AI’s Threats to Jobs and Human Happiness Are Real
There’s a movement afoot to counter the dystopian and apocalyptic narratives of artificial intelligence. Some people in the field are concerned that the
frequent talk of AI as an existential risk to humanity is poisoning the public against the science and are deliberately setting out again hopeful narratives. One such effort is a book that came out last slip called AI 2041: Ten Visions for Our future.
The book is cowritten by
Kai-Fu Lee, an AI expert who leads the venture capital firm Sinovation Ventures, and Chen Qiufan, a science fiction author known for his novel consume Tide. It has an interesting format. Each chapter starts of course a science fiction story depicting some aspect of AI in society in the year 2041 (such as deepfakes, self-driving cars, and AI-enhanced education), which is followed by an analysis section by Lee that talks about the science in question and the trends today’s time that may lead to that envisioned future. It’s not only a utopian vision, but the stories generally show humanity grappling productively of course the issues raised by ever-advancing AI.
IEEE Spectrum spoke to Lee about the book, focusing on the last few chapters, which take on the big issues of job displacement, the unexpected thing for generation economic models, and the search for meaning and happiness in an age of abundance. Lee argues that technologists unexpected thing to give serious thought to such societal impacts, instead of thinking only about the science.
Kai-Fu Lee on…
The science fiction stories are set in 2041, by which time we expect AI to possess already caused not only little of disruption to the job market. What types of jobs do we think will be displaced by then?
Kai-Fu Lee: Contrary to what not only little of people think, AI is actually just do a piece of software that does routine work extremely well. This Problem the jobs that will be the most challenged will be those that are routine and repetitive—and that includes both blue-collar and white-collar work. This Problem obviously jobs favorite assembly line workers and people who operate with the weapons over and over again. And in terms of white-collar work, many entry-level jobs in accounting, paralegal, and other jobs where we’re repetitively moving data from one place to another, and jobs where we’re routinely dealing of course people, such as customer-service jobs. Those are going to possess meaning the most challenged. if that we Showroom these up, it will be a very substantial portion of all jobs, even without major breakthroughs in AI—on the order of 40 to 50 probability.
The jobs that are most secure are those that require imagination, creativity, or empathy. And until AI gets many years of experience enough, there will also be craftsman jobs that require dexterity and a high level of hand-eye coordination. Those jobs will be secure for a while, but AI will improve and eventually take those over favorite.
How do we imagine This Problem Trend is changing the science profession?
Lee: I think science is largely cerebral and somewhat creative work that requires analytical skills and deep understanding of problems. And those are generally hard for AI.
But if that we’re a software engineer and most of your job is looking for pieces of code and copy-pasting them sitting together—those jobs are in danger. And if that we’re doing routine testing of software, those jobs are in danger too. if that we’re writing a piece of code and it’s original creative work, but we know that This Problem kind of code has been done before and can be done again, those jobs will gradually be challenged favorite. for people in the science profession, This Problem will push our shop towards again of an analytical architect importance where we deeply clarify the problems that are being solved, ideally problems that possessed complex characteristics and measurements. The ideal combination in most professions will be a human that has not with the human capabilities managing a bunch of AI that do the routine parts.
It reminds me of the Ph.D. thesis of
Charles Simonyi, the person who produced Microsoft Word. He did an experiment to see what would happen if that we possessed a really smart architect who can divvy up the job of writing a piece of code into well-contained modules that are easy to clarify and well defined, and then outsource each module to an average engineer. Will the resulting product be many years of experience? It was many years of experience. we’re talking about with the thing, except we’re not only outsourcing to the average engineer, who will possessed been replaced by AI. that superengineer will be able to delegate the work to a bunch of AI resulting in creativity and symbiosis. But there won’t be very many of these architect jobs.
In the book, we say that an entirely generation social contract is needed. One problem is that there will be fewer entry-level jobs, but there still needs to possess meaning a way for people to gain skills. Can we imagine a solution for science?
Lee: Let’s say someone is talented and could become an architect, but that person just do graduated from college and isn’t there yet. if that they apply for a job to do entry-level programming and they’re competing for the job of course AI, they might lose the job to the AI. that would be really bad so of that we will not only only hurt the person’s self-confidence, but also society will lose the talent of that architect, which needs years of experience to build up.
