{
  "_note": "Field Dispatch claims-graph fragment — PROTOTYPE covering one theme of six. This file is the data model the rendered pages are built from, and the payload a rebuilt Confab ingests. Every claim resolves to a talk/clip/timestamp. timing:'interpolated' = char-offset estimate within a diarised paragraph (±10–20s), to be reconciled in the full build's word-level pass.",
  "report": {
    "slug": "ai-engineer-melbourne-2026",
    "title": "The Field Dispatch — AI Engineer Melbourne 2026",
    "version": "prototype-0.2",
    "pages": {
      "landing": "index.html — what is this / how it was made (frank LLM disclosure) / state-of-the-field essay linking all six themes / theme index",
      "themes": ["theme-01.html — built end-to-end", "themes 02–06 — queued for full build"],
      "one_pager": "one-pager.html",
      "shared_assets": ["dispatch.css", "dispatch.js — citation travel: flash, back-pill, per-citation ¶ backrefs"]
    },
    "home": "fieldreports.webdirections.org/ai-engineer-melbourne-2026/",
    "sources": {
      "sessions": "data.webdirections.org/ai-engineer/sessions.json",
      "videoedits_project": "0b6bd836-3495-41b1-a1ee-a8cf3f3ac9cb",
      "interviews": "interviews.webdirections.org",
      "timelines": "agents-conffab/scripts/*.timeline.md"
    },
    "join_manifest": {
      "seeds": 14,
      "clips": 89,
      "clips_with_mux_playback": 2,
      "note": "Every session block is clipped per-talk in videoedits with exact boundaries. Only the Day-1 keynote block and one talk are published to Mux so far; remaining citations carry clip ids and resolve as clips publish."
    }
  },
  "theme": {
    "id": "harness-beats-model",
    "number": 1,
    "title": "The harness beats the model",
    "claim": "The base model is becoming a commodity. The durable engineering — and the moat — is everything wrapped around it: routing, context, cascades, and knowing when a smaller model (or no model at all) does the job.",
    "qualification": "6 captured sources across 3 rooms (Keynote, AI Engineering, Leadership) — exceeds the ≥3 talks / ≥2 tracks derivation rule.",
    "evidence": [
      {
        "claim_id": "frame-model-to-harness",
        "grade": "slide",
        "quote": "AI is increasingly not just Models. Model → Harness",
        "speaker": "George Cameron",
        "talk_title": "State of the AI Model Landscape (keynote)",
        "source": { "talk_id": "eee65054-2c79-4f20-b5e8-1be2f96c43b0", "seed_id": "54abf5d2-5249-4922-b303-70f44a6a0a21", "clip_id": "d909b41b-feb2-4a62-9e0d-b1749dc28667", "t_rel_s": 400, "timing": "interpolated" },
        "mux": { "playback_id": "l7TED02Q9CCXrICK02m02fUlbmSLtcRPNxwpuPHcnuo7ZM", "playback_offset_s": 1084, "note": "talk sits inside the published Day-1 keynote block; offset = t_abs − 195" }
      },
      {
        "claim_id": "price-collapse",
        "grade": "spoken",
        "quote": "It's not two-x cheaper — you can go ten-x cheaper, in many cases, by choosing a cheaper model that has more recently been released.",
        "speaker": "George Cameron",
        "talk_title": "State of the AI Model Landscape (keynote)",
        "source": { "talk_id": "eee65054-2c79-4f20-b5e8-1be2f96c43b0", "seed_id": "54abf5d2-5249-4922-b303-70f44a6a0a21", "clip_id": "d909b41b-feb2-4a62-9e0d-b1749dc28667", "t_abs_s": 2020.8, "t_rel_s": 741.8, "timing": "interpolated" },
        "mux": { "playback_id": "l7TED02Q9CCXrICK02m02fUlbmSLtcRPNxwpuPHcnuo7ZM", "playback_offset_s": 1825.8 }
