Last year, China saw a boom in foundation models, the do-everything large language models that underpin the AI revolution. This year, the focus has shifted to AI agents—systems that are less about responding to users’ queries and more about autonomously accomplishing things for them.  There are now a host of Chinese startups building these general-purpose digital tools, which can answer emails, browse the internet to plan vacations, and even design an interactive website. Many of these have emerged in just the last two months, following in the footsteps of Manus—a general AI agent that sparked weeks of social media frenzy for invite codes after its limited-release launch in early March.  These emerging AI agents aren’t large language models themselves. Instead, they’re built on top of them, using a workflow-based structure designed to get things done. A lot of these systems also introduce a different way of interacting with AI. Rather than just chatting back and forth with users, they are optimized for managing and executing multistep tasks—booking flights, managing schedules, conducting research—by using external tools and remembering instructions.  China could take the lead on building these kinds of agents. The country’s tightly integrated app ecosystems, rapid product cycles, and digitally fluent user base could provide a favorable environment for embedding AI into daily life.  For now, its leading AI agent startups are focusing their attention on the global market, because the best Western models don’t operate inside China’s firewalls. But that could change soon: Tech giants like ByteDance and Tencent are preparing their own AI agents that could bake automation directly into their native super-apps, pulling data from their vast ecosystem of programs that dominate many aspects of daily life in the country.  As the race to define what a useful AI agent looks like unfolds, a mix of ambitious startups and entrenched tech giants are now testing how these tools might actually work in practice—and for whom. Set the standard It’s been a whirlwind few months for Manus, which was developed by the Wuhan-based startup Butterfly Effect. The company raised $75 million in a funding round led by the US venture capital firm Benchmark, took the product on an ambitious global roadshow, and hired dozens of new employees.  Even before registration opened to the public in May, Manus had become a reference point for what a broad, consumer‑oriented AI agent should accomplish. Rather than handling narrow chores for businesses, this “general” agent is designed to be able to help with everyday tasks like trip planning, stock comparison, or your kid’s school project.  Unlike previous AI agents, Manus uses a browser-based sandbox that lets users supervise the agent like an intern, watching in real time as it scrolls through web pages, reads articles, or codes actions. It also proactively asks clarifying questions, supports long-term memory that would serve as context for future tasks. "Manus represents a promising product experience for AI agents," says Ang Li, cofounder and CEO of Simular, a startup based in Palo Alto, California, that’s building computer use agents, AI agents that control a virtual computer. “I believe Chinese startups have a huge advantage when it comes to designing consumer products, thanks to cutthroat domestic competition that leads to fast execution and greater attention to product details.” In the case of Manus, the competition is moving fast. Two of the most buzzy follow‑ups, Genspark and Flowith, for example, are already boasting benchmark scores that match or edge past Manus’s.  Genspark, led by former Baidu executives Eric Jing and Kay Zhu, links many small “super agents” through what it calls multi‑component prompting. The agent can switch among several large language models, accepts both images and text, and carries out tasks from making slide decks to placing phone calls. Whereas Manus relies heavily on Browser Use, a popular open-source product that lets agents operate a web browser in a virtual window like a human, Genspark directly integrates with a wide array of tools and APIs. Launched in April, the company says that it already has over 5 million users and over $36 million in yearly revenue. Flowith, the work of a young team that first grabbed public attention in April 2025 at a developer event hosted by the popular social media app Xiaohongshu, takes a different tack. Marketed as an “infinite agent,” it opens on a blank canvas where each question becomes a node on a branching map. Users can backtrack, take new branches, and store results in personal or sharable “knowledge gardens”—a design that feels more like project management software (think Notion) than a typical chat interface. Every inquiry or task builds its own mind-map-like graph, encouraging a more nonlinear and creative interaction with AI. Flowith’s core agent, NEO, runs in the cloud and can perform scheduled tasks like sending emails and compiling files. The founders want the app to be a “knowledge marketbase”, and aims to tap into the social aspect of AI with the aspiration of becoming “the OnlyFans of AI knowledge creators”. What they also share with Manus is the global ambition. Both Genspark and Flowith have stated that their primary focus is the international market. A global address Startups like Manus, Genspark, and Flowith—though founded by Chinese entrepreneurs—could blend seamlessly into the global tech scene and compete effectively abroad. Founders, investors, and analysts that MIT Technology Review has spoken to believe Chinese companies are moving fast, executing well, and quickly coming up with new products.  Money reinforces the pull to launch overseas. Customers there pay more, and there are plenty to go around. “You can price in USD, and with the exchange rate that’s a sevenfold multiplier,” Manus cofounder Xiao Hong quipped on a podcast. “Even if we’re only operating at 10% power because of cultural differences overseas, we’ll still make more than in China.” But creating the same functionality in China is a challenge. Major US AI companies including OpenAI and Anthropic have opted out of mainland China because of geopolitical risks and challenges with regulatory compliance. Their absence initially created a black market as users resorted to VPNs and third-party mirrors to access tools like ChatGPT and Claude. That vacuum has since been filled by a new wave of Chinese chatbots—DeepSeek, Doubao, Kimi—but the appetite for foreign models hasn’t gone away.  Manus, for example, uses Anthropic’s Claude Sonnet—widely considered the top model for agentic tasks. Manus cofounder Zhang Tao has repeatedly praised Claude’s ability to juggle tools, remember contexts, and hold multi‑round conversations—all crucial for turning chatty software into an effective executive assistant. But the company’s use of Sonnet has made its agent functionally unusable inside China without a VPN. If you open Manus from a mainland IP address, you’ll see a notice explaining that the team is “working on integrating Qwen’s model,” a special local version that is built on top of Alibaba’s open-source model.  