High-frequency trading (HFT) can create feedback loops that increase market volatility ( Kirilenko et al., 2017 ). When an HFT algorithm identifies a downward price trend, it triggers automatic sell orders that push prices down even more. This leads other automated systems to sell as well, worsening the decline. Such mechanisms played a role in events like the 2010 Flash Crash when stock markets fell nearly 1000 points in minutes before quickly recovering ( Kirilenko et al., 2017 ). In inflation, the anticipation of higher prices leads to even more price hikes ( Krugman, 2020 ).

Similarly, artificial intelligence develops through self-reinforcing feedback loops that can amplify its impact ( Ferrara, 2024 ). AI advancements drive further innovations, creating a compounding effect ( Brynjolfsson & McAfee, 2017 ). Advancements in AI create a positive feedback loop, where progress in one area drives growth in others, leading to rapid, exponential development ( Cowan, 2024 ). Then, there is algorithmic bias, where an AI’s predictions affect decisions that subsequently reinforce those biases. For instance, predictive policing algorithms based on historical crime data may direct law enforcement to areas with high reported crime rates ( Lum & Isaac, 2016 ). This results in more arrests in those areas, further convincing the AI that they are high-crime zones despite a more balanced actual crime distribution. Without oversight, AI may become so autonomous and complex that it surpasses human comprehension, resulting in unintended consequences ( Bostrom, 2014 ). Krugman (2020) points out that addressing runaway inflation often needs decisive intervention to regain stability, suggesting that a similar approach may be necessary to regulate AI development. Bostrom (2014) notes that AI’s ability to improve itself creates a self-perpetuating cycle similar to runaway inflation, which can lead to significant challenges.

Runaway inflation harms economies by lowering money value, distorting market signals, and eroding savings and investments. This results in reduced purchasing power decreased consumer confidence, and slower economic growth ( Mishkin, 2019 ). Similarly, unchecked AI development can disrupt labor markets through automation, potentially displacing millions of workers. Brynjolfsson and McAfee (2017) caution that rapid automation may worsen income inequality by concentrating wealth among those who control AI technologies. Inflation disrupts economies by creating uncertainty, discouraging long-term investments, and hampers growth ( Mishkin, 2019 ). Likewise, rapid AI automation can destabilize labor markets by diminishing the demand for specific skills and increasing inequality ( Brynjolfsson & McAfee, 2017 ).

Economic instability heavily impacts vulnerable populations, whether due to inflation or unregulated AI development. Inflation reduces purchasing power, making essential goods and services harder to afford, and hyperinflation can lead to social unrest, as seen in Zimbabwe and Venezuela ( Krugman, 2020 ). Unregulated AI also deepens social divides by limiting technological benefits to a select few and marginalizing others. A significant issue is algorithmic bias, which can reinforce existing inequalities. Adversarial examples—inputs designed to confuse AI—can increase bias, resulting in discrimination in hiring, lending, and criminal justice ( Szegedy, 2013 ). These flaws erode trust in AI systems and disproportionately affect historically disadvantaged communities.

Automation-driven job displacement is a significant societal issue. As AI takes over routine and skilled labor tasks, millions of workers may face unemployment, leading to economic insecurity and dissatisfaction ( Acemoglu & Restrepo, 2019 ). Just like hyperinflation can cause social unrest by increasing economic disparities, unregulated AI could worsen social inequalities, limiting economic opportunities for marginalized groups ( Krugman, 2020 ; Acemoglu & Restrepo, 2019 ). This highlights the urgent need for proactive AI governance to prevent technology-driven inequalities and ensure that AI benefits everyone, not just a privileged few.

Runaway inflation does not raise ethical concerns but can worsen social inequalities if government policies fail to protect low-income groups ( Mishkin, 2019 ). In contrast, AI development involves serious ethical issues like algorithmic bias, privacy violations, and the risk of misuse ( UNESCO, 2023 ). A significant problem is algorithmic confounding, where the interaction between an AI system’s predictions and the assessment data creates biased outcomes ( Ferrara, 2024 ). This bias can influence future datasets, making it hard to distinguish actual trends from AI-induced distortions. Consequently, discriminatory practices can be reinforced, especially in hiring, lending, and law enforcement ( Ferrara, 2024 ).

