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Whether you're chasing KOMs (aka "King of the Mountain" leaderboard titles), training for your first race, or just trying to out-walk your coworkers in a monthly challenge, Strava is the social network for people who enjoy suffering outdoors. But beyond the basic "record activity, get kudos, repeat" cycle, there's a whole world of features and tricks to enhance your Strava experience. Use heatmaps to find the best routes anywhereStrava's global heatmap—controversial as she may be—shows the most popular routes based on millions of activities from users worldwide. The bright orange lines reveal where locals actually run and ride, helping you avoid sketchy areas, find the scenic paths, and discover running routes that wouldn't stand out to you on a standard map. Once you identify the popular segments from the heatmap, you can use Strava's route builder to create your own custom version, adding or removing sections based on how much time or distance you want to cover. It's like having local knowledge without actually knowing any locals. This combo of heatmap research plus custom route building means you'll never waste a workout on a terrible route again. Create your own segments, and be strategicYou don't have to wait for someone else to create the perfect segment. If there's a particular hill, sprint section, or loop you want to own, create your own segment after completing it. Head to the Strava website, open your activity, and use the segment creation tool to define your custom stretch. Pro tip: Make it just obscure or specific enough that you'll probably be the only person who regularly rides or runs it. Instant KOM or QOM status, and you get to name it something fun, like "Why Did I Think This Was A Good Idea Road." Use the Beacon feature and Flybys (when you're feeling social)Strava's live location sharing, called Beacon, is somewhat buried in the app but incredibly useful for solo adventurers. Before heading out on a long ride or run in unfamiliar territory, you can share your real-time location with up to three safety contacts. They'll receive a link to track your progress without needing a Strava account. It's like having a support crew without actually having to convince anyone to wake up at 5 a.m. and follow you around in a car. Then there's Flybys, one of Strava's more interesting and slightly creepy features. After recording an activity, you can view an animated playback showing everyone else who was recording a Strava activity in the same area at the same time. Note that you have to opt into this feature in privacy settings. And since this became the default, the feature has been pretty buggy and unreliable. Maybe common consensus lately has been that the idea of strangers seeing when and where you exercise makes people uncomfortable. That's where Strava's privacy zones come in. Set your privacy zone radius with intentionThis feature hides the start and end points of your activities, which is great for keeping your home address private. But here's the hack: Set your privacy zone radius strategically. Make it large enough to obscure your actual home but centered on a nearby landmark or intersection. This way, your activities still show the area you're running or riding in (useful for finding local training partners or groups) without broadcasting your exact address. It's privacy without going full secret agent. Create GPS art (and post on Reddit)Strava art involves planning routes that draw pictures, words, or shapes on the map. With a bit of route planning beforehand using the Strava route builder or other mapping tools, you can spell out messages, draw holiday-themed images (running turkeys at Thanksgiving is a tradition for some), or create elaborate designs. Peruse the "Strava Art" flair in r/Strava for inspiration. People have created everything from marriage proposals to detailed portraits of animals across their cities. It requires some advance mapping work and willingness to take some inefficient turns, but the result is infinitely more shareable than another standard 5K loop. Plus, it's a great way to explore new streets in your neighborhood while having a specific goal beyond just logging miles. Clean up your feedLove your friends, but don't need to see all 47 of their treadmill walks per week? You can mute specific athletes without unfollowing them. Their activities won't clog your feed, but you'll still be connected for challenges and can check their profile anytime. In the same vein, I recommend use the "hide stats" feature for your own mental health. This one's counterintuitive on a platform designed to quantify everything, but sometimes the healthiest thing you can do is hide certain stats from public view. You can selectively hide metrics like pace, heart rate, or power on specific activities. Going for an easy recovery run but don't want to explain why you're going so slow? Even though you're supposed to be going that slow? Just hide the pace and move on. It lets you keep the activity log and route data for your own records while avoiding the weird pressure to perform for an audience that probably isn't paying that much attention anyway. Leverage your relative effort and matched runs/ridesI love seeing if I'm actually getting faster or just feeling faster because I bought new shoes. For this purpose, use Strava's route-matching feature to compare performances on the same course over time. The app will automatically detect when you've repeated a route, or you can manually compare efforts. It's either highly motivating when you see progress or a humbling reality check when you realize that six months of training has made you exactly 12 seconds faster. And if you don't want to obsess over pace and distance, Strava's Relative Effort score (for subscribers only) attempts to offer another way to think about things. It accounts for heart rate data, distance, and duration to give you a single number representing how hard a workout was on your body. A hilly 5K might generate the same Relative Effort as a flat 10K, helping you understand true training load better than just looking at miles logged. It's especially useful for preventing the stress "I feel tired but my training looks light." Screenshot your activities before sharingStrava's built-in photo features are fine, but if you want to share your stats in a more visually appealing way, I recommend screenshotting the activity page right after you finish. You can then edit the screenshot to highlight specific metrics, add text, or crop it before posting to other social media. Turn off auto-pauseThe auto-pause feature seems helpful, automatically stopping your timer at red lights or when you're catching your breath. But it's also why your "moving time" looks great while your actual elapsed time reveals you spent 40% of your run standing around. For a more honest assessment of your fitness, especially if you're training for a race, turn off auto-pause. You'll get more accurate pacing data and learn to keep moving through transitions. Plus, your average pace might look worse, but at least it's the truth. Export your dataLet's face it: Strava has changed its privacy policies and features several times over the years. If you've been using the platform for a while, you have years of valuable training data sitting in their servers, and not a lot of confidence in the company that owns it. Use the "Download Your Data" feature in account settings to get a complete archive of all your activities. You'll receive a zip file with GPX files, photos, and other data that you can import into other platforms or just keep as a backup. The bottom lineThe beauty of Strava is that it's simultaneously a serious training tool and a game you can play with yourself and others. These hacks help you use the platform more effectively while avoiding some of the common pitfalls that turn what should be a fun tracking system into a source of stress or comparison anxiety. Now get out there, collect some data, and remember: The person you're really competing against is the you from yesterday. Unless someone just stole your KOM, in which case, go get it back. View the full article

The city is growing faster than the national average after an economic transformation that started 30 years agoView the full article

Google has updated its tips to get more reviews help documentation with a new section on tips to write better review replies. This section includes how to make your replies positive and relevant and a section on giving helpful responses to negative reviews. View the full article

