- Geopolitical Strategist
Norway, Intermediary
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Episode notes
Two of Wellington's tech experts join host Thomas Mucha to discuss the rapidly evolving AI landscape and separate hype from reality, highlighting everything from the industries most likely to be impacted to AI's geopolitical implications.
Key topics:
2:45 – What is generative AI?
5:00 – Artificial intelligence investment opportunities
7:05 – Potential long-term winners
10:00 – Surprising pace of AI adoption
10:55 – Leveraging AI in investment processes
12:25 – ESG and ethical concerns
14:00 – Geopolitics and the regulation of AI
22:45 – Economic implications
24:30 – Industry risks and opportunities
27:15 – What does generative AI mean for humanity?
Transcript
BARBETTA: So to say, “You can’t build this technology here,” is not going to prevent the technology from being built. It’s not going to prevent jobs that are likely to be at risk being at risk. But it will prevent the economic growth that occurs on the other side. So you mentioned a balance, I think there will be more growth than destruction. And you want to have that growth I think if you’re a regulator.
MUCHA: Today we’re exploring the transformative potential of generative artificial intelligence or generative AI. First for context a bit of political history. Late changes to top-level geopolitical meetings are uncommon barring a sudden emergency or catastrophic event. This year, however, the agenda for the G7 summit in Hiroshima, Japan was altered a few weeks before the meeting with the addition of generative AI. This is somewhat extraordinary given the far more urgent discussion topics like the war in Ukraine and China relations, but world leaders are so concerned about the disruptive potential of this emerging technology that they wanted to set regulations as soon as possible. My recent conversations with policy makers across Washington also indicate a growing concern about the socioeconomic implications of AI particularly as it relates to income inequality. Then of course there are the myriad national security implications from AI-embedded weapon systems to an accelerating arms race in AI as great power competition dominates the global policy and geopolitical backdrops. So to sort out hype from reality and to help us think through the many aspects of this suddenly hot market issue I’m joined today by two of Wellington’s leaders on the subject, Michael Masdea, head of our risk and investment science team, and Brian Barbetta, a global industry analyst and a technology expert here at Wellington. So guys, thank you both for joining me on WellSaid.
MASDEA: Thanks for having us, we’re excited to be here.
BARBETTA:Thank you.
MUCHA: Brian, let’s start with you. So generative AI has taken the world and the markets by storm in just a few months it seems so let’s get a couple of basic definitions out of the way, what is it and how is it being used?
BARBETTA:Thank you, that’s a good question and it sure has seemed to have taken the world by storm here in just a few months. So generative AI is really a branch of machine learning algorithms that generate content based on a user prompt. Examples range from prompting a program to generate a cartoon-based on a description to generating a series of written answers based on a series of questions. Early commercial cases are being built around summarizing medical appointments or generating legal briefs. Helping to plan a vacation. Really a lot of different creative implementations of this technology. The algorithms are based on large language models or LLMs which are trained on large data sets to solve common language problems.
MUCHA: So they’re basically just reading a bunch of words and making sense of it?
BARBETTA:So reading would be one way to think about it. We would read a bunch of words. What the computer is doing, what this program is doing is taking in all those words, applying what are called tokens so trying to analyze what each sentence says, and then as it gives content back, as it gives answers back it’s trying to forecast what’s the most likely token or series of words or word to follow what it’s generated or what you’re prompting it with. So the reason I push back on this notion of reading is we use the phrase artificial intelligence and these algorithms can appear to be intelligent but they don’t actually read the way that a human being would read. And the example I’d give around that is if you think about how long it takes the average human to learn how to drive, a teenager can learn how to drive in somewhere between 25 and 50 hours. Whereas a computer needs to drive millions if not billions of miles to be able to drive a car and you could imagine a human looking at 10 or 15 pictures of a cat and a dog and understanding the difference, whereas you have to train a computer on again thousands or hundreds of thousands or in some instances millions of images. So I wouldn’t necessarily use the phrase reading but you could think about it as similar to reading.
MUCHA: Michael, this is potentially the biggest technological disrupter since the Internet with implications for nearly every facet of society. For our economy to national security, to the development of future technology. So I’d like to focus first on the investment angle, what’s the current investment opportunity set for generative AI as you see it?
