# AI Shockwave Timeline — the dated shocks (Jan 2024 – May 2026).
# A "shock" is an event that materially reset a frontier assumption: how much
# capability per unit of compute, how much reasoning at inference time, or how
# far the bottleneck moved into memory, interconnect, and protocols.
#
# All metric values are official vendor-reported figures unless independently
# reproduced. They are labeled as reported, not neutral field measurements.
#
# impact[] is the heart of the artifact: each entry carries a before -> after
# delta on one axis (capability | economics | systems). One entry per shock is
# flagged headline: true and surfaces inline on the node.

shocks:
  - id: gemini-1-5
    name: Gemini 1.5
    date: "2024-02-15"
    vendor: Google
    logo_domain: deepmind.google
    archetype: context
    layers: [software]
    magnitude: high
    magnitude_score: 85
    magnitude_rationale: >
      Turned long context from a niche demo into a serious product and
      infrastructure design axis.
    confidence: high
    concise_description: >
      A new MoE architecture with 128K standard context, a 1M-token preview,
      and report evidence of near-perfect retrieval beyond 10M tokens.
    assumption_changed: "Long context becomes a production capability, not a demo."
    impact:
      - axis: capability
        label: Standard context window
        before: "32K"
        after: "1M"
        delta: "31x"
        unit: tokens
        direction: up
        headline: true
        source_ref: src-gemini-1-5
      - axis: systems
        label: Long-context retrieval recall
        after: ">99%"
        direction: up
        source_ref: src-gemini-1-5
      - axis: capability
        label: Tested retrieval ceiling
        after: "10M tokens"
        direction: up
        source_ref: src-gemini-1-5
    downstream:
      short: Long-document and long-video workflows become practical.
      medium: RAG assumptions weaken as more state fits in-context.
      long: Context length becomes a pricing and systems-architecture variable.
    parents: []
    source_refs: [src-gemini-1-5]

  - id: nvidia-blackwell
    name: NVIDIA Blackwell & GB200 NVL72
    date: "2024-03-18"
    vendor: NVIDIA
    logo_domain: nvidia.com
    archetype: efficiency
    layers: [hardware, networking]
    magnitude: high
    magnitude_score: 88
    magnitude_rationale: >
      Made the rack, not the single GPU, the practical unit of frontier
      inference and training economics.
    confidence: high
    concise_description: >
      Fifth-generation NVLink and a 72-GPU rack exposing 130 TB/s of
      low-latency GPU communication, with claims of 30x real-time
      trillion-parameter inference vs the H100 era.
    assumption_changed: "The rack becomes the accelerator."
    impact:
      - axis: economics
        label: Real-time LLM inference
        before: "1x (H100)"
        after: "30x"
        delta: "30x"
        direction: up
        headline: true
        source_ref: src-nvidia-blackwell
      - axis: economics
        label: Cost & energy per inference
        before: "1x"
        after: "0.04x"
        delta: "25x lower"
        direction: down
        source_ref: src-nvidia-blackwell
      - axis: systems
        label: Intra-rack GPU bandwidth
        after: "130 TB/s"
        direction: up
        source_ref: src-nvidia-blackwell
    downstream:
      short: Hyperscaler capex pivots toward rack-scale systems.
      medium: Rack-scale "AI factory" design becomes standard.
      long: Interconnect bandwidth becomes a first-class competitive moat.
    parents: []
    source_refs: [src-nvidia-blackwell]

  - id: deepseek-v2
    name: DeepSeek-V2
    date: "2024-05-06"
    vendor: DeepSeek
    logo_domain: deepseek.com
    archetype: efficiency
    layers: [software]
    magnitude: high
    magnitude_score: 84
    magnitude_rationale: >
      First clear open-source proof that memory efficiency and sparse
      activation move the economics frontier, not just the benchmark frontier.
    confidence: high
    concise_description: >
      A 236B-total / 21B-active MoE with Multi-head Latent Attention and
      DeepSeekMoE, reporting dramatic KV-cache and training-cost reductions.
    assumption_changed: "KV-cache compression changes the economics of serving."
    impact:
      - axis: economics
        label: KV cache footprint
        before: "100%"
        after: "6.7%"
        delta: "-93.3%"
        direction: down
        headline: true
        source_ref: src-deepseek-v2
      - axis: economics
        label: Training cost vs DeepSeek 67B
        before: "100%"
        after: "57.5%"
        delta: "-42.5%"
        direction: down
        source_ref: src-deepseek-v2
      - axis: systems
        label: Max generation throughput
        before: "1x"
        after: "5.76x"
        direction: up
        source_ref: src-deepseek-v2
    downstream:
      short: Cheaper open serving on commodity GPU fleets.
      medium: KV compression and MLA become design references.
      long: Architecture shifts toward memory-aware inference efficiency.
    parents: []
    source_refs: [src-deepseek-v2]