      },
      {
        "claim_id": "model-and-harness",
        "grade": "spoken",
        "quote": "Coding agents — they're not the model, they're the model and the harness. Both are drivers of performance.",
        "speaker": "George Cameron",
        "talk_title": "State of the AI Model Landscape (keynote)",
        "source": { "talk_id": "eee65054-2c79-4f20-b5e8-1be2f96c43b0", "seed_id": "54abf5d2-5249-4922-b303-70f44a6a0a21", "clip_id": "d909b41b-feb2-4a62-9e0d-b1749dc28667", "t_abs_s": 2198.2, "t_rel_s": 919.2, "timing": "interpolated" },
        "mux": { "playback_id": "l7TED02Q9CCXrICK02m02fUlbmSLtcRPNxwpuPHcnuo7ZM", "playback_offset_s": 2003.2 }
      },
      {
        "claim_id": "moat-verbatim",
        "grade": "slide",
        "quote": "State lives in files, so you can swap the brain and keep the agent. The model is a commodity; the harness around it is the moat.",
        "speaker": "Navan Tirupathi",
        "talk_title": "Beyond Forgetful Bots: Architectural Patterns for Persistent Agents",
        "source": { "talk_id": "2d53e279-d883-4038-bc12-9b2594324b15", "seed_id": "3fe1ebb9-3b02-4f18-aed7-de45fe89deec", "clip_id": "92e68932-5289-4d06-a700-17941f2d29a4", "t_rel_s": 720, "timing": "interpolated", "slide_block_time": "00:31:40–00:32:20" },
        "mux": null
      },
      {
        "claim_id": "plan-execute-split",
        "grade": "slide",
        "quote": "Strong models plan. Light models execute.",
        "speaker": "Navan Tirupathi",
        "talk_title": "Beyond Forgetful Bots: Architectural Patterns for Persistent Agents",
        "source": { "talk_id": "2d53e279-d883-4038-bc12-9b2594324b15", "seed_id": "3fe1ebb9-3b02-4f18-aed7-de45fe89deec", "clip_id": "92e68932-5289-4d06-a700-17941f2d29a4", "t_rel_s": 720, "timing": "interpolated", "slide_block_time": "00:31:40–00:32:00" },
        "mux": null
      },
      {
        "claim_id": "harness-engineering-named",
        "grade": "spoken",
        "quote": "This sort of discipline has been termed harness engineering. It's basically how we build systems around models to turn them into work engines.",
        "speaker": "Michael Hart",
        "talk_title": "Flue: The Agent Harness Framework",
        "source": { "talk_id": null, "talk_id_note": "not matched in sessions.json — resolve in full build", "seed_id": "b9249996-fa41-4f06-bc02-563bf1354654", "clip_id": "3f86d6a8-b96e-4c9d-8ec0-083c68a4df9e", "t_abs_s": 6344.6, "t_rel_s": 82.9, "timing": "interpolated" },
        "mux": null
      },
      {
        "claim_id": "we-default",
        "grade": "slide",
        "quote": "We don't choose. We default.",
        "speaker": "Stephen Sennett",
        "talk_title": "Orbital Lasers vs For Loops: Economically Matching Models to Tasks",
        "source": { "talk_id": "83e651e5-cccf-4e3b-b345-9400c2ad999a", "seed_id": "b9249996-fa41-4f06-bc02-563bf1354654", "clip_id": "fc36336b-f38a-4389-ac4a-8fa1cc919260", "t_rel_s": 180, "timing": "interpolated", "slide_block_time": "01:03:00–01:03:20" },
        "mux": null
      },
      {
        "claim_id": "midrange-70pc",
        "grade": "spoken",
        "quote": "When I build these things out, I tend to get about 70% of my value out of those mid-range models.",
        "speaker": "Stephen Sennett",
        "talk_title": "Orbital Lasers vs For Loops: Economically Matching Models to Tasks",
