- 4 months ago
Who will own ASEAN’s AI future?
Malaysia is moving fast — launching a National AI Office, securing US$4.2B in Microsoft & Google cloud investments, and upgrading the grid with RM43B from Tenaga Nasional.
Yet, 5.5% annual brain drain and 20% SME AI adoption threatens to leave much of the economy behind.
NIAGA SPOTLIGHT features Sharala Axryd, Founder & CEO, CADS and Dr Chris Daniel Wong, Emeritus Chair, MDCC on how to lock in sovereignty, plug the talent leaks, and ensure no business is left behind.
Malaysia is moving fast — launching a National AI Office, securing US$4.2B in Microsoft & Google cloud investments, and upgrading the grid with RM43B from Tenaga Nasional.
Yet, 5.5% annual brain drain and 20% SME AI adoption threatens to leave much of the economy behind.
NIAGA SPOTLIGHT features Sharala Axryd, Founder & CEO, CADS and Dr Chris Daniel Wong, Emeritus Chair, MDCC on how to lock in sovereignty, plug the talent leaks, and ensure no business is left behind.
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00:00Hello and welcome to Nyaga Spotlight with me, Tamina Kaosji.
00:11Nyaga Spotlight takes us through the week in economic analysis and future affairs.
00:15Now, this week on future affairs, we spotlight the topic of AI governance and talent pipelines for ASEAN.
00:21Now, Malaysia is building the digital rails that could define ASEAN's AI future.
00:26Billions are pouring in. Microsoft with 2.2 billion USD, Google with 2 billion USD and Tanaga Nasnil.
00:34On the other hand, Tanaga is investing over 43 billion ringgit to power it all.
00:39Malaysia also just hosted the inaugural ASEAN AI Malaysia Summit,
00:42a first of its kind regional convergence to catalyze collaboration, governance and innovation in artificial intelligence.
00:49But here's the big question. Will we own this ASEAN AI future or lease it from global giants?
00:57The AI talent race is just as fierce.
00:59Malaysia's annual AI talent brain drain sits at 5.5%, well above the global average.
01:05And while the digital economy could hit 25.5% of our GDP this year,
01:11fewer than 1 in 5 Malaysian SMEs are using AI at all.
01:15That is a red light for the economy.
01:17Niaga Spotlight today discusses how to lock in AI investments for long-term growth,
01:22plug the talent leaks while ensuring that no business is left behind.
01:26This is not just a tech story.
01:28It's one that involves power, economics as well as AI sovereignty.
01:32So joining me online now for this very timely discussion are
01:35Sharla Axrid, founder and CEO with the Centre of Applied Data Science, CADS,
01:40together with Dr. Chris Daniel Wong,
01:41Emeritus Chair with the Malaysia Digital Chamber of Commerce, the MDCC.
01:46Very good morning to the both of you.
01:48Thank you so much for making time.
01:49How are you both doing?
01:51Hi, Pam.
01:52Hi, Dr. Chris.
01:54Wonderful.
01:56Glad to have the both of you with us.
01:58So I've got an opening salvo for the both of you.
02:01Now, one thing that is so clear to anyone who uses AI,
02:04Sharla as well as Dr. Chris,
02:06AI model capacity is doubling roughly every seven months.
02:09So this is far outpacing the speed of legislation.
02:12That's how we're getting these regular chat GPT and et cetera,
02:15updated versions, right?
02:17And despite this lightning speed of innovation,
02:19even the bottled water that you drink
02:22actually has stricter regulations around it.
02:24So the question remains that when we are looking at the fact
02:28that nobody is pausing on AI development,
02:32in ASEAN's context,
02:34how do we then create a governance and talent blueprints
02:37that keep up with the pace of this velocity?
02:41Perhaps I could ask maybe Dr. Chris first,
02:44ensuring that Malaysian platforms can compete globally
02:46while meeting both these local and international standards.
02:49A few opening thoughts.
02:50Well, I think first and foremost,
02:54we need to look at the legislation
02:56that govern the usage of AI.
03:00Back in the European Union,
03:02I believe that they have passed on an AI regulation,
03:07if I'm not mistaken, around August 2024.
03:10And Malaysia echo the same thing,
03:12where at the end of 2024,
03:14we actually set up a national AI office
03:18to look into the legislation of the usage.
03:20of AI and the governance of AI.
03:23But this needs to be accelerated.
03:27The main reason why I say that
03:29this needs to be accelerated,
03:31many scientists were very surprised
03:34to see that the language model learning capacity of AI
03:39is actually way ahead of what they had in mind.
03:44For example,
03:45the model language capacity of AI
03:47AI is actually several months ahead
03:49than what it's supposed to be.
03:52So this actually pose a real-time challenge
03:54in terms of governance.
03:56We thought that the AI
03:57will actually learn something new
03:59by end of the year,
04:01just to keep it in context.
04:02But what they should learn end of the year,
04:05they are learning now actually.
04:06And therefore,
04:08we see that there's a gap
04:10between the industrial usage
04:13versus the governance
04:14or the law that's supposed to be policing it.
04:17And that is why
04:18many fraud,
04:20or besides fraud,
04:23many, what we call that,
04:25inability to manage
04:27such a rapid growth of AI.
04:31Thank you for that, Dr. Chris.
04:33Shah, Sharala,
04:34on your side,
04:35would you tell us a little bit more
04:37about as an opening salvo,
04:38perhaps looking at the AI
04:40and data talent pipeline?
04:42That is, of course,
04:42a crucial issue,
04:44particularly for us here in Malaysia.
04:48Oh, yes.
04:49You know,
04:49like you just talked about,
04:51you know,
04:51whether we can keep up,
04:53do we need to have governance?
04:54And Dr. Chris rightfully
04:55talked about,
04:57you know,
04:57Europe having,
04:58you know,
04:58the AI policy in place.