  - id: gpt-4o
    name: GPT-4o
    date: "2024-05-13"
    vendor: OpenAI
    logo_domain: openai.com
    archetype: context
    layers: [software]
    magnitude: high
    magnitude_score: 82
    magnitude_rationale: >
      Reset the user expectation for latency and made real-time multimodal
      interaction feel native rather than stitched together.
    confidence: high
    concise_description: >
      An end-to-end omni model for text, image, and audio with sub-second
      audio response latency and a 50% cheaper API than GPT-4 Turbo.
    assumption_changed: "Latency drops into conversation range."
    impact:
      - axis: capability
        label: Audio response latency
        before: "~2800 ms"
        after: "320 ms"
        delta: "~9x faster"
        direction: down
        headline: true
        source_ref: src-gpt-4o
      - axis: economics
        label: API price vs GPT-4 Turbo
        before: "100%"
        after: "50%"
        delta: "-50%"
        direction: down
        source_ref: src-gpt-4o
      - axis: capability
        label: Modalities in one model
        after: "text + image + audio"
        direction: up
        source_ref: src-gpt-4o
    downstream:
      short: Voice agents and live multimodal UX become credible.
      medium: Separate modality stacks look increasingly obsolete.
      long: Real-time multimodal inference becomes a baseline expectation.
    parents: []
    source_refs: [src-gpt-4o]

  - id: llama-3-1-405b
    name: Llama 3.1 405B
    date: "2024-07-23"
    vendor: Meta
    logo_domain: meta.com
    archetype: open_frontier
    layers: [software]
    magnitude: high
    magnitude_score: 80
    magnitude_rationale: >
      Narrowed the frontier/open gap enough to change enterprise procurement
      and experimentation behavior.
    confidence: high
    concise_description: >
      The "first frontier-level open source AI model," with 128K context,
      training on over 15T tokens across more than 16,000 H100 GPUs.
    assumption_changed: "Open weights reach the frontier."
    impact:
      - axis: capability
        label: Open-weight frontier parity
        after: "first frontier-level open model"
        direction: up
        headline: true
        source_ref: src-llama-3-1
      - axis: systems
        label: Context window
        before: "8K (Llama 2)"
        after: "128K"
        delta: "16x"
        direction: up
        source_ref: src-llama-3-1
      - axis: economics
        label: Training scale
        after: ">15T tokens / 16k H100"
        direction: up
        source_ref: src-llama-3-1
    downstream:
      short: Open-weight pilots expand inside enterprises.
      medium: Synthetic-data generation and distillation accelerate.
      long: Open weights place structural pricing pressure on closed APIs.
    parents: []
    source_refs: [src-llama-3-1]

  - id: openai-o1
    name: OpenAI o1
    date: "2024-09-12"
    vendor: OpenAI
    logo_domain: openai.com
    archetype: reasoning
    layers: [software]
    magnitude: high
    magnitude_score: 88
    magnitude_rationale: >
      Made inference-time scaling a roadmap category in its own right.
    confidence: high
    concise_description: >
      A reasoning series designed to "spend more time thinking," with smooth
      gains from both train-time and test-time compute.
    assumption_changed: "Reasoning gets its own compute budget."
    impact:
      - axis: capability
        label: AIME 2024 (pass@1)
        before: "13.4% (GPT-4o)"
        after: "74.4%"
        delta: "+61 pts"
        direction: up
        headline: true
        source_ref: src-openai-o1
      - axis: capability
        label: GPQA Diamond (pass@1)
        after: "77.3%"
        direction: up
        source_ref: src-openai-o1
      - axis: systems
        label: New scaling axis
        after: "test-time compute"
        direction: up
        source_ref: src-openai-o1
    downstream:
      short: Separate "reasoner" tiers appear in model menus.
      medium: Thought-budget controls and model routing proliferate.
      long: Inference compute allocation rivals pretraining scale.
    parents: []
    source_refs: [src-openai-o1]

