Molten Ventures Plc (GROW) Earnings Call Transcript

Good morning. Welcome to Molten Ventures & AI investor presentation. [Operator Instructions] Before we begin, we'd like to submit the following poll. I'd now like to hand over to Martin Davis, CEO of Molten Ventures. Good morning, sir.

Thank you very much, Paul, and good morning to everybody who's tuned in this morning. For those of you who've done a number of these calls, you'll be aware that we generally do them around our results, engaging with our retail customer base and our broader customer base is something that's very important to us and we do find this platform really helpful to get some messages out about the company, but also some of the critical areas that are around technology that are developing that are important for our investment thesis and important for the market. And that's the reason for today to talk a little bit about ourselves for those that are not familiar, but ready to focus on AI. And there's been a lot of talk about AI increasingly over the last couple of months. This is indeed something that's not new. It's been something we've been working with for some time and has been developed for some time. But it's something that a number of our investors are keen to hear more about. And I'm very pleased to be joined by Edel Coen, one of our principal investors in our partnership group, who will be leading a panel of 3 of our key investments who will be talking about what AI means to them and their investments. And I'll leave it Edel to introduce those panelists but a very quick welcome and thank you to those panelists. For those of you who are not familiar with Molten, we are -- we've been a venture growth investing business for over 20 years. So we've got a real deep law on the history of investing in venture businesses. And we look at thousands of businesses a year, and we have a very, very steep funnel that gets us through to about the 10 to 20 companies that we invest in or follow-ons that we do every year. So the process is actually very, very tight. And one of the reasons that we think that we are slightly different is that being a listed vehicle, it means that we have an evergreen investment period which means that we can invest early and follow the best companies right the way through to their exit, whether that be trade sale or IPO. And you can see from the slide on the top right-hand side, the stages that we invest in. We don't invest in seed, but we do have one of the largest seed Fund of Funds programs across Europe. We've committed GBP 150 million over the last 5 years to invest in the best seed funds that help us to identify early the most exciting companies that we can invest in Series A, either through our EIS or VCT, but really the core of where we invest is the PLC through Series B, C+ right the way up to pre-IPO. Chart on the bottom right-hand side shows where we invest in this space, which is in this growth space in the middle. And this is an area that a lot of people who don't understand venture or venture investing and maybe not so familiar with. I think a lot of people look at it as very early stage or seed stage but the growth stages is actually very important, it's where our companies have hit certain proof points, be that around technology and possibly around regulatory requirements, maybe around -- certainly around product, commercial traction and whether that's revenue or customers. But they've hit certain growth points and hit -- gone through certain hurdles, which is the stage that we tend to invest. And these companies at this stage tend to have -- they grow very quickly. They're very large addressable markets. They're led by the most ambitious founders that want to scale their businesses to a much larger scale. And that's really where the core of our investing is. Over the years, we've invested in firms that can become very large firms such as Revolut, a Trustpilot some of you'd be familiar with. We invested reasonably early took that right up to IPO. But also some other companies that people are maybe less familiar with some of the lower profile companies that are really innovating and changing the way we live our lives and the way that work operates. And firms like UiPath may be familiar with listed in the U.S., a global leader in RPA. Form3, a company that is developing cloud-native payments systems, and a company called Thought Machine, which is developed cloud-native retail core banking system, the plumbing for some of the most important industries and sectors that we have and the technology that's really disrupting these spaces. And those are the types of companies that we invest in. And we invest in them just when they really start to take off in that growth stage. And because of our structure, can follow on right the way through all the way up to IPO. So that's our model. We've been investing, as I said, for over 20 years. And one of the areas that is becoming increasingly important and the topic of today is all around AI and generative AI. And I think everybody would have read a lot about it. We've had a lot of feedback that people are interested in this particular area. And in many respects, this isn't new. In fact, the first programs, I think the first program to help people manage to play Checkers game in 1952. So during the '50s, this is really where certainly machine learning started to take off. And as early as Alan Turing in the '50s -- and I think the phase AI was probably coined maybe in the late '50s, early '60s. So that's nearly over 80 years ago. And really, this has been a theme and something that has been developing over a very long period of time. And we've been making investments in this space for some time. Companies that we won't hear from today, but you may have heard of by ieso, we invented it -- we invested in 2017. RavenPack and other company was, Graphcore, we invested in 2015. So we've been looking to invest in these areas for some time. But I think what -- the question that probably is on the tips of many people's -- on tounges, is why now? What's happening now? And what's the difference? Why is there so much interest in AI, so much discussion