Timeline of artificial intelligence risks in global finance

The following article is a broad timeline of the course of events related to artificial intelligence risks in global finance.

The AI boom has led to concerns including the existential risk from artificial intelligence, as the uptake on applications of artificial intelligence increases. By late 2025, global finance and artificial intelligence were "deeply intertwined".[1] A June 2025 Menlo Ventures report raised concerns about the sustainability of future revenue and long-term profitability of AI, given the relatively low rate of consumer monetization.[2][3]

2017

  • 30 November—The New York Times said that new AI reports by McKinsey & Company, the National Bureau of Economic Research, and an AI Index created by university researchers, indicated an early AI boom.[4] The Index built on a project—"The One Hundred Year Study on Artificial Intelligence" launched in 2014.[5]

2018

  • —2018 was a year of incremental AI growth in finance.[6]

2022

  • —The release of ChatGPT by OpenAI became the catalyst for an artificial intelligence boom that continues to remake the global economy. [7][8]
  • — According to a European Central Bank report, public interest in AI increased rapidly as evidenced with rising Google searches, AI jobs, models, patents, and innovations since late 2022. At that time Europe led the US in the size of its AI workforce.[9]

2023

  • The regulatory body, the International Monetary Fund (IMF), published their report, "Generative Artificial Intelligence in Finance: Risk Considerations", drawing attention to oversight gaps and the need for regulations.[10] The report explores the risks posed by using generative artificial intelligence (GenAI) systems in the financial sector including "broader risks to financial stability."[10]: 5 

2024

  • January 12 —In January 2024 Bloomberg's published its list of the "Magnificent Seven" Big Tech companies on the stock market based on their strength, size and market capitalization—Apple, Microsoft, Alphabet (Google), Amazon, Meta Platforms (Facebook), Nvidia, and Tesla.[11]
  • 21 June —During the AI boom, Nvidia became the world's most valuable company, surpassing Microsoft, as its value increased to over US$4 trillion.[12][13]
  • — In 2023 and 2024, the "Magnificent Seven" stocks were the primary drivers behind the increase in equity indexes, according to Reuters.[14]

2025

January

  • 23 January —President Donald Trump's AI policy was announced calling for United States global leadership in artificial intelligence.[15] The Economist noted that this politic shift in which the United States seeks "global dominance" in AI includes trimming regulations and assisting in expansion of infrastructure and increase in number of AI workers. Governments of Gulf nations were also investing trillions of dollars in AI.[16]
  • 27 January —Against the backdrop of a tech war between China and the United States over AI dominance, within days of the launch of China's free DeepSeek App, it was the most downloaded app in the United States,[17] rising to the first place in the Apple app store.[18] President Trump responded immediately, saying this "sudden rise" should be a "wake-up" call to the United States, and called on US companies to be more competitive.[18]

June

  • 26 June —In their June 2025 report, Menlo Ventures estimated that only about 3% of consumers paid for artificial intelligence-related services, representing about $USD12 billion in annual spending. This is relatively low in contrast to the massive capital expenditure by AI infrastructure companies, which raises concerns about revenue sustainability and long-term profitability.[2][3]

July

  • 23 July —The Trump administration launched the US AI Action Plan, positioning the United States in a high-stakes technological race with China for global dominance in artificial intelligence, emphasizing that neither nation can afford to fall behind due to the exponential nature of AI advancement.[19] The plan, a new government website and policy speech called for accelerated AI adoption across federal agencies, and a number of initiatives to make is easier for AI infrastructure expansion, and other measures to ensure American leadership in AI standards. Some leading experts warned that the administration failed to provide sufficient regulations and safeguards for AI safety. Concerns were raised about the negative impacts of cuts to research funding and tightened visa policies for scientists, potentially undermining public trust and America's ability to compete internationally.[20]