05:01But for me,
05:02it's really clear
05:03you can't,
05:04you know,
05:04you can't get it perfect.
05:07And having rules
05:09and policies around it,
05:10we have to be careful
05:11about not curbing innovation,
05:13which I think you'll be talking
05:14about it a bit more.
05:15And without the right talent
05:17to build policy,
05:19you also need the right skills
05:21to execute these policies, right?
05:23And the policies cannot be static.
05:25And now your office,
05:26like what Dr. Chris has said,
05:28it's amazing,
05:29but it cannot be static.
05:30It should be ongoing
05:31and it should also not be curbing.
05:34So in all these,
05:36then you're right
05:36that talent and skills
05:38become so crucial
05:40from creating a policy
05:42to executing the policy
05:44and also getting the right talents
05:46and keeping the right talents
05:47in the country.
05:48Exactly.
05:49Getting the right talents
05:50and also keeping them.
05:51I will dig a little bit deeper
05:52into that in just a few more minutes.
05:54Thank you for that, Shah.
05:55Dr. Chris,
05:56so let's look at
05:57a reality check first.
05:58Now, Malaysia, of course,
05:59we're chairing ASEAN this year.
06:01And if the ASEAN Digital Economy
06:03Framework Agreement,
06:04the DEFA,
06:05what they're targeting
06:06is one trillion USD
06:09in added GDP by 2030.
06:11So what would you say
06:12is a sort of a minimum
06:14viable compliance stack
06:16that MDCC is hoping
06:19that every Malaysian SME
06:20is able to meet,
06:22maybe even by next year.
06:24So we are able to sell
06:25across ASEAN
06:27without any kind
06:28of legal backlash.
06:30Well, if you look at
06:32the minimal viable compliance stack,
06:34I would look at 12 things.
06:3612 things in particular.
06:38And then first,
06:38we talk about governance.
06:40Second, we need to talk about,
06:42look at data mapping.
06:44Third is privacy and consent.
06:46And then cross-border transfers,
06:49vendor management,
06:50security baselines,
06:52bridge readiness,
06:53data retention,
06:55marketing and focus.
06:56Focus that's left over
06:58in this internet explorer,
07:00any browser,
07:01rights handling,
07:02training and evidence pack.
07:04I think these are the things
07:05that's,
07:06these 12 things
07:06are the things
07:07that's very crucial
07:08in keeping the minimum
07:10viable stack.
07:11In terms of governance,
07:13in terms of policing,
07:14these are the,
07:16as I said,
07:17these are the framework
07:19that needs to be in depth
07:20of the digital economy framework.
07:23I mean,
07:24Malaysia is right now
07:25being driven
07:26almost 30%
07:27of this GDP
07:28by digital economy.
07:30And as we move forward,
07:32the number is going
07:32to keep on increasing.
07:33And there's a need
07:34to look into
07:36these 12 points
07:37that I mentioned just now
07:38so that SMEs
07:40not only can sell
07:41across ASEAN,
07:43but they can sell
07:43across without
07:44data law conflicts.
07:46And the 10 ASEAN countries,
07:48including EU,
07:49has to come up
07:50with what we call
07:51common framework,
07:52covering data transfer,
07:54consent,
07:55bridge alerts,
07:56so that everything
07:57is aligned.
07:58And one more thing
07:59that I want to add on
08:00is that Malaysia
08:01has its own PDPA.
08:02And I thought
08:03that the PDPA
08:04has to be amended
08:05in line with the DEFA
08:06frameworks of ASEAN frameworks
08:08and also EU frameworks.
08:10That's what I said
08:11earlier on,
08:12so that when businesses
08:13sell across borders,
08:15especially ASEAN,
08:16there is no data law
08:17conflicts.
08:18That will be not as well.
08:20Well said indeed.
08:21Now, of course,
08:22our PDPA amendments
08:23for this year,
08:24they also add
08:25data portability,
08:26mandatory DPOs
08:28and also breach notification.
08:29And this style
08:30of adequacy style
08:31transfer regime,
08:32it's good for
08:34an SME starter pack,
08:35but also then
08:36comes the enforcement
08:38part of things, right?
08:39Thanks for that,
08:40Dr. Chris.
08:41Shah, going back
08:42into speaking about talent.
08:44Now, also,
08:45Microsoft says
08:45its Malaysia plan
08:46includes mass scaling
08:47hundreds of thousands
08:48and CADs, of course,
08:50aided in scaling
08:51earlier programs.
08:52So I think you'd be
08:53the perfect person
08:54to post this to.
08:55So how do you multiply
08:56certified data scientists
08:57from thousands
08:58to hundreds of thousands
09:00without diluting quality?
09:03And this is what
09:03we're going to need
09:04in the medium term.
09:06This is not even
09:06a long-term issue, right?
09:08Yes.
09:09Yeah, this is my
09:11favourite, favourite topic, right?
09:13Mass scaling fails
09:15if it's based on tools.
09:17So a very unpopular
09:18opinion,
09:20but tools come and go.
09:22It's very, very important
09:24we enable skills
09:25in every level
09:26the country has
09:27from schools,
09:30from universities.
09:31So we make sure
09:34that skills like,
09:35you know,
09:35knowing where your data is,
09:37how you collect them,
09:39how you use them
09:39to solve problems.
09:40Even data storytelling
09:42becomes so important
09:44for every level
09:46at work,
09:47graduates that come out.
09:49Responsible AI,
09:50ethical AI,
09:51how you use the data,
09:53whether it's correct
09:53to use the data
09:54becomes really important.
09:56So if you look at
09:58how the world
09:59has moved now
10:00from data scientists
10:01centralised in a company,
10:03they have democratised skills
10:05to every role.
10:06So that's the only way
10:08we're going to guarantee
10:08quality
10:09versus volume.
