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Speaker 1: I have four three two one mission engine.

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Speaker 2: Full power and left off. Go Starlink, Go direct us out.

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Speaker 1: This week the Business of Tech powered by two Degrees Business.

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Speaker 1: I'm back in California to witness my first rocket launch

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Speaker 1: and find out more about the satellite to mobile services

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Speaker 1: coming to New Zealand that those satellites put into orbit

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Speaker 1: this week by SpaceX will soon be provided.

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Speaker 2: I'm Peter Griffin and I'm Ben Moore, and it's a

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Speaker 2: seriously innovation packed episode this week where rocket launchers, satellites

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Speaker 2: and autonomous vehicles and robots will be very soon. Hearing

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Speaker 2: from a special guest, doctor Alex Kendall, a Kiwi and

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Speaker 2: the chief executive of UK based startup Way, which raised

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Speaker 2: over a billion US dollars in funding earlier this year

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Speaker 2: to pursue Kendall's vision for autonomous vehicles and robot you.

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Speaker 1: Waive is one of the hottest AI startups in the

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Speaker 1: world at the moment, and there's a New Zealander driving it,

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Speaker 1: which is very cool.

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Speaker 2: Peter, your interview with Alex Kendall is coming up, but

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Speaker 2: first this week, we've both been at swanky industry events.

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Speaker 2: Although I think your Californian rockets and satellites definitely beat

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Speaker 2: my Auckland payments conference. So Peter, let's talk about this

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Speaker 2: SpaceX rocket launch. This wasn't another starship going up, was

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Speaker 2: it the one they caught with mechanical chop sticks a

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Speaker 2: month or so back.

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Speaker 1: No, Actually, this is a real conventional Falcon nine rocket,

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Speaker 1: which is really the workhorse of the SpaceX fleet, so

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Speaker 1: that puts satellites into orbit on a regular basis. This one,

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Speaker 1: in particular, was putting up around twenty styling satellites to

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Speaker 1: join that constellation is about six thousand of those in

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Speaker 1: orbit now, but it also put up thirteen so called

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Speaker 1: direct to sell satellites. Now, these are forming their own

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Speaker 1: much smaller constellation, a few hundred satellites with the specific

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Speaker 1: aim of allowing people on the ground to send a

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Speaker 1: text message and eventually a call and send data directly

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Speaker 1: from a standard mobile handset to a satellite and then

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Speaker 1: out to the world. Connectivity in places that you don't

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Speaker 1: have a cell site, which in New Zealand is about

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Speaker 1: forty percent of the country, and even out at sea,

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Speaker 1: this will be available out to twenty kilometers out to sea,

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Speaker 1: So the ability to send a text message when you're

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Speaker 1: effectively in a black spot, a mobile black spot. That's

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Speaker 1: what this is about. SpaceX has been putting up these

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Speaker 1: satellites all year and it's getting to the point now

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Speaker 1: it's got three launches left. They told us where they

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Speaker 1: will then flesh out that constellation of satellites, which allows

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Speaker 1: One New Zealand here, which is their partner, their customer,

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Speaker 1: to start launching that service.

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Speaker 2: Yeah, and that's the service that's been had a little

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Speaker 2: bit of controversy around it, isn't it, with One New

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Speaker 2: Zealand making claims in a campaign that it would provide

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Speaker 2: one hundred percent coverage across New Zealand. But the ComCom

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Speaker 2: has kind of come back and said, no, we think

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Speaker 2: you were a little bit misleading there.

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Speaker 1: It is a bit controversial. The Commics Commission claims they've

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Speaker 1: reached the Fair Trading Act by making these claims not

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Speaker 1: being nuanced enough in their advertising. They said in their advertising.

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Speaker 1: This was about eighteen months ago when they rebranded, they

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Speaker 1: really chose to use mobile to satellite as the big

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Speaker 1: game changing technology to align with the One New Zealand brand.

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Speaker 1: So they did the big ad campaign. Now basically said

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Speaker 1: by the end of twenty twenty four we'll have this

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Speaker 1: in the market now. Time is running short on that

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Speaker 1: they're testing it. The only drawback or the thing that's

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Speaker 1: holding it up is Federal Communications Commission approval for SpaceX.

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Speaker 1: So all of the local authorizations have been achieved, so

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Speaker 1: that's not a problem. They're allowed to test it under

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Speaker 1: a license from SpaceX, but they need to get that

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Speaker 1: signed off in the US before they can commercially launch

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Speaker 1: the service, so that may roll over now, most likely

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Speaker 1: into the new year. On the coverage itself, yeah, so

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Speaker 1: this is a contentious thing. If you're inside a house,

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Speaker 1: chances are you're not going to be able to get connectivity.

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Speaker 1: It really is the line of sight with the sky.

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Speaker 1: So you start getting into issues off what if you're

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Speaker 1: on there's some coverage of trees, you fall down into

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Speaker 1: a gully, you want to send a text message because

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Speaker 1: you've broken your leg. Will it be able to get out?

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Speaker 1: And the testing they've done, they're saying that there are

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Speaker 1: some situations where you can, but officially it's a line

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Speaker 1: of sight with the sky. So Tiger Governor, who's the

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Speaker 1: general manager of mobile networks at one end zet this

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Speaker 1: is what he had to say on that coverage issue.

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Speaker 3: The service will work anywhere you see the sky, but

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Speaker 3: we have tested it indoors, so near windows it works absolutely.

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Speaker 3: We've tested it in cars. Works in the car works

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Speaker 3: really well because clearly, you know, there's not a lot

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Speaker 3: of blockhead from the windscreen, so really impressive from a

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Speaker 3: coverage perspective. Now, also important to note is that you

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Speaker 3: will move onto this network. It'll say one and said

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Speaker 3: SpaceX network, so it's you know, you've kind of roamed

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Speaker 3: into the network. So you'll be very clearly aware that

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Speaker 3: you're in the network, and you'll only go onto that

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Speaker 3: network where there's no usable signal on the one insid network.

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Speaker 3: The algorithms that's you know, in the phone and the

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Speaker 3: network will make sure that happens all seamlessly in the background,

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Speaker 3: so you'll be able to roam onto the network under

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Speaker 3: the right conditions.

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Speaker 1: That's interesting.

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Speaker 2: Yeah, it reminds me a little bit of the old

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Speaker 2: days of the CDM a network which Telecom used originally,

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Speaker 2: and if you were in a pine forest, essentially the

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Speaker 2: needles scattered it and you can connect in a forest.

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Speaker 2: It is just a known limitation sometimes of these connectivity technologies.

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Speaker 2: But you know, it sounds like They've put some thought

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Speaker 2: into it, testing it in windows and things like that.

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Speaker 2: So I would imagine if you are trapped under you know,

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Speaker 2: touchwood rubbled in an earthquake might not be super helpful,

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Speaker 2: but if you could just manage to get into the

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Speaker 2: right area, you might be able to get something out.

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Speaker 1: I think we'll see all sorts of scenarios play out there,

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Speaker 1: but ultimately the majority of scenarios I think will be

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Speaker 1: someone who's like, oh, I need to I need to

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Speaker 1: get a message out urgently to warn people or tell

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Speaker 1: people where I am. I'm going to climb up to

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Speaker 1: the top of that hill or at least get out

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Speaker 1: from under the undergrowth, so I could see those sorts

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Speaker 1: of scenarios. The reality is about this is that the

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Speaker 1: more satellites that they have up there, and this is

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Speaker 1: starting to come together now. But the key use case

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Speaker 1: of this is really, you know, disasters when the rest

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Speaker 1: of the network is down for whatever reason, like we

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Speaker 1: saw with cyclone Gabrielle, and we saw a case of

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Speaker 1: this a few months ago in the US and Florida

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Speaker 1: where the hurricane went through took out a lot of

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Speaker 1: the terrestrial mobile infrastructure, and so SpaceX actually turn it on.

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Speaker 1: They said, look, this is available if you've got a

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Speaker 1: cell phone, try and connect to it, see if you

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Speaker 1: can use it. And they did. This is what Tigan

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Speaker 1: had to say about that.

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Speaker 3: They did a really loude scale test with the hurricanes

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Speaker 3: in Florida recently. The numbers they shared with US, and

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Speaker 3: I think it's public as well, is during that time

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Speaker 3: they had twenty seven thousand devices connect onto the network

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Speaker 3: and they sent two hundred and fifty thousand texts over

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Speaker 3: a few days. That's two hundred and fifty thousand people

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Speaker 3: communicated that would not have been communicating. It's pretty awesome

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Speaker 3: during an event where there was no coverage.

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Speaker 2: So that's a lot of people connecting in one go,

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Speaker 2: two hundred and fifty thousand text messages being sent in

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Speaker 2: that case, So, you know, a terrible disaster, tragedy for

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Speaker 2: a lot of people, which is incredibly sad, but a

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Speaker 2: demonstration of how these kinds of technologies can help to

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Speaker 2: mitigate some of the impact of that disaster.

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Speaker 1: Yeah, that was literally the biggest test to date. I mean,

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Speaker 1: they've been testing it in the US with T Mobile.

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Speaker 1: They've been doing some testing now in New Zealand and

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Speaker 1: apparently the New Zealand. Testing is going pretty well, but

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Speaker 1: it's not at the level yet where they're happy for

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Speaker 1: a commercial launch. The number of texts that have gone

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Speaker 1: through successfully, they need to get that up a bit,

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Speaker 1: so they will continue to work on that. But yeah,

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Speaker 1: in terms of the competitive dynamic in this market, there

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Speaker 1: are a bunch of companies that are trying to do this,

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Speaker 1: and they range from Global Star, which is the satellite

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Speaker 1: operator that underpins Apple's Emergency SOS service, which is available

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Speaker 1: in New Zealand. So you can see in a text

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Speaker 1: message if you're in trouble via an Apple device and

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Speaker 1: iPhone and it will go to some call center and

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Speaker 1: they will alert the emergency services. So that exists at

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Speaker 1: the moment. But the ones that are trying to go

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Speaker 1: a little bit further, there's a few companies. One is

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Speaker 1: called ast which is growing in terms of numbers of

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Speaker 1: customers that's got on board link We've talked about in

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Speaker 1: relation to two degrees in Spark, So that's the other

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Speaker 1: provider that is launching services here, but they haven't got

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Speaker 1: nearly as many satellites up there yet as SpaceX does,

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Speaker 1: so some question marks over them. This is what Tiger

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Speaker 1: had to say about that particular technology from Link.

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Speaker 3: I'm not sure if it's well one that we tested

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Speaker 3: with them, probably a couple of years back now, I

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Speaker 3: personally tested went out to gisabonan and tested their service.

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Speaker 3: At the time, it was a two G service and

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Speaker 3: it wasn't very reliable. You had to stand like three

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Speaker 3: meters away from the mobile device because it might lose connectivity,

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Speaker 3: and it was pretty clunky at the time, no doubt.

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Speaker 3: You know, they have improved since, and it was at

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Speaker 3: that point that I said, yeah, they're not for us.

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Speaker 3: I'm recommending we go with SpaceX, and Space said nothing

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Speaker 3: at the time, but we chose to back a horse

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Speaker 3: who had capability than some underwared technology. So definitely Link

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Speaker 3: started earlier. And if you listen to their CEO, they'll

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Speaker 3: say they were the first to prove out the technology,

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Speaker 3: to prove the physics and whatever, and they've got a

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Speaker 3: whole lot of patents around it, which they do, but

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Speaker 3: they certainly don't have the financial backing. And if you

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Speaker 3: google them recently, they haven't said anything for months, which

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Speaker 3: is pretty you know, speaks a.

