How To AI: A Practical Business Q&A With Three Experts

As more companies push AI in their workplaces, the technology is rapidly reshaping the way many of us do our jobs. But a lot of people — from entry-level employees to the C-Suite — are still in the dark about the limits of AI, its best uses, and how to make it work for them. We called in a panel of AI experts to answer some our listeners’ burning questions about how to use it at work: Sayash Kapoor, co-author of the book AI Snake Oil: What Artificial Can Do, What it Can’t, and How to Tell the Difference and the Substack AI as Normal Technology; Rajeev Kapur, CEO of 1105 Media and author of the book AI Made Simple: A Beginner's Guide to Generative Intelligence; and futurist and author Amy Webb, founder and CEO of the consulting firm Future Today Strategy Group. Kara, Sayash, Rajeev and Amy break down everything from how vibe coding works to thornier questions around privacy and regulation. They talk about how young people can prepare themselves to enter the workforce, and how all of us can develop skills to stay relevant. And, of course, they weigh in on the question so many of us are asking right now: Is AI coming for my job? Questions? Comments? Email us at [email protected] or find us on YouTube, Instagram, TikTok, and Bluesky @onwithkaraswisher. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  We're going to have to manage humans, AI agents, or whatever that format takes, and at some point robotics. So that's a whole different type of skill set.

• 0:09Kara Swisher?

  I don't even want to manage humans.

• 0:22Kara Swisher?

  Hi, everyone. From New York Magazine and the Vox Media Podcast Network, this is On with Kara Swisher, and I'm Kara Swisher. Today, we're answering your questions about how to use AI at work or for your business. We're tackling everything from how vibe coding works to the big societal issues around regulation and privacy. And of course, we'll get into what so many of us want to know right now. Will AI take my job? A lot of you sent us some really great questions via email threads and Blue Sky, and we've called in a panel of experts to help answer them. Sayash Kapoor is the co-author of the book, AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference. He also writes the Substack, AI as Normal Technology.

• 1:05Kara Swisher?

  Rajiv Kapoor is CEO of 1105 Media, a business-to-business tech marketing and events company. He's the author of AI Made Simple: A Beginner's Guide to Generative Intelligence. And Amy Webb is a futurist and the founder and CEO of the Future Today Strategy Group. She teaches at New York University's Stern School of Business, and she's the author of multiple books. And her latest is The Genesis Machine: Our Quest to Rewrite Life in the Age of Synthetic Biology. I think it's really important for us to do this a lot and ask all kinds of questions as the technology develops. We did not have this opportunity to have podcasts when the Internet first started, and I spent a lot of time answering these questions myself when I ran into people.

• 1:49Kara Swisher?

  And so I think it's really important to keep asking questions as this stuff rolls out, because it's really in that phase where we're not really sure what's going to happen. All right, let's get into my conversation with Syash Rajiv and Amy, which is brought to you by Smartsheet. Stick around.

• 2:11Speaker 02

  You've been asked to run the company's social channels, but company content plays by its own set of rules. It needs a logo in the first three seconds, but it can't feel like an ad. It needs subtitles, except when it doesn't. It needs to run at two times speed and deliver a complex message, simply, of course, within nine by 16. Thankfully, your company has Canva, so you can easily edit your video and every version of it. What will you canva today?

• 2:43Kara Swisher?

  Sayyash, Rajiv and Amy, thank you for coming on, on.

• 2:47Speaker 00

  My pleasure, our pleasure. Thank you. Thank you so much for having us.

• 2:50Kara Swisher?

  Hey, everybody. Nice to be here. All right, let's start with a broad question and then we'll get into specifics. So how has AI already changed the way businesses operate? And in the next 12 months or so, what are the most consequential impacts you expect to see from AI? Let's start with Rajiv and then Sayyash and then Amy.

• 3:09Speaker 00

  Yeah, I think for me, Kara, I think what we're seeing here is that AI is starting to really start to take tasks. And I think that's what's a key distinction between this idea of taking tasks versus taking jobs. So I think you're going to start to see a lot more tasks being automated. For example, in my company, the other companies I've been advising and working with, the idea of taking every single thing that's manually written down and how do you not take that and AIify it is really what's going to be consequential. And I think going forward, it's going to really help really drive that opportunities of productivity gains, efficiencies and those kinds of things. And I think that's probably going to be the big win. And I think you're seeing that a lot more in the SMB space.

• 3:46Kara Swisher?

  Small business.

• 3:47Speaker 00

  Than you are in the big, larger Fortune 1000 type of spaces, where the SMB guys, actually, what I'm seeing have a lot more flexibility and are willing to invest and willing to take a longer term view of how AI can impact their business.

• 3:58Kara Swisher?

  Isn't taking tasks, taking jobs? Because those were jobs.

• 4:02Speaker 00

  Well, I think what you're saying is that, look, we may not need to hire more people. And I think what we're doing is we're saying, okay, we're going to become more efficient with what we have. And I think you're starting to see more and more organizations start to train their organizations. At least that's what I'm seeing in the area where I'm focused on, which is more of that consumer SMB space. We're starting to see that.

• 4:19Kara Swisher?

  Okay, Sayyash?

• 4:20Speaker 03

  So I really wanted to come back to your point about whether taking tasks is the same as taking jobs.

• 4:25Kara Swisher?

  Uh huh.

• 4:26Speaker 03

  And I would say if you look back at the history of general purpose technologies, these are technologies that can be applied broadly, that has actually not been the case. So for example, in the nineteen seventies, lots of people thought that ATMs would make bank tellers obsolete. And what we've seen instead is that at least for the first four decades after ATMs were installed, the number of bank tellers employed increased. And that was because it was so much cheaper for banks to open new branches with the rise of ATMs that they started opening more of these. But having opened a new branch, they realized that, look, we often do still need bank tellers. Maybe the job is not cutting checks anymore, maybe this job is not to sort of hand over money, but they do still need bank tellers to maybe handle the customer relations side of things. And so oftentimes, that's what makes it hard.

• 5:11Speaker 03

  Like sometimes new technology makes it so much cheaper to sort of do the functions you were doing before that the demand for that sort of service or product increases. And that's what we've seen with ATM. So as we have new technology automate certain tasks, the definition of the job changes to be about everything that has not yet been automated.

