Real promise, real risk, and democratic alternatives to Big Tech AI
As artificial intelligence reshapes societies and economies, this course moves beyond legislative fixes to explore practical, within-reach alternatives for developing and governing AI in ways that promote equity and collective well-being. It centers grounded, community-based, democratic alternatives to the dominant Big Tech model: cooperatives and community-owned initiatives, often built in collaboration with governments, municipalities, universities, and social movements. It deliberately expands beyond the U.S.-focused perspectives that dominate academic and activist tech spaces, with case studies from Latin America, Sub-Saharan Africa, South and Southeast Asia, and Indigenous contexts.
Who it's for
Students, cooperative practitioners, labor organizers, technologists, policymakers, artists, cultural workers, and community leaders building democratic, people-centered alternatives to Big Tech AI.
What you'll leave with
- A critical understanding of AI's political economy
- Familiarity with cooperative and solidarity-based alternatives
- Connections to an international network of practitioners
- A framework for evaluating AI beyond profit and efficiency
Learning objectives
- Understand the historical and current debates around AI, ownership, and equity
- Grasp how extractive AI systems entrench global inequalities
- Examine the role of digital infrastructure in maintaining technopower
- Analyze cooperative alternatives emerging globally
- Assess AI's impact on labor rights, platform governance, and the solidarity economy
- Explore alternative AI governance models centered on democratic decision-making
- Engage with open-source AI tools and cooperative-led digital initiatives
Workload
Required readings are intentionally brief: roughly 30 pages per week. Past participants report 1–6 hours of prep depending on pace and engagement. Suggested readings, films, and podcasts offer further paths for those who want to go deeper.
Remote learning norms
Video on, laptop or desktop only, no multitasking. Arrive on time, mute when not speaking, participate actively through voice or chat. Treat the virtual room as you would a physical one.
Generative AI policy
AI can be a useful conversational partner for testing arguments and clarifying drafts, but you may not submit work that is primarily AI-generated. Submitting AI-written material without meaningful revision or critical engagement is treated as academic misconduct. You're responsible for the accuracy, independent thought, and integrity of everything you submit.
Class schedule
The course unfolds in four interconnected phases: critique, political responses, building alternatives, and movement building. Dates and topics below are shared across all three tracks; each track’s reading load and assignments differ (see Undergraduate / Graduate / Community tabs). Full suggested-reading lists, podcasts, films, guiding questions, and guest-speaker bios also appear in each track’s syllabus PDF on the Resources tab.
Two additional online sessions are optional: the Workshop on Friday, September 25, and Architectures of Participation on Friday, October 9 (9:00–10:50 AM ET). There will be no class on November 12 (Solidarity AI Conference, Bangkok) or November 26 (Thanksgiving).
Note: every off-schedule Friday session runs on a separate Zoom link, not the regular class Zoom; you must register in advance for each one (links below).
Foundations
Orientation to the course structure, digital workspace (Hylo), and discussion norms. After a walkthrough of the syllabus and expectations, students surface perceived harms of AI in breakouts and begin co-creating a shared glossary of key terms.
Beyond logistics, the session introduces AI as a socio-technical system shaped by political, economic, and infrastructural forces, and asks why people-centered alternatives are urgently needed.
Start here → ✓ Checklist: Week 1 (join Hylo, sign the Community Agreement, set up your submission folder, and more).
Learning goals
- Understand AI as a socio-technical system shaped by political, economic, and infrastructural forces.
- Begin co-creating a shared glossary of key terms.
- Reflect on how AI and digital infrastructures operate differently across contexts.
Required readings
- Crawford, Kate. “Introduction.” In Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. New Haven: Yale University Press, 2021, pp. 1–22.
- Digital Watch. “AI That Serves Communities, Not the Other Way Round.” Digital Watch Observatory, 2025.
Suggested readings
- Monett, D. A Collection of Books on Topics Strongly Related to Critical AI (14th ed., 2026).
- Hao, Karen. “Disaster Capitalism.” In Empire of AI. New York: Penguin Press, 2025.
