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#743: The Frugal Architect w/ Werner Vogels: The Ocean Cleanup's mantra: Start simple and iterate relentlessly
00:40:45
When Boyan Slat found more plastic than fish on a dive in Greece, he asked a simple question: "Why c
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This is episode 743 of the AWS.
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Podcast, released on October 27, 2025.
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Hello, everyone and welcome back to the AWS Podcast. Simon Leish here with you. Great to have you back, joined by two very special guests. And this is a special episode, one of our Frugal Architect episodes, which I know a lot of you really hang out for. And of course, you can't have a Frugal Architect episode without our VP and CTO of Amazon.com, verna Vogels. G', day, Verna. How you doing?
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C
Hi, Simon. Yeah, I'm doing well. I'll be having a good time here.
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It's good to have you back to talk about the very important topic of frugality. And to help us with that is an amazing customer guest. We're going to be speaking with Boyan Slat. Boyan founded the Ocean cleanup at age 18 with just 2 to €300 of saved pocket money. After encountering more plastic bags and fish while diving in Greece at the age of 16, that's confronting. What started as a viral tedx talk in 2013 has grown into a global operation that's removed over 70.5 million pounds of trash from aquatic ecosystems worldwide. His approach to engineering is defined by constraints driven thinking. So he's in the right place, starting with limitations rather than capabilities, iterating relentlessly and letting mission guide technical decisions. Under his leadership, the Ocean Cleanup has developed systems from passive Ocean collectors to AI powered river interceptors, operating across 20 rivers in nine countries while targeting 1,000 rivers by 2040. Boyan, welcome to the podcast.
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B
Thank you for having me.
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A
That's a hell of a journey.
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B
Yeah, and actually, I wanted to say we've actually, I think we're at about 91 million pounds of trash collected now.
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I was going to say, but who's counting? But clearly it's important to count.
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B
That's right.
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C
Well, you know, you were quite young when you started this.18.
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At Delft.
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C
I assume you were still there. So tell us a bit about story. What drove you to get this going at all?
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Sure, yeah. Look, I think I've always been a bit of an inventor. All my life I've been very passionate about building things, engineering, going all the way back to when I was 2, 3 years old when I remember building my own chair out of wood and nails just because I thought that the more fun to sit on than one my parents would give me. And then it went into my next passion was computers. So assembled my own tower and then was into chemistry and it's building sort of explosives and smoke bombs and one day almost burnt down the house because I was distilling ammonium nitrate on the stove. I kind of lost track of time.
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C
Yeah.
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B
The entire house was in smoke, which was. My mom really loved that phase. And then I was into model rockets. And then when it was 12, 13, I decided to set a Guinness world record, launching more than 200 of them at the same time. So really I've never been bored is the short summary there. But it all wasn't very useful. It's just sort of having an idea and then making that real. I think there's just an immense amount of satisfaction I get out of that. But then, yeah, when I was 16, I went scuba diving in Greece and I was hoping to see beautiful nature. I went underwater and I came across more plastic bags than fish. And sort of the natural thing for my brain to ask myself was, well, why can't we just clean this up? And once an idea like that or a question like that start brewing in my head, I really cannot stop thinking about it. So. And then I went to Delft to study aerospace engineering. But yeah, at the same time I still was obsessed with this idea. And then after half a year there's this point when I said, well, I can't really do both, so let's give this a try. If it doesn't work out, it can always re enroll after half a year. But then a few months later there was a statics presentation I had given that went viral, was shared millions of times, and then allowed me to build an initial team, raised the first few million dollars and yeah, so got us going. So yeah, that's.
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C
So it's talking about funding there originally crowdfunding, but. So how did you get from crowdfunding to having a, let's say, sustainable income for your business, such as you can continue to win it.
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Of course, the challenge, what we're trying to solve is essentially we're creating a tremendous amount of value. So when you look at the harm done to ecosystem services by plastic is estimated to be between 500 billion and 2 and a half trillion a year.
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So.
