{ "kind": "all", "value": "all", "collectionKey": "slides:all:all:all-document-kinds:all-producers:all-orientations", "filters": { "documentKinds": [], "sourceTypes": [], "orientations": [] }, "total": 319975, "page": 1809, "pageSize": 30, "pageCount": 10666, "rows": [ { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 8, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/8", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-8", "loopMatches": [ { "to": 10, "from": 8, "name": "Hypothesis Test", "slug": "42-hypothesis-test", "bestFor": "Strategy validation, due diligence, research presentations", "matchId": "019dd95a-07fe-70ce-8d3d-9a5a35c5c606", "evidence": "Section divider 'Reviewing our predictions from 2021' followed by predictions revisited.", "position": 1, "objective": "Score 2021 predictions against what actually happened", "structure": "The Hypothesis -> Test 1 (Supports/Refutes) -> Test 2 (Supports/Refutes) -> Conclusion (Validated/Pivoted)", "confidence": 80, "description": "State a hypothesis upfront, then systematically prove or disprove it with evidence" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 10, "from": 5, "beatId": "019dd95a-0682-776c-8e35-305cd1447ec9", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Setup & Stakes", "beatSlug": "mountain-setup-stakes", "evidence": "Definitions, exec summary, scorecard frame the year.", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 9, "slideType": "comparison_table", "function": "summarize", "notes": "The slide uses a color-coded 'Grade' column (green for Yes, red for No) to summarize the outcome of past predictions.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/9", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-9", "loopMatches": [ { "to": 10, "from": 8, "name": "Hypothesis Test", "slug": "42-hypothesis-test", "bestFor": "Strategy validation, due diligence, research presentations", "matchId": "019dd95a-07fe-70ce-8d3d-9a5a35c5c606", "evidence": "Section divider 'Reviewing our predictions from 2021' followed by predictions revisited.", "position": 1, "objective": "Score 2021 predictions against what actually happened", "structure": "The Hypothesis -> Test 1 (Supports/Refutes) -> Test 2 (Supports/Refutes) -> Conclusion (Validated/Pivoted)", "confidence": 80, "description": "State a hypothesis upfront, then systematically prove or disprove it with evidence" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 10, "from": 5, "beatId": "019dd95a-0682-776c-8e35-305cd1447ec9", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Setup & Stakes", "beatSlug": "mountain-setup-stakes", "evidence": "Definitions, exec summary, scorecard frame the year.", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 10, "slideType": "appendix_data", "function": "summarize", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/10", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-10", "loopMatches": [ { "to": 10, "from": 8, "name": "Hypothesis Test", "slug": "42-hypothesis-test", "bestFor": "Strategy validation, due diligence, research presentations", "matchId": "019dd95a-07fe-70ce-8d3d-9a5a35c5c606", "evidence": "Section divider 'Reviewing our predictions from 2021' followed by predictions revisited.", "position": 1, "objective": "Score 2021 predictions against what actually happened", "structure": "The Hypothesis -> Test 1 (Supports/Refutes) -> Test 2 (Supports/Refutes) -> Conclusion (Validated/Pivoted)", "confidence": 80, "description": "State a hypothesis upfront, then systematically prove or disprove it with evidence" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 10, "from": 5, "beatId": "019dd95a-0682-776c-8e35-305cd1447ec9", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Setup & Stakes", "beatSlug": "mountain-setup-stakes", "evidence": "Definitions, exec summary, scorecard frame the year.", "position": 1, "confidence": 55, "parentBeatName": "Setup", "parentBeatSlug": "setup" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 11, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/11", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-11", "loopMatches": [], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 12, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide describes a human-AI collaborative workflow for mathematical discovery.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/12", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-12", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 13, "slideType": "case_study", "function": "illustrate_case", "notes": "Part of a series of slides tracking 2021 predictions.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/13", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-13", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 14, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide tracks a previous year's prediction and provides a technical explanation of the breakthrough, supported by a bar chart comparing speed-ups.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/14", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-14", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 15, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide details the application of RL to magnetic coil control in fusion reactors (TCV/ITER).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/15", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-15", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 16, "slideType": "industry_trends", "function": "summarize", "notes": "The chart shows a significant jump in predicted structures in 2022 compared to historical published data.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/16", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-16", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 17, "slideType": "case_study", "function": "illustrate_case", "notes": "Includes a bar chart showing perplexity decrease with model size and a table of ESM-2 performance metrics.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/17", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-17", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 18, "slideType": "case_study", "function": "illustrate_case", "notes": "Includes logos of research institutions at the bottom.