From Sand to Superintelligence — a visual journey Reports Lab Chapters Reference My study
From Sand to Superintelligence  ·  Reference
Reference

Bibliography

152 sources cited across the book — papers, manuals, archives, whitepapers — organized by chapter and unique URL.

Chapter 1 · The Mineral

  1. vast quantities of silicahttps://investornews.com/critical-minerals-rare-earths/cmi-masterclass-the-re…

Chapter 2 · Fire and Carbon

  1. Producing one ton of metallurgical-grade siliconhttps://www.energycentral.com/energy-biz/post/mining-and-refining-pure-silico…

Chapter 3 · The Nine-Nines Problem

  1. Over the course of several dayshttps://fpt-semiconductor.com/blogs/a-guidance-to-silicon-wafer-manufacturing…

Chapter 4 · Growing a Perfect Crystal

  1. A century laterhttps://www.waferworld.com/post/silicon-wafer-manufacturing-from-sand-to-silicon

Chapter 5 · From Log to Mirror

  1. Wafer slicing is a high-volume arthttps://www.sumcosi.com/english/products/process/step_02.html

Chapter 6 · Designing the Impossible

  1. nanosheet transistorshttps://www.tomshardware.com/tech-industry/semiconductors/tsmc-begins-quietly…

Chapter 8 · Light at 13.5 Nanometers

  1. Decades of lithographyhttps://www.uprtek.com/en/blogs/photolithography
  2. The whole process is repeated fifty thousand times per secondhttps://eureka.patsnap.com/report-what-challenges-do-euv-lithography-processe…
  3. A modern EUV scannerhttps://www.asml.com/news/stories/2021/semiconductor-manufacturing-process-steps
  4. high-numerical-aperture EUVhttps://www.eenewseurope.com/en/tsmc-shuns-high-na-euv-lithography/

Chapter 12 · CoWoS and the 2.5D Revolution

  1. By HBM4, the interface has doubled to 2,048 bits.https://introl.com/blog/nvidia-vera-rubin-platform-8-exaflops-infrastructure
  2. CoWoShttps://anysilicon.com/cowos-package/
  3. Roadmaps are paced by it.https://newsletter.semianalysis.com/p/vera-rubin-extreme-co-design-an-evolution

Chapter 13 · The Vera Rubin Superchip

  1. The whole module contains roughly seventeen thousand individual componentshttps://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer
  2. Olympus Arm v9 architecturehttps://developer.nvidia.com/blog/inside-the-nvidia-rubin-platform-six-new-ch…

Chapter 14 · The NVL72 Rack

  1. In the NVL72 designhttps://www.cnbc.com/2026/02/25/first-look-at-nvidias-ai-system-vera-rubin-an…
  2. 260 terabytes per secondhttps://www.signalintegrityjournal.com/articles/4183-nvidia-kicks-off-the-nex…
  3. Assembly time per tray drops from ~2 hours to about 5 minutes.https://developer.nvidia.com/blog/nvidia-vera-rubin-pod-seven-chips-five-rack…

Chapter 15 · Burn-In and Reliability

  1. Burn-inhttps://resources.system-analysis.cadence.com/blog/msa2020-conduct-burn-in-te…
  2. HTOLhttps://www.kessystemsinc.com/resources/the-pivotal-role-of-burn-in-testing-i…
  3. second-generation RAS enginehttps://www.nvidia.com/en-us/data-center/technologies/rubin/

Chapter 16 · The AI Factory

  1. DGX SuperPOD with DGX Vera Rubin NVL72https://blogs.nvidia.com/blog/dgx-superpod-rubin/
  2. An AI factory's electrical servicehttps://blog.se.com/datacenter/2026/04/09/building-ai-factories-why-integrate…
  3. 10× compared to Blackwellhttps://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer

