door opens and then

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Speaker 1: the robot just sits there and it's it's done because

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Speaker 1: it's done what it was supposed to do. There's one

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Speaker 1: where the robot opens the door and then kind of

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Speaker 1: perks up like, oh, I did what I wanted to do.

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Speaker 1: There was one where the robot fails to open the

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Speaker 1: door and then does nothing UH. And then there was

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Speaker 1: one where the robot fails open the door and then

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Speaker 1: slumps down a little bit as if to say, oh,

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Speaker 1: I'm disappointed I didn't succeed. They then asked people to

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Speaker 1: judge which robots they thought were the most intelligent, and

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Speaker 1: everyone said the robot that failed but showed disappointment was

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Speaker 1: more intelligent than the robot that succeeded but didn't show

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Speaker 1: any expression at all. And when you think about that again,

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Speaker 1: it's holding robots to a standard that doesn't necessarily apply

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Speaker 1: to them because of the human element this human robot interaction.

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Speaker 1: We're holding autonomous cars to a similar standard that perhaps

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Speaker 1: is not fair. We're holding robots to a standard that's

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Speaker 1: not fair, But that means that people who are designing

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Speaker 1: autonomous cars and people who are designing robots have to

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Speaker 1: take that into consideration because that's the way humans are. Well,

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Speaker 1: this is interesting because you're you're mentioning specifically imitating human behavior.

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Speaker 1: And this comes up in an article that I read

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Speaker 1: in I let't see it was in the Verge, and

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Speaker 1: the Verge you might have read the same thing article.

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Speaker 1: It really is and a person by the name of

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Speaker 1: Jennifer Haroun. She's the head of business operations for Google

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Speaker 1: self driving project. And by the way, let's come back

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Speaker 1: to the details of the accident and just a moment,

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Speaker 1: we'll describe what happened. But she says that, well, you

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Speaker 1: know what, maybe I need to back this up just

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Speaker 1: a second here. How about this, Let's describe the accident

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Speaker 1: and then we'll talk about what she said, because it

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Speaker 1: plays perfectly into it, so and helping understand what happened.

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Speaker 1: So here's here's how you can imagine it. All right,

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Speaker 1: You've got an intersection, uh in Mountain View, California, which

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Speaker 1: is where Google's headquarters is located, and you've got the

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Speaker 1: Google self driving car. Correct me if I get any

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Speaker 1: of this wrong, Scott, I'm going from memory. I'm doing

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Speaker 1: mostly the same. It's so the Google self driving cars

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Speaker 1: in the right lane and at once it's playing on

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Speaker 1: making a right turn at this intersection. Now, at the

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Speaker 1: corner of the intersection, there were some sandbags that were

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Speaker 1: a partial obstruction of the lane. Then I think you're

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Speaker 1: blocking a sewer entry a great maybe or something. Right,

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Speaker 1: So the Google car detected that there was an obstruction,

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Speaker 1: and so it had to plan an alternate way to

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Speaker 1: make its right turn. It still wanted to follow the

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Speaker 1: route that it had planned, so the change would have

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Speaker 1: been for it to kind of edge into the next

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Speaker 1: lane over, the next lane to the left before making

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Speaker 1: a right turn. Behind the Google car, approaching at a

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Speaker 1: blistering speed of fifteen miles per hour, was a bus.

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Speaker 1: And so the Google car recognized there was a bus coming.

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Speaker 1: It was moving at a very slow speed at two

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Speaker 1: miles per hour. The Google car said, well, based upon

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Speaker 1: my programming, what I should expect happen is that the

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Speaker 1: bus will slow down, allow me to move through. I'll

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Speaker 1: clear the intersection, the bus will continue. What actually happened

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Speaker 1: was the Google Car made the move into the lane,

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Speaker 1: the bus continued, and there was a low speed collision

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Speaker 1: and there were no injuries. No one was hurt. There

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Speaker 1: was in fact a driver behind the wheel of the

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Speaker 1: Google car. It's just the driver wasn't in control. The

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Speaker 1: autonomous system was in control. And some people might say, well,

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Speaker 1: you know, the bus driver just a let the car end.

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Speaker 1: But Google actually said, and this is important. Google came

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Speaker 1: out and said, we accept responsibility for this. This is

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Speaker 1: something that uh, it's it's valuable that this information has

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Speaker 1: come to light because it means that we need to

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Speaker 1: revisit this particular part of the autonomous car programming. Now

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Speaker 1: I thought that was really interesting. First of all, I

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Speaker 1: have never heard of a company accepting responsibility for something

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Speaker 1: so fast in my life. Well they did, and they didn't.

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Speaker 1: I mean, there's a couple of versions of this. Now,

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Speaker 1: you got the details of the accident, correct, Although I

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Speaker 1: did hear, and this this is a bit confusing. I

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Speaker 1: didn't hear that the lanes in this particular part of

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Speaker 1: town are extremely wide, and so what happened was the

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Speaker 1: car uh kind of edged itself over toward the curb.

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Speaker 1: So it was I guess mimicking human behavior again. And

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Speaker 1: I'll get to that in just a minute. But um,

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Speaker 1: you know, once, once this accident happened and they said,

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Speaker 1: you know, we do need to investigate this. They did

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Speaker 1: that to the tune of about thirty five hundred new

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Speaker 1: tests that they have now implemented since this accident, that said,

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Speaker 1: we're gonna watch for this. You know that we need

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Speaker 1: to understand a little more deeply that some of these

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Speaker 1: larger vehicles may have a different and more difficult times

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Speaker 1: stopping in traffic than a smaller vehicle will. And and

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Speaker 1: that's the reason why some of these bigger vehicles like

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Speaker 1: to continue on their path and think, well, maybe someone

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Speaker 1: behind me will let them in. Google did say we

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Speaker 1: were relying on an element of human kindness UM to

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Speaker 1: let us into that lane. And that's normally what happens.

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Speaker 1: It really does. Usually there's a back and forth or

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Speaker 1: you know, maybe there's always gonna be that off handed

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Speaker 1: time where you know someone does cut through and they're

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Speaker 1: just like they're like, no, I need to get through

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Speaker 1: that intersection in this light cycle, and no one is

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Speaker 1: gonna stop me. Yeah. I mean I'm gonna be ten

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Speaker 1: feet ahead of you when all this happens. And that's

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Speaker 1: where I you know, that's that's my goal. But uh,

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Speaker 1: usually what happens is it's an alternating pattern and they

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Speaker 1: expected that to happen, and it didn't happen in this case.

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Speaker 1: And this is where it plays right into what UM

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Speaker 1: Jennifer had mentioned. Jennifer Harun, who is the head of

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Speaker 1: business operations for Google self driving project, explained at and

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Speaker 1: I think she was at the south By Southwest Kind

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Speaker 1: Prince as well, and she said that the Lexus, it's

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Speaker 1: a Lexus vehicle. It was outfitted with this gear so

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Speaker 1: that it struck the bus in part because it was

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Speaker 1: imitating human behavior. And that's I found that interesting. That

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Speaker 1: she she kind of is deferring the fault here and

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Speaker 1: to say, well, we were just imitating what we see

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Speaker 1: on the streets and that's that's kind of what happened.

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Speaker 1: So it's a it's a double deferral in a way, right,

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Speaker 1: because first they say we were counting on an element

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Speaker 1: of human kindness, which is already kind of a deferral

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Speaker 1: in itself. You're you're essentially saying, well, we were concert

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Speaker 1: we were thinking that the bus driver would be a

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Speaker 1: decent human being and not a Lexus hating scumbag. That's

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Speaker 1: I'm paraphrasing what they said. Obviously, I'm taking a little liberty.

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Speaker 1: And then in this one you're saying she's also saying, well,

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Speaker 1: we designed the car to behave the way we see

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Speaker 1: actual cars behaving on the road. So in both cases

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Speaker 1: you're almost it's a little bit of backing away from

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Speaker 1: taking full responsibility exactly. And this imitating human behavior that

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Speaker 1: she's talking about was that they had recently taught the

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Speaker 1: eals to hug the right hand side of that lane

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Speaker 1: when they're making that right hand turn, and that that's

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Speaker 1: when it's encountered the sandbags that were unexpected, and so

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Speaker 1: this is what I find interesting is if it's a

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Speaker 1: wide lane and she's saying that it was hugging the

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Speaker 1: right hand side of that lane, trying to make the

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Speaker 1: turn as most humans do. If it was in the

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Speaker 1: center of the lane, she's saying, if it had just

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Speaker 1: behaved as they normally would do it, you know that

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Speaker 1: it would be in the exact dead center of that lane.

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Speaker 1: The bus wouldn't have had the gap, I guess, and

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Speaker 1: try to try to make it through that gap, so

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Speaker 1: it would have just been behind the car. Never would

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Speaker 1: have happened. So she's saying, in effect, because we're trying

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Speaker 1: to make it mimic human behavior, and we were hugging

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Speaker 1: that right side, that's why this accident happened, maybe we

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Speaker 1: should have done that. But then again they come back

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Speaker 1: and say that that's absolutely necessary for them to mimic

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Speaker 1: human behavior, because if they don't, that causes trouble as well.

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Speaker 1: There's proather issues that are right, like if a if

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Speaker 1: a vehicle, let's say that it's an autonomous car heading

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Speaker 1: toward an intersection, the light goes from green to amber,

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Speaker 1: and there's technically enough space for the car to break

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Speaker 1: safely and come to a complete stop as the light

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Speaker 1: turns red, knowing that most humans would just gun it,

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Speaker 1: or at least just continue at the same speed to

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Speaker 1: go through the intersection while it's still amber. You might

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Speaker 1: want to think about that when you're designing your autonomous

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Speaker 1: car so that you don't cause a pile up behind

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Speaker 1: you like you don't. If the person directly behind the

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Speaker 1: autonomous car expects the car in front of them to

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Speaker 1: continue through the intersection, you could potentially get rear ended.

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Speaker 1: That happens a thousand times around. I mean more than that,

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Speaker 1: but it happens all over the world, especially in Atlanta,

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Speaker 1: where the rule is that the light turns red, three

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Speaker 1: cars get to go through. It is so true, isn't it.

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Speaker 1: It seems like once one goes through, two more follow you.

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Speaker 1: That's crazy. Yeah, I've seen it happen in multiple places

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Speaker 1: around the city. There's some neighborhoods where it's worse. I'm

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Speaker 1: not gonna name any names, Buckhead, but I'm just saying,

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Speaker 1: never accelerate immediately on a green light anywhere in this area,

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Speaker 1: because you can expect there's gonna be that that odd

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Speaker 1: ball car that comes through it, right, and there's gonna

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Speaker 1: be like a car that's been waiting to turn left

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Speaker 1: the whole time, and they say, I'm not waiting another

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Speaker 1: light cycle, I'm just going now, like even if they're

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Speaker 1: behind the stop line. Yeah, I'm sure. I'm sure it

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Speaker 1: happens everywhere. It's it's particularly bad in these congested areas. Yes,

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Speaker 1: So to your point, it is important to take those

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Speaker 1: things into consideration when designing the autonomous car. You don't

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Speaker 1: want an autonomous car to drive like an inconsiderate jerk

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Speaker 1: of a driver. But at the same time, you can't

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Speaker 1: have it be so clinically precise that it is standing

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Speaker 1: out from all the other drivers. The only way that

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Speaker 1: works is if you get to a point where you're

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Speaker 1: at a saturation point with autonomous cars on the road,

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Speaker 1: where then you can affect the behavior on a mass

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Speaker 1: scale across a fleet of cars and not have that

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Speaker 1: issue of human drivers having awful interactions with robotic drivers exactly.

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Speaker 1: Here's here's the way they state at these These spokesman

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Speaker 1: stated and say it's vital for us to develop advanced

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Speaker 1: skills that respect not just the letter of the traffic code,

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Speaker 1: but the spirit of the road. I think that's a

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Speaker 1: good way to put it. The spirit of the road.

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Speaker 1: I understand that. I completely get that when I read

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Speaker 1: it is that, Yeah, there's little rules here and there

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Speaker 1: that we'd bend, but everybody bends them, and you you

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Speaker 1: expect you you understand how other drivers are gonna behave

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Speaker 1: in the same situation, and you expect that to happen,

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Speaker 1: and you behave in that way, and it all works.

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Speaker 1: But when something comes in, a spoiler comes in, um

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Speaker 1: and it follows exactly to the letter of the log

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Speaker 1: the way that's supposed to happen, that person maybe the

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Speaker 1: you know, the standout right. Another great example of that

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Speaker 1: in Atlanta would be, uh. We have a couple of

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Speaker 1: different highways that run through the city and one that

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Speaker 1: runs around the city and is often thought of as

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Speaker 1: the type of highway that if you get on it,

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Speaker 1: you have to speed. You cannot go the speed limit

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Speaker 1: on two five. It's just too dangerous because everyone else

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Speaker 1: is going above the speed limit. Massive truck traffic, yeah

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Speaker 1: and yeah, and there are enormous, enormous semis uh rushing

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Speaker 1: down there, and you don't want to get you don't

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Speaker 1: want to be poking along when they come up behind you.

