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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. Oscar Pulido joins leaders of BlackRock to discuss how AI will impact the investing landscape of today.

The Bid - AI's 3 Investing Phases

Episode description:

AI has been dominating investing headlines for almost two years, but it's not just a buzzword. It's a powerful technology that's poised to revolutionize industries and economies on a global scale. Investors are asking how will AI reshape job markets, productivity and economic growth? Nicholas Fawcett, a senior economist in the BlackRock Investment Institute joins Oscar to explore what AI means for the broad economy and the different stages of AI's evolution.

Sources: Productivity estimates based on Brynjolfsson, Li, and Raymond (2023), Dell’Acqua et al. (2023) , Cui et al. (2024); Capex spend from BlackRock Investment Institute, Reuters, October 2024; Agricultural data based on IPUMS USA, October 2024

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.

Reference to the names of each company mentioned in this communication is merely for explaining the investment strategy and should not be construed as investment advice or investment recommendation of those companies. For full disclosures go to Blackrock.com/corporate/compliance/bid-disclosures

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TRANSCRIPT

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 Oscar Pulido.

AI has been dominating investing headlines for almost two years, but it's not just a buzzword. It's a powerful technology that's poised to revolutionize industries and economies on a global scale. Investors are asking how will AI reshape job markets, productivity and economic growth? And more importantly, who stands to reap the benefits of this technological revolution.

Joining me is Nicholas Fawcett, a senior economist in the BlackRock Investment Institute. Together we'll explore what AI means for the broad economy and the different stages of AI's evolution. Nicholas will provide an overview of the possible changes across sectors and the strategies investors can use to position themselves for success in this new landscape.

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

Nicholas Fawcett: Thanks for having me back, Oscar.

Oscar Pulido: So, Nicholas, we've been talking about AI or artificial intelligence for some time. The BlackRock Investment Institute has, labeled it as one of the mega forces, which means it's a big structural driver of returns. We've talked about the history of AI on The Bid, we've looked into the investible themes that it creates. But curious, how do you see AI playing out?

Nicholas Fawcett: Absolutely right. It's burst into global consciousness a couple of years ago when Chat GPT came out. One of the reasons it's captivated so many people is that AI is trying to embody and then amplify or scale human intelligence. That's why it's so different from the regular ICT boom that we saw from the 1970s and the 1990s, that put a desktop computer on our desks and then a phone in our pockets.

 It's the general technology that could rival some of the most important breakthroughs of the past couple of hundred years, like steam power, electrification. It's something that can be embedded in a whole range of different applications and avenues that could transform some of these things in the future.

But there's a lot that we don't know. So, we have to be really humble in, every step of the way because they're so complex, these AI models, and we don’t know how much more complex they're going to become. We don’t know how much energy they're going to need. And we don't know really at this stage what effect they might have on the economy. Is it going to transform a lot of sectors broadly? Is it going to be a more narrow impact? The world could look very different depending on the answer to these questions.

So, in order to get in the game, we think we need to have a good BAT. I'm not talking about sports, I'm talking about a framework for laying out how we see this evolving. And the BAT in question is three things, three phases if you like.

First is build-out, the second is adoption, and the third is transformation. The first phase is where we are now, the build-out phase where we're seeing, in a sense, a race to build-out AI infrastructure - data centers, the chips that are needed and so on.

The second adoption phase is really to come. It's as AI matures and firms start to accelerate their adoption of AI in their regular processes, that's going to require a lot of investment from those firms. And then third is the most interesting long-term impact is transformation.

This is where firms really could unlock the value of ai and there's a story to tell about the economics of all of that there's an important question of what it means for investors because the opportunities are here now, even if some of the kind of longer term structural changes are going to play out over decades.

Oscar Pulido: And so, you mentioned the build-out phase, which is the first of those the three phases that you talked about in BAT, as you cleverly labeled that in the build-out phase. What we have tended to see is a handful of stocks that have really dominated market performance. I'm thinking about companies like Nvidia and the Magnificent seven, top companies in the S&P 500. Is there room to go in terms of the performance of some of those companies?

Nicholas Fawcett: We think there is. You mentioned Nvidia, that's captured the imagination of the tech community because the key provider of all of these chips, underlying AI models, we don't think that rules out further growth. And fundamentally, we're only at the dawn of the AI era companies are racing to build-out ever more complex models that requires a lot more computing power than even we have at the moment. That's going to continue as the technology improves.

 What we've noticed is that the underlying GPT models have become an order of magnitude more intelligent with each evolution. So chat GPT3, 4, and in the future 5, 6, 7. And each time you jump up an order of magnitude, you need a lot more computing power. And you can map out with reasonable confidence that the demand is going to be there for the foreseeable future at least. And in a sense, for the firms that are investing in this some of the large tech firms, it's a question of survival rather than should you invest at the margin in data centers or chips or so on, because those who don't compete are risking losing out all of their market share in terms of this opportunity. And the scale that's necessary in order to invest in AI requires you to be a really big player in the first place because the CapEx is so expensive, then you need to continue leveraging that scale in order to continue investing.

It's very difficult for new entrants to get into that market right now because you need a big balance sheet, and that's what these firms have. There's a lot of investor, questions that we hear about whether AI is in a boom bust cycle right now. in a sense, that misses the bigger picture.

History tells us that transformations always create winners and losers. But we think that because AI is potentially so transformational, it has a much broader potential to create quite a lot of value across a whole swathe of the economy and ignoring that could be really missing out on a lot of the gains.

Oscar Pulido: And you touched on CapEx or capital expenditures and the fact that these big tech companies are the big investors in the AI infrastructure. You mentioned they have big balance sheets, and this reminds me of something that Tony Kim also talked about when we talked to him earlier this year about this theme, which is that while the amounts that companies are spending are quite staggering, they have the cash to be able to do it. So, he thinks it's going to continue. How much more will this continue, this capital expenditure boom that we've seen? Where do you see that going?

Nicholas Fawcett: In terms of long-term projection So that's a really big number. Underpinning that is this point that tech needs, computing power needs of AI models are so much bigger than anything that we've seen before. The internet or the search engine technology, that we've seen over the last couple of decades is a lot less computer processing power heavy than AI models. chip costs just directly are going to be a big part of the increasing CapEx spend that we see.

And it's not just that, in fact, it's also the cost of data centers, these chips run like physically really hot, so you need more powerful air conditioning. You need a lot of energy in order to supply the computers. There's a whole ecosystem around the underlying chip that has to be built out too.

In fact, as Tony talked about when he was last, with you, the energy needs are potentially so big that energy suppliers may be struggling to keep up. And so in terms of the risks that we would identify, one of the big near term risks is that they simply can't find enough energy in order to supply that. That's where you see some of the large tech companies investing heavily in even nuclear power stations right now in order to guarantee consistency of energy supply over the coming decades.

Oscar Pulido: And what you're pointing out is that while we think of AI as a technology, it has implications for many different sectors and it truly is transformational. It even impacts the energy sector as well and the demand for energy. Let's stick with Tony Kim for a second, Tony's very passionate about this topic and he's talked about the productivity gains that he thinks it brings to the personal assistant market. but when does AI start to deliver productivity gains for the broader economy?

Nicholas Fawcett: It comes back to the fact that AI is about intelligence, and intelligence is embodied in more, less everything we do, it could significantly boost productivity over the long term.

We already saw a bit of that in the ICT revolution. So, from the mid-nineties to mid-two thousands in the US in particular, ICT was responsible for a big bump up in productivity growth. that's meaningful. In the case of ai, there's a lot of uncertainty, the size and the speed of productivity gains, we just don't quite know yet.

It really boils down to two things. First of all, how far does AI improve the efficiency of specific tasks that you would do within a job, and secondly, how broadly across your job does it help you do things? which is quite big. Think about 10 to 30% time saving on being able to do a task. If you adopt that across every corner of the economy, then it could boost growth by one to two percentage points a year.

That's a slightly abstract number, but when, I was last on, we talked about, demographics being a drag on growth. All of the different drags on growth from the mega forces mean that economies can hope to grow, between one and 2% a year, even as it stands. So a one to two percentage point boost from AI is transformational. but the problem is in reality, that's probably a bit too optimistic.

