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Over the past decade, Figma has transformed how people within companies collaborate to turn software ideas into polished products. Now the company is itself being transformed by AI. The technology is beginning to show its potential to take on much of the detail work that has required human attention in design, coding, and other domains. But the end game involves far more than typing chatbot-style prompts and waiting for the results. I spoke with Figmas head of AI, David Kossnickone of Fast Companys AI 20 honorees for 2025about what the company has accomplished so far and where hes trying to steer it. We’re still in chapter one, maybe the start of chapter two, he told me. This Q&A is part of Fast Companys AI 20 for 2025, our roundup spotlighting 20 of AIs most innovative technologists, entrepreneurs, corporate leaders, and creative thinkers. It has been edited for length and clarity. Talk a little bit about what your work at Figma encompasses and how you came to have this job. Anything that has AI in it, I and my team touch in some way. It’s everything from traditional AI tools like search, which we’ve rebuilt using multimodal embeddings, to some of our newer, AI-forward workflows. Figma Make is an example of that. As to how I came to get this job, I’ll give you a short version. I knew a lot of the Figma team for a long time. The chief product officer, Yuhki [Yamashita], and I went to college together. He was at my wedding. I did a startup of my own, and one of our board members was John Lilly, who was also on the board of Figma. I actually met [Figma cofounder/CEO] Dylan [Field] when there was, like, a 20-person Figma team, because we were building a game engine, and Figma is basically a game engine, with all sorts of custom renderings. [Lilly] was like, You guys should compare notes. So I’ve known the team for a long time, and it’s a product I’ve used a lot. And then, about a year and a half ago, when I joined, I’d been working on AI at Coda, which was then acquired by Grammarly. As a big Figma user, I also felt like there was just such a huge opportunity for Figma, and it had barely gotten started. So I was thinking about what’s next and sharing it with Yuhki: Theres a lot you guys could do. He was like, I know, we just don’t have the right team here yet. You wanna come? I was like, That sounds amazing. Is there a particular Figma philosophy about AI and how to put it into this experience that’s been around for a while, and which people choose to use because they like it, in most cases? There’s been a couple of learnings, both from our own team and from working with customers. A lot of our biggest customers are technology companies themselves. Many are integrating AI themselves. And so we’ve learned through themwhat’s working and what they’re trying. There have been two industry trends, and we’ve done both here. One is trying to find existing workflows that you can add AI to, to save users time, to delight them, to give them new capabilities. And also building totally new experiences that have AI as the core of the workflow. Interestingly, we’ve actually done some market research and surveys of users and other companies. People understand and value the new AI for workflows even more. I think that is counterintuitive. You think you have such big products, and adding efficiencies to them is very viable. And it is. But often, AI is a little more invisible there. Kt’s embedded in a workflow that you’re used to, and so the thing that is forefront in your mind is the workflow itself That’s good. We don’t want to get in people’s way. Figma Designs canvas is kind of like the Google homepage or Facebook news feed, where a single pixel of friction literally slows down millions of people every day. Which makes for interesting challenges. How do you introduce things so they dont bother people? But on the flip side, there’s a lot of new workflows and new tools. Peopleespecially our type of customersare always experimenting. And so they’re very open to trying a totally different approach. Historically, Figma has been this thing that human beings use to collaborate with other human beings to create stuff from scratch, and often very carefully considered stuff. What’s the experience like of integrating tools that take some of that heavy lifting off their shoulders? I think it’s super exciting. It feels and looks different for different user types. So as an example, we actually just finished up a $100,000 hackathon, our first ever, for Figma Make. It was totally inspiring seeing all the range of things people have made. There were students. There were people who never learned to code. There were designers who code a lot, and its just helping them do it faster. There were hobbyists. For a lot of those user types, a very common theme was, Wow, I just couldn’t have done this before. The other way it feels is as a kind of thought partner to experts. I feel this myself as a [product manager] when I chat with Figma Make or ChatGPT. I have a problem. I have a solution in mind. And actually, there are some other solutions I hadn’t thought about, because I was so focused on this one solution. It can help you pull back and see a wider solution space, and explore a few other threads in a very cheap way before you go too deep. Its like Doctor Strange, where he has this magic crystal that lets him look into all the different possible futures. Expert users are always running simulations in their heads. What if I move this button over here? How’s the user behavior going to change? What does that mean for the next part of the experience? We’re finding that these types of AI tools make that loop so much faster, where it’s like, I’m just going to try exploring a bunch. I’m going to literally make them, but make them 10 times as quickly, and play out all those different end states. How far is Figma down the continuum from having no AI to AI being everywhere and doing everything AI could possibly do? It’s an interesting question. There’s AI today and AI in the future. If all research was frozen, there would probably still be five years of new product experiences that the industry could build from current models. But the pace of model improvement is still really high as well. For us, I’d say we’re still in chapter one, maybe the start of chapter two. And chapter one was, We’re going to do a bunch of basic features, get our feet wet, save time in your workflows. Chapter two is, We’re doing some new AI-first experiences. Figma Make, that whole category of prompt-to-app, is very, very new. As the models get better and faster and cheaper, what other new workflows are going to become available? Today, things like autocomplete, as an example, are hard to make fast, and hard to make cheap, and hard tomake high quality. And, you know, we’re still using many interfaces in the industry that feel like typing at a terminal from the ’60s. That’s not the final interface. That’s not the final workflow. I think the interfaces are going to become more visual, more exploratory. It’s part of why I’m so excited about Figma and why I came here. As AI gets better, what you want the experience of working with an AI to feel like is going to be more and more similar to what you want the experience of working with a human to feel like. You’re going to want to brainstorm with the AI before it goes off and thinks for 10 hours and then builds something. You’re going to want to work through the big trade-offs. Youre going to want your teammates in there too, not just the AI. I think that’ll be a super exciting place, where things like code become implementation details that AIs are more and more capable of driving, with humans reviewing.
