Speaker 1: Mhm
Speaker 1: Hello, you're listening to Kobe time a podcast series on
Speaker 1: markets and economies from dBS group research on camera break.
Speaker 1: Chief economist, Welcome to our 73rd episode
Speaker 1: today we will take a break from current affairs and
Speaker 1: talk about something for the medium term,
Speaker 1: specifically the direction of technology to better our lives and
Speaker 1: the kind of work that has been done here in
Speaker 1: Singapore by
Speaker 1: Interpret entrepreneurs to achieve that. Our guest is married Bhandari
Speaker 1: Ceo and founder of Vulcan Ai, an artificial intelligence company
Speaker 1: that focuses on building safety and productivity solutions for industry.
Speaker 1: 4.0 will elaborate on all that.
Speaker 1: Previously. Manic was a senior partner at EY where he
Speaker 1: set up and ran the analytics business for Asean and
Speaker 1: delivered a i powered productivity projects across government.
Speaker 1: Agriculture, transport and logistics sectors. Manic and very welcome to
Speaker 1: Covid Time.
Speaker 1: Good
Speaker 2: morning tomorrow. Thanks for having me on into the conversation.
Speaker 2: Great
Speaker 1: to have you here. I have had you know, small
Speaker 1: chit chat about this issue with you in the past
Speaker 1: but I look forward to having an in depth chat today.
Speaker 1: Money let's begin by setting some context as a tech
Speaker 1: entrepreneur based in Singapore, you're part of the local ecosystem.
Speaker 1: So even before we talk about your company and your products,
Speaker 1: tell us about your experience with the open innovation program
Speaker 1: and I N. D. A. And also for the benefit
Speaker 1: of the listeners introduced to them what these programs are.
Speaker 2: Yeah Tamar in Singapore. You know, there's a whole lot
Speaker 2: of Corporates, there are government agencies which have various problem
Speaker 2: statements like they could be dealing with issues regarding to manpower, safety, security,
Speaker 2: many different issues and problems statements
Speaker 2: Now what happens is sometimes, you know, when you are
Speaker 2: looking for an answer to that problem statement,
Speaker 2: there may not be a ready solution for that. And
Speaker 2: if you want the smartest brains to work on it,
Speaker 2: you would typically do a, you know an RFP and
Speaker 2: a big consulting company will come in and they will
Speaker 2: custom build a very nice solution for you and it
Speaker 2: turns out to be pretty expensive, takes a year
Speaker 2: and it's, it's, I mean and it may still fail, right?
Speaker 2: So
Speaker 2: so that kind of is one of the reasons why
Speaker 2: an open innovation program works quite well. So what the
Speaker 2: government has tried to do is they have multiple problem
Speaker 2: statements which they collect sometimes from government agencies, sometimes from
Speaker 2: Corporates and then they have an ecosystem of startups as
Speaker 2: well as companies
Speaker 2: And they connect the two.
Speaker 2: So there are problem solvers and there are problems statement
Speaker 2: owners so they host these challenges every few months. They
Speaker 2: lived down the problem statements and then as a startup
Speaker 2: you can take a look at it
Speaker 2: and then two things can happen. One is, you may
Speaker 2: look at the problem statement and say, hey, I've, I've
Speaker 2: done something like that before and I can take what
Speaker 2: I have changed it and I think I can solve
Speaker 2: that problem or the second is you look at it
Speaker 2: and then you say which is what we did is
Speaker 2: that this problem seems worth solving
Speaker 2: right, if we can solve this problem, I think we
Speaker 2: can make a lot of money because a lot of
Speaker 2: other companies will have similar problem statements so you go
Speaker 2: in with that mindset and what the government does, I
Speaker 2: am doing what they do is they essentially give a
Speaker 2: little bit of a prize money to do a POC.
Speaker 2: So you get 2000, Yes, so you get a proof
Speaker 2: of concept, so you get some seed money, so it's
Speaker 2: like a seed round but without any state,
Speaker 2: but basically I m d puts up some money,
Speaker 2: the problem order puts up some money and that that
Speaker 2: gets you going and then once it's proven like you
Speaker 2: know it works, the product works, then the company may
Speaker 2: adopt it or they may actually help fund it further
Speaker 2: so that you can actually product is the solution and
Speaker 2: sort of go to market with it.
Speaker 2: And I think the reason it's a very good program
Speaker 2: because what it does is basically
Speaker 2: channeling the best thought leadership and capability to a problem
Speaker 2: statement which is very relevant
Speaker 2: and it's, it's not like just for one person, it
Speaker 2: may actually help the whole industry, so it's a good
Speaker 2: utilization of
Speaker 2: resources, you know, fast prototyping fail fast and basically startups
Speaker 2: also get the exposure because sometimes startups may not have
Speaker 2: the business acumen or the network to actually understand what
Speaker 2: the real problem statements are from the industry. So this
Speaker 2: connecting of the industry with some young and sometimes, you know,
Speaker 2: not so young entrepreneurs
Speaker 2: to solve problems. It's a it's a pretty good way
Speaker 2: of doing things.
Speaker 1: Is this a unique to Singapore? Because I can't think
Speaker 1: in the US for example, the U. S. Government providing
Speaker 1: interesting incentives and not just for Silicon Valley companies is
Speaker 1: a sink or swim situation. There is has Israel and
Speaker 1: stuff like this or is it something that is very
Speaker 1: special to Singapore?
Speaker 2: I think Israel does a bit of that.
Speaker 2: Plus there are,
Speaker 2: I guess there are accelerators and then the accelerators are
Speaker 2: basically some like it's like a informal JV between some
Speaker 2: big corporate is and then some vcs and they tried
Speaker 2: to do that, right? So they identify some startups and
Speaker 2: then they post those problem statements. But yeah, I've not
Speaker 2: come across as maybe there are other programs, but the
Speaker 2: I N. D. Open innovation program,
Speaker 2: I think now it's in its seventh round and I
Speaker 2: think there are like 100 problem statements which which have
Speaker 2: been put forward and it is operating at scale and
Speaker 2: the good thing is they are basically unlocking startups capabilities
Speaker 2: across the world, so it's not just, you know,
Speaker 2: Singapore startups can apply like one of the challenges which
Speaker 2: we won. We were selected from 18 other startups around
Speaker 2: the world,
Speaker 2: right? So it is not just that just because you're
Speaker 2: a singaporean startup, you get some extra credit. But um,
Speaker 2: but yeah, it's good
Speaker 1: fascinating money. We'll talk about the innovations and the solutions
Speaker 1: that your company is doing, but can you at the
Speaker 1: top of your head think of a few other problem
Speaker 1: statements that were done that you didn't go for but
Speaker 1: you thought were interesting and others are engaging.
Speaker 2: Yeah, I think one of the ones I remember were
Speaker 2: sort of Vancouver 19 came out
Speaker 2: and you know, there was you know, social distancing and
Speaker 2: you know, contact tracing which was supposed to be done.