So it's unlikely that every single job is going to be affected in exactly the same way. Some jobs are a lot more amenable to using AI than others. It could be that maybe a fifth of tasks are impacted.

But again, compared to a baseline in which you're growing somewhere between 1 and 2%, that's a meaningful uplift. Part of the answer is focusing on how AI can help us in existing tasks and existing jobs. some of the really exciting potential is for AI to create jobs that we don't know exist yet and can't even imagine existing.

 That's not new if we take the advent of, motorcar, for example, people didn't ride around on horses anymore but it also created the motorway service station and motel network, because all of a sudden people could drive everywhere. It creates new jobs. And in fact, even more recently, and more prosaically, the rise of e-commerce, for example, since the mid 2010s has seen a really rapid boom in employment in warehousing and logistics in the US in particular. There's still potential for AI to create new tasks and new jobs, there's a lot of uncertainty, but that's potentially where a lot of the excitement comes from.

Oscar Pulido: And since you touched on the last time you visited us on the podcast and we did talk about demographics. And as I recall, the one of the ways that economies can try and offset that drag on growth from aging populations is through technology. Innovation and maybe some of the productivity gains that can bring. and that's what you're touching on with what AI could bring in the future. Maybe let's stick with that theme about the labor force and how it impacts workers. How does AI then change the makeup of the economy going forward?

Nicholas Fawcett: So I think AI adoption could massively change the makeup because where labor's deployed, what labor does, that could all change. Even if you don't have a massive improvement in productivity on an aggregate scale, individual kind sectors are really going to be at the forefront of all of the change.

I talked about the shifts that we've seen before, during the, advent of the motorcar. we've seen similar shifts before. just to take a case in point, during the industrial revolution, in So the idea that we've seen big structural transformations is not new in history.

 Another great stat that I like half of the jobs that exist today didn't exist in 1950. The idea that all of this technological transformation is going to change the makeup is pretty easy to understand and motivate.

What we've seen over the past and what we think is going to happen is the nature of our jobs is going to change so that instead of doing things alone, we do things with AI as a tool, it's not going to view everyone out of a job. It's just going to make, people more productive, within jobs, even though. Some sectors are going to change quite a bit. So, what it boils down to again, is this question of will AI create new tasks, new jobs, that we can't conceive of right now? And we think that there's a lot of scope for that to happen. And so the makeup is going to change, but it's not going to mean that, we can just do away with labor.

Oscar Pulido: We've talked about the build-out phase, right? that first phase of the AI revolution that you're describing and how it has benefited certain companies in terms of their stock market performance. As we continue along this journey with ai, what are the industries, what are the sectors that you're seeing that are going to benefit? Is it still the ones that have been winning, or does it start to broaden out a bit?

Nicholas Fawcett: Yeah, it's a great question. So this is where we come back to bat. So build-out adoption and transformation. We're still in this build-out phase, and so we still see more room for these build-out related firms, to, to benefit.

And in particular, we talked about some of the chip producers. These are complex chips. They're still going to be made by similar kind of companies that we're seeing at the moment, and we think that it's difficult to get into that market right now. So, there's still ample demand there. we also see an opportunity in the data center and physical architecture around all of these chips.

So, the upstream industries related to that, like industrials, energy, and so on. As we move into the adoption phase, that's where we start to see things broadening out. So some of the companies that can start employing AI to improve their production, and some businesses are already starting to do that.

We're seeing some firms use AI to scan earnings transcripts, so they can assimilate the collect. Assessment of companies at the moment. some people are using it for marketing kind of core centers as a replacement for shrinking workforces and also a lot in the tech industry around coding.

So you can use AI to help you code, even if you know the language already it just massively increases your productivity and those early adopters present opportunities. Now, of course. further out, in this third phase of transformation, we think that there is going to be a wide range of applications, that firms are going to build and that's going to emerge.

And really the focus is going to shift on the industries that have the most potential, the most, AI exposed. Tasks that we can try and find. And the sectors I would pick out there are things like finance, retail, education, healthcare. All areas where there are a lot of jobs that people currently do on their own that could be improved with ai, but not in a way that would do them out of jobs necessarily.

That would just make existing labor more productive. And so you could dramatically increase. I. The potential for those, industries. And we're already seeing it push the boundaries of human knowledge. So there's a widely reported example of using AI tools to try and predict the way proteins fold.

And you can use that to try and then work out how to create new medicines. That's pretty groundbreaking and we're only in the early days and already there are, a lot of examples where there's no way that humans could have been able to make this much progress just on their own.

Oscar Pulido: If you're an investor and you're thinking about the investment landscape and everything that you've touched on, how does somebody best position themselves to benefit from this megaforce?

Nicholas Fawcett: Yeah. And absolutely right. That's where the back comes in again. you don't need to wait is the most important message.

You don't need to wait for the productivity improvements to come in the real economy. Eventually, you can look around and see investment opportunities now. So it's more concentrated right now on, the build-out phase. Even with the rapid evolution of some of the top players in the sheer volume of information, that we're getting and, the limited visibility that we have in some of the emerging tech means that you need to be pretty well informed in order to make a good investment decision.

That's why we think deep expertise is really important. Especially in the environment where there could be a couple of really big winners who win big.

And so relying on broad benchmarks doesn't really fit the bill in this case. in a way it's a kind of existential point. If we don't know what the world could look like in decades to come, the benchmark of, two- or three-decades time is going to look completely different to the benchmark of today anyway.

So what it means is you need to go active, you need more granular insights into, your investment approach and try and identify what the most promising opportunities are. you also have to think about firms that haven't yet floated firms that are still private. 'cause that could be, if you get them right at the early stage, that could be where some of the biggest value gains are to be had. Knowing how to go into that requires a lot of expertise. So that's why we'd say we favor being active and taking an active investment approach, because that's the way to navigate all of the uncertainty and the expertise that you need in order to make the right decision.

Oscar Pulido: Right, be active. And this is a space that is going to continue to evolve for many years. And you've introduced us a new acronym bat, which we will, now internalize and keep in mind build-out, adoption and transformation. And maybe we'll have you back at some point, Nicholas, to see where we are along that journey. Thank you for, sharing this information and thank you for doing it here on The Bid.

Nicholas Fawcett: Thanks for having me. See you again soon.

Oscar Pulido: Thanks for listening to The Bid. If you've enjoyed this conversation, check out my episode with Tony Kim, tech and AI investing trends, a stock pickers take where we discuss the evolving conversation and emerging opportunities for investing in the AI theme.

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Spoken disclosures at end of each episode:

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

MKTGSH1124U/M-3999705

AI's three investing phases

Nicholas Fawcett, a senior economist in the BlackRock Investment Institute joins Oscar to explore what AI means for the broad economy and the different stages of AI’s evolution and the strategies investors can use to position themselves for success in this new landscape

The Bid - “How to Invest in AI vs Tech In a Portfolio”

Episode Description:

AI investing is a hot topic of conversation. But how should investors be considering both tech and AI in their portfolios - as part of the same allocation, or should they be separate? Jay Jacobs, US Head of Thematic and Active ETFs at BlackRock joins host Oscar Pulido to discuss whether investors should consider AI and tech as separate allocations, different investing approaches to AI and specific sectors to explore within the AI ecosystem.

Sources: The Economist “IT companies log strong revenue growth outside key North America market, May 1, 2024; “Profiles of the Future: An Inquiry into the Limits of the Possible”, Arthur C. Clarke, 1962

Written Disclosures

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.

TRANSCRIPT

<<THEME MUSIC>>

Oscar Pulido: AI investing is a hot topic of conversation. We recently discussed AI and tech investing with Tony Kim and his insights from his annual Silicon Valley tech tour. But how should investors be considering both tech and AI in their portfolios - as part of the same allocation, or should they be separate?

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 Oscar Pulido

Jay Jacobs, US Head of Thematic and Active ETFs at BlackRock joins me to discuss whether investors should consider AI and tech as separate allocations, different investing approaches to AI and specific sectors to explore within the AI ecosystem.

