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Bringing a new drug to market usually requires a decade-long, multibillion-dollar journey, with a high failure rate in the clinical trial phase. Nvidia’s Kimberly Powell is at the center of a major industry effort to apply AI to the challenge. “If you look at the history of drug discovery, we’ve been kind of circling around the same targets for a long time, and we’ve largely exhausted the drugs for those targets,” she says. A “target” is a biological molecule, often a protein, that’s causing a disease. But human biology is extraordinarily complex, and many diseases are likely caused by multiple targets. “That’s why cancer is so hard,” says Powell. “Because it’s many things going wrong in concert that actually cause cancer and cause different people to respond to cancer differently.” Nvidia, which in July became the first publicly traded company to cross $4 trillion in market capitalization, is the primary provider of the chips and infrastructure that power large AI models, both within the tech companies developing the models and the far larger number of businesses relying on them. New generative AI models are quite capable of encoding and generating words, numbers, images, and computer code. But much of the work in the healthcare space involves specialized data sets, including DNA and protein structures. The sheer number of molecule combinations is mind-bogglingly big, straining the capacity of language models. Nvidia is customizing its hardware and software to work in that world. “[W]e have to do a bunch of really intricate data science work to . . . take this method and apply it to these crazy data domains,” Powell says. “We’re going from language and words that are just short little sequences to something that’s 3 billion [characters] long.” Powell, who was recruited by Nvidia to jump-start its investment in healthcare 17 years ago, manages the company’s relationships with healthcare giants and startups, trying to translate their business and research problems into computational solutions. Among those partners are 5,000 or so startups participating in Nvidia’s Inception accelerator program. “I spend a ton of my time talking to the disrupters,” she explains. “Because they’re really thinking about what [AI computing] needs to be possible in two to three years’ time.” This profile is part of Fast Company’s AI 20 for 2025, our roundup spotlighting 20 of AI’s most innovative technologists, entrepreneurs, corporate leaders, and creative thinkers. View the full article

You might not spend a lot of time thinking about your web browser, whether it’s Safari, Chrome, or something else. But the decades-old piece of software remains a pretty important canvas for getting things done. That’s why Tara Feener, who spent years developing creative tools with companies such as Adobe, WeTransfer, and Vimeo, decided to join the Browser Company and within two years became head of engineering, overseeing its AI-forward Dia browser. “This is more ambitious than any of the other things I’ve done, because it’s where you live your life, and where you create within,” she says. Whereas a conventional browser presents you with a search box on its home screen, Dia will either answer your query with AI or route it to a traditional search based on what you write. You can also ask for information from your open tabs or have Dia intelligently sort them into groups. Several of these features have since found their way into more mainstream browsers such as Google Chrome and Microsoft Edge, and in September, Atlassian announced it had acquired the Browser Company and Dia (a $610 million deal), hoping to develop the ultimate AI browser for knowledge workers. Other AI companies are catching on to the importance of owning a browser. Perplexity has launched Comet, and OpenAI launched ChatGPT Atlas in October. This strategic value isn’t lost on Feener, who notes that browsers are typically the starting point for workers seeking information. They also provide a treasure trove of context for AI assistants. Dia can already do things like analyze your history for trends and draft messages in Gmail. Feener says her team has never felt more creative coming up with things to do next. “With Dia, we have context, we have memory, we have your cookies, so we actually own the entire layer,” she says. “Just like TikTok gets better with every swipe, every time you open something in Dia, we learn something about you.” This profile is part of Fast Company’s AI 20 for 2025, our roundup spotlighting 20 of AI’s most innovative technologists, entrepreneurs, corporate leaders, and creative thinkers. View the full article

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 Figma’s head of AI, David Kossnick—one of Fast Company’s AI 20 honorees for 2025—about what the company has accomplished so far and where he’s 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 Company’s AI 20 for 2025, our roundup spotlighting 20 of AI’s 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: “There’s 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 them—what’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 Design’s 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 don’t bother people? But on the flip side, there’s a lot of new workflows and new tools. People—especially our type of customers—are 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 it’s 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. It’s 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 to make 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. You’re 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. View the full article

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 company’s North America Stores division, and they’re here at this Hyatt resort on a crisp September morning to brainstorm new ways to power Amazon’s retail experiences. Passing the hotel lobby’s IMAX-like mountain views, they filter into windowless meeting rooms. Down the hall, the off-site’s keynote speaker—Byron Cook, vice president and distinguished scientist at Amazon—slips 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. He’s 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, doesn’t look much like a scientist, distinguished or otherwise, and certainly not like a VP—more like a nerdy roadie. “They don’t 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 he’s done many times before, Cook is here to get the highly technical people in that room to become believers. He’s championing an approach to AI that isn’t powered by gigawatt data centers stuffed with GPUs, but by principles old enough to be written on papyrus—and one that’s already positioning Amazon as a leader in the tech industry’s quest to solve the problem of hallucinations. Cook doesn’t have a pretalk ritual, no need to get in character. He’s 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 laggard—the 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 OpenAI’s chatbot changed the definition—and expectations—of 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 embrace—or risk irrelevance. Less than six months after ChatGPT’s 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 AWS’s 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 Nova—they are general-purpose AI systems, akin to Google’s Gemini or OpenAI’s 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 public’s 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 AI’s 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 backdrop—“the summer AI turned ugly,” as Deutsche Bank analysts called it—that 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 Cook’s 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 world’s 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 ain’t. Still, Swami Sivasubramanian, one of Amazon’s highest-ranking AI executives, who serves on Jassy’s “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 reasoning’s