10:10and we look at skills,
10:13we train thinkers,
10:14not button pushers.
10:16So I think it's very important
10:18that companies
10:19and universities realise
10:20that, you know,
10:21just getting AWS,
10:22Microsoft is important
10:24as a tool to learn,
10:26but that's not going
10:27to guarantee
10:28what the future
10:28is going to be
10:29because tools come and go
10:31and they expire.
10:33Yeah.
10:34Yeah.
10:35Well, highly agree with that.
10:36And at the same time,
10:37we're also seeing
10:37on the other hand,
10:39globally speaking,
10:40Facebook's Meta's
10:42Mark Zuckerberg,
10:43Sam Altman of OpenAI,
10:44they are bidding
10:45in the hundreds of millions
10:46to get the right
10:47technical AI
10:49knowledge skill workers
10:51at the moment.
10:52So that also,
10:53I think,
10:53speaks to wider trends,
10:55perhaps even in the region,
10:56if not Malaysia,
10:57that salaries are going
10:59to have to be
10:59commensurate enough
11:00to ensure that
11:02trained professionals
11:03also want to stay
11:04in ASEAN and Malaysia.
11:06Thanks for that.
11:08Dr. Chris.
11:08But Tam,
11:09I need to kind of,
11:10I'm sorry,
11:11I need to add that,
11:12right?
11:12You're perfect
11:13in saying that,
11:15you know,
11:15the young talent globally,
11:17right?
11:17We have China,
11:18we have India,
11:18ASEAN is so perfectly
11:21positioned to be
11:22the third largest
11:23young population,
11:24which we are right
11:26there in the sweet spot
11:27to get great data
11:29and AI talent
11:30to be skilled
11:31and to be used
11:32and exported
11:33for the same reason
11:34all these companies
11:35are bidding for.
11:36Absolutely.
11:38I mean,
11:38that youth pool
11:40is unparalleled,
11:41particularly given
11:42the levels of education
11:43that are quite regularized
11:45and uniform
11:45across ASEAN as well.
11:47Dr. Chris,
11:48moving along from there,
11:49now let's look at
11:51powering AI
11:52without brownouts,
11:54those temporary reductions
11:55in power services.
11:56Now,
11:56data center build out
11:58around Malaysia
11:59is absolutely surging.
12:00As mentioned,
12:02Google $2 billion invested,
12:04Microsoft $2.2 billion,
12:06ByteDance are close to
12:08in the range
12:08of 10 billion ringgit,
12:10while TNB is also planning
12:12a 43 billion ringgit
12:13grid update
12:14and this will be by 2029
12:16and in Malaysia.
12:18So now,
12:19this will also let operators
12:20buy clean power directly.
12:22In your opinion
12:23and from MDCC's side,
12:25what guardrails
12:26should tie incentives
12:27to energy discipline?
12:30Because this is going
12:31to be an increasing
12:32cause of concern
12:34as the year goes on.
12:38I think this is
12:39one of my favorite topics
12:41that I would like to touch on.
12:43I think that yesteryears,
12:45there was so much
12:45bust around
12:49when data center
12:49was set up in Malaysia.
12:52But we feel
12:53both the private sectors
12:55and public sectors
12:56feel
12:56and that is why
13:00there was such
13:01a big deal
13:02at the beginning
13:03of the year
13:04when TNB
13:05starts to
13:06implement
13:07new electricity
13:09tariffs.
13:09And of course,
13:11we see some
13:11reductions
13:12among the
13:13households,
13:16but generally
13:16we see more
13:17upwards of
13:19electricity bills
13:19for the businesses
13:20because of
13:21the finances
13:23that needed
13:24to improve
13:25on the grid.
13:26So looking at
13:26I would mention
13:28six things.
13:29I think first and foremost,
13:30before any
13:31new data centers
13:33being set up,
13:35there must be
13:36an energy efficiency
13:38what we call
13:39studies,
13:40right?
13:41How much
13:42power usage
13:43that these
13:45businesses
13:45data centers
13:46would need to
13:48require to use
13:49when they set up
13:50the businesses
13:51on the data center.
13:52So this is about
13:53power usage
13:54effectiveness.
13:55So we need to
13:55cap that change
13:56because if you
13:57don't cap that
13:58and then
13:59this data center
13:59keep on
14:00pulling electricity
14:01and use it
14:02in a big way,
14:03it will affect
14:04households
14:05and it will
14:06affect businesses
14:07because the load
14:08is getting heavier.
14:10And when you
14:11mentioned about
14:12clean power
14:13sourcing,
14:14this is the
14:15second point.
14:16Now we
14:16will recommend
14:18to the policy makers
14:19that about
14:2180 to 100%
14:22of the power
14:23to be backed
14:24via renewable
14:25energy.
14:26So 80 to 100%
14:28is actually
14:28a very tall order.
14:30But we need to
14:30set the right framework,
14:31the right policy
14:32so that we just
14:34don't rely
14:35on coal energy
14:36but instead of
14:37clean new energy
14:39which is actually
14:40renewable energy
14:41and that must be
14:42measured.
14:43And then we also
14:43look at water
14:44use.
14:45We need huge
14:46amount of water
14:47use and we
14:47are talking about
14:49raw water
14:50that also
14:50will stress
14:51out the water
14:52usage both
14:53the household
14:54and also
14:55the businesses.
14:56And if you
14:56recycle the water
14:57after being used,
14:59how effective
15:00in terms of
15:01water usage
15:01effectiveness
15:02will then be
15:03used on the
15:04recyclable water
15:05and not only
15:05new water.
15:06and then we
15:08also need to
15:08look at
15:09grid impact
15:09checks.
15:10When the
15:10grid is
15:11overloaded
15:12during
15:13peak demand
15:13there will
15:14be stability
15:15issue on
15:16the grid
15:16also will
15:18infect household
15:19and businesses.
