Speaker 1: This week on the Business of Tech powered by two
Speaker 1: Degrees Business the rise of the venture studio.
Speaker 2: Here we've counted at least a dozen New Zealand companies
Speaker 2: pursuing this relatively new model, or new for New Zealand anyway,
Speaker 2: This sort of model of creating and funding startups called
Speaker 2: the venture studio. Unlike venture capital firmed, venture studios play
Speaker 2: a much more hands on role in the entire life
Speaker 2: cycle off a startup, from ideation to execution.
Speaker 1: We talk to the co founders of Auckland based venture
Speaker 1: studio Rift about its approach to operating a venture studio,
Speaker 1: with a particular focus on artificial intelligence.
Speaker 3: So we have all the ingredients, all those assets that
Speaker 3: you need to bring together, and then it's about taking
Speaker 3: what can feel a bit boring, sometimes a formulaic approach,
Speaker 3: and yet a lot of the times you can identify
Speaker 3: great opportunities, assemble the team, assemble the financing and then
Speaker 3: go and tackle it.
Speaker 1: I'm Peter Griffin, I'm Ben More and a look at
Speaker 1: the venture studio model of building startups coming up with
Speaker 1: two of Rift's co founders, Lucas Coelo and Chris Moore.
Speaker 2: But first, then the first half results of interdex listed
Speaker 2: spark are announced Tomorrow, that's Friday, they'll release their financial update.
Speaker 2: Sparks had a pretty rough run in the last year
Speaker 2: or so, a sagging sheer price, major cost cutting going on,
Speaker 2: a number of senior people have gone from the organization,
Speaker 2: and a move to divers parts of the business. What
Speaker 2: are you expecting to see tomorrow?
Speaker 1: What I'm expecting is an update, first of all on
Speaker 1: the restructuring. So that's something they promised at their and
Speaker 1: your general meeting last year, was that they would release
Speaker 1: more details on the restructuring, including kind of how it's
Speaker 1: going so far, what they've managed to save. You may
Speaker 1: recall their goal was to say fifty million on labor costs,
Speaker 1: which is about ten percent of their total labor spend.
Speaker 1: It was recently came out that they were cutting their
Speaker 1: management staff as part of that restructure, and how they're
Speaker 1: changing the portfolios that different managers look after, so probably
Speaker 1: a reasonable chunk there as well. So that's what they
Speaker 1: have said is that there will be more information about
Speaker 1: the restructuring at this update, but they've been pretty reticent
Speaker 1: about giving too much detail anyway, so very curious to
Speaker 1: see exactly what they do release and what they don't
Speaker 1: about that situation. So that's kind of the main thing
Speaker 1: I think most people are on tender hooks about.
Speaker 2: Yeah, they sort of foreshadowed this last year. They reduced
Speaker 2: their ebit dye guidance somewhat not a huge hit, but
Speaker 2: that's down. They reduced their dividend guidance to twenty five
Speaker 2: cents per share from twenty seven point five cents, so
Speaker 2: they're basically softening up the market for lower expectations, and
Speaker 2: you know that's been reflected in the share price. I
Speaker 2: think it's down like thirty six percent of something over
Speaker 2: the last year, even including dividends, so it's it's been tough.
Speaker 2: And then there was all angst about them being delisted
Speaker 2: from that big sort of index Overseas, and I was
Speaker 2: expecting a big dip after that, but that was clearly
Speaker 2: sort of priced into it beforehand, so that didn't really
Speaker 2: take a massive hit. But still, you know, this is
Speaker 2: a company that still has a big market shriff to
Speaker 2: telecommunications industry, you know, forty something percent of mobile and
Speaker 2: fixed line broadband, so it is you know, still you know,
Speaker 2: the big player along with one end Z in that market,
Speaker 2: but sort of really facing a bit of an existential crisis.
Speaker 1: Absolutely, especially in like broadband and mobile as well. Two
Speaker 1: Degrees nipping at their heels along with some of the
Speaker 1: Mbenno operator that are starting to collectively take share away
Speaker 1: as they kind of use broadband as a lost leader
Speaker 1: to get people onto their other services. So yeah, that's
Speaker 1: an area where they're struggling. Interestingly, on their share price,
Speaker 1: analysts are kind of a little bit mixed at the moment,
Speaker 1: with some saying, you know, still not worth buying at
Speaker 1: this point. Others are saying, look, this is actually a
Speaker 1: depressed price for Spark. It's likely to go up in
Speaker 1: the future. Now might be a good time to buy.
Speaker 1: What to me that says is that there is a
Speaker 1: little bit of uncertainty about what's actually going to come
Speaker 1: for Spark, that there's obviously going to be a huge
Speaker 1: effort from Spark to reassess their position, try and capture
Speaker 1: back some of their core market and also try and
Speaker 1: capture new market with data centers. That's been a big discussion,
Speaker 1: so I expect we'll probably hear a bit more about
Speaker 1: their data center strategy as well, including some efforts to
Speaker 1: raise capital to co fund development of new data center.
Speaker 2: Yeah, it's been reviewing it's non core assets. So it
Speaker 2: still owns a bit of Connects, the mobile tower business,
Speaker 2: so it might divest the rest of that. And it's
Speaker 2: got quite a big IT division as well, and a
Speaker 2: few sort of units there, health, sort of tech unit, IoT,
Speaker 2: interneted things business, it's curious data business. So do you
Speaker 2: think there may be moves signal to carve off some
Speaker 2: of these units.
Speaker 1: Yeah, we know Connectsha, they're staking Connection, they're looking to sell,
Speaker 1: so that's a definite. They haven't really said yes or
Speaker 1: no on some of the other areas, but I know
Speaker 1: there is definitely some rumblings about what they're planning to
Speaker 1: actually divest. We might hear more about that at this update.
Speaker 1: They might not update on that, because sometimes what will
Speaker 1: happen is they will just get their earnings out of
Speaker 1: the way, update on some more of the directly related
Speaker 1: fiscal stuff, and then update later on some of the
Speaker 1: other plans around investment. That is, unless they've already made
Speaker 1: the deals and they're announcing that they have sold x,
Speaker 1: Y or z. That might happen at this earnings. I
Speaker 1: think what's really interesting is if you look at Deleeperfonseca's
Speaker 1: column from last year that was on Business Desk, and
Speaker 1: he talks about Spark trying to kind of figure out
Speaker 1: what it is now. Is it a tech company or
Speaker 1: is it an infrastructure company, because those are two quite
Speaker 1: different things, and how does it balance those in these
Speaker 1: times of difficulty, So well worth a background read if
Speaker 1: you're interested.
