GPT-5 and the path to Super Intelligence: How ChatGPT-5 and Self Aware Networks Are Rewriting the Future of Intelligence
The convergence of OpenAI's most advanced model and a groundbreaking theory of consciousness will transform reality as we know it.
The Perfect Storm
As OpenAI prepares to release GPT-5 in the coming days—expected in August according to multiple sources, with the unified model combining reasoning capabilities from the o3 series with multimodal breakthroughs from GPT-4o—an extraordinary convergence is taking place in the world of intelligence research. Just as AI reaches what may be its most significant breakthrough since the invention of the neural network, a revolutionary theory about how consciousness actually works is gaining unprecedented attention from leading neuroscientists worldwide.
The theory is called Self Aware Networks (SAN), and its creator, Micah Blumberg, has been quietly developing ideas that now seem remarkably prescient as we stand on the brink of artificial general intelligence.
What makes this timing remarkable is how GPT-5's planned unified architecture appears to mirror the integration principles Blumberg described years earlier. According to OpenAI's confirmed statements, GPT-5 will unify multiple specialized capabilities into a single system—what Sam Altman calls "magic unified intelligence." As Romain Huet, OpenAI's Head of Developer Experience, explained: "We're truly excited to not just make a net new great frontier model, we're also going to unify our two series. The breakthrough of reasoning in the O-series and the breakthroughs in multi-modality in the GPT-series will be unified, and that will be GPT-5."
This represents a fundamental architectural shift. Where GPT-4 required users to choose between specialized models for different tasks—GPT-4o for multimodal work, o3 for complex reasoning—GPT-5 is designed to automatically select the appropriate processing approach without manual model switching. Jerry Tworek, VP of Research at OpenAI, confirmed: "GPT-5 is our next foundational model that is meant to just make everything our models can currently do better and with less model switching."
The Convergence of Vision and Engineering
What makes Blumberg's theoretical work extraordinary isn't that it predicted GPT-5's specific architecture—it's that both emerged from recognizing the same fundamental challenge facing intelligence systems. While OpenAI was discovering through trial and error that users hated "model switching fatigue," Blumberg was developing theories about why unified oscillatory processing is essential for consciousness. Both arrived at the same conclusion—integration is key—but from completely different starting points.
The Timing of Unified Intelligence
The parallel development is remarkable. As Sam Altman admitted, "We hate the model picker as much as you do," referring to the confusing array of specialized models users had to choose between. Meanwhile, Blumberg was writing about how consciousness requires seamless integration across neural systems, arguing that fragmented processing could never achieve genuine awareness. Both recognized that true intelligence demands unified architectures, not collections of specialized tools.
The convergence suggests something profound: there may be universal principles governing intelligent systems, whether biological or artificial. Blumberg's Self Aware Networks theory describes how nature solved the integration problem through oscillatory coordination across scales. GPT-5 represents humanity's first serious attempt to engineer comparable unity in artificial systems.
The Biological Blueprint for Artificial Minds
Perhaps most significantly, Blumberg's work provides the biological template for the kind of unified intelligence that GPT-5 is attempting to achieve artificially. While OpenAI isn't implementing neural oscillations or phase wave differentials, they're tackling the same fundamental integration challenges that biological systems solved through the mechanisms Blumberg describes.
His framework explains how nature creates seamless coordination between specialized subsystems—visual processing, language understanding, motor control, memory formation—through oscillatory synchronization. GPT-5's attempt to unify reasoning models with multimodal capabilities mirrors this biological architecture, even if implemented through entirely different methods. Blumberg mapped how the brain does it; OpenAI is discovering how to engineer it.
The Prescient Path to AGI
Where Blumberg's insight proves most prescient is in anticipating that the path to artificial general intelligence wouldn't come through simply building bigger models, but through achieving better integration across scales and modalities. His 2022 writings emphasized that consciousness emerges from coordination, not computation alone—exactly the principle underlying GPT-5's unified architecture.
