Novel Divergent Thinking: Critical Solutions for the Future

The Necessity of Advanced Technologies for Achieving a 10% Sustainable World GDP Growth Rate

by | Apr 2, 2025 | Catalyzer_Think_Tank, Ipvive, Lenzu, myAshisuto, UP_Electromods

Co-developed by the Catalyzer Think Tank divergent thinking and Gemini Deep Research too.

The aspiration for a sustained 10% compound annual growth rate (CAGR) in world GDP represents an extraordinarily ambitious objective, one that would signify a remarkable acceleration in global economic activity. Such a transformative rate of expansion holds the promise of dramatically improving living standards, alleviating poverty on a massive scale, and providing the resources needed to address pressing global challenges. We believe achieving this ambitious goal necessitates the convergence and synergistic deployment of several cutting-edge technologies. These include relational intelligence, personalized artificial intelligence leveraging hyperbolic geometry lensing and granular to long chain of thought geometric associative memory (GGAM), distributed personal privacy, security, and data management facilitated by blockchain infrastructure like the InterPlanetary File System (IPFS), quantum sensing and computing, and the relational intelligence mesh of today’s edge Internet of Things (IoT), computing, and communication technologies. 

To ascertain the plausibility of this vision, it is crucial to first examine the historical context of global economic growth and the projections for the future under current conditions. Following this, a detailed analysis of each of the proposed technological pillars and their potential contributions to economic productivity and innovation is essential. Finally, the role of Ipvive’s WiSE technology and the limitations and challenges associated with relying solely on these technologies to achieve such an ambitious growth target must be carefully evaluated.

Historical and Projected Global GDP Growth Rates

Understanding the feasibility of a sustained 10% CAGR in world GDP requires a thorough examination of historical growth trends. Data spanning from 1961 to 2023 reveals the typical pace of global economic expansion.1 Over this period, annual global GDP growth rates have fluctuated, with notable peaks generally falling within the 4-6% range. For instance, in 1964, the global GDP growth rate reached 6.57% 1, and in 1973, it stood at 6.41%.2 However, these periods of higher growth have not been sustained over extended durations. Analyzing the average growth rates across different decades further illustrates this point, indicating that a 10% CAGR would represent a significant departure from historical norms. The absence of any prolonged period of such rapid expansion suggests that the underlying mechanisms driving global economic activity have historically operated at a more moderate pace. Achieving the user’s target would therefore necessitate a fundamental transformation in these mechanisms, potentially through the revolutionary impact of the proposed technologies.

Periods of both high growth and significant contraction offer valuable insights into the factors influencing global economic trends. The negative growth rates observed in 2009 1, -1.36% 2) and 2020 1, -2.93% 2) underscore the impact of global crises on economic activity. Conversely, periods of robust growth often coincide with significant technological advancements and favorable global conditions. However, even the most transformative technological waves of the past have not propelled the global economy to a sustained 10% CAGR, indicating that the technologies envisioned by the user would need to exert an influence far exceeding that of previous innovations to achieve such a remarkable outcome.

Current and near-future projections from major international institutions paint a picture of continued moderate global economic growth. The World Bank anticipates a stable but relatively low growth rate of around 2.7% in 2024, holding steady at 2.7% for both 2025 and 2026.3 This projection is below the historical average of 3.7% observed between 2000 and 2019.7 Notably, the World Bank highlights that developing economies, which contribute significantly to global growth, are facing their weakest long-term growth outlook since the turn of the century.4 The International Monetary Fund (IMF) projects a slightly higher global growth rate of 3.3% for both 2025 and 2026 7, which is also below the 3.7% average of the 2000-2019 period. Similarly, the Organisation for Economic Co-operation and Development (OECD) forecasts global growth to be around 3.1% in 2025 and 3.0% in 2026 10, with concerns about increasing trade restrictions potentially hindering economic momentum.12 Even longer-term projections from Trading Economics suggest a trend around 3.00% in 2026 and 2.80% in 2027.13 The consistent projection of moderate growth across these leading economic forecasting bodies underscores the significant gap between current expectations and the user’s ambitious 10% CAGR target. Achieving such a high growth rate would require a disruption to existing trends of a magnitude not currently anticipated by economic experts.

Deconstructing the Technological Pillars

Relational intelligence in the context of artificial intelligence refers to the application of data analytics, machine learning, and other advanced technologies to decipher and leverage the intricate web of relationships between individuals, groups, or entities.14 Unlike emotional intelligence, which focuses on understanding and managing emotions, relational intelligence emphasizes the functional aspects of relationships, aiming to provide actionable insights into patterns, behaviors, and potential opportunities.17 This capability to analyze complex networks of connections within vast datasets holds the potential to unlock hidden patterns and correlations, leading to more informed decision-making, optimized resource allocation, and the identification of novel avenues for innovation and economic expansion.