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Up in the Cascade Mountains, 90 miles east of Seattle, a group of high-ranking Amazon engineers gather for a private off-site. They hail from the companys North America Stores division, and theyre here at this Hyatt resort on a crisp September morning to brainstorm new ways to power Amazons retail experiences. Passing the hotel lobbys IMAX-like mountain views, they filter into windowless meeting rooms. Down the hall, the off-sites keynote speakerByron Cook, vice president and distinguished scientist at Amazonslips into an empty conference room to have some breakfast before his presentation. Cook is 6-foot-6, but with sloping shoulders that make his otherwise imposing frame appear disarmingly concave. Hes wearing a rumpled version of his typical uniform: a thick black hoodie and loose black pants hanging slightly high at the ankles. An ashy thatch of hair points in whatever direction his hands happen to push it. Cook, 54, doesnt look much like a scientist, distinguished or otherwise, and certainly not like a VPmore like a nerdy roadie. They dont know who I am yet, he tells me between bites of breakfast, referring to the two dozen or so engineers now taking their seats. Despite his exalted title, Cook has faced plenty of rooms like this in his self-made role as a kind of missionary within Amazon, spreading the word about a powerful but obscure type of artificial intelligence called automated reasoning. As hes done many times before, Cook is here to get the highly technical people in that room to become believers. Hes championing an approach to AI that isnt powered by gigawatt data centers stuffed with GPUs, but by principles old enough to be written on papyrusand one thats already positioning Amazon as a leader in the tech industrys quest to solve the problem of hallucinations. Cook doesnt have a pretalk ritual, no need to get in character. Hes riffing half-seriously to a colleague about the pleasures of riding the New York subway in the summertime when someone mentions that the session is about to begin. He immediately drops his fork and strides out. His next batch of converts awaits. When ChatGPT hit the world with asteroid force in November 2022, Amazon was caught flat-footed just like everyone else. Not because it was an AI laggardthe tech giant had recently overhauled nearly all of its divisions, including its massive cloud-computing arm, AWS, to leverage deep learning. Amazon also dominated the smart-home market, with 300 million devices connected to Alexa, its AI-powered assistant. It had even been researching and building large language models, the tech behind ChatGPT, for multiple years, as CEO Andy Jassy told CNBC in April 2023. But OpenAIs chatbot changed the definitionand expectationsof AI overnight. Before, AI was still a mostly invisible ingredient in voice assistants, facial recognition, and other relatively narrow applications. Now it was suddenly seen as a prompt-powered genie, an infinitely flexible do-anything machine that every tech company needed to embraceor risk irrelevance. Less than six months after ChatGPTs debut, Amazon launched Bedrock, its own AWS-hosted generative AI service for enterprise clients, a list that currently includes 3M, DoorDash, Thomson Reuters, United Airlines, and the New York Stock Exchange, among others. Over the next two years, Amazon injected generative AI into product after product, from Prime Video and Amazon Music (where it powers content recommendation and discovery tools) to online retail pages (where sellers can use it to optimize their product listings), and even into internal tools used by AWSs sales teams. The company has released two chatbots (a shopping assistant called Rufus and the business-friendly Amazon Q), plus its own set of foundation models called Novathey are general-purpose AI systems, akin to Googles Gemini or OpenAIs line of GPTs. Amazon even caught the industry fever around so-called AGI (artificial general intelligence, a yet-to-be-achieved version of AI that does any cognitive task a human can) and in late 2024 launched AGI Lab, a flashy internal incubator led by David Luan, an ex-OpenAI researcher. Still, none of it captured the publics imagination like the stream of shiny objects emitted by OpenAI (reasoning models!), Anthropic (chatbots that code!), and Google (AI Overviews! Deep Research!). Like Apple, Amazon was unable to turn its early lead in AI assistants into an advantage in this new era. Alexa and Siri simply cannot compete. But maybe that has been for the best, because 2025 was the year that AIs sheen suddenly started to come off: GPT-5 fell flat, vibe coding went from killer app to major risk, and an MIT study rattled the industry by claiming that 95% of businesses get no meaningful return on their AI pilot projects. It was against this backdropthe summer AI turned ugly, as Deutsche Bank analysts called itthat Amazon publicly released Automated Reasoning Checks, a feature promising to minimize AI hallucinations and deliver up to 99% verification accuracy for generative AI applications built on AWS. The product was Cooks brainchild; in a nutshell, it snuffs out hallucinations using the same kind of computerized logic that lets mathematicians prove 300-page-long theorems. (In fact, a 1956 automated reasoning program called Logic Theorist is considered by some experts to be the worlds first AI system, finding new and shorter versions of some of the proofs in Principia Mathematica, one of the most fundamental texts in modern mathematics.) Sexy, it aint. Still, Swami Sivasubramanian, one of Amazons highest-ranking AI executives, who serves on Jassys S-team of direct advisers, was impressed enough to call Automated Reasoning Checks a new milestone in AI safety in a LinkedIn post. Matt Garman, CEO of AWS, referred to it as game-changing. [carousel_block id=”carousel-1763954270090″] Automated reasonings promise of quashing AI misbehavior with math has quietly become an essential part of Amazons strategy around agentsthose LLM-powered workbots that are supposed to transform enterprise productivity [checks watch] any day now. Apparently, businesses have serious side-eye about that, too: Earlier thi year, Gartner predicted that more than 40% of agentic AI projects will be ditched within the next two years due to inadequate risk controls. The company told me recently that it predicts that 30% to 60% of the projects that do go forward will fail due to hallucinations, risk, and lack of governance. Thats not a prophecy Amazon can afford to let come truenot with a potential market for AI agents that Gartner estimates to be worth $512 billion by 2029. One way or another, hallucinations have got to go. The question is how. Agents are just souped-up LLMs, which means they can and will go off the railsin fact, as OpenAI itself recently admitted following an internal study, they cant not. What Cook helped Amazon realize, just months after ChatGPTs release, was that they already had a secret weapon for extinguishing hallucinations, hidden in plain sight. Automated reasoning is the polar opposite of generative AI: old, stiff, and hard to use. Many at Amazon had never heard of it. But Cook knew how to wield it, having brought it to Amazon nearly 10 years ago as a way of rooting out hidden security vulnerabilities within AWS. And hed been amassing what he estimates to be the largest group of automated reasoning experts in the tech industry. Now that investment is set to pay off in a way that Amazon never expected. Automated Reasoning Checks is just the first of many products that the company plans to release (on a timetable it wont specify) that fuse the flexibility of language models with the proven reliability of automated reasoning. The latest, called Policy in Amazon Bedrock Agentcore and previewed this week at AWS’s annual Re:Invent conference, uses automated reasoning to stop agents from taking actions they’re not allowed to (such as issuing customer refunds based on fraudulent requests). If this combined approachknown as neuro-symbolic AIcan reduce the potential failure rate of agentic AI projects by even a fraction of a percent, it would be worth hundreds of millions of dollars, say analysts at Gartner. And Amazon knows it. To realize the transformative potential of AI agents and truly change the way we live and work, we need that trust, Sivasubramanian says. We believe the foundation for trustworthy, production-ready AI agents lies in