Uses of advanced artificial intelligence are changing how societies organize labor, govern, produce knowledge, and make meaning. In light of these developments, this essay argues that AI models, tools, and systems pose three interrelated imperatives for social science: they demand renewed attention to social theories of how technology, human experience, and social order are entangled; they require study as objects of inquiry in their own right; and they offer capabilities that may transform—or upend—the practice of social investigation itself. From Weber’s analysis of rationalization to Du Bois’s study of technology and inequality to contemporary scholarship on algorithmic governance, the essay examines what social science distinctively offers: the capacity to historicize the apparently unprecedented, to trace connections across scales, and to center those most affected by technological change. It identifies how algorithmic systems are remaking the distribution of opportunity and risk as a central task of social inquiry and asks what futures social science might help bring into being.
Automate the C-Suite - The Ideas Letter
Weatherby argues that Benanav’s and Morozov’s essays on AI and the left miss a deeper reality: Today’s AI is not a neutral tool operating within capitalism but the materialization of…
On AI and building your own GIS software
Of the thousands of essays over the past two years surrounding AI and its implications are some very thought provoking ones with enormous implications for GIS. Some of the essays are around the the…
The Socialist Charcuterie Board - The Ideas Letter
Taking issue with Benanav’s institutional vision of a democratic political economy of technology, Morozov argues that generative AI exposes a deeper problem that socialism has yet to solve: how to…
A Real Political Economy of Technology - The Ideas Letter
Two technological futures are competing for political and material priority: generative AI and the green transition. Benanav argues that while AI is marketed as a world-reordering breakthrough, its productivity gains…
The Missing Institution: A Global Dividend System for the Age of Transformative AI — Digitalist Papers
How do we share prosperity in society if work no longer sits at the center of people’s lives? In this essay, Anna Yelizarova explores one provocative possibility: a “global dividend.” If transformative AI shows little respect for national borders, with effects that spill across countries, then we ne
Labor market impacts of AI: A new measure and early evidence
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
AI as Normal Technology
The normal technology frame is about the relationship between technology and society. It rejects technological determinism, especially the notion of AI itself as an agent in determining its future. It is guided by lessons from past technological revolutions, such as the slow and uncertain nature of technology adoption and diffusion. It also emphasizes continuity between the past and the future trajectory of AI in terms of societal impact and the role of institutions in shaping this trajectory.
Mixture of Experts Explained
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
New_ Public | After the Feed
What happens after the social media feed? A presentation on the future of conversation, community, and social connection, and how AI will affect the everyday lives of billions.
AI Safety Is a Narrative Problem · Special Issue 5: Grappling With the Generative AI Revolution
This op-ed explores power and narrative dynamics around AI. Drawing on pop-culture references, the professional experiences of the author and examples from 2023’s “Great AI Safety Hype Roadshow,” this piece draws on the literary criticism technique of practical criticism to consider how speeches and announcements from both Silicon Valley executives and research scientists to interrogate the media-friendly nature of p(doom) discourse—which focuses on the existential risks of AI (PauseAI, 2023)—and its likely consequences. The complexities of AI and its numerous social impacts can be difficult for even the most expert analyst to unpack. In spite of this, the potential of “existential threats” has successfully cut through to become a mainstay of mainstream media coverage over the last year. This piece will make the case that this is an effective narrative conceit that has achieved a number of ends that traditional science communication tends to find difficult, if not impossible, to achieve. Firstly, it is easy to understand. Simplification of this nature—that removes jargon and complexity and focuses on a single outcome—is much easier to fit on a TV rolling news ticker or on the cover of a tabloid newspaper than more well-balanced, representative opinions. Secondly, it inherits prior assumptions from well-known dramatic forms. P(doom) plays to stories familiar from Greek tragedy through to Marvel movies, in which lone male heroes battle ineluctable forces. Thirdly, it is imbued with urgency and so becomes difficult to ignore.
The Future of AI Should Serve People, Not Platforms - The Liquid Frontier
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In this essay, Carson Gross discusses his advice to young people interested in computer science worried about the future given the advancements in AI.
The hidden cost of AI: In conversation with Professor Mark Graham
The hidden cost of AI: Countless humans toil in monotonous, low-paid jobs to power the algorithms behind our tech. Professor Mark Graham exposes this digital sweatshop, calling for accountability and worker rights.
AI, I tried to love you – Writings and rehearsals by Nathan Schneider
Among my peers who study the cultures and economies surrounding technology, I often find myself more tech-positive than most. On the whole I like computers, like the internet, and like in general the emergent human creativity that these things have enabled and revealed. A source of my appreciation is that I expend a lot of effort on the practice of “slow computing,” which is to say, choosing tools and technical communities that I can feel in right relationship with. I tend to approach new socio-technologies (like gig economies, blockchains, and social media) not from a perspective of why is this so bad? so much as asking how could we do this right?
After the feed with Eli Pariser, Ben Smith, and Jasmine Sun
Andrew Ng: The Biggest Opportunities in AI Aren't Where You Think