Senior Gameplay EngineerActivisionWoodland Hills California 91367 United States of America1 hour agoApplyJob Title:Senior Gameplay EngineerRequisition ID:R025343Job Description:Your PlatformFounded in 2002, Infinity Ward is the original studio behind the Call of Duty® franchise. The titles developed by Infinity Ward have won more than 200 "Game of the Year" awards and 100 "Editor's Choice" awards, among many other industry accolades. Some of our previous titles include: Call of Duty®: Ghosts , Call of Duty®: Infinite Warfare and most recently, Call of Duty®: Modern Warfare . Infinity Ward employs more than 250 people in Woodland Hills, Calif., just outside of Los Angeles.A critically acclaimed studio, Infinity Ward is proud to have one of the hardest-working and versatile development teams in the industry. We love to have as much fun as the games we create, and it shows in our relaxed, yet diligent, workplace environment. We believe that making the best games relies on having the best people, so we like to look after ours. We enjoy having fun with top-notch events that everyone gets involved with.Infinity Ward is wholly owned by Activision. To learn more about our studio, please visit us at https://www.infinityward.com/, on Facebook at https://www.facebook.com/InfinityWard/, and @InfinityWard on X.Your MissionInfinity Ward is seeking a talented and passionate Senior Gameplay Engineer to oversee gameplay development. As the Senior Gameplay Engineer, you'll work with our design and content teams to push our gameplay systems to new heights of AAA fun and visual fidelity. Priorities can often change in a fast-paced environment like ours, so this role includes, but is not limited to, the following responsibilities:Independently contribute to the design of our gameplay systemsDevelop, modify, and debug gameplay software in script and C/C++Deploy, optimize, and test gameplay software on PC and console platformsCollaborate with other internal departments (art, design, audio, QA, etc.)Proactively identify issues and fix bugs.Balance immediate needs with a focus on long-term scalability.Player ProfileRequirements:8+ years of gameplay development experience.1 shipped product working as a senior gameplay engineer.Fluency in 3D math, related to game programming.Strong design sensibilities and outstanding communication skills.Strong knowledge of C++ programming and practices.Pluses:Experience with AAA play-control logic, especially on an FPS title.Experience with modern animation techniquesExperience with game physics simulation.Experience with networked game logic.Experience with game AI.Our WorldActivision Blizzard, Inc., is one of the world's largest and most successful interactive entertainment companies and is at the intersection of media, technology and entertainment. We are home to some of the most beloved entertainment franchises including Call of Duty®, World of Warcraft®, Overwatch®, Diablo®, Candy Crush™ and Bubble Witch™. Our combined entertainment network delights hundreds of millions of monthly active users in 196 countries, making us the largest gaming network on the planet!Our ability to build immersive and innovative worlds is only enhanced by diverse teams working in an inclusive environment. We aspire to have a culture where everyone can thrive in order to connect and engage the world through epic entertainment. We provide a suite of benefits that promote physical, emotional and financial well-being for ‘Every World’ - we’ve got our employees covered!The videogame industry and therefore our business is fast-paced and will continue to evolve. As such, the duties and responsibilities of this role may be changed as directed by the Company at any time to promote and support our business and relationships with industry partners.We love hearing from anyone who is enthusiastic about changing the games industry. Not sure you meet all qualifications? Let us decide! Research shows that women and members of other under-represented groups tend to not apply to jobs when they think they may not meet every qualification, when, in fact, they often do! We are committed to creating a diverse and inclusive environment and strongly encourage you to apply.We are committed to working with and providing reasonable assistance to individuals with physical and mental disabilities. If you are a disabled individual requiring an accommodation to apply for an open position, please email your request to accommodationrequests@activisionblizzard.com. General employment questions cannot be accepted or processed here. Thank you for your interest.We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, gender identity, age, marital status, veteran status, or disability status, among other characteristicsRewardsWe provide a suite of benefits that promote physical, emotional and financial well-being for ‘Every World’ - we’ve got our employees covered! Subject to eligibility requirements, the Company offers comprehensive benefits including:Medical, dental, vision, health savings account or health reimbursement account, healthcare spending accounts, dependent care spending accounts, life and AD&D insurance, disability insurance;401(k) with Company match, tuition reimbursement, charitable donation matching;Paid holidays and vacation, paid sick time, floating holidays, compassion and bereavement leaves, parental leave;Mental health & wellbeing programs, fitness programs, free and discounted games, and a variety of other voluntary benefit programs like supplemental life & disability, legal service, ID protection, rental