An engineer overseeing ByteDance’s work on developing an agent, who spoke to MIT Technology Review anonymously to avoid sanction, said that the absence of Claude Sonnet models “limits everything we do in China.” DeepSeek’s open models, he added, still hallucinate too often and lack training on real‑world workflows. Developers we spoke with rank Alibaba’s Qwen series as the best domestic alternative, yet most say that switching to Qwen knocks performance down a notch. Jiaxin Pei, a postdoctoral researcher at Stanford’s Institute for Human‑Centered AI, thinks that gap will close: “Building agentic capabilities in base LLMs has become a key focus for many LLM builders, and once people realize the value of this, it will only be a matter of time." For now, Manus is doubling down on audiences it can already serve. In a written response, the company said its “primary focus is overseas expansion,” noting that new offices in San Francisco, Singapore, and Tokyo have opened in the past month. A super‑app approach Although the concept of AI agents is still relatively new, the consumer-facing AI app market in China is already crowded with major tech players. DeepSeek remains the most widely used, while ByteDance’s Doubao and Moonshot’s Kimi have also become household names. However, most of these apps are still optimized for chat and entertainment rather than task execution. This gap in the local market has pushed China’s big tech firms to roll out their own user-facing agents, though early versions remain uneven in quality and rough around the edges.  ByteDance is testing Coze Space, an AI agent based on its own Doubao model family that lets users toggle between “plan” and “execute” modes, so they can either directly guide the agent’s actions or step back and watch it work autonomously. It connects up to 14 popular apps, including GitHub, Notion, and the company’s own Lark office suite. Early reviews say the tool can feel clunky and has a high failure rate, but it clearly aims to match what Manus offers. Meanwhile, Zhipu AI has released a free agent called AutoGLM Rumination, built on its proprietary ChatGLM models. Shanghai‑based Minimax has launched Minimax Agent. Both products look almost identical to Manus and demo basic tasks such as building a simple website, planning a trip, making a small Flash game, or running quick data analysis. Despite the limited usability of most general AI agents launched within China, big companies have plans to change that. During a May 15 earnings call, Tencent president Liu Zhiping teased an agent that would weave automation directly into China’s most ubiquitous app, WeChat.  Considered the original super-app, WeChat already handles messaging, mobile payments, news, and millions of mini‑programs that act like embedded apps. These programs give Tencent, its developer, access to data from millions of services that pervade everyday life in China, an advantage most competitors can only envy. Historically, China’s consumer internet has splintered into competing walled gardens—share a Taobao link in WeChat and it resolves as plaintext, not a preview card. Unlike the more interoperable Western internet, China’s tech giants have long resisted integration with one another, choosing to wage platform war at the expense of a seamless user experience. But the use of mini‑programs has given WeChat unprecedented reach across services that once resisted interoperability, from gym bookings to grocery orders. An agent able to roam that ecosystem could bypass the integration headaches dogging independent startups. Alibaba, the e-commerce giant behind the Qwen model series, has been a front-runner in China's AI race but has been slower to release consumer-facing products. Even though Qwen was the most downloaded open-source model on Hugging Face in 2024, it didn’t power a dedicated chatbot app until early 2025. In March, Alibaba rebranded its cloud storage and search app Quark into an all-in-one AI search tool. By June, Quark had introduced DeepResearch—a new mode that marks its most agent-like effort to date.  ByteDance and Alibaba did not reply to MIT Technology Review’s request for comments. “Historically, Chinese tech products tend to pursue the all-in-one, super-app approach, and the latest Chinese AI agents reflect just that,” says Li of Simular, who previously worked at Google DeepMind on AI-enabled work automation. “In contrast, AI agents in the US are more focused on serving specific verticals.” Pei, the researcher at Stanford, says that existing tech giants could have a huge advantage in bringing the vision of general AI agents to life—especially those with built-in integration across services. “The customer-facing AI agent market is still very early, with tons of problems like authentication and liability,” he says. “But companies that already operate across a wide range of services have a natural advantage in deploying agents at scale.”

“Is that concrete all around, or is it in my head?” asked Ian Hunter in “All the Young Dudes,” the song David Bowie wrote for Mott the Hoople in 1972. Concrete is all around us, and we haven’t quite wrapped our heads around it. It’s one of the indispensable materials of modernity; as we try to decarbonize the built environment, it’s part of the problem, and innovations in its composition may become part of the solution. Understanding its history more clearly, the Skyscraper Museum’s new exhibition in Manhattan implies, just might help us employ it better. Concrete is “the second most used substance in the world, after water,” the museum’s founder/director/curator Carol Willis told AN during a recent visit. For plasticity, versatility, and compressive strength, reinforced concrete is hard to beat, though its performance is more problematic when assessed by the metric of embodied and operational carbon, a consideration the exhibition acknowledges up front. In tall construction, concrete has become nearly hegemonic, yet its central role, contend Willis and co-curator Thomas Leslie, formerly of Foster + Partners and now a professor at the University of Illinois, Urbana-Champaign, is underrecognized by the public and by mainstream architectural history. The current exhibition aims to change that perception. The Skyscraper Museum in Lower Manhattan features an exhibition, The Modern Concrete Skyscraper, which examines the history of material choices in building tall towers. (Courtesy the Skyscraper Museum) The Modern Concrete Skyscraper examines the history of tall towers’ structural material choices, describing a transition from the early dominance of steel frames to the contemporary condition, in which most large buildings rely on concrete. This change did not happen instantly or for any single reason but through a combination of technical and economic factors, including innovations by various specialists, well-recognized and otherwise; the availability of high-quality limestone deposits near Chicago; and the differential development of materials industries in nations whose architecture grew prominent in recent decades. As supertalls reach ever higher—in the global race for official height rankings by the Council on Tall Buildings and Urban Habitat (CTBUH) and national, corporate, or professional bragging rights—concrete’s dominance may not be permanent in that sector, given the challenge of pumping the material beyond a certain height. (The 2,717-foot Burj Khalifa, formerly Burj Dubai, uses concrete up to 1,987 and steel above that point; Willis quotes SOM’s William Baker