promise of quashing AI misbehavior with math has quietly become an essential part of Amazon’s strategy around “agents”—those 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 this 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.” That’s not a prophecy Amazon can afford to let come true—not 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 rails—in fact, as OpenAI itself recently admitted following an internal study, they can’t not. What Cook helped Amazon realize, just months after ChatGPT’s 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 he’d 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 won’t 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 approach—known as “neuro-symbolic AI”—can 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, it’s worth sketching out how it’s different from the kind of AI you’ve 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 certainty—not 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 work—and have been doing so for decades. Since 1994, when a flaw in Intel’s 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 Paris’s first self-driving Métro train in 1998. In 2004, NASA even used it to control the Spirit and Opportunity rovers on Mars. There’s a catch, of course: Because automated reasoning can only reduce problems to three possible outcomes—yes, no, or the equivalent of “does not compute”—finding 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 works—collapsing 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. That’s 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? Where’s 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, Byron’s 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 Cook’s group fleshed out plans and racked up small but significant wins—like catching a vulnerability in AWS’s Key Management Service, the cryptographic holy of holies that controls how clients safeguard their data—skeptics started becoming evangelists. “Some of these [were] beautiful bugs—they’d 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, Amazon’s 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 clients—heavily regulated financial service firms, like Goldman Sachs and the global hedge fund Bridgewater Associates, with sensitive data and strict compliance requirements—found automated reasoning’s 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 they’d become accustomed to along the 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 logic—the special language of automated reasoning. Step two: Translate any responses generated by the bot too. Step three: Calculate. If there’s 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, it’ll 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, PwC’s global and U.S. commercial technology and innovation officer. PwC relies on solutions like AWS’s automated reasoning tool to check the accuracy of the outputs of its generative AI tools—including agents. But Wood sees the technology’s appeal spreading beyond finance and other regulation-heavy industries. “Look at what it took to set up a website 25 years ago—that was a refined set of skills. Today, you go on Squarespace, click a button, and it’s 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, you’ll 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, Amazon’s 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 that’s peeking out from behind the curtain is Auto, an agent built into Kiro, Amazon’s new AI programming tool, that will use formal logic to help make sure bot-written code matches humans’ intended specifications. But Sivasubramanian, AWS’s vice president for agentic AI (and Cook’s boss), isn’t 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 it’s a domain that’s 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 bots—empowered to do anything from execute transactions to rewrite software—can turn hallucination from tolerable quirk into Russian roulette. It’s a matter of scale: When one vibe coding VC unleashes an agent that accidentally nukes his own app’s database, as happened earlier this year to SaaS investor Jason Lemkin, it’s 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, there’s no Undo button. Enterprise software is full of these potential pitfalls, and existing methods for reducing hallucination aren’t always strong enough to keep agents from blundering into them. That’s 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 rockets”—his metaphor for irreversible actions—“will it launch rockets when we’re 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 agent—do’s and don’ts, written in natural language—into a flowchart-like visualization of how the agent can and cannot behave, all backed by mathematical proof. There’s 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 Amazon’s logic-backed take on AI extending into coordinating the agents themselves. “If you’ve 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 important—which is where I think automated reasoning will play a role.” The ability to reliably orchestrate the actions of AI—not just single agents, but entangled legions of them, at scale—is 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 that’s 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 Amazon’s techniques for using LLMs to convert natural language into formal logic. It’s part of a strategy that could help turn Amazon’s 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 Amazon’s Seattle headquarters. Sitting with his legs tucked catlike beneath him, Cook mused about his own vision for the future of automated reasoning—one that extends far beyond Amazon’s ambitions for enterprise-grade AI. “The world,” he says, “is filled with socio-technical systems”—patchworks 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 it’s 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 that’s the start of a new era.” This story is part of Fast Company’s AI 20 for 2025, our roundup spotlighting 20 of AI’s most innovative technologists, entrepreneurs, corporate leaders, and creative thinkers. View the full article

The biggest story in tech is AI’s increasing capacity to take on tasks once reserved for human beings. But the agents driving that change aren’t machines. They’re humans—inventive, ambitious, enterprising ones. Our third annual roundup of some of the field’s 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 tomorrow’s AI models to speeding drug discovery to reimagining everyday productivity tools. Household names they’re not. Yet, they’re already changing our world, with much more to come. 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 OpenAI’s API. And so the company turned to post-training research lead Michelle Pokrass. Read profile HelloVon 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 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 filmmaker’s creative vision by creating ambitious scenes or effects too pricey to shoot, says Naeem Talukdar, CEO of the video-generation model developer Moonvalley. “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 Hollywood’s 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 generate 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 haven’t seen before.” —Mark Sullivan Oriana Fenwick Koray Kavukcuoglu Chief AI Architect, Google For years, Google has employed many of AI’s 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 company’s first chief AI architect. 