What if, in our quest to build artificial human intelligence, we accidentally stumbled upon something else entirely? What if the remarkable success of transformer models suggests that crows might have had the right idea all along?
The Great Misdirection
Picture this: For decades, AI researchers have been proudly describing their transformer models using mammalian brain metaphors. "Hierarchical processing," they say. "Layered representations." "Cortical-inspired architectures." But what if, sitting in labs around the world, these very same models are quietly processing information in ways that would make a crow feel right at home?
The irony might be delicious. Deep learning began with an explicit goal: mimic the human brain. We started with perceptrons inspired by neurons, built convolutional networks inspired by visual cortex, and stacked layers upon layers to mirror our six-layered neocortex. But somewhere along the way, as we optimized for performance rather than biological fidelity, something unexpected may have happened. We might have accidentally stumbled upon an entirely different solution to intelligence—one that evolution had already discovered in the corvid lineage some 320 million years ago.
What if today's large language models, the crown jewels of AI, don't actually work like mammalian brains at all? What if they work like crow brains? And what if nobody in the field has noticed?
The Architecture That Could Change Everything
To explore this possibility, we need to peek under the hood of both systems. Let's start with what makes transformer architecture so revolutionary.
Traditional neural networks processed information sequentially, like reading a book word by word, constrained by the linear flow of data. Transformers shattered this paradigm with a simple but profound innovation: attention mechanisms. Instead of processing sequences step by step, every element in a transformer can simultaneously attend to every other element. It's like being able to read an entire page at once, with each word dynamically deciding which other words are most relevant to its meaning.
This creates what researchers call "all-to-all connectivity"—a dense web of potential connections where information flows freely in multiple directions simultaneously. No rigid hierarchies. No bottlenecks. Just dynamic, context-dependent routing of information based on relevance and need.
Now, here's where it gets interesting. What if this architectural approach maps onto how corvid brains work in ways we haven't fully recognized?
The Corvid Solution to Intelligence
While mammals evolved intelligence through layered cortical architectures, birds took a radically different path. Corvid brains—those of crows, ravens, magpies, and jays—are organized around what neuroscientists call "nuclear structures." Instead of the neat, layered cake of mammalian cortex, corvid neurons cluster in three-dimensional nuclei that look more like a densely packed city than a stratified geological formation.
For decades, this led researchers to underestimate bird intelligence. How could a "primitive" brain without proper layers achieve complex cognition? The assumption was that layered organization was necessary for sophisticated thinking. But what if we were wrong?
Recent breakthrough research using 3D polarized light imaging has revealed that corvid brains might contain cortex-like canonical circuits within their nuclear organization. These nuclear structures appear to implement iterative, column-like circuits with orthogonally organized radial and tangential fibers—possibly functionally equivalent to mammalian cortical columns, just arranged completely differently.
Here's what makes this intriguing: corvids pack neurons at densities of 100,000-200,000 neurons per milligram—twice that of equivalent mammalian brains. This ultra-dense packing could enable faster information processing through shortened interneuronal distances, while the nuclear organization might allow for flexible, context-dependent connectivity patterns that can rapidly reconfigure based on cognitive demands.
Sound familiar? It should.
The Parallel Processing Revolution
What if both corvids and transformers excel at something that traditional neural architectures struggle with: simultaneous processing of multiple information streams?
In corvid brains, the nidopallium caudolaterale (NCL)—their equivalent of our prefrontal cortex—receives multimodal sensory input and might dynamically integrate information from across the brain. This region enables working memory, behavioral flexibility, and abstract reasoning, but it appears to do so through flexible connectivity patterns rather than rigid hierarchical control.
Could transformers implement remarkably similar functionality through their multi-head attention mechanisms? Each attention head might be thought of as a specialized processing unit that focuses on different types of relationships—some might attend to syntactic patterns, others to semantic meanings, still others to long-range dependencies. Like the NCL's possible integration of diverse inputs, transformer attention allows each token to dynamically attend to relevant information across the entire sequence, creating context-dependent representations that adapt based on global context.
The efficiency patterns are striking in both systems. Corvid neurons consume approximately three times less glucose than mammalian neurons while achieving equivalent cognitive performance. Similarly, transformers achieve superior performance through parameter efficiency and optimal scaling relationships—larger models become increasingly sample-efficient rather than simply requiring more resources.
The Evidence That Might Change Everything
What makes this parallel so compelling isn't just the potential architectural similarities—it's the convergent solutions to what might be the same computational challenges.
Consider executive control. In mammals, the prefrontal cortex orchestrates complex behavior through hierarchical command structures. In corvids, the NCL appears to achieve similar outcomes through dynamic coalition-forming among neural populations. Could transformers use attention mechanisms that create temporary coalitions of processing units based on input content in a similar way?