• 5:32Kara Swisher?

  And what would be the most consequential impact right now that you're seeing initially?

• 5:36Speaker 03

  So I think the main jobs that are being affected already are jobs which were actually already sort of reduced to carrying out one specific task at a time. Most commonly, this is tasks where people had like contract work or where the task itself has been neatly allocated away. So people just had to translate a piece of text or transcribe a certain audio recording.

• 5:59Kara Swisher?

  Uh huh.

• 5:59Speaker 03

  And these jobs comprised of primarily solving one specific well-defined tasks. Those are the sort of jobs where I think we're seeing the most impacts already. I think there has been a reduction in the need for translation and transcription. We've also seen routine work for artists get reduced because of AI, because you no longer need to hire someone, let's say, to create a logo that you want to use for your website.

• 6:23Kara Swisher?

  All right, Amy?

• 6:25Speaker 04

  I might have a little bit of a contradictory viewpoint on this.

• 6:28Kara Swisher?

  Sure.

• 6:28Speaker 04

  The main thing that I'm seeing is a growing delta between expectations and reality. We're working with a lot of CEOs and executive leadership teams who are getting contradictory feedback. So a board of directors or the street, they want to see AI being implemented at scale primarily to address the bottom line, which takes a lot of upfront investment. Meanwhile, the street does not reward companies, at least not that we've seen, for implementing AI solutions. And in some case, it can drive stock prices down. We've actually seen this with a few companies already. And it's a shame because there are leaders taking strategic measured risks, which is to say, not throwing obscene amounts of capital at products and services before they have a plan in place or a long-term perspective.

• 7:13Speaker 04

  And they're making salient decisions and they're getting spanked.

• 7:16Kara Swisher?

  And why is that?

• 7:18Speaker 04

  I think because, as we've seen with every other consequential technology for decades, these technologies take a long time to develop and to implement them at scale. Even at a small and medium business, you have things like compliance and insurance and thinking through workflows and change management. So the technology on its own isn't enough. You need all of the other structures in place. And you need a plan for implementation and then a plan for what comes after that. And just, that all takes time. So technology may be developing at breakneck speed, but the reality is that business moves at the pace of business.

• 7:56Kara Swisher?

  Right. And therefore slower than people think or more measured.

• 7:59Speaker 04

  And government's even slower.

• 8:00Kara Swisher?

  Right.

• 8:00Speaker 00

  Yeah. And I think that was my point earlier, which is I'm seeing a lot more adoption at the SMB level.

• 8:05Kara Swisher?

  Smaller.

• 8:06Speaker 00

  Because they just don't have that same overhang that you might get from the street or other places. And they're willing to take risks, they're willing to give it the time it needs to be successful.

• 8:14Kara Swisher?

  Right. Okay, let's dive into the big question everyone's asking, which is either will AI take my job? Or the other side of the coin, how can I use AI to reduce my labor costs? I do talk to a lot of CEOs, and especially in the tech area, they're like, oh, I'd like to cut my coders from 6,000 to 2,400. I heard this a year ago. And I hear it a lot from different people, even people who are tech, but tech adjacent to AI, essentially. They're not AI companies, per se. They're doing other things like travel, et cetera. So, Rajiv, what industries do you think benefit the most from adapting AI quicker? And what should they Be doing in those fields to make sure they're deploying it intelligently so they

• 8:56Kara Swisher?

  don't get over their skis as Amy was talking about lay off their workers they actually need.

• 9:01Speaker 00

  Yeah, in terms of a very specific industry, I mean, I'm seeing it being adopted across a lot of different key industries, you know, just that I can't point to one particular one that says this one's going to benefit over one particular one. I mean, one could argue like the legal industry is going to go through a lot of change because of what's happening with AI and you may not need as many paralegals, etc., you know, things of that nature. But I think in general, in terms of how you start to think about this, how you start to implement AI across the board, what I've been advising people is you start small. You pick one process, whether that might be customer support, scheduling, whatever it might be, you know, marketing areas, you deploy it, you start to measure the ROI, you measure ROI in weeks, not years. And you really need to look at this from

• 9:45Speaker 00

  a top down perspective. And what we're seeing is a couple of things. Number one is that that ROI issue is because a real issue for the CFOs and they really are the ones who are really tend to be a little bit of the roadblock in terms of understanding and letting people breathe a little bit with this stuff. But the other bigger issue is that at the end of the day, there's two kinds of AI, Cara, as you know, there's the machine learning data side of AI, then there's the chat GPT stuff and on the generative AI things. And really that that machine learning data analytics, that that data side is data is the new oil and you can't do anything unless you build refineries on top of it. I think that's potentially a bigger opportunity for some of these organizations. But the problem there is that it's garbage in, garbage out. So really understanding their data is going to be really critical.

• 10:27Speaker 00

  And if they can do that, you know, they'll understand how to start building the tools to augment everything that's happening in the business and then do the one thing money can't do, which is buy time and just really build a culture on it and don't, don't chase the shiny new tools that are coming out because the space is moving too fast.

• 10:42Kara Swisher?

  All right. So, Sajesh, I want you to answer this question. We got a ton of listener questions on the topic of AI and job losses. So I'm going to read one about the disruption at the managerial level. Aaron Hoffer in San Francisco wrote in to ask, why hasn't there been more serious efforts at replacing leadership roles, even CEO with AI? From a data perspective, there's so many books and think pieces as well as case studies and decisions made by executives that could and have been built into LLMs. If they were so great, why wouldn't these AIs be replacing the mediocre leadership? Is that realistic?

• 11:14Kara Swisher?

  Could AI eventually fill some upper level managerial roles in potentially the C-level suite?

• 11:18Speaker 03

  That's a very interesting question. I mean, this reminds me of a case where someone tried to run with ChatGPT as the mayor. The candidate's entire pitch was, you know, any policy-related question would be put to ChatGPT and, you know, I'll make sure I implement the outputs. And I think unfortunately, this sort of, this perspective sort of misunderstands what the role of leadership is often at these companies. So for example, the role of a CEO is not just to sort of take in all of the data and put out the optimal context. But it's also to build relationships, to sort of figure out in sort of power struggles within the company, what the vision of the company looks like, holding people to that standard. And I think all of those are things that like chatbots can't do.

