(TO BE UPGRADED) The Productivity Paradox: Why AI-Driven Growth Requires Generative Economics

 

The Productivity Paradox: Why AI-Driven Growth Requires Generative Economics

There has been much talk about productivity capacity of Generative AI " In a recent report about the economic impact of generative AI, Google visiting fellow and MIT Sloan principal research scientist Andrew McAfee makes the case that generative AI is not only a game-changing general-purpose technology but could also spur change far more quickly than preceding innovations due to its accessibility and ease of diffusion" {https://mitsloan.mit.edu/ideas-made-to-matter/impact-generative-ai-a-general-purpose-technology?utm_source=mitsloanlinkedin&utm_medium=social&utm_campaign=genpurpose}

Economists and technocrats are equally enthusiastic about the expected boom that will be made possible by this technology. "In terms of being an engine for economic growth, experts predict serious gains. Goldman Sachs estimates that generative AI will be responsible for a 0.4 percentage point increase in GDP growth in the United States over the next decade. There are also ramifications beyond growth statistics. By automating mundane tasks, generative AI will allow people to do more meaningful work, whether that’s enabling physicians to spend less time on paperwork and more time caring for patients or helping professionals dig into upskilling and training."{https://mitsloan.mit.edu/ideas-made-to-matter/impact-generative-ai-a-general-purpose-technology?utm_source=mitsloanlinkedin&utm_medium=social&utm_campaign=genpurpose}

Another aspect about which economists and technocrats are excited is the speed with which such leap in productivity will be made possible by Generative AI. " In a recent report about the economic impact of generative AI, Google visiting fellow and MIT Sloan principal research scientist Andrew McAfee makes the case that generative AI is not only a game-changing general-purpose technology but could also spur change far more quickly than preceding innovations due to its accessibility and ease of diffusion. " and "Past general-purpose technologies took time to have a transformational impact, mainly because they required a new infrastructure. For example, electrical transmission networks needed to be in place to take advantage of electrification. In addition, most advantages associated with past technologies materialized only after users had had the chance to ideate and implement complementary innovations, McAfee writes In contrast, generative AI’s effects will manifest more quickly because much of the required infrastructure — internet-connected devices — is immediately available and already widely used. Generative AI doesn’t require mastery of computer skills or proficiency in a programming language, as people use natural human language to interact with the system" {https://mitsloan.mit.edu/ideas-made-to-matter/impact-generative-ai-a-general-purpose-technology?utm_source=mitsloanlinkedin&utm_medium=social&utm_campaign=genpurpose}

However what that is not being talked about is how this productivity is going to change the world and more importantly how that accelerated boom is going to fundamentally or even substantially improve the lot of the people. Another related things that are kept out of syllabus include what policy prescriptions are required to make economy more generative for the common people- is the Generative AI productivity being associated with with any Generative Economic boom?

Here we must revisit the pages the POWER AND PROGRESS written by Nobel laureates Daron Acemoglu and Simon Johnson which reads as “But what if there is a fly in the ointment? What if AI fundamentally disrupts the labor market where most of us earn our livelihoods, expanding inequalities of pay and work? What if its main impact will not be to increase productivity but to redistribute power and prosperity away from ordinary people toward those controlling data and making key corporate decision? What if along this path, AI also impoverishes billions in the developing world? What if it reinforces existing biases—for example, based on skin color? What if it destroys democratic institutions?” “The evidence is mounting that all these concerns are valid. AI appears set on a trajectory that will multiply inequalities, not just in industrialized countries but everywhere around the world. Fueled by massive data collection by tech companies and authoritarian governments, it is stifling democracy and strengthening autocracy.”……… “it is profoundly affecting the economy even as, on its current path, if it is doing little to improve our productive capabilities.” “We should not assume that the chosen path will benefit everybody, for the productivity bandwagon is often weak and never automatic. What we are witnessing today is not inexorable progress toward the common good but an influential shared vision among the most powerful technology leaders. This vision is focused on automation, surveillance, and mass-scale data collection, undermining shared prosperity and weakening democracies. Not coincidentally, it also amplifies the wealth and power of this narrow elite, at the expense of most ordinary people.” “This dynamic has already produced a new vision oligarchy—a coterie of tech leaders with similar backgrounds, similar worldviews, similar passions, and unfortunately similar blind spots. This is an oligarchy because it is a small group with a shared mind-set, monopolizing social power and disregarding its ruinous effects on the voiceless and the powerless. This group’s sway comes not from tanks and rockets but because it has access to the corridors of power and can influence public opinion.” “The vision oligarchy is so persuasive because it has had brilliant commercial success. It is also supported by a compelling narrative about all the abundance and control over nature that new technologies, especially the exponentially increasing capabilities of artificial intelligence, will create. The oligarchy has charisma, in its nerdy way. Most importantly, these modern oligarchs mesmerize influential custodians of opinion: journalist, other business leaders, politicians, academicians, and all sorts of intellectuals. The vision oligarchy is always at the table and always at the microphone when important arguments are being made.” “It is critical to rein in this modern oligarchy, and not just because we are at a precipice. This is the time to act because these leaders have one thing right: we have amazing tools at our disposal, and digital technologies could amplify what humanity can do. But only if we put these tools to work for people. And this is not going to happen until we challenge the worldview that prevails among our current global tech bosses. This worldview is based on a particular—and inaccurate –reading of history and what that implies about how innovation affects humanity. Let us start by reassessing this history.”

