Hasse Storebakken (00:00)
we have a war situation in Europe and the best competence globally on toxins are in Russia.

And a lot of water suppliers around in Europe are worried these days for willful contamination of their water.

Lars Rinnan (00:23)
I think this was in London, the level of analysis is equal to dipping a bucket into an Olympic-sized swimming pool, then analyzing the content of the bucket.

Hasse Storebakken (00:34)
There are concerns right now with Forever Chemicals that are not monitored at all at most water utilities. And plastics, microplastics, and both of these may cause, be the cause behind lots of young people not being able to...

make children these days.

Lars Rinnan (00:59)
So every year, over 2 billion people worldwide drink contaminated water. Right now, invisible toxins could be seeping into your glass. So welcome back to The World in 2029, the podcast that takes you inside the boldest innovations shaping our future. I'm Lars, and today we are diving into the hidden dangers lurking in our drinking water and the breakthrough technology that's fighting back.

I'm thrilled to welcome Hasse Storribakken, founder and CEO of AquaAlarm, a company pioneering real-time AI-driven water contamination detection. Hasse, thanks for joining us.

Hasse Storebakken (01:43)
Thank you for having me. Really looking forward to this.

Lars Rinnan (01:46)
So am I. So let's dive into this. Quite literally. So what if your water in your own tap is slowly poisoning you without any warning? Hasse, is this really the case? And why are Waterworks not analyzing the water quality properly?

Hasse Storebakken (02:09)
Well, first things first, is it the case? To a certain degree, yes. There are concerns right now with Forever Chemicals that are not monitored at all at most water utilities. And plastics, microplastics, and both of these may cause, be the cause behind lots of young people not being able to...

make children these days.

So that's serious and it's an area of investigation but it's worrying data that is coming out of it.

Lars Rinnan (02:45)
So is that the microplastic spark? This is hindering pregnancy?

Hasse Storebakken (02:48)
Pifas

are these very forever chemicals that last very long. They are made to make your rainwear watertight and make your frying pan less sticky. But they really, since they last forever, they chewed up in smaller and smaller bits and pieces and also end up in water.

As for microplastics, it's a little bit of the same. Also being, you know, put into micro, micro, micro small pieces flowing around in our bloodstreams, ending up in places we don't want them. So these are being discussed and considered in the water industry these days, and some solutions are coming up, but it's far away from being controlled. These are new

challenges. On the tougher side is the fact that

Well, we have a war situation in Europe and the best competence globally on toxins are in Russia.

And a lot of water suppliers around in Europe are worried these days for willful contamination of their water.

That would really scare people a lot and take down trust to authorities.

Lars Rinnan (04:18)
This is really terrorism. Do we have any way of stopping this or really seeing that it's actually happening?

Hasse Storebakken (04:20)
This is

It is very demanding because being able to monitor on all kinds of these toxins being microbiological or chemical is extremely hard. To do so, we would need to have a lot of intelligence data. Actually, Aqualarm has been connected with intelligence services and we are working on these kinds of solutions.

services will be provided, but this differs our normal work, which is not as urgent time-wise as this. So a solution will not be in place for the next month, to put it like that. Our core is in bacteria, which is the common problem, and being monitored these days by...

Lars Rinnan (05:11)
Hmm.

Hasse Storebakken (05:22)
water utilities with weekly or bi-weekly samples that are sent to a lab and data is returning back after water has been consumed. So it's sort of a very old fashioned compliance systematic, which is insufficient. It's slow. It is slow because...

Lars Rinnan (05:40)
And it also sounds very slow.

Hasse Storebakken (05:46)
Regulators in all countries have been lazy and let this continue. It has been possible to do something about it for some time. That's where we are doing a lot of work to get control of it. At present, say, in the most modern parts of the world, 3 to 5 % of the population will annually have a...

gut problem and have to visit the loo more often than the normal practice every year. And for small children and for elderly, this can actually be deadly. These numbers are not so often counted because you can't prove what happened unless there has been a large situation where a lot of people actually end up dying. We've had those situations in Norway also with small children dying one, two, three.

doctors start talking with each other and in those cases you can sometimes really prove what has happened. But otherwise an incident will most often flow through and be gone, cannot be proven.

