🤖 Standards in the Age of AI

articles/2025-12-28-standards-and-ai/index.md

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+---
+title: Standards Matter More Than Ever in the Age of AI
+subtitle: Rethinking scientific UX, incentives, and agency in an AI-mediated world
+abstract: |
+  Advances in artificial intelligence have led to claims that formal standards in scientific communication are becoming obsolete. If AI systems can parse PDFs, extract figures, infer metadata, and answer questions from unstructured text, why invest in shared formats and schemas at all? This framing misunderstands the role standards play. Standards do not merely serve machines; they shape interfaces, incentives, and social norms, determining what kinds of scientific work are produced, shared, and rewarded.
+
+  There is a parallel in software development: AI-assisted coding succeeds not because of chat interfaces alone, but because it builds on decades of tooling—IDEs, language servers, testing frameworks, and structured project context. By contrast, today’s scientific AI is forced to operate on PDFs, a print-era artifact that erases structure and constrains what researchers share. Chat interfaces layered on top of PDFs may improve access, but they do not directly change the underlying incentive system nor support or encourage richer, more reliable scientific workflows.
+
+  Meaningful transformation requires standards that treat research outputs as modular, contextualized objects—linking data, code, analyses, provenance, and computation as first-class components. Such standards enable new interfaces, support AI agents, and create pathways for social change by reshaping credit, transparency, and reproducibility. In an AI-mediated future, standards are not less relevant; they become the primary substrate through which scientific values are encoded and sustained.
+---
+
+A quiet claim has been gaining traction: \
+**If AI can read anything, do we still need standards?**
+
+Language models can parse PDFs, extract figures from images, summarize papers, infer missing metadata, and answer questions about almost any format. When a growing share of scientific interaction takes place through chat interfaces, it’s tempting to imagine a future where structure doesn’t matter—where AI simply interprets the mess for us.
+
+But this argument misses something fundamental. \
+**Standards don’t just shape machines. They shape people.** \
+They structure how research is expressed, how it is reviewed, how it is rewarded, and ultimately what gets made.
+
+> The interface we offer to the world determines the science the world produces.
+
+AI changes _how_ we interact with scientific knowledge, but it does not directly change the social forces that govern it. It does gives us a new chance to reshape those forces—**if we build the right substrate underneath.**
+
+## The Interfaces We Create Determine the Incentives We Reinforce
+
+Researchers don’t publish static PDFs because they love them.
+They publish PDFs because they are the _interface_ the system expects—and because the incentives, credit structures, and evaluation mechanisms are designed around them.
+
+A paper doesn’t include code because the interface of “the paper” was never designed to hold it.
+A multi-panel figure is designed for print because print is intertwined with the incentive models.
+Supplementary materials exist on the margins because the format put them there.
+
+> UX creates norms. Norms create incentives. Incentives create behavior.
+
+If our scientific interfaces only allow narrative, we will continue producing narrative-first science.
+If they make data second-class, data will remain second-class.
+If they hide computation, computation will remain invisible.
+
+AI chat interfaces do not solve this problem.
+They sit _on top_ of it.
+
+Without standards, AI simply learns to imitate the old incentives more efficiently. If the ground truth remains a PDF-shaped artifact, AI will produce a PDF-shaped scientific culture—just accessed via a chatbot.
+
+Formatless science is not a revolution. It is varnish.
+
+To change the _culture_ of science, we must change the _interfaces_ of science—and interfaces are built on standards[^standards].
+
+## Standards Are Levers for Social Change, Not Just Technical Scaffolding
+
+When we introduce a new standard for research components—one that defines datasets, analyses, figures, code, protocols, and narrative as _first-class, modular, referenceable objects_—we create:
+
+New Forms Of Credit
+: When data, code, figures, or workflows can be cited independently, the system begins to reward those contributions.
+
+New Pathways For Recognition
+: Early-career researchers who build tools, notebooks, analyses, or reusable components gain visibility beyond traditional authorship.
+
+New Expectations For Transparency
+: When interactive figures or executable notebooks are supported natively, they stop being exceptions and become norms.