15:19and perhaps
15:21the agency
15:23that's
15:23promoting
15:24businesses
15:25like for
15:26example you
15:26mentioned
15:27Google,
15:27by tenders,
15:28Microsoft and so
15:29forth,
15:30there must be
15:30a form of
15:31tax break
15:32and tax
15:33incentive
15:33like for
15:34example a
15:35gold tier
15:35tax incentive
15:36that allows
15:38operator to
15:39only claim
15:40tax incentive
15:40when they meet
15:41all this
15:42KPI
15:42that I
15:43mentioned
15:43just now
15:44and last
15:45of all
15:45of course
15:45the relevant
15:46border
15:46is to monitor
15:47and produce
15:48transparent
15:49report
15:49and I do
15:50believe
15:50the
15:51Parliament
15:51Accountability
15:53Committee
15:53the PSE
15:54needs to
15:55publish
15:56this data
15:56periodically
15:57and
15:58transparently
15:58so that
15:59the
15:59lawmaker
15:59and also
16:00the people
16:01that work
16:01the lawmaker
16:02will be
16:02able to
16:03know
16:03what is
16:04happening
16:05in the
16:05industries
16:05so that
16:06they also
16:07have
16:07to input
16:08into
16:08future
16:09policies
16:09and that
16:10would be
16:10my
16:11thanks a
16:13lot
16:13Dr Chris
16:13I think
16:14you really
16:14hit the
16:14nail on
16:15a couple
16:15of those
16:15most urgent
16:16issues
16:16and of
16:17course
16:17zooming
16:17in a
16:18little
16:18closer
16:18when it
16:19comes to
16:20in particular
16:21water
16:21of course
16:22as an
16:22essential
16:23resource
16:23when it
16:24comes for
16:24the cooling
16:25purposes
16:25etc
16:26perhaps
16:26one
16:27very
16:28important
16:29parameter
16:30that is
16:30still
16:31missing
16:31overall
16:32is the
16:32fact
16:33that
16:33Malaysia
16:33currently
16:33also
16:34actually
16:35allows
16:36for the
16:36use
16:36of
16:36portable
16:37water
16:37for
16:38cooling
16:38so that
16:39would
16:39perhaps
16:39be the
16:40most
16:40important
16:40thing
16:41to
16:41urgently
16:41legislate
16:42particularly
16:42considering
16:43the
16:43concentration
16:44of
16:44data
16:44centres
16:45when it
16:45comes
16:45to
16:46high
16:47volume
16:47usage
16:48states
16:48such as
16:49Solango
16:49as well
16:50as Johor
16:50thanks a lot
16:51for that
16:51Dr Chris
16:52so
16:53Shah
16:53now
16:54digging
16:54deeper
16:54into
16:55looking
16:55at
16:56the
16:56talent
16:56pool
16:57itself
16:57now
16:58of course
16:58Malaysia's
16:59diaspora
16:59was
16:59sited
17:00at
17:00roughly
17:01around
17:011.86
17:02million
17:03people
17:04that is
17:045.5%
17:05of the
17:05population
17:06looking
17:07specifically
17:08at
17:08AI
17:09and
17:09the
17:10talent
17:10workforce
17:11what
17:12retention
17:12levers
17:13in your
17:13experience
17:14or in
17:14your
17:15perspective
17:15would
17:16work
17:16in
17:17practice
17:17for
17:18mid-career
17:19AI
17:19talent
17:20this
17:22is
17:23this
17:24is
17:24an
17:24issue
17:24right
17:25so
17:25I'm
17:25equally
17:26kind
17:27of
17:27that
17:27you
17:27know
17:27brain
17:28drain
17:28that
17:28Malaysia
17:29has
17:29faced
17:29I
17:29left
17:30the
17:30country
17:30and
17:30I
17:30came
17:30back
17:31and
17:32to
17:32start
17:32CATS
17:33and
17:33you
17:34know
17:345.5%
17:36we have
17:37that
17:37on
17:38brain
17:39drain
17:40but
17:40we
17:40have
17:40about
17:4111%
17:41of
17:42what
17:44employee
17:45graduate
17:45unemployment
17:46in the
17:47country
17:47now
17:48the
17:48key
17:48thing
17:49about
17:49retaining
17:50talent
17:51it's
17:51never
17:52really
17:52about
17:53salary
17:53it's
17:54always
17:54about
17:54giving
17:55opportunity
17:55for
17:56growth
17:56giving
17:57opportunity
17:58to work
17:59on
17:59world-class
17:59projects
18:00but
18:00having
18:01said
18:01that
18:02it's
18:02also
18:03mindset
18:03of
18:04managers
18:04accepting
18:05the
18:05fact
18:06that
18:06experts
18:07kids
18:07could
18:08possibly
18:09earn
18:09more
18:09than
18:10what
18:10they
18:10are
18:11earning
18:11like
18:11even
18:12in
18:12my
18:12company
18:12you
18:13know
18:13special
18:14skills
18:14data
18:15skills
18:15AI
18:16skills
18:16they
18:16earn
18:16more
18:16than
18:17even
18:17what
18:17I
18:17draw
18:18as a
18:18salary
18:18right
18:19so
18:19retention
18:20comes
18:20in
18:20many
18:21ways
18:21but
18:21I
18:21think
18:21it's
18:22important
18:22to
18:22recognize
18:23paying
18:24market
18:25roles
18:25for
18:25skills
18:25we
18:26are
18:26willing
18:27to
18:27hire
18:27foreigners
18:28and
18:29pay
18:29more
18:29than
18:30recognizing
18:31local
18:31talents
18:32you know
18:33maybe not
18:34having
18:34all the
18:35skills
18:35that's
18:35required
18:36but
18:36work
18:37on
18:37them
18:37and
18:37paying
18:38for
18:38the
18:38upskilling
18:38and
18:39giving
18:39them
18:39opportunity
18:40to
18:40grow
18:40and
18:41also
18:41I