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Speaker 1: So again, an autonomous car would need to have that

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Speaker 1: information and take that into account unless you've got to

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Speaker 1: a point where you had so many autonomous vehicles on

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Speaker 1: the road that it was no longer a concern. Yeah,

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Speaker 1: and this is where we we discussed this yesterday because

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Speaker 1: we're talking off air about this just a little bit

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Speaker 1: too prep for today. And uh, the idea would be

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Speaker 1: that's kind of like schooling, almost like fish schooling, UM,

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Speaker 1: and that the cars know where the other one is

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Speaker 1: at all times and they can communicate between them. The

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Speaker 1: problem is when you throw in the human driver element

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Speaker 1: into that mix, or you know, if if if you

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Speaker 1: have just one autonomous car among all humans. That's the

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Speaker 1: other problem with the other issue, and right now, that's

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Speaker 1: the battle that they're fighting right right. So once we

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Speaker 1: get to a point where there's that that tipping point

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Speaker 1: one way or the other, then things will be very different.

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Speaker 1: But there's going to be some growing paints. And this

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Speaker 1: also leads into something that I talked about earlier in

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Speaker 1: UM when I went to ce S Toyota had their

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Speaker 1: big AI discussion. You know, they're they're investing millions of

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Speaker 1: dollars in AI research for autonomous cars and beyond. And

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Speaker 1: one of the things they've talked about was how autonomous

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Speaker 1: cars in general are really really good at handling all

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Speaker 1: the mundane stuff that you would typically encounter on a

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Speaker 1: normal day driving from point A to point B. What

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Speaker 1: they are not good at is dealing with stuff that's

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Speaker 1: outside of that norm, and the sandbags that we talked

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Speaker 1: about earlier would be a great example of that. It's

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Speaker 1: some form of obstruction that's partially blocking off part of

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Speaker 1: the road and that ends up causing a different scenario.

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Speaker 1: And sometimes the card behaves in a way that works

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Speaker 1: out for everybody. In this case, it didn't, And it's

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Speaker 1: not it's not that the car couldn't handle the situation.

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Speaker 1: It's just that the method that the car used turned

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Speaker 1: out to not be reliable. Yeah, this was an extremely

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Speaker 1: slow speed crash, as we've said, and the bus was

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Speaker 1: traveling fifteen miles per hour in the other lane trying

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Speaker 1: to get through that gap. But the Google car was

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Speaker 1: traveling i think they said to two miles per hour. Yeah,

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Speaker 1: very very slow, very slow speed. So the thing is

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Speaker 1: like with the with the the compensation for this, you know,

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Speaker 1: the thirty five hundred tests that they're now going to

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Speaker 1: run additional tests, uh to determine or to to find

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Speaker 1: a way around that situation. Said, it's never gonna happen again.

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Speaker 1: We're gonna do everything we can, but to to think

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Speaker 1: about it that way, to say, the thirty five hundred

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Speaker 1: tests that are gonna allow this vehicle to to think

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Speaker 1: about that exact situation and never let it happen again.

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Speaker 1: Where where just kind of noses out into uh, into

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Speaker 1: a lane that appears open. That's remarkable. I mean it

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Speaker 1: just lets you know that, uh, they're there are tens

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Speaker 1: of thousands of of UM programs are or or thoughts,

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Speaker 1: I don't know how to say it, that are they're

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Speaker 1: going through this thing at all times. You know, um,

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Speaker 1: all these calculations, calculations and parameters and just you know,

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Speaker 1: if this then that you know, those scenarios are being

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Speaker 1: run all the time. It's just incredible, mind boggling, it

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Speaker 1: really is. And and I was looking into you said

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Speaker 1: one one point four or five million miles have been driven,

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Speaker 1: uh flawlessly, right, I mean they hadn't have any problems,

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Speaker 1: you know at at fault. I guess the autonomous vehicle

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Speaker 1: Do you know how much they test on a daily

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Speaker 1: basis and on actually on a daily basis. Okay, well,

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Speaker 1: let's see, I've got I got a note here I

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Speaker 1: should have looked for that reading. Okay, here we go.

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Speaker 1: All right, So, actually this is a per week and

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Speaker 1: then a per day thing. They drive ten thousand miles

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Speaker 1: per week and that's like, you know, somebody in a

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Speaker 1: vehicle on the road, ten thousand miles per week. Per day, though,

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Speaker 1: this number is incredible. Per day, they're driving three million

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Speaker 1: miles of computer simulation miles, so three million the equivalent

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Speaker 1: of three million miles. That's because they can quickly just

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Speaker 1: go through that and have multiple systems running these things,

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Speaker 1: you know. So so the amount of testing that they

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Speaker 1: do in a year is just unbelievable. I don't have

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Speaker 1: yearly stats or anything, but you can extrapolate those numbers

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Speaker 1: to that. Well and and uh, the other point in

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Speaker 1: the Toyota press conference that was interesting to me, and

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Speaker 1: this goes back to what you were saying at the

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Speaker 1: beginning of the show about holding autonomous cars at a

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Speaker 1: different standard than we hold human drivers. Uh. They talked

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Speaker 1: about how you offer a lot of the autonomous car

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Speaker 1: industry talks about the hundred million mile um benchmarks, saying

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Speaker 1: that you want you want a hundred million miles traveled

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Speaker 1: of proven safety, and they said, you know that's not enough.

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Speaker 1: You need to go much bigger than that, a hundred

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Speaker 1: billion miles. And I thought, wow, that is I mean,

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Speaker 1: I get it for you want that many miles so

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Speaker 1: that you can encounter as many possible different situations as

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Speaker 1: you might encounter on the road. Because obviously, if you

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Speaker 1: if you plan a system and it's great for handling

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Speaker 1: percent of the situations, that's fine until you run into

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Speaker 1: that one percent. And when you do figure out how

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Speaker 1: many cars are on the road in the United States

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Speaker 1: alone traveling on any given day, you realize the odds

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Speaker 1: are Eventually, I mean, statistics show statistics proved like odds are,

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Speaker 1: sooner rather than later, one of those autonomous cars will

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Speaker 1: encounter a situation that would have been impossible to anticipate

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Speaker 1: in the programming phase. So I get it. On one hand.

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Speaker 1: On the other hand, I get frustrated because I really

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Speaker 1: want to see this feature get here as soon as possible.

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Speaker 1: But I totally understand the need for that level of

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Speaker 1: of precision that's demanded so that you can be sure

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Speaker 1: that nothing catastrophic happens when a car encounters something that

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Speaker 1: the programmers just did not anticipate. Now, Chris again, he

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Speaker 1: was the what was his title, I think his director

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Speaker 1: of the self driving car project director, that's right that

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Speaker 1: I couldn't remember if he's director or not, but he

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Speaker 1: um he did say, and this is I don't you

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Speaker 1: can find this troubling, I guess if you want to.

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Speaker 1: But I understand what he's saying. He says that, you know,

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Speaker 1: of course the February fourteenth was a tough day for

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Speaker 1: his team, obviously, but he says, and I thought this

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Speaker 1: was interesting. He said, We've got to be prepared for

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Speaker 1: more days just like that if we're gonna ever succeed

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Speaker 1: in creating this this project, you know, making this work,

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Speaker 1: and we're actually gonna have worse days than that. And

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Speaker 1: when I when I hear that, you know we're gonna

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Speaker 1: have worse days than that, of course you think, you

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Speaker 1: know the worst. Do you think that it's going to

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Speaker 1: be involved in an accident that is fatal or you know,

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Speaker 1: harms somebody, anybody in any way, And of course that

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Speaker 1: would be an awful day, that'd be a worse day

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Speaker 1: than what we've seen. But you kind of have to

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Speaker 1: expect something like that. Is gonna happen if you're traveling,

430
00:22:45,520 --> 00:22:48,120
Speaker 1: If you're traveling three hundred billion miles like you said,

431
00:22:48,240 --> 00:22:51,000
Speaker 1: or you know whatever, the the enormous number of miles

432
00:22:51,040 --> 00:22:53,040
Speaker 1: on the road that they want to travel is um

433
00:22:53,160 --> 00:22:55,879
Speaker 1: I would guess that when you're talking about three or

434
00:22:55,920 --> 00:22:58,600
Speaker 1: three million or billion or whatever it was, those might

435
00:22:58,640 --> 00:23:01,560
Speaker 1: be computer simular to miles, because you know, the three million,

436
00:23:01,680 --> 00:23:03,879
Speaker 1: three million a day is a is an enormous number,

437
00:23:03,960 --> 00:23:06,240
Speaker 1: and that adds up quickly. But you know, ten thousand

438
00:23:06,400 --> 00:23:09,080
Speaker 1: miles per week of actual you know, physical drawn the

439
00:23:09,160 --> 00:23:12,120
Speaker 1: road testing, that's that's pretty impressive still, But how long

440
00:23:12,160 --> 00:23:15,679
Speaker 1: would that take to get up to million? And you know,

441
00:23:15,720 --> 00:23:17,720
Speaker 1: I think somebody who laid it out pretty clearly here

442
00:23:17,800 --> 00:23:20,399
Speaker 1: is the U S Transportation Secretary. His name is Anthony Fox,

443
00:23:21,000 --> 00:23:23,720
Speaker 1: and you know he's the one who said where I

444
00:23:23,880 --> 00:23:26,760
Speaker 1: initially read, I guess, don't don't compare these two perfection

445
00:23:27,040 --> 00:23:29,000
Speaker 1: You can't do that. And one of the quotes here

446
00:23:29,040 --> 00:23:31,200
Speaker 1: in an article that I read from the BBC says

447
00:23:32,040 --> 00:23:34,359
Speaker 1: that he says, it's not as surprised that at some

448
00:23:34,400 --> 00:23:36,120
Speaker 1: point to be a crash when they've got this brand

449
00:23:36,119 --> 00:23:38,159
Speaker 1: new technology in the road. But what I what I

450
00:23:38,160 --> 00:23:39,920
Speaker 1: would challenge anyone to do is to look at the

451
00:23:40,000 --> 00:23:41,919
Speaker 1: number of crashes that occurred on the same day that

452
00:23:42,040 --> 00:23:44,640
Speaker 1: the result of human behavior, and that gets right back

453
00:23:44,640 --> 00:23:46,880
Speaker 1: to what you were saying, and that you know, there's

454
00:23:46,920 --> 00:23:49,840
Speaker 1: so many miles driven every day just here in the US,

455
00:23:49,880 --> 00:23:53,119
Speaker 1: around the world, all over the place, that you just

456
00:23:53,240 --> 00:23:55,680
Speaker 1: you know that bad stuff is happening all the time,

457
00:23:55,680 --> 00:23:58,440
Speaker 1: every minute literally. All right, But this is a great

458
00:23:58,520 --> 00:24:01,960
Speaker 1: opportunity for me to transition from the Google story, which

459
00:24:02,000 --> 00:24:05,159
Speaker 1: is you know it's again it has huge implications for

460
00:24:05,200 --> 00:24:10,639
Speaker 1: the autonomous car industry. H even though it was in

461
00:24:10,640 --> 00:24:14,000
Speaker 1: in the grand scheme of things, a minor accident, it

462
00:24:14,119 --> 00:24:18,440
Speaker 1: was something that uh once once you realize, oh they're

463
00:24:18,440 --> 00:24:22,280
Speaker 1: not perfect, then it starts raising some questions. These talks

464
00:24:22,280 --> 00:24:24,719
Speaker 1: were a bit more subdued after that point. Yeah, and

465
00:24:24,720 --> 00:24:27,359
Speaker 1: the south By Southwest like that was definitely happening, although

466
00:24:27,440 --> 00:24:29,760
Speaker 1: I went to a couple of different panels about autonomous

467
00:24:29,760 --> 00:24:31,520
Speaker 1: cars where they didn't even bring it up. They were

468
00:24:32,040 --> 00:24:34,840
Speaker 1: gung ho. I mean, the general feeling and south By

469
00:24:34,880 --> 00:24:38,919
Speaker 1: Southwest is that autonomous cars are a definitive future that

470
00:24:39,000 --> 00:24:43,320
Speaker 1: are coming and that uh that most likely there will

471
00:24:43,359 --> 00:24:47,200
Speaker 1: be some form of shared services model for autonomous cars.

