How AI redefines your workplace

AARON LEVIE: An AI agent again, doesn’t know, “Did I just join SpaceX, or did I join Patagonia?” You’re going to have to tell the agent effectively like, “Who are you right now?”

CLARA SHIH: The underlying substrate of our resources is continually shifting, and so we kind of always have to be on our feet and on our toes, literally just to continually reinvent ourselves and our companies.

ANEESH RAMAN: We’re in this liminal moment where you’re past the end of the beginning, but the beginning of the new hasn’t started yet. We’re transitioning from the industrial age to the entrepreneurial age. We’re going to have to go through these growth moments and have these conversations.

BOB SAFIAN: That’s Aaron Levie, CEO of Box, Meta head of business AI, Clara Shih, and LinkedIn chief economic opportunity officer, Aneesh Raman. Recorded live at the Masters of Scale Summit in San Francisco on October 8th, we all gathered on stage to talk about how AI is changing work and the future of the workplace from the redefining of jobs to the impact of AI work slop. Aaron, Clara, and Aneesh are AI insiders in different ways, and they share their optimism, their uncertainty, and their advice for navigating an unprecedented situation. It’s a thought-provoking boisterous exchange, so let’s get to it. I’m Bob Safian and this is Rapid Response.

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I’m going to do a stand-up meeting here about AI and the workplace. We have three very human leaders that I’m going to bring out here to have this conversation. Aneesh Raman, the chief economic opportunity officer for LinkedIn. Aaron Levie, the CEO of Box, and Clara Shih, the head of business AI at Meta. So I’m going to go around the table. I’m going to ask you each a question. Aneesh, I’m going to start with you.

Copy LinkThe case for AI-optimism in the workplace

There’s a real debate about the impact of AI on work and some say it will be positive, some say it will be negative. You’ve said it will be spectacular. So what is that? Why?

RAMAN: Well, I hate the setup because it makes me sound like an optimist, and I know that means you all will find everything I say not credible. So my optimism about AI is born from extreme levels of pessimism, cynicism, and skepticism about how work has worked to date for humans. At LinkedIn, we are if nothing else, a platform of people. We go where the species goes. So for two years I have thought about human intelligence as my core focus. Two things that give me cause for optimism. First, for humans, work has kind of sucked since the industrial age, we have turned ourselves into efficiency machines, whether you’re at the assembly line or sending emails, it’s more, better, faster. More, better, faster. More, better, faster. That isn’t who humans are.

So that leads you to thing two, which is cause of optimism: AI is going to out-efficiency us, robots are going to out efficiency us. That’s okay. We became the apex species, not because we were the most efficient, but because we were the most imaginative, the most innovative. We turned fire into this whole new world around us. We looked at birds and said, Why can’t we do that? Humans at our best are not about efficiency, so I’m optimistic because AI is going to force us finally, at least from the past couple of centuries, to broaden our view of human intelligence. I’m pessimistic about our state of being ready for that because it’s entire new systems of education, employment, entrepreneurship, but we’re going to have to fix that and that’s great, I think.

Copy LinkWill AI require fewer workers?

SAFIAN: Now, I want to ask Aaron this because Aaron, you and I talked, and when you’re talking about efficiency, Aaron, you said people think that, “Oh, because AI will make us more efficient, we won’t need to have as many people.” You said, “No, that’s a misunderstanding, that actually the AI is going to make you want more people because the ROI of each employee is better with AI.”

LEVIE: If you were to go back 30 or 40 years ago and you probably looked at the process for doing advertising and you were like, “Oh my God, it takes two weeks to get an advertisement out.” There’s all these meetings and people are drawing things, and we eventually are able to take this to print and scale it up, and then you showed somebody Photoshop and you said, “Now we’re going to be able to go digitize this workflow.” And then you show somebody the early forms of maybe a CRM system and you’d say, “Okay, we can go and scale this message to all of our customers.” I think if you were to go back 40 years and you showed everybody those tools, they would say like, “Well, wait a second, we’re probably going to wipe out 80% of all jobs related to sales and marketing, because you just took this amount of work and you’ve made it 80% faster and maybe 90% cheaper, and so this is obviously going to cause this huge economic problem.”