insurance, and others;If the Company requires that you move geographic locations for the job, then you may also be eligible for relocation assistance.Eligibility to participate in these benefits may vary for part time and temporary full-time employees and interns with the Company. You can learn more by visiting https://www.benefitsforeveryworld.com/ .In the U.S., the standard base pay range for this role is $101,000.00 - $186,754.00 Annual. These values reflect the expected base pay range of new hires across all U.S. locations. Ultimately, your specific range and offer will be based on several factors, including relevant experience, performance, and work location. Your Talent Professional can share this role’s range details for your local geography during the hiring process. In addition to a competitive base pay, employees in this role may be eligible for incentive compensation. Incentive compensation is not guaranteed. While we strive to provide competitive offers to successful candidates, new hire compensation is negotiable. Create Your Profile — Game companies can contact you with their relevant job openings. Apply

I experimented with Japanese-inspired textures to give Yatai Master a cozier feel. For gameplay, I tried to make dish quality depend on ingredients, cooking, and player choices—hoping small decisions would feel meaningful in this little street food simulator. Posted by oht on May 4th, 2025 Redesigning Yatai Master’s aesthetic and quality system Visual Redesign During the early stages of development, the game's interface used solid, smooth colors, following a functional but somewhat cold aesthetic. That was the effect I was aiming for—something simple, minimal, and austere. However, as the project progressed, I felt the need to add a warmer, more inviting touch, something that evoked the texture and ambiance of physical materials. That's when I began experimenting with a visual dithering pattern subtly inspired by traditional Japanese designs known as "wagara"—decorative motifs used in fabrics and garments like kimonos. Interestingly, this change wasn't the result of deliberate planning but rather a series of casual experiments while exploring the new features of Pinta 3.0, a free, cross-platform image editor that aspires to be the "Paint.NET" for all operating systems. This version of the software introduced dithering tools, which allow applying halftone patterns to colored surfaces. While playing with this feature, I discovered a texture that vaguely resembled fabric or burlap, offering a softer, more artisanal feel—perfect for the spirit of the Yatai. I believe this dithering adds some "visual personality" and helps break the rigidity of flat colors, creating a more intimate and less virtual atmosphere. I hope this change better represents the idea of a small street stall, typically made with humble materials yet full of character. Rethinking Quality When I started developing my game, one of the first mechanics I defined was dish preparation. I envisioned that ingredients would have a "quality" rating, and customers would evaluate the food based on that. At first, it seemed like a straightforward decision: assign a value from 1 to 10 and calculate an average. Done. How hard could it be? Turns out, quite a bit… Many Variables As I integrated more elements—proper cooking, features like Fast Cooking, ingredients with different attributes—the quality scoring system stopped being a simple sum and became a small decision-making machine. What if a player uses only excellent ingredients? How to penalize poor cooking without being unfair? And how to prevent dishes with more ingredients from having an unfair advantage simply due to more components? I soon realized that a good scoring system not only has to work, but also must balance the game and clearly communicate to the player what’s happening. It’s not just about numbers but what those numbers represent: effort, choices, mistakes, and improvements. After several adjustments, I ended up building a system that considers: The ingredient’s base quality. Whether it was cooked correctly (yes, even that adds or subtracts). A special adjustment if the player uses fast cooking (Fast Cooking). And a final mapping that converts the technical score into a comprehensible evaluation: Poor, Good, Excellent, etc. Markets and Quality One of the elements I was most excited to design was the market system. Just like in real life, not all ingredients come from the same place, nor do they have the same quality or price. In Yatai Master, each market offers different ingredients with variations in their base quality, which directly impacts the final dish’s score. Buying a higher-quality ingredient usually means paying more, but that doesn’t guarantee success—it also needs to be cooked well, have sufficient stock, and require budget management with cunning. On the other hand, opting for cheaper ingredients may allow for more dishes to be prepared but with a lower quality score… and that can affect customer satisfaction. With this mechanic, I aimed to simulate the daily decisions of a small food business owner. And this, without fully planning it at first, helped me more deeply connect gameplay with the experience of running aYatai. You may never see all these calculations as a player, but they’re there, making every decision matter. The most interesting part is that I never imagined how complex designing something as seemingly simple as a score could be. Is it worth all this effort for a number? I think so. Thanks for reading this far.

