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“The real question is whether all data is good all the time for the capital management”
– Peter Marber, Aperture Investors
Speakers: Larry Tabb (Bloomberg Intelligence), Peter Marber (Aperture Investors), Harry Mendell (Federal Reserve lender of New York), Barry Star (Wall Street Horizon, TMX Group), Alison Rooney (Google), Jake Katz (London Stock Exchange)
Larry Tabb of Bloomberg Intelligence initiates the conversation by underscoring the importance of source credibility in capital decisions, highlighting a common concern regarding the reliability of AI-generated information. This sentiment is echoed by Peter Marber of Aperture Investors, who emphasizes that the quality of data-feeding AI models fundamentally impacts their effectiveness. He articulates concerns about the “tyranny of incumbents” in an increasingly consolidated asset management field, suggesting that major players may continue to dominate due to their superior access to proprietary information.
Harry Mendell from the Federal Reserve lender of New York shares practical challenges encountered when attempting to train AI models using high-end systems like Bloomberg’s event news alert system. Due to contractual limitations, such proprietary systems are not readily accessible for AI training, underscoring a barrier to democratizing AI in financial study.
Larry Tabb also reflects on the internet’s initial promise of democratization and its eventual consolidation into the hands of a few major companies. He draws a parallel to AI in capital management, predicting a similar trajectory where large infrastructures and investments lead to consolidation rather than widespread accessibility.
Barry Star from Wall Street Horizon, representing smaller entities, argues that quality, not quantity, of data should be the focus. He notes the competitive edge that can be achieved with superior data, a view that resonates with the historical and ongoing importance of unique, actionable information in financial markets.
Further, Alison Rooney of Google introduces the concept of “prompt engineering” in AI, emphasizing how the formulation of queries significantly influences AI outputs. This leads to a broader reflection on the customization and individualization of AI tools based on specific user inputs and contexts.
Peter Marber then suggests viewing AI as an augmentative tool rather than a replacement for human intelligence, analogous to agricultural advancements. Jake Katz of the London Stock Exchange supports this perspective, seeing the personalization of AI inputs as a feature that enhances competitive advantages for individual users.
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