- O’Neill, Cathy. “Civilian Casualties” and “Ineligible to Serve.” In Weapons of Math Destruction. New York: Crown, 2016, chs. 5–6.
Unpacking the Myths of AI
Challenges the myth of AI as a singular, autonomous intelligence and establishes a shared vocabulary (foundation models, transformers, inference, training, fine-tuning, embeddings, open-weight vs. open-source, agents). We frame AI as a global infrastructure rooted in resource extraction, labor exploitation, and algorithmic control, and confront the limits of regulation.
Learning goals
- Identify the foundational myths that sustain current AI development, and who they serve.
- See how algorithmic, cultural, and environmental harms show up in real-world applications.
- Understand why existing laws aren’t enough and what stands in the way of meaningful regulation.
Required readings
- Webb, Amy. “Mind and Machine – A Very Brief History of AI.” In The Big Nine, ch. 1. New York: PublicAffairs, 2019.
- “Localizing AI in the Global South.” Nature Machine Intelligence 7 (2025): 675.
Suggested readings
- Turner, Fred. “The Texan Ideology.” The Baffler, no. 84, 2025.
- Kerr, Dara. “Trump’s DoJ Intervenes to Back Elon Musk in Datacenter Pollution Lawsuit.” The Guardian, 2026.
- Pope Leo XIV. Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence. Vatican City, 2026.
- Webb, Amy. “The Insular World of AI’s Tribes.” In The Big Nine, ch. 2. 2019.
- Kinder, Molly. “Hollywood Writers Went on Strike to Protect Their Livelihoods from Generative AI.” Brookings Institution, 2024.
- Podcast: The Radical AI Podcast. “Love, Challenge, and Hope … with Ruha Benjamin.” 2020.
- Film: Buolamwini, Joy. “The Coded Gaze: Bias in Artificial Intelligence.” 2019; and “Les sacrifiés de l’IA,” 2025.
Guest speaker: Alek Tarkovski, a leading voice in the public AI movement, building AI infrastructure that serves the public interest rather than corporate profit.
Stories from Cooperative Tech Futures: Workshop 1
Mandatory: this session replaces the class of Thursday, September 10. Held Friday, September 11, 9:00–10:20 AM EDT (online).
A public ICDE workshop inspired by Melike Kaplan and Max Haiven’s Worker as Futurist project. Participants map existing cooperative and community-owned tech initiatives: platform cooperatives, data cooperatives, feminist tech projects, community-owned infrastructure, and community land trusts, and imagine the futures these seeds could grow into beyond Big Tech.
Facilitators: Max Haiven, Melike Kaplan (ICDE), and Alessandro Longo.
Colonialism Reloaded
Examines how AI is entangled with extractive systems that echo colonial legacies, particularly across the Global South. Through regional critiques, lived experiences, and artistic interventions, we explore how digital technologies intersect with labor, sovereignty, and cultural power.
Learning goals
- Analyze how AI reinforces or reconfigures colonial and extractive power structures.
- Identify alternative imaginaries or governance models emerging from the Global South.
- Ask who benefits, and who is made invisible, when AI systems are deployed globally.
Required readings
- Singh, Deepa. “AI Ethics in and for the Global South: Universal vs. Situated Approaches.” Medium, 2025.
Suggested readings
- Vimalendiran, Shav. “Cultural Bias in LLMs.” Blog, 2024.
- Couldry, Nick, and Ulises A. Mejias. “The Coloniality of Data Relations.” In The Costs of Connection, ch. 3. 2019.
- Gurumurthy, Anita, and Nandini Chami. The Wicked Problem of AI Governance. FES, 2019.
- Genç, Kaya. “Desperate for Work, Translators Train the AI That’s Putting Them Out of Work.” Rest of World, 2025.
- Rikap, Cecilia. “South America’s Sovereignty Is Being Lost in Big Tech’s Clouds.” openDemocracy, 2025.
- Du, Michelle, and Chinasa T. Okolo. “Reimagining the Future of Data and AI Labor in the Global South.” Brookings Institution, 2025.