Researchers are trying to determine the identity of the shipwreck. Image: Screenshot, CBC/YouTube Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. A Canadian kid is proof that major scientific discoveries don’t always have to come from grizzled researchers with fancy equipment. Two years ago, then-8-year-old Lucas Atchison went on a family trip to Point Farms Provincial Park in Ontario. Armed with a metal detector he had just received as a birthday present, Atchison dutifully scanned the area, hoping to hear that coveted “beep.”  Eventually, he did. Eagerly digging into the site, Lucas uncovered a metal spike, which his father initially dismissed as something used to tie up boats. But the budding archaeologist insisted they dig further. Soon, they uncovered another spike attached to a piece of wood. The father-son pair had likely stumbled on a two-century-old shipwreck.  “We were on the beach, we got our metal detector out, and as soon as we set it up, ding! It was a spike from the shipwreck,” Lucas recently told the CBC. Lucas’ find led to a full-on excavation  Once the duo realized what they had found, they alerted the provincial parks staff, who then contacted the nonprofit Ontario Marine Heritage Committee (OMHC). The OMHC met with Lucas in 2023, but it took several years to obtain the necessary regulatory permits to excavate the area further. Finally, earlier this month, they began digging. Though still in the early stages, marine archaeologist Scarlett Janusas told the CBC that the shipwreck’s “double frames” suggest it is of the schooner variety. Work still needs to be done to definitively confirm the ship’s identity, but archaeologists working on the project believe they may have a likely contender in the St. Anthony. That vessel wrecked near Lake Huron in 1856 during a trip transporting wheat between Chicago and Buffalo. Researchers at the site are currently creating sketches of the wreck from above and from the side to analyze it in more detail. They are also reportedly reviewing 19th-century insurance requirements for ships, which could provide clues about the vessel’s identity and when it sank. Those documents outline specifications for ships at the time, such as the number of fasteners required in the frames. By cross-referencing those details against records of missing ships, they hope to definitively identify the wreck. You never know what you might find  Lucas’ discovery is a nice reminder that, even today, in our seemingly mapped-out, modernized world, you never know what you might find with a trusty metal detector. His case is far from unique. In just the past few months, a Romanian man used a metal detector to uncover an ancient hoard of Roman coins. Even more recently, a Scottish man discovered a 4,000-year-old axe buried on his family farm. Sometimes, people may not even realize the significance of their finds until much later. Case in point: an Australian prospector named David Hole found a large rock with a metal detector in 2016, believing it to be a gold nugget. Only recently did he learn that it was actually a far rarer 4.6 billion-year-old meteorite. As for the Ontario shipwreck, researchers say they eventually plan to re-bury the find in an anaerobic, oxygen-free environment. That setting could help preserve it by protecting it from natural degradation caused by parasites and other organisms. Lucas, meanwhile, may be just beginning a long journey with his trusted metal detector.

When arsenic is mentioned, many people associate it with a bygone poison. The type of quiet killer used in a murder mystery set on a train somewhere in Victorian England. It seems like a problem of the past.However, arsenic is a naturally occurring substance that can contaminate groundwater and food irrigated with tainted water. And in an alarming new study, researchers have found that climate change is impacting the level of arsenic that people are ingesting. Given that arsenic is a carcinogen, it’s primed to become a lethal killer. Billions of people are at risk of developing arsenic-induced diseases.Why Arsenic Levels are Rising in Rice In a 2025 study in The Lancet, an international team of researchers revealed the results of a study that measured two components associated with climate change — increased carbon dioxide (CO2) levels and surface temperatures — in rice paddies in China.“One important chemical aspect that has been known about rice is its ability to accumulate arsenic, leading to an important and crucial question: Can CO2 and temperature affect arsenic concentration, and if so, what are the health consequences?” says Lewis H. Ziska, an associate professor in environmental health sciences at the Mailman School of Public Health at Columbia University. The study was the first of its kind. Although scientists have long understood how both inorganic and organic arsenic can make their way into food or water, they weren’t sure how climate change might influence arsenic levels.To find out, the researchers grew rice in controlled paddies in China from 2014 to 2023. To a certain extent, the research team expected to find some arsenic in the rice because it grows in flooded paddies and absorbs arsenic from the water.The research team also expected the CO2 levels and surface temperatures would increase because climate change scientists have charted a projection of the likely increase. But would increased CO2 levels and temperatures lead to higher arsenic levels? And what might that mean if people eat the tainted crop? Impacts of Eating ArsenicThe study found that the increased CO2 levels and temperatures indeed