Cloud Reset – The Podcast | Episode 11: AI for 2030 Wrap Episode
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Episode Summary:
After four heavyweight conversations with Australia’s tech and business leaders, Jono and Naran take the mics for a final time this season to unpack the real standout moments that stuck with them. This wrap-up episode captures the energy, optimism, and deep practical insight from:
✅ Steven Worrall (Microsoft)
✅ Peter James (Macquarie Technology Group, DroneShield)
✅ Michael Reid (Megaport)
✅ Angela Fox (Dell Technologies)
In this debrief, your hosts revisit the big ideas—from digital tradies and agent farmers, to boardroom AI strategies, geopolitical leapfrogging, and the rise of “mad scientists” inside your own org. They share their Top 3 takeaways on what matters right now for any business navigating AI.
You’ll hear:
- Why lived experience with AI trumps theory
- The importance of data readiness and building internal capability
- The emerging leadership roles that will define the AI-powered enterprise
It’s unscripted, real talk—and yes, there’s a sign now. Tune in for laughs, insights, and what’s next for Australian businesses riding the AI wave.
Watch or listen now?
#CloudReset #AI #Leadership #DigitalTransformation #TechStrategy #MacquarieCloudServices #Podcast
Episode Transcript:
Welcome to Cloud Reset. This is our recap episode Jono, where we are gonna reflect on the amazing conversations that we had with Steven Worrall, Peter James, Michael Reid, and Angela Fox, our four heavy hitters from the Australian industry.
But before we do that, should we address the elephant in the room? We have a sign.
We have a sign. Now you can’t have a podcast without a sign. I think that’s brilliant. I don’t think we really need to say much at all other than just to be next to the sign and enjoy its warmth,
I feel like. Yep. I, I, I’m right there with you. Alright, let’s find it
out. Now that we’ve covered that off. So what are we here to talk about?
Like, here’s what we’re gonna do. We’re gonna reflect on every conversation that we had where we were time travelers and we went forward five years. Because all the decisions that we made beforehand worked everything, all the best laid plans have produced outcomes via generative AI that has made us either more relevant in market, more efficient as a business, et cetera.
So everything’s worked. And we’re asking our four champion leaders, what decisions did they make to arrive at this wonderful oasis of an outcome as promised by generative AI. So shout out to our guests. Thank you so much. We thoroughly enjoyed speaking to each and every one of you, and we learned heaps from all of you.
Yeah, so let’s talk about that. Let’s get into it.
Uh, probably the first thing that really stood out to me from this series was the importance placed upon lived experience when it comes to AI and getting your hands dirty.
Yes, indeed. And so. Clearly, there’s just a wealth of information that’s out there. There’s a lot of hype. You’ve got generative AI, YouTubers, sprouting, all sorts of things.
New LLMs dropping, new models, promising this, that, and the other, et cetera, et cetera. And every business, every certainly prospect that we speak to is trying to figure out what to do and how to go from the hype and the promise to something tangible.
Exactly right. You know, Steven Worrall was pretty insightful around this.
I think in our first conversation and just talking about, you know, uh, if you have an opinion, it’s not okay to have an opinion unless you’ve actually got some practical experience to inform your opinion around the way that you can leverage AI to improve your business, improve the experience of your customers, make you faster, better.
Stronger, more competitive. Yep. And, uh, and to me that was a huge takeaway. I’ve actually, uh, I’ve actually embraced this. I’ve written the entire script using Grok Really? No, I’m kidding.
I’m so sure about this. We want fresh human insights from you.
Yeah, no, look, I’m still a real person, but I’ll tell you what, I’ve gone deep, deep down, uh, the LLM rabbit hole.
Mm. And, uh, and yeah, it, it’s amazing.
Yeah. Look, and I think. Lived experience within our own business as well. And I know we spoke about this with Peter James, and I know we spoke about it with Steven Worrall as well, reflecting on our own projects and the fact that when we landed, uh, on the outcome for one key generative AI project, there’s no way that we would’ve scoped that the way it ended up and that is that the learnings that happen in real time, every day, every week there were new models dropping agent specific, ’cause we’re of course living in an agentic world now. You see this is the way we need to speak. Yes. And so we have all these different agents linked to different data sources with different LLMs producing different outcomes.
There’s no way our team could have architected that the way it ended up without playing with it.