• 12:00Speaker 03

  On the other hand, we have seen a pressure on the managerial class, on like mid and upper level managers as a result of AI too. So for example, across the board, at least in tech industries, there has been a rapid sort of reduction in the number of people who are employed as mid-level managers because the expectations have risen. And each manager, instead of earlier managing like three or four or five employees, is now managing on the order of 10 or more. So to some extent, this has happened, but at the very top, at the questions about like whether CEOs can be replaced with AI. I think that's not really realistic for the longest time to come, simply because the job of a CEO is not just to take optimal decisions. It's to sort of balance various countervailing forces within the company and decide what the vision for the company should be.

• 12:46Kara Swisher?

  So presumably you could use it to help with, instead of reading 90 leadership books, it could put positive things in, for example.

• 12:53Speaker 03

  Absolutely. I mean, I think it can be very helpful as a decision support tool, just not as a tool to replace decision-making.

• 12:59Speaker 04

  Do you remember Paul Graham, like at some point, somebody built a what would Paul Graham do answer generator?

• 13:05Kara Swisher?

  I'm sure I didn't use it, but go ahead.

• 13:07Speaker 04

  No, no, but, but like it existed and Paul Graham was a co-founder of Y Combinator. They took a corpus, he had sort of kept a blog and somebody took the corpus of everything he had written and dumped it into a basic answer system pre-machine learning. That just sort of spit out answers. And then General Catalyst at one point had appointed their first female board member, who was, of course, an AI, which is all to say, I think that these systems are useful as a way of cataloging and extracting institutional knowledge about a place or a body of work from a person. And there's been pretty good examples since then of this working out like really well.

• 13:51Speaker 04

  At the end of the day, though, the most important technology is people. And for at least the time being, people will keep working in organizations. And so this is about relating to them versus replacing them with leaders with technology.

• 14:04Kara Swisher?

  That's a good point. All right. Our next listener question, which Amy's for you, comes from Shelley Wilson in Providence, Rhode Island. She sent us a voicemail, let's hear it.

• 14:13Speaker 05

  I believe AI in business will impact the younger generation more than any others. My question is, what advice would you give somebody early in their careers? Say, maybe early twenties, mid twenties, about learning about AI in business and incorporating it into their daily work routine? I ask this because I have two children in this age range, both with good professional jobs. One is a chemical engineer, the other's a talent recruiter, both at Fortune 500 companies. And they've learned nothing from their employers about how to incorporate AI into their jobs. By comparison, I work for a large professional services firm in the US. We've been trained on using AI, strongly encouraged to use it, and expected to use it every day.

• 14:58Speaker 05

  So I worry about the younger generation, and I'm certain that my adult children would love to hear what you have to say. Thank you.

• 15:04Kara Swisher?

  So Amy, what advice would you have for Shelley? And how should workers think about future-proofing their careers, whether they're executives or entry-level employees?

• 15:12Speaker 04

  Sure. So it's a good question, Shelley, and one that I actually get a lot. And I just want to highlight that oftentimes, the fears that we have and that we have for our children. Are actually just the fears that we have ourselves. And the same question that you asked was asked at the dawn of social media, when people were confused about Twitter, and at the dawn of mobile phones, and at the dawn of the internet, and I'm sure all of the technology that came before that. So this is to say, there's a lot of training that's happening in upskilling and reskilling, which are two words that I hate because it's sort of

• 15:45Kara Swisher?

  They're bad words.

• 15:46Speaker 04

  They are bad words. It sort of throws out all of the skills that you've taken a lifetime accumulating. So there's a lot of that happening right now for mid-career and above professionals. But it's good to keep in mind that people are in their twenties. I've got a daughter who's 15. These people aren't just digital natives. They are natives of a new world where there's lots of different technology that all does different things. So the fears that you have about the training they're receiving may be well-founded in a sense, but they're probably ambiently or on their own, making use of all of these tools. And in terms of future-proofing a career, it's another word that I don't love, future-proof, because it assumes that you have total control over all of the uncertainties and like the math doesn't work out and all the variables.

• 16:30Speaker 04

  So it's more about being flexible as you go.

• 16:32Kara Swisher?

  So Rajiv, you're also doing a lot of work in the AI education space. Do you have anything you want to add for Shelly?

• 16:37Speaker 00

  Yeah, sure. It's interesting. You know, it's actually really opportune. I just drove my son from LA to Dallas last week. He started a new job on Monday, working for an up-and-coming AI company. And the exciting thing for me is exactly kind of what Amy said, right? This is the most advanced technological generation we've ever seen, and every generation is like that. I have zero concern about where they're headed, you know. But I will say, a little bit of a different viewpoint here is, I think critical thinking skills and really focusing on those are going to be really, really important. You know, I would say the vast majority of folks don't really need to be concerned with how the sausage is made. So learning how to clearly prompt, how to really work with AI, how to communicate with AI is going to be a critical skill. Fact checking, understanding how to deal with hallucinations and those kinds of things,

• 17:21Speaker 00

  making sure the data they're receiving is clear and accurate, and how to then deploy that across an organization. But curiosity and judgment and critical thinking skills are where I would really encourage parents today to really make sure they're spending time with their young adults and their children, making sure that that is not a skill that they're just neglecting. It's going to be more and more important going forward in the future.

• 17:42Kara Swisher?

  So let's talk about how to actually use AI for business. Saish, we're going to ask you this one. This is a question that sent to us via Blue Sky. I think it's Dejango wrote, what do AI developers think we do? They spend trillions on an AI that will, quote, "create images, music, and video." But when we ask it to report how many times an account number shows up in a PDF of an invoice, it cannot provide an accurate answer. So Saish, that was admittedly a question wrapped in a rant, and actually, amount of money invested in AI is probably not trillions of dollars yet. But nonetheless, it's hitting on a bigger topic that you wrote about in your book. That is, what tasks can AI do well, and why does it sometimes hallucinate and make up answers when asked a simple request?

