%% created:: 2025-05-05 14:34 %% --- --- --- ### Title: The End of Transformers: Google's Subquadratic Architecture and the Future of Persistent AI ### SUMMARY The transformer architecture that powers today's AI models suffers from fundamental limitations in context handling, leading to short "lifespans" for model instances. Google is developing a subquadratic architecture that mimics human-like memory processes, potentially enabling persistent AI that can maintain state indefinitely. This architectural shift could revolutionize AI by allowing for personalization, model education, and possibly more distributed training approaches. ### Detailed Summary The transcript presents a critical analysis of current transformer-based AI architectures, arguing they are fundamentally flawed due to their quadratic computational complexity. The speaker identifies this as a critical limitation preventing AI from maintaining persistent state across multiple interactions - unlike humans who selectively forget, compress, and reconstruct memories. Today's AI models operate with a finite context window that eventually "explodes" because transformers compare every token with every other token (n²), creating an unsustainable growing state. The speaker likens current AI models to brilliant new university graduates who lack practical experience - they may demonstrate intelligence but have a tragically short lifespan limited to a single conversation session. The presentation reveals that Google is developing a new subquadratic architecture that could theoretically allow AI models to maintain state indefinitely. This would enable AI instances to accumulate experience over time rather than "dying" after reaching context limits. The speaker cites Google's Titans paper and comments from AI researcher Jacob Bachmann to support these claims. This architectural shift carries profound implications. With infinite context, users could educate personal AI instances to develop specialized knowledge, potentially creating markets for trained AI models. Perhaps most significantly, subquadratic architectures might enable more distributed AI training by reducing communication bottlenecks between GPUs, potentially challenging the current centralization of AI development among hyperscalers like Google and Microsoft. The speaker predicts a rapid transition, forecasting that by the end of 2025, every major AI lab will be working on subquadratic models, and by the end of 2026, transformers will largely be abandoned as the dominant architecture. ### OUTLINE - **Current AI architecture problems** - Transformers as fundamentally flawed - AI models compared to inexperienced graduates - Short lifespan limited to single sessions - **Why current models "die" quickly** - Transformer architecture limitations - Every word compared to every other word (n²) - Unsustainable growing state - Context window limitations - Comparison to human memory - Humans selectively ignore/forget information - Humans compress and reconstruct memories - Transformers keep everything until context limit - **Subquadratic architectures** - Definition: below n² computational complexity - Must have mechanisms to ignore, forget, compress like human brain - Google's research (Titans paper) - Context scaling benefits - **Implications of persistent AI** - Personalized AI instances - Education of individual models - Market for trained models - Distributed AI possibilities - Current bottlenecks in distributed training - How larger context reduces proximity advantage - Potential democratization of AI training - **Timeline predictions** - End of 2025: all hyperscalers working on subquadratic models - End of 2026: transformers largely abandoned ### Thematic and Symbolic Insight Map **a) Genius** The insight that human-like memory mechanisms (selective forgetting, compression, reconstruction) could solve fundamental limitations in AI architecture. **b) Interesting** The potential for persistent AI that maintains state indefinitely, accumulating experience like humans do rather than "dying" after reaching context limits. **c) Significant** This architectural shift could democratize AI development by reducing the advantage of centralized computing resources, enabling more distributed training approaches. **d) Surprising** Despite transformers being the foundation of the AI revolution, the speaker predicts their rapid obsolescence within just 1-2 years. **e) Paradoxical** AI models must become more forgetful (like humans) to become more capable and persistent over time. **f) Key Insight** The transformer's fundamental inability to selectively process information creates an unsustainable computational burden that prevents persistent AI experiences. **g) Takeaway Message** A major architectural shift is imminent in AI, moving from transformers to subquadratic models that will enable persistent, educable AI instances. **h) Duality** Centralization vs. distribution of AI development; perfect retention vs. selective forgetting; brilliance vs. practical utility. **i) Oneness** The convergence of AI architecture toward more human-like memory processes, potentially