We Need a New Kind of Insurance for AI Job Loss
AI is poised to leave a lot of us unemployed. We need to rethink social welfare.
Rapid advances in artificial intelligence promise to reshape industries, redefine job roles, and fundamentally alter the landscape of the global economy. No longer confined to manufacturing, AI-driven tools are encroaching on white-collar professions once thought immune to automation — law, accounting, marketing, finance, design, insurance, human resources, and even software engineering.
Yet, the United States’ social insurance framework remains outdated and ill-equipped to handle this disruption. Workers, their families, and entire communities stand on the edge of an economic cliff, vulnerable to sudden AI-driven job displacement. Rather than scramble to respond after mass layoffs occur, policymakers must begin modernizing social insurance now. The Social Security Act of 1935 was designed for a different era—one that bears little resemblance to the economic realities of 2024.
Even if AI ultimately spurs job creation, AI’s rapid acceleration risks leaving displaced workers in a prolonged period of unemployment before new opportunities emerge—if they emerge at all.
History offers many examples of disruptive technology reshaping the labor market faster than regulators could respond. The spinning jenny, for instance, devastated Britain’s weaving workforce within a generation. However, researchers today hold widely differing opinions on whether AI will displace jobs at a similarly rapid pace and our ability to adapt. Some at MIT anticipate gradual displacement, while Goldman Sachs predicts that 300 million jobs globally will be lost or substantially degraded by 2030. There are some like Elon Musk who believe AI will eventually render all jobs unnecessary. On the other hand, economist David Autor cites historical precedent to argue that AI will actually lead to significant new job creation, noting that 60% of today’s jobs emerged after the mid-20th century.
Despite this range of perspectives, it would be imprudent to gamble on optimistic forecasts. Unlike previous waves of automation, which primarily replaced physical labor, AI’s ability to replicate cognitive and decision-making tasks means it threatens an unprecedented share of the economy. Even if AI ultimately spurs job creation, AI’s rapid acceleration risks leaving displaced workers in a prolonged period of unemployment before new opportunities emerge—if they emerge at all.
Economic incentives overwhelmingly favor automation. AI systems do not demand wages, benefits, or time off. This will create a self-reinforcing cycle: as businesses automate to stay competitive, demand for human labor will shrink, causing the labor force to decline – driving further AI adoption.
Left unchecked, this cycle will deepen economic inequality, weaken consumer purchasing power, and destabilize entire industries. Policymakers can’t afford to bet that the labor market will naturally correct itself. Even in a best-case scenario, the transition will be turbulent in the short term, requiring a robust safety net to prevent widespread economic hardship. AI Displacement Insurance (ADI) would prevent disruption from escalating into a full-blown crisis by ensuring that workers have financial stability and retraining support.
This “displacement time bomb” may detonate even sooner than the demographic one, eroding the financial foundations of social safety nets at an unprecedented pace.
The basic mechanics of social insurance involve the pooling of resources through contributions from workers, employers, or both, into a common fund. This fund is then used to provide benefits to individuals who qualify under certain conditions, typically during periods when they are unable to work.
Under the current system, individuals who lose their jobs can apply to the government for benefits that replace a portion of their lost income. This helps them meet basic living expenses while they search for new employment. The funding structure of social insurance programs typically requires participation from a large majority of the workforce in order to ensure the amount of money paid into the fund is large enough to support those drawing benefits. Social programs have varying eligibility criteria, but they generally include factors such as the reason for unemployment, the length of time an individual has been employed, and the amount of contributions they have made to the fund. Benefits are usually time-limited, providing support for a specific period, such as six months to a year, depending on the program and the individual’s circumstances. Despite their importance, social insurance programs often suffer from financial shortages.
Current social insurance programs are inadequate in several ways. First, there are participation gaps. These programs depend on broad participation to remain solvent, yet many workers — especially freelancers and gig laborers — neither pay in nor can expect to receive benefits. What's more, while employers' contributions to some programs increase with layoffs, there are often caps on these increases. This means that companies with high layoff rates may not bear the full cost of their impact on the system, contributing to a precarious balance between the program's income and expenditures. AI could accelerate these stressors on social insurance – for instance, by driving more people into the gig economy and shrinking the base of contributors.
Beyond participation gaps, social insurance systems face deeper, systemic risks. Aging populations in developed nations are tilting the balance between contributors and beneficiaries, placing immense pressure on programs like Social Security and Medicare. Alongside this demographic shift, AI presents a parallel disruption: as automation displaces workers, it could dramatically reduce the number of people paying into these programs. This “displacement time bomb” may detonate even sooner than the demographic one, eroding the financial foundations of social safety nets at an unprecedented pace.
Unlike traditional job losses that allow for sector-based rebounds, AI-driven unemployment presents unique challenges to existing social insurance systems. If AI automation renders entire industries obsolete, displaced workers won’t be able to simply wait for market conditions to improve or transition into similar roles elsewhere. Instead, they would require extensive retraining to shift into entirely new fields, a process that demands far greater financial and structural support than current unemployment programs are designed to provide. This instability threatens the steady flow of contributions that social insurance programs rely upon, making their long-term viability even more uncertain.