        "source": { "talk_id": "83e651e5-cccf-4e3b-b345-9400c2ad999a", "seed_id": "b9249996-fa41-4f06-bc02-563bf1354654", "clip_id": "fc36336b-f38a-4389-ac4a-8fa1cc919260", "t_abs_s": 4271.1, "t_rel_s": 433.5, "timing": "interpolated" },
        "mux": null
      },
      {
        "claim_id": "tokens-poor-engineering",
        "grade": "spoken",
        "quote": "If you're just looking at this saying we can just always buy more tokens, that is fundamentally poor engineering practice. We cannot just always spend more tokens.",
        "speaker": "Stephen Sennett",
        "talk_title": "Orbital Lasers vs For Loops: Economically Matching Models to Tasks",
        "source": { "talk_id": "83e651e5-cccf-4e3b-b345-9400c2ad999a", "seed_id": "b9249996-fa41-4f06-bc02-563bf1354654", "clip_id": "fc36336b-f38a-4389-ac4a-8fa1cc919260", "t_abs_s": 4535.8, "t_rel_s": 698.2, "timing": "interpolated" },
        "mux": null
      },
      {
        "claim_id": "context-beats-model",
        "grade": "slide",
        "quote": "Right context beats more model. Cheaper models with access to documentation can outperform more expensive models.",
        "speaker": "Stephen Sennett",
        "talk_title": "Orbital Lasers vs For Loops: Economically Matching Models to Tasks",
        "source": { "talk_id": "83e651e5-cccf-4e3b-b345-9400c2ad999a", "seed_id": "b9249996-fa41-4f06-bc02-563bf1354654", "clip_id": "fc36336b-f38a-4389-ac4a-8fa1cc919260", "t_rel_s": 780, "timing": "interpolated", "slide_block_time": "01:13:00–01:13:20" },
        "mux": null
      },
      {
        "claim_id": "demo-vs-2am",
        "grade": "slide",
        "quote": "A demo is one document, on a good day, while everyone is watching. Production is thousands of documents, at two in the morning, when nobody is watching.",
        "speaker": "Avni Bhatt",
        "talk_title": "When a Small Language Model Beat Our LLM in Production",
        "source": { "talk_id": "665cc753-e86b-4d2a-bd73-44b618481ef8", "seed_id": "c7671565-ea8b-43b1-8520-d91f394e7008", "clip_id": "f7e116f0-4ff6-47a9-b56e-95cf6b29a51a", "t_rel_s": 440, "timing": "interpolated", "slide_block_time": "00:28:00–00:28:40" },
        "mux": null
      },
      {
        "claim_id": "slm-cascade",
        "grade": "spoken",
        "quote": "By default, go to SLM… if it is not [compliant], escalate to LLM… so 98% of the documents will go through SLM.",
        "speaker": "Avni Bhatt",
        "talk_title": "When a Small Language Model Beat Our LLM in Production",
        "source": { "talk_id": "665cc753-e86b-4d2a-bd73-44b618481ef8", "seed_id": "c7671565-ea8b-43b1-8520-d91f394e7008", "clip_id": "f7e116f0-4ff6-47a9-b56e-95cf6b29a51a", "t_abs_s": 2055.8, "t_rel_s": 586.7, "timing": "interpolated" },
        "mux": null
      },
      {
        "claim_id": "10x-result",
        "grade": "slide",
        "quote": "10X Cheaper. 10X Faster. Errors ~1 in 20.",
        "speaker": "Avni Bhatt",
        "talk_title": "When a Small Language Model Beat Our LLM in Production",
        "source": { "talk_id": "665cc753-e86b-4d2a-bd73-44b618481ef8", "seed_id": "c7671565-ea8b-43b1-8520-d91f394e7008", "clip_id": "f7e116f0-4ff6-47a9-b56e-95cf6b29a51a", "t_rel_s": 690, "timing": "interpolated", "slide_block_time": "00:32:00–00:32:40" },
        "mux": null
      },
      {
        "claim_id": "bert-heart",
        "grade": "spoken",
        "quote": "The heart of Yara is not an LLM. We went with BERT.",
        "speaker": "Dave Hall",