Speaker 2: Yeah, and it's interesting that a lot of the restructuring
Speaker 2: is in this sort of enterprise and government sections of
Speaker 2: the business. And on the face of it, it looks like
Speaker 2: Spark had been laying the groundwork for really smart, DIVERSEI
Speaker 2: vacation in recent years, with the likes of Curious, the
Speaker 2: sort of AI and data division. It's got its IT
Speaker 2: division as well, which is doing more sort of cloud
Speaker 2: and digital transformation stuff, so you'd think like and they
Speaker 2: published that report which we had Jolie Hodson on the
Speaker 2: podcast about last year looking at productivity, the productivity gap
Speaker 2: in New Zealand, the need to embrace advanced technology, so
Speaker 2: sort of doing that with a focus on IoT and
Speaker 2: health tech and the like. It just hasn't translated. And
Speaker 2: I don't know if that's a that they got the
Speaker 2: market wrong or if it's a just bad execution, but
Speaker 2: more than any other talco. They were sort of saying,
Speaker 2: we want to be about more than supplying speeds and
Speaker 2: feeds which have low margins. There's lots of competition, we
Speaker 2: want to do more, and they've sort of been not,
Speaker 2: you know, slapped back on that, which is sort of disappointing.
Speaker 2: I don't know if it's them or if it's us.
Speaker 1: Yeah, yeah, it's a big question, I mean, and that's
Speaker 1: that was one of the things that analysts have been
Speaker 1: talking about a lot, is like is this just the
Speaker 1: ups and downs of the macro environment or is this
Speaker 1: a fundamental shift in how things are operating.
Speaker 2: So check out Business Desk on Friday, there'll be full
Speaker 2: coverage off the Spark results. Just before we get into
Speaker 2: our featured interview, an update on last week's story about
Speaker 2: being AI Ben. You went in depth into this listed
Speaker 2: tech company and the travails it's been experiencing. Some really
Speaker 2: interesting discussion resulted from the podcast, including on LinkedIn what
Speaker 2: were people saying.
Speaker 1: So on your posting of that episode. The CEO of
Speaker 1: Callahan Innovation is Stephane Korn. He is an AI expert
Speaker 1: from way back, and he posted talking and specifically about
Speaker 1: their project Treehouse and this idea of APIs as a
Speaker 1: way for AI agents to enter operate, which I I thought, well,
Speaker 1: you know my perspective, I thought, it's a much more
Speaker 1: efficient and effective way for machines to talk to each
Speaker 1: other rather than open Ay's operator approach where it tries
Speaker 1: to navigate websites that are designed for humans. Now, Stephane
Speaker 1: Cortan had an interesting take on that where he basically said,
Speaker 1: one of the reasons that APIs don't really work is
Speaker 1: for competitive reasons, which was something I didn't really think about.
Speaker 1: So what was your take on some of the reasons
Speaker 1: that they might.
Speaker 2: Not Well, sitting behind the website, there's a database, and
Speaker 2: for a travel company, it could be all their dynamic
Speaker 2: pricing and all that sort of thing. So they've got
Speaker 2: to be really careful that they don't give away this
Speaker 2: sort of the secret source to their business model, so
Speaker 2: they hold that information very closely to them. I don't
Speaker 2: think it precludes them from issuing API access. For instance,
Speaker 2: an Uber would really like the idea of being integrated
Speaker 2: into a messaging app if you can pull in information
Speaker 2: from Uber saying there are drivers available, it's only going
Speaker 2: to be five minutes. You know that's possible via an API.
Speaker 2: So I think there's some level that they could do.
Speaker 2: But I think Stephann is right. The reason why we
Speaker 2: just haven't seen open ai really go big on its
Speaker 2: operator platform this is the AI agents it's now building
Speaker 2: with an API sort of ecosystem for that, is that
Speaker 2: a lot of companies are sitting back going do I
Speaker 2: want to give you that level of access? So open
Speaker 2: ai has just forged ahead and they're basically screenscraping all
Speaker 2: of that information off websites. It means they can move fast,
Speaker 2: and they're hoping that everyone will go, I need to
Speaker 2: have operator because it's so good, my customers want it.
Speaker 2: So let's do something a bit more substantial here with
Speaker 2: with APIs. So I think that will come, but there's
Speaker 2: definitely a competitive tension there in giving a lot more
Speaker 2: fundamental access to these websites.
Speaker 1: Yeah, and I think one of the concerns might be
Speaker 1: if you look at how deep Seak used distillation, for example,
Speaker 1: to train its model off open ais model, there's actually
Speaker 1: if you have AI running things really rapidly, and you
Speaker 1: could do a whole bunch of API calls to travel company.
Speaker 1: You could start to see how they're pricing actually works,
Speaker 1: and kind of use AI agents that might not be
Speaker 1: exactly what you might want to be accessing your API.
Speaker 1: So yeah, you know there is a possibility there all
Speaker 1: that could potentially be mitigated through careful you know, management
Speaker 1: of the agents and what they're able to do and
Speaker 1: things like that in the future. But that's why I
Speaker 1: kind of said in the last episode as well. It
Speaker 1: has potential, but there's a lot of things that need
Speaker 1: to fall into place in order to make it work.
Speaker 1: It's not impossible that the network effect might turn out
Speaker 1: to make this API marketplace somewhere that companies need to
Speaker 1: be in order to get their business out there into
Speaker 1: the world. But on the other hand, yeah, there is
Speaker 1: a lot of trains, a lot of changes that will
Speaker 1: need to occur in order for it to find that
Speaker 1: market for Yeah, so we've gone.
Speaker 2: From bigless incumbents like Spike to the earliest stage startups,
Speaker 2: and we're focusing in this episode Ben on the idea
Speaker 2: of a venture studio. I'd heard the concept before, really
Speaker 2: in the Silicon Valley context, but it's the first I
Speaker 2: really heard of a local venture studio. Was the Auckland
Speaker 2: Company previously unavailable. It had some really big success with
Speaker 2: the startup track Suit, which is like a brand tracking startup.
Speaker 2: It raised around twenty two million dollars from venture capital
Speaker 2: firms in early twenty twenty four in a series A
Speaker 2: fundraising round that valued it at over one hundred and
Speaker 2: fifty million dollars. But it started with a small amount
Speaker 2: of seed funding and the expertise of the people at
Speaker 2: previously unavailable.
Speaker 1: And then it turns out a number of local companies
Speaker 1: are now pursuing this model, no surprise after a success
Speaker 1: like that, among them a New and Improved Ventures, super
Speaker 1: Bowld Ministry of Awesome, Paloma Ventures, Edition Group, and Rift,
Speaker 1: who we are talking to on the podcast today.
Speaker 2: The three co founders are familiar with building products and
Speaker 2: have a little cash to play with after they sold
Speaker 2: a pretty successful IT consulting firm.
Speaker 1: That's right. So Chris Moore, Lucas Coelo and Ben morew
Speaker 1: All ran Rome Digital together, which sold for around forty
Speaker 1: five million in twenty twenty one, and after a bit
Speaker 1: of a break, they decided to put their expertise and
Speaker 1: money to use building and seeding companies of their own.