The personalization of artificial intelligence through techniques like hyperbolic geometry lensing and GGAM could further amplify these benefits. Hyperbolic geometry, with its inherent capacity to efficiently represent hierarchical data and complex relationships with less distortion than traditional Euclidean space 19, offers a powerful tool for creating more accurate and nuanced AI models. Applying this to personalization could lead to more effective recommendations, targeted interventions, and ultimately boost productivity and innovation across sectors such as e-commerce, education, and healthcare. GGAM, or granular to long chain of thought geometric associative memory, represents a machine learning approach that enables AI systems to build complex associative memories by linking fine-grained data points into extended chains of thought using geometric principles.29 This enhanced associative capability could allow AI to process information in a more sophisticated, human-like manner, fostering breakthroughs in scientific discovery, technological innovation, and problem-solving, all of which are critical for substantial economic growth. While current estimates suggest a modest boost to labor productivity from AI adoption (around 0.5-0.6%) 30, the transformative potential of AI lies in its ability to augment human capabilities, automate tasks, and drive innovation across a wide range of industries, paving the way for significantly higher economic growth in the future.32 The application of relational intelligence in areas like supply chain optimization, healthcare, and finance 39 further underscores its broad applicability and potential to address specific challenges and enhance efficiency across key economic sectors.

Blockchain infrastructure, exemplified by systems like IPFS, plays a crucial role in enhancing data management, security, and computational capabilities. Blockchain technology provides a distributed, immutable ledger for recording data in a secure and transparent manner.42 The InterPlanetary File System (IPFS) is a peer-to-peer, content-addressed system designed for decentralized file storage and sharing, enhancing data resilience and resisting censorship.47 While IPFS is not a blockchain itself, it complements blockchain technologies by providing a robust and secure infrastructure for managing the vast amounts of data generated by AI and IoT systems. This secure and resilient data management foundation fosters trust and enables more efficient data sharing, which is essential for accelerating AI development and facilitating economic collaboration and innovation.

Quantum technologies, encompassing both sensing and computing, offer the potential for significant advancements across various sectors relevant to GDP growth. Quantum sensors possess the capability to perform measurements with unprecedented levels of precision in fields such as navigation, medical diagnostics, materials science, and environmental monitoring.53 These enhanced sensing capabilities could lead to breakthroughs in scientific understanding, the development of novel technologies, and improvements in existing industries, all with the potential to drive economic growth. Quantum computing, with its ability to solve complex problems far faster than classical computers, holds immense promise for accelerating innovation in computationally intensive areas like drug discovery, financial modeling, materials science, and optimization.58 The anticipated economic impact of quantum computing is substantial, with projections suggesting a potential contribution of $1 trillion by 2035.64 This highlights the significant role that quantum technologies are expected to play in future economic expansion.

The integration through relational intelligence of today’s edge IoT, computing, and communication technologies is also poised to contribute to increased efficiency and innovation across industries. Edge computing involves processing data closer to its source, offering benefits such as reduced latency, improved bandwidth utilization, enhanced data privacy and security, and increased reliability.66 This capability for real-time data processing and decision-making at the edge is crucial for many IoT applications. The Internet of Things generates vast amounts of data from connected devices, providing real-time insights, enabling predictive maintenance, optimizing resource management, and improving customer experiences.70 Advancements in communication technologies, particularly the rollout of 5G networks, are critical for providing the high-speed, low-latency connectivity required to support the efficient operation of edge computing and IoT deployments.74 Furthermore, the synergistic relationship between edge IoT, computing, communication, and artificial intelligence is particularly powerful. Deploying AI algorithms at the edge allows for intelligent processing and decision-making at the device level, further enhancing efficiency and enabling new autonomous applications across various sectors.79

Ipvive’s WiSE Deep Tech: An In-Depth Analysis

Ipvive’s WiSE deep technology is presented as a personalization engine and complex adaptive system architecture that leverages machine learning, the mathematics of personalization and relational intelligence, hyperbolic lensing, and GGAM to understand hidden relationships and cycles within data.29 WiSE aims to provide a holistic understanding of complex systems by uncovering non-obvious connections, potentially offering a more insightful perspective than traditional analytical methods. The platform employs a “fast cycle” architecture for identifying divergent triggers and a “slow cycle” architecture for integrating established scientific principles and enabling guided learning of unknown-unknowns.29 This dual-cycle approach suggests a system capable of both rapid response and long-term understanding, which could be valuable for managing and accelerating global economic growth.

