Artificial intelligence is beginning to challenge one of the basic assumptions of professional employment: that young workers enter an organisation, perform increasingly complex tasks and eventually move into management. Comments from Goldman Sachs and Singapore’s financial regulator suggest that this progression may no longer operate in the same way across banking and other professional services.
Kevin Sneader, who leads Goldman Sachs’s business in Asia Pacific outside Japan, said at the Milken Institute Asia Summit in Singapore that new finance employees could be supervising artificial intelligence agents from the beginning of their careers. “When our young folks now start work, they’re managing agents,” Sneader said. “They have to make the most of this virtual army that they’ve now got.”
The significance of that statement is not limited to whether AI eliminates a particular number of jobs. It points to a change in how organisations distribute responsibility, train employees and build management capacity. For decades, junior employees in banking and professional services typically spent years on analysis, preparation and operational work before progressing into roles that involved supervising people. If software performs much of that preparatory work, the route from entry-level employment to management could become shorter, less predictable or less widely available.
The immediate uncertainty concerns middle managers. Sneader said the redeployment of existing managers would be a “generational challenge for many, many segments”. He also said that management work could move closer to the front line. “That management task no longer sits with the middle manager; it sits with the front line,” he said. “We don’t quite know what’s going to happen to that group.”
This is a different question from the familiar debate about mass unemployment. The comments reported from the summit focus on how jobs will be reorganised, rather than presenting a forecast of total workforce reduction. Across the financial industry, however, thousands of roles are expected to be affected as AI changes the type of work available in the future, according to the report.
The urban importance of this shift lies in the way large financial institutions shape professional labour markets. Banking centres depend on a steady supply of graduates, analysts, operations staff, managers and specialist service providers. Their office districts, education pipelines and household economies are connected to the expectation that professional careers will expand through layers of responsibility. A change to that ladder could affect not only individual firms but also the institutions that prepare workers for them.
Goldman Sachs president John Waldron has described the firm’s operations as a “human assembly line” that is suitable for automation. The phrase captures the type of work most exposed to the transition: repeatable tasks performed in sequence, often by large numbers of junior employees. It also explains why the impact may be felt first in recruitment and training rather than only in established senior roles.
Sandra Peterson, an operating partner at investment firm Clayton, Dubilier & Rice, said the traditional career structure of professional services may not survive. Such firms, she said, used to operate as pyramids in which employees joined at the bottom and moved up level by level. Her concern was that if AI handles much of the routine work, firms may not require as many entry-level positions.
That creates a potential institutional problem. Entry-level work is not only a source of employment; it is also how organisations identify talent and develop practical judgement. The supplied report does not establish how banks will replace that training function, or whether the technology will create new roles at a scale comparable to those it changes. It does, however, show that employers and regulators are already considering the consequences for the structure of professional work.
The Monetary Authority of Singapore sees a similar transition. Chia Der Jiun, the regulator’s managing director, said operations staff would need to become managers and supervisors of AI agents. He also said banks would need fewer fresh graduates for analysis and preparatory work. These two developments could occur at the same time: some existing employees may gain responsibility for overseeing automated systems, while fewer new employees are recruited into the traditional starting roles.
Singapore’s response, as described in the report, has focused on skills and education. The city-state has been training bank staff in AI capabilities and working with universities to prepare graduates for the changing employment environment, Chia said. This places workforce adaptation within an institutional framework involving employers, regulators and higher education rather than treating it as a matter for individual workers alone.
The policy challenge is therefore broader than encouraging people to learn how to use new software. Organisations will have to determine what supervision means when the supervised workforce includes AI agents, how performance is assessed, and which decisions must remain with human employees. The supplied material does not provide specific regulatory rules or implementation timelines for these changes. It does show that Singapore’s financial regulator considers the transformation significant enough to involve both banks and universities.
The numbers in the report are limited but important. It refers to thousands of financial-industry jobs being set to change and to fewer fresh graduates potentially being needed for analysis and preparatory work. It does not provide a precise estimate of jobs lost, jobs created, the number of firms affected or the timeframe for the transition. Those omissions matter because the difference between job displacement and job redesign cannot be assessed through broad warnings alone.
The absence of a detailed forecast also makes the middle-management question particularly important. If AI reduces the volume of junior work, the traditional pyramid may narrow at its base. If new supervisory responsibilities are created, some employees may move more quickly into roles overseeing automated systems. But a faster promotion path for some workers would not necessarily compensate for a smaller intake of graduates. It could also change what employers expect from people entering the sector.
For cities with large concentrations of finance and professional services, the transition could alter the relationship between education, employment and office-based economic growth. The report does not quantify effects on office demand, salaries, commuting or local economies, so those consequences cannot be established from the available evidence. The immediate signal is institutional: employers and regulators are questioning whether the existing organisation of professional work can continue unchanged.
There is also a potential shift in the meaning of management itself. In the older model described by the speakers, management was associated with progressing through organisational layers and supervising larger teams of people. In the emerging model, a young employee could be responsible for directing or checking the output of multiple AI systems. That may expand the importance of judgement, verification and accountability, even as it reduces the amount of routine work through which employees traditionally gained experience.
The report also records a more positive interpretation from Sneader. He said the future could be favourable for entrepreneurs because fewer resources are needed to start a business. That possibility suggests that AI may lower some barriers to creating enterprises, even as it disrupts established employment pathways. The material does not show whether entrepreneurial opportunities will offset reduced entry-level hiring in finance, but it does present this as one possible benefit of the changing technology landscape.
What the evidence confirms is a transition in the way financial institutions are thinking about work. Goldman Sachs executives and Singapore’s financial regulator are not describing AI only as a tool for efficiency. They are discussing its effect on recruitment, management, training and the career ladder itself. What remains uncertain is the scale, speed and distribution of those effects.
The next stage will depend on how banks redesign entry-level roles, how they redeploy middle managers and how universities adjust to changing requirements. Until firms publish more detailed workforce plans and regulators establish clearer frameworks, the central fact is not a settled prediction of mass job losses. It is that the organisational model supporting professional careers is being actively reconsidered.
