Goldman Sachs Questions the $1 Trillion Potential of Generative AI Market

Goldman Sachs puts a $1 trillion question mark over Generative AI

A KPMG survey in the US found that executives expect Generative AI to have an enormous impact on business, but most say they are unprepared for immediate adoption.Generative artificial intelligence (AI) has become the latest buzzword across industries and businesses. Most agree that it is going to revolutionise businesses. Yet serious questions hang over its uses and impact. A KPMG survey in the US found executives expect Generative AI to have an enormous impact on business, but most say they are unprepared for immediate adoption. Now a Goldman Sachs report has raised questions over the use of generative AI in business. Tech giants and beyond are set to spend over $1 trillion on AI capex in coming years, with so far little to show for it, the report says. It questions if this large spend will ever pay off? In the report, many experts have expressed doubts over any revolutionary impact of AI in the short term. A few other experts are more optimistic about AI's economic potential and its ability to ultimately generate returns beyond what they call the current "picks and shovels" phase when AI's "killer application" hasn't emerged. "But despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst," says the report. How productive can Generative AI be? In an interview with Goldman Sachs, Daron Acemoglu, Institute Professor at MIT, who has written several books, including 'Why Nations Fail: The Origins of Power, Prosperity, and Poverty' and his latest, 'Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity', argued that the upside to US productivity and growth from generative AI technology over the next decade -- and perhaps beyond -- will likely be more limited than many expect. Acemoglu estimates that only a quarter of AI exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks. And he doesn't take much comfort from history that shows technologies improving and becoming less costly over time, arguing that AI model advances likely won't occur nearly as quickly -- or be nearly as impressive -- as many believe. Acemoglu also questions whether AI adoption will create new tasks and products, saying these impacts are "not a law of nature." He estimates that total factor productivity effects within the next decade should be no more than 0.66% -- and an even lower 0.53% when adjusting for the complexity of hard-to-learn tasks. And that figure roughly translates into a 0.9% GDP impact over the decade. "Every human invention should be celebrated, and generative AI is a true human invention," Acemoglu says. "But too much optimism and hype may lead to the premature use of technologies that are not yet ready for prime time. This risk seems particularly high today for using AI to advance automation. Too much automation too soon could create bottlenecks and other problems for firms that no longer have the flexibility and trouble-shooting capabilities that human capital provides." Return on investment Jim Covello is Head of Global Equity Research at Goldman Sachs, argues that to earn an adequate return on costly AI technology, AI must solve very complex problems, which it currently isn't capable of doing, and may never be. "My main concern is that the substantial cost to develop and run AI technology means that AI applications must solve extremely complex and important problems for enterprises to earn an appropriate return on investment (ROI)," he says. "We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low-wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I've witnessed in my thirty years of closely following the tech industry." "Many people attempt to compare AI today to the early days of the internet," Covello says. "But even in its infancy, the internet was a low-cost technology solution that enabled e-commerce to replace costly incumbent solutions. Amazon could sell books at a lower cost than Barnes & Noble because it didn't have to maintain costly brick-and-mortar locations. Fast forward three decades, and Web 2.0 is still providing cheaper solutions that are disrupting more expensive solutions, such as Uber displacing limousine services. While the question of whether AI technology will ever deliver on the promise many people are excited about today is certainly debatable, the less debatable point is that AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn't designed to do." Covello doesn't think that technology costs decline dramatically as technology evolves due to lack of competition as Nvidia is the only company currently capable of producing the GPUs that power AI, and because the starting point for costs is so high that even if costs decline, they would have to do so dramatically to make automating tasks with AI affordable. Read the full report here.