Now question arises what do we mean by GENERATIVE ECONOMIC BOOM? It is simply an economic boom which translates into a boom in economic capability of general masses, that means an economy where everyone or at least most people are prospering at an accelerated speed keeping pace with Generative AI boom.

This is absolutely necessary to fetch the benefits of productivity that is expected to gain by Generative AI. Otherwise the fruits of accelerated productivity due to Generative AI will be cornered by elites and Generative AI will become a historical villain in the history of mankind by throwing millions and millions of common people into the deep well of inequality and sufferings.

During the feudal age, the wealthiest people were kings and merchants who enjoyed royal patronage. The primary source of income in those days was taxes on agricultural produce, which was the main output of the economy. At that time, society could bear the brunt of inequality.

However, with the advent of the Industrial Revolution, agricultural products were gradually replaced as the main output of the economy by industrially produced commodities and related services. The primary driver of income shifted to the market, though taxation remained an important source of revenue.

Now, the question arises: if AI-driven productivity leads to heightened inequality, who will be the consumers in the market for the increased output, and who will sustain the current high taxation system? Under such circumstances, the market may falter, if not collapse, and productivity may fail to generate an economic boom.

Examining the Argument in Historical and Economic Contexts

1. The Feudal Economy and Agricultural Basis of Wealth

- In feudal economies, land was the main source of wealth, and agriculture was the predominant economic activity. Wealth and power were concentrated in the hands of kings, nobles, and landowners who controlled vast estates. The primary income source for rulers was taxation on agricultural produce, collected either directly from peasants or through feudal lords who managed estates. This concentration of wealth, though unequal, was relatively stable because most of the population was engaged in subsistence farming, and social mobility was limited.

- Economic theories, particularly those of classical economists like Adam Smith, argued that such economies were constrained by limited productive capacity. Growth was minimal, and there was little surplus to reinvest in ways that could generate significant wealth for society as a whole.

2. Industrial Revolution: Shift from Agrarian to Industrial Production

- The Industrial Revolution transformed economies by shifting the basis of wealth from agriculture to industrial production. Commodities and manufactured goods became the major economic outputs, and markets replaced feudal patronage as the primary driver of income and wealth. Industrialists, rather than kings or feudal lords, became the new economic elite.

- Theories by economists like Karl Marx and Friedrich Engels focused on the inequalities inherent in capitalist production. Marx predicted that capitalist systems would inherently create class divisions, with capitalists (those who own means of production) accumulating wealth while workers remained in poverty. This “industrial inequality” was tolerated as long as workers had wages to spend in markets, which sustained demand and kept the economy functional.

