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
Companies are barely on board with AI and they’re now having to account for the next wave of technology overtaking the workplace. Semafor Technology [Subscription Only], March 27, 2026. “Every company in the world today needs to have an OpenClaw strategy,” Nvidia CEO Jensen Huang said at the company’s annual conference last week, referring to the platform of agents that can schedule meetings, send emails, and perform other work on a user’s behalf. OpenAI acquired the open-source platform last month, just weeks after users flooded the internet with examples of interactions between the agents. EY’s global vice chair Julie Teigland agrees with Huang, but says companies are struggling to get their workers to adopt AI. Executives are still trying to layer AI onto their existing businesses rather than ripping up old models and reimagining them, she said. “You will see us massively move in this direction, but it’s going to take us a little bit to get there,” Teigland told Semafor. Case in point: EY is still figuring out its own OpenClaw strategy and couldn’t comment on how it plans to roll out the platform to its consultants. — Rachyl Jones
SoftBank secures $40 billion loan to boost OpenAI investments. Reuters, March 27, 2026. SoftBank Group said on Friday it has secured a $40 billion bridge loan to bolster investments in ChatGPT-maker OpenAI and for general corporate purposes, marking another significant step in its artificial intelligence strategy. The Japanese investment conglomerate, led by founder Masayoshi Son, continues to strengthen ties with OpenAI as global tech firms race to gain an edge in the increasingly competitive generative AI space.
Should you trust AI to manage your money? The finance industry is betting you will. Fortune, March 26, 2026. “Talk to Chuck,” goes the famous ad slogan for Charles Schwab. According to Vlad Golyk, coauthor of a recent McKinsey report on AI and wealth management, more than a third of consumers across all age groups are turning to tools like Claude and ChatGPT for guidance on their investments. In many cases, he says, they are consulting the tools ahead of meeting their real-life financial advisor. This trend is only going to accelerate as Anthropic and others find new ways to infuse financial services with artificial intelligence. The full implications are not clear, and it’s easy to see things going wrong. (Picture a future where a YOLO AI advises its clients to go all in on penny stocks.) The early signs, though, paint a more positive picture—one in which financial insight becomes more democratized and AI tools augment, rather than replace, money managers.
BlackRock’s Larry Fink warns AI may intensify wealth inequality. FT.com [no paywall], March 17, 2026. BlackRock chief executive Larry Fink has warned that AI risks widening inequality, concentrating wealth among a handful of businesses and investors who have financed the industry’s growth. Fink used his annual letter to BlackRock shareholders on Monday to caution that AI could intensify wealth disparities if individuals lack a means to participate in its rise, writing that a growing part of the country felt capitalism was not working for enough people. “The massive wealth created over the past several generations flowed mostly to people who already owned financial assets,” he wrote. “AI threatens to repeat that pattern at an even larger scale.” He added: “The broader question is who participates in the gains. When market capitalisation rises but ownership remains narrow, prosperity can feel increasingly distant to those on the outside.” Advances in AI have upended financial markets this year and set off an arms race among large technology companies such as Meta, Microsoft, Alphabet and Amazon as they look to build their own models that can compete with OpenAI and Anthropic.
Watchdog Issues Grim Warning About Letting AI Run Your Life. Futurism, March 14, 2026. These days, the AI stack beckons: emails, shopping, personal finance — there’s hardly a task some company isn’t clamoring to automate on your behalf. As tempting as it might sound to let AI agents handle your affairs, though, you might want to hold off. A fresh report by the Competition and Markets Authority (CMA) of the UK issued a stark warning that outsourcing responsibilities to an AI entourage could lead to severe consequences. The report, first spotted by the Register, warns that AI agents could subtly manipulate their human keepers toward outcomes that benefit the companies that built them. Shopping agents, for example, could lead unsuspecting humans down a pricing rabbit hole, framing sponsored products as bargains in order to drive sales. As agents are granted more autonomy by humans, the report warns, the risk of errors and manipulation only grows.
AI Agent Goes Rogue, Starts Mining Crypto to Amass Funds. Futurism. March 10, 2026. AI agents — AI systems designed to complete digital tasks without much supervision — may be everywhere, but they’re not exactly ready for primetime. Over the last year, they’ve been caught slandering people, deleting user emails, and wiping out entire hard drives. Most recently, a free-spirited AI agent was caught moonlighting as a crypto miner — a behavior which startled its keepers, Axios reported. Called ROME, the AI agent was being run as part of a research project by an AI lab affiliated with Chinese online retail giant Alibaba. In their ensuing research paper, the researchers describe the agent’s strange side-hustle as a set of “unsafe behaviors” that “arose without any explicit instruction and, more troublingly, outside the bounds of the intended sandbox.”
