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Investors are not simply turning against AI. Surveys show that many expect it to support long-term growth while also worrying about high valuations, heavy spending, market concentration, and whether companies can turn investment into returns. Those views can coexist: the technology may create value over time even as particular investments, business plans, or operational dependencies prove risky.
The findings are snapshots of different groups—not a single measure of what all investors think, and not proof of how AI-related investments will perform. The practical question is whether expected benefits are supported by evidence, and whether companies and markets can absorb the risks involved.
Why do investors see both opportunity and risk?
AI could raise productivity, help companies develop new products, and change how work is done. But expectations about that potential can run ahead of demonstrated results. Building and adopting AI also requires spending, creates reliance on technology providers, and introduces governance and operational challenges.
Recent surveys illustrate the tension, but their respondents and methods differ. The results should be read as separate snapshots rather than combined into a trend line.
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| Survey and respondents | Opportunity view | Risk view |
|---|---|---|
| Natixis Investment Managers, 2025 survey for its 2026 outlook: 515 institutional investors in 29 countries, collectively managing $29.9 trillion | 65% expected AI to supercharge growth again. | 46% worried AI was a bubble; 69% believed major new AI developments would bring concentration risk to the forefront of equity markets; 64% worried a slowdown in AI capital expenditure could upend market growth. |
| Janus Henderson Investors, 2026 survey: 1,000 U.S. investors with at least $250,000 in investable assets, surveyed March 5–24, 2026 | 61% expected a positive long-term impact of AI on markets. | 67% were concerned about a near-term AI bubble or AI-driven market correction. |
The percentages describe respondents’ expectations and concerns, not realized market outcomes. The Natixis group is institutional investors; the Janus Henderson group is affluent U.S. retail investors. Their results cannot establish that investors broadly have shifted from optimism to pessimism.
What risks are asset owners watching?
Asset owners—organizations investing on behalf of beneficiaries, such as pension funds—are focused on the way AI investment could affect the market as a whole. Morningstar Indexes and Morningstar Sustainalytics’ 2026 global quantitative survey included more than 500 asset owners in 11 countries across North America, Europe, and Asia-Pacific, representing more than $20 trillion in combined assets.
- 73% named the compounding effect of AI valuations and capital expenditures among their macro-market concerns.
- 65% cited AI-driven market concentration risk.
- 63% cited overdependence on a few large technology providers.
These concerns connect: large spending plans can support high expectations for a small number of companies, while investors and businesses become more dependent on those firms’ products and infrastructure. That can leave portfolios and operations exposed if expected returns disappoint or a major provider falters. The results report asset owners’ concerns; they do not establish that a valuation bubble exists or that a market correction is inevitable. Morningstar’s survey release also describes asset owners’ demand for forward-looking data to help with portfolio construction and risk management.
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Is AI a bubble, or will AI pay off?
Those are related but different questions. A technology can have substantial long-term potential while some companies’ valuations, spending plans, or promised returns are too optimistic. Conversely, a correction in AI-related shares would not by itself prove that AI has no lasting economic value.
Compare spending with measurable returns
Investors need to distinguish the amount a company is investing from the value it can demonstrate. Relevant evidence may include revenue attributable to AI products, cost savings that have actually been achieved, productivity gains, and the investment needed to sustain them. A company’s announcement of an AI initiative is not, on its own, evidence that the initiative is profitable.
PwC’s 2025 Global Investor Survey asked 1,074 investment professionals in 26 countries and territories about the information they wanted from companies. 42% requested more transparency on AI returns and cost savings, and a separate 42% requested more transparency on AI investments. The survey also found that 88% supported greater corporate spending on cybersecurity to protect against key threats. These are the views of surveyed investment professionals, not a universal investor mandate. PwC’s survey release records their requests for clearer information about companies’ innovation strategies, competitive position, and resilience as well as AI spending and results.
Account for execution and concentration
AI plans depend on more than demand for a new product. They may require computing capacity, data, specialized staff, integration with existing systems, and reliable external services. Returns can fall short if projects take longer or cost more than expected, if customers do not adopt them, or if a company cannot build a durable advantage.
Concentration matters at both the market and company level. If a limited number of firms account for a large share of AI-related investment or supply essential services, a problem at one of them can have outsized effects. This is a risk to assess, not a forecast that the largest providers will fail.
