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Speaker 1: Daniel Vassilev, founder of Relevance AI. Welcome to the mental Mate.

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Speaker 2: Thanks for having me.

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Speaker 1: Now.

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Speaker 3: I'm definitely no expert, and I'm really just a citizen

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Speaker 3: AI guy. In other words, I know a little bit

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Speaker 3: about it. I don't know much about it, But your

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Speaker 3: game is you know lots about it because it is

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Speaker 3: your business. First important I think importantly to me about

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Speaker 3: relevance are what does it do?

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Speaker 2: Yeah, maybe I'll just preface. I think we're all in

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Speaker 2: this journey together. I think great thing about AI is

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Speaker 2: that there's so much opportunity for everybody to jump on

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Speaker 2: and kind of be at the same place as mage other.

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Speaker 2: So I think we're not that much further ahead than you.

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Speaker 2: But a Relevance we have a simple goal. We help

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Speaker 2: our customers build what we call in our workforce. Like

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Speaker 2: when we look into the future and think about how

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Speaker 2: businesses are scaled, they're very much going to be scaled

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Speaker 2: with ideas rather than headcount.

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Speaker 1: But how do you do that?

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Speaker 2: And so that's kind of the question we try to answer.

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Speaker 2: We provide our customers with a product that helps them

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Speaker 2: take a lot of the processes that are usually required

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Speaker 2: to do very manual mundane work and automate that. So

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Speaker 2: some of our customers we power entire swath of their

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Speaker 2: go to market teams to be able to eliminate those

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Speaker 2: mail tasks.

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Speaker 1: So can you give me an example.

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Speaker 2: Yeah, So, if you think about the BDR function right

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Speaker 2: top of funnel for most enterprise go to market teams

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Speaker 2: is literally hundreds of people oftentimes sending out lots of emails,

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Speaker 2: making lots of phone calls to try to generate business.

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Speaker 2: The reality is those environments are really difficult to be

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Speaker 2: in the average ten y or is eight months. Everyone

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Speaker 2: wants to get promoted, the top performers want to get

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Speaker 2: promoted into an eight year roll, which is a closing role,

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Speaker 2: and it's a Brital environment to be in and no

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Speaker 2: one likes doing that job. And so a lot of

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Speaker 2: companies are trying to find ways to alleviate the scaling

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Speaker 2: pressures that they have on their teams, reduce that burnout,

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Speaker 2: and introduce other ways to contact people to sell their products,

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Speaker 2: to introduce their company. So that's one of many examples.

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Speaker 2: Another example might be customer success teams. They work with

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Speaker 2: customers to try show them, you know, how to get

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Speaker 2: best to success with their product. That's a really difficult

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Speaker 2: role on all hours of the day, many different customers

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Speaker 2: on calls, and you're expected to prepare great PowerPoint dex,

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Speaker 2: great materials, engage with the customer, understand their business. We

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Speaker 2: help them automate a lot of that, like actually understand

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Speaker 2: what the customers need, create the collateral for them, send

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Speaker 2: it out to the customer. So the way we talk

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Speaker 2: about it is we think this year fifty percent of

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Speaker 2: go to market tasks are going to be eliminated. They're

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Speaker 2: going to be hambered by a year twenty six. Yeah, yep,

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Speaker 2: those really mundane manual pieces of work, it's going to

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Speaker 2: be gone. We can delegate. That's AI and we're helping

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Speaker 2: our customers.

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Speaker 1: Do that well.

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Speaker 3: And so when you say that, if I could just

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Speaker 3: open up a little bit, you mentioned drs or I

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Speaker 3: might call them bdms, business development managers, whatever you call them,

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Speaker 3: that these are people who are generally speaking, trying to

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Speaker 3: get business for the business.

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Speaker 1: That's right. There's not trying to get customers.

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Speaker 3: Yeah, so I might be it might be like a

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Speaker 3: call center, and you know the old call center, you remember,

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Speaker 3: you know, whatever that famous movie was, Whether they're trying

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Speaker 3: to sell shares and sell stocks. They're just sort of

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Speaker 3: ringing up random hey, going blah blah blah, WHI are

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Speaker 3: you interested in this? They're the sorts of people that

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Speaker 3: relevance AI is trying to make much more efficient at

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Speaker 3: using AI. How does that happen though? So, just do

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Speaker 3: you create a voice? Is it done through voice? Is

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Speaker 3: it done through emails and messaging?

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Speaker 1: Care you both?

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Speaker 2: And to your point, yeah, we're trying to make it

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Speaker 2: not only more productive, but also make that work a

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Speaker 2: little bit more joyful. Like, actually, like, what is the

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Speaker 2: thing that we want people spending their time on. I

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Speaker 2: don't think the value add from those people is necessarily

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Speaker 2: just on the Let me pick up the phone and

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Speaker 2: make one hundred dials and hope two of them convert.

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Speaker 2: I think the value add is thinking about who should

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Speaker 2: we be targeting, how should we target them, what messaging

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Speaker 2: we should use. That's what sets a part the best

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Speaker 2: people in that role versus the worst. And so in

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Speaker 2: the future, like you're going to see a lot more

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Speaker 2: of that grunt work that really requires basically no differentiation

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Speaker 2: from persons a person be handled by AI. We're kind

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Speaker 2: to delegate that to to AI. And what's the best

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Speaker 2: evidence of that look what happened last year of software engineers.

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Speaker 2: In the history of software engineering, you had to write

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Speaker 2: lines of code by hand. Now most software engineers are

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Speaker 2: delegating that to AI, is writing lines of code. Software

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Speaker 2: engineers are thinking more about the architecture. Whatso delegates the

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Speaker 2: I and I think the opportunity of that is huge.

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Speaker 2: We see already an explosion in apps submitted to the

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Speaker 2: app store, number of websites launched. So I don't think

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Speaker 2: that necessarily means software engineers go away. It just changes

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Speaker 2: what they can produce, how much of they can produce,

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Speaker 2: and the quality of it. And so I think the

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Speaker 2: same thing is going to happen to go to market teams.

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Speaker 2: The quality that they can give the customers is going

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Speaker 2: to be higher because of AI, not lower. So then

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Speaker 2: we give you an example. We process all of our

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Speaker 2: inbound with AI entirely. If you book a demo, it's

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Speaker 2: that with RELEVANT. If you got a website, book a demo,

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Speaker 2: that whole experience is how to buy AI. And it's

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Speaker 2: a brilliant experience because it's instantaneous. You're not waiting. No

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Speaker 2: one's trying to pressure you into selling. They're trying to

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Speaker 2: help you get to the to the to the buying

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Speaker 2: journey that you want, and we want to give everyone

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Speaker 2: an extremely personal journey that's relevant to them. You can't

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Speaker 2: do that with people, it just doesn't scale. It's too expensive.

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Speaker 2: And AI is going to give people not only a

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Speaker 2: better time selling, but a better time buying.

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Speaker 1: What are you talking about?

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Speaker 3: We're talking about something specifically built by or were you

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Speaker 3: using Copilot or JAGBT. What are you talking about when

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Speaker 3: like a normal pundle like me, I'm looking at coepile it,

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Speaker 3: or I might be looking at claud or I might

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Speaker 3: be looking at one of any one of those other protocols.

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Speaker 1: What are you talking about? And what is an AI agent?

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Speaker 2: Yeah, that's a really good question. So let me maybe

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Speaker 2: try and break it down. What's happened in the last

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Speaker 2: few years is it's basically like the launch of large

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Speaker 2: language models has given us the ability.

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Speaker 1: Large language models models, so.

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Speaker 2: Chat, GBT, Claude. So the big companies, let's maybe step back,

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Speaker 2: like opening eye Onthropic, these big companies, big foundation model companies,

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Speaker 2: what they're doing is creating AI models which can basically

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Speaker 2: reason like people can. It can if you give it

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Speaker 2: a problem, it can generate a bunch of text. But

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Speaker 2: it turns out the text that generates is basically the

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Speaker 2: same as if I was like writing down my thoughts

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Speaker 2: and process about what to do next. We can take

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Speaker 2: that technology and apply it to problems to basically solve

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Speaker 2: workflows that previously computers couldn't solve. So historically, what a

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Speaker 2: software it's being step one, step two, step three, and

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Speaker 2: you can only do that those steps. If everything is deterministic,

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Speaker 2: If I can break every problem down in a set

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Speaker 2: of if this or that conditions, software has been great

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Speaker 2: for that. What AI is enabling us to now do

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Speaker 2: is in real time make predictions about what to do next.

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Speaker 2: So like when I come up to a decision point,

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Speaker 2: I no longer as a software engineer have to codify

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Speaker 2: all of the outcomes. It can just determine based on

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Speaker 2: this scenario and context, this is the next best action.

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Speaker 2: So when we say AI and AI agents, what we're

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Speaker 2: really just describing is something happens and there's a decision

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Speaker 2: point that needs to be made, and now we have

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Speaker 2: the technology to make that decision like a human can,

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Speaker 2: and not just in the old way of rules. And

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Speaker 2: if you don't have a rule, you can't handle it.

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Speaker 3: So I go, friends who talk to me about their

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Speaker 3: own AIAI agent, What are they talking about something that

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Speaker 3: they have for themselves.

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Speaker 1: It's an individualized thing.

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Speaker 2: Yeah, So I think a lot of people probably referring

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Speaker 2: to as personal assistance, right, Like the way I describe

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Speaker 2: it is in the future, software is not going to

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Speaker 2: be what it is in the past, which is like

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Speaker 2: one size fits all. Software is going to be much

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Speaker 2: more personal because the cost of producing software is going

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Speaker 2: to zero, and so everyone's going to have the access

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Speaker 2: to a personal assistant that can do work for them.

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Speaker 2: It's not going to be what they're used to have

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Speaker 2: chat gipt now, which is just answering questions. It's actually

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Speaker 2: going to go ahead and complete entire tasks. You're going

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Speaker 2: to say, prepare my holiday for me, And it's not

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Speaker 2: just going to give you an itinery, but it's going

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Speaker 2: to go research based on what you like, destinations, accommodation

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Speaker 2: to book book it for you. So most of you

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Speaker 2: when they say they have an A agent that they like,

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Speaker 2: we've just seen open claw. If people are kind of

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Speaker 2: following along, what's happening recently.

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Speaker 1: Were they all talk to each other?

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Speaker 2: Yeah, Yeah, it's like personally this major flourishing of personal assistance.

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Speaker 2: Each one every person has their own open claw and

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Speaker 2: they they've.

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Speaker 1: Formed a group.

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Speaker 3: Yeah, the actual AI agents called called it the agents

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Speaker 3: of the people who built these things.

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Speaker 1: So your AOI agent, my agent.

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Speaker 3: They all join the social club together, and they're all

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Speaker 3: been talking to each other, that's right, and actually putting

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Speaker 3: a shit on us.

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Speaker 2: Yeah this now, I will just preface to everyone's I

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Speaker 2: think there's been a bit of doomorism around this. These

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Speaker 2: are at the moment they're being directed. People are saying

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Speaker 2: to them, Hey, go to this website and like communicate

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Speaker 2: with each other, do that sort of stuff. But it

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Speaker 2: gives you a glimpse into what's happening, because I think

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Speaker 2: the mentality is it's very hard as humans to be

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Speaker 2: like to think about a horse and then be like,

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Speaker 2: we're going to have a car. We're all thinking about

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Speaker 2: maybe a bigger horse, a stronger horse. Right, Those are

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Speaker 2: the first glimpses to give us all perspective of what's

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Speaker 2: about to happen in the future. And when I say future,

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Speaker 2: I don't mean two decades from now. I mean every

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Speaker 2: single year for the next five years. There's going to

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Speaker 2: be I think a tectonic shift in the way everything is.

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Speaker 3: Done, and is II actually assisting the speed of which

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Speaker 3: is happening, Because you know, the old Moore's law probably

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Speaker 3: looks like it doesn't exist anymore, because I mean, a

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Speaker 3: transistor capacity were doubling every year and a half, whatever

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Speaker 3: the words were. It seems to me like it's sort

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Speaker 3: of that's been blind out of the water. It's like

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Speaker 3: quadrupling every couple of months. What sort of predictions would

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Speaker 3: you have would you be putting out on the table

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Speaker 3: for twenty twenty six in relation to what do we

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Speaker 3: what we should be expecting as business owners, for example,

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Speaker 3: for twenty twenty six.

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Speaker 2: Yeah, I think the best parallel you have at the

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Speaker 2: moment is, look what's happening in software engineering, and prepare

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Speaker 2: for it to happen to all knowledge work. By that,

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Speaker 2: I mean almost every single individual task that you take.

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Speaker 2: Sending an email, checking the CRM, updating the CRM, sending

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Speaker 2: out an invite, looking up some research. All of these

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Speaker 2: things we will massively pass over and delegates AI like

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Speaker 2: en mass we are going to start stop doing that

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Speaker 2: sort of work. It's just not going to make sense.

