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July 16, 2025 37 mins

Ambition interrupted: Building AI without losing control.

As the digital economy accelerates, Canada faces a critical inflection point. The infrastructure needed to power AI, data centers, and digital services is rapidly outpacing our policy frameworks, energy systems, and trust protocols. From energy-hungry servers to questions of data sovereignty and public confidence, this episode explores how Canada can build the physical and invisible infrastructure needed to thrive in a digital world — and remain in control of our future.  

With guests Glenda Crisp, President and CEO of the Vector Institute, and Audrey Ancion, Partner, AI & Data at Deloitte Canada, in conversation with Edward Greenspon, co-chair of the Future of Canada Centre. 

Disclaimer: Opinions expressed are genuine and reflect the guest's views; the guest is also a client of Deloitte Canada.

 

 

A French transcript of this episode can be read here: https://deloi.tt/4lX6dKm

 

 

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Ambition interrompue : Bâtir l’IA sans perdre le contrôle

À mesure que l’économie numérique s’accélère, le Canada se trouve à un point d’inflexion critique. L’infrastructure nécessaire pour alimenter l’IA, les centres de données et les services numériques prend de vitesse nos cadres politiques, nos systèmes énergétiques et nos protocoles de confiance. Qu’il s’agisse de serveurs énergivores, de questions de souveraineté des données ou de la confiance du public,

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Transcript

Episode Transcript

Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:01):
Welcome to another edition of FullCircle, the Leapfrog series, where we
explore the opportunity side of thegeopolitical gyrations going on in the
world and what they mean for Canada.
I'm Edward Greenspon.
Today's conversation is about a definingtechnological force of our time.
Artificial Intelligence is foundational.

(00:23):
What economists like to call a generalpurpose technology, think of electricity
at the turn of the 20th century orthe internet over the past 50 years.
They reshaped the competitiveadvantage of industries and
nations, and they changed our lives.
Is Canada set up for success in terms ofresearch talent, digital infrastructure?
Will we be able to parlay theadvantage that we have into a

(00:45):
global position of consequence?
With us today, Glenda Crisp is CEO of theVector Institute, a national AI Research
Center of Excellence based in Toronto.
Glenda brings deep insight from heryears on the front lines of data and
technological transformation, mostof it guiding the financial services
industry into the new tomorrow.

(01:06):
And Audrey Ancion, as an AI strategypartner, she works with clients to
develop skills systems and safeguardsneeded for Canadian businesses
to be among the leaders,not the followers.
Together we'll explore what it will takefor Canada to leapfrog in AI and whether
in fact, anyone can even think of pullingoff a leapfrog without getting AI right.

(01:27):
A warm welcome to you both.
Glenda, let's start with you. And II, I'd just like to get a sense of
the scope of what we're talking about.
Just how big is AI, how profoundlywill it, or is it already changing
the world and changing our lives?
So, I think your intro was bang on.

(01:48):
It is here.
It is changing, um, pretty much allaspects of our economy and so, you know,
my background in financial services,the banks have been moving in this
space in Canada for over 10 years.
Um, you know, things like fraudprevention, risk management, uh, customer
service, but it's not just the banks.

(02:08):
So we see large pharma companies, lifesciences companies that are using AI
to really help with drug discovery,uh, better patient care pathways.
Uh, we see telecom companies thatare using it for logistics, uh,
for preventative maintenance.
We're seeing rail companies use it forpreventative maintenance for logistics.

(02:30):
So I think there's really not any sectorof the economy that will not be impacted
by AI in some way, shape, or form.
So
just drawing perhaps on your financialservices background that you mentioned,
can you just give us an example of
how it is changing the productivityof the industry, how it's changing the

(02:51):
experience of, of, you know, consumers?
I mean just, you know, kind of specificexample in your career of, okay, AI, did
this solve this problem, made this better.
Well, you know, I'm actuallygoing to use an example from
an Australian bank I worked at.
So I was working there during COVIDand there were, as in Canada, many
government programs to support business.

(03:12):
So one of the things that we used AI forwas speech analytics to ensure that our
customer contact center was in compliancewith the government programs, so that in
real time we could make sure that we weregiving the best advice to our customers.
So.
The contact center, uh, managers were ableto coach and better train their employees

(03:34):
that we would give the best advice.
Now, without AI, we would only havebeen able to spot check randomly.
Whereas with AI we could make sure thatevery call, every customer interaction was
good, was in the benefit of the customer,was compliant with the legislation.
And so that's just one exampleand that was hugely productive.

