The Old-Age and Survivors Insurance and Disability Insurance trust funds are projected to deplete their assets by 2033, as stated in the Social Security Board of Trustees' annual 2025 report. At that time, only about 77% of scheduled benefits will be payable.
The projected depletion year for the combined Social Security trust funds is 2034, at which time only 81% of the benefits will be payable. Similarly, the Hospital Insurance fund of the Medicare program is projected to be depleted as soon as 2033.
This emerging retirement insecurity is prompting many younger Americans, particularly Millennials and Gen Z, to explore alternatives beyond conventional savings, as per reports dated April 2025.
Survey reveals new data
The survey also found that 20% of respondents from Gen Z and Generation Alpha would accept their pension in whole or in part in cryptocurrency, with 78% of respondents trusting alternative retirement savings options more.
Furthermore, 60% of Gen Z and millennials plan to increase their crypto holdings, and two-thirds aim to expand their investments; over half of them already allocate retirement assets to cryptocurrencies.
With 62% of respondents intending to engage in Fidelity's crypto-oriented IRA, the future holds a closer integration of cryptocurrency in retirement strategies.
With 21% of Americans already dedicating more savings to crypto than to conventional stocks, almost half of Americans allocate a sizable amount—10% to 20%—of their retirement money to cryptocurrencies.
However, enthusiasm for cryptocurrency hasn't been matched by mainstream financial professionals and regulators, including the U.S. Department of Labor, which has warned against using cryptocurrency for retirement accounts, citing concerns about volatility, fraud, and valuation issues, according to Investopedia.
Retirement advisor Ric Edelman recently advised holding crypto of about 10% to 40% as a small part of a retirement portfolio.
Social security funds are running out, new data shows first appeared on TheStreet on Jun 18, 2025
BUT WAIT THAT'S NOT ALL!
The Old-Age and Survivors Insurance and Disability Insurance trust funds are projected to deplete their assets by 2033, as stated in the Social Security Board of Trustees' annual 2025 report. At that time, only about 77% of scheduled benefits will be payable. The projected depletion year for the combined Social Security trust funds is 2034, at which time only 81% of the benefits will be payable. Similarly, the Hospital Insurance fund of the Medicare program is projected to be depleted as soon as 2033. What is the projected robotic replacements of jobs in the USA market by 2033 and if the Old-Age and Survivors Insurance and Disability Insurance trust funds would be replaced by having employers using robots (serial number by government) pay into the trust fund using a formula that would provide necessary funding to continue the program?
- Historical Context and Estimates:
- A 2020 study by MIT researchers Acemoglu and Restrepo found that between 1990 and 2007, adding one robot per thousand workers reduced employment by about 5.6 workers in a local labor market and 3.3 workers nationwide, with a corresponding wage decline of 0.25–0.5%.
- The study noted that robots primarily impact industries like manufacturing (especially automotive, electronics, and plastics/chemicals), with less-educated, lower-skilled workers in routine manual jobs being most vulnerable.
- A Forrester report suggests that by 2032, automation could displace 11 million jobs (7% of US jobs), but new job creation in sectors like professional services, renewable energy, and smart infrastructure could offset this, reducing net job losses to about 1.5 million.
- Projections for 2033:
- No precise figure exists for 2033, but scaling from historical data and considering the accelerating adoption of robotics and AI, job displacement could range from 10–15 million jobs by 2033, assuming automation continues to target routine, repetitive tasks across manufacturing, customer service, logistics, and administrative roles.
- The Pew Research Center (2014) noted that experts are divided, with some predicting automation will displace more jobs than it creates, while others expect new roles to emerge, particularly in creative, problem-solving, or human-centric fields.
- Uncertainties:
- The Bureau of Labor Statistics (BLS) finds little evidence of dramatic job loss trends in occupations vulnerable to automation, suggesting that economic expansion and new job creation may counterbalance displacement.
- Factors like international competition, policy interventions, and workforce retraining will influence the net impact.
- Concept: A robot tax would levy contributions on employers based on robotic productivity or usage, redirecting automation’s economic gains to Social Security. Zheng Gongcheng, a Chinese social security expert, suggested taxing robotic productivity gains to complement contributions.
- Potential Formula:
- Tax Base: The tax could be based on the number of robots, their productivity (e.g., output value), or the wages of displaced workers. For instance, if one robot replaces 5.6 workers (per Acemoglu and Restrepo), the tax could approximate the lost FICA contributions (e.g., 12.4% of the average wage of displaced workers).
- Adjustments: The tax rate could scale with robot productivity or be tiered by industry (e.g., higher for manufacturing, lower for service sectors). Alternatively, a flat tax per robot or a percentage of automation-related profits could be applied.
- Revenue Potential:
- To close the OASDI’s 3.82% payroll deficit, additional revenue of ~$100–150 billion annually (escalating with inflation) would be needed by 2033, based on current projections.
- A robot tax covering millions of robots could contribute significantly, especially if automation displaces 10–15 million jobs. For example, taxing 2 million robots at $41,664 each yields $83.3 billion/year, covering over half the deficit.
- Combining a robot tax with other measures (e.g., raising the payroll tax cap above $176,100 or increasing the payroll tax rate to 7.2%) could fully address the shortfall.
- Challenges:
- Implementation: Defining “robots” (e.g., industrial robots vs. AI software) and measuring their impact on jobs is complex. Taxing manufacturers versus employers using robots raises questions about fairness and innovation incentives.
- Economic Effects: A robot tax could slow automation adoption, potentially reducing productivity gains or encouraging firms to relocate to lower-tax regions.
- Equity: Low-skilled workers face the highest displacement risk, and a robot tax could fund retraining or universal basic income to support them, enhancing social equity.
- Immigration: Increasing net immigration by 100,000 annually could improve the OASDI actuarial balance by 0.1% of payroll, as foreign-born workers contribute taxes but often claim fewer benefits.
- Tax Cap Removal: Eliminating the $176,100 payroll tax cap could significantly boost revenue, as high earners would contribute more.
- Benefit Adjustments: Reducing benefits (e.g., via means-testing) or raising the payroll tax rate could complement a robot tax but faces political resistance.
