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AI could wipe out entire Wall Street teams. Here’s who will have staying power.‘The number of people used, whether it be a junior banker, analyst or a salesperson, you just don’t need to throw bodies at it anymore’
By
Laila Maidan
Published: June 28, 2025, 8:30 a.m. ET
 “The general rule is, anything that can be automated, will be automated,” one technology strategist said. Photo: MarketWatch illustration/iStockphoto
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For 156 years, American companies have paid investment bankers at Goldman Sachs to guide them through the process of holding an initial public offering and listing their shares on stock exchanges. But earlier this year, Goldman Sachs CEO David Solomon suggested that a lot of that work can now be done by artificial intelligence.
According to Solomon, the drafting of an IPO prospectus would normally take a six-person team at Goldman Sachs Group Inc.
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two weeks to complete, but AI can do 95% of the job in minutes. “The last 5% now matters because the rest is now a commodity,” Solomon told the Financial Times earlier this year.
Solomon is not the only Wall Street CEO assessing the impact of AI on his firm and its business. Navid Mahmoodzadegan, the incoming CEO of Moelis & Co.
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, said on an investor call in June that the bottom layer of his firm’s pyramid of investment bankers will likely shrink because AI will do a chunk of the work previously done by young and ambitious financiers.
“We’re never not going to have a pyramid,” Mahmoodzadegan said. “Can we be a little more efficient on the pyramid down the road if technology develops the way I think it will? I think there’s a possibility of that.”
Across Wall Street, bankers and investment managers are assessing AI and gauging what the next few years will mean for financial institutions — and for the people who seek to work for them.
Artificial intelligence is taking off across corporate America. In a note to employees last week, Amazon Inc.
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CEO Andy Jassy said that the company’s adoption of AI tools means fewer people will be needed for certain jobs. Meanwhile, New York state requires employers to disclose whether a layoff is a result of technological innovation related to AI — a sign of the times as economists, governments and employees try to navigate the impact of the new technology.
Finance companies have been some of the fastest and biggest adopters of AI tools as they became available in the last few years, said Prasanna Tambe, an associate professor at Wharton and co-director of human-AI research at the business school. But so far, a lot of the adoption has been experimental, as firms begin to understand where AI tools will fit into business processes and how reliable they are. Additionally, some of these tools are getting upgraded weekly, which is rapidly increasing their capabilities and making it difficult to project with any certainty their full impact on the labor market, he added.
“The general rule is, anything that can be automated, will be automated,” said Ted Mortonson, managing director and technology strategist at Baird, an investment bank based in Milwaukee.
Mortonson told MarketWatch that his firm was in the discovery phase when it came to how AI tools will be integrated into processes across different divisions. The firm’s business leaders have been working with the information technology department to test out OpenAI’s enterprise offerings and refine the tools to better fit their needs, a process Mortonson has been part of.
Mortonson, who oversees technology sector research, noted that traditionally, the information-gathering process has been done manually, with massive amounts of data collected and organized. But the AI tools he has been testing are allowing him to automate much of that work, and that means he can do the same amount of work with a smaller team.
“The number of people used, whether it be a junior banker, analyst or a salesperson, you just don’t need to throw bodies at it anymore,” Mortonson said. “You can actually optimize it through technology. So hiring is down. Business units aren’t yelling for bodies. They’re yelling for technology.”
AI’s place on Wall Street
AI tools can be trained to automatically and efficiently scrape news, earning results and press releases, organize that data and have it ready for a portfolio manager to pull from when needed, Mortonson said. Even tasks related to researching and constructing financial models or building PowerPoint presentations won’t require as big of a team, because many of those steps can also be automated, requiring only a few humans in the loop to oversee the process. For Mortonson’s role as a tech strategist, he previously needed a team of five to 10 people working a significant number of hours to create a comprehensive presentation that covers all sectors of technology. Now, he said, he only needs two to three people.
Matt Ober, a former Wall Street data scientist turned venture capitalist, told MarketWatch that the investment memos and pitch decks he creates for startups used to take him weeks to put together. Now, ChatGPT does it automatically. After training the chatbot by uploading his firm’s past memos, he can feed it company financials, meeting transcripts, emails and even his texts with startup founders, then prompt it to produce a memo.
On Monday, Goldman Sachs announced its firmwide launch of GS AI Assistant, an internal conversational platform that links to different large language models — the term used for AI tools that can generate and interact with human language. While it’s a fresh development, about 10,000 employees have had access to AI tools since January.
Goldman is still in the process of experimenting and deploying additional AI tools. So far, its AI capabilities include tools that assist the company’s developers with coding, a person familiar with the matter told MarketWatch. The AI tools can also assist employees with retrieving information, creating first drafts and summaries, proofreading, language translation and drafting emails. Goldman is also building out what it calls Banker Copilot, which compiles relevant research and provides investment bankers with a conversational interface. It’s currently being used by a small group that is optimizing it before it is deployed more broadly, a person familiar with the matter said.
Alexander Fleiss, CEO of Rebellion Research, an AI financial adviser and hedge fund, says LLMs are also shortening research time for testing trading strategies. For example, a lot of money is made around strategies built on mean variance reversions, which is the measured likelihood that an asset’s price returns to its moving average in a certain period of time after deviating. Instead of conducting what’s known as a back test for every query a researcher has — simulating trades using historical data — they can ask the LLM for the probability measures around an asset’s price moves. Those with higher likelihoods would then be worth a researcher’s time to conduct a back test. The result is that more ground can be covered in less time and with fewer people.
