WEBVTT - How the venture studio model is changing the game 

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<v Speaker 1>This week on the Business of Tech powered by two

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<v Speaker 1>Degrees Business the rise of the venture studio.

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<v Speaker 2>Here we've counted at least a dozen New Zealand companies

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<v Speaker 2>pursuing this relatively new model, or new for New Zealand anyway,

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<v Speaker 2>This sort of model of creating and funding startups called

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<v Speaker 2>the venture studio. Unlike venture capital firmed, venture studios play

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<v Speaker 2>a much more hands on role in the entire life

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<v Speaker 2>cycle off a startup, from ideation to execution.

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<v Speaker 1>We talk to the co founders of Auckland based venture

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<v Speaker 1>studio Rift about its approach to operating a venture studio,

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<v Speaker 1>with a particular focus on artificial intelligence.

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<v Speaker 3>So we have all the ingredients, all those assets that

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<v Speaker 3>you need to bring together, and then it's about taking

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<v Speaker 3>what can feel a bit boring, sometimes a formulaic approach,

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<v Speaker 3>and yet a lot of the times you can identify

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<v Speaker 3>great opportunities, assemble the team, assemble the financing and then

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<v Speaker 3>go and tackle it.

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<v Speaker 1>I'm Peter Griffin, I'm Ben More and a look at

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<v Speaker 1>the venture studio model of building startups coming up with

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<v Speaker 1>two of Rift's co founders, Lucas Coelo and Chris Moore.

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<v Speaker 2>But first, then the first half results of interdex listed

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<v Speaker 2>spark are announced Tomorrow, that's Friday, they'll release their financial update.

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<v Speaker 2>Sparks had a pretty rough run in the last year

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<v Speaker 2>or so, a sagging sheer price, major cost cutting going on,

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<v Speaker 2>a number of senior people have gone from the organization,

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<v Speaker 2>and a move to divers parts of the business. What

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<v Speaker 2>are you expecting to see tomorrow?

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<v Speaker 1>What I'm expecting is an update, first of all on

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<v Speaker 1>the restructuring. So that's something they promised at their and

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<v Speaker 1>your general meeting last year, was that they would release

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<v Speaker 1>more details on the restructuring, including kind of how it's

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<v Speaker 1>going so far, what they've managed to save. You may

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<v Speaker 1>recall their goal was to say fifty million on labor costs,

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<v Speaker 1>which is about ten percent of their total labor spend.

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<v Speaker 1>It was recently came out that they were cutting their

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<v Speaker 1>management staff as part of that restructure, and how they're

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<v Speaker 1>changing the portfolios that different managers look after, so probably

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<v Speaker 1>a reasonable chunk there as well. So that's what they

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<v Speaker 1>have said is that there will be more information about

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<v Speaker 1>the restructuring at this update, but they've been pretty reticent

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<v Speaker 1>about giving too much detail anyway, so very curious to

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<v Speaker 1>see exactly what they do release and what they don't

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<v Speaker 1>about that situation. So that's kind of the main thing

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<v Speaker 1>I think most people are on tender hooks about.

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<v Speaker 2>Yeah, they sort of foreshadowed this last year. They reduced

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<v Speaker 2>their ebit dye guidance somewhat not a huge hit, but

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<v Speaker 2>that's down. They reduced their dividend guidance to twenty five

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<v Speaker 2>cents per share from twenty seven point five cents, so

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<v Speaker 2>they're basically softening up the market for lower expectations, and

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<v Speaker 2>you know that's been reflected in the share price. I

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<v Speaker 2>think it's down like thirty six percent of something over

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<v Speaker 2>the last year, even including dividends, so it's it's been tough.

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<v Speaker 2>And then there was all angst about them being delisted

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<v Speaker 2>from that big sort of index Overseas, and I was

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<v Speaker 2>expecting a big dip after that, but that was clearly

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<v Speaker 2>sort of priced into it beforehand, so that didn't really

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<v Speaker 2>take a massive hit. But still, you know, this is

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<v Speaker 2>a company that still has a big market shriff to

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<v Speaker 2>telecommunications industry, you know, forty something percent of mobile and

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<v Speaker 2>fixed line broadband, so it is you know, still you know,

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<v Speaker 2>the big player along with one end Z in that market,

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<v Speaker 2>but sort of really facing a bit of an existential crisis.

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<v Speaker 1>Absolutely, especially in like broadband and mobile as well. Two

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<v Speaker 1>Degrees nipping at their heels along with some of the

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<v Speaker 1>Mbenno operator that are starting to collectively take share away

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<v Speaker 1>as they kind of use broadband as a lost leader

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<v Speaker 1>to get people onto their other services. So yeah, that's

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<v Speaker 1>an area where they're struggling. Interestingly, on their share price,

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<v Speaker 1>analysts are kind of a little bit mixed at the moment,

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<v Speaker 1>with some saying, you know, still not worth buying at

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<v Speaker 1>this point. Others are saying, look, this is actually a

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<v Speaker 1>depressed price for Spark. It's likely to go up in

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<v Speaker 1>the future. Now might be a good time to buy.

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<v Speaker 1>What to me that says is that there is a

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<v Speaker 1>little bit of uncertainty about what's actually going to come

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<v Speaker 1>for Spark, that there's obviously going to be a huge

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<v Speaker 1>effort from Spark to reassess their position, try and capture

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<v Speaker 1>back some of their core market and also try and

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<v Speaker 1>capture new market with data centers. That's been a big discussion,

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<v Speaker 1>so I expect we'll probably hear a bit more about

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<v Speaker 1>their data center strategy as well, including some efforts to

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<v Speaker 1>raise capital to co fund development of new data center.

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<v Speaker 2>Yeah, it's been reviewing it's non core assets. So it

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<v Speaker 2>still owns a bit of Connects, the mobile tower business,

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<v Speaker 2>so it might divest the rest of that. And it's

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<v Speaker 2>got quite a big IT division as well, and a

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<v Speaker 2>few sort of units there, health, sort of tech unit, IoT,

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<v Speaker 2>interneted things business, it's curious data business. So do you

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<v Speaker 2>think there may be moves signal to carve off some

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<v Speaker 2>of these units.

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<v Speaker 1>Yeah, we know Connectsha, they're staking Connection, they're looking to sell,

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<v Speaker 1>so that's a definite. They haven't really said yes or

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<v Speaker 1>no on some of the other areas, but I know

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<v Speaker 1>there is definitely some rumblings about what they're planning to

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<v Speaker 1>actually divest. We might hear more about that at this update.

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<v Speaker 1>They might not update on that, because sometimes what will

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<v Speaker 1>happen is they will just get their earnings out of

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<v Speaker 1>the way, update on some more of the directly related

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<v Speaker 1>fiscal stuff, and then update later on some of the

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<v Speaker 1>other plans around investment. That is, unless they've already made

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<v Speaker 1>the deals and they're announcing that they have sold x,

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<v Speaker 1>Y or z. That might happen at this earnings. I

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<v Speaker 1>think what's really interesting is if you look at Deleeperfonseca's

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<v Speaker 1>column from last year that was on Business Desk, and

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<v Speaker 1>he talks about Spark trying to kind of figure out

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<v Speaker 1>what it is now. Is it a tech company or

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<v Speaker 1>is it an infrastructure company, because those are two quite

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<v Speaker 1>different things, and how does it balance those in these

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<v Speaker 1>times of difficulty, So well worth a background read if

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<v Speaker 1>you're interested.

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<v Speaker 2>Yeah, and it's interesting that a lot of the restructuring

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<v Speaker 2>is in this sort of enterprise and government sections of

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<v Speaker 2>the business. And on the face of it, it looks like

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<v Speaker 2>Spark had been laying the groundwork for really smart, DIVERSEI

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<v Speaker 2>vacation in recent years, with the likes of Curious, the

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<v Speaker 2>sort of AI and data division. It's got its IT

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<v Speaker 2>division as well, which is doing more sort of cloud

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<v Speaker 2>and digital transformation stuff, so you'd think like and they

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<v Speaker 2>published that report which we had Jolie Hodson on the

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<v Speaker 2>podcast about last year looking at productivity, the productivity gap

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<v Speaker 2>in New Zealand, the need to embrace advanced technology, so

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<v Speaker 2>sort of doing that with a focus on IoT and

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<v Speaker 2>health tech and the like. It just hasn't translated. And

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<v Speaker 2>I don't know if that's a that they got the

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<v Speaker 2>market wrong or if it's a just bad execution, but

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<v Speaker 2>more than any other talco. They were sort of saying,

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<v Speaker 2>we want to be about more than supplying speeds and

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<v Speaker 2>feeds which have low margins. There's lots of competition, we

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<v Speaker 2>want to do more, and they've sort of been not,

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<v Speaker 2>you know, slapped back on that, which is sort of disappointing.

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<v Speaker 2>I don't know if it's them or if it's us.

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<v Speaker 1>Yeah, yeah, it's a big question, I mean, and that's

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<v Speaker 1>that was one of the things that analysts have been

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<v Speaker 1>talking about a lot, is like is this just the

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<v Speaker 1>ups and downs of the macro environment or is this

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<v Speaker 1>a fundamental shift in how things are operating.

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<v Speaker 2>So check out Business Desk on Friday, there'll be full

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<v Speaker 2>coverage off the Spark results. Just before we get into

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<v Speaker 2>our featured interview, an update on last week's story about

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<v Speaker 2>being AI Ben. You went in depth into this listed

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<v Speaker 2>tech company and the travails it's been experiencing. Some really

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<v Speaker 2>interesting discussion resulted from the podcast, including on LinkedIn what

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<v Speaker 2>were people saying.

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<v Speaker 1>So on your posting of that episode. The CEO of

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<v Speaker 1>Callahan Innovation is Stephane Korn. He is an AI expert

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<v Speaker 1>from way back, and he posted talking and specifically about

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<v Speaker 1>their project Treehouse and this idea of APIs as a

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<v Speaker 1>way for AI agents to enter operate, which I I thought, well,

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<v Speaker 1>you know my perspective, I thought, it's a much more

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<v Speaker 1>efficient and effective way for machines to talk to each

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<v Speaker 1>other rather than open Ay's operator approach where it tries

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<v Speaker 1>to navigate websites that are designed for humans. Now, Stephane

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<v Speaker 1>Cortan had an interesting take on that where he basically said,

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<v Speaker 1>one of the reasons that APIs don't really work is

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<v Speaker 1>for competitive reasons, which was something I didn't really think about.

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<v Speaker 1>So what was your take on some of the reasons

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<v Speaker 1>that they might.

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<v Speaker 2>Not Well, sitting behind the website, there's a database, and

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<v Speaker 2>for a travel company, it could be all their dynamic

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<v Speaker 2>pricing and all that sort of thing. So they've got

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<v Speaker 2>to be really careful that they don't give away this

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<v Speaker 2>sort of the secret source to their business model, so

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<v Speaker 2>they hold that information very closely to them. I don't

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<v Speaker 2>think it precludes them from issuing API access. For instance,

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<v Speaker 2>an Uber would really like the idea of being integrated

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<v Speaker 2>into a messaging app if you can pull in information

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<v Speaker 2>from Uber saying there are drivers available, it's only going

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<v Speaker 2>to be five minutes. You know that's possible via an API.

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<v Speaker 2>So I think there's some level that they could do.