        "talk_title": "Not Everything Needs an LLM",
        "source": { "talk_id": "c1f5da3b-4003-4e79-aa86-d212354d38f7", "seed_id": "789ee714-64e6-40cf-a0d1-bb8b3a396736", "clip_id": "21f96349-29fe-41b1-bc30-1b8abe4168cd", "t_abs_s": 658.4, "t_rel_s": 500.9, "timing": "interpolated" },
        "mux": null
      },
      {
        "claim_id": "30k-vs-212",
        "grade": "spoken",
        "quote": "If I was running this system with everything running through Opus — [$]30,000 US per year. Using BERT: $212.",
        "speaker": "Dave Hall",
        "talk_title": "Not Everything Needs an LLM",
        "source": { "talk_id": "c1f5da3b-4003-4e79-aa86-d212354d38f7", "seed_id": "789ee714-64e6-40cf-a0d1-bb8b3a396736", "clip_id": "21f96349-29fe-41b1-bc30-1b8abe4168cd", "t_abs_s": 1339.4, "t_rel_s": 1181.9, "timing": "interpolated" },
        "mux": null
      },
      {
        "claim_id": "ladder",
        "grade": "spoken",
        "quote": "Start on the bottom rung of the ladder. Start with an if statement or ten. If you can't solve the problem there, have a look at classical ML… go with a text classifier and then reach for an LLM as your last resort. Your budget will thank you.",
        "speaker": "Dave Hall",
        "talk_title": "Not Everything Needs an LLM",
        "source": { "talk_id": "c1f5da3b-4003-4e79-aa86-d212354d38f7", "seed_id": "789ee714-64e6-40cf-a0d1-bb8b3a396736", "clip_id": "21f96349-29fe-41b1-bc30-1b8abe4168cd", "t_abs_s": 1417.6, "t_rel_s": 1260.1, "timing": "interpolated" },
        "mux": null
      }
    ],
    "interviews": [
      {
        "guest": "Sarah Sachs",
        "interview_id": "fb1e3a8b-cc0e-43bb-a523-76eed05dd24f",
        "passages": [
          { "start_ms": 144760, "quote": "Don't lock yourself into one option or one vendor. Understand that you have the ability to change, and don't sleep on open source… And not every model needs to do every job." },
          { "start_ms": 179585, "quote": "Just switching everything over to the updated model is a really lazy way to be selecting models for your customers — you're giving them a more expensive and slow experience." },
          { "start_ms": 470090, "quote": "A lot of it isn't modeling. A lot of it is engineering. It's infrastructure. It's permissions. It's security." }
        ]
      },
      {
        "guest": "Chris [Dr Christian Dandre]",
        "interview_id": "51d0e4bb-c2fa-474f-9188-31228c732719",
        "passages": [
          { "start_ms": 486170, "quote": "When I look at client solutions, they don't want token-consuming monsters as part of their AI workflow." },
          { "start_ms": 503960, "quote": "They need micro language models — very focused reasoning on a very specific, narrow application." }
        ]
      }
    ],
    "dissent": [
      {
        "position": "Harness minimalism",
        "holder": "George Cameron (interview)",
        "interview_id": "874f8bda-64d3-47e2-8afc-4e2d393eec2f",
        "start_ms": 196100,
        "quote": "People are overbuilding in terms of harnesses and the code around AI… agent harnesses — the simpler, the better… way too much hype is around complicated harnesses that are not necessary and actually degrade performance.",
        "note": "The same speaker whose keynote framed 'model AND harness' warns off-stage against harness maximalism. Both positions are live; what nobody defended was the unexamined default to the biggest model."
