When people do work with generative AI, they frequently do knowledge work. But will broad adoption of AI *democratize* knowledge work?
In a new perspective in @natcomputsci.nature.com, we lay out technical + social factors shaping access to and use of AI for knowledge work around the world.
Civil society resistance to AI is not just about capacity. It has anchors in deep and serious concerns around data centers, intellectual property, the lack of regulation, the displacement issues, and more.
As always, these challenges require social as well as technical interventions. What, exactly, the social interventions should be changes, though, depending on the extent to which skill-biased or equalizing effects prevail.
ICYMI, the proposed USPS rules that follow the March mail voting EO are live for comment until July 2. A couple thoughts🧵 www.cnn.com/2026/06/10/p...
State election officials could soon face a stark choice: hand over voter lists to the Trump administration or risk losing Postal Service delivery for mail-in ballots.
Anthropic is embedding AI fellows at nonprofits around the United States. Think Code for America, Peace Corps, or Americorps, but make it AI. It's called Claude Corps.
2. Access and usefulness is also constrained in low-resource languages and where knowledge work is a small portion of the economy.
In the absence of intervention, we risk an AI divide not just in who accesses and uses AI for knowledge work, but also in who benefits versus who is harmed.
1. The distribution of AI's benefits depends on the task.
- Equalizing tasks are standardized, common in training data, and have objective success criteria.
- Skill-biased tasks are open-ended, creative, and where success hinges on user discretion (or taste!)
