August 27, 2025 • 34 mins
New Zealand’s workforce is bracing for the rise of artificial intelligence, but University of Auckland economist and organisational behaviour expert Dr Kenny Ching argues it’s not the jobs apocalypse many fear.
AI will reshape roles rather than eliminate them, and thanks to a resilient agriculture-based economy, the country is better positioned than most to adapt and even lead in agricultural innovation.
“AI is definitely coming for jobs,” said Ching on this week’s episode of The Business of Tech.
“But it’s going to hit the services industry particularly hard and earlier than other industries.”
He believes smarter investment in agritech and a renewed focus on uniquely human skills, like judgment and connection, will be key to future success. Ching recently outlined his thinking on AI and the future of work in Aotearoa in a piece published on The Conversation.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Cura and welcome to the Business of Tech powered by
Two Degrees Business. I'm Peter Griffin and on this week's episode,
once again we're looking at artificial intelligence, but this week
we drill into its impact on our workforce here in
New Zealand. It seems like anyone who really understands AI
is telling us that the change is coming to how
(00:23):
we work as a result of the adoption of AI
are monumental and we aren't doing enough as a nation
to prepare for them. But as you sit in your
car on the commute to the office, or if you're
working at it to gym, you may also be thinking,
right now, it ain't making a dent at my workplace,
and given how clunky and unreliable chat, GPT and co pilot,
(00:46):
are my job safe for the foreseeable future. Well, this
week's guest argues that yes, actually AI is coming for jobs,
particularly in the services industry. White collar rolls that AI
is quickly getting a lot better at not only augmenting
but replacing entirely. But doctor Kenny Ching, an organizational behavioral
(01:08):
expert and economists at the University of Auckland, argues that
New Zealand is going to be better placed than most
other Western nations to absorb the labor impacts of AI.
That's because agriculture is the base of our economy. Primary
sector only employees five point two percent of New Zealanders,
(01:28):
but it's responsible for nearly seventy percent of our export revenue.
Automation is happening in agriculture, but won't take hold nearly
as quickly as it is in the office. A primary
sector anchored economy, Smaller and flatter firms and more tangible,
less codified work mean automation diffuses over longer investment cycles
(01:53):
in orchards, shared construction sites, and supply chains, buying a
window to build capability before the curve steepens. For the
services industry, think marketing, banking, and insurance finance type jobs,
all the corporate admin that goes on across our businesses
all over the country, the threat to jobs is very real.
(02:16):
The latest biannual survey of businesses conducted by Victoria University
researchers in conjunction with the AI Forum, finds that fourteen
percent of organizations are now attributing job losses to AI.
This has doubled in the last six months, so AI
is starting to eat jobs six months ago, they're basically
(02:36):
saying we're not hiring.
Speaker 2 (02:38):
As much as we used to.
Speaker 1 (02:39):
Now they're actually attributing job losses to AI. That's a
big shift and it's only going to accelerate. What's going
on is really well summed up by Moe Gorditt, the
author and former chief business officer of Google x that's
the search giant's secretive research and development arm. Gord It
recently appeared on the Diary of a CEO podcast. Here's
(03:03):
what you had to say about AI's disruptive potential in
the services in Illustrie. There's why collar jobs. I've been
talking about.
Speaker 3 (03:10):
AI is going to replace the grain of a human
and when the West, and it's interesting virtual colonies that
I call it basically outsourced or labor to the to
the developing nations. What the West publicly said at the
time is we're going to be a services economy where
we're not interested in making things and stitching things, and
(03:33):
so let the Indians and Chinese and you know, Bengalis
and Vietnamese do that. We're going to do more defined jobs.
Knowledge workers. We're going to Knowledge workers are people who
work with information and click on a keyboard and move
the mouse, and you know, sitting meetings and all we
produce in the Western societies is what works right or
(03:55):
designs maybe sometimes, but everything we produce can be produced by.
Speaker 1 (03:59):
Any So pivoting to the knowledge economy for decades was
considered to be the thing to do to achieve higher
paying salaries for workers. But as it turns out, we're
lucky to produce a lot of actual stuff, physical stuff
that the world wants to buy and as it happens,
pay more for than they used to. Milk and meat
(04:20):
prices are looking pretty healthy at the moment, but Kenny
Ching suggests that this is not a reason to ignore
AI's applications in agriculture or be complacent about it. In fact,
we should embrace AI and become the best at developing
it in our agrotech sector, because AI is coming for
(04:41):
that sector too, just at a slower pace. Given we've
got great expertise in growing wool, meat, milk, Kiwi fruit
apples were well placed to become world leaders in agritech
and the application of AI in those industries. So here's
the interview with University of All Kenny Ching, which I
(05:01):
recorded last week after you published really interesting essay on
AI's potential impact in a tea. I really enjoyed your
conversation piece from a couple of weeks ago.
Speaker 2 (05:19):
Really interesting.
Speaker 1 (05:20):
You know, you're essentially saying there that AI is definitely
coming for jobs, but it's going to hit you know,
the services industry particularly hard and earlier than other industries,
and you know, with our agricultural base, that may be
an advantage for us. So we'll get into that what
that actually means. But Kenny, in terms of how AI
(05:43):
is affecting the workforce, so far as you do a
horizon scan around the world, you know, what exactly are
you saying. I'm just reading a World Economic Forum report
that is basically saying, so far, it looks like software
development is getting hammered. That's how they put it. You've
got the likes of GitHub, which is a big repository
(06:04):
for software. It has four hundred and twenty million repositories
now and a lot of them are public, so there's
a lot of code there that people can use and
automate their workflows. So that's why we've seen big tech
companies laying off a relatively a lot of staff compared
to other industries. Customer support is another one they call
(06:24):
a setting duck AI automation in contact centers, for instance,
and finance that heavily employees. Machine learning and algorithmic trading
is on the front line of it as well. But
in terms of all the research you're seeing coming out
of various countries, what does it say about the real
impact on jobs when it comes to AI at the moment?
Speaker 4 (06:45):
So, first of all, a lot of the data and
evidence that we have is pretty much within the rear.
Speaker 2 (06:50):
At this moment.
Speaker 4 (06:51):
We don't quite get no for sure, how it's all
going to pan out, but I think gradually we're getting
a good sense of what's going on. As I argue,
a lot of it's going to be centered around the
service industry, and that's where most of the disruption is
going to take place.
Speaker 2 (07:09):
I think.
Speaker 4 (07:10):
So some people have responded to my piece, for example,
saying that, Okay, so many of our New Zealand design
in the service sector, and most of the white college
in a white college jobs, so within that cosmes unemployment,
I don't quite think so.
Speaker 2 (07:24):
I think there are a couple of reasons why.
Speaker 4 (07:26):
One is that the best evidence that we have seems
to suggest that the way AI displacement's going to work
is that it's going to reconfigure jobs more than it
completely replaces them. So maybe about sixty to seventy percent
of what activities we're going to see some automation. But
it doesn't mean it disappears completely outright that overnight.
Speaker 2 (07:49):
It just doesn't happen that way.
Speaker 4 (07:51):
Maybe a third of them might might happen that way,
but it's not going to be all the jobs just disappear.