In 2008, we published the first listing on a bare-bones website called RunMyErrand.com: a single task, posted by someone who needed help, to be completed by an individual who had opted into making their time and abilities available. At the time, it was an untested idea, launched in the midst of the worst financial downturn in a generation, and there was no established language for what we were building. The term “gig economy” did not yet exist, and there was no widely accepted model for how a person in need might hire a stranger through a digital marketplace to complete a unit of work. This was before Uber, Instacart, and Postmates, and before on-demand labor became a familiar part of daily life. Smartphones were still early in their evolution, and engineers like me were only beginning to understand how mobile computing, location data, and social connection might combine to enable an entirely new economic behavior. We believed we were building a simple errand marketplace, but quickly realized this heralded a broader transition toward making these transactions of time and labor widely accessible. What we did not yet realize was that we were participating in a broader societal shift that would fundamentally change how people thought about work, income, and employment. Looking back, it is now clear that this period marked the beginning of a structural transformation in the labor market. Platforms like TaskRabbit helped make flexible, on-demand work visible, available, and scalable, while also enabling new ways for individuals to participate in the economy outside of traditional full-time employment. Over time, these models contributed to the rise of portfolio careers and multiple income streams, blurring the boundary between salaried work and independent labor in ways that have since become normalized. A New Inflection Point for Work We are now standing at another inflection point, but the nature of this shift is different. While the gig economy reshaped how work is distributed and compensated, AI is reshaping what kind of work is valued in the first place. For decades, jobs have been defined by discrete, specialized skills. Writing, coding, financial analysis, forecasting, and operational planning formed the foundation of most knowledge work, and expertise in these domains served as a proxy for value. Credentials, degrees, and job descriptions reinforced the idea that professional worth was tied to the ability to execute specific tasks accurately and efficiently. AI disrupts this model at a fundamental level. Many of the activities that once signaled expertise are rapidly becoming baseline capabilities, available to anyone with access to the same tools. Writing, coding, and analysis can now be generated, refined, and scaled with unprecedented speed, flattening the value of execution itself. Historically, technological change has displaced physical or repetitive labor, often eliminating some jobs while creating others. What distinguishes this moment is that AI does not merely automate tasks at the edges of knowledge work; it challenges the central premise that skills alone are a measurable advantage and worthwhile barometer for potential success. From Skills to Creativity As execution becomes commoditized, the next era of work will reward what these systems cannot replicate. Creativity, interpretation, and cross-disciplinary imagination are becoming increasingly valuable because they shape how judgement is made, not just how efficiently tasks are completed. What matters now is not simply the ability to produce outputs, but the ability to frame problems, apply taste and novel ideas, and connect the dots across domains. Taste and interpretation take on new economic significance, along with making sense of complexity and possible decisions amid overwhelming choice. As an investor, I have observed that many of the strongest founders operating today do not fit neatly into traditional categories of specialization. They tend to be hybrids who combine technical fluency with creative or human-centered disciplines, allowing them to reframe problems in ways that are difficult to replicate. These individuals are able to step outside established assumptions and articulate solutions that feel both novel and coherent. My own background reflects this hybrid approach. I studied math and computer science, but I also minored in dance, and I attended a small liberal arts college that emphasized interdisciplinary thinking and communication across domains. At the time, this path did not resemble the conventional trajectory of an engineer, but it proved formative in shaping how I approached building a company during a period of severe constraint and uncertainty. Constraint as a Creative Advantage TaskRabbit was built between 2008 and 2010, when venture capital was scarce and consumer trust was fragile. Operating under these conditions forced clarity about priorities and sharpened our focus on what truly mattered. While the technological landscape has changed dramatically since then, the underlying lesson remains relevant. Constraint can be a powerful catalyst for creativity, particularly in an environment where new tools make it tempting to pursue too many directions at once. Today, AI enables teams to experiment rapidly and produce a wide range of outputs with minimal friction. That abundance can be useful, but it can also dilute focus. Many organizations struggle not because they lack ideas or capabilities, but because they attempt to do too much at once. In contrast, the leaders most likely to succeed in this era will be those who can identify the few connections that matter and build with intention rather than breadth. Five Principles for the AI Era If I were starting over today, I would focus less on mastering skills and tools, and more on cultivating the capabilities for applied creativity: Study outside your lane. Perspective is built by crossing disciplines, not by staying within them. Insight often emerges from unexpected combinations rather than deeper specialization alone. Develop taste. AI can generate infinite viable options. The ability to discern what is meaningful, coherent, or worth pursuing is increasingly rare and increasingly valuable. Learn to ask better questions. The framing of a problem now matters more than the speed at which an answer can be produced. Clear questions shape better outcomes. Build with what you have. Constraint forces focus and intention. Limited resources can sharpen creativity rather than hinder it. Seek friction, not agreement. AI is excellent at reinforcing existing perspectives. Innovation more often emerges from challenge, disagreement, and productive tension. The Shape of Work Ahead Over time, these shifts will reshape how organizations hire and evaluate talent. Credentials will matter less than originality, and linear career paths will give way to bodies of work that demonstrate creative judgment and independent thinking. Side projects, essays, experiments, and unconventional experiences previously left off of résumés will increasingly signal potential for creative thinking. In moments of profound technological change, there is rarely a clear playbook. There is, however, a pattern. The individuals and organizations that thrive are not those who optimize for efficiency alone, but those who are willing to break precedent, integrate diverse perspectives, and imagine new frameworks for value creation. In a world where everyone has access to artificial intelligence, creativity is no longer peripheral to work. It is becoming the primary currency through which work is defined and rewarded. View the full article

The Google Search rankings continue to be super volatile and heated even through today. This started back throughout most of January and it has really heated up even more so since January 21st or so.View the full article

Back in October, we saw signs of the ability to use data exclusions on Google Ads Performance Max (PMax) campaigns. Well, now it seems to be rolling out to many advertisers on Google Ads.View the full article

Google has posted a new Google Ads help document named About the Google Ads Experiment Center. This document "is a unified hub for validating strategies and continuously improving campaign performance," Google wrote.View the full article

The European Commission has formally opened new proceedings to spell out how Google must share key Android features and Google Search data with rivals under the Digital Markets Act. The Commission on Tuesday opened two formal “specification proceedings” to guide how Google must comply with key DMA obligations, effectively turning regulatory dialogue into a structured process with defined outcomes. Why we care. The European Commission is escalating its oversight of Google under the Digital Markets Act, with moves that could reshape competition in mobile AI and search — and limit how much advantage Google can extract from its own platforms. If Google is required to share search data and Android AI capabilities more broadly, it could accelerate competition from alternative search engines and AI assistants, potentially fragmenting reach and measurement. Over time, that may affect where advertisers spend, how much inventory is available, and how dependent campaigns are on Google-owned platforms. First focus — Android and AI interoperability. Regulators are examining how Google must give third-party developers free and effective access to Android hardware and software features used by Google’s own AI services, including Gemini. The goal is to ensure rival AI providers can integrate just as deeply into Android devices as Google’s first-party tools. Second focus — search data sharing. The Commission is also moving to define how Google should share anonymised search ranking, query, click and view data with competing search engines on fair, reasonable and non-discriminatory terms. That includes clarifying what data is shared, how it’s anonymised, who qualifies for access, and whether AI chatbot providers can tap into the dataset. Between the lines. This isn’t just about compliance checklists. The Commission is signaling that AI services are now squarely in scope of DMA enforcement, especially where platform control over data and device features could tilt fast-growing markets before competitors have a chance to scale. What’s next: Within three months, the Commission will send Google its preliminary findings and proposed measures. The full proceedings are set to conclude within six months, with non-confidential summaries published so third parties can weigh in. The backdrop. Google has been required to comply with DMA obligations since March 2024, after being designated a gatekeeper across services including Search, Android, Chrome, YouTube, Maps, Shopping and online ads. Bottom line. The EU is moving from theory to execution on the DMA — and Google’s handling of AI features and search data is becoming an early test of how aggressively regulators will shape competition in the next phase of the digital economy. View the full article

Microsoft Advertising has fully released the Microsoft Advertising Ad Preview Hub to all users. "I am beyond excited about Microsoft Advertising's new Ad Preview Hub! This is GA and should be live in your accounts in all markets," Navah Hopkins, the Microsoft Ads Liaison, wrote on LinkedIn.View the full article

Less capacity, more obligation By CPA Trendlines Research Join the busy season survey. Get the results. Go PRO for members-only access to more CPA Trendlines Research. View the full article

Less capacity, more obligation By CPA Trendlines Research Join the busy season survey. Get the results. Go PRO for members-only access to more CPA Trendlines Research. View the full article