MASDEA: Yeah, there are a few areas that we see opportunities, you did say investment not trade, and there’s a lot of trades going on, but I think from an investment perspective we see a few areas, the first one being some of the larger tech companies, and I think it’s important to say why. And these are the companies that have both the dollars to really invest in this kind of technology and importantly the access to the data. The data is critical in the process of generative AI. So that creates barriers, it also creates kind of a positive flywheel where the better your data the more you can evolve your systems and your algorithms, and it actually allows you to sort of separate from the pack. So I think their advantage grows with time as we’ve seen and we saw something similar in machine learning. The second area is some of these innovative smaller companies and what they really do is they’re coming up with cases that are use cases that maybe the larger tech companies haven’t thought through. And I think they’re going to find niches that can be quite lucrative. And finally some of the key enablers and this is the companies like the semiconductor chip companies and some of the infrastructure companies that are enabling this. We see opportunities in a lot of these. And of course as the technology matures over time I think we’re going to see more and more use cases and more and more opportunities and certainly for Brian and I one of the fun things that we do is looking for industries that are taking these technologies and using them in differentiated ways and ways that we would have never thought of years before and that’s really where there’s going to be some fun opportunities in the future.
MUCHA: So that’s a broad opportunity set that you just laid out. How confident are you that the market fully understands what we’re getting into here?
MASDEA: So, what we typically see in this early phase there’s just this tremendous amount of sort of euphoria and sort of creative thinking, and it kind of precedes what’s really possible in the short term. But the flip side is that long-term these technologies actually tend to be more transformative than anybody can really think about. You know, with new use cases and ways to do things that we haven’t thought about at all. But it takes some maturity of the technology and we’re still waiting for that.
MUCHA: Brian, building on what Michael just said, obviously we’ve seen this a lot with hot new tech sub-sectors, it’s creating a lot of price volatility, lofty valuations, so how do you see this market evolving from here? And what should we be looking for in terms of who are the long-term winners, who are the flash in the pan losers?
BARBETTA:That’s a great question. And it’s something that we obviously spend quite a bit of time thinking about. So we continue to see leaps forward in the capabilities of the technology that is being invented around machine learning and AI today. So, I think it’s critical to keep a very open mind about what is next. While generative AI is capturing our attention and imaginations today for good reason, it’s certainly not the first nor will it be the last innovative breakthrough we see in the development of AI technology.
MUCHA: Yeah, this is just a little piece of it.
BARBETTA:Right, it’s interesting to have studied the space for so long and to have known so many of the practitioners for so long, they see this as another big step forward. And if you think about this as a sort of staircase of technology it’s certainly a big stair that has been climbed here. But it’s by no means coming out of left field or something that happened overnight. And it’s certainly not where everyone’s going to stop. It’s really how do we continue to build on this, how do we have this become an important input into what is a broader goal in the study of machine learning and artificial intelligence. The end state eventually being some type of artificial general intelligence that opinions range from that’s something we’ll have in this decade to something we’ll never have. And we won’t know the answer till we know the answer, it’s not something I think we can forecast. But what we can forecast and what we can say with confidence today is there continues to be a lot of innovation here. So, with that in mind when we’re early in a development of a technology we think the companies enabling the technology tend to be a good place for focus. That would be the semiconductor companies that are enabling the training and the usage of generative AI today. They seem to be some of the clearest winners, these are businesses that have had strong demand for years behind these trends and that has now accelerated. The growth as is often the case in technology can be explosive. And when you’re dealing with hardware can be a little bit episodic. So you do tend to see some surges and then some deceleration in growth. But we do think these enablers are going to be a very good place to spend time and should be great opportunities in the market from time to time.
The next level that we’re focused on is the platforms and the companies who are building this technology who should benefit as more consumer and enterprise-facing products roll out. And here I think it’s important to remember that the technology is not static.So someone who has a lead today doesn’t necessarily have a lead tomorrow. We’ve seen large shifts in share between platform businesses over time. So we’re trying to think about who has durable competitive advantages, that can be unique data as Michael mentioned or it can be having a large user or customer base that allows you to innovate, experiment, and iterate rapidly to be able to build the next great product and having a number of customers can be a very large durable advantage as you try to build against this new technology.
MUCHA: So Michael, you know, like Brian you’re a veteran, what surprises you most about this? What’s different about this event versus the other hype cycles we’ve seen?
