The next presentation is going to be on future proofing your business with Kevin Roose. Kevin is an award winning tech columnist at The New York Times and bestselling author of three books, future proof young money and the unlikely disciple. His column, the shift, seeks to explore the intersection of technology, business and culture and the way generative eyes go. And it kind of feels like we're in that shift right now, huh? Worried that the world wasn't ready for the changes that I and other automation might bring. Kevin did what any great journalist would do. He interviewed experts, read books and papers, and went on the hunt for answers. And the result is his book, futureproof. A guide to surviving the technological future. So join me in giving Kevin a warm welcome. Good morning. Hello I was going to deliver this lecture in the form of a freestyle rap, but Harry, max Stole my shtick, so I guess I'll just use these slides. So I'm a tech columnist. I work for the New York times, and I'm also the author of future proof, which I believe there are going to be copies of available afterword. And I have spent the last five years of my life investigating this, what I think is one of the most important questions we face as a society, which is how we as humans and businesses and organizations and communities, can survive and navigate a new world of advanced AI. And today I'm going to tell you some of what I learned. But first, I want to start with a question. Why are so many people scared of AI now? I in this room I'm talking with a group of optimists and AI enthusiasts and that's great. I share a lot of that excitement and optimism. But if you go outside of this bubble, outside of San Francisco, maybe even, it's a very different story. So recently, Pew Research did a survey about Americans attitudes on AI. They found that 37% of the people they surveyed were more concerned than excited about the use of AI in their daily lives. Only about 18% were more excited than concerned, and 45% were equally excited and concerned. So about twice as many people are more scared than excited about what's coming. And I know this firsthand because I was the first reporter to ever use the phrase generative AI in the New York Times last October. And since then, I have gotten a flood of emails from all over the country, all over the world, of people who are worried and nervous about what this technology is going to mean for their life, for their livelihoods, for their jobs and for their communities. I've heard from artists who are worried that apps like dolly, too, in stable diffusion in mid journey are going to destroy income streams for them. I've heard from lawyers, I talked to one lawyer who said I tried that chat chapter and I thought, Oh my god, this thing is a better legal writer than me. I'm out of a job. My favorite email, I think, came from a pastor who said that he had been playing around with generative AI tools and that he was really worried that no one was going to show up to Bible study anymore because they could just put the Bible verses into the chat, JPT or some other Jennifer AI program and get back like a perfect summary and analysis. You can already see the headlines like generative AI disrupts God. So I thought this was really interesting and I started hearing so much of this sentiment that I really thought that I should come up with a term for it. So this is my term for this feeling. It's called fellow fear of looming obsolescence. I would estimate that. So I have I actually created a Gmail folder that I now send all of these emails into and it's gotten, you know, dozens a week more. So it keeps growing. So this is clearly a widespread sentiment. And I think a lot of the reason that follow or whatever you want to call it is so prevalent is because we've been giving people bad advice for so long. We were telling people that the way to avoid technological disruption, to stay employed and relevant, we told them a few things. One of them was get a better degree, right? We saw all these articles and TV segments and things where it was like AI and automation are going to disrupt the working class. It was really seen as a blue collar threat to construction workers and truck drivers and retail cashiers. But a funny thing happened, which is that I came for the White collar jobs first. So this is a graph from a recent study that the Brookings Institution did with Stanford, where they took it was actually sort of an interesting methodology. They took the text of AI patents and they compared it systematically to the text of job openings and job descriptions and found phrases that appeared in both like make prediction or analyze results. And they categorized all of these different jobs by their sort of degree of AI exposure. And they found, interestingly, that the highest exposure jobs, the jobs with the biggest chance of being disrupted by AI were jobs that required bachelor's degrees or even graduate and professional degrees. They were in high tech fields. They were good paying. Jobs maybe like some of the ones represented in this room today. The next thing we told people if you wanted to be. Avoid being disrupted by. I was. You should. Learn to code, right. We heard this over and over and over again. I remember there were news stories about how they were going to take coal miners and train them in JavaScript and that was going to save them from becoming obsolete. Well, some bad news for those coal miners. The coding jobs are getting automated, too, right? We now have things like github, copilot, rip lit ghostwriter, these generative AI tools that can work with code. And right now, they are mostly helping programmers be more efficient. But eventually these jobs will. A lot of them will be automated. One funny story on this is Google. A team of engineers at Google were recently playing around with chat tripped and they actually decided to give it their entry level coding exam for new applicants. Catchy beat got hired as an L3 engineer at Google with a starting salary of $180,000. So these tools are powerful. They're getting more powerful. The jobs in programming and engineering that we thought were safe from disruption may not be so safe after all. The third thing we told people is do creative work. Creative work was the exclusive domain of humans machines, and I would never be able to do that. If you wanted a job that was going to be safe