automated reasoning. To understand why Amazon is banking on automated reasoning, its worth sketching out how its different from the kind of AI youve already heard of. Unlike neural networks, which learn patterns by ingesting millions or even billions of examples, automated reasoning relies on a special language called formal logic to express problems as a kind of arithmetic, based on principles that date back to ancient Greece. Computers can use this rule-based approach to calculate the answers to yes-or-no questions with mathematical certaintynot probabilistic best guesses, as deep learning does. Think of automated reasoning like TurboTax for solving complex logical problems: As long as the problems are expressed in a special language, computers can do most of the workand have been doing so for decades. Since 1994, when a flaw in Intels Pentium chips cost the company half a billion dollars to fix, nearly all microchip manufacturers have used automated reasoning to prove the correctness of designs in advance. The French government used it to verify the software for Pariss first self-driving Métro train in 1998. In 2004, NASA even used it to control the Spirit and Opportunity rovers on Mars. Theres a catch, of course: Because automated reasoning can only reduce problems to three possible outcomesyes, no, or the equivalent of does not computefinding ways to apply this logically bulletproof but incredibly rigid style of AI to the real world can be difficult and expensive. But when automated reasoning works, it really workscollapsing vast, even unknowable possibilities into a single mathematical guarantee that can compute in milliseconds on an average CPU. And Cook is very, very good at getting automated reasoning to work. Cook began his career building a formidable scientific reputation at Microsoft Research, where he spent a decade applying automated reasoning to everything from systems biology to the famously unsolvable halting problem in computer science. (Want a foolproof way to tell in advance if any computer program will run normally or get stuck in an infinite loop? Sorry, not possible. Thats the halting problem.) But by 2014, he was looking to put his findings, many of which have been published as peer-reviewed research, to work outside the lab. I was figuring out: Where is the biggest blast radius? Wheres the place I could go to foment a revolution? he says. I watched everyone moving to the cloud, and was like, I think AWS is the place to go. The first problem Amazon aimed Cook at was cloud security. Reporting directly to then chief information security officer Stephen Schmidt, Cook and his newly formed Automated Reasoning Group (ARG) painstakingly translated AWS security protocols into the language of mathematical proofs and then used their logic-based tools to surface hidden flaws. Once those flaws were corrected, those same tools could then prove with certainty that the system was secure. Some at AWS were dubious at first. When you look mad scientist up in the dictionary, Byrons picture is in the margin, says Eric Brandwine, an Amazon distinguished engineer who at the time worked on security for AWS. Early on, I challenged [him] on a lot of this stuff. But as Cooks group fleshed out plans and racked up small but significant winslike catching a vulnerability in AWSs Key Management Service, the cryptographic holy of holies that controls how clients safeguard their dataskeptics started becoming evangelists. Some of these [were] beautiful bugstheyd been there for years and never been found by our best experts, and never been found by bad guys, says James Hamilton, a legendary distinguished engineer within Amazon who now directly advises Andy Jassy. And yet, automated reasoning found them. From 2018 onward, Amazons automated reasoning experts worked with engineers to encode the technology into nearly every part of AWS, from analytics and storage to developer tools and content delivery. One particular niche of cloud-computing clientsheavily regulated financial service firms, like Goldman Sachs and the global hedge fund Bridgewater Associates, with sensitive data and strict compliance requirementsfound automated reasonings promise of provable security extremely compelling. When ChatGPT appeared and the world flung itself headfirst into generative AI, these companies did too. But they still wanted to keep the one small thing, Cook says, that theyd become accustomed to along th way: trust. That customer feedback spurred Cook to imagine how LLMs and automated reasoning might fit together. The solution that he and his collaborators prototyped in the summer of 2023 works by leveraging the same logical framework that worked so well for squishing security bugs in AWS. Step one: Take any policy meant to inform a chatbot (say, a stack of HR documentation, or zoning regulations) and translate it into formal logicthe special language of automated reasoning. Step two: Translate any responses generated by the bot too. Step three: Calculate. If theres a discrepancy between what the LLM wants to say and what the policy allows, the automated reasoning engine will catch it, flag it, and tell the bot to try again. (For humans in the loop, itll also provide logical proof of what went wrong and how, and suggest specific fixes if needed.) We showed that to senior leadership, and they went nuts for it, says Nadia Labai, a senior applied scientist at AWS who partnered with Cook on the project. The demo went on to become Automated Reasoning Checks, which Amazon previewed at its annual Re:Invent conference in December 2024. PwC, one of the Big Four global accounting and consulting firms, was among the first AWS clients to adopt it. We do a lot of work in pharmaceutical, energy, and utilities, all of which are regulated, says Matt Wood, PwCs global and U.S. commercial technology and innovation officer. PwC relies on solutions like AWSs automated reasoning tool to check the accuracy of the outputs of its generative AI toolsincluding agents. But Wood sees the technologys appeal spreading beyond finance and other regulation-heavy industries. Look at what it took to set up a website 25 years agothat was a refined set of skills. Today, you go on Squarespace, click a button, and its done, he says. My expectation is that automated reasoning will follow a similar path. Amazon will make this easier and easier: If you want an automated reasoning check on something, youll have one. Amazon has already embarked on this path with its own enterprise products and internal systems. Rufus, the AI shopping assistant, uses automated reasoning to keep its responses relevant and accurate. Warehouse robots use it to coordinate their actions in close quarters. Nova, Amazons fleet of generative AI foundation models, uses it to improve so-called chain of thought capabilities. And then there are the agents. Cook says the company has multiple agentic AI projects in development that incorporate automated reasoning, with intended applications in software development, security, and policy enforcement in AWS. One is Policy in AgentCore, which Amazon released after this story was reported. Another thats peeking out from behind the curtain is Auto, an agent built into Kiro, Amazons new AI programming tool, that will use formal logic to help make sure bot-written code matches humans intended specifications. But Sivasubramanian, AWSs vice president for agentic AI (and Cooks boss), isnt coy about the commitment Amazon is making. We believe agentic AI has the potential to be our next multibillion-dollar business, he says. As agents are granted more and more autonomy . . . automated reasoning will be key in helping them reach widespread enterprise adoption. Agents are part of why Cook is touting automated reasoning to his engineer colleagues from the North American Stores division at their off-site in the mountains. Retail might not seem to have much in common with finance or pharma, but its a domain thats full of decisions with real stakes. (While onstage at re:Invent 2025, Cook said that “giving an agent access to your credit card is like giving a teenager