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<v Speaker 2>But I think Stephann is right. The reason why we

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<v Speaker 2>just haven't seen open ai really go big on its

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<v Speaker 2>operator platform this is the AI agents it's now building

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<v Speaker 2>with an API sort of ecosystem for that, is that

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<v Speaker 2>a lot of companies are sitting back going do I

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<v Speaker 2>want to give you that level of access? So open

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<v Speaker 2>ai has just forged ahead and they're basically screenscraping all

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<v Speaker 2>of that information off websites. It means they can move fast,

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<v Speaker 2>and they're hoping that everyone will go, I need to

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<v Speaker 2>have operator because it's so good, my customers want it.

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<v Speaker 2>So let's do something a bit more substantial here with

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<v Speaker 2>with APIs. So I think that will come, but there's

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<v Speaker 2>definitely a competitive tension there in giving a lot more

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<v Speaker 2>fundamental access to these websites.

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<v Speaker 1>Yeah, and I think one of the concerns might be

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<v Speaker 1>if you look at how deep Seak used distillation, for example,

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<v Speaker 1>to train its model off open ais model, there's actually

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<v Speaker 1>if you have AI running things really rapidly, and you

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<v Speaker 1>could do a whole bunch of API calls to travel company.

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<v Speaker 1>You could start to see how they're pricing actually works,

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<v Speaker 1>and kind of use AI agents that might not be

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<v Speaker 1>exactly what you might want to be accessing your API.

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<v Speaker 1>So yeah, you know there is a possibility there all

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<v Speaker 1>that could potentially be mitigated through careful you know, management

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<v Speaker 1>of the agents and what they're able to do and

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<v Speaker 1>things like that in the future. But that's why I

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<v Speaker 1>kind of said in the last episode as well. It

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<v Speaker 1>has potential, but there's a lot of things that need

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<v Speaker 1>to fall into place in order to make it work.

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<v Speaker 1>It's not impossible that the network effect might turn out

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<v Speaker 1>to make this API marketplace somewhere that companies need to

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<v Speaker 1>be in order to get their business out there into

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<v Speaker 1>the world. But on the other hand, yeah, there is

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<v Speaker 1>a lot of trains, a lot of changes that will

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<v Speaker 1>need to occur in order for it to find that

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<v Speaker 1>market for Yeah, so we've gone.

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<v Speaker 2>From bigless incumbents like Spike to the earliest stage startups,

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<v Speaker 2>and we're focusing in this episode Ben on the idea

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<v Speaker 2>of a venture studio. I'd heard the concept before, really

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<v Speaker 2>in the Silicon Valley context, but it's the first I

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<v Speaker 2>really heard of a local venture studio. Was the Auckland

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<v Speaker 2>Company previously unavailable. It had some really big success with

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<v Speaker 2>the startup track Suit, which is like a brand tracking startup.

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<v Speaker 2>It raised around twenty two million dollars from venture capital

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<v Speaker 2>firms in early twenty twenty four in a series A

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<v Speaker 2>fundraising round that valued it at over one hundred and

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<v Speaker 2>fifty million dollars. But it started with a small amount

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<v Speaker 2>of seed funding and the expertise of the people at

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<v Speaker 2>previously unavailable.

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<v Speaker 1>And then it turns out a number of local companies

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<v Speaker 1>are now pursuing this model, no surprise after a success

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<v Speaker 1>like that, among them a New and Improved Ventures, super

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<v Speaker 1>Bowld Ministry of Awesome, Paloma Ventures, Edition Group, and Rift,

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<v Speaker 1>who we are talking to on the podcast today.

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<v Speaker 2>The three co founders are familiar with building products and

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<v Speaker 2>have a little cash to play with after they sold

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<v Speaker 2>a pretty successful IT consulting firm.

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<v Speaker 1>That's right. So Chris Moore, Lucas Coelo and Ben morew

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<v Speaker 1>All ran Rome Digital together, which sold for around forty

0:13:25.679 --> 0:13:28.600
<v Speaker 1>five million in twenty twenty one, and after a bit

0:13:28.640 --> 0:13:30.800
<v Speaker 1>of a break, they decided to put their expertise and

0:13:30.960 --> 0:13:34.359
<v Speaker 1>money to use building and seeding companies of their own.

0:13:34.280 --> 0:13:37.800
<v Speaker 2>And so Rift was born. Let's go to Ben's interview

0:13:37.960 --> 0:13:41.480
<v Speaker 2>with Rifts Lucas Coelo and Chris Moore talking about the

0:13:41.559 --> 0:13:45.040
<v Speaker 2>venture studio model, their company Rift and air approach to

0:13:45.080 --> 0:13:47.040
<v Speaker 2>building AI products.

0:13:52.840 --> 0:13:55.440
<v Speaker 1>Hello, and welcome to the Business of Tech. Thank you

0:13:55.480 --> 0:13:58.360
<v Speaker 1>so much for joining us. We're here to talk about

0:13:58.600 --> 0:14:02.120
<v Speaker 1>Rift AI. That's your guy project that you've been working

0:14:02.160 --> 0:14:06.000
<v Speaker 1>on for a while. Now you're out here pushing out

0:14:06.000 --> 0:14:09.440
<v Speaker 1>this studio venture studio model in New Zealand, which is

0:14:09.440 --> 0:14:13.360
<v Speaker 1>a reasonably well known model now internationally and is starting

0:14:13.400 --> 0:14:16.040
<v Speaker 1>to pick up pace in New Zealand as well. So

0:14:16.679 --> 0:14:18.839
<v Speaker 1>why didn't you introduce yourselves to start with?

0:14:19.160 --> 0:14:23.360
<v Speaker 4>Yeah? Cool, Lucas co founded a Rift Ventures before that,

0:14:23.920 --> 0:14:26.680
<v Speaker 4>Ravens in about eight years ago, and before that it

0:14:26.720 --> 0:14:30.160
<v Speaker 4>was heavily involved in the startup scene in Brazil, the

0:14:30.200 --> 0:14:31.720
<v Speaker 4>startup in design and technology.

0:14:31.760 --> 0:14:38.400
<v Speaker 3>Seeing Chris so I was a co founder in Rome Digital,

0:14:38.400 --> 0:14:43.360
<v Speaker 3>which was a product design studio. Actually the missing third

0:14:43.360 --> 0:14:46.080
<v Speaker 3>party here is Ben who's also part of Rift. So

0:14:46.600 --> 0:14:49.480
<v Speaker 3>we've pulled together kind of the same team that we

0:14:49.560 --> 0:14:53.120
<v Speaker 3>had previously. Then we exited that in twenty twenty one

0:14:53.200 --> 0:14:58.480
<v Speaker 3>to a ten billion dollar US listed company, took a

0:14:58.480 --> 0:15:00.560
<v Speaker 3>bit of time off and took a bit of the

0:15:00.600 --> 0:15:03.160
<v Speaker 3>cash and then got the old team back together and

0:15:03.520 --> 0:15:05.480
<v Speaker 3>kicked off rift of the inches go.

0:15:05.760 --> 0:15:08.760
<v Speaker 1>And that story we talked about, we've talked about before.

0:15:08.920 --> 0:15:11.160
<v Speaker 1>I've got a story up on business Desk that listeners

0:15:11.360 --> 0:15:14.160
<v Speaker 1>can read if they want a bit more background. How

0:15:14.200 --> 0:15:16.960
<v Speaker 1>does it work with a venture studio? Are you guys

0:15:17.400 --> 0:15:19.600
<v Speaker 1>developing the products? Are you investing in the products? What

0:15:19.640 --> 0:15:20.440
<v Speaker 1>does it actually mean?

0:15:21.640 --> 0:15:24.160
<v Speaker 4>In summary, there are a couple of entities within sort

0:15:24.160 --> 0:15:26.680
<v Speaker 4>of the studio model. We have the studio itself that

0:15:26.760 --> 0:15:29.200
<v Speaker 4>produces the ideas. So we have a backlog of ideas.

0:15:29.200 --> 0:15:34.000
<v Speaker 4>Were always fishing for ideas, even within our immediate sort

0:15:34.000 --> 0:15:37.640
<v Speaker 4>of team. Our team now it's composed at about eleven people

0:15:38.800 --> 0:15:44.880
<v Speaker 4>with doing engineering, design, growth, marketing, and we always sort

0:15:44.880 --> 0:15:48.320
<v Speaker 4>of have a large backlog of ideas. Ideas are free

0:15:48.440 --> 0:15:50.920
<v Speaker 4>right that we keep adding ideas in there for us

0:15:51.000 --> 0:15:54.080
<v Speaker 4>to take a look at. And one thing that we

0:15:54.160 --> 0:15:55.840
<v Speaker 4>have is kind of like sort of like a playbook

0:15:55.880 --> 0:15:58.520
<v Speaker 4>on a few of the criterias of how we evaluate

0:15:58.600 --> 0:16:01.520
<v Speaker 4>ideas in order to turn them into opportunities, move them

0:16:01.600 --> 0:16:05.920
<v Speaker 4>up onto the pipeline. And that's when we start doing

0:16:05.920 --> 0:16:08.760
<v Speaker 4>a little bit of a desk research, understanding more of

0:16:08.760 --> 0:16:13.160
<v Speaker 4>the vertical that that idea plays in the industry, sort

0:16:13.160 --> 0:16:16.600
<v Speaker 4>of the product can look like, and then we start prototyping,

0:16:16.720 --> 0:16:20.840
<v Speaker 4>testing and see what sort of products or or companies

0:16:20.920 --> 0:16:25.040
<v Speaker 4>Mike can come out of that. So, for example, last

0:16:25.080 --> 0:16:28.760
<v Speaker 4>year our backlog of ideas had about one hundred or

0:16:28.760 --> 0:16:32.320
<v Speaker 4>something ideas. It's ideas that from the time of Rome

0:16:33.440 --> 0:16:37.840
<v Speaker 4>ideas that were sall online from other companies doing in overseas.

0:16:38.520 --> 0:16:42.160
<v Speaker 4>We also did some vertical sort of days or weeks

0:16:42.160 --> 0:16:44.640
<v Speaker 4>that we're going to speak with people within a certain vertical,

0:16:44.760 --> 0:16:47.320
<v Speaker 4>for example health tech to understand sort of their problems

0:16:47.400 --> 0:16:50.360
<v Speaker 4>and what things could we tackle, problems could we solve

0:16:50.400 --> 0:16:53.280
<v Speaker 4>there with with sort of the heavy sort of II mindset.

0:16:55.160 --> 0:16:58.200
<v Speaker 4>And then from quite a few of those we we

0:16:58.280 --> 0:16:59.960
<v Speaker 4>did a first sort of like a hot or no

0:17:01.040 --> 0:17:04.480
<v Speaker 4>quick rating on those ideas, thinking about what is the

0:17:04.520 --> 0:17:07.639
<v Speaker 4>competition locally? Can we build this in New Zealand? Is

0:17:07.720 --> 0:17:09.760
<v Speaker 4>there an entry way here that we can build this

0:17:09.920 --> 0:17:13.720
<v Speaker 4>fast in order to expand with the product later on?

0:17:14.000 --> 0:17:17.320
<v Speaker 4>The future sets are later on, especially because of a

0:17:17.440 --> 0:17:20.080
<v Speaker 4>little limited resource of of inentro studio. Do you want

0:17:20.080 --> 0:17:23.680
<v Speaker 4>to grab something that is too grandios or too expensive

0:17:23.680 --> 0:17:26.600
<v Speaker 4>to bute that requires millions and millions of dollars to

0:17:26.640 --> 0:17:28.360
<v Speaker 4>but so it needs to be something that we can

0:17:28.960 --> 0:17:31.280
<v Speaker 4>uh that can we can buy a big chunk off

0:17:31.600 --> 0:17:36.640
<v Speaker 4>with our with our limited capability at least at the beginning.

