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AI Skepticism Grows Among Stock-Credit Investors

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  Published in Stocks and Investing on Sunday, November 16th 2025 at 0:18 GMT by Bloomberg L.P.

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AI Skepticism Is Gaining Ground With Stock‑Credit Investors
Bloomberg News, 7 Nov 2025

In the past year, the financial sector has witnessed a rapid expansion of artificial‑intelligence (AI) tools—from generative language models that can produce earnings‑call transcripts to algorithms that scour social‑media sentiment and corporate filings. While many investment firms have touted AI as a game‑changer, a growing contingent of “stock‑credit” investors—those who blend equity analysis with credit risk assessment—are beginning to question the reliability of AI‑driven insights. Bloomberg’s latest newsletter (dated 7 November 2025) tracks this trend, delves into its root causes, and looks at the implications for the credit‑rating ecosystem.

1. From Hype to Reality: Why AI Is Under Scrutiny

Early success stories give way to high‑profile missteps.
At the start of 2025, a handful of hedge funds announced “unprecedented” alpha from AI‑augmented fundamental models. Yet within months, several AI‑powered earnings‑forecast models produced wildly divergent numbers for the same firms, and a few high‑profile companies—most notably a mid‑cap biotech that had been “AI‑positive” in its pre‑release reports—missed their quarterly earnings by more than 15 %. The resulting market wobble led to sharp volatility in the associated credit‑default swap (CDS) spreads, sending investors scrambling.

The “AI bias” problem.
Bloomberg highlights a statistical review conducted by the University of Oxford’s Department of Economics, which found that AI models trained on historical data exhibit a bias toward past trends, under‑weighting disruptive events. For credit‑sensitive firms, this translates into systematic underestimation of default risk. “When AI learns from a decade of growth but ignores a decade of tightening regulation, it’s going to underestimate the real risk,” says Dr. Lila Kumar, a senior risk‑modeling analyst at the university.

Governance and transparency gaps.
Many AI models are proprietary, making it difficult for investors to audit or understand the assumptions embedded in the output. Bloomberg reports that only 12 % of the major proprietary models used by institutional investors are subject to third‑party validation—a statistic that has prompted calls for more robust governance frameworks.

2. The Credit‑Investor Perspective

A unique cross‑section of concerns.
Stock‑credit investors are distinct from traditional equity or bond specialists because they monitor both price movements and underlying debt metrics. This dual lens magnifies the impact of any AI misjudgment: a price spike based on an over‑optimistic earnings forecast can mask deteriorating credit fundamentals.

Case in point: The “Blue‑Chip” conundrum.
A Bloomberg‑exclusive interview with John Meyers, portfolio manager at Horizon Capital, illustrates the dilemma. “We were chasing the AI model that predicted a 20 % rise in revenue for a tech giant,” Meyers says. “When the company posted a 5 % decline, the stock fell 12 % and the CDS spread widened 45 bp overnight. Our traders had to re‑evaluate the entire risk model.” The incident prompted Horizon to stop relying on AI‑generated revenue numbers and instead integrate traditional due‑diligence checks.

Sector‑specific disparities.
Bloomberg notes that the skepticism is most pronounced in high‑growth, high‑volatility sectors such as biotech, fintech, and renewable energy. In contrast, more mature industries like utilities and consumer staples still largely rely on AI for data aggregation but are cautious about letting it dictate core valuations.

3. How Credit Rating Agencies Are Responding

Mixed signals from the rating front.
Major rating agencies (S&P Global, Moody’s Investors Service, and Fitch Ratings) have begun to acknowledge the limitations of AI in their own research pipelines. S&P’s latest update, released earlier this month, announced a “hybrid model” that blends machine‑learning predictions with human oversight. “The AI component provides a first pass, but the final rating decision is still made by analysts who contextualize the data,” says S&P’s Chief Data Officer, Maria Torres.

Regulatory pressure mounts.
The U.S. Securities and Exchange Commission (SEC) released a draft guidance in September requiring rating agencies to disclose the use of AI, including the data sources and the risk of model error. This draft was adopted by the European Union’s Markets in Financial Instruments Directive (MiFID II) as a regulatory recommendation in October. The resulting compliance costs have spurred a debate over whether the benefits of AI outweigh its governance burden.

New entrants and open‑source alternatives.
In response to the perceived opacity of proprietary models, a consortium of universities and fintech firms announced the launch of an open‑source credit‑risk platform in late October. The platform, “Credit‑AI,” offers transparency on model assumptions, a community‑reviewed code base, and a sandbox environment for stress testing. While still in early adoption, several mid‑cap firms have begun to pilot Credit‑AI for internal research, signaling a potential shift away from vendor‑locked solutions.

4. Market Dynamics: How Investor Skepticism is Shaping Behavior

Shift from “AI‑only” to “AI‑augmented” strategies.
According to Bloomberg’s proprietary data, 48 % of hedge funds that had adopted AI in their credit research during Q1 2025 have now reduced the weight of AI outputs by an average of 32 %. Meanwhile, a parallel increase of 18 % is noted in “human‑in‑the‑loop” reviews of model predictions.

Cost of due diligence rises.
Investors are spending more on third‑party data vendors and external auditors to verify AI model outputs. The cost of data acquisition for AI models has reportedly risen by 23 % year‑over‑year, as vendors compete for the limited pool of high‑quality, labeled datasets.

Investor confidence and market liquidity.
Bloomberg’s market‑wide sentiment index for credit spreads dropped by 5 % in November, coinciding with a spike in AI‑related earnings miss announcements. Liquidity in corporate bond markets dipped slightly, prompting some banks to adjust pricing spreads to account for perceived AI risk.

5. The Road Ahead: Balancing Innovation and Prudence

A call for better model governance.
Bloomberg’s editorial team argues that the industry cannot simply abandon AI; instead, it must invest in robust governance frameworks that combine algorithmic transparency, continuous model validation, and human oversight. “The promise of AI is undeniable, but the risk of unverified, opaque models in credit markets is too great to ignore,” writes the editorial lead.

Potential regulatory outcomes.
Both the SEC and the European Securities and Markets Authority (ESMA) are expected to formalize AI governance requirements by mid‑2026. These regulations may mandate regular model audits, public disclosure of performance metrics, and standardized error‑budget reporting.

A silver lining for investors.
If implemented correctly, AI can still enhance credit research by speeding up data ingestion and flagging subtle signals that may escape human analysts. “The key is to use AI as a tool, not a replacement,” says Dr. Kumar. “When integrated responsibly, it can improve both speed and depth of analysis.”

Bottom line: AI’s rise in finance has sparked a wave of skepticism among stock‑credit investors, who now demand more transparency, governance, and human oversight. While the technology still holds promise, its current implementation—particularly in credit‑related research—has exposed significant risks. Regulatory bodies are stepping in, and the industry is pivoting toward hybrid models that marry machine learning with seasoned human judgment. For investors, the lesson is clear: embrace AI’s efficiency, but never at the expense of rigorous risk controls.