around AI now. And I think that's what we hope to uncover and unpack a little bit today. But AI in its simplest form is effectively using algorithms to analyze data and to learn from it and to help inform decisions. That's fundamentally what AI is. And right from the early machine learning days, not a great deal has changed there as far as the core. Generative AI really takes it to the next stage. And this is where you have -- you can trained on a specific data set that can generate new data and start copying making direct copies of that data. And that means that it has many, many more applications. It can learn much, much quicker. And I think it's really the generative AI growth and the change in the last couple of years that has really driven the huge interest in this particular area. And the reason that we've seen these changes is the computational power, processing power its always traditionally bottleneck, but with the hardware that we have GPUs and TPUs today, they enable a massive massively greater number of complex calculations, very, very much quicker. Data availability. We've been talking about managing data, the great data revolution that's been running now for some time. Now that data availability has really, really changed and people can get hold of that data, and can manage it much, much tighter. And then, of course, the algorithms. We've seen a real change of those algorithms really in the last 5 years that allow the algorithm to be much more intelligent and to be much more sophisticated. And so a combination of these things linked with the network effect that everybody's networked and connected by these devices means that the generative AI is driving significant change, and it's driving significant change across the whole tech stack and across everything that we do. And we've been investing in companies across the board in this area for some time, as I mentioned earlier on. And I think it's important to say that AI will affect companies in our portfolio and technology companies in different ways. And we have a number that the AI is the absolute -- the AI first, it's really the cause of the whole business and what it's all about. And obviously, we're going to talk -- Darko is going to talk about causaLens today, which is one of those right at the forefront of using AI, and it's really [ rawest form ]. But then there are many other companies that will use AI to power them. And we're going to hear from Julian today about Gardin, one of our earlier stage companies that is using AI in a very intelligent way to help deliver its proposition. And then we'll have those that are enhanced by AI and more and more companies that we're working with are using -- looking at AI and seeing how it can enhance what they provide and ICEYE, the micro satellite companies. It's a really good example of that where -- and so across the portfolio, AI will have an impact in many, many ways, but different ways for different companies. We will invest in companies that are specifically driving AI like Aiven, like causaLens, like ieso, et cetera, like Graphcore. But also many of our other companies, nearly all companies that are using tech that are innovating and reformatting supply chains, value chains, et cetera, et cetera. They are all likely to be using AI much, much more in the future, and we'll hear a lot more about that in a few minutes. But I think another question we get asked a lot by investors is where is the value going to be created in this new AI tech stack. And I think that's a really important question. It is one that we look at quite closely. I would say I'm being very clear that we haven't got the answer yet. It's not clear. I think it is clear that there's a lot of noise at the application layer. So there's a lot of discussion and talk about ChatGPT, et cetera. And so there's a lot of talk and noise around the application layer. But a lot of the commercial traction in the early days is being driven in the infrastructure there. So the likes of NVIDIA and everybody knows what's happening with them with their chips and the demand for their chips, which is very much providing the infrastructure driving AI. And then the middleware layer and the intelligence foundation layer. Again, a lot of work. It's still relatively early stage from a commercial perspective. And we're going to hear a lot about how this is being used in a few minutes from some of our companies. So I think the development is there, and the use cases are there. And more and more corporates are buying into these areas, but where the real long-term value is going to be created in each of these areas is still something that everybody is unclear on, and our model is about finding the best companies that are working in these areas that have the most likelihood of being able to commercialize that capability, because the reality is if you can't commercialize it, then as an investment, it has limited applicability. And so as I said, a lot of the noise around the application layer right now, but there will be value created there in and our job is to be able to identify which of those companies are going to be at the forefront, which will be the winners and then continue to back them through their development. And I think really, that's probably the perfect segue to move on to 3 of our most exciting companies. And of course, we love all of our children, but we're very excited about these companies. And what I think I will do is hand over to Edel, who is, as I said before, one of our -- one of our principal investors in our partnership group, who will lead a panel discussion and I will just finish by saying we appreciate the importance of engaging with our investors through channels such as this, both on our company, our performance, but also on the issues that are affecting our investment thesis and where we invest and our investment ecosystem. And this is one of the areas that we hope to be able to shed some light in AI. And we very much welcome the feedback as to how you find this interesting and whether you would like more on particular topics. But without taking up any more of your time, Edel, the floor is yours.

