This semi-monthly column highlights news, government documents, NGO/IGO papers, conferences, industry white papers and reports, academic papers and speeches, and central bank actions on the subject of AI’s fast paced impact on the banking and finance sectors. The chronological links provided are to the primary sources, and as available, indicate links to alternate free versions.
NEWS:
Study looking at AI chatbots in 7,000 workplaces finds ‘no significant impact on earnings or recorded hours in any occupation’. Fortune
Since OpenAI rolled out ChatGPT just over two years ago, AI chatbots have become the fastest-adopted technologies in history, rivaling the PC three decades ago. Their popularity has created and destroyed entire job descriptions and sent company valuations into the stratosphere—then back down to earth. And yet, one of the first studies to look at AI use in conjunction with employment data finds the technology’s effect on time and money to be negligible. “AI chatbots have had no significant impact on earnings or recorded hours in any occupation,” economists Anders Humlum and Emilie Vestergaard wrote in a National Bureau of Economic Research working paper released this week.
Saudi Arabia’s new artificial intelligence platform HUMAIN plans to launch a $10 billion venture fund and build out data center capacity worth as much as $77 billion, its CEO said. The company, backed by the Public Investment Fund, is also seeking investment from US tech giants, and is in talks about joint projects with OpenAI, Elon Musk’s xAI, and Andreessen Horowitz, Chief Executive Tareq Amin told the Financial Times. HUMAIN will house Saudi Arabia’s AI services, data centers, cloud capabilities, and an Arabic large language model.
The kingdom is working to establish its tech dominance: Riyadh has emerged as the fastest-growing market for data centers in the Middle East, and aims to soon catch up with Abu Dhabi and Dubai in the race to build up the power and capacity needed to fuel the AI boom.
Something Alarming Is Happening to the Job Market – The Atlantic no paywall: “Something strange, and potentially alarming, is happening to the job market for young, educated workers. According to the New York Federal Reserve, labor conditions for recent college graduates have “deteriorated noticeably” in the past few months, and the unemployment rate now stands at an unusually high 5.8 percent. Even newly minted M.B.A.s from elite programs are struggling to find work. Meanwhile, law-school applications are surging—an ominous echo of when young people used graduate school to bunker down during the great financial crisis. What’s going on? I see three plausible explanations, and each might be a little bit true. The first theory is that the labor market for young people never fully recovered from the coronavirus pandemic—or even, arguably, from the Great Recession. “Young people are having a harder time finding a job than they used to, and it’s been going on for a while, at least 10 years,” David Deming, an economist at Harvard, told me. The Great Recession led not only to mass layoffs but also to hiring freezes at many employers, and caused particular hardships for young people. After unemployment peaked in 2009, the labor market took time to heal, improving slowly until the pandemic shattered that progress. And just when a tech boom seemed around the corner, inflation roared back, leading the Federal Reserve to raise interest rates and cool demand across the economy. White-collar industries—especially technology—were among the hardest hit. The number of job openings in software development and IT operations plunged. The share of jobs posted on Indeed in software programming has declined by more than 50 percent since 2022. For new grads hoping to start a career in tech, consulting, or finance, the market simply isn’t that strong.
A second theory points to a deeper, more structural shift: College doesn’t confer the same labor advantages that it did 15 years ago. According to research by the San Francisco Federal Reserve, 2010 marked a turning point, when the lifetime-earnings gap between college grads and high-school graduates stopped widening. At the same time, the share of online job postings seeking workers with a college degree has declined. To be clear: College still pays off, on average. The college wage premium was never going to rise forever, and the fact that non-college workers have done a little better since 2010 isn’t bad news; it’s actually great news for less educated workers. But the upshot is a labor market where the return on investment for college is more uncertain. The third theory is that the relatively weak labor market for college grads could be an early sign that artificial intelligence is starting to transform the economy..”