That’s exactly right. And. You know, off of the back of that, I think having more informed opinions through practical use of this technology, whether it be, you know, selecting a, a really great use case, a defined use case, and running a pilot to your point, uh, or just using it for your own personal use.
You know, whether it’s to create a budget for your household Yes. Or having practical experience with how this tech works is so important. Yeah. And as leaders. That practical experience is going to help you make better decisions around where to place your investments ahead of, ahead of the curve.
Yes.
And I know, uh, Peter James actually had a great analogy around this.
This is the Wayne Gretzky. Yeah. I’ve gotta do this justice. So he said Wayne Gretzky’s famous for saying good players will play where the puck is. Great players will play where the puck is going to be.
Exactly. We spoke a lot about that. Really, and it sounds so, uh, intuitive. Mm. You know, but I think, um, I think for business leaders and boards trying to, you know, plot the next move, understanding, and, and every leader spoke about this, uh, whole situation being a race.
Mm. Whether it’s within your own market. Or geopolitically or, yes. Lots of different opinions on that. And we should get into that.
The geo just on that too, the geopolitical thing. So I know Steven talked about, um, let’s say, uh, lesser developed nations using generative AI as a means to leapfrog and put themselves in a much stronger position.
Um, I thought that was really interesting as well. He didn’t obviously name specific nations in, in saying so, but we could speculate as to who he’s referring to there. Of course.
Yeah, of course. Well, uh, yeah, we spoke about that with Peter James as well. Hmm. And, uh, I think at the time we recorded that episode, uh, you know, deep seek hit the market caused a bit of a panic.
Yes. You know, um, Nvidia share prices dropping and bouncing back and creating volatility. Super interesting, interesting times. And for business leaders trying to navigate. This space.
Mm-hmm.
Uh, having some kind of informed opinion about where the puck might be going Yeah. Is gonna be a really, uh, core tenet of, of being able to be successful in this space.
Indeed. So, uh, really interesting. And then we talked about skills and. You know, there was so much coming out in terms of skills.
Yeah. We talk more about that. Well, IJI love the, the term that was used was, was it digital tradesmen?
Yeah, digital tradies. I think that was, uh, Steven Worrall talking about the skills gap.
Right. In Australia, I think it was something like predicted to be 300,000 jobs Short. Yeah. By 2030. Yeah. And some of the huge investments that the New South Wales government’s making in conjunction with some big software vendors. Like Microsoft. Yep. I think Salesforce is in the mix as well. And the university sector.
Yep. And, uh, producing the, um, the digital training academy out of Meadowbank, the old Meadowbank TAFE campus. Yes. In Sydney. Um, and you know, the amount of. Yeah, upskilling that’s going to be required to support this wave of AI adoption in Australia is, is incredible.
I love this digital tradesmen. It cast an image in my mind.
I’m wondering, I mean, this is about bringing generative AI back to the working man, you know? Do you think these tradesmen still drive Utes and they yell at cyclists and tell them to get off the road of it? Is that, is that what we’re talking about?
I don’t know. No. Do you have, uh, firsthand experience with that
nursing?
Some pain there? We should probably move on. Um, but look, just in terms of skills and, and identifying talent, let’s talk about the mad scientists.
Yeah. The mad scientists. So I, I think this analogy started to emerge, um, off of the back of talking about practical experience and how do you get it mm-hmm. And what’s the best way to fund it?
And, and where should you place a bet? How do you discern between what’s a good idea and a not so good idea? Talk about these small pilots and what everybody seemed to identify was. There’s, there’s huge untapped potential within your own business and your own networks for people who’ve got great ideas about how to leverage this technology to improve your business or improve the experience that your customers have.
Now, the obvious question would be, do most people have the freedom to move sufficiently laterally to embrace these opportunities? And if I think about our own business, I know that we are lucky enough to have roles and, and let’s say some spare cycles or some freedom to explore these things such that the talent bubbled up naturally.
Within our business, and there were people who showed a keen interest to begin with. They had an aptitude as well. They happened to be the same people that had dev skills, dev background to be able to lean into LLMs and RAG and all these specific concepts. So you got this combination of their keen interest, the dev skills, but the freedom to embrace a project and afford them the opportunity to work on a proof of concept.
This feels to me like a luxury.
That’s right. We did speak about that. I think, uh, particularly tech businesses. Mm. With a bit of a, like a, a balance sheet for innovation Yep. Where tech is their core business, of course.
Yep.