Lars Rinnan (06:49)
This is very under communicated. You never read about these things in the media or at least I haven't noticed anything.

Hasse Storebakken (06:57)
Well, actually for us that, you know, monitor for this news, they are there every week around, but not very common in any place. 99.6 % of the samples that are taken are showing no problem. It's those, you know, for 0.4 % that comes and goes in different places. If they come very often in one place, well, then they'll be.

Lars Rinnan (07:04)
Okay.

Hasse Storebakken (07:27)
more stormy waters. But since it happens fairly seldom, it's not so pointed and so much talked about in general.

Lars Rinnan (07:37)
But you also mentioned that they do the waterworks do the analysis very seldomly and it takes a long time to analyze it and to get to the bottom of it. So there's a huge delay here. And you told me once that you use this kind of swimming pool metaphor and it really stuck with me because you said something like

that the level of analysis, I think this was in London, the level of analysis is equal to dipping a bucket into an Olympic-sized swimming pool, then analyzing the content of the bucket.

So is that accurate?

Hasse Storebakken (08:16)
It is accurate, dependent on the local practices, say, do they take a sample a week or every second week or every third week. But that's a vulnerability with the whole concept of these samples that actually water qualities may vary more often and the low frequency of sampling and, you know...

a practice that has grown common in the industry where you take a sample and if it shows something's wrong, then the rule is take another one. And the probability that the second one will find those very rare samples or situations that may actually become serious. You won't find the same thing again unless there is something really stagnant, a dead animal lying there creating a constant problem, for example. So...

The tool package was extremely good 100 years ago. It is outdated now. We do not intend to change it. It will take time and new technologies will, after some time, convince authorities it's time to change regulations. We think differently on implementing this, which is basically helping water companies gain better control at lower costs. So we help them.

not working in the blind, using a lot of efforts to try to improve something that I can't really measure and using a lot of costs on that.

Lars Rinnan (09:45)
Hmm.

Hmm.

Yeah, I actually went a little bit further with the swimming pool analysis. So I kind of fed the story into ChatGPT and asked, well, what's the percentage of the analysis being done? And I'm not sure if it's accurate, but it came out with 2.5 million liters of water in the swimming pool and like a 25 liter bucket.

So that would mean 0.001 % of the water being analyzed.

Hasse Storebakken (10:18)
You probably need to shift it

even one or two steps more into zero. yes, know, yeah, well, they don't take a bucket. They take four, five deciliters. So that's a 20th.

Lars Rinnan (10:23)
Is it?

Exactly. Yeah,

so it's basically nothing. So I mean, they can't find most of the contamination, I guess.

Hasse Storebakken (10:43)
Well, if the cause is persistent, and many other situations may be like that, there's a dead animal lying in a drinking water storage facility, it will continue and you will grasp it. But there are other incidents that can still be hazardous, which is more a short-term situation.

Lars Rinnan (10:49)
Hmm.

Hasse Storebakken (11:13)
So there are lots of technicalities that could reason this. I won't go into them right now, but it is ⁓ possible for us to identify those situations and also understand them by having continuous monitoring.

Lars Rinnan (11:21)
Yeah.

Yeah, but it sounds very old fashioned and outdated. There's probably reasons for this, right? I mean, like expensive sensors or maybe an industry with very low levels of digitization.

Hasse Storebakken (11:42)
Yeah.

Well, food industries, process industries have taken these steps and follow up has been tighter. So why the water suppliers, drinking water supplies haven't been followed up with updated regulations? I can't really answer, but it is definitely outdated. It comes from the law.

from the regulators. They could have looked at what is happening in a food plant where regulations are tougher, for example.