3. AI-Driven Productivity and Potential Economic Inequality - In the current digital age, AI and automation are anticipated to significantly boost productivity by automating tasks across various industries. However, there are concerns that AI could exacerbate inequality by concentrating wealth and power in the hands of those who own or control AI technology (e.g., tech companies and investors), while displacing a significant portion of the workforce. - Economic theories such as those by Joseph Stiglitz and Amartya Sen argue that unchecked inequality can destabilize economies by reducing aggregate demand. If a large share of wealth is held by a few, consumption could fall because the majority, with diminished purchasing power, cannot afford to buy goods and services. Sen’s capability approach emphasizes the importance of expanding capabilities for all individuals to foster economic growth and social progress. If AI-driven productivity is accompanied by widespread job loss, it could limit individuals' economic capabilities and hinder overall growth. 4. Potential Market Collapse and the Risk to Economic Stability - In a market-driven economy, consumer demand is essential for sustained economic growth. If AI-driven inequality means fewer people have the purchasing power to buy goods and services, demand could fall, leading to market stagnation or collapse. This scenario aligns with Keynesian economic theories, which emphasize the role of aggregate demand in driving economic growth. Keynes argued that when demand falls, economies tend toward recession and stagnation, as producers cut back on production and investment. - The classical “underconsumption” theory, notably discussed by economists like Malthus and Hobson, suggests that when income is concentrated among the rich, consumption falters because the wealthy tend to save more and spend less, while the poor, with limited income, are unable to spend adequately. In a modern context, if AI productivity benefits a small elite while many lose their jobs, underconsumption could lead to economic stagnation, hindering the growth potential of AI. 5. Taxation and Redistribution as Stabilizing Mechanisms - High levels of inequality can put pressure on government systems to fund social welfare, infrastructure, and public goods, often through progressive taxation. However, if AI-driven productivity leads to significant inequality, the capacity for taxation might diminish as fewer people and companies hold concentrated wealth, while others have limited or no income.

- Economists like Thomas Piketty argue that progressive taxation and wealth redistribution are essential for addressing inequality. Without redistribution, inequality grows, which can lead to social instability, reduced economic mobility, and hindered economic growth. In the AI economy, if wealth becomes concentrated, tax policies and wealth redistribution will be essential to ensure that benefits of AI productivity reach a broader population and sustain consumer demand.

Raghuram Rajan, an economist and former Governor of the Reserve Bank of India, has expressed strong views on the critical role of demand in sustaining economic productivity and the impact of inequality on this dynamic. His perspective can be understood in the following key points:

1. Role of Demand in Economic Growth:

- Rajan emphasizes that a healthy level of demand is essential for economic growth. Without sufficient demand, businesses cannot sell their products or services, leading to reduced production, fewer investments, and lower productivity in the long run.

- He argues that demand does not only come from wealthy consumers but requires a broad, stable middle class with the purchasing power to support continuous economic activity. When demand is weakened, it creates a negative feedback loop that hampers investment, productivity, and overall economic growth.

2. Inequality and Demand:

- Rajan believes that high levels of inequality undermine demand. When wealth is concentrated in the hands of a few, the majority of the population has limited purchasing power, which reduces overall demand in the economy.

- Wealthier individuals typically save more rather than spend proportionally on consumer goods, meaning that income concentrated at the top does not contribute as effectively to sustaining market demand as income spread across a broader base. This "demand gap" can hinder economic growth by reducing the number of consumers who can actively participate in the market.

3. The Importance of Inclusive Growth:

- Rajan advocates for "inclusive growth"—economic growth that benefits a wide swath of society, not just the wealthy elite. He argues that inclusive growth helps to sustain demand, as it enables more people to contribute to and participate in the economy.

- According to Rajan, policies that focus on equalizing opportunities, such as improving access to education and healthcare, as well as implementing progressive taxation, can help reduce inequality and boost demand. This inclusive growth approach ensures that productivity gains are shared more broadly, creating a stable foundation for continued economic growth.

4. Long-term Economic Stability and Inequality: - Rajan warns that persistent inequality can destabilize markets in the long term. When inequality rises, it creates economic imbalances that lead to social unrest, political polarization, and market distortions. These consequences can create an unstable environment for businesses and investors, ultimately reducing economic productivity. - He advocates for policies that reduce inequality to maintain economic and social stability, which are essential for sustaining demand, fostering innovation, and supporting long-term productivity. 5. Relationship with Financial Crises: - In his book "Fault Lines", Rajan links inequality to financial crises, arguing that stagnant middle-class incomes and rising inequality in developed economies have pressured governments to promote easy credit as a substitute for income growth. This approach can create asset bubbles and financial instability, as seen in the 2008 global financial crisis. - Rajan argues that sustained demand from a healthy middle class is essential for avoiding reliance on debt-fueled growth. Addressing inequality can thus prevent the buildup of financial imbalances that threaten the economy.