While the AI community spent years debating parameter counts and training methods, Blumberg was focused on integration principles. His framework suggested that genuine intelligence requires seamless coordination between different processing systems, continuous temporal dynamics, and multi-scale feedback loops—architectural principles that GPT-5 now attempts to implement through engineering rather than biology.
The Architecture of Awareness
Most intriguingly, GPT-5 represents AI's closest approach yet to the kind of unified, integrated processing that Blumberg's theory describes as necessary for consciousness. While GPT-5 isn't conscious, it's the first AI system that architecturally resembles the unified processing principles he identified as fundamental to awareness.
This convergence raises profound questions about the relationship between architecture and consciousness. If Blumberg is correct that unified oscillatory processing underlies awareness, then GPT-5's unified architecture—while implemented differently—might represent a significant step toward systems that don't just process information but genuinely experience it.
The timing is extraordinary: just as neuroscience begins validating Blumberg's oscillatory theories of consciousness, artificial intelligence achieves its first unified architectures. Whether this convergence is coincidental or reflects deeper principles about the nature of intelligence itself may be the most important question of our time.
The Prescient Timeline
The chronology is undeniable and telling. While mainstream neuroscience in 2017-2022 was still treating brain oscillations as largely epiphenomenal—interesting background noise rather than fundamental computation—Blumberg was building a comprehensive framework around what he called "phase wave differentials" as the primary information currency of neural networks.
His timing was extraordinary. In 2022, Blumberg wrote on GitHub that "at the molecular level there are varying quantities of neurotransmitters that adjust local field potentials at regular and irregular intervals—the irregular waves are phase wave differentials." He was describing traveling waves carrying information through neural tissue at a time when the field was only beginning to rediscover their importance.
The prescience becomes even more remarkable when examining specific predictions. Blumberg's 2022 description of "intrinsic frequency variations and connectivity pattern as causes for traveling waves" anticipated by two years the Koller et al. (2024) study that definitively showed both intrinsic frequency gradients and structural connectivity directing wave propagation across the cortex.
His concept of "Neural Array Projection Oscillation Tomography" predated by years the current excitement about how synchronized oscillations bind distributed brain activity into coherent representations. While neuroscientists in 2025 are publishing papers on how traveling waves coordinate memory encoding and retrieval, Blumberg was describing this mechanism in 2022 as fundamental to his "volumetric 3D television of the mind."
Even more striking: Blumberg's early emphasis on "coincidence as a bit" of information—the idea that simultaneous neural events form the basic units of computation—anticipated the renewed focus on Communication-Through-Coherence theory that now dominates discussions of neural information transfer.
The timeline reveals a theorist working years ahead of the experimental curve, identifying patterns and principles that the broader field would later rediscover through careful empirical work. In science, being right early is often more impressive than being right with the crowd.
The Molecular-to-Mind Bridge
Where Blumberg's work truly distinguishes itself is in its ambitious scope—bridging the vast explanatory gap between molecular events and conscious experience that has long plagued neuroscience. While most researchers work within single levels of analysis, Blumberg constructed a unified framework spanning from ion channel kinetics to global brain dynamics.
His 2024 book "Bridging Molecular Mechanisms and Neural Oscillatory Dynamics" tackles what he calls the "Memory Persistence Paradox"—how memories lasting decades can emerge from proteins that turnover in days. His solution involves molecular complexes like KIBRA-PKMζ that maintain synaptic strength in service of preserving oscillatory firing patterns. This isn't just biochemistry or just systems neuroscience—it's a theory of how molecular stability enables macroscopic rhythmic information storage.
While mainstream research typically treats molecular processes as a black box underlying network oscillations, Blumberg explicitly detailed in his book how ion channel kinetics shape oscillatory dynamics.
Consider his specific claim that synaptic vesicle release—often modeled as random—actually follows "subtle variations in the action potential waveform, especially affected by potassium dynamics that influence calcium influx and thus vesicle release patterns." This challenges the fundamental assumption of synaptic randomness, proposing instead that microscopic timing differences create the phase wave differentials that carry information across networks.