18:42think
18:42it's
18:42important
18:42to
18:43understand
18:44and
18:45build
18:46a
18:46growth
18:47pathway
18:48for
18:48them
18:48right
18:49what's
18:49your
18:49next
18:49career
18:50progression
18:50where
18:51would
18:51you
18:51be
18:51what are
18:52the
18:52projects
18:53you've
18:53got to
18:53have
18:54and
18:54more
18:55and
18:55more
18:55companies
18:55are
18:56involving
18:56in
18:57great
18:57technologies
18:58and
18:59projects
18:59and
18:59I
18:59think
18:59that
19:00will
19:00be
19:00key
19:00to
19:01retain
19:02talent
19:02right
19:02recognizing
19:03them
19:04giving
19:04them
19:05opportunity
19:05to
19:05grow
19:06but
19:06also
19:07paying
19:07what
19:08is
19:08the
19:09market
19:09rate
19:09for
19:09these
19:09skills
19:10it's
19:11still
19:11cheaper
19:11to
19:11have
19:12a
19:12local
19:12to
19:12hire
19:13than
19:14getting
19:14a
19:14foreigner
19:14unless
19:15that
19:15talent
19:16that
19:16particular
19:17skill
19:17is
19:17not
19:18available
19:18but
19:19trying
19:19it
19:19in
19:20making
19:20sure
19:20that
19:21they
19:21are
19:21mentored
19:22to
19:22grow
19:23into
19:23that
19:23role
19:23absolutely
19:25now
19:25also
19:26you've
19:26I think
19:26shared
19:27some
19:27of
19:27these
19:27three
19:28top
19:28retention
19:29levers
19:29which
19:30may
19:30actually
19:31change
19:31decisions
19:32when it
19:32comes
19:32to
19:32the
19:33people
19:33themselves
19:34now
19:34companies
19:35also
19:35share
19:35they
19:35promise
19:36jobs
19:36but
19:36R&D
19:37budgets
19:37and
19:37clear
19:38career
19:38tracks
19:39obviously
19:39matter
19:40more
19:40is
19:41there
19:41the
19:41possibility
19:42of
19:42more
19:42certification
19:44of
19:45domestic
19:46placements
19:47by
19:48players
19:48such as
19:48CADS
19:49of course
19:49so
19:50employers
19:50can
19:51then
19:51credibly
19:51promise
19:52higher
19:53value
19:53roles
19:53to
19:54the
19:54existing
19:55talent
19:55in
19:56Malaysia
19:56that
19:57also
19:57does
19:57need
19:57some
19:58upskilling
19:58in
20:00every
20:01quarter
20:01of the
20:01year
20:02literally
20:02looking
20:02at
20:02the
20:03timeline
20:03of
20:03how
20:03quick
20:04innovations
20:04are
20:04happening
20:05happening
20:05absolutely
20:07right
20:08it's
20:08you know
20:09the word
20:10lifelong
20:10learning
20:11is so
20:11loosely
20:12used
20:12all the
20:13time
20:13but
20:14you know
20:14companies
20:15are
20:15investing
20:16but the
20:16way
20:16they are
20:17doing
20:17it
20:17it's
20:17very
20:18ad hoc
20:18like
20:19how we
20:19used
20:19to do
20:20all
20:20this
20:20while
20:21about
20:22you know
20:22you know
20:23you need
20:23something
20:23and you
20:23upskill
20:24them
20:24then you
20:24let it
20:25go
20:25but
20:25today
20:26more
20:26and more
20:26companies
20:27are looking
20:27at
20:28upskilling
20:28using
20:29AI
20:29as a
20:30strategic
20:31way
20:32of
20:32you know
20:33monitoring
20:34recognizing
20:35what skills
20:36that needs
20:36to be
20:37there
20:37and
20:37enabling
20:38individuals
20:39to be able
20:40to consume
20:41skills
20:42as they
20:43go
20:43so no
20:44more
20:44like you
20:44know
20:45fix
20:45framework
20:45and you
20:46know
20:46hope and
20:47pray for
20:47the best
20:47because even
20:48those
20:49upskilling
20:49framework
20:50needs to
20:50be agile
20:51and continuously
20:52updated
20:52and the
20:53only way
20:53you can
20:54do that
20:54is having
20:55AI
20:55embedded
20:56in that
20:56so
20:57absolutely
20:57you are
20:58spot on
20:59about
20:59having
21:00the right
21:00certification
21:01I think
21:01I think
21:02the part
21:02of certification
21:03becomes
21:04really
21:04important
21:04to ensure
21:05people
21:05have
21:05you know
21:06gone
21:06through
21:07and
21:07finish
21:07it
21:07but
21:08more
21:08so
21:09applying
21:09these
21:10skills
21:10in
21:10companies
21:11this
21:12year
21:13nearly
21:13all
21:13the
21:13companies
21:14are
21:14looking
21:14at
21:15ROI
21:15return
21:16of
21:16investment
21:17of
21:17all
21:17the
21:17upskilling
21:18efforts
21:18we have
21:19done
21:19have
21:19people
21:20going
21:20back
21:21to
21:21their
21:21tables
21:22and
21:22offices
21:23and
21:23applying
21:23this
21:24and
21:24delivering
21:25outcome
21:25like
21:26process
21:27optimization
21:28new
21:28revenue
21:29opportunity
21:30and that's
21:31what
21:31companies
21:31are looking
21:32at
21:32not just
21:33upskilling
21:33but making
21:34sure that
21:34it's
21:34applied
21:35and they
21:35see a
21:36return
21:36in whatever
21:37they've
21:37learned
21:37clearly that
21:39lifelong
21:39learning
21:40model it
21:40has to
21:41be applied
21:41I think
21:41even more
21:42urgently
21:42when it
21:43comes to
21:43AI and
21:44upskilling
21:44thank you
21:45Dr.
