The second annual “AI in Design” report from Designer Fund and Foundation Capital has been circulating in the press with a familiar frame: designers are now builders, AI has made them faster, and the gap between design and engineering is closing. The topline numbers support that reading. Weekly AI usage for design tasks jumped from 54% to 91% year over year, the average designer now uses seven off-the-shelf AI tools instead of three, and half of surveyed designers — across product and brand design, not just design engineers — have shipped AI-generated code to production, according to Designer Fund. That is a fast shift in what a design job entails. But it is not the most interesting number in the report, and treating it as the headline skips a trade-off the survey’s own authors describe more plainly than the coverage does.
The number under the number
Buried a few sections into the report is a statistic that complicates the productivity story: designers reporting decreased team collaboration rose from 5% to 20% year over year, according to Designer Fund, and roughly a third of respondents say collaboration has become “messier,” with ownership less clear than before, according to Foundation Capital. That is not a rounding error. One in five designers is telling researchers, in a survey built to capture AI’s effect on their discipline, that working with other humans has gotten worse since AI entered the workflow. The report’s own explanation is unusually specific: AI tools built for solo output rather than shared workflows are recreating, in design, something close to version-control silos — the fragmented, hard-to-reconcile parallel work that software engineering has spent two decades building tooling to prevent. Design, historically organized around crit sessions and handoff rituals meant to keep output legible to a group, now runs a growing share of its work through tools built for a single person moving fast alone.
This matters more than the raw usage figures because it points to a mechanism, not an outcome. AI clearly makes individual designers faster, but the tools reshaping the discipline were not built with its social structure in mind, and the friction shows up as a measurable collaboration decline for a fifth of the field. The report also notes that 36% of projects now start from an AI-generated prototype rather than a written brief, and 43% of companies expect designers to hand over working prototypes instead of static mocks, compressing the moment a team would traditionally align on scope and ownership before work begins, per Foundation Capital. When the starting artifact is already a built thing rather than a discussed intention, the crit that used to happen before work started increasingly happens after, if at all.
Rebuilding the shared rituals that made a design team more than a set of fast individuals is harder and slower, and it is getting the least attention.
Output expectations are moving faster than the org chart
The report captures a second gap that reinforces the first: 65% of designers say they are taking on more PM and engineering tasks, and 40% say their PMs and engineers are doing more design work, a genuine blurring of the job description in both directions, per Designer Fund. Meanwhile 73% of designers say output expectations are rising, but only 28% of design leaders report having formally updated evaluation or compensation policies. The job is expanding and the question of who owns which piece of the outcome is getting hazier, exactly as the mechanisms meant to track that — reviews, leveling, pay — have mostly stayed still. Twenty percent of surveyed designers now self-identify as “design engineers,” a new title for a real shift in daily work, but titles are easier to adopt than compensation frameworks are to rewrite.
| Category | % of respondents |
|---|---|
| Taking on more PM/eng tasks | 65 |
| PMs/engineers doing more design work | 40 |
| Output expectations rising | 73 |
| Formally updated eval/comp policies | 28 |
| Self-identify as design engineers | 20 |
None of this is presented in the report as a verdict against AI adoption. The research includes case studies from design leaders at Anthropic, Stripe, Linear, and Shopify, among others, and Stripe’s head of design, Katie Dill, offers an optimistic counterpoint, quoted on the report’s own site, stateofaidesign.com:
“AI is sparking a creative renaissance in design. With new instruments, it’s our chance to compose wholly new music.”
That framing describes what individual designers can now do alone, precisely the part of the story the tools were built to optimize. The harder question — the one the collaboration numbers keep surfacing — is whether design teams, as opposed to individuals, are getting better at working together. Sixty-two percent of designers already cite inconsistent AI output as their single biggest challenge, and 80% say reliable quality is what they most want from their tools, according to Foundation Capital. Fixing output quality is a tractable engineering problem vendors are already working on. Rebuilding the shared rituals that made a design team more than a set of fast individuals is harder and slower, and it is getting the least attention.
