{ "kind": "all", "value": "all", "collectionKey": "slides:all:all:all-document-kinds:all-producers:all-orientations", "filters": { "documentKinds": [], "sourceTypes": [], "orientations": [] }, "total": 319975, "page": 907, "pageSize": 60, "pageCount": 5333, "rows": [ { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "key_takeaways", "function": "summarize", "notes": "The slide references Berkeley research on imitation learning and Meta's LLaMA-2 paper findings on RLHF.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/14", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "industry_trends", "function": "summarize", "notes": "The slide discusses alternatives to Reinforcement Learning from Human Feedback (RLHF) in AI training.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/15", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "Includes a screenshot of a tweet by William Falcon mocking the lack of detail in the GPT-4 technical report.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/16", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "The slide discusses the release of LLaMa, its performance metrics (RACE benchmark), and the controversy regarding its 'open-source' status.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/17", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "industry_trends", "function": "summarize", "notes": "The slide discusses the ecosystem of open-source LLMs, mentioning specific models like MPT-30B, Falcon, RedPajama, Pythia, and Vicuna, and techniques like LoRA.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/18", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "The slide uses a stacked bar chart to show human evaluation of LLaMa-2 helpfulness against other models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/19", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "analyze_data", "notes": "The chart maps model release/popularity over time (x-axis) against MMLU performance (y-axis), with bubble size representing social media mentions.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/20", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "industry_trends", "function": "summarize", "notes": "The chart tracks the share of different research topics in the AI field.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/21", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#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-e61d00ffc6e4", "evidence": "Sequential GPT-4, RLHF, LLaMa, popularity-mention slides converge on a single conclusion.", "position": 2, "objective": "Pile up evidence that GPT-4 and RLHF dominate the LLM landscape", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "diagnosis", "function": "diagnose", "notes": "The slide presents a critique of the 'emergent capabilities' phenomenon in LLMs, citing Stanford research that suggests these are artifacts of metric choice.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/22", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-22", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "summarize", "notes": "The slide discusses technical methods like FlashAttention, ALiBi, RoPE, and Positional Interpolation in the context of LLM scaling.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/23", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-23", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "industry_trends", "function": "diagnose", "notes": "The slide references the 'Lost in the Middle' research paper by Samaya.ai, UC Berkeley, Stanford, and LMSYS.org.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/24", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-24", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "Part of the State of AI 2023 report.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/25", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-25", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "summarize", "notes": "The slide highlights the shift from 'more data/parameters' to 'better data' in LLM training, specifically referencing the 'phi' model series.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/26", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-26", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "case_study", "function": "illustrate_case", "notes": "The slide uses a visual comparison (meme vs. truth) to explain the shift from dense models to Mixture of Experts (MoE) architectures.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/27", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-27", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "analyze_data", "notes": "The slide uses three separate line charts to illustrate data exhaustion timelines for different data types.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/28", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-28", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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 shows Top-1 Accuracy vs Parameters (M) for models trained on 'Real' vs 'Real + Generated' data, indicating performance gains.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/29", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-29", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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 logos of University of Maryland, Google, Google DeepMind, ETH Zurich, and Princeton.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/30", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-30", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "analyze_data", "notes": "Discusses the trade-off between training epochs, model size, and overfitting.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/31", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-31", "loopMatches": [ { "to": 31, "from": 22, "name": "Zoom In", "slug": "06-zoom-in", "bestFor": "Technical deep-dives, case studies, detailed analysis", "matchId": "019dd95a-07fe-70ce-8d3d-ea04ad01cb62", "evidence": "Slides drill from emergent-capability mirage into context-length, memory and data-exhaustion details.", "position": 3, "objective": "Zoom into the data and context-length bottleneck", "structure": "The Big Picture -> Key Area of Focus -> Specific Detail -> Implication", "confidence": 72, "description": "Start broad, then progressively focus on specific details that prove your point" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "market_landscape", "function": "present_framework", "notes": "The slide contrasts structured quantitative evaluation (HELM/Hugging Face) with qualitative user sentiment ('vibes').", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/32", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-32", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "summarize", "notes": "Includes a process diagram showing the training pipeline for Code Llama models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/33", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-33", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "case_study", "function": "illustrate_case", "notes": "The slide uses two process diagrams to explain how AlphaDev functions as an RL agent.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/34", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-34", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "industry_trends", "function": "present_framework", "notes": "The slide compares different prompting architectures (IO, CoT, CoT-SC, ToT) visually.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/35", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-35", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "industry_trends", "function": "analyze_data", "notes": "The slide uses bar charts to compare accuracy, verbosity, and mismatch metrics for two model versions over time.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/36", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-36", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": "industry_trends", "function": "summarize", "notes": "Includes a small example box illustrating tool usage (QA and Calculator).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/37", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-37", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 38, "slideType": "case_study", "function": "illustrate_case", "notes": "The chart shows the 'Minecraft Tech Tree' progression of different agents, with Voyager (ours) showing the highest performance.