describing it as “the tallest steel building with a concrete foundation of 156 stories.”) For the moment, however, concrete is ahead of its chief competitors, steel and (on a smaller scale) timber. Regardless of possible promotional inferences, Willis said, “we did not work with the industry in any way for this exhibition.” “The invention of steel and the grid of steel and the skeleton frame is only the first chapter of the history of the skyscraper,” Willis explained. “The second chapter, and the one that we’re in now, is concrete. Surprisingly, no one had ever told that story of the skyscraper today with a continuous narrative.” The exhibition traces the use of concrete back to the ancient Roman combination of aggregate and pozzolana—the chemical formula for which was “largely lost with the fall of the Roman Empire,” though some Byzantine and medieval structures approximated it. From there, the show explores comparable materials’ revival in 18th-century England, the patenting of Portland cement by Leeds builder Joseph Aspdin in 1824, the proof-of-concept concrete house by François Coignet in 1856, and the pivotal development of rebar in the mid-19th century, with overdue attention to Ernest Ransome’s 1903 Ingalls Building in Cincinnati, then the world’s tallest concrete building at 15 stories and arguably the first concrete skyscraper. The exhibition includes a timeline that depicts concrete’s origins in Rome to its contemporary use in skyscraper construction. (Courtesy the Skyscraper Museum) Baker’s lectures, Willis reported, sometimes pose a deceptively simple question: “‘What is a skyscraper?’ In 1974, when the World Trade Center and Sears Tower are just finished, you would say it’s a very tall building that is built of steel, an office building in North America. But if you ask that same question today, the answer is: It’s a building that is mixed-use, constructed of concrete, and [located] in Asia or the Middle East.” The exhibition organizes the history of concrete towers by eras of engineering innovation, devoting special attention to the 19th- and early-20th-century “patent era” of Claude Allen Porter Turner (pioneer in flat-slab flooring and mushroom columns) and Henry Chandlee Turner (founder of Turner Construction), Ransome (who patented twisted-iron rebar), and François Hennebique (known for the re-inforced concrete system exemplified by Liverpool’s Royal Liver Building, the world’s tallest concrete office building when completed in 1911). In the postwar era, “concrete comes out onto the surface [as] both a structural material and aesthetic.” Brutalism, perhaps to some observers’ surprise, “does not figure very large in high-rise design,” Willis said, except for Paul Rudolph’s Tracey Towers in the Bronx. The exhibition, however, devotes considerable attention to the work of Pier Luigi Nervi, Bertrand Goldberg (particularly Marina City), and SOM’s Fazlur Khan, pioneer of the structural tube system in the 1960s and 1970s—followed by the postmodernist 1980s, when concrete could express either engineering values or ornamentation. The exhibition highlights a number of concrete towers, including Paul Rudolph’s Tracey Towers in the Bronx. (Courtesy the Skyscraper Museum) “In the ’90s, there were material advances in engineering analysis and computerization that helped to predict performance, and so buildings can get taller and taller,” Willis said. The current era, if one looks to CTBUH rankings, is dominated by the supertalls seen in Dubai, Shanghai, and Kuala Lumpur, after the Petronas Towers (1998) “took the title of world’s tallest building from North America for the first time and traumatized everybody about that.” The previous record holder, Chicago’s Sears (now Willis) Tower, comprised steel structural tubes on concrete caissons; with Petronas, headquarters of Malaysia’s national petroleum company of that name, a strong concrete industry was represented but a strong national steel industry was lacking, and as Willis frequently says, form follows finances. In any event, by the ’90s concrete was already becoming the standard material for supertalls, particularly on soft-soiled sites like Shanghai, where its water resistance and compressive strength are well suited to foundation construction. Its plasticity is also well suited to complex forms like the triangular Burj, Kuala Lumpur’s Merdeka 118, and (if eventually completed) the even taller Jeddah Tower, designed to “confuse the wind,” shed vortices, and manage wind forces. Posing the same question Louis Kahn asked about the intentions of a brick, Willis said, with concrete “the answer is: anything you want.” The exhibition is front-loaded with scholarly material, presenting eight succinct yet informative wall texts on the timeline of concrete construction. The explanatory material is accompanied by ample photographs as well as structural models on loan from SOM, Pelli Clarke & Partners, and other firms. Some materials are repurposed from the museum’s previous shows, particularly Supertall! (2011–12) and Sky High and the Logic of Luxury (2013–14). The models allow close examination of the Burj Khalifa, Petronas Towers, Jin Mao Tower, Merdeka 118, and others, including two unbuilt Chicago projects that would have exceeded 2,000 feet: the Miglin-Beitler Skyneedle (Cesar Pelli/Thornton Tomasetti) and 7 South Dearborn (SOM). The Burj, Willis noted, was all structure and no facade for a time: When its curtain-wall manufacturer, Schmidlin, went bankrupt in 2006, it “ended up going to 100 stories without having a stitch of glass on it,” temporarily becoming a “1:1 scale model of the structural system up to 100 stories.” Its prominence justifies its appearance here in two models, including one from RWDI’s wind-tunnel studies. Eero Saarinen’s only skyscraper, built for CBS in 1965 and also known as “Black Rock,” under construction in New York City. (Courtesy Eero Saarinen Collection, Manuscripts, and Archives, Yale University Library) The exhibition opened in March, with plans to stay up at least through October (Willis prefers to keep the date flexible), with accompanying lectures and panels to be announced on the museum’s website (skyscraper.org). Though the exhibition’s full textual and graphic content is available online, the physical models alone are worth a trip to the Battery Park City headquarters. Intriguing questions arise from the exhibition without easy answers, setting the table for lively discussion and debate. One is whether the patenting of innovations like Ransome bar and the Système Hennebique incentivized technological progress or hindered useful technology transfer. Willis speculated, “Did the fact that there were inventions and patents mean that competition was discouraged, that the competition was only in the realm of business, rather than advancing the material?” A critical question is whether research into the chemistry of concrete, including MIT’s 2023 report on the self-healing properties of Roman pozzolana and proliferating claims about “green concrete” using alternatives to Portland cement, can lead to new types of the material with improved durability and lower emissions footprints. This exhibition provides a firm foundation in concrete’s fascinating history, opening space for informed speculation about its future. Bill Millard is a regular contributor to AN.