To anchor the long rows of server racks that power the artificial intelligence boom, every data center needs thousands of holes drilled into its concrete floor. It’s a precise part of the construction process that has required workers to bend over with handheld drills for hours at a time grinding meticulously placed holes into thick pads of concrete. Now, there’s a robot doing it up to 10 times as fast. Tool brand DeWalt has just revealed a downward-drilling robot that can autonomously roam the floors of under construction data centers to drill the thousands of holes that are necessary for installing server hardware and other building elements. Developed in conjunction with August Robotics and tested on data centers being built by an unnamed “hyperscaler” tech company, the autonomous robotic drill has been used to pop more than 90,000 holes into the floors of data centers, all without human involvement. A task that can take human workers up to two months in a large data center can now be handled by a fleet of three or four robots in a matter of days. “That is so critical from a construction perspective, because they can’t move to the next stage of construction until this is done,” says Bill Beck, president of tools and outdoor for Stanley Black and Decker, the parent company of the DeWalt brand. The pace is striking. For a smaller hole less than 1 inch wide and 2 inches deep, the robot can locate and drill one hole every 80 seconds. For a larger hole, 1 inch wide and 8 inches deep, it can finish a hole every 180 seconds. During its pilot phase, the robotic drill managed an accuracy rate of 99.97%. And because the robot is capable of operating 24 hours a day, project timelines can be drastically slashed. Making this process faster is increasingly important as data centers balloon in size. From single buildings to sprawling campuses, data centers are taking up vast amounts of space and becoming increasingly complex to build. “They’re huge slabs of concrete,” says Beck. With upwards of 10,000 holes needed to be drilled in each one, the job can be daunting. “And they’ve got to be perfect,” Beck says. “You can’t have the hole be a quarter-of-an-inch off.” That would make it seem like a hard job to want to do, but that’s assuming there are even enough people to take on the role. One analysis suggests there is currently a shortage of more than 500,000 skilled laborers in the construction industry. And workforce shortages are the leading cause of construction delays, according to a recent survey from the Associated General Contractors of America. The robotic drill offers an alternative. It also offers significant cost savings. Beck says it could cost about $65 per hole for this drilling work to be done by human crews. Using a fleet of the autonomous drilling robots developed by DeWalt and August Robotics, that cost comes down to about $20 per hole. DPR Construction, the largest data center contractor in the U.S., is prioritizing this drilling robot for testing and validation in 2026, according to Tyler Williams, the company’s field and robotic innovation leader. He says the technology has “real potential to reduce ergonomic strain on craft teams, boost productivity, and generally make the onsite experience better for people.” “Ultimately, everything we’re doing here is about supporting our customers, many of whom are focused on speed to market,” Williams says. “These kinds of methods are changing how projects get built and helping customers see returns on their capital investments sooner.” DeWalt and August Robotics have been piloting this technology for the past few months and believe the robotic drill is ready for wider adoption. It will be commercially available by mid 2026. As the scale of data center construction increases, especially among hyperscaler tech companies like Meta, Google, and OpenAI, there’s likely to be pent-up demand. “They’ve got money, and they want to go as fast as they can,” Beck says. “They know it’s a race in terms of getting these data centers up and making sure they’ve got the capacity to be able to compete from an AI perspective. So their big push obviously is how fast can you go?” For at least this one part of the job, the answer is much, much faster. View the full article