(03:55):
It it
It made the bank feel comfortablethat they were actually compliant,
um, completely with whatthey were trying to achieve.
That, you know, there was no human error,if you will. But it also reduced the cost
because previously to try and monitorevery single call, every single day
like that, that would've been enormouslyexpensive and frankly, unfeasible.

(04:19):
OK, well, that's very helpful.
Let's pull the camera back a littlebit for a second and, um, maybe
you could help us understand how AIfits into the challenge that, you
know, Canada's facing at the moment.
You know, we increasingly need to
depend on ourselves.
We need to forge new global relationships.

(04:42):
Um, we need to strengthen oureconomy, keep the country secure.
So how mission critical is AI topulling off this kind of leapfrog?
I, I don't know how, how weactually become a much more
self-sufficient, independent
sovereign country.
I think it is hugely critical to ourfuture, but I also think we have the AI

(05:05):
talent, we have the research, we havethe innovation, and we have like strong
natural resources and great companies.
We have everything we need to leapfrog.
What we need to do is actuallyhave the will to take it forward.
And that's, you know, that's critical.
Like AI embedded in business actuallyamplifies a business' strategy.

(05:29):
It doesn't replace it, itjust amplifies it, makes it
stronger or makes it go further.
And I think the same is truefor our economy and our country.
I think there's a lot of good thatcan come out of AI societally.
So it's not just about the economy,it's about benefits and health.
Uh, it's about benefitsto the environment.

(05:51):
So I think it actually informsand shapes the future of our
country from the ground up.
You know, in, in your answer,you used the word sovereignty and
one hears the word sovereigntyoften now around data, around AI.
What do we mean?
You know, because this is a technologythat bridges all, you know, certainly

(06:14):
the internet with which we're now allfamiliar is a global technology, not,
uh, one confined to national states.
So when we're talking about sovereignty,what are we really talking about?
I think we're talking, at least in myopinion, we're talking about control
over our destiny. So meaning sovereigncompute, where the, the actual

(06:38):
infrastructure resides here in Canadaand that a foreign country can't decide
to turn it off. That we have controlover, whether it's on or off. That we
have control over how our sovereign dataassets, for example, how our health data.
Is used, how our publicsector data is used.

(07:00):
And so to me, sovereignis all around control.
Control of Canadians,
for Canadians, right?
So, by Canadians, if you will.
So that to me is why it matters.
Um, the other reason why youwant sovereign compute is, um,
and sovereign data, frankly, isit's a huge factor in retaining.

(07:22):
AI talent advantage we have.
So I have built several AI data scienceteams and companies, and I can tell
you I need three things to keep them.
So once their pay is kind of competitive,I'm not talking about paying the
astronomical sums in the market,
I need compute,

(07:42):
I need data, and I needinteresting problems to solve.
So I have no problem with the thirditem, but compute and data is what
helps me retain my team at companies,and that is true on the national level.
When we look at our AI researchers,we need software compute.
We can then dedicate not just to ourresearchers, but to new business,

(08:06):
small business, not-for-profits.
Um, and we need sovereign data setsthat again, um, we can control how
they're used and for what purpose.
Well, I, I'm, I'm fascinated by theinterplay of those three things.
Correct me if this is wrong, that, youknow, research doesn't exist in a vacuum.

(08:27):
Researchers wanna be on a cutting edge of Yes.
Of practice as well, ifthey're gonna do their work.
So do we have both sides of that equation?
We have both theoretical andapplied researchers basically.
So if, if I take safe AI, for example,Mila and Montreal and Amy and Edmonton are

(08:48):
other two, my sister research institutes,if you will have, um, researchers that
are at the forefront, the leading edge,the frontier of safe AI. At Vector
we have many researchers that areapplied researchers in terms of safe AI.
So it's, it's both.
And, and the thing with retainingthat AI talent advantage that

(09:09):
we have is that the talent wantsto go where the opportunity is.
And opportunity is now increasingly beingdefined as a commercial option, right?
So the, the researchers want to beable to get their innovation, their
research into a commercial adoption.
And if Canadian businesses aren't gonnapick up, if Canadian businesses aren't