The rise of superintelligent AI poses serious ethical risks. Bostrom (2014) warns that highly autonomous AI systems could develop goals misaligned with human values, leading to harmful decisions for society. Unlike inflation, which affects fairness indirectly, AI decision-making directly impacts societal outcomes, making ethical oversight essential. AI can entrench inequalities, violate privacy, and damage public trust without proper governance. To address these issues, clear regulatory frameworks that ensure AI aligns with human values and promotes ethical principles rather than deepening societal divides are needed.

Table 1 shows the similarities between runaway inflation and artificial intelligence, highlighting their self-reinforcing and destabilizing nature. This analysis stresses the need for effective governance frameworks to manage risks and ensure stability. For inflation, central banks use monetary policy as a control tool. Similarly, governing artificial intelligence requires proactive strategies like international collaboration, ethical guidelines, and risk forecasting to align AI development with societal objectives and values. The table highlights the similarities between runaway inflation and AI, as both can be self-reinforcing and destabilizing. However, it is crucial to note their key differences. Unlike inflation, which is destructive, AI has the potential to drive significant economic and societal benefits through value creation and innovation.

Dual Nature of Impact

Inflation harms economic stability and growth. AI has a dual impact: If developed irresponsibly, it can cause societal issues and ethical problems, but with proper regulation, it can transform industries and help solve significant global challenges.

Governance frameworks are essential to ensure that AI’s advantages surpass its risks. Unlike inflation control, which centers on stabilization, AI governance should focus on balancing innovation with harm reduction ( Walter, 2024 ).

Capacity for Value Creation

Inflation reduces purchasing power and creates inefficiencies, decreasing consumer spending and economic decline. Rising prices distort price signals, misallocate resources, and increase uncertainty for investors and consumers ( Alnasser, 2023 ). These issues can stifle innovation and productivity, worsening economic stagnation.

By contrast, AI improves productivity by enabling human workers to concentrate on more complex and creative tasks. This boosts economic growth, fosters innovation across industries, and creates new job opportunities in emerging fields ( Gambelin, 2020 ). AI technologies tackle complex issues like climate change modeling ( Olorunsogo, 2024 ), healthcare ( Bajwa, 2021 ), and global supply chain optimization ( Atadoga, 2024 ), generating value that goes beyond just economic benefits to include societal progress.

Potential for Innovation

The discussion underscores the importance of customized regulatory approaches that acknowledge AI’s transformative potential and address its risks. AI’s ability to create value and drive innovation presents a unique challenge, necessitating governance strategies that extend beyond traditional economic models.

1) Establishing an “AI Governance Body” (Similar to Central Banks)

A regulatory authority, either at the global or national level, should be established to oversee AI development. This authority’s role would include setting guidelines, assessing risks, and intervening when AI advancements pose ethical, social, or economic threats.

In late 2023, the United Kingdom established the AI Safety Institute, which is entrusted with the responsibility of testing and evaluating the safety of advanced artificial intelligence models. Functioning as a quasi-regulatory entity, the Institute conducts thorough assessments of large language models, focusing on identifying risks such as bias, hallucinations, and potential for misuse. This initiative aligns with the proposal for a centralized governance body for artificial intelligence, similar to the role of central banks in financial oversight.

Like inflation targeting, this body could implement “development targeting” to prioritize safe and beneficial AI applications while limiting harmful or uncontrolled growth. A national AI governance body could establish a licensing framework for AI-driven financial tools like robo-advisors and high-frequency trading systems. To obtain a license, firms must meet ethical standards, ensure transparency in algorithmic decision-making, and implement strong risk assessment practices. This framework aims to prevent AI technologies in finance from worsening economic inequality or causing market instability, similar to the regulatory measures taken by central banks to uphold financial stability.

