AI UGC vs Human UGC Decision Sheet
A decision sheet for AI UGC versus hired creators: cost and cycle-time bands, when humans still win, hybrid split by job, rights/disclosure, and the kill criteria that stop you using the wrong tool.
AI UGC and hired creators are different jobs, not a morality contest. This decision sheet is the split: cost and cycle-time bands, when a human still wins, how to hybrid a month, and the rights/disclosure rows so you do not buy the wrong tool for a 3-second hook test.
Do not compare a subscription to a hero film
On a per-finished-ad basis, generated UGC is usually cheaper by an order of magnitude than a commissioned creator video, once you count product, shipping, briefing time and a revision week. That sentence is true and still useless if the job is ‘unscrew this lid on camera’ or ‘this dermatologist is the offer’. Software wins when the job is volume: 3 hooks × 2 faces, then another round 48–72 hours later because frequency on cold is climbing through 2.5–3.5. Humans win when the job is proof you cannot synthesise: hands, fit, texture, a real expert, a real body. If your annual plan is four polished films and no testing, a creator roster is rational and a generator will feel like a toy. Most paid-social accounts are the opposite: they need a new first three seconds before they need a manifesto.
Cycle time is the real currency of a test account
A losing hook you can kill the same afternoon costs you the media, not a week of calendar. A human brief is usually days to get a yes, a week-plus to a cut, then a revision cycle — during which the live ad is already fatiguing. That gap is the whole testing argument. It is also why ‘AI underperformed our creator’ is often a sample-size story: the creator had one film and a prayer; the generator had twelve near-duplicates in one ad set and no isolation. Volume without a readout is noise. The sheet forces a job label on each cell (hook test, proof, expert, demo) and a method. Mixing those jobs in one invoice is how agencies over-pay for talking heads and under-pay for the only demo that would have converted.
Hybrid is a split of labour, not ‘a bit of both’
The hybrid that works: humans shoot the unfakeable proof (hands, product-in-use, expert, real before/after if allowed), then that B-roll is the body while AI faces and voices hunt hooks at test volume. The hybrid that fails: an AI talking head with no product on screen, plus a creator video you cannot iterate, plus a founder who will not pick a winner. Rights differ too. A creator contract must say paid-usage, platforms, term, and whether paid amplification of the organic post is allowed. A generated face needs a commercial licence from the tool and a disclosure path if a reasonable viewer would think a real customer is speaking. Those are different folders. Do not paste a creator release onto a synthetic file and call it done.
How to use the tables on this month’s plan
Label every planned cell with a job from the first table. If the job is hook volume, it is AI unless a human is already on retainer for that SKU. If the job is unfakeable proof, it is human, and AI is banned from pretending. Then read cost, cycle time, and rights for that row so the invoice matches the job. When the volume row says 8–20 variants this week, a product-URL pass in Klip Kanvas is the AI side of the hybrid; still book the one human shoot for the hands the generator cannot do. The last table is the kill: stop using AI for a demo it cannot perform, and stop using humans as a hook factory you cannot afford.
6 decision tables inside: job → method grid, cost and cycle-time bands (observed ranges), performance/sample rules, hybrid monthly split by spend, rights and disclosure artefacts, and the stop-using-this-tool kill list.
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