AI

On average, a ChatGPT query needs nearly 10 times as much electricity to process as a Google search. In that difference lies a coming sea change in how the US, Europe, and the world at large will consume power — and how much that will cost.  For years, data centers displayed a remarkably stable appetite for power, even as their workloads mounted. Now, as the pace of efficiency gains in electricity use slows and the AI revolution gathers steam, Goldman Sachs Research estimates that data center power demand will grow 160% by 2030. At present, data centers worldwide consume 1-2% of overall power, but this percentage will likely rise to 3-4% by the end of the decade. In the US and Europe, this increased demand will help drive the kind of electricity growth that hasn’t been seen in a generation. Along the way, the carbon dioxide emissions of data centers may more than double between 2022 and 2030. How much power do data centers consume? In a series of three reports, Goldman Sachs Research analysts lay out the US, European, and global implications of this spike in electricity demand. It isn’t that our demand for data has been meager in the recent past. In fact, data center workloads nearly tripled between 2015 and 2019. Through that period, though, data centers’ demand for power remained flattish, at about 200 terawatt-hours per year. In part, this was because data centers kept growing more efficient in how they used the power they drew, according to the Goldman Sachs Research reports, led by Carly Davenport, Alberto Gandolfi, and Brian Singer. https://www.goldmansachs.com/infographics/v2/flourish/data-center-power-demand/index.html?auto=1 But since 2020, the efficiency gains appear to have dwindled, and the power consumed by data centers has risen. Some AI innovations will boost computing speed faster than they ramp up their electricity use, but the widening use of AI will still imply an increase in the technology’s consumption of power. A single ChatGPT query requires 2.9 watt-hours of electricity, compared with 0.3 watt-hours for a Google search, according to the International Energy Agency. Goldman Sachs Research estimates the overall increase in data center power consumption from AI to be on the order of 200 terawatt-hours per year between 2023 and 2030. By 2028, our analysts expect AI to represent about 19% of data center power demand. https://www.goldmansachs.com/infographics/v2/flourish/data-centers/index.html?auto=1 In tandem, the expected rise of data center carbon dioxide emissions will represent a “social cost” of $125-140 billion (at present value), our analysts believe. “Conversations with technology companies indicate continued confidence in driving down energy intensity but less confidence in meeting absolute emissions forecasts on account of rising demand,” they write. They expect substantial investments by tech firms to underwrite new renewables and commercialize emerging nuclear generation capabilities. And AI may also provide benefits by accelerating innovation — for example, in health care, agriculture, education, or in emissions-reducing energy efficiencies. US electricity demand is set to surge Over the last decade, US power demand growth has been roughly zero, even though the population and its economic activity have increased. Efficiencies have helped; one example is the LED light, which drives lower power use. But that is set to change. Between 2022 and 2030, the demand for power will rise roughly 2.4%, Goldman Sachs Research estimates — and around 0.9 percent points of that figure will be tied to data centers. https://www.goldmansachs.com/infographics/v2/flourish/growth-in-power-demand/index.html?auto=1 That kind of spike in power demand hasn’t been seen in the US since the early years of this century. It will be stoked partly by electrification and industrial reshoring, but also by AI. Data centers will use 8% of US power by 2030, compared with 3% in 2022. US utilities will need to invest around $50 billion in new generation capacity just to support data centers alone. In addition, our analysts expect incremental data center power consumption in the US will drive around 3.3 billion cubic feet per day of new natural gas demand by 2030, which will require new pipeline capacity to be built. Europe needs $1 trillion-plus to prepare its power grid for AI Over the past 15 years, Europe’s power demand has been severely hit by a sequence of shocks: the global financial crisis, the covid pandemic, and the energy crisis triggered by the war in Ukraine. But it has also suffered due to a slower-than-expected pick up in electrification and the ongoing de-industrialization of the European economy. As a result, since a 2008 peak, electricity demand has cumulatively declined by nearly 10%. https://www.goldmansachs.com/infographics/v2/flourish/it-load-map-2/index.html?auto=1 Going forward, between 2023 and 2033, thanks to both the expansion of data centers and an acceleration of electrification, Europe’s power demand could grow by 40% and perhaps even 50%, according to Goldman Sachs Research. At the moment, around 15% of the world’s data centers are located in Europe. By 2030, the power needs of these data centers will match the current total consumption of Portugal, Greece, and the Netherlands combined. Data center power demand will rise in two kinds of European countries, our analysts write. The first sort is those with cheap and abundant power from nuclear, hydro, wind, or solar sources, such as the Nordic nations, Spain and France. The second kind will include countries with large financial services and tech companies, which offer tax breaks or other incentives to attract data centers. The latter category includes Germany, the UK, and Ireland. https://www.goldmansachs.com/infographics/v2/flourish/power-grids/index.html?auto=1 Europe has the oldest power grid in the world, so keeping new data centers electrified will require more investment. Our analysts expect nearly €800 billion ($861 billion) in spending on transmission and distribution over the coming decade, as well as nearly €850 billion in investment on solar, onshore wind, and offshore wind energy. 

