In a few years, AI will destroy the entire system of the global market economy

The idea that artificial intelligence (AI) could destroy the entire market economy system is a profound and complex hypothesis to consider. In such a scenario, several key elements and events could occur that would impact the fundamentals of the economy.

The hypothesis that artificial intelligence could destroy the entire market economy system can be analyzed by examining in detail its possible impact on various economic and social structures.

The possible destruction of the market economic system by artificial intelligence can be described as a multi-phase process affecting various aspects of economic and social life:

Phase 1: Rise of automation and job losses

Phase 1: Rise of automation and job losses

Job automation: AI could lead to a radical change in the employment landscape. With advanced algorithms and automation, many jobs, from manufacturing to services, could become obsolete. This would initially lead to massive unemployment as human labor would no longer be competitive in many areas. AI systems, particularly machine learning and robotics, have already led to significant job losses in industries such as automotive and electronics manufacturing. One example is the introduction of AI-controlled robots in automobile factories, capable of assembling vehicles with greater precision and efficiency than human workers. According to a 2013 Oxford University study, up to 47% of U.S. jobs could be automatable in the next two decades.

  • Early stage: Simple, repetitive tasks are automated, as is the case with assembly line work in the manufacturing industry. AI-driven systems are beginning to replace human workers in areas such as data entry and customer support.
  • Middle stage: More advanced AI systems are taking on more complex tasks, such as analyzing financial markets, leading to a decline in demand for financial analysts.
  • Late stage: Almost complete automation of complex and creative jobs. AI is surpassing human capabilities in areas such as medical diagnostics, leading to a redefinition of expert knowledge.
Initial automation and job losses
  • Initial integration: AI systems are initially used for simple tasks. Examples include robots in manufacturing or chatbots in customer service.
  • Expansion to include skilled workers: AI is gradually replacing qualified activities, such as accounting, with automated software.
  • Replacing expert knowledge: Advanced AI systems take on complex tasks that were previously reserved for experts, e.g. diagnoses in healthcare using AI like IBM's Watson.
  • Impact on the labor market: Simple, repetitive tasks are increasingly being taken over by AI systems, resulting in a shift in demand for human labor. The first areas affected are typically manufacturing industries and simple services.
  • Worker adaptation: Workers need to retrain and upskill in areas that require more complex, creative or interpersonal skills.
  • Economic dynamism: Companies that quickly invest in AI technologies can reduce costs and increase their competitiveness, leading to a redistribution of market shares.

Phase 2: Capital concentration and market dominance

Phase 2: Capital concentration and market dominance

Concentration of capital: In a market economy system increasingly dominated by AI, companies that use AI efficiently could gain a disproportionate share of the market. This could lead to increased monopoly formation and even greater concentration of capital among those who own and control the AI ​​technology. Companies like Amazon and Google are already demonstrating how the use of AI can lead to enormous market power. These companies are investing heavily in AI to analyze consumer behavior and optimize their services, displacing smaller competitors. The report "The Future of Employment: How susceptible are jobs to computerization?" supports this thesis and shows that technology leaders could receive a disproportionate share of the economic pie.

  • Early stage: Large companies are investing heavily in AI technologies, leading to competitive advantages and marginalizing smaller competitors.
  • Middle stage: Oligopolies emerge in key industries where a few dominant players control the market, empowered by proprietary AI technologies.
  • Late stage: Extreme concentration of capital among tech giants capable of influencing entire markets, leading to destabilization of the free market economy.

Market concentration and control

Beginning of market consolidation: Large companies that invest in AI technologies are displacing smaller competitors.

Emergence of oligopolies: A few companies dominate entire industries through their advanced AI systems.

Monopolization and market power: These companies exert enormous influence on prices and market dynamics, which disadvantages smaller players and consumers.

Strategic business decisions: AI systems can analyze large amounts of data to identify trends and support business decisions, resulting in optimization of operations.

Personalized services: In marketing and sales, AI enables a highly personalized customer approach, which can increase customer loyalty and sales figures.

Barriers to entry: The need for advanced AI for competitive analysis can lead to higher barriers to entry and thus influence market dynamics.