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Speaker 2: Lot, a little bit of a spicy comment there. Clearly

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Speaker 2: clearly very much found that SpaceX was you know, like

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Speaker 2: that was kind of the comment that One New Zealand

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Speaker 2: was making when they first made that agreement. Yes, Link

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Speaker 2: has the proof of concept is a little bit ahead,

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Speaker 2: but SpaceX is infrastructure and funding is so much stronger

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Speaker 2: they go to overtake it. It sounds like that may

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Speaker 2: be very well becoming true.

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Speaker 1: It's interesting, you know Tigan Governeder from One end Z,

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Speaker 1: the gm A for Mobile network, you know, saying there, look,

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Speaker 1: I was the one who really made this call. I

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Speaker 1: wasn't happy with the trials I saw with Link, so

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Speaker 1: I've recommended we go with this different technology from SpaceX,

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Speaker 1: which they have now done Link. It's going to be

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Speaker 1: interesting to see in the next few months. They still

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Speaker 1: have this merger to do this, this listing that they

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Speaker 1: were going to do to raise more capital that's been

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Speaker 1: delayed during the year. Apparently it's still on track. But

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Speaker 1: the bottom line is that, you know, when two degrees

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Speaker 1: and Spark get this going, it's going to be a

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Speaker 1: fair amount of time behind SpaceX's arrangement with One end

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Speaker 1: Z and how many satellites will they have. What will

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Speaker 1: that mean for the delay and getting a message up there?

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Speaker 1: And back Link was the one that came up with

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Speaker 1: this technology. But as we've seen many times in history,

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Speaker 1: it's not always the one it's first with the best

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Speaker 1: technology that actually wins.

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Speaker 2: Yeah, it's the one with the most money. Yeah, in

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Speaker 2: which it happens to be the case with SpaceX the

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Speaker 2: other one.

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Speaker 1: I said to the team at one end, Z, you

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Speaker 1: know what happens if Apple decides to expand from what

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Speaker 1: they're doing at the moment they're sending an SOS message,

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Speaker 1: would they move into providing voice data services via this

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Speaker 1: network of satellites that they're actually investing with Global Star

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Speaker 1: a lot more money in Tigan doesn't seem to think

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Speaker 1: so that there are a few barriers to doing that.

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Speaker 1: This is what he said about that.

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Speaker 3: Like, we still own the customer relationship. So you know,

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Speaker 3: Apple can do over the top, Stuffy can even do

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Speaker 3: over the top voice, but you need a lot of

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Speaker 3: satellites to do voice. I don't see it scanting well,

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Speaker 3: but you know that's my prediction, and certainly I think

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Speaker 3: they will. They will never compete with the architecture that

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Speaker 3: SpaceX have when it comes to scale.

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Speaker 2: It's I think, you know, he's identified that they had

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Speaker 2: different use cases. Really that in terms of what one

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Speaker 2: is aiming to do, sorry, in terms of what Apple

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Speaker 2: is aiming to do with their you know, emergency contact

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Speaker 2: service is a different situation to creating a full connectivity

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Speaker 2: in space cell tower solution. And yeah, I still think

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Speaker 2: it's interesting because you know, they're they're they're they are

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Speaker 2: a company, and they're growing and they're doing more in

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Speaker 2: that space. So it could potentially build out that capability.

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Speaker 2: But you know, SpaceX has such a dominant position in

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Speaker 2: so many ways that it might be difficult to contend

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Speaker 2: with them.

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Speaker 1: Yeah, which I think is on the mind of regulators

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Speaker 1: in the US. Okay, space X is already massive in

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Speaker 1: satellite broadband. They're they're really building out this network of

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Speaker 1: you know, direct to sell satellites as well, either going

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Speaker 1: to own the game entirely. That's not what the US

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Speaker 1: government wants. It wants a diversity off players there. But

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Speaker 1: I think Tiger's right about the fact that it's unlikely

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Speaker 1: Apple would go over the top. They're more likely to

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Speaker 1: partner with the local provider because the local provider has

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Speaker 1: the primary relationship, the billing relationship really with the customer.

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Speaker 1: It's hard to go around that. You also need local

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Speaker 1: spectrum rights to run these services as well. So I

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Speaker 1: don't see that happening. But the big question might really

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Speaker 1: is what's the business case here? So they paid presumably

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Speaker 1: a lot of money, they haven't said how much, but

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Speaker 1: it's a significant investment to SpaceX to have exclusive rights

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Speaker 1: for a period of time to run the service. They've

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Speaker 1: said that the service will only be available on higher

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Speaker 1: end postpaid plans. They haven't said which ones yet, but

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Speaker 1: presumably you know you're gonna have to pay. At the moment.

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Speaker 1: Their entry level plan is about forty five dollars on

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Speaker 1: postpaid mobile, so presumably you might have to go to

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Speaker 1: a sixty five dollars plan to get this, and it

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Speaker 1: would be available on the eighty five dollars plan as well.

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Speaker 1: So are there enough people out there who work in

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Speaker 1: rural areas where connectivity is really patchy, where safety is

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Speaker 1: a big priority, where the boss will say, I can't'm

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Speaker 1: putting everyone on the next plan up. You'll get more

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Speaker 1: mobile data, sure, but you'll also get this ability to

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Speaker 1: send back to head office text messages and then eventually

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Speaker 1: make calls and send low levels of data from way

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Speaker 1: out in the boondocks of New Zealand. That's really the

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Speaker 1: play here, and either enough people between business people, agricultural

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Speaker 1: workers and then the enthusiasts going out into the great

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Speaker 1: wide open, and maybe bodies as well who are willing

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Speaker 1: to go on to a higher plan or stay on

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Speaker 1: a higher plan to take advantage of this. That's where

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Speaker 1: one in z is actually going to make money to

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Speaker 1: subsidize and pay for the service.

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Speaker 2: Yeah, and can they do it at a way that

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Speaker 2: is more affordable than just having portable starlink that you

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Speaker 2: can roll out onto a site for all your workers

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Speaker 2: if you're doing you know, agriculture or something.

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Speaker 1: Starlink Mini is coming as well, but a battery powered

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Speaker 1: version of that you can put in the backpack and

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Speaker 1: take with you as well, so there's more diversity. I

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Speaker 1: guess they would argue. We're also a reseller of that

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Speaker 1: as well. With Space six. This is now dominant. SpaceX

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Speaker 1: has suddenly become in the telecommunications industry in the space

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Speaker 1: of a couple of years. It's extraordinary, isn't it. And

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Speaker 1: you know I didn't really five years ago.

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Speaker 2: I did not have on my Bengo card Elon musk

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Speaker 2: owns worldwide Telecommunications, but maybe I should have, you know,

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Speaker 2: all right, so we will keep an eye on progress

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Speaker 2: as one New Zealand approaches the launch of satellite to

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Speaker 2: mobile services as well as all those other satellites to

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Speaker 2: mobile services that have been promised and now let's go

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Speaker 2: from satellites two Autonomous cars. Wave is a London based

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Speaker 2: company founded by Canterbury born engineer and entrepreneur doctor Alex Kendall,

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Speaker 2: who studied at University of Auckland before heading to the

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Speaker 2: UK on a scholarship to the University of Cambridge.

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Speaker 1: That's where he really started pursuing the idea of using

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Speaker 1: artificial intelligence to control autonomous systems like cars. He came

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Speaker 1: up with the idea for a sort of general purpose

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Speaker 1: AI that learns from its surroundings rather than relying on

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Speaker 1: exhaust of programming and Wave was born.

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Speaker 2: Yeah, Wave's billion dollar rays in May, broke records for

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Speaker 2: a UK startup, and it's now moving to the US.

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Speaker 2: Has a partnership with Uber, as well as online grocery

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Speaker 2: firms like the UK chain Asda. Peter You caught up

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Speaker 2: with Alex to talk about Wave's unique approach to the

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Speaker 2: autonomous vehicle challenge and how it differentiates itself from the

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Speaker 2: likes of Tesla, Weimo and Cruz. So here's your interview

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Speaker 2: with Alex Kendall right after the.

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Speaker 1: Brak Alex, Welcome to the Business of Tech. Thanks so

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Speaker 1: much for coming on the show all the way from London. Hey,

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Speaker 1: glad to be here, and what an amazing year you

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Speaker 1: have had. Just reflecting back, we had that announcement in

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Speaker 1: May which completely my jaw hit the floor here in

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Speaker 1: New Zealand reading the New Zealand Herald to learn that

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Speaker 1: you'd raised the best part of New Zealand dollars one

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Speaker 1: point seven billion, around a billion dollars soft Bank, in Vidia, Microsoft,

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Speaker 1: some huge names in there. Probably the biggest capital raise

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Speaker 1: at least for a New Zealand startup founder so far,

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Speaker 1: and I think she soon acted. The Prime Minister at

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Speaker 1: the time said it anchors the UK's position as an

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Speaker 1: AI superpower. Was one of the biggest AI capital raises

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Speaker 1: in Europe at that point. Just reflect back on that,

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Speaker 1: you know, seven or eight months ago, getting that deal

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Speaker 1: over the line, what was that like, what was that

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Speaker 1: feeling when you got that capital secured?

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Speaker 4: Because a super exciting moment for our whole team. But

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Speaker 4: raising money is a bit of a lagging indicator of success,

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Speaker 4: and for us, it's a bit like reaching the start line.

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Speaker 4: So we've been a deep tech company for well seven

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Speaker 4: years since we incorporated. I've been working on these ideas

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Speaker 4: for many, many more years than that, and I think

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Speaker 4: this represents this special moment where we're transferring from being

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Speaker 4: an R and D focused company into a product company.

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Speaker 4: And what's exciting about this money that we're raised is

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Speaker 4: it gives us the ability to go and take the

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Speaker 4: embodied AI that we're building and go and get it

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Speaker 4: shipped into a product that can actually delight people around

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Speaker 4: the world. And being able to switch from that R

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Speaker 4: and D focus and move into a product journey is

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Speaker 4: a special moment. But because of that, it does feel

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Speaker 4: like the start line. So we've got a lot ahead

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Speaker 4: of us, and we're excited to get stuck in.

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Speaker 1: Yeah, And I think when a significant amount of money

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Speaker 1: like that is raised, that's when you're on the mainstream

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Speaker 1: radar of a lot of people who potentially could be driving.

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Speaker 1: These guys already had the likes of Bill Gates in

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Speaker 1: the backseat of driverless coronets, so people in the industry

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Speaker 1: know the journey you've been on, but it's now become

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Speaker 1: clear to everyone else where it's going. But take us

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Speaker 1: into you've called this the space race of my generation

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Speaker 1: self driving vehicles. What inspired you to focus on this

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Speaker 1: particular field of technology.

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Speaker 4: Well, I remember when I was going into Auckland engineering school.

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Speaker 4: You know, you had the choice of do you want

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Speaker 4: to do buy medical, mechanical, civil engineering. All of the

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Speaker 4: different like that all really fascinated me, and I ended

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Speaker 4: up doing mechatronics robotics engineering because it was the intersection

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Speaker 4: of pretty much everything electrical, mechanical software and you know,

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Speaker 4: for me, robotics and bodied eye self driving cars, these

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Speaker 4: kind of things are just the nexus of so many

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Speaker 4: different disciplines even today. A huge part of it is

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Speaker 4: everything from like public policy, regulation as well as of

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Speaker 4: course simulation, machine learning, and all of the engineering behind

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Speaker 4: these kinds of system So it's a really cross functional challenge.