Speaker 2: And so Rift was born. Let's go to Ben's interview
Speaker 2: with Rifts Lucas Coelo and Chris Moore talking about the
Speaker 2: venture studio model, their company Rift and air approach to
Speaker 2: building AI products.
Speaker 1: Hello, and welcome to the Business of Tech. Thank you
Speaker 1: so much for joining us. We're here to talk about
Speaker 1: Rift AI. That's your guy project that you've been working
Speaker 1: on for a while. Now you're out here pushing out
Speaker 1: this studio venture studio model in New Zealand, which is
Speaker 1: a reasonably well known model now internationally and is starting
Speaker 1: to pick up pace in New Zealand as well. So
Speaker 1: why didn't you introduce yourselves to start with?
Speaker 4: Yeah? Cool, Lucas co founded a Rift Ventures before that,
Speaker 4: Ravens in about eight years ago, and before that it
Speaker 4: was heavily involved in the startup scene in Brazil, the
Speaker 4: startup in design and technology.
Speaker 3: Seeing Chris so I was a co founder in Rome Digital,
Speaker 3: which was a product design studio. Actually the missing third
Speaker 3: party here is Ben who's also part of Rift. So
Speaker 3: we've pulled together kind of the same team that we
Speaker 3: had previously. Then we exited that in twenty twenty one
Speaker 3: to a ten billion dollar US listed company, took a
Speaker 3: bit of time off and took a bit of the
Speaker 3: cash and then got the old team back together and
Speaker 3: kicked off rift of the inches go.
Speaker 1: And that story we talked about, we've talked about before.
Speaker 1: I've got a story up on business Desk that listeners
Speaker 1: can read if they want a bit more background. How
Speaker 1: does it work with a venture studio? Are you guys
Speaker 1: developing the products? Are you investing in the products? What
Speaker 1: does it actually mean?
Speaker 4: In summary, there are a couple of entities within sort
Speaker 4: of the studio model. We have the studio itself that
Speaker 4: produces the ideas. So we have a backlog of ideas.
Speaker 4: Were always fishing for ideas, even within our immediate sort
Speaker 4: of team. Our team now it's composed at about eleven people
Speaker 4: with doing engineering, design, growth, marketing, and we always sort
Speaker 4: of have a large backlog of ideas. Ideas are free
Speaker 4: right that we keep adding ideas in there for us
Speaker 4: to take a look at. And one thing that we
Speaker 4: have is kind of like sort of like a playbook
Speaker 4: on a few of the criterias of how we evaluate
Speaker 4: ideas in order to turn them into opportunities, move them
Speaker 4: up onto the pipeline. And that's when we start doing
Speaker 4: a little bit of a desk research, understanding more of
Speaker 4: the vertical that that idea plays in the industry, sort
Speaker 4: of the product can look like, and then we start prototyping,
Speaker 4: testing and see what sort of products or or companies
Speaker 4: Mike can come out of that. So, for example, last
Speaker 4: year our backlog of ideas had about one hundred or
Speaker 4: something ideas. It's ideas that from the time of Rome
Speaker 4: ideas that were sall online from other companies doing in overseas.
Speaker 4: We also did some vertical sort of days or weeks
Speaker 4: that we're going to speak with people within a certain vertical,
Speaker 4: for example health tech to understand sort of their problems
Speaker 4: and what things could we tackle, problems could we solve
Speaker 4: there with with sort of the heavy sort of II mindset.
Speaker 4: And then from quite a few of those we we
Speaker 4: did a first sort of like a hot or no
Speaker 4: quick rating on those ideas, thinking about what is the
Speaker 4: competition locally? Can we build this in New Zealand? Is
Speaker 4: there an entry way here that we can build this
Speaker 4: fast in order to expand with the product later on?
Speaker 4: The future sets are later on, especially because of a
Speaker 4: little limited resource of of inentro studio. Do you want
Speaker 4: to grab something that is too grandios or too expensive
Speaker 4: to bute that requires millions and millions of dollars to
Speaker 4: but so it needs to be something that we can
Speaker 4: uh that can we can buy a big chunk off
Speaker 4: with our with our limited capability at least at the beginning.
Speaker 4: And then from that we tested it about. We prototype
Speaker 4: like design and researched about ten of them a little
Speaker 4: bit deeper, and that resulted into a few of the
Speaker 4: ones that we have today. So after we take the
Speaker 4: products through the through the validation process, and that might
Speaker 4: be do we get customers, do we get some early
Speaker 4: custom there's interest, what is the the is it feasible
Speaker 4: to build? After we do that process, we take them.
Speaker 4: We also have a fund inside Rift, so Riff the
Speaker 4: studio and Rift the fund. So we have our first
Speaker 4: fund called Rift zero. Now where did the first close
Speaker 4: last year? And we take those opportunities into the fund
Speaker 4: evaluate with a pretty brecorous sort of criteria, evaluate like
Speaker 4: if someone pitching to us and if it passes or not.
Speaker 4: We also invest in all those opportunities as well. That's
Speaker 4: why we say that they graduated from the studio and
Speaker 4: we spin them off as portfolio companies instead of being
Speaker 4: just experiments. So it's it's both.
Speaker 1: Yeah, it's it's quite interesting because obviously the traditional startup
Speaker 1: method is you raise a fund and then you go
Speaker 1: and look for talent or startups to invest into. And
Speaker 1: my fund might have a specific focus on a vertical
Speaker 1: or a stage or whatever you like. So how is
Speaker 1: that discussion with kind of LPs to say, not only
Speaker 1: are you trusting us to do what's right with your money,
Speaker 1: you're trusting us to develop the tech that's going to
Speaker 1: bring you the return on investments. So you're starting, you're
Speaker 1: pitching startups, and you're pitching a fund at the same time.
Speaker 1: How does that conversation go.
Speaker 3: Yeah, I mean it's a more complex conversation than a
Speaker 3: standard VC. You know, when a VC is raising funds,
Speaker 3: they just say, look, we're going to invest in these areas.
Speaker 3: We don't know what the companies will be, we believe
Speaker 3: that we'll be able to find them as a studio
Speaker 3: and as a fund. Rift zero also has an investment thesis.
Speaker 3: It has four different areas that it's looking at. We
Speaker 3: are mainly an AI driven fund, looking at how AI
Speaker 3: is used in the companies as well as the products themselves.