Wall Street Banks, Executives and U.S. Regulators Raise Warnings over Lack of AI Security Reuters, May 7, 2025. A growing chorus of large U.S. banks, hedge fund managers and regulators are sounding alarm bells over rapid spread of artificial intelligence across businesses and the lack of security and risks posed by the innovative technology. The worries expressed by the U.S. financial industry range from the lack of proper security protocols by companies deploying AI in their organizations to the concentration risk firms face from engaging with only a handful of AI service providers. Most alarming are what some see as an “imminent threat” to large segments of society. “The competitive dynamic is so intense among the companies, and geopolitically between Russia and China, that there’s no agency, no ability to stop and say, maybe we should think about what actually we’re creating the building here,” hedge fund veteran Paul Tudor Jones told the business network CNBC in an interview. Tudor Jones, a billionaire hedge fund manager who founded Tudor Investment Corporation in 1980, also leads the Robin Hood Foundation, a non-profit that focuses on poverty reduction. Speaking about a tech conference he recently attended which included the country’s leading AI developers, Tudor Jones said: “We just have to credit all these folks in AI and what they are telling us. We’re creating something that’s really dangerous. It’s going to be really great, too, but we’re helpless to do anything about it. That, to their credit, is what they’re telling us, and yet we’re doing nothing right now. It’s really disturbing.” One of the biggest risks is the ability for bad actors to obtain a so-called “bio hack,” Tudor Jones said. “As fast as these models are growing, and how quickly they’re commoditizing knowledge, and how easily they’re making it accessible, someone could buy a hack — a bio hack, because that’s where the real weakness is. A (biological) weapon that could take out half a humanity.”
Rising Adoption of Artificial Intelligence in Financial Operations – Tabb Forum, April 29, 2025. AI is no longer a future play in financial operations. It’s embedded, evolving, and under scrutiny. From reconciliation to regulatory reporting, firms are layering AI onto core systems to boost speed, accuracy, and oversight, without losing control, writes financial reporter Sean Cramer. As adoption rises, the real battle isn’t about technology. It’s about governance, fit, and readiness to scale. In this TabbFORUM report, Mr. Creamer explains where AI is gaining ground, and where the biggest risks and opportunities still lie.
PAPERS:
Understanding and Mitigating Risks of Generative AI in Financial Services. Sebastian Gehrmann, Claire Huang, Xian Teng, Sergei Yurovski, Iyanuoluwa Shode, Chirag S. Patel, Arjun Bhorkar, Naveen Thomas, John Doucette, David Rosenberg, Mark Dredze, David Rabinowitz
To responsibly develop Generative AI (GenAI) products, it is critical to define the scope of acceptable inputs and outputs. What constitutes a “safe” response is an actively debated question. Academic work puts an outsized focus on evaluating models by themselves for general purpose aspects such as toxicity, bias, and fairness, especially in conversational applications being used by a broad audience. In contrast, less focus is put on considering sociotechnical systems in specialized domains. Yet, those specialized systems can be subject to extensive and well-understood legal and regulatory scrutiny. These product-specific considerations need to be set in industry-specific laws, regulations, and corporate governance requirements. In this paper, we aim to highlight AI content safety considerations specific to the financial services domain and outline an associated AI content risk taxonomy. We compare this taxonomy to existing work in this space and discuss implications of risk category violations on various stakeholders. We evaluate how existing open-source technical guardrail solutions cover this taxonomy by assessing them on data collected via red-teaming activities. Our results demonstrate that these guardrails fail to detect most of the content risks we discuss.
Large Language Models, Small Labor Market Effects – – Working Paper 33777. DOI 10.3386/w33777. Issue Date
We examine the labor market effects of AI chatbots using two large-scale adoption surveys (late 2023 and 2024) covering 11 exposed occupations (25,000 workers, 7,000 workplaces), linked to matched employer-employee data in Denmark. AI chatbots are now widespread—most employers encourage their use, many deploy in-house models, and training initiatives are common. These firm-led investments boost adoption, narrow demographic gaps in take-up, enhance workplace utility, and create new job tasks. Yet, despite substantial investments, economic impacts remain minimal. Using difference-in-differences and employer policies as quasi-experimental variation, we estimate precise zeros: AI chatbots have had no significant impact on earnings or recorded hours in any occupation, with confidence intervals ruling out effects larger than 1%. Modest productivity gains (average time savings of 3%), combined with weak wage pass-through, help explain these limited labor market effects. Our findings challenge narratives of imminent labor market transformation due to Generative AI.