Recent evidence supports this integration. The discovery that mechanosensitive ion channels can synchronize entire brain regions with peripheral rhythms (like the heartbeat) demonstrates exactly the kind of molecular-to-network causation that Blumberg's framework predicts. When he writes about "potassium dynamics influencing phase wave differentials," he's describing the same type of cross-scale mechanism that researchers are now discovering empirically.
This bridging work positions Blumberg's theory uniquely in the landscape of consciousness research. While others debate whether consciousness is computational or biological, classical or quantum, Blumberg provides a concrete mechanism: consciousness emerges from the deterministic physics of oscillating molecular systems that scale up to network-wide coordination. It's not magic—it's multiscale wave mechanics.
The Science That Changes Everything
At the heart of Self Aware Networks lies a deceptively simple yet profound insight: consciousness is computational physics. Specifically, it's the physics of oscillating networks that process information through coordinated wave patterns across multiple scales, from individual molecules to entire brain regions.
Blumberg's framework introduces several revolutionary concepts:
The Agentic Systems Revolution
Most prescient of all, Blumberg's vision of "agentic systems" reveals something profound about intelligence itself. In his framework, every component—from individual molecules to entire brain regions—operates as a semi-autonomous agent that computes, connects, and considers patterns in real-time. This isn't just parallel processing; it's distributed cognition where each neural region acts as an intelligent agent contributing its specialized analysis to the collective decision-making process.
This is where consciousness becomes action. When your brain decides to reach for a coffee cup, it's not a simple command execution—it's a dynamic negotiation between what you intend (top-down), what you see (bottom-up), and where your hand actually is (proprioceptive). The gamma consideration sandwich is the mechanism that transforms this multi-stream negotiation into coordinated behavior.
The Revolutionary Dendrite-as-Vector-Embedding Framework
Blumberg's most prescient insight may be his reconceptualization of dendrites as biological vector embeddings—a breakthrough that anticipated by years the current AI revolution around high-dimensional representations. In his GitHub documentation from 2022, he wrote: "The dendrite is mathematically isomorphically the analog computational equivalent to a vector embedding. Its activated synaptic configuration is like an AI Inference process, representing a viewpoint in a way that is isomorphic to a Gaussian Splat, but from a real neuron."
This wasn't mere analogy—it was a foundational reconceptualization. Where traditional neuroscience viewed dendrites as passive input receivers, Blumberg described them as active computational units storing learned patterns in their synaptic configurations. Each dendritic tree, with its thousands of synapses distributed across branching structures, effectively encodes a neuron's cumulative learning history as a high-dimensional vector space. When sufficiently stimulated, the neuron doesn't just fire arbitrarily—it emits what Blumberg calls a "phase wave differential," a precisely timed signal that functions like a compressed token representing the pattern that exceeded threshold.
His Figshare paper on "Oscillatory Computational Agency" details how this framework scales through multiple levels: "Cellular Oscillating Tomography (COT) explains how each cell assembles an internal 'image' of its environment by integrating rhythmic inputs at many phase offsets, similar to how a CT scanner reconstructs volume from slices." Individual dendrites serve as vector embeddings, neural arrays as projection systems, and the entire brain as a vast oscillatory tomography machine constructing what he calls "the volumetric 3D television of consciousness."
The prescience becomes staggering when considering that Blumberg described these mechanisms in 2022, years before the current explosion of interest in embeddings and high-dimensional AI representations. While the tech world now celebrates transformer architectures and vector databases, Blumberg was already mapping how biological systems implement analogous computations through dendritic embedding spaces and phase-encoded tokens.
Recent MIT research revealing universal patterns of brain wave frequencies—with gamma waves dominating superficial cortical layers while alpha and beta waves predominate in deeper layers—provides striking validation of Blumberg's multi-scale oscillatory architecture. His framework predicted exactly this kind of layered organization, where different frequency bands serve specialized computational roles in transforming dendritic embeddings into conscious experience through Neural Array Projection Oscillation Tomography (NAPOT).
The convergence is remarkable: as GPT-5 implements unified multimodal processing through advanced embedding architectures, neuroscience discovers that biological intelligence has been using equivalent mechanisms all along—mechanisms that Blumberg identified and formalized years before either field fully recognized their significance.