Um, I think it’s table stakes Yeah. To be nurturing those opportunities. Mm. Within your own business, it’s how you’re gonna compete.
How many businesses do you think, other than tech businesses or MSPs would have the roles and the freedoms to do that, do you think is an open question. I, I,
I think there’s a lot of, uh, curiosity. Mm-hmm. I, I, I doubt. I doubt many organisations have really thought deeply about. You know, the right frameworks and guardrails to put in place to give people some freedom.
Yep.
Uh, to be able to experiment with this technology, uh, safely. Yes. And you know, I think we spoke to Peter about this. You know, if you don’t have the luxury, and he mentioned, you know, he sits across a lot of boards.
Yep.
Very experienced, uh, business person. And a lot of the times a businesses backs it to the wall.
Yeah.
They’re trying to manage costs, they’re trying to bring sales in that. Yeah. You know, and so there isn’t really the space for this. And I think what rose to the surface there was the importance of partnership. Yes. You know, and being able to lean on strong partners. You don’t, you don’t have to do it all yourself.
No. Uh, you should innovate from within because only you know your business as well as you do. Yes. So those mad scientists are still there. Yes. But don’t be afraid to partner, to help nurture, nurture them, and turn their idea into something. Mm. Uh. Practical and usable.
So we can’t avoid the generative AI discussion within our business because the products that we buy have features that are baked in.
So that’s happening anyway, right? As all the various analytics platforms that we use, security offerings, et cetera. They’ve all now got either AI or machine uh, learning influenced features and capabilities. So whether we like it or not, it’s in our world. Um, we also know that, and we asked Peter before around focus as well, if there’s operational efficiencies and then these, you go to market and the products that you would then sell and, and take to market. So, um, we can’t really avoid embracing it to be a modern MSP and these are the questions and the decisions for us to reach our five year goal. We know that we’ve gotta do this. It’s a question then for us as to how do we then industrialise that and take that forward. All those internal gains that made us leaner and meaner sufficient for our customers to benefit from our learnings.
That’s exactly right. And I think, uh, you know, competition in our space is, is gonna drive that.
Mm-hmm.
And, and accelerate that.
Yep.
Uh, where it’s going to get interesting again, is for people who aren’t in the business
Yep.
Of selling tech. Yeah. But tech is underpinning their business
Interesting.
And so partnerships I think will be important, but also, uh, being a bit deliberate around some frameworks that you can put in place to discern between what is a good idea and a good way.
A good place you should make a bet.
Mm-hmm.
Um, and may and maybe not so much, and I think Angela Fox spoke. A lot about that. You know, you can go back and listen to that episode, but Dell has quite a mature framework, uh, that they’ve put in place. Because you can imagine, you know, the number of employees they’ve got, they’ve got exponentially more mad scientists than we do.
And obviously there’s so many great ideas. How do you decide? Yep. So that’s the other end of the, the spectrum as well. Yeah,
I’ve heard, um, there’s two really good examples of the use of this technology. I did, uh, hosted a security roundtable yesterday and there was a, um, prominent university, uh, represented there.
The CIO. Um, and I asked him about his lived experience for generative AI and it was consistent with, um, uh, another AI business that we know that’s done a lot of work with banks, uh, and where their product was, which customers are gonna leave. Yeah. And so having the insight sufficient to discern and the accuracy, I think was a one in three.
Now I asked. The CIO of the university, same question. He said they’ve got this key milestone with students. Um, I’m not gonna, I’m not gonna do this justice, but there was a name that he gave to a key milestone and that is whether a student will continue with their degree or whatever it is that they’re studying versus leave.
And they’re trying to figure out can we foresee or predict those students that are gonna stay and those students are gonna leave. And it was the same discipline that our mates who work with the banks went through and getting similar sort of accuracy as well where churn. Right. This desire to either hang on to students or customers was the primary use case, and that’s what he led into.
And I thought that was fascinating. Such a consistent approach.
Yeah. I think a lot of people are thinking about how can you use generative AI to serve your customers? Better and retain your customers.
Retain. Yeah. I mean, look, it’s a thing for us too, isn’t it? Right? So our customer insights project is exactly the same.
It’s about retention of those customers, but also how to better serve them. How to offer up better service. Let’s be honest. Can we sell them more? Are they other products that they would benefit from, et cetera.
So, yeah. Well I think creating more value is what creates loyalty and Yes. And that’s what drives a, a, a good business having a meaningful
conversation much like this one.