But I think there's another critical point, which is that
the way AI displacement is going to happen is that,
for example, if a company is typically is going to
hire five new hires for the hiring season, they will
probably hire three, right, and they expect that those three
(08:14):
will be augmented by AI tools. So and so the
effect of this placement shows up very gradually. It's not
gonna be a mass so disappearance of jobs. Rather, you're
going to see slower hiring and maybe hiring freezers.
Speaker 2 (08:27):
That's all things.
Speaker 4 (08:28):
But you're not going to see incredibly, like you know,
sixty percent of the of jobs just disappear overnight.
Speaker 1 (08:34):
Yeah, you talk in your conversation piece, you mentioned David
Grabner's concept of bullshit jobs. Yeah, absolutely, He called them
rolls that add little genuine value. And between twenty twenty eighteen,
most knit job growth came from these low productivity service
(08:54):
sector jobs. We're talking about marketing, consulting, corporate administration, and
it's happened in New Zealand as well. You know, we
saw an expansion in those sectors. So I guess we
have a lot of jobs in the services sector, and
that is a big sway of things that services technically
cover everything from healthcare to retail to those sorts of
(09:18):
marketing roles. But I guess you know, we've got unemployment
at five point two percent. What you're saying there, and
this is what I'm hearing from the tech vendors who
are selling artificial intelligence systems. They're saying it will augment
your job, it will change your job. It won't necessarily
eliminate sways of jobs. But as you said, you're not
(09:39):
necessarily going to be hiring as many people in future.
And if you look at you know, for instance, Spark
just laid off thirteen hundred people in the last year.
Other companies have had significant cuts as well. It seems
to me that the main impact of AI so far
is signaling to companies we don't need to hire as
(09:59):
a aggressively as we have in the past, which I
guess is a bit of a concern for our return
to growth that the government wants that unemployment isn't necessarily
going to reduce as quickly because the need to hire
people has listened.
Speaker 4 (10:14):
I think that's absolutely the case. I don't think there's
any other country that is not facing these similar issues
as you've described. I think what my argument is that
New Zealand's probably is a little bit different from other
countries in the sense that we tend to have our
companies tend to be a little bit smaller compared to
say the UK or the US. So I got some
(10:36):
stats here, for example that our firms on average has
about five employees compared to about thirteen for across the OECD,
and that and sense suggests that our companies are a
little bit flatter, few duplicated roles, you are more people
wearing multiple hats, and I would argue again that the
jobs tend to be closer to the real economy right,
(10:58):
so I ignore absolutely that will be tremendous impact on
the AI displayson is going to have tremendous impowers, should say,
but my argument is that New Zealand probably experience it
a little bit less compared to many other economies around world.
Speaker 1 (11:15):
And a lot of people will be happy to hear that.
You know, we've got about two point eight million jobs
in New Zealand according to Inframetrics, which is looking at
stats n Z labor data. Construction around ten percent is
actually one of the biggest that may have come down
a bit because construction is a bit soft at the moment,
followed by healthcare and social assistance that's around ten percent,
(11:38):
then professional, scientific and technical services, followed by manufacturing. So
when you get down to the ones that the World
Economic Forum is saying is really at the front line
of this, financial services in New Zealand two point seven
percent and information media telecommunications you know, one point five percent.
So these are relatively small sectors for New Zealand. You've
(12:00):
argued in your conversation piece that our strengthen agriculture, which
is the majority of our exports currently, is going to
see us well through this wave of automation because literally
a lot of that stuff can be automated.
Speaker 2 (12:14):
Yeah, so you're exactly right.
Speaker 4 (12:16):
My argument is that the way that New Zealan economy
is organized with some people have mocked it in the
past that it sounds agrariant, but I think it's actually
the strength, right, that there's so much of economy is
actually real.
Speaker 2 (12:29):
In both the food sectors.
Speaker 4 (12:31):
The agriculture sectors, as well as domestic services, they all
tend to be performing real, high productivity stuff that's not
related to bureaucracy. And that's why I argue comparatively, New
Zealand's going to emerge probably a little bit better than
many other economies that have swung almost overbearingly towards so
(12:53):
much of the low productivity, bureaucratic sort of sectors.
Speaker 1 (12:57):
Having said that, according to this data, five point two
percent of those jobs are in agriculture. So over the
last few decades we've done a very good job at
increasing productivity on farms, reducing the number of people that
physically need to be on an orchard or on a
dairy farm, for instance. So the number of jobs that
(13:21):
actually generate a lot of that value is relatively low.
Speaker 2 (13:25):
That's a good thing.
Speaker 1 (13:26):
That shows that we're actually highly productive producers and therefore
our products are very popular around the world in terms
of the future automation that can be done in the
primary sector. What's your take on how we're doing. We
hear about automated tractors, automated milking sheds and the like.
Are we actually innovating to a great degree in those areas?
Speaker 4 (13:49):
You know, primary industries is always going to remain New
Zealand's backbone. I think, as you said, we've seen some
bright spot We've definitely seen a lot more animation. For example,
in the grading of fruits, supply chain optimization. We're seeing
very interesting startups. For example, I'm using some of them
in my own teaching. For example, the company Halter using
(14:10):
AI powered callers to help farmers virtually fence.
Speaker 2 (14:14):
And manage hurts. I think that's super interesting.
Speaker 4 (14:16):
But relative to many other countries, like my favorite probably
in Netherlands or Israel, in terms of their investment into
R and D, we are definitely not quite there. So
for example, just to put things on context, is really
invests about six percent of their GDP into R and D.
(14:38):
Netherlands is I think it's definitely more than two percent.
We're only investing about one and a half percent of
our GDP into R and d My argument is that
we should be investing a lot more in sectors like agritech.
And to your point earlier that even though the number
of people that's actually hired in agriculture it may not
seem that much on the surface, but I argue, if
(15:00):
more effort is being done to what's really investing in
the innovation mechanisms and the machinery behind agriculture, we're going
to see a lot more employment of being picked up there. Right,
So we could be chanted we could have having a
lot more technicians testing equipment, testing new innovations in agriculture.
(15:21):
We could have a lot more veterinarians, we could have
a lot more highly skilled people that want to work
in innovating for agriculture.
Speaker 2 (15:31):
Yeah, yeah, that's what I argue.
Speaker 1 (15:33):
And look, we're seeing a resurgence and commodity prices for
our farmers at the moment, so that's one of the
few bright spots in the economy.
Speaker 2 (15:43):
Exactly.
Speaker 1 (15:43):
It does employ a relatively small number of New Zealander's currently.
What you're saying there is there's room for expansion there exactly,
And even though it's an area that is harder to automate,
you're also saying we shouldn't neglect artificial intelligence and robotic
automation in that sector. Actually, if we want to maintain
our competitive advantage, we actually need to really embrace it.
Speaker 4 (16:06):
I think we should absolutely embrace it. We shouldn't be
afraid of AI. I think that we know that AI
is going to be there. Instead, we should be embracing
the fact that, for example, in certain niches, in certain industries,
New Zealand has a lot of strength in it. We
could be the global leaders in developing AI solutions for
those sectors. And I'm saying this for maybe the tenth time,
(16:28):
but I think for example, agritech, right, we could absolutely
will lead.