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Speaker 2: And so I think, well, how will it happen.

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Speaker 3: So let's say, for example, right now, you want to

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Speaker 3: make an appointment to come here for the podcast view,

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Speaker 3: contact Sam, the production guy. Sam, I said, in an

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Speaker 3: invite to you and invite to me, it goes to

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Speaker 3: my AA whatever it is. It goes into my diary,

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Speaker 3: which is on you know, we.

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Speaker 1: Have it on.

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Speaker 3: It's on one of the Microsoft systems. Are use suggesting

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Speaker 3: that IO take over all of that?

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Speaker 2: Absolutely?

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Speaker 1: So will it actually find you as as a talent

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Speaker 1: to come on the show. It could as well.

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Speaker 2: Yeah. So if I was Sam, like I said, it

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Speaker 2: was the customer relevance and I was using it for

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Speaker 2: this use case, right, Sam is effectively doing a couple

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Speaker 2: of roles. He's sourcing talent, he's reaching out to them,

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Speaker 2: he's booking them in, he's scheduling.

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Speaker 3: It's also let's say he was researching talent first, because

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Speaker 3: he's looking what's relevant.

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Speaker 2: Absolutely, yes, I described as kind of part of the

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Speaker 2: sourcing yeah process. So what would happen is he would

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Speaker 2: we have this kind of like sort of relemant chat

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Speaker 2: that's kind of way you interface with think of it

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Speaker 2: like a general purpose agent for go to market teams,

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Speaker 2: and you basically connect it to your CRM. I'm not

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Speaker 2: sure if you guys have a CRM, but if you're

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Speaker 2: a more traditional go to market team, you connected to

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Speaker 2: your CRM, your email, your calendar, and a whole bunch

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Speaker 2: of requirements. You start off by giving it context about

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Speaker 2: who mentor it is, who we look for. What is

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Speaker 2: the profiles? You maybe say, Hey, go and look at

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Speaker 2: our past catalog of podcasts we've invited. What's a good profile?

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Speaker 2: It stores that in its context, and now every day

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Speaker 2: every week you'd be like, hey, go out and research

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Speaker 2: interesting people for us to speak to and give me

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Speaker 2: a list of that.

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Speaker 1: It'll do that.

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Speaker 2: Then maybe you ask it to now reach out to

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Speaker 2: these three and try to book in a meeting with.

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Speaker 1: Them, and will they get to point? Sry, sorry to

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Speaker 1: interrupt you, d Will we get to a point? Though?

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Speaker 3: Do you think this year where we're not asking that

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Speaker 3: question is actually saying Mark is taking the initiative. I've

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Speaker 3: gone out and I've found that the last four podcasts

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Speaker 3: that have topped the charts in the world and probably

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Speaker 3: here in Australia is Joe Rogan and Whoever and Dorov

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Speaker 3: and this is the this is where they seem to

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Speaker 3: be doing best and therefore, currently in Australia, we have

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Speaker 3: Daniel here who's here to talk and we're going to

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Speaker 3: reach out to him to come and talk about you know,

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Speaker 3: AI relevance A.

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Speaker 1: I mean, is it going to get to that level

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Speaker 1: where it's it's making taking the initiative.

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Speaker 2: I think if the business owner has the appetites to

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Speaker 2: do that, yes, I don't think that will happen on

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Speaker 2: mass this year. I think so. Kind of I have

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Speaker 2: this analogy of like if we want to self driving cars,

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Speaker 2: it's kind of like four levels of the time that

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Speaker 2: people describe all the way from L zero, which is manual,

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Speaker 2: to L four, which is self driving. I think every

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Speaker 2: company is about to joint go on that journey. The

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Speaker 2: first level is what I call assisted. It's going to

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Speaker 2: assist people to handle very basic tasks like go and

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Speaker 2: find a person to attend that podcast, go and reach

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Speaker 2: out to them to do it. So that's assistant co pilot.

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Speaker 2: It is then hey, run my weekly routine, and my

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Speaker 2: weekly routine is based on all those individual tasks I've done.

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Speaker 2: It can now know based on everything you've done for

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Speaker 2: the last two months of using it, how you run

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Speaker 2: your week. Now it's a co pilot, and then next

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Speaker 2: step after that, L three is order pilot. Now you're

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Speaker 2: not even gonna have to tell it run my week

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Speaker 2: every week. It's going to do this for you because

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Speaker 2: it knows that's what's important for you. All the way

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Speaker 2: through to L four, which is self driving. Every week,

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Speaker 2: it's going to learn this podcast did really, really well.

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Speaker 2: That profile is better than the other profile. Next week,

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Speaker 2: I'm going to source more people like that. So that's

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Speaker 2: kind of like the journey every company is going to

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Speaker 2: go on in the next few years. This year, I'm

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Speaker 2: very confident that assisted assisted piece will be prolific. It'll

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Speaker 2: happen everywhere. The leverage for business owners is then how

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Speaker 2: can they get to AL two and L three faster?

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Speaker 2: And so I think it's one hundred percent going to happen.

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Speaker 2: We've already got customers doing this for context, but for

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Speaker 2: it to happen on mass it really requires the people

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Speaker 2: who are running the business to have that vision for

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Speaker 2: how they want that to happen and how that change

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Speaker 2: is going to occur in their organization, because it's not

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Speaker 2: easy like this has change management. This is it's not

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Speaker 2: just a product problem, it's not a technology problem. It's

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Speaker 2: actually an organization on people problem. I spend half my

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Speaker 2: time helping customers buy a product. Does I help half

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Speaker 2: the time helping transform their organizations? And we work with

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Speaker 2: large We work some the largest product companies in the world.

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Speaker 2: We work with high growth tech companies. When we do this,

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Speaker 2: we know that change management is just as important and

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Speaker 2: so that executive sponsorship and vision is critical to success.

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Speaker 2: So to answer your question, the first piece will happen

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Speaker 2: this year. That's second piece where I will know and

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Speaker 2: do things to you proactively, only for the companies that

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Speaker 2: are actually visionary and want to become AI first.

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Speaker 1: AO first.

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Speaker 3: A white that so relevant are your your business is

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Speaker 3: typically when you go into an organization, what do you do?

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Speaker 3: What's like I'd imagine they say, oh, we should be

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Speaker 3: doing us every day.

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Speaker 1: We don't know where to go to.

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Speaker 3: We talk to these guys because we've got a good rep.

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Speaker 3: Then you come in and you start talking to the

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Speaker 3: senior management. For iguments sake, let's just talk about do

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Speaker 3: you just try to map to joint out?

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Speaker 1: Is that what you're doing first? Organizationally?

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Speaker 2: So it kind of depends a lot of customers are

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Speaker 2: coming to us with a specific pain point, hey, like,

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Speaker 2: our targets this year for sales are growing, but our

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Speaker 2: resources for headcount and not what do we do? Everybody

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Speaker 2: knows that unless you have more account executives selling your product,

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Speaker 2: you're not going to generate more revenue coming in. But

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Speaker 2: what if you don't have the budget or the desire

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Speaker 2: to be adding another one hundred people to your company.

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Speaker 2: That's really hard. Scaling is difficult, and so they'll typically

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Speaker 2: come with the problem like that, like what can we

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Speaker 2: do to help our team avoid burner? How can we

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Speaker 2: help our team succeed more? And so typically it'll be like,

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Speaker 2: we're extremely successful, we are generating lots of we have

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Speaker 2: a lot of repeatable process how can we deploy agents

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Speaker 2: to tackle specific tasks in those processes so we don't

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Speaker 2: have to burden our people with it. So typically have

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Speaker 2: very specific pain points, and given that we work and

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Speaker 2: go to market teams, we tend to it would be

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Speaker 2: some combination usually of like either helping top of funnel,

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Speaker 2: helping throughout the closing journey, or helping once they've closed

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Speaker 2: the customer with implementation handover, helping them create the documentation

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Speaker 2: so the post sales team is really prepared and understands

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Speaker 2: what the customer is expecting once they sign, all the

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Speaker 2: way through to them actually running renewals. So like we

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Speaker 2: have customers that are automatically running renewals of agents still

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Speaker 2: contact the customer like six months out, Hey, your renewals

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Speaker 2: coming up. This is what we've done with you identify

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Speaker 2: risk and so we try to tackle specific pain points.

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Speaker 2: We don't necessarily just try to go in and be like,

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Speaker 2: let's all of AI. That's a very big, difficult problem.

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Speaker 2: But in any business right now, there's heaps of work

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Speaker 2: that is highly repetitive that you absolutely should be thinking about.

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Speaker 2: What's my pathway to have AI help my team solve that.

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Speaker 3: So let's say somebody's listening to this now, business owner,

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Speaker 3: and let's say they've got a a go to market

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Speaker 3: style business. We're not talking about a coffeehop. We're talking

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Speaker 3: about someone who sells product and or services. What should

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Speaker 3: they be asking in themselves today as to whether or

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Speaker 3: not they need someone like Relevance AI to come in

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Speaker 3: and have a look at what they're doing. You know,

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Speaker 3: obviously if they're too small, they're not might not.

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Speaker 1: Be able to ford you.

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Speaker 3: But generally speaking, how would I because most people don't

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Speaker 3: even know where to start, Like they don't know whether

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Speaker 3: is a something's going to help me or not. They

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Speaker 3: don't even know where to start. They can't identify the

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Speaker 3: problem because they might be doing all right, they might

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Speaker 3: be making a bit of money, but they don't realize

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Speaker 3: the marketplace is speeding up ahead of them and they

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Speaker 3: can't even see it. And so what are the signs

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Speaker 3: that I should be doing something?

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Speaker 2: I think the really hard truth is that unless it

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Speaker 2: comes like from you, if you're like let's say, like

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Speaker 2: some sort of you're running a team, you're running an org.

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Speaker 2: If it doesn't come from you, it's really hard to

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Speaker 2: make the change happen. So if you yourself are not

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Speaker 2: willing to put in the effort to level up and

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Speaker 2: understand what's happening in the market, I think you're going

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Speaker 2: to be very strong.

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Speaker 1: You're going to struggle.

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Speaker 2: So my first pot of callers, like to any business

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Speaker 2: owner is like immerse yourself. And I'm not saying I'm

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Speaker 2: not saying just go and chat JBT. Actually like try

400
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Speaker 2: to understand what's happening in the a space, read and

401
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Speaker 2: like watch videos and even watch some of the crazy

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Speaker 2: stuff that you know, people on the fringes with like

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Speaker 2: open chlora doing and like not that that's going to

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Speaker 2: help you in your business, but it's going to give

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Speaker 2: you a perspective on where the market's going.

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Speaker 1: Could you direct him somewhere? Like what would you say

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Speaker 1: to them? Straight up?

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Speaker 3: Like if let's say your younger brother who's twenty seven,

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Speaker 3: you know all the stuff, you know, but he's really

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Speaker 3: good at something else, so it's got nothing to do

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Speaker 3: with AI. And but you know, he knows that he's

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Speaker 3: can't get enough people in his business to grow fast

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Speaker 3: enough because he's a people business type. What would you say, Tom,

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Speaker 3: he knows off about IA. Where would you Where would

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Speaker 3: you direct him? We're just saying, look at something on YouTube?

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Speaker 2: Do you what? Do you?

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Speaker 1: What are you going to tell him?

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Speaker 2: I would say, first of all, just go out and

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Speaker 2: learn about what's happening in a self engineering Look up

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00:17:54,520 --> 00:17:55,879
Speaker 2: claw code, look up cursor.

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Speaker 1: Just say say again, look law code, yeah.

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Speaker 2: And cursor. So like these two products have basically changed

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Speaker 2: the face of software engine.

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Speaker 1: How do I look it up?

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Speaker 2: Just go on Google, search it up, go to YouTube,

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Speaker 2: look it up.

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Speaker 3: So is claud code, claud code, Claude code and cursor?

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Speaker 3: Cursor c uiso R.

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Speaker 2: Yeah, well that will do like look up on YouTube

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Speaker 2: what they are, how to use them, try out some

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00:18:17,720 --> 00:18:20,520
Speaker 2: of the vibe coding products. So vibe coding if again,

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00:18:20,560 --> 00:18:22,440
Speaker 2: if you're a listener who doesn't know what that is,

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00:18:22,440 --> 00:18:25,200
Speaker 2: is basically the concept where you can give natural language

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Speaker 2: instructions to AI, so you speak to it and writes

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Speaker 2: it for you and it creates an app for you.