0:17:37.640 --> 0:17:40.879
<v Speaker 4>And then from that we tested it about. We prototype

0:17:40.960 --> 0:17:44.000
<v Speaker 4>like design and researched about ten of them a little

0:17:44.000 --> 0:17:47.080
<v Speaker 4>bit deeper, and that resulted into a few of the

0:17:47.080 --> 0:17:50.080
<v Speaker 4>ones that we have today. So after we take the

0:17:50.320 --> 0:17:54.920
<v Speaker 4>products through the through the validation process, and that might

0:17:54.960 --> 0:17:59.640
<v Speaker 4>be do we get customers, do we get some early

0:17:59.680 --> 0:18:04.320
<v Speaker 4>custom there's interest, what is the the is it feasible

0:18:04.320 --> 0:18:08.320
<v Speaker 4>to build? After we do that process, we take them.

0:18:08.400 --> 0:18:12.080
<v Speaker 4>We also have a fund inside Rift, so Riff the

0:18:12.119 --> 0:18:15.399
<v Speaker 4>studio and Rift the fund. So we have our first

0:18:15.400 --> 0:18:19.280
<v Speaker 4>fund called Rift zero. Now where did the first close

0:18:19.359 --> 0:18:24.080
<v Speaker 4>last year? And we take those opportunities into the fund

0:18:24.160 --> 0:18:28.679
<v Speaker 4>evaluate with a pretty brecorous sort of criteria, evaluate like

0:18:28.720 --> 0:18:33.199
<v Speaker 4>if someone pitching to us and if it passes or not.

0:18:33.359 --> 0:18:37.000
<v Speaker 4>We also invest in all those opportunities as well. That's

0:18:37.000 --> 0:18:41.359
<v Speaker 4>why we say that they graduated from the studio and

0:18:41.400 --> 0:18:44.760
<v Speaker 4>we spin them off as portfolio companies instead of being

0:18:44.840 --> 0:18:47.560
<v Speaker 4>just experiments. So it's it's both.

0:18:48.040 --> 0:18:51.560
<v Speaker 1>Yeah, it's it's quite interesting because obviously the traditional startup

0:18:51.600 --> 0:18:54.240
<v Speaker 1>method is you raise a fund and then you go

0:18:54.280 --> 0:18:59.760
<v Speaker 1>and look for talent or startups to invest into. And

0:19:00.040 --> 0:19:03.800
<v Speaker 1>my fund might have a specific focus on a vertical

0:19:04.040 --> 0:19:09.680
<v Speaker 1>or a stage or whatever you like. So how is

0:19:09.720 --> 0:19:14.240
<v Speaker 1>that discussion with kind of LPs to say, not only

0:19:14.240 --> 0:19:16.800
<v Speaker 1>are you trusting us to do what's right with your money,

0:19:16.840 --> 0:19:19.879
<v Speaker 1>you're trusting us to develop the tech that's going to

0:19:20.000 --> 0:19:22.280
<v Speaker 1>bring you the return on investments. So you're starting, you're

0:19:22.320 --> 0:19:24.760
<v Speaker 1>pitching startups, and you're pitching a fund at the same time.

0:19:25.480 --> 0:19:26.960
<v Speaker 1>How does that conversation go.

0:19:28.440 --> 0:19:31.760
<v Speaker 3>Yeah, I mean it's a more complex conversation than a

0:19:31.800 --> 0:19:35.880
<v Speaker 3>standard VC. You know, when a VC is raising funds,

0:19:35.920 --> 0:19:38.440
<v Speaker 3>they just say, look, we're going to invest in these areas.

0:19:38.520 --> 0:19:41.440
<v Speaker 3>We don't know what the companies will be, we believe

0:19:41.440 --> 0:19:44.240
<v Speaker 3>that we'll be able to find them as a studio

0:19:44.280 --> 0:19:47.560
<v Speaker 3>and as a fund. Rift zero also has an investment thesis.

0:19:47.600 --> 0:19:50.520
<v Speaker 3>It has four different areas that it's looking at. We

0:19:50.560 --> 0:19:54.359
<v Speaker 3>are mainly an AI driven fund, looking at how AI

0:19:54.600 --> 0:19:57.919
<v Speaker 3>is used in the companies as well as the products themselves.

0:19:58.359 --> 0:20:01.320
<v Speaker 3>So looking at how we can stretch the But those

0:20:01.320 --> 0:20:06.879
<v Speaker 3>four areas are across productivity, security, compliance, health and finance

0:20:07.800 --> 0:20:11.840
<v Speaker 3>as for getting people to sort of understand like does

0:20:11.960 --> 0:20:15.840
<v Speaker 3>Rift have that capability and skill. You know, we spent

0:20:15.880 --> 0:20:20.560
<v Speaker 3>the last ten years building companies and products for a

0:20:20.600 --> 0:20:24.320
<v Speaker 3>lot of startups and corporates. So we built companies for

0:20:24.640 --> 0:20:28.920
<v Speaker 3>X fifteen, which is CBA's accelerator program. We built them

0:20:28.960 --> 0:20:33.080
<v Speaker 3>for Westpac Ventures, we built them for ASB, B and Z,

0:20:34.400 --> 0:20:38.520
<v Speaker 3>the Warehouse Group, Mercury Energy, sky City. You know, we've

0:20:38.560 --> 0:20:41.440
<v Speaker 3>been doing this type of product building for ten years

0:20:41.600 --> 0:20:44.359
<v Speaker 3>as a group and before that. We also have that

0:20:44.440 --> 0:20:47.399
<v Speaker 3>personal experience of building companies in Silicon Valley and in

0:20:47.440 --> 0:20:53.240
<v Speaker 3>the UK, in South America. You know, for us, the

0:20:53.320 --> 0:20:56.879
<v Speaker 3>building and the technology and all those parts aren't really

0:20:56.960 --> 0:21:01.399
<v Speaker 3>the risk. Adventure Studio is there to de risk things

0:21:01.840 --> 0:21:05.320
<v Speaker 3>around repeatable process which we have from time at Rome

0:21:06.200 --> 0:21:09.560
<v Speaker 3>skilled entrepreneurs that we have within the studio as well

0:21:09.600 --> 0:21:12.760
<v Speaker 3>as within our wider network. You know, we would have

0:21:12.800 --> 0:21:15.600
<v Speaker 3>had about two hundred and fifty employees come through Rome

0:21:15.760 --> 0:21:17.320
<v Speaker 3>at the time when we sold it, we had one

0:21:17.400 --> 0:21:20.879
<v Speaker 3>hundred and fifty, So it's a large network across Australia

0:21:20.880 --> 0:21:25.639
<v Speaker 3>and New Zealand, Singapore as well as reach into the US.

0:21:25.880 --> 0:21:28.280
<v Speaker 3>So we have all the ingredients, all those assets that

0:21:28.320 --> 0:21:31.199
<v Speaker 3>you need to bring together, and then it's about taking

0:21:31.640 --> 0:21:36.000
<v Speaker 3>what can feel a bit boring, sometimes a formulaic approach,

0:21:37.000 --> 0:21:40.080
<v Speaker 3>and yet a lot of the times you can identify

0:21:40.119 --> 0:21:43.760
<v Speaker 3>great opportunities, assemble the team, assemble the financing, and then

0:21:43.840 --> 0:21:46.800
<v Speaker 3>go and tackle it. It's actually more similar to how

0:21:47.480 --> 0:21:52.560
<v Speaker 3>Silicon Valley startups are funded. Someone sees an idea or

0:21:52.560 --> 0:21:56.280
<v Speaker 3>an opportunity, they assemble the team, they assemble the capital,

0:21:56.359 --> 0:21:58.680
<v Speaker 3>and then they go and target and tackle the market.

0:22:00.080 --> 0:22:02.280
<v Speaker 3>In the past have seen a lot of New Zealand startups.

0:22:02.280 --> 0:22:04.479
<v Speaker 3>It's kind of they've been in the market for a while,

0:22:05.400 --> 0:22:07.919
<v Speaker 3>scraping along a little bit of friends and family funding,

0:22:08.040 --> 0:22:10.120
<v Speaker 3>and they just happen to hit an inflection point where

0:22:10.119 --> 0:22:13.960
<v Speaker 3>the market needs the product they have. This style is

0:22:14.320 --> 0:22:17.879
<v Speaker 3>quite different, I mean.

0:22:18.080 --> 0:22:20.919
<v Speaker 1>And that's one of the questions as well that I

0:22:20.960 --> 0:22:24.960
<v Speaker 1>was interested in, which is, you know, you have the capability,

0:22:25.000 --> 0:22:27.800
<v Speaker 1>and you have the processes, and you have the experience

0:22:27.840 --> 0:22:31.080
<v Speaker 1>and the talent, but the X factor, the key thing

0:22:31.280 --> 0:22:34.800
<v Speaker 1>is finding the right place to apply those and that's

0:22:34.800 --> 0:22:37.960
<v Speaker 1>something that's talked about significantly in New Zealand. We have

0:22:38.080 --> 0:22:42.840
<v Speaker 1>limited resources in this country, we have limited capital, and

0:22:42.880 --> 0:22:45.320
<v Speaker 1>so we need to be quite specific about how we

0:22:45.400 --> 0:22:51.080
<v Speaker 1>are targeting our solutions. So how do you think through that?

0:22:52.800 --> 0:22:56.760
<v Speaker 4>Yeah, I think it's the what I was saying before.

0:22:56.960 --> 0:22:59.640
<v Speaker 4>Like when we're looking at the vertical just for example,

0:23:00.359 --> 0:23:03.840
<v Speaker 4>a product that we have like chain, we start thinking

0:23:03.880 --> 0:23:07.080
<v Speaker 4>about the first idea, remember that was there on the

0:23:07.119 --> 0:23:15.040
<v Speaker 4>backlog was AI power job management software tool. There are

0:23:15.080 --> 0:23:17.720
<v Speaker 4>a few of them, a few of them are very successful.

0:23:17.800 --> 0:23:19.919
<v Speaker 4>Every once in a while they get acquired by larger

0:23:19.960 --> 0:23:24.240
<v Speaker 4>companies that which means that sometimes the customer service is

0:23:24.280 --> 0:23:27.639
<v Speaker 4>not what it used to be. And therefore, especially on

0:23:27.800 --> 0:23:30.600
<v Speaker 4>the SMBs, they start looking for all right, so the

0:23:30.640 --> 0:23:32.800
<v Speaker 4>prices went up. What else can I start using it?

0:23:32.800 --> 0:23:36.959
<v Speaker 4>It might be interesting to have something that, especially with

0:23:37.200 --> 0:23:40.560
<v Speaker 4>vertical AI businesses, you're always trying to look into, let's

0:23:40.600 --> 0:23:44.200
<v Speaker 4>look at automating things that they're not properly automated on

0:23:44.640 --> 0:23:47.280
<v Speaker 4>sort of the traditional job management tools, and let's start

0:23:47.960 --> 0:23:51.840
<v Speaker 4>let's focus on the product on that. But then while

0:23:51.880 --> 0:23:55.199
<v Speaker 4>we were taking that idea through the validation process and

0:23:55.720 --> 0:23:58.159
<v Speaker 4>we had a prototype of the tool, we had a

0:23:58.240 --> 0:24:00.399
<v Speaker 4>couple of sort of design screens and starts check with

0:24:01.040 --> 0:24:04.280
<v Speaker 4>so traders, with builders, with treadees, we're always trying to

0:24:04.320 --> 0:24:08.320
<v Speaker 4>think about an angle, that is, what is the what

0:24:08.520 --> 0:24:12.120
<v Speaker 4>is the line of this experience that will deliver them

0:24:12.920 --> 0:24:15.560
<v Speaker 4>instance of r O I that will solve an immediate

0:24:15.600 --> 0:24:18.920
<v Speaker 4>problem or or something that they want out to make today.

