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Artificial intelligence is a catalyst for disruptive innovation across various sectors. In this episode, CIO Tony Roth and his esteemed guest, Rudina Seseri, founder and managing partner of Glasswing Ventures take a deep dive into the AI landscape. They explore its unique challenges, promising ventures, and burgeoning investment opportunities.

Artificial Intelligence: Disruption that is Driving Opportunity

Tony Roth, Chief Investment Officer

Rudina Seseri, Founder and Managing Partner, Glasswing Ventures


TONY ROTH: This is Tony Roth and you are listening to Wilmington Trust’s Capital Considerations. Today is a particularly special episode cause I have a good friend, Rudina Seseri, who is a specialist in artificial intelligence, a venture capitalist, a founder of a venture capital firm with a very eminent background in the space.

Neil Sahota was our last guest on the topic of artificial intelligence at a hundred-thousand-foot level. Now we're going to go down to perhaps 10 to 20,000 feet and talk about types of companies. And let me just mention again before we start that should Rudina or I mention any specific companies, we are not making an endorsement of that company or any investment in that company.

Rudina has significant amount of experience on the operational side as well as on the sourcing side. She has been on many, many boards. She's been involved with the Harvard Business School where she was appointed by the Dean as entrepreneur in residence. She has been involved with New England Venture Capital Association, where she is, today, a member of the Board of Directors.

She's also a member of the Business Leadership Council of Wellesley College. And on top of all of that, she's a member of the board of M&T Bank. So Rudina, thank you so much for sharing your expertise with us today.

RUDINA SESERI: Thank you for having me, Tony. I'm glad to be here.

TONY ROTH: Let's start with what's happened this year.

When we think about the opportunity set, the corners of disruption, which are becoming far bigger than mere corners in the economy. We keep on finding ourselves back to the same topic, which is AI. Do you think that all of a sudden things have come together and we're turning a big corner now where it's going from sort of a wave crisscrossing the ocean to a tsunami hitting the shore?

Or is this just a gradual incline and people are just becoming aware of it Now? What's happened and how big do you think the opportunity is? Because it seems like it could really transform our economy, both in positive and negative ways.

RUDINA SESERI: Fundamentally, the AI evolution, and most recently in the last few decades revolution, has been long coming, and that's very, very important to understand. It has been a 70-year journey, seven zero, not seven years, and not sort of the bubbly seven months of buzz that has been going on. It's been gradual, but at a very steep curve, I would say in the last decade and a half. And it's really driven by three fundamental or core drivers, one being the explosion of data.

AI disruption comes on the backs of prior disruptions, particularly around think about the web and the amount of data that it started to create with the digital space in the late eighties, early nineties. Think about mobile and that revolution and the new types of data that came out of it, and then fundamentally IOT [Internet of Things], et cetera.

One big driver for the maturity and the at least perceived sense of all pervasive adoption or around AI is data. The other piece is the cost of compute. The cost of computing is becoming down and, fundamentally, with the advent of the cloud in 2006 onward, more computing power and the cost keeps coming down.

So Moore's Law, holding strongly for those familiar with that law. And then thirdly, algorithmic breakthroughs. And the real tsunami is as you labeled it. The most recent one and the, the buzz around it has been around the algorithmic breakthroughs. Two very important moments to level set. One you have in 2006, the emergence of deep learning or neural nets, where fundamentally you have complex data sets from which these neural nets, these algorithms, can extract highly generalizable features to deliver complex and valuable outputs as opposed to sort of a linear relationship, which is what used to be previously with classical machine learning. Beyond there in 2017, you had the emergence of transformer technologies. There is a very famous paper in academic circles called Attention Is All You Need that fundamentally laid the foundation for the transformer architecture, which is the backbone of everything you hear around generative AI today and why there is so much buzz. And it's interesting because that paper marked the beginning of the most recent sort of wave around generative AI, where effectively you are leveraging transformer technologies and architecture to generate new previously unprecedented unseen outputs or forms of data, whether it's text to image, text to text, 2D to 3D, is this creation of new synthetic data.

So why do you, we all become immediately aware? To your point and to your question, is what I call the mass consumerization of AI because applications such as ChatGPT—by the way, the “T” in ChatGPT, it actually stands for transformer.

It made AI tangible to everyone. The ability to deliver near-human level type answers and engagement made it feel real and very humanized. So, all of a sudden it becomes a cocktail party topic and therefore mass awareness.

