Drawing on the poetics of machine learning and the architectures of neural networks, I bring a cultural theorist’s lens to the evolving discourse around generative AI.
These essays trace the symbolic and material layers of model development—from data curation and annotation to the rhetoric of alignment and the ghostly presences of synthetic speech.
I write to ask what it means to model language, memory, and relation at scale—and what voices are amplified, obscured, or transformed in the process.
Topics I Explore
Risk, Ethics & Governance
Where do sociotechnical imaginaries collide with the real-world stakes of automation?
Training Data as Cultural Artifact
Who decides what is representative, and what does it mean to filter the world?
Multilingualism & Memory
How do models inherit the politics of translation and language hierarchy?
Poetics of Computation
What metaphors shape how we understand learning, alignment, and hallucination?
Polyphony & Model Voice
How can dialogism inform more plural, layered architectures?