ZBrush, Maya, Marvelous Designer & Substance Painter Workflow with Antonio MossuccaLearn the entire process of bringing a character to life from 2D concept to 3D final render. This 8-hour workshop by Antonio Mossucca, a lookdev and character artist for Scanline VFX, Framestore and Axis Studios, shares the culmination of his skills learned throughout his career working at top studios. This workshop is intended for anyone who would like to gain a deeper understanding of how an anthropomorphic character/creature is created, from the initial ideation and reference gathering through to the final render. Foundational knowledge of ZBrush, Maya, Substance Painter, and Arnold is recommended. Antonio kicks off this workshop with a look at his approach to preparing for a 3D character project. He then demonstrates head and creature sculpting in ZBrush, instructs on how to create realistic clothing using Marvelous Designer, and explains how to texture the character using Substance 3D Painter. He then walks through the process of rendering the character in Arnold to create the final images for a portfolio piece.If you are passionate about character and creature art and want to know how to push your skills to the next level, or simply curious and want to learn more about how it’s done, this tutorial will provide essential insights to help you level up your 3D character/creature creation. Antonio provides his ZBrush User Interface and shortcuts as well as his Aces CG LUT files to use in Photoshop, plus the useful scripts he used in XGen and mGear for character rigging.WATCH NOW

Last year, China saw a boom in foundation models, the do-everything large language models that underpin the AI revolution. This year, the focus has shifted to AI agents—systems that are less about responding to users’ queries and more about autonomously accomplishing things for them.  There are now a host of Chinese startups building these general-purpose digital tools, which can answer emails, browse the internet to plan vacations, and even design an interactive website. Many of these have emerged in just the last two months, following in the footsteps of Manus—a general AI agent that sparked weeks of social media frenzy for invite codes after its limited-release launch in early March.  These emerging AI agents aren’t large language models themselves. Instead, they’re built on top of them, using a workflow-based structure designed to get things done. A lot of these systems also introduce a different way of interacting with AI. Rather than just chatting back and forth with users, they are optimized for managing and executing multistep tasks—booking flights, managing schedules, conducting research—by using external tools and remembering instructions.  China could take the lead on building these kinds of agents. The country’s tightly integrated app ecosystems, rapid product cycles, and digitally fluent user base could provide a favorable environment for embedding AI into daily life.  For now, its leading AI agent startups are focusing their attention on the global market, because the best Western models don’t operate inside China’s firewalls. But that could change soon: Tech giants like ByteDance and Tencent are preparing their own AI agents that could bake automation directly into their native super-apps, pulling data from their vast ecosystem of programs that dominate many aspects of daily life in the country.  As the race to define what a useful AI agent looks like unfolds, a mix of ambitious startups and entrenched tech giants are now testing how these tools might actually work in practice—and for whom. Set the standard It’s been a whirlwind few months for Manus, which was developed by the Wuhan-based startup Butterfly Effect. The company raised $75 million in a funding round led by the US venture capital firm Benchmark, took the product on an ambitious global roadshow, and hired dozens of new employees.  Even before registration opened to the public in May, Manus had become a reference point for what a broad, consumer‑oriented AI agent should accomplish. Rather than handling narrow chores for businesses, this “general” agent is designed to be able to help with everyday tasks like trip planning, stock comparison, or your kid’s school project.  Unlike previous AI agents, Manus uses a browser-based sandbox that lets users supervise the agent like an intern, watching in real time as it scrolls through web pages, reads articles, or codes actions. It also proactively asks clarifying questions, supports long-term memory that would serve as context for future tasks. "Manus represents a promising product experience for AI agents," says Ang Li, cofounder and CEO of Simular, a startup based in Palo Alto, California, that’s building computer use agents, AI agents that control a virtual computer. “I believe Chinese startups have a huge advantage when it comes to designing consumer products, thanks to cutthroat domestic competition that leads to fast execution and greater attention to product details.” In the case of Manus, the competition is moving fast. Two of the most buzzy follow‑ups, Genspark and Flowith, for example, are already boasting benchmark scores that match or edge past Manus’s.  Genspark, led by former Baidu executives Eric Jing and Kay Zhu, links many small “super agents” through what it calls multi‑component prompting. The agent can switch among several large language models, accepts both images and text, and carries out tasks from making slide decks to placing phone calls. Whereas Manus relies heavily on Browser Use, a popular open-source product that lets agents operate a web browser in a virtual window like a human, Genspark directly integrates with a wide array of tools and APIs. Launched in April, the company says that it already has over 5 million users and over $36 million in yearly revenue. Flowith, the work of a young team that first grabbed public attention in April 2025 at a developer event hosted by the popular social media app Xiaohongshu, takes a different tack. Marketed as an “infinite agent,” it opens on a blank canvas where each question becomes a node on a branching map. Users can backtrack, take new branches, and store results in personal or sharable “knowledge gardens”—a design that feels more like project management software (think Notion) than a typical chat interface. Every inquiry or task builds its own mind-map-like graph, encouraging a more nonlinear and creative interaction with AI. Flowith’s core agent, NEO, runs in the cloud and can perform scheduled tasks like sending emails and compiling files. The founders want the app to be a “knowledge marketbase”, and aims to tap into the social aspect of AI with the aspiration of becoming “the OnlyFans of AI knowledge creators”. What they also share with Manus is the global ambition. Both Genspark and Flowith have stated that their primary focus is the international market. A global address Startups like Manus, Genspark, and Flowith—though founded by Chinese entrepreneurs—could blend seamlessly into the global tech scene and compete effectively abroad. Founders, investors, and analysts that MIT Technology Review has spoken to believe Chinese companies are moving fast, executing well, and quickly coming up with new products.  Money reinforces the pull to launch overseas. Customers there pay more, and there are plenty to go around. “You can price in USD, and with the exchange rate that’s a sevenfold multiplier,” Manus cofounder Xiao Hong quipped on a podcast. “Even if we’re only operating at 10% power because of cultural differences overseas, we’ll still make more than in China.” But creating the same functionality in China is a challenge. Major US AI companies including OpenAI and Anthropic have opted out of mainland China because of geopolitical risks and challenges with regulatory compliance. Their absence initially created a black market as users resorted to VPNs and third-party mirrors to access tools like ChatGPT and Claude. That vacuum has since been filled by a new wave of Chinese chatbots—DeepSeek, Doubao, Kimi—but the appetite for foreign models hasn’t gone away.  