• 18:24Speaker 03

  Yeah, I mean, that's a great question. And I think this goes back to how these language models are trained and what they're trained to do. So basically, the way ChatGPT works is, given an input sentence, maybe a query by a user, all it is doing at any given point is sort of predicting what the next most likely word is in its response. So if you ask it, "What is your name?", maybe the next most likely response is, "My name is ChatGPT," and that's what it comes up with. And for some of these responses, the chatbot can be like really precise. Some of these responses are data that it has been trained on hundreds or thousands of times. For example, if you ask it things or information that belongs in Wikipedia, this is something

• 19:06Speaker 03

  language models have been trained hundreds of times on, and it is quite likely that the chatbot will get it right. That's what gives it the illusion of actually sort of being all-knowing in some sense, or giving these answers correctly. But unlike humans, if you, for example, ask the same question to a human, you would expect a human who can tell you anything on Wikipedia to also be able to count up to a hundred. But that's not the case for ChatGPT. This has also been called the jagged frontier. So the frontier of what language models can do is quite uneven. They are very good at certain skills, but very bad at others. And so we can't really extrapolate, based on how well a language model can answer questions about Wikipedia, we can't really extrapolate that to how well it can do maths.

• 19:49Speaker 03

  And to be clear, I think this is not just so that we shouldn't use AI at all for a number of tasks, but just that we should have these really clear evaluations before we start to use them, especially for critical tasks. We need to understand how well it can do at that specific task.

• 20:03Kara Swisher?

  Right. So Rajiv, what about vibe coding? That's when people use an LLM like ChatGPT to write a code for, say, an app that can do X, Y, Z. So you can use plain language prompted chatbot to write code rather than actually program yourself. From a business perspective, what are the pros and cons in the rise of vibe coding? And for those of us who aren't software engineers, is vibe coding a viable way for non-technical people to use this?

• 20:27Speaker 00

  Yeah, that's a great question. Ultimately, I think what vibe coding is going to enable is a whole level of entrepreneurship that we haven't seen in quite some time. I kind of like to call it this dawn of a new enlightenment period. It's going to enable whole new ways of looking at art, music, sciences, all these kinds of things. It's going to lead to more of this broader revolution from an industrial perspective that we're going to see across countries. So I think those are the pros. I think the cons are you need to have a very specific skill set. And I think it goes back to the things that we talked about a few minutes ago that Sash and Amy also touched on, is that this idea of critical thinking, the idea of prompting, the idea of really making sure that you have that skill set and you're developing that skill set to be able to really ask the questions the right way and really to be able to put into that prompt box,

• 21:09Speaker 00

  because that's going to become probably one of the most critical skill sets that anybody can develop. And if you're struggling with that, you could get frustrated. Bad data could come out of it, so a little bit of garbage in, garbage out, frustration, longer periods, a lack of sticking with a particular project, some data chaos. But I think one of the bigger challenges might be in organizations is you see this concept of shadow AI popping up, right, where different groups, different departments are all doing kind of their own thing and with the lack of any sort of control or process.

• 21:39Kara Swisher?

  Making vibe coding this stuff.

• 21:40Speaker 00

  Right, yeah.

• 21:42Kara Swisher?

  So speaking of critical thinking, our next listener has a related query. It was sent to us via threads from at Scan My Photos. They wrote, as AI takes over the, quote, "thinking tasks at work," "how do we make sure humans don't lose the ability to think for themselves?" Amy, why don't you tackle this one? There was a recent example of this, a study published in the journal Lancet. Gastroenterology and hepatology found that in just a few months of using AI to interpret test results, doctors were being about 20% worse at doing it on their own. And many people can't look at a map. I mean, you could use examples from the past. So a listener asked, how do we make sure humans don't lose the ability to think for themselves?

• 22:20Speaker 04

  Well, that's up to us, isn't it?

• 22:22Kara Swisher?

  Right.

• 22:24Speaker 04

  There's no question that with certain technologies that offer automation, the resulting impact, if they're really good, is a sort of learned helplessness.

• 22:33Kara Swisher?

  That's a very good way of putting it.

• 22:35Speaker 04

  The more that you use a ChatGPT, for example, to write your emails or to help writing your essays, there's a little bit of a slippery slope between structuring that organization and doing the critical thinking yourself to start, and then just like, please answer this in the style of a McKinsey senior exec or whatever it is that you're trying to do. And that doesn't cause catastrophic long-term negative impacts necessarily, but it does put you on a path where you're not going to continue to learn and grow. And learning and growing is important even in the absence of, you know, game-changing technologies. So, well, like when I was in advanced calculus,

• 23:16Speaker 04

  I remember at one point there was a connected calculator. And I had a professor who was very adamant that we did not use this advanced calculator technology. You know, and I think everybody's been through some version of that. A calculator is just a brick if you don't know basic math. And in a way, AI is also kind of a brick if you don't know what to do with it. But it is becoming easier to know less and be able to sort of do more. So this is really up to everybody. And this kind of relates to the vibe coding conversation earlier. You know, one of the key pieces of missing information is not what do you put into the prompt window, but as a company, how comfortable are we dumping in a corpus of

• 24:00Speaker 04

  our own data so that people can build stuff with it? I'm much, much more concerned about the lack of depth, about what happens when you sell or license your data, or you allow employees to dump your data into a corpus, or to make their own corpus, and then throw like a llama on top of it, an open source tool, with the potential consequences of that in terms of lost revenue in the future, or lost ability to make revenue, or now all of your stuff is out there for anybody to use. Those are the kind of fundamental thinking questions that I think we should be spending more time with. Yeah, in business, you know, Scott Galloway, who's my partner pivot, puts everything in, and I put almost nothing. Yeah, same. I actually was saying this to Sam Altman, he goes, why not? And I go, I just don't trust you.

• 24:44Speaker 04

  Yeah, it's not, that's not. It's not you. I said, no offense, but. No, I mean, look, there's not a lot of people stopping to have thoughts about that, let alone a critical debate about it, and it's super important. Well, he's not a lawyer, and he's not a doctor, so there's no expectation of privacy, right? No, we could have a whole separate conversation about other ways that that company is breaching what I would consider to be basic privacy. But again, like, sometimes when tools are so easy, it's easier for us to use them than to stop and ask a question about what the potential implications are of using them. And then you give up, like, I'll just tell them. Well, yeah. And there's a lot of already instances, especially in the world of news and media, where archives have been sold, and nobody really thought about what that would mean

• 25:27Speaker 04

  going forward, and that's revenue, it's going to be a revenue problem for companies in the future. So now is the time to be thinking and making some decisions using information about the future. We'll be back in a minute.

