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Speaker 1: We go New Zealand absolutely flat out at the top

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Speaker 1: of the line.

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Speaker 2: They're going to struggle to control the boat with those

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Speaker 2: big foils. From here.

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Speaker 3: France on the.

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Speaker 4: Insight should lead at mark one, but it's going to.

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Speaker 3: Be a sprint, and we are often racing here in

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Speaker 3: New Zealand.

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Speaker 1: Ah.

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Speaker 2: Yes, nothing says summer like watching Team New Zealand foiling

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Speaker 2: its way across the hierarchy Gulf in sale GP.

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Speaker 3: That's right. And although the Aussies ended up winning sale

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Speaker 3: GP headed by New Zealand, America's cup legend. So Russell

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Speaker 3: Coots is not only a triumph of innovation and yacht design,

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Speaker 3: it's one of the most high tech sports in the world.

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Speaker 3: Without official intelligence playing an increasingly important role in the

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Speaker 3: sale GP action.

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Speaker 1: Some of the athletes look at it as a sailboat.

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Speaker 1: I look at the mercer and IoT device. So the

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Speaker 1: amount of information that we can get off one catamound,

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Speaker 1: we can use AI to be able to predict what

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Speaker 1: they're doing right and what they're doing wrong, it's pretty amazing,

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Speaker 1: it really is.

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Speaker 2: We're back with season three of the Business of Tech,

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Speaker 2: and this week we catch up with the Chief Technology

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Speaker 2: Officer of cel GP Warren Jones. Now he had a

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Speaker 2: very successful tournament in Auckland over the weekend where the

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Speaker 2: IF fifty foiling catamarans were on display for some pretty

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Speaker 2: intense racing, which Ben you got to see firsthand from

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Speaker 2: the sale GP chase boat.

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Speaker 3: I did. I did. I was very fortunate to be

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Speaker 3: able to catch the fourth race of the first day

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Speaker 3: of racing from the water and it was awesome to

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Speaker 3: be honest, like, I'm not a big sailing guy. I'm

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Speaker 3: not even a big sports guy in general, but there

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Speaker 3: is something about sale GP that they have really captured

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Speaker 3: some magic in a bottle there. It's a combination of

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Speaker 3: the marketing, the format, I think the money behind the

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Speaker 3: event as well. It was just it was almost a

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Speaker 3: festival like event at the venue with the grant these

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Speaker 3: massive grandstands and there were people all around excited having

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Speaker 3: a good time, and you get up into the grandstands

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Speaker 3: as I it was lucky enough to do as well,

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Speaker 3: had a really great view, completely sold out, buzzing people

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Speaker 3: cheering for Auckland and waving flags for their respective countries.

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Speaker 3: It really was an exciting and fun event to be

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Speaker 3: part of and I do hope that it does come

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Speaker 3: back to Auckland again.

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Speaker 2: Well, that's the thing, because we had that debacle down

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Speaker 2: in Littleton where we had racing postponed because of the

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Speaker 2: dolphins in the harbor, which was what Russell Coots and

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Speaker 2: companies signed up to. They knew that this was a possibility,

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Speaker 2: but it left a really bad taste and suggested maybe

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Speaker 2: that we might not see sell GP back. But it's

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Speaker 2: in Auckland at a time when economic gloom is everywhere.

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Speaker 2: It's just really good to see some big event like

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Speaker 2: that that has those elements like performance, yachting technology, A

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Speaker 2: lot of people coming to see this great event. We

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Speaker 2: sort of need more of that.

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Speaker 3: Yeah, absolutely, it did draw crowds to the waterfront and

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Speaker 3: to win you'd quart it, which is one of my

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Speaker 3: favorite parts of Auckland as well. So great to see.

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Speaker 3: And I was also very fortunate to be able to

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Speaker 3: get a tour of the behind the scenes as well

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Speaker 3: for SALGB through their tech tent, their tech base, i

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Speaker 3: should say, and I got close up with the foils,

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Speaker 3: these new titanium teafoils, and I got to go into

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Speaker 3: the data room and check out all of the dashboards

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Speaker 3: and panels and talk to some of the people there

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Speaker 3: on the ground working with this immense amount of data,

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Speaker 3: and it was quite eye opening and really impressive to

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Speaker 3: understand how the digital world is built into this sport

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Speaker 3: from the ground up. The CTO, Warren Jones, as you're

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Speaker 3: here in the interview, he was there from day one

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Speaker 3: figuring out how to make this a global event, how

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Speaker 3: to make it the most technology forward event. So we'll

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Speaker 3: have a listen to that interview later in the episode,

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Speaker 3: and I think it's a really interesting one.

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Speaker 2: Yeah, so much tech involved, and as you'd expect with

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Speaker 2: Larry Ellison, the Oracle founder, he was the one behind

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Speaker 2: a lot of the America's Cup campaigns against Team New

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Speaker 2: Zealand and is the co founder of SLGP, so very

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Speaker 2: much infused with that sort of tech philosophy. So yeah,

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Speaker 2: looking forward to that. First, though, we've got to talk

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Speaker 2: about the big news of the week. Trump's inauguration surreal

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Speaker 2: for so many reasons, not least of which was the

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Speaker 2: side of the tech titans. Tim Cook from Apple, Jeff

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Speaker 2: Bezos from Amazon, Sundhaaripachai from Google, of course Elon Musk there.

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Speaker 2: Those are just the ones that were directly behind the president,

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Speaker 2: so obviously had a front row seat to the inauguration

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Speaker 2: and Ben all there really to bend the knee to

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Speaker 2: the new president.

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Speaker 3: Yeah. Absolutely, And you know, it reminds me of that

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Speaker 3: kid in high school who the popular kids would all

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Speaker 3: rally around because they could get him to do whatever

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Speaker 3: they wanted. And Trump is that kid. You know, He's

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Speaker 3: so hungry for power and money that he will just

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Speaker 3: kind of do whatever he is And this is obviously

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Speaker 3: my opinion, he will do whatever he's paid to do.

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Speaker 3: And the biggest, most money rich people in the world

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Speaker 3: are all there to take advantage of that. And for

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Speaker 3: the current world that we live in, that means technology,

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Speaker 3: because tech companies are the biggest and richest companies in

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Speaker 3: the world.

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Speaker 2: Yeah. Well, I think it definitely is a striking shift

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Speaker 2: that has gone on in Silicon Valley, and this really

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Speaker 2: illustrated it this week. You know, four years ago, many

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Speaker 2: of those Silicon Valley executives viewed Trump with skepticism, if

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Speaker 2: not outright hostility. Eight years ago, he pulled them all

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Speaker 2: into Trump Tower just after he'd won the election, and

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Speaker 2: to twenty sixteen very awkward meeting that they had because

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Speaker 2: they didn't trust them, they didn't take him seriously. They

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Speaker 2: thought he was going to be a one hit wonder.

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Speaker 2: Pretty soon after that, the anti trust stuff really kicked in.

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Speaker 2: That happened actually under Trump's rain, it really accelerated under

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Speaker 2: Biden and then we saw the for instance, Google being

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Speaker 2: declared a monopoly by US district court. So they've got

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Speaker 2: these regulatory concerns looming, anti trust investigations in court cases.

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Speaker 2: They're worried about that. They see financial opportunities as well.

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Speaker 2: Just as we're recording this, there's news breaking about so

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Speaker 2: called Stargate in the US, this massive AI investment up

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Speaker 2: to five hundred billion dollars at the likes of Open

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Speaker 2: Ai and others are committing to artificial intelligence. Trump is

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Speaker 2: launching that. So he's basically saying to these companies, get

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Speaker 2: on my bandwagon the whole America first, make America great again,

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Speaker 2: and you will profit from it as well. And then

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Speaker 2: it's really a matter of I think, political survival. If

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Speaker 2: you're not in the tent with Trump, you're seen as

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Speaker 2: his enemy and he will use whatever lever he has

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Speaker 2: to go against you. So I guess it's slightly cynical,

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Speaker 2: but it's strategic. By the likes of Zuckerberg and Bezos,

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Speaker 2: to really align with Trump. They realize that if they don't,

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Speaker 2: their massive empires could be dismantled.

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Speaker 3: Yeah. I mean, it's just good business, isn't it, to

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Speaker 3: be honest like these people they I think it does

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Speaker 3: go to show that despite all of the issues maybe

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Speaker 3: with some of the previous administrations, that they were actually

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Speaker 3: managing to keep somewhat of a cap on these massive

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Speaker 3: super corporations, that they were actually exercising some power to

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Speaker 3: make the likes of Matter be a bit more responsible

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Speaker 3: around their impact on the population that are using them,

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Speaker 3: and that once that, once that administration is gone, all

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Speaker 3: bets are off. You know. So clearly, although you can

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Speaker 3: criticize whatever, you criticize the previous administrations as much as

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Speaker 3: you want, it is a really clear indicator that they

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Speaker 3: were actually doing something. You know, Mark Zuckerberg wouldn't have

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Speaker 3: taken off the energy mask and revealed his true self

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Speaker 3: had things continued as they were. You know, I don't know.

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Speaker 3: It's a terrifying situation for me because I've beat the

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Speaker 3: drum for a long time about the importance of technology,

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Speaker 3: as somebody who's been reporting on technology for some time,

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Speaker 3: about how much it underpins everything we do. In the

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Speaker 3: world everything we are now as a global and smaller

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Speaker 3: national communities, and now the people who are controlling technology

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Speaker 3: are coming out and in behind the man who has

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Speaker 3: you know, tried to put ridiculous medical pseudomedical day phoinicians

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Speaker 3: around gender about a man, you know, one of the

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Speaker 3: richest men in the world, the technology leader, coming out

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Speaker 3: onto a stage and doing what is very clearly a

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Speaker 3: Nazi salute. And I don't think that that can be

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Speaker 3: explained away really because if he is, you know, either

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Speaker 3: he's the mega genius that everyone that a lot of

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Speaker 3: people say he is, and if that's so, he wouldn't

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Speaker 3: have accidentally done a Nazi salute. But so if he's

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Speaker 3: not a mega genius, then he just oh, whoops, I

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Speaker 3: did it by accident.

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Speaker 2: Well, he's a student of history. I mean, he's read

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Speaker 2: all the Greek and Roman philosophy, knows about European history

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Speaker 2: and World War Two, and you know, I sort of

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Speaker 2: wonder if he knows what he's doing.

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Speaker 4: And he came up in apartheid South Africa. Yeah, like,

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Speaker 4: you know, there's no way, there is no way he

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Speaker 4: didn't have some sense of what he was doing, but

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Speaker 4: he knew that he could get away with it because

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Speaker 4: of the halo effect that comes from being associated with

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Speaker 4: the Trump administration and the US media's shyness around making accusations.