Speaker 1 (16:31):
Us for that.
Speaker 4 (16:32):
And the risk is that if we don't embrace it,
we could end up being price takers.
Speaker 2 (16:38):
That would be really, really tough. Yeah, right, If.
Speaker 4 (16:41):
You are actually not, if you're actually going to be
using other countries AI tools in the future and they
will come, you're going to end up purely as a
price taker. That would be a massive problem going forward
because we have literally no route out of this transformation
in the economy.
Speaker 1 (16:59):
The World Economic Forum and others are basically saying at
the moment, what we're seeing is the industries and sectors
that are being automated very rapidly are the ones that
have really good data. Software development has great data because
there's a lot of code repositories, the large language models
have crawled all of that code, so they're very good
(17:20):
at creating code automatically. Finance also has a lot of
data available, a lot of it's proprietary data, but it's there.
Other sectors is not so much healthcare. There's a lot
of privacy issues here, a lot of fragmentation of data, repositories, construction,
which is a big employer in New Zealand, manufacturing, and others.
(17:42):
There's that whole truth for you that the ones that
have a lot of data and it's been made available
so companies have done a lot of work to build
data warehouses and tap into all of their data, those
are the ones that can take advantage of automation that
much quicker.
Speaker 4 (17:55):
I'm a research economist by training, right, so you like
to look for data sets that's easy for us to
do the analysis on, and so anything that has more
qualified data that's just put into nice data sheets and
I could just run my regressions absolutely right. So those
(18:17):
who exactly it's the same dynamics here for AI. Anything
that's easily that the AI program can easily make use
of any data, those will be the sectors that AI
is more easily going to.
Speaker 2 (18:29):
To take over.
Speaker 4 (18:31):
And that's why any sectors that rely much more on
less qualified data, more on tangible hands on work that's
not so easy to code right away and once in zeros,
those are the sectors that AI would take a little
bit longer to displace. But at the same time, because
we have that knowledge, we know how to do it,
(18:53):
we could be the leaders in the indie AI solutions
for those sectors.
Speaker 1 (18:57):
Yeah, and that very much goes to the egg tech
story and likes a wholtery a Halter is now a
world leader and managing herds of of dairy and beef
cows in the US and New Zealand and Australia because
it has so much data on every single cow.
Speaker 4 (19:15):
And the fact that we have we have great experience, yeah,
trying to raise cattle.
Speaker 2 (19:20):
That's why you're even able to attempt a solution like
this because you have the experience of doing that.
Speaker 4 (19:26):
It's not quite the same as just doing AI systems
for trading on stock markets.
Speaker 1 (19:31):
Yes, No, that's a good point. It's very different decades
of experience being world class at breeding sheep or milking
cows and creating dairy products with those AI tools, that's
our competitive advantage in terms of the service industry. A
lot of people listening to this podcast will be sitting
(19:53):
there in marketing roles and communications, office administration, business strategy,
and con eltancy and they're probably thinking, well, you just
tell me I have a bullshit job. So what should
they be thinking? A lot of them will be in
their forties and fifties have been doing the same thing
for decades. Now are they about to be washed away
(20:15):
in the AI tsunami?
Speaker 4 (20:17):
I think the first thing you acknowledge and this cuts
very close to my heart because as an educator, I'll
be very honest. I'm seeing firsthand and these are bright
young students, very motivated. They're really struggling to lend any
roles right now because basically firms are saying, hey, we
don't need five juniors, which does need three? Right? And
(20:38):
you know, we're just going to give that AI tools
to do the rest. And it's tough. I totally empathize
with that. Having said that, you know, and the other
thing as well is that I definitely empathize because I'm
really not good at the stuff that I think is
going to matter the most in this new world. I
actually really am bad at things like networking. I almost
really dislike it. So I understand how I'm the boy
(21:00):
is going to be because what I'm going to say
here is that this world is going to lean into
human connections back again and judgment, right, because if everything
can be automated, then what really makes the difference is
that you've got to really demonstrate that you are not AI,
that you are actually human. The things that's going to
matter again would be human connections and judgment. That's where
(21:23):
many of the folks who are afraid of AI displacement
in that sense, I think maybe they don't have that
much to fear because they're probably I think the people
who are a bit more scared would be those that
have been working for a good number of years. As
we're describing, they actually have a lot of advantages because
(21:44):
they have all those human connections, they have all those
human judgment, they have empathy that you know, it's not
easily a machine replaceable in that sense. I think their jobs,
you know, I wouldn't want to say it's secure, but
I would say they probably don't have to.
Speaker 2 (22:02):
Be that worry.
Speaker 4 (22:03):
My bigger fear, bigger worry I should say, is with
actually the freshies that's actually coming on the job market,
because they're getting into a job market is dramatically different,
and my prediction is not even that profound, is that
hiring is just going to keep slowing down. And my
(22:25):
bigger worry is that these students will be so demotivated
by what they're seeing and what they're experiencing, and we
have no easy solution for that.
Speaker 1 (22:34):
Yeah, this is born out in my conversations, particularly with
tech companies. A lot of them have internships and they
take on board students fresh out of university or polytechnics
that have some sort of IT qualifications. It might be
a computer science degree, it might be a diploma or
(22:55):
something like that. So typically they would spend a year
or two doing the sort of grunt work helping out
the more senior code is maybe doing testing now with
AI agents able to do a lot of those roles
with human oversight. A lot of software development people are
saying to me, I don't really know what I'm going
to be doing with our fresh interns and new graduates
(23:17):
exactly soon they're basically having to say to them, you
need to become AI engineers, so you're going to oversee
the AI systems and interact with them. That is going
to be your future and software development. So I guess
if you're just coming out of a computer science degree,
that's a huge shift from probably what you were told
(23:38):
when you started that degree.
Speaker 2 (23:40):
Yeah.
Speaker 4 (23:40):
Absolutely, and I think not even just computer science, which
so for example, was just talking to a friend of
mine with a partner in a measure consulting company, and
he was describing to me that typically junior analysts, when
they hire them, they'll be doing sort of like almost
like runy taking minutes doing the market research and that
(24:04):
sort of thing. Those are definitely going to be replaced
by AI. The worries about that, you know, what's going
to happen to they're probably not going to hire junior
analysts at least not at the same.
Speaker 2 (24:16):
Rate as before.
Speaker 4 (24:18):
But I think he brought up a more subtle point,
which is something that I.
Speaker 2 (24:22):
Worry a little bit more on a longer term basis.
Speaker 4 (24:25):
Is that those grant work that they give to the
junior analysts it's partly also to allow them to get
to know the organizations, get them to form human connections,
get them to network, get them to know more people,
right because eventually that's going to be the basis of why,
and that's going to be the most important point and
(24:50):
source of their capabilities.
Speaker 2 (24:52):
Right, It's that.
Speaker 4 (24:53):
They're able to form connections, able to know people, They're
able to understand problems and network essentially.
Speaker 2 (24:59):
So I worry that in a.
Speaker 4 (25:02):
New era where so much of these junior work so
to speak, are displaced and being outsourced to AI systems,
we're not giving young people the chance to develop human
connections and learning opportunities just by doing these work. That
is something that don't really have easy solutions for.