472
00:24:47,560 --> 00:24:53,600
Speaker 1: I think most people UH agreed that personal ownership UH

473
00:24:53,840 --> 00:24:58,400
Speaker 1: is going to slowly phase out, largely because younger generations

474
00:24:58,920 --> 00:25:02,960
Speaker 1: don't necessarily see the necessity of owning a car. And

475
00:25:03,000 --> 00:25:06,359
Speaker 1: there were some interesting statistics too. I saw a panel

476
00:25:06,400 --> 00:25:10,280
Speaker 1: it's called Robot Cars and sharing, road rage or smooth

477
00:25:10,400 --> 00:25:14,960
Speaker 1: sailing and UH. This was This had three panelists on it,

478
00:25:15,040 --> 00:25:18,840
Speaker 1: a moderator and two panelists. One was the moderator was

479
00:25:18,880 --> 00:25:23,360
Speaker 1: Frederick Sue of a company called NATO. NATO creates an

480
00:25:23,359 --> 00:25:26,600
Speaker 1: app and a camera set up where you can essentially

481
00:25:26,720 --> 00:25:30,359
Speaker 1: upgrade your car into a smart car, not an autonomous car,

482
00:25:30,520 --> 00:25:34,400
Speaker 1: but a smart car where it's able to use information

483
00:25:34,440 --> 00:25:38,040
Speaker 1: from the camera and run it through some algorithms that

484
00:25:38,080 --> 00:25:40,320
Speaker 1: are on the back end of the data system that

485
00:25:40,480 --> 00:25:43,480
Speaker 1: then transmits to your app to let you know things

486
00:25:43,520 --> 00:25:47,399
Speaker 1: like how well, how good of a driver is the driver,

487
00:25:47,800 --> 00:25:49,639
Speaker 1: that kind of stuff. So it's also used for like

488
00:25:49,720 --> 00:25:53,320
Speaker 1: fleet management. UH. You can use it to figure out

489
00:25:53,600 --> 00:25:56,520
Speaker 1: if the driver you've just hired to be one of

490
00:25:56,520 --> 00:25:59,800
Speaker 1: your your employees, if that was a good choice or

491
00:25:59,840 --> 00:26:01,720
Speaker 1: not it or maybe you need to rethink that. That

492
00:26:01,800 --> 00:26:06,399
Speaker 1: kind of stuff based, yeah, and uh, and it pulls

493
00:26:06,440 --> 00:26:08,520
Speaker 1: information from a lot of different sources, but the camera

494
00:26:08,840 --> 00:26:12,840
Speaker 1: is the primary one. He was the moderator. And then

495
00:26:12,920 --> 00:26:17,159
Speaker 1: you had Shad Laws from Renault. Uh and who was

496
00:26:17,160 --> 00:26:19,919
Speaker 1: funny because he talked about Renault is a brand that

497
00:26:20,080 --> 00:26:22,520
Speaker 1: is famous around the world but not here in the US,

498
00:26:22,600 --> 00:26:26,920
Speaker 1: but you might know our partner Nissan. And then uh, yeah,

499
00:26:26,960 --> 00:26:31,720
Speaker 1: we knew Renault back in what the mid eighties I think, yeah, yeah.

500
00:26:31,920 --> 00:26:34,920
Speaker 1: And then there was Mark Plattchen from BMW, who was

501
00:26:34,920 --> 00:26:37,480
Speaker 1: actually a substitute. Originally it was supposed to be Maryanne

502
00:26:37,600 --> 00:26:40,959
Speaker 1: Wu of ge Ventures. But we'll have a little bit

503
00:26:40,960 --> 00:26:42,840
Speaker 1: more to say about BMW in just a minute, because

504
00:26:42,880 --> 00:26:46,880
Speaker 1: I can't wait to tell Scott about this. So one

505
00:26:46,960 --> 00:26:49,760
Speaker 1: one some let me throw you some some statistics at you,

506
00:26:49,920 --> 00:26:52,520
Speaker 1: or some of the facts at you that Sue brought up.

507
00:26:52,760 --> 00:26:55,000
Speaker 1: So one of the things he said was that the

508
00:26:55,080 --> 00:27:02,560
Speaker 1: typical American car spends of its life part of its

509
00:27:02,600 --> 00:27:05,639
Speaker 1: life park That's an enormous chunk of time. Yeah, so

510
00:27:05,680 --> 00:27:09,960
Speaker 1: only four of your typical American car, knowing that there

511
00:27:09,960 --> 00:27:13,480
Speaker 1: are cases outside on either end, uh, four percent of

512
00:27:13,520 --> 00:27:17,320
Speaker 1: it is actually used driving around. So with that, when

513
00:27:17,359 --> 00:27:19,800
Speaker 1: you when you hit someone with that. Assuming that that

514
00:27:19,920 --> 00:27:23,639
Speaker 1: is in fact correct, I don't know where his source

515
00:27:23,760 --> 00:27:27,439
Speaker 1: was for, but assuming that is in fact correct, you

516
00:27:27,480 --> 00:27:30,320
Speaker 1: can start to see an argument for a fleet of

517
00:27:30,359 --> 00:27:34,840
Speaker 1: autonomous vehicles that can drive around on demand and pick

518
00:27:34,880 --> 00:27:37,440
Speaker 1: someone up and drop them off, because that means you

519
00:27:37,440 --> 00:27:40,080
Speaker 1: could free up the space that would be taken by

520
00:27:40,119 --> 00:27:45,280
Speaker 1: a parked car and use it for something else, because

521
00:27:45,800 --> 00:27:49,439
Speaker 1: I mean, a lot of our spaces are reserver for parking.

522
00:27:49,440 --> 00:27:52,600
Speaker 1: In fact, there are regulations for office buildings about how

523
00:27:52,680 --> 00:27:55,760
Speaker 1: much square footage you have to set aside for parking

524
00:27:56,359 --> 00:27:59,680
Speaker 1: in certain cities. It depends on the city. But imagine

525
00:27:59,720 --> 00:28:02,840
Speaker 1: that you of a world where people are relying on

526
00:28:02,880 --> 00:28:05,119
Speaker 1: autonomous cars to pick them up and drop off, you

527
00:28:05,160 --> 00:28:07,720
Speaker 1: don't need that space for parking anymore. You can actually

528
00:28:07,760 --> 00:28:12,000
Speaker 1: dedicate that to something else and make becausa money. I

529
00:28:12,000 --> 00:28:13,760
Speaker 1: think it's the way they put it. But anyway, plant

530
00:28:13,760 --> 00:28:15,919
Speaker 1: a tree, plant a tree, you could also do that.

531
00:28:16,160 --> 00:28:19,920
Speaker 1: Come on, you tree hugger, um No, I also think

532
00:28:19,920 --> 00:28:22,679
Speaker 1: that would be awesome. So one of the things that

533
00:28:22,720 --> 00:28:30,439
Speaker 1: I thought was shocking it. I think the effect on

534
00:28:30,520 --> 00:28:34,680
Speaker 1: me was not what the speaker was planning. Shad laws

535
00:28:34,720 --> 00:28:40,120
Speaker 1: of Renault was talking about the the safety factor of

536
00:28:40,160 --> 00:28:45,360
Speaker 1: autonomous cars, and his argument was that um safety a

537
00:28:45,440 --> 00:28:47,720
Speaker 1: Thomas cars. First of all, we can't determine that they're

538
00:28:47,760 --> 00:28:51,160
Speaker 1: more safe than human driven cars yet because we don't

539
00:28:51,240 --> 00:28:53,600
Speaker 1: have enough information. We don't have enough autonomous cars on

540
00:28:53,640 --> 00:28:56,480
Speaker 1: the road, we haven't had enough scenarios to really tell.

541
00:28:57,000 --> 00:28:59,360
Speaker 1: But then he also said safety is really not as

542
00:28:59,520 --> 00:29:02,520
Speaker 1: big as as you might think, because the safety benchmark

543
00:29:03,280 --> 00:29:06,600
Speaker 1: is to try and have fewer than one fatality per

544
00:29:06,600 --> 00:29:11,760
Speaker 1: one million kilometers driven. Now, in the United States it

545
00:29:11,920 --> 00:29:15,800
Speaker 1: is one point oh eight fatalities for one million miles.

546
00:29:15,800 --> 00:29:18,480
Speaker 1: But a mile is longer than a kilometer, right, one

547
00:29:18,520 --> 00:29:21,600
Speaker 1: mile is one point six kilometers, so it's still below

548
00:29:21,960 --> 00:29:25,200
Speaker 1: that one fatality per one million kilometers. And then he

549
00:29:25,240 --> 00:29:27,920
Speaker 1: said for most countries that's the case. There are a

550
00:29:27,920 --> 00:29:30,040
Speaker 1: few that are above it, but not many. So is

551
00:29:30,040 --> 00:29:32,840
Speaker 1: this an unrealistic standard to be to be held to? Well,

552
00:29:32,840 --> 00:29:34,640
Speaker 1: I think I think what he was trying to say

553
00:29:34,760 --> 00:29:39,280
Speaker 1: is that human drivers are pretty safe already, and therefore

554
00:29:40,280 --> 00:29:43,040
Speaker 1: you can't sell autonomous cars on the promise of safety

555
00:29:43,040 --> 00:29:46,160
Speaker 1: because we're so safe already. I would counter that argument

556
00:29:46,480 --> 00:29:49,840
Speaker 1: by saying more than thirty thousand people died last year

557
00:29:49,960 --> 00:29:53,680
Speaker 1: as a result of car accidents, as thirty fewer people

558
00:29:54,000 --> 00:29:58,680
Speaker 1: around today because of a car accident, and more. Something

559
00:29:58,680 --> 00:30:01,040
Speaker 1: around the order of ninet percent of car accidents are

560
00:30:01,080 --> 00:30:03,400
Speaker 1: at the fault of the human of a human driver,

561
00:30:03,640 --> 00:30:07,480
Speaker 1: at least one human driver. And so my counter to

562
00:30:07,520 --> 00:30:11,520
Speaker 1: that argument is that it may be statistically speaking a

563
00:30:11,760 --> 00:30:14,600
Speaker 1: safe thing, but when you get down to actual numbers,

564
00:30:14,680 --> 00:30:18,480
Speaker 1: with real human being lives attached to it, I would

565
00:30:18,560 --> 00:30:22,480
Speaker 1: argue that the autonomous vehicles so far have proven to

566
00:30:23,080 --> 00:30:26,920
Speaker 1: be a really good move in the right direction to

567
00:30:27,040 --> 00:30:30,120
Speaker 1: reduce that number dramatically. This is dangerous territory you're waiting

568
00:30:30,120 --> 00:30:32,840
Speaker 1: into here, because on our show on Car Stuff, we

569
00:30:33,000 --> 00:30:37,360
Speaker 1: sometimes talk about, you know, the uh, the incredible rise

570
00:30:37,560 --> 00:30:41,800
Speaker 1: in in fatalities on Georgia highways last year because there

571
00:30:41,840 --> 00:30:44,800
Speaker 1: was a huge increase increase or something, you know, year

572
00:30:44,840 --> 00:30:47,560
Speaker 1: over year, and it was really big and it was

573
00:30:47,600 --> 00:30:49,560
Speaker 1: the first time in a long, long time, long stretch

574
00:30:49,600 --> 00:30:51,640
Speaker 1: of time where um, you know, it had had actually

575
00:30:51,680 --> 00:30:53,560
Speaker 1: been on the rise. It it was going down up

576
00:30:53,600 --> 00:30:56,000
Speaker 1: until that point, and then suddenly this big spike and

577
00:30:56,400 --> 00:30:58,400
Speaker 1: trying to figure out why, and we're talking about distraction

578
00:30:58,440 --> 00:31:00,360
Speaker 1: and all that stuff, you know, smartphones and things behind

579
00:31:00,400 --> 00:31:03,240
Speaker 1: the wheel and trying to just, you know, guess why

580
00:31:03,280 --> 00:31:06,480
Speaker 1: it is happening that way. And of course somebody writes

581
00:31:06,480 --> 00:31:08,400
Speaker 1: in and says, well, thirty eight thousand people is not

582
00:31:08,440 --> 00:31:11,040
Speaker 1: that many people. And you say, well, that that's a

583
00:31:11,080 --> 00:31:14,080
Speaker 1: lot of people that die on on US highways. And

584
00:31:14,120 --> 00:31:16,080
Speaker 1: they say, now, now, back in the nineteen fifties, the

585
00:31:16,160 --> 00:31:19,479
Speaker 1: number was like forty four thousand, and that you know that,

586
00:31:19,560 --> 00:31:21,200
Speaker 1: and that was with less drivers on the road. And

587
00:31:21,200 --> 00:31:24,000
Speaker 1: they give you all these stats about population and number

588
00:31:24,000 --> 00:31:26,080
Speaker 1: of miles driven and all that night, and I get

589
00:31:26,120 --> 00:31:28,240
Speaker 1: I gotta be honest, I get kind of confused with

590
00:31:28,240 --> 00:31:31,160
Speaker 1: with that, you know, with that angle, like trying to

591
00:31:31,200 --> 00:31:33,720
Speaker 1: compare apples to apples, you know, back then, you know,

592
00:31:34,040 --> 00:31:36,640
Speaker 1: sixty years ago to today. That's it's kind of tough

593
00:31:36,720 --> 00:31:39,680
Speaker 1: to do well, especially you know, there's so many other

594
00:31:39,760 --> 00:31:41,880
Speaker 1: factors there. Right, Yeah, you might have fewer drivers on

595
00:31:41,920 --> 00:31:45,320
Speaker 1: the road, but your safety regulations weren't anything like they

596
00:31:45,320 --> 00:31:48,440
Speaker 1: are today years ago exactly. That was one thing and

597
00:31:48,480 --> 00:31:51,440
Speaker 1: we always argue that point too. There were no crumple zones,

598
00:31:51,600 --> 00:31:53,480
Speaker 1: there were no air bags, there's none of that stuff

599
00:31:53,520 --> 00:31:55,560
Speaker 1: going on, so maybe that it counts for it, but

600
00:31:55,600 --> 00:31:58,680
Speaker 1: then they counter with another argument. So I'm just saying that,

601
00:31:58,720 --> 00:32:01,480
Speaker 1: you know, I feel that somebody out there is going

602
00:32:01,520 --> 00:32:03,760
Speaker 1: to have some kind of issue with you know, mentioning

603
00:32:03,800 --> 00:32:06,200
Speaker 1: that thirty. You know, thirty a huge number. That was

604
00:32:06,240 --> 00:32:08,719
Speaker 1: what it was last year in the U. S. Alone.