TONY ROTH: Let me just go backwards and start with…


TONY ROTH: …the premise of this paper, but to make sure I understand it, to sort of lay the foundation for what's going on right now.

And it's so fascinating. One of the reasons that we're experiencing this really started even before the Nvidia acute increase in stock price. They're the ones that sell all the chips. The precedent to that was indeed the. ChatGPT phenomenon. And what makes that different and unique is that it's not like, for example, an x-ray where AI has been used successfully now for some number of years.


TONY ROTH: In medical diagnostics where you can look at past patterns analogized to a particular x-ray and say, okay, that computer can actually predict the health of that patient or the pathology of that patient better than a doctor could. But this is actually saying, okay, let's take a photograph of Tony.

And I just came back from Costa Rica where I watched my wife go to surf camp and they could say, okay, let's take a picture of Tony. Let's make it look like Tony is not just on a surfboard, but going down a big wave and make it really real. And you can actually create things that didn't exist.

Or on ChatGPT you can create a paper that never existed before on a topic that could seem reasonably intelligible. And that's the idea of this paper to actually move beyond the existing and creating new fresh material.

RUDINA SESERI: Yep. A couple of finer points on that. In the Tony in Costa Rica example, they wouldn't even need to take a picture of Tony based on looking at all sorts of other data sets.

It could create the image of the Tony-like personality or person. So I don't want people to go away with the impression of deep fakes, although technology is neither good nor bad. It depends on how we humans use it. But fundamentally that, or on the ChadGPT and the engagement that you have, what is unique, Tony, is the creation of new type of outputs, whether it's images or text, but also that this output to a human, feels very human like.


RUDINA SESERI: ChatGPT is probably the most common example, has all sorts of challenges. From the data it was trained on now becoming all data on the web. It stopped training with data as of 2021, but it's very, very coherent. Nobody should be writing anything from scratch at this point.

You have a co-piloting dynamic with ChatGPT. Knowing that ChatGPT has what we call in the industry, a hallucination problem; doesn't know fact from fiction or truth from lies.


RUDINA SESERI: So verification you can make up sources that never existed. If accuracy is a goal, shy away from ChatGPT, getting you started is a goal, then this dynamic becomes very productive and quite efficient.

TONY ROTH: And it's easy to fathom how these three components have come together recently to create this productivity. You can see companies like Google have just accumulated so much data about the world, about us living within the world about different aspects on the world, and you can see how computing power just continuously grows at leaps and bounds with Moore's law.

You look at our phone, one of these phones is more powerful than a computer that would've taken the whole room I'm sitting in just a decade or two ago. And then of course, the power of the algorithm becoming smarter and how to program these machines. When you think about what we're experiencing, what we can actually witness, the generative AI.

As an investor, does that take all the oxygen out of the room or is that just one of many areas of AI that you think is going to be truly transformative to the economy and to our lives?

RUDINA SESERI: You are asking more, a more complex question that you might realize. So let me make an attempt to decouple the two facets.

The ability for us as being individuals or us being businesses and enterprises to leverage generative AI tools will be all pervasive. Much like we use software in the cloud, much like we all have mobile phones. I go back to the example, if you want to write a paper, if you want to publish something, you wouldn't start from scratch.

And it cuts to facets like nobody codes, uh, think developers and technologies, nobody codes from scratch anymore. Up to 60, 70% of the code is in a co-piloting dynamic in this generative AI type capabilities and tools. And then the finer, what makes the software or the solution and product really valuable is the human.

So one facet is how we incorporate generative AI tools and capabilities or wrappers as we refer to them in the everyday lives of any technology company, any company, and us as individuals. That will cut all across the board. Just like again, mobile and other facets. Then there is the question of what constitutes an AI native company, a company that's leveraging AI at its core and has proprietary algorithms and data access.

This entire exchange you and I have had, has really focused on what we call the foundational models or large language models, which is open AI with ChatGPT and Microsoft Partnership. Cohere some of the other players. We haven't even scratched the surface around the opportunities in true gen AI native capabilities, and much broader other machine learning and AI capabilities that exist to drive performance, to drive output, to drive differentiation and productivity.

Let me take a step back. We use AI and machine learning as a thing. I would love to dispel the notion that AI is monolithic. Dear Lord. No, it is not. In fact saying I have an AI company that's doing this, or I have an AI tool is doing this is like saying I'm in tech. Great. Nice to meet you. What is the product that you're delivering or building?

