The Cognitive Revolution: Navigating the Algorithmic Age of Artificial Intelligence
The Cognitive Revolution is not about Artificial Intelligence; it is about us and what makes us deeply unmistakably human.
We are living through a profound shift in which artificial intelligence is reshaping our industries, economies, and the very ways we think, decide, and connect.
This book invites readers to pause and reflect on that transformation ā not with fear or fascination, but with clarity and intention.
The Cognitive RevolutionĀ explores the exponential pace of technology with the deliberate, human pace of wisdom. Drawing on systems thinking, emotional intelligence, and strategic foresight, Diallo offers a grounded framework for navigating complexity with calm and purpose.
Through precise analysis and human-centered reflection, the book examines AIās potential to elevate or erode what matters most ā our empathy, shared values, and sense of meaning in work and society.
This is not a book of predictions, but of perspective. It is a call to thoughtful action ā to lead, design, and govern in ways that keep humanity at the center of innovation.
In this algorithmic age,Ā The Cognitive RevolutionĀ reminds us that our most extraordinary intelligence remains our capacity for compassion, clarity, and purpose.
The Cognitive Revolution: Navigating the Algorithmic Age of Artificial Intelligence
The Cognitive Revolution is not about Artificial Intelligence; it is about us and what makes us deeply unmistakably human.
We are living through a profound shift in which artificial intelligence is reshaping our industries, economies, and the very ways we think, decide, and connect.
This book invites readers to pause and reflect on that transformation ā not with fear or fascination, but with clarity and intention.
The Cognitive RevolutionĀ explores the exponential pace of technology with the deliberate, human pace of wisdom. Drawing on systems thinking, emotional intelligence, and strategic foresight, Diallo offers a grounded framework for navigating complexity with calm and purpose.
Through precise analysis and human-centered reflection, the book examines AIās potential to elevate or erode what matters most ā our empathy, shared values, and sense of meaning in work and society.
This is not a book of predictions, but of perspective. It is a call to thoughtful action ā to lead, design, and govern in ways that keep humanity at the center of innovation.
In this algorithmic age,Ā The Cognitive RevolutionĀ reminds us that our most extraordinary intelligence remains our capacity for compassion, clarity, and purpose.
For six months, Anna had watched her four-year-old son, Leo, fade. A mysterious illness had left him perpetually exhausted, baffling a team of dedicated pediatric specialists. After countless tests yielded no answers, one of the doctors proposed a last resort: a new AI diagnostic platform. The system ingested Leo's entire medical history, his genetic data, and the latest clinical research from around the globe. In under an hour, it returned a result that had eluded the human experts for months. It identified a rare, newly discovered genetic disorder and, remarkably, pointed to a new, AI-designed precision drug that could treat it. For the first time in months, Anna felt a surge of hope.
The relief, however, was short-lived. The treatment was astronomically expensive. When they submitted the request, their insurance provider's own AI-powered cost-benefit analysis system reviewed the case. Trained on millions of historical claims, the algorithm calculated the long-term cost-effectiveness of the treatment for such a rare condition and, in a fraction of a second, issued an automated denial.
One AI had offered her son a future; another had just taken it away.
Anna's story is not an outlier. It is a postcard from the presentāand a warning about the future we are all entering.
The Accelerating Disruption
The contemporary era is defined by profound, accelerating technological disruption, whose speed and scale challenge the very foundations of societal governance. The advent of advanced Artificial Intelligence (AI), particularly the emergence of powerful generative models, represents not merely an incremental advance in the digital age but a fundamental societal transformation on par with the great technological revolutions of human history. Yet, this revolution operates according to a logic entirely new to human experience.
The core dynamic that makes this moment unique was captured in a stark observation by technology leaders Aza Raskin and Tristan Harris: "nukes don't make stronger nukes, but AI makes stronger AI".Ā¹ This simple statement powerfully encapsulates the recursive, self-accelerating nature of artificial intelligence that lies at the heart of the contemporary challenge. This recursive power is the engine of what futurist Ray Kurzweil terms "The Law of Accelerating Returns," which posits that technological change is not linear, but exponential.Ā² As technology progresses, our ability to improve upon it also progresses, creating a synergistic feedback loop where Innovations build on each other at an ever-faster rate.
