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

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Speaker 1: Degrees Business the rise of the venture studio.

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Speaker 2: Here we've counted at least a dozen New Zealand companies

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Speaker 2: pursuing this relatively new model, or new for New Zealand anyway,

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Speaker 2: This sort of model of creating and funding startups called

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Speaker 2: the venture studio. Unlike venture capital firmed, venture studios play

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Speaker 2: a much more hands on role in the entire life

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Speaker 2: cycle off a startup, from ideation to execution.

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Speaker 1: We talk to the co founders of Auckland based venture

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Speaker 1: studio Rift about its approach to operating a venture studio,

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Speaker 1: with a particular focus on artificial intelligence.

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Speaker 3: So we have all the ingredients, all those assets that

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Speaker 3: you need to bring together, and then it's about taking

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Speaker 3: what can feel a bit boring, sometimes a formulaic approach,

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Speaker 3: and yet a lot of the times you can identify

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Speaker 3: great opportunities, assemble the team, assemble the financing and then

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Speaker 3: go and tackle it.

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Speaker 1: I'm Peter Griffin, I'm Ben More and a look at

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Speaker 1: the venture studio model of building startups coming up with

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Speaker 1: two of Rift's co founders, Lucas Coelo and Chris Moore.

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Speaker 2: But first, then the first half results of interdex listed

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Speaker 2: spark are announced Tomorrow, that's Friday, they'll release their financial update.

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Speaker 2: Sparks had a pretty rough run in the last year

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Speaker 2: or so, a sagging sheer price, major cost cutting going on,

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Speaker 2: a number of senior people have gone from the organization,

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Speaker 2: and a move to divers parts of the business. What

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Speaker 2: are you expecting to see tomorrow?

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Speaker 1: What I'm expecting is an update, first of all on

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Speaker 1: the restructuring. So that's something they promised at their and

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Speaker 1: your general meeting last year, was that they would release

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Speaker 1: more details on the restructuring, including kind of how it's

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Speaker 1: going so far, what they've managed to save. You may

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Speaker 1: recall their goal was to say fifty million on labor costs,

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Speaker 1: which is about ten percent of their total labor spend.

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Speaker 1: It was recently came out that they were cutting their

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Speaker 1: management staff as part of that restructure, and how they're

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Speaker 1: changing the portfolios that different managers look after, so probably

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Speaker 1: a reasonable chunk there as well. So that's what they

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Speaker 1: have said is that there will be more information about

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Speaker 1: the restructuring at this update, but they've been pretty reticent

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Speaker 1: about giving too much detail anyway, so very curious to

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Speaker 1: see exactly what they do release and what they don't

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Speaker 1: about that situation. So that's kind of the main thing

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Speaker 1: I think most people are on tender hooks about.

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Speaker 2: Yeah, they sort of foreshadowed this last year. They reduced

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Speaker 2: their ebit dye guidance somewhat not a huge hit, but

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Speaker 2: that's down. They reduced their dividend guidance to twenty five

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Speaker 2: cents per share from twenty seven point five cents, so

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Speaker 2: they're basically softening up the market for lower expectations, and

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Speaker 2: you know that's been reflected in the share price. I

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Speaker 2: think it's down like thirty six percent of something over

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Speaker 2: the last year, even including dividends, so it's it's been tough.

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Speaker 2: And then there was all angst about them being delisted

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Speaker 2: from that big sort of index Overseas, and I was

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Speaker 2: expecting a big dip after that, but that was clearly

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Speaker 2: sort of priced into it beforehand, so that didn't really

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Speaker 2: take a massive hit. But still, you know, this is

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Speaker 2: a company that still has a big market shriff to

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Speaker 2: telecommunications industry, you know, forty something percent of mobile and

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Speaker 2: fixed line broadband, so it is you know, still you know,

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Speaker 2: the big player along with one end Z in that market,

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Speaker 2: but sort of really facing a bit of an existential crisis.

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Speaker 1: Absolutely, especially in like broadband and mobile as well. Two

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Speaker 1: Degrees nipping at their heels along with some of the

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Speaker 1: Mbenno operator that are starting to collectively take share away

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Speaker 1: as they kind of use broadband as a lost leader

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Speaker 1: to get people onto their other services. So yeah, that's

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Speaker 1: an area where they're struggling. Interestingly, on their share price,

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Speaker 1: analysts are kind of a little bit mixed at the moment,

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Speaker 1: with some saying, you know, still not worth buying at

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Speaker 1: this point. Others are saying, look, this is actually a

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Speaker 1: depressed price for Spark. It's likely to go up in

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Speaker 1: the future. Now might be a good time to buy.

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Speaker 1: What to me that says is that there is a

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Speaker 1: little bit of uncertainty about what's actually going to come

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Speaker 1: for Spark, that there's obviously going to be a huge

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Speaker 1: effort from Spark to reassess their position, try and capture

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Speaker 1: back some of their core market and also try and

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Speaker 1: capture new market with data centers. That's been a big discussion,

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Speaker 1: so I expect we'll probably hear a bit more about

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Speaker 1: their data center strategy as well, including some efforts to

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Speaker 1: raise capital to co fund development of new data center.

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Speaker 2: Yeah, it's been reviewing it's non core assets. So it

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Speaker 2: still owns a bit of Connects, the mobile tower business,

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Speaker 2: so it might divest the rest of that. And it's

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Speaker 2: got quite a big IT division as well, and a

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Speaker 2: few sort of units there, health, sort of tech unit, IoT,

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Speaker 2: interneted things business, it's curious data business. So do you

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Speaker 2: think there may be moves signal to carve off some

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Speaker 2: of these units.

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Speaker 1: Yeah, we know Connectsha, they're staking Connection, they're looking to sell,

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Speaker 1: so that's a definite. They haven't really said yes or

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Speaker 1: no on some of the other areas, but I know

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Speaker 1: there is definitely some rumblings about what they're planning to

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Speaker 1: actually divest. We might hear more about that at this update.

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Speaker 1: They might not update on that, because sometimes what will

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Speaker 1: happen is they will just get their earnings out of

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Speaker 1: the way, update on some more of the directly related

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Speaker 1: fiscal stuff, and then update later on some of the

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Speaker 1: other plans around investment. That is, unless they've already made

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Speaker 1: the deals and they're announcing that they have sold x,

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Speaker 1: Y or z. That might happen at this earnings. I

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Speaker 1: think what's really interesting is if you look at Deleeperfonseca's

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Speaker 1: column from last year that was on Business Desk, and

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Speaker 1: he talks about Spark trying to kind of figure out

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Speaker 1: what it is now. Is it a tech company or

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Speaker 1: is it an infrastructure company, because those are two quite

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Speaker 1: different things, and how does it balance those in these

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Speaker 1: times of difficulty, So well worth a background read if

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Speaker 1: you're interested.

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Speaker 2: Yeah, and it's interesting that a lot of the restructuring

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Speaker 2: is in this sort of enterprise and government sections of

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Speaker 2: the business. And on the face of it, it looks like

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Speaker 2: Spark had been laying the groundwork for really smart, DIVERSEI

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Speaker 2: vacation in recent years, with the likes of Curious, the

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Speaker 2: sort of AI and data division. It's got its IT

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Speaker 2: division as well, which is doing more sort of cloud

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Speaker 2: and digital transformation stuff, so you'd think like and they

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Speaker 2: published that report which we had Jolie Hodson on the

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Speaker 2: podcast about last year looking at productivity, the productivity gap

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Speaker 2: in New Zealand, the need to embrace advanced technology, so

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Speaker 2: sort of doing that with a focus on IoT and

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Speaker 2: health tech and the like. It just hasn't translated. And

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Speaker 2: I don't know if that's a that they got the

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Speaker 2: market wrong or if it's a just bad execution, but

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Speaker 2: more than any other talco. They were sort of saying,

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Speaker 2: we want to be about more than supplying speeds and

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Speaker 2: feeds which have low margins. There's lots of competition, we

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Speaker 2: want to do more, and they've sort of been not,

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Speaker 2: you know, slapped back on that, which is sort of disappointing.

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Speaker 2: I don't know if it's them or if it's us.

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Speaker 1: Yeah, yeah, it's a big question, I mean, and that's

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Speaker 1: that was one of the things that analysts have been

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Speaker 1: talking about a lot, is like is this just the

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Speaker 1: ups and downs of the macro environment or is this

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Speaker 1: a fundamental shift in how things are operating.

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Speaker 2: So check out Business Desk on Friday, there'll be full

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Speaker 2: coverage off the Spark results. Just before we get into

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Speaker 2: our featured interview, an update on last week's story about

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Speaker 2: being AI Ben. You went in depth into this listed

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Speaker 2: tech company and the travails it's been experiencing. Some really

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Speaker 2: interesting discussion resulted from the podcast, including on LinkedIn what

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Speaker 2: were people saying.

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Speaker 1: So on your posting of that episode. The CEO of

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Speaker 1: Callahan Innovation is Stephane Korn. He is an AI expert

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Speaker 1: from way back, and he posted talking and specifically about

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Speaker 1: their project Treehouse and this idea of APIs as a

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Speaker 1: way for AI agents to enter operate, which I I thought, well,

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Speaker 1: you know my perspective, I thought, it's a much more

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Speaker 1: efficient and effective way for machines to talk to each

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Speaker 1: other rather than open Ay's operator approach where it tries

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Speaker 1: to navigate websites that are designed for humans. Now, Stephane

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Speaker 1: Cortan had an interesting take on that where he basically said,

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Speaker 1: one of the reasons that APIs don't really work is

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Speaker 1: for competitive reasons, which was something I didn't really think about.

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Speaker 1: So what was your take on some of the reasons

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Speaker 1: that they might.

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Speaker 2: Not Well, sitting behind the website, there's a database, and

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Speaker 2: for a travel company, it could be all their dynamic

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Speaker 2: pricing and all that sort of thing. So they've got

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Speaker 2: to be really careful that they don't give away this

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Speaker 2: sort of the secret source to their business model, so

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Speaker 2: they hold that information very closely to them. I don't

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Speaker 2: think it precludes them from issuing API access. For instance,

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Speaker 2: an Uber would really like the idea of being integrated

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Speaker 2: into a messaging app if you can pull in information

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Speaker 2: from Uber saying there are drivers available, it's only going

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Speaker 2: to be five minutes. You know that's possible via an API.