lead to higher concentrations of arsenic. The researchers’ rice paddies were representative of rice paddies in Asia, which means that what happened in their paddies is likely happening in others around the region.Higher arsenic rates in rice are a major health concern. Arsenic is a known carcinogen. When found in drinking water, there is an increased risk of bladder and skin cancer. Other studies have found that ingesting arsenic can lead to cancer in the digestive tract, hematopoietic system, kidneys, liver, or lymphatic system.Problematically, billions of people can now be considered at risk for arsenic-related cancers. More than half of the world’s population depends on rice as a staple food. Scientists are concerned that the increased arsenic in a staple food like rice could lead to a spike in future cancer cases throughout Asia.Rice Consumption in the U.S. But what does this mean for the U.S. food supply? Rice consumed in the U.S. comes from the Arkansas Grand Prairie, Gulf Coast, Mississippi Delta, or Sacramento Valley. These crops have not been tested by scientists to determine how CO2 levels and surface temperatures are impacting the arsenic concentrations. But if climate change can raise arsenic levels in Asia, Ziska says it can happen in the U.S. as well.“It seems likely given the biogeochemistry of how rice can accumulate arsenic when grown under flooded (paddy) conditions,” Ziska says.It’s a research area that Ziska says needs more attention, both in terms of long-term study support and public awareness. Many people don’t realize that climate change has the ability to alter the food supply by creating conditions that are ripe for toxins.“Climate change isn’t just about wildfires and sea-level rise and storms of the century. It’s also affecting a food staple — rice — that is consumed by half the world’s population every day. Maybe we should pay attention,” he says. This article is not offering medical advice and should be used for informational purposes only.Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:National Cancer Institue. ArsenicEmilie Lucchesi has written for some of the country's largest newspapers, including The New York Times, Chicago Tribune and Los Angeles Times. She holds a bachelor's degree in journalism from the University of Missouri and an MA from DePaul University. She also holds a Ph.D. in communication from the University of Illinois-Chicago with an emphasis on media framing, message construction and stigma communication. Emilie has authored three nonfiction books. Her third, A Light in the Dark: Surviving More Than Ted Bundy, releases October 3, 2023, from Chicago Review Press and is co-authored with survivor Kathy Kleiner Rubin.

At Salone del Mobile in 2023, Armani Casa presented something new. Or, perhaps it would be more accurate to say that they re-created their classics. The brand introduced three different techniques that transformed their furniture into new works of art, both bold in color and material, and subtly naturalistic in aesthetic.In a statement, Mr. Armani said the collection's Venus console and Virgola bookcase shelves "feature a new type of hand-painted and lacquered glass." And at a Tuesday night event, partygoers saw the new techniques live and in-person at the brand’s Madison Avenue Flagship in New York. Indeed, Armani noted that the objects are "reminiscent of the uniqueness and beauty of high-end jewelry, made with a craftsmanship that is only possible in Italy." Treatment One: Orsini GlassLorenzo BaroncelliA demonstration of the Orsini Method.The Orsini Method involves artisans painting pieces of glass, creating a series of marbled bands. The brand noted, “This finish is obtained by hand painting the back of a tempered extra-clear glass with a special paint mixing different pastel tones, using a large soft brush.”The colors were inspired by the blue green walls in the Neoclassical Palazzo Orsini in Rome. Though the method is innovative, the painterly process and the use of temperas and pastels still make it feel completely classical.Items re-imagined from the Armani Casa collection with the Orsini Method include: Venus 2024 and Virgola 2024.Treatment Two: Shell Mosaic Lorenzo BaroncelliA demonstrationIn this mother-of-pearl mosaic, experts remove the outer layer of an oyster shell to find a the shiny whites, grays, and blues beneath. The Shell Mosaic, with this high end construction, reflects the ties between Armani's Casa collections and his clothing lines. Said Mr. Armani, “The strongest link between Armani/Casa and my fashion collections is the need to be functional and comfortable without sacrificing luxurious materials and a strong sense of style.” The Shell Mosaic is available in the following pieces from the Armani CASA Collection: Antoinette 2023, Camilla 2023, Sofia 2023, and Vega 2024.Treatment Three: Midollino Courtesy ArmaniThe Midollino Technique on the Riesling Bar Cabinet.Wicker will never go out of style, and Armani Casa’s iteration on the traditional form is reflective of the expertise and craftsmanship constant across the brand's fashion and furniture.The Midollino technique features a carefully woven, but thick and high quality grain. It happens to be the same method also used for a number of Armani handbags, capturing the relationship between the past and present reflective across the collection. Items from the Armani Casa Collection featuring the Midollino Technique Include: Melrose 2023, Riesling 2022, and Sharon 2023.Dorothy ScarboroughDorothy Scarborough (she/her) is the assistant to the Editor in Chief of Town & Country and Elle Decor. 