The evidence of this acceleration is all around us. Some analyses suggest that the computing power required for AI doubles every 100 days.Ā³ The societal adoption of these new capabilities is occurring at a blistering pace; ChatGPT reached over 50 percent of the US population within approximately two years of its launch, a level of penetration that took the internet 17 years to achieve.ā“ This compression of adaptation lines represents a profound rupture. In past revolutions, societies had decades, generations, or even centuries to adjust their institutions, educational systems, and social norms. Today, that window has shrunk to a matter of months.āµ
This is not just an acceleration of past trends but a "higher-order disruption".ā¶ In contrast, the Industrial Revolution was defined by the mechanization of physical labor, and the Digital Revolution by the connection of information; the AI revolution is the first to automate and augment cognition itselfāthe very engine of all other human progress.ā· When AI can write code, draft legal documents, and accelerate scientific discovery, it is not just changing our tools; it is changing the process of Innovation itself.āø The gravity of this moment is underscored by unprecedented warnings from the revolution's architects. Luminaries such as Geoffrey Hinton and Yoshua Bengio, who received the Turing Award for their foundational work, have publicly cautioned that the trajectory of AI development, if left unchecked, poses catastrophic and even "existential risks to humanity".ā¹ Their concerns, echoed in open letters signed by thousands of experts, have elevated the discourse from one of economic disruption to one of civilizational security, framing the challenge as a global priority on par with pandemics and nuclear war.Ā¹ā°
The unique, recursive nature of AI is the direct cause of this modern governance crisis. Our societal systems are designed to manage change driven by external human Innovation rather than by a technology's internal, self-improving logic. Past transformative technologies, like the steam engine or the semiconductor, were powerful but functionally static; their improvement depended entirely on the pace of human ingenuity and discovery.Ā¹Ā¹ AI, by contrast, is a tool for automating invention. It can be used to design more efficient semiconductor chips, write more powerful algorithms, and analyze vast datasets, thereby improving its performance and creating a powerful, self-reinforcing feedback loop.Ā¹Ā² This recursive engine is what drives the exponential pace of change.
Our governance systemsāour legal, ethical, and regulatory frameworksāare, by design, dominated by balancing feedback loops. They rely on processes like deliberation, precedent, and consensus-building, which intentionally introduce time delays to ensure stability and prevent hasty, ill-conceived actions in a world of linear, human-driven change.Ā¹Ā³ The result is a fundamental mismatch. Our technology changes exponentially, while our laws and institutions change incrementally. This explains why simply urging regulators to "go faster" is a futile strategy; it fails to address the underlying systemic mismatch.
The Central Challenge: The Governance Gap
The central problem this book addresses is the direct consequence of this structural mismatch: a "pacing problem," defined as the widening chasm between the exponential pace of AI development and the linear, reactive cadence of our ethical, legal, and regulatory frameworks.Ā¹ā“ It is not simply a matter of law struggling to keep up with Innovation; it is a fundamental conflict in their core operating principles.Ā¹āµ
This challenge is dangerously exacerbated by the "Collingridge Dilemma."Ā¹ā¶ This dilemma posits that in the early stages of a technology's development, its societal impacts are not yet foreseeable, making control difficult to justify. Yet, by the time the impacts become apparent and acute, the technology is so deeply entrenched in our economic and social structures that meaningful control has become prohibitively complex, expensive, and politically fraught.Ā¹ā· This dynamic explains why our traditional "wait-and-see" approach to regulationāobserving the emergence of a new technology, waiting for its negative externalities to become undeniable, and then, often after decades of accumulated harm, developing laws to mitigate themāis destined to fail in the AI era.Ā¹āø
The result of this governance gap is a dual crisis. On the one hand, it creates a landscape of unmanaged risk, where negative consequencesāfrom the systemic automation of bias and the erosion of democratic discourse to the proliferation of autonomous weaponsācan become rapidly entrenched. On the other hand, it creates a landscape of missed opportunities, where the immense potential of AI to solve humanity's most significant challenges is stifled by legal uncertainty, a lack of public trust, and the absence of a clear, shared vision for its responsible deployment.