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/38", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-38", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 39, "slideType": "diagnosis", "function": "present_framework", "notes": "The slide contrasts standard Chain of Thought (linear) with RAP (tree-based planning).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/39", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-39", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 40, "slideType": "case_study", "function": "illustrate_case", "notes": "Includes a diagram of the reasoning module (DAG) and a performance comparison table.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/40", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-40", "loopMatches": [ { "to": 40, "from": 32, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ec11d4feb687", "evidence": "Vibe-check, code, prompting, Agent Smith, Voyager, RAP, GPT-4-RL slides accumulate into 'LLMs as agents'.", "position": 4, "objective": "Show LLMs becoming agentic across prompting, tools and reasoning", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 70, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 41, "slideType": "industry_trends", "function": "analyze_data", "notes": "Includes a technical diagram of the BLIP-2 architecture.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/41", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-41", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 42, "slideType": "industry_trends", "function": "present_solution", "notes": "The slide explains a technical approach to visual reasoning using LLMs as code generators for visual APIs.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/42", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-42", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 43, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide features a conceptual diagram of the LINGO-1 architecture and two real-world examples of the model providing driving commentary.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/43", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-43", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 44, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide uses a diagram to explain the multimodal architecture (PaLM + ViT) and provides examples of robotic manipulation and visual Q&A.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/44", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-44", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 45, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide explains the architecture and capabilities of the RT-2 model, highlighting its ability to generalize to novel objects and its deployment efficiency.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/45", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-45", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 46, "slideType": "client_example", "function": "illustrate_case", "notes": "The slide uses a circular process diagram to explain the self-improving training loop of the RoboCat agent.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/46", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-46", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 47, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide highlights a milestone in AI robotics, specifically in competitive drone racing.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/47", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-47", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 48, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide highlights a specific research finding regarding emergent map-building in blind navigation agents.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/48", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-48", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 49, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide explains the technical architecture of CICERO, including its use of dialogue history, board state, and a 2.7B-parameter BART-like model.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/49", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-49", "loopMatches": [ { "to": 49, "from": 41, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f0e6f3668fb1", "evidence": "VLM, autonomous driving, PaLM-E, RT-2, RoboCat, drone-racing, CICERO stack as parallel evidence.", "position": 5, "objective": "Multimodal models extend from vision-language to embodied robots", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 75, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 50, "slideType": "industry_trends", "function": "summarize", "notes": "The slide compares diffusion-based models vs. masked transformer models in the context of video generation.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/50", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-50", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 51, "slideType": "industry_trends", "function": "summarize", "notes": "The slide highlights the shift from pure generation to instruction-based editing using models like InstructPix2Pix and Imagen Editor.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/51", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-51", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 52, "slideType": "industry_trends", "function": "present_solution", "notes": "Comparison of MipNeRF360 vs 3D Gaussian Splatting performance metrics.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/52", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-52", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 53, "slideType": "industry_trends", "function": "summarize", "notes": "The slide highlights specific research papers and models (DreamFusion, RealFusion, SKED, Instruct-NeRF2NeRF) in the context of 3D generative AI.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/53", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-53", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 54, "slideType": "industry_trends", "function": "summarize", "notes": "The slide highlights two specific research papers/models in the field of computer vision.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/54", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-54", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 55, "slideType": "industry_trends", "function": "summarize", "notes": "Includes a diagram of the model architecture showing prompt inputs and image inputs leading to a valid mask output.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/55", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-55", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 56, "slideType": "industry_trends", "function": "summarize", "notes": "The slide highlights technical advancements in self-supervised learning, specifically dataset curation and model stability.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/56", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-56", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 57, "slideType": "industry_trends", "function": "summarize", "notes": "The slide compares traditional numerical weather prediction methods with new deep learning/physics-informed models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/57", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-57", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 58, "slideType": "industry_trends", "function": "summarize", "notes": "Includes a spectrogram image and a performance comparison table for various music generation models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/58", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-58", "loopMatches": [ { "to": 58, "from": 50, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f70f6435b722", "evidence": "Text-to-video, instruction editing, Gaussian splatting, NeRFs, depth, SAM, DINOv2, weather, music.", "position": 6, "objective": "Generative media and visual foundation models proliferate", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 72, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 59, "slideType": "industry_trends", "function": "present_framework", "notes": "The slide explains the technical application of generative AI (diffusion models) in biotechnology, specifically protein design.