In summary, Raghuram Rajan views strong demand as essential for economic growth, with inequality posing a serious threat to sustained productivity. He advocates for inclusive growth policies to ensure that wealth and income are more equitably distributed, creating a stable demand base and reducing the risk of financial crises and economic instability.

Abhijit Banerjee and Esther Duflo, Nobel Prize-winning economists, have extensively researched poverty and inequality, especially through their work with the Abdul Latif Jameel Poverty Action Lab (J-PAL). Their views on poverty, inequality, and the role of demand in sustaining markets and productivity can be summarized as follows:

1. Multidimensional Nature of Poverty

- Banerjee and Duflo argue that poverty is a complex, multidimensional issue. They emphasize that poverty is not just a lack of income, but also a lack of access to essential services, resources, and opportunities.

- Through their research, they show that poverty is often perpetuated by structural barriers, such as poor access to education, healthcare, and stable employment. They advocate for targeted interventions to address these specific barriers to help lift people out of poverty sustainably.

2. Impact of Inequality on Economic Growth and Social Stability

- Banerjee and Duflo believe that high levels of inequality can stifle economic growth and hinder social stability. They argue that inequality prevents large sections of the population from accessing opportunities and realizing their potential, which in turn limits overall economic productivity.

- By concentrating wealth and resources in the hands of a few, inequality restricts the spending capacity of a large part of the population, leading to weaker demand and slowing economic growth. They advocate for policies that address inequality, such as progressive taxation and social safety nets.

3. Importance of Demand in Sustaining Markets and Productivity

- Banerjee and Duflo highlight the critical role of demand in sustaining markets and productivity, especially in developing economies. They argue that low-income households tend to spend more on necessities, which can stimulate demand across various sectors and boost productivity.

- Through J-PAL's randomized control trials (RCTs), they demonstrate that providing financial support to low-income households, such as cash transfers or subsidies, can lead to higher consumption. This increase in demand can drive production, employment, and economic growth, especially in low-resource settings.

- Banerjee and Duflo thus advocate for policies that enhance the purchasing power of low-income households, which can create a virtuous cycle of demand-driven growth.

4. Effective Government Intervention and Social Safety Nets

- They are proponents of well-designed government interventions to reduce poverty and inequality. According to them, social safety nets such as cash transfers, healthcare access, and education subsidies are essential for empowering low-income individuals and families, enabling them to participate in the economy as consumers and producers.

- Banerjee and Duflo argue that government support is crucial for creating a foundation of social and economic stability, which allows individuals to take risks, invest in education, and improve their economic circumstances.

5. Behavioral Insights and Poverty Traps - In their book "Poor Economics", Banerjee and Duflo examine the behavioral aspects of poverty, showing how individuals trapped in poverty often face difficult decisions that reinforce their situation. They argue that understanding these behaviors is crucial to designing effective policies. - For example, they emphasize the importance of “nudges” or small interventions that can help people make better financial and health decisions. These nudges, such as providing free vaccinations or small incentives for savings, can help break the cycle of poverty and allow individuals to contribute more effectively to the economy. 6. Redistribution to Enhance Economic Growth - Banerjee and Duflo argue that redistribution can support economic growth by boosting the purchasing power of the poor and reducing extreme inequality. They propose that redistribution is not just about fairness but is essential for building sustainable demand in the economy. - In their work, they argue that well-designed redistribution policies, such as targeted subsidies and tax credits for low-income households, can improve economic efficiency by ensuring that more people participate in the market, supporting a healthy demand for goods and services. 7. Focus on Evidence-Based Policies - Banerjee, Duflo, and J-PAL advocate for evidence-based policy-making through randomized controlled trials (RCTs) to understand what interventions are most effective in addressing poverty. Their research has shown that some simple interventions, like improving access to education or providing small financial incentives, can have a significant impact on economic productivity and poverty reduction. - By tailoring interventions based on empirical data, they believe governments and organizations can achieve more sustainable outcomes in poverty alleviation.

Banerjee and Duflo’s research suggests that poverty and inequality are critical challenges to economic growth and productivity, as they limit the purchasing power of a large segment of the population. They argue that boosting demand from low-income households can have a positive ripple effect on markets and productivity, sustaining economic growth. They advocate for targeted, evidence-based interventions to address the structural causes of poverty and inequality, emphasizing that inclusive policies and social safety nets are essential for long-term, stable economic development.

