As the adoption and utilization of artificial intelligence (AI) becomes increasingly widespread, law firms and accounting firms are reducing the number of new lawyers and accountants they hire. The same trend can be observed in software development companies, where coding is the primary work.
At this year's World Economic Forum (WEF) in Davos, the CEOs of Google DeepMind and Anthropic, both leading companies in the AI sector, issued explicit warnings. Demis Hassabis of DeepMind stated that AI would begin to impact entry-level and junior positions, as well as internships, starting this year. Dario Amodei of Anthropic reaffirmed earlier projections that up to 50% of entry-level jobs could disappear within the next five years.
In response, Molly Kinder, a Senior Fellow at the Brookings Institution, published an article titled "To save entry-level jobs from AI, look to the medical residency model." She argued that if these predictions prove accurate, the traditional method of nurturing young talent in knowledge work-hiring entry-level employees to assign them simple and repetitive tasks so they gain experience-will not be sustainable once AI takes over such work. She also proposed several alternatives.
Kinder asserted that the career ladder needs to be redesigned, and that a solution could be found in the medical field's residency system. She explained that the residency model is a structured, mentorship-based program where learning itself constitutes the work. She suggested that white-collar professions, shaken by AI, could also implement their own residency models.
AI excels at knowledge work performed on computers-such as research, writing, calculations, and coding-but it cannot replace trial lawyers (in the United States, for example) who read jurors' reactions and deliver persuasive closing arguments, or managers who handle sensitive layoffs. Such roles require judgment, intuition, and a sense of presence-qualities that cannot be digitized and are instead developed through extensive hands-on experience.
The problem is that the entry-level jobs that once helped cultivate these abilities-drafting documents, preparing reports and presentations, conducting analyses-are exactly the kinds of work that can now be replaced by AI. If companies stop offering these roles, the pathways to advanced expertise and to becoming future managers and leaders will be severed. Entry-level jobs have not only served to get work done, but have also prepared employees for higher-level roles.
The medical residency system takes the opposite approach. Residents are doctors in training, but they also treat real patients. From the outset, they examine patients and propose treatment plans, making actual medical decisions under supervision. Senior doctors review cases together, explain their reasoning, and gradually grant more autonomy. Crucially, learning is not a side benefit-it is the work itself.
Kinder argued that white-collar professions could adopt this approach as well. For example, in the legal field, instead of having new lawyers spend years reviewing documents and drafting contracts, they could accompany negotiations, practice courtroom arguments, and gradually take the lead on cases. Mentors would review choices and decisions together afterward. The goal would not be billable hours, but capability development. In consulting, residents could participate in client presentations from day one, observe how senior colleagues read the room and handle objections, receive coaching, and gradually take on more responsibility.
Such training comes at a cost. In medicine, society as a whole benefits from skilled doctors, so U.S. taxpayers help subsidize hospitals' training costs through Medicare. While it may not be necessary to fund white-collar training with taxes, companies that reap substantial productivity gains from AI have a responsibility to help maintain the talent pipeline on which those gains depend.
Kinder also proposed the creation of an AI workforce reinvestment fund. Companies that automate entry-level jobs would contribute to the fund, which would support residency programs across industries. This is not a penalty for innovation, but a mechanism to reinvest part of the efficiency gains into the next generation of talent. The United Kingdom already operates a "use it or lose it" system, imposing a small wage levy on large companies, which can only be reclaimed through approved training programs. Unlike basic income or retraining programs based on the premise that jobs are permanently lost, this approach maintains pathways into professional careers that young people have prepared for.
Kinder also argued that the philanthropic sector could play a role. Foundations interested in labor market changes could support standardized curricula or intermediary infrastructure that individual companies would struggle to build alone. Governments could also convert youth public service programs into residency models, placing young talent in legal aid organizations, public agencies, or nonprofit tech teams, thereby contributing to the public good while providing practical expertise and mentorship.
Kinder believes that if neither companies, philanthropies, nor governments take action, the cost will be shifted to young individuals. Graduates may have to choose between pursuing additional degrees, enrolling in expensive bootcamps, or relying on personal networks for internships. The productivity gains from AI would accrue to shareholders and senior staff, while young people would bear the cost of developing their own careers.
Kinder concluded, "If we want future leaders, we must invest in the complex, mentorship-driven processes that produce them," adding, "Policymakers, companies, and philanthropies must begin now to build the infrastructure that enables a new career ladder centered on learning and capabilities that AI cannot replace."
Whether Kinder's proposals are realistic remains to be seen, but it is clear that the shock of shrinking entry-level and junior positions due to AI is significant enough to prompt such ideas.
Kinder is a nationally recognized expert on AI, economic inequality, and the present and future of work. As a Senior Fellow at the Brookings Institution, she leads multi-year projects analyzing the impact of generative AI on work and workers, as well as policy responses. She holds a Master of Public Administration in International Development from the Harvard Kennedy School.
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