First, a confession: I tried writing this essay with A.I.
I started with ChatGPT’s “deep research” mode, asking it to compile a report on what new jobs for humans might be created by the rise of A.I. It asked a few follow-up questions and then set off, returning with a 6,000-word report, broken down by industry. I fed that report into ChatGPT 4o — along with the original assignment memo from my editor and a few other recent industry reports on the future of work — and asked for an article in the style of The New York Times Magazine.
It was done within 90 minutes. The article was lively and informative, and while some of its imagined future careers were a bit fanciful (a “synthetic relationship counselor” apparently will be someone who can step in when you’re in love with your A.I.), it also covered an interesting spectrum of plausible jobs and featured some delightful turns of phrase. To the average reader, it likely would have come across as a breezy Sunday read with just enough interesting points to warrant a bit of reflection.
So why aren’t you reading that version? Well, for starters, it would have gotten me fired: Almost all quotes and experts in the article were entirely made up. But I had a deeper, more philosophical concern. Even if the A.I.-written version of this piece was entirely factual, submitting it to my editors would have represented a fundamental misunderstanding of why they hired me. In freelance journalism, as in many fields where the work product is written text, you aren’t just being paid for the words you submit. You’re being paid to be responsible for them: the facts, the concepts, the fairness, the phrasing. This article is running with my byline, which means that I personally stand behind what you’re reading; by the same token, my editor is responsible for hiring me, and so on, a type of responsibility that inherently can’t be delegated to a machine.
Commentators have become increasingly bleak about the future of human work in an A.I. world. The venture-capitalist investor Chris Sacca recently went on Tim Ferriss’s podcast and declared that “we are super [expletive].” He suggested that computer programmers, lawyers, accountants, marketing copywriters and most other white-collar workers were all doomed. In an email to his staff, Fiverr’s chief executive, Micha Kaufman, added designers and salespeople to the list of the soon-to-be-damned.
Such laments about A.I. have become common, but rarely do they explore how A.I. gets over the responsibility hurdle I’m describing. It’s already clear that A.I. is more than capable of handling many human tasks. But in the real world, our jobs are about much more than the sum of our tasks: They’re about contributing our labor to a group of other humans — our bosses and colleagues — who can understand us, interact with us and hold us accountable in ways that don’t easily transfer to algorithms.
This doesn’t mean the disruptions from A.I. won’t be profound. “Our data is showing that 70 percent of the skills in the average job will have changed by 2030,” said Aneesh Raman, LinkedIn’s chief economic opportunity officer. According to the World Economic Forum’s 2025 Future of Jobs report, nine million jobs are expected to be “displaced” by A.I. and other emergent technologies in the next five years. But A.I. will create jobs, too: The same report says that, by 2030, the technology will also lead to some 11 million new jobs. Among these will be many roles that have never existed before.
If we want to know what these new opportunities will be, we should start by looking at where new jobs can bridge the gap between A.I.’s phenomenal capabilities and our very human needs and desires. It’s not just a question of where humans want A.I., but also: Where does A.I. want humans? To my mind, there are three major areas where humans either are, or will soon be, more necessary than ever: trust, integration and taste.
Trust
Robert Seamans, a professor at New York University’s Stern School of Business who studies the economic consequences of A.I., envisions a new set of roles he calls A.I. auditors — people who dig down into the A.I. to understand what it is doing and why and can then document it for technical, explanatory or liability purposes. Within the next five years, he told me, he suspects that all big accounting firms will include “A.I. audits” among their offerings.
A related job he imagines is an A.I. translator: someone who understands A.I. well enough to explain its mechanics to others in the business, particularly to leaders and managers. “The A.I. translator helps to interface between something that’s super-technical and what a manager knows and understands — and what they need to know in order to make a decision,” Seamans said.
In a sense, both of Seamans’s visions fall into a broader category of “trust.” I didn’t submit my A.I.-generated article in part because that would have betrayed my editors’ trust, but also because I didn’t trust it — trust that it was true, trust that it got the facts right. Because I hadn’t done the work and the thinking myself, I couldn’t tell if it was being fair or reasonable. Everyone who tries to use A.I. professionally will face a version of this problem: The technology can provide astonishing amounts of output in an instant, but how much are we supposed to trust what it’s giving us? And how can we know?
As A.I. continues to become more influential in our jobs and organizations, we’re going to develop a lot of these trust issues. Solving them will require humans.
Under the “trust” umbrella will be a whole new breed of fact checkers and compliance officers. Legal documents, annual reports, product specifications, research reports, HVAC contracts — all of these will soon be written by A.I., and all will need humans to review and verify them with an eye toward the surprising and weird mistakes A.I. is prone to make.
