{"id":"svpg","name":"Silicon Valley Product Group","kind":"organization","aliases":["SVPG"],"summary":"Silicon Valley Product Group, founded by Marty Cagan, publishes essays and coaching material on product management and product leadership.","url":"https://www.svpg.com/","referenceUrls":["https://www.svpg.com/","https://www.svpg.com/feed/"],"sourceIds":["svpg"],"topicIds":["work-and-agency"],"version":"entity-catalog-v1","identity":null,"path":"/entities/svpg","json":"/entities/svpg.json","markdown":"/entities/svpg.md","relationships":{"sourceIds":["svpg"],"noteIds":[],"perspectiveIds":[],"parallelIds":[],"topicIds":["work-and-agency"]},"publications":"/api/pipeline/publications?entity=svpg","profile":{"schemaVersion":1,"identityId":"svpg","tracked":null,"contributorContexts":[],"affiliations":[],"statements":[],"hostedStatements":[],"questions":[],"readings":[],"relations":[],"boundary":"Escape Meridian selects and paraphrases these records. A mention, shared topic or affiliation does not establish authorship, endorsement, agreement or causation. Missing positions remain unknown."},"currentReadings":{"entityId":"svpg","items":[{"id":"5b3d811141742106b459b9d24d4df7e5c475fb27c5d8152c3255f4ec8132fa32","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","path":"/readings/silicon-valley-product-group-argues-experimentation-should-5b3d8111","sourceUrl":"https://www.svpg.com/experimentation-ai-and-the-product-model/","publisher":"Silicon Valley Product Group","originalPublished":"2026-10-21T15:53:41+00:00","dateLabel":"Oct 21, 2026","relationship":"publisher","relationshipLabel":"Publisher","topicIds":["work-and-agency"],"sourceParticipation":[{"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"}}],"roles":[],"participationCoverage":{"total":2,"returned":1,"complete":false,"fullAssertions":"source-metadata-endpoint"}},{"id":"59e92687bf28e02e8480f80fee6a2f4efa8bf15d199067d863336b9fa4755059","title":"Silicon Valley Product Group argues the real trade-off is predictability versus outcomes","summary":"In “Predictability vs Outcomes,” Silicon Valley Product Group argues that the central choice for many companies is not output versus outcomes, but predictability versus outcomes. The article says companies often prioritize predictability, hoping that shipping promised work will produce outcomes, while product teams may lack responsibility for those outcomes. It argues that this priority can leave output as a fallback measure of progress and weaken trust when predictability goals are missed.\n\nThe article says outcomes require accepting that many roadmap ideas will not work, acknowledging what is unknown, and using product discovery to find solutions that can generate the needed outcome. It contrasts that approach with pushing untested ideas onto roadmaps with expected dates or timeframes. The author does not reject predictability: the article says some situations require it, and frames the issue as how companies choose between two worthy priorities. It presents this choice as central to company culture and to the difference between the project model and the product model, including how companies use AI. The article does not provide evidence quantifying how common these practices are or how much they affect outcomes.\n\nWhat criteria can a company use to decide when predictability is critical and when outcomes should take priority?","path":"/readings/silicon-valley-product-group-argues-the-real-trade-off-is-59e92687","sourceUrl":"https://www.svpg.com/predictability-vs-outcomes/","publisher":"Silicon Valley Product Group","originalPublished":"2026-10-12T15:24:29+00:00","dateLabel":"Oct 12, 2026","relationship":"publisher","relationshipLabel":"Publisher","topicIds":["work-and-agency"],"sourceParticipation":[{"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/predictability-vs-outcomes/","locator":"document/html[2]/head[0]/script[81]@5752.@graph[0].author"},"assertionId":"b94b2cce2664664f6eccabbb3b5f86b3b717b3ea2bcd4bd9feadb4a58333a39e:participation:0","identity":{"version":"entity-identity-v1","status":"unresolved","actorId":null,"canonicalActorId":null,"candidateActorIds":[],"assertions":[],"sourceObservedAt":"2026-11-05T04:25:22.337Z","knowledgeAsOf":null},"sourceVersion":{"snapshotId":"b94b2cce2664664f6eccabbb3b5f86b3b717b3ea2bcd4bd9feadb4a58333a39e","sourceUrl":"https://www.svpg.com/predictability-vs-outcomes/","contentSha256":"ee2d8bb4f4e47ddfcf817c28d4edf75200a7e10adbfa1e80ec11d39de512ad84","textHash":"46ea345b682bad04cbba1197384bb152f4b147a0ce24fdb5c3d7ae734e3dc186","observedAt":"2026-11-05T04:25:22.337Z"}}],"roles":[],"participationCoverage":{"total":2,"returned":1,"complete":false,"fullAssertions":"source-metadata-endpoint"}}],"scanned":120,"truncated":false,"scanLimit":500,"checkedAt":"2026-11-05T15:23:28.390Z"},"currentReadingsUrl":"/entities/svpg.json"}