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AI may radically change our conception of “who is in an org”.
Historically, there is a sharp delineation between an org’s staff (on the “inside”), versus its users (on the “outside”). The relationship of a fulltime employee of an org is radically different than that of one of its customers. This is especially true for consumer tech startups & companies: think Google, Meta, Reddit, X, now perhaps OpenAI and Anthropic.
With AI however, the bar for an external user to be able to contribute, drops drastically. At the same time, employing and compensating this user will be much easier, as AI obliterates transaction costs. Overall, I expect org boundaries to become much more porous: more part-time workers, more volunteers, more collectives and communes.
Stepping back: why is it the case that orgs have a relative few fulltime employees? What makes it hard for a single org to stretch to encompass more humans? I’ll go through four major historical drivers, and how AI might change each.
First, it’s hard to hire and onboard. Hiring requires a lot of trust. An org wants to know that a person is a good fit, worth paying for. The job market is famously a market for lemons. Orgs try to vet candidates, but resumes are low-signal; interviews cost precious staff time, and also select for people good at interviewing. Ideally you would see the person doing the job — in a work test or work trial.
AI can change this by making search and matching problems much more efficient. And this is not just “pair up existing resumes & job listings”. AI can form a much more detailed model of a human based on all their public outputs, and a role’s requirements based on all of its outputs. Or: have your Claude apply for jobs, have their Claude interview your Claude to see if the fit is good.
Second, larger teams incur higher communication costs. The number of pairwise relationships grows quadratically with team size; Dunbar famously pointed to a cap of ~150 people per stable community. Startup literatu