Generative AI is being heralded as one of the most transformative innovations in human history.
And AI optimism has become one of the market's biggest drivers.
Companies are estimated to devote to over $1 trillion to AI-related spending in the coming years.
So will the benefits and returns of the technology justify the cost?
We're a couple years into this and there's not a single thing that this is being used for that's cost-effective at this point.
I'm Allison Nathan and this is Goldman Sachs exchanges.
Every month I speak with investors, policymakers, and academics about the most pressing market-moving issues for our top of my report from Goldman Sachs research.
This month I looked at Generative AI.
I think we're all familiar with the bull case by now.
My colleague Joseph Briggs from our Global Economics Research Team estimates that Generative AI could ultimately automate a quarter of all work tasks.
And boost US productivity by 9% and US GDP growth by 6.1% cumulatively over the next decade.
And investors have certainly bought into the theme with the big tech firms at the center of it all, accounting for over 60% of the S&P 500 indexes year-to-date return.
But given the enormous cost to develop and run the technology with no so-called killer application for it yet found, questions about whether the technology will ever sufficiently deliver on this investment have grown.
I spoke to two people who are skeptical.
Darren Asimoglu, an Institute professor at MIT, is the author of several books including Power and Progress are 1000-year struggle over technology and prosperity.
In a recent paper he estimated that only a quarter of AI exposed tasks will be cost-effective to automate over the next 10 years, and implying that AI will impact less than 5% of all work tasks and boost US productivity by only 0.5% and US GDP about 1% cumulatively over the next decade.
I asked him to explain. Part of the reason why I wrote the papers is because I did see a lot of enthusiasm, some quantitative, some qualitative, from commentators, some experts, others, observers of the tech industry about the transformative effects that AI is going to have very quickly on the economy.
And I think economic theory actually puts a lot of discipline on how some of these effects can work.
Once we leave out those things like amazing new products coming online, something much better than silicon coming, for example, in 5 years.
That's, of course, if that happens, all right, that's big.
But once you leave those out, the way that you're going to get productivity effects is you look at what fraction of the things that we do in the production process are impacted and how that impact is going to change our productivity or reduce our costs.
So those are the two ingredients.
And my prior, even before looking at the data was that the number of tasks that are going to be impacted by Gen AI is not going to be so huge in the short run because a lot of the things that humans do are very multifaceted.
So almost all of the things that we do in transport, manufacturing, mining utilities has a very central component of interacting with the real world.
And AI ultimately can help with that as well, but you can't imagine that being a big thing within the next few years.
So therefore, my intuition was that from the beginning, it's going to be pure mental tasks that are going to be affected and those are not trivial, but they're not huge either.
So the way that I go about doing that is I rely on one of the most comprehensive studies by a lone dude, a Michigan and rock that codes what the current AI technology, generative AI technology combined with other AI technologies and computer vision.
Could ultimately do what they did seemed fairly, fairly solid.
So I decided to start from what they did.
So if you take their numbers, it suggests that something like 20% in terms of value added share in terms of the economic importance of the tasks that we do in the production process could be ultimately transformed or heavily impacted by AI.
But that's a timeless prediction ultimately, but when will that ultimately be realized?
And in that there is another paper by Neil Thompson and Martin Fleming and co-authors, which looks at a subset of these technologies, the computer vision technologies, where we understand how they work and the costs a little bit better.
And for the computer vision technologies, they come up with some numbers as well, but more importantly, they make an effort in thinking about how quickly these things are going to be cost effective because something being ultimately done by generative AI, which sufficient improvements doesn't mean that it's going to be a big deal within five, six, seven years.
They come up with a number for computer vision technologies again, which looking into the details seemed reasonable, which is that about 20 to 25% of what is ultimately doable can be cost effectively automated by computer vision technologies within 10 years.
So then I combine these two estimates and I forecast that about 4.5, 4.6% of the dose 23% is going to be done in the short run that within the 10 year horizon.
And that's the basis of my sort of fairly uncertain.
Of course, we cannot be certain about any of these things, but baseline estimate of what can be achieved with J.N.A.I within a 10 year horizon.
When we think about applying AI technology to various tasks to improve productivity and increase cost savings, we have seen in the past that over time as technology evolves, you end up being able to do harder things and you end up being able to do them in a less costly way.
Do you expect that to be the case for AI absolutely absolutely I expect that, but I am less convinced that we're going to get there very quickly by just throwing more GPU capacity.
So in particular, I think there is one view and this is again another open area.
That's why any estimate of what can be achieved within any time horizon is going to be very uncertain, but there's a view among some people in the industry that there's like a scaling law, you double the amount of data, you double the amount of compute capacity, say number of GPU units are processing power and you're going to double the capacities of the AI model.
