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AI stock valuations aren't wrong--they're just not right ... yet, says JPMorgan assets boss | Fortune

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  Published in Stocks and Investing on Tuesday, October 28th 2025 at 9:47 GMT by Fortune
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Valuation Explosion and the “AI Bubble” Narrative

The article opens by outlining the staggering uptick in AI‑focused company valuations over the past two years. Publicly traded AI firms—ranging from large cloud providers to niche robotics and natural‑language‑processing start‑ups—have seen their price‑to‑earnings (P/E) ratios climb to unprecedented levels. In some cases, the P/E has exceeded 100, dwarfing the averages for the broader technology sector. Fortune notes that this spike is driven not just by real‑world deployments (autonomous vehicles, advanced analytics, and AI‑augmented customer service) but also by speculative bets on future earnings potential.

“Valuations have been driven by an expectation that AI will not only grow but transform entire industries,” one JPMorgan analyst comments in a cited interview. The narrative is reminiscent of the dot‑com boom, but analysts warn that unlike the previous bubble, AI’s underlying technology has a more concrete and potentially disruptive value proposition. The article stresses that this nuance complicates valuation models, as the line between realistic and inflated expectations is blurred.

JPMorgan’s “Prudential Premium” Framework

A significant portion of the piece is devoted to JPMorgan’s newly released “Prudential Premium” framework. The bank has adjusted its multiples for AI firms by applying a higher discount to earnings growth, arguing that many of the projected earnings are derived from untested AI algorithms and speculative use‑cases. The framework also introduces a “technology‑risk premium,” designed to account for the higher probability of obsolescence or regulatory setbacks. JPMorgan’s model suggests that, when applied, the median P/E for AI stocks would drop to around 45, a figure that the bank considers a more realistic upper bound.

The article quotes the lead JPMorgan AI strategist, who explains that the framework incorporates both macro‑economic factors—such as rising interest rates and tightening fiscal policy—and micro‑economic variables, including intellectual‑property ownership and data‑access advantages. “We’re not discounting AI outright; we’re discounting the hype,” the strategist says.

Goldman Sachs and the “Tech‑Integrated Asset” Approach

Goldman Sachs, in contrast, presents a “Tech‑Integrated Asset” model that treats AI as an infrastructure component rather than a standalone growth engine. In this view, AI’s real value lies in its integration across diverse business lines—manufacturing, logistics, healthcare, and finance. The bank argues that such cross‑sector deployments justify higher valuation multiples because AI can be embedded in existing revenue streams, reducing the risk profile compared to pure play AI startups.

The article summarizes Goldman’s perspective by noting that its analysts have adjusted the beta of AI stocks upward to reflect increased systemic risk, yet still project a higher risk‑adjusted return. The firm’s latest research includes a scenario analysis that shows a 15% upside for AI equities in a moderate growth environment and a 7% downside in a contraction scenario.

Deutsche Bank’s “Data‑Centric Valuation” Focus

Deutsche Bank’s analysis, as outlined in the coverage, takes a different tack, focusing on data availability and usage. DBs Group argues that the scarcity of high‑quality, labeled data sets is a significant bottleneck for AI advancement. The bank’s valuation model applies a “Data‑Scarcity Discount” to companies whose growth is heavily dependent on proprietary data sources, while offering a premium to those with access to broad, public datasets or strong data‑sharing partnerships.

This data‑centric lens has led DBs to revise the valuation of several AI firms upward, particularly those involved in cloud‑based AI services. “Data is the new oil, and those who control it can command higher multiples,” the DBs analyst notes.

Regulatory Environment and Ethical Concerns

Beyond valuation models, Fortune’s article examines how regulatory scrutiny is shaping investor sentiment. The European Union’s AI Act, set to go into effect early next year, imposes strict compliance requirements on high‑risk AI systems, including transparency, explainability, and bias mitigation. U.S. regulators are also considering similar frameworks, potentially increasing compliance costs for AI companies. The piece highlights that these regulatory hurdles could materially affect profitability and, consequently, valuations.

Ethical concerns around privacy and algorithmic bias also loom large. The article cites a survey of AI executives, revealing that 68% anticipate significant reputational risk if their algorithms are perceived as unfair or invasive. This risk is factored into JPMorgan’s discount model, while Goldman and DBs consider it an additional cost of capital.

Market Sentiment and Investor Behavior

Fortune captures the dichotomy in investor behavior: a segment of retail investors, attracted by headlines about GPT‑4 and autonomous vehicles, continues to pour money into AI stocks, while institutional investors are increasingly cautious. The article references data from brokerage platforms showing that retail trading in AI equities has increased by 120% year‑to‑date, while institutional allocation has plateaued at 30% of the tech sector’s total equity investment.

JPMorgan’s research indicates that institutional investors are now demanding a higher return on risk, pushing valuations downward. Meanwhile, the article notes that a “price‑in the future” mindset is prevalent among retail investors, who focus more on narrative than fundamentals.

Bottom Line

Fortune’s coverage concludes that while AI’s potential to reshape industries is undeniable, the market’s valuation of AI companies remains highly sensitive to a range of macro‑economic, regulatory, and technological factors. JPMorgan’s “Prudential Premium” and Goldman Sachs’ “Tech‑Integrated Asset” frameworks offer investors two distinct lenses to assess risk and reward, while Deutsche Bank’s data‑centric focus underscores the importance of data ownership in the AI value chain.

The article underscores that the next few quarters will be pivotal. As regulatory frameworks take shape, AI firms must demonstrate sustainable, data‑driven growth to justify high multiples. Institutional capital will likely continue to act as a counterbalance to retail enthusiasm, potentially tempering the current valuation highs. In the rapidly evolving AI landscape, both investors and companies will need to navigate a delicate balance between hype and tangible, repeatable value creation.