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Speaker 2: So I think there's some level that they could do.

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Speaker 2: But I think Stephann is right. The reason why we

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Speaker 2: just haven't seen open ai really go big on its

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Speaker 2: operator platform this is the AI agents it's now building

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Speaker 2: with an API sort of ecosystem for that, is that

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Speaker 2: a lot of companies are sitting back going do I

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Speaker 2: want to give you that level of access? So open

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Speaker 2: ai has just forged ahead and they're basically screenscraping all

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Speaker 2: of that information off websites. It means they can move fast,

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Speaker 2: and they're hoping that everyone will go, I need to

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Speaker 2: have operator because it's so good, my customers want it.

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Speaker 2: So let's do something a bit more substantial here with

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Speaker 2: with APIs. So I think that will come, but there's

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Speaker 2: definitely a competitive tension there in giving a lot more

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Speaker 2: fundamental access to these websites.

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Speaker 1: Yeah, and I think one of the concerns might be

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Speaker 1: if you look at how deep Seak used distillation, for example,

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Speaker 1: to train its model off open ais model, there's actually

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Speaker 1: if you have AI running things really rapidly, and you

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Speaker 1: could do a whole bunch of API calls to travel company.

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Speaker 1: You could start to see how they're pricing actually works,

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Speaker 1: and kind of use AI agents that might not be

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Speaker 1: exactly what you might want to be accessing your API.

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Speaker 1: So yeah, you know there is a possibility there all

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Speaker 1: that could potentially be mitigated through careful you know, management

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Speaker 1: of the agents and what they're able to do and

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Speaker 1: things like that in the future. But that's why I

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Speaker 1: kind of said in the last episode as well. It

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Speaker 1: has potential, but there's a lot of things that need

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Speaker 1: to fall into place in order to make it work.

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Speaker 1: It's not impossible that the network effect might turn out

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Speaker 1: to make this API marketplace somewhere that companies need to

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Speaker 1: be in order to get their business out there into

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Speaker 1: the world. But on the other hand, yeah, there is

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Speaker 1: a lot of trains, a lot of changes that will

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Speaker 1: need to occur in order for it to find that

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Speaker 1: market for Yeah, so we've gone.

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Speaker 2: From bigless incumbents like Spike to the earliest stage startups,

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Speaker 2: and we're focusing in this episode Ben on the idea

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Speaker 2: of a venture studio. I'd heard the concept before, really

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Speaker 2: in the Silicon Valley context, but it's the first I

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Speaker 2: really heard of a local venture studio. Was the Auckland

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Speaker 2: Company previously unavailable. It had some really big success with

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Speaker 2: the startup track Suit, which is like a brand tracking startup.

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Speaker 2: It raised around twenty two million dollars from venture capital

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Speaker 2: firms in early twenty twenty four in a series A

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Speaker 2: fundraising round that valued it at over one hundred and

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Speaker 2: fifty million dollars. But it started with a small amount

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Speaker 2: of seed funding and the expertise of the people at

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Speaker 2: previously unavailable.

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Speaker 1: And then it turns out a number of local companies

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Speaker 1: are now pursuing this model, no surprise after a success

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Speaker 1: like that, among them a New and Improved Ventures, super

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Speaker 1: Bowld Ministry of Awesome, Paloma Ventures, Edition Group, and Rift,

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Speaker 1: who we are talking to on the podcast today.

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Speaker 2: The three co founders are familiar with building products and

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Speaker 2: have a little cash to play with after they sold

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Speaker 2: a pretty successful IT consulting firm.

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Speaker 1: That's right. So Chris Moore, Lucas Coelo and Ben morew

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Speaker 1: All ran Rome Digital together, which sold for around forty

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Speaker 1: five million in twenty twenty one, and after a bit

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Speaker 1: of a break, they decided to put their expertise and

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Speaker 1: money to use building and seeding companies of their own.

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Speaker 2: And so Rift was born. Let's go to Ben's interview

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Speaker 2: with Rifts Lucas Coelo and Chris Moore talking about the

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Speaker 2: venture studio model, their company Rift and air approach to

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Speaker 2: building AI products.

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Speaker 1: Hello, and welcome to the Business of Tech. Thank you

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Speaker 1: so much for joining us. We're here to talk about

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Speaker 1: Rift AI. That's your guy project that you've been working

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Speaker 1: on for a while. Now you're out here pushing out

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Speaker 1: this studio venture studio model in New Zealand, which is

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Speaker 1: a reasonably well known model now internationally and is starting

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Speaker 1: to pick up pace in New Zealand as well. So

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Speaker 1: why didn't you introduce yourselves to start with?

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Speaker 4: Yeah? Cool, Lucas co founded a Rift Ventures before that,

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Speaker 4: Ravens in about eight years ago, and before that it

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Speaker 4: was heavily involved in the startup scene in Brazil, the

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Speaker 4: startup in design and technology.

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Speaker 3: Seeing Chris so I was a co founder in Rome Digital,

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Speaker 3: which was a product design studio. Actually the missing third

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Speaker 3: party here is Ben who's also part of Rift. So

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Speaker 3: we've pulled together kind of the same team that we

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Speaker 3: had previously. Then we exited that in twenty twenty one

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Speaker 3: to a ten billion dollar US listed company, took a

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Speaker 3: bit of time off and took a bit of the

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Speaker 3: cash and then got the old team back together and

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Speaker 3: kicked off rift of the inches go.

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Speaker 1: And that story we talked about, we've talked about before.

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Speaker 1: I've got a story up on business Desk that listeners

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Speaker 1: can read if they want a bit more background. How

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Speaker 1: does it work with a venture studio? Are you guys

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Speaker 1: developing the products? Are you investing in the products? What

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Speaker 1: does it actually mean?

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Speaker 4: In summary, there are a couple of entities within sort

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Speaker 4: of the studio model. We have the studio itself that

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Speaker 4: produces the ideas. So we have a backlog of ideas.

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Speaker 4: Were always fishing for ideas, even within our immediate sort

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Speaker 4: of team. Our team now it's composed at about eleven people

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Speaker 4: with doing engineering, design, growth, marketing, and we always sort

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Speaker 4: of have a large backlog of ideas. Ideas are free

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Speaker 4: right that we keep adding ideas in there for us

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Speaker 4: to take a look at. And one thing that we

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Speaker 4: have is kind of like sort of like a playbook

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Speaker 4: on a few of the criterias of how we evaluate

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Speaker 4: ideas in order to turn them into opportunities, move them

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Speaker 4: up onto the pipeline. And that's when we start doing

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Speaker 4: a little bit of a desk research, understanding more of

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Speaker 4: the vertical that that idea plays in the industry, sort

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Speaker 4: of the product can look like, and then we start prototyping,

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Speaker 4: testing and see what sort of products or or companies

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Speaker 4: Mike can come out of that. So, for example, last

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Speaker 4: year our backlog of ideas had about one hundred or

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Speaker 4: something ideas. It's ideas that from the time of Rome

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Speaker 4: ideas that were sall online from other companies doing in overseas.

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Speaker 4: We also did some vertical sort of days or weeks

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Speaker 4: that we're going to speak with people within a certain vertical,

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Speaker 4: for example health tech to understand sort of their problems

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Speaker 4: and what things could we tackle, problems could we solve

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Speaker 4: there with with sort of the heavy sort of II mindset.

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Speaker 4: And then from quite a few of those we we

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Speaker 4: did a first sort of like a hot or no

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Speaker 4: quick rating on those ideas, thinking about what is the

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Speaker 4: competition locally? Can we build this in New Zealand? Is

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Speaker 4: there an entry way here that we can build this

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Speaker 4: fast in order to expand with the product later on?

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Speaker 4: The future sets are later on, especially because of a

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Speaker 4: little limited resource of of inentro studio. Do you want

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Speaker 4: to grab something that is too grandios or too expensive

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Speaker 4: to bute that requires millions and millions of dollars to

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Speaker 4: but so it needs to be something that we can

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Speaker 4: uh that can we can buy a big chunk off

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Speaker 4: with our with our limited capability at least at the beginning.

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Speaker 4: And then from that we tested it about. We prototype

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Speaker 4: like design and researched about ten of them a little

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Speaker 4: bit deeper, and that resulted into a few of the

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Speaker 4: ones that we have today. So after we take the

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Speaker 4: products through the through the validation process, and that might

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Speaker 4: be do we get customers, do we get some early

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Speaker 4: custom there's interest, what is the the is it feasible

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Speaker 4: to build? After we do that process, we take them.

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Speaker 4: We also have a fund inside Rift, so Riff the

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Speaker 4: studio and Rift the fund. So we have our first

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Speaker 4: fund called Rift zero. Now where did the first close

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Speaker 4: last year? And we take those opportunities into the fund

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Speaker 4: evaluate with a pretty brecorous sort of criteria, evaluate like

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Speaker 4: if someone pitching to us and if it passes or not.

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Speaker 4: We also invest in all those opportunities as well. That's

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Speaker 4: why we say that they graduated from the studio and

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Speaker 4: we spin them off as portfolio companies instead of being

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Speaker 4: just experiments. So it's it's both.

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Speaker 1: Yeah, it's it's quite interesting because obviously the traditional startup

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Speaker 1: method is you raise a fund and then you go

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Speaker 1: and look for talent or startups to invest into. And

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Speaker 1: my fund might have a specific focus on a vertical

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Speaker 1: or a stage or whatever you like. So how is

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Speaker 1: that discussion with kind of LPs to say, not only

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Speaker 1: are you trusting us to do what's right with your money,

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Speaker 1: you're trusting us to develop the tech that's going to

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Speaker 1: bring you the return on investments. So you're starting, you're

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Speaker 1: pitching startups, and you're pitching a fund at the same time.

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Speaker 1: How does that conversation go.

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Speaker 3: Yeah, I mean it's a more complex conversation than a

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Speaker 3: standard VC. You know, when a VC is raising funds,

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Speaker 3: they just say, look, we're going to invest in these areas.