Lars Rinnan (12:22)
Yeah, absolutely. So what about climate change? Is that also affecting this and to what measure?

Hasse Storebakken (12:33)
to a large degree. So drought is serious. Too much rain is serious. when we have 40 milliliter downfalls during a 24 hour period, all those systems that were built for a fifth of this will be challenged. So it means that... ⁓

storage tanks for clean water, intake of water will be changed. Small leakages will bring in lot of earth dust in some cases also bacteria sometimes. If animals walked on these surfaces you can guess what will happen.

Lars Rinnan (13:10)
Yeah. And what about the infrastructure? Is that also quite old and outdated?

Hasse Storebakken (13:20)
Yeah, you know, in most of the Western world, lot of investments were made 40 to 100 years ago. And that hasn't been repeated. So these pipes are not looking like shiny, glossy steel pipes on the inside anymore. There is corrosion, biofilm in... Yeah. You wouldn't like to think of how it looks.

It's a habitat, I usually call it. A lot of bacteria set up the residence in there. And sometimes if climate change changes the quality of water, they will travel somewhere else. Meaning they let go of the walls, the biofilm moves away, and it ends up maybe in a water reservoir, creating a situation there that then will distribute.

poor water, sometimes dangerous water to a lot of residents.

Lars Rinnan (14:21)
Yeah. So if you combine those things we talked about just now, mean, aging infrastructure, know, very, very little of the water being analyzed, very seldom, you know, climate change, obviously increasing. It seems that we are going to see more and more contamination over drinking water, unless something is done. And of course, this is this is where

Aqua Alarm comes into play. So how does your sensor network catch those toxin spikes in the moment they occur?

Hasse Storebakken (15:00)
Well, we started up with this 10 years ago and the initial thinking was a lot directed towards sensor devices. I and others were also at that time working on early AI solutions. And to make a solution that can be distributed along

kilometers and kilometers of pipelines with all these different facilities. Monitoring all these systems will be extremely costly unless the system is designed to keep costs down. So to avoid having too many sensors, we rely also on the data analytics.

It means that we can gather information from the customers about, say, factors that cause problems. So that could be core things in water pressure. If there's a leak in a pipe and the water pressure is positive, it means the leak goes outward. That's costly. You lose a lot of water that you paid a lot to make, but it's not necessarily dangerous. If the water pressure is low,

that leak will come back with a soup of whatever was out there. Maybe that was another pipe bringing sewer, possibly a challenge. So this is sort of a core example of how information from the customer systems will inform us. Okay, there might have been some low pressure situations at pipes that are 90 years old and probably leaky.

and these pipes lie next to a sewer pipe that is also the same age. Okay, then we will, if we see a low pressure situation and our sensor, which is very rough, indicates more bacteria, well, it's logical and we can point to that.

Lars Rinnan (16:41)
Hmm.

Hasse Storebakken (16:55)
It means that it is enough to count cells, but not knowing exactly which cells, because we could guess that from the data with that sewer pipe, and what kind of cells would be in that. So these are examples of how...

Say water science combined with sensing science and data science together can make a fairly cheap simple logic supporting an early warning system.

Lars Rinnan (17:27)
Yeah, so this is not necessarily so complex. So why isn't there anything, why hasn't there been anything on the market for the last, let's say, five years, 10 years?

Hasse Storebakken (17:44)
Maybe partly because the specialists that are working on this have all been trained with laboratory work and they've been very geared and focused on having, for example, sensing devices that know exactly what kind of cell is this and that. And they've invented and tried to sell a lot of sensing equipment which is very costly. And that kills the business case. So that may be...

cause apart reasoning for this situation.

It is also because the power of AI has only become evident for many now. We started 10 years ago and we're working hard on it so that we're ready for huge market actors now. Others are just starting to understand, wow, there is ways around those costs. AI can actually ensure you have good actionable advice at a lower cost.