A New Cognitive Toolkit: The Four Lenses of Navigation
Navigating this unpaved road requires more than just faster laws or greater technical expertise; it demands a new Cognitive toolkit āa more sophisticated way of understanding and shaping our complex reality. This book provides that toolkit through four interconnected analytical lenses. They are not independent concepts but a synthesized framework for analysis and action, designed to equip leaders, policymakers, and citizens with the wisdom needed to steer the Cognitive revolution. While others have focused on the technical or ethical dimensions in isolation, this book demonstrates that only an integrated cognitive framework can address a challenge that is simultaneously systemic, human, and exponential.
Lens 1: Systems Thinking
The first lens is systems thinking, a holistic approach that moves beyond analyzing isolated events to comprehending the intricate web of interconnections, feedback loops, and emergent properties that constitute a complex whole.Ā¹ā¹ A systems thinker resists the urge for simple, linear conclusions, instead asking not just "what is the immediate problem?" but "what are the underlying structures and mental models generating this pattern of problems over time?".Ā²ā° When applied to AI, this perspective is transformative. It allows us to see artificial intelligence not as a discrete tool but as a potent catalyst embedded within our deepest societal, economic, and geopolitical systems.Ā²Ā¹ A systems view helps map the reinforcing feedback loops that can amplify harm, for instance, how biased historical data is used to train a hiring algorithm, which then produces biased outcomes that are recorded as new data, creating a vicious cycle that systematically entrenches discrimination.Ā²Ā² It also helps identify "leverage points"āplaces within a complex system where a small, well-focused intervention can produce significant, enduring, and often cascading positive change.Ā²Ā³
Lens 2: Emotional Intelligence
The second lens is emotional intelligence (EQ), which is the capacity to recognize, understand, and manage one's own emotions, as well as recognize, understand, and influence others' feelings.Ā²ā“ It encompasses core competencies such as empathy, self-awareness, collaboration, and the ability to make sound, value-driven judgments, particularly under pressure.Ā²āµ As AI commoditizes routine analytical and procedural tasks, EQ ascends from a "soft skill" to a critical, strategic asset.Ā²ā¶ Its application to AI governance is essential for three reasons. First, it is the foundation of ethical reasoning. AI can process data and calculate probabilities, but it lacks genuine empathy and cannot make the nuanced, value-aligned moral judgments that are indispensable for its responsible deployment.Ā²ā· Second, EQ is the bedrock of trust. Public and organizational trust in AI is not primarily a function of its technical performance but of the perceived integrity and empathy of the people and institutions deploying it.Ā²āø Third, emotional intelligence is the key to leading organizations and societies through the profound, often fearful, cultural and operational shifts that AI precipitates.Ā²ā¹
Lens 3: Strategic Foresight
The third lens is strategic foresight, a systematic and disciplined practice for exploring, anticipating, and shaping multiple plausible futures, rather than attempting to predict a single, deterministic one.Ā³ā° Its goal is not prediction but enhanced preparedness. Strategic foresight is the essential tool for shifting governance from a reactive to a proactive posture. In the context of AI, it allows policymakers to move beyond responding to today's crises and begin preparing for tomorrow's challenges. By employing established methods such as horizon scanning and scenario planning, leaders can "stress-test" current policies and strategies against a range of plausible AI-driven futuresāfor example, futures characterized by mass technological unemployment versus those defined by seamless human-AI collaboration.Ā³Ā¹ This process enables the design of more resilient, robust, and adaptive strategies capable of navigating deep uncertainty.