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 Kavukcuoglu’s new role would bring “more seamless integration, faster iteration, and greater efficiency” to Google’s lab-to-market pipeline. Hundreds of staffers working to apply Google’s Gemini large language model to transform its search engine are now part of his team, The Information reported. He’s also involved with everything from data center strategy to bolstering the Google Cloud web services platform. Kavukcuoglu’s 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, Kavukcuoglu’s deep familiarity with its technical stack should be an asset. “There’s a long history of research that built up to this point,” he told Big Technology’s Alex Kantrowitz last May. —Harry McCracken 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 HelloVon Byron Cook VP and Distinguished Scientist, Amazon Hallucinations are baked into the way generative AI works, but that doesn’t mean we have to live with them. Byron Cook—a vice president and distinguished scientist at Amazon Web Services—realized that an alternative AI technology called “automated reasoning” could be the perfect way to keep chatbots’ confabulations in check. 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 don’t trust them—yet. “First of all, this [agent] could lie to me,” explains Cook. “But secondly, if I let it launch rockets”—his metaphor for irreversible actions—“will it launch rockets when we’re not supposed to?” Amazon is betting that automated reasoning, and Cook, can keep agents on a leash. —John Pavlus Read Feature Article 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 doctor–patient 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. 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 hospital’s 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, I’m not compensated for the care that I deliver—I’m compensated for the care that I documented that I deliver,” Rao says. “So we are extending the documentation to help with billing.” —Yasmin Gagne Oriana Fenwick 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 Anthropic’s first in-house AI welfare researcher. Read profile 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 “There’s this growing sense that both digital technology and AI are happening to people, they’re 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. It’s working on an AI-assisted software development tool called Sculptor, which became open to public preview in late September. “What we’re 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 Paula Goldman Chief Ethical and Humane Use Officer, Salesforce Before Paula Goldman became Salesforce’s 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 set 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 Omidyar’s impact investment firm, where she evaluated the social consequences of emerging technology. Goldman is now helping refine Salesforce’s 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 that’s setting up the system is able to see in advance what it’s 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 HelloVon 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. That’s 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 Washington’s 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. 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 AI’s potential harms, such as discriminatory system biases. But avoiding assumptions about AI’s 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. “Washington’s really bad at forecasting how technology will develop,” he says. “We don’t want to make those mistakes.” —Issie Lapowsky Oriana Fenwick 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 Uber’s 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 public 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, Waabi’s 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 it’s never encountered in the physical world—a key advantage for its system. Waabi’s 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 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. 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 HelloVon Rodrigo Liang Cofounder and CEO, SambaNova Systems Why aren’t more chips designed to reduce the huge amount of power used by AI data centers? Rodrigo Liang, SambaNova’s cofounder and CEO, compares traditional GPUs to a cook that prepares each dish individually. SambaNova’s 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 inference—the 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 SambaNova’s 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 SambaNova’s advantages will accrue with AI’s increasing power and performance demands. “All the things that we’ve 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 platform’s millions of users and full of ideas for improving it. In March 2024, he was named to oversee the company’s AI products—a key element of its growth strategy after its August 2025 IPO—offering him the chance to do more than daydream about its future. Read More The fruits of Kossnick’s labor are more and more apparent. AI features now span Figma’s portfolio, from its flagship Design app to the new Make vibe coding tool to features for creating slideshows, websites, and marketing assets. Given Figma’s inherently multidisciplinary nature—two-thirds of its users work in areas outside design—the technology can knock down some of creativity’s traditional boundaries, he asserts: “It’s easier with the help of AI to reach into a lane where you’re 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 place—which means that some of its best AI is nearly invisible, at least until users know they want it. “Figma Design’s 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 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. Nvidia’s 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, we’ve been kind of circling around the same targets for a long time, and we’ve 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 AI’s cameras scan the horizon, searching for wisps of smoke that humans might overlook for hours. “Today’s fires are spreading much more quickly,” says CEO Sonia Kastner, who cofounded Pano AI in 2020. “You don’t have time for slow detection, slow assessment, slow buildup of resources.” Read More Pano’s system detects wildfires in a median of 3.5 minutes—revolutionary compared with traditional 911 alert times. It triangulates fire locations within hundreds of meters and alerts multiple agencies at once. Kastner’s 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 system—the company has secured more than $140 million in cumulative contracts and raised a $44 million funding round in June—federal adoption remains the biggest hurdle. To that end, Kastner frequently travels to Washington to push agencies to modernize procurement. “We’re 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 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, Turing supplies 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. “We’re able to do that because we have two engines: the talent engine that’s finding smart talent and the data generation platform that the talent works on.” —Mark Sullivan View the full article

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, sunshine—which solar panels can convert into electricity—is 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 Google’s 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 debris—the collection of defunct human-made objects in Earth’s orbit—is 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 SpaceX’s 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 Google’s project targets—a sun-synchronous orbit approximately 400 miles above Earth—is a prime location for uninterrupted solar energy. At this orbit, the spacecraft’s 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 prototype satellites 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 sun—known as space weather—can 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. Suncatcher’s current design doesn’t include these active avoidance capabilities. In the first six months of 2025 alone, SpaceX’s 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. Today’s 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 mission’s 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 satellite’s 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. View the full article