Manus, for example, uses Anthropic’s Claude Sonnet—widely considered the top model for agentic tasks. Manus cofounder Zhang Tao has repeatedly praised Claude’s ability to juggle tools, remember contexts, and hold multi‑round conversations—all crucial for turning chatty software into an effective executive assistant. But the company’s use of Sonnet has made its agent functionally unusable inside China without a VPN. If you open Manus from a mainland IP address, you’ll see a notice explaining that the team is “working on integrating Qwen’s model,” a special local version that is built on top of Alibaba’s open-source model.  An engineer overseeing ByteDance’s work on developing an agent, who spoke to MIT Technology Review anonymously to avoid sanction, said that the absence of Claude Sonnet models “limits everything we do in China.” DeepSeek’s open models, he added, still hallucinate too often and lack training on real‑world workflows. Developers we spoke with rank Alibaba’s Qwen series as the best domestic alternative, yet most say that switching to Qwen knocks performance down a notch. Jiaxin Pei, a postdoctoral researcher at Stanford’s Institute for Human‑Centered AI, thinks that gap will close: “Building agentic capabilities in base LLMs has become a key focus for many LLM builders, and once people realize the value of this, it will only be a matter of time." For now, Manus is doubling down on audiences it can already serve. In a written response, the company said its “primary focus is overseas expansion,” noting that new offices in San Francisco, Singapore, and Tokyo have opened in the past month. A super‑app approach Although the concept of AI agents is still relatively new, the consumer-facing AI app market in China is already crowded with major tech players. DeepSeek remains the most widely used, while ByteDance’s Doubao and Moonshot’s Kimi have also become household names. However, most of these apps are still optimized for chat and entertainment rather than task execution. This gap in the local market has pushed China’s big tech firms to roll out their own user-facing agents, though early versions remain uneven in quality and rough around the edges.  ByteDance is testing Coze Space, an AI agent based on its own Doubao model family that lets users toggle between “plan” and “execute” modes, so they can either directly guide the agent’s actions or step back and watch it work autonomously. It connects up to 14 popular apps, including GitHub, Notion, and the company’s own Lark office suite. Early reviews say the tool can feel clunky and has a high failure rate, but it clearly aims to match what Manus offers. Meanwhile, Zhipu AI has released a free agent called AutoGLM Rumination, built on its proprietary ChatGLM models. Shanghai‑based Minimax has launched Minimax Agent. Both products look almost identical to Manus and demo basic tasks such as building a simple website, planning a trip, making a small Flash game, or running quick data analysis. Despite the limited usability of most general AI agents launched within China, big companies have plans to change that. During a May 15 earnings call, Tencent president Liu Zhiping teased an agent that would weave automation directly into China’s most ubiquitous app, WeChat.  Considered the original super-app, WeChat already handles messaging, mobile payments, news, and millions of mini‑programs that act like embedded apps. These programs give Tencent, its developer, access to data from millions of services that pervade everyday life in China, an advantage most competitors can only envy. Historically, China’s consumer internet has splintered into competing walled gardens—share a Taobao link in WeChat and it resolves as plaintext, not a preview card. Unlike the more interoperable Western internet, China’s tech giants have long resisted integration with one another, choosing to wage platform war at the expense of a seamless user experience. But the use of mini‑programs has given WeChat unprecedented reach across services that once resisted interoperability, from gym bookings to grocery orders. An agent able to roam that ecosystem could bypass the integration headaches dogging independent startups. Alibaba, the e-commerce giant behind the Qwen model series, has been a front-runner in China's AI race but has been slower to release consumer-facing products. Even though Qwen was the most downloaded open-source model on Hugging Face in 2024, it didn’t power a dedicated chatbot app until early 2025. In March, Alibaba rebranded its cloud storage and search app Quark into an all-in-one AI search tool. By June, Quark had introduced DeepResearch—a new mode that marks its most agent-like effort to date.  ByteDance and Alibaba did not reply to MIT Technology Review’s request for comments. “Historically, Chinese tech products tend to pursue the all-in-one, super-app approach, and the latest Chinese AI agents reflect just that,” says Li of Simular, who previously worked at Google DeepMind on AI-enabled work automation. “In contrast, AI agents in the US are more focused on serving specific verticals.” Pei, the researcher at Stanford, says that existing tech giants could have a huge advantage in bringing the vision of general AI agents to life—especially those with built-in integration across services. “The customer-facing AI agent market is still very early, with tons of problems like authentication and liability,” he says. “But companies that already operate across a wide range of services have a natural advantage in deploying agents at scale.”

Text embedding and reranking are foundational to modern information retrieval systems, powering applications such as semantic search, recommendation systems, and retrieval-augmented generation (RAG). However, current approaches often face key challenges—particularly in achieving both high multilingual fidelity and task adaptability without relying on proprietary APIs. Existing models frequently fall short in scenarios requiring nuanced semantic understanding across multiple languages or domain-specific tasks like code retrieval and instruction following. Moreover, most open-source models either lack scale or flexibility, while commercial APIs remain costly and closed. Qwen3-Embedding and Qwen3-Reranker: A New Standard for Open-Source Embedding Alibaba’s Qwen Team has unveiled the Qwen3-Embedding and Qwen3-Reranker Series—models that set a new benchmark in multilingual text embedding and relevance ranking. Built on the Qwen3 foundation models, the series includes variants in 0.6B, 4B, and 8B parameter sizes and supports a wide range of languages (119 in total), making it one of the most versatile and performant open-source offerings to date. These models are now open-sourced under the Apache 2.0 license on Hugging Face, GitHub, and ModelScope, and are also accessible via Alibaba Cloud APIs. These models are optimized for use cases such as semantic retrieval, classification, RAG, sentiment analysis, and code search—providing a strong alternative to existing solutions like Gemini Embedding and OpenAI’s embedding APIs. Technical Architecture Qwen3-Embedding models adopt a dense transformer-based architecture with causal attention, producing embeddings by extracting the hidden state corresponding to the [EOS] token. Instruction-awareness is a key feature: input queries are formatted as {instruction} {query}<|endoftext|>, enabling task-conditioned embeddings. The reranker models are trained with a binary classification format, judging document-query relevance in an instruction-guided manner using a token likelihood-based scoring function. The models are trained using a robust multi-stage training pipeline: Large-scale weak supervision: 150M synthetic training pairs generated using Qwen3-32B, covering retrieval, classification, STS, and bitext mining across languages and tasks. Supervised fine-tuning: 12M high-quality data pairs are selected using cosine similarity (>0.7), fine-tuning performance in downstream applications. Model merging: Spherical linear interpolation (SLERP) of multiple fine-tuned checkpoints ensures robustness and generalization. This synthetic data generation pipeline enables control over data quality, language diversity, task difficulty, and