The current generation of large language models (LLMs) is astonishingly capable.
But it is also astonishingly inefficient.
Not because these systems reason poorly — but because they reconstruct what they already know, again and again, at enormous computational and energetic cost.
As AI adoption accelerates, this inefficiency is no longer theoretical. It shows up in:
– massive power consumption
– extreme cooling requirements
– surging demand for high-bandwidth memory
– rising inference costs that scale poorly with usage
This is not an unavoidable property of intelligence. It is a consequence of architecture.
The hidden cost: recomputation at scale
Today’s LLMs treat knowledge as something that must be re-derived through computation, rather than retrieved from storage. Ask a simple factual question — a definition, a well-known relationship, a standard procedure — and a trillion-parameter system may:
– activate dozens of layers
– move gigabytes of weights and KV cache
– perform billions of floating-point operations
Even though the answer is stable, shared, and has been generated countless times before.
This design made sense when models were small and usage was limited.
At global scale, it becomes wasteful.
Compute is no longer the main problem — data movement is
Modern AI inference is increasingly memory-bound, not compute-bound.
Much of the energy cost comes from:
– moving weights through memory hierarchies
– maintaining and reading large attention caches
– synchronizing data across accelerators
In many cases, moving the data costs more energy than the math itself.
Yet we still force models to “think” their way back to basic knowledge instead of remembering efficiently.
A shift in perspective: knowledge as infrastructure
A more scalable framing is not “how do we make models smarter?”
It is: “Why do intelligent systems refuse to store and retrieve shared knowledge efficiently?”
Human societies solved this centuries ago:
– we don’t rewrite books every time we need information
– we build libraries, indices, catalogues, and references
– we reconstruct meaning from compact representations
AI systems could do the same.
Where DeepSeek’s Engram fits — and its limits
Recent work on Engram memory by DeepSeek is interesting precisely because it moves in this general direction. Engram introduces a sparse, conditional memory mechanism that allows models to retrieve certain static patterns cheaply, instead of rebuilding them through dense transformer computation.
If it works as described, it can:
– reduce redundant computation
– lower inference cost for knowledge-heavy queries
– partially separate “knowing” from “reasoning”
Three architectural stages of AI knowledge use
Stage 1 — Monolithic transformer (today)
User Query
↓
[ Massive Transformer ]
↓
Answer
Characteristics
– Knowledge implicitly encoded in weights
– Same facts recomputed endlessly
– High FLOPs and high memory traffic
– Expensive and energy-intensive at scale
However — and this matters — Engram is a small step, not a paradigm shift.
It does not yet provide:
– a universal, structured knowledge layer
– full deduplication of shared facts
– robust indexing, versioning, or provenance
– guarantees against drift or corruption
It suggests the right direction, but it does not yet redefine the architecture.
Stage 2 — Retrieval-augmented systems (transitional)
User Query
↓
[ Retriever ] → Documents → Tokens
↓
[ Transformer ]
↓
Answer
Improvements
– External knowledge reduces hallucinations
– Model size grows slower
Limitations
– Retrieved documents still consume tokens
– Latency and bandwidth increase
– The model still has to “read” everything
Stage 3 — Knowledge-centric architecture (emerging ideal)
┌────────────────────────┐
User Query ───▶ │ Knowledge Layer │
│ – Canonical facts │
│ – Relations & schemas │
│ – Indices & metadata │
│ – Compressed traces │
└──────────▲─────────────┘
│
Sparse Retrieval
│
┌──────────┴─────────────┐
│ Reasoning Engine │
│ (Transformer) │
└──────────▲─────────────┘
│
Generation
│
Answer
Key properties
– Knowledge stored once, reused globally
– Retrieval cheaper than recomputation
– Reasoning reserved for uncertainty and synthesis
– Compute, memory, and storage each do what they’re best at
Why this matters for energy, cost, and scale
The biggest efficiency gains will not come from:
– ever larger models
– longer context windows
– brute-force hardware scaling
They will come from not forcing trillion-parameter systems to reason about trivial, already-known facts.
In practical terms:
– fewer GPU cycles per answer
– less memory bandwidth pressure
– lower power and cooling requirements
– more predictable inference costs
This is not about user memory or personalization.
It is about shared, non-personal knowledge — safely reusable, auditable, and energy-efficient.
What the big players are likely doing
Organizations like OpenAI, Google, and Anthropic are clearly investing in memory — but so far mainly at the system and product layer (context management, retrieval, orchestration). The harder shift — treating storage, indexing, and retrieval as architectural peers to neural compute — is still emerging.
When it happens, it may quietly deliver more real-world impact than another jump in parameter count.
Final thought
The future of AI scalability is unlikely to be defined by systems that think harder.
It will be defined by systems that stop thinking about things they already know — and start remembering them properly.
That is not a question of intelligence.
It is a question of architecture.