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Speaker 3: So yeah, and.

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Speaker 2: That's the and that's the real worry, this concentration of power.

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Speaker 2: You've got the PayPal mafia sort of there. You've got

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Speaker 2: Peter Thiel, You've got David Sachs, You've got Elon Musk,

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Speaker 2: You've got all these other people who see themselves as

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Speaker 2: tech utopians or tech optimists. That's Mark Andresen, the venture

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Speaker 2: capitalist who is donkey deep with with Trump, has spent

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Speaker 2: the last few months at Mara Lago advising him. So really,

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Speaker 2: you know what's in it for them? Sure, they want deregulation,

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Speaker 2: particularly around AI and crypto, and we've seen Trump jump

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Speaker 2: into crypto himself with his own mean coin. Millennia has

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Speaker 2: her own mean coin as well, so they've been working

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Speaker 2: on him and influencing him as well. And you know,

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Speaker 2: to some extent, AI and these technologies will underpin some

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Speaker 2: of the biggest transformations this century, and healthcare and battling

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Speaker 2: climate change and that as well. But what they don't

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Speaker 2: seem to talk about or really accept is the concentration

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Speaker 2: of power into the hands of basically them and a

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Speaker 2: few of their friends, and how bad that is. They

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Speaker 2: keep preaching the tech will solve the world's challenges, but

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Speaker 2: who controls it and who gets to profit from that?

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Speaker 2: They're quite comfortable with the idea of someone having four

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Speaker 2: hundred billion dollars in wealth being worth more than a

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Speaker 2: mid sized country and that is just the free market

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Speaker 2: working for them.

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Speaker 3: Yeah, we've lost the distaste that we had for the

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Speaker 3: idea of for profit companies basically controlling the world. The

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Speaker 3: US went through it already in the age of the

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Speaker 3: Rockefellers and these kinds of things, and they had to

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Speaker 3: really clamp down and turn around and say no. But

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Speaker 3: it looks like this time they're just heading the gas

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Speaker 3: on that whole concept. Maybe they'll hit a wall, and

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Speaker 3: you know, maybe the next four years will create some

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Speaker 3: much needed institutional change in the following administration after this one,

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Speaker 3: But who knows. You know, maybe it's just reached a

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Speaker 3: point where hope is so gone from a lot of

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Speaker 3: these people who are struggling in the US that they're

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Speaker 3: just going to kind of throw their lot in with

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Speaker 3: whoever promises them the most radical change and quite frankly,

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Speaker 3: the most radical change that was promised in the US

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Speaker 3: is Trump.

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Speaker 2: Yeah, for the tech CEOs. Now there is a price

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Speaker 2: to this allegiance. You know, we're already seeing the policy

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Speaker 2: shifts that for instance, Meta has made around fact checking

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Speaker 2: and reverting back to freedom of speech, which is very

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Speaker 2: much Trump and Musks sort of lends on freedom of speech.

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Speaker 2: They've had to lend financial support Elon Musk to the

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Speaker 2: tune of two hundred five fifty million dollars, but they've

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Speaker 2: all chipped into Trump's inaugural fund, and no doubt future

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Speaker 2: campaign efforts there will be an expectation that they fund him.

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Speaker 2: They're making platform changes. This is just the start really,

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Speaker 2: as the ideology really starts to set in, how far

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Speaker 2: do they have to go before they sort of sort

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Speaker 2: of get really uncomfortable about it. So there's that dynamic.

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Speaker 2: I guess the dynamic for us also, you know, at

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Speaker 2: the bottom of the world is what does it mean

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Speaker 2: for us? What are the global implications of this? And

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Speaker 2: I think the key one really is any efforts that

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Speaker 2: we are trying to make in this part of the world,

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Speaker 2: in New Zealand and Australia to rain in big tech.

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Speaker 2: All of that is probably going to be put on

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Speaker 2: ice or watered down significantly for fear that Trump will

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Speaker 2: retaliate with some sort of trade sanctions or tariffs or

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Speaker 2: a lessening of the relationship between the US and our

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Speaker 2: country trees. That's literally the game that he plays. It's

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Speaker 2: what he's playing with Canada, Panama and Greenland. At the moment.

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Speaker 2: New Zealand does not want to get on his ship list.

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Speaker 3: Absolutely. Yeah, we've been focusing on the US as an

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Speaker 3: export market for a really long time because the US

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Speaker 3: has kind of been wooing us to keep us away

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Speaker 3: from China as much as possible. But you know, it's

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Speaker 3: only going to take it's only going to take a

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Speaker 3: word in the ear to Trump for all of that

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Speaker 3: to potentially change, and so then we might have to

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Speaker 3: re look at China as you know, a really close

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Speaker 3: source of export revenue because we may not have as

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Speaker 3: much of a choice, which would be a pity because

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Speaker 3: it's you know, like choosing kind of which which choosing

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Speaker 3: which knife stab yourself in the foot with. I guess

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Speaker 3: maybe that's a bit extreme, but you know it is.

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Speaker 3: It's just a sad state of affairs. And obviously the

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Speaker 3: US has never been. No country is perfect, no country

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Speaker 3: is without faults. But I think that when you so

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Speaker 3: closely intertwine you're like politics with corporate interests and then

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Speaker 3: kind of identity politics as well, in particular targeting of

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Speaker 3: certain identities as a as a other group as a scapegoat,

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Speaker 3: then you start to get into trouble. And yeah, I'm

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Speaker 3: just I don't know. I feel that the influence of technology,

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Speaker 3: these technology titans, these these oligarchs is going to spiral,

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Speaker 3: and I am concerned for what it means for New Zealand,

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Speaker 3: particularly given the kind of investment that the government has

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Speaker 3: into the likes of Microsoft and Amazon. In the terms

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Speaker 3: of our systems. Yeah, hopefully this year will be a

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Speaker 3: big call for considering New Zealand's tech industry, how we

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Speaker 3: can bolster it, and how we can maybe put a

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Speaker 3: little bit of a fence around ourselves from big tech

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Speaker 3: as much as we can.

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Speaker 2: Yeah, I think we really need to think strategically about

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Speaker 2: where our place in the world is. We're exporting some

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Speaker 2: fantastic technology which the US are buying, you know, lots

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Speaker 2: of subscribers to zero and other companies over there, but

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Speaker 2: how do we actually cement the infrastructure and the pipeline

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Speaker 2: to make sure that we continue to do more of

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Speaker 2: this and really compete on the global stage. You know,

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Speaker 2: I think we're sort of past the point where we

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Speaker 2: can sort of sit on the fence and be the

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Speaker 2: Switzerland of the South Pacific. I think in this new

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Speaker 2: sort of era of deglobalization, we do need to have

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Speaker 2: strong relationships with one camp or the other, but we've

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Speaker 2: also got to have something to give, something to differentiate ourselves.

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Speaker 2: And you know, I think this malaiser in at the

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Speaker 2: moment is really about we're struggling to find out what

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Speaker 2: our edge is. We need to figure identify it and

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Speaker 2: really go after it.

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Speaker 3: Absolutely so.

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Speaker 2: One person who wasn't in the room at the inauguration

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Speaker 2: earlier this week was Larry Ellison, one of the other

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Speaker 2: richest men in the world, the founder of Oracle.

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Speaker 3: Yeah. He's got a long association with the America's Cup

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Speaker 3: as the owner of Oracle Team USA.

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Speaker 2: Which famously beat US in twenty thirteen in San Francisco

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Speaker 2: after clawing its way back from being seven points down.

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Speaker 2: Jimmy Spiddle got one up on us in the biggest

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Speaker 2: comeback in sporting history.

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Speaker 3: After that, he and Russell Kurtz formed a new tournament

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Speaker 3: sale GP which featured these fast foiling catamarans and is

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Speaker 3: really intended to be the formula one of say large audience,

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Speaker 3: quick and exciting races, fast paced entertainment.

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Speaker 2: And it is fast. The boats reach top speeds of

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Speaker 2: around one hundred kilometers per hour, which is pretty incredible

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Speaker 2: for foiling boat.

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Speaker 3: It is. Yeah, and the foiling technology which did evolve

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Speaker 3: from the America's Cup along with the carbon fiber design

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Speaker 3: of the boats is incredible. But so too is the

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Speaker 3: digital tech on the boats that monitors every aspect of

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Speaker 3: the yacht and crew performance.

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Speaker 2: Yeah. Literally billions, billions of data points created in every

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Speaker 2: single race, So a huge it operation. It really underpins

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Speaker 2: sell GP YEP.

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Speaker 3: And the man behind it is Warren Jones. He's a

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Speaker 3: brit based in London who was previously the director of

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Speaker 3: Technology at Oracle Racing, so also has an America's Cup association.

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Speaker 2: And being caught up with Warren following the racing action

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Speaker 2: of the past weekend in Auckland. Here's his interview with

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Speaker 2: sale GP tech mastermind Warren Jones.

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Speaker 3: Hi, Warren, welcome to the Business of Tech podcast. Thank

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Speaker 3: you so much for joining.

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Speaker 1: Us now, thank you for inviointing me.

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Speaker 3: So sale GP. We just had the Auckland event this weekend,

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Speaker 3: the second event for the year, and from what I understand,

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Speaker 3: quite a successful one as well. So we were jam packed,

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Speaker 3: sold out at the grand stands in the Auckland Harbor,

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Speaker 3: and I was lucky enough to come along and see

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Speaker 3: a few of the races and just a high paced,

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Speaker 3: adrenaline filled kind of experience, which is I guess for

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Speaker 3: me a little bit different from the traditional sailing experience

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Speaker 3: when it comes to sport. The America's Cup obviously exciting,

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Speaker 3: but there is a next level of fervor and kind

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Speaker 3: of speed that comes with sale GP. So I guess,

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Speaker 3: from your perspective, how does sale GP kind of differ

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Speaker 3: from your traditional sailing experience.

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Speaker 1: So from my perspective on the two technology officers, so

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Speaker 1: I look at it differently. Rather than some of the

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Speaker 1: athletes look at it as a sail boat, I look

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Speaker 1: at them as an IoT device. So the amount of

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Speaker 1: information that we can get off one catam around one

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Speaker 1: f fifty. What that information is if what the athletes

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Speaker 1: are doing, where they're going, what they're where they've come from,

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Speaker 1: where the other boats are going where we can use

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Speaker 1: AI to be able to predict what they're doing, what

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Speaker 1: they're doing right, and what they're doing wrong.

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Speaker 5: It's yeah, it's pretty amazing, it really is.

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Speaker 1: So Yeah, so I call them extreme I IoT devices.

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Speaker 3: They are probably maybe the fastest IoT devices are around.

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Speaker 1: Probably, Yes, yeah, I agree with that.

