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Innovation thesis

Creative futures

What we believe about the next era of creator tools — and where we are putting the work. Five beliefs. The evidence for each. What we are building because of them.

San Antonio, Texas
The Creative Futures · Phazur Labs
The thesis

Machines will make everything. They will not know what is good.

So the scarce thing stops being production and starts being judgment. Judgment is taught by people. People need footage they own, consent that holds, and a record of what they chose. Nobody is building that record.

We are. We clear creator video for AI, licence it more than once, and keep what it teaches.

What we do

Three verbs. That is the whole company.

01
Clear
We take video from working creators and make it legal to train on.
  • Consent signed for AI training, at upload
  • Likeness signed apart from footage
  • Rights written into the file, auditable
Live · Today
02
Licence
We rent that video to AI companies. More than once.
  • Non-exclusive. Time-boxed. Field-limited.
  • Renewed against next year's footage
  • The archive never leaves our hands
Live · Q2 2027
03
Learn
We record what creators approve and reject, and train a model on it.
  • Every decision captured with its reason
  • Becomes a model that scores creative work
  • Sold by API. Never depletes.
Live · 2028

Everyone else is building the printer. We are building the editor — and we own the paper.

The five beliefs

What we believe.

Belief 01

Generation falls to zero. Judgment does not.

Two curves moved in opposite directions. They already crossed.

20232030 VALUE OF A VERIFIED MINUTE → 430 COST TO GENERATE → 1
Indexed to 100 in 2023 — directional, not forecast.
Belief 02

The machine runs furthest ahead where the money is.

Years of schooling between the question asked and the answer given. Anthropic Economic Index, June 2026.

Image & graphics
+2.6
Games
+1.9
Apps & sites
+1.7
Email
+0.3
Papers
0.0
Blogs
−0.1
Belief 03

The interface sets autonomy. Not the model.

Which means the winnable ground is design, and small teams win design.

Chat surface~62%
Agentic surface~88%
Identical model underneath. 0.26 autonomy points apart.
A blog post takes 13 rounds on one surface. One prompt on the other.
Belief 04

Consent stopped being polite. It became the supply.

You can scrape a video. You cannot scrape a signature.

100+

copyright suits open and unresolved

EU AI Act

now demands proof of where training data came from

$0

paid to individual creators so far — every cheque went to an institution

Belief 05

Models learned from cinema. The market wants the opposite.

Ask a frontier model for something real and you get a costume.

What models trained on

Stock. Cinema. Crews, lighting rigs, colourists.

What advertisers pay for

A phone. A kitchen. Someone who is not acting.

The gap between those two rows is the most expensive unsolved problem in commercial video AI.
What follows

Five beliefs point at one gap.

Nobody owns the layer between what a machine makes and what a human will stand behind.

01Generation → zero
02Gap widest in pictures
03Interface sets autonomy
04Consent is supply
05Vernacular is scarce
The gap

Consented, vernacular, performance-labelled creative work — and a model trained on what humans chose. We are building both. One feeds the other.

Where the money sits

A small market inside a very large one.

$5.7B
2026
$12.4B
2030E
$22.6B
2034E
AI training data licensing, $B.
$310B

Creator economy in 2026. Headed past $500B.

200M+

People making content. Eight million make a living.

15–30%

What brokers take from rights holders today.

70/30

Our split — in the creator's favour, published.

What we are building

The three verbs, built as three layers.

Clear feeds the corpus we licence. Verify feeds Judge. Judge makes Verify better.

Clear

Rights at the source
  • Consent signed at upload, not rebuilt later
  • Likeness is a separate permission
  • Provenance written into the file
  • Opt-out reaches backwards

Verify

The review surface
  • Agent roster with named scope
  • Autonomy slider, set per task
  • Visual diff for motion, colour and cut
  • Approval gates on anything public

Judge

The model
  • Scores brief fit, register and audience
  • Trained on our own approvals and rejections
  • Served by API to platforms and agents
  • Never trained on anything unconsented
How it works

Inside one session.

A brand needs forty cutdowns by Friday. Here is what happens.

01
Human
Brief in.

Creator drops the brief. The roster reads it and proposes a shot plan.

02
Human
Slider set.

Creator picks the autonomy level. Draft only. Draft and cut. Or full pass.

03
System
Agents run.

Agents pull cleared footage, cut, grade, caption. Every asset carries its rights.

04
System
Gate opens.

Forty cuts land side by side. Visual diff shows what changed and why.

05
Human
Judgment.

Creator approves eleven, kills the rest. Each call is captured with its reason.

Step 05 is the product. Twenty-nine rejections and eleven approvals is not a chore. It is a labelled dataset nobody else is collecting.

The data

What we capture that nobody else does.

Raw footage is pixels. This is taste, written down.

Brief

what was asked for

+
Candidates

every variant produced

+
Choice

which one shipped

+
Reason

why the others died

+
Outcome

how it performed

= one preference pair
Raw video
$1–$4 / min

Priced by the pound.

A preference pair
No public price

Almost nobody has collected one at scale.

Why it matters
Taste, labelled

You cannot train a model to judge without watching humans judge first.

The model

We are not training a generator.

Generators cost a billion dollars and we would lose. Judges cost a fraction and nobody has one.

Goes in
  • The brief
  • The candidate cut
  • Audience and channel
  • Brand register
The Judge
  • A reward model, trained on our pairs
  • Fine-tuned on open weights
  • Cheap to run
  • Ours to keep
Comes out
  • Brief fit — does it answer the ask
  • Register — does it sound like the brand
  • Authenticity — real or costume
  • Predicted performance

Fine-tuning open weights is now a managed service priced on compute. A specialised model is a project, not a moonshot.

