Collective action strategies in the age of AI w/ Nick Vincent from Data Leverage

Mar 4, 2026

Nick Vincent, assistant professor at Simon Fraser University and author of Data Leverage, studies how data can be used as bargaining power. He explores data strikes, poisoning, and redirecting contributions as collective tactics. He discusses agentic coding tools, risks from local data and sandboxing, and how LLMs’ reliance on collective data creates leverage for organized pressure.

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INSIGHT

Coding Agents Create New Collective Data Opportunities

Coding agents mark a step-change in AI utility because they keep rich transcripts and run locally, enabling new collective data strategies.
Nick warns agents can read all files, so transcripts stored on-device open opportunities for pooled sharing or leverage.

ADVICE

Sandbox Agents Before Granting File Access

Try coding agents yourself but limit risk: sandbox them in VMs, containers, or air-gapped hardware before giving file access.
Nick recommends cautious experimentation now while defaults and deletion tooling mature.

INSIGHT

Three Questions That Determine Junior Engineer Risk

Whether AI kills entry-level software jobs depends on three questions: true capabilities, industry hiring norms, and whether education teaches irreducible reasoning.
Nick stresses evaluation contamination risks because models may memorize answers from web sources like Stack Overflow.

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I spoke to Nick Vincent, assistant professor of computing science at Simon Fraser University and author of the Data Leverage substack, about what it actually means that AI systems are built on the collective output of humanity's digital labor and what we can do about it.

Nick has spent years researching how data functions as a bargaining tool, and we get into the nitty gritty of what that looks like in practice: data strikes, data poisoning, and redirecting your data to competitors as forms of collective action. We also discuss the explosion of agentic coding tools like Claude Code, what they mean for software engineers and entry-level workers, and why the very thing that makes LLMs so powerful — their dependence on collective human data — is also what makes them uniquely vulnerable to organized pressure from below.

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