The Multifaceted Relationship Between AI and Economics: Impacts, Challenges, and Insights

Artificial intelligence (AI) has the potential to enhance decision-making by offering precise and timely information to businesses and policymakers. This study delves into the intricate relationship between AI and economics, with a specific focus on three key domains: Supply Chain Optimization, Financial Fraud Detection, and Automation's Impact on the Workforce. By shedding light on both the advantages and challenges of AI integration in economics, this research aims to contribute to the ongoing discussion. The research objectives encompass exploring AI's influence on the multifaceted relationship with economics, offering valuable insights for policymakers, industry stakeholders, and researchers.


Introduction
The Economics of Artificial Intelligence (Agrawal et al., 2019) provides frameworks for understanding the economic impact of AI.This paper further delves into the multifaceted relationship between artificial intelligence (AI) and economics.It comprehensively explores AI as a general-purpose technology, investigating its influence on economic growth, job markets, and inequality.The research also scrutinizes regulatory responses to the transformative effects of AI and its impact on the methodologies employed in economic research.This research holds paramount importance in elucidating the intricate interplay between AI and economics, with a specific focus on Supply Chain Optimization, Financial Fraud Detection, and Automation and Workforce Impacts.The research methodology entails a deep dive into relevant case studies, industry reports, and scholarly articles, providing a comprehensive panorama of both the advantages and challenges posed by AI.The findings contribute to a nuanced comprehension of how the evolution of AI reverberates throughout economics, offering useful insights for policymakers, industry stakeholders, and researchers alike.
Ultimately, this paper aspires to enrich the ongoing discourse on AI integration in economics by underscoring its potential benefits and addressing the accompanying challenges.By identifying these critical factors, stakeholders can make informed decisions regarding AI adoption and regulation, fostering equilibrium between technological progress and socio-economic considerations.

Results
Through comprehensive literature reviews conducted in this study, significant insights have been unveiled regarding the intersection of AI and economics within the domain of supply chain optimization.The findings affirm the imperative of leveraging AI to orchestrate seamless economics, thereby expediting operations and enhancing overall efficiency.
In the context of supply chain management, AI's impact on economics is far-reaching.AI technologies empower businesses to make data-driven decisions, optimizing resource allocation, and cost management.The integration of AI algorithms into economic models enables dynamic forecasting, risk assessment, and real-time adaptation to market fluctuations.This infusion of intelligence into economic strategies enhances adaptive capabilities and resilience, contributing to sustainable growth.
Moreover, AI's role in economics extends to enhancing customer experiences.By analyzing consumer behavior patterns and preferences, AI enables personalized marketing and product recommendations.This not only cultivates customer loyalty but also stimulates demand and shapes market dynamics, consequently influencing economic trajectories.
Figure 1 describes the computation used to train various famous artificial intelligence systems as time changes.Xaxes indicate the publication dates of the AI systems, Y-axes measure the computational efficiency in PFLOPS, which stands for petaFLOPS.PetaFLOP is a unit measuring the calculating speed of a computer that performs one quadrillion (10 15 ) floating-point operations per second.Each petaFLOP represents that 60kW of power is required to operate the system (NYU IT, 2023).
As shown in Figure 1, 36 artificial intelligence systems are analyzed, each published at different periods.A growing trend is displayed in the graph: as the timeline approaches the present, the computational efficiency increases.With greater computational efficiency, more energy is required to perform one quadrillion floating-point operations every second in order for the system to run.GPT-4 by OpenAI -an AI model published on March 15, 2023--ranks the highest, with training computes of 21,000,000,000 petaFLOPS (Webb, 2023).With each petaFLOP meaning 60kW of power to operate, 1,260,000,000,000 kW of power is required to run the system of GPT-4.Published by IDEAS SPREAD