Chapter 17 · The Electron's Choice

  1. Pure silicon's behaviour is conditionalhttps://www.nobelprize.org/prizes/physics/1956/summary/
  2. 1.12 electron-voltshttps://www.pveducation.org/pvcdrom/pn-junctions/band-gap
  3. low as 1013 dopant atoms per cubic centimeterhttps://en.wikipedia.org/wiki/Doping_(semiconductor)
  4. Bell Labs in work culminating in February 1940https://www.computerhistory.org/siliconengine/silicon-pn-junction-is-discovered/

Chapter 18 · The Transistor as a Valve

  1. 336 billionhttps://nvidianews.nvidia.com/news/rubin-platform-ai-supercomputer
  2. 0.4–0.7 V in modern deviceshttps://nanohub.org/resources/5780/download/2009.01.20-ece606-l28.pdf
  3. 15 nanometershttps://semiwiki.com/semiconductor-services/the-international-roadmap-for-dev…
  4. Dennard scaling's slow deathhttps://en.wikipedia.org/wiki/Dennard_scaling

Chapter 19 · From Switch to Logic

  1. Claude Shannon's 1937 master's thesishttps://en.wikipedia.org/wiki/A_Symbolic_Analysis_of_Relay_and_Switching_Circ…
  2. CMOS logichttps://www.computerhistory.org/siliconengine/cmos-circuits-eclipse-bipolar-a…
  3. ten billion such additions per second per corehttps://www.intel.com/content/www/us/en/developer/articles/technical/intel-sd…

Chapter 20 · Adders, Latches, Memory

  1. carry-lookaheadhttps://en.wikipedia.org/wiki/Carry-lookahead_adder
  2. carry-selecthttps://en.wikipedia.org/wiki/Carry-select_adder
  3. multipliershttps://www.cs.cmu.edu/~410/doc/segments/book.pdf
  4. tens of megabytes of cachehttps://www.tomshardware.com/news/intel-core-i9-13900k-review

Chapter 21 · The Clock

  1. tens of millions in a typical CPUhttps://en.wikipedia.org/wiki/Clock_signal
  2. 10% of the chip's total powerhttps://patents.google.com/patent/US20060277509A1/en
  3. Pentium 4 Prescott (2004) ran a 31-stage pipelinehttps://en.wikipedia.org/wiki/Pentium_4
  4. branch predictorhttps://en.wikipedia.org/wiki/Branch_predictor

Chapter 22 · Fetch, Decode, Execute

  1. AMD Zen 5https://www.amd.com/en/products/cpu/amd-ryzen-9-7950x
  2. M4 in a new MacBookhttps://www.apple.com/newsroom/2024/10/new-macbook-pro-features-m4-family-of-…
  3. µopshttps://en.wikipedia.org/wiki/Micro-operation
  4. von Neumann architecturehttps://en.wikipedia.org/wiki/Von_Neumann_architecture

Chapter 23 · From Transistors to ISA

  1. Intel x86-64 manualhttps://www.intel.com/content/www/us/en/developer/articles/technical/intel-sd…
  2. Arm ARMv9 referencehttps://developer.arm.com/documentation/ddi0487/latest
  3. Hennessy and Patterson's Computer Architecture textbookhttps://www.cs.cmu.edu/~410/doc/hennessy-patterson.pdf
  4. Pentiumhttps://en.wikipedia.org/wiki/Pentium_(original)
  5. PTX (Parallel Thread Execution)https://docs.nvidia.com/cuda/parallel-thread-execution/

Chapter 24 · Memory's Pyramid

  1. Computer Architecture: A Quantitative Approachhttps://www.elsevier.com/books/computer-architecture/hennessy/978-0-12-811905-1
  2. Intel's optimization manualhttps://www.intel.com/content/www/us/en/docs/intrinsics-guide/index.html
  3. 64 byteshttps://en.wikipedia.org/wiki/CPU_cache#Cache_entries
  4. subfield of OS performance workhttps://en.wikipedia.org/wiki/Translation_lookaside_buffer