605
00:32:09,400 --> 00:32:11,240
Speaker 1: That's a huge number, no matter how you look at it.

606
00:32:11,240 --> 00:32:14,200
Speaker 1: I mean, even if even if there're more people driving,

607
00:32:14,240 --> 00:32:17,080
Speaker 1: I think my response to anyone who would argue like

608
00:32:17,520 --> 00:32:20,320
Speaker 1: that that this is less than in the past, I

609
00:32:20,360 --> 00:32:23,640
Speaker 1: would say, that's good, but it could be lower and

610
00:32:24,040 --> 00:32:27,120
Speaker 1: lower number of people who die as a result of

611
00:32:27,160 --> 00:32:29,560
Speaker 1: car accidents. I think it's hard to argue that that's

612
00:32:29,640 --> 00:32:32,560
Speaker 1: a bad thing. No, you certainly want that number to

613
00:32:32,600 --> 00:32:34,520
Speaker 1: be as low as close to zero as you could

614
00:32:34,560 --> 00:32:37,520
Speaker 1: possibly make it. Of course, automakers strive for that. They're trying,

615
00:32:37,520 --> 00:32:40,360
Speaker 1: they're they're trying everything they can to make essentially a

616
00:32:40,360 --> 00:32:42,360
Speaker 1: death proof car. I mean, you can't, you know, you

617
00:32:42,360 --> 00:32:46,400
Speaker 1: can't account for every situation, every single situation, but they're

618
00:32:46,400 --> 00:32:49,160
Speaker 1: doing their best to make what is essentially a death

619
00:32:49,200 --> 00:32:51,720
Speaker 1: proof car. And there's several that, you know, several marks

620
00:32:51,720 --> 00:32:55,640
Speaker 1: that they've got they've gone years without a fatality, and

621
00:32:55,680 --> 00:32:58,320
Speaker 1: they've got you know, the stats somewhere back on my desk.

622
00:32:58,360 --> 00:33:00,360
Speaker 1: But there there's a few that have gone. I'm gonna

623
00:33:00,400 --> 00:33:02,400
Speaker 1: guess here, just based off my memory, it was like

624
00:33:02,440 --> 00:33:06,840
Speaker 1: five or six years without fatality caused by a fault

625
00:33:06,840 --> 00:33:09,360
Speaker 1: in a system in their vehicle, right well, And and

626
00:33:09,400 --> 00:33:11,720
Speaker 1: that also leads me to a different panel that I saw.

627
00:33:11,760 --> 00:33:13,520
Speaker 1: We'll come We'll come back to the road rage one

628
00:33:13,560 --> 00:33:17,640
Speaker 1: because we got to get to the BMW's But the

629
00:33:17,640 --> 00:33:19,720
Speaker 1: other panel I saw that was related to this was

630
00:33:19,760 --> 00:33:22,680
Speaker 1: called looking Forward to Rush Our the Future of Transit

631
00:33:22,960 --> 00:33:25,320
Speaker 1: looking Forward to Russia. Yeah, this was from a couple

632
00:33:25,360 --> 00:33:29,080
Speaker 1: of industrial designers without to it design talking about the

633
00:33:29,080 --> 00:33:32,840
Speaker 1: future of transportation, and it wasn't just autonomous cars or

634
00:33:32,880 --> 00:33:35,960
Speaker 1: even just the future of cars. That was one half

635
00:33:36,680 --> 00:33:39,240
Speaker 1: of the panel, and that was done by a guy.

636
00:33:39,840 --> 00:33:42,920
Speaker 1: The guy who who led that part was not Dan Dorley,

637
00:33:42,920 --> 00:33:46,240
Speaker 1: but there was also Chip Walters who did the other half,

638
00:33:46,280 --> 00:33:49,520
Speaker 1: which was more about the hyper loop. Also fascinating, but

639
00:33:49,600 --> 00:33:51,920
Speaker 1: that we're not talking about the hyper loop today. So

640
00:33:52,200 --> 00:33:54,280
Speaker 1: switching back over to Dorley, one of the things Dorley

641
00:33:54,320 --> 00:33:57,880
Speaker 1: said that I thought was really interesting was that once

642
00:33:57,880 --> 00:33:59,960
Speaker 1: you get to a level where you have a lot

643
00:34:00,040 --> 00:34:03,600
Speaker 1: of autonomous cars on the road, like let's say the

644
00:34:03,640 --> 00:34:07,960
Speaker 1: majority of cars on the road or autonomous, and you

645
00:34:08,080 --> 00:34:12,280
Speaker 1: have proof I mean, obviously this only works if everything

646
00:34:12,360 --> 00:34:15,400
Speaker 1: is working properly, but you have proof that because of

647
00:34:15,440 --> 00:34:17,720
Speaker 1: the number of autonomous vehicles on the road, the number

648
00:34:17,760 --> 00:34:23,279
Speaker 1: of crashes decreases dramatically, the number of deaths decreased dramatically.

649
00:34:23,840 --> 00:34:26,160
Speaker 1: Then you can start to play around with other stuff

650
00:34:26,280 --> 00:34:30,600
Speaker 1: because if the autonomous cars are approven technology that's safe,

651
00:34:31,239 --> 00:34:35,360
Speaker 1: you can let up on some of the major safety

652
00:34:35,480 --> 00:34:37,480
Speaker 1: considerations you've had to put into place over the last

653
00:34:37,480 --> 00:34:40,000
Speaker 1: few years. In order to minimize that number that we

654
00:34:40,080 --> 00:34:44,000
Speaker 1: talked about, that thirty thousand or higher number. You could

655
00:34:44,000 --> 00:34:47,920
Speaker 1: remove crumple zones. You can make cars smaller and lighter,

656
00:34:48,000 --> 00:34:51,880
Speaker 1: so which is especially important if your cars also are electric,

657
00:34:52,400 --> 00:34:54,680
Speaker 1: because the battery will have less weight to have to

658
00:34:54,719 --> 00:34:59,040
Speaker 1: move around. That will extend the driving range of your vehicle.

659
00:34:59,800 --> 00:35:02,080
Speaker 1: But because you've made your vehicle lighter, not that not

660
00:35:02,120 --> 00:35:04,080
Speaker 1: that the battery has gotten any better, but it doesn't

661
00:35:04,120 --> 00:35:06,480
Speaker 1: have to push as much weight around. And again, this

662
00:35:06,640 --> 00:35:08,920
Speaker 1: this only works though, if every vehicle out there is

663
00:35:09,000 --> 00:35:11,040
Speaker 1: the same. Yeah, you have to have you have to

664
00:35:11,080 --> 00:35:15,040
Speaker 1: have enough autonomous vehicles, at least the majority, if not

665
00:35:16,440 --> 00:35:20,120
Speaker 1: of them out there, so that you can be confident

666
00:35:20,560 --> 00:35:24,959
Speaker 1: that by eliminating those safety features that are important right now,

667
00:35:25,760 --> 00:35:28,960
Speaker 1: it's not going to make any difference. Um, and I

668
00:35:29,000 --> 00:35:31,839
Speaker 1: think that, I think we're pretty far away from that,

669
00:35:32,120 --> 00:35:34,000
Speaker 1: but I thought it was an interesting point. He also

670
00:35:34,080 --> 00:35:38,000
Speaker 1: talked about more man car manufacturers creating a sort of

671
00:35:38,000 --> 00:35:41,920
Speaker 1: a universal chassis where lots of different bodies of vehicles

672
00:35:41,920 --> 00:35:45,160
Speaker 1: could fit on top of the same basic chassis, leading

673
00:35:45,200 --> 00:35:48,640
Speaker 1: to a future where ultimately you can and you can

674
00:35:48,680 --> 00:35:51,360
Speaker 1: do this now. Actually, if you've got enough money, you

675
00:35:51,400 --> 00:35:55,200
Speaker 1: can go to certain specialty companies and three D print

676
00:35:55,560 --> 00:35:58,400
Speaker 1: a car design. You could design a car if you

677
00:35:58,400 --> 00:36:01,799
Speaker 1: wanted to, and three D print a car body that

678
00:36:01,880 --> 00:36:05,239
Speaker 1: fits on top of a particular chassis and motor drive

679
00:36:05,280 --> 00:36:08,480
Speaker 1: train configuration, and uh so you can have your own.

680
00:36:09,160 --> 00:36:11,080
Speaker 1: Like people would say, well, what kind of cars as

681
00:36:11,320 --> 00:36:14,399
Speaker 1: that's my car. I call it a Strickland. Yeah, it's

682
00:36:14,680 --> 00:36:21,560
Speaker 1: it's a Strickland. It doesn't drive anywhere. Um, yeah, that's

683
00:36:21,640 --> 00:36:24,000
Speaker 1: that's a joke about me not driving. But yeah, that

684
00:36:24,120 --> 00:36:26,600
Speaker 1: I thought it was interesting that he was looking into

685
00:36:26,719 --> 00:36:32,839
Speaker 1: implications of autonomous cars well beyond safety, well beyond the

686
00:36:32,920 --> 00:36:35,879
Speaker 1: shared model. He was looking at au Thomas cars like, well,

687
00:36:35,920 --> 00:36:38,480
Speaker 1: what does that do to the design of the car itself.

688
00:36:38,560 --> 00:36:41,000
Speaker 1: That's interesting that, you know, you could eliminate the things

689
00:36:41,040 --> 00:36:43,520
Speaker 1: that we find that we have to have now. And

690
00:36:43,520 --> 00:36:45,360
Speaker 1: that's that's an interesting way to think about it, Like

691
00:36:45,400 --> 00:36:48,680
Speaker 1: if if, if it's just not necessary, what could you

692
00:36:48,719 --> 00:36:51,560
Speaker 1: really pair that design down to? What what what smart

693
00:36:51,600 --> 00:36:53,959
Speaker 1: things could you do with that to make it work

694
00:36:54,000 --> 00:36:57,480
Speaker 1: better as an electric platform, as an autonomous platform. You know,

695
00:36:57,640 --> 00:37:00,200
Speaker 1: it all makes it makes good sense. But again you're

696
00:37:00,239 --> 00:37:04,560
Speaker 1: you're counting on you participation in this. Yeah, you need to.

697
00:37:04,760 --> 00:37:07,439
Speaker 1: You would need to have enough buy in so that

698
00:37:07,440 --> 00:37:10,839
Speaker 1: there isn't a risk of having something like we saw

699
00:37:10,920 --> 00:37:13,520
Speaker 1: with the previous Google tests. You know, we talked about

700
00:37:13,520 --> 00:37:17,480
Speaker 1: there were more than a dozen accidents involving Google self

701
00:37:17,560 --> 00:37:23,279
Speaker 1: driving cars previously only only the previous ones until before

702
00:37:23,360 --> 00:37:27,240
Speaker 1: February two thousand sixteen. They were all at the fault

703
00:37:27,280 --> 00:37:30,400
Speaker 1: of a of a human driver, either the person manually

704
00:37:30,440 --> 00:37:34,080
Speaker 1: controlling the self driving car or another driver. So the

705
00:37:34,120 --> 00:37:36,400
Speaker 1: same thing is true if you're in an autonomous vehicle

706
00:37:36,719 --> 00:37:38,560
Speaker 1: and there are human drivers on the road, then there's

707
00:37:38,600 --> 00:37:41,440
Speaker 1: a chance that one of them could make a terrible

708
00:37:41,920 --> 00:37:43,480
Speaker 1: like there could just be an accident, it could be

709
00:37:43,520 --> 00:37:47,080
Speaker 1: a failure, it could be a distracted driver, drunk driver,

710
00:37:47,239 --> 00:37:52,680
Speaker 1: could be anything. And until you eliminate those possibilities, it

711
00:37:52,760 --> 00:37:56,120
Speaker 1: is pretty dangerous to just say, let's eliminate crumple zoneserous

712
00:37:56,160 --> 00:37:58,480
Speaker 1: you could do other stuff like imagine, you know, you

713
00:37:58,560 --> 00:38:02,200
Speaker 1: have no need for controls, so you free up all

714
00:38:02,200 --> 00:38:05,360
Speaker 1: that space in the front that would be normally be

715
00:38:05,480 --> 00:38:08,920
Speaker 1: dedicated to steering wheel and pedals and that kind of stuff.