Lens 4: Anticipatory Governance
The fourth and final lens, anticipatory governance, represents the practical synthesis of the other three. It is a proactive, adaptive, and collaborative governance model designed to address emerging technological risks before they escalate into full-blown crises.Ā³Ā² It is the operational framework for escaping the Collingridge Dilemma. Anticipatory governance integrates foresight directly into the policymaking process, favoring flexible mechanisms that can co-evolve with technology. These include regulatory sandboxes, which provide controlled environments for innovators and regulators to co-design and test new technologies, and tiered, risk-based rules, such as those found in the European Union's AI Act, which apply stricter oversight to higher-risk applications.Ā³Ā³ By building the institutional capacity to learn, adapt, and act at a speed commensurate with the technology it seeks to oversee, anticipatory governance offers a viable path to closing the pacing gap.Ā³ā“
These four lenses, when combined, reveal more profound truths about the transition to AI. The very technical limitations of AI, particularly its lack of genuine emotional intelligence, are not just a problem to be solved; they define the space where uniquely human skillsāsuch as leadership, collaboration, ethical judgment, and creativityābecome irreplaceable and economically scarce.Ā³āµ Emotional intelligence is thus the key to unlocking this "human advantage." At the same time, the open-source ethos that fuels much of AI's rapid Innovation also creates the "Pandora's Box" dilemma of dual-use proliferation, making it nearly impossible to control the spread of dangerous capabilities.Ā³ā¶ A central governance challenge, therefore, is to manage this paradox: how to foster the open Innovation needed for progress while simultaneously creating safeguards against the proliferation it enables. This requires a nuanced, tiered approach to governance, as explored in Part III of this book, rather than a one-size-fits-all solution.
Figure 1 ā A New Cognitive Toolkit: The Four Lenses of Navigation
The following table provides a concise summary of this integrated governance framework.
Table 1: The Four Lenses of AI Governance
Lens
Definition
Core Principles
Primary Application to AI Governance
Systems Thinking
A holistic approach that views problems as part of an overall system, focusing on interconnections and feedback loops.Ā³ā·
Interconnections, Emergence, Feedback Loops, Causality, time Delays.Ā³ā·
Analyzing AI's ripple effects, identifying leverage points for policy, and understanding how small biases can amplify into systemic discrimination.Ā³ā·
Emotional Intelligence
The ability to recognize, understand, and manage one's own emotions and influence those of others.Ā³āø
Self-Awareness, Empathy, Relationship Management, Ethical Judgment.Ā³āø
Building public trust, ensuring human values guide AI development, mitigating bias through empathetic design, and leading through change.Ā³āø
Strategic Foresight
A systematic discipline for exploring, anticipating, and shaping multiple plausible futures.Ā³ā¹
Horizon Scanning, Scenario Planning, Identifying Weak Signals, Stress-Testing Strategies.Ā³ā¹
Moving policy from a reactive to a proactive stance, preparing for a range of AI-driven societal shifts, and identifying long-term risks and opportunities.Ā³ā¹
Anticipatory Governance
A proactive governance model that integrates foresight and stakeholder engagement to address emerging tech risks beforethey become crises.ā“ā°
Proactivity, Adaptiveness, Inclusivity, Continuous Learning.ā“ā°
Creating flexible, co-evolving regulatory frameworks (e.g., the EU AI Actā“Ā¹) that can keep pace with exponential technological change.ā“ā°
The Journey Ahead: An Overview of the Book's Structure
This book is structured as a comprehensive journey, guiding the reader from a foundational understanding of the AI disruption to a practical framework for action. It is organized into four distinct parts.
