The Emergence of the “Useless Class”: The Socio-Economic Challenges of an AI-Driven Future

Disclaimer: This article was inspired by and developed with the assistance of OpenAI’s ChatGPT, a state-of-the-art artificial intelligence language model. While the AI has provided valuable insights and ideas, the content has been reviewed and adapted by the author to ensure accuracy and relevance to the target audience. Any errors or omissions are the sole responsibility of the author. We would like to extend our gratitude to OpenAI for their innovative technology and its contribution to the creation of this article.

In the wake of rapid technological advancements, we’re facing a scenario where millions might become part of what’s been termed as the “useless class,” a population segment seemingly without the capacity to contribute significantly to an economy driven by artificial intelligence (AI). As AI continues its march into industries far and wide, the economic landscape is changing, creating the potential for mass unemployment and a socio-political shift.

Understanding the Phenomenon

A World Economic Forum report from 2022 revealed a startling projection: by 2025, up to 52% of all current work tasks could be automated, up from the 29% they estimated in 2018. This number is not only significant but also a possible precursor to the formation of the “useless class.”

This term refers to individuals whose skills are not just obsolete but may also find it challenging to retrain to stay relevant in an economy where AI and automation are increasingly prevalent. Indeed, as AI and robotics become more advanced, they can complete tasks once thought exclusive to humans, like driving vehicles, writing articles, analyzing complex data sets, and even diagnosing diseases.

A Looming Crisis?

In the US, the Bureau of Labor Statistics (2023) reported that industries most affected by AI and automation, such as manufacturing and transportation, make up approximately 22% of the national employment. When extrapolated, we could be looking at tens of millions of jobs potentially disappearing over the next decade.

In contrast, a McKinsey Global Institute study estimated that the adoption of AI technologies could create between 60 million to 200 million new jobs globally by 2030. However, the catch here is these new jobs demand different skills, most of which are not currently possessed by the workforce in threatened industries.

The issue is twofold: those who cannot transition into the new jobs might form the “useless class,” while those with the necessary skills could help form a new “techno-elite.”

Tackling the Challenge

To prevent this socio-economic divide, governments and communities should consider several strategies.

  1. Invest in Education and Retraining: It’s paramount to focus on building a future workforce that can participate in an AI-driven economy. Governments should invest heavily in education and retraining programs focused on skills relevant to this new job market, such as data analysis, programming, and AI ethics.
  2. Universal Basic Income (UBI): As automation may lead to job losses, governments could implement UBI, a financial safety net ensuring everyone has a minimum income level.
  3. Encourage Entrepreneurship: Entrepreneurship can help foster economic diversification, potentially creating new sectors where humans can contribute.
  4. Implement Effective Legislation: Governments must enact appropriate regulations to prevent the concentration of power due to technological capabilities. Laws should focus on encouraging competition, ensuring privacy, and preventing misuse of user data.

Expanding on the proposed strategies to tackle the rise of the “useless class”, one of the key focus areas is enhancing our ability to make data-driven investment decisions through the application of AI and data science models. Here are some practical models that can be adopted:

Predictive Analysis for Investment in Education and Retraining

AI and machine learning models can be used to perform predictive analysis, identifying future skills gaps in the economy. Governments can utilize this information to invest strategically in education and retraining programs. For instance, using Natural Language Processing (NLP), we could analyze job postings to identify the most in-demand skills.

Data-Driven Universal Basic Income

AI can also be instrumental in implementing Universal Basic Income (UBI) effectively. By analyzing socio-economic data such as employment status, income levels, cost of living, and more, a machine learning model could predict the optimum UBI that can support different demographics, thereby ensuring a more fair and just implementation.

AI in Encouraging Entrepreneurship

Governments can use AI to identify sectors with high growth potential and incentivize entrepreneurship in these areas. AI could analyze market trends, consumer behavior, and economic indicators to predict promising sectors. Similarly, a recommendation system could match prospective entrepreneurs with opportunities that best fit their skills and experiences.


AI can be used to monitor compliance with regulations and identify potential breaches. For example, machine learning models could analyze corporate data to ensure compliance with competition laws, or monitor social media platforms to safeguard privacy and prevent misuse of user data.

AI-Driven Impact Investing

Lastly, governments can use AI to drive their own investment strategies. Machine learning models can predict the societal impact and return on investment of different policies, allowing governments to invest in areas that provide the most significant societal benefit.

Techno-elites and Centralized Lobby Power

The rise of the “techno-elites” – individuals or corporations holding immense power due to their technological capabilities – poses a threat to balanced governance. Their influence could extend into policymaking, leading to an unfair advantage and an unbalanced society.

To prevent this, we need transparency and stronger regulations. Governments should demand transparency about AI algorithms and decision-making processes. New legislation could prevent these techno-elites from exercising undue influence over politicians and lawmakers. Independent oversight boards with the power to penalize wrongdoers could also help keep these entities in check.

Examples of Influence

To illustrate how the techno-elite might wield influence, consider the lobbying activities in policy-making. They could potentially use AI-powered persuasion models to shape public opinion subtly or even use advanced data analytics to identify and target key decision-makers based on their susceptibility to certain arguments or incentives.

Moreover, these entities could exploit AI’s predictive capabilities to anticipate regulatory changes and adapt their strategies accordingly, giving them an unfair advantage over competitors and the ability to shape legislation in their favor.

Algorithmic Transparency

AI-driven technologies play an increasingly significant role in our lives, from recommendation algorithms to advanced decision-making systems. To ensure fair use, governments could require organizations to make their AI algorithms transparent and understandable. This would involve sharing not only the code but also the data used for training and the specific techniques used in the model’s development.

AI systems for explainability, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive exPlanations), can be utilized to interpret the decision-making process of complex models. This facilitates greater understanding and prevents misuse of AI for undue influence.

AI-enhanced Regulatory Compliance

Governments can leverage AI to monitor compliance with regulations and detect potential violations. AI can analyze vast amounts of data from multiple sources – financial transactions, social media interactions, corporate communications – to spot irregularities indicative of lobbying or exertion of undue influence.

Independent Oversight and AI Audit

Establishing independent oversight boards with the power to conduct AI audits could provide an extra layer of defense against undue influence by techno-elites. These audits could assess the fairness, transparency, and privacy-preserving measures of AI systems, penalizing those who fall short of required standards. AI auditing tools such as IBM’s AI Fairness 360 or Google’s What-If Tool could be employed for this purpose.


As we navigate the complexity of an AI-driven future, mitigating the rise of the “useless class” and managing the influence of the techno-elite require our undivided attention. The strategies proposed here aim to provide a comprehensive response to these challenges. By investing in education and retraining, implementing effective UBI programs, and promoting entrepreneurship, we can create a resilient workforce equipped to participate in the AI-driven economy, thus avoiding the formation of a “useless class.”

On the other hand, it is equally important to manage the rise of the techno-elite. Through the enforcement of transparency in AI algorithms, the use of AI-enhanced regulatory compliance mechanisms, and the establishment of independent oversight boards, we can ensure that the power conferred by technological expertise is not misused.

The goal is not merely to mitigate the challenges posed by AI and automation but also to leverage these technologies to foster an inclusive, dynamic, and resilient society. In a world where change is the only constant, our ability to adapt and innovate will define our collective future.

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