{"id":"5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32","title":"Silicon Valley Product Group argues experimentation should shape what product teams build","argument":"Silicon Valley Product Group’s article argues that as AI makes building and deploying product capabilities faster and cheaper, deciding what to build becomes more important. It distinguishes the project model, in which teams deliver assigned projects and features, from the product model, in which teams solve problems and are responsible for outcomes.\n\nThe article says experimentation should extend beyond optimization—testing ways to improve an existing product—to innovation: learning what to build and why. It describes qualitative experiments, such as testing prototypes with users, and quantitative experiments, which use data to assess outcomes, as complementary ways to learn. AI can help with tasks such as analyzing user data and evaluating tests, the article says, but experiments still inform decisions and strategy.\n\nFor AI products, the article notes that evaluations, or evals, add a layer to product discovery; it says quantitative tests can be more expensive, take longer, and yield less durable conclusions. It advocates a hybrid model: product teams own experiments with coaching and assistance from experimentation professionals. The proposed shift is from counting tests to assessing business and customer impact. The article does not offer evidence for its claims about high failure rates or show that the proposed organizational approach produces better outcomes. What results","topicIds":["work-and-agency"],"domain":"work","claimType":"attributed reading","limits":"The article asserts that quickly built and released products have had high failure rates and serious consequences, but supplies no figures or supporting cases in the provided text. The article advocates the hybrid model but provides no comparative results showing whether it improves business or customer outcomes. The author is not identified in the provided article text.","speaker":{"name":"Marty Cagan","roleAtSource":"Article author"},"source":{"title":"Experimentation, AI and the Product Model | Silicon Valley Product Group","publisher":"Silicon Valley Product Group","format":"Article","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","publishedAt":"2026-10-21T15:53:41+00:00","dateBasis":"Date stated by the publisher."},"evidence":[{"text":"The article argues that AI’s lower building and deployment costs make choosing what to build a more critical product skill.","excerpt":"As AI makes the actual building and deployment of product capabilities faster and cheaper, everything depends on what we choose to build.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article distinguishes the project model, which assigns teams projects and features, from the product model, which gives teams problems to solve and responsibility for outcomes.","excerpt":"In contrast, in the product model, product teams are instead given problems to solve, and they are then empowered to come up with a solution that delivers the necessary business results (outcomes).","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article argues experimentation should contribute to innovation and product discovery, not just optimization of existing products.","excerpt":"It means expanding experimentation beyond optimization to innovation, and beyond proving outcomes, to influencing outcomes.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article advocates a hybrid model in which product teams own experiments and experimentation professionals coach and assist them.","excerpt":"It lets the product teams own the experimentation, and take responsibility for outcomes, yet with the assistance and coaching of experimentation professionals.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article says quantitative tests for AI products may be costlier, slower, and less durable as a basis for conclusions.","excerpt":"Product discovery includes the eval process, and many of the judgements about the product are subjective. Moreover, an AI product changes the calculus for the use of quantitative tests, as they are generally more expensive and take longer, and the conclusions may not be as durable.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"}],"sourceMedia":{"status":"found","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","checkedAt":"2026-11-05T03:52:28.876Z","items":[{"type":"image","url":"https://www.svpg.com/wp-content/themes/svpg2022/app/img/svpg-social.jpg","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","attribution":"Silicon Valley Product Group","basis":"publisher-metadata","availability":"verified","verifiedAt":"2026-11-05T03:52:29.014Z"}],"note":"Media declared by the original publisher; availability checked separately from the editorial reading."