For household products maker Procter & Gamble, the highest goal is to delight the consumer, according to Chairman, President, and CEO Jon Moeller. And that colors how he sees new artificial intelligence technologies. The point is not simply to find ways to use AI, but rather to find ways that AI can be used for that outcome — to create delight, Moeller said in a discussion with Goldman Sachs Asset Management. “The outcomes are consumer, customer, employee, societal, and shareholder delight, period,” Moeller said on June 6 at The Forum with Ashish Goyal from the Fundamental Equities US Equity Team in GSAM. The Forum is a daily meeting that convenes leading experts to discuss global trends that impact GSAM’s investments. “If AI helps us drive those outcomes better and sooner, we should embrace it. If it doesn’t, it’s not as important to me.” He cited the ability of technology, including new AI tools, to improve quality control processes. Existing methods for establishing that a product meets standards might involve testing it in a warehouse weeks after it’s manufactured. That has obvious drawbacks, since the company might have to scrap or rework an entire production run after a problem is uncovered. Better to incorporate sensors and algorithms that can identify quality problems as they occur.  “We want everything done in real time, on the line, with technology, so that we know instantly if something is wrong,” Moeller explained. “And with AI, we can start trending toward knowing instantly — knowing before it happens, not just when it happens.”  In Procter & Gamble’s business, delighting the consumer means coming up with products that are clearly superior in a specific category, he said. This might be a diaper that doesn’t leak as often, or a laundry detergent that works better in cold water so that less energy is used in washing. Moeller said the company wants to sell products with “irresistible or noticeable superiority from first use,” and he talks about commitments to such products as “superiority investments.” Innovation in consumer products can be time consuming and very expensive, he said. Conceptualizing an innovative idea may be the easy part, but then the company has to figure out how to make the product on an industrial scale, building a production line that “operates faster than your eye can even follow.”  Then there is the expense of bringing a product to market and figuring out how to reach the consumer. Here again, Moeller said there may be a role for AI and a potential advantage. A large company like Procter & Gamble has deep experience with advertising and extensive data on what works. “Which means we can much better train an AI model to help us, again on a preliminary basis, evaluate characteristics of advertising,” Moeller said. “Is this likely to break through? Is it likely to drive market growth?” Moeller said there may also be potential for the use of AI in Procter & Gamble’s laboratories. “We’re increasingly moving innovation from the lab bench to very sophisticated computers,” he said. This may help the company to speed up molecular discovery, explore more areas for innovation, and come up with more successful product ideas. Even as he described how AI may speed up processes that take a lot of employee hours today, Moeller suggested that AI is unlikely to lead to lower employment. “Probably the reverse is true,” he said. “If you have tools that make people more effective and more efficient, those people become more valuable.” Goldman Sachs Research has predicted that generative AI adoption won’t lead to large job losses and may even boost employment as the technology creates new opportunities. Moeller, who was Procter & Gamble’s CFO before he became CEO, compared what’s happening with the implementation of new AI tools to what happened in financial and accounting realms with the adoption of spreadsheets several decades ago. Employees in financial roles could suddenly do things they had never been able to do before, he said, “and we hired more of them.” Moeller’s views on this topic can be seen in his efforts to promote “digital acumen” among Procter & Gamble’s employees. He identified that as one of his high-priority focus areas for the company. His point is that the continuous improvement of the digital skills of the people who work for and lead Procter & Gamble will be key to keeping up with new technologies, including generative AI.  “We’re not going to be able to take full advantage of the technology that’s available to us if people don’t know what’s possible and don’t know what questions they should be asking to their teams,” Moeller said.

play_circle Download Video Transcriptdownload Graphcore co-founder and CEO Nigel Toon set out to revolutionize machine intelligence with a chip specifically designed for AI. In this session of Goldman Sachs Talks, he joins Clif Marriott of Global Banking & Markets to discuss how advances in semiconductors will power an AI-enabled future.  This episode was recorded on May 14, 2024.