0:24:20.200 --> 0:24:22.800
<v Speaker 4>What can we build that will be fast to build,

0:24:22.920 --> 0:24:25.400
<v Speaker 4>that we can solve that problem. They will they will

0:24:25.440 --> 0:24:28.040
<v Speaker 4>pay for us to solve that problem, and then we

0:24:28.080 --> 0:24:31.800
<v Speaker 4>can build the rest of the capability afterwards, after the

0:24:31.880 --> 0:24:35.199
<v Speaker 4>proof that that piece of that piece of the of

0:24:35.359 --> 0:24:38.879
<v Speaker 4>the of the solution. And that's when, for example, we

0:24:39.000 --> 0:24:42.560
<v Speaker 4>start testing the in this case, the I think we

0:24:42.640 --> 0:24:48.240
<v Speaker 4>call a builder boost the the job management software lending page.

0:24:48.280 --> 0:24:51.040
<v Speaker 4>We have a couple of prototype screens, and after every

0:24:51.119 --> 0:24:54.760
<v Speaker 4>interview they will say, yeah, it's really cool, it looks

0:24:54.800 --> 0:24:59.160
<v Speaker 4>really cool, but not what my problem is? I hate

0:24:59.160 --> 0:25:01.840
<v Speaker 4>doing this, I doing the admin work. I hate all

0:25:01.880 --> 0:25:04.119
<v Speaker 4>of the tools that already exists. Yeah, this looks nice,

0:25:04.160 --> 0:25:07.239
<v Speaker 4>but I just want to I just can't pick up

0:25:07.240 --> 0:25:09.480
<v Speaker 4>my phone while am I work and I lose jobs

0:25:09.520 --> 0:25:11.840
<v Speaker 4>because of it. Can you solve someone? Do you know

0:25:11.920 --> 0:25:14.000
<v Speaker 4>anyone that can do that and then it qualify the

0:25:14.080 --> 0:25:17.119
<v Speaker 4>leads for us. And that's when Jane came about.

0:25:17.200 --> 0:25:17.360
<v Speaker 2>Right.

0:25:17.400 --> 0:25:20.200
<v Speaker 4>They were like, yeah, that's something that we can solve,

0:25:20.240 --> 0:25:22.439
<v Speaker 4>and we can solve reasonably fast.

0:25:22.920 --> 0:25:26.440
<v Speaker 1>And Jane, is your l M powered super intelligent the

0:25:26.480 --> 0:25:30.160
<v Speaker 1>voicemail that can understand the context of what's being spoken

0:25:31.880 --> 0:25:32.919
<v Speaker 1>verbally through the phone.

0:25:33.640 --> 0:25:37.679
<v Speaker 4>Yeah. Yeah, we're looking at now at the vision of

0:25:37.760 --> 0:25:40.960
<v Speaker 4>being this working towards being on the vision being this

0:25:41.119 --> 0:25:45.520
<v Speaker 4>Oh encompassy our encompassing sort of inbound lead qualification for

0:25:45.600 --> 0:25:49.600
<v Speaker 4>Assouli traders. Right, so not only qualifying leads that come

0:25:49.640 --> 0:25:54.359
<v Speaker 4>through the phone, it also can schedule sort of appointments

0:25:54.880 --> 0:25:58.240
<v Speaker 4>through the phone. It's on your Google calendar and in

0:25:58.320 --> 0:26:03.680
<v Speaker 4>your tool of choice and also integrating with WhatsApp. Business

0:26:04.720 --> 0:26:08.880
<v Speaker 4>noticed that it's a lot of small businesses that their

0:26:08.960 --> 0:26:13.639
<v Speaker 4>value proposition is helping some traders set up their sas

0:26:13.640 --> 0:26:17.359
<v Speaker 4>tools and we're like, okay, so this is a this

0:26:17.520 --> 0:26:19.399
<v Speaker 4>is really a gap in the market, Like people are

0:26:19.400 --> 0:26:23.760
<v Speaker 4>trying to solve this by being a consultancy to to

0:26:23.960 --> 0:26:26.200
<v Speaker 4>this is how that's how hard it is and how

0:26:28.560 --> 0:26:32.119
<v Speaker 4>uh that that it shows the friction that exists with

0:26:32.560 --> 0:26:38.080
<v Speaker 4>the things that exist today and how without without even

0:26:38.480 --> 0:26:41.200
<v Speaker 4>everyone that we spoke with they're like, yeah, we don't.

0:26:41.280 --> 0:26:43.919
<v Speaker 4>We want to spend less time doing that so I

0:26:43.960 --> 0:26:49.240
<v Speaker 4>can spend more time just doing the work getting paid.

0:26:49.920 --> 0:26:53.320
<v Speaker 1>Interesting. Yeah, you're a New Zealand based business, you have

0:26:53.480 --> 0:26:57.680
<v Speaker 1>international ties, so I'm assuming and the question isn't will

0:26:57.760 --> 0:27:01.000
<v Speaker 1>New Zealand does pay for this, but it's will people

0:27:01.119 --> 0:27:03.480
<v Speaker 1>somewhere pay for this? And where is that? So that

0:27:03.560 --> 0:27:05.240
<v Speaker 1>must be another consideration as well.

0:27:05.760 --> 0:27:07.960
<v Speaker 3>Yeah, you know, New Zealand market is far too small,

0:27:08.480 --> 0:27:11.320
<v Speaker 3>so if anything would have to be a minimum of

0:27:11.320 --> 0:27:15.159
<v Speaker 3>a regional market Australia and New Zealand, say Asia, so

0:27:15.240 --> 0:27:19.760
<v Speaker 3>apax but predominantly of course we're targeting the US, but

0:27:20.040 --> 0:27:22.120
<v Speaker 3>you have to start somewhere where it's easy to get

0:27:22.160 --> 0:27:25.480
<v Speaker 3>some initial feedback. But you don't want to stay with

0:27:25.680 --> 0:27:28.119
<v Speaker 3>that story of New Zealand being the trial market and

0:27:28.280 --> 0:27:31.040
<v Speaker 3>just two years later it's still your trial market. It's

0:27:31.400 --> 0:27:34.560
<v Speaker 3>really just used during the validation phase. Then once the

0:27:34.600 --> 0:27:37.880
<v Speaker 3>portfolio company is really to be launched, you're straight into

0:27:37.920 --> 0:27:38.359
<v Speaker 3>the US.

0:27:38.880 --> 0:27:43.160
<v Speaker 4>Yeah. I find the New Zealand market, especially coming from overseas,

0:27:43.440 --> 0:27:47.760
<v Speaker 4>a very interesting market for tech in comparison to what

0:27:47.880 --> 0:27:50.000
<v Speaker 4>I'm used to in the US or in Brazil. I

0:27:50.040 --> 0:27:53.400
<v Speaker 4>think in the US, of course, depending on a little

0:27:53.400 --> 0:27:56.280
<v Speaker 4>bit of the state, but same as in Brazil. But

0:27:56.440 --> 0:28:01.119
<v Speaker 4>people tend to swarm more into new things, and in

0:28:01.160 --> 0:28:04.800
<v Speaker 4>New Zealand, I feel it's a little bit the majority

0:28:04.840 --> 0:28:09.159
<v Speaker 4>of the customers they wait someone to test it first

0:28:09.200 --> 0:28:12.400
<v Speaker 4>before they jump into it. So I tend to think

0:28:12.440 --> 0:28:15.840
<v Speaker 4>that a few of these things, especially on the products,

0:28:15.880 --> 0:28:18.439
<v Speaker 4>if you can sell it here to New Zealanders and

0:28:18.480 --> 0:28:22.119
<v Speaker 4>can convince a convince a very traditional sort of trade

0:28:22.359 --> 0:28:25.800
<v Speaker 4>to adopt that into its workflow, that's a good signal

0:28:25.880 --> 0:28:29.360
<v Speaker 4>that you can probably get some more adoption overseas.

0:28:29.960 --> 0:28:33.760
<v Speaker 1>That's really interesting. Yeah, and I guess because you aren't

0:28:33.840 --> 0:28:37.199
<v Speaker 1>thinking of New Zealand as your primary market. You know,

0:28:37.280 --> 0:28:40.360
<v Speaker 1>I've heard stories before of startups who have had great

0:28:40.400 --> 0:28:45.320
<v Speaker 1>ideas that could have great ROI for trade's but they

0:28:45.360 --> 0:28:48.400
<v Speaker 1>just weren't able to convince the trades to actually adopt

0:28:48.400 --> 0:28:51.240
<v Speaker 1>it at a scale that was able to provide some

0:28:51.560 --> 0:28:56.200
<v Speaker 1>assurance to investors before unfortunately the companies collapsed. Not because

0:28:56.200 --> 0:28:59.040
<v Speaker 1>you guys have the venture model, because you aren't seeing

0:28:59.080 --> 0:29:01.320
<v Speaker 1>New Zealand as your prime market, but as a kind

0:29:01.360 --> 0:29:04.720
<v Speaker 1>of like trial market. That allows you to then go, well,

0:29:04.760 --> 0:29:07.320
<v Speaker 1>look now we have the set, we can throw it

0:29:07.360 --> 0:29:10.560
<v Speaker 1>somewhere that's a bit more swarmy and see and see

0:29:10.560 --> 0:29:11.440
<v Speaker 1>what happens there.

0:29:11.800 --> 0:29:12.320
<v Speaker 4>Yeah.

0:29:12.400 --> 0:29:16.120
<v Speaker 3>The venture studio model also allows you to play with

0:29:16.160 --> 0:29:19.440
<v Speaker 3>the different leavers of engineering marketing spend across all of

0:29:19.480 --> 0:29:23.480
<v Speaker 3>your portfolio companies. So if you have a traditional startup,

0:29:23.960 --> 0:29:26.120
<v Speaker 3>you hire an engineering team or you've got some co

0:29:26.200 --> 0:29:28.920
<v Speaker 3>founders as engineers, and that's great as you get a

0:29:28.920 --> 0:29:31.880
<v Speaker 3>bit of scale. Then every month you're paying for the

0:29:31.920 --> 0:29:34.160
<v Speaker 3>engineering team and you just got to keep feeding them

0:29:34.200 --> 0:29:37.080
<v Speaker 3>with work to be done. Now, some of the time

0:29:37.120 --> 0:29:39.160
<v Speaker 3>it's actually you don't need more features. What you need

0:29:39.200 --> 0:29:41.280
<v Speaker 3>to do is be selling what you have and get

0:29:41.320 --> 0:29:43.880
<v Speaker 3>the feedback from market. I mean, I think you know,

0:29:43.920 --> 0:29:45.720
<v Speaker 3>there's there's a lot of stats on I think it's

0:29:45.760 --> 0:29:48.480
<v Speaker 3>like forty percent of the features you build aren't even

0:29:48.480 --> 0:29:51.520
<v Speaker 3>the ones that customers adopt. And I've seen it myself

0:29:52.320 --> 0:29:55.160
<v Speaker 3>now with a venture studio, because you have a portfolio

0:29:55.240 --> 0:29:57.240
<v Speaker 3>of these, you can pull back some of the engineering

0:29:57.280 --> 0:30:00.560
<v Speaker 3>talent from one of the portfolio companies should into the

0:30:00.600 --> 0:30:04.080
<v Speaker 3>second one. While you're looking for the right market signal

0:30:04.200 --> 0:30:08.320
<v Speaker 3>waiting for market adoption. AI is one that you know,

0:30:08.400 --> 0:30:11.320
<v Speaker 3>in some cases is fast and other cases is slow.