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So there are not many companies with that level, if any, at that level of revenue. But of course, most of that value is not value that's captured in the GDP of human civilization. Right. It's things like clean air, coastal protection, being able to go to a coast and having clean beaches, having marine life, all of that. So of course we tap part of that through tourism, fisheries, et cetera. But a lot of it is not captured. So I think if something can be a for profit, it should be a for profit, because then there's such a beauty in having this alignment of incentives between your team members, shareholders, wider society. But the problem is, of course, when you're doing something like this, which is really outside of the market, you don't have that. So that's why I opted to start a nonprofit. When I started this, I really didn't see any other way to get it off the ground. And crowdfunding, Actually, the first crowdfunding campaign was $89,000, and then the second one, a year later was $2 million. But that got us started, but of course wasn't of the scale that was required to really make this work. But thankfully, over time, I think the way we've approached this is almost like investment round. So basically, we raise some money and then we get results, and then with those results, we get to the next level of credibility. And I think your level of credibility has to be proportional to the amount of money you can raise or automatically becomes that. Right.
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C
But you're also building things. So for that, you absolutely need capital to be able to do that.
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B
Yeah, exactly. So I think then over time, we were fortunate enough that both individuals and companies stepped up to support us. So you got people like Marc Benioff, salesforce, Joe Gabia from Airbnb, as well as literally millions of other people who decided, hey, I want to be part of this. This is like an Apollo project for the Earth. This is really exciting and we want this to happen. And then over time, of course, at the beginning, it was this very high risk, high reward project. But over time, as risk came down and now it's at this moment, it's not a question anymore whether we can do it. We know exactly what to do. It's really just the scaling challenge. Companies also started to get involved. And there, of course, is more than philanthropy. It's really an exchange of value, because for corporates partnering with us, it allows them to have this variable story. Increases brand value, increases awareness. So these partnerships is something I'm really excited about. And the journey is a business type of transaction.
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C
Recently, I heard a lot about microplastics that are inside people and inside fish and things like that. Is there a relationship between what you are trying to clean up and those microplastics?
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For sure, yeah. The fortunate thing is that only a very small fraction of plastic in the ocean is currently microplastics. It's in order of a few percent. Most of the plastic is actually large Stuff. And of course what we do is unfortunately the tiniest pieces, they're really too small to extract. Fortunately, over time they will wash up on shoreline, so they won't be there forever. But of course, by removing everything that's not microplastic, all the larger objects, you prevent the creation of those microplastics because they are really coming from the degradation of larger plastics. And then on top of that, by what we do in rivers, we prevent the inflow of new plastic into the ocean. I think what we do is we really prevent this ticking time bomb of the amount of microplastic increasing tenfold, hundredfold in the next few decades if we don't clean this up.
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A
So Bori, let me take you back to the early, early, early days. And your first system called Wilson was, which I understand didn't last too long before it sort of reached its limit. Tell us about that and what that really how that changed your thinking.
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B
So the original vision to clean up the Great Pacific Garbage Patch was to have a giant U shaped barrier that was passive, that was just free floating. It would be pushed forth by wind and waves and would corral the plastic inside of it. So we'd only have to come by periodically and empty it out and bring the plastic to land for recycling, which was a very elegant idea on paper, but in reality it didn't collect plastic. So we took it out and then we deployed it and then it didn't collect plastic. And really just a few months later it broke into two. And obviously we learned a tremendous amount from that. And yeah, after that we kind of completely changed the approach and then we eventually got to a system that worked. But yeah, looking back, I think what I would have done differently is that obviously we learned a lot, but I think we could have learned the same lessons in a cheaper way we were. So we truly believed that that was the system that was going to clean our ocean. It was always like this, this tunnel.
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Vision sort of thing.
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And then ultimately, I mean, you're going to fail along the way, right? When you're doing something new, you're going to have so many failures. But I think your job as an innovator is really to make sure that you can recover from those failures and that you make mistakes in an as cheap and fast way as possible. So it was great what we learned, but I think we could have learned it with a system that was maybe a tenth of the scale and maybe close to shore, rather than actually in the middle of the Great Pacific Garbage Patch. So that's kind of the Thing I would have done differently.
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C
So you started off with trying to attack the biggest problem there was.
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B
Yeah, right. So it wasn't really this MVP approach, I would say. And before that we had done scale model tests and we had done prototypes near shore. But the actual thing of catching the plastic, which is again quite an important requirement for an ocean cleanup system.
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We.
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Only really put that to the test at full scale in the real environment. While maybe we could have done that with simulated plastic or something closer to sure.
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These are lessons where there's a famous T shirt I'm sure you've all seen that says, I don't often test, but when I do it's in production. That's sort of the experience there.
