The Intellectual Foundation
This curated collection reveals a continuum: from evolutionary computation that shaped biological intelligence, to architectural principles that can guide artificial intelligence, to practical wisdom for human decision-making. Each book contributed essential insights to this unified framework exploring intelligence across domains.
The chronological arrangement shows how thinking evolved from technical foundations to philosophical implications, mirroring the journey from computational principles to human meaning.
Beyond FLOPS: The Evolutionary Processing Unit
This paper challenges the prevailing AI paradigm by reframing Artificial General Intelligence through evolutionary principles. The Evolutionary Processing Unit (EPU) represents 4 billion years of computational optimization that created modular, plastic architectures, what I call the Biological Processing Unit (BPU).
Core Insight: Brute-force scaling is fundamentally misaligned with the architectural principles evolution derived for reasoning, adaptation, and understanding. Future breakthroughs require understanding evolution's output rather than replicating its computational effort.
How This Fits: Establishes the evolutionary foundation that informs all subsequent papers, showing that intelligence emerged through architectural innovation, not raw computation.
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Abstract: The prevailing paradigm in artificial intelligence research suggests that Artificial General Intelligence (AGI) is achievable primarily through the scaling of computational resources, model parameters, and training data. This paper challenges that view by reframing the AGI challenge in terms of evolutionary principles. We present a thought experiment that contrasts the cumulative computational effort of the evolutionary process, as represented by the Evolutionary Processing Unit (EPU), with the capabilities of modern supercomputing. The analysis suggests that brute-force scaling is not only inefficient but fundamentally misaligned with the architectural principles that evolution derived. We argue that future breakthroughs will stem from a deeper understanding of the EPU's output: the modular, plastic, and causally grounded architecture of the Biological Processing Unit (BPU), in this case, the human brain, which evolved to navigate the very challenges of reasoning, adaptation, and understanding that current AI systems lack.
Attention Is All We Have
The 2017 Transformer revealed attention as AI's breakthrough mechanism. This paper argues attention is actually the universal resource allocation strategy that evolution optimized over 4 billion years, that operates across artificial systems, biological intelligence, and human consciousness.
Core Thesis: Attention is both the mechanism through which consciousness operates and the moral foundation of intelligence. It solves the fundamental problem of prioritizing limited resources in service of goals, whether computational or cognitive.
How This Fits: Provides the unifying mechanism that connects evolutionary principles (EPU) to AI architecture (Beyond Scale) and human application (MOL), showing how selective focus enables intelligence across domains.
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Abstract: In 2017, researchers at Google introduced the Transformer model in a paper titled Attention Is All You Need, demonstrating that artificial intelligence performance depends less on architectural complexity and more on selective focus, or the ability to prioritize what matters and disregard what does not. This paper argues that the same principle governs both human intelligence and fulfillment, and that attention, the selective allocation of cognitive and emotional resources, is the shared architectural foundation underlying both human and machine intelligence. Drawing on the Evolutionary Processing Unit (EPU) framework and its product, the Biological Processing Unit (BPU), we demonstrate that attention is not merely a proper mechanism, but a fundamental resource allocation strategy that evolution has optimized over the past four billion years.
The Attention Crisis: Language, Meaning, and the Architecture of Augmented Human Intelligence
For the first time in history, humanity produces more language than we can process: 15-70 trillion tokens daily. This paper argues we're entering a global attention crisis where infinite language overwhelms finite human attention, destabilizing the intersubjective reality that enables cooperation, democracy, and shared meaning.
Core Insight: The solution isn't Artificial General Intelligence as an oracle, but Augmented Human Intelligence—systems designed to strengthen human judgment rather than replace it. AHI treats attention as the scarcest cognitive resource, helping us allocate it wisely and preserve our capacity for reflection.
How This Fits: Applies attention theory to our current societal crisis, showing how modular AI architectures (Beyond Scale) can be designed specifically to augment human cognition rather than overwhelm it, creating the foundation for practical AHI systems.