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

Jay Jacobs: Thank you for having me.

Oscar Pulido: Jay, as we have five mega forces that we've been talking about that the BlackRock Investment Institute highlighted as structural drivers of return over the long term. These include things like, the aging population, the transition to a low carbon economy, and also artificial intelligence. So, when you hear artificial intelligence being dubbed a mega force, do you agree with that?

Jay Jacobs: Absolutely. And there's a few reasons why this is a mega force. I would say one reason to just brute force it is going to have trillions of dollars of impact on the global economy. So, it's big, it's powerful.

But I think secondly, this isn't just a narrow theme. This is something that is going to impact a wide range of sectors and industries as they start to adopt artificial intelligence. I think that's critical to any megaforce is that it should really have broad implications.

And then I think the third reason is that it's going to impact investor portfolios. Whether you intentionally allocate to the AI theme or not, the impact that it's having on the technology sector, the impact it's having on the energy sector, this means that it is going to impact basically all portfolios in the world one way or another. And it really is important for investors to consider that impact as they build their portfolios for the future.

Oscar Pulido: So, Jay, I'm curious to hear, what are you hearing in terms of the sentiment towards this topic from the investors that you meet with?

Jay Jacobs: there's a lot of excitement around AI right now, and I think a lot of this stems from the fact that we've really hit a new catalyst for artificial intelligence. Now, taking a step back, the idea of artificial intelligence has been around for a very long time. The Turing test, which was designed to determine if something was a computer or a human, was created in 1950. We've even had, Siri on our phones for over 10 years, but now we've hit this inflection point with ChatGPT and generative AI platforms that make it more accessible and frankly, more useful than ever before. So, I think a lot of investors are just trying to figure out, what does this mean, what does this mean for my portfolios, what does this mean for my everyday lives and how do I prepare for this?

Now what we've seen, in these first few months of this AI revolution though, is a lot of focus on just a few names, a lot of kind of concentration on household names in the AI space that's common in early days of a technological revolution. But you're starting to see now more interest in thinking about what's next, what's inning number two for artificial intelligence from an investing lens and from what this means for my everyday life.

Oscar Pulido: And we've learned a lot from folks like Jeff Shen about the history of AI and while it feels very recent to us as investors or the topic being talked about in markets, it's been around for a while, but it still feels to me like we're early in the theme in terms of capturing the investment opportunity. I don't know if you'd agree with that. And maybe if you do, how early in the theme are we?

Jay Jacobs: We're still in really early stages of the AI theme. Chat. GPT was released to the world in November 2022. We're a year and change beyond that at this point, really powerful mega forces can take decades to fully, I develop and have the full impact of it felt. So, what are some of the other things to evolve over the next decade or so? I think one is they'll continue to fine tune this technology. Large language models could get more complex and more nuanced than ever before. two, we're seeing the growth of data at an exponential rate and at its very fundamental basis, AI is just about leveraging data to generate more valuable outputs.

And the third thing is that we're getting more computing power than ever before. The semiconductors are getting more powerful. We're seeing hundreds of billions of dollars invested in data centers and really aggregating all of this compute into artificial intelligence. So, if you have better models, more data, more computing power, you really start to see exponential growth in artificial intelligence and an exponential increase in the value of artificial intelligence.

So, you put that all together and the fact that we've been at this for 20 months, 21 months, it's still very early in this mega force.

Oscar Pulido: And from your observation, what are the industries or the companies that have been most impacted by this early stage, mega force, either positively or negatively?

Jay Jacobs: Well, some of the earliest companies are in the technology space, which are actually in the software space because you could think about how, generative AI creates content, some of that content can be actually coding and programming languages for software development. So, some of the companies that have been the earliest adopters of artificial intelligence are just software companies.

Legal and consulting services, so if you can get more efficient in creating documents, more efficient in creating PowerPoints, more efficient in creating strategy documents, those segments of the economy have been affected by this already. But also, if you could look at the healthcare space, we see a ton of opportunity for artificial intelligence, how hospitals are managed to get more efficient, usage of doctors and nurses and medical devices. You look at pharmaceutical development, which is really an exercise in data, in terms of getting all this genetic and personal data and understanding how different drug compounds are going to interact with that to develop revolutionary drugs, there's billions of dollars of opportunity there as well.

So, this isn't an exhaustive list, but if you just take it as technology, healthcare professional services across legal and consulting, you're already talking about hundreds of billions of dollars of opportunity across the economy.

Oscar Pulido: And I know you're not an AI skeptic, but believe it or not, there are some of those people out there and it's natural whenever we have new technology that is unknown and we don't really know the future ramifications, but what do you say to somebody who is a skeptic or when you think about some of the industries that maybe have been negatively impacted so far.

Jay Jacobs: I guess that I would question the nature of the skepticism. I think it's fair for people to say this technology will take time to be adopted, that some of the use cases might be more valuable than other use cases, that maybe, we're being overpromised artificial intelligence in the short term.

But if you really take a long-term structural view of this theme, there's just a lot of conviction that this is going to have a major economic impact. You can see it when you use these tools, how there's really a brilliance, almost a magic to some of these artificial intelligence tools. A quote that I love is that any substantially advanced technologies indistinguishable from magic, that's where we are with generative AI today. Whether you're asking it to write a poem about financial investing or if you're, asking it to develop some strategy document for you, it's really a very powerful tool. And so, I think we learn how to harness this magic and make it an everyday tool, the use cases and the economic impact are going to be massive.

Oscar Pulido: There's a lot of interest, there's a lot of evolution. It seems like when you want to invest in AI, people think about investing in technology and I'll, maybe I'll just invest in the technology sector. are those the same things? If you want to invest in AI and just investing in the tech sector,

Jay Jacobs: A lot of AI companies are in the tech sector, but the tech sector isn't just AI. And one of the terms we use, and apologies, this is like a business consulting, a term here, but MECE mutually exclusive, collectively exhaustive.

This is how the sector world works. Every company has to be tied to one sector and every sector has to be unique in that no company can be tagged to two sectors. So, what that means in the artificial intelligence space is sometimes you have companies that are in the consumer discretionary space, but actually run really powerful cloud computing platforms that are essential to AI, or some companies maybe create software, which is really useful to AI, which is in the tech sector, but other companies might be tagged as a communication services company. So, if you just look at AI from a traditional sector lens, you're not necessarily capturing all the right companies that could exist outside of IT. And you're also going to capture companies within IT that are not AI: printer companies or companies that are, developing glass for smartphones.

Oscar Pulido: So, it's more nuanced is what you're saying. While we like to Break things up into nice, neat categories and definitions, when you talk about something like AI, it sounds like it can cut across numerous sectors.

Jay Jacobs: It does. So, you really have to look from the bottoms up with a fresh lens of identifying what are the AI companies around the world today, regardless of sector, regardless of geography. But really try to understand where these companies fit in the AI value chain.

Oscar Pulido: So, you mentioned the value chain and the AI space can cut across sectors. And so, when you think about the investment opportunities, where should people be looking?

Jay Jacobs: Right now, a lot of people are looking really at the mega cap tech names, specifically in the United States. And some of these companies have the most resources, the most data, the most software engineers, but that's really a very concentrated and limited view of AI. If you look under the hood, I think one of the most compelling opportunities right now is in the infrastructure layer of AI. What we mean by the infrastructure layer is semiconductors, digital infrastructure, even power infrastructure.

Because it's still early days, there could be different platforms that succeed in AI. There could be different products that succeed in AI, but regardless of what platform or product or a large language model of artificial intelligence wins, we know there's a common need, which are those semiconductors, the data centers, and the power.

Right now, AI is going to really drive a lot of power, demand growth. It's going to drive a lot of demand for semiconductors particularly. General processing graphics, processing units, it could even drive a lot of demand for things like copper and other materials that are necessary for data centers and digital infrastructure. So, it's really about not just trying to predict the winners 10 years from now, but who are the companies that form that base layer of infrastructure today and are benefiting from this build out.

