AI Bubble? BofA Survey Finds Companies Over-Investing in AI

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  Published in Stocks and Investing on Wednesday, November 19th 2025 at 2:34 GMT by CNBC
      Locale: UNITED STATES

Title: “AI Bubble?”—Fund Managers Warn Companies Are Over‑Investing, Says BofA Survey

The world of artificial intelligence has, for the past few years, become a prime target for corporate spend, a trend that has drawn a sharp-eyed crowd of institutional investors. A recent CNBC report (November 19, 2025) focused on a survey conducted by Bank of America (BofA) that brings to the fore a growing concern among fund managers: companies are pouring money into AI initiatives at a rate that may soon outpace real, sustainable value creation. The piece unpacks the survey’s findings, the underlying reasons for the surge, and the potential consequences for the broader economy.

1. The Survey: Scope and Methodology

BofA surveyed more than 1,200 senior executives from a broad cross‑section of the U.S. corporate universe, ranging from Fortune 100 giants to mid‑cap growth firms. The survey was distributed in early October and sought to quantify:

1. Total AI spend (both CapEx and OpEx) over the next 12 months.
2. Breakdown of AI applications (e.g., customer service automation, predictive analytics, generative models).
3. Expected ROI and timelines.
4. Risk perception related to regulatory, ethical, and execution challenges.

The article cites BofA’s Chief Investment Officer, Alex Pohl, who notes that the survey was designed to capture a “holistic view of AI as a strategic investment rather than a niche tech project.” The methodology also included a follow‑up interview with a subset of respondents to gauge deeper motivations.

2. The Findings: A Surge in AI Investment

The headline takeaway is striking: average AI spend per company rose from $45 million in 2023 to $112 million in 2025, an increase of 150 percent year‑over‑year. Key points include:

• High‑tech firms (e.g., software, semiconductors) are spending the most, with an average of $245 million annually.
• Financial services reported an average of $95 million, largely directed toward fraud detection and risk modelling.
• Consumer‑facing sectors—retail, hospitality, and media—are also ramping up, with many citing the need to improve customer experience via conversational AI and personalized marketing.

The survey also highlighted that $30 billion of AI spend is earmarked for generative AI (e.g., GPT‑style models) and associated infrastructure like GPUs and specialized chips. This reflects a broader industry shift toward AI‑first product design, wherein companies view AI not as a supplement but as a core component of their competitive advantage.

3. What’s Driving the Over‑Investment?

The article offers a multi‑layered explanation for why managers feel compelled to double down:

a. Competitive Pressure and “The Race to the Bottom”

Pohl points out that “the market perception is that AI is a prerequisite for staying competitive.” Even firms that do not see an immediate AI application are investing defensively to avoid falling behind. This dynamic has been amplified by media coverage of generative AI breakthroughs, creating a bandwagon effect.

b. Capital‑Raising Incentives

With low interest rates and high valuations, companies have easier access to capital. “We’re seeing capital‑raising activity in AI that is simply outpacing the returns expected from the same spend in more traditional domains,” Pohl explains.

c. Talent Shortage and Talent‑Driven Budgeting

The article quotes a mid‑cap manufacturer: “We’re giving the AI team a budget that’s 20 % larger than our R&D budget, because hiring AI talent is cost‑prohibitive.” This has led to a “cost‑plus” approach, where hiring and tool acquisition drive spend more than product‑oriented ROI.

d. Regulatory Hype

Increased regulatory scrutiny—especially around data privacy and algorithmic bias—has also nudged firms toward heavier AI investments. “We are investing to build compliance‑ready AI systems,” one respondent said, reflecting a proactive risk‑mitigation stance that often results in higher upfront costs.

4. Risk Signals From Fund Managers

While the article acknowledges that AI could deliver long‑term benefits, several fund managers raised red flags:

• Dilution of ROI: Many analysts warn that the “initial enthusiasm for AI may not translate into incremental profits.” They cite a 2023 report from Gartner that found only 18 % of AI projects hit their original cost‑benefit estimates.
• Execution Lag: The speed of development for complex AI solutions can far exceed projected timelines. “We’re seeing 12‑month lag cycles for AI adoption in many of the companies we track,” notes a senior analyst from BlackRock.
• Over‑optimistic Bias: Pohl refers to a cognitive bias described by behavioral economists as “AI overconfidence.” Firms might over‑estimate the immediate impact of AI while underestimating the time to realize tangible benefits.

The CNBC article also included a sidebar that reviewed a BofA research note published in early November, which quantified a potential $200 billion “excess capacity” in AI spend across the U.S. corporate landscape. This figure is projected to rise if the current pace persists.

5. The Broader Economic Implications

An over‑investment in AI could have ripple effects beyond the balance sheets:

a. Capital Misallocation

Investors may divert capital away from other productive sectors. “We might see underinvestment in infrastructure or green tech as funds chase AI,” Pohl warned.

b. Productivity Paradox

Historical data suggests that technology adoption often precedes a productivity boost by several years. In the interim, firms could experience “cost overruns” and stagnant revenue growth. The article cites the 2012 “AI productivity gap” study from MIT Sloan, which found that AI investments did not correlate strongly with productivity increases until 2020.

c. Regulatory Backlash

Excessive AI spending could invite regulatory scrutiny, especially if companies fail to meet compliance or ethical standards. The piece quotes a legal expert who warns of “potential fines or operational constraints” that could erode the cost‑benefit calculus.

6. What Comes Next? Potential Signals for Investors

Fund managers are now keeping an eye on several key indicators:

1. AI Spend as a Share of Total CapEx: A steep increase could signal over‑investment.
2. Actual Deployment Metrics: The number of AI products live, measured against projected milestones.
3. Profitability Attribution: How much of a company’s earnings can be directly linked to AI initiatives.
4. Regulatory Landscape Updates: New rules or guidelines that could affect AI spend.

The article concludes with a cautionary note: “While the AI wave promises transformative potential, investors must scrutinize whether companies are investing for value or for validation.” It encourages a balanced approach, combining the enthusiasm for AI’s future with disciplined due diligence on its current and near‑term financial impact.

7. A Look Ahead: Follow‑On Research

To deepen understanding, the CNBC piece follows up on several external sources:

• BofA’s AI Spend Forecast Report (Nov 2025) – provides an updated projection of AI spend growth and industry break‑downs.
• Gartner’s “AI Adoption and ROI” (2024) – outlines typical ROI timelines for AI projects across sectors.
• MIT Sloan’s “Productivity Paradox” (2012 & 2023 updates) – tracks historical patterns of tech investment versus productivity outcomes.

These references offer a broader context for investors and corporate leaders alike, underscoring the importance of aligning AI initiatives with solid business outcomes rather than chasing hype.

In sum, the CNBC article and the accompanying BofA survey reveal a corporate climate in which AI is being embraced at an unprecedented rate, driven by competitive pressure, capital availability, and talent dynamics. Yet, fund managers are raising legitimate concerns that the pace of investment may outstrip the pace of returns, potentially leading to a costly AI bubble. Investors are urged to monitor spending patterns, actual deployment metrics, and regulatory developments to gauge whether companies are truly creating value or merely filling a “fancy” budget line item.