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More UK-based chip designer Arm offers the architecture for systems-on-a-chip (SoCs) that are used by some of the world’s largest tech brands, from Nvidia to Amazon to Google parent company Alphabet and beyond, all without ever manufacturing any hardware of its own — though that’s reportedly due to change this year. And you’d think with a record setting last quarter of $1.24 billion in total revenue, it might want to just keep things steady and keep raking in the cash. But Arm sees how fast AI has taken off in the enterprise, and with some of its customers delivering record revenue of their own by offering AI graphics processing units that incorporate Arm’s tech, Arm wants a piece of the action. Today, the company announced a new product naming strategy that underscores its shift from a supplier of component IP to a platform-first company. “It’s about showing customers that we have much more to offer than just hardware and chip designs. specifically — we have a whole ecosystem that can help them scale AI and do so at lower cost with greater efficiency,” said Arm’s chief marketing officer Ami Badani, in an exclusive interview with VentureBeat over Zoom yesterday. Indeed, as Arm CEO Rene Haas told the tech news outlet Next Platform back in February, Arm’s history of creating lower-power chips than the competition (cough cough, Intel) has set it up extremely well to serve as the basis for power-hungry AI training and inference jobs. According to his comments in that article, today’s data center consume approximately 460 terawatt hours of electricity per year, but that is expected to triple by the end of this decade, and could jump from being 4 percent of all of the world’s energy usage to 25 percent — unless more Arm power-saving chip designs and their accompanying optimized software and firmware are used in the infrastructure for these centers. From IP to platform: a significant shift As AI workloads scale in complexity and power requirements, Arm is reorganizing its offerings around complete compute platforms. These platforms allow for faster integration, more efficient scaling, and lower complexity for partners building AI-capable chips. To reflect this shift, Arm is retiring its prior naming conventions and introducing new product families that are organized by market: Neoverse for infrastructure Niva for PCs Lumex for mobile Zena for automotive Orbis for IoT and edge AI The Mali brand will continue to represent GPU offerings, integrated as components within these new platforms. Alongside the renaming, Arm is overhauling its product numbering system. IP identifiers will now correspond to platform generations and performance tiers labeled Ultra, Premium, Pro, Nano, and Pico. This structure is aimed at making the roadmap more transparent to customers and developers. Emboldened by strong results The rebranding follows Arm’s strong Q4 fiscal year 2025 (ended March 31), where the company crossed the $1 billion mark in quarterly revenue for the first time. Total revenue hit $1.24 billion, up 34% year-over-year, driven by both record licensing revenue ($634 million, up 53%) and royalty revenue ($607 million, up 18%). Notably, this royalty growth was driven by increasing deployment of the Armv9 architecture and adoption of Arm Compute Subsystems (CSS) across smartphones, cloud infrastructure, and edge AI. The mobile market was a standout: while global smartphone shipments grew less than 2%, Arm’s smartphone royalty revenue rose roughly 30%. The company also entered its first automotive CSS agreement with a leading global EV manufacturer, furthering its penetration into the high-growth automotive market. While Arm hasn’t disclosed the EV manufacturer’s precise name yet, Badani told VentureBeat that it sees automotive as a major growth area in addition to AI model providers and cloud hyperscalers such as Google and Amazon. “We’re looking at automotive as a major growth area and we believe that AI and other advances like self-driving are going to be standard, which our designs are perfect for,” the CMO told VentureBeat. Meanwhile, cloud providers like AWS, Google Cloud, and Microsoft Azure continued expanding their use of Arm-based silicon to run AI workloads, affirming Arm’s growing influence in data center compute. Growing a new platform ecosystem with software and vertically integrated products Arm is complementing its hardware platforms with expanded software tools and ecosystem support. Its extension for GitHub Copilot, now free for all developers, lets users optimize code using Arm’s architecture. More than 22 million developers now build on Arm, and its Kleidi AI software layer has surpassed 8 billion cumulative installs across devices. Arm’s leadership sees the rebrand as a natural step in its long-term strategy. By providing vertically integrated platforms with performance and naming clarity, the company aims to meet increasing demand for energy-efficient AI compute from device to data center. As Haas wrote in Arm’s blog post, Arm’s compute platforms are foundational to a future where AI is everywhere—and Arm is poised to deliver that foundation at scale. What it means for AI and data decision makers This strategic repositioning is likely to reshape how technical decision makers across AI, data, and security roles approach their day-to-day work and future planning. For those managing large language model lifecycles, the clearer platform structure offers a more streamlined path for selecting compute architectures optimized for AI workloads. As model deployment timelines tighten and the bar for efficiency rises, having predefined compute systems like Neoverse or Lumex could reduce the overhead required to evaluate raw IP blocks and allow faster execution in iterative development cycles. For engineers orchestrating AI pipelines across environments, the modularity and performance tiering within Arm’s new architecture could help simplify pipeline standardization. It introduces a practical way to align compute capabilities with varying workload requirements—whether that’s running inference at the edge or managing resource-intensive training jobs in