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

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Show Notes

Rebecca Hinds, author of "Your Best Meeting Ever" and Head of the Work AI Institute at Glean, breaks down the surprising findings from the new Work AI Index 2026 report surveying 6,000 workers. While 87% now use AI and report saving 13 hours per week, only 13% say their organization is performing significantly better—a paradox explained by two new concepts: "botsitting" (the hidden labor of making AI useful) and "botshitting" (delivering AI-generated work you can't defend). They discuss practical solutions including better-integrated AI systems, smarter AI detection policies, and aligning work to meaningful missions.

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CHAPTERS:

(00:00) About the Episode

(03:22) Grounding AI adoption

(06:46) Methodology and Glean

(12:25) Productivity paradox emerges (Part 1)

(20:31) Sponsor: Claude

(22:22) Productivity paradox emerges (Part 2)

(25:36) Bot sitting burden

(34:00) Hidden time savings

(39:56) Meaning versus automation

(47:14) Enterprise graph potential

(53:13) Detecting bot slop

(01:00:32) Retention and incentives

(01:07:54) Transformation and mission

(01:20:06) AI teammate model

(01:26:01) Future organizational design

(01:32:31) Research and meetings

(01:41:43) Episode Outro

(01:45:07) Outro

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Transcript

This transcript is automatically generated; we strive for accuracy, but errors in wording or speaker identification may occur. Please verify key details when needed.

Introduction

[00:00] 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 the bubble.

The headline numbers are genuinely strange. 87% of workers now use AI, 73% say it makes them more productive, and on average they report saving 13 hours per week—a third of a work week. And yet only 13% say their organization is performing significantly better as a result.

The report contributes two new terms to the AI discourse: "botsitting" and "botshitting."

Botsitting is all the unglamorous, untracked labor required to make AI useful—feeding it context, debugging its outputs, cleaning up its messes—which the report finds consumes 6.4 hours per week, or roughly half of all the time AI supposedly saves.

For those who are being asked to automate parts of their work that they'd rather do themselves – such as a customer 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 botshitting, 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. 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 people are asked to do.

Obviously one part of the solution is more integrated AI systems which have the context they need. My experience with my own "Deep Context" system is that it's dramatically reduced my own time spent botsitting, and 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 with this show, as you may know, is mostly to learn and 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’re playing at the end of each episode. I’ve really enjoyed the comments people have sent about these, and I encourage you to stay tuned at the end of today’s episode for a legitimately catchy tune with some outstanding, poignant AI written lyrics. It did require quite a bit of botsitting to get it just right, but I really do enjoy the final product and hope you do too.

With that, I hope you enjoy this grounding 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.

Main Episode

[03:22] Nathan Labenz: Rebecca Hines, head of the Work AI Institute at Glean and author of the new Work AI Index 2026 report, Welcome to the Cognitive Revolution.

[03:32] Rebecca Hinds: Thank you so much for having me, Nathan.

[03:33] Nathan Labenz: I'm looking forward to this conversation. I think. People like me who live very much in the AI bubble, which is kind of a social bubble, albeit a very online one, and also a day-to-day work bubble, that's just like how I use my computer, I think is 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. So 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? Do 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.

[05:11] Rebecca Hinds: It's a great question. I think, in many ways, what we're seeing with AI is not unlike what we've seen with previous technologies, in particular when we think about the change management. And that is what we're seeing above all else. This is a human change just as it is a technology change. And as we've seen with every other technology change, we underestimate the human piece. We underestimate resistance. We underestimate fear. Now, AI has this very unique element in that the fear is more visceral. And I think, you know, in large part, as trivial as it might seem, the naming, you know, it comes back to the naming. The fact that we've named this thing artificial intelligence, etymology, it's fundamentally in tension with human intelligence. And I don't think organizations take that seriously enough. The fact that even if the technology works objectively, if employees don't see it as a teammate, if they don't see it in the context of something that can amplify or augment their skill set, well, they're going to either resist or if they're going to symbolically use the technology as opposed to meaningfully using it. And that's what we're seeing. That's what we're seeing on the ground is we're seeing pockets of excellence. We're seeing individuals, teams, and organizations fundamentally transform themselves with this technology in very, very exciting ways. But we're also seeing the vast majority of, in particular, teams and organizations struggle. Struggle to translate the individual productivity gains, which pretty much everyone is getting on some level to real business and team level outcomes.

[06:47] Nathan Labenz: Makes sense. I might have a couple little follow-up questions regarding your expectations as we go. But Tell me about the methodology. I mean, everything we're going to discuss here in terms of findings is downstream of basically 2 main data sources, as I understand it. One is a big survey, which you can tell us more about. And then the other that caught my eye in the report is aggregated telemetry from the Glean platform. And I'm interested in what that actually looks like and understanding a little bit better the substance of that data as well.

[07:18] Rebecca Hinds: Sure. And there, the main bulk of the data is the survey data. This was something that has been in the works for months and months. We fielded it December and January 2025, 2026. And I think what's novel about how we collected the survey data, so it's 6,000 knowledge workers, 3,000 in the US, and then 1.5K in each of the UK and Australia. It was developed in partnership with our eight founding members of the Work AI Institute, which is our internal research center at Glean. And what's special is each one of these experts, these eight experts comes at the AI conversation a little bit differently. Some are very much focused on the psychology, the mindset around AI. Others are more focused on the technology, you know, digital transformation. Others are more focused on org design. You know, how do we think about this from a systematic, you know, org design perspective? We thought it would be important, given the transformative nature of the technology to have a pulse into all of these different dimensions. So that's the survey component. A lot of conversations, you know, we're talking with our customers at Glean as well as, you know, organizations broadly in terms of what works, what doesn't work. And so that informed a lot of the narrative. And then the aggregated anonymous Glean telemetry data is something that, is very exciting because it's objective. And, triangulating the data in terms of both the subjective survey data, which is, inherently biased for many different reasons, pairing that with something that is objective, we thought was important. And in particular, looking at how we're seeing adoption happen on the Glean platform. You know, the importance of cross-functional adoption, the importance of, you know, whether your manager adopts or a team member or cross-functional team member adopts, the network effects associated with this technology are massive. And when we think of change management, overwhelmingly right now we're seeing top-down change happen, mandates, memos telling employees to use AIRLs. Well, the best organizations, the most effective ones, they're having a bifurcated strategy. Yes, top-down change is important. We absolutely need a policy. We absolutely need principles. We absolutely need to see the CEOs and executives using the technology. But bottom-up change is just as important and finding these AI influencers or champions within your organization is so essential to activate and activate meaningful change and not just the symbolic change because the CEO has told us to use the technology.

[10:01] Nathan Labenz: Moment to just take a beat on Glean and what Glean does. I think our audience is generally AI obsessed. I think that's the one commonality that we all share. So everybody has heard of Glean at least. I will confess though, as an individual operator, I'm obviously not in the sweet spot of the target market. And so I've never actually used it. And I don't know too much about what the experience is like, although I certainly have a sense. And I also don't know how you go to market, whether it's a sort of enterprise sales model versus, a kind of product-led bottoms up, or maybe it's a hybrid. But maybe give us like the double click on Glean so people can understand, in a little bit more of a functional procedural way what that, how that data is being generated.

[10:50] Rebecca Hinds: Sure. So Glean is a work AI platform. Historically, we started in enterprise AI search, solving the problem of how do we find the right relevant information within an organization. This was pre-AI, pre-mainstream AI. Now our platform has an intelligent assistant as well. So every employee has that intelligent teammate that deeply understands not only how they work, but also how the organization works. So starting to give you recommendations in terms of what you should be prioritizing each day, as well as when you ask it questions, knowing enough about you and your job function to provide intelligent answers. And then agents are a big part of the platform as well. So automated workflows But again, have that organizational context and able to streamline tasks and cross-functional tasks across the organization. And the real, I think, bread and butter of the platform is context. There's a lot of conversations right now around the importance of the context graph. Well, our data model enables us to surface that context and feed off of it in a really exciting way when AI is able to truly understand your work, your team's work, and the organization's work, well then you avoid generic answers and you start to get into this really exciting territory of predictive AI and proactive AI telling you what matters right now in the moment of your day-to-day work.