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Speaker 4: I also can't understate how hard it is to build

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Speaker 4: an AI, let alone embody it in the physical world.

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Speaker 4: I mean the thing about of course, we're seeing AI

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Speaker 4: each impact earlier in area where from search engines to

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Speaker 4: knowledge retrieval to chatbots, because these systems, you know, don't

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Speaker 4: have the physical world integration or the level of safety

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Speaker 4: required to operate. But you know, what we're building at

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Speaker 4: Wave is an embodied AI that can bring AI into

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Speaker 4: the physical world. It can enable AI to be trusted

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Speaker 4: to do tasks for us, whether it's driving our car

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Speaker 4: or eventually in areas like manufacturing or domestic robotics. And

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Speaker 4: you know, what this represents is an opportunity for us

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Speaker 4: to develop the next level of tools. I mean, we've

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Speaker 4: built as humanity, We've built the wheel, calculated the computer.

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Speaker 4: But you know, largely speaking, a lot of the you know,

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Speaker 4: machines that we use are still relatively dumb, and I

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Speaker 4: think the opportunity to bring AI into them will let

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Speaker 4: us trust them, delegate more tasks to them, and ultimately

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Speaker 4: free up our time for what matters most. So it's

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Speaker 4: a long way of saying this is a huge opportunity,

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Speaker 4: but it's just so hard because of the level of

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Speaker 4: safety required. Let's take driving as an example. It's not

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Speaker 4: like it's a constrained area. You could have conceivably any

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Speaker 4: weather occur. You could have vulnerable road users or other

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Speaker 4: cars behave in a way that you can't predict. And

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Speaker 4: so what that requires is a system that has the

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Speaker 4: general purpose intelligence and reasoning to deal with things it's

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Speaker 4: never seen before. And to build that level of common sense,

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Speaker 4: reasoning and robustness on a robot that's out there in

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Speaker 4: the physical world in ways that you can't control once

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Speaker 4: you've shipped it. That is an extraordinary challenge.

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Speaker 1: And so.

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Speaker 4: You know, that's that's really what we mean when it's

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Speaker 4: when I say it's a space race of a generation.

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Speaker 4: I think it truly is one of the greatest engineering

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Speaker 4: challenges out there today, one that I'm passionate to be

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Speaker 4: spending my entire life working on.

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Speaker 1: Yeah, and this idea of embodied AI another term you

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Speaker 1: use end to end learning. I mean the narrative around

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Speaker 1: autonomous vehicles. It's sort of been dominated by Tesla to date.

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Speaker 1: There's been other companies like Mobilized, a great Israeli company

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Speaker 1: I've visited many years ago. You've got Waymo vehicles. I've

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Speaker 1: seen around the streets of San Francisco, which is quite

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Speaker 1: surreal seeing a car there with no one behind the

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Speaker 1: steering wheel. You're probably used to that, but I find

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Speaker 1: it a bit freaky. Your approach. How is it fundamentally

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Speaker 1: differing or is it differing fundamentally from the companies that

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Speaker 1: sort of come before.

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Speaker 4: Well, we've had a really contrar and approach this problem.

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Speaker 4: So when we say we use end to end learning,

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Speaker 4: what that means is that we have a machine learning

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Speaker 4: model in neural network that learns to drive our cars

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Speaker 4: all the way from input to output. So what that

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Speaker 4: means is that it's a data driven stack. We don't

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Speaker 4: program rules it'say if a red light stop or for

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Speaker 4: green light go. Rather, we just feed the system a

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Speaker 4: lot of data and let it learn those patterns in

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Speaker 4: the data. And so the result is that the scale

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Speaker 4: is a lot better, it's a lot more cost effective.

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Speaker 4: We don't need armies of engineers that program the exact

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00:24:13,000 --> 00:24:16,720
Speaker 4: rules or matt that the the robot of self driving

436
00:24:16,760 --> 00:24:20,359
Speaker 4: car needs to follow. Instead, we can learn concepts that

437
00:24:20,960 --> 00:24:23,480
Speaker 4: more complex than humans can hand engineer. And the really

438
00:24:23,520 --> 00:24:28,040
Speaker 4: interesting thing is this has been a very contrarian approach,

439
00:24:28,080 --> 00:24:31,280
Speaker 4: which is something that New Zealand's certainly used to, and

440
00:24:32,000 --> 00:24:35,320
Speaker 4: it was I don't know, I really enjoyed it in

441
00:24:35,359 --> 00:24:38,800
Speaker 4: the early days of building Wave because I essentially I

442
00:24:38,880 --> 00:24:41,200
Speaker 4: finished my PhD and was convinced that this would be

443
00:24:41,280 --> 00:24:43,440
Speaker 4: the way that the future of robotics would work, that

444
00:24:43,520 --> 00:24:47,840
Speaker 4: we would have learning machines that could learn to understand

445
00:24:47,840 --> 00:24:51,159
Speaker 4: their world and interact in a way that would harness

446
00:24:51,200 --> 00:24:53,359
Speaker 4: the power of machine learning. And at the time in

447
00:24:53,359 --> 00:24:56,639
Speaker 4: twenty seventeen, this was an idea that was almost completely

448
00:24:56,760 --> 00:25:00,720
Speaker 4: rejected by the industry. We'd just seen billions of dollars

449
00:25:00,840 --> 00:25:02,920
Speaker 4: raised by the big tech companies to go and build

450
00:25:03,119 --> 00:25:07,480
Speaker 4: self driving cars. We've seen leaders predict that this technology

451
00:25:07,520 --> 00:25:12,240
Speaker 4: would be widespread in the next couple of years. And equally,

452
00:25:12,320 --> 00:25:14,520
Speaker 4: when I went and pitch these ideas to these folks,

453
00:25:14,800 --> 00:25:17,119
Speaker 4: they said, oh, into any will ever be safe and

454
00:25:17,240 --> 00:25:20,320
Speaker 4: gave a bunch of dismissive arguments. So I guess that

455
00:25:20,760 --> 00:25:22,560
Speaker 4: led me to go, go build the company, to go

456
00:25:23,600 --> 00:25:28,120
Speaker 4: go make this technology a reality. And today, probably last year,

457
00:25:28,200 --> 00:25:31,360
Speaker 4: I think we saw the turning point where a lot

458
00:25:31,440 --> 00:25:35,080
Speaker 4: of folks started to shift their position. And I wouldn't

459
00:25:35,080 --> 00:25:37,440
Speaker 4: say this is the incumbent approach day, far from it.

460
00:25:37,480 --> 00:25:39,040
Speaker 4: We've still got a lot to prove, but at least

461
00:25:39,080 --> 00:25:42,080
Speaker 4: it's no longer being dismissed out of hand. And the

462
00:25:42,160 --> 00:25:44,440
Speaker 4: exciting thing for us is that in terms of the

463
00:25:44,560 --> 00:25:48,040
Speaker 4: car companies that we licensed this technology to a year ago,

464
00:25:48,160 --> 00:25:50,440
Speaker 4: we couldn't even get a meeting with their executives, and

465
00:25:50,520 --> 00:25:53,479
Speaker 4: now we have CEOs of all the major car companies

466
00:25:53,480 --> 00:25:56,440
Speaker 4: around the world lining up to come experiences technology and

467
00:25:56,440 --> 00:25:59,159
Speaker 4: when they'd ride in it, they get this chat GPT

468
00:25:59,320 --> 00:26:01,240
Speaker 4: reaction going, oh my god, I want this and my

469
00:26:02,040 --> 00:26:05,840
Speaker 4: product yesterday. And that's a that's a really you know,

470
00:26:06,280 --> 00:26:10,520
Speaker 4: a really exciting moment. But for us from here, it's

471
00:26:11,240 --> 00:26:13,760
Speaker 4: it's all about, well, we've still got a lot, we've

472
00:26:13,800 --> 00:26:15,520
Speaker 4: got a lot of work to do, but at least

473
00:26:16,080 --> 00:26:18,480
Speaker 4: the core ideas I think are starting to play out

474
00:26:18,880 --> 00:26:22,560
Speaker 4: and our thesis certainly you know, still holds from from

475
00:26:22,600 --> 00:26:23,920
Speaker 4: when we started seven years ago.

476
00:26:24,400 --> 00:26:29,040
Speaker 1: Yeah, just jumping back to the University of Auckland. Interestingly,

477
00:26:29,160 --> 00:26:32,960
Speaker 1: you went into the second year off your mechatronics engineering

478
00:26:34,119 --> 00:26:36,080
Speaker 1: degree there. What was that like sort of was that

479
00:26:36,160 --> 00:26:37,760
Speaker 1: a bit freaky sort of going in with all those

480
00:26:37,800 --> 00:26:40,800
Speaker 1: second year students as your first experience of university.

481
00:26:42,080 --> 00:26:44,760
Speaker 4: Oh, look, they have some of my some of my

482
00:26:44,840 --> 00:26:45,520
Speaker 4: best friends now.

483
00:26:45,680 --> 00:26:46,320
Speaker 1: So it was.

484
00:26:48,080 --> 00:26:49,560
Speaker 4: I don't know, I don't I don't spend too much

485
00:26:49,560 --> 00:26:52,840
Speaker 4: time getting hung up about the whole imposter syndrome. I've

486
00:26:53,000 --> 00:26:55,480
Speaker 4: made enough of a fall of myself in life.

487
00:26:55,560 --> 00:26:56,399
Speaker 1: So it was.

488
00:26:58,160 --> 00:27:00,320
Speaker 4: It was an adventure right, Like I. I I don't

489
00:27:00,359 --> 00:27:03,560
Speaker 4: think I've ever had this great, you know, strategic plan

490
00:27:03,720 --> 00:27:06,200
Speaker 4: for how my life's going to play out. I've I've

491
00:27:06,520 --> 00:27:10,600
Speaker 4: certainly been fortunate enough with you know, the scholarships that

492
00:27:10,640 --> 00:27:12,399
Speaker 4: I've been able to win to Auckland or to the

493
00:27:12,520 --> 00:27:15,560
Speaker 4: University of Cambridge that have given me some of that flexibility,

494
00:27:16,080 --> 00:27:20,600
Speaker 4: which I certainly recognized. But beyond that, I've yeah, I've

495
00:27:20,680 --> 00:27:25,440
Speaker 4: just really tried to prioritize my time of whatever's most interesting,

496
00:27:26,320 --> 00:27:30,280
Speaker 4: what's what's what's the biggest adventure. And I think I

497
00:27:30,280 --> 00:27:35,040
Speaker 4: actually came to Auckland and ended up not doing starting

498
00:27:35,080 --> 00:27:38,800
Speaker 4: off in first year and I you know, I think

499
00:27:38,840 --> 00:27:40,479
Speaker 4: a couple of weeks in I started to look at

500
00:27:40,480 --> 00:27:43,440
Speaker 4: the material and second year and I thought it was fascinating,

501
00:27:43,520 --> 00:27:45,200
Speaker 4: interesting and sort of want to get stuck into that.

502
00:27:45,359 --> 00:27:47,560
Speaker 4: And so thankfully the dean let me in, let me

503
00:27:47,640 --> 00:27:50,639
Speaker 4: jump into that, and it was a it was an

504
00:27:50,640 --> 00:27:53,879
Speaker 4: awesome couple of years. What I really liked about it

505
00:27:54,240 --> 00:27:56,359
Speaker 4: the course, was just how practical and varied it was.