And then what actually happens is it turns out that the world didn’t have actually the amount of sales and marketing and products that ultimately it could use, so all of the companies that previously couldn’t afford that advertising campaign or couldn’t build up their sales team or couldn’t target all their marketing now finally could afford to do that. And so as we saw software digitize a whole bunch of practices and workflows over a 30 or 40-year period; AI does the same for forms of labor and intelligence that were otherwise very scarce and hard to access. And so I just look at even within our organization, there are many areas where if we could make a certain function 20% or 30% or 50% more efficient, I wouldn’t go and remove 20% or 30% or 50% of the dollars from that function. You might actually scale even faster because all of a sudden the productivity gain and the ROI from that function is so much higher.

So I think every company is going to be somewhere different on that continuum or each organization will look different, but even if you just imagine let’s say the small businesses that always have a structural disadvantage versus large companies because of their lack of access to talent and resources, just if you imagine them all being weaponized with the same expert lawyer and expert marketing and expert product development and software engineer as any kind of mid or large size company, what’s going to happen next is you’re going to have just a tremendous amount of growth of new organizations emerge with lots more productivity in a number of categories that we don’t see productivity in today, and that alone I think would support the job growth from there.

Copy LinkThe rise of AI “work slop”

SAFIAN: So I want to ask all of you this, but maybe, Clara, you can take it first. We’ve heard this term that’s become very popular, work slop, that AI-generated work is creating more work. It’s like volume over quality, and I guess my question is like, is work slop just a phase for AI? Is it like a bug that we’re going to fix or is this a feature and something that we’re always going to have to be vigilant about when we’re in a world where so much can be created so simply, so easily?

SHIH: I think it’s a really great question. I think there’s always been work slop. I’ve been responsible for some work slop, especially earlier in my career. I’ve managed a lot of people. I think certainly AI makes it easier to create lots of work slop, and so just like any new technology, I can imagine the very first spreadsheets when they were invented, people weren’t sure how to use it. The features probably didn’t include checking the formulas, and so there were probably some really bad, incorrect spreadsheets.

The same thing is happening now, and so I think we’ll learn to design better products. This is what I talk to my team about all the time is, before someone clicks Save in Business AI, let’s not let them save an instruction that doesn’t compute, that doesn’t make sense. So I think there’s a number of things we can do through smart UI/UX. We’re still in very early days, and I think the design aspect of this of where do you introduce friction, where do you include extra checks? That’s a really, I think, wide open area of opportunity.

SAFIAN: Either of you have anything else you want to say about work slop? I’ll keep going.

RAMAN: Like any tool, use it. Do not misuse it. Do not overuse it. There’s already lots of research. MIT has great research with brain scans that if you overuse AI, you’ll deplete your ability to grow critical thinking skills, creativity. AI helps individuals get started, but if everyone’s using it across a team, sameness creeps in. So it’s all about how you use it. Ask it to give you 100 ideas based on your idea and riff off of that rather than say, “Solve this thing for me and I’m going to copy and paste it.” I think work slop is great because we’re having this conversation now. We’re in this liminal moment, philosophers or neuroscientists will know, where you’re past the end of the beginning, but the beginning of the new hasn’t started yet. We’re transitioning from the industrial age to the entrepreneurial age. We’re going to have to go through these growth moments and have these conversations.

SAFIAN: Because it’s early as you’re all saying, there are trends that you guys see that you think may turn out a different way. Aaron, I’m thinking about how you founded Box. You know how important and distinctive a culture can be in an organization. Each company is different. All of our cultures, each one is different. When we add AI agents to our work, do we need to orient them like we do new employees? If we all use off-the-shelf AI, are we going to end up with off-the-shelf culture? How do you think about how bringing that technology in meshes with the human?

LEVIE: Certainly, the answer’s in the question there fortunately. And by the way, I’m actually fine with lots of work slop because all I see is lots of files that are going to get generated. They have to be like I want as much—

SAFIAN: That’s good for you. That’s okay.

RAMAN: Scale the work slop.

LEVIE: We want as much vibe, knowledge work as possible. That’s just like the amount of data generated from that. I think everybody here is familiar with context engineering. It’s sort of a simple analogy, which is if you have an employee off the street, super-intelligent person, doesn’t know kind of what job they’re in, they just appeared and you’re like, “Okay, today you’re a lawyer and the next day you’re a marketer and the next day you’re a coder.” That’s kind of what an AI model is. And so you have to give it the context necessary to be able to perform its tasks.