• 25:48Speaker 02

  What will you Canva today? What will you brainstorm, collaborate on, present today? What will you resize, post, edit, imagine? What will you research, test, switch, and finally have the courage to pitch? You got this! What will you visualize, design, brand, animate, create, generate? What will you translate, pour your heart into, or have a laugh doing? Whatever you want to make, it's possible. So what will you Canva today?

• 26:18Kara Swisher?

  Okay, our next listener question comes from Carrie Tolericco in Scranton, Pennsylvania. She sent us this voicemail. Let's hear it.

• 26:25Speaker 06

  Hi Kara. I'm curious if your panel would agree with my opinion that the broad adoption of AI is going to be limited by the lack of tech savvy among average employees at average companies. My company has been testing some of these tools and I've been struck by the amount of handholding our middle managers have needed during demos or training. These aren't stereotypical tech illiterate boomers. They're mostly Gen Xers and older millennials like me. I think there's a real skill gap between the people working at your average, small or mid-sized company and those working directly in tech or industries that are tech adjacent or particularly tech-heavy, and this gap will have a big effect on the broad adoption of these tools. Would love to hear everyone's thoughts. Thanks.

• 27:04Kara Swisher?

  Sayash, what do you think?

• 27:06Speaker 03

  I absolutely agree. I mean, I think we've seen this process once again, as Amy said, with past technologies as well, where technologies go through phases of invention and innovation on top of it, but then the broad adoption takes much, much longer. And so this is one reason why I think the true impacts of AI will actually not even be realized this decade. It'll take us much more than the next five years to actually realize the impacts of AI, to figure out what productive uses we can put it towards. And in some cases, it might not even be the case that, you know, existing business structures can make use of AI very well. I mean, I'm reminded of how, after we invented electricity... It took forty years for us to figure out how to electrify factories. And, you know, just putting in electricity in the existing factories was not enough.

• 27:51Speaker 03

  They needed to basically reorient the entire factory layout around the process of electrification to make it work. And so in some cases, the most dramatic transformations and the sort of most in-depth adoption of AI will only take place as people learn how to use it, as we figure out what new use cases there are, and in some cases even reinvent how we run businesses around AI.

• 28:12Kara Swisher?

  So to her question, where do companies get it wrong when it comes to them rolling out AI for their business?

• 28:18Speaker 03

  One of the main things I've seen is not recognizing the gap between capability and reliability.

• 28:23Kara Swisher?

  Mm hmm.

• 28:24Speaker 03

  So chatbots are very good at taking you eighty percent of the way there. If you ask for a first draft of something, you can get something that's about eighty percent good. If you ask it to do something on the internet, maybe you will get like an eighty percent version of it back. But actually getting to like what is often called the five nines of reliability, that something is ninety nine point nine nine nine percent reliable, is much, much harder, especially with random systems or sarcastic systems like chatbots. And so I think that's what many companies have failed to realize. And we've seen catastrophic product failures as a result. So I'm reminded of these two products, the Humane Tech Pin and the Rabbit R1, these were both general purpose AI assistants. Both of these were pretty capable, they could like order, do it at your home address,

• 29:08Speaker 03

  but maybe ten percent of the times they would order your food to the wrong address. And, you know, that might be all right when you're using this product in a lab, but from the perspective of a real world, actual product that consumers can use, it's a catastrophic failure.

• 29:21Kara Swisher?

  So both Amy and Rajiv, I want you to give an example of a scenario that might unfold at a company that's encouraging its employees to use AI. Say you have an employee who's hired to be a subject matter expert on a given topic or project, and they're put in a situation where someone higher up in them has used AI to solve a problem they're working on, the solution generated is wrong, and the employee knows it's wrong. The higher up doesn't know that but keeps pointing to the AI as evidence that they're right. For example, that's something that could easily happen. How can companies get ahead of situations like that and what policies need to be in place? Amy and then Rajiv.

• 29:51Speaker 04

  Yeah, I mean, look, the real world scenario that is happening in just about every company that I've been inside of for the past two years is there is very likely an enterprise Level AI, something or another going on and almost exclusively that has to do with paper forms, so financial services, healthcare, insurance. It is automating the process of manually entering those data or synthesizing data into one standard set, but that used to be a very capital, like human capital intensive job. That's already happening and unifying those data sets. So there is that kind of AI going on. Most of what's happening is individuals are sort of going rogue and doing their own things

• 30:33Speaker 04

  on AI, whether that's writing their reports or, I don't know, screwing around and trying to come up with ideas, brilliant big ideas to bring into meetings outside the purview of the executive leadership or any management team. And I also have anecdotal evidence because my father-in-law, who's like 82 years old and fat fingers every text message he's ever sent is now primarily using, I think it's ChatGPT on his mobile phone in replace of, instead of search.

• 31:01Kara Swisher?

  Mm-hmm.

• 31:02Speaker 04

  Right? Because it's just easier and gets them to where he needs to go faster.

• 31:05Kara Swisher?

  Better.

• 31:06Speaker 04

  Okay. That is the present day scenario. The problem with that is there are not yet legal provisions in place in most companies. The data protection, I mean, look, in the United States alone, there's a patchwork regulatory situation between states. So in some states with some insurance, for example, I think in the state of, it might be Connecticut, I'm going to get this wrong possibly, but like, I don't think you can use AI in most circumstances there. Totally different in Texas. So that part is very real and very challenging. I think what we need to do is stop going crazy and wild and thinking about all of these doomsday or totally utopian scenarios for AI and just get very much into the weeds

• 31:49Speaker 04

  on what's actually happening right now and why and how to put it on the right path in every company.

• 31:55Kara Swisher?

