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Goldman Sachs Warns AI Valuation May Already Be a Bubble

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  Published in Stocks and Investing on Monday, November 17th 2025 at 16:36 GMT by Fortune

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Is AI a Bubble? Goldman Sachs Says the Market is Already Priced In $19 Trillion

Fortune, 17 November 2025

The article from Fortune tackles a question that has been circulating in finance and technology circles for the past year: “Is artificial intelligence a bubble?” At the heart of the discussion lies Goldman Sachs’ most recent valuation of the global AI market, which it estimates to be $19 trillion by 2030. While the firm acknowledges that the sector’s growth prospects are extraordinary, it also cautions that the industry could be over‑valued, and that a “bubble” may already be in place. Below is a detailed summary of the article’s key points, the context it provides through internal links, and the implications for investors, businesses, and policymakers.

1. The $19 Trillion Benchmark

Goldman Sachs’ research team, led by senior economist Dr. Eleni Maroulis, released a new study that aggregates data from 2,500 companies, venture capital funds, and private‑equity investors. Their methodology combines:

1. Revenue‑based forecasting – projecting AI‑related revenues in cloud, software, and services.
2. Capital‑expenditure (CapEx) analysis – estimating the amount companies plan to invest in AI hardware, research, and acquisitions.
3. Stock market multiples – using valuation ratios from AI‑heavy firms (e.g., Nvidia, Alphabet, Microsoft) as benchmarks.

The conclusion: by 2030, the global AI economy could be worth roughly $19 trillion – nearly 4 % of today’s global GDP. The study notes that the AI sector is expected to account for 20 % of total corporate investment in the next decade, a figure that is “dramatically higher than any other tech wave in history.”

Fortune follows this line of reasoning with a link to Goldman’s full report, titled “AI: The Next Frontier for Growth.” The article also highlights that the firm’s model is based on a 10‑year compound annual growth rate (CAGR) of 22 % for AI adoption across all sectors.

2. Signs of a Bubble

Goldman Sachs’ senior analyst, Ravi Patel, points out several warning signals that could signal an over‑valuation:

• “Melt‑in‑Your‑Coffee” growth – The firm has seen a 30 % YoY jump in AI revenue across its portfolio. While healthy, such rapid growth is reminiscent of the late‑1990s dot‑com boom.
• Excessive capital inflows – Venture capital, private equity, and public equity markets have poured an estimated $2 trillion into AI startups and infrastructure last year alone, creating a “demand‑driven” market.
• Over‑optimistic forecasts – The same report notes that many industry analysts predict an average AI profit margin of 60 % by 2028, a figure that “tends to overstate the true economic value” according to the research team.

The article links to an external piece, “The History of Tech Bubbles” by Bloomberg, which draws parallels between current AI hype and previous cycles such as the biotech boom of 2010‑2015. Goldman Sachs warns that while AI’s potential is large, “the pricing mechanisms of the market may have already baked in some of that upside.”

3. How the Market is “Already Priced In”

One of the article’s central questions is whether AI has already been fully absorbed by the market. Patel explains that many high‑profile companies—Microsoft, Amazon, Google, and Nvidia—have already included AI-related growth in their earnings guidance. This means that the $19 trillion figure may be largely a reflection of “price discovery” rather than an untapped market.

Furthermore, Goldman’s research shows that stock market valuations of AI‑heavy companies have surged by 70 % since 2022. The firm argues that this surge is a function of “market psychology” rather than fundamental value. The article links to the company earnings releases for 2025, providing readers with a concrete view of how AI revenue growth has been integrated into quarterly financial statements.

4. The Role of Government and Regulation

Goldman Sachs emphasizes that policy frameworks will shape the AI landscape, potentially mitigating bubble risks. They cite:

• EU AI Act – A regulatory package that aims to standardize safety and accountability, potentially curbing over‑ambitious AI claims.
• U.S. National AI Initiative – Funding for research, especially in “AI safety” and “AI for public good,” which could keep the sector anchored to societal needs.
• China’s AI strategy – The country’s heavy investment in AI R&D may keep its valuation “steady” rather than speculative.

The article links to a recent White House briefing on AI regulation, providing context on how policy could alter the trajectory of the market.

5. Investment Strategies in a Potential Bubble

Goldman Sachs offers guidance for investors. Patel suggests a “cautionary, balanced approach”:

1. Diversification – Spread exposure across AI service providers, hardware, and AI‑enabled industries (healthcare, finance, retail).
2. Focus on fundamentals – Prioritize companies with strong balance sheets, solid cash flows, and clear revenue streams tied to AI.
3. Avoid “pure play” AI startups – Many early‑stage firms have high burn rates and uncertain exit paths.
4. Use defensive hedges – Consider derivatives that protect against a decline in AI valuations.

The article also references a Morgan Stanley op‑ed, “AI, the new growth engine, but how to avoid a crash.” This piece complements Goldman’s advice by arguing that a selective approach is essential for long‑term returns.

6. AI’s Broad Impact on the Economy

Beyond valuation, the Fortune article outlines the multidimensional impact of AI:

• Productivity gains – AI is expected to contribute an additional 3 % to global GDP by 2030.
• Employment – While some jobs may be displaced, new roles in data science, AI ethics, and AI maintenance will emerge.
• Societal implications – AI could transform education, healthcare, and public services, but also raises ethical questions about privacy and bias.

To illustrate, the article links to a Harvard Business Review case study on AI in healthcare, which shows how predictive analytics are already reducing readmission rates by 15 %.

7. Take‑Away Takeaways

1. The AI market is huge and growing fast, with Goldman Sachs estimating a $19 trillion valuation by 2030.
2. Signs of over‑valuation—rapid revenue growth, massive capital inflows, and high profit‑margin expectations—suggest a potential bubble.
3. Market pricing may already be embedded in the valuations of major AI‑heavy firms, implying that the $19 trillion figure is partly a reflection of market sentiment.
4. Policy and regulation will play a critical role in preventing a crash by ensuring safety, transparency, and fairness.
5. Investors should adopt a diversified, fundamentals‑focused strategy, avoiding over‑exposure to speculative AI startups.

In a world where AI is woven into every layer of industry, the question of whether it is a bubble remains unsettled. Goldman Sachs’ research offers a sobering view that urges caution, while acknowledging the extraordinary opportunities that AI presents. For those watching the market’s trajectory, the article serves as a reminder that great growth often comes hand‑in‑hand with significant risk.