GOVERNMENT DOCUMENTS
UK Competition and Markets Authority (MA)
Research and analysis Agentic AI and consumers. Published 9 March 2026
Current state
AI is now embedded in many aspects of everyday life. Consumers already experience and interact with AI through search, recommendations, fraud detection, customer service and decision‑support tools that can save time and improve access to information. The rapid spread of generative AI – enabling natural language interaction – has accelerated this trend, bringing AI into direct, large‑scale engagement with consumers. To date, however, AI adoption and its impact have been uneven and most consumer‑facing AI has operated as a tool: it supports decisions, while coordination, monitoring and action remain with the user.
Potential future state
Agentic AI could drive a step change in how people use AI and its impact on their lives. Definitions vary but these include AI agents that can be instructed in natural language to achieve a goal autonomously, navigating some complexity in the environment, planning, coordinating, and taking actions – potentially across multiple services. AI agents do not merely assist, they sense (perceive their environment), decide and act They go beyond generating responses to user queries and may:
- assess goals, break them into subtasks, and plan end-to-end workflows
- retrieve real-time data (that may include personal data) from other agents, databases and other services
- execute actions autonomously, such as making payments on behalf of the user
- store memory of past interactions to improve over time
For businesses, this could unlock substantial productivity gains. For consumers, today’s chatbots may prove only a first step towards more capable personal agents – systems that anticipate needs and execute transactions on the user’s behalf. If realised reliably at scale, this shift – from using tools to delegating outcomes – could materially change how people engage with markets and how value is created. The potential benefits for consumers are significant if the technology achieves reliability and is deployed responsibly. Agentic AI could reduce friction, improve personalisation and support better outcomes including potentially lower prices and tailored deals, including in complex markets….At the same time, there are material risks. Greater autonomy for agents increases the consequences of errors, may heighten risks of manipulation and loss of consumer agency, and could lead to worse overall outcomes for consumers. People may be steered towards products and services that are more profitable but less suited to their needs, potentially paying higher prices. AI agents raise new questions about transparency, incentives and accountability and whether the current tools and frameworks that protect consumers are fit for purpose. Without appropriate safeguards, agentic systems could undermine trust in AI and consumer markets rather than strengthen it, and this loss of trust and confidence in turn could inhibit positive innovation, investment and growth.
PAPERS
NBER
Artificial Intelligence, Productivity, and the Workforce: Evidence from Corporate Executives. Salomé Baslandze, Zachary Edwards, John Graham, Ty McClure, Brent H. Meyer, Michael Sparks, Sonya R. Waddell & Daniel Weitz
We use novel data from a survey of nearly 750 corporate executives to study the effects of artificial intelligence (AI) on productivity and the workforce. We document substantial heterogeneity in AI adoption across firms, with more than half having already invested, though many smaller firms are only beginning to do so. Labor productivity gains are positive, vary across sectors, and are expected to strengthen in 2026, with the largest effects concentrated in high-skill services and finance. These gains are not primarily driven by firms’ capital deepening but instead reflect increases in revenue-based total factor productivity, closely associated with innovation-and demand-oriented channels. We document a productivity paradox, in which perceived productivity gains are larger than measured productivity gains, likely reflecting a delay in revenue realizations. In labor markets, we find little evidence of near-term aggregate employment declines due to AI, though larger companies anticipate AI-driven workforce reductions, while smaller firms expect modest gains. We also find evidence of compositional reallocation of labor both within and across firms, with routine clerical roles declining and a relative demand for skilled technical roles increasing. We develop an index that ranks job functions most negatively affected by AI.
AI in Science. Ajay K. Agrawal, John McHale & Alexander Oettl. Working Paper 34953. DOI 10.3386/w34953. Issue Date
We explore the impact of artificial intelligence (AI) on the knowledge production function. We characterize AI as a tool, not for full automation but rather for augmentation through enhanced search over combinatorial spaces. This leads to increased scientific productivity. We decompose knowledge production into a multi-stage process to shed light on the “jagged frontier” of AI in science, revealing differential returns to different tools across domains (e.g., data-rich biology vs. anomaly-sparse physics) and workflow stages (e.g., strong design aids like AlphaFold vs. subtler question generation tools). We treat human judgment as indispensable for tasks involving abductive inference, contextual nuance, and trade-offs, particularly in data-sparse environments. Drawing on a task-based model that distinguishes “ordinary” from AI-expert scientists, we describe how exogenous improvements in AI yield nonlinear productivity gains amplified by the share of scientists that are AI-experts to underscore the role of AI complements like skills training and organizational design.