more—resulting in a high degree of coverage and relevance in low-resource settings. Performance Benchmarks and Insights The Qwen3-Embedding and Qwen3-Reranker series demonstrate strong empirical performance across several multilingual benchmarks. On MMTEB (216 tasks across 250+ languages), Qwen3-Embedding-8B achieves a mean task score of 70.58, surpassing Gemini and GTE-Qwen2 series. On MTEB (English v2): Qwen3-Embedding-8B reaches 75.22, outperforming other open models including NV-Embed-v2 and GritLM-7B. On MTEB-Code: Qwen3-Embedding-8B leads with 80.68, excelling in applications like code retrieval and Stack Overflow QA. For reranking: Qwen3-Reranker-0.6B already outperforms Jina and BGE rerankers. Qwen3-Reranker-8B achieves 81.22 on MTEB-Code and 72.94 on MMTEB-R, marking state-of-the-art performance. Ablation studies confirm the necessity of each training stage. Removing synthetic pretraining or model merging led to significant performance drops (up to 6 points on MMTEB), emphasizing their contributions. Conclusion Alibaba’s Qwen3-Embedding and Qwen3-Reranker Series present a robust, open, and scalable solution to multilingual and instruction-aware semantic representation. With strong empirical results across MTEB, MMTEB, and MTEB-Code, these models bridge the gap between proprietary APIs and open-source accessibility. Their thoughtful training design—leveraging high-quality synthetic data, instruction-tuning, and model merging—positions them as ideal candidates for enterprise applications in search, retrieval, and RAG pipelines. By open-sourcing these models, the Qwen team not only pushes the boundaries of language understanding but also empowers the broader community to innovate on top of a solid foundation. Check out the Paper, Technical details, Qwen3-Embedding and Qwen3-Reranker. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Coding Guide to Building an Iterative AI Workflow Agent Using LangGraph and GeminiAsif Razzaqhttps://www.marktechpost.com/author/6flvq/From Clicking to Reasoning: WebChoreArena Benchmark Challenges Agents with Memory-Heavy and Multi-Page TasksAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Mistral AI Introduces Mistral Code: A Customizable AI Coding Assistant for Enterprise WorkflowsAsif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA AI Releases Llama Nemotron Nano VL: A Compact Vision-Language Model Optimized for Document Understanding

Insights 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support AI-first strategies, leaders must progress through a deliberate journey: building foundational AI knowledge, cultivating an AI-first mindset, and honing AI-related skills, before confidently leading with AI. View the infographic Digital IntelligenceFuture of WorkTransformation Share this resource Share on LinkedIn Share on Facebook Share on X Share on WhatsApp Email this Page Connect with us Change isn’t easy, but we can help. Together we’ll create informed and inspired leaders ready to shape the future of your business. Contact us Latest Insights Strategic Alignment Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units Harvard Business Publishing announced the launch of Harvard Business Impact, a new brand identity for… Read more: Harvard Business Publishing Unveils Harvard Business Impact as New Brand for Corporate Learning and Education Units News Digital Intelligence Succeeding in the Digital Age: Why AI-First Leadership Is Essential While AI makes powerful operational efficiencies possible, it cannot yet replace the creativity, adaptability, and… Read more: Succeeding in the Digital Age: Why AI-First Leadership Is Essential Perspectives Digital Intelligence 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation AI has become a defining force in reshaping industries and determining competitive advantage. To support… Read more: 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation Infographic Talent Management Leadership Fitness Behavioral Assessment In our study, “Leadership Fitness: Developing the Capacity to See and Lead Differently Amid Complexity,”… Read more: Leadership Fitness Behavioral Assessment Job Aid The post 4 Keys to AI-First Leadership: The New Imperative for Digital Transformation appeared first on Harvard Business Impact.

Last year, China saw a boom in foundation models, the do-everything large language models that underpin the AI revolution. This year, the focus has shifted to AI agents—systems that are less about responding to users’ queries and more about autonomously accomplishing things for them.  There are now a host of Chinese startups building these general-purpose digital tools, which can answer emails, browse the internet to plan vacations, and even design an interactive website. Many of these have emerged in just the last two months, following in the footsteps of Manus—a general AI agent that sparked weeks of social media frenzy for invite codes after its limited-release launch in early March.  These emerging AI agents aren’t large language models themselves. Instead, they’re built on top of them, using a workflow-based structure designed to get things done. A lot of these systems also introduce a different way of interacting with AI. Rather than just chatting back and forth with users, they are optimized for managing and executing multistep tasks—booking flights, managing schedules, conducting research—by using external tools and remembering instructions.  China could take the lead on building these kinds of agents. The country’s tightly integrated app ecosystems, rapid product cycles, and digitally fluent user base could provide a favorable environment for embedding AI into daily life.  For now, its leading AI agent startups are focusing their attention on the global market, because the best Western models don’t operate inside China’s firewalls. But that could change soon: Tech giants like ByteDance and Tencent are preparing their own AI agents that could bake automation directly into their native super-apps, pulling data from their vast ecosystem of programs that dominate many aspects of daily life in the country.  As the race to define what a useful AI agent looks like unfolds, a mix of ambitious startups and entrenched tech giants are now testing how these tools might actually work in practice—and for whom. Set the standard It’s been a whirlwind few months for Manus, which was developed by the Wuhan-based startup Butterfly Effect. The company raised $75 million in a funding round led by the US venture capital firm Benchmark, took the product on an ambitious global roadshow, and hired dozens of new employees.  Even before registration opened to the public in May, Manus had become a reference point for what a broad, consumer‑oriented AI agent should accomplish. Rather than handling narrow chores for businesses, this “general” agent is designed to be able to help with everyday tasks like trip planning, stock comparison, or your kid’s school project.  Unlike previous AI agents, Manus uses a browser-based sandbox that lets users supervise the agent like an intern, watching in real time as it scrolls through web pages, reads articles, or codes actions. It also proactively asks clarifying questions, supports long-term memory that would serve as context for future tasks. “Manus represents a promising product experience for AI agents,” says Ang Li, cofounder and CEO of Simular, a startup based in Palo Alto, California, that’s building computer use agents, AI agents that control a virtual computer. “I believe Chinese startups have a huge advantage when it comes to designing consumer products, thanks to cutthroat domestic competition that leads to fast execution and greater attention to product details.” In the case of Manus, the competition is moving fast. Two of the most buzzy follow‑ups, Genspark and Flowith, for example, are already boasting benchmark scores that match or edge past Manus’s.  