[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-music-producers-show-how-to-build-ai-products-en":3,"article-related-music-producers-show-how-to-build-ai-products-en":30,"series-tools-485acabc-5fb4-4084-97a5-e44ac702de05":85},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":22,"views":26,"created_at":27,"published_at":28,"topic_cluster_id":29},"485acabc-5fb4-4084-97a5-e44ac702de05","music-producers-show-how-to-build-ai-products-en","Music producers show how to build AI products","\u003Cp data-speakable=\"summary\">This breaks down the 60\u002F30\u002F5 rule into an editing-first AI product template.\u003C\u002Fp>\u003Cp>I've been building with AI tools long enough to be annoyed by the same bad demo over and over. Someone wires up a model, types a prompt, gets a flashy output, and then acts like that output is the product. It isn’t. In real work, the model is usually the messy first draft. The thing that actually matters is whether a person can shape it, reject it, correct it, and keep moving without losing their mind.\u003C\u002Fp>\u003Cp>That’s why this Forbes Council post hit me. It takes music production, which is one of the few creative workflows where taste, iteration, and accountability are impossible to fake, and uses it to explain how AI products should really work. I’ve seen the same pattern in coding tools, writing tools, and design tools: the best users do not want full automation. They want control with less friction. That distinction matters more than all the demo theater in the world.\u003C\u002Fp>\u003Cp>The source is Sourabh Pateriya’s Forbes article, \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fcouncils\u002Fforbestechcouncil\u002F2026\u002F06\u002F01\u002Fthe-60305-rule-what-music-producers-can-teach-about-building-ai-products\u002F\">“The 60\u002F30\u002F5 Rule: What Music Producers Can Teach About Building AI Products”\u003C\u002Fa>. It’s a Council Post, not a research paper, but it gives me a useful lens: most value sits in ideation and collaboration, and only a tiny slice sits in full delegation.\u003C\u002Fp>\u003Ch2>Stop pretending users want the robot to finish the song\u003C\u002Fh2>\u003Cblockquote>“60% use AI as an ideation tool... 30% use it as a co-producer... Only 5% delegate full production to AI.”\u003C\u002Fblockquote>\u003Cp>What this actually means is simple: most professionals do not want AI to replace the workflow they already trust. They want AI to help them get unstuck, explore options, and move faster inside the process they already own.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780667344107-c2ay.png\" alt=\"Music producers show how to build AI products\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Pateriya points to a shape that shows up everywhere. In music, producers use AI to generate melodies, chord progressions, and arrangement starters. Then they keep working. They don’t hand over the whole track and go home. They prompt, reject, tweak, drag, replace, and listen again. That’s not a bug. That’s the job.\u003C\u002Fp>\u003Cp>I ran into this exact problem when I watched teams build “write the whole thing” copilots for internal knowledge work. The first version always looked magical in a demo and useless in practice. People didn’t trust the output enough to ship it, and they hated the feeling of being trapped in someone else’s draft. The tool had output, but no ownership.\u003C\u002Fp>\u003Cp>How to apply it: stop optimizing your product around the fantasy of one-shot generation. Build for the 60 and the 30 first. Let users start with a suggestion, then edit it in place. Make regeneration cheap, obvious, and local to the part they want to change. If your product can’t support fast rejection, it’s probably too eager.\u003C\u002Fp>\u003Cul>\u003Cli>Default to drafts, not finals.\u003C\u002Fli>\u003Cli>Make partial acceptance a first-class action.\u003C\u002Fli>\u003Cli>Show alternatives side by side instead of hiding them behind another prompt.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Judgment is the product, not the output\u003C\u002Fh2>\u003Cblockquote>“Producers are paid for taste, not output.”\u003C\u002Fblockquote>\u003Cp>What this actually means is that the expensive part of creative work is not typing notes into a box. It’s knowing what to keep, what to cut, and what feels wrong even when it looks technically correct.\u003C\u002Fp>\u003Cp>This is the line in the article that I kept coming back to. The author says when a producer rejects an AI-generated chord change, that rejection is the actual work. I love that because it kills a lazy assumption: that the value in AI products comes from volume. It doesn’t. Value comes from helping a person exercise judgment faster.\u003C\u002Fp>\u003Cp>That matters in software too. I’ve watched engineers use \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffeatures\u002Fcopilot\">GitHub Copilot\u003C\u002Fa> \u003Ca href=\"https:\u002F\u002Fgithub.com\u002F\">GitHub\u003C\u002Fa> to autocomplete chunks of code, then ignore the “build me an app” fantasy prompts because those prompts are too blunt to trust. The real win is not that the model can write code. The win is that the engineer can inspect, edit, and keep control.