Speaker 1 (25:23):
Yeah, I really like the concept of getting out from
behind your desk so you're spending less time writing research
reports and minutes and summarizing meetings and inwardly focused all
the admin that goes with being in a modern organization
and actually getting out talking to customers, partners, networking that
face to face stuff, really understanding what your clients need.
(25:45):
That's actually I think can be a really empowering thing.
But it will be a transition for businesses I'm not
sure if they've got their head around it, and frankly,
as a country, I'm not sure if we have as well.
We now have an artificial intelligence strategy for the country.
It was released a couple of months back by the government,
but it got a lot of criticism, particularly for you know,
(26:07):
it's lack of focus on skills development, reskilling, upskilling, preparing
our workforce for what's coming. Do you think that we're
sort of really light on that in a strategic sense.
Speaker 4 (26:20):
I think yes, I know, I do think that New
Zealand's already doing a pretty decent job in the sense
that at least we're recognizing and I think here at
the university side, we're seeing a lot of momentum for sure,
that there's a lot of focused now thinking about AI.
How do we incorporate that into our teaching, how to
incorporate that in research, how do we propel students.
Speaker 2 (26:41):
If you're comparing.
Speaker 4 (26:42):
New Zealand to other countries, I'm sure we can find
better examples of even being more of countries being even
more proactive. I think Singapore, for example, extremely proactive in
thinking about AI. I think these as far back as
maybe five years ago, they've already been talking about this,
so even before the recent sort of attention due to
(27:08):
all these generative AI tools, the synophore is already well
preparing for this era of automation that's going to come.
I think one of the interesting things I think that
you might find of interest is that Singapore has this
pretty successful scheme called Skills Future, where basically they give
every adult credits basically for them to take courses so
(27:31):
they could reskill. And I think that many adults actually
taking this up right now because they fear what's going
to happen in the future or there or they may
be optimistic about what's going to happen in the future,
so they're picking up using these credits to learn things
like hey, how do I do marketing using gen AI tools?
And also even using these credits to learn things like hey,
(27:54):
how do I understand what are AI ethics?
Speaker 2 (27:57):
Or how do I use AI tools in the more
go manner that sort of thing.
Speaker 4 (28:02):
I think New Zealand should do something like this, and
I think it's definitely doable. We're not a huge population
and a five point nine million people, we definitely could
do something like this. So give every adult some subsidized
credit for them to undertake courses. It could be in universities,
it could be in vocational institutes. Just for them to
(28:26):
just get up to speed with all these AI tools
that's been coming on and all the issues that are
coming on.
Speaker 2 (28:33):
I think those would be very, very helpful.
Speaker 1 (28:35):
You took yeah there about Singapore. Actually that's really where
your career started, wasn't it, Kenny. You did a stint
at the Economic Development Board of Singapore. Incredible success in
tech and science, in the service industry and financial services
in Singapore. What was it like in that sort of
early days where they were getting some of these schemes together.
(28:56):
We saw the rise of a star, really really valuable
body up in Singapore as well. What was that like
and really what do you think we can learn from
the success of Singapore.
Speaker 4 (29:08):
I came from Singapore originally. I'm born and bred actually
in Singapore. If I went overseas for my higher education
and then somehow found my way here. I started my career,
as you said, working in Singapore Economic development aboard Singapore.
And one of the things that Singapore is extremely good
at is this coordination. The country is super united in
(29:34):
the sense of that we have a very capable government
that sets in place targets that's with this way that
they want to achieve, and everything just spins into place
in terms of the coordination that we need in terms
of the structure and the resources and so on, your
message together, and we execute really well on that front.
(29:57):
It's probably not model that every other country could aspire to,
because there are natural advantages as Singapore has that most
other countries don't. The fact that it's a small country.
You're very for We are very fortunate to have a
government that's extremely most skill knowledgeable and benign. It's very
(30:19):
difficult for every country to be able to replicate. But
things like being coordinated, things like thinking in terms of
long term planning, like thinking that there are going to
be challenges that's going to be coming.
Speaker 2 (30:33):
We shouldn't run from it. That's just think what's the
best we could do about it. Those are things.
Speaker 4 (30:37):
These are qualities that I'm sure every other country should
it really aspire to.
Speaker 1 (30:42):
We are increasing our collaboration with Singapore. There's not much
new science funding coming through at the moment, but a
couple of the projects that have been topped up or
new projects funded recently are I think health tach collaborations
with Singapore, so it's just a natural partner to collaborate
with it. To our part of the world, we're really
focused on Asia Pacific. Singapore has particularly in the biotech space.
(31:06):
Singapore is doing some great stuff on the future of food,
so there's a lot of synergies there.
Speaker 4 (31:11):
You know, Singapore basically they treat innovations as survival right,
so because Singapore doesn't have natural resources, basically make ourselves indispensable.
So in terms of like the amount of resources they're
poor into R and D is definitely much higher than
New Zealand.
Speaker 2 (31:28):
So they do about two.
Speaker 4 (31:29):
And a half percent in two point two too and
a half percent, so that's significantly more than what New
Zealand is doing.
Speaker 2 (31:36):
So again there's something that New Zealand should aspire to.
Speaker 4 (31:39):
I would also say that Singapore. There are certain things
about Singapore that I also have some reservations on. So
for example, I will get sing points. So much as
society as built a lot of expectations, right you you
sort of you. You want to do well in school,
get the right degree, join the right sector. You're almost
on the street half the success. So much of Singapore
(32:02):
has been built on that pop down self thinking. But
that works in the world where careers are very stable.
Speaker 2 (32:09):
But now AI is.
Speaker 4 (32:10):
Really reshaping the labor market in very unpredictable ways. I
worry that for a system that's been built so much
around certainty, around clear letters of success, that's going to
be huge cultural adjustments. I think in New Zealand we
don't have that quite the same level, you know, structure
or investment, but we're probably a little bit more used
(32:31):
to zigzagging a little bit more that you know. There's
not a straight line success, straight path to success or
of thinking, and I think that's why it, Luke gives
me a lot of hope that New Zealand will we'll
be able to navigate the next few years.
Speaker 2 (32:47):
Well, well, that's good to hear, Kenny.
Speaker 1 (32:49):
Thanks so much for coming on the business of take
great posting the conversation. We'll link to that in the
show notes, and you keep the insights coming.
Speaker 2 (32:57):
Thank you very much. Appreciate the time.
Speaker 1 (33:04):
That was. Kenny Cheng, economist and organizational behavior scholar, on
what AI is really doing to work for New Zealand.
He clearly sees opportunity lean into primary sector innovation while
scaling AI literacy across services, back agrotech, R and D
and data infrastructure think herd management, grading of fruit, logistics,
(33:27):
that sort of stuff. So local firms build proprietary data
sets and solutions instead of renting them from abroad. He
suggests to business leaders map roles to tasks and target
augmentation not blanket replacement. Protect entry level learning by redesigning
the on ramps. AI takes over the grunt work and
(33:49):
invest in upscaling, credits and partnerships that build capability ahead
of the next adoption wave. As he pointed out, Singapore's
Skills Future program gives every single Pourian upskilling opportunities. It's
a really good example of what we could do at
a national level to ready the workforce for AI. Anyway,
that's it for the Business of Tech this week. Follow
(34:12):
rate and share the podcast on iHeartRadio or your favorite
podcast app. Check the show notes out at Businessdesk, dot
co dot NZ. You'll find Kenny's conversation piece and examples
mentioned in the episode there too. Thanks so much for listening.