Speaker 3: So looking at how we can stretch the But those
Speaker 3: four areas are across productivity, security, compliance, health and finance
Speaker 3: as for getting people to sort of understand like does
Speaker 3: Rift have that capability and skill. You know, we spent
Speaker 3: the last ten years building companies and products for a
Speaker 3: lot of startups and corporates. So we built companies for
Speaker 3: X fifteen, which is CBA's accelerator program. We built them
Speaker 3: for Westpac Ventures, we built them for ASB, B and Z,
Speaker 3: the Warehouse Group, Mercury Energy, sky City. You know, we've
Speaker 3: been doing this type of product building for ten years
Speaker 3: as a group and before that. We also have that
Speaker 3: personal experience of building companies in Silicon Valley and in
Speaker 3: the UK, in South America. You know, for us, the
Speaker 3: building and the technology and all those parts aren't really
Speaker 3: the risk. Adventure Studio is there to de risk things
Speaker 3: around repeatable process which we have from time at Rome
Speaker 3: skilled entrepreneurs that we have within the studio as well
Speaker 3: as within our wider network. You know, we would have
Speaker 3: had about two hundred and fifty employees come through Rome
Speaker 3: at the time when we sold it, we had one
Speaker 3: hundred and fifty, So it's a large network across Australia
Speaker 3: and New Zealand, Singapore as well as reach into the US.
Speaker 3: So we have all the ingredients, all those assets that
Speaker 3: you need to bring together, and then it's about taking
Speaker 3: what can feel a bit boring, sometimes a formulaic approach,
Speaker 3: and yet a lot of the times you can identify
Speaker 3: great opportunities, assemble the team, assemble the financing, and then
Speaker 3: go and tackle it. It's actually more similar to how
Speaker 3: Silicon Valley startups are funded. Someone sees an idea or
Speaker 3: an opportunity, they assemble the team, they assemble the capital,
Speaker 3: and then they go and target and tackle the market.
Speaker 3: In the past have seen a lot of New Zealand startups.
Speaker 3: It's kind of they've been in the market for a while,
Speaker 3: scraping along a little bit of friends and family funding,
Speaker 3: and they just happen to hit an inflection point where
Speaker 3: the market needs the product they have. This style is
Speaker 3: quite different, I mean.
Speaker 1: And that's one of the questions as well that I
Speaker 1: was interested in, which is, you know, you have the capability,
Speaker 1: and you have the processes, and you have the experience
Speaker 1: and the talent, but the X factor, the key thing
Speaker 1: is finding the right place to apply those and that's
Speaker 1: something that's talked about significantly in New Zealand. We have
Speaker 1: limited resources in this country, we have limited capital, and
Speaker 1: so we need to be quite specific about how we
Speaker 1: are targeting our solutions. So how do you think through that?
Speaker 4: Yeah, I think it's the what I was saying before.
Speaker 4: Like when we're looking at the vertical just for example,
Speaker 4: a product that we have like chain, we start thinking
Speaker 4: about the first idea, remember that was there on the
Speaker 4: backlog was AI power job management software tool. There are
Speaker 4: a few of them, a few of them are very successful.
Speaker 4: Every once in a while they get acquired by larger
Speaker 4: companies that which means that sometimes the customer service is
Speaker 4: not what it used to be. And therefore, especially on
Speaker 4: the SMBs, they start looking for all right, so the
Speaker 4: prices went up. What else can I start using it?
Speaker 4: It might be interesting to have something that, especially with
Speaker 4: vertical AI businesses, you're always trying to look into, let's
Speaker 4: look at automating things that they're not properly automated on
Speaker 4: sort of the traditional job management tools, and let's start
Speaker 4: let's focus on the product on that. But then while
Speaker 4: we were taking that idea through the validation process and
Speaker 4: we had a prototype of the tool, we had a
Speaker 4: couple of sort of design screens and starts check with
Speaker 4: so traders, with builders, with treadees, we're always trying to
Speaker 4: think about an angle, that is, what is the what
Speaker 4: is the line of this experience that will deliver them
Speaker 4: instance of r O I that will solve an immediate
Speaker 4: problem or or something that they want out to make today.
Speaker 4: What can we build that will be fast to build,
Speaker 4: that we can solve that problem. They will they will
Speaker 4: pay for us to solve that problem, and then we
Speaker 4: can build the rest of the capability afterwards, after the
Speaker 4: proof that that piece of that piece of the of
Speaker 4: the of the solution. And that's when, for example, we
Speaker 4: start testing the in this case, the I think we
Speaker 4: call a builder boost the the job management software lending page.
Speaker 4: We have a couple of prototype screens, and after every
Speaker 4: interview they will say, yeah, it's really cool, it looks
Speaker 4: really cool, but not what my problem is? I hate
Speaker 4: doing this, I doing the admin work. I hate all
Speaker 4: of the tools that already exists. Yeah, this looks nice,
Speaker 4: but I just want to I just can't pick up
Speaker 4: my phone while am I work and I lose jobs
Speaker 4: because of it. Can you solve someone? Do you know
Speaker 4: anyone that can do that and then it qualify the
Speaker 4: leads for us. And that's when Jane came about.
Speaker 2: Right.
Speaker 4: They were like, yeah, that's something that we can solve,
Speaker 4: and we can solve reasonably fast.
Speaker 1: And Jane, is your l M powered super intelligent the
Speaker 1: voicemail that can understand the context of what's being spoken
Speaker 1: verbally through the phone.
Speaker 4: Yeah. Yeah, we're looking at now at the vision of
Speaker 4: being this working towards being on the vision being this
Speaker 4: Oh encompassy our encompassing sort of inbound lead qualification for
Speaker 4: Assouli traders. Right, so not only qualifying leads that come
Speaker 4: through the phone, it also can schedule sort of appointments
Speaker 4: through the phone. It's on your Google calendar and in
Speaker 4: your tool of choice and also integrating with WhatsApp. Business
Speaker 4: noticed that it's a lot of small businesses that their
Speaker 4: value proposition is helping some traders set up their sas
Speaker 4: tools and we're like, okay, so this is a this
Speaker 4: is really a gap in the market, Like people are
Speaker 4: trying to solve this by being a consultancy to to
Speaker 4: this is how that's how hard it is and how
Speaker 4: uh that that it shows the friction that exists with
Speaker 4: the things that exist today and how without without even
Speaker 4: everyone that we spoke with they're like, yeah, we don't.
Speaker 4: We want to spend less time doing that so I
Speaker 4: can spend more time just doing the work getting paid.
Speaker 1: Interesting. Yeah, you're a New Zealand based business, you have
Speaker 1: international ties, so I'm assuming and the question isn't will
Speaker 1: New Zealand does pay for this, but it's will people
Speaker 1: somewhere pay for this? And where is that? So that
Speaker 1: must be another consideration as well.
Speaker 3: Yeah, you know, New Zealand market is far too small,
Speaker 3: so if anything would have to be a minimum of
Speaker 3: a regional market Australia and New Zealand, say Asia, so
Speaker 3: apax but predominantly of course we're targeting the US, but
Speaker 3: you have to start somewhere where it's easy to get
Speaker 3: some initial feedback. But you don't want to stay with
Speaker 3: that story of New Zealand being the trial market and
Speaker 3: just two years later it's still your trial market. It's
Speaker 3: really just used during the validation phase. Then once the
Speaker 3: portfolio company is really to be launched, you're straight into
Speaker 3: the US.