21:46Chris
21:46let's
21:46dig
21:47deeper
21:47into
21:47something
21:48that
21:48you
21:48had
21:48of
21:48course
21:48briefly
21:49touched
21:49on
21:49earlier
21:50in
21:50the
21:50opening
21:50the
21:51quandary
21:52that
21:52we're
21:52facing
21:53now
21:53of
21:53building
21:54once
21:54but
21:54complying
21:55twice
21:56now as
21:56mentioned
21:57the
21:57EU
21:57AI
21:57Act
21:58it
21:58entered
21:58into
21:58force
21:59late
21:592024
22:00August
22:01and
22:02imposing
22:02duties
22:03on
22:03high
22:03risk
22:03AI
22:04Malaysia
22:04of course
22:05we did
22:05launch
22:05our
22:06NAYO
22:07our
22:07national
22:07AI
22:07office
22:08also in
22:09December
22:092024
22:09steering
22:10standards
22:11now on
22:11MDCC
22:12side
22:12what
22:13would
22:13be
22:13a
22:14blueprint
22:14perhaps
22:15so
22:15Malaysian
22:16platforms
22:17can
22:17ship
22:18AI
22:18features
22:19that
22:19satisfy
22:20both
22:20EU
22:21as
22:21well
22:21as
22:21ASEAN
22:22rules
22:22without
22:23having
22:24to
22:24duplicate
22:24costs
22:25well
22:27in my
22:28opinion
22:28we need
22:29to adopt
22:29an AI
22:30governance
22:30system
22:31for
22:31example
22:32ISO
22:33or
22:34ITC
22:34standards
22:35of
22:3642001
22:37that
22:39can
22:39be
22:40mapped
22:40to
22:41EU
22:41AI
22:42act
22:43obligation
22:43and
22:44there
22:44are
22:45certain
22:45AI
22:46governance
22:46principle
22:46so
22:47when
22:48we
22:49are
22:49able
22:49to
22:49adopt
22:50in
22:50a
22:50standard
22:51AI
22:52governance
22:52system
22:53therefore
22:54both
22:54of
22:55the
22:55processes
22:55both
22:56regime
22:56can
22:57be
22:57made
22:58into
22:58one
22:58regime
22:59and
22:59then
23:00both
23:00processes
23:00are
23:01met
23:01so
23:02then
23:02we
23:02are
23:02able
23:02to
23:03manage
23:03the
23:03scope
23:04of
23:04risk
23:04the
23:05scope
23:05of
23:05risk
23:06where
23:07we
23:07are
23:08able
23:08to
23:08use
23:08EU
23:09high
23:10risk
23:10category
23:10as
23:11a
23:11standard
23:11baseline
23:12and
23:12then
23:13the
23:13rest
23:14of
23:14the
23:14ARCEN
23:14will
23:14then
23:15follow
23:15it
23:15naturally
23:16because
23:16they
23:17have
23:17a
23:17chance
23:17so
23:18this
23:19year
23:19as
23:19you
23:20said
23:20it
23:20presents
23:21a
23:21good
23:22opportunities
23:22for
23:23Malaysia
23:23to
23:24mapping
23:26up
23:27this
23:27standard
23:27baseline
23:28so
23:28we
23:29need
23:29this
23:30so
23:31that
23:31we
23:32are
23:32able
23:32to
23:32cover
23:33not
23:33only
23:33EU
23:34but
23:35also
23:35ASEAN
23:36as well
23:36so
23:37in
23:37all
23:37we
23:38want
23:39to
23:39encourage
23:40the
23:41policy
23:41maker
23:42to
23:42create
23:42the
23:43standard
23:43AI
23:44technical
23:44file
23:45template
23:45that
23:46is
23:46recognized
23:46by
23:46both
23:47EU
23:47and
23:48also
23:48ASEAN
23:49and
23:49as
23:50I
23:50said
23:50once
23:50it
23:50is
23:50unified
23:51right
23:52we
23:53can
23:53monitor
23:54together
23:54and
23:55importantly
23:56we
23:56are
23:56also
23:56able
23:57to
23:57share
23:57the
23:57same
23:58registry
23:59fields
23:59because
24:00the
24:01standards
24:02and
24:02the
24:02systems
24:03are
24:03both
24:03the
24:03same
24:04then
24:05this
24:05is
24:06what
24:06you
24:06meant
24:07by
24:08built
24:09once
24:09but
24:10comply
24:10twice
24:10and
24:11if
24:12we
24:12are
24:13able
24:13to
24:13do
24:13this
24:13not
24:14only
24:14we
24:14comply
24:14twice
24:14but
24:15because
24:15of
24:15the
24:16forward
24:16looking
24:16we
24:17are
24:17then
24:17able
24:18to
24:18comply
24:18three
24:19times
24:19actually
24:20so
24:20this
24:21is
24:21something
24:21that
24:21we
24:21need
24:22to
24:22look
24:22ahead
24:23not
24:23only
24:23five
24:23years
24:24eight
24:24years
24:24but
24:25ten
24:25years
24:25ahead
24:25on
24:26how
24:26to
24:26unify
24:27the
24:27system
24:27between
24:28regions
24:28Absolutely
24:30now
24:30comprehensive
24:31AI
24:31regime
24:32with
24:32risk
24:33tired
24:33obligations
24:34that
24:34really
24:34is
24:34the
24:34way
24:35to
24:35go
24:35and
24:35time
24:36is
24:36really
24:36not
24:36on
24:37anyone's
24:37side
24:38thank
24:38you
24:38for
24:38that
24:38Shah
24:39now
24:40looking
24:40at
24:40diversity
24:41which
24:42is
24:42of course
24:42an
24:43economic
24:43lever
24:43now
24:44CAD
24:48actually
24:48improves
24:49female
24:50retention
24:51into
24:51more
24:52senior
