How much does AI video cost per finished minute?
AI video, cost per finished minute · $/fin-min · measured 2025.4–2026.6 · fitted live from 3 observations by the Many Minded cost-curve engine.
The last measured value is $2.40K/min (2026.6, vendor API price sheets (Google/OpenAI/Runway/Luma) + practitioner cost reports). Over the fitted window the series falls 34.7% a year (80% interval −70.9% to +46.7%) — and no halving time is honest here: that interval spans zero, so this window does not resolve the direction. Carried forward on that fit, 2031: $369/min (80%: $6.65/min–$20.5K/min).
Estimate-grade series. This one is constructed, not read off an authoritative sheet — capability-adjusted $ per finished usable MINUTE of 1080p narrative-grade AI video — flagship per-second API price × the measured ~100:1 takes ratio (raw seconds generated per finished second), NOT the raw sticker price; dated within-year points, not annual medians. Treat the level as an estimate and the slope as the claim.
Thin series — n=3. Under six observations the interval is carried by the Farmer–Lafond drift-volatility prior rather than by this series' own measured volatility. The band is wide because the evidence is thin, and that is the correct response.
the curve
what this series measures
capability-adjusted $ per finished usable MINUTE of 1080p narrative-grade AI video — flagship per-second API price × the measured ~100:1 takes ratio (raw seconds generated per finished second), NOT the raw sticker price; dated within-year points, not annual medians.
the gate — what this threshold unlocks
Central fit: ~2027. 80% window: 2028–2032 · est-grade · n=3. That is 0.3 halvings away at today's value. (The window is computed on whole-year horizons; for a series with mid-year points it can round past the central fit — read it as "within the next year or two, probably.")
what the fit says
| quantity | value | how it is computed |
|---|---|---|
| fitted rate | −34.7%/yr (80%: −70.9% to +46.7%) | Farmer–Lafond drift over the trailing window · n=3 points, 1.2 years |
| tempo | unresolved | the 80% interval on the rate spans zero at this window |
Not shown for this curve, because the machinery returns nothing: Wright's law (no cumulative-deployment series for this technology); the accelerating/decelerating check (needs ≥8 observations, this has 3); a regime break (needs ≥12 observations).
the projection, with its interval
Log cost as a random walk with drift: the forecast variance grows with horizon (τ + τ²/m), which is why these bands widen instead of staying parallel. The middle column is the least useful number on this page; the interval is the claim.
| year | central fit | 80% interval | |
|---|---|---|---|
| 2027 | $2.02K/min | $1.06K/min to $3.87K/min | near horizon |
| 2028 | $1.32K/min | $283/min to $6.19K/min | near horizon |
| 2029 | $864/min | $80/min to $9.32K/min | near horizon |
| 2030 | $564/min | $23/min to $13.8K/min | the band is already wide here |
| 2031 | $369/min | $6.65/min to $20.5K/min | the band is already wide here |
| 2032 | $241/min | $1.93/min to $30.1K/min | the band is already wide here |
| 2033 | below $200/min | — | past the point where a number would be theater |
The table stops at $200/min — a decade below the gate. Past that, quoting a number would be theater rather than forecast.
questions this page answers
How much does AI video cost per finished minute?
$2.40K/min as of 2026.6, the latest measured value in the series (vendor API price sheets (Google/OpenAI/Runway/Luma) + practitioner cost reports). The fitted trend has it falling 34.7% a year, with an 80% interval of −70.9% to +46.7%. This is an estimate-grade series: the level is constructed, so read the slope as the claim.
How fast is the cost of a finished minute of AI video falling?
The fitted rate is −34.7% a year, but the 80% interval (−70.9% to +46.7%) spans zero — at this window the direction itself is not resolved, so no doubling or halving time is honest.
What will the cost of a finished minute of AI video be in 2031?
The central fit says $369/min, inside an 80% interval of $6.65/min to $20.5K/min. The interval is the forecast; the middle number is only its midpoint. Bands widen with horizon because shocks accumulate — a constant-width band would be overconfident.
When will the cost of a finished minute of AI video reach ≤ $2,000/fin-min (a $1K 30-second spot)?
The central fit says ~2027, with an 80% window of 2028–2032, on an estimate-grade series from 3 observations. Crossing it is what the engine calls "a broadcast-grade ad spot for ≈ $1K", firing: one-person studios — the film cost structure inverts. Treat the window, not the year, as the claim.
Where does this data come from?
vendor API price sheets (Google/OpenAI/Runway/Luma) + practitioner cost reports. 3 observations spanning 2025.4–2026.6. The series is estimate-grade — constructed from vendor sheets and practitioner reports, and labeled as such wherever it appears. Both the observation ledger and the fitting code are public, and the engine publishes its own calibration score and its misses.
the raw numbers
Every observation behind the fit, unrounded by us and unsmoothed. This table is here on purpose: graphs make people underestimate exponential change, and the raw series beside the curve is the one correction shown to work.
| year | $/fin-min | change |
|---|---|---|
| 2025.4 | $4.00K/min | — |
| 2025.7 | $2.40K/min | −81.8% |
| 2026.6 | $2.40K/min | +0.0% |
where this comes from
- Source: vendor API price sheets (Google/OpenAI/Runway/Luma) + practitioner cost reports.
- Basis: estimate-grade. The series is constructed from vendor sheets and practitioner reports, labeled as such everywhere it appears, and never presented as a measured benchmark.
- Observations: n=3, spanning 2025.4–2026.6. Ledger last updated 2026-09-07.
- Fit: the Farmer–Lafond stochastic model — log cost as a random walk with drift over a 10-year trailing window, Student-t tails, and (for series under 15 points) a volatility floor at their drift-volatility prior. The code is readable source; the observation ledger is public JSON.
- Reuse: this compiled series and the numbers on this page are published under CC BY 4.0 — take them, cite the page. The underlying source keeps its own terms.
- Accuracy: the engine grades its own intervals in public — every feasible hindcast re-fitted on data as it stood, scored for whether the actual landed inside the 80% band. The calibration score is on the engine page, misses included.
the rest of the graph
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