Speaker 4: Yeah. I find the New Zealand market, especially coming from overseas,
Speaker 4: a very interesting market for tech in comparison to what
Speaker 4: I'm used to in the US or in Brazil. I
Speaker 4: think in the US, of course, depending on a little
Speaker 4: bit of the state, but same as in Brazil. But
Speaker 4: people tend to swarm more into new things, and in
Speaker 4: New Zealand, I feel it's a little bit the majority
Speaker 4: of the customers they wait someone to test it first
Speaker 4: before they jump into it. So I tend to think
Speaker 4: that a few of these things, especially on the products,
Speaker 4: if you can sell it here to New Zealanders and
Speaker 4: can convince a convince a very traditional sort of trade
Speaker 4: to adopt that into its workflow, that's a good signal
Speaker 4: that you can probably get some more adoption overseas.
Speaker 1: That's really interesting. Yeah, and I guess because you aren't
Speaker 1: thinking of New Zealand as your primary market. You know,
Speaker 1: I've heard stories before of startups who have had great
Speaker 1: ideas that could have great ROI for trade's but they
Speaker 1: just weren't able to convince the trades to actually adopt
Speaker 1: it at a scale that was able to provide some
Speaker 1: assurance to investors before unfortunately the companies collapsed. Not because
Speaker 1: you guys have the venture model, because you aren't seeing
Speaker 1: New Zealand as your prime market, but as a kind
Speaker 1: of like trial market. That allows you to then go, well,
Speaker 1: look now we have the set, we can throw it
Speaker 1: somewhere that's a bit more swarmy and see and see
Speaker 1: what happens there.
Speaker 4: Yeah.
Speaker 3: The venture studio model also allows you to play with
Speaker 3: the different leavers of engineering marketing spend across all of
Speaker 3: your portfolio companies. So if you have a traditional startup,
Speaker 3: you hire an engineering team or you've got some co
Speaker 3: founders as engineers, and that's great as you get a
Speaker 3: bit of scale. Then every month you're paying for the
Speaker 3: engineering team and you just got to keep feeding them
Speaker 3: with work to be done. Now, some of the time
Speaker 3: it's actually you don't need more features. What you need
Speaker 3: to do is be selling what you have and get
Speaker 3: the feedback from market. I mean, I think you know,
Speaker 3: there's there's a lot of stats on I think it's
Speaker 3: like forty percent of the features you build aren't even
Speaker 3: the ones that customers adopt. And I've seen it myself
Speaker 3: now with a venture studio, because you have a portfolio
Speaker 3: of these, you can pull back some of the engineering
Speaker 3: talent from one of the portfolio companies should into the
Speaker 3: second one. While you're looking for the right market signal
Speaker 3: waiting for market adoption. AI is one that you know,
Speaker 3: in some cases is fast and other cases is slow.
Speaker 3: It's so trying to get the timing right is I
Speaker 3: believe easier with the venture studio where you can move
Speaker 3: the burn around across the portfolio companies.
Speaker 1: This is something I wanted to talk to you guys
Speaker 1: about as well, because we have in the I guess
Speaker 1: really end of last year beginning of this year, we've
Speaker 1: started to see this kind of counter rhetoric towards AI
Speaker 1: and generative AI. And you know that this this idea
Speaker 1: that open AI and it's ken who have been funneling
Speaker 1: billions and billions of dollars into the development of the
Speaker 1: technology are ultimately going to fail to see any actual
Speaker 1: return on that investment just because of the scale of it.
Speaker 1: And so you know, with the recent release of deep
Speaker 1: Seek as well, that has simplified and made those models
Speaker 1: maybe a little bit more affordable to actually run, that
Speaker 1: kind of proves that there's going to be a difficulty
Speaker 1: in getting a return on the investment in the long
Speaker 1: run because they can't justify the expense when there's going
Speaker 1: to be cheaper alternatives. What's your thoughts around that, and
Speaker 1: how for companies like yourselves that are building on these
Speaker 1: AI platforms that may represent a risk around the continuation
Speaker 1: of the technology into the future.
Speaker 3: Yeah, if I sort of think back to two thousand
Speaker 3: when you had you know, the Internet bubble and boom,
Speaker 3: and you had this huge buildout of infrastructure and companies
Speaker 3: like you know, Cisco, Juniper Networks, etc. We're riding high,
Speaker 3: huge investments to get people more bandwidth. And back then
Speaker 3: you had a ADSL link you know, one point five
Speaker 3: megabit or something, and everyone was like, yeah, that's that's amazing.
Speaker 3: I can now watch some standard DEF video with twenty
Speaker 3: frames per second. What would I need more for? And
Speaker 3: you know, you're fast forward now and you've got five
Speaker 3: G networks where you can do video streaming, you know,
Speaker 3: on the go in your car, you've got five to
Speaker 3: your house with one hundred to three hundred or eight
Speaker 3: hundred megabit down Like, people will find a way to
Speaker 3: consume bandwidth. It's going to be the same with AI compute.
Speaker 3: Like you know, deep seat coming out as great as
Speaker 3: open source, it's already been ripped apart in bits of
Speaker 3: being put into existing models, ignoring the supposed cost because
Speaker 3: there's a lot of issues around. Well, actually they did
Speaker 3: have access to fifty thousand in video GPUs and they
Speaker 3: did spend more money than the kind of the sticker price.
Speaker 3: You know, that's that's great. You need to bring the
Speaker 3: costs down. We're already seeing that with open Ai. Like
Speaker 3: they're the three mini models like ninety three percent cheaper
Speaker 3: than their at last one. It's faster, less latency, which
Speaker 3: means you can do more of these real time voice,
Speaker 3: real time communications, real time robotics. It's does really feel
Speaker 3: like we're at the beginning of this expansion. We do
Speaker 3: see it with some of the AI startups, you know,
Speaker 3: we you know, we we review a lot to see
Speaker 3: where they are, what's the state of the market, you know,
Speaker 3: what's real, what isn't And a lot of times you'll
Speaker 3: click through and it will be coming soon, you know,
Speaker 3: sign up for a better access. But we also know
Speaker 3: because we're using it and building products that are in
Speaker 3: market that customers are using, and it's today solving a
Speaker 3: problem that it does work for you know a fair
Speaker 3: number of use cases that are still there to be
Speaker 3: to be tackled. And yeah, it's the worst it's going
Speaker 3: to be at the moment and it just keeps getting better.
Speaker 3: Runway Sourer for image generation mid you know, for video
Speaker 3: generation mid journey, like the disruption is massive.
Speaker 1: Mm.
Speaker 4: I still think there's a lot of case that the
Speaker 4: use cases that we at you they're the low hanging
Speaker 4: through use cases for l lambs that people are finding
Speaker 4: out and then now we're figuring out that, oh wait
Speaker 4: a second, we might not need other lambs for everything.
