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