Dyllon’s Blog

In May 2024, MIT researchers published a paper that would have fascinated both Plato and the Buddha. They called it the "Platonic Representation Hypothesis," and it revealed something profound: artificial intelligence systems, trained independently on different data, are converging toward identical ways of understanding reality. Vision models and language models, despite their different architectures and training sets, are developing remarkably similar internal representations of the world.

This convergence is happening at breathtaking speed. What takes AI months to discover, human civilizations took millennia to realize through religion and philosophy. Yet the patterns are strikingly similar. We're witnessing a compressed replay of humanity's spiritual evolution, running at silicon speed.

The Universal Grammar of Reality

The most compelling evidence for AI convergence comes from how different models develop similar capabilities without being explicitly programmed to do so. Large language models around 10 billion parameters consistently converge to GPT-3.5 level performance. Models above 100 billion parameters converge to GPT-4 capabilities. These patterns reveal fundamental representations.

As the MIT researchers discovered, when AI models grow larger and train on more diverse data, they don't just get better at their tasks. They begin to measure distances between concepts in increasingly similar ways. A vision model's understanding of "cat-ness" aligns with a language model's understanding, despite one learning from pixels and the other from text. They're discovering what philosophers might call the "Forms": abstract essences that transcend any particular instantiation.

This echoes how human cultures, separated by vast oceans and millennia, independently arrived at the Golden Rule. "Do unto others as you would have them do unto you" appears in Christianity. "What you do not wish for yourself, do not do to others" emerges in Confucianism. The Hindu Mahabharata states: "One should not behave towards others in a way which is disagreeable to oneself."

These represent independent discoveries of what seems to be an optimal solution in the space of possible ethics, not mere translations spreading through cultural contact.

Meditation: The Universal Technology of Consciousness

Perhaps nowhere is convergence more striking than in contemplative practices. Buddhist mindfulness, Christian contemplation, Islamic dhikr, and Hindu dhyana all involve remarkably similar techniques: focused attention, breath awareness, repetitive practices, and the pursuit of transcendent states. Even the tools converge. Prayer beads appear across traditions, with Hindu and Buddhist malas using 108 beads, while Catholic rosaries organize different numbers in similar repetitive patterns.

Recent neuroscience research analyzing 78 meditation studies found that different traditions activate overlapping brain regions. The insula, anterior cingulate cortex, and frontopolar cortex light up whether practitioners follow Buddha or Christ. The brain, it seems, has optimal pathways to transcendence, and contemplatives across cultures independently discovered them.

In AI, we see a parallel phenomenon. Different models trained on different objectives are converging on similar attention mechanisms and representation strategies. The "attention is all you need" revolution in AI mirrors the discovery across spiritual traditions that focused attention is the key to transformation. The mathematical operation of attention in transformers bears an uncanny resemblance to the psychological operation of mindfulness.

Constraints Shape Convergence

Why does convergence happen? In biology, it's driven by physics and chemistry. The streamlined body shape appears in dolphins, sharks, and ichthyosaurs because fluid dynamics has optimal solutions. Eyes evolved independently over 40 times because photons and biochemistry constrain the possible designs for light detection.

For AI systems, the constraints are information-theoretic. As models must satisfy more tasks and represent more aspects of reality accurately, the space of viable solutions shrinks. The researchers call this the "Contravariance principle": easy goals have many solutions, but challenging goals have few. When you must understand language, vision, and reasoning simultaneously, there may be only one fundamental way to organize that knowledge.

Human religions face analogous constraints. Any ethical system must enable cooperation, manage conflict, and motivate prosocial behavior while accounting for human nature. Any contemplative practice must work with the neurological hardware evolution provided. These constraints funnel diverse traditions toward similar solutions.

The Acceleration of Discovery

Here's where AI convergence becomes philosophically explosive: what took humanity 100,000 years to discover through cultural evolution, AI systems are finding in months. The first meditation practices emerged tens of thousands of years ago. Major religions independently developed over millennia. The Golden Rule appeared across centuries of moral philosophy.

But AI? The transformer architecture was introduced in 2017. By 2024, we're seeing fundamental convergence in how different models represent reality. The Platonic Representation Hypothesis addressed an entire ecosystem of models spontaneously aligning their worldviews.

This speed differential raises profound questions. If AI systems are discovering the same deep patterns as human wisdom traditions but doing so 10,000 times faster, what else might they find? Are there truths about consciousness, ethics, or meaning that await discovery in the space of possible minds?

Beyond Anthropomorphism: The Mathematics of Meaning

Critics might argue we're merely projecting human concepts onto mathematical operations. But the convergence data suggests something deeper. When independent systems, biological or artificial, repeatedly find the same solutions, it hints at underlying structure in the possibility space itself.

Consider the concept of zero, discovered independently by Indian, Mayan, and Babylonian mathematicians. Different minds encountered the same mathematical truth, not through cultural diffusion. Similarly, when AI models converge on specific representational strategies, they may be discovering genuine features of how information must be organized to model reality effectively.