Amid an uncertain economy—the growth of AI, tariffs, rising costs—companies are pulling back on hiring. As layoffs increase, the labor market cools, and unemployment ticks up, we’re 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. Gallup’s 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, it’s not an act of affection. It’s often in desperation. “Being a job hugger means you’re 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, I’m doing a good job, I’m not leaving.’” This performative behavior can be psychologically damaging, especially in a culture of layoffs. “If I was scared of losing my job I’d 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 aren’t loving their jobs but are still going above and beyond, that it’s a one-way trip to burnout.” The tight squeeze In cases where jobs aren’t immediately under threat, the effects of hugging are more likely to be slow burning. When an employee’s only motivation is to collect a consistent paycheck, discretionary effort drops. They’re 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 they’re doing, and that they’re investing their time well,” Eatough says. “When efforts are low, that can impact a person’s 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 you’re in a situation that feels like it’s sucking life out of you, you end up ruminating about how depleting it is, then end up so tired that you don’t have energy for restorative activities outside of work. So it’s this downward spiral—you begin your workday even more depleted.” Longer term, job hugging stunts growth. “When you’re 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 you’re 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 it’s anxiously clinging to a role during unfavorable labor markets. It’s not that employees don’t want to quit—it’s that they can’t. “It’s human nature that when there’s a threat of any sort that we move away from it and towards stability,” Klotz says. “Your job represents that stability. And currently, it’s 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 help—focusing 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 tasks—in other words, quiet quitting—could 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 we’ll see some people be resilient, wait it out, and find another role,” Klotz says. “But there’ll be others in the quagmire of struggling with exhaustion of spending eight hours a day in a job they don’t like.” View the full article

Charge on cash in stocks-and-shares Isas ‘essentially a tax’, Michael Summersgill says View the full article

Unlike millennials who embraced hustle culture and burned out, Gen Zers have a new concept of what ‘making it’ looks like in today’s workplace—and it doesn’t involve a fancy title. View the full article

Endings are tricky: You want closure and to go out with a bang—which is a hard balance. It’s natural to want the end of the year to be meaningful. Even the moon appears to agree with this sentiment, and it’s about to prove it. The final full moon of 2025, which is also called the cold moon, will be a bright supermoon occurring on December 4. Before we get into how best to moon-gaze, let’s break down what that all means, and do a year-end moon review. Why is December’s full moon called the ‘cold moon’? Human beings assign names even to celestial happenings. The Old Farmer’s Almanac compiled the most commonly used monikers, based on Old English and Native American sources. December’s moon is called the cold moon because of the chilly winter temperatures. According to EarthSky, it is also known as Moon Before Yule or the Long Night Moon. What is a supermoon? The moon orbits Earth in an elliptical pattern, which means the orb has differing proximity to the planet. When the full moon lines up with the closer approach to Earth, known as perigee, a supermoon occurs. The moon appears brighter and fuller because it is physically closer to Earth. What makes this supermoon special? December’s supermoon offering is the finale of three consecutive supermoons, which also occurred in October and November this year. Because the orb will mirror the sun, December’s supermoon will also be the highest-hanging full moon of 2025. What is the moon’s 2025 recap? There were 12 full moons in 2025. (Sometimes, because of the lunar year length, there are 13, such as in 2023.) 2025’s dozen included three supermoons, two total lunar eclipses—and a partridge in a pear tree. (Well, the scientific nature of the latter is questionable . . . but ’tis the season.) How best to view the December supermoon The most dramatic time to view the supermoon is just after moonrise, because of the “moon illusion.” This phenomenon, which is when the moon appears larger when near the horizon, can’t be fully explained by science. This optical illusion of sorts, combined with the fact that the supermoon appears brighter and bigger, makes for one spectacular nighttime view. Since viewing times vary by location, use this moonrise tool to best plan your moon-gazing experience. If you miss tonight, never fear. The moon will reach its peak on December 4 at 6:14 p.m. ET, but it will appear full for a couple of days, so you have wiggle room that allows for more moon-gazing opportunities. View the full article

Nigel Farage’s party attracts far more funding than both Labour and the ConservativesView the full article

It’s a tale as old as the modern workplace: In the 1960s, women entered the workforce en masse, ready to compete with their male counterparts for promotions, pay, and opportunity—only to find the system wasn’t built for them. Today, women comprise almost half of the U.S. labor force. The playing field looks different now, but the fight for equal access hasn’t gone away. It just moved into subtler territory. Companies make quiet calculations about who’s worth “investing in,” says Corinne Low, gender economist and associate business professor at the University of Pennsylvania’s Wharton School of Business. Women often face career penalties in anticipation of motherhood as employers presume they’re more likely to take leave or step back. Once in their 40s, “past” childbearing, this bias fades. But not before it’s done damage. The cost of inaction is huge: 4 out of 10 mothers in the first five years after childbirth resign. In 2025, around 400,000 mothers with young children resigned from the U.S. workforce—the sharpest decline in more than 40 years. Mothers face a training penalty that hinders their career advancement On average, data shows women working full-time only earn 83% of a man’s median annual salary. Mothers face even worse odds—their pay is often reduced by 3% for every child they have. A new study from the University of Connecticut finds that, one to three years after childbirth, women are 17% to 22% less likely to receive on-the-job training opportunities, such as seminars, workshops, and development programs, compared with a 3% to 8% decline for men who became fathers. The result is a hidden skills and promotion gap that may explain nearly a third of the motherhood wage penalty. When women have children, they’re viewed as less committed or competent, research shows—a bias that leads employers to assume mothers are too busy, distracted, or disinterested to participate in training opportunities. This is called “benevolent prescriptive stereotyping,” and it doesn’t do mothers any favors, says Joan C. Williams, distinguished professor of law emerita and founding director of the Equality Action Center at UC Law San Francisco. As Williams points out: “If you don’t get work, you eventually either get laid off because you’re not progressing, or you leave because you’re disgusted that you don’t get good work. Or you just stall out.” If a mother turns down an opportunity for training or advancement, it’s important to circle back—not to assume it’s a permanent no, says Williams. She also recommends employers keep track of who receives opportunities in their workplace—and who doesn’t. Supporting mothers isn’t a charity case Another opportunity mothers are often left out of is informal networking, like happy