Phase 3: Devaluation of human knowledge

Phase 3: Devaluation of human knowledge

Devaluation of human knowledge: Human knowledge and skills could be devalued as AI is able to learn faster, adapt, and perform tasks with greater precision. Education and experience, the cornerstones of individual progress in a market economy, may become less important. AI-driven systems, such as IBM's Watson, have demonstrated capabilities that can surpass human knowledge in specific areas such as diagnosis in medicine. This calls into question the relevance of decades of experience and expertise. A McKinsey Global Institute report estimates that about 800 million global workers could be replaced by automation by 2030.

  • Early stage: AI is beginning to outperform human performance in specific niche areas, such as games of chess or Go.
  • Middle stage: AI systems like IBM Watson demonstrate superiority in multiple intellectual areas, challenging traditional expertise.
  • Late stage: AI systems make contributions to science and other knowledge domains, reducing the need for human expertise and challenging education systems.

Declassification of human intelligence

Initial superiority in niche areas: AI beats humans in specialized games like chess and Go.

Taking over intellectual pursuits: AI is beginning to surpass human performance in various academic and professional fields.

Displacement of human expertise: AI contributes to research and development, changing the role of human researchers and influencing the educational landscape.

Phase 4: Change in consumption dynamics

Phase 4: Change in consumption dynamics

Change in consumption dynamics: With falling incomes and rising unemployment, the demand for goods and services would change. This could lead to deflation as overproduction, driven by efficient AI systems, meets a shrunken market with low purchasing power. In an AI-dominated economy, demand for luxury goods could increase while demand for basic goods stagnates as AI reduces production costs. This could lead to a shift in economic growth from a quantitative to a qualitative model, as indicated by the increasing preference for personalized and high-quality products in mature markets.

  • Early stage: AI enables personalized marketing strategies and is beginning to influence consumer behavior.
  • Middle stage: Production becomes so efficient through AI and automation that the cost of many goods falls, changing consumer preferences.
  • Late stage: A shift towards qualitative growth and luxury goods while the value of standard goods declines.

Change in consumption habits

Personalized marketing: AI uses big data to analyze individual buyer behavior and tailor marketing strategies accordingly.

Price deflation due to overproduction: Production efficiency increased by AI reduces costs, changes perceptions of value and could lead to overproduction.

Shift to luxury goods: As standard products become cheap and ubiquitous, luxury goods and personalized services could become more important.

Changing consumer needs: The availability of new products can change consumer needs and thus influence the direction of market segments.

Phase 5: Social and ethical challenges

Phase 5: Social and ethical challenges

Social and ethical challenges: Social structures may be under pressure to redefine themselves. Issues such as universal basic income or new forms of social protection could come to the fore to address the economic disparities caused by AI. The introduction of a universal basic income as a response to unemployment caused by AI is already the subject of serious discussion. The experiment in Finland testing a universal basic income could be a precursor for future social policy initiatives.

  • Early stage: Initial calls for a universal basic income arise in response to unemployment caused by AI.
  • Middle stage: Experiments with universal basic income are taking place, like in Finland, with mixed results.
  • Late stage: A new social safety net may be required to ease the social tensions caused by technological unemployment.

Social disruption and ethical dilemmas

The beginning of the social challenges: Unemployment is rising and calls for a universal basic income are getting louder.

Experiments with social safety nets: Some countries are testing new social systems such as universal basic income to compensate for job losses.

Establishment of new social models: New models for social protection and income distribution may be required to compensate for the unemployment caused by AI.

Social impact: The role of people in many professions could fundamentally change, which could lead to social tensions and the need for adjustments in the education system.

Independent operations: Some companies may begin to deploy autonomous AI systems that can operate without human intervention, further increasing efficiency.

Legal and ethical issues: This raises questions about accountability and control over AI systems, which must lead to new laws and ethical guidelines.

Loss of traditional jobs: Widespread automation could lead to significant job losses, forcing labor market restructuring.

Social protection systems: There is a need for new forms of social protection as traditional models could collapse under the pressure of automation.

Adjustment of consumer behavior: Consumption could shift as fewer people have regular income, leading to a reassessment of the value of work and leisure.

Phase 6: Change in corporate governance

Phase 6: Change in corporate governance

Changing corporate governance: Companies could become autonomous entities controlled by AI systems, with decisions made based on data and algorithms. This would represent a new form of economic management, potentially eliminating the need for human intuition and decision-making. AI-driven decision-making systems could reduce the need for human management. Algorithms could make decisions based on real-time data and predictive models, which in some cases may be more effective than human intuition, as is the case with algorithmic trading in financial markets.