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"What results would show whether the hybrid model improves business and customer outcomes compared with other approaches?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Silicon Valley Product Group argues experimentation should shape what product teams build","summary":"Silicon Valley Product Group’s article argues that as AI makes building and deploying product capabilities faster and cheaper, deciding what to build becomes more important. It distinguishes the project model, in which teams deliver assigned projects and features, from the product model, in which teams solve problems and are responsible for outcomes.\n\nThe article says experimentation should extend beyond optimization—testing ways to improve an existing product—to innovation: learning what to build and why. It describes qualitative experiments, such as testing prototypes with users, and quantitative experiments, which use data to assess outcomes, as complementary ways to learn. AI can help with tasks such as analyzing user data and evaluating tests, the article says, but experiments still inform decisions and strategy.\n\nFor AI products, the article notes that evaluations, or evals, add a layer to product discovery; it says quantitative tests can be more expensive, take longer, and yield less durable conclusions. It advocates a hybrid model: product teams own experiments with coaching and assistance from experimentation professionals. The proposed shift is from counting tests to assessing business and customer impact. The article does not offer evidence for its claims about high failure rates or show that the proposed organizational approach produces better outcomes. What results","question":"What results would show whether the hybrid model improves business and customer outcomes compared with other approaches?","claims":[{"text":"The article argues that AI’s lower building and deployment costs make choosing what to build a more critical product skill.","excerpt":"As AI makes the actual building and deployment of product capabilities faster and cheaper, everything depends on what we choose to build.","support":"source-stated"},{"text":"The article distinguishes the project model, which assigns teams projects and features, from the product model, which gives teams problems to solve and responsibility for outcomes.","excerpt":"In contrast, in the product model, product teams are instead given problems to solve, and they are then empowered to come up with a solution that delivers the necessary business results (outcomes).","support":"source-stated"},{"text":"The article argues experimentation should contribute to innovation and product discovery, not just optimization of existing products.","excerpt":"It means expanding experimentation beyond optimization to innovation, and beyond proving outcomes, to influencing outcomes.","support":"source-stated"},{"text":"The article advocates a hybrid model in which product teams own experiments and experimentation professionals coach and assist them.","excerpt":"It lets the product teams own the experimentation, and take responsibility for outcomes, yet with the assistance and coaching of experimentation professionals.","support":"source-stated"},{"text":"The article says quantitative tests for AI products may be costlier, slower, and less durable as a basis for conclusions.","excerpt":"Product discovery includes the eval process, and many of the judgements about the product are subjective. Moreover, an AI product changes the calculus for the use of quantitative tests, as they are generally more expensive and take longer, and the conclusions may not be as durable.","support":"source-stated"}],"limitations":["The article asserts that quickly built and released products have had high failure rates and serious consequences, but supplies no figures or supporting cases in the provided text.","The article advocates the hybrid model but provides no comparative results showing whether it improves business or customer outcomes.","The author is not identified in the provided article text."],"mediaId":"12252e0b8bda636f6749d974da0f2c9124bde2d271aec86517afbd90af4a2a84","correctionReason":null,"id":"5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32","publicationId":"264aeac9-d630-436f-8f7a-9c138a120029","version":1,"sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","sourceTitle":"Experimentation, AI