Absolutely. And, uh, you know, I think, um. I think that that led us to Okay, we’re, we’re in the future. And we did ask a lot of our guests to consider what their leadership teams might look like. Mm. You know, are different kinds of people actually operating businesses. And there were two key insights that came away, uh, from those conversations or themes for me.
One of them was around the democratisation of knowledge. Right. And how, how AI. Is going to continue to reshape hierarchies yes. In business and flatten these structures. Yes. I know we were talking about that before, but you had some interesting takes on this.
Yeah. Well, and so, I mean, everybody now has the information, uh, in the palm of their hand, right?
And so everybody’s capable of making these decisions, which is like a democratisation of leadership and a flattening of hierarchies and structures. And this will then create new roles and new opportunities. And I think like Michael Reid had an amazing example of a leadership role. Um, let’s talk about that.
Yeah.
That was. That was mind blowing to me. That was one of the most interesting insights that came out. Imagine, imagine, uh, a world where, uh, leaders of organisations are now experts at managing AI agents like a farm of agents, teams of AI agents. Yes. And, and he spoke about, you know, a, a possibility. Mm.
Whereby this, this theoretically has infinite scale.
Yes.
You know, and, and what does that look like to manage? Manage an organisation that looks like that and teams of AI agents taking over entire business processes and functions. Yeah, indeed. Incredible indeed. And
look timely too. Right? So while you were in London, obviously Manus was dropped.
What was Manus, right? Manus was the binding together of a number of agents. But also with the ability to spawn off processes in a consistent and coordinated, or I should say orchestrated manner. So the ability to, uh, go off to the web and do research, to pull data back to produce charts and reports and compile information.
I think the question that I saw online was produce a report covering Tesla stock over the, the last six months, for example. And it’s creating processes. It’s bringing information back. It’s showing you the process in real time, and then you get to see the output being produced as well. And it was stunning and amazing.
And so you could have multiple Manus type instances where agents are performing functions and the leader, the new leader in Michael Reid’s new model is owning the outcome of any number of these agents. And maybe it’s six, maybe it’s that classic, don’t have more than six reports. Maybe it’s six instances of Manus doing God knows what.
Yeah. And you can foresee a future whereby, uh, in terms of the skills market, mm. People who’ve developed and managed certain types of agents, bring them with them. Mm, my agent’s better than your agent, right? I’m an expert at this. And it becomes part of your, uh. Your professional value proposition For
sure.
Well, I, it’s like, so again, lived experience, maybe what we’re talking about here is the lived experience of agents and retraining, fine tuning. Um, like I know with our soc, digital twin, our optimiser, frankly, it’s only as good as the way we’ve tuned it as well. Like if I think about the way that performs today versus six months or 12 months ago, distinctly different.
Yes. Right. And our meantime to respond and closure metrics have gotten lower and lower, but more importantly, there’s less hallucinations and the quality’s improved. So maybe you bring the lived experience of agents and a manager who’s capable of continuing to retrain his pets, so to speak, or however you wanna describe.
Yeah. Uh, it’s really, really interesting and, you know, underpinning all of this, you know, a, a common theme across. Across every conversation was the importance of data.
Yes, indeed. And look, the emergence of fabric as well, I’m not gonna talk about that. Obviously, you know, big Microsoft partner, but gees fabric’s exploding.
Um, and if nothing else, a catalyst for organisations to have a good look at data platform, have a look at their structured and unstructured data. Think about what state does that need to be in sufficient to start working with, you know, your ETLs, your pipelines and producing outcomes. Obviously you need the data in a usable, um, state.
Yeah, so important. And then the security of that, where it lives, governance around that. Oh gosh, who’s got access to it? Like there’s a whole other Yeah, there’s an entire, uh, market that’s emerging in terms of demand. For these kinds of services, and probably the big takeaway is encouraging business leaders to start Yes.
Thinking about their data if they haven’t already. Yes. Right. The first place, yes. To make an investment if you haven’t done this, is getting your data into shape. Yeah. Because none of this is worth anything. Without the raw material, which is data.
You know what I can foresee? So maybe we saw the adapt research not that long ago and uh, investments in generative AI projects as an initiative was higher up in the list to security initiatives, which I found fascinating.
So I’m wondering, with all the training modules that we have to do internally now as good security citizens, and you’re only as good as your weakest end user, and we are end users ourselves, so we go through this nausea of all this security training and awareness, et cetera. I’m wondering whether.