(09:33):
gonna partner with them, then theywill go to a country where those
companies will, and right now we lag.
So the global AI adoptionrate for business is 34%.
In Canada, it's 26%. And we lag theother G7 countries in this.
So, um, we, we have serious workto do in our business community in

(09:58):
picking up our socks in terms ofreally driving hard into AI adoption.
'cause that is also key for howwe keep that talent advantage
that we've spent years building.
Audrey, when you hear that adoption
globally is 34%,
in Canada, it's only 26%, and youknow you're on the front lines with
a lot of different companies as theytry to adapt into the world of AI,

(10:24):
do those numbers surprise you?
Unfortunately, they do not surprise me.
This is a problem we'vehad for many years.
The adoption in Canada and it, itis linked to our risk appetite and
Unfortunately, this lack of appetiteis translating in very small demand,
which as Glenda highlighted, ithurts our DEI sector as a whole.

(10:47):
'cause if we don't have localdemand for Canada-made AI and Canada
talent, we will lose this advantage.
And to what do you attribute, youknow, the blockage? Like what's in
the way of them being faster adopters?
I think one of the, the gaps we'reseeing is the lack of a holistic

(11:07):
approach towards the technology.
So think people think firstand foremost, this is a, a
technology that will do magic.
Um, but unfortunatelythat's not the way it works.
Um, you need good data
to use AI. You need people. Peoplefundamentally use the technology and if
you put, uh, a bot in between, uh, orAI in between two manual tasks, you're

(11:34):
not, you've not redesigned your process,you've not improved things forward.
So what we're finding is, uh,people or organizations tend to
lack this holistic approach, which,where technology is one part of the
puzzle, but it's not the only one.
And, Ed, do you mind if Iweigh in on that as well?
Because I agree with, uh, whatAudrey is saying, but I would

(11:56):
also add, uh, maybe a plus one.
I think there is a legitimate concern
around the safe, responsible use of AI.
Like I do think there are companies thatare legitimately worried about it, and
I would have two things to say to them.
Number one, like I mentioneda bit earlier, we have world

(12:17):
expertise in the safe use of AI.
You know, Mila, Amy Vector, we'reall in that space and we are ready
to help, um, vector produced, uh,trustworthy, safe AI principles years ago.
So there is help for Canadiancompanies if that's their concern.
And then the second thing thatI would point to is that we

(12:38):
have highly regulated companies.
Like banks, like insurance companies,like life sciences companies
that are dealing with incrediblysensitive data that have adopted AI.
And so I guess what I would sayto other sectors of the economy is
if they can do it, you can do it.
And so reach out.

(12:59):
Talk to your networks, talk to people inthe banks, talk to people in the insurance
and the, and the pharma companies.
Find out how they got over thehurdle of safe and responsible.
Um, and, and start digging inand start doing and learning.
And, and again, nobody's asking you tolike AI a-fi, everything tomorrow, right?
It's start, uh, get going,build your knowledge, build

(13:21):
your skill, and then scale up.
I wanna talk about the, um, safe AIproposition and the, the resistance to
AI in a, in, in a couple of moments.
But I also wanna stick with the kindof holy trinity that you described

(13:44):
earlier of Talend compute and data.
Let's just dig a little bitdeeper into talent for a moment
before we move forward there.
So Vector published a snapshot ofOntario's AI system just last month in
June, and it showed Canada ranked firstin the G seven in terms of talent growth.

(14:04):
And of course, as you were saying earlier,talent attracts investment and and usage.
So are we where we wanna be?
I mean, is is talent growth
uh, the building block, the takeoff point?
Um, so look here, here's, uh, Ithink the word I would use for our,
our state of play in AI is paradox.

(14:24):
So we are leading, absolutely leadingin AI talent growth and other,
and other countries are trying toplay catch up, but we are lagging
In business adoption as I've been saying.
So we're both ahead and we're behind.
So it's it, that's the contradictionof the moment that we sit at.
But there are encouraging signs.Like our recent report shows in

(14:46):
the last year more than 17,000 AIjobs were created in Ontario, and
that's double over the prior year.
Um.
I would also note that the privateinvestment in Ontario AI hit $2.6 billion
last year, and that's a 69% increase.

