Future Proof Part 2: Blue Collar Workers

The Blue-Collar Worker and AI

The ongoing integration of artificial intelligence and robotics into the labor market is increasingly affecting blue-collar occupations, i.e. jobs that traditionally involve manual labor. While some industries are already seeing significant progress, other areas are still facing challenges that make full automation difficult.

A key issue in this context is the shortage of skilled workers, which poses significant problems for many industries. Demographic change is leading to an aging society, with fewer and fewer young people entering the labor market. At the same time, quality and efficiency requirements are increasing, which in turn increases the demand for qualified workers. In this context, the use of AI and robotics offers a promising solution to close the gap between supply and demand. The necessity of using AI and robotics in care arises from the fact that without these technologies, essential tasks in the care of older people can no longer be adequately fulfilled. The growing shortage of nursing staff and the increasing number of people in need of care make the use of robotics and AI almost indispensable to ensure high-quality care and to relieve the burden on nursing staff.

In the distant future, the approach of longevity or “longevity escape velocity” (longevity escape velocity (LEV) is a concept from life extension research that refers to the point at which medical advances are progressing so rapidly that a person's life expectancy increases by more than a year per year. This means that every medical innovation increases the remaining lifetime faster than biological aging progresses, so that theoretically an unlimited life would be possible.) a veritable instrument to meet the demographic challenge in a futuristic way by - to put it casually - rejuvenating people and making aging controllable, but these are theoretical developments whose research offers hope but is by no means certain.

The use of AI-supported doctors and diagnostic systems could therefore currently provide people in underdeveloped countries with access to excellent medical treatment. These technologies make it possible to bring medical knowledge and expertise to regions that have been underserved, thus reducing health inequalities. By automating diagnostic processes and providing telemedicine services, patients in remote areas can be treated more efficiently and effectively. Just imagine: even today, it is possible to use small language models to bring locally running SLMs to even the most remote regions, which provide adequate answers to simple to moderately difficult medical questions, including the analysis of blood values. In difficult cases, LLM's can provide significantly better expertise, which already outperforms human experts. 

This will lead to a drastic increase in life expectancy in nations with already high birth rates. And this in turn will further the imbalance between Western nations, which have hardly any offspring, and so-called developing countries, which are experiencing a baby boom. Without wanting to make an ethical or moral judgment, AI will, however, lead to a situation in which, in African and Southeast Asian countries in particular, where there is already high unemployment, even more people will stay in the labor market for longer and these nations will be even more exposed to the pressure of competition in the market.

And the developing field of robotics is likely to intensify this trend on a global scale. While robotics is a necessary solution to the shortage of skilled workers in Western nations, it will lead to even more competition in the labor market in developing nations.

In the automotive industry, for example, robots have been an integral part of production lines for decades. However, we are currently seeing a new generation of robots that can act much more flexibly and adaptably thanks to AI. One example is the humanoid robot “Figure 02”, which is used at the BMW plant in Spartanburg. This robot supports employees in physically demanding and repetitive tasks, which not only increases efficiency but also improves working conditions for employees. 

Tesla's humanoid robot, known as “Optimus,” was first introduced in 2021 and has since made significant progress. Optimus is currently being used in Tesla's manufacturing facilities to perform simple and repetitive tasks, such as sorting battery cells. This use serves both to increase efficiency and to relieve human workers.

Elon Musk has announced that Optimus will be used on a larger scale in Tesla production from 2025. From 2026, Tesla plans to make the robot available to other companies for use in various industries such as logistics, manufacturing and the service sector. 

In the long term, Tesla envisions a wide range of applications for Optimus that go beyond industrial tasks. The robot could assist in households with everyday tasks, such as shopping, gardening or even childcare. Elon Musk emphasizes that Optimus has the potential to fundamentally change the world of work and contribute to a “future of abundance.” For 2026, Musk promises the production of 1000 Tesla Optimus per month - in exponentially increasing numbers.

Despite this progress, the widespread use of AI in the automotive industry is still far from complete. A 2019 study by the Capgemini Research Institute found that only 12% of German automotive companies use AI extensively, while the US (25%) and the UK (14%) are ahead. However, the trend is clearly rising.

Another example of the successful use of AI and robotics can be found in the logistics industry. Companies like Hermes are using the robotic dog “Spot” to relieve human employees in the logistics center. “Spot” can work around the clock and take on tasks that are difficult or dangerous for humans, such as monitoring the supply system or detecting gas leaks. Such technologies help to increase efficiency while also improving employee safety.