Oscar Pulido: And that's consistent with what you talked about earlier. You just said copper. you talked about semis, which is the technology sector. But I'm recalling a conversation that we had with Will Su from the fundamental equities team and his discussion around AI was the demand for energy, that it's going to create. And we talked about data centers as well. So I go back to this question that when you think about AI and investing in AI, it is really different than just investing in the technology sector. Maybe you can elaborate, a bit more on that.

Jay Jacobs: It is. And so, part of that is really thinking across the entire AI value chain. You absolutely have technology companies that are some of the leading voices and developers and products in the AI space today, but if you look below that, that there's companies involved in real estate that are involved in AI.

These are the data centers that own valuable properties that can host, cloud computing services and a lot of compute power in them, and have all the security and, cooling that's necessary to run those. you can look at the energy companies that are going to be supplying power to those data centers.

You can look at companies that own transmission lines that are going to connect the power to those data centers, the semiconductor developers. It's really this entire ecosystem. And so, what I would really suggest that investors do is not get laser focused on just mega cap tech. You really have to look broadly across the economy to understand what is feeding into artificial intelligence.

Not just what is feeding into it, but also what are some of the critical choke points. I think energy could be a choke point. We really have to rapidly scale energy production in the US to be able to feed all these data centers. The physical real estate itself is a choke point. It takes time to build new data centers.

It takes permitting, it takes licenses, it takes materials and labor. Even semiconductors where you see really long supply chains, where there's wait times for some of these really powerful semiconductors, that's a choke point as well. So, it's about considering the entire value chain, it's about looking at who's benefiting in the value chain today, which is really that infrastructure layer. And it's about understanding what are those pivotal choke points where there can be really powerful economic pricing because these companies really have a scarce resource.

Oscar Pulido: As you're describing AI I think of it as a technology, but you're making me think of it more as a theme, that really touches on a lot of parts of the economy and a lot of different sectors.

And so, if you're an investor and you're building a portfolio, it sounds like you have to think about an allocation to AI as distinct from an allocation to tech. And maybe should investors be incorporating both of these in their portfolio or how would you ask, how would you think an investor should think about allocating in this space?

Jay Jacobs: Well, it’s not necessarily either or, but I think because there's overlap between the technology sector and AI, people just have to consider, what is that intersection? we know that investors that are looking just broad benchmarks across US equities or global equities are going to have a lot of tech exposure 'cause these are some of the biggest companies by market cap. But if you really want to have granular exposure to AI and really benefit from this mega force, you have to really make an intentional allocation.

So, one way that we're seeing investors do this is selling down technology exposure and replacing it with an artificial intelligence basket. That's just one way to fund it, we see other people maybe even selling down core exposure and buying an artificial intelligence basket. but you really have to think about how these two areas are intersecting because there is overlap between the two.

Oscar Pulido: And is there a geographical bias when you allocate to AI, do you, inherently allocate more to one part of the world than another? Or is it pretty diverse geographically as well?

Jay Jacobs: Right now, the artificial intelligence theme is very US driven. it could broaden out and you could see more companies around the world enter into the AI space as this theme develops. But if you look right now, the leaders across these different parts of the value chain from some of the chip designers, from some of these LLM platforms, to the data centers, to the energy, a lot of that still is very US focused today.

Oscar Pulido: But presumably the second order effect the beneficiaries of AI are probably more global in nature, and maybe in that case, not just in the US.

Jay Jacobs: Oh, absolutely. technology is a huge export from the United States. If you look specifically at the tech sector, 60% of its revenues come from overseas. So, I would absolutely expect that artificial intelligence will be one of those exports going forward. in a lot of ways this is part of the artificial intelligence theme is this global race for artificial intelligence. And I think if you look at valuations today, a lot of what's baked into some of these artificial intelligence companies is the expectation that this is a global theme, not just a US theme.

Oscar Pulido: And Jay, you spend a lot of time, talking about this theme and thinking about it and helping investors think about how to allocate in their portfolios. But what's that one thing you would want to leave the audience with when they're thinking about artificial intelligence in their portfolios.

Jay Jacobs: I think the one takeaway for investors is to really consider, do they have exposure to the full value chain of artificial intelligence? I'm willing to bet most investors have some exposure just because of how dominant some of the mega cap tech names in the United States are in broad us, US or global benchmarks, but likely investors are missing that longer tail of artificial intelligence names. Again, the semiconductors, the digital infrastructure names, some of the energy names that are going to benefit from this theme. So, it's really, you have to look across the entire value chain rather than just a concentrated handful of mega cap tech stocks.

Oscar Pulido: And you alluded to this, but the BlackRock Investment Institute has also recently talked about the fact that there's a lot of money being invested in this space in AI and all the value chain to use your term, but the return on investment from that might not be immediate. It might take some time before companies benefit from that investment. And I wonder, do you share that viewpoint and how long do you think people have to wait before we really start to see that return on investment?

Jay Jacobs: It depends on what part of the value chain you're looking at. if you look at this massive amount of Capex that's being spent by some hyper-scalers to develop artificial intelligence models, they're spending money today on building data centers, on securing energy, on getting semiconductors.

So, some of those companies are making money right now in artificial intelligence. I think where some of the lag is going to be is in the enterprise or government or in the consumer space, really starting to use these artificial intelligence products, the output from all that Capex, that could take time. It takes time for people to change their habits, it takes time for enterprises to adopt a new technology, to feel comfortable with it, to roll it out to their employees, to encourage its use. There will be a lag, but I think it's a well-deserved lag in the sense that its people taking their time to adopt a new technology and understand how to best use it.

Oscar Pulido: And perhaps, tying it to your comments about the investment opportunities, the companies that are benefiting from it right now are maybe the ones you hear more about. They're getting an immediate benefit, but the ones that have more of a lagged impact of it, it might be the future investment opportunity when it starts to improve their productivity and, when it's accretive to the growth of the overall company.

Jay Jacobs: I think that's right. If we look at today's winners, it's really focused on those artificial intelligence infrastructure companies. It's the semiconductors that are. Very powerful data processors that are powering kind of the training behind these large language models. it's the data centers that are in short supply as we look to rapidly scale computing power across the country.

it's some of the energy companies which are finding really a new growth avenue that they haven't had for the last 15 years in terms of. Growing energy demand from artificial intelligence. Those are today's winners that are collecting revenue really in this near-term timeframe. But if we look longer term, you know that healthcare use case for pharmaceuticals, it might be five years before we realize profitability from that.

Because you have to develop a new drug, you have to develop a new drug, cheaper or better than before. By harnessing this artificial intelligence power that's going to take a little bit of time. So, there's absolutely a, a. A different timescale that some of these companies are operating on in terms of when they will realize the benefit from this theme.

Oscar Pulido: And Jay, how are you finding artificial intelligence? Enter your day-to-day life or has it yet? do you find yourself. Using it or being impacted by it in your personal life or maybe even in your work life.

Jay Jacobs: I'm never going to create an itinerary for a trip again. I, that is something I fully outsource to, to the large language models. even writing emails, it, it takes a little bit of rewiring how you think about using this new tool.

I think we take for granted tools sometimes, we, we know how to use calculators, we know when to use calculators. We know how to interact with them. in a lot of ways AI is just another tool, but we have to get used to it. We have to start thinking about how we use it in our everyday lives to incorporate it, and then we can start to really enjoy the productivity gains of outsourcing some of the less efficient tasks we do, and creating more time for the more complicated, nuanced tasks that we do every day.

Oscar Pulido: In fairness, you might be a little bit ahead of me in terms of the adoption of AI in your everyday life. I still feel like I'm coming up the curve.

But I'm sure we'll hear more about the applications of AI in the world and how to think about it in investor portfolios. in the meantime, Jay, we really appreciate you joining us on the Bid.

Jay Jacobs: Thanks, Oscar.

<<SPOKEN DISCLOSURES>>

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

MKTGSH0924U/M-3850136

How to invest in AI vs tech in a portfolio

Jay Jacobs, U.S. Head of Thematic and Active ETFs, joins Oscar to discuss whether investors should consider AI and tech as separate allocations, different investing approaches to AI, and specific sectors to explore within the AI ecosystem.

