AI podcast mini-series

Oscar Pulido: Welcome to The Bid, where we break down what's happening in the markets and explore the forces changing the economy and finance. I'm your host, Oscar Pulido.

Artificial intelligence, or AI, is more than just a buzzword. It's a transformative force that has revolutionized industries and reshaped the way we live, work, and interact with technology. But before the age of self-driving cars, virtual assistants and smart homes, there is a fascinating history of research, trials and tribulations that laid the foundation for the AI driven world we inhabit today.

In this episode, we'll delve deep into the origins of AI, tracing back to its theoretical beginnings and early aspirations. We'll uncover the pivotal moments that triggered significant shifts in investment sentiment, and we'll analyze the turning points in AI's history that propelled the technology from mere possibilities to tangible investment realities.

Here with me today is Jeff Shen Co-CIO, and Co-head of Systematic Active Equity at BlackRock, an investment team that emphasizes the use of data-driven insights and cutting-edge technology in their approach. Jeff and his team have been at the forefront of innovation in AI for decades from their role in helping to start BlackRock's AI labs several years ago to the strides that his team is making in leveraging the latest AI advancements in their investment process today.

Jeff, thank you so much for joining us on The Bid.

Jeff Shen: Thanks for having me.

Oscar Pulido: So, Jeff, I'd like to start by asking you to talk a little bit how you got started in this field of systematic investing and then what is your interpretation of some of the most recent developments in artificial intelligence?

Jeff Shen: Absolutely. So, I started in graduate school, got a PhD in finance and had been in the investment world for the last, 25- 30 years. I'll say that it's certainly been extraordinarily exciting to see some of the most recent developments. Some people like to call it the age of AI, the age of big data, the age of machine learning, and we're going to get into a little bit of what all of these things mean. I'll say, given what's going on in the world, this is certainly a bit of a golden age. We've also never seen so many developments in different algorithms to interpret this interesting data. And then eventually relative to the investors I think the most important thing is how do you make sense of all of this data and what you can do with this data and eventually lead to a better investment outcome?

Clearly, we're going to get into Generative AI, large language models, If I have to step back a little bit, we've been looking at numbers for the last 40, 50 years. And large language model or natural language processing in general certainly allows us to be really smart readers of any of the texts out there. Whether it's earning transcript, whether it's a broker calls, whether it's news articles. Now we can read smartly as an investor. And I think that's a revolutionary step that can impact how AI can be applied in the investment world.

Oscar Pulido: And that's interesting that you use the term, golden age for data, for technology and how it's maybe helping you do your job. But maybe if we could just, take a step back and tell us a little bit about maybe some of the technology milestones that led to the development of artificial intelligence. Just wind the clock back and help us understand where we've come from.

Jeff Shen: Now we can wind back the clock a lot if we want, because when you think about artificial intelligence it is really a field that encompasses many different subfields, if you go back to logic, go back to Aristotle, or if you go back to normal distribution, At the same time, the artificial intelligence field as we know it today probably can date Back a bit around 1940s and about 80 plus years of research and development. When we think about in 1969, Marvin Minsky and John McCarthy were given Turin award. A big part of it is actually about how do we think about representation? How do we think about reasoning in a machine intelligence way? And when we say machine intelligence, essentially you can really think about an intelligent agent would get a bunch of inputs and then the agent would go through a rational set of calculation- what we call algorithm. And the output will be a set of rational actions and behaviors that is actually desirable in a particular type of environment or context.

The development here is about better input, meaning better representation, better sensing technology, better algorithm, how do you process this information better? And also, how you measure the success is really to think about the output or the behavior coming from this kind of, intelligent agent, whether that can learn in a new environment, whether it can adapt to a new environment, so how good the output is. So, it is really about better sensing, better processing, or better algorithm, and then eventually leading to better output.

Oscar Pulido: I didn't realize you would take us back to Greek philosophers in winding the clock back, but you also made the point that a lot of the research and development has been taking place over the last 80 years. Certainly, a long period of time there've been advancements in this field. Have there been certain breakthroughs or milestones that triggered a shift in the investment trends in artificial intelligence where it started to get the attention of those who wanted to again, invest in this field and weren't just observing it from afar.