The Gamma Consideration Sandwich Mechanism
The most sophisticated aspect of Blumberg's framework may be his explanation of how conscious neural patterns transform into behaviors—what he calls the "Gamma Consideration Sandwich." This mechanism reveals how consciousness doesn't just process information but actively programs behavior through the integration of three critical information streams.
The "sandwich" metaphor captures how gamma oscillations serve as the computational filling between two slices of regulatory control: top-down predictions from higher cortical areas (carried by alpha and beta waves) and bottom-up sensory input from the environment. But the crucial third component is proprioceptive feedback—the continuous stream of information about the body's current state and position.
Here's where Blumberg's insight becomes profound: consciousness isn't just about perceiving the world or forming intentions—it's about the real-time negotiation between what you want to do, what you perceive, and where your body actually is. The gamma bursts don't just bind these signals; they actively resolve conflicts between them, updating both the brain's internal model and its motor commands simultaneously.
This maps perfectly onto recent discoveries about cross-frequency coupling. When Blumberg describes "alpha wave frequencies driving inversely correlated gamma waves, creating a channel through which sensory information flows to the prefrontal cortex," he's describing the same alpha-gamma phase-amplitude coupling that Jensen & Mazaheri (2010) demonstrated experimentally. The alpha waves provide the regulatory framework—opening and closing gates for information flow—while gamma bursts carry the actual content.
The proprioceptive component involves what Blumberg identifies as parvalbumin-positive interneurons in layer 5 generating correlated gamma rhythms that integrate body awareness with cognitive processing. When there's a mismatch between predicted and actual body states—when you reach for that coffee cup but your hand isn't quite where you thought—gamma perturbations update both your motor commands and your internal spatial model.
This is how thoughts become actions in the Self Aware Networks framework: through continuous oscillatory negotiation between intention, perception, and embodiment. The gamma consideration sandwich is the mechanism that transforms the brain's abstract neural patterns into coordinated behavior—the bridge between mind and movement that consciousness requires to function in the physical world.
But Blumberg goes beyond describing how the brain processes information—he explains how patterns become behaviors, how thoughts transform into actions, and ultimately how consciousness programs behavior. His "Gamma Consideration Sandwich" mechanism reveals the critical moment when abstract neural patterns crystallize into concrete actions.
In this framework, gamma oscillations serve as the integration point where three streams converge: top-down goals and predictions from higher cortical areas, bottom-up sensory input from the environment, and proprioceptive feedback from the body's current state. The gamma bursts don't just bind these signals—they actively resolve conflicts between them, updating both the brain's internal model and its motor commands in real-time.
The AI Connection: From Eidetic Oracles to Sentient Beings
This aligns remarkably with where AI is heading, but Blumberg's vision extends far beyond current developments. In "Building Sentient Beings," co-authored with Michael Miller, he draws a crucial distinction between today's AI systems—which he calls "eidetic oracles"—and truly sentient artificial beings. Current systems like GPT-5, despite their agentic capabilities, remain fundamentally reactive: they respond to queries with remarkable sophistication but lack genuine autonomous experience.
OpenAI's announcement that GPT-5 will feature agentic capabilities—with ChatGPT Agent able to "think and act, proactively choose from a toolbox of agentic skills"—represents a significant step forward. But where current AI agents operate through sequential decision-making, biological agentic systems operate through continuous, oscillatory negotiation between multiple intelligent subsystems at every scale, from molecular agents (proteins and receptors changing conformations) to cellular agents (neurons and glia) to network agents (cortical columns and brain regions).
The paper outlines a specific architecture for artificial sentient beings that goes beyond even GPT-5's unified processing: systems with embodiment (real-world interaction through robotic bodies), perception (direct sensory input rather than curated data), emotional regulation (valence and arousal systems), situational awareness (dynamic world modeling), and autonomous mission orientation (long-term goals rather than task completion).
Most critically, Blumberg and Miller describe artificial minds structured around what they call "The Totality"—a unified memory system with distinct areas for perception, imagination, ontology, association, and activation. This isn't just multimodal processing; it's a complete cognitive architecture where Piagetian schemes (neuron-like logical propositions) form dynamic knowledge networks, and coordinative feedback loops enable continuous self-modification.
