AI didn’t just change how content gets made. It changed what matters when building a sustainable content strategy.
For years, creative teams were constrained by production — time, budget, resources. AI removed those constraints almost overnight. Now, content can be generated faster than it can be briefed. Entire campaigns spin up in hours. The bottleneck, at least on the surface, is gone.
But something else replaced it: uncertainty.
The real question teams are sitting with is: “Can we actually use this content everywhere, safely, and over time?”
That uncertainty is now the constraint. And it’s reshaping how modern creative teams operate.
The Sora moment wasn’t a failure — it was a preview
When OpenAI pulled Sora after a surge of attention and adoption, it caught a lot of teams off guard. Viral traction, rapid output, a seemingly endless stream of AI-generated video. But the decision wasn’t about technology falling short. It was about everything around it not being ready — and in many cases, not being controllable.
Concerns around deepfakes, misuse, and content authenticity weren’t edge cases. They were inevitable outcomes of a system optimized for volume without accountability. As outputs scaled, so did the noise: content that was easy to generate, impossible to verify, and increasingly unusable in any environment where standards actually matter.
Questions around ownership, licensing, and long-term viability remained unresolved. Not unclear, but unresolved. That distinction matters. Because it means the risk isn’t temporary. It’s structural.
The signal: Creative capability is outpacing creative responsibility. In an AI-first world, the risk isn’t just what you generate, it’s what you can’t defend.
Most AI-generated content comes with unclear, or entirely absent, indemnification. That’s not a small detail. It signals that even the platforms generating the content aren’t fully standing behind it. Ownership is ambiguous. Liability is being pushed downstream to the teams using it.
So the question isn’t just “can we use this?” It’s “if this is challenged, are we protected?” And for many teams right now, the answer is no.
The AI music problem is bigger than most teams realize
If AI video raised questions about rights and attribution, AI-generated music brought those questions to a head.
In June 2024, Universal Music Group, Sony Music, and Warner Music Group jointly filed lawsuits against AI music platforms Suno and Udio, alleging large-scale copyright infringement through unauthorized use of copyrighted recordings as training data. The RIAA framed it as one of the most significant copyright cases the music industry has seen in a generation.
Source: RIAA v. Suno, Inc. and RIAA v. Uncharted Labs, Inc. (Udio), U.S. District Court, June 2024
The core issue isn’t only whether the AI-generated output sounds similar to a protected recording — though that does matter, especially in music, since copyright infringement can take place whether you intentionally infringe on someone’s copyrighted work or not. It also concerns how these models were trained, often using copyrighted material without license, consent or compensation. That training-data liability follows the output and it follows the teams deploying it commercially.
The U.S. Copyright Office has been equally direct: AI-generated content, absent meaningful human authorship, is not eligible for copyright protection. That’s not just a theoretical gap, it means the entity that trained the model may not be able to fully stand behind the license they’re offering you.
Source: U.S. Copyright Office, “Copyright and Artificial Intelligence” Policy Study, Parts 1 & 2, 2023–2024
Stock audio checklist:
When evaluating any stock media library for AI-generated audio content, ask:
- Does the library clearly separate AI-generated tracks from human-licensed ones?
- Without clear labeling it’s hard to know which tracks carry which protections. If a platform can’t tell you without a doubt, that’s a red flag.
- Is the training data for AI-generated music publicly disclosed?
- If a platform can’t tell you what their AI models were trained on, you can’t assess whether that training involved unlicensed recordings.
- If a track is challenged, who bears the liability?
- Many platforms offering AI-generated music pass the legal risk downstream to the team using it. Look for specific indemnification language, not just a license grant.
- Are usage rights perpetual, or tied to an active subscription?
- If your rights expire when your subscription does, any content already deployed becomes a liability the moment you are offboard. Look for a provider that provides perpetual licenses.
- Is it safe for broadcast?
- Music that makes it to broadcast carries a huge risk to your organization. If you intend to use stock audio for broadcast (today or in the future), you must be certain that it is approved for that type of distribution.
What actually changed — and why most teams haven’t adjusted yet
AI didn’t remove the complexity of scaling content. It relocated it.
Production is faster, cheaper, and more accessible than it’s ever been. But speed in production doesn’t automatically translate to speed in execution. Once content is created, it still has to move through the rest of the system: legal review, brand standards, channel requirements, and long-term usage considerations.
This is where AI-driven content strategies start to break down. Not because teams can’t produce content — but because they can’t consistently move that content from creation to deployment without friction.
Where licensing becomes the limiting factor
AI content moves quickly through production, only to slow down — or stop entirely — when it needs to be approved, reused, or extended. Rights aren’t always clear. Usage isn’t always consistent. What was fast to create becomes slow to operationalize.
AI content can be created quickly, rapidly accelerating production timelines. But as Legal teams continue to be uncertain on the terms and provenance of AI-created work, and as production teams struggle to clear that work for broad use cases, a new bottleneck arises. Now we no longer have to wait to create the asset – but can we actually use it?
That creates a recognizable pattern:
- Content gets generated but not fully utilized because teams are forced to weigh where it’s safe (or too risky) to actually deploy it
- Legal becomes a bottleneck — not by choice, but by necessity
- More content to manage means more approvals to navigate and more risk to evaluate
Without a clear licensing foundation, scale introduces friction instead of efficiency.
EU context: The EU AI Act (effective August 2024) now requires providers of general-purpose AI models to disclose training data used in content generation. This is creating new pressure on AI media platforms to clarify provenance — and new exposure for teams operating without that clarity.