      }
    ],
    "do_next": {
      "individual": "Route by task, not by habit: default to a mid-tier model and retry upward on failure. Sennett's benchmark — ~70% of value from mid-range. Trade-off: retries add latency to the failure path.",
      "team_lead": "Make 'why this model for this task?' a review question with a required answer. Bhatt's cascade (SLM default, escalate the strange 2%) is the reference pattern.",
      "org_leader": "Ask for per-task unit economics, not the vendor lineup. Hall's router: ~$30k/yr on a frontier model vs $212 on BERT. If nobody can produce that comparison, that's the gap.",
      "stop_doing": "Auto-upgrading every workload to each new frontier model. Sachs: 'a really lazy way to be selecting models.'"
    },
    "questions": {
      "_note": "Role-tagged reflection/discussion prompts — the 'what you do with this report' layer. Each derives from cited evidence; the discussion prompt derives from the theme's dissent.",
      "individual": [
        "Do you know the cost per task and failure rate of the workflow you run most? If you swapped in a model a tier down tomorrow, what would catch its failures?",
        "If your provider deprecated your default model next month, what would you have to rewrite? Whatever you just pictured is the harness — is it yours, or the vendor's?"
      ],
      "team_lead": [
        "Where in your process would 'why this model for this task?' actually get asked — design review, PR template, eval sign-off? Right now, is it asked anywhere?",
        "Bhatt's cascade escalates the strange 2% to the big model. What's your escalation boundary — and who decided it?"
      ],
      "org_leader": [
        "Who in your org could produce the $30k-vs-$212 comparison for your three biggest AI workloads? If the answer is nobody, what would it take?",
        "Is your model spend a line item someone owns, or a surprise at the end of the quarter?"
      ],
      "discussion": "Cameron says harnesses are overbuilt; Tirupathi says the harness is the moat. Run the argument for your own stack: which failure mode are you closer to — orchestration you don't need, or defaults you haven't examined?"
    }
  },
  "themes_02_06": [
    {
      "id": "context-isnt-memory", "number": 2, "title": "Context isn't memory", "page": "theme-02.html",
      "claim": "Agents forget everything, and a bigger context window doesn't fix it — context and memory are different things. Real memory is the field's most honestly unsolved problem.",
      "talks": [
        {"title": "Why Your Coding Agent Forgets Everything", "speaker": "Igor Costa", "seed": "54abf5d2-5249-4922-b303-70f44a6a0a21", "clip": "fb09023d-c710-4f8e-8e65-3c2594cd6fab", "start_s": 5140.5, "end_s": 6050.7, "mux_playable": true},
        {"title": "Memory Breaks Them in Production", "speaker": "Ananya Roy", "seed": "da0ea3f9-1f97-40a3-aeb0-244f500eeb69", "clip": "9c2406fc-84b5-4545-a04d-87dd165e7660", "start_s": 5028.6, "end_s": 6190.8},
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        {"title": "AI Agents Are Distributed Systems", "speaker": "Lovee Jain", "seed": "da0ea3f9-1f97-40a3-aeb0-244f500eeb69", "clip": "d501a45b-6718-4986-b306-27311947837c", "start_s": 6250.4, "end_s": 7301.6},
        {"title": "Towards Long-Horizon Tasks", "speaker": "Zixuan Li", "seed": "f5521b3e-3b41-4427-a989-878602da807f", "clip": "41b1f56e-f5b8-457b-898d-83736ccb8ebe", "start_s": 5242.3, "end_s": 6425.2}
      ],
      "dissent": "Where memory lives (files vs platforms vs in-model, three-way); whether bigger contexts dissolve it (even the sceptical lab still grows context); persistence vs forgetting-by-design.",
      "notes": "Moss Ebeling's 'Close your agentic loop' evaluated and dropped — no memory content (feedback-loops theme)."