(15:09):
And then the last number I wouldshare is there were 70, so 7 0, 70
new AI companies launched in Ontario.
Last year.
So these are all extremely positiveindicators that we have strong foundations
for a leapfrog moment, if you will.
Um, but frankly, you know,I worry at night that we're

(15:31):
still not moving fast enough.
We're still not drivinghard enough in this space.
Maybe you couldn't weigh in on thatparadox and the kinds of things
you're doing to help companies.
I think one way to help companiesis to help them prioritize where to
start with AI. Um, because those willdetermine, um, the rest of the pathway.

(15:53):
And so, um, getting information,knowledge, and experience on.
Within your industry, whatare the proven use cases?
Where do you see real advantage?
And also asking yourself as acompany, where do we have data?
And so reflecting on that andhaving a first purposeful step is

(16:15):
very important for organizations.
The the, the research andthe data points are there.
Um, and that's a great place to start.
It seems to me thateven where we have data.
Um, we create obstaclesto using that data.
And I think, you know, it's, uh, youknow, nothing's more obvious on the
healthcare system where data is divided upinto, you know, different jurisdictions

(16:39):
that even within those jurisdictions,certain people don't wanna release
the data, have it speak to each other.
How are we gonna get our data,
Glenda?
Well Ed, if I had the magic answerto that Um, look, there are
initiatives underway right now.
Um, there is an initiative called Vital,which is actually looking at creating

(17:03):
a health data set, um, that wouldthen basically enable us to advance
in terms of applying AI in clinicaltrials, drug discovery, et cetera.
So,
there are initiatives that are underway.
Um, we do need interprovincialcooperation on this.
We do need mechanisms forthe data to be standardized

(17:24):
across jurisdictions for sure.
But there are ways to do this,uh, and, and like, look, privacy
enhancing technology is not new.
It has been around for a while.
Um, and you know, that's again, an areaof research that continues to unfold.
But there are techniques to basicallyde-identify data so that we can do

(17:46):
these things safely and securely.
But this is where we need provincialgovernments coming together to tear
down barriers between provinces.
This is where we need real leadership.
And so, you know, I would encouragefolks to look at the vital
Initiative.
It's an area that we all needto be pushing our governments,

(18:07):
our, our communities, to get intofor the benefit of Canadians,
not just for random purposes.
Audrey, maybe you could also just explain someof the, um, obstacles, you know, you
know, for instance, I hear, um, you know,I've been in the news business and I've
been a creator of news and I like my IP.

(18:30):
Um, uh, but
there's an argument that, you know, thatthe protection of that IP interferes with
the, uh, advancement of, of AI and, anddata are. Are these kinds of problems
that you, um, that you run into ascompanies are trying to work this through?
I think though that's an issue,but I would say it's, it's not the

(18:52):
biggest one that organizations raise.
Um, because for example, one ofthe ecosystem player we, uh, we
work with is called Scale AI.
And scale AI brings together a
consortium of organizations thatcome together and have a common
pain point and solve this together.
So I, I wanna say on this point,ed, I, I'm actually seeing great

(19:13):
examples of collaboration in Canada.
Um, and this, this Scale AI,one is just one of those
and they share their data.
There is a consortium agreementwhere IP rights are established
and, and negotiated, but it's turnedinto, uh, a strength for Canadians.
And one of the great data points we sawin the Vector report also is the number

(19:37):
of patents that's increasing in Canada
while it
outside of Canada, thisnumber is decreasing for AI.
So, um, I, I would like usto think positively when we
think IP and we think patents.
We do have a Canadianadvantage there as well.
Okay, well I don't wanna be a negative,uh, person in here, so I'm ha I'm happy
you gave me something to grab onto there,

(19:59):
that's, uh, that's quite positive.
So, I've been talking about obstacles.
Maybe you could explain.
Why it is that we're moving aheadon patents while other people seem
to be in some retreat and the paceof, uh, of new patents, Glenda?
I think that actually is linked tothe new jobs and the new companies
that are forming in Ontario.