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Speaker 3: We don't know what the companies will be, we believe

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Speaker 3: that we'll be able to find them as a studio

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Speaker 3: and as a fund. Rift zero also has an investment thesis.

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Speaker 3: It has four different areas that it's looking at. We

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Speaker 3: are mainly an AI driven fund, looking at how AI

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Speaker 3: is used in the companies as well as the products themselves.

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Speaker 3: So looking at how we can stretch the But those

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Speaker 3: four areas are across productivity, security, compliance, health and finance

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Speaker 3: as for getting people to sort of understand like does

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Speaker 3: Rift have that capability and skill. You know, we spent

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Speaker 3: the last ten years building companies and products for a

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Speaker 3: lot of startups and corporates. So we built companies for

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Speaker 3: X fifteen, which is CBA's accelerator program. We built them

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Speaker 3: for Westpac Ventures, we built them for ASB, B and Z,

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Speaker 3: the Warehouse Group, Mercury Energy, sky City. You know, we've

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Speaker 3: been doing this type of product building for ten years

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Speaker 3: as a group and before that. We also have that

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Speaker 3: personal experience of building companies in Silicon Valley and in

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Speaker 3: the UK, in South America. You know, for us, the

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Speaker 3: building and the technology and all those parts aren't really

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Speaker 3: the risk. Adventure Studio is there to de risk things

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Speaker 3: around repeatable process which we have from time at Rome

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Speaker 3: skilled entrepreneurs that we have within the studio as well

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Speaker 3: as within our wider network. You know, we would have

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Speaker 3: had about two hundred and fifty employees come through Rome

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Speaker 3: at the time when we sold it, we had one

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Speaker 3: hundred and fifty, So it's a large network across Australia

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Speaker 3: and New Zealand, Singapore as well as reach into the US.

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Speaker 3: So we have all the ingredients, all those assets that

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Speaker 3: you need to bring together, and then it's about taking

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Speaker 3: what can feel a bit boring, sometimes a formulaic approach,

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Speaker 3: and yet a lot of the times you can identify

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Speaker 3: great opportunities, assemble the team, assemble the financing, and then

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Speaker 3: go and tackle it. It's actually more similar to how

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Speaker 3: Silicon Valley startups are funded. Someone sees an idea or

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Speaker 3: an opportunity, they assemble the team, they assemble the capital,

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Speaker 3: and then they go and target and tackle the market.

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Speaker 3: In the past have seen a lot of New Zealand startups.

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Speaker 3: It's kind of they've been in the market for a while,

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Speaker 3: scraping along a little bit of friends and family funding,

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Speaker 3: and they just happen to hit an inflection point where

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Speaker 3: the market needs the product they have. This style is

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Speaker 3: quite different, I mean.

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Speaker 1: And that's one of the questions as well that I

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Speaker 1: was interested in, which is, you know, you have the capability,

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Speaker 1: and you have the processes, and you have the experience

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Speaker 1: and the talent, but the X factor, the key thing

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Speaker 1: is finding the right place to apply those and that's

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Speaker 1: something that's talked about significantly in New Zealand. We have

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Speaker 1: limited resources in this country, we have limited capital, and

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Speaker 1: so we need to be quite specific about how we

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Speaker 1: are targeting our solutions. So how do you think through that?

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Speaker 4: Yeah, I think it's the what I was saying before.

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Speaker 4: Like when we're looking at the vertical just for example,

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Speaker 4: a product that we have like chain, we start thinking

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Speaker 4: about the first idea, remember that was there on the

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Speaker 4: backlog was AI power job management software tool. There are

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Speaker 4: a few of them, a few of them are very successful.

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Speaker 4: Every once in a while they get acquired by larger

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Speaker 4: companies that which means that sometimes the customer service is

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Speaker 4: not what it used to be. And therefore, especially on

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Speaker 4: the SMBs, they start looking for all right, so the

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Speaker 4: prices went up. What else can I start using it?

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Speaker 4: It might be interesting to have something that, especially with

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Speaker 4: vertical AI businesses, you're always trying to look into, let's

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Speaker 4: look at automating things that they're not properly automated on

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Speaker 4: sort of the traditional job management tools, and let's start

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Speaker 4: let's focus on the product on that. But then while

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Speaker 4: we were taking that idea through the validation process and

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Speaker 4: we had a prototype of the tool, we had a

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Speaker 4: couple of sort of design screens and starts check with

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Speaker 4: so traders, with builders, with treadees, we're always trying to

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Speaker 4: think about an angle, that is, what is the what

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Speaker 4: is the line of this experience that will deliver them

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Speaker 4: instance of r O I that will solve an immediate

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Speaker 4: problem or or something that they want out to make today.

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Speaker 4: What can we build that will be fast to build,

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Speaker 4: that we can solve that problem. They will they will

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Speaker 4: pay for us to solve that problem, and then we

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Speaker 4: can build the rest of the capability afterwards, after the

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Speaker 4: proof that that piece of that piece of the of

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Speaker 4: the of the solution. And that's when, for example, we

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Speaker 4: start testing the in this case, the I think we

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Speaker 4: call a builder boost the the job management software lending page.

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Speaker 4: We have a couple of prototype screens, and after every

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Speaker 4: interview they will say, yeah, it's really cool, it looks

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Speaker 4: really cool, but not what my problem is? I hate

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Speaker 4: doing this, I doing the admin work. I hate all

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Speaker 4: of the tools that already exists. Yeah, this looks nice,

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Speaker 4: but I just want to I just can't pick up

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Speaker 4: my phone while am I work and I lose jobs

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Speaker 4: because of it. Can you solve someone? Do you know

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Speaker 4: anyone that can do that and then it qualify the

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Speaker 4: leads for us. And that's when Jane came about.

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Speaker 2: Right.

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Speaker 4: They were like, yeah, that's something that we can solve,

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Speaker 4: and we can solve reasonably fast.

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Speaker 1: And Jane, is your l M powered super intelligent the

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Speaker 1: voicemail that can understand the context of what's being spoken

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Speaker 1: verbally through the phone.

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Speaker 4: Yeah. Yeah, we're looking at now at the vision of

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Speaker 4: being this working towards being on the vision being this

436
00:25:41,119 --> 00:25:45,520
Speaker 4: Oh encompassy our encompassing sort of inbound lead qualification for

437
00:25:45,600 --> 00:25:49,600
Speaker 4: Assouli traders. Right, so not only qualifying leads that come

438
00:25:49,640 --> 00:25:54,359
Speaker 4: through the phone, it also can schedule sort of appointments

439
00:25:54,880 --> 00:25:58,240
Speaker 4: through the phone. It's on your Google calendar and in

440
00:25:58,320 --> 00:26:03,680
Speaker 4: your tool of choice and also integrating with WhatsApp. Business

441
00:26:04,720 --> 00:26:08,880
Speaker 4: noticed that it's a lot of small businesses that their

442
00:26:08,960 --> 00:26:13,639
Speaker 4: value proposition is helping some traders set up their sas

443
00:26:13,640 --> 00:26:17,359
Speaker 4: tools and we're like, okay, so this is a this

444
00:26:17,520 --> 00:26:19,399
Speaker 4: is really a gap in the market, Like people are

445
00:26:19,400 --> 00:26:23,760
Speaker 4: trying to solve this by being a consultancy to to

446
00:26:23,960 --> 00:26:26,200
Speaker 4: this is how that's how hard it is and how

447
00:26:28,560 --> 00:26:32,119
Speaker 4: uh that that it shows the friction that exists with

448
00:26:32,560 --> 00:26:38,080
Speaker 4: the things that exist today and how without without even

449
00:26:38,480 --> 00:26:41,200
Speaker 4: everyone that we spoke with they're like, yeah, we don't.

450
00:26:41,280 --> 00:26:43,919
Speaker 4: We want to spend less time doing that so I

451
00:26:43,960 --> 00:26:49,240
Speaker 4: can spend more time just doing the work getting paid.

452
00:26:49,920 --> 00:26:53,320
Speaker 1: Interesting. Yeah, you're a New Zealand based business, you have

453
00:26:53,480 --> 00:26:57,680
Speaker 1: international ties, so I'm assuming and the question isn't will

454
00:26:57,760 --> 00:27:01,000
Speaker 1: New Zealand does pay for this, but it's will people

455
00:27:01,119 --> 00:27:03,480
Speaker 1: somewhere pay for this? And where is that? So that

456
00:27:03,560 --> 00:27:05,240
Speaker 1: must be another consideration as well.

457
00:27:05,760 --> 00:27:07,960
Speaker 3: Yeah, you know, New Zealand market is far too small,

458
00:27:08,480 --> 00:27:11,320
Speaker 3: so if anything would have to be a minimum of

459
00:27:11,320 --> 00:27:15,159
Speaker 3: a regional market Australia and New Zealand, say Asia, so

460
00:27:15,240 --> 00:27:19,760
Speaker 3: apax but predominantly of course we're targeting the US, but

461
00:27:20,040 --> 00:27:22,120
Speaker 3: you have to start somewhere where it's easy to get

462
00:27:22,160 --> 00:27:25,480
Speaker 3: some initial feedback. But you don't want to stay with

463
00:27:25,680 --> 00:27:28,119
Speaker 3: that story of New Zealand being the trial market and

464
00:27:28,280 --> 00:27:31,040
Speaker 3: just two years later it's still your trial market. It's

465
00:27:31,400 --> 00:27:34,560
Speaker 3: really just used during the validation phase. Then once the

466
00:27:34,600 --> 00:27:37,880
Speaker 3: portfolio company is really to be launched, you're straight into

467
00:27:37,920 --> 00:27:38,359
Speaker 3: the US.