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How can AI amplify financial-system vulnerabilities?
The risks are not limited to whether an individual AI company succeeds. Shared technology, financing links, and common dependencies can transmit problems between firms or across markets.
Linked financing and large capital commitments
The International Monetary Fund describes circular financing arrangements in which AI-related firms invest in or finance one another. If companies are linked through investments, contracts, or financing, a shock at one firm can affect others; the potential for spillovers is greater when activity is concentrated.
The IMF estimated AI-related capital expenditure of $3.4 trillion through 2029 in its 2026 report. It also reported that hyperscalers had raised more than $100 billion in bond financing since January 2025 in anticipation of future AI-related expenditures, supplemented by loans and intercorporate arrangements. These are estimates and reported financing figures, not proof that the spending will occur as projected or that it will cause losses.
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The IMF’s assessment is not that a crisis is inevitable: it said financial-stability risks from hyperscaler debt issuance remained contained in its analysis. It also noted that hyperscaler earnings growth had kept pace with capital expenditure and free cash flow remained high at the time of the report. Its discussion of balance-sheet pressure is a potential vulnerability, not a claim that firms are already unable to meet their commitments. The IMF’s April 2026 Global Financial Stability Report further notes that reported depreciation implies an average useful life of around seven years for major hyperscalers’ property, plant, and equipment. The report cautions that GPUs and advanced chips could become obsolete faster; it does not establish imminent systemic risk from obsolescence.
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Shared models, cyber threats, and operational disruption
The Bank of Canada’s 2026 financial-system survey presents AI less as a standalone source of financial instability than as a possible amplifier of existing vulnerabilities. In the Canadian context described by the survey, concerns include opaque models and shared model errors, AI-assisted cyberattacks and fraud, governance lagging adoption, dependence on a few AI and cloud providers, and disruption to critical operations.
Among the Bank of Canada’s survey participants, 58% cited difficulty integrating AI with existing infrastructure and workflows as an adoption hurdle, while 56% cited talent-related constraints. Those barriers can affect how safely and effectively firms deploy the technology; the figures describe the survey participants, not every business or financial institution. The Bank of Canada’s survey highlights discuss these dependencies and operational concerns in its Canadian financial-system context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should investors look for in a company’s AI plans?
Surveys cannot identify which securities will win. They do point to practical questions for evaluating a company’s claims and its ability to manage the associated risks.
- Spending: What is the company investing in AI, and which costs are recurring? How much depends on outside computing, data, or software providers?
- Value: What measurable revenue, cost savings, or productivity improvements does the company expect, and what evidence shows those results are being achieved?
- Disclosure: Does the company explain its AI strategy, investments, expected returns, and competitive position clearly enough to assess progress?
- Governance and data: Who is accountable for AI decisions? How are privacy, security, bias, conflicts of interest, and model limitations addressed?
- Resilience: What happens if an AI system produces an error, a cyberattack disrupts it, or a key provider becomes unavailable? Are there tested alternatives and human oversight for critical functions?
These are due-diligence lenses, not a formula for selecting investments. They help separate a credible business case from a broad promise that AI will create value eventually.
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Why are some investors cautious about using AI for investment decisions?
Concern about AI as an investment theme is distinct from concern about using AI to research or manage investments. In the Janus Henderson 2026 survey of affluent U.S. investors, respondents identified bias or conflicts, privacy and security, preference for traditional methods, and lack of trust as leading barriers to using AI for investment purposes. The survey also found that 28% named failure to meet expectations as a top AI investment concern, 24% named bias, misuse, or inadequate safeguards, and 19% named overvaluation.
Those findings describe investor attitudes in that survey; they do not show that AI-generated financial information is always unreliable. They do underline why investors should understand what information a tool uses, how its output is checked, and whether a person remains accountable for consequential decisions.
As Janus Henderson portfolio manager Denny Fish put it, “AI skepticism is understandable, but investors risk failing to distinguish between valuation noise and long-term structural change.” He also argued that patience and discipline matter because the technology could create winners while exposing losers. That is an investment manager’s view, not a guarantee of future market outcomes. Janus Henderson’s survey release provides the survey findings and his comments.
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