There’ll be, uh, learning modules for data governance and data for classification. And I’ve just produced an artifact and is this highly sensitive and where is this document gonna go and who’s gonna see it? Yeah. And all the rest of it. And we’re all gonna get trained on this ’cause we’re all data domain custodians and we’re all responsible for the artifacts we produce and the sensitivity of them and the flow through of policies and data exfiltration and all the rest of this stuff that is important, not withstanding being able to.
Suck out a generative AI outcome from this stuff,
or uploading everything you just produced into Grok,
which we may have done today.
Okay. And look, this has been heaps of fun. Heaps of fun, and I think, uh. I think, um, I think we’re, we’re starting to look semi-professional at this, but I know behind the scenes are really different.
Do we even need to speak
now that we have a sign? Uh, basically the sign says it all. We can just look at the camera with the sign,
but, you know, be, before we really wrap this up today, um, maybe some of the things that surprised us, what was, what was your most surprising, uh, comment or. Insight out of this series.
I tell you what was not surprising was that Steven Worrall wouldn’t answer my question around what is this definition of generative AI that exists between Open AI and Microsoft? It is your
question, isn’t it?
Well, Mike, they know about it. He didn’t have a clue what I was talking about, so that didn’t surprise me in the slightest.
So that’s the opposite of that. So, um, what surprised me, look, I, I was blown away by Michael Reid’s comment about like, leaders managing AI agents. ’cause that makes perfect sense to me, right? And so you, you hire somebody whose job it is to oversee this. Stuff and the outcomes associated with them. Um, I’ve got a visualisation in my head of what this person looks like.
They don’t look like you and I, by the way. They’re probably coming into the office with a backwards hat and a fluro coloured top or something. They’re different people.
Yeah. Right. Yeah. Maybe in the future they look like a Tesla robot
possibly. Yeah, exactly. So that blew me away. What about you?
Yeah. Look, there, there were, there was so much stuff, but I, um, I was really surprised by the number of jobs that were gonna be short.
Right, right. The opportunity that’s ahead of. People Mm. Who want to participate in this industry is massive. It feels like the early two thousands again.
Yeah.
You know, uh, back when the network engineering was cool. Right, right. We spoke about that with Michael Reid, but
yeah.
Um, some, you know, 300,000 jobs.
Mm.
The upskilling opportunities, the new markets that are emerging.
Mm-hmm.
Uh. Michael Reid spoke about the number of Silicon Valley unicorns going from zero to a hundred million. ARR one. Just got the one he mentioned. It’s got acquired recently by Google. Yes. For billions of dollars.
Yes.
Right. This is, this is now, it’s real.
It’s massive. And, uh, and I think there’s a huge opportunity for people to come and have a go. Yes.
Question for you would be like, let’s say Microsoft’s approach in that you’ve got copilot studio for those people who, this is the low code, no code option, right? Where let’s say layman like you and I, Jono can compile something together that produces a reasonable outcome where we have no real
dev or coding backgrounds, and I’m probably being unfair to you. Maybe you do. Right? No, that’s perfectly fair. Okay. Right. And so you, you should be able to produce a generative AI outcome by using the tools afforded to you. Yeah, right. But the ma, the mad scientists within our business had dev backgrounds.
So this is how the whole AI foundry approach that Microsoft would say, where you, you can get involved directly with the models in Azure, for example, to produce an outcome. But these are people who were. Dev minded, dev inspired had those skills and they’re able to cobble together, in our case, a 20 LLM, you know, multifaceted agentic solution.
Now that’s not you and I and there’s probably something for everybody in between those models. Yeah,
yeah, I agree. Huge opportunity for everybody.
Mm-hmm.
And, uh, look, I know we. We, uh, this is, this is all live Unscripted.
Yeah.
Uncut. What didn’t make the cut? What should we talk about there? Should we give any behind the scenes insights?
Do I don’t think our audience has any appreciation for how little preparation you and I do for these episodes? Master the disgusted about producer. Yeah.
Shout out to producer Pru. Shout out to Pru. Uh, yeah. In incredible. Yeah. Um. Look, there’s a lot of preparation that doesn’t happen. Mm. Therefore it doesn’t make the cut.
That’s right. But frankly, this is, I Well, look, would you and I ever do a scripted thing? I don’t think so. No. Do people want a scripted thing?
Yeah, I don’t think so. I’m feeling pretty good about it.
Yeah.
