Goldman Sachs also comes to cool down AI: The trend of AI adoption is slowing down

As super giants continue to invest hundreds of billions of dollars into the AI hardware sector, the latest data from Goldman Sachs, Apollo, and MIT reveals a disturbing trend: the adoption of AI in enterprises is slowing down, with large companies even experiencing a decline in adoption rates.

According to a report released by Goldman Sachs Chief Economist Jan Hatzius on September 8, despite the acceleration of AI hardware investments, the growth rate of AI adoption among U.S. companies has slowed in the third quarter.

Data shows that by the third quarter of 2025, the proportion of U.S. companies using AI has only slightly increased from 9.2% in the second quarter to 9.7%. Although it is still growing, the growth rate has slowed. This stands in stark contrast to the hot investments in the AI infrastructure sector, with the report noting that super giants have invested hundreds of billions of dollars in building AI data centers, and these massive capital expenditures urgently need to prove their return on investment.

The report also indicates that industries such as finance and real estate have seen the largest increases in AI adoption rates, while the education services sector has experienced a decline. In terms of the labor market, Goldman Sachs believes the impact remains "moderate," but job displacement has already occurred in areas such as technology, design, and customer service. Since the last update, layoffs caused by AI have affected 10,375 workers.

More Severe Signals: Declining AI Adoption Rates Among Large Enterprises

Even more concerning than the "slowing growth" revealed in the Goldman Sachs report is the possibility that the AI adoption rate among large enterprises may have peaked and begun to decline.

Wall Street Insight reported that, according to an analysis by Torsten Sløk, Chief Economist at Apollo Global Management, the AI adoption among large enterprises is slowing down. This analysis is based on survey data from the U.S. Census Bureau covering 1.2 million companies, showing that enterprises with more than 250 employees are experiencing a downward trend in AI adoption rates.

This trend may indicate that after an initial phase of enthusiastic experimentation, large enterprises are entering a "technology disillusionment phase," beginning to reassess the actual value of AI tools and the challenges of integration.

95% of AI Investment Returns are Zero?

The fundamental reason for the obstacles in enterprise AI adoption may be found in an earlier MIT report that triggered a market crash. A report titled “The Generative AI Gap: The State of Business AI in 2025” found that as much as 95% of enterprises receive zero returns from their generative AI investments.

The main author of the report, Aditya Challapally, pointed out that the core issue is not the AI models themselves, but rather the existence of "learning gaps" and integration strategy flaws within enterprises. For example, many general-purpose tools designed for individual users (such as ChatGPT) perform poorly in enterprise environments that require adaptation to specific workflows In addition, the report found that the success rate of "purchasing" mature AI tools (about 67%) is far higher than that of companies "building" their own systems (about one-third), which poses a direct challenge to those companies that have invested heavily in developing proprietary AI systems.

Market Panic May Spread

These negative data have had a substantial impact on market sentiment. After the MIT report was released in August, it triggered a significant sell-off in tech stocks, with the Nasdaq index recording its largest single-day drop for the month (-1.4%), and Nvidia's stock price falling by 3.5%. Wallstreetcn reported that a trader at the time stated, "This story is causing panic among people."

For investors, a series of signals from Goldman Sachs' "slowing growth" to Apollo's "declining adoption rate," and finally to MIT's revelation of the "zero return" dilemma clearly indicate that the commercialization path of AI is far more complex and lengthy than expected. Against the backdrop of the Nasdaq 100 index's expected price-to-earnings ratio still being nearly one-third higher than the long-term average, investors need to shift their focus from the fervent pursuit of technological breakthroughs to a cautious assessment of companies' actual implementation capabilities and profitability