I'll catch you next week with another episode of the
Business of Tech.
Cura and welcome to the Business of Tech powered by
Two Degrees Business. I'm Peter Griffin and on this week's episode,
once again we're looking at artificial intelligence, but this week
we drill into its impact on our workforce here in
New Zealand. It seems like anyone who really understands AI
is telling us that the change is coming to how
(00:23):
we work as a result of the adoption of AI
are monumental and we aren't doing enough as a nation
to prepare for them. But as you sit in your
car on the commute to the office, or if you're
working at it to gym, you may also be thinking,
right now, it ain't making a dent at my workplace,
and given how clunky and unreliable chat, GPT and co pilot,
(00:46):
are my job safe for the foreseeable future. Well, this
week's guest argues that yes, actually AI is coming for jobs,
particularly in the services industry. White collar rolls that AI
is quickly getting a lot better at not only augmenting
but replacing entirely. But doctor Kenny Ching, an organizational behavioral
(01:08):
expert and economists at the University of Auckland, argues that
New Zealand is going to be better placed than most
other Western nations to absorb the labor impacts of AI.
That's because agriculture is the base of our economy. Primary
sector only employees five point two percent of New Zealanders,
(01:28):
but it's responsible for nearly seventy percent of our export revenue.
Automation is happening in agriculture, but won't take hold nearly
as quickly as it is in the office. A primary
sector anchored economy, Smaller and flatter firms and more tangible,
less codified work mean automation diffuses over longer investment cycles
(01:53):
in orchards, shared construction sites, and supply chains, buying a
window to build capability before the curve steepens. For the
services industry, think marketing, banking, and insurance finance type jobs,
all the corporate admin that goes on across our businesses
all over the country, the threat to jobs is very real.
(02:16):
The latest biannual survey of businesses conducted by Victoria University
researchers in conjunction with the AI Forum, finds that fourteen
percent of organizations are now attributing job losses to AI.
This has doubled in the last six months, so AI
is starting to eat jobs six months ago, they're basically
(02:36):
saying we're not hiring.
Speaker 2 (02:38):
As much as we used to.
Speaker 1 (02:39):
Now they're actually attributing job losses to AI. That's a
big shift and it's only going to accelerate. What's going
on is really well summed up by Moe Gorditt, the
author and former chief business officer of Google x that's
the search giant's secretive research and development arm. Gord It
recently appeared on the Diary of a CEO podcast. Here's
(03:03):
what you had to say about AI's disruptive potential in
the services in Illustrie. There's why collar jobs. I've been
talking about.
Speaker 3 (03:10):
AI is going to replace the grain of a human
and when the West, and it's interesting virtual colonies that
I call it basically outsourced or labor to the to
the developing nations. What the West publicly said at the
time is we're going to be a services economy where
we're not interested in making things and stitching things, and
(03:33):
so let the Indians and Chinese and you know, Bengalis
and Vietnamese do that. We're going to do more defined jobs.
Knowledge workers. We're going to Knowledge workers are people who
work with information and click on a keyboard and move
the mouse, and you know, sitting meetings and all we
produce in the Western societies is what works right or
(03:55):
designs maybe sometimes, but everything we produce can be produced by.
Speaker 1 (03:59):
Any So pivoting to the knowledge economy for decades was
considered to be the thing to do to achieve higher
paying salaries for workers. But as it turns out, we're
lucky to produce a lot of actual stuff, physical stuff
that the world wants to buy and as it happens,
pay more for than they used to. Milk and meat
(04:20):
prices are looking pretty healthy at the moment, but Kenny
Ching suggests that this is not a reason to ignore
AI's applications in agriculture or be complacent about it. In fact,
we should embrace AI and become the best at developing
it in our agrotech sector, because AI is coming for
(04:41):
that sector too, just at a slower pace. Given we've
got great expertise in growing wool, meat, milk, Kiwi fruit
apples were well placed to become world leaders in agritech
and the application of AI in those industries. So here's
the interview with University of All Kenny Ching, which I
(05:01):
recorded last week after you published really interesting essay on
AI's potential impact in a tea. I really enjoyed your
conversation piece from a couple of weeks ago.
Speaker 2 (05:19):
Really interesting.
Speaker 1 (05:20):
You know, you're essentially saying there that AI is definitely
coming for jobs, but it's going to hit you know,
the services industry particularly hard and earlier than other industries,
and you know, with our agricultural base, that may be
an advantage for us. So we'll get into that what
that actually means. But Kenny, in terms of how AI
(05:43):
is affecting the workforce, so far as you do a
horizon scan around the world, you know, what exactly are
you saying. I'm just reading a World Economic Forum report
that is basically saying, so far, it looks like software
development is getting hammered. That's how they put it. You've
got the likes of GitHub, which is a big repository
(06:04):
for software. It has four hundred and twenty million repositories
now and a lot of them are public, so there's
a lot of code there that people can use and
automate their workflows. So that's why we've seen big tech
companies laying off a relatively a lot of staff compared
to other industries. Customer support is another one they call
(06:24):
a setting duck AI automation in contact centers, for instance,
and finance that heavily employees. Machine learning and algorithmic trading
is on the front line of it as well. But
in terms of all the research you're seeing coming out
of various countries, what does it say about the real
impact on jobs when it comes to AI at the moment?
Speaker 4 (06:45):
So, first of all, a lot of the data and
evidence that we have is pretty much within the rear.
Speaker 2 (06:50):
At this moment.
Speaker 4 (06:51):
We don't quite get no for sure, how it's all
going to pan out, but I think gradually we're getting
a good sense of what's going on. As I argue,
a lot of it's going to be centered around the
service industry, and that's where most of the disruption is
going to take place.
Speaker 2 (07:09):
I think.
Speaker 4 (07:10):
So some people have responded to my piece, for example,
saying that, Okay, so many of our New Zealand design
in the service sector, and most of the white college
in a white college jobs, so within that cosmes unemployment,
I don't quite think so.
Speaker 2 (07:24):
I think there are a couple of reasons why.
Speaker 4 (07:26):
One is that the best evidence that we have seems
to suggest that the way AI displacement's going to work
is that it's going to reconfigure jobs more than it
completely replaces them. So maybe about sixty to seventy percent
of what activities we're going to see some automation. But
it doesn't mean it disappears completely outright that overnight.
Speaker 2 (07:49):
It just doesn't happen that way.
Speaker 4 (07:51):
Maybe a third of them might might happen that way,
but it's not going to be all the jobs just disappear.
But I think there's another critical point, which is that
the way AI displacement is going to happen is that,
for example, if a company is typically is going to
hire five new hires for the hiring season, they will
probably hire three, right, and they expect that those three
(08:14):
will be augmented by AI tools. So and so the
effect of this placement shows up very gradually. It's not
gonna be a mass so disappearance of jobs. Rather, you're
going to see slower hiring and maybe hiring freezers.
