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 alternate links with no paywall, as available.

NEWS:

Goldman Sachs and Morgan Stanley see their stocks soar as the AI boom fuels big banks. Quartz, April 16, 2025. Goldman Sachs and Morgan Stanley earnings provide a clear picture of Wall Street collecting its cut of virtually every relevant AI transaction. Talk about a real-time snapshot: Bank earnings season, unfolding this week, has offered perhaps the clearest picture yet of which sectors of the economy are benefitting from this historic capex binge brought on by the AI buildout. Banking is, at it happens, not least among them. Goldman Sachs and Morgan Stanley results together provide a particularly clear picture of Wall Street collecting its cut of virtually every relevant transaction. Unsurprisingly, the primary financing mechanism is debt. Major tech companies have announced more than $700 billion in capital expenditures for 2026 alone — an eye-watering 70% increase even over last year’s historic numbers — and with such enormous spending, there’s simply no way that cash could cover it all, were it even the case that corporations wanted to finance the buildout that way. This means bond markets are a major channel, and growing at similarly impressive rates, year over year. As an example, the five major AI hyperscalers issued $121 billion in U.S. corporate bonds in 2025, compared to an average of $28 billion per year in the five years prior. Estimates for 2026 range now from $175 billion to $300 billion, which means the question is whether the market will grow a mere 50% or, at the upper end of the range, nearly triple. Of course, someone has to help out by underwriting those bonds, trading them, advising on the deals, and managing the resulting assets (not to mention helping newly wealthy AI founders manage their newfound wealth). Enter Wall Street’s biggest firms. Here’s an even more granular look at the dynamics.

Wall Street Banks Cut 5,000 Jobs Even as They Notched Record Profits. A stunning show of first quarter earnings for big financial firms has been accompanied by the upended lives of 5,000 bank employees terminated in the same period. Bloomberg [no paywall], April 15, 2026. The dismissals come after the banks shrank headcount last year as well, feeding fears that even companies that are performing well won’t be looking to add staff as they pour money into artificial intelligence. None of the bank executives touting profits this week linked their job reductions to AI. Still, last quarter’s cuts were far greater than a year earlier, when the lenders collectively reduced headcount by 707 employees. “AI gives us places to go,” Bank of America Chief Executive Officer Brian Moynihan said Wednesday to analysts. “We’re still in the early stages of what all this will do. But we’re seeing real benefits out of it today.”

OpenAI Acquires Hiro Finance: The Push into Vertical AI Personal Finance. MarketMinute, April 14, 2026. On April 14, 2026, OpenAI officially announced the acquisition of Hiro Finance, a cutting-edge startup specializing in autonomous personal finance. The deal, described by industry insiders as a strategic “acquihire,” brings one of the most sophisticated financial reasoning teams in the fintech world under the OpenAI umbrella. As part of the transition, Hiro Finance will sunset its independent application on April 20, with its core technology and talent moving to bolster OpenAI’s emerging vertical applications division. The move marks a pivotal moment for OpenAI as it shifts from providing general-purpose artificial intelligence to offering specialized, high-stakes services like financial advisory. By integrating Hiro’s specialized math-heavy models, OpenAI aims to transform ChatGPT from a conversational assistant into a proactive “Super-Assistant” capable of managing complex household balance sheets, optimizing tax strategies, and executing multi-step financial plans with mathematical precision.

Oracle Financial Services Extends Agentic AI Platform to Corporate Banking. MarketWatch, April 14, 2026. Oracle Financial Services is transforming corporate banking with new embedded AI capabilities and agents. With these advancements, financial institutions and corporate banks can now benefit from an enterprise-class suite of AI-infused applications and pre-built AI agents for treasury, trade finance, credit, and lending. These enhancements automate mission-critical processes and speed decision-making to help banks and their corporate clients navigate market volatility and risk, accelerate loan processing, and unlock new opportunities for enhanced growth and service. By enabling AI experience agents to engage directly with clients and bankers, and domain agents to collaborate across workflows, Oracle is helping corporate banks transition from fragmented, manual operations to a unified, real-time, data-driven system of intelligence that improves efficiency, compliance, and client experiences. “Corporate banking runs on precision, resiliency, and trust,” said Sovan Shatpathy, senior vice president, product management and development, Oracle Financial Services. “Our AI-powered platform embeds intelligence directly into mission-critical processes, accelerating decisions and strengthening governance so banks can serve clients with greater speed and confidence.”