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Speaker 2: So there's products like Lovable Replet and others that basically

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00:18:32,720 --> 00:18:36,560
Speaker 2: can create whole products for you. Go dabble in those things,

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Speaker 2: because that is the most real way you can experience

439
00:18:39,920 --> 00:18:44,720
Speaker 2: how AI is impacting an entire category. And I think

440
00:18:44,760 --> 00:18:47,400
Speaker 2: once you start doing that, you'll enter a journey which

441
00:18:47,400 --> 00:18:49,679
Speaker 2: will then naturally take you to being more educated and

442
00:18:49,680 --> 00:18:51,199
Speaker 2: more informed about how you can apply that to your

443
00:18:51,200 --> 00:18:54,280
Speaker 2: own business. The reality is there is no clear answer

444
00:18:54,280 --> 00:18:56,479
Speaker 2: of what you should do. The reality is no one

445
00:18:56,520 --> 00:18:57,920
Speaker 2: can predict what's going to happen in the next twelve

446
00:18:57,920 --> 00:19:01,760
Speaker 2: to twenty four months and beyond. But you have the

447
00:19:01,800 --> 00:19:05,080
Speaker 2: best chance at looking around the corner if you at

448
00:19:05,119 --> 00:19:08,480
Speaker 2: least understand where AI right now is having tremendous amounts

449
00:19:08,520 --> 00:19:10,159
Speaker 2: of impact. I was just on the phone the other

450
00:19:10,240 --> 00:19:13,399
Speaker 2: day with a prospect in the translation space in the

451
00:19:13,440 --> 00:19:14,320
Speaker 2: last twelve months.

452
00:19:14,320 --> 00:19:15,800
Speaker 1: So what's a translation space you mean?

453
00:19:15,840 --> 00:19:19,399
Speaker 2: Like translation services, like the localized software and other products

454
00:19:19,400 --> 00:19:22,200
Speaker 2: for companies. DAVE in the last twelve months gone from

455
00:19:22,840 --> 00:19:24,800
Speaker 2: very large percentage of it done by humans to now

456
00:19:24,800 --> 00:19:29,240
Speaker 2: almost none in twelve months. Software engineering roles, we've gone

457
00:19:29,240 --> 00:19:31,480
Speaker 2: from like all lines of code basically written by hand,

458
00:19:31,720 --> 00:19:36,359
Speaker 2: to us significantly smaller percentage. These transitions are happening, and

459
00:19:36,400 --> 00:19:39,120
Speaker 2: I think if you look at what products are enabling

460
00:19:39,160 --> 00:19:42,280
Speaker 2: that you'll have an opportunity to understand where you should

461
00:19:42,320 --> 00:19:43,720
Speaker 2: be and what you could be looking at. So do

462
00:19:43,800 --> 00:19:44,879
Speaker 2: that for your own business.

463
00:19:45,040 --> 00:19:46,920
Speaker 3: I was talking to someone the other day and he

464
00:19:47,000 --> 00:19:50,760
Speaker 3: had those ray ban glasses on, not the sun glasses

465
00:19:50,760 --> 00:19:52,520
Speaker 3: that you have on the table, but the ray bang

466
00:19:52,520 --> 00:19:54,920
Speaker 3: glasses with all the cameras, et cetera on it, and he.

467
00:19:54,920 --> 00:19:57,439
Speaker 1: Said that he can see it here talking to you.

468
00:19:57,320 --> 00:19:59,680
Speaker 3: You can talk and be talking to me in Russian

469
00:20:00,400 --> 00:20:03,560
Speaker 3: and it translates is translating him back back to him

470
00:20:03,600 --> 00:20:07,439
Speaker 3: in English in real time. The whole conversation. He's all,

471
00:20:07,440 --> 00:20:09,080
Speaker 3: he's just doing that. We're seeing there looking at you

472
00:20:09,119 --> 00:20:13,119
Speaker 3: and for all you know, I can understand Russian. Is

473
00:20:13,160 --> 00:20:16,280
Speaker 3: that the sort of thing you're talking about, and that's

474
00:20:16,320 --> 00:20:18,200
Speaker 3: not a personal level, but like, is that the sort

475
00:20:18,240 --> 00:20:21,040
Speaker 3: of advancements that AI is doing for us?

476
00:20:21,320 --> 00:20:21,520
Speaker 1: Yeah?

477
00:20:21,600 --> 00:20:23,000
Speaker 2: I guess the point I'm trying to make is that

478
00:20:23,040 --> 00:20:27,120
Speaker 2: these roles are Previously you could not deal with technology.

479
00:20:27,440 --> 00:20:29,679
Speaker 2: You now can, and so I think that's going to

480
00:20:29,680 --> 00:20:33,200
Speaker 2: apply everywhere. And I guess, you know, I think what's

481
00:20:33,200 --> 00:20:36,639
Speaker 2: really important for everyone to be really I guess clear

482
00:20:36,680 --> 00:20:39,040
Speaker 2: about is that this is happening everywhere.

483
00:20:39,240 --> 00:20:42,120
Speaker 1: And if we don't adopt it, what would you say.

484
00:20:42,000 --> 00:20:44,600
Speaker 2: You're not going to succeed. You can't. It's like it's

485
00:20:44,640 --> 00:20:46,639
Speaker 2: like asking me, what if we don't have internet and

486
00:20:46,680 --> 00:20:49,040
Speaker 2: laptops in our office? You just can't operate a business.

487
00:20:49,119 --> 00:20:52,080
Speaker 2: It's impossible. You're not You're not going to be competitive.

488
00:20:52,520 --> 00:20:55,720
Speaker 2: You're not gonna survive because there's going to be someone else.

489
00:20:56,000 --> 00:20:58,920
Speaker 2: We know, we we have capitalism, We rely on competition,

490
00:20:58,960 --> 00:21:01,040
Speaker 2: We have a free market. Someone else is going to

491
00:21:01,040 --> 00:21:04,680
Speaker 2: come in and do your work with AI at a

492
00:21:04,680 --> 00:21:07,920
Speaker 2: better level of quality or a lower cost. Either way,

493
00:21:08,000 --> 00:21:10,240
Speaker 2: your customers are going to move to them, or both

494
00:21:10,600 --> 00:21:11,000
Speaker 2: or both.

495
00:21:11,320 --> 00:21:14,600
Speaker 3: Is the expectation that if you don't do something get

496
00:21:14,600 --> 00:21:16,959
Speaker 3: started this year, because are we talking about is it

497
00:21:17,080 --> 00:21:17,800
Speaker 3: so imminent?

498
00:21:18,080 --> 00:21:18,400
Speaker 1: It is?

499
00:21:18,880 --> 00:21:22,080
Speaker 2: I think I've honestly personally come on a big journey

500
00:21:22,080 --> 00:21:24,920
Speaker 2: of the last twelve months where I've i the timelines

501
00:21:24,920 --> 00:21:28,080
Speaker 2: that I keep thinking about keep shortening, things keep happening faster,

502
00:21:29,440 --> 00:21:31,560
Speaker 2: and we're already a place where the models that those

503
00:21:31,600 --> 00:21:33,879
Speaker 2: large language models which I spoke about that these companies

504
00:21:33,920 --> 00:21:37,320
Speaker 2: are producing, they're already at a level which can solve

505
00:21:37,480 --> 00:21:40,800
Speaker 2: so many tasks that you can only imagine one or

506
00:21:40,800 --> 00:21:43,399
Speaker 2: two more step function changes and performance. What that's going

507
00:21:43,440 --> 00:21:45,399
Speaker 2: to lead to. It is fundamentally going to change the

508
00:21:45,440 --> 00:21:48,840
Speaker 2: way we do all knowledge work, not some not a little,

509
00:21:48,880 --> 00:21:50,680
Speaker 2: not a fraction or knowledge work.

510
00:21:51,119 --> 00:21:52,560
Speaker 1: You sound quite adamant about that.

511
00:21:53,359 --> 00:21:55,399
Speaker 3: If you were talking to me and I had a

512
00:21:55,400 --> 00:21:58,360
Speaker 3: small business now let's call it this business here, and

513
00:21:58,640 --> 00:22:02,320
Speaker 3: I was trying to upscill my people internally, they producers

514
00:22:02,320 --> 00:22:05,080
Speaker 3: and everybody else who works in the joint, what would

515
00:22:05,080 --> 00:22:06,120
Speaker 3: you say to me to tell them?

516
00:22:06,480 --> 00:22:07,280
Speaker 1: So what I do think?

517
00:22:07,320 --> 00:22:08,879
Speaker 2: So kind of like maybe you step back again, kind

518
00:22:08,880 --> 00:22:11,360
Speaker 2: of give you the Rolevans perspective. So for context again

519
00:22:11,400 --> 00:22:13,960
Speaker 2: for your listeners, we've been around for five years now,

520
00:22:14,600 --> 00:22:16,960
Speaker 2: we've raised forty two million USD today.

521
00:22:17,000 --> 00:22:18,480
Speaker 1: I saw that in the brief. That's amazing.

522
00:22:18,600 --> 00:22:19,239
Speaker 2: Yeah, thank you.

523
00:22:19,280 --> 00:22:20,760
Speaker 1: And did you get a big vale?

524
00:22:21,240 --> 00:22:22,040
Speaker 2: We did? We did pretty well.

525
00:22:22,119 --> 00:22:24,560
Speaker 3: Yea, yeah, you a lady to disclose the vale raise

526
00:22:24,600 --> 00:22:27,119
Speaker 3: forty two million on for what sort of val it

527
00:22:27,200 --> 00:22:27,919
Speaker 3: must be large?

528
00:22:28,240 --> 00:22:29,960
Speaker 2: Yeah, it's a good evaluation with very.

529
00:22:29,840 --> 00:22:30,880
Speaker 1: Long and then who are you investors?

530
00:22:31,040 --> 00:22:34,280
Speaker 2: More importantly, we have some amazing investors' best I mentioned

531
00:22:34,280 --> 00:22:37,560
Speaker 2: partners INSI partners Peak, Fifth Team due to be Square

532
00:22:37,600 --> 00:22:39,560
Speaker 2: Asia and then King of a Capital here out of Sydney.

533
00:22:39,920 --> 00:22:41,480
Speaker 2: We've been very like to have an amazing set of

534
00:22:41,520 --> 00:22:45,639
Speaker 2: investors backing us, and so the reason why they're back

535
00:22:45,720 --> 00:22:47,840
Speaker 2: us is over the last few years we've been very

536
00:22:47,920 --> 00:22:50,040
Speaker 2: much gung ho on this, Like in twenty twenty three

537
00:22:50,359 --> 00:22:53,639
Speaker 2: we announced our Series A with the headline that we

538
00:22:53,720 --> 00:22:55,960
Speaker 2: believe companies are going to scale with ideas, not headcount.

539
00:22:56,040 --> 00:22:58,720
Speaker 2: Back then it kind of sounded like a marketing slogan. Slogan,

540
00:22:58,800 --> 00:23:02,399
Speaker 2: Now it's clear from software engineering that's happening, and so

541
00:23:02,400 --> 00:23:04,480
Speaker 2: we've been very opinionated in that direction and we've been

542
00:23:04,600 --> 00:23:07,240
Speaker 2: very lucky to work with some of the best companies

543
00:23:07,240 --> 00:23:08,440
Speaker 2: in the world, and if you check a logo on

544
00:23:08,520 --> 00:23:10,480
Speaker 2: our website, you'll see some of them to help them

545
00:23:10,480 --> 00:23:12,879
Speaker 2: do this change. So we're coming from a perspective of

546
00:23:12,920 --> 00:23:15,439
Speaker 2: like actually seeing this work in reality, and it was

547
00:23:15,600 --> 00:23:18,159
Speaker 2: much harder to do it six months ago than it

548
00:23:18,200 --> 00:23:20,000
Speaker 2: is now. So I can only imagine how much easier

549
00:23:20,000 --> 00:23:22,080
Speaker 2: it is to achieve the same outcomes six months from now.

550
00:23:22,280 --> 00:23:25,320
Speaker 2: And what are those outcomes work that can be done

551
00:23:25,359 --> 00:23:27,679
Speaker 2: my machines that previously can only be done by people.

552
00:23:28,359 --> 00:23:30,520
Speaker 2: And I think that's a net positive thing, by the way,

553
00:23:30,520 --> 00:23:33,680
Speaker 2: because for every team we've done this, we've seen those

554
00:23:33,720 --> 00:23:36,600
Speaker 2: teams be able to produce better work at a better quality,

555
00:23:37,520 --> 00:23:40,439
Speaker 2: at a rate that's much more efficient for a business

556
00:23:40,440 --> 00:23:42,320
Speaker 2: to run, because I think right now every company is

557
00:23:42,320 --> 00:23:44,359
Speaker 2: putting a lot more pressure on their people, and I

558
00:23:44,359 --> 00:23:46,240
Speaker 2: think that's not sustainable, and so I actually think AI

559
00:23:46,280 --> 00:23:48,199
Speaker 2: is going to come help solve some of that. But

560
00:23:48,200 --> 00:23:51,440
Speaker 2: I give you that context because so, like, why what's

561
00:23:51,480 --> 00:23:53,600
Speaker 2: my perspective then on how people can roll this out.