Goldman Sachs puts a $1 trillion question mark over Generative AI

A KPMG survey in the US found executives expect Generative AI to have an enormous impact on business, but most say they are unprepared for immediate adoption.Generative artificial intelligence (AI) has become the latest buzzword across industries and businesses. Most agree that it is going to revolutionise businesses. Yet serious questions hang over its uses and impact. A KPMG survey in the US found executives expect Generative AI to have an enormous impact on business, but most say they are unprepared for immediate adoption. Now a Goldman Sachs report has raised questions over the use of generative AI in business. Tech giants and beyond are set to spend over $1 trillion on AI capex in coming years, with so far little to show for it, the report says. It questions if this large spend will ever pay off? In the report, many experts have expressed doubts over any revolutionary impact of AI in the short term. A few other experts are more optimistic about AI's economic potential and its ability to ultimately generate returns beyond what they call the current "picks and shovels" phase when AI's "killer application" hasn't emerged. "But despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst," says the report. How productive can Generative AI be? In an interview with Goldman Sachs, Daron Acemoglu, Institute Professor at MIT, who has written several books, including 'Why Nations Fail: The Origins of Power, Prosperity, and Poverty' and his latest, 'Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity', argued that the upside to US productivity and growth from generative AI technology over the next decade -- and perhaps beyond -- will likely be more limited than many expect. Acemoglu estimates that only a quarter of AI exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks. And he doesn't take much comfort from history that shows technologies improving and becoming less costly over time, arguing that AI model advances likely won't occur nearly as quickly -- or be nearly as impressive -- as many believe. Acemoglu also questions whether AI adoption will create new tasks and products, saying these impacts are "not a law of nature." He estimates that total factor productivity effects within the next decade should be no more than 0.66% -- and an even lower 0.53% when adjusting for the complexity of hard-to-learn tasks. And that figure roughly translates into a 0.9% GDP impact over the decade. "Every human invention should be celebrated, and generative AI is a true human invention," Acemoglu says. "But too much optimism and hype may lead to the premature use of technologies that are not yet ready for prime time. This risk seems particularly high today for using AI to advance automation. Too much automation too soon could create bottlenecks and other problems for firms that no longer have the flexibility and trouble-shooting capabilities that human capital provides." Return on investment Jim Covello is Head of Global Equity Research at Goldman Sachs, argues that to earn an adequate return on costly AI technology, AI must solve very complex problems, which it currently isn't capable of doing, and may never be. "My main concern is that the substantial cost to develop and run AI technology means that AI applications must solve extremely complex and important problems for enterprises to earn an appropriate return on investment (ROI)," he says. "We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low-wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I've witnessed in my thirty years of closely following the tech industry." "Many people attempt to compare AI today to the early days of the internet," Covello says. "But even in its infancy, the internet was a low-cost technology solution that enabled e-commerce to replace costly incumbent solutions. Amazon could sell books at a lower cost than Barnes & Noble because it didn't have to maintain costly brick-and-mortar locations. Fast forward three decades, and Web 2.0 is still providing cheaper solutions that are disrupting more expensive solutions, such as Uber displacing limousine services. While the question of whether AI technology will ever deliver on the promise many people are excited about today is certainly debatable, the less debatable point is that AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn't designed to do." Covello doesn't think that technology costs decline dramatically as technology evolves due to lack of competition as Nvidia is the only company currently capable of producing the GPUs that power AI, and because the starting point for costs is so high that even if costs decline, they would have to do so dramatically to make automating tasks with AI affordable. Read the full report here.

Goldman Sachs puts a $1 trillion question mark over Generative AI - ET Telecom

Internet 4 min read Goldman Sachs puts a $1 trillion question mark over Generative AI Jim Covello is Head of Global Equity Research at Goldman Sachs, argues that to earn an adequate return on costly AI technology, AI must solve very complex problems, which it currently isn't capable of doing, and may never be. Generative artificial intelligence (AI) has become the latest buzzword across industries and businesses. Most agree that it is going to revolutionise businesses. Yet serious questions hang over its uses and impact. A KPMG survey in the US found executives expect Generative AI to have an enormous impact on business, but most say they are unprepared for immediate adoption. Now a Goldman Sachs report has raised questions over the use of generative AI in business. Tech giants and beyond are set to spend over $1 trillion on AI capex in coming years, with so far little to show for it, the report says. It questions if this large spend will ever pay off? In the report, many experts have expressed doubts over any revolutionary impact of AI in the short term. A few other experts are more optimistic about AI's economic potential and its ability to ultimately generate returns beyond what they call the current "picks and shovels" phase when AI's "killer application" hasn't emerged. "But despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst," says the report. How productive can Generative AI be? In an interview with Goldman Sachs, Daron Acemoglu, Institute Professor at MIT, who has written several books, including 'Why Nations Fail: The Origins of Power, Prosperity, and Poverty' and his latest, 'Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity', argued that the upside to US productivity and growth from generative AI technology over the next decade -- and perhaps beyond -- will likely be more limited than many expect. Acemoglu estimates that only a quarter of AI exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks. And he doesn't take much comfort from history that shows technologies improving and becoming less costly over time, arguing that AI model advances likely won't occur nearly as quickly -- or be nearly as impressive -- as many believe. Acemoglu also questions whether AI adoption will create new tasks and products, saying these impacts are "not a law of nature." He estimates that total factor productivity effects within the next decade should be no more than 0.66% -- and an even lower 0.53% when adjusting for the complexity of hard-to-learn tasks. And that figure roughly translates into a 0.9% GDP impact over the decade. "Every human invention should be celebrated, and generative AI is a true human invention," Acemoglu says. "But too much optimism and hype may lead to the premature use of technologies that are not yet ready for prime time. This risk seems particularly high today for using AI to advance automation. Too much automation too soon could create bottlenecks and other problems for firms that no longer have the flexibility and trouble-shooting capabilities that human capital provides." Return on investment Jim Covello is Head of Global Equity Research at Goldman Sachs, argues that to earn an adequate return on costly AI technology, AI must solve very complex problems, which it currently isn't capable of doing, and may never be. "My main concern is that the substantial cost to develop and run AI technology means that AI applications must solve extremely complex and important problems for enterprises to earn an appropriate return on investment (ROI)," he says. "We estimate that the AI infrastructure buildout will cost over $1tn in the next several years alone, which includes spending on data centers, utilities, and applications. So, the crucial question is: What $1tn problem will AI solve? Replacing low-wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I've witnessed in my thirty years of closely following the tech industry." "Many people attempt to compare AI today to the early days of the internet," Covello says. "But even in its infancy, the internet was a low-cost technology solution that enabled e-commerce to replace costly incumbent solutions. Amazon could sell books at a lower cost than Barnes & Noble because it didn't have to maintain costly brick-and-mortar locations. Fast forward three decades, and Web 2.0 is still providing cheaper solutions that are disrupting more expensive solutions, such as Uber displacing limousine services. While the question of whether AI technology will ever deliver on the promise many people are excited about today is certainly debatable, the less debatable point is that AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn't designed to do." Covello doesn't think that technology costs decline dramatically as technology evolves due to lack of competition as Nvidia is the only company currently capable of producing the GPUs that power AI, and because the starting point for costs is so high that even if costs decline, they would have to do so dramatically to make automating tasks with AI affordable.