September

  • 7 September —The Economist cautioned that AI revenues are relatively modest compared to the high cost and investments in the creation of new data centers.[16] Even Sam Altman—OpenAI CEO and one of the leading figures of the AI boom,[21]—raised concerns about investors' outsized hopes for financial returns. At the same time, history has shown that new technologies, like railways and electricity, endured and spread after the initial hype faded.[16]
  • 12 September —Economists warn that U.S. households' direct and indirect investments—mutual funds or retirement plans—in the stock market reached an unprecedented historically high level, now representing 45% of all financial assets, or about $USD51.2 trillion.[22] Compared to the Dot-com bubble this represents a sharp increase in exposure. This makes U.S. households vulnerable to market downturns which in turn would result in decreasing consumer spending.[22] U.S. household net worth rose to a record $176.3 trillion in the second quarter, an increase of $7.3 trillion since early 2025 and about $46 trillion higher than before the pandemic. Federal Reserve data attribute the surge primarily to gains in stock markets and housing values. However, the rise in wealth on paper coincided with increased household borrowing and growing government debt.[23]
  • 18 September —Questions were being raised about how quickly the data centers, chips, servers, and GPUs assets of major AI companies will depreciate in value.[24][25] Comparisons have been made to the Railway Mania in the aftermath of the stock market bubble where a valuable physical infrastructure remained standing, and the telecoms crash after the dot-com bubble which left fiber networks.[25]
  • 28 September —There were warnings that record-high American stock ownership during the AI-fueled market boom is a red flag for systemic risk, as the current concentration in equities exceeds levels seen before the dot-com bubble burst in 2000, and could amplify the impact of any future stock market correction.[22]

October

  • 3 October —In 2025 alone, venture capitalists invested almost $USD200 billion in the artificial intelligence sector.[26]
  • 29 October —Nvidia was the first company in the world to be valued at US$5 trillion,[27] largely due to AI demand and strategic partnerships with leading technology and AI firms.[28] Nvidia's increase in value was "meteoric".[7]

November

  • 2 November —Forbes reported that, since April, the 'Magnificent Seven' tech giants together contributed over 40% of the S&P 500's return, highlighting their outsized influence and the growing impact of AI on market valuations.[29] CNN warned that while there is a current benefit to investors, with such a high concentration in the S&P 500, they are highly exposed to the fate of the Mag Seven.[22]
  • 2 November —Globally there are 11,000 datacentres—huge campuses for AI infrastructure, including thousands of chips, GPUS, and servers.[30] This represents a 500% increase over the last two decades.[31] It is anticipated that $3USDtn more will be spent on increasing that number over the next two or three years.[31]
  • 5 November —Concerns about the potential for a market bubble were raised as six of the AI-related Big Tech "Magnificent Seven"—that contribute to the AI boom—reported losing ground in the stock market.[1] Global markets and artificial intelligence have become "deeply intertwined", according to a Reuters report.[1]
    • —As of November 2025, more than 50% of the 20 largest S&P firms were deeply exposed to AI. In contrast, in 2000, the 20 S&P 500 firms represented 39% of its total value only 11 of these companies were exposed to the internet. If AI fails to deliver strong returns on their investments, these top S&P firms would be significantly impacted, according to the Economist.[32]
    • —Analysts suggest that the AI market in 2025 may not behave like a traditional one, as investors are simultaneously aware of the risks and driven by the potential for outsized rewards. Leading AI labs may believe that the first company to achieve artificial general intelligence (AGI)—when an AI system surpasses all human cognitive abilities and becomes capable of self-improvement—could dominate the future of technology and finance. While some have estimated that the potential value of such a breakthrough could be as high as $1.46 quadrillion, this figure is speculative and widely debated.[25]
  • 5 November —Bloomberg described Nvidia's H100 Hopper-Blackwell AI chips as the "King of AI chips".[33] Nvidia dominates the AI chip market with over 78% of the market share because of both speed and cost. According to Business Insider, the technical superiority and widespread developer familiarity with Nvidia's platform resulted in H100 chips becoming the preferred choice for demanding AI workloads.[34]
  • 7 November —Andrew Bailey, Governor of the Bank of England called attention to the risk to the market in light of the lack of certainty about future earnings in AI versus AI companies "very positive productivity contribution".[35]
  • 10 November —The first report of the Forecasting Research institute's (FRI) Longitudinal Expert AI Panel (LEAP) was published, providing insights into the projected high-stakes impact of AI by 2030. The panel includes hundreds of leading experts from computer science, economics, and AI policy—including superforecasters. LEAP experts forecast that around 18% of work hours in the US will be assisted by generative AI by 2030, up sharply from approximately 2% in 2025. This reflects a major anticipated integration of AI technologies into daily work and productivity.[36][37]
  • 11 November —By late fall 2025, these large AI-related tech companies—including Meta, Microsoft, Amazon, and Google—had begun using loans and financial instruments—special purpose vehicles (S.P.V.), asset-backed securities (A.B.S.)—to obtain the capital they need for large investments in their new data centers and AI infrastructure.[3] Elon Musk's xAI and Meta used S.P.V.s, rather than relying only on their own cash flow, to acquire tens of billions in debt for major investments.[3] Citing the June Menlo report on low consumer interest in paid AI use combined with huge capital expenditure by large AI companies, a New York Times article cited growing concerns about revenue sustainability, long-term profitability, and the potential risk factor for credit markets, as tech firms take on increasing amounts of debt to finance acquisition of billions of dollars of Nvidia chips, for example.[2][3]