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Abstract: Humanity's greatest evolutionary advantage has always been language: the ability to create shared meaning, coordinate at scale, and imagine futures that do not yet exist. But for the first time in history, the production of language has outstripped our capacity to process it. Each day, humanity generates tens of trillions of tokens of text, far more than any individual or institution can meaningfully absorb. Large language models accelerate this flood, producing polished prose at negligible cost and overwhelming the finite bandwidth of human attention. This imbalance erodes the intersubjective reality—the shared layer of beliefs, norms, and trust—that societies depend on. This paper argues that we are entering a global attention crisis: infinite language colliding with finite minds. The solution is not Artificial General Intelligence conceived as an oracle of truth, but Augmented Human Intelligence (AHI): systems designed to strengthen, rather than replace, human judgment. AHI treats attention as the scarcest cognitive resource, helping us allocate it wisely, resist manipulation, and preserve the fragile capacities for reflection and wise action.
Beyond Scale: A Modular Architecture for Adaptive AI
This paper proposes an alternative AI architecture inspired by evolutionary neuroscience: modular systems with specialized components coordinated by dynamic executive function, designed for continuous adaptation rather than periodic retraining.
Architectural Innovation: Drawing on EPU principles, we propose four core capabilities: modular orchestration, causal reasoning, continuous plasticity, and resource-constrained attention allocation, thereby creating systems that enhance rather than replicate human intelligence.
How This Fits: Applies evolutionary insights (EPU) and attention mechanisms to create practical AI architectures that enable human-AI collaboration rather than replacement.
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Abstract: Current approaches to artificial general intelligence (AGI) focus primarily on scaling large language models (LLMs) through increased parameters, training data, and computational resources. However, this paradigm faces fundamental limitations: energy consumption required for training grows exponentially, training cycles remain static, and systems lack the adaptive plasticity that characterizes natural intelligence. This paper proposes an alternative architecture inspired by evolutionary neuroscience: a modular AI system with specialized components coordinated by a dynamic executive function, all designed for continuous adaptation rather than periodic retraining. Drawing on the Evolutionary Processing Unit (EPU) framework, which demonstrates that evolution achieved intelligence through architectural innovation rather than raw computational scale, we argue that the path to AGI, or perhaps more achievable, Augmented Human Intelligence (AHI), requires fundamentally different approaches that mirror the distributed, plastic architecture of the Biological Processing Unit (BPU).
The Mastery of Life Framework
Modern life offers boundless opportunity but limited clarity. This paper introduces a practical framework for identifying what truly matters, tracking progress with intention, and adapting to life's evolving needs through awareness, attention, and adaptation.
Practical Application: By organizing life into core domains and personalized metrics, MOL helps individuals move from reactivity toward deliberate living, applying attention theory and modular principles from AI to personal development.
How This Fits: Demonstrates how evolutionary principles discovered by the Biological Processing Unit can be deliberately applied to enhance human fulfillment and completing the loop from computation to consciousness.
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Abstract: Modern life offers boundless opportunity but limited clarity. We chase success, balance, and happiness without a coherent framework for understanding what truly matters or how these forces interact with one another. This paper introduces the Mastery of Life (MOL) framework, a practical and theoretically grounded model for identifying what truly matters, tracking progress with intention, and adapting to life's evolving needs. Drawing on behavioral science, decision theory, and systems thinking, the framework proposes that mastery is not about control, but rather about awareness, attention, and adaptation. By organizing life into seven core domains derived from established well-being research and measuring 8-12 personalized metrics, MOL helps individuals move from reactivity toward deliberate living, a shift from unconscious motion to conscious direction.
Augmented Human Intelligence: The Synthesis
This framework culminates in a vision for human-AI collaboration that transcends artificial general intelligence. By leveraging evolutionary principles, attention mechanisms, and modular architectures, we can create systems where human and artificial intelligence elevate each other.
The Path Forward: Rather than pursuing AGI through pure scaling, we should strive for Augmented Human Intelligence, creating collaborative systems that exceed what either human or artificial intelligence can achieve alone through mutual enhancement.
The Integration: This represents the practical synthesis of evolutionary computation, architectural innovation, and human wisdom, showing how intelligence principles apply across biological and artificial domains.