Lars Rinnan (18:51)
Exactly. So cost must be a very critical element of this because this is you're combining hardware sensors that goes into the water network with software and analysis, you know, algorithms. And it could be expensive to develop those algorithms, but once you have developed them, you can of course implement that in large numbers, but the sensors, they're hardware. So

They need to be made anyway. So how are your hardware sensors different than the existing ones on the market?

Hasse Storebakken (19:29)
Well, getting the cost down as much as possible was the core design criteria. And we ended up with a spectrometer that roughly counts cell. It sounds very expensive with a spectrometer, but actually the core technology, if you pick it apart and simplify it as much as possible, it's fairly low cost.

There's also lot of work in making sure that any flow channel that you measure something in will have the same problem as the pipes. There will be biofilm there. It will over time have poorer function. So for us, very much was about simplifying the tech as much as possible and reducing as much as possible the...

say livability for biofilm inside our measurement systematics. That took some years, you it wasn't easy. So that it helps us a bit with competition. But of course, others will probably do this smarter in the next year. We will also do it smarter every year. That's how it goes. We have to improve, be as smart as possible.

Lars Rinnan (20:24)
Exactly.

Yeah, maybe I have a head start. It really sounds like

that. So what, how, how is the cost of your sensors compared to what is currently on the market?

Hasse Storebakken (20:56)
Some of the sensors in the market may have a cost three to six times our sensing devices. At the same time our sensing devices will probably go down to a third of what they are now in the future with more mass production. ⁓ Production numbers are not that high yet.

The core for us is actually to make sure that the business case becomes more and more valuable and we actually don't sell sensors. They are our responsibility. What we sell is the actionable advice. So we only sell a front end which tells our customers, this is happening, this is what you should do now, next week, next year. That's what I buy. And we have the responsibility to...

make sure that our hardware is working, costing as little as possible.

Lars Rinnan (21:49)
Yeah, that makes sense. I mean, you need both. You need the sensors to capture all the data and then you need the software and the algorithms to analyze all those methods of data points and turn them into actionable alerts, of course.

Hasse Storebakken (22:04)
Yeah, Maybe in the future we won't need that many sensors. Maybe we have so much knowledge about the other data that most of the customers already have. Like I talked about, water pressure, flow, and some other factors that I always, very often, already have in the systems.

Lars Rinnan (22:25)
Yeah. So could you give us some examples of, let's say municipalities or industrial users who have adopted the system and what kind of differences made for them?

Hasse Storebakken (22:40)
Yeah, we've been testing our hardware with water utilities in Scandinavia along in the process of developing this. Right now we're rolling out to a UK. ⁓

water supplier, they serve under three million population. So it's a large actor. We're very happy with doing that in the UK because there's an extra incentive to get new solutions in place because the authorities fine these water suppliers very hard.

when they do not meet quality requirements. So it motivates them.

Lars Rinnan (23:28)
What does that mean

concretely? How big are these fines?

Hasse Storebakken (23:35)
Well, let me take one example which is extreme and which is not about water quality which we are focused on, but it is about sewer and handling sewer and avoiding it to getting into the right places. Thames Water, so the water supplier for London, got a fine in August last year on...

103 million pounds. Whoops. So that was about 10 % of their total turnaround.

Lars Rinnan (24:03)
Oops.

wow, that hurts.

Hasse Storebakken (24:10)
So

the similar fines for our customers could easily be 10 to 15 million pounds that are relevant for us and that we can help the customers take away. This is the UK. These are unusual numbers internationally, but for us it's a very good market to develop our models, which can then be reused in other markets where

the saving potentials are extremely much smaller but still significant because if you run some of the biggest process facilities globally because look at taking in raw water, treating it, sending it to a storage and then to customer or maybe to several storages and pipes and then to customers. These are the biggest process facilities in the world.