Speaker 2 (08:27):
That's all things.
Speaker 4 (08:28):
But you're not going to see incredibly, like you know,
sixty percent of the of jobs just disappear overnight.
Speaker 1 (08:34):
Yeah, you talk in your conversation piece, you mentioned David
Grabner's concept of bullshit jobs. Yeah, absolutely, He called them
rolls that add little genuine value. And between twenty twenty eighteen,
most knit job growth came from these low productivity service
(08:54):
sector jobs. We're talking about marketing, consulting, corporate administration, and
it's happened in New Zealand as well. You know, we
saw an expansion in those sectors. So I guess we
have a lot of jobs in the services sector, and
that is a big sway of things that services technically
cover everything from healthcare to retail to those sorts of
(09:18):
marketing roles. But I guess you know, we've got unemployment
at five point two percent. What you're saying there, and
this is what I'm hearing from the tech vendors who
are selling artificial intelligence systems. They're saying it will augment
your job, it will change your job. It won't necessarily
eliminate sways of jobs. But as you said, you're not
(09:39):
necessarily going to be hiring as many people in future.
And if you look at you know, for instance, Spark
just laid off thirteen hundred people in the last year.
Other companies have had significant cuts as well. It seems
to me that the main impact of AI so far
is signaling to companies we don't need to hire as
(09:59):
a aggressively as we have in the past, which I
guess is a bit of a concern for our return
to growth that the government wants that unemployment isn't necessarily
going to reduce as quickly because the need to hire
people has listened.
Speaker 4 (10:14):
I think that's absolutely the case. I don't think there's
any other country that is not facing these similar issues
as you've described. I think what my argument is that
New Zealand's probably is a little bit different from other
countries in the sense that we tend to have our
companies tend to be a little bit smaller compared to
say the UK or the US. So I got some
(10:36):
stats here, for example that our firms on average has
about five employees compared to about thirteen for across the OECD,
and that and sense suggests that our companies are a
little bit flatter, few duplicated roles, you are more people
wearing multiple hats, and I would argue again that the
jobs tend to be closer to the real economy right,
(10:58):
so I ignore absolutely that will be tremendous impact on
the AI displayson is going to have tremendous impowers, should say,
but my argument is that New Zealand probably experience it
a little bit less compared to many other economies around world.
Speaker 1 (11:15):
And a lot of people will be happy to hear that.
You know, we've got about two point eight million jobs
in New Zealand according to Inframetrics, which is looking at
stats n Z labor data. Construction around ten percent is
actually one of the biggest that may have come down
a bit because construction is a bit soft at the moment,
followed by healthcare and social assistance that's around ten percent,
(11:38):
then professional, scientific and technical services, followed by manufacturing. So
when you get down to the ones that the World
Economic Forum is saying is really at the front line
of this, financial services in New Zealand two point seven
percent and information media telecommunications you know, one point five percent.
So these are relatively small sectors for New Zealand. You've
(12:00):
argued in your conversation piece that our strengthen agriculture, which
is the majority of our exports currently, is going to
see us well through this wave of automation because literally
a lot of that stuff can be automated.
Speaker 2 (12:14):
Yeah, so you're exactly right.
Speaker 4 (12:16):
My argument is that the way that New Zealan economy
is organized with some people have mocked it in the
past that it sounds agrariant, but I think it's actually
the strength, right, that there's so much of economy is
actually real.
Speaker 2 (12:29):
In both the food sectors.
Speaker 4 (12:31):
The agriculture sectors, as well as domestic services, they all
tend to be performing real, high productivity stuff that's not
related to bureaucracy. And that's why I argue comparatively, New
Zealand's going to emerge probably a little bit better than
many other economies that have swung almost overbearingly towards so
(12:53):
much of the low productivity, bureaucratic sort of sectors.
Speaker 1 (12:57):
Having said that, according to this data, five point two
percent of those jobs are in agriculture. So over the
last few decades we've done a very good job at
increasing productivity on farms, reducing the number of people that
physically need to be on an orchard or on a
dairy farm, for instance. So the number of jobs that
(13:21):
actually generate a lot of that value is relatively low.
Speaker 2 (13:25):
That's a good thing.
Speaker 1 (13:26):
That shows that we're actually highly productive producers and therefore
our products are very popular around the world in terms
of the future automation that can be done in the
primary sector. What's your take on how we're doing. We
hear about automated tractors, automated milking sheds and the like.
Are we actually innovating to a great degree in those areas?
Speaker 4 (13:49):
You know, primary industries is always going to remain New
Zealand's backbone. I think, as you said, we've seen some
bright spot We've definitely seen a lot more animation. For example,
in the grading of fruits, supply chain optimization. We're seeing
very interesting startups. For example, I'm using some of them
in my own teaching. For example, the company Halter using
(14:10):
AI powered callers to help farmers virtually fence.
Speaker 2 (14:14):
And manage hurts. I think that's super interesting.
Speaker 4 (14:16):
But relative to many other countries, like my favorite probably
in Netherlands or Israel, in terms of their investment into
R and D, we are definitely not quite there. So
for example, just to put things on context, is really
invests about six percent of their GDP into R and D.
(14:38):
Netherlands is I think it's definitely more than two percent.
We're only investing about one and a half percent of
our GDP into R and d My argument is that
we should be investing a lot more in sectors like agritech.
And to your point earlier that even though the number
of people that's actually hired in agriculture it may not
seem that much on the surface, but I argue, if
(15:00):
more effort is being done to what's really investing in
the innovation mechanisms and the machinery behind agriculture, we're going
to see a lot more employment of being picked up there. Right,
So we could be chanted we could have having a
lot more technicians testing equipment, testing new innovations in agriculture.
(15:21):
We could have a lot more veterinarians, we could have
a lot more highly skilled people that want to work
in innovating for agriculture.
Speaker 2 (15:31):
Yeah, yeah, that's what I argue.
Speaker 1 (15:33):
And look, we're seeing a resurgence and commodity prices for
our farmers at the moment, so that's one of the
few bright spots in the economy.
Speaker 2 (15:43):
Exactly.
Speaker 1 (15:43):
It does employ a relatively small number of New Zealander's currently.
What you're saying there is there's room for expansion there exactly,
And even though it's an area that is harder to automate,
you're also saying we shouldn't neglect artificial intelligence and robotic
automation in that sector. Actually, if we want to maintain
our competitive advantage, we actually need to really embrace it.
Speaker 4 (16:06):
I think we should absolutely embrace it. We shouldn't be
afraid of AI. I think that we know that AI
is going to be there. Instead, we should be embracing
the fact that, for example, in certain niches, in certain industries,
New Zealand has a lot of strength in it. We
could be the global leaders in developing AI solutions for
those sectors. And I'm saying this for maybe the tenth time,
(16:28):
but I think for example, agritech, right, we could absolutely
will lead.
Speaker 1 (16:31):
Us for that.
Speaker 4 (16:32):
And the risk is that if we don't embrace it,
we could end up being price takers.
Speaker 2 (16:38):
That would be really, really tough. Yeah, right, If.
Speaker 4 (16:41):
You are actually not, if you're actually going to be
using other countries AI tools in the future and they
will come, you're going to end up purely as a
price taker. That would be a massive problem going forward
because we have literally no route out of this transformation
in the economy.