[12:26] Nathan Labenz: Cool, interesting. The mix of company context and individual context is definitely a pretty live discussion. I'm thinking of Dan Shipper and every team and how they've kind of experimented with various versions of this and already evolved their approach in a meaningful way. I have my own pretty elaborate personal AI infrastructure at this point that is kind of an extension of me in one lane and then also increasingly like trying to get it to be sort of an employee that can do its own thing with more and more autonomy as we go over time as well. Maybe let's again come back to that. I want to make sure we don't bury the lead on the headline of the report, which I do think is interesting food for thought at a minimum. So I'll give you the headline and you give me the expanded version of it. 87% of people that you surveyed are using AI. 73% say it makes them more productive. And the one that really blew me away, on average, they say they save 13 hours a week thanks to their use of AI. That's 1/3 of a work week. Some caveats to come, obviously, but that's a lot of time, like a really strikingly large number. But then the flip side of this is only 13% of the survey respondents say that their organization as an organization is performing much better than it used to be based on all this AI that's happening. Those are the big headlines that jumped out to me. Give me the double click on how we should start to understand that.

[14:01] Rebecca Hinds: Sure, and I think you've hit the nail on the head with your previous set up to the question and that, we're seeing this massive disconnect between individual productivity gains and organizational performance. Now, this is survey data, so it's, inherently biased in terms of how people are reporting. Depending on how we ask multiple questions around time savings and, depending on the question, we saw ranges from 10 hours per week to 14 hours per week. On average, what we're seeing is people are reporting that based on the work output, they have fully automated with AI. It's about 11 hours of time savings. But when you ask them about the organizational performance, do they see their organization perform significantly better as a result of the technology? We see this gap, just 13%. The fact that 13% of employees only 13% are saying that their organizations are significantly performing better or performing better significantly because of the technology. And it's a big disconnect that we've seen in multiple different studies. We've certainly seen in the headlines the budgets that don't pay off, the ROI that doesn't pay off. What's novel about this report, this Work AI Index is where we're theorizing the time savings is going. And we're coining this phrase bot sitting as the hidden human labor that is required to make the technology usable. I think we all feel it on some level. It's the feeding AI context. It's the overseeing the AI. It's the debugging the AI. It's the cleaning up after the AI. And it's a massive chunk of labor, on average, upwards of six hours per week workers are spending on this bot sitting activity that is often tedious. You make a point in the report to point out not all bot sitting is negative, certainly. There are healthy forms of bot sitting, but more often than not, it is negative, it is tedious, it is exhausting, it is not rewarded, and it's not appreciated or tracked or measured and certainly not incentivized within the organization. And I think that is feeding a lot of this failed expectations, the an execution gap that we see across the board, not recognizing the human labor and recognizing that this isn't in most cases a problem in terms of the individual employee. This is a systematic issue where we've invested in tools that don't have that context. We've invested in tools in silos that now we're seeing AI sprawl and agent sprawl. And it's a whole host of different factors that are coming from multiple different directions to feed this bot sitting which then turns into what we're calling bot ********. Bot ******** is the dissipation of work that hasn't been checked, that workers can't explain. We see 40 to 41% of employees saying they ship AI work that they couldn't explain if asked. This is bot ******** and I think it's pervasive. We often see the polished nonsense come where it looks polished, it looks finished, it looks like we've done the assignment. When we dive a little bit deeper, it's actually quite hollow. There's very little substance. That's part of it. That's the most visible form. But it's also, it's the shadow AI. It's the using AI that you can't explain in terms of the output. It's a whole host of different dimensions as well.

[17:40] Nathan Labenz: I guess to give you my personal bot sitting and near miss of bot ******** experience, just in preparing for this conversation, I have an agent that's runs every day and it looks for new podcast bookings on my calendar. And then its job is to, and it does have pretty good access, but this anecdote goes to show just how important connecting all the dots is. because for lack of 1 connection, the result came out not so good. But I've given this agent access to my drive and in my drive, I've got hundreds of these previous documents where I've written up these outlines of questions. So it can go in and look at all those that I've previously done and use those for inspiration. Of course, it can go search the web and whatever. And so it saw you pop up on the calendar and then it went and did its thing and did a big, pretty extensive deep dive into your background and your book and which we can touch on a little later as well. And the previous reports and things you've been involved with. And it missed one point, which is that there's this new report, which I had gotten a copy of in e-mail from one of your teammates, not specifically sent by you. And so basically the whole thing was not gonna work. It was like maybe 10 to 20% of that was the conversation we actually were gonna wanna have, but it missed. plus percent of the substance of what we were really going to be talking about. And I think I know better and I didn't send that off without noticing that. But if I had, it would have been, I would think textbook bot ******** behavior, which I do think we should all be very much watching out for. Now I got a much better result when I said, hey, you missed something really crucial, which is the report we're actually going to be focusing on. And then still there's another round where I feel like to do a good job, to be respectful to you as a guest, to be respectful of the audience who's trusting me with their time, which is obviously the one resource they can't possibly get more of, is I have to come having internalized that information and ready to make it my own. So even though I did get a great jump and a very meaningful assist from the agent, And in some sense, it was maybe good enough as an artifact that I could have shared and you would have been like, this is fine. Good thorough outline of questions. I still need to do that extra work to be able to show up and have the interaction in a way that hopefully allows me to do a good job and not feel like I'm making it up or reading it cold as I go. So I definitely relate to those two different things. I think I've had fortunately very few instances where I've let something get away from me without noticing that the bots have gone haywire. And I wonder, what do you think is the root of this problem, especially the bot ******** one, where people are just putting work out there into their team environment that they can't even defend? What's the root of that? Is it that they don't understand that the AI is not always going to do a good job? Is it that they're just alienated in the 1st place and fundamentally don't care? Are they low? Obviously, there's an issue in general with not everybody is super conscientious. So maybe this is just a new flavor of not doing a very good job for some people. What can you tell me about the psychology or how do you understand the mistake people are making when they do that?

[20:31]Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

Main Episode

[23:00] Rebecca Hinds: I think as most things are, it's multifaceted. And in the report, we theorize the cycle at play here. I do think it starts with bot sitting. I do think it starts with all of this manual work that is often a reflection of all of the AI that's being deployed in our organization and the pressure to adopt. Organizations are under massive pressure to adopt this technology and implement it. So you start to get more pressure to adopt. You have more. need to bot sit the technology, that is exhausting. It's exhausting because it takes up a lot of time, but it's also exhausting because you're not rewarded for it. And there's really not very much incentive in many organizations to bot sit well. And what we start to see is good enough. In the research, they sometimes call it satisficing. Once you see an AI output that is good enough, well, that's often permission to ship it. your case, you're an expert, you're skilled at your craft, right? You're recognizing that good enough isn't good enough. And so that's a big, I think the bot sitting is a precursor to the bot ********. It's the exhaustion. And employees hit a breaking point. They can no longer bot sit and they start to bot **** more and more. I think a big part of this is the lack of context. The lack of context in the AI tools, the fact that so many AI tools don't speak to one another. In so many organizations, employees want to use different models and different tools. And that's why I'm excited about Glean as a platform as well, because there's no situation in my mind that I can envision where we have a single model a single tool environment. Employees want that choice. We're seeing models leapfrog each other left and center. Employees want different models, different tools for different use cases. It becomes very complicated if you don't have that contextual layer to connect them and make sense of them. And not just in terms of how are the dots connected, but also in terms of recency. Knowing that this report is published this week, this month versus a report that was published 2 months ago, two years ago, that should be given different treatment. The authoritativeness of the content as well, very hard to discern in an enterprise context. So I think context is the big feeder as well from a technical standpoint. And then you have all these perverse incentives in organizations. The token maxing, the rewarding, the clicks of the tool, that is a big contributing factor as well.

[25:37] Nathan Labenz: Yeah, I used to tell people, When I did any sort of AI advisory consulting, I would say you could do a lot worse than as a leader just watching your token consumption, but definitely don't tell the team that's how you're gonna be measuring them. So it's really super easy to cheat on that. It's amazing that's actually, that's honestly been probably years ago that I was saying that, and it's funny to see that people are still shooting themselves in the foot that way. I guess in terms of understanding my fellow human. I struggle a little bit with the idea that this bot sitting work is so onerous. My attitude, which I don't expect everybody to share, but I just will give it to you for compare and contrast, is like, I used to have to do stuff and now I get to have AI largely in many cases do this stuff for me. I still have to make a contribution. But I definitely get a lot more done a lot faster. And I also get to learn a lot more about AI and what it can do, which I find to be always a very interesting question unto itself. And just a lot more because I'm able to take on so many more different things. I'm able to learn much more broadly and satisfy my curiosity in all kinds of ways that I never could before AI. So if you combine that with a And I would say mine is even more. I was at this AI event recursive last weekend and the question of people in the audience was, if your team had to replace you plus AI as it exists today, how many of you unaided by AI would they have to hire to get the same output? The median answer in that room was like basically two. In other words, people thought that they were twice as productive thanks to AI as they would be if they were unaided. And that's pretty much where I put myself as well. So even though, leaving that aside, okay, so people are reporting 13 hours gained. There's another stat in the report that says, let me make sure I get it exactly right. People are spending 6.4 hours, basically half of that time savings on this bot sitting activity. reviewing outputs, connecting things, different products that don't connect. And I think anybody listening to this show certainly had that experience of, okay, I got a clawed prompt, I got a clawed report or plan or whatever, but now I got to put that into copy and paste. So the phrase you just use is the human becomes the integration layer. And I felt that, and it is certainly tedious, although it's honestly also just a part of general computer work, right? We've all got kind of Slack and then this other task tracker and then there's e-mail. And so it's all a little bit disjointed anyway. All that to say, I don't really get it. Like, why is it so bad to be responsible for babysitting the bots or bot sitting? What is it that's really bothering people so much or alienating them so much about that?