If you put them together, you know, it's easy to understand costs for operating this and costs for replacing hardware are extreme. If you're doing that without modern process control, well, we know that you have unnecessary costs between five and 30 % higher than you need to. So those five to 30 % is what we basically promised the customers. That's what we can take away.

Lars Rinnan (25:35)
It's a really attractive offer. And if you can avoid those fines, they were pretty hefty. Yeah, yeah. And this is the UK that's signing the biggest fines right now. So it's not like a EU regulation. It's a UK regulation.

Hasse Storebakken (25:42)
That's an addition to this,

Yes.

UK

has an unusual situation. They privatized their water sector many years ago and it hasn't... It has created a lot of frustration with a lot of money being taken out of the sector instead of using it for upgrading technology and improving services. So they established an authority to focus on this and they are... They ended up with becoming extremely tough.

Lars Rinnan (26:11)
Hmm.

Hasse Storebakken (26:21)
For us, that's super valuable. I sort of feel with some of our customers, but well, things are changing. We have very motivated customers in that market. But we're also working right now with a customer in Italy, which is typical of this time. If you look now on the Glaciers in Switzerland, they are melting away. It means that the...

Po area of north Italy has had dry summers year after year, they have to reuse water. So now we're involved in a project where reusing water, so cleaning potatoes, vegetables, fruits with water until now they have only been allowed to use the drinking water once. We're being part of trying to help them reuse it again and again and again. So some kind of treatment and then...

sensing, checking quality, using, repeat, repeat.

Lars Rinnan (27:19)
That's very good. I love that. So let's go a little bit back to the AI part. You know, I love the AI stuff, of course, and I definitely like to hear a little bit more about it. So using AI to kind of predict the contamination. So this means that you at some point need to kind of set some AI thresholds.

So when do you sound an alarm to the waterworks and when don't you? I mean, it's a bit like false positives versus false negatives. So false alarms versus missed contamination. So there must be some kind trade-offs in that. So how do you balance that sensitivity with reliability?

Hasse Storebakken (28:14)
Yeah, it's a good and very important question and we see that our strategics differed to very many others that deliver, for example, all the sensors and the question will always come. What does this mean? Where does it come from? Those are the answers that we have to answer and we answer those by combining the sensing data with all this operational data at the customer. And then for.

say a reservoir that holds treated water. like in Norway on the coast, soon coming there now in June, there will be lots of people coming to these coastal areas and the drinking reservoir will some days almost be emptied because people come to the cottage, they will shower, they will water their lawn and the population increases by 150%. And at that stage,

The drinking water reservoir may actually be almost empty. And what's on the bottom of a reservoir? Well, it's all those residues that have fallen down, which is then whirled up and maybe with some more bacteria and brought to the welcoming the tourists. We have situations like this in different versions around the world. So we need to understand

causal factor like this, which was about the level of water in the reservoir, the speed of production, and the last time there was cleaning on the bottom floor of the reservoir. These are examples of risk factors, causal factors. So our vectors, our algorithms, our expressions of such risk understanding pictures of the causes

for something that will give some extra trips to the Louvre for some of these tourists coming back at that stage.

So with a sound systematic there and with a good water scientific starting point building our algorithms, we have a fairly high certainty of what is happening. So when this theory, this water theory is combined with sensing data, we have very few false alarms.

Had we only had the sensing data showing an increased peak, that could be induced by so many other factors. And this is the history. These kind of what we call weak indicators have been attempted as solutions for some 20, 30 years. That is, for example, high temperature, grayish water, more oxygen in the water.

All this indicate a good place for bacteria to live and thrive, but not necessarily the fact that they are living and thriving. This has been the unsuccessful implementations of systems like this over 25 years, which the water utilities hate and also those delivering them because there's so many false alarms.

Lars Rinnan (31:31)
Yeah, exactly. But do you also kind of integrate a human in the loop in these systems? And at what level do they operate?