The Bid – “AI And The Energy Grid: Solving for AI’s Power Needs”

Episode Description

The world remains abuzz over artificial intelligence, but rapid advancement and adoption of the technology is poised to drive a significant increase in power demand, and this demand could redefine energy consumption as we know it. Today we ask the critical question: is the energy sector equipped for the AI power revolution?

Will Su, of BlackRock's Fundamental Equities team, is one of BlackRock’s leading voices on all things energy. Will walks us through the sector’s pivotal role in the build-out and future of AI and digs into the potential investment opportunities and challenges.

Sources: “Electricity Mix” Our world in energy, January 2024; “What is U.S. electricity generation by energy source?” Energy Information Administration, “OpenAI Presents GPT-3, a 175 Billion Parameters Language Model” Nvidia, 2020; GPT-4 Details Revealed, Patrick McGuinness, 2023; Data Centers Around The World, United States International Trade Commission 2021; “North America Data Center Trends H2 2023”, CBRE 2024; “Electric power sector CO2 emissions drop as generation mix shifts from coal to natural gas” EIA, 2021; “Electravision” JPMorgan, March 2024; “Fuel Mix” Ercot, March 2024; “Television, capturing America's attention at prime time and beyond” US bureau of Labor Statistics, September 2018.

Written Disclosures

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 Oscar Pulido.

The world remains a buzz over artificial intelligence, but rapid advancement and adoption of the technology is poised to drive a significant increase in power, demand, and this demand could redefine energy consumption as we know it today, we ask the critical question. Is the energy sector equipped for the AI power revolution?

Today I'm joined by Will Sue from BlackRock's Fundamental Equities team. Will is one of BlackRock's leading voices on all things energy. He'll walk us through the sector's pivotal role in the build out and future of AI, as well as dig into the potential investment opportunities and challenges Will, thank you for joining us on The Bid.

Will Su: Thank you, Oscar. Great to be here.

Oscar Pulido: So, Will, we've talked about artificial intelligence on the podcast a lot, and it seems like there's no limits to the growth of this technology except the fact that it consumes a lot of energy and maybe that's the constraint. Tell us a little bit about why AI consumes so much power.

Will Su: The simple answer to that extremely complicated question is that information processing is energy, and we are processing more information today than we've ever thought of, even from just a few years ago. At its most fundamental level, computations are just moving electrons around a semiconductor chip, but when you multiply that very small electric current by trillions of calculations, the energy demand adds up very, very quickly.

I think Rob Goldstein mentioned this the concept of AI is not really anything new. The MIT AI lab was started in the late 1950s, we did have a breakthrough moment in 2017 when a team of researchers wrote a paper about the transformer, which then became the architecture for today's large language models or LLMs. Now, these models are being trained on trillions of parameters and tokens that make them high quality, high capacity, and able to contextualize the questions that they're being asked.

And just to give you an idea of how big the computational power we're talking about is here. ChatGPT4 was trained on about 70,000 Zetta flops of compute power. That's 70 trillion trillion operations per second. Mind bending numbers. And as that number grows over time, that's why we're seeing this recent interest in meeting the power demand of AI.

Oscar Pulido: Did you say Zetta flops? 'Cause I'm going to need a glossary. I think as we talk more about artificial intelligence, it feels like the terminology is new to a lot of people. And when you talk about power and the quantity, help us understand like, how much are we talking about on a global scale?

Will Su: So as anyone who tried to model this out can tell you it's very hard to have a lot of confidence for 10, 20 years down the road when you're looking at something with such exponential growth. That being said, we did build our own model because as they say, all models are wrong, but some are useful. In building this model, it's helped us understand what the key variables are and maybe how the shape of that future power demand might look like.

And the punchline is, we think there could be up to 1000 terrawatt hours of incremental electricity demand for AI by 2030, and that would be about 3% of global electricity. And keep in mind that the internet today already consumes 2 to 3% of global electricity for things like data centers, networking transmissions and increasingly for blockchains. In aggregate you could see total internet demand, including AI, make up 6 to 7% of global electricity demand by 2030.

Oscar Pulido: And how is the world going to manage that power demand because it's incremental on top of what is already the demand for power, right?

Will Su: Right. I think we can first dig a little bit into what is driving that AI demand. There's really three roughly equal buckets in our 2030 outlook.

One is for training. So that's the power that it takes to train these very large models. And again, just to give you an idea of the scale in 2022 Chat GPT-3 came out. It was trained on 175 billion parameters and 300 billion tokens. And the amount of energy it took to train could power about 90,000 US homes for a year.

Now you fast forward to 2023 when Chat GPT-4 came out, that model was reportedly trained on 1.8 trillion parameters and 13 trillion tokens. And the energy it took to train that could power 2.5 million U.S. homes for a year, and these models are getting bigger by the day.

And the good news there is with each generation of semiconductors, each generation becomes about 50% more power efficient. So, it takes half the amount of power for one calculation. It's not enough to offset just how quickly the models are getting bigger, and then remember, more players are entering this game, globally, not just in the US but also in Europe and Asia. So, you add it all together and training really represents the bulk of the power growth that we see for AI in the coming few years.

The second bucket for demand is something called querying. So that's when consumers, businesses, and other computers start to ask questions to these trained large language models. And in our model, we think you could see up to 30 billion AI queries per day by 2030. For comparison today, we make about 10 billion internet searches per day. But you have to remember that not all queries are created equally, right? A text-based query takes about the same amount of power as an internet search, but an AI generated photo takes up to 30 times more power, and a 60 second AI generated video takes up to 7,000 times more power than a text query. And video is big, it's 57% of all internet traffic today. So how the consumer adapts to AI video is really one of the key variables that'll determine just how much energy we're going to require to power AI.

And then the third bucket is really for data center operations, mainly for cooling, because when you're doing trillions of calculations per second, these chips run really hot.

So yes, 1000 terrawatt hours by 2030. That is a big number. I think it's a challenging task to meet that demand, but not an impossible one.

Oscar Pulido: And maybe you can expand there because you shared a lot of numbers. you said the word trillions a couple times. the percentage increases that you've cited, particularly when you talked about how we use artificial intelligence to query, was quite large. So, what role do renewables play in this energy demand? I'm thinking about things like wind solar, are they the major component or are there other, sources of energy that we're going to rely on?

Will Su: So, renewables are by far the fastest growing source of power generation. In the last 20 years. They've gone from almost nothing to 13% of global power generation. And they will continue to grow at a very fast pace.

Without a doubt, renewables are going to play a big part, in powering AI, but also in powering this overall theme of electrification of our energy systems. Now renewables have one really big drawback when it comes to powering AI, which is intermittency. Right? Let's zoom into the Ercot grid in Texas, which is the largest wind market and the second largest solar market in the U.S.

So, it has a lot of renewables, and if you just zoom in on a typical day, the solar power tends to peak out between 8:00 AM and 7:00 PM when the sun's shining. And the wind peaks when the wind speeds are the highest, which is usually from midnight to 7:00 AM when you wake up. Peak demand really happens in the hours of 8:00 PM to midnight. That's when people are at home relaxing, watching TV, streaming, checking their social media. And you'll see that during that period, natural gas demand really increases to meet that gap that can't be met by wind and solar.

And this is probably a good time to talk about nuclear, which people don't think of a lot, but it's actually today the largest source of carbon free power generation. It makes up about 9% of global power.

But I think as governments around the world start to realize how much electricity growth there's going to be, there's starting to be a change in thinking. And in countries like South Korea, Japan, Italy, and here in the U.S. you're seeing regulators extending previously planned shutdowns of nuclear plans, and even in some cases, allowing them to restart after they've already been shut. So definitely don't count nuclear out in this low carbon way to power AI going forward.

Oscar Pulido: So, it sounds like the, the demand is so significant that it is causing even some sources of energy that in the past that felt like, were becoming less of a priority to reenter the focus. Ultimately what you've said is there's a lot of different sources of energy that are going to help, power the AI demand. You mentioned nuclear gas, but also renewables. And if I could focus you on the US for just a second, artificial intelligence is not just the US topic, but it is the part of the world where the build out is really gaining a lot of momentum and therefore, how is the U.S. thinking about the power supply for artificial intelligence?