and the Product Model | Silicon Valley Product Group","publisher":"Silicon Valley Product Group","publishedAt":"2026-10-21T15:53:41+00:00","originalPublished":{"value":"2026-10-21T15:53:41+00:00","precision":"instant","basis":"publisher_page"},"firstObservedAt":"2026-11-04T12:33:18.400Z","createdAt":"2026-11-05T03:53:08.444Z","historical":true,"entityIds":["svpg"],"topicIds":["work-and-agency"],"media":[{"id":"12252e0b8bda636f6749d974da0f2c9124bde2d271aec86517afbd90af4a2a84","kind":"article","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-11-05T03:52:29.014Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."},{"id":"83a6feda0ba80eb81db6f609502fdf81f3db532830f24c283e10ffd166296191","kind":"image","url":"https://www.svpg.com/wp-content/themes/svpg2022/app/img/svpg-social.jpg","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","label":"View source image","provenance":"publisher_page","verification":{"status":"verified","checkedAt":"2026-11-05T03:52:29.014Z","method":"publisher_association_and_head","contentType":"image/jpeg","cacheUntil":1793851049014},"recommendationReason":"Observed on the acquired publisher source; availability checked."}],"sourceMedia":{"status":"found","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","checkedAt":"2026-11-05T03:52:28.876Z","items":[{"type":"image","url":"https://www.svpg.com/wp-content/themes/svpg2022/app/img/svpg-social.jpg","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","attribution":"Silicon Valley Product Group","basis":"publisher-metadata","availability":"verified","verifiedAt":"2026-11-05T03:52:29.014Z"}],"note":"Media declared by the original publisher; availability checked separately from the editorial reading."},"editorialDecision":{"outcome":"accept","reasonCodes":["useful_perspective"],"rubricVersion":"stream-admission-v5","mediaId":"12252e0b8bda636f6749d974da0f2c9124bde2d271aec86517afbd90af4a2a84","confidence":0.86,"provider":"typesafe","model":"jev-1.13.0","inputHash":"b6292b5b99f9cf4b30f2db25b40ddd8b34be0186adbc0259439a29034fdb9141","decidedAt":"2026-11-05T03:52:29.723Z"},"provenance":{"textHash":"cbb5b07cadd4da648e009e256700cd04ac23f30a4df84bbf34dd539ed38ba011","observationId":"fe921170edb21268e8a724b22bcf59f7cc7f7c7923a78dd589d17b85b72ba33c","retrievedAt":"2026-11-05T03:52:28.876Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"d8d0c202-e1e2-4229-8064-59e49edd9e49"},"reviewer":{"model":"gpt-6-luna","runId":"1feb6d2c-12e8-497f-844e-feb05ce6c5ae"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-05T03:53:08.444Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Marty Cagan","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Marty Cagan","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Marty Cagan","status":"stated","provenance":{"basis":"article-jsonld","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","locator":"document/html[2]/head[0]/script[79]@5880.@graph[0].author"},"assertionId":"8f743d635a9d62a31f0b141641561ac30b87a47ef0d1319b286cc91a5c1cc587:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-05T03:52:28.876Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"8f743d635a9d62a31f0b141641561ac30b87a47ef0d1319b286cc91a5c1cc587","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","contentSha256":"08ca7882a23f2d2ff69c63ea83d50e2dfc05e71b9e5e4462e4c627685148a553","textHash":"cbb5b07cadd4da648e009e256700cd04ac23f30a4df84bbf34dd539ed38ba011","observedAt":"2026-11-05T03:52:28.876Z"}}],"participationCoverage":{"total":2,"returned":1,"complete":false,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","snapshotId":"8f743d635a9d62a31f0b141641561ac30b87a47ef0d1319b286cc91a5c1cc587","contentSha256":"08ca7882a23f2d2ff69c63ea83d50e2dfc05e71b9e5e4462e4c627685148a553","textHash":"cbb5b07cadd4da648e009e256700cd04ac23f30a4df84bbf34dd539ed38ba011","observedAt":"2026-11-05T03:52:28.876Z","basis":"publisher-page-metadata","sourceLinks":7,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32/source"}},"canonicalUrl":"https://escapemeridian.com/readings/silicon-valley-product-group-argues-experimentation-should-5b3d8111","formats":{"html":"/readings/silicon-valley-product-group-argues-experimentation-should-5b3d8111","markdown":"/readings/silicon-valley-product-group-argues-experimentation-should-5b3d8111.md","json":"/readings/silicon-valley-product-group-argues-experimentation-should-5b3d8111.json"},"attribution":"Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.","maintainedGuideLinks":[],"readerContext":{"version":"reader-context-v1","attribution":"Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.","terms":[{"id":"term-evals","name":"Evals","explanation":"Structured