Fernando Alcala Jr.

Too often, the story told about generative technology is one of acceleration

faster workflows, automated outputs, reduced costs. This is important, but it is also incomplete. By focusing only on efficiency, we risk overlooking the capacity of these tools to create spaces of transformation. . . . what anthropologists might call liminal spaces.

A liminal experience is a threshold state: a place between stability and change, where old roles no longer apply and new ones have not yet solidified. It is uncomfortable, yes, but also profoundly generative. When professional sectors learn to integrate technologies that open these spaces intentionally, they can move beyond transactional utility toward experiences of growth, creativity, and meaning.

Three Windows into Liminality & Generativity

1. Education and Entrepreneurship

Research on entrepreneurship education shows how students occupy a liminal space between being just learners and becoming entrepreneurs. In this threshold, uncertainty and identity tension are not barriers but catalysts for experimentation and growth.

“Transformation in the liminal space ‘in between’ student and entrepreneur” (2024). Read here

2. Generative AI in Research

Recent scholarship on generative AI in academic research argues that these systems don’t simply speed up data analysis. Instead, they create liminal opportunities between what is already known and what might emerge. By generating patterns, hypotheses, and visualizations, AI positions researchers in a fertile “in-between” a place where meaning is not yet fixed but waiting to be discovered.

Perkins, M., Roe, J., et al. “Generative AI Tools in Academic Research” (2024). Read here

3. Identity Work in Academia

Another study on the labor process in universities describes how academics often occupy liminal positions: simultaneously teacher, researcher, and administrator, yet never fully defined by any one role. In these states of ambiguity, identity work emerges as new roles and practices are negotiated.

Oladeinde, O. “Academic Labour Process and ‘Identity Work’ Construction” (2022). Read here

Implications for Professional Sectors

Healthcare, law, design, and education all face similar choices. Do we integrate generative technologies only as engines of productivity or as partners in shaping meaning?

• Healthcare: AI could be used not just for diagnostics, but to help patients and clinicians explore multiple narratives of care.
• Design: Generative tools can force practitioners into ambiguous territory where “getting the right design” and “getting the design right” demand different modes of engagement. (Hong et al., 2023)
• Education: Generative systems can provoke curiosity and uncertainty pushing students beyond rote answers into the liminal work of discovery.

Across these examples, the thread is the same: technology, if deliberately designed, can generate spaces where identity, practice, and understanding are in motion.

The philosophical tension remains unresolved. Should inspiration and meaning be treated as emergent properties of complex human-technology systems, or as fundamental human qualities that must be safeguarded in every system we build?

What is clear is that liminal experiences hold power. When professional sectors integrate generative technology not just for speed but for depth, they create economies that invite reflection, transformation, and growth. The challenge, and opportunity lies in daring to cultivate these thresholds, rather than smoothing them away.

Works Cited

• Transformation in the liminal space ‘in between’ student and entrepreneur. International Journal of Management Education (2024). ScienceDirect
• Perkins, M., Roe, J., et al. (2024). Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies. arXiv Preprint. arXiv PDF
• Oladeinde, O. (2022). Academic Labour Process and ‘Identity-Work’ Construction: Liminal Experience of Academics in the University. Journal of Scientific Research & Reports. JSRR
• Hong, M. K., Hakimi, S., Chen, Y.-Y., Toyoda, H., Wu, C., & Klenk, M. (2023). Generative AI for Product Design: Getting the Right Design and the Design Right. arXiv Preprint. arXiv Abstract