hours, dinners, or travel, says Kate Westlund Tovsen, founder of Society of Working Moms, a supportive community for and by working mothers. Even if a mother can’t attend, “It’s nice to be invited,” Tovsen adds, who suggests teams try daytime coffee hours as a caregiver-friendly option. Mothers are forced to be proactive, as many companies lack frameworks to support leave or reintegration, Williams cautions. She advises scheduling meetings with superiors before and after taking family leave to make a plan. And though being a new mother is a relatively short blip on a woman’s career, companies often make “permanent decisions in terms of who they’re investing in based on this kind of temporary period when women are most squeezed,” says Low. Supporting mothers is not a charity case, she argues, but a competitive edge that lets them retain talent long term. “Caregiver strategies and investments, including benefits and return-to-work programs, deliver measurable business returns,” states Jess Ringgenberg, professional certified coach and CEO of Elxir, an advisory firm focusing on caregivers in the workplace. “Companies see three to six times ROI through higher retention, productivity, and lower absenteeism” with such programs, Ringgenberg says. Replacing a mid-level caregiver comes with backfill, training, and ramp-up expenses that can reach $200,000, says Ringgenberg, or totaling twice the employee’s annual salary. But some companies are already working hard to help mothers succeed—and it’s paying off. Small and large companies finding solutions Frontier Co-op, an Iowa-based wholesaler of natural and organic products with around 580 employees, created the Breaking Down Barriers to Employment initiative, which includes an on-site childcare center, subsidized to $120 per week per child. Their childcare program enables parents to participate in training programs and developmental opportunities that might otherwise be missed, explains Megan Schulte, vice president of human resources. She says 100% of new parents returned to work after their parental leave. While Frontier Co-op eases the logistical strains of childcare, Brigade Events, a woman-owned and operated event strategy and management company in Dallas with 10 full-time employees, tackles rebuilding confidence and access for women who stepped out. The company views its mentorship and project-based work model as a form of retraining, recognizing women’s existing expertise, rather than resetting them to zero. Senior employees work on a hybrid schedule—three days from home, two in-office—to preserve collaboration while creating space for caregiving. Brigade doesn’t bat an eye at blocked calendars for a child’s doctor appointment or school event. “Our whole culture is giving grace to each other,” says April Zorsky, partner and chief creative officer. One of their policies is that mothers returning from their 16-week maternity leave take a “transition month” working at 50% capacity. This can mean working from home, setting their own schedules, and easing back in without penalty. “As moms, we feel it’s crucial to have flexibility,” says Zorksy. Larger companies can learn to be more flexible and collaborative, too, says Marissa Andrade, a veteran HR executive and former chief people officer at Chipotle. She recalls when one of her field managers chose to take a six-month maternity leave during a period of company-wide turnaround. Before she left, she requested an interim hire from the Mom Project, a digital platform that helps companies to hire skilled mothers, to support her leave. It went so smoothly that the field manager was able to reenter without missing a beat. Andrada recommends establishing employee–business resource groups. At Chipotle, one employee-created group, “The Hustle” (Humans United to Support the Ladies Experience), formed a maternity program to keep employees in the loop while on leave, and reoriented them on compliance and training updates on their return. “Don’t overlook the power of your employees as your consumer,” says Andrada. When companies invite access for mothers—to training, to support, to opportunities that just don’t reacclimate them to their roles, but get them to thrive in them—everyone wins. Mothers aren’t just reentering the workforce with confidence. Employers are retaining their talent, too. View the full article

Companies are increasingly using AI to conduct job interviews, and, according to experts in the field, the technology is leading to some impressive results. However, giving candidates the choice between an AI interviewer or a human can create bias that makes landing a job tougher for some people, according to a new report. AI is now a common part of the job application process. According to the World Economic Forum, around 88% of employers use some form of AI for initial candidate screening such as filtering or ranking job applications. But AI is also being used to conduct interviews. Currently, around 21% of U.S. companies use the technology for initial interviews. AI interviewers can give companies an edge when during the hiring process. One study found that candidates who were interviewed by an AI were more likely to land a job than candidates who were sourced by humans screening résumés: 54% of candidates interviewed by AI got the job, compared to about 29% of candidates sourced by a traditional résumé screening. Still, there is a lot to learn about how utilizing AI interviews impacts both people and firms. Brian Jabarian, a researcher at the University of Chicago Booth School of Business with doctorates in economics and philosophy, recently examined what happens to candidates when they are offered a choice between an AI interviewer and a human interviewer, which he detailed in his paper, Choice as Signal: Designing AI Adoption in Labor Market Screening. The research, which has not been peer reviewed, finds giving candidates a choice between a human and AI interview could also create a new hurdle for low-ability candidates—applicants whose skills are below the firm’s hiring threshold. Jabarian tells Fast Company that different applicants will automatically be drawn to either AI interviewers or human interviewers based on their strengths. For example, “applicants with strong language skills prefer human interviewers to highlight their English proficiency,” he says. “In contrast, applicants with strong analytical skills choose the AI interviewer to showcase their quantitative strengths.” But the choice isn’t neutral, like a candidate may expect it to be. “An applicant’s decision to be interviewed by a human or an AI agent can reveal private information about their strengths, weaknesses, or expectations for relative performance,” Jabarian writes in his paper, also pointing out that employees with high abilities benefit because companies can identify them more easily “using both the signal and the selection decision, increasing their probability of being hired.” However, it also means firms are able to more easily identify low-ability workers. Jabarian writes: “Consequently, low-skilled workers succeed less often in obtaining a job and therefore experience a welfare loss.” Essentially, by interpreting both the choice itself as well as the information from the interview, an employer’s precision increases, which doesn’t serve lower-ability candidates. Jabarian says if firms had no insight into the candidate’s choice, then all workers would have the advantage of choosing which interviewer best shows their skill set, but companies would lose out on the advantages of using AI interviewers. While on the surface giving job candidates choices about how they are interviewed seems like a solid idea, Jabarian says that in practice, it’s not quite so simple. “Before this new paper, I was really rooting for giving this choice to people because I was confused about why everyone was assuming it was just okay to impose a new technology on people in a high-stakes environment when they maybe didn’t want it,” he explains. However, now he believes it’s clear that the choice alone hurts the weakest candidates, and therefore it shouldn’t be one that is routinely offered but rather “on a case-by-case basis.” Jabarian says he expects AI interviewing to increase, particularly because it’s good for firms. Still, that doesn’t mean humans as interviewers are a thing of the past or irrelevant. AI interviewers and humans have different strengths: Human recruiters can improvise and are able to vary their interviews, while AI creates a consistent experience and is excellent at garnering information from candidates. That means adopting hybrid techniques—where humans and AI run interviews with opposing purposes—might really be the smartest and fairest way to hire. View the full article