Will Su: we really should talk about one of the biggest unsung triumphs in the energy transition so far, which is the U.S. Power grid has decarbonized itself by a third. Over the last 20 years, and about 60% of that came in the form of cheap and abundant natural gas as a result of the shale revolution that allowed us to substitute out much more polluting coal.

 You saw a coal share in the last 20 years drop from 50% to 16%. Natural gas went up from 19% of U.S. Power generation to 42. The other 60, the other 40% came from renewables. So, renewables, again, grew from almost nothing 20 years ago to 14% of the US power grid today. So, there's already a really strong track record of partnerships between natural gas and renewables to combine and help us decarbonize.

Now, when you think about AI, and you think about data centers. The U.S. has about one third of the total data center capacity in the world, and I'm very confident that share will grow over time because we have the leading technology companies that are leading this AI revolution. And then we are also blessed with abundant resources, both traditional and renewable.

If you look at a map of where these data centers are located in the U.S., you'll see that they're mostly in these big clusters that are located close to population centers. So almost half of all data centers in the U.S. are in Virginia. They're almost all in this six square mile tiny area called Data Center Alley near Arlington.

There's other big clusters like Hillsborough, near Portland, Oregon. there's also growing clusters around Ohio, and you'll see a problem if you juxtapose that map onto one where the renewable resources are best in this country. The source of greatest solar radiation is in the southwest U.S., so that's places like Southern California, Nevada, New Mexico, and where the wind speeds are the highest are down the middle of the U.S. In the windy corridor that goes from the Dakotas down to Northern Texas.

And they don't really overlap with where the data centers are located today and where the most growth is likely to happen in the coming years. And then to make matters worse, this country's really falling behind in making long distance transmission investments. We're making one eighth the mileage of new transmission lines than we did 10 years ago.

That's a result of a number of regulatory and economic challenges with interstate infrastructure, and this is where natural gas is going to come in. It's a proven, mature technology. It's much cleaner than coal. It plugs easily into different grids, so it shapes my view that I think at least half, if not more of the incremental power for AI in the U.S. will come from natural gas and the balance will mostly be met by new renewable developments.

Oscar Pulido: Data Center Alley doesn't sound as glamorous as like Silicon Valley, but it seems like it's also very important. Let's come back to your role as an investor. You spend your day thinking about companies to invest in, and if you follow the markets over the last couple years, it's been all about technology. But we're having a discussion about the energy space, and so presumably that means there's investment opportunities in the energy sector. Where are those?

Will Su: Absolutely. So, as a value minded income investor, I have thought for a long time that energy is an undervalued sector because the market under appreciates both the volume and the duration for which the world needs oil and gas for the decades to come.

And I think this recent focus on how do we power AI just shines yet another spotlight on how power hungry our world really is. And over time, I think that will help this sector rerate higher. Now, aside from that, I think the energy sector actually might be one of the most underappreciated beneficiaries for all the technological gangs that'll come with better generative AI.

Some of the world's largest supercomputers are actually owned by large energy companies. Why? Because they perform a number of very computationally intensive tasks. Things like asset optimization, algorithmic trading, four D seismic imaging for new resource discoveries.

And I'll give you one specific example, which is the industry is using more and more of what's called a digital twin. So, this is like a virtual replica of a real-world asset, something like a refinery or an offshore platform. It's just got so much data inside of it that you can do a lot of really interesting and exciting things. Things like predictive maintenance, fixing things before they break, things like stress, testing them for severe weather events or identifying methane leaks and reducing emissions that way. So, I think there's more than one way to win with energy when it comes to the theme of AI that's greatly underappreciated by the market today.

I think the other sector that deserves some airtime here is utilities. So, utilities are a yield driven high dividend paying sector that's been somewhat out of favor in the last few years in a rising rate environment. But as the U.S. Grid goes from not having much growth for the last 20 years to needing to grow one to 2% per year going forward, there's a big opportunity for these utilities.

It'll come after an initial period of heavy investments now, which utilities will win depends very strongly on what regulatory regime and what geography they operate in.

Oscar Pulido: And it's interesting just to hear you talk about energy and utilities. I'm reminded we spoke to your colleague Carrie King, who reminded us that while it has been a very tech-driven market, in the last couple of years, there are opportunities that are starting to appear. And you're zooming in on the energy and utility sector as a function of artificial intelligence and power demand. But for an investor who is looking at this space, what should they be considering as they think about investing?

Will Su: The energy sector contributes about 10% of the S&P 500's net income, but it makes up less than 4% of the index by market cap. And I think that valuation disconnect is driven by this persistent, and in my view, misplaced fear that this sector has no long-term growth. Because I think as we sit here talking about breakthrough technologies like generative AI, it is important for us to remember that there's many different poles for incremental energy demand in this world, and all or nothing approach to energy just isn't going to work.

We have to find ways to help the traditional energy sources become cleaner and more responsibly sourced. At the same time, we scale up our renewables portfolio together, and only together will they be able to power the world forward in a pragmatic energy transition.

Oscar Pulido: Right, the world is evolving, where the demand for energy will come from is changing. With the number of statistics that you've been able to cite about the energy sector and artificial intelligence, where does this passion come from? How did you get interested in this space?

Will Su: Oscar, I'm having flashbacks to 16 years ago when I started my career at a large investment bank in the equity research department, and my recruiter said, you can either join the internet team or the energy team. And I had no hesitation. I said, energy, it's supply-demand driven. It's quantitative. The world needs this stuff. And you fast forward to today, and I think the internet index has outperformed energy by about 1,100%. But if you gave me a time machine to go back, I will make the same choice over again.

This job has taken me to really exciting places all over the world. Offshore Norway, the Permian Basin in Texas, the Bakken in North Dakota, or deep into the Amazon jungle in Guyana. That's a country that's going to go from the second poorest in South America to having the same GDP per capita as Brazil in less than a decade because of their resource development. So, it's been a really thrilling ride so far and I look forward to more of what's to come.

Oscar Pulido: We're glad you made that decision 16 years ago and that you would make it again, if you went back in time. Thanks for sharing all this insight on the energy sector, on artificial intelligence, and thank you for doing it here on The Bid.

Will Su: Thank you, Oscar,

Oscar Pulido: Thanks for listening to this episode of The Bid. If you've enjoyed this episode, check out our episode with Rob Goldstein and Lance Bronstein. Where they discuss AI through a COO lens and what business leaders are considering as AI is advancing.

<<SPOKEN DISCLOSURES>>

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

MKTGSH0524U/M-3571844

AI and the energy grid: Solving for AI’s power needs

Will Su, of BlackRock's Fundamental Equities team, is one of BlackRock’s leading voices on all things energy. Will is walks us through the sector’s pivotal role in the build-out and future of AI and digs into the potential investment opportunities and challenges.

The Bid Ep 164. GenAI From a COO Lens


Episode Description:

Join host Oscar Pulido as he explores the transformative power of AI and its impact on the financial services industry. Rob Goldstein, Chief Operating Officer of BlackRock, and Lance Braunstein, Head of Aladdin Engineering, share their optimistic perspectives on Gen AI and the evolution of technology over the past year. They discuss how AI is reshaping work patterns, empowering individuals through natural language interfaces, and revolutionizing client expectations. Discover how AI can be harnessed at the industry level to enhance productivity, leverage data, and drive better investment outcomes, while still emphasizing the crucial role of human supervision. With a focus on talent development and the democratization of data, they envision a future where AI augments human capabilities, making organizations more efficient and individuals more adaptable.

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.

The advent of AI has radically altered the landscape of work, reshaping the fabric of industries and prompting a monumental shift in how tasks are executed, and decisions are made within the financial services industry. AI has induced both awe and apprehension. The financial world stands on the precipice of an AI driven transformation where the balance between machine intelligence and human ingenuity heralds a new era of possibilities, challenges, and responsibilities.