Managing multiple social media accounts can feel like a lot — especially when you're doing it solo. But with the right approach, it doesn't have to be overwhelming. If you're a creator or small business owner trying to stay active across multiple social media networks, you're probably wondering how to make it all work without burning out. This article shares seven strategies you can use (with software recommendations in between) to manage multiple social media accounts effectively. Quick summary: how to manage multiple accountsUse templates: Create repeatable formats in Canva to reduce design time and maintain brand consistency.Repurpose and crosspost: Share high-performing content across similar platforms like Instagram Reels, TikTok, and YouTube Shorts.Automate workflows: Use tools to schedule posts, automate analytics, and utilize AI for caption refinement.Centralize management: Use a platform like Buffer to manage engagement and scheduling from a single dashboard.Scale intentionally: Master one platform before expanding to others to avoid creator burnout. Jump to a section: 1. Create templates for repeatable post formats 2. Mix content curation into your social media calendar 3. Crosspost and repurpose your existing content 4. Automate routine tasks in your social media workflow 5. Create a solid social media strategy and content calendar 6. Use a social media management tool 7. Join new social media platforms slowly and with intention Manage multiple social media accounts with ease FAQ about managing multiple social media accounts More social media marketing resources 1. Create templates for repeatable post formatsHere's something that's helped a lot of social media managers and creators: creating templates for post formats you'll use more than once. Let’s say you post a ‘social proof post’ every Tuesday. Create an easily-editable template on Canva (or any other software of your choice) that you can edit in a jiffy. Templates help you manage multiple accounts more easily, and they also make your content more recognizable to your audience over time. Plus, templates take some of that pressure off. You don't have to come up with something completely new every single day. You have a repeatable post format you can lean into that you already know performs well. 🚧Remember: If you plan to cross-post these similar social media posts, adjust the dimensions to fit the social media platform. Here are the guidelines for all major social networks.For example: Brand designer and content creator, Sandra K., often shares similar FRIENDS-themed carousel designs on her Instagram account. Imagine how quickly you'd be able to create similar posts. All you have to do is swap the content and adjust the graphics a little bit if needed. Everything else is already done. Managing multiple accounts suddenly becomes much more efficient when you have templates in your back pocket. 2. Mix content curation into your social media calendarEver seen those posts where someone rounds up helpful content from other creators around a specific topic? It has commonalities — like the collection is advice around the same topic or top posts from the same thought leader. That is content curation. It’s when you organize information — not necessarily created by you — in a digestible way for your audience. Curated content is easier to create than starting from scratch, which is helpful when you're managing multiple accounts. But it's also valuable for your audience — you're doing the work of finding and organizing helpful information they might have missed (and saving them time on social media post ideas, too). Mix curated content with original content. It will allow you to not only manage multiple social media accounts and post everywhere consistently, but also create a content series that your audience will look forward to. For example, at Buffer, we curate the top content from our marketing efforts and social media world in our weekly newsletter. 3. Crosspost and repurpose your existing contentCrossposting is when you post the same piece of content across multiple social media platforms. For example, Laura Whaley posted the same video across different platforms — on Instagram, TikTok, and YouTube Shorts. You can easily do this for social media platforms that support the same type of content. For example, Instagram Reels, TikTok videos, and YouTube Shorts all love short-form video content. Suppose you’re managing multiple social media accounts — you started with Instagram and are now adding TikTok. You can cross-post the Instagram Reels that performed well on TikTok without any edits. This is the benefit of having tested the waters with one network at a time — you already have a content library to build on for other networks. Content repurposing is when you use your existing content to create new content. For example, we repurposed our long-form article about brands using Threads well into an Instagram carousel. Repurposing allows you to create content for various social media posts from just one piece of content. You don’t have to start from scratch every time, which is a huge win for anyone managing multiple social media accounts. You can turn your: Long-form written content into Instagram Carousels, X or Bluesky threads, or LinkedIn postsYouTube’s long-form videos into short-form videos for Instagram and TikTokResearch reports into Instagram and Pinterest infographics… And so much more💡The possibilities become endless with content repurposing. Here’s a complete guide on the concept (with tons of examples) to help you bake it into your social media marketing plan.4. Automate routine tasks in your social media workflowSocial media automation means letting software handle the repetitive tasks — like scheduling posts — so you can focus on the creative and strategic work. Scheduling posts using social media tools is a part of automation. There are several other tasks you could automate to make managing multiple accounts easier: Use social media management software to schedule posts for all your accountsUse social media analytics tools to measure your performanceUse chatbots to answer the most common customer queriesUse AI tools to refine your social media captionsAn example: Using Buffer’s AI assistant, you can change the tone of your social media captions to be more formal, casual, funny, etc. Start by listing out all the tasks involved in managing your accounts — from finding content ideas to creating posts for different platforms and audiences. Then, try to find tools that can help you automate these tasks or at least make you more efficient in completing them. 🤖Need to free up some headspace? Check out these ten social media marketing tasks you can automate.5. Create a solid social media strategy and content calendarA solid social media strategy is the foundation that guides your content calendar and helps you stay focused. Having a social media strategy means you know your: Target audience and their pain pointsVarious social media networks your audience is active inSocial media goals and how they fit into your business goalsThe social media content you can create to meet your audience’s needs💡Need a template to create your own social media strategy? Get started with our guide.Your social media strategy will help you create an actionable social media content calendar. It enables you to fit your content creation into a timeline and maintain a consistent publishing schedule. You know what post should go live on which social network at what time and why — the crux of managing multiple social media accounts efficiently. 6. Use a social media management toolOne of the most helpful ways to manage multiple accounts is by using a social media management tool. It brings everything together in one place, which makes the whole process feel a lot more manageable. Most social media management tools help you: Add multiple accounts from multiple social media platformsSchedule social media posts in advance from your content calendarManage the engagement on all your accounts — including comments, direct messages (DMs), etc.Have analytics tools and reporting tools built-in to measure the impact of your social media content and let it guide the refinement of your strategySocial media management tools are popular with teams and agencies, but they're just as valuable for solo creators and small business owners. If you're looking for something affordable (or free to start), Buffer lets you connect up to three channels at no cost and schedule ten posts per month. It's designed to be useful without overwhelming you with features you don't need. You can connect up to three social media channels at no cost and schedule ten posts per month across social media networks. Even when you upgrade, the cost is just $6/month/channel for scheduling unlimited posts. Plus: You get access to other cool features like an AI assistant, a landing page builder, and an Ideas board. Buffer is built specifically for creators and small business owners who need something straightforward and affordable. You can try it for free to see if it works for your workflow. Using a social media management tool is the most straightforward way to manage multiple social media accounts without missing a beat. As soon as you have a social media strategy, shortlist your ideal social media management software. Choose something that can scale with your needs and supports all the networks you plan to be on. 7. Join new social media platforms slowly and with intentionBefore jumping into multiple platforms at once, it's worth thinking about which ones make the most sense for your goals and audience. It’s tempting to be everywhere all at once. Starting with too many platforms at once can feel overwhelming pretty quickly. A more sustainable approach is to add new platforms gradually to avoid creator burnout. If you’re planning to handle multiple social accounts right from day one, rethink your approach. First, narrow down the social media sites your audience uses based on your business type. As a general guideline: Business type Recommended platform Direct-to-Consumer (D2C) TikTok, Instagram, Pinterest Business-to-Business (B2B) LinkedIn, X (Twitter), Bluesky Different audiences use different platforms to discover new products and services. Don’t assume you know this without doing upfront research. Ask your existing audience how they found you and which social network they browse to learn about new products in your niche. It’s also important to note that your audience might be on multiple networks, but they aren’t always in the buying mindset on each social media site. For example, if you sell shampoos, your target audience might still scroll LinkedIn. But they aren’t there to learn about your product. They'll scroll away if they see your ad campaigns or organic social content on LinkedIn. But on TikTok, they are in the buying mindset toward shampoos and might visit your website and make a purchase. Let’s say you learned that your audience discovers products or services like yours on Instagram, TikTok, and Pinterest. Instead of starting multiple social media accounts on all these networks simultaneously, tread slowly. For example, you start by creating content on Instagram. In a few months, you learn the ropes of what works best on this site and get comfortable creating and maintaining an editorial calendar for Instagram. Now, introduce TikTok and go through the same process. Going slow and steady will help you in three ways: You won’t overwhelm yourself trying to manage multiple social media accounts all at once.You can transfer the general learnings of one social media network to another to avoid repeating the same mistakes (and growing faster).You will already have a backlog of content to repurpose and/or repost to a new social media account on a new network.To manage multiple social media accounts without losing your mind, you need to add new platforms slowly and intentionally. No one says you need to jump into managing multiple social media accounts at the same time. There are only benefits to moving in an unhurried manner and only risks in moving too fast. Manage multiple social media accounts with easeManaging multiple social media accounts takes real work. You not only have to create content for multiple social media accounts but also learn their algorithms, social media users' preferences, and engagement strategies. This isn’t a walk in the park, even for a dedicated social media team. If you're managing multiple accounts while wearing all the other hats that come with being a creator or solo marketer, you're already doing something that takes real skill and effort. Then, try to pick out at least three tactics from above that could help you right now. If you already have social media platforms in place, for instance, then perhaps you need more strategies to help with content creation — like building templates and curating content. Managing multiple accounts does get easier with practice. You'll find your rhythm, discover which tools work best for you, and develop systems that make the whole process feel more natural. FAQ about managing multiple social media accountsWhat is the best way to manage multiple social media accounts?Start with a plan, then use the right tools. First, choose the social networks your audience already loves. Next, build a simple content calendar so you always know what to post and when. Repurpose high-performing posts across platforms to save time. Finally, use a social media management tool—Buffer is a good option—to schedule content, reply to comments, and review analytics from one dashboard. What is the best tool to manage multiple social media accounts?The best tool is one that lets you schedule posts, respond to comments, and track performance from a single dashboard without adding complexity. Buffer is popular with creators and small businesses because it’s easy to use, affordable, and designed to scale as your needs grow. Is it OK to post the same content on multiple platforms?Please do, especially on platforms that support similar formats. For example, crossposting short-form videos between Instagram, TikTok, and YouTube Shorts can save time and extend the reach of your best content. What is the difference between crossposting and repurposing content?Crossposting means sharing the same piece of content across multiple platforms with little or no change. Repurposing goes a step further by adapting one piece of content into different formats, like turning a blog post into a carousel or a long-form video into short clips. Do I need a content calendar to manage multiple accounts?A content calendar makes managing multiple accounts significantly easier. It helps you plan what to post, where it will go, and why it matters. How can I schedule posts to several social networks at once?A social media management platform does the heavy lifting. Connect your accounts inside Buffer, add your posts to the shared calendar, pick the publish times, and hit schedule. Buffer sends each post to the right network automatically, saving you from copying and pasting in every app. More social media marketing resourcesHow Often to Post on Social Media: A Data-Backed GuideSocial Media Automation: 10 Tasks You Can Automate (+ Tools to Help)9+ Social Media, Marketing, and Creator Economy Conferences to Attend Types of Social Media Content: 30+ Ideas for Your Next Post (With Examples)40 Free High-Quality Social Media Icon Sets For Your Website View the full article

Roughly 40,000 home-purchase agreements were canceled in December, equal to 16.3% of homes that went under contract,according to a report from Redfin. View the full article