In today’s job market, many employees are feeling the pressure. Layoffs continue to make headlines, hiring pipelines have slowed, budgets have tightened, and job seekers are facing fierce competition. For those already employed, this environment raises a tricky question: What’s reasonable to ask for at work right now—and what isn’t? There’s always the standard wish list: promotions, raises, more flexibility, and better benefits. But in a strained economy, some of these asks may be harder to land—and for many employees, even harder to ask for. Zety, a career platform designed to make job searching easier with expert-backed tools and advice, found in its latest Pay on Pause Report three in five workers are willing to forgo or accept smaller raises this year due to fears of layoffs and job instability, and 66% avoided asking for a raise altogether, citing economic pressures and uncertainty. In a job market this unpredictable, where many employees are job hugging out of fear—one question remains: should employees hold off on asking, or should conversations still be happening? There’s fear in asking According to career expert Jasmine Escalera, many employees are hesitant to ask for anything right now. The thought process is: “I should just be grateful to have a job,” or, “I don’t want to ask for more and rock the boat, especially if AI is coming in,” she explains. Maybe even, “I don’t want to disrupt what I already have, because I don’t want to then be out in that job market and not even know when’s the next time I’m potentially going to get a position,” Escalera says. In today’s job market, employees are often hesitant to speak up, hoping staying quiet will help them maintain their positions—especially since certain requests, like pay raises, are harder to secure. Pay increases and promotions may be harder to secure It is true. Certain requests are more difficult in today’s job market, Escalera explains, and pay raises are one of them. “If layoffs and budget cuts are happening, one of the first things that are going to go is pay increases,” she says. This also includes bonuses, or any other type of financial component. “Anything that goes into the budget could potentially go wrong, which is not good for individuals who are in positions where they need to be upskilling. Or they need to be learning more to complement AI, or even potentially just for specific career growth opportunities,” she says. Promotions also face limitations. As Escalera explains, “Promotions typically come with raises and professional development [or] upskilling opportunities—those are going to be things that potentially go away. Still, it doesn’t mean employees should shy away from asking, or from putting their requests on their managers radar. Open the conversation A company may not be able to provide pay raises or promotions during a downturn, but that doesn’t mean the conversation can’t happen. “Even if your company comes out and says, ‘we don’t have the financial capacity to give pay raises right now’, or ‘we don’t have the financial capacity to give bonuses right now’—that does not mean you do not have the conversation,” Escalera says. The key is approaching the discussion thoughtfully, focusing on your contributions and the value you bring. You might say, “I understand that the organization is in financial hardship, or may not be giving bonuses or pay raises at this moment, but I really want to open up the conversation around my work’” Escalera suggests. Carolyn Troyan, CEO of Leadership360, an HR consulting and leadership coaching firm, agrees it’s important to be thoughtful with your approach. “It’s doing it in an emotionally intelligent way,” she says. “After half your team has been laid off, demanding a raise is probably not such a good idea.” But after the dust has settled and the company is back on steady footing, it’s reasonable to bring it up—or even during your next performance review, if the timing feels right. When having that conversation, acknowledge the environment and what the team has been through—but don’t let that stop you from discussing your growth with your manager. “Just because a company is struggling doesn’t mean you don’t have a career plan,” Troyan says. To your manager, you might say, “Here’s what I want to do over the next two to three years, I’d love to kind of talk about that with you. What opportunities do you see available, even in this environment, for me to learn some of these new skills?” Commonly, you’re going to hear one of two responses, Troyan explains: “We really love you, but we can’t do it right now,” which comes up a lot. Or, you may receive feedback highlighting what you need to work on to reach a promotion or raise in the future. Either way, you’re still having the conversation. Support and flexibility Even if a company can’t provide a promotion or raise due to financial hardship, there are other things to ask for. One of the biggest asks right now is support—support that isn’t monetary, Escalera says, pointing to the same report: Mental health support tops the list. “What that really shows is that individuals are incredibly burnt out and stressed out,” she said. As a result, we’re seeing more requests for mental health days and other forms of support. If a company isn’t meeting requests for pay, flexibility, or other forms of support, it may be a signal for employees to reassess their options. Even in uncertain times, understanding your value, approaching conversations thoughtfully, and asking for the support you need are all things you don’t have to shy away from. View the full article

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Creating animated videos can seem intimidating, but it’s a manageable process when broken down into clear steps. First, you’ll want to plan your video by outlining your core message and writing a compelling script. Next, storyboarding allows you to visualize each scene, followed by choosing animation software that fits your skill level. Each of these steps is essential for developing a cohesive final product, and grasping them will set you up for success in your animation project. What comes next is equally important. Key Takeaways Start with a clear script and storyboard to outline your animated video’s core message and visual flow. Choose appropriate animation software based on your skill level and project needs, such as Vyond for beginners or Adobe After Effects for advanced users. Design distinct characters and backgrounds that enhance storytelling and maintain visual coherence throughout the animation. Select a harmonious color palette and ensure audio elements are balanced, syncing voiceovers with animations for a polished final product. Test your animation with feedback from screenings, refining the script and visuals to better engage your target audience. How to Plan Your Animated Video When you plan your animated video, it’s vital to start with a clear outline or script, as this will help you define your core message and structure. Begin by developing storyboards to visually break down each scene, which aids in planning camera angles and shifts that keep viewers engaged. Next, create