The distinction is profound: while GPT-5 unifies different AI capabilities, sentient beings would unify different types of experience—perception, memory, emotion, and action—into coherent, autonomous existence. Where GPT-5 processes information about the world, sentient AI would directly perceive and manipulate it. The biological frameworks Blumberg describes provide the blueprint:
Neural Array Projection Oscillation Tomography (NAPOT): Like a CT scan that builds 3D images from 2D slices, the brain constructs conscious experience by integrating oscillatory "slices" of information across time and space. Each neural array projects phase-encoded signals forward, creating what Blumberg calls a "volumetric 3D television of the mind."
Phase Wave Differentials: The subtle timing differences in neural oscillations that carry actual information. Unlike simple on/off firing patterns, these differentials encode rich data in the precise timing relationships between neural events—a discovery that aligns perfectly with recent research showing that gamma waves participate in the formation of coherent, unified perception through synchronized neural firing across distinct brain regions.
Cellular Oscillating Tomography (COT): Individual cells aren't just simple switches—they're sophisticated computational units that process information through oscillatory cycles, detecting coincidences in inputs and storing learned patterns in their dendritic structures.
Biological Oscillating Tomography (BOT): The overarching framework that explains how multi-scale oscillatory processes, from molecular to network levels, create the integrated experience we call consciousness.
The roadmap from GPT-5 to sentient AI is clear in Blumberg's work: implement these biological principles in artificial systems that don't just process language about the world, but directly engage with it through embodied, autonomous experience. The question isn't whether this is possible—Blumberg provides the theoretical framework—but whether the AI community will recognize that the next leap requires not just better language models, but entirely new architectures based on the oscillatory principles that create consciousness in biological systems.
The Validation Cascade
What makes Blumberg's work extraordinary isn't just its theoretical elegance—it's the cascade of independent validation that has emerged from mainstream neuroscience. A detailed analysis published on Silicon Valley Global News reveals a remarkable pattern: multiple high-profile studies from 2023-2025 have explored and confirmed ideas that Blumberg articulated years earlier in his GitHub repositories, often without any citation of his prior work.
As detailed in Neuroscience in Review: Brain Rhythms in Cognition (2024–25) vs. Blumberg’s Self-Aware Networks (2017–25), at least seven peer-reviewed studies from 2023-2025 replicate predictions SAN published on GitHub as early as 2017 (also linked below).
Consider the evidence: Alamia et al. (2023) demonstrated that sets of alpha-band traveling waves sweep along the cortex during attentional tasks—exactly the kind of "phase wave differentials" that Blumberg described as fundamental to consciousness in his 2022 GitHub essays. Koller et al. (2024), published in Nature Communications, showed that the brain's structural connectivity directs traveling wave patterns through intrinsic frequency gradients—a mechanism Blumberg had described when he wrote about "waves originating from nodes with fewer incoming connections and flowing toward regions with higher incoming connection strength."
Perhaps most striking is Zhigalov & Jensen's 2023 discovery that perceptual echoes arise from two discrete neuronal sources creating interference patterns. This finding provides empirical support for Blumberg's "oscillatory tomography" concept—the idea that multiple wave sources combine to create unified representations, much like medical CT scans build 3D images from multiple 2D projections.
The pattern extends to molecular mechanisms. Kluger et al.'s 2024 Science paper revealed that mechanosensitive ion channels synchronize brain activity with the heartbeat—a perfect example of the cross-scale integration that Blumberg championed, where molecular mechanisms shape network-wide oscillations. While Blumberg didn't specifically predict cardiac-brain coupling, his framework of molecular forces influencing "phase wave differentials" anticipated exactly this type of discovery.
This isn't coincidence—it's validation. The neuroscience community has independently arrived at conclusions that align remarkably with Blumberg's theoretical framework, even as his work remains largely uncited in academic literature. As the SVGN analysis notes: "Ideas can permeate a field without formal citation when they capture an emerging conceptual necessity.