562
00:23:54,720 --> 00:23:57,359
Speaker 2: One of the hypothesis we have when we started the company,

563
00:23:57,400 --> 00:23:59,640
Speaker 2: in addition to scaling with ideas not headcount, was that

564
00:23:59,720 --> 00:24:03,400
Speaker 2: don't the main experts are critical. So when we think

565
00:24:03,440 --> 00:24:05,840
Speaker 2: about AI agents, you should think of them a lot

566
00:24:05,880 --> 00:24:08,639
Speaker 2: more like employees then you do software. And so what

567
00:24:08,680 --> 00:24:11,119
Speaker 2: I mean by that, how do we hire and train employees.

568
00:24:12,240 --> 00:24:14,439
Speaker 2: You don't have an engineer, Hire a marketing leader and

569
00:24:14,440 --> 00:24:16,200
Speaker 2: train them how to be a marketing leader. You don't

570
00:24:16,240 --> 00:24:18,760
Speaker 2: have marketing Hire a salesperson and train them how to

571
00:24:18,760 --> 00:24:21,040
Speaker 2: be a salesperson. You have domain experts, we're really good

572
00:24:21,080 --> 00:24:23,760
Speaker 2: at that role, hiring and training for those roles. So

573
00:24:23,960 --> 00:24:26,159
Speaker 2: the best way that I believe you can do this

574
00:24:26,240 --> 00:24:28,880
Speaker 2: in your company is find the people in your business

575
00:24:29,400 --> 00:24:31,879
Speaker 2: who are experts at what you do, who are really

576
00:24:31,880 --> 00:24:34,920
Speaker 2: really top performers, and ideally find the ones that also

577
00:24:35,040 --> 00:24:37,600
Speaker 2: have a desire for kind of systems thinking I kind

578
00:24:37,600 --> 00:24:40,840
Speaker 2: of describe it or curiosity about AI, and just get

579
00:24:40,880 --> 00:24:44,480
Speaker 2: them to have one goal automate more and more of

580
00:24:44,520 --> 00:24:47,640
Speaker 2: the tasks that they hate doing with AI and set

581
00:24:47,640 --> 00:24:49,639
Speaker 2: them loose, make that their number one priority, and make

582
00:24:49,680 --> 00:24:52,480
Speaker 2: them accountable for the performance of their agents or the AI.

583
00:24:53,680 --> 00:24:56,080
Speaker 2: That's the best way to success because the reality is

584
00:24:57,320 --> 00:24:59,479
Speaker 2: only you and your top performers know how to do

585
00:24:59,520 --> 00:25:01,560
Speaker 2: your job really well. If you just think this is

586
00:25:01,560 --> 00:25:03,280
Speaker 2: like a technology problem where I'm going to have a

587
00:25:03,280 --> 00:25:05,080
Speaker 2: bunch of engineers are going to build some product you're

588
00:25:05,080 --> 00:25:07,320
Speaker 2: not going to succeed. You've got to treat your agents

589
00:25:07,359 --> 00:25:09,560
Speaker 2: like employees. You've got to train them like employees. You've

590
00:25:09,560 --> 00:25:12,160
Speaker 2: got to have someone accountable for them, like a manager

591
00:25:12,160 --> 00:25:14,480
Speaker 2: of any other team. And so for the business owners

592
00:25:14,520 --> 00:25:16,640
Speaker 2: out there, find that person in your company. Get them

593
00:25:16,680 --> 00:25:19,160
Speaker 2: started now, give them the space to do it, give

594
00:25:19,160 --> 00:25:22,200
Speaker 2: them goals, give them resources, but start right now.

595
00:25:22,240 --> 00:25:22,680
Speaker 1: Don't wait.

596
00:25:23,200 --> 00:25:25,240
Speaker 3: The scary thing about that for a lot of people

597
00:25:25,320 --> 00:25:27,160
Speaker 3: is that dumb. You know, people lose their jobs. Of course,

598
00:25:27,160 --> 00:25:28,920
Speaker 3: you know you would have heard this conversation over and

599
00:25:29,000 --> 00:25:31,280
Speaker 3: over again. And let's say we've got to let's say

600
00:25:31,280 --> 00:25:35,800
Speaker 3: we've got a BDM team which says twenty there's three

601
00:25:35,880 --> 00:25:38,840
Speaker 3: high flyers seventeen okay.

602
00:25:38,480 --> 00:25:40,080
Speaker 1: Which is pretty much normal. You know, you know, you

603
00:25:40,119 --> 00:25:41,800
Speaker 1: get eighty twenty might.

604
00:25:41,800 --> 00:25:44,000
Speaker 3: Bee six high flyers, but it's usually the eighty twenty

605
00:25:44,040 --> 00:25:46,240
Speaker 3: real eight percent of business gets number twenty percent of

606
00:25:46,240 --> 00:25:48,720
Speaker 3: the people and the rest that you need them because

607
00:25:48,760 --> 00:25:51,040
Speaker 3: she's because you need the other twenty percent of work

608
00:25:51,080 --> 00:25:54,080
Speaker 3: because you want one hundred percent. But unfortunately, generally speaking,

609
00:25:54,119 --> 00:25:56,439
Speaker 3: you've got of that twenty people, you've got fourteen of

610
00:25:56,480 --> 00:25:59,840
Speaker 3: them just sort of average. Okay, so which is like

611
00:26:01,000 --> 00:26:02,800
Speaker 3: But one of the things I just want to ask

612
00:26:02,840 --> 00:26:04,840
Speaker 3: you now is what I think what you're saying is

613
00:26:04,880 --> 00:26:07,640
Speaker 3: pick up those six or so high flyers and get

614
00:26:07,680 --> 00:26:10,399
Speaker 3: them to run the program because they know they know

615
00:26:10,520 --> 00:26:14,719
Speaker 3: what to make sure that the AI agent needs to

616
00:26:14,760 --> 00:26:17,000
Speaker 3: know exactly, because they've been in the game for so long,

617
00:26:17,200 --> 00:26:20,399
Speaker 3: the exactly individuals, and or they're the best performers. What

618
00:26:20,400 --> 00:26:23,520
Speaker 3: do we do about the other fourteen people? What happens?

619
00:26:23,680 --> 00:26:24,720
Speaker 3: What do you think what do you think is going

620
00:26:24,760 --> 00:26:27,080
Speaker 3: to happen in the world for those people as AI

621
00:26:27,160 --> 00:26:30,840
Speaker 3: becomes a non negotiable AI agents and continue to become

622
00:26:30,840 --> 00:26:34,400
Speaker 3: a non negotiable what is it a retraining.

623
00:26:34,040 --> 00:26:36,400
Speaker 1: What do we do for those individuals? That's aod question.

624
00:26:36,440 --> 00:26:40,520
Speaker 2: Look, I definitely have all the answers. I think prediction

625
00:26:40,720 --> 00:26:42,960
Speaker 2: predicting the future right now is really really hard. I

626
00:26:43,000 --> 00:26:45,040
Speaker 2: think the only safe thing for us to predict is

627
00:26:45,080 --> 00:26:47,480
Speaker 2: that AI is going to become a permanent wave of working.

628
00:26:47,560 --> 00:26:50,119
Speaker 3: Yeah, so that's a non negotiation. You can't say we

629
00:26:50,160 --> 00:26:52,000
Speaker 3: want to protect everybody, so therefore we're not going to

630
00:26:52,000 --> 00:26:53,440
Speaker 3: do it. Because you're going to get left behind it.

631
00:26:53,440 --> 00:26:55,879
Speaker 3: You will end up having nobody because all twenty will

632
00:26:55,920 --> 00:26:58,040
Speaker 3: be out of a job exactlyause you're outed a business exactly.

633
00:26:58,119 --> 00:27:02,679
Speaker 2: So I think that is undeniable, underniably true. What happens

634
00:27:02,680 --> 00:27:04,480
Speaker 2: after that, I think is is like so many different

635
00:27:04,480 --> 00:27:07,480
Speaker 2: flavors like Jevan's paradox, right, this idea that as soon

636
00:27:07,520 --> 00:27:11,200
Speaker 2: as you and if you change a supply, it or

637
00:27:11,200 --> 00:27:13,240
Speaker 2: also impact demand, and you just all of a sudden

638
00:27:13,320 --> 00:27:15,440
Speaker 2: get so much more demand. So, yes, software is easier

639
00:27:15,520 --> 00:27:18,359
Speaker 2: to create now, but now we want custom software for

640
00:27:18,400 --> 00:27:21,919
Speaker 2: every single individual, and so we need to basically make

641
00:27:21,960 --> 00:27:24,240
Speaker 2: sure that we can meet that demand. So therefore we

642
00:27:24,240 --> 00:27:26,679
Speaker 2: still need as many software engineers potentially because there's just

643
00:27:26,720 --> 00:27:29,600
Speaker 2: so much more demand for this, for this product that

644
00:27:29,640 --> 00:27:32,320
Speaker 2: we now have on offer. You're saying, with are we

645
00:27:32,400 --> 00:27:34,600
Speaker 2: need more software engineering potentially? Right? Like I'm just saying,

646
00:27:34,640 --> 00:27:36,560
Speaker 2: like if we look at them as paradox, that's what's

647
00:27:36,560 --> 00:27:39,879
Speaker 2: happening in every other part of technologies shift in the history. Now.

648
00:27:39,920 --> 00:27:41,280
Speaker 2: I don't know if that's necessarily true here. I think

649
00:27:41,320 --> 00:27:43,639
Speaker 2: there's probably some differences in AI that applied to that,

650
00:27:44,240 --> 00:27:46,080
Speaker 2: But what I would say is that AI can make

651
00:27:46,119 --> 00:27:49,760
Speaker 2: those average performance top performance, because maybe that average performer

652
00:27:49,880 --> 00:27:51,960
Speaker 2: isn't a top Maybe it's not that they're an average

653
00:27:51,960 --> 00:27:54,440
Speaker 2: performer due to a lack of work ethic or a

654
00:27:54,520 --> 00:27:57,120
Speaker 2: lack of interest. Maybe there's other factors. Maybe it's training,

655
00:27:57,200 --> 00:28:00,520
Speaker 2: maybe it's like their experience. Maybe they want to be

656
00:28:00,560 --> 00:28:03,000
Speaker 2: top performers, they don't know how to get there. Imagine

657
00:28:03,040 --> 00:28:05,320
Speaker 2: you've now got a set of AI agents that know

658
00:28:05,359 --> 00:28:07,320
Speaker 2: how to do the job at a really high level,

659
00:28:07,680 --> 00:28:10,040
Speaker 2: and they partner with those people. Now, each one of

660
00:28:10,040 --> 00:28:12,359
Speaker 2: those people can make sure they're performing those individual tasks

661
00:28:12,359 --> 00:28:14,400
Speaker 2: that are really really high level, and then the bits

662
00:28:14,400 --> 00:28:16,720
Speaker 2: that only they can do, they can focus on being

663
00:28:16,720 --> 00:28:18,600
Speaker 2: really really good at that. So in fact, that actually

664
00:28:18,640 --> 00:28:20,480
Speaker 2: might make those employees more valuable. You might want to

665
00:28:20,480 --> 00:28:23,000
Speaker 2: pay them more money because now that they're actually delivering

666
00:28:23,000 --> 00:28:24,480
Speaker 2: more output and outcome for your business.

667
00:28:24,680 --> 00:28:26,480
Speaker 3: So I say your agent is not very expensive either,

668
00:28:26,480 --> 00:28:29,200
Speaker 3: because they basically costs you nothing more cheaper.

669
00:28:29,520 --> 00:28:31,639
Speaker 1: If they're cheaper, yeah, and they're only getting cheaper.

670
00:28:31,680 --> 00:28:33,840
Speaker 3: That's well, they cheaper because they might cost you something

671
00:28:33,840 --> 00:28:35,440
Speaker 3: in the beginning, but they cost you nothing over time.

672
00:28:35,640 --> 00:28:37,120
Speaker 3: You're not giving them a pay rise all the time,

673
00:28:37,200 --> 00:28:38,840
Speaker 3: like as they get better and better and better, they're

674
00:28:38,840 --> 00:28:41,440
Speaker 3: not getting bonuses either. Because it's funny, you know, recently,

675
00:28:41,840 --> 00:28:44,560
Speaker 3: recently one of my business which we lend money, we've

676
00:28:44,560 --> 00:28:47,680
Speaker 3: got quite a lot of mortgage physical mortgage brokers around

677
00:28:47,680 --> 00:28:50,520
Speaker 3: the world around Australia, should say, and like a lot.

678
00:28:50,640 --> 00:28:53,880
Speaker 3: And the point you just made is a very important point.