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Speaker 3: Yeah, I mean it's fantastic because I'm talking to some

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Speaker 3: of the tech people on the day. They were saying

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Speaker 3: that there's one hundred and twenty five sensors on each boat.

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Speaker 1: Is that correct, Yeah, yeah, exactly.

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Speaker 3: And something like thirty two thousand data points per second

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Speaker 3: that come off those boats.

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Speaker 5: No, three hundred and fifty thousand per second.

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Speaker 3: I was off by an order of magnitude three hundred

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Speaker 3: thousand data points per second.

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Speaker 1: Yeah. We manage around within the Oracle OCI Oracle cloud,

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Speaker 1: we manage around fifty three billion data requests go every afternoon,

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Speaker 1: So it's it's pretty monumental the amount of information that

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Speaker 1: we process. And then on the back of that is

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Speaker 1: that we need to get that information out as quick

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Speaker 1: as possible. So that information we have, there's about ten

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Speaker 1: buckets of stakeholders that would need that information. Going from

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Speaker 1: the broadcast the teams to be able to coach social media,

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Speaker 1: the surgier by tech team to look at the boat

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Speaker 1: and see how the boat is sailing on there so

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Speaker 1: it goes on and on, so move that information around

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Speaker 1: the world in milliseconds. Yeah, it's incredible.

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Speaker 3: It is truly incredible because one of those sets of

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Speaker 3: stakeholders is in fact the umpires, who I believe are

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Speaker 3: based in the UK. And so there you're having a

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Speaker 3: sail boat race moving up to one hundred ks an

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Speaker 3: hour in extreme cases, with three hundred and fifty thousand

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Speaker 3: data points per second that are not only streaming out

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Speaker 3: of these boats, but they're going all the way over

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Speaker 3: to the United Kingdom to be assessed by the umpires.

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Speaker 3: That must be quite cutting edge in terms of the

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Speaker 3: technology that's involved there. Where did you even go about

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Speaker 3: starting that kind of project? What were the kind of

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Speaker 3: thought processes?

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Speaker 1: So it's all started in twenty eighteen, So you know

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Speaker 1: what I decided, I wanted to have a remote because

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Speaker 1: of our global nature, we were traveling around the world.

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Speaker 1: I wanted consistency to be able to where people would

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Speaker 1: be able to sit in the same seat or be

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Speaker 1: able to have the same equipment doing things Previously, you know,

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Speaker 1: if we traveled from from New Zealand to San Francisco,

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Speaker 1: the equipment is not the same equipment because it's on

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Speaker 1: a boat. It's on it, it gets off the motor

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Speaker 1: transport and it's it's it's different transfer. So yeah, so

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Speaker 1: having an Oracle Cloud where we have security, we have

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Speaker 1: power management, we have always on as well. So you know,

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Speaker 1: previously we had an equipment that had to be strapped

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Speaker 1: down to travel from destination to destination. Now, within within

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Speaker 1: the o C, I we it's on all the time,

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Speaker 1: or it's on how much we want.

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Speaker 5: It to be on.

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Speaker 1: So it's yeah, it's it's pretty it's it's pretty crazy.

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Speaker 1: But yeah, there's a product called within Oracle called Oracle

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Speaker 1: fast Connect, so extends your data center to anywhere around

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Speaker 1: the world.

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Speaker 5: So we build.

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Speaker 1: High availability links between London and in your case, Auckland

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Speaker 1: and then we send the data down there. So I

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Speaker 1: think in Auckland we are about one hundred and sixty

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Speaker 1: two milliseconds back to London, so it's a blink of

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Speaker 1: an eye. It's pretty incredible. But Oracle fast Connect then

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Speaker 1: extends that out, so we use a data center within

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Speaker 1: London called London.

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Speaker 5: South Data Center.

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Speaker 1: We utilize that one because it's halfway in the middle

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Speaker 1: of the world, so when we go east and when

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Speaker 1: we go west is sort of halfway through there, and

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Speaker 1: it's one hundred percent of a new world as well,

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Speaker 1: So they use they use all the different power that

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Speaker 1: this station. And I gotta say rain has to be

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Speaker 1: part of that because it rains a lot in London ablutely. Yeah.

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Speaker 3: I guess one of the challenges when you have such

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Speaker 3: a high technologically dense sport is that there are going

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Speaker 3: to be issues, and we saw this weekend there was

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Speaker 3: an issue with the sensor on the Black Foils boat,

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Speaker 3: which Peter Birling attributed to some of his late starts,

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Speaker 3: or at least one of his late starts at How

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Speaker 3: do you navigate that challenge when you do want everything

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Speaker 3: to be the same, and that is a key aspect

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Speaker 3: of the sport. You want everything to be the same

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Speaker 3: each race, but you are going to have these like

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Speaker 3: you say, no, even even if two sensors are the

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Speaker 3: same product on different boats, there is some level of

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Speaker 3: transformation between them by dint of you know, physics.

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Speaker 1: Yeah, it's it's unfortunate, but it's it happens, and the

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Speaker 1: boats are rigorously checked and failed tested it in every

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Speaker 1: every way. We use a product called Oracle Anomaly Detection.

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Speaker 1: So out of those fifty three billion data requests that

439
00:25:53,119 --> 00:25:56,320
Speaker 1: we get, we scan them overnight and it looks for

440
00:25:56,320 --> 00:26:00,000
Speaker 1: anomalies within the data and we can find out fault

441
00:26:00,480 --> 00:26:03,679
Speaker 1: on on, if something's coming, if something's going to fail

442
00:26:04,040 --> 00:26:06,280
Speaker 1: and it's failed before, then we can see that and

443
00:26:06,320 --> 00:26:08,159
Speaker 1: we can see the anomalies within it and then we

444
00:26:08,200 --> 00:26:10,920
Speaker 1: can change it out. But unfortunately it was a new

445
00:26:11,960 --> 00:26:15,040
Speaker 1: vault that we found within the black foils, and therefore

446
00:26:15,080 --> 00:26:17,680
Speaker 1: then you know, we couldn't be able to do that.

447
00:26:17,720 --> 00:26:20,960
Speaker 1: But it we check and check and check and test

448
00:26:21,000 --> 00:26:24,600
Speaker 1: and test and test, and it's just it sucks that,

449
00:26:25,160 --> 00:26:27,760
Speaker 1: you know, we have to pay it. We're talking about

450
00:26:27,800 --> 00:26:31,919
Speaker 1: this rather we're talking about than the athletes navigating the

451
00:26:31,960 --> 00:26:33,600
Speaker 1: waters as as they do.

452
00:26:34,800 --> 00:26:37,800
Speaker 3: Yeah, but I suppose when it comes to higher levels

453
00:26:37,800 --> 00:26:40,000
Speaker 3: of technology, there's always going to be these these small

454
00:26:40,040 --> 00:26:42,440
Speaker 3: points of failure. And so if we can get to

455
00:26:42,520 --> 00:26:46,400
Speaker 3: kind of ninety nine percent, that's still pretty good. There

456
00:26:46,440 --> 00:26:48,000
Speaker 3: is the unfortunate time when you're going to have that

457
00:26:48,000 --> 00:26:48,679
Speaker 3: one percent.

458
00:26:49,000 --> 00:26:52,040
Speaker 1: But we're trying for one hundred percent that's our goal.

459
00:26:52,080 --> 00:26:54,800
Speaker 1: We want to be able to say that the boats.

460
00:26:54,840 --> 00:26:56,840
Speaker 1: You know, we're very lucky in a way that all

461
00:26:57,000 --> 00:26:59,520
Speaker 1: eleven boats that sailed in Auckland do exactly the same.

462
00:27:00,080 --> 00:27:00,200
Speaker 5: You know.

463
00:27:00,280 --> 00:27:03,560
Speaker 1: They have the same sensors, the same weight, the same

464
00:27:04,280 --> 00:27:09,000
Speaker 1: uh rudders, the same t foils, they have everything the same.

465
00:27:09,080 --> 00:27:13,000
Speaker 1: And therefore then when the athletes then look at the data,

466
00:27:13,119 --> 00:27:15,600
Speaker 1: they can all look at their each other's data as well,

467
00:27:15,720 --> 00:27:20,120
Speaker 1: because it's you know, that type of technology that can

468
00:27:20,160 --> 00:27:24,520
Speaker 1: share a manstall competing teams. It's it's a shame, but yeah,

469
00:27:24,640 --> 00:27:28,800
Speaker 1: they'll they'll hopefully, they'll, they'll, they'll, they'll get back in

470
00:27:28,840 --> 00:27:30,200
Speaker 1: Sydney and have a good race there.

471
00:27:30,560 --> 00:27:33,200
Speaker 3: Yeah, And that is actually quite a unique thing about

472
00:27:33,200 --> 00:27:35,720
Speaker 3: sale GP, the fact that all of the data between

473
00:27:35,800 --> 00:27:39,520
Speaker 3: the teams is actually shared. And not only that, but

474
00:27:39,640 --> 00:27:44,600
Speaker 3: it's actually kept within the sale GP company itself. So

475
00:27:44,640 --> 00:27:47,240
Speaker 3: there's a lot of your analysts are within sale GP,

476
00:27:47,640 --> 00:27:50,840
Speaker 3: and a lot of the technological specialists are actually within

477
00:27:50,920 --> 00:27:53,160
Speaker 3: sale GP, so you have some of them on the teams.

478
00:27:54,000 --> 00:27:59,879
Speaker 3: But the disbursement of the data isn't It is wide

479
00:28:00,119 --> 00:28:03,800
Speaker 3: and broad and highly available for all of the teams.

480
00:28:04,359 --> 00:28:06,280
Speaker 3: I guess, first of all, do you want to talk

481
00:28:06,320 --> 00:28:10,400
Speaker 3: through kind of the decision around that and what that

482
00:28:10,480 --> 00:28:14,040
Speaker 3: means in a highly technological sport. To have that data

483
00:28:14,320 --> 00:28:17,400
Speaker 3: broadly available, I think.

484
00:28:17,320 --> 00:28:20,760
Speaker 1: It's pretty huge and I don't see any sports or

485
00:28:20,760 --> 00:28:24,000
Speaker 1: any other federation being able to do that, let alone

486
00:28:25,359 --> 00:28:30,600
Speaker 1: give that data to each competitor. I remember in season

487
00:28:30,840 --> 00:28:35,960
Speaker 1: three the Canadian team joined CLGP. Within their third race,

488
00:28:36,000 --> 00:28:38,960
Speaker 1: they won a race, and they would ask the question,

489
00:28:39,200 --> 00:28:41,120
Speaker 1: how you know your third race?

490
00:28:41,200 --> 00:28:42,720
Speaker 5: You win a race? How did you do that?