716
00:38:08,920 --> 00:38:12,440
Speaker 1: You could have a workstation or an entertainment station. Because

717
00:38:12,480 --> 00:38:15,920
Speaker 1: you're not driving, you don't even necessarily have to face forward. Yeah,

718
00:38:15,960 --> 00:38:18,120
Speaker 1: you can face backwards. I had a discussion about this

719
00:38:18,160 --> 00:38:22,239
Speaker 1: on Forward Thinking and Lauren immediately said, yeah, I could

720
00:38:22,280 --> 00:38:24,360
Speaker 1: never do that. I'd be yakking all over the inside

721
00:38:24,400 --> 00:38:26,160
Speaker 1: of that car. I think a lot of people have

722
00:38:26,239 --> 00:38:28,680
Speaker 1: their trouble on a train already or a buss, you know,

723
00:38:28,719 --> 00:38:32,839
Speaker 1: in certain situations. But imagine if you could sit sideways. Uh,

724
00:38:33,320 --> 00:38:35,520
Speaker 1: the design of the vehicle could just be so radically

725
00:38:35,520 --> 00:38:38,440
Speaker 1: different that none of that really matter. You could you

726
00:38:38,440 --> 00:38:40,680
Speaker 1: could probably design you know, those honeycomb systems where you

727
00:38:40,680 --> 00:38:42,080
Speaker 1: could sleep in the car if you want it. Yeah,

728
00:38:42,120 --> 00:38:46,279
Speaker 1: it's kind of funny because it actually opens up an

729
00:38:46,480 --> 00:38:51,879
Speaker 1: enormous opportunity for designers, right, unprecedented opportunity, because you would

730
00:38:51,880 --> 00:38:54,880
Speaker 1: be completely transforming the interior of a car. All the

731
00:38:54,920 --> 00:38:58,960
Speaker 1: things we associate as being well, not all, but a

732
00:38:59,000 --> 00:39:01,480
Speaker 1: lot of the things we are so as being the

733
00:39:01,520 --> 00:39:03,520
Speaker 1: definition of what an inside of a car would look

734
00:39:03,520 --> 00:39:06,279
Speaker 1: like go out the window, I mean figuratively speaking. And

735
00:39:06,320 --> 00:39:10,480
Speaker 1: so you could then have all kinds of different configurations

736
00:39:10,520 --> 00:39:13,279
Speaker 1: and designs almost like almost more like home design really

737
00:39:13,360 --> 00:39:17,520
Speaker 1: or room design and some sort of interior design for vehicles. Yeah,

738
00:39:17,520 --> 00:39:19,960
Speaker 1: it's strange, strange thought. Hey, by the way, I want

739
00:39:20,000 --> 00:39:22,600
Speaker 1: to clarify one thing really, just something that's been bugging

740
00:39:22,640 --> 00:39:25,200
Speaker 1: me for last ten minutes. I do know that there

741
00:39:25,200 --> 00:39:27,400
Speaker 1: were were no cars on US roads prior to the

742
00:39:27,440 --> 00:39:29,759
Speaker 1: nineteen eighties. I was just mentioning their brief comeback, you know,

743
00:39:29,800 --> 00:39:33,200
Speaker 1: with the Alliance lineup and uh and and the kind

744
00:39:33,239 --> 00:39:35,960
Speaker 1: of I guess I'm going to mention the failure that

745
00:39:35,960 --> 00:39:38,360
Speaker 1: that was. It was, it was not not all that

746
00:39:38,400 --> 00:39:41,320
Speaker 1: well received. Yeah, but I think most of my listeners

747
00:39:42,000 --> 00:39:44,839
Speaker 1: are most of them let me let me clarify, most

748
00:39:44,840 --> 00:39:49,239
Speaker 1: of my listeners in the US are probably unfamiliar with

749
00:39:49,280 --> 00:39:52,560
Speaker 1: the brand Rent. Yeah, probably because I'm I'm guessing many

750
00:39:52,640 --> 00:39:54,799
Speaker 1: of them were born in the eighties. Yeah, it's a

751
00:39:55,000 --> 00:39:59,000
Speaker 1: It is a seldom seen vehicle on the roads here

752
00:39:59,000 --> 00:40:00,719
Speaker 1: in the United States, but in other parts of the

753
00:40:00,719 --> 00:40:02,680
Speaker 1: world it is a very popular. Yeah, I mean a

754
00:40:02,680 --> 00:40:05,720
Speaker 1: lot like pougeot or something like that. You know, there's reasons,

755
00:40:05,760 --> 00:40:08,920
Speaker 1: but not the right show. It's so funny when you

756
00:40:08,960 --> 00:40:13,080
Speaker 1: start throwing around, uh, car manufacturer names and car brand names,

757
00:40:13,680 --> 00:40:16,960
Speaker 1: and then you come to that weird realization that in

758
00:40:17,000 --> 00:40:19,920
Speaker 1: other parts of the world they're totally different ones that

759
00:40:20,160 --> 00:40:22,160
Speaker 1: and some some of which that are prevalent in the

760
00:40:22,239 --> 00:40:25,600
Speaker 1: United States are largely unknown in certain parts of the world.

761
00:40:26,080 --> 00:40:28,080
Speaker 1: And it just reminds you like, oh, yeah, that's right.

762
00:40:28,160 --> 00:40:30,560
Speaker 1: The whole world isn't the US. Yeah, it's it's it

763
00:40:30,640 --> 00:40:33,200
Speaker 1: is strange. And once you travel outside and you see that,

764
00:40:33,280 --> 00:40:35,640
Speaker 1: you see the same vehicle but it's named something different

765
00:40:35,760 --> 00:40:38,200
Speaker 1: or something like that, it's just unusual. It's just it's

766
00:40:38,239 --> 00:40:42,160
Speaker 1: it is eye opening. Really. Now I've been teasing this

767
00:40:42,560 --> 00:40:47,440
Speaker 1: for the whole episode. But let's get back to BMW

768
00:40:47,880 --> 00:40:50,560
Speaker 1: and Mark Platchen. Is this intended to hurt me? No,

769
00:40:50,640 --> 00:40:52,360
Speaker 1: it's not tend to hurt you. I just want to

770
00:40:52,400 --> 00:40:56,120
Speaker 1: see what your reaction is. Scott. I. Scott and I

771
00:40:56,160 --> 00:41:00,200
Speaker 1: started talking about this off microphone yesterday, and as I

772
00:41:00,280 --> 00:41:04,600
Speaker 1: was talking, a little voice in my head said, shut up, Jonathan,

773
00:41:05,040 --> 00:41:07,759
Speaker 1: save it for the show. So that's what we're gonna do.

774
00:41:07,960 --> 00:41:10,840
Speaker 1: It's not really you might just shrug and say, oh,

775
00:41:10,880 --> 00:41:14,120
Speaker 1: all right. But in order to set this up first,

776
00:41:14,800 --> 00:41:18,080
Speaker 1: what is bmw slogan? Now they're known as It's It's

777
00:41:18,080 --> 00:41:21,640
Speaker 1: a driver's car, right, It's a it's um the ultimate

778
00:41:21,719 --> 00:41:25,240
Speaker 1: driving machine means the ultimate driving machine, the ultimate driving machine.

779
00:41:25,320 --> 00:41:27,359
Speaker 1: So you would think that, you know, of course they're

780
00:41:27,360 --> 00:41:30,440
Speaker 1: gonna dabble in autonomous systems like you know, maybe adaptive

781
00:41:30,440 --> 00:41:33,600
Speaker 1: cruise control something like that. But I just I've had

782
00:41:33,640 --> 00:41:37,839
Speaker 1: a hard time all along seeing BMW going fully autonomous

783
00:41:37,880 --> 00:41:39,879
Speaker 1: because of the way they market their company. Right now,

784
00:41:39,880 --> 00:41:42,680
Speaker 1: it's it is the ultimate driving machine. It's a driver's vehicle.

785
00:41:42,920 --> 00:41:45,920
Speaker 1: If you want something that's fun to drive, that's an experience,

786
00:41:46,560 --> 00:41:48,799
Speaker 1: you get a BMW. You get you know something that's uh,

787
00:41:48,800 --> 00:41:51,560
Speaker 1: it's it's top of the line, it's expensive, it's plush,

788
00:41:51,960 --> 00:41:54,600
Speaker 1: it's it's it's a well handling car, it's powerful, it's

789
00:41:54,680 --> 00:41:58,399
Speaker 1: it's everything you want, and again, ultimate driving machines. So

790
00:41:58,400 --> 00:42:01,440
Speaker 1: so why are they messing around with autonomous vehicles? That's

791
00:42:01,480 --> 00:42:07,600
Speaker 1: that's my thoughts. Plantation works specifically with the autonomous vehicle

792
00:42:08,120 --> 00:42:12,640
Speaker 1: section in BMW, and his response to the first part

793
00:42:12,719 --> 00:42:15,759
Speaker 1: would be, I imagine I'm putting some words into his mouth,

794
00:42:15,880 --> 00:42:17,920
Speaker 1: so take this with a grain of salt, But I

795
00:42:17,800 --> 00:42:21,480
Speaker 1: imagine he would say, it's where the future of vehicles

796
00:42:22,200 --> 00:42:25,919
Speaker 1: definitely happens to be completely understand that and you cannot

797
00:42:25,960 --> 00:42:28,960
Speaker 1: ignore it. If you do, you'll be left behind. Yes,

798
00:42:30,120 --> 00:42:36,480
Speaker 1: he said that the company was at a real um.

799
00:42:36,640 --> 00:42:39,279
Speaker 1: They were in a quandary. He actually said that. I

800
00:42:39,280 --> 00:42:42,560
Speaker 1: think it was last year he was brought in to talk,

801
00:42:42,640 --> 00:42:43,920
Speaker 1: or maybe it was a few years ago. He was

802
00:42:43,960 --> 00:42:47,920
Speaker 1: taught brought in to talk about the concepts they needed

803
00:42:47,920 --> 00:42:51,680
Speaker 1: to talk about in an upcoming conversation. They were gonna

804
00:42:51,719 --> 00:42:55,120
Speaker 1: hit like some corporate milestone, and they wanted to talk

805
00:42:55,160 --> 00:42:59,160
Speaker 1: about what are the next one years of BMW going

806
00:42:59,200 --> 00:43:02,440
Speaker 1: to look like? Now? Anyone who's listened to forward thinking.

807
00:43:02,560 --> 00:43:05,560
Speaker 1: You know, predicting the future is hard. Predicting five years

808
00:43:05,560 --> 00:43:08,360
Speaker 1: out is hard. Predicting a hundred years out is impossible.

809
00:43:08,400 --> 00:43:11,879
Speaker 1: I can't. Yeah. Uh. The only thing I can predict

810
00:43:11,920 --> 00:43:13,520
Speaker 1: tomorrow is that if I don't wear some block, I

811
00:43:13,560 --> 00:43:16,040
Speaker 1: will be sunburnt. That's it, because I know I'm gonna

812
00:43:16,040 --> 00:43:19,680
Speaker 1: be outside a lot. But he said it was his

813
00:43:19,760 --> 00:43:23,160
Speaker 1: job to try and help coordinate this, this vision of

814
00:43:23,200 --> 00:43:27,840
Speaker 1: BMW for the next one years, and taking into account

815
00:43:27,880 --> 00:43:31,279
Speaker 1: the fact that autonomous cars are I mean, everyone is

816
00:43:31,280 --> 00:43:33,319
Speaker 1: stuff byself. West was talking about them as if it's

817
00:43:33,360 --> 00:43:35,879
Speaker 1: a foregone conclusion. That's the future, that's where we're going.

818
00:43:37,280 --> 00:43:40,319
Speaker 1: So taking that as part of it, they actually had

819
00:43:40,680 --> 00:43:46,040
Speaker 1: serious internal discussions what does this mean with our slogan

820
00:43:46,480 --> 00:43:51,440
Speaker 1: the Ultimate driving Machine? What do we do? Do we rebrand?

821
00:43:52,000 --> 00:43:56,040
Speaker 1: How do we rebrand? This is something we pride ourselves upon.

822
00:43:56,160 --> 00:43:59,680
Speaker 1: It is a corporate identity. It's it's kind of the

823
00:43:59,719 --> 00:44:03,520
Speaker 1: center role mantra of the company's it's the DNA of

824
00:44:03,520 --> 00:44:07,840
Speaker 1: the company. They started playing with alternatives to the slogan,

825
00:44:07,920 --> 00:44:10,960
Speaker 1: like maybe we change it to something else, and they

826
00:44:10,960 --> 00:44:14,560
Speaker 1: tried a few different things out and all internally, and

827
00:44:14,640 --> 00:44:17,080
Speaker 1: no one liked them. No one liked them. And then

828
00:44:17,160 --> 00:44:22,839
Speaker 1: finally someone said, well, technically it's a driving machine. It's

829
00:44:22,840 --> 00:44:27,200
Speaker 1: a machine that's driving. It is the driving machine. We

830
00:44:27,280 --> 00:44:31,400
Speaker 1: can make the ultimate driving machine. So it's still the

831
00:44:31,440 --> 00:44:36,879
Speaker 1: same slogan. The context is redefined. Yeah, boy, I don't know.

