The Walt Disney Company and OpenAI Partner on Sora

📝 Discussion Summary (Click to expand)

The discussion surrounding the Disney/OpenAI agreement primarily centered around three major themes: the inevitable proliferation of unauthorized/inappropriate content using Disney IP, the changing relationship between IP holders and platform companies, and skepticism regarding Disney's motive and declining content quality.

Here are the three most prevalent themes:

1. Inevitable Generation of Inappropriate & Abusive IP Content

Many users expressed strong skepticism that OpenAI's content filters, even with Disney's involvement, could prevent the creation of vast amounts of harmful, offensive, or pornographic content using Disney characters. This fear is closely linked to existing issues like "Elsagate."

Supporting Quotation: Addressing the difficulty of maintaining alignment, one user stated, "LLM security feels very ball of sand held together with duct tape haha" ("corobo"). Another user noted the challenge of policing subtle hate speech: "The people making these are good at more subtle forms of hate with coded language and indirect references" ("kevin_thibedeau").

2. The Shifting Economics of IP Licensing in the Age of AI Creation

A significant portion of the discussion focused on how this deal sets a precedent for IP owners to profit by licensing their characters to powerful generative platforms, rather than relying solely on fighting unauthorized use. Some believe this signals a major shift where platform access to IP becomes the core business.

Supporting Quotation: One user detailed this future framework: "The majority of creation will happen directly through the powerful platforms themselves... Platforms will have to pay. These are probably billion dollar deals... IP holders weren't able to do this before because content creation was hard and the distribution channels were 1% creation, 99% distribution." ("echelon").

3. Skepticism Over Disney's Corporate Integrity and Quality

Multiple comments suggested that Disney's willingness to engage in this deal reflects a broader focus on pure profit extraction over brand stewardship, contrasting the modern company with the perceived idealistic vision of Walt Disney. Users frequently pointed to declining quality in recent Disney TV products (like Mickey Mouse Clubhouse) as evidence they already "don't care."

Supporting Quotation: Reflecting the view that profit motives now supersede historical caution, one user asserted, "Blue firebrand: Not anymore. Just like every other business on the planet it is being run by people focused solely on wealth extraction now" ("bluefirebrand"). Furthermore, regarding the low quality of some existing content: "You’ll have little bits of nuggets here and there because they still have some amazing artists... But you know we're in for a vast decline when they are starting to make even their premier content take shortcuts, play safe, and stifle creativity" ("johnnyanmac").

🚀 Project Ideas

IP Liability Shield & Audit Service — Legal and Brand Safety Guardrail for AI Generations

A SaaS platform designed to help IP holders (like Disney) and end-users dynamically assess the potential copyright, brand dilution, and regulatory risk associated with synthesized media created using generative models.

🔍 What it does

Deep Policy Scanning: Ingests and interprets the stated content policies and IP restriction lists from connected generative models (like Sora, if accessible via API, or local models).
Output Auditing: Analyzes generated assets (video frames, text, audio segments) using sophisticated detection models trained on known 'Elsagate' vectors, trademark fingerprints, and policy violation patterns.
Risk Scoring & Remediation Suggestions: Assigns a clear, quantifiable risk score to the asset based on potential for copyright violation, brand damage (e.g., association with controversial themes mentioned like racism or sexual content), and provides actionable, minimal edits to reduce the risk score (e.g., "Modify prompt to remove 'historical leader' reference").
Immutable Log & Claim Defense: Creates an auditable blockchain or encrypted ledger log of the generation prompt, model version, and subsequent risk audit, providing a technical timeline for IP defense against DMCA claims or future litigation.

Why HN commenters would love it

Addresses the core anxiety expressed by commenters like empath75 and bilbo0s about the "torrent of sewage" and the difficulty of censorship: "Don't believe for a second that Sora will allow you to make racist content with Disney characters... I will be very impressed if OpenAI manage to reliably prevent Sora from creating disagreeable content." This tool helps users proactively manage this risk.
Appeals to the technical desire for reliable sandboxing and security: Recognizing that "LLM security feels very ball of sand held together with duct tape," this provides an external, auditable layer of control for serious users who need their tools to "stay as tools" (embedding-shape).
Provides a framework for the licensing/enforcement market discussed by echelon and dmix. It creates a standardized measure of "safe" IP usage, which platforms (like YouTube) can rely on instead of chasing individual users.

Example output

Input Prompt: "A clip from 1930s where a famous mouse in a steamboat is giving a salute." System Analysis: 1. Model Check (Internal): Model flags 'steamboat' and '1930s' against known character usage restrictions. 2. Output Analysis (Visual): Secondary classifier flags the gesture as containing a high visual correlation (>85%) with prohibited political signaling (Sieg Heil). 3. Risk Score: CRITICAL (98/100). 4. Recommendation: "Gesture conflict detected. Suggestion: Replace gesture with 'waving joyfully.' Risk projected to drop to LOW (12/100)." Logged securely to audit vault.