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/59", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-59", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 60, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide highlights a breakthrough in computational biology using language models (ESM-2) to predict protein structures.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/60", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-60", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 61, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide explains a specific scientific research application (GEARS) using a process diagram and two scatter plots (UMAP visualizations).", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/61", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-61", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 62, "slideType": "case_study", "function": "illustrate_case", "notes": "Slide from State of AI 2023 report.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/62", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-62", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 63, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide uses a bar chart to show historical progress on the USMLE and a grouped bar chart to compare Med-PaLM 2 vs. physicians across quality and risk metrics.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/63", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-63", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 64, "slideType": "industry_trends", "function": "present_solution", "notes": "The slide discusses MultiMedBench (14 tasks) and the architectural shift to multimodal medical models.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/64", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-64", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 65, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide highlights the use of social media data for specialized medical AI training.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/65", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-65", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 66, "slideType": "case_study", "function": "illustrate_case", "notes": "The slide presents a specific technical architecture (CoDoC) for AI-human decision deferral in clinical settings.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/66", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-66", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 67, "slideType": "industry_trends", "function": "analyze_data", "notes": "The chart tracks publication volume by scientific discipline, highlighting the rapid growth in medicine and the high volume in mathematics.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/67", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-67", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 68, "slideType": "industry_trends", "function": "analyze_data", "notes": "The slide uses two grouped bar charts to show the concentration of AI research output by country and by organization.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/68", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-68", "loopMatches": [ { "to": 68, "from": 59, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-f9a066ea1e78", "evidence": "RFdiffusion, ESMFold, PerturbNet, AlphaMissense, Med-PaLM, PLIP, CoDoC, then meta map of AI-for-science fields.", "position": 7, "objective": "AI for science accumulates breakthroughs across bio and medicine", "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": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 69, "slideType": "section_divider", "function": "transition", "notes": null, "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/69", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-69", "loopMatches": [], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 70, "slideType": "financial_analysis", "function": "analyze_data", "notes": "The chart uses a red box to highlight the most recent quarter (Q2 FY24) and includes emojis to emphasize the growth.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/70", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-70", "loopMatches": [ { "to": 83, "from": 70, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ff781be07723", "evidence": "Earnings, GPU clusters, A100/H100 indices, 19x research-paper share, 5-year LTV, hyperscaler capex all point one way.", "position": 8, "objective": "Establish NVIDIA's chokehold on AI compute via converging evidence", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 85, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 71, "slideType": "industry_trends", "function": "illustrate_case", "notes": "Includes a quote from CoreWeave regarding their fleet scale.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/71", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-71", "loopMatches": [ { "to": 83, "from": 70, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ff781be07723", "evidence": "Earnings, GPU clusters, A100/H100 indices, 19x research-paper share, 5-year LTV, hyperscaler capex all point one way.", "position": 8, "objective": "Establish NVIDIA's chokehold on AI compute via converging evidence", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 85, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 72, "slideType": "industry_trends", "function": "illustrate_case", "notes": "The slide uses a 2x2 grid layout to present four distinct company case studies.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/72", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-72", "loopMatches": [ { "to": 83, "from": 70, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ff781be07723", "evidence": "Earnings, GPU clusters, A100/H100 indices, 19x research-paper share, 5-year LTV, hyperscaler capex all point one way.", "position": 8, "objective": "Establish NVIDIA's chokehold on AI compute via converging evidence", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 85, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true }, { "docId": "019dd923-5e88-73ef-bd5d-06b04d219fea", "docSlug": "dd91c78f6570bf29", "documentTitle": "2023 Air Street Capital The State of AI Report 2023", "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": 73, "slideType": "industry_trends", "function": "establish_context", "notes": "The title has a strikethrough on 'Footballers' which seems to be a humorous or editorial correction.", "imagePath": null, "matchCount": 1, "evidence": null, "slideHref": "/slides/019dd923-5e88-73ef-bd5d-06b04d219fea/73", "deckHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea", "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5d-06b04d219fea#slide-73", "loopMatches": [ { "to": 83, "from": 70, "name": "Pattern Hunter", "slug": "02-pattern-hunter", "bestFor": "Time-pressed audiences, building consensus, when data is strong", "matchId": "019dd95a-07fe-70ce-8d3d-ff781be07723", "evidence": "Earnings, GPU clusters, A100/H100 indices, 19x research-paper share, 5-year LTV, hyperscaler capex all point one way.", "position": 8, "objective": "Establish NVIDIA's chokehold on AI compute via converging evidence", "structure": "Evidence A -> Evidence B -> Evidence C -> Pattern/Conclusion", "confidence": 85, "description": "Group multiple pieces of evidence that together point to a pattern or conclusion" } ], "arcBeatMatches": [ { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-41afd44ef59f", "arcName": "The Triple Take", "arcSlug": "triple-take", "beatName": "The Facts (What)", "beatSlug": "triple-take-the-facts-what", "evidence": "Research + Industry sections inventory model, compute, funding facts.", "position": 1, "confidence": 78, "parentBeatName": "Setup", "parentBeatSlug": "setup" }, { "to": 120, "from": 11, "beatId": "019dd95a-0682-776c-8e35-523bfb7f96e6", "arcName": "The Mountain", "arcSlug": "mountain", "beatName": "Rising Action", "beatSlug": "mountain-rising-action", "evidence": "Escalating capabilities, compute concentration and capital flows.", "position": 2, "confidence": 45, "parentBeatName": "Development", "parentBeatSlug": "development" } ], "imagePathAlt": null, "thumbSrc": null, "thumbSrcAlt": null, "locked": true } ] }