One thing CEOs, knowledge workers, and investors seem to agree on is that AI is a threat to jobs, the economy, safety, and human meaning. But if you talk to anyone in the AI industry—or to early adopters outside of it—you'll hear the same thing we've noticed internally here at Every: There's more work to do than ever.
I don't believe there will be a tipping point where things flip and the jobs are gone. The new reality is the opposite—the more we automate, the more expert human work there is to do.
Here's why: AI commoditizes the residue of human expertise—whatever can be made explicit enough to train on. That collapses the value of default model output and creates demand for what's different. Demand for what's different is demand for human experts, even as we approach artificial general intelligence (AGI).
We are a team of almost 30 people, and we haven't fired all of our employees in favor of agents. We haven't ditched software-as-a-service (SaaS) products in favor of vibe-coded apps. We still hire humans to do customer service (with a lot of agent assistance), and we still hire human writers and editors and engineers.
Sure, employee agents take over more of the stable, repeatable, well-framed layer of work. But there is a lot of work that still requires a human being in the loop. We've found over and over that for any kind of complex task, the best way to get great work is to have an AI and a human going back and forth in the same workspace.
Humans are still vital. In every example, the agent needs a human in order for the work to, well, work. The further away an agent gets from a human who is in charge of making sure it works well, the less well it works. Someone has to point it at the right thing, decide whether the output is good, catch the places where it is wrong, and turn the result into a real-life decision or process.
That's because the current generation of models only knows about work that has been done. Humans know about what needs to be done, right now. Humans are alive to a specific time, customer, codebase, or conversation in a way the training corpus isn't yet. The aliveness isn't just having more current data. We come to the moment from somewhere, with a continuous, constantly new perspective of our own—running wants, running concerns, and a running read on what matters, which changes what we see.
Making expert work cheaper does not therefore simply replace experts. It creates more situations where expert judgment is needed.
