Dan Shipper
CEO of Every
Published May 21, 2026
There is a paradox at the heart of AI.
At Every, we’ve automated everything we can. We use Codex and Claude Code across coding, writing, design, customer service, and more. We alpha-test all of the new models from OpenAI, Anthropic, and Google before they come out. We are riding the exponential boom in model intelligence and automation as far and as fast as possible.
And yet it seems like, for us, there’s more human work to do than ever. 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.
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There’s no tipping point coming 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).
To understand why this is, we have to go beyond the graphs, and look at how AI is used for work today. That will help us see, in a more grounded light, the paradox—and its resolution.
Here are some thoughts:
This article is important because it challenges the simplistic narrative that AI will replace psychologists. It correctly argues that automation creates new demand for expert judgment and contextual sensitivity. Psychotherapy also requires relational presence and ethical accountability, as I have argued. Psychologists should take heart from this. Our core skills, including ethical reasoning, relationship building, sensitivity to diversity, and case conceptualization, are not being automated away. They are becoming more valuable.
However, psychologists should also be wary. The article underestimates the cognitive burden of supervising AI, ignores the need for formal training, and downplays the subtle ways that fluent but fallible AI can exploit human heuristics (known as automation bias). It also fails to address data privacy, algorithmic bias, deskilling, and the potential for AI to widen inequities in access to quality psychological care.
The practical takeaway is this. Psychologists should learn to use AI as a tool for editing, treatment planning possibilities, and case conceptualization, but they must remain accountable in all areas of practice. We should advocate for training programs that teach AI literacy as a core competency. We must insist on AI tools that are transparent, privacy preserving, and validated on diverse populations. And we must remember that the heart of our work, the healing relationship between two human beings, lies entirely outside the space that any current AI can occupy. That is not a limitation to be overcome. It is the enduring reason human psychologists matter.