for a long time. You should become an artist or a musician or a journalist because those jobs required ingenuity and human creativity. We know how that turned out. We now have programs like Dolly to an MIT journey and stable diffusion and all of these other ones, including Jasper, that can do a lot of the work the creative professionals have been doing for many years. So this is how we try to equip people for the oncoming AI revolution. And it really has left a lot of people feeling adrift like they don't know what to do. They're thinking to themselves, if degrees, technical skills and creativity won't save us, what will? So starting about five years ago, I decided to try to answer this question. I went out, I talked to many of the leading researchers, and I talked to historians and economists. I read about 300 years worth of industrial history books. And I, I distilled all of that advice and all those teachings into three types of work that I think are safe from AI replacement. And there's an asterisk there until agi, artificial general intelligence, at which point all bets are off and we're going to have much bigger fish to fry than whether or not we keep our jobs. So these three types of work that I believe are safe from AI replacement. I call them surprising social and scarce. So the first category, surprising work. You can also think of his work that I can't do. So I it really likes regularity and structure and rules. It's why I is very good at playing chess, for example. It's the same game every time you can play it a million times. It will have exactly the same rules and you can get a little bit better every time. But there are a lot of jobs that aren't like that. There are a lot of jobs that are full of surprises. They are irregular or chaotic. They are physical. They involve the physical world and atoms moving atoms around, not pixels or bits. These jobs are hard to codify. They're hard to sort of say, here's a 10 step manual for how to accomplish this task, which makes them very hard to program into an AI. So some of the jobs that I think could fit into this category are things like chefs at a busy restaurant. That's that's a very chaotic job, if you watched any shows about that. Plumbers, I think, are very safe because we're going to keep having sewage systems. They're going to keep breaking. And no two houses, septic systems are exactly the same. Also, childcare providers, I think, are going to be fairly safe going forward. I've got an almost one-year-old son at home. And I think that is a job taking care of him. That is not going to be automated any time soon. Although if any of you out there are working on an AI babysitter that can keep him from trying to eat rocks and stick his fingers into electrical sockets, I will be your first customer. So that's the surprising jobs, the work that I right now can't do. The next category is what I call social jobs. This is the jobs that I won't do. And why won't it do these jobs? Well because this work fulfills social and emotional needs. It taps into sort of hardwired human instincts for connection and friendship and empathy. It's not about making things. It's about making people feel things. Some of these jobs are what I call experiential. They involve creating experiences for people. One job that I actually think is going to be surprisingly durable is the barista. Now, the barista on its face might seem like a very easy job to automate. Everyone's got a coffee maker in their house. If you go if any of you flew into SFO, you've probably seen the little like robot coffee thing that they have there that, you know, takes your cup and fills it up, and serves it to you. But I think if you go down any street in San Francisco, you'll see tons of coffee shops there, any number of places that will charge you $13 to slowly pour over a cup of single origin coffee for you. And the reason that we're still willing to pay for that, even though we have better and more efficient options, is because we're not actually paying for the coffee. We're paying for the experience of going into a coffee shop, having someone greet us with a friendly smile, having a place where we can have a conversation with friends or get some work done. We're paying for the experience, not the product. And so as a result, automating that job really only really doesn't take care of the whole need. It doesn't meet that need. I also think there's going to be a Renaissance in what I call artisanal work. Now, I'm not just talking about like, you know, craftsmanship. I think there's going to be a whole sort of sub economy that booms alongside the generative boom of work that we value because it's not automated, because it's done by humans rather than machines. Yann lecun, the head of AI research at meta, gave this great talk a few years ago where he gives the example of a DVD player versus a ceramic Bowl. So a DVD player is on one level a very complicated product. It's got lasers and rare Earth metals and all kinds of stuff. But because it's made by machines, you can get one for like 40 or 50 bucks. They're very cheap. Whereas if you want a good handmade ceramic bowl, which is not complicated technology, it's actually technology that's been around for thousands of years. You're probably going to have to pay $100 or $200 or upwards of that for a really good ceramic Bowl. And they use this as a way to illustrate the fact that things that are done by machines gradually decline in value, perceived and real, and things that are done by humans increase in value, perceived and real. And so I think we will start to see as AI in meshes itself more and more in our economy, that there will be jobs that get their value from the fact that humans, not robots, are doing them. The third type of work that I think is safe from AI is what I call scarce work. And this is work that we won't let I do in every technological revolution since the Industrial Revolution. We as a society have decided to impose limits on new technology, things that we're not going to let it do. My favorite example of this is in 1865, just after the invention of steam power, they called them road locomotives. They were sort of the precursors to cars. They were steam powered vehicles that could drive on roads. The UK parliament, under pressure from the very threatened stagecoach driver lobby, passed a law known as the locomotive act, which required that every