[28:41] Rebecca Hinds: So it's the taking away from the meaningful work. And in the report, we look at three categories of interaction with AI. One is the bot sitting. One is the using the technology. So using the technology to do real work, you prompt it and It gives you the answer or it asks you a follow-up question in a way that you're moving work forward, iterating with the technology, as opposed to you're asking it a prompt. It doesn't have the context, so you're re-prompting it. We're seeing about 36% of all AI sessions fail, meaning a worker goes to use the technology and it's not successful. They either have to start completely from scratch or do significant rework. Imagine if it was right on the first time. Imagine if you could put those 6.4 or hours into either using the technology to drive work forward or the third category, which is learning or building agents, the time savings would be significantly higher. And so I think, again, there's a small component of bot sitting that I think is healthy. And for people who are curious, this is less of a problem. And I love to believe in human curiosity. I think the reality for many, and I think Nathan, you and I are probably an outlier here, the reality is employees are too to be curious right now. They're too overwhelmed with work to spend those one, two hours tinkering with the technology and prompting 4 different. tools and picking the right answer. And that's the problem. The fact that it's not meaningful work and it's not meaningful learning with the technology. We also look at along the different dimensions of bot sitting, what is most exhausting. In the report, we call it the exhaustion multiplier. And what we see is the highest exhaustion multiplier is associated with feeding AI context, right? Because that is in the best case, something your AI should know. know where the documents are, which documents are authoritative, and you should not be supplying that as a human in most cases. The other one is the debugging. The debugging. see an output, it's wrong, but because of the nature of LLMs, you're not quite sure why it's broken, what's wrong. You try to tweak one thing because the nature of the technology is probabilistic, it's not deterministic, you're not really sure what you tweaked that worked and didn't. That is the biggest contributor to this exhaustion multiplier.

[31:12] Nathan Labenz: Yeah, that's interesting. The idea that I do have this experience sometimes. One thing I've been really enjoying doing lately is creating songs for each episode of the podcast. You can start thinking about if you want to request a genre. I try, but I can't always promise to be able to make something great in any given genre. It's really striking how sometimes I'll just run my kind of produce episode Claude code skill, which includes coming up with an idea for a song, writing lyrics, prompting Suno with that. Sometimes I show up and it's like banger immediately. And then other times I find myself sitting there and it is this kind of black box thing where you're like, you know, I tweak the style prompt, maybe I tweak the lyrics a little bit, go again. And for some reason it's just not working. It's just not landing. It's just not giving me what I want. And that can be definitely a sort of exhausting thing, especially because sometimes I get in this spot where I'm like, who even cares about these Like, am I doing this for anyone? Although actually I do get remarkably a lot of positive commentary on the songs. Anyway, I can relate to that sort of exhaustion point. I can definitely also relate to the shoveling context point. I used to have before my, you know, now much more integrated setup, I used to have just a single PDF that had a bunch of intro essays that I'd previously done for the podcast. And I found it was kind of exhausting, although this is like such a baby first world thing to say. Even just to go find that PDF every time and put it into the web UI so Claude would have it to use as examples. And it's like, man, that's really not much to complain about. And yet somehow it feels so much better now that those sort of moving of files and context around have been mostly automated away. Just on the time though, I mean, okay, so I'm empathizing with some of these problems, some of these pain points for sure, but it still seems like there's something, I guess one of the hypotheses we should always keep in mind is people maybe just in many cases don't like their jobs that much. I think this is something that the AI discourse broadly should remember much more than it does. And I've probably got on my soapbox enough times about that already, but that's for sure an ingredient in this overall recipe. But it was just striking still that like, okay, 13 hours saved, a little under half of that sort of reconsumed by doing this copying and pasting and double checking sort of work. But that still gives you almost a full workday back, right? Am I reading that right? Are people like, have we created a four day work week that we're just not ready to talk about?

[34:01] Rebecca Hinds: So here's the problem, Nathan. And when we look at the individual level, we're seeing all of this exciting productivity gain. Even if we consider bot sitting, the net net is positive. The problem is, where is that time savings going is 1 aspect. And what we do see, especially in organizations that haven't communicated an AI strategy and employees aren't confident in their organization's AI strategy, employees are taking it for themselves, right? The amount of non-disclosure, the hiding of the AI usage to managers, it's rampant and we have some data points in the report. And so they're managing the perception. They're managing the perception of how they're using the technology because in many cases, you tell your manager or your organization you're getting 6 hours back, probably you're going to then get 6 hours more of work. And that is the wrong calculus. But I actually think the bigger problem is what happens at the individual level often does not translate to the team and organization. In the research, it's sometimes called coordination neglect, right? And I think the most concrete example of this is you can have me as an individual worker, I take a single bullet point, I convert it into a five page report, I send it to my colleague. A colleague takes that five-page report it's too long, turns it into a single bullet point, right? Each person looks productive each person looks like they've saved massive amounts of time, but when you... put the pieces together was just this hamster wheel of AI slop, work slop, lot ******** in a way that does very little service to the organization. And I think that's the disconnect here. It's very hard to measure, but it's certainly something we're seeing in practice in conversations with executives. And some of the data speaks to it certainly in terms of its individual productivity gains not translating to real team and organizational gains.

[36:03] Nathan Labenz: Part of me feels like that might be healthy. It could be more healthy perhaps, but a four day work week would be a nice stepping stone perhaps on the road to the AI future.

[36:15] Rebecca Hinds: I don't want to present the perception that I think in many ways we should be giving time savings back to the employee to be innovative, to be creative, to have better work-life balance. The problem is we don't have, most organizations in part because of the context problem, don't understand where the time savings is going and how to extract meaning from it at the team and organizational level. And that's the problem because we start to see, you know, that's I think feeding a lot of this more is more pressure. You don't know how X translates to Y, Y being business outcomes, KPIs. the knee-jerk reaction is the only thing I can associate is more tokens, more clicks in the tool is more output is a proxy for productivity, and that becomes very dangerous.

[37:08] Nathan Labenz: Do we see harm? It's one thing to say people are saving 13 hours a week. They have to give half of those back to these sort of somewhat annoying tasks. Maybe they save a bunch of time, they get more leisure, they, I don't know, do more social media at work or whatever. at least as long as nobody's asking too many questions. Only 13% though say their organization is doing much better, but is there a sort of corresponding stat for, are some people saying their organization is doing much worse? Because I think one, the sort of optimistic read of the results so far would be like, as people often say, it's still the worst it'll ever be. A survey was done at a point in time which was coinciding with a by many anecdotal accounts, at least another step change advance in models' ability to do search and assemble their own context on the fly, effectively, at least if given access programmatically to do it. And if you were to say, hey, only 13% are doing notably better, but like we're not really seeing anybody doing like terribly worse, then I'd say, hey, that's like a, we're off to a pretty good start. But you might say, actually, no, there's like a bunch of people who are saying the organization is like outright suffering as a result of this. So what do we see on that side of the ledger?

[38:23] Rebecca Hinds: It's a great question. So this particular question, and again, the time savings range from 10 hours to 13 hours. The automation is 11 hours per week. But in terms of the 13%, it's a Likert scale. So we essentially ask, do you strongly agree, disagree? So we definitely see all aspects of this spectrum. I think the optimism in me says you're right in the sense of we shouldn't expect transformative gains from this technology too quickly. I think what gives me pause and what does concern me is the volume of not just the bot sitting, but also the bot shooting, the shadow AI, the not disclosing, not being transparent to the organization, right? That is not going to get better if all else remains similar, it is only going to get worse. But I think we shouldn't rush to transform our organizations so quickly. I think that's also a really dangerous area to be in. the most effective organizations, they're measuring. And as part of that measurement, they're baking in failure. In the best cases, we're seeing executives pinpoint 80% of AI initiatives failing because failing is part of innovation. that I think we need to keep in mind too is a lack of transformation in certain areas is very healthy because it indicates that in the best cases, you're taking well-intentioned risks and benefits and hopefully learning from those failures as well.