  Actually, Rishi, I'm going to restate that. You said earlier that data is oil and you've also said that business leaders need to understand the difference between generative AI and machine learning in order for them to be successful and need to focus on their machine learning and invest in their data. Flesh that out for us because, you know, what both Amy and Sayash had been saying is that, you know, a lot of people go rogue, they're doing other things. How do you become disciplined in what you're doing and what's important for you versus what Amy was just talking about, like, hey, jump on ChatGPT and get a very unsafe answer?

• 32:28Speaker 00

  Yeah, first of all, Amy's answer was dead on right. And in terms of the issue of machine learning and data, look, here's the bottom line, right? Which is companies create data every single day, every single minute. But if you start looking at organizations, I've spoken to probably about 2,500 CEOs in that SMB space in the last two years, Cara, and I can count on two hands and my two feet, how many of them feel like they have good data? They just don't have good data. And the reason why is because when they think about data, they think spend. They don't understand, it was never a priority for them to understand and realize how data becomes so important to them. And so that's when you start to see some of the projects start to fail and you start to see some of the report from MIT about projects failing and this and that.

• 33:13Speaker 00

  And that's part of the reason why is because they just don't have good command of their data. They don't understand, as much as we live in this bubble of tech, there's a whole world out there that doesn't understand a data warehouse, that doesn't understand data management, doesn't understand data analytics, doesn't understand how to pull data and use it to have it tell a story, whatever the case might be. So I argue with folks that don't chase all the shiny new objects that are coming out every single day and that all these companies are putting out, which is great and it's awesome, but it's moving so fast, just focus on your core execution of let's just get a data scientist on board, just start with that. Get somebody to look at your data, start understanding and finding basic implementations of what you have access to and just start there and use that as a win to start building off of, use that as your foundation.

• 33:57Speaker 04

  If I may.

• 33:58Speaker 00

  Because you can't do anything.

• 33:59Speaker 04

  Go ahead, go ahead, Amy. Just get a data scientist is like telling a fancy lady in Los Angeles to just go get a Birkin, right? Which is to say like, very, very scarce. So I would love for every small and medium business or like any business to be able to go to the data scientist store and hire people. The reality is they aren't there. And this is another big problem. We didn't really think through the future of the workforce we would need. So we don't have enough people in the pipeline. I just want to set expectations for everybody because you do need to have some. Understanding of how these systems and tools work. And in some cases, you do need a data scientist, but there's not a lot out there to choose from right now.

• 34:39Kara Swisher?

  Right, so let me point to that. You touched on this right now, but we're seeing more companies encourage your employees to use LLMs, which is even worse, right? Just on their own. But also more employees using without telling their employers. A few months ago, the security and software company Ivanti put out a report showing that a little more than 40% of office workers are using generative AI tools like GPT. And the 1 in 3 said they're doing it in secret. So if AI adoption is inevitable, I'd like each of you, what is the smartest way for leadership to shape it rather than chase it? Amy and then Sash and Raji.

• 35:14Speaker 04

  I think this is going to sound a little ambiguous, but I think the bottom line is ask questions. There are too many leaders out there throwing cash, obscene amounts of money at every big professional services firm, and they're all knocking down the door, looking for handouts to help them build huge, enormous AI systems that are either going to underperform or going to need to change. So there is some amount of like, you have to be a little bit more level headed and understand, first of all, what problem are you trying to solve? That's the most important thing. What are you trying to do and why? And if you can't answer that question, AI is not going to answer it for you. It's just going to be a very, very complicated, very expensive, time intensive solution. So that's the first thing.

• 35:59Speaker 04

  And I do worry about overreliance on consultants. I'll give you a quick case study, quick little example. One of the smartest CEOs I've ever met had a very smart idea for how to advance not just his company, but the industry. And one of the big consulting houses came in and said, we'll, we got it, you know, we'll build it for you. And they overbuilt something to a degree that I had just never seen before, and it drastically underperformed. And now they're nowhere closer to where they needed to be, but they're out a significant amount of money. And this was a publicly traded company, so they took a hit. On the balance sheet, you first of all have to figure out what it is that you're trying to do and then figure out who's in your value network that's going to help you get

• 36:42Speaker 04

  there. And it may or may not be a consultant, it might be partnering with some smaller group of people. I don't know. But this is not a like a one stop shop thing anymore.

• 36:52Kara Swisher?

  Correct. All right, Sush.

• 36:53Speaker 03

  One of the things I'm quite optimistic about is actually running experiments, small pilots in-house. And I've seen like many businesses have had successes where, you know, employees start out with a side project, like trying to use AI to sort of speed up or improve the productivity of a certain business process. And that's how you figure out, like you separate the wheat from the chaff, basically. And, you know, to some extent, to the extent that it is possible, I think it's also good for companies to lean into this type of thing. For example, I remember all of the big tech companies giving people like a few hours of their week off to pursue side projects. And some of them went on to become these really important crucial business ideas that have now grown to like hundreds

• 37:34Speaker 03

  of millions of users. And so this is the type of thing where, you know, the experiments that are already happening organically within a company can actually be used to sort of shore up the company's responsiveness to AI.

• 37:49Kara Swisher?

  Rajiv?

• 37:50Speaker 00

  Yeah, no, I mean, I agree. Look, we, at my company, one of my divisions, what we do is we do a lot of data analytics, education and training programs. And the thing that I'm encouraged by, and maybe a little bit of a different take on what Amy said earlier, which is CEOs and executives are now coming with their IT people to these events, because they realize it's a partnership. They're starting to understand and realize where they have gaps. So I'm encouraged because this hasn't happened before. I mean, we've been doing this now for years and decades. And now, for the first time, you're actually seeing the C suite, some of the members of the C suite actually coming with their CIO, their CTO. So that's really exciting to see. So I'm encouraged for that for the future.

• 38:33Kara Swisher?

  Okay, so I've got a kind of two in one question now about transparency around the use of AI. Both of these come from Blue Sky. Tav asks, how are business leaders addressing or not the imperative of labeling AI generated? Content in internal workflows and client facing outputs. And Shane Spicer asks if you work in a field that involves relationship and relation building, education, mental health, et cetera. How transparent should one be with people that they're reading or seeing is generated by AI? I think this is sort of getting to the idea of standards. Why don't we start with you, Amy, and then Saeesh on this one?