road locomotive had to have a staff of three people a driver, a Stoker and a guy to stand out front and wave a red flag as the car slowly made its way down the road. So that was a protected job through regulation lasted for about 30 years, actually, until they repealed the law. But there are also jobs that we won't let be automated for all kinds of reasons moral, ethical, societal. These jobs tend to have a few qualities to them. They're either sort of high stakes jobs with low fault tolerance is one category. So think about the 9/11 operator. You know, if, God forbid, something were to happen to me in the next few minutes and one of you kind people called 911, the person who picked up on the other end of that line would be a human. It's not because we don't know how to automate that job. We've just decided collectively as a society that job is too important to entrust to an AI. It has very low fault tolerance, and we want to know that when we're in distress and we call someone for help, the person on the other end of that line is going to have intuition and common sense, and they're going to be able to route our requests to the right people as quickly as possible. It also includes jobs with what I call observable excellence. So this is a little bit of a funny thing, but I was spurred to this because I was thinking about the fact that even though for many years now, AI has been better than even the best human grandmasters in chess, we still have grandmasters. They didn't go away. Magnus Carlson is still out there, you know, every day playing chess. And there are lots of chess players that make a living from that profession, even though technically they're second class now, because the air is better than all of them. I think there are a lot of jobs like this out there that we of, you know, keep around because we like seeing people who are excellent at their work doing that work. Right we cheer for Olympic swimmers, even though speedboats go faster. And so I think there are a number of jobs that involve observable excellence, people who are star performers in their field. I don't think Taylor Swift has much to worry about from an algorithm or an AI, although I think some of the studio musicians who back her might. We like having role models. So what I like about these categories of work, surprising social and scarce, is that they're industry agnostic. It doesn't matter what your job title is. You can make your job or your company more surprising social and scarce. You can also make it less surprising, social and scarce, and make yourselves more vulnerable to replacement by air. You can be a doctor who just reads scans in a radiology lab all day. Or you can be a beloved pediatrician who makes friends with all of the families in their community and is considered irreplaceable by the community. There's also no barrier to entry. You don't need a specialized degree. You don't need to go to coding boot camp. All you need to do is work on these surprising social and scarce skills. And it works for individuals, for teams and for entire businesses. One of my favorite examples of someone who has made themselves less replaceable by working on these skills is my accountant. He's a guy named Russ Garofalo. He runs a firm called Brass taxes, and he realized he's an amazing accountant. But that's not actually why I hire him. I hire him because he's a former comedian. Before he was when it got to CPA and became an accountant. He was a stand up comedian and comedy writer. And he has built a firm full of funny accountants like my wife does not believe me, but I actually enjoyed doing my taxes. I'm like, yeah, I'm ready for some itemized deductions this year. Like, let's go. And I asked him what they said, Russ, why do you do this? Because he also not only does he hire funny people, but he pays for them to take improv comedy lessons. And I want to ask him about that. So why do you do this? What does this have to do with tax preparation? They said, well, our advantage is the conversation we have with you. I can't compete with H&R Block on efficiency or price. I can't compete with turbotax, certainly. But what I can compete, where I can standout, where I can form unique relationships with our customers is by having a good experience with you, by turning tax preparation from something that you dread into something that you actually enjoy. And that's made his firm a standout. And they're growing as a lot of other firms in his field are contracting. This strategy also works for big businesses. One of the examples that I write about in the book is what happened at Best Buy. Several years ago, they were undergoing a real challenge from Amazon and other online retailers. And their CEO, a guy named Hubert joly, decided to do something radical and unexpected where he didn't just want to automate their warehouses and stuff a bunch of, you know, efficiency tools into their stores. He started an in-home advisor program where now you can go if you need a new stereo for your patio or a new flat screen TV, you can call Best Buy and they'll send someone out to your house and sit with you for a long time, as long as it takes to figure out what you need. They basically turn themselves into tech therapists or tech in in-home tech support. And it really changed the trajectory of that business. So the thought that I want to leave you with today. And then we'll do some questions is that this model, this surprising social and scarce model of developing these uniquely human skills is how we protect ourselves from unwanted technological disruption. And it also means for people like the ones in this room who are using and deploying AI inside businesses that we need to be think thoughtful about how we do this. Future proofing ourselves doesn't mean just using a bunch of tools to take out 50% of the back office. It doesn't mean using it to write all our emails and losing that spark of human connection. It means using AI to become more human, more surprising, more social, more scarce to do things that machines can't do. If we just compete with the AI at the things that it's good at, we lose. If we allow the AI to make us more robotic, more like it, we also lose. We have to lean in and accentuate the things that make us uniquely human. In order to be future proof, we have to let AI be AI and humans be humans. Thank you.

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