A tide of investment is pouring into generative artificial intelligence. Will it be worth it? “That’s the center of all debate right now,” says Sung Cho of Goldman Sachs Asset Management. Investment is flowing into everything from the silicon underpinning the training of artificial intelligence models to the power companies that supply electricity to acres of data centers. To see where the industry is headed, Cho and Brook Dane, portfolio managers on the Fundamental Equity team in Goldman Sachs Asset Management, met with executives from 20 leading technology companies driving AI innovation. Those conversations — with public and private firms, from semiconductor makers to software giants — indicate that some companies are already generating returns from AI, and some would buy even more AI hardware if they could get their hands on it. “Our confidence continues to increase that this technology cycle is real,” Dane says. “It’s going to be big, as they say.” But there are also risks. Cho and Dane say it’s possible that the large language models being built by a handful of companies will find they are competing in a winner-takes-all market. The use cases, or killer apps, that fully justify the intense investment are yet to emerge. They also point out that, in a year in which US stock indexes have set successive record highs, no rally in tech stocks ever goes in a straight line. “You get these waves of both investment-digestion and hype-reality,” Dane says. “And the two of them play out across a multi-year horizon.” Cho and Dane’s insights come as Goldman Sachs Research recently published an examination of the immense amount of investment in AI, featuring interviews with Daron Acemoglu, Institute Professor at MIT, and Jim Covello, Goldman Sachs’ Head of Global Equity Research. The report, titled “Gen AI: too much spend, too little benefit?” notes that mega tech firms, corporations, and utilities are set to spend around $1 trillion on capital expenditures in the coming years to support AI. We spoke with Goldman Sachs Asset Management’s Cho and Dane about the prospects for the industry’s return on its investments, whether this trend is primarily playing out in public or in private markets, and the companies that can hope to capitalize on the AI boom. How much investment are you seeing in these models? And do you think it’s realistic to see a return on investment that justifies that capital anytime soon? Sung Cho: That’s the center of all debate right now. Brook Dane: The biggest question in the marketplace right now is: Are we getting a return on the investment? I’m reasonably comfortable that we are seeing that return. And there are a couple of data points I’m looking at that give me comfort. First: We spent a lot of time on this trip talking with the CFO of a hyperscaler who had just come back from their strategic planning, where they’re doing their one-, three-, five-year forward looks. This person talked very openly, not with any kind of numbers whatsoever, about how they were doing the RoI calculations across the clusters where they were deploying GPUs and how they were finding it very accretive from a return standpoint. Now, this company is already running massive inferencing (using already-trained AI models to reason or make predictions) workloads across their infrastructure for recommendation engines. They’re seeing results in terms of increases in time spent on their platforms, as these models have predicted, with the next piece of content. So for them, the RoI calculation is probably the simplest to calculate, because you can deploy a cluster, you can do a more sophisticated algorithm that can then lead to more time spent, which can lead to more advertising surface, which can then drive revenue. The second thing, and this is from following the industry over the long term and having had lots of recent discussions with another hyperscaler around their capital spending plans: We know how disciplined they have always been historically, and how they’re seeing both incremental revenue pick up, and seeing the incremental returns that they get out of their capital spending. This CFO is emphatic that they have the money, and if they could get more GPUs to deploy they would. Having known this person for 20 years, and understanding how they approach capital budgets, how they spend their capital — this person wouldn’t be doing that if there wasn’t a genuine, real, tangible return that they can see in front of them. And they’re pretty emphatic. But it’s early, and the other downside is that for these frontier models you can’t fall off the front end of the wave. You can’t be the fourth frontier model that doesn’t spend the incremental $1 billion dollars to get your model to be better. So for those guys, there’s a bit of an arms race here, and there’s a little bit of a leap of faith embedded in that. Sung, what’s your view on the RoI question? Sung Cho: This is one of the most important questions. And that’s what’s going to dictate the direction of markets over the next six to 12 months at least, and whether tech continues to outperform or not. Obviously with any RoI question, you have to understand the scale of what’s been invested so far. If you look at NVIDIA’s revenues in calendar year 2022, they did $26 billion dollars in revenue. And they did $26 billion dollars in revenue this most recent quarter. So in basically two years, NVIDIA has quadrupled its revenues. If you compare what’s being spent on NVIDIA to total cloud capital expenditures, nearly 50% is going into NVIDIA chips. So the investment in AI has been massive. And if you think about RoI, the starting point is around the “I,” which has been very, very high. If you are a bull, the most important thing here, and Brook mentioned this, is that right now there’s a race to see who can build the best foundational model (general purpose models that can be applied