  • Early stage: Companies are starting to use AI for data-driven decisions.
  • Middle stage: AI systems are introduced into boardrooms, resulting in a data-driven and less intuitive leadership culture.
  • Late stage: Business management could be largely automated, with AI systems taking over strategic decisions.

Transformation of corporate governance

Data-driven business decisions: Companies use AI to optimize strategic decisions.

Integration into company management: AI systems are given advisory roles on boards and influence company management.

Automation of the management level: In some scenarios, AI systems could also replace decision-makers and make strategic decisions autonomously.

New forms of companies: There could be a rise in decentralized companies controlled by AI systems, leading to a change in the corporate landscape.

Shifting power structures: Large technology companies that master AI could become even more powerful, which could lead to an imbalance in the economic order.

Democratization of technologies: On the other hand, AI could also lead to a democratization of technologies as open source projects and collaborative development models become more important.

Adjustment of the legal framework

Phase 7: Adjustment of the legal framework

Adjusting the legal framework: Laws and regulations would need to be revised to address the new realities of an AI-driven economy. This could include defining AI as legal entities or introducing new forms of taxation and regulation. The European Union has already introduced the General Data Protection Regulation (GDPR), which serves as a framework to control the collection and use of data by AI and other technologies. Such regulations will likely need to evolve to keep pace with advances in AI technology.

  • Early stage: Initial laws and regulations, such as GDPR, are being introduced to govern the use of AI and data-driven technologies.
  • Middle stage: Need to develop new laws to deal with the ethical and economic challenges of AI.
  • Late stage: International agreements may be required to set global standards for the use of AI.

Legal and regulatory adjustments

First waves of regulation: Laws such as the EU General Data Protection Regulation (GDPR) are beginning to regulate the use of AI.

Development of specific AI laws: As AI becomes more widespread, new legal frameworks must be created.

International AI agreements: Ultimately, global agreements may be necessary to standardize the use of AI worldwide.

Changing ownership structures: The importance of physical ownership may diminish as data and algorithms become the primary forms of capital.

Power of data: Access to data and the ability to use it could become the decisive factor for economic success.

New forms of value creation: Companies could generate revenue by licensing algorithms and AI services, leading to new business models.

Phase 8: Reshaping the global economic order

Phase 8: Reshaping the global economic order

Global Impact: The global balance of power could shift as nations with advanced AI technology may dominate, while less developed countries could fall even further behind. Stanford University's "AI Index 2019" shows that countries such as the USA and China are leaders in AI research and application. This could lead to a bifurcation of the global economy, with AI-leading nations expanding their economic and political influence while others are left behind.

  • Adjusting international trade rules: The role of AI in manufacturing and services is leading to a renegotiation of trade agreements as comparative advantages need to be redefined.
  • Global competition for AI dominance: Nations compete for dominance in AI development, which could lead to a technological arms race similar to the space race of the 20th century.
  • Emergence of an AI-led economic philosophy: A new economic theory could emerge that takes into account the role of AI in all aspects of economic activity and challenges the classical understanding of supply and demand and labor market theory.
  • Transforming monetary and financial systems: AI could accelerate the need to develop new forms of currencies and financial instruments that are better adapted to a highly automated, AI-integrated economy. This could also lead to greater adoption of cryptocurrencies and a redefinition of the role of central banks.
  • New forms of social security: As traditional jobs continue to disappear, entirely new systems of social security could emerge based on a different distribution of wealth generated by AI-driven economies.

These points illustrate that the impact of AI on the market economy is profound and complex. The complete collapse of the system is not inevitable, but significant transformation seems inevitable. The challenge is to control AI developments so that they contribute to the well-being of society and do not lead to its destabilization.

It is important to emphasize that although this scenario is theoretically possible, it also involves many uncertainties. Actual development depends on numerous factors, including political decisions, ethical considerations and the adaptability of human society. AI has the potential to increase efficiency and productivity, which could theoretically lead to higher living standards if the benefits are distributed fairly. The challenge is to design and regulate technology so that it benefits society as a whole rather than dividing it.