tests of what an AI system can do or how it behaves, run before or after release.","referenceUrl":"https://developers.google.com/machine-learning/glossary#evals"}],"entities":[{"id":"svpg","name":"Silicon Valley Product Group","kind":"organization","context":"Silicon Valley Product Group, founded by Marty Cagan, publishes essays and coaching material on product management and product leadership.","referenceUrls":["https://www.svpg.com/","https://www.svpg.com/feed/"],"path":"/entities/svpg","json":"/entities/svpg.json","markdown":"/entities/svpg.md"}]},"researchQuestions":[{"id":"ai-product-management-work","title":"What happens to product management work?","question":"How is AI changing product management work, and where does a product manager’s advantage now lie?","url":"/questions/ai-product-management-work","edition":"product-management-read-2026-11-04"}],"withdrawn":false,"references":{"schemaVersion":1,"target":{"type":"reading","id":"5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32","revision":"264aeac9-d630-436f-8f7a-9c138a120029","revisionKind":"publication-id","title":"Silicon Valley Product Group argues experimentation should shape what product teams build","url":"https://escapemeridian.com/readings/silicon-valley-product-group-argues-experimentation-should-5b3d8111"},"source":{"type":"source","id":"https://www.svpg.com/experimentation-ai-and-the-product-model/","revision":"cbb5b07cadd4da648e009e256700cd04ac23f30a4df84bbf34dd539ed38ba011","revisionKind":"sha256-source-text","title":"Experimentation, AI and the Product Model | Silicon Valley Product Group","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."},"revisions":[{"id":"5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32","title":"Silicon Valley Product Group argues experimentation should shape what product teams build","argument":"Silicon Valley Product Group’s article argues that as AI makes building and deploying product capabilities faster and cheaper, deciding what to build becomes more important. It distinguishes the project model, in which teams deliver assigned projects and features, from the product model, in which teams solve problems and are responsible for outcomes.\n\nThe article says experimentation should extend beyond optimization—testing ways to improve an existing product—to innovation: learning what to build and why. It describes qualitative experiments, such as testing prototypes with users, and quantitative experiments, which use data to assess outcomes, as complementary ways to learn. AI can help with tasks such as analyzing user data and evaluating tests, the article says, but experiments still inform decisions and strategy.\n\nFor AI products, the article notes that evaluations, or evals, add a layer to product discovery; it says quantitative tests can be more expensive, take longer, and yield less durable conclusions. It advocates a hybrid model: product teams own experiments with coaching and assistance from experimentation professionals. The proposed shift is from counting tests to assessing business and customer impact. The article does not offer evidence for its claims about high failure rates or show that the proposed organizational approach produces better outcomes. What results","topicIds":["work-and-agency"],"domain":"work","claimType":"attributed reading","limits":"The article asserts that quickly built and released products have had high failure rates and serious consequences, but supplies no figures or supporting cases in the provided text. The article advocates the hybrid model but provides no comparative results showing whether it improves business or customer outcomes. The author is not identified in the provided article text.","speaker":{"name":"Marty Cagan","roleAtSource":"Article author"},"source":{"title":"Experimentation, AI and the Product Model | Silicon Valley Product Group","publisher":"Silicon Valley Product Group","format":"Article","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","publishedAt":"2026-10-21T15:53:41+00:00","dateBasis":"Date stated by the publisher."},"evidence":[{"text":"The article argues that AI’s lower building and deployment costs make choosing what to build a more critical product skill.","excerpt":"As AI makes the actual building and deployment of product capabilities faster and cheaper, everything depends on what we choose to build.