468
00:27:38,880 --> 00:27:43,160
Speaker 4: Yeah. I find the New Zealand market, especially coming from overseas,

469
00:27:43,440 --> 00:27:47,760
Speaker 4: a very interesting market for tech in comparison to what

470
00:27:47,880 --> 00:27:50,000
Speaker 4: I'm used to in the US or in Brazil. I

471
00:27:50,040 --> 00:27:53,400
Speaker 4: think in the US, of course, depending on a little

472
00:27:53,400 --> 00:27:56,280
Speaker 4: bit of the state, but same as in Brazil. But

473
00:27:56,440 --> 00:28:01,119
Speaker 4: people tend to swarm more into new things, and in

474
00:28:01,160 --> 00:28:04,800
Speaker 4: New Zealand, I feel it's a little bit the majority

475
00:28:04,840 --> 00:28:09,159
Speaker 4: of the customers they wait someone to test it first

476
00:28:09,200 --> 00:28:12,400
Speaker 4: before they jump into it. So I tend to think

477
00:28:12,440 --> 00:28:15,840
Speaker 4: that a few of these things, especially on the products,

478
00:28:15,880 --> 00:28:18,439
Speaker 4: if you can sell it here to New Zealanders and

479
00:28:18,480 --> 00:28:22,119
Speaker 4: can convince a convince a very traditional sort of trade

480
00:28:22,359 --> 00:28:25,800
Speaker 4: to adopt that into its workflow, that's a good signal

481
00:28:25,880 --> 00:28:29,360
Speaker 4: that you can probably get some more adoption overseas.

482
00:28:29,960 --> 00:28:33,760
Speaker 1: That's really interesting. Yeah, and I guess because you aren't

483
00:28:33,840 --> 00:28:37,199
Speaker 1: thinking of New Zealand as your primary market. You know,

484
00:28:37,280 --> 00:28:40,360
Speaker 1: I've heard stories before of startups who have had great

485
00:28:40,400 --> 00:28:45,320
Speaker 1: ideas that could have great ROI for trade's but they

486
00:28:45,360 --> 00:28:48,400
Speaker 1: just weren't able to convince the trades to actually adopt

487
00:28:48,400 --> 00:28:51,240
Speaker 1: it at a scale that was able to provide some

488
00:28:51,560 --> 00:28:56,200
Speaker 1: assurance to investors before unfortunately the companies collapsed. Not because

489
00:28:56,200 --> 00:28:59,040
Speaker 1: you guys have the venture model, because you aren't seeing

490
00:28:59,080 --> 00:29:01,320
Speaker 1: New Zealand as your prime market, but as a kind

491
00:29:01,360 --> 00:29:04,720
Speaker 1: of like trial market. That allows you to then go, well,

492
00:29:04,760 --> 00:29:07,320
Speaker 1: look now we have the set, we can throw it

493
00:29:07,360 --> 00:29:10,560
Speaker 1: somewhere that's a bit more swarmy and see and see

494
00:29:10,560 --> 00:29:11,440
Speaker 1: what happens there.

495
00:29:11,800 --> 00:29:12,320
Speaker 4: Yeah.

496
00:29:12,400 --> 00:29:16,120
Speaker 3: The venture studio model also allows you to play with

497
00:29:16,160 --> 00:29:19,440
Speaker 3: the different leavers of engineering marketing spend across all of

498
00:29:19,480 --> 00:29:23,480
Speaker 3: your portfolio companies. So if you have a traditional startup,

499
00:29:23,960 --> 00:29:26,120
Speaker 3: you hire an engineering team or you've got some co

500
00:29:26,200 --> 00:29:28,920
Speaker 3: founders as engineers, and that's great as you get a

501
00:29:28,920 --> 00:29:31,880
Speaker 3: bit of scale. Then every month you're paying for the

502
00:29:31,920 --> 00:29:34,160
Speaker 3: engineering team and you just got to keep feeding them

503
00:29:34,200 --> 00:29:37,080
Speaker 3: with work to be done. Now, some of the time

504
00:29:37,120 --> 00:29:39,160
Speaker 3: it's actually you don't need more features. What you need

505
00:29:39,200 --> 00:29:41,280
Speaker 3: to do is be selling what you have and get

506
00:29:41,320 --> 00:29:43,880
Speaker 3: the feedback from market. I mean, I think you know,

507
00:29:43,920 --> 00:29:45,720
Speaker 3: there's there's a lot of stats on I think it's

508
00:29:45,760 --> 00:29:48,480
Speaker 3: like forty percent of the features you build aren't even

509
00:29:48,480 --> 00:29:51,520
Speaker 3: the ones that customers adopt. And I've seen it myself

510
00:29:52,320 --> 00:29:55,160
Speaker 3: now with a venture studio, because you have a portfolio

511
00:29:55,240 --> 00:29:57,240
Speaker 3: of these, you can pull back some of the engineering

512
00:29:57,280 --> 00:30:00,560
Speaker 3: talent from one of the portfolio companies should into the

513
00:30:00,600 --> 00:30:04,080
Speaker 3: second one. While you're looking for the right market signal

514
00:30:04,200 --> 00:30:08,320
Speaker 3: waiting for market adoption. AI is one that you know,

515
00:30:08,400 --> 00:30:11,320
Speaker 3: in some cases is fast and other cases is slow.

516
00:30:11,560 --> 00:30:14,800
Speaker 3: It's so trying to get the timing right is I

517
00:30:14,840 --> 00:30:17,320
Speaker 3: believe easier with the venture studio where you can move

518
00:30:17,400 --> 00:30:20,080
Speaker 3: the burn around across the portfolio companies.

519
00:30:20,600 --> 00:30:22,120
Speaker 1: This is something I wanted to talk to you guys

520
00:30:22,160 --> 00:30:24,960
Speaker 1: about as well, because we have in the I guess

521
00:30:25,000 --> 00:30:27,200
Speaker 1: really end of last year beginning of this year, we've

522
00:30:27,240 --> 00:30:35,520
Speaker 1: started to see this kind of counter rhetoric towards AI

523
00:30:35,840 --> 00:30:40,400
Speaker 1: and generative AI. And you know that this this idea

524
00:30:40,560 --> 00:30:46,080
Speaker 1: that open AI and it's ken who have been funneling

525
00:30:46,120 --> 00:30:49,160
Speaker 1: billions and billions of dollars into the development of the

526
00:30:49,160 --> 00:30:53,840
Speaker 1: technology are ultimately going to fail to see any actual

527
00:30:53,840 --> 00:30:57,160
Speaker 1: return on that investment just because of the scale of it.

528
00:30:57,320 --> 00:31:00,680
Speaker 1: And so you know, with the recent release of deep

529
00:31:00,720 --> 00:31:05,959
Speaker 1: Seek as well, that has simplified and made those models

530
00:31:05,960 --> 00:31:10,160
Speaker 1: maybe a little bit more affordable to actually run, that

531
00:31:10,920 --> 00:31:14,680
Speaker 1: kind of proves that there's going to be a difficulty

532
00:31:14,720 --> 00:31:17,840
Speaker 1: in getting a return on the investment in the long

533
00:31:17,880 --> 00:31:20,400
Speaker 1: run because they can't justify the expense when there's going

534
00:31:20,480 --> 00:31:23,719
Speaker 1: to be cheaper alternatives. What's your thoughts around that, and

535
00:31:23,760 --> 00:31:27,280
Speaker 1: how for companies like yourselves that are building on these

536
00:31:27,360 --> 00:31:31,480
Speaker 1: AI platforms that may represent a risk around the continuation

537
00:31:32,200 --> 00:31:34,080
Speaker 1: of the technology into the future.

538
00:31:34,880 --> 00:31:38,160
Speaker 3: Yeah, if I sort of think back to two thousand

539
00:31:38,280 --> 00:31:40,880
Speaker 3: when you had you know, the Internet bubble and boom,

540
00:31:40,960 --> 00:31:44,400
Speaker 3: and you had this huge buildout of infrastructure and companies

541
00:31:44,480 --> 00:31:48,560
Speaker 3: like you know, Cisco, Juniper Networks, etc. We're riding high,

542
00:31:49,280 --> 00:31:53,080
Speaker 3: huge investments to get people more bandwidth. And back then

543
00:31:53,200 --> 00:31:56,160
Speaker 3: you had a ADSL link you know, one point five

544
00:31:56,240 --> 00:31:59,560
Speaker 3: megabit or something, and everyone was like, yeah, that's that's amazing.

545
00:31:59,560 --> 00:32:03,640
Speaker 3: I can now watch some standard DEF video with twenty

546
00:32:03,760 --> 00:32:07,160
Speaker 3: frames per second. What would I need more for? And

547
00:32:07,320 --> 00:32:09,480
Speaker 3: you know, you're fast forward now and you've got five

548
00:32:09,600 --> 00:32:13,680
Speaker 3: G networks where you can do video streaming, you know,

549
00:32:13,760 --> 00:32:16,720
Speaker 3: on the go in your car, you've got five to

550
00:32:16,800 --> 00:32:18,880
Speaker 3: your house with one hundred to three hundred or eight

551
00:32:18,960 --> 00:32:23,240
Speaker 3: hundred megabit down Like, people will find a way to

552
00:32:23,320 --> 00:32:27,400
Speaker 3: consume bandwidth. It's going to be the same with AI compute.

553
00:32:27,960 --> 00:32:30,200
Speaker 3: Like you know, deep seat coming out as great as

554
00:32:30,280 --> 00:32:32,600
Speaker 3: open source, it's already been ripped apart in bits of

555
00:32:32,680 --> 00:32:39,360
Speaker 3: being put into existing models, ignoring the supposed cost because

556
00:32:39,360 --> 00:32:41,560
Speaker 3: there's a lot of issues around. Well, actually they did

557
00:32:41,600 --> 00:32:45,360
Speaker 3: have access to fifty thousand in video GPUs and they

558
00:32:45,360 --> 00:32:48,160
Speaker 3: did spend more money than the kind of the sticker price.