- Raval, Noopur. “Interrupting Invisibility in a Global World.” Interactions 28, no. 4 (2021).
- Podcast: Marx, Paris. “AI Hype Enters Its Geopolitics Era w/ Timnit Gebru.” Tech Won’t Save Us, 2025.
- Art project: Ghana Think Tank.
Guest speaker: Sandeep Mertia, Assistant Professor at Stevens Institute of Technology; anthropology of computing, data-driven governance, and technology futures, with a focus on South Asia and the majority world.
The Environmental Cost of AI
Retraces every stage of AI’s material life: lithium and rare-earth extraction, megawatt-hungry data centers, water-cooled GPUs, and mounting e-waste. In breakouts, students apply cooperative principles to draft “eco-manifestos.”
Learning goals
- Map the full ecological costs of AI, from extraction to e-waste.
- Explore how cooperative principles could reshape AI’s environmental footprint.
- Ask what it would take to build truly sustainable, low-carbon AI, and who is responsible.
Required readings
- Hao, Karen. “Plundered Earth.” In Empire of AI. New York: Penguin Press, 2025.
- Bhat, Divsha. “Big Tech Is Building AI in the Desert. The Water May Not Last.” Rest of World, 2025.
Suggested readings
- Görüçü, S. “Meme-tivism: Rethinking AI’s Environmental Impact.”
- Berreby, David. “As Use of A.I. Soars, So Does the Energy and Water It Requires.” Yale Environment 360, 2024.
- Parshley, Lois. “The Hidden Environmental Impact of AI.” Jacobin, 2024.
- Saitō, Kōhei. Slow Down: The Degrowth Manifesto. Astra House, 2024 (selection).
- Nafus, Dawn, and Laura Watts. “Federated Fair Trade Energy.” LIMITS ’26, 2026.
- “Water for Data.” People & Power, Al Jazeera, 2025 (video).
- Falk, Sophia. “More Than Carbon: The Full Environmental Footprint of LLMs.” LUMEN, 2025.
Guest speaker: Frank Pasquale, Professor of Law at Cornell Tech and Cornell Law School; a leading scholar of algorithmic accountability and the law of AI; author of The Black Box Society and New Laws of Robotics.
AI and the Appropriation of Creative Labor
Examines how generative AI automates artistic work and trains on scraped creative labor without consent. We focus on authorship, ownership, attribution, compensation, and economic survival, and students draft policy memos or fair-compensation proposals.
Learning goals
- Weigh how far AI companies should be held accountable for using copyrighted material, and what compensation models are viable at scale.
- Ask whether “style” is a form of intellectual property deserving protection.
- Design a fair, enforceable system for protecting artists, musicians, and writers.
Required readings
- Wang, Judy, and Nicol Turner Lee. “AI and the Visual Arts: The Case for Copyright Protection.” Brookings Institution, 2025.
Suggested readings
- O’Brien, Matt, and Sarah Parvini. “ChatGPT’s Viral Studio Ghibli-Style Images Highlight AI Copyright Concerns.” Associated Press, 2025.
- Lab for AI Ethics & Creative Labor. “F(r)ictions: Creative Work in an Age of AI.” Horizontal TNS2.
- Camnitzer, Luis. “Artificial Utopia.”
- World Economic Forum. Technology Convergence Report 2025. Geneva: WEF, 2025.
Guest speaker: Marcelo Vieta, Associate Professor at the University of Toronto; social and solidarity economy, worker-recuperated enterprises, and new cooperativism.
Automation’s Backbone
Exposes the illusion of autonomous AI by spotlighting the hidden human labor behind it (content moderation, annotation, data labeling) through the myth of the 18th-century Mechanical Turk.
Learning goals
- See how the myth of autonomous AI obscures the human labor behind machine learning.
- Weigh the ethical and economic consequences of hiding data workers, especially in the Global South.
- Ask what it would take to make this labor visible, valued, and collectively owned.
Required readings
- Muldoon, James, Callum Cant, and Mark Graham. “The Annotator.” In Feeding the Machine, ch. 1. New York: Bloomsbury, 2024.
