Blockchain, Artificial Intelligence, and Future Research

Post-Launch Public Interest in DeepSeek vs. ChatGPT: A Comparative Google Trends Analysis in Indonesia and the Philippines

2025-02-03T15:18:00+07:00

This study investigates the post-launch public interest in DeepSeek and ChatGPT through a comparative analysis using Google Trends data from Indonesia and the Philippines. Employing a quantitative approach, the research examines temporal trends and regional variations to understand public engagement patterns with these AI tools. The findings reveal that ChatGPT consistently garners higher public interest in both countries, driven by its global recognition, multilingual capabilities, and extensive integration into educational and professional contexts. In contrast, DeepSeek shows sporadic increases in interest, particularly within niche communities and academic environments. The analysis highlights the influence of cultural, linguistic, and technological factors on AI adoption, emphasizing that while global brand strength supports widespread engagement, localized strategies are crucial for fostering sustained interest in emerging technologies. This study contributes to the understanding of AI adoption dynamics in Southeast Asia, offering insights for developers, educators, and policymakers to optimize AI integration in diverse socio-cultural contexts.

Mapping Public Interest in Cryptocurrency in Indonesia (2021–2024): Analyzing Geographical Disparities and Temporal Trends

2025-02-05T21:49:23+07:00

This study investigates public interest in cryptocurrency in Indonesia, focusing on related queries, geographical distribution, and temporal trends. Utilizing a quantitative descriptive approach with secondary data from Google Trends (January 2021–December 2024), the findings reveal that cryptocurrency is predominantly perceived as a trading tool, with terms like trading and crypto dominating search queries. Geographical analysis highlights significant regional disparities, with Bali demonstrating the highest interest due to its global connectivity and digitally literate population, while Nusa Tenggara Timur reflects challenges such as limited infrastructure and financial literacy. Temporal trends show fluctuations aligned with global market events and regulatory developments, stabilizing in 2023–2024 as public perceptions matured. These results emphasize the need for public education to broaden understanding beyond speculative trading and for inclusive strategies to address regional and educational gaps. This study contributes to the literature by offering a novel framework integrating geographical and temporal analyses, providing actionable insights for policymakers and stakeholders to foster equitable and sustainable cryptocurrency adoption in emerging markets.

Onchain Analysis: A Comparative Study of Decentralized Exchange (DEX) Activities on Ethereum, Solana, and Binance Smart Chain (BSC)

2025-02-13T16:18:15+07:00

The rapid expansion of decentralized exchanges (DEXs) has reshaped cryptocurrency trading, yet comparative cross-chain evaluations remain scarce, particularly in transaction efficiency, liquidity sustainability, and economic viability. This study conducts an on-chain comparative analysis of Uniswap (Ethereum), Raydium (Solana), and PancakeSwap (BSC) by examining transaction count, trade volume, protocol revenue, and total value locked (TVL). Using real-time blockchain data from DefiLlama, Dune Analytics, Artemis, Token Terminal, and Arkham, this research applies quantitative cross-chain modeling to assess the performance of each ecosystem. Findings reveal that Ethereum leads in trade volume and liquidity depth, driven by institutional adoption despite high transaction costs. Solana exhibits superior transaction efficiency, attracting high-frequency traders, though its lower TVL suggests liquidity retention challenges. BSC, once a major DeFi player, now faces stagnation, with declining trade volume and fragmented liquidity. Unlike prior studies focusing solely on transaction metrics, this research integrates protocol revenue as a sustainability indicator, offering a broader understanding of DEX viability. The results underscore the need for cross-chain liquidity bridges and Layer-2 scaling solutions to mitigate liquidity fragmentation and enhance DeFi efficiency. This study contributes to both academia and industry by providing empirical insights for blockchain developers, investors, and policymakers, emphasizing the importance of scalability, liquidity incentives, and revenue optimization. The findings support future research on multi-chain liquidity integration, AI-driven market-making, and governance frameworks, guiding the next evolution of decentralized financial ecosystems.

Will Artificial Intelligence Reshape the Global Workforce by 2030? A Cross-Sectoral Analysis of Job Displacement and Transformation

2025-02-24T18:51:52+07:00

The rapid advancement of artificial intelligence (AI) is transforming the global labor market, presenting both opportunities and challenges. This study investigates the extent of AI-driven job displacement and task transformation across industries, highlighting sector-specific vulnerabilities and workforce perceptions. Using secondary data from Statista’s global surveys (2022–2023), involving 22,816 employees, 1,684 business leaders, and 803 corporations, the study employs descriptive statistical analysis to identify patterns of job disruption and skill adaptation. The findings reveal that AI primarily reshapes job functions rather than eliminating entire occupations, with 57% of respondents reporting task augmentation and 36% expressing concern about job loss. Routine-based sectors, such as manufacturing and customer service, face higher displacement risks, while knowledge-based professions, including healthcare, education, and creative industries, experience AI as a complementary tool. Additionally, disparities in AI adoption are evident between large corporations and small-to-medium enterprises (SMEs), often due to resource limitations and varying digital readiness. The study concludes that successful AI integration hinges on proactive strategies, including continuous workforce reskilling, adaptive education systems, and ethical AI deployment. Policymakers, industry leaders, and educational institutions must collaborate to ensure an inclusive transition, prioritizing digital literacy and skills development. Future research should explore regional variations, firm-level case studies, and the long-term socio-economic impacts of AI adoption. Ultimately, this study underscores the importance of balancing technological advancement with workforce resilience to foster sustainable economic growth in an AI-driven era.

Integrating Artificial Intelligence with Blockchain: A Literature Review on Opportunities, Challenges, and Applications

2025-02-27T22:41:34+07:00

The integration of Artificial Intelligence (AI) and Blockchain represents a paradigm shift in digital transformation, offering enhanced security, scalability, and automation. While previous research has explored these technologies independently, this study provides a comprehensive review of their convergence, synthesizing insights across multiple domains such as finance, healthcare, and supply chain management. The findings highlight the bidirectional enhancement of AI-Blockchain integration: Blockchain reinforces AI’s reliability by ensuring data immutability and transparency, whereas AI optimizes Blockchain efficiency through intelligent consensus mechanisms and fraud detection. However, significant challenges remain, including scalability constraints, computational overhead, and regulatory concerns. This study contributes to the theoretical understanding of AI-Blockchain synergy by integrating concepts from Computational Trust Theory and Decentralized Ledger Theory. Practically, it provides actionable insights for industry stakeholders, particularly in decentralized finance, privacy-preserving AI models, and secure digital transactions. The novelty of this research lies in its examination of AI-Blockchain integration through geographical and temporal trends, revealing disparities in adoption and regulatory responses. Despite its potential, real-world implementation remains limited, necessitating further empirical validation and exploration of emerging technologies such as quantum computing and the Internet of Things (IoT). By addressing these gaps, this study serves as a foundation for future research and policy development, advocating for interdisciplinary collaboration to ensure secure, efficient, and ethical AI-Blockchain ecosystems. The implications extend beyond academia, offering strategic guidance for practitioners and policymakers navigating the complexities of this technological convergence.