491
00:28:42,840 --> 00:28:46,320
Speaker 1: And they come back and said, data using the Oracle data,

492
00:28:46,400 --> 00:28:50,120
Speaker 1: looking at the Oracle dashboards, looking at teams that are

493
00:28:50,160 --> 00:28:53,360
Speaker 1: sailing the boat correctly, not what they would be doing

494
00:28:53,400 --> 00:28:56,640
Speaker 1: if they didn't have that data, they'd be sailing it incorrectly.

495
00:28:57,200 --> 00:29:00,000
Speaker 1: They're having that information is a great start and level

496
00:29:00,160 --> 00:29:02,360
Speaker 1: out the play and field. To be honest that the

497
00:29:02,640 --> 00:29:05,800
Speaker 1: probably the top teams don't like it, but the teams,

498
00:29:06,320 --> 00:29:09,880
Speaker 1: the emergent teams coming through love it because it gives

499
00:29:09,880 --> 00:29:12,840
Speaker 1: them that that it neutralizes everything from that point of view.

500
00:29:12,840 --> 00:29:17,520
Speaker 1: But it's so powerful now the data, because you could

501
00:29:17,560 --> 00:29:20,480
Speaker 1: be you could be going down. You could say, okay,

502
00:29:20,480 --> 00:29:22,080
Speaker 1: then this is how I want to sail the boat

503
00:29:22,400 --> 00:29:25,120
Speaker 1: and it could be right, it could be wrong. But

504
00:29:25,160 --> 00:29:28,760
Speaker 1: then you have other teams being able to do something

505
00:29:28,920 --> 00:29:31,320
Speaker 1: or try something, so you're all looking at each other's

506
00:29:31,400 --> 00:29:34,240
Speaker 1: data to be able to find that sweet spot. It's

507
00:29:34,280 --> 00:29:35,800
Speaker 1: pretty powerful and.

508
00:29:35,640 --> 00:29:37,560
Speaker 3: I guess, well, it sounds like what that's going to

509
00:29:37,600 --> 00:29:41,640
Speaker 3: do is kind of result in a return to the mean,

510
00:29:41,720 --> 00:29:45,280
Speaker 3: and so everybody's doing the same thing. In reality, these

511
00:29:45,360 --> 00:29:48,040
Speaker 3: athletes are pushing the boundaries. They're trying new things to

512
00:29:48,080 --> 00:29:51,080
Speaker 3: try and get that edge, and so there's a constant

513
00:29:51,400 --> 00:29:57,640
Speaker 3: feedback loop of trying different little tiny maneuvers to see

514
00:29:57,640 --> 00:29:59,520
Speaker 3: if they can push it. And then everybody can see

515
00:29:59,520 --> 00:30:01,840
Speaker 3: how six for those maneuvers were.

516
00:30:02,960 --> 00:30:05,000
Speaker 1: Going back and say, we're very lucky in a way

517
00:30:05,000 --> 00:30:07,520
Speaker 1: that sailing is that. You know, if we generate fifty

518
00:30:07,560 --> 00:30:13,960
Speaker 1: three billion data requests, there's equivalent of fifty three billion options.

519
00:30:14,920 --> 00:30:17,320
Speaker 1: So there's a lot of things to do and a

520
00:30:17,400 --> 00:30:20,840
Speaker 1: lot of things to change, and you could change it.

521
00:30:20,880 --> 00:30:24,200
Speaker 1: For one, in Auckland, the setting on the boat could

522
00:30:24,240 --> 00:30:26,520
Speaker 1: be fantastic, But then when you get to Sydney, there's

523
00:30:26,560 --> 00:30:30,640
Speaker 1: different current, there's different wind, there's different courses, there's different

524
00:30:31,120 --> 00:30:34,200
Speaker 1: so many other things that you need to change to

525
00:30:34,240 --> 00:30:35,720
Speaker 1: be able to sail those conditions.

526
00:30:36,800 --> 00:30:40,120
Speaker 3: Going back to the text, So, staying on this shared

527
00:30:40,200 --> 00:30:43,560
Speaker 3: data theme, one of the things about data is that

528
00:30:43,600 --> 00:30:46,200
Speaker 3: really it's a stream of numbers when you get down

529
00:30:46,240 --> 00:30:50,080
Speaker 3: to its core, which is not particularly helpful for people

530
00:30:50,080 --> 00:30:52,920
Speaker 3: who are not data scientists. And like you said, you

531
00:30:52,960 --> 00:30:55,240
Speaker 3: have ten buckets of stakeholders that need to be able

532
00:30:55,280 --> 00:30:58,040
Speaker 3: to access and understand this data. So there must have

533
00:30:58,120 --> 00:31:01,960
Speaker 3: been a user interface challenge in terms of how you

534
00:31:02,040 --> 00:31:05,160
Speaker 3: communicate this data to all the different people involved. Do

535
00:31:05,160 --> 00:31:07,040
Speaker 3: you want to talk a bit about that side of it.

536
00:31:08,400 --> 00:31:13,080
Speaker 1: Yeah, So the main component is Oracle stream analytics. So

537
00:31:13,960 --> 00:31:17,440
Speaker 1: we we didn't want to create we didn't want to

538
00:31:17,480 --> 00:31:21,360
Speaker 1: get the data and then get data ten times from

539
00:31:21,360 --> 00:31:23,480
Speaker 1: the boat, so we just get one source of data

540
00:31:23,640 --> 00:31:27,200
Speaker 1: and then manipulate that data to whoever needs that information

541
00:31:27,280 --> 00:31:30,600
Speaker 1: and what they do. So stream analytics gives us stability

542
00:31:30,680 --> 00:31:33,880
Speaker 1: to get the series and ones, as you said, and

543
00:31:33,960 --> 00:31:38,600
Speaker 1: then be able to create a metric like Team New Zealand,

544
00:31:39,440 --> 00:31:44,040
Speaker 1: what's the distance between the finishing line to the boundary

545
00:31:44,080 --> 00:31:46,840
Speaker 1: to Team A to Team B to Team C. So

546
00:31:47,000 --> 00:31:49,080
Speaker 1: you know, we know that if you put an f

547
00:31:49,080 --> 00:31:51,239
Speaker 1: fifty on the water is going to generate data. So

548
00:31:51,280 --> 00:31:53,000
Speaker 1: once it gets to the data, then you can work

549
00:31:53,000 --> 00:31:58,720
Speaker 1: out where they are. We have very very complex information

550
00:31:58,800 --> 00:32:01,160
Speaker 1: on the boats and we can generate We know where

551
00:32:01,240 --> 00:32:05,920
Speaker 1: each f fifty years within one centimeter in the spatial world,

552
00:32:06,400 --> 00:32:08,960
Speaker 1: so we can then work out where that boat is

553
00:32:09,000 --> 00:32:13,400
Speaker 1: comparison to where everything else is on the racetrack. So

554
00:32:14,200 --> 00:32:18,960
Speaker 1: within stream analytics you create metrics called patterns, so we

555
00:32:19,040 --> 00:32:22,200
Speaker 1: have around I think it's about three thousand patterns within it.

556
00:32:22,360 --> 00:32:24,320
Speaker 1: So as soon as that data comes in, then it

557
00:32:24,360 --> 00:32:27,640
Speaker 1: converts it into this information and then it goes off

558
00:32:27,680 --> 00:32:33,640
Speaker 1: to the individual stakeholders. So for broadcast it would go

559
00:32:33,680 --> 00:32:40,560
Speaker 1: into liveline. So liveline is augmented reality package for the coaches.

560
00:32:40,600 --> 00:32:44,360
Speaker 1: It goes back into the venue and therefore the coaches

561
00:32:44,440 --> 00:32:48,840
Speaker 1: have access to a dashboard and then live video. We

562
00:32:48,920 --> 00:32:52,240
Speaker 1: also recreate the wing screen, so each each f fifty

563
00:32:52,280 --> 00:32:56,200
Speaker 1: has two screens in the wing and it has information

564
00:32:56,360 --> 00:33:01,680
Speaker 1: about about the start, about the boundaries and things like that.

565
00:33:01,760 --> 00:33:03,800
Speaker 1: So we recreate that then to the coaches, so the

566
00:33:03,800 --> 00:33:06,200
Speaker 1: coaches can see real time what the sailors have seen

567
00:33:06,240 --> 00:33:09,680
Speaker 1: on the boat as well. So there's the huge amounts

568
00:33:09,680 --> 00:33:12,560
Speaker 1: of information going around back and forth around the world,

569
00:33:12,800 --> 00:33:14,480
Speaker 1: and it's just you know.

570
00:33:15,000 --> 00:33:16,160
Speaker 5: What information you need.

571
00:33:16,200 --> 00:33:20,160
Speaker 1: And the coaches at a great point really because historically

572
00:33:20,240 --> 00:33:22,480
Speaker 1: the coaches were always on the water, they were looking

573
00:33:22,480 --> 00:33:26,400
Speaker 1: at the boat, they were communicating, and SLGP wanted to

574
00:33:26,520 --> 00:33:29,080
Speaker 1: change the way that was. We thought that was inefficient

575
00:33:29,160 --> 00:33:31,560
Speaker 1: and we have so much information that we can help them.

576
00:33:31,920 --> 00:33:35,200
Speaker 1: We didn't want to use fossil fuel engines on the water,

577
00:33:35,360 --> 00:33:38,160
Speaker 1: just running around chasing out the boats and burning fuel

578
00:33:38,480 --> 00:33:41,960
Speaker 1: and necessary. So bringing them on the shore is something

579
00:33:41,960 --> 00:33:45,600
Speaker 1: that we really wanted and from And you asked one

580
00:33:45,640 --> 00:33:47,120
Speaker 1: of the coaches now if they want to go back

581
00:33:47,160 --> 00:33:49,400
Speaker 1: on the water, and they go, nope, we're pretty happy

582
00:33:49,400 --> 00:33:49,960
Speaker 1: here we are.

583
00:33:50,960 --> 00:33:54,680
Speaker 3: Yeah, what are the kind of advantages of being a

584
00:33:54,720 --> 00:33:57,720
Speaker 3: sthetic on the sideline rather than on the water.

585
00:34:00,080 --> 00:34:02,960
Speaker 1: For me, I'm not a coach, and I've talked to

586
00:34:03,000 --> 00:34:05,160
Speaker 1: many coaches on there, but I think it's the amount

587
00:34:05,200 --> 00:34:08,759
Speaker 1: of informations asvailable to them. I think when you're on

588
00:34:08,800 --> 00:34:13,000
Speaker 1: a boat, you're you're holding on to your life with

589
00:34:13,080 --> 00:34:15,160
Speaker 1: one hand and then you're trying to look at a

590
00:34:15,200 --> 00:34:18,480
Speaker 1: phone or a tablet on it within the other just

591
00:34:18,520 --> 00:34:21,760
Speaker 1: as a work. But having that information and being able

592
00:34:21,840 --> 00:34:23,960
Speaker 1: to have that real time and also talk to the

593
00:34:23,960 --> 00:34:28,000
Speaker 1: boat as well. We're seeing the role of the coach

594
00:34:28,080 --> 00:34:32,680
Speaker 1: is changing, it's evolving, it's having there, they're part of

595
00:34:32,680 --> 00:34:35,719
Speaker 1: the team now, they're giving information, real time information and

596
00:34:35,719 --> 00:34:37,280
Speaker 1: things like that which they didn't before.