- Smith, Robert, and Alexandra Heal. “Spies, Spinners, Solicitors: Builder.ai’s Creditor List in Full.” Financial Times, 2025.
Suggested readings
- Tinn, Honghong. “TSMC and the New Geopolitics of the Twenty-First Century.” In Island Tinkerers. MIT Press, 2025.
- Gray, Mary L., and Siddharth Suri. “Algorithmic Cruelty” and “Working Hard …” In Ghost Work. 2019.
- Casilli, Antonio A. “Will Humans Replace Robots?” In Waiting for Robots, ch. 1. Univ. of Chicago Press, 2025.
- Crawford, Kate. “Labor.” In Atlas of AI, ch. 2. 2021.
- Buolamwini, Joy. “Daughter of Art and Science.” In Unmasking AI, ch. 1. 2023.
- Mwema, Esther, and Abeba Birhane. “Undersea Cables in Africa.” First Monday 29, no. 4 (2024).
- Echchaibi, Rajabi, and Schneider. Sacred Stack: The Art of Cyborg Community. 2023.
- Prabhu, Vinay Uday, and Abeba Birhane. “Large Image Datasets: A Pyrrhic Win for Computer Vision?” arXiv, 2020.
Guest speaker: Kenzo Soares Seto, ICDE Fellow and Resident Fellow at Yale Law School; AI regulation in Latin America, digital sovereignty, and platform economies.
Regulate, Enforce, Build Worker Power
Examines how legislation and public procurement could support a democratic, people-centered AI ecosystem. At stake is whether laws can restrain corporate monopolies and state surveillance, or merely formalize inequality under the banner of “ethics.”
Learning goals
- Imagine legislative frameworks that could truly enable people-centered AI.
- Assess how the EU AI Act and U.S. Executive Order fall short.
- Weigh the risks of relying on policy alone in an era of techno-authoritarianism.
Required readings
- Jozak, Tom. 2024 EU AI Act: A Detailed Analysis. SSRN, 2025.
Suggested readings
- Vertesi, boyd, Taylor, and Shestakofsky. “Reckoning with the Political Economy of AI.” arXiv, 2026.
- Benkler, Y. “Practical Anarchism: Peer Mutualism, Market Power, and the Fallible State.” Politics & Society 41, no. 2 (2013).
- Cuéllar, Mariano-Florentino, and Aziz Z. Huq. The Democratic Regulation of Artificial Intelligence. Knight Institute, 2022.
- Ramos, Maria Elisabete, et al. “Cooperatives and the Use of Artificial Intelligence.” Sustainability 15, no. 1 (2023).
- Lazar, Seth, and Mariano-Florentino Cuéllar. “AI Agents and Democratic Resilience.” Knight Institute, 2025.
- “Community-Aligned AI Benchmarks.” Aspen Digital, 2025–2026.
- McQuillan, Dan. Resisting AI. Bristol University Press, 2022 (selection).
Guest speaker: Dorleta Urrutia Oñate, ICDE Fellow and professor at Mondragon University; AI-driven financing and governance frameworks for platform cooperatives.
Digital Independence
Case Study Analysis Paper due (Undergraduate).
Examines digital sovereignty in an age when a handful of US-based companies dominate operating systems, cloud, AI models, and communications, and asks what sovereignty means once AI agents act on our behalf. We examine the economics of AI infrastructure and alternative, community-accountable models.
Learning goals
- Analyze how the concentration of digital infrastructure affects autonomy, resilience, and democracy.
- Explore steward-owned, employee-owned, and Indigenous AI models.
- Ask what sovereignty means when systems act autonomously on our behalf.
Required readings
- Nagappa, A., and D. Angus. “Nearly Everything We Use Online Is Owned by Big Tech.” The Conversation, 2026.
- Anthropic. “Statement on the US Government Directive to Suspend Access to Fable 5 and Mythos 5.” 2026.
- Broer, S. M. L. “The 28th Regime as a Pathway for Non-Extractive Business Models.” N-EXTLAW Working Paper No. 5/2026.