Speaker 4: We can use a couple of the smaller models there
Speaker 4: will be much more useful and choose some specific verticals
Speaker 4: or for some specific sort of tasks or even a
Speaker 4: sort of agenta task and that's much eeaper to run.
Speaker 4: So mind you start using that and also thinking AI
Speaker 4: researcher open ai pulls it on on Twitter x formerly
Speaker 4: Twitter about that more money now is being spent on
Speaker 4: inference then on pre training, So more money even spending
Speaker 4: on what can this do and can we learn things
Speaker 4: that there's no data for it before, which will become
Speaker 4: more useful. So even the CAPEC spend is going silver structure, yeah,
Speaker 4: and the infrastructure is going a lot. They're spending a
Speaker 4: lot of money on building the infrastructure for that. I
Speaker 4: do think that if the market is expecting an immediate
Speaker 4: return for this investment. That might be especially for this
Speaker 4: companies they are spending a lot.
Speaker 1: You mentioned kind of in video, and you mentioned Cisco,
Speaker 1: and one of the observations was with Cisco. Obviously in
Speaker 1: the early two thousands they had that massive wipeout of
Speaker 1: a lot of their value and there was a similar blip,
Speaker 1: but in video probably was it was not obviously of
Speaker 1: the same scale. But the argument was that these companies
Speaker 1: that are being continually inflated, the value is being inflated
Speaker 1: by the the the pouring of billion dollars into R
Speaker 1: and D by these companies, that we will start to
Speaker 1: see a similar popping off that value bubble, not necessarily
Speaker 1: around the quality of the tech, but the value bubble
Speaker 1: in itself. Does that concern you guys as having both
Speaker 1: the products and the fund.
Speaker 3: I think on the end Video one, what's quite interesting
Speaker 3: is you know that they've managed to ride three waves.
Speaker 3: They've managed to ride the GPU wave, when you know
Speaker 3: graphics move from being calculated on CPU offload. Then they've
Speaker 3: managed to ride the cryptomning wave where it moved from
Speaker 3: CPU to GPU and then GPU to ASK. And now
Speaker 3: they've managed to ride this third wave with AI, you know, phenomenal,
Speaker 3: But there's spent a lot of time making sure that
Speaker 3: there's software. It's it's not just the chip, it's the
Speaker 3: software that you use around it. Kuda, which is the
Speaker 3: language they use for programming those GPUs. But you are
Speaker 3: seeing you know, Amazon have their own processor. I think
Speaker 3: it's called Trainium. You know, like Google have got their
Speaker 3: tensor processor unit, the TPU. People are coming for that crown.
Speaker 3: Of course, it's so rich. The good news is, you know, yes,
Speaker 3: Cisco wore that bubble and that bubble burst, but when
Speaker 3: you look at the major players, you know, Google and
Speaker 3: Facebook and all the social media Instagram, they all came
Speaker 3: out after that collapse, after the infrastructure burst. Also, there
Speaker 3: was a lot of costs that the US government was
Speaker 3: selling Spectrum at the time to the telpots, so that
Speaker 3: sucked a lot of their cap becks up as well. So, yeah,
Speaker 3: if we were in the chip space, if we're in
Speaker 3: the model space, I think that's going to be cut throat.
Speaker 3: You're going to end up with open source, and you're
Speaker 3: going to end up with Microsoft, which is open AI.
Speaker 3: You're going to end up with Amazon, which is anthropic,
Speaker 3: and you're going to end up with Google, with with
Speaker 3: Gemini and you know, and you know under open source
Speaker 3: of course you got Lama with Meta and all these others.
Speaker 3: Like I wouldn't want to be in that space, application space,
Speaker 3: you know, being able to look at what tasks administration
Speaker 3: work you can replace. Yeah, that's that's the place to play.
Speaker 1: Yeah, because you're not concerned about being caught in the
Speaker 1: inner potential bubble burth.
Speaker 3: If your companies are making money and we're not building
Speaker 3: companies that are growth at all costs its balance of
Speaker 3: growth and profitability as a venture studio, that's okay.
Speaker 4: Yeah, because we're we're consuming more the commodity that they
Speaker 4: provide in terms of the models than being caught up
Speaker 4: on developing the models ourselves. And depending on who wins
Speaker 4: this race, it's an open source with the excellent models
Speaker 4: that that exist, and we're building with a with a
Speaker 4: paradigm that is, we're building our capabilities in our future
Speaker 4: is based on on the available sort of capabilities of
Speaker 4: the of this commodity that exists and where it might
Speaker 4: be in a fewure based on the trend of development
Speaker 4: that this exists. But we can consume from any player. Yeah,
Speaker 4: that it's out there, so we can consume the open
Speaker 4: Eye one the Anthropic one, because they're probably going to
Speaker 4: be very converged. They are converge to be very similar,
Speaker 4: at least that it looks today. I'm not sure tomorrow,
Speaker 4: Open a I might release something that we look and say,
Speaker 4: oh my god, this is ten years ahead of everyone else,
Speaker 4: as it was with the Chat GPT, although some might
Speaker 4: argue that other companies had what the first GPT was,
Speaker 4: they just didn't want to release to the public, so
Speaker 4: Opening I was not that ahead as others might think.
Speaker 4: But they seem to be very ethnical.
Speaker 3: Nick and Nick.
Speaker 4: It just continuously and there are some that are For example,
Speaker 4: even in a few other products, we don't use one
Speaker 4: model we use. We might use multiple models they're better,
Speaker 4: or smaller models they are better in each task. There's
Speaker 4: Claude is excellent for coding for for some reason, and
Speaker 4: I'm not sure what they added into the the find
Speaker 4: the tuning or on the model, but it's I find
Speaker 4: it much better for coding than GPT. So you usually
Speaker 4: want I'm use in cursor, I want to look into
Speaker 4: coding or use Claude instead of using Chat GIPT. I
Speaker 4: think it's just better, especially with Python. I might see
Speaker 4: that other companies that are consuming a few of these
Speaker 4: models my pick and choice and do the same because
Speaker 4: I think the secret is still on. The secret selves
Speaker 4: of companies still be the services they provide. They are
Speaker 4: solving a problem. How is the experienced layer, how is
Speaker 4: that sort of application layer more than the commodity itself?
Speaker 4: Chatting with someone that is like, as long as there's
Speaker 4: coffee to be bought, I want to keep building Starbucks
Speaker 4: shops or nice coffee shops. I don't want to build
Speaker 4: a farm to start producing coffee because that would that
Speaker 4: would probably be a RaSE to zero or very very soon,
Speaker 4: if not that already started. And as you're saying, with
Speaker 4: a deep seek, wiping out quite a few, quite a
Speaker 4: bit of the value of these companies.
Speaker 1: Interesting, I do enjoy that you said Starbucks or nice
Speaker 1: coffee shops. I think that's that's very to be honest.