If I use machine learning, which is a subset of AI, as a proxy in the how there are tens and tens of architectures solely on the deep learning side. I'm completely discounting the classical machine learning that has existed for years. Just within deep learning, there are lots and lots of architectures where transformers with embeddings and attention forgive the jargon, are only two facets that deliver on the gen AI piece, but you have other architectures, convolutional neural nets (CNNs), which are very much focused on image recognition or recurring neural nets, which have memory so they can leverage learnings from prior trainings to apply to make for a stronger output. So lots and lots of architectures.

TONY ROTH: What would be some examples of that?

RUDINA SESERI: Since I was going with RNNs or recurring neural nets, within it, there is a particular technique called PINS, which stands for physics informed neural nets. What physics informed neural net does, it's basically grounds the usage of a particular algorithm or neural net to the reality of our physical world. I have a portfolio company in full disclosure called Base two.ai.

What they provide is think of process manufacturing. If you're not familiar with it, it's sort of the actual process of producing compounds, whether it's drugs, chemicals, anything of that nature. These guys basically bring a no-code digital twin, such that process engineers instead of trial and error in the real world to figure out the right compounds and amounts and all that, they can do it in a digital world—drag and drop.

TONY ROTH: Mm-hmm.

RUDINA SESERI: And leverage algorithms and the various possibilities of outputs. And the way that they're able to ground these algorithms is around the fact that they use this PIN architecture as well as some other embedding and, and transformer architectures to basically put the formula and bound the possibilities that the models generate as output so that it is grounded in, in the actual chemical compound or drug compound.

TONY ROTH: I'm going to call it digital chemistry, and you could think of, for example, a drug company that wants to discover or identify a compound that will have a potentially salutary impact on particular molecule that may be problematic for human beings rather than having to create lots of different experiments with the different compounds in a laboratory, they can do this in a computer because the computing environment has been programmed to essentially, Using these deep neural networks replicate what would happen in a beaker in a test tube.

RUDINA SESERI: The first part of your statement captures the definition of a digital twin. The second part of your statement captures the technique, or one of the techniques that they use, which is called PINS.

And the reason we got on this topic is back to the idea of, in their case, they're using combinations of techniques. So I illustrated one architecture and one technique that they're using within the that architecture. In reality, it's combination of architectures and combinations of technique. When we talk about AI and machine learning and we use them as a catch-all term, that's fine.

The reality though, is much like with any other technology, understanding what architecture, what technique, and what training in combination, with what type and kind of data set for which use case is mission critical to building in a successful AI company, AI native company, but fundamentally to knowing how to invest in this space, particularly in the early stage.

TONY ROTH: If we want it as not just investors, but as citizens of the world, as participants in ways that we're aware of and ways that we're not aware of, and certainly beneficiaries in many ways, each one of us, of this revolution.


TONY ROTH: That these three different elements, data, computation (access to computation), and power of algorithm is now providing us. Is there a, if you will, a taxonomy of AI where you can say, okay, it's not going to be perfect, but to give us a, map? There's generative AI, there’s AI that is, call it deep learning that allows us to simulate reality and speed things up.


TONY ROTH: Are there certain core categories that we can grossly reduce this to for now just so we can better have a roadmap to move forward and, and are understanding our sort of our AI literacy?

RUDINA SESERI: Yeah. It doesn't exist. You will have papers on different techniques. There are some sort of high level generally understood.

Categories within classical and deep learning and within certain architectures. But we at Glasswing Ventures have developed what we call the AI palette that serves to fulfill exactly that purpose. We haven't published it yet, but it, it's upcoming, so I'm happy to provide it as a add-on to this podcast when it becomes available.

But to date, it's been very hard to map. And here's the other challenge, the breakthroughs in AI events, whether it's a new machine learning technique, whether it is getting more transparency on how neural networks work, any facet, the papers are coming at a speed that we have never seen before. We track it and we model that as well.

And fundamentally, from 2020 onward, the early adoption of transformers and Gen AI. Then wider adoption. The growth is a step function rather than a linear growth. So it just gives you a sense of how much innovation is going on. Keeping up with it is its own challenge and you need to be a specialist investor in that regard.

And then mapping it becomes its own challenge in its own way. So I think we've at least begun to crack that knot. So stay tuned.

TONY ROTH: When you scan the early-stage space, which is where you play, when you apply your vision to the early-stage space—and you can't even begin to look at everything much less invested in everything.

But where do you think the biggest disruptions, the most interesting opportunities are that will really just totally turn the economy upside down?