The loop

The tool is not the product. The tool is the pump.

  1. 01Community

    Creators bring consented work.

  2. 02Verify

    They approve, they reject.

  3. 03Pairs

    Every call is captured.

  4. 04Judge

    The model learns taste — and feeds it back to the community.

Sold outward: the corpus · the benchmark · the API
Glowing vermilion circuit loop on near-black
How value compounds

The same hour, four times over.

Every step is something we do to footage we already hold. Gross, per hour, over a 24-month window.

$0
Raw
Unlicensable. No consent on file.
$180
Cleared
One non-exclusive licence.
$720
Multi-licensed
Four buyers. Same footage.
$2,160
Labelled
Brief, drafts, choice, outcome.

Twelve times the return, without shooting a single new frame.

How it pays

Five lines. Each one buys the next.

#LineTypeLiveWe keep
01Benchmark accessscore your model against oursRecurringQ1 202730%
02Corpus licencenon-exclusive, 24 months, field-limitedDealQ2 202730%
03Managed collectionwe shoot it, cleared, to your specServicesQ2 202740%
04Corpus subscriptionthe flow, committed annuallyRecurringQ4 202730%
05Judgment APIpriced per evaluationRecurring202885%

Lines 01 to 04 pay for the model. Line 05 is the only one that does not spend the asset it sells.

Pricing

The rate card.

Labelled footage prices at four times raw. Same pixels. Different product.

ProductUnitPrice
Benchmark accessPer seat, annual$30,000
Corpus licence — standard footagePer minute, non-exclusive$3.00
Corpus licence — preference chainsPer minute, non-exclusive$12.00
Managed collectionPer finished hour, cleared$9,000
Corpus subscriptionAnnual commit, 200 hrs minimumfrom $200,000
Judgment APIPer 1,000 evaluations$40

Anchored to observed market rates of $1–$4 per minute. Re-verify before any negotiation.

Unit economics

Follow one hour of footage.

Over a 24-month licence window. Every number below is per single hour.

Raw hour
Cost to clear$45
Licensed 4× at $3/min$720
Creator takes 70%$504
We keep 30%$216
Contribution$171
Labelled hour
Cost to clear and label$180
Licensed 3× at $12/min$2,160
Creator takes 70%$1,512
We keep 30%$648
Contribution$468

A labelled hour returns 2.7× a raw hour. Same footage. We just wrote down what the human decided.

The key term

Sell it once, or rent it four times.

Modelled on 1,200 cleared hours. The most consequential line in any contract we sign.

$288K
Exclusive, once
$432K
Non-excl ×2
$648K
Non-excl ×3
$864K
Non-excl ×4
Exclusive modelled at a $4/min premium. Non-exclusive at $3/min.
The build

Recurring takes over in year three.

2027
2028
2029
Gross bookings 0.67 → 2.29 → 6.20 Our net 0.23 → 0.86 → 3.07 To creators 0.44 → 1.43 → 3.14
$M. Assumes ~1,200 cleared hours post-remediation, non-exclusive licensing to multiple buyers, API live H2 2028. Replace with real figures before circulation.
71%

of 2029 net is recurring — subscription and API

$5.0M

cumulative to creators by 2029, published and audited

30/70

our share against theirs, on every licensing dollar

Where we stand

Everyone owns a piece. Nobody can referee.

Judges across models →
Owns consented supply →
Thinking Machines
Higgsfield
Adobe
Data brokers
Creative Futures
How value should flow

The 70% is not a cost. It is the moat.

Brokers in this market take 15 to 30 percent. We take less and we publish it.

Nobody can match it

A fund-backed rival carrying margin expectations cannot publish 70/30. We can. The supply is the strategy.

It keeps the tap open

Take the standard cut and the community works it out in eighteen months. Then there is no second act.

It sells itself

A creator who sees what their work earned tells another creator. That is our sales team.

Creators licence through us. They never sell to us. Likeness is signed apart from footage. Opt-out reaches backwards. The community decides who we are allowed to sell to.

Where we are putting the work

Rights first. Everything waits.

90 days — Clear the ground
  • Audit the archive — hours, codec, consent
  • Run the consent campaign at 70/30
  • Label every brief, draft and outcome
  • Publish the benchmark
Year one — First money
  • Two non-exclusive licences signed
  • Verify shipped and instrumented
  • First managed collection contract
  • Past 70% cleared
Years 2–3 — The judge
  • Judgment model v1 trained
  • API live with platform partners
  • Flow revenue passes archive revenue
  • Benchmark cited by a frontier lab
What would make us wrong

A thesis that cannot fail is not a thesis.

You should hear this from us first.

Our rights are not clean yet

Old releases never mentioned AI. Assume today the archive cannot be licensed.

Remediation is week one. Cleared percentage reported monthly until it passes 70.

Too few buyers

Maybe a dozen firms can write a real video data cheque, and they move slowly.

Collection and the API exist to cut our dependence on lab procurement.

Platforms build their own judge

Adobe and the agent frameworks will ship evaluation natively.

Inside their own walls. A judge that scores rivals has to be neutral. They cannot be.

Kill criterion

If the visual gap narrows toward zero across the next four Economic Index reports, we are wrong and we will say so.

In one line

Machines will make everything. We will own the record of what was worth making.

Clear the footage. Licence it more than once. Keep what it teaches.
Start in San Antonio, because consent is a relationship and relationships are local.

Put the work where we are.

Talk to us →