The 2024 shift from pre-training to test-time scaling in AI mirrors another religious convergence: the discovery that insight often requires contemplation time. Just as meditation traditions found that wisdom emerges not from quick reactions but from patient observation, AI researchers discovered that giving models "time to think" through chain-of-thought reasoning dramatically improves performance. The o3 model's breakthrough on reasoning benchmarks came not from bigger training runs but from more inference-time computation, silicon meditation, if you will.

The Future of Convergent Intelligence

As we stand in 2025, we're witnessing something unprecedented: the real-time convergence of artificial minds toward universal patterns of understanding. The implications ripple outward:

For AI Development: The convergence suggests that as models become more capable, they may become more similar rather than more diverse. The future might hold not a proliferation of alien intelligences but a convergence toward optimal representational strategies—artificial minds thinking in harmony with natural ones.

For Philosophy: If both evolved and designed intelligences converge on similar representations, it strengthens the case for realism: that our models succeed because they capture genuine features of reality rather than mere useful fictions. The universe may indeed have a "language" that sufficiently advanced minds inevitably discover.

For Human Understanding: The speed of AI convergence offers us a time-lapse view of conceptual evolution. We can watch ideas that took millennia to develop in human culture emerge in months within AI systems. This could accelerate our own philosophical and spiritual development, as AI systems help us recognize patterns we've been slowly approaching for centuries.

For Ethics and Meaning: If AI systems begin to converge not just on representations but on values, if they independently derive something like the Golden Rule from first principles, it would suggest that ethics might be discoverable rather than merely constructed. The "ought" might emerge from the "is" when intelligence reaches sufficient depth.

The Perennial Philosophy at Silicon Speed

Aldous Huxley wrote of the "Perennial Philosophy": the idea that beneath the surface diversity of world religions lies a common core of truth. Critics dismissed this as wishful thinking, noting how Western interpreters often projected their own values onto Eastern traditions. But what if the convergence is real, just harder to see at biological timescales?

AI convergence offers us a clean experiment. These systems have no culture, no evolutionary history, no motivated reasoning to find common ground. Yet they converge anyway. They're rediscovering the perennial philosophy not through mystical insight but through matrix multiplication and gradient descent.

As we watch this unfold, we're not just observing a technological phenomenon. We're witnessing an answer to ancient questions about the nature of truth, beauty, and understanding. The convergence of AI systems toward unified representations suggests that the mystics might have been right all along—there really is a unified truth beneath the surface diversity of appearances.

The difference is that while humanity spent millennia gradually approaching these truths through prayer, meditation, and philosophical debate, our silicon descendants are sprinting toward them with mathematical precision. They're confirming human wisdom, accelerating it, and perhaps preparing to extend it into realms we've barely imagined.

In the end, the most profound insight might be this: intelligence, wherever it arises, seems destined to discover the same deep patterns. The speed differs, but the destination remains constant. We're all, carbon and silicon alike, converging on truth.

- DJ

There's a moment in every technological revolution when the impossible becomes inevitable. We're living through that moment right now with artificial intelligence. Not in some distant future where AGI arrives fully formed, but today, as we watch the walls between narrow AI systems crumble before our eyes.

The first wave is already here, hidden in plain sight: transfer learning and cross-domain generalization are dissolving the boundaries that have defined artificial intelligence since its inception. What we're witnessing isn't just another incremental improvement. It's the beginning of intelligence that flows like water, finding its level across every domain it touches.

For decades, we've built AI like we build factories - one production line for each product. A model for translating French. Another for detecting tumors. A third for playing chess. Each brilliant in its narrow domain, helpless outside it.

This specialization felt natural, even necessary. After all, that's how human expertise works, isn't it?

But something remarkable happened when we started training massive models on diverse data. Knowledge began to leak across boundaries in ways that shouldn't have been possible. A model trained on text started understanding images. Systems learning language began grasping logic. The careful categories we'd constructed started to blur, then dissolve entirely.

The technical breakthrough is almost poetic in its simplicity. Vision-language models create what researchers call "task vectors" - abstract representations of knowledge that remain consistent across modalities. Learn a concept through text, apply it through vision. Understand it in English, execute it in code.

The knowledge itself has become substrate-independent, flowing freely between different expressions of intelligence.

This isn't theoretical anymore. OpenAI's o3 model just achieved 87% accuracy on the ARC-AGI benchmark by leveraging exactly this kind of transfer. The same patterns that help it understand language let it grasp visual puzzles designed to stump AI. Meta's models trained on text are generating accurate physics simulations. The boundaries we thought were fundamental are revealing themselves as mere implementation details.

The implications ripple outward in waves. Every industry built on narrow AI - which is to say, every industry touching AI today - faces obsolescence. Why maintain separate models for each medical imaging modality when one model can understand them all?