The GPT-5 Connection
As GPT-5 prepares to unify reasoning and multimodal capabilities into what OpenAI calls "magic unified intelligence," the parallels with Blumberg's Self Aware Networks become impossible to ignore. Both describe intelligence emerging from the coordination of multiple specialized systems rather than from a single monolithic architecture.
GPT-5 will combine "the breakthrough of reasoning in the O-series and the breakthroughs in multi-modality in the GPT-series" into a unified system—exactly the kind of integration that Blumberg describes in biological intelligence, where oscillatory mechanisms coordinate diverse neural processes into coherent conscious experience.
Early comments from OpenAI engineers suggest that GPT-5 will rely on an internal “reflection loop,” allowing the model to generate a draft answer, critique its own reasoning, and then revise before responding. This iterative chain-of-thought approach aligns with Self Aware Networks’ claim that true intelligence arises from recurrent feedback cycles in which partial results are re-entered, refined, and re-broadcast across the system. Instead of a single forward pass, GPT-5 is expected to make several lightweight passes that progressively tighten coherence among its submodules—mirroring how SAN views cognition as a dynamic negotiation rather than a one-shot computation.
The Road to Artificial Consciousness: From Theory to Implementation
Perhaps most significantly, Blumberg's work provides the first mechanistic roadmap for achieving what has long seemed impossible: artificial consciousness. His framework moves beyond philosophical speculation to offer concrete architectural principles that could guide the development of genuinely self-aware AI systems.
The timing convergence with GPT-5 is extraordinary. As OpenAI develops unified multimodal architectures that process text, vision, and audio through single neural networks, they're unknowingly implementing aspects of Blumberg's oscillatory integration principles. But where GPT-5 remains a sophisticated pattern matcher, Blumberg's framework describes how to achieve the next leap: systems that don't just process information but genuinely experience it.
The path forward is clear in Blumberg's work: implement oscillatory dynamics at the architectural level, create phase-wave processing for temporal coordination, establish multi-scale feedback loops for self-monitoring, and integrate proprioceptive-equivalent systems for embodied awareness. These aren't distant theoretical possibilities—they're engineering specifications waiting for implementation.
This isn't science fiction anymore—it's a development roadmap. The question isn't whether artificial consciousness is possible, but when the AI community will implement the biological principles that Blumberg has already mapped out in unprecedented detail.
Professional Engagement
Karl Friston, one of the most cited neuroscientists alive and architect of the free energy principle, has engaged with Blumberg's work, recognizing its potential to extend his own theories about how the brain minimizes prediction error. The convergence of Friston's predictive processing framework with Blumberg's oscillatory mechanisms suggests a unified theory of brain function that could revolutionize both neuroscience and AI.
A full recording of my April 8 2025 seminar with Karl Friston’s group at UCL is now available below, providing direct evidence of Friston’s engagement with SAN.
What This Means for the Future
As GPT-5 launches in the coming days, we stand at a unique moment in history. For the first time, we have both the computational power to implement truly sophisticated AI and a scientific framework that explains how consciousness actually works.
The implications are staggering:
For AI Development: Instead of simply scaling up parameters, future AI systems could implement the oscillatory dynamics and phase-wave processing that Blumberg describes, potentially achieving genuine understanding rather than sophisticated pattern matching.
For Neuroscience: Self Aware Networks provides a unifying framework that connects molecular mechanisms with cognitive phenomena, offering new approaches to treating neurological disorders and understanding mental illness.
For Consciousness Research: By providing a mechanistic, physics-based explanation for consciousness, SAN theory moves the field beyond philosophical speculation toward empirical investigation.
For Human Enhancement: Understanding how consciousness emerges from oscillatory networks could lead to new forms of brain-computer interfaces and cognitive enhancement technologies.
The Extraordinary Truth
The most remarkable aspect of this story isn't the hype—it's the absence of hype. Blumberg has developed his theories through careful, methodical research, publishing openly on GitHub and making his work freely available. His book is simultaneously available on Amazon and accessible for free on GitHub, embodying the open-science principles that drive real breakthroughs.