UNC Charlotte unveils M.S. in Financial Engineering and Fintech to meet rising demand for AI-driven finance talent. The Business Journals. April 15, 2026. For more than two decades, UNC Charlotte has helped fuel the region’s financial services growth through its nationally recognized M.S. in Mathematical Finance (MAFI) program. Now, as the industry undergoes rapid transformation driven by artificial intelligence, data science and digital assets, the university is marking a new chapter: the evolution of the program into the M.S. in Financial Engineering and Fintech. This isn’t just a name change. It reflects a shift already underway across the financial sector.

The new, AI-powered Google Finance is expanding to more than 100 countries. Google Blog, April 8, 2026. Starting today, the new, AI-powered Google Finance is going global. April 6, 2026. Over the coming weeks, we’re rolling out the experience to 100+ countries — including Australia, Brazil, Canada, Indonesia, Japan, Mexico and more — with full local language support to help you track the markets in the language you speak. This reimagined experience (already live in the U.S. and India) offers powerful capabilities to help you better understand the financial world.

• AI-powered research: Ask anything, from complex questions about the market to details on individual stocks. You’ll get a comprehensive AI response, with links to learn more.
• Advanced visualizations: New charting tools allow you to go beyond basic performance by toggling technical indicators like moving average envelopes and candlestick charts.
• Real-time intel: A revamped news feed and expanded data for commodities and cryptocurrencies keep you informed as markets move.
• Live earnings: Follow corporate earnings calls with live audio, synchronized transcripts and AI-generated insights.

Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell summoned Wall Street leaders to an urgent meeting on concerns that the latest artificial intelligence model from Anthropic PBC will usher in an era of greater cyber risk. BloombergLaw, April 10, 2026. Bessent and Powell assembled the group at Treasury’s headquarters in Washington on Tuesday to make sure banks are aware of possible future risks raised by Anthropic’s Mythos and potential similar models, and are taking precautions to defend their systems, according to people familiar with the matter who asked not to be identified citing the private discussions. Many of the executives were in town already for a meeting of the Financial Services Forum, an advocacy group made up of the biggest lenders. A representative for the Treasury didn’t immediately respond to a request for comment. A spokesperson for the Fed declined to comment.

How AI is breaking the financial services learning curve. Banks have widely adopted AI, but many still struggle to use it in deal workflows, creating a growing skills gap across. American Banker, April 8, 2026.

• Key insights: With AI tech developing quickly, about two-thirds of banks are relying on informal staff training to help employees use new models.
• What’s at stake: AI, particularly agentic commerce, poses authentication and other security risks that can be enhanced by an internal learning curve.
• Forward look: Experts suggest experimental sandboxes and the appointment of early adopters to spot experts in using new forms of AI.

2026 Report on Employer Firms: Findings from the 2025 Small Business Credit Survey. March 03, 2026. Use of artificial intelligence:

• Nearly half of firms (46%) reported that their business or its employees currently use AI, while an additional 15% planned to begin using it in the next 12 months. One-third of firms have no plans to use AI.
• Of those that use AI, about half said their business is experimenting with AI, while another 44% had partially integrated AI into their business processes. Just 7% of AI users had fully integrated AI into their business.
• The most common tasks for which businesses reported using AI are writing or marketing (83%), followed by individual productivity (61%) and planning or analysis (51%).
• While the vast majority of firms that use AI experienced no change in their labor costs because of AI, 71% said its use led to increased productivity, 39% noted improved quality of goods and services, and 31% reported higher sales.
• For AI users, the top challenges were accuracy (46%) and adapting tools to meet business needs (43%). For firms that plan to use AI in the next 12 months, the top challenges were finding tools to meet business needs (54%) and the time required to implement or train employees on AI (37%).
• Among the 33% of businesses with no plans to adopt AI, over half reported that it is not applicable to their business, while 30% said they prefer not to use it.