[39:57] Nathan Labenz: One thing I wanted to follow up on was your comment on meaning. And because I thought that this was a I would say a striking apparent contradiction in the report is the observation that the people who feel most threatened by AI seem to be most eager to adopt it and use it more and more. And they're going so far as to automate work that they'd rather keep. And I'd love to hear a little bit more that it's obviously very connected to this question of meaning versus alienation. Could you give us some examples of things that you heard from individuals on how you end up in a spot where this is the part of my job that I actually liked, but I'm feeling pressured to have AI do it. What does that actually look like? I'd love to get a couple sketches if you could.

[40:53] Rebecca Hinds: It's such an insightful question. And we unpack several of these paradoxes or contradictions because there are many of them, right? There are many of them at play in part because this is such a psychological technology. And the fact that we're treating it as a human adds a whole bunch of different complexities. And one of these paradoxes, one of these contradictions is around why do we see the people who are most fearful of the technology, most worried about being replaced or displaced, in more. And I think a lot of it is the perception. You feel a threat, you don't fully understand the technology, perhaps you don't have the support of your organization, and you want to look AI native, you want to look like you're transforming. And so the natural knee jerk reaction in too many cases is to automate as much as possible. And unfortunately, in many cases, if you're looking to automate and visibly show that you're transforming with the technology, you're probably going to point it at parts of work that you are most familiar with. And in not all cases, but in many cases, the parts of work that you're most familiar with are probably the parts of work that give you the most meaning. Not always, but certainly a case. So concretely, what we're seeing is around relationships with other people, customers service, for example, these amazing customer service representatives who have spent years, decades developing that craft of the personal relationship, the long-term relationship, all of a sudden you have a technology that can in theory automate some of that relationship building, perhaps to get completely off your plate so that you're no longer interacting with a human. That's what gives you joy and meaning at work. That is very dangerous. And that is what we're seeing. And not just in this survey, there was a fascinating at a Stanford a while back that found 41% of Y Combinator AI startups are automating things that people would prefer to keep human. And again, this again boils down to the psychology of this. We can't just assume that because the technology can do something, because it can automate something, it should be automated. We know that so much of work and so much of the process is meant to be messy. It's meant to be full of friction because that friction, sometimes it's called the IQ effect, right? When we build something ourselves, when we do the hard work of doing the thing, well, that builds ownership, it builds good judgment, it builds purpose, it builds pride. And these are not feel good, nice to haves. These are hard drivers of performance. And it's a very difficult calculus because it differs for every person, every team, every organization, but it's absolutely essential that we think about, okay, what is this division of labor between humans and AI. And the calculus should not just be, can the AI do the thing? It also needs to take into account this human piece. What does the employee find meaningful? Because that meaning is going to drive their best work and it's going to drive situations where when they do get the time savings, they're reinvesting it into the betterment of themselves, their teams, and the organizations rather than more clicks of the tool or taking the time savings for themselves and not the organization.

[44:19] Nathan Labenz: That customer service example is a really good one. And I do think it goes to show how tough this is going to be in a lot of ways, because I can totally imagine being a person who is in that kind of job because you like talking to people, you're a people person, and that is what gets you going every day. And then to think, okay, you're not going to do that anymore, but instead you're going to get to sit in front of this agent builder UI or whatever and try to string together what you used to do and watch out for its failures. And it's like, I didn't sign up for this. I don't really, I never would have wanted this job in the 1st place if I had to do this, but here I am. And at the same time, If you put yourself in the leadership standpoint or honestly in the customer position as well, I think the logic of it is like pretty unavoidable that if only for responsiveness. I mean, I've looked at, for example, my company's response times when Fin is active on our intercom versus when it's a human. And we do a great job on customer service and we do have, you know, real people, people and our customers have always really spoken very highly of our customer success team. And yet the immediate response of fitness, in many cases, it's like, it's a real value driver for the customer too, because they get out of there in a couple minutes instead of, with the longer back and forth of a human could be, 30 minutes plus the doing it the old way. So I think that is really tough. If you think about, I've seen this said recently that companies are graphs of algorithms, and that I think did not come from Glean, but it very much rhymes with some of the recent releases around the enterprise graph. How do you think people should be thinking about this from a sort of leadership executive competitive dynamic standpoint? I think it's hard to say Maybe we wanna keep some humans in customer service because whatever, we got super high value customers, they're gonna value it, there's something intangible. But I think it's hard for most companies to really make an argument that we shouldn't take a 90% cost savings and the ability to be instantly responsive to all of our customers because people like doing it the old way. But then there are, that's one algorithm in the graph of algorithms that constitute a company. But then there may be other ones where you maybe do have a better reason to keep it more human. How do you think leadership should be decomposing their organizations and thinking for all the different parts of it, what they have to accept the tides of history and where they maybe want to hold on to things for special reasons?

[47:15] Rebecca Hinds: So this is where I can really geek out because I think it's so exciting. And when we think about the enterprise graph in particular, the enterprise graph isn't just a connection of people and tasks and documents, right? It's a collection of everything from the mission of the company to the goals, to the projects, to the tasks, to the people, to the documents, to the technology. And you can easily imagine a world where if, and we're starting to see it certainly with our customers at Glean, if you have an AI platform that understands enough about your organization, and we'll take this customer service example, it can do two things that are really exciting. One, it can understand, given all the interactions you've had with customers your sales team has had or anyone else, and understand, okay, what is, for example, the level of complexity in those interactions? what did the customer or client want in those interactions? Did they want that fast response or was it a long-term relationship building conversation? It can then make a determination or recommendation to you in terms of, okay, this is a predominantly human warranted interaction. This is a human in the loop type interaction. This should be completely automated and we should be be making the trade off ourselves of absolutely, I don't want to live in organizations that over index on keeping work meaningful for employees. In the best case, you sometimes automate parts of work that employees do find meaningful, but you absolutely replace that with parts of work that employees can now take on that they find just as meaningful. not more meaningful in the best case. So that's one dimension of this. The second dimension is if you have an understanding of not just the enterprise ENA, but also how individuals work, their skill sets, their career ambitions, You can start to make recommendations using AI in terms of, okay, what is the task allocation now for this specific person, given their expertise, given the goals of the organization, given the development of the technology, given their career ambitions, and it becomes incredibly exciting. We're seeing this in terms of how some of our customers will now staff project teams in fundamentally different ways. Previously, we've relied on the static org chart to staff our projects. If AI knows enough about your organization, it can make a dynamic recommendation in real time. Who across your 1000, 2000, 10,000 employee base is the right mix of people based on skill sets, based on expertise, based on career ambitions and passions, bandwidth, and do a complex calculus that we as humans could never do. That is, in my opinion, the North Star of enterprise AI. It's the power of the enterprise graph. And we're starting to see snippets of this come to life in a way that I think is incredibly exciting.

[50:26] Nathan Labenz: Yeah, I like that notion of, boy, it's been a long time since I've worked at a big company. But when I did, I had a lot of outside the box ideas. briefly, when I did briefly, I should say, I had a lot of outside the box ideas of how to use things like internal markets or, auction mechanisms to figure out how to allocate people to the best and highest uses. And those mostly fell on deaf ears and for understandable reasons, because there was going to be, even if even if you assume buy in, which we didn't have, there was still going to be a lot of like time spent operating those mechanisms. And I could just see why, it just wouldn't happen. The younger me didn't understand those things quite as well. But this is, because you can have the AIs grind through. This is sort of the, in a sense, it's like the positive version of the mass surveillance use case, right? Like we used to be safe for mass surveillance because there just wasn't enough human brain power to process all the logs. Now we've got that. problem solved in a potentially very problematic way. But here you can actually imagine going through your full roster and really trying to tinker with all the different assignments and configurations that you might spin up. And you can imagine how that could really unlock a lot of potential that nobody would have had the time, certainly, perspective probably as well to be able to do in a pre-AI era. So I do think that is like pretty exciting.