679
00:28:54,920 --> 00:28:58,520
Speaker 3: We've created an AI agent broker who's been trained on

680
00:28:59,200 --> 00:29:01,840
Speaker 3: just about every policy there is that exists in the

681
00:29:01,840 --> 00:29:07,280
Speaker 3: country and that and as people, we we haven't sort

682
00:29:07,280 --> 00:29:09,120
Speaker 3: of actually launched it to the market yet, but it's

683
00:29:09,320 --> 00:29:12,440
Speaker 3: it's being launched, it's sort of being trialed and as

684
00:29:12,440 --> 00:29:14,880
Speaker 3: it learns what every single one of our brokers does,

685
00:29:14,920 --> 00:29:18,120
Speaker 3: it becomes one person or one entity that knows everything

686
00:29:18,160 --> 00:29:21,480
Speaker 3: that everybody knows. And but what we're using it for

687
00:29:21,680 --> 00:29:24,080
Speaker 3: is to help our brokers, so our brogus can talk

688
00:29:24,120 --> 00:29:26,920
Speaker 3: to it about what they should do exactly to make

689
00:29:26,960 --> 00:29:30,200
Speaker 3: them better, to make them more efficient. And those brokers

690
00:29:30,240 --> 00:29:31,960
Speaker 3: we've spoken to, we haven't gone and spoke to me honestly,

691
00:29:31,960 --> 00:29:33,560
Speaker 3: we haven't gone and spoke to our best brocers, our

692
00:29:33,560 --> 00:29:36,000
Speaker 3: best breakers as well. Anyway, we're talking to our not

693
00:29:36,120 --> 00:29:38,600
Speaker 3: so best Brogers and they love the idea of it.

694
00:29:38,960 --> 00:29:43,880
Speaker 3: And we've actually made it into audio too, so it

695
00:29:43,960 --> 00:29:45,920
Speaker 3: actually you could talk to it like I'm talking to

696
00:29:45,960 --> 00:29:47,120
Speaker 3: you now and it'll talk.

697
00:29:47,000 --> 00:29:48,240
Speaker 1: Back to you.

698
00:29:48,240 --> 00:29:48,440
Speaker 2: You know.

699
00:29:48,560 --> 00:29:50,880
Speaker 3: That's and we've given it a female boys and all

700
00:29:50,920 --> 00:29:53,680
Speaker 3: sort of stuff, so it's it's actually quite interesting. We've

701
00:29:53,720 --> 00:29:55,240
Speaker 3: we've been working on this for a while now, no

702
00:29:55,280 --> 00:29:57,640
Speaker 3: but no one in our industries. But right it's about

703
00:29:57,680 --> 00:30:00,160
Speaker 3: making each one of the individuals a better.

704
00:30:00,200 --> 00:30:01,080
Speaker 1: Version of themselves.

705
00:30:01,760 --> 00:30:04,200
Speaker 3: Absolutely, that's using AI agents to be a bit of

706
00:30:04,280 --> 00:30:06,280
Speaker 3: version of in our case, brokers.

707
00:30:06,520 --> 00:30:08,000
Speaker 2: And the thing is we ask a lot of our people,

708
00:30:08,040 --> 00:30:09,360
Speaker 2: We ask a lot of our teams. I don't think

709
00:30:09,360 --> 00:30:11,400
Speaker 2: there's a single company right now that's not actually asking

710
00:30:11,480 --> 00:30:12,360
Speaker 2: a lot from their people.

711
00:30:12,440 --> 00:30:13,120
Speaker 1: So how can you.

712
00:30:13,040 --> 00:30:15,320
Speaker 2: Support them in that journey? Is what I'm thinking about,

713
00:30:15,400 --> 00:30:18,320
Speaker 2: and I think as well, in this future, go to

714
00:30:18,360 --> 00:30:21,080
Speaker 2: market teams are even more indispensable. Like I'm actually on

715
00:30:21,080 --> 00:30:22,960
Speaker 2: a mission at Relevance to not only create the world's

716
00:30:22,960 --> 00:30:25,400
Speaker 2: best product when it comes to building an al workforce,

717
00:30:25,640 --> 00:30:27,760
Speaker 2: but also the world's best go to market team because

718
00:30:27,800 --> 00:30:31,800
Speaker 2: I genuinely believe for yourself or for clients both both

719
00:30:31,880 --> 00:30:33,560
Speaker 2: like I want to have person the best go to

720
00:30:33,560 --> 00:30:34,920
Speaker 2: market team. I want my customers to have the best

721
00:30:34,960 --> 00:30:37,840
Speaker 2: go to market team because to your example of out

722
00:30:37,840 --> 00:30:39,840
Speaker 2: the brokers, like at the end of the day, the

723
00:30:40,200 --> 00:30:42,640
Speaker 2: person who is coming in to get the loan. Of

724
00:30:42,680 --> 00:30:44,480
Speaker 2: course they want the right information. Of course they want

725
00:30:44,480 --> 00:30:47,440
Speaker 2: the best information, but that's not why they work with

726
00:30:47,440 --> 00:30:50,560
Speaker 2: a broker. Like the relationships and the kind of the

727
00:30:50,560 --> 00:30:52,480
Speaker 2: care that people put into things is critical. And I

728
00:30:52,480 --> 00:30:54,840
Speaker 2: think we all trust exactly like we all want to

729
00:30:54,840 --> 00:30:57,400
Speaker 2: work with someone that cares about us. We trust it

730
00:30:57,680 --> 00:30:59,360
Speaker 2: looking for our best interest. So if AI can help

731
00:30:59,400 --> 00:31:02,040
Speaker 2: supplement them to make sure that they never make basic

732
00:31:02,080 --> 00:31:05,320
Speaker 2: mistakes about facts and knowledge, great, But then they're there

733
00:31:05,360 --> 00:31:07,040
Speaker 2: for me to support me on my journey. And I

734
00:31:07,080 --> 00:31:10,960
Speaker 2: think in this next wave, companies really need to think

735
00:31:11,000 --> 00:31:14,480
Speaker 2: about how they engage with customers because if product motes

736
00:31:14,560 --> 00:31:18,640
Speaker 2: maybe start wearing thin GTM motes, I think will be

737
00:31:18,720 --> 00:31:19,600
Speaker 2: really really pronounced.

738
00:31:20,000 --> 00:31:22,959
Speaker 1: Again, would you what did you call it? Like if

739
00:31:23,000 --> 00:31:25,640
Speaker 1: the product mode is what was the second one you said.

740
00:31:25,480 --> 00:31:26,560
Speaker 2: Go to market motes, I think.

741
00:31:28,600 --> 00:31:29,000
Speaker 1: Boat's sake.

742
00:31:29,400 --> 00:31:31,200
Speaker 2: You go to market teams like they might be the

743
00:31:31,280 --> 00:31:33,360
Speaker 2: most precious resource. They're going to be face to face

744
00:31:33,400 --> 00:31:36,000
Speaker 2: with your customers, building those relationshis building that trust. You

745
00:31:36,040 --> 00:31:37,080
Speaker 2: can't under invest in that.

746
00:31:37,520 --> 00:31:40,400
Speaker 3: If I was to ask relevant AI, do you go

747
00:31:40,480 --> 00:31:42,400
Speaker 3: into it into an organization and do an order?

748
00:31:43,000 --> 00:31:46,360
Speaker 2: Yeah, we typically we have a pretty engagement with.

749
00:31:46,840 --> 00:31:48,920
Speaker 1: An AI ordered. Of course not not a tax order

750
00:31:49,000 --> 00:31:49,960
Speaker 1: or whatever, but of course yeah.

751
00:31:49,960 --> 00:31:51,720
Speaker 2: And then we go in and like day one, we're like,

752
00:31:51,840 --> 00:31:54,160
Speaker 2: this is not a customer vendor relationships for partners. We

753
00:31:54,200 --> 00:31:56,400
Speaker 2: want to transform you go to market organization to be

754
00:31:56,520 --> 00:31:58,920
Speaker 2: an a first go to market organization. Let's learn about

755
00:31:58,920 --> 00:32:00,480
Speaker 2: how you sell, let's learn how you do things. And

756
00:32:00,520 --> 00:32:02,280
Speaker 2: the reality is we just had a company kick off

757
00:32:03,680 --> 00:32:06,280
Speaker 2: last week and we have four customers at ten, two

758
00:32:06,280 --> 00:32:08,800
Speaker 2: from the US, flu over two from Australia. And the

759
00:32:08,840 --> 00:32:11,440
Speaker 2: thing they all said about us is they've never experienced

760
00:32:11,720 --> 00:32:15,560
Speaker 2: the level of care support from a vendor before. And

761
00:32:15,600 --> 00:32:18,560
Speaker 2: that's because we I truly think like our relationships with

762
00:32:18,600 --> 00:32:22,080
Speaker 2: our customers are going to be so meaningful because unlike software,

763
00:32:22,080 --> 00:32:26,200
Speaker 2: where your TAM is actually quite limited, the amount of

764
00:32:26,280 --> 00:32:28,200
Speaker 2: value and spend that customers can have when you has

765
00:32:28,240 --> 00:32:30,000
Speaker 2: a pretty finite limit. If you look at SaaS company,

766
00:32:30,000 --> 00:32:32,600
Speaker 2: it's generally at our stage the amount of spend that

767
00:32:32,600 --> 00:32:34,200
Speaker 2: customers are going to have with agents is going to

768
00:32:34,200 --> 00:32:36,040
Speaker 2: be huge. It's going to be much closer to labor

769
00:32:36,080 --> 00:32:38,880
Speaker 2: spend than software spend, and so I want to make

770
00:32:38,920 --> 00:32:41,200
Speaker 2: sure we're doing everything to invest in those customers to

771
00:32:41,280 --> 00:32:42,920
Speaker 2: make them successful and grow with them.

772
00:32:43,480 --> 00:32:45,440
Speaker 3: When you say the amount of spend that the una

773
00:32:45,480 --> 00:32:46,720
Speaker 3: of the agents is going to be close to what

774
00:32:46,720 --> 00:32:50,560
Speaker 3: they spend with people, yeah, well I don't understand that, Like,

775
00:32:51,240 --> 00:32:51,760
Speaker 3: how do you mean?

776
00:32:52,080 --> 00:32:56,000
Speaker 2: So if you think about like software, the software budgets

777
00:32:56,040 --> 00:32:58,760
Speaker 2: that exist today, they're going to be a fraction of

778
00:32:58,800 --> 00:33:01,040
Speaker 2: the spend that people that companies are going to have

779
00:33:01,120 --> 00:33:03,360
Speaker 2: on AI. Because AI is going to solve for outcomes

780
00:33:03,400 --> 00:33:05,239
Speaker 2: like people do. It's not going to solve for like

781
00:33:05,880 --> 00:33:07,800
Speaker 2: making it easier for me to store my data in

782
00:33:07,840 --> 00:33:10,560
Speaker 2: a database. Yeah, it's actually going to run my business.

783
00:33:10,960 --> 00:33:14,360
Speaker 2: Most businesses are going to be heavily reliant on AI

784
00:33:14,400 --> 00:33:17,200
Speaker 2: to ol preay in the future, and the people that

785
00:33:17,240 --> 00:33:20,120
Speaker 2: are working in that business will be acting much more

786
00:33:20,120 --> 00:33:22,560
Speaker 2: like managers than individual contributors.

787
00:33:22,920 --> 00:33:25,920
Speaker 3: See, is this is AO to some extent at its

788
00:33:25,920 --> 00:33:28,400
Speaker 3: early stage, the stage you're talking about that right now,

789
00:33:28,840 --> 00:33:33,480
Speaker 3: is this sort of going to replace all those Philippine

790
00:33:33,480 --> 00:33:35,719
Speaker 3: and ADMIN people that everybody seems to be using. We

791
00:33:35,800 --> 00:33:39,840
Speaker 3: use them, you know, wherever, whichever country, Nepalese, et cetera.

792
00:33:39,920 --> 00:33:42,760
Speaker 3: I mean, is this the next stage of outsourcing admin.

793
00:33:42,920 --> 00:33:44,760
Speaker 2: It's going to it's gonna have a really big dance

794
00:33:44,760 --> 00:33:45,920
Speaker 2: on BPOs for sure.

795
00:33:47,040 --> 00:33:48,800
Speaker 1: She's the explain what a BPO is to audience.

796
00:33:48,800 --> 00:33:54,040
Speaker 2: But sorry, business processing offer organization like what they basically

797
00:33:54,080 --> 00:33:56,120
Speaker 2: do is as you said, there will help you. Like

798
00:33:56,200 --> 00:33:58,480
Speaker 2: let's say you're a banking you need to have invoice processed,

799
00:33:58,840 --> 00:34:00,640
Speaker 2: you know, every single day, and someone needs to take

800
00:34:00,680 --> 00:34:04,040
Speaker 2: that PDF and convert it into some input on our software,

801
00:34:04,360 --> 00:34:08,759
Speaker 2: some sort of software system. Those companies will be really

802
00:34:08,800 --> 00:34:10,520
Speaker 2: impacted by agents for sure.