December

  • 10 December —In October 2025, a workshop on “AI and financial stability” in the Financial Industry Forum on Artificial Intelligence II (FIFAI II), co‑hosted by the Office of the Superintendent of Financial Institutions, the Department of Finance Canada, the Bank of Canada, and the Global Risk Institute, brought together over 50 Canadian and international experts from the financial sector, regulators, academia, and business. An interim report dated 23 November 2025 stated that 44% of participants viewed autonomous or so-called agentic AI systems as the most likely current source of AI‑related systemic risk in finance.[38]

2026

  • 12 January —Moody's rating agency notes that as financial institutions make greater use of AI in their day‑to‑day operations, their exposure to cyber incidents and operational disruptions increases. Differing approaches such as the European Union’s AI Act and China’s licensing regime are likely to raise compliance costs and complicate the cross‑border deployment of AI systems.[39]
  • 26 January —Analysts at the Bank for International Settlements caution that as firms invest heavily in AI infrastructure such as data centres, they increasingly rely on debt and private credit rather than traditional bank loans, shifting risks into more opaque parts of the financial system. They warn that this funding structure, combined with wider adoption of similar AI models and digital‑finance platforms, could intensify and speed up familiar financial‑stability risks.[40] They argue that this may occur by increasing correlated behaviour,[a] contagion[b] and procyclicality[c] while making emerging vulnerabilities harder for supervisors to detect and manage.[40][41]
  • 23 February Goldman Sachs chief economist Jan Hatzius stated that, despite large-scale investment in generative AI infrastructure, AI had contributed "basically zero" to U.S. GDP growth in 2025, challenging earlier claims that AI spending was a major driver of recent economic performance.[42]
  • 24 February CNBC and Reuters reported that Anthropic's new Claude AI legal plug-in—Claude Cowork—triggered a broad sell‑off in software and data providers, raising fears that AI could undercut key software business models and revenues.[43] Because software firms are major borrowers in the roughly three‑trillion‑dollar private credit market, this AI‑driven shock to software valuations fed through to private credit lenders’ share prices and revived concerns that opaque, highly leveraged loans to AI‑exposed software companies could become a source of emerging credit risk in global finance.[44]
  • 26 February 2026—Analysts from Reuters warn that Nvidia’s AI chips are improving faster than the memory needed to support them, causing shortages and large price increases for that memory and fueling worries that memory suppliers will take a larger share of future profits, even as Nvidia's shares remain much more expensive than those of companies facing the same boom‑and‑bust risk in the AI market. Nvidia's basic business of making and selling chips is extremely profitable, with only about 25 cents of each sales dollar needed to cover the direct costs of producing its products. Nvidia is still very profitable, but these analysts caution that the real money might gradually shift toward the companies that control the scarce, critical High Bandwidth Memory (HBM) chips that AI systems depend on, such as South Korea's SK hynix.[45]
  • 28 February 2026 —Another example of systemic risk is the way in which AI data‑centre boom has caused a global RAM shortage. Investment in AI data centers consumes much of the available memory‑chip supply, which has led to a global shortage of RAM and (HBM), according to CBC News.[46] Only three firms—Samsung, SK Hynix, Micron—dominate RAM production. as of February both were sold out of HBM for 2026. AI data‑center investment had absorbed so much RAM and HBM capacity that major electronics makers warned of higher prices and delays.[46]