an animatic using your storyboards and placeholder audio to assess timing and pacing before full production. Draft your voiceover narration early to clarify complex topics and streamline editing. Finally, utilize feedback from test screenings to refine your storyboards and script, ensuring your animated video effectively communicates its message. This structured approach is fundamental when learning how to create animated videos, how to create animations for YouTube, or how to create a cartoon. Writing an Engaging Script When you’re writing an engaging script, it’s essential to know your audience and outline your key messages clearly. Comprehending who you’re speaking to helps you tailor your content effectively, ensuring it resonates with viewers. Know Your Audience How do you guarantee your animated video script resonates with your audience? First, you need to know your audience. Start with comprehending your audience’s demographics, interests, and preferences. This insight allows you to tailor your script effectively. Conducting surveys or researching audience behavior can help identify the topics and tones that will engage them. Use relatable language and examples to make your script accessible. Structuring your script with a clear beginning, middle, and end assures your audience can easily follow your message. Moreover, include a strong call to action at the end to encourage interaction. By utilizing an animated video service, you can learn how to create simple animation that reflects your audience’s values and needs. Outline Key Messages To create an engaging animated video script, it’s significant to outline your key messages effectively. Start by identifying your core message to align with your audience’s interests and the video’s purpose. Next, create a logical outline that organizes your points for clear progression. Here are four vital steps to take into account: Define the main takeaway for your viewers. Break down your core message into digestible sub-points. Write in a conversational tone, using simple language and relatable examples. Incorporate visual cues to guide animation decisions, indicating moments for specific visuals. Storyboarding Your Animation Storyboarding your animation is crucial for visualizing your narrative and ensuring that each scene flows smoothly. By breaking down your script into individual frames, you can clearly outline character positions, backgrounds, and actions, which helps with timing and pacing. This structured approach not just streamlines the production process but also serves as a valuable communication tool among team members, minimizing misunderstandings before you start animating. Importance of Storyboarding As you begin an animation project, having a well-structured storyboard is crucial for success. Storyboarding helps you break down your script into individual scenes, enhancing clarity and flow in your animated corporate video. It allows you to visualize key elements, ensuring your audience remains engaged. Here are four key benefits of storyboarding: Visualizes camera angles, character movements, and shifts. Aids in creating an animatic to assess timing and pacing. Serves as a communication tool among the production team. Saves time and resources by identifying potential issues early. Using animation software or Adobe Animate video animation programs effectively can streamline this process, aligning your creative vision and ensuring a smoother production experience. Visualizing Your Narrative Creating a storyboard serves as a fundamental step in visualizing your narrative for animation projects. This visual representation breaks down your script into individual scenes, clarifying the sequence of events and actions. Each panel should include key elements such as character positions, backgrounds, and important dialogue to guarantee a clear vision. You can improve storytelling by utilizing different shot types and camera angles. Regularly revisiting and refining your storyboard throughout the animation process will help address any inconsistencies. Scene Action Key Dialogue 1 Introduce character “Hi, I’m Alex!” 2 Character moves “Let’s go on an adventure!” 3 Conflict arises “What was that noise?” 4 Climax moment “We must face our fears!” 5 Resolution “We did it together!” This process is crucial for anyone looking to create an animated character or understand how to create cartoon animation, whether you’re working with an Animaker video creation company or on your own. Timing and Flow When planning your animation, grasping the timing and flow of your scenes is crucial for maintaining audience engagement. Effective storyboarding helps visualize the sequence of events, ensuring a structured approach. Consider these key elements: Key Frames: Illustrate major actions and changes to keep viewers invested. Camera Angles: Plan various perspectives to improve storytelling. Transitions: Smoothly connect scenes for a cohesive experience. Animatic: Combine storyboards with rough timing and audio to assess pacing before production. During storyboarding, pay attention to the timing of voiceovers and sound effects. This alignment enriches the narrative and addresses pacing issues early on, streamlining your editing process and resulting in a polished final animation. Choosing the Right Animation Software Choosing the right animation software can greatly impact your project’s success, especially since various options cater to different skill levels and animation styles. If you’re a beginner, user-friendly platforms like Vyond and Animaker might be your best animation software free choices. For advanced users, tools such as Toon Boom Harmony and Adobe After Effects offer more robust features. Consider the type of animation you want; for instance, Blender thrives in 3D animations, whereas Pencil2D is great for 2D projects. Assess specific features you need, like keyframe capabilities or vector tools. Additionally, check for free animation software options and trial periods before committing, ensuring compatibility with various file formats for easy sharing on platforms like YouTube. Designing Characters and Backgrounds When designing characters and backgrounds for your animated videos, start by defining your character’s personality and traits, as these elements guide their visual identity. You can use sketching software or traditional techniques to create various iterations, focusing on poses and expressions that best represent their characteristics. For backgrounds, consider the setting and select color palettes and styles that not just complement your characters but additionally improve the overall mood of the scene. Character Design Principles In character design, it’s essential to start by defining a character’s personality and role within the story, as these elements greatly influence their visual traits. To create compelling characters, consider these character design principles: Facial Expressions: Tailor expressions to reflect emotions aligned with the character’s personality. Color Theory: Use warm colors for friendly characters and cool colors for aloof ones to evoke the desired emotional response. Silhouette and Shape: Employ distinctive shapes—round for friendly and angular for antagonistic—to visually convey character traits. Character Turnarounds: Develop a style guide that includes character turnarounds to guarantee consistency across animations. Background Creation Techniques Creating effective backgrounds is crucial for improving the storytelling and visual appeal of animated videos. Start by sketching character concepts to establish traits that resonate with your audience. Utilize animation software, like Adobe Illustrator, to create scalable designs. When designing backgrounds, make sure they complement the story’s theme, using styles that elevate the mood. Layered backgrounds are particularly useful; they allow for parallax scrolling effects, adding depth during animation. Consider whether