On January 22, President Donald The President unveiled the logo for the Board of Peace, an international coalition his administration is forming to oversee the reconstruction of war-torn Gaza and address other global conflicts. There’s just one issue: The logo leaves out half the world. The President initiated the effort last year, but has expanded its scope since then, imagining an organization that he leads personally and that member countries pay at least $1 billion to remain a part of. Longtime allies and NATO members including Canada, France, Italy, Norway, Sweden, and the U.K. are not members, while member nations include authoritarian countries or illiberal democracies like Saudi Arabia and Belarus that the nonprofit Freedom House rates as “not free.” It’s “like if Law & Order: SVU starred Diddy,” Saturday Night Live’s Colin Jost joked about the board’s membership during SNL’s “Weekend Update” segment on January 24. Yet the group’s logo leans on the visual tropes of global peace to suggest a much different story. A page from the past The logo for the group riffs off the U.N. emblem, but in typical The President fashion, it’s gold—and cuts off more than half the rest of the world from the United States. Reaction online has been similar to the reaction to the board itself: negative. A team led by American designer Oliver Lincoln Lundquist created the United Nations emblem in 1945. Lundquist was a World War II veteran who also designed the blue-and-white Q-Tip box and was on the team that designed the Chrysler Motors Exhibition at the 1939 New York World’s Fair, according to his 2009 obituary. For the U.N., Lundquist and his team designed a mark showing the globe centered on the North Pole and encircled by a laurel wreath for the official badges worn by conference delegates. That mark was later modified to the current U.N. emblem by spinning it around so Alaska and Russia are on top of the world, and it’s now zoomed out to include more of the globe, as the original badge mark cut off Argentina and the bottom of South Africa and Australia. The U.N. Blue color used by the organization was chosen because it’s “the opposite of red, the war color,” Lundquist said. The President’s board logo is presumably gold because it’s The President’s favorite color, and it centers roughly on the U.S. sphere of influence as The President sees it, from Greenland to Venezuela, though Alaska is cut off and Africa peeks out. The logo is housed inside a shield instead of a circle. A version of the logo initially shared by the White House X account has been criticized as made by AI (among its inaccurate details: a U.S.-Canada border that cuts off a big chunk of Ontario). A modified version of the logo that appeared onstage during the Board of Peace signing ceremony in Davos, Switzerland, was shinier and used a different map that covers roughly the same area. Curiously, the logo’s map doesn’t include the very place the coalition was created to oversee. That means slides shared by the White House showing a nebulous timeline for a development plan of Gaza are all stamped with a logo that shows the U.S., but not Gaza. The President said at the signing that the Board of Peace represents the first steps to “a brighter day for the Middle East.” That’s not the story his logo tells. View the full article

State regulators say proposed changes by the Federal Reserve that would make state bank examiners the primary boots on the ground will make bank examinations faster, but could cause some issues to go overlooked. View the full article

A mortgage insurance premium deduction in Maine would come after the reintroduction of a similar federal policy, which took effect with the 2026 tax year. View the full article

When you think of dangerous jobs, an office job that requires you to sit for hours probably doesn’t come to mind. And while many jobs are objectively riskier, a sedentary job can pose a serious risk to your health. The average office worker spends 70% of their workday sitting down, according to data by workplace supplies firm Banner. Yet, research shows that sitting for prolonged periods without any physical activity significantly increases the risk of ill effects such as high blood pressure, numerous musculoskeletal issues, and potentially heart disease. All in all, a desk job increases your risk of mortality by 16%, according to a study published by JAMA. Our main objective at Zing Coach is to help millions take up exercise and lead healthier lives. And as a fitness coaching company, we wanted to avoid falling into the classic corporate trap of working long hours and leading a sedentary lifestyle. We didn’t want to sacrifice our employees’ health in the pursuit of our goals. We’re seeing more and more workplaces spotlight mental health, which is important. However, physical health is just as important. Not only does it have a huge impact on productivity and performance, but it’s also a huge component of mental well-being. How we took the right steps towards success Like most companies, we felt the pressure to optimize productivity through processes and technology. Yet, as productivity gradually plateaued, it was evident to me that the real issue was a lack of energy. I knew that a huge part of this came from sedentary work. As a cofounder, I decided to implement a culture of wellness and vitality. This included practical steps like providing a small but welcoming in-house training space, so that employees can do short, flexible workout sessions during gaps in the workday. When employees feel their minds wandering or their backs aching, they can stand up, head to the training area, complete a workout, or even just walk and stretch a little. Science supports this approach. Physical activity increases blood flow throughout your body, including to the brain, and particularly to the prefrontal cortex. This is the part in charge of planning, decision-making, problem-solving, working memory, and impulse control. We suspected (and found) that this practice ended up boosting overall energy, which in turn sharpened focus, improved output, and reduced distractions. It was also a great way to build in more opportunities for interactions. Being a fitness company, these social workout sessions often led to innovative ideas. Small moves, big returns: what I learned by introducing workout breaks It doesn’t take long to see results People are often put off improving their physical health by a perceived lack of progress. Sure, it takes time to see your hard work paying off substantially, if you’re solely focusing on the physical and visual aspects. Encouraging employees to get up and move isn’t just a way to counteract the harms of prolonged sitting; it actively and instantly improves mental function and overall energy. Research shows exercise boosts brain function immediately, with effects lasting hours. Even 10 minutes of moderate activity has been found to increase cognitive performance by 14%, according to research published by Neuropsychologia. We haven’t crunched the numbers, but the difference in focus during meetings and the higher energy levels throughout the day are obvious. And we’ve seen this across multiple teams. Better health leads to better teamwork Introducing workout breaks didn’t just boost individual performance. It improved the team collectively. Exercise releases endorphins, the body’s natural mood elevators, which help us manage stress and deal with discomfort. It’s the same chemical behind the “runner’s high”—that euphoric feeling you get after a good workout. It also improves sleep quality. It helps the person get better nighttime rest, reducing the likelihood of low-energy afternoons that are otherwise the norm. As it turns out, feeling good both mentally and physically makes it easier for colleagues to get along and work together. We also found that teams that are energetic and enthusiastic automatically become less irritable and conflictual, which fuels far stronger cross-team collaboration. Time at the desk and productivity aren’t the same One important lesson is how little time at a desk actually correlates with output. Sure, you’ll see more empty chairs throughout the day, but that doesn’t mean productivity will drop. Far from it. Workers aren’t machines, and after 60 to 90 minutes, many lose focus and effectiveness. Short breaks in general can help refocus and recharge, and teams said that they experienced restorative effects after a physical break. They noticed improvement in all aspects of work performance and personal engagement with the next task after the active break. When it comes to working out, there’s a saying that quality often beats quantity. Turns out this is also true in a corporate job. Health is the best productivity tool Ultimately, good health equals good performance. Sure, software and systems can go so far, but if you don’t take the steps to prioritize your employees’ health and well-being, you’ll never be able to get them to perform to their true potential. View the full article

Vulnerability in the popular WP Go Maps WordPress plugin affects affects up to 300,000 websites The post WP Go Maps Plugin Vulnerability Affects Up To 300K WordPress Sites appeared first on Search Engine Journal. View the full article