Genspark, led by former Baidu executives Eric Jing and Kay Zhu, links many small “super agents” through what it calls multi‑component prompting. The agent can switch among several large language models, accepts both images and text, and carries out tasks from making slide decks to placing phone calls. Whereas Manus relies heavily on Browser Use, a popular open-source product that lets agents operate a web browser in a virtual window like a human, Genspark directly integrates with a wide array of tools and APIs. Launched in April, the company says that it already has over 5 million users and over $36 million in yearly revenue. Flowith, the work of a young team that first grabbed public attention in April 2025 at a developer event hosted by the popular social media app Xiaohongshu, takes a different tack. Marketed as an “infinite agent,” it opens on a blank canvas where each question becomes a node on a branching map. Users can backtrack, take new branches, and store results in personal or sharable “knowledge gardens”—a design that feels more like project management software (think Notion) than a typical chat interface. Every inquiry or task builds its own mind-map-like graph, encouraging a more nonlinear and creative interaction with AI. Flowith’s core agent, NEO, runs in the cloud and can perform scheduled tasks like sending emails and compiling files. The founders want the app to be a “knowledge marketbase”, and aims to tap into the social aspect of AI with the aspiration of becoming “the OnlyFans of AI knowledge creators”. What they also share with Manus is the global ambition. Both Genspark and Flowith have stated that their primary focus is the international market. A global address Startups like Manus, Genspark, and Flowith—though founded by Chinese entrepreneurs—could blend seamlessly into the global tech scene and compete effectively abroad. Founders, investors, and analysts that MIT Technology Review has spoken to believe Chinese companies are moving fast, executing well, and quickly coming up with new products.  Money reinforces the pull to launch overseas. Customers there pay more, and there are plenty to go around. “You can price in USD, and with the exchange rate that’s a sevenfold multiplier,” Manus cofounder Xiao Hong quipped on a podcast. “Even if we’re only operating at 10% power because of cultural differences overseas, we’ll still make more than in China.” But creating the same functionality in China is a challenge. Major US AI companies including OpenAI and Anthropic have opted out of mainland China because of geopolitical risks and challenges with regulatory compliance. Their absence initially created a black market as users resorted to VPNs and third-party mirrors to access tools like ChatGPT and Claude. That vacuum has since been filled by a new wave of Chinese chatbots—DeepSeek, Doubao, Kimi—but the appetite for foreign models hasn’t gone away.  Manus, for example, uses Anthropic’s Claude Sonnet—widely considered the top model for agentic tasks. Manus cofounder Zhang Tao has repeatedly praised Claude’s ability to juggle tools, remember contexts, and hold multi‑round conversations—all crucial for turning chatty software into an effective executive assistant. But the company’s use of Sonnet has made its agent functionally unusable inside China without a VPN. If you open Manus from a mainland IP address, you’ll see a notice explaining that the team is “working on integrating Qwen’s model,” a special local version that is built on top of Alibaba’s open-source model.  An engineer overseeing ByteDance’s work on developing an agent, who spoke to MIT Technology Review anonymously to avoid sanction, said that the absence of Claude Sonnet models “limits everything we do in China.” DeepSeek’s open models, he added, still hallucinate too often and lack training on real‑world workflows. Developers we spoke with rank Alibaba’s Qwen series as the best domestic alternative, yet most say that switching to Qwen knocks performance down a notch. Jiaxin Pei, a postdoctoral researcher at Stanford’s Institute for Human‑Centered AI, thinks that gap will close: “Building agentic capabilities in base LLMs has become a key focus for many LLM builders, and once people realize the value of this, it will only be a matter of time.” For now, Manus is doubling down on audiences it can already serve. In a written response, the company said its “primary focus is overseas expansion,” noting that new offices in San Francisco, Singapore, and Tokyo have opened in the past month. A super‑app approach Although the concept of AI agents is still relatively new, the consumer-facing AI app market in China is already crowded with major tech players. DeepSeek remains the most widely used, while ByteDance’s Doubao and Moonshot’s Kimi have also become household names. However, most of these apps are still optimized for chat and entertainment rather than task execution. This gap in the local market has pushed China’s big tech firms to roll out their own user-facing agents, though early versions remain uneven in quality and rough around the edges.  ByteDance is testing Coze Space, an AI agent based on its own Doubao model family that lets users toggle between “plan” and “execute” modes, so they can either directly guide the agent’s actions or step back and watch it work autonomously. It connects up to 14 popular apps, including GitHub, Notion, and the company’s own Lark office suite. Early reviews say the tool can feel clunky and has a high failure rate, but it clearly aims to match what Manus offers. Meanwhile, Zhipu AI has released a free agent called AutoGLM Rumination, built on its proprietary ChatGLM models. Shanghai‑based Minimax has launched Minimax Agent. Both products look almost identical to Manus and demo basic tasks such as building a simple website, planning a trip, making a small Flash game, or running quick data analysis. Despite the limited usability of most general AI agents launched within China, big companies have plans to change that. During a May 15 earnings call, Tencent president Liu Zhiping teased an agent that would weave automation directly into China’s most ubiquitous app, WeChat.  Considered the original super-app, WeChat already handles messaging, mobile payments, news, and millions of mini‑programs that act like embedded apps. These programs give Tencent, its developer, access to data from millions of services that pervade everyday life in China, an advantage most competitors can only envy. Historically, China’s consumer internet has splintered into competing walled gardens—share a Taobao link in WeChat and it resolves as plaintext, not a preview card. Unlike the more interoperable Western internet, China’s tech giants have long resisted integration with one another, choosing to wage platform war at the expense of a seamless user experience. But the use of mini‑programs has given WeChat unprecedented reach across services that once resisted interoperability, from gym bookings to grocery orders. An agent able to roam that ecosystem could bypass the integration headaches dogging independent startups. Alibaba, the e-commerce giant behind the Qwen model series, has been a front-runner in China’s AI race but has been slower to release consumer-facing products. Even though Qwen was the most downloaded open-source model on Hugging Face in 2024, it didn’t power a dedicated chatbot app until early 2025. In March, Alibaba rebranded its cloud storage and search app Quark into an all-in-one AI search tool. By June, Quark had introduced DeepResearch—a new mode that marks its most agent-like effort to date.  ByteDance and Alibaba did not reply to MIT Technology Review’s request for comments. “Historically, Chinese tech products tend to pursue the all-in-one, super-app approach, and the latest Chinese AI agents reflect just that,” says Li of Simular, who previously worked at Google DeepMind on AI-enabled work automation. “In contrast, AI agents in the US are more focused on serving specific verticals.” Pei, the researcher at Stanford, says that existing tech giants could have a huge advantage in bringing the vision of general AI agents to life—especially those with built-in integration across services. “The customer-facing AI agent market is still very early, with tons of problems like authentication and liability,” he says. “But companies that already operate across a wide range of services have a natural advantage in deploying agents at scale.”