As companies explore new generative artificial intelligence technologies, immense spending on the infrastructure to make it possible is underway. At the same time, investors are debating the extent to which reality will keep pace with the extraordinary hopes for AI technology. In Europe, some of that money is being spent to boost digital enablers — companies with technology vital to build the chips powering the data centers that run the AI models. Double digit boosts to longer-term earnings power for certain digital enablers in Europe are possible, with beneficiaries in Germany, France, and especially the Netherlands, according to Goldman Sachs Research. We talked to Goldman Sachs Research analyst Alexander Duval about the impact of the AI buildout on the European technology ecosystem. How is AI capital spending affecting the European technology sector? The sheer scale of investment on the hardware side globally is clearly having an impact on a regional basis in Europe, affecting the digital enablers — the companies needed to make the AI expansion possible. We see technology giants set to spend more than a trillion dollars on AI-related capital expenditures in the coming years. And that’s starting right now. Near-term investment levels are pretty substantial. The largest US tech companies are spending around $200 billion already this year. To put that in context, that would be equivalent to roughly a quarter of the entire European continent’s capex for all sectors. So it’s truly a phenomenal amount of money. What are they spending it on? They need to beef up their AI data centers to do the parallel processing that’s needed. Those are US companies, though, and the semiconductor foundries themselves are often in Taiwan and Korea. Where do European companies fit in? Two specific angles. First, semiconductor equipment. If you want to make semiconductors that are this powerful, you need to buy lithography machines that can print the detailed circuitry onto the chip. There’s basically only one place you can go for these extreme ultraviolet (EUV) lithography machines: You can only get them from ASML in the Netherlands. Beyond that, there are things like atomic layer deposition, a very precise way of putting materials onto wafers to build up transistors, and another company in the Netherlands does that. For all of these players, we are talking about a pretty substantial boost. Another company in this country is a leader in hybrid bonding, which is needed to make AI chips more power- and heat-efficient. All this stuff from an infrastructure perspective is kicking off already. 1. Link; 2. Based on our US team’s forecasts for US Hyperscalers; 3. Nvidia, 4. BESI, 5. Link; 6. Reuters, 7. ASML Source: Compiled by Goldman Sachs Global Investment Research What other technologies or companies may get a boost? Another aspect is the amount of power that’s required to run the AI infrastructure. That’s a major issue. A German company produces semiconductors that regulate the flow of power into the data centers, and they have some quite innovative solutions to help with efficiency. Equally we see a need for extra sensors and microcontrollers at the edge of the network, which can be supplied by French technology. More generally, what are you watching for in terms of AI technology? The challenge, if we think more broadly about AI, is that we don’t exactly know how the contact-with-reality scenario will play out. We don’t know what issues it may bring up. There might need to be regulation; there probably needs to be guardrails put around it. That’s more about the application of the technology and how we program the software, as opposed to issues with the hardware per se. What potential use cases are you watching? You can already envisage how a lot of this could be used. If you can distill the sum total of the internet into a bunch of vectors, you can basically do that for anything. You can do this for music. You can do this for proteins, so you could use AI for protein engineering. You could do this for enzymes. You could ensure that certain crops will grow better. You could generate healthcare solutions. There definitely are things that, with a bit of imagination, could be pretty interesting and easy to access. Further work is clearly required, but clearly there are enterprises looking at ways of leveraging AI to control cost in multiple domains.  How will the best uses be sorted out? Investors are keen to see that there is a return. Some companies have a sensible plan as to what can be done with this technology and a clear idea about how they will get a return on investment. I think investors are happy with that. Some other companies have just said they’re spending money on AI, up to several billion dollars, and the stocks have been selling off.  We caution in the report that we need to ensure that these use cases are coming through. Your report cites the potential for a trillion dollars of investment. What is the timeframe? Over the next handful of years, I would say. The $200 billion this year is supposed to actually go up. It’s not one-off for this year. Obviously, we have to see how these use cases take off. Can we really engineer proteins? Can we really use AI to advance carbon capture? Can enterprises truly leverage it to save meaningful cost or drive substantial productivity gains? Several semiconductor players are forecasting extraordinary AI-related revenue growth and by implication extremely large capex plans. Clearly some of the most important technologists on the planet are of the view that this is something which has got a lot of momentum for the next few years. But in our report, we look out to 2030. If we don’t see the return on investment in that timeframe, that could suggest a more bearish scenario. How does that affect the outlook for European chip companies overall? We have constructed long-term scenarios for a slate of European companies. For a number of them, we can see

