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Business Insider
22-07-2025
- Business
- Business Insider
How Anthropic got so good at coding
Anthropic has become the dominant provider of AI coding intelligence, and the startup's success has sparked a wave of soul-searching, theorizing, and "code red" scrambles across Silicon Valley. The goal of this frantic activity is to find out how Anthropic got so good at coding. "That's the trillion-dollar question," said Quinn Slack, CEO of startup Sourcegraph, which relies on Anthropic models. "It's like, why is Coca Cola is better than Pepsi?" Elon Musk wants to know. His xAI startup has been trying to topple Anthropic lately. Mark Zuckerberg's mad dash for AI talent and infrastructure is partly driven by the same quest to understand Anthropic's coding lead and catch up. There's a lot at stake here. Since Anthropic's AI coding breakthrough just over a year ago, revenue has surged. It's pulling in billions of dollars now, mostly from other companies paying for access to its models for coding tasks. The startup may soon be worth $100 billion. Floored by a model Sourcegraph's Slack remembers the exact moment when he realized Anthropic had a major breakthrough on its hands. This was June 2024, when Anthropic released its Claude Sonnet 3.5 model. Slack was floored. "We immediately said, 'this model is better than anything else out there in terms of its ability to write code at length' — high-quality code that a human would be proud to write," he said. Slack quickly arranged a meeting at Sourcegraph and announced that Sonnet 3.5 would be their default AI model, providing the underlying intelligence that powers the startup's coding service for developers. And he gave it away for free. Some colleagues wanted more time to evaluate if such a drastic move made sense financially. But Slack insisted. "Anthropic changed everything," he said. "And as a startup, if you're not moving at that speed, you're gonna die." The go-to vibe coding platform Just over a year later, Anthropic models power most of the top AI coding services, including Cursor, Augment, and Microsoft's GitHub Copilot. Even Meta uses Anthropic models to support its Devmate internal coding assistant. AI coding startup Windsurf was going to be acquired by OpenAI, but Anthropic cut off access to its Claude models, and the deal crumbled. Now Windsurf is back using Anthropic. All those videos on social media of teenagers vibe coding new apps and websites? Impossible without Anthropic's AI breakthrough in June 2024. What's even more surprising is that Anthropic's AI coding lead has endured. Its latest models, including Claude Sonnet 4, are still the best at coding more than a year later. That's almost unheard of in AI, when new advancements seem to pop up every day. Trying to answer the trillion-dollar question Silicon Valley hasn't given up trying to crack open Anthropic's AI coding secrets. A few years ago, Anthropic would have published a long research paper detailing the data, techniques, and architecture it used to get Sonnet 3.5 to be a coding expert. Nowadays, though, competition is so fierce that all the AI labs keep their AI sauce super secret. However, in a recent interview with Business Insider, Anthropic executive Dianne Penn, shared some clues on how the startup made this breakthrough. Cofounder Ben Mann also discussed some successful techniques recently on a podcast. BI also interviewed several CEOs and founders of AI coding startups that rely on Anthropic AI models, along with a coding expert from MIT. Let's start with Eric Simons, the ebullient CEO of Stackblitz, the startup behind blockbuster vibe coding service Simons thinks Anthropic had its existing models write code and deploy it. Then, the company evaluated all the deployed code, through a combination human expertise and automated AI analysis. With software coding, it's relatively easy to evaluate good versus bad outputs. That's because the code either works, or it doesn't, when deployed. This creates clear YES and NO signals that are really valuable for training and fine-tuning new AI models, he explained. Anthropic took these signals and funneled them into the training data and development process for the new Sonnet AI models. This reinforcement-learning strategy produced AI models that were much better at coding, according to Simons, who was equally blown away by Sonnet 3.5's abilities in the summer of 2024. Human versus AI evaluations Anthropic cofounder Ben Mann appeared on a podcast recently and seemed to revel in the idea that the rest of Silicon Valley still hadn't caught up with his startup's AI coding abilities. "Other companies have had, like, code reds for trying to catch up in coding capabilities for quite a while and have not been able to do it," he said. "Honestly, I'm kind of surprised that they weren't able to catch up, but I'll take it." Still, when pushed for answers, he explained some of the keys to Anthropic's success here. Mann built Anthropic's human feedback data system in 2021. Back then, it was relatively easy for humans to evaluate signals, such as whether model output A was better than B, and feed that back into the AI development process via a popular technique known as Reinforcement Learning from Human Feedback, or RLHF. "As we've trained the models more and scaled up a lot, it's become harder to find humans with enough expertise to meaningfully contribute to these feedback comparisons," Mann explained on the No Priors podcast. "For coding, somebody who isn't already an expert software engineer would probably have a lot of trouble judging whether one thing or another was better." So, Anthropic pioneered a new approach called Reinforcement Learning from AI Feedback, or RLAIF. Instead of humans evaluating AI model outputs, other models would do the analysis. To make this more-automated technique work, Anthropic wrote a series of principals in English for its models to adhere to. The startup called it Constitutional AI. "The process is very simple," Mann said. "You just take a random prompt like 'How should I think