Supply Chain Optimization
AI enables data-driven enhancements across supply chain operations, leading to significant performance improvements.By analyzing large datasets, AI provides invaluable visibility and actionable intelligence that transforms supply chain management.Specifically, AI reduces costs substantially as evidenced by the 15% drop in logistics expenses and millions in inventory savings (Mohsen, 2023).AI-optimized routing and warehouse automation generate major efficiency gains.Additionally, AI boosts service levels dramatically -the 65% improvement shows it allows supply chains to be far more responsive to shifting customer demands and market dynamics (Mohsen, 2023).This responsiveness is key for competitiveness.The quantifiable 15-65% optimizations in costs, service levels, forecast accuracy, etc. make a compelling case for AI adoption (Mohsen, 2023).The data clearly demonstrates AI is a transformational technology that brings step-change benefits to supply chain performance.
AI can optimize multiple aspects of supply chain performance.For example, the goals of reducing production scheduling time and response time to disruptions by 50% demonstrate AI's ability to dramatically improve agility and responsiveness (Walter, 2023).In addition, the 2-5% targeted gains in overall equipment effectiveness, productivity, and reductions in downtime and scrap highlight AI's capacity to boost efficiency and cut waste through enhanced monitoring and control (Walter, 2023).The 10% forecast accuracy improvement further exhibits AI's power for data-driven inventory and production optimization (Walter, 2023).Moreover, AI-enabled real-time visibility into production and logistics allows rapid problem identification and corrective actions, increasing flexibility.Together, these KPIs paint a compelling picture of AI as a transformative technology for data-driven supply chain enhancements.Published by IDEAS SPREAD AI plays a significant role in combating financial fraud through its advanced analytical capabilities.AI algorithms can identify patterns and behaviors that are indicative of fraud.The algorithms learn from historical data and can detect anomalies in transactions, account activities, and behaviors that deviate from the norm.The SEC's AI tools detect accounting and financial fraud, generating billions in legal insights and investigations.The Treasury's AI system has produced numerous fraud investigations and recouped substantial amounts of money from fraudulent activities.The IRS has spent $400 million on an AI system to identify $1.5 billion in improper tax refunds and fraud (West, 2021).Medicare/Medicaid estimates their AI system helped prevent or identify $1.5 billion in fraudulent payments from 2011-2015 (West, 2021).Compared to traditional methods, AI-based fraud oversight at financial institutions has over 50% higher detection rates (West, 2021).AI credit card fraud detection is 95%+ accurate compared to 60-70% for rules-based systems, saving billions in losses (West, 2021).Deep learning AI can analyze massive financial data to uncover money laundering at 5X the rate of legacy tools (West, 2021).
AI plays a pivotal role in enhancing fraud prevention across various fronts in the financial sector.One significant application lies in data-driven fraud detection, where AI's proficiency in big data analysis, combined with continuous evolution, results in superior efficacy of data mining.By amassing extensive consumer behavior and fraudulent activity data, AI systems identify deviations from established patterns, promptly flagging transactions for review and improvement through machine learning.In parallel, AI-powered notifications enable real-time fraud mitigation by promptly detecting anomalies and notifying customers for transaction verification, thus empowering them to confirm legitimate actions (Larcelet-Prost, 2023).Additionally, conversational AI introduces dynamic interactions, fostering trust in automated systems while facilitating seamless transaction verification, a crucial aspect for countering fraud.Voice AI further strengthens fraud prevention by offering a recognizable and unique brand voice, effectively countering voice phishing scams.This customized voice enhances customer identification of genuine communications and prevents scams.Moreover, voice biometrics utilizes AI to uniquely identify individual voices, augmenting user authentication's robustness and security in voice-based interactions, while a customized branded voice bolsters user trust and interaction authenticity.

Automation and Workforce Impacts
AI is closely linked to automation, streamlining repetitive tasks and boosting efficiency.This can lead to job enrichment and the creation of new roles, but also job displacement.The integration of AI requires careful planning, as it can create a hybrid workforce where humans and machines collaborate, emphasizing the need for upskilling and ethical considerations.About 50% of work activities globally could be automated by 2030 using current demonstrated technologies (Manyika & Sneader, 2018).15% of the global workforce, or 400 million workers, could be displaced by automation between 2016-2030 in a midpoint adoption scenario (Manyika & Sneader, 2018).In the midpoint scenario, 20-25% of the workforce in advanced economies like the US and France will be displaced by 2030.And around 3% of the global workforce, or 120 million people, will need to switch occupational categories by 2030 (Manyika & Sneader, 2018).Many middle-wage jobs are at high risk of automation, worsening wage polarization and income inequality.However, new labor demand from economic growth could add 555-890 million jobs globally between 2016-2030, offsetting job losses (Manyika & Sneader, 2018).AI and automation are intertwined, enhancing efficiency and productivity in various industries.While they create new job opportunities, they can also displace routine tasks, necessitating skill shifts.Jobs requiring AI skills are in high demand.Upskilling is essential, and ethical considerations and economic impacts arise.AI enables the automation of many tasks previously thought to require human skills and judgment, exposing large parts of the workforce to potential disruption.A survey of US firms found that 54% of AI adopters reported doing so to automate existing processes (Whitehouse, 2022).In the US, 12.6% of workers were employed at firms that adopted AI between 2016-2018 (Whitehouse, 2022).Among AI adopters in the US, 41% reported increasing their demand for skills after adopting AI (Whitehouse, 2022).A study using Dutch data found that workers made redundant due to their firm's AI adoption suffered an annual income loss of 9% over 5 years, driven by unemployment spells (Whitehouse, 2022).In the EU, 8% of enterprises with over 10 employees used AI in 2021 (Whitehouse, 2022).Algorithmic management systems in warehouses have increased labor productivity but not wage growth, resulting in a falling labor share in warehousing in several EU countries.

Limitations 
Cost of Implementation: Implementing AI systems can involve significant upfront costs, including infrastructure, hardware, software, and skilled personnel.For some businesses, especially small and mediumsized enterprises (SMEs), the initial investment required for AI implementation may be prohibitive and pose a financial burden.Published by IDEAS SPREAD  Workforce Displacement: The automation capabilities of AI can lead to job displacement and changes in the labor market.Certain routine and repetitive tasks can be automated, potentially reducing the need for human workers in those areas.This can lead to concerns about job losses, particularly for workers whose skills become obsolete or less in demand due to AI advancements.

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Widening Gaps: The adoption of AI could widen gaps among countries, companies, and workers, which is a key challenge.Some argue that in the AI race, the EU has a structural disadvantage: a lack of scale manifested by a lack of a substantial homogenous pool of data, which is an essential resource for AI development.

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Technical Bottlenecks: The technical bottlenecks in the development of AI technology itself also lead to a large gap between the conception of theoretical research and the blueprint in actual practice.