And so maybe actually ironically, if anything, you’ll have to get more context than the person would. It’s actually very easy for an employee to pick up the general cultural sort of norms and work practices because they can just look over at one other person and say, “Oh, I see the way that you just collaborated over there.” And so we’re more of a collaborative culture versus we just make really quick decisions and then move forward. An AI agent again doesn’t know, “Did I just join SpaceX or did I join Patagonia?” I’m assuming those are two different ends of the cultural spectrum. So you’re going to have to tell the agent effectively like, “Who are you right now? And here are the norms in our organization, and here is the context about the business process that you’re involved in.”

SAFIAN: But whose responsibility is that? Is it a tech function? Is that an HR function? Do we even know what—

SHIH: Yes, it’s both.

LEVIE: Yeah, that’s right.

SHIH: I mean, it’s so interesting. So I had the pleasure of working with small businesses all day and they don’t have a lot of their standard operating procedures written down. They don’t have a formal knowledge base. Or if they do, it gets outdated very quickly. And so I think one really neat application of LLMs is to actually help extract what is the tacit culture, what are the values, what are the policies? And use that to almost help surface what you would need to provide as context.

Copy LinkHow AI is impacting employment

SAFIAN: Aneesh, we’ve heard predictions about white collar bloodbath or difficulties for entry-level jobs drawing up. Within LinkedIn on the platform, is there fresh data you’re seeing that sort of gives us any clue about whether any of that is all myth or whether there’s anything real in that?

RAMAN: There’s a lot of data. This isn’t a moment where anyone with absolute certainty can tell you what’s going to happen and where. We know historically technological disruption has led to a net gain in employment. We know this is technology unlike any we’ve had before. My best guess is that net-new businesses is going to be the key metric for societies to look at if they want net employment to go up. But the biggest signal to me is this one number, we have 70%. So we’ve got data across skills, jobs, learning courses, all of it. 70% is what we predict the average job we’ll see in terms of skills change by 2030. So if you are not changing your job, your job is changing on you at a really fundamental level, and that’s really what we should all be focused on.

Yes, there’s going to be some jobs that are going to get displaced and they already are, software engineering, customer service, but there are human skills and net-new skills that those humans can transition into jobs as they change and new jobs as they emerge. You ask who’s in charge, talent or HR? Moderna has a new job title. I was just on a panel with her recently where she’s in charge of talent and tech. They’ve just merged it. My job title got made up by our CEO a year ago for me.

LEVIE: Slop jobs moments.

RAMAN: So that’s all coming. MIT has a stat, 60%, 60, of employment in 2018 did not exist in 1940. So all of it’s going to change. It’s going to change for everyone. There’s some basic things you can do, see your job as a set of tasks. Some tasks AI is going to do or robots are going to do, tasks you’re going to do with them. That means be AI-fluent, know what the latest tool is for what you do, and this third bucket is the exciting one. It’s the stuff you’re going to do unique as a human, this sort of entrepreneurialism you’re going to bring to work as an employee, as work as an entrepreneur, start seeing the conveyor belt in your job, whether you’re CEO or right out of college and you’ll be able to track this and build the job you want.

Copy LinkThe evolving landscape for entry-level talent

LEVIE: What are you seeing in the younger generation, let’s say the data? It seems like it’s all over the place. From my perspective, I think it actually should be one of the better times to be coming out of college. Obviously, there is a hiring crunch. Unfortunately, you have some macro elements that are also driving that kind of unrelated AI, but for us, I get blown away by the resourcefulness, agency, and energy of the next generation coming in because they’re AI native and the tools that they’re using. And so to me when I talk to large companies, it’s like you actually want to really try and get that generation to shake up your organization.

SAFIAN: 100%.

LEVIE: Learn everything you can from them because they’re going to most likely show up in your organization and be completely fed up with how you operate and the amount of just process and bureaucracy when they know that something is totally different. So I don’t know if that’s showing up in the data yet.

RAMAN: Yeah. Well, it is in that there’s no either-or right now. So it is the best of times and the worst of times to be a new grad. Our data shows they’re the most pessimistic and, we know, facing the hardest employment situation, but we also know they over index on resilience, adaptability, they are natives to these technologies. They will come in and push your company to disrupt yourself from within, but I think the failure really has been at a leader level across sectors to tell the story right. I mean, humans process change through stories and we’ve all been out there living a story of AI as an end in itself and chasing the latest AI data of capability. That is change that’s going to keep changing. We are not doing enough storytelling for entry-level grads, but also for everyone about how there is a better place here.