Speaker 2: Then there was a startup which I think very quickly
Speaker 2: started using some, you know, barely devices which the workers
Speaker 2: at construction sites could basically put on
Speaker 2: and then you could keep track of, you know, people
Speaker 2: who are supposed to be in zone versus zone B
Speaker 2: and when they come close to each other, they it
Speaker 2: buzzes and it is basically on the prevention side. So
Speaker 2: I think that was pretty good. I think the technology
Speaker 2: was being used in cruises and casinos and other sort
Speaker 2: of use cases, they quickly took that and adapted it.
Speaker 2: And then I think now thousands of workers are already
Speaker 2: using it.
Speaker 1: Right, right. I'm pretty sure that in terms of solutions
Speaker 1: for saying climate change related activities, whether it's an easy
Speaker 1: to use current calculator or you know, not just people
Speaker 1: can have on a daily basis in terms of pursuing
Speaker 1: responsible behavior. I've seen
Speaker 1: applications like that all over the world and sometimes it
Speaker 1: is sort of useful to have a
Speaker 1: non commercial body sort of, you know, nudge the privacy thing,
Speaker 1: you can actually make money out of it and and
Speaker 1: do good at the same time. And, and of course,
Speaker 1: you know, money as we go deeper into the conversation
Speaker 1: and I'll talk about this more. So let's talk about
Speaker 1: your company Vulcan. Ai I'm in case you're curious about
Speaker 1: the genesis of the name and why and how you
Speaker 1: set it up.
Speaker 2: Well, I think I'll start with the name because I
Speaker 2: mean if anybody, if anybody has watched a star trek,
Speaker 2: right? So then then you know, Spock comes to mind
Speaker 2: And but that was not the reason why we call
Speaker 2: it Vulcan, I think when I started Vulcan I was
Speaker 2: wanted to focus on industry 4.0
Speaker 2: And because my point of view was that that you know,
Speaker 2: as we digitize, I think we there is a new
Speaker 2: way of doing things, whether it's improving the processes or
Speaker 2: improving yield and or now more recently digitizing of human work. Right?
Speaker 2: So you're focusing on industry 4.2. And then I was
Speaker 2: looking at like who was the
Speaker 2: like God of, you know, manufacturing or metal works and
Speaker 2: then welcome was that? And then welcome click. I, I
Speaker 2: immediately thought, hey, that's also like a spot
Speaker 2: and his whole point was that, you know, that alien
Speaker 2: sort of race was about, um, not going with, you
Speaker 2: know gut or emotion, but every decision they make is
Speaker 2: very logical and fact based and that is what, that
Speaker 2: is the cultural change we wanted to drive in industry
Speaker 2: four point oh so it was a combination of those
Speaker 2: two
Speaker 2: ideas and then how that's how I came up with
Speaker 2: the name balcony.
Speaker 1: So
Speaker 2: Yeah. So yeah. So the longer story is, I mean
Speaker 2: I've been in consulting nearly 20 years and we, it
Speaker 2: was fun. I mean, but you moved from client to
Speaker 2: client and you solve very interesting problems and clients pay
Speaker 2: money
Speaker 2: to solve those problems and do that work because it's
Speaker 2: too difficult to do it for themselves or it may
Speaker 2: not be poor to them. Right? So I had been
Speaker 2: working interestingly, I mean initially mostly work was with telcos
Speaker 2: and banks and that's mostly consumer data. So it was
Speaker 2: becoming quite mature already but it's the same thing, how
Speaker 2: do we sell more? How do we manage risk? That
Speaker 2: is the problem statement essentially.
Speaker 2: Then when I switched gears to another industry, so the
Speaker 2: whole agriculture and forestry industry was going through a early
Speaker 2: stages of some big transformation.
Speaker 2: So the drones were becoming more and more pervasive. But
Speaker 2: the problem with drones was basically when you fly drones
Speaker 2: on these huge plantations or forests, uh it's hours and
Speaker 2: hours of flying data that you've collected and you traditionally
Speaker 2: had people looking at that,
Speaker 2: I'm holding it and then saying, okay that
Speaker 2: three there may have an issue. It doesn't look that green. Right?
Speaker 2: So we did the math for one of the biggest
Speaker 2: paper companies in the world. And if they actually processed
Speaker 2: all the drone imagery manually, it would have taken them
Speaker 2: 100 people in 100 years to go through it. So
Speaker 2: it was just not possible.
Speaker 2: So one of the innovations we did was basically used
Speaker 2: of ai
Speaker 2: to automatically process all this imagery which is being collected
Speaker 2: and then pinpoint exactly which tree had an issue so
Speaker 2: they could do something about it. Now this project
Speaker 2: because 12 months,
Speaker 2: 20 people $3 million.
Speaker 2: So of course for a big company, I mean, which
Speaker 2: is like a $10, $12 billion dollar company, this is
Speaker 2: still pretty good on the way.
Speaker 2: Then we started thinking that hey, if the same thing,
Speaker 2: if we have to democratize it and make it available
Speaker 2: for a small farmer somewhere,
Speaker 2: this is not going to work. Right? So how do
Speaker 2: we make that shift? So the reason I started working,
Speaker 2: I was then how do we take some of the
Speaker 2: best kind of solutions and ideas which we had conceptualized
Speaker 2: for big MNCs big companies, billion dollar companies
Speaker 2: and build it into products which are bite sized, which
Speaker 2: even the small guy on the street or the small
Speaker 2: farmer can start using. So so coincidentally and I think
Speaker 2: that was a pretty good meeting of the minds. One
Speaker 2: of my biggest clients which is a big Indonesian
Speaker 2: family business which have interests in oil palm and paper
Speaker 2: and forestry. They said that's great. Like you build it
Speaker 2: for us and then can be monetized it and open
Speaker 2: it up for others because this should become a utility.
Speaker 2: This should not, this does not have to be a
Speaker 2: secret competitive advance. This should be a utility and everybody
Speaker 2: should benefit from it. Right.
Speaker 2: So that's how we started. We started as like a
Speaker 2: JV between
Speaker 2: me and another big family business and now we are
Speaker 2: at a stage where we have built these products and
Speaker 2: we are opening up for other companies and we are
Speaker 2: making the product so easy to use that and so
Speaker 2: easy to deploy that smaller smaller companies. And the vision
Speaker 2: is that a small farmer saying Bangladesh somewhere can just
Speaker 2: open an app and get access to the same technology
Speaker 2: which just five years back people were paying millions of
Speaker 2: dollars to access.
Speaker 1: All right. So among the products that your company sells.
Speaker 1: So would that be the first one that looking at
Speaker 1: satellite imagery of agriculture feeds?
Speaker 2: Yes, that's right. So I think our first basically flagship
Speaker 2: product is and I will tell you a little bit
Speaker 2: of a story right? Because it's important to visualize how
Speaker 2: why it is important. So like one client of ours
Speaker 2: is 500,000 hectares. That's the amount of plantation they have,
Speaker 2: They have. So which is I think 20 times the
Speaker 2: size of Singapore. So
Speaker 1: that's a good and scaling point. Yes.