(20:20):
I think the two go hand in hand.
I think, uh, maybe perhapssome of our Canadian fellows
are waking up to, to patents.
And look, I was thrilled to seethat we had a 10% increase year
on year versus others, but Iwould still be maybe a bit of,
I, I don't mean to be a downer, but it'son a relatively small base, so we need

(20:43):
to be moving even faster in that space.
We need to be, you know,increase our, our patents by 20-
25% year on year to catch up.
Um, and I think, uh, I'm, I'm, I amencouraged by the signs and like I said,
I think a lot of the numbers coming outof this report are, are very encouraging,

(21:05):
but uh, there's still so much to do.
So, let's go to the third pointon the Trinity, which is compute.
And I'll ask both of youin succession about that.
Compute s a strange wordto start with for people.
It's a new word in, in the vocabulary.
So maybe glad you just start byexplaining what compute is and then

(21:26):
both of you may be, will, uh, tellus where we stand on compute and
what the implications of that are.
Sure.
So, look, the best way to thinkabout AI compute is to think
about it like a highway or a road.
It's gotta be part of ournational infrastructure that
makes our businesses hum and grow.

(21:47):
So that's how you think about it.
What, what is it?
So essentially to create some ofthese sophisticated AI models, you
actually need huge, um, AI compute,which is the processor, the memory,
and the story, or the storage story,all working together in concert.
And so think about your phone.

(22:08):
Every few years, you know, thebattery doesn't work as well,
it's not as fast as all the otherones, and you have to upgrade.
Compute is exactly the same.
It's something that we need to refactorrefresh on a recurring annual basis.
Um, so not that every single chip needsto be refactored every year, but on an
annual basis, we need to beuplifting, probably a third

(22:31):
to half of our AI compute.
So, the reason why we care about compute,like I said, it's it's big in terms
of attracting and retaining talent.
Um.
And I am encouraged the federal governmenthas put aside investment for it.
So that is great news.
Um, but what we really need isthe provinces to step up and for

(22:53):
And for the provinces to view this as anation building infrastructure.
And they need to playa role in this as well.
Um.
And, and frankly, what we'llfind is if, I've got a researcher
here, so here's why you care.
If I've got a researcher in Torontoand they're working on some really
great breakthrough around healthAI, for example, if they don't

(23:15):
have access to compute, tobasically power their research,
they will go to where the compute is.
So they will leave, they will go toSilicon Valley, they will go to the UK,
they will go to France, they will goto where the compute is, so they can
do their research and their innovation.
And so then you might say, well,Glenda, it's just one researcher.
There's lots.

(23:35):
Right?
Well, no, it's, it's that researcher.
It's the team of postdoc and gradstudents that work around them.
But more importantly, it's theresearch and innovation that
they're creating that we lose.
And so we actually, then it createsthis ripple effect where we lose the
economic growth that could result ofthe fact of that one researcher's work.

(23:57):
And so, kind of like, you know, for lackof a, of a nail, we lost the horse s
shoe.
So it's, it's, we need to get the computein order to get, um, the economic growth.
And is the nationalism that we see inCanada and some of the concerns about
the United States, is that shiftingthe equation or, or is it really, you

(24:21):
know, not big enough in the idea thatI wanna have a fulfilling career?
I. I think it's a shift in the equation.
I think the geopoliticalenvironment plays a role for sure.
I think Canada's in an, uh, aspot where there's the potential
to attract, uh, top researchers,but we need to have the resources.
We need to have the compute, and weneed to have a business community

(24:43):
that's saying yes, and we'regonna wanna adopt what you build.
We're gonna want to invest in it, we'regonna wanna advance it to the next level.
Audrey, from your perspective,do you run into what I'm just
gonna call a compute deficit?
We saw our clients welcome theannouncement from the federal
government to invest in the computestrategy with over $2 billion.

(25:04):
And I love Glenda's, uh,metaphor of the highway.
Canadians should know that we nowhave very strong, uh, highways for
AI and we encourage them to use them.
We need to, to draw the attention to that.
So of of course we have a deficit.
And if you look at numbers, I thinkthat we are, um, you know, our
capacity is 10 times lower thanthe US on a GDP adjusted basis.

(25:28):
So still ways to go.
Um, but I think the optimist inme is welcoming the investment.
And encouraging Canadian and Canadianleaders, um, to not use this as an
excuse to, to delay investments.
Um, there is enough compute capacity formany organizations here in Canada to,
to use it for, in an applied fashion.

(25:50):
So, um, let's use the
highways please.
If I could just plus one on Audrey.
There are, uh, companies that arebuilding out, uh, software compute
more cloud computing optionsthat are coming online this year.
So there is going to be moreand more compute coming.
It's just, I guess my messageis we need to keep going.
Like, let's, let's not assumethis is a one and done.