Cloud computing sales are expected to rise to $2 trillion by the end of the decade, according to Goldman Sachs Research. Generative artificial intelligence is forecast to account for about 10-15% of the spending. The total addressable market for cloud services is poised to expand at a 22% compound annual growth rate from 2024 to 2030, writes Kash Rangan, head of US software research in Goldman Sachs Research, in the team’s report. Generative AI could constitute $200 billion to $300 billion of cloud spending, as investment moves beyond mega technology companies and foundation model providers. Companies spending on digital transformation and cloud modernization will contribute to the surge in cloud computing sales, writes Rangan. Only about 30% of workloads have moved to the cloud, according to a recent survey by Goldman Sachs Research. The estimate for cloud revenue growth is also based on recent historical precedent — the market more than doubled between 2019 and 2023 to $496 billion, representing a 26% compound annual growth rate. At the same time, Rangan anticipates that spending for, and adoption of, generative AI will broaden out to more companies. As that happens, it will be a further catalyst for the cloud sector. AI investment poised to broaden beyond semiconductors Much of the recent technology spending, and subsequent rise in stock prices, has centered on infrastructure companies like semiconductor makers. The next phase is expected to generate opportunities in platform companies that allow the best use of that infrastructure while providing building blocks for next-generation applications, as well as software companies that create generative AI applications, Rangan writes. “We are starting off with infrastructure and that should lead to growth in platforms, which can help manage all the data and facilitate processing by applications,” he writes. Rangan forecasts infrastructure as a service (IaaS) will account for $580 billion of the cloud market by 2030, or 29%. Platform as a service (PaaS) is expected to make up $600 billion in that same time period, or roughly 30%, while software as a service (SaaS) is expected to contribute $780 billion for 41% of the market. https://www.goldmansachs.com/infographics/v2/flourish/forecast-split/index.html?auto=1 “The infrastructure layer is poised to be the initial beneficiary, as we are already observing with the AI revenue ramp from the hyperscalers,” he writes. “This should be followed by the platform and application layers, respectively. An inherent tethering exists between PaaS and SaaS —  where PaaS solutions are needed to support the emergence of a killer application but the value in the platform layer can’t compound until more compelling applications emerge.” The next phase of generative AI Five of the biggest US technology companies are forecast to spend $215 billion on generative AI this year (up from $125 billion in 2022). But sky-high capital expenditures for generative AI are expected to gradually decline. Lowering costs isn’t entirely straightforward, as Rangan points out that some aspects of model training are relatively fixed. But the team still expects companies to eventually produce models that extract more efficiency from the hardware, for model training to give way to using the models (known as inferencing), and for smaller and specialized models to emerge. The software sector, which has had three straight years of decelerating growth, is set to potentially re-accelerate. The uptick will be driven by declining interest rates (lowering the hurdle rate for some IT projects), more certainty about economic policies after the US election in November (which has delayed some spending decisions), and key software conferences in the fall that will provide insight on generative AI products. IT budgets for generative AI are resilient Even as the investor outlook for generative AI gyrates from excitement about its prospects to skepticism about its viability, there are signs that investment in the technology is resilient. Goldman Sachs Research’s survey of IT buyers shows respondents expect 9% of their budgets to be potentially allocated to generative AI in three years, up from 7% indicated in an earlier survey in January.  https://www.goldmansachs.com/infographics/v2/flourish/future-it-budget/index.html?auto=1 The history of cloud computing can be instructive for understanding the development of generative AI. Rangan points out that it took time for the killer applications that underpin cloud computing to reach mainstream status. The return on investment for generative AI is hard to quantify, just as it was in the early stages of cloud computing. “Going through transition points like this can be very unnerving,” he writes. There are other parallels with cloud computing, Rangan writes. It took time for cloud-based applications to become more robust than their on-premises counterparts, but now they have more functionality. Likewise, once training has produced AI models of sufficient maturity, that will pave the way for more sophisticated applications.  That said, if generative AI doesn’t materialize as a disruptive force, software company valuations could rise, as there will be less competition for IT budgets, and there’s less risk of displacement, Rangan writes. But that’s not his base case. “There is a much greater probability that the generative AI opportunity is indeed real and that software applications and platform companies are able to re-invent and therefore re-accelerate growth — especially as interest rates start to come down,” he writes. “With or without generative AI, software platforms and applications companies are in a very good position to deliver attractive returns for investors over the next several years.”