Speaker 2: Yeah and it's an oil pump. So the way it
Speaker 2: works and basically you plant a tree and then that
Speaker 2: plant gives you food for the next 25 years. Right?
Speaker 2: And then uh you don't need to do much actually,
Speaker 2: you just have to give some fertilizer once or twice
Speaker 2: a year. And then every day basically there are harvesters
Speaker 2: who are going to the tree and harvesting the fruit
Speaker 2: and then the fruit is then sent to the mill
Speaker 2: where it's converted into oil and there's the same oil
Speaker 2: which is you know if you eat the Mcdonald's the
Speaker 2: soft serve cone or french fries, it's the same oil
Speaker 2: which is being used. Palm oil is pretty prevalent, We
Speaker 2: may not know it, but it's there.
Speaker 2: And even if you have some beauty clean products, it's
Speaker 2: their great
Speaker 1: shampoos, toothpaste.
Speaker 2: That's right, it's everywhere. Right. And so the biggest things
Speaker 2: you can do to sort of change the the yield
Speaker 2: in terms of if I have 500,000 hectares, how do
Speaker 2: I make more money. The only thing you can do
Speaker 2: is make sure that tree is healthy and it's producing
Speaker 2: the fruit, right? That's the and the quality of the
Speaker 2: fruit is good
Speaker 2: and the levers there are mostly whether you cannot control, right?
Speaker 2: So whether it is what it is fertilization you can control.
Speaker 2: And then the second thing you control is harvesting, you
Speaker 2: have to harvest is that the right thing?
Speaker 2: Like it's simple, right? So if you have a garden
Speaker 2: then you if you're planting tomatoes you will you know
Speaker 2: pluck it when it's like ripe and very because you
Speaker 2: want to use it immediately but with fruit it's a
Speaker 2: bit different. So right so the timing of the harvesting
Speaker 2: is is really important. So they have 30,000 workers, right?
Speaker 2: So they have to then
Speaker 2: Basically go and walk this 500,000 hectares
Speaker 2: and look at the trees and then figure out whether
Speaker 2: the trees are healthy or not. It's hot. There are
Speaker 2: there is wildlife, there are snakes. So for a regular
Speaker 2: person who's in the village and who's helping run this
Speaker 2: plantation life is pretty tough. You may be walking tens
Speaker 2: of kilometers every day trying to find issues but
Speaker 2: in the end you may not still find it because
Speaker 2: you don't know where to look,
Speaker 2: right? So the big change we did was get the
Speaker 2: satellite image, we do the processing. Then we give the
Speaker 2: location of each tree where there could be an issue.
Speaker 2: Then this guy goes to that particular location and verifies
Speaker 2: whether it's correct or not and then takes a picture,
Speaker 2: sends it back and say yes there is an issue.
Speaker 2: Either we need to spray some pesticide
Speaker 2: more, more fertilizer.
Speaker 2: So it's basically completely changing the way things used to
Speaker 2: operate earlier where people are just going around randomly walking
Speaker 2: and tough life. Whereas now they only go to the
Speaker 2: places where they actually need to go. So it's a
Speaker 2: much more targeted and less work. But the impact is
Speaker 2: a lot more.
Speaker 1: Can you give us a sense of what kind of
Speaker 1: impact we're talking about? Is it like a 5% increase
Speaker 1: in yield or more than that?
Speaker 2: Yeah. So
Speaker 2: so the the the yield is it
Speaker 2: Would be between 2-5%.
Speaker 2: Which is and it's just coming from simple I would
Speaker 2: say fertilizer
Speaker 1: optimization. Right?
Speaker 2: So basically we can actually tell you that, you know,
Speaker 2: the nitrogen level of this particular tree is a bit
Speaker 2: low support more fertilizer.
Speaker 2: So that's the 5%. So if you give the fertilizer
Speaker 2: right time the food production will go up right at
Speaker 2: the same time. You also don't want to put too
Speaker 2: much fertilizer because it's not good for the environment. And
Speaker 2: the soil is right. So we also find out trees
Speaker 2: which are good meaning you don't need to fertilize. And
Speaker 2: So so one of our clients spends $300 million dollars
Speaker 2: A year on fertilizer and we save like 20%.
Speaker 2: So which is that 16 million to 16 million a year.
Speaker 2: But at the same time it's great for the environment
Speaker 2: because you're not putting chemicals into the soil, right? So
Speaker 2: it's a
Speaker 2: so on one side you're increasing year but you're also
Speaker 2: doing it in a more sustainable way.
Speaker 1: But it could be be a little more regular without
Speaker 1: giving away your trade secret as it. How is this
Speaker 1: processing done? I mean is it just a photo image analysis?
Speaker 1: Like looking at the spectrum that the trees have?
Speaker 2: Yes, you're right. Nearly there. You guessed it. So basically
Speaker 2: when you take an image from the satellite and it
Speaker 2: basically measures the reflected its so when the sun bounces
Speaker 2: off and it has basically infrared and red edge and
Speaker 2: the visible spectrum.
Speaker 2: So you have sort of energy bands across
Speaker 2: the spectrum and then what is bouncing back which they
Speaker 2: call it reflecting its that will have a very unique
Speaker 2: spectral sort of profile for different things. So what we've
Speaker 2: done is we've taken like millions of trees
Speaker 2: and these trees have been labeled by ground proving that
Speaker 2: whether it's healthy or not healthy and then we sort
Speaker 2: of we have matched So the ai matches the spectrum
Speaker 2: profile with what's good, what's bad and
Speaker 2: I'm simplifying it, but basically that's what it does. But
Speaker 2: it has evolved to such an extent that not only
Speaker 2: does it tell whether it's healthy or not, you can
Speaker 2: even tell the type of disease
Speaker 2: which is suffering from. So it could be gannon, normal
Speaker 2: leaf eating caterpillar. And the beauty of that is once
Speaker 2: we tell that the person doesn't even have to go
Speaker 2: and check,
Speaker 2: You can straightaway go with the right intervention and the
Speaker 2: insecticide that you have to spray right there and the
Speaker 2: sooner you fix it, right? So one tree is infected,
Speaker 2: it's like COVID-19 it can spread.
Speaker 2: So if I please have it and you don't intervene
Speaker 2: on time that disease will spread and that's a massive
Speaker 2: impact on yield. So with satellite imagery you're getting more
Speaker 2: recurring every three months. What's wrong? You take the action
Speaker 2: and you sort of nip it in the bud in
Speaker 2: a way it's like contact tracing
Speaker 1: just hearing this. It sounds like it's a perfect solution
Speaker 1: for a very large farm.
Speaker 1: Is this also useful for a small farm?
Speaker 2: Yes, I mean there are so there is obviously an
Speaker 2: inflection point where it may not make sense for a
Speaker 2: very small farm because there's a very small farm farmer,
Speaker 2: you're walking your farm and your whole livelihood depends on it.