Allowing AI-driven technological advancements to concentrate wealth and power among a small elite can lead to a vicious cycle of poverty, inequality, and economic decline. This scenario unfolds in several interconnected stages, which together threaten to destabilize both markets and productivity:

1. Concentration of Wealth and Power

- As AI automates processes and increases productivity, those with the resources to develop and control AI technologies—often large corporations and wealthy individuals—stand to gain disproportionately. This concentration of economic power leads to monopolies and oligopolies, where a few companies dominate entire sectors.

- With AI in the hands of a powerful few, they control not only the flow of money but also decision-making power. This centralization limits opportunities for smaller businesses and reduces the potential for economic diversification, leading to a stagnation of innovation outside these elite circles.

2. Rising Inequality and Reduced Economic Mobility

- Concentrated wealth means that income and resources are distributed unevenly. Workers in low-skilled jobs or those vulnerable to automation may find themselves unemployed or underpaid, while the wealthy elite who control AI technologies continue to amass wealth.

- This disparity creates a society of "haves" and "have-nots," where economic mobility becomes nearly impossible for the lower classes. Social stratification deepens, as opportunities for education, health, and overall well-being become limited for the less privileged.

- Inequality also reduces social cohesion, as resentment builds within the broader population. This can lead to political instability, social unrest, and even violence, further destabilizing economic and social systems.

3. Decline in Demand - When wealth is concentrated at the top, the purchasing power of the majority diminishes. Since low- and middle-income groups typically spend a larger proportion of their earnings on goods and services, their reduced income results in lower demand across various sectors of the economy. - Unlike the affluent elite, who are more likely to invest or save their wealth, lower-income groups drive consumption. As demand declines, businesses suffer from falling revenues, leading to layoffs, reduced investment in new products or services, and an overall economic slowdown. - This reduction in demand creates a downward spiral, where businesses cut back on production due to lower sales, which in turn leads to further layoffs and even weaker demand. 4. Market Frailty and Instability

- Furthermore, as fewer people hold significant buying power, the market becomes overly reliant on the consumption patterns of a wealthy minority. Such reliance is inherently unstable, as it can lead to abrupt market fluctuations based on the financial decisions of a few.

- A decline in demand creates instability within the market. As businesses struggle to sell their products, prices may drop, leading to deflation. Deflation further discourages investment and consumption, as people delay purchases in anticipation of further price decreases.

- In the long term, an economy dependent on AI-generated productivity may face severe setbacks as the concentration of wealth leads to a distorted, unsustainable market structure. Small and medium-sized enterprises, unable to compete with AI-driven giants, may shut down, reducing diversity and resilience in the economy.

5. Erosion of Productivity and Innovation - Productivity initially surges with AI but begins to falter as demand declines and market instability grows. With fewer consumers able to afford new goods or services, companies lose incentives to innovate and invest in further AI developments. - As AI technology owners focus on maximizing short-term profits, they may neglect broader productivity gains that could benefit society. The lack of investment in skill development, infrastructure, and broader innovation reduces the long-term growth potential of the economy. - In addition, a society divided by inequality experiences a "brain drain," where skilled workers either emigrate to more equitable economies or lack motivation to innovate within an unfair system. Over time, this leads to an erosion of talent and creativity, both essential for sustainable productivity. 6. Self-Perpetuating Economic Decline - With productivity faltering, inequality increasing, and demand continuing to fall, the economy risks entering a self-perpetuating cycle of decline. Without broad-based demand, companies cut back on production, leading to job losses and further declines in consumer spending. - This cycle of reduced spending, market frailty, and declining productivity creates a stagnant or shrinking economy, where poverty becomes entrenched, and inequality becomes even more pronounced. - In such an environment, government revenues also decline, as fewer people contribute to tax systems. Reduced tax income undermines public services, which exacerbates social and economic divides, further trapping low-income individuals in poverty. 7. Implications for Society and Democracy - Extreme inequality and economic concentration undermine the democratic process. Wealthy individuals and corporations wield disproportionate influence over policy-making, using their power to shape regulations, taxes, and labor laws in their favor, often at the expense of the broader population. - The erosion of democratic institutions further entrenches inequality, as it limits accountability and promotes policies that serve the elite. Social welfare, education, and healthcare investments are deprioritized, while policies that maintain the wealth and power of the elite are reinforced. - This creates a vicious cycle where inequality and poverty deepen, democratic institutions weaken, and public trust erodes. In the long term, such trends can lead to social instability, weakening both economic and political foundations.