the cloud. These engineers, often juggling system uptime and cost-performance tradeoffs, may find more clarity in mapping their orchestration logic to predefined Arm platform tiers. Data infrastructure leaders tasked with maintaining high-throughput pipelines and ensuring data integrity may also benefit. The naming update and system-level integration signal a deeper commitment from Arm to support scalable designs that work well with AI-enabled pipelines. The compute subsystems may also accelerate time-to-market for custom silicon that supports next-gen data platforms—important for teams that operate under budget constraints and limited engineering bandwidth. Security leaders, meanwhile, will likely see implications in how embedded security features and system-level compatibility evolve within these platforms. With Arm aiming to offer consistent architecture across edge and cloud, security teams can more easily plan for and enforce end-to-end protections, especially when integrating AI workloads that demand both performance and strict access controls. The broader effect of this branding shift is a signal to enterprise architects and engineers: Arm is no longer just a component provider—it’s offering full-stack foundations for how AI systems are built and scaled. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Energy vs. hearts.Last week, Duolingo announced a major shift: they’re replacing their long-standing hearts system with a new “energy” mechanic. The company frames this as a way to make learning more motivating by focusing on rewarding correct answers rather than punishing mistakes.But after spending 48 hours thinking about this change and analyzing reactions across the internet, I’ve come to see it as something far more nuanced: a masterclass in behavioral psychology that serves both users and Duolingo’s bottom line in fascinating ways.What’s actually changingUnder the old hearts system, users started with five hearts and lost one for each mistake. When all hearts were depleted, you couldn’t continue learning until you either waited (hearts regenerated over time), watched ads, practiced old content to earn hearts back, or paid for premium.The new energy system gives users 25 energy units. Each lesson costs one unit to start, and mistakes also cost energy. The key difference? Users can earn bonus energy at random intervals when answering correctly.As Moses Wayne, a senior staff engineer at Duolingo, explains to The Verge: “We feel like this is a way that we can motivate you to focus on things you’re getting right rather than penalizing for the things that you’re making mistakes on.”It sounds positive on the surface. But there’s much more happening under the hood.The psychological switch: from loss to expenditureThe hearts system was built on loss aversion (our tendency to feel losses more intensely than equivalent gains). Users would desperately try to avoid mistakes to keep their hearts. In psychological terms, this creates anxiety that can inhibit learning, especially for a complex skill like language acquisition.The energy system cleverly shifts the frame from “losing hearts through mistakes” to “spending energy to learn.” This slight repositioning transforms the psychological experience. Instead of punishment, it’s now an investment.But here’s where it gets particularly interesting, and perhaps concerning.At time of writing, users will still be seeing this when they run out of hearts.Variable rewards: the slot machine mechanismThe new system incorporates one of the most powerful psychological techniques in existence: variable reward schedules, or what behavioral scientists call “intermittent reinforcement.”When completing lessons, users now receive random bonus energy for consecutive correct answers. This unpredictability is the same mechanism that makes slot machines so addictive. As behavioral psychologist B.F. Skinner discovered decades ago, unpredictable rewards create stronger habit loops than predictable ones.The uncertain nature of “when will I get bonus energy?” creates a dopamine response that keeps users engaged far longer than the more predictable hearts system. One psychology research article noted that “variable ratio schedules have two benefits: They result in the most instances of the behavior than any of the other schedules… and they result in behaviors that are ‘hard to extinguish,” meaning the behavior persists even when rewards decrease.Duolingo isn’t the first app to use this technique. It’s everywhere in modern digital products, from social media (random likes triggering dopamine) to games (loot boxes with variable rewards). What’s notable is how Duolingo is implementing it in an educational context.The inverse energy model: Finch vs. DuolingoTo understand what makes Duolingo’s approach unique, it’s worth comparing it to another popular app that uses an energy system: Finch.In Finch, a self-care app with a virtual pet, users earn energy by completing wellness tasks. As one reviewer describes it: “My effort gives [my pet] energy, but my lack of effort takes none away.” The energy you earn through self-care allows your pet to go on adventures and grow.Energy in Finch and Duolingo are both symbolized by a ⚡Duolingo takes the inverse approach, i.e. you spend energy to engage with the product. This fundamental difference reveals contrasting philosophies:Finch’s model: Do real-world activities → earn in-app energy → enjoy rewardsDuolingo’s model: Spend in-app energy → engage with content → occasionally get energy backWhile both use similar terminology, the direction of energy flow completely changes the behavioral dynamics. One rewards external action; the other creates a closed loop that keeps users in the app.The business of artificial scarcityLet’s not overlook the monetization angle. Super Duolingo subscribers get unlimited energy, just as they previously got unlimited hearts. The new system maintains the same fundamental constraint: free users eventually hit a wall where they need to either wait, work harder, or pay up.What the energy system might do more effectively is increase the perceived value of the premium subscription. By creating a more engaging core loop