Core Viewpoint - The article discusses the current state of the AI revolution, indicating that it is still in the "installation phase" characterized by excessive investment and potential for a market bubble before entering a "golden age" [1][3][8]. Investment Trends - Major tech companies like Google, Amazon, Microsoft, and Meta are projected to invest up to $750 billion in data centers over the next two years to support AI models [3]. - Morgan Stanley forecasts that global spending in this sector will reach $3 trillion by 2029 [3]. Market Concerns - There is growing anxiety among investors regarding the returns on massive capital expenditures, with a report from MIT revealing that 95% of surveyed companies have not seen returns from their generative AI investments [4]. - OpenAI's CEO, Sam Altman, expressed concerns about potential losses for some investors, indicating a lack of optimism regarding the existence of an AI bubble [4]. Historical Context - The article references Carlota Perez's identification of five major technological revolutions, suggesting that the current AI revolution is an extension of the information technology revolution that began in the 1970s [4][5]. - Historical patterns indicate that each technological revolution experiences a cycle from investment frenzy to bubble burst before achieving a golden age [6][9]. Unique Characteristics of the AI Revolution - Unlike previous revolutions, the AI revolution is primarily driven by software, which allows for rapid global scaling through network effects, exemplified by OpenAI's ChatGPT reaching 700 million users in under three years [11]. - The competitive landscape is intensified by digital globalization, which increases both opportunities and risks, as seen with cheaper AI models affecting investor confidence in U.S. tech stocks [12]. Future Implications - AI companies are positioned to directly benefit from the economic value they create, potentially transforming sectors like healthcare, drug discovery, and autonomous vehicles [13][14]. - The article emphasizes the need for civil society to shape the revolution to serve public interests, drawing parallels to historical actions taken to regulate powerful companies and address labor market disruptions [15][16]. Challenges Ahead - Current issues such as dysfunctional financial markets, concentrated corporate power, rising populism, and climate change present significant challenges that could impact the trajectory of the AI revolution [17][19].

Group 1 - The core viewpoint is that the current AI revolution is in a "frenzied deployment phase," which historically precedes a bubble burst before entering a golden age [1][4] - Major tech companies like Google, Amazon, Microsoft, and Meta are projected to invest up to $750 billion in data centers to support AI models over the next two years, with global spending in this area expected to reach $3 trillion by 2029 [1][2] - A report from MIT indicates that 95% of surveyed companies have not seen any returns from their generative AI investments, raising concerns about the sustainability of current investments [1][4] Group 2 - Historical patterns show that technological revolutions, including the current AI wave, follow a predictable cycle characterized by initial over-investment and market bubbles [2][3] - The AI revolution is unique as it is driven by both software and hardware, altering financial dynamics and enabling rapid global expansion through network effects [5][6][7] - The competitive landscape is intensified by digital globalization, which increases both opportunities and risks, as seen with the impact of cheaper AI models on investor confidence [8] Group 3 - To transition into a golden age, society must actively shape the revolution to serve its purposes, similar to historical efforts to regulate powerful companies and address labor market disruptions [9] - Current challenges such as dysfunctional financial markets, concentrated corporate power, and climate change pose significant hurdles that need to be addressed for the AI revolution to fulfill its potential [9]