Joining me today is Rob Goldstein, BlackRock's, Chief operating Officer, and Lance Bronstein, head of Aladdin engineering, BlackRock's Portfolio Management software. Rob and Lance will help us consider the issues facing business operators. From how to harness this technology to amplify human capabilities, redefine roles, upskill the workforce and recalibrating approaches to risk management and client interactions.

Lance and Rob, thank you for joining us on the podcast,

Rob Goldstein: Awesome. Great to be here.

Lance Braunstein: Thank you.

Oscar Pulido: Lance, this is your first time, I think you're what we refer to as a longtime listener, first time caller. And Rob, it turns out a little fun fact is that you were actually the first guest on the first ever Bid podcast that we recorded, the topic was around FinTech, the sort of intersection between finance and technology, so it's very appropriate to have you back.

Rob Goldstein: I actually assumed I was coming to get my royalty checks. Is that not what's happening here?

Oscar Pulido: We know you've been busy, so we took a little bit of time in welcoming you back. That was about five years ago, you didn't spend a lot of time talking about AI on that episode at the time, but a lot's changed. It's 2024 and AI is top of the list of topics that we've been addressing on the podcast, and this is a really great opportunity to hear from two business leaders at BlackRock about how it's impacting the business.

So, Rob, maybe I can start with you. I'd love to hear your perspective on Gen AI, and just how it's evolved over the past year because it seems like things are evolving quickly.

Rob Goldstein: Taking a bunch of steps back, AI as a concept is not a new concept. In fact, sometime in the 1950s MIT started its AI lab. So as a broad concept, AI's been around for a long time.

So, it's January 2024, but if you really think about it, from the period of time, more or less of Davos last year, so in January 2023 till now was actually, in my opinion, one of the most extraordinary times in the history of technology.

And there were major, major, major step functions in terms of technology, but more importantly, it's less that there's sort of new math. It's this confluence of data, compute power methods that have existed for a long time all coming together in a way that effectively has enabled or really started the transformative journey to enable English to be how people interact with computers.

People have grown accustomed to interacting with computers in certain ways, they've grown accustomed to interacting with them, with a mouse, with a keyboard, through your phone, with your thumbs. But for the first time, there's enough data, there's enough compute power, there's enough technology to fit models that enable you to talk to a computer, and to have it talk back to you. That capability, that step function is actually, in my opinion, one of the most transformative technology step functions we will see in our lifetime, this is going to really change how people think of technology.

Oscar Pulido: I'm glad you mentioned that AI is not A new concept. It's popular now, but that it's something that's been around for decades, and we actually talked about that on a few episodes, last year with some of our investment leadership. People like Jeff Shen and Brad Betts, who talked about, Dr. Steven Boyd and, the AI labs that we've set up. I'm also fascinated that you've been in the industry and with BlackRock for 30 years, very close to technology, and that you're saying in the past year is some of the most transformational change that you're seeing. So, how does that impact the financial services industry. You're the COO for BlackRock so you're the pacesetter for change. so, what are financial services company doing to try and stay up with that change?

Rob Goldstein: It's actually a super fascinating question because when you go to meetings and start talking about this stuff, people want to start talking about humanity, robots, when are the robots taking over humanity. So often I'm the one saying that's super important, but we need to focus on this through the lens of being in our jobs in a company.

The way I like to think about it, this whole concept of language and English being the way people interact, is a very different way technology is used and it's a way that really impacts work patterns. It's a way that really impacts how companies view productivity, efficiency, those broad concepts.

I'll give you a couple of examples. Lance and I were, leading a group of people as we presented this to the board. We wrote a presentation, and in the presentation, we spoke about a variety of the aspirations that we had for BlackRock, but the key theme is that all of the technology tools we build, people should be able to just type in what they want the tool to do, and the tool should be able to do that. That's number one.

Number two is we produce a lot of client reports. performance evaluations, credit write-ups, prospectuses, whatever it is. That first draft why can't that be done by a computer? Normally the work pattern would be someone would then take that away and four days later you'd get a draft of the letter. Why can't, right after that meeting, we get a draft of a letter that's from a computer and then we all comment on it.

So, what we did for the board presentation is we wrote a PowerPoint presentation explaining what we were going to do, the strategy, the risks, all of those components, everything you would imagine. Then, we actually fed it to ChatGPT within what we call a walled garden, basically our own version of effectively running the ChatGPT models, but within our own infrastructure, so we're not leaking any information. So, we took that presentation, put it through the model, and we said write a 1000-word executive summary, because in addition to submitting a PowerPoint presentation, we also would submit a, an executive summary more in like Microsoft words, something that's more, literal than a deck.

I got the memo coming out of it, and I gave it to two people who are among the most critical people in terms of looking at memos. One was Larry Fink, and one was Martin Small, our CFO. Martin came into my office and Martin said, I don't know why you didn't mention this, it's missing this, the tone's a little, you should be more confident in what we've done. And I was like, “Oh, Martin, it was written by a computer.” He was like, “Oh, really?” He had no idea. And then with Larry, it was the same thing. “Hey, the tone of this is off.” And I said, Larry, it was written by a computer. We could set that tone.

I could have said, Here's a PowerPoint presentation, here's 10 memos. Give me a memo in a thousand words that summarizes this PowerPoint presentation in the tone of these other memos.

And I think if you look at it through the lens of A COO, the productivity that unlocks is beyond imagination. If you look at it through the lens of a normal person in terms of helping you in daily life through being able to talk to a computer and have the computer talk back to you, it really is a remarkable, transformative opportunity.

Oscar Pulido: And that word productivity, the two of you are senior leaders and any time saved is very helpful, but it helps people across an organization. The example you just gave time saved and being able to invest it elsewhere. And Lance, you sit at the forefront of the technology platform that BlackRock runs and what has what Rob has discussed around the evolution of gen AI mean for an organization that has a tech platform that employs engineers, what do those engineers do now when the gen AI tools can do the kind of stuff that Rob described?

Lance Braunstein: Before I get to that, let me riff on the idea that this is really expansive. So, when we say that this will impact productivity across job families, we mean that quite literally. We talked about executive presentation, getting to a first draft of a PowerPoint, a Word document. But imagine getting to a first draft of an application. Having a software engineer, not start with a blank slate, but say, do the thing that is like this other thing that we've done before, or an analyst summarizing broker, documents or a salesperson summarizing all of the interaction notes. The idea here is that this democratization of data and models really is expansive across the enterprise. It's every job family, HR professionals, legal professionals, compliance professionals will work differently. How does this impact the technology world? in a couple of ways. So, if you have a technology platform, it will change the way that you think about the user interface and the user experience.

First, the standard will become a chat interface, exactly what Rob was describing, which is an English language, natural language interface to the computer rather than code or rather than complex application navigation.

Second, the way that you think about your information architecture, this is the way that information and data interrelates changes. So instead of having to navigate four different applications to determine my risk to book a trade, now I can simply ask a chat interface like 'Tell me what my risk is and on the strength of that risk rebalance my portfolio in the following ways.' That changes the navigation paradigm in a pretty profound way.

And then finally, I think this notion of democratization of data and models is really powerful. The idea that more people will participate in a broader set of data-driven decisions than they have historically. And I'm not talking about data scientists, and I'm not talking about PhDs. I'm talking about every single person involved in the investment life cycle now will have more data at their fingertips than they ever have before. That is hugely powerful.

So, if you are at the helm of a technology platform, thinking about the interface, thinking about the democratization of data, thinking about your information architecture, changes.

Oscar Pulido: I have two kids, I'm listening to you both talk and you're describing this world where, things seem a little bit easier. Like I can get to a more advanced part of the work or the project sooner. And when I think about my kids, part of how they learn is trial and error. They learn from mistakes that they make, and we learn from our mistakes. So, is the environment that you're both painting one where it's different in terms of how you develop talent because they don't get to learn as much from their mistakes? And maybe I'm not thinking about it, right, but what does that mean for talent development in an organization?