Speaker 2: So you can see visibly
Speaker 2: if anything is going wrong, right? So it may not
Speaker 2: make sense in that context. But having said that there
Speaker 2: are certain things which the satellite imagery gives you, which
Speaker 2: the human eye cannot. Like I was saying as a human,
Speaker 2: you only see the visible spectrum. So you see what
Speaker 2: you see.
Speaker 2: But the satellite has more spectral band data coming in
Speaker 2: and in fact a lot of the nutrients and health
Speaker 2: information is captured in those banks. So the tree may
Speaker 2: look perfectly fine from outside to a naked eye.
Speaker 2: But actually there may be something wrong. So it helps
Speaker 2: still helps with early detection. So it does make sense.
Speaker 2: I think the only constraint probably is the order size.
Speaker 2: Let me rewind a little bit. So like six years,
Speaker 2: seven years back.
Speaker 2: Um satellite imagery was really expensive and the big governments
Speaker 2: of the world would use it for defense and intelligence. Right?
Speaker 2: So now with the satellite space booming and it's pretty
Speaker 2: congested out there, the cost of satellite imagery is going down.
Speaker 2: So they have reduced the order size.
Speaker 2: But even now if you want to get satellite in
Speaker 2: misery you have to get at least
Speaker 2: 100 square kilometers.
Speaker 2: All right, so it would not make sense to get
Speaker 2: that kind of satellite imagery for the small farmer.
Speaker 2: But co operatives and governments are sort of getting together
Speaker 2: source sourcing it at scale and then distributing the insides
Speaker 2: to the farmers because it does help the economy and
Speaker 2: the GDP
Speaker 1: fascinating. Okay, so from agriculture to other products that you
Speaker 1: have some of the stuff that you and I have
Speaker 1: talked in the past to share with us some of
Speaker 1: the other things that Vulcan Ai is producing and marketing.
Speaker 2: Sure. The other thing we focused on is what I
Speaker 2: would call is sort of the worker two point oh
Speaker 2: I think we talked about industry four point Oh I
Speaker 2: think there's a lot of talk about ai automatic automating
Speaker 2: things and you know, robots taking over. But there are
Speaker 2: certain things which are,
Speaker 2: you require humans to do that,
Speaker 2: right? So whether it's like in construction or you're lashing
Speaker 2: containers on a on a ship
Speaker 2: or you know, cleaning toilets. I mean of course they're
Speaker 2: robots but they can't get everywhere. So my I mean
Speaker 2: I think the point of view I have is that
Speaker 2: yes, I think productivity gains will happen, but humans are
Speaker 2: pretty essential and there will be a lot of things,
Speaker 2: especially physical work, which which may not be completely automated.
Speaker 2: So how do we make it work and more enriched,
Speaker 2: safer and better for for blue collar workers. Right, So
Speaker 2: like during covid 19, your go to Starbucks, but
Speaker 2: and you can work from there. But then along when
Speaker 2: you go there, they could also be a cleaner who's
Speaker 2: cleaning the road, right? So life is pretty tough for them.
Speaker 2: So I consciously decided that we should be using ai
Speaker 2: not to just replace humans, but make lives better for
Speaker 2: the more sort of blue collar workers whose life is
Speaker 2: pretty tough. So
Speaker 2: so we developed a solution which basically taps into
Speaker 2: variables
Speaker 2: and there is a i in it which sort of
Speaker 2: gives nudges
Speaker 2: to them if they are sort of not taking care
Speaker 2: of their
Speaker 1: health
Speaker 2: or if they fall down during the course of work,
Speaker 2: then an alert is sent to the supervisors so they
Speaker 2: can send help
Speaker 2: or it could be keeping track of their exertion because
Speaker 2: it's physical exertion and giving them nudges that hey, you
Speaker 2: may be dehydrated, can you rest a little bit? So
Speaker 2: it gives them those notches so they can take control
Speaker 2: over their health and sort of fatigue and exhaustion while
Speaker 2: they're working. But more importantly,
Speaker 2: we're also giving visibility to the enterprise level
Speaker 2: to the employer to basically point out areas or teams
Speaker 2: which may be overworked and may be at risk of
Speaker 2: workplace accidents
Speaker 2: because sometimes people,
Speaker 2: You know, you may have 50 people working at a
Speaker 2: construction site
Speaker 2: And there may be somebody who has a preexisting heart
Speaker 2: condition and you may not even know that. And you've
Speaker 2: given him the same amount of work as somebody else
Speaker 2: and then you but you don't want people to just
Speaker 2: die on your watch. Right? So this gives them that. Okay, fine,
Speaker 2: I have the same 50 people but this person I can,
Speaker 2: you can reallocate resources based on how they react
Speaker 2: to physical stress or exertion
Speaker 2: because what we can now show employers is that same
Speaker 2: amount of exertion. Physical exertion, which is captured based on
Speaker 2: physical movement and heart rate.
Speaker 2: What is the impact on fatigue? What is the impact
Speaker 2: on their heart rate and you can rebalance the workload.
Speaker 2: So this solution is, you know, targeted at the construction sector,
Speaker 2: maritime sector facilities management. We are now starting to use
Speaker 2: it for aircraft cleaning because the other thing we do
Speaker 2: is
Speaker 2: just like in your foot bed
Speaker 2: basically using your hand motion. it can detect whether you're
Speaker 2: exercising or not. We have work activity detection
Speaker 2: meaning if you're doing some work
Speaker 2: it can quantify that work actively.
Speaker 2: So like in agriculture when you're spraying fertilizer you spray
Speaker 2: like that. It's a very unique and motion.
Speaker 2: We use AI which is embedded in the variable to
Speaker 2: convert it into fertilization then if the plantation owner wants
Speaker 2: to see okay which part of my plantation was fertilized?
Speaker 2: Which one was not because most of it is still
Speaker 2: manual application. We can see
Speaker 2: we can see that that and we can sort of
Speaker 2: have a geospatial view of that. So the idea is
Speaker 2: to make the person's job safer make him more in
Speaker 2: charge of his own health and and fitness but at
Speaker 2: the same time also given aggregated view to the employers.
Speaker 2: So they know where the risk and the hotspots are
Speaker 2: in their workforce so they can manage it better
Speaker 1: naive question which is is by wearable. Do you mean
Speaker 1: a watch or does it have to be or could
Speaker 1: be something else?
Speaker 2: So right now we have our own watch
Speaker 2: and so we have basically optimized because the one thing
Speaker 2: we wanted to do was we didn't want to send
Speaker 2: the data out of the watch.
Speaker 2: And so we needed like a
Speaker 2: smartwatch like with some inherent sort of a good processor
Speaker 2: so that we can embed our aI inside it. So
Speaker 2: it's always listening in terms of the sensor data and
Speaker 2: processing it and only when the person has an incident
Speaker 2: or needs help, we send an alert back
Speaker 1: and what's been the experience so far in terms of
Speaker 1: overall health and well being of workers in the pilots
Speaker 1: or the actual cases that you've looked at so far.