- Kuslikis, A., and S. Dudley. “TCUs Positioned to Lead a Community of Indigenous AI Practice.” Tribal College Journal 37, no. 4 (2026).
Guest speaker: Phakin Nimmannorrawong, ICDE Fellow and PhD candidate in Sociology at the University of Cambridge; leftist politics and blockchain communities.
From Cooperative AI to the Solidarity Stack
Introduces the Solidarity Stack: AI infrastructure built through alliances among cooperatives, public institutions, unions, climate justice networks, and Indigenous data-sovereignty campaigns. Students compare cooperative, steward-owned, nonprofit, municipal, and public ownership models across every layer of AI’s supply chain.
Learning goals
- Contrast member-owned data centers with corporate platforms on control, ownership, and accountability.
- Identify design choices that support data sovereignty, democratic governance, and resilience.
- Explain why cooperatives alone can’t confront AI’s structural harms, and what cross-movement work looks like.
Required readings
- Hunt-Hendrix, Leah, and Astra Taylor. “Solidarity Beyond Borders.” In Solidarity. New York: Pantheon Books, 2024.
- Wilson, David, and Andrew E. G. Jonas. “The People as Infrastructure Concept.” Urban Geography, 2021.
Suggested readings
- Scholz, R. Trebor, and Mark Esposito. “Building a Solidarity Ecosystem for AI.” Stanford Social Innovation Review, 2026.
- Kawano, Emily, and Julie Matthaei. “System Change: A Basic Primer to the Solidarity Economy.” Nonprofit Quarterly, 2020.
- Nicole, Sarah, et al. How Can Data Cooperatives Help Build a Fair Data Economy? Project Liberty Institute, 2025.
- Laville, Jean-Louis. “Origins and Histories of the Social and Solidarity Economy.” In Encyclopedia of the SSE. 2023.
- Zimmermann, Annette. Democratizing AI.
- Public AI Network; Current AI; “The Politics of Open Infrastructures” (PDF).
- Vieta, Marcelo, et al. “Editorial: The New Cooperativism.” Affinities 4, no. 1 (2010).
- Nosengo, Nicola, et al., eds. AI Models in the Global South. Nature Collection, 2025.
- Bria, Timmers, and Gernone. EuroStack – A European Alternative for Digital Sovereignty. Bertelsmann Stiftung, 2025.
Guest speaker: Morshed Mannan, Lecturer in Global Law and Digital Technologies at Edinburgh Law School and ICDE Affiliate Faculty; blockchain and cooperative governance.
Applying Cooperative Principles to AI Ethics
Asks what it would concretely mean to apply the seven cooperative principles, first articulated in 1844, to the design, governance, and ethics of AI. We engage directly with READ-COOP and Transkribus and develop practical criteria for evaluating democratic governance, participation, accountability, and community benefit.
Learning goals
- Apply the seven cooperative principles to AI design, governance, and deployment.
- Assess READ-COOP and Transkribus as a model for democratically governed AI.
- Explore how federated cooperative systems can scale solidarity-based alternatives.
- Evaluate whether an AI system is genuinely democratic, accountable, sustainable, and beneficial.
Required readings
- Terras, Melissa, et al. “The Artificial Intelligence Cooperative: READ-COOP, Transkribus, and the Benefits of Shared Community Infrastructure.” Open Research Europe 5, no. 16 (2025).
Suggested readings
- Tangen, Ihenacho, and Nanda. “Why Employee Share Ownership Matters for Long-Term Value Creation.” NBIM, 2026.
- Mannan, Pek, and Papadimitropoulos. “The Cooperative Governance of Artificial Intelligence.” In Global Cooperative Economics and Movements. Routledge, 2025.
- Srivastava, Lina. “Building Community Governance for AI.” Stanford Social Innovation Review, 2024.
- Scholz, R. Trebor. “The Coming Data Democracy.” In Own This! Verso, 2023.
- Video: Oxford Union. “THW End the Private Ownership of Social Media Companies.” 2026.
Guest speaker: Melissa Terras, Professor of Digital Cultural Heritage at the University of Edinburgh and a founding director of READ-COOP, the cooperative behind Transkribus.