0:30:11.560 --> 0:30:14.800
<v Speaker 3>It's so trying to get the timing right is I

0:30:14.840 --> 0:30:17.320
<v Speaker 3>believe easier with the venture studio where you can move

0:30:17.400 --> 0:30:20.080
<v Speaker 3>the burn around across the portfolio companies.

0:30:20.600 --> 0:30:22.120
<v Speaker 1>This is something I wanted to talk to you guys

0:30:22.160 --> 0:30:24.960
<v Speaker 1>about as well, because we have in the I guess

0:30:25.000 --> 0:30:27.200
<v Speaker 1>really end of last year beginning of this year, we've

0:30:27.240 --> 0:30:35.520
<v Speaker 1>started to see this kind of counter rhetoric towards AI

0:30:35.840 --> 0:30:40.400
<v Speaker 1>and generative AI. And you know that this this idea

0:30:40.560 --> 0:30:46.080
<v Speaker 1>that open AI and it's ken who have been funneling

0:30:46.120 --> 0:30:49.160
<v Speaker 1>billions and billions of dollars into the development of the

0:30:49.160 --> 0:30:53.840
<v Speaker 1>technology are ultimately going to fail to see any actual

0:30:53.840 --> 0:30:57.160
<v Speaker 1>return on that investment just because of the scale of it.

0:30:57.320 --> 0:31:00.680
<v Speaker 1>And so you know, with the recent release of deep

0:31:00.720 --> 0:31:05.959
<v Speaker 1>Seek as well, that has simplified and made those models

0:31:05.960 --> 0:31:10.160
<v Speaker 1>maybe a little bit more affordable to actually run, that

0:31:10.920 --> 0:31:14.680
<v Speaker 1>kind of proves that there's going to be a difficulty

0:31:14.720 --> 0:31:17.840
<v Speaker 1>in getting a return on the investment in the long

0:31:17.880 --> 0:31:20.400
<v Speaker 1>run because they can't justify the expense when there's going

0:31:20.480 --> 0:31:23.719
<v Speaker 1>to be cheaper alternatives. What's your thoughts around that, and

0:31:23.760 --> 0:31:27.280
<v Speaker 1>how for companies like yourselves that are building on these

0:31:27.360 --> 0:31:31.480
<v Speaker 1>AI platforms that may represent a risk around the continuation

0:31:32.200 --> 0:31:34.080
<v Speaker 1>of the technology into the future.

0:31:34.880 --> 0:31:38.160
<v Speaker 3>Yeah, if I sort of think back to two thousand

0:31:38.280 --> 0:31:40.880
<v Speaker 3>when you had you know, the Internet bubble and boom,

0:31:40.960 --> 0:31:44.400
<v Speaker 3>and you had this huge buildout of infrastructure and companies

0:31:44.480 --> 0:31:48.560
<v Speaker 3>like you know, Cisco, Juniper Networks, etc. We're riding high,

0:31:49.280 --> 0:31:53.080
<v Speaker 3>huge investments to get people more bandwidth. And back then

0:31:53.200 --> 0:31:56.160
<v Speaker 3>you had a ADSL link you know, one point five

0:31:56.240 --> 0:31:59.560
<v Speaker 3>megabit or something, and everyone was like, yeah, that's that's amazing.

0:31:59.560 --> 0:32:03.640
<v Speaker 3>I can now watch some standard DEF video with twenty

0:32:03.760 --> 0:32:07.160
<v Speaker 3>frames per second. What would I need more for? And

0:32:07.320 --> 0:32:09.480
<v Speaker 3>you know, you're fast forward now and you've got five

0:32:09.600 --> 0:32:13.680
<v Speaker 3>G networks where you can do video streaming, you know,

0:32:13.760 --> 0:32:16.720
<v Speaker 3>on the go in your car, you've got five to

0:32:16.800 --> 0:32:18.880
<v Speaker 3>your house with one hundred to three hundred or eight

0:32:18.960 --> 0:32:23.240
<v Speaker 3>hundred megabit down Like, people will find a way to

0:32:23.320 --> 0:32:27.400
<v Speaker 3>consume bandwidth. It's going to be the same with AI compute.

0:32:27.960 --> 0:32:30.200
<v Speaker 3>Like you know, deep seat coming out as great as

0:32:30.280 --> 0:32:32.600
<v Speaker 3>open source, it's already been ripped apart in bits of

0:32:32.680 --> 0:32:39.360
<v Speaker 3>being put into existing models, ignoring the supposed cost because

0:32:39.360 --> 0:32:41.560
<v Speaker 3>there's a lot of issues around. Well, actually they did

0:32:41.600 --> 0:32:45.360
<v Speaker 3>have access to fifty thousand in video GPUs and they

0:32:45.360 --> 0:32:48.160
<v Speaker 3>did spend more money than the kind of the sticker price.

0:32:49.240 --> 0:32:51.280
<v Speaker 3>You know, that's that's great. You need to bring the

0:32:51.320 --> 0:32:54.800
<v Speaker 3>costs down. We're already seeing that with open Ai. Like

0:32:54.840 --> 0:33:00.120
<v Speaker 3>they're the three mini models like ninety three percent cheaper

0:33:00.120 --> 0:33:04.240
<v Speaker 3>than their at last one. It's faster, less latency, which

0:33:04.280 --> 0:33:06.520
<v Speaker 3>means you can do more of these real time voice,

0:33:06.600 --> 0:33:13.200
<v Speaker 3>real time communications, real time robotics. It's does really feel

0:33:13.240 --> 0:33:16.880
<v Speaker 3>like we're at the beginning of this expansion. We do

0:33:17.040 --> 0:33:19.600
<v Speaker 3>see it with some of the AI startups, you know,

0:33:19.640 --> 0:33:22.520
<v Speaker 3>we you know, we we review a lot to see

0:33:22.680 --> 0:33:26.200
<v Speaker 3>where they are, what's the state of the market, you know,

0:33:26.240 --> 0:33:28.480
<v Speaker 3>what's real, what isn't And a lot of times you'll

0:33:28.480 --> 0:33:30.680
<v Speaker 3>click through and it will be coming soon, you know,

0:33:30.800 --> 0:33:35.200
<v Speaker 3>sign up for a better access. But we also know

0:33:35.320 --> 0:33:37.560
<v Speaker 3>because we're using it and building products that are in

0:33:37.680 --> 0:33:40.360
<v Speaker 3>market that customers are using, and it's today solving a

0:33:40.400 --> 0:33:44.440
<v Speaker 3>problem that it does work for you know a fair

0:33:44.520 --> 0:33:47.320
<v Speaker 3>number of use cases that are still there to be

0:33:47.520 --> 0:33:51.200
<v Speaker 3>to be tackled. And yeah, it's the worst it's going

0:33:51.240 --> 0:33:53.200
<v Speaker 3>to be at the moment and it just keeps getting better.

0:33:53.960 --> 0:33:57.480
<v Speaker 3>Runway Sourer for image generation mid you know, for video

0:33:57.560 --> 0:34:02.400
<v Speaker 3>generation mid journey, like the disruption is massive.

0:34:03.120 --> 0:34:03.479
<v Speaker 1>Mm.

0:34:04.120 --> 0:34:06.920
<v Speaker 4>I still think there's a lot of case that the

0:34:07.000 --> 0:34:11.760
<v Speaker 4>use cases that we at you they're the low hanging

0:34:11.800 --> 0:34:14.800
<v Speaker 4>through use cases for l lambs that people are finding

0:34:14.840 --> 0:34:17.759
<v Speaker 4>out and then now we're figuring out that, oh wait

0:34:17.760 --> 0:34:19.960
<v Speaker 4>a second, we might not need other lambs for everything.

0:34:20.040 --> 0:34:22.160
<v Speaker 4>We can use a couple of the smaller models there

0:34:22.440 --> 0:34:25.760
<v Speaker 4>will be much more useful and choose some specific verticals

0:34:25.840 --> 0:34:29.360
<v Speaker 4>or for some specific sort of tasks or even a

0:34:29.440 --> 0:34:31.720
<v Speaker 4>sort of agenta task and that's much eeaper to run.

0:34:32.239 --> 0:34:36.319
<v Speaker 4>So mind you start using that and also thinking AI

0:34:36.400 --> 0:34:42.880
<v Speaker 4>researcher open ai pulls it on on Twitter x formerly

0:34:42.920 --> 0:34:47.239
<v Speaker 4>Twitter about that more money now is being spent on

0:34:48.360 --> 0:34:51.600
<v Speaker 4>inference then on pre training, So more money even spending

0:34:51.640 --> 0:34:54.919
<v Speaker 4>on what can this do and can we learn things

0:34:54.960 --> 0:34:58.239
<v Speaker 4>that there's no data for it before, which will become

0:34:58.320 --> 0:35:04.120
<v Speaker 4>more useful. So even the CAPEC spend is going silver structure, yeah,

0:35:04.120 --> 0:35:07.799
<v Speaker 4>and the infrastructure is going a lot. They're spending a

0:35:07.800 --> 0:35:11.160
<v Speaker 4>lot of money on building the infrastructure for that. I

0:35:11.239 --> 0:35:15.719
<v Speaker 4>do think that if the market is expecting an immediate

0:35:15.760 --> 0:35:20.640
<v Speaker 4>return for this investment. That might be especially for this

0:35:20.719 --> 0:35:21.799
<v Speaker 4>companies they are spending a lot.

0:35:23.200 --> 0:35:26.360
<v Speaker 1>You mentioned kind of in video, and you mentioned Cisco,

0:35:26.560 --> 0:35:30.799
<v Speaker 1>and one of the observations was with Cisco. Obviously in

0:35:30.840 --> 0:35:34.080
<v Speaker 1>the early two thousands they had that massive wipeout of

0:35:34.120 --> 0:35:38.400
<v Speaker 1>a lot of their value and there was a similar blip,

0:35:38.560 --> 0:35:41.080
<v Speaker 1>but in video probably was it was not obviously of

0:35:41.120 --> 0:35:45.440
<v Speaker 1>the same scale. But the argument was that these companies

0:35:45.440 --> 0:35:50.719
<v Speaker 1>that are being continually inflated, the value is being inflated

0:35:51.280 --> 0:35:56.560
<v Speaker 1>by the the the pouring of billion dollars into R

0:35:56.600 --> 0:35:58.799
<v Speaker 1>and D by these companies, that we will start to

0:35:58.800 --> 0:36:02.960
<v Speaker 1>see a similar popping off that value bubble, not necessarily

0:36:03.040 --> 0:36:05.560
<v Speaker 1>around the quality of the tech, but the value bubble

0:36:05.560 --> 0:36:09.080
<v Speaker 1>in itself. Does that concern you guys as having both

0:36:09.120 --> 0:36:10.960
<v Speaker 1>the products and the fund.

0:36:12.600 --> 0:36:15.040
<v Speaker 3>I think on the end Video one, what's quite interesting

0:36:15.160 --> 0:36:17.800
<v Speaker 3>is you know that they've managed to ride three waves.