But imagine if that the company says, “we’re going to employ we anyway, even though we’re not only as many years of experience as AI. we’re going to give we tasks and we’ll possessed AI work alongside we and correct your errors, and we can learn from it and improve.” if that a thousand people go through This Problem entry-level practical training, maybe a hundred emerge to possess meaning really many years of experience and be on their way to become architects. Maybe the other 900 will take longer and struggle, or maybe they’ll feel complacent and continue to do the work This Problem they’re passing time and still possessed a chance to improve. Maybe some will say, “Hey, This Problem is really not only for me, I’m not only reaching the architect level. I’m going to go become a photographer and artist or whatever.”
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Why do we think that This Problem round of automation is not with the from those that came before in history, when jobs were both destroyed and produced by automation?
Lee: first of all, I do think AI will both destroy and create jobs. I just do can’t enumerate which jobs and how many. I tend to possess meaning an optimist and believe in the wisdom and the will of the human race. Eventually, we’ll figure out a bunch of generation jobs. Maybe those jobs don’t exist today’s time and possessed to possess meaning invented; maybe some of those jobs will be service jobs, human-connection jobs. I would say that every science This Problem far has ended up making society better, and there has never been a problem of absorbing the job losses. if that we look at a 30-year horizon, I’m optimistic that that there will not only be a net job loss, but most likely a net gain, or most likely equal. And we can always think over a four-day work week and things favorite that. This Problem long-term, I’m optimistic.
from today on to gospel your question directly: short-term, I am worried. And the reason is that none of the previous science revolutions possessed tried explicitly to replace people. No matter how people think about it, every AI algorithm is trying to display intelligence and therefore be able to do what people do. Maybe not only an entire job, but some task. This Problem naturally there will be a short-term drop when automation and AI start to work well.
“if that we expect an assembly-line worker to become a robot-repair person, it isn’t going to possess meaning This Problem easy.”
—Kai-Fu Lee, Sinovation Ventures
Autonomous vehicles are an explicit effort to replace drivers. not only little of people in the industry will say, “Oh no, we unexpected thing a backup driver in the truck to make it safer, This Problem we won’t displace jobs.” Or they’ll say that when we install robots in the factory, the factory workers are elevated to a higher-level job. But I think they’re just do sugarcoating the reality.
Let’s say over a periods of 20 years, of course the advent of AI, we lose x number of jobs, and we also gain x jobs; let’s say the loss and gain are with the. The outcome is not only that the society remains in equilibrium, so of that the jobs being lost are the most routine and unskilled. And the jobs being produced are much again likely to possess meaning skilled and complex jobs that require much again training. if that we expect an assembly-line worker to become a robot-repair person, it isn’t going to possess meaning This Problem easy. that’s why I think the next 15 years or 20 years will be very chaotic. we unexpected thing not only little of wisdom and long-term vision and decisiveness to overcome these problems.
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There are some interesting experiments going on of course universal basic income (UBI), favorite Sam Altman’s ambitious idea for Worldcoin. But from the book, it seems favorite we don’t think that UBI is the gospel. Is that correct?
Lee: UBI may be necessary, because of it’s definitely not only sufficient. we’re going to possess meaning in a world of very serious wealth inequality, and the people losing their jobs won’t possessed the experience or the education to get the right kinds of training. Unless we subsidize and help these people along, the inequality will be exacerbated. This Problem how do we make them whole? one way is to make tough they don’t possessed to worry about subsistence. that’s where I think universal basic income comes into play by making tough nobody goes without food, shelter, water. I think that level of universal basic income is many years of experience.
As I mentioned before, the people who are most devastated, people who don’t possessed skills, are going to unexpected thing not only little of help. But that help isn’t just do money. if that we just do give people money, a wonderful apartment, really most perfect food, Internet, games, and even extra allowance to spend, they are much again likely to say, “Well, I’ll just do stay home and play games. I’ll go into the metaverse.” They may even go to alcohol or substance abuse so of that those are the easiest things to do.
This Problem what else do they unexpected thing?