Economic downturns further complicate matters. During recessions, demand for benefits surges as unemployment rises, yet contributions to these programs simultaneously shrink. This can quickly deplete the reserves meant to buffer these fluctuations, particularly if they weren’t sufficiently built up during periods of economic growth. AI-accelerated automation could make these cycles even more volatile, leading to sharper job losses in downturns and a slower recovery of the workforce. Companies may find it easier to shed jobs during recessions and less urgent to rehire once conditions improve, extending periods of economic imbalance and placing additional pressure on social insurance funds.
Finally, current social insurance programs can be ineffectual due to significant administrative hurdles. Cumbersome eligibility determinations, slow processing times, and inefficient benefit distribution erode public trust in the system. While AI holds potential for streamlining these administrative hurdles, its broader economic impact could overwhelm existing infrastructures, flooding them with claims and creating increasingly complex eligibility cases. AI may push these programs toward a breaking point, and demand that we rethink the fundamentals of how they function in an economy increasingly shaped by automation.
The unique risks posed by pace, scale, and nature of AI-driven job displacement merit the creation of a new form of social insurance, referred to here as AI Displacement Insurance (AIDI). This program would be specifically designed to support workers whose jobs are displaced by AI technologies, offering them financial assistance and resources to transition into new roles in the evolving economy.
In order to avoid the shortcomings of traditional social insurance programs, the design of AIDI should focus on three key areas: increasing participation, tailoring benefits to recipients’ economic status, and implementing novel funding mechanisms to enhance the program’s financial resilience.
AIDI should be designed to include all workers, regardless of their employment status or industry. This would require mandatory participation for both full-time employees and independent contractors, including gig workers and freelancers.
To ensure compliance, contributions to the AIDI fund would be integrated into existing payroll systems for employees, while independent contractors would contribute through quarterly tax payments. The program could also offer incentives, such as tax deductions or government-matched contributions, for voluntary contributions from workers who are not required to participate, such as those working in non-traditional roles.
AIDI should also recognize that the impact of job displacement can vary widely depending on an individual’s circumstances and offer benefits that are tailored to a recipient’s economic needs and status.
The program could offer a tiered benefits structure, where the amount and duration of benefits are based on factors such as the recipient’s previous earnings, their likelihood of finding new employment, and the level of retraining required.
In addition to financial benefits, AIDI could also provide career counseling, job placement services, educational opportunities, and other resources. These services would help recipients transition into new roles more effectively, reducing the likelihood of long-term unemployment and ensuring that they remain productive members of the workforce.
To enhance the financial resilience of the AIDI program, policymakers should consider implementing novel funding mechanisms that align with the unique challenges posed by AI.
One such mechanism is AI-Usage-Based Contributions, which would require that companies with high rates of AI adoption contribute more to the insurance fund.
This would align the costs of the insurance program with the benefits of AI adoption, creating a more equitable distribution of financial responsibility. Companies that automate large portions of their workforce would contribute more, helping to offset the displacement caused by their actions. It would also serve as a deterrent to companies considering excessive automation without regard for the social impact, encouraging them to adopt AI in a more balanced and responsible manner.
However, it could discourage innovation and the adoption of AI technologies, particularly among smaller businesses that may already face financial constraints. If the costs of contributing to the insurance fund are too high, companies might delay or avoid implementing AI, potentially stifling economic growth and innovation.
/odw-inline-subscribe-cta
Another innovative funding mechanism could involve the use of Income Sharing Agreements (ISAs) for recipients of AIDI who go on to earn high incomes within five years of receiving benefits. For example, beneficiaries who earn more than $400,000 annually would be required to allocate a portion of their income back to the insurance fund. This would create a self-replenishing system where those who benefit from the program during their transition contribute to its sustainability once they are financially stable.
The primary advantage of this approach is that it helps ensure the long-term financial viability of the program without relying solely on contributions from employers or taxpayers. It also promotes a sense of reciprocity and social responsibility among beneficiaries, who are asked to contribute back to the system that supported them during a difficult period.
However, implementing ISAs could present challenges, such as ensuring compliance and accurately tracking the income of beneficiaries. There may also be concerns about the fairness of requiring high earners to contribute a portion of their income, particularly if they feel that their success is due to their efforts rather than the support they received. To address these concerns, the program could include caps on the total amount that beneficiaries are required to repay.
As AI’s applications continue to grow, the risk of job displacement will likely increase, particularly in fields that were previously considered stable and secure. AIDI is a forward-thinking approach to social insurance specifically designed to address the unique risks posed by AI. By increasing participation, tailoring benefits to the needs of recipients, and implementing innovative funding mechanisms, this program can provide a robust safety net for workers facing displacement while ensuring the long-term sustainability of the fund.
As we consider the future of work in an AI-dominated economy, our society should be discussing AIDI and along with even more radical solutions. For instance, if AI significantly reduces the need for human labor across multiple sectors, Universal Basic Income would provide a baseline level of financial security for all citizens. While considered radical compared to traditional social insurance models, in a world where traditional employment becomes increasingly scarce, it could help maintain economic stability and social cohesion.
Before we adopt any new social insurance program, we need to address several critical questions. How can we balance the drive for innovation with the social costs of AI adoption? What regulatory frameworks will ensure that AI benefits society as a whole? How can we design social insurance programs that adapt to a rapidly evolving economic landscape? How should our conceptions of work and social value evolve as traditional employment becomes less central to our society? The dialogue on these challenges should involve policymakers, businesses, and workers, and begin immediately so we are prepared to meet our new economic reality head-on.
.png)