(26:12):
This is an ongoinginvestment we need to make.
Okay.
Let's circle back to safety. AndI think again, maybe it'd be good
to explain what we mean by safety.

Do we mean like in 2001 (26:32):
A Space Odyssey? Like, Hal taking command of the ship. And you
know, preventing that kind of thing.
Is that what we're talking about?
Or safety, or is that a myth?
And then I also wanna put it togetherwith resistance to the concern about jobs.
That people have about jobsand we hear now about agents.

(26:54):
And so Glenda, I just want you to doa little bit of an education, uh, for
us to start about, about what we meanby safety, what we mean about agents,
and then, you know, we can assess,you know, where we are in both those
places and whether public confidence,how we establish public confidence
at a level that will be necessary.

(27:16):
So look, safety is a broad topic,so it absolutely, if you're looking
at the frontier research doesmean let's prevent a Hal 2001.
Let's, let's make sure thatas AI is progressing, that it
is doing so in a safe manner.
Um, but for most businesses,frankly, we're, we're not.
We're not at that levelof using that type of AI.

(27:39):
So it's really about making surethat as a company, you actually
have established what your ethicalprinciples around the use of data
and AI are, and that you've embedded
those principles into your, uh,operations in how you select and use
AI. And does the AI that you eitherpurchase or build line up to those

(28:02):
ethical standards that you have set?
Um, and there are manycompanies that have done that.
Um, there's now an ISO standard that youcan get certified on for AI governance.
So there are clear
uh, guardrails that are out therein the public that you can adopt.
Um, so really it's around how do you doit so that, how do, how do you adopt AI

(28:24):
such that it drives benefit to your, uh,to your business, to your customer, to
your employees? Um, but you do it in sucha way that is ethical and responsible?
I. So I think that's more the, the dayto day question for most companies. Now
agents or Agentic AI, as it is oftencalled, is where the computer now is

(28:45):
and, and the AI is now making decisions.
And so it's not just understandinglike, you know, uh, the question, it's
also proposing the answer, if you will.
And so when you start looking at adoptat adopting Agentic AI, you wanna make
sure you've got a human in the loop,meaning a human is part of the process

(29:09):
and checking and making sure that theright answers are going to the customers,
or that the right action is being taken.
So it's not about lettingthe AI go and not monitoring.
You absolutely need to be monitoring it.
So these are different types ofjobs that are now getting created.
Um, so your comment on jobs,yes, there will be an impact.

(29:31):
Um, I mean, I'm gonna date myself,but I remember when spreadsheets
were the big thing in the ninetiesand it was like a huge thing.
If you were an expert in Excel.
Um, now it's gonna be how, howstrong are you in using Chat GPT?
How strong are you in using theseand in using them appropriately

(29:51):
so that you get the right results?
I, I do think we'll see a shift andthere'll be different types of jobs.
Um.
Certainly, I actually think there willbe a lot of jobs around data and how
do we make sure that we've got gooddata, clean data, that it's structured
in a way that can be used, um, by ourai? Because AI is 100% based on data.

(30:13):
So good data leads to good AI.
So I think there's going to bea lot of new jobs that I can't
even imagine sitting here today.
I don't know what they will be, but youknow, the new one that came up a couple
years ago was prompt engineers, right?
Like.
Five years ago, no one hadheard of what that was.
Right In the first time Iheard it, I'm like, oh my word,
somebody's made up a new, a new job title.

(30:35):
So it's, it's how do you actually use AI,um, such that it drives the benefit and
does so ethically, responsibly, safely?
Audrey, we hear a lot, um, ofcriticism from time to time that
too much attention is paid to 494 d0:30:56,354 --> 00:31:01,064 Defense when we talk about AI and perhaps not enough attention is paid

(31:01):
to offense, the productivity gain,you know, the business gains, the
lifestyle quality of life gains.
How
do you assess the safety issues?
The development and promotion ofAI can be a strength for Canada.
We have, um, a combination ofresearchers and organizations that

(31:22):
are applying leading practices todevelop trustworthy AI in a way
that doesn't stifle innovation.
I'm very proud of our Canadianorganizations in this, in this area,
and I think this is a place whereCanada can shine on the global stage.
Glenda, I wanna finish up on jobs,which is the other issue we raised.

(31:43):
You know, if there's anything that I thinkmakes Canadians anxious that s the idea
that some technology's gonna come andtake their job. And I know we can look
at economic history and say that that'snot actually how it works, but even then,
there's always a period of disruption.Should people be worried about their job?

