AI Is a Five-Layer Cake

energy → chip → infrastructure → models → applications

Examples

  • Chatbots & AI agents
  • Robotaxis & robotics
  • Enterprise AI & coders
  • Drug discovery platforms

Model Types

  • LLM — Large Language Model
  • VLM — Vision Language Model
  • SSM — State Space Model
  • Protein AI, Chemical AI

Components

  • Land & power delivery
  • Cooling systems
  • Networking fabric
  • Orchestration software

CPU vs GPU

  • CPU: sequential, low-latency
  • GPU: parallel, high-throughput
  • GPUs power AI training

Why It Matters

  • Every token = electrons moving
  • Bounds intelligence output
  • No abstraction layer below
Layer 01 · Foundation
⚡ Energy
Electricity · Heat · Computation
Layer 02
🔲 Chip
CPU · GPU · Silicon
Layer 03
🏭 Infrastructure
AI Factories · Data Centers
Layer 04
🧠 Models
LLM · VLM · GPT · SSM
Layer 05 · Top
🚀 Applications
Where economic value is created

Key Insight

  • A robot & a drug platform can share the same stack
  • Same layers, different outcomes

Domains

  • Language, Biology, Chemistry
  • Physics, Finance, Medicine
  • Robotics & autonomous systems

Key Insight

  • Not storing information
  • Manufacturing intelligence
  • Tens of thousands of processors as one

Core Concepts

  • Core: independent physical unit
  • Thread: virtual instruction stream
  • Memory bandwidth = data speed

Scale

  • Real-time intelligence = real-time power
  • AI factories near power plants
  • Energy is an AI policy issue

CPU vs GPU — The Chip Layer Explained

CPU — Central Processing Unit

HOST · Latency-Oriented · Sequential

Architecture Latency-oriented; completes individual tasks with low delay
Core Count Small — typically 4 to 64 cores
Processing Serial execution — instructions run one after another
Control Complex control unit: manages branching, scheduling, instruction execution
Cache Large L1, L2, L3 cache to store frequently used data
Best for OS tasks, program control, sequential operations

GPU — Graphics Processing Unit

DEVICE · Throughput-Oriented · Parallel

Architecture Throughput-oriented; processes large amounts of data in parallel
Core Count Large — typically hundreds to thousands of cores
Processing Parallel execution — multiple instructions at exactly the same time
Control Reduced control unit; optimized for repeated parallel operations
Memory Smaller cache; uses high-bandwidth memory for fast data transfer
Best for Matrix computations, image processing, machine learning

Pre-Recorded Software

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Real-Time Intelligence