access to your credit card… You might end up owning a pony or a warehouse full of candy.”) And in that environment, relying on autonomous botsempowered to do anything from execute transactions to rewrite softwarecan turn hallucination from tolerable quirk into Russian roulette. Its a matter of scale: When one vibe coding VC unleashes an agent that accidentally nukes his own apps database, as happened earlier this year to SaaS investor Jason Lemkin, its a funny story. (He got the data back.) But if Fortune 500 companies start deploying swarms of agents that accidentally mislead customers, destroy records, or break industry regulations, theres no Undo button. Enterprise software is full of these potential pitfalls, and existing methods for reducing hallucination arent always strong enough to keep agents from blundering into them. Thats because agents shift the definition of hallucination itself, from errors in word to errors in deed. First of all, this thing could lie to me, explains Cook. But secondly, if I let it launch rocketshis metaphor for irreversible actionswill it launch rockets when were not supposed to? Back in his hotel room after the keynote, Cook is reviewing the contents of a confidential slide deck about how automated reasoning can solve this rocket-launching problem. The demo, which he hurriedly mentioned in his talk (he ran out of time before being able to show it), describes a system that can transform safety policies for an agentdos and donts, written in natural languageinto a flowchart-like visualization of how the agent can and cannot behave, all backed by mathematical proof. Theres even an Attempt to Fix button to use if the system detects an anomaly. Cook calls the demo a concept car, but some of its ideas made it into Policy in AgentCore, which is already available in preview to some AWS customers. PwC, for one, sees Amazons logic-backed take on AI extending into coordinating the agents themselves. If youve got agents building other agents, collaborating with other agents, managing other agents, agents all the way down, says Wood, then having a way of forcing consistency [on their behavior] is going to be really, really importantwhich is where I think automated reasoning will play a role. The ability to reliably orchestrate the actions of AInot just single agents, but entangled legions of them, at scaleis a target that Amazon has squarely in its sights. But automated reasoning may not be the only way to get the job done. EY, another Big Four firm, recently launched its own neuro-symbolic solution to AI hallucinations, EY Growth Platforms, which fuses deep learning with proprietary knowledge graphs. A startup called Kognitos offers business-friendly agents backed by a deterministic symbolic program, dubbed English as Code. Others, like PromptQL, forgo neuro-symbolic methods altogether, preferring the simulated reasoning of frontier LLMs. But even they still attack the agent hallucination problem much like Amazon does: by using generative AI to translate business processes into a special internal language thats easy to audit and control. That translation process is where Amazon built a 10-year lead with automated reasoning. Now it has to maintain it. Nadia Labai is currently working on ways to improve Amazons techniques for using LLMs to convert natural language into formal logic. Its part of a strategy that could help turn Amazons brand of customer-driven, business-friendly AI into a new class of industry- defining infrastructure. A few days before the off-site, I met with Cook in a conference room at Amazons Seattle headquarters. Sitting with his legs tucked catlike beneath him, Cook mused about his own vision for the future of automated reasoningone that extends far beyond Amazons ambitions for enterpise-grade AI. The world, he says, is filled with socio-technical systemspatchworks of often-abstruse rules that only highly paid experts can easily navigate, from civil statutes to insurance policies. Right now, rich people get [to take advantage of] that stuff, he continues. But if the rest of us had a way to manipulate these systems in natural language (thanks, LLMs) with an underlying proof of correctness (thanks, automated reasoning), a workaday kind of superintelligence could be unlocked. Not the kind that helps us colonize the galaxy, as Google DeepMind CEO Demis Hassabis envisions, but one that simply helps people navigate the complexity of everyday life, like figuring out where its legal to build housing for an aging relative or how to get an insurance company to cover their expensive medication. You could have an app that, in an hour of your own time, would get answers to questions that before would take you months, Cook says. That democratizes, if you will, access to truth. And thats the start of a new era. This story is part of Fast Companys AI 20 for 2025, our roundup spotlighting 20 of AIs most innovative technologists, entrepreneurs, corporate leaders, and creative thinkers.
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.read-more { display: flex; justify-content: right; font-weight: 700; font-family: var(--font-centra); color: #000000; font-size: 13px; line-height: 14px; letter-spacing: 1.4px; text-transform: uppercase; flex-wrap: nowrap; } /* Stronger selector to override other styles */ .read-more a { white-space: nowrap; border-bottom: 5px solid transparent !important; cursor: pointer; text-decoration: none !important; } /* .read-more a:hover, .read-more a:focus { color: black !important; border-bottom-color: black !important; text-decoration: none !important; } */ .read-even-more { display: flex; justify-content: right; font-weight: 700; font-family: var(--font-centra); color: #000000; font-size: 13px; line-height: 14px; letter-spacing: 1.4px; text-transform: uppercase; flex-wrap: nowrap; padding-top: 8px; } /* Stronger selector to override other styles */ .read-even-more a { white-space: nowrap; border-bottom: 5px solid transparent !important; cursor: pointer; text-decoration: none !important; } /* .readeven-more a:hover, .read-even-more a:focus { color: black !important; border-bottom-color: black !important; text-decoration: none !important; } */ The biggest story in tech is AIs increasing capacity to take on tasks once reserved for human beings. But the agents driving that change arent machines. Theyre humansinventive, ambitious, enterprising ones. Our third annual roundup of some of the fields most intriguing players includes scientists and ethicists, CEOs and investors, big-tech veterans and first-time founders. These 20 innovators are tackling challenges from training tomorrows AI models to speeding drug discovery to reimagining everyday productivity tools. Household names theyre not. Yet, theyre already changing our world, with much more to come. [Illustration: Oriana Fenwick] Michelle Pokrass Technical Staff Member, OpenAI Last year, OpenAI decided it had to pay more attention to its power users, the ones with a knack for discovering new uses for AI: doctors, scientists, and coders, along with companies building their own software around OpenAIs API. And so the company turned to post-training research lead Michelle Pokrass. Read profile [Illustration: HelloVon; source image: Carlton Canary] Rachel Taylor Product Manager, Sesame Rachel Taylor began her career as a creative director in the advertising business, a job that gave her plenty of opportunity to micromanage the final product. I had control of the script, she remembers. I could think about the intonation, and I could give the actor notes. Read profile [Source photo: Joelle Grace Taylor] Naeem Talukdar Cofounder and CEO, Moonvalley The rise of AI-generated actress Tilly Norwood may have been a stunt, but Hollywood is indeed embracing generative AI, a threat to those who owe their livelihoods to the movies. Still, AI could also expand a filmmakers creative vision by creating ambitious scenes or effects too pricey to shoot, says Naeem Talukdar, CEO of the video-generation model developer Moonvalley. Read More Every project you see on the big screen is a result of an endless amount