0:36:17.840 --> 0:36:20.680
<v Speaker 3>They've managed to ride the GPU wave, when you know

0:36:20.719 --> 0:36:25.080
<v Speaker 3>graphics move from being calculated on CPU offload. Then they've

0:36:25.080 --> 0:36:29.640
<v Speaker 3>managed to ride the cryptomning wave where it moved from

0:36:29.640 --> 0:36:32.799
<v Speaker 3>CPU to GPU and then GPU to ASK. And now

0:36:32.800 --> 0:36:39.880
<v Speaker 3>they've managed to ride this third wave with AI, you know, phenomenal,

0:36:39.880 --> 0:36:41.360
<v Speaker 3>But there's spent a lot of time making sure that

0:36:41.360 --> 0:36:43.880
<v Speaker 3>there's software. It's it's not just the chip, it's the

0:36:43.960 --> 0:36:46.680
<v Speaker 3>software that you use around it. Kuda, which is the

0:36:46.760 --> 0:36:50.879
<v Speaker 3>language they use for programming those GPUs. But you are

0:36:51.000 --> 0:36:54.960
<v Speaker 3>seeing you know, Amazon have their own processor. I think

0:36:54.960 --> 0:36:59.960
<v Speaker 3>it's called Trainium. You know, like Google have got their

0:37:00.080 --> 0:37:04.960
<v Speaker 3>tensor processor unit, the TPU. People are coming for that crown.

0:37:05.000 --> 0:37:09.399
<v Speaker 3>Of course, it's so rich. The good news is, you know, yes,

0:37:09.640 --> 0:37:13.120
<v Speaker 3>Cisco wore that bubble and that bubble burst, but when

0:37:13.160 --> 0:37:15.560
<v Speaker 3>you look at the major players, you know, Google and

0:37:15.719 --> 0:37:18.879
<v Speaker 3>Facebook and all the social media Instagram, they all came

0:37:18.920 --> 0:37:24.080
<v Speaker 3>out after that collapse, after the infrastructure burst. Also, there

0:37:24.160 --> 0:37:27.040
<v Speaker 3>was a lot of costs that the US government was

0:37:27.080 --> 0:37:31.880
<v Speaker 3>selling Spectrum at the time to the telpots, so that

0:37:31.960 --> 0:37:35.399
<v Speaker 3>sucked a lot of their cap becks up as well. So, yeah,

0:37:35.400 --> 0:37:37.719
<v Speaker 3>if we were in the chip space, if we're in

0:37:37.760 --> 0:37:40.359
<v Speaker 3>the model space, I think that's going to be cut throat.

0:37:40.400 --> 0:37:42.480
<v Speaker 3>You're going to end up with open source, and you're

0:37:42.480 --> 0:37:45.279
<v Speaker 3>going to end up with Microsoft, which is open AI.

0:37:45.400 --> 0:37:47.680
<v Speaker 3>You're going to end up with Amazon, which is anthropic,

0:37:47.840 --> 0:37:49.400
<v Speaker 3>and you're going to end up with Google, with with

0:37:49.600 --> 0:37:53.279
<v Speaker 3>Gemini and you know, and you know under open source

0:37:53.280 --> 0:37:55.360
<v Speaker 3>of course you got Lama with Meta and all these others.

0:37:56.480 --> 0:37:59.160
<v Speaker 3>Like I wouldn't want to be in that space, application space,

0:37:59.360 --> 0:38:02.840
<v Speaker 3>you know, being able to look at what tasks administration

0:38:03.080 --> 0:38:06.440
<v Speaker 3>work you can replace. Yeah, that's that's the place to play.

0:38:06.960 --> 0:38:10.759
<v Speaker 1>Yeah, because you're not concerned about being caught in the

0:38:11.239 --> 0:38:14.000
<v Speaker 1>inner potential bubble burth.

0:38:14.400 --> 0:38:18.320
<v Speaker 3>If your companies are making money and we're not building

0:38:18.360 --> 0:38:21.120
<v Speaker 3>companies that are growth at all costs its balance of

0:38:21.480 --> 0:38:26.480
<v Speaker 3>growth and profitability as a venture studio, that's okay.

0:38:26.920 --> 0:38:31.279
<v Speaker 4>Yeah, because we're we're consuming more the commodity that they

0:38:31.320 --> 0:38:35.840
<v Speaker 4>provide in terms of the models than being caught up

0:38:35.880 --> 0:38:39.920
<v Speaker 4>on developing the models ourselves. And depending on who wins

0:38:39.920 --> 0:38:43.720
<v Speaker 4>this race, it's an open source with the excellent models

0:38:43.760 --> 0:38:47.000
<v Speaker 4>that that exist, and we're building with a with a

0:38:47.120 --> 0:38:51.600
<v Speaker 4>paradigm that is, we're building our capabilities in our future

0:38:51.680 --> 0:38:56.360
<v Speaker 4>is based on on the available sort of capabilities of

0:38:56.440 --> 0:38:59.239
<v Speaker 4>the of this commodity that exists and where it might

0:38:59.320 --> 0:39:02.120
<v Speaker 4>be in a fewure based on the trend of development

0:39:02.200 --> 0:39:05.360
<v Speaker 4>that this exists. But we can consume from any player. Yeah,

0:39:05.560 --> 0:39:08.040
<v Speaker 4>that it's out there, so we can consume the open

0:39:08.080 --> 0:39:11.800
<v Speaker 4>Eye one the Anthropic one, because they're probably going to

0:39:11.880 --> 0:39:16.600
<v Speaker 4>be very converged. They are converge to be very similar,

0:39:17.480 --> 0:39:20.000
<v Speaker 4>at least that it looks today. I'm not sure tomorrow,

0:39:20.000 --> 0:39:22.239
<v Speaker 4>Open a I might release something that we look and say,

0:39:22.239 --> 0:39:25.680
<v Speaker 4>oh my god, this is ten years ahead of everyone else,

0:39:25.960 --> 0:39:29.239
<v Speaker 4>as it was with the Chat GPT, although some might

0:39:29.360 --> 0:39:33.160
<v Speaker 4>argue that other companies had what the first GPT was,

0:39:33.239 --> 0:39:36.080
<v Speaker 4>they just didn't want to release to the public, so

0:39:36.120 --> 0:39:39.000
<v Speaker 4>Opening I was not that ahead as others might think.

0:39:39.040 --> 0:39:41.600
<v Speaker 4>But they seem to be very ethnical.

0:39:41.719 --> 0:39:42.399
<v Speaker 3>Nick and Nick.

0:39:42.560 --> 0:39:47.040
<v Speaker 4>It just continuously and there are some that are For example,

0:39:47.040 --> 0:39:49.399
<v Speaker 4>even in a few other products, we don't use one

0:39:49.520 --> 0:39:52.680
<v Speaker 4>model we use. We might use multiple models they're better,

0:39:53.600 --> 0:39:55.919
<v Speaker 4>or smaller models they are better in each task. There's

0:39:57.760 --> 0:40:00.799
<v Speaker 4>Claude is excellent for coding for for some reason, and

0:40:00.800 --> 0:40:04.000
<v Speaker 4>I'm not sure what they added into the the find

0:40:04.120 --> 0:40:07.879
<v Speaker 4>the tuning or on the model, but it's I find

0:40:07.880 --> 0:40:14.240
<v Speaker 4>it much better for coding than GPT. So you usually

0:40:14.280 --> 0:40:16.760
<v Speaker 4>want I'm use in cursor, I want to look into

0:40:16.800 --> 0:40:19.279
<v Speaker 4>coding or use Claude instead of using Chat GIPT. I

0:40:19.280 --> 0:40:22.960
<v Speaker 4>think it's just better, especially with Python. I might see

0:40:23.000 --> 0:40:26.040
<v Speaker 4>that other companies that are consuming a few of these

0:40:26.080 --> 0:40:29.680
<v Speaker 4>models my pick and choice and do the same because

0:40:29.719 --> 0:40:33.759
<v Speaker 4>I think the secret is still on. The secret selves

0:40:33.800 --> 0:40:37.600
<v Speaker 4>of companies still be the services they provide. They are

0:40:37.680 --> 0:40:40.680
<v Speaker 4>solving a problem. How is the experienced layer, how is

0:40:40.719 --> 0:40:45.160
<v Speaker 4>that sort of application layer more than the commodity itself?

0:40:45.320 --> 0:40:48.120
<v Speaker 4>Chatting with someone that is like, as long as there's

0:40:48.200 --> 0:40:51.279
<v Speaker 4>coffee to be bought, I want to keep building Starbucks

0:40:51.800 --> 0:40:55.640
<v Speaker 4>shops or nice coffee shops. I don't want to build

0:40:55.640 --> 0:40:58.440
<v Speaker 4>a farm to start producing coffee because that would that

0:40:58.480 --> 0:41:02.839
<v Speaker 4>would probably be a RaSE to zero or very very soon,

0:41:02.920 --> 0:41:05.680
<v Speaker 4>if not that already started. And as you're saying, with

0:41:05.800 --> 0:41:09.000
<v Speaker 4>a deep seek, wiping out quite a few, quite a

0:41:09.000 --> 0:41:10.960
<v Speaker 4>bit of the value of these companies.

0:41:11.680 --> 0:41:14.480
<v Speaker 1>Interesting, I do enjoy that you said Starbucks or nice

0:41:14.520 --> 0:41:19.640
<v Speaker 1>coffee shops. I think that's that's very to be honest.

0:41:19.760 --> 0:41:26.240
<v Speaker 4>Yeah, I don't mind star Yeah.

0:41:24.200 --> 0:41:26.279
<v Speaker 1>That's great. I mean, I know we're probably over is that,

0:41:26.320 --> 0:41:28.719
<v Speaker 1>But I just one more question I want to ask

0:41:29.400 --> 0:41:33.000
<v Speaker 1>around that idea as well. Was there's also a commentary

0:41:33.040 --> 0:41:35.440
<v Speaker 1>that we might be hitting a ceiling in terms of

0:41:35.520 --> 0:41:42.560
<v Speaker 1>capability around L l ms, talking in particular around hallucinations.

0:41:43.160 --> 0:41:45.200
<v Speaker 1>You know, it's a feature, it's not something we can

0:41:45.239 --> 0:41:51.560
<v Speaker 1>get rid of talking about the cognitive in quotes capabilities

0:41:51.680 --> 0:41:54.759
<v Speaker 1>of these lms that the rate of increase is now

0:41:54.880 --> 0:41:59.160
<v Speaker 1>so minimal that actually it looks like we might have

0:41:59.680 --> 0:42:03.400
<v Speaker 1>hit the ceiling on that, and so building with the

0:42:03.480 --> 0:42:05.480
<v Speaker 1>idea that there's going to be something better in the

0:42:05.480 --> 0:42:09.920
<v Speaker 1>future may be a mistake, if that makes sense. What's

0:42:10.000 --> 0:42:11.400
<v Speaker 1>your take on that perspective.

0:42:12.760 --> 0:42:16.279
<v Speaker 3>I guess one thing is the companies we're building at

0:42:16.280 --> 0:42:19.640
<v Speaker 3>the moment take advantage of the current state of the art.

0:42:20.000 --> 0:42:25.000
<v Speaker 3>We're very cautious about building for the crossover point of

0:42:25.160 --> 0:42:28.760
<v Speaker 3>some capability that might come in eighteen twenty four months.

0:42:29.800 --> 0:42:31.640
<v Speaker 3>So I think, yeah, if you're building companies, you need

0:42:31.680 --> 0:42:34.360
<v Speaker 3>to make sure you're building with what can be achieved today.