Now the difficulty there is at least threefold one is what does it mean to double AI capabilities because what we're talking about here, for example, open ended things like customer service or understanding and summarizing text.
There isn't a very clear metric that I get twice as good, so that's one complication.
The other one is doubling data, what does that mean if we throw in more data from Reddit into the next version of GPT, that will be useful in improving prediction of the next word when you are engaged in some sort of informal conversation, but it won't necessarily make you much better at helping customer that are having problems with their telephone or with their
data. So you need higher and higher quality data and it's not clear what that data is going to come from and worry about comes it's going to be easily and cheaply available to generative AI.
So the doubling data part is also not very clear and then the final one is I think there is the possibility that there could be very severe limits to where we can go with the current architecture.
Human cognition doesn't just rely on a single mode it involves many different types of cognitive processes, different types of sensory inputs, different types of reasoning.
So the current architecture of the large language models has proven to be more impressive than many people would have predicted, but I think it still takes a big leap of faith to say that just on this architecture of predicting next word we're going to get something that's as smart as you know how in 2001 Odyssey so those are all the sort of the uncertainties which to
me says if you're thinking about the next few years we know already the month.
So anything that's invented or big breakthroughs it's not going to have a huge effect within the next few years.
You've said many times it's really about the horizon, but ultimately there are people out there arguing that this technology is paving the way for super intelligence that can really accelerate innovation broadly in the economy.
Are you questioning that at all.
Well again for the current paper we're talking about all I need to say that is a time horizon issue so I don't think anybody seriously is arguing or can make a serious argument that within five to 10 years we're going to have super intelligence now going beyond the 10 year horizon.
I would also question the premise that we are on a path towards some sort of super intelligence precisely because of the reasons that I tried to articulate a second ago that I think this one particular way of understanding as summarizing information is only a small part of what human cognition does it's going to be very difficult to imagine that a large language
model is going to have the kinds of capabilities to pose the questions for itself develop the solutions then test those solutions find new analogies and so on and so forth like for example I am completely open to the idea that within a 2030 or horizon.
The process of science could be revolutionized by AI tools but the way that I would see that is that humans have to be at the driving seat they decide and where there is great social value for additional information and how AI can be used then AI provide some inputs then humans have to come in and start bringing other types of information and other real world interactions
for testing those and then once they are tested some of those reliable models.
So those reliable models have to be taken into other things like drugs or new products and another round of testing and so on and so forth have to be developed so if you're really talking about super intelligence then you must think that all of these different things can be done by an AI model within a 2030 or horizon again I find that not very likely.
So what do you think about the AI model?
So what do you think about the AI model?
So what do you think about the AI model?
So my scenario for doing that right would be exactly by creating new tasks for scientists rather than scientists using intuition for coming up say new materials which then they test in various different ways if AI models can be trained to do part of that process then humans can then be trained to become more specialized and provide better interactions better inputs
into the AI models. And that's the kind of thing that I think would ultimately lead to much better possibilities for human discovery.
Right. Well the investment in AI is absolutely surging we have equity analysts forecasting a trillion dollar spend is that money going to go to waste?
That's a great question and I don't know so my paper and basic economic analysis suggests that there should be an investment because many of the things that we are using AI for some sort of automation that means that we are substituting algorithms and capital for human labor and that should lead to an investment.
So that's the reason why my numbers for GDP increases are twice as large as the productivity increases because economic theory suggests there should be an investment boom.
But then reality supervines on that and says when you have an investment boom some of it is going to get wasted because some of it is going to be driven by things that you cannot do yet but people attempted some of it may be driven by hype some of it may be driven by too optimistic about how quickly you can integrate AI into your existing organization on the other hand.
Salah it is going to be super useful because it's going to lay the seeds all that next phase where much better things can be done.
So I think the devil is in the detail.
So I don't have a very strong prior as to how much of that big investment boom is going to get wasted and how much of it is going to lay the seeds for something much better.
But I expect both will happen.
I then spoke to Jim Kavello the head of global equity research at Goldman Sachs and a long time watcher of technology trends as a former well known semiconductor analyst.
He drew some pretty surprising insights from past tech spending cycles when I asked him whether tech companies are currently over spending on AI.
The biggest challenge is that over the next several years alone we're going to spend over a trillion dollars developing AI you know around the infrastructure whether it's the data center infrastructure whether it's utilities infrastructure whether it's the applications a trillion dollars.
And that is the issue in my mind what trillion dollar problem is AI going to solve this is different from every other technology transition that I've been a part of over the last 30 years that I've closely followed the technology industry.