Chapter 25 · Boot

  1. 0xFFFFFFF0https://wiki.osdev.org/Real_mode
  2. UEFIhttps://uefi.org/specifications
  3. GRUBhttps://www.gnu.org/software/grub/
  4. The Linux x86 boot protocolhttps://www.kernel.org/doc/html/latest/x86/boot.html
  5. kernel boothttps://www.kernel.org/doc/html/latest/admin-guide/bootconfig.html
  6. systemdhttps://systemd.io/

Chapter 26 · The OS as Conductor

  1. Modern Operating Systems by Tanenbaumhttps://www.cs.vu.nl/~ast/books/mos3/
  2. The Linux mmap man pagehttps://man7.org/linux/man-pages/man7/mmap.7.html
  3. three hundred and fifty system callshttps://man7.org/linux/man-pages/man2/syscalls.2.html
  4. Completely Fair Schedulerhttps://www.kernel.org/doc/html/latest/scheduler/sched-design-CFS.html

Chapter 27 · The Translation Stack

  1. CPython's ast modulehttps://docs.python.org/3/library/ast.html
  2. its own ASThttps://clang.llvm.org/docs/IntroductionToTheClangAST.html
  3. LLVM IRhttps://llvm.org/docs/LangRef.html
  4. LLVM's developer meetingshttps://llvm.org/devmtg/
  5. ELFhttps://refspecs.linuxbase.org/elf/elf.pdf
  6. Intel's optimization guidehttps://www.intel.com/content/www/us/en/develop/documentation/cpp-compiler-de…

Chapter 28 · The GPU's Different Mind

  1. The CUDA C++ Programming Guidehttps://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html
  2. NVIDIA's Volta whitepaperhttps://images.nvidia.com/content/volta-architecture/pdf/volta-architecture-w…
  3. Hopperhttps://resources.nvidia.com/en-us-tensor-core/nvidia-tensor-core-gpu-datasheet
  4. Tritonhttps://github.com/openai/triton
  5. FlashAttentionhttps://github.com/Dao-AILab/flash-attention
  6. occupancy calculatorshttps://developer.nvidia.com/cuda-toolkit

Chapter 29 · A Neural Network Lives in Numbers

  1. a Llama modelhttps://huggingface.co/meta-llama
  2. a Mistral modelhttps://huggingface.co/mistralai
  3. Attention Is All You Needhttps://arxiv.org/abs/1706.03762
  4. FlashAttentionhttps://github.com/Dao-AILab/flash-attention
  5. mixed-precision training paperhttps://docs.nvidia.com/deeplearning/performance/mixed-precision-training/ind…

Chapter 30 · A Thought, Token by Token

  1. tiktokenhttps://github.com/openai/tiktoken

Chapter 31 · The Second Wire

  1. stock tickerhttps://www.britannica.com/technology/stock-ticker
  2. patenthttps://www.loc.gov/collections/alexander-graham-bell-papers/articles-and-ess…
  3. Internet Protocolhttps://www.rfc-editor.org/rfc/rfc791
  4. "Information Management: A Proposal"https://www.w3.org/History/1989/proposal.html
  5. REST APIshttps://en.wikipedia.org/wiki/REST
  6. Radarhttps://radar.cloudflare.com/
  7. Routing layers like OpenRouterhttps://openrouter.ai/

Chapter 32 · Tokens on the Wire

  1. tiktokenhttps://github.com/openai/tiktoken
  2. SentencePiecehttps://github.com/google/sentencepiece
  3. Anthropichttps://www.anthropic.com/pricing
  4. OpenAIhttps://openai.com/pricing
  5. OpenAI's embeddings guidehttps://platform.openai.com/docs/guides/embeddings
  6. Sentence-Transformershttps://www.sbert.net/
  7. OpenAI's function-calling APIhttps://platform.openai.com/docs/guides/function-calling
  8. Anthropic's tool-usehttps://docs.anthropic.com/en/docs/build-with-claude/tool-use

Chapter 33 · Latency Is Cognition

  1. Nielsen's classic response-time workhttps://www.nngroup.com/articles/response-times-3-important-limits/
  2. Speculative decodinghttps://arxiv.org/abs/2305.13245
  3. Leviathan et al. (2022)https://arxiv.org/abs/2211.17192