with the variable rewards, users may be more likely to convert when they hit energy limits.The strategy is brilliant, if perhaps ethically complex. By transforming a negative experience (punishment for mistakes) into a more positive ne (spending a resource with occasional bonuses), Duolingo has maintained its monetization constraints while making them feel less punitive.Critics of the heart system have long pointed out that unlimited hearts are one of the main selling points of Duolingo’s premium tier, suggesting the constraint exists primarily to drive conversions rather than improve learning. The energy system continues this pattern, just with more sophisticated psychological underpinnings.The learning science questionThe most important question is whether this change actually improves learning outcomes.Duolingo claims the energy system helps users “get through more lessons,” as the data shows users can do more with the new system. But completing more lessons doesn’t necessarily mean better language acquisition.Language learning requires making mistakes. It’s a fundamental part of the process. The old heart system discouraged risk-taking by heavily penalizing errors, potentially leading users to stick with easier content where they wouldn’t lose hearts.The energy system might ease this anxiety somewhat — making a mistake costs the same as starting a new lesson. But the fundamental constraint remains: you still have a limited resource that depletes when you make errors.A truly learning-optimized approach might involve no penalties for mistakes at all, focusing instead on spaced repetition, comprehensible input, and other evidence-based language acquisition methods. But such an approach might not drive the same level of engagement or monetization.Duloingo falls somewhere in the center of the bottom right quadrantUsers vs. learnersThis tension highlights a key challenge for educational technology: the gap between what creates engaged users and what creates successful learners.The techniques that make an app sticky (intermittent reinforcement, streaks, gamification) don’t always align with optimal learning science. In fact, they can sometimes work against it by encouraging shallow engagement over deep processing.Duolingo has always walked this line, using game mechanics to keep people engaged with language learning when they might otherwise quit. The energy system represents an evolution of this approach, using more sophisticated behavioral psychology to maintain the same fundamental constraints while making them feel better.The verdict: clever design for multiple objectivesAfter analyzing this change from multiple angles, I’m left impressed by its cleverness while somewhat ambivalent about its implications.The energy system:Creates a more positive psychological framing than heartsImplements powerful variable rewards that increase engagementMaintains the same fundamental monetization pressurePotentially allows for more mistakes, which is better for learningUses randomness to create stronger habit loopsIt’s a change that serves both user experience and business goals, likely increasing both engagement and conversion to premium. Whether it truly serves learning outcomes remains to be seen, though it’s probably at least marginally better than the hearts system in this regard.What fascinates me most is how this change reveals the sophistication of behavioral design in today’s digital products. What looks like a simple mechanic change is actually a carefully engineered system to shape behavior through psychological principles.The next time you find yourself compulsively swiping for another lesson, hoping for that bonus energy to drop, remember that this feeling isn’t accidental… it’s by design.And while I appreciate the brilliance of that design, I find myself wishing more of this psychological ingenuity was directed toward optimizing learning outcomes rather than engagement metrics. Perhaps that’s the next frontier for language learning apps: finding ways to make evidence-based learning as addictive as a slot machine.Sam Liberty is a gamification expert, applied game designer, and consultant. His clients include The World Bank, Click Therapeutics, and DARPA. He teaches game design at Northeastern University. He is the former Lead Game Designer at Sidekick Health.Duolingo’s small UI switch that changes everything was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI is creating value for everyone — from researchers in drug discovery to quantitative analysts navigating financial market changes. The faster an AI system can produce tokens, a unit of data used to string together outputs, the greater its impact. That’s why AI factories are key, providing the most efficient path from “time to first token” to “time to first value.” AI factories are redefining the economics of modern infrastructure. They produce intelligence by transforming data into valuable outputs — whether tokens, predictions, images, proteins or other forms — at massive scale. They help enhance three key aspects of the AI journey — data ingestion, model training and high-volume inference. AI factories are being built to generate tokens faster and more accurately, using three critical technology stacks: AI models, accelerated computing infrastructure and enterprise-grade software. Read on to learn how AI factories are helping enterprises and organizations around the world convert the most valuable digital commodity — data — into revenue potential. From Inference Economics to Value Creation Before building an AI factory, it’s important to understand the economics of inference — how to balance costs, energy efficiency and an increasing demand for AI. Throughput refers to the volume of tokens that a model can produce. Latency is the amount of tokens that the model can output in a specific amount of time, which is often measured in time to first token — how long it takes before the first output appears — and time per output token, or how fast each additional token comes out. Goodput is a newer metric, measuring how much useful output a system can deliver while hitting key latency targets. User experience is key for any software application, and