Superagency: What Could Go Right?

Perhaps Hoffman's most provocative argument: pausing AI development doesn't make the world safer. If responsible actors stop while adversaries continue, the pause only benefits those unconcerned with safety. The safest path is responsible innovation — building safety into systems through real-world deployment and iteration.

Hoffman's narrative sounds egalitarian: everyone with access becomes a superagent. In practice, the gap between "AI tourists" and "AI natives" is widening. The people who benefit most already have clear goals, domain knowledge, and a habit of structured experimentation. They know which questions to ask, which outputs to ignore, and when to stop automating and start exercising judgment.

Teaching "how to think with AI" (meta-skills, evaluation, risk awareness) matters more than any specific tool or prompt recipe. See our Prompt Engineering and AI Critical Thinking courses.

Where reality diverged: the thesis assumes a broad, even distribution of superagency as models get cheaper. What we actually see is concentration of advantage. Organizations that invested early in AI literacy, internal tooling, and data pipelines now pull away from the pack, while late adopters struggle just to write "AI strategy" PDFs.

Individual power users are dramatically more effective than casual users because they build personal toolchains, not just one-off interactions. For our Academy, this leads to a clear design principle: we don't just describe superagency — we train learners to become the kind of operators who can wield it responsibly.