+
+New Incentives For Better Science
+: If reproducible bundles become the default, reproducibility becomes achievable—not aspirational.
+
+New UX That Reshapes Behavior
+: When researchers _see_ modular, computational objects, they begin to _produce_ modular, computational objects.
+
+Social change follows interface change.
+
+And interface change follows standards.
+
+## Chat Is Not Enough
+
+Conversational interfaces are invaluable and completely transformative. They improve accessibility, lower barriers to entry, and empower researchers to interrogate the literature with unprecedented ease.
+
+But chat is not a neutral interface.
+It is a **view**, not a **structure**.
+
+Chat hides complexity. \
+Chat summarizes. \
+Chat smooths over gaps.
+
+Chat does not:
+
+- expose provenance
+- ensure or encourage reproducibility
+- offer structured credit
+- provide version guarantees
+- power interactive data exploration
+- reflect the modularity of modern research
+- surface computational steps or experimental protocols
+- enable multi-agent validation or analysis
+
+Without standards, chat becomes a universal UI sitting on top of a fractured, inconsistent, ambiguous world. Science cannot rest on answers that change from run to run.
+The scientific record must be inspectable, referenceable, and stable—**not probabilistic**.
+
+With standards, chat becomes a **gateway** into a rich ecosystem of modular, interoperable, trustworthy, reusable scientific components.
+
+## AI Agents Make Standards Socially Essential
+
+The next decade will depend not just on single LLMs but ecosystems of AI agents that:
+
+- analyze figures
+- validate protocols
+- compare results
+- run computations
+- audit statistical methods
+- check materials against RRIDs
+- link data to results
+- recommend related work
+- construct experimental timelines
+- verify provenance
+- assist in reviewing and curation
+
+Agents amplify incentives. \
+They reinforce the patterns available to them. \
+They can accelerate good norms—or ossify bad ones.
+
+Agents cannot collaborate reliably without shared standards[^agents].
+And when agents become part of the scientific workflow, **the standards we choose will determine the social shape of science.**
+
+[^agents]: Agent collaboration will rely on a hybrid stack: structured content, well-defined APIs, and AI-mediated access patterns such as RAG pipelines, MCP-style servers, or their successors. While the interfaces may evolve, the underlying requirement remains constant: agents need shared, machine-actionable representations of scientific objects to coordinate, reason, verify results, and build on each other’s work.
+
+## Science Needs "IDEs"
+
+A useful comparison comes from evolving tooling and products in software development, from simple text-editors, to Integrated Development Environments (IDEs), to AI-assisted or automated development and vibe-coding.
+
+Modern programming happens in sophisticated environments like [VS Code](https://en.wikipedia.org/wiki/Visual_Studio_Code) and [Cursor](<https://en.wikipedia.org/wiki/Cursor_(code_editor)>) that now integrate AI directly into the interface, alongside language server protocols, linting, static analysis, automated testing, dependency graphs, and version control. Even when these interfaces are completely abstracted away (e.g. [Lovable](<https://en.wikipedia.org/wiki/Lovable_(company)>), [V0](https://en.wikipedia.org/wiki/Vercel)), the foundational technologies still power the experience. These programming tools predate modern LLMs, yet they are precisely what made AI-assisted coding powerful rather than fragile demo-ware.
+
+> AI did not replace these environments. It amplified them. It abstracted them.
+
+Those interfaces work because they operate on **rich context**:
+
+- structured source code
+- explicit project boundaries
+- dependency graphs and build systems
+- externalized as well as bundled resources
+- typed languages and schemas
+- tests, linters, and automated checks
+- sophisticated version control systems
+- long-lived, machine-readable artifacts
+
+If we had jumped directly from [Windows Notepad](https://en.wikipedia.org/wiki/Windows_Notepad) to chat‑based code generation—skipping IDEs, compilers, and tooling—the result would have been very different. Stunted. AI systems would lack the structural context and automated corrections they rely on. They would be far more error‑prone, and would be incapable of supporting the industries that now depend on them.
+
+Today, scientific AI is being asked to reason over PDFs. That is the equivalent of trying to build modern software tooling on plain-text files alone.
