The AI Daily Brief: Artificial Intelligence News and Analysis
Botsitting: The Work Draining AI Gains
26 Jun 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is botsitting and why is it important in the AI workplace?
Today on the AI Daily Brief, we're talking about botsitting and the hidden labor that comes with the AI transformation of work. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Super Intelligent, Mission Cloud, and OutSystems.
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Today, we're talking about a new report from Glean and the Work AI Institute that's part of their Work AI Index for 2026, and it's all about something called botsitting, or the hidden human labor of AI at work.
Now, one of the things that you may or may not have noticed this year is that I've done a little bit less coverage of studies from, for example, consulting firms or enterprise-focused research houses, and there is an actual specific reason for that.
There's actually a couple of reasons, but they all come back to my feeling that the paradigm has shifted so much between non-agentic and agentic work that anything that's interacting with non-agentic work is largely irrelevant. Now, of course, if you are an enterprise AI leader, that's not the case.
There are still lots of use cases that are non-agentic that are going to be valuable and productivity enhancing. But you guys know that I have a very strong bias towards being interested in opportunity AI, not just efficiency AI, and the big changes that I see happening in terms of how we work, not just doing the same stuff we've always done a little bit faster.
This report, however, starts to get into and name some new types of work that surround AI and agents that I think is really valuable to call out and start to explore. So that's what we're going to get into. Now let's start with the statistics that they use to set everything up.
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Chapter 2: How does AI impact individual productivity versus organizational performance?
In fact, the report found that frequent bot sitters, which they define as respondents who spend 40% or more of their AI time on bot sitting activities, i.e. above the median for AI users, 73% were more likely to be actively hunting for another job. Now, in terms of who is botsitting more, one culprit is simply higher volume of work.
Heavy AI users are more likely to report frequent botsitting than light users, which I think makes intuitive sense. But the report argues that actually tools sprawl. In other words, the number of different AI tools workers use is another big culprit, with workers who use multiple AI tools 35% more likely to report frequent botsitting.
They also found that right now, 60% of workers are rerunning the same prompt across multiple tools because the first output wasn't good enough, all of which adds up to what they call the AI toggle tax. Now, outside of just feeling overwhelmed or burning out, there is another even more pernicious outcome of bot sitting, which the report labels bot shitting.
Now, I had my editors bleep it there, but I don't think it takes too much imagination to add the H to bot sitting to understand the term. Effectively, the process of botsitting turning into botshitting is when workers start to cognitively offload too much to AI. Now, this can happen even without botsitting.
We've all seen others and felt probably in ourselves the temptation to hand over more and more of our thinking and judgment to the AIs we use. As we start to trust the outputs more, especially around common or routine tasks, we stop checking the outputs. We stop verifying the sources.
And in an enterprise context, many admit that they start to ship the first output that looks good enough instead of pushing for one they can actually explain, defend, and stand behind. Now, of course, these things can happen even if we're not spending a ton of time bot sitting.
But when you add the additional layer of burnout and frustration that comes with that bot sitting work, this can become even more likely. The report writes, bot shitting is rarely a single bad decision or a reckless click. It's usually a slow surrender of agency, one shortcut at a time. First, workers stop fully understanding the output. Then they stop interrogating it.
Eventually, they stop feeling responsibility for it at all. And not only does this come with offloading understanding or offloading judgment, people also offload responsibility. The report writes that when AI-generated work fails, 40% of workers blame AI and only 29% admit that it was their own fault.
They call this an example of moral disengagement, writing, it's the gradual mental process by which people stop holding themselves accountable for harmful or careless behavior. Heavy AI users, they found, are 3.4 times more likely than light users to blame the tool when something goes wrong. So the cycle that they're identifying looks something like this.
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