This week’s roundup of critical developments in AI for secondary markets and capital markets infrastructure: from new trading models and regulatory updates to energy demands powering the AI boom, here’s what’s shaping the trading landscape this week:
🔗 Bloomberg Expands Chatbots in IB for Easy Collaboration Across Firms
Bloomberg has announced a significant update to its Instant Bloomberg (IB) chat platform, introducing “IB Connect: Cross-Firm Chatbots.” This new feature allows users to deploy custom chatbots across firms, bringing real-time updates from order management systems, research summaries, and client trade ideas directly into chat rooms. This move integrates automation into traders’ workflows, enhancing collaboration and efficiency. While Symphony has offered similar capabilities, Bloomberg’s initiative is seen as a strategic step to maintain its competitive edge, especially as LSEG explores similar integrations through its partnership with Microsoft Teams.
🔗 Bloomberg Expands Chatbots in IB for Easy Collaboration Across Firms
🚀 Baiont Unveils End-to-End AI Quant Trading Framework
Chinese quantitative fund Baiont, managing approximately $970 million, has fully embedded artificial intelligence into every stage of its trading lifecycle—from strategy design to execution and risk management. Founder Feng Ji emphasized the transformative role of AI in quant trading, stating that firms not adopting AI within three years may become obsolete. Baiont’s approach involves a holistic AI-driven model that integrates all stages of the trading process using a single foundation model, marking a decisive pivot toward AI-native trading architectures in Asia.(Financial Times)
🔗 Read more on Financial Times
🏛️ ICMA Responds to IOSCO on AI Risks
The International Capital Market Association (ICMA) has submitted its response to the International Organization of Securities Commissions (IOSCO) regarding AI’s role in capital markets. Key highlights from ICMA’s response include:
- Clear Global Definitions: Advocating for standardized terminology to ensure consistency across jurisdictions.
- Transparency on AI Risks: Emphasizing the need for clarity on potential risks associated with AI applications in capital markets.
- Proportionate Oversight: Urging regulators to adopt oversight measures that balance innovation with risk management, avoiding unnecessary hindrances to technological advancement.
ICMA also highlighted the importance of maintaining a “human in the loop” for AI systems, especially in areas like settlement processes and risk management. Additionally, concerns were raised about the use of synthetic data and the environmental impact of large-scale AI deployments.
🔗 Read the full ICMA response (PDF)
🧭 IMF Webinar: AI’s Role in Capital Markets
On May 20, 2025, the International Monetary Fund (IMF) hosted a high-level webinar titled “Advances in Artificial Intelligence: Implications for Capital Market Activities.” The session delved into the transformative effects of AI on capital markets, with discussions focusing on:CEF
- Market Surveillance Improvements: Exploring how AI can enhance the monitoring and analysis of market activities.
- Automated Investment Advisory: Discussing the rise of AI-driven advisory services and their impact on investment strategies.
- Regulatory Gaps: Identifying areas where current regulations may lag behind rapid AI advancements, emphasizing the need for agile regulatory frameworks.
Speakers included Benjamin Mosk and Puja Singh, Senior Financial Sector Experts at the IMF, who provided insights into the efficiency gains achieved through automation and advanced data analysis, as well as the potential risks to financial stability and market transparency.IMF+4CEF+4Center of Excellence in Finance+4
🔗 Watch the webinar and access related materials
🔋 The Hidden Cost of AI: Energy & Emissions
A powerful new investigation from MIT Technology Review reveals the soaring environmental toll of AI infrastructure.
🧾 Key stats from the report:
- AI data centers = 4.4% of U.S. electricity in 2024; projected to hit 12% by 2028
- Generating a 5-second video = 3.4 million joules (🛵 ~38 miles on an e-bike)
- GPT-4 training = $100M and 50 GWh of energy (enough to power San Francisco for 3 days)
💸 Who pays? Utility contracts with tech firms may shift the burden to consumers—some U.S. households could see $37.50/month increases in electricity bills.
🔍 Closed-source models like GPT-4 remain opaque, frustrating regulators and researchers alike.
The AI revolution is reshaping markets—but also energy policy, regulation, and competition. As infrastructure grows and model capabilities expand, expect regulatory alignment, cost scrutiny, and cross-platform innovation to dominate headlines.
As always, thanks for reading! Let me know what you liked, didn’t and what you would like to see more of.
Many thanks
Rebecca