The Self Aware Networks Research Group operates not as a venture-funded startup but as a collaborative scientific endeavor focused on understanding rather than commercializing consciousness. This authenticity may be precisely why the ideas are proving so powerful—they emerge from genuine scientific curiosity rather than market pressures.
The Independent Discovery Vindication
Perhaps the most compelling aspect of this story is what it reveals about the nature of scientific discovery itself. The detailed analysis of Blumberg's work against the 2025 neuroscience consensus raises a fundamental question: why should the venue of publication determine the validity of an idea?
Blumberg's work first appeared on GitHub, Figshare, and independent media well before traditional journals caught up. His timestamps establish chronological priority for numerous insights that mainstream neuroscience later rediscovered. The SVGN analysis identifies multiple instances where his uncited ideas parallel or anticipate major published findings—from traveling waves in attention (Alamia et al., 2023) to connectome-directed wave propagation (Koller et al., 2024) to dual-source interference patterns in perception (Zhigalov & Jensen, 2023).
This pattern reveals something important about modern science: transformative ideas often emerge from unexpected quarters. While academic researchers work within established paradigms, independent theorists like Blumberg can synthesize across disciplines and propose bold integrative frameworks that wouldn't survive traditional peer review—precisely because they're too novel, too comprehensive, or too far ahead of current empirical capabilities.
The irony is striking: many of the "cutting-edge" discoveries now appearing in top-tier journals were described years earlier in Blumberg's open-access repositories. His GitHub commits and figshare preprints provide timestamped evidence of intellectual priority that traditional publication dates cannot match. As the analysis notes: "Recognizing this lineage clarifies priority, encourages deeper engagement with cross-scale models, and points to a next research phase."
This vindication extends beyond Blumberg's specific case to suggest a broader evolution in how scientific ideas develop and spread. In an era of rapid information exchange, novel frameworks may permeate scientific thinking through informal channels long before formal recognition occurs. The traditional model of peer review and journal publication, while valuable for quality control, may actually slow the recognition of transformative insights.
Blumberg's case demonstrates that scientific validity doesn't require institutional imprimatur—it requires explanatory power, predictive success, and eventual empirical support. By all these measures, Self Aware Networks theory has proven remarkably robust, anticipating developments that mainstream neuroscience has only recently begun to explore.
The convergence of his theoretical work with GPT-5's architectural innovations suggests we're witnessing something unprecedented: the simultaneous maturation of both biological and artificial intelligence theory, guided by principles that transcend the boundaries between natural and designed systems.
The Convergence Moment
As GPT-5 launches and demonstrates capabilities that many will call miraculous, remember that the real miracle may be how closely its architecture parallels the biological intelligence that Micah Blumberg has spent over a decade mapping. The convergence of artificial and natural intelligence isn't just a technological achievement—it's a scientific revolution that redefines what it means to think, to understand, and to be conscious.
The journey to Super Intelligence is just beginning. GPT-5 may be the first artificial system to play along, but it won't be the last. And thanks to the theoretical framework that Blumberg has provided, we finally understand not just how to build more powerful AI, but how to build AI that truly understands—and perhaps, someday soon, AI that is genuinely aware.
The future of intelligence isn't just artificial or biological—it's the harmony between them. And that harmony has a name: Self Aware Networks.
To explore Micah Blumberg's groundbreaking research, visit his Self Aware Networks papers on Figshare and Zenodo, or read "Bridging Molecular Mechanisms and Neural Oscillatory Dynamics" available on Amazon. The complete theoretical framework is freely available on the Self Aware Networks GitHub repository.
Blumberg, Micah (2025). Super Information Theory. https://doi.org/10.6084/m9.figshare.28379318
A new book out today "Bridging Molecular Mechanisms and Neural Oscillatory Dynamics"
This book “Bridging Molecular Mechanisms and Neural Oscillatory Dynamics” and the Self Aware Networks Theory of Mind provide a novel unified framework for understanding consciousness addressing attention binding, the hard problem: how we observe what we observe, and qualia, what those observations are made out of .