597
00:34:38,440 --> 00:34:41,160
Speaker 3: How long did it actually take you to architect this

598
00:34:41,280 --> 00:34:45,320
Speaker 3: whole system, because it sounds incredibly, credibly complex. I imagine

599
00:34:45,360 --> 00:34:47,560
Speaker 3: there must have been a few few men hours put

600
00:34:47,600 --> 00:34:50,120
Speaker 3: into getting everything the way that you needed it to be.

601
00:34:51,440 --> 00:34:51,640
Speaker 5: Well.

602
00:34:51,719 --> 00:34:56,840
Speaker 1: I remember in two and late twenty seventeen, early twenty eighteen,

603
00:34:56,960 --> 00:34:59,360
Speaker 1: I sat down and they took me about three months

604
00:34:59,360 --> 00:35:02,919
Speaker 1: to go through our calls catalogue of services, and.

605
00:35:02,840 --> 00:35:03,960
Speaker 5: I was cherry picking.

606
00:35:04,400 --> 00:35:06,920
Speaker 1: Yeah, I want that, that's what that's what we need,

607
00:35:06,960 --> 00:35:09,120
Speaker 1: that's what we should be doing, that's what we could do.

608
00:35:09,560 --> 00:35:10,759
Speaker 5: And we we I.

609
00:35:10,840 --> 00:35:14,759
Speaker 1: Cherry picked up about a dozen applications that we thought

610
00:35:14,760 --> 00:35:17,160
Speaker 1: we could use. Some of the applications we had to

611
00:35:17,200 --> 00:35:20,000
Speaker 1: modify or oracle modified for us because the user case

612
00:35:20,239 --> 00:35:22,560
Speaker 1: was not what it was supposed to be. But they

613
00:35:22,600 --> 00:35:25,600
Speaker 1: were the teams were happy to to change that. But

614
00:35:25,640 --> 00:35:29,400
Speaker 1: it Yeah, it was about three months. There's there's they

615
00:35:29,440 --> 00:35:31,719
Speaker 1: we talk about threety three billion data recrafts. They have

616
00:35:31,800 --> 00:35:35,680
Speaker 1: fifty three billion products, so it's it's it's huge, the

617
00:35:35,719 --> 00:35:38,600
Speaker 1: amount of the products and the things they do. So yeah,

618
00:35:38,640 --> 00:35:41,680
Speaker 1: we sat down and we built it out. But we

619
00:35:41,680 --> 00:35:44,640
Speaker 1: we always wanted to have a cloud instance that's where

620
00:35:44,680 --> 00:35:48,879
Speaker 1: we we thought the future was, and that's that's where

621
00:35:48,920 --> 00:35:50,160
Speaker 1: it's that's where it's gone.

622
00:35:50,200 --> 00:35:50,360
Speaker 5: You know.

623
00:35:50,440 --> 00:35:52,560
Speaker 1: We we I talked to you at the beginning. I'm

624
00:35:52,600 --> 00:35:55,360
Speaker 1: in London with our broadcasts and we sat with the umpires,

625
00:35:55,400 --> 00:35:57,759
Speaker 1: we sat with the people who were producing the show.

626
00:35:58,040 --> 00:36:02,400
Speaker 1: The content, all the cameras, all four t eight cameras,

627
00:36:02,440 --> 00:36:05,839
Speaker 1: go back to London, We create a show, we distribute

628
00:36:05,840 --> 00:36:08,319
Speaker 1: them to one hundred and sixty eight broadcasters around the world.

629
00:36:08,400 --> 00:36:11,920
Speaker 1: From London, we send that feedback to you to watching

630
00:36:11,960 --> 00:36:14,680
Speaker 1: the Adrenaline Lounge, to the big screens, and that's all

631
00:36:14,719 --> 00:36:20,480
Speaker 1: coming from London within three hundred milliseconds. It's yeah, it's crazy.

632
00:36:20,480 --> 00:36:22,480
Speaker 3: It is crazy. It's it's almost that. I mean, if

633
00:36:22,520 --> 00:36:26,120
Speaker 3: you go back five ten years, unimaginable, you just couldn't

634
00:36:26,360 --> 00:36:27,359
Speaker 3: couldn't have imagined it.

635
00:36:27,880 --> 00:36:28,879
Speaker 1: So yeah, the.

636
00:36:28,880 --> 00:36:31,480
Speaker 3: Capability to do it, it does feel very cutting edge,

637
00:36:31,920 --> 00:36:34,920
Speaker 3: and it sounds like you were, you know, set on

638
00:36:35,000 --> 00:36:39,120
Speaker 3: Oracle from very very early on. Was there a technological

639
00:36:39,360 --> 00:36:40,200
Speaker 3: reason as well?

640
00:36:42,560 --> 00:36:44,719
Speaker 1: We have we have a we have a relationship with them,

641
00:36:44,760 --> 00:36:46,920
Speaker 1: But if I was if we didn't have a relationship

642
00:36:46,960 --> 00:36:49,399
Speaker 1: with them, I would have chosen them due to their

643
00:36:50,120 --> 00:36:53,200
Speaker 1: their oci locations around the world. If you look where

644
00:36:53,239 --> 00:36:55,239
Speaker 1: we go around the world, that they have a data

645
00:36:55,239 --> 00:36:58,239
Speaker 1: center around the corner. It's you know that that's one

646
00:36:58,280 --> 00:37:01,840
Speaker 1: of the big things that us. We need low latency,

647
00:37:01,920 --> 00:37:05,560
Speaker 1: We need data to get to its end location as

648
00:37:05,680 --> 00:37:10,400
Speaker 1: quick as possible, and Oracle are very very good at that.

649
00:37:11,960 --> 00:37:16,000
Speaker 3: What were some of the most tricky challenges for you

650
00:37:16,080 --> 00:37:19,319
Speaker 3: and the team in setting this up, whether whether in

651
00:37:19,360 --> 00:37:22,560
Speaker 3: the setup or ongoing. What are the kind of what

652
00:37:22,600 --> 00:37:27,840
Speaker 3: are the technological limits that you're budding up against?

653
00:37:28,160 --> 00:37:32,920
Speaker 1: You know, Auckland is probably the furthest away from London

654
00:37:32,960 --> 00:37:35,560
Speaker 1: as you could possibly go. I think if you go

655
00:37:35,600 --> 00:37:40,080
Speaker 1: a bit further, you're coming home, aren't you. So it

656
00:37:40,120 --> 00:37:43,640
Speaker 1: gets to its limit there, so we utilize Oracle has

657
00:37:43,640 --> 00:37:49,800
Speaker 1: an edge component in Sydney, so we utilize that. But

658
00:37:49,920 --> 00:37:56,719
Speaker 1: it's we're generating about four times more data today than

659
00:37:56,760 --> 00:38:01,560
Speaker 1: we were in twenty nineteen. We're it, and we need

660
00:38:01,560 --> 00:38:04,520
Speaker 1: that data as fast fast, So it's it's we with

661
00:38:05,640 --> 00:38:07,920
Speaker 1: and how do you You can generate data as long

662
00:38:07,920 --> 00:38:09,880
Speaker 1: as you want it, doesn't you know, you're always going

663
00:38:09,920 --> 00:38:12,360
Speaker 1: to be getting information. But what does that mean? Does it?

664
00:38:12,480 --> 00:38:14,680
Speaker 1: Does it mean something? Is it giving you more insight

665
00:38:14,719 --> 00:38:18,160
Speaker 1: to what's happening? Is it giving you more the ability

666
00:38:18,239 --> 00:38:20,480
Speaker 1: to make the boats go faster, to make the boats

667
00:38:20,520 --> 00:38:23,759
Speaker 1: more reliable? So it's it's given that context of what

668
00:38:23,800 --> 00:38:27,399
Speaker 1: that data means as well. So that's more information, more dashboards,

669
00:38:27,480 --> 00:38:30,760
Speaker 1: more there. But then we have to train people because

670
00:38:30,800 --> 00:38:33,360
Speaker 1: some of the information now is getting as granular as possible.

671
00:38:33,400 --> 00:38:36,720
Speaker 1: And you talked about we have we have a data

672
00:38:36,760 --> 00:38:40,000
Speaker 1: scientists working for sales GP, but most of them are

673
00:38:40,000 --> 00:38:43,279
Speaker 1: all sailors as well. So having that ability to look

674
00:38:43,280 --> 00:38:46,279
Speaker 1: at the data from the X and Y, but then

675
00:38:46,320 --> 00:38:48,520
Speaker 1: also look at what they represent on a on a

676
00:38:48,640 --> 00:38:50,440
Speaker 1: on a racecourse as well is invaluable.

677
00:38:51,000 --> 00:38:56,120
Speaker 3: Yeah, I could imagine that. How how does that how

678
00:38:56,120 --> 00:38:58,960
Speaker 3: does it translate into the real world boat? So I mean,

679
00:38:59,160 --> 00:39:02,279
Speaker 3: because these are standsized boats, you've got things like your

680
00:39:02,320 --> 00:39:07,560
Speaker 3: major upgrades like the tfoil for example, was did you

681
00:39:07,800 --> 00:39:09,960
Speaker 3: obviously you must have used the data to be able

682
00:39:10,000 --> 00:39:12,879
Speaker 3: to say, well, you know, we can recognize that it's

683
00:39:12,880 --> 00:39:15,720
Speaker 3: giving us all these advantage, but there's a physical aspect

684
00:39:15,719 --> 00:39:17,399
Speaker 3: to it as well. So when it comes to making

685
00:39:17,440 --> 00:39:22,719
Speaker 3: decisions about fleet upgrades, changes to the fleet, how does

686
00:39:22,760 --> 00:39:25,000
Speaker 3: it go from data to action?

687
00:39:27,239 --> 00:39:34,160
Speaker 1: We we have a we have a we have an

688
00:39:34,160 --> 00:39:39,880
Speaker 1: application that we can generate it's called a BPP so

689
00:39:41,800 --> 00:39:44,120
Speaker 1: to be able to look at the boats. So you

690
00:39:44,120 --> 00:39:45,880
Speaker 1: can put t foils on there, you can put the

691
00:39:46,040 --> 00:39:48,280
Speaker 1: l foils on there, you can make the boat longer, shorter,

692
00:39:48,719 --> 00:39:50,920
Speaker 1: and it will give you what like. It's basically a

693
00:39:50,920 --> 00:39:52,960
Speaker 1: CFD which lives.