[52:00] Rebecca Hinds: And there's a small finding in the report that I'm incredibly excited. We have one of our co-authors, Aruna from Berkeley, she made a note as soon as we put this in the report, wow, this is so exciting. I think so too. We don't foreground it because there's so much, but what we see in the most effective organization, so the 13% who have employees saying that significant productivity gains are occurring, They measure. They measure a lot of different things. In particular, they don't just measure productivity, but they also put that data disproportionately more in the hands of employees. And this is not new. I saw it with collaboration technology too. I saw it with remote and hybrid work and aging type situations. If employees have access to that data, it is transparent to them. Everyone is better off. Unfortunately, we're seeing cases of that not happening in organizations. Again, another benefit of the enterprise graph is everyone has access to it, right? Everyone is able to query the graph and understand the state of play within the organization in a way that I think is going to drive much better outcomes for both organization and individual.

[53:14] Nathan Labenz: So speaking of measurement, one thing that I was kind of pondering myself as I was imagining myself leading an organization through these challenges based on all the findings in the report, and I don't know if we said this yet, but 69% of people admit to some form or some amount of bot ******** which I think has like a couple different definitions, but for me, it's like passing off AI work in, down the line somewhere where you yourself cannot defend the quality of the work. That's a very high rate. So it does strike me that, again, kind of my earlier thought, in a way, that's maybe a really optimistic thing for how well AI is working, because if you already have two-thirds of people just doing AI outputs blindly, sending them down the line, and the wheels aren't falling off entirely, that's like kind of an amazing finding. But then if I'm trying to manage that as a leader, I'm thinking, how do I detect who's doing it? And how do I detect where it's actually working? And maybe I do want to share that with with employees, maybe I do want to partially share it. I'm not sure exactly what the right level of transparency would be there. But have you seen anybody, you know, I've seen also recently, historically, my attitude or my synthesis of available information has been AI detectors don't work. So like when I go to present to a group of teachers, I'll say, don't do AI detectors, or at least if you do, you know, you certainly can't they can be wrong. So you can't trust them too much. These days, it does seem like Pangram Labs is getting a lot of praise in the general discourse around being pretty reliable. So I'm kind of wondering, should I be, if I'm a leader, adopting like something like Pangram Labs and having, you know, all these intermediate work outputs evaluated such that I can potentially both realize who's doing this, but also like maybe where which, again, this is kind of an angle on which nodes in my graph of activity that constitute my enterprise, which ones can I actually just have AI do? Like I potentially already have a lot of answer to that question in the work product that people have, the AI work product that people have passed off as their own. Do you see anything like that?

[55:32] Rebecca Hinds: Yes, and I think it's so early. And also there's not going to be a right answer for every organization. A lot of it depends on the psychological safety within the organization. I've long followed Amy Edmondson's great work and I think psychological safety has never been more important in our organization because ideally you have the people raising their hands and saying, this is bot **** or I contributed to bot **** and here is why. I think AI detectors, you know, there is for sure, and I'm seeing it from multiple different angles, you know, there will be a world where all of our AI tools will flag the level of uncertainty associated with the response as well as the likelihood of purely AI generation versus human to AI. I think what's exciting about the more context you have as well is you can feed it more data points. You can feed it not just generically is this likely to be generated by AI. If it knows enough about you as an individual, it can know, okay, Rebecca's default writing style as a human is this versus doing something generic. And I think I think that's why we're seeing a lot of detectors not work. Why I think this isn't necessarily and certainly isn't strictly a technology issue or solution is organizations need to understand the why. The why behind the bot ******** is just as important as the bot ********. In particular, when we think about using shadow AI tools, right? That is a form of bot ********. You're injecting risk into the organization by using an unapproved tool or a sanction in an unapproved way, we're seeing a cohort of organizations crack down and punish employees for doing that. And then we're seeing a portion that definitely have guardrails and repercussions for unsanctioned use of the technology, but not strictly that. It's understanding why are employees using unapproved tools or why are they coloring outside the lines because usually it's the highest performers that are doing that. And usually they're doing that because the existing tech stack isn't working for them. They see so much potential in the technology and they're making that calculus in their head. Hey, I would rather get the productivity gains. In the best cases, hopefully get the gains for the organization. And they're doing this trade off. Ideally, organizations recognize that and make the safe path, the more efficient path. That is the gold standard. I think part of it is a technology piece, but part of it is deeply human.

[58:14] Nathan Labenz: Another stat that jumped out at me was, I believe both the people that do the most bot sitting. So doing, first thing that should be said is doing more bot sitting is associated with being more successful with AI. So both probably because You need to do it to get good results and because you are going to naturally do more of it the more you use AI in the simplest analysis. But then also the more bot sitting seems to be associated with higher likelihood of being on the job market and actively looking for your next phase of your career. And the same is also true on the bot ******** side. People that are doing more of that are again more likely to be looking for a job. That sounds scary and it might be scary. I guess another way I might interpret it if I think about the person who was in the traditional sort of customer service or customer success role and is now being asked to babysit bots, is maybe some of this turnover could in fact be healthy. If people are like doing something they don't want to do, I guess a big question in general with AI is like, Obviously, is it going to create more jobs than it destroys? But even more locally and focused on specific organizations and teams and individuals, is the new job that gets created, whether it's greater or less than one per job that gets eliminated, is it something that the person that had the original job can pivot into or would want to pivot into? And I guess my read on some of this stuff is like, it sort of suggests that in a lot of cases, the answer is no. People that are doing a lot of this AI stuff, good and bad, it sounds like are kind of voting with their feet that this is not really what they want. Maybe they don't see themselves being successful in this new world. And so I guess I wonder, do you see that as a sign, do you see that as something that like leadership is doing wrong? Or do you see it as sort of an actual signal of just change is often not what people want. And so they're kind of reacting in a ultimately what might be a perfectly sensible way.

[1:00:33] Rebecca Hinds: So I'll hypothesize here based on the data, because we certainly don't know causation. These are correlations more than anything else. I think when we think about bot sitting, it's very different than bot ********. Bot sitting, what we're seeing in the data, what I'm certainly seeing in practice is there are two big links between the act of bot sitting and the desire to leave the organization. One is, and we have an amazing co-author on the report, Paul Leonardi, who's done foundational, impactful work on what he calls digital exhaustion. The digital employee experience is increasingly the employee experience. We often trivialize it, but the reality is overwhelmingly we're seeing If employees are exhausted by technology, they're wanting to leave the organization. And when we think about bot sitting in particular, what I'm seeing is if employees are spending all of this time manually feeding context to the AI, that doesn't instill a whole bunch of confidence in the employee that their employer, the organization has a strategy or a good one around the technology. And I think that's a big part of it. If you're wasting your days bot sitting when your organization is vocalizing in all hands in town halls that this is a transformative technology and we're transforming. There's a disconnect that if you're seeing all else being equal in other organization that is investing in technologies that do have context, you're going to choose the latter. So I think that's the bot sitting piece. Certainly not the whole piece, but I think that those are reasonable conclusions to hypothesize. The bot ******** I think is different. The bot ******** is, in my opinion and what I'm seeing in conversations, you've decided that you're disengaged from the organization, perhaps because you're spending so much time bot sitting and you're wanting to leave the organization is a reflection of that broader disengagement, right? If you're bot sitting and no longer feeling a sense of ownership over your AI generated work, probably that's part of a bigger disengagement picture and feeling and sentiment within the organization. I certainly don't think that's the case for every employee, but I do think that's a part of it as well. Aruna, who I mentioned before, she hypothesizes in the report that perhaps it's also that people have bot sitted so or they become such an expert in the technology that they've realized their market value is higher outside the organization than inside of the organization. And that's a reflection of the disengagement, both in terms of bot ******** and the desire to leave the organization, your market value has increased. I certainly think that's for a subset of employees, the association as well.

[1:03:36] Nathan Labenz: Yeah, so basically the key is if you're leading an organization, you got to tell who are the good AI users and who are the bot ********. And you're going to want to take steps to retain your top performers. You need to both use your AI detector to know who's using AI, but then also you need like a quality score to know who's actually using it effectively. And those people that are using it a lot, but using it effectively are ones that you're at risk of losing if you don't level up your game in one way or another, which could be better AI tools for them, could be better comp for them, perhaps. The ones on the other end that are just passing off AI work that they can't defend, probably in the end, you're going to have to make your peace with this is part of ways with these people is going to be the cost of transformation. How do you see people telling the difference? And do you see on the upside, obviously in deep in the AI bubble, I'm not this central in the AI bubble, but we've got all these stories of basically sports star, pop star incomes for top end ML researchers. I don't expect that is happening in quite such an extreme way in enterprises. But are we seeing like retention plays where people are getting like meaningfully rewarded in terms of comp if they are really on the cutting edge of helping their organization with AI transformation, if only in terms of embodying it and bringing it to their own role in an elite way.