  - id: mcp
    name: Model Context Protocol
    date: "2024-11-25"
    vendor: Anthropic
    logo_domain: anthropic.com
    archetype: interoperability
    layers: [networking, software]
    magnitude: medium
    magnitude_score: 65
    magnitude_rationale: >
      Smaller immediate effect than a frontier model launch, but it changed
      how developers think about integration debt in agent systems.
    confidence: high
    concise_description: >
      An open standard for secure, two-way connections between AI tools and
      data sources, with a spec/SDK and reference servers.
    assumption_changed: "Model-to-tool connectivity standardizes."
    impact:
      - axis: systems
        label: Model-to-tool integration
        before: "bespoke per-tool connectors"
        after: "one open protocol"
        direction: up
        headline: true
        source_ref: src-mcp
      - axis: capability
        label: Reference servers at launch
        after: "Drive, Slack, GitHub, Git, Postgres, Puppeteer"
        direction: up
        source_ref: src-mcp
    downstream:
      short: Bespoke connector duplication starts to look wasteful.
      medium: MCP server ecosystems expand across vendors.
      long: Tools and data become modular "ports" for any model.
    parents: []
    source_refs: [src-mcp]

  - id: deepseek-v3
    name: DeepSeek-V3
    date: "2024-12-26"
    vendor: DeepSeek
    logo_domain: deepseek.com
    archetype: efficiency
    layers: [software, hardware, networking]
    magnitude: high
    magnitude_score: 90
    magnitude_rationale: >
      Made algorithm-framework-hardware co-design legible as an open
      competitive strategy, not just a hyperscaler secret.
    confidence: high
    concise_description: >
      671B total / 37B active with FP8 mixed-precision training, multi-token
      prediction, and cross-node MoE communication overlap.
    assumption_changed: "Open frontier quality at a visible GPU-hour cost."
    impact:
      - axis: economics
        label: Full training compute
        after: "2.788M H800 GPU-hours"
        direction: down
        headline: true
        source_ref: src-deepseek-v3
      - axis: economics
        label: Decoding throughput (MTP)
        before: "1x"
        after: "1.8x"
        direction: up
        source_ref: src-deepseek-v3
      - axis: systems
        label: Training precision
        before: "BF16"
        after: "FP8 mixed"
        direction: down
        source_ref: src-deepseek-v3
    downstream:
      short: API price pressure rises across the market.
      medium: Open infra stacks copy FP8, MTP, and comm overlap.
      long: '"Model quality per GPU-hour" becomes a core frontier KPI.'
    parents: [deepseek-v2]
    source_refs: [src-deepseek-v3]

  - id: deepseek-r1
    name: DeepSeek-R1
    date: "2025-01-20"
    vendor: DeepSeek
    logo_domain: deepseek.com
    archetype: reasoning
    layers: [software]
    magnitude: high
    magnitude_score: 95
    magnitude_rationale: >
      The canonical "DeepSeek moment": an open reasoning model that
      materially changed market expectations for price, openness, and
      distillability.
    confidence: high
    concise_description: >
      R1 and R1-Zero showed large-scale RL could induce reasoning, with a
      cold-start multi-stage pipeline and six distilled smaller models.
    assumption_changed: "Open reasoning reaches parity and becomes distillable."
    impact:
      - axis: capability
        label: AIME 2024 (pass@1)
        after: "79.8%"
        direction: up
        headline: true
        source_ref: src-deepseek-r1
      - axis: economics
        label: Reasoning openness
        before: "closed (o1)"
        after: "open weights + 6 distilled"
        direction: up
        source_ref: src-deepseek-r1
      - axis: capability
        label: MATH-500
        after: "97.3%"
        direction: up
        source_ref: src-deepseek-r1
    downstream:
      short: A reasoning price war begins.
      medium: Dense distilled reasoners proliferate.
      long: Open reasoning becomes a research baseline and commodity.
    parents: [deepseek-v3]
    source_refs: [src-deepseek-r1]

