You sit through a research session, watch a participant get stuck, hear the exact words they use to describe their confusion — and that moment stays with you in a way no summary of it ever will. That gap is the quiet subject of two essays Nielsen Norman Group published eight days apart in July 2026, and read together they pull design documentation in opposite directions. On 24 July, Tony Alicea argued that “the output of research and design shifts from documents written for humans to curated context that guides AI” , proposing a UX.md — research synthesis, user models, a domain glossary — as design’s new deliverable, modeled on Google’s already-shipped DESIGN.md. On 17 July, Maria Rosala made the opposite case about the same artifacts: “a team that outsources research to AI gets a report, but it doesn’t get the learning” . Neither is wrong. They’re describing two different jobs a design document has always done at once, and the machine-readable version only does one of them.
Google already shipped the machine-readable half
Alicea’s proposal isn’t speculative — it’s modeled on something real. In April 2026, Google open-sourced DESIGN.md , a draft specification letting teams export design rules as a plain file coding agents can import, so “AI agents can know exactly what a color is for” instead of guessing, in engineer Cassia Xu’s phrasing. It’s already being forked, as community projects packaged whole brands’ visual identity into installable DESIGN.md files within months of the format going public. Alicea’s UX-context design extends the same logic upstream, into research: he defines it as “the practice of discovering and curating what an organization knows and wants into the context that guides everything its AI tools generate,” and argues research output “needs to be made ‘AI-ready.’” Given that 91% of surveyed designers now use AI weekly, according to the AI in Design Report 2026 , a file a model can actually ingest isn’t a fringe idea — it’s where the reading has already gone.
A design document always did two jobs at once, and the machine-readable version only does one of them.
A report was never just a delivery mechanism
Rosala’s argument doesn’t deny that AI-ready context works as transmission — it denies that transmission was ever the whole point of a research deliverable. “We were all there. We all saw it. We all heard it. We all lived it,” she writes, describing why sitting through sessions together, not reading the eventual document, was what “pushed our design forward.” She’s not being sentimental about it: she cites the self-generation effect — people retain what they produce themselves better than what they merely consume — plus Princeton research showing a listener’s brain activity mirrors a storyteller’s, and Michigan State/UC Santa Barbara findings that personal narratives activate more of the brain than technical manuals do. A comprehensive report, in her framing, creates “an illusion of learning” — everyone nods at the deck, and nobody actually absorbed what happened in the room. That’s a strong parallel to a problem this magazine has flagged in synthetic UX research : a system optimized to deliver a clean answer can hide exactly the friction a team needed to sit with.
The floor was already low before AI touched it
The counter that has to be taken seriously is that human-readable research reports were frequently ignored anyway — stakeholders skim a deck, absorb a headline finding, and move on, which means a file a model actually reads and applies every time it generates something may raise research’s floor rather than lower it. Alicea himself doesn’t pretend the curation problem is solved: “a curation decision made for today’s models may be wrong for next year’s,” he writes, and he’s careful to frame UX.md as downstream of research a team still has to do and understand well enough to distill — not a replacement for doing it. That’s a narrower claim than it first sounds, and it happens to be exactly the learning Rosala is defending; his UX.md only works if someone already went through the process she describes. But narrowing the claim doesn’t answer who owns the curation itself, an editorial judgment closer in kind to the compliance question this magazine raised about whether agents reliably follow the AGENTS.md files teams already write for them than to a simple export step. Nobody in either essay is assigned that job, and a task with no owner tends to get done inconsistently or not at all.
That’s the real fork in the road, and it isn’t between writing for humans or writing for machines — most teams will end up doing both, a UX.md alongside the sessions that produced it. It’s whether the organization treats curating that context as a new, ongoing discipline with someone accountable for it, or as a byproduct any tool can generate on the way out the door. Skip that distinction, and a team gets a file its AI reads faithfully every time, built from a version of its own research that nobody quite remembers living through.