Speaker 2: I think surprisingly the workers have been very welcoming. I
Speaker 2: think they are quite complementary. They,
Speaker 2: I mean there's two things which literally like one is
Speaker 2: they think that I mean and rightly so that the
Speaker 2: company is investing in their safety and
Speaker 1: health. So
Speaker 2: that's good. It's like generally the thing is that, you know,
Speaker 2: we're just working, working, working, nobody cares enough and you know,
Speaker 2: finally the company has bought
Speaker 2: and then is now keeping track of my safety.
Speaker 2: The second thing, which is more I would say
Speaker 1: on
Speaker 2: a more lighthearted way is that they also feel very
Speaker 2: happy when they go back to their family and then say, hey,
Speaker 2: I have a new smartwatch because a smart watch, it
Speaker 2: has two modes. It has a work mode and it
Speaker 2: has like a life mode. So when you go to work,
Speaker 2: it's running all those ai things to keep you safe
Speaker 2: and productive. But when you check out our work, it's
Speaker 2: just another smartwatch and you can watch youtube listen to music,
Speaker 2: you can do all of that. So it comes also
Speaker 2: as an employee perk. So the reception has been quite good.
Speaker 2: I think on the corporate side
Speaker 2: there is some resistance, I won't say to the idea,
Speaker 2: but some resistance to
Speaker 2: like what the business cases,
Speaker 2: right? Because like if you're a company which provides cleaners
Speaker 2: say to um BFC
Speaker 2: then they will say I am footing the bill but
Speaker 2: it's not like DBS is paying me for this, right?
Speaker 2: So because I am also a service provider to somebody.
Speaker 2: So the angle we are taking in and this is
Speaker 2: with the government and the the union is that we
Speaker 2: need to educate the service buyers. The end clients to
Speaker 2: basically tell them that you know, safety is important. You
Speaker 2: don't want a cleaner to slip and break his hip
Speaker 2: while he's working at, you know, mbf see tower one, right?
Speaker 2: They say yeah of course we don't want that.
Speaker 2: Then you say, okay then you're cleaning company. You tell
Speaker 2: them that you know, they need to equip these solutions
Speaker 2: in their workforce and to be honest it's like 5
Speaker 2: $10 a month per person is hardly anything. So if
Speaker 2: the solution is there and if people wanted people will
Speaker 2: definitely take it. Right? So that was one the second
Speaker 2: one
Speaker 2: which I don't think is the right attitude. But we
Speaker 2: do come across
Speaker 2: some people, I was talking to a person who manages
Speaker 2: like 100 cleaners
Speaker 2: and his point, I think he was a bit too
Speaker 2: transparent was that if I give this watch,
Speaker 2: then we may discover people who have heart trade issues
Speaker 2: and then we may have to find replacements for them.
Speaker 2: Which is true.
Speaker 1: Yes, of course.
Speaker 2: Which is true. But when you're in a constrained labor
Speaker 2: market you don't want to unearth suddenly and then you're
Speaker 2: applying to saying, hey, you know then of your cleaners
Speaker 2: are not healthy enough to do this kind of work
Speaker 2: I need younger, I need healthier. So there is that
Speaker 2: you know fear which people have and I think this
Speaker 2: fear is quite valid for the person who's wearing it.
Speaker 2: Also
Speaker 2: like if I have if my sole livelihood
Speaker 2: it can be impacted just because I'm not healthy. Like
Speaker 2: for you and me it doesn't make a difference, right?
Speaker 2: So if I have a hypertension or something and you
Speaker 2: know I will go to the doctor but I can
Speaker 2: still continue to my work. But if you're like a
Speaker 2: small breadwinner and somebody gives you a watch all good.
Speaker 2: But the watch tells you that actually you're a very
Speaker 2: high risk of getting a heart attack while you're working
Speaker 2: and hence we have to find somebody else. It's pretty scary,
Speaker 1: very interesting. I mean yeah it's that wasn't sort and
Speaker 1: I think that you know of course you know we
Speaker 1: want all employers to do the right thing which is
Speaker 1: looking after the well being of their employees.
Speaker 1: But an employee perspective when they're the sort of bread winner,
Speaker 1: you are posing a very very interesting dilemma money. Are
Speaker 1: there other implicated sort of applications of the same model?
Speaker 2: We were doing a lot of work on sort of
Speaker 2: general improving productivity using a
Speaker 2: like one of the solutions we have with J. D. C.
Speaker 2: Town council is around a state inspection.
Speaker 2: So you know, they are one of the biggest landlords
Speaker 2: in in Singapore and they also maintain or basically inspect
Speaker 2: the roads regularly. So given that there is not enough
Speaker 2: people in Singapore were not able to inspect the roads
Speaker 2: as regularly as you want and you would have noticed
Speaker 2: over the years the number of potholes has has has
Speaker 2: gone up
Speaker 2: and the portals don't get fixed
Speaker 2: as soon and it's not just about a comfort, comfortable ride, right?
Speaker 2: It's a safety risk because people sometimes may swerve out
Speaker 2: to avoid the Porthole and accident. Plus we have delivery riders.
Speaker 2: So one of the things we did was we basically
Speaker 2: took our Ai and embedded into like a small sized
Speaker 2: device
Speaker 2: this edge devices now being put in busses
Speaker 2: and other vehicles as you're just driving around the AI
Speaker 2: is automatically detecting portals, tagging it with the location and
Speaker 2: then sending it back. So instead of having somebody inspected
Speaker 2: once a year. So we have like memorized the road
Speaker 2: inspection process. So that's another
Speaker 2: pretty cool project which we're now scaling to the rest
Speaker 2: of Singapore
Speaker 1: back in the States in the middle of the night.
Speaker 1: They have this ad it's called I fallen and can't
Speaker 1: get up at. So it's about the elderly and a
Speaker 1: signal goes out to the nurse or the caregiver or
Speaker 1: their Children that you know, the, the elderly person has
Speaker 1: fallen and they need help. I think you have something
Speaker 1: like that up your sleeve as well, correct?
Speaker 2: Yes, that's right.
Speaker 2: And in fact we were, we have tried to move
Speaker 2: away from this may be giving away too much, but
Speaker 2: basically we're trying to move away from the variable because
Speaker 2: the elderly may not wear it at night and at
Speaker 2: night is when you have most of these incidents. So
Speaker 2: we're trying to count with some hacks where you basically
Speaker 2: stick our device next to the bed and then basically
Speaker 2: it is able to then figure out whether you're on
Speaker 2: the bed or you're not on the bed.
Speaker 2: So a simple rule which basically says, hey, it's nighttime
Speaker 2: and you've been away from your bed for more than
Speaker 2: one hour. Send an alert
Speaker 2: to your family or to somebody else because you should
Speaker 2: be in your bed. So like simple tweaks like that
Speaker 2: with technology and you can solve big problems.