Historically we've always had a very cheap solution replacing a very expensive solution here you have a very expensive solution that's meant to replace low cost labor and that doesn't even make any sense from the jump right and that's my biggest concern on AI at this point.
But isn't technology always expensive in its nascent stage and then you improve you evolve you iterate and the cost comes down dramatically.
Yeah not always let's take e commerce in the internet as the best example of this from the get go right you had a very cheap technology e commerce replacing a very expensive brick and mortar retail solution.
Amazon was able to sell books from the first day that I started selling books on the internet because it was cheaper to sell over the internet that it was for Barnes and Noble to have retail stores that was cheaper from the beginning like so there's a real life example of arguably the most important technology development of our generation e commerce that was cheaper
from day one fast forward 30 years right and it's still cheaper we still have a cheaper solution replacing a more expensive solution take.
You know uber replacing limousine services right so you started cheaper and 30 years later the internet is still enabling things to be cheaper than what the incumbent solution is there's nothing about AI that's cheap today right and you're starting from a very high cost based so that part I think there's a lot of revision of history on about how things always start
expensive and get cheaper nobody started with a trillion dollars and there's examples of when there's a monopoly on the bottleneck of the technology the technology cost don't always come down.
I'll give you an example you know the main bottleneck in making a semiconductor is lithography and there's only one company in the world as some lithography that can make advanced lithography tools.
Lithography systems when I covered semis 20 years ago were in the tens of millions of dollars now a single lithography system can cost in the hundreds of millions of dollars because there's only one person that can do it and right now in video is the only person that can provide GPUs that power AI and that's why AI is so expensive it's really the GPU costs the number
that you have to use in order to run the data centers and then how much the chips cost I think a big determination is the only thing that can provide.
The determination in whether AI costs ever become affordable is going to be whether there are other players that come in that can provide chips alongside of Nvidia if one wants to argue that we're going to see costs come down significantly it's going to be because other providers like Intel and AMD come alongside Nvidia and are able to make GPUs that can be used in data centers
and or the data center the hyper scale providers themselves like Google and Microsoft and Amazon are going to.
They're going to make their own chips I think that's a big leap from where we are today there's certain pockets of semiconductors that those companies can compete with Nvidia and but they haven't been able to take over the dominant GPU position that would enable a more competitive cost environment where Nvidia would have to be more accommodative on pricing and so I
think that's a big question mark I think there's a lot of complacency on the part of the tech world that costs are going to come down and I don't think that's going to be a good thing to do.
And I don't think that's a foregone conclusion even if it does come down the starting point of how expensive this technology is means cost have to come down an unbelievable amount to get to the point where this is actually affordable to automate some of these technologies right and ultimately you don't really have a lot of expectation that it will be able to perform
in terms of cognitive ability close to humans do you see real limits to the technology relative to the promise that some people are purporting.
Many people want to say this is the biggest technology invention of their lifetime I just think that's to me almost the silly starting point for all this right how can someone say this is bigger than when we first put a cell phone in someone's hand or when we first put the internet in front of someone or frankly when we first put a laptop in front of someone right like
those were transformative technologies that were fundamentally enabling you to do something different that you had ever done before you couldn't make a phone call from wherever you were you couldn't compute.
From wherever you were and you know relative the internet you could buy something over the internet that you used to have to go to a brick and mortar store for.
Got it, but when we first you know came up with cell phones I don't think anyone understood how transformative it could be so why are you so confident that this won't be just as or more transformative.
I think that's revisionist history to like I covered semiconductors when the smartphone was invented and I sat through hundreds of semiconductor presentations where they showed the roadmap right away like from day one of the smartphone here's everything that this is eventually going to be able to do and it was out in the future that we were going to be able to do
them but we had identified the things that we were going to be able to do like immediately upon the advent of the smartphone people so we're going to have our GPS and the smartphone right because at the time you will be able to do that.
At the time you would have your hurts rental cars that had those clunky GPS systems and they would show that and they would show your iPhone and they would say here's the roadmap of what this is eventually going to be able to do same with health applications same with internet same with a lot of these things but AI is pie in the sky big picture if you build that they will come.
I trust this because technology always evolves and we're a couple years into this and there's not a single thing that this is being used for that's cost effective at this point.
I think there's an unbelievable misunderstanding of what the technology can do today the problems that it can solve aren't big problems there is no cognitive reasoning in this like people act like if we just tweak it a little bit it's somehow going we're not even in the same zip code of where this needs to be.
If the benefits and maybe even the returned don't justify the cost do the big tech companies that are spending this money today have any choice but to engage in the a arms race given the competitive pressures.