[1:05:09] Rebecca Hinds: I love this question. No, not in a meaningful way. Should they? Yes. And this is not new, right? My colleague Rob Cross, who has been an inspiration for me for many years, some of his research shows that high performing organizations, they're up to 5.5 times more likely than lower effective ones, less effective ones in measuring and rewarding effective collaboration. The great organizations do this. They build some sort of measurement. It looks different for every organization. Sometimes it's tied to pay, sometimes it's more informal. But fundamentally, the most successful organizations, the most enduring organizations, they do something to reward the collective and not just the individual. I think that could not be more important with AI when we're starting to see this tragedy of the commons, hyper focus on individual productivity in a way that's not translating. I'm seeing it in bits and spurts is one example where they take something like a hackathon or an agentathon and they think very carefully about what are we going to incentivize. And they're not just giving prizes for the biggest business impact as most organizations do. They also give prizes for improvement, best before and after prompt. They give prizes for co-creation feedback, peer feedback, not just are you using the technology to drive your individual productivity gains, but are you creating value together? Those types of things we're seeing in effective organizations, but we're not seeing it at scale in a way that I think we should. And again, if I think 10 steps ahead, if you do have a context graph, if you do have an enterprise graph, well, it can help you make that determination because it will know where value has been created and it can start to give you recommendations and we see it in the data where, A large portion of employees are saying already their organizations are using AI to inform performance management, hiring and firing too. And when we ask, what is your expectation? Not surprisingly, it's even higher. I think we're, I hope, this is the hopeful part of me, I hope we see a world where not only can AI help us drive more objective, fair performance reviews when they've been riddled with biases for forever. And it can help us feed the performance evaluation with a bigger focus on the collective as opposed to the individual. That becomes incredibly exciting. And I think we're going to see it because we are already seeing bits and spurts of it.

[1:07:55] Nathan Labenz: So tell me a little bit more about how you think leaders should approach this culture building. I have said that many times myself. it's got to be a cultural thing. And, you mentioned like, we need to see executives using AI. That's, I think, a very sensible starting point, but it's got to go farther than that. And in some cases, it's going to be in a pretty tough messaging environment, let's say, right? Because we do have quite a few rounds of layoffs from tech companies, mostly so far, being attributed to AI. Tell me if you think this will play out differently, but I suspect that we're going to see a pretty similar phenomenon extended to the rest of the economy and probably a few successive waves over the not too long of a time horizon. And certainly my sense is that public company CEOs feel like, geez, whether I like it or not, I'm under a lot of pressure to figure out how to make this stuff work. And I'm told that my competitors are gonna be dramatically cutting their costs and can I really compete if I don't? So they're gonna be presumably in a pretty tough spot where they're like, okay, here's me as the CEO using some prompts. At the same time though, we are cutting headcount by 10 to 20% or whatever. And we're going to work better together. And it seems like it's going to be a very tough messaging plane to land. What advice do you have for people that are facing this difficult challenge?

[1:09:33] Rebecca Hinds: This is an area where we do know so much about what works. Unfortunately, we don't see it implemented. And employees can call ******** from a mile away. And if they see a talk track that's different from what's happening in practice, that does not instill confidence and you start to see a whole bunch of this symbolic use of the technology. Transparency is so important right now for so many reasons, but in particular to understand the why. The why behind organizational changes in either direction, the why helps people understand whether they're willing to believe in it and willing to commit to it in a reasonable way. Now, what I'm seeing overwhelmingly is performative theater at every level of the organization, but in particular with executives. And I spend a lot of time with executives and the amount of conversations I've been part of where one executive decides that they're going to cut 15% of headcount. And then there's a conversation around, should it be 16%, should it be 14%? And there's this very, you know, very disturbing narrative that there's a one-size-fits-all for every organization. There is not, and we've seen efforts to change organizations and dismantle hierarchy, flatten the org chart time and time again. Often they don't work because they're short term, perceived short term fixes that don't help the underlying problem. This is an organizational design transformation in its fullest sense. having an understanding of what is your culture, what sorts of behaviors does your culture embody, does it reward, does it punish? That is very important right now. And articulating that to employees in the context of AI as well. What is AI in relation to humans is very important. And what we see overwhelmingly is in these organizations, and we talk about in the report, employees are significantly more likely in these transformative organizations to view AI as a teammate and how they interact with it. But not as an employee on the same level as them, because that becomes dangerous when we're starting to deflect blame on the AI tool. The blame, the onus, the responsibility rests with humans. But ideally, the organization is positioning AI both psychologically and in the tools and technologies they invest in as a teammate in a way that employees can grok, how might I interact with this technology? How might I start to delegate work to? It's a multi, multifaceted problem. But the answer is not to make cuts or make sweeping changes without understanding the impact of those changes on a smaller level. And I think that's why coinciding with these these layoffs, these cuts is a lot of regret because we don't fully understand what employees do. And we've seen this in customer service cases where you cut the human agents, you realize, wow, those human agents did a really important work in building long-term relationships with customers. You then bring them back. Again, this is the power of an enterprise type graph is you can do that proactively and you can understand the real value of the humans in a way that is much more thoughtful and is much more long-term thinking than just doing the thing before understanding the ramifications. It's not easy, but the answer is not to move quickly without understanding the foundations.

[1:13:20] Nathan Labenz: It strikes me that the importance of mission and mission buy-in might really be at a premium in the near term, because I can just imagine a hugely different reaction if you sort of imagine your scene from The Office where it's kind of generic widget code, paper co, indistinguishable from tons of other competitors, where if you ask, if we don't do it, who will? And the answer is like, well, lots of other people and they'll do it pretty much just as well and the world won't really be that much different. That seems like a really hard place to manage this sort of transformation from. Whereas I do know some organizations where they're really in a very meaningful way, bought in on the mission and the success of the organization. And then when things like this happen, even if it does result in somewhat painful change, if people believe that the mission itself is going to be advanced by this change, then presumably their tolerance for certain pain is dramatically higher. I guess the tough thing is it's hard to synthesize a mission where there really isn't a very compelling one. But any thoughts on the mission premium, we might call it?

[1:14:37] Rebecca Hinds: So it's super insightful. And so one of our other contributors to the report and one of our founding members, Bob Sutton and I, we're working on a piece right now, hopefully it'll be in Harvard Business Review, on if you are going to flatten, how do you do it well? And what are the pieces you need in place to do it well? This is not necessarily job cuts. It's strictly looking at flattening and hierarchy. And one of the core arguments backed by research and evidence is you can't flatten well If employees don't understand the mission, because what you're starting to do with job cuts, but also just flattening in general, is you're dismantling hierarchy and hierarchy gives employees. a sense of what to do in situations of uncertainty. If you don't have that hierarchy, you need to replace it with something else. And the mission is a key way to do that, right? If employees have a strong sense of what is the company's mission and they believe in it and invest in it, they're going to make the right decisions when no one's watching and no one's surveilling them. If you don't have that, even if you have a super strong company mission, but employees can't make the connection between their work and the company mission, that's just as detrimental. You can't do any of this well. And I think we underestimate, and I love that you called this out. I think it's incredibly insightful and important that we need to think about the bigger picture. And when we think about the DNA of an organization, the mission is at the top. And again, the enterprise graph of it knows enough about that. It can start to give you proactive insights in terms of, okay, does the work we're doing, do employees understand the mission? Does the work they do at an individual level ladder up to the mission or is it completely disconnected from the rest of the enterprise graph? That again becomes very exciting. But I think we underestimate just how important purpose and pride and meaning at work is. If nothing else, Without that, it drives certainly more bot ******** within the organization.

[1:16:45] Nathan Labenz: How much of an impact do you think that Elon's Twitter takeover and subsequent managerial decisions have had on, let's say, executive culture broadly? I guess my read on that is It sure looks like it basically worked. I was asking AIs about this in preparation and something like 7,500 employees was what they had when he took over. He took that down to something like 1000, maybe as an absolute low, maybe 1,500. Did hire some people back, has hired some people. It seems like the team has grown a little bit since then, but it's maybe 1/3 of what it used to be. They did lose a lot of revenue. I would say that was mostly because advertisers didn't like him slash his content policies slash just the general vibe and kind of walked away. But on all the other metrics, they've kept their users, the site. I'm old enough to remember when people said the site wasn't going to work anymore. It continues to work. If anything, I think recently they've started to accelerate some things with a really nice new API that I'm building on where I'm like, oh, this thing is actually really well done. Elon's obviously a special case in many ways that has options seemingly available to him financing and otherwise that not everybody has. But do you think that example has, does it entice executive thinking broadly throughout the country to think, geez, could I do something similar? Or is it just so far out that it's not, doesn't even really register with most company leaders?