Source: EU AI Act, Regulation (EU) 2024/1689, Title IX obligations on GPAI model providers
Framework 1: What breaks vs. what scales in an AI-first workflow
What breaks at scale
- Production without licensing clarity: content gets created quickly but slows down in approval or can’t be reused
- Assets tied to platforms or evolving terms: usage depends on where and how the content was created, and those terms can change
- Content built for single use: teams recreate instead of reuse, increasing cost and cycle time
- Unverifiable content sources: legal and compliance can’t confidently approve or defend usage
- Late-stage rights validation: risk is discovered after production, not before
What actually scales
- Clear, upfront licensing frameworks: content moves from creation to deployment without friction
- Assets designed for reuse across channels and campaigns: production investment compounds over time
- Source transparency and traceability: teams can confidently approve and defend usage
- Rights that are consistent and durable: no need to re-clear content as workflows or tools change
- Early alignment between creative, legal, and procurement: fewer downstream blockers, faster time to market
Takeaway: Scaling production only creates value if what you produce can actually be used — again and again.
Framework 2: The future-state creative organization
Before: Production-centric teams
- Measured on output volume
- Licensing handled at the end of the process
- Legal involved as a checkpoint
- Content treated as one-time deliverables
Now: Hybrid creative teams
- Producing more content with AI support
- Experiencing more friction in approvals and reuse
- Starting to question ownership and rights — especially for music and video
Next: System-oriented teams
- Licensing is built into workflows from the start
- Content is treated as a reusable asset system
- AI is used intentionally, not indiscriminately
- Creative, legal, and procurement operate in alignment
Takeaway: The most advanced teams aren’t just faster. They’re more structured, more intentional, and more defensible.
Framework 3: Generation layer (speed) vs. foundation layer (scale)
AI changed how content can be created — but not what’s required to use it at scale.
Generation layer (AI tools) = speed. Used for ideation, iteration, and rapid workflows. This is where speed comes from.
Foundation layer (licensed content) = scale. Used for consistent brand expression, cross-channel deployment, long-term usage, and risk management. This is where scale comes from.
The two layers aren’t in competition. AI tools serve the generation layer well. But without a solid foundation layer underneath (i.e. content with documented provenance, clear licensing, and meaningful indemnification) the speed has nowhere stable to land.
Takeaway: Creativity and workflows drive content production. A strong legal foundation determines whether that content can scale.
Framework 4: The five question “can we actually use this?” filter
Before content moves from production into execution, high-performing teams apply a simple filter of five questions. Not to slow things down — but to prevent friction later.
- Source: Do we know where this content originated? Can we verify it if needed?
- Rights: Are usage rights clear, documented, and consistent?
- Continuity: Will this content remain usable if tools or vendors change?
- Risk: Are we protected if something is challenged? Look for meaningful IP indemnification — the number matters.
- Scalability: Can this be reused across campaigns, teams, and channels?
Takeaway: If any of these answers are unclear — that content isn’t ready to scale. This applies to video. It applies to imagery. And it especially applies to music, where the legal landscape is actively shifting.
Where Storyblocks fits into the shift
As AI continues to evolve, one thing becomes more valuable: certainty. At Storyblocks, we’ve built our model around that principle.
A transparent content foundation
All content in our library is created by and licensed from real creators — not scraped, inferred, or trained on undisclosed catalogs. Our library is entirely human-created, which means there’s no training-data liability attached to it. When you use Storyblocks music or video, you’re not inheriting the legal uncertainty that follows AI-generated content from platforms that can’t account for what their models were trained on.
The same principle applies to our AI Toolkit, and it’s where the AI distinction really matters – which we provide complete transparency on.
When you use the Storyblocks AI Toolkit, you’re starting with a clip already in our library that’s been documented, contributor-sourced, and cleared for AI use. You’re modifying a known, covered asset – meaning the rights chain is intact from the start.
Indemnification and accountability
With up to $1 million in indemnification for business users, we give teams a layer of protection that AI-generated outputs typically lack. That’s not a small distinction. It’s the difference between a license and a liability transfer.
Clear, durable licensing
You know exactly how content can be used under our Business license — without shifting terms or ambiguity. That clarity doesn’t expire when a tool changes, an AI policy gets updated, or a platform decides to revise what’s covered.
Usage rights that extend beyond subscriptions
Content you license remains usable even as your stack evolves. No need to re-clear assets when platforms change or vendors shift their terms.
Built for hybrid workflows
Whether your team is using AI tools for ideation, licensed stock for deployment, or original production for hero content — Storyblocks acts as the stable layer underneath. In an AI-first world, the foundation matters more than the tool.
What comes next for AI and content strategy
AI will continue to improve. But the advantage won’t come from generating more, it will come from making that content usable at scale.
The legal landscape around AI-generated content, and AI music in particular, is still being written. The RIAA lawsuits, the Copyright Office’s ongoing AI study, the EU AI Act — these are signals, not edge cases. They’re shaping what enterprise content governance looks like for the next decade.
The teams that move ahead will be the ones building AI workflows that don’t break under legal pressure, creating content that holds up over time, and aligning around clarity instead of assumptions.
Content is no longer scarce. What’s scarce is content you can actually use.
In that world, the teams that win won’t be the ones who can create anything. They’ll be the ones who can use everything — confidently, defensibly, and at scale.