Part I, The AI Disruption as a Complex Adaptive System, deconstructs the fundamental nature of this revolution. We will apply the lens of systems thinking to analyze past technological transformationsāfrom the agricultural revolution's reconfiguration of our relationship with the land to the industrial revolution's "science of simplification" and the digital revolution's creation of a new information ecosystem. This historical perspective allows us to understand the timeless patterns of disruption and the unique, accelerating dynamics of the AI era. We will then map the interconnected physical, digital, and economic engine that powers this change āfrom semiconductor chips and energy-hungry data centers to the rise of the "inference economy"āand reveal the self-reinforcing feedback loops that drive its exponential growth.ā“Ā²
Part II, The Transformation of Work, Worth, and the Workforce, explores the profound and personal impact of AI on human labor. We will move beyond the simplistic and polarized narrative of mass job replacement to analyze the complex rebalancing of tasks, the rise of human-AI augmentation, and the critical need for a lifelong learning ecosystem. This part examines the displacement of routine Cognitive tasks, the creation of entirely new job categories, and the emergence of the "new-collar" worker. Here, we will establish emotional intelligence not as a soft skill but as a core strategic asset for individuals, organizations, and societies navigating this transition. We will explore the proactive strategies that corporations and labor unions are adopting to co-design the future of work.ā“Ā³
Part III, The Widening Gyre: Ethical Frontiers and Governance Frameworks for the Algorithmic Age, confronts the "governance gap" head-on. We will systematically analyze the multifaceted ethical and legal challenges of the algorithmic ageāfrom the automation of bias and the erosion of truth to the proliferation of dual-use capabilities and the specter of existential risk. Each of these Frontiers is examined through our four-lens framework to reveal its deeper systemic complexities. We will then synthesize these insights into a concrete, actionable framework for anticipatory governance that can bridge the chasm between Innovation and regulation, blending the strengths of emerging global models like the EU's AI Act and the U.S. Executive Order on AI.ā“ā“
Part IV, Architecting a Virtuous Cycle for Humanity, applies this governance framework to our most pressing global challenges. We will map AI's dual potential to either accelerate or inhibit progress on the United Nations' Sustainable Development Goals, framing the future as a deliberate choice between a "virtuous cycle" of shared prosperity and a "vicious cycle" of deepening inequality. This section outlines the collaborative architecture that guides this technology, identifying the key roles of entrepreneurs, policymakers, and emotionally intelligent strategists. It culminates in a clear, stakeholder-specific call to action, providing a roadmap for collectively steering the Cognitive revolution toward a more equitable, sustainable, and human-centric future.ā“āµ
The Choice Before Us
The future trajectory of artificial intelligence is not a predetermined technological outcome to be passively awaited. It is a product of our collective choices, investments, and policies. The evidence presented throughout this book will make clear that we stand at a critical juncture, facing a choice between two profoundly different futures. The path of inaction, fragmentation, and reactive governance leads toward a vicious cycle of deepening inequality, unmanaged risk, and social instability. The path of proactive, collaborative, and systems-aware stewardship leads toward a virtuous cycle of responsible Innovation, shared prosperity, and sustainable human development. This book's ultimate purpose is to provide the analytical tools, strategic frameworks, and actionable wisdom necessary to make that choice deliberately and wisely. It is intended as a guide for the architects of the 21st centuryāthe policymakers, business leaders, educators, innovators, and citizensāwho are now tasked with designing a future where our most powerful technology serves our most profound human values.
Footnotes
Ā¹ Aza Raskin, in "The AI Dilemma," Your Undivided Attention (podcast), Center for Humane Technology, March 2023. Transcript available at: https://www.humanetech.com/podcast/the-ai-dilemma.
Ā² Ray Kurzweil, "Kurzweilās Law (aka 'the law of accelerating returns')," Edge.org, January 12, 2004. https://www.writingsbyraykurzweil.com/the-law-of-accelerating-returns, accessed June 10, 2025.
Ā³ Andrew Rocco for Zacks, "Nuclear's Moment: Securing US AI Supremacy," Nasdaq, last modified July 14, 2025. https://www.nasdaq.com/articles/nuclears-moment-securing-us-ai-supremacy, accessed June 10, 2025.
This article attributes the claim to the World Economic Forum. Other analyses report different doubling times. For example, research from Our World in Data suggests that since 2010, the computation used for training notable AI systems has doubled approximately every six months. See Charlie Giattino and Veronika Samborska, "Since 2010, the training computation of notable AI systems has doubled every six months," Our World in Data, October 16, 2023, https://ourworldindata.org/data-insights/since-2010-the-training-computation-of-notable-ai-systems-has-doubled-every-six-months.