\u003C\u002Fp>\u003Cp>How to apply it: design your AI UI around decisions. Add explicit accept, reject, and revise states. Track what users change after generation. If they always rewrite the same section, that’s not user failure. That’s product intelligence telling you where the model is weak.\u003C\u002Fp>\u003Cul>\u003Cli>Expose confidence where it matters.\u003C\u002Fli>\u003Cli>Let users annotate the model’s mistakes.\u003C\u002Fli>\u003Cli>Use edits as training signals for your product roadmap.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Bidirectional workflows beat one-shot prompts\u003C\u002Fh2>\u003Cblockquote>“The work is bidirectional. The AI is the junior collaborator, and the producer is still the artist.”\u003C\u002Fblockquote>\u003Cp>What this actually means is that the best AI tools don’t behave like vending machines. They behave like a junior teammate who can draft quickly, take feedback, and try again without sulking.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780667311681-xerp.png\" alt=\"Music producers show how to build AI products\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Pateriya describes power users doing the unglamorous stuff: prompt a melody, regenerate it twice, drag it into the DAW, replace the bassline by hand, ask for a different drum feel, accept half, reject the rest. That’s the actual workflow. It’s not one prompt and victory. It’s back-and-forth.\u003C\u002Fp>\u003Cp>I’ve seen the same dynamic in writing tools. If a system only works when I ask for a perfect answer, I stop trusting it fast. But if it helps me move between rough idea, structured draft, and final polish, I keep using it. The friction disappears because the tool fits the way I already think.\u003C\u002Fp>\u003Cp>How to apply it: build multi-step generation into the product. Don’t force users to restart from scratch for every change. Preserve state. Keep the user’s prior edits visible. Make regeneration scoped, not global. The more your interface supports iteration, the less your users feel like they’re wrestling a slot machine.\u003C\u002Fp>\u003Ch2>Identity is why people resist full automation\u003C\u002Fh2>\u003Cblockquote>“The people who chose music as a career did not do it so they could become editors of machine output.”\u003C\u002Fblockquote>\u003Cp>What this actually means is that people do not adopt creative tools just to become janitors for a model. They want speed, but they also want authorship.\u003C\u002Fp>\u003Cp>This is the part a lot of AI product teams keep missing. They assume resistance to automation is mostly about skill gaps or fear. Sometimes it is. But often it’s identity. People care about how the work feels and what it says about them. If your product turns them into a passive reviewer, they’ll use it reluctantly or not at all.\u003C\u002Fp>\u003Cp>I’ve felt this while testing AI tools that promise to “do the work for you.” The phrase sounds efficient in a pitch deck and insulting in practice. Most serious users want augmentation, not replacement. They want to feel sharper, not erased.\u003C\u002Fp>\u003Cp>How to apply it: ask whether your product preserves the user’s signature. Does it let them shape style, tone, and constraints? Can they see their own decisions reflected in the result? If not, you’re building a machine that outputs content, not a tool people want to live in.\u003C\u002Fp>\u003Ch2>Build for rescue, not just creation\u003C\u002Fh2>\u003Cblockquote>“They are using stem separation to rescue an old recording, AI-assisted mixing to balance a chaotic session or MIDI generation to break a writer’s block at 3 o’clock in the morning.”\u003C\u002Fblockquote>\u003Cp>What this actually means is that the highest-value AI use cases are often boring, specific, and deeply practical. They fix broken work. They do not just create new work from nothing.\u003C\u002Fp>\u003Cp>This is where the article gets especially useful for builders. The examples are not “generate a masterpiece.” They are “save the session,” “clean up the mix,” and “get unstuck at 3 a.m.” That’s the kind of use case people actually pay for because it saves time, reduces pain, and keeps momentum alive.\u003C\u002Fp>\u003Cp>I like this framing because it gives product teams a better filter. If your feature only looks good in a cold start demo, it’s probably weak. If it helps recover a messy project, preserve prior work, or repair something halfway done, it has a better shot at becoming habit.\u003C\u002Fp>\u003Cp>How to apply it: map your product to rescue moments. Where do users lose time? Where do they get stuck? Where do they need cleanup, not inspiration? Build AI features for those moments first, then widen out to generation once trust is earned.\u003C\u002Fp>\u003Ch2>The 60\u002F30\u002F5 rule is a product strategy, not a music metaphor\u003C\u002Fh2>\u003Cblockquote>“The bulk of the value lives in ideation. A meaningful middle lives in collaboration. A thin sliver lives in full delegation.”\u003C\u002Fblockquote>\u003Cp>What this actually means is that AI product design should be organized around the percentage of work people actually want to hand over. Most of it should support thinking. Some of it should support co-creation. Very little should insist on total automation.