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A skeptical look at AI investment

发布时间 2024-09-08 07:34:15    来源

摘要

Tech giants and beyond are set to spend an estimated $1 trillion on AI capex in coming years. Will this investment pay off? And if it doesn’t, what does that mean for businesses and investors? MIT’s Daron Acemoglu and Goldman Sachs Research’s Jim Covello explain why reality may not match the hype on the latest episode of Goldman Exchanges, which explores the latest Top of Mind report, Gen AI: too much spend, too little benefit? https://www.goldmansachs.com/intelligence/pages/gen-ai-too-much-spend-too-little-benefit.html Date of recordings June 11, June 13, July 11, 2024 This podcast should not be copied, distributed, published or reproduced, in whole or in part. The information contained in this recording was obtained from publicly available sources, has not been independently verified by Goldman Sachs, may not be current, and Goldman Sachs has no obligation to provide any updates or changes. All price references and market forecasts are as of the date of recording. This podcast is not a product of Goldman Sachs Global Investment Research and the information contained in this podcast is not financial research. The views and opinions expressed in this podcast are not necessarily those of Goldman Sachs and may differ from the views and opinions of other departments or divisions of Goldman Sachs and its affiliates. Goldman Sachs is not providing any financial, economic, legal, accounting, or tax advice or recommendations in this podcast. The information contained in this podcast does not constitute investment advice or an offer to buy or sell securities from any Goldman Sachs entity to the listener and should not be relied upon to evaluate any potential transaction. In addition, the receipt of this podcast by any listener is not to be taken to constitute such person a client of any Goldman Sachs entity. Neither Goldman Sachs nor any of its affiliates makes any representation or warranty, express or implied, as to the accuracy or completeness of the statements or any information contained in this podcast and any liability therefore (including in respect of direct, indirect or consequential loss or damage) is expressly disclaimed.

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