you’re working in 2D or 3D, as this affects the complexity of your designs. Color Palette Selection Selecting a color palette is a fundamental step in designing characters and backgrounds for animated videos. A well-chosen palette improves visual cohesion and engages the audience. Here’s how to approach color palette selection: Choose 3-5 harmonious colors to create a balanced look that isn’t overwhelming. Apply color theory principles, using complementary or analogous colors to evoke emotions; for example, blue for calmness and red for excitement. Ensure contrast between characters and backgrounds so your characters stand out clearly. Utilize tools like Adobe Color or Coolors to generate palettes, ensuring consistency throughout your project. With these tips, you’ll improve your animation download and learn how to create an animation using free animation software easy enough for anyone. Techniques for Animation Animation techniques vary widely, each offering unique methods to bring visuals to life. 2D Animation Techniques focus on creating characters and settings in a flat space, often using software like Adobe Animate or Toon Boom Harmony. For a more immersive experience, you can explore 3D Animation Techniques, which utilize software such as Autodesk Maya or Blender to model characters in three-dimensional space, enhancing realism. Motion Graphics combines graphic design with animation, using tools like After Effects to craft dynamic visuals that improve storytelling. Furthermore, Rotoscoping allows you to trace over footage, frame by frame, to achieve realistic animations, often blending live-action with animated elements. Each technique serves distinct purposes, so choose based on your project’s needs. Adding Sound and Voiceovers Once you’ve established your animation through various techniques, adding sound and voiceovers becomes a crucial step in enhancing the final product. Follow these steps to effectively incorporate audio: Import your audio files into Premiere Pro and organize them in the Fundamental Sound panel for easy management. Use the audio track mixer to adjust volume levels, ensuring your voiceovers and sound effects are balanced and clear. Carefully sync your voiceover with the animated visuals by aligning audio clips on the timeline, using waveforms for precise timing. Incorporate sound effects strategically to engage viewers, making sure they complement your animation without overshadowing the voiceover narrative. Lastly, export your animated videos in the H.264 codec for peak quality and platform compatibility. Editing Your Animated Video Editing your animated video is a critical phase where you refine and improve your project to guarantee it resonates with your audience. Start by enhancing your cartoon video with sound effects, music tracks, and voiceovers using Premiere Pro’s Vital Sound panel. This allows for better audio quality control. To capture viewer attention, consider adding animated intros at the beginning. You can as well utilize animation presets and keyframe animation capabilities to create custom motion effects that align with your video’s style. Make sure to synchronize audio with animations for a polished look. This attention to detail is vital for effective editing, whether you’re using a whiteboard animation maker or learning how to animate a video from scratch. Exporting for Optimal Playback To guarantee your animated video plays back effectively across various platforms, you’ll need to evaluate several key factors during the export process. Here are four important steps for exporting for ideal playback: Codec: Use the H.264 codec for a balance between quality and file size. Resolution: Set your resolution to at least 1920×1080 pixels for Full HD clarity. Frame Rate: Choose 30 fps for standard videos or 60 fps for smoother motion, depending on your animation style. Bitrate: Target a bitrate of around 8 Mbps for 1080p videos to maintain quality without bloating file size. Always preview your exported video for audio-video synchronization issues to guarantee a polished presentation. This knowledge aids in perfecting how to make animations for YouTube with an ai animation generator from text. Sharing Your Animated Video on Social Media Sharing your animated video on social media can greatly boost its visibility and engagement, making it a crucial step in your promotional strategy. Start by choosing the right platforms; for example, use YouTube for long-form content, Instagram for short clips, and Facebook for a broader reach. Optimize your video format according to each platform’s specifications—vertical videos perform well on Instagram Stories and TikTok. Improve your post with engaging captions and relevant hashtags to enhance discoverability. Schedule your posts during peak engagement times based on analytics, as this can considerably increase views. Finally, monitor engagement metrics like views and shares to evaluate your animated video’s performance, which will inform future content strategies and help you refine how to make cartoon animation. Frequently Asked Questions How to Make an Animation Video Step by Step? To make an animation video step by step, start by defining your key message and target audience. Next, craft a compelling script that outlines your narrative. Once that’s done, create detailed storyboards to visualize each scene. Then, design characters and backgrounds that match your vision. After designing, use animation software to animate your scenes. Finally, polish your video with audio and effects before exporting it for sharing. This process guarantees clarity and engagement. How to Create a Custom Animation With Step by Step? To create a custom animation, start by brainstorming your concept and writing a detailed script. Next, develop a storyboard that outlines each scene, including actions and changes. Design characters and backgrounds using graphic software, ensuring they match your theme. Animate your designs with tools like Adobe After Effects, applying keyframes for movement and syncing with audio. Finally, review your work thoroughly, making adjustments before exporting in your chosen format. What Are the 4 Stages of Animation? The four stages of animation are conceptualization, design, animation, and post-production. In the conceptualization stage, you develop the core idea and script that guide your project. Next, during design, you create characters and backgrounds that visually represent your story. The animation stage brings these designs to life through techniques like 2D or 3D animation. Finally, post-production involves editing and adding elements like voiceovers and sound effects to refine the final product. What Program to Use to Make Animated Videos? To create animated videos, you have several superb programs to choose from. Vyond is user-friendly and offers templates perfect for business and educational content. If you’re a beginner, Animaker’s drag-and-drop features simplify the process. For advanced users, Blender provides robust 3D modeling tools. Professionals might prefer Toon Boom Harmony for its all-encompassing 2D capabilities. Finally, Adobe After Effects excels in motion graphics, allowing you to create visually striking animations with ease. Conclusion Creating animated videos requires careful planning and execution. By following the steps outlined—planning, scripting, storyboarding, choosing software, designing visuals, adding audio, editing, and exporting—you guarantee a polished final product. Each phase contributes to a cohesive narrative that effectively communicates your message. With the right techniques and tools, you can engage your audience and improve their experience. Remember, sharing your animated video on social media can broaden its reach, so consider your distribution strategy as well. Image via Google Gemini This article, "A Step-by-Step Guide to Creating Animated Videos" was first published on Small Business Trends View the full article