  - id: claude-3-7-sonnet
    name: Claude 3.7 Sonnet
    date: "2025-02-24"
    vendor: Anthropic
    logo_domain: anthropic.com
    archetype: reasoning
    layers: [software]
    magnitude: medium
    magnitude_score: 70
    magnitude_rationale: >
      Did not originate the reasoning trend, but made "fast mode vs deep-think
      mode" a practical product interaction pattern.
    confidence: high
    concise_description: >
      The "first hybrid reasoning model," with a visible extended-thinking
      mode, API control over thinking budget, and Claude Code.
    assumption_changed: "Thinking budget becomes product UI."
    impact:
      - axis: capability
        label: Thinking mode
        before: "implicit"
        after: "user-controlled budget to 128K"
        direction: up
        headline: true
        source_ref: src-claude-3-7
      - axis: systems
        label: Agentic coding
        after: "Claude Code (terminal)"
        direction: up
        source_ref: src-claude-3-7
    downstream:
      short: Product teams expose thought-budget controls.
      medium: Agentic coding workflows mature.
      long: Unified fast+slow models replace fragmented model menus.
    parents: []
    source_refs: [src-claude-3-7]

  - id: a2a
    name: Agent2Agent Protocol
    date: "2025-04-09"
    vendor: Google
    logo_domain: google.com
    archetype: interoperability
    layers: [networking, software]
    magnitude: medium
    magnitude_score: 66
    magnitude_rationale: >
      Early, but strategically important: it treats a multi-agent estate as a
      network problem rather than a single-model problem.
    confidence: medium
    concise_description: >
      A protocol with 50+ launch partners using HTTP, SSE, and JSON-RPC,
      positioned as complementary to MCP for multi-agent tasks.
    assumption_changed: "Agents become network peers."
    impact:
      - axis: systems
        label: Cross-agent interoperability
        before: "single-vendor orchestration"
        after: "vendor-neutral protocol"
        direction: up
        headline: true
        source_ref: src-a2a
      - axis: capability
        label: Launch partners
        after: "50+"
        direction: up
        source_ref: src-a2a
    downstream:
      short: Enterprise pilots for cross-agent workflows rise.
      medium: Vendor-neutral orchestration becomes easier.
      long: Agent meshes become an architecture category.
    parents: []
    source_refs: [src-a2a]

  - id: nvlink-fusion
    name: NVLink Fusion
    date: "2025-05-18"
    vendor: NVIDIA
    logo_domain: nvidia.com
    archetype: interoperability
    layers: [hardware, networking]
    magnitude: medium
    magnitude_score: 64
    magnitude_rationale: >
      Significance is architectural and ecosystem-level; real deployment
      effects lag the announcement.
    confidence: medium
    concise_description: >
      Opened the NVLink ecosystem to semi-custom AI infrastructure, letting
      custom CPUs and ASICs pair with NVIDIA rack-scale systems.
    assumption_changed: "Custom silicon plugs into the NVIDIA fabric."
    impact:
      - axis: systems
        label: Custom silicon in NVIDIA fabric
        before: "NVIDIA CPUs/GPUs only"
        after: "custom CPU/ASIC pairing"
        direction: up
        headline: true
        source_ref: src-nvlink-fusion
      - axis: economics
        label: Scale-out networking
        after: "800 Gb/s"
        direction: up
        source_ref: src-nvlink-fusion
    downstream:
      short: Sovereign and custom AI stack designs become plausible.
      medium: CPU/ASIC heterogeneity grows inside NVIDIA fabrics.
      long: The fabric becomes the platform, not only the GPU.
    parents: [nvidia-blackwell]
    source_refs: [src-nvlink-fusion]