0:42:34.719 --> 0:42:36.760
<v Speaker 3>That's why we see a lot that are hidden behind

0:42:36.880 --> 0:42:40.600
<v Speaker 3>sign ups and join the wait lists. They were also

0:42:40.640 --> 0:42:44.080
<v Speaker 3>waiting for a tech shift. So that's pretty critical. And

0:42:44.160 --> 0:42:46.080
<v Speaker 3>it's not to say that the evolution of the product

0:42:46.600 --> 0:42:51.560
<v Speaker 3>cannot look to future capabilities, but your sellable kind of

0:42:51.680 --> 0:42:54.640
<v Speaker 3>wedge in today should be able, well not should, it

0:42:54.680 --> 0:42:57.520
<v Speaker 3>has to actually work. That's one of the things as

0:42:57.560 --> 0:43:01.600
<v Speaker 3>far as have we hit the limits, I think what's

0:43:01.640 --> 0:43:04.200
<v Speaker 3>interesting is, you know you have something like deep Seat

0:43:04.239 --> 0:43:05.640
<v Speaker 3>come out and it's got a whole bunch of these

0:43:05.719 --> 0:43:09.879
<v Speaker 3>kind of clever optimizations you know in ways of doing

0:43:09.960 --> 0:43:14.480
<v Speaker 3>things like people are going to continually find these. So

0:43:14.480 --> 0:43:17.440
<v Speaker 3>if you can use it to train your model at

0:43:17.440 --> 0:43:20.120
<v Speaker 3>a tenth the price, well, now you can train a

0:43:20.160 --> 0:43:23.760
<v Speaker 3>model that's ten times as large. Can you find more data?

0:43:23.960 --> 0:43:25.799
<v Speaker 3>Like I said, that's one of the big sources. So

0:43:25.800 --> 0:43:30.160
<v Speaker 3>then you start looking at synthetic data careful of hallucinations.

0:43:32.080 --> 0:43:33.880
<v Speaker 3>There's a lot of smart people, there's a lot of money.

0:43:34.080 --> 0:43:36.839
<v Speaker 3>I just don't see us hitting a wall in the

0:43:36.880 --> 0:43:40.960
<v Speaker 3>next next few years. The same as bandwidth. Again it's

0:43:40.960 --> 0:43:43.799
<v Speaker 3>a weird analogy, but remember thinking, you know, one point

0:43:43.840 --> 0:43:46.240
<v Speaker 3>five megabit was so fast because you had a twenty

0:43:46.239 --> 0:43:50.560
<v Speaker 3>eight K modem before that, and you know you used

0:43:50.600 --> 0:43:52.600
<v Speaker 3>to get like a say in New Zealand, a T

0:43:52.760 --> 0:43:54.560
<v Speaker 3>one or it's the US T one or an E

0:43:54.680 --> 0:43:56.480
<v Speaker 3>one line two megabit, and it would cost you ten

0:43:56.520 --> 0:43:59.200
<v Speaker 3>thousand dollars a month to connect Auckland to Wellington. It

0:43:59.400 --> 0:44:03.480
<v Speaker 3>take thirty five calls, and that just seems so quaint,

0:44:04.000 --> 0:44:05.640
<v Speaker 3>but at the time it would have been well, that's

0:44:05.640 --> 0:44:07.920
<v Speaker 3>the fastest that we can get it across the network,

0:44:07.960 --> 0:44:11.359
<v Speaker 3>and all of these other reasons where there's demand, you know,

0:44:11.440 --> 0:44:14.879
<v Speaker 3>money and innovation follows. I don't think we've been hitting

0:44:14.920 --> 0:44:15.759
<v Speaker 3>limits soon.

0:44:17.000 --> 0:44:20.239
<v Speaker 4>And then's the bit about we're talked about building with

0:44:20.400 --> 0:44:24.080
<v Speaker 4>the future capability in mind, with CRUs Is saying as well,

0:44:24.160 --> 0:44:27.520
<v Speaker 4>is that there's a lot of the not a lot,

0:44:27.560 --> 0:44:29.600
<v Speaker 4>but they are parts of the workflows of a few

0:44:29.640 --> 0:44:32.680
<v Speaker 4>of the products that we're working with that they're just

0:44:33.080 --> 0:44:36.760
<v Speaker 4>ultimately like that llms are not capable of doing right now.

0:44:36.800 --> 0:44:38.080
<v Speaker 4>And then you what it do is that you do

0:44:38.280 --> 0:44:41.000
<v Speaker 4>that part of workflow with your standard automation sort of

0:44:41.400 --> 0:44:45.200
<v Speaker 4>standard code there. But it generates is that it starts

0:44:45.280 --> 0:44:48.120
<v Speaker 4>when people start using it generates a very specific sort

0:44:48.160 --> 0:44:52.360
<v Speaker 4>of the amount of data that you can use a

0:44:52.640 --> 0:44:55.839
<v Speaker 4>small model to be very good at that very specific

0:44:56.400 --> 0:44:59.960
<v Speaker 4>sort of workflow, which is something that starts building different

0:45:00.480 --> 0:45:03.440
<v Speaker 4>right It'll be very hard to build differentiation if you're

0:45:03.480 --> 0:45:07.840
<v Speaker 4>just wrapping your company around whatever is the large the

0:45:07.920 --> 0:45:10.680
<v Speaker 4>data set the lms are trained on, because then anyone

0:45:10.719 --> 0:45:12.680
<v Speaker 4>that just pluts into the data set will be able

0:45:12.760 --> 0:45:17.319
<v Speaker 4>to to roughly perform the same task. But when you

0:45:17.480 --> 0:45:21.200
<v Speaker 4>viewed something that solves a very specific sort of touch

0:45:21.239 --> 0:45:26.040
<v Speaker 4>point and you start getting your model into it and

0:45:26.239 --> 0:45:29.799
<v Speaker 4>then using that data to train your capability, I think

0:45:29.880 --> 0:45:33.960
<v Speaker 4>then you start creating differentiation and something that it's more defensible,

0:45:34.120 --> 0:45:38.279
<v Speaker 4>especially in the in a world where anyone can go

0:45:38.320 --> 0:45:43.040
<v Speaker 4>to cursor or go to what's the zero from A

0:45:43.200 --> 0:45:47.640
<v Speaker 4>and build a SaaS company in a in a week

0:45:47.760 --> 0:45:51.160
<v Speaker 4>or so, it would be very interesting to see how

0:45:51.160 --> 0:45:51.560
<v Speaker 4>it goes.

0:45:52.800 --> 0:45:54.400
<v Speaker 1>It will be it will be very interesting.

0:45:54.640 --> 0:46:00.080
<v Speaker 4>Yeah, very interesting. In the same moment that said that

0:46:00.120 --> 0:46:07.920
<v Speaker 4>the first billion dollar company of one person should should

0:46:09.040 --> 0:46:10.160
<v Speaker 4>should arrive very soon.

0:46:10.360 --> 0:46:11.680
<v Speaker 1>Some helpments ad a lot of things.

0:46:19.360 --> 0:46:22.000
<v Speaker 2>We'll put a couple of articles in the show notes,

0:46:22.040 --> 0:46:27.239
<v Speaker 2>some really interesting articles, including from New Zealand companies sort

0:46:27.280 --> 0:46:31.240
<v Speaker 2>of explaining the pros, mainly the pros of the venture

0:46:31.320 --> 0:46:34.359
<v Speaker 2>studio model. You know, in my mind, really I think

0:46:34.400 --> 0:46:38.200
<v Speaker 2>the big advantage here and I think tracks who really

0:46:39.040 --> 0:46:42.400
<v Speaker 2>illustrates this. You have, you know, an advertising guru like

0:46:42.520 --> 0:46:47.200
<v Speaker 2>James Herman previously unavailable, Simon Pound, people like that who

0:46:47.280 --> 0:46:53.359
<v Speaker 2>really understand marketing, advertising branding. They come across some entrepreneurs

0:46:53.360 --> 0:46:56.040
<v Speaker 2>who want to set up something in that space, and

0:46:56.080 --> 0:47:00.799
<v Speaker 2>they have deep integral knowledge of that and and what

0:47:00.920 --> 0:47:03.600
<v Speaker 2>works and what doesn't. So you invite them in, maybe

0:47:03.600 --> 0:47:06.280
<v Speaker 2>give them a little bit of seed funding, but mainly

0:47:06.440 --> 0:47:11.319
<v Speaker 2>take equity and return for nurturing this company, giving it

0:47:11.440 --> 0:47:15.360
<v Speaker 2>your expertise. That seems to be the venture studio model,

0:47:15.560 --> 0:47:18.319
<v Speaker 2>and it then allows a company to get to a

0:47:18.360 --> 0:47:23.120
<v Speaker 2>point where it's really attractive to venture capital companies. And

0:47:23.160 --> 0:47:25.280
<v Speaker 2>I think that's a really good model for New Zealand,

0:47:25.320 --> 0:47:29.359
<v Speaker 2>where we do have these areas of deep expertise, but

0:47:29.400 --> 0:47:31.799
<v Speaker 2>we don't necessarily have a lot of seed funding. So

0:47:32.280 --> 0:47:37.200
<v Speaker 2>you're seeing these people who maybe have created successful companies

0:47:37.920 --> 0:47:40.680
<v Speaker 2>exited them, have a little bit of capital and are

0:47:40.719 --> 0:47:44.520
<v Speaker 2>willing to back these teams and do numerous teams at once,

0:47:44.560 --> 0:47:47.640
<v Speaker 2>so not just put everything into one company. You have

0:47:47.800 --> 0:47:51.319
<v Speaker 2>half a dozen of these companies under your roof, maybe

0:47:51.360 --> 0:47:55.080
<v Speaker 2>even hot desking or sharing space, looking over the shoulder,

0:47:55.160 --> 0:47:59.880
<v Speaker 2>helping them, mentor them, introduce them to your networks and

0:48:00.560 --> 0:48:02.799
<v Speaker 2>business context. It seems to be quite a good model

0:48:02.840 --> 0:48:03.440
<v Speaker 2>for New Zealand.

0:48:03.920 --> 0:48:04.120
<v Speaker 4>Yeah.

0:48:04.120 --> 0:48:08.080
<v Speaker 1>Absolutely, And also it does help to slightly address the

0:48:08.280 --> 0:48:11.680
<v Speaker 1>talent issue as well. Because if you have it, like

0:48:11.719 --> 0:48:14.120
<v Speaker 1>they say, they have a group of engineers within Rift

0:48:14.360 --> 0:48:18.440
<v Speaker 1>that they can then choose how to where to kind

0:48:18.440 --> 0:48:22.040
<v Speaker 1>of direct their talent. So two different different companies say

0:48:22.080 --> 0:48:24.000
<v Speaker 1>they've got one thing over here, and they're like, oh,

0:48:24.040 --> 0:48:26.760
<v Speaker 1>we really need a new feature on that asap. Let's

0:48:26.800 --> 0:48:31.120
<v Speaker 1>move sixty percent of our engineering over to that. For

0:48:31.160 --> 0:48:34.160
<v Speaker 1>the meantime, you don't have to suddenly scale up and

0:48:34.239 --> 0:48:36.799
<v Speaker 1>get a bunch of contractors and figure, you know, get

0:48:36.800 --> 0:48:38.719
<v Speaker 1>them to get on board with the systems and learn

0:48:38.760 --> 0:48:41.560
<v Speaker 1>new things. It's just this kind of this core at

0:48:41.640 --> 0:48:44.600
<v Speaker 1>rift that can do things as they need them to do.

0:48:44.640 --> 0:48:46.960
<v Speaker 1>So another really great way of addressing some of New

0:48:47.040 --> 0:48:48.040
<v Speaker 1>Zealand's shortfalls.

0:48:48.680 --> 0:48:54.400
<v Speaker 2>Yeah, I guess looking at the potential cons of this model.