of creative compromises from the directors and the filmmakers, he says.Moonvalley, which has raised $154 million, works with four of Hollywoods biggest studios, advising them on how to integrate AI into productions and reskill workers. Its model is trained on licensed, high-resolution content and is capable of production-grade video generation.Over the past year, Moonvalley has shifted its focus to developing world models, which geneate video that accurately portrays the complex physics of something like a car crash. As these models grow, says Talukdar, they start to be able to reason on things that they havent seen before. Mark Sullivan [Illustration: Oriana Fenwick; source image: Google Deep Mind] Koray Kavukcuoglu Chief AI Architect, Google For years, Google has employed many of AIs brightest minds. Yet it was burdened with a reputation for ineffectiveness when it came to turning its breakthroughs into products. Recently, however, CEO Sundar Pichai has made dramatic moves to overcome that unfortunate legacy. A big one came in June 2025 when he named Koray Kavukcuoglu the companys first chief AI architect. Read More A onetime Google summer intern and veteran of DeepMind, the British AI startup Google acquired in 2014, Kavukcuoglu helped manage the 2023 merger of DeepMind and Google Brain, another research arm. He remains CTO of the combined entity, Google DeepMind, but now he reports directly to Pichai, who announced the promotion in a memo explaining that Kavukcuoglus new role would bring more seamless integration, faster iteration, and greater efficiency to Googles lab-to-market pipeline. Hundreds of staffers working to apply Googles Gemini large language model to transform its search engine are now part of his team, The Information reported. Hes also involved with everything from data center strategy to bolstering the Google Cloud web services platform. Kavukcuoglus background is in the science of AI, not turning it into offerings that appeal to billions of people. Still, as Gemini-powered features increasingly show up in Google mainstays such as search, Android, and Gmail, investors have grown more optimistic that Google will be a titan of the AI era rather than a victim of it. As the company strives to keep that momentum going, Kavukcuoglus deep familiarity with its technical stack should be an asset. Theres a long history of research that built up to this point, he told Big Technologys Alex Kantrowitz last May. Harry McCracken [Illustration: HelloVon] Justine and Olivia Moore Partners, Andreessen Horowitz Andreessen Horowitz investors (and identical twins) Justine and Olivia Moore have been in venture capital since their days at Stanford University, where, in 2015, they cofounded an incubator to help students pursue business ideas. Read profile [Illustration: HelloVon; source photo: Bee Lavender] Byron Cook VP and Distinguished Scientist, Amazon Hallucinations are baked into the way generative AI works, but that doesnt mean we have to live with them. Byron Cooka vice president and distinguished scientist at Amazon Web Servicesrealized that an alternative AI technology called automated reasoning could be the perfect way to keep chatbots confabulations in check. Read More The product he spearheaded in 2024, called Automated Reasoning Checks, acts like Mr. Spock for language models, using rigid logic to catch and correct up to 99% of hallucinations.Now Cook is applying automated reasoning to agents: autonomous, LLM-powered enterprise apps. Many businesses dont trust themyet. First of all, this [agent] could lie to me, explains Cook. But secondly, if I let it launch rocketshis metaphor for irreversible actionswill it launch rockets when were not supposed to?” Amazon is betting that automated reasoning, and Cook, can keep agents on a leash. John Pavlus Read Feature Article [Source photo: Abridge] Shiv Rao Cofounder and CEO, Abridge A cardiologist at the University of Pittsburgh Medical Center (UPMC), Shiv Rao is the cofounder of Abridge, an AI-driven platform that records doctorpatient conversations in real time. The AI works across more than 100 languages and can distinguish when a doctor, patient, or translator is speaking to make the most accurate records. Read More Abridge is also integrated into medical platforms such as Athenahealth and Wolters Kluwer, where it can fill out forms and expedite tasks like insurance pre-authorization or writing prescriptions.Rao, who has experience as a tech investor with UPMC, developed the idea while making his rounds. His hospitals proximity to Carnegie Mellon, a tech hub, gave him a firsthand look at machine learning. That led him to found his company in 2018, long before ChatGPT came around. Abridge, which has raised a total of approximately $800 million, is currently in use at more than 150 U.S. health systems, including Johns Hopkins Medicine, the Mayo Clinic, Kaiser Permanente, and Duke Health. The less time physicians spend on paperwork, the more time they have to focus on their patients.As a doctor, Im not compensated for the care that I deliverIm compensated for the care that I documented that I deliver, Rao says. So we are extending the documentation to help with billing. Yasmin Gagne [Illustration: Oriana Fenwick; source image: Kyle Fish] Kyle Fish Research Scientist, Anthropic What if the chatbots we talk to every day actually felt something? What if the systems writing essays, solving problems, and planning tasks had preferences, or even something resembling suffering? And what will happen if we ignore these possibilities? Those are the questions Kyle Fish is wrestling with as Anthropics first in-house AI welfare researcher. Read profile [Source photo: Kelly Nyland] Kanjun Qiu Cofounder and CEO, Imbue Before most people started thinking about generative AI, Imbue cofounder and CEO Kanjun Qiu was worrying about its future. Qiu had established a co-living community in San Francisco called the Archive, where she counted among her housemates several working in AI, providing her with an early sense of how AI might further consolidate power among the big tech companies. Read More Theres this growing sense that both digital technology and AI are happening to people, theyre not necessarily happening with us or for us, she says.Imbue, which emerged from stealth in late 2022, aims to help people create their own AI tools. Its working on an AI-assisted software development tool called Sculptor, which became open to public preview in late September.What were trying to do is create a tool that lets you feel the structure of your software and understand it, says Qiu, by enabling it to remember context across different projects and suggesting ways to refine users code. While other AI software development startups such as Bolt and Replit offer stand-alone products, Sculptor acts as an interface for Claude Code, allowing developers to run multiple agents in parallel. Jared Newman [Source photo: Chloe Jackman Photography] Paula Goldman Chief Ethical and Humane Use Officer, Salesforce Before Paula Goldman became Salesforces first in-house ethicist in 2019, she earned a PhD in anthropology at Harvard. That training remains central to her work at the business software giant, which now includes helping product teams se guardrails for AI behavior, testing tools for safety, and engaging policymakers on trustworthy AI. Read More Goldman had already been immersed in these questions at eBay founder Pierre Omidyars impact investment firm, where she evaluated the social consequences of emerging technology. Goldman is now helping refine Salesforces ethical principles around the deployment and testing of generative AI and agentic tools. Her team has helped develop systems to ensure AI follows instructions, avoids toxic behavior, and stays within established ethical guidelines.Those types of tools are increasingly important as AI takes on more autonomy, she says. You want to make sure that the person thats setting up the system is able to see in advance what its going to produce.But while cloud technology has continued to evolve, Goldman says one thing has not: establishing