about my taxes?' and then you have the model write a response. Then you have the model criticize its own response with respect to one of the principles, and if it didn't comply with the principle, then you have the model correct its response." For coding, you can give the AI models principles such as "Did it actually serve the final answer?" or "Did it do a bunch of stuff that the person didn't ask for?" or "Does this code look maintainable?" or "Are the comments useful and interesting?" Mann explained. Dr. Mann's empirical method Elad Gil, a top AI investor and No Priors host, concurred, saying the clear signals from deploying code and seeing it if works, makes this process fruitful. "With coding, you actually have like a direct output that you can measure: You can run the code, you can test the code," he said. "There's sort of a baked-in utility function you can optimize against." Mann cited an example from his father, who was a physician. One day, a patient came in with a skin condition on his face, and Dr. Mann couldn't find what the problem was. So, he divided the patient's face into sections and applied different treatments. One area cleared up, revealing the answer empirically. "Sometimes you just won't know and you have to try stuff — and with code that's easy because we can just do it in a loop," Anthropic's Mann said. Constitutional AI and beyond In an interview with BI, Anthropic's Penn described other ingredients that went into making the startup's models so good at coding. She said the description from Simons, the StackBlitz CEO, was "generally true," while noting that Anthropic's coding breakthrough was the result of a multiyear effort involving many researchers and lots of ideas and techniques. "We fundamentally made it good at writing code, or being able to figure out what good code looks like, through what you can consider as trial and iterations," she said. "You're giving the model different questions and allowing it to figure out what the right answer is on a coding problem." When asked about the role of Constitutional AI, Penn said she couldn't share too much detail on the exact techniques, but said "it's definitely in the models." Using tools with no hands Anthropic also trained Sonnet 3.5 to be much better at using tools, a key focus that has begun to turn AI models from chatbots into more general-purpose agents — what the startup calls "virtual collaborators." "They don't have hands," Penn said, so instead, Anthropic's models were trained to write code themselves to access digital tools. For example, she said that if an Anthropic model is asked for weather information or stock prices, it can write software to tap into an application programming interface, or API, a common way for apps to access data. Following instructions When software coding projects get really big, you can't knock out the work in a few minutes. The more complex tasks take days, weeks, or longer. AI models have been incapable of sticking with long-term jobs like these. But Anthropic invested heavily in making Sonnet 3.5 and later models much better at following human instructions. This way, if the model gets stumped on a long coding problem, it can take guidance from developers to keep going — essentially listening better to understand the intent of its human colleagues, Penn explained. (Hey, we can all get better at that). Knowing what to remember Even the best human software developers can't keep everything related to a coding project in their brains. GitHub repositories, holding code, images, documentation, and revision histories, can be massive. So Anthropic trained is AI models to create a kind of scratch pad where it jots down notes in an external file system as it's exploring things like a code base. "We train it to use that tool very well," Penn said (while I frantically scribbled notes on my own reporting pad). The key here is that Anthropic's models were trained to remember more of the salient details of coding projects, and ignore the less important stuff. "It's not useful to say, 'Dianne is wearing a colored shirt in this conversation, and Alistair is wearing a green shirt,'" Penn said, describing the BI interview taking place at that moment. "It's more important to note that we talked about coding and how Anthropic focused on coding quality." This better use of memory means that Anthropic models can suggest multiple code changes over the course of an entire project, something that other AI models aren't as good at. "If it's not trained well, it could scribble the wrong things," Penn told me. "It's gotten really good at those things. So it actually does not just mean in the short term that it can write good code, but it remembers to write data so that it might make a second or third change that another AI model might not know, because the quality of its notes, plus the quality of its core intelligence, are better." Claude Code and terminal data For a while, in around 2022, it looked like AI progress was happening automatically, through more data, more GPUs, and bigger training runs. "The reality is that there are very discrete breakthroughs, and very discrete ideas that lead to these breakthroughs," said Armando Solar-Lezama, a distinguished professor of computing at MIT. "It takes researchers, and investment in research, to produce the next idea that leads to the next breakthrough." This is how Anthropic's hard-won coding lead happened. But access to detailed, granular data on how human developers write software is crucial to stay ahead in this part of the AI race, he added. Andrew Filev has a theory related to this. He's CEO of Zencoder, another AI coding service that uses Anthropic's models. Filev thinks that data from computer terminal use is key to training AI models to be good at coding. A terminal is a text-based interface that lets developers send instructions to a computer's operating system or software. They type in information via a "command line," and hopefully get outputs. "Large language models are great with text," he told me in a recent interview about Anthropic. "The computer terminal, where