+
+PDFs are not a stable foundation. They erase structure, hide context, and collapse rich computational work into artifacts shaped by print-era incentives. Worse, they constrain what researchers share in the first place: if the unit of evaluation must fit inside a PDF-shaped box, then the incentives for sharing data, code, workflows, and intermediate results are marginalized[^margins].
+
+[^margins]: That is, they are _literally_ in the margins and footers of scientific repositories and our leading journals.
+
+## Adding Context to Research Objects
+
+To build genuinely transformative scientific interfaces, we need standards that treat research outputs more like evolving continuous projects rather than a static document.
+
+That means first-class access to:
+
+- underlying data
+- executable analyses and notebooks
+- example visualizations
+- computational environments
+- intermediate results
+- provenance and lineage
+- modular components that can be reused and recombined
+
+With this context, entirely new experiences become possible: running new analyses directly on prior work, exploring alternative visualizations, launching agents to test hypotheses, or comparing methods across studies without starting from scratch.
+
+This is what will actually transform science—not hallucinated LaTeX rendered in a chat window, but the science itself, connected to the data, code, experiments, and decisions that produced it.
+
+We need standards that work for humans as well as for an AI-mediated future. The good news is that the _translations_ into a new format is now abundantly possible; _the tools exist today_. No matter the system, articles will not be translated on the fly, but put into _some_ sort of structure that exist beneath the surface. The real question is whether those standards are good —whether they enable new capabilities, reinforce the incentives we want, and reflect the values we hold as a scientific community.
+
+## Our Choice
+
+AI gives us the chance to break free from legacy constraints—print-era formats, PDF-centric workflows, incentive systems that reward narrative over substance, and siloed artifacts that undermine reproducibility.
+
+But we only get that future if we define:
+
+- what scientific objects _are_
+- how they link together
+- how they are reused, remixed, and composed
+- how they are cited
+- how they are versioned
+- how they are validated
+- how they surface in tools
+- how they appear in chat interfaces
+- how agents operate on them
+
+> These are not just technical questions. \
+> They are **social** ones.
+
+The standards we build now will shape what scientists create for decades to come.
+
+## The Social Shape of Science
+
+AI makes standards the _primary instrument of social and cultural transformation_ in science.
+
+If we want to move beyond static papers, beyond non-executable figures, beyond buried supplements, beyond ambiguous provenance, and beyond incentive structures rooted in print culture, we need standards that support the science we hope to see.
+
+AI lets us hide complexity from authors and readers. \
+Standards let us transform the ecosystem around them. \
+_Together_, they allow us to redesign the incentives, workflows, and UX of scientific communication.
+
+This is the work of the [Open Exchange Architecture](https://oxa.dev):
+to build the shared substrate that supports richer interfaces, better incentives, more transparent research, and a scientific culture aligned with what we value—not just what we inherited.
+
+The future is not formatless. \
+The future is modular, structured, referenceable, computational—and socially transformative.
+
+[^standards]: Throughout this piece, “standards” need not imply a single, universal specification or a top-down committee process. In practice, standards often begin locally: within a research group, a company, a tool, or even a single application API. What matters is not universality at the outset, but consistency and reuse. As these shared data structures, schemas, and interfaces are adopted more widely—across teams, tools, repositories, or entire segments of open-access science—their value compounds. Capabilities improve, interoperability emerges, tooling stabilizes, and new forms of reuse and attribution become possible, including citation and credit at the level of individual figures, datasets, analyses, or workflows. Standards scale socially as much as technically.

articles/2025-12-28-standards-and-ai/myst.yml

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+# See docs at: https://mystmd.org/guide/frontmatter
+version: 1
+extends:
+  - ../blog.yml
+project:
+  id: 45b71826-f7b2-458d-abde-9edaab99274a
+  title: Standards Matter More Than Ever in the Age of AI
+  subtitle: Rethinking scientific UX, incentives, and agency in an AI-mediated world
+  description: AI can read anything, but without shared standards it only reinforces PDF-era incentives. The interfaces we build shape what science becomes, and transforming scientific culture requires modular, structured, contextual foundations beneath every interface.
+  date: 2025-12-28
+  authors:
+    - id: rowan