Copy LinkHow business leaders should approach integrating AI

SAFIAN: Clara, I wanted to ask you, when I talked to one CEO last week who said he’s being pressured to adopt AI in areas that he’s not sure it’s actually beneficial for the business, but he feels like, “Oh, I’m getting this push from all different parts of the organization, investors, and the board.” How do leaders strike the balance between, “I got to be in this,” versus, “It’s not really showing any measurable impact now yet”?

SHIH: It’s such an important question. I mean, I see this all the time from various leaders that I meet with. I think it’s first being hands-on and really getting in there and understanding the capabilities because I think with that judgment, with that firsthand experience, only then can leaders really know, “Okay, I want to apply it here but not here.” Another really great success formula is splitting up the team, having people focus on immediate use cases. What can I unlock today that will show me ROI this quarter, next quarter, versus what are the bigger bets where just I see the secular trend and we have to skate to where the puck is going but it’s going to take-

SAFIAN: And whether I can be more patient.

SHIH: I can be-

SAFIAN: And I should be patient or-

SHIH: Not patient, but just know that it’s going to take longer and more experiments to asymptotically, hopefully get to the right answer. And then just having space and time to live in both time horizons simultaneously. What’s the day to day, what’s the quarter, and where do I want to be? How would I be completely screwed in six months or 12 months if I don’t have this tiger team that’s incubating experiments at startup speed?

SAFIAN: Aaron, you’re nodding.

LEVIE: One pitfall that a lot of existing organizations will fall into, like us included when I walk around and try and talk about what kind of processes we can bring automation to, I think there is this idea that we have this sort of end-state utopian view of AI can do anything and can automate anything. The reality is that if you drop AI into today’s business process, it’s going to actually do very little and you have to actually re-engineer your workflow and you have to re-engineer your process. I think a lot of times, unfortunately, you’ll see people try and drop in AI into a process where then even with the best automation, you’re going to get a 10% gain or whatnot of that workflow.

SAFIAN: It’s not world-changing.

SHIH: Yeah.

LEVIE: It’s not world-changing, but it’s because you didn’t think about re-engineering the workflow for AI. And we thought AI would work how we do. It turns out it might be the case that we have to work how AI does and we have to be actually in service of the agent to make it most productive as opposed to this unfortunate reality where we probably thought it would make us productive.

SAFIAN: It’s a little terrifying. Your job is to make the AI more productive?

LEVIE: Totally. Well, so there was some offsite 15 years ago where one of our engineering managers told me about the inverted pyramid. His job is to enable all the people below him technically in a hierarchy, but to be as productive as possible. And it was sort of this inversion of like he works for them to make them as effective as possible. And the reality is that that’s kind of what we’re going to be doing with AI agents for quite some time because we’re going to be working to make them effective, give them the context they need, give them the specifications and rules and guardrails. And if you look at the five and 10 and 20-person start-ups that are AI-native fully, they don’t have a single business process, that’s basically what they’re doing. They’re working to support agents to be super effective, and that’s just a totally different way to work.

RAMAN: And it is, make them more productive to make you more impactful.

SHIH: Yeah, help you help me.

LEVIE: Obviously, at the end it’s going to make you more productive.

RAMAN: I think we have to be affirmatively pro-human. It’s hard to state how radical it is.

LEVIE: That was not pro-human the way I phrased it. Yes, yes.

SAFIAN: Aaron’s admission that he could have been more pro-human in his description gets a laugh, but what he and the others are talking about is no laughing matter. When Aneesh says, “It’s hard to state how radical it is to rethink our way of working,” he isn’t kidding. As Clara puts it, we could be screwed if we take the wrong tack and it’s going to be hard to get to the right answer. So what habits and leadership traits will help us most as we move through this transition? We’ll talk about that more after the break. Stay with us.

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Before the break, we heard Box CEO, Aaron Levie, Meta head of business AI, Clara Shih, and LinkedIn chief economic opportunity officer, Aneesh Raman live at the Masters of Scale Summit, talk about how AI is impacting work and the workplace. Now they take audience questions about leadership from an executive at IBM’s Institute for Business Value and about education from the Chancellor of Vanderbilt University, plus Clara’s prediction on the future of organizational structures and Aneesh’s prediction on the future of careers. Let’s jump back in.

Copy LinkThe skill that’s most needed for this AI era

I’m going to expand our stand-up meeting and bring some folks in the audience into this. This is Cindy Anderson-

SHIH: I feel like I’m in a game show.