AI can also be used in the stock-picking process. A common way investment funds find stocks is by narrowing the universe of companies using a filter that lists names with certain financial ratios or stocks that have been trending higher. These filters tend to be rigid, using the same requirements over decades. LLMs can broaden out the filtering options by allowing money managers to ask more tailored and qualitative questions, Fleiss said. For example, AI can be asked to pull up stocks of companies with earnings calls that feature the most positive language while also having increasing profit margins, among other factors. That information may not be traded on, but it’s another layer of information that can be added to the stock-picking process. Fleiss noted that, for now, LLMs are more helpful for mutual funds or value investment strategies, and for any firm where stockbrokers rely more on a company’s financial data to pick stocks that would be held for longer periods.
So who will be left?
The answer to who will be left working on Wall Street and how many jobs could be cut depends on who you ask.
It’s very early to say for certain how great the impact on Wall Street’s labor force will be, Tambe said. Firms must decide how reliable the AI tools are, how to integrate them into the decision-making process and what legalities may be tied to their use within investment banking. Until more of this becomes clear, it will be difficult to project with any certainty the full scope of the impact on the financial-services industry.
However, the ability of LLMs to take over many administrative and research tasks naturally means that certain skills will be in less demand. What Mortonson refers to as the “busy body work” — research tasks like reading a company’s annual 10-K financial filing or putting together financial models and memos — could be done by AI tools. Since that work is generally associated with junior employees, Wall Street’s managers will need to adjust how they keep people flowing up the ranks at their firms.
Additionally, sector or industry analysts focusing on areas such as technology, banking, healthcare or industrials could see much of what they do carried out by agentic AI, which is able to make decisions and take actions that can be customized. In the future, these agents can then be assigned to a sector specialist, Mortonson said.
“It’s very taxing on the brain to try to absorb all the sector data coming out,” Mortonson said. Because there is so much information, an analyst can spend most of the day trying to find a needle in a haystack, and at least 60% of what’s important still gets missed, he noted. An AI agent can scan for sector-relevant data 24 hours a day, seven days a week, reducing the manpower required to find and absorb data. It’s a huge advantage that he estimates will be in full effect in two to three years.
In a 2024 interview with CNBC, Citadel CEO Ken Griffin said that his firm uses LLMs to a degree that has changed how capital is invested, and that he expects AI tools to bring about “incremental gains” in areas where it can produce written work and manage confidential data. However, he said it won’t be able to pick stocks or replace thoughtful active money managers.
Mortonson’s observations reflect some of what Griffin spoke about. Because of the tasks that AI can and can’t do, there will be a bifurcation between people with skills that will become less relevant and those with skills that will become more important, Mortonson said. With regard to the former group, he believes fewer junior personnel will be needed across all roles on Wall Street, including sales, research, banking and trading.
In the latter group, there are three types of employees who will have stronger staying power and potentially bigger salaries, he said: department heads with tremendous expertise in their sector; employees who have relationships with clients, since things like friendship, trust and integrity can’t be authentically replicated by AI; and employees who can add human value through their experience, opinions and personal interpretation of data. Broadly speaking, with the use of AI tools, headcount will drop by around 30%, and those in key positions will be very relationship-focused, he added.
“It’s the intuitive nature of the human brain that makes people money,” Mortonson said. AI theoretically mimics the process of decision-making, but where significant capital implications are involved, it can’t replace a human. His advice to junior bankers or those hoping to land jobs on Wall Street is to get really good at being efficient with the AI tools now available, which includes getting very familiar with the different LLMs like OpenAI, Grok and Claude.ai and understanding the scope of their capabilities. Junior employees already working at financial firms can accelerate their career path if they demonstrate proficiency in the continually evolving AI tools, while more senior employees who don’t do so will fall behind, he said.
Ober echoed that insight, adding that the skill sets required on Wall Street will shift. It used to be that the top up-and-coming analysts weren’t merely good with financial markets, but they were also efficient because they could manage and analyze data with Excel or code with Python or R. None of that will matter as much now, since AI is capable of handling these tasks in less time, he said. On one hand, he said, this will make entry-level jobs on Wall Street easier because an employee won’t need to be a coder, but it will also raise the bar for getting a job because it will increase competition amid a drop in demand for junior hires.
Ober also points out that these tools could enable more productivity, allowing money managers, researchers and analysts to cover more ground, test more strategies or explore more companies. This is the approach currently being taken by Goldman Sachs and Morgan Stanley.
David Wu, head of Morgan Stanley’s
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AI product and architecture strategy, said in an email to MarketWatch that his firm is working to integrate generative AI tools — the term used for AI that creates text, images, video and audio — across its businesses to enhance productivity.
“The tools we roll out have a ‘human in the loop’ freeing up our employees to spend more time on value-add projects and enhance how we serve our clients,” Wu said.
JPMorgan Chase & Co.
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-0.57%
is already using AI tools across its divisions to drive efficiencies, according to executives who spoke at the firm’s investor conference in May. Marianne Lake, CEO of consumer and community banking, said that due to AI innovation, headcount will trend down by about 10% in the next five years, even as her business unit continues to grow by over 25%.
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About the Author
Laila Maidan
Laila Maidan is a Tech and Investing reporter at MarketWatch. She covers the rapid developments in the sector along with actionable investment recommendations and deep dives into valuations.
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