Hasse Storebakken (31:45)
We do not provide an automated system at present. We don't trust our systems enough for that. So what we provide is the actionable advice, but we also provide an overview of all those causal factors that are influenced and how much they are influenced. So we provide online continuous updates on what is happening, what is about to happen, and it shifts the whole operation and work.

situation from people working on a water supplier that are used to reactive work, used to having overtime four days a week with running and trying to fix things that has happened to a situation where they actually can act before there is a deviation. Adjust, tune it a little bit better so that they have control. They will lose some salaries.

but their marriages will be more happy, I guess.

Lars Rinnan (32:47)
which is definitely more important, of course. And I'm also guessing that there's like a feedback loop into this, so there's learning built into the system.

Hasse Storebakken (32:59)
⁓ because our algorithms, they are based on scientific guesses, I like to call them. They are never correct at first. So of course we have to adjust them. And that's a machine learning challenge. We're not so happy with what we have because machine learning will sometimes make up things and we don't understand why. So we're presently testing a lot of tools with explainable machine learning. So...

documenting why this change, why this change. This is a difficult part. Solutions are coming our way. We're listening to a lot of actors now, so we expect to have that better in place shortly.

Lars Rinnan (33:39)
Yeah, well, it sounds really interesting and going absolutely the right way. So I'm thinking, one thing is analyzing a single facility, but is it really possible to protect millions or even billions of people? And what kind of changes in terms of challenges when you kind of scale up to

let's say potentially a global scale for these kind of systems.

Hasse Storebakken (34:09)
This has been an important question for us because when we decided to go into this business, we looked at World Health Organization diarrhea death data. And we saw that, well, this is a global problem. It's big in the Western world and developed world. It's even bigger in other areas. And we saw that

a good spread of a technology like this, once the cost has come far enough down, our aspiration is to save 40,000 lives from this. And it's a tough motivation, but it has been important in the tough work of developing something like this over 10 years. So, a part of our solution

is how we want to sell this. Right now, for example in the UK and in Scandinavia, we sell directly to large water utilities. And we also have a lot of interest from some of the biggest actors that already deliver technology to the water industry. So these are pump providers, pipe providers that also have started delivering some software. And our way of

thinking on this is to let them sell our technology and in that way reach globally. So that will be the core method of making sure this is reachable all over the world.

Lars Rinnan (35:44)
So saving 40,000 lives is massive. I mean, those 40,000 lives have, you know, brothers, sisters, parents, know, siblings, whatever, you it has a huge impact. Is this globally or is this some kind of regional estimate?

Hasse Storebakken (36:01)
This is a global number

and the proof points are tough. It's an internal motivation. It's an not internal now, I see, internal North Star because it was hard to pick numbers and be really certain based on these deaths from diarrhea problems. These are waves that go through all communities globally every year.

Lars Rinnan (36:13)
Not anymore.

Hasse Storebakken (36:29)
and also with a lot of weaknesses because many of them are not really detected. So the number may be wrong, but the potential is definitely very large and it is very motivating and you need that kind of motivation if you want to make an industrial software. It's a tough game. It's a tough game.

Lars Rinnan (36:49)
It's a tough game, absolutely. But

this is perhaps the best motivation you can possibly have. I'm also guessing that more data probably means better analysis, right? what kind of, let's say, amount of data or amount of sensors would be, let's say, enough for reliable analysis? And I know that this is a stupid question, but let's start there.

Hasse Storebakken (36:59)
Definitely.

Well, at least after just one year of data from...

A of our customers will be more data than has ever been assembled regarding the detailed status of water distribution networks and the data elements that we are collecting. And that also makes us sort of, okay, we have to respect that there's so much valuable information here that we have to make sure that there's a lot of research and development connected to it.

Basically opening up for many kinds of partnerships to make sure that this is happening. Investors that we have that are very enthusiastic also push a lot on that. Don't limit push, push, push, open up. Make sure this is growing in all directions possible.