506
00:27:56,480 --> 00:27:59,040
Speaker 4: I I spent most of my time in the mechatronics

507
00:27:59,119 --> 00:28:01,840
Speaker 4: lab three D printing robots and building drones and all

508
00:28:01,880 --> 00:28:04,640
Speaker 4: these kind of things and some late night sleeping under

509
00:28:04,640 --> 00:28:05,320
Speaker 4: the desks there.

510
00:28:05,400 --> 00:28:06,639
Speaker 1: But it was, it was.

511
00:28:07,119 --> 00:28:10,200
Speaker 4: It was just a lot of fun and the friends

512
00:28:10,240 --> 00:28:12,320
Speaker 4: and memories I made were invaluable.

513
00:28:12,960 --> 00:28:17,440
Speaker 1: Yeah, and you were a resident advisor at Arour Hall.

514
00:28:17,880 --> 00:28:19,000
Speaker 1: What was that experience like?

515
00:28:20,400 --> 00:28:24,560
Speaker 4: Oh, look, that was just I think again coming back

516
00:28:24,600 --> 00:28:27,600
Speaker 4: to the theme of a bit of an adventure. For me,

517
00:28:28,320 --> 00:28:33,280
Speaker 4: being an RA was the most amazing year of teamwork,

518
00:28:33,400 --> 00:28:36,800
Speaker 4: of fun, pranks and everything like that. But whether it

519
00:28:36,880 --> 00:28:39,720
Speaker 4: was that or playing hockey for the university, I think

520
00:28:41,320 --> 00:28:44,280
Speaker 4: being part of those teams and I guess developing some

521
00:28:44,400 --> 00:28:47,680
Speaker 4: early schools of leadership have carried through to how I

522
00:28:47,840 --> 00:28:50,720
Speaker 4: aim to lead our teamate Wave today, you know, where

523
00:28:51,200 --> 00:28:53,600
Speaker 4: over three hundred people and the leadership challenges are certainly

524
00:28:53,640 --> 00:28:56,640
Speaker 4: demanding there and hopefully I learned a thing or two

525
00:28:58,080 --> 00:28:59,280
Speaker 4: through some of those experiences.

526
00:29:00,160 --> 00:29:03,440
Speaker 1: Yeah, And I guess around about that time that you

527
00:29:03,520 --> 00:29:06,120
Speaker 1: were studying or just about to graduate, you know, we

528
00:29:06,200 --> 00:29:08,560
Speaker 1: had to like to Power by Proxy had been acquired

529
00:29:08,600 --> 00:29:12,200
Speaker 1: by Apple, you know, huge deal in terms of that

530
00:29:12,320 --> 00:29:17,400
Speaker 1: technology being featured in and iPhones, you know, whireless charging technology,

531
00:29:17,520 --> 00:29:21,000
Speaker 1: fantastic achievement for that company. UNI Services were spinning off

532
00:29:21,080 --> 00:29:24,480
Speaker 1: other companies. Was the kernel in your mind then that

533
00:29:25,000 --> 00:29:26,320
Speaker 1: I want to be a startup founder?

534
00:29:29,200 --> 00:29:30,880
Speaker 4: As I said, I don't think it was some grand

535
00:29:30,960 --> 00:29:35,360
Speaker 4: strategic plan. It was And actually, I you know, when

536
00:29:35,400 --> 00:29:36,840
Speaker 4: people come to me and say I want to found

537
00:29:36,840 --> 00:29:40,440
Speaker 4: a startup for I guess, you know, finding a startup sake,

538
00:29:40,880 --> 00:29:45,480
Speaker 4: I think that's often I really don't look on that favorably.

539
00:29:45,520 --> 00:29:48,480
Speaker 4: I think it's a The problem with that is that

540
00:29:49,080 --> 00:29:53,480
Speaker 4: it's focusing on the outcome, not the journey. And because

541
00:29:54,480 --> 00:29:59,080
Speaker 4: these these experiences are so uncertain and so meandering and

542
00:29:59,160 --> 00:30:01,960
Speaker 4: how they go, you really need gristin stick ability, and

543
00:30:02,000 --> 00:30:04,480
Speaker 4: I think that really comes from being problem driven. So

544
00:30:05,080 --> 00:30:09,640
Speaker 4: what I was fascinated with is is building and creating

545
00:30:09,680 --> 00:30:14,160
Speaker 4: impact with with this technology. And I wanted to solve

546
00:30:14,200 --> 00:30:17,160
Speaker 4: the problem. Now, look, if there was amazing, an amazing

547
00:30:17,720 --> 00:30:19,600
Speaker 4: environment for me to work and to be able to

548
00:30:19,680 --> 00:30:21,360
Speaker 4: do that, I probably would have gone there. But the

549
00:30:21,840 --> 00:30:26,480
Speaker 4: reality was was that you know, in academia there was

550
00:30:26,760 --> 00:30:29,520
Speaker 4: no speed or resource and all of the industry if

551
00:30:29,560 --> 00:30:31,480
Speaker 4: it's at the time, we're taking the other approach, and

552
00:30:31,600 --> 00:30:35,280
Speaker 4: so you know that that you know, it was a

553
00:30:35,280 --> 00:30:36,840
Speaker 4: problem I had to solve and and led me to

554
00:30:37,160 --> 00:30:39,280
Speaker 4: starting a company to go get the resource and go

555
00:30:39,480 --> 00:30:42,959
Speaker 4: go create it myself. And so I think I think

556
00:30:43,000 --> 00:30:46,400
Speaker 4: it's always I've always had a problem driven mindset towards

557
00:30:47,760 --> 00:30:50,640
Speaker 4: towards building and and you know whatever I'm working on.

558
00:30:53,320 --> 00:30:53,520
Speaker 1: Yeah.

559
00:30:53,640 --> 00:30:58,280
Speaker 4: So, but of course seeing people and being inspired by

560
00:30:58,360 --> 00:30:59,960
Speaker 4: what people have built it as a key part of it.

561
00:31:00,120 --> 00:31:01,760
Speaker 4: And I was fortunate enough to have a lot of

562
00:31:01,800 --> 00:31:03,800
Speaker 4: that around at the University of Cambridge where there was

563
00:31:03,880 --> 00:31:07,640
Speaker 4: a lot of entrepreneurship, but even in Auckland. I mean

564
00:31:07,640 --> 00:31:10,960
Speaker 4: I was at a dinner that Robbie from Ice House

565
00:31:10,960 --> 00:31:14,400
Speaker 4: put on a couple of weeks ago in London, and

566
00:31:15,000 --> 00:31:17,680
Speaker 4: seeing just the breadth of deep tech coming out of

567
00:31:17,680 --> 00:31:20,560
Speaker 4: New Zealand right now is inspiring. And the wave of

568
00:31:21,000 --> 00:31:23,560
Speaker 4: success stories that are now turning into angel investors that

569
00:31:23,600 --> 00:31:27,120
Speaker 4: are supporting their ecosystem. I think these kind of cycles,

570
00:31:27,600 --> 00:31:30,280
Speaker 4: they're fairly incompressible. You need to go through the winds

571
00:31:30,360 --> 00:31:32,440
Speaker 4: and failures and the success cycles to be able to

572
00:31:32,480 --> 00:31:34,560
Speaker 4: create that kind of ecosystem. You know, Silicon Valley in

573
00:31:34,600 --> 00:31:38,280
Speaker 4: the US has had forty fifty years of a head start,

574
00:31:38,720 --> 00:31:41,600
Speaker 4: but without a doubt New Zealand is going to grow

575
00:31:41,720 --> 00:31:44,120
Speaker 4: this as a muscle and hopefully it's something that I'll

576
00:31:44,120 --> 00:31:46,720
Speaker 4: be able to contribute to one day. But I better

577
00:31:46,800 --> 00:31:50,200
Speaker 4: get heads down and get a product ship first.

578
00:31:50,560 --> 00:31:53,640
Speaker 1: Yeah, make this a massive, massive success and hopefully that

579
00:31:53,720 --> 00:31:55,479
Speaker 1: will then come as it has done for a lot

580
00:31:55,560 --> 00:31:58,560
Speaker 1: of New Zealand investors have done the start ups. That's

581
00:31:58,600 --> 00:32:01,480
Speaker 1: a great thing we are. Robbie has been integral to

582
00:32:01,600 --> 00:32:05,120
Speaker 1: that is, you know, leveraging some of those people who've

583
00:32:05,160 --> 00:32:06,840
Speaker 1: been through it and done it, the likes of Rod

584
00:32:06,920 --> 00:32:10,440
Speaker 1: Drury and Sam Morgan and Rowan Simpson, those sorts of

585
00:32:10,480 --> 00:32:13,840
Speaker 1: people who have then put their money back in very

586
00:32:14,080 --> 00:32:16,240
Speaker 1: different scale. As you said, Cambridge is at the height

587
00:32:16,280 --> 00:32:18,720
Speaker 1: of one of the biggest R and D sort of

588
00:32:19,800 --> 00:32:23,440
Speaker 1: areas in the world, has some amazing companies here. How

589
00:32:23,520 --> 00:32:25,720
Speaker 1: did that sort of change your perspective or impact it

590
00:32:26,320 --> 00:32:30,960
Speaker 1: in terms of autonomous vehicles and how to approach it

591
00:32:31,440 --> 00:32:35,040
Speaker 1: being exposed during your PhD to all this amazing R

592
00:32:35,120 --> 00:32:36,360
Speaker 1: and D expertise there.

593
00:32:38,080 --> 00:32:42,040
Speaker 4: Yeah, it's there was this amazing experience where so I

594
00:32:42,120 --> 00:32:43,880
Speaker 4: want to I was fortunate enough to get this the

595
00:32:43,920 --> 00:32:48,080
Speaker 4: Wolffisher Scholarship which sent me over to Cambridge, and I've

596
00:32:48,120 --> 00:32:49,760
Speaker 4: never been to Europe before, and I turned up at

597
00:32:49,840 --> 00:32:51,880
Speaker 4: the university and look, if you haven't been, this place

598
00:32:51,960 --> 00:32:54,960
Speaker 4: is like a castle. I turned up and was told

599
00:32:55,240 --> 00:32:59,000
Speaker 4: down this corridor was Isaac Newton's old study, you know

600
00:32:59,120 --> 00:33:03,160
Speaker 4: by the way, you know Ernst Rutherford. This is where

601
00:33:03,200 --> 00:33:05,320
Speaker 4: he did his experiments to split the atom and all

602
00:33:05,360 --> 00:33:09,040
Speaker 4: of this kind of stuff. And I also I moved

603
00:33:09,040 --> 00:33:11,680
Speaker 4: into this room that was on the top floor of

604
00:33:12,080 --> 00:33:15,040
Speaker 4: a place that literally had cast layers on the on

605
00:33:15,160 --> 00:33:18,560
Speaker 4: the balcony. You know, there was no shower, only a bath.