Allowing AI to concentrate wealth and power without safeguards can lead to a society marked by inequality, weakened demand, economic instability, and diminished productivity. Addressing this requires proactive policies that prevent wealth concentration, promote inclusive economic growth, and ensure broad-based demand. By doing so, society can harness the productivity gains of AI while creating a more stable and equitable economic system.

Neoliberalism in the Age of Generative AI: A Danger to the Common Good

The adoption of neoliberal economic and political frameworks in the modern digital age, particularly with the rise of generative AI, presents significant risks to societal welfare and the well-being of common people. Neoliberalism emphasizes free markets, minimal government intervention, and privatization, which can amplify inequalities and destabilize public services, often at the expense of the most vulnerable in society. Here’s why neoliberalism can be especially harmful in this context, along with real-world cases demonstrating these effects.

1. Exacerbation of Inequality and Wealth Concentration

Wealth and Power Consolidation: Neoliberal policies lead to wealth consolidation among powerful corporations, often in the tech sector. This concentration marginalizes smaller businesses and common workers, deepening wealth inequality.

Case Study: United States and Big Tech

In the U.S., neoliberalism has allowed companies like Amazon, Google, and Meta to achieve near-monopolistic control over markets. The tech giants accumulate wealth and data, which they use to further dominate the market through targeted ads and data-driven services. For instance, Amazon’s influence over e-commerce, cloud services, and logistics has left many small businesses struggling to compete, driving them out of business. This has led to income disparities in affected regions, especially in areas reliant on small and medium enterprises (SMEs).

2. Surveillance Capitalism and Erosion of Privacy

Data Exploitation: Generative AI operates on vast amounts of data derived from user interactions. Neoliberal policies prioritize minimal regulation on data collection, allowing private corporations to commoditize personal data.

Case Study: China’s Surveillance Economy

Though China does not have a pure neoliberal model, its market-driven approach to data is significant. With major tech companies collecting and using data for targeted consumer profiling, many are concerned about how this data is used to influence and shape citizen behavior. The lack of privacy protections in China shows how market-driven AI can lead to a complete erosion of individual privacy. Though China represents an extreme case, its surveillance economy illustrates what can happen when data and AI are used unchecked to control, monitor, and manipulate citizens.

3.Job Displacement and Wage Stagnation

Labor Market Disruptions: Neoliberal frameworks favor companies that cut labor costs, often by adopting AI-driven automation, which displaces low-skill jobs

.

Case Study: India’s IT Sector and Automation

In India, a combination of neoliberal policies and technological advancements has led to massive adoption of automation in the IT sector. This shift has resulted in the displacement of lower-skill jobs as companies increasingly employ AI systems for back-office functions, customer service, and even software development. Many entry-level jobs, which were once a staple for the Indian middle class, have either disappeared or transformed into gig roles with low pay and minimal benefits, exacerbating job insecurity and economic instability.

4. Undermining of Public Goods and Services

Privatization of Essential Services: Neoliberal policies advocate for the privatization of healthcare, education, and even AI-driven public services, making these services more expensive and less accessible to the common people.

Case Study: Chile’s Healthcare and Education Systems

Under Chile’s neoliberal policies introduced in the 1980s, the country privatized many public services, including healthcare and education. The result has been significant social inequity, with high-quality services available only to those who can afford them. The 2019-2020 protests in Chile were, in large part, driven by these inequities, as citizens demanded accessible healthcare and quality education for all. The resulting unrest led to constitutional reforms, emphasizing a public-centered approach over privatized services. This example shows how neoliberalism can compromise social equity, making essential services inaccessible to ordinary citizens.

5. Environmental Degradation and Lack of Sustainable Practices

Unsustainable Resource Consumption: Neoliberalism’s growth-focused policies encourage corporations to pursue short-term gains with little regard for environmental consequences.

Case Study: Brazil’s Amazon Deforestation

Brazil’s neoliberal policies under President Jair Bolsonaro weakened environmental protections, leading to significant deforestation of the Amazon. These actions were aimed at enabling businesses to exploit the Amazon for agriculture and mining. The environmental degradation not only has catastrophic global implications but also directly impacts Indigenous communities who rely on the forest for their livelihoods. This example highlights the dangers of a neoliberal approach that prioritizes corporate interests over ecological balance and the well-being of marginalized populations.