Superfast chips are in high demand — not just by the artificial intelligence industry, but also by companies that work in computer graphics, robotics, autonomous vehicles, or drug discovery. “It’s fun to see all these amazing applications being created,” says Jensen Huang, the CEO of Nvidia. Speaking to David Solomon, the CEO of Goldman Sachs, at the Communacopia + Technology conference in San Francisco, Huang explained how computer graphics, for example, rely heavily on AI infrastructure. “We compute one pixel, and we infer the other 32,” he says in an edition of Goldman Sachs Talks. “Computing one pixel takes a lot of energy. Inferring the other 32 takes very little energy, and you can do it very fast. And the image quality is incredible.” Given this speed and flexibility, this infrastructure more than pays for itself, Huang says, responding to a question from Solomon about returns on investment for customers. By spending on such equipment, “the computing cost goes up a little bit — maybe it doubles,” Huang says. “But you reduce the computing time by a factor of about 20. You get 10x savings.” How Huang sees the data center market Chips that accelerate computing are everywhere, but there is no such thing as a universal accelerator, Huang says. Instead, every time a chip company enters a new market, it must learn new algorithms. They differ according to purpose; the algorithm for image processing would be different from the algorithm to model fluid dynamics. “Usually, some 5-10% of the code represents 99.999% of the run time,” Huang says. “So if you take that 5% of the code and offloaded it onto an accelerator, then technically you should be able to speed up the application a hundred times.” The promise of this kind of accelerated computing has led to keen investor interest in the data center market, Huang says. He thinks this infrastructure can yet be improved. For one thing, the average data center is “super-inefficient, because it’s filled with air, and air is a lousy conductor of electricity.” Making data centers denser — eliminating the air, in other words — will make them cheaper and more energy efficient. Another revolution lies in how data centers now understand not just how to process data but the meaning of the data itself, and how to translate one form of data to another, Huang says: “English to images, images to English, English to proteins, proteins to chemicals.” The chip supply chain needs to be resilient The ecosystem of manufacturers and suppliers to the chip industry is sprawling and complex, and particularly concentrated in Asia. As a result, Nvidia tries to design diversity and redundancy into every aspect of its supply chain. Companies need to have “enough intellectual property” to be able to shift their manufacturing from one “fab” — or chip-making facility — to another if they have to, Huang says. “Maybe the process technology won’t be as great, or you won’t get the same level of performance or cost, but you will still be able to provide the supply.”

With the world’s biggest companies racing to build the most sophisticated artificial intelligence, questions about how far and how fast the technology can be scaled are coming into focus, according to Goldman Sachs Research.  The advent of generative AI has created a surge of excitement about the future of the technology. Unlike other types of AI, gen AI can create its own outputs in natural language. Because it’s “multimodal,\” it can also generate responses in other formats, including text, numbers, videos, and sound. Large tech companies are citing initial use cases, and some enterprise players see scope for major productivity gains in certain areas such as code writing, which could make the most valuable employees even more productive. However, the exact size of future economic benefits is the subject of debate. The cost of building gen AI at scale is extremely high, with big tech companies investing hundreds of billions of dollars — although the cost per query has come down considerably since the technology first launched. At the inaugural Goldman Sachs European Virtual AI and Semis Symposium, 20 speakers — from CEOs and technologists to macro economists — came together to assess the prospects for AI. In particular, they discussed key topics including use cases, the total addressable market, challenges for further development and adoption of the technology, and implications for European hardware and semiconductors. We talked to Alexander Duval, head of Europe Tech Hardware & Semiconductors in Goldman Sachs Research, about the main findings of the symposium. What role are the large tech companies known as “hyperscalers” playing in the development of AI? So far, US tech giants have been at the vanguard of generative AI use. They\’ve been developing large language models, which they can use both for their existing business and also potentially in creating new business tools. The symposium heard how the technology is generating a quarter of one hyperscaler’s code and saving meaningful engineering time for another. Broader use cases in the real economy include its use to predict the structure of proteins, and even to “de-age” an actor\’s appearance in a movie. Those are striking examples. But it\’s worth bearing in mind that hyperscalers have been spending hundreds of billions on this. Together, they have spent around $200 billion on AI this year, and that will probably increase to $250 billion next year. Developing large language models can cost tens or hundreds of millions of dollars. And that\’s why at this symposium, we really wanted to look at whether it\’s feasible or desirable that the technology could scale to address many more use cases. These hyperscalers have a lot of free cash flow, and we are starting to see examples of use cases, but a number of industry observers believe that at some point we need to see a return on investment for a broader array of use cases and verticals. Have any key use cases emerged for artificial intelligence in the broader economy? Because generative AI is multimodal, it could theoretically apply to multiple fields: customer support, coding, medical analysis, marketing and many others. Given that there is a very significant level of investment in AI, the aggregate benefit of such use cases will need to be demonstrated in order to justify a solid return on investment. That being said, some participants at the symposium said that it might not be imperative for AI to scale in one particular area — in other words, a single key use case may not be necessary — as long as the economic benefits from all the different