24:52AI
24:52roles
24:53in
24:53Malaysia
24:54judging
24:55from
24:55what
24:55you
24:55have
24:56been
24:56involved
24:56in
24:56most
24:57recently
24:57I
24:59think
24:59there's
24:59a lot
25:00of
25:00females
25:00now
25:01leading
25:02you know
25:03chief
25:03data
25:03officers
25:04chief
25:04AI
25:05officers
25:05it's
25:06amazing
25:06but
25:07I
25:08think
25:08what
25:08would
25:09really
25:09help
25:09is
25:10have
25:10more
25:10sponsorship
25:11at
25:12work
25:12than
25:13mentorship
25:13right
25:14I
25:14always
25:14say
25:15women
25:15should
25:16need
25:16more
25:16visibility
25:17in
25:17the
25:17organization
25:18and
25:18that's
25:19the
25:19only
25:19way
25:19that's
25:19going
25:19to
25:19happen
25:20is
25:20get
25:20more
25:21internal
25:21sponsorship
25:22for
25:22them
25:22to
25:22grow
25:23and
25:23give
25:23the
25:24support
25:24I
25:25mean
25:25we
25:25have
25:25all
25:25the
25:26re-entry
25:27program
25:27the
25:27government
25:27has
25:28deployed
25:29over
25:30the
25:30years
25:30coming
25:31back
25:31to
25:31work
25:32all
25:33that
25:33is
25:33fantastic
25:34and
25:34the
25:35issue
25:35has
25:35always
25:36been
25:36not
25:37getting
25:38women
25:38being
25:39in
25:39STEM
25:39or
25:40AI
25:40it's
25:40always
25:40about
25:41keeping
25:41them
25:41in
25:42the
25:42workforce
25:43right
25:43visibility
25:44is
25:45another
25:46thing
25:46I
25:46think
25:47a
25:47lot
25:47of
25:47these
25:47amazing
25:48female
25:49leaders
25:49in
25:50data
25:51and
25:51AI
25:51are
25:52not
25:52very
25:53visible
25:53out
25:53there
25:54and
25:54I
25:54would
25:54encourage
25:55more
25:55and
25:55more
25:55women
25:55to
25:56go
25:56out
25:56and
25:56talk
25:57and
25:57share
25:58what
25:58they've
25:58been
25:59doing
25:59and
25:59how
25:59they
25:59grow
26:00and
26:00you
26:01know
26:01women
26:02need
26:02more
26:02role
26:03models
26:03you
26:04know
26:04and
26:05that's
26:05really
26:05important
26:06to
26:06go
26:07out
26:07there
26:07as
26:08uncomfortable
26:08as
26:09it
26:09might
26:09be
26:09you
26:11know
26:11because
26:11you
26:12know
26:13lack
26:13of
26:13a
26:13better
26:14word
26:14right
26:14monkey
26:14see
26:15monkey
26:15do
26:15not
26:15that
26:15we
26:16are
26:16monkeys
26:16but
26:17having
26:17like
26:18you
26:18know
26:18seeing
26:18more
26:19of
26:19us
26:19out
26:19there
26:20having
26:20families
26:21having
26:21career
26:22and
26:23having
26:23difficulties
26:24to
26:24you
26:25know
26:25being
26:25the
26:25super
26:26woman
26:27that
26:27we
26:27always
26:28expected
26:28to
26:28be
26:29it's
26:30not
26:30as
26:31rosy
26:32as
26:32a lot
26:32of
26:32people
26:33think
26:33and
26:33sharing
26:33it
26:34becomes
26:35really
26:35crucial
26:35so
26:36women
26:36would
26:37stay
26:38in AI
26:39when
26:39they get
26:40to see
26:40the path
26:41and
26:41get
26:41the
26:41support
26:42they
26:42get
26:42the
26:43sponsorship
26:43to
26:44do
26:44that
26:44not
26:45just
26:45coddling
26:47them
26:47and
26:47giving
26:48them
26:48maternity
26:49leave
26:49and
26:49support
26:50for
26:51children
26:51I
26:51think
26:51it's
26:52more
26:52women
26:52need
26:52to
26:53go out
26:53and
26:53be
26:54visible
26:54about
26:54the
26:55career
26:55and
26:55the
26:55job
26:55they're
26:56doing
26:56clearly
26:57so
26:58and
26:58absolutely
26:59always
26:59thank
27:00you
27:00for
27:00doing
27:01more
27:01than
27:01your
27:01part
27:02if
27:02I
27:02may
27:02say
27:03because
27:03when
27:04it
27:04comes
27:04to
27:04the
27:05global
27:05talent
27:05data
27:06pool
27:06women
27:07still
27:07fall
27:07behind
27:08at
27:08anywhere
27:08between
27:09as
27:09low
27:09as
27:0922%
27:10to
27:1030%
27:11depending
27:11on
27:12the
27:12market
27:12and
27:12CADS
27:13has
27:13also
27:13been
27:13running
27:13targeted
27:14fellowship
27:14as well
27:15as
27:15certification
27:16programs
27:17right
27:17so
27:18a lot
27:18more
27:18to be
27:19done
27:19but
27:19it
27:20does
27:20look
27:20like
27:21this
27:21is
27:21a
27:21time
27:22to
27:26Dr
27:27Chris
27:27moving
27:28along
27:28from
27:28there
27:29into
27:29looking
27:29at
27:30AI
27:31fraud
27:31before
27:32we
27:32close
27:32AI
27:34powered
27:34scams
27:35are
27:35really
27:36hammering
27:36consumers
27:372024
27:37wise
27:38Malaysia
27:39logged
27:39close
27:40to
27:402
27:40billion
27:41in
27:41online
27:42fraud
27:42losses
27:43police