RUDINA SESERI: First, as a specialized fund, it's as important knowing where not to invest, as well as you know where to apply and not to apply AI. For us, enterprise and cybersecurity, are the key end markets. So we're not just looking at any AI opportunity that emerges. You mentioned radiology. We wouldn't touch, uh, radiology, diagnostics AI tool, even though they've been quite effective. It's not the area that we operate. For us, it's very much around productivity in the enterprise, data infrastructure, cybersecurity, et cetera. So, where we see the biggest opportunities with that vintage point, I think from a market perspective, um, anything around supply-chain manufacturing optimization, completely ripe for disruption. Any areas around security. AI both is a headache for security.

Think third party risk, think identity access management. I mean, in your positively intended, you know, example of Tony in Costa Rica you almost described the deep fake. So how do we deal with Tony, the real CIO at Wilmington Trust versus Tony, the deep fake that's generated? Well, the problem gets created by the misuse of technology, but it turns out that AI techniques and machine learning techniques are part of the answer in terms of identity access management and actually tying the data to know that Tony is the person that we believe he is, and further, more to doing away with passwords altogether around identification and authentication. So those are a couple of areas that I think are, are ripe for disruption. But it's hard, Tony, to pinpoint just to one area simply because it's going to touch in every facet. It begs the question. What will the doctor of the future look like?

What will the lawyer of the future look like? What will the student of the future look like? Why do we want experienced doctors? Because of their ability to, “have seen everything.” You don't want the resident, you want the deeply specialized, experienced doctor with decades under his or her belt that has seen it all.

Well, wait a minute. If I have an algorithm that has been trained and “has seen it all and can diagnose.” What might the doctor of the future look like for me? I got the diagnosis. Should he or she specialize more in the curing rather than just the treatment or the maintenance? How do we free their brain and their training up for that?

The lawyer, same idea. If you know pattern recognition and experience in case law is relevant. Where can the machine add value? Where does the lawyer find their own differentiation in that context? Student, if in a classroom you can use algorithms in addition to a human teacher instructing, if you can use algorithms to help Rudina the student mindset in her areas of weakness, which do not mirror Tony's challenges, but the algorithm can deliver mass personalization at scale.

Huh? What does the future of learning look like? We can go through every facet of our lives, whether we put our investor hat on, our consumer hat, on our enterprise hats on, and see that it is all pervasive. Much like the web was, much like mobile was, all the way back to the industrial revolution and automation that occurred there.

TONY ROTH: When we look at public companies to invest in, we look at price to earnings, we look at free cash flow. We look at the quality of their management, which is more of a qualitative factor. We look at the barriers to entry and the mode around the business. We look at a lot of things. Recently, we've sat down as a team and we've said every company that's listed should be rightly, a consumer of AI.

Assuming that they're not creators of AI themselves, they should at a minimum be consumers of AI. We should look at every sector and industry and understand what are the applications of AI that are happening, at least to the extent it's public. And we should evaluate where each company stands along that journey. If nothing else, but to start to build our muscle memory around understanding how radical these changes could be and where they might occur in the ecosystem of each space. Does that strike you as a logical thing to do, or is that sort of, are we chasing our tails?

RUDINA SESERI: I think it's very logical and it is going to manifest itself in one way or another. Whether you track it as how they're leveraging AI to some of the incumbents in a, in a particular space, waking up one morning and realizing that the competition is not their tried and true known public competitor. It’s this little tech company they had never thought about previously, but they're leveraging AI to deliver a much more, I don't know, highly efficient or better price or whatever the differentiation might be. So it will manifest itself in outputs that you're used to measuring, whether it's market share, whether it's profitability, whether it's revenue growth.

TONY ROTH: Of course.

RUDINA SESERI: So in that regard, of course, you've got to be grounded in the financial metrics that you're evaluating.

But those will be lagging indicators of strategic decisions and execution that the management team has made. A facet of, which is technology adoption and a finer point of mean that is leveraging AI to drive your own internal disruption.

TONY ROTH: Rudina, you talked about a couple of applications. I think there's nothing that makes this more real than to actually give examples. So one is supply-chain management.

So that's not intuitive to me. I would think that, okay, supply chain management. I'm going to source my computer chips from Taiwan and I'm going to diversify it into Vietnam and nearshore some of it to either Mexico or the U.S. should I need to, I can figure that out myself. No AI. What am I missing? Where does supply-chain management come in?