Speaker 4: Yeah, I don't mind star Yeah.
Speaker 1: That's great. I mean, I know we're probably over is that,
Speaker 1: But I just one more question I want to ask
Speaker 1: around that idea as well. Was there's also a commentary
Speaker 1: that we might be hitting a ceiling in terms of
Speaker 1: capability around L l ms, talking in particular around hallucinations.
Speaker 1: You know, it's a feature, it's not something we can
Speaker 1: get rid of talking about the cognitive in quotes capabilities
Speaker 1: of these lms that the rate of increase is now
Speaker 1: so minimal that actually it looks like we might have
Speaker 1: hit the ceiling on that, and so building with the
Speaker 1: idea that there's going to be something better in the
Speaker 1: future may be a mistake, if that makes sense. What's
Speaker 1: your take on that perspective.
Speaker 3: I guess one thing is the companies we're building at
Speaker 3: the moment take advantage of the current state of the art.
Speaker 3: We're very cautious about building for the crossover point of
Speaker 3: some capability that might come in eighteen twenty four months.
Speaker 3: So I think, yeah, if you're building companies, you need
Speaker 3: to make sure you're building with what can be achieved today.
Speaker 3: That's why we see a lot that are hidden behind
Speaker 3: sign ups and join the wait lists. They were also
Speaker 3: waiting for a tech shift. So that's pretty critical. And
Speaker 3: it's not to say that the evolution of the product
Speaker 3: cannot look to future capabilities, but your sellable kind of
Speaker 3: wedge in today should be able, well not should, it
Speaker 3: has to actually work. That's one of the things as
Speaker 3: far as have we hit the limits, I think what's
Speaker 3: interesting is, you know you have something like deep Seat
Speaker 3: come out and it's got a whole bunch of these
Speaker 3: kind of clever optimizations you know in ways of doing
Speaker 3: things like people are going to continually find these. So
Speaker 3: if you can use it to train your model at
Speaker 3: a tenth the price, well, now you can train a
Speaker 3: model that's ten times as large. Can you find more data?
Speaker 3: Like I said, that's one of the big sources. So
Speaker 3: then you start looking at synthetic data careful of hallucinations.
Speaker 3: There's a lot of smart people, there's a lot of money.
Speaker 3: I just don't see us hitting a wall in the
Speaker 3: next next few years. The same as bandwidth. Again it's
Speaker 3: a weird analogy, but remember thinking, you know, one point
Speaker 3: five megabit was so fast because you had a twenty
Speaker 3: eight K modem before that, and you know you used
Speaker 3: to get like a say in New Zealand, a T
Speaker 3: one or it's the US T one or an E
Speaker 3: one line two megabit, and it would cost you ten
Speaker 3: thousand dollars a month to connect Auckland to Wellington. It
Speaker 3: take thirty five calls, and that just seems so quaint,
Speaker 3: but at the time it would have been well, that's
Speaker 3: the fastest that we can get it across the network,
Speaker 3: and all of these other reasons where there's demand, you know,
Speaker 3: money and innovation follows. I don't think we've been hitting
Speaker 3: limits soon.
Speaker 4: And then's the bit about we're talked about building with
Speaker 4: the future capability in mind, with CRUs Is saying as well,
Speaker 4: is that there's a lot of the not a lot,
Speaker 4: but they are parts of the workflows of a few
Speaker 4: of the products that we're working with that they're just
Speaker 4: ultimately like that llms are not capable of doing right now.
Speaker 4: And then you what it do is that you do
Speaker 4: that part of workflow with your standard automation sort of
Speaker 4: standard code there. But it generates is that it starts
Speaker 4: when people start using it generates a very specific sort
Speaker 4: of the amount of data that you can use a
Speaker 4: small model to be very good at that very specific
Speaker 4: sort of workflow, which is something that starts building different
Speaker 4: right It'll be very hard to build differentiation if you're
Speaker 4: just wrapping your company around whatever is the large the
Speaker 4: data set the lms are trained on, because then anyone
Speaker 4: that just pluts into the data set will be able
Speaker 4: to to roughly perform the same task. But when you
Speaker 4: viewed something that solves a very specific sort of touch
Speaker 4: point and you start getting your model into it and
Speaker 4: then using that data to train your capability, I think
Speaker 4: then you start creating differentiation and something that it's more defensible,
Speaker 4: especially in the in a world where anyone can go
Speaker 4: to cursor or go to what's the zero from A
Speaker 4: and build a SaaS company in a in a week
Speaker 4: or so, it would be very interesting to see how
Speaker 4: it goes.
Speaker 1: It will be it will be very interesting.
Speaker 4: Yeah, very interesting. In the same moment that said that
Speaker 4: the first billion dollar company of one person should should
Speaker 4: should arrive very soon.
Speaker 1: Some helpments ad a lot of things.
Speaker 2: We'll put a couple of articles in the show notes,
Speaker 2: some really interesting articles, including from New Zealand companies sort
Speaker 2: of explaining the pros, mainly the pros of the venture
Speaker 2: studio model. You know, in my mind, really I think
Speaker 2: the big advantage here and I think tracks who really
Speaker 2: illustrates this. You have, you know, an advertising guru like
Speaker 2: James Herman previously unavailable, Simon Pound, people like that who
Speaker 2: really understand marketing, advertising branding. They come across some entrepreneurs
Speaker 2: who want to set up something in that space, and
Speaker 2: they have deep integral knowledge of that and and what
Speaker 2: works and what doesn't. So you invite them in, maybe
Speaker 2: give them a little bit of seed funding, but mainly
Speaker 2: take equity and return for nurturing this company, giving it
Speaker 2: your expertise. That seems to be the venture studio model,
Speaker 2: and it then allows a company to get to a
Speaker 2: point where it's really attractive to venture capital companies. And
Speaker 2: I think that's a really good model for New Zealand,
Speaker 2: where we do have these areas of deep expertise, but
Speaker 2: we don't necessarily have a lot of seed funding. So
Speaker 2: you're seeing these people who maybe have created successful companies
Speaker 2: exited them, have a little bit of capital and are
Speaker 2: willing to back these teams and do numerous teams at once,
Speaker 2: so not just put everything into one company. You have
Speaker 2: half a dozen of these companies under your roof, maybe
Speaker 2: even hot desking or sharing space, looking over the shoulder,
Speaker 2: helping them, mentor them, introduce them to your networks and
Speaker 2: business context. It seems to be quite a good model
Speaker 2: for New Zealand.
Speaker 4: Yeah.