PAPERS:

Trade in AI-Related Products – Michael E. Waugh. Working Paper 35053. DOI 10.3386/w35053. Issue Date April 2026

This paper documents facts about international trade in AI-related products. I develop a large language model (LLM) classification tool that maps HS10 codes in U.S. trade data to products used in the construction and operation of AI infrastructure. AI-related products account for 23 percent of U.S. imports in 2025, and imports of these products have grown by 73 percent since 2023. Over the same period, imports of non-AI-related products have grown by only 3 percent, with the divergence between the two categories beginning in early 2024. Mexico is a key market on both the import and export side, and together with Taiwan these two countries account for about half of all U.S. trade in AI-related products. Trade policy has treated these products lightly with product-level exemptions shielding much of AI-related imports from tariffs. Absent the AI boom, a simple accounting exercise suggests that the U.S. goods trade deficit would have been nearly $200 billion smaller in 2025.

Forecasting the Economic Effects of AI – Ezra Karger, Otto Kuusela, Jason Abaluck, Kevin A. Bryan, Basil Halperin, Todd R. Jones, Connacher Murphy, Philip Trammell. Working Paper 35046. DOI 10.3386/w35046. Issue Date April 2026

We elicit forecasts of how AI will affect the U.S. economy, comparing the beliefs of five groups: academic economists, employees at AI companies, policy researchers focused on AI, highly accurate forecasters, and the general public. The median respondent in each group expects substantial advances in AI capabilities by 2030, small declines in labor force participation consistent with demographic shifts, and an annual GDP growth rate of 2.5%, which exceeds both the typical medium-run (2.0%) and long-run (1.7%) baseline forecasts from government agencies and private-sector forecasters. Conditional on a “rapid” AI progress scenario, in which AI systems surpass human performance on many cognitive and physical tasks, experts forecast substantial, though not historically unprecedented, economic shifts: annualized GDP growth rising to around 4% and the labor force participation rate falling from its current level of 62% to 55% by 2050, with roughly half of that decline—equivalent to around 10 million lost jobs—attributable to AI. A variance decomposition suggests that expert disagreement about these effects is driven primarily by different beliefs about the economic effects of highly capable AI systems rather than by disagreement about the pace of AI progress. These forecasts map onto notably different policy preferences across groups: experts strongly favor targeted measures such as worker retraining, whereas the general public supports both targeted programs and broader interventions, including a job guarantee and universal basic income.

AI Patents in the United States and China: Measurement, Organization, and Knowledge Flows – Hanming Fang, Xian Gu, Hanyin Yan & Wu Zhu. Working Paper 35022 DOI 10.3386/w35022. Issue Date April 2026. We develop a high-precision classifier to measure artificial intelligence (AI) patents by fine-tuning PatentSBERTa on manually labeled data from the USPTO’s AI Patent Dataset. Our classifier substantially improves the existing USPTO approach, achieving 97.0% precision, 91.3% recall, and a 94.0% F1 score, and it generalizes well to Chinese patents based on citation and lexical validation. Applying it to granted U.S. patents (1976–2023) and Chinese patents (2010–2023), we document rapid growth in AI patenting in both countries and broad convergence in AI patenting intensity and subfield composition, even as China surpasses the United States in recent annual patent counts. The organization of AI innovation nevertheless differs sharply: U.S. AI patenting is concentrated among large private incumbents and established hubs, whereas Chinese AI patenting is more geographically diffuse and institutionally diverse, with larger roles for universities and state-owned enterprises. For listed firms, AI patents command a robust market-value premium in both countries. Cross-border citations show continued technological interdependence rather than decoupling, with Chinese AI inventors relying more heavily on U.S. frontier knowledge than vice versa.