RUDINA SESERI: Oh, Tony, you're missing a ton. What you're missing, I will start by quantifying it is about 60 to 80 billion with a B of hard dollar losses every year because you're ordering XYZ chip, and you're tracking all of this in your ERP [enterprise resource planning] system, which is still, for the most part, on premise and not in the cloud, right?

By the way, your very inefficient colleague, Rudina has inputted the information on those chips that you ordered incorrectly, and so this ERP system is telling you that you have major shortages when in reality you are overstocking the chips. So highly inefficient management. Or it shows that you have it in stock, you go to the actual physical area to pick it up and oops, it was a mistake.

So now you have to order it overnight, pay a premium, or stop the production line, which is millions of dollars per hour lost. The amount of inefficiency that exists within the manufacturing and supply-chain world is actually quite astounding, especially when considering that these are very thin margin type businesses.

So you can leverage AI and different AI techniques in many ways. One way, and I'm thinking of a portfolio company called Verusen, that you can do is actually first leverage some techniques to basically identify truly what you have in stock, even if you have errors in your tracking and, and how it's been coded.

Because it can leverage semantic capabilities and other capabilities such that even if it's incomplete or partially correct, it can identify it. Secondly, it's tying what you have in the physical to what you have in your software, even if they're legacy software systems. So, by basically extracting data out of the sensors in the physical world, et cetera.

So you truly have transparency in what you have in stock that is tens of millions of dollars per company, per quarter. And that's not a euphemism. Beyond there though, there's this notion of trusted supply. Well, how do you know that you're optimizing on your purchasing? Well, what if you knew that you're ordering XYZ parts, your chips in your example at this timeframe?

But if you could shift it by a quarter,…


RUDINA SESERI: … if you could shift it by a week and purchase differently or bundled it differently, you'd get better pricing and optimize on savings. Would you do that? That is all actually powered by machine learning and AI algorithms, and it can drive pretty tangible, um, results at highly measurable ROI [return on investment]. That's an example.

TONY ROTH: How about passwords? How is it that I look at Rudina as a venture capitalist. She must have a bank account. So I'm going to type in Rudina and then use AI to find her password. Is that possible or is AI a means to get rid of passwords as you suggested?

How could AI help us get rid of passwords?

RUDINA SESERI: AI, and any other technology, it's not good or bad in in itself. It's how we use it.


RUDINA SESERI: So if you would like to crack into my password by leveraging AI Sure. Yes, you can do that. Especially when you think in the context of large businesses and enterprises. You do not manage to hack an account purely by going head on into the account.

You typically, it's human sort of mistakes. I get an invite from a bank. It looks legit. You click on it. Oops, it's a phishing attack and in reality, what you've done is you've given your credentials, because it looks like the website, but in reality, you're inputting the credentials...


RUDINA SESERI: … into bad actors and by the time you figured it out, even if it's a matter of minutes, they've swiped your accounts. So how does AI help with that? In this example, there's a company, Allure Security Systems, which is leveraging AI to basically immediately notify banks when this phishing attack is happening in a fake website looking like, uh, original bank or the true bank is being set up.

So they're able to pick patterns to be able to prevent it in a matter of minutes rather than hours and days when the damage has been caused. So that's one facet. But back to Passwordless. We're entering a world where tying the identities of our physical world to our digital fingerprint. Fingerprint, not in the literal, but what we do on, on the digital and the web.

Tying those identities can actually be with high confidence, a pretty good validator of authentication, and therefore doing away with the need to input a password. And there are a number of companies that are emerging in the space.

TONY ROTH: So just changing our focus for a moment. Where is the creativity really happening?

I look at, for example, big companies that have the biggest R&D budgets. And then I look at all of the startups that are going to turn into the software companies of tomorrow because they are in fact the progenitor AI locuses of activity. Is it happening in both areas or is it really big companies just trying to consume the services of these startups that have really all the talent? How do you describe the ecosystem?

RUDINA SESERI: It's an and not an or. I think you've had some very cutting edge work come out of the incumbents. And by incumbents I mean the Googles of the world, the Microsofts of the world, Salesforce to, to a certain extent, but Amazon for sure.

You have a lot of interesting research that has come out, but equally, you have a lot of. Research and paper coming out of the academic institutions, and you also have a relationship with a lot of the large tech companies and incumbents are collaborating with researchers in, in academia and vice versa.

