NEWS:
Are we ready to hand AI the key – MIT Technology Review, no paywall, June 12, 2025. On May 6, 2010, at 2:32 p.m. Eastern time, nearly a trillion dollars evaporated from the US stock market within 20 minutes—at the time, the fastest decline in history. Then, almost as suddenly, the market rebounded. After months of investigation, regulators attributed much of the responsibility for this “flash crash” to high-frequency trading algorithms, which use their superior speed to exploit moneymaking opportunities in markets. While these systems didn’t spark the crash, they acted as a potent accelerant: When prices began to fall, they quickly began to sell assets. Prices then fell even faster, the automated traders sold even more, and the crash snowballed. The flash crash is probably the most well-known example of the dangers raised by agents—automated systems that have the power to take actions in the real world, without human oversight. That power is the source of their value; the agents that supercharged the flash crash, for example, could trade far faster than any human. But it’s also why they can cause so much mischief. “The great paradox of agents is that the very thing that makes them useful—that they’re able to accomplish a range of tasks—involves giving away control,” says Iason Gabriel, a senior staff research scientist at Google DeepMind who focuses on AI ethics…
Disrupted or displaced? How AI is shaking up jobs. New technology is starting to have a profound effect on work and employment – FT.com no paywall. Now employees, bosses and policymakers are trying to decipher what exactly the benefits of generative AI look like. “This latest generation of AI could change every job. I don’t think that is too much of an exaggeration,” said Peter Cheese, chief executive of the Chartered Institute of Personnel and Development, the UK’s professional body for HR and people development. “Of course you can see examples where AI in different forms is already making a difference to their workforce, but it’s still early days for many companies.” It is primarily changing roles not eliminating them, enabling humans to focus on more value-add elements of their jobs Many employers are cutting jobs under the guise of economic and political uncertainty. But high profile examples of AI-driven lay-offs in recent months, from technology company IBM to language learning app Duolingo, are fuelling questions about whether a slash and burn of white-collar roles is under way.
AI: Rogo’s AI Analysts Disrupting Junior Bankers and Empowering Wall Street. Fintech Blueprint, May 15, 2025. Finally, a pause in the Wall Street recruiting frenzy. Apollo told prospective investment-banking candidates that it won’t interview or extend offers to the class of 2027 this year, Bloomberg reports, after bank executives complained about investment firms raiding their junior employees. If others follow, the move would restore a bit of sanity to a process that has become nonsensical over the past decade, with private-equity firms dangling future-dated job offers to grads just weeks out of college. JPMorgan has complained loudly about the practice, which CEO Jamie Dimon has called “unethical,” and threatened to fire anyone who accepted such an offer. But there’s another angle worth watching: Financial firms are grappling with how AI will change their need for junior employees, whose bread-and-butter work — making PowerPoints, running Excel models — is already being done by AI agents. Delaying their recruiting buys time.
NGS/IGOs
BIS.org – Starting with the basics: a stocktake of gen AI applications in supervision FSI Briefs | No 26 | 12 June 2025 by Jermy Prenio – PDF full text
Highlights
- Many financial authorities are already experimenting with, developing or using generative artificial intelligence (gen AI) applications for supervision purposes.
- Financial authorities seek to leverage the new technology to find information more efficiently, but their gen AI activities are hampered by outdated information technology (IT) infrastructure, data security concerns and a lack of technical skills.
- Most of the reported gen AI applications in supervision can be grouped into three categories: (i) basic document processing; (ii) knowledge management; and (iii) document review. Most “in use” applications fall into the first category; development work is spread out across the three categories; and experiments are concentrated in the second and third categories.
- The main challenges identified in integrating gen AI applications in supervision are user acceptance and inaccuracies in information provided. These challenges will likely intensify as financial authorities move to more complex gen AI use cases.
BIS.org – Central banks – opportunities and implications posed by artificial intelligence. Speech by Mr Yannis Stournaras, Governor of the Bank of Greece, at the ECONDAT Conference on “Economics with nontraditional data and analytical tools”, London, 6 June 2025. Today, I would like to discuss how central banks can harness the transformative potential of artificial intelligence (AI) in their mission to safeguard monetary and financial stability. My remarks will unfold along three dimensions, focusing on several important issues, but without being exhaustive. First, on the ways that AI intersects with our monetary policy strategy at the European Central Bank (ECB). Second, on the opportunities AI offers to central banks for efficiency gains in areas such as communication and economic analysis. Third, on the implications posed by AI for price stability, monetary policy transmission and financial stability.
PAPERS:
NBER. Artificial Intelligence and Technological Unemployment. Working Paper 33867. DOI 10.3386/w33867. Issue Date How large is the impact of artificial intelligence (AI) on labor productivity and unemployment? This paper introduces a labor-search model of technological unemployment, conceptualizing the generative aspect of AI as a learning-by-using technology. AI capability improves through machine learning from workers and in turn enhances their labor productivity, but eventually displaces workers if wage renegotiation fails. Three distinct equilibria emerge: no AI, some AI with higher unemployment, or unbounded AI with sustained endogenous growth and little impact on employment. By calibrating to the U.S. data, our model predicts more than threefold improvements in productivity in some-AI steady state, alongside a long-run employment loss of 23%, with half this loss occurring over the initial five-year transition. Plausible change in parameter values could lead to global and local indeterminacy. The mechanism highlights the considerable uncertainty of AI’s impacts in the presence of labor-market frictions. In the unbounded-AI equilibrium, technological unemployment would not occur. We further show that equilibria are inefficient despite adherence to the Hosios condition. By improving job-finding rate and labor productivity, the optimal subsidy to jobs facing the replacement risk of AI can generate a welfare gain from 26.6% in the short run to over 50% in the long run.
Starobinsky, Mark, Ontology-Enhanced AI: Redefining Trust and Adaptability in Artificial Intelligence (April 17, 2025). Available at SSRN: https://ssrn.com/abstract=5237202 or http://dx.doi.org/10.2139/ssrn.5237202 – This paper introduces Ontology-Enhanced AI, a patent-pending architecture designed to augment large language models (LLMs) with symbolic reasoning, real-time trust scoring, and regulatory compliance. Unlike traditional probabilistic-only approaches, this framework overlays LLM pipelines with a dynamic ontology-driven reasoning layer that is auditable, explainable, and adaptive.
Key innovations include:
- A Bayesian trust engine that evaluates output reliability in real time
- Symbolic trace export for transparency and compliance mapping
- Federated ontology feedback to evolve system knowledge without retraining
This enterprise-ready platform addresses core challenges in hallucination control, governance, and legal risk within AI deployment — offering a modular, API-accessible solution for regulated industries. The system has been independently developed and is currently live as a working pilot under the OntoGuard AI, LLC entity.