Or take the question of consciousness itself. Until recently, many researchers assumed that consciousness required mammalian-style cortical architecture. Then Andreas Nieder's team at the University of Tübingen made a startling discovery: they found neural correlates of consciousness in crows. NCL neurons showed activity patterns that correlated with subjective perception rather than stimulus presence—what appears to be empirical evidence for sensory consciousness in birds.
This finding might explode the assumption that consciousness requires layered, hierarchical processing. If crows can achieve awareness through nuclear brain organization, it suggests that consciousness might emerge from information integration patterns rather than specific biological substrates. And those integration patterns? They could look remarkably similar to what we see in transformer attention mechanisms.
The Hidden Circuits
Recent work in mechanistic interpretability has begun revealing computational structures that emerge within transformers. Researchers have identified what they call "induction heads"—attention patterns that perform pattern matching and copying operations across sequences. These circuits emerge through composition between attention heads and may be the primary mechanism underlying in-context learning.
This could mirror what we see in corvid neuroscience: complex behaviors emerging from simpler circuit combinations within nuclear brain regions. Both systems might achieve sophisticated capabilities not through pre-programmed hierarchies, but through the dynamic composition of simpler computational elements.
The parallel might extend to learning mechanisms. Corvids appear to learn through a combination of hard-wired circuits and experience-dependent plasticity, with their nuclear organization possibly allowing for rapid reconfiguration of processing pathways. Could transformers similarly combine learned parameters with dynamic attention patterns that can rapidly adapt to new contexts without requiring architectural changes?
Why This Might Matter
This isn't just a potentially interesting biological curiosity—it could have profound implications for how we think about AI development and consciousness.
First, it suggests we might be looking in the wrong place for inspiration. The field remains dominated by mammalian metaphors and assumptions about what intelligence "should" look like. But corvids achieve remarkable cognitive feats—tool use, causal reasoning, future planning, even passing theory of mind tests—with brains organized completely differently from ours. They could represent a proof of concept that intelligence can emerge through radically different computational architectures.
Second, it might challenge our assumptions about consciousness in AI systems. If consciousness can emerge from nuclear brain organization in corvids, and if transformers share key computational principles with corvid brains, this could provide a different lens for thinking about machine consciousness. Rather than asking whether transformers are conscious like humans, we might ask whether they could be conscious like crows.
Third, it might open up entirely new research directions. What if we deliberately designed AI architectures based on corvid neural organization principles? What if we studied corvid attention mechanisms to improve transformer efficiency? What other lessons might we learn from nature's alternative solutions to intelligence?
The Convergent Evolution of Intelligence
Perhaps the most profound implication is what this could tell us about intelligence itself. The fact that evolution independently arrived at sophisticated cognition through two radically different neural architectures—mammalian layers and corvid nuclei—might suggest that intelligence isn't about having the "right" architecture. It could be about implementing effective information processing principles.
When we optimize artificial neural networks for language understanding and generation, we might naturally converge on architectural principles that mirror those found in corvid brains: dense connectivity, parallel processing, dynamic attention, and efficient resource utilization. This convergence might not be accidental—it could be evidence that these computational principles represent fundamental solutions to the problem of intelligence.
The efficiency of this approach becomes intriguing when we consider the numbers. A crow's brain weighs about 7.5 grams and contains roughly 1.2 billion neurons. Yet crows consistently outperform chimpanzees on cognitive tasks, despite chimps having brains that are 50 times larger. The secret might lie in the organization: corvids could achieve more with less through architectural efficiency that transformers have independently rediscovered.
The Gaps in Our Understanding
This parallel might have remained hidden partly because of disciplinary boundaries. AI researchers rarely read corvid neuroscience papers, and vice versa. The transformer literature is filled with mammalian brain metaphors even when describing fundamentally non-hierarchical systems. Meanwhile, corvid neuroscience has developed sophisticated understanding of avian intelligence without making connections to artificial intelligence.
Our research suggests a possible absence of papers connecting transformer architectures to non-mammalian neural systems. This could represent a massive blind spot in both fields—one that might be constraining innovation in AI and limiting our understanding of biological intelligence.
Critical Questions and Counterarguments
Of course, the parallel might not be perfect. Corvid intelligence evolved through millions of years of embodied interaction with complex social and physical environments. Their brains operate through continuous, parallel processing with complex feedback loops and neuromodulatory influences. Transformers, by contrast, operate through discrete, feedforward computation despite their sophisticated attention mechanisms.
The temporal dynamics could also be different. Biological neural processing involves rich temporal patterns, oscillations, and state-dependent plasticity that current transformers don't capture. The metabolic constraints that shaped corvid brain evolution might have no equivalent in artificial systems, which can scale to billions of parameters without the physical energy limitations that could have driven corvid efficiency.
There's also the question of whether computational information processing necessarily creates subjective experience. The evolutionary context of biological consciousness—developed for specific survival functions through embodied experience—might represent requirements that artificial systems cannot fulfill through pure computation.