See also

Notes

  1. ^ In this context, “correlated behaviour” refers to many market participants or institutions responding in similar ways to the same models, signals or shocks, increasing the likelihood of joint stress or losses.
  2. ^ In finance, “contagion” commonly refers to the spread of market disturbances or stress from one institution, market segment or country to others, for example through balance‑sheet linkages, fire sales or shifts in risk sentiment.
  3. ^ "Procyclicality" describes financial dynamics that reinforce the ups and downs of the broader economy, such as when abundant credit and rising asset prices in booms encourage more risk‑taking, while tighter credit and falling prices in downturns lead to forced deleveraging and amplify stress in the financial system.

Citations

  1. ^ a b c Culp 2025.
  2. ^ a b c Menlo Ventures 2025.
  3. ^ a b c d e Frisch 2025.
  4. ^ Lohr 2017.
  5. ^ Stanford University 2016.
  6. ^ Statt 2018.
  7. ^ a b Mickle 2025.
  8. ^ Biino 2024.
  9. ^ Leitner et al. 2024.
  10. ^ a b Ghiath Shabsigh 2023.
  11. ^ Bloomberg 2024.
  12. ^ Hart 2024.
  13. ^ Duffy 2024.
  14. ^ Krauskopf 2025.
  15. ^ The White House 2025.
  16. ^ a b c The Economist 2025.
  17. ^ Hoskins & Edwards 2025.
  18. ^ a b Ingram 2025.
  19. ^ White House 2025.
  20. ^ Wilner 2025.
  21. ^ Weil 2023.
  22. ^ a b c d Towfighi 2025.
  23. ^ Spirlet 2025.
  24. ^ The Economist 2025a.
  25. ^ a b c The Economist 2025c.
  26. ^ Chapman 2025.
  27. ^ Berkowitz 2025.
  28. ^ Kaye 2025.
  29. ^ Stone 2025.
  30. ^ Bubley 2025.
  31. ^ a b Milmo 2025.
  32. ^ The Economist 2025b.
  33. ^ King 2025.
  34. ^ Kim 2025.
  35. ^ Leask 2025.
  36. ^ Research_FRI 2025.
  37. ^ The Economist 2025d.
  38. ^ "FIFAI II: A Collaborative Approach to AI Threats, Opportunities, and Best Practices, Workshop 3 - AI and Financial Stability". Canadian Office of the Superintendent of Financial Institutions (OSFI). 14 November 2025. Retrieved 2 February 2026.
  39. ^ "Digital economy 2026 executive summaries: Artificial intelligence, digital finance, cyber risk, and data centers". Retrieved 22 February 2026.
  40. ^ a b Zhang 2026.
  41. ^ Aldasoro, Doerr & Rees 2026.
  42. ^ Ovide 2026.
  43. ^ "Anthropic touts new AI tools weeks after legal plug-in spurred market rout". Reuters. 24 February 2026. Retrieved 24 February 2026.
  44. ^ Shan, Lee Ying (9 February 2026). "Private credit worries resurface in $3 trillion market as AI pressures software firms". CNBC. Retrieved 22 February 2026.
  45. ^ Cyran, Robert (25 February 2026). "Breakingviews - Nvidia is dogged by memories of cycles past". Reuters. Retrieved 27 February 2026.
  46. ^ a b Benchetrit, Jenna (28 February 2026). "Why a memory chip shortage is wreaking havoc on the consumer electronics industry". CBC News. Retrieved 28 February 2026.

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