[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-jagged-frontier::en":3,"gloss-cluster-jagged-frontier::en":23,"gloss-next-jagged-frontier::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"jagged-frontier","core-ai","Jagged Frontier","The jagged frontier is the idea that AI capability has an uneven, unpredictable boundary: tasks that look equally hard to a human fall on opposite sides of it. A model may draft a nuanced strategy memo flawlessly, then fail a simple counting or ordering task a child could do. The line between \"AI is great at this\" and \"AI quietly fails at this\" is jagged, not a neat difficulty gradient, and it doesn't match human intuitions about what's easy or hard. The term comes from a 2023 study of knowledge workers using AI. For builders, the practical implication is that you cannot reason about whether a model will handle a given task by analogy to a similar-seeming one — apparent difficulty is a poor predictor. Practical note: test each capability you plan to ship on real examples rather than assuming that strength on one task transfers to a neighboring one. Map where your specific use case sits on the frontier empirically, and re-check when you change models.","The jagged frontier is AI capability's uneven boundary: a model nails a nuanced strategy memo, then fails a counting task a child could do.",null,[11,14,17,20],{"slug":12,"name":13},"foundation-model","Foundation Model",{"slug":15,"name":16},"hallucination","Hallucination",{"slug":18,"name":19},"llm-benchmark","LLM Benchmark",{"slug":21,"name":22},"reasoning-model","Reasoning Model",[24,28,32,36,39,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":58,"category":5,"name":59,"updated_at":35},"deep-learning","Deep Learning",{"slug":61,"category":5,"name":62,"updated_at":27},"diffusion-model","Diffusion Model"]