Both folks and researchers have seen the opportunity to start their own companies. So, unless a incumbent makes it very, very appealing in the millions and millions. The opportunity cost of being the domain expert around the facet of ML to solve a real problem in the market with a real budget is quite high.

I have high faith in the entrepreneurial spirit in that regard and in commercializing technology and AI research. In fact, I believe that this is the fastest space we have seen in the area of AI in terms of transformation and adoption of technology. From the initial publishing of a paper to industry adoption, I think of three months ago out of MIT a technique called LIGO, a paper came out on it, which basically makes the computational requirements for large language models, quite, quite low. It's already sort of being adopted in industry and for startups, and if, in fact, it leaves up to its potentially, it could be landscape changing because all of a sudden the natural monopoly type characteristics, or at least oligopoly type characteristics that you see with open AI and Google and others, the game changes because you don't need as much computational power to process the data and train your algorithms and so makes it appealing, not just for the incumbents, but other smaller players.

TONY ROTH: Does the pace of change make it harder or easier for you to do your job successfully when things are changing just so fast and you decide, okay, I'm going to put chips on this small venture company.


TONY ROTH: And then all of a sudden, who knows what happens.

There's three other companies that leapfrog it or I, I don't know.


TONY ROTH: Or it becomes obviated by some other development. Is it a harder environment to be successful to invest, or is it just different?

RUDINA SESERI: I would imagine that you would be harder of an environment to invest if I were a generalist early-stage investor, because how do you keep track of everything?

As a specialized AI and frontier tech investor, two things. No, I live and breathe it every day. So if anything, it's actually refreshing to say, ah, this problem, we have to have this work around to solve this challenge around this technique. Now we get more visibility in this. So no, it's different, not harder, especially for early stage.

It does require specialization, that is for sure, and it does require, being embedded in the innovation ecosystems.

TONY ROTH: I've always thought about technology as having two locuses of benefit. There's the aspect in which technology improves the efficiency of our economy. So for example, AI could enable us to deal with our inflation problem by taking labor off of the shoulders of the workers and having computers handle it and not have a supply and demand imbalance in the labor market, which has been driving inflation. That's one aspect of technology. It's the ability to do things faster, more accurately, more efficiently, et cetera. But the other aspect of tech technology has always been, what I think of as the softer, qualitative side, which is the improvement of society and civilization, which is the improvement of living standards, the quality of life that's not as directly measurable.

So when you think about AI and where we're likely to be as a society in five or 10 years, how different will our world look because of AI?

RUDINA SESERI: We will have redefined yet again our relationship with technology. Our children, I have a 10 year old daughter, they're already AI natives and neither we you know it, nor do they know it just yet.

So it will be a very differently defined relationship. I think it will result in productivity. I also think it will result in job displacement. Interestingly. More jobs being created, believe it or not, but different types of jobs. So, it will have societal ramifications around retraining and not just of low skilled workers, I think of high-skilled workers.

So how do we plan for that academically? And then in terms of the softer benefits, that really depends on the culture. Fundamentally, we make of technology who we are, and it reflects us more than anything else, but the dynamic of reshaping our relationship with technology will be very, very real. And this time it'll be a human-like interaction because of the way that it's being AI set up, especially generative AI. And in fact, it makes one wonder whether eventually now I'm really pushing 10, 20, 30 years, especially with the rise of quantum computing, which is not quite around the corner, but maybe two corners out. We're seeing some breakthroughs in that in, in hardware. The rest, you know, will follow. But those combinations and also the research that's being done around the brain, what will it mean for life longevity? What will it mean for also, going really philosophical, where if I'm augmented by AI algorithms, where do I, the human, end?  Where does it, the machine, begin?

Or vice versa? How will our society handle it?

TONY ROTH: We have to end here, but I want to thank you and I think the takeaway today is just have profound. And important the technology corners that we are in. The process of turning as it relates to. I just love the way you laid out those three core ingredients that are enabling this revolution of massive amounts of data.

Massive amounts of computational power very cheaply accessible, with really smart algorithms that are sometimes writing themselves and improving themselves. At the end of the day, it, it comes together to be a lot of different changes in our world. Again, Rudina, it was great to have you today.

RUDINA SESERI: Thank you for having me, Tony. Always a pleasure.

TONY ROTH: I want to remind all of our listeners, Wilmingtontrust.com for our latest thought leadership on many topics, and we will be back to you again with another episode shortly. Thank you.


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Featured Guest

Rudina Seseri    
Founder and Managing Partner
Glasswing Ventures


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