832
00:44:37,200 --> 00:44:39,960
Speaker 1: That's why I went, yeah, I don't know about this

833
00:44:41,800 --> 00:44:44,399
Speaker 1: all right. Well, so it's almost like you're putting the

834
00:44:44,400 --> 00:44:47,239
Speaker 1: the emphasis on the on the other part. I don't

835
00:44:47,239 --> 00:44:48,400
Speaker 1: know how how do you even look at that? I

836
00:44:48,400 --> 00:44:50,719
Speaker 1: guess it's just how you would say that. I'd say

837
00:44:50,719 --> 00:44:54,680
Speaker 1: the emphasis. The emphasis previously was on driving because you

838
00:44:54,719 --> 00:44:57,520
Speaker 1: think of driving as a verb that people indulge. So

839
00:44:57,560 --> 00:45:01,120
Speaker 1: now it's the ultimate driving machine. I okay, Well, a boy,

840
00:45:01,200 --> 00:45:04,080
Speaker 1: that's so subtle. So if you make And this was

841
00:45:04,120 --> 00:45:06,799
Speaker 1: also an interesting discussion because people ask the questions and

842
00:45:07,480 --> 00:45:10,759
Speaker 1: what happens to brand identity in a future of autonomous

843
00:45:10,840 --> 00:45:14,120
Speaker 1: vehicles that are likely not going to be owned by

844
00:45:14,239 --> 00:45:17,000
Speaker 1: individuals but will be in some form of shared economy.

845
00:45:18,120 --> 00:45:21,720
Speaker 1: And they had a really good response for this. They said, well,

846
00:45:23,160 --> 00:45:26,200
Speaker 1: you could argue that all autonomous cars would essentially be

847
00:45:26,200 --> 00:45:30,120
Speaker 1: alike that one you know, robo uber car would be

848
00:45:30,160 --> 00:45:33,280
Speaker 1: the same as the next robot uber car, except eventually

849
00:45:33,320 --> 00:45:34,960
Speaker 1: someone would come along and say, you know what, We're

850
00:45:34,960 --> 00:45:37,160
Speaker 1: going to make a different robot uber car that has

851
00:45:37,760 --> 00:45:42,399
Speaker 1: X features in it, which appeals to why demographic somebody

852
00:45:42,440 --> 00:45:44,640
Speaker 1: will pay a premium for that feature, right, Because if

853
00:45:44,680 --> 00:45:48,800
Speaker 1: you're like, hey, we noticed that, uh that young people

854
00:45:48,880 --> 00:45:50,920
Speaker 1: between the ages of such and such and such and such,

855
00:45:50,960 --> 00:45:53,279
Speaker 1: they really care about these things and they don't care

856
00:45:53,280 --> 00:45:56,319
Speaker 1: about these other things. Let's make some cars that go

857
00:45:56,560 --> 00:45:59,640
Speaker 1: straight straight to what they care about, and we'll be

858
00:45:59,680 --> 00:46:02,239
Speaker 1: able to dominate that market. And then you get competition

859
00:46:02,360 --> 00:46:04,760
Speaker 1: there because other companies will follow, which means you still

860
00:46:04,880 --> 00:46:08,280
Speaker 1: end up getting that differentiation. You still get the brand identity.

861
00:46:08,320 --> 00:46:12,040
Speaker 1: The question is how do they define themselves so that

862
00:46:12,080 --> 00:46:15,720
Speaker 1: the experience of being in, say a BMW autonomous car

863
00:46:16,280 --> 00:46:19,440
Speaker 1: is different from being an Alexis autonomous car. And now

864
00:46:19,480 --> 00:46:21,560
Speaker 1: we know all they do is put the emphasis on machine.

865
00:46:22,480 --> 00:46:24,680
Speaker 1: That's it, right, But I thought, I guess it beats

866
00:46:24,719 --> 00:46:31,239
Speaker 1: like BMW we give up or BMW it was fun

867
00:46:31,280 --> 00:46:35,840
Speaker 1: well at last, Yeah, yeah, you can come up with

868
00:46:35,880 --> 00:46:37,960
Speaker 1: a bunch of funny slogans. I'm sure for it. But

869
00:46:37,960 --> 00:46:41,200
Speaker 1: but honestly like to stick with what they have. Really,

870
00:46:41,200 --> 00:46:43,160
Speaker 1: I think maybe if if that's what they're gonna do,

871
00:46:43,160 --> 00:46:46,080
Speaker 1: and they're gonna push it that way, that may be

872
00:46:46,200 --> 00:46:48,000
Speaker 1: exactly what they do. They may you may hear that

873
00:46:48,080 --> 00:46:52,000
Speaker 1: emphasized machine over over driving, which we are now. So

874
00:46:52,239 --> 00:46:54,120
Speaker 1: that's gonna be really weird, isn't it. I think so.

875
00:46:54,239 --> 00:46:56,360
Speaker 1: I mean, I also think it's gonna be weird to

876
00:46:56,400 --> 00:46:59,560
Speaker 1: be in a world where, assuming that the shared car

877
00:47:00,120 --> 00:47:03,439
Speaker 1: approach is what what wins out, it will be weird

878
00:47:03,480 --> 00:47:05,760
Speaker 1: to live in that world for lots of different reasons,

879
00:47:05,800 --> 00:47:07,680
Speaker 1: because a lot of us are very used to having

880
00:47:07,680 --> 00:47:10,759
Speaker 1: our own personal vehicle for multiple reasons, not just for

881
00:47:10,800 --> 00:47:14,279
Speaker 1: the convenience sake, but convenience outside of just I have

882
00:47:14,360 --> 00:47:17,120
Speaker 1: a car whenever I need to go someplace, assuming that's

883
00:47:17,120 --> 00:47:21,719
Speaker 1: not broken down. What about all the stuff that's in

884
00:47:21,760 --> 00:47:24,480
Speaker 1: a car, Like a lot of people have stuff that

885
00:47:24,520 --> 00:47:27,919
Speaker 1: they keep in their car, and it might equipment or

886
00:47:27,960 --> 00:47:31,200
Speaker 1: you know that diapers, Yeah, stuff like a new parents

887
00:47:31,239 --> 00:47:33,279
Speaker 1: might have a box of diapers in the car so

888
00:47:33,320 --> 00:47:36,120
Speaker 1: that when they travel places they have their supply right there.

889
00:47:36,160 --> 00:47:37,640
Speaker 1: If they need to run out to the car, they

890
00:47:37,640 --> 00:47:41,800
Speaker 1: can or but in the future if you have shared vehicles,

891
00:47:41,800 --> 00:47:43,919
Speaker 1: obviously you can't just keep stuff in a car. You'd

892
00:47:43,960 --> 00:47:47,359
Speaker 1: have to carry everything you need with you all the time. Uh,

893
00:47:47,360 --> 00:47:50,120
Speaker 1: and you either have to pare down the stuff so

894
00:47:50,160 --> 00:47:52,799
Speaker 1: that you're saying, well, I might not be prepared for

895
00:47:52,800 --> 00:47:55,600
Speaker 1: certain situations. But yeah, but it's a trade off. Isn't

896
00:47:55,640 --> 00:47:57,400
Speaker 1: it really nice to just kind of leave an umbrella

897
00:47:57,480 --> 00:47:58,960
Speaker 1: in your car and have it when you need it

898
00:47:59,000 --> 00:48:01,360
Speaker 1: and you don't have to remember every single time you

899
00:48:01,360 --> 00:48:03,960
Speaker 1: go out the door. It was funny because Plain actually said, well,

900
00:48:04,000 --> 00:48:08,280
Speaker 1: maybe we'll have services where you could actually store your stuff.

901
00:48:09,160 --> 00:48:11,799
Speaker 1: Uh and what would account what would end up being

902
00:48:11,800 --> 00:48:13,920
Speaker 1: like a mobile storage unit and you could just call

903
00:48:14,040 --> 00:48:15,719
Speaker 1: upon it to come to you whenever you needed it.

904
00:48:15,760 --> 00:48:18,480
Speaker 1: And I thought that puts more cars on the road.

905
00:48:19,040 --> 00:48:21,160
Speaker 1: That's a terrible idea. Yeah, I I'll just tell you

906
00:48:21,239 --> 00:48:23,319
Speaker 1: right now, that's a bad idea. Joe and Lauren both

907
00:48:23,320 --> 00:48:26,880
Speaker 1: agree with you, and I do too. I also thought like, well,

908
00:48:26,920 --> 00:48:30,000
Speaker 1: that doesn't sound like that's ideal. So yeah, there's obviously

909
00:48:30,040 --> 00:48:32,239
Speaker 1: some huge trade offs that would happen. We'll imagine a

910
00:48:32,280 --> 00:48:34,480
Speaker 1: block of lockers, you know, that's would it be a

911
00:48:34,520 --> 00:48:37,080
Speaker 1: block of lockers driving down the road with your stuff

912
00:48:37,120 --> 00:48:39,400
Speaker 1: and everybody else's stuff in it, And what if some

913
00:48:39,480 --> 00:48:41,560
Speaker 1: went across town. Need I know that they would probably

914
00:48:41,800 --> 00:48:44,520
Speaker 1: keep it in a central area, central area, but people

915
00:48:44,560 --> 00:48:47,000
Speaker 1: don't all like Like, let's say that my next door

916
00:48:47,000 --> 00:48:49,000
Speaker 1: neighbor and I both use the same unit because we

917
00:48:49,080 --> 00:48:52,120
Speaker 1: both live next door to each other. We don't necessarily

918
00:48:52,160 --> 00:48:54,640
Speaker 1: work anywhere close to each other, so he might work

919
00:48:54,640 --> 00:48:56,480
Speaker 1: on the other side of town. He needs his umbrella.

920
00:48:56,520 --> 00:48:59,680
Speaker 1: I need my umbrella. That car, I mean, easily you

921
00:48:59,680 --> 00:49:03,160
Speaker 1: could see problems with that model. There was a similar

922
00:49:03,200 --> 00:49:04,960
Speaker 1: model that I also. Let me see what you think

923
00:49:05,000 --> 00:49:09,640
Speaker 1: about this one by Scott so talking about shared cars. Now,

924
00:49:09,800 --> 00:49:12,320
Speaker 1: in the examples I've been giving so far, it's essentially

925
00:49:12,320 --> 00:49:15,279
Speaker 1: a fleet of service vehicles, something along the lines of

926
00:49:15,280 --> 00:49:18,200
Speaker 1: an uber or a lift, only with no human drivers. Right.

927
00:49:20,080 --> 00:49:23,160
Speaker 1: One of the alternatives I heard, Shad Law actually mentioned

928
00:49:23,160 --> 00:49:28,439
Speaker 1: this possibility, which I think is uh almost as bad

929
00:49:29,080 --> 00:49:35,080
Speaker 1: as the traveling locker idea. What if instead of it

930
00:49:35,239 --> 00:49:40,640
Speaker 1: being a fleet car, it's a communal car among multiple households,

931
00:49:40,680 --> 00:49:44,520
Speaker 1: and you own like a sixth of that car. Can

932
00:49:44,560 --> 00:49:48,400
Speaker 1: you imagine that working out? How would you guarantee that

933
00:49:48,480 --> 00:49:53,400
Speaker 1: the car would be available for all the households. Well, okay,

934
00:49:53,440 --> 00:49:56,200
Speaker 1: this isn't as bad an idea is, and I understand

935
00:49:56,239 --> 00:49:58,600
Speaker 1: that it's it's not great, and there's a lot of

936
00:49:58,600 --> 00:50:01,200
Speaker 1: flaws to this one as well. But um, isn't this

937
00:50:01,280 --> 00:50:03,800
Speaker 1: kind of the idea behind you know, the the companies

938
00:50:03,840 --> 00:50:05,839
Speaker 1: that allow you to have kind of a lease on

939
00:50:06,440 --> 00:50:08,920
Speaker 1: three different types of vehicles at one time and you

940
00:50:08,920 --> 00:50:11,400
Speaker 1: can use the one that you need when you need it.

941
00:50:11,480 --> 00:50:14,719
Speaker 1: So you lease, um, you know, a a sedan, you

942
00:50:14,800 --> 00:50:17,160
Speaker 1: lease a compact car that's very good, you know with mileage,

943
00:50:17,560 --> 00:50:20,160
Speaker 1: and you lease, you know, a pickup truck, and when

944
00:50:20,200 --> 00:50:22,200
Speaker 1: you need to pickup truck on the weekend, you can

945
00:50:22,200 --> 00:50:24,000
Speaker 1: rent that. You can you can have that brought to you,

946
00:50:24,120 --> 00:50:26,000
Speaker 1: or you can go get it. Um use it for

947
00:50:26,040 --> 00:50:27,920
Speaker 1: that amount of time. But what if somebody else is

948
00:50:28,000 --> 00:50:31,080
Speaker 1: using that that that sedan when you need it, you know,

949
00:50:31,120 --> 00:50:32,600
Speaker 1: they need it for the week and you also need

950
00:50:32,600 --> 00:50:34,040
Speaker 1: it for the week. I mean, how does that all work?