6. Political Influence and Erosion of Democratic Processes

Corporate Influence in Politics Neoliberal policies allow large corporations to wield considerable influence over political decisions through lobbying and campaign contributions.

Case Study: Corporate Lobbying in the European Union

In the EU, corporate lobbying has become a powerful force shaping policy decisions. Tech companies like Google and Meta have invested heavily in lobbying efforts to influence digital and data privacy regulations. These corporations often push for policies that favor their interests over those of consumers, limiting the effectiveness of data protection and privacy laws like GDPR. Such lobbying practices erode public trust in democratic processes, as citizens see policies skewed in favor of corporations rather than the common good.

7. Loss of Human Agency and Ethical Concerns

AI-Driven Decision-Making without Ethical Constraints: Neoliberal policies often lack the ethical regulations needed to manage the social biases that AI systems can amplify.

Case Study: Predictive Policing in the United States

Predictive policing systems in the U.S., which use AI to analyze data and predict crime hotspots, have come under scrutiny for perpetuating racial and socioeconomic biases. These systems, influenced by a neoliberal focus on cost efficiency, often lead to over-policing in low-income, marginalized communities. By relying on biased data without accountability, these technologies reinforce existing social inequalities, showing how neoliberal policies that prioritize efficiency over ethics can harm vulnerable populations.

The neoliberal framework, especially when applied to generative AI, amplifies existing social, economic, and environmental vulnerabilities. Through real-world examples, we see how neoliberal policies, with their focus on deregulation, market dominance, and profit maximization, deepen inequality, reduce access to essential services, and compromise democratic accountability. Without careful regulation and policy intervention aimed at balancing economic efficiency with social equity, the unchecked adoption of neoliberal policies could deepen existing inequalities, erode democratic values, and threaten societal well-being, especially for common people.

These cases illustrate how neoliberalism, in the context of generative AI, can reinforce a system that prioritizes profit over the public interest, often at the expense of the vulnerable and marginalized. A shift toward policies that consider social good, sustainable development, and ethical AI governance is essential for building a more inclusive and equitable digital future.

Policy Recommendations to Prevent Economic Collapse

To prevent AI-driven productivity from collapsing markets, policymakers could consider measures to ensure broad-based economic benefits:

Universal Basic Income (UBI): As suggested by economists like Milton Friedman, a UBI could help maintain demand by providing all citizens with a basic income, allowing them to participate in the economy even if they are displaced by AI.

Skills and Workforce Training Programs: Programs to reskill workers could help people adapt to jobs in the AI economy, fostering capability-building as envisaged by Amartya Sen.

Progressive Taxation on AI Companies and Wealthy Elites: Higher taxation on AI companies and ultra-wealthy individuals could support welfare programs and economic redistribution, helping to balance wealth disparities.

Encouraging Ethical AI Development: Policymakers could enforce regulations to ensure AI is developed in ways that benefit society as a whole, minimizing job displacement and promoting sustainable economic growth.

Democracy’s Role in Regulating AI-driven Productivity

- Democracy, with its checks and balances, could play a crucial role in ensuring that AI’s benefits are widely shared rather than concentrated. Democratic processes allow citizens to demand policies that favor economic inclusivity and prevent the monopolization of AI technology.

- In line with Acemoglu, Robinson, and Stiglitz’s ideas, democracy can prevent the concentration of power and wealth in AI, ensuring that society's collective interests are prioritized. Through democratic institutions, regulations can be enacted to address AI-related inequality, ensuring that the productivity gains benefit all and mitigate risks of economic collapse.

In conclusion, while AI-driven productivity offers significant economic potential, unchecked inequality could lead to underconsumption, reduced aggregate demand, and market instability. To prevent these risks, economic theories and historical examples suggest the need for redistribution, democratic regulation, and capability-building to create an economy where AI benefits all.