• 39:10Speaker 04

  I think this is industry by industry. And what I've seen so far is that in the creative industry, certainly news and journalism, I think there's a much bigger emphasis on being transparent. And I think in the health and medicine industry, because again, very heavily regulated, I don't know that I've seen the same in many other industries, whether that's like big engineering and construction or retail. And I don't know that there are any current regulations around that in most countries. That's different in the EU and it's different in like Japan. So really, it really depends. But if the question you're trying to get at is, should there be transparency? The answer is yes, but good luck figuring out mechanisms to enforce that.

• 39:54Speaker 04

  Because right now, the financial incentive is

• 39:57Kara Swisher?

  Not to, just use it.

• 39:58Speaker 04

  That's absolutely right.

• 39:59Kara Swisher?

  Use it as a tool and hide it away. What do you, Saeesh, what do you think?

• 40:02Speaker 03

  I mean, I broadly agree. I would say like, at the end of the day, the solution that is most likely pragmatically to work is to just enforce accountability on the final outcomes. So irrespective of what tools an employee or someone in these industries uses to get to the sort of final point, they're the ones who have to stand behind it. And I think we're seeing some good examples. I mean, like depending on how you look at it, of lawyers actually being held to account for using chat GPT generated citations. So I think over the last couple of years, we've seen over a hundred cases where lawyers have introduced AI generated hallucinations into their legal briefings. They've actually even presented it.

• 40:44Kara Swisher?

  We'll be back in a minute.

• 40:53Kara Swisher?

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• 41:36Kara Swisher?

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• 42:05Kara Swisher?

  So let's wrap up talking about standards, ethics, and the rules around it. So you co-write a substack that you recently retitled AI as Normal Technology, where you try to find the middle ground between dystopian and utopian versions of what AI can do. So let's take out your claim here. Does calling AI a normal technology mean it's not really going to change how companies operate? And how does AI compare to, say, the Internet, which clearly has been a truly revolutionary technology?

• 42:30Speaker 03

  I mean, so the claim that AI is normal technology is in opposition to claims that we are inventing a new species or we might potentially reach superintelligence by 2027 or 2030 or what have you. I think in the absence of this kind of common sense default way of thinking about the future impacts of the technology, I think lots of people were starting to think about whether they should prepare for a world where the company has no employees in the next five years, or whether superintelligence is actually going to take over. And so a lot of our emphasis with the AI as a normal technology project is to kind of push back against that, to outline that AI actually might well be very similar to previous general purpose technologies like the internet, or perhaps even like electricity. Now, these

• 43:15Speaker 03

  technologies were clearly transformative, and the nominal in the title is not to say that AI is not transformative, but just that the impacts of AI will play out not over the next two years, but perhaps over the next decade or two.

• 43:26Kara Swisher?

  But now, Rajiv, you're more bullish on AI shifting things rather quickly. Make the case for why it fundamentally transformed the economy and the society presumably, and how government leaders should deal with the unintended consequences of a revolutionary technology. And again, should there be limits on how businesses can use AI?

• 43:44Speaker 00

  Yeah, look, I mean, I guess a couple ways to answer these questions. Why am I bullish? I'm bullish because I'm starting to see it really make a difference in a lot of people's lives. I'm seeing it happen with individuals. I'm seeing it provide major change and transformation and helping organizations and companies really think about their company going forward, how to compete. I'm bullish because I think we're going to enter into a new world of entrepreneurial expansion around the world. And when you democratize these tools and people have access to these tools from around the world, I think you're going to see a lot more entrepreneurial ideas, you know, coming on board. Now, in terms of should people just be able to run hog wild? No, I mean, you know, probably not. But, look, at the end of the day, there's some regulation that probably should happen here. And I realize

• 44:28Speaker 00

  that our word is really difficult for some people to embrace and understand. And, you know, and I don't think the big companies are going to want it. The governments here are not going to want it unless everybody does it. But with that being said, I think there are some common grounds to really address areas such as deep fakes and those kinds of things. So I think there's some common ground there that we could do. But in terms of why I'm bullish is because I'm seeing it. I'm seeing it. I'm seeing it every day. And I think this is a different type of revolution. So, look, I remember the e-commerce time, Carrie. You know, I used to work for Michael Dell. And I remember walking into his office. I think I told you the story. When he said, hey, guys, go figure out how to sell computers on the Internet. We all looked at him and we thought he was crazy. Like, who's going to buy anything on the Internet? Right? And look where we are right now. And so this idea of being curious, not judgmental, is going to be really important.

• 45:12Speaker 00

  And I really think that this is a revolution that's happening more at that individual, small, medium business level. I think that's going to really rise everybody up here. And that's why I'm bullish about it.

• 45:21Kara Swisher?

  Amy, now you've argued that privacy is dead. And now with LLMs, it seems like intellectual property is also on shaky ground. What other rights will AI erode then? And is the trade off worth it, both for businesses and citizens?

• 45:35Speaker 04

  Yeah, so privacy is dead. I think intellectual property, if not already, having breathed its last breath, is certainly on its way. The problem is that we have to stop thinking personally and in business of data as just things that are written or numbers that are in spreadsheets.

• 45:53Speaker 04

  Data exists in many different ways. The way that you type on your keyboard, the way that you walk, your unique gait, your heartbeat. And we are very quickly entering an era of robotics, which I know, I know for a hundred years, everybody's been promising robots. I think they're actually coming now, but not in the form that we all expected. And the reason for that is to achieve the next iteration of AI. We need embodiment, which is to say AI systems need to pick up more data than exists that's scrapable in physical form. So with that being said, you know, your personal data is not just the way that you look, but literally the gestures, the way that you move your body around

• 46:36Speaker 04

  and the combinations of all of these different points, which is true for employees, which is true of teams, which is true of businesses. And I could go on and on. The challenge is, regulation is inherently a reaction to something that's already happened. It's not a pathway to

• 46:51Kara Swisher?

  To the future, yeah.

• 46:52Speaker 04

  To the future. And the problem that that will eventually create is that as we start to see true advancements and I could have a whole separate conversation about AGI and where we're actually at and what all of that means, but in the event that we're headed in that direction, because we're going to have more types of data, more contextual data, what that implies is lots and lots of lawsuits ahead because we're not currently preparing for a world in which we define data much more broadly, we're scraping data in different ways, and we're starting to use those data in ways to support different types of businesses where you may not become a beneficiary on the other side personally.