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Yeah. And I think, yeah, when we started out, we really didn't. I think we went through six or seven conceptual iterations over the years and it was really like the way I imagine it is like a landscape full of hills and your goal is to climb that, get on top of the highest, the tallest hill, and then there's this dense fog and you can only look 3 to 5 meters ahead of you. And at some point you know you're climbing a hill, but you don't know yet whether it's actually going to be the right hill, the one that you actually want to end up on. So it does take a certain degree of trial and error to find that hill. But again, it's really just about trying to limit the cost of that experimentation.
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Absolutely.
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I guess it's always very hard to know, am I climbing the right hilltop here? I think one thing that I noticed is when you're actually climbing the right hill, things seem to get simpler and simpler over time. Well, when you're climbing the wrong hill, actually things get spiral into complexity. So I remember when we were still trying to make that passive system work, there was this challenge of these contradicting design requirements where on one hand you want the system to be flexible, to be able to follow the waves so that it doesn't break under sort of fatigue loads. But on the other hand, you also want to keep the system open because you don't want this U to collapse because otherwise there's plastic can't enter. Right. So then at some point in time, we got to the point that we were doing engineering studies on literally an Eiffel tower sized steel structure that was submerged below the water level that would act like a spreader to keep that U open. And then at some point in time I was thinking like, guys, what are we doing here? This is Insane. We're creating a literal Eiffel Tower here, like a steel truss structure. It's kind of this, you might be familiar with this idea of epicycles. Before Copernicus figured out that everything was rotating around the sun, there were these other models that kind of mathematically you could make work. But basically you got these little circles on circles and it just was not simple at all, but you could, it.
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Was all too hard.
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But I think that when you see Epicycle, it's kind of a hint that, okay, I'm not on the right track here, I'm climbing the wrong hill.
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A
So in a lot of the early systems, in the sort of starting off point, you were using a lot of sort of off the shelf hardware and open source models for computer vision, etc. And things have changed a lot since then. But what did those experiences teach you that you still apply today?
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B
Yeah. So computer vision is general sensing is really a core part of our solutions. If you think about it, the key to our success is being there where the plastic is. That applies to both what we do in the great Pacific Garbage Patch in the middle of the ocean, as well as in rivers, where in this great Pacific Garbage Patch, you got an area three times the size of France, where on average the plastic is super dispersed. But then when you take a sailboat through it, there are days when you see hardly any plastic and there's days when you see tons and tons of, of plastic around you. So being able to determine where these hotspots are, which by the way are dynamic, they come and go all the time in different places, different times, that is really the key because otherwise you're just basically sieving the ocean. It's really about pinpointing those places where the plastic is. And similarly in rivers, what we found is that less than 1% of the world's rivers is responsible for 80% of plastic pollution. So being in the correct rivers is the key to being able to do this effectively. So what that means is really when we go into these coastal cities, it's deploying hundreds of sensors around the city, being able to determine which are the, you know, what's the Pareto here? Which rivers do most of those emissions and where in those rivers should we deploy. And for example, some rivers you got vessel traffic as well, right. So you can't block off the entire river with our systems. So there we release GPS trackers that mimic the flow of plastic so we can really again strategically deploy there where the plastic goes while giving enough space for or boats to pass so it's all about being at the right place at the right time. And even though the cleanup system, the hardware is simple and it must be simple because complex hardware is very hard to scale. It breaks down, it's hard to maintain, it's hard to deploy, et cetera. So you want to keep the hardware as simple as possible. And then basically all the complexity, you know, that's really applied to more the, what we call the software side. Right. So really the intelligence of especially upfront in terms of determining where you deploy.
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C
So why is it, Ashley, that these 20 rivers are the ones that are causing most pollution? I mean, is that because, is it humans, is it factories, is it, I mean, how come there's only these 20 rivers that actually take 80% of plastic?