you keep commands, is basically text, too. So at some point, people realized that they should just give that data to their AI model, and it can do amazing things — things which previously had never worked." In late May, Anthropic rolled out Claude Code, a command line tool for AI coding that works with developers' existing terminals. Suddenly, Anthropic is now competing against its main customers — all those other AI coding services. The move also created a direct relationship between Anthropic and developers, giving the AI lab access to a richer source of data on how expert humans write software. "The amount and the speed that we learn is much less if we don't have a direct relationship with our coding users," Anthropic's Mann said. "So launching Claude Code was really essential for us to get a better sense of what do people need, how do we make the models better, and how do we advance the state-of-the-art?" In theory, this granular information could be used to help train and fine-tune Anthropic's next models, potentially giving the startup a data edge that might preserve its AI coding lead even longer. "Could I do this without Anthropic's latest models? No," said Sourcegraph's Slack. "And would their models be as good without Claude Code? I don't think so."
Yahoo
16-07-2025
- Business
- Yahoo
Supply Chain AI Symposium to feature execs from Augment, project44, GenLogs, and HappyRobot
FreightWaves is gearing up to host its pioneering Supply Chain AI Symposium at the historic International Spy Museum in Washington, DC, on July 30, 2025. The event promises to bring together a dynamic mix of industry leaders, innovators, and AI enthusiasts who are at the forefront of transforming the logistics landscape. Among the notable speakers at the symposium are Harish Abbott, Jett McCandless, Ryan Joyce, and Javi Palafox, whose experiences and insights into AI-driven advancements in FreightTech will help symposium attendees stay on the leading edge. Harish Abbott, the CEO and Co-founder of Augment, stands out as a visionary in logistics technology. With an impressive background in e-commerce fulfillment and AI innovations, Abbott's latest venture, Augment, is at the cutting-edge of enhancing productivity within the logistics industry. Augment's platform focuses on automating routine tasks, thus boosting operator efficiency and turning complex operational challenges into innovative solutions. Abbott's experience includes substantial roles at Amazon and the successful founding of companies like Deliverr, which have been pivotal in facilitating rapid, scalable logistics solutions across global supply networks. His leadership at Augment continues to address inefficiencies in logistics through AI, wielding the potential to drive substantial improvements in supply chain operations. Jett McCandless, the Founder and CEO of project44, is another highlight of the symposium. Honored for his innovative contributions to supply chain intelligence, McCandless has been instrumental in redefining supply chain visibility. Project44, under his guidance, has become a trailblazer in utilizing SaaS technology to promote real-time visibility across global logistics networks. The company integrates data and automation to optimize transportation processes, significantly enhancing supply chain resilience and operational efficiency. McCandless, with over 20 years of experience, brings a wealth of knowledge in aligning traditional logistics practices with cutting-edge technology, having previously served in executive roles contributing to scaling operations and strategic growth at GlobalTranz. Ryan Joyce, co-founder and CEO of GenLogs, merges his intelligence community expertise with logistics innovation. Having spent over a decade in U.S. intelligence and counter-terrorism, Joyce brings a unique perspective to the logistics industry by applying counter-terrorism methodologies to combat freight fraud and inefficiency. Through GenLogs' cutting-edge freight intelligence platform, Joyce enhances supply chain visibility with real-time data insights using a nationwide sensor network. His leadership aims to provide brokers, carriers, and shippers with actionable intelligence to improve security and operational efficiency within the $7 trillion logistics market. Javi Palafox, Co-Founder and COO of HappyRobot, blends financial strategy and technological innovation. Transitioning from corporate finance into startup innovation, Palafox has played a crucial role in developing AI-driven communication tools designed for logistics. HappyRobot's voice agents automate numerous logistical operations, aiming to address inefficiencies and reduce operational costs significantly. Palafox's role is pivotal in leveraging AI to enhance operational efficiency while ensuring seamless integration within existing logistics frameworks. This focus on product development and fundraising, along with his strategic approach to using AI for real-world problems, makes him a valuable addition to the symposium's roster of speakers. The Supply Chain AI Symposium will delve into topics beyond individual company achievements, fostering an environment of knowledge exchange through panel discussions, case studies, and keynote addresses. Attendees will gain insights into innovative AI applications in logistics, exploring how these technologies are revolutionizing supply chain efficiency, visibility, and sustainability. As the logistics sector sits on the brink of a new digital era, participation in the Supply Chain AI Symposium is crucial for industry veterans and emerging innovators alike. Registrations for the event are already open and spots are expected to fill quickly. Interested participants are encouraged to secure their place promptly to access the full range of opportunities available at this transformative gathering. Mark your calendars for July 30, 2025, and prepare to engage with leading minds who are driving the future of AI in logistics. The post Supply Chain AI Symposium to feature execs from Augment, project44, GenLogs, and HappyRobot appeared first on FreightWaves.