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/18", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-18", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 19, "slideType": "case_study", "function": "illustrate_case", "notes": "Includes a time-lapse visual of plastic degradation and a schematic of the ML model used for enzyme engineering.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/19", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-19", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 20, "slideType": "industry_trends", "function": "diagnose", "notes": "The slide references a Princeton study on data leakage in 329 papers across 17 fields.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/20", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-20", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 21, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide explains the methodology of training an Inverse Dynamics Model (IDM) to scale up training data for a foundation model.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/21", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-21", "loopMatches": [ { "to": 21, "from": 12, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-9f07f56c535f", "evidence": "DeepMind math, fusion, AlphaFold, protein LMs, plastic enzymes, Minecraft agents — all evidence of one pattern.", "position": 2, "objective": "Compile case studies showing ML breakthroughs in physical/life sciences", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 80, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 22, "slideType": "industry_trends", "function": "summarize", "notes": "The slide highlights the competitive landscape of LLMs applied to software engineering tasks.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/22", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-22", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 23, "slideType": "industry_trends", "function": "analyze_data", "notes": "The slide features a Venn diagram mapping various 'Efficient Transformer' research papers into categories like Recurrence, Memory/Downsampling, Low Rank/Kernels, Learnable Patterns, and Sparse.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/23", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-23", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 24, "slideType": "key_takeaways", "function": "summarize", "notes": "Includes a specific example of a math problem solved by Minerva.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/24", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-24", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 25, "slideType": "industry_trends", "function": "summarize", "notes": "The slide includes three small scatter plots showing different benchmark saturation dynamics.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/25", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-25", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 26, "slideType": "industry_trends", "function": "analyze_data", "notes": "The chart illustrates the relationship between model parameters and compute (FLOPs), comparing DeepMind's findings with previous OpenAI scaling laws.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/26", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-26", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 27, "slideType": "industry_trends", "function": "analyze_data", "notes": "The slide discusses the non-linear performance gains in LLMs as they scale, using four specific task examples (Arithmetics, Fig. of speech, Multi-task NLU, Transliteration).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/27", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-27", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 28, "slideType": "industry_trends", "function": "illustrate_case", "notes": "The chart compares truthfulness and informativeness across different model sizes and prompting strategies, highlighting WebGPT's performance.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/28", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-28", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 29, "slideType": "industry_trends", "function": "analyze_data", "notes": "The chart uses a logarithmic scale for the y-axis to visualize the massive acceleration in compute requirements.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/29", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-29", "loopMatches": [ { "to": 29, "from": 22, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a20c665fe3de", "evidence": "Codex, Transformer alternatives, Minerva, BIG-Bench, Chinchilla, emergence, tool-use, compute eras stack as evidence.", "position": 3, "objective": "Survey LLM research frontier — transformers, scaling, emergence, compute eras", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 30, "slideType": "industry_trends", "function": "summarize", "notes": "Includes a list of academic paper titles as a visual reference for the trend.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/30", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-30", "loopMatches": [ { "to": 33, "from": 30, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a4c263216829", "evidence": "Diffusion takeover, DALL-E 2/Imagen/Parti race, Stability/Midjourney emergence, text-to-video kickoff.", "position": 4, "objective": "Document the diffusion / text-to-image explosion across labs", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 31, "slideType": "industry_trends", "function": "illustrate_case", "notes": "The slide uses visual examples to demonstrate the capabilities of different AI models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/31", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-31", "loopMatches": [ { "to": 33, "from": 30, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a4c263216829", "evidence": "Diffusion takeover, DALL-E 2/Imagen/Parti race, Stability/Midjourney emergence, text-to-video kickoff.", "position": 4, "objective": "Document the diffusion / text-to-image explosion across labs", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 32, "slideType": "industry_trends", "function": "illustrate_case", "notes": "The slide uses visual evidence (image generation examples) to support the claim of competitive parity.