Jeff Shen: Yeah, I think the investors', attitude towards this field certainly have gone through I call it a potential three phases. Initially, there's always a sense of skepticism. How can an intelligent system be better than humans in behaving or delivering certain output?

So, there's a certainly a phase of skepticism, which is normal for any type of new technology. Then I think it can go through a bit of a period of hype. There's a lot of excitement, maybe sometimes too much excitement, and then that may eventually lead to a bit of a crash of excitement in any type of new technology.

And I think for AI, I'll say that back in 1950 s, there was a prediction that AI system would beat the world champion in chess in about 10 years. So, you're thinking late 1970s AI would be able to beat the world champion. That did not happen until 30 years later. we all know the story of Deep Blue and Gary Kasparov in 1997.

So initially there was certainly a bit of hype then in the 1980s there was certainly a bit of an AI winter and that caused a lack of enthusiasm in the sector. And then clearly, given some of the latest development, I think right now we're certainly seeing quite a bit of excitement, quite a bit of hype. So, I think, from an investment perspective I would like to think a thoughtful approach in any of these technologies, it's important, because there could be hype, there could be skepticism, there could be crash. It's always important to think about what is really going to impact, the future, what is really going to survive some of the hype, and I think the thoughtful approach is certainly an approach that BlackRock likes to take.

Oscar Pulido: And presumably this cycle that you highlighted, and I'd never heard the term AI winter, but I get the sense, of course those are periods where there was more pessimism towards this technology, but a lot of technology goes through that cycle, perhaps that you've described the hype, the pessimism so with respect to artificial intelligence, why is it that it has reemerged as a viable investment opportunity? I think it's safe to say we're out of an AI winter.

Jeff Shen: Yes, the 1980s is quite behind us. I want to take us back a little bit to think about some of the games that AI has actually delivered. I talked about the 1997 chess match. Deep Blue won over Gary Kasparov the World Champion back in that day. People also may remember that 2011, the IBM Watson system actually end up winning Jeopardy. and we thought Jeopardy's a very human type of endeavor and, for folks in Asia, the game Go has been around for a couple thousand years.

And the Google DeepMind developed this algorithm called AlphaGo. And that essentially, it is an AI system, that actually defeated Lee Sedol who was the world champion back in 2016.

If you don't know what the game Go is, it's a game with a board and you put black and white pebbles on top of it. And the whole objective of the game is to occupy as much territory on the board as possible. The difficulty of the game is that the number of possibilities of the moves is more than the atoms in the universe.

What Google DeepMind did was trying to use essentially what we call is machine learning. It is actually learning, along the way. So to a certain extent, the chess, what they did was a bit of a brute force. They imagined all the possibilities and they went for it. The game of goal, the issue is that you cannot get to the end by imagining all the possibilities.

The beautiful thing here is really that the machine came out with some of the moves that the human Lee Sedol in particular thought was pretty dumb moves, but it turned out to be brilliant moves10 minutes into the game. so, that was a surprise in a sense that it won without mimicking human. It won because it went with a machine way of thinking about what's most rational move under the circumstance and it was adaptive along the way. So, I think that was probably the most surprising part when we see artificial intelligence, that was probably the manifestation of the artificial intelligence because it had nothing to do with human intelligence.

Just to dwell on this, they developed another system called Alpha Zero, which did not even know the rules of the game and essentially self-simulated, so it was playing against itself try to learn the game. That was a major breakthrough because it did not rely on any of the prior human knowledge and it basically self-simulated, so played against itself such that it knows the objective eventually want to occupy as much territory as possible. Other than that, it did not know anything and, it ends up actually winning, against some of the world champions. so that's the part that's most exciting.

So, I would say that some of these big triumphs of AI systems that actually defeat, human or world champion in particular games certainly has been an extraordinary excitement, both in terms of thinking not only what AI can do, but also, the potential for AI system to surpass human capabilities.

That's certainly creates a lot of excitement, but also creates some level of anxiety. And I would say that leads both towards potential opportunities, but also there's quite a bit of a risk that people are considering.

Oscar Pulido: And so just bringing it back to the investment landscape, what are some of the types of companies in the AI space that are attracting the attention of investors?