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B
Yeah, well, so we are currently deployed in 20 rivers, but the 1% actually represents in the order of a thousand rivers. So it's still significant amount. But I guess when we say rivers, a more accurate word would be waterways. Most of them are pretty small drains. What we see is that these rivers are concentrated in coastal cities and middle income countries where you got a combination of a, basically a high concentration of population close to the coast and you have basically very poor waste management infrastructure. These are countries that are rapidly developing economically. People now have enough wealth to buy a lot of stuff wrapped in plastic, but the government basically does not have the money yet to do proper waste collection and disposal. What's quite interesting is that there's really no correlation between the amount of plastic that a country consumes and the amount of plastic that it emits to the ocean. Essentially there's this thing called the Kuznets curve which is like an inverted U where the poorest country, they hardly emit any plastic because they don't consume any plastic. The richest countries, they consume most of the plastic. So the world's richest countries, they consume about a third of all the plastic. But they're responsible for less than half a percent of global plastic emissions. Because here in Western Europe and US and Japan, Korea, we of course have good systems to collect the waste and dispose of it. It's really this middle ground. Places like Jakarta, Manila, Mumbai, Accra, a place like that, where you have a lot of people, a lot of plastic now getting cashewed, but basically no way to collect and dispose of it.
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C
I was very fortunate a few weeks ago in the Amazon and no plastic in the Amazon.
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B
I think what's quite interesting as well is that most plastic that ends up in rivers never makes it to the ocean. So whatever plastic gets we actually tested this again with a GPS tracker experiment. So a year ago we released I think about 100 GPS trackers in Manaus in the Amazon. None of them made it to the ocean. And that's really because it's so far inland. And of course the slope of the river is also, it's very thick, shallow, right. It's like an ocean almost. It's not like a very aggressive, fast flowing river. That plastic just gets sort of beached on the riverbanks and gets stuck in twigs. But that stuff, yeah, really doesn't make it to the ocean. So that's of course also when we started out back in 2017, our first global river model actually predicted that the Amazon would be a key contributor. But back then we, we didn't really understand the, the physics yet. We didn't know this, this, this fact. And now when we take that into account, actually the Amazon just completely drops off our, our list of priority, which is good because it is saying insanely big version. I'm very glad we are to, to do that one.
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A
So, Bojan, tell us a bit more about something the team did using some artificial intelligence where you cut data transmission costs by 99.975%. That's a lot of cuttage. Can you walk us through how you used AI as a practical tool rather than a silver bullet?
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B
We developed something we call ADIs. It's the automatic Debris Identification System, which are these cameras that we attach to, to ships, just merchant vessels, like cargo ships, container ship that go around the world all the time. And whenever they end up, whenever they enter port, they send a data package to headquarters here. And it actually tells us the concentration of plastic along the path in the ocean that they traveled. And we use this to better determine the location of the plastic in the Great Pacific Garbage Patch. But also as a global thermometer for how we're doing. Right, because ultimately we make it very explicit, we want to help ourselves out of business. But for that we need to know when we're done and we really need to be able to track what is the trend in terms of how much plastic is in the ocean. So by having the ships continuously traversing the ocean getting that data, we are able to have this global plastic thermometer.
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C
So these models run on the camera itself. So all the processing is done locally at the edge and you basically get the data, the resulting data, amazing.
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A
So that's such an impressive way of doing things. Now one thing you touched on here is the global nature of the work you're doing, which Introduces a whole lot of extra complexity. I mean, we talked about 20 rivers, nine countries, you've got ships going everywhere. Like there's a lot of stuff going on. How do you design your systems so that you don't need a specialist engineer everywhere? Because I can imagine that could get quite expensive if you went down that track.
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B
Yeah, So I guess that's another one of those items for the list of mistakes I made, which is the original system that we developed to intercept plastic in rivers was a really cool machine. It was fully solar powered, fully autonomous and like a trash eating robot essentially. And then we deployed them in places like Thailand, Indonesia. And then you discover. Oh well, actually first of all, it's very hard to import those things because how do you classify it? Is it a boat, is it a barge? What is it? There's no sort of line item, there's.
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No tick box for trash eating robot.
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B
Yeah. And then you need the permits to deploy. Again, same problem. Then installing it requires specialized installation vessels and it's quite difficult. Then you need to train people to operate it. Again, pretty difficult. You need people to, you need to have the maintenance and then you have these PLCs on board and then a tiny thing breaks and you have to air freight something from Germany to fix it. And it's just a big mess. Right. So, so then we got to the conclusion that okay, maybe people are underrated and actually creating jobs in these places is actually also something that is sort of a positive side effect to what we do. So then we kind of backtracked on that and went to more simpler hardware, simpler ways using excavators for extraction rather than, you know, conveyor belts. And yeah, I mean that's working quite well. Of course now the key is, so the technology is quite simple. But now we still, you know, looking ahead, right now we're doing about one deployment per quarter next year we want to get to a point where we're deploying one interceptor per week roughly. So that scaling ramp, it really requires us to rethink the way we're doing things. It's still too much reinventing the wheel every time. Seeing everything as a unique project. So it's all about building this project factory now where we standardize everything from the contracts we set up with the operators to all the individual hardware components to the installation manuals. It all has to be standardized because otherwise we will not be able to get to that deployment cadence.