559
00:32:49,240 --> 00:32:51,280
Speaker 3: You know, that's that's great. You need to bring the

560
00:32:51,320 --> 00:32:54,800
Speaker 3: costs down. We're already seeing that with open Ai. Like

561
00:32:54,840 --> 00:33:00,120
Speaker 3: they're the three mini models like ninety three percent cheaper

562
00:33:00,120 --> 00:33:04,240
Speaker 3: than their at last one. It's faster, less latency, which

563
00:33:04,280 --> 00:33:06,520
Speaker 3: means you can do more of these real time voice,

564
00:33:06,600 --> 00:33:13,200
Speaker 3: real time communications, real time robotics. It's does really feel

565
00:33:13,240 --> 00:33:16,880
Speaker 3: like we're at the beginning of this expansion. We do

566
00:33:17,040 --> 00:33:19,600
Speaker 3: see it with some of the AI startups, you know,

567
00:33:19,640 --> 00:33:22,520
Speaker 3: we you know, we we review a lot to see

568
00:33:22,680 --> 00:33:26,200
Speaker 3: where they are, what's the state of the market, you know,

569
00:33:26,240 --> 00:33:28,480
Speaker 3: what's real, what isn't And a lot of times you'll

570
00:33:28,480 --> 00:33:30,680
Speaker 3: click through and it will be coming soon, you know,

571
00:33:30,800 --> 00:33:35,200
Speaker 3: sign up for a better access. But we also know

572
00:33:35,320 --> 00:33:37,560
Speaker 3: because we're using it and building products that are in

573
00:33:37,680 --> 00:33:40,360
Speaker 3: market that customers are using, and it's today solving a

574
00:33:40,400 --> 00:33:44,440
Speaker 3: problem that it does work for you know a fair

575
00:33:44,520 --> 00:33:47,320
Speaker 3: number of use cases that are still there to be

576
00:33:47,520 --> 00:33:51,200
Speaker 3: to be tackled. And yeah, it's the worst it's going

577
00:33:51,240 --> 00:33:53,200
Speaker 3: to be at the moment and it just keeps getting better.

578
00:33:53,960 --> 00:33:57,480
Speaker 3: Runway Sourer for image generation mid you know, for video

579
00:33:57,560 --> 00:34:02,400
Speaker 3: generation mid journey, like the disruption is massive.

580
00:34:03,120 --> 00:34:03,479
Speaker 1: Mm.

581
00:34:04,120 --> 00:34:06,920
Speaker 4: I still think there's a lot of case that the

582
00:34:07,000 --> 00:34:11,760
Speaker 4: use cases that we at you they're the low hanging

583
00:34:11,800 --> 00:34:14,800
Speaker 4: through use cases for l lambs that people are finding

584
00:34:14,840 --> 00:34:17,759
Speaker 4: out and then now we're figuring out that, oh wait

585
00:34:17,760 --> 00:34:19,960
Speaker 4: a second, we might not need other lambs for everything.

586
00:34:20,040 --> 00:34:22,160
Speaker 4: We can use a couple of the smaller models there

587
00:34:22,440 --> 00:34:25,760
Speaker 4: will be much more useful and choose some specific verticals

588
00:34:25,840 --> 00:34:29,360
Speaker 4: or for some specific sort of tasks or even a

589
00:34:29,440 --> 00:34:31,720
Speaker 4: sort of agenta task and that's much eeaper to run.

590
00:34:32,239 --> 00:34:36,319
Speaker 4: So mind you start using that and also thinking AI

591
00:34:36,400 --> 00:34:42,880
Speaker 4: researcher open ai pulls it on on Twitter x formerly

592
00:34:42,920 --> 00:34:47,239
Speaker 4: Twitter about that more money now is being spent on

593
00:34:48,360 --> 00:34:51,600
Speaker 4: inference then on pre training, So more money even spending

594
00:34:51,640 --> 00:34:54,919
Speaker 4: on what can this do and can we learn things

595
00:34:54,960 --> 00:34:58,239
Speaker 4: that there's no data for it before, which will become

596
00:34:58,320 --> 00:35:04,120
Speaker 4: more useful. So even the CAPEC spend is going silver structure, yeah,

597
00:35:04,120 --> 00:35:07,799
Speaker 4: and the infrastructure is going a lot. They're spending a

598
00:35:07,800 --> 00:35:11,160
Speaker 4: lot of money on building the infrastructure for that. I

599
00:35:11,239 --> 00:35:15,719
Speaker 4: do think that if the market is expecting an immediate

600
00:35:15,760 --> 00:35:20,640
Speaker 4: return for this investment. That might be especially for this

601
00:35:20,719 --> 00:35:21,799
Speaker 4: companies they are spending a lot.

602
00:35:23,200 --> 00:35:26,360
Speaker 1: You mentioned kind of in video, and you mentioned Cisco,

603
00:35:26,560 --> 00:35:30,799
Speaker 1: and one of the observations was with Cisco. Obviously in

604
00:35:30,840 --> 00:35:34,080
Speaker 1: the early two thousands they had that massive wipeout of

605
00:35:34,120 --> 00:35:38,400
Speaker 1: a lot of their value and there was a similar blip,

606
00:35:38,560 --> 00:35:41,080
Speaker 1: but in video probably was it was not obviously of

607
00:35:41,120 --> 00:35:45,440
Speaker 1: the same scale. But the argument was that these companies

608
00:35:45,440 --> 00:35:50,719
Speaker 1: that are being continually inflated, the value is being inflated

609
00:35:51,280 --> 00:35:56,560
Speaker 1: by the the the pouring of billion dollars into R

610
00:35:56,600 --> 00:35:58,799
Speaker 1: and D by these companies, that we will start to

611
00:35:58,800 --> 00:36:02,960
Speaker 1: see a similar popping off that value bubble, not necessarily

612
00:36:03,040 --> 00:36:05,560
Speaker 1: around the quality of the tech, but the value bubble

613
00:36:05,560 --> 00:36:09,080
Speaker 1: in itself. Does that concern you guys as having both

614
00:36:09,120 --> 00:36:10,960
Speaker 1: the products and the fund.

615
00:36:12,600 --> 00:36:15,040
Speaker 3: I think on the end Video one, what's quite interesting

616
00:36:15,160 --> 00:36:17,800
Speaker 3: is you know that they've managed to ride three waves.

617
00:36:17,840 --> 00:36:20,680
Speaker 3: They've managed to ride the GPU wave, when you know

618
00:36:20,719 --> 00:36:25,080
Speaker 3: graphics move from being calculated on CPU offload. Then they've

619
00:36:25,080 --> 00:36:29,640
Speaker 3: managed to ride the cryptomning wave where it moved from

620
00:36:29,640 --> 00:36:32,799
Speaker 3: CPU to GPU and then GPU to ASK. And now

621
00:36:32,800 --> 00:36:39,880
Speaker 3: they've managed to ride this third wave with AI, you know, phenomenal,

622
00:36:39,880 --> 00:36:41,360
Speaker 3: But there's spent a lot of time making sure that

623
00:36:41,360 --> 00:36:43,880
Speaker 3: there's software. It's it's not just the chip, it's the

624
00:36:43,960 --> 00:36:46,680
Speaker 3: software that you use around it. Kuda, which is the

625
00:36:46,760 --> 00:36:50,879
Speaker 3: language they use for programming those GPUs. But you are

626
00:36:51,000 --> 00:36:54,960
Speaker 3: seeing you know, Amazon have their own processor. I think

627
00:36:54,960 --> 00:36:59,960
Speaker 3: it's called Trainium. You know, like Google have got their

628
00:37:00,080 --> 00:37:04,960
Speaker 3: tensor processor unit, the TPU. People are coming for that crown.

629
00:37:05,000 --> 00:37:09,399
Speaker 3: Of course, it's so rich. The good news is, you know, yes,

630
00:37:09,640 --> 00:37:13,120
Speaker 3: Cisco wore that bubble and that bubble burst, but when

631
00:37:13,160 --> 00:37:15,560
Speaker 3: you look at the major players, you know, Google and

632
00:37:15,719 --> 00:37:18,879
Speaker 3: Facebook and all the social media Instagram, they all came

633
00:37:18,920 --> 00:37:24,080
Speaker 3: out after that collapse, after the infrastructure burst. Also, there

634
00:37:24,160 --> 00:37:27,040
Speaker 3: was a lot of costs that the US government was

635
00:37:27,080 --> 00:37:31,880
Speaker 3: selling Spectrum at the time to the telpots, so that

636
00:37:31,960 --> 00:37:35,399
Speaker 3: sucked a lot of their cap becks up as well. So, yeah,

637
00:37:35,400 --> 00:37:37,719
Speaker 3: if we were in the chip space, if we're in

638
00:37:37,760 --> 00:37:40,359
Speaker 3: the model space, I think that's going to be cut throat.

639
00:37:40,400 --> 00:37:42,480
Speaker 3: You're going to end up with open source, and you're

640
00:37:42,480 --> 00:37:45,279
Speaker 3: going to end up with Microsoft, which is open AI.

641
00:37:45,400 --> 00:37:47,680
Speaker 3: You're going to end up with Amazon, which is anthropic,

642
00:37:47,840 --> 00:37:49,400
Speaker 3: and you're going to end up with Google, with with

643
00:37:49,600 --> 00:37:53,279
Speaker 3: Gemini and you know, and you know under open source

644
00:37:53,280 --> 00:37:55,360
Speaker 3: of course you got Lama with Meta and all these others.

645
00:37:56,480 --> 00:37:59,160
Speaker 3: Like I wouldn't want to be in that space, application space,

646
00:37:59,360 --> 00:38:02,840
Speaker 3: you know, being able to look at what tasks administration

647
00:38:03,080 --> 00:38:06,440
Speaker 3: work you can replace. Yeah, that's that's the place to play.

648
00:38:06,960 --> 00:38:10,759
Speaker 1: Yeah, because you're not concerned about being caught in the

649
00:38:11,239 --> 00:38:14,000
Speaker 1: inner potential bubble burth.

650
00:38:14,400 --> 00:38:18,320
Speaker 3: If your companies are making money and we're not building

651
00:38:18,360 --> 00:38:21,120
Speaker 3: companies that are growth at all costs its balance of

652
00:38:21,480 --> 00:38:26,480
Speaker 3: growth and profitability as a venture studio, that's okay.