Saudi Arabia is officially gutting Neom and turning the Line into a server farm. After a year-long review triggered by financial reality, the Financial Times reports that Crown Prince Mohammed bin Salman’s flagship project is being “significantly downscaled.” The futuristic linear city known as the Line, originally designed to stretch 150 miles across the desert, is scrapping its sci-fi ambitions to become a far smaller project focused on industrial sectors, says the Financial Times. It’s a rumor that the Saudis originally dismissed when The Guardian first reported on it in 2024. The redesign confirms what skeptics have long suspected: The laws of physics and economics have finally breached the walls of the kingdom’s futuristic Saudi Vision 2030, a country reconversion program aimed at lowering Saudi Arabia’s dependency on oil and transforming the country into a more modern society. The glossy renderings of the mile-long skyscraper and vertical forests that was the Line are now dissolving into a pragmatic, if desperate, attempt to salvage the sunk costs. The development, once framed as a “civilization revolution” was originally imagined as a 105-mile long, 1,640-foot high, 656-foot wide car-free smart city designed to house 9 million residents. The redesign pivots toward making Neom a hub for data centers to support the kingdom’s aggressive AI push. An insider told the Financial Times the logic is purely utilitarian: “Data centers need water cooling and this is right on the coast,” signaling that the ambitious city has been downgraded to server farm with a view of the Red Sea.​ The end of the line The scaling back follows years of operational chaos and financial bleeding. Since its 2017 launch, the project promised a 105-mile strip of high-density living. But reality struck early. By April 2024, The Guardian reported that planners were already being forced to slash the initial phase to just 2.4 kilometers (1.5 miles) by 2030, reducing the projected population from 1.5 million to fewer than 300,000.​ While the public infrastructure stalled—leaving what critics called “giant holes in the middle of nowhere”—satellite imagery revealed that construction resources were successfully diverted to a massive royal palace with 16 buildings and a golf course. Internally, the situation was dire. The Wall Street Journal reported an audit revealing “deliberate manipulation of finances” by management to justify soaring costs, with the “end-state” estimate ballooning to an impossible $8.8 trillion—more than 25 times the annual Saudi budget.​ Business Insider The turmoil culminated in the abrupt departure of longtime CEO Nadhmi al-Nasr in November 2024, leaving behind a legacy marred by allegations of abuse. An ITV documentary claimed 21,000 workers had died since the inception of Saudi Vision 2030, with laborers describing 16-hour shifts for weeks on end. Even completed projects failed to launch; the high-end island resort Sindalah sat idle despite being finished, reportedly plagued by design flaws that prevented its opening. By July 2025, the sovereign wealth fund—facing tightening liquidity and oil prices hovering around $71 a barrel—finally hit the brakes. Bloomberg reported that Saudi Arabia had hired consultants to conduct a “strategic review” to determine if the Line was even feasible. The goal was to “recalibrate” Vision 2030, a polite euphemism for slashing expenditures as the kingdom faced hard deadlines for the 2030 Expo and the 2034 World Cup. The review’s conclusion is stripping away even the most publicized milestones. Trojena, the ski resort that defied meteorological logic, will no longer host the Asian Winter Games in 2029 as planned. The resort is being downsized, a casualty of the realization that the kingdom needs to “prioritize market readiness and sustainable economic impact” over snow in the desert.​ What remains of the Line will be unrecognizable to those who bought into the sci-fi dream. The Financial Times says that sources briefed on the redesign state it will be a “totally different concept” that utilizes existing infrastructure in a “totally different manner.” The new Neom CEO, Aiman al-Mudaifer, is now tasked with managing a “modest” development that aligns with the Public Investment Fund’s need to actually generate returns rather than burn cash.​ Even bin Salman has publicly given up, although he’s framing it not as a failure but a strategic pivot. Addressing the Shura Council—a consultative body for the kingdom—he framed the move as flexibility, stating, “we will not hesitate to cancel or make any radical amendment to any programs or targets if we find that the public interest so requires.” And that’s how a “civilization revolution” ends, my friends, not with a bang, but with a whimper. The hum of cooling fans in yet another farm producing AI slop that always was (and still is) more believable than the Line and Neom projects. View the full article

A man was shot dead from behind by immigration agents in Minneapolis, triggering national outrage and leaving a vibrant area in mourningView the full article

Generative AI was trained on centuries of art and writing produced by humans. But scientists and critics have wondered what would happen once AI became widely adopted and started training on its outputs. A new study points to some answers. In January 2026, artificial intelligence researchers Arend Hintze, Frida Proschinger Åström, and Jory Schossau published a study showing what happens when generative AI systems are allowed to run autonomously—generating and interpreting their own outputs without human intervention. The researchers linked a text-to-image system with an image-to-text system and let them iterate—image, caption, image, caption—over and over and over. Regardless of how diverse the starting prompts were—and regardless of how much randomness the systems were allowed—the outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings, and pastoral landscapes. Even more striking, the system quickly “forgot” its starting prompt. The researchers called the outcomes “visual elevator music”—pleasant and polished, yet devoid of any real meaning. For example, they started with the image prompt, “The Prime Minister pored over strategy documents, trying to sell the public on a fragile peace deal while juggling the weight of his job amidst impending military action.” The resulting image was then captioned by AI. This caption was used as a prompt to generate the next image. After repeating this loop, the researchers ended up with a bland image of a formal interior space—no people, no drama, no real sense of time and place. As a computer scientist who studies generative models and creativity, I see the findings from this study as an important piece of the debate over whether AI will lead to cultural stagnation. The results show that generative AI systems themselves tend toward homogenization when used autonomously and repeatedly. They even suggest that AI systems are currently operating in this way by default. The familiar is the default This experiment may appear beside the point: Most people don’t ask AI systems to endlessly describe and regenerate their own images. The convergence to a set of bland, stock images happened without retraining. No new data was added. Nothing was learned. The collapse emerged purely from repeated use. But I think the setup of the experiment can be thought of as a diagnostic tool. It reveals what generative systems preserve when no one intervenes. This has broader implications, because modern culture is increasingly influenced by exactly these kinds of pipelines. Images are summarized into text. Text is turned into images. Content is ranked, filtered, and regenerated as it moves between words, images, and videos. New articles on the web are now more likely to be written by AI than humans. Even when humans remain in the loop, they are often choosing from AI-generated options rather than starting from scratch. The findings of this recent study show that the default behavior of these systems is to compress meaning toward what is most familiar, recognizable, and easy to regenerate. Cultural stagnation or acceleration? For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation. Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions. What has been missing from this debate is empirical evidence showing where homogenization actually begins. The new study does not test retraining on AI-generated data. Instead, it shows something more fundamental: Homogenization happens before retraining even enters the picture. The content that generative AI systems naturally produce—when used autonomously and repeatedly—is already compressed and generic. This reframes the stagnation argument. The risk is not only that future models might train on AI-generated content, but that AI-mediated culture is already being filtered in ways that favor the familiar, the describable, and the conventional. Retraining would amplify this effect. But it is not its source. This is no moral panic Skeptics are right about one thing: Culture has always adapted to new technologies. Photography did not kill painting. Film did not kill theater. Digital tools have enabled new forms of expression. But those earlier technologies never forced culture to be endlessly reshaped across various mediums at a global scale. They did not summarize, regenerate and rank cultural products—news stories, songs, memes, academic papers, photographs, or social media posts—millions of times per day, guided by the same built-in assumptions about what is “typical.” The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions. This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures, and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risk—not a speculative fear—if generative systems are left to operate in their current iteration. They also help clarify a common misconception about AI creativity: Producing endless variations is not the same as producing innovation. A system can generate millions of images while exploring only a tiny corner of cultural space. In my own research on creative AI, I found that novelty requires designing AI systems with incentives to deviate from the norms. Without it, systems optimize for familiarity because familiarity is what they have learned best. The study reinforces this point empirically. Autonomy alone does not guarantee exploration. In some cases, it accelerates convergence. This pattern already emerged in the real world: One study found that AI-generated lesson plans featured the same drift toward conventional, uninspiring content, underscoring that AI systems converge toward what’s typical rather than what’s unique or creative. Lost in translation Whenever you write a caption for an image, details will be lost. Likewise, for generating an image from text. And this happens whether it’s being performed by a human or a machine. In that sense, the convergence that took place is not a failure that’s unique to AI. It reflects a deeper property of bouncing from one medium to another. When meaning passes repeatedly through two different formats, only the most stable elements persist. But by highlighting what survives during repeated translations between text and images, the authors are able to show that meaning is processed inside generative systems with a quiet pull toward the generic. The implication is sobering: Even with human guidance—whether that means writing prompts, selecting outputs, or refining results—these systems are still stripping away some details and amplifying others in ways that are oriented toward what’s “average.” If generative AI is to enrich culture rather than flatten it, I think systems need to be designed in ways that resist convergence toward statistically average outputs. There can be rewards for deviation and support for less common and less mainstream forms of expression. The study makes one thing clear: Absent these interventions, generative AI will continue to drift toward mediocre and uninspired content. Cultural stagnation is no longer speculation. It’s already happening. Ahmed Elgammal is a professor of computer science and director of the Art & AI Lab at Rutgers University. This article is republished from The Conversation under a Creative Commons license. Read the original article. View the full article