Speaker 1: Yeah, fine line between being too intrusive and being cognizant
Speaker 1: of one's well being. I think that's where you have
Speaker 1: to do a lot of tweaking.
Speaker 2: Yes. I mean the way we deal with it is
Speaker 2: that because in the consumer setting, you can,
Speaker 2: you can assign who you want the alert to go to.
Speaker 2: So the power is, is with the person who's like,
Speaker 2: so you stick the device then you say, okay, this
Speaker 2: is the person I want to send, So it's like
Speaker 2: your emergency contact. Right? So it's you who's saying who
Speaker 2: needs to be alerted?
Speaker 1: That's right, That's right. But I was gonna ask you
Speaker 1: a question about the lessons for the for the medium
Speaker 1: term challenges in terms of, you know, bring the customers,
Speaker 1: but I think more or less covered that. I want
Speaker 1: to stay with this point because I'm picking up this
Speaker 1: team from your response is that you are very much
Speaker 1: in favor of ethical use of Ai, which is a
Speaker 1: big issue out there and you are a contributing author
Speaker 1: to Singapore's first
Speaker 1: Ai ethics body of knowledge. Can you expand on that?
Speaker 2: Yeah, I think that's a great question. So as a
Speaker 2: I becomes more pervasive, I think we come across um
Speaker 2: a little bit of ethical dilemmas once in a while,
Speaker 2: like I was talking to somebody who was doing a
Speaker 2: project for one of the big casinos
Speaker 2: and they had, they had developed an Ai model
Speaker 2: whose objective function was to obviously maximize profit? Right, So
Speaker 2: it was more around that. How do you optimize the,
Speaker 2: like the line of credit, like if they want to
Speaker 2: gamble and how much can you give and how do
Speaker 2: you manage risk?
Speaker 2: And but
Speaker 2: what was missing from that objective function was that are
Speaker 2: we making this person a,
Speaker 2: you know habitable
Speaker 2: gambler and
Speaker 2: you know, and then that and can you really afford it? Right. So,
Speaker 2: so now do you do that or do you do
Speaker 2: the profit optimization? So there is this trade off of course,
Speaker 2: in pure profit optimization, you also don't want that person
Speaker 2: to go completely
Speaker 2: um over the top so that, you know, he goes
Speaker 2: bankrupt and doesn't come back. So there's always advance but
Speaker 2: there are these kind of questions which are coming up
Speaker 2: more and more because in the traditional world it's a
Speaker 2: human and the human uses some judgment. Part of it
Speaker 2: is emotional part of it is, you know, profit seeking
Speaker 2: and
Speaker 2: you try to balance and you do it but different
Speaker 2: people may react to it in different ways
Speaker 2: and the way I work has been basically trained on
Speaker 2: decisions which have been made in the past. So unless
Speaker 2: you also tell whether this decision was good about the
Speaker 2: way I will learn from whatever decisions have been made
Speaker 2: in the past. So there are some obviously some parameters
Speaker 2: which we can set
Speaker 2: to improve the ethical implementation we have. So with that
Speaker 2: in mind basically the Singapore Government and the Singapore Computer
Speaker 2: society and it was announced in the World Economic Forum,
Speaker 2: it's the first sort of body of knowledge of its
Speaker 2: kind where
Speaker 2: um there are, you know, case studies in terms of
Speaker 2: what could go wrong, but they also specify the things
Speaker 2: that we have to keep in mind to make sure
Speaker 2: that we're doing the right thing
Speaker 2: and it has basically four different dimensions. So one is
Speaker 2: around governance and the governance is basically, you know, in
Speaker 2: the ai value chain there are different players, it could
Speaker 2: be the business owner, there's the data guy, there's the
Speaker 2: Ai guy and then the person who executes it,
Speaker 2: so who governs that? How do you formulate the right
Speaker 2: problem statement?
Speaker 2: And it should also become part of the board agenda
Speaker 2: because things can really go wrong if you don't implement
Speaker 2: it correctly. So it talks a little bit about governance
Speaker 2: around that. Then the second important point is around human
Speaker 2: centric city, which is basically saying hey we still need
Speaker 2: to put humans at the center of it
Speaker 2: and when we design something, it has to be designed
Speaker 2: with the humans best interests in mind.
Speaker 2: So then there are sort of steps on how to
Speaker 2: do that. The third pieces around operations management and operations
Speaker 2: management is more around over a period of time, circumstances change,
Speaker 2: environment changed data changes and the model made drift. So
Speaker 2: when you put an Ai model into production, how do
Speaker 2: you make sure it is not biased, it is fair
Speaker 2: and there should be like statistical measures put in place
Speaker 2: to manage the operations of that.
Speaker 2: And lastly it talks about stakeholder communication, so like how
Speaker 2: do you communicate to um every user in the summer?
Speaker 2: The ecosystem. Right. So basically if you are in the
Speaker 2: gambling example, if you're refusing to give credit widely doing
Speaker 2: that and how do you explain it? Because people may say, hey,
Speaker 2: I didn't get it, but my friend
Speaker 2: got it, so it doesn't have all the answers, but
Speaker 2: at least it gives you a toolkit to start thinking,
Speaker 2: start sensitizing yourself to ethics and some frameworks to be
Speaker 2: more smart about ethical implementation.
Speaker 1: I think it's extremely important to do that money, I
Speaker 1: mean the world is almost flat in terms of
Speaker 1: social media apps and various uh you know, a wearable
Speaker 1: apps that are ubiquitous developing country, developed country across societies,
Speaker 1: but the capacity to deal with some of these ethical
Speaker 1: ramifications seems to be very uneven, so I'm glad that
Speaker 1: you know, you and your colleagues in Singapore have sort
Speaker 1: of pushed it through and I hope that it's not
Speaker 1: just a question of Singaporean companies
Speaker 1: benefiting from it, but it has
Speaker 1: some degree of global reach because you know, it affects
Speaker 1: all of us. Facebook is not just for some rich countries,
Speaker 1: it's everywhere. Uh and and similarly I want to talk
Speaker 1: a bit about your
Speaker 1: personal experience as an entrepreneur in general and also in
Speaker 1: the context of, of being an entrepreneur in Singapore
Speaker 1: now, you know, it's a small nation,
Speaker 1: it is wealthy, it is well educated, has world class
Speaker 1: infrastructure notwithstanding the potholes that you were talking about, but
Speaker 1: the markets here are small by global standards and so
Speaker 1: is the talent for, So how should a local entrepreneur
Speaker 1: deal with this scaling issue as far as Singapore is concerned?
Speaker 2: Yeah, I think what Singapore has is
Speaker 2: I think a very
Speaker 2: forward looking government
Speaker 2: and it's, I think they catch
Speaker 2: the uptrends pretty early on, like I think right now
Speaker 2: it's tech, it's a, i it's you know, all the
Speaker 2: consumer apps, so I think there is and we're now
Speaker 2: switching very quickly to make sure that we have enough
Speaker 2: talent um to you know, serve that demand.