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C
You'Re generating or collecting an enormous amount of data with all the ships. And also the things that you have yourself what do you do with the data? I mean, beyond just running your company and beyond making decisions yourself, is this something that you share with others as well?
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B
Yeah, for sure. So. So actually, we're just going through a review of some of the next cities we're going to tackle with our Interceptors this morning. And it's amazing, for basically an entire city, we mapped the entire river network. We know exactly where all the landfills are. We know exactly where we need to deploy the trash flux out of each of the waterways. It's amazing. And then, of course, even post deployment, we are collecting tons of data because we're able to see, okay, what's the trash? We're collecting the composition, the quantity, and of course, we're using all this data to deploy the best possible solutions and to further optimize their efficacy and improve the next set of deployments. We're also using it to, of course, convince funders, partners to show. Here we go. What we're doing. And we have a good plan to tackle a given city, but we're also sharing it with the local government because they are actually able to make better decisions with that data. For example, in Jamaica, one of the things we found is that the bulk of the trash was Styrofoam packaging. We shared that data with the government, and then they themselves decided to ban those Styrofoam clamshell takeout packages in Jamaica just because they saw, like, okay, actually, the majority of trash coming down the rivers is.
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A
That's what it is. Yeah.
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B
So, yeah. And I guess a broader point there is that what we're doing, of course, is not the ultimate solution. Ultimately, we don't want plastic to even flow down these rivers. We hope one day those interceptors are not necessary anymore, but that's not going to happen tomorrow. But it's not either. Or. Right. What we're doing through the data we collect, through the visibility we generate for the problem, we're actually able to help catalyze that upstream change as well.
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C
So do you think you'll ever be done?
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B
Yeah, for sure. And I think there's two levels of done. I think there is the point when the oceans are clean, which I think we should be able to express in years, not decades. So right now, we're stopping between 2 and 5% of global plastic pollution. We've doubled last year. We're doubling again this year. So just mathematically, we. You don't need that many more doublings to get to 100%. So, yeah. And I think now with this 30 cities program, we want to tackle a third of emissions in the next few years. So that should allow us to maintain this annual 100% year over year growth. So I think pretty quickly we'll be able to get to the point that, hey, great, oceans are clean again. At that point. However, those interceptors are still necessary. Right, because if you stop operating, then the oceans will get polluted again and stuff happens again. Yeah, and I think that will be true for a while. I think before the entire developing world is as pristine as Singapore or something, I think will take a few decades. And of course, we need to make sure that the interceptors will keep operating during that time. Of course, what we do envision is that after a few years, the government can really step up in essentially integrating our interceptors into their waste management infrastructure. So it's basically just. Yeah, just like they have garbage trucks in the streets, they need to operate interceptors in their rivers. So, yeah, I think clean oceans very soon. But then the day that we can take out all the interceptors and it's truly done, I think that will be sort of decades away.
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A
Now, you spoke about the growth rate and sort of how much we need to double, et cetera. And obviously you want to maintain cost control to make sure you can grow in terms of the collection. And you've often said that cost per kilogram of plastic removed is one of the KPIs you really most value. Tell us about how you apply that metric to your technical decision making.
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B
Yeah, of course, the unit economics are very important. So it's. So we use this to, to really prioritize where we deploy. And it's also a metric that we use to drive continuous improvement, both in terms of initial investment, operational cost. It's something we use to judge the performance of our operator. So the local contracted company that actually runs the interceptors. So, yeah, it's definitely a key metric. It's not the only metric. And I think one nuance that we're now recently started making is that it's really not cost per kilo of plastic, but it really costs per unit of impact. Because in some places you can imagine, say two hypothetical cities, one city that emits a thousand tons a year, but where it's basically just mud floods around the city. And then there's another city that Maybe emits just 100 tons a year, but those hundred tons go straight into a coral reef with a tourism hotspot, where of course, the social environmental damage would be much greater than in the first scenario. So we're now able to actually Put a dollar value to the ecosystems around those cities where the plastic goes, so we're able to better, again, make better decisions in terms of where we should prioritize our deployments.