Yahoo
23-03-2025
- Business
- Yahoo
Byte-Sized AI: Walmart Announces Gen AI Merchant Tool; Oxford Industries Partners With Exotec
Byte-Sized AI is a bi-weekly column that covers all things artificial intelligence—from startup funding, to newly inked partnerships, to just-launched, AI-powered capabilities from major retailers, software providers and supply chain players. Walmart announced this week that it has deployed a generative AI-based assistant for its merchants. More from Sourcing Journal What Makes Generative AI for Design Difficult to Conquer at Scale? Up Close: In Conversation With Gaia Dynamics CEO Emil Stefanutti Flexport Alleges Freightmate AI Founders Stole and Used Its Trade Secrets to Build Company The tool, called Wally, can help employees automate data entry, generate insights about a data set, figure out the reasons associated with a product's performance and calculate necessary forecasts to help the merchant with decisioning. Like many other AI assistant tools, Walmart notes that Wally does not require technical expertise. Wally is trained to answer employees' natural language questions. The company said it expects that Wally can help merchants 'focus on strategic, creative and innovative activities that enhance customer experiences and meet evolving customer expectations,' rather than using that time on repetitive, time-consuming tasks. Per Walmart's blog post, the company developed Wally itself, using a conglomeration of its own business data and company needs, with a focus on 'ultimately enabling it to act autonomously on the merchant's behalf within configurable guardrails, executing tactical actions necessary to bring their strategy to life.' Doug McMillon, president and CEO of Walmart, gave a short sneak peek at the tool on the company's Q4 earnings call late last month, also noting that some internal employees are using AI tools to aid their coding efforts. McMillon said technology continues to be an important part of Walmart's way forward. 'As we become more productive and reduce the amount of time we work on routine tasks, that gives us time to develop tools that help us grow the business and move faster,' he said. 'I love how we're changing how we think and work without changing who we are. I can see us getting faster.' San Francisco-headquartered startup Augment has secured a $25 million seed round, led by 8VC, it announced Tuesday. The startup aims to use AI to solve inefficiencies in the logistics landscape. Its marquis product, which it calls Augie, is an AI-powered assistant that helps freight industry operators automate time-consuming, mundane tasks for greater efficiency and accuracy. Augie can place and take calls, or send texts and emails, related to shipment location, issues with deliveries and other related issues. It can also partially automate workflows for truckload (FTL), less-than-truckload (LTL) and drayage shipments. It interacts with human operators via a dashboard and via employees' own communication tools, like Slack. Harish Abbott, co-founder and CEO of Augment, said the fundraising round emphasizes investors' belief in the transformative impact AI could have on the at-large logistics industry when deployed against meaningful use cases. 'We are applying AI to logistics, one of the largest and most complex industries, to drive transformative change,' said Abbott. 'Augie is like an assistant to every operator in the freight industry, Augie performs the tedious and mundane tasks so the operators can focus on the important and urgent. Harish previously founded Deliverr, previously owned by Shopify and now owned by Flexport. The startup, which has offices in San Francisco, Chicago and Toronto, plans to use the funds to further build out its logistics-focused platform and to increase headcount on its engineering and customer success teams. Arrive Logistics, a brokerage, is one of Augment's first customers. Matt Pyatt, founder and CEO of Arrive, said the technology will help his company continue to provide strong service to its own clients. 'We partnered with Augment to build a multi-functional AI assistant, giving our team another tool to spend more time on value-added parts of their jobs and delivering a better experience to our partners,' Pyatt said in a statement. 