This may give rise to a title that could be called trust authenticator or trust director. And such jobs will need to be adjacent to other new roles, which are essentially variations on an A.I. ethicist. It will be these ethicists’ jobs to build chains of defensible logic that can be used to support decisions made by A.I. (or by hybrid A.I.-and-human teams) to a wide variety of interested parties, including investors, managers, customers and perhaps even judges and juries. “Many companies have played around with the idea of an ‘ethics board,’” Seamans said. “I think that you could imagine a future where these A.I. ethics boards are empowered a lot more than they tend to be today.”
At its core, trust is about accountability — and this is where a human in the loop is most critical. In everything from contracts to nuclear-launch systems, we need humans to be accountable. “There should be a human who ultimately takes responsibility,” said Erik Brynjolfsson, director of the digital economy lab at the Stanford Institute for Human-Centered Artificial Intelligence and also a founder of the A.I. consulting company Workhelix. “Right now if a car crashes, you have to sort out: Is it the antilock brakes? Was it the driver? Was there something wrong in the road? If it’s the antilock brakes, who was it who made that part? And they trace it back to who ultimately is responsible for that thing. It may be a complex chain of causality, and it’s going to get that much more complicated with A.I., but ultimately you have to trace it back to somebody who takes responsibility.”
In a number of fields, from law to architecture, A.I. will be able to do much of the basic work customers need, from writing a contract to designing a house. But at some point, a human, perhaps even a certified one, needs to sign off on this work. You might call this new role a legal guarantor: someone who provides the culpability that the A.I. cannot. Ethan Mollick, a professor at the Wharton School of Business and the author of “Co-Intelligence: Living and Working With A.I.,” refers to such jobs as the “sin eaters” for A.I. — the final stop in the responsibility chain.
Another new role will be some type of consistency coordinator. A.I. is good at many things, but being consistent isn’t one of them. Can a fashion house be assured that a particular dress will be accurate and consistently represented across dozens of A.I.-generated photographs? In manufacturing, can a virtual twin manager — someone who manages and tweaks software versions of real-world objects and systems — be sure that A.I.-made digital replicas will stay consistent as new changes are implemented? And when A.I. isn’t consistent, it can’t be trusted. This is where a dedicated role, one that can accept accountability, will be needed to validate consistency across systems and organizations.
One more possibility: escalation officer. In an essay titled “What Will Remain for People to Do?” the writer and economist Daniel Susskind points out that there are roles that humans simply prefer other humans to perform. He brings up the fact that professional chess remains popular despite the fact that A.I. has long been able to trounce the best chess players. But our preferences will almost certainly also require someone to step in when the A.I. just feels … inhuman. In customer service, when the A.I. has been going around and around, people will want to speak with a human capable of empathy and understanding. Such roles will also most likely be important in education, where students and parents feel the need for human intervention when something goes wrong.
Integration
Given the complexity of A.I., many of the new jobs will be technical in nature. There will be a great need for people who deeply understand A.I. and can map that knowledge into business needs.
Seamans calls this group the A.I. integrators: experts who figure out how to best use A.I. in a company, then implement it. “A C.E.O. might say on an earnings call, ‘We’re investing in A.I.,’” Seamans told me. “But to do what? Is it some back-office functions like bill-pay and collections? Is it employment and screening? Is it some sort of work flow with your white-collar workers for whatever your business is?” Figuring this out takes someone who knows both the technology and the company.
This includes people who fix the A.I. when it breaks, which will look a little different than traditional I.T. specialists. As A.I. becomes more “agentic” — meaning that A.I. agents are out solving complex tasks on their own — the systems will become more deeply layered. When something goes awry, this will require someone who can dig through the network to find what went wrong, why it went wrong and how to repair it: an A.I. plumber, so to speak, who must snake the pipes of the entire system.
Deciding which tools to use, and when, is a complex problem. The learning-and-tutoring empire Khan Academy, for example, has deeply integrated A.I. models into its business and products, building virtual tutors to help children with everything from algebra to essay writing. Just keeping track of the models — how they’ve improved, how much they lie or “hallucinate,” which ones are currently better at language or math — is a continuing chore. “These models are constantly changing,” said Sal Khan, who founded Khan Academy. “You’re constantly making perceived improvements to features, but you need to evaluate whether you’re regressing.” Roles created simply to evaluate the latest and greatest models might simply be called A.I. assessors.
But this is still just the beginning. “When my students ask me, ‘What kind of company should I be starting?’ I often steer them in the direction of, ‘Be the person who connects customer problems to the power of the technology,’” Brynjolfsson said.