use cases are sufficient in aggregate. You could see efficiency gains across the board. Some speakers pointed out there are a number of examples of very large successful tech businesses where you could argue that there wasn\’t a key use case at first. Take the example of ride hailing apps. There was already a perfectly good solution: Walking to the end of the street and hailing a taxi physically. But by leveraging software and network effects, you could create very large economic benefits, as well as benefits to consumers. Is there still room for smaller technology companies to compete? Some speakers at the symposium had interesting insights on small language models. At first, technology players were focused on building large language models — and those are still important. But there is also a trend of developing smaller and more efficient models. Small language models are easier to fine tune, they may have lower energy consumption, and they can be customized to meet an enterprise\’s specific requirements in a given domain (such as legal, medicine, or finance). They’re also generally less expensive, because they\’re smaller and use less power. Large language models will remain important, and tech behemoths have the resources, free cash flow, and balance sheets to drive the development of those. But speakers pointed out that there will be other, perhaps smaller players in the ecosystem who can innovate and develop small language models that will sit on top of those larger models. Some speakers thought this presented an opportunity for small companies to drive innovation at the top of the stack and highlighted the large number of companies being founded daily to do so. Could the high cost of generative AI hold back development? Training LLMs requires very high levels of capital investment. You need to build a data center, you need all the semiconductors — that includes both GPUs and memory chips — and you need hardware, power, and utilities. Speakers mentioned that the cost per query in some domains is multiple times higher than for a regular search algorithm.  That said, there has been steady progress on reducing costs. The cost of a generative AI query at some large tech companies has come down significantly since the launch of the technology, and a new gen AI company has said that revenue generated by the latest generation of LLMs exceeded the cost of training prior models. While some speakers stated there could be a risk that spending on AI could reduce if significant returns

More than two years after ChatGPT’s debut, Goldman Sachs Chief Information Officer Marco Argenti says the potential for generative artificial intelligence is coming into focus. The development of increasingly powerful large language models (LLMs), supercharged by advances in robotics will, in Argenti’s view, begin to bring sweeping changes to everything from employment to regulation of the technology in 2025. Argenti, the former vice president of technology of Amazon Web Services, makes five predictions about how AI could evolve and interact with businesses and society in the near future: The new hybrid workforce: AI systems are becoming more like humans. So why not employ them like humans? The capabilities of AI models to plan and execute complex, long-running tasks on humans’ behalf will begin to mature. This will create the conditions for companies to eventually “employ” and train AI workers to be part of hybrid teams of humans and AIs working together. The question becomes: How best to get humans and AI to work together. Companies will reskill human managers to oversee a hybrid workforce. The role of human resources will evolve into a department for human and machine resources. The first AI “layoffs” could eventually emerge, in which AI models will be replaced by better AI tools or humans if they perform poorly compared to their peers. The emergence of expert AI: The AI version of PhDs will arrive. Companies will integrate AI with their proprietary data, either with retrieval-augmented generation (RAG) — an architecture that can connect LLMs to external, specialized datasets — or via a process known as fine-tuning, which involves enhanced training of an LLM with a smaller, specialized dataset. As a result, expert AI systems, or large expert models, will gradually emerge with advanced capabilities and industry-specific knowledge — for example, specialized models for medicine, robotics, finance, or material sciences. Robotic breakthroughs powered by AI: So far, AI models have been trained by reading essentially all the books in the world. What if they\’re trained on the world itself? Children learn to walk before they learn to read. In the same way, the intersection of LLMs and robotics will increasingly bring AI into, and enable it to experience, the physical world, which will help enable reasoning capabilities for AI. At the same time, these models will transform commodity hardware into specialized components capable of performing far beyond their default capabilities. Advanced cameras using cheap sensors, studio-quality microphones using low-cost transducers, and off-the shelf mechanical joints capable of performing complex manipulation tasks will drive down costs for combining advanced AI with robotics and will speed up innovation. Regulation goes from global to local: As the world awaits regulatory clarity, responsible AI principles will take center stage for CEOs and boards.   In addition to (and somewhat separate from) national, state, or sectoral regulations, companies across sectors will continue to see the benefit of implementing proper controls, such as responsible AI principles (i.e., a form of self-regulation). Responsible AI will become an even bigger priority for CEOs and boards of major companies. Large model consolidation: The Formula One experience arrives for AI. Given the cost and complexity of engine development in the Formula One motorsport competition, there are many cars but only a few engine makers. Likewise, the investment required to train and maintain large frontier models (those that are the largest and most advanced) for AI will eventually result in only a handful of providers. Consolidation will mirror what has taken place in cloud infrastructure, databases, and operating systems, where the total number of companies developing large AI engines will be countable on one hand. Startups that are now “model-centric” will shift towards building solutions that are model-agnostic, focusing instead on other aspects of AI such as compliance, safety, data integration, orchestration, automation, and user experience.