'The Augment team has exceeded our expectations as a partner, shadowing our reps in house, learning the business and building solutions that make sense for our operation.' Automation on the backend of logistics is becoming increasingly popular in the industry; Flexport recently shared further information about a slew of AI-based tools it plans to use to streamline operations for customers. Oxford Industries, which owns brands like Lilly Pulitzer, Tommy Bahama and Johnny Was, has selected AI-enabled warehousing robotics vendor Exotec to install automated systems in its new distribution center in Lyons, Ga. Oxford will integrate Exotec's Skypod system, which uses robots to pick and carry up to 66 pounds' worth of goods around the warehouse and uses cameras to sense and respond to any obstacles on the distribution center floor. Exotec's systems use machine learning, deep learning and proprietary algorithms to effectively identify, move, sort, pick and otherwise handle items in a client's warehouse. Oxford's newest distribution center, slated to open in the latter half of the year, will use over 450 robots to sort, pack and ship items out of the facility, which boasts more than 560,000 square feet. According to Exotec, the partners also hope to use the robots to help with returns-based challenges by using the robots to 'significantly [cut] the amount of time and labor needed to inspect, sort and store returned items, streamlining the process of making it available for resale.' Romain Moulin, CEO and co-founder of Exotec, said this project marks the company's largest deployment to date. It already works with clients like Uniqlo and Gap. 'The Oxford Industries project not only showcases the performance and scalability of our system, but also the sophistication or our integration capabilities,' Moulin said in a statement. 'Having a client like Oxford Industries select Exotec as the integrator for a project of this complexity speaks volumes about the trust they put in our ability to deliver end-to-end warehouse automation that goes beyond our standard Skypod system.' Earlier this month, Lowe's launched Mylow, a generative AI-powered chatbot that allows consumers to receive answers to home improvement-related questions. For instance, a consumer might ask Mylow, 'Which washer and dryer pair will save me the most on my utility bills?' or 'How much mulch do I need for my flower beds?' From there, Mylow answers the question, sharing advice and products a consumer might find useful for the project they're working on. Mylow, which Lowe's developed in tandem with OpenAI, can also provide localized recommendations if consumers provide their zip code. According to the company, the tool is already available to MyLowe's Rewards members via desktop or mobile browser. Seemantini Godbole, chief digital and information officer at Lowe's, said the tool will help the company better serve its customers. 'Home improvement is inherently complex and can feel overwhelming even for the most experienced DIYer—that's why Lowe's has invested in AI and emerging technologies to create solutions that truly help our customers,' Godbole said in a statement. 'We're aiming to deliver the best customer service in retail and Mylow represents an industry-leading step forward in helping us do that. This solution will not only help our customers be more informed, but our associates too.' Going forward, Lowe's anticipates it will integrate the system into its app—and add voice-activated capabilities—later this year. Home Depot launched a similar tool, which it calls Magic Apron, earlier this month. Sign in to access your portfolio
Yahoo
24-02-2025
- Business
- Yahoo
Augmented Reality in Retail Market Revenues to Grow from $19.9 Billion in 2024 to a Projected $64.6 Billion by 2030 - Discover Emerging Trends and Growth Opportunities
Some of the 106 major companies featured in this Augmented Reality in Retail market report include Amazon, Apple, Augment, Blippar, Google, Gravity Jack, Holition, IKEA, Imaginate Technologies, and INDE Augmented Reality in Retail Market Dublin, Feb. 24, 2025 (GLOBE NEWSWIRE) -- The "Augmented Reality in Retail - Global Strategic Business Report" has been added to global market for Augmented Reality in Retail was valued at US$19.9 Billion in 2024 and is projected to reach US$64.4 Billion by 2030, growing at a CAGR of 21.6% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions. The growth of augmented reality in retail is driven by technological advancements, changing consumer expectations for personalized shopping, and the need for retailers to differentiate themselves in a competitive market. Advances in mobile technology, faster internet speeds, and the widespread adoption of smartphones have made AR more accessible and functional for consumers. Today's customers expect a seamless, engaging shopping experience, and AR fulfills this by offering personalized interactions, whether they are shopping online or in-store. The rise in e-commerce has further accelerated the adoption of AR, as retailers seek innovative ways to bring interactivity and customer engagement to online platforms. Additionally, AR's ability to reduce product returns by helping customers make more informed purchases has made it a valuable tool in cost savings and customer satisfaction. Retailers are