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A
That's amazing. Where does the plastic go? Like you're collecting this plastic, what happens to it all?
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B
The places where we go to tend to be the places where of course, where the biggest leakage occurs and where the greatest leakage occurs tends to be the places where the poorest waste management are. Infrastructure exists. Right. So it is always quite a puzzle to figure out, okay, what do we do with this waste? And in most cases we are able to find suitable responsible destinations for this waste. So this can be, of course, and there's this hierarchy of waste destination. So ideally what's at the top is recycling. So in some places there's quite a large pet fraction, which is the type of plastic that's most valuable, easiest to recycle. So there you have a big chunk that gets recycled and then below that you essentially got landfill and incineration. Where of course, we audit those destinations to make sure that those landfills don't leak, that the employment conditions are responsible and yeah, to really make sure that that waste never ends up back in the environment and is staying there in a responsible way. Where sometimes we actually have to decide, okay, there's no good solution here yet. However, there's just so much pollution, we just have to go deploy. And then we actually ourselves invest in improvements of the infrastructure to make sure that ultimately we can guarantee that none of our waste ever goes back in the environment. So that could be investing in sorting centers. It can be investing in something as simple as fencing to around a landfill to make sure that the landfill doesn't leak into a river that's next to it is really. Yeah. Like we believe it's our responsibility to make sure that everything we collect never ends up back in the ocean. So that's the story for the Riverways, which is actually the more complicated story than for what we take out of the great Pacific garbage. Because there actually we're able to recycle almost everything. And we've made, together with our partner Kia, we've made components of their electric cars out of our plastic. And Coldplay's latest record has a special edition made of our plastic. And I think but with more and more partners coming on board, yeah, we're doing kind of fun and exciting things together with them to really tell the story of the ocean cleaner.
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A
So Bojan, one last question. Could be an easy one, could be a Hard one. What's the most important thing that you've learned or something you've changed your mind about since you started the ocean cleanup?
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C
Don't do it.
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B
Actually, I keep a list of things that change my mind about in my phone, so let me.
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A
Well, that's a good. That's. Actually, there's a tip right there. A podcast listener tip. Keep a list of things you change your mind about. Because one of the important things is to be flexible enough to change your mind. You know, when the facts change, I change my mind. What do you do?
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B
I personally heard Jeff Bezos say the same thing at a conference where it's like, you have to be very stubborn about the goal. You have to be very flexible in terms of how you get there. Right.
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A
And stubborn on the vision, flexible on the details.
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C
Well, I mean, your. The way that you solve this is you have a lot of hardware, and making hardware is always way more difficult than making software.
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B
It's almost. It's a long iteration cycle. So I think throughout my life, I think the connecting thread has been that I get an immense amount of satisfaction from having an idea, having a vision, and then seeing that become reality. And I think in the first few years, that mental image that was super crisp and sharp, and I could see that like a movie. That picture in my head was always about seeing that first cleanup system in action or seeing that first river system in action. And, of course, I was able to make those pictures a reality together, of course, with the team and everyone that all our parties, et cetera. But actually, the lesson there has been that it was the wrong picture that I had in my head. I shouldn't have dreamt about this particular machine. I should have dreamt about a clean ocean, essentially. And I think as an inventor, it's very easy to get sort of an emotional attachment to your ideas, to your, you know, to your inventions. And that can stand in the way of being flexible enough when it comes to how you get to the goal, because the goal is not having a thousand Interceptors on Earth. The goal is to have a clean ocean. And that's a subtle difference because, of course, we need one to get to the other. But it matters a tremendous deal.
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A
Amazing. Bojan, thanks so much for coming on and telling us about the journey.
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C
Yeah, thank you. It's great. Great story. Great company, you guys. Doing amazing work.
- [40:23]
B
Thanks so much.
- [40:25]
A
Absolutely. And, Bernard, thanks for co piloting again. As we roll through, hearing some of these great stories, I mean, it just shows that frugality affects the world in which we live in if applied properly. And of course we do love to get your feedback. AWSpartcast@Amazon.com is the place to do it. And until next time, keep on building.