Transcription
Artificial intelligence is currently developing at enormous speed from a supporting tool to an independently acting system. While classical software has so far primarily automated repetitive tasks, so-called AI agents are increasingly emerging, which make decisions independently, plan complex workflows, analyze data, communicate with other systems, and take over hitherto irreplaceable knowledge work. AI models from Open May, Anthropic, and Google already show today that AI is no longer just taking over simple routine tasks, but is capable of performing activities that were previously reserved for academic or commercial professions. Precisely because of this, a societal debate is arising that goes beyond technical questions. If AI agents replace millions of human labor in the future, the question inevitably arises as to who will then finance the welfare state at all. Our social security system in Germany is essentially based, in simplified terms, on people working, companies paying wages, employees and employers paying social contributions, and pensions, health, long-term care, and unemployment insurance being financed from these contributions. This model has worked for decades because human labor was the central factor of production in economies. But precisely this fundamental assumption could be called into question by AI agents, because if companies employ significantly fewer people in the future, but at the same time become more productive through AI, large parts of the previous revenues of the welfare state could collapse. The economic motivation behind the use of AI agents is obvious. A human employee not only incurs pure wage costs, but also additional social contributions, holiday entitlements, sick days, office space, hardware costs, insurance, and numerous other ancillary costs. Even at a low minimum wage level, total costs for companies quickly amount to several thousand euros per month. For example, for a customer service employee with about 160 working hours per month, total costs can amount to approximately €4,000. In contrast, AI agents have operating costs that are often much lower. Today's AI systems work on a token basis. Depending on the provider and model, the costs per request and response range from fractions of a cent to a few cents. Even powerful agentic systems only incur a few hundred euros in monthly operating costs in many scenarios. In addition, there is a crucial difference. AI agents theoretically work around the clock. They do not need breaks, no holidays, no sick days, and they are not unionized. A single AI agent can process hundreds of customer inquiries simultaneously, which would be impossible for human employees. Precisely because of this, there is high economic pressure in numerous professional fields. Particularly repetitive or standardized activities are considered particularly at risk. These include, for example, case processing, customer service, call centers, research, translations, administrative tasks, or simple programming work. The temptation is therefore great to replace support departments partly or entirely with AI agents. Small law firms could automate certain legal routine tasks in the future, marketing agencies could have standardized content generated by AI, and software companies could also automate simple development tasks. From a purely business perspective, many companies therefore ask themselves why they should pay several thousand euros per month for human labor when an AI agent can perform similar tasks for a fraction of the cost. It is precisely at this point that the idea of mandatory social security contributions for AI agents or a labor tax for AI comes in. This is not about an AI agent itself being insured for health or receiving a pension. Rather, the idea is that companies that replace human labor with AI must provide financial compensation to at least partially offset the loss of social contributions. Because if, for example, 100 support employees, 50 case workers, and 10 developer positions are replaced by AI in a company, potentially millions in annual social contributions and indirectly also consumer taxes could be lost because purchasing power could be lost. The welfare state would thus be under considerable pressure in the long term. This idea is strongly reminiscent of a proposal by Bill Gates from 2017. Gates argued at the time that the work of robots should be taxed similarly to human labor. If a machine replaces an employee, at least a portion of the saved social contributions should continue to flow back into the social system. This concept can also be applied to AI agents. Computing power and tokens could, in the long term, take on a similar role to human working hours in industrial society. Computers would thus increasingly become productive labor themselves. A lack of AI social contributions or regulation could have significant societal consequences. These include, for example, massive staff reductions, because AI systems could be significantly cheaper than human employees in many areas. Declining wages, less employment, falling social contributions, and growing economic inequality could be the long-term consequences of this development. Especially in aging societies like Germany, whose social security system is already heavily burdened, this could cause significant problems. However, AI agents are not humans. They have no consciousness, do not age, do not get sick, and need neither a pension nor health insurance. Social security systems historically arose primarily to cover human risks, not to tax productivity. From this perspective, the demand for social contributions for AI appears as questionable as mandatory pension insurance for excavators, assembly lines, or tractors. In fact, machines have already replaced a great deal of human labor in early industrial revolutions. A tractor can take over the work of many field workers, and an excavator replaces hundreds of hours of physical labor. Nevertheless, the machines do not pay social contributions, although other insurances naturally apply, such as liability, operational, maintenance, or machine insurance, as well as ongoing costs for energy, repairs, and maintenance. One could simply view AI agents as another stage of technological automation. In addition, there is the concern that an AI social contribution could slow down innovation. Even today, it is often criticized that Europe is lagging behind the USA and China in the field of AI. Additional taxes on AI could particularly burden startups and smaller companies. If every use of an AI system had immediate tax or social security implications, companies could reduce investments, delay innovations, or increasingly relocate development abroad. Young companies in particular would be particularly affected, as they often rely heavily on automation. Through mandatory social security contributions for AI, Europe could fall further behind technologically due to excessive regulation. Another problem is a clear legal definition. What exactly is an AI agent? Is a simple chatbot already an agent, or is it an autonomously acting system? What about classical machine learning models, large language model-based workflows, or Excel macros? The boundaries between classical software and AI are very blurred. Today's software systems often consist of hybrid components, where classical automation and AI merge. A clear regulatory separation would therefore be technically and legally extremely difficult. Furthermore, it is often underestimated that AI agents are by no means free. While the pure API costs often seem low, in practice numerous indirect costs and technical debts arise. Hallucinations, incorrect decisions, security problems, data protection risks, human monitoring, quality control, and maintenance sometimes cause considerable additional effort. Agentic systems in particular can quickly become very complex and unmanageable. Current research even shows that the token consumption of agent systems is partly difficult to predict and autonomous AI agents can cause surprisingly high operating costs in certain scenarios. Nevertheless, in many cases, a significant cost advantage over human labor remains. So far, hardly anyone is seriously demanding that social security contributions be paid for AI agents. However, an automation tax or AI labor tax, where providers, operators, or companies that replace human labor on a large scale with AI pay, would be conceivable. Another approach would be a token tax, i.e., a small levy per token quantity used, comparable to a CO2 tax. The basic idea here is that computing power will, in the long term, become new productive resources for modern economies, and companies therefore have to give back a portion of their AI-based productivity to society. However, many technical and political questions remain open. How can it be enforced internationally? How should open-source models or locally operated AI systems be handled? How can workarounds be prevented? And how high could such a levy even be without endangering innovation and competitiveness? Ultimately, the actual core question may not be whether AI agents should be subject to social security contributions. The more crucial question might be how a welfare state is to be financed at all if human labor is no longer the central factor of production in the long term. However, our system is based almost entirely on this to this day. If AI were to take over large parts of productive work, it could trigger one of the most profound societal upheavals since the industrial revolution. In the end, this debate inevitably leads to some satirically appearing thought experiments. Will an AI agent eventually need a 40-hour week? Will there be a minimum wage per million tokens in the future? Will AI unions emerge at some point? Does an agent report sick when the GPU overheats? Will context overflow eventually be considered digital burnout? And after how many tokens of runtime does an AI agent have a claim to well-deserved retirement? Even if these questions sound like science fiction and should be taken with a wink, probably not many would have believed a few years ago that autonomous AI agents could one day seriously replace large parts of human knowledge work. M.