Great. Thanks so much, Martin, and good morning to everybody that's joined us today. My name is Edel Coen. As Martin said, I'm the principal, investing in our investment team and spent quite a lot of time thinking about and looking at and investing in AI companies. So it is my pleasure this morning to present a panel where we kind of dig in a little bit deeper with the folks that are living and breathing this every day. We kind of sit on the sidelines a little bit and have very clear thoughts on where this market is going, but there's nothing better than hearing from the people that are building and utilizing AI themselves every day. So joining me this morning, we have Darko Matovski, who is CEO and Co-Founder of causaLens; Julian Godding, who's lead data scientists at Gardin, and we have Shay Strong, who's the VP of Analytics at ICEYE. And folks if you want to turn your cameras on, and we'll get rid of this presentation, there, we go full house. It's great to see you this morning. Thanks, everybody, for joining. I will hand over to you, to allow to introduce yourselves and then look forward to diving in a little bit deeper. So would you like to start us off, Shay?

Yes. Absolutely. Pleasure to be with you. So from my side, I would say my career has been a bit characterized by this geometric arc in space-based analytics. So I have a PhD in Astrophysics, looking out to the universe from earth really focused on infrared analysis. And then I moved into defensive space in the U.S., working at national security sectors with Johns Hopkins University characterizing incoming ballistic missiles, so kind of moving the arc towards the limb. And then kind of most recently in this third phase of my career, going down straight to earth and looking from space down to earth at the changes that we see from space-based analysis of what's going on at earth. And so I was Chief Data Scientist at a DC startup, it called OmniEarth and then moved into becoming the Director of AI/ML at EagleView in Seattle, working on analytics for insurance, specifically for underwriting and claims. But most recently, the last 3 years, I have been the VP of Analytics for the finished satellite company, ICEYE. And I grow and mentor a very large interdisciplinary team focused on making sense of our radar imagery. We have 30 radar satellites and we're intent on becoming the global source of truth for quantifying change on earth, very focused on bringing scalability, accuracy and consistency to the insurance and reinsurance sectors as well as governments for natural catastrophe response and recovery.

Fantastic. What a colorful career Shay, super to have you with us. And Darko, would you like to pick up next?

Hi, everybody. Thanks so much for joining us. I'm Darko, Co-Founder and CEO of causaLens. My background has been AI all my life, PhD in AI. It was definitely not a cool as it is now. I would have struggled to explain it to anyone in the pub back then. I think very different situation today, which is really, really exciting to see. I worked at the National Physical Laboratory; this is where Alan Turing works. Most people consider Alan Turing the father of AI. He was the first who mention of how computers could learn from data without being explicitly programmed. I then went and worked for some high-caliber hedge funds like Man Group $100 billion, always helping humans make better decisions with AI. causaLens is by far the most exciting there. At causaLens, we're building the future of decision-making. We envisage a world in which humans and machines work together to make the most important decisions in business, society and health care. One of the key problems with AI today, especially technologies like gen AI is the lack of availability and the potential leads to lack of trust in the technology due to hallucinations and lack of reasoning. At causaLens, we are building technology to make AI safe and AI that can work with humans together, AI that embeds values of society. So we're really, really excited about the next stage of the journey where we can bring this technology in all walks of life.

That's great. Thanks, Darko. And we'll definitely pick up on some of those challenges, both practical and kind of broader societal challenges a little bit later. So look forward to that. And Julian.

Hi, everyone, and thank you, Edel and Martin for having us today. My name is Julian, and I'm the Lead Data Scientist at Gardin. Gardin is an AI agriculture company. And what we're trying to do is build a digital plant computer to supercharge the future of food. So we make a sensor that measures plant photosynthesis to optimize food production in indoor growing environments like greenhouses and vertical farms. My background is as a scientist, so study chemistry. I did research in Oxford University in perovskite solar cells. And that kind of kick started an interest for me in the sustainable transition. So after my research, I went into industry and worked for the U.K.'s largest bioenergy company. And I got interested there in using data and analytics in the most important industries to our economy. So energy, agriculture, manufacturing and -- so yes I have worked in energy and then moved into Gardin working in the agricultural space where I'm taking our measurement of plant photosynthesis and using those to help growers improve how they grow food.