Chapter 34 · Agents

  1. Anthropic's Claude with computer usehttps://www.anthropic.com/news/claude-3-5-sonnet
  2. OpenAI's Operatorhttps://openai.com/index/operator/
  3. OpenHandshttps://github.com/All-Hands-AI/OpenHands
  4. SWE-bench Verifiedhttps://www.swebench.com/
  5. AgentBenchhttps://arxiv.org/abs/2308.03688
  6. Cursorhttps://www.cursor.com/
  7. GitHub Copilot Workspacehttps://github.com/features/copilot
  8. Aiderhttps://aider.chat/
  9. Intercom's Finhttps://www.intercom.com/fin

Chapter 35 · Swarm

  1. multi-agent systemshttps://en.wikipedia.org/wiki/Multi-agent_system
  2. AutoGenhttps://github.com/microsoft/autogen
  3. LangGraphhttps://github.com/langchain-ai/langgraph
  4. Multi-Agent Research Systemhttps://www.anthropic.com/news/research
  5. Du et al. (2023)https://arxiv.org/abs/2305.14325
  6. OpenRouterhttps://openrouter.ai/
  7. Agent2Agent (A2A) protocolhttps://google.github.io/A2A/

Chapter 36 · Protocols of Trust

  1. OpenAIhttps://platform.openai.com/docs/guides/function-calling
  2. Anthropichttps://docs.anthropic.com/en/docs/build-with-claude/tool-use
  3. Model Context Protocolhttps://modelcontextprotocol.io/
  4. ODBChttps://en.wikipedia.org/wiki/Open_Database_Connectivity
  5. Agent2Agent (A2A) protocolhttps://google.github.io/A2A/
  6. Agent Communication Languageshttps://arxiv.org/abs/2402.08164
  7. Decentralized identifiershttps://www.w3.org/TR/did-core/

Chapter 37 · The Memory Commons

  1. FAISShttps://github.com/facebookresearch/faiss
  2. Pineconehttps://www.pinecone.io/
  3. Weaviatehttps://weaviate.io/
  4. Qdranthttps://qdrant.tech/
  5. pgvectorhttps://www.pgvector.org/
  6. chunking strategyhttps://arxiv.org/abs/2312.10997
  7. Knowledge graphshttps://en.wikipedia.org/wiki/Knowledge_graph
  8. GraphRAGhttps://github.com/microsoft/graphrag

Chapter 38 · The Browser Becomes the Worker

  1. robotic process automationhttps://en.wikipedia.org/wiki/Robotic_process_automation
  2. computer usehttps://www.anthropic.com/news/claude-3-5-sonnet
  3. Operatorhttps://openai.com/index/operator/
  4. WebDriverhttps://www.w3.org/TR/webdriver2/
  5. Chrome DevTools Protocolhttps://chromedevtools.github.io/devtools-protocol/
  6. WebArenahttps://webarena.dev/

Chapter 39 · Markets of Models

  1. OpenRouterhttps://openrouter.ai/
  2. Togetherhttps://www.together.ai/
  3. Fireworkshttps://fireworks.ai/
  4. Replicatehttps://www.replicate.com/
  5. Groqhttps://groq.com/
  6. Anthropic's Haiku 4.5https://www.anthropic.com/news/claude-haiku-4-5
  7. OpenAI's smaller GPT-5 variantshttps://openai.com/pricing

Chapter 40 · The Compounding

  1. Metcalfe's lawhttps://spectrum.ieee.org/metcalfes-law-is-wrong
  2. Cursorhttps://www.cursor.com/
  3. Shumailov et al. (2023)https://arxiv.org/abs/2305.17493

Chapter 41 · Where Value Reroutes

  1. BLShttps://www.bls.gov/
  2. Indeed Hiring Labhttps://www.hiringlab.org/
  3. long-tail publishershttps://www.theatlantic.com/