606
00:33:18,640 --> 00:33:21,000
Speaker 4: There was no Wi Fi, only wide intoet. So there

607
00:33:21,040 --> 00:33:23,920
Speaker 4: were some things that was you know, it was more medieval,

608
00:33:24,360 --> 00:33:28,720
Speaker 4: but the people and the conversations and the sort of

609
00:33:29,120 --> 00:33:33,120
Speaker 4: space I had to go and get immersed in some

610
00:33:33,280 --> 00:33:36,640
Speaker 4: ideas was just awesome. And part of what I was

611
00:33:37,400 --> 00:33:41,640
Speaker 4: really enjoyed doing was bringing some ideas that hadn't been

612
00:33:41,640 --> 00:33:45,560
Speaker 4: connected together before, and you know, bring them together because

613
00:33:46,960 --> 00:33:50,640
Speaker 4: when I started to read and explore, machine learning was

614
00:33:50,680 --> 00:33:54,280
Speaker 4: starting to take a foot but hadn't yet really impacted robotics.

615
00:33:55,280 --> 00:33:57,400
Speaker 4: And so seeing some of the deep learning technology that

616
00:33:57,680 --> 00:33:59,800
Speaker 4: some people around me were working on and being able

617
00:33:59,840 --> 00:34:04,400
Speaker 4: to take that into problems like slam or localization or

618
00:34:05,000 --> 00:34:07,440
Speaker 4: figure teaching a robot to figure out where they are,

619
00:34:07,560 --> 00:34:10,040
Speaker 4: what's going to happen next, or even working with a

620
00:34:10,120 --> 00:34:13,640
Speaker 4: colleague that was working on how to measure the uncertainty

621
00:34:13,719 --> 00:34:15,600
Speaker 4: or safety of such a system and bring that in.

622
00:34:16,080 --> 00:34:19,920
Speaker 4: I think that cross pollination was really fantastic, But then

623
00:34:19,960 --> 00:34:22,960
Speaker 4: it was just a lot of really you know, share,

624
00:34:23,120 --> 00:34:27,719
Speaker 4: hard work and effort. I'm also a big believer that

625
00:34:28,000 --> 00:34:29,640
Speaker 4: there's no such thing as Eureka moments.

626
00:34:29,920 --> 00:34:30,640
Speaker 1: It's not like you.

627
00:34:32,920 --> 00:34:36,200
Speaker 4: It's not like you sort of sit in a bath,

628
00:34:36,239 --> 00:34:37,880
Speaker 4: spill some water and run down the street naked and

629
00:34:37,960 --> 00:34:41,279
Speaker 4: think that that's how the world works. It's sort of

630
00:34:41,320 --> 00:34:44,840
Speaker 4: sustained effort against something over many, many years is what

631
00:34:44,960 --> 00:34:48,799
Speaker 4: allows you to solve hard problems. And you know, if

632
00:34:48,800 --> 00:34:50,759
Speaker 4: you count that PhD time and wave now it's been

633
00:34:50,760 --> 00:34:53,800
Speaker 4: a decade of work on working on embodied AI and

634
00:34:53,840 --> 00:34:58,239
Speaker 4: we're still starting to scratch the surface. I also had

635
00:34:58,280 --> 00:35:01,400
Speaker 4: the chance to hit out and spend six months or

636
00:35:01,440 --> 00:35:03,680
Speaker 4: so with the startup in Silicon Valley, and I think

637
00:35:03,719 --> 00:35:06,880
Speaker 4: that experience was awesome because I got to see what

638
00:35:07,239 --> 00:35:10,960
Speaker 4: create looked like. I got to see an amazing robotic

639
00:35:11,040 --> 00:35:15,000
Speaker 4: startup sku Ideo that built drones take off quite quite

640
00:35:15,080 --> 00:35:18,320
Speaker 4: literally and learn from so many people there and be

641
00:35:18,440 --> 00:35:20,520
Speaker 4: mentored and inspired by them. And actually that was a

642
00:35:20,560 --> 00:35:22,120
Speaker 4: bit of a full circle for me because the first

643
00:35:22,360 --> 00:35:27,640
Speaker 4: robots was building in my family's farm back in christ

644
00:35:27,719 --> 00:35:30,640
Speaker 4: Church were drones, and I remember putting these things together

645
00:35:30,800 --> 00:35:33,640
Speaker 4: chasing a few sheep around the paddock. I actually the

646
00:35:33,960 --> 00:35:36,480
Speaker 4: final year engineering research project I did in Auckland was

647
00:35:36,800 --> 00:35:39,439
Speaker 4: a drone that we took to the domain and taught

648
00:35:39,480 --> 00:35:42,840
Speaker 4: it to fly around and follow people wearing an orange beanie.

649
00:35:43,160 --> 00:35:46,279
Speaker 4: So this kind of technology was quite fun to play

650
00:35:46,320 --> 00:35:49,920
Speaker 4: with back then. I actually entered that into the Velocity

651
00:35:50,000 --> 00:35:53,560
Speaker 4: Challenge in the final year of Auckland. We made the finals.

652
00:35:53,719 --> 00:35:59,120
Speaker 4: But I'll never forget one experience when we were pitching

653
00:35:59,160 --> 00:36:01,440
Speaker 4: a crowd of about three hundred people with our business idea.

654
00:36:01,680 --> 00:36:04,560
Speaker 4: It was actually to use this drone to automate understanding

655
00:36:04,600 --> 00:36:08,680
Speaker 4: pasture length around a farm. And look, I think Holt

656
00:36:08,680 --> 00:36:13,200
Speaker 4: has done a much better job at agriculture automation than

657
00:36:13,239 --> 00:36:15,200
Speaker 4: we were trying to do there. But long story short,

658
00:36:15,640 --> 00:36:17,719
Speaker 4: in front of this crowd, I flew the drone up

659
00:36:17,760 --> 00:36:20,799
Speaker 4: and I must have had a few too sleepless nights

660
00:36:20,800 --> 00:36:23,360
Speaker 4: because one of the roads fell off midflight. It spiraled

661
00:36:23,400 --> 00:36:26,040
Speaker 4: and crashed in front of that whole crowd and gone.

662
00:36:26,120 --> 00:36:28,799
Speaker 4: Gone was that business idea, but probably for the better.

663
00:36:30,440 --> 00:36:32,440
Speaker 1: Well, what you're working on is probably a bit more

664
00:36:32,680 --> 00:36:35,680
Speaker 1: scalable in terms of the problem you're trying to solve,

665
00:36:35,800 --> 00:36:39,640
Speaker 1: and I understand that in terms of the physical testing.

666
00:36:39,680 --> 00:36:42,719
Speaker 1: A few technology started on the streets of Cambridge while

667
00:36:42,800 --> 00:36:45,400
Speaker 1: using delivery vehicles that were going around the area. You

668
00:36:45,520 --> 00:36:46,560
Speaker 1: started to automate them.

669
00:36:48,680 --> 00:36:53,919
Speaker 4: Yeah, So today we have a fleet in the UK,

670
00:36:54,360 --> 00:36:56,680
Speaker 4: in the US and soon some other parts of the

671
00:36:56,719 --> 00:37:01,720
Speaker 4: world as well, and we're test this technology on public

672
00:37:01,800 --> 00:37:05,680
Speaker 4: roads with safety drivers. So we're still sort of monitoring

673
00:37:05,760 --> 00:37:10,040
Speaker 4: the system, but the technology is really developing quite rapidly.

674
00:37:10,120 --> 00:37:12,239
Speaker 4: It's able to drive in places it's never been to

675
00:37:12,360 --> 00:37:15,120
Speaker 4: before without a high definition map. This is something that

676
00:37:15,200 --> 00:37:17,040
Speaker 4: the industry struggled with and is a problem that we're

677
00:37:17,360 --> 00:37:20,279
Speaker 4: able to overcome. It can drive with just cameras or

678
00:37:20,360 --> 00:37:23,319
Speaker 4: camera radar, but essentially a really lean camera, a lean

679
00:37:23,400 --> 00:37:26,280
Speaker 4: census stack, the kind of thing you find on modern

680
00:37:26,520 --> 00:37:28,960
Speaker 4: luxury vehicles that you can buy today and a growing

681
00:37:29,040 --> 00:37:32,680
Speaker 4: number of consumer vehicles. And then we've also done some

682
00:37:32,760 --> 00:37:35,879
Speaker 4: tests with the largest grocery companies in the UK doing

683
00:37:35,920 --> 00:37:40,439
Speaker 4: grocery delivery. So all of this experience we are using

684
00:37:40,560 --> 00:37:44,000
Speaker 4: to train to make our system safer, to make it

685
00:37:44,080 --> 00:37:48,279
Speaker 4: more performant. But the first product that we're launching is

686
00:37:48,320 --> 00:37:52,200
Speaker 4: going to be with consumer vehicles. So very soon you'll

687
00:37:52,239 --> 00:37:55,040
Speaker 4: be able to buy a new car and it'll have

688
00:37:55,120 --> 00:37:57,480
Speaker 4: waves AI on it. And the awesome thing that we'll

689
00:37:57,480 --> 00:37:59,959
Speaker 4: do is improve the safety. It'll mean that that vehicle

690
00:38:00,120 --> 00:38:05,680
Speaker 4: or will anticipate and prevent a number of collisions that

691
00:38:05,800 --> 00:38:08,120
Speaker 4: might have happened otherwise. It will provide you with an

692
00:38:08,120 --> 00:38:10,919
Speaker 4: AI that you can collaborate with that can help you drive,

693
00:38:11,000 --> 00:38:13,439
Speaker 4: take the stress off your drive, and have a hands

694
00:38:13,480 --> 00:38:17,960
Speaker 4: off driving experience. And you know, as we grow an experience,

695
00:38:18,440 --> 00:38:20,160
Speaker 4: we want to as quickly as we can make this

696
00:38:20,239 --> 00:38:22,359
Speaker 4: a hands off, eyes off experience where you can pass

697
00:38:22,400 --> 00:38:24,840
Speaker 4: the liability of driving to the car. And that's the

698
00:38:24,920 --> 00:38:27,799
Speaker 4: ultimate point where you can, I guess, get value back

699
00:38:27,920 --> 00:38:31,320
Speaker 4: and when the vehicle is provably safe enough to be

700
00:38:31,440 --> 00:38:35,040
Speaker 4: a thing that we in society can trust. But as

701
00:38:35,040 --> 00:38:36,880
Speaker 4: a global product, and we want to bring this to

702
00:38:36,880 --> 00:38:39,880
Speaker 4: the entire world. And my family keep pestering me, I

703
00:38:39,960 --> 00:38:42,200
Speaker 4: can't of course, can't wait till it can drive around

704
00:38:42,200 --> 00:38:43,080
Speaker 4: the streets in New Zealand.

705
00:38:43,880 --> 00:38:48,040
Speaker 1: Yeah, well, you know, that is an interesting question. I

706
00:38:48,120 --> 00:38:52,600
Speaker 1: think the former Transport Secretary Mark Harper, when he was

707
00:38:52,640 --> 00:38:54,719
Speaker 1: talking about way if he was saying we could have

708
00:38:55,719 --> 00:38:59,600
Speaker 1: autonomous vehicles on UK roads by twenty twenty six, at

709
00:38:59,640 --> 00:39:03,239
Speaker 1: least some those roads. But you know, much better infrastructure

710
00:39:03,280 --> 00:39:06,839
Speaker 1: in places like the US and the UK. I mean,

711
00:39:06,880 --> 00:39:10,040
Speaker 1: in terms of our our roads are one lane sort

712
00:39:10,080 --> 00:39:12,560
Speaker 1: of highways, and that sort of thing probably take a

713
00:39:12,640 --> 00:39:15,480
Speaker 1: bit longer, I guess. And of course the regulations have

714
00:39:15,640 --> 00:39:17,800
Speaker 1: to change as well. In the UK, I think have

715
00:39:17,920 --> 00:39:21,560
Speaker 1: been very proactive. The US, state by state have facilitated

716
00:39:21,600 --> 00:39:24,839
Speaker 1: a lot of testing of self driving vehicles and certain

717
00:39:24,920 --> 00:39:28,040
Speaker 1: levels off autonomy. I guess it's only natural that those

718
00:39:28,080 --> 00:39:31,239
Speaker 1: bigger centers are going to be the first places to

719
00:39:31,320 --> 00:39:33,759
Speaker 1: actually put these in the hands of consumers.