The Bhasmasur Paradox: Ancient Wisdom for Modern Technology In an era where artificial intelligence, unprecedented wealth, and technological power seem to promise unlimited potential, an ancient Hindu mythological tale serves as a profound warning about the perils of unchecked ambition. The story of Bhasmasur, passed down through generations of Indian storytelling, resonates with surprising relevance in today's rapidly evolving technological landscape. The narrative begins with Bhasmasur, an asura (demon) whose name would become synonymous with destructive ambition. Like many modern-day tech pioneers and industry titans, Bhasmasur sought power through extraordinary means. His path to power, however, took the form of severe penance dedicated to Lord Shiva. Through extreme austerities and unwavering dedication, Bhasmasur demonstrated a single-minded pursuit of his goal – a trait often celebrated in today's entrepreneurial culture. Lord Shiva, impressed by this dedication, granted Bhasmasur an audience and offered him a boon of his choosing. This pivotal moment mirrors contemporary discussions about breakthrough technologies: when we achieve the power we seek, do we fully understand its implications? Bhasmasur's request was seemingly simple yet profound – the power to turn anyone to ashes by merely touching their head. This ability, much like today's powerful technologies, had the potential for both creation and destruction. The parallel with modern technological development is striking. Just as Bhasmasur gained his power through legitimate means (his penance), many of today's most potent technologies – from artificial intelligence to genetic engineering – are developed with legitimate intentions. However, the story takes a dark turn when Bhasmasur's power corrupts his judgment, a phenomenon we've witnessed repeatedly in the history of technological and corporate power. Testing his newfound ability, Bhasmasur began a reign of terror, turning many to ashes and inspiring fear among gods and mortals alike. His power, divorced from wisdom and ethical constraints, became a force of pure destruction. This phase of the story eerily reflects modern concerns about unregulated technological advancement and the potential for powerful tools to be misused when divorced from ethical considerations. The climax of the tale arrives when Bhasmasur's arrogance reaches its peak – he decides to test his power on Lord Shiva Himself, the very source of his ability. This moment of supreme hubris parallels contemporary warnings about technological singularity and the potential for created systems to exceed and possibly threaten their creators. The cosmic chase that ensues, with Lord Shiva fleeing from Bhasmasur, symbolizes humanity's potential loss of control over its creations. The resolution comes through Lord Vishnu's intervention, appearing as Mohini, an enchantress of exceptional beauty. Bewitched by Her beauty, Bhasmasur was completely captivated. Mohini engaged him in a dance and, using Her divine charm, convinced him to mimic Her movements. Bhasmasur, oblivious to the trap being laid, copied each of Her gestures exactly. The dance reached its crucial moment when Mohini placed Her hand upon Her own head – and Bhasmasur, still following Her every move, did the same. In that instant, by the power of his own boon, he was reduced to ashes by his own hand. This divine intervention demonstrates how intelligence and grace can overcome even the most formidable threats. The demon's downfall comes not from external force but from his own power turned against him – a poignant reminder that unchecked ambition often contains the seeds of its own destruction. This ancient tale holds several crucial lessons for our modern world. First, it warns against the blind pursuit of power without corresponding wisdom and ethical framework. In an age where technological capabilities advance exponentially, the story reminds us that the ability to do something doesn't necessarily mean we should. Second, it highlights the importance of building in safeguards and limitations – something modern tech developers might consider as they create increasingly powerful systems. The story also emphasizes the value of wisdom over raw power. Lord Vishnu's solution, utilizing intelligence and strategy rather than force, suggests that our approach to managing powerful technologies should similarly prioritize wisdom and foresight over mere capability. This is particularly relevant as we grapple with questions about AI safety, ethical technology development, and responsible innovation. Moreover, the tale serves as a metaphor for the potential consequences of technological hubris. Just as Bhasmasur's power ultimately led to his destruction, unchecked technological advancement without proper consideration of consequences could lead to serious societal or even existential risks. In conclusion, the story of Bhasmasur transcends its mythological origins to offer vital insights for our contemporary world. As we stand on the brink of unprecedented technological capabilities, this ancient tale reminds us that true progress requires not just power but wisdom, not just capability but responsibility. The intelligence and grace demonstrated by Lord Vishnu in the story represent qualities we desperately need today – thoughtful consideration, ethical framework, and the wisdom to know when and how to apply our capabilities. The lesson is clear: whether in the realm of artificial intelligence, corporate power, or technological advancement, we must balance ambition with wisdom, progress with responsibility, and power with purpose. Otherwise, like Bhasmasur, we risk falling victim to our own unbridled ambitions, trapped in a dance of our own making that leads to our undoing.​​​​​​​​​​​​​​​​

                 (The Productivity Paradox: Why AI-Driven Growth Requires Generative Economics

AI Policy Framework. This image has been generated by Claude 3.5)