694
00:39:52,719 --> 00:39:53,680
Speaker 5: Within the OCI.

695
00:39:53,760 --> 00:40:00,680
Speaker 1: It's a cloud based tool, so so you can you

696
00:40:00,880 --> 00:40:03,520
Speaker 1: like like like whether you design cars or you can

697
00:40:03,719 --> 00:40:05,120
Speaker 1: just sign.

698
00:40:06,120 --> 00:40:07,760
Speaker 5: We have our own there with our.

699
00:40:07,640 --> 00:40:10,800
Speaker 1: Physics in there, so have the ability to be able

700
00:40:10,840 --> 00:40:14,080
Speaker 1: to test the foils. So we start off within the

701
00:40:14,400 --> 00:40:16,880
Speaker 1: virtual world and work out what that looks like from

702
00:40:16,920 --> 00:40:18,759
Speaker 1: from that point of view, and then they test it.

703
00:40:18,800 --> 00:40:22,359
Speaker 1: They test it then the simulation simulation, so we use

704
00:40:22,400 --> 00:40:24,680
Speaker 1: our we have our own simulator that the team is

705
00:40:24,800 --> 00:40:27,040
Speaker 1: used to be able to do that, so we test

706
00:40:27,080 --> 00:40:30,719
Speaker 1: it what what and we use real time data so

707
00:40:31,239 --> 00:40:34,799
Speaker 1: from from Auckland. We'll get the data, we'll put that

708
00:40:34,880 --> 00:40:38,799
Speaker 1: into our platform and then we run the simulations then

709
00:40:38,960 --> 00:40:42,920
Speaker 1: using the wind, the tide the conditions of what that was,

710
00:40:42,960 --> 00:40:45,920
Speaker 1: and then you could work out then what the numbers were.

711
00:40:46,360 --> 00:40:48,040
Speaker 1: So it gets to a point there where we think

712
00:40:48,080 --> 00:40:51,279
Speaker 1: that yes, and I'm not a designer, just given the

713
00:40:51,360 --> 00:40:53,439
Speaker 1: tools to be able to do it. And there comes

714
00:40:53,440 --> 00:40:55,560
Speaker 1: a time when you think that yes, I think we

715
00:40:55,600 --> 00:40:59,200
Speaker 1: can start building physical models, or we can build test

716
00:40:59,239 --> 00:41:02,279
Speaker 1: models and stuff that and then then it goes out there.

717
00:41:02,320 --> 00:41:04,920
Speaker 1: But then you need software then to be able to

718
00:41:05,040 --> 00:41:07,640
Speaker 1: control the boards, to be able to control the forces,

719
00:41:08,040 --> 00:41:10,239
Speaker 1: to the loads and things like that as well. And

720
00:41:11,080 --> 00:41:14,640
Speaker 1: the performance engineering team at SALGP they will manage all that.

721
00:41:14,760 --> 00:41:17,120
Speaker 1: So they build, they rocked the code to be able

722
00:41:17,160 --> 00:41:20,400
Speaker 1: to drop the boards, to be able to manage the loads,

723
00:41:20,440 --> 00:41:23,160
Speaker 1: all these other things as well. So it's a it's

724
00:41:23,200 --> 00:41:28,640
Speaker 1: a load of it's it's multi department, it's everybody working

725
00:41:29,560 --> 00:41:31,680
Speaker 1: looking at data and trying to work out how to

726
00:41:32,080 --> 00:41:32,640
Speaker 1: move that forward.

727
00:41:32,680 --> 00:41:34,439
Speaker 5: It's yeah, it's pretty pretty tough.

728
00:41:34,840 --> 00:41:37,040
Speaker 3: Yeah, I can imagine. So, so basically you have a

729
00:41:37,040 --> 00:41:40,000
Speaker 3: digital twin of the boats and then you use the

730
00:41:40,080 --> 00:41:43,600
Speaker 3: data from the boats to inform that digital twin and

731
00:41:43,680 --> 00:41:46,560
Speaker 3: then make changes and then bring it slowly into the

732
00:41:46,560 --> 00:41:51,359
Speaker 3: real world to simplify things drastically. But there's a lot

733
00:41:51,440 --> 00:41:54,400
Speaker 3: of stages and software and engineering that goes into that

734
00:41:54,440 --> 00:41:55,640
Speaker 3: whole process.

735
00:41:56,280 --> 00:41:59,080
Speaker 1: Huge, huge amounts, and you know, you change one thing,

736
00:41:59,760 --> 00:42:02,360
Speaker 1: then another thing changes as well, and that batterfly effect

737
00:42:02,360 --> 00:42:04,840
Speaker 1: then is happening on there. So it's trying to limit

738
00:42:04,880 --> 00:42:09,319
Speaker 1: that case and go from there. But we're young as well.

739
00:42:09,360 --> 00:42:11,440
Speaker 1: We've only been doing this for five years, five six

740
00:42:11,520 --> 00:42:14,040
Speaker 1: years as well, so there are a couple of steps

741
00:42:14,080 --> 00:42:17,239
Speaker 1: that we don't know that we should be doing as well.

742
00:42:17,320 --> 00:42:19,280
Speaker 1: So we're learning and we're trying to move this forward

743
00:42:19,320 --> 00:42:21,759
Speaker 1: as well. So it's a long way to go.

744
00:42:22,160 --> 00:42:25,640
Speaker 3: Yeah, I can imagine speaking of that, what are you

745
00:42:25,960 --> 00:42:28,880
Speaker 3: hoping for in the future, What are your pipe dreams?

746
00:42:28,880 --> 00:42:29,839
Speaker 3: What are you hoping for?

747
00:42:31,160 --> 00:42:33,360
Speaker 1: So we utilize a lot of AI at the moment.

748
00:42:33,400 --> 00:42:36,080
Speaker 1: As you know, we mentioned that that fable number, that

749
00:42:36,120 --> 00:42:40,239
Speaker 1: fifty three billion, that a human mind can't comprehend what

750
00:42:40,280 --> 00:42:44,320
Speaker 1: that is. So you need you need mL you need AI,

751
00:42:44,480 --> 00:42:46,800
Speaker 1: you need large language models to be able to handle

752
00:42:46,840 --> 00:42:50,520
Speaker 1: that now. So we're working with Oracle on really cool

753
00:42:50,560 --> 00:42:54,440
Speaker 1: stuff on what to do that the automated cameras now

754
00:42:54,440 --> 00:42:59,439
Speaker 1: that we're working on now that that is no other

755
00:42:59,520 --> 00:43:02,480
Speaker 1: federation or sport is being able to do that. But

756
00:43:03,440 --> 00:43:07,760
Speaker 1: that's all underlined on data. So you know those those

757
00:43:07,920 --> 00:43:10,360
Speaker 1: sensors that you mentioned there, we know when the boat

758
00:43:10,520 --> 00:43:15,879
Speaker 1: is going to capsize, all the hull is forty two

759
00:43:15,880 --> 00:43:18,879
Speaker 1: percent from the water. So therefore then we can tell

760
00:43:18,920 --> 00:43:21,960
Speaker 1: the cameras to go and go and film this boat

761
00:43:22,000 --> 00:43:24,960
Speaker 1: here and things like that, so we you know, predictive

762
00:43:25,040 --> 00:43:26,839
Speaker 1: AI to be able to work out where the boats

763
00:43:26,840 --> 00:43:29,239
Speaker 1: are going, if there's going to be a collision or

764
00:43:29,680 --> 00:43:32,240
Speaker 1: something's happening, go and move the cameras to this location.

765
00:43:32,880 --> 00:43:35,960
Speaker 1: So at the moment, all the cameras are controlled from London.

766
00:43:36,080 --> 00:43:37,080
Speaker 5: So each boat.

767
00:43:36,880 --> 00:43:40,040
Speaker 1: Has an agile camera on the back of the boat

768
00:43:40,440 --> 00:43:42,839
Speaker 1: and the operators are in London control in it over

769
00:43:42,840 --> 00:43:43,360
Speaker 1: the weekend.

770
00:43:43,480 --> 00:43:47,320
Speaker 5: So it's a pretty tough job there. So to have.

771
00:43:48,600 --> 00:43:50,800
Speaker 1: AI to be able to go and find these shots

772
00:43:50,800 --> 00:43:52,480
Speaker 1: for you and then you can frame it then is.

773
00:43:54,680 --> 00:43:55,560
Speaker 5: Exactly what we want.

774
00:43:56,719 --> 00:43:59,080
Speaker 3: So just that I can kind of understand what you

775
00:43:59,760 --> 00:44:02,840
Speaker 3: mean is that there's a AI program that on the

776
00:44:02,920 --> 00:44:07,200
Speaker 3: camera that is able to understand when a shot is

777
00:44:07,640 --> 00:44:09,960
Speaker 3: what you might be looking for, and so rather than

778
00:44:09,960 --> 00:44:13,279
Speaker 3: having to manually adjust constantly and find what you're looking for,

779
00:44:13,680 --> 00:44:17,080
Speaker 3: it just can go oh, yeah, this is good here

780
00:44:17,120 --> 00:44:17,680
Speaker 3: for a while.

781
00:44:19,239 --> 00:44:22,720
Speaker 1: Yeah, or you just say follow Follow Follow Team New Zealand,

782
00:44:22,719 --> 00:44:25,800
Speaker 1: and the camera just follows it everywhere it goes. It's

783
00:44:26,040 --> 00:44:29,759
Speaker 1: it's it's pretty. It's amazing, it really is. But a

784
00:44:29,760 --> 00:44:34,920
Speaker 1: lot of the industry is running on vision, object recognition

785
00:44:34,960 --> 00:44:37,880
Speaker 1: and things like that, but we have abundance of information,

786
00:44:37,960 --> 00:44:40,640
Speaker 1: events of data, and real time data as well, so

787
00:44:40,680 --> 00:44:44,359
Speaker 1: we we we're using data to follow the story rather

788
00:44:44,440 --> 00:44:48,320
Speaker 1: than the division, but we also use an augment minted

789
00:44:48,400 --> 00:44:51,959
Speaker 1: reality and we we can we have what we call

790
00:44:52,239 --> 00:44:55,239
Speaker 1: athlete tracking, so we have the camera at the back end,

791
00:44:55,520 --> 00:44:58,400
Speaker 1: we have the athletes coming from side to side, and

792
00:44:58,440 --> 00:45:01,880
Speaker 1: we built a model with Oracle skeletons, so we have

793
00:45:01,920 --> 00:45:07,839
Speaker 1: good facial recognition models. But they have helmets on, they

794
00:45:07,880 --> 00:45:10,000
Speaker 1: have goggles on, so we can't really see them. So

795
00:45:10,360 --> 00:45:14,080
Speaker 1: we modeled each athletes from their skeleton and how they run,

796
00:45:14,320 --> 00:45:16,799
Speaker 1: and therefore then we can use AI to match that

797
00:45:16,840 --> 00:45:19,520
Speaker 1: so when they're running past, then we can work out

798
00:45:19,560 --> 00:45:23,640
Speaker 1: within I think it was su fifteen milliseconds who they

799
00:45:23,640 --> 00:45:25,600
Speaker 1: are and then we can put a little name strap

800
00:45:25,640 --> 00:45:28,120
Speaker 1: above them and tell tell the audience this is this

801
00:45:28,200 --> 00:45:29,160
Speaker 1: person or that person.