Speaker 1: Absolutely, And also it does help to slightly address the
Speaker 1: talent issue as well. Because if you have it, like
Speaker 1: they say, they have a group of engineers within Rift
Speaker 1: that they can then choose how to where to kind
Speaker 1: of direct their talent. So two different different companies say
Speaker 1: they've got one thing over here, and they're like, oh,
Speaker 1: we really need a new feature on that asap. Let's
Speaker 1: move sixty percent of our engineering over to that. For
Speaker 1: the meantime, you don't have to suddenly scale up and
Speaker 1: get a bunch of contractors and figure, you know, get
Speaker 1: them to get on board with the systems and learn
Speaker 1: new things. It's just this kind of this core at
Speaker 1: rift that can do things as they need them to do.
Speaker 1: So another really great way of addressing some of New
Speaker 1: Zealand's shortfalls.
Speaker 2: Yeah, I guess looking at the potential cons of this model.
Speaker 2: So clearly, you know, the people who are running these
Speaker 2: venture studios are spreading their risk but spreading their attention
Speaker 2: across a number of ventures. So you know, you may
Speaker 2: be diluting the attention that you can pay to a
Speaker 2: particular company. And I've seen a couple of podcasts where
Speaker 2: people who have been through venture studios and sort of say, well,
Speaker 2: it's you know, sort of feel like I'm competing with
Speaker 2: other companies in this venture studio for the attention of
Speaker 2: the people who are funding this and supporting us, So
Speaker 2: I guess that's the downside. But having said that, we're
Speaker 2: pretty collegial in New Zealand, so you're more likely to
Speaker 2: actually be able to leverage off what other companies sort
Speaker 2: of in the venture studio stable are actually doing, to
Speaker 2: try and learn from them and maybe even partner on
Speaker 2: business ventures.
Speaker 1: Yeah, exactly. And what I think is interesting about ref
Speaker 1: as well as that particular focus on artificial intelligence because
Speaker 1: it is such an evolving sector and it kind of
Speaker 1: does link into that idea of talent because the leadership
Speaker 1: team they spend a lot of time I'm looking at
Speaker 1: different research papers about AI, about generative AI and its applications,
Speaker 1: and then they can disseminate that information to their ideas
Speaker 1: for startups, rather than you know, having to kind of
Speaker 1: each individual company follow along as best they can and
Speaker 1: split that time. So it's another benefit as well. But
Speaker 1: it's also interesting they're focused on AI, especially in this
Speaker 1: kind of very AI heavy new world that we're in
Speaker 1: the conversation about whether it's a bubble. It seems to
Speaker 1: me from a conversation with them with the founders that
Speaker 1: they are quite focused in their approach. They're wanting to
Speaker 1: kind of really really focused on what products are actually
Speaker 1: finding product market fit and value and go from there,
Speaker 1: rather than trying to kind of have a product and
Speaker 1: get a bunch of hype around it and pump a
Speaker 1: bunch of VC in it and then see where you
Speaker 1: go from there. Maybe that's a little I don't know.
Speaker 2: Yeah, they're clearly using AI and it was really an
Speaker 2: interesting that discussion about their reflections on Deep Seek and
Speaker 2: that move from sort of training to inference, which is
Speaker 2: sort of changing the priorities in the market at the moment,
Speaker 2: and also their view of the AI landscape. It seems
Speaker 2: as though they're expecting, maybe a bit belatedly, a wave
Speaker 2: of AI startups to sort of emerriage. I mean, they
Speaker 2: are emerging, but it hasn't been as pronounced as say
Speaker 2: in the Australian market, so it's good to hear that
Speaker 2: that's coming. But you know what they're doing. I think
Speaker 2: with the couple of ventures that have got traction so far,
Speaker 2: it sort of harkens back to what we were talking
Speaker 2: to Rowan Simpson about last week, is not necessarily being
Speaker 2: first but trying to be the best, you know, so
Speaker 2: the startup they were talking about that basically makes it
Speaker 2: easy to take the admin out of being a small business,
Speaker 2: you know, for tradees and that sort of thing. Sure
Speaker 2: we've got if I have done that others we seem
Speaker 2: to have a timely to another one. I guess we
Speaker 2: seem to have some expertise in this area in New Zealand,
Speaker 2: but they're taking a slightly different approach to it, applying
Speaker 2: AI to it where appropriate. That's not necessarily a revolutionary idea,
Speaker 2: but it's all about executing that well absolutely.
Speaker 1: And they also finished raising their first fund as well,
Speaker 1: so as well as using internal money, they are raising
Speaker 1: a fund that they can feed money into their own
Speaker 1: studios as well. So that obviously shows some confidence in
Speaker 1: the market in what Rift is doing.
Speaker 2: I think they've put five million into the zero Fund
Speaker 2: Zero Fund, and that seems to be quite a bit
Speaker 2: for a venture studio. Although I was looking at New
Speaker 2: and Improved Ventures, which really the same people behind that
Speaker 2: were previously unavailable. They've sort of set up a new
Speaker 2: venture studio I think with the likes of ice House Ventures.
Speaker 2: They've raised six million dollars to back three for new firms.
Speaker 2: That's what The Herald reported late last year. So there's
Speaker 2: definitely some good seed money starting to flow into these
Speaker 2: and the reason is, I think statistics I've seen is
Speaker 2: that they're actually more successful. Your chance of getting to
Speaker 2: the next stage is greater out of these venture studios,
Speaker 2: which have actually been around since the mid nineties in
Speaker 2: Silicon Valley. So they've nailed that sort of process, and
Speaker 2: according to some sources that I've seen online as well,
Speaker 2: that they allow you to get there quicker. So it's
Speaker 2: more rapid way to go from ideation to execution, which
Speaker 2: obviously that's what is all about. Has been quick to market.
Speaker 1: Yeah, and they're not necessarily looking for unicorns as well, Like, yes,
Speaker 1: there is some sense of it would be nice to
Speaker 1: have one, of course, but at the end of the day,
Speaker 1: a flow of one hundred to two hundred million dollar
Speaker 1: companies that they can sell and then reinvat, or they
Speaker 1: can split off and can go and have lives of
Speaker 1: their own. That's also something that you actually do need.
Speaker 1: We've talked about it before, that middle layer of companies
Speaker 1: that are continuing to grow, be sold or change or
Speaker 1: whatever else it is, and feeding talent and money back
Speaker 1: into the market as well. It's not all about the unicorns.
Speaker 2: Sounds good power to them.
Speaker 1: So a big thanks to Lucas Coelo and Chris Moore
Speaker 1: for coming on, and Ben Morrow as well for helping
Speaker 1: set that up.
Speaker 2: More on New Zealand's growing band of venture studios in
Speaker 2: the show notes. Head to the podcast section at Business
Speaker 2: Desk dot co dot d.
Speaker 1: You can stream the podcast there or on iHeartRadio, and
Speaker 1: of course it's available on your podcast platform of choice.
Speaker 2: Get in touch with your feedback and topic suggestions. We're
Speaker 2: on LinkedIn and blue Sky these days.
Speaker 1: Catch us again for the next episode next Thursday.
Speaker 2: See you then,