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- [00:00]
Rajiv Kapoor
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.
- [00:09]
Kara Swisher
So I don't even want to manage humans. Hi everyone from New York Magazine and the Vox Media Podcast network.
- [00:25]
Podcast Announcer
This is on with Kara Swisher and I'm Kara Swisher.
- [00:29]
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 bluesky and we've called in a panel of experts to help answer them. Saish 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. 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 latest is the Genesis 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.
- [01:44]
Podcast Announcer
Have podcasts when the Internet first started.
- [01:45]
Kara Swisher
And I spent a lot of time answering these questions myself when I ran into people. 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 Sayash, Rajiv and Amy, which is.
- [02:01]
Podcast Announcer
Brought to you by Smartsheet. Stick around. Support for this show comes from Smartsheet. There's a common saying out there, Jack of all trades, master of none. But Smartsheet is changing that. With its intelligent work management platform, Smartsheet can embed AI into your workflow. So so you can tackle any project without needing to be an expert. Whether you're setting up your first project, building a complex formula, or analyzing your data, the AI powered features of Smartsheet are there to work alongside you to Help you move faster and get work done. Go to smartsheet.com Vox to learn more. It is on Sayash.
- [02:48]
Kara Swisher
Rajeev and Amy, thank you for coming on on.
- [02:52]
Rajiv Kapoor
My pleasure. Our pleasure. Thank you.
- [02:53]
Saish Kapoor
Thank you so much for having us.
- [02:55]
Amy Webb
Hey everybody. Nice to be here.
- [02:56]
Kara Swisher
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 Rajeev and then Saesh and then Amy.
- [03:14]
Rajiv Kapoor
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, 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 AIFY it is really what's going to be consequential. And I think going forward it's going to really help really drive that opportunity to 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, small business 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.
- [04:03]
Kara Swisher
Isn't taking tasks taking jobs because those were jobs.
- [04:06]
Rajiv Kapoor
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 of where I'm focused on, which is more that consumer SMB space. We're starting to see that.
- [04:23]
Saish Kapoor
Okay, Saeesh, so I really wanted to come back to your point about whether taking tasks is the same as taking jobs. 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 1970s, 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 their job is not cutting checks anymore. Maybe their 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. 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.
- [05:37]
Kara Swisher
And what would be the most consequential impact right now that you're seeing initially?
- [05:41]
Saish Kapoor
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 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. 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. 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.
- [06:27]
Amy Webb
All right, Amy, I might have a little bit of a contradictory viewpoint on this.
- [06:32]
Kara Swisher
Sure.
- [06:32]
Amy Webb
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, 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, and they're making salient decisions and they're getting spanked.
- [07:21]
Kara Swisher
And why is that?
- [07:22]
Amy Webb
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.
- [08:00]
Podcast Announcer
Right.
- [08:00]
Kara Swisher
And therefore slower than people think or more measured.
- [08:03]
Amy Webb
And government's even slower.
- [08:05]
Podcast Announcer
Right?
- [08:05]
Rajiv Kapoor
Yeah. And I think that was my point earlier, which is I'm seeing a lot more adoption at the S and B level. Smaller, 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.
- [08:18]
Podcast Announcer
Right.
- [08:19]
Kara Swisher
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 doing other things like travel, et cetera. So, Rashid, 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 don't get over their skis, as Amy was talking about, Lay off their workers they actually need.
- [09:06]
Rajiv Kapoor
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, et cetera, 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, marketing areas, you deploy it, you start to measure the roi. You measure the ROI in weeks, not years. And you really need to look at this from a top down perspective. And what we're seeing is a couple things. Number one is that that ROI issue is because it's a real issue for the CFOs and they really are the ones who 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, Kara. As you know, there's the machine learning data side of AI, then there's the ChatGPT GPT stuff and on the generative AI things and really that machine learning data analytics. That data side 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. And if they can do that, 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 chase the shiny new tools that are coming out because the space is moving too fast.
- [10:46]
Kara Swisher
All right, so Saiyash, I want you to answer this question. We get 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? Could AI eventually fill some upper level managerial roles, even potentially the C level suite?
- [11:23]
Saish Kapoor
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 any policy related question would be put to ChatGPT and I'll make sure I implement the outputs. And I think unfortunately 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. On the other hand, we have seen a pressure on the managerial class on 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 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 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 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.