Lance Braunstein: First, I think it's what Rob said a minute ago, this notion of getting to a first draft sooner. Part of the power of these large language models is prompting the model often in a very precise way. So, like when Rob talked about tone, you could create the initial draft and then go back and say, I'd like it to be in the tone of this other document, or I'd like you to refine this into a set of bullet points, et cetera, et cetera.

So, in terms of development and learning, the idea that you could get to that first draft of your term paper of your science project faster by harnessing more data and more models, I think is a powerful learning tool. That is not cheating and getting to the Wikipedia of result sooner, it is actually harnessing more information.

Then the interactivity that you have with that first draft or with the model, I think is another learning opportunity. So, the ability to prompt in an increasingly precise way to me, drives a greater analytic mindset. Rob and I talk about this notion that we all are going to become developers, when you think about the human computer interface becoming a prompt in English, that means all of us are writing code. We may not think of it as code, those prompts may not look like code, they may look like an English language sentence, but they're code. And they will generate code in some cases. So, the precision with which you have to prompt the computer increases over time. So that learning to be more precise, more analytic, more data-driven, is a talent opportunity. It's a learning and development opportunity.

Rob Goldstein: Lemme just add, we try very hard to be tech optimists and it's amazing how many things through the years you could point to as given this new technology, this means humanity's going to become much more stupid and humanity is doomed. But I'll give a couple of examples through my own personal lens.

So, my dad was a financial advisor my dad growing up would work almost every night, but at nine o'clock, he would stop working because it was not polite to call people at home, after nine o’clock, and once email came about, if you get an email at nine o'clock and you don't answer it by like 10 o'clock, you're considered rude.

So, everything just adapted, and if anything, expectations change. It created a huge productivity boom in theory. As opposed to having to FedEx documents, you can now email documents, that's a productivity boom. But it didn't seem like the amount of work went down, just expectations changed. And I think what happens with technology is as it empowers this new productivity and these productivity step functions, it brings with it changing expectations.

People can look at these technologies through different lenses. I look at it through the lens, I think there's going to be a giant productivity boom. I think there's going to be a giant expectations boom. And I think that how people get smart will change. I don't exactly know how, but I know that humans are very adaptable. Technology's a tool that actually makes them even more adaptable. And I think that combination, I have confidence that people are going to get smarter, not less smart.

Oscar Pulido: You've given examples of the productivity boom, right? You mentioned the board presentation and the memo and, but now you just talked about the boom in expectations, and you touched on your dad being a financial advisor. That's a very client-oriented profession as most of financial services is. So, talk a bit more about how does this change what clients. Expect now that generative AI is more interwoven in business.

Rob Goldstein: Absolutely. And, if you zoom out for a second at the state of the industry, on the wealth side, most clients have websites or apps that they could access. They could see things in real time, but the truth is you get reporting once a month. And on the institutional side, it's equally the same, if not even more so once a month.

 Oscar, if I said to you in the year 2030, do you get reporting, once a month? I think you'd be, hmm, I don't think so. Ultimately, you could imagine, every day, every hour, every trade, whatever, if you want a summary of your portfolio and how it's changed, you have access to one. I think it's going to be very hard to fulfill that expectation with people. I think it's going to be very easy to fulfill that expectation with technology.

And that is why that English component, the ability to talk to the computer in English and have the computer talk to you in English, that is why that is a whole new unlock with regard to technologies that will be profoundly impactful in terms of the Day-to-Day lives of people, in ways that are unimaginable.

Oscar Pulido: You both mentioned this point a couple times, so worth reiterating that the language that you use to interface with computers has been coding languages for many years. But what you're saying is that now it's English is that language to interface with the computer and the coding goes on in the background, but by, having that shift, more people can interact with the machines that are increasing productivity in businesses or the economy.

Lance Braunstein: Yeah, that's correct. the question often comes up because we've lowered the barrier to the human computer interface, do professions like software engineering, software development, system engineering, data analytics go away? I believe the answer is A hard no. Not only do they not go away, but the burden that we put on our servers, on our computers, as we expand the aperture of the user base- in this case, the prompt engineers, who is every human who will interface with a large language model- actually grows the burden on resilience, scale performance security increases.

So, the need for really talented engineers who could construct the backends. of all of these systems that now have quite an elegant low barrier to entry as a co-pilot or a chat, I think that need grows over time. Now I am like Rob, a tech optimist and pragmatist, I am thinking the hard next 12 to 24 months, there are jobs to be done, they will be enabled by these generative AI technologies and these models, but they are jobs that we could predict.

Rob Goldstein: If we were having this conversation pre covid, the technology that we would be talking about was autonomous driving. And it was like, why would your kids get a driver's license? Don't buy a new car. Everything is going to be autonomous driving.

Then if you think about what happened, you basically had a once in a hundred years, scenario, where two people couldn't get into the car with each other unless they were in the same family pod. So, if ever there was a time for autonomous driving to take over, it would've been then. Instead, what happened was driving trucks wound up being so in demand that in certain countries, they had to call up their National Guard to actually drive trucks because it became a matter of national security and national infrastructure.

And I think as you listen to the subtext of what Lance and I are saying here the subtext is, this isn't about the computer alone, it's about the person being much more empowered, much more productive by the computer, but the person in a very similar scenario to autonomous driving, still being part of that process, still being the critical control, still looking at what the computer is doing. I think where people get confused is they look at a world with no people, we look at a world with people who are enabled to be better by the power of the technology.

Oscar Pulido: You've both touched on the fact that you're both tech optimists and I think, it's always good to end and pragmatists. but always good to end on an optimistic note. I think as we talk about any topic. So, I'd love to just get your thoughts on, the next year or two, what lies ahead, like what haven't you discussed that gives you even more optimism about ai, in a business setting?

Rob Goldstein: I have a vision that I believe will come true. Which is right now there's this concept of the prompt engineer, no one knew what that was a year ago, now it's going to be the job of the future. I have a different perspective on it, I'm very fortunate, I have two children. One of them is graduating this year of college, and one of them is a sophomore in college. And I think for my daughter Sadie, who's a sophomore in college by the time she graduates, I believe two things. One, the concept of a prompt engineer won't really exist. And two, whatever it was supposed to do, she will naturally know how to do from being in college for the next two years. This goes back to your question about training, sometimes you're training yourself and you don't even know it. I think a lot of what we're talking about here is just going to be natural. It's going to be in the water, and we won't even know it.

Lance Braunstein: And I would just extend that Getting everybody into generative ai, teaching them the concepts, teaching them the prompting. Is going to enhance our ability to just run a better investment process to be a better technology company. The thing that excites me in this next year, and I am really thinking about from now, is getting people more into ai. Getting people like hands-on into co-pilots and chat assistance and enabling them to get to that first draft that we described earlier, faster with higher quality. That's thing one. I think thing two is there will be a number of jobs that we want to automate, that we want to create greater automation.

And again, not in the, not to, to the exclusion of the human supervisor, but getting those rote tasks to a greater place of automation, I think is immediate and exciting for me. So more of us becoming sort of gen AI enabled and empowered. And more of us doing the highest value work rather than the rote tasks that is near and present and super exciting for me.

Oscar Pulido: And it sounds like more people becoming students of technology,

it sounds like the evolution in AI is going to push everybody in that direction. And guys, I want to thank you for, joining us, on the podcast as almost the professors of technology that we will look to, I'm sure down the road. Again, Rob, Lance, thanks for joining us.

Rob Goldstein: Great. Thank you.

Lance Braunstein: Thanks.

Oscar Pulido: Thanks for listening to this episode of The Bid. If you've enjoyed this episode, check out our two-parter on AI featuring Brad Betts and Jeff Shen, where we look at the history of investing in ai, and potential future applications in finance. Subscribe to The Bid wherever you get your podcasts. Subscribe to The Bid wherever you get your podcasts.

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AI from a COO perspective

Join host Oscar Pulido as he explores the transformative power of Generative AI and its impact on the financial services industry. Rob Goldstein, Chief Operating Officer of BlackRock, and Lance Braunstein, Head of Aladdin Engineering, share their optimistic perspectives on Gen AI and the evolution of technology over the past year.

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