Speaker 1 (16:59):
The World Economic Forum and others are basically saying at
the moment, what we're seeing is the industries and sectors
that are being automated very rapidly are the ones that
have really good data. Software development has great data because
there's a lot of code repositories, the large language models
have crawled all of that code, so they're very good
(17:20):
at creating code automatically. Finance also has a lot of
data available, a lot of it's proprietary data, but it's there.
Other sectors is not so much healthcare. There's a lot
of privacy issues here, a lot of fragmentation of data, repositories, construction,
which is a big employer in New Zealand, manufacturing, and others.
(17:42):
There's that whole truth for you that the ones that
have a lot of data and it's been made available
so companies have done a lot of work to build
data warehouses and tap into all of their data, those
are the ones that can take advantage of automation that
much quicker.
Speaker 4 (17:55):
I'm a research economist by training, right, so you like
to look for data sets that's easy for us to
do the analysis on, and so anything that has more
qualified data that's just put into nice data sheets and
I could just run my regressions absolutely right. So those
(18:17):
who exactly it's the same dynamics here for AI. Anything
that's easily that the AI program can easily make use
of any data, those will be the sectors that AI
is more easily going to.
Speaker 2 (18:29):
To take over.
Speaker 4 (18:31):
And that's why any sectors that rely much more on
less qualified data, more on tangible hands on work that's
not so easy to code right away and once in zeros,
those are the sectors that AI would take a little
bit longer to displace. But at the same time, because
we have that knowledge, we know how to do it,
(18:53):
we could be the leaders in the indie AI solutions
for those sectors.
Speaker 1 (18:57):
Yeah, and that very much goes to the egg tech
story and likes a wholtery a Halter is now a
world leader and managing herds of of dairy and beef
cows in the US and New Zealand and Australia because
it has so much data on every single cow.
Speaker 4 (19:15):
And the fact that we have we have great experience, yeah,
trying to raise cattle.
Speaker 2 (19:20):
That's why you're even able to attempt a solution like
this because you have the experience of doing that.
Speaker 4 (19:26):
It's not quite the same as just doing AI systems
for trading on stock markets.
Speaker 1 (19:31):
Yes, No, that's a good point. It's very different decades
of experience being world class at breeding sheep or milking
cows and creating dairy products with those AI tools, that's
our competitive advantage in terms of the service industry. A
lot of people listening to this podcast will be sitting
(19:53):
there in marketing roles and communications, office administration, business strategy,
and con eltancy and they're probably thinking, well, you just
tell me I have a bullshit job. So what should
they be thinking? A lot of them will be in
their forties and fifties have been doing the same thing
for decades. Now are they about to be washed away
(20:15):
in the AI tsunami?
Speaker 4 (20:17):
I think the first thing you acknowledge and this cuts
very close to my heart because as an educator, I'll
be very honest. I'm seeing firsthand and these are bright
young students, very motivated. They're really struggling to lend any
roles right now because basically firms are saying, hey, we
don't need five juniors, which does need three? Right? And
(20:38):
you know, we're just going to give that AI tools
to do the rest. And it's tough. I totally empathize
with that. Having said that, you know, and the other
thing as well is that I definitely empathize because I'm
really not good at the stuff that I think is
going to matter the most in this new world. I
actually really am bad at things like networking. I almost
really dislike it. So I understand how I'm the boy
(21:00):
is going to be because what I'm going to say
here is that this world is going to lean into
human connections back again and judgment, right, because if everything
can be automated, then what really makes the difference is
that you've got to really demonstrate that you are not AI,
that you are actually human. The things that's going to
matter again would be human connections and judgment. That's where
(21:23):
many of the folks who are afraid of AI displacement
in that sense, I think maybe they don't have that
much to fear because they're probably I think the people
who are a bit more scared would be those that
have been working for a good number of years. As
we're describing, they actually have a lot of advantages because
(21:44):
they have all those human connections, they have all those
human judgment, they have empathy that you know, it's not
easily a machine replaceable in that sense. I think their jobs,
you know, I wouldn't want to say it's secure, but
I would say they probably don't have to.
Speaker 2 (22:02):
Be that worry.
Speaker 4 (22:03):
My bigger fear, bigger worry I should say, is with
actually the freshies that's actually coming on the job market,
because they're getting into a job market is dramatically different,
and my prediction is not even that profound, is that
hiring is just going to keep slowing down. And my
(22:25):
bigger worry is that these students will be so demotivated
by what they're seeing and what they're experiencing, and we
have no easy solution for that.
Speaker 1 (22:34):
Yeah, this is born out in my conversations, particularly with
tech companies. A lot of them have internships and they
take on board students fresh out of university or polytechnics
that have some sort of IT qualifications. It might be
a computer science degree, it might be a diploma or
(22:55):
something like that. So typically they would spend a year
or two doing the sort of grunt work helping out
the more senior code is maybe doing testing now with
AI agents able to do a lot of those roles
with human oversight. A lot of software development people are
saying to me, I don't really know what I'm going
to be doing with our fresh interns and new graduates
(23:17):
exactly soon they're basically having to say to them, you
need to become AI engineers, so you're going to oversee
the AI systems and interact with them. That is going
to be your future and software development. So I guess
if you're just coming out of a computer science degree,
that's a huge shift from probably what you were told
(23:38):
when you started that degree.
Speaker 2 (23:40):
Yeah.
Speaker 4 (23:40):
Absolutely, and I think not even just computer science, which
so for example, was just talking to a friend of
mine with a partner in a measure consulting company, and
he was describing to me that typically junior analysts, when
they hire them, they'll be doing sort of like almost
like runy taking minutes doing the market research and that
(24:04):
sort of thing. Those are definitely going to be replaced
by AI. The worries about that, you know, what's going
to happen to they're probably not going to hire junior
analysts at least not at the same.
Speaker 2 (24:16):
Rate as before.
Speaker 4 (24:18):
But I think he brought up a more subtle point,
which is something that I.
Speaker 2 (24:22):
Worry a little bit more on a longer term basis.
Speaker 4 (24:25):
Is that those grant work that they give to the
junior analysts it's partly also to allow them to get
to know the organizations, get them to form human connections,
get them to network, get them to know more people,
right because eventually that's going to be the basis of why,
and that's going to be the most important point and
(24:50):
source of their capabilities.
Speaker 2 (24:52):
Right, It's that.
Speaker 4 (24:53):
They're able to form connections, able to know people, They're
able to understand problems and network essentially.
Speaker 2 (24:59):
So I worry that in a.
Speaker 4 (25:02):
New era where so much of these junior work so
to speak, are displaced and being outsourced to AI systems,
we're not giving young people the chance to develop human
connections and learning opportunities just by doing these work. That
is something that don't really have easy solutions for.
Speaker 1 (25:23):
Yeah, I really like the concept of getting out from
behind your desk so you're spending less time writing research
reports and minutes and summarizing meetings and inwardly focused all
the admin that goes with being in a modern organization
and actually getting out talking to customers, partners, networking that
face to face stuff, really understanding what your clients need.
(25:45):
That's actually I think can be a really empowering thing.