- [12:50]
Kara Swisher
So presumably it could use it to help Instead of reading 90 leadership books, it could posit things in, for example.
- [12:57]
Saish Kapoor
Absolutely. I mean I think it can be very helpful as a decision support tool, just not as a tool to replace decision making.
- [13:03]
Amy Webb
Do you remember Paul Graham? Like at some point somebody built a what would Paul Graham do? Answer generator.
- [13:09]
Kara Swisher
I'm sure I didn't use it, but go ahead.
- [13:11]
Amy Webb
No, no, 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, 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 really well. 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.
- [14:09]
Kara Swisher
That's a good point. All right, our next listener question, which, Amy, is for you, comes from Shelley Wilson in Providence, Rhode Island. She sent us a voicemail moment. Let's hear it.
- [14:17]
Shelley Wilson
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 20s, mid-20s, 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 is 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. 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.
- [15:09]
Kara 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?
- [15:16]
Amy Webb
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 and upskilling and reskilling, which are two words that I hate because it's sort of.
- [15:49]
Kara Swisher
They're bad words.
- [15:51]
Amy Webb
There 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 20s. 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 is 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 in all the variables. So it's more about being flexible as you go.
- [16:37]
Kara Swisher
So, Rajeev, you're also doing a lot of work in the AI education space. Do you have anything you want to add for Shelley?
- [16:41]
Rajiv Kapoor
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 upand cominging 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. 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, 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.
- [17:47]
Kara Swisher
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 BlueSky. I think it's Dejango wrote, what do AI developers think we do? They spend trillions on an AI that will 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 Zayesh, that was admittedly a question wrapped in Iran. 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 task can AI do well and why does it sometimes hallucinate and make up answers when asked a simple request.
- [18:29]
Saish Kapoor
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 really precise. Some of these responses are data that 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 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 100. 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 math. And to be clear, I think this is not to say 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.
- [20:08]
Podcast Announcer
Right.
- [20:08]
Kara Swisher
So, Rajeev, what about Vibe coding? That's when people use an LLM like ChatGPT to write a code for, say, an app that can do xyz 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?
- [20:31]
Rajiv Kapoor
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 the country. 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 Sesh 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 skillset to be able to really ask the questions the right way and really to be able to put into that prompt box, 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, you know, 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 making vibe coding this stuff. Right? Yeah.
- [21:46]
Kara Swisher
So speaking of critical thinking, our next listener has a related query. It was sent to us via threads from 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 became 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 asks, how do we make sure humans don't lose the ability to think for themselves?
- [22:25]
Amy Webb
Well, that's up to us, isn't it?
- [22:27]
Kara Swisher
Right?
- [22:28]
Amy Webb
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.
- [22:38]
Kara Swisher
That's a very good way of putting it.
- [22:39]
Amy Webb
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 game changing technologies. So when I was in advanced calculus, 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. 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 do more. So this is really up to everybody. And it 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 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 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.
- [24:38]
Kara Swisher
Yeah. In business, you know, Scott Galloway, who's my partner at Pivot, puts everything in and I put almost nothing.
- [24:43]
Amy Webb
Yeah, same.
- [24:44]
Kara Swisher
I actually was saying this to Sam Alton. He goes, why not? And I go, I just don't trust you.
- [24:49]
Amy Webb
Yeah, that's not.
- [24:50]
Kara Swisher
It's not you. I said no offense, but no.
- [24:53]
Amy Webb
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.
- [25:00]
Kara Swisher
Well, he's not a lawyer and he's not a doctor. So there's no expectation of privacy, Right?
- [25:05]
Amy Webb
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.
- [25:21]
Kara Swisher
Like I'll just tell them, well yeah.
- [25:25]
Amy Webb
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 going forward. And that's revenue. That'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.
- [25:43]
Kara Swisher
We'll be back in a minute.
- [25:54]
Podcast Announcer
Support for on with KERASwisher comes from Smartsheet. Most people listening know what it means to be burned out when you feel overwhelmed by a new project, a complex spreadsheet or a data heavy report. Sometimes the work keeps stacking up, up and doesn't stop, halting your creativity and blocking you up. Smartsheet wants that work to flow. Their AI powered platform is designed for you to spend less time struggling, freeing you up for your most creative, impactful work. How it all starts with a simple conversation. Their AI powered onboarding helps you set up a project in minutes, even if you're not an expert. Need a formula? Just describe what you want and Smartsheet AI generates it for you. Want to understand your data better? Smartsheet AI analyzes it and creates dynamic charts you can add to dashboards to track your projects in real time. It's personalized, it's intuitive, and it makes work more efficient for everyone so you can break through the distractions, find your focus and start working with flow. Go to smartsheet.com box to learn more.