2) Risk Assessment and Monitoring (Similar to Inflation Forecasting)

Regularly assessing AI risks through predictive models and audits can help identify potential disruptions early on. Maintaining transparency and reporting can enhance central banks’ public engagement. Like inflation forecasts, predictive risk assessments could provide warnings of harmful AI developments, allowing regulators to act proactively ( Brynjolfsson & McAfee, 2017 ).

The U.S. Food and Drug Administration’s (FDA) framework for Software as a Medical Device (SaMD) encompasses both premarket evaluation and post-market surveillance of artificial intelligence tools utilized in healthcare, including diagnostic systems. This approach is designed to ensure ongoing safety and efficacy, paralleling the methods employed by central banks to manage inflation through forecasting.

An AI governance body that monitors healthcare AI systems using predictive risk models. For instance, a diagnostic AI that recommends treatments based on patient data is regularly audited for bias, accuracy, and security. If an audit finds potential bias against minority groups, the body requires immediate adjustments before further deployment. This proactive approach is similar to inflation forecasting, where risks are addressed before they can cause problems.

3) International Collaboration (Similar to the IMF)

International agreements and treaties are crucial for regulating AI development across borders and preventing regulatory arbitrage. Global organizations like the UN or OECD should take on a role similar to that of the IMF in managing global AI policy. Global cooperation in AI governance resembles the alignment seen in monetary policy during economic crises, as it fosters consistency and reduces the chances of regulatory gaps ( Bostrom, 2014 ).

GPAI, established in 2020, is an international initiative that includes Canada, France, India, and the US. It aims to promote responsible AI development by harmonizing policy, sharing data responsibly, and addressing ethical challenges across borders.

These standards mandate real-time monitoring of AV systems and outline protocols for managing accidents or failures. By harmonizing regulations internationally, this framework prevents companies from taking advantage of regulatory gaps in different jurisdictions, much like how coordinated international monetary policies help avert currency crises.

4) Ethical and Societal Goals (Similar to Economic Stability Goals)

Central banks aim to control inflation while supporting employment and growth. In the same way, AI governance should focus on maximizing societal benefits, like reducing inequality, while minimizing risks such as bias and misuse. Governance frameworks must ensure AI development aligns with societal values, just as monetary policy promotes stable economic growth and employment ( Krugman, 2020 ).

Licensing and Certification: Organizations developing advanced AI must comply with ethical and safety standards similar to those used by central banks to regulate financial institutions.

Development Caps: Limit certain AI functions until their risks are thoroughly evaluated, like adjusting interest rates to control inflation. Restricting high-risk AI applications could prevent disruptive results, similar to how raising interest rates controls inflation ( Acemoglu & Restrepo, 2019 ).

In 2024, officials from the United States and China entered into preliminary discussions to establish measures to prevent the deployment of autonomous artificial intelligence in military conflicts. This dialogue reflects an increasing awareness of the necessity for development constraints in high-risk domains ( Reuters, 2024 ). This includes restrictions on capabilities such as autonomous decision-making in military and large-scale surveillance. These limits are akin to adjusting interest rates to control inflation, allowing for a careful pace of AI development.

The differences in AI applications underscore the need for customized regulatory approaches that acknowledge AI’s potential while managing its risks. AI can create value and drive innovation but presents unique challenges that require governance beyond traditional economic models. Key strategies include forming independent governing bodies, implementing predictive monitoring, setting development limits, and promoting global cooperation to ensure responsible and ethical AI development aligned with society’s long-term goals.

The idea of an AI governance body is appealing, but significant challenges exist.

An independent AI governance body could be crucial in managing AI risks and promoting ethical development, similar to how central banks handle monetary stability. By overseeing AI’s societal and economic impacts, this body would ensure that AI innovation serves the public interest, drawing from frameworks like the EU AI Act, OECD AI Principles, and NIST AI Risk Management Framework.