Goldman Sachs' survey shows that the AI adoption rate among U.S. companies in the second quarter has significantly surged from 7.4% in the fourth quarter of last year to 9.2%, with large enterprises employing over 250 employees reaching an adoption rate of 14.9%. The most important signal is that the revenue expectations for the Semiconductor Industry are set to increase by 36% by the end of 2026 compared to current levels, and the 2025 revenue forecast has been raised.
While Wall Street continues to debate the AI bubble, the latest data on AI adoption rates among U.S. companies and revenue expectations for Semiconductor companies seem to reveal the true nature of this AI revolution.
On June 6, according to reports from the Feng Trade Platform, Goldman Sachs' AI adoption tracking report for the second quarter of 2025 shows that the AI adoption rate among U.S. companies has significantly risen from 7.4% in the fourth quarter of last year to 9.2%, with large enterprises employing over 250 employees achieving an adoption rate of 14.9%.
Goldman Sachs' chief Analyst Jan Hatzius, Joseph Briggs, and others stated in the report that the most important signal for the market is that revenue expectations for the Semiconductor Industry are expected to grow by 36% by the end of 2026 compared to current levels, and analysts have raised the revenue forecasts for the Semiconductor Industry for 2025 and AI hardware companies.
Analysts point out that this adjustment in expectations reflects the sustainability of the AI investment boom.
AI investment growth remains strong.
Semiconductor companies are still the biggest beneficiaries of the AI investment wave.
Goldman Sachs stated in the report that since the release of ChatGPT, analysts have raised the revenue forecast for the Semiconductor Industry for the end of 2025 by 200 billion dollars, and forecasts for other AI hardware companies by 105 billion dollars.
Apart from semiconductors, the revenue forecasts for cloud service providers and utility companies have also been upgraded by Analysts.
Investment in AI-related hardware and software in the U.S. accelerated in the first quarter, but Goldman Sachs believes that this growth may be underestimated due to the methodological issues with how the U.S. Department of Commerce's Bureau of Economic Analysis views semiconductors and cloud services as intermediate inputs.
Goldman Sachs stated that this statistical blind spot reveals the limitations of the traditional economic statistics framework in capturing the true scale of AI investment, and explains why the AI boom is not as clearly reflected in macroeconomic data as expected.
The adoption rate of AI in enterprises is accelerating.
Data at the enterprise level is particularly striking.
The report indicates that by the second quarter of 2025, significant progress in enterprise AI adoption is expected, with 9.2% of U.S. enterprises currently using AI to produce commodities or services, a considerable increase from 7.4% in the fourth quarter.
In terms of industry distribution, the sectors of education, information, finance, and Professional Services reported the largest increases in adoption rates, growing over 3 percentage points compared to the previous quarter.
Broadcast and telecommunications companies expect the largest increase in AI adoption rates in the next six months. Goldman Sachs observed that sub-industries where work tasks are more susceptible to AI automation have higher adoption rates, and this correlation remains strong.
From the perspective of company size, large enterprises with more than 250 employees continue to maintain the highest adoption rate of 14.9%, while medium-sized enterprises with 100-249 employees expect the largest increase in adoption rate in the next six months, rising by 4.7 percentage points to 14.6%. The adoption rate of medium-sized enterprises with 150-249 employees has also accelerated.
Despite the rapid growth of AI adoption, its impact on the labor market remains limited, with most labor market Indicators showing no significant signs of impact.
The report states that AI-related job vacancies currently account for 24% of all IT job vacancies, and 1.5% of all job postings.
In addition, Goldman Sachs pointed out that in the limited areas where generative AI has been deployed, there has been a significant increase in labor productivity. Data shows that academic research indicates an average productivity increase of 23%, while corporate examples show an efficiency improvement of about 29%.
Goldman Sachs believes that the paradox of limited adoption with significant effects may suggest that the true disruptive impact of AI has not yet been fully unleashed.