SAFIAN: I know. She helps run the IBM Institute for Business Value. Does a lot of research about AI. Do you have a question for them?

CINDY ANDERSON: I do. If humans are going to be responsible for making AI more productive, what’s one skill that you think is going to get much more important as humans are doing that, as well as continuing to manage other humans?

SHIH: I’m happy to go first. I think entrepreneurship, which is defined by pursuit of opportunity without regard to resource constraint because the underlying substrate of our resources is continually shifting. And so we have to be constantly our feet and on our toes, literally, just to pay attention to these trends and continually reinvent ourselves and our companies.

RAMAN: I would say curiosity. I mean, I think that curiosity about AI, curiosity about what’s coming. I believe the entrepreneurial mindset is going to be core to work. And so we’ve sort of made up this nomenclature of the five C’s. So curiosity, compassion, creativity, courage, communication. We all have to get better at all of those. And then you have the sort of habits of resilience, adaptability, learn how to learn quick, learn how to fail fast. The best signal that we’re starting to do that as a system, anyone who took an AP in the U.S. or has kids who are taking an AP, they’re in 72% of high schools, they’re launching a new set of AP courses on entrepreneurship and financial literacy. We’re going to teach these five C’s, and they’re going into the career and vocational tech space. So we’re starting to see that happen. But if you have to pick one, I think just be curious.

SHIH: I love that, but I don’t like that it’s confined to APs. It should be to everybody.

RAMAN: Yeah, 100%.

SHIH: Really.

RAMAN: 100%. Yes.

SHIH: Because think about it’s like the top percentage of students who take AP.

RAMAN: Yes, which they’re also trying to change with it, but I agree with you, it should be core curriculum. Yes.

Copy LinkPreparing education systems for AI

SAFIAN: You guys have led me to the perfect next question. This is Daniel Diermeier. He’s the Chancellor of Vanderbilt University.

RAMAN: Oh, wow.

SHIH: All right.

SAFIAN: A great football season so far, by the way, Chancellor.

RAMAN: Yes.

SAFIAN: What’s your question for our group?

DANIEL DIERMEIER: Let’s keep it going. So we’ve been thinking about what to do as a university context. About two years ago we said, look, we’re going to take computer science out of engineering, create a whole new college, which we call College of Connected Computing. The vision is kind of computing for all. The students love it. They’re all over it. The faculty love it. It’s literally from astrophysics to archeology. The parents are all freaked out. So their worry is, “Oh my God, these jobs are going to go away.” So if you were in my shoes right now, how do we prepare graduates from the leading universities to thrive in this new environment?

RAMAN: I’ll do a quick interdisciplinary study. So bringing computer science with philosophy gets you partly there. And then I’m a big believer we’re about to see a new era of the humanities because those are so core to all of this, critical thinking, ethical reasoning, the thing that humanities have to do, and you can help lead the way. If you think about math in the end of the 19th century, it was totally abstract. It wasn’t until engineering degrees got created that engineers could be hired. We’ve got to make applied humanities like a real thing. We’ve got to come up with these degrees that are easy for employers to get that this is what I’m going to get, but those are my two things I would say.

SAFIAN: Aaron?

LEVIE: I’m trying to be as pro-human as possible. I do think actually you have one of the toughest jobs of the next decade because we’re in this period where we’re going to put a lot of pressure and expectation and emphasis on who’s coming through the school system, and are they ready on the other end for this next world?

SAFIAN: Which, we don’t really know what that world is.

LEVIE: We don’t know what that world is.

SHIH: And it’s changing.

SAFIAN: It’s different.

LEVIE: I mean, I would guess I’m making this up, but probably in the past 50 years, you probably have not had more people saying different conflicting things about that. And so I just don’t think in 2007, we were doing panels about, “What’s the future of every job category?” So the entire economy and all of academia, including literally the university itself. So we all are wishing you the best.

RAMAN: Just go with football. You got to think—

LEVIE: I think it’s sports, it’s going to be sports.

RAMAN: Football.

LEVIE: We’re all going to play football. So I’ll probably be totally proven wrong about this in five years, hopefully this is not live-streaming or something, right?

SAFIAN: No, no one will ever see this again.