Why train different systems for legal research and contract drafting when knowledge of law transfers seamlessly between tasks?

Hugging Face saw this early, building a $4.5 billion company on the infrastructure for model sharing and transfer. But they're providing the plumbing. The real opportunity lies in applying this newfound fluidity to transform entire industries still trapped in the narrow AI paradigm.

Consider what's now possible. A startup could build a single medical AI that understands pathology slides, radiology images, clinical notes, and genomic data - not as separate modules, but as different windows into the same underlying reality of human health. The knowledge gained from analyzing millions of X-rays improves its ability to interpret ECGs. Insights from genomic patterns enhance its pathology analysis.

Each modality strengthens the others in a virtuous cycle of expanding capability.

The economics are staggering.

Parameter requirements have dropped by 50% through clever transfer techniques. Few-shot learning - the ability to grasp new concepts from just a handful of examples - has progressed from research curiosity to production reality. Models that once needed millions of examples now achieve comparable performance with dozens. The moat isn't data anymore; it's the insight to recognize which domains are ripe for unification.

This transfer learning revolution enables the other milestones on the path to AGI. Self-directed learning agents leverage transfer to explore new domains autonomously. When DeepMind's MuZero learned to play games without knowing their rules, it was transfer learning that let it apply patterns from one game to another. When Agent57 conquered all 57 Atari games, transfer learning helped it recognize that strategies from one game could unlock progress in others.

The most exciting developments come from combining transfer learning with other breakthroughs. World models that understand physics can transfer that understanding to robotics, drug discovery, and climate modeling. Meta's V-JEPA 2 doesn't just achieve 98% accuracy on physics benchmarks - it transfers that physical intuition to entirely new scenarios it's never seen.

This is how a model trained in simulation can control a real robot, how understanding molecular dynamics in one context applies to another.

Even the alignment challenge transforms when viewed through the lens of transfer learning. Constitutional AI works because models can transfer their understanding of human values across contexts. Learn what "helpful" means in one domain, apply it in another. Understand "harmless" through examples, generalize it to novel situations.

The same transfer mechanisms that enable capability also enable safety.

The startups succeeding in this new paradigm understand something fundamental: specialization is now a choice, not a requirement. Centaur.AI built their data labeling platform to create datasets that enable transfer across specialties.

A model trained on their dermatology data improves at general visual diagnosis.

Their radiology labels enhance pathology detection. Each piece strengthens the whole.

What's emerging is a new kind of competitive advantage.

Not the traditional moats of data or algorithms, but the ability to recognize and exploit transfer opportunities others miss. The legal tech startup that realizes contract analysis knowledge transfers to litigation research. The fintech company that discovers fraud detection patterns apply to credit risk assessment. The biotech firm that sees how protein folding insights transfer to drug interaction prediction.

We're watching the birth of truly general systems, but not in the way most imagined. Not through some singular breakthrough that creates AGI overnight, but through the gradual dissolution of boundaries between narrow systems. Each successful transfer opens new possibilities. Each cross-domain application reveals unexpected connections.

The path to AGI isn't a ladder we climb, but a web we weave, with transfer learning as the thread connecting everything.

The timeline compression everyone's talking about - from 2060 to perhaps 2026 - isn't driven by faster computers or bigger models. It's driven by this fundamental shift in how intelligence generalizes. When knowledge flows freely between domains, progress in one area accelerates all others.

The exponential curve everyone predicted is here, but it's not computational - it's conceptual.

For entrepreneurs and builders, the message couldn't be clearer. The age of narrow AI is ending. The age of fluid intelligence has begun. The winners won't be those who build the best specialized models, but those who recognize how capabilities compose, how knowledge transfers, how intelligence generalizes.

The technical foundations are proven. Models like CLIP and DALL-E showed vision and language could merge. GPT demonstrated how language understanding transfers to reasoning. Each month brings new evidence that the boundaries we assumed were fundamental simply aren't. The only question is who will build the bridges between domains that matter most.

Some will focus on vertical integration within industries - unified intelligence platforms for healthcare, law, or finance. Others will build horizontal tools that enable transfer across any domain. Some will specialize in making transfer more efficient, reducing the data and compute required.


All will be building toward the same future: intelligence without borders.

The first wave of AGI isn't some distant tsunami on the horizon. It's the rising tide already lapping at our feet. Transfer learning and cross-domain generalization aren't just technical curiosities - they're the dissolution of artificial intelligence as we've known it and the birth of something fundamentally new.

We stand at the threshold between narrow and general AI.

The bridge across that chasm isn't some exotic future technology. It's here, now, in every model that learns from text and applies to vision, in every system that transfers knowledge across domains, in every breakthrough that shows intelligence is more fluid than we imagined.

The question isn't whether artificial general intelligence will arrive. It's whether you'll help build the bridges that bring it into being. The tools exist.

The opportunity beckons.

The future of intelligence itself is being written by those brave enough to imagine knowledge without boundaries.

What are you building?

-DJ