Nuclear power will be a key part of a suite of new energy infrastructure built to meet surging data-center power demand driven by artificial intelligence. But nuclear can’t meet all of the increased data-center power needs. Natural gas, renewables, and battery technology will also have a role to play, according to Goldman Sachs Research. Several big tech companies looking for low carbon, round-the-clock energy signed contracts for new nuclear capacity in the last year, and there could be more such deals ahead. Those efforts come as electricity usage by data centers is expected to more than double by 2030, according to reports led by Brian Singer, Jim Schneider, and Carly Davenport.  In total, the team forecasts 85-90 gigawatts (GW) of new nuclear capacity would be needed to meet all of the data center power demand growth expected by 2030 (relative to 2023). But well less than 10% will be available globally by 2030. As power needs ramp up, the efficiency gains of data center infrastructure are beginning to slow, according to Davenport, a US utilities research analyst in Goldman Sachs Research. “Growth from AI, broader data demand, and a deceleration of power efficiency gains is leading to a power surge from data centers,” she writes. How much is AI power consumption expected to rise? Power demand from data centers is on track to grow more than 160% by 2030, compared to 2023 levels, Goldman Sachs Research projects. A scenario in which 60% of that increased demand was met by thermal sources such as natural gas would lead to an expected emissions increase of 215-220 million tons globally, equivalent to 0.6% of the world’s energy emissions. While renewables have the potential to meet most of the increased power needs from data centers at some times of day, they don\’t produce power consistently enough to be the only energy source for data centers, explains Schneider, a digital infrastructure analyst in Goldman Sachs Research. “Our conversations with renewable developers indicate that wind and solar could serve roughly 80% of a data center\’s power demand if paired with storage, but some sort of baseload generation is needed to meet the 24/7 demand,” Schneider writes. He adds that nuclear is the preferred option for baseload power, but the difficulty of building new nuclear plants means that natural gas and renewables are more realistic short-term solutions. Nuclear energy has almost zero carbon dioxide emissions — although it does create nuclear waste that needs to be managed carefully. But the scarcity of specialized labor, the challenges of obtaining permits, and the difficulty of sourcing sufficient uranium all pose a challenge to the development of new nuclear power plants. By the 2030s, though, new nuclear energy facilities and developments in AI could start to bring down the overall carbon footprint of AI data centers. In the meantime, companies trying to supply energy for new data centers are likely to focus on a mix of power sources, writes Singer, global head of GS SUSTAIN in Goldman Sachs Research. “Our outlook on power demand growth warrants an ‘and’ approach, not an ‘or’ approach, as we see ample opportunities for generation growth across sources,” he writes. How much will nuclear power increase? Recent contracts for nuclear energy facilities along with signs of countries’ greater appetite for nuclear power suggest a significant increase of investment in the next five years, and a corresponding rise in power supply in the 2030s. The proliferation of AI data centers has boosted investor confidence in future growth in electricity demand at the same time as big tech companies are looking for low-carbon reliable energy. This is leading to the de-mothballing of recently retired nuclear generators, as well as consideration for new larger-scale reactors. In the US alone, big tech companies have signed new contracts for more than 10 GW of possible new nuclear capacity in the last year, and Goldman Sachs Research sees potential for three plants to be brought online by 2030. Meanwhile, governments are also broadly more supportive of nuclear power. Switzerland is reconsidering the use of nuclear generators for its electricity supply, while nuclear power enjoys bipartisan support in the US, and the Australian opposition party has put forward plans to introduce nuclear reactors. Participants at the COP28 conference in late 2023, an annual summit convened by the UN to address climate change, agreed to triple global nuclear capacity by 2050. Building the ‘green’ data center Green energy sources are also receiving considerable investment from AI providers. The team forecasts that 40% of the new capacity built to support increased power demand from data centers will be renewables. The supply cost of renewable energy sources is cheaper than generating electricity from natural gas, before taking into account transmission considerations and filling gaps when the sun isn’t shining and the wind isn’t blowing. Analysis by Goldman Sachs Research shows that, at face value, the average cost of energy for onshore wind hosted on the site of a data center is $25 per megawatt hour in the US, while solar energy costs $26/MWh, and combined cycle natural gas (the most fuel-efficient type of gas-fired power plant) costs $37/MWh before accounting for the cost of carbon capture. In practice, though, utility-scale solar plants only run around 6 hours per day on average, while wind plants run for an average of 9 hours per day. There is also day-to-day volatility in the capacity of these sources, depending on the radiance of the sun and the strength of the wind. Transmission costs are also a consideration for data center operators. Because renewable energy sources often take up a much greater land footprint than natural gas or nuclear, they are more likely to be located away from big cities, where much of the energy that they generate is used. As a result, the energy they generate may have to travel further before it is used. On the other hand, thermal plants — such as those powered by nuclear reactors or combined cycle natural gas — can run throughout the day, without hourly intermittency challenges.