Lars Rinnan (38:06)
Yeah, it's a good point. But you probably also need more data, also more diverse data, because I'm guessing that different regions have kind of different, different challenges in their water works. So you probably need data from all those different regions also to be, let's say to claim that this is a global solution.

Hasse Storebakken (38:30)
true, our initial customers have to be what we call digitally mature. They have to have fairly much data in their systems. So these are very often drinking water suppliers that serve 500,000 persons and up. And we want to reach a lot of those others also. And the core understanding is that the more data we

managed to gather the less data that gather now and use for our models, the less data will be needed from each customer in the future. So we believe we will gradually be able to distribute this more widely. Maybe seeking satellite data to get overviews locally and from

that kind of data, just on surface data, being able to understand also what is happening with distribution systems. Just one example of data sources that we are looking at, but are not going deep into at present.

Lars Rinnan (39:34)
What about regulatory hurdles? So I assume that going into different regions, different countries, you will be faced with different standards and different ways of getting approvals in different countries. Is that something you have encountered already?

Hasse Storebakken (39:52)
We're trying to sneak around that with not promising compliance. So the customer story is about helping a customer whether they want to clean apples with the same water again and again, they have a compliance requirement. And on the other side, we're basically saying, okay, we can help you keep that compliance requirement.

but we don't take away the other rules so you have to negotiate with the authorities yourself. So processes like that are going on. But we can basically help them deliver at that requirement with the lowest possible costs because we can help them adjust being proactive instead of having huge problems every now and then and having to fix things at overtime and report a lot of faults etc etc.

Lars Rinnan (40:45)
Yeah. So then it boils down to cost, perhaps cost versus benefits. So different municipalities or waterworks need to, of course, justify their investments and the, let's say the economic value or avoiding a contamination crisis. I don't know how they do this, but maybe you do.

Hasse Storebakken (41:07)
Yeah, actually we're not arguing so much about avoiding the contamination crisis, but maybe using less resources on being over certain of the control. For example, chlorination chemicals is something that...

are necessary in many regions and save lives. At the same time, with better control of what is the actual stage, it's very often also possible to reduce the level of contamination when you know that enough is enough, which we can monitor with our sensors that monitor directly on a spectrological situation. So if you place a sensor at the...

nursery water intake or at elderly homes intake, very vulnerable groups, and you see that, well, the number of cells are controlled, it's good enough, then you can reduce. That's a very simplified example, having sharp microbiome sensing has an added value and that can create huge savings.

Lars Rinnan (42:19)
Yeah, yeah, absolutely. So moving on to 2029. Of course, this podcast is called The World of 2029. And this is what we're doing. We're looking into the future and the different companies addressing huge important issues for the future.

So how is the future of drinking water looking? I mean, right now it seems from everything you've told me that drinking water is not safe at all. How will this look like in 2029 in your opinion? Will water safety be as immediate and pervasive as the air we breathe? Or will the problem have escalated out of control?

Hasse Storebakken (43:09)
Water is such an urgent element of life, of making food, clothes, the basis for people looking at cat videos, because you have to cool the data centers with water. Water is crucial everywhere, and the value of water, the awareness of the value of water will increase.

Water scarcity will touch most parts of the world. And with that, there will be huge changes soon. And it means different pricing methods, different payment methods for different qualities of water. When you wash your car, you can't use drinking water. So how is that priced? How is it used? For...

industries requiring a lot of water, they will have to pay a lot to use drinking water, pushing them over to reusable water.

So water scarcity will be common and we will have different ways of understanding it and paying for it in the future. And ⁓ quite another awareness of the value of water.

For us, we believe that we will also be using AI more to support, know, optimized operations of facilities. And we're struggling with the ethics and making sure that AI doesn't take control. So we have this loose plan of...

identifying values, the best values that can be from the best entrepreneurs in the world and try to include this into programming of ethics, meaning if the operators come in the morning and talk with a night watch, which will be a persona AI, it will be a woman taking care. the programming will be based on lots of interviews with that kind of women, mostly.