WiSE incorporates several “Super” functionalities, including Super Sensing© (combining imperfect and dynamic multimodal data), Super Judgement© (building a map of inherent complexity from multiple perspectives), Ultra-Personalized Nudge© (providing customized guidance), Super Verification© (comparing and verifying multiple perspectives), and Guided Learning© (discovering divergence and illuminating paths to future patterns).29 These features collectively aim to create “Relational Intelligence” and enable human-machine synergies for improved comprehension and decision-making. The claimed benefits of WiSE include personalized understanding, improved human-machine collaboration, enhanced decision-making, unlocking hidden potential, driving innovation, and navigating complex situations.29 These benefits align with the key requirements for achieving a substantial and sustainable increase in global GDP growth.

WiSE’s focus on relational intelligence and personalized insights could potentially provide a crucial layer of coordination and optimization for the other technologies discussed, maximizing their combined impact on economic growth. It is conceivable that WiSE could act as a unifying architecture, orchestrating the synergistic deployment of AI, blockchain, and quantum technologies towards the goal of accelerated GDP growth. However, it is important to note that the current information available lacks specific details on how WiSE integrates with blockchain infrastructure like IPSF and quantum technologies.29 

Alternative Perspectives, Limitations, and Challenges Associated with Relying Solely on These Specific Technologies

While the convergence of relational intelligence, personalized AI, blockchain, quantum technologies, and edge IoT/computing/communication, potentially orchestrated by WiSE, holds significant promise for driving economic growth, it is crucial to consider alternative perspectives and potential limitations. The widespread deployment of these technologies could lead to unintended consequences, such as job displacement due to automation, potentially increasing economic inequality.33 Furthermore, equitable access to these advanced technologies across different countries and populations remains a significant challenge, potentially widening the digital divide and limiting their global impact on GDP growth.74

Relying solely on technological solutions without addressing fundamental economic, social, and political factors could also limit the achievement of a 10% sustainable global GDP growth rate. Factors such as education, infrastructure, governance, global cooperation, and resource availability play critical roles in economic development.84 Achieving such a high growth rate likely requires a holistic approach that encompasses progress in these broader areas alongside technological innovation. The resource requirements, including energy and materials, for the widespread deployment and operation of these technologies must also be considered to ensure long-term sustainability.13 Additionally, the economic benefits of technology adoption may experience diminishing returns if not accompanied by complementary investments in human capital and organizational changes.31

Ethical considerations surrounding AI, data privacy, and quantum technologies also need careful management to ensure public trust and prevent negative societal consequences that could hinder economic progress.84 The integration of new technologies into the economy also involves “adjustment costs” related to initial investments and organizational changes, which could offset some of the immediate economic benefits.33 Finally, geopolitical tensions and protectionist trade policies could create headwinds that limit the potential for technology-driven economic growth.5 Given these complexities and the historical context, achieving a sustained 10% CAGR remains an exceptionally ambitious goal that likely requires a confluence of factors extending beyond the deployment of these specific technologies alone.

Conclusion

In conclusion, the aspiration for a sustained 10% world GDP growth rate is historically unprecedented and faces significant headwinds based on current economic projections. While relational intelligence, personalized AI leveraging hyperbolic geometry lensing and GGAM, blockchain infrastructure like IPSF, quantum technologies (sensing and computing), and edge IoT/computing/communication possess substantial potential to drive innovation, efficiency, and productivity across the global economy, achieving such a dramatic and sustained acceleration solely through their deployment is highly improbable. Ipvive’s WiSE deep tech offers a promising unifying architecture with its focus on relational intelligence and personalized insights, but the current lack of detailed information regarding its integration with key technologies like blockchain and quantum computing raises questions about its readiness to serve as the fundamental missing engine for such a transformative outcome.

A more realistic scenario involves a notable increase in the rate of global GDP growth driven by the synergistic effects of these technological advancements, potentially orchestrated by a platform like WiSE. However, reaching a sustained 10% CAGR would likely necessitate significant and concurrent progress in global cooperation, economic policy, resource management, and addressing ethical considerations. Continued research, development, and responsible implementation of these technologies, alongside a holistic approach to global challenges, will be crucial for maximizing their potential to foster sustainable and inclusive global prosperity, even if the ambitious target of a 10% sustained growth rate remains a considerable challenge.

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