Jeff Shen: I think, right now generative AI, especially, related to large language model is certainly all the excitement. Language is so central to our civilization, to our culture, and if you have a AI system that can have language, essentially at its own disposal and being able to not only mimic a human, but also, in a lot of tests, you can see that it's actually, whether it's a AP test for some of the high school graduates or some of the GMAT or GRE type of test, it certainly has demonstrated very strong capabilities. That part is actually generating a lot of excitement.

I think the second part is also if you're providing tools for the AI revolution whether it's chips or whether it's systems, whether it's cloud computing. So, the tools companies are also benefiting, from this overall AI trend.

That's probably the two big ones. Clearly, the outlook for this space is there will be certainly a few winners, but I think there's also could be quite a few losers because this is a very competitive space.

Oscar Pulido: And on the generative AI, side of it that you mentioned, as artificial intelligence becomes more mainstream and more people talking about it, one of the things that gets raised, and I think you touched on this, there's ethical concerns, there's questions around the biases that might exist in the data that AI is processing, there's data privacy concerns. So how do you see these generative AI companies dealing with those issues, or are they waiting for public policy to step in and regulate this in their own way?

Jeff Shen: Yeah, I'll come back. regulation second. I think just looking at the state of the world I’ll make a couple of observations.

One is that biased data will lead to biased output. Data integrity is very important and making sure that the data is covering all different aspects of the world so that we can get a more balanced output. So, I think the integrity of data is certainly critical for the success of the AI system.

The second part is some of these algorithms that's been run are extraordinarily powerful. Millions and billions of parameters that's actually being put into the system. The challenge there is really that the transparency aspect of it is actually very difficult to achieve just given how complex these systems can be. Probably some people may have heard about the neuro network that is underlying a lot of these generative AI systems. When you get into the billions of parameters, it's very difficult to figure out how the decisions is actually being made, that level of transparency can be of concern.

So come to the regulation. I think this is also the field that the industry, the technology is certainly ahead of where the government policies are. And this is actually where education for the policy makers, making sure that people really understand not only the power, but also the risk associated with the systems are very critical.

I think it's also a competitive game whether it's a competition within the industry, competition around the globe, it’s also forcing industry to need to go very fast. On one hand, this is a very powerful tool that can be transformational in a lot of industries, certainly in the investment industry. At the same time, the regulation is one or two steps behind, so I think that's the conundrum that we face.

Oscar Pulido: And so, Jeff, you took us back many years in terms of the history and some of the milestones that we've seen that have helped the development of artificial intelligence. You're using this in your day-to-day job as a systematic investor in present day. So, take us forward into the future. What are some of the developments that you see happening in AI and what are the investment opportunities for investors?

Jeff Shen: I would call out three things that I think for people to think about. I'll say that big data. Big model and big crowd on the big data front there's certainly a lot more data available and from an investment perspective, this is certainly about how do you take as much data as possible in your decision making. And it's a scale game, it's a bit of a race, on how much data you can absorb, how much data you can process. But I think the race is on, and I think that's also where the excitement can be because if you can measure things, you can make better decisions. And data is really the new oil for the AI system.

On the big model front, we talk about generative AI, we talk about large language model. These models are getting to be more and more powerful, also more and more flexible. I'll give you one specific example today we can measure many things; we can also combine them in a way that is actually optimal. So, it is not only about measure many different things in the economy in the world, but it's also combining them so that you can form a holistic decision through a big model by combining features together. So, I think from that perspective the big model not only allows you to combine different things in efficient fashion, but also allow you to tackle multiple objectives. You may care about, the investment outcome you may care about, draw down characteristics. You may care about the climate impact. So, this kind of big model really allows you to combine things in efficient way, but also allow for considering different objectives.

And the big crowd aspect of it. If you think about how generative AI is actually coming from. The human input is becoming more and more important in getting the AI system to be better. So, it is interesting that even though we call it artificial intelligence, human intelligence is actually quite important element of artificial intelligence. So, I think this idea of human in the loop is certainly something that I think is here to stay. We are making the system better and to allow the system to produce more rational response.

The new world it's not only about generating new ideas, we can see that from a generative AI perspective we can generate a lot more. But I think the human input here is also really allows us to think about how we judge these things. How do we discriminate the good output in relation to mediocre output? So, I think the human, aspect of it is actually also evolved from going from purely at a job of generating things to one that's also about discriminating different outcome. So, I'll see that big data, big model, and big crowd, are some of the exciting things as we look forward.