trust with customers. Obviously, we are a business, and being commercially successful is very important, she says. Also, we know that trust is what makes that possible. Steven Melendez [Illustration: HelloVon; source photo: Lee Towndrow] Tara Feener Head of Engineering, the Browser Company You might not spend a lot of time thinking about your web browser. But the decades-old app remains an important canvas for getting things done. Thats why Tara Feener, who spent years developing creative tools at the likes of Adobe and Vimeo, joined the Browser Company. Within two years, she was head of engineering for its AI-forward Dia browser. Read profile Read Q&A Dean Ball Senior Fellow, Foundation for American Innovation In Washingtons scramble to govern artificial intelligence, few have had as much influence as Dean Ball. A former research fellow at the Mercatus Center, a libertarian think tank, Ball was the principal author of the AI Action Plan, which the White House released in July. Read More Depending on whom you ask, the document will either secure the United States lead in AI or unleash reckless proliferation.The plan focuses on accelerating innovation through deregulation, streamlining the construction of data centers, and driving the adoption of American-made AI tools abroad. It includes popular provisions like embracing open-source AI, along with divisive ones such as requiring federal agencies to work only with LLM developers whose AI models are free from top-down ideological bias and withholding AI funding from states that pass AI laws the administration deems burdensome.Even as the industry has praised the document, critics have panned it for failing to curb AIs potential harms, such as discriminatory system biases. But avoiding assumptions about AIs future is the point, says Ball, who left the White House in August and is now a fellow at the conservative Foundation for American Innovation. Washingtons really bad at forecasting how technology will develop, he says. We dont want to make those mistakes. Issie Lapowsky [Illustration: Oriana Fenwick; source photo: Waabi] Raquel Urtasun Founder and CEO, Waabi After decades of AI research, Waabi CEO Raquel Urtasun believes she has learned how to build a better self-driving truck. Urtasun began her career in academic research about 25 years ago, focusing much of it on autonomous-driving technologies such as object detection. There was a lot of innovation that needed to happen in order to enable the revolution that we see today, she says. Read More Following a stint as chief scientist at Ubers self-driving car unit, Urtasun launched Waabi in 2021 to build a verifiable, human-interpretable AI model for autonomous driving. Waabi-enabled big rigs have been on pulic roads since 2023 and are slated for driverless operation by the end of 2025. Though many autonomous truck systems are limited to highways and depots, Waabis technology is designed to carry goods all the way to their final destinations on surface streets. The company has raised more than $280 million to date.Urtasun also remains a computer science professor at the University of Toronto, where her graduate students conduct doctoral research at Waabi through a unique arrangement. Some recent research involves simulation, allowing Waabi to now let its AI practice in situations its never encountered in the physical worlda key advantage for its system.Waabis AI has shown that it can quickly react to novel conditions, even seamlessly managing its first encounter with rain, which it had never practiced for. It was kind of nerve-racking, says Urtasun, who was in that vehicle with some investors. But it was amazing to see. Steven Melendez Read Q&A [Source photo: Karrie Karahalios] Karrie Karahalios Professor, MIT Media Lab For years, the feeds on Facebook, Instagram, and TikTok have devoured our attention. Mediated by opaque algorithms, they reduce users to passive consumers of content whose likes and shares tell the platform how to keep them scrolling and viewing ads. Karrie Karahalios is well-known for her research on the fairness of these social algorithms, studying their inputs and outputs. Read More Since joining the MIT Media Lab in September, she has been expanding her research into ways of empowering individuals and communities to fight back against algorithmic overreach. This has led her to focus on contestable systems, which let human users talk back to algorithms, perhaps to contest a content moderation decision that may at first seem final. This could be through a set of preference settings to control the content of a social feed, or it might be through an AI voice or chat interface that allows a user to engage the algorithm in a plain language dialogue. If no solution is reached, the issue might be bumped up to a human moderator.As we build these systems, and they seem to be permeating our society right now, one of my big goals is not to ignore human intuition and not to have people give up agency, Karahalios says. Mark Sullivan [Illustration: HelloVon; source photo: Lisa DeNeffe] Rodrigo Liang Cofounder and CEO, SambaNova Systems Why arent more chips designed to reduce the huge amount of power used by AI data centers? Rodrigo Liang, SambaNovas cofounder and CEO, compares traditional GPUs to a cook that prepares each dish individually. SambaNovas Reconfigurable Dataflow Units (RDUs), in contrast, operate like an assembly line that processes each part of an AI request in sequence. Read More RDUs compete with traditional GPUs for AI inferencethe application of trained models to new data that happens when we use AI apps. The goal: to slash inference power requirements, while also reducing latency. Customers with strict privacy requirements can run servers with SambaNovas RDUs on site, or they can have the company manage them in the cloud. We found it hard to believe that we had to rely on an architecture that was started 25 years ago, 30 years ago, and primarily focused on graphics and gaming, Liang says.SambaNova raised $676 million at a $5.1 billion valuation in April 2021, yet challenges remain, most notably the dominance and mindshare of large players such as Nvidia. Still, Liang believes SambaNovas advantages will accrue with AIs increasing power and performance demands. All the things that weve designed natively into the product are going to become more and more important, he says. Jared Newman David Kossnick Senior Director and Head of AI Products, Figma Before David Kossnick joined Figma, he was one of the design platforms millions of users and full of ideas for improving it. In March2024, he was named to oversee the companys AI productsa key element of its growth strategy after its August 2025 IPOoffering him the chance to do more than daydream about its future. Read More The fruits of Kossnicks labor are more and more apparent. AI features now span Figmas portfolio, from its flagship Design app to the new Make vibe coding tool to features for creating slideshows, websites, and marketing assets. Given Figmas inherently multidisciplinary naturetwo-thirds of its users work in areas outside designthe technology can knock down some of creativitys traditional boundaries, he asserts: Its easier with the help of AI to reach into a lane where youre not as familiar with the details and bring the context, the intuition, the insight that you have.At the same time, the company has been careful not to mess up elements of its experiences that people liked in the first placewhich means that some of its best AI is nearly invisible, at least until users know they want it. Figma Designs canvas is kind of like the Google homepage or Facebook newsfeed, says Kossnick. A single pixel of friction literally slows down millions of people every day. Harry McCracken Read Q&A [Illustration: Oriana Fenwick] Kimberly Powell VP of Healthcare, Nvidia Bringing new drugs to market requires decade-long, multibillion-dollar journeys, with a high failure rate in the clinical trial phase. Nvidias Kimberly Powell is at the center of a major effort to apply AI to the challenge. If you look at the history of drug