Integration jobs are already on the rise, according to LinkedIn’s Raman, even if their titles are fairly staid. “‘Head of A.I.’ jobs are up, I think, three times in the last five years,” he said. “A.I. engineers are the fastest growing role in the U.S., followed by A.I. consultants.” In the future, they might carry more specific titles, like integration specialist.
You can reliably expect these types of integration jobs to become more specific as A.I. progresses. Companies are already using A.I. models that are highly customized to the organization. These models may be built on general tools like Claude or ChatGPT, but they also have access to and train on the company’s proprietary data. This potentially creates two new roles. One is simply the A.I. trainer: the person whose job it is to help the A.I. find and digest the best, most useful data a company has and then teach it to respond in accurate and helpful ways.
The other role emerges from the fact that these custom A.I.s will interact with employees all over the organization and possibly even customers. This creates an unusual issue: What is your company’s “A.I. personality?” Is it cloying and overly complimentary, as some recent A.I. models have been? Is it sardonic and grumpy, like ChatGPT’s Monday model? An A.I. personality director will fine-tune these issues, and in the future, an organization’s A.I. personality could become as core to its brand as its logo.
There are, of course, very complicated industries where A.I. holds enormous promise but also enormous risks. Perhaps at the top of that list is health care. It’s not hard to imagine this field requiring many different kinds of integration roles, such as a drug-compliance optimizer — a person who develops A.I.-driven systems to make sure patients take the right medications at the correct time. In such complicated industries, we’ll also very likely see an A.I./human evaluation specialist: someone who determines where A.I. performs best, where humans are either better or simply needed and where a hybrid team might be optimal.
To highlight the importance of getting integration right, Seamans tells a story from robotics, another field he studies. Like A.I., robotics is an automation technology positioned to displace human workers. But research shows that manufacturers who incorporate robotics usually end up with more human employees, not fewer. Why? “Nobody knows,” Seamans said. He suspects the reason is that as robotic factories grow and thrive, they steal business from other factories, which enables them to expand. “It looks like what’s happening is that growth is coming at the expense of firms that are not adopting robots,” Seamans said.
But manufacturers, Seamans explains, are not robotic experts, and incorporating robots requires someone specialized not just in robots but in reconstructing manufacturing lines to accommodate them. This has given rise to specialized robot integrators. And these integrators are vital, though they are not evenly available geographically. Some places have them, and some places don’t.
“Imagine two places that are equally dense, with these types of manufacturers,” Seamans said. “If one of them has a local integrator, that one is much more likely to have robots than the other area.” And the one that has robots may succeed and grow, while the other may shrink and die. “It points to the really important need of someone who can sort of bridge the technology and the industry expertise,” Seamans said.
Taste
It will remain a human’s job, of course, to tell the A.I. what to do. But telling A.I. what to do requires having a vision for exactly what you want. In a future where most of us have access to the same generative tools, taste will become incredibly important.
There is a viral “60 Minutes” clip that’s worth considering on this point, an interview of the music producer Rick Rubin by Anderson Cooper. In it, Cooper tries to understand what, exactly, Rubin does.
“Do you play instruments?” Cooper asks.
“Barely,” Rubin answers.
“Do you know how to work a sound board?” Cooper asks.
“No,” Rubin says. “I have no technical ability. And I know nothing about music.”
After a bit more back-and-forth, Cooper asks, “So what are you being paid for?”
Rubin answers the question: “The confidence I have in my taste, and my ability to express what I feel, has proven helpful for artists.”
This undoubtedly undersells what Rubin does, but the idea of primarily being valued for your very confident taste resonates in an A.I. future. As A.I. expands, for better or worse, we will start to see a form of creativity without craft.
One reason I wasn’t ready to let A.I. write this article for me came down to a trust issue. But I can see a future in which that won’t necessarily be the case. In that future, provided my editor and I can trust the A.I., the job of writing this article may very well come down to selecting the inputs, then picking and choosing phrases, paragraphs and lines of reasoning offered by Claude, ChatGPT, Gemini and others. I will still be the “author” of the article, but perhaps not the writer.
When creative options are nearly limitless, people with the ability to make bold, stylish choices will be in demand. And this will be true not just for creative industries such as writing, filmmaking and advertising but for business of all kinds. Knowing what you want — and having a sense of what will resonate with customers — will be the core human role in developing products and systems.
Given this, it might seem surprising that “designer” is often on the list of jobs that A.I. is expected to replace. It’s true that graphic designers, for example, won’t need to point and click their way to compelling layouts or perfectly kerned typefaces; the A.I. will do that. But at their most fundamental level, what designers actually do is marshal creative choices to a desired outcome. This requires making a whole bunch of choices based on taste: What is needed from a logo or page design? How do you know when it’s good? How do you know when it will have impact? How do you even know when it’s finished? Rather than go away, in the future, the term “designer” might actually grow to cover a whole range of jobs in which a person’s main function is to steer A.I. to create something compelling — a product, a service, a process — based largely on their taste.