Oscar Pulido: Well, that's an easy way to remember your outlook and that last point, is a good example of just as technology evolves and it appears to be displacing humans in, what they do, humans adapt and humans contribute to that technology and work alongside, that technology. So, I'm going to go out on a limb, Jeff, and say, this is not the last time we're going to talk about artificial intelligence on the podcast. So, we look forward to having you back at some point in the future. And thanks so much for joining us today.

Jeff Shen: Thank you so much, Oscar.

Oscar Pulido: Thanks for listening to this episode of The Bid. If you enjoyed this episode, be sure to check back next week for part two of our deep dive into AI, where I chat with Brad Betts, former NASA scientist and global equity researcher for BlackRock's systematic investment team, where we look to the future of this technology and explore the potential applications for AI in the investment world.

Subscribe to The Bid wherever you get your podcasts.Disclosure

This material is intended for information purposes only, and does not constitute investment advice, a recommendation or an offer or solicitation to purchase or sell any securities, funds or strategies to any person in any jurisdiction in which an offer, solicitation, purchase or sale would be unlawful under the securities laws of such jurisdiction. The opinions expressed are as of the date of publication and are subject to change without notice. Reliance upon information in this material is at the sole discretion of the reader. Investing involves risks. BlackRock does and may seek to do business with companies covered in this podcast. As a result, readers should be aware that the firm may have a conflict of interest that could affect the objectivity of this podcast.​

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The Bid - Navigating The Future of Investing with AI | AI pt. 2

Episode Description:

In Part 2 of our deep dive into AI, Brad Betts, former NASA scientist, Stanford Professor and global equity researcher for BlackRock Systematic, joins Oscar to help us understand what AI means for investing and the opportunities that lie ahead for the investing landscape.

Written Disclosures in Episode Description:

This content is for informational purposes only and is not an offer or a solicitation. Reliance upon information in this material is at the sole discretion of the listener.

For full disclosures go to Blackrock.com/corporate/compliance/bid-disclosures

TRANSCRIPT:

<<THEME MUSIC>>

Oscar Pulido: Welcome to The Bid, where we break down what's happening in the markets and explore the forces changing the economy and finance. I'm your host, Oscar Pulido.

Artificial intelligence can be described as the simulation of human intelligence by machines. As we heard in the previous episode in this two-part look at AI, we heard how AI has been evolving for decades. Today we're going to take a look at the implications of AI for the investing landscape. Here with me today is Brad Betts, global equity researcher for BlackRock Systematic, an investment team that emphasizes the use of data-driven insights and cutting-edge technology in their approach.

Brad focuses his research efforts on using artificial intelligence, machine learning, and natural language processing to generate alpha. In other words, he uses AI to attempt to outperform the markets, and he's been researching these technologies for decades from his time as a scientist at NASA to the pivotal role he's played in bringing AI innovations to BlackRock. Brad will help us understand what AI means for investing and the opportunities that lie ahead.

Brad thank you so much for joining us on The Bid.

Brad Betts: Thanks for having me.

Oscar Pulido: So, Brad AI is seemingly everywhere right now. We're hearing about it on company earnings calls. We're hearing about it in news headlines, but you've been researching and using these technologies, really throughout your career. So, I'd love to hear more about the evolution of AI in terms of how you've seen it and how it pertains to the investment management industry.

Brad Betts: Yeah, my pleasure. I was an intern in the summer of 1989. I was working in a military research lab and one of the senior members of the lab had gone to a conference and came back and gave us all a briefing, and it was the first time that I had heard about artificial neural networks. And these neural networks are the key architecture behind things like ChatGPT. it was a particular demonstration. of a truck, a big rig, you know, an 18-wheeler being backed up.

Here I was seeing a computer learning how to do it. It wasn't being instructed explicitly by humans. It was learning how to do it, and this fascinated me. and it could do it even from a position that you called jackknifing a big rig, which is a really tricky position to get a truck out of. And it was riveting and so that was my first exposure.

Back in those days, we had nowhere near the computing power, nowhere near the volume of data, nowhere near the accelerated computing hardware that we have now, and nowhere near the sophistication of the algorithms. But it captivated me and it in no small part led to the great fortune I had in that next step of my career, which was to do my doctorate in something called the information systems lab at Stanford.