0:48:54.719 --> 0:48:58.279
<v Speaker 2>So clearly, you know, the people who are running these

0:48:58.360 --> 0:49:02.480
<v Speaker 2>venture studios are spreading their risk but spreading their attention

0:49:02.600 --> 0:49:07.000
<v Speaker 2>across a number of ventures. So you know, you may

0:49:07.040 --> 0:49:10.680
<v Speaker 2>be diluting the attention that you can pay to a

0:49:10.680 --> 0:49:12.879
<v Speaker 2>particular company. And I've seen a couple of podcasts where

0:49:12.880 --> 0:49:17.439
<v Speaker 2>people who have been through venture studios and sort of say, well,

0:49:17.440 --> 0:49:20.160
<v Speaker 2>it's you know, sort of feel like I'm competing with

0:49:20.280 --> 0:49:24.400
<v Speaker 2>other companies in this venture studio for the attention of

0:49:24.120 --> 0:49:27.360
<v Speaker 2>the people who are funding this and supporting us, So

0:49:27.400 --> 0:49:30.520
<v Speaker 2>I guess that's the downside. But having said that, we're

0:49:30.560 --> 0:49:33.000
<v Speaker 2>pretty collegial in New Zealand, so you're more likely to

0:49:33.040 --> 0:49:36.759
<v Speaker 2>actually be able to leverage off what other companies sort

0:49:36.760 --> 0:49:39.160
<v Speaker 2>of in the venture studio stable are actually doing, to

0:49:40.080 --> 0:49:42.600
<v Speaker 2>try and learn from them and maybe even partner on

0:49:42.680 --> 0:49:44.280
<v Speaker 2>business ventures.

0:49:44.800 --> 0:49:48.080
<v Speaker 1>Yeah, exactly. And what I think is interesting about ref

0:49:48.239 --> 0:49:52.320
<v Speaker 1>as well as that particular focus on artificial intelligence because

0:49:52.320 --> 0:49:54.840
<v Speaker 1>it is such an evolving sector and it kind of

0:49:54.840 --> 0:49:58.680
<v Speaker 1>does link into that idea of talent because the leadership

0:49:58.719 --> 0:50:00.879
<v Speaker 1>team they spend a lot of time I'm looking at

0:50:00.920 --> 0:50:05.760
<v Speaker 1>different research papers about AI, about generative AI and its applications,

0:50:06.239 --> 0:50:10.360
<v Speaker 1>and then they can disseminate that information to their ideas

0:50:10.400 --> 0:50:15.160
<v Speaker 1>for startups, rather than you know, having to kind of

0:50:15.239 --> 0:50:18.799
<v Speaker 1>each individual company follow along as best they can and

0:50:18.800 --> 0:50:21.880
<v Speaker 1>split that time. So it's another benefit as well. But

0:50:22.000 --> 0:50:24.879
<v Speaker 1>it's also interesting they're focused on AI, especially in this

0:50:25.040 --> 0:50:29.759
<v Speaker 1>kind of very AI heavy new world that we're in

0:50:29.880 --> 0:50:35.200
<v Speaker 1>the conversation about whether it's a bubble. It seems to

0:50:35.239 --> 0:50:38.520
<v Speaker 1>me from a conversation with them with the founders that

0:50:38.560 --> 0:50:41.840
<v Speaker 1>they are quite focused in their approach. They're wanting to

0:50:41.920 --> 0:50:45.640
<v Speaker 1>kind of really really focused on what products are actually

0:50:45.680 --> 0:50:50.040
<v Speaker 1>finding product market fit and value and go from there,

0:50:50.719 --> 0:50:54.360
<v Speaker 1>rather than trying to kind of have a product and

0:50:54.480 --> 0:50:56.200
<v Speaker 1>get a bunch of hype around it and pump a

0:50:56.239 --> 0:50:58.120
<v Speaker 1>bunch of VC in it and then see where you

0:50:58.160 --> 0:51:00.759
<v Speaker 1>go from there. Maybe that's a little I don't know.

0:51:01.280 --> 0:51:03.760
<v Speaker 2>Yeah, they're clearly using AI and it was really an

0:51:03.880 --> 0:51:07.799
<v Speaker 2>interesting that discussion about their reflections on Deep Seek and

0:51:07.840 --> 0:51:12.160
<v Speaker 2>that move from sort of training to inference, which is

0:51:12.200 --> 0:51:15.680
<v Speaker 2>sort of changing the priorities in the market at the moment,

0:51:16.800 --> 0:51:20.000
<v Speaker 2>and also their view of the AI landscape. It seems

0:51:20.000 --> 0:51:23.600
<v Speaker 2>as though they're expecting, maybe a bit belatedly, a wave

0:51:23.719 --> 0:51:26.000
<v Speaker 2>of AI startups to sort of emerriage. I mean, they

0:51:26.000 --> 0:51:28.719
<v Speaker 2>are emerging, but it hasn't been as pronounced as say

0:51:28.880 --> 0:51:31.440
<v Speaker 2>in the Australian market, so it's good to hear that

0:51:31.440 --> 0:51:34.560
<v Speaker 2>that's coming. But you know what they're doing. I think

0:51:34.600 --> 0:51:38.480
<v Speaker 2>with the couple of ventures that have got traction so far,

0:51:39.239 --> 0:51:41.000
<v Speaker 2>it sort of harkens back to what we were talking

0:51:41.040 --> 0:51:44.680
<v Speaker 2>to Rowan Simpson about last week, is not necessarily being

0:51:44.719 --> 0:51:47.759
<v Speaker 2>first but trying to be the best, you know, so

0:51:48.480 --> 0:51:54.040
<v Speaker 2>the startup they were talking about that basically makes it

0:51:54.080 --> 0:51:57.400
<v Speaker 2>easy to take the admin out of being a small business,

0:51:57.440 --> 0:51:59.200
<v Speaker 2>you know, for tradees and that sort of thing. Sure

0:51:59.200 --> 0:52:01.879
<v Speaker 2>we've got if I have done that others we seem

0:52:01.920 --> 0:52:04.239
<v Speaker 2>to have a timely to another one. I guess we

0:52:04.280 --> 0:52:08.200
<v Speaker 2>seem to have some expertise in this area in New Zealand,

0:52:08.200 --> 0:52:11.440
<v Speaker 2>but they're taking a slightly different approach to it, applying

0:52:11.520 --> 0:52:16.480
<v Speaker 2>AI to it where appropriate. That's not necessarily a revolutionary idea,

0:52:16.560 --> 0:52:19.799
<v Speaker 2>but it's all about executing that well absolutely.

0:52:19.880 --> 0:52:22.520
<v Speaker 1>And they also finished raising their first fund as well,

0:52:22.640 --> 0:52:25.400
<v Speaker 1>so as well as using internal money, they are raising

0:52:25.400 --> 0:52:28.680
<v Speaker 1>a fund that they can feed money into their own

0:52:28.760 --> 0:52:34.000
<v Speaker 1>studios as well. So that obviously shows some confidence in

0:52:34.040 --> 0:52:36.640
<v Speaker 1>the market in what Rift is doing.

0:52:37.200 --> 0:52:40.760
<v Speaker 2>I think they've put five million into the zero Fund

0:52:41.200 --> 0:52:42.920
<v Speaker 2>Zero Fund, and that seems to be quite a bit

0:52:43.000 --> 0:52:45.160
<v Speaker 2>for a venture studio. Although I was looking at New

0:52:45.200 --> 0:52:48.840
<v Speaker 2>and Improved Ventures, which really the same people behind that

0:52:49.120 --> 0:52:53.000
<v Speaker 2>were previously unavailable. They've sort of set up a new

0:52:53.360 --> 0:52:56.160
<v Speaker 2>venture studio I think with the likes of ice House Ventures.

0:52:56.200 --> 0:53:01.320
<v Speaker 2>They've raised six million dollars to back three for new firms.

0:53:01.440 --> 0:53:05.520
<v Speaker 2>That's what The Herald reported late last year. So there's

0:53:05.560 --> 0:53:09.960
<v Speaker 2>definitely some good seed money starting to flow into these

0:53:10.040 --> 0:53:12.880
<v Speaker 2>and the reason is, I think statistics I've seen is

0:53:12.920 --> 0:53:17.319
<v Speaker 2>that they're actually more successful. Your chance of getting to

0:53:17.440 --> 0:53:20.400
<v Speaker 2>the next stage is greater out of these venture studios,

0:53:20.400 --> 0:53:22.640
<v Speaker 2>which have actually been around since the mid nineties in

0:53:22.719 --> 0:53:26.399
<v Speaker 2>Silicon Valley. So they've nailed that sort of process, and

0:53:26.719 --> 0:53:29.480
<v Speaker 2>according to some sources that I've seen online as well,

0:53:29.520 --> 0:53:33.840
<v Speaker 2>that they allow you to get there quicker. So it's

0:53:33.920 --> 0:53:40.160
<v Speaker 2>more rapid way to go from ideation to execution, which

0:53:40.200 --> 0:53:43.840
<v Speaker 2>obviously that's what is all about. Has been quick to market.

0:53:44.400 --> 0:53:48.399
<v Speaker 1>Yeah, and they're not necessarily looking for unicorns as well, Like, yes,

0:53:48.480 --> 0:53:50.359
<v Speaker 1>there is some sense of it would be nice to

0:53:50.360 --> 0:53:53.760
<v Speaker 1>have one, of course, but at the end of the day,

0:53:53.840 --> 0:53:56.680
<v Speaker 1>a flow of one hundred to two hundred million dollar

0:53:56.719 --> 0:54:00.360
<v Speaker 1>companies that they can sell and then reinvat, or they

0:54:00.400 --> 0:54:02.120
<v Speaker 1>can split off and can go and have lives of

0:54:02.160 --> 0:54:06.200
<v Speaker 1>their own. That's also something that you actually do need.

0:54:06.200 --> 0:54:09.160
<v Speaker 1>We've talked about it before, that middle layer of companies

0:54:09.160 --> 0:54:13.680
<v Speaker 1>that are continuing to grow, be sold or change or

0:54:13.680 --> 0:54:16.879
<v Speaker 1>whatever else it is, and feeding talent and money back

0:54:16.920 --> 0:54:19.719
<v Speaker 1>into the market as well. It's not all about the unicorns.

0:54:19.880 --> 0:54:23.200
<v Speaker 2>Sounds good power to them.

0:54:22.520 --> 0:54:25.279
<v Speaker 1>So a big thanks to Lucas Coelo and Chris Moore

0:54:25.320 --> 0:54:28.120
<v Speaker 1>for coming on, and Ben Morrow as well for helping

0:54:28.160 --> 0:54:28.759
<v Speaker 1>set that up.

0:54:29.200 --> 0:54:31.960
<v Speaker 2>More on New Zealand's growing band of venture studios in

0:54:31.960 --> 0:54:34.920
<v Speaker 2>the show notes. Head to the podcast section at Business

0:54:34.920 --> 0:54:36.120
<v Speaker 2>Desk dot co dot d.

0:54:36.440 --> 0:54:40.000
<v Speaker 1>You can stream the podcast there or on iHeartRadio, and

0:54:40.120 --> 0:54:43.040
<v Speaker 1>of course it's available on your podcast platform of choice.

0:54:43.120 --> 0:54:45.600
<v Speaker 2>Get in touch with your feedback and topic suggestions. We're

0:54:45.640 --> 0:54:47.760
<v Speaker 2>on LinkedIn and blue Sky these days.

0:54:48.160 --> 0:54:50.719
<v Speaker 1>Catch us again for the next episode next Thursday.

0:54:51.080 --> 0:54:51.560
<v Speaker 2>See you then,