720
00:39:35,760 --> 00:39:39,600
Speaker 4: Yeah, and look, the future of transportation is going to

721
00:39:39,600 --> 00:39:43,319
Speaker 4: be multimodal, from walking so I cycled to work every day,

722
00:39:43,440 --> 00:39:47,200
Speaker 4: or micromobility or public transport or vehicles, and I think

723
00:39:47,200 --> 00:39:51,200
Speaker 4: there's an opportunity for embodied AI to improve the safety

724
00:39:51,239 --> 00:39:53,840
Speaker 4: and efficiency of not just cars but also public transport

725
00:39:54,600 --> 00:39:57,960
Speaker 4: are making it more frequent customizable. You know, you might

726
00:39:58,000 --> 00:39:59,520
Speaker 4: not even need to follow the buses set route, but

727
00:39:59,560 --> 00:40:03,080
Speaker 4: it might be you know, might have shared mobility that

728
00:40:03,120 --> 00:40:06,279
Speaker 4: can go to many different actually drop you off your

729
00:40:06,320 --> 00:40:08,200
Speaker 4: door or things like this. I think there's a lot

730
00:40:08,239 --> 00:40:11,239
Speaker 4: of disruption we're going to see a mobility with autonomy.

731
00:40:12,160 --> 00:40:15,840
Speaker 4: When you think about also how it's going to transform cities.

732
00:40:16,560 --> 00:40:18,920
Speaker 4: Cars today utilized about three percent of the time, and

733
00:40:20,200 --> 00:40:22,960
Speaker 4: with autonomy, hopefully we can enable them so they don't

734
00:40:23,000 --> 00:40:25,040
Speaker 4: just sit parked all day, but they can actually do

735
00:40:25,239 --> 00:40:28,720
Speaker 4: useful things or go and park in some low value

736
00:40:28,719 --> 00:40:31,080
Speaker 4: piece of real estate and free up all the car

737
00:40:31,200 --> 00:40:36,560
Speaker 4: parking spaces. Of course, reduce the congestion which is largely

738
00:40:36,640 --> 00:40:40,880
Speaker 4: caused by by road accidents. I also think there's other

739
00:40:41,719 --> 00:40:44,799
Speaker 4: side effects that they might be able to accelerate electrification,

740
00:40:45,000 --> 00:40:48,640
Speaker 4: and I really do hope that New Zealand can benefit

741
00:40:48,680 --> 00:40:51,200
Speaker 4: more from electrification of vehicles. I know it can be

742
00:40:51,280 --> 00:40:53,560
Speaker 4: challenging on some of the long journeys you know, through

743
00:40:53,760 --> 00:40:57,000
Speaker 4: South Island and things like this, but certainly in cities

744
00:40:57,080 --> 00:41:02,239
Speaker 4: like Auckland, you know seeing seeing conversion to electrification over

745
00:41:02,800 --> 00:41:06,400
Speaker 4: dominant electrification transport network over the next decade is for

746
00:41:06,520 --> 00:41:09,200
Speaker 4: me a must and I hope autonomy can just accelerate that.

747
00:41:09,760 --> 00:41:14,080
Speaker 1: Yeah, it'll happen. And the fleet is growing gradually and

748
00:41:14,320 --> 00:41:17,719
Speaker 1: more charging stations are being rolled out. The government wants

749
00:41:17,760 --> 00:41:19,800
Speaker 1: to roll out ten thousand off them, so that range

750
00:41:19,800 --> 00:41:23,640
Speaker 1: anxiety I think will dissipate. In terms of the where

751
00:41:23,680 --> 00:41:27,600
Speaker 1: we're at in the artificial intelligence sort of evolution, There's

752
00:41:27,640 --> 00:41:30,080
Speaker 1: been a lot of angst over the last couple of

753
00:41:30,160 --> 00:41:34,360
Speaker 1: years since the rise of generative AI, and you know,

754
00:41:34,680 --> 00:41:38,760
Speaker 1: the likes of Jeffrey Hint and esteemed researcher and Google

755
00:41:38,800 --> 00:41:42,520
Speaker 1: employee until recently have sort of sounded the alarm about it.

756
00:41:42,600 --> 00:41:44,920
Speaker 1: I guess it's one thing for chat GPT to be

757
00:41:45,040 --> 00:41:48,880
Speaker 1: spitting out misinformation or hallucinating. It's another thing when it

758
00:41:49,000 --> 00:41:51,600
Speaker 1: is embodied AI, when it's in part of a physical

759
00:41:52,480 --> 00:41:57,160
Speaker 1: you know, robot essentially that could but you know, potentially

760
00:41:57,239 --> 00:42:00,120
Speaker 1: hurt someone if it goes wrong. What's year view on

761
00:42:00,680 --> 00:42:02,640
Speaker 1: how quickly we are moving? You want to get the

762
00:42:02,640 --> 00:42:05,920
Speaker 1: stuff to market quickly, obviously into the hands of people.

763
00:42:06,360 --> 00:42:10,000
Speaker 1: Striking that balance of safety and speed to market. How

764
00:42:10,040 --> 00:42:13,680
Speaker 1: do you get it right? Yeah? Look, I.

765
00:42:15,520 --> 00:42:18,120
Speaker 4: For sure respect what Jeff is saying in those regards,

766
00:42:18,200 --> 00:42:21,360
Speaker 4: but I think that I'd much rather be part of

767
00:42:21,560 --> 00:42:24,160
Speaker 4: building the future and building the right future in a

768
00:42:24,280 --> 00:42:29,320
Speaker 4: dulous technology, rather than, you know, just being worried about

769
00:42:29,920 --> 00:42:33,480
Speaker 4: some I think it is a it's a non zero

770
00:42:33,560 --> 00:42:36,399
Speaker 4: but low likelihood outcome of how this technology might be used.

771
00:42:36,719 --> 00:42:40,000
Speaker 4: But let's take autonomous driving it as an example. The

772
00:42:40,640 --> 00:42:45,120
Speaker 4: level of safety, and this is a really highly regulated

773
00:42:45,160 --> 00:42:47,200
Speaker 4: space already, right, and I know that there's something that

774
00:42:47,600 --> 00:42:50,040
Speaker 4: regulators have very switched on to. I have a lot

775
00:42:50,120 --> 00:42:53,080
Speaker 4: of ongoing conversations in that regard of how we can

776
00:42:53,120 --> 00:42:57,680
Speaker 4: make this technology safe. So, firstly, the amount of testing

777
00:42:58,040 --> 00:43:00,800
Speaker 4: and structured thinking that needs to happen before these systems

778
00:43:00,840 --> 00:43:05,880
Speaker 4: go on public road is immense ways that we systematically

779
00:43:06,040 --> 00:43:09,120
Speaker 4: and statistically prove a level of safety that will be

780
00:43:09,160 --> 00:43:12,280
Speaker 4: acceptable to the public. Ultimately that there's a political decision,

781
00:43:12,800 --> 00:43:16,480
Speaker 4: But where that's currently landing is these systems need to

782
00:43:16,520 --> 00:43:21,319
Speaker 4: be significantly safer than a safe and competent human driver.

783
00:43:21,600 --> 00:43:24,360
Speaker 4: So let's put impaired or drunk or those kind of

784
00:43:24,440 --> 00:43:26,279
Speaker 4: drivers out of the way and actually look at what

785
00:43:26,400 --> 00:43:29,280
Speaker 4: an attentive human drivers like we need to be significantly

786
00:43:29,280 --> 00:43:31,520
Speaker 4: safer than that, and it's a very high bar, but

787
00:43:32,160 --> 00:43:35,200
Speaker 4: an important bar to exceed and one that we can

788
00:43:36,400 --> 00:43:39,080
Speaker 4: statistically prove. And then when it comes to areas like

789
00:43:39,200 --> 00:43:45,920
Speaker 4: cybersecurity or vehicle integration, you know, there are some very

790
00:43:45,960 --> 00:43:49,040
Speaker 4: well known methods that can be used to make sure

791
00:43:49,080 --> 00:43:55,160
Speaker 4: that this is a secure and integrated deployment of this technology.

792
00:43:56,360 --> 00:43:59,920
Speaker 4: And so ultimately I think that this is going to

793
00:44:00,160 --> 00:44:03,000
Speaker 4: be technology that will be trusted, will be safe, will

794
00:44:03,000 --> 00:44:06,120
Speaker 4: be able to operate within a given environment, and have

795
00:44:06,320 --> 00:44:10,759
Speaker 4: the level of oversight needed that it's you know, the

796
00:44:10,840 --> 00:44:12,600
Speaker 4: way that it is built, no way it's going to

797
00:44:12,640 --> 00:44:16,840
Speaker 4: go off pieced and you know, do something beyond what

798
00:44:17,000 --> 00:44:20,560
Speaker 4: it's trained and authorized to do. And then when it

799
00:44:20,640 --> 00:44:24,120
Speaker 4: comes to actually improving upon the level of road safety

800
00:44:24,200 --> 00:44:26,480
Speaker 4: we see today, you know, these vehicles can see three

801
00:44:26,520 --> 00:44:29,040
Speaker 4: sixty degrees at once, They can see with different senses

802
00:44:29,080 --> 00:44:32,920
Speaker 4: aside from just vision, like radar or things like this,

803
00:44:33,840 --> 00:44:37,200
Speaker 4: and of course can make decisions faster than a human can.

804
00:44:37,360 --> 00:44:40,960
Speaker 4: And so the potential to drive in a safer way

805
00:44:41,000 --> 00:44:43,799
Speaker 4: and give you the New Zealand Police an easier job.

806
00:44:43,960 --> 00:44:46,840
Speaker 4: It's something that society should require because it's just going

807
00:44:46,880 --> 00:44:47,920
Speaker 4: to improve our lives so much.

808
00:44:48,320 --> 00:44:50,239
Speaker 1: When you look at how many millions of people are

809
00:44:50,280 --> 00:44:54,960
Speaker 1: killed on the road due to human error around the world.

810
00:44:55,160 --> 00:44:57,200
Speaker 1: It's just it is a no brainer. It's going to

811
00:44:57,239 --> 00:45:00,200
Speaker 1: take us decade probably to get there, but hopefully, I mean,

812
00:45:00,360 --> 00:45:02,799
Speaker 1: could you're not using or not required to use such

813
00:45:02,920 --> 00:45:06,839
Speaker 1: specialized technology with your method, Hopefully we'll see a lower

814
00:45:06,960 --> 00:45:09,919
Speaker 1: barrier to entry in terms of price as well. When

815
00:45:10,520 --> 00:45:15,239
Speaker 1: a BMW or another provider or automobile maker incorporates this in,

816
00:45:16,120 --> 00:45:17,840
Speaker 1: it's going to be sort of commoditized. It's going to

817
00:45:17,880 --> 00:45:20,960
Speaker 1: be maybe a couple of thousand dollars premium rather than

818
00:45:21,160 --> 00:45:22,040
Speaker 1: ten thousand dollars.