Natural Language Processing and Innovation Research – – Working Paper 33821. DOI 10.3386 w33821. Issue Date
Innovation is central to models in economics, strategy, management, and finance, yet it remains difficult to measure due to its intangible and knowledge-based na ture. Recent advancements in Natural Language Processing offer new methods to analyze textual artifacts, providing empirical insights into previously hard-to-measure aspects of innovation. This paper provides an overview of the current applications of these methods in empirical innovation research, highlights their transformative potential for reshaping how researchers study and quantify innovation, and discusses the critical challenges that accompany their use.
NGOs/IGOs
Expecting job replacement by GenAI: effects on workers’ economic outlook and behavior. BIS Working Papers | No 1269 | 20 May 2025 by Yusuke Aoki, Joon Suk Park, Yuya Takada and Koji Takahashi
Focus – Our research explores how people’s expectations about the impact of generative AI (gen AI) on jobs shape their broader economic outlook and behaviour. To investigate this, we conducted surveys and randomised experiments in the United States and Japan. Participants were randomly given expert estimates showing that gen AI might replace either 14% or 47% of current jobs. We measured their beliefs about job replacement due to AI both before and after they received this information. Additionally, we asked them to forecast economic indicators such as GDP growth and inflation rates, and to report their willingness to learn and use AI in the workplace.
Contribution – While many studies have examined how people feel about AI’s impact on the labour market, there has been little research on how these perceptions influence economic expectations and decisions. This is important because people’s beliefs – especially about technology – can influence their behaviour, investment decisions and even inflation or growth trends. By filling this gap, our study provides new insights into the broader consequences of public expectations about gen AI, particularly how they might affect inflation forecasts, labour market participation and AI adoption.
Findings – We find that people do revise their views about gen AI’s impact when presented with expert information. In Japan, higher expected job replacement due to gen AI led to higher inflation expectations and increased intentions to use gen AI at work, especially among those in creative jobs. These shifts may reflect growing expectations of investment in AI technologies. In the United States, however, the impact was different: although people also updated their beliefs, this did not lead to greater gen AI adoption. Instead, those with higher education levels expected reduced demand for labour and required skill in their current jobs. This highlights key differences in how expectations influence economic behaviour across countries.
Artificial intelligence and human capital: challenges for central banks. BIS Bulletin | No 100 | 24 April 2025 by Sarah Bell, Blaise Gadanecz, Leonardo Gambacorta, Fernando Perez-Cruz and Vatsala Shreeti
Key takeaways
- Artificial intelligence (AI) is changing how central banks use human capital. Two scenarios illustrate the uncertainty around the trajectory of AI development: “AI copilots”, which augment rather than replace human skills, and “AI agents”, which automate specific central bank tasks and can act as substitutes for human roles.
- Central banks are already integrating “AI copilots” in their daily operations. These tools enhance staff productivity without fundamentally altering how their work is conducted. In contrast, “AI agents” could transform workflows in the next decade, though human oversight will remain essential to ensure their responsible and ethical adoption.
- To successfully transition toward AI-intensive workflows under either scenario, a focus on retraining and upskilling existing staff, attracting new talent and fostering a culture that embraces innovation is warranted
- Online appendix
Federal Reserve Bank of San Francisco. Anton Korinek | The Economics of Transformative AI
Summary – Anton Korinek, Professor at the University of Virginia, Department of Economics and Darden School of Business, delivered a live presentation on the economics of transformative AI on April 22, 2025. Following his presentation, Professor Korinek answered live and pre-submitted questions with our host moderator, Huiyu Li, co-head of the EmergingTech Economic Research Network (EERN) and research advisor at the Federal Reserve Bank of San Francisco.