That lab was buzzing with ideas of information theory and signal processing, and cryptography and optimization and compression and medical imaging. It was just a wonderful time and there were giants of the 20th century, that were there. But there was another powerful force in this information Systems lab at Stanford, and it was a gentleman named Bernie Widrow - Bernard Widrow. And Bernie Widrow, had developed the first practical use of a neural network. If you made a phone call for many decades in the US you were using artificial neural networks, it was being used behind the scenes to do things like cancel echoes on the line.

Anybody that remembers older phone calls and you could sometimes get a really annoying echo. Well, that was technology that had been developed by Professor Widrow and others, something called Adeline. Professor Widrow, and others, built Adeline in 1960 before I was born, so that history of this goes well back into the 1950s. And it was the first practical use of a neural network. And again, these neural networks are what are underpinning so much of the progress and excitement.

I then had the good fortune to use what I had been trained and taught at places like NASA, at startups in Silicon Valley, and then close to 16 years ago now at BlackRock. The evolution has been for me, one of extraordinary excitement and privilege to see the changes in these technologies, to see the advent of more data, of more computation, of increased sophistication and algorithms, has led to this remarkable excitement that we're seeing now.

Oscar Pulido: Well, it's interesting because for many of us, common folk we think of AI as a recent development but I think you started your story with an internship back in 1989, this really has been an evolution over multiple decades, and you mentioned a couple terms, by the way, you mentioned neural networks, machine learning. I know we talk about like large language models, natural language processing, how do you think about those terms? What do they mean? How do they apply to the investment industry? Maybe we can go there.

Brad Betts: The field is definitely filled with jargon acronyms and terminology. Artificial intelligence, is a very broad and ambitious field of endeavor, which is obviously looking to try to mimic human levels of intelligence.

Machine learning is another term, and you can think of it perhaps as a sort of a subset of artificial intelligence. that tends to be more focused on performing a particular task. It's using computers to learn from historical data to take advantage of a historical data to make predictions about the future. That's a common use of machine learning. And its roots are in the signal processing and computer science and applied mathematics, the applied mathematics of optimization community.

You mentioned natural language processing, another term that people will use for that is also text analysis but text analysis, natural language processing, NLP, is trying to have computers understand and take advantage to perform some task, human speech, human writing.

You mentioned large language models, ChatGPT is an example of a large language model. We call them large language models because they as rule of thumb, are trained with large amounts of text data, and also they have a very large number of parameters. you can just think of them as dials. you have one dial on your thermostat at home, imagine having billions of dials. So how you set those dials is critically important. The last one, you asked about, Neural networks, the, maybe the slightly more correct people usually just shorten it to neural network. we really say artificial neural network to distinguish it from the biological neural networks in our own human brains. These artificial neural networks we're meant to be very crude, of approximations of our human brains, of the neurons and synaptic connections and it turns out that these techniques pioneered by people like Bernie Widrow and many others have proven, over many decades now to be staggeringly successful.

Under all of those terms you've mentioned, natural language processing, machine learning, artificial intelligence, large language models. Under all of it is large amounts of data. Large amounts of computing power to take advantage of that data. And then also this accelerated computing. It's not just the standard computers people tend to have at home. There's specialized hardware that is well-suited to machine learning, natural language processing, artificial intelligence tasks in particular. It actually grew out of graphics processing, but it turns out it was well suited to the mathematics of this of this area. So, I hope that gives a flavor and we've only touched some of it.

Oscar Pulido: Well, one of the added terms that comes to mind when you think of Ai, and you mentioned it, one of the most widely publicized headlines around AI when ChatGPT was released at the end of 2022, and I think you put it in the category of Large Language Models. So how does the large language model that ChatGPT uses compare to what you would use in investment analysis?

Brad Betts: We use large language models for investment at BlackRock, but we use them very differently. So, we take these large language models and we tune them to make forecasts for the returns that companies will generate in their stock. So, in effect, you can think of this model being tuned and becoming a real specialist. This a big piece of computer code powered by massive amounts of data and computation and becomes a specialist in making forecasts of, what will the return be to Apple over the next week, over the next quarter, over the next month.