819
00:45:22,600 --> 00:45:24,239
Speaker 4: Well, that's a key point to make, right, is that

820
00:45:24,360 --> 00:45:26,680
Speaker 4: the current technology you see driving around in Beijing or

821
00:45:26,719 --> 00:45:30,200
Speaker 4: San Francisco, they follow high definition maps, which is infrastructure

822
00:45:30,239 --> 00:45:36,320
Speaker 4: that is expensive and challenging to roll out, certainly globally,

823
00:45:36,400 --> 00:45:39,240
Speaker 4: and that's why the technology is starting in affluent places

824
00:45:39,280 --> 00:45:44,040
Speaker 4: like Silicon Valley. And what we're building gives a vehicle

825
00:45:44,120 --> 00:45:45,960
Speaker 4: that comes off a mass production line, you know, the

826
00:45:46,040 --> 00:45:48,560
Speaker 4: kind of new vehicles you buy today that have cameras

827
00:45:48,600 --> 00:45:50,400
Speaker 4: on them. Gives the ability for a vehicle like that

828
00:45:50,520 --> 00:45:52,960
Speaker 4: to be able to drive anywhere, to drive anywhere in

829
00:45:53,040 --> 00:45:56,840
Speaker 4: a way that's so including roads that it might not

830
00:45:56,960 --> 00:46:01,279
Speaker 4: have been to in the very end of I don't

831
00:46:01,280 --> 00:46:03,279
Speaker 4: know South Island, New Zealand where I'm from, or something

832
00:46:03,360 --> 00:46:05,520
Speaker 4: like this. So I do hope that it can bring

833
00:46:05,560 --> 00:46:09,000
Speaker 4: this technology to a global scale, and ultimately embodied a

834
00:46:09,080 --> 00:46:12,040
Speaker 4: Eye is going to go well beyond our road vehicles.

835
00:46:12,080 --> 00:46:16,719
Speaker 4: We're going to see it improve goods and delivery applications,

836
00:46:16,760 --> 00:46:23,600
Speaker 4: commercial vehicles. We should see it improve manufacturing, and you know,

837
00:46:23,680 --> 00:46:29,080
Speaker 4: hopefully this can make manufacturing possible at a much greater scale.

838
00:46:29,400 --> 00:46:32,640
Speaker 4: You know, today manufacturing really centers or where say labor

839
00:46:32,800 --> 00:46:36,080
Speaker 4: is cheaper. But you know, in the future, when you

840
00:46:36,160 --> 00:46:39,600
Speaker 4: have robot workers, you know, the cost of a cost

841
00:46:39,680 --> 00:46:43,799
Speaker 4: of a robot manufacturing system should be fairly consistent around

842
00:46:43,840 --> 00:46:46,800
Speaker 4: the world, and so manufacturing will now start to depend

843
00:46:46,880 --> 00:46:49,520
Speaker 4: on things like electricity or supply chain, and I think

844
00:46:49,600 --> 00:46:52,239
Speaker 4: New Zealand might have more of advantage there with a

845
00:46:52,320 --> 00:46:56,520
Speaker 4: renewable energy that we have, or equally in domestic tasks.

846
00:46:57,400 --> 00:47:00,760
Speaker 4: I think having embodied AYE systems that can help improve

847
00:47:00,840 --> 00:47:03,640
Speaker 4: our quality of living. I think all of these systems

848
00:47:03,840 --> 00:47:06,160
Speaker 4: ultimately are going to just accelerate the things that we're

849
00:47:06,160 --> 00:47:08,880
Speaker 4: capable of doing. I think we should be clear that

850
00:47:10,040 --> 00:47:11,680
Speaker 4: the tasks that we ask them to do should be

851
00:47:11,760 --> 00:47:13,680
Speaker 4: ones that we want to free up our time around

852
00:47:13,760 --> 00:47:15,640
Speaker 4: and and we should be able to delegate what we

853
00:47:15,719 --> 00:47:19,880
Speaker 4: want but contain keep the tasks that we want as society.

854
00:47:20,920 --> 00:47:24,560
Speaker 4: But ultimately, it's going to be like giving an accountant

855
00:47:24,560 --> 00:47:29,880
Speaker 4: a calculator or you know, it's just another it's a

856
00:47:29,960 --> 00:47:32,880
Speaker 4: new tool, but it's an intelligent tool that's going to

857
00:47:33,280 --> 00:47:35,879
Speaker 4: allow us to do so much more. And that's something

858
00:47:35,880 --> 00:47:37,560
Speaker 4: that's the future that I get really excited about.

859
00:47:46,040 --> 00:47:50,200
Speaker 1: You've been entrusted with a lot of investor money now,

860
00:47:50,400 --> 00:47:54,320
Speaker 1: so that's huge confidence that those companies have put a

861
00:47:54,400 --> 00:47:58,000
Speaker 1: new a lot of founders along the way. Often a

862
00:47:58,080 --> 00:48:03,000
Speaker 1: technical founder will either be paired up with someone who's

863
00:48:03,239 --> 00:48:06,439
Speaker 1: got real commercial now so or sometimes even replaced. You'll

864
00:48:06,440 --> 00:48:09,560
Speaker 1: see a technical founder sort of shunt it aside into

865
00:48:09,600 --> 00:48:12,560
Speaker 1: a CTO role or something like that. What is the

866
00:48:12,680 --> 00:48:17,400
Speaker 1: key in your journey to winning that confidence showing that

867
00:48:17,520 --> 00:48:19,960
Speaker 1: you understand this technology and you have a vision for it.

868
00:48:20,000 --> 00:48:22,640
Speaker 1: A bit like Peter Beck, who is a technical founder

869
00:48:23,040 --> 00:48:26,960
Speaker 1: but has been able to attract a lot of capital

870
00:48:27,120 --> 00:48:29,319
Speaker 1: to the business, so shows that he has business now

871
00:48:29,520 --> 00:48:32,239
Speaker 1: and as a leader of people as well. What's been

872
00:48:32,280 --> 00:48:34,600
Speaker 1: the journey that you've been on that has allowed you

873
00:48:34,719 --> 00:48:36,440
Speaker 1: to tick all of those boxes.

874
00:48:36,960 --> 00:48:40,359
Speaker 4: Yeah, well, I look, I started in the CTO mind

875
00:48:40,440 --> 00:48:42,960
Speaker 4: just building out the technology, and it's been fascinating how

876
00:48:43,280 --> 00:48:46,319
Speaker 4: my role has changed every year, from writing the first

877
00:48:46,360 --> 00:48:50,000
Speaker 4: code to building a team, finding commercial partners, raising investment,

878
00:48:50,040 --> 00:48:52,960
Speaker 4: and now leading an executive team. And I've loved this

879
00:48:53,120 --> 00:48:56,600
Speaker 4: because it's almost like as soon as I get good

880
00:48:56,640 --> 00:48:58,960
Speaker 4: at one level of scale, all of a sudden, we

881
00:48:59,040 --> 00:49:03,040
Speaker 4: outgrow it and lenge comes. So that's been That's been

882
00:49:04,080 --> 00:49:07,399
Speaker 4: I guess, very rewarding personally to go through that growth.

883
00:49:07,440 --> 00:49:08,000
Speaker 1: And I think.

884
00:49:10,400 --> 00:49:13,120
Speaker 4: Perhaps one of the things that has allowed me to

885
00:49:13,600 --> 00:49:21,360
Speaker 4: do that has been a mix between being very strong

886
00:49:21,440 --> 00:49:24,600
Speaker 4: minded and passionate about ideas, but also being I guess

887
00:49:24,680 --> 00:49:27,239
Speaker 4: humble with a growth mindset. And what I mean by

888
00:49:27,280 --> 00:49:30,640
Speaker 4: that is that, look, I can be incredibly passionate and opinionated,

889
00:49:30,719 --> 00:49:37,399
Speaker 4: but also very open to feedback, very curious to learn,

890
00:49:37,480 --> 00:49:40,520
Speaker 4: and I think some of those values that we embody

891
00:49:40,560 --> 00:49:44,160
Speaker 4: as a company now at Wave, I think have been

892
00:49:44,280 --> 00:49:46,920
Speaker 4: key to success. So look, I had a vision at

893
00:49:46,960 --> 00:49:49,920
Speaker 4: the start that everyone was telling me this was crazy,

894
00:49:50,000 --> 00:49:53,279
Speaker 4: this would never work, and I had the resilience and

895
00:49:53,360 --> 00:49:58,120
Speaker 4: belief to stick with it. But also the way we've

896
00:49:58,160 --> 00:50:00,879
Speaker 4: implemented or solved this vision has just and changed over

897
00:50:00,880 --> 00:50:03,319
Speaker 4: the years as we've learned more. And you know, I've

898
00:50:03,400 --> 00:50:08,320
Speaker 4: had the humility to to be able to make hard decisions,

899
00:50:08,400 --> 00:50:11,160
Speaker 4: forego some costs, and and and learn along the way

900
00:50:11,200 --> 00:50:14,840
Speaker 4: and grow. And I think I think those those that

901
00:50:15,000 --> 00:50:18,200
Speaker 4: mindset and those skills and experiences have been crucial. And

902
00:50:18,280 --> 00:50:21,440
Speaker 4: you know, ones that I think were important to my

903
00:50:21,600 --> 00:50:26,359
Speaker 4: family growing up in the South Island, New Zealand, whether

904
00:50:26,400 --> 00:50:28,920
Speaker 4: it was a sense of adventure we had around the

905
00:50:29,239 --> 00:50:32,919
Speaker 4: Southern Alps, or you know, the all of the things

906
00:50:32,960 --> 00:50:35,200
Speaker 4: that we were able to learn and do growing up

907
00:50:35,280 --> 00:50:39,000
Speaker 4: in in New Zealand. And I think they have they

908
00:50:39,040 --> 00:50:42,200
Speaker 4: have largely been the key to I won't call it

909
00:50:42,280 --> 00:50:43,960
Speaker 4: success yet because we've got a lot of work to do,

910
00:50:44,080 --> 00:50:45,880
Speaker 4: but the key to to get into the stage that

911
00:50:45,920 --> 00:50:46,400
Speaker 4: we're at now.

912
00:50:46,640 --> 00:50:51,080
Speaker 1: And your father engineer as well, so thinking about projects,

913
00:50:51,160 --> 00:50:53,680
Speaker 1: tinkering on things from a very early age with him

914
00:50:53,719 --> 00:50:54,920
Speaker 1: as well, probably helped.

915
00:50:56,440 --> 00:50:59,719
Speaker 4: Yeah, and I'm really grateful to my mom and my

916
00:50:59,800 --> 00:51:03,920
Speaker 4: dad for I think again, having that that gross mindset, like,

917
00:51:05,080 --> 00:51:08,120
Speaker 4: you know, praising the effort that I did, not necessarily

918
00:51:08,320 --> 00:51:11,920
Speaker 4: the outcomes, you know, giving me the ability to to

919
00:51:12,080 --> 00:51:14,239
Speaker 4: learn and explore, and the sacrifices they made for that.