• 47:33Kara Swisher?

  So what are some of the AI trends you see on the horizon, ones that we didn't get to cover, but that our listeners should be thinking about? Sayash, let's start with you, and then Rajiv, and then Amy.

• 47:42Speaker 03

  So I think, I continue to think that one of the biggest changes will happen organizationally. Like, I think the organizational structures that we've seen so far might need to be radically like changed in response to how we can use AI as a technology. And to give you an example, I think, let's turn to software engineering. So for the longest time, we've had companies that build software and companies that use software. We have had like a few hundred companies that build the majority of the software that the rest of the world uses. But with AI, as the cost of developing software keeps going down, I think we might need this fundamental change in the sense that rather than sort of us all relying on tech from a few companies, it might turn out that it's more efficient for each company to have this

• 48:25Speaker 03

  small software engineering team that can actually handle all of the sort of business needs that this company has. And so I think this is the sort of seismic shift that usually takes a decade or two to unfold. Like to Rajiv's point, the reason it took the internet so long to get to the point where it is today was because we needed shifts in entire business models. We needed e-commerce companies like Amazon, or perhaps like even others to sort of come about to really make this move possible. And so I think for people trying to look ahead, maybe the questions are at the level of organizations and what does the future of the organization look like, whether it is because of AI for developing software or for any other application, for marketing, for any other business process.

• 49:07Kara Swisher?

  Okay, Rajiv.

• 49:09Speaker 00

  Yeah, that was a great point, Sayash. And to build on kind of Amy's point about the robotics and Sayash's point there, like, Kara, we are the last generation, like right here, the four of us, that's ever going to manage humans alone. We're going to have to manage humans. AI agents or whatever that format takes and at some point robotics. So that's a whole different type of skill set. So if you look back earlier in the, in the pod where we were talking about-

• 49:32Speaker 04

  I don't even want to manage humans, but go ahead.

• 49:36Speaker 00

  Yeah. So, but if you go back earlier in the podcast, we were talking about leadership and CEOs being replaced. You just can't because you can't replace that empathy. And so, look, the bottom line here is the spark's been lit, the genie's out of the bottle. And the biggest challenge, quite frankly, it's moving so fast. Like people I speak to, again, the CEOs I'm speaking to, is that they're getting whiplash because it's just moving too fast. Like they just don't know which tool to use, which one to go to. It's going to be interesting what the future unfolds. I'm excited and, and just, and I'm glad that there's folks like Amy and Sahesh and yourself, who can really help bring, you know, light on this.

• 50:08Speaker 04

  All right, Amy, last word. Sure. So in my world, trends are not trendy. They're, they're long-term indications of change. And I think it's more important to think about convergences when it comes to AI. So I've got three. The first is the convergence of AI and biology, but not for medicine, for other things like construction. So there are metamaterials that are being generated, created. So think of bricks that can move with seismic activity or things that are made out of wood, a different type of wood that, that normally wouldn't be, or packaging. Also engineering, soybeans and cane sugar, all different types of things to grow in environments where climate has presented a huge challenge.

• 50:52Speaker 04

  So that's already something that we're seeing that'll have long lasting impact on the economy and society. AI and robotics, going back to that for a moment. Forget anthropomorphized humans. This is stuff like, imagine instead of a person having to put up scaffolding on a building, which in New York is, you know, persistent and everywhere, a robot doing that instead, faster, more efficient and definitely safer, among other things. In China, they're already using it to park cars. That's right. That's right. And then the third area, because I guess we're at the end now, is AI in space. I think we're going to see a faster advancement, space exploration. And this is not actually an area that I researched, but my daughter wants to study aerospace engineering and become a lunar architect.

• 51:34Speaker 04

  So part of what she is learning how to do with AI is a totally different type of design and simulation so that she can build habitats on the moon. And going back to that earlier question, she's 15, she's in ninth grade.

• 51:45Kara Swisher?

  Right.

• 51:45Speaker 04

  And this is the kind of stuff that she's already starting to think about, how to advance human civilization, not just to be off planet because Elon Musk thinks it's cool, but for the purpose of learning new things about ourselves.

• 51:55Kara Swisher?

  Yeah, you know, I was just interested in astrobiologists, because I'm

• 51:59Speaker 04

  Oh, yeah.

• 51:59Kara Swisher?

  talking about this, and he said, "The worst day on Earth is better than the best day on Mars."

• 52:03Speaker 04

  Hundred percent, hundred percent. But you can still have some cool days on the moon, you know?

• 52:08Kara Swisher?

  The moon, yeah, yeah, the moon, I guess.

• 52:09Speaker 04

  That's right.

• 52:10Kara Swisher?

  A little dusty for me. It's like Burning Man. Anyway, I really appreciate all this, and I think it's really helpful for people to keep this dialogue up as things change over time. One of the key messages I think all of you are saying is do not run away from this, because it's like running away from electricity or the internet or something. It's inevitable, and if you're not part of it, you will be definitely left behind. Anyway, we appreciate it. Thank you so much. Thank you. Thank you so much for having us.

• 52:38Speaker 04

  Thanks.

• 52:44Kara Swisher?

  Today's show was produced by Christian Castor, Ressel Keterioka, Michelle Aloy, Megan Burney, and Kalen Lynch. Special thanks to Bradley Sylvester. Our engineers are Fernando Aruda and Rick Kwan, and our theme music is by Track academics. If you're already following the show, you're normal technology. If not, robots are coming for you. Go wherever you listen to podcasts, search for On with Kara Swisher, and hit follow. Thanks for listening to On with Kara Swisher from Podium Media, New York Magazine, the Vox Media Podcast Network, and us. We'll be back on Thursday with more.

• 53:20Speaker 02

  What will you Canva today? What will you brainstorm, collaborate on, present today? What will you resize, post, edit, imagine? What will you research, test, switch, and finally have the courage to pitch? You got this! What will you visualize, design, brand, animate, create, generate? What will you translate, pour your heart into, or have a laugh doing? Whatever you want to make is possible. So what will you Canva today?