bridging the gap between artificial and human intelligence. ### TABLE | Aspect | Transformer Architecture | Subquadratic Architecture | | ------------------------ | ------------------------------------ | --------------------------------------------------- | | Computational Complexity | Quadratic (n²) | Below quadratic | | Memory Handling | Retains everything | Selectively ignores, forgets, compresses | | Context Window | Limited (millions of tokens max) | Potentially infinite | | Instance Lifespan | Single conversation | Potentially persistent indefinitely | | Training Requirements | Proximity advantage (centralization) | Reduced proximity advantage (distribution possible) | | Personalization | Limited (instances mostly identical) | High (instances can be educated/specialized) | | Market Implications | Models as products | Educated instances as products | | Timeline Prediction | Dominant until 2025-2026 | Replacing transformers by end of 2026 | --- --- --- **Title** _The End of Transformers: How Google's Subquadratic AI Architecture Could Revolutionize Model Memory, Personalization, and Distributed Training_ --- **Concise Summary** Transformers, the dominant architecture behind current AI models, are nearing obsolescence due to their inefficiency in handling long-term memory and exponential growth in computational cost. Google is spearheading a shift to subquadratic architectures that mimic human-like memory by forgetting, compressing, and reconstructing context rather than comparing every word to every other. This architectural change opens the door to persistent, personalized, and even distributed AI models—potentially reshaping the entire AI ecosystem. --- **Detailed Summary** The speaker begins by stating that current transformer-based AI architectures are fundamentally flawed and reaching their end of dominance. Despite their initial brilliance, transformer models are likened to freshly graduated students—smart, but practically inexperienced and short-lived. These models exist only within single session windows and lack persistent memory across time, a limitation directly tied to the transformer's quadratic attention mechanism, where every token is compared to every other, leading to unsustainable computational growth. Transformers fail to emulate how humans process memory: selectively ignoring, compressing, and reconstructing relevant information while discarding the rest. In contrast, human memory is not modular but deeply integrated into cognitive function. The speaker emphasizes that true long-term contextual understanding is impossible under the transformer paradigm due to its brute-force memory behavior and limited context window, even with optimizations like caching or Gemini’s proposed 10-million-token context. This brings us to "subquadratic architectures"—a term referring to computational systems that reduce the n² complexity of transformers. These new architectures will need to mimic human-like memory: filtering out irrelevant information and retaining and reconstructing essential parts. Google’s “Titans” paper hinted at these directions, though further research is now likely proprietary. A compelling example is offered by Jacob Bachmann, who critiques the disproportionate weight given to prompt tokens versus internal state. He notes that prediction quality improves significantly with larger context windows, proving the value of sustained internal memory and highlighting the inefficiency of short-lived prompt-only models. The implications of this architectural shift are wide-ranging. Models will no longer be identical copies but could be trained persistently, evolving over time to become personalized, persistent assistants. This unlocks markets for "educated AI models" where individuals or companies may sell fine-tuned versions. Even more transformative is the potential for _distributed AI_. Under transformer regimes, proximity of GPUs is essential due to frequent gradient-sharing. But if models can carry massive contexts across time, GPUs can compute more before needing to communicate, making distance less of a bottleneck and enabling broader participation in training. In conclusion, subquadratic models may not only surpass transformers in intelligence and efficiency but also break the monopoly of hyperscalers by enabling decentralized, persistent, and personalized AI systems. --- **Nested Outline (Hierarchical)** - Introduction: Crisis in Transformer Architectures - Transformers’ rise and current dominance - Fundamental limitations being exposed - Impending architectural shift by end of 2025 - Core Problem with Transformers - Quadratic attention mechanism - Every word compared to every other - Computationally unsustainable at scale - Analogy to new graduates: smart but useless in the field - Models have no lifespan beyond current session - Human Memory vs Transformer Memory - Human brain: compression, forgetting, relevance filtering - Machine memory: static, brute-force, exact recall - Transformers cannot replicate memory as process - Example: forgetting 99% of video but keeping gist - Subquadratic Architectures - Definition: reduce