Goldman Sachs aims to ensure that new artificial intelligence tools don’t lead to a loss of knowledge within the institution, Chris Churchman, head of the company’s digital platform for institutional clients, Marquee, said, CNBC reported Monday (Aug. 24).
Speaking during an episode of Goldman Sachs’ podcast, Exchanges, Churchman said an overreliance on AI could lead to traders becoming passive operators and junior traders losing the opportunity to learn the “tacit and intuitive knowledge” held by senior traders, according to the report.
While embedding AI in trading and banking processes will make the industry more profitable today, there is a risk that it could reduce the industry’s apprenticeship culture, per the report.
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“There’s a huge danger here that in the era of AI, we outsource our reasoning to these models, and we have cognitive atrophy that stops us being able to reason from first principles ourselves,” Churchman said in the report.
Goldman Sachs is still figuring out how it will manage this while transitioning more systems to AI, per the report.
Goldman Sachs published research Wednesday (Aug. 19) that found that AI displacement could be stronger among entry-level employees. The report also found that employment in highly exposed industries such as call centers, software publishing, advertising services and management consulting have seen slower job openings growth since the second half of 2022.
In June, Goldman Sachs estimated that over 9% of U.S. jobs could be displaced by generative AI over the next 10 years. The company also estimated that artificial intelligence will generate new jobs over the long run and add to the 25 million to 35 million new jobs that are already created each year by the U.S. economy. Together, these trends mean that the peak unemployment rate impact of AI would be less than 1%, per the report.
The PYMNTS Intelligence report “Financial Services Pulls Ahead in the Enterprise AI Race” found that financial services firms are “going all in” on AI and that these firms have deeply embedded AI into revenue recognition, credit scoring and sales forecasting.
“The industry’s most adopted use cases cluster in structured, auditable back-office functions: the internal operations that keep a business running but that customers never directly see,” the report said.
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