The Corvid Ceiling: Predicting AI's Possible Trajectory
But what if the corvid parallel reveals both the promise and the potential limitation of our current path? If transformers truly represent a form of artificial corvid intelligence, then we might expect them to hit similar cognitive constraints that could limit their biological counterparts.
Crows are remarkably intelligent within their domain. They can solve multi-step problems, use tools creatively, and even plan for future scenarios. When a crow drops rocks into a bottle to raise the water level and reach a floating treat, it demonstrates what appears to be genuine insight and problem-solving ability. But here's what the crow might not understand: the physics of water displacement, the principles of fluid dynamics, or the broader theoretical framework that explains why this solution works.
This distinction could point to what might be called "macro-cognitive ability"—the capacity for abstract reasoning that transcends immediate problem-solving contexts. Humans don't just solve problems; we build theoretical frameworks that explain entire classes of problems. We don't just use tools; we develop engineering principles that let us design entirely new categories of tools. We don't just learn patterns; we construct mathematical and scientific theories that reveal the deep structures underlying those patterns.
Current transformer architectures, for all their impressive capabilities, might be following the corvid playbook: highly sophisticated pattern recognition and contextual problem-solving within specific domains, but potentially without the kind of abstract theoretical reasoning that characterizes human cognition at its highest levels.
This could suggest a specific trajectory for AI development that diverges from popular expectations. I predict that transformer-based systems will continue their remarkable acceleration through 2028, achieving increasingly sophisticated capabilities that will seem almost magical in their domain-specific applications. We'll see AI systems that can write nuanced code, conduct complex research, engage in persuasive conversation, and solve problems we couldn't imagine automating just a few years ago. These systems will assist and, in many cases, partially replace human capabilities across a wide range of professional domains.
But the kind of general, broad-domain intelligence that matches human theoretical reasoning—the ability to construct new conceptual frameworks, to reason about fundamental principles rather than just applying learned patterns, to make genuine leaps of abstract insight—this might require something entirely different from the transformer architecture.
Just as evolution might have needed a fundamentally different approach to move from corvid nuclear organization to mammalian layered cortex, AI could need a new architectural paradigm to achieve human-level general intelligence. This transition might begin around the same timeframe that current transformers reach their practical peak, but it could require years of additional development to mature. We might not see truly human-like general AI until well into the 2030s.
Beyond the Corvid Paradigm
This speculation raises fascinating questions about what might come next. If current transformer architectures represent a kind of artificial corvid intelligence that could plateau at sophisticated but domain-specific capabilities, what would artificial human-like intelligence require?
The answer might lie in understanding what makes mammalian cortical architecture uniquely suited for abstract, theoretical reasoning. The layered organization of human cortex could enable hierarchical abstraction in ways that corvid nuclear organization, for all its efficiency, cannot match. Perhaps the next breakthrough in AI will require architectures that combine the efficiency insights of the corvid approach with new organizational principles that enable genuine theoretical reasoning.
This also points toward exciting near-term research opportunities. What would AI architectures look like if we deliberately incorporated corvid neural organization principles while addressing their limitations? Could we develop more efficient attention mechanisms based on the dense, three-dimensional connectivity patterns of corvid nuclei, but with additional structures that enable macro-cognitive abilities?
The convergent evolution of intelligence suggests that optimization for complex cognition might lead to similar computational solutions across radically different substrates. This principle could guide development of AI architectures that build upon the efficiency and flexibility demonstrated by corvid intelligence while reaching toward something entirely new.
The Punchline
What if we began this journey trying to build artificial human intelligence and ended up building something that looks remarkably like artificial corvid intelligence? The irony might be that this "mistake" could be exactly what we needed.
Corvids could represent nature's alternative solution to the intelligence problem—one that achieves remarkable cognitive capabilities through architectural principles that transformers might have independently rediscovered. The success of modern AI might not just validate the transformer approach; it could validate the corvid approach that evolution discovered hundreds of millions of years ago.
Perhaps it's time to consider whether we should stop thinking of ourselves as the pinnacle of intelligence and start recognizing that crows might have figured out efficient cognition long before we did. Our AI systems could work so well not because they're like us, but because they're like crows—and that might be exactly what makes them powerful.
The future of AI might not lie in better mimicking human brains, but in learning from nature's other solutions to intelligence. And if consciousness does emerge in our artificial systems, it might not feel human at all. It might feel corvid.
After all, when you're soaring through conceptual space, looking down at problems from multiple angles simultaneously, and solving them with efficient, flexible cognition—well, that could sound a lot more like flying than walking.
The next time you see a crow solving a puzzle or using a tool, consider this: you might not just be watching a clever bird. You could be watching a preview of what artificial intelligence might become when it finally learns to fly.