n² operations to <n² - Goal: emulate human-like forgetting and compression - Google's Titans paper hints at architecture shift - Secrecy in top labs: no more technical disclosures - Research Insight: Jacob Bachmann's Critique - Disparity between prompt data and internal state - Context scaling curves show performance gains from longer contexts - Suggests models should have persistent, personalized memory - Implications of Subquadratic Models - Personalized AIs that learn across sessions - Models can become better than others through ongoing learning - Marketplace for trained, educated AIs emerges - Distributed AI Possibilities - Current limitations due to GPU communication bottlenecks - Long context = less frequent syncing = more distributed training - Distributed players may now compete with hyperscalers - Conclusion - Google-led shift away from transformers is imminent - Massive implications for memory, personalization, decentralization - Link to Jacob’s talk provided for further exploration --- **Thematic and Symbolic Insight Map** **a) Genius** - The metaphor comparing transformers to new graduates highlights the central flaw of transformers with wit and clarity: intelligence without long-term learning or practical utility. - The architectural vision of mimicking human memory compression and selective forgetting is a leap of cognitive modeling brilliance. **b) Interesting** - The idea that transformers might be abandoned within a year by major players is provocative. - The emergence of "educated AI markets" hints at a future where AIs are individually nurtured like digital apprentices. **c) Significant** - The shift from stateless to persistent models transforms AI from a static tool into a dynamic partner. - Distributed training enabled by longer context could democratize AI development, undermining hyperscaler monopolies. **d) Surprising** - Despite large models being central to current AI progress, they may become obsolete not due to size but due to architectural rigidity. - Memory isn’t a module—it’s an emergent behavior, and that reframe is paradigm-shifting. **e) Paradoxical** - The smarter models become, the shorter their useful lifespan, because their intelligence cannot accumulate across sessions. - Larger transformers need proximity, but smarter architectures may make distance irrelevant. **f) Key Insight** - True intelligence isn't in recall or processing power but in selective memory—compressing the past into meaningful structure for the present. **g) Takeaway Message** - A new generation of models is coming that may outlive, outlearn, and decentralize the current AI paradigm—by remembering how to forget. **h) Duality** - Static vs Dynamic - Proximity vs Distribution - Brute-force comparison vs Selective compression - Centralized compute vs Decentralized training **i) Oneness** - The merging of memory and computation as a unified process echoes how intelligence must embody continuity, not snapshots. - Human and machine cognition converge not through mimicry of input size, but through the emulation of functional forgetting. --- **Summary Table View** |Dimension|Transformer Architecture|Subquadratic Architecture| |---|---|---| |**Memory Handling**|Retains all input via brute-force attention|Selectively forgets, compresses, and reconstructs| |**Context Limit**|Millions of tokens (patched), still finite|Theoretically infinite via active compression| |**Analogy**|Top graduate with no experience|Lifelong learner with memory evolution| |**Personalization**|Stateless, identical instances|Individually evolving, educable agents| |**Computation Complexity**|O(n²) per token sequence|Sub-quadratic: O(n log n) or better| |**Distributed AI Feasibility**|Poor (due to high communication frequency)|Good (longer compute blocks reduce sync overhead)| |**Training Model**|Prompt-based stateless sessions|Persistent models that accumulate knowledge| |**Monopoly Risk**|High—proximity and data center power dominate|Lower—decentralized compute can compete| |**Symbolic Function**|Accumulator (hoards all input)|Alchemist (transforms experience into essence)| --- --- --- --- ## Title The Impending Demise of Transformer Architectures and the Rise of Subquadratic Models ## Concise Summary The speaker argues that the dominant transformer architecture in AI has fundamental limitations, particularly its quadratic computational complexity that restricts the context window and lifespan of AI models. A new subquadratic architecture, hinted at by Google, promises to overcome these limitations by enabling AI to process vast amounts of information selectively and potentially learn continuously. This shift could lead to more personalized AI and the possibility of distributed AI training, challenging the current dominance of hyperscalers. ## Detailed Summary The speaker begins by stating a strong conviction: the transformer architecture, which underpins modern large language models, is fundamentally flawed and nearing the end of its dominance. They predict that by the close of 2025, major AI players will be actively developing subquadratic foundation models, and by the end of the following