Babysitting the Machine: Glean's Rebecca Hinds on the Hidden Human Labor of AI at Work

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Today, my guest is Rebecca Hinds, author of the bestseller Your Best Meeting Ever and head of the Work AI Institute at Glean, which has just published the new Work AI Index 2026 report, which draws on a survey of 6,000 digital workers to describe the state of AI as it's used and experienced by employees at companies that are operating well outside of the AI bubble.
The headline numbers are genuinely strange. 87 percent of workers now use AI. Seventy-three percent say it makes them more productive, and on average they report saving 13 hours per week, a third of a full work week. And yet only 13 percent say their organization is performing significantly better as a result. The report contributes two new terms to the AI discourse, bot-sitting and bot-shitting. Bot-sitting is all the unglamorous, untracked labor required to make AI useful, feeding it context, debugging its outputs, and cleaning up its messes, which the report finds consumes 6.4 hours per week, or roughly half of all the time that AI supposedly saves. For those who are being asked to automate parts of their work that they'd rather do themselves, such as, for example, a customer service representative who enjoys talking to people but is now being asked to supervise agents, this can be especially painful. Such alienation predicts both reduced engagement and increased turnover, and helps explain bot-shitting, which is when people deliver AI-generated work that they can't explain or defend. In the extreme, business becomes farce, a perpetual motion machine of AI slop, and shockingly, in the survey, 69% admit to doing it, a number that reflects both the incredible progress that AIs have made, and perhaps the amount of bullshit work that people are asked to do.
Obviously, one part of the solution is more integrated AI systems, which have the context that they need. My experience with my own deep context system is that it's dramatically reduced my own time spent bot-sitting. We discuss how Glean's Enterprise Graph product is playing a similar role for enterprises. Beyond that, we also consider what organizations can do to create a more functional AI culture, including how to use AI detection to protect the business without discouraging positive use, rewarding people monetarily for effectively collaborating on AI solutions, and perhaps most powerfully, aligning work to a meaningful shared mission.
My mission for this show, as you may know, is mostly to learn and to help others learn as much as possible. But lately, I've also been trying to entertain and delight you with original songs, made with Suno, which we've been playing at the end of each episode. I've really enjoyed the comments that people have sent about these, and I encourage you to stay tuned to the end of this episode for a legitimately catchy tune with some outstanding, poignant AI-written lyrics. It did require quite a bit of bot-sitting to get it just right, but I do enjoy the final product, and I hope you do too. With that, I hope you enjoy this groundbreaking look at AI as it's practiced in large-scale organizations throughout the English-speaking world. With Rebecca Hinds, head of the Work AI Institute at Glean.
Rebecca Hinds, head of the Work AI Institute at Glean, and author of the new Work AI Index 2026 Report. Welcome to The Cognitive Revolution.

**Rebecca Hinds** (3:32)
Thank you so much for having me, Nathan.

**Nathan Labenz** (3:34)
I'm looking forward to this conversation. I think people like me who live very much in the AI bubble, which is a social bubble, yet a very online one, and also a day-to-day work bubble. It's just like how I use my computer, I think it's kind of diverged pretty significantly from what the rest of the world is doing. I think people like me sort of run a bit of a risk of getting detached from, especially because I work by myself largely these days, kind of run a risk of getting detached from what's going on in the real world, at real companies that are actually driving most of the economy, and where not everybody has the luxury or the inclination to be a bleeding edge early adopter with all of the, I'd say more ups than downs certainly, but certainly a mix of ups and downs that come with that. Today's conversation is going to be a really good exercise in grounding and calibrating myself and understanding in the bigger world, what is the current state of play. I would love to start just by getting a little bit of grounding from you though.
In terms of how you understand AI, I think so many AI conversations kind of diverge early or people can sort of talk past each other if they're expecting very different things in the near-term future of AI, and that's not necessarily put on the table beforehand. So what is your kind of expectation? Like are you an AGI or short timelines, AGI soon person? You make analogies to other technology waves, give us kind of your zoomed out view, and then we'll zoom in on the report itself.