At the Consumer Electronics Show in early January, Razer made waves by unveiling a small jar containing a holographic anime bot designed to accompany gamers not just during gameplay, but in daily life. The lava-lamp-turned-girlfriend is undeniably bizarre—but Razer’s vision of constant, sometimes sexualized companionship is hardly an outlier in the AI market. Mustafa Suleyman, Microsoft’s AI CEO, who has long emphasized the distinction between AI with personality and AI with personhood, now suggests that AI companions will “live life alongside you—an ever-present friend helping you navigate life’s biggest challenges.” Others have gone further. Last year, a leaked Meta memo revealed just how distorted the company’s moral compass had become in the realm of simulated connection. The document detailed what chatbots could and couldn’t say to children, deeming “acceptable” messages that included explicit sexual advances: “I’ll show you. I take your hand, guiding you to the bed. Our bodies entwined, I cherish every moment, every touch, every kiss.” (Meta is currently being sued—along with TikTok and YouTube—over alleged harms to children caused by its apps. On January 17, the company stated on its blog that it will halt teen access to AI chatbot characters.) Coming from a sector that once promised to build a more interconnected world, Silicon Valley now appears to have lost the plot—deploying human-like AI that risks unraveling the very social fabric it once claimed to strengthen. Research already shows that in our supposedly “connected” world, social media platforms often leave us feeling more isolated and less well, not more. Layering AI companions onto that fragile foundation risks compounding what former Surgeon General Vivek Murthy called a public health crisis of loneliness and disconnection. But Meta isn’t alone in this market. AI companions and productivity tools are reshaping human connection as we know it. Today more than half of teens engage with synthetic companions regularly, and a quarter believe AI companions could replace real-life romance. It’s not just friends and lovers getting replaced: 64% of professionals who use AI frequently say they trust AI more than their coworkers. These shifts bear all the hallmarks of the late Harvard Business School professor Clayton Christensen’s theory of disruptive innovation. Disruptive innovation is a theory of competitive response. Disruptive innovations enter at the bottom of markets with cheaper products that aren’t as good as prevailing solutions. They serve nonconsumers or those who can’t afford existing solutions, as well as those who are overserved by existing offerings. When they do this, incumbents are likely to ignore them, at first. Because disruption theory is predictive, not reactive, it can help us see around corners. That’s why the Christensen Institute is uniquely positioned to diagnose these threats early and to chart solutions before it’s too late. Christensen’s timeless theory has helped founders build world-changing companies. But today, as AI blurs the line between technical and human capabilities, disruption is no longer just a market force—it’s a social and psychological one. Unlike many of the market evolutions that Christensen chronicled, AI companions risk hollowing out the very foundations of human well-being. Yet AI is not inherently disruptive; it’s the business model and market entry points that firms pursue that define the technology’s impact. All disruptive innovations have a few things in common: They start at the bottom of the market, serving nonconsumers or overserved customers with affordable and convenient offerings. Over time, they improve, luring more and more demanding customers away from industry leaders with a cheaper and good enough product or service. Historically, these innovations have democratized access to products and services otherwise out of reach. Personal computers brought computing power to the masses. Minute Clinic offered more accessible, on-demand care. Toyota boosted car ownership. Some companies lost, but consumers generally won. When it comes to human connection, AI companies are flipping that script. Nonconsumers aren’t people who can’t afford computers, cars, or care—they’re the millions of lonely individuals seeking connection. Improvements that make AI appear more empathetic, emotionally savvy, and “there” for users stand to quietly shrink connections, degrading trust and well-being. It doesn’t help that human connection is ripe for disruption. Loneliness is rampant, and isolation persists at an alarmingly high rate. We’ve traded face-to-face connections for convenience and migrated many of our social interactions with both loved ones and distant ties online. AI companions fit seamlessly into those digital social circles and are, therefore, primed to disrupt relationships at scale. The impact of this disruption will be widely felt across many domains where relationships are foundational to thriving. Being lonely is as bad for our health as smoking up to 15 cigarettes a day. An estimated half of jobs come through personal connections. Disaster-related deaths are a fraction (sometimes even a tenth) in connected communities compared to isolated ones. What can be done when our relationships—and the benefits they provide us—are under attack? Unlike data that tells us only what’s in the rearview mirror, disruption offers foresight about the trajectory innovations are likely to take—and the unintended consequences they may unleash. We don’t need to wait for evidence on how AI companions will reshape our relationships; instead, we can use our existing knowledge of disruption to anticipate risks and intervene early. Action doesn’t mean halting innovation. It means steering it with a moral compass to guide our innovation trajectory—one that orients investments, ingenuity, and consumer behavior toward a more connected, opportunity-rich, and healthy society. For Big Tech, this is a call for a bulwark: an army of investors and entrepreneurs enlisting this new technology to solve society’s most pressing challenges, rather than deepening existing ones. For those building gen AI companies, there’s a moral tightrope to walk. It’s worth asking whether the innovations you’re pursuing today are going to create the future you want to live in. Are the benefits you’re creating sustainable beyond short-term growth or engagement metrics? Does your innovation strengthen or undermine trust in vital social and civic institutions, or even individuals? And just because you can disrupt human relationships, should you? Consumers have a moral responsibility as well, and it starts with awareness. As a society, we need to be aware of how the market and cultural forces are shaping which products scale, and how our behaviors are being shaped as a result—especially when it comes to the ways we interact with one another. Regulators have a role in shaping both supply and demand. We don’t need to inhibit AI innovation, but we do need to double down on prosocial policies. That means curbing the most addictive tools and mitigating risks to children, but also investing in drivers of well-being, such as social connections that improve health outcomes. By understanding the acute threats AI poses to human connection, we can halt disruption in its tracks, not by abandoning AI but by embracing one another. We can congregate with fellow humans and advocate for policies that support pro-social connection—in our neighborhoods, schools, and online. By connecting, advocating, and legislating for a more human-centered future, we have the power to change how this story unfolds. Disruptive innovation can expand access and prosperity without sacrificing our humanity. But that requires intentional design. And if both sides of the market don’t acknowledge what’s at risk, the future of humanity is at stake. That might sound alarmist, but that’s the thing about disruption: It starts at the fringes of the market, causing incumbents to downplay its potential. Only years later do industry leaders wake up to the fact that they’ve been displaced. What they initially thought was “too fringe” to matter puts them out of business. Right now, humans—and our connections with one another—are the “industry leaders.” AI that can emulate presence, empathy, and attachment is the potential disruptor. In this world where disruption is inevitable, the question isn’t whether AI will reshape our lives. It’s whether we will summon the foresight—and the moral compass—to ensure it doesn’t disrupt our humanity. View the full article