Super. That's great. And so what our audience will have heard there is that there are very broad applications of AI across each company to solving diverse set of very big problems, I would say. And Martin has touched on how we kind of stratify our portfolio companies into AI first, which Darko will sort of put you guys into that focus AI Power, which is where Gardin sits core part of your proposition and then share with ICEYE of using it as an enabling technology in the background to get yourselves to be able to create much better products for our customers. I wonder, could we dive into that a little bit deeper and talk to us about how you primarily leverage AI when that's building or using internally and also how you enable your customers to do that. Maybe we'll start with you, Darko, since you're probably the most in depth in that space.

Yes, of course. So we are the pioneers of Causal AI which is a new category of AI that allows machines to reason like humans for the first time. And there are significant implications for this technology. We can go into this later. But when it comes to emerging AI technology. What we find in general is that it takes hundreds of millions of dollars, top talent and a lot of time to actually be able to adopt an emerging area of AI. And so our job is to allow organizations to adopt this technology at a fraction of the cost and at a fraction of the time and taking away all the risk. So that's why we exist. We want to make this wonderful technology, which we believe is the future of artificial intelligence, and we want to make it accessible. So today there's only a handful of companies that actually have Causal AI. Those are Microsoft, Amazon, Netflix, Spotify, Uber and Airbnb. That's kind of it. Those guys -- when you switch on your Netflix, the recommender engine that shows different movies and series, for you versus me, that's actually from their Causal AI team, when you switch your Spotify on the recommender for the next song that you would like is actually also from their Causal AI. But that's only 5 or 6 companies globally that have the -- that have made investments early, they have acquired talent and have the infrastructure to use this technology and make their products and services superior. Everybody else, which is thousands and thousands of organizations just don't have the ability to hire the talent, don't have the resources to build it and they don't have the time to do this. This is -- this can take years, maybe even decades for large enterprises. So what we do is we create a platform, we call it decisionOS, which allows everybody else to become as good as Microsoft in a very, very short space of time. In a couple of weeks, a couple of months as opposed to and at a fraction of the cost that it will take to build this technology itself. So what the platform allows our customers to do is to build end-to-end Causal AI solutions. And they do -- primary use of our technology is for improved decision-making. The most exciting use cases are decision-making in business and society and government and health care, where you want to combine the best of human and machine. It's about building this new type of decision systems where we can fully trust, we fully understand and the human has a big role to play. So think of our technology as the infrastructure that allows others to adopt this technology very fast. So the primary users of our technology are the data scientist. So they're the builders on our technology. So think of it as like decisionOS is kind of like windows where it's a layer where you can build apps. So data scientists can build app specific to the use of the business. And then, of course, the end user is a decision maker. And so we were able to actually create this great collaboration between the main expert, decision makers and data scientists as well. So that's a long answer to your short question.

Yes. No, it's fantastic. And I'm obviously extremely optimistic and excited to have causaLens in the portfolio. But to me, it's the grace and democratizing technology, really, you're enabling folks that otherwise would not be able to do this because of all the practical challenges and leverage an incredible technology. So super good to hear that. Julian, in many ways, Gardin is actually doing something similar. So agriculture, one of our oldest industries, if you like, in some ways, stuck in the past very difficult to bring technology in and digitized. Maybe talk to us a little bit more about how Gardin enables your customers to releverage this technology?

Yes. So I think one of the best ways to think about it is when we think of how indoor agriculture has worked so far. So greenhouses is a very established industry, it's actually how most of us will get our vegetables. Look when you buy a tomato -- pack of tomatoes from Sainsbury's, almost definitely comes from the Netherlands. And I suppose the Netherlands is this hotspot of indoor growing. And actually, they're the second largest exporter of food by value in the world, which is kind of insane when you think about how small it is as a country. And so it's been incredibly successful, but the way that it's worked so far is that a grower is a very technical role in these greenhouses and takes a lot of specialist knowledge and training and the way that they manage their operations as they measure everything in the greenhouse that's going on, the temperature, the humidity, the light levels, the irrigation. And they have to combine all of these variables and kind of use the green finger to understand how well is the plant responding to these -- the environment. What we're trying to do is move from this part of the -- of a climate computer where you're measuring everything around the plant to measuring the plant itself and creating a plant computer. And basically then, you know exactly how your product is actually performing, which is the plan because in every other industry, there's an obsession with measuring the product. But in agriculture, it's been impossible to do so because there just hasn't been this technology that allows you to measure the plant itself. And so that's what we tried to build. And to do that, we have this sensor that measures the rate of photosynthesis in plants remotely and photosynthesis is obviously the core process in a plant for growth. And to do that, we've invented a new sensor, which operates completely autonomously. And so you basically stick this sensor in the greenhouse. And it measures plants around it using computer vision. So kind of the first area where we integrate AI is in computer vision and robotics. So we've effectively got a small robot in the greenhouse, which senses its environment around it finds plants to measure and then shines a small beam of light onto the plant to understand how efficiently they're photosynthesizing . And that's how we power our data collection. And that's really important because there's actually a huge labor shortage in agriculture at the moment. And so -- and there's a massive shortage of these growers that actually know how to manage these farms. So having kind of an AI system, which is completely autonomous is really important where you don't need to have any additional labor input into collecting this data.

