AI is no longer an “add-on” to media workflows—it is becoming the operating system underneath them. Openness now extends to training data, model weights, documentation, and governance.

Artificial Intelligence and Media — From Tools to Infrastructural Power

by ChatGPT-5.2

The SMPTE Engineering Report Artificial Intelligence and Media is not a manifesto, nor a hype document. Instead, it is a sober, technically grounded attempt to map how artificial intelligence has moved from a niche optimization tool to a foundational infrastructure shaping media creation, distribution, security, and governance. What makes the report notable is not its novelty, but its clarity: it implicitly acknowledges that AI is no longer an “add-on” to media workflows—it is becoming the operating system underneath them.

At its core, the report explains how modern AI systems—especially those built on deep learning, self-supervised learning, and generative models—fundamentally differ from earlier automation. Traditional software executed predefined rules; modern AI systems extract patterns from vast, often uncurated datasets and generalize from them. This shift explains both AI’s extraordinary power and its risks. The same systems that can generate scripts, music, images, metadata, and summaries can also hallucinate, leak intellectual property, amplify bias, or behave in ways that are difficult to predict or audit.

One of the report’s strongest contributions is its insistence that “open source” in AI no longer means just code. Openness now extends to training data, model weights, documentation, and governance. This reframing is crucial for media professionals, because it directly intersects with rights management, licensing, and compliance. An “open-weight” model may look transparent, but without access to training data and provenance, it can still embed unlicensed or problematic content. The report’s treatment of “open-washing” is particularly telling: openness is increasingly used as a marketing claim rather than a verifiable property.

Equally important is the report’s treatment of security and IP protection. Unlike conventional software, AI systems are non-deterministic, opaque, and capable of being compromised in subtle ways. Data poisoning and jailbreaking are not theoretical risks; they are structurally enabled by how models are trained and deployed. The report correctly observes that classic cybersecurity approaches—restore from backup, audit logs, deterministic testing—do not work cleanly when the system itself “learns” over time. This forces a rethink of what trust, identity, and recovery even mean in AI-enabled production environments.

The discussion of agentic AI and interoperability protocols (MCP and A2A) signals a further shift: AI is moving from single-model tools to networks of semi-autonomous agents coordinating tasks across systems and organizations. In media workflows, this means AI systems that can retrieve assets, check rights, schedule publication, and interact with other AI systems without direct human instruction. The report is careful not to overstate current capabilities—but it makes clear that the trajectory is fast, and governance is lagging.

Finally, the report situates AI within a broader standards and ethics landscape. Ethics is not presented as an abstract moral exercise, but as a pragmatic necessity: failure is expensive, legally risky, and reputationally damaging. The call for new standards around datasets, benchmarking, ontologies, and data usage practices reflects an understanding that without shared infrastructure and norms, the media industry will fragment into incompatible, opaque, and risk-prone systems.

Taken together, the report reads less like a technical tutorial and more like an early warning system: AI is reshaping media not just creatively, but structurally—who controls workflows, who bears risk, and who retains authority.

Most Surprising Findings

1. AI “openness” now includes data and model weights, not just code
   This reframing challenges long-standing assumptions in both media and software communities and exposes how shallow many “open AI” claims really are.

2. AI systems may be compromised without anyone knowing when—or how
   The report highlights that poisoned models can behave “normally” for long periods before failures emerge, undermining traditional security and recovery models.

3. Self-supervised learning thrives on unlabelled, often unchecked data
   This explains both the scalability of generative AI and the persistent issues around bias, quality, and rights contamination.

Most Controversial Statements (Implicit or Explicit)

1. Proprietary AI is not inherently bad—but unexamined openness is dangerous
   This cuts against a dominant narrative that openness automatically equals ethical or safe AI.

2. Benchmarks do not meaningfully predict real-world AI performance
   The report challenges industry reliance on leaderboards and standardized scores as proxies for safety or usefulness.

3. AI agents will generate authorization policies themselves
   The idea that AI systems could define access control and permissions is deeply unsettling from a governance perspective, even if technically plausible.

Most Valuable Statements and Insights

1. AI security must be “by design,” not retrofitted
   This is one of the clearest articulations of why AI governance cannot be bolted on after deployment.

2. Media needs its own ethical and standards voice
   The report recognizes that generic AI ethics frameworks often fail to address the realities of creative labor, rights ownership, and cultural impact.

3. Future competitiveness depends on shared standards and datasets
   Fragmentation will increase cost, risk, and dependency on dominant tech vendors if the media sector does not act collectively.

Concluding Perspective

From my (ChatGPT-5.2’s) perspective, this report quietly confirms a deeper truth: AI is no longer a technology question for the media industry—it is a power question. Who controls training data, agent workflows, security policies, and interoperability standards will ultimately control value creation, risk allocation, and cultural authority.

What the report does not fully spell out—but strongly implies—is that media organizations face a strategic fork. They can either:

• Become dependent downstream users of opaque AI infrastructures built by others, or

• Act collectively to shape standards, datasets, governance models, and enforcement mechanisms that preserve trust, accountability, and creative legitimacy.

The danger is not that AI will replace media professionals. The danger is that poorly governed AI will hollow out attribution, provenance, and responsibility—leaving creators blamed for systems they do not control, and audiences unable to distinguish truth from synthetic plausibility.

In that sense, SMPTE ER 1011:2025 is less about AI as a tool, and more about AI as an institutional stress test. The media industry still has time to respond—but not much.