653
00:38:26,920 --> 00:38:31,279
Speaker 4: Yeah, because we're we're consuming more the commodity that they

654
00:38:31,320 --> 00:38:35,840
Speaker 4: provide in terms of the models than being caught up

655
00:38:35,880 --> 00:38:39,920
Speaker 4: on developing the models ourselves. And depending on who wins

656
00:38:39,920 --> 00:38:43,720
Speaker 4: this race, it's an open source with the excellent models

657
00:38:43,760 --> 00:38:47,000
Speaker 4: that that exist, and we're building with a with a

658
00:38:47,120 --> 00:38:51,600
Speaker 4: paradigm that is, we're building our capabilities in our future

659
00:38:51,680 --> 00:38:56,360
Speaker 4: is based on on the available sort of capabilities of

660
00:38:56,440 --> 00:38:59,239
Speaker 4: the of this commodity that exists and where it might

661
00:38:59,320 --> 00:39:02,120
Speaker 4: be in a fewure based on the trend of development

662
00:39:02,200 --> 00:39:05,360
Speaker 4: that this exists. But we can consume from any player. Yeah,

663
00:39:05,560 --> 00:39:08,040
Speaker 4: that it's out there, so we can consume the open

664
00:39:08,080 --> 00:39:11,800
Speaker 4: Eye one the Anthropic one, because they're probably going to

665
00:39:11,880 --> 00:39:16,600
Speaker 4: be very converged. They are converge to be very similar,

666
00:39:17,480 --> 00:39:20,000
Speaker 4: at least that it looks today. I'm not sure tomorrow,

667
00:39:20,000 --> 00:39:22,239
Speaker 4: Open a I might release something that we look and say,

668
00:39:22,239 --> 00:39:25,680
Speaker 4: oh my god, this is ten years ahead of everyone else,

669
00:39:25,960 --> 00:39:29,239
Speaker 4: as it was with the Chat GPT, although some might

670
00:39:29,360 --> 00:39:33,160
Speaker 4: argue that other companies had what the first GPT was,

671
00:39:33,239 --> 00:39:36,080
Speaker 4: they just didn't want to release to the public, so

672
00:39:36,120 --> 00:39:39,000
Speaker 4: Opening I was not that ahead as others might think.

673
00:39:39,040 --> 00:39:41,600
Speaker 4: But they seem to be very ethnical.

674
00:39:41,719 --> 00:39:42,399
Speaker 3: Nick and Nick.

675
00:39:42,560 --> 00:39:47,040
Speaker 4: It just continuously and there are some that are For example,

676
00:39:47,040 --> 00:39:49,399
Speaker 4: even in a few other products, we don't use one

677
00:39:49,520 --> 00:39:52,680
Speaker 4: model we use. We might use multiple models they're better,

678
00:39:53,600 --> 00:39:55,919
Speaker 4: or smaller models they are better in each task. There's

679
00:39:57,760 --> 00:40:00,799
Speaker 4: Claude is excellent for coding for for some reason, and

680
00:40:00,800 --> 00:40:04,000
Speaker 4: I'm not sure what they added into the the find

681
00:40:04,120 --> 00:40:07,879
Speaker 4: the tuning or on the model, but it's I find

682
00:40:07,880 --> 00:40:14,240
Speaker 4: it much better for coding than GPT. So you usually

683
00:40:14,280 --> 00:40:16,760
Speaker 4: want I'm use in cursor, I want to look into

684
00:40:16,800 --> 00:40:19,279
Speaker 4: coding or use Claude instead of using Chat GIPT. I

685
00:40:19,280 --> 00:40:22,960
Speaker 4: think it's just better, especially with Python. I might see

686
00:40:23,000 --> 00:40:26,040
Speaker 4: that other companies that are consuming a few of these

687
00:40:26,080 --> 00:40:29,680
Speaker 4: models my pick and choice and do the same because

688
00:40:29,719 --> 00:40:33,759
Speaker 4: I think the secret is still on. The secret selves

689
00:40:33,800 --> 00:40:37,600
Speaker 4: of companies still be the services they provide. They are

690
00:40:37,680 --> 00:40:40,680
Speaker 4: solving a problem. How is the experienced layer, how is

691
00:40:40,719 --> 00:40:45,160
Speaker 4: that sort of application layer more than the commodity itself?

692
00:40:45,320 --> 00:40:48,120
Speaker 4: Chatting with someone that is like, as long as there's

693
00:40:48,200 --> 00:40:51,279
Speaker 4: coffee to be bought, I want to keep building Starbucks

694
00:40:51,800 --> 00:40:55,640
Speaker 4: shops or nice coffee shops. I don't want to build

695
00:40:55,640 --> 00:40:58,440
Speaker 4: a farm to start producing coffee because that would that

696
00:40:58,480 --> 00:41:02,839
Speaker 4: would probably be a RaSE to zero or very very soon,

697
00:41:02,920 --> 00:41:05,680
Speaker 4: if not that already started. And as you're saying, with

698
00:41:05,800 --> 00:41:09,000
Speaker 4: a deep seek, wiping out quite a few, quite a

699
00:41:09,000 --> 00:41:10,960
Speaker 4: bit of the value of these companies.

700
00:41:11,680 --> 00:41:14,480
Speaker 1: Interesting, I do enjoy that you said Starbucks or nice

701
00:41:14,520 --> 00:41:19,640
Speaker 1: coffee shops. I think that's that's very to be honest.

702
00:41:19,760 --> 00:41:26,240
Speaker 4: Yeah, I don't mind star Yeah.

703
00:41:24,200 --> 00:41:26,279
Speaker 1: That's great. I mean, I know we're probably over is that,

704
00:41:26,320 --> 00:41:28,719
Speaker 1: But I just one more question I want to ask

705
00:41:29,400 --> 00:41:33,000
Speaker 1: around that idea as well. Was there's also a commentary

706
00:41:33,040 --> 00:41:35,440
Speaker 1: that we might be hitting a ceiling in terms of

707
00:41:35,520 --> 00:41:42,560
Speaker 1: capability around L l ms, talking in particular around hallucinations.

708
00:41:43,160 --> 00:41:45,200
Speaker 1: You know, it's a feature, it's not something we can

709
00:41:45,239 --> 00:41:51,560
Speaker 1: get rid of talking about the cognitive in quotes capabilities

710
00:41:51,680 --> 00:41:54,759
Speaker 1: of these lms that the rate of increase is now

711
00:41:54,880 --> 00:41:59,160
Speaker 1: so minimal that actually it looks like we might have

712
00:41:59,680 --> 00:42:03,400
Speaker 1: hit the ceiling on that, and so building with the

713
00:42:03,480 --> 00:42:05,480
Speaker 1: idea that there's going to be something better in the

714
00:42:05,480 --> 00:42:09,920
Speaker 1: future may be a mistake, if that makes sense. What's

715
00:42:10,000 --> 00:42:11,400
Speaker 1: your take on that perspective.

716
00:42:12,760 --> 00:42:16,279
Speaker 3: I guess one thing is the companies we're building at

717
00:42:16,280 --> 00:42:19,640
Speaker 3: the moment take advantage of the current state of the art.

718
00:42:20,000 --> 00:42:25,000
Speaker 3: We're very cautious about building for the crossover point of

719
00:42:25,160 --> 00:42:28,760
Speaker 3: some capability that might come in eighteen twenty four months.

720
00:42:29,800 --> 00:42:31,640
Speaker 3: So I think, yeah, if you're building companies, you need

721
00:42:31,680 --> 00:42:34,360
Speaker 3: to make sure you're building with what can be achieved today.

722
00:42:34,719 --> 00:42:36,760
Speaker 3: That's why we see a lot that are hidden behind

723
00:42:36,880 --> 00:42:40,600
Speaker 3: sign ups and join the wait lists. They were also

724
00:42:40,640 --> 00:42:44,080
Speaker 3: waiting for a tech shift. So that's pretty critical. And

725
00:42:44,160 --> 00:42:46,080
Speaker 3: it's not to say that the evolution of the product

726
00:42:46,600 --> 00:42:51,560
Speaker 3: cannot look to future capabilities, but your sellable kind of

727
00:42:51,680 --> 00:42:54,640
Speaker 3: wedge in today should be able, well not should, it

728
00:42:54,680 --> 00:42:57,520
Speaker 3: has to actually work. That's one of the things as

729
00:42:57,560 --> 00:43:01,600
Speaker 3: far as have we hit the limits, I think what's

730
00:43:01,640 --> 00:43:04,200
Speaker 3: interesting is, you know you have something like deep Seat

731
00:43:04,239 --> 00:43:05,640
Speaker 3: come out and it's got a whole bunch of these

732
00:43:05,719 --> 00:43:09,879
Speaker 3: kind of clever optimizations you know in ways of doing

733
00:43:09,960 --> 00:43:14,480
Speaker 3: things like people are going to continually find these. So

734
00:43:14,480 --> 00:43:17,440
Speaker 3: if you can use it to train your model at

735
00:43:17,440 --> 00:43:20,120
Speaker 3: a tenth the price, well, now you can train a

736
00:43:20,160 --> 00:43:23,760
Speaker 3: model that's ten times as large. Can you find more data?

737
00:43:23,960 --> 00:43:25,799
Speaker 3: Like I said, that's one of the big sources. So

738
00:43:25,800 --> 00:43:30,160
Speaker 3: then you start looking at synthetic data careful of hallucinations.

739
00:43:32,080 --> 00:43:33,880
Speaker 3: There's a lot of smart people, there's a lot of money.

740
00:43:34,080 --> 00:43:36,839
Speaker 3: I just don't see us hitting a wall in the

741
00:43:36,880 --> 00:43:40,960
Speaker 3: next next few years. The same as bandwidth. Again it's

742
00:43:40,960 --> 00:43:43,799
Speaker 3: a weird analogy, but remember thinking, you know, one point

743
00:43:43,840 --> 00:43:46,240
Speaker 3: five megabit was so fast because you had a twenty

744
00:43:46,239 --> 00:43:50,560
Speaker 3: eight K modem before that, and you know you used

745
00:43:50,600 --> 00:43:52,600
Speaker 3: to get like a say in New Zealand, a T

746
00:43:52,760 --> 00:43:54,560
Speaker 3: one or it's the US T one or an E

747
00:43:54,680 --> 00:43:56,480
Speaker 3: one line two megabit, and it would cost you ten

748
00:43:56,520 --> 00:43:59,200
Speaker 3: thousand dollars a month to connect Auckland to Wellington. It

749
00:43:59,400 --> 00:44:03,480
Speaker 3: take thirty five calls, and that just seems so quaint,

750
00:44:04,000 --> 00:44:05,640
Speaker 3: but at the time it would have been well, that's

751
00:44:05,640 --> 00:44:07,920
Speaker 3: the fastest that we can get it across the network,

752
00:44:07,960 --> 00:44:11,359
Speaker 3: and all of these other reasons where there's demand, you know,

753
00:44:11,440 --> 00:44:14,879
Speaker 3: money and innovation follows. I don't think we've been hitting

754
00:44:14,920 --> 00:44:15,759
Speaker 3: limits soon.