Speaker 2: So that's one plus there is a lot of support,
Speaker 2: I would say both financial support as well as
Speaker 2: um you know, just guidance and and getting local entrepreneurs
Speaker 2: connected with local companies to do some schoolwork, prior new solutions,
Speaker 2: but also globalized. I mean the way they have architected
Speaker 2: the whole value chain is pretty amazing, right? So, very
Speaker 2: early stage you have these innovation programs where people give
Speaker 2: problem statements and you do that. So we also benefited
Speaker 2: from that. Right? So some of our products are coming
Speaker 2: out of
Speaker 2: that, so you do the problem when you do the
Speaker 2: proof of concept, you have the prototype, there is some
Speaker 2: seed funding, you do that.
Speaker 2: Then the next step is basically you have, so I
Speaker 2: M B A will help with that then, as it
Speaker 2: starts to get some traction, there is enterprise Singapore which
Speaker 2: focuses on local smes right? So as a local startup
Speaker 2: will qualify as a local sme initially. Right? So then
Speaker 2: they have programs where they would even give you 80%
Speaker 2: of the cost of doing a pilot with any other
Speaker 2: company in Singapore.
Speaker 2: So now you sort of have a few pilot clients
Speaker 2: and the product market fit is established. The next step
Speaker 2: is basically you want to globalize now the Singapore government
Speaker 2: also has sort of funding and support available to open
Speaker 2: offices overseas and again, do those pilots now, once you've
Speaker 2: done that, then obviously then the,
Speaker 2: the VCS and the institutional investors and kicking and they
Speaker 2: also helped make those connections.
Speaker 2: So the infrastructure is there, the funding is there? I
Speaker 2: think the talent we're slowly getting there. So it's it's
Speaker 2: actually a pretty good place to, you know, have a
Speaker 2: startup and do things, but but you're right in terms
Speaker 2: of scale,
Speaker 2: you may not
Speaker 2: have that scale. Right? So, so it's a great place
Speaker 2: to start a startup, but you have to have the
Speaker 2: mindset that I need to make it global to really
Speaker 2: make it big. So even when you start in conceptualizing you,
Speaker 2: you always have to put the lens on that. Is
Speaker 2: this is this problem statement, something which can apply to
Speaker 2: other markets in hardware
Speaker 2: globalized. So I think that, so it's just a good springboard,
Speaker 2: I could, I would say a test bed to do things,
Speaker 2: but you're definitely, but the capital is here, but you
Speaker 2: have to think global, if you have to be successful,
Speaker 1: how open
Speaker 1: is the rest of Asia to Singapore entrepreneurs, so if
Speaker 1: you develop a product here um introducing and marketing it
Speaker 1: in Southeast Asia or South Asia or even the holy
Speaker 1: grail of all china, I mean is that a considerable
Speaker 1: realistic dream and and other examples where companies have gone big,
Speaker 2: so I will give two data points on that and
Speaker 2: this may not represent
Speaker 2: You know everything. I think one is
Speaker 2: I think the reputation of Singapore as a tech hub
Speaker 2: is still not there, I think it's not like if
Speaker 2: you talk about Israel then, oh wow, that anything which
Speaker 2: is coming out of here out of Israel is good,
Speaker 2: so we are not like that, I think
Speaker 2: what were known in the market is more for like
Speaker 2: valuation capital and people raising big rounds and things like that, right?
Speaker 2: But I think in terms of tech, I think our
Speaker 2: branding is, is not there yet, so other markets may
Speaker 2: not
Speaker 2: immediately think that just because you're a Singapore startup, you
Speaker 2: must be good in tech, that is not the connection
Speaker 2: they will make. But having said that, what I realized
Speaker 2: is that if you're playing in the industries which Singapore
Speaker 2: is going for like you can be a tech company,
Speaker 2: which is saying the real estate or construction sector,
Speaker 2: you will be taken more seriously because Singapore is known
Speaker 2: for that, if you're in Fintech
Speaker 2: and yep you will be taken more seriously because Singapore
Speaker 2: is a financial help, right? And we have the, you know,
Speaker 2: one of the world's biggest ports and maritime industry is big, right?
Speaker 2: So if you do something tech in maritime again, you
Speaker 2: will be taken very seriously.
Speaker 2: Same thing with the airlines, you have Singapore airlines, changi airport,
Speaker 2: So if you, so if you are in a tech startup,
Speaker 2: which is focusing on these industries, which Singapore is famous for,
Speaker 2: then I think you have a much better chance of
Speaker 2: being sort of appreciated and taken seriously than if you
Speaker 2: like for us
Speaker 2: for sure. Like when I talk about agriculture, people will say, hey,
Speaker 2: what I mean, Singapore doesn't even have agriculture, what are you,
Speaker 2: have you? Right? So they will not take place. But
Speaker 2: of course we counter that because we have years of
Speaker 2: experience and even though the team is here, our experiences
Speaker 2: outside Singapore, right? We don't have a client in Singapore,
Speaker 2: but everybody's outside.
Speaker 2: So I think that's the challenge. The other thing I've,
Speaker 2: I think, which is very, some is
Speaker 2: we have talked to some disease and we talked about like,
Speaker 2: you know, the ease of getting funded in Singapore and,
Speaker 2: and so this was some Malaysia based VC and their
Speaker 2: point was, and I'm not sure whether it's right or
Speaker 2: wrong or even politically correct, but what he was saying
Speaker 2: that don't take money from sort of Singapore because
Speaker 2: you may end up getting fat and lazy, right? So
Speaker 2: basically there's a lot of easy money out there and
Speaker 2: he's saying over the last 10 years, he's seeing so
Speaker 2: many startups, it's so easy to make money that there,
Speaker 2: it is not the scene where, you know, there is
Speaker 2: a startup guy and you know, he's worrying about his
Speaker 2: next paycheck and he just has to get it right.
Speaker 2: So that kind of
Speaker 2: aggression, desperation is missing from the Singapore startups
Speaker 2: and his point of view is that, you know, Singapore
Speaker 2: startups have it easy, there's a lot of government support, right?
Speaker 1: It's it's interesting, right? So you could argue that today,
Speaker 1: even in Silicon Valley, there is so much money and
Speaker 1: you were talking about accelerators earlier, it may not be
Speaker 1: driven by the public sector, but there are these very,
Speaker 1: very large accelerators out there which also make capital available
Speaker 1: to just about any single idea that's out there,
Speaker 1: which also leads to a massive boom bust cycle. So
Speaker 1: I think, I don't think that isn't necessarily specific to
Speaker 1: public support for entrepreneurism, private sector also overreaches and over
Speaker 1: invest and invest in ridiculous things many times as well. Um,
Speaker 1: but no, I, you know, from the very beginning of
Speaker 1: this conversation, I've been struck by the proactive role the
Speaker 1: government of Singapore plays
Speaker 1: indulging entrepreneurs in going in the right direction, as long as,
Speaker 1: you know, just producing value for the society, I see
Speaker 1: nothing wrong with that. Um you briefly touched upon skills
Speaker 1: and labor. So let's get into that. We've been reading
Speaker 1: a lot about it lately.