802
00:45:29,320 --> 00:45:34,480
Speaker 3: Oh my gosh, that's that's incredible, quite a really cutting edge.

803
00:45:35,160 --> 00:45:37,600
Speaker 3: And so are you when are you expecting that to

804
00:45:37,640 --> 00:45:39,120
Speaker 3: be available.

805
00:45:38,560 --> 00:45:42,400
Speaker 1: To it's live? Yeah, So if you can have a

806
00:45:42,400 --> 00:45:47,360
Speaker 1: look at the recording of the Auckland eventuals see I

807
00:45:47,360 --> 00:45:50,719
Speaker 1: think day two they had it on a couple of

808
00:45:50,719 --> 00:45:52,600
Speaker 1: times there, so it's a camera at the back and

809
00:45:52,840 --> 00:45:56,319
Speaker 1: they do it I think Spain and Australia.

810
00:45:57,160 --> 00:46:01,239
Speaker 3: Fantastic. Wow. And so that kind of thing. We'll just

811
00:46:01,280 --> 00:46:07,399
Speaker 3: continue to expand where you're able to understand things more

812
00:46:07,480 --> 00:46:12,480
Speaker 3: fully I guess, and then use that information to provide

813
00:46:13,120 --> 00:46:16,399
Speaker 3: deeper insights but also gain some predictive knowledge about what

814
00:46:16,840 --> 00:46:18,439
Speaker 3: might be going on with the race.

815
00:46:19,400 --> 00:46:20,120
Speaker 5: Yeah exactly.

816
00:46:20,200 --> 00:46:23,239
Speaker 1: And you know, we we've got at the moment, within

817
00:46:23,280 --> 00:46:28,080
Speaker 1: the OCI, we've got about two trillion lines of data

818
00:46:28,200 --> 00:46:31,360
Speaker 1: available to us. So that's every race that sales GPS

819
00:46:31,400 --> 00:46:34,040
Speaker 1: competed in. We keep every bit of data available. So

820
00:46:34,520 --> 00:46:38,200
Speaker 1: when we create a new metric in today's world, we

821
00:46:38,280 --> 00:46:40,239
Speaker 1: can go back and then create it from the other

822
00:46:40,320 --> 00:46:42,800
Speaker 1: races as well and then test it. So we're always

823
00:46:43,239 --> 00:46:46,239
Speaker 1: using that information and testing on models and moving that on.

824
00:46:46,320 --> 00:46:49,400
Speaker 1: But yeah, there's a lot of there's a lot of information,

825
00:46:49,480 --> 00:46:52,960
Speaker 1: and it's that the autonomous databases are just amazing. They

826
00:46:53,040 --> 00:46:55,560
Speaker 1: just they just keep on going. It's just you know,

827
00:46:55,960 --> 00:46:59,719
Speaker 1: fifty three billion data lines of code in it. It's just incredible.

828
00:47:00,840 --> 00:47:04,920
Speaker 3: Just to round us out, you mentioned large language models

829
00:47:05,360 --> 00:47:08,359
Speaker 3: and so you know I'm assuming, then correct me if

830
00:47:08,360 --> 00:47:11,800
Speaker 3: I'm wrong. If you've got this extraordinary amount of data,

831
00:47:11,840 --> 00:47:14,480
Speaker 3: are you using the large language models to actually interrogate

832
00:47:14,560 --> 00:47:17,759
Speaker 3: that data to say, going to show me what would

833
00:47:17,840 --> 00:47:21,120
Speaker 3: you know all of the wing data from this race

834
00:47:21,320 --> 00:47:24,560
Speaker 3: or any Is it that advanced it is?

835
00:47:24,760 --> 00:47:28,080
Speaker 1: You can you can compare, you can compare races, you

836
00:47:28,080 --> 00:47:32,160
Speaker 1: can compare boats, you can compare area everything. There's there's

837
00:47:32,200 --> 00:47:36,759
Speaker 1: there's things that we're we've got plans to do and

838
00:47:36,760 --> 00:47:38,759
Speaker 1: and use that. But it's it's a huge amount of

839
00:47:38,800 --> 00:47:40,600
Speaker 1: information and the huge amounts of a bit of data.

840
00:47:40,680 --> 00:47:43,080
Speaker 1: But we think that there's things that we're not thinking

841
00:47:43,120 --> 00:47:45,920
Speaker 1: about at the moment. And like if you if you

842
00:47:46,000 --> 00:47:49,480
Speaker 1: find a metric today and you've you know, this is

843
00:47:49,520 --> 00:47:53,960
Speaker 1: the new metric. We we do a chasing within season

844
00:47:53,960 --> 00:47:57,840
Speaker 1: twenty twenty five, we have a chasing target, so it

845
00:47:58,000 --> 00:48:00,560
Speaker 1: works out that if a boat has chasing another boat

846
00:48:00,600 --> 00:48:03,279
Speaker 1: and it brings that down. That was only done for

847
00:48:03,360 --> 00:48:06,439
Speaker 1: this season, but because we have the data, we could

848
00:48:06,480 --> 00:48:08,719
Speaker 1: go back to season one and be able to work

849
00:48:08,760 --> 00:48:11,279
Speaker 1: that that that out from there because we have this

850
00:48:11,920 --> 00:48:14,280
Speaker 1: as you as you reckon, as you said, the zeros

851
00:48:14,280 --> 00:48:16,759
Speaker 1: are ones that that's available to us, so we can

852
00:48:16,800 --> 00:48:17,959
Speaker 1: really create that as well.

853
00:48:18,239 --> 00:48:21,399
Speaker 3: Wow, right, we need to wrap up because I'm aware

854
00:48:21,400 --> 00:48:23,480
Speaker 3: you've probably got a lot of sleep to catch up

855
00:48:23,520 --> 00:48:27,160
Speaker 3: on after a busy weekend, or maybe some more work

856
00:48:27,200 --> 00:48:30,120
Speaker 3: to do one of the two. So just the final question,

857
00:48:31,000 --> 00:48:33,399
Speaker 3: is there anything that we haven't covered that you think

858
00:48:33,520 --> 00:48:40,280
Speaker 3: is super interesting, super unique to sal GP technological technology wise?

859
00:48:42,239 --> 00:48:44,480
Speaker 1: Really, I think we've been pretty it's been pretty good

860
00:48:44,520 --> 00:48:47,319
Speaker 1: actually with everything. It's just we're you know, we're very

861
00:48:47,440 --> 00:48:49,640
Speaker 1: lucky in a way that that that that the CEO

862
00:48:49,800 --> 00:48:55,960
Speaker 1: of of c l GP, Sir Russell Cootz, he he

863
00:48:56,080 --> 00:48:59,839
Speaker 1: really believes in technology and he wants the tech to.

864
00:48:59,040 --> 00:48:59,920
Speaker 5: To go forward.

865
00:48:59,920 --> 00:49:04,120
Speaker 1: He's he's incredibly he's saying in knowledge, as you know

866
00:49:04,280 --> 00:49:07,200
Speaker 1: from Olympic Gold Medals and America's Cups and things like that.

867
00:49:07,239 --> 00:49:09,759
Speaker 1: But to understand the tech as well is really good.

868
00:49:09,800 --> 00:49:12,319
Speaker 1: So to have a champion pushing us forward, to be

869
00:49:12,360 --> 00:49:15,360
Speaker 1: able to use this tech, to be able to find

870
00:49:15,480 --> 00:49:17,879
Speaker 1: nuances to be more efficient and how we.

871
00:49:17,800 --> 00:49:19,879
Speaker 5: Do it is is pretty cool.

872
00:49:19,960 --> 00:49:22,399
Speaker 1: So yeah, we're you know, we we've got a load

873
00:49:22,440 --> 00:49:25,279
Speaker 1: of a load of new new things that we've got

874
00:49:25,320 --> 00:49:27,799
Speaker 1: coming out over the season now and you know, thanks

875
00:49:27,840 --> 00:49:29,960
Speaker 1: to Oracle, they're they're helping us push these through.

876
00:49:37,120 --> 00:49:40,839
Speaker 2: So being one of the really interesting things that cel

877
00:49:40,960 --> 00:49:44,920
Speaker 2: GP has decided to do, which Warren talked about in there,

878
00:49:45,080 --> 00:49:47,880
Speaker 2: is share all of the data off the boats with

879
00:49:48,080 --> 00:49:51,880
Speaker 2: every team. So here you have the super competitive group

880
00:49:51,960 --> 00:49:56,800
Speaker 2: of racing syndicates. Normally they're hiding everything and keeping everything

881
00:49:56,960 --> 00:49:59,960
Speaker 2: close to their chists, and with sel GP, they said, look,

882
00:50:00,360 --> 00:50:06,840
Speaker 2: we're about innovation and going faster, trying to put on

883
00:50:06,880 --> 00:50:09,000
Speaker 2: a really good show here. The best way to facilitate

884
00:50:09,040 --> 00:50:12,560
Speaker 2: that is to give everyone full visibility and what the

885
00:50:12,680 --> 00:50:13,640
Speaker 2: competitors are doing.

886
00:50:14,040 --> 00:50:16,960
Speaker 3: Yeah, it's such an interesting choice that really does define

887
00:50:17,239 --> 00:50:21,200
Speaker 3: sale GP as a sport because it does become more

888
00:50:21,200 --> 00:50:24,040
Speaker 3: about the sailing and you know, I had a chat

889
00:50:24,080 --> 00:50:28,080
Speaker 3: to very brief chat to the strategists from the Great

890
00:50:28,080 --> 00:50:31,399
Speaker 3: Britain team and the following the press conference. Her name's

891
00:50:31,400 --> 00:50:36,440
Speaker 3: Hannah Mills, incredible sailing talent, and she was basically like,

892
00:50:36,680 --> 00:50:40,560
Speaker 3: it is just really exciting to be able to access

893
00:50:40,600 --> 00:50:42,600
Speaker 3: all this data, to be able to work with all

894
00:50:42,600 --> 00:50:44,879
Speaker 3: of it, and then to balance that with the kind

895
00:50:44,920 --> 00:50:48,279
Speaker 3: of vibes out there on the water to figure out

896
00:50:48,360 --> 00:50:50,520
Speaker 3: kind of how we actually approach things. It turns it

897
00:50:50,560 --> 00:50:56,680
Speaker 3: into a thoughtful strategic sport broadly across the entire fleet,

898
00:50:57,080 --> 00:50:59,600
Speaker 3: rather than just saying who can afford the best technology

899
00:50:59,600 --> 00:51:00,680
Speaker 3: and the best analysts.