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article distinguishes the project model, which assigns teams projects and features, from the product model, which gives teams problems to solve and responsibility for outcomes.","excerpt":"In contrast, in the product model, product teams are instead given problems to solve, and they are then empowered to come up with a solution that delivers the necessary business results (outcomes).","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article argues experimentation should contribute to innovation and product discovery, not just optimization of existing products.","excerpt":"It means expanding experimentation beyond optimization to innovation, and beyond proving outcomes, to influencing outcomes.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article advocates a hybrid model in which product teams own experiments and experimentation professionals coach and assist them.","excerpt":"It lets the product teams own the experimentation, and take responsibility for outcomes, yet with the assistance and coaching of experimentation professionals.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"},{"text":"The article says quantitative tests for AI products may be costlier, slower, and less durable as a basis for conclusions.","excerpt":"Product discovery includes the eval process, and many of the judgements about the product are subjective. Moreover, an AI product changes the calculus for the use of quantitative tests, as they are generally more expensive and take longer, and the conclusions may not be as durable.","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","relationship":"Source-stated"}],"sourceMedia":{"status":"found","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","checkedAt":"2026-11-05T03:52:28.876Z","items":[{"type":"image","url":"https://www.svpg.com/wp-content/themes/svpg2022/app/img/svpg-social.jpg","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","attribution":"Silicon Valley Product Group","basis":"publisher-metadata","availability":"verified","verifiedAt":"2026-11-05T03:52:29.014Z"}],"note":"Media declared by the original publisher; availability checked separately from the editorial reading."},"assumptions":[],"openQuestion":{"attribution":"Open question","text":"What results would show whether the hybrid model improves business and customer outcomes compared with other approaches?"},"whatWouldChangeView":{"attribution":"Change condition not recorded","text":""},"editorialComparisons":[],"live":{"title":"Silicon Valley Product Group argues experimentation should shape what product teams build","summary":"Silicon Valley Product Group’s article argues that as AI makes building and deploying product capabilities faster and cheaper, deciding what to build becomes more important. It distinguishes the project model, in which teams deliver assigned projects and features, from the product model, in which teams solve problems and are responsible for outcomes.\n\nThe article says experimentation should extend beyond optimization—testing ways to improve an existing product—to innovation: learning what to build and why. It describes qualitative experiments, such as testing prototypes with users, and quantitative experiments, which use data to assess outcomes, as complementary ways to learn. AI can help with tasks such as analyzing user data and evaluating tests, the article says, but experiments still inform decisions and strategy.\n\nFor AI products, the article notes that evaluations, or evals, add a layer to product discovery; it says quantitative tests can be more expensive, take longer, and yield less durable conclusions. It advocates a hybrid model: product teams own experiments with coaching and assistance from experimentation professionals. The proposed shift is from counting tests to assessing business and customer impact. The article does not offer evidence for its claims about high failure rates or show that the proposed organizational approach produces better outcomes. What results","question":"What results would show whether the hybrid model improves business and customer outcomes compared with other approaches?","claims":[{"text":"The article argues that AI’s lower building and deployment costs make choosing what to build a more critical product skill.","excerpt":"As AI makes the actual building and deployment of product capabilities faster and cheaper, everything depends on what we choose to build.","support":"source-stated"},{"text":"The article distinguishes the project model, which assigns teams projects and features, from the product model, which gives teams problems