Tesla's planned Cybercab, a fully autonomous two-seater vehicle without a steering wheel or pedals, could in turn significantly change the taxi market. With a target price of under $30,000 and low operating costs per kilometer, Tesla aims to appeal to both private individuals and fleet operators. The introduction of such robotaxis could reduce operating costs and increase efficiency, putting pressure on traditional taxi companies to adapt their business models. However, regulatory and technological challenges still stand in the way of implementation, and it remains to be seen how quickly and comprehensively this innovation will penetrate the market. What can be stated unequivocally, however, is that self-driving cars will not only have a significant impact on private transport, but will also displace taxi drivers in particular.

Nevertheless, the automation of blue-collar jobs is often more complex than that of white-collar jobs. Many manual tasks require fine motor skills, situational adaptability and interpersonal interaction – abilities that are difficult for machines to imitate. A striking example is the care of the elderly. In view of demographic change and the associated shortage of skilled workers, the use of robotics and AI in care is being discussed. Robots can assist with routine tasks such as cleaning rooms, transporting objects or preparing meals, thus relieving the burden on care workers. In addition, AI-based assistance systems can remind older people to take their medication or provide support with everyday tasks.

Furthermore, such technologies have their limitations when it comes to emotional support and human contact, aspects that we have already discussed in more detail above, but which are essential in care. Although there are developments such as the robot “Paro”, which is used therapeutically in the form of a seal, such devices cannot replace human interaction.

As research and development progresses, other blue-collar areas could benefit from AI and robotics in the near future. In logistics, for example, AI-supported robots are already being used to pick products independently, thus increasing efficiency in warehouses. 

Despite significant advances in robotics and artificial intelligence, there are still fields of work in which machines are far from full automation. This is mainly due to the challenges of fine motor skills, situational adaptability and interaction with unstructured environments. Professions in which precise manual work, tactile feedback and creative problem solving play a central role are particularly affected. Here a few examples:

1. Surgery and medical procedures
   In modern medicine, robots such as the DaVinci surgical robot are already an established part of minimally invasive surgery. These systems enable more precise cuts and minimize tremors, but they are not autonomous – they act as tools under the control of an experienced surgeon. Highly complex procedures that require a high degree of tactile feedback and situational judgment, such as emergency surgery or organ transplants, still present an insurmountable hurdle for AI-based systems. The challenges lie in the ability to adapt spontaneously to unexpected complications, to manipulate soft tissue flexibly and to guide surgical instruments with a sensitivity that combines human senses and experience.
   

2. Goldsmithing and watchmaking
   Professions such as goldsmithing and watchmaking require an extraordinary degree of precision and skill. Working with precious metals or assembling delicate watch mechanisms requires a fine motor sensitivity that current robotic systems can only replicate with difficulty. Although there are machines that automate individual production steps, the interplay of dexterity, visual feedback and creative design remains firmly in the hands of humans.
   

3. Orthopedic technology and prosthesis construction
   Fitting prostheses or orthopedic aids requires a combination of biomechanical knowledge, manual dexterity and empathy for the individual needs of the patient. While 3D printing and machine milling are now taking over parts of the manufacturing process, the final fitting by a specialized orthopedic technician remains essential. A robot is not yet able to provide the haptic impression, the sense of pressure distribution on the skin and the communication with the patient for optimal adjustment.
   

4. Construction and restoration
   The construction sector is increasingly benefiting from robotics – with autonomous construction machines, bricklaying robots and 3D printers for concrete walls. Nevertheless, there are numerous activities that cannot be easily automated. Restoration work on historical buildings, technical adjustments to complex constructions or the processing of a wide variety of materials under varying conditions require human expertise. In these cases, sensory feedback, intuitive corrections and dealing with unexpected challenges play a key role.

The main reason why robotics cannot be used trivially in these occupations is sensory integration. Humans combine haptic feedback with visual and auditory information to perform highly precise movements. Machines, on the other hand, require elaborate sensor technology to achieve even rudimentary levels of similar sensitivity. 

However, it must be emphasized at this point that the analysis only represents a snapshot. At present, it is difficult for robots to take on the above-mentioned tasks and occupations. However, methods such as Isaac-Sim from NVIDIA make it possible to train and continuously improve humanoid robots in a variety of situations. Problems that humanoid robots face today can be solved in the near future using synthetic training data.

Conclusion

Humanoid robots are already being used in production. Jensen Huang, CEO of NVIDIA, recently said that the ChatGPT moment of robotics is imminent. Insiders estimate that in 3-5 years, humanoid robots will be so advanced that they can easily be used at home for housework. This will make a human dream come true. However, today's humanoid robots are still limited and many blue-collar jobs can still only be done by humans. This is especially true in areas where fine motor skills are required. But here, too, it must be emphasized that the extrapolation into the future extends only a few years into the future. Whether humanoid robotics with the help of AI will not have replaced a large proportion of human blue-collar workers in just five years is simply impossible to calculate. However, in the following, we will take a look at the current state of research and focus on the empirical evidence.

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