[1:18:17] Rebecca Hinds: So I'll comment generally on this because I think we're seeing multiple different instances of CEOs making pretty radical changes and executives jumping on the bandwagon and wanting the promised gains for their organization. That is natural. Absolutely. We see it with any high profile CEO. We're certainly seeing what Jensen at Nvidia. Very interesting leadership strategies. I admire him deeply in so many ways. What works for Jensen and Nvidia doesn't work for 99.9% of other organizations. Part of it is mission as boss, right? They do a lot of work on meetings. The fact that Jensen doesn't have one-on-one meetings with his direct reports, that does not work in most organizations. It works in Nvidia because they have this mission as boss, right? Every employee at least the report of Jensen, his direct reports, they have a super, super clear sense as far as I can gather of what the mission of the company is and they're fully bought in. So they don't need as much that one-on-one interaction with the boss. I think this holds for AI as well. What works for one organization, especially when we think about AI that is, again, so deeply psychological, isn't going to work for your organization. I think we can take inspiration and certainly I think we underestimate the power of a leader who motivates and a leader who makes bold changes during this time in a way that employees feel a sense of energy around them. But I do think it's very dangerous to think that what works, especially in the headlines and the external perceptions for one organization can be translated, copy and pasted into your organization.

[1:20:06] Nathan Labenz: One thing I also wanted to double back on and get a little more color commentary is you said that the most successful organizations, you're seeing this pattern of people relating to the AIs as teammates, but not necessarily peers. I'd love to hear a little bit more about that. And maybe you could fill in some of the details with what is your experience of using Glean at Glean like today. You obviously have human teammates, you have AIs, presumably these AIs have as good of an integration into the deep context of Glean as any AIs anywhere could possibly have. So what is your mental model for like when you go to an AI, when you go to a person, when you start a group chat between you and another person and the AI? What's the difference also between the sort of you, Rebecca, extension AI versus the, I don't know, is it a central like Glean bot that is the company-wide version? I guess paint a picture of your present is everybody else's future, right? So tell us what's going on at Glean now so people can have a little more concrete sense of what they're going to be walking into.

[1:21:17] Rebecca Hinds: Sure, and this is a nuanced argument for sure. And I think I've done enough research on this sort of mental model I did in a previous life. I did some research with colleagues with Carol Dweck at Stanford, her lab on mindsets and the importance of this psychological framing. What we're seeing in practice, and I'm not convinced this won't change, But when we think about the mental model of a teammate, that is extremely valuable right now because it gives employees something concrete to explain, how do I think about this thing in my day-to-day work? And in particular, we see when employees adopt that teammate mentality, They're not treating the technology as transactional, right? It's not like a hammer or a calculator, something you can pick up, use and put down, right? Treating it as a teammate means you're not expecting the perfect answer. You're not expecting the technology to work perfectly. The teammate is in the interaction. If it knows enough about me, so the Glean assistant is my personal assistant, my teammate that I go to every hour of the day for things I need help with and to move work forward. The danger of the teammate mentality in practice when we think about the psychological aspect is deflecting blame. This isn't a human teammate where if the AI makes a mistake, we can blame the human. And Paul Leonardi, who I mentioned, he's done fascinating research to show when a human screws up, if I have an assistant and the assistant makes a mistake, a human assistant, we blame the human assistant. When an AI makes a mistake, we blame the human as well. If we're on the receiving end, unfortunately, as we're deploying these tools, so often we think that, oh, this person, this AI is agentic, it has agentic capabilities, we can deflect blame. No, that's not the case. So that's the danger of the metaphor. But in terms of Glean, the assistant knows enough about me. It also, I use it all the time when I'm wanting to do a lot of executive type communication, upward reporting, for example, every executive likes to consume information in a different way. And so being able to ask my glean assistant, okay, what is the preferred mode of consumption for this executive versus this executives is very helpful. As I was joining the organization, I'm about 11 years, 11 months in, feels like 11 years some days. It's, I'm able to query the AI and understand the institutional context that came before me. I'm able to, I wrote an article on this, I'm able to understand not only what was a decision made around this, why did we launch this specific feature, who are our customers, what's our product roadmap, all of those things are super important. But I also asked it, what makes a successful employee successful at Glean? And it was able to understand from the culture, this is what the successful employees do differently. And I fundamentally acted a little bit differently because of that, knowing what the culture rewards. So that becomes really exciting.

[1:24:27] Nathan Labenz: Could you share a little bit, I don't know if it's too close to the secret sauce, but could you share a little bit more about what Glean AI said makes an employee successful at Glean?

[1:24:37] Rebecca Hinds: I wrote an article because I used my exact queries, but it was something around the team mentality. And I think what we've talked about today, it's not a culture of rewarding individual performance, it's a culture of rewarding the team. It's also a culture of long-term thinking as opposed to other organizations. And so that innovative spirit, and I feel this every day, it's rewarded when you raise your hand and share the weird, wacky idea, share the future of what's possible with the enterprise graph. Those types of things that do differ case by case were some of the things I asked it in my early days. And now it knows enough about how I work where it's able to produce information that is unique to me. I'm also able to choose different models depending on the task at hand. And it has memory capabilities that it's constantly learning with every query, with every day what my priorities are. It'll proactively flag when I haven't followed up on an e-mail, which I am working at Asana for many years, I've become horrible at e-mail. So that's really helpful. And again, proactively telling me when I've missed something, when there's an action item in a doc that I've forgotten is helpful in making sure I don't drop the ball on too many things.

[1:26:01] Nathan Labenz: So on that note of long-term thinking, tough question here for sure, but if you try to see through the fog and imagine maybe what Glean looks like in a few years and more broadly like what large companies in general look like a few years from now. What do you see? I think we kind of have the easiest time imagining a version of the world that's pretty similar to the current one, but where like a lot of stuff is automated and so the savings kind of get passed on to the consumer. Like, maybe companies are both more profitable and their stuff is cheaper and people are able to buy more because everything got so much more efficient. It does seem like though that idea of efficiency and both higher profits and higher consumer surplus is kind of premised on major labor cost savings, AKA companies are going to be smaller in terms of headcount than they are now. There's a lot of people who want to tell me a different story about how it's not going to be like that and it's really going to be about growth. And then I'm kind of always like, well, sure, it's going to be growth for some, but I also kind of see a trend in general in business that starts again with technology toward kind of winner take all dynamics. it seems like we're seeing more and more market concentration in a lot of different places. So I do believe that like some companies will really win through growth, but a lot of that's probably going to be taking share from other companies. So is there another vision that you find compelling Aside from that default vision, or is that ultimately kind of the future you think we're headed toward?

[1:27:46] Rebecca Hinds: The 2 separate questions. A vision I think is compelling is different than I think the reality that will play out. The answer everyone loves to give is AI frees us up for higher order work and we're going to have more jobs than ever before. I think we will. There's certainly going to be jobs created that have never existed before and that's exciting. I don't think costs are going to decrease in the short term at all when it comes to using the technology. And you speak with executives every week where they've blown their AI budgets in a very short time for the year. Sometimes they're blow. And we've seen headlines where you blow your token budget for a year and a month. The costs are not decreasing. And I think that is a very real concern for every organization. And that's why I think the future is not a single model platform. a multimodal, multi-tool platform. In the best cases, the AI is able to proactively route the task to the right model given a multitude of different dimensions, efficiency, complexity, cost as well. So that I'm pretty confident in. I think we are seeing smaller and smaller teams. And I think that makes sense because if we're using AI in the right way, A, it should reduce the massive coordination tax of work. we have slightly smaller teams, we should probably need fewer managers if a core part of the managerial role has been to manage this coordination tax that can now be taken on with AI. That becomes exciting in many ways because we are seeing an opportunity for specialists to become more generalists. We're certainly seeing roles rebundle where, and I've seen this in practice, in particular through my PhD, I saw the org chart pressure tested and organizations start to think about, okay, where can AI give me insights across silos in a way that now makes sense to redraw roles that previously lived in two, three, four different roles, now in a single role. I think we're going to see that in the companies that survive and thrive. I hope for a world where we're spending more work on the deeply human parts of work and we have a much clearer sense of what is the right division of labor. I'm starting to see enough evidence where I think that's a possibility. I spend a decent amount of time with an amazing CHRO at one of the largest healthcare organizations and they've used AI to task map all their roles and just determine that 70% of all tasks are overlapping with other roles across roles. And so in those sorts of situations, you can easily imagine the enterprise type graph making the determination in terms of, okay, what does make sense to live in a human role versus what makes sense to live in an agent role? How many agents is the right agent to human ratio? That is certainly going to be in flux. And I think organizations are going to grapple with that. Those are the things that I'm pretty confident In terms of beyond that, I think a lot of it is up for grabs and I think a lot of it depends on the level of intentionality that organizations put into the human aspect. This is not a technology that's inherently good or bad. It's not going to inherently make our organizations better or worse. It's about how we enact them. And my optimism says that, yes, this is going to make for much better organizations in terms of delivering customer value, delivering employee value, but I think we're seeing enough evidence that if you're not invested in the human piece, this can easily create a world that's worse for the individual team and organization. I hope that's not the case, and I think I'm seeing enough evidence it isn't. Not if we're not intentional about it.