755
00:44:17,000 --> 00:44:20,239
Speaker 4: And then's the bit about we're talked about building with

756
00:44:20,400 --> 00:44:24,080
Speaker 4: the future capability in mind, with CRUs Is saying as well,

757
00:44:24,160 --> 00:44:27,520
Speaker 4: is that there's a lot of the not a lot,

758
00:44:27,560 --> 00:44:29,600
Speaker 4: but they are parts of the workflows of a few

759
00:44:29,640 --> 00:44:32,680
Speaker 4: of the products that we're working with that they're just

760
00:44:33,080 --> 00:44:36,760
Speaker 4: ultimately like that llms are not capable of doing right now.

761
00:44:36,800 --> 00:44:38,080
Speaker 4: And then you what it do is that you do

762
00:44:38,280 --> 00:44:41,000
Speaker 4: that part of workflow with your standard automation sort of

763
00:44:41,400 --> 00:44:45,200
Speaker 4: standard code there. But it generates is that it starts

764
00:44:45,280 --> 00:44:48,120
Speaker 4: when people start using it generates a very specific sort

765
00:44:48,160 --> 00:44:52,360
Speaker 4: of the amount of data that you can use a

766
00:44:52,640 --> 00:44:55,839
Speaker 4: small model to be very good at that very specific

767
00:44:56,400 --> 00:44:59,960
Speaker 4: sort of workflow, which is something that starts building different

768
00:45:00,480 --> 00:45:03,440
Speaker 4: right It'll be very hard to build differentiation if you're

769
00:45:03,480 --> 00:45:07,840
Speaker 4: just wrapping your company around whatever is the large the

770
00:45:07,920 --> 00:45:10,680
Speaker 4: data set the lms are trained on, because then anyone

771
00:45:10,719 --> 00:45:12,680
Speaker 4: that just pluts into the data set will be able

772
00:45:12,760 --> 00:45:17,319
Speaker 4: to to roughly perform the same task. But when you

773
00:45:17,480 --> 00:45:21,200
Speaker 4: viewed something that solves a very specific sort of touch

774
00:45:21,239 --> 00:45:26,040
Speaker 4: point and you start getting your model into it and

775
00:45:26,239 --> 00:45:29,799
Speaker 4: then using that data to train your capability, I think

776
00:45:29,880 --> 00:45:33,960
Speaker 4: then you start creating differentiation and something that it's more defensible,

777
00:45:34,120 --> 00:45:38,279
Speaker 4: especially in the in a world where anyone can go

778
00:45:38,320 --> 00:45:43,040
Speaker 4: to cursor or go to what's the zero from A

779
00:45:43,200 --> 00:45:47,640
Speaker 4: and build a SaaS company in a in a week

780
00:45:47,760 --> 00:45:51,160
Speaker 4: or so, it would be very interesting to see how

781
00:45:51,160 --> 00:45:51,560
Speaker 4: it goes.

782
00:45:52,800 --> 00:45:54,400
Speaker 1: It will be it will be very interesting.

783
00:45:54,640 --> 00:46:00,080
Speaker 4: Yeah, very interesting. In the same moment that said that

784
00:46:00,120 --> 00:46:07,920
Speaker 4: the first billion dollar company of one person should should

785
00:46:09,040 --> 00:46:10,160
Speaker 4: should arrive very soon.

786
00:46:10,360 --> 00:46:11,680
Speaker 1: Some helpments ad a lot of things.

787
00:46:19,360 --> 00:46:22,000
Speaker 2: We'll put a couple of articles in the show notes,

788
00:46:22,040 --> 00:46:27,239
Speaker 2: some really interesting articles, including from New Zealand companies sort

789
00:46:27,280 --> 00:46:31,240
Speaker 2: of explaining the pros, mainly the pros of the venture

790
00:46:31,320 --> 00:46:34,359
Speaker 2: studio model. You know, in my mind, really I think

791
00:46:34,400 --> 00:46:38,200
Speaker 2: the big advantage here and I think tracks who really

792
00:46:39,040 --> 00:46:42,400
Speaker 2: illustrates this. You have, you know, an advertising guru like

793
00:46:42,520 --> 00:46:47,200
Speaker 2: James Herman previously unavailable, Simon Pound, people like that who

794
00:46:47,280 --> 00:46:53,359
Speaker 2: really understand marketing, advertising branding. They come across some entrepreneurs

795
00:46:53,360 --> 00:46:56,040
Speaker 2: who want to set up something in that space, and

796
00:46:56,080 --> 00:47:00,799
Speaker 2: they have deep integral knowledge of that and and what

797
00:47:00,920 --> 00:47:03,600
Speaker 2: works and what doesn't. So you invite them in, maybe

798
00:47:03,600 --> 00:47:06,280
Speaker 2: give them a little bit of seed funding, but mainly

799
00:47:06,440 --> 00:47:11,319
Speaker 2: take equity and return for nurturing this company, giving it

800
00:47:11,440 --> 00:47:15,360
Speaker 2: your expertise. That seems to be the venture studio model,

801
00:47:15,560 --> 00:47:18,319
Speaker 2: and it then allows a company to get to a

802
00:47:18,360 --> 00:47:23,120
Speaker 2: point where it's really attractive to venture capital companies. And

803
00:47:23,160 --> 00:47:25,280
Speaker 2: I think that's a really good model for New Zealand,

804
00:47:25,320 --> 00:47:29,359
Speaker 2: where we do have these areas of deep expertise, but

805
00:47:29,400 --> 00:47:31,799
Speaker 2: we don't necessarily have a lot of seed funding. So

806
00:47:32,280 --> 00:47:37,200
Speaker 2: you're seeing these people who maybe have created successful companies

807
00:47:37,920 --> 00:47:40,680
Speaker 2: exited them, have a little bit of capital and are

808
00:47:40,719 --> 00:47:44,520
Speaker 2: willing to back these teams and do numerous teams at once,

809
00:47:44,560 --> 00:47:47,640
Speaker 2: so not just put everything into one company. You have

810
00:47:47,800 --> 00:47:51,319
Speaker 2: half a dozen of these companies under your roof, maybe

811
00:47:51,360 --> 00:47:55,080
Speaker 2: even hot desking or sharing space, looking over the shoulder,

812
00:47:55,160 --> 00:47:59,880
Speaker 2: helping them, mentor them, introduce them to your networks and

813
00:48:00,560 --> 00:48:02,799
Speaker 2: business context. It seems to be quite a good model

814
00:48:02,840 --> 00:48:03,440
Speaker 2: for New Zealand.

815
00:48:03,920 --> 00:48:04,120
Speaker 4: Yeah.

816
00:48:04,120 --> 00:48:08,080
Speaker 1: Absolutely, And also it does help to slightly address the

817
00:48:08,280 --> 00:48:11,680
Speaker 1: talent issue as well. Because if you have it, like

818
00:48:11,719 --> 00:48:14,120
Speaker 1: they say, they have a group of engineers within Rift

819
00:48:14,360 --> 00:48:18,440
Speaker 1: that they can then choose how to where to kind

820
00:48:18,440 --> 00:48:22,040
Speaker 1: of direct their talent. So two different different companies say

821
00:48:22,080 --> 00:48:24,000
Speaker 1: they've got one thing over here, and they're like, oh,

822
00:48:24,040 --> 00:48:26,760
Speaker 1: we really need a new feature on that asap. Let's

823
00:48:26,800 --> 00:48:31,120
Speaker 1: move sixty percent of our engineering over to that. For

824
00:48:31,160 --> 00:48:34,160
Speaker 1: the meantime, you don't have to suddenly scale up and

825
00:48:34,239 --> 00:48:36,799
Speaker 1: get a bunch of contractors and figure, you know, get

826
00:48:36,800 --> 00:48:38,719
Speaker 1: them to get on board with the systems and learn

827
00:48:38,760 --> 00:48:41,560
Speaker 1: new things. It's just this kind of this core at

828
00:48:41,640 --> 00:48:44,600
Speaker 1: rift that can do things as they need them to do.

829
00:48:44,640 --> 00:48:46,960
Speaker 1: So another really great way of addressing some of New

830
00:48:47,040 --> 00:48:48,040
Speaker 1: Zealand's shortfalls.

831
00:48:48,680 --> 00:48:54,400
Speaker 2: Yeah, I guess looking at the potential cons of this model.

832
00:48:54,719 --> 00:48:58,279
Speaker 2: So clearly, you know, the people who are running these

833
00:48:58,360 --> 00:49:02,480
Speaker 2: venture studios are spreading their risk but spreading their attention

834
00:49:02,600 --> 00:49:07,000
Speaker 2: across a number of ventures. So you know, you may

835
00:49:07,040 --> 00:49:10,680
Speaker 2: be diluting the attention that you can pay to a

836
00:49:10,680 --> 00:49:12,879
Speaker 2: particular company. And I've seen a couple of podcasts where

837
00:49:12,880 --> 00:49:17,439
Speaker 2: people who have been through venture studios and sort of say, well,

838
00:49:17,440 --> 00:49:20,160
Speaker 2: it's you know, sort of feel like I'm competing with

839
00:49:20,280 --> 00:49:24,400
Speaker 2: other companies in this venture studio for the attention of

840
00:49:24,120 --> 00:49:27,360
Speaker 2: the people who are funding this and supporting us, So

841
00:49:27,400 --> 00:49:30,520
Speaker 2: I guess that's the downside. But having said that, we're

842
00:49:30,560 --> 00:49:33,000
Speaker 2: pretty collegial in New Zealand, so you're more likely to

843
00:49:33,040 --> 00:49:36,759
Speaker 2: actually be able to leverage off what other companies sort

844
00:49:36,760 --> 00:49:39,160
Speaker 2: of in the venture studio stable are actually doing, to

845
00:49:40,080 --> 00:49:42,600
Speaker 2: try and learn from them and maybe even partner on

846
00:49:42,680 --> 00:49:44,280
Speaker 2: business ventures.