Yeah great question I really think that's an important one and I think the answer right now is no they don't have a choice right which is why we're going to see the build out continue for now that's sort of what the technology industry does like look at virtual reality.
This would not be the first technology that didn't meet the hype right that's the other part of historical context that I think is so important people act like those of us who think this might not be as big as some other people think it is or naysayers on technology no there's just a historical context there was a period where nobody was ever going to need to go see a house
again in person if they were buying it because they were going to use virtual reality glasses to look at that blockchain was supposed to be a big technology.
Metaverse was supposed to be all the money that got spent on metaverse those things are not exist today from a technology use case standpoint and just because the tech industry hype something up doesn't really mean a lot but to your exact point we're going to keep building this for the time being in the eyes of the tech industry and frankly in the eyes of a lot of enterprises
if it does work and they haven't position themselves for it they're going to be way behind so there's a huge phomo element to this which is powering all the hype and I don't think the hype is really going to end anytime soon.
Right and then companies outside the tech sector have also started spending a lot of money on AI capabilities what do the early results from that investment show.
I think that almost universally it's showing that there's not a lot that I can do today and again people on different parts of the spectrum of anywhere from we shouldn't expect it to do anything today it's such early stages and technology evolves and finds away to the other side of the spectrum is we're several years into this and by this time it was supposed to be doing
so. And everybody's on a different part of that continuum right now there's very limited applications for how this can be used effectively very few companies are actually saving any money at all doing this and that's where I think you get into the how long do we have to go before people start to really question.
So what does all that we've discussed mean for investors that are focused on AI over the near medium and long term.
I think it's all in the infrastructure side it gets back to the point that we were just talking about where we're still going to keep building a I don't think we're anywhere near done building it right the world so convinced that this is going to be something significant that there's nobody's even remotely close.
In my opinion to scaling back on the build and so what I've been saying for two years is what I continue to say keep buying the infrastructure providers is it really expensive absolutely.
But I've never seen a stock that goes down only because it's expensive it got expensive for reason people believe in the fundamental growth outlook if the stock collapses it's going to be because there's a problem with the fundamental growth not because of the valuation.
And ultimately if you are right we are building all this infrastructure and capacity that at some point won't really be in demand right very new on you right yeah it looks bad it looks exactly 2001 2 and 3.
For the internet build out like people again it's a relevant discussion right when they want to talk about that if you build that they will come and when we built the internet and them 30 years later we developed Uber and all those things are true right it ends badly when you build things that the world's not ready for right and I don't know that it's as problematic simply
because a lot of the company spending money today are better capitalized and some of the companies that were spending money then.
But when you wind up with a whole bunch of access capacity because you built something that isn't going to get utilized it takes a while that the world has to then grow back into that supply demand balance so it ends badly if we're right that this isn't going to have the adoption that everybody thinks.
But I would say one of the biggest lessons I've learned over 25 years here is bubbles take a long time to burst so the build of this could go on a long time before we see any kind of manifestation of the problem I'm very respectful of how long they can go on and that's why I have this sort of more nuance you have look keep owning the infrastructure providers because we're
still building it what should investors be focused on to see a changing of the tea leaves here that you expect to come back to the market.
That you expect to come eventually I think it'll be fascinating to see how long people can go with the if you build that they will come approach right at some point in the next 12 to 18 months you would think there has to be a bunch of applications that show up that people can see and touch and feel that they feel like OK I get it now here's how we're going to use AI because again investors
are trying to use this in their everyday life and there was a period a year ago where everybody was pretty excited about how asset managers could utilize AI and I think if you interviewed an asset manager for this most of the time and I'm going to tell you the same thing which is we're struggling on how to figure out how to use it we can't really find applications that make
a ton of sense again there's isolated examples models and things in that nature but nothing significant and so I think the longer it goes without any applications that are obvious to people or significant applications that are obvious to people the more challenge the bigger thing to me is the corporate profit issue right that's what I would watch if our investors
corporate profits and as long as corporate profits are great companies have money to try experiments but negative our OI experiments are the first things to go and corporate profits low down so that's what I would really have my eye on so some really provocative thoughts from Jim I should mention that I also spoke to my Goldman Sachs research colleagues cash ring
in and Eric Sheridan who see it very differently Eric our US Internet equity research analyst says that current cat back spend as a share of revenues doesn't look markedly different from prior tech investment cycles and cash ads that the potential for returns from this cat back cycle seems more promising than even previous cycles given that incumbents with low
cost of capital and massive distribution networks and customer bases are leading it but Eric does warn that if AI's killer application fails to emerge in the next six to 18 months he'll become more concerned about the ultimate payoff of all the investment we're currently seeing we'll leave it there for now thank you for listening to this episode of Goldman Sachs
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