And the process by which we do that is one called fine tuning. So, when people are interfacing with something like ChatGPT, they tend to be putting in text and they get out text. Whereas when we use them for investment, we tend to be putting in lots of text, and what comes out are numbers, forecasts of returns, and we use those forecasts of returns to conduct trading.

Oscar Pulido: Brad, one of the criticisms perhaps of ChatGPT is you put in text and. The text, you get back may or may not be accurate, it might have provided a lot of convenience, there's a bit of like, how do we know what it's telling me is true?

How do you then, assess that with the fine tuning that you described when you put in text and it gives you numbers, and do you know how believable that data is in order to make your forecast?

Brad Betts: You put your finger, of course, on a very difficult and challenging part of our job, which is exactly what you said. how do you trust, how do you tune the neural network properly, appropriately to do what it is that you want it to do?

So, this fine tuning process, think of it as, as, picking up an initial base model, that sort of understands a language, say English, it understands English very well. It can generate tokens, maybe not quite as well as ChatGPT but it can do it pretty well.

And then what we do is we feed in new text and we give this text a label. And this label is used by the neural net to understand if it's done something well or poorly.

Say you take the transcript of an earnings call, or say you take a broker report from some sell-side firm, this is the phase where you're training your neural net, then you're going to use the neural net for new data. But initially what you're doing during this fine tuning is using history to make the net much more specialized. And so you put in a piece of text and you give it this label. you can use the future returns that a company will receive over some horizon.

Then the neural net takes that information, feeds it forward through its architecture, and it produces a forecast of what the return it thinks will be achieved. We reward or penalize the neural network on how well it does. And then the neural network, the algorithm I'm describing it so I'm personifying it, of course it's just math and code, computer code, but what happens is the neural network then adjusts its weights.

I mentioned earlier that notion of like maybe having a billion dials, how do you set those? This part of the algorithm is now called back-propagating. The neural net says, “okay, I took that piece of text. I think that the firm in the future, over the next say month or so, isn't going to do very well. Oh, but you're actually telling me it did do very well. Thanks for giving me that feedback via that label. I'm now going to go and adjust my weight slightly to try to close that gap.”

But you're doing this with huge amounts of information and you're doing it very rapidly. And it's not just one piece of text. It's taking in billions of these items and it becomes very skilled at matching what happened. And now what you can do once you have trained it in this fashion now you can use new pieces of information that it hasn't seen, so nobody knows because now you're talking about a real prediction, you're talking about the future, the neural net can take that piece of text and say, based on everything that I've learned, that I've been trained with, I think that this firm is going to do well. and again, it's not using any one piece of text.

By the way, Oscar, one of the things that allows us to do this fine tuning so powerfully is a brilliant algorithm called ADMM, the alternating direction method of multipliers. Under the covers, a lot of this comes down to doing optimization well, and this ADMM technique, was brilliant. And we have developed and tuned an ADMM implementation for so many years. that under the covers allows us to do this fine tuning because I described it as it's just matching returns. we actually get it to match exactly the investment problem or as close as we can make it to what we are really going to do.

So that is to say we want the neural network to learn to maximize returns while minimizing risk and minimizing transaction costs and minimizing borrow costs. And we're able to do this because of the brilliance of ADMM.

Oscar Pulido: Can I go back to something you mentioned, and that was fascinating how you painted that, image of the neural network learning being given feedback. Isn't the data that you are providing, historical data, and we're in a new investment regime, the great moderation is over, that period of low interest rates and low inflation and growth being more stable and predictable. Well, now we're in a new investment regime, of more volatility, so how do you think about the predictive nature of these models when maybe we're at an inflection point and some of the real-time data that you are feeding, it, it might not be trained for that. or does the data go back so far that it has plenty of history to have picked up on various economic cycles?

Brad Betts: Yeah, a huge change was the pandemic. As humans, we hadn't roughly seen a pandemic at this scale for, around a hundred years. Think back to March of 2020, how many of us really predicted how sharp the rebound would be in markets. And whether you are a computer, Or whether you're a human, A shift of that kind of magnitude is an incredible challenge.

The markets are a very dynamic system. And the nature of a dynamic system is It keeps changing. it will continue to be true. algorithms, humans have to be nimble, they have to be able to take advantage of what information they have available to do the best job that they can for prediction.

And we'll see, it is a fascinating time to, to be alive, to see, who is able to adapt more effectively to these changes that that will always be there in the markets.