    },
    {
      "id": "evals-are-the-new-tests", "number": 3, "title": "Evals are the new tests", "page": "theme-03.html",
      "claim": "You can't ship non-deterministic systems on vibes. Evals — with observability underneath — are taking the place tests and ops took. The live argument is how much to trust the LLM judge.",
      "talks": [
        {"title": "Evaluation Precedes Evolution", "speaker": "Tanya Dixit", "seed": "3fe1ebb9-3b02-4f18-aed7-de45fe89deec", "clip": "5ac6f321-8f44-40bf-ba27-584658810493", "start_s": 263.3, "end_s": 1296.6},
        {"title": "Our AI Hallucinated in Production", "speaker": "Yicheng Guo (REA Group)", "seed": "b9249996-fa41-4f06-bc02-563bf1354654", "clip": "d4efd6d6-59e5-462b-badf-6663b4d5e19b", "start_s": 1458.7, "end_s": 2234.7},
        {"title": "Agent Observability at Internet Scale", "speaker": "Daniel Nadasi (Google)", "seed": "b9249996-fa41-4f06-bc02-563bf1354654", "clip": "c01bd8cb-ca11-447d-b73a-7b3766e8dbcd", "start_s": 252.4, "end_s": 1400.8},
        {"title": "Agentic Support with Langfuse", "speaker": "Sergey & Sahil (Canva)", "seed": "f85c77e6-4b5c-416d-9d3a-5d6fd730d966", "clip": "e64ce6a9-2f27-4e82-8ac3-35da4185a000", "start_s": 6084.8, "end_s": 7231.2},
        {"title": "Multi-Armed Bandits: The Scientific Shotgun for Evals", "speaker": "Ron Au", "seed": "ee299910-ae76-44bd-8d46-300145545ff7", "clip": "39f92317-b00a-43e4-9d2c-4ba144b40467", "start_s": 5760.3, "end_s": 6768.1},
        {"title": "Stop vibing your agents to production", "speaker": "Justin Barias", "seed": "777a4a17-0356-45c5-83bd-77db32dd71e7", "clip": "c6c3c33a-4694-4638-aa4a-ba5f707b20e5", "start_s": 2452, "end_s": 3563.7},
        {"title": "Terabytes of AI coding agent logs", "speaker": "Dave Slutzkin (Cadence)", "seed": "777a4a17-0356-45c5-83bd-77db32dd71e7", "clip": "8e836365-5c58-4de4-8fbf-6a6050f16af9", "start_s": 5020.7, "end_s": 6068.7}
      ],
      "dissent": "LLM-as-judge as scaling mechanism (REA, Canva) vs rationed last resort (Dixit, Barias, Au, Rudenko's same-family bias finding, Hall's turtles-all-the-way-down).",
      "notes": "Clip title says '12TB of logs'; on stage Slutzkin says ~3.5TB — page title softened to 'Terabytes'."
    },
    {
      "id": "spec-is-the-source", "number": 4, "title": "Spec is the source", "page": "theme-04.html",
      "claim": "When code can be regenerated on demand, the thing worth keeping is the statement of intent — the spec. Code becomes a build artifact. The most productively contested claim of the conference.",
      "talks": [
        {"title": "Spec driven AI development (Compiling Intent)", "speaker": "Nick Beaugeard", "seed": "02b8e916-9e8b-4507-ba74-58f21e611b8c", "clip": "b6eae040-6615-40b4-88d3-3ba36c165ccb", "start_s": 234.7, "end_s": 1268.3},
        {"title": "AGENTS.md is the wrong conversation", "speaker": "Jakub Riedl", "seed": "02b8e916-9e8b-4507-ba74-58f21e611b8c", "clip": "39066174-b55c-43b9-81b4-eaa7728c6e83", "start_s": 2437.4, "end_s": 3505.8},