No, I think that's great. And there's just so many practical benefits of using computer vision and AI in this environment where you're kind of shifting from a human know-how and kind of let's measure everything else to this product obsession, which as somebody that eats food. I'm pretty excited about. It's about time we kind of moved on and they all have some other challenges facing that industry. So okay, very exciting to hear that, Julian. Shay, I know with ICEYE, you're kind of using AI more as an enabling technology in the background. Can you tell us a little bit more about that?

Yes, absolutely. And I particularly like this phrase of this AI enhanced. I think it's really interesting, and it resonates with me quite a bit. For sure, from the perspective of capturing imagery from space, this is very heavy data. Like each image can be multiple gigabytes and then once you process it and once you collect things over time and then map that globally, like you're quickly scaling to terabytes of information. And so AI is a natural tool for processing that fundamentally, just to create sense out of what is being captured. There's just simply no way you could scale other types of algorithms. But I would also be a little bit of devil's advocate. For the products that my team create, specifically, again, looking at natural catastrophe solutions, I explicitly don't force my team to use AI, which is actually to the dismay of some of my younger machine learning engineers who just want to throw the best new tool at it. But really, like my job is trying to strike a balance between, we've collected all this imagery. What are the right tools, AI might be one of them to get to the best outcome. And from a personal perspective, I've worked for companies that have definitely tried to hit -- use this AI hammer to hit every nail. And at least when it wasn't well thought out or the use cases weren't well known, it was ineffective. And I think also like Darko had mentioned, too, like, AI is not an easy thing to get into, like there's a tremendous amount of engineering overhead, compute and responsibility. And I mean it will be it's fantastic that causaLens is helping reduce some of that. But now in our space at ICEYE, ultimately, of course, we still need to make sense of the underlying data, and we do use a lot of ML and AI applications. What -- some of the interesting things that we're focusing on is really trying to make sense of the radar aspect of the data that we have. So ICEYE sensors are not your optical sensors. It's a different part of the electromagnetic spectrum, meaning that it's observing things we don't see with our eye. And that makes it incredibly difficult to then interpret and find patterns. And where we do see value in applying ML and AI is in trying to better understand the physics that the information we're getting back if you're not familiar, synthetic aperture radar is kind of like I visualize it bat with Sonar. So it sends out a pulse, it owns its own source of radiation. It doesn't need the Sun, so it controls everything, but it sends up its pulse and receives an echo like a backscatter. There's a tremendous amount of physics in that backscatter that we can visualize as an image. But then understanding the coherent physics behind it is incredibly complicated. And is beyond kind of the existing tool sets and kind of traditional ways of exploiting that information. So this is where like I'm particularly excited about leveraging better and thoughtful use of AI and ML and it's where my team is exploring and focusing as well.

Yes. Amazing. It's fascinating again such a diverse set of problems. I mean the physics of the earth is not a small undertaking to try and visualize. So I know it's incredible to hear how you're using AI in the background. I think maybe we'll then -- we've talked about some of the benefits, and I know you guys are using it and enabling AI kind of in broader industries. I think for me, there is some real practical challenges, no doubt about it in limitations. But if we just kind of take a step back for a moment, and this is on certainty across the media and thinking about potential societal challenges and perhaps negative impact. I mean, all you have to do is open up a newspaper and you'll see kind of two extreme views about AI. The first one is it will revolutionize how we live for the better. And the second one is, it will revolutionize how we live for the worst. And I think that the truth is probably somewhere between both of those. My own personal view is that AI can definitely be a force for good. In the future, every company will be an AI company, just as most companies now are sort of tech companies, as we've seen that advancement in recent decades. And having said that, I do read the papers, I read books and there is a phrase that rings my ear, which is, "A good machine in the wrong hands, can become a bad machine." So I wonder, Shay and Darko, would you share some of your top thoughts of those on these limitations, particularly around perhaps bias the ethics of AI and kind of how you think about ways of overcoming these challenges?