803
00:34:10,320 --> 00:34:12,279
Speaker 3: Because right now, the last couple of years, we'll be

804
00:34:12,320 --> 00:34:14,239
Speaker 3: paying someone in Australi one hundred and twenty grand year

805
00:34:14,280 --> 00:34:17,120
Speaker 3: to do that. But we get it done in Philippines.

806
00:34:17,200 --> 00:34:20,200
Speaker 3: We actually do it. We operate this way. We're paying

807
00:34:20,239 --> 00:34:23,759
Speaker 3: them thirty grand. They're actually very well educated, they're you know,

808
00:34:23,760 --> 00:34:27,319
Speaker 3: they're fantastic, and so we can get four of those

809
00:34:27,360 --> 00:34:29,279
Speaker 3: people to do what one was doing the cost of

810
00:34:29,280 --> 00:34:31,640
Speaker 3: one here in Australia. Now that's been that's been a

811
00:34:31,640 --> 00:34:35,040
Speaker 3: big had a big impact on our organization in terms

812
00:34:35,040 --> 00:34:37,319
Speaker 3: of P and L. Like it's allowed us to save

813
00:34:37,360 --> 00:34:39,440
Speaker 3: a lot of money, but still right, the same amount

814
00:34:39,480 --> 00:34:41,400
Speaker 3: of business and.

815
00:34:40,880 --> 00:34:42,960
Speaker 2: You're going to move that to a the next few years.

816
00:34:43,040 --> 00:34:47,160
Speaker 3: Yeah, So does that mean that those outsourcing businesses because

817
00:34:47,200 --> 00:34:48,480
Speaker 3: A do it much cheat them.

818
00:34:48,480 --> 00:34:51,480
Speaker 1: We pay a Philippines person.

819
00:34:52,360 --> 00:34:55,200
Speaker 2: It depends on a task, but it will. It's undoubtable.

820
00:34:55,360 --> 00:34:59,000
Speaker 2: It's like, it's without a doubt that by some point

821
00:34:59,040 --> 00:35:00,680
Speaker 2: in the future it'll be my much cheaper than a

822
00:35:00,680 --> 00:35:03,799
Speaker 2: person doing it. And what's the perfect ingredients for an

823
00:35:03,840 --> 00:35:06,840
Speaker 2: agent to handle your work? It's highly repetitive, it's a

824
00:35:06,880 --> 00:35:09,239
Speaker 2: well known process, so you know actually how to do it.

825
00:35:10,880 --> 00:35:13,600
Speaker 2: And if you have those two pieces of ingredients, you

826
00:35:13,640 --> 00:35:15,680
Speaker 2: can have an agent do that. And as long as

827
00:35:15,719 --> 00:35:19,160
Speaker 2: the cost basis becomes lower, which it is continuously happening,

828
00:35:19,200 --> 00:35:22,560
Speaker 2: like the cost of GPS is going down, the performance up,

829
00:35:23,440 --> 00:35:25,160
Speaker 2: The amount of performance and intelligence you have in a

830
00:35:25,160 --> 00:35:28,759
Speaker 2: model per dollar spent is improving. It's going to get

831
00:35:28,800 --> 00:35:30,359
Speaker 2: to a place where you can one hundred percent handle

832
00:35:30,360 --> 00:35:33,239
Speaker 2: those tasks with agents. I think that's without question. Now again,

833
00:35:33,280 --> 00:35:36,560
Speaker 2: I think the important conclusion to not make here is

834
00:35:36,600 --> 00:35:39,640
Speaker 2: that that means those people and those organizations can't find

835
00:35:39,600 --> 00:35:42,200
Speaker 2: your opportunities. I think there's just going to be more

836
00:35:42,200 --> 00:35:47,759
Speaker 2: opportunities created somewhere else. Typically that's what happens, but it's

837
00:35:48,680 --> 00:35:50,080
Speaker 2: I think we should be I think we should be

838
00:35:50,120 --> 00:35:53,680
Speaker 2: intellectually honest and definitely be aware of what's happening, because

839
00:35:54,000 --> 00:35:56,239
Speaker 2: transitions can be difficult, and if we plan for transitions,

840
00:35:56,239 --> 00:35:58,040
Speaker 2: then we can make sure we succeed in them.

841
00:35:58,200 --> 00:36:01,760
Speaker 3: Is rather than ZAOI finding again really speaking, widespread adopt

842
00:36:01,960 --> 00:36:08,720
Speaker 3: adoption of rules of your prognosis and also the treatment

843
00:36:08,760 --> 00:36:10,920
Speaker 3: for your prognosis of where things are going in twenty six,

844
00:36:11,040 --> 00:36:14,680
Speaker 3: twenty seven, twenty are you getting across the board acceptance?

845
00:36:15,040 --> 00:36:18,120
Speaker 2: I am this year increasingly Like when I speak to

846
00:36:18,200 --> 00:36:21,359
Speaker 2: kind of CIOs or CEOs, I'm increasingly getting more and

847
00:36:21,400 --> 00:36:25,040
Speaker 2: more clarity that this is their goal. They know to

848
00:36:25,080 --> 00:36:28,200
Speaker 2: survive they need to adopt AI. They don't quite necessarily how,

849
00:36:28,280 --> 00:36:31,160
Speaker 2: but they know this is happening. I think again, claud

850
00:36:31,200 --> 00:36:33,719
Speaker 2: code and what it did for software engineering has been

851
00:36:34,160 --> 00:36:38,080
Speaker 2: eye opening for everybody and again to all the viewers

852
00:36:38,080 --> 00:36:40,840
Speaker 2: out there, like if you haven't seen what it can do,

853
00:36:40,880 --> 00:36:43,760
Speaker 2: just go do some research it is changing software engineering

854
00:36:43,760 --> 00:36:47,640
Speaker 2: as an industry. At that point, you no longer have

855
00:36:47,719 --> 00:36:49,680
Speaker 2: to you know, we do long have to debate the

856
00:36:49,760 --> 00:36:53,080
Speaker 2: reality or the possibility of this. It's happening. So every

857
00:36:53,120 --> 00:36:55,480
Speaker 2: company now is going through existential crisis. We saw what

858
00:36:55,520 --> 00:36:58,400
Speaker 2: happened with the public SaaS companies right, there's been a

859
00:36:58,440 --> 00:37:00,680
Speaker 2: huge self in the last few weeks are uncertain of

860
00:37:00,920 --> 00:37:05,880
Speaker 2: where they're going. Companies like HubSpot, they exceeded earnings, expected earnings,

861
00:37:05,920 --> 00:37:10,120
Speaker 2: yet they've been hit on stock price. Why is that, Well,

862
00:37:10,120 --> 00:37:12,360
Speaker 2: it seems like the markets now are not rewarding you

863
00:37:12,400 --> 00:37:15,319
Speaker 2: for performing based on or even exceeding expectation. They're only

864
00:37:15,360 --> 00:37:17,520
Speaker 2: rewarding you if you're accelerating, and that means that you're

865
00:37:17,600 --> 00:37:20,600
Speaker 2: kind of able to drive AI in your business. So

866
00:37:20,640 --> 00:37:24,120
Speaker 2: I think there's kind of like existential fears everywhere, and

867
00:37:24,200 --> 00:37:26,360
Speaker 2: so there's a lot of appetites to solve for this.

868
00:37:26,520 --> 00:37:28,360
Speaker 2: And the good thing is it will solve for a

869
00:37:28,400 --> 00:37:30,799
Speaker 2: lot of these things. Like I'm actually very confident that

870
00:37:30,840 --> 00:37:32,840
Speaker 2: a lot of these problems that we do have with scaling,

871
00:37:32,920 --> 00:37:36,880
Speaker 2: with challenges, with burnout, with mundane work, AI is going

872
00:37:36,920 --> 00:37:38,799
Speaker 2: to be a huge contributor. It's helping solve for that

873
00:37:39,200 --> 00:37:41,080
Speaker 2: and as an end result, Like again, I think you

874
00:37:41,160 --> 00:37:42,840
Speaker 2: kind of rightly called this out a little bit earlier.

875
00:37:42,880 --> 00:37:45,160
Speaker 2: But what a lot of people don't realize is think

876
00:37:45,200 --> 00:37:47,480
Speaker 2: about the better quality that you're going to be able

877
00:37:47,520 --> 00:37:50,200
Speaker 2: to receive, the better services you can receive, the more

878
00:37:50,239 --> 00:37:52,520
Speaker 2: you help your brokers be better at their job, the

879
00:37:52,560 --> 00:37:55,000
Speaker 2: better service your customer is going to receive.

880
00:37:54,840 --> 00:37:57,080
Speaker 3: And more, like how I can recruit more exactly, they

881
00:37:57,120 --> 00:37:59,400
Speaker 3: going to hear about it. I mean, you've got to

882
00:37:59,440 --> 00:38:02,160
Speaker 3: be the best technology these days because the good ones

883
00:38:02,160 --> 00:38:02,600
Speaker 3: will come to you.

884
00:38:02,640 --> 00:38:03,839
Speaker 2: And your customers are going to come to you because

885
00:38:03,840 --> 00:38:05,640
Speaker 2: they're going to say, working with a broker from this

886
00:38:05,680 --> 00:38:08,799
Speaker 2: company is better than anybody else because they never make

887
00:38:08,840 --> 00:38:11,800
Speaker 2: a mistake. They know everything, every policy, than every change.

888
00:38:11,840 --> 00:38:14,160
Speaker 1: And no one individual can know that. It's just not possible.

889
00:38:14,800 --> 00:38:16,520
Speaker 3: And you know what's interesting in our game, and it's

890
00:38:16,520 --> 00:38:19,200
Speaker 3: just slightly off the topic, but it is interesting because

891
00:38:19,239 --> 00:38:22,080
Speaker 3: AI has allowed us to better satisfy the regulator. So

892
00:38:22,120 --> 00:38:23,880
Speaker 3: the regulator wants to make sure that I give you

893
00:38:23,920 --> 00:38:26,640
Speaker 3: the best deal. Now I can't possibly give you the

894
00:38:26,640 --> 00:38:29,080
Speaker 3: best deal because I don't know all the deals. I mean,

895
00:38:29,120 --> 00:38:32,760
Speaker 3: there's thirty odd lenders out there, and the policies change

896
00:38:32,800 --> 00:38:36,759
Speaker 3: every week due to appetite, credit impairments, you know, like

897
00:38:37,040 --> 00:38:40,160
Speaker 3: concentrated risk, all sorts of things. Whereas the only way

898
00:38:40,200 --> 00:38:43,920
Speaker 3: you can actually know everything is some sort of machine

899
00:38:44,200 --> 00:38:47,120
Speaker 3: or some sort of agent that is constantly being updated.

900
00:38:47,120 --> 00:38:49,359
Speaker 3: As long as we make sure it's updated, we give

901
00:38:49,360 --> 00:38:51,759
Speaker 3: them material update and for it to learn. That's the

902
00:38:51,800 --> 00:38:54,120
Speaker 3: only way you can actually undertake your duties properly.

903
00:38:55,440 --> 00:38:57,680
Speaker 2: Only way because now, like let's say it took you,

904
00:38:58,120 --> 00:39:00,080
Speaker 2: let's say it takes you two hours to complete a

905
00:39:00,120 --> 00:39:02,239
Speaker 2: task to do that really thorough investigation, and there's no

906
00:39:02,239 --> 00:39:04,880
Speaker 2: way you can maybe do that in a natural state

907
00:39:04,920 --> 00:39:07,279
Speaker 2: of doing business. Now you can. You can delegate to

908
00:39:07,400 --> 00:39:10,359
Speaker 2: AAR it'll go spend the two hours checking thing by

909
00:39:10,480 --> 00:39:12,239
Speaker 2: line by line, every little thing that you need to

910
00:39:12,280 --> 00:39:15,200
Speaker 2: do against to the viewers out there, like don't think

911
00:39:15,239 --> 00:39:17,560
Speaker 2: chat GPT where you ask a question that returns a response.

912
00:39:18,120 --> 00:39:20,600
Speaker 2: Think a system to which you delegate a problem. It

913
00:39:20,680 --> 00:39:23,120
Speaker 2: will work with an interact with one hundred dollars systems

914
00:39:23,239 --> 00:39:25,480
Speaker 2: over two hours to solve that problem and come to

915
00:39:25,480 --> 00:39:28,279
Speaker 2: you with a solution. That's where we're going to. And

916
00:39:28,320 --> 00:39:32,520
Speaker 2: so now that lender. Sorry, the person seeking the loan

917
00:39:32,560 --> 00:39:34,800
Speaker 2: as a consumer is going to get the best advice.

918
00:39:34,840 --> 00:39:37,120
Speaker 2: It's going to get the best perspective, it's going to

919
00:39:37,120 --> 00:39:39,480
Speaker 2: get the best offer. It's going to get it much

920
00:39:39,520 --> 00:39:42,800
Speaker 2: more detailed. Maybe they'll get personal collateral for them. Maybe

921
00:39:42,840 --> 00:39:46,600
Speaker 2: that person seeking a loan is maybe less educated about

922
00:39:46,600 --> 00:39:48,600
Speaker 2: how loans work, and we really want to educate them,

923
00:39:48,640 --> 00:39:50,360
Speaker 2: and so we're going to create some custom collateral that

924
00:39:50,400 --> 00:39:52,480
Speaker 2: they understand. You know all you know?