LEVIE: I cringe up a little bit when I see people say like, “Don’t learn software engineering, don’t learn X, Y, Z thing,” because the software engineers that I know that were the best software engineers in the deep practice and every element of it are also the best AI coding engineers, and they’re getting the most output from AI and they’re getting the most done. And I think it’s incredible what a lot of the low-code kind of no-code AI coding tools are doing because it’s opening up the audience of who can actually build things, and that’s an incredible gift to humanity, and it’ll cause so many more people to be curious and creative and all the other C’s.

But when you have somebody use AI to build a complex system, you better be sure that the person who’s pressing commit, that code also has somebody who knows how to review it and ensure that all of the dependencies are not going to blow up the site and DDoS you and lead us to a security vulnerability and they know how to connect the dots to the rest of the system. And so I think a lot of the fundamentals of computer science remain even in a world where AI is generating a significant portion of the code. We don’t know how to have 100% non-hallucinated code generated. And so you extrapolate that and you say, “In a world where we don’t know how to do 100% of the job, we know how to automate parts of the tasks of the job,” then it’s an interesting question of like what really changes in the fundamental lessons that you would give young people?

And that’s why I kind of get hard-pressed because you still probably want people to go through law school, learning all of the critical thinking required to be a lawyer. And you probably still want people going through business school understanding, how do you sell the customers? Why do you decide what they want? All of the fundamentals of each of these domains. So I don’t think that really changes. I just think AI is giving you leverage. Maybe we see a collapse of specialization to some extent because now I can take on three jobs or five jobs worth of work as defined by 2020, 2021 work. But again, I don’t know. I think probably the way you teach changes, obviously, I mean everybody already knows that, but it’s not obvious to me that there’s entire, just, eliminate entire categories. And so thus I think anybody coming out of college, you’re still going to probably learn a lot of those core skills.

Copy LinkPredictions for how AI will impact work

SAFIAN: I’m going to ask Clara and Aneesh each to make a prediction for me. Let me start with Clara. What does the future workplace hierarchy look like with AI? Will it look familiar to us from today?

SHIH: No. I think to Aaron’s point, there’s going to be a reduction in specialization, more generalists. If you think about the role of a corporation, ultimately it boils down to three sets of jobs at a very high level. There’s building a product, there’s selling the product, and then there’s running the corporation to support those activities. And today that is split up into hundreds of subspecialties. AI will increasingly help us collapse that and create more small businesses. I think we’re going to see just an explosion in the number of businesses that get started.

SAFIAN: All right, Aneesh, your prediction is what a future career will look like?

RAMAN: I put money on it, and we’ll see what happens in five years. Jobs are going to be about tasks, not titles. Titles just like already don’t matter. Careers are going to be climbing walls, not ladders. You’re going to go up, down, around. Companies are going to be work charts, not org charts, completely blurred line to function, built around projects that are happening. And then ultimately, economies are going to be engines of human innovation that are succeeding, not scaled of efficiency. And I think that that’s where the career opportunities for a young person, but for any person, you’re going to have more agency. If you lean into curiosity, you start to define yourself, be pro you.

The other end of this is an era where no one beats you at being you. You’ll have a bunch of agents and you’ll have hopefully the right systems and structures at school and at work to support that. But we are not just badass as humans. The human brain is incredible. But you are incredible as you are as an individual. You have lived and learned experience that shapes a very unique perspective. And then you’re going to bring that to your job. You’re going to build your career around it. And as you do that and you get to nobody beats you at being you, you’re going to be able to run the game in a way that very few people got to across human history. And that’s what I think if we lean in with curiosity, you can get yourself to see that.

SAFIAN: Well, I want to thank you, all of you, for being you on the stage with us and taking us through this. Let’s hear it for Aneesh, Aaron, and Clara. Thank you very much.

RAMAN: Thank you.

SAFIAN: I had so many thoughts rolling through my head during that dialogue and listening to it afterward, I’m still buzzing. There’s a tone of optimism coming from all three of them, and there’s also acknowledgement that so much is changing and we really don’t know for certain where it will land. How will AI and work mix? The answer may be pretty varied based on where you are, who you are, and what works best for you. As Aneesh puts it, “You being you.” There were plenty of other brain-buzzing discussions at the Masters of Scale Summit, which we’ll continue to share here on Rapid Response, including a dynamic and unexpected exchange with the commissioner of the National Women’s Soccer League and the CEO of The Honest Company, and a pairing of the CEOs of Chobani and Patagonia that goes deep into how values and commerce connect. You can also catch up with videos from the Masters of Scale Summit on the Rapid Response YouTube channel. I’m Bob Safian. Thanks for listening.