847
00:49:44,800 --> 00:49:48,080
Speaker 1: Yeah, exactly. And what I think is interesting about ref

848
00:49:48,239 --> 00:49:52,320
Speaker 1: as well as that particular focus on artificial intelligence because

849
00:49:52,320 --> 00:49:54,840
Speaker 1: it is such an evolving sector and it kind of

850
00:49:54,840 --> 00:49:58,680
Speaker 1: does link into that idea of talent because the leadership

851
00:49:58,719 --> 00:50:00,879
Speaker 1: team they spend a lot of time I'm looking at

852
00:50:00,920 --> 00:50:05,760
Speaker 1: different research papers about AI, about generative AI and its applications,

853
00:50:06,239 --> 00:50:10,360
Speaker 1: and then they can disseminate that information to their ideas

854
00:50:10,400 --> 00:50:15,160
Speaker 1: for startups, rather than you know, having to kind of

855
00:50:15,239 --> 00:50:18,799
Speaker 1: each individual company follow along as best they can and

856
00:50:18,800 --> 00:50:21,880
Speaker 1: split that time. So it's another benefit as well. But

857
00:50:22,000 --> 00:50:24,879
Speaker 1: it's also interesting they're focused on AI, especially in this

858
00:50:25,040 --> 00:50:29,759
Speaker 1: kind of very AI heavy new world that we're in

859
00:50:29,880 --> 00:50:35,200
Speaker 1: the conversation about whether it's a bubble. It seems to

860
00:50:35,239 --> 00:50:38,520
Speaker 1: me from a conversation with them with the founders that

861
00:50:38,560 --> 00:50:41,840
Speaker 1: they are quite focused in their approach. They're wanting to

862
00:50:41,920 --> 00:50:45,640
Speaker 1: kind of really really focused on what products are actually

863
00:50:45,680 --> 00:50:50,040
Speaker 1: finding product market fit and value and go from there,

864
00:50:50,719 --> 00:50:54,360
Speaker 1: rather than trying to kind of have a product and

865
00:50:54,480 --> 00:50:56,200
Speaker 1: get a bunch of hype around it and pump a

866
00:50:56,239 --> 00:50:58,120
Speaker 1: bunch of VC in it and then see where you

867
00:50:58,160 --> 00:51:00,759
Speaker 1: go from there. Maybe that's a little I don't know.

868
00:51:01,280 --> 00:51:03,760
Speaker 2: Yeah, they're clearly using AI and it was really an

869
00:51:03,880 --> 00:51:07,799
Speaker 2: interesting that discussion about their reflections on Deep Seek and

870
00:51:07,840 --> 00:51:12,160
Speaker 2: that move from sort of training to inference, which is

871
00:51:12,200 --> 00:51:15,680
Speaker 2: sort of changing the priorities in the market at the moment,

872
00:51:16,800 --> 00:51:20,000
Speaker 2: and also their view of the AI landscape. It seems

873
00:51:20,000 --> 00:51:23,600
Speaker 2: as though they're expecting, maybe a bit belatedly, a wave

874
00:51:23,719 --> 00:51:26,000
Speaker 2: of AI startups to sort of emerriage. I mean, they

875
00:51:26,000 --> 00:51:28,719
Speaker 2: are emerging, but it hasn't been as pronounced as say

876
00:51:28,880 --> 00:51:31,440
Speaker 2: in the Australian market, so it's good to hear that

877
00:51:31,440 --> 00:51:34,560
Speaker 2: that's coming. But you know what they're doing. I think

878
00:51:34,600 --> 00:51:38,480
Speaker 2: with the couple of ventures that have got traction so far,

879
00:51:39,239 --> 00:51:41,000
Speaker 2: it sort of harkens back to what we were talking

880
00:51:41,040 --> 00:51:44,680
Speaker 2: to Rowan Simpson about last week, is not necessarily being

881
00:51:44,719 --> 00:51:47,759
Speaker 2: first but trying to be the best, you know, so

882
00:51:48,480 --> 00:51:54,040
Speaker 2: the startup they were talking about that basically makes it

883
00:51:54,080 --> 00:51:57,400
Speaker 2: easy to take the admin out of being a small business,

884
00:51:57,440 --> 00:51:59,200
Speaker 2: you know, for tradees and that sort of thing. Sure

885
00:51:59,200 --> 00:52:01,879
Speaker 2: we've got if I have done that others we seem

886
00:52:01,920 --> 00:52:04,239
Speaker 2: to have a timely to another one. I guess we

887
00:52:04,280 --> 00:52:08,200
Speaker 2: seem to have some expertise in this area in New Zealand,

888
00:52:08,200 --> 00:52:11,440
Speaker 2: but they're taking a slightly different approach to it, applying

889
00:52:11,520 --> 00:52:16,480
Speaker 2: AI to it where appropriate. That's not necessarily a revolutionary idea,

890
00:52:16,560 --> 00:52:19,799
Speaker 2: but it's all about executing that well absolutely.

891
00:52:19,880 --> 00:52:22,520
Speaker 1: And they also finished raising their first fund as well,

892
00:52:22,640 --> 00:52:25,400
Speaker 1: so as well as using internal money, they are raising

893
00:52:25,400 --> 00:52:28,680
Speaker 1: a fund that they can feed money into their own

894
00:52:28,760 --> 00:52:34,000
Speaker 1: studios as well. So that obviously shows some confidence in

895
00:52:34,040 --> 00:52:36,640
Speaker 1: the market in what Rift is doing.

896
00:52:37,200 --> 00:52:40,760
Speaker 2: I think they've put five million into the zero Fund

897
00:52:41,200 --> 00:52:42,920
Speaker 2: Zero Fund, and that seems to be quite a bit

898
00:52:43,000 --> 00:52:45,160
Speaker 2: for a venture studio. Although I was looking at New

899
00:52:45,200 --> 00:52:48,840
Speaker 2: and Improved Ventures, which really the same people behind that

900
00:52:49,120 --> 00:52:53,000
Speaker 2: were previously unavailable. They've sort of set up a new

901
00:52:53,360 --> 00:52:56,160
Speaker 2: venture studio I think with the likes of ice House Ventures.

902
00:52:56,200 --> 00:53:01,320
Speaker 2: They've raised six million dollars to back three for new firms.

903
00:53:01,440 --> 00:53:05,520
Speaker 2: That's what The Herald reported late last year. So there's

904
00:53:05,560 --> 00:53:09,960
Speaker 2: definitely some good seed money starting to flow into these

905
00:53:10,040 --> 00:53:12,880
Speaker 2: and the reason is, I think statistics I've seen is

906
00:53:12,920 --> 00:53:17,319
Speaker 2: that they're actually more successful. Your chance of getting to

907
00:53:17,440 --> 00:53:20,400
Speaker 2: the next stage is greater out of these venture studios,

908
00:53:20,400 --> 00:53:22,640
Speaker 2: which have actually been around since the mid nineties in

909
00:53:22,719 --> 00:53:26,399
Speaker 2: Silicon Valley. So they've nailed that sort of process, and

910
00:53:26,719 --> 00:53:29,480
Speaker 2: according to some sources that I've seen online as well,

911
00:53:29,520 --> 00:53:33,840
Speaker 2: that they allow you to get there quicker. So it's

912
00:53:33,920 --> 00:53:40,160
Speaker 2: more rapid way to go from ideation to execution, which

913
00:53:40,200 --> 00:53:43,840
Speaker 2: obviously that's what is all about. Has been quick to market.

914
00:53:44,400 --> 00:53:48,399
Speaker 1: Yeah, and they're not necessarily looking for unicorns as well, Like, yes,

915
00:53:48,480 --> 00:53:50,359
Speaker 1: there is some sense of it would be nice to

916
00:53:50,360 --> 00:53:53,760
Speaker 1: have one, of course, but at the end of the day,

917
00:53:53,840 --> 00:53:56,680
Speaker 1: a flow of one hundred to two hundred million dollar

918
00:53:56,719 --> 00:54:00,360
Speaker 1: companies that they can sell and then reinvat, or they

919
00:54:00,400 --> 00:54:02,120
Speaker 1: can split off and can go and have lives of

920
00:54:02,160 --> 00:54:06,200
Speaker 1: their own. That's also something that you actually do need.

921
00:54:06,200 --> 00:54:09,160
Speaker 1: We've talked about it before, that middle layer of companies

922
00:54:09,160 --> 00:54:13,680
Speaker 1: that are continuing to grow, be sold or change or

923
00:54:13,680 --> 00:54:16,879
Speaker 1: whatever else it is, and feeding talent and money back

924
00:54:16,920 --> 00:54:19,719
Speaker 1: into the market as well. It's not all about the unicorns.

925
00:54:19,880 --> 00:54:23,200
Speaker 2: Sounds good power to them.

926
00:54:22,520 --> 00:54:25,279
Speaker 1: So a big thanks to Lucas Coelo and Chris Moore

927
00:54:25,320 --> 00:54:28,120
Speaker 1: for coming on, and Ben Morrow as well for helping

928
00:54:28,160 --> 00:54:28,759
Speaker 1: set that up.

929
00:54:29,200 --> 00:54:31,960
Speaker 2: More on New Zealand's growing band of venture studios in

930
00:54:31,960 --> 00:54:34,920
Speaker 2: the show notes. Head to the podcast section at Business

931
00:54:34,920 --> 00:54:36,120
Speaker 2: Desk dot co dot d.

932
00:54:36,440 --> 00:54:40,000
Speaker 1: You can stream the podcast there or on iHeartRadio, and

933
00:54:40,120 --> 00:54:43,040
Speaker 1: of course it's available on your podcast platform of choice.

934
00:54:43,120 --> 00:54:45,600
Speaker 2: Get in touch with your feedback and topic suggestions. We're

935
00:54:45,640 --> 00:54:47,760
Speaker 2: on LinkedIn and blue Sky these days.

936
00:54:48,160 --> 00:54:50,719
Speaker 1: Catch us again for the next episode next Thursday.

937
00:54:51,080 --> 00:54:51,560
Speaker 2: See you then,