951
00:50:34,080 --> 00:50:38,359
Speaker 1: I don't Again, same set of problems. I think, yeah,

952
00:50:38,760 --> 00:50:40,920
Speaker 1: maybe a smaller scale and the one that I'm talking about, mean,

953
00:50:40,960 --> 00:50:44,960
Speaker 1: maybe if there's maybe the only way I can see

954
00:50:44,960 --> 00:50:47,520
Speaker 1: it working. Is that you again go back to the

955
00:50:47,560 --> 00:50:50,640
Speaker 1: fleet of cars. So you've got a fleet of autonomous cars.

956
00:50:51,360 --> 00:50:54,160
Speaker 1: You've got a group of people who have essentially collectively

957
00:50:54,280 --> 00:50:58,399
Speaker 1: invested so that they own the quote unquote owned one

958
00:50:58,440 --> 00:51:01,239
Speaker 1: of those cars it and actually own a car. They

959
00:51:01,280 --> 00:51:04,600
Speaker 1: just own a share share. Yeah, it's like a timeshare

960
00:51:04,640 --> 00:51:07,839
Speaker 1: for those vehicles that are on demand. So if I

961
00:51:07,920 --> 00:51:10,240
Speaker 1: call for a car and my neighbor calls for a car,

962
00:51:10,280 --> 00:51:13,000
Speaker 1: and they're both and we're both on this this plan,

963
00:51:13,400 --> 00:51:16,760
Speaker 1: two different cars come because of the way we've agreed

964
00:51:16,800 --> 00:51:20,520
Speaker 1: with this fleet, and it's the purchase price of the

965
00:51:20,600 --> 00:51:24,600
Speaker 1: vehicle that ends up covering the cost of the individual

966
00:51:24,640 --> 00:51:28,000
Speaker 1: trip as opposed to doing a trip, you know, a

967
00:51:28,040 --> 00:51:31,520
Speaker 1: fee per trip, like you like a typical rental car. Now. Yeah,

968
00:51:31,640 --> 00:51:34,879
Speaker 1: so essentially it would be like, all right, well, collectively,

969
00:51:35,080 --> 00:51:39,480
Speaker 1: we all got together and we put in thirty dollars

970
00:51:39,520 --> 00:51:42,719
Speaker 1: to quote unquote buy a car. What that really does

971
00:51:42,800 --> 00:51:47,200
Speaker 1: is give us unlimited travel using this service within its

972
00:51:47,400 --> 00:51:51,640
Speaker 1: range of service, you know, assuming that it isn't you know,

973
00:51:51,880 --> 00:51:55,399
Speaker 1: state or a countrywide or whatever. And I can see

974
00:51:55,440 --> 00:51:58,600
Speaker 1: it working that way maybe, but I can't see it

975
00:51:58,640 --> 00:52:02,319
Speaker 1: working in such a way where you actually physically have

976
00:52:02,760 --> 00:52:06,080
Speaker 1: one car to share between the multiple house That would

977
00:52:06,080 --> 00:52:08,719
Speaker 1: never work, just wouldn't work out. There's gotta be a

978
00:52:08,719 --> 00:52:10,279
Speaker 1: way around it, Like you said, it's gotta be there's

979
00:52:10,280 --> 00:52:12,840
Speaker 1: gotta be a pool of vehicles to draw from. It

980
00:52:12,960 --> 00:52:15,320
Speaker 1: just wouldn't work. But this was the I wish you

981
00:52:15,360 --> 00:52:18,399
Speaker 1: could have gone because I wish you could have seen, uh,

982
00:52:18,520 --> 00:52:20,399
Speaker 1: panels like these and some of the other ones too.

983
00:52:20,400 --> 00:52:23,200
Speaker 1: Like I was only able to go to three panels total,

984
00:52:23,680 --> 00:52:27,280
Speaker 1: but there were so many there were, all about autonomous vehicles.

985
00:52:27,320 --> 00:52:30,040
Speaker 1: It sounds fascinating. I really didn't know until some of

986
00:52:30,040 --> 00:52:31,799
Speaker 1: the you know, the reports that have been reading just

987
00:52:31,840 --> 00:52:34,799
Speaker 1: for this podcast, uh, that that this show is so

988
00:52:34,880 --> 00:52:37,520
Speaker 1: focused on that type of technology. I tend to think

989
00:52:37,520 --> 00:52:40,520
Speaker 1: of more of cs to be like that rather than

990
00:52:40,560 --> 00:52:43,479
Speaker 1: this south By Southwest. Yeah, it's it's It's interesting because

991
00:52:43,520 --> 00:52:47,960
Speaker 1: south By Southwest Interactive for many years was focused on

992
00:52:48,480 --> 00:52:51,400
Speaker 1: mobile apps, like that was the big thing, mobile apps

993
00:52:51,440 --> 00:52:56,120
Speaker 1: and some gaming. But usually you're talking about the next Twitter.

994
00:52:56,600 --> 00:52:59,080
Speaker 1: You know, a mere Cat and Periscope both came out

995
00:52:59,080 --> 00:53:04,759
Speaker 1: in mere Cat went under. Periscope is still around because

996
00:53:04,760 --> 00:53:09,279
Speaker 1: it's owned by Twitter. Uh. Anyway, that's the kind of

997
00:53:09,280 --> 00:53:11,880
Speaker 1: stuff you would expect. But they had different tracks of

998
00:53:11,920 --> 00:53:16,080
Speaker 1: programming under Interactive and one of the tracks was titled

999
00:53:16,120 --> 00:53:19,279
Speaker 1: Intelligent Future, and that's where all the robotics and autonomous

1000
00:53:19,360 --> 00:53:23,560
Speaker 1: vehicles and AI all those discussions fell under that. And

1001
00:53:23,719 --> 00:53:25,319
Speaker 1: uh so a lot of it had to do with

1002
00:53:25,880 --> 00:53:28,960
Speaker 1: the future of cars, and again not just autonomous cars,

1003
00:53:29,000 --> 00:53:32,520
Speaker 1: but the idea of, uh what what is it going

1004
00:53:32,520 --> 00:53:36,879
Speaker 1: to look like? From multiple standpoints. I think autonomous played

1005
00:53:36,880 --> 00:53:38,800
Speaker 1: a huge role because everyone just assumes that's going to

1006
00:53:38,880 --> 00:53:41,799
Speaker 1: be part of the future, no matter how it turns out. Yeah,

1007
00:53:41,840 --> 00:53:43,719
Speaker 1: you know, And one thing we should probably point out

1008
00:53:43,719 --> 00:53:46,080
Speaker 1: here is that we always talk about how it's happening.

1009
00:53:46,080 --> 00:53:48,719
Speaker 1: It's it's incrementally happening. What's going on, and we're we're

1010
00:53:48,719 --> 00:53:50,480
Speaker 1: getting little bits and pieces of it now and we

1011
00:53:50,520 --> 00:53:53,160
Speaker 1: see it, you know, in our everyday cars, but not

1012
00:53:53,280 --> 00:53:56,040
Speaker 1: the whole package yet. And the whole package they always

1013
00:53:56,120 --> 00:53:57,799
Speaker 1: it seems like it's always ten years out, is what

1014
00:53:57,840 --> 00:54:00,400
Speaker 1: they say. But I'm seeing estimates now that rain anywhere

1015
00:54:00,400 --> 00:54:03,600
Speaker 1: from three years to thirty years, and and those are

1016
00:54:03,640 --> 00:54:05,960
Speaker 1: all to be honest, those are all realistic. I mean,

1017
00:54:05,960 --> 00:54:08,680
Speaker 1: it could take thirty years. It could be faster than that,

1018
00:54:08,719 --> 00:54:12,400
Speaker 1: it could be we could I have this by you

1019
00:54:12,440 --> 00:54:14,640
Speaker 1: never know. Yeah, I think I think three years is

1020
00:54:14,680 --> 00:54:17,360
Speaker 1: probably what we would we would start to see actual

1021
00:54:17,480 --> 00:54:20,480
Speaker 1: vehicles make their way onto the roads. Thirty years is

1022
00:54:20,480 --> 00:54:22,680
Speaker 1: where you get to the point where you're at saturation. Yeah,

1023
00:54:22,680 --> 00:54:25,440
Speaker 1: and you know, I know on this podcast, even especially

1024
00:54:25,440 --> 00:54:27,279
Speaker 1: in car stuff, but on on this one, we've we've

1025
00:54:27,280 --> 00:54:30,319
Speaker 1: mentioned before they're already out there their cars that can

1026
00:54:30,400 --> 00:54:32,520
Speaker 1: drive you home from work without you touching the wheel

1027
00:54:32,600 --> 00:54:36,120
Speaker 1: or doing anything. You but they simply can't say it's

1028
00:54:36,120 --> 00:54:38,400
Speaker 1: an autonomous You have to be sitting down the wheel

1029
00:54:38,440 --> 00:54:40,640
Speaker 1: and allow it to You can allow it to do it,

1030
00:54:40,920 --> 00:54:42,040
Speaker 1: but you have to be there, and you have to

1031
00:54:42,080 --> 00:54:44,360
Speaker 1: be ready to take control at any moment. And often

1032
00:54:44,480 --> 00:54:48,000
Speaker 1: often you'll get a little little beep asking you to

1033
00:54:48,040 --> 00:54:50,040
Speaker 1: make sure you make contact with the wheel to prove

1034
00:54:50,120 --> 00:54:52,640
Speaker 1: that you're still paying attention and everything exactly, you're not

1035
00:54:52,840 --> 00:54:54,399
Speaker 1: taking a nap on the way home. You don't wanna,

1036
00:54:54,719 --> 00:54:57,280
Speaker 1: you know, those companies don't want to be liable for

1037
00:54:57,440 --> 00:54:59,960
Speaker 1: a terrible accident. So the three to thirty years were

1038
00:55:00,040 --> 00:55:03,120
Speaker 1: talking about is where the companies are actually confident enough

1039
00:55:03,160 --> 00:55:06,359
Speaker 1: to say, this is an autonomous you know, self driving car,

1040
00:55:06,560 --> 00:55:10,680
Speaker 1: Let it do it. Yeah, and and uh, I'm so

1041
00:55:10,719 --> 00:55:12,880
Speaker 1: glad you're able to join me on this this episode

1042
00:55:12,880 --> 00:55:15,640
Speaker 1: and talk about this kind of stuff. I know that

1043
00:55:15,719 --> 00:55:17,560
Speaker 1: I come across as I love to needle you with

1044
00:55:17,640 --> 00:55:20,600
Speaker 1: these because you're the car guy and it's fun. It's fun.

1045
00:55:20,719 --> 00:55:25,000
Speaker 1: I should also mention that pretty much everyone's agreed that

1046
00:55:26,280 --> 00:55:29,080
Speaker 1: personal car ownership is not ever going to go away

1047
00:55:29,200 --> 00:55:31,920
Speaker 1: entirely in the United States, that no one seemed to

1048
00:55:31,960 --> 00:55:35,640
Speaker 1: believe that that was the case. Uh. People said that

1049
00:55:35,880 --> 00:55:39,480
Speaker 1: it may be that fewer people own their own vehicles,

1050
00:55:39,760 --> 00:55:42,719
Speaker 1: but you'll you'll still be allowed to own and operate

1051
00:55:42,760 --> 00:55:45,600
Speaker 1: your own vehicle. I could see that happening, especially for

1052
00:55:45,640 --> 00:55:48,319
Speaker 1: things like rural areas. It doesn't make sense to have

1053
00:55:48,640 --> 00:55:52,560
Speaker 1: an autonomous car service out serving way all the way.

1054
00:55:52,719 --> 00:55:55,440
Speaker 1: Rural areas, like you know, cattle ranchers aren't going to

1055
00:55:55,440 --> 00:55:58,080
Speaker 1: have any need for that. Now, this is a congested

1056
00:55:58,120 --> 00:56:01,719
Speaker 1: city situation. Yes, this is for been dense urban environments

1057
00:56:01,719 --> 00:56:06,440
Speaker 1: and not it's not ideal for other situations. So, but

1058
00:56:06,600 --> 00:56:09,040
Speaker 1: one guy did say that he could envision a future

1059
00:56:09,080 --> 00:56:13,480
Speaker 1: in which car ownership of like an actual car owner

1060
00:56:14,239 --> 00:56:19,319
Speaker 1: will be about as rare as horse owners are today. Yeah,

1061
00:56:19,719 --> 00:56:23,000
Speaker 1: so there's plenty of people who still own horses, just

1062
00:56:23,400 --> 00:56:27,160
Speaker 1: not the general population. There's a there's a lot of

1063
00:56:27,239 --> 00:56:29,680
Speaker 1: wide open space out there, and I think that you know,

1064
00:56:29,719 --> 00:56:32,160
Speaker 1: that's where they will be still used. Of course, and

1065
00:56:32,440 --> 00:56:35,640
Speaker 1: maybe in cities, you know, I hate to say it,

1066
00:56:35,680 --> 00:56:37,360
Speaker 1: but there may be a point where you know, you

1067
00:56:37,360 --> 00:56:39,279
Speaker 1: can't drive into the city in your own personal vehic.