Bangkok Conference
No synchronous session. Prof. Scholz is at the Solidarity AI 2026 Conference, Chulalongkorn University, Bangkok (Nov 12–15).
What Coops Are Already Doing
AI’s development remains tightly controlled by firms like OpenAI, Microsoft, and Meta. This session surveys real cooperative alternatives already operating (IFFCO, FrieslandCampina, MIDATA, Pescadata, READ-COOP, the Gamayyar African Tech Workers’ Cooperative), and what it takes to build a cooperative AI initiative.
Learning goals
- Map where cooperatives and solidarity-economy actors already intervene in AI, and their impact.
- Identify the structural or legal shifts that would let cooperatives do more.
- Ask how broader solidarity networks provide the scale needed to challenge corporate AI.
Required readings
- Scholz, Trebor, and Stefano Tortorici. “5 Ways Cooperatives Can Shape the Future of AI.” Harvard Business Review, 2025.
Suggested readings
- Scholz, T., and S. Tortorici. Cooperative AI? SSRN, 2025.
- Hubbard, Sarah. “Cooperative Paradigms for Artificial Intelligence.” Ash Center, Harvard Kennedy School, 2024.
- Free Knowledge Institute. “Cooperative Clouds: How LaSuite.coop Is Building a New Model for Digital Sovereignty.”
- Poliks, J., and S. Trillo. “Convergence.” Carrier Bag, 2026.
- REWE Group. “AI Manifesto.” Digital Day 2020, Cologne.
Guest speaker: TBD.
Movement Building
Explores why cooperatives alone cannot meaningfully confront the systemic challenges of AI, and why they must align with tech-worker unions, climate justice networks, and Indigenous data-sovereignty campaigns rather than stay apolitical.
Learning goals
- Explain the limits of cooperatives working in isolation.
- Picture meaningful cross-movement collaboration among co-ops, unions, climate activists, and Indigenous groups.
- Ask how shared governance, mutual aid, and collective ownership can shape AI beyond capitalist or state-centered models.
Required readings
- Hunt-Hendrix, Leah, and Astra Taylor. “Solidarity Beyond Borders.” In Solidarity. Pantheon Books, 2024.
- Kawano, Emily, and Julie Matthaei. “System Change: A Basic Primer to the Solidarity Economy.” Nonprofit Quarterly, 2020.
Suggested readings
- Vieta, Marcelo, et al. “Editorial: The New Cooperativism.” Affinities 4, no. 1 (2010).
What Stays With Us: Closing Circle and Collective Commitments
Final Paper due.
A closing lecture and reflection on the semester. We revisit the concepts that stayed with us, examine sample papers, return to the peer library, and close with feedback and collective commitments.
Undergraduate track
Undergraduate students follow the full 15-week schedule with graded assignments, papers, and instructor feedback throughout.
Grading breakdown
Assignments
Reading Responses (6 total)
500 words, written on your Google Doc and posted to the Hylo Discussion thread for that week. Due Tuesdays at 11:59 PM ET before the class session for that week's readings.
Case Study Analysis Paper
1,500–2,000 words, Chicago style, ≥5 scholarly sources. One cooperative AI initiative (e.g. READ-COOP, MIDATA).
Final Paper or Project
2,500–3,000 word research paper, or applied project, on an instructor-approved topic synthesizing the course.
Graduate track
Graduate students follow a parallel, more advanced syllabus with additional readings and a full research paper.
Grading breakdown
Assignments
Reading Responses (3 total)
1,000–1,200 words each. Critical engagement across multiple readings, integrating outside scholarly sources. Due Sundays at 11:59 PM ET.
Final Research Paper Proposal
1–2 pages: research question, methodology, preliminary bibliography. Feedback shapes the final paper.
Annotated Bibliography
Bring a complete draft to class for structured peer feedback before submitting the final paper.
Final Research Paper
4,000–5,000 words, original argument, ≥10 scholarly sources.
Add yourself to the Cross Connection Document, it's how classmates find each other for peer feedback and discussion.
