Why static-first architecture matters more than schema in a post-search world
Schema is not the answer. Why static-first architecture (Astro, edge) wins for llm retrieval, search crawlers, and agentic browsing in a post-search world.
Field Notes
Observations, arguments, frameworks-in-progress. Not corporate, not polished, but rigorous.
Schema is not the answer. Why static-first architecture (Astro, edge) wins for llm retrieval, search crawlers, and agentic browsing in a post-search world.
AI feels cheap. It feels cheap because investor capital is subsidising it. Four structural forces are converging that reframe the trajectory as an open question with significant downside.
When AI told a consumer 'you have a good deal, don't switch', it wasn't providing information. It was giving advice. The RBA's data reveals a fundamental shift.
AI surfaces are not just answering questions; they're making recommendations, naming winners, providing decision-ready verdicts. Brands that don't appear are invisible at the point of decision.
ChatGPT is starting to test advertising in the U.S. This does not change current marketing in Australia, but it indicates where conversational AI is heading.
On the surface, the OAIC's first compliance sweep looks narrow. It is not. The regulator is shifting from reactive complaint handling to proactive market enforcement.
2025 was the year the old marketing playbook stopped working. 2026 won't be about incremental adaptation. It will be about reconstruction.
The intent is sound. The mechanism is blunt. Once minors present as adults to evade the ban, the safety systems that protect them stop working.
Marketing effectiveness is being eroded by a structural decline in signal quality across the major platforms. The performance impact is happening before the regulatory impact.
AI-generated avatars as artificial customers in advertising erode trust and breach Australian Consumer Law. A note on what this means for marketers and platforms.
Token Oriented Object Notation is a compact, human-readable format for feeding structured data into LLMs. It's not a JSON replacement, but for the right shapes, it earns its place.
Click-through rates are falling, and everyone's trying to figure out what it means. The signal is not decline. It is redistribution.
Hidden manipulation of AI agents through covert instructions. The user sees nothing unusual, but the agent reads an extra layer of meaning and acts on it.
Analysts are no longer just interpreters of data. They're builders, product thinkers, and technologists. Four signals from a day at MeasureCamp.
GraphQL defines what is requested. gRPC defines how it is delivered. SOAP once defined the terms. None yet define why. That missing layer is what comes next.
MCP is the internal wiring that makes the agent capable. AIP is the diplomatic code that makes the agent trustworthy. One enables thought. The other governs action.
Search isn't declining; it's fragmenting. Mapping the twelve discovery surfaces where intent now meets information.
Reasoning is less a human privilege and more a universal function of structured systems. Humans reason through awareness and intent; machines reason through scale and structure.
Crawling is built on the assumption that information is public and predictable. The web isn't that anymore. APIs are replacing crawling as the dominant model for how data moves.
By 2030, the transactional layer of the web will be dominated by autonomous agents negotiating with brand APIs. Websites won't disappear, but they'll become backend infrastructure.
Three definitions for concepts emerging in the near future. UX is where meaning meets mechanism. AX is where machine trust is built. AIP is how they talk.
How the emerging agentic internet separates use, intelligence, and control. UX is human trust. AX is machine trust. AIP is institutional trust.
A practical model for evaluating how data is used in marketing. Plot consumer value against data sensitivity. Decide what to proceed with, what to redesign, and what to retire.
Consent, Utility, Value. A simple, repeatable method for evaluating whether a data use case is ethical, valuable, and legally defensible.