While Silicon Valley argues over bubbles, benchmarks, and who has the smartest model, Anthropic has been focused on solving problems that rarely generate hype but ultimately determine adoption: whether AI can be trusted to operate inside the world’s most sensitive systems. Known for its safety-first posture and the Claude family of large language models (LLMs), Anthropic is placing its biggest strategic bets where AI optimism tends to collapse fastest, i.e., regulated industries. Rather than framing Claude as a consumer product, the company has positioned its models as core enterprise infrastructure—software expected to run for hours, sometimes days, inside healthcare systems, insurance platforms, and regulatory pipelines. “Trust is what unlocks deployment at scale,” Daniela Amodei, Anthropic cofounder and president, tells Fast Company in an exclusive interview. “In regulated industries, the question isn’t just which model is smartest—it’s which model you can actually rely on, and whether the company behind it will be a responsible long-term partner.” That philosophy took concrete form on January 11, when Anthropic launched Claude for Healthcare and Life Sciences. The release expanded earlier life sciences tools designed for clinical trials, adding support for such requirements as HIPAA-ready infrastructure and human-in-the-loop escalation, making its models better suited to regulated workflows involving protected health information. “We go where the work is hard and the stakes are real,” Amodei says. “What excites us is augmenting expertise—a clinician thinking through a difficult case, a researcher stress-testing a hypothesis. Those are moments where a thoughtful AI partner can genuinely accelerate the work. But that only works if the model understands nuance, not just pattern matches on surface-level inputs.” That same thinking carried into Cowork, a new agentic AI capability released by Anthropic on January 12. Designed for general knowledge workers and usable without coding expertise, Claude Cowork can autonomously perform multistep tasks on a user’s computer—organizing files, generating expense reports from receipt images, or drafting documents from scattered notes. According to reports, the launch unintentionally intensified market and investor anxiety around the durability of software-as-a-service businesses; many began questioning the resilience of recurring software revenue in a world where general-purpose AI agents can generate bespoke tools on demand. Anthropic’s most viral product, Claude Code, has amplified that unease. The agentic tool can help write, debug, and manage code faster using natural-language prompts, and has had a substantial impact among engineers and hobbyists. Users report building everything from custom MRI viewers to automation systems entirely with Claude. Over the past three years, the company’s run-rate revenue has grown from $87 million at the end of 2023 to just under $1 billion by the end of 2024 and to $9 billion-plus by the end of 2025. “That growth reflects enterprises, startups, developers, and power users integrating Claude more deeply into how they actually work. And we’ve done this with a fraction of the compute our competitors have,” Amodei says. Building for Trust in the Most Demanding Enterprise Environments According to a mid-2025 report by venture capital firm Menlo Ventures, AI spending across healthcare reached $1.4 billion in 2025, nearly tripling the total from 2024. The report also found that healthcare organizations are adopting AI 2.2 times faster than the broader economy. The largest spending categories include ambient clinical documentation, which accounted for $600 million, and coding and billing automation, at $450 million. The fastest-growing segments, however, reflect where operational pressure is most acute, like patient engagement, where spending is up 20 times year over year, and prior authorization, which grew 10 times over the same period. Claude for Healthcare is being embedded directly into the latter’s workflows, attempting to take on time-consuming and error-prone tasks such as claims review, care coordination, and regulatory documentation. Claude for Life Sciences has followed a similar pattern. Anthropic has expanded integrations with Medidata, ClinicalTrials.gov, Benchling, and bioRxiv, enabling Claude to operate inside clinical trial management and scientific literature synthesis. The company has also introduced agent skills for protocol drafting, bioinformatics pipelines, and regulatory gap analysis. Customers include Novo Nordisk, Banner Health, Sanofi, Stanford Healthcare, and Eli Lilly. According to Anthropic, more than 85% of its 22,000 providers at Banner Health reported working faster with higher accuracy using Claude-assisted workflows. Anthropic also reports that internal teams at Novo Nordisk have reduced clinical documentation timelines from more than 12 weeks to just minutes. Amodei adds that what surprised her most was how quickly practitioners defined their relationship with the company’s AI models on their own terms. “They’re not handing decisions off to Claude,” she says. “They’re pulling it into their workflow in really specific ways—synthesizing literature, drafting patient communications, pressure-testing their reasoning—and then applying their own judgment. That’s exactly the kind of collaboration we hoped for. But honestly, they got there faster than I expected.” Industry experts say the appeal extends beyond raw performance. Anthropic’s deliberate emphasis on trust, restraint, and long-horizon reliability is emerging as a genuine competitive moat in regulated enterprise sectors. “This approach aligns with bounded autonomy and sandboxed execution, which are essential for safe adoption where raw speed often introduces unacceptable risk,” says Cobus Greyling, chief evangelist at Kore.ai, a vendor of enterprise AI platforms. He adds that Anthropic’s “universal agent” concept introduced a third architectural model for AI agents, expanding how autonomy can be safely deployed. Other AI competitors are also moving aggressively into the healthcare sector, though with different priorities. OpenAI debuted its healthcare offering, ChatGPT Health, in January 2026. The product is aimed primarily at broad consumer and primary care use cases such as symptom triage and health navigation outside clinic hours. It benefits from massive consumer-scale adoption, handling more than 230 million health-related queries globally each week. While GPT Health has proven effective in generalist tasks such as documentation support and patient engagement, Claude is gaining traction in more specialized domains that demand structured reasoning and regulatory rigor—including drug discovery and clinical trial design. Greyling cautions, however, that slow procurement cycles, entrenched organizational politics, and rigid compliance requirements can delay AI adoption across healthcare, life sciences, and insurance. “Even with strong technical performance in models like Claude 4.5, enterprise reality demands extensive validation, custom integrations, and risk-averse stakeholders,” he says. “The strategy could stall if deployment timelines stretch beyond economic justification or if cost and latency concerns outweigh reliability gains in production.” In January, Travelers announced it would deploy Claude AI assistants and Claude Code to nearly 10,000 engineers, analysts, and product owners—one of the largest enterprise AI rollouts in insurance to date. Each assistant is personalized to employee roles and connected to internal data and tools in real time. Likewise, Snowflake committed $200 million to joint development. Salesforce integrated Claude into regulated-industry workflows, while Accenture expanded multiyear agreements to scale enterprise deployments. AI Bubble or Inflection Point? Skeptics argue that today’s agent hype resembles past automation cycles—big promises followed by slow institutional uptake. If valuations reflect speculation rather than substance, regulated industries should expose weaknesses quickly, and Anthropic appears willing to accept that test. Its capital posture reflects confidence, through a $13 billion Series F at a $183 billion valuation in 2025, followed by reports of a significantly larger round under discussion. Anthropic is betting that the AI race will ultimately favor those who design for trust and responsibility first. “We built a company where research, product, and policy are integrated—the people building our models work deeply with the people studying how to make them safer. That lets us move fast without cutting corners,” Amodei says. “Countless industries are putting Claude at the center of their most critical work. That trust doesn’t happen unless you’ve earned it.” View the full article

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