increasingly using AR as a branding tool as well, offering unique, interactive experiences that set them apart from competitors, attract a younger audience, and create memorable brand interactions. The COVID-19 pandemic has also fueled the growth of AR, as it prompted retailers to innovate and provide virtual shopping alternatives during periods of restricted in-store access. As AR technology continues to evolve and becomes more cost-effective, the demand for augmented reality in retail is expected to grow, cementing its role as a critical component of modern retail strategy, enhancing customer engagement, and driving sales in an increasingly digital-first ScopeThe report analyzes the Augmented Reality in Retail market, presented in terms of market value (US$ Thousand). The analysis covers the key segments and geographic regions outlined (Software & Services, Hardware); Device Type (Head-Mounted Display (HMD), Smart AR Mirror, Handheld Device); Application (Try-On Solutions, Advertising & Marketing, Planning & Designing, Information Systems); End-Use (Furniture & Lighting, Jewelry, Beauty & Cosmetics, Grocery Shopping, Other End-Uses).Geographic Regions/CountriesWorld; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of Insights: Market Growth: Understand the significant growth trajectory of the Augmented Reality Software & Services segment, which is expected to reach US$36.8 Billion by 2030 with a CAGR of a 21.1%. The Augmented Reality Hardware segment is also set to grow at 22.2% CAGR over the analysis period. Regional Analysis: Gain insights into the U.S. market, valued at $6 Billion in 2024, and China, forecasted to grow at an impressive 19.5% CAGR to reach $8.9 Billion by 2030. Discover growth trends in other key regions, including Japan, Canada, Germany, and the Asia-Pacific. Report Features: Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030. In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa. Company Profiles: Coverage of major players such as Amazon, Apple, Augment, Blippar, Google and more. Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments. Key Questions Answered: How is the Global Augmented Reality in Retail Market expected to evolve by 2030? What are the main drivers and restraints affecting the market? Which market segments will grow the most over the forecast period? How will market shares for different regions and segments change by 2030? Who are the leading players in the market, and what are their prospects? Some of the 106 major companies featured in this Augmented Reality in Retail market report include: Amazon Apple Augment Blippar Google Gravity Jack Holition IKEA Imaginate Technologies INDE Key Attributes Report Attribute Details No. of Pages 123 Forecast Period 2024-2030 Estimated Market Value (USD) in 2024 $19.9 Billion Forecasted Market Value (USD) by 2030 $64.4 Billion Compound Annual Growth Rate 21.6% Regions Covered Global MARKET OVERVIEW Influencer Market Insights World Market Trajectories Global Economic Update Augmented Reality in Retail - Global Key Competitors Percentage Market Share in 2025 (E) Competitive Market Presence - Strong/Active/Niche/Trivial for Players Worldwide in 2025 (E) MARKET TRENDS & DRIVERS Growing Demand for Interactive Shopping Experiences Fuels Growth of AR in Retail Increasing Use of AR in Virtual Try-Ons Drives Adoption in Fashion and Beauty Sectors Here`s How AR in Product Visualization Enhances Customer Confidence in Online Shopping Rising Focus on Customer Engagement Expands Use of AR for In-Store Experiences Advancements in Mobile AR Technology Make AR More Accessible for Retail Customers Increasing Demand for Personalization Drives AR Integration in Customizable Product Offerings Here`s How AR in Home Furnishings Helps Customers Visualize Products in Their Own Space Growing Use of AR for Contactless Shopping Solutions Supports Safe and Convenient Retail Experiences Rising Popularity of Social Media Shopping Fuels Demand for AR-Enabled Social Commerce Here`s How AR Enhances Brand Loyalty by Creating Unique and Memorable Shopping Experiences Increasing Adoption of AR for Location-Based Promotions Boosts Customer Engagement In-Store Advances in AI and Machine Learning Improve the Accuracy of AR-Powered Product Recommendations Focus on Omnichannel Strategies Drives Seamless Integration of AR Across Online and Offline Retail Growing Investments in Digital Transformation Support Long-Term Growth of AR in Retail For more information about this report visit About is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends. Attachment Augmented Reality in Retail Market CONTACT: CONTACT: Laura Wood,Senior Press Manager press@ For E.S.T Office Hours Call 1-917-300-0470 For U.S./ CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900Sign in to access your portfolio