900
00:51:01,040 --> 00:51:04,799
Speaker 2: Yeah, obviously Oracle is all over this as it was

901
00:51:04,920 --> 00:51:11,120
Speaker 2: the America's Cup were Oracle racing in particular. But you

902
00:51:11,120 --> 00:51:12,759
Speaker 2: look at it from their point of view. Sure they

903
00:51:12,800 --> 00:51:16,319
Speaker 2: love their technology being used, but the insights they're getting here,

904
00:51:16,320 --> 00:51:18,880
Speaker 2: like the stuff you're talking about digital twins, you know,

905
00:51:18,920 --> 00:51:21,759
Speaker 2: being able to build a digital twin off an f

906
00:51:21,760 --> 00:51:25,759
Speaker 2: fifty boat and every aspect of its design and its

907
00:51:25,800 --> 00:51:28,319
Speaker 2: performance all the way down to the crew. They can

908
00:51:28,440 --> 00:51:34,440
Speaker 2: track through their skeletal shape exactly who's moving around a boat.

909
00:51:34,600 --> 00:51:38,920
Speaker 2: Where So the granularity of detail that they've got on this,

910
00:51:39,040 --> 00:51:42,840
Speaker 2: they can then apply to other types of industries, anything

911
00:51:42,880 --> 00:51:47,719
Speaker 2: that's fast moving, high performance, where there's potential health and

912
00:51:47,760 --> 00:51:51,360
Speaker 2: safety issues. They can take this and put it into factories,

913
00:51:51,480 --> 00:51:53,719
Speaker 2: put it into transport systems. It's got to be a

914
00:51:53,800 --> 00:51:57,560
Speaker 2: hugely valuable thing in terms of intellectual property that it

915
00:51:57,600 --> 00:51:58,480
Speaker 2: creates for Oracle.

916
00:51:59,000 --> 00:52:02,439
Speaker 3: Definitely. Yeah, and you know that the talk about the

917
00:52:02,480 --> 00:52:06,920
Speaker 3: capabilities of the Oracle Cloud to actually do these this

918
00:52:07,040 --> 00:52:09,600
Speaker 3: kind of project. I would imagine the learnings that the

919
00:52:09,600 --> 00:52:12,400
Speaker 3: Oracle team is taking from working with sale GP to

920
00:52:13,120 --> 00:52:16,040
Speaker 3: you know, get these this massive amount of data from

921
00:52:16,040 --> 00:52:17,960
Speaker 3: one side of the world to the other as fast

922
00:52:17,960 --> 00:52:21,680
Speaker 3: as possible, to run some real time analytics, some post

923
00:52:22,680 --> 00:52:26,200
Speaker 3: post capture analytics over it, to stream it into different places.

924
00:52:26,440 --> 00:52:28,560
Speaker 3: That's all going to be really useful for Oracle to

925
00:52:28,600 --> 00:52:31,760
Speaker 3: take back into its team and apply to other areas

926
00:52:31,760 --> 00:52:34,520
Speaker 3: of industry as well. So it's definitely a win win

927
00:52:34,640 --> 00:52:36,760
Speaker 3: situation for Larry Ellison.

928
00:52:37,239 --> 00:52:43,200
Speaker 2: Yeah, and AI and machine learning playing a role less

929
00:52:43,200 --> 00:52:45,839
Speaker 2: so at the moment, but definitely seems as though they're

930
00:52:45,880 --> 00:52:48,239
Speaker 2: exploring the use of that for future applications.

931
00:52:49,000 --> 00:52:52,279
Speaker 3: Yeah. Yeah, really interesting what they're talking about there in

932
00:52:52,360 --> 00:52:55,279
Speaker 3: terms of being able to predict where a boat might

933
00:52:55,320 --> 00:52:57,719
Speaker 3: be headed and be able to say, look, you're on

934
00:52:57,760 --> 00:53:01,560
Speaker 3: a collision course. And not only can that, as Warren

935
00:53:01,640 --> 00:53:04,239
Speaker 3: Jones said, mean like let's get the cameras over there immediately,

936
00:53:04,320 --> 00:53:07,120
Speaker 3: but also it can actually make it safer. So if

937
00:53:07,239 --> 00:53:09,200
Speaker 3: you know, if you get a flag as the driver

938
00:53:09,600 --> 00:53:12,440
Speaker 3: of a boat saying we need to we need to

939
00:53:12,440 --> 00:53:15,000
Speaker 3: be careful because you're on a collision course, there's you know,

940
00:53:15,239 --> 00:53:17,120
Speaker 3: the tides are coming this way, your boat's doing this,

941
00:53:17,320 --> 00:53:20,320
Speaker 3: so we can predict that this course is going to

942
00:53:20,360 --> 00:53:22,960
Speaker 3: be a concern, make it just make a change. Now,

943
00:53:23,040 --> 00:53:25,200
Speaker 3: that's actually going to make it safer for the athletes

944
00:53:25,200 --> 00:53:28,279
Speaker 3: there out on the water as well. So that requires

945
00:53:28,440 --> 00:53:33,000
Speaker 3: some pretty hefty on board compute as well. So when

946
00:53:33,000 --> 00:53:36,360
Speaker 3: I talked to the Oracle representative Alistair Green, he was

947
00:53:36,360 --> 00:53:40,359
Speaker 3: saying that there wills there will need to be some

948
00:53:40,440 --> 00:53:43,560
Speaker 3: work on making those inference models as small as possible,

949
00:53:43,880 --> 00:53:46,239
Speaker 3: because you can't have too much equipment on a boat

950
00:53:46,239 --> 00:53:48,600
Speaker 3: because you can't have too much weight. So you'll need,

951
00:53:48,680 --> 00:53:50,960
Speaker 3: you know, if you can throw a powerful but light

952
00:53:51,120 --> 00:53:54,440
Speaker 3: gpu on there and have a really small inferencing model

953
00:53:54,440 --> 00:53:57,640
Speaker 3: that can run on a small amount of space, then

954
00:53:57,680 --> 00:53:59,920
Speaker 3: you can start to get some really exciting things happening.

955
00:54:00,920 --> 00:54:03,680
Speaker 2: Yeah, and from the viewer's point of view, you know,

956
00:54:04,280 --> 00:54:06,680
Speaker 2: broadcasting is a big part of this. So the rich

957
00:54:06,719 --> 00:54:09,520
Speaker 2: experience that they're able to create, the augmented reality and

958
00:54:09,560 --> 00:54:12,040
Speaker 2: all that sort of thing is really cool. I guess

959
00:54:12,040 --> 00:54:14,520
Speaker 2: part of me feels And this was an argument about

960
00:54:14,520 --> 00:54:18,360
Speaker 2: the America's Cup. It got two technology centric, you know,

961
00:54:18,440 --> 00:54:22,200
Speaker 2: this this race for more and more innovation, being the fastest,

962
00:54:22,200 --> 00:54:26,000
Speaker 2: being the most impressive. You know, at some point, you know,

963
00:54:26,160 --> 00:54:28,480
Speaker 2: you just want to get back to the basics, getting

964
00:54:28,480 --> 00:54:30,680
Speaker 2: out in the boat, not having looking at a screen

965
00:54:30,760 --> 00:54:32,960
Speaker 2: and the sensors and all that sort of thing. Just

966
00:54:33,040 --> 00:54:37,239
Speaker 2: the prowess of human beings sailing, putting all that experience

967
00:54:37,800 --> 00:54:40,920
Speaker 2: to good use sort of. I think part of me

968
00:54:41,040 --> 00:54:43,840
Speaker 2: craves that, and we're getting further and further away from it.

969
00:54:44,440 --> 00:54:47,239
Speaker 3: Yeah, that's a fair and that's a fair observation, I think.

970
00:54:48,560 --> 00:54:50,759
Speaker 3: But I would say that that is that is just

971
00:54:50,800 --> 00:54:54,680
Speaker 3: not what sale GP is, you know, and maybe that's

972
00:54:54,680 --> 00:54:57,600
Speaker 3: an opportunity for up. You can start your own sailing competition.

973
00:54:57,719 --> 00:55:03,319
Speaker 3: That's a class. Yeah, hands in the Wellington Harbor, that's right.

974
00:55:03,560 --> 00:55:08,520
Speaker 2: Yeah, Well, that's it for the Business of Tech this week,

975
00:55:08,719 --> 00:55:13,440
Speaker 2: Thanks so much to Warren Jones fantastic technology that he

976
00:55:13,520 --> 00:55:17,080
Speaker 2: showcased here. Lots of big issues to get our teeth

977
00:55:17,080 --> 00:55:19,479
Speaker 2: into in twenty twenty five on the Business of Tech,

978
00:55:19,600 --> 00:55:22,400
Speaker 2: and we've got some other great guests lined up for

979
00:55:22,440 --> 00:55:23,279
Speaker 2: the podcast too.

980
00:55:23,560 --> 00:55:26,080
Speaker 3: Next week we'll hear from a New Zealand software startup

981
00:55:26,120 --> 00:55:30,920
Speaker 3: that has one hundred million users internationally, perhaps the greatest

982
00:55:30,960 --> 00:55:32,839
Speaker 3: reach of Kiwi Tech so far.

983
00:55:33,280 --> 00:55:36,440
Speaker 2: It's a great success story and there's plenty more like

984
00:55:36,440 --> 00:55:39,640
Speaker 2: that to come. To catch all of our episodes, subscribe

985
00:55:39,640 --> 00:55:42,720
Speaker 2: to the Business of Tech on your podcast platform of choice.

986
00:55:42,719 --> 00:55:44,960
Speaker 2: We're also streaming on iHeartRadio.

987
00:55:45,360 --> 00:55:48,200
Speaker 3: Get in touch with us with your feedback and topic suggestions.

988
00:55:48,239 --> 00:55:50,920
Speaker 3: We're on LinkedIn and blue Sky, and catch.

989
00:55:50,719 --> 00:55:53,600
Speaker 2: Us again for the next episode next Thursday.

990
00:55:54,040 --> 00:55:54,600
Speaker 3: See you later.