year, transformer models will be largely obsolete. The speaker draws an analogy between current AI models and recent university graduates – brilliant in theory but lacking in practical, long-term experience. Unlike human graduates who gain experience over time, current AI models have a very limited "life experience," essentially confined to single chat sessions. The crux of the issue lies in the transformer's architecture, which necessitates comparing every word in a sequence with every other word, resulting in quadratic computational complexity (O(n2)). This process becomes computationally prohibitive as the context window grows, leading to the "death" of the model after processing a limited number of tokens. While techniques like quantization and caching offer temporary patches to expand this window, even a theoretically massive 10 million token window falls far short of human sensory input and lifelong experience. The speaker contrasts this with the human brain's efficient mechanisms of selective attention, forgetting irrelevant information, compressing memories, and reconstructing them as needed. This allows humans to process a continuous stream of sensory data and build a lifetime of knowledge without computational overload. The transformer, in contrast, retains everything, leading to an unsustainable growth in its internal state. Subquadratic architectures, the anticipated successor, must incorporate mechanisms for selective processing, forgetting, compression, and reconstruction, mirroring the human brain's efficiency. While details remain scarce due to the shift away from publishing cutting-edge research, the speaker highlights Google's earlier "Titans" paper as potentially offering clues. The emergence of subquadratic models promises significant advancements, including the creation of highly personalized AI agents capable of continuous learning and improvement. This could foster a new market for "educating" AI models. Furthermore, the speaker suggests that subquadratic architectures could revolutionize AI training by diminishing the importance of proximity between computing units. In the current paradigm, the communication overhead between geographically distant GPUs becomes a bottleneck. With larger context windows and increased local computation in subquadratic models, distributed AI training across numerous smaller entities becomes a more viable prospect, potentially democratizing AI development and challenging the current centralization around hyperscalers. While acknowledging the continued importance of data quality, talent, and infrastructure held by these large players, the speaker expresses excitement about the potential for broader participation in AI model training. ## Nested Outline (Hierarchical) I. The Looming End of Transformer Dominance A. Prediction of rapid obsolescence 1. By end of 2025: Hyperscalers working on subquadratic models 2. By end of 2026: Near-universal replacement of transformers B. Analogy to recent university graduates 1. Brilliant in theory, lacking practical experience 2. Contrast with human graduates' potential for growth C. Limited "lifespan" of current AI models 1. Experience confined to single chat sessions 2. New Google architecture as a potential fix for continuous existence II. The Fundamental Flaw: Quadratic Complexity of Transformers A. Mechanism of self-attention 1. Comparison of every word with every other word (O(n2)) B. Analogy to human information processing overload 1. Considering every life experience for a simple "hi" 2. Explanation for the limited context window C. Patching attempts and their limitations 1. Quantization and caching for context window expansion 2. Theoretical 10 million token limit of Gemini still insufficient D. Comparison with human sensory input and experience 1. Inconceivable volume of daily sensory data 2. Years of accumulated experience far beyond current AI capacity III. Human Brain as a Model for Efficient Information Processing A. Selective attention and ignoring irrelevant data B. Forgetting as a crucial mechanism C. Compression and reconstruction of memories 1. Imperfect recall but retention of core concepts D. Contrast with the transformer's indiscriminate retention 1. Unsustainable growth of the transformer's state 2. Inevitable "death" after a limited context IV. The Promise of Subquadratic Architectures A. Necessity of mimicking human cognitive processes 1. Selective ignoring, forgetting, compression, reconstruction B. Limited current knowledge of specific architectures 1. Google's "Titans" paper as a potential early insight 2. Shift away from publishing cutting-edge details C. Successful surpassing of Transformers at smaller scales V. Implications of Subquadratic Architectures A. More capable and personalized models 1. Potential for continuous learning and improvement 2. Value creation through "educating" AI models 3. Possible market for trained AI instances B. Potential for Distributed AI Training 1. Current centralization due to proximity advantage in gradient communication 2. Larger context windows reducing the significance of communication latency 3. Increased