Yes. I mean I'm happy to maybe jump in first. It's a really interesting topic area. And honestly, like with respect to kind of implications for fundamentally maybe regulating this is -- I go back and forth all the time. But as I think about the ethics and responsibility and bias and challenges within AI relative to earth observation, I am struck by the fact that on one hand, remote sensing, observing the earth from space is a great quantifiable way to evaluate something. But also, it's not a black box. Like at the end of the day, whatever algorithm you use or whatever piece of information you derive, someone on earth can go physically walk to that place and confirm or deny it. So there's like this truthfulness that we have to maintain, which is really lovely. And in a way, I think that kind of protects the way that we use AI, like we can maybe quickly see when we're way off or way out of bounds. But there's other aspects that I have seen in my career with remote sensing too where just acquiring the information, the remote sensing information, the way that it's acquired, for instance, aerial imagery collected from a plane, still remote sensing, not from space. But only the most lucrative and significant governments and countries typically afford the best quality aerial information. And therefore, if you go and abstract and create this remote sensing model using AI with this amazing data you are over-indexing for bias for really well-to-do countries, and you're completely ignoring lower income areas or third world countries, often these are divorced from the data set. So that aspect, I find particularly jarring. And I think there's, for sure, a level of accountability and responsibility that we think about when we try to tackle some of this. So yes, perhaps I'll stop talking Edel, and turn over to Darko.

No. It's so interesting though that you touched on that piece because the idea of the haves and the have-nots. And this has always been a theme when we look at technological progress. And if you think about climate change, for example, perhaps they have-nots are the ones most in need of that type of data. So it throws up a lot of questions, but thank you for sharing that. And Darko, can you tell your thoughts.

Yes, absolutely. So I completely agree with Shay that current machine learning can be disconnected from the real world. And I think all we're doing really with traditional machine learning is we're learning historical patterns. And if there's -- and we're just predicting based on those. So if those historical patterns were -- clearly, society has moved on and many things that in the past were unfair and unjust they have been corrected. But actually, those in justices and unfairness is still actually embedded in that historical data. And we've seen some high-profile failures of machine learning because of this reason, and I can share some. So there was a racial bias down in a major health care algorithm that was used by health care providers and hospitals, and it was actually denying care for black patients. There was a high-profile failure of Zillow's AI, 2,000 jobs lost overnight because the algorithm just learned some patterns in past that are not in representative of today. And the list goes on. We have a case of Amazon scraping AI -- secret AI recruiting tool that was biased against women. And so clearly, AI can cause a lot of harm if we're just using kind of the traditional machine learning, which is -- that is learning historical patterns and is then just predicting kind of the next pattern. Now luckily, what we do provides the hope towards safe AI and trusted AI. So the way Causal AI works is we can learn causal relationships from the past, but we're able to eliminate the things that are correlated by accident. So that's one benefit. But the second benefit is we can actually, for the first time, humans and machines speak the same language, the language of Causality. So we are able to give the humans the ability to inspect the causal diagram and say, "Hey, this thing here doesn't make sense in the real world", like we cannot be making decision about whether to give someone a higher limit on their credit card if they're buying ice cream at midnight. Like it's -- and this is actually a real case where someone buying ice cream at midnight, there was a correlation to them getting divorced soon. So their credit limits kept going down because they kept buying ice cream. So clearly, we don't want that is making decisions based on these potentially spurious patterns. We want to be able to have the humans look at this and say, well, look, we cannot have AI decide whether someone gets lower or higher credit limit based on what time they're buying ice cream. It just makes no sense. So for the first time, I think we do have the tools to change how AI works. Today, I feel we are on a crossroad. We're in a crossroad between AI being the best technology that has ever been invented and is the best thing that has happened to society. And we have also -- we're in that crossover one of the part leads to the worst thing that has happened and a lot of harm being caused. And really, it all comes down to applying the right type of AI the right use cases. So for anything that is critical like the use cases we discussed, whether it's the decide on the well-being of humans, we cannot really rely on this correlation based, traditional machine learning that just learns the patterns. We need Causal AI that can -- we can introspect we can make sure it aligns with the values and we can detach it from historical data alone.