ā“ Elon University News Bureau, "Survey: 52% of U.S. adults now use AI large language models like ChatGPT," Today at Elon, March 12, 2025. https://www.elon.edu/u/news/2025/03/12/survey-52-of-u-s-adults-now-use-ai-large-language-models-like-chatgpt/, accessed June 10, 2025. The survey, conducted in January 2025, found that 52% of U.S. adults use large language models. The comparison to the internet's adoption rate is based on the observation that this level of penetration was achieved in just over two years since ChatGPT's public launch in November 2022, a pace far exceeding previous technologies.
āµ Ibid. The rapid adoption timeline described in the survey supports the conclusion that societal adaptation windows have dramatically compressed.
ā¶ Tristan Harris and Aza Raskin, "The AI Dilemma," Your Undivided Attention (podcast), Center for Humane Technology, March 2023. The term reflects the unique nature of AI as a technology that automates cognition, distinguishing it from previous technological revolutions.
ā· Ibid.
āø Ibid.
ā¹ Center for AI Safety, "Statement on AI Risk," May 30, 2023. https://www.safe.ai/work/statement-on-ai-risk, accessed June 10, 2025. Geoffrey Hinton and Yoshua Bengio are the first two signatories to this statement.
Ā¹ā° Ibid. The full text of the statement is: "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war."
Ā¹Ā¹ Donella H. Meadows, Thinking in Systems: A Primer, ed. Diana Wright (White River Junction, VT: Chelsea Green Publishing, 2008), 25ā49. Meadows' analysis of systems with balancing feedback loops, which seek stability, contrasts with the reinforcing loops driving AI.
Ā¹Ā² Ray Kurzweil, "Kurzweilās Law (aka 'the law of accelerating returns')," Edge.org, January 12, 2004, accessed June 10, 2025. Kurzweil describes this as a core feature of technological evolution: "Each stage of evolution provides more powerful tools for the next." https://www.edge.org/response-detail/10600.
Ā¹Ā³ Donella H. Meadows, Thinking in Systems: A Primer, 25ā34. Meadows describes balancing feedback loops as stabilizing, goal-seeking structures that are fundamental to social and political systems designed for deliberation and stability.
Ā¹ā“ Adam Thierer, "The Pacing Problem, the Collingridge Dilemma & Technological Determinism," Technology Liberation Front, August 16, 2018. https://techliberation.com/2018/08/16/the-pacing-problem-the-collingridge-dilemma-technological-determinism/, accessed June 10, 2025. Thierer discusses the concept, originally articulated by Larry Downes, that "technology changes exponentially, but social, economic, and legal systems change incrementally."
Ā¹āµ Ibid.
Ā¹ā¶ David Collingridge, The Social Control of Technology (New York: St. Martin's Press, 1980).
Ā¹ā· Ibid. As summarized by Evgeny Morozov, Collingridge argued: "When change is easy, the need for it cannot be foreseen; when the need for change is apparent, change has become expensive, difficult and time consuming." See Evgeny Morozov, "The Collingridge Dilemma," response to the "Edge Question 2012," Edge.org, 2013, https://www.edge.org/response-detail/10898, accessed June 10, 2025.
Ā¹āø This conclusion is a synthesis of the two preceding concepts. See Collingridge, The Social Control of Technology, and Thierer, "The Pacing Problem."
Ā¹ā¹ Donella H. Meadows, Thinking in Systems: A Primer, ed. Diana Wright (White River Junction, VT: Chelsea Green Publishing, 2008), 2ā7.
Ā²ā° Ibid., 89.
Ā²Ā¹ Ibid., 12ā16.
Ā²Ā² Ibid., 56ā58. Meadows explains how reinforcing feedback loops ("vicious cycles") can amplify small initial biases into systemic outcomes.