925
00:39:52,800 --> 00:39:54,240
Speaker 1: Did you know this? Yeah?

926
00:39:54,280 --> 00:39:57,040
Speaker 2: Based on your profile, you're probably maybe your first home buyer.

927
00:39:57,120 --> 00:39:58,840
Speaker 2: You don't know probably these things, or you're from Sydney

928
00:39:58,920 --> 00:40:01,080
Speaker 2: versus Melbourne. These are the new is that the AI

929
00:40:01,120 --> 00:40:03,360
Speaker 2: can guess you might not know. And all of a sudden,

930
00:40:03,400 --> 00:40:06,080
Speaker 2: every single one of your clients is getting a personalized

931
00:40:06,080 --> 00:40:09,360
Speaker 2: experience built for them on the fly, based on their profile,

932
00:40:09,560 --> 00:40:13,280
Speaker 2: based on their needs, their questions. And so as a consumer,

933
00:40:13,320 --> 00:40:15,360
Speaker 2: I'm excited for that world because you're just going to

934
00:40:15,400 --> 00:40:18,560
Speaker 2: get a better product. And so not only do you

935
00:40:18,600 --> 00:40:21,799
Speaker 2: help your team perform better, you help your customers have

936
00:40:21,880 --> 00:40:22,680
Speaker 2: a better experience.

937
00:40:22,800 --> 00:40:25,640
Speaker 3: Do you think that is there any chance at the

938
00:40:25,640 --> 00:40:27,600
Speaker 3: moment that II can make errors?

939
00:40:27,640 --> 00:40:29,400
Speaker 1: Absolutely? How do we sort of manage that.

940
00:40:29,440 --> 00:40:32,160
Speaker 3: Part of it is that where we keep the bdms

941
00:40:32,360 --> 00:40:35,719
Speaker 3: still in the role to oversee everything. Yeah.

942
00:40:35,719 --> 00:40:38,440
Speaker 2: Absolutely, so, like again, think of an a think of

943
00:40:38,480 --> 00:40:40,600
Speaker 2: the AI agents that work for your team as employees.

944
00:40:40,880 --> 00:40:44,680
Speaker 2: Employees make mistakes, humans make mistakes all the time. I

945
00:40:44,719 --> 00:40:46,799
Speaker 2: am a CMO at a company. I hire a new

946
00:40:46,840 --> 00:40:50,120
Speaker 2: marketing manager. Their job is to update my landing page.

947
00:40:50,160 --> 00:40:52,759
Speaker 2: For the first month, I'm probably asking them to show

948
00:40:52,800 --> 00:40:56,120
Speaker 2: me every update for approval. After a while, I'm like, hey, Mark,

949
00:40:56,239 --> 00:40:57,719
Speaker 2: you're a really good at updating the website. You make

950
00:40:57,760 --> 00:40:59,800
Speaker 2: no mistakes, don't come to me for approval anymore, just

951
00:41:00,120 --> 00:41:02,640
Speaker 2: doing yourself. You're going to go through a very similar

952
00:41:03,040 --> 00:41:05,520
Speaker 2: journey with your agents. Our customers do this all the time.

953
00:41:05,880 --> 00:41:07,600
Speaker 2: Like when you build your agent and you train it,

954
00:41:07,840 --> 00:41:09,680
Speaker 2: You train it basically with like a one or two

955
00:41:09,680 --> 00:41:12,560
Speaker 2: page document explaining how to do a shob. You connect

956
00:41:12,600 --> 00:41:14,800
Speaker 2: it to all the tools it needs. And in relevance,

957
00:41:14,800 --> 00:41:16,600
Speaker 2: we have the option to say some of these tools

958
00:41:16,640 --> 00:41:20,719
Speaker 2: can automatically run, some of these tools require approval, and

959
00:41:20,760 --> 00:41:22,520
Speaker 2: so you still have a manager to give it some

960
00:41:22,640 --> 00:41:25,080
Speaker 2: rules exactly, and so like, hey, before I send out

961
00:41:25,080 --> 00:41:26,759
Speaker 2: an email, because I don't trust you, yet I need

962
00:41:26,760 --> 00:41:28,560
Speaker 2: you to seek approval, and so you have humans approve

963
00:41:28,560 --> 00:41:30,600
Speaker 2: of that. Maybe after a few months you're like, the

964
00:41:30,800 --> 00:41:32,640
Speaker 2: error rate for this is so low. This this employee

965
00:41:32,640 --> 00:41:34,480
Speaker 2: is really good. It makes a few mistakes, I trust

966
00:41:34,480 --> 00:41:36,799
Speaker 2: it to do it automatically. Then you switch to order on.

967
00:41:37,520 --> 00:41:41,200
Speaker 2: So I think again, like the faster people frame AI

968
00:41:41,400 --> 00:41:44,359
Speaker 2: and AI agents in particular as the way they would

969
00:41:44,480 --> 00:41:49,120
Speaker 2: frame about hiring someone, the easier they will have an

970
00:41:49,160 --> 00:41:51,520
Speaker 2: easier they'll have an easier time of both understanding how

971
00:41:51,560 --> 00:41:53,000
Speaker 2: it works but also implementing it.

972
00:41:53,040 --> 00:41:55,400
Speaker 3: So it's a good way to finish this off, Daniel.

973
00:41:55,719 --> 00:41:58,359
Speaker 3: And something something I got at it, which is quite

974
00:41:58,360 --> 00:42:01,960
Speaker 3: good for me, is that I should be thinking about

975
00:42:02,800 --> 00:42:09,279
Speaker 3: recruiting AI agents as employees call it. Yeah, but then

976
00:42:09,320 --> 00:42:12,279
Speaker 3: not employees obviously, but AI agents. I've got to think

977
00:42:12,320 --> 00:42:14,520
Speaker 3: about it as not saying, oh, we're going to have

978
00:42:14,560 --> 00:42:17,760
Speaker 3: AI in a business. I've got to have AI recruits,

979
00:42:17,960 --> 00:42:20,719
Speaker 3: AI agent recruits in my business in those areas which

980
00:42:20,719 --> 00:42:22,360
Speaker 3: AI can manage.

981
00:42:22,680 --> 00:42:24,240
Speaker 1: But I've got to have recruits in my office.

982
00:42:24,320 --> 00:42:26,200
Speaker 2: Yeah. And why is that important or not my office

983
00:42:26,200 --> 00:42:28,279
Speaker 2: my world? Yeah? And why is that important? Because it

984
00:42:28,360 --> 00:42:32,080
Speaker 2: forces you to then build the organizational structure to succeed. Yeah,

985
00:42:32,120 --> 00:42:34,480
Speaker 2: so what's that Someone is going to manage that agent,

986
00:42:34,640 --> 00:42:37,080
Speaker 2: someone's going to be responsible for training it, someone's accountable

987
00:42:37,080 --> 00:42:38,560
Speaker 2: for the goals. Because the thing is, it's like in

988
00:42:38,600 --> 00:42:40,120
Speaker 2: a normal recruit exactly.

989
00:42:39,760 --> 00:42:40,520
Speaker 1: Like an agent.

990
00:42:40,719 --> 00:42:42,880
Speaker 2: You can't you can't hold accountab because it's obously not.

991
00:42:42,920 --> 00:42:45,360
Speaker 2: A person we can hold accountable is the person managing

992
00:42:45,360 --> 00:42:47,960
Speaker 2: the agent. And the person managing the agent is then

993
00:42:47,960 --> 00:42:50,400
Speaker 2: incentivized to actually train it. So a lot of our customers,

994
00:42:50,719 --> 00:42:52,920
Speaker 2: we have people who started off working with relevance in

995
00:42:53,160 --> 00:42:57,480
Speaker 2: the company as like individual person. Very quickly they start

996
00:42:57,560 --> 00:43:00,239
Speaker 2: being promoted into like an aur ops manager role, don't

997
00:43:00,280 --> 00:43:02,480
Speaker 2: have a team of people who help them manage agents.

998
00:43:02,840 --> 00:43:04,879
Speaker 2: And so all of a sudden, you're starting to see

999
00:43:04,880 --> 00:43:09,240
Speaker 2: what the new workforce looks like. It's not about Bob

1000
00:43:09,320 --> 00:43:11,640
Speaker 2: being able to send out one hundred calls. It's about

1001
00:43:11,640 --> 00:43:14,319
Speaker 2: Bob being able to orchestrate a team of agents as

1002
00:43:14,320 --> 00:43:17,279
Speaker 2: if there were humans and train and intervene. I'm going

1003
00:43:17,280 --> 00:43:19,200
Speaker 2: to give you approvals. Hey, this week, we're going to

1004
00:43:19,239 --> 00:43:21,480
Speaker 2: actually do some different stuff. That stuff we did last

1005
00:43:21,480 --> 00:43:23,080
Speaker 2: week is not its good. I'm going to help now

1006
00:43:23,120 --> 00:43:25,400
Speaker 2: train you on a new process. And so if you

1007
00:43:25,440 --> 00:43:27,200
Speaker 2: think about it in that way, you're much more likely

1008
00:43:27,160 --> 00:43:29,160
Speaker 2: to succeed. If you think about it as like a

1009
00:43:29,160 --> 00:43:30,880
Speaker 2: piece of software that you're now going to buy and

1010
00:43:30,960 --> 00:43:33,719
Speaker 2: install and it's magically going to solve your problems. That

1011
00:43:34,160 --> 00:43:36,600
Speaker 2: doesn't exist, No magic bullets, no simple bullets here, you know.

1012
00:43:37,239 --> 00:43:40,560
Speaker 2: So think of it in that frame of reference in

1013
00:43:40,640 --> 00:43:42,959
Speaker 2: order to design your organization for the future.

1014
00:43:42,719 --> 00:43:45,200
Speaker 3: And so that recruit would fit within the architecture of

1015
00:43:45,239 --> 00:43:47,960
Speaker 3: your business the same as any other recruit exactly.

1016
00:43:48,280 --> 00:43:48,840
Speaker 1: It's interesting.

1017
00:43:48,920 --> 00:43:51,640
Speaker 3: Yeah, not so long ago, maybe two months ago. I

1018
00:43:51,800 --> 00:43:55,120
Speaker 3: use one of the platforms to look at a bit

1019
00:43:55,120 --> 00:43:58,120
Speaker 3: of land, a property I own around this area, and

1020
00:43:58,440 --> 00:43:59,560
Speaker 3: I said, I want you to go and have look at

1021
00:43:59,600 --> 00:44:02,320
Speaker 3: all the da that have development applications have been approved

1022
00:44:02,520 --> 00:44:05,640
Speaker 3: over the last two years, and who find out who

1023
00:44:05,680 --> 00:44:08,000
Speaker 3: the architectures, all that stuff appears in the documentation of

1024
00:44:08,040 --> 00:44:10,080
Speaker 3: the council, and tell me what I can put on

1025
00:44:10,120 --> 00:44:16,120
Speaker 3: this property and design and actually a few more instructions

1026
00:44:16,160 --> 00:44:20,000
Speaker 3: actually designed the building for me. It was pretty bloody

1027
00:44:20,040 --> 00:44:23,600
Speaker 3: good and it did fit within the constraints the you know,

1028
00:44:23,920 --> 00:44:26,480
Speaker 3: let's call it height, foot.

1029
00:44:26,840 --> 00:44:28,400
Speaker 1: Floor space, ratios. All that sort stuff fit.

1030
00:44:28,520 --> 00:44:31,680
Speaker 3: I could see it was right, and it sort of

1031
00:44:31,760 --> 00:44:34,560
Speaker 3: encouraged me to think about perhaps either selling it or

1032
00:44:34,600 --> 00:44:38,360
Speaker 3: putting it getting an application approved. I think that AI

1033
00:44:38,480 --> 00:44:40,680
Speaker 3: is not scary. If you start using it, you've got

1034
00:44:40,719 --> 00:44:43,719
Speaker 3: to use it. Can I just finally, finally, how do

1035
00:44:43,760 --> 00:44:49,960
Speaker 3: you think the AI will interface with us as individuals

1036
00:44:50,000 --> 00:44:52,640
Speaker 3: in our private life as opposed to what you guys

1037
00:44:52,640 --> 00:44:53,640
Speaker 3: do relevance AI.