        {"title": "Engineering without reading code", "speaker": "Ben Taylor (Stile)", "seed": "02b8e916-9e8b-4507-ba74-58f21e611b8c", "clip": "512f84e1-e3dd-4a3c-84dc-239cea5add4c", "start_s": 3570.8, "end_s": 4761.1},
        {"title": "The Death of Documentation", "speaker": "Josh Gillies", "seed": "02b8e916-9e8b-4507-ba74-58f21e611b8c", "clip": "7474c1aa-e990-4e21-9a0a-322e80a43017", "start_s": 4797, "end_s": 5980.1},
        {"title": "Keynote", "speaker": "Jeremy Howard", "seed": "f5521b3e-3b41-4427-a989-878602da807f", "clip": "212b5ce8-01fc-4c16-9c0c-336c2d9b996d", "start_s": 361.2, "end_s": 2350.3},
        {"title": "Craft in the Time of Agents", "speaker": "Annie Vella", "seed": "f5521b3e-3b41-4427-a989-878602da807f", "clip": "3f8dedb5-02ec-4a86-9a29-1b6ccea5f247", "start_s": 2571.1, "end_s": 3537.1}
      ],
      "dissent": "Keep the spec (Beaugeard) vs keep the learning loop (Riedl) vs the code speaks for itself and humans keep only the why/ADRs (Gillies); Howard's 200k-lines caution.",
      "notes": "'We've been backing up the wrong files' = Nick Beaugeard, verified (slide 'We have been…' / spoken 'We've all been…', ~t_abs 301s, clip b6eae040 t_rel ~66s). Four core talks ran consecutively in one room."
    },
    {
      "id": "the-economics-broke", "number": 5, "title": "The economics broke", "page": "theme-05.html",
      "claim": "Token spend is an engineering constraint with a budget line — compute strategy is product strategy. Prices per capability collapse while total spend rises; both true at once.",
      "talks": [
        {"title": "Token Town", "speaker": "Sarah Sachs (Notion)", "seed": "54abf5d2-5249-4922-b303-70f44a6a0a21", "clip": "3dfcfa7d-003f-45f8-80d2-bdacb30a8dc8", "start_s": 2633.8, "end_s": 3736.1, "mux_playable": true},
        {"title": "Everything Is a Factory", "speaker": "Geoff Huntley", "seed": "54abf5d2-5249-4922-b303-70f44a6a0a21", "clip": "81e42fff-33da-47ba-a82b-a2bf9408dab8", "start_s": 3831.4, "end_s": 5050, "mux_playable": true},
        {"title": "State of the AI Model Landscape", "speaker": "George Cameron", "seed": "54abf5d2-5249-4922-b303-70f44a6a0a21", "clip": "d909b41b-feb2-4a62-9e0d-b1749dc28667", "start_s": 1279, "end_s": 2498.1, "mux_playable": true},
        {"title": "How Many Agents Are Too Many?", "speaker": "Anannya Roy Chowdhury", "seed": "3fe1ebb9-3b02-4f18-aed7-de45fe89deec", "clip": "8ba66dc0-d631-4fdc-a4d8-f69a3f04ea3a", "start_s": 5060.7, "end_s": 6272.8},
        {"title": "The AI Tax", "speaker": "Krishna kanth Mundada", "seed": "789ee714-64e6-40cf-a0d1-bb8b3a396736", "clip": "d042663a-dbfe-4126-85b3-843fbdd5250b", "start_s": 1796.1, "end_s": 3580.6}
      ],
      "dissent": "Ride the price collapse (Cameron: 10–100x falls, 'use the best model and pay the price') vs spend discipline now (Sachs, Mundada); Huntley's token-max abundance vs Sachs' outcome-max.",
      "notes": "Candidates corrected: 'Kill the God Agent' (Gairola) is a security talk; 'Designing Inference-Native Systems' (Kamal) is a design-paradigm talk — both removed from this theme. Uber budget story corroborated independently in two rooms (Sachs slide; Mundada slide sourced to Fortune, May 2026)."