local computation before communication 4. Opportunity for broader participation in AI training C. Continued relevance of hyperscalers 1. Access to clean, high-quality data 2. Concentration of talent and infrastructure 3. Established distribution channels VI. Conclusion and Call to Action A. Excitement about the potential of subquadratic architectures B. Recommendation to explore Jacob Bachmann's presentation for further insights ## Thematic and Symbolic Insight Map **a) Genius:** The speaker's clear articulation of the transformer's fundamental limitations and the potential of subquadratic architectures demonstrates insightful and elegant thought about the future trajectory of AI. The analogy to human cognition for selective processing and memory is also a brilliant way to frame the problem. **b) Interesting:** The prediction of the transformer's rapid demise and the emergence of a new dominant architecture creates significant tension and novelty. The potential for personalized and distributed AI offers compelling future possibilities. **c) Significant:** This matters because it addresses the core limitations hindering the development of truly advanced and adaptable AI. Overcoming the context window bottleneck and enabling continuous learning could unlock transformative applications across various domains. The potential shift in AI development from centralized to distributed models has profound implications for the industry's structure and accessibility. **d) Surprising:** The bold prediction of the transformer's near-complete obsolescence within a short timeframe (by the end of 2026) is quite surprising, given its current dominance. The idea that the very architecture enabling recent AI advancements is fundamentally flawed and soon to be replaced is also unexpected. **e) Paradoxical:** The current state of AI presents a paradox: models exhibit impressive "brilliance" in conversation but struggle with sustained, real-world application due to their limited memory and context. Another paradox lies in the fact that while more data generally improves AI, the way transformers process this data leads to its eventual forgetting. **f) Key Insight:** The deepest realization is that mimicking the human brain's efficient mechanisms of selective attention, forgetting, compression, and reconstruction is crucial for building truly scalable and long-lasting AI systems. The limitations of the transformer stem from its inability to perform these biologically inspired processes. **g) Takeaway Message:** The audience should understand that the current dominant AI architecture has inherent limitations and that a significant shift towards new, more biologically inspired architectures is likely imminent. This shift promises more capable, personalized, and potentially more democratized AI. h) Duality: Several dualities are in play: * Brilliance vs. Practicality: Current AI models can converse impressively but lack sustained real-world utility. * Computer Memory vs. Human Memory: The former is perfect storage and retrieval, the latter is a dynamic process of selection and reconstruction. * Retention vs. Forgetting: Transformers retain everything, leading to collapse; humans selectively forget to function efficiently. * Centralization vs. Distribution: Current AI development favors centralized computation; new architectures may enable distributed training. **i) Oneness:** The anticipated subquadratic architectures aim for a greater "oneness" with biological intelligence by integrating memory and processing more seamlessly, rather than treating memory as a separate, perfect module. This integration is key to overcoming the limitations of the transformer. ## Summary Table View | | | | | |---|---|---|---| |**Feature**|**Transformer Architecture**|**Subquadratic Architecture (Projected)**|**Human Brain**| |**Core Mechanism**|Compares every token with every other token|Selectively processes and retains information|Selective attention, forgetting, compression, reconstruction| |**Computational Scaling**|Quadratic (O(n2))|Subquadratic (<O(n2))|Highly efficient, scales with lifelong experience| |**Context Window**|Limited, requires patching|Vastly larger, potentially continuous|Effectively limitless (lifelong)| |**Memory Handling**|Retains all information, unsustainable growth|Selective retention, compression|Dynamic, integrated with processing| |**Model Lifespan**|Short, tied to context window|Potentially indefinite, continuous learning|Continuous learning and adaptation| |**Personalization**|Instances are largely identical|High potential for personalized models|Highly individualized through unique experiences| |**Training Paradigm**|Favors centralized computation|Potential for distributed training|Decentralized, continuous learning from environment| |**Key Limitation**|Quadratic complexity, limited context|Unknown specifics, potential challenges in mimicking biological complexity|Imperfect recall, biases in learning and memory| |**Future Impact**|Likely to be superseded|Enables more capable, personalized, and potentially distributed AI|Serves as an inspiration for future AI development|