Mutual Shaping of Technology and Gender

In the AI Story

Applied to AI, the framework reveals that the tools shaping contemporary work are being developed within institutional cultures whose temporal, social, and demographic composition encodes specific assumptions about users. Wajcman's empirical research at the Alan Turing Institute documented persistent gender disparities in the emerging AI workforce: in jobs, qualifications, seniority, industry distribution, and even self-confidence. Her analysis of UK data found that between 2012 and 2022, eighty percent of total venture capital invested in AI was raised by all-male founding teams. All-female teams raised 0.3 percent. When female-founded AI startups secured funding, they received on average six times less capital per deal.

This distribution is not merely a reflection of existing inequality — it is actively producing the AI that will reshape work for the next generation. Wajcman conceptualizes the pattern as male-lens investing: the predominantly male venture capital ecosystem evaluates opportunities through a lens shaped by its own social position, recognizing certain kinds of innovation and certain kinds of founders as more credible. When the funding ecosystem systematically favors male founders, it systematically favors the problems they identify, the solutions they envision, and the workflows male-dominated teams design.

The consequence is that AI tools — even when designed without any discriminatory intent — encode the temporal assumptions of the environments that produced them. Claude Code's design, for instance, assumes a user who can sustain a long, iterative conversation — building, testing, refining, building again. The tool is optimized for the kind of extended session typical of developers whose temporal resources are consolidated. This is an excellent design for the user whose time permits it. It is a less excellent design for the user whose sessions are interrupted every thirty minutes by obligations the tool cannot see.

The mutual-shaping framework is distinct from both technological determinism (the view that technology autonomously produces social effects) and pure social constructivism (the view that technology is whatever societies make of it). It holds that technologies have material properties that constrain their possible uses, but that those properties are themselves shaped by the social relations of design — and that users actively interpret, adapt, and sometimes resist the tools they encounter. The analysis requires attending simultaneously to the tool and to the social context, without reducing either to the other.

Origin

Wajcman developed the mutual-shaping framework through the 1980s and 1990s in response to both technological determinism (dominant in mainstream sociology) and earlier feminist accounts that treated technology as monolithically patriarchal. Her Feminism Confronts Technology (1991) established the synthesis, drawing on Judy Wajcman's own engineering background, Donna Haraway's cyborg theorizing, and the sociology of scientific knowledge.

The framework was subsequently adopted across feminist technology studies and became foundational to the field now known as gender and technology studies. Its application to AI extends three decades of consistent methodological commitment: the insistence that the analysis must hold both the tool and the social relations of its production in view simultaneously.

Key Ideas

Neither determinism nor constructivism. The framework rejects both the view that technology autonomously shapes society and the view that technology is infinitely malleable by user interpretation.

Design encodes social position. Tools designed by institutions with specific demographic compositions encode assumptions that favor users who share those demographics, regardless of explicit intent.

Male-lens investing shapes AI's trajectory. The 80/0.3 distribution of AI venture capital means the problems AI addresses, the founders who build it, and the workflows it assumes are being selected through a systematically gendered filter.

Mutual shaping is continuous. At every stage — research, funding, design, deployment, use — the social and technical are being co-produced, which means intervention is possible at every stage.

User adaptation matters but has limits. Users can resist and reinterpret tools, but they cannot redesign the assumptions encoded in the architecture — structural change requires intervention in the production process.

Debates & Critiques

Critics from the techno-optimist tradition argue that mutual-shaping analysis overstates the influence of design relations on actual use — that users routinely repurpose tools in ways designers never intended, and that the framework underestimates human agency. Wajcman's response is that while users do adapt and resist, the baseline friction of using a tool whose design doesn't match your circumstances is itself a cost that distributes along lines of existing inequality, and that celebrating user agency can obscure the structural asymmetries the framework exists to illuminate.