Why building trust matters more than building components.There’s a scene in Field of Dreams that regularly comes to mind. Ray Kinsella (Kevin Costner) is standing at the edge of his cornfield ballpark, full of doubt about his ball field project. And then Terrence Mann (James Earl Jones) delivers this quiet but powerful monologue…James Earl Jones as Terrence Mann gives an inspiring speech“People will come, Ray. They’ll come to Iowa for reasons they can’t even fathom… They’ll arrive at your door as innocent as children, longing for the past… They’ll pass over the money without even thinking about it — for it is money they have and peace they lack.”It’s powerful. And there’s temptation for designers to think this way. That, if we just build it properly, people will come and use it. It’ll be a raving success. But that kind of thinking rests on emotion and hope. “People will come, Ray.”That got me thinking about my work with design systems. Because early on, that’s exactly how I imagined it would work.No…they won’t just come.I thought if you just built a great design system, people would come running from all over to use it. Designers would geek out. Engineers would contribute. PMs would rally around the time savings and consistency. I thought the system would basically sell itself. Job done.Except not. Not at first. In fact, most of those people actively resisted it. And it left me frustrated trying to figure out what was going on.That’s when it hit me: people don’t come just because you built something. They come because you built something that includes them. This isn’t a fictional baseball field built on faith. They don’t come because they don’t know it. It’s foreign. And as humans, we tend to run away from what we don’t understand.That’s why the people who work on design systems are so important. So that you can be known and your systems can be known. So that a relationship can be established. Trust can be created and people can see that this system will work for them. In fact, it’s been purpose-built for them.Design Systems are political (it’s true)I never would have imagined how political design systems would be. They seem logical and straightforward. It’s everything you need to build interfaces and consistent experiences. So, I thought the quality of the work would speak for itself. But once your system meets the real world, things get complicated. Real fast.Everyone already has established working patterns and people aren’t usually inclined to change. Plus, designers have their opinions. Engineers have priorities. Product managers have launch dates. And everyone already has a “good-enough solution”. So, now your carefully crafted design system feels more like a threat or liability than the life-changing birthday present you thought it’d be.You thought you were offering something amazing. They hear restriction. They see more steps. And they’re annoyed — at you — for meddling in their perfectly stable world.Why? Well, people love their tools. And, I mean LOVE them. They deeply cherish them. I think of the carpenter who’s used the same, trusty 20oz wood-handled hammer for 40 years. It’s weathered difficult projects and has dents and stains from 1,000s of projects. How do you convince someone to leave that hammer behind and pick up a new, unproven hammer for their work? It usually happens in community…in relationships.So, it turns out, building the system was the easy part. Getting people to care about it? That’s the real work. And that’s where building relationships comes in.The real work: building relationshipsTrue adoption doesn’t happen through documentation or a flashy campaign.So you can’t rely on a “build it and they will come” mentality. It means you need to make time to understand the people you’re building for and with. Because if it isn’t theirs, it won’t matter how good it is. Every group has different motivations, pain points, and goals. If you want them on board, you have to speak to them, about them, and how your system will help them.Designers need to see how the system supports creativity, not stifles it. Demonstrate how it will help them be more effective in their work.Engineers care about stability, performance, and clean code. Show them the efficiency that it brings to their development pipelines.PMs are focused on delivery. Make the system reduce friction and risk.Executives want the business case to be clear. Show them how your system enables faster velocity, better consistency, and reduced maintenance cost.Establishing how individual goals come together as shared goals is critical.Sounds selfish, right? Not really. They’re paid to do their job, and if you want your design system to be successful, it has to make them successful. So if you can’t answer a need to any of your partners or stakeholders, go back and figure out how to create that kind of value…or start with the people where the value already is.It’s the relationships that shift the dynamic. Suddenly it’s not “my design system vs. their priorities”…it’s shared ownership…our collective win.You can’t enforce your way to powerful adoptionYour first instinct might be to skip the relationships and rely on the “because I said so” method. So you add mandates. Governance councils. Approval gates. But none of them really work. You’ll have people subvert the system, or hold so fearfully tight to it that it hurts the product experience in the end.People don’t adopt systems because they have to. They adopt them because they believe in them. And belief is earned, not enforced. Your job is to find the intrinsic motivation that will cause them to jump on board and be a raving fan spreading the good news of your system. And that can’t be a marketing slogan or tagline; it has to be built into the design system.The goal isn’t to control usage…it’s to cultivate trust, leading to usage. It’s better to have ten enthusiastic partners than a hundred reluctant rule-followers. Because those ten partners? They’ll advocate for you. They’ll give real feedback. They’ll make the system better.It sounds uncomfortable, but it’s best to trade policing behavior for building partnerships. That’s the moment your system will truly grow. Because trust is a currency that earns interest.Trust is the real foundationDesign systems rely on a strong foundation: principles, guidelines, tokens, styles, components, and more. But that foundation must be built on something critical: trust.Trust is what makes someone choose the system instead of rolling their own. It’s what keeps your system on their radar when everything’s on fire. It’s what makes people reach out to work it out together, instead of working around you and subverting the whole thing.Without trust, your system is just a nice idea. It’s relegated to pixel art. But with relationships that foster trust…they see how the system becomes indispensable. It’s their go-to secret weapon for success.Trust and relationships are the ground that all foundations are built on.I’ve been on both sides. I’ve had teams avoid the system because they didn’t trust it…because they didn’t trust me. Here’s the secret: I know them or how to serve them…no trust. On the flip side, I’ve seen teams move faster, smoother, and more confidently because we’d built a foundation of partnership over time.Trust isn’t a side effect. It’s not a nice-to-have. It’s the ground that holds the whole thing up. Without it, your design system is drifting in the outer cosmos. Relationships are the gravity that keeps your design system grounded in trust.They’ll come… but only if you earn itSo, I hate to break it to you, but the truth is…no one’s coming just because your system is well-made. There’s already a plethora of well-made systems out there ready to leave you disappointed.They’re busy with the newest top priority. They’ve been burned by “that type of system” before. They see you as a risk, not a partner. But…if you spend time knowing them… if you listen… if you build trust… if you make it feel like it’s theirs… well then… “…they will come…”They’ll message you before they start a new feature. They’ll advocate for tokens during sprint planning. They’ll tell others it saved them time. They’ll ask how they can help improve it.They won’t come for the components. They’ll come for what the system gives them: clarity. Consistency. Relief.And if you’ve done the hard, human work behind the system? Well then, you might just look up one day and realize that there’s a whole bunch of people in your Iowa ball field.The politics of Design Systems was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Here are the answers for The New York Times Mini Crossword for June 5.