[1:31:39] Nathan Labenz: Yeah. There's probably going to be a healthy amount or perhaps an unhealthy amount of creative destruction in any case. I do imagine we'll see a lot of organizations that look great, but there's going to be probably a strong survivor bias in the organizations that continue to exist in however many years time. That's obviously another cycle. All these cycles are getting shorter and shorter.

[1:32:01] Rebecca Hinds: 100%. And I think what I'm also seeing is what works for an AI native company does not work for a legacy company. I think there's going to be two models. Unfortunately, I think for many reasons, AI native companies have a leg up. They don't have that existing baggage. They don't have the existing legacy org chart, but don't try to copy and paste AI native business model onto a legacy organization. Figure out what has been working for so long and figure out what needs to evolve versus what shouldn't evolve.

[1:32:32] Nathan Labenz: So thank you for giving me so much of your time today. Just a couple of final questions. One on your own work. If you just imagine the next couple of cycles of this and whether it's the biggest companies on the stock market getting displaced or the model release cycle, it seems like everything is competing up. In work like this, my gut says that this is like You probably already used AI pretty extensively in producing this report, but it feels like we might be hitting a phase change moment for you where the way in which you can study organizations is probably changing pretty dramatically. So I would imagine that you might imagine, I would imagine that you might be planning to use AI interviewers in the future and data analysis and maybe this sort of thing becomes less of a one-off report and more of like a rolling index where there's like a monthly update or even sort of real-time sort of vibe to it because certainly everybody is going to want to be as close to the moment as they can in terms of understanding what is really going on. How do you anticipate your own work changing over the next couple cycles?

[1:33:43] Rebecca Hinds: So we are, this is planning to be, we're planning to have this be a pulse type survey, which I've long been excited about. And especially because we're looking at it from multiple different dimensions every six months or so, the goal is to be able to track this longitudinally. And I think when we think about what will the bot sitting percentage look like, the bot sitting, how do we see roles evolving, that becomes incredibly exciting and uniquely possible with AI in many ways, in particular because of the data. I think we were writing this report, the co-authors and myself as one of them, were very passionate about human narrative and human storytelling. And so there are parts of your research process that absolutely are 100% human. But there are parts that now we can do much more quickly and effectively because of AI. So I think that aspect's exciting. I, as a researcher, I'm trained in a methodology called ethnography, which essentially means you embed yourself in an organization and watch over many months and years how things change. I actually don't think that's going to change a whole lot with AI. I certainly think field notes and some of the ins and outs of how you do the method change, but these are long-term changes and I think There are parts of this change that we will never be able to glean fully from survey data. We're going to need to have the lived experience of the people on the ground. Interviews can get you some of the way there, but I think there's no substitute for embedding yourself in an organization doing truly grounded research and figuring out all of the unexpected ways that this is going to change our organization. because I think we as humans don't even see it a lot of the time. So that aspect I don't think is going to change much. The importance of really grounded research where you're taking the time and this is a technology that you can't learn everything from subjective survey data or even the exciting telemetry data that gets you a piece of it. There's no substitute for that sort of multi-method research approach in my view.

[1:35:57] Nathan Labenz: Is there any place organization, whatever that you would love to embed in that would be your dream deep research environment. Another version of that, I guess, would be like, I don't know that there were any experiments. All of this was pretty observational, and organizations obviously got enough on their plate without letting academics like you come in and do controlled experiments. But what do you think would be, if somebody really wanted to offer themselves up as tribute, What would be the kind of big questions you think are hardest to answer that if you were really able to get the right kind of access or structure and experiment in the right way, would shed the most light on the biggest questions that are currently open for you?

[1:36:40] Rebecca Hinds: Gosh, this is so tempting. And I'll give a cheesy answer, which one of the reasons I joined Glean was we do have this bias for experimentation. And while we don't have any experiments in the traditional sense in the report, we do have a whole bunch of experiments going on. And ones that I'm excited to talk about in the short term in terms of how are we pressure testing AI alongside some of our practices meetings is one where we've done experiments around how do employees, Glean employees respond to AI now being brought into our meetings in a way that is deeply psychological and exciting. So that's an exciting piece. I think the org chart transformation is top of mind for me and Bob Sutton, who I work very closely with. I would love to embed it in a traditional, sometimes it's called extreme cases where organizations will do something completely radical and observe on the ground how this is happening. I would love to go in a legacy organization that has decided they're going to fundamentally rewire the org chart and watch on the ground.

[1:37:44] Nathan Labenz: I should also mention your book, the best meeting of your life, right? That's what it's called. Your best meeting ever. Your best meeting ever. You just reminded me of it in that comment about how bots are being brought into meetings. What's new in meetings? How would you update your advice as we see evolving cultural expectations practices around meetings?

[1:38:07] Rebecca Hinds: Oh gosh, this is an hour question. I think meetings are not unlike any other work practice in many ways. They're the most dysfunctional work practice. But what we're seeing with every work practice is AI is helping in some places and AI is making things more dysfunctional. I'm incredibly excited about the potential of AI injected in meetings to help us have a better sense of the health of a meeting. And I'm seeing so many examples working with organizations all across the world of people using AI in fundamentally different ways to Understand whether the dynamics in the meeting are healthy. Understand whether executives are dominating the airtime. Automatically auto delete meetings from calendars when they fail to have a good meeting design and agenda or people accept. Even measuring collaboration, understanding are people in the right collaborative mode for creativity versus coordination? That is all. I could nerd out on that forever. Where I'm seeing a lot of dysfunction is cognitively offloading to AI and sending our digital twins to meetings instead of showing up ourselves. And we have, I don't know if these stats made it into the end report. They certainly, we have a UK piece where we see this on another level and certainly there's some stats on the sheer. amount of people who have sent digital twins to lead meetings on their behalf and the sheer volume of meetings that now have at least one note taker, that's where I become very nervous and frustrated because meetings are so expensive. They're one of these activities that the power and potential of them is that they are deeply human. If we think that we can send a note taker bot to a meeting instead of showing up ourselves, well that is a symptom of we didn't need the meeting. And that is something that maybe AI can fix. Maybe if it has enough information it can fix. But some of these things, ground zero fundamentals, having an understanding of what deserves to be a meeting in your organization is the first step. It's largely independent of AI in terms of fundamentally rethinking having the right purpose for a meeting and ensuring that it truly does warrant that live synchronous time that is incredibly expensive. And the last point I'll say around this is we often, it's being referred to in the research right now as mental proof around the technology, the fact that We are watching how people use AI very carefully. And we're starting to be able to tell when people are bot ******** not always, but when people are bot ******** versus not. And when you see someone appear, certainly a manager, but anyone bot **** that triggers a sense of they don't value your time, right? The opposite is also true. If we see someone has invested enough time into designing a meeting, understanding how to use AI in a disciplined way, it's a reflection of the person. And I think that's what we're seeing in meetings as well. You send your digital note taker bot to a meeting, it signals that you don't value other people's time in a way that is a deeply human phenomenon. And I think that's another big piece of this whole picture inside and outside of meetings as well.

[1:41:31] Nathan Labenz: Cool. Thank you. time again, the scarcest resource. I appreciate how much you've been willing to share with me today. Rebecca Hines, thank you for being part of the Cognitive Revolution.

[1:41:41] Rebecca Hinds: Thank you for having me.

Outro

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