Oscar Pulido: That race between man and machine, brings up something that we touched on in our previous episode, which are some of the ethical concerns, that arise from artificial intelligence, and what the impact is going to be on industries and society. And there seems to be two, schools of thought here. One is very fearful about the rise of these technologies and there's another side of the camp that's a little bit more I reverent. So, what's your view in terms of the business impact, the impact on society from artificial intelligence?

Brad Betts: It's my life's work. I don't go to bed worrying at night around a machine takeover. The opposite for me. I'm excited by the opportunities that these present the opportunities. You know, I,I feel that sometimes the conversation gets a bit focused on the risk and that's human. but sometimes it misses the extraordinary opportunity, the opportunity that will come, you look at algorithms like alpha fold. a neural network that learned the 3D folding structure of proteins, the opportunity that will afford for drug discovery is wonderful.

I fall in the camp of being more excited. I think that the impact businesses will see is around, automation. I think a lot of the ways initially and unsurprisingly businesses will see these impact will be, in productivity enhancing tools, for example, on things like email, in code generation, in the translation of old computer code into more modern languages to reduce operational risks, to put them into a more modern, framework.

How many of us really go to bed at night achieving inbox zero? Imagine if you had an agent that was sitting there that was summarizing the emails for you, was summarizing presentations, was drawing your attention over time to the ones that it's starting to learn, that you're more interested in that it feels are more worthy of your attention, that are able to summarize.

I think that is a way, and firms like Microsoft are working very hard and highly incentivized, of course to bring those kind of capabilities, to many enterprises. So I think these opportunities for automation are a path where we're going to see, a flurry of activity around the adoption of these technologies.

Oscar Pulido: I think the term inbox zero would resonate with a lot of people. I know it resonates with me. It seems like an impossibility unless I delete everything and then surely I'll have missed a lot of important things. But while you work in the investment industry, it sounds like a lot of the examples you gave were actually just about productivity, automation you mentioned healthcare as well. Are there other opportunities for artificial intelligence in in other industries or other, just, parts of our life that we're not thinking about, that you think about when it comes to artificial intelligence?

Brad Betts: You see them more and more in things like, chip design, the layout of chips on silicon, on car aircraft, spacecraft design, improvements in advancements in simulation, environments that, that come from these technologies.

There's a lot. And I don't want to give short shrift to investment, my goodness, there's so much opportunity that we have taken advantage of at BlackRock and will continue to take advantage of at BlackRock through these technologies. Our allegiance, Oscar, is to whatever is efficacious. Our mission at BlackRock is to do right by our clients. It's to do right and to bring them those investment results that they want and need.

 What it really excites me, the opportunity for these technologies in an investment context is that opportunity to continue to improve the outcome ultimately for our clients to execute the mission.

And also quickly, I mentioned that it feels very personal for me. my mom grew up one of seven kids. They were of modest means and she put herself through high school and she put herself through nursing school and. And she raised three boys. And she and my dad built a home for us and, saved their money and bought me and my brothers, our first computer, a Commodore 64 that took savings from them, that took effort. And I've been obsessed with computers and math since I was a little boy. And Oscar, if II can put, my life's study of math and computer science, if I can put that to use, to help our clients to, then for me that's a life well lived. That, that idea that maybe I can try to help a family that has never had a vacation. there's an extra basketball. It as a birthday gift or something, all the things that, that my mom never had. and for me that's, that's a life well lived.

Oscar Pulido: I love that story. I'm sure a lot of our listeners will say the same. Brad, I know you're based in San Francisco. I'm just thinking about your comments. I've been through the office but there's a quote somewhere that says something like the art of investing has become the science of investing. I'm wondering whether you put that up there when you first joined?

Brad Betts: Well done, Oscar. Good for you for remembering. That comes from one of the most brilliant, researchers I've had the privilege to work with, Ron and Richard Grinold.

Oscar Pulido: Brad, thanks for all the insights and, most importantly, thanks for joining us on The Bid.

Brad Betts: Thanks for having me, Oscar. It was a real privilege.

Oscar Pulido: Thanks for listening to this episode of The Bid. If you enjoyed this episode, check out part one where we took a look into AI's history and its inflection points for investing. Subscribe to The Bid wherever you get your podcasts.

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