- [27:00]
Kara Swisher
Okay, our next listener question comes from Carrie Tolorico in Scranton, Pennsylvania. She sent us this voice memo. Let's hear it.
- [27:06]
Carrie Tolorico
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. We'd love to hear everyone's thoughts.
- [27:44]
Amy Webb
Thanks.
- [27:46]
Kara Swisher
Saish, what do you think I absolutely agree.
- [27:49]
Saish Kapoor
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 users we can put it towards. And in some cases, it might not even be the case that existing business structures can make use of AI very well. I mean, I'm reminded of how after we invented electricity, it took 40 years for us to figure out how to electrify factories. And just putting in electricity in the existing factories was not enough. We 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.
- [28:54]
Kara Swisher
So to her question, where do companies get it wrong when it comes to them rolling out AI for their business?
- [28:59]
Saish Kapoor
One of the main things I've seen is not recognizing the gap between capability and reliability. So chatbots are very good at taking you 80% of the way there. If you ask for a first draft of something, you can get something that's about 80% good. If you ask it to do something on the Internet, maybe you'll get like an 80% version of it back. But actually getting to what is often called the five nines of reliability, that something is 99.999% reliable is much, much harder, especially with random systems or stochastic 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 order doordash to your home address, but maybe 10% of the times they would order your food to the wrong address. And 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.
- [30:02]
Kara Swisher
So both Imi and Rajeev, 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, a subject matter expert on a given topic or project and they're put in a situation where someone higher up than 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 Yeah, I mean.
- [30:33]
Amy Webb
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 that, 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 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. Right. Because it's just easier and gets him to where he needs to go faster. 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 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 on what's actually happening right now and why and how to put it on the right path in every company.
- [32:37]
Kara Swisher
Actually, Rasheed, 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, need to focus on their machine learning and invest in their data. Flesh that out for us. Because what both Amy and Sayash had been saying is that 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.
- [33:09]
Rajiv Kapoor
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, Kara, 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. 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 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, get a data scientist on board. Just start with that. Just 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, if I may.
- [34:39]
Kara Swisher
If you can't do anything, go ahead, go ahead, Amy.
- [34:42]
Amy Webb
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. And 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.
- [35:21]
Kara Swisher
Right, so let me point to that. You touched on this right now, but we're seeing more companies encourage 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 ChatGPT. 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 Saish and Raji.
- [35:56]
Amy Webb
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. And I do worry about overreliance on consultants. I'll give you a quick case study, quick little example. One of the biggest, 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 got it, we'll build it for you. And they overbuilt something to a degree that I 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 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 like a one stop shop thing anymore, correct?
- [37:34]
Saish Kapoor
All right, Sej, One of the things I'm quite optimistic about is actually running experiments, small pilots in house. And I've seen many businesses have had successes where employees start out with a side project, 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, you separate the wheat from the chaff, basically. And 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 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 of millions of users. And so this is the type of thing where 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.
- [38:30]
Kara Swisher
Rajiv?
- [38:32]
Rajiv Kapoor
Yeah, no, I mean, I agree. Look at my company, one of my divisions, what we do is we do a lot of data analytics, education and training programs.
- [38:40]
Amy Webb
We.
- [38:40]
Rajiv Kapoor
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.
- [39:14]
Kara 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 Bluesky. 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 Sayesh on this one.
- [39:51]
Amy Webb
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 Japan. So 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 because right now the financial incentive is not to just use it. That's absolutely right.
- [40:41]
Kara Swisher
Use it as a tool and hide it away. Zayash, what do you think?
- [40:44]
Saish Kapoor
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 ChatGPT generated citations. So I think over the last couple of years We've seen over 100 cases where lawyers have introduced AI generated hallucinations into their legal briefings. They've actually even presented it.
