Bio

Bio

I am a political economist and doctoral candidate at UMass Amherst, researching the geographically uneven development of global capitalism from a comparative-relational approach in historical perspective. My work bridges classical political economy and contemporary state theory, with special focus on Latin America in the shadow of U.S. imperialism. I build datasets, formal models, and digital archives—not just to understand history, but to contribute to transforming the present.

Generative AI and the Political Economy of Digital Capitalism

Course, University of Massachusetts Amherst, 2026

Note: This syllabus was prepared for an application and has not yet been taught.

Course Description

This course examines generative AI technologies such as ChatGPT, Stable Diffusion, and Bard through a political-economy perspective on contemporary digital capitalism. Students combine hands-on engagement with generative AI tools and critical analysis of their economic, social, and ethical implications. Core themes include the technical foundations of large language models, platform power, data extraction, cloud infrastructure, intellectual monopoly, labor, governance, and AI ethics within global circuits of digital capitalism.

Required Textbook

  • Kulkarni, A., Gudivada, D., Kulkarni, A., & Shivananda, A. (2023). Applied Generative AI for Beginners. Springer Nature.

Selected Readings

  • Rikap, C. (2021). Capitalism, Power and Innovation: Intellectual Monopoly Capitalism Uncovered. Routledge (selected extracts).
  • Lehdonvirta, V. (2022). Cloud Empires: How Digital Platforms Are Overtaking the State and How We Can Regain Control. MIT Press (selected extracts).
  • Additional journal articles on AI governance, circuits of extraction, and AI ethics will be provided during the semester.

Requirements and Grading

ComponentWeight
Midterm Prompt Engineering Project25%
Final Poster + Prompt Set25%
Class Participation & Reading Responses20%
Take-Home Final Exam (AI Ethics)30%

Assignments

The midterm project focuses on collaborative prompt engineering. Student teams design a generative AI system oriented toward a creative or practical task, including goal definition, prompt logic, evaluation rubric, and outputs. Final deliverables must be reproducible.

The final project consists of a poster and a complete prompt set that can be independently executed by users. Posters are presented in a university-wide poster session.

Participation and reading responses include engagement in lectures, group-formation discussions, two short response papers, and peer feedback. The take-home final exam is a reflective analytical essay on AI ethics and governance.

AI-Guided Group Formation

During Weeks 1–2, class time is dedicated to designing a survey that determines how an AI system assigns project groups. Students collectively decide which attributes (e.g., skills, availability, interests) are included and how they are weighted.

Academic Honesty and Use of AI

All submitted work must be original. Students may not present another person’s work, including AI-generated text, as their own. If generative AI tools are used to support coursework, students must follow the ethical principles outlined by Cheng et al. (2025):

  1. Transparency: Clearly disclose any AI assistance.
  2. Critical review: Edit AI output for accuracy, originality, and coherence.
  3. Verification: Confirm the validity of facts, citations, and references.
  4. Accountability: Responsibility for the final product remains with the student.

Failure to comply may constitute plagiarism and result in a failing grade.

Accessibility and Accommodations

Students with disabilities or learning differences are encouraged to contact Disability Services and inform the instructor as early as possible to ensure appropriate accommodations.

Make-Up Policy

Permission for make-up exams must be obtained in advance and is granted only for documented reasons such as illness or family emergency. Travel plans or social obligations do not qualify.

Topics by Week

  1. Introduction to Generative AI — Applied Generative AI ch. 1
  2. Tokens, Transformers, and Large Language Models — ch. 2
  3. ChatGPT, Bard, Claude — ch. 3
  4. Diffusion Models: Images and Audio — ch. 4
  5. Prompt Engineering I & Group Discussion — ch. 5
  6. Prompt Engineering II & Team Finalization — ch. 6
  7. Team Project Coaching — ch. 7
  8. Midterm Project Presentations
  9. Platforms, Capital, and Data — Lehdonvirta (selected extracts)
  10. Intellectual Monopoly and Cloud Control — Rikap (selected extracts)
  11. AI Labor and Extraction — Rikap (selected extracts)
  12. Governance and Public Infrastructure — Lehdonvirta (selected extracts)
  13. Poster Preparation and Mock Demos
  14. Poster Presentations (University-Wide Session)
  15. AI Ethics and Take-Home Final Exam — Rikap & Lehdonvirta (selected extracts)