to solve and responsibility for outcomes.","excerpt":"In contrast, in the product model, product teams are instead given problems to solve, and they are then empowered to come up with a solution that delivers the necessary business results (outcomes).","support":"source-stated"},{"text":"The article argues experimentation should contribute to innovation and product discovery, not just optimization of existing products.","excerpt":"It means expanding experimentation beyond optimization to innovation, and beyond proving outcomes, to influencing outcomes.","support":"source-stated"},{"text":"The article advocates a hybrid model in which product teams own experiments and experimentation professionals coach and assist them.","excerpt":"It lets the product teams own the experimentation, and take responsibility for outcomes, yet with the assistance and coaching of experimentation professionals.","support":"source-stated"},{"text":"The article says quantitative tests for AI products may be costlier, slower, and less durable as a basis for conclusions.","excerpt":"Product discovery includes the eval process, and many of the judgements about the product are subjective. Moreover, an AI product changes the calculus for the use of quantitative tests, as they are generally more expensive and take longer, and the conclusions may not be as durable.","support":"source-stated"}],"limitations":["The article asserts that quickly built and released products have had high failure rates and serious consequences, but supplies no figures or supporting cases in the provided text.","The article advocates the hybrid model but provides no comparative results showing whether it improves business or customer outcomes.","The author is not identified in the provided article text."],"mediaId":"12252e0b8bda636f6749d974da0f2c9124bde2d271aec86517afbd90af4a2a84","correctionReason":null,"id":"5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32","publicationId":"264aeac9-d630-436f-8f7a-9c138a120029","version":1,"sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","sourceTitle":"Experimentation, AI and the Product Model | Silicon Valley Product Group","publisher":"Silicon Valley Product Group","publishedAt":"2026-10-21T15:53:41+00:00","originalPublished":{"value":"2026-10-21T15:53:41+00:00","precision":"instant","basis":"publisher_page"},"firstObservedAt":"2026-11-04T12:33:18.400Z","createdAt":"2026-11-05T03:53:08.444Z","historical":true,"entityIds":["svpg"],"topicIds":["work-and-agency"],"media":[{"id":"12252e0b8bda636f6749d974da0f2c9124bde2d271aec86517afbd90af4a2a84","kind":"article","url":"https://www.svpg.com/experimentation-ai-and-the-product-model/","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","label":"Read original","provenance":"source_link","verification":{"status":"verified","checkedAt":"2026-11-05T03:52:29.014Z","method":"acquired_source","contentType":"text/html"},"recommendationReason":"Recommended by Jev for understanding this source; checked by Luna."},{"id":"83a6feda0ba80eb81db6f609502fdf81f3db532830f24c283e10ffd166296191","kind":"image","url":"https://www.svpg.com/wp-content/themes/svpg2022/app/img/svpg-social.jpg","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","label":"View source image","provenance":"publisher_page","verification":{"status":"verified","checkedAt":"2026-11-05T03:52:29.014Z","method":"publisher_association_and_head","contentType":"image/jpeg","cacheUntil":1793851049014},"recommendationReason":"Observed on the acquired publisher source; availability checked."}],"sourceMedia":{"status":"found","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","checkedAt":"2026-11-05T03:52:28.876Z","items":[{"type":"image","url":"https://www.svpg.com/wp-content/themes/svpg2022/app/img/svpg-social.jpg","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","attribution":"Silicon Valley Product Group","basis":"publisher-metadata","availability":"verified","verifiedAt":"2026-11-05T03:52:29.014Z"}],"note":"Media declared by the original publisher; availability checked separately from the editorial reading."