- [41:26]
Podcast Announcer
We'll be back in a minute. Support for on with Kara Swisher comes from Smartsheet. Work is taxing. It's the reason it's called work. But if you're struggling with endless new projects, complex spreadsheets and data, heavy reporting, then work isn't even the right word. It's become a problem and it's stopping you in your tracks. That's why there's smartsheet Their AI powered platform is designed to get your work flowing so you can spend less time struggling and more time doing the things you're actually excited about. It starts with a simple conversation. AI powered onboarding helps you set up a project in minutes, even if you're not an expert. Need a formula? Just describe what you want and Smartsheet AI generates it for you. Want to understand your data better? Smartsheet AI analyzes it and creates dynamic charts and dashboards in real time. It's personalized, it's intuitive, and it makes work more efficient for everyone. So you can break through distractions, find your focus and start working with flow. Go to smartsheet.com Vox to learn more.
- [42:36]
Kara Swisher
So let's wrap up talking about standards, ethics and the rules around it. Saesh, 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 stake out your claim here. Does calling AI 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?
- [43:00]
Saish Kapoor
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 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 technologies were clearly transformative. And the normal 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.
- [43:56]
Kara 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 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?
- [44:14]
Rajiv Kapoor
Yeah, look, I mean, I guess a couple way 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, that probably should happen here and I realize that our word is really difficult for some people to embrace and understand. And you know, and if 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 deepfakes and, you know, 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, Kara. 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, Kara. 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? Look where we are right now. And so this idea of being curious, not judgmental, I think it's going to be really important. 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.
- [45:51]
Kara 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?
- [46:05]
Amy Webb
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, 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 100 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, your personal data is not just the way that you look, but literally the gestures, the way that you move your body around 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 the future. Yeah, 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.
- [48:03]
Kara 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? Syash, let's start with you and then Rajeev and then Amy.
- [48:12]
Saish Kapoor
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 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 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.
- [49:38]
Kara Swisher
Okay, Rajiv.
- [49:39]
Rajiv Kapoor
Yeah, that was a great point, Seish. And to build on kind of Amy's point about the robotics and Saish'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.
- [50:02]
Kara Swisher
Talking about I don't even want to manage humans, but go ahead.
- [50:06]
Rajiv Kapoor
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' which tool to use, which one to go to. It's going to be interesting what the future unfolds. I'm excited and I'm glad that there's folks like Amy and Saish and yourself who can really help bring light on this.
- [50:38]
Kara Swisher
All right, Amy, last word.
- [50:40]
Amy Webb
Sure. So in my world, trends are not trendy, 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 in 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 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. 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 persistent, and everywhere a robot doing that instead. Faster, more efficient, and definitely safer. Among other things.
- [51:44]
Kara Swisher
In China they're already using to park cars, these things.
- [51:46]
Amy Webb
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 gonna see a faster advancement, space exploration. And this is not actually area that I research, but my daughter wants to study aerospace engineering and become a lunar architect. So part of what she's 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, 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.
- [52:26]
Kara Swisher
Yeah, you know, I was just interested in astrobiologists because I'm talking about this. And he said the worst day on Earth is better than the best day on Mars.
- [52:34]
Amy Webb
100%. 100%. But you can still have some cool days on the moon, you know?
- [52:38]
Kara Swisher
The moon? Yeah, yeah, the moon. I guess it's 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.
- [53:06]
Rajiv Kapoor
Thank you.
- [53:07]
Saish Kapoor
Thank you so much for having us. Thanks.
- [53:14]
Kara Swisher
Today's show was produced by Christian Casserole, Kateri Yoko Michelle Eloi, Megan Burney and Kalyn Lynch. Special thanks to Bradley Sylvester.
- [53:23]
Podcast Announcer
Our engineers are Fernando Arruda and Rick.
- [53:25]
Kara Swisher
Kwan and our theme music is by Trackademics. 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.
- [53:36]
Podcast Announcer
For on with Kara Swisher and hit follow.
- [53:38]
Kara Swisher
Thanks for listening to on with Kara Swisher from Podium Media, New York Magazine, the Vox Media Media Podcast network, and us.
- [53:44]
Podcast Announcer
We'll be back on Thursday with more. Thank you to smartsheet for supporting this episode. Technology is moving faster than ever. To stay current with work's changing landscape, you want to partner with a platform that adapts with it, AI that can empower you to move faster, think bigger and work with flow. While the AI capabilities of smartsheets can help you cut through complexity and focus on what matters most, Find out more@smartsheet.com Vox that's smartsheet.com Vox.