Sora Died and OpenAI Deleted Everything — The Free AI Video Stack Nobody Can Shut Down

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: Sora Died and OpenAI Deleted Everything — The Free AI Video Stack Nobody Can Shut Down
- URL: https://www.youtube.com/watch?v=6R5Rsv15adI
- Topic: Interfaces + Open Design
- My current learning frame: Install one of the two free cockpits (ComfyUI or Wan2GP via Pinokio), download Wan 2.2's Apache-licensed weights sized to your VRAM, render a 5-second clip, and finish it with the free post kit — SeedVR2 upscaling, RIFE interpolation, and MMAudio soundtrack.
- Why this matters: New playlist item from Hyperautomation Labs; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:22 / Evidence 1: "to the partnership. The Journal reports Disney found out less than an hour before the rest of us. And here is a detail nobody screenshotted. After the export window closes, OpenAI permanently deletes everything users ever made. Leftover..."
- 2:07 / Evidence 2: "Google's ultra tier, the one with serious video volume, is $249.99 a month. A realistic creator stack, Runway Pro plus Kling Pro plus, Google AI Pro, lands around $92 a month. Over $1,100 a year. And notice what..."
- 3:41 / Evidence 3: "One is void across entire continents. And some so-called open source models do not exist as downloads at all. So, for each model in this stack, I will give you three things. What it does, what hardware it..."
- 5:33 / Evidence 4: "tools charge extra for. It generates the video and the synchronized audio together in a single pass. Dialogue, lip sync, sound effects, stereo. It was the first production-ready open model to pull that off. And the full version..."
- 8:02 / Evidence 5: "You do not need nine subscriptions to run these models. You need one of two free cockpits. Comfy UI is the industry standard, node-based with official support for 1, 1U1, and LTX. It looks intimidating for about an..."
- 10:13 / Evidence 6: "The catch, 12-hour session caps and idle disconnects that wipe your loaded model. Lane three is the sleeper. Kaggle. A published 30 GPU hours per week, double what Colab usually gives you. 12-hour sessions and you can attach..."
- 13:24 / Evidence 7: "PDF, the owned stack. Every repo and model link, the license table with the revenue caps and the region bands, the VRAM ladder, every free cloud quota, and the real render times. Comment the word owned, and I..."

Video-aware target:
- Prompt lane: Creative automation
- Mechanism to extract: Extract the creative production loop, especially where the human keeps taste, selection, and final judgment.
- Artifact to produce: A creative production board with source inputs, prompt recipe, selection criteria, edit pass, and taste-review checkpoints.
- Artifact must include: brief; source inputs; generation recipe; selection criteria; edit/review checkpoint

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Extract the creative production loop, especially where the human keeps taste, selection, and final judgment. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A creative production board with source inputs, prompt recipe, selection criteria, edit pass, and taste-review checkpoints.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Brief -> Source material -> Generation -> Selection -> Edit -> Taste review -> Reusable recipe
   - answers to these source questions: What asset is being produced? | What inputs and tools drive it? | Where does human taste intervene?
   - 3 concrete examples that apply the video idea to real agentic work, such as Claude-generated video campaign; image-to-site workflow; voice or video editing loop
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: mistaking novelty for quality; no source/brief discipline; shipping generated media without taste review
   - a checklist for the next real workflow, focused on: brief, inputs, generation, selection, critique
   - one practical exercise with a clear done signal: Build one reusable creative recipe and define what would make the result rejectable.
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "Sora Died and OpenAI Deleted Everything — The Free AI Video Stack Nobody Can Shut Down", not a generic Interfaces + Open Design essay.
- Anchor each creative step to transcript evidence about inputs, model/tool choices, iteration, editing, or critique.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: generic content advice; tool hype; creative output without selection criteria.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.