  - id: nvidia-rubin
    name: NVIDIA Rubin & Vera Rubin NVL72
    date: "2026-01-05"
    vendor: NVIDIA
    logo_domain: nvidia.com
    archetype: efficiency
    layers: [hardware, networking]
    magnitude: medium
    magnitude_score: 72
    magnitude_rationale: >
      Potentially enormous, but still early in the deployment cycle; partner
      availability is in the second half of 2026.
    confidence: medium
    concise_description: >
      72 Rubin GPUs and 36 Vera CPUs with 260 TB/s rack bandwidth, 20.7 TB GPU
      memory, and claims of 10x lower inference token cost than Blackwell.
    assumption_changed: "Context memory and token economics dominate infra."
    impact:
      - axis: economics
        label: Inference token cost vs Blackwell
        before: "1x"
        after: "0.1x"
        delta: "10x lower"
        direction: down
        headline: true
        source_ref: src-nvidia-rubin
      - axis: economics
        label: GPUs to train MoE
        before: "1x"
        after: "0.25x"
        delta: "4x fewer"
        direction: down
        source_ref: src-nvidia-rubin
      - axis: systems
        label: Rack bandwidth
        before: "130 TB/s"
        after: "260 TB/s"
        direction: up
        source_ref: src-nvidia-rubin
    downstream:
      short: Roadmap resets for hyperscale and neocloud buyers.
      medium: Long-context and agentic inference infra is redesigned.
      long: Context-memory and storage fabrics join the model stack.
    parents: [nvlink-fusion]
    source_refs: [src-nvidia-rubin]

  - id: deepseek-v4
    name: DeepSeek-V4 Preview
    date: "2026-04-24"
    vendor: DeepSeek
    logo_domain: deepseek.com
    archetype: context
    layers: [software, hardware]
    magnitude: high
    magnitude_score: 86
    magnitude_rationale: >
      Pushes million-token context, open weights, and agent orientation into
      one package and makes them feel default rather than exotic.
    confidence: medium
    concise_description: >
      V4-Pro and V4-Flash with a 1M-token standard context (1.6T/49B active
      for Pro) at a fraction of the inference FLOPs and KV cache of V3.2.
    assumption_changed: "1M context becomes default and open."
    impact:
      - axis: capability
        label: Standard context window
        before: "128K (V3)"
        after: "1M"
        delta: "8x"
        direction: up
        headline: true
        source_ref: src-deepseek-v4
      - axis: economics
        label: Single-token inference FLOPs vs V3.2
        before: "100%"
        after: "27%"
        delta: "-73%"
        direction: down
        source_ref: src-deepseek-v4
      - axis: economics
        label: KV cache vs V3.2 at 1M context
        before: "100%"
        after: "10%"
        delta: "-90%"
        direction: down
        source_ref: src-deepseek-v4
    downstream:
      short: Long-context agent apps become practical.
      medium: Hybrid full-context + retrieval replaces pure-RAG defaults.
      long: 1M context becomes a mainstream premium/open tier.
    parents: [deepseek-v3]
    source_refs: [src-deepseek-v4]

  - id: claude-opus-4-8
    name: Claude Opus 4.8
    date: "2026-05-28"
    vendor: Anthropic
    logo_domain: anthropic.com
    archetype: reasoning
    layers: [software]
    magnitude: medium
    magnitude_score: 66
    magnitude_rationale: >
      Anthropic frames it as a modest improvement; the genuine shift is agentic
      autonomy — long-running, low-supervision multi-agent execution — plus
      cheaper fast-mode serving.
    confidence: high
    concise_description: >
      A modest-but-tangible Opus upgrade focused on reliability and agentic
      judgment, launched with effort control and a Claude Code "dynamic
      workflows" mode that runs hundreds of parallel subagents for
      codebase-scale migrations.
    assumption_changed: "Frontier models run long, low-supervision agentic work reliably."
    impact:
      - axis: capability
        label: Unflagged code flaws vs Opus 4.7
        before: "1x"
        after: "0.25x"
        delta: "4x fewer"
        direction: down
        headline: true
        source_ref: src-claude-opus-4-8
      - axis: economics
        label: Fast-mode price vs prior models
        before: "1x"
        after: "0.33x"
        delta: "3x cheaper"
        direction: down
        source_ref: src-claude-opus-4-8
      - axis: systems
        label: Agentic scale (Claude Code)
        after: "100s of parallel subagents"
        direction: up
        source_ref: src-claude-opus-4-8
    downstream:
      short: Effort control and parallel-subagent orchestration spread in agentic coding.
      medium: Long-running, low-supervision agent workflows move from demo to default.
      long: Frontier competition shifts from chatbot quality to autonomous-agent reliability.
    parents: [claude-3-7-sonnet]
    source_refs: [src-claude-opus-4-8]