1068
00:56:39,520 --> 00:56:41,920
Speaker 1: You maybe have a giant parking lot in the outskirts

1069
00:56:41,920 --> 00:56:43,320
Speaker 1: of the city and you get out from there and

1070
00:56:43,360 --> 00:56:47,600
Speaker 1: then you take your city city approved transportation once you're inside.

1071
00:56:47,600 --> 00:56:49,040
Speaker 1: And I wouldn't that be something. I mean, it'd be

1072
00:56:49,480 --> 00:56:52,480
Speaker 1: a dramatic change in the way that we do things now.

1073
00:56:52,480 --> 00:56:55,960
Speaker 1: I mean it would significantly changed the well, the entire

1074
00:56:55,960 --> 00:56:59,680
Speaker 1: city escape really everything would be different. So um, it's fascinating.

1075
00:56:59,719 --> 00:57:02,120
Speaker 1: Top and again, thanks for inviting me in today to

1076
00:57:02,160 --> 00:57:03,759
Speaker 1: do this. I always have fun talking with you, and

1077
00:57:03,800 --> 00:57:05,360
Speaker 1: I know you like to rib me a little bit

1078
00:57:05,440 --> 00:57:08,319
Speaker 1: about car ownership and you know the way it's going,

1079
00:57:08,800 --> 00:57:10,440
Speaker 1: and uh, you know, I agree on a lot of

1080
00:57:10,480 --> 00:57:11,880
Speaker 1: this stuff. I mean, I think we we can have

1081
00:57:12,120 --> 00:57:15,240
Speaker 1: a decent conversation back and forth about Um, I understand

1082
00:57:15,320 --> 00:57:18,520
Speaker 1: that you know things are moving towards autonomous vehicles, but

1083
00:57:18,600 --> 00:57:20,640
Speaker 1: I also and I'm glad that you said it too,

1084
00:57:20,720 --> 00:57:23,080
Speaker 1: that you know it's never gonna go completely away. Well,

1085
00:57:23,120 --> 00:57:28,040
Speaker 1: and and to be fair, we're so so so in

1086
00:57:28,080 --> 00:57:31,600
Speaker 1: the baby stage of this, right, we're in the earliest

1087
00:57:31,640 --> 00:57:37,160
Speaker 1: stages of this autonomous era. That making any definitive statement

1088
00:57:37,400 --> 00:57:41,120
Speaker 1: such as autonomous cars will completely replace manual cars, or

1089
00:57:41,160 --> 00:57:44,560
Speaker 1: that car ownership will completely become a thing of the past,

1090
00:57:45,160 --> 00:57:48,800
Speaker 1: or even that manual cars will no longer be allowed

1091
00:57:49,240 --> 00:57:52,400
Speaker 1: in within city limits, any of those, It's so premature

1092
00:57:52,440 --> 00:57:56,800
Speaker 1: to make any kind of statement like that, Um. And honestly,

1093
00:57:57,080 --> 00:57:59,600
Speaker 1: it may turn out that we just see that the

1094
00:57:59,640 --> 00:58:03,360
Speaker 1: idea old mix is somewhere in the middle, with a

1095
00:58:03,400 --> 00:58:06,800
Speaker 1: mixture of autonomous cars and manually driven cars. We don't know,

1096
00:58:07,360 --> 00:58:11,400
Speaker 1: you know, the mathematical models suggests that if you went

1097
00:58:11,480 --> 00:58:13,840
Speaker 1: all autonomous, you avoid a lot of problems, but that's

1098
00:58:13,880 --> 00:58:16,120
Speaker 1: not necessarily the way it will actually shake out in

1099
00:58:16,160 --> 00:58:18,040
Speaker 1: real life. Well, I'm with you. I try to avoid

1100
00:58:18,040 --> 00:58:19,800
Speaker 1: the predictions because it just ends up making you look

1101
00:58:19,840 --> 00:58:22,560
Speaker 1: like a fool later, right when when what happens eventually?

1102
00:58:22,960 --> 00:58:25,720
Speaker 1: But that's pretty much status quo for me. Well, you

1103
00:58:25,800 --> 00:58:28,960
Speaker 1: kind of have to though, you know, anyways, I really

1104
00:58:29,000 --> 00:58:30,720
Speaker 1: I don't like to do that, but that I like

1105
00:58:30,800 --> 00:58:33,520
Speaker 1: to just kind of sit back and and kind of

1106
00:58:33,560 --> 00:58:35,520
Speaker 1: just take it all in because there's so many changes

1107
00:58:35,560 --> 00:58:38,680
Speaker 1: happening right now. It's it's actually pretty exciting. Yeah, I mean,

1108
00:58:39,240 --> 00:58:42,720
Speaker 1: and you know, you got to remember the autonomous cars,

1109
00:58:42,720 --> 00:58:45,520
Speaker 1: if they've become a thing, like a real serious thing,

1110
00:58:45,880 --> 00:58:49,360
Speaker 1: like most people believe, there are implications well beyond the

1111
00:58:49,360 --> 00:58:53,400
Speaker 1: auto industry that things that could really be effective, like

1112
00:58:53,400 --> 00:58:56,800
Speaker 1: like airline industry. You know, if you're able to jump

1113
00:58:56,840 --> 00:58:59,680
Speaker 1: into an autonomous autonomously driven car and you can do

1114
00:59:00,520 --> 00:59:02,760
Speaker 1: or you can go to sleep and you are not

1115
00:59:03,440 --> 00:59:06,000
Speaker 1: you don't need to get to whatever your destination is

1116
00:59:06,040 --> 00:59:09,000
Speaker 1: within a couple of hours, that could really impact a

1117
00:59:09,000 --> 00:59:12,120
Speaker 1: lot of airline travel. So well, yeah, I mean, okay,

1118
00:59:12,240 --> 00:59:14,440
Speaker 1: I know we gotta wrap up here, but you're you're

1119
00:59:14,480 --> 00:59:16,600
Speaker 1: making me think of you know, the the uh, the

1120
00:59:16,680 --> 00:59:19,360
Speaker 1: kind of pros cons you you wait, if you're making

1121
00:59:19,400 --> 00:59:21,400
Speaker 1: a short trip on a plane, you know, if you're

1122
00:59:21,400 --> 00:59:24,320
Speaker 1: flying from here to or Lando, sure, and it's like

1123
00:59:24,320 --> 00:59:26,160
Speaker 1: like from here, it's like an hour a little a

1124
00:59:26,160 --> 00:59:28,000
Speaker 1: little less than an hour and a half flight. Yeah,

1125
00:59:28,040 --> 00:59:29,800
Speaker 1: but then then then you have to take a new can.

1126
00:59:29,840 --> 00:59:31,320
Speaker 1: You gotta get up early, you gotta pack the car,

1127
00:59:31,360 --> 00:59:33,000
Speaker 1: you gotta get to the airport and park and all

1128
00:59:33,000 --> 00:59:35,600
Speaker 1: that stuff. It ends up taking more than half of

1129
00:59:35,640 --> 00:59:38,040
Speaker 1: the day. But you could just drive there too, And

1130
00:59:38,080 --> 00:59:40,560
Speaker 1: if you can do that in in a way that

1131
00:59:40,600 --> 00:59:43,280
Speaker 1: doesn't it's not taxing on you. It's it's it's actually

1132
00:59:43,360 --> 00:59:46,480
Speaker 1: comfortable and you're in your own your own car. It's

1133
00:59:46,480 --> 00:59:48,800
Speaker 1: a lot more comfortable than being crammed into an airplane.

1134
00:59:50,280 --> 00:59:52,360
Speaker 1: Why would you not do that? You have the opportunity

1135
00:59:52,440 --> 00:59:55,280
Speaker 1: to stop at a specific place to have food rather

1136
00:59:55,320 --> 00:59:58,160
Speaker 1: than just buying whatever little snack box happens to be

1137
00:59:58,200 --> 01:00:00,360
Speaker 1: on the plane. Yes, so you're right. It does change

1138
01:00:00,400 --> 01:00:03,360
Speaker 1: even you know that short distance travel. Yeah, Now for

1139
01:00:03,400 --> 01:00:07,880
Speaker 1: long distances, obviously, I think air travel. Unless you're determined

1140
01:00:07,920 --> 01:00:11,680
Speaker 1: to do the Great autonomous American road trip, I think

1141
01:00:11,920 --> 01:00:15,680
Speaker 1: you're The airlines will still be very much a strong

1142
01:00:15,760 --> 01:00:18,360
Speaker 1: player in that, but it will affect their bottom line,

1143
01:00:18,560 --> 01:00:22,320
Speaker 1: and that will affect how they route planes, how they

1144
01:00:22,360 --> 01:00:27,680
Speaker 1: design planes, how they how they price tickets. So there's

1145
01:00:27,720 --> 01:00:32,120
Speaker 1: some big potentially disruptive things that could happen ripple out

1146
01:00:32,280 --> 01:00:35,400
Speaker 1: from the automotive industry out into many other ones. So

1147
01:00:35,520 --> 01:00:40,800
Speaker 1: it's pretty interesting stuff. So, Scott, it's been fantastic. Thank

1148
01:00:40,800 --> 01:00:42,560
Speaker 1: you so much for joining. Well, thank you for having

1149
01:00:42,600 --> 01:00:45,080
Speaker 1: me again. I appreciate it. I'm I I'd be happy

1150
01:00:45,120 --> 01:00:46,880
Speaker 1: to come back and talk about stuff like this anytime

1151
01:00:46,880 --> 01:00:49,160
Speaker 1: you want. Well, I'll definitely be having you back on

1152
01:00:49,240 --> 01:00:52,040
Speaker 1: here before too long, I'm sure. And of course people

1153
01:00:52,040 --> 01:00:55,280
Speaker 1: can listen to your show car stuff you and Ben

1154
01:00:55,360 --> 01:01:00,400
Speaker 1: bowl and tackle all things vehicular. Uh, and that's it's

1155
01:01:00,400 --> 01:01:02,320
Speaker 1: amazing show. So if you guys haven't listened to that,

1156
01:01:02,360 --> 01:01:05,280
Speaker 1: make sure you go and subscribe to it because, uh,

1157
01:01:05,360 --> 01:01:08,320
Speaker 1: if there ever happened to be a question you had

1158
01:01:08,440 --> 01:01:14,640
Speaker 1: about vehicles, whether it's how they work or the design

1159
01:01:14,680 --> 01:01:18,000
Speaker 1: stories behind them, or even stuff like I still love

1160
01:01:18,440 --> 01:01:22,240
Speaker 1: the series you did about Coast to coast races. I

1161
01:01:22,320 --> 01:01:27,080
Speaker 1: loved it very good. I didn't know your listener. Well,

1162
01:01:27,080 --> 01:01:29,680
Speaker 1: I appreciate the you know, the plug for our show there.

1163
01:01:29,680 --> 01:01:32,560
Speaker 1: That's that's very nice. And and we have something like

1164
01:01:32,600 --> 01:01:34,640
Speaker 1: seven hundred and fifty episodes. So if you go to

1165
01:01:34,680 --> 01:01:37,200
Speaker 1: our you know, our our website which is car Stuff

1166
01:01:37,200 --> 01:01:40,200
Speaker 1: Show um dot com and you can find every one

1167
01:01:40,200 --> 01:01:42,560
Speaker 1: of the podcasts we've ever done there. I think iTunes

1168
01:01:42,640 --> 01:01:45,920
Speaker 1: limits it to what like two or three the latest

1169
01:01:45,960 --> 01:01:48,320
Speaker 1: two hundreds. So if you go to car Stuff Show

1170
01:01:48,360 --> 01:01:50,240
Speaker 1: dot com, then you can check out all of them

1171
01:01:50,320 --> 01:01:52,960
Speaker 1: and yeah, you know, take a look, find some that

1172
01:01:53,440 --> 01:01:57,560
Speaker 1: immediately tickle your fancy, listen to them, and then I

1173
01:01:57,600 --> 01:01:59,680
Speaker 1: guarantee you're gonna get hooked, and then you're just gonna

1174
01:02:00,120 --> 01:02:02,560
Speaker 1: go on a binge. Shucks. I appreciate it. Thank you.

1175
01:02:02,880 --> 01:02:05,280
Speaker 1: So guys. If you have any questions for me, any

1176
01:02:05,360 --> 01:02:08,000
Speaker 1: suggestions for future episodes or guests I should have on

1177
01:02:08,040 --> 01:02:10,440
Speaker 1: the show, anything like that, send me a message. The

1178
01:02:10,560 --> 01:02:14,760
Speaker 1: email address is tech stuff at how stuff works dot com,

1179
01:02:14,880 --> 01:02:17,520
Speaker 1: or drop me a line on Facebook or Twitter at

1180
01:02:17,560 --> 01:02:20,560
Speaker 1: both of those to handle is tech Stuff H. S W.

1181
01:02:21,320 --> 01:02:28,640
Speaker 1: And I will talk to you again really soon for

1182
01:02:28,760 --> 01:02:31,080
Speaker 1: more on this and thousands of other topics. Is it

1183
01:02:31,160 --> 01:02:42,080
Speaker 1: how stuff Works dot com