Lars Rinnan (45:18)
Hmm.

Hasse Storebakken (45:18)
So

that's not a fantasy. It's a loose plan at present.

Lars Rinnan (45:24)
Yeah, but this you know, you talked about different Water qualities. I mean like drinking water probably on the top of the pyramid and then like gray water or or Whatever you would call it to to wash your car or to to water your lawn So how does that mean that we need to Rebuild our water networks Do we need some kind of smart house technologies to

to kind of keep control of different defined water qualities and will also payment systems be necessary to have, you know, so that you pay, you know, various amounts for various water qualities. Is that what we're looking for?

Hasse Storebakken (46:07)
The small steps and the first steps, you can see them if you travel around the world. People are taking care of rainwater, making sure they are collected and reused for watering.

I believe that us common men will still be able to use the water we need for our households, above a certain threshold in most communities, pricing will go up drastically. So it means that if you have a pool, it'll come at a cost, a high cost, as an example.

So '29 isn't all that far away, but I think that these things will start to come through shortly. And like I'm saying, if you look around a bit, it is already step by step being visible.

Lars Rinnan (47:01)
And with your solutions, with sensors, with smart algorithms, giving alerts for contamination of drinking water, you anticipate to have saved those 40,000 lives by 2029 then.

Hasse Storebakken (47:16)
No, don't think so. This is a complex stage and as we have mentioned on the way here, distributing this to the areas where the needs are the highest, which are, for example, all the mega cities that attract millions and millions around the world right now and where there's absolutely not enough water and the quality problems are huge and the cost problems are huge. These are our main targets to be able to, yeah, let me take an example.

Well, there are cities in South America and Africa with more than 20 million people and not having good city planning. We need to find ways to get our systems into those areas. It's hypercritical right now.

Lars Rinnan (48:07)
Yeah. So what are the cities that are most at risk, do you think?

Hasse Storebakken (48:16)
So many. So listing large cities in.

Lars Rinnan (48:17)
All of them, perhaps.

Hasse Storebakken (48:23)
whichever part, at least in Africa, South America, most of the cities that have more than 10 million population also struggle a lot with this.

Lars Rinnan (48:35)
Yeah, yeah. And also, you know, going back to what you said about about Thames water and London, which is perhaps a city that most people in the world look at as very modern and you know, very civilized, they must have really good drinking water and really good drinking water, let's say control. But they don't. Of course, if you kind of take other cities as well, then

Hasse Storebakken (48:56)
Well, they were like in

many other places. They were very early. They built systematics that were very good. Of course, a thousand years later than they did in Rome. But still, all these investments are a bit demanding now because it's time to change and time to rebuild and that's hard underneath an enormous city.

Lars Rinnan (49:19)
Yeah, yeah, and it's costly, of course. Very expensive. Hmm, interesting. So I think we're going to see an interesting future anyway. I mean, I do really hope that your system gets into the, you know, the mega cities of around the world, be it Lagos, be it Cairo, be it Tokyo or whatever.

Hasse Storebakken (49:22)
It is very costly.

Lars Rinnan (49:44)
And I also really hope that you achieve your target of 40,000 saved lives. I think that's a fantastic target to have. Maybe it will be even more. It has anyway been really, really eye-opening, And from learning about invisible toxins to predictive AI, so AQUA ALARM is rewriting the rules of water safety. I love that.

I'm looking forward to having safe drinking water in my own tap. So thank you so much for the groundbreaking and super important work that you're doing and for sharing your insights with our audience. Thank you so much.

Hasse Storebakken (50:25)
Thank you, I really enjoyed this. Thank you very much.

Lars Rinnan (50:30)
So that's it for today's episode of The World in 2029. So if you enjoyed this deep dive into water quality, be sure to subscribe, leave a review, and remember, the future is better than you think.