Ā²Ā³ Donella H. Meadows, "Leverage Points: Places to Intervene in a System," The Sustainability Institute, 1999. This concept was later integrated into the posthumously published Thinking in Systems.
Ā²ā“ Daniel Goleman, Emotional Intelligence: Why It Can Matter More Than IQ (New York: Bantam Books, 1995), 33ā45.
Ā²āµ Ibid.
Ā²ā¶ Ibid., 148ā163. Goleman argues that as work becomes more complex and collaborative, EQ becomes a primary determinant of professional success.
Ā²ā· Ibid., 3ā29. Goleman's core thesis is that the "emotional mind" provides the value-based context that the "rational mind" lacks.
Ā²āø Ibid. Trust is presented as an outcome of relational competencies rooted in emotional intelligence, such as empathy and integrity.
Ā²ā¹ Ibid.
Ā³ā° Andy Hines and Peter Bishop, Thinking about the Future: Guidelines for Strategic Foresight, 2nd ed. (Houston, TX: Hinesight, 2015).
Ā³Ā¹ Ibid.
Ā³Ā² David H. Guston, "Understanding 'anticipatory Governance'," Social Studies of Science 44, no. 2 (April 2014): 218ā242. Guston defines it as "a broad-based capacity extended through society that can act on a variety of inputs to manage emerging knowledge-based technologies while such management is still possible." See also Organisation for Economic Co-operation and Development (OECD), "Anticipatory Innovation Governance: Shaping the future through proactive policy making," OECD, accessed June 10, 2025, https://www.oecd.org/en/topics/anticipatory-governance.html.
Ā³Ā³ Guston, "Understanding 'anticipatory Governance'"; European Commission, "The AI Act," accessed June 10, 2025, https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai.
Ā³ā“ OECD, "Anticipatory Innovation Governance."
Ā³āµ Daniel Goleman, Emotional Intelligence: Why It Can Matter More Than IQ, 148ā163.
Ā³ā¶ Center for AI Safety, "Statement on AI Risk," May 30, 2023. The proliferation of powerful, open-source models is a key driver of the risks that signatories like Bengio and Hinton have warned about.
Ā³ā· Donella H. Meadows, Thinking in Systems: A Primer, ed. Diana Wright (White River Junction, VT: Chelsea Green Publishing, 2008).
Ā³āø Daniel Goleman, Emotional Intelligence: Why It Can Matter More Than IQ (New York: Bantam Books, 1995).
Ā³ā¹ Andy Hines and Peter Bishop, Thinking about the Future: Guidelines for Strategic Foresight, 2nd ed. (Houston, TX: Hinesight, 2015).
ā“ā° David H. Guston, "Understanding 'anticipatory Governance'," Social Studies of Science 44, no. 2 (April 2014): 218ā242; OECD, "Anticipatory Innovation Governance."
ā“Ā¹ European Commission, "The AI Act," accessed June 10, 2025, https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai.
ā“Ā² Ray Kurzweil, "Kurzweilās Law (aka 'the law of accelerating returns')," Edge.org, January 12, 2004.
ā“Ā³ The White House, "Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence," Federal Register 88, no. 209 (November 1, 2023): 75191ā75226. Section 7 specifically addresses "Supporting Workers."
ā“ā“ European Commission, "The AI Act"; The White House, "Executive Order 14110."
ā“āµ The White House, "Executive Order 14110," Section 1, "Purpose."
Bibliography
Center for AI Safety. "Statement on AI Risk." May 30, 2023. https://www.safe.ai/work/statement-on-ai-risk. Accessed June 10, 2025.
Collingridge, David. The Social Control of Technology. New York: St. Martin's Press, 1980.
Elon University News Bureau. "Survey: 52% of U.S. adults now use AI large language models like ChatGPT." Today at Elon, March 12, 2025. https://www.elon.edu/u/news/2025/03/12/survey-52-of-u-s-adults-now-use-ai-large-language-models-like-chatgpt/. Accessed June 10, 2025.