Speaker 1: And as a, you know, a company that is growing,
Speaker 1: that you run manic, I'm sure you recruit regularly. So
Speaker 1: what are you seeing in terms of the homegrown supply
Speaker 1: of labor
Speaker 1: and
Speaker 1: maybe going even once to further, what can be done
Speaker 1: to get singaporeans ready for this tech heavy future that
Speaker 1: is inevitably out there.
Speaker 2: Yeah, I think it's quite clear that there are not
Speaker 2: enough singaporeans to meet the current tech demand.
Speaker 2: Um, we've seen, you know, salary expectations changing within days.
Speaker 2: It's like when you interview somebody a week ago, his
Speaker 2: salary expectations are X. And then by the time you
Speaker 2: get around to making an offer to three weeks later,
Speaker 2: he's already got three or four other offers and now
Speaker 2: he's expecting to X.
Speaker 2: Right? So that is just not
Speaker 2: sustainable. Right? And I think there is this huge divergence
Speaker 2: which is happening between the tech haves and the tech
Speaker 2: have not, right? Because at the same time, I, you know,
Speaker 2: I take grab rights and then I'm talking to some
Speaker 2: of these drivers and you know, some of them have
Speaker 2: been displaced.
Speaker 2: Um, and they had, you know, regular jobs before covid
Speaker 2: and they got displaced and,
Speaker 2: and smart guys, you know, but the, it's so much
Speaker 2: easier to basically for the short term, like if you
Speaker 2: have a two year blip something like a covered to
Speaker 2: basically just become a grab driver, do delivery and you know,
Speaker 2: you can sort of, you can support your family. Right? So,
Speaker 2: but that displaced group and I think in the earlier
Speaker 2: days we had semiconductor and other industries. Right? I think
Speaker 2: that to us offers a much bigger pool
Speaker 2: of people and opportunity and that can change in six months. Right?
Speaker 2: So if you address the talent from like let's start
Speaker 2: teaching quoting in schools and then let's have, you know,
Speaker 2: six new programs saying n tur in us for computer science,
Speaker 2: it will take time. It will take you know, four
Speaker 2: or 56 years to see that
Speaker 2: come on board.
Speaker 2: But if you take some of these other people who
Speaker 2: are professionals, they're very serious. They are very hard working
Speaker 2: and you know between 30 to like 50 years of age.
Speaker 2: And if we can tap into that and get them
Speaker 2: some sort of basic um tech skills that unlocks a
Speaker 2: huge opportunity for us. Right? So,
Speaker 2: so I think that if we have, we have to
Speaker 2: focus on that. But I think it needs
Speaker 1: some
Speaker 2: education because the programs are there. Right. You have the
Speaker 2: skills future and you have a whole lot of subsidy
Speaker 2: available but we need to do more in terms of
Speaker 2: education that computer science doesn't need to be that complicated right?
Speaker 2: With some, it's and there are a lot of no
Speaker 2: code platforms. So you may not even need to cold
Speaker 2: and there are other aspects of that you can get into,
Speaker 2: it could be designed,
Speaker 2: it could be testing, it could be other things where
Speaker 2: you may not necessarily have a code, so if we
Speaker 2: tell people that, you know,
Speaker 2: you can be a non techie but still do a
Speaker 2: good tech role, I think that may unlock a lot
Speaker 2: of the sort of
Speaker 2: talent which we have, but traditionally we've, we've not been
Speaker 2: able to tap into
Speaker 1: no code platform. That's a very, very nice way of
Speaker 1: putting it to Monica and I think that would be
Speaker 1: music to the ears of many
Speaker 1: who out there are keen to embrace the
Speaker 2: future
Speaker 1: where technology is everywhere, but they feel that, you know,
Speaker 1: their math skills are not good enough or their coding
Speaker 1: skills are never going to be good enough and therefore
Speaker 1: they probably will get left behind. And I think to
Speaker 1: your point that there is a whole ecosystem that is
Speaker 1: beyond just the engineering aspect, but it's also very um
Speaker 1: high value added and people can have for the carriers
Speaker 1: and make lots of money and be
Speaker 1: part of the whole tech side. Guys know manic looking
Speaker 1: at the video, it looks like you're in your office,
Speaker 1: so you're not working from home.
Speaker 2: Um no, I've, I've been working from the
Speaker 2: office more and more in the last few months. Yeah,
Speaker 1: Alright. Why I thought people like you can work anywhere
Speaker 1: and have given on the concept of office or am
Speaker 1: I misjudging that.
Speaker 2: So I so we have team A team B and
Speaker 2: I like to spend time with each of the teams
Speaker 2: and I think some of the brainstorming work white boarding,
Speaker 2: I think it's best done face to face and but
Speaker 2: what we've done for our team is we have no
Speaker 2: prescribed regime as in who needs to come and when
Speaker 2: they need to come
Speaker 2: we let our ports figure out when they want to
Speaker 2: come
Speaker 2: and so it's pretty flexible and we are now using
Speaker 2: the office more as a
Speaker 2: obviously like a social brainstorming kind of facility where people
Speaker 2: just come in and your white board and because we
Speaker 2: have physical infrastructure, right? So like we have the watch
Speaker 2: and we have sensors and all that. So it's become
Speaker 2: more like a lab. So you basically come here, you
Speaker 2: try out different things and it's a good people want
Speaker 2: to come here.
Speaker 2: Right? So I think when the government allowed people to
Speaker 2: come back, they were so happy everybody was, everybody was back. Yeah,
Speaker 1: I'm so happy to hear that. Especially your point that
Speaker 1: you know the workplace is also becoming a
Speaker 1: point of sort of social interaction for the team to
Speaker 1: come together. So yes, some of the work may be
Speaker 1: doable from home. Maybe a lot of the work but
Speaker 1: at the end of the day you work with human
Speaker 1: beings and you need to see them in person
Speaker 1: to get full synergy. I think that's a great note
Speaker 1: to conclude our discussion. Monique Landry, thank you so much
Speaker 1: for your time and insights.
Speaker 2: Thanks thanks
Speaker 1: also to our listeners. Kobe time was produced by ken
Speaker 1: diverge from Spice Studios. This is his first episode of
Speaker 1: Kobe time. Welcome ken,
Speaker 1: daisy Sharma and violently also provided additional assistance.
Speaker 1: Kobe time is for information only and does not represent
Speaker 1: any trade ideas or recommendations. All 73 episodes of Kobe
Speaker 1: time are available on Youtube and all major podcast platforms
Speaker 1: including apple google and Spotify. As for our research publications,
Speaker 1: webinars and live streams. You can find them all by
Speaker 1: googling Devious Research Library. Have a great day.