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/32", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-32", "loopMatches": [ { "to": 33, "from": 30, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a4c263216829", "evidence": "Diffusion takeover, DALL-E 2/Imagen/Parti race, Stability/Midjourney emergence, text-to-video kickoff.", "position": 4, "objective": "Document the diffusion / text-to-image explosion across labs", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 33, "slideType": "industry_trends", "function": "establish_context", "notes": "Slide from State of AI 2022 report.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/33", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-33", "loopMatches": [ { "to": 33, "from": 30, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-a4c263216829", "evidence": "Diffusion takeover, DALL-E 2/Imagen/Parti race, Stability/Midjourney emergence, text-to-video kickoff.", "position": 4, "objective": "Document the diffusion / text-to-image explosion across labs", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 82, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 34, "slideType": "timeline", "function": "illustrate_case", "notes": "The slide highlights the speed at which the open-source community has replicated or improved upon proprietary landmark models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/34", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-34", "loopMatches": [ { "to": 36, "from": 34, "name": "Tale Two Worlds", "slug": "04-tale-two-worlds", "bestFor": "Competitive analysis, benchmarking, case for change", "matchId": "019dd95a-07fe-70ce-8d3d-aaf1439dbefa", "evidence": "Three repeated timelines 'Closed for 14/15/35 months' framing closed vs open-source worlds.", "position": 5, "objective": "Contrast closed-source releases vs open-source community catch-up speed", "structure": "Current State -> Desired State / Benchmark -> The Gap & Implication", "confidence": 85, "description": "Show the gap between two states to drive urgency or highlight opportunity" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 35, "slideType": "timeline", "function": "illustrate_case", "notes": "The timeline highlights the speed at which the open-source community (e.g., DALL-E mini/Craiyon, Stable Diffusion) caught up to or surpassed closed-source models from OpenAI, Meta, and Google.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/35", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-35", "loopMatches": [ { "to": 36, "from": 34, "name": "Tale Two Worlds", "slug": "04-tale-two-worlds", "bestFor": "Competitive analysis, benchmarking, case for change", "matchId": "019dd95a-07fe-70ce-8d3d-aaf1439dbefa", "evidence": "Three repeated timelines 'Closed for 14/15/35 months' framing closed vs open-source worlds.", "position": 5, "objective": "Contrast closed-source releases vs open-source community catch-up speed", "structure": "Current State -> Desired State / Benchmark -> The Gap & Implication", "confidence": 85, "description": "Show the gap between two states to drive urgency or highlight opportunity" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 36, "slideType": "timeline", "function": "summarize", "notes": "The slide highlights the speed of open-source community adoption and improvement of proprietary AI models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/36", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-36", "loopMatches": [ { "to": 36, "from": 34, "name": "Tale Two Worlds", "slug": "04-tale-two-worlds", "bestFor": "Competitive analysis, benchmarking, case for change", "matchId": "019dd95a-07fe-70ce-8d3d-aaf1439dbefa", "evidence": "Three repeated timelines 'Closed for 14/15/35 months' framing closed vs open-source worlds.", "position": 5, "objective": "Contrast closed-source releases vs open-source community catch-up speed", "structure": "Current State -> Desired State / Benchmark -> The Gap & Implication", "confidence": 85, "description": "Show the gap between two states to drive urgency or highlight opportunity" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "docSlug": "cc8183ec02431b7a", "documentTitle": "2022 Air Street Capital The State of AI Report 2022", "authorId": "AirStreetCapital", "authorName": "Air Street Capital", "documentKindSlug": "consulting-deck", "documentKindLabel": "Consulting deck", "sourceTypeSlug": "vc_research", "sourceTypeLabel": "VC research", "presentationDate": null, "orientation": "landscape", "aspectRatio": 1.777, "pageNumber": 37, "slideType": "client_example", "function": "illustrate_case", "notes": "Includes visual diagrams of the SayCan decision process and success metrics.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc/37", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-01cf0d8a8fbc#slide-37", "loopMatches": [ { "to": 43, "from": 37, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-afe811e71ad6", "evidence": "SayCan, ConvNets vs ViT, vision-language unification, Gato, IRIS, NeRFs as parallel evidence.", "position": 6, "objective": "Show transformers becoming the universal cross-modality architecture", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 78, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 80, "from": 5, "beatId": "019dd95a-0682-776c-8e35-2557e3799e96", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Definitions, exec summary, Research and Industry sections inventory the state of AI.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 93, "from": 11, "beatId": "019dd95a-0682-776c-8e35-35abf646aa8b", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Research, Industry, Politics sections accumulate signals.", "position": 2, "confidence": 55, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true } ] }