# Against Scale: Provocations and Resistances to Scale Thinking

arXiv: https://arxiv.org/abs/2010.08850 · HTML: https://mlsanity.com/paper/2010.08850/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap](https://mlsanity.com/paper/2306.01941/) (2023)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support](https://mlsanity.com/paper/2112.05675/) (2021)
- [Handling and Presenting Harmful Text in NLP Research](https://mlsanity.com/paper/2204.14256/) (2022)
- [Identifying Implicit Social Biases in Vision-Language Models](https://mlsanity.com/paper/2411.00997/) (2024)
- [Position: Measure Dataset Diversity, Don't Just Claim It](https://mlsanity.com/paper/2407.08188/) (2024)
- [Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP](https://mlsanity.com/paper/2405.17159/) (2024)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp](https://mlsanity.com/paper/2405.08209/) (2024)
- [Scaling Laws Do Not Scale](https://mlsanity.com/paper/2307.03201/) (2023)
- [Risks of AI Foundation Models in Education](https://mlsanity.com/paper/2110.10024/) (2021)
- [Bringing AI Participation Down to Scale: A Comment on Open AIs Democratic Inputs to AI Project](https://mlsanity.com/paper/2407.11613/) (2024)
- [Irresponsible AI: big tech's influence on AI research and associated impacts](https://mlsanity.com/paper/2512.03077/) (2025)
- [Big AI is accelerating the metacrisis: What can we do?](https://mlsanity.com/paper/2512.24863/) (2025)
- [Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm](https://mlsanity.com/paper/2411.13546/) (2024)
- [Toward Substantive Intersectional Algorithmic Fairness: Desiderata for a Feminist Approach](https://mlsanity.com/paper/2508.17944/) (2025)
- [The Market in the Model: Latent Diffusion as Neural Economy](https://mlsanity.com/paper/2606.19151/) (2026)

## Abstract (arXiv)

At the heart of what drives the bulk of innovation and activity in Silicon Valley and elsewhere is scalability. This unwavering commitment to scalability -- to identify strategies for efficient growth -- is at the heart of what we refer to as "scale thinking." Whether people are aware of it or not, scale thinking is all-encompassing. It is not just an attribute of one's product, service, or company, but frames how one thinks about the world (what constitutes it and how it can be observed and measured), its problems (what is a problem worth solving versus not), and the possible technological fixes for those problems. This paper examines different facets of scale thinking and its implication on how we view technology and collaborative work. We argue that technological solutions grounded in scale thinking are unlikely to be as liberatory or effective at deep, systemic change as their purveyors imagine. Rather, solutions which resist scale thinking are necessary to undo the social structures which lie at the heart of social inequality. We draw on recent work on mutual aid networks and propose questions to ask of collaborative work systems as a means to evaluate technological solutions and guide designers in identifying sites of resistance to scale thinking.