},"editorialDecision":{"outcome":"accept","reasonCodes":["useful_perspective"],"rubricVersion":"stream-admission-v5","mediaId":"12252e0b8bda636f6749d974da0f2c9124bde2d271aec86517afbd90af4a2a84","confidence":0.86,"provider":"typesafe","model":"jev-1.13.0","inputHash":"b6292b5b99f9cf4b30f2db25b40ddd8b34be0186adbc0259439a29034fdb9141","decidedAt":"2026-11-05T03:52:29.723Z"},"provenance":{"textHash":"cbb5b07cadd4da648e009e256700cd04ac23f30a4df84bbf34dd539ed38ba011","observationId":"fe921170edb21268e8a724b22bcf59f7cc7f7c7923a78dd589d17b85b72ba33c","retrievedAt":"2026-11-05T03:52:28.876Z","kind":"publisher-page","preparer":{"model":"gpt-6-luna","runId":"d8d0c202-e1e2-4229-8064-59e49edd9e49"},"reviewer":{"model":"gpt-6-luna","runId":"1feb6d2c-12e8-497f-844e-feb05ce6c5ae"}},"audit":{"status":"uncertain","reviewedAt":"2026-11-05T03:53:08.444Z","model":"anthropic/claude-opus-5.5"},"sourceAttribution":{"authors":[{"name":"Marty Cagan","url":null,"kind":"unknown"}],"authorRole":"article-author","hosts":[],"status":"stated","authorRoster":{"truncated":false,"omitted":0,"assertionGroupsOmitted":0},"participation":[{"actor":{"name":"Marty Cagan","url":null,"kind":"unknown","actorId":null},"role":"author","scope":{"kind":"work-credit","locator":null},"literalCredit":"Marty Cagan","status":"stated","provenance":{"basis":"article-jsonld","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","locator":"document/html[2]/head[0]/script[79]@5880.@graph[0].author"},"assertionId":"8f743d635a9d62a31f0b141641561ac30b87a47ef0d1319b286cc91a5c1cc587:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-05T03:52:28.876Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"8f743d635a9d62a31f0b141641561ac30b87a47ef0d1319b286cc91a5c1cc587","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","contentSha256":"08ca7882a23f2d2ff69c63ea83d50e2dfc05e71b9e5e4462e4c627685148a553","textHash":"cbb5b07cadd4da648e009e256700cd04ac23f30a4df84bbf34dd539ed38ba011","observedAt":"2026-11-05T03:52:28.876Z"}}],"participationCoverage":{"total":2,"returned":1,"complete":false,"fullAssertions":"source-metadata-endpoint"},"distribution":null,"publisher":null,"sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","snapshotId":"8f743d635a9d62a31f0b141641561ac30b87a47ef0d1319b286cc91a5c1cc587","contentSha256":"08ca7882a23f2d2ff69c63ea83d50e2dfc05e71b9e5e4462e4c627685148a553","textHash":"cbb5b07cadd4da648e009e256700cd04ac23f30a4df84bbf34dd539ed38ba011","observedAt":"2026-11-05T03:52:28.876Z","basis":"publisher-page-metadata","sourceLinks":7,"linksTruncated":false,"metadataUrl":"/api/pipeline/stream/5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32/source"}}}],"audits":[{"publicationId":"264aeac9-d630-436f-8f7a-9c138a120029","verdict":"uncertain","findings":[{"dimension":"attribution","status":"pass","explanation":"The draft attributes the argument to Silicon Valley Product Group as publisher, consistently uses 'the article argues/says', and notes that the author is not named in the text. No source assertion is presented as established fact.","excerpt":"This talk is focused on the product organization, the people responsible for building the products the company sells."},{"dimension":"summary","status":"uncertain","explanation":"The content faithfully covers the AI-lowers-build-cost thesis, the project vs product model, the shift from optimization to innovation, qualitative and quantitative experiments, evals, the hybrid model, and the shift from counting tests to measuring impact. However, the summary ends with a dangling fragment, 'What results', which appears to be a truncated copy of the question. This leaves the reading malformed. Smallest repair: delete the trailing fragment. The title and question otherwise follow from the source.","excerpt":"This is why the hybrid model is often the most effective, and what we generally advocate."},{"dimension":"date","status":"pass","explanation":"originalPublished 2026-10-21T15:53:41Z comes from the publisher page and matches the feed entry. Nothing in the text points to an earlier publication of this narrative version. The keynote delivery date is not given and does not affect the publication date.","excerpt":null},{"dimension":"support","status":"pass","explanation":"Each claim is matched by an exact excerpt. The draft's 'can be more expensive' and 'less durable' wording stays close to the source's 'generally' and 'may not be as durable' hedging. The limitations correctly note that the high-failure-rate assertion and the hybrid model's effectiveness are unsupported by data in the text. The question is open and not leading.","excerpt":"Initially companies thought they could simply build quickly, release into the wild, and see what sticks, but the high failure rates come with serious consequences"},{"dimension":"media","status":"pass","explanation":"The selected media is the verified direct link to the original SVPG article. It is the most direct way to reach the source.","excerpt":null}],"createdAt":"2026-11-05T03:53:08.444Z","model":"anthropic/claude-opus-5.5"}],"correctionUrl":"https://escapemeridian.com/corrections/"}