The New Face of Automation on Wall Street
Once, the biggest fear among Wall Street’s junior analysts was a typo in a spreadsheet or a late-night model error. Now, it’s something much larger: Sam Altman’s OpenAI.
The San Francisco-based company, whose technology already powers millions of knowledge workers, is rapidly extending its reach into finance. Its newest generation of enterprise models, insiders say, can perform tasks traditionally assigned to first- and second-year analysts – from building valuation models to summarizing research reports and generating pitch decks.
The result is a looming shift in the finance industry’s talent pipeline. AI isn’t just assisting analysts anymore. It’s replacing them.
The Entry-Level Squeeze
According to internal memos from major investment banks reviewed by Millionaire MNL, firms including Goldman Sachs, Morgan Stanley, and Citigroup have quietly begun testing OpenAI-powered copilots in their operations departments and analyst teams.
These tools can pull financial data, generate discounted cash flow models, and even draft internal memos summarizing client activity – tasks that once took entry-level staff hours to complete.
“AI isn’t just about productivity anymore,” said one senior banker who requested anonymity. “It’s about headcount. Why hire ten analysts when one with an AI assistant can do the same work?”
This mirrors a trend already sweeping other white-collar sectors, from law to consulting, but the speed of adoption in finance has caught many by surprise.
The Altman Effect
OpenAI CEO Sam Altman has long positioned his company’s mission as more than just technological. His vision: to integrate AI deeply into the infrastructure of human decision-making.
“Financial analysis is structured, data-rich, and repetitive – which makes it a perfect use case,” said Greg Brockman, OpenAI’s president, in a recent interview. “Our goal isn’t to eliminate analysts but to amplify them to 10x performance.”
But as automation climbs up the value chain, even mid-level employees are beginning to feel uneasy. “It starts as an assistant,” said one hedge fund manager. “Then it becomes the analyst. Then, eventually, it’s the associate.”
Wall Street’s AI Experiments Go Mainstream
At Morgan Stanley, internal teams have already integrated GPT-based tools to assist financial advisors in interpreting complex research data. Meanwhile, JPMorgan Chase has filed patents for its own AI models designed to draft investment recommendations and identify arbitrage opportunities.
The shift is part of a broader arms race in financial AI, with firms racing to capture the productivity gains while minimizing reputational and compliance risks.
“The problem isn’t whether AI can do the work,” said Clara Shih, CEO of Salesforce AI. “It’s whether regulators and clients are ready to accept it.”
Regulators Watching Closely
The Securities and Exchange Commission (SEC) has begun informally monitoring how financial firms deploy AI in analysis and trading. Concerns center on transparency, accountability, and systemic bias, especially if models trained on historical data reinforce flawed investment assumptions.
“We’re entering an era where algorithms are writing the financial story,” said Gary Gensler, SEC Chair, in a recent speech. “The question is, who’s checking their math?”
Even so, few expect the agency to halt adoption. The economic pressure to deploy AI tools remains overwhelming, particularly as firms look to cut costs after years of rising compensation and compliance expenses.
A Vanishing Rite of Passage
For generations, Wall Street’s entry-level roles have been grueling yet formative – long hours, high stress, and intense learning curves that shaped future dealmakers.
Now, that path looks uncertain.
“The traditional analyst grind is disappearing,” said Jeffrey Appelbaum, managing director at a major investment firm. “AI doesn’t need to stay up until 3 a.m. fixing PowerPoint slides. It just gets it done.”
That shift has cultural consequences. Without those foundational years of training, firms risk producing leaders who never truly learned the business from the ground up. “You can’t teach instinct through automation,” Appelbaum added. “AI is brilliant at logic but terrible at judgment.”
The New Skillset for Finance’s Future
Despite the disruption, experts say AI won’t eliminate analyst roles entirely, it will redefine them.
Future analysts will spend less time building models and more time interpreting insights, stress-testing assumptions, and strategizing around human behavior, skills machines can’t replicate.
“The analyst of 2030 will look more like a data scientist than an Excel jockey,” said Karen Karniol-Tambour, co-CIO of Bridgewater Associates. “The question is whether today’s firms can adapt fast enough to attract that kind of talent.”
To prepare, business schools are rapidly expanding AI-in-finance curricula, and firms are investing in internal retraining programs focused on prompt engineering and model validation.
The Bottom Line: A New Era of Meritocracy – or Mechanization?
As AI continues to permeate finance, the industry faces an uncomfortable paradox: the technology promising greater efficiency may also erase the very human apprenticeship that built it.
Altman, for his part, insists that change is inevitable. “Every industry that deals in information will be transformed,” he said. “Finance just happens to be one of the first.”
For the young and ambitious, the message is clear: the next great Wall Street career might start not with a spreadsheet — but with a prompt.