European Commission. "The AI Act." Accessed June 10, 2025. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai.
Goleman, Daniel. Emotional Intelligence: Why It Can Matter More Than IQ. New York: Bantam Books, 1995.
Guston, David H. "Understanding 'anticipatory Governance'." Social Studies of Science 44, no. 2 (April 2014): 218ā242.
Hines, Andy, and Peter Bishop. Thinking about the Future: Guidelines for Strategic Foresight. 2nd ed. Houston, TX: Hinesight, 2015.
Kurzweil, Ray. "Kurzweilās Law (aka 'the law of accelerating returns')." Edge.org, January 12, 2004. https://www.writingsbyraykurzweil.com/the-law-of-accelerating-returns. Accessed June 10, 2025.
Meadows, Donella H. Thinking in Systems: A Primer. Edited by Diana Wright. White River Junction, VT: Chelsea Green Publishing, 2008.
Organisation for Economic Co-operation and Development (OECD). "Anticipatory Innovation Governance: Shaping the future through proactive policy making." Accessed June 10, 2025. https://www.oecd.org/en/topics/anticipatory-governance.html.
Rocco, Andrew, for Zacks. "Nuclear's Moment: Securing US AI Supremacy." Nasdaq. Last modified July 14, 2025. https://www.nasdaq.com/articles/nuclears-moment-securing-us-ai-supremacy. Accessed June 10, 2025.
Thierer, Adam. "The Pacing Problem, the Collingridge Dilemma & Technological Determinism." Technology Liberation Front, August 16, 2018. https://techliberation.com/2018/08/16/the-pacing-problem-the-collingridge-dilemma-technological-determinism/. Accessed June 10, 2025.
The White House. "Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence." Federal Register 88, no. 209 (November 1, 2023): 75191ā75226. https://www.federalregister.gov/documents/2023/11/01/2023-24283/safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence. Accessed June 10, 2025.
This is a provocative, well-constructed, and thorough narrative which chronicles progress through innovation from a balanced perspective weighing the opportunities it presents with realities that pose risks due to the speed and scale of acceleration. It is brought into perspective as the author, Diallo illustrates the difference between widespread proliferation of ChatGPT in worldwide usage over the past year versus the 17 years it took to reach that level of adoption with the internet. The focus on the possibilities for amplification of intelligence as opposed to the previous more utilitarian functions of connectivity and mechanization of the internet is a notable differentiation in strategic value. He describes this transformation of the cognitive dimension as more pervasive and personal and issues a stark warning that along with other measures of progress, there is a mental health crisis associated with algorithmic engagement and other challengewhen faced with max uncertainty. The bookās outlining of the structural geopolitical interdependencies and the impending inference economy is forward-thinking and not without challenges, a timely, complex topic worth further exploration.
The emphasis on systems thinking and feedback loops is an essential aspect of a digital society as is the innovative governance models that can adapt to changing circumstances. He differentiates between novel outputs that are patterns of training data and the kinds of original human ability to generate creative and culturally meaningful forms of expression is of critical importance in maintaining a human in the proactive process of co-creation.
It supports the priciples of building trustworthiness by outlining the different frameworks, laws, and international governing bodies role in establishing ethics, principles to protect human rights, freedoms, and increase transparency, and environmental protections, and calls for a new social contract. It does not rely on any one source, but recommends a multi-stakeholder approach across diverse roles in society and institutions. The examples of virtuous cycles further highlight how these technologies can be applied towards specific examples of SDG's.
As information on this topic is saturated, this assimilation of topics is thorough, balanced, and includes depth of supporting research. It portrays the information in an accessible format that is a suggested 'must read' for organizational leaders who need to wrangle through these issues by mitigating risks and designing solutions that can best meet some of the most urgent and critical issues as well as advancement through innovation. In addition to the accuracy and thoroughness, Dallio brings a level of humanness in approaching the Cognitive Revolution that people are desperately seeking that builds confidence about one's relevancy in a fast-changing paradigm.