AI's missing revenues

Why it matters: The U.S. stock market continues to hit new highs, driven in large part by optimism surrounding the coming AI revolution. Between the lines: Either way, billions of dollars in capital is almost certain to be incinerated. The big picture: Newly published reports from Goldman Sachs, Barclays, and Sequoia Capital have crunched the numbers on how much has been and will be spent on AI-related infrastructure, and how much extra revenue companies will need to make all that spending worth it. Between the lines: "Overbuilding things the world doesn't have use for, or is not ready for, typically ends badly," Goldman Sachs head of global equity research Jim Covello warns. Follow the money: Goldman Sachs projects that companies and utilities will spend about $1 trillion on AI capex in the coming years. Zoom in: The lack of revenue is at the core of the skepticism. Reality check: Barclays estimates that AI capex by 2026 will be sufficient to support 12,000 AI products of the scale of ChatGPT. Zoom out: Other unknowns include whether AI tech will become cheap enough to generate significant cost savings, whether it'll solve the kind of highly complex problems that would make it worth the price, and whether we'll be able to supply the energy needed to keep up with AI's growth. The other side: Tech leaders don't see "overbuilding" as a dirty word. The bottom line: Generative AI's topline benefits are not arriving anytime soon, realists argue.

Roadblocks to Generative AI Market Expansion

What are the main ethical concerns hindering the growth of the generative AI market? The primary ethical concerns revolve around issues such as data privacy, bias, misinformation, and accountability. Generative AI systems require vast amounts of data to function effectively, often leading to concerns about how this data is sourced, stored, and utilized. Privacy issues arise when sensitive or personal data is used without explicit consent, potentially violating privacy rights. Bias in AI systems is another significant issue, as these models can perpetuate or even amplify existing biases present in their training data, leading to unfair or discriminatory outcomes. How does the lack of standardized regulations affect the adoption of generative AI technologies? The absence of standardized regulations creates uncertainty and risk for businesses looking to adopt generative AI technologies. Without clear regulatory frameworks, companies face difficulties in understanding their legal obligations and the potential liabilities associated with using AI. This uncertainty can deter investment and innovation, as businesses may be wary of the potential for future regulatory changes that could impact their operations. Additionally, the lack of standards can lead to inconsistent practices across the industry, resulting in uneven quality and safety of AI applications. What are the technical challenges impeding the progress of generative AI? Technical challenges in generative AI include issues related to data quality and quantity, model complexity, computational resources, and interpretability. Generative AI models require vast amounts of high-quality data to train effectively, but acquiring and curating such data can be difficult and costly. Additionally, these models are often highly complex, with millions or even billions of parameters, making them challenging to develop, fine-tune, and deploy. The computational resources needed to train and run these models are immense, necessitating significant investments in hardware and infrastructure. This can be a barrier for smaller companies or those with limited budgets. Another critical technical challenge is the interpretability of generative AI models. How do economic factors influence the growth of the generative AI market? Economic factors play a significant role in shaping the growth trajectory of the generative AI market. High development and operational costs are among the primary economic barriers. Building and maintaining generative AI systems require substantial investments in talent, technology, and infrastructure. Skilled AI researchers and engineers command high salaries, and the computational resources needed for training large models are expensive. Additionally, businesses must invest in data acquisition, storage, and processing capabilities. These costs can be prohibitive, particularly for startups and small to medium-sized enterprises (SMEs), limiting their ability to compete and innovate in the AI space. What role does public perception play in the adoption of generative AI technologies? Public perception significantly impacts the adoption of generative AI technologies. Public trust is essential for the widespread acceptance and use of any new technology, and generative AI is no exception. Concerns about the ethical implications, potential misuse, and impact on jobs and society can lead to skepticism and resistance. High-profile incidents of AI failures or misuse, such as deepfake scandals or biased decision-making, can exacerbate these concerns and erode trust. Additionally, the general public may lack understanding of how generative AI works and its potential benefits, leading to fear and uncertainty.