Community track
No grades, no required written work: built for cooperative practitioners, organizers, technologists, and community leaders who want the same critical conversation without academic credit.
Four participation pathways
Undergraduate students (New School)
Follow the undergraduate syllabus with graded assignments and full instructor feedback.
Graduate students (New School)
Follow the graduate syllabus: more advanced readings, papers, and instructor guidance.
Community participants (synchronous)
Attend live sessions, join discussions and breakouts. No assignments, no grades.
Community participants (asynchronous)
Watch recordings and engage on your own schedule, with office hours timed for Asia and Southeast Asia. Must declare this track by Week 3; no certificate available for this path.
To receive your certificate
Attend at least 12 of the 15 sessions, prepared to participate. Arriving more than 10 minutes late, or leaving more than 15 minutes early, counts as an absence. There are no written assignments or grades, though you're welcome to share reflections on Hylo.
Community norms
Built on mutual respect, curiosity, and trust. Engage with others kindly, challenge ideas without attacking people, and keep what's shared in class private unless clearly meant for wider circulation. We follow the spirit of the Chatham House Rule: share what you learn freely, but never link ideas or comments back to a specific participant's identity or affiliation.
Asynchronous track
For community participants who can't attend live sessions. There's no separate syllabus for this track; it follows the regular community syllabus, on your own schedule.
What you get
- Full access to the course platform and discussion spaces
- Recorded class sessions to watch on your own time
- All course materials, the same as live participants
- Scheduled instructor office hours, including sessions timed for participants in Asia and Southeast Asia
What's different
- No assignments, no grades, no required written work
- No individual written feedback
- No certificate available for this track
Everything you need lives in one place: Resources for recordings, slides, and Hylo; Schedule for weekly topics and readings; Office Hours for how to reach the instructor.
Office hours
Prof. R. Trebor Scholz · tscholz@cyber.harvard.edu (community) or scholzt@newschool.edu (undergraduate / graduate)
In New York City: walk-and-talk
For students residing in New York City, office hours happen outdoors, meeting at the office at 79 Fifth Avenue and walking toward Washington Square Park. It's a relaxed way to talk through course topics while getting some fresh air.
Everywhere else: virtual
Email to arrange a time, and we'll schedule a virtual session that works for both of us.
Scheduled office hours, Fall 2026
These are collective office hours: everyone joins together, there are no individual time slots, and questions are taken in the order people sign up. Add your name, email, and what you'd like to discuss on the sign-up sheet for each date.
- Tuesday, Sept 1 · 9:00–10:00 AM ET · Sign up
- Wednesday, Sept 9 · 4:00–5:00 PM ET · Sign up
- Monday, Sept 14 · 9:00–10:00 AM ET · Sign up
- Wednesday, Sept 23 · 4:00–5:00 PM ET · Sign up
- Tuesday, Oct 6 · 9:00–10:00 AM ET · Sign up
- Tuesday, Oct 20 · 9:00–10:00 AM ET · Sign up
- Wednesday, Nov 4 · 4:00–5:00 PM ET · Sign up
- Wednesday, Dec 2 · 4:00–5:00 PM ET · Sign up
All sign-up sheets also live in this folder.
For asynchronous participants
Office hours include sessions specifically timed to be accessible for participants in Asia and Southeast Asia; see the Asynchronous tab for the rest of what's available on this track.
Course resources
The forum, shared documents, and full syllabi for AI Without Bosses, Fall 2026.
Join Hylo
Sign up for the course discussion group.
DiscussionHylo: AWB FA 2026
The group's home once you've joined.
RequiredCommunity Agreement
Terms of agreement form, complete before the first session.
ArchiveVideo Recordings
Recorded class sessions for review or asynchronous viewing.
ArchiveLecture Slides
Slide decks from each week's session.
CohortCross Connection Document
Meet your classmates and set up peer feedback.
ReferenceGlossary
Shared key terms, co-created starting Week 1.
CohortPeer Library
Alternate readings suggested by students, revisited in Week 15.
MatriculatedRoles Sign-Up
Matriculated students sign up for a session role here.