discovery, weve been kind of circling around the same targets for a long time, and weve largely exhausted the drugs for those targets, she says. Read profile Read Q&A Sonia Kastner Cofounder and CEO, Pano AI From mountaintop perches across 13 states, Pano AIs cameras scan the horizon, searching for wisps of smoke that humans might overlook for hours. Todays fires are spreading much more quickly, says CEO Sonia Kastner, who cofounded Pano AI in 2020. You dont have time for slow detection, slow assessment, slow buildup of resources. Read More Panos system detects wildfires in a median of 3.5 minutesrevolutionary compared with traditional 911 alert times. It triangulates fire locations within hundreds of meters and alerts multiple agencies at once.Kastners eight-person AI team has spent five years training models to spot fires and distinguish smoke from dust or clouds. Quietly, computer vision has gotten really, really good, she says. While enterprises (and more and more states) have embraced the systemthe company has secured more than $140 million in cumulative contracts and raised a $44 million funding round in Junefederal adoption remains the biggest hurdle. To that end, Kastner frequently travels to Washington to push agencies to modernize procurement. Were serving as a bridge between the technology sector and emergency managers on the front lines of these ever-worsening natural disasters, she says. Jeremy Caplan [Illustration: HelloVon] Jonathan Siddharth Cofounder and CEO, Turing In early 2023, Jonathan Siddharth foresaw the coming AI arms race. He expanded the mission of his company, Turing, a recruiting platform that matched companies with remote workers. We went from finding smart software engineers to finding smart humans in every field and building a platform that could extract that human knowledge and skills and distill it into an LLM, he says. Read More Today, Turingsupplies training data for eight of the nine companies developing the largest general-purpose AI models. The shift has also turned Turing into a quiet but central player in the artificial intelligence ecosystem, shaping what the next generation of AI systems will know. Turing is profitable and valued at roughly $2.2 billion.As models have advanced, generic data (often scraped from the web) is no longer good enough to achieve further intelligence gains. AI researchers need a regular supply of data that captures deep subject-matter expertise across domains from STEM to healthcare, Siddharth says. Were able to do that because we have two engines: the talent engine thats finding smart talent and the data generation platform that the talent works on. Mark Sullivan
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The rapid expansion of artificial intelligence and cloud services has led to a massive demand for computing power. The surge has strained data infrastructure, which requires lots of electricity to operate. A single, midsize data center here on Earth can consume enough electricity to power about 16,500 homes, with even larger facilities using as much as a small city. Over the past few years, tech leaders have increasingly advocated for space-based AI infrastructure as a way to address the power requirements of data centers. In space, sunshinewhich solar panels can convert into electricityis abundant and reliable. On November 4, 2025, Google unveiled Project Suncatcher, a bold proposal to launch an 81-satellite constellation into low Earth orbit. It plans to use the constellation to harvest sunlight to power the next generation of AI data centers in space. So instead of beaming power back to Earth, the constellation would beam data back to Earth. For example, if you asked a chatbot how to bake sourdough bread, instead of firing up a data center in Virginia to craft a response, your query would be beamed up to the constellation in space, processed by chips running purely on solar energy, and the recipe sent back down to your device. Doing so would mean leaving the substantial heat generated behind in the cold vacuum of space. As a technology entrepreneur, I applaud Googles ambitious plan. But as a space scientist, I predict that the company will soon have to reckon with a growing problem: space debris. The mathematics of disaster Space debristhe collection of defunct human-made objects in Earths orbitis already affecting space agencies, companies, and astronauts. This debris includes large pieces, such as spent rocket stages and dead satellites, as well as tiny flecks of paint and other fragments from discontinued satellites. Space debris travels at hypersonic speeds of approximately 17,500 mph in low Earth orbit. At this speed, colliding with a piece of debris the size of a blueberry would feel like being hit by a falling anvil. Satellite breakups and anti-satellite tests have created an alarming amount of debris, a crisis now exacerbated by the rapid expansion of commercial constellations such as SpaceXs Starlink. The Starlink network has more than 7,500 satellites providing global high-speed internet. The U.S. Space Force actively tracks more than 40,000 objects larger than a softball using ground-based radar and optical telescopes. However, this number represents less than 1% of the lethal objects in orbit. The majority are too small for these telescopes to identify and track reliably. In November 2025, three Chinese astronauts aboard the Tiangong space station were forced to delay their return to Earth because their capsule had been struck by a piece of space debris. Back in 2018, a similar incident on the International Space Station challenged relations between the U.S. and Russia, as Russian media speculated that a NASA astronaut may have deliberately sabotaged the station. The orbital shell Googles project targetsa sun-synchronous orbit approximately 400 miles above Earthis a prime location for uninterrupted solar energy. At this orbit, the spacecrafts solar arrays will always be in direct sunshine, where they can generate electricity to power the onboard AI payload. But for this reason, sun-synchronous orbit is also the single most congested highway in low Earth orbit, and objects in this orbit are the most likely to collide with other satellites or debris. As new objects arrive and existing objects break apart, low Earth orbit could approach Kessler syndrome. In this theory, once the number of objects in low Earth orbit exceeds a critical threshold, collisions between objects generate a cascade of new debris. Eventually, this cascade of collisions could render certain orbits entirely unusable. Implications for Project Suncatcher Project Suncatcher proposes a cluster of satellites carrying large solar panels. They would fly with a radius of just 1 kilometer, each node spaced less than 200 meters apart. To put that in perspective, imagine a racetrack roughly the size of the Daytona International Speedway, where 81 cars race at 17,500 mph while separated by gaps about the distance you need to safely brake on the highway. This ultradense formation is necessary for the satellites to transmit data to each other. The constellation splits complex AI workloads across all its 81 units, enabling them to think and process data simultaneously as a single, massive, distributed brain. Google is partnering with a space company to launch two prototypesatellites by early 2027 to validate the hardware. But in the vacuum of space, flying in formation is a constant battle against physics. While the atmosphere in low Earth orbit is incredibly thin, it is not empty. Sparse air particles create orbital drag on satellites; this force pushes against the spacecraft, slowing it down and forcing it to drop in altitude. Satellites with large surface areas have more issues with drag, as they can act like a sail catching the wind. To add to this complexity, streams of particles and magnetic fields from the sunknown as space weathercan cause the density of air particles in low Earth orbit to fluctuate in unpredictable ways. These fluctuations directly affect orbital drag. When satellites are spaced less than 200 meters apart, the margin for