MASDEA: Yeah. Brian and I have watched a lot of these play out over our decades and I think the speed of adoption of this one has been a bit surprising and Brian mentioned it that this has been going on for a while like the use of generative AI has existed in the marketplace, companies have been using it, but all the stars aligned and we really inflected and that’s on a funding basis, that’s on a usage basis on a number of different aspects. And those inflections are really hard to predict and they almost always surprise us, but they often come and we’re looking for those every day. And what comes after is a little bit less surprising and we were starting to see that already and it’s really this dynamic of expectations get really really high really really fast. And it tends to exceed the technology at first. But again longer-term the technology tends to have a lot more capabilities than anybody’s dreaming up today.
MUCHA: Yeah stuff we’re not thinking about.
MASDEA: Stuff we’re not thinking about. Use cases we’re not thinking about. Technology innovations and breakthroughs that are still yet to happen. And that’s what we’re continuing to look for.
MUCHA: So how is your investment science team using generative AI and machine learning in our research process? And who do you collaborate with here at Wellington in that process?
MASDEA: Great question. So we’re fortunate to have many machine learning data experts here, including those who are steeped in generative AI, and understand what’s going on, and are practicing it. Some of which have been around here for decades, and actually have seen how this has all unfolded. And so, we’re leveraging their capabilities in every way we can think of and we’re doing it across the whole investment process. So if you think about investing, there’s an idea generation process, and then there’s an implementation process. And we’re looking at all those areas, including summarizing research, extracting insights from all of our data that we have, driving efficiency in various ways, so there’s a lot going on. But as you expect, I assume a lot of firms are actually doing a lot of these things. So, part of what we need to think a lot about is how do we differentiate, and what makes us different, and one advantage we have at Wellington is we have the size to invest, so we have these experts that can really tap into the work that a lot of these big tech companies are doing. And then we also have breadth. So we have exposure to all asset classes, hundreds [of] investors, all market cap sizes, all parts of the world. What that creates is a lot of proprietary data, a lot of proprietary insights and understanding. And when you have that that can be a pretty big advantage for things like generative AI. Like Brian and I were talking about earlier, if you have data that no one else has, you have a real potential advantage. So we’re trying to lean into that, and really understand, how can we tap into that in ways that the rest of the market can’t?
MUCHA: Yeah that’s interesting, the variety of perspectives can lead to better outcomes, because you’re mixing more ideas in it.
MASDEA: That’s right.
MUCHA: So Brian, what are some of the chief ethical or ESG concerns with generative AI?
BARBETTA:I think it will be very hard to stay ahead of all the potential challenges that this technology will create. So while we don’t know for sure I think it’s fair to say there’s likely going to be some similarities to the challenges here that we have seen with the scaling of the Internet and the scaling of mobile technology. The second-order effects again are challenging so ride-sharing became very large as a result of everyone having a mobile device in their pockets. And that has had social implications for labor, that’s had implications for the value of a taxi medallion, and I think very few people likely forecasted that when we all started buying cell phones. So there are always going to be second- and third-order effects that are hard to forecast. I think here it’s clear that unintended biases in algorithms are likely to have some challenges. And then as I think about the potential of the technology for misuse I think we’ll see large language models leveraged for hacking so that’s certainly something we see as there’s new technology developments. And then from a misinformation campaign perspective I think the ability to engineer a lot of what I’d say are fake users to essentially achieve a goal so to have say you create 50 million social media accounts with the intent of spreading a particular disinformation, it’ll be a lot harder to detect that those social media accounts are fake. Because there’s more creativity in how the answers are generated. I think it’ll be important for us to figure out how to leave fingerprints in the technology so that we can detect what is a human and what is one of these generative AI-controlled bots.