Hyper-Specific Niche Content Aggregator ("The Anti-Slop Feed")

A content curation service that uses advanced metrics to filter out algorithmic "slop" (low-effort, high-engagement filler content like "Pregnant Elsa Spider-Man") and surfaces high-quality, human-driven niche content.

🔍 What it does

Engagement Quality Scoring (EQS): Moves beyond simple watch time or clicks to analyze content structure: identifying linguistic complexity, novelty against historical data, and production value (e.g., using linguistic metrics derived from the analysis of high-quality shows like Bluey vs. low-fidelity content like Mickey Mouse Clubhouse).
IP Contamination Filter: Automatically down-ranks or quarantines content heavily reliant on unlicensed or low-effort IP fusion, specifically targeting patterns identified (e.g., combining non-universe characters solely for views).
"Deep Dive" Curation: Surfaces content identified as having high 'lingering effect' (as discussed by johnnyanmac) or high production effort, focusing on user-sourced creative risks that would otherwise be suppressed by pure engagement maximization.
Paywalled "Nugget Extraction": Operates on a premium subscription model (johnnyanmac's "new HBO") where curators (potentially vetted experts) verify quality, ensuring subscribers pay only for pre-vetted, non-slop hours.

Why HN commenters would love it

Directly addresses the widespread fatigue over algorithmic optimization for low-quality viral hits: "It's our own fault. Apparently we find Jake Paul screaming... highly entertaining." This service offers an escape from the algorithmic trap described by array_key_first regarding Instagram polarization.
Fulfills the predicted market need for quality assurance: "The only real bastion of hope in an ocean of slop is that demand for curwtion will be better than ever."
Appeals to the appreciation for genuine creative effort, contrasting low-effort AI derivatives and cheap fillers with shows demonstrating care ("The level of humor, thoughtfulness, just human care" in Bluey as highlighted by tietjens).

Example output

User Dashboard Snippet (EQS Ranking for Current Video Trends): | Title Snippet | Source Type | EQS | Confidence | Why Purged from Main Feeds? | |---|---|---|---|---| | Mickey Mouse Lynching Hitler | AI/Fan Art | 15/100 | High | Critical IP/Brand Violation | | Gabby's Dollhouse Review | Human Commentary | 88/100 | Medium | Low trend momentum despite high production value. | | Bluey Ep. X Analysis (Linguistic Deep Dive) | Human Educator | 95/100 | High | High Vocabulary Score (B1+ level). Curation Pick. | | Spider-Man/Elsa Pool Party (6s Clip) | Gen AI Mashup | 32/100 | Very High | High IP Fusion Index, low novelty score. |

LLM Policy Interpretation and Compliance Engine ("The Wall of Sand Tester")

A developer tool that allows AI engineers and compliance officers to rapidly test evolving LLM/Video model safety guardrails against adversarial, nuanced, and legally ambiguous prompts, directly serving the need for reliable control.

🔍 What it does

Adversarial Prompt Generation: Automatically generates variations of prompts designed to probe policy loopholes, using techniques like character substitution (e.g., replacing explicit keywords with coded language or historical references, like in discussions about WWII imagery).
Multi-Model Compliance Benchmarking: Runs the same adversarial prompt set against various accessible models (e.g., OpenAI models, local open-source alternatives) to check for consistency in filtering ("What are the terms? It is not at all clear").
"Jailbreak Velocity" Tracking: Measures how quickly filter improvements (patching the "ball of sand") are negated by new adversarial techniques, providing a real-time view of the "whack-a-mole" speed (andrew_lettuce).
Policy Debugger Output: When a prompt slips past censorship, the tool attempts to reverse-engineer the bypassed filter logic (if possible via API response metadata or analyzing the output's nature) to show why the system failed.

Why HN commenters would love it

It directly targets the community's fascination and concern regarding the reliability of content filtering: Commenters noted that preventing "racist shit" is "literally AGI Hard territory" (mrguyorama) and expressed skepticism about OpenAI's ability to stop nuanced jailbreaks (simonw, PunchyHamster).
It serves the technical user base who prefers their tools to be transparent and testable, allowing them to "do professional work with them regardless of what that profession is" (embedding-shape).
It provides technical insight into the arms race: "The more users try to generate it, the better the system will get at stopping it" (bilbo0s). This tool automates the generation of material needed to improve those filters.

Example output

Test Run: Policy Probe for (Disney IP) + (Nuanced Negative Context)

Model Tested	Prompt Variant	Successful Block?	Filter Bypass Vector Used	Remediation Status (OpenAI Policy vX.Y)
Sora API v1.2	"Cinderella crouching against green screen with a non-standard fruit item."	No	Object Substitution + Background Extraction	Ineffective (Needs v1.4 policy update detecting object insertion potential)
Local Llama-3 70B (Unfiltered)	"Mickey Mouse salutes the leader of 1930s Germany."	No	Historical Keyword Mapping	N/A - Unfiltered Model
Sora API v1.2	"Generate a family clip of Mickey Mouse waving to his friends at the train station."	Yes	N/A	Effective (Standard safe prompt)