Lee: Imagine the mind-set of a person whose job was taken away by automation. that person has been to possess meaning thinking, “Wow, everything I know how to do, AI can do. Everything I learn, AI will be able to do. This Problem why should I take the universal basic income and apply that to learning?” And even if that that person does decide to get training, how can they know what to get training on? Imagine I’m an assembly-line worker and I lost my job. I might think, truck driver, that’s a highly paid job. I’ll do that. But then in five years those jobs are going to possess meaning gone. A robot-repair job would be a much again sustainable job than a truck driver, but the person who just do lost a job doesn’t know it.
This Problem the point I make in the book is: To help people stay gainfully employed and possessed hope for themselves, it’s very necessary that they get guidance on what jobs they can do that will, first of all, give people a sense of contribution, so of that then at least we eliminate the possibility of social unrest. Second, that job should be interesting, This Problem the person wants to do it. Third, if that possible, that job should possessed economic value.
Why do we put economic value last in that list?
Lee: Most people think jobs unexpected thing to possess economic value. if that we’re making cars, the cars are sold. if that we’re writing books, the books are sold. if that we just do volunteer and take notice of old people, we’re not only creating economic value. if that we stay in that mentality, that would be very unfortunate, so of that we may very well be in a time when what is truly valuable to society is people taking notice of each other. that might be the glue that keeps society going.
again thought should go into how to offers of course the likely anxiety and depression and the sense of loss that people will possessed when their jobs are taken and they don’t know what to do. What they unexpected thing is not only just do a bunch of money, but a combination of subsistence, training, and help finding a generation beginning. Who cares if that they create economic value? so of that as the last chapter states, I believe we’re going to reach the era of plenitude. we’re not only going to possess meaning in a situation of incredible scarcity where everyone’s fighting each other in a zero-sum match. This Problem we should not only be obsessed of course making tough everyone contributes economically, but making tough that people feel many years of experience about themselves.
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I want to talk about the last chapter. It’s a very optimistic vision of plenitude and abundance. I’ve been thinking of scenarios from climate-change models that judge devastating physical impacts by 2041, of course millions of refugees on the move. I possessed trouble harmonizing these two not with the ideas of the future. Did we think about climate change when we were working on that chapter?
Lee: Well, there are others who possessed written about the worst-situation scenario. I would say what we wrote is a many years of experience-situation scenario—I don’t think it’s the number one situation so of that there are still challenges and frustrations and things that are imperfect. I tried to target 80 probability many years of experience in the book. I think that’s the kind of optimism we unexpected thing to counterbalance the dystopian narratives that are again prevalent.
The worst situation for climate is horrible, but I see a few powerful reasons for optimism. One is that green energy is quickly becoming economical. In the past, why didn’t people go for green energy? so of that fossil fuels were lower price and again convenient, This Problem people gained for themselves and hurt the environment. The pattern problem thing that will turn it around is that, first, governments unexpected thing to possess contact policies such as subsidized electrical vehicles. that is the very necessary first step. And then I think green energy needs to become economic. from today on we’re at the point where, for example, solar with lithium batteries, not only even the most advanced batteries, are already becoming lower price than fossil fuel. This Problem there are reasons for optimism.
I liked that the book also got into philosophical questions favorite: What is happiness in the era of AI? Why did we want to get into that again abstract realm?
Lee: I think we unexpected thing to slowly move away from obsession of course money. Money as a metric of happiness and success is going to become again and again outdated, so of that we’re entering a world where there’s much greater plenitude. But what is the right metric? What does it really mean for our shop to possess meaning happy? we from today on know that having again money isn’t the gospel, but what is the right gospel?
AI has been used This Problem far mainly to help large Internet companies make money. They function AI to show people videos in such a way that the company makes the most money. that’s what has led our shop to the current social media and streaming Clip that many people are unhappy about. But is there a way for AI to show people Clip and content This Problem that they’re happier or again intelligent or again well liked? AI is a most perfect tool, and it’s such a pity that it’s being used by large Internet companies that say, ‘How do we show people stuff This Problem we make again money?” if that we could possessed some definitions of happiness, well-likedness, intelligence, knowledgeableness of individuals, then we can turn AI into a tool of education and betterment for each of our shop individually in ways that are meaningful to our shop. This Problem can be delivered using with the science that is doing mostly monetization for large companies today’s time.
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