There are some titles we already have, like product designer, that will simply grow to encompass a whole lot more. In the future, product designers will have a much greater ability to own products, from top to bottom. The role will be not just about the big picture but also about all the choices that bring that big picture to life.
And there are other design qualifiers that will likely come into vogue. I might, for example, not be a writer but an article designer. Story designer might become a more popular title in film and TV. We could see a lot more world designers in everything from marketing — where a person fabricates an entire universe, complete with fictional characters and locations, which then feeds all the images and videos of a campaign — to video games. Many of these roles will be more focused on style than on technical execution.
But these are creative industries. Things get perhaps more interesting when you consider such roles in noncreative fields. You might see a human resources designer who can more thoroughly control everything from training materials to detailed benefits-and-leave policies, giving them a more pronounced ability to personally shape the entire culture of an organization. We might see civil designers, who are more focused on the creative part of the job than the math and physics, favored over civil engineers.
“Designer” may not end up being the preferred nomenclature, but it usefully signifies the shift. More and more people will be tasked with making creative and taste decisions, steering the A.I. where they want it to go. And these people will be lower and lower on the seniority chart. One of the major concerns with A.I. today is that it is taking the lower-level jobs, which are traditionally focused on the kind of rote work that A.I. excels at. Raman wrote a guest essay about this issue in The Times, but to me, he also pointed out a potential solution: A.I. can help novice workers overcome their inexperience, helping them fill in deficiencies in everything from writing and research to design and development.
This means that rather than have rookie employees compile reports or write memos — things the A.I. is good at — you might have them start, say, creating new ideas for products right away. Traditionally, this kind of work would be reserved for deeply experienced workers, but it won’t need to stay that way. By empowering young, inexperienced workers, A.I. can enable them to be more entrepreneurial, faster. And this means that a greater range of the organization — with a wider range of perspectives — can be hunting for new great ideas or new areas for growth rather than busying themselves with repetitive office tasks. “As this starts to take off, we’ll find ourselves in a new economy,” Raman said. “Something like the ‘innovation economy,’ with entrepreneurialism as its core.”
Creative decision-making will also most likely become core to a company’s competitiveness. Businesses where intelligence and expertise are the differentiating traits will have to pivot. Take financial-services firms, for example. Once all these firms have access to the same powerful intelligence, how do they stand out from one another? The answer may be found in “taste” roles — how they communicate and market themselves, how they show up to customers, their creative philosophies. You could see a role here for a differentiation designer, whose remit combines branding, philosophy, product, risk tolerance and creative execution.
There’s plenty of reason to lament the loss of craft, of course. It’s grim to imagine an age when our writers don’t write, our musicians don’t play instruments and our illustrators don’t draw. But that’s not really the age we’re entering; the act of craft, after all, will always have a huge impact on thinking. Mollick sees this even when it comes to his academic writing. “I will have it do research in advance, but I will never let it write before I write,” Mollick said of A.I. “I have to write messily to think something through. Otherwise, the A.I. will dominate my thoughts.”
The reality is that people will continue to draw, write and play instruments out of preference and out of need — it’s how they work and think best. But as we enter a radically abundant age in terms of creation, we are certain to see a lot more avenues to creative output that don’t involve the same level of craft. These will come with pitfalls, yes, but also advantages.
Seamans uses Pixar as an analogy to explain the potential benefits of the shift. Before Pixar, he said, “there were these folks who were really high-end in terms of their craft.” Animators put a lot of energy into the drawings in each frame. But once computers could automate that work, the role of the animators shifted. “They were able to spend a lot more time — and, for that matter, put a lot more resources toward — thinking about storytelling and plot development.”
The A.I. future holds the possibility that one day you won’t need to know how to do everything in order to do everything. “We’re all going to be C.E.O.s of a small army of A.I. agents,” Brynjolfsson said. “We have to think, OK: What is it we really want to accomplish? What are the goals here? And we have to think a little bit more deeply about that than we have in the past.”
There are a lot of legitimate concerns about where A.I. is taking us. But if we think carefully enough — if we are intentional about the things we ask of A.I. — our future could look very bright. In other words, we are the designers of our A.I. future. Let’s hope we have great taste.
Read by Malcolm Hillgartner
Narration produced by Tanya Pérez and Krish Seenivasan
Engineered by Joel Thibodeau
The post A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You. appeared first on New York Times.