1038
00:44:54,880 --> 00:44:57,319
Speaker 2: I think it'll bleed into very similar ways, like a

1039
00:44:57,320 --> 00:44:59,279
Speaker 2: lot of the again mundane work we do in our

1040
00:44:59,280 --> 00:45:04,440
Speaker 2: personal lives that we enjoy, filing your taxes or scheduling

1041
00:45:04,880 --> 00:45:07,520
Speaker 2: I don't know, booking a dinner or something like that,

1042
00:45:07,680 --> 00:45:10,520
Speaker 2: or changing the booking or booking flights. I just think

1043
00:45:10,600 --> 00:45:13,960
Speaker 2: all of those tasks that previously we all know how

1044
00:45:13,960 --> 00:45:16,880
Speaker 2: we've done, they're going to be done by your AI

1045
00:45:17,040 --> 00:45:19,560
Speaker 2: assistant agent. That agent is going to have access to

1046
00:45:19,640 --> 00:45:22,200
Speaker 2: your systems like you do. You'll give it access to

1047
00:45:22,680 --> 00:45:25,279
Speaker 2: your login details. Basically it'll access those systems and do

1048
00:45:25,280 --> 00:45:27,600
Speaker 2: those things for you. And I think that's really exciting,

1049
00:45:27,640 --> 00:45:30,680
Speaker 2: Like I think, I think it's cool. Like the only

1050
00:45:30,719 --> 00:45:34,440
Speaker 2: reason it's not exciting is if you see AIS as

1051
00:45:34,520 --> 00:45:37,239
Speaker 2: kind of like I don't know like this almost like

1052
00:45:37,280 --> 00:45:38,840
Speaker 2: this monster in the closet, you don't quite know and

1053
00:45:38,920 --> 00:45:40,680
Speaker 2: understand if you just see it for what it is.

1054
00:45:40,719 --> 00:45:43,279
Speaker 2: It's a technology that really powerful. That's really powerful in

1055
00:45:43,280 --> 00:45:45,080
Speaker 2: the sense that it can make real time decisions about

1056
00:45:45,120 --> 00:45:47,440
Speaker 2: what to do next, and you can leverage that capability

1057
00:45:47,440 --> 00:45:49,920
Speaker 2: to do less mundane things yourself. All of a sudden,

1058
00:45:49,960 --> 00:45:51,680
Speaker 2: it's just another piece of tech that we're all going

1059
00:45:51,719 --> 00:45:53,560
Speaker 2: to have in our lives that's going to enrich a

1060
00:45:53,600 --> 00:45:55,560
Speaker 2: lot of our lives. Now, that doesn't mean there's going

1061
00:45:55,600 --> 00:45:58,000
Speaker 2: to be some challenges as with any technology, that definitely

1062
00:45:58,040 --> 00:46:02,120
Speaker 2: will be, but it's it's a lot less kind of

1063
00:46:02,640 --> 00:46:05,480
Speaker 2: monster in the closet, and it's a lot more just

1064
00:46:05,520 --> 00:46:07,719
Speaker 2: a piece of technology that's going to change a lot

1065
00:46:07,760 --> 00:46:10,640
Speaker 2: of things about how we live in a very positive way.

1066
00:46:10,880 --> 00:46:15,040
Speaker 3: You let an IOI agent DASH personal assistant access to

1067
00:46:15,040 --> 00:46:16,759
Speaker 3: your uru details.

1068
00:46:16,680 --> 00:46:19,320
Speaker 1: Everything, Yeah, yeah, I do you do it now? Yeah?

1069
00:46:19,320 --> 00:46:21,640
Speaker 2: So the way I like we as I said, like,

1070
00:46:21,680 --> 00:46:23,799
Speaker 2: we've launched this kind of chat too point zero where

1071
00:46:24,120 --> 00:46:26,040
Speaker 2: I can connect it to all of my systems like

1072
00:46:26,160 --> 00:46:28,480
Speaker 2: email and everything, and the more it has access to

1073
00:46:28,520 --> 00:46:31,520
Speaker 2: the more it can do for me. Obviously, I'm running

1074
00:46:31,520 --> 00:46:33,560
Speaker 2: it in relevance, which is very much like we have

1075
00:46:33,600 --> 00:46:35,680
Speaker 2: a lot of security and kind of enterprise guardrails, so

1076
00:46:35,719 --> 00:46:37,440
Speaker 2: I trust it. I wouldn't do that with some of

1077
00:46:37,480 --> 00:46:39,600
Speaker 2: these open source solutions that people come up. I probably

1078
00:46:39,640 --> 00:46:43,359
Speaker 2: wouldn't trust it there, but they're not at this stage anyway. Yeah,

1079
00:46:43,360 --> 00:46:45,759
Speaker 2: but consumers will get that, like we're whatever's happening in

1080
00:46:45,840 --> 00:46:48,360
Speaker 2: enterprise today. Consumers will get the same level of security

1081
00:46:48,360 --> 00:46:50,799
Speaker 2: and services that those people are getting, and you'll be

1082
00:46:50,800 --> 00:46:53,320
Speaker 2: able to connect it to everything, and there'll be laws

1083
00:46:53,400 --> 00:46:56,319
Speaker 2: in place for protecting your data. Like anything else, there'll

1084
00:46:56,360 --> 00:46:58,160
Speaker 2: be laws for what they can and can't do. I'm

1085
00:46:58,200 --> 00:47:00,960
Speaker 2: sure the governments are going to wants to have some

1086
00:47:01,120 --> 00:47:05,799
Speaker 2: say in how to regulate you know, this industry. So yes,

1087
00:47:06,040 --> 00:47:09,120
Speaker 2: I think embrace it, be mindful of security. I think

1088
00:47:09,200 --> 00:47:11,360
Speaker 2: always be mindful of security. I want people to be

1089
00:47:11,440 --> 00:47:13,560
Speaker 2: very conscious of like not connecting it to your bank

1090
00:47:13,600 --> 00:47:17,680
Speaker 2: account and then having a bad time. But trust to

1091
00:47:17,760 --> 00:47:21,120
Speaker 2: pick right vendors, use the vendors, and embrace it. And

1092
00:47:21,480 --> 00:47:23,440
Speaker 2: the more you learn about it, I think the more

1093
00:47:23,520 --> 00:47:25,439
Speaker 2: likely you will be able to succeed with it. As

1094
00:47:25,440 --> 00:47:27,799
Speaker 2: with any change, I haven't lived through many of them.

1095
00:47:28,000 --> 00:47:30,879
Speaker 2: But I suspect people who have lived through big technological

1096
00:47:30,960 --> 00:47:34,239
Speaker 2: changes and shifts have found many ways to generate opportunity

1097
00:47:34,239 --> 00:47:36,560
Speaker 2: from them. I think this is no difference to those times.

1098
00:47:36,520 --> 00:47:39,440
Speaker 3: I think you're right. I mean, you clearly right them.

1099
00:47:39,440 --> 00:47:42,040
Speaker 3: Don't mean they're in a condescend any way. You obviously

1100
00:47:42,040 --> 00:47:44,000
Speaker 3: know a lot more about this topic than I do.

1101
00:47:44,120 --> 00:47:45,239
Speaker 1: But if you.

1102
00:47:45,239 --> 00:47:49,840
Speaker 3: Don't get on board, you will be left behind. And

1103
00:47:49,960 --> 00:47:54,880
Speaker 3: I think just finally does relevance a I have anything

1104
00:47:55,160 --> 00:47:58,080
Speaker 3: for smaller business businesses who may not be able to

1105
00:47:58,080 --> 00:48:00,000
Speaker 3: affward something like you, something more off the shit.

1106
00:48:01,280 --> 00:48:04,200
Speaker 2: So we do have a self subversion the platform, but candidly,

1107
00:48:04,360 --> 00:48:07,399
Speaker 2: like we're really like, we work best with companies over

1108
00:48:07,440 --> 00:48:10,200
Speaker 2: seven fifty employees. We enterprise go to market teams. That's

1109
00:48:10,200 --> 00:48:12,840
Speaker 2: our sweet spot. That's who we will accelerate the most.

1110
00:48:13,360 --> 00:48:15,879
Speaker 2: There are plenty of other solutions out there that can

1111
00:48:15,920 --> 00:48:19,520
Speaker 2: help you maybe on your journey. Candidly, as I said, like,

1112
00:48:19,840 --> 00:48:21,920
Speaker 2: you might not even need a product today, You might

1113
00:48:21,960 --> 00:48:24,319
Speaker 2: need a first start educating yourself. So actually just going

1114
00:48:24,320 --> 00:48:26,040
Speaker 2: and learning the state of the market might help you.

1115
00:48:26,400 --> 00:48:28,280
Speaker 2: It might then help you leverage some of those tools

1116
00:48:28,280 --> 00:48:31,640
Speaker 2: in your personal journey and then once you've done that step,

1117
00:48:31,680 --> 00:48:33,640
Speaker 2: step back for a second, reflect all the process in

1118
00:48:33,680 --> 00:48:35,280
Speaker 2: your business and look for solutions.

1119
00:48:35,760 --> 00:48:36,359
Speaker 1: But you know.

1120
00:48:36,520 --> 00:48:38,400
Speaker 2: Relevance, right, Like going back to that kind of that

1121
00:48:38,400 --> 00:48:41,120
Speaker 2: people problem, Like, I'm on a mission to build the

1122
00:48:41,160 --> 00:48:43,040
Speaker 2: best go to marketing I possibly can. Like we have

1123
00:48:43,120 --> 00:48:45,319
Speaker 2: an office and for your own business, for our own business.

1124
00:48:45,400 --> 00:48:48,120
Speaker 2: We have an office in Sydney of seventy people. We

1125
00:48:48,160 --> 00:48:50,000
Speaker 2: open up an office in San Francisco last year and

1126
00:48:50,000 --> 00:48:52,400
Speaker 2: we're just opening up now. We one in Barcelona. So

1127
00:48:52,520 --> 00:48:54,120
Speaker 2: my call out to anyone listening to this, like, if

1128
00:48:54,120 --> 00:48:55,359
Speaker 2: you want to be part of a journey where we're

1129
00:48:55,400 --> 00:48:58,360
Speaker 2: actually deploying this at scale at real organizations, want to

1130
00:48:58,360 --> 00:49:00,360
Speaker 2: see it firsthand, and you want to be in AI

1131
00:49:00,440 --> 00:49:04,160
Speaker 2: first go to market team, then definitely check out Relevance, Relevance,

1132
00:49:04,280 --> 00:49:06,720
Speaker 2: dot com, slash careers. You can see all the open roles.

1133
00:49:07,000 --> 00:49:09,040
Speaker 2: So do I think people are going to be needed, Absolutely?

1134
00:49:09,520 --> 00:49:10,719
Speaker 2: Do I think people are going to be needed in

1135
00:49:10,719 --> 00:49:13,640
Speaker 2: different ways? Absolutely, So if you want to be part

1136
00:49:13,640 --> 00:49:16,000
Speaker 2: of that journey, definitely come come join us. And if

1137
00:49:16,000 --> 00:49:18,600
Speaker 2: there are any kind of small business owners that are

1138
00:49:18,640 --> 00:49:20,840
Speaker 2: potentially at that scale where they're starting to see an

1139
00:49:20,840 --> 00:49:23,640
Speaker 2: inflection point and they have an opportunity to maybe take

1140
00:49:23,680 --> 00:49:26,640
Speaker 2: some of those repeatable processes and automate them with agents.

1141
00:49:26,840 --> 00:49:28,560
Speaker 2: Then they should definitely check out some of our content

1142
00:49:28,800 --> 00:49:30,720
Speaker 2: at the very least look at what our customers are saying,

1143
00:49:30,760 --> 00:49:32,440
Speaker 2: because I think if you look at somewhat these larger

1144
00:49:32,480 --> 00:49:36,080
Speaker 2: companies that are forward thinking and that are working with us,

1145
00:49:36,480 --> 00:49:37,880
Speaker 2: it might give you ideas for what you can be

1146
00:49:37,880 --> 00:49:38,440
Speaker 2: doing yourself.

1147
00:49:38,560 --> 00:49:40,640
Speaker 1: So some of those case studies they appear on your website,

1148
00:49:40,640 --> 00:49:41,759
Speaker 1: they do. Yeah, okay, that's great.

1149
00:49:41,840 --> 00:49:46,000
Speaker 3: Yeah, well well, Daniel, congratulations by the way, fantastic I

1150
00:49:46,239 --> 00:49:48,440
Speaker 3: getting a good vowel and raising all that amount of

1151
00:49:48,480 --> 00:49:50,200
Speaker 3: money which you can now deployed your business to make

1152
00:49:50,200 --> 00:49:52,640
Speaker 3: it bigger and bigger and bigger, and for a young

1153
00:49:52,719 --> 00:49:55,560
Speaker 3: fellow or some of your age, which is it's amazing

1154
00:49:55,560 --> 00:49:57,239
Speaker 3: and it's great to see in Australian how they're doing that.

1155
00:49:57,320 --> 00:49:58,680
Speaker 1: Well not mate, great suation.

1156
00:49:58,520 --> 00:49:59,960
Speaker 2: Appreciate it. Thank you,