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Regulation tracker

The COAI team is tracking how AI and tech regulation is changing

President-elect Trump has appointed Silicon Valley veteran David Sacks as the White House AI czar, a role focused on establishing U.S. leadership in artificial intelligence and addressing concerns about Big Tech bias. Sacks, who brings 25 years of technology industry experience including leadership roles at PayPal and Yammer, will be tasked with shaping federal AI policy and protecting free speech online. The appointment signals a potential shift in how artificial intelligence will be regulated and promoted at the federal level under the incoming administration.

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What’s happening in AI right now

Major financial institutions are racing to deploy AI

Tech giants may dominate AI headlines, but financial institutions are quietly becoming some of the most sophisticated AI adopters. This week brought a flurry of revelations about how major banks and financial services firms are implementing AI at massive scale.

The leaders emerge

JPMorgan Chase has already deployed its internal AI assistant to over 200,000 employees while migrating nearly 1,000 applications to AWS. The banking giant's "LLM Suite" is now being used across multiple business functions, showcasing how AI can be practically implemented within highly regulated industries.

Similarly, Ally Financial launched its proprietary AI platform Ally.ai, built on AWS and Azure OpenAI. The company developed a structured Technology Operating Model (ATOM) to guide AI initiatives through five careful stages - a framework other financial firms are likely to emulate.

Data infrastructure: the hidden challenge

These successes haven't come easily. Financial institutions face unique hurdles in AI adoption, particularly around data management. Many operate with outdated systems struggling to handle diverse data types across multiple siloed departments. Legacy infrastructure often can't provide the high-quality, accessible data that modern AI requires.

This challenge has created opportunities for specialized AI solutions targeting front, middle and back office operations. The financial services industry appears to be at an inflection point where the gap between traditional infrastructure and modern requirements is driving rapid innovation.

Innovation on the customer interface

While back-end transformations continue, financial institutions are also experimenting with novel customer-facing AI applications. IDFC FIRST Bank in India recently unveiled an AI-powered holographic avatar of Amitabh Bachchan that provides interactive banking services through touch-based interactions. Though currently limited to a single Mumbai branch, the bank plans nationwide expansion.

This reflects a broader trend of banks exploring immersive AI technologies to enhance customer engagement while maintaining a personal touch. The success of such initiatives could influence other institutions to adopt similar approaches.

The crypto wild card

The intersection of AI and blockchain technology presents another frontier. Industry forecasts suggest that by 2025, we'll see AI-operated crypto wallets, autonomous chatbots, and new organizational structures like DUNA for DAOs. Rather than creating entirely new solutions, the focus appears to be on improving existing infrastructure and practical implementations.

Looking ahead

Those that get it right stand to capture enormous value. Early moves by JPMorgan Chase, Ally Financial and others suggest that contrary to popular belief, traditional financial institutions may end up being some of the most sophisticated and successful AI adopters of all.

We publish daily research, playbooks, and deep industry data breakdowns. Learn more here

In Bagel’s most recent article, they reveal how large language models are evolving from prediction tools to cognitive agents. From breaking down math problems to understanding everyday logic, this is AI's next frontier.

Key takeaways:

  • Training-time methods: These approaches, including fine-tuning and curriculum learning, focus on enhancing AI during development by tailoring its abilities to solve complex tasks and building specialized skills for specific problem areas.

  • Inference-time methods: Techniques like chain of thought and self-consistency optimize AI’s reasoning and decision-making in real time, enabling more accurate and dynamic problem-solving without requiring retraining.

In other words, Bagel’s article explains how AI is evolving from simple prediction tools to smarter systems that can reason through problems and make better decisions in real time.

📬 Join 30,000+ readers exploring the cutting edge of AI research. Read Bagel’s most recent article to understand how reasoning will define the next leap in AI’s evolution.

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