    },
    {
      "id": "the-org-is-the-bottleneck", "number": 6, "title": "The org is the bottleneck", "page": "theme-06.html",
      "claim": "The limiting factor isn't the technology — it's organisations: governance built for a different risk shape, leadership miscalibrated about its own workforce, and what the shift feels like twenty years into a career.",
      "talks": [
        {"title": "Your engineers aren't afraid of AI — they're afraid of becoming junior again", "speaker": "Andy Kelk", "seed": "789ee714-64e6-40cf-a0d1-bb8b3a396736", "clip": "a849ee5b-15e6-481a-b46e-3c4fac29c272", "start_s": 3676.6, "end_s": 5299.2},
        {"title": "Stop Blocking, Start Building", "speaker": "Hamish Songsmith", "seed": "fd290c73-7c05-4688-a1e1-be5fe45efcbb", "clip": "46e349c2-f30a-4291-b57c-88bc67d34e20", "start_s": 1675.9, "end_s": 2830.9},
        {"title": "Beyond Silicon Valley", "speaker": "Aubrey Blanche", "seed": "fd290c73-7c05-4688-a1e1-be5fe45efcbb", "clip": "b5ebee6c-bca9-41c8-9bbd-cfbfca1e1f16", "start_s": 164.2, "end_s": 1627.7},
        {"title": "Empowering non-technical builders", "speaker": "Inga Pflaumer", "seed": "98789184-cdae-4d36-bbe7-1f5956d23740", "clip": "ed0ff559-a2d5-4792-a189-ae2d9b881100", "start_s": 2817.7, "end_s": 4432.2},
        {"title": "From AI survey to production: the readiness gap", "speaker": "Dr Christian Dandre", "seed": "89e26ec1-9d3c-465d-9f03-3bbcfe83cc36", "clip": "47fdb160-6f23-4b3e-b981-858bca860e9e", "start_s": 149.7, "end_s": 1678.4},
        {"title": "Culture-first AI adoption at scale", "speaker": "Eric Grigson & Paul Hughes (Culture Amp)", "seed": "89e26ec1-9d3c-465d-9f03-3bbcfe83cc36", "clip": "2c489a95-7c0a-4ff8-ba53-dfeb914b16c7", "start_s": 1733.8, "end_s": 3363.3}
      ],
      "dissent": "Adopt-now vs governance-first (Songsmith vs Blanche, same stage); mandate vs grassroots (Kelk vs Culture Amp's reluctant reversal); regulation vs transparency (Blanche vs Dandre).",
      "notes": "Nick Lothian's talk is a privacy-tech survey — panel quotes only for this theme. Inga Pflaumer's delivered title differs from the printed program. Krishna's surname unverified in recordings — check roster before publish."
    }
  ],
  "anomalies": [
    "'We fired our LLM judge' is APOCRYPHAL (verified 6 July 2026): the phrase appears nowhere in any transcript, slide or interview. Likeliest source of the garble: Tanya Dixit's slide 'LLM judge only FIRES for what genuinely can't be checked deterministically.' Removed from all report copy; the correction is disclosed on the theme-03 page.",
    "Slide-timeline clocks are offset from seed/clip time per session block (keynotes ≈ +195s, AI Eng D1S1 ≈ +211s, SW Eng D2S1 ≈ +193s, Leadership D2S1 ≈ +9s, etc.) — apply offsets when deep-linking from slide blocks.",
    "Shubh Chatterjee's 'Model-Agnostic Systems' was pulled from the program before the event (confirmed by John, 5 July 2026) — no clip, no transcript, no slides. Removed from the report's talk lists; retained here so the pipeline doesn't go looking for it.",
    "sessions.json places Cameron's keynote on Day 2 ('State of the Model Market'); the recordings put it on Day 1 as 'State of the AI Model Landscape'. The clip inventory (derived from actual recordings) is treated as authoritative; running order diverged from the printed schedule in several rooms.",
    "Spoken-quote timestamps within single-paragraph diarised transcripts are char-offset interpolations (±10–20s) pending the full build's word-level reconciliation."
  ]
}