But it will be a transition for businesses I'm not
sure if they've got their head around it, and frankly,
as a country, I'm not sure if we have as well.
We now have an artificial intelligence strategy for the country.
It was released a couple of months back by the government,
but it got a lot of criticism, particularly for you know,
(26:07):
it's lack of focus on skills development, reskilling, upskilling, preparing
our workforce for what's coming. Do you think that we're
sort of really light on that in a strategic sense.
Speaker 4 (26:20):
I think yes, I know, I do think that New
Zealand's already doing a pretty decent job in the sense
that at least we're recognizing and I think here at
the university side, we're seeing a lot of momentum for sure,
that there's a lot of focused now thinking about AI.
How do we incorporate that into our teaching, how to
incorporate that in research, how do we propel students.
Speaker 2 (26:41):
If you're comparing.
Speaker 4 (26:42):
New Zealand to other countries, I'm sure we can find
better examples of even being more of countries being even
more proactive. I think Singapore, for example, extremely proactive in
thinking about AI. I think these as far back as
maybe five years ago, they've already been talking about this,
so even before the recent sort of attention due to
(27:08):
all these generative AI tools, the synophore is already well
preparing for this era of automation that's going to come.
I think one of the interesting things I think that
you might find of interest is that Singapore has this
pretty successful scheme called Skills Future, where basically they give
every adult credits basically for them to take courses so
(27:31):
they could reskill. And I think that many adults actually
taking this up right now because they fear what's going
to happen in the future or there or they may
be optimistic about what's going to happen in the future,
so they're picking up using these credits to learn things
like hey, how do I do marketing using gen AI tools?
And also even using these credits to learn things like hey,
(27:54):
how do I understand what are AI ethics?
Speaker 2 (27:57):
Or how do I use AI tools in the more
go manner that sort of thing.
Speaker 4 (28:02):
I think New Zealand should do something like this, and
I think it's definitely doable. We're not a huge population
and a five point nine million people, we definitely could
do something like this. So give every adult some subsidized
credit for them to undertake courses. It could be in universities,
it could be in vocational institutes. Just for them to
(28:26):
just get up to speed with all these AI tools
that's been coming on and all the issues that are
coming on.
Speaker 2 (28:33):
I think those would be very, very helpful.
Speaker 1 (28:35):
You took yeah there about Singapore. Actually that's really where
your career started, wasn't it, Kenny. You did a stint
at the Economic Development Board of Singapore. Incredible success in
tech and science, in the service industry and financial services
in Singapore. What was it like in that sort of
early days where they were getting some of these schemes together.
(28:56):
We saw the rise of a star, really really valuable
body up in Singapore as well. What was that like
and really what do you think we can learn from
the success of Singapore.
Speaker 4 (29:08):
I came from Singapore originally. I'm born and bred actually
in Singapore. If I went overseas for my higher education
and then somehow found my way here. I started my career,
as you said, working in Singapore Economic development aboard Singapore.
And one of the things that Singapore is extremely good
at is this coordination. The country is super united in
(29:34):
the sense of that we have a very capable government
that sets in place targets that's with this way that
they want to achieve, and everything just spins into place
in terms of the coordination that we need in terms
of the structure and the resources and so on, your
message together, and we execute really well on that front.
(29:57):
It's probably not model that every other country could aspire to,
because there are natural advantages as Singapore has that most
other countries don't. The fact that it's a small country.
You're very for We are very fortunate to have a
government that's extremely most skill knowledgeable and benign. It's very
(30:19):
difficult for every country to be able to replicate. But
things like being coordinated, things like thinking in terms of
long term planning, like thinking that there are going to
be challenges that's going to be coming.
Speaker 2 (30:33):
We shouldn't run from it. That's just think what's the
best we could do about it. Those are things.
Speaker 4 (30:37):
These are qualities that I'm sure every other country should
it really aspire to.
Speaker 1 (30:42):
We are increasing our collaboration with Singapore. There's not much
new science funding coming through at the moment, but a
couple of the projects that have been topped up or
new projects funded recently are I think health tach collaborations
with Singapore, so it's just a natural partner to collaborate
with it. To our part of the world, we're really
focused on Asia Pacific. Singapore has particularly in the biotech space.
(31:06):
Singapore is doing some great stuff on the future of food,
so there's a lot of synergies there.
Speaker 4 (31:11):
You know, Singapore basically they treat innovations as survival right,
so because Singapore doesn't have natural resources, basically make ourselves indispensable.
So in terms of like the amount of resources they're
poor into R and D is definitely much higher than
New Zealand.
Speaker 2 (31:28):
So they do about two.
Speaker 4 (31:29):
And a half percent in two point two too and
a half percent, so that's significantly more than what New
Zealand is doing.
Speaker 2 (31:36):
So again there's something that New Zealand should aspire to.
Speaker 4 (31:39):
I would also say that Singapore. There are certain things
about Singapore that I also have some reservations on. So
for example, I will get sing points. So much as
society as built a lot of expectations, right you you
sort of you. You want to do well in school,
get the right degree, join the right sector. You're almost
on the street half the success. So much of Singapore
(32:02):
has been built on that pop down self thinking. But
that works in the world where careers are very stable.
Speaker 2 (32:09):
But now AI is.
Speaker 4 (32:10):
Really reshaping the labor market in very unpredictable ways. I
worry that for a system that's been built so much
around certainty, around clear letters of success, that's going to
be huge cultural adjustments. I think in New Zealand we
don't have that quite the same level, you know, structure
or investment, but we're probably a little bit more used
(32:31):
to zigzagging a little bit more that you know. There's
not a straight line success, straight path to success or
of thinking, and I think that's why it, Luke gives
me a lot of hope that New Zealand will we'll
be able to navigate the next few years.
Speaker 2 (32:47):
Well, well, that's good to hear, Kenny.
Speaker 1 (32:49):
Thanks so much for coming on the business of take
great posting the conversation. We'll link to that in the
show notes, and you keep the insights coming.
Speaker 2 (32:57):
Thank you very much. Appreciate the time.
Speaker 1 (33:04):
That was. Kenny Cheng, economist and organizational behavior scholar, on
what AI is really doing to work for New Zealand.
He clearly sees opportunity lean into primary sector innovation while
scaling AI literacy across services, back agrotech, R and D
and data infrastructure think herd management, grading of fruit, logistics,
(33:27):
that sort of stuff. So local firms build proprietary data
sets and solutions instead of renting them from abroad. He
suggests to business leaders map roles to tasks and target
augmentation not blanket replacement. Protect entry level learning by redesigning
the on ramps. AI takes over the grunt work and
(33:49):
invest in upscaling, credits and partnerships that build capability ahead
of the next adoption wave. As he pointed out, Singapore's
Skills Future program gives every single Pourian upskilling opportunities. It's
a really good example of what we could do at
a national level to ready the workforce for AI. Anyway,
that's it for the Business of Tech this week. Follow
(34:12):
rate and share the podcast on iHeartRadio or your favorite
podcast app. Check the show notes out at Businessdesk, dot
co dot NZ. You'll find Kenny's conversation piece and examples
mentioned in the episode there too. Thanks so much for listening.
I'll catch you next week with another episode of the
Business of Tech.
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