the same goes for AI factories. High throughput means smarter AI, and lower latency ensures timely responses. When both of these measures are balanced properly, AI factories can provide engaging user experiences by quickly delivering helpful outputs. For example, an AI-powered customer service agent that responds in half a second is far more engaging and valuable than one that responds in five seconds, even if both ultimately generate the same number of tokens in the answer. Companies can take the opportunity to place competitive prices on their inference output, resulting in more revenue potential per token. Measuring and visualizing this balance can be difficult — which is where the concept of a Pareto frontier comes in. AI Factory Output: The Value of Efficient Tokens The Pareto frontier, represented in the figure below, helps visualize the most optimal ways to balance trade-offs between competing goals — like faster responses vs. serving more users simultaneously — when deploying AI at scale. The vertical axis represents throughput efficiency, measured in tokens per second (TPS), for a given amount of energy used. The higher this number, the more requests an AI factory can handle concurrently. The horizontal axis represents the TPS for a single user, representing how long it takes for a model to give a user the first answer to a prompt. The higher the value, the better the expected user experience. Lower latency and faster response times are generally desirable for interactive applications like chatbots and real-time analysis tools. The Pareto frontier’s maximum value — shown as the top value of the curve — represents the best output for given sets of operating configurations. The goal is to find the optimal balance between throughput and user experience for different AI workloads and applications. The best AI factories use accelerated computing to increase tokens per watt — optimizing AI performance while dramatically increasing energy efficiency across AI factories and applications. The animation above compares user experience when running on NVIDIA H100 GPUs configured to run at 32 tokens per second per user, versus NVIDIA B300 GPUs running at 344 tokens per second per user. At the configured user experience, Blackwell Ultra delivers over a 10x better experience and almost 5x higher throughput, enabling up to 50x higher revenue potential. How an AI Factory Works in Practice An AI factory is a system of components that come together to turn data into intelligence. It doesn’t necessarily take the form of a high-end, on-premises data center, but could be an AI-dedicated cloud or hybrid model running on accelerated compute infrastructure. Or it could be a telecom infrastructure that can both optimize the network and perform inference at the edge. Any dedicated accelerated computing infrastructure paired with software turning data into intelligence through AI is, in practice, an AI factory. The components include accelerated computing, networking, software, storage, systems, and tools and services. When a person prompts an AI system, the full stack of the AI factory goes to work. The factory tokenizes the prompt, turning data into small units of meaning — like fragments of images, sounds and words. Each token is put through a GPU-powered AI model, which performs compute-intensive reasoning on the AI model to generate the best response. Each GPU performs parallel processing — enabled by high-speed networking and interconnects — to crunch data simultaneously. An AI factory will run this process for different prompts from users across the globe. This is real-time inference, producing intelligence at industrial scale. Because AI factories unify the full AI lifecycle, this system is continuously improving: inference is logged, edge cases are flagged for retraining and optimization loops tighten over time — all without manual intervention, an example of goodput in action. Leading global security technology company Lockheed Martin has built its own AI factory to support diverse uses across its business. Through its Lockheed Martin AI Center, the company centralized its generative AI workloads on the NVIDIA DGX SuperPOD to train and customize AI models, use the full power of specialized infrastructure and reduce the overhead costs of cloud environments. “With our on-premises AI factory, we handle tokenization, training and deployment in house,” said Greg Forrest, director of AI foundations at Lockheed Martin. “Our DGX SuperPOD helps us process over 1 billion tokens per week, enabling fine-tuning, retrieval-augmented generation or inference on our large language models. This solution avoids the escalating costs and significant limitations of fees based on token usage.” NVIDIA Full-Stack Technologies for AI Factory An AI factory transforms AI from a series of isolated experiments into a scalable, repeatable and reliable engine for innovation and business value. NVIDIA provides all the components needed to build AI factories, including accelerated computing, high-performance GPUs, high-bandwidth networking and optimized software. NVIDIA Blackwell GPUs, for example, can be connected via networking, liquid-cooled for energy efficiency and orchestrated with AI software. The NVIDIA Dynamo open-source inference platform offers an operating system for AI factories. It’s built to accelerate and scale AI with maximum efficiency and minimum cost. By intelligently routing, scheduling and optimizing inference requests, Dynamo ensures that every GPU cycle ensures full utilization, driving token production with peak performance. NVIDIA Blackwell GB200 NVL72 systems and NVIDIA InfiniBand networking are tailored to maximize token throughput per watt, making the AI factory highly efficient from both total throughput and low latency perspectives. By validating optimized, full-stack solutions, organizations can build and maintain cutting-edge AI systems efficiently. A full-stack AI factory supports enterprises in achieving operational excellence, enabling them to harness AI’s potential faster and with greater confidence. Learn more about how AI factories are redefining data centers and enabling the next era of AI.