920
00:51:14,800 --> 00:51:17,919
Speaker 4: My Yeah, my dad was an engineer and he left

921
00:51:18,560 --> 00:51:25,120
Speaker 4: big corporate to you know, build a software consultancy in

922
00:51:25,239 --> 00:51:29,520
Speaker 4: christ Church that uh, you know, made some sacrifices for

923
00:51:29,680 --> 00:51:31,640
Speaker 4: for our family, but was also a bit of an

924
00:51:31,719 --> 00:51:34,400
Speaker 4: entrepreneur's in regard. And my mum was an entrepreneur, uh

925
00:51:34,880 --> 00:51:38,560
Speaker 4: starting various businesses from our school lunches at primary school

926
00:51:38,640 --> 00:51:43,600
Speaker 4: to a blanket importing business business to other other work

927
00:51:43,640 --> 00:51:46,239
Speaker 4: that she does now. And so I, you know, I

928
00:51:46,800 --> 00:51:49,840
Speaker 4: love that environment and very feel very fortunate to have

929
00:51:50,640 --> 00:51:54,120
Speaker 4: had their had their had them have had their mentorships

930
00:51:54,360 --> 00:51:58,440
Speaker 4: as parents, and yeah, it was it was awesome and

931
00:51:58,520 --> 00:51:59,960
Speaker 4: I couldn't ask for anything more.

932
00:52:00,560 --> 00:52:03,560
Speaker 1: Well, it's been a huge year for you, Alex, huge

933
00:52:03,600 --> 00:52:07,360
Speaker 1: capital raising, lots of stuff going on on the technical

934
00:52:07,440 --> 00:52:10,040
Speaker 1: front as well, three hundred at least people and growing

935
00:52:10,200 --> 00:52:14,600
Speaker 1: as well to accommodate your vision and your plans. Congratulations

936
00:52:14,719 --> 00:52:18,320
Speaker 1: on an epic year. Good luck for next year, and

937
00:52:18,920 --> 00:52:22,239
Speaker 1: hopefully we will start to see autonomous vehicles really hitting

938
00:52:22,239 --> 00:52:24,600
Speaker 1: the roads at least in some of the big cities

939
00:52:24,600 --> 00:52:27,680
Speaker 1: around the world, including places like London and San Francisco.

940
00:52:27,840 --> 00:52:30,040
Speaker 1: So thanks so much for being on the business of tech.

941
00:52:30,320 --> 00:52:32,160
Speaker 4: Wash the space and hopefully you can ride the wave soon.

942
00:52:32,520 --> 00:52:33,000
Speaker 4: Chess better.

943
00:52:36,520 --> 00:52:39,640
Speaker 2: I really want to try an autonomous car. That's what

944
00:52:39,760 --> 00:52:42,000
Speaker 2: I'm getting from a lot of the conversation lately, like

945
00:52:42,480 --> 00:52:45,440
Speaker 2: everybody's talking about it, and I'm just at that stage

946
00:52:45,480 --> 00:52:47,480
Speaker 2: now where you know, you hear about a technology so

947
00:52:47,560 --> 00:52:49,719
Speaker 2: many times and you start itching to just check it

948
00:52:49,760 --> 00:52:52,279
Speaker 2: out for yourself. I think what's interesting about it in

949
00:52:52,360 --> 00:52:55,520
Speaker 2: the way that you've described it is how normal it

950
00:52:55,640 --> 00:52:57,720
Speaker 2: feels after the first couple of times.

951
00:52:58,280 --> 00:53:02,520
Speaker 1: Yeah, I'm back in at the moment, obviously after visiting

952
00:53:02,600 --> 00:53:08,440
Speaker 1: this SpaceX launch, and I'm really dying to get back

953
00:53:08,480 --> 00:53:11,360
Speaker 1: in one when I get to Los Angeles because I

954
00:53:11,560 --> 00:53:15,200
Speaker 1: really love the experience. But what Alex Kendall is doing

955
00:53:15,440 --> 00:53:19,440
Speaker 1: is very different to what the lights of Weimo have

956
00:53:19,600 --> 00:53:24,399
Speaker 1: done with their driverless car. It has one model, one

957
00:53:24,560 --> 00:53:28,280
Speaker 1: single large model that learns all of these individual tasks.

958
00:53:28,440 --> 00:53:31,080
Speaker 1: Now that's a very different approach to what has been

959
00:53:31,200 --> 00:53:37,680
Speaker 1: taken before, and he's pitching this as suitable for autonomous vehicles,

960
00:53:37,800 --> 00:53:41,800
Speaker 1: but the same fundamental model will be useful for robots

961
00:53:41,800 --> 00:53:45,799
Speaker 1: as well, maybe even plain so autonomous planes and humanoid

962
00:53:45,960 --> 00:53:51,120
Speaker 1: robots that like optimis. You know the Tesla robot we

963
00:53:51,200 --> 00:53:55,320
Speaker 1: saw sort of debuted demoed a few weeks back. So

964
00:53:55,760 --> 00:53:59,520
Speaker 1: this is a fundamental different approach. It's obviously attracted a

965
00:53:59,560 --> 00:54:02,279
Speaker 1: lot of and a lot of interest, and there are

966
00:54:02,320 --> 00:54:04,960
Speaker 1: a lot of carmakers who are keen to get involved.

967
00:54:06,120 --> 00:54:09,920
Speaker 1: So is this safer? Is this as reliable fundamentally? Will

968
00:54:09,960 --> 00:54:13,640
Speaker 1: this win the autonomous vehicle race? Still to be seen,

969
00:54:13,760 --> 00:54:17,720
Speaker 1: but boys made some huge progress so far. Yeah, definitely.

970
00:54:18,160 --> 00:54:20,520
Speaker 2: I remember the first ever tech conference I went to

971
00:54:20,920 --> 00:54:27,759
Speaker 2: was in Videos GtC twenty eighteen, and during that conference

972
00:54:28,440 --> 00:54:31,080
Speaker 2: in video was talking about exactly this kind of thing

973
00:54:31,520 --> 00:54:35,240
Speaker 2: about the future where you can use GPUs to basically

974
00:54:35,440 --> 00:54:40,520
Speaker 2: simulate terrain and you know, cities and countries potentially and

975
00:54:40,600 --> 00:54:43,359
Speaker 2: then tweak different things. You'll go to add more rain

976
00:54:43,560 --> 00:54:46,320
Speaker 2: or sleep or hail or or a car crash, and

977
00:54:46,400 --> 00:54:49,280
Speaker 2: you can do run simulations many many times and train

978
00:54:49,840 --> 00:54:53,600
Speaker 2: models to recognize different and react in different ways to

979
00:54:53,760 --> 00:54:58,200
Speaker 2: different situations. So it sounds like that Alex has really

980
00:54:58,239 --> 00:55:01,200
Speaker 2: taken that concept and it is working really hard to

981
00:55:01,239 --> 00:55:02,880
Speaker 2: put that into into practice.

982
00:55:03,680 --> 00:55:08,520
Speaker 1: It's happening, and yeah, so he now has to prove

983
00:55:08,600 --> 00:55:11,200
Speaker 1: this with a large number of vehicles. You know, you've

984
00:55:11,239 --> 00:55:15,359
Speaker 1: got Waimo's doing one hundred thousand robotaxi rides a week

985
00:55:15,400 --> 00:55:19,200
Speaker 1: in in San Francisco, LA and Phoenix, China. You know,

986
00:55:19,360 --> 00:55:23,880
Speaker 1: Baido's Apollo Go Services is doing hundreds of thousands of

987
00:55:24,080 --> 00:55:26,600
Speaker 1: rides as well. So he's certainly not the first to this.

988
00:55:27,400 --> 00:55:31,759
Speaker 1: There are others out there doing autonomous taxi rides and that,

989
00:55:32,080 --> 00:55:34,560
Speaker 1: so he's got some catching up to do. But you know,

990
00:55:34,560 --> 00:55:37,560
Speaker 1: in an industry that's full of hype and over promising,

991
00:55:38,160 --> 00:55:41,560
Speaker 1: he seems to have got a lot of obviously financial

992
00:55:41,640 --> 00:55:44,400
Speaker 1: backing a billion in capital that's going to help him

993
00:55:44,480 --> 00:55:47,120
Speaker 1: accelerate everything, and he's already been doing that this year.

994
00:55:47,680 --> 00:55:50,239
Speaker 1: But it also that that appetite from the carmakers I

995
00:55:50,280 --> 00:55:52,600
Speaker 1: think is really important where they are seeing this is

996
00:55:52,719 --> 00:55:56,239
Speaker 1: the one that may hit that sweet spot of not

997
00:55:56,360 --> 00:55:59,640
Speaker 1: being too expensive but being safe and a good user

998
00:55:59,680 --> 00:56:02,560
Speaker 1: experien That's what they want for their customers, so they

999
00:56:02,640 --> 00:56:04,359
Speaker 1: seem to be showing a lot of interest in sort

1000
00:56:04,360 --> 00:56:08,359
Speaker 1: of lining up to trial his technology now. Very cool.

1001
00:56:08,719 --> 00:56:10,600
Speaker 2: Hopefully we're the key we at the Helm. We might

1002
00:56:10,840 --> 00:56:12,920
Speaker 2: see it land on our shores sometime soon.

1003
00:56:13,239 --> 00:56:13,479
Speaker 3: Yeah.

1004
00:56:13,680 --> 00:56:16,040
Speaker 1: And just what a down to earth guy, you know.

1005
00:56:16,320 --> 00:56:20,800
Speaker 1: He's had a great experience obviously at Auckland UNI, but

1006
00:56:21,120 --> 00:56:23,799
Speaker 1: also growing up in Canterbury as well, his parents very

1007
00:56:23,920 --> 00:56:28,759
Speaker 1: influential and just running this big business now and still

1008
00:56:28,800 --> 00:56:31,120
Speaker 1: having his mates around to sleep on his couch in London.

1009
00:56:32,560 --> 00:56:34,640
Speaker 2: I think if you're a key we in London, that's

1010
00:56:34,719 --> 00:56:37,279
Speaker 2: just something you've got to live with from my understanding.

1011
00:56:40,120 --> 00:56:42,839
Speaker 1: That's it for another episode off the Business of Tech.

1012
00:56:43,000 --> 00:56:45,680
Speaker 1: Thanks very much to one in Z, general manager of

1013
00:56:45,840 --> 00:56:51,120
Speaker 1: Mobile network Tiger Governor and Wave founder doctor Alex Kendall.

1014
00:56:51,480 --> 00:56:53,239
Speaker 2: Show notes for the Business of Tech are in the

1015
00:56:53,280 --> 00:56:56,520
Speaker 2: podcast section at Business desk dot co dot z, where

1016
00:56:56,520 --> 00:56:59,359
Speaker 2: you can stream this podcast in Phil every week for free.

1017
00:56:59,840 --> 00:57:03,920
Speaker 2: Are available from iHeartRadio or your podcast platform off choice.

1018
00:57:04,560 --> 00:57:06,960
Speaker 1: Get in touch with your feedback and we'd love to

1019
00:57:07,040 --> 00:57:10,719
Speaker 1: hear your suggestions for upcoming guests. To email Ben on

1020
00:57:10,960 --> 00:57:13,480
Speaker 1: benat Businessdesk dot co dot MZ.

1021
00:57:13,920 --> 00:57:16,160
Speaker 2: You can find both of us occasionally on x but

1022
00:57:16,280 --> 00:57:19,200
Speaker 2: mostly on LinkedIn, where you can also follow the Business

1023
00:57:19,280 --> 00:57:21,360
Speaker 2: of Tech page for all of our updates.

1024
00:57:21,800 --> 00:57:24,240
Speaker 1: That's it for this week. Another episode coming your way

1025
00:57:24,400 --> 00:57:25,360
Speaker 1: next Thursday.

1026
00:57:25,560 --> 00:57:27,160
Speaker 2: Until then, have a great week.