error evaporates. A single impact could not only destroy one satellite but also send it blasting into its neighbors, triggering a cascade that could wipe out the entire cluster and randomly scatter millions of new pieces of debris into an orbit that is already a minefield. The importance of active avoidance To prevent crashes and cascades, satellite companies could adopt a leave no trace standard, which means designing satellites that do not fragment, release debris, or endanger their neighbors, and that can be safely removed from orbit. For a constellation as dense and intricate as Suncatcher, meeting this standard might require equipping the satellites with reflexes that autonomously detect and dance through a debris field. Suncatchers current design doesnt include these active avoidance capabilities. In the first six months of 2025 alone, SpaceXs Starlink constellation performed a staggering 144,404 collision-avoidance maneuvers to dodge debris and other spacecraft. Similarly, Suncatcher would likely encounter debris larger than a grain of sand every five seconds. Todays object-tracking infrastructure is generally limited to debris larger than a softball, leaving millions of smaller debris pieces effectively invisible to satellite operators. Future constellations will need an onboard detection system that can actively spot these smaller threats and maneuver the satellite autonomously in real time. Equipping Suncatcher with active collision-avoidance capabilities would be an engineering feat. Because of the tight spacing, the constellation would need to respond as a single entity. Satellites would need to reposition in concert, similar to a synchronized flock of birds. Each satellite would need to react to the slightest shift of its neighbor. Paying rent for the orbit Technological solutions, however, can go only so far. In September 2022, the Federal Communications Commission created a rule requiring satellite operators to remove their spacecraft from orbit within five years of the missions completion. This typically involves a controlled de-orbit maneuver. Operators must now reserve enough fuel to fire the thrusters at the end of the mission to lower the satellites altitude, until atmospheric drag takes over and the spacecraft burns up in the atmosphere. However, the rule does not address the debris already in space, nor any future debris, from accidents or mishaps. To tackle these issues, some policymakers have proposed a use tax for space debris removal. A use tax or orbital-use fee would charge satellite operators a levy based on the orbital stress their constellation imposes, much like larger or heavier vehicles paying greater fees to use public roads. These funds would finance active debris-removal missions, which capture and remove the most dangerous pieces of junk. Avoiding collisions is a temporary technical fix, not a long-term solution to the space debris problem. As some companies look to space as a new home for data centers, and others continue to send satellite constellations into orbit, new policies and active debris-removal programs can help keep low Earth orbit open for business. Mojtaba Akhavan-Tafti is an associate research scientist at the University of Michigan. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Amid an uncertain economythe growth of AI, tariffs, rising costscompanies are pulling back on hiring. As layoffs increase, the labor market cools, and unemployment ticks up, were seeing fewer people quitting their jobs. The implication: Many workers will be job hugging and sitting tight in their roles through 2026. Put more pessimistically: Employees are going to feel stuck where they are for the foreseeable future. In many cases, that means staying in unsatisfying jobs. Gallups 2025 State of the Global Workforce report shows that employee engagement has fallen to 21%. And a March 2025 study of 1,000 U.S. workers by advisory and consulting firm Fractional Insights showed that 44% of employees reported feeling workplace angst, despite often showing intent to stay. So if these employees are hugging their current roles, its not an act of affection. Its often in desperation. Being a job hugger means youre feeling anxious, insecure, more likely to stay but also more likely to want to leave, says Erin Eatough, chief science officer and principal adviser at Fractional Insights, which applies organizational psychology insights to the workplace. You often see a self-protective response: Nothing to see here, Im doing a good job, Im not leaving. This performative behavior can be psychologically damaging, especially in a culture of layoffs. If I was scared of losing my job Id try everything to keep it: complimenting my boss, staying late, going to optional meetings, being a good organizational citizen, says Anthony Klotz, professor of organizational behavior at the UCL School of Management in London. But we know that when people arent loving their jobs but are still going above and beyond, that its a one-way trip to burnout. The tight squeeze In cases where jobs arent immediately under threat, the effects of hugging are more likely to be slow burning. When an employees only motivation is to collect a consistent paycheck, discretionary effort drops. Theyre less productive. Engagement takes a huge hit. Over time, that gradually chips away at their well-being. Humans want to feel useful, that they care about the work theyre doing, and that theyre investing their time well, Eatough says. When efforts are low, that can impact a persons sense of value. The effects stretch beyond the workplace, too. Frustrated and reluctant stayers can quickly end up in a vicious cycle, Klotz says, noting, When youre in a situation that feels like its sucking life out of you, you end up ruminating about how depleting it is, then end up so tired that you dont have energy for restorative activities outside of work. So its this downward spiralyou begin your workday even more depleted. Longer term, job hugging stunts growth. When youre looking out for yourself, rather than the team or organization, your investment in working relationships begins to break down, Eatough says. Over time, staying in that situation means youre more likely to become deeply cynical, which hurts the individual and their career trajectory. When hugging becomes clinging Feeling stuck is nothing new. At some point in their careers, most workers will be in a situation where if they could leave for a better role, they would, says Klotz, who predicted the Great Resignation. But what distinguishes job hugging is that its anxiously clinging to a role during unfavorable labor markets. Its not that employees dont want to quitits that they cant. Its human nature that when theres a threat of any sort that we move away from it and towards stability, Klotz says. Your job represents that stability. And currently, its not a great time to switch jobs. There are few options for job huggers. The first is speaking up and working with a manager to improve the situation. But this might be unlikely for employees who feel trapped or lack motivation in the first place. Klotz says cognitive reframing can helpfocusing purely on the positive aspects of a draining role, such as a friendly team, and tuning out the rest. Finally, slowly backing away from extra tasksin other words, quiet quittingcould mean workers can redraw work-life boundaries in the interim at least. Otherwise, beyond Stoic philosophy or a benevolent boss, there is little choice but to wait it out. In some cases, a job hugger may eventually turn it around, ease their grip, and become quietly content in their role. But more often, wanting to quit usually leads to actually quitting. In effect, job hugging is damage control: hanging on until the situation changes. I think well see some people be resilient, wait it out, and find another role, Klotz says. But therell be others in the quagmire of struggling with exhaustion of spending eight hours a day in a job they dont like.
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E-Commerce
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