(32:04):
What I would say is I would stronglyencourage people to go out and learn.
To not be fearful, but to go educateyourself, learn how to use these tools
and build some expertise in them. Forcompanies, especially small companies,
I know that can be a daunting task ifyou're thinking about, you know, you've

(32:24):
got maybe 50 people in your company andhow do you make that that transition?
Um.
All of the institutes areworking on driving adoption
across the business sector.
And at Vector we actually have a programspecifically focused on small companies.
So companies less than 500 people.
It's called Fastlane.
And one of the things we do is wework with those small companies to

(32:48):
help them upskill their employees.
And in some cases, um, weactually help them hire their
first machine learning person.
And help them actually move theirproduct, their service more towards AI.
Um, and so I would say,
you know, look at it as an opportunityto learn something new and get involved.

(33:12):
Like, don't, don't wait forthe change to happen to you.
You figure out how you want to manage thechange, how you wanna, um, participate.
Because AI's here, it's not a fad.
It's not going away.
So let's all just get in and startlearning and trying on new things.
Last question, Audrey, and thenGlenda, if you had one thing

(33:36):
that you'd like to see happen tomake Canada more competitive in AI
to allow us to be more sovereign
what would that one thing be, Audrey?
I, I'd love for our countryto have targets in terms of a
adoption so that we have somethingas a country to aim towards.

(33:57):
It's hard to improve something withoutmeasuring it and, and having a goalpost,
um, having something to look forward to sothat we can rally, um, people around it,
I think that could help act as acatalyst in the next few months.
Glenda? Alright, I'll try and keep thisshorted, but here's, here's my pitch.

(34:17):
I've heard my whole career thatCanadians are nice and polite, and I
always tell people, not on the ice.
You put a hockey stick in a Canadian'shand, they are not nice on the ice.
What I would like to see is the ambition,the drive to win that we have when we
play hockey, when we curl, which is mysport when we do any of our winter sports.
I would like to see thatambition, that drive to win, move

(34:40):
into our business sector in AI.
Please take that will to win.
Please think about slamming yourglobal competitors into the boards.
Let's look to win in the AI space.
Well, I think, uh, those areboth doable things, so let's
get on and get it done, and
I wanna thank you both forbeing with us today on Full

Circle (35:02):
The Leapfrog series.
Thank you.
Thanks, Ed.
Okay, Audrey, we're back for aminute to, uh, do a bit of a recap.
So what would your main takeawaysbe from today's discussion?
I really love that Glendaused that hockey analogy.

(35:24):
We do wanna encourageevery Canadians to play.
Right now we have a few very, verystrong teams in large enterprises,
but we think there's an opportunityto infuse AI into every business
out there in every organization.
We also think there's a bigrole for the public sector.
We talked about healthcareon this podcast, but there's
also many other areas.

(35:45):
So we, we just wanna encouragepeople to pick up the sport.
OK, ,so we wanna encourage everyone to geton the ice, and therefore we'll have,
um, more people who will both benefit intheir own, uh, experiences, but, um, more
candidates to rise up to gold medalists.
A
hundred percent.
And like every sport,it requires training.

(36:05):
You can't just become a greathockey player like that.
It requires training, rigor,and discipline, and some inputs.
We've talked about data, we've talkedabout the importance of people and risks.
There are risks when you're onthe ice, so you need to be aware
of those and mitigate those.
So you don't, you don't justshow up on the ice unprepared.
Um, there's things to do inadvance, but it's very doable.

(36:26):
Okay.
Well Audrey, I wanna thank you for thatand thank you for the work you're doing.
Thanks, Ed.
Alright,
so everyone, AI is moving fast andyou know, so is the world around it,
which makes it even more imperative.
It's integral to any Canadian leapfrogstrategy and the history of general
purpose technologies is that they'redisruptive, but that's followed by

(36:51):
higher standards, living better
quality of life.
Glenda Crisp and Audrey Ancion have mappedout a route in today's discussion, and
I thank them both for helping us hereon Full Circle: The Leapfrog series.
If you enjoyed today's episode, shareit with a colleague and subscribe
for more conversations on theissues shaping Canada's future.

(37:12):
Until next time, I'm Edward Greenspon.
Thanks for listening to FullCircle: The Leapfrog series.
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