\u003C\u002Fp>\u003Cp>That is the cleanest takeaway from the piece. The 60\u002F30\u002F5 rule is not really about music. It’s about how professionals behave when tools get smarter. They don’t rush to surrender control. They use AI where it compresses the boring parts and leaves their judgment intact.\u003C\u002Fp>\u003Cp>I think this is why so many AI products feel off. Teams start with capability and work backward. That’s the wrong direction. Start with the user’s willingness to delegate, then build the product around the level of trust they actually have.\u003C\u002Fp>\u003Cp>How to apply it: classify every feature into one of three buckets. Idea generation, collaborative refinement, or full delegation. Then check your roadmap. If everything lives in bucket three, you’re probably building for demos, not daily use.\u003C\u002Fp>\u003Ch2>The template you can copy\u003C\u002Fh2>\u003Cpre>\u003Ccode># Editing-first AI product template\n\n## Product principle\nAI should help the user think, draft, and refine before it tries to finish anything.\n\n## Design rules\n1. Default to drafts, not finals.\n2. Make accept \u002F reject \u002F revise actions explicit.\n3. Keep generation scoped to a small part of the work.\n4. Preserve user edits and show what changed.\n5. Support fast regeneration without losing context.\n6. Treat user corrections as product feedback.\n\n## Workflow structure\n- Step 1: User defines intent, constraints, and style.\n- Step 2: AI generates 3-5 options or a rough draft.\n- Step 3: User edits, rejects, or combines pieces.\n- Step 4: AI revises only the selected sections.\n- Step 5: User finalizes and exports.\n\n## Product questions to ask\n- Where do users get stuck?\n- What do they usually fix after generation?\n- What should the model never decide alone?\n- Which parts need the user's taste most?\n- What is the smallest useful AI action?\n\n## Feature checklist\n- [ ] Inline editing\n- [ ] Partial acceptance\n- [ ] Regenerate selected section only\n- [ ] Version history\n- [ ] Visible diffs\n- [ ] User feedback capture\n- [ ] Human-owned final export\n\n## Copy-ready positioning line\nWe help users move from rough idea to final work without giving up control.\n\n## Prompt pattern\nYou are a junior collaborator. Generate a draft, offer alternatives, and wait for revision. Do not finalize anything without user edits.\n\n## Evaluation metric\nMeasure how often users edit, keep, or reject AI output before shipping.\n\n## Anti-patterns\n- Full auto-complete with no review\n- One-shot generation as the main workflow\n- Hidden model changes with no explanation\n- Outputs that cannot be edited in place\n- Interfaces that treat the user like a spectator\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>The source article is \u003Ca href=\"https:\u002F\u002Fwww.forbes.com\u002Fcouncils\u002Fforbestechcouncil\u002F2026\u002F06\u002F01\u002Fthe-60305-rule-what-music-producers-can-teach-about-building-ai-products\u002F\">here on Forbes\u003C\u002Fa>. My breakdown is original commentary built from that post, plus my own product experience; the 60\u002F30\u002F5 framing and the quoted lines belong to Sourabh Pateriya.\u003C\u002Fp>","A practical breakdown of the 60\u002F30\u002F5 rule for AI product design, plus a copy-ready template for editing-first workflows.","www.forbes.com","https:\u002F\u002Fwww.forbes.com\u002Fcouncils\u002Fforbestechcouncil\u002F2026\u002F06\u002F01\u002Fthe-60305-rule-what-music-producers-can-teach-about-building-ai-products\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780667344107-c2ay.png","tools","en","6ef273f0-b81e-440b-a793-4a94af6c6682",[17,18,19,20,21],"ai product design","music production","human-in-the-loop","generative ai","workflow design",[23,24,25],"Most users want AI to draft and suggest, not fully replace their workflow.","Good AI products center judgment, editing, and partial acceptance.","The strongest use cases often rescue broken work instead of creating from zero.",0,"2026-06-05T13:48:02.935672+00:00","2026-06-05T13:48:02.919+00:00","a7343b93-37cc-4634-a2bc-707f6275bdb6",{"tags":31,"relatedLang":44,"relatedPosts":48},[32,35,38,40,42],{"name":33,"slug":34},"AI product design","ai-product-design",{"name":36,"slug":37},"generative AI","generative-ai",{"name":18,"slug":39},"music-production",{"name":41,"slug":19},"Human-in-the-loop",{"name":21,"slug":43},"workflow-design",{"id":15,"slug":45,"title":46,"language":47},"60305-rule-editing-first-ai-products-zh","60\u002F30\u002F5 讓 AI 產品先會改稿","zh",[49,55,61,67,73,79],{"id":50,"slug":51,"title":52,"cover_image":53,"image_url":53,"created_at":54,"category":13},"21c79843-1132-4b8b-9891-481a58d4be91","ai-coding-agents-2026-practical-roadmap-en","AI Coding Agents in 2026: What Changes Next","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780677198321-hoz2.png","2026-06-05T16:32:41.831612+00:00",{"id":56,"slug":57,"title":58,"cover_image":59,"image_url":59,"created_at":60,"category":13},"c52930db-c317-440e-8375-4c436e38b848","how-to-use-petros-ch32h417m-alef-board-en","How to use the Petros CH32H417M Alef board","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1780663685207-99w8.png","2026-06-05T12:47:33.023332+00:00",{"id":62,"slug":63,"title":64,"cover_image":65,"image_url":65,"created_at":66,"category":13},"48cf00b3-5b41-4de3-868d-94c540e9295c","midjourney-web-app-ai-art-en","Midjourney’s web app changes how people use AI 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