27:43probed
27:44roughly
27:45around
27:45500
27:46or so
27:46deep
27:46fake
27:47cases
27:47with
27:48commensurate
27:48losses
27:49of close
27:49to
27:493
27:50million
27:50ringgit
27:50so
27:51what
27:51kind
27:51of
27:51concrete
27:52asks
27:53does
27:53MDCC
27:54have
27:55for
27:552025
27:56to
27:57make
27:58sure
27:59that
27:59this
27:59is
27:59not
28:00something
28:00which
28:00continues
28:01on
28:01an
28:01uptick
28:02but
28:02it
28:02can
28:03be
28:03managed
28:04well
28:06our
28:07recommendation
28:07is to
28:08close
28:08the
28:09block
28:09loop
28:09right
28:10so
28:10when
28:10you
28:10close
28:11the
28:11block
28:11loop
28:11we
28:12need
28:12to
28:12look
28:12at
28:13verify
28:13merchants
28:14detect
28:15and
28:16remove
28:16the
28:16scam
28:17as
28:17fast
28:17as
28:17possible
28:18and
28:19trace
28:19the
28:19AI
28:20content
28:20and
28:20ban
28:21it
28:21and
28:22then
28:22we
28:22need
28:22to
28:23protect
28:23compliant
28:23platforms
28:24and
28:24of course
28:25educating
28:25policies
28:27I
28:28mean
28:29educating
28:29the
28:29public
28:29I
28:30will
28:31look
28:31at
28:31three
28:32things
28:32very
28:32importantly
28:33I
28:33think
28:34number
28:34one
28:34we
28:34need
28:35to
28:35look
28:35at
28:3624
28:37hours
28:38take
28:38down
28:38platform
28:39must
28:39remove
28:40scams
28:42within
28:4312
28:44hours
28:45once
28:46there's
28:46a
28:46complaint
28:47in
28:47launch
28:47I
28:48know
28:48industry
28:49has
28:49been
28:49screaming
28:50because
28:50once
28:50we
28:50do
28:51it
28:51the
28:51cost
28:52of
28:52compliance
28:52will be
28:52very high
28:53but
28:53there's
28:54no
28:54choice
28:54right
28:54there's
28:55no
28:55choice
28:55we have
28:55to do
28:56it
28:56because
28:56there's
28:56so
28:56much
28:57scam
28:57in
28:58the
28:58market
28:58right
28:59now
28:59right
29:00so
29:0024
29:00hours
29:01response
29:01time
29:02and
29:02second
29:02we
29:03need
29:03to
29:03provide
29:04safe
29:04harbor
29:04and
29:04rapid
29:05response
29:05there
29:06must
29:06be
29:06legal
29:06protection
29:07for
29:07the
29:07platform
29:08and
29:08also
29:08legal
29:09protection
29:09for
29:10the
29:10consumer
29:10as
29:11well
29:11we
29:11have
29:12heard
29:12of
29:12depositors
29:13lost
29:14their
29:14money
29:14in
29:14the
29:14bank
29:14and
29:15the
29:15bank
29:15refused
29:15to
29:16pay
29:16and
29:16went
29:16to
29:17court
29:17and
29:17so
29:17forth
29:18so
29:18there
29:18must
29:19be
29:19rapid
29:19response
29:20safe
29:20harbor
29:21policy
29:21and
29:22the
29:24third
29:24thing
29:24is
29:24AI
29:25content
29:25labeling
29:25anything
29:26that
29:27people
29:27use
29:27which
29:27is
29:28not
29:28originally
29:29recorded
29:29with
29:30human
29:30but
29:31is
29:31generated
29:34by AI
29:34a
29:35computer
29:35there
29:36must
29:36be
29:36a
29:37water
29:37marking
29:37just
29:37for
29:38example
29:38you
29:39buy
29:39a
29:39bottle
29:39of
29:40wine
29:40right
29:41or
29:41a
29:42bottle
29:42of
29:42alcohol
29:43and
29:43that
29:43is
29:44a
29:44particular
29:44custom
29:45what
29:46we call
29:46a
29:47sticker
29:47or
29:47stamp
29:47that's
29:48being
29:48stick
29:48on
29:49the
29:49bottle
29:49to
29:50show
29:51that
29:51it's
29:51genuine
29:51so
29:52likewise
29:52for
29:53AI
29:53content
29:54there
29:54must
29:54be
29:54a
29:55labeling
29:55that
29:56this
29:56is
29:56actually
29:57genuine
29:57but
29:58of course
29:58somebody
29:58has to
29:59pay
29:59for
29:59it
29:59but
30:00there's
30:00no
30:00choice
30:04thanks
30:09very
30:09much
30:10for
30:10the
30:10insight
30:10so
30:11far
30:11and
30:11I
30:11think
30:12it's
30:12clearly
30:12pointed
30:13to
30:13the
30:13fact
30:13that
30:14AI
30:15will
30:15be
30:15defining
30:16ASEAN's
30:16competitiveness
30:17but
30:17only if
30:18we
30:18own
30:18the
30:18infrastructure
30:19keep
30:20our
30:20talent
30:20and
30:20also
30:21ensure
30:21that
30:21businesses
30:22are
30:22not
30:22left
30:23behind
30:23thank
30:23you
30:24so
30:24much
30:24for
30:24the
30:24conversation
30:25today
30:25I'm
30:25Sharala
30:26as
30:26well
30:26as
30:26Dr.
30:27Chris
30:27well
30:27that's
30:28all
30:28we
30:28have
30:28time
30:28for
30:29today
30:29on
30:29Nyaga
30:29Spotlight
30:30and
30:31with
30:34insights
30:35here's
30:35to a
30:36productive
30:36week
30:37ahead
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