MUCHA: So a lot of what you just mentioned intersects with the geopolitical and national security concerns not just of generative AI but of AI broadly speaking, which of course is my interest here. So first, you know, AI broadly speaking, it’s squarely moved into the policy maker focus particularly with regard to the potential for labor market impacts and what this means to economic inequality, social cohesion, the resulting political impacts. That comes right out of the mouths of policymakers when you start talking to them about this so, I think there’s an education gap here that’s really important from the policymaker perspective. Lawmakers are racing first to try to understand the technology. And then second to figure out ways to regulate it. So I was in DC this week, meeting with US senators and representatives and it’s clear to me that there’s a very steep learning curve on the policy side that we’re going to have to deal with from the investment perspective. Now by contrast, and this is similar to what you said about this being around for a while, the national security world has been on this for years for a lot of good reasons. And that’s because AI is expected to be, you know, what they think of as a transformative technology as it relates to national security, broadly speaking but particularly in the great power context and this accelerating competition with China and Russia who are also key players and rapid adopters of artificial intelligence. And this is a long list of issues whether that’s infusing weapon systems with human decision making or military networking and communications. It’s opening up all these new cyber vulnerabilities across business and government, to the emerging risk of nonstate actors or individuals getting their hands on the more nefarious aspects of this technology to what you just mentioned, AI-enabled social media algorithms, how that’s going to impact inflaming internal political debates and division, to data harvesting of sensitive personal information, this is a long list of nightmares here.
BARBETTA:There’s good stuff too. Just to be clear.
MUCHA: You can get to that, I’m not done with the bad stuff yet.
BARBETTA:It helps plan a dinner party. And we shouldn’t underestimate the value of that.
MUCHA: So, in that national security perspective now the Pentagon has put down some internal markers particularly in terms of how AI is applied to weapon systems but naturally there are concerns here that other countries won’t adopt similar protections, particularly in such a competitive and combative geopolitical environment. So I think geopolitical realism, these ongoing shifts in military doctrines and strategy is also driving an acceleration of these AI factors. It mirrors a lot of what’s been happening in the markets. Right. There’s a lot of activity and a lot of enthusiasm on the national security side with this technology. I think these national security concerns need to be a bigger part of the conversation as this investment gold rush accelerates. So my question to you, Brian, you know, with that as the backdrop and I know I threw a lot at you there, how do you expect emerging regulations to affect, let’s say the investment landscape first?
BARBETTA:Thus far we’ve seen a number of regulators who have tried to tackle this problem and at least thought about it. So Japan has taken an approach of saying that you can use copyrighted material to train algorithms. Which was viewed as a very friendly maneuver towards technology companies to hopefully accelerate the development of this technology. Interestingly it does have challenges in different languages. So that seems to be an olive branch if you will from the Japanese government to try to encourage technology companies to invest more there whereas we’ve seen Europe take an earlier stance on trying to craft some regulations around how to make sure the growth of this technology occurs in a way that’s consistent with the values I would say that the EC has really put forward in their technology regulations. For better or worse and I don’t know the answer of whether it’s better or worse I do think regulators remain behind in regulating technology for the most part. And these are regulations that are extremely challenging to craft and I think for better it’s worth being a little late perhaps as opposed to being early and I’d use the ride-sharing example as one where again you would not have forecasted I don’t think the explosive growth in ride-sharing when we all started carrying cell phones but it’s great to look back and make adjustments to the labor policies around ride-sharing rather than preventing that technology from growing the way that it has and creating the amount of economic growth that it has. On the labor front we’ve seen creative destruction from technology consistently over the last decades, I mean really that’s the growth of any economy involves some level of creative destruction. And I don’t necessarily think this next phase from generative AI is going to be substantially more disruptive than what’s already happened over the last 20 or 30 years. I think there’ll be a lot more good than bad, notwithstanding all the bad you highlighted. Though from a national security perspective I hope we are early not late in trying to understand these issues and hopefully there’ll be a lot of common ground there that politicians can agree on.
MUCHA: Yeah, and I do hear a lot of those positive aspects from lawmakers as well, I mean the first thing is like yep, we’re worried about jobs, we’re worried about social instability, we’re worried about national security. At the same time they say AI is going to enable all sorts of advancements in learning and education, etc., etc. So yeah, like any technology, there needs to be a balance. There’s been a lot of talk in my circles about increasing policy coordination in the great power context, right, on semiconductors and other industries. AI has been a huge focus of this great power competition push. And I’m curious if you think we’re going to see the kind of increased coordination across countries in regulating this, or do you think the national differences and the technological differences and competing interests might get in the way of that?