Yeah. What el what else? What else did we have fun with? Uh,
there different personalities as well, weren’t there.
Right. And so it’s all, all walks of life, you know, like, uh, all our leaders from Steven, Peter, et cetera. Um, but all. Hugely optimistic as well, weren’t they? Like with everything that’s going on in the world right now, there’s just everybody’s sort of motivated and buoyed and there’s much more positivity about where we’re heading than, than anything else.
And I think that was encouraging.
Yeah, hugely encouraging a, a total net positive for everybody. Yes. You know, and I think it’s important to look at it through that lens with a growth mindset. Mm-hmm. That’s what I appreciated was the growth mindset.
Yes.
From all of the leaders that we. That we were privileged enough to speak to and probably, you know, another shout out to them how grateful, um, I am.
Yes. And I know you are hugely for having the opportunity to, to learn from them.
Yes.
I know our listeners appreciate it. Yeah. Um, it was super meaningful. And uh, I’m just also encouraged by, you know, when we, when we pick up the phone and ask them if they want to do it, they all say Yes,
I know.
Yeah. You’re just gonna keep trying that.
Well, they could have said no. They all said yes. It, I mean, makes me wonder who else we’re gonna ask.
Yeah, well that’s exactly, you’re probably gonna have to stay tuned to make sure you subscribe. Indeed. Yeah. But before we get into that. Uh, top three takeaways. Top three takeaways for our listeners.
Um, the number one thing for me is that, um, you have to find an opportunity to. Source your own lived experience, either with your own clever people, um, or through a partner. Um, find a use case within your business and play around and produce something because you will have a very different opinion on not just that lifecycle, but also what generative AI can do for you.
The quality of the output, the results, et cetera. And then living with that solution is just as important from a lived experience. If I think about our own products and solutions, they’ve evolved markedly in the months. We’ve had them in play within our business, so that’s huge for me. You know, move beyond the YouTube, beyond the hype, beyond the news, beyond NVIDIA’s, share price, and everybody else.
Like just have a crack. The tools, the solutions are there. Um, there’s gotta be someone in your business that can do that, and hopefully you’ll afford them the cycles to do it. If not, partner with someone and just produce something. Do something such that your opinion is based on a real outcome that helped you somehow.
Yep. Hugely important. Mm. Partnerships. Practical experience. Yeah. Informing your opinions. Yes. So you can help make better decisions ahead of the curve. Mm-hmm. Where the puck is going. That’s right. And if you haven’t figured any of that out yet, set aside some investment and some thought and put that to your data.
That’s right. Start thinking about data. Absolutely. And if you need some help in that with that, find some people who, find some people
who can help you. Absolutely. Yeah. I think
it’s the organisations that understand their data.
Mm
quickly. Are the ones who are going to get ahead.
That’s right. I would find it surprising how many organisations in Australia are there that couldn’t benefit from some sort of generative AI capability, right?
Like we’ve all got customers, whether we’re B2C, B2B, et cetera. There is bound to be something that could be improved, uh, made more efficient with gendered ai. So this feels to me like all businesses in Australia. Yeah,
absolutely. I think it’s a huge opportunity for everybody. Mm-hmm. Uh, I think it’s time to thank our listeners.
Indeed. Thank you so much for coming on this journey with us. It’s been amazing. You know, what did Monty Burns say? What’s that gentle reader? You enjoyed this as much as we,
we go, we go for another Simpson’s analogy. We love doing this. Yeah. Did we We had a great time. It’s great. We have lots of fun. Uh, we’re grateful for all the subscribers.
Uh, this thing continues to grow and we’re gonna keep doing our best to. Yes, we are. To make it interesting and bring you value into that end. Make sure you subscribe, comment if you want to see something. Uh, see us investigate a topic. If you want us to, uh, make a call and see if someone will join the podcast, we’re happy to, to do that too.
I’ll call anyone,
anyone at all. It’s hilarious.
Set us the challenge. Yes. Uh, where can you find the full series?
So, uh, clearly on our website, Macquarie Cloud Services website, but every single episode is on Spotify. It’s on YouTube, wherever you find your podcast, clearly we are blasting this everywhere on LinkedIn.
If you’ve been able to avoid it so far, good luck. We’re gonna continue to find you. Um, you’ll get us anywhere.
Yeah, we’re everywhere. That’s a wrap. We’re so everywhere. We have a sign. We have a sign listening, and see you all next time. See you soon.