BARBETTA:I would draw a distinction between regulating the development of the technology and regulating where the hardware enabling the training of this technology is able to be sold. I think we’ll continue to see more not less regulation around semiconductor supply chains. What types of technology are able to be sold in what regions. We’ve already seen a significant impact in the GPU market or the graphical processing units which enable a lot of these technologies, you now can’t buy the leading-edge technology in China and those regulations become more onerous in the years to come. I think you’ll continue to see those types of regulations including limiting the ability of the machines that make the semiconductors being shipped into China so again I do think you’ll see more of that. Regulating the technology itself, regulating software development in general I think is very hard if not impossible. And the technology will find a way to be created so if you were to say for instance, “You can’t build generative AI that we think may have an impact on job creation or on existing jobs,” that technology will likely be built in another region and the jobs created will accrue to that region and will be lost in the region that regulates. I think that’s a lesson that the European Union has likely learned with the vast majority of market capitalization in the technology sector occurring really in the west coast of the United States that being Internet and software companies. So to say, “You can’t build this technology here,” is not going to prevent the technology from being built. It’s not going to prevent jobs that are likely to be at risk being at risk. But it will prevent the economic growth that occurs on the other side. So you mentioned a balance, I think there will be more growth than destruction. And you want to have that growth I think if you’re a regulator. So hopefully there’ll be thoughtfulness around that.
MUCHA: Do you think from an investment perspective these differing regulatory frameworks that might emerge is a point of differentiation in picking winners from losers?
MASDEA: I think in areas like this where it has much more geopolitical and much more national security interest impact, I think the regulatory frameworks will play a much bigger role than they have in a lot of other areas. I do think it’s something that we have to navigate.
MUCHA: Yeah, I found that the national security implications are imbuing a lot of the other industries as well.
BARBETTA:So I would agree with Michael and certainly getting the direction of regulation here is going to be very important. We have the incredible benefit of working with folks like you, Thomas, who are spending their time with regulators and meeting with the decision makers and that’s access we get that’s rare and that allows us to forecast some of where this regulatory travel is going.
MUCHA: Yeah, in such a fluid environment I think that’s a critical aspect of it. Now Michael, we’ve sort of touched upon some of the socioeconomic implications here but I’m curious how you’re thinking about the long-term sort of nuts and bolts economic implications of this technology and do you think it has the potential to accelerate GDP for example? Or maybe help central bankers foresee shifts and make better policy decisions? I mean, is there a straight line here between this technology and economic impacts?
MASDEA: I’d say yes to both of them but there’s kind of asterisk to both answers so as I think you know well, Thomas, demographically globally we’re moving in the wrong direction for GDP and that’s slowing global GDP over time. There’s a much bigger premium put on the productivity piece of what’s happening in the world. And generative AI has tremendous potential to provide a productivity boost. The tricky thing is that, we’ve talked about this a little bit but the impact it could potentially have on the labor market. And as Brian said earlier, humankind has a lot of history with these major innovations and we ultimately evolve, but that evolution needs to happen and it may not be the smoothest and prettiest evolution but what our skills are and what our labor does will need to evolve as generative AI plays more of a role in the economy but net-net I think it should be a productivity enhancer which is what we need globally for GDP to continue to grow. And certainly, generative AI has the potential to forecast better. That’s one of the potential real powers of this technology and I think that will benefit a lot of the central bankers and as they’re trying to forecast what’s going to happen. Of course, the subject matter of which they’re trying to forecast is going to evolve too because of the influence of generative AI so, how that nets out and how quickly that’s positive versus confusing for the central bankers I think still remains to be seen.
MUCHA: Yeah, and I think we’re quite a ways away from FedGPT. So Brian, you know, what sectors or industries do you think are going to be most harmed by AI? And this is me being pessimistic again, and which ones do you think will be the most positively impacted?
BARBETTA:So I think the shadow of the sectors that do well are likely the sectors and roles that will be challenged. So as I think about generating content the ability to generate far more content at a far lower cost should lead to an explosion in what we can interact with, what we can view, what we can listen to. What we can read. And the flip side of that will be some of the content creators who have those roles today are likely to find themselves marginalized.
MUCHA: So the Hollywood writers’ guild and musicians and artists. Do you have a pessimistic view on their futures?
BARBETTA:I think the people that are at the forefront there so if you’re writing a novel that’s read by a number of people or you’re building top-tier movies I think they’re likely to be better off not worse as they’re able to do more. It’s sort of a superpower. If you can craft that incredible story or direct that incredible film this should be a tool you’re able to use to do that better. On the flip side if you’re building ad copy for instance that is used in a digital world that isn’t a big brand campaign but it’s trying to convince someone to buy a certain color pair of sneakers those are roles I think are more likely to be automated. And it’s hard to know. I mean it’s something I think a lot about but so hard to know what’s next and as we were sitting here I was thinking about what if I had a digital assistant that knows how my week is going, that knows whether or not I slept well last night or didn’t, and is then able to tell me what is the best thing for me to eat today. As we were talking about breakfast before we kicked off, I was thinking about it and thinking it’d be great if there was a generative AI tool that said, “Brian, you’re going to work out this afternoon instead of yesterday when you worked out in the morning so you should have this for lunch. You know, you’ll be less likely to have heartburn or it’ll improve your performance at the gym.” Which I desperately need and then help me with selecting my meal for dinner, for instance, and coming up with a recipe, and saying here are the three places you could go get takeout from. And is there a restaurant chain that leans into that? Is there a food delivery company that leans into that? Is it crazy, and nobody wants that? You know, we don’t know yet, and I think we’ll see just an incredible number of products built around this, and they’ll be obvious in hindsight, you know, how did we not see ridesharing coming? How did we not see homesharing coming, the way that they’ve taken off with the explosion of the internet? But, you know, there’ll be billions of dollars created by those who go out and build it.
MUCHA: My digital assistant is screaming cheeseburger in my ear right now.
MUCHA: So let me wrap up this conversation with more of a philosophical, or existential type question. So, you know, we have an idea about what generative AI is, thanks to this conversation and all the work that you guys are doing in the topic. But what does it mean for our future lives?
BARBETTA: I’m going to think about it a little differently, if you’ll give me permission to answer a slightly different question. Which is, what will it continue to mean? I would just zoom out a little bit from just generative AI, and say that machine learning has already changed so much about how we interact with the world. Whether people recognize it or not, the television show you choose to watch tonight is going to be recommended by a very expensive and thoughtful computer program that is getting very good at forecasting what you will or will not like, when you turn on the TV. One of the companies that introduced one of those recommendation engines saw explosive growth in the amount of time people spent watching content once an algorithm started suggesting what might be next. Generative AI is going to open up a new front in content creation and digital assistance that should lead us to hand over more decision making to technology, and should lead us to likely spend more time with technology, whether or not you view that as good, I think is a whole other podcast. So those are the things I think we’ll continue to see. And the big question I have is how are we able to marry this generative AI technology we’ve built today, these forecasting engines, with the next breakthrough in machine learning? I do think it needs to be rooted in some version of truth, truth being knowable facts, like mathematics. And to your early point about reading, this should give a machine learning algorithm more of an ability to truly read, rather than ingest large amounts of data. And that’s where a lot of research is happening today, how can we get to a point where we can point these programs at a subject matter and say, go learn, go research this science, go study physics? And come up with some type of new conclusion, and new areas of research, and that’s what’s next, I think it’ll be something we’re talking about hopefully in the not too distant future, and we’ll continue to see this technology have more of an impact on our lives.
MUCHA: Michael, we’ll give you the last word.
MASDEA: I would just add that, you know, this is a technology, right? It’s a tool for humanity, and for humankind. It’s not inherently evil or inherently good. I think it is a tool. And I think a lot of what comes of it will be of how we decide to use it. And I think it’s important to make that point. And the other important point is how we as humans evolve around that technology. And so, I think that’s going to be the key is how we react as humans, how we really evolve around this, and how we decide that we want to use this. And so I think it’s still early in that, and we watch how it unfolds.
MUCHA: All right, that’s a great place to end. And dear listener, I can assure you that neither of these guys used ChatGPT in any of their answers. So thank you both for being here on WellSaid, once again, Michael Masdea, head of our risk and investment science team, and Brian Barbetta, a global industry analyst and technology expert.
BARBETTA:Thank you Thomas.
MASDEA: Thank you, Thomas.
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Views expressed are those of the speaker(s) and are subject to change. Other teams may hold different views and make different investment decisions. For professional/institutional investors only. Your capital may be at risk. Podcast produced June 2023.
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Co-Head, Risk & Investment Science
Global Industry Analyst
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